PROFESSIONAL AGEISM: The Impact of Ageism among Research and Healthcare Professionals on Older Patients: A Systematic Review

The Impact of Ageism among Legal and Financial Professionals on Older People: Systematised Reviews

Susan Markham

A thesis in fulfilment of the requirements for the degree of Master of Science (Research)

School of Psychiatry Faculty of Medicine

September 2020

1 Thesis Title PROFESSIONAL AGEISM: The Impact of Ageism among Research and Healthcare Professionals on Older Patients: A Systematic Review The Impact of Ageism among Legal and Financial Professionals on Older People: Systematised Reviews

ABSTRACT Ageism – stereotyping, prejudice and discrimination towards people on the basis of chronological age – is ubiquitous in society and has a significant impact on older people across many aspects of their lives, including the provision of critical services. However, the impact of ageist behaviours on the part of key frontline professionals who interact closely and regularly with older people – healthcare, legal and financial professionals – has never been synthesised. Aim: The aim of this project was to examine how ageism influences professional behaviour among these providers and subsequently affects outcomes for older people. The overall hypothesis was that ageist actions and decisions by healthcare, legal and financial professionals would adversely impact the care and management of older people, including the provision of clinical care, services and advice. Methods: The project involved three components: a systematic review of the medical literature (the major component) and two systematised reviews of the legal and financial literature. The systematic review of the medical literature was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines in order to identify, evaluate and synthesise research focusing on age-based actions by research and healthcare professionals and consequences for older patients. Multiple electronic databases were searched including PubMed, Embase, Web of Science core collection, Medline via EBSCO, PSYCHInfo, CINAHL, Scopus, Ageline and ProQuest Central. Additional data were gathered via hand searches of relevant reference lists and leading journals. No date or geographical restrictions were set. The systematised reviews of the financial and legal literature were conducted using key elements of systematicity (i.e. a priori specification of the research hypotheses, comprehensive search of multiple databases using defined search terms and determination and application of explicitly defined inclusion and exclusion criteria). While less comprehensive than a systematic review, this method enables a degree of consistency in methodological approach across the three research areas. Similarly, no date or geographical restrictions were set. Results: The systematic review in health care yielded 73 studies. The review found clear associations between age-based management among clinical researchers/healthcare professionals and adverse impacts on older patients. Compared with younger patients, older patients were under- represented or excluded from clinical trials and evidence-based treatments in the majority (68) of studies. The systematised review in law found eleven eligible studies, nine of which showed older people were treated more leniently that younger people in sentencing judgements, potentially due to positive ageism; two studies found negative consequences for older people in terms of unequal justice for older victims of crime and ageism in adult protection, guardianship and conservatorship. Two studies were identified in finance and showed that older people were directly or indirectly mistreated by financial institutions, through pressure to buy financial products and a failure to address future financial needs or investigate indicators of possible financial abuse. Conclusion: Collectively, the evidence found that older people either experienced ageism through arbitrary age barriers, were subject to differences in management compared with younger people, or received a lower standard of care than was warranted. While these findings were associated with largely negative outcomes for older people, there were some positive outcomes associated with age-based decision-making. For example, the majority of legal studies found that more leniency was shown to older defendants in sentencing decisions. Overall, decisions about the care and management of older people should be based on each person’s individual situation, tolerance and needs.

INCLUSION OF PUBLICATIONS STATEMENT TABLE OF CONTENTS

Acknowledgements 7 List of figures 8 List of tables 8 Abstract 9

PART 1: INTRODUCTION 11 1.1 Background 11 1.2 Ageing population 13 1.3 Prevalence of ageism 14 1.4 Rationale 14 1.5 Aims 15 1.6 Hypotheses 15 1.7 Thesis structure and approach 15 References 17

PART 2: THE IMPACT OF AGEISM AMONG RESEARCH AND HEALTHCARE PROFESSIONALS ON OLDER PATIENTS: A SYSTEMATIC REVIEW 20

Chapter 1: Introduction 20 1.1 Background 20 1.2 Rationale 21 1.3 Aims 22 1.4 Hypotheses 22

Chapter 2: Literature review 23 1.1 Definitions of ageism 23 1.2 Labels and definitions of ‘older’ 24 1.3 Theoretical framework of ageism 27 1.4 Ageism in health care: Research approaches and settings 30 1.5 Impact of ageism in clinical research and health care 31 1.6 Ageing, ageism and human rights 34 1.7 Conclusion 35

2 Chapter 3: Methods 36 3.1 Search process 36 3.2 Data sources and search strategy 36 3.2.1 Search terms 38 3.2.2 Database search string example 39 3.2.3 Automatic search updates 39 3.3 Inclusion and exclusion criteria 39 3.3.1 Inclusion criteria 40 3.3.2 Exclusion criteria 40 3.4 Data extraction 40 3.5 Data analysis and synthesis 41 3.6 Assessment of quality and risk of bias 42 3.7 Ethics approval 42

Chapter 4: Results 43 4.1 Search results 43 4.2 Study characteristics 45 4.2.1 Methods 45 4.2.1.1 Statistical methods 45 4.2.2 Focus 45 4.2.3 Year of publication 46 4.2.4 Geographical location 46 4.2.5 Study sites 47 4.2.6 Disease focus 47 4.3 Synthesis of findings 48 4.2.1 Terminology of ageism 48 4.2.2 Definitions of ‘older’ or ‘elderly’ 48 4.3.3 Exclusions from clinical research 50 4.3.4 Differential healthcare receipt 54 4.3.5 Increasing disparity with increasing age 55 4.3.6 Inappropriate medication management 55 4.3.7 Under-representation of older patients 55 4.3.8 Changes over time 56 4.3.9 Clinical outcomes in different settings and locations 57 4.3.10 Management of older patients and human rights 57 4.3.11 Differences in guidelines-based care 57

3 4.4 Lack of ageism 58 4.5 Assessment of additional variables 58 4.6 Quality and risk of bias assessment 59

Chapter 5: Discussion 62 Limitations 70 Future directions 71 Conclusion 73 References 74

PART 3: THE IMPACT OF AGEISM AMONG LEGAL PROFESSIONALS ON OLDER PEOPLE: A SYSTEMATISED REVIEW 87

Chapter 1: Introduction 87 1.1 Background 87 1.2 Rationale 89 1.3 Aims 90 1.4 Hypothesis 90

Chapter 2: Methods 91 2.1 Approach 91 2.2 Methodology 91 2.3 Inclusion and exclusion criteria 92 2.3.1 Inclusion criteria 92 2.3.2 Exclusion criteria 92

Chapter 3: Results 93 3.1 Search results 93 3.2 Synthesis of eligible studies 94 3.2.1 Age-based sentencing of older offenders 94 3.2.2 Age-based inequity of access to justice 95 3.2.3 Older age and adult protection, guardianship and conservatorship 96 3.3 Conclusion 97

Chapter 4: Discussion 103 Limitations 106 Conclusion 107 References 108

4 PART 4: THE IMPACT OF AGEISM AMONG FINANCIAL PROFESSIONALS ON OLDER PEOPLE: A SYSTEMATISED REVIEW 111

Chapter 1: Introduction 111 1.1 Background 111 1.2 Rationale 112 1.3 Aims 112 1.4 Hypothesis 113

Chapter 2: Methods 114 2.1 Approach 114 2.2 Methodology 114 2.3 Inclusion and exclusion criteria 115 2.3.1 Inclusion criteria 115 2.3.2 Exclusion criteria 116

Chapter 3: Results 117 3.1 Search results 117 3.2 Findings 117 3.2.1 Mistreatment by financial institutions 117 3.2.2 Lack of safeguards in lending 118 3.2.3 Response to financial abuse 119

Chapter 4: Discussion 122 Limitations 124 Conclusion 125 References 126

PART 5: FINAL DISCUSSION 129 5.1 Individual review findings 130 5.1.1 Healthcare professionals 130 5.1.2 Legal professionals 131 5.1.3 Financial professionals 131 5.2 Synthesis of results 132 5.2.1 Similarities and differences 132 5.3 Future directions 135 5.4 Conclusion 136 References 137

5 Appendices 138 Appendix 1. Systematic review studies: Tabulated data 138 Appendix 2. Risk of bias assessment tool used to assess observational studies 178 Appendix 3. Systematic review: Included studies 181

6 ACKNOWLEDGEMENTS

Writing this thesis during the COVID-19 pandemic has highlighted how serious the consequences of ageism can become, and has reinforced my desire to investigate ways of identifying and overcoming ageism in our society.

Many thanks to my supervisors, Professor Carmelle Peisah and Professor Prue Vines, for their support and expertise. Thanks to Dr Nicola Gates for her time and advice about the systematic review process, to Professor Philip Ward for his timely guidance, and to my brother, Paul Markham, for his ongoing support and IT assistance.

To my three favourite people in the world – Joss, Christian and Luke Hawthorn – for their amazing encouragement, unconditional love and support, and for cheering me on at every step. In particular, thanks to Joss for his inspiration, expertise, valuable insights and constant encouragement; to Christian for his calm and steadfast presence, kindness and gentleness, and willingness to discuss all things medical; and to Luke for the fabulous food, fun and laughter, fashion discussions, and reminder that we are nothing without a song or a dance. You are all simply the best.

Finally, this thesis is dedicated to my mother, Helen Moore, for showing me that age is not a reason to stop doing anything.

7 List of figures PART 2 Figure 4.1. PRISMA diagram illustrating procedure for selecting studies 44 Figure 4.2. Year of publication of included studies 46 Figure 4.3. Single/dual study locations 47 Figure 4.4. Definitions of ‘older’ and ‘elderly’ in included studies 50

List of tables PART 2 Table 3.1. Search sources 37 Table 3.2. Database search strings 38 Table 3.3. SIGN explanations for assessment of risk of bias 42 Table 4.1. Studies that examined inclusion of older people in clinical trials/ 53 clinical trial protocols Table 4.2. Number of studies that addressed contraindications, confounding 59 variables, patient preference, study limitations Table 4.3. Ageism-healthcare findings and quality/risk of bias ratings 61

PART 3 Table 3.1. Eligible studies examining ageism among legal professionals 98 and outcomes for older people

8 ABSTRACT

Ageism – stereotyping, prejudice and discrimination towards people on the basis of chronological age – is ubiquitous in society and has a significant impact on older people across many aspects of their lives, including the provision of critical services. However, the impact of ageist behaviours on the part of key frontline professionals who interact closely and regularly with older people – healthcare, legal and financial professionals – has never been synthesised.

Aim: The aim of this project was to examine how ageism influences professional behaviour among these providers and subsequently affects outcomes for older people. The overall hypothesis was that ageist actions and decisions by healthcare, legal and financial professionals would adversely impact the care and management of older people, including the provision of clinical care, services and advice.

Methods: The project involved three components: a systematic review of the medical literature (the major component) and two systematised reviews of the legal and financial literature. The systematic review of the medical literature was conducted in accordance with the Preferred Reporting Items for Systematic Reviews and Meta- Analysis (PRISMA) guidelines in order to identify, evaluate and synthesise research focusing on age-based actions by research and healthcare professionals and consequences for older patients. Multiple electronic databases were searched including PubMed, Embase, Web of Science core collection, Medline via EBSCO, PSYCHInfo, CINAHL, Scopus, Ageline and ProQuest Central. Additional data were gathered via hand searches of relevant reference lists and leading journals. No date or geographical restrictions were set. The systematised reviews of the financial and legal literature were conducted using key elements of systematicity (i.e. a priori specification of the research hypotheses, comprehensive search of multiple databases using defined search terms and determination and application of explicitly defined inclusion and exclusion criteria). While less comprehensive than a systematic review, this method enables a degree of consistency in methodological approach across the three research areas. Similarly, no date or geographical restrictions were set.

Results: The systematic review in health care yielded 73 studies. The review found clear associations between age-based management among clinical researchers/healthcare professionals and adverse impacts on older patients. Compared with younger patients, older patients were under-represented or excluded

9 from clinical trials and evidence-based treatments in the majority (68) of studies. The systematised review in law found eleven eligible studies, nine of which showed older people were treated more leniently that younger people in sentencing judgements, potentially due to positive ageism; two studies found negative consequences for older people in terms of unequal justice for older victims of crime and ageism in adult protection, guardianship and conservatorship. Two studies were identified in finance and showed that older people were directly or indirectly mistreated by financial institutions, through pressure to buy financial products and a failure to address future financial needs or investigate indicators of possible financial abuse.

Conclusion: Collectively, the evidence found that older people either experienced ageism through arbitrary age barriers, were subject to differences in management compared with younger people, or received a lower standard of care than was warranted. While these findings were associated with largely negative outcomes for older people, there were some positive outcomes associated with age-based decision-making. For example, the majority of legal studies found that more leniency was shown to older defendants in sentencing decisions. Overall, decisions about the care and management of older people should be based on each person’s individual situation, tolerance and needs.

10 PART 1 INTRODUCTION

1.1 Background Ageism – stereotyping, prejudice and discrimination towards people on the basis of chronological age (WHO, 2018) – is a highly prevalent problem that may be more pervasive than racism or sexism, particularly in the public domain (Levy & Banaji, 2002).

Ageism can affect people of any age, but older people are common targets. Ageism directed towards older people can be negative or positive, but negative stereotypes, attitudes and behaviours are far more pervasive (Ng et al, 2015). Never has this been more evident than in the COVID-19 pandemic, in which ageism has proliferated with calls for sacrificing older people for the good of society (Ayalon et al, 2020). Healthcare and financial crises can amplify socially constructed dividing practices such as age stratifications and categorisations of people, reinforcing the concept of ‘othering’ or ‘us versus them’ (Phelan, 2020). Public discussions about the value of older people and assumptions that everyone who is older is alike, perpetuate negative ageism stereotypes and undermine the heterogeneity of ageing (Ayalon et al, 2020).

Ageism acts to legitimise and reinforce social inequalities (WHO, 2015); it is typically more pronounced towards older women, older people who are poor and those with dementia (Ayalon et al, 2014; Biggs et al, 2019). Stereotypes about ageing discourage older people from participating in the workforce or undertaking social activities, and contribute to the social isolation of older generations (Donizzetti et al, 2019).

Ageism may be implicit or explicit, self-directed or other-directed, and can be expressed on multiple levels (Iversen et al, 2009; de São José & Amado, 2017). Distinctions, stratifications and judgements based on a person’s age are subtly woven into patterns of thinking such that, at the individual level, these predominantly negative stereotypes are internalised from a young age, primed via repeated exposure to negative depictions of ageing and older people, and manifested as ageist actions and behaviours towards others as well as towards oneself (Ayalon & Tesch-Romer, 2017; Macnicol, 2006; Reynolds, 2020). At the societal/structural level, ageism is expressed across multiple settings, systems and contexts. It forms part of societal norms and

11 cultural practices, and can underpin policies, laws and regulations (Henricks, 2005). The interaction between individual and societal/structural ageism further maintains and bolsters its occurrence (Ayalon & Tesch-Romer, 2018a).

Unlike other forms of prejudice such as racism and sexism, ageism is socially accepted and does not typically provoke shame among its perpetrators (Levy & Banaji, 2002). Ageism has been described as the most socially acceptable form of prejudice – the last acceptable ‘ism’ – even though it is one that will likely affect everyone who lives long enough (WHO, 2015). Ageism is unique in that it often instigated by people who will eventually become the target demographic – it is prejudice against our feared future selves (Nelson, 2005).

In the healthcare arena, some of the key barriers to developing good public health policy on ageing are negative attitudes and assumptions about older people (WHO, 2015). Older people are excluded from clinical research due to arbitrary age barriers and other criteria that disproportionately limit their participation (Cherubini et al, 2011; Murthy et al, 2004). Ageism can impact the attitudes and behaviours of healthcare professionals towards older patients (Ben-Harush et al, 2017; Chang et al, 2020; Ouchida & Lachs, 2015). For example, it can lead to clinical decisions that limit access to care based on chronological age rather than health needs (Gewirtz-Meydan & Ayalon, 2017; Helmes & Gee, 2003; Schroyen et al., 2018; Uncapher & Arean, 2000). Stereotypical assumptions about older patients can also limit the medical information and healthcare choices that older patients are presented with (Buttigieg et al., 2018; Grant, 1996).

Ageism within law, policy and practice may involve decisions or actions that have a differential impact on the older population as a whole, or a negative impact on specific segments of the older population (Spencer, 2009). Ageist views about older people may mean that older people are excluded from discussions about their legal needs or removed from decision-making opportunities, robbing older people of their autonomy (LCO, 2012; O’Neill & Peisah, 2019).

Similarly in the financial services field, ageist attitudes can manifest as a failure to give sufficient attention to the financial needs of older people or to involve older people in discussions about their financial needs (Gibson & Rochford, 2008). Ageist stereotypes

12 about the abilities of older people contribute to the perception that older people are unable to manage their own financial affairs (Phelan, 2020). The ageist assumption that older people are a financial drain on society can lead to a lack of appropriate financial advice and may lead to conditions enabling subsequent financial exploitation (Harbison, 2016).

Across all fields, ageism can manifest as indirect or implicit microaggressions (e.g. helping behaviours that cultivate or affirm weakness, vulnerability or helplessness), compassionate ageism (e.g. positive advocacy for a subgroup of older people that, although well-intentioned, facilitates negative generalisations about all older people), belittling behaviour (e.g. paternalism, elderspeak, derogatory actions), disparate care and attention (e.g. compared with younger people), under-treatment or suboptimal management, inattention, disinterest or disregard (Reynolds, 2020; Wyman, 2018).

1.2 Ageing population The COVID-19 pandemic was the first pandemic to occur since the global population consisted of more people aged over 65 years than under five years (UN, 2019). Globally, the fastest growing age group is those aged 65 and older, who are projected to account for 16% of the world’s population by 2050, or one in four of those living in Europe and the USA (UN, 2019). In 2017 there were 3.8 million Australians aged 65 and over (15% of the total population). This is predicted to increase to 8.8. million (22% of the total population) by 2057 (ABS, 2014 & 2017). Globally, the number of people aged 80 years or older is projected to triple, from 143 million in 2019 to 426 million in 2050 (UN, 2019).

In many countries around the world, ageing is perceived as a negative experience highlighted by decline and deterioration (Horton et al, 2007; Ory et al, 2003). However, ageing is complex and highly diverse. It is dependent on a range of interactive components, including individual factors such as physiological age, health status, economic and social circumstances, as well as cultural and regional influences (WHO, 2015). The interplay of these factors gives rise to substantial variability in function, ability and quality of life for older individuals, perhaps even more so than at other stages in the lifespan (Garrison-Diehn et al, 2020). Neglecting or overlooking these differences and applying perceived age-based group characteristics to individuals, regardless of each person’s own characteristics, leads to generalisations and

13 stereotypical assumptions about older people and ageing (Ayalon & Tesch-Romer, 2018b; Macnicol, 2006).

1.3 Prevalence of ageism Almost fifty years since ageism was conceptualised by Robert Butler in 1969, it remains highly prevalent in many societies and is predicted to increase further as the population ages (Ayalon, 2014; Officer et al, 2016). The exact prevalence of ageism is unknown, but available data suggests that between 48% and 91% of older people have experienced ageism in some form (Kim et al, 2015; Palmore, 2004). Ageist views are held by people of many ages, including older people themselves (Dobbs et al, 2008; Giles & Reid, 2005; Hagestad & Uhlenberg, 2005).

For many older people, ageism is an everyday encounter. Because it is often implicit or subconscious, ageism commonly goes unrecognised and unchallenged (Cuddy et al, 2005). An Australian survey involving people aged 65 and older reported a range of personal experiences of ageism, including being told a joke about older people (57%), being patronised (38%) and being ignored (37%) (The Benevolent Society, 2017). Over 25% of people stated that they did not get a job because of their age, while 14% stated that they did not get a job promotion because of their age.

1.4 Rationale Ageism can have a significant impact on older people across many aspects of their lives including employment, social interactions, governing policies and procedures and the provision of critical services. However, the impact of ageist behaviours on the part of key frontline professionals who interact closely and regularly with older people – healthcare, legal and financial professionals – has never been synthesised. In a contemporary society where ageism remains highly prevalent, it is essential to understand the consequences of professional ageism on older people. In addition, given that ageism is evident across all aspects of society, research in ageism should be multidisciplinary, involving investigation and collaboration between professional groups (Doron, 2019; Phelan, 2020). This project provides a unique perspective on ageism in that it examines and synthesises evidence in health, law and finance in order to determine the impact of ageism among the professionals who practise in these fields. The systematic review in health focuses on actions by researchers and clinicians – something that has not been a sole focus of previous studies which have

14 taken a broader perspective in examining the impact of ageism. There is also a dearth of empirical ageism research in law and finance.

With a rapidly ageing population and a pandemic-induced proliferation of ageist attitudes, greater attention to ageism is timely and warranted. This study could potentially inform clinical practice guidelines, health service auditing and professional education, as well as facilitate an important change in the way that age is used by professionals to guide management of older people.

1.5 Aims This thesis attempts to redress the gaps in knowledge regarding the impact of ageism among healthcare, legal and financial professionals by conducting a systematic review of the medical literature, together with systematised reviews of the legal and financial literature, to examine how ageism influences professional behaviour and subsequently affects outcomes for older people.

1.6 Hypotheses Systematic review hypotheses: (1) That ageism among research and healthcare professionals will adversely impact the care and management of older people (2) That the adverse effects of ageism will manifest in a range of clinical, research and policy settings.

Systematised reviews hypotheses: (1) That ageism among legal professionals will adversely impact interactions with older people, including the provision of services and advice (2) That ageism among financial professionals will adversely impact interactions with older people, including the provision of services and advice.

1.7 Thesis structure and approach Following this Introduction (Part 1), Part 2 of the thesis forms the major component of the project. It is a systematic review of the medical literature to assess the association between ageism, clinical decision-making by healthcare professionals and researchers, and consequences for older patients. The focus is on actual actions by healthcare professionals and outcomes for older patients. Therefore, the review does

15 not include studies of attitudes, potential actions or hypothetical scenarios, or self- directed or self-reported ageism.

Parts 3 and 4 of the thesis are smaller sections examining ageism among legal and financial professionals. Part 3 is a systematised review of the literature to assess the association between ageism among legal professionals and the provision of legal services to older people. Similarly, Part 4 is a systematised review of the literature to assess the association between ageism among financial professionals and the provision of financial services to older people.

Systematised review was the chosen method for the investigations in law and finance. This type of review is similar to a systematic review in that it uses elements of systematicity: i.e. the a priori specification of the research hypotheses, comprehensive searches of multiple databases using defined search terms, as well as determination and application of explicitly defined inclusion and exclusion criteria (Grant & Booth, 2009). Although less comprehensive than a systematic review, it maintains a degree of consistency in methodological approach across the three research areas.

Part 5 presents the overall conclusion of the project, examining the impacts of ageism among the three frontline professional groups, as well as the similarities and differences in findings and future directions for research.

16 References Australian Bureau of Statistics 2014. Australian historical population statistics, 2014. ABS cat. no. 3105.0.65.001. Canberra: ABS.

Australian Bureau of Statistics 2017. Australian Demographic Statistics, Jun 2016. ABS cat. no. 3101.0. Canberra: ABS.

Ayalon L. Perceived age, gender, and racial/ethnic discrimination in Europe: results from the European social survey. Educ Gerontol 2014; 40: 499–17.

Ayalon L, Tesch-Römer C. Taking a closer look at ageism: self- and other-directed ageist attitudes and discrimination. Eur J Ageing 2017; 14: 1–4.

Ayalon L, Tesch- Römer C. On the Manifestations and Consequences of Ageism. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham. [2018a]

Ayalon L, Tesch- Römer C. Ageism: Concepts and origins. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland. [2018b]

Ayalon L, Chasteen A, Diehl M, et al. Aging in Times of the COVID-19 Pandemic: Avoiding Ageism and Fostering Intergenerational Solidarity. J Gerontol B Psychol Sci Soc Sci 2020 Apr 16: doi.org/10.1093/geronb/gbaa051.

Baethge C, Goldbeck-Wood S, Mertens S. SANRA—a scale for the quality assessment of narrative review articles. Research Int Peer Rev 2019; 4: doi.org/10.1186/s41073-019-0064-8.

Biggs S, Carr A, Haapala I. Dementia as a source of social disadvantage and exclusion. Austral J Age Ageing 2019; 38: 26–33.

Cherubini A, Oristrell J, Pla X, et al. The persistent exclusion of older patients from ongoing clinical trials regarding heart failure. Arch Intern Med 2011; 171: 550–56.

Cuddy AJC, Norton MI, Fiske ST. This old stereotype: The pervasiveness and persistence of the elderly stereotype. J Soc Issues 2005; 61: 267–85.

De São José J, Amado C. On studying ageism in long- term care: A systematic review of the literature. Int Psychogeriatr 2017; 29: 373–87.

Dobbs D, Eckert JK, Rubinstein B. An Ethnographic Study of Stigma and Ageism in Residential Care or Assisted Living. Gerontologist 2008; 48: 517–26.

Donizzetti AR. Ageism in an aging society: the role of knowledge, anxiety about aging, and stereotypes in young people and adults. Int J Environ Res Public Health 2019; 16: 1329.

Doron I. 25 Years of Elder Law: An Integrative and Historical Account of the Field of Law and Aging. Theoretical Inquiries in Law 2019; 21: 1–24.

Garrison-Diehn C, Au YH, Scherer K. Ageism in Behavioral Healthcare. In: Benuto L, Duckworth M, Masuda A, O'Donohue W (eds); Prejudice, Stigma, Privilege, and Oppression. 2020: 410–12. Springer, Cham, Switzerland.

17 Giles H, Reid SA. Ageism across the lifespan: Towards a self-categorization model of ageing. J Social Issues 2005; 61: 389–404.

Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Lib J 2009; 26: 91–108.

Hagestad GO, Uhlenberg P. The Social Separation of Old and Young: A Root of Ageism Journal of Social Issues 2005; 61: 343–60.

Horton S, Baker J, Deakin JM. Stereotypes of aging: their effects on the health of seniors in North American Society. Educational Gerontology 2007; 33: 1021–35. Iversen TN, Larsen L, Solem PE. A conceptual analysis of ageism. Nordic Psychology 2009; 61: 4–22.

Kim IH, Noh S. Chun H. Mediating and moderating effects in ageism and depression among the Korean elderly: the roles of emotional reactions and coping responses. Osong Publ Health Res Perspect 2015; 7: 3–11.

Levy BR, Banaji MR. Implicit ageism. In: TD Nelson (ed). Ageism stereotyping and prejudice against older persons. 2002: 49–75. Cambridge, MA: MIT Press.

Macnicol, J. Age Discrimination: A Historical and Contemporary Analysis. 2006; New York: Cambridge University Press.

Murthy VH, Krumholz HM, Gross CP. Participation in cancer clinical trials: Race-, sex-, and age-based disparities. JAMA. 2004; 291: 2720–26.

Nelson TD. Ageism: Prejudice Against Our Feared Future Self. J Social Issues 2005; 61: 207–21.

Ng R, Allore HG, Trentalange M, et al. Increasing Negativity of Age Stereotypes across 200 Years: Evidence from a Database of 400 Million Words. PLoS ONE 2015; 10: e0117086.

Officer A, Schneiders ML, Wu D, et al. Valuing older people: time for a global campaign to combat ageism. Bull World Health Organ 2016; 94: 710.

Ory M, Hoffman MK, Hawkins M, et al. Challenging aging stereotypes: strategies for creating a more active society. Am J Prev Med 2003; 25 (Suppl 2): 164–71.

Palmore EB. Research note: ageism in Canada and the United States. J Cross Gerontol 2004; 19: 41–46.

Phelan A. In: Advances in Elder Abuse Research: Practice, Legislation and Policy. Phelan A (ed). 2020: 1–11. Springer, Cham, Switzerland.

Reynolds L. The COVID-19 Pandemic Exposes Limited Understanding of Ageism. J Aging & Social Policy 2020; 32: 499–505.

The Benevolent Society. The drivers of ageism Foundational research to inform a national advocacy campaign tackling ageism and its impacts in Australia. Summary report: 2017.

United Nations, Department of Economic and Social Affairs, Population Division. World Population Prospects 2019: Highlights (ST/ESA/SER.A/423).

18 World Health Organization. Ageing and life-course. Geneva: World Health Organization; 2018. Available at: http://www.who.int/ageing/ageism/en/; accessed 15 June, 2020.

World Health Organization. World report on ageing. Geneva: World Health Organization; 2015. ISBN 978 92 4 156504 2.

Wyman MF, Shiovitz-Ezra S Bengel J. Ageism in the Health Care System: Providers, Patients, and Systems. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland.

19 PART 2 THE IMPACT OF AGEISM AMONG RESEARCH AND HEALTHCARE PROFESSIONALS ON OLDER PATIENTS: A SYSTEMATIC REVIEW

CHAPTER 1: INTRODUCTION

1.1 Background Ageism is a key determinant of health (Chang et al, 2020). It permeates the attitudes and behaviours of clinical researchers and healthcare professionals across many health disciplines and has been associated with a range of adverse outcomes for older patients at both the individual and structural levels (Ben-Harush et al, 2017; Chang et al, 2020; Cherubini et al, 2011; Ouchida & Lachs, 2015).

An Australian Human Rights Commission study found that almost half of all Australians feel that age discrimination is present within the healthcare system (AHRC, 2013). A US study that assessed healthcare discrimination – defined as receiving poorer service or treatment than other people by doctors or hospitals – found that one in five people aged 50 years or older experienced discrimination in healthcare settings (Rogers et al, 2015).

When it comes to developing ageing and healthcare policies, ageist assumptions can limit the way that problems are conceptualised and prioritised (Officer & de la Fuente- Nunez, 2018). As a result, increasing age is seen as a reason to prioritise the needs of younger people and to minimise the role of older people in policy discussions. There is a double hit for older people because ageism itself is also under-recognised and under-acknowledged at the policy level (WHO, 2015).

Ageist attitudes and beliefs about ageing and older people can manifest as a lack of interest in working with older populations, where health professionals may consider working with older patients as less prestigious or less attractive than working with younger patients (Grant, 1996; Rosowsky, 2005). Stereotypical assumptions about older patients can limit the medical information and healthcare choices that older patients are presented with (Buttigieg et al, 2018; Grant, 1996). Attitudes and beliefs about older people can lead to arbitrary age barriers in clinical trials, barring people over a certain age from participation, while additional exclusion criteria 20 disproportionately restrict the enrolment of older people in research (Cherubini et al, 2011; Murthy et al, 2004).

Ageist communication includes the use of ‘elderspeak’ by healthcare professionals, which infantilises older patients (Gendron et al, 2015). For example, providers may automatically speak slowly and loudly to older patients, using exaggerated intonation and simpler vocabulary and grammar (Ouchida & Lachs, 2015). At other times, providers may speak directly to a caregiver about the older patient instead of talking with the patient, or they may stereotypically assume that an older patient is cognitively impaired and talk about them with other staff as though the patient wasn’t there.

The issue of ageism and its causal role in the state of Australian aged care – described as a ‘shocking tale of neglect’ – has come to the fore in the light of the Royal Commission into Aged Care (Royal Commission, 2019). The report states that, “As a nation, Australia has drifted into an ageist mindset that undervalues older people and limits their possibilities.” (Royal Commission, 2019; p1). Such ageist attitudes and behaviours are often a manifestation of the prevailing societal view of ageism, subjective perceptions regarding the value of an older person’s life, and a stereotypical assumption that quality of life deteriorates with age.

The World Health Organisation (WHO) is developing a global campaign to combat ageism, which aims to provide a platform to change attitudes, assumptions and behaviours towards ageing and older people (WHO, 2017).

1.2 Rationale It is important to examine ageism within the healthcare setting, an environment of great salience to the older person. However, while ageist attitudes towards older people have been extensively investigated in health care, the impact of discriminatory actions by clinical researchers and healthcare professionals towards older patients has received less attention (Ben-Harush et al, 2017; Gewirtz-Meydan & Ayalon, 2017). At the same time, ageist attitudes and beliefs are not necessarily synonymous with actual behaviours (Gewirtz-Meydan & Ayalon, 2017). It is important to not only examine healthcare professionals’ ageist attitudes and beliefs, but to determine how ageism affects the real-world clinical care of older patients.

21 A recent systematic review by Chang et al (2020) established that individual (internal age stereotypes) and structural ageism in health care is associated with wide-reaching global adverse outcomes. What is now needed is greater drilling down into the way in which structural ageism impacts the health care of older people and the processes that mediate denial of access to health care and research. Notably, despite the acknowledged increase in prevalence of ageism-health associations over time, studies of structural-level ageism have decreased over the last decade (Chang et al, 2020). Moreover, the global extent of ageism has been exposed during the COVID-19 pandemic, rendering it urgent and imperative to address (Ayalon et al, 2020). If we are to address ageism in health care, it is these very structural processes that need to be understood.

The systematic review focuses on an important aspect of structural ageism: the impact of age-based actions by researchers and professionals on the management of older patients, which has not been a sole focus of previous studies.

1.3 Aims This thesis attempts to redress the gaps in knowledge by conducting a systematic review of the literature to assess the evidence examining ageism among healthcare professionals and the subsequent outcomes for older patients. This systematic review is the major component of a multidisciplinary investigation into ageism among frontline workers who interact closely with older people: healthcare, legal and financial professionals.

1.4 Hypotheses (1) That ageism among research and healthcare professionals will adversely impact the care and management of older people (2) That the adverse effects of ageism will manifest in a range of clinical, research and policy settings.

22 CHAPTER 2: LITERATURE REVIEW

1.1 Definitions of ageism There is no one agreed definition of ageism. The term was first proposed by Robert Butler in 1969, when he described it as, “age discrimination or age-ism, prejudice by one age group toward other age groups” (Butler 1969; p243). Over subsequent years, Butler expanded his original definition to describe ageism as prejudicial attitudes and assumptions about older people, discriminatory practices, or structural elements that perpetuate stereotypes about older people (Butler, 1980). Butler later added that he was just as concerned with older people’s negativism towards younger people (Butler, 1989). Other researchers support definitions that do not single out older people, but rather describe ageism as a concept that attributes certain qualities and abilities to people based on their age (Angus & Reeve, 2006; Laws, 1993). The World Health Organization (WHO) defines ageism as, “stereotyping, prejudice and discrimination towards people on the basis of age” (WHO, 2018).

Overall, definitions of ageism are typically underpinned by three classic components: prejudice (the affective component), stereotyping (the cognitive component) and discrimination (the behavioural component) (Cuddy & Fiske, 2002). McMullin and Marshall suggested that this represents, “two interconnected dimensions of ageism: an ageist ideology, which includes negative stereotypes, beliefs, and attitudes, and age discrimination, which is behavior that excludes certain people and places them in a disadvantaged situation relative to others on the basis of their chronological age” (McMullin & Marshall, 2001).

Several researchers have included additional key dimensions and components to describe ageism, although there remains no general consensus on the definition or the ways in which ageism is measured in research (de Sao Jose et al, 2019).

Iversen et al (2009) outlined a comprehensive conceptualisation of ageism that includes four dimensions. The first dimension combines the three classic components, the second dimension is the positive/negative aspect, the third is the conscious/unconscious aspect, and the fourth is the multiple levels over which ageism can be expressed.

23 Iversen et al (2009) define ageism as: “Negative or positive stereotypes, prejudice and/or discrimination against (or to the advantage of) elderly people on the basis of their chronological age or on the basis of a perception of them as being “old” or “elderly”. Ageism can be implicit or explicit and can be expressed on a micro-, meso- or macro-level” (Iversen et al, 2009; p15).

However, Snellman (2016) suggested that the use of the words ‘elderly’, ‘their’ and ‘them’ in the above definition highlights ageism as an ‘us versus them’ problem, and argued that it doesn’t account for ageism towards younger people. De São José & Amado (2017) agreed, and also proposed that an additional fifth dimension should be added: that of self-directed or other-directed ageism, adapting Iversen’s definition as follows: “Negative or positive stereotypes, prejudice and/or discrimination against (or to the advantage of) us on the basis of our chronological age or on the basis of a perception of us as being ‘old’, ‘too old’, ‘young’ or ‘too young’. Ageism can be self-directed or other-directed, implicit or explicit and can be expressed on a micro, meso or macro-level” (de São José & Amado, 2017; p375).

Many theorists and researchers have highlighted the particular importance of implicit ageism, which is insidious and may underpin many ageist practices (Levy & Banaji, 2002). Levy argues that anyone who has internalised their culture’s age stereotypes is likely to engage in implicit ageism, making it widespread but also hidden.

1.2 Labels and definitions of ‘older’ The use of labels to describe older people is often underpinned by a normalised and accepted devaluing of older adults and negative attitudes about ageing (Gendron et al, 2018). For example, ‘old’ is often used in a derogatory way while ‘young’ is positive. Young is even applied to older people to make them look or sound ‘better’, for example, in describing someone as “young at heart” or “looking young for your age”. Age labels overshadow the individual, foster stereotypes and encourage us to regard someone as ‘other’ (Richardson et al, 2011).

24 ‘Old age’ is a relative term and does not serve as a descriptor of needs (Reed et al, 2006). It is also conceptualised differently depending on a person’s vantage point – research shows that people have different notions of what constitutes ‘older’ depending on where they sit on the age continuum and how they perceive their own ageing (Giles & Reid, 2005). For example, studies of self-perceived or subjective age have found that adults over the age of 40 often feel 20% younger than their chronological age (Rubin & Berntsen, 2006). People who are satisfied with their own ageing and feel younger than they are may experience more positive well-being in late life (Kotter-Grühn et al, 2009; Mock & Eibach, 2011).

Definitions of ‘older’, ‘old age’ and ‘elderly’ are not straightforward or universally determined. While a chronological age of 65 is often used to define ‘older’ at the population level, at the individual level a person does not necessarily become frail or dependent at this age or any age. In addition, whatever cut-off is used, the terms ‘older’ ‘old age’ and ‘elderly’ group together people who are up to 35 years apart in age.

There is no agreed definition of what constitutes ‘older’ or ‘elderly’ in health literature. Some studies define older as a chronological age of 65 years old or older, while others use this cut-off to define elderly. Some refer to those aged 65–74 years as ‘early elderly’ and those aged over 75 years as ‘late elderly’ (Orimo et al, 2006). An Australian study suggested that, while the term ‘elderly’ can have a variety of definitions, it is generally used to describe people aged 70–75 years or older (Jennens et al, 2006).

In addition to shifting definitions in clinical research, there may be no definition provided at all. A study examining how ‘elderly’ patients are defined and considered in Australian clinical guidelines on the use of pharmacotherapy found that 17 of 20 guidelines (85%) did not define ‘elderly’ in any way, while the remaining three (15%) used chronological age as a definition (Singh & Bajorek, 2014). Two of these defined those aged 65 or older as ‘elderly’, while one guideline used 75 years the cut-off for ‘elderly’.

25 Similar variability is seen across clinical management studies. Two studies examining breast cancer management in older women – published in 1987 and 2013, respectively – both defined ‘elderly’ as aged 70 years and older (Greenfield et al, 1897; Malik et al, 2013), while a trial examining the participation of older patients in cancer clinical trials defined those aged over 65 years as ‘elderly’ (Dunn et al, 2017). A study examining stroke management used an age cut-off of 75 to define ‘older’ (Bhalla et al, 2004), while a meta-analysis examining attitudes towards younger and older adults defined the ‘old-old’ as 75 and older and the ‘young-old’ as 55–64 years of age (Kite et al, 2005).

National and international research and health organisations tend to use 65 as the cut- off for ‘older’, including the Australian Institutes of Health and Welfare (2018) and the US National Institutes of Health (2018). The WHO does not define ‘older’ but outlines a conceptual model that is underpinned by intrinsic capacities (physical and mental) and functional abilities of older people. The WHO states that intrinsic capacity and functional ability do not remain constant but typically decline with age as a result of underlying diseases and the ageing process (WHO, 2017a).

Given the diversity of ageing, some health organisations are moving away from chronological age towards the concept of frailty as a way to identify people who may be at greater risk of morbidity, future hospitalisation, residential care admission or death (NHS, 2020). To this end, Britain’s National Health Service (NHS) is working to reframe frailty as a long-term condition to be prevented, identified and managed alongside other long-term conditions. However, the terms ‘frail’ and ‘frailty’ can also have negative consequences, and may similarly serve to label older people.

With increasing longevity and an ageing society, the concept of ‘old age’ has shifted. However, despite the diversity and evolution of ageing as a biopsychosocial construct, it continues to be stereotyped – predominantly negatively – which further reinforces ageism (Dionigi, 2015).

26 1.3 Theoretical framework of ageism Researchers have proposed several theories of how age stereotypes develop and impact the health of older people, although many clinical studies do not reference these theoretical frameworks or expressly define ageism. As such, it has been noted that much of the literature on ageism shows a disconnect between empirical research and theory (Buttigieg et al, 2018). A further disconnect between research, theory and translational impact might be added to this, suggesting a need for direct application of ageism research to real-world health settings.

As noted earlier, Butler outlined three separate but related constructs of ageism, namely: prejudice towards older people, including attitudes held by older people themselves; discriminatory practices against older people; and structural or institutionalised stereotypes about older people, resulting in policies and practices that cause harm (Butler, 1980).

A concept proposed by Levy (2009), known as stereotype embodiment theory, suggests that age stereotypes “are embodied when their assimilation from the surrounding culture leads to self-definitions that, in turn, influence functioning and health” (Levy, 2009; p333). According to this theory, observations of the way older people are treated, together with age beliefs expressed in a culture, are assimilated at a young age and reinforced over time, often unconsciously. This involves detrimental treatment of older people (age discrimination), negative age stereotypes (beliefs) about older people, and negative self-perceptions of ageing. Such beliefs become more important with personal resonance and can operate across multiple pathways (i.e. behavioural, psychological and physiological) (Levy, 2009). These stereotypes operate from the top down – from society to the individual – as well as over the lifespan.

Stereotype threat theory has been studied to understand how internalised (self- concept) ageist mechanisms operate alongside external environmental factors to affect health outcomes (Steele & Aronson, 1995). This theory proposes that presenting familiar negative stereotypes associated with a certain group will subsequently threaten members of this group into a situation where they must deal with the possibility of “being judged or treated stereotypically, or of doing something that would inadvertently confirm the stereotype” (Steele & Aronson, 1998; p403). Research suggests that this threat may be reduced when people have a sense of control over

27 the situation. In the case of older patients, this may include being involved in decision- making regarding their health (Scholl & Sabat, 2005), a direct challenge to the passivity often expected of them.

Terror management theory as applied to ageism draws on people’s evolutionary fear of death, where older people remind others, usually younger than themselves, that life is transient, the body is fallible and death is inevitable (Martens et al, 2005). Younger people deal with these fears by dissociation – creating a distance between themselves and older people – and expressing negative attitudes and behaviours toward older people because they fear ageing and death.

Social identity theory proposes that people act as members of the reference groups they identify with, not just on the basis of their personal characteristics or interpersonal relationships. People delineate their group from others through biases and distinctions (in-groups and out-groups). Age can be one criterion for group membership (Tajfel & Turner, 1979). During the COVID-19 pandemic, age-based intergenerational divisions were stark. This type of age distancing or ‘othering’ may subsequently affect younger people’s self-stereotypes about ageing and their very own ageing processes as they internalise negative attitudes and beliefs about older people (Ayalon et al, 2020; Levy, 2009).

Social identity theory can also be demonstrated within groups, for example, when some older people dissociate themselves from other older people who are frailer than themselves: “I’m not one of them”. On a wider level, this dissociation may clarify why older people often accede to societal ageist perceptions. Studies of implicit ageism (unconscious bias) have shown that older people exhibit greater unconscious identification with younger people than older people, suggesting that it may override in-group preferences (Levy, 2005; Nosek et al, 2001).

The stereotype content model proposes that cultural age stereotypes about older people revolve around them being king/warm/friendly rather than competent/intelligent/efficient (Fiske et al, 2002). This is a type of paternalistic or ambivalent prejudice, where older people are considered essentially harmless, but also require assistance from others to make decisions and take responsibility for them. In practice, this high-warmth, low-competence perception of older people

28 manifests itself throughout discriminatory communication and treatment of older people (Cuddy & Fiske, 2002).

Gendron and colleagues describe a theory of relational ageism in the context of ageist language (Gendron et al, 2018). The widespread use of ageist language results from normalising and accepting the devaluing of older adults. When ageist statements are made, they may be reinforced by encouragement from individuals or groups, for example, via laughter, applause or verbal agreement. Relational ageism is a dynamic process that describes both the participatory behaviour (the ageist statement) and interpersonal responses to ageism, demonstrating a “pathway in which ageism is expressed and perpetuated through positive reinforcement from others” (Gendron et al, 2018; p247).

The succession, identity and consumption model was developed by North and Fiske (2012, 2013) and proposes that ageism may be underpinned by intergenerational tensions over resources and prescriptive expectations of older adults' roles and behaviours in society. According to this model, ageism arises in younger people because older people are perceived to be in the way or fail to step aside when expected (succession), they consume more than their fair share of limited resources in society (consumption) and they fail to act their age (identity). These issues may be amplified during healthcare or financial crises when decisions about resource allocations can be critical.

Several researchers have suggested that ageing and age stereotypes is best viewed and researched via a lifespan approach (Levy & Macdonald, 2016; Ram´ırez & Palacios-Espinosa, 2016). A lifespan approach to ageing proposes that ageing is a lifelong issue influencing people along the age continuum. Views on ageing develop as the result of dynamic, ongoing, complex and interacting factors including biological- evolutionary, psychological and societal-contextual factors. These factors are multidirectional, and the relative impact of each changes across a person’s lifespan (Kornadt et al, 2019; Levy & Macdonald, 2016; WHO, 2015). This theory supports the concept that even though the age stereotypes held by different people may be similar, there are individual differences that shape these (Hummert, 2003). In addition, while age stereotypes can predict perceived discrimination, experiencing discrimination may also change views on ageing (Voss et al, 2017).

29 1.4 Ageism in health care: Research approaches and settings A range of concepts and terms have been used to describe ageism in healthcare research, often depending on the context in which it is studied (Buttigieg et al, 2018). Studies examining the consequences of ageism have described age-based bias, age disparities or differences, age-based exclusions, age discrimination, age-based inequalities or inequities, under-treatment, under-representation, unmet needs, lack of treatment access and suboptimal management.

Research settings include public and private hospitals and medical centres, acute and chronic care facilities, general practice and specialty clinics, area health networks and trusts, single and multi-centre facilities, as well as data generated from clinical databases and registries, clinical audits and case notes.

Studies looking at ageism in health care have been carried out across six continents (Chang et al, 2020), with the majority of studies conducted in Europe (including the UK) and North America. In terms of research design and methodology, most studies are either qualitative or quantitative; there are very few mixed-methods trials.

Many studies are framed around the importance of exploring differences in the provision of health care between age groups. Some examine exclusions or unmet needs in older age groups only, while others combine exploration of differences in management of older people with population-based prevalence data to highlight exclusions and differences in care.

Several quantitative studies examining the attitudes and beliefs of healthcare professionals towards older people use ageism assessment scales. Ones used commonly in healthcare research include the Fraboni Scale of Ageism (Fraboni et al, 1990; Rupp et al, 2005), the Facts on Aging Quiz (Palmore, 1977), the Attitudes Towards Older People Scale (Kogan, 1961), the Aging Semantic Differential Scale (Rosencranz & McNevin, 1969), the Reactions on Aging Questionnaire (Gething, 1994), and the Image of Aging Scale (Levy et al, 2004). While there are multiple validated scales available, a systematic review of eleven ageism scales found that only one met the minimum requirements for psychometric validation (the Expectations Regarding Aging Scale), but it did not cover all dimensions of ageism (Ayalon et al, 2019). The review found that many scales were supported by low-quality evidence. In addition,

30 these ageism scales were designed to measure ageism towards older persons in general, and do not specifically assess ageism by healthcare professionals towards older patients.

Many quantitative studies have used vignettes (hypothetical case scenarios) to examine healthcare professionals’ intentions regarding treatment of older patients. These types of experimental studies document proposed management rather than real-life clinical care – they do not show actual actions and behaviours –thus they are limited in what they reveal regarding outcomes for older patients.

Qualitative studies have also examined the attitudes and beliefs about ageism among healthcare professionals. These studies can generate richer information regarding perceptions and reasons behind actions, and may reveal the complexities of management that quantitative research does not.

1.5 Impact of ageism in clinical research and health care Studies of ageism in health care have proceeded since ageism was first defined by Butler in 1969, although to a lesser extent than research into racial and gender-based prejudices (Nelson, 2016).

Ageist attitudes and behaviours have been associated with a range of adverse outcomes for older people at both the individual and societal/structural levels (Chang et al, 2020). For example, health professionals’ knowledge and attitudes about ageing has been shown to affect how accurately and sensitively they distinguish normal changes associated with ageing from acute illness and chronic disease (Ouchida & Lachs, 2015). Stereotypical assumptions about the functional and cognitive abilities of older patients can lead to the withholding of key medical information and treatments (Buttigieg et al, 2018; Chang et al, 2020). Such assumptions can also limit the healthcare choices that older patients are presented with and therefore the decisions they make about their own care (Grant, 1996).

Ageism among research and healthcare professionals can lead to decisions that limit access to care based on chronological age rather than health needs (Gewirtz-Meydan & Ayalon, 2017; Helmes and Gee, 2003; Schroyen et al, 2018; Uncapher and Arean, 2000). This includes limiting or excluding the participation of older people in clinical

31 trials, a common example of ageism that has been explored across many health disciplines. (Bellera et al, 2013; Dunn et al, 2017; Hamaker 2014). Studies examining the age range of people in clinical trials compared to those in the general population with the same condition have found large age gaps between trial participants and those in the community, even in trials of diseases that predominantly affect older people (Murthy et al, 2004).

Research has found that self-directed ageism, in which older adults have negative attitudes about ageing, can shorten their lifespan by 7.5 years compared with those who have positive attitudes towards ageing (Levy et al, 2002). This applies across the lifespan. For example, a study found that young adults aged 18 to 39 years with negative age stereotypes were twice as likely to have a cardiovascular event after the age of 60 compared with those in the same age group who had positive attitudes towards ageing (Levy et al, 2009). The English Longitudinal Study of Ageing found that perceived age discrimination is common and is associated with increased odds of poor self-rated health and risk of incident serious health problems (Jackson et al, 2019). The Baltimore Longitudinal Study of Aging found that, over a 38-year period, individuals with more negative age stereotypes showed significantly worse memory performance compared to those with less negative age stereotypes (Levy et al, 2012). The adverse effects on memory were significantly greater when the age stereotypes were relevant to participants.

Implicit or covert ageism is an important feature of ageism that has received less attention in the literature compared to more explicit forms of ageism (de São José & Amado, 2017). According to Levy (2001), implicit ageist stereotypes and attitudes towards older people may exist and operate without conscious awareness, intention or control. People who internalise the age stereotypes of their culture are likely to engage in implicit ageism.

Many qualitative studies have found that ageist practices are not straightforward and are multifactorial (Ben-Harush et al, 2017; Billings, 2006). For example, a study found that while there was a perception of denying referrals and access to specialist care for older patients, access could be restricted by the institution or specialty unit, sometimes based on an age cut-off and sometimes due to preferential treatment for younger patients (Billings, 2006). Some healthcare professionals found it difficult to

32 separate age discrimination from decisions based on frailty, and recognised the challenges of making clinical decisions for older patients with multiple comorbidities and treatment regimens (Billings, 2006). A study investigating ageism amongst physicians, nurses and social workers found that the provision of treatment to older patients was associated with challenges such as dealing with older patients’ families, working with older patients who wanted to be involved in decision-making and dealing with older patients who were considered demanding and unpleasant at times (Ben- Harush et al, 2017). Healthcare professionals admitted that they adopted some ageist communication patterns of elderspeak and provided little information simply for convenience or to save time. Ethical dilemmas have also been highlighted by qualitative research into ageism, particularly consideration of invasive treatments and procedures versus quality of life for older patients (Ben-Harush et al, 2017).

The economic cost of ageism is another area that has received little attention in research. The first study to examine the economic cost of ageism on health was conducted in 2017 (Levy et al, 2020). The study used health economic modelling techniques to analyse data derived from national surveys, demographic data and ageism research. The models calculated the cost of ageism on eight health conditions for all people aged 60 years or older in the United States during a one-year period. The study found that the one-year cost of ageism was US$63 billion, equal to one in every seven dollars spent on the eight health conditions. Researchers also found that ageism was responsible for over 17 million cases of the eight most expensive health conditions during the same time period.

A significant and major contribution to the literature has been a recent systematic review examining the global impact of ageism on broader health outcomes across 11 health domains in 45 countries (Chang et al, 2020). Findings included widespread ageism-health effects in all countries, with a greater prevalence of significant ageism- health effects amongst less educated people, and in less-developed countries, probably related to resource limitations.

33 1.6 Ageing, ageism and human rights Ageism is a human rights issue. The Report of the United Nations’ High Commissioner for Human Rights to the Economic and Social Council (UNHCHR, 2012) identified several substantial gaps in international protections of older people’s human rights, including ageism. At the United Nations’ 26th International Day of Older Persons (2016), the Commissioner stated that, “Ageism should not be downplayed: it is an infringement of older person’s human rights” (United Nations Human Rights Office of the High Commissioner, 2016).

Ageism infringes on older people’s human rights in many ways, for example, by failing to provide equitable access to quality health care, giving inadequate attention to older people’s needs and aspirations, and failing to provide sufficient autonomy and control (Peisah et al, 2012; United Nations Human Rights Office of the High Commissioner, 2016).

Many countries and international bodies have worked to protect older people’s rights through legislation and regulations. Australia’s Age Discrimination Act (2004) makes it unlawful to discriminate against any person on the basis of age, including in the provision of goods and services, employment, education and accommodation. The Act also highlights the need to tackle the negative stereotypes that lead to age discrimination. In 2018, Australia’s Aged Care Quality and Safety Commission Act established a regulatory framework that aims to, “protect and enhance the safety, health, well-being and quality of life of aged care consumers; and promote aged care consumers’ confidence and trust in the provision of aged care services and Commonwealth-funded aged care services” (ACQSC 2018; p2). The US National Institutes of Health (NIH) charter states that older adults must be included in NIH- conducted or supported human subjects research unless there are scientific or ethical reasons not to include them (NIH, 2018).

The United Nations General Assembly established the Open-Ended Working Group on Ageing (OEWG) in 2010, in an effort to establish concrete ways to strengthen the protection of people’s human rights as they age. In 2016 the ‘Global strategy and action plan on ageing and health’ was adopted by the World Health Assembly, with a key aim to develop health systems that support the needs of older people (WHO, 2017).

34 Despite these measures, a human rights perspective has often been absent from the development of global ageing policies, and a comprehensive, international human rights framework for older people is still missing (de Pauw et al, 2018). During emergency situations such as the COVID-19 pandemic, older adults face significant risks in terms of compromised autonomy and violation of their human rights (UN Human Rights Office of the High Commissioner, 2020). Australia’s Age Discrimination Commissioner stated that, “Within a human rights context, it is important to acknowledge that COVID-19 is a test of governments, communities and individuals” (Patterson, 2020). Much more needs to be done to address ageism in this context.

1.7 Conclusion Since the concept of ageism was first conceptualised in 1969, there has been a plethora of research exploring its origins and impacts in healthcare settings. However, the clinical evidence has not been systematically reviewed with a defined focus examining the impact of ageism amongst healthcare professionals and researchers on clinical decision making and outcomes for older patients. These aspects of ageism need to be explored further.

35 CHAPTER 3: METHODS

3.1 Search process A systematic review of the literature was conducted to comprehensively identify, evaluate and synthesise research that focused on age-based actions by healthcare professionals and researchers and assess the impact and consequences on older patients. Database searches, data extraction and synthesis, and quality assessments were undertaken by a single reviewer (the candidate).

The systematic review process adhered to a strict study design, which was based on explicit, pre-specified and reproducible methods (CRD, 2009). The review has been written in accordance with the recommended protocol set forth by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. PRISMA is an evidence-based minimum set of items for reporting in systematic reviews and meta-analyses, promoting transparent and complete reporting of research (Moher et al, 2009). Since publication in 2009, PRISMA guidance has been cited over 54,000 times and endorsed by more than 400 peer-reviewed journals (Page & Moher, 2017). The current guideline comprises a 27-item checklist covering title, abstract, introduction, methods, results, discussion and funding sources.

3.2 Data sources and search strategy All searches were conducted over January and February 2019. Multiple electronic databases and other data sources were searched to capture the optimal number of relevant studies (Table 3.1). Several journals were hand-searched as they were likely to contain potentially relevant international research on ageism in health care. Reference lists of relevant studies as well as references for related literature reviews and meta-analyses were hand searched to identify any further sources of data. The reference lists of all clinical studies included in this systematic review were also hand searched. Full texts of potentially relevant articles were assessed against inclusion/exclusion criteria.

Decisions regarding the ideal selection of databases to search were based on the findings of a study examining the optimal combination of databases needed to conduct efficient searches in biomedical systematic reviews (Bramer et al, 2017). The study found that the combination of Medline, Embase, Web of Science and Google

36 Scholar (the first 200 references) is the minimum recommended combination to achieve maximum recall of references. The researchers also found that additional specialised databases provide further unique results when they are aligned to systematic review topics. Therefore, in addition to the four databases listed above, additional databases relevant to the review topic were included.

A detailed account of searches and data collection procedures was maintained to keep track of all searches. Citations and abstracts of articles identified via database searches were downloaded into referencing software (Bookends Inc), which was used to collate all references and identify and discard duplicates. After abstract screening full-text articles were located primarily through the University of NSW library’s online database collection and were saved in an electronic folder. A duplicate copy of all research papers was saved on a USB which was stored in a secure location.

Table 3.1. Search sources.

Database searches Other data sources

Medical databases Hand searches

PubMed Journals Embase European Journal on Ageing Web of Science core collection The Gerontologist Medline via EBSCO Age and Ageing PSYCHInfo Journal of Ageing Studies CINAHL International Psychogeriatrics Scopus Ageing and Society Ageline Reference lists ProQuest Central Key papers and reviews

All reference lists of eligible studies

Trial registers Grey literature

Cochrane CENTRAL (via the Cochrane Google Scholar Library) Open Grey PROSPERO Grey Literature Report Clinicaltrials.gov Grey Matters World Health Organisation International Clinical Trials Registry Platform (WHO ICTRP)

37 3.2.1 Search terms A combination of four fields of MeSH terms, key words and/or text strings were developed and tailored as appropriate to each database searched (Table 3.2).

Table 3.2. Database search strings.*

String 1 String 2 String 3 String 4

Ageism, Impact Health/healthcare Old, older, older Ageist, outcome, professionals, adults, older age consequences, healthcare personnel, people, discrimination, approach, health/healthcare older patients, age bias, perspective, practitioners, allied elders, elderly, age stereotype, perceptions, health personnel, elderly adults, age prejudice, reaction, stance, medical personnel, elderly people, age disparities, response, medical staff, medical elderly patients, age inequalities result, professionals, elderly care, treatment, healthcare providers, geriatric patients, assessment, healthcare workers, seniors, aged, management, clinicians, physicians, aged care, behaviour, care nurses, doctors, aged 80 and psychologists, older, psychiatrists, hospital ageing staff, clinical trial, researchers

*Adapted as required for each database searched.

38 3.2.2 Database search string example An example of a PubMed search is as follows: ((((((((ageism[MH]) OR "ageism") OR "age discrimination") OR "age bias") OR "attitudes towards ageing") OR "discrimination, age") OR "prejudice against older people") OR "ageist") AND (((((((((((((((((((("Attitude of health personnel"[MH]) OR "unprofessional behavior") OR "behavior") OR "demeanor") OR "approach") OR "perspective") OR "reaction") OR "stance") OR "response") OR "impact") OR "outcome") OR "consequence") OR "consequences" OR "treatment" OR "assessment" OR "Management" OR "result" OR "Response" OR "care")) AND (((((((((((((((((((((("health personnel"[MH]) OR "health personnel") OR “healthcare personnel”) OR "allied healthcare personnel") OR "health professional") OR "healthcare professional") OR "health practitioner") OR "medical personnel") OR "medical staff") OR "healthcare provider") OR "healthcare worker") OR "clinician") OR "physician") OR "nurse") OR "doctor") OR "psychologist") OR "psychiatrist") OR "hospital staff") OR “clinical trial”) OR “researcher”) AND (“old” OR "older adults" OR "older people" OR "elderly" OR “elders”) OR "old people" OR “older patients” OR “seniors” OR “aged” OR “aged 80 and older”))) AND Humans[Mesh] AND English[lang]

3.2.3 Automatic search updates In order to obtain the most up-to-date results, automatic monthly searches were run via PubMed and Google Scholar between February 2019 and February 2020. Results were reviewed monthly and full texts of potentially relevant articles were assessed against inclusion/exclusion criteria.

3.3 Inclusion and exclusion criteria In order to minimise bias, inclusion and exclusion criteria were closely linked to the review question and were piloted on five studies to determine that they could be reliably applied. No date restrictions or geographical restrictions were set.

While many studies have examined beliefs and attitudes towards older people, the aim of this review was to focus on the clinical outcomes of ageism among healthcare professionals and researchers; therefore, medical students and other healthcare students were excluded from the analysis, as they do not typically drive decisions about care.

39 This review only included studies that examined the potential link between ageism and real-world clinical outcomes/clinical practice decisions. Studies based on hypothetical scenarios or case vignette experiments were excluded because they do not report on actual actions or outcomes.

Aggregate studies that examined the exclusion of older people from clinical trials (e.g. systematic reviews) were eligible because they generated primary data on age disparities and exclusions. The individual trials included in these studies did not assess age exclusions, so were not eligible for inclusion.

3.3.1 Inclusion criteria • Studies that essentially or exclusively focus on ageism/age bias/age disparities/age-related inequalities • Ageism on the part of medical/healthcare professionals or researchers • Studies that report the impact (outcomes, consequences) of ageism by healthcare professionals on older patients • Original quantitative research • Articles published in peer-reviewed journals • Articles published in English.

3.3.2 Exclusion criteria • Studies not focused on ageism/age bias/age disparities/age-related inequalities • Studies focused on ageism on the part of non-medical professionals, healthcare/medical students, general community members (including reports by patients about perceived ageism/self-perceptions about ageism) • Investigation of ageism towards younger people • Studies that do not report any actual outcomes • Qualitative studies • Non-original research e.g. reviews, commentary, letters • Studies not published in a peer-reviewed journal • Articles not in English.

3.4 Data extraction Relevant data were extracted from included studies using a data extraction form. This was pilot tested on five initial articles, after which it was refined. The final version of

40 the form includes the following items: author, date and title; study population and setting; study aims; focus terminology; study design; main outcomes; findings; and limitations/other comments. The final populated table is shown in Appendix 1.

3.5 Data analysis and synthesis Significant diversity in the included studies was anticipated, specifically in terms of healthcare specialty areas, healthcare professionals involved, definitions of ageism/age discrimination/age bias, healthcare settings, types of studies and outcome measures. Therefore, it was decided a priori that a narrative synthesis would be the most appropriate approach to analyse results.

The methods of data synthesis and analysis followed the Economic and Social Research Council (ESRC) Methods Programme guidance on the conduct of narrative synthesis within systematic reviews, as outlined in the Centre for Reviews Dissemination (CRD) guidance for undertaking reviews in health care (CRD, 2009). This involved developing a preliminary synthesis of findings, analysing the relationships within and between studies, and critically assessing the quality (robustness) of the findings.

To examine the first hypothesis – that ageism among research and healthcare professionals will adversely impact the care and management of older people – the review looked at whether there was evidence of age-related clinical decisions, actions and consequences across a variety of outcome domains. These included the provision of differential health care between age groups, the use of age cut-offs to guide participation and care, occurrences of unmet needs in older patients, provision of evidence-based care and differences in management of older patients versus population-based prevalence data.

To examine the second hypothesis – that adverse effects of ageism will manifest in a range of clinical, research and policy settings – the review looked at whether there was evidence of age-related clinical decisions, actions and outcomes in international medical research and clinical environments, hospitals, area health districts, specialist clinicals and general practice settings, as well as in institutional practices and procedures.

41 3.6 Assessment of quality and risk of bias Quality and risk of bias assessments of included observational studies (n=61) was conducted using modified Scottish Intercollegiate Guidelines Network (SIGN) methodology checklists for cohort and case control studies (SIGN) (Appendix 2). The checklist includes nine key domains to assess quality and risk of bias in observational studies: selection, exposure, outcome assessment, confounding, loss to follow-up, analysis, selective reporting, conflicts of interest and other comments (Wang et al, 2019). Several included studies (n=12) were systematic literature reviews. An existing validated tool (ROBIS) for assessing risk of bias in systematic reviews was used to asses bias in these studies (Whiting et al, 2016). SIGN ratings of overall methodological quality were used to grade each study as high quality, acceptable or low quality (Table 3.3).

Table 3.3. SIGN explanations for assessment of risk of bias.

Quality/risk of bias Explanation

High quality Majority of criteria met. Little or no risk of bias. Results unlikely to be changed by further research. Acceptable Most criteria met. Some flaws in the study with an associated risk of bias. Conclusions may change in the light of further studies. Low quality Either most criteria not met, or significant flaws relating to key aspects of study design. Conclusions likely to change in the light of further studies.

3.7 Ethics approval As this study was based on analysis of existing literature, ethics approval was not required.

42 CHAPTER 4: RESULTS

4.1 Search results Articles identified through systematic database searches were selected as per the Preferred Reporting Items for Systematic Reviews and Meta-analysis (PRISMA). A total of 2,456 publications were identified through database searches, and an additional 52 references were identified via hand-searching of key journals and reference lists. After removing duplicates, clearly irrelevant articles and those not in English, 1,569 titles and abstracts were screened. A further 1,428 references were removed, primarily because they were not focused on the topic of interest, did not include patient outcomes, were not focused on healthcare professionals or were not quantitative research. Full-text articles were obtained for the remaining 141 articles, which were screened for evaluation against the inclusion/exclusion criteria. A further 73 articles were assessed as ineligible for inclusion. During follow-up searches, an additional five eligible articles were identified. A total of 73 articles met the inclusion criteria (Figure 4.1).

43 Figure 4.1. PRISMA diagram illustrating procedure for selecting studies.

44 4.2 Study characteristics The methods, focus and terminology used to describe ageism or age differences differed widely among the 73 included studies.

4.2.1 Methods Thirty-one studies, including 11 systematic reviews, analysed clinical trial data to determine whether older patients were under-represented in clinical research across a variety of therapeutic areas. Established registries or databases were used in 23 studies to examine potential associations between patient age and clinical management. Seven studies performed clinical audits and six trials completed case note reviews to assess whether age was a factor in treatment decisions. Four prospective cohort studies examined age differences in the provision of clinical care and subsequent patient outcomes, while one quantitative survey explored involvement of older patients in decision making. One study performed a descriptive analysis to examine whether clinical guidelines included recommendations for older patients.

4.2.1.1 Statistical methods The majority of studies used multivariate logistic regression analyses to evaluate the influence of age on the use of treatments or management decisions, to examine associations between selected variables and the presence of age-based exclusions, as well as to evaluate changes over time in the frequency of trial age restriction eligibility criteria. A minority of studies included only unadjusted analyses, for example to calculate the proportion of patients receiving treatment across different age groups. Several studies used Kaplan-Meier survival curves or the Cox proportional hazards model to examine survival differences. One audit used Poisson probability to compare findings between two populations. The majority of studies used chi-square tests, t- tests, Wilcoxon tests and/or Fisher’s exact test to assess categorical and continuous variables.

4.2.2 Focus The 31 studies that examined the involvement of older people in clinical trials and clinical trial protocols focused on under-representation of older participants via several means, most commonly exclusions based on arbitrary age cut-offs. Seventeen of these studies also examined representation of older trial participants compared with the distribution of the same patients in the general population or in large

45 population-based registries, while twelve included investigation of more subtle forms of ageism (e.g. exclusions based on comorbidity, polypharmacy).

Twenty-five studies were focused on exploring differential healthcare receipt between multiple age-groups (e.g. ≤65 years, 66–75 years, 76–85 years, >85 years). Fifteen studies compared medical management between two groups who were defined as being older or younger than a designated age (e.g. 65 years). One study focused on management of older patients compared with recommended guideline management, while one study assessed potentially inappropriate medication prescribing in older patients.

4.2.3 Year of publication Included studies were published between 1987 and 2019. The vast majority of studies were published from 2000 onwards (n=66). Of these, 36 were published between 2010 and 2019 (Figure 4.2).

Figure 4.2. Year of publication of included studies.

4.2.3 Geographical location The largest number of studies (n=26) included participants from multiple countries, predominantly because they involved analysis of international databases and multiple clinical trials (e.g. systematic reviews). The largest number of single-country studies

46 were conducted in the UK (n=15) and the USA (n=14). Four studies were conducted in Canada and three took place in the Netherlands. One study was conducted across Australia/New Zealand and one across Canada/USA. The remaining studies were conducted in Australia (1), Ireland (1), Belgium (1), France (1), Italy (1), Sweden (1), Switzerland (1), Israel (1) and Korea (1) (Figure 4.3).

4.2.4 Study sites Eight studies were conducted at one site, e.g. a single hospital setting. Seven studies involved multiple sites, e.g. several hospitals or clinics within a region or country. Forty-one studies were conducted using data from a single database or registry, while 17 studies used data from more than one database or registry.

Figure 4.3. Single/dual nation study locations.*

*In addition, 26 studies involved international databases and registries with participants from six continents.

4.2.5 Disease focus The most common area of investigation into ageism was cancer (n=23), of which nine trials included multiple cancer types and eight focused on breast cancer. Other cancers included colon (1), rectal (1), haematological (1), lung (1), chronic myeloid leukemia (1) and non-Hodgkin lymphoma (1).

47 Twenty-one trials examined ageism in cardiovascular disease, including myocardial infarction (5), stroke (5), acute coronary syndrome (3) and heart failure (3). Other cardiovascular areas included ischaemic heart disease (n=2), atrial fibrillation (1), hypercholesterolaemia (1) and cardiac arrest (1).

Eight studies included multiple disease states, five focused on musculoskeletal conditions and five examined age-related management in critical care/trauma care. Other disease states included dementia (3), epilepsy (1), diabetes (1), Parkinson’s disease (1), chronic kidney disease (1), asthma (1), psychosis (1), incontinence (1) and hyperparathyroidism (1).

4.3 Synthesis of findings Data from studies that met all inclusion criteria were abstracted and summarised descriptively, including data on first author, publication date and title, study population and setting, study aims, definition of ageism or related terms (i.e. focus terminology), study design, main outcomes, key findings and study limitations/other comments. The full table containing the summarised data from the 73 included studies is shown in Appendix 1.

Overall, ageism-healthcare associations were seen in 68 (93.2%) of the 73 studies included in the review. As predicted by the first hypothesis, these associations were evident across all types of study designs and specialty areas. Five studies found no associations between age and medical management. As predicted by the second hypothesis, ageism and treatment disparities were seen in a variety of international healthcare settings and regions including hospitals, general practice, specialist centres, regional health districts and multinational studies.

4.3.1 Terminology of ageism Multiple terms were employed to describe ageism or ageist behaviours and actions. No dominant terminology emerged, although ‘ageism’ was used infrequently as the main focus term (8 studies) compared with other approaches such as ‘exclusions based on age’ (15 studies), ‘age disparities’ (12 studies), ‘age differences’ (12 studies) or ‘under-representation’ (11 studies). Other focus terminology included ‘under- treatment’ (5), ‘age-related inequalities’ (3), ‘age bias’ (3), ‘age discrimination’ (2), ‘age gap’ (1) and ‘inappropriate management of older patients’ (1).

48

The terms ‘ageism’ or ‘ageist’ were mentioned in 20 out of 73 studies; of these, 10 studies mentioned ageism in the title of the paper. In three of the studies in which ageism was in the title, it was never mentioned again in the paper (Briggs et al, 2012; Grant et al 2000; Gnavi et al, 2007). In three other papers where ageism was in the title, it was only mentioned once in the rest of each paper and not expanded upon in relation to the aims of the research or findings (Bond et al, 2003; DeWilde et al, 2003; Wonnacott et al, 2012). In several studies, ageism was listed as a keyword but not mentioned in the actual paper. No papers referred to any theoretical or conceptual framework of ageism.

4.3.2 Definitions of ‘older’ or ‘elderly’ As noted in Chapter 1, there is no agreed definition of how ‘older’ or ‘elderly’ is determined in society or in health research. As such, the use of the terms ‘older’ and ‘elderly’ varied in the studies included in this review. Twenty-five studies did not include a definition at all, largely because they were looking at age disparities across different age groups. The most common definition of ‘older’ was 65 and older (17 studies). However, the most common definition of ‘elderly’ was also 65 and older (11 studies) (Figure 4.4). In the latter group, six studies were published between 2000– 2009 and three since 2010.

Few studies explained their definitions of ‘older’ or ‘elderly’. One study looking at trauma care between older and younger patients said that the categorisation of ‘young’ and ‘old’ was based on contingency tables reflecting the univariate association between age group and transport to trauma centre (Ryb et al, 2011). One stroke study stated that selecting age 75 as the reference age for comparisons between younger and older patients was arbitrary, but that previous research had showed that patients aged over 75 have poorer outcomes and are less likely to receive optimal stroke care (Bhalla et al, 2004).

49 Figure 4.4. Definitions of ‘older’ and ‘elderly’ in included studies.*

18 16 14 12 10 Older 8 Elderly 6 Very Elderly 4 Number of studies of Number 2 0 60 65 70 75 80 85 Age (years)

*Not defined in 25 studies; years shown denote definitions of the year ‘and older’ e.g. 65 = ‘65 years and older’.

4.3.3 Exclusions from clinical research All 31 studies that examined under-representation in clinical research trials or trial proposals found that older people were directly or indirectly excluded from participating in these trials (Table 4.1). Older people were excluded from trials in oncology, cardiovascular diseases, diabetes, Alzheimer’s disease, musculoskeletal disorders, haematology, neurology, urology and health risk behaviours.

Over 80% of studies (n=25) found that an arbitrary upper age limit was used to exclude older participants from clinical trials and protocols. Exclusions based on an arbitrary age limit ranged from 13.7% in a study of hospital research proposals (Briggs et al, 2012) to 65.7% in a study of age exclusions in type 2 diabetes trials (Cruz- Jentoft et al, 2013). A trial examining exclusions of older people from health risk behaviour trials found that 53% excluded people over the age of 65; this increased to 72% for those over the age of 75 (Levy et al, 2006). Exclusions did not decline over the 14 years studied. A systematic review of randomised controlled trials in non- Hodgkin lymphoma found that 49% of trials had a maximum age cut-off; patients older than 65 years were excluded from participating in 25% of trials overall, even though the disease primarily affects older adults (Bellera et al, 2013).

50

Seventeen studies found that participants in clinical research were markedly younger than those with the same condition in the general population. For example, a systematic review of stroke rehabilitation trials found that the mean age of trial patients was 64.3 years, almost a decade younger than the mean age of patients seen by stroke physicians in daily practice (Gaynor et al, 2014). A study examining representation of older patients in trials of Alzheimer’s disease found that 78% of trial participants were younger than 80 years, despite the fact that 72% of people with Alzheimer’s disease are aged 80 or older. Only eight percent of clinical trial participants were aged 85 years or older, compared with 38% in the general population (Banzi et al, 2016). A study examining cancer registration trials reported overall age proportions of 36% for those aged over 65 years, 20% for those aged 70– 75 years and 9% for those aged over 75 years; this contrasted to proportions in the general population of 60%, 46% and 31%, respectively (Talarico et al, 2004). The gap between the age of trial participants and the corresponding population increased progressively with older age.

Six studies analysed the proportion of trials designed solely for older people and found that these types of trials were in the minority. For example, a study of 440 trials investigating treatment for type 2 diabetes found that only six trials (1.4%) were designed specifically for older adults, and only one trial talked about strategies to improve the recruitment of older people (Cruz-Jentoft et al, 2013). A study examining trial protocols for low back pain found that only 2.99% of trials were exclusively for people older than 65 years of age (Carvalho do Nascimento et al, 2019). The study by Bellera et al (2013) examining non-Hodgkin lymphoma trials found that 10.3% were designed exclusively for patients aged 65 years or older, despite a median age of 66 years at diagnosis. A study looking at trials for haematological malignancies found that 5% focused exclusively on older or ‘unfit’ patients (Hamaker et al, 2014).

More subtle (implicit) forms of ageism were also associated with trial participation. Eleven studies examined additional exclusion criteria that can covertly disadvantage older participants, including the use of polypharmacy, presence of comorbidities (physical and psychological), levels of physical or cognitive function, travel requirements, performance status, visual or hearing deficits and reduced life expectancy. All 11 studies found that such exclusion criteria were barriers that

51 disproportionately restricted the enrolment of older people in clinical trials (Table 4.1). For example, an evaluation of a large cancer registry involving haematological trials found that 69% of trials either explicitly or implicitly excluded older patients: 27% excluded patients based on age, 16% excluded patients based on performance status and 51% excluded patients based on stringent organ function restrictions (Hamaker et al, 2014). The same study found that one-third of trials that excluded older patients based on age allowed inclusion of younger patients with poor performance status. A study examining heart failure trial exclusions found that 43.4% of trials had at least one poorly justified exclusion criteria that could limit the participation of older people (Cherubini et al, 2011).

52

Table 4.1. Studies that examined inclusion of older people in clinical trials/clinical trial protocols.

Author, year Study type Therapeutic area Assessment* Banzi et al, 2016 Systematic review Alzheimer’s disease A Bellera et al, 2013 Systematic review Cancer (non-Hodgkin B, C, D lymphoma) Briggs et al, 2012 Audit Mixed B, C Carvalho do Cross-sectional Musculoskeletal (LBP) B, D Nascimento et al, 2019 review Cherubuni et al, 2011 Registry analysis Cardiovascular disease B, C (HF) Cruz-Jentoft et al, 2013 Registry analysis Diabetes B, C, D Dodd et al, 2011 Systematic review Cardiovascular disease A, B (ACS) Dunn et al, 2017 Systematic review Cancer (multiple) A Fitzsimmons et al, 2012 Database analysis Parkinson’s disease B Gaynor et al, 2014 Review of reviews Stroke A, B, C Gurwitz et al, 1992 Systematic review Cardiovascular disease B (AMI) Hamaker et al, 2014 Registry analysis Cancer (haematological) B, C, D Hutchins et al, 1999 Database analysis Cancer (multiple) A, D Javid et al, 2012 Prospective Cancer (multiple) B, C survey Jennens et al, 2006 Registry analysis Cancer (multiple) A, B Konrat et al, 2012 Literature review Medications (4 common) A Lee et al, 2001 Systematic review Cardiovascular disease A, B (ACS) Leinonen et al, 2015 Systematic review Alzheimer’s disease A, B Levy et al, 2006 Systematic review Health risk behaviours B Lewis et al, 2003 Database analysis Cancer (multiple) A, B, C Ludmir et al, 2019a Database analysis Cancer (multiple) B, D Ludmir et al, 2019b Database analysis Cancer (multiple) A, B, C McGarvey et al, 2017 Literature review Musculoskeletal A, B, C (osteoporosis) Morse et al, 2004 Database analysis Urology (incontinence) A, B Paeck et al, 2014 Systematic review Musculoskeletal (LBP) B Schoenmaker & Van Literature review Dementia A Gool, 2004 Talarico et al, 2004 Database analysis Cancer (medications) A Thake & Lowry, 2017 Systematic review Mixed: trials in 4 journals B Trimble et al, 1994 Database analysis Cancer (multiple) A, B Yee et al, 2003 Registry analysis Cancer (multiple) A, B Zulman et al, 2011 Systematic review Mixed: trials in 5 journals B, C

*A: Includes comparison to general population (comparative under-representation) B: Includes arbitrary age limits C: Includes indirect exclusions of older people D: Includes trials designed exclusively for older people

53 4.3.4 Differential healthcare receipt Forty studies examined differences in clinical management between age groups. Of these, 25 studies examined differential healthcare receipt across several different age groups and 15 compared management differences between a younger and an older age group (defined as older or younger than a certain reference age, which varied across studies: range 60–80 years).

Thirty-five of the 40 studies reported differences in care between older and younger patients. Older patients were not provided with the same diagnostic services or treatment options as younger patients in the fields of cancer, cardiovascular disease, critical care, rheumatoid arthritis, dementia, asthma, diabetes, epilepsy, incontinence, hyperparathyroidism, Parkinson’s disease and psychosis.

Studies found that, compared with younger patients, older patients were managed less intensively, (Bajorek et al, 2012; Barakat et al, 2009; Grant et al, 2000), treated for a shorter duration of time (Wiel et al, 2018), not offered the same types of treatments, including evidence-based options (Greenfield et al, 1987) under-treated (Fairhead & Rothwell, 2006), under-diagnosed (Jung et al, 2009), under-triaged (Lehmann et al, 2009; Ryb et al, 2011), less involved in decision making (Chambaere et al, 2012), and referred less often for specialist services or follow-up (Mitford et al, 2009). Older patients were also under-investigated compared to younger patients. One study looked at diagnostic testing across age groups and found that older patients (aged over 65 years) with ischaemic heart disease were denied access to the same diagnostic testing as younger patients, even when indications for testing were present (Bond et al, 2003). In addition, older hospital patients with indications for further investigation were less likely than younger patients to be referred for exercise tolerance tests and for cardiac catheterisation and angiography.

Two studies highlighted the potential consequences of age-based clinical management on mortality. For example, in a study examining treatment with imatinib for chronic myeloid leukemia, imatinib use was inversely associated with increasing patient age, but older patients who received imatinib survived significantly longer than those who did not (Wiggins et al, 2010). A UK national study of lung cancer care found significant inverse correlations between age and any active treatment and survival, independent of case-mix factors and non-cancer causes of death (Peake et al, 2003).

54 4.3.5 Increasing disparity with increasing age Increasing age was associated with increasing treatment disparities. One research group described “a clear pattern of decreasing ‘diagnostic zeal’ and active treatment with increasing age” (Peake et al, 2003; p175).

Studies that examined care across multiple age groups found that, with increasing age, there were incremental age-related declines in the provision of appropriate medications (DeWilde et al, 2003; Hood et al, 2000; Wiggins et al, 2010), diagnostic investigations and active treatment (Peake et al, 2003), guidelines-based treatments including surgery and adjuvant treatment (Lavelle et al, 2007; Rudd et al, 2007), delays from diagnosis to surgery (Wu et al, 2010) and discharge instructions (Forman et al, 2009).

As a result, the greatest treatment disparities were seen in the ‘oldest old’. For example, a study examined non-standard management of breast cancer in the UK and found that, compared with women aged 65–69, women in their 80s with operable (stage 1–3a) breast cancer were less likely to receive triple assessment, primary surgery, axillary node surgery or steroid receptor testing (Lavelle et al, 2007). Failure to investigate receptor status among older patients resulted in treatment decisions being made without fundamental information.

4.3.6 Inappropriate medication management One study used a screening tool to assess potentially inappropriate medication prescribing in patients aged 65 years or older (Bruin-Huisman et al, 2017). This longitudinal study found that, based on the STOPP/START screening criteria, 34.7% of patients aged 65 years and older were prescribed medications that were potentially inappropriate. In addition, potentially beneficial medications were absent in 84.8% of patients.

4.3.7 Under-representation of older patients One study examined representation of older patients in clinical guidelines for five common chronic conditions: diabetes, hypertension, heart failure, osteoporosis and stroke (Cox et al, 2011). The study found that, while 12 of 14 guidelines included some recommendations for patients aged 65 years and older, only five included recommendations for those aged 80 years and older. Only three guidelines provided

55 specific sections on prevention and management of the conditions in patients aged over 65 years. Analysis of all references used to inform the guidelines found that, of the studies that provided a mean age of participants, only 1.4% of reported a mean age of 80 years and older.

As shown in Section 4.3.1, 100% of studies looking at clinical trial participation found that older people were under-represented. Of these, 54% examined age differences between trial participants and the general population and found that the age of trial participants did not represent the actual distribution of patients with the same condition in the general population. This under-representation of older trial participants was evident even in trials that specifically examined treatments for older people. In addition, very few studies were specifically designed for older people.

4.3.8 Changes over time Nineteen studies examined changes in management or representation of older patients over time, with mixed results. A study assessing the representation of older patients enrolled in colorectal cancer and lung cancer chemotherapy trials between two decades found that the median age of trial patients with colorectal cancer remained constant between the two decades (62.0 and 62.2 years), whereas the median age of the population with colorectal cancer increased from 68.4 to 70.2 years (Jennens et al, 2006). In the same study, the median age of patients in lung cancer trials increased from 59.8 to 61.8 years, whereas the median age of the lung cancer population increased from 67.4 to 70.4 years. The study found that while fewer trials set an upper age limit for eligibility in the second decade analysed, there was an increasing age discrepancy between patients in trials and the general population. More recently, Ludmir et al (2019a) found that upper age restriction criteria in cancer clinical trials appears to be falling, but the same research group found that age-based disparities among cancer trial participants compared with the general population may be widening over time (Ludmir et al, 2019b).

A systematic review examining the exclusion of older people from randomised controlled trials found only modest improvements over a 17-year time period (Thake & Lowry, 2017). In the study of inappropriate medication use by Bruin-Huisman et al (2017), the percentage of patients with one or more potential prescribing omissions showed a statistically significant decrease over eight years, but the proportion of

56 patients given at one least one potentially inappropriate medication did not change significantly over time. In a study examining age-dependent inequalities in the management of myocardial infarction over time, patients aged 75 years and older remained less likely to receive evidence-based medical care and reperfusion therapy over the 24-year study period (Nauta et al, 2013).

4.3.9 Clinical outcomes in different settings and locations Differences in management across age groups were reported in hospitals, including intensive care units (Nauta et al, 2013) and emergency departments (Grant et al, 2000), general practice (Hood et al, 2000), specialist centres (Reuber et al, 2010) and regional health districts (Peake et al, 2003; Rudd et al, 2007). Ageism was reflected in age-based policies and protocols, shown by the exclusion of older patients from clinical trials based on arbitrary age limits. Ageism was associated with policies and procedures used to guide patient management, demonstrated by the under- representation of older patients in clinical guidelines (Bruin-Huisman et al, 2017). Ageism-health associations were seen in around the world, including studies conducted in single/dual nations (n=15 countries) as well as international multicentre studies, which were conducted over six continents.

4.3.10 Management of older patients and human rights Three of the 73 studies briefly mentioned human rights in the context of health care for older people (Bond et al, 2003; Cherubini et al, 2011; Thake & Lowry, 2017). The latter two studies mentioned the PREDICT study aims, which included production of a charter to champion the rights of older people to participate in clinical trials. One additional study mentioned the “pressing practical and moral reasons to ensure that the recruitment of participants in rehabilitation trials mirrors more closely that seen in clinical practice in both age and clinical profile” (Gaynor et al, 2014; p430).

4.3.11 Differences in guidelines-based care Studies in this review showed that implementing guidelines-based practice is inconsistent across age groups and is less likely in older patients (Gottlieb et al, 2011). For example, a study examining age differences in adherence to treatment guidelines for patients following ST elevation myocardial infarction found that adherence to recommended treatment, including primary reperfusion and use of evidence-based medications, was less common for patients aged over 75 compared to younger

57 patients (Gottlieb et al, 2011). In the same study, guidelines implementation was associated with a better one-year outcome across all age groups, but the absolute benefit was greater in older patients, as shown by a lower number needed to treat (NNT). A study examining access to stroke care found that fewer older patients were sent to a stroke unit than younger patients, despite available evidence and guidance showing that all patients with stroke do better in stroke units than in a general ward, irrespective of age (Rudd et al, 2007).

4.4 Lack of ageism Five of the 73 included studies did not find ageism-health associations. One cross- sectional study examined management of patients with rheumatoid arthritis (Harrison et al, 2005); one cross-sectional study assessed decisions made by 10 specialists regarding the optimal location for critical care management (Hubbard et al, 2003); one audit examined cardiac arrest calls and deaths following open-heart surgery (Mackay et al, 2004); one cohort study assessed stroke management (Saposnik et al, 2009); and one case note review examined specialist intervention rates for patients with chronic kidney disease (Wonnacott et al, 2012).

4.5 Assessment of additional variables When assessing differential healthcare receipt, it is important to examine additional factors that may bias the results or affect interpretation of the results (Salway et al, 2017). This can be challenging in observational studies because analysis is limited by the available data. The majority of studies that focused on differential healthcare receipt mentioned or accounted for several additional factors such as contraindications, confounding variables (including comorbidity), patient preference or study limitations (Table 4.2). Overall, 36 studies included adjustment for confounding variables; contraindications were accounted for in seven studies and were not mentioned in 26 of 40 studies; patient preference was not mentioned in 30 studies and was accounted for in one study; limitations were discussed in 29 studies.

In the studies that controlled for confounding variables, differences in treatment patterns between older and younger patients persisted. For example, a study that found older patients were comparatively under-treated concluded that, while it is difficult to determine the extent to which under-treatment was the result of less aggressive management, it continued even when other key physiological variables

58 were taken into account (Barakat et al, 1999). One study investigating age-related disparities in colon cancer treatment found that, after adjusting for confounding factors, patients aged 60–69, 70–79 and 80+ years were significantly less likely to receive surgery than those aged <60 years (Hayes et al, 2019). A study of breast cancer management found that older women were investigated less intensively and were less likely to receive potentially curative surgery than younger patients (Lavelle et al, 2007). After accounting for tumour characteristics, differences between age groups remained, with older women less likely to receive standard management. In a study looking at ageism in the management of lung cancer, there was still a clear pattern of decreasing diagnostic intervention and active treatment after adjusting for multiple case mix factors (Peake et al, 2003).

Table 4.2. Number of studies* that addressed contraindications, confounding variables, patient preference, study limitations.

Factors that may affect Mentioned Accounted Not interpretation of results for in analysis mentioned Contraindications 7 7 26

Confounding variables 2 36 2

Patient preference 9 1 30

Study limitations** 29 N/A 11

*Total studies = 40; ** defined as mentioned or not mentioned

4.6 Quality and risk of bias assessment The SIGN checklists for quality and risk of bias assessments in case control and cohort studies covering nine domains and 19 categories were used to assess quality and risk of bias in the 61 observational studies (Appendix 2). The remaining 12 studies were systematic reviews, which were assessed using a validated systematic review risk of bias tool (ROBIS). The SIGN risk of bias assessment criteria were used to rate the overall risk of bias in all studies (Table 3.3). Studies judged as high quality had little or no risk of bias; acceptable studies had some flaws with an associated risk of bias; low quality studies had significant flaws and a high risk of bias.

59 Overall, 48 studies were judged to be high quality, 21 were considered acceptable and four were considered low quality (Table 4.3). Two of the five studies that did not find evidence of ageism were judged as low quality. The low-quality ratings were given to studies that drew conclusions from combining very diverse data, made broad generalisations based on a small cohort of selected patients, lacked information about the selection of clinicians involved in decision making, and a systematic review that only assessed 40% of eligible studies in the analysis.

60 Table 4.3. Ageism-healthcare findings and quality/risk of bias ratings.

Author, year Ageism- Quality Author, year Ageism- Quality healthcare rating healthcare rating association association Bajorek et al, 2012 Y + Konrat et al, 2012 Y + Banzi et al, 2016 Y ++ Lavelle et al, 2007 Y ++ Barakat et al, 1999 Y ++ Lee et al, 2001 Y ++ Bellera et al, 2013 Y + Lehmann et al, Y + 2009 Bergman et al, 1992 Y ++ Leinonen, et al Y ++ 2015 Bhalla et al, 2004 Y 0 Levy et al, 2006 Y ++ Bond et al, 2003 Y ++ Lewis et al, 2003 Y ++ Briggs et al, 2012 Y + Ludmir et al, 2019a Y ++ Bruin-Huisman et al, Y + Ludmir et al, 2019b Y ++ 2017 Carvalho do Y ++ Mackay et al, 2004 N ++ Nascimento et al, 2019 Chambaere et al, Y + Malik et al, 2013 Y + 2012 Cherubuni et al, 2011 Y ++ McGarvey et al, Y + 2017 Cox et al, 2011 Y ++ Mitford et al, 2010 Y ++ Cruz-Jentoft et al, Y ++ Morse et al, 2004 Y ++ 2013 DeWilde et al, 2003 Y ++ Nauta et al, 2013 Y ++ Dodd et al, 2011 Y ++ Nguyen et al, 2010 Y ++ Dunn et al, 2017 Y + Paeck et al, 2014 Y 0 Fairhead et al, 2006 Y ++ Peake et al, 2003 Y ++ Fitzsimmons et al, Y ++ Reuber et al, 2010 Y + 2012 Forman et al, 2009 Y + Rudd et al, 2007 Y ++ Gaynor et al, 2014 Y ++ Ryb et al, 2011 Y + Gnavi et al, 2007 Y + Saposnik et al, N ++ 2009 Gottlieb et al, 2010 Y ++ Schoenmaker & Y ++ Van Gool, 2004 Grant et al, 2000 Y ++ Sin et al, 2001 Y ++ Greenfield et al, 1987 Y ++ Talarico et al, 2004 Y ++ Gurwitz et al, 1992 Y ++ Thake & Lowry, Y + 2017 Hamaker et al, 2014 Y ++ Trimble et al, 1994 Y ++ Harrison et al, 2005 N 0 Wang et al, 2010 Y ++ Hayes et al, 2019 Y ++ Weaver et al, 1991 Y ++ Hood et al, 2000 Y + Wiel et al, 2018 Y ++ Hubbard et al, 2003 N 0 Wiggins et al, 2010 Y + Hutchins et al, 1999 Y ++ Wonnacott et al, N + 2012 Javid et al, 2012 Y ++ Woodard et al, Y ++ 2003 Jennens et al, 2006 Y + Wu et al, 2010 Y ++ Jin et al, 2014 Y + Yee et al, 2003 Y ++ Joerger et al, 2013 Y ++ Zulman et al, 2011 Y + Jung et al, 2009 Y ++

++ High quality: Majority of criteria met. Little or no risk of bias. Results unlikely to be changed by further research. + Acceptable: Most criteria met. Some flaws in the study with an associated risk of bias. Conclusions may change in the light of further studies. 0 Low quality: Either most criteria not met, or significant flaws relating to key aspects of study design. Conclusions likely to change in the light of further studies.

61 CHAPTER 5: DISCUSSION

As the global population ages, there is a growing need to identify ageist behaviours and actions in health care. The evidence of a parallel increase in ageism with the COVID-19 pandemic, where the notion that health care should be rationed based on age, illustrates why this is a significant issue that should be investigated (Ayalon et al, 2020; Carrieri et al, 2020).

The purpose of this systematic review was to examine how ageism on the part of healthcare professionals, researchers and healthcare systems impacts older patients. In particular, the review aimed to assess the association between ageism, actual clinical decision-making in health care and outcomes for older patients. This was achieved by reviewing the literature to identify, evaluate and synthesise research that focused on age-based actions in health care.

This review found clear associations between age-based management among healthcare professionals/healthcare systems and adverse impacts on older patients. Overall, ageism-healthcare associations were seen in 68 (93.2%) of the 73 studies included in the review. As predicted by the first hypothesis, ageism-health associations were evident through a number of structural and behavioural processes. As predicted by the second hypothesis, these effects were seen across a range of settings.

Ageism associations were particularly evident in the studies examining the participation of older people in clinical trials, demonstrated by use of an arbitrary age cut-off as a barrier to participation, as well as more subtle ways of limiting the enrolment of older people (Cherubini et al, 2011; Hamaker et al, 2014). Older people were routinely excluded from participating in clinical trials, even in trials that specifically examined treatments for older people (Bellera et al, 2013). These exclusions are among the most overt and widespread examples of ageism in health care. All studies that examined this issue found evidence of explicit and/or potentially implicit ageism. Arbitrary age cut-offs were used to exclude older patients from trials across many therapeutic areas and in every year studied. Multiple studies found that participants in clinical research were markedly younger than those with the same condition in the general population (Banzi et al, 2016; Gaynor et al, 2014). Current trial

62 protocols continue to have unwarranted age limits. For example, a study examining prospective protocols planning interventions for low back pain found that approximately 50% of the registered trials were planning to include participants older than 65 years; of those that excluded older people, 93.6% did so via an arbitrary upper age limit (Carvalho do Nascimento et al, 2019).

Other exclusion criteria are frequently used that ultimately minimise the participation of older people, including polypharmacy, comorbidity, hearing and visual deficits, and physical and cognitive impairment (Cherubini et al, 2011; Lewis et al, 2003; Ludmir et al, 2019b). Researchers with the PREDICT study have highlighted that exclusions based on other criteria such as these are not justified unless they will cause danger to participants or affect interpretation of results (Cherubini et al, 2011).

Few clinical trials are specifically designed for older people. Six of the 31 studies that assessed clinical trial exclusions also analysed the proportion of trials designed solely for older people. They found that these types of trials were in the minority (range 1.4%–10.3%), even in disease states that predominantly affect older people (Bellera et al, 2013; Hamaker et al, 2014).

Older patients and carers consider it age discrimination to determine trial participation based on age (Bartlam et al, 2012). The PREDICT study, which was established to investigate the extent of trial exclusions in European countries, performed a mixed methods study to explore the views of older people and their carers; the majority said that exclusions were ageist and infringed on their rights (Bartlam et al, 2012). There is an ethical responsibility to study treatments in the populations they affect (Ilgili et al, 2014). The concept of medical justice, as described by the Declaration of Helsinki, states that medical research is only justified if there is a reasonable likelihood that the populations involved in the research stand to benefit from the results of the research (WMA, 2001).

Several regulatory bodies including the US National Institutes of Health (NIH) have developed charters stipulating that older adults must be included in clinical research unless there are scientific or ethical reasons not to include them (NIH, 2018). However, exclusions continue to occur. Several studies examined trends over time and found that, while there have been modest declines in setting an upper age limit for eligibility,

63 there are increasing age discrepancies between patients in clinical trials and the general population (Jennens et al, 2006; Ludmir et al, 2019a; Ludmir et al, 2019b). A systematic review examining the exclusion of older people from randomised controlled trials found that older patients remain under-represented, with only modest improvements over a 17-year period (Thake & Lowry, 2017). These results suggest that, while there may be greater awareness of inappropriate age criterion, it is not being translated into clinical practice.

Exclusions from clinical research have significant knock-on effects. The main consequence is that older adults have limited access to evidence-based interventions because the majority of pharmacological and non-pharmacological interventions being investigated are not tested in a representative sample of end users (Carvalho do Nascimento et al, 2019; Cherubini et al, 2011). Treatment recommendations have to be extrapolated from results of research involving younger populations, limiting the generalisability (external validity) of the trial results and causing difficulties for both clinicians and patients (Cherubini et al, 2011). Clinicians may not be comfortable with prescribing when there is less robust evidence available. For others, the evidence gap may be used as justification to limit treatment (Rudd et al, 2007).

Safety concerns are also an issue. For example, if a treatment for Alzheimer’s disease is trialled without testing it in those who will be using the medication, the impact of the drug on pharmacokinetic and pharmacodynamic parameters as shown in the trial population may be quite different from actual users of the drug, impacting both efficacy and safety. Clinicians may over- or under-estimate the benefits and harms of the medication based on evidence from non-representative samples (Banzi et al; Cherubini et al, 2011).

Another consequence is the under-representation of older patients in clinical guidance. A study that examined representation of older patients in clinical guidelines for common chronic conditions found very few (3 out of 14) included sections on prevention and management in patients aged over 65 years (Cox et al, 2011). Any guidance for people aged 80 and over was scarce. When guidelines have been developed for the management of older people, they recognise the dearth of clinical trial evidence and resort to expert opinion and extrapolation from research involving younger populations (Brown et al, 2003).

64 Why are older people excluded from clinical research? Investigators may hold stereotypical opinions about older people as a group , i.e. that they are too vulnerable or frail to undergo a clinical trial, or they may deem older people unsuitable due to the presence of comorbid conditions and/or the requirement for multiple medications (McMurdo et al, 2005). Some researchers may believe that screening older people, obtaining consent to participate or relying on carers to assist participants are factors that are too time-consuming. Or, researchers may want a homogenous trial population to generate ‘clean’ science and ensure low drop-out rates (McMurdo et al, 2005). Yet, a high percentage of older people report that they are willing to participate in clinical trials if given the chance (Peterson et al, 2005). A study examining physician and patient decision-making regarding enrolment in breast cancer trials found that if a trial is available and the patient is eligible, older and younger patients enrol at the same rate (Javid et al, 2012). However, the same study found that physicians were less likely to discuss clinical trial participation with older patients compared with younger patients, even when older patients were eligible. Physician concerns about the treatment regimens or toxicity profile were profile were similar for older and younger patients.

Medical care should be guided by clinical evidence and guidelines. Preventing certain patient groups from taking part in research makes this difficult, especially when those patients are among the biggest users of health care. In Australia, the majority of Pharmaceutical Benefits Scheme prescriptions are dispensed to people aged 65 and over. In 2016–17, people aged 80–84 years had the highest rate of script dispensing per 1,000 people, followed by people aged 75–79 years. This is consistent with the relatively higher proportion of hospital and other health services used by older patients (AIHW 2016; AIHW 2018).

Excluding older people from clinical trials because of their age is one of the most poorly justified barriers to trial participation (Cherubini et al 2011; Crome et al, 2011; Lewis et al, 2003; Murthy et al, 2004). Removing age-based barriers to trial enrolment would improve the generalisability of trial results and allow a more meaningful and representative population of patients to be recruited – including those who may benefit most from the treatments being investigated (Unger et al, 2016). Zulman et al (2010) outlines several ways to improve representation of older people in clinical trials, including providing clear justification for exclusions, minimising eligibility criteria that

65 may disproportionately exclude older adults, including outcomes that are highly relevant to older patients and reducing the burdens of recruitment and enrolment to enable involvement of a more diverse range of participants.

The majority of studies that examined differential healthcare receipt across age groups or between a younger and an older cohort found clear differences in care that adversely impacted older patients: older patients received less intensive and shorter treatments (Bajorek et al, 2012; Barakat et al, 2009; Fairhead & Rothwell, 2006; Greenfield et al, 1987; Wiel et al, 2018), fewer investigations (Bond et al, 2003; Peake et al, 2003), fewer specialist referrals (Mitford et al, 2009) and had more limited access to specialised treatments (Grant et al, 2000; Rudd et al, 2002). Multiple studies found that older patients did not receive the same evidence-based therapies as younger patients (Hayes et al, 2019; Joerger et al, 2013. Malik et al, 2013; Rudd et al, 2007).

Older patients were less likely to undergo investigations, or to be triaged to the same level as younger patients (Bond et al, 2003; Grant et al, 2000; Mitford et al, 2009). Studies also found that implementing guidelines-based practice is inconsistent across age groups and is less likely in older patients (Gottlieb et al, 2011). This is concerning, given that following guidelines was associated with better outcomes across age groups (Gottlieb et al, 2011). Other studies have found that health outcomes do not vary by age when guideline-based care is provided (SUTC, 2007). For example, a large meta-analysis examining statin efficacy and safety in older people found that statin therapy produces significant reductions in major vascular events irrespective of age, including in those aged over 75 years (CTTC, 2019).

In addition, studies found that the oldest patient groups experienced the greatest disparities in care. Increasing age was associated with incremental age-related declines in the provision of appropriate medications (DeWilde et al, 2003; Hood et al 2000; Wiggins et al, 2010), diagnostic investigations and active treatment (Peake et al, 2003), guidelines-based treatments (Lavelle et al, 2007; Rudd et al, 2007), delays from diagnosis to surgery (Wu et al, 2010) and discharge instructions (Forman et al, 2009). In other words, the ‘oldest old’ were treated less vigorously and appropriately than ‘younger old’ patients and younger patients overall.

66 Such differences in healthcare receipt can have serious impacts for older patients. When older patients are under-treated or subject to less effective interventions it leads to suboptimal outcomes (Peake et al, 2003; Wiggins et al, 2010). This may in turn reinforce providers’ views about older patients and how well they respond to treatment, further perpetuating a cycle of stereotypical beliefs and discriminatory actions.

Patient preference can and should play a role in clinical decision-making. Only 10 of 40 studies comparing management across age groups made reference to patient preference, and one included it in the analysis. Other research has yielded mixed results on whether older patients want the same level of interventions as younger patients. A study that found patient preference plays a role in treatment also found that women aged 80 and older were over three times as likely to have radiotherapy omitted after breast conservation surgery than women ages 67–79 years, independent of patient preference (Mandleblatt et al, 2000). Other studies have shown that older patients are just as likely to agree to aggressive therapy (Yellen et al, 1994). A large UK survey found that older people with cancer are no more likely to refuse cancer treatment than younger patients (MacMillan, 2015). Studies have found that older patients want as much information about disease and management as younger patients (Girones et al, 2012).

Additional qualitative data may be a valuable addition in exploring the role of patient preference in clinical decision-making. Regardless, patient preference is less likely to influence initial diagnostic assessments, and there were differences in this aspect of care between older and younger age groups (Lavelle et al, 2007; Peake et al, 2003). Failure to fully investigate older patients to the same degree as younger patients means that management decisions are being made without key information.

The studies that examined differential healthcare receipt highlighted differences in treatment patterns across age groups. The majority of these studies attempted to control for confounders where possible, and differences in health care between age groups persisted. Are these results examples of ageism in health care? Age differences may infer age-based treatment practices, but causation is difficult to establish in observational studies, and differences cannot be conclusively attributed to ageism (see Limitations). Several studies acknowledged the limitations of their

67 analyses and made cautious conclusions about findings (Jin et al, 2010; Jung et al, 2009). Others documented differences in care between age groups or between younger and older patients and concluded that these differences were unwarranted and based on age (Peake et al, 2003; Wiggins et al, 2010).

Health care is highly complex, and it can be challenging to distinguish between careful decision-making and inappropriate, age-based care, both of which are affected by multiple individual- and structural-level factors (Wang et al, 2010; Wyman et al, 2018). Actions on the part of healthcare professionals may be a manifestation of the prevailing societal view of ageism, subjective perceptions regarding the value of an older person’s life, or ageist assumptions that quality of life deteriorates with age (Lev et al, 2018, de São José & Amado, 2017). Older patients may not be considered worthy of the same level of active treatment as younger patients (Wyman et al, 2018). As is evident from the COVID-19 pandemic response, there is still a belief that if resources are scarce and potentially need to be rationed then younger patients should be first in line (Fraser et al, 2020; Rudd et al, 2007). Healthcare professionals’ attitudes about their own ageing may also play a role (Ben-Harush et al, 2017; Liu et al, 2012). Paternalism – “I know what’s best for you” – may encourage ageism, even in those with good intentions (Kagan & Maloney, 2017). This type of ageism is also seen in society where age stereotypes are often veiled in humour and jokes (North & Fiske, 2012; Palmore 2005).

The differences in healthcare receipt between older and younger patients may be examples of implicit ageism, which is subtle, may be unconscious and is difficult to identify (Levy & Banaji, 2002; de São José & Amado, 2017). Levy (2001) states that anyone who has internalised their culture’s age stereotypes is likely to engage in implicit ageism, making it widespread but also hidden. While exclusions from clinical trials based on arbitrary age limits can be considered explicit ageism, more subtle forms of ageism may involve exclusions because of criteria that typically disadvantage older people (Cherubini et al, 2011).

Failure to include older patients in decision-making, less access to diagnostic services than younger patients, under-treating and treating less aggressively are all examples of implicit ageism if the providers simply presume that older patients cannot tolerate interventions. Where efficacy or knowledge of expected effects are uncertain, implicit

68 mechanisms of management may come into play (Gnavi et al, 2007). Clinical practice is also influenced by familiarity, and some clinicians may operate on existing stereotypical practices (Wyman et al, 2018). Explicit ageism is easier to see and thus easier to address on human rights, ethical, economic or clinical grounds. In comparison, implicit ageism is harder to shed light on and address.

Institutional ageism may be built into policies and procedures and be invisible to healthcare professionals (Sellman, 2009; Wyman et al, 2018). When it comes to developing ageing and healthcare policies, ageist assumptions can limit the way that problems are framed, conceptualised and prioritised, and therefore the solutions that are offered (Officer & de la Fuente-Nunez, 2018). For example, a study examining access to stroke care across the UK found that, compared with younger patients, older patients were more commonly sent to a general ward than a specialised stroke unit, despite available evidence and government guidance showing that patients of all ages had better outcomes when treated in stroke units (Rudd et al, 2007). Institutional ageism can operate independently from ageism at the individual level, but it can reinforce and magnify age stereotypes (Voss et al, 2018).

Healthcare utilisation and costs can be higher among older people compared with younger people, and may increase even further as the population ages. This can reinforce beliefs that older patients are a burden on the healthcare system (Beard & Bloom, 2015) and may contribute to differential treatment and age-based policies, including age cut-offs for funding of screening programs as well as a lack of access to innovative therapies (Ayalon & Tesch-Romer, 2017; Kagan & Maloney, 2018; Wiggins et al, 2010).

Institutional ageism also exists in global health policy and can influence the development of targets and policies that favour younger people (Lloyd-Sherlock et al, 2016). The United Nations General Assembly has set a target to reduce premature mortality, which is defined in Australia as death before the age of 75 (AIHW 2016; UN 2019). This approach to health suggests that people who die before a certain age are dying prematurely, and health policies should try to combat early deaths. However, it also implies that survival after that age is fundamentally less important (Lloyd-Sherlock et al, 2016). These policies can subsequently discourage research and data collection in older age groups, and ultimately limit the attention given to issues that affect older

69 people including palliative care, polypharmacy and the management of more complex health presentations (Lloyd-Sherlock et al, 2016; WHO, 2015).

Overall, institutional ageism is a double blow for older people, because ageism itself is under-recognised and under-acknowledged at the policy level, even though it has been shown to be highly prevalent (Ayalon 2014; WHO, 2015).

Limitations This review has several limitations. Using data from observational studies, including studies that rely on existing registry data, limits the information available for analysis. This is particularly important in studies examining differences in healthcare receipt (healthcare inequities or disparities), where associations between age and management choices can be inferred but causation cannot be demonstrated (Salway et al, 2017).

During the course of this review, a seminal and comprehensive systematic review by Chang et al (2020) was published. It was the first systematic review to examine the consequences of both structural- and individual-level ageism in health care. Included studies examined ageist attitudes and beliefs about older people, ageist behaviours by healthcare professionals and students as well as self-directed and self-reported ageism. Chang et al reported ageism-healthcare associations in studies of differential healthcare receipt, studies involving hypothetical case scenarios, self-reported ageism studies and clinical trials examining under-representation of older people.

Many studies included by Chang et al and in this review suggest that differences in care across age groups and between younger and older patients may reflect ageism. However, based on the type of data available it is difficult to conclude that age-based decision-making is the cause of the observed differences in all settings and all types of studies. For example, while results from studies employing hypothetical scenarios may suggest ageist intent, actual management is not captured in these studies.

This systematic review concludes that studies of clinical trial exclusions based on arbitrary age cut-offs are evidence of ageism, while observational studies of differential healthcare receipt illustrate treatment patterns that may suggest ageism but can’t pin-point ageism as the main factor influencing management. The

70 differences in provision of health care across different age groups warrants further investigation.

This review did not include qualitative or mixed-methods research. Qualitative research can provide valuable insights into why certain behaviours and actions occur, and would help to elucidate the reasons behind clinical decision-making across older and younger age groups. Patient preference was not accounted for and is likely to play a role in healthcare receipt. This would also be a valuable addition to further mixed-methods ageism research.

Identification, screening and evaluation of eligible studies were performed by the candidate alone, so it is possible that some relevant articles were missed, or that errors in data collection were made. In addition, the marked heterogeneity of papers in terms of aims, definitions, concepts and methodologies limited this review to a narrative synthesis of results.

Future directions Greater clarity about how older patients are defined is important in future research endeavours. The terms ‘older’ and ‘elderly’ were commonly used in included studies, usually with no obvious clinical purpose or explanation. Research published in 2020 during the COVID-19 pandemic continued to use either or both terms; some of this research referred only to ‘elderly’ people/patients without clearly defining what age group the term applied to (e.g. Cheng & Williamson 2020; Holt et al, 2020; Rothan & Byrareddy, 2020).

The use of the term ‘elderly’ has been strongly discouraged by some medical journals including the Journal of the American Geriatrics Society (JAGS), which has adopted the American Medical Association’s style guide when it comes to talking about older people. The guide also discourages the use of other terms such as ‘the aged’, ‘elders’ and ‘seniors’, because they signify discrimination and negative stereotyping of older people (Lundebjerg, et al 2017). JAGS has advised authors to refer to people aged 65 and older as ‘older adults’ and asks authors to use a specific age range when describing their research or when making specific healthcare recommendations.

71 Many journals including The Lancet, the British Medical Journal, the Journal of the American Medical Association and the New England Journal of Medicine follow the International Committee of Medical Editors’ Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals, which state that, “Authors should use neutral, precise, and respectful language to describe study participants and avoid the use of terminology that might stigmatize participants” (ICME 2019; p17). The high impact factor of such journals means that they are ideally placed to promote and adopt the use of neutral language when it comes to age.

There is a dearth of studies examining ageism in the provision of mental health services compared to other areas of health (Bodner et al, 2018). Existing research shows that clinicians may consider certain conditions such as depression to be a normal part of ageing and older age as a time of life where people are less satisfied (Laidlaw & Pachana, 2009). Mental health clinicians may be less willing to work with older adults and have negative assumptions about the effectiveness of psychotherapy with older adults (Bodner et al, 2018). Further research is needed in this area so that appropriate mental health strategies can be developed for older patients.

There are currently few mixed-method studies investigating ageism in health care. Research that combines qualitative and quantitative research would be an effective way to further examine decision-making and subsequent outcomes for older patients. There is also a need for more intervention research. Current studies are predominantly observational, and more research is required to examine active interventions aimed at reducing or eliminating ageism.

This review has shown that ageism and age-based management is evident in clinical research and health care. Therefore, education for healthcare students, researchers and practising healthcare professionals is a key area for intervention. Education should highlight the diversity and heterogeneity of ageing, elucidate the concepts of ageism, raise awareness about the impacts of ageism for older patients as well as healthcare systems, and encourage students to reflect on their own prejudices and biases when it comes to ageing and older people. This could create a better understanding about ageing and older patients and may help to minimise ageist stereotypes, attitudes and behaviours.

72

Finally, studies of good practice would be very valuable. A minority of included studies found no evidence of ageism or age differences in care, and further research may help to shed light on the factors that underpin more equitable decision making. It is important to explore advocacy approaches to addressing ageism among those in health care vested with such responsibility, including geriatricians and gerontologists from a range of healthcare disciplines who support older people to live their lives with dignity and autonomy.

Conclusion This review focused on examining how age-based actions of healthcare professionals, researchers and healthcare systems impact older patients. The review found that ageism-health associations were apparent at the structural level and across multiple therapeutic areas, most overtly through the use of arbitrary age barriers. The population is ageing rapidly, but there is no parallel increase in research including older people. As a result, older patients are discriminated against even before they receive treatment, because they are under-represented in clinical research. Evidence is subsequently lacking to underpin management decisions.

The review also found clear differences in health care between older and younger patients, which may result from age-based actions by healthcare professionals and within healthcare systems. Such actions have significant consequences. Lower quality care leads to poorer outcomes for patients, which in turn may further perpetuate stereotypical beliefs and actions among providers. Further investigation is warranted in order to provide greater insights into how treatment decisions are made across different age groups.

The heterogeneity of the ageing process and among older people makes age-based exclusions and differential health care based on age unwarranted. If age remains a deciding factor in determining management, older patients will continue to have less access to evidence-based interventions than their younger counterparts, leading to suboptimal medical care and adverse consequences. Decisions about health care should be based on each patient’s individual situation, tolerance and needs.

73 References Angus J, Reeve P. Ageism: A threat to “aging well” in the 21st century. J App Gerontol 2006; 25: 137–52.

Australian Government. Age Discrimination Act 2004 No.68, 2004; Compilation No. 41: Compilation date: 1 April 2019.

Australian Government. Aged Care Quality and Safety Commission Act 2018. No. 149. Compilation No. 1. Available at https://www.legislation.gov.au/Details/C2020C00046; accessed 19 May 2020.

Australian Human Rights Commission. Fact of fiction? Stereotypes of older Australians. Research report; 2013.

Australian Institute of Health and Welfare 2016. Australia’s health 2016. Australia’s health series no. 15. Cat. no. AUS 199. Canberra: AIHW.

Australian Institute of Health and Welfare. Australia’s health 2018. Australia’s health series no. 16. AUS 221. Canberra: AIHW.

Australian Institutes of Health and Welfare. Older Australia at a glance. 2018. Available at https://www.aihw.gov.au/reports/older-people/older-australia-at-a- glance/contents/summary; accessed 22 January 2020.

Ayalon L, Chasteen A, Diehl M, et al. Aging in Times of the COVID-19 Pandemic: Avoiding Ageism and Fostering Intergenerational Solidarity. J Gerontol B Psychol Sci Soc Sci. 2020 Apr 16: doi.org/10.1093/geronb/gbaa051.

Ayalon L, Dolberg P, Mikulionienė S, et al. A systematic review of existing ageism scales. Ageing Res Rev 2019; 54: 100919.

Ayalon L, Tesch-Römer C. Taking a closer look at ageism: self- and other-directed ageist attitudes and discrimination. Eur J Ageing 2017; 14:1–4.

Ayalon L. Perceived age, gender, and racial/ethnic discrimination in Europe: results from the European social survey. Educ Gerontol 2014; 40: 499–17.

Bajorek BV, Ren S. Utilisation of antithrombotic therapy for stroke prevention in atrial fibrillation in a Sydney hospital: then and now. Int J Clin Pharm 2012; 34: 88–97.

Banzi R, Camaioni P, Tettamanti M, et al. Older patients are still underrepresented in clinical trials of Alzheimer's disease. Alzheimers Res Ther 2016; 8: 32.

Barakat K, Wilkinson P, Deaner A, et al. How should age affect management of acute myocardial infarction? A prospective cohort study. Lancet 1999; 353: 955–59.

Bartlam B, Crome P, Lally F, et al. The views of older people and carers on participation in clinical trials: the PREDICT Study. Clinical Investigation 2012; 2: 327– 36.

Beard J, Bloom D. Towards a comprehensive public health response to population ageing. Lancet 2015; 385: 658–61.

Bellera C, Praud D, Petit-Monéger A, et al. Barriers to inclusion of older adults in randomised controlled clinical trials on Non-Hodgkin's lymphoma: a systematic review. Cancer Treat Rev 2013; 39: 812–17.

74 Ben-Harush A, Shiovitz-Ezra S, Doron I, et al. Ageism among physicians, nurses, and social workers: findings from a qualitative study. Eur J Ageing 2017; 14: 39–48.

Bergman L, Kluck HM, van Leeuwen FE, et al. The influence of age on treatment choice and survival of elderly breast cancer patients in South-eastern Netherlands: A population-based study. Eur J Cancer 1992; 28A: 1475–80.

Bhalla A, Grieve R, Tilling K, et al. Older stroke patients in Europe: stroke care and determinants of outcome. Age and Ageing 2004; 33: 618–24.

Billings J. Staff perceptions of ageist practice in the clinical setting: Quality in Ageing and Older Adults 2006; 7: 33–45.

Bodner et al, 2018. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland.

Bond M, Bowling A, McKee D, et al. Does ageism affect the management of ischaemic heart disease? J Health Serv Res Policy 2003; 8: 40–47.

Bramer WM, Rethlefsen ML, Kleijnen J, Franco OH. Optimal database combinations for literature searches in systematic reviews: a prospective exploratory study. Systematic Reviews 2017; 6: 245.

Briggs R, Robinson S, O'Neill D. Ageism and clinical research. Ir Med J 2012; 105: 311–12.

Brown AF, Mangione CM, Saliba D, Sarkisian CA, California Healthcare Foundation/American Geriatrics Society panel on improving care for elders with diabetes. J Am Geriatr Soc 2003; 51(5 Suppl): S265–80.

Bruin-Huisman L, Abu-Hanna A, Van Weert HCPM, et al. Potentially inappropriate prescribing to older patients in primary care in the Netherlands: a retrospective longitudinal study. Age and Ageing 2017; 46: 614–19.

Butler RN. Age-ism: another form of bigotry. Gerontologist. 1969; 9: 243–46.

Butler RN. Ageism: a foreword. J Soc Issues 1980; 36:8–11. doi: http://dx.doi.org/10.1111/j.1540-4560.1980.tb02018.x

Butler RN. Dispelling ageism: The cross-cutting intervention. Annals of the AAPSS 1989; 503: 138–47.

Buttigieg SC, Ilinca S, de Sao Jose JMS, Larsson AT. Researching ageism in health- care and long-term care. In: Ayalon L, Tesch-Römer C. (eds) Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland.

Carrieri D, Peccatori FA, Boniolo G. COVID-19: A plea to protect the older population. Int J Equity Health 2020; 19: 72.

Carvalho do Nascimento PR, Ferreira ML, Poitras S, et al. Exclusion of older adults from ongoing clinical trials on low back pain: a review of the WHO trial registry database. J Am Geriatr Soc 2019; 67: 603–08.

75 Centre for Reviews and Dissemination. Systematic Reviews: CRD's guidance for undertaking systematic reviews in health care. University of York; 2009. Available a: www.york.ac.uk/inst/crd/pdf/Systematic_Reviews.pdf; accessed 13 March 2020.

Chambaere K, Rietjens JAC, Smets T, et al. Age-based disparities in end-of-life decisions in Belgium: A population-based death certificate survey. BMC Public Health 2012; 12: 447.

Chang E-S, Kannoth S, Levy S, et al. Global reach of ageism on older persons’ health: A systematic review. PLoS ONE 2020; 15: e0220857.

Cheng AC, Williamson DA. An outbreak of COVID‐19 caused by a new coronavirus: what we know so far. Med J Aust 2020; 212: 393–94.

Cherubini A, Oristrell J, Pla X, et al. The persistent exclusion of older patients from ongoing clinical trials regarding heart failure. Arch Intern Med 2011; 171: 550–56.

Cholesterol Treatment Trialists’ Collaboration. Efficacy and safety of statin therapy in older people: a meta-analysis of individual participant data from 28 randomised controlled trials. Lancet 2019; 393: 407–15.

Cox L, Kloseck M, Crilly R, et al. Underrepresentation of individuals 80 years of age and older in chronic disease clinical practice guidelines. Can Fam Physician 2011; 57: e263–69.

Crome P, Lally F, Cherubini A, et al. Exclusion of Older People from Clinical Trials: Professional Views from Nine European Countries Participating in the PREDICT Study. Drugs Aging 2011; 28: 667–77.

Cruz-Jentoft AJ, Carpena-Ruiz M, Montero-Errasquín B, et al. Exclusion of older adults from ongoing clinical trials about type 2 diabetes mellitus. J Am Geriatr Soc 2013; 61: 734–38.

Cuddy AJC, Fiske ST. Doddering but dear. In Nelson TD (ed). Ageism: Stereotyping and prejudice against older persons. 2002: 3–26. Cambridge, MA: MIT Press.

De Pauw M, Sleap B, Georgantzi N. Ageism and age discrimination in international human rights. In: Doron I, Georgantzi N (eds): Ageing, Ageism and the Law. 2018; Edward Elgar Publishing. ISBN: 9781788972109.

De São José J, Amado C. On studying ageism in long- term care: A systematic review of the literature. Int Psychogeriatr 2017; 29: 373–87.

De São José J. Ageism in healthcare. In: Gu D, Dupre ME (eds). Encyclopedia of Gerontology and Population Aging. 2019; Springer Nature, Switzerland.

DeWilde S, Carey IM, Bremner SA, et al. Evolution of statin prescribing 1994–2001: A case of agism but not of sexism? Heart 2003; 89: 417–21.

Dionigi RA. Stereotypes of Aging: Their effects on the health of older adults. J Geriatr 2015; 1–9. 10.1155/2015/954027.

Dodd KS, Saczynski JS, Zhao Y, et al. Exclusion of older adults and women from recent trials of acute coronary syndromes. J Am Geriatr Soc 2011; 59: 506–11.

76 Dunn C, Wilson A, Sitas F. Older cancer patients in cancer clinical trials are underrepresented. Systematic literature review of almost 5000 meta- and pooled analyses of phase III randomized trials of survival from breast, prostate and lung cancer. Cancer Epidemiol 2017; 51: 113–17.

Fairhead JF, Rothwell PM. Underinvestigation and undertreatment of carotid disease in elderly patients with transient ischaemic attack and stroke: comparative population based study. BMJ 2006; 333: 525–27.

Fiske ST, Cuddy AJC, Glick P, Xu J. A model of (often mixed) stereotype content: Competence and warmth respectively follow from perceived status and competition. J Pers Soc Psychol 2002; 82: 878–902.

Fitzsimmons PR, Blayney S, Mina-Corkill S, et al. Older participants are frequently excluded from Parkinson's disease research. Parkinsonism Relat Disord 2012; 18: 585–89.

Forman DE, Cannon CP, Hernandez AF, et al. Influence of age on the management of heart failure: findings from Get With the Guidelines-Heart Failure (GWTG-HF). Am Heart J 2009; 157: 1010–17.

Fraboni M, Saltstone R, Hughes S. The Fraboni Scale of Ageism (FSA): an attempt at a more precise measure of ageism. Can J Aging 1990; 9: 56–66.

Fraser S, Lagace M, Bongue B, et al. Ageism and COVID-19: what does our society’s response say about us? Age and Ageing 2020; 00: 1–4.

Gaynor EJ, Geoghegan SE, O'Neill D. Ageism in stroke rehabilitation studies. Age Ageing 2014; 43: 429–31.

Gendron TL, Inker J, Welleford EA. A theory of relational ageism: A discourse analysis of the 2015 White House Conference on Aging. Gerontologist 2018; 58: 242–50.

Gething L. Health professional attitudes towards ageing and older people: preliminary report of the Reactions to Ageing Questionnaire. Aust J Ageing 1994; 1377–81.

Gewirtz-Meydan A, Ayalon L. Physicians’ response to sexual dysfunction presented by a younger vs. an older adult. Int J Geriatr Psychiatry 2017; 32: 1476–83.

Giles H, Reid SA. Ageism across the lifespan: Towards a self-categorization model of ageing. J Social Issues 2005; 61: 389–404.

Girones R, Torregrosa D, Gomez-Codina J, et al. Lung cancer chemotherapy decisions in older patients: the role of patient preference and interactions with physicians. Clinical and Translational Oncology 2012; 14: 183–89.

Gnavi R, Migliardi A, Demaria M, et al. Statins prescribing for the secondary prevention of ischaemic heart disease in Torino, Italy. A case of ageism and social inequalities. Eur J Public Health 2007; 1: 492–96.

Gottlieb S, Behara S, Schwartz R, et al. Age differences in the adherence to treatment guidelines and outcome in patients with ST-elevation myocardial infarction. Arch Gerontol Geriatr 2011; 52: 118–24.

77 Grant LD. Effects of ageism on individual and health care providers’ responses to healthy aging. Health and Social Work 1996; 21: 9–15.

Grant PT, Henry JM, McNaughton GW. The management of elderly blunt trauma victims in Scotland: Evidence of ageism? Injury 2000; 31: 519–28.

Greenfield S, Blanco DM, Elashoff RM, Ganz PA. Patterns of care related to age of breast cancer patients. JAMA 1987; 257: 2766–70.

Gurwitz JH, Col NF, Avorn J. The exclusion of the elderly and women from clinical trials in acute myocardial infarction. JAMA 1992; 268: 1417–22.

Hamaker ME, Stauder R, van Munster BC. Exclusion of older patients from ongoing clinical trials for hematological malignancies: an evaluation of the National Institutes of Health Clinical Trial Registry. Oncologist 2014; 19: 1069–75.

Harrison MJ, Kim CA, Silverberg M, Paget SA. Does age bias the aggressive treatment of elderly patients with rheumatoid arthritis? J Rheumatol 2005; 32: 1243–48.

Hayes L, Forrest L, Adams J, et al. Age-related inequalities in colon cancer treatment persist over time: a population-based analysis. J Epidemiol Community Health 2019; 73: 34–41.

Helmes E, Gee S. Attitudes of Australian therapists toward older clients: educational and training imperatives. Educ Gerontol 2003; 29: 657–70.

Holt NR, Neumann JT, McNeil JJ, Cheng A. Implications of COVID-19 in an ageing population. Med J Aust 2020. Preprint 6 May 2020.

Hood S, Taylor S, Roeves A, et al. Are there age and sex differences in the investigation and treatment of heart failure? A population-based study. Br J Gen Pract 2000; 50: 559–63.

Hubbard RE, Lyons RA, Woodhouse KW, et al. Absence of ageism in access to critical care: a cross-sectional study. Age and Ageing 2003; 32: 382–87.

Hummert ML. Commentaries on “Mind matters: cognitive and physical effects of aging self-stereotypes” and author's reply: When is an age stereotype an aging self- stereotype? A commentary. J Gerontol 2003 (Series B); 58: 212–16.

Hutchins LF, Unger JM, Crowley JJ, et al. Underrepresentation of patients 65 years of age or older in cancer-treatment trials. N Engl J Med 1999; 341: 2061–67.

Ilgili O, Arda B, Munir K. Ethics in geriatric medicine research Turk Geriatri Derg 2014; 17: 188–95.

International Committee of Medical Editors’ Recommendations for the Conduct, Reporting, Editing, and Publication of Scholarly Work in Medical Journals. 2019. Available at http://www.icmje.org/recommendations/; accessed 8 June 2020.

Iversen TN, Larsen L, Solem PE. A conceptual analysis of ageism. Nordic Psychology 2009; 61: 4–22.

Jackson SE, Hackett RA, Steptoe A. Associations between age discrimination and health and wellbeing: cross-sectional and prospective analysis of the English Longitudinal Study of Ageing. Lancet Public Health 2019; 4: e200–08.

78

Javid SH, Unger JM, Gralow JR, et al. A prospective analysis of the influence of older age on physician and patient decision-making when considering enrollment in breast cancer clinical trials (SWOG S0316). Oncologist 2012; 17: 1180–90.

Jennens RR, Giles GG, Fox RM. Increasing underrepresentation of elderly patients with advanced colorectal or non-small-cell lung cancer in chemotherapy trials. Int Med J 2006; 36: 216–20.

Jin X-M, Lee J, Choi N-K, et al. Utilization patterns of disease-modifying antirheumatic drugs in elderly rheumatoid arthritis patients. J Korean Med Sci 2014; 29: 210–16.

Joerger M, Thürlimann B, Savidan A, et al. Treatment of breast cancer in the elderly: A prospective, population-based Swiss study. J Geriatr Oncol; 2013; 4: 39–47.

Jung B, Pahlman L, Johansson R, et al. Rectal cancer treatment and outcome in the elderly: an audit based on the Swedish rectal cancer registry 1995–2004. BMC Cancer 2009, 9: 68.

Kagan SH, Maloney KW. Cancer screening and early detection in older people: considerations for nursing practice. Seminars in Oncology Nursing 2017; 33, 199–207.

Kite ME, Stockdale GD, Whitley BR, Johnson BT. Attitudes toward younger and older adults: An updated meta-analytic review. J Social Issues 2005; 61: 241–66.

Kogan N. Attitudes toward old people: the development of a scale and an examination of correlates. J Abnorm Soc Psychol 1961; 62: 44–54.

Konrat C, Boutron I, Trinquart L, et al. Underrepresentation of elderly people in randomised controlled trials. The example of trials of 4 widely prescribed drugs. PLoS One 2012; 7: e33559.

Kornadt AE, Kessler E-M, Wurm S, et al. Views on ageing: a lifespan perspective. Eur J Ageing https://doi.org/10.1007/s10433-019-00535-9.

Kotter-Grühn D, Kleinspehn-Ammerlahn A, Gerstorf D, et al. Self-perceptions of aging predict mortality and change with approaching death: 16-year longitudinal results from the Berlin Aging Study. Psychol Aging 2009; 24: 654–67.

Laidlaw K, Pachana NA. Ageing, mental health, and demographic change: Challenges for psychotherapists. Prof Psychol: Res Pract 2009; 40: 601–08.

Lamont RA, Swift HJ, Abrams D. A review and meta-analysis of age-based stereotype threat: negative stereotypes, not facts, do the damage. Psychol Aging 2015; 30: 180– 93.

Lavelle K, Todd C, Moran A, et al. Non-standard management of breast cancer increases with age in the UK: a population based cohort of women ≥65 years. Br J Cancer 2007; 96: 1197–203.

Laws G. “The Land of Old Age”: Society’s changing attitudes toward urban built environments for elderly people. Ann Assoc Am Geog 1993; 83: 672–93.

Lee PY, Alexander KP, Hammill BG, et al. Representation of elderly persons and women in published randomized trials of acute coronary syndromes. JAMA 2001; 286: 708–13.

79

Lehmann R, Beekley A, Casey L, et al. The impact of advanced age on trauma triage decisions and outcomes: a statewide analysis. Am J Surg 2009; 197: 571–75.

Leinonen A, Koponen M, Hartikainen S. Systematic review: representativeness of participants in RCTs of Acetylcholinesterase Inhibitors. PLoS One 2015; 10: e0124500.

Lev S, Wurm S, Ayalon L. Origins of Ageism at the Individual Level. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland.

Levy B. Stereotype embodiment: A psychosocial approach to aging. Curr Dir Psychol Sci 2009; 18: 332–36.

Levy BR. Eradication of ageism requires addressing the enemy within. Gerontologist 2001; 41: 578–79.

Levy BR. Unconscious ageism. In: Palmore EB, Branch L, Harris D (eds). Encyclopedia of ageism. 2005; Binghamton: Haworth Press.

Levy BR, Banaji MR. Implicit ageism. In Nelson TD (ed). Ageism stereotyping and prejudice against older persons. 2002: 49–75. Cambridge, MA: MIT Press.

Levy BR, Kasl SV, Gill TM. Image of aging scale. Percept Mot Skills 2004; 99: 208–10.

Levy BR, Kosteas J, Slade M, Myers L. Exclusion of elderly persons from health-risk behavior clinical trials. Prev Med 2006; 43: 80–85.

Levy BR, Slade MD, Chang ES, et al. Ageism amplifies cost and prevalence of health conditions. Gerontologist 2020; 60: 174–81.

Levy BR, Slade MD, Kunkel SR, Kasl SV. Longevity increased by positive self- perceptions of aging. J Pers Soc Psychol 2002; 83: 261–70.

Levy BR, Zonderman A, Slade MD, Ferrucci L. Negative age stereotypes held earlier in life predict cardiovascular events in later life. Psychol Sci 2009; 20: 296–98.

Levy BR, Zonderman AB, Slade MD, Ferrucci L. Memory shaped by age stereotypes over time. J Gerontol B Psychol Sci Soc Sci 2012; 67: 432–36.

Levy SR, Macdonald JL. Progress on understanding ageism. J Soc Issues 2016; 72: 5–25.

Lewis JH, Kilgore ML, Goldman DP, et al. Participation of patients 65 years of age or older in cancer clinical trials. J Clin Oncol 2003; 21: 1383–89.

Liu YE, While AE, Norman IJ, Ye W. Health professionals’ attitudes toward older people and older patients: a systematic review. J Interprof Care. 2012; 26: 397–409.

Lloyd-Sherlock PG, Ebrahim S, McKee M, Prince JM. Institutional ageism in global health policy. BMJ 2016; 354: i4514.

Ludmir EB, Mainwaring W, Lin TA, et al. Factors associated with age disparities among cancer clinical trial participants. JAMA Oncol 2019 Jun 3. doi: 10.1001/jamaoncol.2019.2055.

80

Ludmir EB, Subbiah IM, Mainwaring W, et al. Decreasing incidence of upper age restriction enrollment criteria among cancer clinical trials. J Geriatr Oncol 2019; 11: 451–54.

Lundebjerg NE, Trucil DE, Hammond EC, et al. When it comes to older adults, language matters: Journal of the American Geriatrics Society adopts modified American Medical Association style. JAGS 2017; 65: 1386–88.

Mackay JH, Powell SJ, Charman SC, Rozario C. Resuscitation after cardiac surgery: Are we ageist? Eur J Anaesthesiol 2004; 21: 66–71.

MacMillan Cancer Support. Exploring the attitudes and behaviours of older people living with cancer. Ipsos MORI 2015.

Malik MK, Tartter PT, Belfer R. Undertreated breast cancer in the elderly. J Cancer Epidemiol 2013; 893104.

Mandelblatt JS, Hadley J, Kerner JF, et al. Patterns of breast carcinoma treatment in older women: Patient preferences and clinical and physician influences. Cancer 2000; 89: 561–73.

Martens A, Goldenberg JL., Greenberg J. A terror management perspective on ageism. J Social Issues 2005; 61: 223–39.

McGarvey C, Coughlan T, O’Neill D. Ageism in studies on the management of osteoporosis. J Am Geriatr Soc 2017; 65: 1566–68.

McMurdo MET. Including older people in clinical research. BMJ 2005; 331: 1036–37.

Meisner BA. A meta-analysis of positive and negative age stereotype priming effects on behavior among older adults. J Gerontol B Psychol Sci Soc Sci 2012; 67: 13–17.

Mitford E, Reay R, McCabe K, et al. Ageism in first episode psychosis. Int J Geriatr Psychiatry 2010; 25: 1112–18.

Moher D, Liberati A, Tetzlaff J, et al. Preferred reporting items for systematic reviews and meta-analyses: The PRISMA statement. PLoS Med 2009; 6: e1000097.

Morse AN, Labin LC, Young SB, et al. Exclusion of elderly women from published randomized trials of stress incontinence surgery. Obstet Gynecol 2004; 104: 498–503.

Murthy VH, Krumholz HM, Gross CP. Participation in cancer clinical trials: Race-, sex-, and age-based disparities. JAMA. 2004; 291: 2720–26.

National Health Service. Improving care for older people. Available at: https://www.england.nhs.uk/ourwork/clinical-policy/older-people/improving-care-for- older-people/; accessed 10 February 2020.

National Institutes of Health. Looking for the latest definition for terms relating to human subjects and clinical trials? August 2018. Available at https://nexus.od.nih.gov/all/2018/08/07/human-subjects-and-clinical-trial-glossary- updates/; accessed 13 February 2020.

81 Nauta SJ, Deckers JW, Akkerhuis KM, et al. Age-dependent care and long-term (20 year) mortality of 14,434 myocardial infarction patients: Changes from 1985 to 2008. Int J Cardiol 2013; 167: 693–97.

Nelson TD. The age of ageism. J Social Sciences 2016; 72: 191–98.

Nguyen HL, Goldberg RJ, Gore JM, et al. Age and sex differences, and changing trends, in the use of evidence-based therapies in acute coronary syndromes: Perspectives from a multinational registry. Cor Art Dis 2010, 21: 336–44.

North MS, Fiske ST. An inconvenienced youth? Ageism and its potential intergenerational roots. Psychol Bull 2012; 138: 982–97.

North MS, Fiske ST. Act your (old) age: Prescriptive, ageist biases over succession, consumption, and identity. Personality Social Psychol Bull 2013; 39: 720–34.

Nosek BA, Banaji MR, Greenwald AG. Harvesting implicit group attitudes and beliefs from a demonstration website. Group Dynamics 2001; 6: 101–15.

Officer A, de la Fuente-Nunez V. A global campaign to combat ageism. Bull World Health Organ 2018; 96: 299–300.

Orimo H, Ito H, Suzuki T, et al. Reviewing the definition of “elderly”. Geriatr Gerontol Int 2006; 6: 149–58.

Ouchida KM, Lachs MS. Not for doctors only: Ageism in healthcare. Generations 2015; 39: 46–57.

Paeck T, Ferreira ML, Sun C, et al. Are older adults missing from low back pain clinical trials? A systematic review and meta-analysis. Arthritis Care Res 2014; 66: 1220–26.

Page MJ, Moher D. Evaluations of the uptake and impact of the preferred reporting items for systematic reviews and meta-analyses (PRISMA) statement and extensions: A scoping review. Syst Rev 2017; 6: 263.

Palmore E. Facts on aging: A short quiz. The Gerontologist 1977; 17: 15–20.

Palmore EB, Branch L, Harris D (eds). Encyclopedia of ageism. 2005; Binghamton: Haworth Press.

Patterson K. Australian Human Rights Commission: Ageism and COVID-19. 5 May 2020. Available at: https://humanrights.gov.au/about/news/ageism-and-covid-19; accessed 22 May 2020.

Peake MD, Thompson S, Lowe D, Pearson MG. Ageism in the management of lung cancer. Age Ageing 2003; 32: 171–77.

Peisah C, Brodaty H, O’Neill N. The mental health and rights of mentally ill older people. In: Dudley M, Silove D, Gale F (eds). Mental health and human Rights: Vision, praxis, and courage. Oxford University Press, Oxford UK, June 2012.

Peterson ED, Lytle BL, Alexander KP, Coombs LP. The willingness of under- represented groups to participate in clinical trials. J Am Coll Cardiol 2005; 39: 435A.

Ram´ırez LF, Palacios-Espinosa X. Stereotypes about old age, social support, aging anxiety and evaluations of one’s own health. J Soc Issues 2016; 72: 47–68.

82

Reed J, Cook G, Cook M, et al. Specialist services for older people : issues of negative and positive ageism. Age Society 2006; 26: 849–65.

Reuber M, Torane P, Mack C. Do older adults have equitable access to specialist epilepsy services? Epilepsia, 2010; 51: 2341–43.

Richardson S, Karunananthan S, Bergman H. I may be frail but I ain't no failure. Can Geriatr J 2011; 14: 24–28.

Rogers SE, Thrasher AD, Miao Y, et al. Discrimination in healthcare settings is associated with disability in older adults: Health and retirement study, 2008–2012. J Gen Intern Med 2015; 30: 1413–20.

Rosencranz HA, McNevin TE. A factor analysis of attitudes toward the aged. Gerontologist 1969; 9: 55–59.

Rosowsky E. Ageism and professional training in aging: Who will be there to help? Generations 2005; 29: 55–58.

Rothana HA, Byrareddy SN. The epidemiology and pathogenesis of coronavirus disease (COVID-19) outbreak. J Autoimmun 2020 May; 109: 102433.

Royal Commission into Aged Care Quality and Safety. Interim Report: Neglect; Volume 1. October 2019.

Rubin DC, Berntsen D. People over forty feel 20% younger than their age: Subjective age across the lifespan. Psychon Bull Rev 2006; 13: 776–80.

Rudd AG, Hoffman A, Down C, et al. Access to stroke care in England, Wales and Northern Ireland: The effect of age, gender and weekend admission. Age Ageing 2007; 36: 247–55.

Rupp DE, Vodanovich SJ, Credé M. The multidimensional nature of ageism: Construct validity and group differences. J Soc Psychol 2005; 145: 335–62.

Ryb GE, Dischinger PC. Disparities in trauma center access of older injured motor vehicular crash occupants. J Trauma 2011; 71: 742–47.

Salway SM, Payne N, Rimmer M, et al. Identifying inequitable healthcare in older people: Systematic review of current research practice. Int J Equ Health 2017; 16: 1232017.

Saposnik G, Black SE, Hakim A, et al. Age disparities in stroke quality of care and delivery of health services. Stroke 2009; 40: 3328–35.

Schoenmaker N, Van Gool WA. The age gap between patients in clinical studies and in the general population: A pitfall for dementia research. Lancet Neurol 2004; 3: 627– 30.

Schroyen S, Adam S, Marquet M, et al. Communication of healthcare professionals: Is there ageism? Eur J Cancer Care 2018; 27.

Sellman D. Ethical care for older persons in acute care settings. Nursing Philosophy 2009; 10: 69–70.

83 SIGN Methodology Checklist 3: Cohort studies. Available at: https://www.sign.ac.uk/checklists-and-notes; accessed 5 January 2020.

Sin DD, Tu JV. Underuse of inhaled steroid therapy in elderly patients with asthma. Chest 2001; 119: 720–25.

Singh S, Bajorek B. Defining ‘elderly’ in clinical practice guidelines for pharmacotherapy. Pharmacy Practice 2014;12: 489.

Snellman F. Whose ageism? The reinvigoration and definitions of an elusive concept. Nordic Psychology 2016; 68: 148–59.

Steele CM, Aronson J. Stereotype threat and the test performance of academically successful African Americans. Pers Soc Psychol 1995; 69: 797–811.

Steele CM, Aronson J. Stereotype threat and the test performance of academically successful African Americans. In: Jencks C, Phillips M (eds). The Black–White test score gap; 1998: 401–427. Brookings Institution Press.

Stroke Unit Trialists’ Collaboration. Organised inpatient (stroke unit) care for stroke. Cochrane Database Syst Rev 2007, Issue 4. Art. No.: CD000197.

Tajfel H, Turner JC. An integrative theory of intergroup conflict. In: Austin WG, Worchel S (eds). The Social Psychology of Intergroup Relations; 1979: 33–47. Monterey, CA: Brooks/Cole.

Talarico L, Chen G, Pazdur R. Enrollment of elderly patients in clinical trials for cancer drug registration: A 7-year experience by the US Food and Drug Administration. J Clin Oncol 2004; 22: 4626–31.

Thake M, Lowry A. A systematic review of trends in the selective exclusion of older participant from randomised clinical trials. Arch Gerontol Geriatr 2017; 72: 99–102.

Trimble EL, Carter CL, Cain D, et al. Representation of older patients in cancer treatment trials. Cancer 1994; 74 (7 Suppl): 2208–14.

Uncapher H, Arean PA. Physicians are less willing to treat suicidal ideation in older patients. J Am Geriatr Soc 2000; 48: 188–92.

Unger JM, Cook E, Tai E, Bleyer A. The role of clinical trial participation in cancer research: Barriers, evidence, and strategies. Am Soc Clin Oncol Educ Book 2016; 35: 185–98.

United Nations Committee on Economic Social and Cultural Rights. The economic, social and cultural rights of older persons. 1995. Available at https://www.refworld.org/pdfid/4538838f11.pdf; accessed 8 June 2020.

United Nations Department of Economic and Social Affairs Programme on Ageing. Health inequalities in old age. 2018. Available at https://www.un.org/development/desa/ageing/; accessed 8 June 2020.

United Nations Economic and Social Council Report of the United Nations High Commissioner for Human Rights. Item 14 (g) of the Provisional Agenda: Social and human rights questions: Human rights. Geneva, 23–27 July 2012. Available at https://documents-dds-

84 ny.un.org/doc/UNDOC/GEN/G12/420/71/PDF/G1242071.pdf?OpenElement; accessed 22 May 2020.

United Nations General Assembly Open-Ended Working Group on Ageing or The Purpose of Strengthening the Protection of The Human Rights of Older Persons; 2010. Available at: https://social.un.org/ageing-working-group/; accessed 23 April 2020.

United Nations Human Rights Office of the High Commissioner. “Unacceptable” – UN expert urges better protection of older persons facing the highest risk of the COVID- 19 pandemic. 27 March 2020. Available at: https:// www.ohchr.org/EN/NewsEvents/Pages/DisplayNews. aspx? NewsID=25748&LangID=E; accessed 21 May 2020.

United Nations Human Rights Office of the High Commissioner. UN International Day of Older Persons. 1 October 2016. Available at https://www.ohchr.org/EN/NewsEvents/Pages/DisplayNews.aspx?NewsID=20859&La ngID=E; accessed 21 May 2020.

Voss P, Bodner E, Rothermund K. Ageism: The relationship between age stereotypes and age discrimination. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland.

Voss P, Wolff JK, Rothermund K. Relations between views on ageing and perceived age discrimination: A domain-specific perspective. Eur J Ageing 2017; 14: 5–15.

Wang J, Kollias J, Boult M, et al. Patterns of surgical treatment for women with breast cancer in relation to age. Breast J 2010; 16: 60–65.

Wang Z, Taylor K, Allman-Farinelli M, et al. A systematic review: Tools for assessing methodological quality of human observational studies. NHMRC; 2019. Available at: https://nhmrc.gov.au/guidelinesforguidelines/develop/assessing-risk-bias; accessed 5 March 2020.

Weaver WD, Litwin PE, Martin JS, et al. Effect of age on use of thrombolytic therapy and mortality in acute myocardial infarction. The MITI Project Group. J Am Coll Cardiol 1991; 18: 657–62.

Westerhof GJ, Miche M, Brothers AF, et al. The influence of subjective aging on health and longevity: A meta-analysis of longitudinal data. Psychol Aging 2014; 29: 793–802.

Whiting P, Savovic J, Higgins JPT, et al. ROBIS: A new tool to assess risk of bias in systematic reviews was developed. J Clin Epidemiol 2016; 69: 225–34.

Wiel E, Di Pompeo C, Segal N, et al. Age discrimination in out-of-hospital cardiac arrest care: A case-control study. Eur J Cardiovascular Nurs 2017; 17: 505–12.

Wiggins CL, Harlan LC, Nelson HE, et al. Age disparity in the dissemination of imatinib for treating chronic myeloid leukemia. Am J Med 2010; 123: 764.e1–9.

Williams A. Intergenerational equity: An exploration of the ‘fair innings’ argument. Health Econ 1997; 6:11732.

85 Wonnacott A, Meran S, Roberts G, et al. Applying estimated glomerular filtration rate to an ageing population: Are we in danger of becoming ageist? Eur J Int Med 2012; 23: 705–10.

Woodard S, Nadella PC, Kotur L, et al. Older women with breast carcinoma are less likely to receive adjuvant chemotherapy: Evidence of possible age bias? Cancer 2003; 98: 1141–49.

World Health Organization. Global strategy and action plan on ageing and health. Geneva: 2017. Licence: CC BY-NC-SA 3.0 IGO. Available at https://www.who.int/ageing/WHO-GSAP-2017.pdf; accessed 22 February 2020.

World Health Organization. Integrated care for older people: Guidelines on community-level interventions to manage declines in intrinsic capacity. Geneva: World Health Organization; 2017a. Licence: CC BY-NC-SA 3.0 IGO.

World Health Organization. World report on ageing. Geneva: World Health Organization; 2015. ISBN 978 92 4 156504 2.

World Medical Association Declaration of Helsinki. Ethical principles for medical research involving human subjects. Bull World Health Organ 2001; 79: 373–74.

Wu B, Haigh PI, Hwang R, et al. Underutilization of parathyroidectomy in elderly patients with primary hyperparathyroidism. J Clin Endocrinol Metab 2010; 95: 4324– 30.

Wyman MF, Shiovitz-Ezra S Bengel J. Ageism in the health care system: providers, patients, and systems. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 19; 2018. Springer: Cham, Switzerland.

Yee KW, Pater JL, Pho L, et al. Enrollment of older patients in cancer treatment trials in Canada: why is age a barrier? J Clin Oncol 2003; 21: 1618–23.

Yellen S, Cella D, Leslie W. Age and clinical decision making in oncology patients. J Natl Cancer Inst 1994; 86: 1766–70.

Zulman DM, Sussman JB, Chen X, et al. Examining the evidence: A systematic review of the inclusion and analysis of older adults in randomized controlled trials. J Gen Intern Med 2011; 26: 783–90.

86 PART 3 THE IMPACT OF AGEISM AMONG LEGAL PROFESSIONALS ON OLDER PEOPLE: A SYSTEMATISED REVIEW

CHAPTER 1: INTRODUCTION

1.1 Background Legal professionals – judges, barristers, solicitors – play a key role in promoting and protecting the autonomy, rights and interests of older people. They are essential in assisting older people to make informed choices about many aspects of their lives, including health care, financial management, advanced care planning and accommodation (Tilse, 2002). However, the legal needs of older people may be challenged by ageism – stereotypical attitudes and behaviours towards people based on age. If legislation or policy is based on age stereotypes, it is likely to have negative effects on older people (LCO, 2012). It is therefore important to explore how ageism affects the ways that legal professionals practise and make decisions concerning older adults (Whitton, 1997).

Ageing is highly diverse, involving a lifelong series of transitions that occur at different times for different people. ‘Older people’ are not a homogenous group, and their legal needs will differ depending on individual circumstances and factors such as health status, economic and social circumstances, and cultural and regional influences (WHO, 2015).

Ageism may operate in the law in several ways (LCO, 2012). Specifically, ageism within law, policy and practice may involve decisions or actions that have a differential impact on the older population as a whole, or a negative impact on specific segments of the older population (Spencer, 2009). Ageism may involve acts or omissions that favour younger people at the expense of older people, failing to recognise diversity within age groups or similarities across age groups.

In some cases, legislation that is particularly relevant to older people may appear neutral, but in practice is ageist because it has a disproportionate impact on older adults (Love et al, 2013). In other instances, laws may be non-ageist, but ageist

87 attitudes and stereotypes among legal professionals who implement the laws can make them ineffective for older people or increase the likelihood that an older person’s autonomy will not be respected (Spencer, 2009; Hall, 2009). For example, if a competent older person attempts to make a decision for themselves but is undermined by a legal professional who thinks that older people should defer to others, then the laws regarding consent and capacity will be ineffective (LCO, 2012).

Evidence from case law has shown that merging lack of capacity and undue influence – which are different concepts – contributes to the idea that older people may automatically lack capacity (Vines, 2018). Ageist views about older people may be implicit in the way that many undue influence cases in probate law are decided. There are also assumptions that older people with cognitive disabilities such as dementia lack capacity, which automatically robs older people of their due autonomy (O’Neill & Peisah, 2019). This type of implicit or subtle ageism is often hard to see, and may be unconscious on the part of those taking action. This has been rarely explored in the literature.

Ageism may manifest as paternalism, for example, by not involving older people in discussions about their legal needs and talking directly to family members instead, or by removing decision-making opportunities for older people under the guise of protecting their interests (LCO, 2012). If this is normalised or dismissed by legal professionals, then key opportunities for advocacy are missed.

The Australian Parliament’s Older People and the Law Report (2007) found that, “the existing legal system is not well equipped to meet the legal needs of older people, who often have complex needs but require low cost solutions that are targeted and delivered in a specific way” (Parliament of Australia, 2007). Policy has not adequately recognised the distinctive nature of the challenges older people face regarding access to justice (Sage-Jacobson, 2015). Despite the existence of anti-age discrimination legislation, the Law Council of Australia’s Justice Project (2018) found that multiple barriers still exist in terms of older people’s access to justice, including ageism, restricted access to legal assistance and power imbalances in modes of representation (LCA, 2018).

88 1.2 Rationale Ageism has been researched more extensively in health care than other fields. However, there is growing opinion among researchers that, because ageism can be found across all aspects of society and within multiple institutions and organisational practices, research and responses to ageism should be multidisciplinary, involving collaboration within and between services such as healthcare, legal and financial services, government and non-government organisations, advocates and policymakers (Doron, 2019; Phelan, 2020).

In Australia, the protection of older people’s rights has been the subject of greater scrutiny as part of the Australian Law Reform Commission Inquiry’s ‘Protecting the Rights of Older Australians from Abuse’, and has led to calls for legal professionals to take on a greater role in safeguarding older people (ALRC, 2016). For these reasons, Barry has argued that, “The professions need to examine their practices when it comes to working with older clients” (Barry, 2017; p268).

Very little empirical legal research has been conducted in this area, despite a growing interest and awareness of issues relating to ageing and ageism (Doron et al, 2018). Legal research is often textual, based on the interpretation of cases and legislation (Doron & Hoffman, 2005). There have been calls to engage in more empirical legal studies that are underpinned by rigorous methodology and data analysis, similar to the methodology used in medical research (Doron, 2019; Eisenberg, 2011; Getman, 1985). In 1985, Getman stated that, “Empirical study has the potential to illuminate the workings of the legal system, to reveal its shortcomings, problems, successes, and illusions” (Getman, 1985; p489). More recently, Doron, a legal scholar in gerontology, argued that there is, “an almost total lack of empirical studies regarding the attitudes of lawyers towards older clients, and regarding experiences of older clients in their encounter with lawyers and with the legal system. More empirical research is needed in this field in order to better understand the inter-connection between law and ageism” (Doron et al, 2018; p316).

89 1.3 Aims This review aims to assess the impact of ageist actions and decisions by legal professionals on older people. A systematised review was chosen as the methodological approach. This type of review, “includes one or more elements of the systematic review process while stopping short of claiming that the resultant output is a systematic review” (Grant & Booth, 2009; p102). The purpose of choosing this approach was to be as consistent as possible with the major section of this project – the systematic review of ageism among healthcare professionals. This review forms part of a multidisciplinary investigation into ageism among frontline workers who interact closely with older people: healthcare, legal and financial professionals.

1.4 Hypothesis The study hypothesis was that ageism among legal professionals will adversely impact interactions with older people, including the provision of services and advice.

90 CHAPTER 2: METHODS

2.1 Approach A systematised review was chosen as the method to review the empirical law literature as it pertains to ageism. This type of review does not represent a compete systematic review but includes several elements of systematicity. In this review, these were: the a priori specification of the research hypotheses, the comprehensive search of multiple databases using defined search terms plus additional hand searches of key journals and reference lists, the determination and application of explicitly defined inclusion and exclusion criteria and the tabulation of data (Grant & Booth, 2009). Like the systematic review in health care, the results were narratively synthesised.

2.2 Methodology A literature search was conducted across multiple Australian and international databases to locate relevant empirical peer-reviewed original research in law. Database searches for English language texts were conducted via HeinOnline, EBSCOhost Legal Source, Informit, ProQuest, Lexis Advance, WestLawAU and JSTOR. Searches took place in March and April 2020. Google Scholar was also searched via an automatic monthly search from February 2019 to April 2020.

Several journals were hand-searched including the Journal of Empirical Legal Studies, the Journal of Elder Abuse and Neglect, Elder Abuse Law Review, Theoretical Inquiries in Law, Journal of International Law, Aging and Policy, Law and Human Behaviour, Journal of Social Welfare and Family Law and the Journal of Law, Medicine and Ethics. Reference lists of included studies as well as key reviews and papers were also hand searched to identify any further sources of data.

In order to be consistent with the systematic review and specifically examine the impact of ageism among legal professionals on older people, a similar combination of four fields of search terms were used: • Ageism, ageist, age discrimination, age bias, age prejudice, age stereotype, age disparities, age inequalities, age exclusion • Legal professional, lawyer, barrister, judge, solicitor, estate planner, prosecutor, legal services, law firm, legal ethics, professions-law and legislation

91 • Old, older, older people/clients/consumers, elder, elderly, elderly people/clients, aged, geriatric, seniors, aged, ageing • Impact, outcome, consequences, approach, perspective, perceptions, reaction, stance, response, management, behaviour, demeanour, attitudes, response.

2.3 Inclusion and exclusion criteria This review only included empirical research that examined the potential link between ageism/age discrimination/age bias and outcomes/management decisions by legal professionals. No date restrictions or geographical restrictions were set. The limited availability of quantitative legal research was apparent during the initial scoping review, so the eligibility criteria included mixed-methods studies.

2.3.1 Inclusion criteria • Research studies that focus on ageism/age bias/age disparities/age-related inequalities • Ageism on the part of legal professionals • Studies that report the impact (outcomes, consequences) of ageism by legal professionals on older people • Original quantitative or mixed-methods research • Articles published in peer-reviewed journals • Articles published in English.

2.3.2 Exclusion criteria • Studies not focused on ageism/age bias/age disparities/age-related inequalities • Studies focused on ageism on the part of other (non-legal) professionals, general community members (including reports by clients or consumers about perceived ageism/self-perceptions about ageism) • Investigation of ageism towards younger people • Studies that do not report any actual outcomes • Qualitative studies • Non-empirical research e.g. case review, commentary, letters • Studies not published in a peer-reviewed journal • Articles not in English.

92 CHAPTER 3: RESULTS

3.1 Search results Compared with health care, very few empirical, peer-reviewed studies met the inclusion criteria. In the course of this review, eleven quantitative or mixed-methods research papers were identified that specifically examined real-life examples of age- based actions among legal professionals and the outcomes for older people (Table 3.1). The majority of studies were from the United States (n=9). One study was from Canada and one was from Northern Ireland. No eligible studies from Australia were identified.

Nine studies focused on age-based sentencing decisions involving older defendants. One study from Northern Ireland examined inequitable access to justice for older victims of crime (Brown & Gordon, 2019), while one study from the United States focused on ageism in adult protection, guardianship and conservatorship statutes and judgments (Whitton, 1997).

Age-based terminology varied across the included studies. For example, one study talked about ageism in the legal profession (Whitton, 1997) while one described ageism in sentencing procedures (Love, 2013). Three studies described ‘age disparities’ or ‘sentencing disparities’, while two studies talked about ‘age differences’. One study focused on ‘unequal access to justice for older people’, one study examined ‘age selectivity’, one described ‘age effects’ and one focused on the ‘impact of older age’.

The definitions of ‘older’ also varied between studies. For example, one study described several ‘older age groups’ as 50–54 years, 55–64 years and 65 years and older; others defined ‘older’ as 55, 60 or 65 years and older.

The majority of studies performed database analyses of cases (n=8). One performed an analysis of quantitative and qualitative data, one developed and mailed customised surveys and one study performed a random sample review of cases.

93 3.2 Synthesis of eligible studies 3.2.1 Age-based sentencing of older offenders Eight North American studies conducted database analyses to examine the impact of age on sentencing and found that age plays a role in sentencing decisions in both federal and state cases, even after controlling for crime-related variables. Age was shown to have an independent effect on sentencing in many cases and also interacts with race and gender (Steffensmeier et al, 1998; Steffensmeier et al, 2017).

The outcomes of studies analysing sentencing across age groups show that older age is generally associated with more lenient treatment in the judicial system. For example, a study looking at sentencing disparities reviewed 175,000 criminal law cases in the US state of Pennsylvania to assess whether older offenders received particularly lenient sentencing compared with younger offenders (Steffensmeier & Motivans, 2000). The study found that defendants who were aged in their 60s received more lenient sentences than those aged 21–49 years. The probability of defendants in their 60s being incarcerated was almost 25% less than defendants in the 21–29 age group; this rose to 30% for those aged 70 years and older. Those aged in their 60s and 70s were given the most lenient sentences overall. The age disparities were greatest in property disputes and violent offending and less evident in drug cases.

Another study examined sentencing for several older age groups (50–54 years, 55–64 years, 65 years and older) and found that those aged 65 and older were significantly less likely to be sentenced to prison than the other groups (Blowers & Doerner, 2015). However, when the older offenders did receive custodial sentences, they were given longer sentences than the 50–54 age group.

A Canadian study examined the impact of older age (defined as 60 years and older) on sentencing by analysing legal texts that specifically addressed the age of the offender in the written judgment (Love et al, 2013). The study included 221 trial and appeal- level decisions over a 4-year period to determine whether age-based factors contributed to sentencing. The results showed that older age mitigated sentencing, usually through shorter prison terms, serving custodial time in the community, house arrest, suspended sentences or conditional sentences. In some cases, age was a factor in granting concurrent rather than consecutive sentences, even for serious crimes. Some judges did not give a reason as to why older offenders received a lighter

94 sentence, but age was listed as a mitigating factor. In other cases, the reasoning was given, for example: older people have less time to live; they find it more difficult to be in prison; and they may die in prison. Poor health was a mitigating factor independent of age. In some cases, age did not favour the older offender, for example, when there was a pattern of criminal behaviour or when it would reward the concealment of crimes. In a few cases, age worked against the offender because the accused “should have known better.” The latter may be a result of negative ageism in that older people were held to a higher standard, but this was a minority response. Considering older people as weakened and unable to cope in prison can also be indicative of ageism.

One study examined why such leniency in sentencing occurs and explored whether judges would acknowledge their observed leniency with older offenders (Smith & Shriver, 2018). Using mailed surveys to capture judicial attitudes of 212 US trial court judges, the study found that only 31% of respondents acknowledged treating older offenders with greater leniency. When asked about factors were more important in sentencing older versus younger offenders, these judges rated ‘offender having cognitive impairment’ and ‘offender’s criminal history’ as the most important factors in their decision-making with older offenders. Other non-legal items considered more important for older offenders compared with younger offenders were current health problems, life expectancy, potential health care costs and ability to adjust to prison (Smith & Shriver, 2018).

3.2.2 Age-based inequity of access to justice Contrasting results were seen in a mixed-methods study from Northern Ireland that used outcome data to examine inequitable access to justice for older victims of crime (Brown & Gordon, 2019). In Northern Ireland, outcome data is considered the most appropriate quantitative indicator to reveal the extent to which procedural justice for victims of crime is obtained. Outcomes include charging the accused or issuing a summons, caution or penalty notice for the offence, or giving the accused a discretionary disposal such as a referral to restorative justice (Brown & Gordon, 2019).

The study found that there were lower outcome rates for common categories of crime committed against older adults (defined as people aged 65 and older) than for similar crimes committed against younger adults. Age differences in legal outcomes were seen in categories of burglary, criminal damage, theft from a vehicle and of a vehicle,

95 and were consistent across 10 years of data. In the qualitative analysis, these findings were associated with a failure to adequately take into account the additional factors that can disproportionately impact older victims’ ability to engage with the justice process, including fear of (or actual) intimidation, self-stereotyping perceptions of not wanting to be a burden and the stress of giving evidence in court.

There were also certain types of crime that disproportionately affected older people, such as elder abuse and burglaries that involve deliberate targeting of homes occupied by older people. The study found that these latter cases involving older people can be particularly challenging for legal professionals in terms of gathering sufficient evidence for a case to proceed to court, for example, if the victim is fearful of reprisals, or embarrassed or ashamed in cases involving breaches of trust or deception (Brown & Gordon, 2019).

3.2.3 Older age and adult protection, guardianship and conservatorship A US study that specifically examined ageism in the legal profession involved a survey of adult protection, guardianship and conservatorship statutes in the US and analysis of a random sample of guardianship, will and trust contest cases (Whitton, 1997). The study looked for signs of characteristic signs of ageism such as prejudicial stereotypes, paternalism and age-based professional interventions. The study found that older age was used independently as justification in adult protection legislation when determining whether someone was impaired, incapacitated or disabled.

The random sample of case law found examples of older age being used to justify imposition of guardianship, and to determine cases of undue influence in trust and will contest cases – in effect, age was used as an independent basis for terminating personal and legal autonomy (Whitton, 1997). Ageist, paternalistic language was also seen in the wording of multiple judgments. The study demonstrates that explicit and implicit ageism can substantially influence the quality of legal services provided to older people.

96 3.3 Conclusion The results of this review demonstrate both negative and positive associations between age-based actions among legal professionals and impacts on older people. The studies that examined sentencing of older versus younger offenders found that older people were given more lenient sentences than younger offenders and were less likely to be sentenced to prison compared with younger offenders. Older offenders were generally given shorter sentences – with the exception of one study that found people aged 65 or older were less likely to receive prison terms than those aged 50– 54 but were given longer sentences when they did receive a custodial sentence. Despite these disparities in sentencing across age groups, a study directly assessing judges’ sentencing decisions found that only 31% of judges acknowledged being more lenient with older offenders (Smith & Shriver, 2018).

In the other two studies – one looking at the outcomes of crimes committed against older people and one examining ageism in adult protection and guardianship – older people had less access to justice or were subject to ageist actions and behaviours, leading to adverse outcomes for older people (Brown & Gordon, 2019; Whitton, 1997).

97

Table 3.1. Eligible studies examining ageism among legal professionals and outcomes for older people.

Author, Study Aims Focus Design, methods Main outcomes Findings year population, terminology setting

Brown & Levels of To explore the Unequal Mixed-methods Most common Lower outcome rates for older victims of Gordon, recorded concept of access to data analysis to categories of crime crime than for similar crimes committed 2019 crime in access to procedural explore the reported to the against younger adults. This was Northern procedural justice interactions of police by older associated with a failure to adequately take Ireland; justice for older people with people; likelihood a into account the additional factors that may outcomes, older victims the criminal crime reported to disproportionately impact older victims’ interviews justice system of the police by an ability to engage with the justice process with older Northern Ireland older person will including self-ageist attitudes and fear of people, legal reach outcome; intimidation officials, factors that may police impact on the outcome rate

Blowers & 18,199 cases To determine Age Database analysis Impact of age on Offenders in the ‘young-old’ category (50– Doerner, compiled by whether disparities looking at age and older offenders; 54 years old) had the highest odds of 2013 the United judges provide other variables differences across incarceration, while the so-called ‘oldest- States leniency when and impact on age groups; which old’ (those aged 65 and over) had the Sentencing sentencing sentencing groups received lowest odds of incarceration. Sentence Commission older prison sentences length: opposite findings i.e. ‘oldest-old’ offenders and length of received longer sentence terms than the sentence ‘young-old’. Researchers unable to ascertain what other factors played into the latter findings

98 Cutshall & 745 criminal To examine Age Analysis of past Sentencing Prosecutors selectively enforced legal Adams, prosecutions age-selectivity selectivity criminal differences across norms against shoplifting; offender's age 1983 in the District at several convictions from different age was a consideration in such decisions. of Columbia, decision one US district groups: 17–25 After controlling for other variables, older USA points for a years; 26–49 years; (aged 50 and older) shoplifters were specific type 50+ years prosecuted less often than those aged 26– of criminal 49 but no less often than younger offense: shoplifters shoplifting

Doerner & 59,897 cases To examine Age Database analysis The effects of Hispanics and blacks, males, and younger Demuth, compiled by independent disparities looking at race/ethnicity, defendants receive harsher sentences than 2010 the United and joint independent gender and age whites, females, and older defendants after States effects of effects of race/ affected sentencing controlling for legal and contextual factors. Sentencing race/ethnicity, ethnicity, gender, outcomes; sentence In terms of age, the youngest defendants Commission gender, and age on outcomes of (aged 18–20) were most likely to receive age on incarceration and specific race/ethnic- prison sentences. The odds of incarceration sentencing sentence length gender-age-specific among defendants aged 60 and over were decisions in defendant approximately 40% lower than those US federal subgroups among defendants aged 18–20 years courts

Love et al, 221 trial and To determine Ageism in Case law review Whether older age Older age mitigated sentencing e.g. 2013 appeal-level how, why, and sentencing using database reduced sentencing through shorter prison terms, serving decisions over in what way practices analysis to find duration; why custodial time in the community, house a 4-year advanced age cases that judges factored in arrest, suspended sentences or conditional period in influences the specifically age in sentencing; sentences. Age was a factor in granting Canada exercise of considered the justifications in concurrent rather than consecutive judicial older age of the sentencing sentences. discretion in defendant in Judges’ reasoning, when given, included: the sentencing sentencing older people have less time to live; they of older find it more difficult to be in prison; and adults. they may die in prison

99 Smith & 212 judges To examine Impact of Mailed surveys; Whether judges 31% of the sample reported that they tend Schriver, presiding over whether older age on Judges randomly treated older to sentence older offenders with more 2018 state criminal judges report sentencing sampled from offenders with more leniency. Of these, the majority cited trials in the being more nationwide leniency; how age cognitive impairment and previous US lenient with database; all is rated as a factor criminal history as being more important older states equally in decision-making in sentencing of older offenders vs offenders and represented younger offenders. Researchers concluded the factors that, to an extent, judges may not like to they consider admit to that they treat groups of offenders in making differently or they might not be aware of sentencing how much they weight personal decisions with characteristics of offenders when making this population decisions about sentencing

Steffensmeier 120,300 To examine Age Used state-wide Whether older Age had a curvilinear relationship with et al, 1995 cases in age differences data to analyse defendants receive sentencing discrimination: young (aged Pennsylvania differences in in sentencing data sentences similar to 18–19) and older (aged 60 and older) sentencing e.g. sentencing variables and those given to offenders received most lenient sentences whether to outcomes across younger compared to those aged in ‘middle’ age incarcerate age groups defendants; whether groups (20–60); the age-sentencing and length of older defendants relationship became linear from about age prison receive harsher 30; older offenders were also less likely to sentence sentences or more receive prison terms lenient sentences.

100 Steffensmeier 139,000 To examine Sentencing Used state-wide Effects of age, Age, race and gender all had independent, et al, 1998 cases in age, gender disparities data to analyse gender and race on significant direct effects on sentence Pennsylvania and race sentencing data sentencing duration outcomes. Gender effects were the largest, differences in variables and and likelihood of followed by age and then race. The sentencing and outcomes across prison time; the influence of age depends on gender: age is how these age groups, interplay of these more influential in the sentencing of male variables gender and race variables in than female offenders. The influence of interact to and the interplay sentencing race among males depends on the affect of these variables offender’s age: race is a more influential sentencing factor among younger males than older males; the small main effect for race masks considerable variation in race differences across age groups. Overall: young black males are sentenced more harshly than any other group

Steffensmeier 175,000 To determine Age Used state-wide Impact of age and Older offenders of both genders were & Motivans, criminal law whether older differences data to generate gender on decisions sentenced less harshly and were less likely 2000 cases in defendants in logit models to to incarcerate; to be imprisoned than younger people; if Pennsylvania receive more sentencing assess effects of impact of age and imprisoned, older defendants received lenient ageing on gender on length of shorter prison terms. sentences than decisions to prison terms The advantage for older people was younger ones; incarcerate and diminished in cases of drug offending examine used ordinary effects of age least squares on sentencing models to assess for male and effects on the female length of-term offenders decisions

101 Steffensmeier 470,000 To examine Age effects Used state-wide Effects of age, Race, ethnicity, gender and age had et al, 2017 criminal law the data to analyse gender and race on substantial main effects on sentence cases in independent sentencing data sentencing duration outcomes. The interaction of these Pennsylvania effects of race, variables and and likelihood of variables significantly influenced ethnicity, outcomes across prison time sentencing. Gender affects were largest; gender and age groups, The interplay of age influences sentence severity in a age on gender and race these variables in curvilinear fashion, with offenders aged sentencing and the interplay sentencing. over 50 and under age 21 given the least outcomes, as of these variables severe sentences, while those aged 21 to well as 34 received the harshest sentences. The important joint main effects of race/ethnicity, gender and effects based age mask considerable variation in the on multiple ways these are influenced by each other defendant characteristics

Whitton, Random To determine Ageism in Random sample Existence of Older age was used independently in adult 1997 sample of case whether the the legal of case law prejudicial protection legislation when determining law manifestations profession involving adult stereotypes, whether someone was impaired, of ageism protection, paternalism and incapacitated or disabled. found in guardianship, and age-based Older age was used to justify imposition of society-at- conservatorship professional guardianship, and to determine cases of large also exist statutes in the interventions undue influence in trust and will contest in the legal United States, will cases. profession and trust contest Paternalistic language was used in the cases and wording of multiple judgements professional practices dealing with guardianship

102 CHAPTER 4: DISCUSSION

The purpose of this systematised review was to examine how ageism on the part of legal professionals impacts older people. This was achieved by performing a literature review to identify and synthesise empirical quantitative or mixed-methods research that focused on age-based decisions in legal practice.

The handful of eligible studies provide some preliminary support for the study’s hypothesis that ageism adversely impacts interactions with older people, including the provision of services and advice, among legal professionals. In included studies, older people were either treated differently compared with younger people, or age was used to justify removal of older people’s autonomy and rights.

The results demonstrate both negative and positive associations between age-based actions among legal professionals and impacts on older people. For example, the results from criminal sentencing studies, which accounted for the majority of the empirical research, showed more positive ageism associations in the treatment of older offenders. In these studies, older people were treated more leniently than younger people overall, generally receiving lighter sentences and fewer custodial sentences, even after controlling for crime-related variables.

While many accounts and descriptions of ageism are negative, ageism can also benefit older adults, as shown in these sentencing trials. Palmore defines ageism as, “any prejudice or discrimination against or in favour of an age group” (Palmore, 1999; p4). Palmore argues that positive ageism exists where positive stereotypes are relied upon, or even where negative stereotypes create a positive result (Palmore, 1999). So- called ‘pseudo-positive’ ageism shows itself in various patronising responses to older adults.

Steffensmeier proposed the focal concerns theory to explain positive ageism in sentencing (Steffensmeier et al, 1998). This theory proposes that judges may be influenced by three focal concerns when making sentencing decisions: blameworthiness, protection of the community, and practical constraints and consequences. Blameworthiness is related to the degree of harm the offender caused and is associated with a retributive philosophy in sentencing. It is considered the most

103 important factor in sentencing decisions overall (Steffensmeier et al, 1998). Protection of the community is related to the risk that the offender will do further harm and the subsequent need to incarcerate offenders to prevent recidivism. Practical constraints and consequences relate to how sentencing decisions impact the functioning of the criminal justice system as well as the circumstances of individual defendants, their families and communities.

The sentencing of older offenders may be impacted by each of these focal concerns and may be tainted by ageist attitudes and actions. Judges may believe that older offenders are less blameworthy due to presumed cognitive decline associated with ageing; they may think that older offenders will cost the judicial system more money because of perceived age-related health declines or think that they pose less of a risk because crime rates are lower for older people. Given that multiple variables play a role in sentencing, some judges may not recognise or wish to acknowledge the impact of an offender’s age on their decision-making (Smith & Shriver, 2018). Certain characteristics of judges such as their own age, experiences, time on the bench and anxieties about ageing may influence their sentencing decisions due to conscious or unconscious age biases (Smith & Schriver, 2018). Paternalism may encourage ageism, even in those with good intentions (Kagan & Maloney, 2017).

As in health care, the provision of legal services and advice is affected by multiple individual- and structural-level factors. In a series of studies by Steffensmeier and colleagues (1998, 2000, 2017), multiple factors including age, race and gender had separate and interactive effects on outcomes. Commonly held perceptions and assumptions about certain groups affected legal attributions of culpability and criminal risk, such that a collection of certain offender characteristics led to severe sentences for some defendants and greater leniency for others (Steffensmeier et al, 2017).

The researchers argue that these factors are best not studied alone because, “these social statuses and their intersectionality are not just individual attributes but cultural categories that shape the distribution of sanctions and criminal punishment” (Steffensmeier et al, 2017). In other words, the intersection of ageism, sexism and racism has a substantial effect on offender outcomes. For example, young adult black or Hispanic males had approximately 25% greater probability of incarceration than

104 older white female defendants, and a 30% greater probability of incarceration than the youngest white, black and Hispanic females.

Conversely, ageism was associated with negative outcomes for older people in the two studies that did not examine criminal sentencing judgments – one study investigated outcomes for crimes committed against older people (Brown & Gordon, 2019) and one examined ageism in adult protection, guardianship and conservatorship statutes as well as will and trust contest cases (Whitton, 1997).

The actions by legal professionals in these two latter studies may reflect a range of ageist views about older people. For example, that older people lack capacity and require others to take over decision-making, resulting in a loss of autonomy for that person. They may indicate paternalism, viewing older people as being frail and vulnerable and in need of protection. They may also reflect the view that older people are less deserving of appropriate and timely care compared with younger people, or demonstrate a disinterest in meeting the legal needs of older people.

The findings of negative outcomes associated with age-based decision-making are consistent with the findings of the healthcare systematic review in which ageism- health associations had overwhelmingly negative consequences for older people. They also align with results from age discrimination cases in employment, which have shown that employers are significantly less likely to hire older workers than younger applicants, older workers in employment have less access to training, and those who face ageism in the workplace are more likely to retire early (Chang et al, 2020).

At the structural level, access to justice policies do not prioritise the distinctive legal needs of older people, and there is a lack of empirical research that has examined what the real barriers are in terms of access to justice for older people (Sage- Jacobson, 2015). Looking at the larger body of evidence it can be argued that ageism on the part of legal and other professionals is associated with different, often negative, outcomes for older people compared with younger counterparts, and that ageist attitudes and approaches can lead to suboptimal management of older people.

105 By and large, the majority of studies focused on sentencing, with a handful of studies examining victims of crime. What was striking was the paucity of studies that examined the professional relationship between lawyers and older clients. What remains unelucidated is the attitudes and professional behaviour of lawyers in relation to older clients. Is there less professional zeal towards older clients? Are societal injustices dismissed as normative? Further research is required to examine these issues.

Limitations Overall, interpretation of the results is limited by the small number of studies that were eligible. In addition, the majority of eligible studies involved US sentencing judgments, which limits generalisability to other regions.

This study was purposefully focused on ageism on the part of legal professionals. Further examination of the wider legal system including human rights frameworks and case law would yield a broader perspective. When it comes to research examining ageism and the law, traditional scholarship is usually normative and based on textual analysis; case law plays a leading role in the development of jurisprudence (Doron, 2019). Quantitative empirical research is not common in law and applying a similar approach used in health care to the field of law is not routine. These factors limit the type and quantity of evidence eligible for inclusion.

In addition, the review only included quantitative or mixed-methods analysis. Quantitative analysis can overlook nuances in judicial reasoning, and obscure important trends in judicial analysis (Blackham, 2020). Inclusion of more qualitative research may have elucidated some of the observed actions seen in the included studies.

However, a key aim of this review was to be consistent with the major section of this project – the systematic review in health care – to ascertain whether similar types of empirical evidence existed in the field of law and if so, what the evidence revealed.

106

Conclusion This systematised review – part of a larger project looking at ageism among frontline professionals – examined age-based actions of legal professionals and how these impact older people. The review found few studies that have performed empirical quantitative or mixed-methods research in this area. Eligible studies showed positive and negative outcomes for older people – in effect, older people were treated differently to younger people. Whether these decisions were driven by ageist attitudes and prejudices cannot be definitively ascertained. However, whether it is via implicit or explicit ageism, stereotyping and discriminating against people on the basis of their age can lead to significant, overt consequences for older people in the community. Further empirical research is warranted to examine the association between ageism and the provision of legal services for older people.

107 References Australian Human Rights Commission. Fact of fiction? Stereotypes of older Australians. Research report; 2013.

Australian Law Reform Commission. Elder Abuse: National Plan. 2016. Available at https://www.alrc.gov.au/wp-content/uploads/2019/08/dp83_02._national_plan.pdf; accessed 14 July 2020.

Barry L. Capacity and Vulnerability: How Lawyers Assess the Legal Capacity of Older Clients. J Law and medicine 2017; 25: 267–82.

Blackham A. Why Do Employment Age Discrimination Cases Fail? An Analysis of Australian Case Law. The Sydney Law Review 2020; 42: 1–35.

Blowers AN, Doerner JK. Sentencing outcomes of the older prison population: An exploration of the age leniency argument. Journal of Crime and Justice 2013; 38: 58– 76.

Brown KJ, Gordon, F. Older victims of crime: Vulnerability, resilience and access to procedural justice. International Review of Victimology 2018; 25: 201–21.

Callan MJ, Dawtry RJ, Olson JM. Justice motive effects in ageism: The effects of a victim's age on observer perceptions of injustice and punishment judgments. J Experimental Social Psychology 2012; 48: 1343–49.

Chang E-S, Kannoth S, Levy S, et al. Global reach of ageism on older persons’ health: A systematic review. PLoS ONE 2020; 15: e0220857.

Cutshall CR, Adams K. Responding to older offenders: Age selectivity in the processing of shoplifters. Criminal Justice Review 1983; 8: 1–8.

Doerner JK, Demuth S. The independent and joint effects of race/ethnicity, gender, and age on sentencing outcomes in U.S. federal courts. Justice Quarterly 2010; 27: 1– 27.

Doron I. 25 Years of Elder Law: An Integrative and Historical Account of the Field of Law and Aging. Theoretical Inquiries in Law 2019; 21: 1–24.

Doron I, Numhauser-Henning A, Spanier B, et al. Ageism and anti-ageism in the legal system: A review of key themes. In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland.

Doron I, Hoffman A. Time for law: Legal literacy and gerontological education, Educational Gerontology 2005; 31: 627–42

Eisenberg T. The Origins, Nature, and Promise of Empirical Legal Studies and a Response to Concerns. Cornell Law Faculty Publications 2011; 974.

Getman JG. Contributions of Empirical Data to Legal Research. J Legal Education 1985; 35: 489–94.

Grant MJ, Booth A. A typology of reviews: an analysis of 14 review types and associated methodologies. Health Info Lib J 2009; 26: 91–108.

108 Hall M. Developing an anti-ageist approach within law: advancing substantive equality for older persons through law, policy and practice. July 2009. Available at: https://www.lco-cdo.org/wp-content/uploads/2014/01/older-adults-commissioned- paper-hall.pdf; accessed 4 August 2020.

Kagan SH, Maloney KW. Cancer screening and early detection in older people: Considerations for nursing practice. Sem Oncol Nursing 2017; 33, 199–207.

Law Council of Australia. The Justice project: Final report. August 2018.

Law Commission of Ontario. A framework for the law as it affects older adults: Advancing substantive equality for older persons through law, policy and practice Toronto: April 2012.

Love H, Kelly F, Doron I. Age and Ageism in Sentencing Practices: Outcomes from a case law review. Can Crim Law Review 2013; 17: 253–79.

Mueller-Johnson K, Toglia M, Sweeney CD, Ceci SJ. The perceived credibility of older adults as witnesses and its relation to ageism. Behav Sci Law 2007; 25: 355–75.

Office of the High Commissioner for Human Rights (OHCHR). Normative standards in international human rights law in relation to older persons – Analytical outcome paper: 2012.

O’Neill N, Peisah C. Capacity and the Law. 3rd Edition. Sydney University Press, 2011.

Palmore E. Ageism: Negative and positive. New York: Springer, 1999.

Parliament of the Commonwealth of Australia. Older people and the law. House of Representatives Standing Committee on Legal and Constitutional Affairs. September 2007 Canberra. Available at: https://www.aph.gov.au/parliamentary_business/committees/house_of_representative s_committees?url=/laca/olderpeople/report.htm; accessed 1 August 2020.

Phelan A. Introduction. In: Advances in Elder Abuse Research: Practice, Legislation and Policy. Phelan A (ed). 2020: 1–11. Springer, Cham, Switzerland.

Sage-Jacobson S. Access to justice for older people in Australia. Ageing Law 2015; 33. Available at https://journals.latrobe.edu.au/index.php/law-in- context/article/view/58; accessed June 15 2020.

Smith MS, Schriver JL. Judges’ sentencing decisions with older offenders. Psychol Crime Law 2018; 24: 105–16.

Spencer C, on behalf of the Law Commission of Ontario. Ageism and the law: emerging concepts and practices in housing and health. Advancing Substantive Equality for Older Persons through Law, Policy and Practice: August 2009. Available at https://www.lco-cdo.org/wp-content/uploads/2014/01/older-adults-commissioned- paper-spencer.pdf; accessed 12 May 2020.

Steffensmeier D, Kramer J, Ulmer J. Age differences in sentencing. Justice Quarterly 1995; 12: 583–602.

109 Steffensmeier D, Kramer J, Ulmer J. The interaction of race, gender, and age in criminal sentencing: the punishment cost of being young, black, and male. Criminology 1998; 36: 763–98.

Steffensmeier D, Motivans M. Older men and older women in the arms of criminal law: offending patterns and sentencing outcomes. J Gerontology: Soc Sci 2000; 55B: S141–51.

Steffensmeier D, Painter-Davis N, Ulmer J. Intersectionality of race, ethnicity, gender, and age on criminal punishment. Sociol Perspect 2017; 60: 810–33.

Tilse C, Setterlund D, Wilson J, Herd B. Legal practitioners and older clients: Challenges and opportunities for effective practice. Elder Law Review 2002; 1: 34–42.

Vines P. Undue influence and testamentary freedom: Which human right for the elderly? Obligations Conference: A biennial conference on the law of obligations. Melbourne; 17–20 July, 2018.

Wasarhaley NE, Golding JM. Ageism in the courtroom: mock juror perceptions of elder neglect. Psychol Crime Law 2017; 23: 874–98.

Whitton LS. Ageism: Paternalism and Prejudice. DePaul L Rev 1997; 46: 453.

World Health Organization. World report on ageing. Geneva: World Health Organization; 2015. ISBN 978 92 4 156504 2.

110 PART 4 THE IMPACT OF AGEISM AMONG FINANCIAL PROFESSIONALS ON OLDER PEOPLE: A SYSTEMATISED REVIEW

CHAPTER 1: INTRODUCTION

1.1 Background Public discourse about the economics of population ageing often highlights the perceived impending threats that older people pose (the so-called ‘grey tsunami’) in terms of spending and allocation of resources (Ayalon, 2020) – in other words, older people are considered an economic threat to society. Ageism is inherent in many descriptions of older people as an unaffordable social and economic burden; such sentiments have been linked to conditions enabling subsequent financial exploitation of older people (Harbison, 2016). Older people are less valued in other economic and financial contexts, for example, in calculations that perform cost‐benefit analyses on remaining years or future productivity (Spencer, 2009). In other ways, older people are portrayed as vulnerable and in need of multiple protections (Ayalon, 2020; Lowenstein et al, 2009).

All such portrayals automatically associate certain qualities with older people simply due to their age. In reality, older people encompass a diverse mix of adults who have made significant financial contributions to society through taxation, consumer spending and other economically valuable activities such as volunteering. In some countries such as the United Kingdom, these contributions may be greater than the costs of pensions, welfare and health care combined (Cook, 2011). In Australia, older people have increased their contribution to the labour force, and financial dependency on the wage-earning population has fallen (Betts, 2014).

Ageism can affect the provision of financial services in several ways. For example, ageist stereotypes may take the form of subtle assumptions about the worth of older people, which may affect not only how professionals deal with older people, but how they consider reports of suspected financial mistreatment (Wasarhaley & Golding, 2017).

111 Ageist attitudes can manifest as a failure to give sufficient attention to the financial needs of older people or to involve older people in discussions about their financial needs (Gibson & Rochford, 2008). Ageist stereotypes about the abilities of older people contribute to the perception that older people are unable to manage their own financial affairs (Phelan, 2020). The ageist assumption that older people are a financial drain on society can lead to a lack of appropriate financial advice and may lead to conditions enabling subsequent financial exploitation (Harbison, 2016).

Ageist societal attitudes that view older people as untapped sources of wealth may play a role in the promotion of inappropriate financial products, where the assets of older people are seen as more important than their financial well-being (Naughton & Drennan, 2016). Inaction – ‘passive ageism’ – on the part of financial institutions can lead to a lack of adequate protections against financial fraud, scams or exploitation (Setzfand & Watson, 2015).

Australia’s Banking Royal Commission recognised that, “there is an asymmetry of knowledge and power between consumers and financial services entities’ (Hayne, 2019). The UK’s Financial Conduct Authority (FCA) stated that, “there are risks that older consumers’ financial services needs are not being fully met, resulting in exclusion, poor customer outcomes and potential harm” (FCA, 2017).

1.2 Rationale As highlighted in earlier chapters, ageism has been researched most extensively in healthcare settings compared with other fields. Research examining older people’s experiences with the financial industry has largely focused on employment discrimination against older employees working at financial firms. Some research has investigated older people’s financial capabilities and needs. Many studies have examined the issue of financial elder abuse. However, empirical research examining ageism on the part of financial professionals and the impact on older customers is scarce.

1.3 Aims This systematised review aimed to assess the available empirical evidence examining ageism among financial professionals and the subsequent outcomes for older people. The purpose of choosing this approach was to be consistent with the major section of

112 this project – the systematic review of ageism in health care – as well as the systematised review in law, and to ascertain whether similar types of empirical evidence existed in the financial literature. This review forms part of a multidisciplinary investigation into ageism among frontline workers who interact closely with older people: health, legal and financial professionals.

1.4 Hypothesis The study hypothesis was that ageism among financial professionals will adversely impact interactions with older people, including the provision of services and advice.

113 CHAPTER 2: METHODS

2.1 Approach A systematised review was chosen as the method to review the empirical finance literature as it pertains to ageism. As noted in Part 3 of the thesis, this type of review includes one or more elements of the systematic review process; in this review, these were: the a priori specification of the research hypothesis, the comprehensive search of multiple databases using defined search terms plus additional hand searches of key journals and reference lists, and the application of explicitly defined inclusion and exclusion criteria (Grant & Booth, 2009).

2.2 Methodology A literature search was conducted across multiple Australian and international databases to locate relevant empirical peer-reviewed original research in finance. Database searches for English language texts were conducted via EBSCO Business Source Ultimate, Informit Business Collection, Econolit, ProQuest Central and JSTOR. Searches took place in March and April 2020. Google Scholar was also searched via an automatic monthly search from February 2019 to April 2020.

Several journals were hand-searched including the Journal of Elder Abuse and Neglect, the Journal of Financial Service Professionals, the Journal of Finance, Economics Letters, Journal of Consumer Affairs, International Review of Finance, the Journal of Adult Protection and the Journal of Financial Counseling and Planning. Reference lists of included studies as well as key reviews and papers were also hand- searched to identify any further sources of data.

114 In order to be consistent with the other parts of the project and to specifically examine the impact of ageism among financial professionals on older people, a combination of four fields of search terms were used, similar to those used in the healthcare systematic review: • Ageism, ageist, age discrimination, age bias, age prejudice, age stereotype, age disparities, age inequalities, age exclusion • Financial professional, financial advice, finance professional, financial management, financial institution, adviser, financial counsellor, bank, banking, bank staff, bank manager, accountants, auditors • Old, older, older people/clients/consumers, elder, elderly, elderly people/clients, aged, geriatric, seniors, aged, ageing • Impact, outcome, consequences, approach, perspective, perceptions, reaction, stance, response, management, behaviour, demeanour, attitudes, response.

2.3 Inclusion and exclusion criteria The focus was on empirical research that examined the potential link between ageism/age discrimination/age bias and outcomes/management decisions by financial professionals. No date restrictions or geographical restrictions were set. The limited availability of quantitative research in finance was apparent during the initial scoping review, so the eligibility criteria included mixed-methods studies.

2.3.1 Inclusion criteria • Research studies that focus on ageism/age bias/age disparities/age-related inequalities • Ageism on the part of financial professionals • Studies that report the impact (outcomes, consequences) of ageism by financial professionals on older people • Original quantitative or mixed-methods research • Articles published in peer-reviewed journals • Articles published in English.

115 2.3.2 Exclusion criteria • Studies not focused on ageism/age bias/age disparities/age-related inequalities • Studies focused on ageism on the part of other (non-financial) professionals, general community members (including reports by clients or consumers about perceived ageism/self-perceptions about ageism) • Investigation of ageism towards younger people • Studies that do not report any actual outcomes • Qualitative studies • Non-original research e.g. general reviews, commentary, letters • Studies not published in a peer-reviewed journal • Articles not in English.

116 CHAPTER 3: RESULTS

3.1 Search results This systematised review did not identify any empirical quantitative or mixed-methods research that specifically examined ageism on the part of financial professionals and the outcomes for older people. Though searches yielded several articles, none matched the eligibility criteria.

Given that no eligible studies met the inclusion criteria, a modified search was conducted to identify quantitative or mixed-methods research that examined the way in which financial transactions and practices may adversely affect or disadvantage older people. The focus remained on studies that reported actions by financial professionals or financial institutions towards older people and included reports by consumers. The revised search yielded two results: one Irish mixed-methods study examining mistreatment of older customers by financial institutions, and one Australian mixed-methods review of reverse mortgage lending practices.

3.2 Findings 3.2.1. Mistreatment by financial institutions Financial mistreatment of older people by financial institutions is an area that has received very little attention in the literature to date, even though these institutions can have substantial influence on their clients’ financial decision making. An Irish mixed- methods study randomly surveyed 2,021 community-dwelling people aged 65 and older (mean age 74.5 years) to gather information about interpersonal mistreatment (defined as elder financial abuse within the context of interpersonal relationships) and financial institution mistreatment (defined as the direct or indirect practices of financial institutions that target or threaten the financial wellbeing of older people) (Naughton & Drennan, 2016). The study found that, in the year prior to the survey, 1.8% of respondents had experienced interpersonal financial abuse, while 1.04% had experienced mistreatment by financial institutions – that is, being pressured to buy or being sold financial products they did not want or understand. The latter rose to 1.9% when considering financial institution mistreatment over time, i.e. since the age of 65 years.

117 The institutions most commonly engaged in these practices were banks (57%) and insurance companies (24%). In a comparison of the two types of financial mistreatment, those who reported financial institution mistreatment had attained higher levels of education, had higher mean scores for physical and mental health measures and lived in less-deprived areas compared to those who had experienced interpersonal abuse (Naughton & Drennan, 2016). While the reasons for this are unknown, it is possible that the providers were targeting older people with a particular asset profile, or that those who disclosed this type of mistreatment are more aware of such practices.

3.2.2 Lack of safeguards in lending The reverse mortgage market has provided a way for older people to access equity in their homes. Financial institutions and governments have an interest in reverse mortgage schemes that allow older people to supplement their retirement income and pay other costs (Tilse et al, 2005). In 2012, the Australian Government introduced enhanced consumer protections to help consumers make more informed choices about reverse mortgages and protect them from potential harm.

Following the introduction of these protections, the Australian Securities and Investment Commission (ASIC) conducted a mixed-methods review of reverse mortgage lending from 2013 to 2017. The most common age group to obtain a reverse mortgage was 65–74 years for women and 65–79 years for men; the average age of borrowers was 75 years (ASIC, 2018).

The review found that, while reverse mortgages may allow older people to improve their financial situation in retirement and remain in their homes, some borrowers struggle to recognise the long-term risks associated with these products and the impact on their future financial needs (ASIC, 2018). Most borrowers had a basic but sometimes limited understanding of how their reverse mortgage worked, and most could not explain specific details about the impact of compound interest and loan size on the overall cost of the loan. The review found that lenders focused primarily on the borrower’s short-term financial objectives, while limited or no attention was paid to clients’ possible future financial needs. Approximately 92% of the loan files reviewed did not record the possible future needs of the borrower in sufficient detail and

118 contained no evidence that the broker or lender had discussed how a loan may affect the borrower’s ability to afford future expenditures.

The contracts from all five large lending groups involved in the study contained terms that had the potential to be unfair to reverse mortgage borrowers. For example, given older people are the recipients of reverse mortgages, it would be prudent to make sure there is a tenancy clause in the contract to protect a spouse if the borrower dies. However, the review found that only one of the five lenders offered consumers an option to include a tenancy protection provision in their loan contract. This only offered protection for one year after the death of the borrower, the lender could refuse any tenancy protection application and the nominated non-borrower resident had to be a relative of the borrower and over 70 years old at the time of the application. While this was the only lender to offer a tenancy protection option to the co-resident, the terms were very restricted and the use of an arbitrary age cut-off was explicitly ageist.

ASIC noted that, “Lenders can do more to improve long-term consumer outcomes and help potential borrowers make informed decisions about their immediate and future financial needs” (ASIC, 2018; p1).

3.2.3 Response to financial abuse The issue of financial elder abuse is a particularly serious concern for older people and frontline professionals. Researchers have suggested that elder abuse is the most visible manifestation of ageism (Phelan & Ayalon, 2020; Podnieks & Thomas, 2017). Elder abuse has the opportunity to flourish when respect for older people is missing (Podnieks & Thomas, 2017).

Like ageism, there is no agreed definition of financial abuse. The WHO defines it as, “the illegal or improper exploitation or use of funds or other resources of the older person" (WHO, 2002). It may involve adverse acts perpetrated by people known to and trusted by the victim, as well as acts perpetrated by strangers and institutions. Financial abuse may involve monetary abuse, legal abuse or property abuse (Vancity, 2014). It may manifest as theft, fraud, or pressure to sign legal documents (Jackson & Hafemeister, 2011). Financial abuse can include a failure to access benefits, inadvertent or deliberate mismanagement of an older person’s assets and opportunistic exploitation of finances by another person (Crosby et al, 2008). Other

119 behaviours that may be considered financial abuse include refusing to repay a loan, living with someone without contributing to living expenses and failing to care for someone after agreeing to do so in exchange for money or property (ALRC, 2017).

The aforementioned ASIC review of reverse mortgage lending practices found that, while lenders had some indirect safeguards in place to protect against the financial abuse of older people, there were multiple instances where lenders had failed to identify and investigate indicators of possible financial abuse. In each case, the lender did not make or did not document further inquiries into whether the consumer was being taken advantage of by a caregiver or family member. Three of the five lenders lacked policies, procedures or guidelines to help lending staff detect instances of financial abuse or to respond to suspected abuse. Two lenders did not provide bank staff with training in financial abuse or capacity. One lender introduced a policy in 2017 focusing on vulnerable consumers in general, and what to do in cases of suspected fraud or scams (ASIC, 2018).

The detection, management and prevention of elder abuse can be challenging. Financial abuse can take many forms, and the onset may be gradual and insidious (Phelan et al, 2018). It may be difficult to differentiate between financial abuse and legitimate transactions – for example, when there is some indication of consent by the older person such as a signed document or gift, or when a person with legitimate power over an older person’s accounts is withdrawing money for apparent expenses (Nerenberg et al, 2012). There may be a fine balance between respecting an older person’s right to privacy and autonomy and the obligation to protect them from suspected abuse (Naughton & Drennan, 2016). Cognitive or sensory impairments may also cloud the distinction between willing assent and financial abuse (Conrad et al, 2010).

Frontline staff in banks and other financial institutions are well placed to detect unusual or suspicious transactions which may indicate financial abuse of older customers (Naughton & Drennan, 2016). In their submission to Australia’s National Response to Elder Abuse report (2017), the Australia Law Reform Commission recommended that the Code of Banking Practice be amended to require banks to take ‘reasonable steps’ to identify and prevent the financial abuse of vulnerable customers (ALRC, 2017). This should include staff training on ways to recognise and

120 respond appropriately to financial abuse and developing systems and protocols that detect unusual transactions and other avenues for abuse. Other financial professionals such as accountants and advisors may have long-term relationships with older clients and their families, and may therefore be prime candidates for identifying signs of potential financial abuse (Peiros & Smyth, 2018).

However, as noted in ASIC’s review of reverse mortgage practices, adequate safeguards against financial abuse were missing, suspected cases were not followed up and almost no consideration was given to how the mortgage would affect older customers’ long-term financial requirements (ASIC, 2018). More research is needed to investigate what underpins such decisions by financial professionals when dealing with older customers, in particular their own views concerning the rights and needs of older people (Wilson & Tilse 2015).

121 CHAPTER 4: DISCUSSION

The purpose of this systematised review was to examine how ageism on the part of financial professionals impacts older people. The review found no eligible studies that specifically examined ageism on the part of financial professionals and the outcomes for older people. Given the influence that financial institutions can have on older people’s financial decision-making, and the seriousness of financial elder abuse, the lack of research examining ageism among financial professionals is surprising. This suggests both a lack of insight into ageism and a lack of reflection and scrutiny of behaviours in the industry.

This is a key area for intervention. Finances are a critical concern because after retirement, older people have little or no ability to generate further income (Gibson & Rochford, 2008). As such, the main financial concern faced by many older people is having enough money to live on now and into the future (Russell et al, 2018). Financial insecurity can have serious knock-on effects including a negative impact on health status, compromised independence, social isolation and the loss of autonomy and dignity (Bisgaier & Rhodes, 2011; Burnett et al, 2016; Phelan, 2020). The financial exploitation of older adults has been recognised by the US Centers for Disease Control and Prevention as a serious public health problem (Hall et al, 2016).

Overall, research examining the conduct of financial institutions regarding the assets of older people is scarce, and little empirical research has investigated the financial mistreatment of older people by financial professionals and institutions (Naughton & Drennan, 2016). Some researchers have suggested that the data may be hiding in plain sight, in the form of financial transaction data (Deane, 2018). However, privacy issues around financial interactions and transactions may pose a barrier to using the data for further investigation.

The available evidence concerning older people and financial management suggests that some older people may be directly or indirectly mistreated by financial institutions. Ageism – in the form of overt acts or more subtle instances of neglect – may play a role in these actions. For example, it can be argued that pressuring older people to purchase financial products they do not want or understand is an example of ageism, where older people are seen as untapped sources of wealth and their

122 assets are viewed as more important than their welfare (Naughton & Drennan, 2016). In other words, ageism enables people to view the financial assets of an older person as disconnected from their financial well-being, increasing the potential for financial mistreatment (Naughton & Drennan, 2016).

Ageism may also be at play in the actions by lenders who failed to identify and investigate indicators of possible financial abuse of older people, and did not document further inquiries into whether older customers were being taken advantage of (ASIC, 2018). Subtle ageist assumptions about the worth of older people can limit the way that problems are conceptualised and prioritised, which can subsequently affect how financial professionals consider and act on reports of suspected financial elder abuse (Wasarhaley & Golding, 2017).

Neglecting to pay adequate attention to the future financial needs of older customers who are taking out a reverse mortgage loan may reflect similar ageist attitudes about older people’s worth. Including contract terms based on an arbitrary age cut-off, as was shown in the review of reverse mortgages, is an overtly ageist measure.

Ageist attitudes and beliefs about older people can also manifest as a lack of interest in working with older populations, and may underpin decisions to provide older clients with a lower standard of care than is warranted. Substandard care was particularly evident in the ASIC review of reverse mortgage lending, where the overwhelming majority of lenders failed to give adequate, or indeed any, attention to the future financial needs of older people. Such ‘passive ageism’ can have serious consequences, particularly in terms of older people’s long-term savings and retirement assets (Setzfand & Watson, 2015).

Underpinning ageist actions against older people is the concept of ‘othering’, where older people are seen as separate from, and often inferior to younger generations, making it easier to treat them differently (Biggs & Haapala, 2013; Wilson et al, 2009). This also enables greater tolerance of neglect and exploitation (Angus & Reeves 2006; Nelson, 2005).

123 However, there has been little investigation into the normative practices of financial institutions that may be ageist or facilitate financial elder abuse. Do incentive structures within banks and other financial institutions play a role? Does the combination of societal ageist attitudes and institutional normative practices act as a permissor of financial mistreatment? These issues have not been empirically tested and further studies are needed to answer these questions.

Limitations Quantitative or mixed-methods empirical research into ageism is uncommon in the field of finance, and applying methodology that is routinely used in other fields limits the availability of eligible studies. However, as highlighted in Part 3 of this thesis, the purpose of choosing this approach was to be consistent with the systematic review in health care, to ascertain whether similar types of empirical evidence existed in financial literature and if so, to assess the available evidence. Given that initially no eligible studies were identified, the criteria were broadened slightly to include studies that examined the way in which financial transactions and practices may adversely affect or disadvantage older people. The focus was on studies that included investigation of actions by financial professionals or financial institutions towards older people. The revised search yielded only two results, highlighting the dearth of research in this area.

In addition, the review only included quantitative or mixed-methods analysis. Inclusion of more qualitative research studies may have revealed more about the complexities of ageism in the financial industry and the management of older people. Evidence of ageist practices by financial professionals may also be more apparent in legal cases concerning financial abuse, fraud and other legal and financial issues affecting older people.

124 Conclusion This systematised review – part of a larger project looking at ageism among frontline professionals – examined age-based actions of financial professionals and how these impact older people. While the review did not identify any eligible studies that met the original inclusion criteria, analysis of the available research highlights the impact that financial professionals and institutions can have on older people’s financial management, including financial mistreatment of older customers, the failure to build adequate safeguards into lending practices and paying insufficient attention to older people’s financial needs.

In light of the importance role that financial wellbeing and autonomy play in the quality of older people’s lives, as well as the seriousness of financial elder abuse, further empirical research is warranted to examine the association between ageism and the provision of financial services for older people.

125 References Angus J, Reeve P. Ageism: A threat to “aging well” in the 21st century. J App Gerontol 2006; 25: 137–52.

Australia Law Reform Commission. ALRC Report 131, Elder Abuse – A National Legal Response. 2017.

Australian Securities and Investment Commission (ASIC). Review of reverse mortgage lending in Australia: Report 586. August 2018.

Ayalon L. Life in a world for all ages: From a utopic idea to a reality. J Elder Policy 2020; 1: 39–67.

Betts KJ. The ageing of the Australian population: triumph or disaster? Technical report 2014. Available at: https://www.researchgate.net/publication/295519852_The_ageing_of_the_Australian_ population_triumph_or_disaster; accessed 2 July 2020.

Biggs S, Haapala, I. Elder mistreatment, ageism, and human rights. Int Psychogeriatr 2013; 25: 1299–306.

Bisgaier J, Rhodes KV. Cumulative adverse financial circumstances: Associations with patient health status and behaviors. Health Soc Work 2011; 3: 129–37.

Burnett J, Jackson SL, Sinha AK, et al. Five-year all cause mortality rates across five types of substantiated elder abuse occurring in the community. J Elder Abuse Neglect 2016; 28: 59–75.

Conrad KJ, Iris M, Ridings JW, et al. Self-report measure of financial exploitation of older adults. Gerontologist 2010; 50: 758–73.

Cook J. The socio-economic contribution of older people in the UK. Working with older people 2011; 15: 141–46.

Crosby J, Clark A, Hayes R, et al. The financial abuse of older people: A review from the literature carried out by the Centre for Policy on Ageing on behalf of Help the Aged. 2008. Available at: http://www.cpa.org.uk/information/reviews/financialabuse240408%5B1%5D.pdf; accessed 15 June 2020.

Deane S. Elder financial exploitation: Why it is a concern, what regulators are doing about it, and looking ahead. June 2018. US Securities and Exchange Commission Office of the Investor Advocate. Available at: https://www.sec.gov/files/elder-financial- exploitation.pdf; accessed 8 July 2020.

Financial Conduct Authority. Ageing population and financial services: Occasional paper 31. November 2017. Available at: https://www.fca.org.uk/publications/occasional-papers/ageing-population-financial- services; accessed 8 July 2020.

Gibson F, Rochford F. Emerging consumer credit issues for older Australians. UWS Law Review 2008;12: 73–102.

Grant MJ, Booth A. A typology of reviews: An analysis of 14 review types and associated methodologies. Health Info Lib J 2009; 26: 91–108.

126 Hall JE, Karch DL, Crosby AE. Elder abuse surveillance: Uniform definitions and recommended core data elements for use in elder abuse surveillance, Version 1.0. Atlanta, GA: National Center for Injury Prevention and Control, Centers for Disease Control and Prevention; 2016.

Harbison R. Contesting elder abuse and neglect: Ageism, risk and the rhetoric of rights in the mistreatment of older people. 2016; Vancouver: UBC Press.

Hayne K. Final Report of the Royal Commission into Misconduct in the Banking, Superannuation and Financial Services Industry, Commonwealth of Australia. 2019; Canberra: 493.

Jackson SL, Hafemeister JD. Financial abuse of elderly people vs. other forms of elder abuse: Assessing their dynamics, risk factors, and society’s response. Available at: https://www.ncjrs. gov/pdffiles1/nij/grants/233613.pdf; accessed 8 July 2020.

Lowenstein A, Eisikovits Z, Band-Winterstein T, Enosh G. Is elder abuse and neglect a social phenomenon? Data from the first national prevalence survey in Israel. J Elder Abuse and Neglect 2009; 21: 253–77.

Naughton C, Drennan J. Exploring the boundaries between interpersonal and financial institution mistreatment of older people through a social ecology framework. Ageing Society 2016; 36: 694–715.

Nelson TD. Ageism: Prejudice against our feared future self. J Social Issues 2005; 61: 207–21.

Nerenberg L, Davies M, Navarro AE. In pursuit of a useful framework to champion elder justice. J Am Soc Aging 2012; 36: 89–96.

Peiros K, Smyth C. Professionals’ role in preventing elder financial abuse. Taxation in Australia 2018; 52: 510–11.

Phelan A, McCarthy S, McKee J. Safeguarding staff’s experience of cases of financial abuse. Br J Social Work 2018; 48: 924–42.

Phelan A, Ayalon L. The intersection of ageism and elder abuse. In: Advances in Elder Abuse Research: Practice, Legislation and Policy. Phelan A (ed). 2020: 11–22. Springer, Cham, Switzerland.

Phelan A. Introduction. In: Advances in Elder Abuse Research: Practice, Legislation and Policy. Phelan A (ed). 2020: 1–11. Springer, Cham, Switzerland.

Podnieks E, Thomas C. The consequences of elder abuse. In: Elder Abuse: Research, Practice and Policy. XinQi Dong (ed). 2017: 109–24. Springer, Cham, Switzerland.

Russell R, Kutin J, Stewart M, Cai RY. Financial wellbeing: Older Australians; 2018. Available at: https://www.anz.com.au/content/dam/anzcomau/documents/pdf/aboutus/wcmmigrat ion/financial-wellbeing-older-aus-2018.pdf; accessed 2 July 2020.

Setzfand J, Watson M. Passive ageism and its effect on older adults’ finances. J Am Soc Aging 2015; 39: 39-45.

127 Spencer C, on behalf of the Law Commission of Ontario. Ageism and the law: Emerging concepts and practices in housing and health. Advancing Substantive Equality for Older Persons through Law, Policy and Practice: August 2009. Available at https://www.lco-cdo.org/wp-content/uploads/2014/01/older-adults-commissioned- paper-spencer.pdf; accessed May 12 2020.

Tilse C, Wilson J, Setterlund D, Rosenman L. Older people’s assets: A contested site. Australasian J Ageing 2005; 24 (Suppl): S51–56.

Vancity. The invisible crime: A report on seniors financial abuse. 2014. Available at: https://www.vancity.com/SharedContent/documents/pdfs/News/Vancity_Seniors_Fin ancial_Abuse_Report.pdf; accessed 1 July 2020.

Wasarhaley NE, Golding JM. Ageism in the courtroom: Mock juror perceptions of elder neglect. Psychol Crime Law 2017; 23: 874–98.

Wilson J, Tilse C, Setterlund D, Rosenman L. Older people and their assets: A range of roles and issues for Social Workers. Aust Social Work 2009; 62; 155–67.

Wilson J, Tilse C. Opening up options: Decision making around older people's assets. Aust Social Work 2015; 68: 153–55.

World Health Organisation. World report on violence and health. 2002. Available at: http://whqlibdoc.who.int/publications/2002/9241545615_eng.pdf?ua=1; accessed 5 July 2020.

128 PART 5 FINAL DISCUSSION

Ageism is highly prevalent in society and can have a significant impact on older people in many aspects of their lives, including the provision of critical services. Ageism on the part of the frontline professionals who interact closely and regularly with older people – healthcare, legal and financial professionals – can pose a substantial threat to older people’s well-being (Chang et al, 2020; LCO, 2012; Setzfand & Watson, 2015). However, empirical research examining professional ageism in these areas has not been synthesised.

This study extends previous research on ageism by conducting a systematic review of the medical literature, together with systematised reviews of the legal and financial literature, to examine the impact of ageism by healthcare, legal and financial professionals.

Ageism has proliferated in response to the COVID-19 pandemic, with calls for older people to sacrifice themselves for the good of society (Ayalon et al, 2020). The pandemic has led to public discussions about the value of older people, with the underlying assumption that all older people are alike, and collectively less important than younger generations. Even encouraging older people to distance themselves from the rest of society, or deciding to keep older people behind closed doors “for their own good”, are ageist actions, because they are based on the supposition that everyone over a certain age – whatever that may be – shares the same vulnerabilities and risks.

Such so-called compassionate or unintentional ageism only serves to cultivate and affirm stereotypical attitudes and prejudices (Reynolds, 2020). Moreover, the paternalistic inferences of determining the best interests of older people robs them of their due self-determinism and autonomy. Society has no greater right to decide on the best interests of older people than it does of younger people.

All of these erroneous assumptions are driven by ageism. Greater attention to ageism is timely and warranted in light of these events and others that may follow.

129 5.1 Individual review findings 5.1.1 Healthcare professionals Since the concept of ageism was first conceptualised in 1969, a growing body of research has explored its origins and impacts in healthcare settings. However, the clinical evidence has not been systematically reviewed with a defined focus examining the impact of ageism amongst healthcare professionals and researchers on real-life clinical decision-making and outcomes for older patients. This project’s major component – the systematic review in health care – aimed to address this gap. The review adhered to a strict study design and has been written in accordance with the recommended protocol set forth by Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines.

The systematic review found clear associations between age-based management among healthcare professionals and adverse impacts on older patients, with negative effects seen in the overwhelming majority of studies. Only five of the 73 eligible studies found no evidence of ageism or age-based differences in care. The most explicit examples of ageism involved the use of arbitrary upper age limits to restrict participation of older people in clinical trials. All studies that examined under- representation in clinical research trials or trial proposals showed that older people were directly or indirectly excluded. Eighty per cent of studies documented arbitrary age cut-offs, while studies that assessed more implicit forms of ageism found that additional exclusion criteria disproportionately restricted the enrolment of older people in research. In addition, all studies that examined age differences between trial participants and the distribution of older patients with the same condition in the general population found that trial participants were markedly younger, even in trials that specifically examined treatments for older people. Overall, very few clinical trials were designed exclusively for older people. This is an important oversight with substantial knock-on effects in terms of safety and quality in health care, equitably owed to older people as to younger people. Exclusions from clinical trials excludes older people from evidence-based interventions and guidance, and accordingly, proper clinical governance.

Clear age differences were also evident in the studies that examined differential healthcare receipt across age groups. Compared with younger people, older patients received less intensive and shorter treatments and were not given the same evidence-

130 based therapies. Increasing age was associated with increasing treatment disparities, with incremental declines in appropriate diagnostic investigations and guidelines- based treatments, including surgery and adjuvant therapies. These actions have potentially serious consequences for morbidity and mortality (Peake et al, 2003).

5.1.2 Legal professionals The results of the systematised review in law revealed contrasting findings. Very little empirical legal research has been conducted in this area, and the search yielded few eligible studies. Nine of the 11 included studies examined judges’ sentencing decisions involving older defendants and found that age impacts these decisions, even after controlling for crime-related variables. However, in contrast to health care, older age was associated with more lenient treatment in the judicial system, where older offenders received fewer custodial sentences and shorter periods of incarceration compared with younger offenders. In some cases, older age was a factor in granting concurrent rather than consecutive sentences, even for serious crimes. Some judges gave reasons as to why older offenders received a lighter sentence, including: older people have less time to live; they find it more difficult to be in prison; they may die in prison. Poor health was a mitigating factor independent of age.

Conversely, negative outcomes were seen in the other two legal studies. One found unequal justice for older victims of crime, with lower outcome rates for common categories of crime committed against older adults than for similar crimes committed against younger adults. The other study revealed ageism in decisions regarding adult protection, guardianship and conservatorship, where older age per se led to assumptions of impairment or lack of capacity to justify imposition of guardianship, and to determine cases of undue influence in trust and will contest cases.

5.1.3 Financial professionals Empirical evidence was rarer still in the systematised review examining ageism among financial professionals, in which no studies met the original inclusion criteria. As a result, the search was broadened slightly, uncovering two studies that examined potentially discriminatory practices within the finance industry. One study examined mistreatment of older people by financial institutions and found that older people were pressured into buying or were sold financial products they did not want or understand.

131 The other study reviewed reverse mortgage lending practices and outcomes, and found that financial institutions providing reverse mortgage products failed to address older borrowers’ future financial needs or identify and investigate indicators of possible financial abuse.

5.2 Synthesis of results Taken together, what do these results say about the care, advice and assistance given to older people by the frontline professionals who play a critical role in their lives? The majority of evidence shows that older people either experienced explicit ageism through arbitrary age barriers, were subject to differences in management compared with younger age groups, or received a lower standard of care than was warranted. Out of 86 studies across the three areas, only five in health care showed neutral results, i.e. no associations between age and management.

The provision of care and advice is affected by multiple individual and structural factors. While the results showing age disparities and poor standards of care may indicate age-based management practices, it is not possible to say that these findings are specifically reflective of ageism. In the healthcare and legal studies comparing outcomes across age groups, the majority attempted to control for confounders where possible, and differences between age groups persisted. Levy (2001) argues that anyone who has internalised their culture’s age stereotypes is likely to engage in implicit ageism, making it widespread but also hidden. Given the pervasive and often elusive nature of ageism across many aspects of society, it is important to accentuate the role it may play in professional relationships and interactions with older people.

5.2.1 Similarities and differences This project employed a similar model to examine ageism across the three professional groups, which, to the candidate’s knowledge, has not previously been attempted. The systematised reviews in law and finance were closely aligned with the systematic review in health care, particularly in terms of the search terms, comprehensive search methodology and eligibility criteria. The aim of this approach was to capture similar types of data so that similarities and differences could be revealed, as well as exposing the gaps in empirical ageism research.

132 A common approach employed in the healthcare and law literature was the use of large databases to study age disparities in management – in this case, medical treatments and judicial sentencing. This enables some evaluation of empirical evidence across these two professions. However overall, there was a marked lack of empirical research examining ageism among legal and financial professionals in terms of their relationships and interactions with older clients. Empirical investigation is very common in healthcare research, and this was evident from the number of studies identified at all stages of the systematic review process. In contrast, normative and interpretive research of case law is a more common approach in legal analysis. If we are to examine professional behaviours – often more a focus of health and psychosocial research – then direct participant research is needed. Researchers have called for more empirical research in the legal field beyond case law examination to better understand the inter-connection between law and ageism (Doron et al, 2018).

Evidence of ageism among legal and financial professionals may be more evident in legal cases concerning elder abuse, fraud, undue influence, capacity, enduring powers of attorney and guardianship, as well as in relation to retirement villages and aged care.

Nonetheless, similar themes emerged. A striking commonality was a collective professional apathy towards older people. In health care it manifested as less aggressive management, in particular, less vigorous diagnostic and treatment regimens that were not guided by evidence-based research. In finance it manifested as a failure to ensure that older people understood lending products and services, and was also demonstrated by the lack of scrutiny and follow-up regarding potential financial abuse. In law it was evident in lower outcome rates for crimes committed against older adults, where there was a failure to adequately take into account the factors that have an impact on older victims’ ability to engage with the justice process. These findings may suggest ageist sentiments that older people should or deserve to be managed less aggressively than others – that they are somehow less deserving of appropriate and timely care compared with younger people.

133 It is implicitly ageist if providers presume that older people do not warrant the same level of assistance or guidance as younger people, would not benefit from the same management opportunities as younger populations, do not have the necessary capability or capacity to engage in decision-making or management, or cannot tolerate interventions simply due to chronological age. Collectively, it is evident from these results across all three areas that older people received less intensive care and management than younger people or did not receive a standard of care that should apply to everyone, regardless of age.

The underestimation of older people is common in society, and assumptions of incompetence and impairment contaminate many professional interactions. For example, in healthcare, legal and financial interactions, it is often assumed that older people lack capacity and require others to take over decision-making, resulting in a loss of autonomy for that person. Equally, ageism may manifest as paternalism, where older people are perceived as frail, vulnerable and in need of protection. These negative ageist stereotypes, along with assumptions that older people are weak and unable to cope, may underlie the more lenient sentencing seen in the legal review – albeit to the advantage of older people – as well as the less vigorous treatment of older people seen in the healthcare review. It is ageist to suggest that older people need special (positive or negative) treatment due to their age, because this reinforces stereotypes and assigns characteristics to older people as a homogenous group.

Many of these patterns appear to increase with increasing age. For example, increasing age was associated with more disparate sentencing in law, where the oldest age groups received the most lenient sentences. Similarly, increasing treatment disparities were seen in health care, with incremental age-related declines in the provision of appropriate treatments – described as, “a clear pattern of decreasing ‘diagnostic zeal’ and active treatment with increasing age” (Peake et al, 2003; p175). Given the ageing population, this does not bode well for the future. Timely intervention to reduce professional ageism is essential.

134 5.3 Future directions Accurate measurement and assessment of ageism and its relationship to outcomes requires empirical research that begins early in the research process (Snellman, 2018). Examination of case law, in combination with more empirical research in law and finance, could be a very valuable way of bringing together theories, methods and data to provide a more complete picture of ageism.

Overall, there is a need to engage in more mixed-methods research in all three professional fields, across different research settings and involving professionals as well as older people and family members/carers. This would enable researchers to validate findings using different methodologies, gain a deeper understanding of ageism and determine the most effective ways to intervene. Mixed-methods research would also provide greater insights into what underpins age-based decisions by professionals, and articulate their own views concerning the rights and needs of older people.

Other gaps in the literature warrant further attention. For example: what are the key factors that influence the development and maintenance of ageism among healthcare, legal and financial professionals? What role does implicit ageism play in interactions with older patients, customers and clients? To what extent does ageism underpin normative practices within institutions and thereby facilitate serious outcomes such as mistreatment and abuse? Further studies are needed to answer these questions.

There is also a need for more intervention research. Current studies are predominantly observational, and more research is required to examine active interventions aimed at reducing or eliminating ageism. In addition, greater cross-disciplinary research using common methodology will allow findings to be synthesised and applied across multiple settings and disciplines. To this end, working towards a unified definition of ageism would allow researchers to work from a common conceptual base.

135 5.4 Conclusion This project – the first to synthesise research of professional ageism across the fields of health care, law and finance – has shown that ageism, age disparities in management and substandard care on the part of healthcare, legal and financial professionals can have significant consequences for older people. Using age as a justification to treat older people differently, often less vigorously or with less care, should not be acceptable in a compassionate society. Ageing is highly diverse and age is a poor correlate of health or ability. Behaviours and actions that derive from ageism must be addressed and overcome, so that decisions about treatment, care and assistance are not underpinned by age, but are based on each individual’s situation, tolerance and needs.

136 References Chang E-S, Kannoth S, Levy S, et al. Global reach of ageism on older persons’ health: A systematic review. PLoS ONE 2020; 15: e0220857.

Law Commission of Ontario. A Framework for the Law as it Affects Older Adults: Advancing Substantive Equality for Older Persons Through Law, Policy and Practice Toronto: April 2012.

Levy BR. Eradication of ageism requires addressing the enemy within. Gerontologist 2001; 41: 578–79.

Levy BR, Slade MD, Kunkel SR, Kasl SV. Longevity increased by positive self- perceptions of aging. J Pers Soc Psychol 2002; 83: 261–70.

Levy BR, Zonderman A, Slade MD, Ferrucci L. Negative age stereotypes held earlier in life predict cardiovascular events in later life. Psychol Sci 2009; 20: 296–98.

Levy BR, Slade MD, Chang ES, et al. Ageism amplifies cost and prevalence of health conditions. Gerontologist. 2020; 60: 174–81.

Peake MD, Thompson S, Lowe D, Pearson MG. Ageism in the management of lung cancer. Age Ageing 2003; 32: 171–77.

Reynolds L. The COVID-19 pandemic exposes limited understanding of ageism. J Aging Soc Pol 2020; 32: 499–505.

Setzfand J, Watson M. Passive ageism and its effect on older adults’ finances. J Am Soc Aging 2015; 39: 39-45.

Snellman F. Normative, empiricist, and interpretive considerations in the ageism research process In: Ayalon L, Tesch-Römer C (eds). Contemporary Perspectives on Ageism. International Perspectives on Aging 2018; 19. Springer, Cham, Switzerland.

137 APPENDIX 1. SYSTEMATIC REVIEW STUDIES: TABLUATED DATA N=73 studies Data: • Author, title • Study population, setting • Aims • Focus terminology • Design • Methods • Main outcomes assessed • Findings

! ! "#"$%$"&'() *&##+'$(

Author, title Study Aims Focus Design Methods Main outcomes Findings Limitations, comments population, terminology assessed setting

Bajorek & Audit of 201 To determine Age Retrospective Conducted an audit Proportion of Of those patients who Small study at one Ren, 2012 patient use of disparities audit of of medical records of patients receiving were deemed ‘eligible’ location on one group records from antithrombotic medical hospital inpatients antithrombotic for warfarin (n=155), only of patients, limiting Utilisation of one Sydney therapy for records aged 65 years and therapy at the point 55% were prescribed generalisability; antithrombotic hospital older patients older with a of discharge from warfarin. Whilst there retrospective study therapy for with AF; to significant diagnosis hospital have been improvements cannot control for stroke compare results of AF over 12-month in the overall utilisation of potential confounding prevention in with findings of period; compared to antithrombotic therapy factors that already atrial fibrillation a prior audit; to previous audit between audits, there are exist; can’t assume all in a Sydney compare results conducted 10 years still significant gaps in the necessary details hospital: then with prior translation of evidence included e.g. and now. Int J recommendatio from clinical trials to contraindications or Clin Pharm ns of the TAG clinical practice other reasons for non- 2012 34: 88–97 clinical warfarin use indicator

Banzi, et al, Systematic To determine Under- Systematic Systematically The proportions of Patients enrolled in Advantage: focuses on 2016 review of 165 the age gap representation literature searched databases participants in clinical trials for recent research trials trials between of older review (MedLine, different age Alzheimer’s disease not Missing some Older patients involving participants in patients EMBASE, the classes in clinical representative of actual information on the are still under- treatment for recent clinical Cochrane Library) trials versus the distribution of patients in proportions of patients represented in Alzheimer’s trials and ClinicalTrials.gov number of patients the general population per age class in clinical clinical trials of disease epidemiological from 2000 to July with Alzheimer’s e.g. people younger than trials due to lack of Alzheimer’s data 2015 to retrieve disease by age class 80 years were highly reporting in some disease. clinical trials testing in the USA represented in clinical studies; potential bias in Alzheimer's pharmacologic population trials (78%), despite the that used only US Research & treatments for fact that those aged 80 comparative population Therapy 2016; Alzheimer’s disease and older make up the data as reference; did 8: 32–41 (excluded current majority (72%) of patients not assess possible treatments i.e. with Alzheimer’s disease. biases affecting the cholinesterase Only 8% of clinical trial internal validity of inhibitors and participants were 85 years included trials memantine) or older Barakat et al, 1225 To assess how Effect of age Prospective Examined Primary endpoint Patients aged 70 years or Selection bias: only one 1999 consecutive age affects the on cohort study management of was death. Survival older took a longer time hospital in one part of patients management of management consecutive patients to hospital to arrive in hospital and London included How should age admitted with patients and risk with acute discharge was were less likely to receive affect acute stratification in myocardial recorded for all thrombolysis or discharge management of myocardial acute MI infarction who were 1225 patients beta-blockers than acute infarction to a admitted to the patients younger than 60 myocardial district coronary care unit of years infarction? A general Newham General prospective hospital in Hospital between cohort study east London 1988 and 1994 Lancet 1999; 353: 955–59

Bellera et al, 87 RCTs To assess the Barriers to Systematic Systematic search of Representation of 49% of trials had upper Only searched one 2013 published representation inclusion/ review Medline database older patients in age cut-off; 25.3% database, which between Jan of older patients under- between Jan 1 2005 RCTs on NHL; directly excluded pts >65 potentially misses Barriers to 2005 and Dec in randomised representation and Dec 31 2011; whether trial years; 10.3% focused relevant data; restrictive inclusion of 2011 controlled trials of older screened 356 records eligibility criteria exclusively on pts >65 inclusion criteria (only older adults in in non- people in English or French; prevent older or years; 54% indirectly phase II and III RCTs) randomised Hodgkin’s included 87 in frail patients from excluded older adults e.g. controlled lymphoma analysis participating; via ECOG status, liver or clinical trials on whether RCT kidney function, Non-Hodgkin’s including older comorbidities; 10.3% did lymphoma: A patients use not directly or indirectly systematic endpoints other exclude pts >65 years, review. Cancer than the usual although two excluded pts Treatment efficacy criteria >70 years. Reviews 2013; such as survival Proportions of older 39: 812–817 time or time to patients included do not response or things reflect disease incidence; such as toxicity, trials including older quality of life or adults were published in geriatric journals with lower assessment scores impact factors; few RCT (autonomy, used appropriate nutrition, endpoints for older adults depression, risk to fall) Bergman et al, 2268 patients To assess the Age-related Population- Used population- Survival across age Patients aged 75 years and Selection bias: Only 1992 with breast influence of age treatment based study based data from 10 groups; treatment older were treated by searched one database cancer on treatment differences using data community hospitals choices across age adjuvant radiotherapy less that serves 7% of the The influence of choice and from a large in the Netherlands to groups often than younger Netherlands; high age on treatment survival registry examine treatment patients; instead, they percentage with choice and pattern and survival received surgery alone or unknown stage in older survival of of older (aged over surgery followed by age group makes elderly breast 55) breast cancer adjuvant hormonal definitive conclusions cancer patients patients in a general therapy. Surgical difficult; registry study in south-eastern population; divided procedures in older means no information Netherlands: A patients into age patients were less on treatment population- groups: 55-64 years, extensive than in younger motives/choice, based study. Eur 65-74 years and 75 patients even though older physical condition, J Cancer 1992; years and older. patients presented with comorbidities 28A: 1475–80 ‘Elderly’ women more advanced stage were defined as 65 cancer overall; relative years and older survival was similar across age groups

Bhalla et al, Data collected To estimate the Age Prospective Analysed hospital Differences in the Some age bias found; Limitations of adjusting 2004 from 13 structure and disparities cohort study and community frequency of mixed results: older for non-randomised hospitals in 10 process of care, services for 3 categorical people more likely to gain comparisons of stroke Older stroke European and identify months after stroke; variables between access to organised stroke outcome and process for patients in countries independent this involved patients older than care but less likely to differences in case mix; Europe: stroke (1,847 factors recording the and younger than receive diagnostic didn’t include all care and patients with associated with available staff time 75 years were investigations, therapy potential variables; only determinants of first stroke) 3-month (doctors, nurses and performed with the input and outpatient included one or two outcome. Age mortality and therapists), mortality χ2 test review. hospitals from each and Ageing functional data from death Overall: while older country plus huge 2004; 33: 618- outcome in certificates, stroke patients had equal variation in models of 24 patients aged functional status or better access to stroke care across over 75 years specialised stroke care, Europe so hard to compared with rates of standard generalize findings; younger stroke diagnostic and therapeutic excluded community- patients across procedures, as well as based patients Europe rehabilitation services, were lower for older patients compared to younger counterparts

Bond et al, Records from To analyse age- Access to care Retrospective Tracked each case Differences in Older hospital patients Sample of the outpatient 2003 712 pts from related access to based on age case note backwards and diagnostic testing with IHD and indications notes was taken due to one English exercise testing, analysis forwards by 12 (exercise tolerance for further investigation time to screen; study Does ageism (NHS) coronary months from the testing, referrals for were less likely to be only at one site and affect the hospital in angiography, patient’s date of cardiac catheter, referred for exercise limited to hospital management of London; revascularisatio entry to the study in angiography) and tolerance tests, cardiac patients, both of which ischaemic heart patients had n procedures order to gather treatment catheterisation and may limit disease? J been and complete data on a (revascularisation, angiography, independent generalisability of Health Services diagnosed thrombolysis for cross-section of receipt of of ender and severity of findings; risk of Res & Policy with hospital patients patients without the thrombolysis) condition. incomplete note-taking 2003; 8: 40–47 cardiovascular diagnosed with risk of biased between four age Older patients not masking other disease cardiovascular behaviour of the groups <65; 65-75; discriminated against re confounders; patient disease treating clinicians; 75-80; >80 years receipt of indicated population may have performed reliability treatments been underestimated checks on coding; (revascularisation or due to reliance on inter-coder reliability thrombolysis) but were coding to identify was extremely high; more likely to have been suitable patients coding took an filtered earlier at the average of 40 investigation stage, so minutes per case selection bias partly note explains this finding when it comes to revascularisation

Briggs et al, Analysis of To investigate Exclusion of Audit of Examined research Recorded exclusion Found that of the 226 Very brief methods 2012 226 clinical whether patients based clinical proposals submitted of patients based on trials, 31 (13.7%) section and paper; trials exclusion of on age research to the Research an arbitrary upper excluded older people inclusion/exclusion Ageism and submitted by older people proposals Ethics committee age limit and based solely on an criteria not shown; no Clinical geriatricians, was prevalent in submitted to covering teaching assessed whether arbitrary upper age limit. discussion of study Research. Ir neurologists, clinical research Dublin hospitals attached to this was justified or The mean upper age limit limitations; only brief Med J. 2012; psychiatrists, proposals teaching Trinity College unjustified used in these trials was explanation of results; 105: 311–12 oncologists, hospitals Dublin over 3-year 69.2 years of age no explanation of how gastroenterolo period from July determined which gists, 2008 to July 2011 exclusions where cardiologists inclusive to assess justified or not justified, the use of upper age making it difficult to limits (not justified assess the validity of the on medical grounds) data; 3-years is to exclude patients comprehensive from clinical trials timeframe Bruin- Data from To investigate Inappropriate Retrospective Analysed routinely Prevalence of at 34.7% of patients aged 65 PIMs and PPOs were Huisman et al, 182,000 potentially management longitudinal collected data over 8 least one PIM and years and older were not clinically evaluated 2017 patients of 49 inappropriate of older study years (2007 to 2014) of at least one PPO prescribed medications in the current study, general prescribing patients using a GP research as proportion of all that were potentially some identified Potentially practitioners (prescriptions database of the eligible patients; inappropriate; potentially PIMs/PPOs might inappropriate in the and omissions) Academic Medical prevalence of PIMs beneficial medications actually be well prescribing to Netherlands; in older patients Center in the and PPOs per were absent in 84.8% of considered instead of older patients in 4537 older Netherlands. individual patients; proportion of inappropriate; only one primary care in patients (aged Database contains STOPP/START patients with at least one database in one area of the Netherlands: 65 years or anonymised records criterion as PIM did not change Amsterdam so results a retrospective older) of a cohort of more proportion of all significantly over time; not necessarily longitudinal than 182,000 included patients percentage of patients generalisable; registry study. age and patients. Used Dutch with one or more study limitations; Ageing 2017; version of the prescribing omissions retrospective study 46: 614–19 STOPP/START showed a statistically cannot control for criteria to determine significant decrease over potential confounding inappropriate the investigated years; factors that already prescribing and percentage of omissions exist; can’t assume all prescribing was much higher than necessary details omissions other studies; percentage included e.g. of inappropriate contraindications or prescribing was similar to other reasons for other studies prescribing

Carvalho do 323 LBP To examine trial Exclusions Cross- Searched for The number of 156 of the 323 clinical Search restricted to Nascimento et clinical trial protocols to based on age sectional prospective ongoing trial protocols for LBP analysis of registers al, 2019 protocols in assess inclusion study protocols planning prospective were not planning to from one database; the World of older adults interventions for registered clinical include patients aged investigated future trials Exclusion of Health LBP with trials on LBP over 65 years; of these, so can’t determine the Older Adults Organization registration dates planning to enrol only 10 were aimed at actual number of older from Ongoing (WHO) from January 2015 older adults younger patients e.g. participants who will be Clinical Trials clinical trial through November athletes, pregnant recruited on Low Back registry 2018. From the women. Pain: A Review protocols of the A total of 167 trials of the WHO eligible studies, were planning to recruit Trial Registry extracted those participants older than Database. J Am planning to include 65 years, however, only Geriatr Soc older adults five of these (2.99%; 2019; 67: 603– pooled sample = 169 08 participants) were designed to target participants specifically older than 65 years. Of the trials that were not including older participants, exclusion was not justified and was imposed through an arbitrary upper-age limit in 93.6% of the protocols

Chambaere et Physicians To assess 1) Various cited Post-mortem Validated Examined Found that age is not a Recall bias; moderate al, 2012 who certified differences in including survey among questionnaire sent to differences in determining factor in the 58.4% response rate; deaths (3,623 2007 in the prejudicial physicians physicians who management rate of end-of-life results provided may Age-based deaths) incidence of attitudes certifying a certified the deaths. decisions across decisions, but plays a role not reflect actual disparities in end-of-life toward the large Lawyer was age groups in terms in the preceding decision practices and ignore the end-of-life decisions across aged, old age, representative involved as of EOL decisions making process (i.e. older complexity of end-of- decisions in age groups; 2) aging process, sample of intermediary and preceding patients were less often life decision making; Belgium: a incidence shifts discriminatory death between responding decision-making included in decision could not ascertain population- between 1998 practices certificates in physicians, processes making for alleviation of patients’ education based death and 2007 in the 2007 researchers and the pain and symptoms); this status which may be certificate different age Flemish Agency for was independent of other confounder/determinant Survey. BMC groups; 3) Care and Health to factors; authors concluded of EOL decision Public Health decision-making guarantee that that “it is thus warranted making process 2012, 12: 447 process; 4) completed to conclude that older whether questionnaires could patients are at higher risk formulation and never be linked to a of paternalism” granting of particular patients or euthanasia physicians requests differ in incidence across age groups

Cherubini et Analysis of To assess extent Age-based Analysis of Assessed presence of Consensus of A significant proportion Only examined trials al, 2011 251 trials of exclusions WHO Clinical upper age limit principal of trials (n=64, 25.5%) submitted to the WHO- investigating underrepresentat Trials (explicit exclusion) researchers limited the participation ICTRP registry; data The persistent treatment of ion of older Registry as well as other regarding of older patients using an about each trial is brief; exclusion of heart failure people in Platform on exclusion criteria justification of arbitrary upper age limit; assessed eligibility from older patients that were ongoing clinical Dec 1 2008 that may result in exclusion; used this was seen in both trial protocols, whereas from ongoing found in the trials for HF; to exclusion of older multivariate pharmacologic and eligibility from the clinical trials WHO Clinical evaluate patients (implicit logistic regression nonpharmacologic trials; actual trial may differ. regarding heart Trials justifications for exclusions) modelling to found high rates of other Analysis of completed failure. Arch Registry exclusions; to identify variables exclusion criteria such as trials cannot control for Internal Med Platform assess significantly the presence of potential confounding 2011; 171: 550– associations associated with comorbidity, cognitive factors that already 56 between trial explicit upper age impairment, physical exist. characteristics exclusion disabilities, Strength: trial protocol and exclusion polypharmacy, analysis removed criteria communication barriers, publication bias; or visual or hearing includes deficits, that might pharmacological and indirectly limit the non-pharmacological inclusion of older management individuals

Cox et al, 2011 14 clinical To determine Exclusion of Descriptive CPGs reviewed for Treatment for those Very low representation Chose 14 Canadian guidelines whether older patients analysis of age-specific aged 65 and older, of pts aged 80 and older guidelines for 5 chronic Underrepresenta covering 5 guidelines and clinical recommendations treatment for those in CPGs and studies used conditions: limited tion of different supporting guidelines e.g. identification or aged 80 and older; for guidelines; 12 of 14 generalisability to other individuals 80 conditions: evidence inclusion of frail treatment for those provided specific conditions or other years of age and diabetes, heart provide older pts, pts older with multiple recommendations for pts populations. older in chronic failure, information for than 80 years and pts chronic conditions. 65 years of age and older; Strengths: examined all disease clinical hypertension, management of with multiple Specific only 5 provided references articles for practice osteoporosis, older patients chronic conditions; recommendations recommendations for pts each guideline (2,559 guidelines. Can stroke (80 years and conducted analysis for those aged 65 aged 80 and older. refs); looked at Fam Physician older) with to distinguish and older, those 2,559 studies used as intersection of age and 2011;57:e263-9 multiple chronic between “younger” aged 80 and older, evidence to support multiple comorbidities conditions older individuals (65 and for those with recommendations; 2,272 to 79 years) those multiple chronic studies provided the mean aged 80 years and conditions age of participants, of older which only 31 (1.4%) reported a mean age of 80 years of age and older Cruz-Jentoft et 440 trials To assess extent Exclusion or Registry Examined the Frequencies of 289 trials (65.7%) Analysis of completed al, 2013 examining of exclusion of under- analysis of proportion of trials exclusion criteria excluded pts via an trials cannot control for treatment for older people representation World Health excluding people via that might limit the arbitrary upper age limit potential confounding Exclusion of type 2 from ongoing of older Organization an arbitrary upper inclusion of older (65 to 85) upper age factors that already Older Adults diabetes clinical trials in people International age limit or other individuals in limits more frequent when exist; only examined from Ongoing type 2 diabetes Clinical Trials exclusion criteria clinical trials. sample sizes were smaller trials submitted to the Clinical Trials mellitus Registry that might indirectly Exclusion criteria than 100 (73.6%). WHO-ICTRP registry; About Type 2 Platform on cause limited were classified as Most common exclusion data about each trial is Diabetes July 31, 2011 recruitment justified or poorly criterion was comorbidity brief; assessed Mellitus. J Am of older individuals justified (338 trials, 76.8%) – only eligibility from trial Geriatr Soc considered justified in protocols, whereas 2013; 61:734– 23.2% of trials. eligibility from the 38 Exclusion for actual trial may differ polypharmacy (29.5% of trials), cognitive impairment (18.4%), short life expectancy (8.9%) and other poorly justified exclusion criteria that could limit the inclusion of older individuals was evident. Only six trials (1.4%) were designed specifically to study older adults

DeWilde et al, Computerised To assess trends Inequitable Database Computerised Odds ratios for Of all variables, age had Potential weakness in 2003 medical data in statin prescribing analysis medical data from receiving a statin strongest association with selection of the from 142 prescribing over (the term 142 general practices prescription statin prescription: 44.9% practices; one author is Evolution of general time; to ‘ageism’ not across England and according to age of patients aged 35–64 a director of a company statin practices examine impact mentioned Wales involving and other variables years received a statin vs that markets data to prescribing across of patient and even though patients aged 35 e.g. gender, type of 10.4% of those aged 75– pharma companies; not 1994–2001: a England and practice factors in title of years and older IHD, time since 84 and only 1.2% of those all data was shown. case of agism Wales on inequitable article) treated for IHD. diagnosis, aged over 85 (1998 data). Strengths: Removed but not of prescribing Examined temporal ACORN, smoking No evidence that the age response bias by sexism? Heart trend from 1994 to status; temporal differences were including all patients 2003; 89: 417– 2001 odds ratios for trends from 1994 to diminishing with registered with all 21 receiving a statin 2001 prescribing rates when included practices prescription in 1998 comparing 2001 vs 1998

Dodd et al, Systematic To determine Exclusion/ Systematic Information on the Age range in trials, 23 trials (29.7%) had Only searched 2011 review whether the under- review of age of trial gender ratios, trial explicit exclusion criteria MEDLINE and the involving participation of representation trials participants, exclusions based based on age. Only 13.8% Cochrane Central Exclusion of 68,016 older adults and of older percentage of on age of study participants were Register of Controlled Older Adults participants in women in patients participants who aged 75 and older, and Trials; small number of and Women 80 trials published were female, and 27.7% were women. trials over limited time from Recent clinical trials of whether there were These percentages are frame; could not Trials of Acute acute coronary specific exclusions below the representation establish proportion of Coronary syndromes has based on age were of all US adults participants aged over Syndromes J increased extracted from experiencing an ACS in 75 in several trials so Am Geriatr Soc included trials. recent years who were used estimates 2011; 59: 506– Evaluated change aged 75 and older 11 over 2-year period (41.9%) or female (May 2007–May (41.5%) 2009)

Dunn et al, Systematic To identify the Under- Systematic Followed PRISMA Age distribution of 4,993 potential papers Selection bias: only 2017 review extent to which representation review of guidelines to identify trial participants in were identified, but only searched one database; involving elderly of older trials that used phase III RCTs of cancer treatment three papers on breast only included papers in Older cancer screening of populations are patients meta- or cancer-specific trials cancer, three on lung English; time-limited to patients in 4,993 represented in pooled treatments with a cancer, and none on 5 years so may have cancer clinical potential cancer treatment analyses primary or prostate cancer presented missed other data; only trials are papers, with clinical trials of secondary outcome the age distribution of included three types of underrepresente final total of the world’s top of overall-survival. their participants. Except cancer trials; only d. Systematic six papers three cancers: Restricted to for one paper of breast included study type of literature review included in breast, prostate research published cancer, participants ≥70 either meta-analyses or of almost 5000 the review and lung within 5 years from years in all other papers pooled analyses; small meta- and May 2016; only were underrepresented number of analyses in pooled analyses included studies with final review (six) of phase III sufficient although represented randomized information on age data on 110,224 trials of survival distribution of participants; almost all from breast, participants studies were conducted prostate and in developed regions lung cancer, which limits Cancer generalisability to Epidemiology developing regions 2017; 51: 113– 17

Fairhead et al, All patients To identify any Under- Comparative Identified all patients Age-specific rates Patients with recent Accounted for multiple 2006 undergoing under- treatment and population- in routine clinical of carotid imaging, carotid TIA or ischaemic confounders, compared carotid investigation under- based study practice who had diagnosed ≥ 50% stroke: 575 in routine to large well-matched Under- imaging for and under- investigation carotid imaging symptomatic clinical practice and 402 population base investigation ischaemic treatment of of older during the study carotid stenosis, in the Oxford vascular and retinal or older patients patients period for a new and subsequent study had carotid undertreatment cerebral TIA TIA and stroke ischaemic cerebral endarterectomy, in imaging, with similar of carotid or stroke from or retinal event by patients with recent rates disease in 1 April 2002 screening all NHS TIA or stroke up to the age of 80. elderly patients to 31 March and private referrals The incidence of ≥ 50% with transient 2005 in the for carotid symptomatic stenosis ischaemic attack Oxford ultrasonography, increased steeply with and stroke: vascular study magnetic resonance age, particularly in those comparative (n = 91 105) angiography, aged ≥ 80. population- and from 1 computed Compared with based study April 2002 to tomography investigations in patients BMJ 2006; 31 March angiography and in the Oxford vascular 333:525–27 2003 in conventional arterial study, the rates of carotid routine or venous imaging, diagnosis of ≥ clinical angiography and 50% symptomatic practice (n = compared to Oxford stenosis and carotid 589,899) vascular study endarterectomy in this age group in routine clinical practice were all substantially lower

Fitzsimmons World Health To assess Exclusion of Systematic Extracted data on PD Trial exclusions 101 studies (49%, equal One large registry used; et al, 2012 Organization potential for older patients database trials from the WHO based on upper age to 30.1% of all patients) did not assess quality of Clinical Trials older patients to analysis International Clinical limits; associations excluded patients based the studies included and Older Registry participate in Trials Registry between study on an arbitrary upper age the relevance of their participants are Platform for Parkinson’s Platform on April 1, characteristics and limit. Mean upper age results to clinical frequently 206 actively disease research 2011 exclusion by age limit for exclusion was practice; assessed excluded from recruiting PD based on upper 79.3 years; study potential for Parkinson's research age limits variables affecting participation rather than disease studies exclusion by upper age actual participation and research. (47,224 limit: statistically cannot predict how Parkinsonism participants) significant in studies with many older patients Relat Disord fewer than 100 were actually recruited 2012; 18: 585– participants 89 Forman et al, 57,937 HF To determine Age-related Database Data were stratified Guideline Found modest age-related Potential selection bias: 2009 admissions differences in management analysis into 3 age groups recommended declines in respect to due to voluntary from January management of differences/ (≤65, 66-75, 76-85, therapies at some but not all evidence- participation; data were Influence of age 2005 through heart failure age and >85 years) discharge: based HF therapies; also collected by medical on the April 2007 in across age discrimination complete discharge found high overall use of chart review and are management of 257 hospitals groups instructions, evidence-based HF limited by heart failure: in USA evaluation of therapies even in the documentation and Findings from LVEF, ACEI or oldest HF patients; found abstraction. Due to Get with the ARB use for LVEF low use of procedures in large number of Guidelines– < 40%; smoking relation to age patients, small changes Heart Failure cessation advice, that are statistically (GWTG-HF). beta-blocker use at significant may not be Am Heart J discharge for clinically significant. 2009; 157: patients with Used database 1010-17 LVSD without supported by grant from contraindications or pharma; no follow up intolerance after discharge

Gaynor et al, 182 stroke To Ageism/lack Systematic All RCTs of stroke Analysed mean Mean age of all patients Only used one database 2014 rehabilitation systematically of inclusion of review rehabilitation entered ages, exclusion was 64 years, almost a RCTs entered review the older patients in the Cochrane criteria and gender decade younger than Ageism in into Cochrane literature to database that ratios of trials those seen by stroke stroke database assess the extent reported mean age related to stroke physicians in daily rehabilitation of ageism in rehabilitation practice and 11–12 years studies. Age and stroke included in the younger than encountered Ageing 2014; rehabilitation Cochrane in hospital practice in 43: 429–31 studies Collaboration Britain. Almost half Reviews (46%) of trials excluded patients with cognitive impairment, almost one- quarter (23%) excluded patients with dysphasia and one-eighth (13%) excluded patients with multiple strokes

Gnavi et al, Records of Aimed to assess Not given, Quantitative: Records of all Prescription of Among those aged >74 Data gathered from 2007 7,446 patients which factors even though Log-binomial 2001/2002 residents statins for years, the prescription rate multiple databases; who had a (social and ‘ageism’ is in models in Turin, Italy, aged secondary was 40% lower than that linkage between Statins diagnosis of clinical) title – not presented as 30–85 years, with a prevention for younger people. databases has potential prescribing for IHD on influence the mentioned prevalence hospital discharge according to social Efficacy uncertain in for error; no private the secondary hospital start of statin again; also, no rate ratios diagnosis of IHD and clinical those age >74 at the time prescribing data prevention of discharge in treatment in mention of were used to were linked to the variables including of the study. Authors included; limited to one ischaemic heart Turin, Italy people with age bias or test statins regional Database of age (3 groups: 30– concluded that age and city with low statin disease in IHD in Turin, discrimination prescription drug prescriptions to 64, 65–74, 75–85 social determinants act in prescribing rates; Torino, Italy. A which has one in the paper associations identify those years), gender, concert to reduce the cannot identify case of ageism of the lowest with clinical persons who, within education level, propensity of physicians prescriptions that are and social statin and socio- 3 months after marital status, main to prescribe statins; not picked up at the inequalities. prescribing rates demographic discharge, had been diagnosis among the elderly, more pharmacy; differences Eur J Public in Europe characteristics prescribed statins revascularisation, disadvantaged persons are in prescribing for those Health 2007; diabetes, discharge less likely to receive aged over 74 years 17, 492–96 ward statins corresponded to a time when statins were not recommended to those aged 75 and older, calling into question any finding of age bias

Gottlieb et al, 1,026 patients To assess age Age-related Observational The number of The number of Adherence to guidelines Attempted to adjust for 2010 from differences in management analysis evidence-based EBM used in including primary differences between age prospective the use of differences medicines used at hospital and at reperfusion and use of 4- subgroups but could not Age differences nationwide ACC/AHA discharge (aspirin, discharge; EBM were provided less exclude the influence of in the adherence Acute treatment beta-blocker, association frequently to older confounding variables to treatment Coronary guidelines and ACEI/ARBs, statins) between age and patients (age > 75 years) on patient management guidelines and Syndrome effects on was assessed for the prescription of compared to younger and outcome. outcome in Israeli Survey mortality in each age subgroup. multiple EBM; patients. Guidelines Strength: unselected patients with (ACSIS) patients with Association between survival at one implementation was patients (consecutive ST-elevation ST-elevation age and prescription year; reperfusion associated with a better 1- admissions process myocardial myocardial of multiple EBM therapy given; in- year outcome across all used); included NNT in infarction. infarction plus survival at one hospital age-subgroups. However, analysis Archives of (STEMI) year was assessed complications the absolute gain Gerontology (measured by NNT) was and Geriatrics significantly higher with 2011; 52:118– advancing age 24

Grant et al, Data relating To assess in- Not given, Prospective Analysed data Differences by Age appears to be an Unable to account for 2000 to 11,545 hospital even though study using routinely collected patient age independent factor in several possible trauma mortality for ‘ageism’ is in data collected by STAG i.e. pre- regarding use of trauma care: had an effect confounding effects e.g. The patients in seriously title; describes by the hospital information, resuscitation room on determining whether pre-morbid status of management of Scotland injured elderly “less Scottish presenting facilities for initial patient would initially be each patient or any elderly blunt patients and to aggressive Trauma Audit physiological and assessment and triaged to resuscitation deterioration in head trauma victims determine management” Group time-based data, management, room. trauma patients making in Scotland: whether age had of older (STAG) details of clinical senior medical staff Age had a significant them less likely to be evidence of an independent patients study; 2-year care and injuries involvement, effect in determining transferred; staffing ageism? Injury effect on the time period sustained, operative admission to whether patient was levels or volume of Int J Care process of from 1 July procedures and intensive care units, transferred to regional patients. Examined Injured 2000; trauma care 1996–30 June transfer details transfers to neurosurgical service. trauma care services in 31: 519–28 1998. Twenty regional Significantly more Scotland, which limits hospitals were neurosurgical unexpected deaths generalisability to other contributing centres and occurred in older pts. regions data to STAG mortality Age did not have at the time of significant effect on the the study seniority medical response from A&E staff, assignment to surgeons or anaesthetists for serious injury surgery

Greenfield et Chart review To determine Age bias Quantitative Patterns of care for Effect of age, Study found statistically Hospitals in one part of al, 1987 of 374 breast whether retrospective breast cancer comorbidity and significant effect of age the US, limiting cancer cases physicians chart review patients; hospital on on treatment of patients generalisability; results Patterns of Care across seven provided less across 7 controversial issues appropriate with breast cancer – even may apply only to Related to Age diverse vigorous hospitals were excluded; treatment when older patients were breast cancer; of Breast hospitals in treatment for examined (including some vigorous, healthy and management of breast Cancer Patients. California, older patients completeness of individual aspects presented with localised cancer has changed in JAMA 1987; USA with breast diagnostic of breast cancer mx breast cancer, were less the 30+ years since this 257: 2766–70 cancer than information, staging e.g. lymph node likely to receive optimal study, limiting younger and initial examination) standard treatment. generalisability of patients management; also The inhibiting effect of results; only one person developed a age began at age 65 years; responsible for hospital comorbidity index results were consistent selection (potential for that would account across all hospitals selection bias) for comorbidities that may influence care Gurwitz et al, 214 trials To assess the Exclusions Systematic Literature search for Extent to which More than 60% of clinical Only one online 1992 involving extent to which based on older review trials between 1960 older patients are trials of drug therapy in database (Medline) 150,920 older patients age and 1991 to identify excluded from drug acute MI excluded older searched; multiple hard The Exclusion participants are excluded all relevant studies trials; factors patients: age cutoffs as copy sources also used of the Elderly from drug trials of specific associated with low as 65 years; and Women for acute MI. pharmacotherapies exclusions; significantly more from Clinical To identify used to treat acute relationship prevalent in later studies. Trials in Acute factors MI between the age Studies involving Myocardial associated with characteristics of thrombolytic agents with Infarction. exclusions and study populations an invasive procedure JAMA 1992; relationship and the proportion were significantly 268:1417–22 between of women associated with age exclusion and participants exclusion; 10 studies representation (4.7%) completely of women excluded women

Hamaker et al, 1,207 trials To determine Age-related Search of NIH clinical trial For included trials, 5% of trials focused Assessed eligibility 2014 from the NIH characteristics exclusions clinical trial registry was the following data exclusively on older or from trial protocols, clinical trial of currently registry at a searched on July 1, were extracted unfit patients; 69% whereas eligibility Exclusion of registry recruiting single time 2013 for currently from explicitly or implicitly from the actual trial Older Patients clinical trials point recruiting phase I, II the registry excluded older patients: may differ; focused from Ongoing with or III clinical trials website: target 27% of trials excluded exclusively on the NIH Clinical Trials hematological with hematological disease entities; patients based on age; clinical trials registry; for patients to malignancies. source of funding; 16% excluded based on researchers limited by Hematological assess inclusion Trial characteristics type of performance status; 51% data provided by trial Malignancies: and exclusion of and study intervention; based on organ function investigators on the An Evaluation older patients objectives were inclusion and restrictions. registry website which of the National extracted from the exclusion criteria One-third of those that may not include all Institutes of registry website with particular excluded older patients inclusion/exclusion Health Clinical focus on age limits, based on age allowed criteria; no consensus Trial Registry. comorbidity, organ inclusion of younger on which cut-off values Oncologist function, and patients with poor represent strict or 2014;19:1069– performance status performance status; 8% moderate restrictions in 75 did not place any organ function (authors restrictions on organ used method from peer function. Over time, there reviewed and was a shift from exclusion frequently cited based on age toward publication) exclusion based on organ function Harrison et al, Analysis of To determine Age bias and Secondary Assessment of Use of RA No evidence that age Multiple selection 2005 data from 273 whether any under- analysis of potential age bias medications affected therapeutic biases: Female patients patients with differences exist treatment of prospectively (undertreatment) of assessed in 4 ways: approach to RA. The only, males were Does age bias RA (49 pts between older patients collected data older patients by number of rheumatologists did not excluded; all patients the aggressive aged over 70 management of from the analysing use of RA DMARDs currently appear to avoid the use of from one tertiary centre; treatment of matched to 49 older patients Hospital for medications between in use, number of multiple and/or more excluded patients elderly patients pts aged under (aged 70 years Special older patients (aged patients currently potent agents. Older without private health with rheumatoid 60) and older) and Surgery 70 years and older) using steroids, patients responded at least insurance. Small cohort arthritis? younger patients Rheumatoid vs younger patients NSAID and as well as younger (49 older and 40 J Rheumatol (60 years and Arthritis (aged 20-60 years) biologic therapy counterparts. No younger patients) – all 2005; 32; 1243– younger) re use Clinical enrolled in a tertiary undertreatment was limit generalisability of 48 of combination Research centre registry. observed findings. disease Registry Exclusion criteria modifying removed a sizeable antirheumatic population of RA drugs patients. Comorbidity (DMARDs), index used may biologic agents, underestimated true corticosteroids, levels of comorbidity NSAIDs and failed to fully control for confounding by health status

Hayes et al, Demographic To assess age- Age-related Population- Age at diagnosis was Outcomes (by age) Results suggest that age Patients all from north 2019 and clinical related inequalities based categorised into <60, related to current remains a major factor in of England which may characteristics inequalities in quantitative 60–69, 70–79 and clinical guidelines treatment decisions. potentially limit Age-related of cohort with colon cancer study 80+ years. for treatment of Age-related inequalities in generalisability of inequalities in colon cancer care (including Performed an colon cancer, i.e.: treatment exist after results (although very colon cancer (n=31,910) consideration of assessment of receipt of cancer- adjustment for large population-based treatment persist comorbidity and treatment across directed surgery confounding factors (e.g. registry 31,000 patients) over time: a other different age groups compared with no comorbidity and stage). which has been found to population- confounders) by gathering data on cancer-directed Patients aged 60–69, 70– have excellent based analysis. J and whether age, sex, area surgery, receipt of 79 and 80+ years were population coverage; Epidemiol these trends deprivation (as a chemotherapy in significantly less likely to database included Comm Health have changed proxy for surgical patients, receive surgery than those patients who started 2019;73:34–41 over time socioeconomic receipt of aged <60 years. treatment so unknown if status, year of chemotherapy in Age-related differences in completed; may have diagnosis, tumour non-surgical receipt of surgery and underestimated true site and stage and patients, receipt of adjuvant chemotherapy levels of comorbidity in treatments received no cancer-directed (but not chemotherapy in the study population for all patients treatment (i.e., no non-surgical patients) due to index used and diagnosed with surgery, narrowed over time failed to fully control colon cancer chemotherapy or (1999–2010) for the for confounding by between 1 January radiotherapy ’younger old’ (aged <80 health status; limited by 1999 and 31 years) but did not registry data e.g. does December 2010 diminish for the oldest not show if treatment from the population- patients aged >80 years vs given was palliative or based Northern and those aged less than 60 curative and does not Yorkshire Cancer years. Older pts were account for Registry (NYCRIS). more likely to receive no patient/family treatment than younger preferences patients

Hood et al, 583 patients To determine Age Population- Preformed a Use of Found that investigation Retrospective design 2000 with a whether age and differences based retrospective case investigations of heart failure with cannot control for diagnosis of sex influenced retrospective note review of (echocardiogram, echocardiography was potential confounding Are there age heart failure in the management case note prevalent cases in 16 chest-X-ray, ECG); less likely in those aged factors that already and sex 16 general of heart failure review general practices in use of primary and over 80 compared to those exist. differences in practices in in the West London. secondary care; aged less than 80; Selection bias: only the investigation West London community Cases for 583 prescription of treatment with ACE undertaken in one, and treatment of patients (57% ACE inhibitor inhibitors decreased with upper-class region of heart failure? women) with a across age groups age London, limiting A population- diagnosis of heart generalisability of based study. Br failure were results. J Gen Pract reviewed Lack of documentation 2000; 50: 559– (in available notes) of 63. drug side effects limits analysis; some degree of recall bias (GPs asked to recall any other patients with HF over and above those found in notes)

Hubbard et al, 4,058 patients To assess Access to care Cross- All sick patients Differences by Results suggest no No information on how 2003 across 5 whether access according to sectional (4,058) were studied patient age substantial age the 10 assessors were hospitals in a to critical care age (ageism in study every 12th day for regarding access to discrimination against selected/their Absence of South Wales in south Wales title) one year; collected critical care across patients who need critical backgrounds, etc ageism in access health is related to age demographic, age groups: care. (selection bias). to critical care: authority; clinical and Age up to 55 years, Many patients on general No information on a cross-sectional 2,287 (56.4%) physiological data; 55–64 years, 65–74 wards may have needs measurements used by study. Age and were being 10 members of the years, 75–84 years, that are better managed on assessors to justify Ageing 2003; cared for on a Welsh Intensive 84 years and older critical care ward, but conclusions. 32: 382–87 general ward; Care Society researchers found these Conducted in one 1,769 in subsequently judged unmet needs were not region of Wales – limits critical care the optimum related to age of patients generalisability to other areas. location of care for countries. each patient. Trained Strong conclusion research nurses (“there is no educated ward staff relationship between in study methods these unmet needs and the age of the patient”) not supported by data provided

Hutchins et al, 16,396 To determine Under- Retrospective Rates of enrolment Rates of enrolment Substantial and significant Limited by referrals to 1999 patients in 164 the rates of representation database of patients aged 65 by age group and under-representation of SWOG, which may be Southwest enrolment of of older analysis years or older were for 15 types of older patients (aged over defined by low referrals Underrepresenta Oncology patients aged 65 patients compared with cancer 65) in trials: 25% vs 63 of older patients to tion of patients Group years or older in corresponding rates percent in the community; those institutions. 65 years of age (SWOG) cancer treatment of patients with under-representation seen Retrospective design or older treatment trials cancer in the general in all types of cancer apart cannot control for In cancer- trials population (derived for lymphoma. potential confounding treatment trials. from the 1990 US Under-representation was factors that already exist N Engl J Med Census and National particularly strong in 1999; 341: Cancer Institute’s trials of treatment for 2061–67 Surveillance, breast cancer (9 percent Epidemiology, and vs. 49 percent, P<0.001) End Results Program). Fifteen types of cancer were included in the analysis

Javid et al, 228 To examine Age bias Prospective Used Patient Compared patient- Found that if a trial is Did not capture 2012 physicians at patient and study Participation and physician- available and the patient patients’ cancer stage eight SWOG provider-driven Questionnaire, perceived barriers is eligible, older and which may impact A Prospective institutions (5 factors Patient Refusal to enrollment to younger patients enroll at decision making. Analysis of the academic and responsible for Questionnaire and cancer clinical the same rate. Physicians Limited to centres with Influence of 3 community the under- the Physician trials by age group were less likely to discuss strong interest in Older Age on based); 909 representation Treatment (65 yrs and older vs clinical trial participation clinical trials and Physician and pts, of whom of older patients Questionnaire to those aged less than with older patients despite women with breast Patient 309 pts were in cancer gauge reasons for 65), including their eligibility. cancer, so may not be Decision- eligible for clinical trials; non-enrollment in demographics, trial Physician concerns over representative of Making When trial focus on clinical trials and availability and study regimen or toxicity general population of Considering participation barriers between analyses across age eligibility, as well profile were similar for cancer patients or Enrollment in (152 older (65 years groups as concerns related older and younger treating physicians Breast participated, or older) and to treatment, patients. Physicians were Cancer Clinical 159 did not). younger patients medical status, age, more likely to cite patient Trials (SWOG family and age alone as a reason for S0316). The financial or not discussing or Oncologist transportation enrolling a patient in a 2012; 17:1180– concerns trial with older patients 90

Jennens et al, 94 CRC trials To quantify Age Retrospective Gathered data from Median age of Difference in median age Compared different 2006 and 81 change over disparities review the Victorian Cancer registry patients vs of patients in VCR vs populations (trial pts NSCLC trials time in median Registry and all those enrolled in those clinical trials was from US/Europe vs Increasing age of patients large randomised clinical trials; approx. 10 years for CRC Aust registry patients). under- in clinical trials chemotherapy trials differences in and NSCLC. No change Had to average median representation for metastatic for advanced CRC variables over time in median age in trials ages from individual of elderly CRC and and NSCLC between despite increasing age in trials rather than patients with NSCLC vs 1982–2001. Only patients i.e. age gap is individual patient data. advanced general included large (n widening. Small change Only used one state colorectal or population; to subjects >100) trials in NSCLC trials with registry; only searched non-small-cell assess that specified the increase in median age of one database (plus lung cancer in proportion of number of patients 2 years but doesn’t keep reviews and meta chemotherapy trials with upper enrolled, median age pace with the rapidly analyses); retrospective trials. Int Med J age limit for and the years the increasing age of the design cannot control 2006; 36: 216– eligibility trial was open to general lung cancer for potential 20 enrolment population confounding factors that already exist Jin et al, 2014 166,388 To investigate Age-related Quantitative Used data from Evaluated Found wide variations in Limited by insurance patients in disease- differences database January 1 2005 to proportion of DMARD prescriptions data registry; conducted Utilization Korea aged 65 modifying review June 30 2006 from DMARD use e.g. based on 8-9 years before Patterns of and older, antirheumatic the Health Insurance accounting for demographic factors and published results during Disease- with a drug (DMARD) Review and demographic medical care utilisation which time prescribing Modifying diagnosis of use in Korean Assessment Service factors, medical status. Male sex, of DMARDs has Antirheumatic rheumatoid older patients claims database to care utilisation advanced age, visiting a changed substantially – Drugs in arthritis with rheumatoid examine DMARD status and primary or secondary care thus generalisability of Elderly arthritis use geographic institution and treated in results questionable Rheumatoid division. rural areas were related to Arthritis Compared a lower number of Patients. rheumatoid factors, DMARD prescriptions. J Korean Med medication use and There was an increasing Sci 2014; 29: comorbidities trend of DMARD use 210–16 between DMARD during the study period users and non- (16 mths) users. Evaluated number of prescriptions with mono and combination DMARD therapies

Joerger et al, Records of To examine Under- Prospective Analysed patient Surgical and non- Proportion of locally Limited by 2013 4,820 breast patterns of care treatment of population- data from medical surgical breast advanced, metastatic and retrospective data cancer of older patients older patients based study records of women cancer treatment incompletely staged collection and lack of Treatment of patients in with breast with a diagnosis of over 5 age groups breast cancer increased outcome data; did not breast cancer in Switzerland cancer and breast cancer; also (<65 years, 65 to with age. assess all potentially the elderly: evaluate sent questionnaires <70, 70 to <75, 75 Higher age was a interacting A prospective, potential to treating clinicians to <80 and ≥80 significant risk factor for comorbidities population- causative for extra data. Study years); the omitting post-BCS based Swiss factors for the period: January 2003 predictive impact radiotherapy, sentinel study. J decrease in BC- to December 2005; of patient age on lymph node dissection Gerontol 2012; specific survival involved 7 specific treatments and adjuvant endocrine 4: 39–47 in this age population-based treatment group cancer registries

Jung et al, Data on To assess the Age- Prospective Analysed data in the Choice of surgical Treatment of rectal cancer Database analysis 2009 15,104 influence of age disparities audit Swedish Rectal strategy, use of was by patient's age: in makes it difficult to patients with on treatment Cancer Registry preoperative pts 75 years or older, assess whether Rectal cancer rectal cancer and outcome of (SRCR) from radiotherapy and distant metastases were treatment differences in treatment and in Sweden rectal cancer patients treated for outcome following diagnosed less frequently older vs younger outcome in the rectal cancer in abdominal surgery than in younger patients; patients is purposive elderly: an Sweden in 1995– in older (75 +) vs older patients underwent audit based 2004 younger patients abdominal surgery less on the Swedish frequently but they had rectal cancer more Hartmann’s registry 1995– procedures than younger 2004. BMC patients; preoperative Cancer 2009; 9: radiotherapy was used for 68 34% of patients ≥75 years vs 67% of younger patients; older patients had lower relative survival 90 days postoperatively and lower relative five-year survival

Konrat et al, 155 RCT To determine Under- Literature Searched PubMed to Representation of Of the 155 RCT reports Only used one database; 2012 reports the representatio review identify all RCTs older patients in included, only 3 were examined only compared to representation n of older indexed from 1966 clinical trials of 4 exclusively about older published reports of Under- large national of older people patients to April 2008 commonly used patients. Proportion of RCTs (no grey representation database of in RCTs evaluating one of the oral drugs patients aged 65 or older literature); only looked of Elderly 1,958,716 involving 4 4 medications; compared with that matched age of those at 4 drugs; only used People in patients medications: compared these to proportion of in clinical practice was data on population use Randomised pioglitazone, estimates of patients aged 65 or 10.8% for pioglitazone of medications from one Controlled rosuvastatin, community-based older treated with trials, 18.2% for country, limiting Trials. The risedronate, treatment population the drugs in risedronate trials, 10.3% generalisability of Example of valsartan determined from a clinical practice for rosuvastatin trials, results; only one author Trials of 4 national health 13.4% for valsartan trials. selected the studies to Widely insurance database The representation of include in the analysis Prescribed (SNIIR-AM) older people did not differ (selection bias) Drugs. PLoS covering by publication date or ONE 2012; 7: approximately 86% sample size e33559 of the French population Lavelle et al, 480 women Aimed to assess Age Population- Identified all women Proportions of Older women were less Retrospective design 2007 aged 65+ whether age disparities based, aged 65 years and patients who likely to receive standard cannot control for with invasive predicts a range retrospective older who were received non- management than potential confounding Non-standard breast cancer of indicators of cohort study resident in Greater standard younger women for all factors that already management of standard Manchester, management indicators investigated. exist. Potential selection breast cancer management of recorded in cancer Compared to women bias as not all cases increases with invasive breast registry with an aged 65–69 years, reviewed; sample from age in the UK: a cancer anniversary date for women aged 80+ years one geographical region population- management invasive breast with operable (stage 1- only, limiting based cohort of after accounting cancer in 1999, 3a) breast cancer had generalisability of women >/equal for tumour performed case note increased odds of not results; patient to 65 years. characteristics reviews to check and receiving triple preferences and health British J Cancer supplement cancer assessment, not receiving status not taken into 2007; 96: 1197– registry information primary surgery, not account 1203 on management, undergoing axillary node tumour variables and surgery and not age undergoing tests for steroid receptors. Women aged 75–79 years had increased odds of not receiving radiotherapy after breast-conserving surgery vs those aged 65–69 years

Lee et al, 2001 Data from 593 To determine Under- Systematic Searched MEDLINE The percentage of The number of RCTs with Limited search RCTs for whether the representation review and Cochrane women enrolled explicit age exclusions increases risk of Representation acute percentage of of older databases for articles and the percentage declined from 58% during selection bias: only of Elderly coronary older patients patients January 1966–March of enrolled patients 1966-1990 to 40% during searched MEDLINE Persons and syndromes and women in 2000. Estimates of who were at least 1991-2000. Trial and Cochrane databases Women in published community-based 75 years of age vs enrolment of patients for English-language Published clinical trials of MI rates came from patients in aged 75 years or older articles; could not Randomized acute coronary the National community increased from 2% during determine if other Trials of Acute syndromes has Registry of 1966-1990 to 9% reasons for exclusions Coronary increased; and Myocardial during1991-2000. This is Syndromes how this Infarction and the still well below patients in JAMA 2001; enrolment Worcester Heart community with MI 286: 708-13. compares with Study (37%) disease prevalence Lehmann et al, Data To evaluate the Age-based Retrospective Analysed the Trauma Patients aged 65 or older Retrospective design 2009 regarding relationship management registry Washington State management/triage were significantly less cannot control for 51,227 trauma between age and analysis Trauma Registry for patterns for older likely to have trauma potential confounding The impact of admissions trauma triage the period January 1, vs younger team activation despite a factors that already advanced age on (27% aged 65 decisions, the 2000, to December patients; impact of similar percentage of exist. Did not analyse trauma triage years or older) need for 31, 2004 these triage severe injuries; they also trauma triage criteria to decisions and emergent practices on more often required see why many of these outcomes: A interventions morbidity and urgent craniotomy and older trauma patients statewide and outcomes mortality orthopaedic procedures. are not being captured analysis. Am J Heart rate and blood by the trauma system Surgery 2009; pressure were not 197: 571–75 predictive of severe injury for older patients. Under triaged older patients had 4 times the mortality rate and discharge disability of younger patients

Leinonen et al, 15,032 To determine Comparative Systematic Conducted literature Differences in age Mean age of participants There was a lack of 2015 patients in 31 whether there representation review review of 3 and sex distribution in RCTs was significantly detailed information on RCTs of are differences databases for articles and comorbidities lower (by 5.8 years) than participants’ age Systematic Acetylcholine in age and sex about double-blind, in RCTs of AD the mean age of 79.7 distribution in RCTs, so Review: sterase distribution and placebo-controlled, drugs vs real-world years in the reference researchers had to use Representative- Inhibitors presence of co- randomised trials of population with population; most articles normal distribution ness of compared morbidities donepezil, AD did not report age assumption to estimate Participants in with 28,093 between rivastigmine and distribution of the proportion of RCTs of patients with participants galantamine participants. The participants in each age Acetylcholinest Alzheimer’s included in proportion of women was group (may over- or erase Inhibitors. in reference RCTs of 63.2% in RCTs vs 67.8% underestimate true PLoS ONE population acetylcholineste in the reference numbers). Reference 2015; 10: rase inhibitors population. Information population from one e0124500 and nationwide on comorbidities and use country only cohort of of concomitant drugs persons with were lacking or poorly Alzheimer’s reported in most articles disease.

Levy et al, 2006 198 health- To examine Exclusion of Systematic Systematically Whether the trial 53% of the 198 health- Only used one database risk behaviour whether older older people review reviewed clinical included persons risk behaviour clinical to conduct literature Exclusion of clinical trials patients are trials targeting the over the age of 65 trials excluded people review. Only included elderly persons excluded from leading health-risk or over the age of over the age of 65; the trials published in the from health-risk health-risk behaviours, as 75 in the study exclusion percentage five most cited journals; behaviour behaviour identified by Healthy cohort increased to 72% for could not determine clinical trials. clinical trials; if People 2010: those over the age of 75. what proportion of older Preventive so, determine tobacco use, The exclusion of older patients were included Medicine 2006; what may be overweight/obesity, people did not decline as only 5% of clinical 43: 80–85 responsible physical inactivity, over the 14 years studied. trials specified the these exclusions substance abuse, This age exclusion pattern proportion of the irresponsible sexual was not explained by the sample that was over behaviour intervention's the age of 65 intrusiveness or whether illness was an exclusion criterion

Lewis et al, 59,300 To evaluate the Inclusion of Retrospective Conducted a Participation of 32% of participants in Did not assess the non- 2003 patients participation of older people analysis retrospective older (65 yrs and phase II and III clinical clinical factors that may enrolled in older patients in analysis using three older) patients in trials were aged 65 or influence elderly Participation of 495 clinical clinical trials databases to assess clinical trials older, compared with 61% participation in cancer patients 65 trials and assess the patient and trial stratified by trial of patients with incident trials (e.g. patient years of age or impact of characteristics for phase (II v III) and cancers in the USA; the preference); older in cancer protocol 59,300 patients by stage of disease degree of under- retrospective design clinical trials. J exclusion enrolled onto 495 (early v late). representation was more cannot control for Clin Oncol criteria on NCI-sponsored, Also assessed pronounced in trials for potential confounding 2003; 21:1383– participation of cooperative group impact of clinical early-stage cancers than factors that already exist 89 older patients trials, active from trial protocol in trials for late-stage 1997 through 2000 exclusions on cancers participation of older patients in trials

Ludmir et al, 742 trials To characterise Exclusion of Clinical trial Database query was Use of age-related Upper age restriction Single clinical trials 2019 (a) involving the incidence older patients database performed through enrollment criteria enrolment criteria seen in registry based in the enrollment of and correlates of analysis the over time in cancer 10.1% of RCTs; the United States may limit Decreasing 449,720 age-restrictive ClinicalTrials.gov RCTs median age cutoff for generalisability incidence of patients eligibility website in restricted trials was 72 internationally; upper age criteria among November 2017 to years; decreasing justifications for age- restriction cancer RCTs; to identify cancer incidence of age restrictive criteria were enrollment examine RCTs for adult restriction criteria over not examined in this criteria among changes in the patients time, e.g. trials initiating analysis; did not analyse cancer clinical utilisation of enrolment in 2002–2005 other criteria that may trials J Geriatr age restrictions had a 16.1% rate of age- affect exclusion of older Oncol 2019; over time restrictive eligibility patient e.g. organ doi.org/10.1016/ criteria, compared with function, history of j.jgo.2019.11.00 7.6% for trials initiating prior malignancy, other 1 enrolment in 2010–2014 comorbidities, which may disproportionately affect trial eligibility for older patients

Ludmir et al, 302 trials To characterise Age Clinical trial Phase 3 clinical The difference in Median age of trial Single clinical trials 2019 (b) involving 262, the age disparities vs database oncology RCTs were median age participants was a mean registry based in the 354 disparities matched analysis identified through between trial of 6.49 years younger United States may limit Factors participants; among population ClinicalTrials.gov – participant median than the population generalisability Associated with 249 trials participants in included multi-arm age and population- median age; age internationally; limited Age Disparities (82.5%) were RCTs of RCTs assessing a based disease-site- disparities were by selection of 4 cancer Among Cancer industry common therapeutic specific median age significantly greater in types; matching Clinical Trial funded cancers and intervention for (using SEER industry-funded trials; population age data Participants identify factors patients with breast, database) was disparities were also from SEER is US data JAMA Oncol associated with prostate, colorectal, determined for greater among trials of and most trials was 2019; wider age or lung cancer (the 4 each trial targeted systemic therapy multinational; SEER doi:10.1001/jam imbalances most common and lung cancer trials; also captures all patients aoncol.2019.205 cancer disease sites) enrolment criteria with the disease, not 5 restrictions based on just those who performance status or age underwent treatment; cutoffs were associated median age information with age disparities; the is limited gap between trial and population median ages is increasing over time Mackay et al, Cardiac arrest To assess effect Age-based Prospective Prospective audit of Reviewed deaths to ‘Do Not Attempt Data only from one 2004 calls of age and other discrimination and cardiac arrest calls verify whether Resuscitation' orders hospital in the UK – following risk factors on (“Are we retrospective following 6,550 CPR had been appeared twice as does not necessarily Resuscitation 6,550 open- the incidence ageist?”) audits consecutive open- attempted prior to frequently in older reflect practice in wider after cardiac heart surgery and outcome of heart surgery cases; death; to determine patients (≥70 years). population or other surgery: are we cases cardiac arrests plus retrospective whether a DNAR However, proportions of clinical environments ageist? Eur J following open- audit of all cardiac order had been deaths without CPR and Anaesthesiol heart surgery. In surgical deaths not implemented; and the organ failure scores 2004; 21: 66–71 addition, to preceded by cardiac to determine the across age groups were retrospectively arrest; compared age number of organ similar, suggesting study the effects and risk groups systems that had severity of illness was of age and 'failed' at the time more important than age organ system of decision to in determining failure score on institute the DNAR resuscitation status the placement of order DNAR request

Malik et al, 2,447 patients To compare Age Retrospective Compared Demographic Over 50% of older Physician and selection 2013 with breast treatment of disparities database management of 382 variables, patients were biases: used one breast cancer breast cancer in review women aged 71 pathologic undertreated. Older cancer database Undertreated older (aged 71 years or older with findings, survival, patients were treated less maintained by one Breast Cancer in years or older) breast cancer with undertreatment aggressively e.g. received doctor only. the Elderly. women with 2,065 women aged fewer mastectomies, less Retrospective design Journal of younger (aged less than 71 with radiation after cannot control for Cancer less than 71) breast cancer; conservation surgery; potential confounding Epidemiology women. patients over 71 seldom received factors that already exist 2013; 893104: years of age who chemotherapy even for doi: were undertreated by node-positive cases. Older 10.1155/2013/8 conventional criteria patients received 93104 were compared to hormonal therapy as their appropriately frequently as younger treated counterparts patients. Outcomes were comparable between younger and older patients, possibly as their cancers were smaller, better differentiated and with fewer involved nodes McGarvey et 102 To assess Ageism Literature Review clinical trials Mean age of The mean age of study Only searched one al, 2017 osteoporosis whether the fact review of osteoporosis participants and participants was 64 years, database; small sample clinical trials that management within exclusion criteria despite the fact that the size (a number of RCTs Ageism in osteoporosis is systematic reviews of trials related to average age at hip fracture did not report mean age Studies on the considered a to identify those that osteoporosis is 83 for women and 84 so were excluded for Management of disease of older reported the mean management that for men. Twenty-four the analysis); did not Osteoporosis. J people was age of participants; had been included (23%) of the 102 trials give actual patient Am Geriatr Soc reflected in compared the in the Cochrane used older age as an numbers included in 2017; 65: 1566– research into findings to the mean Database of exclusion factor. Other trials; did not discuss 68 osteoporosis; age of hip fracture in Systemic Reviews exclusion factors were confounders and to international studies often age-related e.g. time determine the (osteoporosis is the since menopause, extent of ageism leading cause of hip impaired cardiac function, in studies on the fracture) dependent in ambulation, management of any severe comorbidity, osteoporosis dementia or any cognitive impairment

Mitford et al, 537 patients To compare Ageism Quantitative Basic data form was Data were collected Findings show that first- Potential gaps in patient 2010 with a first- incidence, naturalistic completed either by on demographics, episode psychosis in older records may omit episode diagnostic design the patient's diagnosis at people is not uncommon – important data; Ageism in first psychosis groups and consultant presentation and 23% of those who difficulty in identifying episode hospitalisation psychiatrist or by hospital admissions presented with first- first-episode psychosis; psychosis. Int J of two differing PACE staff by age in the first episode psychosis were included patients with Geriatr age groups with (population adjusted year. aged over 65 yrs. some cognitive Psychiatry first-episode clinical The older group were impairment, which may 2010; 25: 1112– psychosis epidemiology). admitted later on after limit generalisability of 18 Differences by presentation, and had findings patient age regarding longer average hospital the access to a range stays compared to of services younger patients appropriate to people with psychosis

Morse et al, 20 studies To review the Under- Clinical trial MEDLINE and Representation of Found that older women Only searched 2 2004 involving published representation database Cochrane Databases older women aged aged 70 and above were databases; assumed 2,018 randomised analysis of Clinical Trials 70 and older in underrepresented in normal age distribution Exclusion of participants trials of the were searched from clinical trials across clinical trials of stress in trials (which would Elderly Women surgical January 1966 to different surgery incontinence surgery likely overestimate from Published treatment of December 2003; types (3.8%) compared to percentage of older Randomized stress urinary performed manual population prevalence of patients in trials); small Trials of Stress incontinence searches of meta- stress incontinence in sample size of trials and Incontinence and estimate the analyses and review women aged 70 and older excluded similar Surgery. Obstet proportion of articles from 2000 to (10–40%), the prevalence number of surgical Gynecol 2004; women 70 years 2003 of women aged 70 and treatment trials based on 104: 498–503 or older enrolled older among those lack of information, in those trials seeking care for stress which may limit incontinence (51%), and generalisability of the proportion of results surgeries for stress incontinence actually performed on women aged 70 or older (16%)

Nauta et al, Management To determine Age- Case Trained physicians Treatment and Older patients were less Makes claims about 2013 of 14,434 whether age- dependent management and nurses mortality in four likely to receive evidence- older pts remaining less consecutive dependent inequalities analysis accustomed to the age groups (pre-55, based medical care and likely to receive Age-dependent patients inequalities in use of standardised 55–65, 65–75, >75 reperfusion therapy evidence-based care but care and long- admitted to care and case report forms years) during the last 24 years, cannot prove causation term (20 year) intensive outcome collected data and although the differences and differences mortality of coronary care changed over a study patients were became smaller over time; decreased over time; 14,434 unit 24-year period categorised into four found that temporal trends also found that, in myocardial for patients groups of patients in 30-day and long-term absolute terms, “older infarction admitted with according to age mortality improved across patients benefited most patients: myocardial all age groups during this from the improved Changes from infarction 24-year period (i.e. were medical care for MI 1985 to 2008. independent of age) during the last 24 Int J Cardiol years”; data are derived 2013; 167: 693– from a single centre 97 which limits generalisability; nature of diagnosis and care changed over the long study period Nguyen et al, 50,096 To assess age Age-related Multi-site Data were provided Receipt of Use of evidence-based Did not have 2010 patients and sex differences observational by 106 hospitals guideline-based medications and CABG information available hospitalised differences in study located in 14 medications and or PCI for ACS on several variables i.e. Age and sex with an ACS the hospital use countries; examined cardiac procedures significantly increased socioeconomic status, differences, and who were of several management across across age groups; over the study time patient preferences changing trends, enrolled in the evidence-based 4 age groups: <65, trends in use of period; however age and which may have in the use of GRACE cardiac 65–74, 75–84, 85 medications and sex gaps in the use of confounded some of the evidence-based registry therapies, as years and older. procedures over most medications observed associations therapies in between April well as receipt time period of remained relatively acute coronary 1999 and of invasive study unchanged over time. syndromes: December cardiac In comparison with men perspectives 2007 procedures; in aged less than 65 years, from a addition, to patients in other age–sex multinational examine strata were less likely to registry. changing trends be treated with these Coronary Artery (1999–2007) in medications and cardiac Disease 2010, the use of procedures. Differences 21: 336–44 medications and between patients of procedures different ages of the same according to sex were larger than the patient’s age differences noted between and sex men and women within the same age strata

Paeck et al, 274 RCTs on To examine the Exclusions Systematic Systematically Inclusion criteria of 41.6% of the included Only searched one 2014 low back pain age-related based on age review and reviewed clinical RCTs on LBP trials excluded people database (PubMed); (46,725 inclusion meta-analysis trials identified via based on the age of aged over 65 years; there used a random sample Are Older participants) criteria clinical query in participants; effects was no change in terms of so did not include all Adults Missing distribution of PubMed; of 1,047 of year of including older patients identified published from Low Back participants in trials identified, publication on age- over time (20 years) trials in the review; Pain Clinical RCTs of low randomly selected related despite the ageing limited by a lack trial Trials? A back pain 400 for assessment inclusion/exclusion population data on the percentage Systematic interventions of eligibility criteria to assess of included participants Review and and to assess changes over time who were age over 65 Meta-Analysis. whether this years Arthritis Care & changes over Research 2014; time 66: 1220–26 Peake et al, Data from To study age- Ageism Prospective Questionnaires given Age and treatments Results demonstrate large Selection bias: Not clear 2003 1,652 patients related audit to treating within 6 months of age-related differences in how 48 hospital trusts across 48 differences in respiratory specialist bronchoscopy management and survival were selected, how Ageism in the trusts in the the outcomes of at onset and one across 3 age in patients with lung many refused to take management of UK lung cancer month after groups: less than 65 cancer, largely part, etc; did not correct lung cancer. patients, bronchoscopy; at 6 yrs, 65–74 yrs, 75 independent of case-mix for smoking status; only Age and Ageing controlling for months, treatment yrs and older factors. Authors describe included patients who 2003; 32: 171– case-mix factors and survival data “a clear pattern of saw specialist, which 77 (performance were recorded by decreasing ‘diagnostic may underestimate age status, medical staff and zeal’ and active treatment bias comorbidities, audit clerks with increasing age.” proxy for stage), In particular, older and to study the patients were less likely to impact of have a positive ageism on lung histological diagnosis cancer patients’ (Including those with survival good performance status and no COPD); less likely to receive surgery even with confirmed diagnosis of ‘potentially operable’ non-small cell cancer of good performance status with no significant COPD. Those with confirmed small cell lung cancer were less likely to receive chemotherapy. Older patients aged over 75 years were 50% more likely to die within 6 months of diagnosis vs those aged under 65

Rudd et al, 246 hospitals To investigate Age-based National audit Royal College of Ageism in the Older patients with stroke Retrospective study so 2007 in England, whether access discrimination (UK) Physicians delivery of acute were less likely to receive relies on existing care Wales and to high-quality Intercollegiate assessment high-quality care whether information; local Access to stroke Northern stroke care is Working Party investigation and managed in a stroke unit clinicians completed the care in England, Ireland, affected by the Stroke used Audit management; or general ward, data without any Wales and involving age or gender of Tool to examine ageism in particularly those over external validation Northern audit of 8,718 the patient, or organisation of in- rehabilitation and age 85 years. Older Ireland: the patients. by weekend patient stroke care longer-term care patients less likely to effect of age, admission and the process of ageism when receive care in line with gender and stroke care in patients are current clinical weekend hospitals managing managed in a guidelines: less likely to admission. Age stroke patients stroke unit; care on receive brain scan within and Ageing weekends vs 24 hours, less likely to be 2007; 36: 247– weekdays; care treated in a stroke unit, 55 based on gender less likely to receive secondary prevention and some aspects of rehabilitation. Found no influence of gender on stroke care; care at weekends was worse for all patients than on weekdays

Rueber et al, Records of To assess Equitable or Audit of Service evaluation Compared the age Found inequity of access Only one area of 2010 846 adults whether inequitable patient based on information distribution of to specialist services: England, so limits under the specialist access based records contained in the patients under care older patients with generalisability of Do older adults current care of epilepsy service on age service records of all with that of the epilepsy not referred as results; did not include have equitable a specialist provides patients under the largest population- often as younger patients enough information access to epilepsy equitable access current care of an based to the specialist service. about data presumably specialist service in to patients with epilepsy service epidemiologic Overall prevalence of gathered or shown epilepsy Sheffield and epilepsy network study of epilepsy epilepsy in the audit area services? Rotherham, regardless of carried out in the very similar to prevalence Epilepsia, 2010; UK age United Kingdom. figures from UK and 51: 2341–43 international studies, suggesting that age results were not due to older patients not presenting to their primary care provider Ryb et al, 2011 35,830 cases Aimed to Age Analysis of Younger and older Likelihood of being Found that, contrary to Lack of adequate used to define evaluate disparities National injured MVC transported to a guidelines to treat older physiologic (blood Disparities in weighted whether older Automotive occupants were trauma centre patients with more pressure and Trauma Center sample of injured motor Sampling compared in terms of based on age older caution and take them to a tachycardia) and Access of Older 7,894,940 vehicular crash System Crash- likelihood of being or younger than 60 trauma centre more comorbidity data in the Injured Motor cases in USA occupants’ worthiness transported to a years readily, injured MVC triage model; unable to Vehicular Crash national access to trauma Data System trauma center. occupants older than 60 adjust for the use of Occupants J database centers reflects Studied confounders years were 25% less medications that may Trauma. 2011; the lower including factors likely to be taken to a influence triaging 71: 742–47. threshold included in the triage trauma center than decisions; could not suggested in decision tree, younger patients with adjust for geographical triaging re- biomechanical similar triage criteria. variations related to commendations factors Tendency to under-triage rural environments, and injury factors older patients remained distance to trauma even after accounting for center, availability occupant and crash and/or use of air biomechanical variables. transport, and presence This trend was even more of a regional trauma accentuated (i.e. 32% less system likely) when injury severity, instead of triage criteria, was used in the adjusted models

Saposnik et al, 3,631 patients To determine Age Prospective Used data from the Indicators of Found that stroke care Findings may be 2009 with ischemic whether access disparities cohort study Registry of the quality stroke care: delivery was similar affected only involving stroke in to stroke care, Canadian Stroke use of across all age groups with dedicated stroke centres Age Disparities Ontario, delivery of Network to evaluate thrombolysis, the exception of slightly – results may not be in Stroke Canada health services, the association dysphagia lower rates of generalisable to Quality of Care and clinical between age and screening, investigations in patients community stroke care; and Delivery of outcomes after stroke management admission to a aged over 80 years unmeasured Health Services stroke are and outcomes across stroke unit, carotid confounders (e.g. time Stroke. 2009; affected by age age groups: younger imaging, to physiotherapy or 40: 3328–35 than 60 years, 60– antithrombotic speech therapy, nurse- 69, 70– 79 and 80 therapy, warfarin to-bed ratio) may have years and older for atrial influenced the results fibrillation at discharge Schoenmaker 35 studies To determine Age Database Gathered data from Age differences in In clinical trials of Multiple selection & Van Gool, (6,953 the age differences analysis plus literature review clinical trials vs dementia, younger biases: Only searched 2004 patients) distribution of literature focused on general population patients are over- one database (PubMed); compared patients review diagnostic methods represented and older limited search to 119 The age gap with the age included in and clinical trials patients are under- predetermined journals; between distribution of studies on and compared age represented. Peak age of limited search period to patients in dementia diagnostic distribution with patients with dementia in 4 years clinical studies patients in the methods and prevalence figures clinical trials is and in the general therapeutic for dementia in the approximately 75 years general population of interventions in Dutch population versus 83 years in the population: a the dementia and general population pitfall for Netherlands compare dementia (n=180,961) findings to the research. Lancet age distribution Neurol 2004; 3: of dementia 627–30 patients in the general population

Sin et al, 2001 6,254 patients To assess Under- Population- Used data from the Receipt of inhaled 40% of patients did not Did not have patient with a recent whether inhaled treatment of based, Canadian Institute of corticosteroids receive ICS within 90 information on possible Underuse of asthma corticosteroids older patients retrospective Health Information (ICS) within 90 days of discharge from adverse reactions to inhaled steroid exacerbation are underused in cohort study (CIHI) on hospital days of discharge initial hospitalisation inhaled steroid therapy therapy in in Ontario, older patients discharges in from hospital for an Patients > 80 years old which may have elderly patients Canada with a recent Ontario to identify asthma were at a greater risk of impacted results. with asthma. asthma patients aged 65 exacerbation across not receiving ICS Retrospective design Chest; 2001; exacerbation years and older who age groups: 65–69 compared to those 65–70 cannot control for 119: 720–25 requiring were discharged years, 70–75 years, years of age. Nonreceipt potential confounding hospitalisation with a diagnosis of 76–80 years, 80 of inhaled steroid therapy factors that already exist asthma between years and older was particularly April 1, 1992 and prominent in the older March 31, 1997 patients with multiple comorbidities. Treatment by primary-care physician was independently associated with elevated risk of not receiving ICS vs specialist care

Talarico et al, Data from To analyse the Age-related Retrospective Data were analysed Rates of enrolment Age distributions in new Unable to determine 2004 28,766 cancer age-related exclusions analyses of according to age in each age group treatment registration proportion of older patients from enrolment of demographic distributions of 65 for each cancer vs trials were: 36% for 65 cancer patients not Enrollment of 55 registration cancer patients data of cancer years or older, 70 the corresponding years or older, 20% for 70 enrolled onto clinical Elderly Patients trials onto registration patients years or older and 75 rates in the US years or older and 9% for trials because of other in Clinical trials of new enrolled onto years or older. Rates cancer population 75 years or older, factors such as Trials for drugs or new registration of enrolment in each compared with 60%, physician’s judgment, Cancer Drug indications trials age group for each 46%,and 31%, patient’s preference or Registration: A approved by the cancer were respectively, in the US eligibility requirements. 7-Year US Food and compared with the cancer population. Retrospective design Experience by Drug corresponding rates Statistically significant cannot control for the US Food Administration in the US cancer under-representation of potential confounding and Drug from 1995 to population older patients was seen factors that already exist Administration 2002 for all cancer treatments J Clin Oncol except breast cancer 2004; 22: 4626– hormonal therapies. 31 Patients aged 70 years or older accounted for most of the under- representation

Thake & 4,341 RCTs in To assess the Exclusion by Systematic All RCTs in BMJ, Specified upper- Of 4,341 included RCTs: Only searched 4 Lowry, 2017 BMJ, Lancet, proportion of age review Lancet, JAMA and age cutoffs in trials; 1,258 (29%) had upper journals and RCTs, JAMA and RCTs that have NEJM from 1998 to changes in age- age limits specified, 1,168 raising potential for A systematic NEJM unexplained 2015 were reviewed related cutoffs over (92.8%) of which did not selection bias. review of trends upper age limits to identify any time (1998-2015). have any explanation for Strength: 18-year in the selective and review specified upper-age the cut off (overall total timeframe to see exclusion of whether this cutoff; proportion of 26.9%). Over the 18-year changes over time older proportion is RCTs with an period there was a limited participants reducing over unexplained cutoff but statistically significant from time was then correlated decrease in the proportion randomised over time to look for of RCTs with unexplained clinical trials. any changes upper age limits, but still Archives of found that, in 2015, Gerontology 22.9% of the total RCTs and Geriatrics reviewed were excluding 2017; 72: 99– older participants with no 102 given explanation

Trimble et al, Over 23,000 To determine Representatio Quantitative Compared the 1992 Incidence of Older patients (men and Limited detail about 1994 patients in the n of older comparative accrual of the accrual of older women aged 65 years or included trials: did not more than 500 representation patients database National Cancer patients to cancer older) were significantly give exact number of Representation clinical trials of older people analysis Institute (NC1)- treatment trials for underrepresented in trials included overall, of Older in clinical trials sponsored Clinical the 5 leading cancer treatment trials; or number of patients Patients in Cooperative Group causes of cancer found that some phase I per trial, or for each Cancer treatment trials death for men and and II trials were cancer site; study Treatment (included more than for women recruiting older patients looked at accrual of Trials Cancer 8,000 older patients) separately; to specifically stratifying for patients which may 1994; for the 5 most ascertain the age and physiologic differ from actual 74: 2208–14 common cancers in number of phase 2 impairment to determine participation men and women and 3 cooperative toxicity and maximum with the 1990 group therapeutic tolerable dose incidence data from trials specifically the NCI's targeting Surveillance, this age group Epidemiology, and End Results program (SEER)

Wang et al, Data for To assess the Treatment Database Used data from the Age at diagnosis in Found consistent Lack of data relating to 2009 31,298 pattern of breast differences analysis National Breast years (<40, 41–50, differences between the patient participating in patients cancer surgery across age Cancer Audit 51–70, >70), and age groups in early decision-making Patterns of after adjusting groups (NBCA) differences in the diagnosis, cancer process Surgical for other major various diagnostic, pathological Treatment for prognostic pathological, and characteristics and the Women with factors in surgical treatment surgical treatment Breast Cancer in relation to parameters across received. After adjusting Relation to Age. patient age the age groups for cancer pathological The Breast characteristics, age was Journal, 2010; independently associated 16: 60–65 with surgical treatment pattern in women with breast cancer. Women aged > 70 were 3 times more likely to receive no surgical treatment compared with other age groups Weaver et al, 3,256 patients To assess the Differences in Observational Used data from Management across Thrombolytic drugs were Did not consider age 1991 hospitalised history, management study consecutive hospital age groups; results prescribed to only 12% of discrimination in for acute treatments and according to admissions for MI adjusted for patients >65 years of age. management choices Effect of age on myocardial outcome in age (therefore unselected confounders/ Overall, 29% of patients but considered many use of infarction older patients group of patients) variables younger than 75 were other potential thrombolytic aged 65 years and assessed treated with a systemic explanations for therapy and and over management thrombolytic drug differences observed mortality in according to age and compared with only 5% acute other variables of patients older than 75. myocardial This is 3 times lower than infarction. the number of older JACC 1991; 18: patients in who therapy 567–62 was indicated. Other medications were also used less often in older patients. The decision to treat older patients is affected by a higher incidence of complicating illness, absence of chest pain on admission and nonspecific ECG abnormalities

Wiel et al, 2018 Data for 4,347 To compare Age Case-control All adult patients Differences in life With similar cardiac Retrospective design matched pairs OHCA care and discrimination study based with cardiac arrest support initiation, arrest conditions cannot control for Age of patients outcomes against older on data recorded between advanced cardiac (matching criteria), the potential confounding discrimination between young patients extracted from July 2011 and May life support study found less basic life factors that already in out-of- patients (<65 the French 2014 were included. duration, MMT support initiation (despite exist; could not hospital cardiac years old) National Each older patient automated chest greater witness presence ascertain comorbidity arrest care: a and older Cardiac Arrest was matched on compression, MMT of arrest), less mobile data which may have case-control patients (aged (CA) registry three criteria: sex, ventilation, MMT medical team (MMT) affected outcomes study. Eur J 65 or older) initial cardiac epinephrine ventilation and less MMT Cardiovasc rhythm and no-flow injection between epinephrine injection Nursing duration. Then younger and older were given to older 2018; 17: 505– compared patients patients 12 management of those aged less than 65 with those aged 65 or older Wiggins et al, Care of 423 To characterise Age Population- Used three large data Characteristics Older patients were not Recall bias: some 2010 chronic chronic myeloid disparities based data sets to assess chronic associated with the treated with imatinib at information obtained by myeloid leukemia sets myeloid leukemia receipt of imatinib the same rate as younger asking treating Age Disparity in leukemia therapies and survival and (the standard first- patients: Imatinib use was physicians for details; the patients in the assess the mortality rates in line therapy at the inversely associated with existing co-morbid Dissemination USA impact of the time periods before time of the study); age: 90%, 75% and 46% conditions were of Imatinib for introduction of and after the survival differences for patients aged 20–59, documented from Treating imatinib on introduction of between those who 60–79 and ≥ 80 years, medical record review Chronic chronic myeloid imatinib; patients received imatinib respectively. and physician Myeloid leukemia were randomly and those who did Older patients who statements and were not Leukemia Am J survival and selected from cancer not received imatinib categorised by severity Med 2010; 123: mortality rates registries in the survived significantly 764.e1-9 Surveillance, longer than those who did Epidemiology, and not receive the drug End Results (SEER) Program

Wonnacott et Analysis of To examine Age Clinical Descriptive Comparison of Did not find any evidence Retrospective review of al, 2012 clinical how disparities record statistical analysis outcomes between that older patients patient notes in a small, records of 546 nephrologists analysis and statistical tests the two age groups managed differently to single NHS trust limits Applying patients (312 manage undertaken to of intervention younger patients generalisability of estimated under 75 years outpatients and compare the results rates, type of results; very short time glomerular old and 234 to see if any between the two interventions frame (9 weeks) of data filtration rate to over 75 years differences in groups of patients (medication collection an ageing old) from a intervention (aged 75 years and changes, referrals, population: are single NHS rates on the over or aged under investigations, we in Trust in South basis of patient 75 years) hospital danger of Wales, United age admission), disease becoming Kingdom stability ageist? Eur J Internal Medicine 2012; 23:705–10

Woodard et al, Data on 480 Aimed to Age Database Participants were Odds of receiving After controlling for Retrospective design 2003 women with illuminate disparities analysis divided into 3 chemotherapy confounders (stage, tumor cannot control for localised the true relation groups: aged less based on age, size, lymph node potential confounding Older Women breast between age and than 50 years (30%), recruitment to involvement, and PR factors that already with Breast carcinoma chemotherapy aged 50–65 years clinical trials status) and effect exist; small number of Carcinoma Are use by (45%), aged over 65 modifiers, found that patients (480) seen at a Less Likely controlling for years (25%) older women were less tertiary centre, so to Receive factors that likely to receive adjuvant potential for selection Adjuvant obscure this chemotherapy compared bias Chemotherapy relation. with younger women Cancer 2003; 98:1141–49

Wu et al, 2010 3,388 patients To examine for Under- Retrospective Data were obtained Parathyroidectomy Found an inverse Inclusion and exclusion with primary differences in treatment cohort study from an integrated and time interval to relationship between age methods employed may Underutilization hyperpara- the use of according to health care delivery surgery across 5 and likelihood of surgery, omit several classes of of Parathyroid- thyroidism; parathyroidecto age system in Southern age groups: 0–49 independent of key patients; dual-energy x- ectomy in 964 my across California years, 50–59, 60– variables. This likelihood ray absorptiometry Elderly Patients underwent different age encompassing 69, 70–79 and 80 decreased linearly among scores available did not with Primary parathyroid- groups approximately 3 years and older patients aged 60 and older cover all sites and all Hyperparathyroi ectomy million individuals. when compared to subjects. Retrospective dism J Clin Patients with patients aged 50–59. design cannot control Endocrinol biochemically Among patients meeting for potential Metab 2010; 95: diagnosed PHPT 2002 consensus criteria confounding factors that 4324–30 during the years for surgical treatment, the already exist 1995–2008 were overall surgery rate was identified to assess 43%, with the highest rate the use of of 59% for 50–59 age parathyroidectomy group. The surgery rate for patients aged 70-plus was significantly lower at 24%. Patients aged 60- plus experienced significantly longer delays from diagnosis to surgery compared to younger patients

Yee et al, 2003 4,174 patients To evaluate the Under- Comparative Number of older Differences in Older patients accounted Only used one (large) enrolled onto enrolment of representation registry and patients enrolled in enrolment onto for 22% of those enrolled database and did not Enrollment of 69 NCIC older patients of older population National Cancer clinical cancer in trials vs 58% of the include pharmaceutical Older Patients CTG trials of (>65 years) in patients analysis Institute of Canada trials in Canada; Canadian population with company-sponsored in Cancer 16 tumour Canadian cancer Clinical Trials comparison with cancer. Differences seen trials; retrospective Treatment types treatment trials Group treatment cancer incidence in in cancer types except for design cannot control Trials in and compare trials between 1993 Canadian and US multiple myeloma. Found for potential Canada: Why is accrual of older and 1996 was populations 15% of older patients confounding factors that Age a Barrier? J patients in compared to the enrolled in adjuvant trials, already exist Clin Oncol Canada and the corresponding rates 25% in metastatic trials, 2003; 21:1618- United States in the general 29% in investigational 23 population of new drug trials, 24% in patients with cancer phase I trials, 21% in aged > 65 years supportive care trials. The obtained from overall proportion of Statistics Canada, older patients enrolled and US Southwest onto Canadian trials Oncology Group (22%) was slightly lower (SWOG) than that in SWOG trials (25%)

Zulman et al, Review of 109 To examine the Exclusions Systematic Conducted PubMed Inclusion in clinical 20.2% of trials excluded Only searched one 2011 clinical trials inclusion and based on age review search to identify trials based on age patients based on upper database; only searched analysis of phase III or IV and other criteria age cutoff; 45.6% of 5 journals; only Examining the complex, older randomised e.g. health status, remaining trials excluded searched in one year Evidence: A adults in controlled trials quality of life patients using criteria that Systematic randomised published in 2007 in could disproportionately Review of the controlled trials 5 major journals: impact older adults; only Inclusion and JAMA, NEJM, 26.6% of trials examined Analysis of Lancet, Circulation outcomes considered Older Adults in and BMJ. Reviewed highly relevant to older Randomized age eligibility, adults. Overall, 38.5% of Controlled average age of study trials performed an age- Trials J Gen population, specific subgroup Intern Med outcomes, exclusion analysis, but fewer than 2001; 7:783–90 criteria and age- half examined potential specific subgroup confounders of analyses differential treatment effects by age, e.g. comorbidities Appendix 2. Risk of bias assessment tool used to assess observational studies

Study ID Type of study Domain Category Questions Assessment Selection Sample Were participants representative Yes representative of of the target population? No target population NA Unsure Comparability of Were the comparison groups Yes exposure and (exposed/unexposed, No comparison groups cases/controls) recruited from NA comparable populations? Unsure Appropriateness of Were inclusion/exclusion criteria Yes eligibility criteria applied equally to all study No groups? NA Unsure Recruitment time Were participants in different Yes frame groups recruited over the same No period of time? NA Unsure Response and non- Is the information regarding the Yes response rate number of participants who were No ineligible or who refused to NA participate adequately reported? Unsure What was the response rate? Percentage NA Unknown What percentage of the original Percentage sample were included in the final NA analysis? Unknown Exposure Validity and Was exposure status measured in Yes reliability of a standardised, valid and/or No exposure reliable way? NA measurement Unsure Outcome Definition Are the outcomes are clearly Yes assessment defined? No NA Unsure Accuracy of Were the outcome measures Yes outcome accurate (valid and reliable)? No measurement NA Unsure Blinding of the Were outcome assessors blinded Yes research staff to the exposure status? No NA Unsure Confounding Description of Is the distribution of confounders Yes confounding clearly described? No variables NA Unsure Accounting for Were confounding variables taken Yes confounding into account in the design and/or No analysis? NA Unsure Lost to Adequacy of length Was follow-up period Yes follow-up of follow-up appropriate/sufficiently long No enough to allow development of NA the outcome? Unsure Amount of loss to Were the numbers and reasons for Yes follow-up participant withdrawals/dropouts No recorded? NA Unsure Handling of loss to Were appropriate statistical Yes follow-up methods used to account for No missing data? NA Unsure Analysis Appropriate Were the statistical methods used Yes statistical methods to analyse the outcomes No appropriate? NA Unsure Selective Selective reporting Were all measured outcomes Yes reporting of outcomes reported? No NA Unsure Conflict of Conflict of interest Were there any funding sources Yes interest (funding, other) or conflicts of interest that may No affect the authors’ interpretation NA of the results? Unsure Other Overall assessment How well was the study done to Very well minimise the risk of bias or Moderately well confounding? Not well Provide examples Unsure Examples: Are the results of this study Yes directly applicable to the patient No group targeted? NA Unsure Taking into account clinical Yes considerations, your evaluation of No the methodology used, and the NA statistical power of the study, do Unsure you think there is clear evidence of an association between main variable (age) and outcome? Overall, do you think the Warranted conclusions are warranted or Not warranted unwarranted? What other factors are missing that could impact on the results? (e.g. patient preference, comorbidities, contraindications, other) Other bias Is the study free of other biases? Yes No NA Unsure

Adapted from SIGN Methodology Checklists 3 (Cohort studies) and 4 (Case-control studies) and Wang Z,

T aylor K, Allman- Farinelli M, Armstrong B, Askie L, Gh ersi D, et al. A systematic r eview:

T ools f or a ssessing methodo log ical qu ality of human o bservati onal stu dies 2019. doi:10.31222/osf. io/pnqmy.

Appendix 3. Systematic review: Included studies

Bajorek BV, Ren S. Utilisation of antithrombotic therapy for stroke prevention in atrial fibrillation in a Sydney hospital: then and now. Int J Clin Pharm 2012; 34: 88–97.

Banzi R, Camaioni P, Tettamanti M, et al. Older patients are still underrepresented in clinical trials of Alzheimer's disease. Alzheimers Res Ther 2016; 8: 32.

Barakat K, Wilkinson P, Deaner A, et al. How should age affect management of acute myocardial infarction? A prospective cohort study. Lancet 1999; 353: 955–59.

Bellera C, Praud D, Petit-Monéger A, et al. Barriers to inclusion of older adults in randomised controlled clinical trials on Non-Hodgkin's lymphoma: a systematic review. Cancer Treat Rev 2013; 39: 812–17.

Bergman L, Kluck HM, van Leeuwen FE, et al. The influence of age on treatment choice and survival of elderly breast cancer patients in South-eastern Netherlands: A population-based study. Eur J Cancer 1992; 28A: 1475–80.

Bhalla A, Grieve R, Tilling K, et al. Older stroke patients in Europe: stroke care and determinants of outcome. Age and Ageing 2004; 33: 618–24.

Bond M, Bowling A, McKee D, et al. Does ageism affect the management of ischaemic heart disease? J Health Serv Res Policy 2003; 8: 40–47.

Briggs R, Robinson S, O'Neill D. Ageism and clinical research. Ir Med J 2012; 105: 311– 12.

Bruin-Huisman L, Abu-Hanna A, Van Weert HCPM, et al. Potentially inappropriate prescribing to older patients in primary care in the Netherlands: a retrospective longitudinal study. Age and Ageing 2017; 46: 614–19.

Carvalho do Nascimento PR, Ferreira ML, Poitras S, et al. Exclusion of older adults from ongoing clinical trials on low back pain: a review of the WHO trial registry database. J Am Geriatr Soc 2019; 67: 603–08.

Chambaere K, Rietjens JAC, Smets T, et al. Age-based disparities in end-of-life decisions in Belgium: a population-based death certificate survey. BMC Public Health 2012; 12: 447.

Cherubini A, Oristrell J, Pla X, et al. The persistent exclusion of older patients from ongoing clinical trials regarding heart failure. Arch Intern Med 2011; 171: 550–56.

Cox L, Kloseck M, Crilly R, et al. Underrepresentation of individuals 80 years of age and older in chronic disease clinical practice guidelines. Can Fam Physician 2011; 57: e263– 69.

Cruz-Jentoft AJ, Carpena-Ruiz M, Montero-Errasquín B, et al. Exclusion of older adults from ongoing clinical trials about type 2 diabetes mellitus. J Am Geriatr Soc 2013; 61: 734–38.

DeWilde S, Carey IM, Bremner SA, et al. Evolution of statin prescribing 1994–2001: a case of agism but not of sexism? Heart 2003; 89: 417–21.

Dodd KS, Saczynski JS, Zhao Y, et al. Exclusion of older adults and women from recent trials of acute coronary syndromes. J Am Geriatr Soc 2011; 59: 506–11.

Dunn C, Wilson A, Sitas F. Older cancer patients in cancer clinical trials are underrepresented. Systematic literature review of almost 5000 meta- and pooled analyses of phase III randomized trials of survival from breast, prostate and lung cancer. Cancer Epidemiol 2017; 51, 113–17.

Fairhead JF, Rothwell PM. Underinvestigation and undertreatment of carotid disease in elderly patients with transient ischaemic attack and stroke: comparative population based study. BMJ 2006; 333: 525–27.

Fitzsimmons PR, Blayney S, Mina-Corkill S, et al. Older participants are frequently excluded from Parkinson's disease research. Parkinsonism Relat Disord 2012; 18: 585–89.

Forman DE, Cannon CP, Hernandez AF, et al. Influence of age on the management of heart failure: findings from Get With the Guidelines-Heart Failure (GWTG-HF). Am Heart J 2009; 157: 1010–17.

Gaynor EJ, Geoghegan SE, O'Neill D. Ageism in stroke rehabilitation studies. Age Ageing 2014; 43: 429-31.

Gnavi R, Migliardi A, Demaria M, Petrelli A, Caprioglio A, Costa G. Statins prescribing for the secondary prevention of ischaemic heart disease in Torino, Italy. A case of ageism and social inequalities. Eur J Public Health 2007; 1: 492-96.

Gottlieb S, Behara S, Schwartz R, et al. Age differences in the adherence to treatment guidelines and outcome in patients with ST-elevation myocardial infarction. Arch Gerontol Geriatr 2011; 52: 118–24.

Grant PT, Henry JM, McNaughton GW. The management of elderly blunt trauma victims in Scotland: Evidence of ageism? Injury 2000; 31: 519–28.

Greenfield S, Blanco DM, Elashoff RM, Ganz PA. Patterns of care related to age of breast cancer patients. JAMA 1987; 257: 2766–70.

Gurwitz JH, Col NF, Avorn J. The exclusion of the elderly and women from clinical trials in acute myocardial infarction. JAMA 1992; 268: 1417–22.

Hamaker ME, Stauder R, van Munster BC. Exclusion of older patients from ongoing clinical trials for hematological malignancies: an evaluation of the National Institutes of Health Clinical Trial Registry. Oncologist 2014; 19: 1069–75.

Harrison MJ, Kim CA, Silverberg M, Paget SA. Does age bias the aggressive treatment of elderly patients with rheumatoid arthritis? J Rheumatol 2005; 32: 1243–48.

Hayes L, Forrest L, Adams J, et al. Age-related inequalities in colon cancer treatment persist over time: a population-based analysis. J Epidemiol Community Health 2019;73:34–41.

Hood S, Taylor S, Roeves A, et al. Are there age and sex differences in the investigation and treatment of heart failure? A population-based study. Br J Gen Pract 2000; 50: 559– 63.

Hubbard RE, Lyons RA, Woodhouse KW, et al. Absence of ageism in access to critical care: a cross-sectional study. Age and Ageing 2003; 32: 382–87.

Hutchins LF, Unger JM, Crowley JJ, et al. Underrepresentation of patients 65 years of age or older in cancer-treatment trials. N Engl J Med 1999; 341: 2061– 67.

Javid SH, Unger JM, Gralow JR, et al. A prospective analysis of the influence of older age on physician and patient decision-making when considering enrollment in breast cancer clinical trials (SWOG S0316). Oncologist 2012; 17: 1180–90.

Jennens RR, Giles GG, Fox RM. Increasing underrepresentation of elderly patients with advanced colorectal or non-small-cell lung cancer in chemotherapy trials. Int Med J 2006; 36: 216–20.

Jin X-M, Lee J, Choi N-K, et al. Utilization Patterns of Disease-Modifying Antirheumatic Drugs in Elderly Rheumatoid Arthritis Patients. J Korean Med Sci 2014; 29: 210–16.

Joerger M, Thürlimann B, Savidan A, et al. Treatment of breast cancer in the elderly: A prospective, population-based Swiss study. J Geriatr Oncol; 2013; 4: 39–47.

Jung B, Pahlman L, Johansson R, et al. Rectal cancer treatment and outcome in the elderly: an audit based on the Swedish rectal cancer registry 1995–2004. BMC Cancer 2009, 9: 68.

Konrat C, Boutron I, Trinquart L, et al. Underrepresentation of elderly people in randomised controlled trials. The example of trials of 4 widely prescribed drugs. PLoS One 2012; 7: e33559.

Lavelle K, Todd C, Moran A, Howell A, Bundred N, Campbell M. Non-standard management of breast cancer increases with age in the UK: a population based cohort of women ≥65 years. Br J Cancer 2007; 96: 1197–203.

Lee PY, Alexander KP, Hammill BG, et al. Representation of elderly persons and women in published randomized trials of acute coronary syndromes. JAMA 2001; 286: 708–13.

Lehmann R, Beekley A, Casey L, Salim A, Martin M. The impact of advanced age on trauma triage decisions and outcomes: a statewide analysis. Am J Surg 2009; 197: 571–75.

Leinonen A, Koponen M, Hartikainen S. Systematic review: representativeness of participants in RCTs of Acetylcholinesterase Inhibitors. PLoS One 2015; 10: e0124500.

Levy BR, Kosteas J, Slade M, Myers L. Exclusion of elderly persons from health-risk behavior clinical trials. Prev Med 2006; 43: 80–85.

Lewis JH, Kilgore ML, Goldman DP, et al: Participation of patients 65 years of age or older in cancer clinical trials. J Clin Oncol 2003; 21: 1383–89.

Ludmir EB, Subbiah IM, Mainwaring W, et al. Decreasing incidence of upper age restriction enrollment criteria among cancer clinical trials. J Geriatr Oncol 2019; 11: 451–54.

Ludmir EB, Mainwaring W, Lin TA, et al. Factors associated with age disparities among cancer clinical trial participants. JAMA Oncol 2019 Jun 3. doi: 10.1001/jamaoncol.2019.2055.

Mackay JH, Powell SJ, Charman SC, Rozario C. Resuscitation after cardiac surgery: are we ageist? Eur J Anaesthesiol 2004; 21: 66–71.

Malik MK, Tartter PT, Belfer R. Undertreated breast cancer in the elderly. J Cancer Epidemiol 2013; 893104.

McGarvey C, Coughlan T, O’Neill D. Ageism in studies on the management of osteoporosis. J Am Geriatr Soc 2017; 65: 1566–68.

Mitford E, Reay R, McCabe K, et al. Ageism in first episode psychosis. Int J Geriatr Psychiatry 2010; 25: 1112–18.

Morse AN, Labin LC, Young SB, Aronson MP, Gurwitz JH. Exclusion of elderly women from published randomized trials of stress incontinence surgery. Obstet Gynecol 2004; 104: 498–503.

Nauta SJ, Deckers JW, Akkerhuis KM, et al. Age-dependent care and long-term (20 year) mortality of 14,434 myocardial infarction patients: Changes from 1985 to 2008. Int J Cardiol 2013; 167: 693–697.

Nguyen HL, Goldberg RJ, Gore JM, et al. Age and sex differences, and changing trends, in the use of evidence-based therapies in acute coronary syndromes: perspectives from a multinational registry. Cor Art Dis 2010, 21: 336–44.

Paeck T, Ferreira ML, Sun C, et al. Are older adults missing from low back pain clinical trials? A systematic review and meta-analysis. Arthritis Care Res 2014; 66: 1220–26.

Peake MD, Thompson S, Lowe D, Pearson MG. Ageism in the management of lung cancer. Age Ageing 2003; 32: 171–-77.

Reuber M, Torane P, Mack C. Do older adults have equitable access to specialist epilepsy services? Epilepsia, 2010; 51: 2341–43.

Rudd AG, Hoffman A, Down C, Pearson M, Lowe D. Access to stroke care in England, Wales and Northern Ireland: the effect of age, gender and weekend admission. Age Ageing 2007; 36: 247–55.

Ryb GE, Dischinger PC. Disparities in Trauma Center Access of Older Injured Motor Vehicular Crash Occupants. J Trauma 2011; 71: 742–47.

Saposnik G, Black SE, Hakim A, et al. Age disparities in stroke quality of care and delivery of health services. Stroke 2009; 40: 3328–35.

Schoenmaker N, Van Gool WA. The age gap between patients in clinical studies and in the general population: a pitfall for dementia research. Lancet Neurol 2004; 3: 627–30.

Sin DD, Tu JV. Underuse of inhaled steroid therapy in elderly patients with asthma. Chest 2001; Mar 2001; 119: 720–25.

Talarico L, Chen G, Pazdur R. Enrollment of elderly patients in clinical trials for cancer drug registration: A 7-year experience by the US Food and Drug Administration. J Clin Oncol 2004; 22: 4626–31.

Thake M, Lowry A. A systematic review of trends in the selective exclusion of older participant from randomised clinical trials. Arch Gerontol Geriatr 2017; 72: 99–102.

Trimble EL, Carter CL, Cain D, et al. Representation of older patients in cancer treatment trials. Cancer 1994; 74 (7 Suppl): 2208–14.

Wang J, Kollias J, Boult M, et al. Patterns of surgical treatment for women with breast cancer in relation to age. Breast J 2010; 16: 60–65.

Weaver WD, Litwin PE, Martin JS, et al. Effect of age on use of thrombolytic therapy and mortality in acute myocardial infarction. The MITI Project Group. J Am Coll Cardiol 1991; 18: 657–62.

Wiel E, Di Pompeo C, Segal N, et al. Age discrimination in out-of-hospital cardiac arrest care: a case-control study. Eur J Cardiovascular Nurs 2017; 17: 505–12.

Wiggins CL, Harlan LC, Nelson HE, et al. Age disparity in the dissemination of imatinib for treating chronic myeloid leukemia. Am J Med 2010; 123: 764.e1–9.

Wonnacott A, Meran S, Roberts G, et al. Applying estimated glomerular filtration rate to an ageing population: are we in danger of becoming ageist? Eur J Int Med 2012; 23: 705– 10.

Woodard S, Nadella PC, Kotur L, Wilson J, et al. Older women with breast carcinoma are less likely to receive adjuvant chemotherapy: evidence of possible age bias? Cancer 2003; 98: 1141–49.

Wu B, Haigh PI, Hwang R, et al. Underutilization of parathyroidectomy in elderly patients with primary hyperparathyroidism. J Clin Endocrinol Metab 2010; 95: 4324–30.

Yee KW, Pater JL, Pho L, et al. Enrollment of older patients in cancer treatment trials in Canada: why is age a barrier? J Clin Oncol 2003; 21: 1618–23.

Zulman DM, Sussman JB, Chen X, et al. Examining the evidence: A systematic review of the inclusion and analysis of older adults in randomized controlled trials. J Gen Intern Med 2011; 26: 783–90.