Essays on Social Disparities in Health Among Older People in Malaysia

Essays on Social Disparities in Health among Older People in Malaysia

A thesis submitted to the University of Manchester for the degree of Doctor of Philosophy in the Faculty of Humanities

2019

Syed Zahiruddin bin Syed Zainulabidin

School of Social Science

Contents

Abstract viii

Declaration ix

Acknowledgement xi

Abbreviations xii

1 Introduction 1 1.1 Why health? 4 1.2 The concept of disparity 6 1.2.1 Health disparities 7 1.2.2 Social disparities in health 8 1.2.3 Older people and disparities 10 1.3 Malaysia 12 1.3.1 Districts in Malaysia 14 1.3.2 The healthcare system 15 1.3.3 The older people in Malaysia 17 1.4 Description of chapters 19

2 Methodology 23 2.1 Theoretical framework 23 2.1.1 The spatial arrangement 24 2.2 The determinants – context and composition 25 2.3 Conceptual outline of the thesis 27 2.4 Data - The National Health and Morbidity Survey 29 2.4.1 Sampling framework 29 2.4.2 Sample size determination 30 2.4.3 Missing data 30 2.5 Analysis technique: Multilevel and spatial modelling 31

3 Social Determinants and Mental Disorders among Older People in Malaysia 34 3.1 Introduction 35 3.2 Data and methods 38 3.2.1 Data 38 3.2.2 Outcome variable: Mental disorders 38 3.2.3 Covariates 39 3.2.4 Method 40

3.3 Results 41 3.4 Discussion 50 3.5 Conclusion 57

4 Unmet Cardiovascular Care Needs among Older People in Malaysia 58 4.1 Introduction 59 4.2 Methods 62 4.2.1 Data 62 4.2.2 The dependent variable: Unmet cardiovascular care needs 62 4.2.3 FRS and SCORE calculations 63 4.2.4 Covariates 63 4.2.5 Method 65 4.3 Results 66 4.4 Discussion 79 4.5 Conclusion 85

5 Does Socioeconomic Status of Older People Define Geographic Variation of Diabetes in Malaysia? 87 5.1 Introduction 89 5.2 Data and method 93 5.2.1 Data 93 5.2.2 The dependent variables/ outcomes 94 5.2.3 Covariates 94 5.2.4 Method 95 5.3 Results 97 5.4 Discussion 107 5.5 Conclusion 112

6 How Spatial Distribution Informed Us about Undiagnosed Non- Communicable Diseases Risks in a Developing Country 113 6.1 Introduction 114 6.2 Methods 118 6.2.1 Study area 118 6.2.2 Data 119 6.2.3 Outcome variable 119 6.2.4 Independent Variables 120 6.2.5 Modelling 120 6.3 Results 124 6.3.1 Descriptive and bivariate analysis 124 6.3.2 Multivariate analysis 128 6.4 Discussion 132 6.5 Conclusion 137

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7 Discussion 139 7.1 Key findings 140 7.2 Surprising non-significant findings 143 7.3 Implications for theory 145 7.4 Policy implications and implementations 147 7.5 Strength of the thesis 148 7.6 Future research 149 7.6.1 The importance of a multilevel perspective 150 7.6.2 A need for longitudinal data and research 151 7.6.3 Considering the spatial context 151 7.7 Concluding remarks 152

Bibliography 154

A. Appendix for Chapter 1 193

B. Appendix for Chapter 3 197

C. Appendix for Chapter 4 200

D. Appendix for Chapter 6 204

E. Syntax E1. Stata code for Chapter 3 207 E2. Stata code for Chapter 4 213 E3. Stata code for Chapter 5 220 E4. R code for Chapter 6 225

List of Tables

1.1 Older People (60+) in Malaysia by Ethnicity, 2000 to 2030 19

3.1 Sample characteristics of 5908 respondents in 104 districts 43 3.2 Bivariate analysis of predictor variables 45 3.3 Determinants of the mental disorders, coefficient 48

4.1 Summary statistics of the sample 68 4.2 Bivariate analysis of unadjusted coefficient, odds ratio and marginal effects 73 4.3 Determinants of unmet care, the coefficient and odds ratio 75

5.1 Descriptive analysis of the NHMS dataset 98 5.2 Bivariate logistic regression of known diabetes and undiagnosed diabetes 101 5.3 Multilevel logistic regression of known diabetes 102 5.4 Multilevel logistic regression of undiagnosed diabetes 103

6.1 Descriptive and unadjusted bivariate analysis 127 6.2 Adjusted multivariate analysis using logistic regression and INLA 129 6.3 Summary statistics of posterior fixed effects using INLA: mean, standard deviation, 95% credible interval including the median, mode, and OR based on the posterior mean 130 6.4 DIC value and posterior marginal variance for the fitted models 131

List of Figures

1.1 Increase in the percentage of the older population aged 65 and above, from 2008 to 2050 in developed and developing countries 3 1.2 Map of Malaysia 12 1.3 The population age pyramids, 2017 and 2050 18 1.4 Old-age dependency ratio in Malaysia 19

2.1 A conceptual framework for understanding how social determinants influence health 28

3.1 The number of older people (%) by districts in 2010 42 3.2 Two-way graphs with standard errors on selected predictor Variables 44 3.3 Estimated probabilities for predictor variables 50 3.4 OADR in quartiles by districts in 2010 55

4.1 Two-way linear prediction plots of 10-year CVD risk score against respondent’s age with 95% CI 69 4.2 Box plots of FRS and SCORE by gender for selected characteristics of ethnicity, education, location and marital status 70 4.3 Median estimates of FRS and SCORE risk scores vs age groups by met and unmet care. Note: the red line indicates the threshold of the high-risk category 71 4.4 Point estimates of logit coefficients with 95% confidence intervals comparing Model 1 (baseline), Model 2 (social determinants) and Model 3 (contextual) 76 4.5 District effects ranking of unmet care needs based on random intercepts in Model 3 78

5.1 Maps of the prevalence of the known diabetes and undiagnosed diabetes among older people in 2015 based on NHMS 99 5.2 The geographic distribution of the adjusted odds ratio of known diabetes between districts at the individual and contextual level 105 5.3 The geographic distribution of the adjusted odds ratio of unknown diabetes between districts at the individual and contextual level 106

6.1 Map of Malaysia 118 6.2 Example of a neighbourhood network in a graph map in the state of Johor, Malaysia 122 6.3 Example of a binary graph for the first three nodes/ districts

in Johor 122 6.4 The mean prevalence of undiagnosed NCDs risks among older people in the Peninsular Malaysia based on the NHMS 125 6.5 Bar plot of diagnosed and undiagnosed NCDs risks among older people by age group and gender 128 6.6 Posterior mean for the district-specific undiagnosed NCDs risks compared with the whole of the study area and posterior probability 132

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Abstract

In the context of developing countries, the health effects due to increases in the number of older people are still not fully understood. Based on the evidence in developed countries, social determinants that influence health could be used to explain the effects as individuals’ health is both a determinant and an outcome of their socioeconomic circumstances. This study contributes to existing research by investigating associations between health outcomes and social disparities that relate to non-communicable diseases (NCDs) occurrences among older people in Malaysia. In addition, the issue of unmet healthcare needs is also investigated, in which older people with NCDs were not receiving appropriate healthcare interventions that they needed. At this point, policymakers in the country should be aware that older people with greater health needs may also be those with fewer means to access healthcare. In this study, social disparities are found to be an important discourse in understanding health differences at multiple levels - individuals and also districts where they resided. These levels are key areas in understanding how individuals and contextual processes operate as well as how their effects distributed along the spatial scale. Here, the estimation models and geographical maps developed by means of multilevel and spatial regression techniques could offer policymakers with systematic approaches to track health differences due to social disparities across districts in a comparable and interpretable manner. The evidence could also help policymakers to formulate effective health policies that are recent, precise and targeted. Furthermore, the findings may be instrumental in realising multi-sectoral efforts to overcome variations and complexities of health issues among older people that are beyond the control and authority of a single health ministry.

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Declaration

No portion of the work referred to in the thesis has been submitted in support of an application for another degree or qualification of this or any other university or other institutes of learning.

1. The author of this thesis (including any appendices and/ or schedules to this thesis) owns certain copyright or related rights in it (the ‘Copyright’) and he has given The University of Manchester, UK certain rights to use such Copyright, including for administrative purposes. 2. Copies of this thesis, either in full or in extracts and whether in hard or electronic copy, may be made only in accordance with the Copyright, Designs and Patents Act 1988 (as amended) and regulations issued under it or, where appropriate, in accordance with licensing agreements which the University has from time to time. This page must form part of any such copies made. 3. The ownership of certain Copyright, patents, designs, trademarks and other intellectual property (the ‘Intellectual Property’) and any reproductions of copyright works in the thesis, for example graphs and tables (‘Reproductions’), which may be described in this thesis, may not be owned by the author and may be owned by third parties. Such Intellectual Property and Reproductions cannot and must not be made available for use without the prior written permission of the owner(s) of the relevant Intellectual Property and/or Reproductions. 4. Further information on the conditions under which disclosure, publications and commercialisation of this thesis, the Copyright and any Intellectual Property and/ or Reproductions described in it may take place is available in the University IP Policy (see http://documents.manchester.ac.uk/display.aspx?DocID=24420), in any relevant thesis restriction declarations deposited in the University Library’s regulations (see https://www.library.manchester.ac.uk/about/regulations/) and in the University’s policy on presentation of thesis.

Acknowledgement

All praise to Allah, the Lord of the Worlds; the Most Beneficent, the Most Merciful. All praise for His blessings and guidance that enable me to finish writing up this thesis.

First of all, I am truly grateful to the Government of Malaysia particularly the Public Service Department of Malaysia for granting me the award of ‘Hadiah Latihan Persekutuan’ or the Federal Training Award that funds my PhD education here in the University of Manchester, UK. Also, I am obliged to the Director- General of Health, Malaysia and in particular to Dr. Tahir bin Aris from the Institute of Public Health for giving me permission to use the NHMS dataset for my thesis research.

Notably, I would like to express my gratitude to my supervisors, Dr. Gindo Tampubolon and Dr. Ronnie Ramlogan for their advice and support in guiding my PhD journey. I am very grateful to Pak Gindo for his patience and persistence in supervising a mature learner like me. Thank you for imparting your valuable time, ideas and knowledge that helped me a lot. I hope I can be a good researcher like you; who is observant and pays greater attention to details.

To my dear colleagues; Asri Maharani, Wulung Hanandita and Devi Femina, who I shared with my cherished moments and trying times here at the University, thank you for your friendships throughout those years.

Most especially, I would like to dedicate this thesis to my late father, Syed Zainulabidin bin Syed Abdul Rani, and my dear mother, Jamiah binti Md. Yusoff. I love both of you very much.

To my darlings and wonderful children; Nabilah, Nadiah, Imran, Iman and Ikram, who serve as my inspiration to pursue this undertaking. I hope that all of you will be more successful than me and strive for the best. Last of all, to my wife, Nor Suhana binti Abdul Wahab, the love of my life. Thank you for being by my side when I needed you most. I could not complete this thesis without you. Love you always ♥♥♥

Abbreviations

AIC Aikake information criterion BIC Bayesian information criterion BMI Body mass index BOR Bed occupancy rate BP Blood pressure CAR Conditional autoregressive CI Confidence interval CHD Coronary heart disease CVD Cardiovascular diseases DIC Deviance information criteria DOSM Department of Statistics, Malaysia DSWM Department of Social Welfare, Malaysia EB Enumeration blocks EPU Economic Planning Unit, Malaysia FBG Fasting blood glucose FRS Framingham score GAD Generalised anxiety disorder Gini Gini coefficient index GLM Generalised linear model GLMM Generalised linear mixed model HH Household HHAS Household average size ICC Intraclass correlation coefficient INLA Integrated nested Laplace approximation LQ Living quarters MCMC Markov chain Monte Carlo MINI The Mini International Neuropsychiatric Interview

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MOH Ministry of Health, Malaysia MOR Median odds ratio OADR Old-age dependency ratio OR Odds ratio PC Public health clinic to population ratio PH Public healthcare facility NA Non-available NCDs Non-communicable diseases or also known as non-infectious diseases NHMS National Health and Morbidity Survey, Malaysia RM Ringgit Malaysia SCORE Systematic coronary risk evaluation SD Standard deviation SE Standard error SES Socioeconomic status UN United Nations WC Waist circumference WHO World Health Organization

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Chapter 1 Introduction

“Our society must make it right and possible for old people not to fear the young or be deserted by them, for the test of a civilization is the way that it cares for its helpless members” ~ American writer and novelist, Pearl Buck (1892-1973), winner of the Pulitzer Prize for her book, ‘The Good Earth,’ in 1932 and the Nobel Prize in literature in 1938.

The world’s population is getting older by the day, and the growth of older people aged 65 and above will be higher in developing countries (UN, 2015; UN, 2018). Globally, the number of older people is expected to be more than double from 841 million in 2013 to more than 2 billion in 2050 (UN, 2013). Primarily, people now live longer as a result of improvements in healthcare, better nutrition and innovative technology (WHO, 2015a). This demographic shift in ageing brings in numerous possibilities to the social and economic structures of a nation and also poses new challenges in health and wellbeing.

Figure 1.1 shows that developing countries are getting older at a higher rate and over a much shorter period of time compared to developed countries (Cauley, 2012). Most of these developing countries, including Malaysia, are still at their lower levels of development (Smith, 2012). Therefore, the speed of the older population’s growth is alarming. In developed countries, the process of population ageing co-occurs with or surpasses a country’s social and economic development (Kinsella and He, 2009). For instance, France took over 115 years to double her population of aged 65 years and above from 7% in 1865 to 14% in 1980. Similarly, the United States took 69 years, while the United Kingdom took 45 years. Japan, as the first developed country in Asia, took 25 years to double her older population aged 65 and above to 14% in 1994 (He et al., 2016). Meanwhile, developing countries such as Thailand will take 20 years to double her aged population from 7% to 14% in 2031, Indonesia is estimated to take 23 years in 2042 and Malaysia will take 28 years in 2046 to reach that percentage figure (Shetty, 2012).

In both developed and developing countries, non-communicable diseases (NCDs) are the leading cause of disability, morbidity, and mortality among older people including cardiovascular and chronic respiratory diseases, as well as mental disorders (WHO, 2013a). Yet, health systems are planned to focus on acute cases and less on the preventive and chronic care of the diseases (Cauley, 2012; He et al., 2016). In 2015, WHO (2015a) reported that healthcare deliveries in developing countries are poorly aligned with the needs of their older populations. In particular, healthcare that meets the needs of older people is not in place to provide the continuum of chronic care required for them. This is even more critical in developing countries that are characterised by widespread undiagnosed diseases, and low awareness of health problems (Smith, 2012).

A civilised country has to care better for her older people. For that reason, Sen (2009) stresses that everybody has the right to be treated with dignity, especially in their old age. Furthermore, policymakers and public health practitioners have long sought not only to improve overall population health but also to reduce or eliminate differences in health based on demography, socioeconomic status, geography and other social factors (Arcaya et al., 2015). Developing countries are running out of time in implementing adequate policies that can address and provide solutions to issues concerning older people, including health (Shetty, 2012). The increase in their proportion would also bring about various social and economic implications. In a paradox, a country’s push to achieve a higher average life expectancy would translate to the need for more resources for the sustenance and support of the aged. This situation is becoming complicated as older people’s health issues intensify the demand and pressures of increasing cost and expenditure on healthcare (Filmer and Pritchett, 1999; Adler and Newman, 2002; Nixon and Ulmann, 2006; Pickett and Wilkinson, 2015).

Figure 1.1 Increase in the percentage of the older population aged 65 and above, from 2008 to 2050 (Cauley, 2012) in developed and developing countries (UN, 2014)

The socioeconomic consequences of NCDs are compounded by the high costs of healthcare treatment that represent a grave financial risk to older people with the diseases. It is estimated that every year more than 100 million people are driven below the poverty line because of catastrophic health expenditures (WHO, 2015a). Health systems in developing countries are overwhelmed by the increasing healthcare costs to manage these conditions. On this note, Adler et al. (2016) cautioned that an increase in medical care spending should not be treated as a genuine investment in public health that can address social determinants. Governments in these countries need to have strategies to ensure the 3

availability of sustained resources for which there will always be competing demands (WHO, 2012a).

The evidence from the Whitehall study in the United Kingdom (Marmot et al., 1991) showed that policies targeting the reduction of socioeconomic inequality in improving income and employment opportunities may also reduce health disparities. Apart from that, individuals could also improve their socioeconomic status of income and occupation by maintaining their good health (Goldman, 2001). Further evidence shows that disparities as a result of uneven distribution of diseases can be diminished with good policies and planning (Woodward and Kawachi, 2000). For example, reallocating resources in order to lessen socioeconomic disparities may result in lower healthcare cost and greater marginal health benefits. More innovative and effective healthcare interventions are also required to reduce health disparities. Nevertheless, informing policymakers will be a major challenge particularly in presenting related evidence clearly and meaningfully without either being simplistic or complicated.

1.1 Why health?

The definition of health as stipulated in the World Health Organization’s constitution is "a state of complete physical, mental and social well-being and not merely the absence of disease or infirmity" (WHO, 2006). This definition then extends to the desire of achieving good health whereby “the enjoyment of the highest attainable standard of health is one of the fundamental rights of every human being without distinctions of race, religion, political, belief, economic or social condition.” Health is important as it constitutes an individual’s wellbeing and enables that person to function as an agent. This can motivate individuals to meet their life expectations and pursue life goals that they value (Anand, 2002; Sen, 2002). Under these circumstances, governments have moral obligations to create conducive conditions for their people to have the freedom to lead lives they have reason to value (Sen, 2009). In addition, improving a society’s health is related less to

how wealthy, or prosperous that society is, but more on how evenly and fairly this wealth or resources are distributed (Graham, 2004).

Adler and Newman (2002) deduce that illness is one of the causes of poverty while poverty is a major health risk and a disadvantage to achieving desired health outcomes. They see illness as an obstacle to overcoming disadvantages, and health disparities that further deprive the present situation of disadvantaged groups. Moreover, Sen (2009) observes health as an essential capability required for individuals to function in societies and at the same time, he signifies suffering as a central feature of the illness. However, he acknowledges that individuals’ health status alone cannot provide an adequate understanding of the dimensions of illnesses. In view of this, many researchers have noted that public health decisions are inadequate in responding to patients’ actual suffering (Filmer & Pritchett, 1999; Eliassen, 2013; Farag et al., 2013) and the experience of healing.

Grossman (1972; 2000) acknowledges health as a capital stock that can depreciate with time and can be increased by investment in health or other related health aspects. This investment combines multiple factors of production, including health interventions, healthy behaviours and medical treatments to prevent and manage ill-health (Grossman, 2000). The demand for health is unlike most other goods because individuals allocate resources in order to both produce and consume health. Sen (2009) and Marmot et al. (2010) suggest that any present health systems need to come up with effective strategies, not only from the perspective of the economy but also looking at the social factors. Such strategies will enhance access to healthcare that produces good health. Concurrently, strategies that promote resources to be well-targeted, distributed and managed effectively, also be able to achieve a fair and equal healthcare delivery and improve population’s health (Filmer & Pritchett, 1999; Farag et al., 2013). As a form of investment, resource distribution within a health system is expected not only to improve access and quality healthcare but also to contribute to social and socioeconomic progress (Nixon & Ullman, 2006; Rasanathan et al., 2011).

Apart from the biological and psychological factors, health is influenced by a wide array of social factors (Davidson, 2002). Thus, it makes sense to focus on health policies on such risk factors that are potentially modifiable. Moreover, new insight on social disparities has emerged – social gradient pattern in health (Marmot, 2015), where greater social advantage corresponds to good health, while social disadvantage leads to ill-health (Pickett and Wilkinson, 2010). These sorts of disparities seem to further persist even after taking into account certain socioeconomic factors at a higher level such as neighbourhood conditions, demographic representation of and accumulated resources in an area (Braveman, 2011). Although higher-level factors may lead to substantial health effects, they are rarely measured in developing countries (Smith, 2012). In developed countries, Braveman et al. (2004) observe that there has been an increasing awareness among policymakers who perceive that healthcare alone cannot effectively improve the overall population health, without addressing the social determinants in health (Marmot et al., 2008, 2015).

1.2 The concept of disparity

The ‘disparity’ is defined as a great difference or a marked distinction in quality or character (“Disparity,” 2018a; 2018b). Disparity occurs when one group is perceived to have greater or lesser outcomes than another group in a population. In research articles, the terms disparity, inequality and inequity are used interchangeably (Braveman, 2006). Inequality is considered unfair and can affect anyone or any group, but its circumstances are avoidable (Kawachi et al., 2002). Inequality is also defined as an unfair situation in a particular society that indicates a lack of opportunities (Carter-Pokras and Baquet, 2002). The key distinction between the terms inequality and disparity is that the former is employed whenever quantities are unequal, while the latter requires passing a moral judgement that the inequality is wrong or immoral (Kawachi et al., 2002).

Correspondingly, disparities appear to occur in the health system, whereby groups of people who share certain socioeconomic disadvantages, such as poverty, low education

and unemployment are lesser in health compared to advantaged groups (Marmot, 2015). Moreover, these disparities may be the outcomes of social inequalities (Raphael, 2009). In this sense, health disparities are termed as systematic differences in health that can be avoided by reasonable means (Marmot et al., 2012). When such health differences are preventable and unnecessary, allowing them to persist is unjust (Whitehead, 1992).

In addition, Braveman (2006) reveals that the definition of ‘health inequality’ that is generally used in related policies does not include any moral judgement on whether observed differences are fair or just. Therefore, Braveman (2006) suggests that ‘health inequity’ or ‘health disparity,’ which is a specific type of health inequality, better signifies unjust differences in health.

1.2.1 Health disparities

Both Whitehead (1992) and Braveman (2006) define health disparities as general differences in health on the premise of being avoidable, unjust and unfair. Such differences can be measured and evaluated in terms of both inequality and inequity since what is unequal is not necessarily inequitable (Carter-Pokras & Baquet, 2002). Kawachi et al. (2002) stress that any measurable aspect of health that varies across individuals or according to social relevant groupings can be called a health inequality. While, health disparities are considered unfair according to some social norms which may vary across countries (de-Looper & Lafortune, 2009). These differences are obvious along various dimensions, including age, gender, geographic areas, race or ethnic groups, and socioeconomic status (Fiscella et al., 2000, Adler & Newman, 2002, Asada et al., 2013).

The WHO recognises health equity as a priority reflected in part by its formation of the Commission on Social Determinants of Health in 2005 (Marmot et al., 2008). The persistence of health differences, for instance, based on age raises moral concerns that impede fairness and justice. However, health differences based on age are unavoidable, and it is difficult to argue that health differences between younger and older people are unjust (Sen, 2002). Unlike the example of age-related health differences, disparities in 7

health outcomes for instance, across ethnic groups or geographical areas can be avoided. While the existence of health disparities is a universal problem, the extent to which social factors matter for health has been shown to vary (WHO, 2008).

Health disparities can be regarded as chains of events indicated by differences in the environment, health status and quality of healthcare deliveries, or health outcomes that deserve further analysis (de-Looper & Lafortune, 2009). Eliassen (2013) adds that health disparities persist because of their usefulness to those who hold and seek to consolidate power. Braveman et al. (2011b) argue that the term health disparities should be used purposefully to focus on a certain subset of differences in individuals’ health that meets specified criteria and relevance to social justice.

Health disparities are found to be associated with a number of attributes. Using an attribute-specific disparities measurement, Asada (2013) found that examining one attribute-specific health disparity for instance race/ ethnicity, could not explain health disparities as a whole. Braveman (2006) proposes that measuring disparities that capture multiple attributes requires three components; (i) an indicator of health or a modifiable determinant of health, (ii) indicators of social position by categorising people into different groups or social strata – gender, income, ethnicity, and (iii) a method for comparing the health indicator across different social strata along the line of the least to most advantaged.

1.2.2 Social disparities in health

An individual’s health is both a determinant and an outcome of their socioeconomic circumstances (Graham, 2004). Health disparities negatively affect groups of people who have systematically experienced greater social or socioeconomic obstacles to health. Marmot et al. (2010) describe health disparities as consequences of social disparities. In short, Pickett and Wilkinson (2010) put these disparities as two unpleasant occurrences which interrelate with one another. Daniels (2008) further clarifies that social disparities can affect health and generate differences between groups within a population. 8

Peter (2004) emphasises that social disparities in health, are immoral because these disparities deviate from fair health outcomes and are a product of unjust social and economic institutions. These disparities are a type of differences in health that are closely linked to social or socio-economic disadvantages. Social disparities place people already disadvantaged by belonging to particular social groups at a further disadvantage with respect to their health (Braveman, 2011a).

Social disparities in health are systematically associated with different levels of underlying social advantages in a social hierarchy (Braveman et al., 2004). Braveman (2011) puts forward a dose-response association between health outcomes and socioeconomic factors that reinforce the debate on the social gradient in health. This further informs the understanding of social disparities in health regarding gender, race/ ethnicity where social factors and socioeconomic status are distinct characteristics but inseparable. Note that health access and behaviours are shaped by higher determinants of education, income and other socioeconomic factors (Woolf and Braveman, 2011). Again, education is also linked to income and wealth, in which greater educational attainment typically translates into increased opportunities for more rewarding and higher-paying employment opportunities (Braveman et al., 2011a) and so, promotes good health behaviours and better access to healthcare.

The Marmot Review (Marmot et al., 2010) found that health tends to be poorer in less equal societies, particularly when the level of disparities are compared between geographic areas. This observation concurs with Braveman (2006; 2011) who already infers that geographic variations in term of difference in socioeconomic levels between areas correspond to diverse health outcomes. Thus, it is imperative to investigate disparities not only across social groups but also across geographical areas to provide important insights into the distributions of health (Oshio, 2018).

An important key to eliminate social disparities that affect health is by reallocating healthcare resources. This measure is modifiable and cost-effective for a government to implement (Woodward and Kawachi, 2000). Braveman (2011) advocates this measure by proposing a policy that targets health disadvantaged social groups in certain areas that

require particular healthcare attention because their underlying social characteristics are likely to make it more difficult for them to be healthy.

1.2.3 Older people and disparities

The term ‘older people’ in this thesis is generally used to indicate the statutory age for retirement in a particular country (Davidson, 2002). In most developing countries, this term describes those aged 60 and above, while in developed countries ‘older people’ represents those aged 65 and above. This term also reflects the preference of old people to be called ‘older people’ or ‘senior citizens’ instead of ‘elderly’ (Davidson, 2002). WHO (2015a) uses the term ‘older person’ in their communication to describe a person whose age has passed the median average life expectancy at birth for a country.

Old age may bring problems of age-related diseases, and this requires additional resources to care for their needs that are provided by the public (Cauley, 2012; Prince et al., 2015). Also, a combination of healthcare advances and quality lifestyle brings along a phenomenon which Fries (1980) coined as ‘compression of morbidity’ in developed countries. In essence, this means that older people are in better health and live longer with most of their disabilities and morbidities concentrated in a short period before their end of life. Thus, the morbidity phase becomes compressed. Yet, it is not fully understood how compression of morbidity can be achieved, especially in developing countries where older people, who are poor and with lower levels of education, fail to benefit from improvement efforts made in healthcare (Prince et al., 2015).

Gale (2013) infers that there is a relationship between health, disparity, and ageing, such that as age progresses, disparity increases and health deteriorates. Though there are several studies focusing on disparities in health status which are well documented, solutions for reducing those disparities are less clear (Meyers, 2007; Voelker, 2008). Moreover, there is also a lack of systematic understanding of health disparity among older people (Kennedy et al., 2013.; Medina and Negroni, 2014; Tøien et al., 2014).

Older people in any health system would encounter social and health disparities (Fiscella and Williams, 2004), not only in terms of health status and access to healthcare but also in terms of disparities in the treatment they receive. Older people often face difficulties in locating appropriate healthcare services particularly specialists and preventive care, and they also utilise these services much less. This group is also associated with a wide range of health problems, including chronic diseases and mental health issues (Kannel et al., 1987; Gale et al., 2011; Gale, 2013). Older people are also vulnerable to changes in their environment, and their health status is more likely than that of younger people to be affected adversely by any social, economic and demographic changes (UN, 2015).

Old age and older people are typically being associated with frailty, disability, and ill- health (Cauley, 2012). The increase in the population of older people brings about various social and health implications (Arokiasamy et al., 2012). At this stage, health is a major concern as there is an increasing number of older people with long term conditions and complex care needs (Cauley, 2012). In developed countries, healthcare utilisation appears to increase with age while this trend is not markedly observed in developing countries, where older people use healthcare significantly less (WHO, 2015a). This low usage perhaps due to barriers to access and lack of appropriate old-aged services as well as focus still remains on services that cater to the younger populations who are economically active.

Note that longer life expectancy means a government needs more resources to sustain and support the aged. Social disparities in health outcomes are important factors that can provide additional information for decision making and appropriate healthcare intervention to reduce avoidable illnesses, disability, and mortality. Therefore, in order to prolong good health, it is imperative to understand older people’s health and problems, relating to their social determinants that may deter or prevent them from accessing healthcare services.

1.3 Malaysia

Malaysia is an independent country with a constitutional monarchy and democratic parliament that together form the federal government structure. The country is a middle- income nation in Southeast Asia (World Bank, 2018). Figure 1.2 shows that Malaysia consists of two non-contiguous regions separated by the South China Sea (Ahmad et al., 2018); (i) Peninsular Malaysia, also known as West Malaysia, and (ii) East Malaysia on the island of Borneo. There are 11 states in West Malaysia with two federal territories; the capital city of Kuala Lumpur and the country’s administrative centre of Putrajaya. The country borders with Thailand at the north, Singapore at the south which is connected by a causeway bridge, and Indonesia’s island of Sumatera at the west, across the Strait of Malacca. East Malaysia has two large states of Sabah and Sarawak together with another federal territory of Labuan, an offshore financial centre. These two states constitute 60% of Malaysia’s landmass and both border with Brunei Darussalam and Kalimantan, a Borneo region of Indonesia.

Figure 1.2 Map of Malaysia

The total population including non-citizens was 32.4 million people in 2018, and the country has a multi-ethnic population (DOSM, 2018). The citizens were about 29 million people with the Malays as the majority ethnic group of about 58% of the Malaysian population followed by the Chinese (~ 23%) and Indians (~ 7%). Another 10 to 11% of Malaysians consisted of the Bumiputera or ‘sons of the soil’ groups comprise of the Orang Asli or aboriginal people in the Peninsular, localised ethnics of predominantly Kadazan, Bajau and Murut in Sabah, as well as Iban, Bidayuh and Melanau in Sarawak. Another 1% of Malaysians are categorised as other minorities. Malaysia is experiencing rapid urbanisation with about 70% of her population living in cities and urban areas, placing the country as the second most urbanised country in Southeast Asia after Singapore (Tey et al., 2015; World Bank, 2018). This trend toward greater urbanisation is indicative of better social amenities and growing economic opportunities in urban areas.

Malaysia is undergoing a demographic transition as her fertility rate and the population of 15 years and below are slowly declining (DOSM, 2017a). Moreover, the average life expectancy at birth was 74.8 years in 2017, an increase of 0.5 years from 2011 (DOSM, 2017a). In 2017, life expectancy at birth for a male was 72.7 years, while a higher life expectancy of 77.4 years for a female. Similar to many other countries around the world, Malaysia is also experiencing rapid growth of the older population. As the overall health standard improves, Malaysia is gradually becoming an aged nation, and by the year 2025, more than 15% of the population will be 60 years of age and above (DOSM, 2017b).

On the other note, there are less than 1% of households living in poverty in Malaysia (EPU, 2015). However, the Gini coefficient of income inequality in Malaysia remains high at 0.401 in 2014 but is steadily declining (World Bank, 2018). For further socioeconomic progress, the government aims to achieve an equitable or inclusive society, and to improve the overall wellbeing, especially by focusing on Malaysians in the bottom 40% households of the income distribution or the B40 group. This lower income group is vulnerable to economic shocks as well as increases in the cost of living. To assist the B40 group, the government has introduced more targeted measures to support the poor and vulnerable, mainly in the form of cash transfers and more of such

targeted approaches are being put forward to enhance socioeconomic sectors, particularly in the healthcare sector.

1.3.1 Districts in Malaysia

In Peninsular Malaysia, a district level is an administrative area below the state level and is administered by a land and district office, headed by a district officer. Under a district, there are a number of mukim, a lower level boundary area that constitutes villages and housing residences. However, a mukim is of less importance with respect to the administration of land and allocation of resources compared to a district.

In East Malaysia, a district is a section of an administrative division or smaller regions in Sabah (5 divisions) and Sarawak (12 divisions). In Sarawak, larger districts are further divided into sub-districts before the mukim level.

A district is typically a mix of different development areas of urban and rural. However, there are fully urbanised districts especially districts with state capitals as well as those with federal territory status. Rural districts are those districts located along the Titiwangsa Range, a chain of mountains that divide the Peninsular into East and West Coasts, and districts in the interior of Sabah and Sarawak. The list of districts is as shown in the Appendix for Chapter 1, each with a total number of population-based on the 2010 Census (DOSM, 2011) and the Gini coefficient index (Gini) for districts in 2014 and 2016 (DOSM, 2016a).

District boundaries are generally coextensive with local government boundaries but in fully urbanised districts, few local governments or municipalities oversee the planning and delivery of municipal services as well as providing and maintaining community infrastructures. As well, few districts are governed by a single local government, for example, both Northeast and Southwest districts of Pulau Pinang or Penang state are administered by the Penang Island City Council.

1.3.2 The healthcare system

Malaysia adopted a British healthcare system upon her independence in 1957, concentrating on primary services of health promotion and preventive care, including public clinics and disease control, as well as secondary services of curative care (WHO, 2012b). This system eventually transformed into a present dual system of a public-funded and thriving private healthcare, co-existing in tandem. In some services mainly in tertiary services that involve advanced medicine and rehabilitative care, they form amicable public-private collaborations (Quek, 2009). In addition, public healthcare extends their services to the rural, inland areas and islands where the populations are small in numbers and sparse.

The private healthcare provides primary care in terms of private clinics and hospitals that cater to secondary care of curative and diagnostic healthcare services and tertiary care typically in urban areas. Most of the primary care in these areas is delivered by private healthcare practitioners; there are only a few public clinics. Also, large numbers of private dental clinics and retail pharmacies provide basic and minimal health checks in urban areas.

Public healthcare services are organised under a civil service structure and are centrally administered by the Ministry of Health (MOH) through a tiered organisational flow of federal (central), state and district offices. The district health offices come under the authority of a State Health Department. The MOH plans and regulates most public sector healthcare services while others such as Defence, Education, Housing and Local Government Ministries, and Aboriginal and Social Welfare Departments also have certain roles in healthcare. The MOH also regulates the pharmaceutical and medical devices industries as well as food safety. At the moment, the MOH exerts slight regulatory control over the private healthcare providers, but there are plans to shift the dual system into a single healthcare system in the future.

In the public sector, all health policies and programmes are centrally formulated and coordinated, in the case of multi-sectoral efforts, in the MOH. The federal government

allocates most of the budget for health to the MOH. The budget will be further channelled to the states’ health departments which then distribute the monies to their respective districts and public hospitals. In this manner, a health facility receives a fixed annual budget, prepared under standard budget lines which are linked to performance indicators. This budget practice promotes the effective implementation of policies and ensures standard and equitable programmes across the country in order to achieve a set of national goals stipulated for health.

Healthcare in Malaysia is heavily subsidised by the government. At a nominal rate of Ringgit Malaysia (RM) 1 or equivalent to about £0.18, an exchange rate of RM5.532 for £1 in 2016 (Poundsterlinglive, 2018), for the first registration in a health clinic or a hospital, a person will receive primary or/and secondary health treatment and care, together with a supply of medicine, if prescribed. Subsequent visits to the health facilities will be free of charge. For tertiary and rehabilitative care, affordable fees compared to private healthcare are charged. For the B40 group and older Malaysian citizens aged 60 and above, they are exempted from paying these fees.

Though the public healthcare provides a form of social protection of universal coverage and affordable care, up till now, it has been overwhelmed by long waiting times, overcrowding at clinics and hospitals, and increasing demands for quality care. The government strives to promote structural reforms, expand their services and upgrade the facilities in order to address those challenges.

The expenditure on healthcare in the country is still low relative to developed countries (MOH, 2018). In 2016, public healthcare spent about 7% of the government’s total budget or 2.2% of the national gross domestic product (GDP). The MOH spent 22.3 billion or about 84% of the overall public health budget. While out-of-pocket spending by healthcare consumers was about 75% of the total private expenditure or about 2% of the GDP. Based on the constant value per capita in 2016, health expenditure for Malaysia was about RM1,636 or £296 per person (MOH, 2018).

1.3.3 The older people in Malaysia

In Malaysia, older people are legally defined as individuals aged 60 and above (DSWM, 1995). Malaysia’s total population of older people in the year 2012 was 8.2% of the total population of the country, or 2.4 million out of 29.34 million population. The population of Malaysia is relatively young compared to the population in developed countries. However, changes in the age structure due to fertility decline and longer life expectancy contribute to the ageing of the population. By 2020, it is estimated that the number of older people will be 5.5 million and by 2030, Malaysia will be in the category of ageing nations with older people constituting more than 15% of the population.

Demographic transition describes a population change over a time period, and factors such as fertility and mortality rates influence this change. An ageing population is a result of a decline in these two rates over time (He et al., 2016). The interrelationships of the two processes have a profound effect on the age structure of Malaysia.

Figure 1.3 shows the 2017 and 2050 age pyramids (UN, 2017) that indicate a changing trend in the demographic profile of Malaysia. The demographic age structure shifts to a more columnar shape with an increasing proportion of older people aged 60 and above, from a broad-based pyramid shape with a high proportion of the younger population in 2017. This shift within the 33-year period indicates a transformation of the country into an ageing nation (Kinsella and He, 2009). Among the older people in 2050, UN (2017) estimated a higher number of older females than males, especially those aged 75 and above. The working adults aged 15 to 64 years proportionately remain the same in 2017 and 2050. However, this proportion may likely lead to an increase in the old-age dependency ratio (OADR) due to a higher number of older people in 2050.

Figure 1.3 The population age pyramids, 2017 and 2050 (UN, 2017)

Figure 1.4 draws an important socioeconomic measure of the headcount ratio, the OADR. The ratio is the number of persons aged 65 and above for every one hundred persons aged 15 to 64. The projection of OADR in Malaysia from 1995 to 2055 by the UN (2017) reveals an increase in the socioeconomic burden of the working adults to sustain the provisions of older people who are considered as a non-productive group. Figure 1.4 also reflects a higher percentage increase in every decade since 1995 and indicates a declining birth rate and slow population growth. Urbanisation and migration will also affect older people where working adults migrate from rural or less-developed areas to more developed areas mostly in urban settings (Chan, 2005), leaving older people in the rural to fend for themselves.

Table 1.1 shows the proportion of the older population between the various ethnic groups in the country from 2000 to 2040. The Malays and Bumiputera will continue to make up the majority of older people in terms of numbers, however, have a lower percentage rate in 2040 compared to the Chinese and Indians. The older Chinese will register the highest percentage rate of population ageing at each decade, which illustrates a profound ageing challenge within their community.

Figure 1.4 Old-age dependency ratio in Malaysia (UN, 2017)

Old-age dependency ratio (ratio of population aged 65+ per 100 population aged 15-64) by year

29.8

20.4 16.1 12.1 8.5 6.3 6.8

1995 2005 2015 2025 2035 2045 2055

Table 1.1 Older People (60+) in Malaysia by Ethnicity, 2000 to 2030 (DOSM, 2011; 2012)

Number (‘000) Percentage (%) Year Malaya Chinese Indian Othersb Malaya Chinese Indian Othersb 2000 804.2 501.0 93.9 12.5 5.64 8.80 5.59 4.62 2010 1,242.90 777.6 150.3 12.0 7.09 12.16 7.88 6.32 2020 1,889.30 1,153.80 254.7 21.1 9.12 16.90 12.15 6.89 2030 2,709.10 1,540.30 373.5 33.9 11.42 21.87 16.82 8.70 2040 3,704.30 1,854.60 473.9 47.0 14.23 26.13 20.99 9.85 a includes the Bumiputera b foreigners, expatriates and immigrants

1.4 Description of chapters

This thesis aims to understand the social structure that considers patterned relationships between the determinants and the outcomes and avoids inferences on individuals’ motives and intentions. The empirical chapters in the thesis collectively define those aged 50 and 19

above as the target population, even though the starting age of older people in Malaysia is officially at 60. This is in line with the convention used by the WHO on the Study on Global Ageing and Adult Health (SAGE) that accounted those aged 50 as older people particularly in developing countries (Anand, 2015; WHO, 2015a). In addition, the English Longitudinal Study of Ageing or ELSA considers age 50 as a baseline cohort for older people (Chou, 2008). Another longitudinal survey, the Health and Retirement Study or HRS in the United States also adopts a similar age baseline (Luo et al., 2012). Apart from these surveys, evidence showed that signs of age-related health conditions including frailty, a progressive age-related physical decline, are strongly present and can be analysed at the age of 50 (Golomb et al., 2012; Beard et al., 2016). In developed countries, the prevalence of multimorbidity in older people is known to be higher among those aged 50 to 60 as well as people with advanced old age (WHO, 2015a). And so, this thesis describes statistical evidence to reveal the true nature of how the older population functions within the health dimensions as early as the age of 50. Moreover, it takes into account social disparities within a spatial context in order to fully inform policymakers of the expanse of health issues experienced by older people.

The originality of this thesis derives from the analysis of social disparities in health in a developing country, focusing on older people aged 50 and above as the main subject, modelled by multilevel and spatial analyses. Health is a broad and complicated subject matter to elucidate entirely, thus, this thesis is further presented in four essays that explore NCDs’ prevalence as its health outcomes. The background and motivation for pursuing this thesis have been described above.

Chapter 2 describes the methodology of this thesis. This includes the theoretical framework, the determinants, the conceptual outline of the thesis, the dataset that includes the sampling process, and the general analysis technique used in the empirical chapters.

The first empirical essay in Chapter 3 discusses the issue of mental disorders of depression and generalised anxiety disorder (GAD) among older people. The secondary dataset used was collected in 2010 and reported by the MOH in 2011. However, the multilevel regression technique has never been applied in previous analyses of the dataset in

investigating older people’s mental health problem. Chapter 3 shows social determinants that are significantly associated with mental disorders at individual and district levels. The health status and physical capacity of individuals are also significant factors that influence mental disorders. This chapter shows that the district level predictor of OADR can be a useful indicator for policy consideration. Mental disorders measured as a single outcome also varies between the districts. The new insights from this chapter can motivate similar further studies on mental disorders in the country and help policymakers to monitor mental health disorders.

Chapter 4 examines the unmet care needs of cardiovascular diseases (CVD) care among older people. The analysis technique used is novel for a developing country by using two established CVD risk measurements; the Framingham score (FRS) (Lloyd-Jones et al., 2002) and the Systematic Coronary Risk Evaluation (SCORE) (Conroy et al., 2003). The data is similarly used in the analysis in Chapter 3. The FRS and SCORE risk results are divided into three categories – low risk, intermediate risk and high risk of CVD. This is further coded as those who were diagnosed or undiagnosed with CVD. Individuals with an intermediate and high risk of CVD, and at the same time undiagnosed will be deemed as those who have unmet care needs. Three multilevel regression models are then fitted. Age groups, gender, ethnicity, and marital status, as well as certain socioeconomic determinants at the individual level, are found to be associated with unmet care needs among older people. At the district level, the Gini coefficient is associated with unmet care needs. This chapter stresses the urgent need for further preventive measures of CVD risk factors and raised health awareness for those aged 50 and above.

Chapter 5 evaluates the likelihood of older people having diabetes, specifically, type 2, and being found undiagnosed of their diabetic conditions, which they are unaware of. This chapter also examines the socioeconomic status (SES) factors that may be associated with the diabetic problem among them, and at the same time, attempt to understand how geographic distribution in known and undiagnosed diabetes varies between districts in Peninsular Malaysia. The secondary dataset used was collected in 2015. The two outcomes of known and undiagnosed diabetes are analysed separately, whereby for each outcome, three multilevel models are fitted. Respectively, the diabetes outcome is

influenced by different SES factors that reveal the overall diabetic problem as dynamic and multidimensional. The findings demonstrate that SES factors at the district level show differing effects on the two diabetes outcomes and such results will not be achieved without leveraging the multilevel approach. The multilevel together with the mapping of the distribution in estimated known and undiagnosed diabetes provides avenues to determine and track progress in reducing the burden of diabetes among older people in the country.

Chapter 6, the last empirical chapter, explores the spatial analytic approach based on a Bayesian framework in estimating the posterior distribution of diseases and their risks. The health outcome, undiagnosed NCDs risks, consists of the three major diseases of diabetes, hypertension and heart disease. The secondary data analysed is similar to the data used in Chapter 5. The Bayesian method permits mapping of geographic areas that show clusters of undiagnosed NCDs risks and provide an advanced understanding of associations between health and spatial dimensions. The analysis takes into consideration the effect of neighbouring areas on a specific district, based on Tobler’s (2004) observation in geography that near things are more related than distant things. This chapter shows that undiagnosed NCDs risks decrease with age and the cluster of elevated undiagnosed risks districts can clearly be distinguished on the output maps. This visual evidence will provide an important tool for policymakers to identify quickly those districts with a high prevalence of undiagnosed NCDs risks among its older population.

To conclude this thesis, chapter 7 summarises the key findings of Chapter 3, 4, 5 and 6. Also, the chapter discusses the contribution of the findings in those chapters and offers directions for future research.

Chapter 2

Methodology

2.1 Theoretical framework

Braveman et al. (2011c) point out the importance of advocating social justice to alleviate social disparity and reduce differences in health. Social justice has long been recognised as a stimulating idea that motivates people to progress; to create more egalitarian societies and to redress capitalistic exploitation of human labour (Sen, 2015). Sen also proposes that human progress should be judged through human capability in pursuing the quality of life and not through economic positions, measured among others by health status. Nevertheless, health disparities do not refer generically to all differences but specifically centred on differences relevant to social justice. Braveman (2011b) further categorises such health disparities as systematic and reasonably avoidable health differences, adversely affecting socially disadvantaged groups, which clearly reflect social disadvantages. In order to reduce these disparities, it is essential that health policies are seen to be fair and just (Wilkinson and Pickett, 2010). Marmot et al. (2010) emphasise that reducing health inequalities is a matter of fairness and social justice, with a fair distribution of health and wellbeing as important social goals.

Sen (2015) proposes that societies should establish and constitute ‘just institutions’ where an individual’s act and behaviour comply entirely with the demands and requirements of these institutions’ functions. Sen perceives that debates about justice that relate to practicalities need to emphasise on comparative arguments, even if agents were unable to identify what constitutes as a perfectly just. On this note, Sreenivasam (2012) puts forward the importance of acknowledging that institutions, and not simply individuals, can be in ‘partial compliance’ in meeting their obligations to implement social justice.

However, Marmot (2015) infers that there will always be competing, conflicting and opposing arguments in the debate about what constitutes social justice and how these ‘just

institutions’ should perform. On one hand, those who embrace the idealisation of perfectly just, require all relevant agents including individuals and institutions to demonstrate full compliance with the demands of justice (Stemplowska, 2008; Valentini, 2012). To achieve these ideal circumstances, the society should live in a favourable circumstance and environment which can socially and economically flourish in order to realise justice. The members of the society are then expected to act or perform willingly on the duties and obligations applied to them.

On the other hand, those who refute the idealisation of perfectly just put forward the rationale of partial compliance, where agents are unwilling to act in full on the duties that apply to them (Sreenivasam, 2012; Valentini, 2012). In terms of social disparities that occur in health, these duties should not discriminate people in terms of their age, gender, and other social or socioeconomic standings. In fact, the partial compliance rationale offers recommendations that are desirable, realistic and achievable (Stemplowska, 2008) in solving the urgent practical questions that confront society. Sreenivasam (2012) foresees that a non-idealist perspective can be tailored into formulating policies and courses of action that are morally permissible, politically possible and likely to be effective.

2.1.1 The spatial arrangement

Hamlin and Stemplowska (2012) offer an idea of ‘institutional design’; the pursuit of social arrangement intended to promote and compromise the differences between ideals. The ‘institutional design’ also attempts to accommodate the partial compliance rationale that operates within social structures of large communities that are complex and involve multiple levels of structural influences. Blau (1993) conceptualised this social structure as a multidimensional space of social positions that indicate differences between individuals in a particular distribution of a population. Among others, Blau came up with a term of ‘graduated parameter’ that refers to continuous distributions of differences in status and resources such as income, education and employment opportunities, which also

reflects the inequality of SES on multiple levels. Such inequality implies a social heterogeneity where Blau anticipated that two individuals with identical social positions have more similarities than individuals with different social positions. In other words, individuals with the same affiliation to a given social or SES category have generally more intense relationships than individuals belonging to different groups.

Galster (2010) articulates Blau’s ‘graduated parameter’ concept, on a universal scale, as a social position of an individual that can be described in terms of the individual’s spatial location. Here, Tobler’s first law of geography (Tobler, 1970; Grasland, 2010) provides the theoretical basis that links spatial location with social issues, where behaviours of individuals and groups can function in spatial contexts; a structural approach of social life that in Tobler’s words, ‘people die, are born and migrate’. Simply, Tobler indicates that locations which are closer with each other would have more similarity in values of attributes than locations which are further apart. Both Blau and Tobler were interested in the spatial arrangement (Grasland, 2010) of the relation between structures and interactions of individuals and groups, in line with the idea of ‘institutional design’, through concepts of distances and opportunities. In this spatial arrangement, the behaviours of individuals and groups can occur in spatial contexts. This arrangement could further provide new insights in understanding social disparities in health and contribute towards formulating preventative policies in narrowing such disparities.

2.2 The determinants – context and composition

An understanding of social disparities in health and the mechanisms these disparities operate might point out something important for us to assess how a society or an institution contributes to health injustices (Peter, 2004). Carter-Pokras and Baquet (2002) anticipate that eliminating disparities will require innovative outlook about the causes of disparities, health, and disease determinants. Therefore, distinguishing compositional against contextual effects is of primary importance for making causal inferences about how those determinants impact health (Kawachi et al., 2002). For instance, concentrated

poverty in an area and many other contextual characteristics may not just impact the average health of a community, but also create health disparities between social groups (Kawachi et al., 2002).

From the point of social disparities in health, we will want to distinguish the contextual from the compositional effects to further elucidate the factors contributing to a certain health issue. We will also want to determine social factors that differ significantly between individuals and groups with better or worse health. It is known that individual actions toward maintaining their health are shaped by higher factors; the contexts where individuals live that appear to strongly affect their health (Braveman et al., 2011b). So, contextual effects refer to the influence a neighbourhood or other type of higher-level unit has on people, while compositional effects are simply reflective of the characteristics of individuals comprising the neighbourhood or another social setting (Subramanian et al., 2003). Contextual factors that affect health include social, and health policies, healthcare resources, and intervention programmes (Kawachi et al., 2002). These are potential targets in efforts to reduce health disparities and improve wellbeing. Contexts such as neighbourhoods or households are shaped by many factors, including geographic factors such that people are not randomly distributed into healthy and unhealthy circumstances (Braveman, 2011).

On the other hand, compositional effects refer to variations in health attributable to the health status of the individuals who are members in a given context (Subramanian et al., 2003). For instance, the poor health status of residents in a particular neighbourhood compared to residents in the surrounding areas may be compositional. Thus, at the individual level, compositional effects can be reflected by characteristics such as ethnicity, gender, occupation and education (Braveman et al., 2004).

The WHO recommends that health indicators need to be reported according to groups, or ‘equity stratifiers’ for the purposes of monitoring health inequities (WHO, 2013b). However, compositional effects of health disparities for example, from the racial/ ethnicity lens can only explain partially the actual circumstances. Individuals’ conditions of inadequate education and low income to unhealthy neighbourhoods can contribute to

these disparities in a complex manner (Woolf and Braveman, 2011). In contrast, those with higher education level and the non-poor with good income fare better in health than those with lower education level or the poor (Braveman, 2011). For example, both average life and disability-free life expectancies vary based on the areas where individuals live in. In the United Kingdom, people living in wealthier areas lived 6 years longer than people living in poorer areas (WHO, 2015a). Besides an individual’s poor condition, the reasons may be due to the lack of healthcare resources and infrastructures that enable an individual with the restricted capacity to improve their health.

The geographic setting, not just a social group, plays an important role in shaping health (Kearns 2002). Observed geographic health disparities may be driven by processes that are rooted in space, place, or both (Arcaya et al., 2015). While ‘space’ relates to individuals’ distance or proximity from a defined source, ‘place’ is used to describe individuals’ memberships in administrative areas, such as districts or states. On these terms, governments implement healthcare policies and programmes, for instance, building healthcare clinics that are specific to these areas’ boundaries. Arcaya et al. (2015) further explain that the health impacts of policies and programmes are not dependent on the residents’ physical location, but on this form of membership. In brief, they believe policies and intervention programmes can be more effective by targeting geographic health disparities.

2.3 Conceptual outline of the thesis

Social disparities in health outcomes provide important information for decision making and appropriate intervention to reduce preventable morbidity and mortality. A critical dilemma among policymakers is whether social disparities in health can be mitigated by injecting more resources in targeted areas of health or placing this additional resources equally in all geographical areas concerned (Kreng and Yang, 2011; Balarajan et al., 2011; Marmot, 2017). In particular, the associations between socioeconomic status (SES) and health outcomes among older people are not well understood, especially in developing

countries (Smith, 2012). Despite this, ideal resource allocation in healthcare should ensure that most people have access to equal health care services when needed. Identifying the associations above can provide more options for policy remedies.

Figure 2.1 A conceptual framework for understanding how social determinants influence health. Amended from the conceptual framework of the Commission on Social Determinants of Health (WHO, 2008; Marmot, 2017).

Figure 2.1 shows an amended framework of social determinants that influence health. Individuals and groups are stratified according to their SES. They will experience different types of material circumstances and psychosocial factors that make these individuals or groups more or less vulnerable to poor health. The responses of healthcare to such experiences in the form of health interventions may also be influenced by the nature and degree of this social stratification. The outcome, health conditions, in return, determine individuals’ and groups’ SES, and their socioeconomic contexts such as geographical areas. The figure also illustrates that social disparities are amenable to intervention through public policies and changes in societal norms and values.

2.4 Data – The National Health and Morbidity Survey

The National Health and Morbidity Survey (NHMS) is a ten-year survey that has been conducted since 1986. However, after the third NHMS in 2006, it becomes a five-year occasion with subsequent surveys in 2011 and 2015. Initially, the survey aims to provide a community-based dataset on the pattern of general health problems, health demands and expenditure on health. This effort was to facilitate the MOH in reviewing their policy priorities, implementing programmes, planning future allocation of resources and evaluating the impact of healthcare strategies. The scopes covered in the earlier surveys were morbidity rates, health service utilisations and its barriers, health expenditure and its sources, immunisation coverage, NCDs of asthma, angina, diabetes, hypertension and acute respiratory illnesses, injuries, and smoking. More scopes were added; load of illnesses, health-seeking behaviours, healthcare consumption cost, health risks such as exercise, alcohol consumption, drug abuse and sexual practices, and specific health problems of hypercholesterolemia, ischaemic heart disease, medically diagnosed cancer, and physical impairments.

2.4.1 Sampling framework

The NHMS covered every district, both urban and rural areas in Malaysia. The target population was non-institutionalised individuals residing in Malaysia, whereas institutionalised individuals who stayed in residences other than their households, such as hostels, hospitals, care and nursing homes were excluded from the survey. The sampling framework for NHMS in 2011 and 2015 are based on the National Population and Housing Census 2010 by the Department of Statistics Malaysia (DOSM, 2011). The sampling involved two stages; the primary sampling unit or Enumeration Blocks (EB), continuous areas in nature with identified boundaries, and the second sampling unit or Living Quarters (LQ) within the EB. There are about 75,000 EB in Malaysia, and each EB consist of between 80 to 120 LQ with an average population of 500 to 600 people.

2.4.2 Sample size determination

The sample size for NHMS is calculated based on the expected prevalence of health- related problems in the population, obtained from the previous NHMS, adjusted for the total number of the target population. For NHMS 2011, the optimum sample size required was 9,528 LQ (MOH, 2011a), and 10,428 LQ for NHMS 2015 (MOH, 2015a). Larger sample sizes were allocated for states with a higher number of population in Selangor, Johor and Sabah, and lesser sample sizes for a smaller number of population in Perlis, Melaka and Putrajaya.

The sampling frame and sampling process for NHMS are also provided by the DOSM. The final weight for NHMS includes adjusted weight and population factor, where adjusted weight takes into account the non-response cases. Population factor or external weight utilises the mid-year population estimates of age group, sex, ethnicity and states as benchmarks. Here, population estimates of 2010 and 2015 are used for NHMS 2011 and 2015 respectively.

2.4.3 Missing data

Because of the nature of self-reported questionnaires in the NHMS datasets, a number of responses is considered as missing randomly or is declared as non-available in the NHMS report. Nevertheless, all outcome variables in the empirical chapters of Chapter 3 to 5 are derived variables (Lorgelly and Lindley, 2008) that are constructed by calculating or categorising health-related variables in the datasets. Cases with missing values are excluded in the outcome variables. For the covariates, the data is analysed based on complete cases. Therefore, the number of samples used in each of empirical chapters are reported as numbers of complete cases.

For chapter 6, the spatial analysis that operates on a Bayesian hierarchical model with a spatially smooth CAR prior already considers the missing observations in its algorithm 30

(Blangiardo & Cameletti, 2015). So, the analysis leverages on the advantage of the Bayesian approach in INLA that takes into account uncertainty in the estimates and its flexibility in dealing with issues such as missing data.

2.5 Analysis technique: Multilevel and spatial modelling

Multivariable analyses are widely used to investigate the relationships of demographic and socioeconomic factors with health outcomes or the likelihood of certain diseases (Arokiasamy et al., 2017). Moreover, it is recognised that the aggregate unit considered or groups at higher levels may conceal important within-groups effects, for instance, the contextual differences among geographic areas (Arcaya et al., 2015). It is important to acknowledge that individual observations within a group cannot be assumed as independent of one another (Subramanian et al., 2003). Therefore, both individual and contextual factors have important influences in the aetiology of health disparities (Graham, 2004).

Taking into account these considerations, multilevel statistical techniques avoid the problem of single-level analysis that obscures the distinct and interactive effects of individual and contextual factors as shown in Figure 1.5. From the standpoint of policymakers, multilevel methods can provide a basis for constructing a socioeconomic context description of an individual’s health outcomes, which can be adopted into policies. Models can be constructed to observe not only differences at the individual level but also differences of SES among social groups. This will enable policymakers to identify those groups with poor health outcomes and target them in terms of policy interventions for their health improvements rather than individually.

The multilevel approach can also avoid errors in interpreting the results of the analysis. A well-known error in multivariate regression is the ecological fallacy or Robinson’s fallacy (Diez-Roux, 1998; Subramanian et al., 2003; Hox et al., 2017). The ecological fallacy is the inference of empirical relationships observed at the group level that are generalised to individuals within the groups or the interpretation of aggregated observations at the 31

individual level. Another type of error is the individualistic fallacy (Hox et al., 2017), in which inferences of observations at the individual level are generalised for the groups or higher levels; the opposite of ecological fallacy.

A multilevel perspective gives clear recognition to the idea of hierarchy and the nesting of people within places (Arcaya et al., 2015). Multilevel modelling can estimate the parameters better than other regression models for observations that are clustered at various levels, and the estimated coefficients are less likely to be biased if the analysis takes into account the data’s nested structure (Snijders and Bosker, 2011). So, older people are nested or reside within an administrative area such as districts, and these districts are considered as homes to the contextual factors. The consequence of failing to recognise hierarchical structures is that standard errors of the regression coefficients will be underestimated, leading to an overstatement of statistical significance (Twisk, 2006; Snijders and Bosker, 2011; Hox et al., 2017). Such standard errors of higher-level predictors will be the most affected when grouping is ignored.

Multilevel analyses combine the regression and the variant components to account for the nested structure of the data. A general two-level model without the independent variables is expressed in Equation 1.1.

Equation 1.1

푦푖푗 = 훽0 + 휇푗 + 푒푖푗

We assume j as groups (e.g. districts) or the context we are measuring with a number of individuals i in each group. At the individual level, we have the dependent variable 푦푖푗 where the subscripts i and j refer to the two levels; 훽0 is the overall mean of the dependent variable across all groups; 휇푗is the random intercept for the groups; and 푒푖푗 is the residual, which is the difference between the ith individual’s particular variable and their group’s mean. The multilevel regression technique is further explained in Chapters 2, 3 and 4. 32

This thesis also applies the Bayesian inference that has become popular in research based on spatial statistics. This inference can model different types of mixed and random effects for spatial and spatiotemporal analysis, drawing the strength of the Markov Chain Monte Carlo (MCMC) technique. However, MCMC can be tedious and attaining the required number of samples may take a longer period of computation. In addition, analyses in this thesis are multivariate and approximating the full posterior distribution may not be possible. Therefore, the analysis in Chapter 5 applied the Integrated Nested Laplace Approximation (INLA) technique that focuses on the posterior marginal for latent Gaussian field (Rue et al., 2009). The R-INLA package in the R programming language (R Core Team 2014) provides an interface to operate INLA, using standard R commands. The results can then be visualised by plotting them on geographical maps. The INLA method is elucidated in Chapter 5.

Chapter 3

Social Determinants and Mental Disorders among Older People in Malaysia

Abstract: Even as the population of Malaysia is ageing, the evidence is lacking on the association between social disparities and mental disorders in older Malaysians. In addition, mental disorders have not been thoroughly investigated by comparing geographical area variations, making more effective targeting impossible. This study sought to identify the association between social determinants and mental health in order to investigate its relationships with contextual-level and individual-level determinants and to examine the variations of these relationships between districts as an administrative area of interest. Data from 5,908 respondents aged 50 and above from the National Health and Morbidity Survey 2011 were extracted for analysis. The mental disorders of depression and general anxiety disorder were combined into a binary outcome variable, while two categories of predictor variables are used. The chosen variables at the individual level were age, gender, ethnicity, marital status, location, household size, education level, employment status, monthly household income, self-rated health and self-rated physical capacity. The chosen variables at the district level were old-age dependency ratio (OADR), household average size (HHAS) and public health clinic to population ratio (PC). Binary logistic regression was used for bivariate and multivariate analysis. Four multilevel models were constructed in the multivariate analysis to assess individual and contextual effects on mental disorders among older people. In the contextual model, being female (β: 0.52, OR: 1.68), Chinese (β: -0.62, OR: 0.54), living in an urban area (β: -0.24, OR: 0.79), self-rated health (β: 0.24, OR: 1.27) and self-rated physical functionality (β: 0.40, OR: 1.50) are significantly associated with mental health disorders, at 5% or less. The contextual determinant of the OADR (β: -0.06, OR: 0.94) also has a similar association. The model accounted for 9% of the total variance being explained by the effects at the district level. Social disparities associated with mental disorders among older people and the differences in mental health vary between districts in Malaysia. This evidence could be used in mitigating mental disorders nationally.

Keywords: mental disorders, multilevel modelling, social determinants

3.1 Introduction

The demographic changes that are likely to unfold in the future and the disparities that these changes possibly will bring, especially among older people, have become pertinent issues for a developing country such as Malaysia to address. If these issues are not addressed adequately, a growing number of older people will be a burden on the public coffers in order to maintain their good health and ensure the delivery of quality healthcare. At present, research on older people and social determinants that may affect the health of this age group in Malaysia are still lacking. Policymakers need to be furnished with new and updated evidence, especially since the proportion of people 60 years and older is steadily increasing: it was 7.9% of the total population in 2010 and will grow to an expected 10.6% by 2020 (EPU, 2015). Therefore, in less than five years, one in ten Malaysians will be at least 60 years of age. This proportion will increase to an estimated 23.6% in 2050 compared to the world average of 21.5% (UN, 2015). Also, the government has emphasised the importance of achieving universal access to quality healthcare by 2020 (EPU, 2015) and thus research on potential health risks among older people has never been more timely.

Social disparities have been established as significant contributors to health decline (Marmot, 2015, Pickett and Wilkinson, 2010), where the lower a person’s social position, the worse that individual’s health (Cummings and Jackson, 2008, Shuey and Willson, 2008). In turn, health differences could widen the gap between groups of people who are more and less advantaged socially (Braveman, 2006) and deemed worthy of attention because of its impact on social values. Among health areas, mental health bears a high likelihood of social disparities, as many of the common mental disorders, especially anxiety and depression, are shaped by the social and physical surroundings in which people live (WHO, 2014b). Mental disorders in Malaysia should not be ignored due to the high burden and morbidity (Maideen et al., 2014). Furthermore, the context of mental health is also complicated to grasp within the multi-cultural, multi-ethnic and multi- religious communities of Malaysia (Haque, 2005). Eventually, mental disorders increase

the risk of contracting communicable and non-communicable illnesses and could also cause physical injuries.

The common mental disorders in Malaysia are depression (Mukhtar and Oei, 2011) and anxiety (Sok Yee and Pei Lin, 2011), and both problems also affect 10% of the world’s population at any given time (Layard et al., 2013). The National Health and Morbidity Survey (NHMS) reports revealed that mental disorders among Malaysians aged 16 to 60 had increased slightly from 10.7% in 1996 to 11.2% in 2006 (Krishnaswamy et al., 2012, Maideen et al., 2014). Other findings estimate the prevalence of depression in Malaysia at between 8% and 12% among adults at any given time (Ng, 2014). While the NHMS in 2011 reported that about 12% of adults were already suffering some form of psychiatric morbidity (MOH, 2011b), the report also stated that there was a much lower depression prevalence of 1.8% among adults 16 years and older. In addition, the NHMS found that the prevalence of generalised anxiety disorder was 1.7% among adults in 2011, even though a local study estimated a higher prevalence between 8.2% and 12.9% (Sok Yee and Pei Lin, 2011). Another study found that depression among adults 18 years and older was associated with living in urban areas, being female, being of Indian ethnicity, being unmarried and having a lower education (Maideen et al., 2014). However, NHMS reports did not reveal much about the prevalence of mental disorders among older people. Nonetheless, a study on severe depression among those 60 years and older estimated that the prevalence was quite high, around 19.2% (Rashid and Tahir, 2015). Still, little is known about mental disorders among older people and often they go unnoticed (Prince et al., 2007) even though symptoms of anxiety and depression are common in older people (Gale et al., 2011).

Empirical studies on mental disorders in Malaysia are mainly centred around clinical, primary care and general community settings (Mohd Sidik et al., 2003a, Mohd Sidik et al., 2003b, Sherina et al., 2004, Imran et al., 2009, Mukhtar and Oei, 2011) and described the problems in terms of symptoms, lifetime or current mental disorders. These studies reported that the prevalence of depressive symptoms among the older people since 2000 ranged from 6.3% and 18%; the results vary depending on the assessment tools used. Moreover, in this age group, mental disorders often go untreated (Mukhtar and Oei, 2011)

as these conditions are usually perceived as part of the ageing process, especially in Malaysia’s rural areas. Besides, most of the mental disorders encountered are treated as medical issues rather than events that are prompted by a whole range of psychosocial factors within the population (Crabtree and Chong, 2000). The likelihood of misclassifying people as having mental disorders, instead of merely experiencing emotional distress, which is a typical response to any adversity, may increase if the social context of those problems is ignored (McNally, 2011).

The World Health Organisation (WHO) has identified that the increased health risks of many common mental disorders are associated with social disparities (WHO, 2014b). Moreover, area differences in health may exist, thus underlining the importance of local geographical context in shaping these problems. Until now, research on older people and mental health in Malaysia has focused mainly on a single geographical area (Sok Yee and Pei Lin, 2011, Maideen et al., 2014). Further findings showed that variation between urban and rural areas was not significant in older people’s psychological well-being (Momtaz et al., 2011) and that successful ageing is relatively free from the effects of the place of residence (Hamid et al., 2012). However, these studies encouraged the need for future research.

To date, few studies have been conducted to determine the possible effects of predictors of mental disorders among older people, using Malaysia as a research context. So far, most of the studies that have investigated mental disorders have focused on a single mental disorder, either anxiety or depression. These studies used total scores of points or scales to assess the severity of the disorder without further identifying the source of the problems (Mukhtar and Oei, 2011). Surprisingly, mental disorders have not been carefully investigated by comparing geographical area variations explicitly at the district or state level. Further, what remains unclear is why mental disorders vary between areas in Malaysia, if at all. We attempt to answer two questions; (i) what are the social determinants associated with mental disorders? (ii) What is the relationship between contextual level and individual-level determinants of mental health disorders? In addition, this paper attempts to understand whether area differences in health compositional factors and contextual effects do exist in Malaysia.

3.2 Data and methods

3.2.1 Data

The NHMS dataset received from the Ministry of Health, Malaysia (MOH, 2011a) was used for the analysis, mainly for variables at the individual level. The survey was conducted throughout 2010 to obtain data on health problems, health needs and health expenditure. NHMS used a sampling frame based on enumeration blocks, which are geographically contiguous areas with identified boundaries which allow a nested structure of individual data according to districts for the analysis. The survey gathered health data from 28,650 respondents and out of this total, the data of 5,908 respondents (20.6% of the total) 50 years and older were extracted for analysis. In addition, two datasets were used to generate variables for the district level; the 2010 Population and Housing Census of Malaysia from the Department of Statistics Malaysia, and the Public Health Facilities Distribution in 2009 from the MOH.

3.2.2 Outcome variable: Mental disorders

NHMS IV had gathered data on mental health among adults 16 years and older to determine the prevalence of two common mental disorders in Malaysia, namely depression and generalised anxiety disorder (hereafter ‘anxiety’). The questionnaires used for this purpose are based on the Mini International Neuropsychiatric Interview (MINI), a psychiatry structured diagnostic interview instrument (Lecrubier et al., 1997). MINI is specially designed to allow non-specialised interviewers to enumerate questionnaires on mental health with the respondents. Hence, an interview can be done briefly and straightforward since respondents are only required to choose between the answers ‘Yes’ or ‘No’ choices.

This analysis considers the distribution of mental disorders and neither considers the pathophysiology and prevalence estimates of anxiety and depression nor their level of

severity. On this note, both problems share numerous risk factors, including increasing age and physical inactivity, and also lead to similar outcomes, especially cardiovascular illnesses (Almeida et al., 2012). Due to these reasons, the term ‘mental disorders’ is used to describe both anxiety and depression and is measured as a single variable, ‘mental’ in the analysis. Therefore, a respondent who answers ‘Yes’ to the screening questions in the domain of either anxiety or depression was regarded as suffering from a mental disorder. This outcome variable was coded as a binary variable, where ‘No’ was given a value of 0 and ‘Yes’ a value of 1.

3.2.3 Covariates

There are two categories of predictor variables: individual variables as level 1 and district variables as level 2. Socio-demographic and socio-economic characteristics were set as predictor variables at level 1. Socio-demographic characteristics are age, gender, ethnicity, marital status and location, while socio-economic characteristics are measured by household size, education level, employment status and monthly household income. Also, two general health characteristics were used as predictor variables, self-rated health and self-rated physical capacity. At level 2, old-age dependency ratio (OADR), household average size (HHAS) for each district and the number of the public health clinics to population ratio (PC) were used.

The ‘age’ variable included respondents from the age of 50 years to 107 years. This continuous variable was centred about its mean. Gender was coded as ‘female’, with ‘male’ given a value of 0, while ethnicity was tabulated into four dummy variables with ‘Malay’ as the reference ethnicity, (i) ‘Chinese’, (ii) ‘Indian’, (iii) ‘Sabah & Sarawak’ specifically for ethnic groups in those states of East Malaysia, and (iv) ‘Other Ethnicities’, including other Asians, Caucasians as well as the Orang Asli, the indigenous people of Peninsular Malaysia. Marital status was arranged as ‘married’, with ‘single’ as the reference, and household size was coded as a dummy variable, ‘alone’, where a household of more than one household member was given a value of 0. The area location, ‘urban’

was coded as a binary variable, with ‘rural’ as a reference, plus the education level, ‘education’ which was ordinal and organised into three categories (i) primary or lower, (ii) secondary, and (iii) tertiary. Employment status was represented as ‘unemployed’, with a value of 0, and ‘employed’ with a value of 1. Monthly household income, ‘income’ was imputed as a log-transformed continuous variable. For the ‘health’ variable, respondents were asked to rate their present health status on a five-point scale from ‘very good’ to ‘very bad’. They were also asked to rate if they had any difficulty in performing work or daily activities during the past 30 days, on a five-point scale from ‘none’ to ‘unable to perform’. This scale was then established as an ordinal variable, ‘physical’. However, none of the 5,908 respondents rated ‘very bad’ for the ‘health’ variable.

The OADR variable (Chan, 2005, World Bank, 2016) was measured by calculating the number of people 65 and older divided by the number of working people from age 15 to 64 in 2010, giving the proportion of dependents for every 100 working people in every district. The HHAS variable was a continuous variable obtained by calculating the total population in a district divided by the number of households in that particular district. The PC variable was also included since mental health promotion and prevention activities were being delivered by public clinics in the districts. As a note, public clinics in the analysis do not include community health clinics and dental clinics. There were officially 126 districts in Malaysia for 2010 and out of these, 104 districts were captured in the NHMS dataset.

3.2.4 Method

The analysis used sampling weights and calculations were carried out using Stata version 13.0 by StataCorp LP, U.S. A number of variables at an individual level had missing values – ‘married’ (0.1%), ‘alone’ (0.3%), ‘income’ (13.5%), ‘education’ (0.8%), ‘health’ (0.4%) and ‘physical’ (0.2%). Although this was the case, the data were analysed based on complete cases. Binary logistic regression was used for both bivariate analysis and multilevel models (Twisk, 2006, Rabe-Hesketh and Skrondal, 2008) to estimate the odds

ratio and probabilities of the outcome variable. Multilevel modelling was considered due to the nested structure of individuals within districts in the dataset. The multilevel approach can estimate the overall relationship between the individual, compositional factors and the outcome ‘mental’ as fixed parameters. A four-step sequential modelling was adopted, with complexity being increased at each step (Duncan et al., 2003). Then, we computed the median odds ratio (MOR) (Rabe-Hesketh and Skrondal, 2012) that estimates the median value of the odds ratios between two individuals from two different districts that are randomly chosen. By measuring MOR between an individual with a larger odds ratio of mental disorder living in a particular district and an individual with a smaller odds ratio living in another district (Larsen and Merlo, 2005), we could determine whether districts’ clustering occurs at an odds ratio scale.

3.3 Results

The data of 5,908 respondents aged 50 and above were extracted from the NHMS dataset. For individual variables as in Table 3.1, about 47% of the respondents were older 60 years and above, while 54% of respondents were women. By ethnicity, Malays consist of 56% of respondents and the ethnicity proportions closely resemble the percentage of ethnic groups in the Malaysian population in 2010 (DOS, 2011). A quarter of the respondents were not married, while only 7% of them had tertiary education. About 54% of the respondents lived in urban areas in the dataset, even though records showed that more than 70% of Malaysians resided in urban areas in 2010 (Siwar et al., 2016). As for the district variables, OADR shows a mean of 8.3 people 65 years and older for every 100 working adults nationally, one public clinic for every 60,482 people and average household size of 4.4 people in 2010. Figure 3.1 shows that a higher percentage of older people lived on the west coast of the Peninsula, which is densely populated and enjoys higher economic prosperity. Meanwhile, the east coast of the Peninsula and East Malaysia had a younger population in 2010, with Sabah, a state in East Malaysia, having the lowest percentage of older people.

In Figure 3.2, the domains of ethnicity, education and location are selected to present the mean of mental disorders between male and female respondents. The two-way graphs indicated that females reported higher means of mental disorders compared to male respondents in all domains. Moreover, females in rural areas reported higher problems than those in urban areas. The Chinese had the lowest mean among all ethnic groups for both genders. It is clear from Figure 3.2 that males at all levels of education displayed nearly no differences in means of mental disorders, and this was also observed among females.

Figure 3.1 The number of older people (%) by districts in 2010

Table 3.2 provides the results of the bivariate analysis between the outcome and predictor variables by applying the bivariate logistic regression method. The individual variables for ‘female’, ‘Chinese’, ‘Indian’, ‘Sabah & Sarawak’, ‘married’, ‘alone’, ‘urban’, ‘income’, ‘health’ and ‘physical’ as well as OADR, are significantly associated with the outcome variable, ‘mental’. No evidence of association was found for the ‘age’ variable, which indicates age is not a significant factor in mental health. As for ethnicity, the ‘Other

Ethnicities’ was not significant compared to the other four main ethnicities, due mainly to the low frequency of 2.9% in the dataset. What is striking in Table 3.2 is that both socioeconomic variables of education and employment had no influence on mental disorders, while household income had significant marginal effects (p<0.01).

Table 3.1 Sample characteristics of 5,908 respondents in 104 districts

Variable N % Mental health disorders Yes 803 13.7 No 5,068 86.3 Age 50-54 1,681 28.4 55-59 1,463 24.8 60-64 997 16.9 65-69 673 11.4 70-74 538 9.1 75+ 556 9.4 Gender Male 2,721 46.1 Female 3,187 53.9 Ethnicity Malay 3,283 55.6 Chinese 1,531 25.9 Indian 471 8.0 Bumiputera 451 7.6 Other Ethnicities 172 2.9 Marital status Unmarried 1,424 24.1 Married 4,481 75.9 Education level Primary or lower 3,790 64.6 Secondary 1,647 28.1 Tertiary 426 7.3 Location Rural 2,740 46.4 Urban 3,168 53.6 mean sda OADR 8.3 2.9 HHAS 4.4 0.5 a standard deviation

Figure 3.2 Two-way graphs with standard errors on selected predictor variables

Next, the multilevel logistic regression analysis was conducted to estimate the probability of mental disorders from 10 predictor variables, which were significantly associated with the outcome variable in the bivariate analysis. The dependent variable was mental disorders, and the independent variables in this analysis were ‘female’, ‘Chinese’, ‘Indian’, ‘Sabah & Sarawak’, ‘married’, ‘alone’, ‘urban’, ‘income’, ‘health’, ‘physical’ and OADR.

Table 3.2 Bivariate analysis of predictor variables

Variable Constant Odds Ratio Marginal Age 0.99 (0.01) -0.01 (0.01) -0.01 Female 1.63 (0.16) 0.49 (0.10) 0.06‡ Chinese 0.49 (0.06) -0.72 (0.13) -0.83‡ Indian 1.49 (0.23) 0.40 (0.15) 0.05‡ Bumiputera 1.55 (0.56) 0.44 (0.15) 0.05‡ Other Ethnicities 1.62 (0.01) 0.48 (0.35) 0.06 Married 0.67 (0.07) -0.40 (0.10) -0.05‡ Alone 1.40 (0.24) 0.34 (0.17) 0.04† Urban 0.68 (0.06) -0.39 (0.10) -0.05‡ Employed 0.95 (0.09) -0.05 (0.10) -0.01 Education 0.89 (0.07) -0.12 (0.08) -0.01 Income 0.65 (0.07) -0.42 (0.10) -0.05‡ Health 1.72 (0.13) 0.54 (0.07) 0.06‡ Physical 1.67 (0.10) 0.51 (0.06) 0.06‡ OADR 0.97 (0.02) -0.03 (0.02) -0.00† HHAS 1.04 (0.09) 0.04 (0.08) 0.00 Standard error in parentheses Sig.: †significant at 5% or less; ‡significant at 1% or less

Then, four models, namely the unconditional, random intercept, random coefficient and contextual model were fitted. Model 1 is an unconditional or null model for the binary response without any predictor variables at level 1 and level 2. Model 1 is expressed as in

Equation 3.1 where 휂푖푗 = 푙표푔𝑖푡(휋(푥푖푗)).

Equation 3.1

퐿푒푣푒푙 1: 휂푖푗 = 훽0푗

퐿푒푣푒푙 2: 훽0푗 = 훾00 + 휇0푗

Model 2 in Equation 3.2 is a random-intercept model, as only intercepts are allowed to vary across districts. The predictor variable, ‘female’ is included to differ across districts in this model.

Equation 3.2

퐿푒푣푒푙 1: 휂푖푗 = 훽0푗 + 훽1푗푓푒푚푎푙푒푖푗

퐿푒푣푒푙 2: 훽0푗 = 훾00 + 휇0푗

훽1푗 = 훾10

Model 3 in Equation 3.3, is a random-coefficient model since both the intercepts and coefficients in the level 1 equation are allowed to vary across districts at level 2.

Equation 3.3

퐿푒푣푒푙 1: 휂푖푗 = 훽0푗 + 훽1푗푓푒푚푎푙푒푖푗 + 훽2푗퐶ℎ𝑖푛푒푠푒푖푗 + 훽3푗퐼푛푑𝑖푎푛푖푗 +

훽4푗푆푎푏푎ℎ&푆푎푟푎푤푎푘푖푗 + 훽5푗푚푎푟푟𝑖푒푑푖푗 + 훽6푗푎푙표푛푒푖푗 + 훽7푗푢푟푏푎푛푖푗 +

훽8푗𝑖푛푐표푚푒푖푗 + 훽9푗ℎ푒푎푙푡ℎ푖푗 + 훽10푗푝ℎ푦푠𝑖푐푎푙푖푗

퐿푒푣푒푙 2: 훽0푗 = 훾00 + 휇0푗

훽1푗 = 훾10 + 휇1푗

Lastly, a contextual model for the binary response, Model 4 as in Equation 3.4 is fitted and included both individual and district-level variables. The OADR variable is then added to the model.

Equation 3.4

퐿푒푣푒푙 1: 휂푖푗 = 훽0푗 + 훽1푗푓푒푚푎푙푒푖푗 + 훽2푗퐶ℎ𝑖푛푒푠푒푖푗 + 훽3푗퐼푛푑𝑖푎푛푖푗 +

훽4푗푆푎푏푎ℎ&푆푎푟푎푤푎푘푖푗 + 훽5푗푚푎푟푟𝑖푒푑푖푗 + 훽6푗푎푙표푛푒푖푗 + 훽7푗푢푟푏푎푛푖푗 +

훽8푗𝑖푛푐표푚푒푖푗 + 훽9푗ℎ푒푎푙푡ℎ푖푗 + 훽10푗푝ℎ푦푠𝑖푐푎푙푖푗

퐿푒푣푒푙 2: 훽0푗 = 훾00 + 훾01푂퐴퐷푅 + 휇0푗

훽1푗 = 훾10 + 휇1푗

Table 3.3 provides the parameter estimates for the fixed effects and random effects of those four fitted models. For Model 1, the intercept (ϒ00) was -1.83, p<0.01, which means the overall average logit of mental disorders across all districts was -1.83. There was a significant between-district variance (τ00) of 0.35, which was the intercept variance across all districts. The MOR is 1.76, which means that if two individuals from two different districts are chosen at random, the odds ratio of mental disorder comparing the two individuals will be equal or more than 1.76 half of the time.

For Model 2, the intercept (ϒ00) was -2.12, p<0.01 which means the estimated logit of mental disorders for males (‘female’= 0) in a district was -2.12. The logit coefficient for

‘female’ (ϒ10) was 0.49, p<0.01, which indicates that the logit of mental disorders for females (‘female’= 1) was 0.49, with an odds ratio of 1.62. To visualise this finding, the empirical Bayes estimates (as shown in the Appendix for Chapter 3) in a dot plot demonstrates how the intercepts in Model 2 vary across the districts.

For Model 3, the logit of mental disorders for females was 0.52, p<0.01 which was 1.68 times greater than males. In other words, being female increases the likelihoods of having a mental disorder by a factor of 1.68. This was slightly higher than the results in Model 2.

The between-district variance (τ00) was 0.32, p<0.05, with 9% of the total variance accounted for by the districts in level 2. Also, the predictor variables of ‘Chinese’, ‘health’ and ‘physical’ were significantly associated with the outcome variable, p<0.01 at multiple levels.

In Model 4, the logit for a female was 0.52, p<0.01, similar to Model 3. In this contextual model, it was indicated that the likelihood of a female having a mental disorder was 1.68 times higher than a male in 2010 when holding the other predictors constant. The result for Model 4 also indicates that the logit for ‘Chinese’ was -0.62, p<0.01, with an odds ratio of 0.54, and this effect was significant. This result means that the Chinese had a lower odds ratio of having a mental disorder by a factor of 0.54 compared to Malays. There was no evidence the Indians and Sabah and Sarawak ethnicities being associated with mental disorders.

The odds ratio for both ‘married’ and ‘alone’ were 0.93 and 1.20 respectively. This finding 47

indicates that the likelihood of a married respondent having a mental disorder was 0.93 times less than the likelihood of a single respondent doing so. At the same time, the odds ratio of a respondent being alone having a mental disorder was 1.20 times greater compared to a respondent living with at least one household member. However, both predictors were not significant in estimating the outcome variable. As these predictors seem to be closely related, the correlation matrix (as shown in the Appendix for Chapter 3) shows that ‘married’ and ‘alone’ had a weak negative linear relationship of -0.36 with one another.

Table 3.3 Determinants of the mental disorders, coefficient

Variable Model 1 Model 2 Model 3 Model 4 Null Random Intercept Random Coefficient Contextual

β (SE) OR β (SE) OR β (SE) OR β (SE) OR Female 0.49 (0.08)‡ 1.62 0.52 (0.10)‡ 1.68 0.52 (0.10)‡ 1.68 Chinese -0.61 (0.13)‡ 0.54 -0.62 (0.13)‡ 0.54 Indian 0.25 (0.16) 1.29 0.24 (0.16) 1.27 Sabah Sarawak 0.33 (0.18) 1.39 0.24 (0.18) 1.27 Married -0.07 (0.11) 0.93 -0.07 (0.11) 0.93 Alone 0.17 (0.17) 1.19 0.18 (0.17) 1.20 Urban -0.16 (0.12) 0.85 -0.24 (0.12)† 0.79 Income -0.01 (0.10) 0.99 -0.03 (0.10) 0.97 Health 0.23 (0.07)‡ 1.26 0.24 (0.07)‡ 1.27 Physical 0.42 (0.07)‡ 1.50 0.40 (0.07)‡ 1.50 OADR -0.06 (0.02)‡ 0.94 Intercept -1.83 (0.08)‡ 0.16 -2.12 (0.09)‡ 0.37 -3.06 (0.36)‡ 0.32 -2.46 (0.44)‡ 0.44 Between- 0.35 (0.09)‡ 0.37 (0.09)‡ 0.32 (0.13)† 0.32 (0.13)† District Variance MOR 1.76 1.78 1.72 1.72 Observations 5,871 5,871 5,113 5,113 Number of 104 104 104 104 groups Standard error in parentheses Sig.: †significant at 5% or less; ‡significant at 1% or less

The logit for ‘urban’ was -0.24, p<0.05, with an odds ratio of 0.79. This result points out that the likelihood of respondents residing in urban areas having a mental disorder was 0.79 times less than the likelihood of those residing in rural areas. Thus, rural residents had a higher likelihood of having a mental disorder. Interestingly, ‘urban’ was not a significant predictor without the district level variable of OADR in Model 4. One explanation for this result might be that ‘urban’ is not an individual level variable in this analysis and represents more of a variable at a higher level. In addition, the odds ratio for monthly household income in Model 4 was 0.97, p>0.05, which was not significant.

The logit for ‘health’ was 0.24, p<0.01, with an odds ratio of 1.27 and ‘physical’ was 0.40, p<0.01, with an odds ratio of 1.50. Both predictors were significantly associated with the variable ‘mental’. For the district level variable, the logit for OADR was -0.06, p<0.01, with an odds ratio of 0.94. This shows that for a one-unit increase in the OADR, the odds ratio of having a mental disorder decreases by a factor of 0.94. On that note, OADR had a significant effect; the OADR values become higher as mental disorders decline.

Further analysis of the variance and covariance components for the random effects in

Model 4 shows that the between-district variance (τ00) was 0.32, p<0.05, which was similar to Model 3. After accounting for only one district-level predictor variable, OADR, the MOR is 1.72 that suggests clustering of districts.

Figure 3.3 illustrates the estimated probabilities of those significant predictors in Model 4 when holding the other predictors at their means. What stands out in Figure 2.3 is that the probability of respondents having a mental disorder becomes higher if they are females, if they are ethnic Malays, if they reside in rural areas and if they rate themselves a lower standing on both health and physical capacity.

Figure 3.3 Estimated probabilities for predictor variables

Adjusted Predictions of Gender with 95% CIs Adjusted Predictions of Ethnicity with 95% CIs

.16

.18

.14

.16

.12

.14

.12

.08 .1

.06

Predicted Mean, Fixed Predicted Only Mean, Portion

Predicted Mean, Fixed Predicted Only Mean, Portion .08 Male Female Malay Chinese Gender Ethnic

Adjusted Predictions of Location with 95% CIs Adjusted Predictions of Self-rated Health with 95% CIs

.18

.16

.14

.12

Predicted Mean, Fixed Predicted Only Mean, Portion Predicted Mean, Fixed Predicted Only Mean, Portion Rural Urban Very Good Good Moderate Not Good Location Self-rated Health

Adjusted Predictions of Self-rated Physical with 95% CIs

0 Predicted Mean, Fixed Predicted Only Mean, Portion none mild moderate severe unable Self-rated Physical Activity for the Past 30 Days

3.4 Discussion

Prior evidence related to the distribution of mental disorders among older people mostly comes from developed countries. This evidence could not best reflect trends in a developing country such as Malaysia, which has a multi-ethnic population of various religious and cultural backgrounds. Due to this distinctiveness, it is relevant to examine

the effects from a multilevel perspective to further understand the extent of mental disorders at multiple levels.

The first question sought to identify social determinants that are associated with mental health disorders. The results underline the importance of socio-demographic factors, especially area, gender and ethnicity at multiple levels as predictors of mental disorders among older people, alongside health status and physical functionality. These correspond strongly with a previous report (Fisher and Baum, 2010, Allen et al., 2014) which suggests that social disparities could lead to an increased risk of having a mental disorder. On the contrary, the results revealed no association between socioeconomic predictors, including education, employment and household income and mental health when taking into account those multiple levels.

Old age is usually accompanied by a decline in health and physical condition. Previous findings have confirmed that both anxiety and depression share a similar risk factor of increasing age (Almeida et al., 2012). However, this paper could not find such an association, and thus, age in itself is not predictive of mental disorders. This inconsistency may arise perhaps due to reason that those people who suffered mental disorders died prior to old age, while those older people who survived into old age had not been pressed by life adversity. Other empirical evidence supports the idea that mental disorders are not a normal part of ageing (Fiske et al., 2009) and are less common in old age. A review of studies on people of Caucasian ethnicity, aged 50 and above and depression also didn’t find any significant tendency of depression with increasing age (Djernes, 2006).

At the individual level, a higher percentage of females reported mental disorders and also rated themselves lower in health and physical capacity compared to males. This percentage accords with a previous study which claimed that mental disorders are more common in females (WHO, 2014b), as they frequently experience social, economic and environmental factors in different ways than males (Prina et al., 2011). Recent research suggested that the biological differences between men and women may be responsible for the difference between men and women (Tampubolon, 2016). A higher proportion of females than males reported mental disorders in a survey in Ireland and also showed

higher levels of current psychological distress (Doherty and Kartalova‐O'Doherty, 2010). The findings showed that females older than 65 years of age were more likely to report mental disorders. In Malaysia, a possible explanation for this might be that older females might experience greater pressure associated with their roles in the household and society compared to males. Females have a higher life expectancy in Malaysia (Mahari, 2011) and are more likely to live alone in old age, which may trigger mental disorders if faced with an adverse situation or stressful environment.

By ethnicity, the Chinese have a lower probability of reporting mental disorders compared to Malays. The rest of the ethnicities – Indians, ethnicities in the states of Sabah and Sarawak, and others are not associated with mental disorders. This comes as no surprise since evidence suggests that Chinese are considered successful agers (Momtaz et al., 2011, Hamid et al., 2012). They are also better off economically and predominantly live in urban areas where healthcare facilities are easily accessible.

The results demonstrate that those who live in rural areas were more prone to mental disorders than those who reside in urban areas. The general view was that mental issues are more prevalent in urban areas, where the lifestyle is more stressful than in rural areas. For instance, a local study on the east coast of Peninsular Malaysia found limited evidence on mental health disorders among the rural population (Sok Yee and Pei Lin, 2011), while others implied that area differences were not significant for successful ageing (Hamid et al., 2012). One possible reason for these contradictory findings is probably that cases of mental disorders may be under-enumerated in rural areas (Mohd Sidik et al., 2003b) and symptoms may be ignored and unnoticed. Another possible reason, as this paper suggests, is that location or place of residence is not supposed to be an individual-level variable but rather a group-level variable. If this is the case, then generalising a group characteristic as an individual characteristic would commit an ecological fallacy (Twisk, 2006). These might explain the differing findings related to area differences. Besides, both public and private healthcare resources are more concentrated in urban areas and could offer adequate social support on health awareness, including mental health and age-friendly amenities that rural areas lack.

The findings also show that both self-rated health and self-rated physical functionality have a strong association with mental disorders, though it is difficult to ascertain whether these conditions result in mental disorders or the other way around. This is consistent with findings from developed countries (Cummings and Jackson, 2008, Shuey and Willson, 2008). Those findings showed that older people with lower socioeconomic status are more likely to experience health risks such as lower levels of self-rated health and physical functionality, which lead to a higher prevalence of mental disorders. Further evidence from the UK suggested that declining functionality and poorer health might increase the risk of mental disorders in disadvantaged individuals aged 60 years or over (Gale et al., 2011).

Although income and education were not found to be associated with mental health disorders among older people, their effects should not be ignored. A local study found that depression in older people was associated with those individuals with no formal education and with a lower family income (Mohd Sidik, 2003a). In addition, individuals aged 60 and above with lower income in Korea were associated with a higher likelihood of having depressive symptoms (Kim et al., 2017). Furthermore, Ladin (2008) inferred that socioeconomic disparities in terms of educational attainment in depression persist all the way through later life. Ladin constructed a multivariate model derived from a cross- sectional study of more than 22,000 men and women of age 50 to 104 years in 10 European countries, and found that educational attainment was a strong predictor of late- life depression with inverse associations across all countries. In addition, Sareen et al. (2011) established that low levels of household income in the United States were associated with lifetime mental health disorders including suicide attempts, and a decline in household income is associated with increased risk of mental disorders.

Araya et al. (2003) found a strong and inverse association between education and common mental disorders in Chile using logistic regression models. However, income was not associated with the prevalence of common mental disorders, after adjusting for other socioeconomic variables. The researchers reported that similar results have been found in other Latin American studies but British studies tend to find that income, and not education, is associated with mental health disorders.

Based on the second aim, this paper has been able to investigate the relationships between contextual level and individual-level determinants of mental disorders. Figure 3.4 shows the distribution of OADR across Malaysia and most high OADR were located on the west coast of the Peninsula, which is more prosperous economically than the rest of the country. Moreover, this is perhaps the most compelling finding that the distribution of OADR for districts varies significantly and can predict the distribution of mental disorders. In other words, districts with higher OADR will notice fewer mental disorders and vice versa. Prior to this paper, no other analysis has documented OADR as a predictor variable for mental health among older people.

There’s also a debate on whether OADR is a good measurement for old-age dependency (Sanderson & Scherbov, 2015, Basten, 2013), and an increase in OADR indicates the added pressures public healthcare has to withstand. A high OADR also implies that economically active people face greater pressure to support older people, who generally are not employed. However, this is not the case in Malaysia. Based on Figure 2.4, most of the lower OADR districts were found in rural areas or places with low economic activities. This coincides with the argument mentioned before regarding the urban-rural observations, particularly the issue of low healthcare resources in these places. This argument is in line with a recent study (Chong et al., 2013) that mental health disorders are more alarming, especially in rural areas due to a lack of community mental health facilities. For the other district-level variables, this paper did not detect any evidence of either the average household size or the public health clinic to population ratio varying significantly with mental disorders.

The results also suggest that district differences in health compositional factors and contextual effects can be turned into a district ranking as shown in the Appendix for Chapter 3. These estimates are taken from the random intercepts across districts in Model 2, where the spread in intercept values is considerable. This district ranking is a useful tool for policymakers to target allocation of resources to districts that needed it the most.

Figure 3.4 OADR in quartiles by districts in 2010

This paper has several strengths as well as limitations. A key strength of this paper is that it leverages the multilevel approach to examine the association of mental disorders at both individual and district levels with social determinants, by making use of a nationally representative sample of the NHMS. As this is secondary data, the analysis is constrained by the breadth and availability of that data. On this note, the NHMS gathers data on a whole-population basis across all ages and not solely focused on older people. Since the outcome and some predictor variables were self-rated, this paper is also exposed to common method bias. This is either at the level of the enumerator, who may make errors in the sampling, or the responder during sample collection, as older people are likely to be incomplete responders and find it difficult to recall specific information. They may have more difficulty, due to age-associated cognitive changes, in perceiving and recalling details, particularly symptoms or health status.

This paper takes into account the nesting structures in the data which examine individuals’ characteristics nested within districts and applies district-level variables such as OADR. Based on these findings, the district is an important factor in predicting mental disorders, as those older people who reside in the same district would have similar characteristics

which influence their lives. However, it is important to bear in mind the possible biases. For example, estimation biases at the individual level might occur, as some districts had lower representations and others had higher representations. Furthermore, some variables were not significantly associated with the outcome variable in the bivariate analysis and were omitted in the subsequent analysis. This could cause omitted variable bias in the models. Finally, the cross-sectional nature of the NHMS dataset might pose a problem of reverse causality problem, particularly amongst those predictor variables of health status and physical functionality.

As Malaysia demographic transition to an ageing nation, the gradual increase in the population of older people seems to be considered an additional burden on the healthcare services, after communicable diseases and non-communicable illnesses. Remarkable progress has been made by the services in maintaining the good health of the overall population, which directly contributes to the increase in life expectancy. This paradox poses multiple challenges to how healthcare should be delivered in meeting the growing demand, mitigating costs and ensuring quality. Still, healthcare is often sought once it is too late, leading to high-cost medical treatment. As mental disorders could not be ignored, this paper offers policymakers innovative approaches to tackling those challenges that take into account the social disparities in health. Understanding these disparities could enable effective healthcare to be provided older people and knowledge in this area will become increasingly necessary in the near future. It is known that successful ageing is more likely to occur among those who are wealthy, more educated and are more economically advantaged than others (Hamid et al., 2012). Targeting healthcare resources based on older people’s needs and according to the geographical area could gradually reduce the steepness of these social disparities. Any public responses toward health risk factors for mental health need to be multi-sectoral and across multiple government levels, given that mental risk factors act at different levels and could also affect changes in social and economic development. This knowledge could also be disseminated at the district level to support action in mitigating mental disorders locally. Thus, this paper provides a clear message for the sectors of housing, welfare, transport, education and healthcare,

among others, to work together and to pool available resources for improving older people’s mental health.

3.5 Conclusion

This paper suggests that social determinants, namely gender, ethnicity and location, are significantly associated with mental disorders at multiple levels. The overall health status and physical capacity of individuals are also important determinants at multiple levels, although these might be exacerbated by mental disorders instead. The findings illustrate that the district-level predictor of OADR could be a useful indicator for the government to plan healthcare resources in mitigating social disparities in health among older people. These findings indicate that social disparities may cause mental disorders in Malaysia and vary between geographical areas, particularly between districts.

Chapter 4

Unmet Cardiovascular Care Needs among Older People in Malaysia

Abstract: Cardiovascular diseases (CVD) pose a substantial challenge to public health, especially in developing countries such as Malaysia, where both communicable and non- communicable diseases still coexist. The rise in CVD prevalence has raised concerns that these diseases might lead to rising levels of unmet needs for cardiovascular care. To date, there is still limited compositional and contextual evidence of unmet cardiovascular care needs from developing countries, especially among older people, which could enable policymakers to propose effective strategies to lessen the impact of CVD epidemic. We investigated the factors that result in unmet cardiovascular care needs among older people. We also examine variations in unmet cardiovascular care needs between districts in Malaysia and contextual determinants that explain those variations. A cross-sectional, nationally representative survey of the National Health and Morbidity Survey (NHMS) is utilised in this study. We determined the risk of CVD among older people aged 50 and above by calculating the individual’s total 10-year CVD risk estimation score using two established methods, the Framingham (FRS) and the Systematic Coronary Risk Evaluation (SCORE). Individuals with undiagnosed intermediate or high CVD risk are considered as having unmet cardiovascular care needs. A progression of three multilevel models was fitted: the baseline, social determinant and contextual models. Covariates of demographic attributes and socioeconomic status were included along with selected district-level determinants of Gini coefficients, old-age dependency ratios (OADR) and public hospitals’ bed occupancy rate (BOR) for each district in the contextual model. The study documented significant associations of certain demographic and socioeconomic covariates, including age group and gender, with unmet cardiovascular care. All contextual determinants were also found to be significantly associated with unmet cardiovascular care needs, a finding which can explain the variations of unmet cardiovascular care needs between districts. A ranking of districts based on the results of the contextual model is illustrated. This study adds to the growing literature of unmet healthcare needs, especially cardiovascular care, among older people in developing countries. The contextual determinants found to be significantly associated with unmet cardiovascular care could further benefit policy discussions on improving older people’s quality of life in these contexts.

Keywords: Framingham, multilevel modelling, risk estimation, SCORE, unmet cardiovascular care needs

4.1 Introduction

Cardiovascular diseases (CVD), which include diseases related to the heart, blood vessels and the cerebrovascular system, have been the leading cause of morbidity and mortality in developing countries (Reddy, 2005; Yusuf et al., 2014). The major modifiable CVD risk factors are high blood pressure, which contributes to 13% of global deaths, followed by tobacco use (9% of deaths), elevated blood glucose (6% of deaths), physical inactivity (6% of deaths) as well as obesity (5% of deaths) (Mendis et al., 2011; Prince et al., 2015). High blood pressure could lead to atherosclerosis (Cooney et al., 2010), a condition which is usually asymptomatic over an extensive period before gradually leading to clinical manifestations of CVD. As a result, fatal complications of CVD are generally observed in older people (WHO, 2007). The WHO (2012a) also recognised that the prevalence of CVD is highly associated with unmodifiable risk factors such as age and gender, where older age and male sex, respectively, are prevailing characteristics.

CVD contributed to about 30.3% of the disease burden in 2010, measured as disability- adjusted life years in people aged 60 and above worldwide (Prince et al., 2015). Since older people have high prevalence rates of CVD (Kannel et al., 1987), the number of older people with these diseases will continue to rise alongside an ageing population (Mosterd and Hoes, 2007). Thus, CVD has become a serious public health concern that poses a risk of increased disability and chronic morbidity for older people (WHO, 2009). More importantly, the healthcare services might not be able to curtail CVD from spreading widely due to resource constraints, high treatment costs and competing for health priorities (Lloyd-Sherlock, 2000; Khan et al., 2013).

CVD risks have been extensively studied in developed countries (Lloyd-Jones et al., 2002; Conroy et al., 2003) and developing countries such as Malaysia are starting to comprehend the ramifications of a CVD epidemic (Selvarajah et al., 2013). In Malaysia, CVD is on the rise (Huxley et al., 2015), with a higher prevalence of morbidity (MOH, 2010), even though mortality rates due to CVD are declining among people aged 60 and above (DOSM, 2016c). A high percentage of Malaysians aged 55–64 were found to have three or more CVD risk factors compared to the younger age groups (Ghazali et al., 2015). In

2010, the prevalence of CVD risk factors for those aged 50 and above was between 11.0% to 13.7% for undiagnosed diabetes, 27.0% to 34.7% for undiagnosed high blood pressure and 31.9% to 37.3% for high blood cholesterol (MOH, 2011b). CVD remained the primary cause of medically certified deaths: it contributed to about 26.1% of total deaths in 2010 and 23.8% in 2014 (DOSM, 2016c). The rise in CVD morbidities among older people points to the likelihood of unmet healthcare needs since the awareness of hypertension as a major risk factor is still low among the public (Rampal et al., 2008). A study in a developed country showed that general unmet healthcare needs in older people were associated with negative health outcomes (Herr et al., 2014). To date, there is still limited evidence from developing countries of unmet healthcare needs especially among older people to help healthcare policymakers lessen the impact of the CVD epidemic.

From a different perspective, unmet healthcare needs can be seen as a useful indicator to measure how well healthcare resources are being allocated (Williams and Doessel, 2011). Since healthcare resources will always be scarce, a practical option for effective service delivery is to target areas with groups of people most in need of these resources. A study of 2,206 respondents aged 16 and above in Malaysia found that individuals from certain ethnic groups and geographical areas had a low utilisation of available healthcare services (Krishnaswamy et al., 2009). Furthermore, this study also found that older people were two to three times more likely to seek medical help compared to younger age groups. Another local study by Momtaz et al. (2012) observed that older people with chronic illnesses had higher rates of unmet healthcare needs. Developed countries such as the US and Canada (Lasser et al., 2006) face similar issues of unmet healthcare needs among their population due to social disparities and economic barriers in accessing quality care. Even in countries with advanced healthcare systems such as Korea (Ko, 2016) and Japan (Murata et al., 2010), socioeconomic reasons were reported for unmet healthcare needs among their older populations.

Numerous factors are associated with unmet healthcare needs that not only can be explained at the individual level but also at higher levels, especially the community level (Nelson and Park, 2006). Evidence shows that CVD risk factors are not only linked to individual factors but also to contextual determinants as well (Prince et al., 2015). Yasin

et al. (2012) pointed out that prevention and management of non-communicable diseases such as CVD should take into consideration strategies that are context-driven. Contextual determinants have also been found to shape individuals’ unmet healthcare needs. A study by Gebreab et al. (2015) found significant variations across geographical areas for poor cardiovascular health in the United States. Cavalieri (2013) observed significant variations within regions in Italy in self-reported reasons for having perceived unmet medical needs. Similarly, Sibley and Glazier (2009) found variations of unmet healthcare needs due to three contextual determinants of accessibility, availability and acceptability across provinces in Canada.

There is a real need to comprehend the demographic, geographic and socioeconomic risk factors in addressing CVD in Malaysia (Rasiah et al., 2013). Therefore, we aimed to understand what factors determine the unmet needs for cardiovascular care among older people in Malaysia. We also aimed to examine whether variations in unmet cardiovascular care between districts exist and what contextual determinants could significantly explain those variations. This will be achieved by, firstly, considering a nationally representative survey gathered in 2010 comprised of health information on Malaysian adults aged 50 and above. Next, we will measure unmet cardiovascular care as an outcome variable. Aggregate CVD risk scores for individuals were obtained using two widely known scores: the Framingham (FRS) (Lloyd-Jones et al., 2002) and the Systematic Coronary Risk Evaluation (SCORE) (Conroy et al., 2003). The total risk scores of CVD will be estimated (Huxley et al., 2015); and using the scores, we will categorise individuals into two distinct groups, those with met or unmet cardiovascular care needs. Lastly, we will elucidate the contributions of the individual- and district-level characteristics to the dependent variable by using multilevel models. By understanding the sources of the variations of unmet cardiovascular care and identifying the contextual determinants associated with it, this study contributes to increase knowledge in related health issues and to better inform the policymakers so that the growing CVD epidemic in Malaysia can be averted.

4.2 Methods

4.2.1 Data

We analysed secondary data extracted from the National Health and Morbidity Survey (NHMS), a cross-sectional, nationally representative survey (MOH, 2011a). The survey was conducted by the Ministry of Health Malaysia in 2011 with 28,650 individuals interviewed and a response rate of 93%. Our final sample comprises 5,908 respondents aged 50 and above, or 21% of the survey respondents, who were not institutionalised. This includes the individual-level (Level 1) determinants of demographic and socioeconomic as predictor variables in the analysis. For the district-level (Level 2) determinants, we used published data from the DOSM and created predictor variables of the Gini index, OADR and BOR.

4.2.2 The dependent variable: Unmet cardiovascular care needs

Unmet cardiovascular care needs (or unmet care needs, henceforth) is coded as a binary variable. This variable was valued as 1 for respondents who scored in the intermediate or high CVD risk category on either the FRS or SCORE and was as yet undiagnosed for CVD and its related risks. We determined undiagnosed respondents to be those who answered ‘no’ to all five health status questions in the NHMS: ‘Have you ever been told by a doctor or medical assistant that you have (i) heart disease, (ii) stroke, (iii) diabetes, (iv) hypertension, and/or (v) high blood cholesterol?’ The unmet care needs variable was valued as 0 for respondents who answered ‘yes’ to one or more of the health status questions.

4.2.3 FRS and SCORE calculations

The FRS and SCORE calculate the individual’s total 10-year CVD risk estimation score. We used both methods to take into account all possible intermediate and high CVD risk individuals in the sample. These methods are recommended for use in clinical practice and can measure CVD risk in a multi-ethnic population such as Malaysia (Selvarajah et al., 2014; Chia et al., 2015).

For the FRS, we operated the simpler 10-year estimation model that is office-based and non-laboratory (D’Agostino et al., 2008). The FRS algorithms, as shown in the Appendix for Chapter 4, are gender-specific that take into account age, systolic blood pressure (BP), whether the respondents receive any treatment for hypertension or not, as well as smoking and diabetes status. To assess the probability of a CVD event, the FRS risk scores are divided into three categories – low risk (<10%), intermediate-risk (10%–20%) and high risk (>20%) (Chia et al., 2015).

For the SCORE, we estimate a person’s 10-year risk of fatal CVD that is derived from a Weibull distribution model (Conroy et al., 2003; Goh et al., 2014). The SCORE algorithms, as shown in the Appendix for Chapter 4, include variables of age, gender, systolic BP, total cholesterol, and smoking status. We used the SCORE high risk instead of the low-risk country model considering that Malaysia is a high CVD-risk country (Ghazali et al., 2015; Huxley et al., 2015). The SCORE risk result is then divided into three categories – low risk (<1%), intermediate-risk (1%–4%) and high risk (≥5%) (Selvarajah et al., 2013).

4.2.4 Covariates

Level 1 consists of categorical variables of demographic and socioeconomic determinants. The demographic variables are age, gender, ethnicity, location and marital status. Age is treated as age groups of 55–59, 60–64, 65–69, 70–74 and 75+ with 50–54 as a reference. There are five categories of ethnicity, with Malay as a reference, Chinese, 63

Indian, Bumiputera (which specifically refers to indigenous ethnic groups in the states of Sabah and Sarawak), and other ethnicities. Gender, location and marital status are coded as dummy variables representing male/female, urban/rural and married/unmarried, respectively. The socioeconomic variables are education, employment and household spending. Education is a three-category variable with no schooling or having only primary education as a reference, followed by having a secondary and tertiary education. Employment is a dummy variable representing those who are employed. Household spending is coded as a quintile category variable with the first quintile, the lowest, as the reference and the fifth quintile as the highest. Household spending is preferred over household income, as consumption is better and more accurate in measuring living standards (Deaton and Zaidi, 2002).

Level 2 consists of three contextual variables namely the Gini index in 2009, OADR and BOR for each district in 2010. The Gini index is the most common measure of income inequality and has been used as a measure of health inequality that captures the distribution of healthcare resources and health risk in a population (Truman et al., 2011). The greater the value of the Gini coefficient, the greater the inequality of a given society. The OADR is calculated by adding up the number of people aged 65 years and above and dividing this number by the total number of people between 15 to 64 years old and then multiplying the total by 100 (World Bank, 2016). The OADR is a standard indicator to assess population ageing (Sanderson and Scherbov, 2015), though it does not take into account demographic changes, particularly falling mortality rates (Spijker and MacInnes, 2013). The BOR is calculated by taking into account the average number of daily utilised public hospital beds divided by the available beds in the district. We used BOR percentage as it measures the demand for healthcare resources as well as points to disparities in allocating healthcare resources in an area (Kreng and Yang, 2011).

4.2.5 Method

First, we fit an unadjusted logistic regression to determine the odds of unmet care needs in individuals. We also compared the unweighted and weighted sample to assess whether the results were robust. Then, we fitted the multilevel logistic regression to take into account the nested structure of the data and to predict unmet care in individual i residing in district j. We applied multilevel logistic regression to avoid mixing different levels of analysis, which might cause misleading interpretations. This would also provide a framework to further understand the relationships between unmet care needs and the demographic, socioeconomic and contextual determinants.

We fitted a progression of hierarchical multilevel models (Twisk, 2006, Rabe-Hesketh and Skrondal, 2008) from Model 1 (baseline), Model 2 (social determinants) to Model 3 (contextual). Multilevel models have been used previously to assess CVD prevalence (Diez-Roux et al., 2000; Maharani and Tampubolon, 2014; Gebreab et al., 2015) in developed and developing countries. This progression would explain how Level 1 and Level 2 predictor variables contribute to variations in the dependent variable. The use of a two-level model will separate the Level 2 effect from the Level 1 effect. Model 1 and Model 2 are fitted as random-coefficient models since both the intercepts and coefficients in Level 1 are allowed to vary across the districts. Model 1 tests the impact of unmodified individual characteristics of age and gender across the districts, and Model 2 tests all Level 1 characteristics. The models are specified as follows:

Equation 4.1

휂푖푗 = 훽0 + 훽푘푋푘푖푗 + 휇푘푗푋푘푖푗 + 휇0푗

In Equation 4.1, we assumed n individuals (i = 1,…,n) nested in N districts (j = 1,…,N) and the outcome depends on k covariates. 휂푖푗 = 푙표푔𝑖푡(휋(푥푖푗)), the logit function (the natural log of the odds) of the dependent variable and 훽0 is the overall mean of the 65

dependent variable. 훽푘 specifies the coefficient to be estimated, 푋푘푖푗 is the covariate’s observed value, 휇푘푗 is the random coefficient, and 휇0푗 is the random intercept. Next, Model 3 is fitted as a contextual model and includes both Level 1 and Level 2 variables. This model tests the district determinants’ impact on the outcome, and is expressed as follows:

Equation 4.2

휂푖푗 = 훽0 + 훽푘푋푘푖푗 + 휇푘푗푋푘푖푗 + 훾푘푗푊푘푗 + 휇0푗

In Equation 4.2, the district variable 푊푗, and 훾푘 as the coefficient are added to the model. Lastly, MOR is calculated to translate the area level variance in terms of OR scale (Rabe- Hesketh and Skrondal, 2012). Merlo et al. (2006) defined MOR as the median value of the odds ratio between an area with the highest risk of the health problem and another area with the lowest risk that are chosen at random. They measured MOR as an increased risk in the median that would have if an individual move from an area to another with a higher risk. To assess whether the models fit the data and to compare models’ performance, the Aikake information criterion (AIC) and Bayesian information criterion (BIC) statistics are calculated (Rabe-Hesketh and Skrondal, 2008). In all of our analyses, a p-value of 5% or less is considered statistically significant. Data management and analysis are performed using the statistical software of Stata/SE version 14.0 by StataCorp

LP, U.S.

4.3 Results

Table 4.1 presents the summary statistics of the sample, which consists of 5,908 respondents aged 50 and above, with a mean age of 61 years and about 54% being female. More than half of the respondents were under 60 (53%), and the main ethnic group in the

sample was Malay (56%) followed by Chinese (26%), Indians (8%), Bumiputeras (7.6%) and other ethnicities (2.9%). The vast majority (80%) of the respondents were married, and female respondents made up 83% of the unmarried population. About 65% of the respondents had no formal education or only attended primary school, and 60% of this group were female. More than half of the respondents resided in urban areas (54%), about 43% of them were employed, and 45% were in the bottom 40% of the household spending group. The average Gini index was about 41% (SD=2.5) and varied more than 11% between districts with the highest and lowest index scores. On average, OADR was 8 (SD=2.9) people aged 65 years and above per 100 people aged 15–64 years. Also, BOR was 65.2% (SD=17.1%), which indicates that the healthcare system, particularly in secondary and tertiary care, was not fully utilised in 2010.

Figure 4.1 plots 10-year CVD risk scores (FRS and SCORE) against age, showing CVD risk increases with age measured with both methods. The average risk scores cut across the threshold of the high-risk category (FRS≥20%; SCORE≥5%) at about age 54 for FRS and 58 for SCORE. The FRS risk scores also show a wider confidence interval at about age 80 and above compared to SCORE.

Figure 4.2 shows box plots of FRS and SCORE by gender. The plots reveal that male respondents had much higher median scores and wider spreads compared to females in the characteristics of ethnicity, education, location and marital status. Both methods show that 50% of the male CVD risk scores in these characteristics are placed in the high-risk category. In contrast, 50% of the female respondents were in the low and intermediate CVD risk groups. The male CVD risk scores in both methods also display considerably more variability.

Moreover, the female CVD risk scores show farther right-skewed distributions than male scores. These observations suggest that male respondents had higher CVD risk scores compare to female. As a comparison, SCORE risk scores have a narrower spread or less variability than FRS. However, they display relatively more outliers.

Table 4.1 Summary statistics of the sample

Variables N % Male Female

Age 5,908 - 2,721 3,187 50–54 1,681 28.4 782 899 55–59 1,463 24.8 653 810 60–64 997 16.9 506 491 65–69 673 11.4 334 339 70–74 538 9.1 247 291 75+ 556 9.4 199 357 Gender Male 2,721 46.1 - - Female 3,187 53.9 - - Ethnicity Malay 3,283 55.6 1,549 1,734 Chinese 1,531 25.9 700 831 Indian 471 8.0 186 285 Bumiputera 451 7.6 214 237 Other ethnics 172 2.9 72 100 Marital status Not married 1,424 24.1 237 1,187 Married 4,481 75.9 2,482 1,999 Education level No school/ Primary 3,790 64.6 1,523 2,267 Secondary 1,647 28.1 901 746 Tertiary 426 7.3 274 152 Location Rural 2,740 46.4 1,289 1,451 Urban 3,168 53.6 1,432 1,736 Employment status Unemployed 3,377 57.2 1,038 2,339 Employed 2,531 42.8 1,683 848 Household spending quintiles 1st (lowest) 1,181 22.4 479 702 nd 2 1,188 22.6 559 629 3rd 1,002 19.0 489 513 4th 1,009 19.1 494 515 5th (highest) 888 16.9 421 467 mean sd min max Gini index (%) 41.34 2.45 34.2 45.4 OADR 8.27 2.91 1 16 BOR 65.17 17.12 20.42 100.00 Note: OADR- Old-aged dependent ratio; BOR- Bed occupancy rate.

Figure 4.1 Two-way linear prediction plots of 10-year CVD risk score against respondent’s age with 95% CI

Figure 4.3 displays the FRS and SCORE risk scores across age groups and care status (met or unmet care). In FRS, the unmet care respondents had lower risk scores in all age groups compared to respondents with met care. Conversely, the unmet care respondents in SCORE had higher risk scores in most age groups, except those aged 65–69. More than 50% of unmet care respondents aged 70–74 and 75+ had high CVD risk scores in both methods.

Table 4.2 presents the unadjusted logistic regression statistics, including the marginal effects of the covariates on the dependent variable, to determine those characteristics that were significantly associated with unmet care needs among older people. By extending the sample to represent the general population, the analysis found that few significant characteristics shown in the unweighted sample retained their significance with unmet care needs in the weighted sample. As shown in the weighted column of Table 4.2, being

of the age groups 70–74 and 75+ had significant marginal effects of 11% and 7% increase on the predicted odds ratio of having unmet care needs compared to the reference group of those aged 50–55. The analysis also showed significantly lower odds of unmet care needs in females, people of Indian and Bumiputera ethnicity as well as those who resided in urban areas with a marginally 1% reduction. For the socioeconomic determinants, significantly lower odds ratios are observed in respondents who had secondary education, with a 4% decrease in marginal effects as well as in respondents in the fifth quintile of household spending, with a 7% decrease in marginal effects.

In contrast, being employed increases the odds ratio of having unmet care needs by 12%. Our analysis of the contextual determinants indicates that each contextual variable could significantly explain the variations of unmet care in districts. Higher-income inequality lowers the odds ratio of unmet care along with higher BOR, which shows less than 1% of the change in marginal effects. On the other hand, the odds ratio increased in districts with higher numbers of older people who were dependents.

Figure 4.2 Box plots of FRS (top four) and SCORE (bottom four) by gender for selected characteristics of ethnicity, education, location and marital status

1 1

.8 .8

.6 .6

.4 .4

.2 .2

0 0

Framingham: 10-yearCVD risk M F M F M F M F M F M F M F M F Malay Chinese Indian Bputera Others No school/Primary Secondary Tertiary

1 1

.8 .8

.6 .6

.4 .4

.2 .2

0 0

Framingham: 10-yearCVD risk M F M F M F M F Rural Urban Not married Married

.8 .8

.6 .6

.4 .4

.2 .2

SCORE: CVD 10-yearCVD SCORE: risk

0 0

M F M F M F M F M F M F M F M F Malay Chinese Indian Bputera Others No school/Primary Secondary Tertiary

.8 .8

SCORE: CVD 10-yearCVD SCORE: risk

M F M F M F M F Rural Urban Not married Married

Figure 4.3 Median estimates of FRS and SCORE risk scores vs age groups by met and unmet care. Note: the red line indicates the threshold of the high-risk category

40 40

30 30

20 20

median of FRS score (%) FRS scoremedian of

10 10

median of SCORE scoreSCORE median (%) of

0 0

50-54 55-59 60-64 65-69 70-74 75+ 50-54 55-59 60-64 65-69 70-74 75+

unmet care met care

Because these covariates are correlated with one another, it is useful to place them together in a model to assess their net contributions to the dependent variable. Table 4.3 shows the parameter estimates from the multilevel logistic models that were fitted. In Model 1, covariates of age and gender were added together to assess the unmodified characteristics for CVD risk. In the category of age, the 70–74 and 75+ groups show high significant relationships (p≤0.01) with estimated logits of 0.78 and 0.85 respectively. Having an age of 70–74 years increases the odds of unmet care by 2.17 times compared to the reference group (50–54), while having an age of 75+ increases the odds by 2.34 times. Being in the age groups of 60–64 and 65–69 would relate significantly with unmet care needs at 5% or less. Therefore, the odds of having unmet care needs increase with age and are statistically significant at age 60 and above. For gender, the results show that being female reduces the chances of having unmet care needs by 81% with an estimated logit of -1.64 (p≤0.01) and an odds ratio of 0.19. The intercept variance across all districts or between–district variance in Model 1 is 2.87 with an intraclass correlation coefficient (ICC) of 47% attributable to differences between districts. This large ICC value clearly shows a district’s effect on the outcome and indicates the high similarity of predicted odds of unmet care among older people in a particular district. The MOR, as a measure of heterogeneity, is 5.03 which is a large odds that suggest high variation between the districts.

Table 4.2 Bivariate analysis of unadjusted coefficient (β), odds ratio (OR) and marginal effects

Variables Unweighted Weighted β OR Marginal β OR Marginal 55–59 years 0.12 1.13 0.02 0.11 1.12 0.02 60–64 years 0.25 1.28 0.05‡ 0.04 1.04 0.01 65–69 years 0.29 1.33 0.05‡ 0.05 1.05 0.01 70–74 years 0.55 1.73 0.10‡ 0.57 1.76 0.11‡ 75+ years 0.46 1.59 0.08‡ 0.37 1.44 0.07† Female -1.54 0.21 -0.27‡ -1.42 0.24 -0.26‡ Chinese -0.19 0.83 -0.04‡ -0.02 0.98 <-0.01 Indian -0.40 0.67 -0.08‡ -0.32 0.72 -0.06† Bumiputera -4.46 0.01 -0.84‡ -5.51 <0.01 -1.07‡ Other ethnics -0.51 0.60 -0.10† -0.49 0.61 -0.10 Married 0.13 1.14 0.02 0.14 1.15 0.03 Urban -0.16 0.85 -0.03‡ -0.06 0.94 -0.01 Secondary -0.15 0.85 -0.03† -0.20 0.82 -0.04† Tertiary <0.01 1.00 <0.01 0.20 1.21 0.04 Employed 0.71 2.03 0.13‡ 0.61 1.85 0.12‡ Household spending quintiles 2nd -0.11 0.90 -0.21 -0.17 0.84 -0.03 3rd -0.17 0.84 -0.03 -0.19 0.82 -0.04 4th -0.20 0.81 -0.04† -0.24 0.79 -0.05 5th (highest) -0.38 0.68 -0.07‡ -0.33 0.72 -0.07† Gini index -0.08 0.93 -0.01‡ -0.10 0.90 -0.02‡ OADR 0.07 1.07 0.01‡ -1.45 1.06 0.01‡ BOR 0.01 1.01 <0.01‡ 0.01 1.01 <0.01‡ Note: OADR- Old-aged dependent ratio; BOR- Bed occupancy rate. Statistics are weighted to population-level using the NHMS weights. Significance: †5% or less; ‡1% or less.

In Model 2, all Level 1 covariates are included to gain adjusted estimates of unmet care needs in explaining its variability among older people. From this model, age groups of 55–59, 65-–69, 70–74 and 75+ each show significant relationships with the dependent variable, and the odds increase with age up to 2.17 times compared to the reference group (50–54). Being female significantly reduces the odds of unmet care by 80% and similar trends are also shown by being Indian (29%), being Bumiputera (98%), being married (41%) and having a secondary education (24%). Being in the third and fifth spending quintiles would indicate lower unmet care needs, with odds ratios of 0.75 and 0.72 respectively. However, being employed increases the odds of unmet care by 1.54 times

over being unemployed. The between-district variance shows a significance of 1.58, which is lower by 45% in this adjusted model. The ICC is 30% less compared to Model 1 with similar trends in AIC (12%) and BIC (10%). This suggests that Model 2 fits the data better.

To improve Model 2 and to determine contextual risk factors at higher levels, the Level 2 covariates of the Gini index, OADR and BOR are added in Model 3. At Level 1, the findings correspond with Model 2, where similar covariates maintain their significant relationships with unmet care needs. At Level 2, the Gini index has a significant negative relationship with unmet care needs, with an estimated logit coefficient of -0.18 (p≤0.01), giving the odds ratio of 0.83. This indicates that districts with the most unequal income distribution had 17% less unmet care needs compared to districts with the most equal. In contrast, both OADR and BOR show significant positive relationships. For OADR, the estimated logit coefficient is 0.16 (p≤0.01), and the odds of unmet care are 1.17 times more for those districts with the highest numbers of people aged 65 and above for every 100 people aged 15–64 than those with the lowest numbers. For BOR, the estimated logit coefficient is 0.02 (p≤0.01), and the odds of having unmet care needs are 2% more in districts with the highest BOR than the lowest. By adding the contextual covariates, the between-district variance further decreases by 17% to 1.31. While the ICC decreases by 5% and the MOR also decreases by 10%. In addition, the AIC and BIC also decrease by 27 points and 7 points each. These figures show that Model 3 fits the data much better than Model 2.

Table 4.3 Determinants of unmet care, the coefficient (β) and odds ratio (OR)

Model 1 Model 2 Model 3 baseline social contextual β OR β OR β OR 55–59 years 0.19 1.20 0.28† 1.32† 0.27† 1.31† 60–64 years 0.26† 1.30† 0.24 1.28 0.25 1.28 65–69 years 0.33† 1.38† 0.38‡ 1.46‡ 0.37† 1.44† 70–74 years 0.78‡ 2.17‡ 0.79‡ 2.20‡ 0.77‡ 2.17‡ 75+ years 0.85‡ 2.34‡ 0.72‡ 2.06‡ 0.72‡ 2.04‡ Female -1.68‡ 0.19‡ -1.61‡ 0.20‡ -1.66‡ 0.19‡ Chinese -0.12 0.88 -0.13 0.88 Indian -0.33† 0.71† -0.36† 0.70† Bumiputera -3.95‡ 0.02‡ -3.13‡ 0.04‡ Other ethnics -0.19 0.83 -0.13 0.88 Married -0.53‡ 0.59‡ -0.54‡ 0.59‡ Urban -0.02 0.98 0.01 1.00 Secondary -0.27‡ 0.76‡ -0.27‡ 0.77‡ Tertiary -0.27 0.76 -0.25 0.78 Employed 0.43‡ 1.54‡ 0.42‡ 1.53‡ Household spending quintiles 2nd -0.20 0.82 -0.19 0.82 3rd -0.28† 0.75† -0.27† 0.76† 4th -0.23 0.79 -0.23 0.80 5th (highest) -0.33† 0.72† -0.30† 0.74† Gini index -0.18‡ 0.83‡ OADR 0.16‡ 1.17‡ BOR 0.02‡ 1.02‡ Intercept -1.52‡ 0.22‡ -0.55‡ 0.01‡ 9.07‡ 63.09‡ ICC 0.47 0.33 0.28 MOR 5.03 3.32 2.98 Between-district variance 2.87‡ 1.58‡ 1.31‡ AIC 4,743 4,158 4,131 BIC 4,795 4,304 4,297 Note: OADR- Old-aged dependent ratio; BOR- Bed occupancy rate; ICC: intraclass correlation coefficient; MOR- Median odds ratio; AIC- Aikake information criterion; BIC- Bayesian information criterion. Significance: †5% or less; ‡1% or less.

Figure 4.4. Point estimates of logit coefficients () with 95% confidence intervals comparing Model 1 (baseline), Model 2 (social determinants) and Model 3 (contextual)

55-59 years 60-64 years 65-69 years 70-74 years 75+ years Female Secondary Tertiary Chinese Indian Bumiputera Other ethnics Married Urban Employed Spending Q2 Spending Q3 Spending Q4 Spending Q5 Gini index OADR BOR -6 -5 -4 -3 -2 -1 0 1 Coefficients

Model 1 Model 2 Model 3

To assess the progression from Model 1 to Model 3, Figure 4.4 displays the point estimates of logit coefficients with 95% confidence intervals. We differentiate the models by plotting different markers for their point estimates. By observing these marker distances relatively between models, we can see that the point estimates for each covariate do not adjust much except the age group of 75+, females and people of the Bumiputera ethnicity. The covariates for females and people aged 75+ display lower effects on unmet care needs after the contextual variables are added in Model 3. However, having a Bumiputera ethnicity has a larger effect, with a wide confidence interval. This is mainly due to the small sample sizes. In all models, covariates of age, employed and OADR are significant.

An important question to policymakers would be: ‘which districts had the highest and lowest likelihood of unmet care among older people in 2011?’ To visualise the districts’

standings in the likelihood of unmet care needs, Figure 4.5 displays the effects ranking of 104 districts based on the random intercepts in Model 3. From this ranking, there appears to be no clear distinction of unmet care needs between urban and rural areas in Malaysia. Distinctively, the state of Selangor had four districts – Gombak, Kuala Selangor, Petaling and Kuala Langat – in the top ten districts with the highest effects, while Kelantan had three – Gua Musang, Kota Bharu and Machang. At the other end of the ranking scale, the capital city of Kuala Lumpur and the federal administrative centre of Putrajaya was listed in the bottom ten districts, with the lowest effects. This also includes the state of Terengganu, with four districts – Kuala Terengganu, Besut, Hulu Terengganu and Marang. The ranking also shows that all districts in both states of Sabah and Sarawak in East Malaysia had shown low negative effects of unmet care. It may be the case, though, that small sample sizes and less variation of unmet care within those states’ districts could also contribute to their below-average effects.

Figure 4.5 District effects ranking of unmet care needs based on random intercepts in Model 3, from low (left) to high unmet cardiovascular care needs (right)

Kubang Pasu Gua Musang Gombak Kuala Selangor Petaling Kota Bharu 100 Machang Sik Kuala Langat Hulu Perak Barat Daya Kota Tinggi Kulim Baling Pasir Mas Klang Sbrg Perai Selatan Perlis Johor Bahru Raub Tumpat Kuala Muda Tanah Merah Kinta Cameron Highlands

80 Timor Laut Bachok Pasir Puteh Jempol Pontian Tampin Melaka Tengah Seremban Sbrg Perai Tengah Alor Gajah Bandar Baharu Kuantan Kluang Jerantut Port Dickson Sbrg Perai Utara Muar Maran Lahad Datu Kapit

60 Sipitang Bentong Labuk&Sugut Kerian Semporna Kota Belud Batang Padang Ranau Jasin Limbang Hilir Perak Kota Marudu Kudat Labuan Keningau Tuaran Yan Kuala Penyu Papar Sri Aman

Ranking (104 districts) (104 Ranking

40 Samarahan Kota Setar Rompin Tenom Sepang Manjung Mukah Lipis Pendang Batu Pahat LMS Penampang Ulu Selangor Temerloh Jelebu Bera Bintulu Sarikei Sandakan Dungun

20 Beaufort Miri Tawau Setiu Sibu Segamat Kota Kinabalu Kemaman Ulu Langat Kuching Kuala Pilah Marang Kuala Kangsar Hulu Terengganu Besut Sabak Bernam Putrajaya Kuala Terengganu Kuala Lumpur

-3 -2 -1 0 1 2 Random effects for district

4.4 Discussion

There is still limited knowledge of contextual effects of unmet needs especially cardiovascular care in developing countries. For that reason, this study uses multilevel models to explain both the individual- and district-level variations in unmet CVD care among older people in Malaysia based on nationally representative data in 2011. We investigated demographic and socioeconomic determinants at the individual level as well as contextual determinants at the district level of the Gini index, the dependency ratio of OADR and BOR.

We found significant differences in unmet care needs between age groups, with greater odds among individuals aged 70–74 followed by those aged 75+, 65–69 and 55–59. This observation builds on our earlier observation of a secular increase in CVD risk scores in later life, and it is likely to be related to low health awareness and promotion as well as lack of healthcare intervention in CVD risk factors. However, the findings in developed countries did not show a similar increasing trend of higher odds in unmet care with advancing age. For instance, Sibley and Glazier (2009) found that unmet healthcare needs were higher in younger age groups in Canada while Brezzi and Luongo (2016) discovered an indistinct trend by which unmet healthcare needs differed significantly between regions in Italy, Spain and the Czech Republic for those aged 65 and above.

We also found that older women had lower CVD risk scores than men that might explain their lower odds of having unmet care needs. The low CVD risks among older women are also in line with a local study which found that the odds of older women having ≥3 CVD risk factors were lower compared to younger women (Ghazali et al., 2015). Another possible explanation for lower odds of unmet care needs among older women might be that women utilise more healthcare services than men, even though both genders had similar average CVD risk scores. On the contrary, Selvarajah et al. (2013) argued that Malaysian women of most age groups were more likely to have elevated CVD risks compared to men. They inferred that their finding might be contributed to by increased central obesity among women than men, and a higher number of younger women found with CVD risk factors. Furthermore, Chen and Hou’s (2002) study in Canada, which

found that women were more likely than men to report unmet healthcare needs about service unavailability.

Other demographic determinants such as ethnicity and marital status also influence unmet care needs. For ethnicity, older people of both Indian and Bumiputera ethnicities had lower odds of unmet care needs compared to older Malays. Though there is no established local evidence for unmet cardiovascular care needs among Indians or Bumiputeras for us to compare with, Amiri et al. (2014) found that adult Indians had lower odds than Malays of having at least one CVD risk factor. On the contrary, Ghazali et al. (2015) suggested that Indian men and women had higher odds of carrying three or more CVD risk factors than other ethnicities in their study.

In terms of marital status, married individuals were more likely to have lower odds of unmet care needs compared to those who were not married, which corresponds with Maharani and Tampubolon’s (2014) findings of Indonesia. This result suggests that married individuals might receive support from their spouses and families to seek care (Yasin et al., 2012), while those unmarried individuals might have less such support and less motivation to seek care.

The social determinants of health are shaped by the unequal distribution of money and resources across all levels, which can be avoided especially within a country (Marmot et al., 2008). Our findings echo this fact, where socioeconomic determinants such as education levels, employment status and household spending at the individual level are found to be associated with unmet care needs among older people. This relationship is consistent with a study by Rasiah et al. (2013), who found a similarly significant relationship between socioeconomic position and CVD risk factors in Malaysia. On the other hand, this relationship is in reverse in developed countries where CVD risk factors in older people are linked with socioeconomic disadvantages (Prince et al., 2015).

Through our observations on socioeconomic determinants, we found that older people with secondary education and household spending in the third and fifth quintiles had lower odds of having unmet care needs. For education, the findings in developed countries were mixed. Those with higher education in Italy had lower odds of having unmet medical 80

needs, however, findings in France and the United Kingdom showed opposite trends (Brezzi and Luongo, 2016). With respect to household spending, more spending power means that affluent older people could afford other options for accessing healthcare through health insurance or out-of-pocket payments. This spending leverage would lower the risks of experiencing unmet care needs. Our reasoning corresponds to Malaysia’s increased out-of-pocket spending for healthcare from 1.08% of the gross domestic product (GDP) in 2000 to 1.76% of the GDP in 2009 (MOH, 2012). To weigh this against disposable income as a standard measure of wealth distribution, Mielck et al. (2009) observed that people aged 50 years and above from lower-income groups had reported more unmet care needs than higher-income groups in France, Germany, Greece and Sweden. In addition, Zhu (2015) noted that financially secure older people in China had a lower risk of experiencing unmet healthcare needs.

One unanticipated finding was that employed older people were more likely to have higher odds of unmet care needs than those who were unemployed. This is probably due to constraints on making time to visit the clinic and other obstacles related to availability issues (Sibley and Glazier, 2009), such as clinic’s distance from home and waiting time, all of which might persuade working older people to delay seeking care. Our findings concur with Brezzi and Luongo’s (2016) observations that being employed in general had a greater effect on the odds of unmet healthcare needs in developed countries.

We did not find a significant association between urban and unmet care needs, however, the influence of the urban-rural socioeconomic gradient should be an important consideration in future research. Possibly, in this analysis, the non-significant association between urban and unmet care needs could be due to the continuous efforts by the health authority in improving the healthcare services in both urban and rural areas. Particularly, more resources are allocated to strengthen the primary healthcare system by increasing preventative health programmes and also the number of healthcare clinics. In countries with greater urban-rural socioeconomic gradient such as in Africa, CVD in the form of peripheral artery disease is found to be more frequent among the older population in urban areas (Desormais, 2015). Nevertheless, Li et al. (2018) found that rural, older patients with CVD were less likely to use inpatient services compared with urban patients in

China. However, they did not find any significant difference regarding outpatient care between the two areas.

Though we found significant effects of socioeconomic determinants on unmet care among older people, this finding should be further investigated. Chen and Hou (2002) revealed that unmet needs for healthcare in Canada were primarily related to problems of service availability, which was not associated with socioeconomic status. In Malaysia, public healthcare fees for patients are heavily subsidised by the government. Older people, especially those in the low-income group, are eligible to receive free healthcare for most services. Thus, older people from any socioeconomic background should not face any problem accessing public healthcare facilities at all levels of care. In this regard, we suspect that other health or/and non-health related reasons may perhaps mediate the relationships between socioeconomic factors and unmet care in Malaysia. Among others are long waiting times for appointments in clinics and insufficient income and savings to support the cost of living with care support, which eventually discourages older people from seeking care. In a developed country, longer waiting times for outpatient care decreased the utilisation of the service and is associated with poorer health outcomes among older people (Pizer and Prentice, 2011).

At the district level, the significant variance between districts in the multilevel models confirms that older people residing in a district shared common explanatory features of unmet care needs and this allows districts to be distinguished from one another. Next, we found all three contextual determinants, namely the Gini index, OADR and BOR, are important factors that explain unmet care needs among older people. These determinants are typically used as indicators for policymakers to allocate resources and determine priorities in implementing development projects and programmes.

The Gini coefficient shows that districts with higher income inequality had lower odds of unmet care among older people. This significant finding is in line with Diez-Roux et al. (2000) who inferred that income inequality might exert a contextual effect on CVD risk factor prevalence. However, Babones (2008) noted their difficulty in determining the causality of the relationship between income inequality and population health. Our

findings suggest that in these districts with higher income inequality, further efforts had been taken to cater to the health needs of poor and low-income groups. Another plausible explanation would be that, compared to older residents in rural areas, older residents in urban areas with higher income inequality (EPU, 2015) had better access to healthcare facilities and thus had a lesser prevalence of unmet care needs.

On the other hand, we found that districts with high numbers of older people tended to have greater unmet care needs. A higher OADR indicates the increasing demand for social and economic provisions from working adults, thus increasing pressure on them as well. However, we should not ignore unemployment among working-age adults and older people who were still employed. There is no definite explanation for this finding, but perhaps in these districts, older people became more dependent on their financial providers, including on their social support. Moreover, slightly higher odds of unmet care needs are also seen in districts with a higher BOR. This might relate to issues of healthcare availability. However, in other developing countries, Maharani and Tampubolon (2014) revealed a non-significant relationship between healthcare resources and unmet CVD care needs. Nevertheless, they stressed the importance of considering the determinants of healthcare utilisation in policymaking. As BOR represents resource utilisation in public hospitals, higher rates indicate patient congestion, an increased length of stay and longer bed availability times, which could deter older people from seeking care.

The three contextual determinants used in our study have illustrated our efforts in understanding the variations in unmet care needs among older people. We then rank the districts as shown in Figure 4.5, which takes into account both contextual and compositional determinants, to compare the districts’ standings in the effects of unmet care needs. Prior to this, Selvarajah et al. (2013) had identified urban-rural differences where better health status was observed in urban and developed areas in Malaysia. So, it is not surprising that well-developed districts such as Kuala Lumpur and Putrajaya display the lowest effects. However, it is also intriguing to discover that Selangor, the richest state in the country, had four out of nine districts placed in the top ten, with higher effects of unmet care needs. Our ranking also shows that less-developed states had fared better, with overall lower effects than developed ones. Terengganu had four out of seven districts in

2011 in the bottom ten, while Sabah and Sarawak had below-average effects for all of their districts. As there are other various factors beyond our study to explain the states’ differences, these observations are important considerations for future research. Nevertheless, this evidence already points to the need for states to consider within-state variations to improve the health outcomes for all residents.

We found that the use of risk estimation methods especially in primary care settings could help in reducing unmet needs, delay CVD onset and further improve health outcomes in older people with CVD risks. The methods that we used to calculate the total CVD risks would also avoid the cost of intervention escalating through timely medical intervention, as CVD is widely known to be the most costly group of diseases to treat (Luengo- Fernandez et al., 2006; Allender et al., 2008; Versteylen et al., 2011). The use of these methods for older people is still debatable and requires caution because the coefficients for the algorithms were mostly derived from a younger population (Neuhauser et al., 2005; Cooney et al., 2010). The FRS can be used up to the age of 75, however, the age range for SCORE is more limited (up to 65) (Cooney et al., 2010). Until a risk estimation method specifically for older people is developed and validated, these systems will be the only established tools available to assess total CVD risk. We see both methods as being simple, accessible and cost-effective at a primary care level, and therefore should be further promoted for health prevention programmes.

Malaysia is a middle-income country with a well-functioning public healthcare system designed primarily for the control of the communicable disease. However, the rise of non- communicable diseases such as CVDs requires new strategies for public healthcare to overcome this epidemiological transition. It is critical to understand by what means the burden of CVD among the aged population is distributed across areas in the country. Our study reveals that some quarters of the aged population were unaware of their CVD risks. For that reason, it is crucial to detect them early, monitor the health of the aged population and impede the progression of CVD.

To begin with, healthcare services could concentrate efforts on reaching aged persons in districts with higher odds of unmet cardiovascular care needs to ensure effective

intervention with the better use of resources. In Malaysia, resources are being allocated by the federal government directly to the departments of health in every state, which then decides the allocation for their respective districts. In this context, the district ranking graph as shown in Figure 4.5 can be used for this purpose, as well as for a monitoring tool to measure improvements.

There are two limitations in this study that merit our attention. First, the NHMS data that we used was a population-based national survey, and it was designed without taking the multilevel perspective into account, specifically the appropriate sample sizes at multiple levels. Because we extracted those respondents aged 50 and above for our analyses, we obtained 104 out of possible 126 districts in the data as groups in Level 2. In addition, most districts in the states of Sabah and Sarawak were being represented by a smaller sample of respondents compared to those in other states. This might pose the problem of wider standard errors and biased estimations in the Level 1 covariates. Second, our study is cross-sectional in line with the data used, and thus precludes causal inference. Analyses using longitudinal data are better to comprehend how district levels vary temporally, infer contextual causation and explain the geographic variations more accurately. Therefore, our findings should be viewed as exploratory and suggestive, with the purpose of raising the importance of viewing smaller administrative areas such as districts in the context of policy for improving the population’s health.

4.5 Conclusion

This study adds to the growing literature on the effect of social factors on health status and healthcare, which relates to issues of an ageing population in developing countries. We suggest that certain demographic and socioeconomic at the individual level could explain the prevalence of unmet cardiovascular care needs among older people. At the district level, the contextual determinant of Gini index shows a negative relationship with the health outcome, while both BOR and OADR show positive relationships. Further studies need to be carried out to establish more compositional and contextual evidence to

comprehend social disparities in unmet healthcare needs. On this note, we stress the urgent need for further prevention measures of CVD risk factors and raised health awareness for those aged 50 and above. The evidence presented here calls for healthcare policies and planning that take into account the specific needs of older people such as developing age- appropriate services and improved health screening programmes. The efforts forward seem uphill for a developing country to advance their healthcare and at the same time fulfil their social obligations in the midst of an aged population. Even so, all of us deserve better prospects of health in later life, and merely this justifies those impending efforts.

Chapter 5

Does Socioeconomic Status of Older People Define Geographic Variation of Diabetes in Malaysia?

Abstract: Malaysia recorded a high prevalence of diabetes: 10.1% among its population in 2013. National data showed an increased prevalence in known and undiagnosed diabetes among older people. As established in other developing countries, socioeconomic factors had been identified as contributing to this increase. To date, there is little local information about the effects of diabetes on socioeconomic status (SES), at individual and district levels. In addition, research on the geographical distribution of both known and undiagnosed diabetes across the country to further understand the diabetes predicament is still negligible. We investigated the likelihood of older people having diabetes and to be found undiagnosed in Malaysia. We also investigated SES factors that might be associated with the diabetes problem among the targeted population and sought to understand how geographic distribution in known and undiagnosed diabetes varies between districts. We analysed the two outcomes of known and undiagnosed diabetes separately, using a cross-sectional dataset of the National Health and Morbidity Survey. A total of 6,227 older people, aged 50 and above, residing in Peninsular Malaysia were included in the analysis. Three multilevel models were fitted: the null, compositional and contextual models. Covariates of demographic, SES and anthropometry measurements were included along with selected contextual determinants of old-age dependency ratio and poverty incidence for each district. Based on the compositional and contextual models for each diabetes outcome, we produced maps that enhance our understanding of the geographic distribution of estimated known and undiagnosed diabetes across districts. In the multilevel models, aged group 50-54, being female, Indian ethnicity, higher waist circumference, the highest household spending quintile and the district’s poverty incidence were significantly associated with known diabetes. Meanwhile, older age groups, Chinese ethnicity, being employed and lower waist circumference were found to be significantly associated with undiagnosed diabetes. By utilising the maps, we showed that the diabetes problem varies between districts, with the pattern of districts clustering and visually displaying diabetes disparity between developed and less developed districts. As they age, older people have a higher likelihood of having known diabetes and a lower likelihood of being undiagnosed. In addition, each diabetes outcome is significantly influenced by a different set of SES factors. Thus, the overall diabetes problem is multidimensional and requires new insights for healthcare planning and multi-sectoral interventions.

Keywords: geographic distribution, multilevel modelling, known diabetes, socioeconomic status, undiagnosed diabetes

5.1 Introduction

Diabetes is rapidly progressing as an immense public health challenge (Nanditha et al., 2016; Ramachandran et al., 2012) that could exacerbate into an epidemic, especially for developing countries (Hossain et al., 2007). The International Diabetes Federation estimated about 425 million adults aged 20 to 79 lived with diabetes in 2017, including 98 million older people aged 65 and above (IDF, 2017). These numbers will reach about 629 million adults (a 48% increase) in 2045, with 191 million older people living with diabetes, a staggering 95% increase. More than 80% of people living with diabetes reside in developing countries (Jaacks et al., 2016, Ramachandran et al., 2012). These countries are expected to experience substantial demographic changes with decreases in infectious disease burdens, higher rates of urbanisation, increases in life expectancy and population ageing (Akter et al., 2012). The changes are likely to worsen the prevalence of diabetes as well as their related complications (Guariguata et al., 2014).

As a developing country, Malaysia recorded a 10.1% prevalence of diabetes among its population in 2013 (Guariguata et al., 2014). In 2014, the country had the highest number of persons affected with the disease among developing countries in the Western Pacific region, which caused public concern (Meneilly and Tessier, 1995; Chan et al., 2014). The national health survey showed that diabetes prevalence among adults aged 18 and above in Malaysia was 15.2% in 2011 (MOH, 2011) and 17.5% in 2015 (MOH, 2015b). Adults with undiagnosed diabetes, those who were not known previously to have diabetes, were estimated at 8% of the population in 2011 and 9.2% in 2015. The survey data also showed a trend in both known and undiagnosed diabetes with increasing age. The prevalence of diabetes among those aged 60 and above rose 3.7 percentage points from 34.4% in 2011 to 38.1% in 2015, while undiagnosed diabetes increased by a 0.5 percentage point for the same period (MOH, 2011; MOH, 2015b). Among others, socioeconomic transition and urbanisation have been identified as significant factors that contribute to this secular trend that has been identified in other developing countries (Ramachandran et al., 2012; Angkurawaranon et al., 2015).

It is established that diabetes prevalence increases with age (Guariguata et al., 2014; Rampal et al., 2010; Jaacks et al., 2016), eventually reaching its peak among very old (Robledo and Gadsby, 2017). This age-related increase in prevalence is alarming because older people have a higher risk of disability and complication associated with diabetes especially diabetes-related macrovascular diseases, such as atherosclerosis, which accounted for 75% of all mortality among diabetic patients (Wallace, 1999). This concern is further aggravated by the fact that a significant number of older Malaysians do not go for health checks (Cheah and Goh, 2017a). Another concern is that most people living with diabetes do not become aware of their condition and remain undiagnosed until the diabetes symptoms emerge (Young and Mustard, 2001; Bagheri et al., 2014). Above all, older people living with undiagnosed diabetes do not receive early medical attention that could prevent further complications (Jaacks et al., 2016). Therefore, additional healthcare resources are needed to care for older people with such health problems (Khan et al., 2013).

Apart from the physiological factors, social and socioeconomic factors including income, education, and occupation are known to affect individuals’ health (Rabi et al., 2006; Agardh et al., 2011): individuals with lower SES have been associated with declining health outcomes (Gary-Webb et al., 2013) while higher-SES individuals are better at accessing healthcare (Townsend et al., 1992; Brown et al., 2004). Moreover, low-income individuals are also likely to have diabetes with higher rates of related complications (Rabi et al., 2006). In addition, these individuals have high chances to be undiagnosed since healthcare is less accessible to them (Agardh et al., 2011). In developed countries, SES has long been linked with the prevalence of diabetes (Krishnan et al., 2010; WHO, 2011; Hill et al., 2013; Calixto and Anaya, 2014) where diabetes and its risk factors were observed to be higher in the lower-SES groups (Bocquier et al., 2011; Hill et al., 2013). A study on a French population showed that a lower SES was associated with diabetes risks, including less physical activity, higher body mass index (BMI) and larger waist circumference (Jaffiol et al., 2013). Among people aged 50 and above, findings based on the English Longitudinal Study of Ageing (ELSA) in the United Kingdom showed that

current SES could be used as an indicator for risk of diabetes, particularly among older men (Demakakos et al., 2012).

Researchers in developing countries have also made notable strides to relate SES to diabetes, although with mixed findings. For instance, Korean researchers found significant associations between diabetes and socioeconomic factors of education and household income (Hwang and Shon, 2014). Similarly, a systematic review of diabetes research articles spanning more than 40 years concluded that increased diabetes risks were observed among people with lower SES in developing countries (Agardh et al., 2011). In contrast, a study in Sri Lanka found an absence of an association between the management of diabetes and patients’ socioeconomic backgrounds measured by an area-level deprivation index and an individual’s social status index (De Silva et al., 2016). On an earlier account, they revealed significant associations of similar socioeconomic determinants with obesity (De Silva et al., 2015), a significant risk factor for diabetes (Hossain et al., 2007; Angkurawaranon et al., 2015). Meanwhile, a study in Malaysia pointed out that SES, measured by education and household income, was not associated with diabetes prevalence (Ismail et al., 2000).

To comprehend the underlying issues that influence diabetes prevalence, an area or a place where individuals live has emerged as an important contextual factor (Calixto and Anaya, 2014) used to explain the association between SES and health outcomes at higher levels. In developed countries, geographic information has been widely used to investigate the effect of area-level SES on diabetes prevalence. For instance, a cross- sectional study in five regions in Germany showed that the SES of municipalities is significantly associated with diabetes prevalence (Maier et al., 2013). Researchers in the United States identified a distinctive geographic distribution of diagnosed diabetes that they identified as a diabetes belt (Barker et al., 2011). This belt is characterised by regions with a higher percentage of African-Americans and is economically focused mostly on the agriculture sector. Another research study in the United States found that socioeconomic determinants at the neighbourhood level could explain the diabetes prevalence among African-American women (Krishnan et al., 2010).

However, research in developing countries using a similar geographical approach is still lacking although they exhibit higher rates of diabetes prevalence (Dagenais et al., 2016). Those few related studies such as those on China (Zhou et al., 2014) found a negative social gradient associated with diabetes prevalence; from urban areas with high SES to rural areas with low SES. A study in Taiwan developed a township disadvantages index which consists of areas’ poverty, social disorder and minority composition measured as a higher-level socioeconomic variable (Chen and Truong, 2012). Their findings showed that areas with higher on the disadvantages index in Taiwan displayed higher obesity rates; a significant risk factor for diabetes.

To tackle diabetes comprehensively requires concerted efforts in prevention and a better understanding of individuals’ behaviour, their socioeconomic position and the contextual factors involved (De Silva et al., 2016) to better plan health interventions. To date, there are few epidemiological studies of diabetes in older people in developing countries (Jan Mohamed et al., 2015; Robledo and Gadsby, 2017; Wu et al., 2017). It might be plausible that many assume that diabetes in older people is a common occurrence and does not warrant further investigation.

To further understand the diabetes problem in Malaysia, we investigate the prevalence of diabetes or known diabetes and undiagnosed diabetes as two separate issues. We categorise individuals as having undiagnosed diabetes if they were not known previously to have diabetes but displayed higher-than-normal readings of blood glucose measured during the survey. We answer three key questions in our study of diabetes: (i) what are the likelihoods of older people having diabetes, and to be found undiagnosed? (ii) do SES factors show significant associations with the diabetes problem among older people? and (iii) how does the geographic distribution in known and undiagnosed diabetes vary between districts?

In our analysis, multilevel logistic regression will be used to identify district-level determinants associated with the two outcomes, known and undiagnosed diabetes, together with individual determinants. We will propose hierarchical models that show how individual and district level determinants influence the two diabetes outcomes. Then,

we will map our findings for a more natural comparison of the disease prevalence between districts. Our findings may offer avenues for policymakers to design effective strategies based on specific area needs that aim to reduce disparity in the treatment and care management of diabetic older people.

5.2 Data and method

5.2.1 Data

We use the National Health and Morbidity Survey (NHMS), a cross-sectional, nationally representative survey (MOH, 2015a) as the secondary data source for individual-level determinants in our analysis. The Ministry of Health Malaysia carried out the survey in 2015 with a response rate of about 97% and with 29,460 individuals interviewed. Our final sample comprises 6,227 respondents aged 50 and above, or 21% of the respondents, who were not institutionalised and resided in Peninsular Malaysia. Detailed information of the survey, especially on the methodology used, is given elsewhere (MOH, 2015a). For the district-level determinants, we used the published data of the Basic Household Income and Amenities Survey from the Department of Statistics, Malaysia (DOSM, 2016a) to construct predictor variables of districts’ median household income and poverty incidence.

Structured questionnaires were used to collect data based on the scope of the survey. There were two types of questionnaires: face-to-face interviews and self-administered questionnaires. For the face-to-face interviews, the pre-tested questionnaire was bilingual (Bahasa Melayu and English) accompanied by a questionnaire manual prepared as a guide for the enumerators. The self-administered questionnaires were prepared in four languages: Bahasa Melayu, English, Mandarin, and Tamil. The face-to-face interview questionnaires were programmed into a software application, and the data collection was done using a tablet computer. The self-administered questionnaires were prepared in hard copy. Trained nurses conducted the clinical assessment, which includes anthropometry

measurements (weight, height, waist circumference) and a biochemistry test for blood measurement using the CardioChek® Professional Analyser device.

5.2.2 The dependent variables/ outcomes

We operate two diabetes outcomes as dependent variables, namely known diabetes in which the individuals knew about their diabetes condition, and undiagnosed diabetes in which the individuals were unaware of their illness. In line with the survey’s report, we presume that diabetes mellitus or Type 2 diabetes is being measured (MOH, 2015b). These two outcomes are analysed separately. First, known diabetes is coded as a binary variable. A value of 1 is assigned to diabetic respondents who measured equal to or more than 7.0 mmol/L for fasting blood glucose, or 11.1 mmol/L for random non-fasting blood glucose (MOH, 2015c). Respondents with the pre-diabetes condition are also considered as diabetics and given a value of 1 if their fasting blood glucose measured between 6.1 to 6.9 mmol/L or non-fasting blood glucose measured between 7.8 to 11.0 mmol/L. A value of 0 is assigned to respondents with a normal fasting (less than 6.1 mmol/L) or non-fasting blood glucose (less than 7.8 mmol/L) measurement. Second, similar to known diabetes, the outcome of undiagnosed diabetes is also coded as a binary dependent variable. We determined undiagnosed respondents as those who answered ‘No’ on the question in NHMS, ‘Have you ever been told by a doctor or medical assistant that you have diabetes?’ and, at the same time, they were identified as diabetics in the biochemistry test. For them, we assigned a value of 1. For those respondents who answered ‘Yes’ on the same question, or measured as having normal blood glucose, we assigned a value of 0.

5.2.3 Covariates

The individual-level consists of categorical variables of demographic and SES. The demographic variables are age, gender, and ethnicity. The SES variables are the individual’s place of residence, education, and employment as well as household spending 94

measured in quintiles with the 1st quintile as the lowest end reference point. We also include the anthropometry measurement of body mass index (BMI) and waist circumference. The district-level consists of two SES variables (DOSM, 2016) namely the old-aged dependent ratio (OADR) calculated based on the World Bank (2016) and the poverty incidence in percentage.

5.2.4 Method

The multilevel modelling enables us to estimate the variance partition between individual and district levels, for further understanding of the relative importance of predicting variables to a health outcome at different levels. Explanations of the modelling approach can be referred to elsewhere (Twisk, 2006; Rabe-Hesketh and Skrondal, 2008; Robson and Pevalin, 2015). We apply a general form of multiple logistic regression in Equation 5.1.

Equation 5.1

휂 = 훽0 + 훽1푋1+ . . + 훽푛푋푛

휋 where 휂 = 푙표푔𝑖푡(휋) = 푙푛 is the logit function for the outcomes of known or 1−휋 undiagnosed diabetes in the ith individual in a jth district; 훽0 is the intercept; 푋1. . . 푋푛 are the covariates or predictor variables, and 훽1 … 훽푛 are the logit coefficients of the predictors. Error terms are only specified at the district-level. On this note,

푋1. . . 푋푛 represent individual-level covariates. Then, in Equation 5.2, we operated the district-level covariates in a two-level multiple logistic regression:

Equation 5.2

휂푖푗 = 훽0푗 + 훽1푗푋1푖푗+ . . + 훽푛푗푋푛푖푗

훽0푗 = 훾00 + 훾01푋1푗+ . . + 훾0푛푋푛푗 + 푢0푗

훽1푗 = 훾10 + 훾11푋1푗+ . . + 훾1푛푋푛푗 + 푢1푗

휋(푥푖푗) where 휂푖푗 = 푙표푔𝑖푡(휋(푥푖푗)) = 푙푛 ; 훾00, 훾01, 훾10, 훾11 are the district-level 1−휋(푥푖푗) coefficients; 푢0푗 is an error term related to the random intercept and 푢1푗 is an error term related to the slope across the districts. Similar equation to Equation 5.2 is then repeated for the subsequent logit coefficients 훽푛푗.

Firstly, we fit an unadjusted logistic regression to determine the odds ratio (OR) for each of the two outcomes: known and undiagnosed. Then, we fitted a progression of multilevel models – the (i) Null, (ii) Compositional and (iii) Contextual models – where we have a set of models for each outcome. Next, the Aikake information criterion (AIC) and Bayesian information criterion (BIC) statistics are calculated to select the best model that fits the data. On all of our analyses, a p-value of 5% or less is considered as statistically significant. From the results of the Compositional and Contextual models, post-estimation predicted probabilities using Bayesian estimator are obtained. Lastly, maps are plotted to visualise the geographic variances of both outcomes. We only analysed data for the peninsular region due to data constraints for the states of Sabah and Sarawak. Data management and analysis are all performed using the statistical software of Stata/SE version 14.0 by StataCorp LP, U.S.

5.3 Results

As shown in Table 5.1, 29.8% of respondents, out of 6,200 older people, were identified as diabetics. This is higher than the national average of 17.5% for adults aged 18 and above in 2015 (MOH, 2015b). The age group of 55–59 displays the highest percentage of individuals with diabetes at 7.3%, and subsequently, the percentages reduce with advancing age. For gender, females with diabetes were 16.5% compared to 13.3% in males. For ethnicity, the Indians have the highest proportion of diabetes with 38.8%, followed by other ethnicities (32.2%), the Malays (29.1%) and the Chinese (28.3%). In the education category, those who either did not attend school or only attended primary school display the highest percentage of 17.1%. Both groups of unemployed and those who resided in urban areas demonstrate higher numbers of people living with diabetes. The fifth quintile of household spending is the highest quintile with diabetes at 6.3%.

Figure 5.1 displays two maps of the prevalence of known and undiagnosed diabetes among older people in 2015 based on the NHMS dataset. Visually, both maps show the disease distribution within districts that vary in known diabetes but shows distinct clusters of districts in the undiagnosed diabetes map. We can notice that many districts show contrasting prevalence in the two outcomes where a district might be low in known diabetes, yet had a high prevalence of unknown diabetes at the same time, and vice versa. This observation justifies that known and undiagnosed diabetes should be dealt with separately.

Furthermore, districts that are more economically developed, particularly in the West Coast and the Southern regions, had a higher prevalence in known but lower in undiagnosed diabetes, compared to the East Coast districts which are less developed. We speculate that older people in the developed districts lead sedentary and unhealthy lifestyles that might contribute to a higher prevalence of known diabetes. On the other hand, they have better access to healthcare services due to a high number of public and private healthcare facilities that result in a lower prevalence of undiagnosed diabetes in these districts.

Table 5.1 Descriptive analysis of the NHMS dataset

Variable Diabetes % Normal % Total % Proportion (%) † Age (n=6,200) 50–54 439 7.1 1,159 18.7 1,598 25.8 27.5 55–59 452 7.3 939 15.1 1,391 22.4 32.5 60–64 325 5.2 816 13.2 1,141 18.4 28.5 65–69 267 4.3 602 9.7 869 14.0 30.7 70–74 194 3.2 381 6.1 575 9.3 33.7 75+ 169 2.7 457 7.4 626 10.1 27.0 Gender (n=6,200) Male 824 13.3 2,044 33.0 2,868 46.3 28.7 Female 1,022 16.5 2,310 37.2 3,332 53.7 30.7 Ethnicity (n=6,200) Malay 1,273 20.5 3,103 50.1 4,376 70.6 29.1 Chinese 339 5.5 861 13.9 1,200 19.4 28.3 Indian 194 3.1 306 4.9 500 8.0 38.8 Others 40 0.6 84 1.4 124 2.0 32.2 Education (n=6,194) No school/ Primary 1,061 17.1 2,424 39.2 3,485 56.3 30.4 Secondary 616 9.9 1,540 24.9 2,156 34.8 28.6 Tertiary 168 2.7 385 6.2 553 8.9 30.4 Employment (n=4,369) Unemployed 564 12.9 1,316 30.1 1,880 43.0 30.0 Employed 694 15.9 1,795 41.1 2,489 57.0 27.9 Strata (n=6,200) Rural 833 13.4 2,115 34.2 2,948 47.6 28.3 Urban 1,013 16.3 2,239 36.1 3,252 52.4 31.2 HH spending (n=6,009) 1st (lowest) 330 5.5 873 14.5 1,203 20.0 27.4 2nd 373 6.2 842 14.0 1,215 20.2 30.7 3rd 374 6.2 851 14.2 1,225 20.4 30.5 4th 337 5.6 837 14.0 1,174 19.6 28.7 5th (highest) 380 6.3 812 13.5 1,192 19.8 31.9

† Numerator (individuals living with diabetes)/ Denominator (total number of individuals in the group)

Figure 5.1 Maps of the prevalence of the known diabetes (left) and undiagnosed diabetes (right) among older people in 2015 based on NHMS

Table 5.2 shows the results of the unadjusted bivariate analyses of known and undiagnosed diabetes using the bivariate logistic regression. Age groups of 55–59 and 70–74 display a more significant (p<0.01) 5 to 6% points increase of known diabetes than the reference group of 50–54. Significant decreases of between 11% and 14.7% points are observed for most of the age groups compared to the age reference in undiagnosed diabetes, except for the 55–59 group. These findings indicate that known diabetes is associated with advancing age and at the same time, individuals are more aware of their conditions as they get older. A similar trend is observed in the gender determinant, whereby the OR of known diabetes is higher in females, although the association is not significant. Meanwhile, undiagnosed diabetes is more pronounced in males. By ethnicity, Indians display higher OR (p<0.01) of known diabetes than Malays. However, they are less likely to be undiagnosed with diabetes (p<0.01). In contrast, Chinese and the other ethnicities are more likely to be found undiagnosed compared to Malays.

Table 5.2 also shows that urban residents are 2.8% more likely to have diabetes than rural residents. Nevertheless, a similar, though the non-significant association is observed in the undiagnosed outcome. Older people with employment are 17.1% points higher in

undiagnosed diabetes than those who are unemployed, prompting speculation that time constraints might discourage employed individuals from getting their health check. The education determinant does not appear to explain the two diabetes outcomes. In the household spending determinant, the highest quintile demonstrates a higher OR (p<0.01) in the two outcomes compared to the lowest quintile. Older people with weight problems and larger waist circumference are more likely to have diabetes and, at the same time, be more aware of their diabetic condition. In the district-level determinants, OADR and poverty incidence are associated with known diabetes (p<0.01) although marginally. For undiagnosed diabetes, only OADR shows a significant relationship (p<0.05) with the outcome.

Table 5.3 shows the progression of multilevel models, from the Null model to the Compositional and Contextual models, fitted to examine adjusted relationships between the known diabetes as the outcome with the adjusted covariates. We use a multilevel logistic regression approach that considers older people nested within their respective districts. This approach could avoid errors due to mixing different levels of analyses. In the Null model without the covariates and also in the Compositional model, the values of between-district variance are significant (p<0.01). The MOR that measures heterogeneity in the Null model is 1.61. The odds are 1.79 in the Compositional model and 1.75 in the Contextual model. The MOR values in the three models show evidence of district clustering in which the individual’s probability of having known diabetes is also determined by the districts’ effects.

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Table 5.2 Bivariate logistic regression of (i) known diabetes and (ii) undiagnosed diabetes

VARIABLE β OR dy/dx β OR dy/dx Known Undiagnosed Level 1 Age 55–59 0.24 1.27 0.05‡ -0.14 0.87 -0.03 60–64 0.05 1.05 0.01 -0.47 0.62 -0.11‡ 65–69 0.16 1.17 0.03 -0.61 0.54 -0.15‡ 70–74 0.30 1.34 0.06‡ -0.46 0.63 -0.11‡ 75+ -0.02 0.98 -0.01 -0.48 0.62 -0.11‡ Female 0.09 1.09 0.02 -0.20 0.82 -0.05‡ Ethnicity Chinese -0.04 0.96 -0.01 0.63 1.89 0.15‡ Indian 0.44 1.55 0.09‡ -0.42 0.66 -0.09‡ Others 0.15 1.16 0.03 0.66 1.93 0.16† Urban 0.14 1.15 0.03‡ 0.03 1.03 0.01 Employed -0.10 0.90 -0.02 0.71 2.04 0.17‡ Education Secondary -0.09 0.91 -0.02 0.05 1.05 0.01 Tertiary -0.01 0.99 -0.01 0.16 1.17 0.04 HH Spending 2 0.16 1.17 0.03 0.16 1.18 0.04 3 0.15 1.16 0.03 0.08 1.09 0.02 4 0.06 1.07 0.01 0.23 1.26 0.06 5 (highest) 0.21 1.24 0.04‡ 0.38 1.47 0.09‡ BMI 0.04 1.04 0.01‡ -0.03 0.97 -0.01‡ Waist 0.02 1.02 0.01‡ -0.02 0.98 -0.01‡ circumference Level 2 OADR -0.03 0.97 -0.01‡ -0.03 0.97 -0.01† Poverty -0.16 0.85 -0.03‡ -0.11 0.89 -0.03

Note: HH- Household; BMI- Body mass index; OADR- Old-aged dependent ratio 2010; Poverty- Poverty incidence (%) 2014. dy/dx notation is the derivative of y with respect to x. Significance: †5% or less, ‡1% or less.

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Table 5.3 Multilevel logistic regression of known diabetes

Model 1 - Null Model 2 - Compositional Model 3 - Contextual Β (SE) OR Β (SE) OR Β (SE) OR Intercept -0.89(0.07) 0.41‡ -3.76(0.36) 0.02‡ -3.53(0.45) 0.03‡ Level 1 Age (50–54) 55–59 0.17(0.10) 1.19 0.17(0.10) 1.19 60–64 0.05(0.11) 1.05 0.06(0.11) 1.06 65–69 0.23(0.13) 1.25 0.23(0.13) 1.26 70–74 0.48(0.17) 1.61‡ 0.48(0.17) 1.62‡ 75+ -0.25(0.25) 0.78 -0.25(0.25) 0.78 Gender (Male) Female 0.18(0.09) 1.19† 0.18(0.09) 1.19† Ethnicity (Malay) Chinese -0.13(0.11) 0.88 -0.14(0.11) 0.87 Indian 0.32(0.14) 1.38† 0.32(0.14) 1.38† Others 0.05(0.27) 1.05 0.04(0.27) 1.04 Education (No School/ Primary) Secondary -0.13(0.09) 0.88 -0.13(0.09) 0.88 Tertiary 0.04(0.13) 1.04 0.033(0.13) 1.03 Employment (Unemployed) Employed 0.01(0.09) 1.01 0.01(0.09) 1.01 Strata (Rural) Urban 0.01(0.10) 1.01 -0.02(0.10) 0.98 HH spending (1st) 2nd 0.18(0.13) 1.20 0.18(0.13) 1.20 3rd 0.16(0.13) 1.18 0.17(0.13) 1.18 4th 0.12(0.13) 1.12 0.11(0.13) 1.12 5th (highest) 0.33(0.15) 1.39† 0.33(0.15) 1.39† BMI -0.02(0.01) 0.98 -0.02(0.01) 0.98 Waist circumference 0.03(0.01) 1.03‡ 0.03(0.01) 1.03‡ Level 2 OADR -0.01(0.03) 0.99 Poverty -0.25(0.12) 0.78† Random Effects Between-district 0.25(0.06) ‡ 0.37(0.09) ‡ 0.34(0.01) variance MOR 1.61 1.79 1.75 AIC 7,398 4,782 4,775 BIC 7,412 4,953 4,940

Note: HH- Household; BMI- Body mass index; OADR- Old-aged dependent ratio 2010; Poverty- Poverty incidence (%) 2014. Significance: †5% or less, ‡1% or less.

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Table 5.4 Multilevel logistic regression of undiagnosed diabetes

Model 1 – Null Model 2 - Compositional Model 3 - Contextual Β (SE) OR Β (SE) OR Β (SE) OR Intercept -0.33(0.08) 0.72‡ 1.60(0.55) 4.98‡ 1.74(0.62) 5.68‡ Level 1 Age (50–54) 55–59 0.02(0.15) 1.02 0.03(0.15) 1.03 60–64 -0.11(0.17) 0.90 -0.10(0.17) 0.90 65–69 -0.43(0.20) 0.65† -0.44(0.12) 0.64 70–74 -0.16(0.25) 0.85 -0.16(0.25) 8.86 75+ -0.27(0.38) 0.77 -0.27(0.38) 0.77† Gender (Male) Female -0.06(0.14) 0.94 -0.05(0.14) 0.95 Ethnicity (Malay) Chinese 0.72(0.18) 2.06‡ 0.71(0.18) 2.04‡ Indian -0.34(0.21) 0.71 -0.33(0.21) 0.72 Others 0.77(0.44) 2.15 0.75(0.44) 2.13 Education (No School/ Primary) Secondary -0.03(0.13) 0.97 -0.04(0.13) 0.96 Tertiary -0.12(0.20) 0.89 -0.12(0.20) 0.89 Employment (Unemployed) Employed 0.63(0.13) 1.87‡ 0.63(0.13) 1.87‡ Strata (Rural) Urban -0.19(0.14) 0.83 -0.28(0.14) 0.80 HH spending (1st) 2nd 0.22(0.20) 1.25 0.22(0.20) 1.24 3rd 0.07(0.20) 1.08 0.07(0.20) 1.08 4th 0.19(0.21) 1.21 0.18(0.21) 1.20 5th (highest) 0.42(0.23) 1.52 0.41(0.23) 1.51 BMI 0.02(0.02) 1.02 0.02(0.02) 1.02 Waist circumference -0.03(0.01) 0.97‡ -0.03(0.01) 0.97‡ Level 2 OADR -0.01(0.03) 0.10 Poverty -0.15(0.14) 0.86 Random Effects Between-district variance 0.34(0.10) ‡ 0.27(0.14) ‡ 0.28(0.14) ‡ MOR 1.75 1.64 1.66 AIC 3,266 2,027 2,029 BIC 3,278 2,171 2,184

Note: HH- Household; BMI- Body mass index; OADR- Old-aged dependent ratio 2010; Poverty- Poverty incidence (%) 2014. Significance: †5% or less, ‡1% or less.

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Significantly, people aged 70–74, females and people of Indian ethnicities could explain the known diabetes compared to their reference groups. The waist circumference OR of known diabetes increased by 1.2% compared to the unadjusted bivariate analysis that indicates that anthropometry examination is vital in conducting health assessment on the risk of diabetes for older people. In measuring the effect of SES, only the highest quintile of household spending group could significantly (p<0.05) explain the outcome in Compositional and Contextual models. As observed in the unadjusted analysis, older people with the highest capacity to spend are more likely to have diabetes, compared to those in the lowest quintile of household spending. In the Contextual model, higher estimates of known diabetes are to be found in districts with lower poverty incidence (p<0.05). Our results in the bivariate and multilevel analyses show that the highest quintile of household spending is significant to explain known diabetes. These results indicate that in Malaysia's circumstances, diabetes is still a disease of affluence among older people. The Contextual model is best fitted to explain the known diabetes as it has the lowest AIC and BIC values, though we should note that the model's between-district variance is no longer significant.

Following the progression of multilevel models for known diabetes, Table 5.4 displays a similar approach to fit models for undiagnosed diabetes as the outcome from Null to Compositional and Contextual. The MOR recorded a value of 1.75 in the Null and reduced values of 1.64 for the Compositional and 1.66 for the Contextual model. Contrary to the results of known diabetes, undiagnosed diabetes is less likely to be found in older groups. We speculate that as older people age, they become aware of their diabetic condition. By ethnicity, Chinese are more likely to have their condition undiagnosed compared to Malays. Similar to known diabetes, the waist circumference covariate shows a strong relationship (p<0.01) with undiagnosed diabetes. The only SES determinant that could explain undiagnosed diabetes at the individual level is employment; it shows significant results in both Compositional and Contextual models. On this note, employed older people are about 86% more likely to be undiagnosed than unemployed individuals. This suggests that time constraints might be one of the factors which discourage employed older people from seeking health checks.

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In addition, we do not find covariates of OADR and poverty incidence at the district level to be significantly associated with undiagnosed diabetes. The between-district variances in all models are significant nonetheless based on the AIC and BIC assessments; we found that the Compositional model is the better model fit for undiagnosed diabetes. In this instance, we suggest that undiagnosed diabetes is more of an individual dependence outcome and not being influenced by factors at higher levels. Thus, intervention to resolve undiagnosed problems should focus more on older people’s health-seeking behaviours.

Figure 5.2 The geographic distribution of the adjusted OR of known diabetes between districts at the individual (left) and contextual level (right)

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Figure 5.3 The geographic distribution of the adjusted OR of unknown diabetes between districts at the individual (left) and contextual level (right)

We present the estimated probabilities of known and undiagnosed diabetes for districts in Peninsular Malaysia based on the Compositional and Contextual models for the two diabetes outcomes. Figure 5.2 displays maps of the geographic distribution of known diabetes between districts at the Compositional (individual) and Contextual level. Although the between-district variance in the Contextual model shows no significance, for the purpose of comparison, it is still useful to show any visual differences of the two adjusted models, particularly when district-level determinants are factored in. However, the differences in known diabetes between districts in these maps are marginal. Both maps indicate that most of the districts in the East Coast and several districts in the Northern region are estimated to present higher probabilities of known diabetes.

Similarly, Figure 5.3 shows maps of the geographic distribution of undiagnosed diabetes between districts. The estimated probabilities of districts with undiagnosed diabetes show marked differences in district variations between the Compositional and Contextual maps, and also as to Figure 5.2. Patterns of district clustering are apparent in the Northern districts with higher probabilities of undiagnosed diabetes, contrary to the Central and

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Southern clusters that show lower probabilities. The East Coast districts with higher probabilities of known diabetes do not follow a similar trend in undiagnosed diabetes. The use of maps as a communication tool, as used here, would allow policymakers to identify those districts in need of healthcare resources and track progress when new data become available.

5.4 Discussion

Older people, aged 50 and above, represent the fastest-growing segment of the population in Malaysia (DOSM, 2017b). Until now, there is still limited research done in investigating their health risk or related health problems such as diabetes. This paucity reflects a slight interest in older people's health, among others, because of the higher rate of diabetes in younger individuals who are economically more active (Chan et al., 2014; Dagenais et al., 2016). Nevertheless, the management of diabetes in older people is challenging and worthy of attention (Meneilly et al., 1995; Kirkman et al., 2012). It is imperative to reduce differences in health across age groups in order to improve the overall population health (Arcaya et al., 2015). We consider the two outcomes, namely known and undiagnosed diabetes, as two separate issues with different factors that might influence them and should be engaged explicitly to resolve the diabetes predicament altogether.

We seek to answer our three research questions. First, we found that as the aged group advances, older people are more likely to have known diabetes and also are less likely to be found undiagnosed. On both accounts, older people are likely to be diagnosed with diabetes at the age when frailty and disability begin to set in. With nearly 50% of the respondents measured with diabetes aged below 60 and economically active, this warrants further investigations into the effectiveness of the healthcare services to identify them much earlier. Second, each outcome is significantly influenced by a different set of SES factors. Older people living in a household with high spending capacity, and in districts with lower poverty incidence are more likely to have known diabetes. By contrast, older

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people with employment are more likely to be found undiagnosed. This shows that the overall diabetes problem is multidimensional and cannot entirely be understood without taking different compositions and contexts into our perspective. Third, we show how estimates of known and undiagnosed diabetes vary between districts. On the maps, patterns of districts clustering appear visibly on both outcomes. In addition, the disparity in the diabetes outcomes between those developed and less developed districts as well as urban and rural areas can also be visualised.

In general, we now understand that the SES of older people has a great influence on their health. Already, low SES individuals have long been associated with a risk of diabetes (Everson et al., 2002). Household spending at an individual level, those households with the highest spending capacity, and poverty incidence at the district level could be used as indicators to assess known diabetes prevalence. People with higher SES could influence their health outcomes by having more options in accessing healthcare services. However, we found that they have higher chances of having diabetes instead.

In a systematic review of diabetes research in East Asian countries, researchers did not find any strong association between employment and known diabetes (Wu et al., 2017). On our data, we did not see any significance in most of the SES factors in explaining undiagnosed diabetes except for employment. This concurs with a local study which found that educated and unemployed older people are more likely to be aware of their diabetic conditions (Cheah and Goh, 2017b).

We also did not find any significant associations between the covariates urban, and the household spending quintiles in both outcomes in the multilevel model except in the highest (fifth quintile) of household spending with known diabetes. For urban, our finding might suggest that there is a consistency, or the difference is nominal, in the prevalence of known diabetes and undiagnosed diabetes between urban and rural Malaysia. In other developing countries such as China, the prevalence of known diabetes was higher in more- urbanised areas, where the study’s sex-stratified multilevel models showed a two-fold higher diabetes prevalence in urban compared to rural areas (Attard et al., 2012).

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For household spending, older people in the household with the highest spending quintile could possibly have more options and opportunities in accessing healthcare services. This finding concurs with a study by Kim et al. (2013) that showed older Koreans aged 65 years and above; those with higher incomes, have higher rates of diabetes screening compared to older Koreans with lower incomes. However, Wang et al. (2013) found that both the contextual-level and individual-level factors of annual household income were found to be negatively associated with the prevalence of diabetes and here, adults had a higher likelihood of diabetes in lower household income groups.

We are also interested in measuring the association of demographic determinants such as age, gender, and ethnicity. These could indirectly be related to older people’s productivity and participation in socio-economic activities. Though age exhibits significant relationships in the unadjusted analysis, their significance diminishes in the adjusted multilevel models, indicating less importance of the determinant when we control other covariates in the nested structure. In ethnicity, the Chinese show a higher probability of undiagnosed diabetes than other ethnics especially the Malays who are likely to undergo blood glucose screening (Cheah and Goh, 2017b).

For gender, our findings suggest females are more aware of their diabetic conditions than males when contextual factors are considered in the multilevel models, though such association is non-significant in undiagnosed diabetes. This concurs with an undiagnosed diabetes study among people aged 52–79 years, in which the researchers found that the overall prevalence of undiagnosed diabetes was significantly higher in men than in women (Pierce et al., 2009). Nevertheless, it is known that adult females were more likely to develop diabetes than adult males with the probability of developing diabetes increases with higher BMI (Kim et al., 2013). In a contextual study that takes into account the individual and regional risk factors, higher area-level deprivation was independently associated with higher diabetes type 2 and obesity prevalence among female respondents (Maier et al., 2014). Conversely, in another study, the prevalence of known diabetes was higher among males aged 35 to 64 years with both microvascular and macrovascular complications were significantly predicted by the male sex factor (Wändell and Gåfvels, 2004).

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Despite the fact that an individual’s education level is an important SES indicator, our study showed that this indicator had no effect on both outcomes in the bivariate and multilevel models. On this note, established evidence showed that adults with low levels of educational attainment, for instance, that is equivalent to lower secondary school or less, were related to a higher prevalence of diabetes type 2 (Espelt et al., 2008; Steele et al., 2017) Still, we cannot dismiss education level as an important variable in any health outcomes study where higher educational attainment could influence better adherence to diet, physical activity and regular clinic follow-up (Sacerdote et al., 2012). However, some evidence showed that educational attainment may not be a good predictor in certain circumstances. Borrell et al. (2006) found that the factor of education levels demonstrated mix effects on diabetes prevalence among a racially and ethnically diverse population in the United States. Lower educational levels among the Whites and Hispanics were associated with higher diabetes prevalence, and similar prevalence was also observed among highly educated Blacks. In another circumstance, Davis et al. (2010) reported that older patients with diabetes type 2 and have higher educational attainment levels should not be regarded as having a low risk of severe hypoglycaemia, a diabetes complication with an abnormally low blood glucose level.

Our data demonstrate that SES factors at the district level showed differing effects on the two diabetes outcomes and such observations will not be realised without considering the multilevel approach. The approach allows us to disentangle the effects (Ludwig et al., 2011) of individual factors from those effects at a higher level. Using both compositional and contextual models, we predict the known and undiagnosed diabetes across districts. Most districts with high probabilities of known and undiagnosed diabetes are found in the East Coast and Northern regions that are predominantly rural and less developed. Areas with lower SES signified by a high poverty incidence (DOSM, 2016a) have a lower level of economic development; it often means less access to healthcare. Examining this further, older people with both known and undiagnosed diabetes might migrate and reside in these areas especially after retirement rather than living in developed areas. This migration might exacerbate the diabetes burden in rural areas.

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Public healthcare in the country is being carried out at the district, state and national levels where resources are allocated at each level. Strategies for allocating healthcare resources may be at risk of being inequitable and inefficient. Identification of districts with a high prevalence of known and undiagnosed diabetes would help in allocating resources equitably and in monitoring any health improvements. The identification process may also lead to the detection of other related factors, especially diabetes risks that might have been unaccounted for previously. On this note, universal and simplistic associations cannot be used to explain the spectrum of diabetes fully. We demonstrate that ecological approaches that do not control for potential compositional and contextual effects are no longer warranted. With better analysis and estimation methods, calls for approaches that recognise the increased risk of diabetes and its complications in advancing age as well as identifying unique healthcare needs in older people can be strongly justified.

Our study has several limitations that could be further improved in future studies. First, a significant limitation is in the use of both known and undiagnosed diabetes as our two separate dependent outcomes. The known diabetes is generated by sample measurement of either fasting or non-fasting blood glucose. We are unsure whether those respondents with fasting blood glucose do follow the 8-hour fasting required before the blood test (MOH, 2015a). Similarly, those with non-fasting blood glucose would probably ingest food with high sugar or carbohydrate content prior to the test. Second, there is also a possible risk of glucose device errors due to improper calibration or incorrect use by the enumerators. These would obscure the actual blood glucose results. The third limitation is about the use of NHMS dataset that is cross-sectional without knowledge of how long the respondents had been diabetics, and whether their diabetes is a type 1 or 2 as they are not easily distinguishable (Wallace, 1999; Jaacks et al., 2016). Fourth, NHMS is designed as a self-reported survey that requires respondents to answer questionnaires asked by the enumerators. The self-reported feature could expose the dataset to errors such as recall bias, inability or refusal to answer, typos or data input errors. Fifth, NHMS was neither explicitly designed to solely survey older people nor consider the nested structure of the data. Due to this, several districts, especially rural districts, are represented by a smaller sample of respondents. Lastly, for the district level data, we are limited in the choice of

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suitable data for our analysis and used readily available data from the public domain. On this note, our findings should be viewed as exploratory and suggestive, with the purpose of raising the importance of leveraging multilevel approaches and examining geographic variations to better monitor and improve population health.

5.5 Conclusion

Diabetes, specifically type 2, is a progressive disease with serious complications if it is not tackled effectively and as early as possible. To understand this disease, policymakers need to acquire new insights for healthcare planning and multi-sectoral interventions, not exclusively focusing on healthcare services. We have shown that known and undiagnosed diabetes among older people are two separate issues with differing factors at the multilevel, especially SES which might influence them. We also understand that as the older people age, they become highly susceptible to diabetes and only become aware of their condition later in their life. Thus, we suggest a multi-sectoral intervention that takes into account the compositional and contextual factors. Likewise, displaying the findings in the form of maps would be useful in visually pinpointing evidence of areas/districts in need of better healthcare services. To a discerning policymaker, our methods of estimating the diabetes problem could be applied and further improved as more data becomes available especially at higher levels. These approaches would allow public health to determine and track progress in reducing the burden of diabetes in the respective districts. It is clear that from now on, the way forward is to confront this disease at a policy level, thus enhancing older people’s health.

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Chapter 6

How Spatial Distribution Informed Us about Undiagnosed Non-Communicable Diseases Risks in a Developing Country

Abstract: Worldwide, it is estimated that half of the people affected by non-communicable diseases (NCDs) remain undiagnosed and untreated. As a result, more attention has been focused on identifying high-risk populations including older people. Malaysia, a developing country, also faces a higher prevalence of the diseases along with an increasingly older population. So, the possibility of a higher number of older people to be found undiagnosed is inevitable. To understand the problem, we analysed nationally represented data, the National Health and Morbidity Survey (NHMS) in 2015, by employing multilevel and spatial modelling. The spatial mapping model has been widely used to identify disease distribution and geographical areas with unusual disease prevalence. Based on the knowledge that an area’s attribute values would eminently influence its neighbours’ attribute values as well, models were formulated using an integrated nested Laplace approximation (INLA) with latent Gaussian field to obtain Bayesian inferences. INLA provides accurate approximations for posterior marginal and shorter computational time compared to Markov chain Monte Carlo (MCMC) method. By operating INLA, we found that individual-level factors of age, employment and residence in urban districts could explain the outcome of undiagnosed diseases among older people. At the district level, the percentage of households within a five-kilometre radius from a public healthcare facility, a measure of distance, also influenced the outcome. Then, we constructed choropleth maps from the models to visualise the posterior distributions of undiagnosed NCDs. We found evidence of spatial patterns where neighbouring districts with a similar level of prevalence clustered together. From this, we showed that undiagnosed NCDs are likely to increase in less developed districts. Here, we suggested that policymakers should consider contextually sensitive healthcare policy that is precise and targeted, to better manage the diseases and allocate resources more effectively.

Keywords: INLA, multilevel modelling, non-communicable diseases, spatial modelling

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6.1 Introduction

Non-communicable diseases or NCDs, such as diabetes, hypertension and heart disease are recognised as a serious public health concern. Based on the World Health Organization’s (WHO) report, NCDs are ranked as the leading causes of 70% of mortality worldwide in 2015 (WHO, 2017). Furthermore, 80% of those mortalities occurred in developing countries (Wagner & Brath, 2012). While these figures are already high, the WHO (2017) further estimated that half of the people affected by NCDs remain undiagnosed and untreated. This is not unusual as evidence shows that diabetes is asymptomatic and undiagnosed in 30% to 80% of cases all over the world (Beagley et al., 2014). Added to this, undiagnosed NCD risks, for instance, high blood pressure, could persist and aggravate into severe complications such as cardiovascular and end-stage kidney diseases (Lackland & Weber, 2015).

Due to the diseases’ dire consequences, more attention has been focused on identifying high-risk populations (Bagheri et al., 2014; Niessen et al., 2018). As such, chronic health conditions are commonly associated with older people in developing countries (Basu & King, 2013; Wu et al., 2013). Here, researchers also found that the NCDs are increasing in prevalence, mainly among populations with lower socioeconomic status: who are poor, less educated and adopt unhealthy lifestyle (Halpin et al., 2010; Maurer and Ramos, 2014; Chatterji et al., 2015; Prince et al., 2015). Contrary to the prior proposition of the diseases linked with affluence (Basu and King, 2013), a recent study suggests an increase in the clustering of NCDs among those groups with low socioeconomic status (Niessen et al., 2018). The researchers also noted that older people in the group are more susceptible to multiple NCDs and this might explain the upsurge trend in the phenomena of frailty with advancing age in developing countries. To make matters worse, they are found to be undiagnosed with NCDs.

Older people with poor health are more likely to experience coexisting chronic illnesses (Beard & Bloom, 2015; Marengoni et al., 2016). A review of about 100 articles on health and ageing in developed countries (Lindgren, 2016) found that most of older people, aged 55 and above, have at least one chronic health condition that requires healthcare

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interventions. It has also been reported that the older population aged 60 and above carried about 25% of all the disease burden worldwide, and again developing countries show a higher burden per person (Chatterji et al., 2015). As this burden increases, older people utilise more healthcare services than other population groups (Beard & Bloom, 2015). Though the NCDs burden can be delayed, older people’s health will inevitably start to deteriorate at some point in time and they are likely to develop other health complications.

Apart from a higher probability of older people with chronic health issues, another concern is that NCDs treatment and care are costly (Bloom & Canning, 2008), even more so if the disease has progressed into complications. And so, health interventions that targeted older people’s health needs for care and medication would preserve older people’s health longer as they aged (Prince, 2015). These interventions would also lower the treatment cost in the long run. Intermediate NCDs risks of elevated blood glucose and cholesterol along with increased blood pressure (BP) are tied with social disadvantages. For instance, poverty and unemployment might increase the prevalence of undiagnosed NCDs (Beaglehole et al., 2011; Wagner & Brath, 2012). Clearly, an improved understanding of social factors would help efforts in disease prevention and broaden the path to cost-effective health interventions.

As a developing country, Malaysia also faces a growing number of older people, characterised as those aged 60 and above (Ambigga et al., 2011). It is estimated that by 2030, the country will become an aged nation, with older people exceeding the threshold of 15% of the population (DOSM, 2016b). A high number of older people with undiagnosed diseases would pose an unprecedented challenge to public healthcare. Based on national records, the overall prevalence of undiagnosed raised blood glucose among adults aged 18 and above had increased from 8% in 2011 to 9.2% in 2015 (MOH, 2011b; MOH, 2015b). The age group of 55 to 59 recorded the highest prevalence of 13.7% in 2011 and after that age group of 65 to 69 at 13.6% in 2015. A similar incremental trend is observed for undiagnosed raised blood cholesterol level: from 26.6% to 38.6% for overall adults, with the age group of 55 to 59 recording the highest prevalence for both years at 37.3% in 2011 and 8.5% in 2015 (MOH, 2011b; MOH, 2015b). Instead,

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undiagnosed elevated BP showed a declining trend, from an overall prevalence of 19.8% to 17.2%, with age group 65 to 69 recording the highest prevalence of 34.7% and 28.7% for both years (MOH, 2011b; MOH, 2015b). With those figures in mind, undiagnosed NCDs risks will continue to conceal the actual threat of the diseases.

To comprehend the NCDs threat, it is important to consider the potential of mapping to reveal geographical patterns of undiagnosed NCD risks among the older population. On this note, Elliot et al. (2002) suggested that diseases and their risks vary across space, due to differences in environmental exposure and the risk-inducing behaviour of the population. Geographical issues, such as health gaps between urban and rural as well as socioeconomic area differences that contribute to social disparities in health, have emerged as an important context in understanding NCDs pattern and distribution (Halpin et al., 2010). Furthermore, within this context, neighbourhood characteristics can be used to reflect socioeconomic standings that might influence health outcomes (Calixto & Anaya, 2014, O’Campo et al., 2015). The effect of spatial proximity must be considered since neighbouring areas are dependent on one another and tend to share certain attributes compared to more distant areas (Tobler, 2004). The proximity also influences the interaction and dependency between neighbouring areas and in turn affects the health outcome in a specific area. The spatial mapping model is already used to identify disease distribution in addition to geographical areas with unusual disease prevalence (Pfeiffer et al., 2008). This model could enable a further understanding of NCDs patterns throughout a region or a country, and guide policy formulation specific to an area’s health needs.

The key benefit of maps is that they will allow policymakers to identify an area or a cluster of areas that exhibit elevated disease risks (Faraway et al., 2018). This, in turn, enables policy options to be recommended that would allocate healthcare resources effectively to populations in real need of it (Darmofal, 2015). To date, there has been no reliable evidence that shows the association between undiagnosed NCD risks among older people and neighbourhood effects in Malaysia. Despite this, a local study found that a significant proportion of older people did not undergo health checks, in order to know their risk factors or early signs of diseases and where they resided determine this likelihood (Cheah & Goh, 2017a). Therefore, the purpose of our study is to narrow this evidence gap. We

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would like to assess the spatial pattern of undiagnosed NCD risks among older people across Peninsular Malaysia and to identify areas characterised by the highest and lowest relative undiagnosed risks.

Our study will map the continuous spatial distribution of undiagnosed NCD risks among older people. We aim (i) to understand how socioeconomic factors associated with undiagnosed NCD risks are spatially distributed, and (ii) to identify geographical patterns of clustering of areas that show spatial dependency. We take into account the hierarchical structure in the data of individuals within their respective districts and the spatial autocorrelation or similarity of a district to its neighbouring districts (Morrison, 2017). We derive our study based on Tobler’s (2004) observation in geography that near things are more related than distant things. This means an area’s attribute values would eminently influence its neighbours’ attribute values as well, where areas closer together are more similar than those from more distant areas (Pfeiffer et al., 2008). The estimations of undiagnosed risks in the study consider this neighbourhood effect. We visualise and describe the spatial patterns via maps and offer an explanation on them. In order to achieve this, we employ a Bayesian approach to measure both the hierarchical properties of individuals nesting within districts and the spatial autocorrelation or dependence of undiagnosed risks between the districts. To capture these complex spatial dependency structures in the data, we operate a theorem of Integrated Nested Laplace Approximation (INLA) (Rue et al., 2009). This allows us to obtain effective posterior probability about the parameters given the data by encoding the likelihood with appropriate prior distributions.

The significance of this study is that it explores the latest spatial modelling through INLA that is able to map such areal clusters of undiagnosed risks. This will provide an invaluable opportunity to advance the understanding of spatial dimensions (Comber et al., 2011) which takes into consideration the effect of neighbouring areas on a specific district. This would guide more contextually sensitive policy interventions (Cummins et al., 2007) which will minimise gaps in healthcare provision. And at the same time, this will provide a tool for policymakers to effectively identify districts with a high prevalence of undiagnosed NCD risks due to its older population.

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6.2 Methods

6.2.1 Study Area

Malaysia is located in South East Asia (Figure 6.1) and comprises two regions separated by the South China Sea, (i) West Malaysia, also known as Peninsular Malaysia, and (ii) East Malaysia in Borneo. Malaysia had a population of about 32 million people in 2017, with people aged 50 and above making up about 19.6% or 6.3 million people (DOSM, 2016b). Healthcare services are provided by both the public and private sectors, and the Federal Government allocated around 2% of the national gross domestic product on public healthcare spending in 2015 (MOH, 2017). Our study area focused on the Peninsular due to data inadequacy for East Malaysia. There are 86 districts or administrative areas in the Peninsular. However, the island district of Langkawi in the north-west is omitted from our analysis due to the island’s disconnectedness with the absence of neighbouring districts.

Figure 6.1 Map of Malaysia

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6.2.2 Data

We sampled 5,432 respondents, aged 50 and above, from the National Health and Morbidity Survey (NHMS), a cross-sectional, nationally representative survey conducted in 2015 (MOH, 2015a). The sample constitutes about 20% of respondents out of the total 29,460 adults, aged 18 and above interviewed, and is about 74% out of the total 7,367 respondents aged 50 and above. We already omitted 1,177 older people due to the exclusion of East Malaysia and the Langkawi district. We also excluded 758 healthy older people without any NCD risks, as we selected those respondents with one or more NCD risks for the analysis. Written informed consent was obtained from the respondents and questionnaires were administered by trained enumerators. From NHMS, we derived our demographic and socioeconomic variables at the individual level. Further details regarding the NHMS methodology can be referred to their published report (MOH, 2015a). In addition, we obtained the district-level data from the public domain of the Department of Statistics, Malaysia (DOSM, 2016a) and spatial data for Malaysia from the Database of Global Administrative Areas website (GADM, 2018).

6.2.3 Outcome variable

We measured undiagnosed NCD risks among older people aged 50 and above as a binary outcome variable. We counted elevated blood pressure as well as raised blood glucose and cholesterol levels as key cardio-metabolic risk factors for NCDs based on recommendations of the WHO (2013a). Following the NHMS methodology (NHMS, 2011a & 2015a), blood samples were collected from every respondent following overnight fasting. When fasting was not possible, blood samples were collected in-situ and considered as a non-fasting blood sample. A respondent was classified as having undiagnosed NCD risks when the respondent was not known or not being told by a clinician to have one or more risk factors, and yet at the same time had; (i) raised blood glucose with higher fasting blood glucose (FBG) of 6.1 mmol/L or non-FBG of 11.1 mmol/L; (ii) elevated BP with higher systolic pressure of 140 mmHg and/ or diastolic 119

pressure of 90 mmHg; and (iii) raised blood cholesterol level of more than 5.2 mmol/L. Respondents with one or more of undiagnosed risks factors are coded with 1, while those already diagnosed with diabetes, hypertension and/or heart disease, are placed in the reference group and coded with 0.

6.2.4 Independent variables

Due to the hierarchical nature of our data and approach, we measured the variables at individual and district levels. This will avoid errors of mixing different level of analyses in our inferences (Robson & Pevalin, 2015). At the individual level, it is well documented that demographic and socioeconomic determinants are strongly associated with disease prevalence (Elliot et al., 2000). For that reason, we included the variables of age, gender, education, employment and strata that are considered in similar studies in other developing countries (WHO, 2010; Arokiasamy et al., 2017). In addition, we considered household spending instead of income (Deaton & Zaidi, 2002) and assessed the household consumption aggregate that better reflected the living standards of older people. Age is categorised as groups of 55–59, 60–64, 65–69, 70–74 and 75+ with 50–54 as the reference. Gender, employment and strata are coded as binary independent variables, respectively the male, the unemployed and the rural. Education is coded as a 4-categorical variable with no schooling as the reference, followed by primary, secondary and tertiary education. Household spending is coded as a quintile 5-categorical variable with the first quintile as the reference. At the district level, we selected the Gini coefficient index (Gini) for 2014 and the percentage of households within a five-kilometre radius from a public healthcare facility (PH) in 2016 (DOSM, 2016a) as a continuous variable.

6.2.5 Modelling

The Peninsula is divided into a set of disjointed areas of districts. These districts interact with one another in terms of health distributions based on their proximity. This interaction 120

of spatial autocorrelation will be stronger among nearer districts, especially those sharing a border, and the interaction effect declines as the distance between them increases (Darmofal, 2015). Therefore, a conditional autoregressive (CAR) random effect is used to obtain a multivariate joint distribution based on a set of neighbouring districts. This random effect also allows for correlations between the observations across the districts (Schmidt & Nobre, 2014). Next, we applied INLA to determine the posterior marginal distributions (Rue et al., 2009). This method provides faster and efficient Bayesian estimations without the need for MCMC sampling (Lindgren et al., 2011). It also added the advantage of measuring the unobserved district-specific effect. This effect is further decomposed as a spatially structured random effect (푢푖) which observed autocorrelation among the districts, and a spatially unstructured (푣푖) component which observed the hierarchical aspect of the model (Morrison, 2017). INLA can thus incorporate both the hierarchical and the spatial dependency structures simultaneously to obtain the overall variance of random effects.

Spatial data often takes the form of polygon entities defined by boundaries that distinguish areas (Elliot et al., 2000). Yet, data that aggregates over a country or a particular region is continuous in nature. The effect of a variable does not end at a border that is shown as lines on a map, in fact, those effects have a continuous influence that carries over into neighbouring areas. In a country, older people who seek healthcare will not stop and acknowledge a district border. In seeking care, the individual would continue to move and visit the nearest healthcare facility located in neighbouring districts. Moreover, socioeconomic factors that influence undiagnosed risks among older people in a district might transcend and influence the health outcomes in neighbouring districts.

To capture those effects in the spatial analysis, INLA method requires a graph specification (Faraway et al., 2018) in a binary format that shows which nodes, or district identification numbers, are neighbours to one another. Based on Figure 6.2 for the state of Johor, we outlined the graph defined in a binary format for the first three of Johor’s districts;

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Figure 6.2 Example of a neighbourhood network in a graph map in the state of Johor, Malaysia

Figure 6.3 Example of a binary graph for the first three nodes/ districts in Johor

10 1 4 3 8 9 10 2 3 4 5 9 3 6 1 4 5 7 9 10

In Figure 6.3, the first number on the top row (10) indicates the number of nodes in Johor, which has ten districts. For the subsequent rows, the first number is the ID dedicated to each district, the second number is the number of neighbouring districts and the following numbers are their respective IDs. For instance, the row 3 6 1 4 5 7 9 10 refers to the Keluang district with ID number (3), which has six (6) neighbouring districts of Batu Pahat (1), Kota Tinggi (4), Kulai Jaya (5), Mersing (7), Pontian (9) and Segamat (10).

For the analysis, first, we describe the sample and then perform an unadjusted bivariate analysis by means of logistic regression, a generalised linear model (GLM). The output will be used to pick the independent variables that are significantly associated with the outcome. The odds ratio (OR) of each variable can be calculated by obtaining the 122

exponent values of the estimated coefficients. Second, for the adjusted multivariate analysis, we fitted a GLM to obtain the maximum likelihood estimations and also fitted another similar model by using INLA for posterior estimations. Again, we only picked those variables that were significantly associated with the outcome for the next analysis. Third, we operated a generalised linear mixed model (GLMM) with INLA to incorporate the hierarchical and spatial dependency structures concurrently. On this note, INLA can be specified in both GLM and GLMM (Schrödle & Held, 2011; Faraway et al., 2018). Lastly, we created maps from the posterior estimations in the GLMM to determine whether areal clusters exist.

We opted for a Bayesian hierarchical model with a spatially smooth CAR prior, to serve our purpose (Schrödle & Held, 2011; Morrison, 2017). A Bayesian approach allows for uncertainty in the estimates and deals with missing observations (Blangiardo & Cameletti, 2015). The posterior probabilities p(θ|x) is operated as in Equation 6.1, where p(x|θ) is the likelihood, while p(θ) is the prior probabilities. p(x|θ) denotes how likely x would be observed conditional on θ, the unknown parameter(s) of interest. For the following steps in the modelling process, please refer to the Appendix for Chapter 6.

Equation 6.1

푝(휃|푥) ∝ 푝(푥|휃)푝(휃)

In addition, we used the deviance information criterion (DIC) to select the best-fitted model, in which a smaller value of DIC is favoured (Darmofal, 2015). The DIC is defined as in Equation 6.2.

Equation 6.2

퐷퐼퐶 = 퐷 + 푝퐷 123

where 퐷 is the posterior estimation at deviance and 푝퐷 is the effective number of parameters (Faraway et al., 2018). The DIC selection approach is similar to the model selection using the AIC that determines the fit and complexity of models in the frequentist approach (Schrödle & Held, 2011).

Further information on the INLA methodology and BYM modelling are available elsewhere (Rue et al., 2009; Lindgren et al., 2011; Morrison, 2017; Faraway et al., 2018; Freni-Sterrantino et al., 2018). Our statistical analysis and modelling technique are computed by using the open-source R-statistical software version 1.0.153, together with a number of R packages that support our analysis (R Core Team, 2018). The R-INLA statistical package can be found at www.r-inla.org.

6.3 Results

6.3.1 Descriptive and bivariate analysis

Figure 6.4 shows a choropleth map of the mean prevalence of undiagnosed NCD risks among the older people across the Peninsular. Most of the 81 districts that we analysed show a large prevalence of between 39 to 58% of the population with undiagnosed risks. A few districts, in white colour, did not show any prevalence due to unavailability of undiagnosed observations. There are eight districts with the lowest prevalence. The highest prevalence districts were dispersed with small and distinct clusters formed in the central, eastern and northern regions indicated by the darkest colour scale.

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Figure 6.4 The mean prevalence of undiagnosed NCDs risks among older people in Peninsular Malaysia based on the NHMS. Note: NA - data unavailable

Table 6.1 presents the descriptive statistics together with the unadjusted bivariate analysis results. Of 5,432 respondents, nearly half of them were found to be undiagnosed. Those aged 50 to 59, who are yet to be considered as older people in Malaysia, represent about 47% of the sample. All age groups show significant associations with the outcome and the odds of undiagnosed diseases are lower with advancing age. There were about 11% more female respondents than male respondents.

NHMS found more than 50% of older people reside in urban areas. These urban residents display lower odds to be undiagnosed with about 21% less than the odds of rural residents. Meanwhile, the OR of employed respondents not knowing that they had disease risks was 87% higher than the OR of unemployed older people, who constituted more than 60% of the sample. Respondents who were better educated and lived in households with greater spending both displayed lower odds of undiagnosed risks. However, these variables are not significantly associated with the outcome.

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At the district-level, Table 6.1 shows that the majority of the older people sampled lived within a 5km radius from a public healthcare facility. Even though this variable is statistically significant, the odds to be found undiagnosed is a slight 1% higher than the odds of older people living outside the 5km radius. However, the other district-level variable, Gini, does not meet the statistical significance.

The bar plots in Figure 6.5 further highlight the difference in diagnosed and undiagnosed diseases for the age groups and genders. In addition, the plots also exhibit the severity of the health problem. Aside from the fact that undiagnosed prevalence decreases with age, each age group displays a proportion of more than 30% of older people who are not aware of their health conditions. Similarly, more than 40% of respondents for both genders were found to be undiagnosed. These percentages of undiagnosed problems correspond to global percentage figures, ranging from 24% to 75% across the regions in 2013 (Beagley et al., 2014).

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Table 6.1 Descriptive and unadjusted bivariate analysis

Descriptive Unadjusted Bivariate Variable N % β SD OR Outcome 5,432 Diagnosed = reference 2,821 51.93 Undiagnosed 2,611 48.07 Age 5,432 50-54 = reference 1,324 24.37 55-59 1,204 22.16 -0.302 0.080 0.739† 60-64 1,019 18.76 -0.412 0.084 0.662† 65-69 791 14.56 -0.569 0.091 0.566† 70-74 536 9.87 -0.740 0.105 0.477† 75+ 558 10.27 -0.684 0.103 0.505† Gender 5,432 male = reference 2,408 44.33 female 3,024 55.67 -0.199 0.055 0.820† Education 5,401 no schooling = reference 627 11.61 primary 2,452 45.40 -0.003 0.080 0.997 secondary 1,842 34.10 -0.020 0.090 0.980 tertiary 480 8.89 -0.163 0.122 0.850 Employment 5,432 unemployed = reference 3,359 61.84 employed 2,073 38.16 0.628 0.057 1.874† Strata 5,432 rural = reference 2,631 48.44 urban 2,801 51.56 -0.232 0.054 0.793† Spending (quintile) 5,266 1 (lowest) = reference 1,060 20.13 2 1,027 19.50 -0.080 0.088 0.923 3 1,083 20.57 -0.065 0.086 0.937 4 1,047 19.88 -0.044 0.087 0.957 5 (highest) 1,049 19.92 -0.166 0.087 0.847

Mean (%) SD (%) PH distance 92.78 6.94 0.011 0.004 1.011† Gini 34.77 3.34 -0.081 0.284 0.922

Note: PH- public healthcare facilities; Gini- Gini coefficient index. Significance: † p-value significant at 5% or less,

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Figure 6.5 Bar plot of diagnosed and undiagnosed NCDs risks among older people by age group (푋2= 85.44, p<0.001) and gender (푋2= 13.04, p<0.001)

6.3.2 Multivariate analysis

Table 6.2 presents the results of the adjusted multivariate analysis, which operates on two methods: logistic regression with a frequentist approach of conventional maximum likelihood estimation and a Bayesian approach by fitting a logistic regression model with INLA. In both methods, we wanted to obtain a reduced model by minimising the number of parameters, especially in INLA so as to reduce the possibility of multicollinearity (Faraway et al., 2018). Therefore, we selected variables that were significantly associated with the outcome in the earlier unadjusted bivariate analysis. Those variables were age group, gender, employment, strata and PH distance.

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Table 6.2 Adjusted multivariate analysis using logistic regression and INLA

Logistic regression INLA Variable β SD Mean SD 2.5% 97.5% Intercept -0.999† 0.385 -1.001 0.385 -1.758 -0.246 Age 50-54 = reference 55-59 -0.220† 0.082 -0.220 0.082 -0.381 -0.060 60-64 -0.261† 0.088 -0.261 0.088 -0.433 -0.089 65-69 -0.416† 0.096 -0.417 0.096 -0.605 -0.229 70-74 -0.544† 0.110 -0.545 0.110 -0.762 -0.329 75+ -0.430† 0.111 -0.430 0.111 -0.648 -0.214 Gender male = reference female -0.046 0.060 -0.046 0.060 -0.165 0.072 Employment unemployed = reference employed 0.496† 0.067 0.496 0.067 0.365 0.628 Strata rural = reference urban -0.267† 0.056 -0.268 0.056 -0.377 -0.158 PH distance 0.012† 0.004 0.012 0.004 0.005 0.020 Significance: † p-value significant at 5% or less

Both methods show similar estimations in the output. With the exception of gender, the other adjusted variables are significantly associated with the outcome in the frequentist method. The 95% credible intervals for gender contain zero, which means gender is not associated with the outcome. The posterior means for age groups and strata show negative effects of the variables, while the effects of employment and PH distance are positive. The variable of PH distance has a 95% credible interval value of 0.005 to 0.02, which signifies a weaker probability with the outcome.

Table 6.3 shows the summary statistics of the two spatial models; the null model and the full model. In the full model, the variable of gender is not included. From the output, the posterior for the fixed effects tend to be approximately normal, as there is no difference shown between the posterior mean, median and mode in the parameters for both models. For age groups, the posterior mean estimations decrease with advancing age and these effects are negatively associated with the outcome. The lowest possibility of being found

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undiagnosed is estimated for the age group of 70–74, while the age group of 55–59 is estimated to have the highest possibility instead, assuming all other variables are fixed. This finding demonstrates that older people are willing to seek and utilise healthcare with increasing age.

Table 6.3 Summary statistics of posterior fixed effects using INLA: mean, standard deviation (SD), 95% credible interval including the median, mode, and OR based on the posterior mean

Model Parameter Mean SD 2.5% Median 97.5% Mode OR

Null β0 0.496 0.01 0.476 0.495 0.516 0.495 1.642

Full β0 0.281 0.157 -0.028 0.280 0.591 0.280 1.324 β 55-59 -0.054 0.020 -0.093 -0.054 -0.015 -0.054 0.947 β 60-64 -0.062 0.021 -0.104 -0.062 -0.021 -0.062 0.940 β 65-69 -0.101 0.023 -0.146 -0.101 -0.056 -0.101 0.904 β 70-74 -0.133 0.026 -0.184 -0.133 -0.082 -0.133 0.875 β 75+ -0.100 0.026 -0.152 -0.100 -0.048 -0.100 0.905 β employed 0.124 0.015 0.095 0.124 0.153 0.124 1.132 β strata -0.054 0.016 -0.085 -0.054 -0.023 -0.054 0.947 β PH distance 0.003 0.002 -0.001 0.003 0.006 0.003 1.003

The output also shows that the 2.5% and 97.5% posterior quantiles for the employed variable are negative, which indicates with 95% probability that the variable’s effect is negative. This means that older people who are employed are likely to be found undiagnosed with the odds of about 12% higher than the unemployed. In addition, older people who reside in urban areas are 5% lower in the odds of undiagnosed risks than those who live in rural areas. The urban’s 95% credible interval values of -0.085 to -0.023 suggest its strong correlation with the outcome. The district-level variable, PH distance, shows a slight increase in the odds of undiagnosed risks by 0.3%. However, the 2.5% scale of the parameter lies below zero, indicating no association between PH distance and the outcome.

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Table 6.4 DIC value and posterior marginal variance for the fitted models

Model Null Full Mean of Deviance, 퐷 7774.7 7614.5 Deviance at Mean, 퐷(휃) 7737.8 7572.0 Effective number of parameters, 푝퐷 36.9 42.6 DIC, 퐷 + 푝퐷 7811.6 7657.1

Posterior marginal variance 0.36 0.33

In Table 6.4, the DIC for the full model is less than that of the null model. As such, we favour the full model with adjusted parameters, which has a better trade-off between fit and model complexity. The posterior marginal variance is 0.33 in the full model, suggesting that 33% of the variability is explained by the spatial structure.

From the output of the full spatial model, we constructed choropleth maps to visualise the distribution of posterior estimations of the districts. Figure 6.6 shows the maps of the distributions of posterior means for each district and also the posterior probability of older people of being found undiagnosed in a particular district. Clusters are clearly distinguished in the posterior probability map with three prominent features. First, clusters with the estimated high-risk prevalence of older people with undiagnosed diseases, represented by the darkest scale, lie at the northern and the southeast of the Peninsula. These districts are a mix of urban and rural areas with a dense population. Second, a large medium-risk cluster, represented by two grey shade scales, is situated in the centre region of the Peninsula. The districts in this cluster are mostly rural areas that are less developed and sparsely populated. And third, a cluster of estimated low-risk districts in the southwest, represented by the lightest scale, where the capital city Kuala Lumpur and a few other large cities are situated. These low-risk districts are more affluent and well- developed.

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Figure 6.6 Posterior mean for the district-specific undiagnosed NCDs risks compared with the whole of the study area (left) and posterior probability (right)

6.4 Discussion

The challenges in managing NCDs among older people in terms of improving ongoing care and support are complicated (Ambigga et al., 2011), even more so if they were earlier undiagnosed and untreated for a long period of time. In order to comprehend these problems in the context of a developing country, we have identified demographic and socioeconomic factors that are associated with undiagnosed NCDs risks among older people. Furthermore, we also identified spatial patterns of geographical clusters that differentiate districts with low and high levels of undiagnosed NCDs, after all the fixed effects had been adjusted for. This we achieved through generating choropleth maps of the Peninsula (Pfeiffer et al., 2008) which showed the distribution of the posterior means and the probabilities. Here, the maps enhanced our knowledge of the latent drivers of the diseases and how it is geographically distributed.

This study adds a number of key findings to the literature. Firstly, our findings reflect the fact that older people’s socioeconomic factors play a crucial part in the underlying problems of undiagnosed conditions. We also found undiagnosed NCD risks tend to 132

decrease with age, in contrast to diagnosed NCDs, which increase with advancing age (Prince et al., 2015). The decrease in the trend of undiagnosed diseases might possibly be that throughout that time, healthcare managed to find and diagnose them, and also that some of the older population eventually succumbed to the disease and died.

We also found that older people who were employed are more likely to be found undiagnosed and a similar observation was reported by local researchers (Cheah & Goh, 2017). Employed people are usually more mobile and yet may visit health clinics less than those who are unemployed, as they feel active and relatively healthy (Arcury et al., 2005). Evidence from China showed that forgone care of chronic diseases was relatively high among older people with low socioeconomic status and that this trend decreases with age (Li et al., 2018). We suspected that time constraints due to working hours and longer waiting times in health clinics might be the causes in Malaysia. In light of this evidence, districts with a higher proportion of employed older people should increase health screening efforts by encouraging older people to go for a health check-up.

In contrast with the findings by Arokiasamy et al. (2017) in developing countries, we found that education levels and greater wealth based on household spending capacity cannot explain the odds of the undiagnosed outcome. This shows that within the context of the country, educated and wealthier people would have a similar chance as those who are poor and less educated to be found undiagnosed. On the other hand, Wu et al. (2013) pointed toward wealthier respondents in China who were more likely to seek healthcare particularly after they noticed or experienced symptoms of NCDs. People with a higher level of education are also found to be more willing to seek better health and living with a healthy lifestyle (Di Cesare et al., 2013). In addition, we did not find gender differences among older people to play a significant part in determining the odds of undiagnosed NCDs risks. In contrast, Jaffol et al. (2013) observed that the prevalence of undiagnosed diabetes was significantly higher in men than women in developed countries.

In our analysis, there is no significant difference between female and undiagnosed NCDs risks in the multivariate models prior to the spatial analysis. A possible explanation for this observation might due to both male and female are almost equally likely to have

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undiagnosed NCDs. Another possibility would be that undiagnosed NCDs among the females might decrease with age and they became aware of their health conditions in their later years, much earlier than males. This should be the case as the inverse coefficients in the models demonstrate that males are more likely to be undiagnosed. On a similar note, Mazza et al. (2007) also reported a non-significant relationship between BMI and overall mortality in older women. Taking into account the spatial context, the male-female factor would exhibit complex social relations and structures in estimating NCDs as pointed out by Weimann et al. (2016) and needs to be examined further.

Secondly, we observed that geographical patterns could enhance our understanding of the distribution of undiagnosed NCDs risks. Based on the findings, the outcome risks were unevenly distributed across the Peninsula, and significant spatial clusters could be identified where similar districts with either high or low posterior estimations clearly stood out. Moreover, we found 33% of the variability in the full model is explained by the spatial structure. This shows spatial dependence on the parameters that determine the prevalence of undiagnosed risks between the neighbouring districts that border each other. This finding highlights the importance of assessing geographical patterns, namely districts clustering that may need distinctive healthcare strategies rather than a general policy for the non-cluster districts. However, the two district-level variables that we used, the Gini and PH distance, are not significantly associated with undiagnosed risks. On the contrary, Bell et al. (2011) implied that neighbourhood effects could define the health status of the residents, and these effects are reinforced by the distance to reach healthcare services, in which a farther distance would reduce utilisation of those services. Future research should find other district-level variables to explain undiagnosed risks occurrence in Malaysia.

Lastly, we found that undiagnosed risks can be viewed as a development problem relating to obstacles that prevent access to healthcare in a particular area (Chand, 2012). We have shown that undiagnosed risks vary across districts, with higher odds of the risks in rural areas that are underdeveloped and with fewer healthcare facilities. Our findings also demonstrate that areas in near proximity to larger cities, especially on the West Coast of the Peninsula have lower odds of undiagnosed risks than other districts. Many researchers

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found that distance plays an important role in health checks uptake (Astell-Burt et al., 2011; Cullinan et al., 2012; Bell et al., 2013).

In developed countries, it is known that older people are less likely to travel long distances to seek healthcare compared to younger populations (Somenahalli & Shipton, 2013). Nemet et al. (2000), who investigated the association between distance and the utilisation of healthcare by older people aged 65 and above in a rural region in the United States, reported that increased distance from a healthcare provider significantly reduces service utilisation. Tsuji et al. (2012) concluded that geographic accessibility in terms of distance to a healthcare facility in one of Japan’s prefectures was an important factor for the utilisation of the services for older people. Other factors such as car ownership and a better income are known to affect healthcare utilisation, especially in rural areas (Arcury et al., 2005, Comber et al., 2011). On a different note, Arokiasamy et al. (2017) highlighted that the high prevalence of undiagnosed and untreated NCDs in developing countries might indicate the inadequacies in the diagnosis and care management of NCDs.

A number of limitations should be mentioned. First, undiagnosed NCDs risks were determined by a self-reporting question on whether a respondent had been told by clinicians that he or she was having the diseases. This self-reporting might expose to risks of under-reporting due to recall bias and misunderstanding of the questions asked. Apart from that, bias and errors due to measurement inaccuracies by the enumerators, especially in getting blood samples, could occur and these are not documented in the NHMS report (MOH, 2015a).

Second, especially in spatial GLMM, covariates that are spatially smooth are often collinear with spatially smooth random effects (Hanks et al., 2015). This is known as spatial confounding that may lead to misleading interpretation. On one hand, the associated NCDs risks for a district being in a cluster might be confounded by the SES disparities between the regions. On the other hand, independent variables having a spatial pattern may confound with the spatial random effects, resulting in fixed-effects estimates that are arbitrary or not useful. One solution to this problem is by operating a restricted spatial regression especially in areal spatial data that is discrete (Prates et al., 2014).

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However, they advise that running spatial alleviating methods such as restricted spatial regression approach may result in different coefficients and inferences. Thus, it is unclear such approach could be meaningful in investigating NCDs occurrences.

Third, it is challenging to come up with a good choice of priors in the BYM model that could determine the posterior estimations of undiagnosed risks distribution. We chose minimally informative priors (Blangiardo & Cameletti, 2015) so that the estimations are being informed by the likelihood more than the priors. However, the choice of priors is diverse and the use of different types of priors would provide varying precision of the estimations.

Lastly, maps are known to have the potential to inform as well as to mislead. Although choropleth maps are widely used in diseases and health risks mapping, they have two inherent problems. As pointed out by Pfeiffer et al. (2008) the component polygons of the study region, which are large and seems to take over the map, could induce bias in the interpretation. As such, the disease would be visualised as more severe and widely spread than the actual distribution. They also noted that a skewed disease distribution is difficult to display on a map using a finite number of colour shading scales. This might lead to misinformation or loss of evidence on the importance of that particular skewed distribution.

The ultimate objective of our line of research is to inform and improve policy formulation. While life expectancy and the number of older people are increasing in Malaysia, the quality of those additional years remains unclear. Public health response needs to be relevant to address the diversity in the health and social aspects of older people. Variations in undiagnosed risks present important challenges for policymakers formulating policies that could improve access to healthcare.

However, rapid changes in the demographic profile of the ageing population and the increased burden of diseases give this research undertaking some urgency. We demonstrated that spatial analysis and hierarchical approach in understanding the data offer greater opportunities in terms of monitoring disease improvement and distributing healthcare resources effectively. The spatial analysis provides a modelling technique that 136

is both data- and knowledge-driven (Pfeiffer et al., 2008; Darmofal, 2015). Nevertheless, understanding posterior estimations of the parameters in this study is only part of the solution. The essential part lies in how best to use this knowledge as priors for future research that perhaps use a similar approach like ours, which hopefully will benefit the health outcomes of the population of interest.

6.5 Conclusion

Over the years, the Malaysian government has managed to provide public healthcare by drawing from available resources that are limited. However, our findings showed that greater improvements are required to reduce geographical variations in terms of undiagnosed risks among the older population. This indicates that the trend of undiagnosed risks is likely to remain or might intensify in the absence of mitigating actions and pose a persistent challenge to the public. This may also indicate that additional resources are needed to implement such actions. At present, there is a lack of studies that investigate undiagnosed risks among older people, particularly in the spatial context. We showed that spatial disparities in undiagnosed risks are likely to increase in less developed districts. At the individual level, older people who are employed are at risk of being undiagnosed and thus, of being untreated. While their conditions remain undiagnosed, older people might face delayed treatment at a higher cost and their conditions may gradually becoming life-threatening.

We applied INLA, a novel spatial analysis approach based on a Bayesian framework that has potential in estimating and mapping posterior distributions of diseases and their risks. It is also an invaluable instrument for further understanding of the spatial context of health outcomes. This method is able to approximate precisely, based on the given data, areas of elevated risk and enable geographic targeting of effective interventions that are area- specific. Moreover, it can be used to track spatial clusters and gauge improvement in interventions as additional data becomes available in the future.

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We conclude that there is a strong justification to better allocate healthcare resources based on districts’ needs in order to manage health risks. Presented with maps, we are inclined to see spatial structures in the form of districts’ clustering. In other words, if policymakers were to improve health conditions, it is crucial for them to understand the geographical patterns of the risks. In addition, our findings also signify the geographical pattern of healthcare utilisation of older people across the Peninsula. Therefore, policymakers should begin to consider contextually sensitive policy interventions that are precise and targeted, especially to mitigate undiagnosed NCD risks among older people. Now, it seems without doubt, that the healthcare policy direction is clear.

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Chapter 7

Discussion

“When you can measure what you are speaking about, and express it in numbers, you know something about it; but when you cannot express it in numbers, your knowledge is of a meagre and unsatisfactory kind” ~ Irish mathematical physicist, William Thomson (1824-1907), also known as Lord Kelvin who introduced the absolute zero temperature scale or Kelvin scale in 1848.

A paramount duty of a good government is to promote social justice. Thus, it is important to recognise social disparities in health as an occurrence that place individuals already in disadvantaged circumstances at a further disadvantage point due to their poor health status (Braveman et al., 2004). For instance, socially disadvantaged groups are more likely to experience adverse, health-harming physical and stressful working conditions, along with disadvantaged living situations associated with their low income. This occurs when segments or classes in society emerged, where social and economic resources and opportunities, systematically sort individuals into healthy and unhealthy living and working conditions (Settersen and Trauten, 2009; Braveman, 2012). Therefore, good health is critical for groups or individuals to escape from their social disadvantages.

This thesis provides evidence that social disparities are an important discourse in understanding health differences, particularly among older people. It highlights the importance of considering health issues of the older people at the heart of ageing research and in all stages of the health policy and implementation process.

Evidence has long been established in developed countries that any efforts to address the burden of diseases require multilevel and multi-strategic praxis in formulating effective policies. Awareness is growing among the policymakers that healthcare alone cannot adequately improve the overall population’s health or reduce health disparities without

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also improving the people’s living conditions and quality of life. In developing countries, very little is known about effective ways to address social disparities in order to improve health and reduce disease prevalence, especially on when, where, and how to intervene.

7.1 Key findings

Chapter 3 examines the two common mental health disorders; GAD or anxiety and depression among older people, which are combined into a single outcome in the analysis. Older people may experience either anxiety or depression at a time, or suffer both concurrently. These disorders can occur for no obvious reason but they can be triggered by certain personal events such as retirement, bereavement, financial difficulties, relationship problems, disability and poor health. The symptoms often develop gradually in older people that can lead to other health problems and a decline in their daily functioning.

The findings in Chapter 3 suggest the need for a healthcare service to focus on older women who have a higher risk of mental health disorders. With a higher average of life expectancy at birth, they are expected to live longer than older men. They may experience loss of support and loneliness that might worsen their health. Apart from that, older people who complain about their poorer health and physical limitation status should also be taken seriously by the clinicians, as warning signs of mental health problems. In terms of ethnicity, the Chinese are less likely to experience mental health disorders than the Malays, however, this should not be an excuse to limit them from accessing related healthcare services. An interesting finding is that older people who live in urban areas are less likely to suffer disorders than those in the rural. One might put forward a reasonable explanation as such that healthcare services are better developed in urban areas. Other than that, explanations could be that urban areas offer better housing, employment opportunities, recreational activities and other amenities that might have lower effects on older people in experiencing mental health risks.

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At the district-level, OADR shows a significant relationship with mental disorders, where districts with higher values of the dependent ratio are more likely to show a higher prevalence of anxiety and depression. This suggests that higher OADR is driven by outmigration of adults from a developing area to a more developed area. Due to the reduced number of adults and young population in the district, older people would probably have less support in the communities, thus suffer emotional distresses.

By considering OADR as a district feature, policymakers would now have better interpretations of the individual-level factors’ significance in explaining the health outcome. For instance, older women may face a greater risk of mental health disorders in districts with higher OADR compare to older women in other districts. In view of this context, more emphasise in providing related resources and mental health services should be given to women in districts with a higher number of older people as dependents.

Chapter 4 analyses the problem of unmet cardiovascular care needs as a health outcome. Here, unmet is a result of older people with high CVD risks, who did not receive any treatment, medication and care for their conditions. Unmet care needs have become an emerging health issue in developed countries and there are many reasons for this. Among them are individual’s financial constraints, lack of transportation mode and inadequate healthcare services in a particular area. Even with such difficulties, local research on the unmet health needs of older people has been limited. Thus, it is important to acknowledge social factors that influence unmet needs in order to reduce the number of older people with poorer health.

By using the two established algorithms; the FRS and the SCORE, the unmet needs among older people in Malaysia is found significant, and the prevalence increases with advancing age. The multilevel models put forward that employed older people are more likely to be found with unmet needs than the unemployed. These findings might be more pronounced in districts with greater OADR and BOR. OADR appears to be a crucial indicator at the district-level in determining the prevalence of unmet cardiovascular care needs, similar to findings in Chapter 3. On the other hand, BOR indicates great importance to assess the efficient usage of valuable healthcare resources. Constant bed shortages in hospitals

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should spark concerns about unmet care needs and such a measure can be a valuable tool to detect the problem in a particular district.

Chapter 5 explores the geographic distribution of Type 2 diabetes among older people that is further characterised into two health outcomes of known and undiagnosed diabetes. The NHMS report published in 2015 also gave a descriptive account of both outcomes with undiagnosed diabetes represent a significant percentage of the disease prevalence. Despite this, undiagnosed diabetes has not been well considered in previous diabetes studies, which gave more weight on known or diagnosed diabetes. This is a concern because nearly 9% of adults including older people with diabetes in 2015 as reported are unaware of their conditions and are likely to have higher risks of diabetes-related complications.

Chapter 5 shows that the estimated prevalence of known diabetes increases with advancing age, while, undiagnosed diabetes decreases with age. The findings also show that each outcome is determined by different socioeconomic factors. For known diabetes, older people in households with the highest spending capacity is an explanatory factor. This indicates that older people with greater wealth have better access to healthcare, and in a way, diabetes is still a disease of affluence. For undiagnosed diabetes, older people who are employed are likely to be found undiagnosed. The finding points toward the problem of long waiting times in health clinics that cause people to delay their health check due to time constraints. For these reasons, each diabetes outcome should be dealt with unique and distinctive policy interventions.

At district-level, known diabetes is explained by the districts’ poverty incidence where older people who live in districts with lower incidence recorded are estimated more likely to be found diagnosed. However, undiagnosed diabetes is not being determined by any other higher-level factor measured in the analysis. It becomes clear that undiagnosed circumstances are inclined toward personal choice in seeking healthcare and not being influenced by factors associated with the area or district. Meanwhile, at a higher level of inference, known diabetes can be explained by the affluent status of a district, where districts with better socioeconomic development that assures improved healthcare services, would reveal a higher number of people with diabetes.

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This chapter also offers visual evidence of maps that display the geographic distribution of both diabetes outcomes based on the estimated prevalence in the multilevel models. Apart from providing geographical information, maps could enable healthcare interventions to be made more precise and targeted. This advantage is critical in order to generate proper estimations of future healthcare needs and costs to overcome the diabetes problem.

Chapter 6 investigates the spatial distribution of undiagnosed NCDs risks of elevated blood pressure, raised blood glucose and cholesterol level among older people. This chapter considers the influence of neighbouring districts’ effects has on the prevalence of undiagnosed NCDs risks. By considering the spatial dependence structure in the data through a Bayesian framework, spillover effects of socioeconomic factors resulting from the sharing of borders and spatial proximity are proven to influence the health outcome in a particular district.

The findings in Chapter 6 hint that certain individual disparities occur. In terms of age, older people in the advanced age groups are less likely to be found undiagnosed with NCDs risks. Also, the effects of residing in urban districts positively affect older people’s motivation to seek healthcare than those who reside in rural districts. However, employed older people are estimated more likely to be unaware of their health conditions compared to unemployed older people. Although none of the district level factors is able to significantly explain undiagnosed NCDs risks, this does not rule out the possibility that the effects of individual-level variables may still differ due to contextual factors.

7.2 Surprising non-significant findings This thesis highlights the need to understand the interaction of social determinants of health, including SES, in influencing the health outcomes that are being investigated, in order to address health problems faced by older people more effectively. This understanding will lead to a better formulation of healthcare strategies aimed at the prevention and treatment of NCDs that include CVD and mental health disorders. This

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will also gradually disseminate into improving other interrelated segments such as gender equity, income level and educational opportunities, and issues of neighbourhood conditions and physical environment among others.

However, the findings in the empirical chapters reveal a number of important social determinants of health do not reach statistical significance for certain health outcomes or problems. In Chapter 3, household income and educational attainment are found to be not significantly associated with mental health disorders, though evidence showed both factors are strong predictors for this health problem. In Chapter 4, the urban factor displays a non-significant relationship with unmet CVD care needs. Yet, the urban-rural socioeconomic gradient is an important discourse to further explain the influence of SES on unmet care needs. In Chapter 5, again, the urban factor together with the household spending, from the lowest to the fourth quintile, do not show any significance with both outcomes of known and undiagnosed diabetes. The findings in this chapter also show that the female variable is a significant factor in known diabetes when contextual factors are considered in the multilevel models. In contrast, the variable shows a non-significant relationship in undiagnosed diabetes. In Chapter 6, the female factor once again is found to be non-significant in determining the undiagnosed NCDs risks.

On one hand, these unexpected and conflicting findings do not necessarily mean that there are no beneficial effects of those factors on the health outcomes measured. For a non- significant female variable, this is likely due to increased awareness of diabetes, persistent efforts to identify individuals with the disease, and improved clinical care resulting in many of those with the previously undetected disease having been diagnosed. At this point, further research is required to explore and understand the conflicting findings in different samples of older people or in other younger populations.

On the other hand, the non-significant factors might indicate that these factors are evidently not essential in estimating the rate or prevalence of certain health problems. However, these considerations should be acknowledged in formulating healthcare strategies and policies. For instance, policy inputs on the female gender, while non- significant, are still necessary for improving the women’s general health status,

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particularly in Malaysia. In this case, information regarding women’s health would bring about the much-needed collaboration of government agencies at multiple levels - municipal, district and state levels, for development planning especially in urban areas in creating better living conditions and reducing social disparities for older women.

7.3 Implications for theory

Improvement in health requires efforts in addressing social disparity by ensuring effective strategies and policies are in place so that resources can be allocated fairly. Moreover, individuals are being given options and opportunities to enhance their quality of life as well as their living arrangements. In view of this, the government has an obligation to advocate social justice in order to alleviate social disparity and reduce differences in health. By considering the human capability perspective in pursuing the quality of life, it is also important to revalue individuals, herein older people, as subjects of their actions and to allow them to progress in their respective social structures. Such strategies and policies could also enable the government to promote ‘just institutions’ via the organisational structure of ministries and departments. These institutions might be in ‘partial compliance’ in meeting their obligations to achieve social justice, in this regard the right of the older people to achieve good health, by implementing various healthcare projects and programmes.

In order for the ‘just institutions’ to comply and perform their duties, they should not be seen to discriminate people in terms of their social groupings and living arrangements. However, it is evident from the findings in the empirical chapters that social and socioeconomic disparities in health still persist among older people at multiple levels – individuals and districts. Though certain social determinants are identified, including some SES factors at the individual level, that have effects on a particular health problem faced by older people, one will still question the duties of these healthcare authorities on their part in addressing the problem. To facilitate the authorities in formulating effective strategies and policies, novel approaches in policymaking such as multilevel regression

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(Chapter 3, 4 and 5) that could estimate health prevalence at a higher level, is greatly needed. Now, policymakers are able to obtain insights into the health problem from both individual- and district-level to formulate effective health policies that are precise and targeted. This approach could also bring about multi-sectoral collaboration with other ministries and departments as well as private sectors and non-governmental organisations in addressing a certain health problem or health difference. The end result from such an undertaking might not be ideal in meeting social justice as more data and information is required. Nonetheless, the policymaking tools that the multilevel approach offer would improve resource allocation which makes certain that most people would have fair and equal access to healthcare.

In addition, this thesis also brings forward the spatial context perspective that we now recognise as an important dynamic in influencing health outcomes. By taking into account this context, policymakers would have a better understanding of distinct behaviours of individuals and groups in different areas. In Chapter 6, the clustering of districts that marked the intensity of the health outcome demonstrates the idea that locations which are closer with each other would have more similarity in values of attributes than locations which are further apart. The operative notion here is that the more the health of older people within an area is similar to one another compared to older people in other areas, the more likely the determinants of health may have to do with the environment and characteristics of that particular area. The spatial boundaries that define the level of analysis here, the districts, could also have strong influences in inspiring older people to adopt a healthy lifestyle. For instance, a higher number of recreational areas would motivate them to do physical activities, and a higher number of stores that sell fresh foods located within their residential areas could encourage them to consume more healthy meals.

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7.4 Policy implications and implementations

Due to limited resources, it is difficult for a social institution such as a health authority to achieve full compliance with the demands of justice. A realistic aim for the agency would be to set a partial compliance approach by targeting policies and intervention efforts on certain areas or communities. As a result, a greater impact on health can be attained with optimal resources through a number of policy implications as well as implementations.

First, the present healthcare planning for the older population by various agencies is done in silos. As a result, the desired outcome may not be holistic, which also makes health monitoring and evaluation quite challenging. Therefore, as put forth in the empirical chapters, multi-sectoral efforts are needed to overcome health issues that are beyond a health authority’s control and jurisdiction. These efforts are also suggested to overcome variation and randomness of social and health determinants that could not possibly be controlled by a single agency.

On the above point, the Health in all Policies (HiAP) initiative set by the WHO (2014a) could be leveraged by the MOH. HiAP is an approach that improves the accountability of policymakers to address health impacts at all levels of policy-making. This approach can strengthen the capacity of MOH to engage with other ministries and governmental agencies by providing a framework of leadership, advocacy, partnership and mediation to focus on health issues. By merging the multilevel perspective together with the spatial context of a certain health issue, agencies including the MOH could identify and acknowledge their roles in the formulation of policies that involve them, collaboration in multi-sectoral efforts and sharing of resources such as human capital, assets and facilities, and technological modalities. Apart from that, HiAP also provides a platform that allows communities, civil societies and other social movements to be involved in the development, implementation, monitoring of strategies and policies, and contribute to improve health literacy, especially for the older population.

Second, the investigation of area or neighbourhood effects offers a unique opportunity for learning how health disparities evolved. This opportunity also highlights the need to

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develop models and strategies that can explain the variation of older people’s health at multiple levels; regions, states, districts, communities and individuals. For example, policymakers need to grasp the idea that higher-level effects eventually trickle down to individuals without disregarding the mutual influences and interdependencies among people, within places, and between people and the places in which they live.

Third, the empirical chapters have identified regions with a high prevalence of older people with diabetes, CVD, mental health disorders and NCDs risks. This information may facilitate policymakers to design urgent programmes for early detection of illnesses and at the same time to raise awareness on the prevention of those health risks especially the CVD. These programmes may include implementation of annual screening programs for early diagnosis of both diabetes and hypertension, and promotion of healthy lifestyles that include modifying dietary habits namely reducing the intake of salt, sugar and high caloric food, controlling the body weight and managing the blood pressure level.

Lastly, the policy directions in this thesis should be two-fold – able to influence individuals to make healthier choices and to ensure health authorities to allocate resources effectively. On a side note, social disparities in health are good measures of how we are performing as a society and as a government. However, due to the fact that the largest group of those who use healthcare services are older people, there is the risk of reinforcing a view of older people as in constant need and passive recipients. This could lead to bias, stereotyping, and discrimination against older people (WHO, 2015a) that may instead have harmful effects on their health. Moreover, there is also the likelihood that care needs are ‘unmet’ or diseases are ‘undiagnosed’ for the reason that of individual personal resistance to seeking healthcare, or other individual preferences such as getting complementary and alternative medicines which are customary in developing countries.

7.5 Strength of the thesis

A key strength of this thesis is the focus of its research on social and health issues of older people. On a related note, little is known about the health status of older people and their 148

health differences between social groupings and administrative areas in Malaysia. Due to the longevity in life-expectancy and the increase in health expenditure, the need to study older people that involves their social and health-related aspects has become necessary. Also, there has been a growing awareness among policymakers to evaluate present strategies and policies in health continuously. This is to ensure healthcare interventions are always relevant and able to address the health challenges faced by the older population.

The methods used here namely the multilevel regression and INLA are novel approaches in the health research of older people. The literature in this field of research using the two methods is still lacking in the country and the findings from this thesis could contribute to add knowledge and better understanding of the similar approaches in future research. By means of the multilevel approach, tools such as district rankings in Chapter 3 and 4, and maps in Chapter 5 can be developed to display the estimates and distribution of the prevalence of a particular health problem in visual and graphical forms. By means of INLA in Chapter 6, this latest spatial modelling is leveraged to map areal clusters of health risks. This provides an invaluable tool in understanding health distributions via a spatial dimension that considers the effect of neighbouring areas on a district. In addition, the technique of multilevel and spatial analysis used is adequate to capture both the complexity and diversity of issues on older people, their health and disparities.

7.6 Future research

Researchers should continue to push forward the research agenda of ‘social disparities in health’ in developing countries especially among older people. The questions are no longer about whether social factors are important influences on their health, but rather about how social factors operate and how to mediate them in activating health-promoting pathways and finding avenues to avert health-damaging ones. Such knowledge would help to further prevent the escalating prevalence of chronic diseases of older people and allay premature disability and morbidity. Up until now, too little attention has been given in investigating

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higher levels of determinants, such as socioeconomic progress, economic development, environmental and other external influences. Although most health research have focused on the downstream social factors (Putnam and Galea, 2008), the fact is that higher level or upstream determinants denote better inferences of the fundamental causes in pathways that influence downstream factors.

Furthermore, too little attention is also given to social disparities involving factors of race and ethnics. Even though this factor is a sensitive issue to address in a developing country such as Malaysia, for the purpose of improving the service delivery, it is crucial for policymakers to comprehend the dynamics of race and ethnics in influencing differences and variations in health among older people. For instance, this thesis found that the Chinese have a better mental health ability compared to the Malays, Indians and other ethnicities that is probably due to their better position in the economy. Another reason possibly is that most Chinese reside in developed areas with easier access to healthcare.

7.6.1 The importance of a multilevel perspective

A multilevel model explicitly recognises that individuals behave in a particular context. However, in developing countries, such contextual framework is still lacking in investigating health and diseases. Correspondingly, multilevel models are able to examine the interplay of individual and higher-level characteristics. By separating out the between- area and within area contributions of area effects, it is possible to see the extent to which they actually explain any differences between areas, once the characteristics of the individuals who reside there have been taken into account (Joshi et al., 2004). Understanding the appropriate level in which contextual processes and individuals operate as well as how their effects distributed along the spatial scale (Cummins et al., 2007) are key research areas. As health is a diverse and intricate field to comprehend fully, it is imperative that evidence guide formulation of contextual driven policy and interventions.

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7.6.2 A need for longitudinal data and research

It should be noted that the findings in this thesis are all stochastic, which occur randomly and may not be predicted precisely, and not deterministic. This is partly due to the nature of the cross-sectional data used, the NHMS that only captures observations at a specific point in time. Moreover, cross-sectional data does not consider the temporal dimension of the health outcome being investigated. Therefore, more life-course research is necessary, including longitudinal studies to generate databases with comprehensive information on demographic, social, socioeconomic and health factors. Longitudinal data is much preferred in order to gain causal relationships and to explore the spatiotemporal structure that would clearly raise the validity of the estimations.

7.6.3 Considering the spatial context

Existing empirical research has put the importance of area analysis in assessing population health and investigating how social disparities affecting health are shaped and persistently occur. However, knowledge beyond the present conceptualisation in policy is necessary to fully comprehend the complicated spatial interdependencies between different levels described variably by different individuals, social and other higher-level groups (Cummins et al., 2007). Here, there is an urgent need for research in this field to shift from a conventional view into a relational one. This thesis found, for example, districts with geographical boundaries drawn at a specific scale are not static and fixed but operate in nodes of networks that are fluid and dynamic. This allows a district attributes in term of health to a certain extent influences its neighbouring districts health attributes. However, a fuller understanding of the effect of neighbourhood’s environment on health will require new data on a specific area or neighbourhood attributes, and studies specially designed to test hypotheses regarding the processes through which neighbourhood or area effects may be mediated.

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Research that involves advancing the measure of social factors is a vital requisite for effective efforts, especially in descriptive studies and monitoring. The causal relationship between these determinants is beyond the scope of this thesis, but it makes up an important insight for future research. A single research study is unable to encompass an entire pathway of upstream and downstream factors. Besides that, ongoing descriptive research is needed to track and monitor changes over time of the distributions of demographic and socioeconomic determinants at various levels, along with the effort to track the population’s health status. The findings can help policymakers to set up future targets and to outline priorities with the purpose of updating policies and interventions according to those changes.

7.7 Concluding remarks

Based on the recommendations by WHO (2015b), policymakers should be mindful that older people with greater health needs may also be those with fewer means to access healthcare. Sen (2009) reminds that social change is an ongoing, random process that cannot be prescriptive. Therefore, policies ought to strengthen the capacity of older people to seek and thrive towards good health according to their capability and functioning; to think through what they are actually able to be and do. Policies, interventions and institutional changes that are feasible and practical, should also be complemented by a ‘local’ focus that accounts, for instance, the local culture and social context (Hamlin and Stemplowska, 2012). This also requires a clear understanding of older people’s health circumstances and focus on what can be done appropriately and effectively to enhance service delivery.

In this thesis, social disparities are found to be an important discourse in understanding health differences at multiple levels - individuals and also districts where they resided. These levels are key areas in understanding how individuals and contextual processes operate as well as how their effects distributed along the spatial line.

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Here, the evidence could also help policymakers to formulate effective health policies that are recent, precise and targeted. While the prevalence of older people, for instance, with NCDs has been increasing over time, it is essential to identify and gather more information on the sociodemographic and socioeconomic groups, and geographical regions (particularly administrative districts) to reduce the NCDs related mortality. In addition, more research is required to investigate social disparities in areas that specifically focus on technological access to services and information due to older peoples’ inability or limited physical function, waiting times and inadequate consultation times with clinicians, early disease screening and detection, and the ageism; a negative perception of older people by younger individuals, healthcare and welfare workers, and society as a whole.

In the context of Malaysia as a developing country, the implications of a changing age structure and an increase in the number of older people are still not fully understood. Moreover, what explicit actions to be taken is also not very clear, as research on ageing and older people in Malaysia is still at its infancy. Yet, the need for evidence-based policymaking is pressing. Population ageing is itself a silent epidemic. So, there is a need to develop integrated and innovative research strategies to provide relevant policies and programmes. More targeted input around specific research and policy areas could be achieved by focussing on fewer important health issues, such as whether healthcare are adapted or developed to older people’s needs, and whether older people have difficulties in accessing healthcare services. Moreover, policymakers should consider contextually- sensitive healthcare policies to better manage adverse health outcomes. On this juncture, new ways of thinking at all policy levels are needed in order to prevent the health crisis of old age in the country.

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A. Appendix

Chapter 1

State/ Gini No. District Population* Region 2014 2016 1 Batu Pahat 417,458 0.292 0.295 2 Johor Bahru † 1,386,569 0.337 0.360 3 Kluang 298,332 0.297 0.376 4 Kota Tinggi 193,210 0.246 0.340 5 Mersing 20,894 0.284 0.357 Johor/ S 6 Muar 247,957 0.289 0.317 7 Pontian 155,541 0.274 0.352 8 Segamat 189,820 0.297 0.319 9 Kulai 251,650 0.248 0.286 10 Tangkak 136,852 0.294 0.310 11 Baling 135,646 0.347 0.404 12 Bandar Baharu 42,341 0.333 0.362 13 Kota Setar † 366,787 0.358 0.413 14 Kuala Muda 456,605 0.366 0.381 15 Kubang Pasu 220,740 0.384 0.445 16 Kulim 287,694 0.346 0.353 Kedah/ N 17 Langkawi 94,777 0.352 0.307 18 Padang Terap 62,896 0.330 0.348 19 Sik 67,378 0.347 0.434 20 Yan 68,319 0.363 0.350 21 Pendang 94,962 0.364 0.405 22 Pokok Sena 49,506 0.318 0.377 23 Bachok 133,152 0.386 0.406 24 Kota Bharu † 491,237 0.394 0.379 25 Machang 93,087 0.416 0.371 26 Pasir Mas 189,292 0.424 0.427 27 Pasir Puteh Kelantan/ 117,383 0.387 0.379 28 Tanah Merah EC 121,319 0.387 0.352 29 Tumpat 153,976 0.370 0.380 30 Gua Musang 90,057 0.300 0.340 31 Kuala Krai 109,461 0.332 0.377 32 Jeli 40,637 0.362 0.396 33 Besut 140,952 0.344 0.342 34 Dungun 154,932 0.359 0.336 35 Kemaman 171,383 0.371 0.328 Terengganu/ 36 Kuala Terengganu † 343,284 0.349 0.325 EC 37 Marang 97,857 0.345 0.301 38 Hulu Terengganu 72,052 0.303 0.231 39 Setiu 55,517 0.299 0.295

193

State/ Gini No. District Population* Region 2014 2016 40 Alor Gajah 182,666 0.318 0.334 41 Jasin Melaka/ C 135,317 0.289 0.367 42 Melaka Tengah † 503,127 0.317 0.330 43 Jelebu 39,200 0.313 0.352 44 Kuala Pilah 66,092 0.349 0.358 45 Port Dickson 115,361 0.337 0.361 Negeri 46 Rembau 43,011 0.346 0.338 Sembilan/ C 47 Seremban † 555,935 0.363 0.385 48 Tampin 84,889 0.325 0.364 49 Jempol 116,576 0.285 0.305 50 Bentong 119,817 0.344 0.313 51 Cameron Highlands 38,471 0.288 0.268 52 Jerantut 91,096 0.346 0.326 53 Kuantan † 461,906 0.348 0.317 54 Lipis 89,730 0.380 0.327 55 Pekan Pahang/ EC 110,633 0.314 0.295 56 Raub 95,506 0.345 0.282 57 Temerloh 165,451 0.382 0.336 58 Rompin 114,901 0.332 0.294 59 Maran 115,424 0.326 0.313 60 Bera 97,882 0.324 0.272 61 Batang Padang 179,494 0.366 0.330 62 Manjung 232,277 0.374 0.367 63 Kinta † 767,794 0.355 0.360 64 Kerian 179,706 0.349 0.341 65 Kuala Kangsar 159,505 0.379 0.364 66 Larut, Matang & Perak/ N 334,073 0.342 0.340 Selama 67 Hilir Perak 208,570 0.380 0.393 68 Ulu Perak 91,218 0.387 0.378 69 Perak Tengah 101,128 0.390 0.389 70 Kampar 98,978 0.356 0.330 71 Gombak 682,226 0.377 0.341 72 Klang † 861,189 0.373 0.361 73 Kuala Langat 224,648 0.321 0.321 74 Kuala Selangor 209,590 0.371 0.369 75 Petaling Selangor/ C 1,812,633 0.387 0.397 76 Sabak Bernam 105,777 0.354 0.328 77 Sepang 211,361 0.373 0.329 78 Hulu Langat 1,156,585 0.334 0.338 79 Hulu Selangor 198,132 0.327 0.245 80 Perlis † Perlis/ N 231,541 0.346 0.327 81 Seberang Perai 371,975 0.317 0.330 Tengah Pulau 82 Seberang Perai Utara 295,979 0.345 0.338 Pinang/ N 83 Seberang Perai 171,045 0.342 0.339 Selatan 194

State/ Gini No. District Population* Region 2014 2016 84 Timur Laut † 520,242 0.395 0.377 85 Barat Daya 202,142 0.359 0.327 86 Tawau 412,375 0.369 0.395 87 Lahad Datu 206,861 0.320 0.300 88 Semporna 137,868 0.412 0.420 89 Sandakan 409,056 0.393 0.383 90 Kinabatangan 150,327 0.357 0.422 91 Beluran 106,632 0.400 0.420 92 Kota Kinabalu † 462,963 0.370 0.406 93 Ranau 95,800 0.390 0.397 94 Kota Belud 93,180 0.416 0.430 95 Tuaran 105,435 0.374 0.396 96 Penampang 125,913 0.347 0.371 97 Papar 128,434 0.356 0.360 98 Kudat Sabah/ EM 85,404 0.442 0.422 99 Kota Marudu 68,829 0.448 0.430 100 Pitas 38,764 0.446 0.434 101 Beaufort 66,406 0.387 0.378 102 Kuala Penyu 19,426 0.329 0.363 103 Sipitang 35,764 0.376 0.433 104 Tenom 56,597 0.328 0.350 105 Nabawan 32,309 0.322 0.291 106 Keningau 177,735 0.346 0.347 107 Tambunan 36,297 0.293 0.354 108 Kunak 62,851 0.394 0.372 109 Tongod 36,192 0.436 0.396 110 Putatan 55,864 0.337 0.386 111 Kuching † 617,887 0.361 0.367 112 Bau 54,246 0.325 0.324 113 Lundu 33,413 0.326 0.360 114 Samarahan 87,923 0.328 0.334 115 Serian 91,599 0.350 0.388 116 Simunjan 39,226 0.325 0.372 117 Sri Aman 66,790 0.383 0.395 118 Lubok Antu 27,984 0.311 0.305 119 Betong 62,131 0.359 0.352 Sarawak/ 120 Saratok 26,541 0.362 0.337 EM 121 Sarikei 58,021 0.383 0.361 122 Meradong 29,441 0.348 0.395 123 Daro 30,671 0.355 0.393 124 Julau 15,816 0.351 0.322 125 Sibu 247,955 0.396 0.388 126 Dalat 19,062 0.364 0.359 127 Mukah 42,922 0.412 0.379 128 Kanowit 28,954 0.388 0.364 129 Bintulu 189,146 0.340 0.349

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State/ Gini No. District Population* Region 2014 2016 130 Tatau 30,383 0.260 0.245 131 Kapit 56,053 0.348 0.303 132 Song 20,595 0.343 0.285 133 Belaga 36,114 0.275 0.354 134 Miri 300,543 0.375 0.354 135 Marudi 64,018 0.345 0.346 136 Limbang 48,186 0.384 0.346 137 Lawas 38,385 0.442 0.455 138 Matu 17,369 0.444 0.417 139 Asajaya 31,874 0.376 0.388 140 Pakan 15,480 0.254 0.298 141 Selangau 22,819 0.350 0.389 142 Kuala Lumpur ± 1,674,621 0.407 0.378 FT/ C 143 Putrajaya 72,413 0.374 0.369 144 Labuan FT/ EM 86,908 0.385 0.398

Note: *population numbers are based on the 2010 census (DOSM, 2011). †districts with the state’s capital. ±capital city of Malaysia. FT – Federal Territory. The regions in Malaysia are South Peninsular (S); East Coast of the Peninsular (EC); Central Peninsular (C); Northern Peninsular (N); and East Malaysia (EM).

196

B. Appendix

Chapter 3

197

Empirical Bayes estimates for random intercepts across the districts in Model 2

Kinta Kota Bharu Lipis Perlis Segamat Tampin Ulu Langat Tanah Merah Pasir Mas Gua Musang Kuala Pilah LMS Hilir Perak Kuantan Batang Padang Timor Laut Sibu Melaka Tengah Limbang Gombak Pontian Samarahan Pasir Puteh Besut Jempol Raub Sbrg Perai Tengah Kota Kinabalu Jasin Manjung Sipitang Kerian Kuala Terengganu Kuala Langat Yan Alor Gajah Sik Kuala Penyu Sbrg Perai Utara Sandakan Penampang Kuching Baling Bera Cameron Highlands Tumpat Setiu Seremban Kuala Kangsar Kuala Muda Kudat Bentong Kemaman Labuk&Sugut Kapit Kota Setar Sarikei Barat Daya Kota Tinggi Muar Kubang Pasu Hulu Terengganu Machang Kuala Lumpur Tawau Petaling Rompin Tenom Kuala Selangor Bachok Kluang Papar Putrajaya Temerloh Bintulu Mukah Dungun Ulu Selangor Jelebu Marang Miri Beaufort Pendang Semporna Bandar Baharu Kulim Klang Kota Belud Batu Pahat Johor Bahru Hulu Perak Lahad Datu Labuan Keningau Port Dickson Jerantut Sri Aman Sbrg Perai Selatan Ranau Maran Sepang Sabak Bernam Kota Marudu Tuaran

0 .5 1 1.5 2 2.5 Random Intercepts

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1.00

0.25

1.00

OADR

0.02 1.00 0.04

Physical

0.00 1.00 0.41 0.06

Health

0.16 1.00 0.10 0.16 0.15

- - - -

Income

1.00 0.14 0.48 0.06 0.13 0.16

Edu

- - - -

0.19 0.01 1.00 0.49 0.10 0.15 0.03

- - -

Employ

0.23 0.40 1.00 0.03 0.19 0.04 0.07 0.39

- - - - -

Urba

.00 .01

1 0.05 0 0.03 0.04 0.04 0.02 0.03 0.06

- - - - -

Alone

0.36 0.19 0.02 1.00 0.00 0.20 0.24 0.05 0.10 0.04

- - - - -

Married

0.00 0.07 0.03 1.00 0.04 0.09 0.04 0.04 0.05 0.03 0.22

------

Ethnic

0.08 0.15 0.02 1.00 0.01 0.34 0.03 0.35 0.34 0.02 0.02 0.00

- - - - -

Female

0.13 0.31 0.03 1.00 0.02 0.04 0.30 0.07 0.38 0.32 0.16 0.25 0.12

Age

------

1.00 0.03 0.02 0.01 0.08 0.04 0.06 0.05 0.01 0.06 0.11 0.15 0.04

0.01 0.01

------

Mental

Mental Alone Edu HH Age Female Ethnic Married Urban Employ Income Heal Physical OADR

Correlation Correlation matrix

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C. Appendix

Chapter 4

Table 4.4 The FRS Simple Office-based Non-laboratory Predictors of CVD Coefficients and Hazard Ratios of 10-year risk (D’Agostino et al., 2008).

Male* (10-year Baseline Survival: So(10) = 0.88431) Variable Beta** p-value Hazard Ratio 95% CI Log of Age 3.11296 <.0001 22.49 (14.80, 34.16) Log of BMI 0.79277 <.0066 2.21 (1.25, 3.91) Log of Systolic BP untreated 1.85508 <.0001 6.39 (3.61, 11.33) Log of Systolic BP treated 1.92672 <.0001 6.87 (3.90, 12.08) Smoking 0.70953 <.0001 2.03 (1.75, 2.37) Diabetes 0.53160 <.0001 1.70 (1.37, 2.11) Female* (10-year Baseline Survival: So(10) = 0.94833) Variable 휷** p-value Hazard Ratio 95% CI Log of Age 2.72107 <.0001 15.20 (8.59, 26.87) Log of BMI 0.51125 <.0609 1.67 (0.98, 2.85) Log of Systolic BP untreated 2.81291 <.0001 16.66 (8.27, 33.54) Log of Systolic BP treated 2.88267 <.0001 17.86 (8.97, 35.57) Smoking 0.61868 <.0001 1.86 (1.53, 2.25) Diabetes 0.77763 <.0001 2.18 (1.63, 2.91)

* The 10-year risk for female can be calculated as 1-0.94833exp(Σßx-26.0145) where ß is the regression coefficient and x is the level for each risk factor; the risk for the male is given as 1-0.88431exp(Σßx-23.9388) ** Estimated regression coefficient

For the FRS, the general algorithm is based on the Simple Office-based Non-laboratory using BMI as shown in Table 4.4 (D’Agostino et al., 2008). The general algorithm is gender-specific that takes into account the systolic BP, smoking (No=0, Yes=1) and diabetes (No=0, Yes=1), and importantly, whether a respondent is receiving treatment for his/ her hypertension condition or not. The specific algorithms for FRS are shown in Equation 4.3 to 4.6

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Equation 4.3 FRS (male) treated = 1 - 0.88431 ^ exp((3.11296(Log of Age) + 0.79277(Log of BMI) + 1.92672(Log of Systolic BP) + 0.70953(Smoking) + 0.5316(Diabetes)) - 26.0145)

Equation 4.4 FRS (male) untreated = 1 - 0.88431 ^ exp((3.11296(Log of Age) + 0.79277(Log of BMI) + 1.85508(Log of Systolic BP) + 0.70953(Smoking) + 0.5316(Diabetes)) - 26.0145)

Equation 4.5 FRS (female) treated = 1 - 0.94833 ^ exp((2.72107(Log of Age) + 0.51125(Log of BMI) + 2.88267(Log of Systolic BP) + 0.61868(Smoking) + 0.77763(Diabetes)) - 23.9388)

Equation 4.6 FRS (female) untreated = 1 - 0.94833 ^ exp((2.72107(Log of Age) + 0.51125(Log of BMI) + 2.81291(Log of Systolic BP) + 0.61868(Smoking) + 0.77763(Diabetes)) - 23.9388)

For the SCORE that estimates the 10-year risk for fatal CVD, firstly, the underlying risks for coronary heart disease (CHD) and for non-coronary (non-CHD) CVD are calculated separately for (i) the current person's age and (ii) his/ her age in ten years, using the values for alpha (a) and p as shown in Table 4.5 (Conroy et al., 2003). Then four sets of calculations; the underlying survival probability of each CHD and non-CHD CVD, S0

(age) and S0 (age + 10) are obtained as in Equation 4.7.

Equation 4.7 푝 푆0(푎푔푒) = exp (−(exp(푎))(푎푔푒 − 20) 푝 푆0(푎푔푒 + 10) = exp (−(exp(푎))(푎푔푒 − 10)

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Table 4.5 Coefficients for Equation 3.7

CHD Non-CHD CVD a p a p Low risk Male -22.1 4.71 -26.7 5.64 Female -29.8 6.36 -31.0 6.62 High risk Male -21.0 4.62 -25.7 5.47 female -28.7 6.23 -30.0 6.42

Next, the coefficients in Table 4.6 are used to calculate the weighted sum (w) of the CVD risk factors of cholesterol level, smoking status and systolic BP. Two w values for CHD and for non-CHD CVD are calculated as in Equation 4.8. Smoking status is coded as ‘1’ for the current smoker and ‘0’ for non-smoker. Total cholesterol is measured in mmol/L and systolic BP is measured in mmHg. The coefficient or weighting for each risk factor is indicated by β.

Table 4.6 Coefficients for Equation 3.8

CHD Non-CHD CVD Smoking 0.71 0.63 Cholesterol (mmol/L) 0.24 0.02 Systolic BP (mmHg) 0.018 0.022

Equation 4.8

푤 = 훽푐ℎ표푙(푐ℎ표푙푒푠푡푒푟표푙 − 6) + 훽푠푦푠푡표푙푖푐 퐵푃(푠푦푠푡표푙𝑖푐 퐵푃 − 120) + 훽푠푚표푘푒(푠푚표푘𝑖푛푔)

In Equation 4.9, the S0 (age) and S0 (age+10) from Equation 4.7 and the w values obtained in Equation 4.8 are used to calculate the probability of survival, S (age) and S (age+10) for CHD and non-CHD CD causes in Equation 4.9.

202

Equation 4.9 exp (푤) 푆(푎푔푒) = (푆0(푎푔푒)) exp (푤) 푆(푎푔푒 + 10) = (푆0(푎푔푒 + 10))

Then, for each cause, the 10-year survival probability based on the S (age) and S (age + 10) is estimated in Equation 4.10.

Equation 4.10

푆(푎푔푒 + 10) 푆 (푎푔푒) = 10 푆(푎푔푒)

Next, the 10-year risk for each cause can be obtained with Equation 4.11.

Equation 4.11

푅𝑖푠푘10 = 1 − 푆10(푎푔푒)

Lastly, both the risks for CHD and non-CHD CVD are combined as in Equation 4.12 to estimate the person’s overall 10-year CVD risk.

Equation 4.12

퐶푉퐷푅𝑖푠푘10(푎푔푒) = [퐶퐻퐷푅𝑖푠푘(푎푔푒)] + [푁표푛 − 퐶퐻퐷푅𝑖푠푘(푎푔푒)]

203

D. Appendix

Chapter 6

Following Equation 6.1, we specified a Besag–York–Mollie (BYM) model which is a mix of two established latent models, namely the i.i.d and the Besag (Freni-Sterrantino et al.,

2018). The hyperparameters for the BYM model are the precision 휏1 of i.i.d (v) and the precision 휏2 of the Besag (u) as in Equation 6.3. This BYM model is commonly used in disease mapping (Andersen et al., 2014). The components of BYM include a fixed intercept, a structured spatial autocorrelation effect, and an unstructured random noise effect. Fixed parameters are optional, however, we did not include them in our modelling. BYM model incorporates CAR effects, which rely on neighbourhood structures that account for spatial autocorrelation and also over dispersion issues (Blangiardo & Cameletti, 2015; Freni-Sterrantino et al., 2018).

Equation 6.3

휃 = (휃1, 휃2) = (푙표푔휏1, 푙표푔휏2)

We assigned minimally informative priors (Blangiardo & Cameletti, 2015; Freni- Sterrantino et al., 2018) and expected that the likelihood will dominate and the priors will be less important. In our case, a logGamma prior with parameters a = 1 and b = 0.001.

Thus, the hyperparameters design is i) the log unstructured effect precision 휏1 with logGamma (1, 0.001) and ii) the log of structured effect precision 휏2 with logGamma (1, 0.001). This permits the data to play a major part in controlling how smooth the function fit of the model is. Then again, the choice of priors is not straightforward (Riebler et al., 2016; Faraway et al., 2018) and may present a considerable impact on the result of our analysis. The data we used is adequate, and we assume the parameters are independent in

204

their prior distributions. However, if the data is sparse, the priors should then be selected carefully (Schrödle & Held, 2011).

For the multilevel modelling, let y be Bernoulli distributed with probability P(y=1) = 휋. Its density is given in Equation 6.4.

Equation 6.4 휋 푓(푦) =푒푥푝 푒푥푝 {푦푙표푔 ( ) + 푙표푔 (1 − 휋)} 1 − 휋

휋 The parameter 휃 equals to the logit of 휋 or (푙표푔 ( ) where the logit link leads to a (1−휋) logistic regression model as in Equation 6.5. For the ith district, the undiagnosed NCDs risks are modelled as:

Equation 6.5

푌푖 ~ 퐵푒푟푛표푢푙푙𝑖 (휋푖) 푙표푔𝑖푡(휋푖) = 훽0 + 훽1푥푖1+ . . +훽푛푥푖푛 + 푢푖 + 푣푖

where 푙표푔𝑖푡(휋푖) is the number of estimated outcome while 훽0 is the average prevalence of undiagnosed risks among older people across all districts. 푢푖 is the spatially structured residual modelled by a CAR specification, while 푣푖 is the unstructured residual effects among all the districts with exchangeable structure and distribution of

2 푣푖 ~ 푁표푟푚푎푙(0, 휎푣 )

For model selection, 푝퐷 in Equation 2 reflects the relative contributions of the data and the priors, by which a higher correlation coefficient or r value indicates that the data plays a major role. In Equation 6.6, 푝퐷 is also dependent on the deviance taken at posterior estimation, 퐷(휃) such as:

205

Equation 6.6

푝퐷 = 퐷 − 퐷(휃)

A smaller value of 퐷 indicates a good model fit while a smaller value of 푝퐷 reflects parsimony (Darmofal, 2015).

206

E. SYNTAX

E1. Stata code for Chapter 3

use "\\nask.man.ac.uk\home$\Desktop\DATA NHMS\1st article\nhms_mental.dta", clear

*Construct new variable from given (more than 1) categorical variables *Variables are: gad1 gad2 gad3 -- generalised anxiety disorder *Original input: yes=1, no=2, not eligible to answer next question=-8, missing=-44 *Create new variables: gad1x, gad2x, gad3x gen gad1x=gad1 gen gad2x=gad2 gen gad3x=gad3

*Original input: yes=1, no=2, not eligible to answer next question=-8, missing=-44 *Assume -8 & -44 as "no"=0 recode gad1x (2=0) //yes=1 recode gad2x (-8=0)(2=0) //yes=1 recode gad3x (-8=0)(2=0) //yes=1

*Generate new variable: gad (binary yes/no) gen gad= gad1x+gad2x+gad3x // values 0,1,2,3 whereby screened for gad is >1

*Variables are: down1 down2 -- down mood Create new variables: down1x down2x gen down1x=down1 gen down2x=down2

*Similar to gad, recode new vars recode down1x (2=0) recode down2x (-8=0)(2=0)

*Generate new variable: down (binary yes/no) gen down= down1x+down2x

*Create dependent variable: mental_bi gen mental_bi=down+gad label variable mental_bi "both symptoms exist (=2)"

*Generate id for each observations generate id = _n order id // to place var id as first column in data editor window generate id = _N // number of observations

*Bivariate analysis with weight & Delta Method for robustness (a random variable about its mean) logit mental_bi cen_age [pweight=weight4] margins [pweight=weight4], dydx(*)

207

logit mental_bi female [pweight=weight4] margins [pweight=weight4], dydx(*) xi: logit mental_bi i.ethnic [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi married [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi alone [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi urban [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi employ [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi edu [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi log_income [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi cen_age [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi health_rated [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi phy_rated [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi oadr [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi hhavrg [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi clinic_ratio [pweight=weight4] margins [pweight=weight4], dydx(*) logit mental_bi cen_age [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi female [pweight=weight4], or margins [pweight=weight4], dydx(*) xi: logit mental_bi i.ethnic [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi married [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi alone [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi urban [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi employ [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi edu [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi log_income [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi cen_age [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi health_rated [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi phy_rated [pweight=weight4], or margins [pweight=weight4], dydx(*)

208

logit mental_bi oadr [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi hhavrg [pweight=weight4], or margins [pweight=weight4], dydx(*) logit mental_bi clinic_ratio [pweight=weight4], or margins [pweight=weight4], dydx(*)

*Model 1 melogit mental_bi || dist_id: outreg2 using chap11out, e(ll chi2) dec(3) long word replace melogit, or estimates store null estimates stats null

*Model 2 melogit mental_bi female|| dist_id: outreg2 using chap11out, e(ll chi2) dec(3) long word append melogit, or estimates store ranint estimates stats ranint

*Model 3 xi: melogit mental_bi female i.ethnic married alone urban log_income health_rated phy_rated || dist_id: female, cov(uns) outreg2 using chap11out, e(ll chi2) dec(3) long word append melogit, or estimates store ranslop1 estimates stats ranslop1

*Model 4 xi: melogit mental_bi female i.ethnic married alone urban log_income health_rated phy_rated oadr || dist_id: female, cov(uns) outreg2 using chap11out, e(ll chi2) dec(3) long word append melogit, or estimates store ranslop2 estimates stats ranslop2

*Likelihood Ratio Test lrtest null ranint lrtest ranint ranslop1 lrtest ranslop1 ranslop2

*Median Odds Ratios (MOR) display exp(sqrt(2* ”level 2 variance value” )*invnormal(3/4))

*Intraclass Correlation (ICC) display ”level 2 variance value” / ( ”level 2 variance value” + (_pi^2/3))

*Model Comparisons using AIC and BIC stats estimates stats null ranint ranslop1 ranslop2

*Intepreting the estimate probabilities xi: quietly melogit mental_bi i.ethnic oadr clinic_ratio hhavrg|| dist_id:

209

margins i.ethnic, atmeans predict (mu fixedonly) vsquish quietly melogit mental_bi urban oadr clinic_ratio hhavrg|| dist_id: margins urban, atmeans predict (mu fixedonly) vsquish quietly melogit mental_bi health_rated oadr clinic_ratio hhavrg|| dist_id: margins self_rated, atmeans predict (mu fixedonly) vsquish

*Create maps shp2dta using MYS_adm2, database(mydb2) coordinates(mycoord2) use mydb2

*Create map using command spmap. Variables are oadr and id. There are four colour groups based on quartiles. *the number of colour groups can be changed by using the option clnumber(#) spmap oadr using "C:\Users\syedzahiruddin\Desktop\DATA NHMS\1st article\mycoord2.dta", id(id) fcolor(Blues)

*Create map for percentage of aged persons (60 years and above) against population spmap aged_pcent using "C:\Users\syedzahiruddin\Desktop\DATA NHMS\1st article\mycoord2.dta", id(id) fcolor(Reds)

* To create a twoway graph and data containing variables mental, female, urban collapse (mean) meanmental= mental (sd) sdmental=mental (count) n=mental, by(female urban) // generate upper and lower values of the confidence interval generate himental = meanmental + invttail(n-1,0.025)*(sdmental / sqrt(n)) generate lomental = meanmental - invttail(n-1,0.025)*(sdmental / sqrt(n)) // asyvars option graph bar meanmental, over(female) over(urban) asyvars // graph twoway, not attractive as graph bar graph twoway (bar meanmental female) (rcap himental lomental female), by(urban)

*Twoway bar command to make a graph that resembles the graph bar command and then combine that with error bars, create new variable urbfem // a combination of urban + female generate urbfem = female if urban == 0 replace urbfem = female+3 if urban == 1 // +3 is to set gap sort urbfem list urbfem urban female, sepby(urban) //overlay the error bars by overlaying a rcap graph twoway (bar meanmental urbfem) (rcap himental lomental urbfem) //overlaying two separate bar graphs, one for each gender twoway (bar meanmental urbfem if female==0) /// (bar meanmental urbfem if female==1) /// (rcap himental lomental urbfem) //insert legend and axis title twoway (bar meanmental urbfem if female==0) /// (bar meanmental urbfem if female==1) /// (rcap himental lomental urbfem), /// legend(row(1) order(1 "Male" 2 "Female") ) /// xlabel( 0.5 "Rural" 3.5 "Urban", noticks) /// xtitle("Location") ytitle("Mean Reported Mental Symptom")

* To create a twoway graph and data containing variables mental, female, ethnic

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collapse (mean) meanmental= mental (sd) sdmental=mental (count) n=mental, by(female ethnic) // generate upper and lower values of the confidence interval generate himental = meanmental + invttail(n-1,0.025)*(sdmental / sqrt(n)) generate lomental = meanmental - invttail(n-1,0.025)*(sdmental / sqrt(n)) // asyvars option graph bar meanmental, over(female) over(ethnic) asyvars // graph twoway, not attractive as graph bar graph twoway (bar meanmental female) (rcap himental lomental female), by(ethnic)

*Twoway bar command to make a graph that resembles the graph bar command and then combine that with error bars, create new variable urbfem // a combination of urban + female generate ethfem = female if ethnic == 1 replace ethfem = female+3 if ethnic == 2 // +3 is to set gap replace ethfem = female+6 if ethnic == 3 // +6 is to set gap replace ethfem = female+9 if ethnic == 4 // +9 is to set gap replace ethfem = female+12 if ethnic == 5 // +12 is to set gap sort ethfem list ethfem ethnic female, sepby(ethnic) //overlay the error bars by overlaying a rcap graph twoway (bar meanmental ethfem) (rcap himental lomental ethfem) //overlaying two separate bar graphs, one for each gender twoway (bar meanmental ethfem if female==0) /// (bar meanmental ethfem if female==1) /// (rcap himental lomental ethfem) //insert legend and axis title twoway (bar meanmental ethfem if female==0) /// (bar meanmental ethfem if female==1) /// (rcap himental lomental ethfem), /// legend(row(1) order(1 "Male" 2 "Female") )/// xlabel( 0.5 "Malay" 3.5 "Chinese" 6.5 "Indian" 9.5 "Sabah Sarawak" 12.5 "Others", noticks) /// xtitle("Ethnic") ytitle("Mean Reported Mental Symptom") collapse (mean) meanmental= mental (sd) sdmental=mental (count) n=mental, by(female edu) generate himental = meanmental + invttail(n-1,0.025)*(sdmental / sqrt(n)) generate lomental = meanmental - invttail(n-1,0.025)*(sdmental / sqrt(n)) graph bar meanmental, over(female) over(edu) asyvars graph twoway (bar meanmental female) (rcap himental lomental female), by(edu) generate edufem = female if edu == 0 replace edufem = female+3 if edu == 1 // +3 is to set gap replace edufem = female+6 if edu == 2 // +6 is to set gap sort edufem list edufem edu female, sepby(edu) //overlay the error bars by overlaying a rcap graph twoway (bar meanmental edufem) (rcap himental lomental edufem) //overlaying two separate bar graphs, one for each gender twoway (bar meanmental edufem if female==0) /// (bar meanmental edufem if female==1) /// (rcap himental lomental edufem) //insert legend and axis title twoway (bar meanmental edufem if female==0) /// (bar meanmental edufem if female==1) ///

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(rcap himental lomental edufem), /// legend(row(1) order(1 "Male" 2 "Female") )/// xlabel( 0.5 "Primary or lower" 3.5 "Secondary" 6.5 "Tertiary", noticks) /// xtitle("Education") ytitle("Mean Reported Mental Symptom") collapse (mean) meanmental= mental (sd) sdmental=mental (count) n=mental, by(dist_id)

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E2. Stata code for Chapter 4

use "\\nask.man.ac.uk\home$\Desktop\DATA NHMS\2nd article\nhms_cvd.dta", clear

*Framingham calculation, example // adopted from: General Cardiovascular Risk Profile for Use in Primary Care: The Framingham Heart Study, D'Agostino R.B. et al.(2008), Circulation

*Calculation for male, using non-lab calculation based on BMI, variable sbptr(sbp treated) & sbpxtr(sbp not treated); smoke(0,1), diabetes(0,1) // general formula: 1-0.88431^ ((exp(3.11296(logage) + 0.79277(logbmi) + 1.85508(logsbpxtr) + 1.92672(logsbptr) + 0.70953(smoke) + 0.5316(diabetes)) - 26.0145)

*Calculation for female // general formula: 1-0.94833^ ((exp(2.72107(logage) + 0.51125(logbmi) + 2.81291(logsbpxtr) + 2.88267(logsbptr) + 0.61868(smoke) + 0.77763(diabetes)) -23.9388)

*Create variable sbpbeta to accomodate diffrent coeff. values for sbp treated and not treated by gender. g020num is a var. being told having HPT (1-yes, 2-no) // male (treat=1.92672, xtreat=1.85508) female (treat=2.88267, xtreat=2.81291). sbpbeta is log(sbp). gen sbpbeta = log(sbp) replace sbpbeta = 1.92672*(sbpbeta) if female==0 & g020num==1 replace sbpbeta = 1.85508*(sbpbeta) if female==0 & g020num==2 replace sbpbeta = 2.88267*(sbpbeta) if female==1 & g020num==1 replace sbpbeta = 2.81291*(sbpbeta) if female==1 & g020num==2 gen exp = 3.11296*(logage)+0.79277*(logbmi)+sbpbeta+0.70953*(smoke)+0.5316*(diabetes) if female==0 replace exp = 2.72107*(logage)+0.51125*(logbmi)+sbpbeta+0.61868*(smoke)+0.77763*(diabetes) if female==1 gen fram=1-0.88431^exp((exp)-23.9388) if female==0 replace fram=1-0.94833^exp((exp)-26.0145) if female==1

*Create Framingham percentage variable based on SCORE high risk chart labelling gen framcat = framperc recode framcat (.=-44) replace framcat = 1 if framcat >0 & framcat<1 replace framcat = 2 if framcat >1 & framcat<3 replace framcat = 3 if framcat >3 & framcat<5 replace framcat = 4 if framcat >5 & framcat<8 replace framcat = 5 if framcat >8 & framcat<17 replace framcat = 6 if framcat >17 & framcat<30 replace framcat = 7 if framcat >30 label variable framcat "Framingham CVD 10-year risk category" label define fram 1 "<1%" 2 "1%-2%" 3 "3%-4%" 4 "5%-7%" 5 "8%-16%" 6 "17%-30%" 7 ">30%" -44 "." label values framcat fram

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recode framcat (-44=.)

*SCORE calculation, example //from: Estimation of ten-year risk of fatal cardiovascular disease in Europe: the SCORE project, Convoy R.M.(2003) European Heart Journal

*STEP 1 -- use table A for coeff. for High Risk region //base age with CHD gen s0chd = exp(-(exp(-21))*(age-20)^4.62) replace s0chd = exp(-(exp(-28.7))*(age-20)^6.23) if female==1

//base age plus 10 years with CHD gen s10chd = exp(-(exp(-21))*(age-10)^4.62) replace s10chd = exp(-(exp(-28.7))*(age-10)^6.23) if female==1

//base age without CHD gen s0nchd = exp(-(exp(-25.7))*(age-20)^5.47) replace s0nchd = exp(-(exp(-30))*(age-20)^6.42) if female==1

//base age plus 10 years without CHD gen s10nchd = exp(-(exp(-25.7))*(age-10)^5.47) replace s10nchd = exp(-(exp(-30))*(age-10)^6.42) if female==1

*STEP 2 -- calculate weighted sum (w) of risk factors of cholesterol (choles), smoking (smoke) and systolic blood pressure (systol), table B for coeff. each risk //weighted sum with CHD gen wchd = (0.24*(choles-6))+(0.018*(sbp-120))+(0.71*(smoke)) gen wnchd = (0.02*(choles-6))+(0.022*(sbp-120))+(0.63*(smoke))

*STEP 3 -- combine the underlying risk of CHD and non-CHD at base age and +10 years with weighted sum gen schd = s0chdêxp(wchd) gen schd10 = s10chdêxp(wchd) gen snchd = s0nchdêxp(wnchd) gen snchd10 = s10nchdêxp(wnchd)

*STEP 4 -- survival probability surchd & surnchd gen surchd = schd10/schd gen surnchd = snchd10/snchd

*STEP 5 -- risk calculation gen rchd = 1-surchd gen rnchd = 1-surnchd

*STEP 6 -- cardiovascular risk 10-year estimates gen score = rchd+rnchd

*create SCORE percentage variable based on SCORE high risk chart labelling gen scorecat = scoreperc recode scorecat (.=-44) replace scorecat = 1 if scorecat >0 & scorecat<1

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replace scorecat = 2 if scorecat >1 & scorecat<3 replace scorecat = 3 if scorecat >3 & scorecat<5 replace scorecat = 4 if scorecat >5 & scorecat<10 replace scorecat = 5 if scorecat >10 & scorecat<15 replace scorecat = 6 if scorecat >15 label variable scorecat "SCORE CVD 10-year risk category" label define score 1 "<1%" 2 "1%-2%" 3 "3%-4%" 4 "5%-9%" 5 "10%-14%" 6 ">15%" -44 "." label values scorecat score recode scorecat (-44=.)

*Create dependent variable gen treat = 0 replace treat = 1 if e020==1 | f020num==1 | g020num==1 | b7001==1 | b7003==1 gen combscore = scorecat /// to generate a var that capture individuals having Fram or/and SCORE replace combscore = 1 if framcat==1 replace combscore= 2 if framcat==2 replace combscore=3 if framcat==3 gen treat = 0 replace treat = 1 if e020==1 | f020num==1 | g020num==1 | b7001==1 | b7003==1 gen unmet1=combscore recode unmet1 (3=1)(2=1)(1=0) replace unmet1 = 2 if treat==1

*Create unmet2 with lowrisk untreated as missing gen unmet2=unmet1 recode unmet2 (0=.)(2=0) recode unmet1 (2=0) /// 0=met (including lowrisk), 1=unmet

*Coeff, odds ratio & marginal effect using logit (weighted) xi: logit unmet i.age1 [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.female [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.edu [pweight=weight4] logit,or margins [pweight=weight4], dydx(*) xi: logit unmet i.ethnic [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.married [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.alone [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.emp [pweight=weight4]

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logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.urban [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.quinspend [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.quincome [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.herate [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.phyrate [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) logit unmet gini [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) logit unmet bor [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) logit unmet oadr [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) logit unmet pclinic [pweight=weight4] logit, or margins [pweight=weight4], dydx(*) logit unmet apcmort [pweight=weight4] logit, or margins [pweight=weight4], dydx(*)

*Coeff, odds ratio & marginal effect using logit (unweighted) xi: logit unmet i.age1 logit, or margins, dydx(*) xi: logit unmet i.female logit, or margins, dydx(*) xi: logit unmet i.edu logit,or margins, dydx(*) xi: logit unmet i.ethnic logit, or margins, dydx(*) xi: logit unmet i.married logit, or margins, dydx(*) xi: logit unmet i.alone logit, or margins, dydx(*) xi: logit unmet i.emp

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logit, or margins, dydx(*) xi: logit unmet i.urban logit, or margins, dydx(*) xi: logit unmet i.quinspend logit, or margins [pweight=weight4], dydx(*) xi: logit unmet i.quincome logit, or margins, dydx(*) xi: logit unmet i.herate logit, or margins, dydx(*) xi: logit unmet i.phyrate logit, or margins, dydx(*) logit unmet gini logit, or margins, dydx(*) logit unmet bor logit, or margins, dydx(*) logit unmet oadr logit, or margins, dydx(*) logit unmet pclinic logit, or margins, dydx(*) logit unmet apcmort logit, or margins, dydx(*)

*Bivariate analysis, pwcorr pwcorr age1 female edu ethnic married urban quinspend quincome herate phyrate gini bor oadr apcmort, obs sig

*Model fit for null model1) random intercept (model2) random slope (model3) contextual with 2nd level variables(model4) and cross-level interactions (model5) melogit unmet1 || district: melogit, or estimates store model1 xi: melogit unmet1 i.female || district: melogit, or estimates store model2 lrtest model1 model2 xi: melogit unmet1 i.age1 i.female i.edu i.ethnic i.married i.urban i.emp i.quinspend || district: female, cov(uns) melogit, or estimates store model3 lrtest model2 model3

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xi: melogit unmet1 i.age1 i.female i.edu i.ethnic i.married i.urban i.emp i.quinspend gini oadr bor || district: female, cov(uns) melogit, or estimates store model4 lrtest model3 model4 estimates stats model1 model2 model3 model4 melogit unmet1 [pw=weight4] || district: melogit, or estimates store model1 xi: melogit unmet1 i.female [pw=weight4]|| district: melogit, or estimates store model2 lrtest model1 model2 xi: melogit unmet1 i.age1 i.female i.edu i.ethnic i.married i.urban i.quinspend [pw=weight4]|| district: female, cov(uns) melogit, or estimates store model3 lrtest model2 model3 xi: melogit unmet1 i.age1 i.female i.edu i.ethnic i.married i.urban i.quinspend gini oadr bor [pw=weight4] || district: female, cov(uns) melogit, or estimates store model4 lrtest model3 model4 gen fem_oadr = female*oadr gen fem_apcmort = female*apcmort gen edu_oadr = edu*oadr gen edu_apcmort = edu*apcmort xi: melogit unmet i.age1 i.female i.edu i.ethnic i.married i.urban quinspend quincome herate phyrate gini bor oadr apcmort fem_oadr fem_apcmort edu_oadr edu_apcmort || district: female, cov(uns) melogit, or estimates store model5 lrtest model4 model5

*quietly, graph melogit unmet i.female gini oadr apcmort || district: margins female, atmeans predict(mu fixedonly) vsquish marginsplot, name(p1) melogit unmet i.edu gini oadr apcmort || district: margins edu, atmeans predict(mu fixedonly) vsquish marginsplot, name(p2) melogit unmet i.urban gini oadr apcmort || district:

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margins urban, atmeans predict(mu fixedonly) vsquish marginsplot, name(p3) melogit unmet i.age1 gini oadr apcmort || district: margins age1, atmeans predict(mu fixedonly) vsquish marginsplot, name(p4) grc1leg p4 p1 p2 p3, ycommon

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E3. Stata code for Chapter 5

use "C:\Users\Admin.THINKPAD.000\Desktop\3rd article\NHMS2015 DM Semenanjung Stata13.dta", clear gen ndistrict = substr(ebid, 1, 4) order ndistrict, b(Strata) generate district=real(ndistrict) describe district ndistrict order district, b(Strata) drop ndistrict

*based on Malaysia geoportal, http://myliis3.mygeoportal.gov.my/upi/ label define District 101 "Batu Pahat" 102 "Johor Bahru" 103 "Kluang" 104 "Kota Tinggi" 105 "Mersing" 106 "Muar" 107 "Pontian" 108 "Segamat" 121 "Kulai" 122 "Tangkak" 201 "Kota Setar"202 "Kubang Pasu" 203 "Pdg Terap" 204 "Langkawi" 205 "Kuala Muda" 206 "Yan" 207 "Sik" 208 "Baling" 209 "Kulim" 210 "Bandar Baharu" 211 "Pendang" 212 "Pokok Sena" 301 "Bachok" 302 "Kota Bharu" 303 "Machang" 304 "Pasir Mas" 305 "Pasir Puteh" 306 "Tanah Merah" 307 "Tumpat" 308 "Gua Musang" 310 "Kuala Krai" 311 "Jeli" 312 "Lojing" 401 "Melaka Tengah" 402 "Jasin" 403 "Alor Gajah" 501 "Jelebu" 502 "Kuala Pilah" 503 "Port Dickson" 504 "Rembau" 505 "Seremban" 506 "Tampin" 507 "Jempol" 601 "Bentong" 602 "Cameron Highlands" 603 "Jerantut" 604 "Kuantan" 605 "Lipis" 606 "Pekan" 607 "Raub" 608 "Temerloh" 609 "Rompin" 610 "Maran" 611 "Bera" 701 "Sbrg Perai Tengah" 702 "Sbrg Perai Utara" 703 "Sbrg Perai Selatan" 704 "Timor Laut" 705 "Barat Daya" 801 "Batang Padang" 802 "Manjung" 803 "Kinta" 804 "Kerian" 805 "Kuala Kangsar" 806 "LMS" 807 "Hilir Perak" 808 "Hulu Perak" 809 "Selama" 810 "Perak Tengah" 811 "Kampar" 901 "Perlis" 1001 "Klang" 1002 "Kuala Langat" 1004 "Kuala Selangor" 1005 "Sabak Bernam" 1006 "Ulu Langat" 1007 "Ulu Selangor" 1008 "Petaling" 1009 "Gombak" 1010 "Sepang" 1101 "Besut" 1102 "Dungun" 1103 "Kemaman" 1104 "Kuala Terengganu" 1105 "Hulu Terengganu" 1106 "Marang" 1107 "Setiu" 1201 "Kota Kinabalu" 1202 "Papar" 1203 "Kota Belud" 1204 "Tuaran" 1205 "Kudat" 1206 "Ranau" 1207 "Sandakan" 1208 "Labuk&Sugut" 1209 "Kinabatangan" 1210 "Tawau" 1211 "Lahad Datu" 1212 "Semporna" 1213 "Keningau" 1214 "Tambunan" 1215 "Pensiangan" 1216 "Tenom" 1217 "Beaufort" 1218 "Kuala Penyu" 1219 "Sipitang" 1221 "Penampang" 1222 "Kota Marudu" 1223 "Pitas" 1224 "Kunak" 1225 "Tongod" 1226 "Putatan" 1301 "Kuching" 1302 "Sri Aman" 1303 "Sibu" 1304 "Miri" 1305 "Limbang" 1306 "Sarikei" 1307 "Kapit" 1308 "Samarahan" 1309 "Bintulu" 1310 "Mukah" 1311 "Betong" 1401 "Kuala Lumpur" 1501 "Labuan" 1601 "Putrajaya"

*To make the dataset for Peninsular only drop if State == 12 //Sabah & Labuan drop if State == 13 //Sarawak

*Create variables normal [<6.1mmol/L:fast, <7.8mmol/L:random], ifg (impaired fasting glucose)[6.1-6.9mmol/L:fast], igt (impaired glucose tolerance)[7.8-11mmol/L:random], diabetem mellitus (dm) [>=7mmol/L:fast, >=11.1mmol/L:random] *For ifg & igt will be known as prediabetes (predm) *Use variables R4010 (fasting glucose, Yes=1, No=2) and R4020 (glucose readings) // fdm= fasting diabetes mellitus, categorical gen fdm = 1 if R4010 ==1 // there were 2,122 respondents with fasting glucose

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recode fdm (1=2) if R4020>=7 // 1=non dm 2=dm recode fdm (1=3) if R4020>=6.1 & R4020<=6.9 // 3=ifg recode fdm (3=2)(2=3) //2=ifg, 3=dm

// ndm= non-fasting diabetes mellitus, categorical gen ndm = 1 if R4010 ==2 // there were 4,078 respondents with non-fasting glucose recode ndm (1=3) if R4020>=7.8 & R4020<=11 // 1=non dm 2=dm recode ndm (1=3) if R4020>7.7 & R4020<11.1 // 3=igt recode ndm (3=2)(2=3) //2=igt, 3=dm

// create interval variable fdm1 = fasting DM (number) & ndm1 = non-fasting DM (number) gen fdm1 = R4020 if R4020 >=0 & R4010==1 gen ndm1 = R4020 if R4020 >=0 & R4010==2

*Generate graph egen meanndm1=mean(ndm1), by(Age) egen meanfdm1=mean(fdm1), by(Age) egen sdndm1=sd(ndm1), by(Age) egen sdfdm1=sd(fdm1), by(Age) gen upndm1= meanndm1+sdndm1 gen londm1= meanndm1-sdndm1 gen upfdm1= meanfdm1+sdfdm1 gen lofdm1= meanfdm1-sdfdm1 twoway lfitci meanndm1 Age if Age >=50 & Age <=90, sort cmissing(n) || lfitci meanfdm1 Age if Age >=50 & Age <=90, sort cmissing(n)

*Generate diabetes variable gen diabetes = . replace diabetes= 1 if fdm ==1 | ndm==1 replace diabetes=2 if ndm==2 | fdm==2 replace diabetes= 3 if fdm==3 | ndm ==3

*Generate known unknown gen unknown=. replace unknown=1 if known_dm==0 & diabetes==1 replace unknown=2 if known_dm==0 & diabetes==2 replace unknown=3 if known_dm==0 & diabetes==3 replace unknown=0 if known_dm==1

*Generate var diabetes status (diabstatus) gen diabstatus=diabetes replace diabstatus = 3 if undiag==0 replace diabstatus =4 if undiag==1 replace diabstatus=5 if undiag==2 recode diabstatus (0=1)(3=2)(1=3)(4=4)(2=5)(5=6)

*Bivariate analysis // outcome: known & unknown DM, var: outcome xi: logit outcome i.Agegroup [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.female [pweight=Weight_Final]

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logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.ethnic [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.married [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.urban [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.region [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.emp [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.edu [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.Qincome [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome i.Qspend [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*)

// outcome: normal & DM, var: outcome2 xi: logit outcome2 i.Agegroup [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.female [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.ethnic [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.married [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.urban [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.region [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.emp [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.edu [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.Qincome [pweight=Weight_Final]

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logit, or margins [pweight=Weight_Final], dydx(*) xi: logit outcome2 i.Qspend [pweight=Weight_Final] logit, or margins [pweight=Weight_Final], dydx(*)

//Create decile glucose measurements xtile Qndm=ndm [aw=Weight_Final], n(10) // decile non-fasting glucose (categorical) tab Qndm xtile Qfdm=fdm [aw=Weight_Final], n(10) // decile fasting glucose tab Qfdm egen Qgluco = rowmax (Qndm Qfdm) // combine two variables tab Qgluco egen diabcat = rowmax (ndm fdm) // combine two variables tab diabcat tab2 Qgluco diabcat, row column cell

*Create variable for fasting glucose and turn into xtile(n=10) gen fast = R4010 recode fast (-9=.)(-7=.)(-6=.)(2=.) gen fastlevel = R4020 if fast ==1 recode fastlevel (-9=.) // missing data, leaves with 1,953 respondents xtile Qfast=fastlevel, n(10)

*Create variable for random glucose and turn into xtile(n=10) gen random = R4010 recode random (-9=.)(-7=.)(-6=.)(1=.) gen randomlevel = R4020 if random ==2 recode randomlevel (-9=.) // missing data, leaves with 4,017 respondents xtile Qrandom=randomlevel, n(10) egen Qread = rowmax (Qfast Qrandom) // combine two variables, 5,970 respondents in Peninsular, 418 missing

*Multilevel analysis, use variables that are significant in bivariate analysis: outcome2 (normal vs. diabetes) & outcome (undiagnosed vs. diagnosed) // use outcome2 variable normal==0 & diabetes(inc. prediabetes/ fasting & non-fasting) ==1 //dependant var: outcome2 (normal=0, DM=1) melogit outcome2 || district: melogit, or estimates store model1 xi: melogit outcome2 Agegroup i.female ethnic i.urban region i.emp edu Qincome || district: i.female, cov(uns) melogit, or estimates store model2 lrtest model1 model2 xi: melogit outcome2 Agegroup i.female ethnic i.urban region i.emp edu Qincome oadr bha2000 mhi2000 || district: i.female, cov(uns) melogit, or estimates store model3 lrtest model2 model3

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//dependant var: outcome (known=0, unknown=1) melogit outcome || district: melogit, or estimates store model1 xi: melogit outcome i.Agegroup i.female i.ethnic i.married i.urban i.region i.emp i.edu Qincome Qspend || district: i.female, cov(uns) melogit, or estimates store model2 lrtest model1 model2 xi: melogit outcome i.Agegroup i.female i.ethnic i.married i.urban i.region i.emp i.edu Qincome Qspend gini2014 oadr bha2000 mhi2000 || district: i.female, cov(uns) melogit, or estimates store model3 lrtest model2 model3

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E4. R code for Chapter 6

### Descriptive analysis ### summary(NHMS15risk_R) attach(NHMS15risk_R) table(age) mean(age) sd(age) detach() ## after analysis ended

### Bivariate analysis (unadjusted), GLM logistic regression ### age.binary<- glm(undiag~factor(age), data=NHMS15risk_R, family=binomial()) round(coef(summary(age.binary)), 3) exp(0.156) ## to obtain odds ratio Qspend.binary<- glm(undiag~factor(Qspend), data=NHMS15risk_R, family=binomial()) round(coef(summary(Qspend.binary)), 3) exp() ## to obtain odds ratio

### Multivariate and spatial analysis, GLMM logistic regression ### library(maptools) library(spdep) library (raster) library(shp2graph) install.packages("INLA", repos=c(getOption("repos"), INLA="https://inla.r-inla- download.org/R/stable"), dep=TRUE) library(INLA) require(rgdal) my2<-getData('GADM', country='Malaysia', level=2) plot(my2) names(my2) unique(my2$NAME_2) unique(my2$NAME_1) malaya<-subset(my2,NAME_1=="Johor" | NAME_1=="Kedah" | NAME_1=="Kelantan" | NAME_1=="Kuala Lumpur" | NAME_1=="Melaka" | NAME_1=="Negeri Sembilan" | NAME_1=="Pahang" | NAME_1=="Perak" | NAME_1=="Perlis" | NAME_1=="Pulau Pinang" | NAME_1=="Putrajaya" | NAME_1=="Selangor" | NAME_1=="Trengganu" ) ## without Sabah & Sarawak plot(malaya) malaya1 <- subset (malaya, !NAME_2=="Langkawi") ## without Langkawi malaya1.map<-poly2nb(malaya1) nb2INLA("malaya1.graph", malaya1.map)

## INLA (mod0 (null) & mod1(full)) ## formula0<-undiag~1+f(ID, model="bym", graph=”malaya1.graph”, scale.model=TRUE, hyper=list(prec.unstruct=list(prior="loggamma", param=c(1,0.001)), prec.spatial=list(prior="loggamma", param=c(1,0.001)))) mod0<- inla(formula0, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE)

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round(mod0$summary.fixed, 3) formula1<- undiag~1+factor(age)+factor(work)+factor(strata)+distpub+ f(ID, model="bym", graph=”malaya1.graph”, scale.model=TRUE, hyper=list(prec.unstruct=list(prior=”loggamma”, param=c(1,0.001)), prec.spatial=list(prior="loggamma", param=c(1,0.001)))) mod1<- inla(formula1, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE) round(mod1$summary.fixed, 3) names <- sort(malaya1$NAME_2) data.UHR <- data.frame(NAME=names) Nareas <- length(data.UHR[,1]) data.UHR$ID <- seq(1,Nareas)

# posterior mean for the random effects # csi <- mod1$marginals.random$ID[1:Nareas] zeta <- lapply(csi,function(x) inla.emarginal(exp,x))

# define the cutoff for zeta # zeta.cutoff <- c(0.85, 0.925, 1.00, 1.075, 1.15)

# transform zeta in categorical variable # cat.zeta <- cut(unlist(zeta), breaks=zeta.cutoff, include.lowest=TRUE, na.rm=FALSE)

# create a dataframe with all the information needed for the map # maps.cat.zeta <- data.frame(ID=data.UHR$ID, cat.zeta=cat.zeta)

# add the categorized zeta to the spatial polygon # data.district <- attr(malaya1, "data") attr(malaya1, "data") <- merge(data.district, maps.cat.zeta, by="ID")

# map posterior mean for the district-specific outcome # library(lattice) trellis.par.set(axis.line=list(col=NA)) spplot(obj=malaya1, zcol="cat.zeta", col.regions=gray(seq(0.9, 0.1, length=4)), asp=1)

# posterior probability # a <- 0 prob.csi <- lapply(csi, function(x) {1 - inla.pmarginal(a, x)}) prob.csi.cutoff <- c(0.05, 0.27, 0.49, 0.70, 0.92) cat.prob.csi <- cut(unlist(prob.csi),breaks=prob.csi.cutoff, include.lowest=TRUE) maps.cat.prob.csi <- data.frame(ID=data.UHR$ID, cat.prob.csi=cat.prob.csi) data.district <- attr(malaya1, "data") attr(malaya1, "data") <- merge(data.district, maps.cat.prob.csi, by="ID") trellis.par.set(axis.line=list(col=NA)) spplot(obj=malaya1, zcol= "cat.prob.csi", col.regions=gray(seq(0.9,0.1,length=4)))

# proportion of variance from marginal posterior # mat.marg0 <- matrix(NA, nrow=Nareas, ncol=100000) m0<- mod0$marginals.random$ID for (i in 1:Nareas){ u0 <- m0[[Nareas+i]]

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mat.marg0[i,] <- inla.rmarginal(100000, u0) } var.u0 <- apply(mat.marg0, 2, var) var.v0<- inla.rmarginal(100000,inla.tmarginal(function(x) 1/x, mod0$marginals.hyper$"Precision for ID (iid component)")) perc.var.u0 <- mean(var.u0/(var.u0+var.v0)) perc.var.u0

### Sensitivity analysis for the choice of priors ###

# priors logGamma (1, 0.01) # formula1.1<- undiag~1+factor(age)+factor(work)+factor(strata)+distpub+ f(ID, model="bym", graph=”malaya1.graph”, scale.model=TRUE, hyper=list(prec.unstruct=list(prior=”loggamma”, param=c(1,0.01)), prec.spatial=list(prior="loggamma", param=c(1,0.01)))) mod1.1<- inla(formula1.1, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE) round(mod1.1$summary.fixed, 3)

# priors logGamma (1, 0.1) # formula1.2<- undiag~1+factor(age)+factor(work)+factor(strata)+distpub+ f(ID, model="bym", graph=”malaya1.graph”, scale.model=TRUE, hyper=list(prec.unstruct=list(prior=”loggamma”, param=c(1,0.1)), prec.spatial=list(prior="loggamma", param=c(1,0.1)))) mod1.2<- inla(formula1.2, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE) round(mod1.2$summary.fixed, 3)

# priors logGamma (0.1, 0.001) # formula1.3<- undiag~1+factor(age)+factor(work)+factor(strata)+distpub+ f(ID, model="bym", graph="malaya1.graph", scale.model=TRUE, hyper=list(prec.unstruct=list(prior="loggamma", param=c(0.1,0.001)), prec.spatial=list(prior="loggamma", param=c(0.1,0.001)))) mod1.3<- inla(formula1.3, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE) round(mod1.3$summary.fixed, 3)

# priors logGamma (0.1, 0.0005) # formula1.4<- undiag~1+factor(age)+factor(work)+factor(strata)+distpub+ f(ID, model="bym", graph="malaya1.graph", scale.model=TRUE, hyper=list(prec.unstruct=list(prior="loggamma", param=c(0.1,0.0005)), prec.spatial=list(prior="loggamma", param=c(0.1,0.0005)))) mod1.4<- inla(formula1.4, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE) round(mod1.4$summary.fixed, 3)

# priors loggamma (1, 0.0005) # formula1.5<- undiag~1+factor(age)+factor(work)+factor(strata)+distpub+ f(ID, model="bym", graph="malaya1.graph", scale.model=TRUE, hyper=list(prec.unstruct=list(prior="loggamma", param=c(1,0.0005)), prec.spatial=list(prior="loggamma", param=c(1,0.0005)))) mod1.5<- inla(formula1.5, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE) round(mod1.5$summary.fixed, 3)

# priors loggamma (0.01, 0.001) #

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formula1.6<- undiag~1+factor(age)+factor(work)+factor(strata)+distpub+ f(ID, model="bym", graph="malaya1.graph", scale.model=TRUE, hyper=list(prec.unstruct=list(prior="loggamma", param=c(0.01,0.001)), prec.spatial=list(prior="loggamma", param=c(0.01,0.001)))) mod1.6<- inla(formula1.6, data=NHMS15risk_R, E=E, control.family=list(initial=1), control.inla=list(h=1e-4), control.compute=list(dic=TRUE, cpo=TRUE), verbose=TRUE) round(mod1.6$summary.fixed, 3)

## to compute the posterior mean and 95% credible interval for the fixed effect b0 on the natural scale as all parameters are on the logarithmic scale ## exp.b0.mean <- inla.emarginal(exp, mod1.6$marginals.fixed[[1]]) exp.b0.mean exp.b0.95ci <- inla.qmarginal(c(0.025, 0.975), inla.tmarginal(exp, mod1.6$marginals.fixed[[1]])) exp.b0.95ci

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