HEALTH WORKFORCE AND LABOUR MARKET DYNAMICS IN OECD HIGH-INCOME COUNTRIES: A SYNTHESIS OF RECENT ANALYSES AND SIMULATIONS OF FUTURE SUPPLY AND REQUIREMENTS
Human Resources for Health Observer Series No 20
HEALTH WORKFORCE AND LABOUR MARKET DYNAMICS IN OECD HIGH-INCOME COUNTRIES: A SYNTHESIS OF RECENT ANALYSES AND SIMULATIONS OF FUTURE SUPPLY AND REQUIREMENTS
Human Resources for Health Observer Series No 20 Health workforce and labor market dynamics in OECD high-income countries: a synthesis of recent analyses and simulations of future supply and requirements (Human Resources for Health Observer, 20) ISBN 978-92-4-151228-2
© World Health Organization 2017 Some rights reserved. This work is available under the Creative Commons Attribution-NonCommercial-ShareAlike 3.0 IGO licence (CC BY-NC-SA 3.0 IGO; https://creativecommons.org/licenses/by-nc-sa/3.0/igo). Under the terms of this licence, you may copy, redistribute and adapt the work for non-commercial purposes, provided the work is appropriately cited, as indicated below. In any use of this work, there should be no suggestion that WHO endorses any specific organization, products or services. The use of the WHO logo is not permitted. If you adapt the work, then you must license your work under the same or equivalent Creative Commons licence. If you create a translation of this work, you should add the following disclaimer along with the suggested citation: “This translation was not created by the World Health Organization (WHO). WHO is not responsible for the content or accuracy of this translation. The original English edition shall be the binding and authentic edition”. Any mediation relating to disputes arising under the licence shall be conducted in accordance with the mediation rules of the World Intellectual Property Organization. Suggested citation. Health workforce and labor market dynamics in OECD high-income countries: a synthesis of recent analyses and simulations of future supply and requirements. Geneva: World Health Organization; 2017 (Human Resources for Health Observer, 20). Licence: CC BY-NC-SA 3.0 IGO. Cataloguing-in-Publication (CIP) data. CIP data are available at http://apps.who.int/iris. Sales, rights and licensing. To purchase WHO publications, see http://apps.who.int/bookorders. To submit requests for commercial use and queries on rights and licensing, see http://www.who.int/about/licensing. Third-party materials. If you wish to reuse material from this work that is attributed to a third party, such as tables, figures or images, it is your responsibility to determine whether permission is needed for that reuse and to obtain permission from the copyright holder. The risk of claims resulting from infringement of any third-party-owned component in the work rests solely with the user. General disclaimers. The designations employed and the presentation of the material in this publication do not imply the expression of any opinion whatsoever on the part of WHO concerning the legal status of any country, territory, city or area or of its authorities, or concerning the delimitation of its frontiers or boundaries. Dotted and dashed lines on maps represent approximate border lines for which there may not yet be full agreement. The mention of specific companies or of certain manufacturers’ products does not imply that they are endorsed or recommended by WHO in preference to others of a similar nature that are not mentioned. Errors and omissions excepted, the names of proprietary products are distinguished by initial capital letters. All reasonable precautions have been taken by WHO to verify the information contained in this publication. However, the published material is being distributed without warranty of any kind, either expressed or implied. The responsibility for the interpretation and use of the material lies with the reader. In no event shall WHO be liable for damages arising from its use.
Design: Blossom Communication. Layout: Zando Escultura. Printed by the WHO Document Production Services, Geneva, Switzerland. TABLE OF CONTENTS
Major contributors and acknowledgements 3 Abbreviations 4 Executive summary 5 1. Introduction 9 1.1 Objectives 9
2. Methods 11 2.1 Synthesis of evidence 11 2.2 Development of simulations of future HRH supply and requirements 12
3. Results – synthesis of available evidence 14 3.1 Jurisdictional focus 14 3.2 Professions included 14 3.3 Date of publication 15 3.4 Analytical timeframes 15 3.5 Types of models used 15 3.6 Assumptions described 16 3.7 Trends and issues identified 16 Ageing populations 16 Chronic disease and comorbidities 16 Ageing workforces 17 Migration 17 Inconsistent use of terms 17 Distribution of resources 17 Interprofessional education and practice 17 Changing care delivery models 18 Changing practice patterns and scopes of practice 18 Evolving regulatory structures 18 Stakeholder engagement 18 Incentives 18
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Technological changes 18 Balancing the private and public sectors 19 Data quality 19 Iterative planning 19 3.8 A methodology for projecting HRH requirements in high-income OECD countries 20 4. Results – development of simulations on future HRH supply and requirements 25 4.1 HRH supply vs. requirements 26 4.2 Impacts of changes in HRH productivity 27 4.3 Impacts of changes in levels of service 27 4.4 Impacts of changes in health status 28 4.5 Impacts of demographic changes 29 4.6 Impacts of changes in HRH activity 29 4.7 Impacts of changes in HRH training 30 4.8 Impacts of changes in HRH participation 30 4.9 Impacts of changes in HRH retention 31 4.10 Impacts of changes in multiple parameters 32
5. Discussion 33 5.1 Synthesis of evidence 33 5.2 Simulations of future HRH supply and requirements 33
6. Conclusions 37 7. References 38 Annex 1: List of websites consulted 45 Annex 2: Data extraction tool for peer and non-peer reviewed literature 48 Annex 3: Stated objectives of included countries’ health-care systems 49
2 Major contributors and acknowledgements
The development of this report was coordinated by Giorgio Cometto (Global Health Workforce Alliance, WHO), under the oversight of James Campbell (Director, Health Workforce, and Executive Director, Global Health Workforce Alliance, WHO).
The analyses and the development of the contents of the report were led by Gail Tomblin Murphy (Director), Stephen Birch, Adrian MacKenzie, Janet Rigby, Stephanie Bradish and Annette Elliott Rose of the WHO/PAHO Collaborating Centre on Health Workforce Planning and Research, Dalhousie University, Halifax, NS, Canada.
The authors would like to express their appreciation for the input from: James Buchan, Queen Margaret University, UK; University of Technology Sydney, Australia Gaetan Lafortune, OECD Tony Scott, University of Melbourne, Australia Martine Morin, University of Sherbrooke, Canada Guilliano Russo, Universidade Nova de Lisboa, Portugal Mario Dal Poz, State University of Rio de Janeiro, Brazil Marjolein Dieleman, Royal Tropical Institute, the Netherlands Jackie Cumming, Victoria University at Wellington, New Zealand Gilles Dussault, Universidade Nova de Lisboa, Portugal Paulo Ferrinho, Universidade Nova de Lisboa, Portugal Edward Salsberg, National Health Policy Forum, United States of America Michel van Hoegaerden, European Union Joint Action on Health Workforce Planning and Forecasting – Belgium/European Union.
Suggested citation. Health workforce and labor market dynamics in OECD high-income countries: a synthesis of recent analyses and simulations of future supply and requirements. Geneva: World Health Organization; 2017 (Human Resources for Health Observer, 20). Licence: CC BY-NC-SA 3.0 IGO.
This analysis was also used as a basis for two separate peer-reviewed publications: Tomblin Murphy G, Birch S, MacKenzie A, Bradish S, Elliott Rose A. A synthesis of recent analyses of human resources for health requirements and labour market dynamics in high-income OECD countries. Human Resources for Health. 2016;14(1):59. Tomblin Murphy G, Birch S, MacKenzie A, Rigby J. Simulating future supply of and requirements for human resources for health in high-income OECD countries. Human Resources for Health. 2016;14(1):77.
Series No 20 3 Human Resources for Health Observer Abbreviations
AG Advisory Group DALY disability-adjusted life years FTE full-time equivalent GHWA Global Health Workforce Alliance HRH human resources for health OECD Organisation for Economic Co-operation and Development PAHO Pan American Health Organization
4 Executive summary
Introduction Simulation of future supply and requirements Based on the criteria identified through the synthesis of existing Human resources for health (HRH) are a core element of health systems. evidence, a simulation of future HRH supply in terms of head counts Their availability, accessibility, quality and performance directly impact was produced using a stock-and-flow approach, which entails adjusting the effectiveness and equity of health-care services. Planning for HRH current HRH stocks according to expected flows in (e.g. new graduates, therefore has a central role in health-care systems. HRH planning inward migration) and out (e.g. retirements, attrition to other sectors, remains a major challenge in many countries despite its centrality to outward migration etc.) of each country’s stock. These head counts the success of global campaigns such as the Millennium Development were then adjusted according to levels of participation (providing direct Goals. The adoption by the World Health Assembly of the Global patient care) and activity (proportion of full-time hours spent providing Strategy on Human Resources for Health: Workforce 2030 for the direct patient care) for different types of HRH. Costs of producing and period 2016–2030 reflects the growing recognition of the importance maintaining these stocks can then be simulated based on average of HRH planning. To inform the development of this strategy, the WHO/ training costs and wages. Pan American Health Organization (PAHO) Collaborating Centre on Health Workforce Planning and Research at Dalhousie University was To inform the process of estimating HRH requirements, a review of the commissioned to conduct a rapid review of recent analyses of HRH objectives of each included country’s health-care system, as described requirements and labour market dynamics in high-income1 countries in documents obtained for the first report, was conducted. Ensuring who are members of the Organisation for Economic Co-operation equitable access to health-care services and maintaining and/or and Development (OECD),2 to identify a methodology to determine promoting the health of their respective populations were the objectives future HRH requirements for these countries, and, on the basis of this shared across most of the included countries. As such, service methodology, to provide simulations of the future supply of and needs- requirements were simulated according to different levels of health based requirements for midwives, nurses and physicians in the included within countries and existing levels of service provision by those levels countries to the year 2030. of health. These service requirements were then converted to simulated HRH requirements based on estimates of the productivity of different Methods types of HRH. This approach is consistent with that described in several previous studies. Synthesis of current evidence Multiple steps were taken to obtain all possible relevant analyses of HRH In this way, the supply of and requirements for midwives, nurses and requirements and labour market dynamics used in the last decade for physicians were simulated for each included country in 2030. The the target countries. These included a systematic search of published simulations were based on data that were readily available, either from peer-reviewed literature, targeted searches of key HRH websites, and existing public online sources (e.g. OECD or WHO indicator databases) multi-stage mining of the references of documents obtained through or through documents gathered as part of Phase 1. In the time available these means. This analysis was undertaken in collaboration with it was not possible to engage with the various HRH stakeholder groups colleagues in the WHO’s Health Workforce department, who contributed in the included countries to obtain additional information or to do to the development of the search parameters, helped identify potential targeted internet searches for information on each individual country relevant websites, and invited prospective members to join an and profession. HRH planners and other relevant stakeholder groups international Advisory Group of key HRH researchers. within the included countries would likely be better positioned to obtain this information. In aggregate, 35% of the data elements required to 1 According to World Bank designations. implement the estimation approach were found. Analysis was focused 2 Included countries: Australia, Austria, Belgium, Canada, Chile, the Czech Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, Ireland, Israel, Italy, only on three types of HRH – midwives, nurses and physicians – due to Japan, Luxembourg, the Netherlands, New Zealand, Norway, Poland, Portugal, Republic of these data limitations. Korea, Slovakia, Slovenia, Spain, Sweden, Switzerland, the United Kingdom and the United States of America.
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In the absence of reliable data on the future values of various model makers in their particular contexts. The results of this review suggest, variables, all except the size and age-sex distribution of the population however, that rarely have explicit policy questions been identified to were held constant for the production of the baseline simulations. guide HRH research methods and analyses; instead available methods However, univariate and multivariate sensitivity analyses were have been adopted with policy “interests” (as distinct from “questions”) conducted modulating the assumptions on each individual model made to fit these methods. parameters, as well as on several parameters simultaneously, to explore and illustrate how the results produced can change according In an attempt to inform improvements to this situation, seven criteria for to different scenarios. These analyses were based on actual variation in identifying an HRH planning approach appropriate to a given jurisdiction parameter values found within Canada, which was used as an example have been presented. Although none of the approaches found through because that was the country for which the most complete data was the review met all of these criteria, several – from Australia, Canada and found, and as such allowed for the best empirical evidence to guide New Zealand – met all but one. Examples of other approaches which sensitivity analyses. met each individual criterion have also been identified so that planners can explore different options depending on the relative importance of Results these criteria given their respective circumstances and health system objectives. Synthesis of current evidence Although over 200 relevant documents were reviewed in detail, Simulations of future supply and requirements collectively they do not include sufficient information to provide a These simulations suggest that, if the current HRH situations in the clear picture of the expected future HRH situation in these countries. included countries continue to 2030, most of the included countries At best, it can be said that the HRH supply in these countries is could face shortfalls of one or more types of HRH; that is, holding generally expected to grow; because different analyses reach constant all the parameters included in the model except population, different conclusions about future HRH requirements in these they would not have enough HRH available to continue to provide their countries, it is not clear whether that growth will be adequate to current levels of health-care services to their respective populations. In meet health system objectives in the future. Although most analyses contrast, some countries may experience surpluses of some types of suggest that the numbers of physicians and nurses required in HRH; that is, they would have more than the number needed to continue the included countries are likely to increase in the future, this view to provide current levels of health-care services to their respective varies across analyses depending on the methods and assumptions populations. In total, these simulations in the baseline scenarios used. Further, most analyses for professions other than nurses and sum to shortfalls of over 45 000 midwives, 1.1 million nurses and physicians suggest that the numbers required are likely to decrease 754 000 physicians across the 31 included countries for 2030. These in the future. The implications of these projected respective surpluses simulated shortfalls are the collective result of simulated future supplies and shortages in terms of meeting health system objectives are not of 157 000 midwives, 6.8 million nurses and 2.4 million physicians clear. against simulated requirements of 202 000 midwives, 7.9 million nurses and 3.2 million physicians. Several recurring themes regarding factors of importance in HRH planning were evident across the documents reviewed. These included: The sensitivity analyses demonstrated the degree to which the ageing populations and health workforces; changes in disease patterns, simulated future physician gap in Canada varied according to models of care delivery, scopes of practice, regulatory structures and different assumed values for various planning parameters, including technologies in health care; migration; incentives; data quality; the population growth, population health status, average levels of distribution of resources; interprofessional education and practice; service provision, HRH productivity, and the training, participation, stakeholder engagement; and balancing the public and private sectors. retention and activity of HRH. The results of these analyses showed They also included important inconsistencies in the use of key HRH simulations of future HRH gaps are highly sensitive to even modest planning terms, which in turn affected the choice of methods in different changes in the assumed future values of different planning documents for HRH planning. parameters. In these examples, the simulated future physician gap ranged from a large shortfall – equivalent to 47% of the future Different approaches to HRH planning will be appropriate for different supply – to an almost equally large surplus equivalent to 43% of jurisdictions depending on their respective contexts and the objectives the future supply. It is important to reiterate that these divergent of their health-care systems and hence the policy questions being scenarios are not based on hypothetical values of planning variables, faced. Based on these objectives, methods need to be adopted that but on actual values that represent real variations within a single produce relevant answers for the precise HRH questions facing policy- country.
6 If the same level of variability found in the sensitivity analyses for by earlier analyses of HRH planning worldwide. First, HRH planning Canadian physicians is applied to the aggregate gaps simulated for must be conducted iteratively, recognizing the constantly evolving all 31 included countries, these could range from shortfalls of 74 000 nature of population health needs, health-care practices and regulatory midwives, 3.2 million nurses and 1.2 million physicians (47% less than structures, and health labour markets, and incorporating ongoing supply) to surpluses of 67 000 midwives, 2.9 million nurses and 1.0 monitoring and evaluation. Second, HRH planning must be conducted million physicians (43% greater than supply) by 2030. Thus, the results by engaging with the relevant stakeholders. Third, HRH planning must of these simulations should not be interpreted as predictions of what will be based on appropriate data, and this includes measures of both happen; instead they are meant to serve as compass bearings, showing the health levels of the population as well as the health-care services the directions in which the HRH situations in the included countries planned to respond to those health levels, together with characteristics are heading, and may continue if their current situations (measured as of the health workforce and the broader economic, sociopolitical, completely as possible with the time and resources available) remain as environmental and technological contexts in which the planning is being they are. done.
Discussion Conclusions
Although online resources such as the OECD’s indicator database The objective of this report was to simulate the supply of and provide a great deal of data relevant to HRH planning in member requirements for midwives, nurses and physicians in high-income OECD countries, a more comprehensive approach, fully consistent with the countries up until 2030, to inform global policy dialogue and strategic criteria outlined in the methodology section, would require additional planning at both international and national levels, such as that occurring information as well as direct engagement with the relevant stakeholder as part of the development of the global strategy on HRH. This was groups in the respective countries. In the absence of this information carried out using publicly available data and using assumptions about and engagement, several assumptions regarding the current and future the values of the various determinants of supply and requirements, values of some planning parameters were required; these assumptions, aside from population projections, remaining constant throughout and their implications in terms of interpreting the results, are discussed that time period. Despite the limitations resulting from challenges in detail in the main body of this report. obtaining some of the necessary data in the time available, the results demonstrate the potential to apply an approach to simulating HRH It is important to note that these scenarios have been presented for supply and requirements in a manner more comprehensive than most of illustrative purposes. For some countries the assumptions described those currently being used in many countries. HRH planners in individual above may be very close to reality; for others they may be quite countries, who have more direct access to data on the relevant planning different. With access to more accurate and comprehensive country- parameters, are best placed to implement such an approach working specific information, planners in individual countries can easily replace with their respective stakeholder groups. these assumptions with relevant data to better reflect their actual HRH situations. Further, despite the above limitations, considerable The results of these simulations suggest that the trajectories of HRH effort was put into estimating the current or “baseline” value of the supplies and requirements vary widely across countries and professions, various parameters used to simulate HRH supply and requirements for with some potentially headed for large surpluses, while others may be midwives, nurses and physicians in the included countries. However, it moving toward large shortfalls. These findings reinforce the concern is not possible to accurately predict what values these parameters will highlighted by the WHO (2015b) in its Global Strategy on Human take on in the future. Resources for Health: Workforce 2030 that many high-income countries are challenged with matching supply and requirements for HRH under The simulations of the supply of and requirements for midwives, nurses existing and future affordability and sustainability constraints, and and physicians suggest that the trajectory of HRH situations varies often experience periodic swings between perceived shortfalls and considerably by profession and by country. Rather than serving as oversupply. The analysis reported here provides evidence that can also predictions of the future HRH situations in these countries, the results inform a global policy dialogue on international imbalances and mobility illustrate the opportunities and the policy levers that decision-makers patterns across countries. The type of HRH planning approach described have to influence the supply of and requirements for HRH and the here also allows the identification of the policy levers with the greatest potential consequences of failing to take timely action. potential to affect trends and prevent the emergence of future gaps, thereby informing policy options that may alleviate or avoid these gaps, These results also underscore the importance of several factors critical such as changes to models of care delivery or to HRH training and/or to effective HRH planning, each of which have been repeatedly identified regulatory structures.
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The sensitivity analyses demonstrate that these simulations are highly on a valid methodology, and engages the relevant stakeholder groups. dependent on the assumptions on current and future values of the Further, it is imperative that these stakeholders collaborate to ensure various variables that determine HRH supply and requirements. These that the types of data necessary for such methodologies are regularly findings highlight the importance of HRH planning that is iterative, based collected and analysed to inform HRH planning.
8 1. Introduction
The backbone of any health-care system is the human resources who a global HRH strategy for the period 2016–2030 (Campbell et al., 2014) deliver care. Thus, human resources for health (HRH) planning has a reflect the growing recognition of the importance of HRH planning. These direct impact on the functioning of health-care systems, which are efforts build on a number of activities undertaken by the WHO to promote critical to ensuring a healthy population. According to one of the early evidence-based HRH planning among member countries. For example, seminal texts on the subject, HRH planning is, the HRH Observer series, published by WHO, has included technical issues on tools for modelling HRH supply and requirements (Dal Poz et al., “the process of estimating the number of persons and the kind of 2010), measuring inequalities in the distribution of HRH (Anand, 2010), knowledge, skills, and attitudes they need to achieve predetermined and analysing HRH labour markets in developing countries (Scheffler et health targets and ultimately health status objectives. Such al., 2012), among other topics relevant to HRH planning. planning also involves specifying who is going to do what, when, where, how, and with what resources for what population groups To inform this process, the WHO/Pan American Health Organization or individuals so that the knowledge and skills necessary for (PAHO) Collaborating Centre on Health Workforce Planning and the adequate performance can be made available according to Research at Dalhousie University was asked to conduct a rapid review predetermined policies and time schedules. This planning must be of recent analyses of HRH requirements and labour market dynamics a continuing and not a sporadic process, and it requires continuous in high-income3 countries who are members of the Organisation for monitoring and evaluation.” (Mejía & Fülöp, 1978). Economic Co-operation and Development (OECD)4 (referred to hereafter as the “included countries”) and to identify criteria and a methodology Effective HRH planning entails matching the HRH supply with the to determine future HRH requirements for these countries. On the basis requirements for HRH necessary to satisfy health-care system objectives. of these criteria, simulations of the future supply of and needs-based In many publicly funded health-care systems these objectives relate to requirements for midwives, nurses and physicians in the included meeting the health-care needs of the population and involve replacing countries to the year 2030 were developed. traditional measures of demand for HRH (determined by a population’s ability and willingness to pay for the services HRH provide) with measures 1.1 Objectives of the HRH required to support service planning and delivery in ways that address a population’s need for care. Under such an approach, the The specific objectives of this report were as follows: quantity and type of services planned to respond to those needs must be determined in the context of a government’s capacity to fund care. Synthesis of evidence It may be that not all needs for care can be met, or that care levels are less than “gold-standard” or evidence-based levels because of resource 1. Identify all analyses of HRH requirements and health labour market limitations, but health-care services, and the HRH required to deliver dynamics for high-income OECD countries published within the past them, are still planned in relation to the levels and distribution of needs 10 years. for care in the population. This is distinct from more common approaches where estimates of HRH requirements are based simply on service levels 2. Categorize the analyses according to the type(s) of models used to observed by demographic characteristics in the population. estimate requirements, the professions included, the timeframes over which they apply, any labour market trends identified, and any HRH planning remains a major challenge in many countries (Dussault et assumptions on which they were based. al., 2010; Matrix Insight, 2012; Ono et al., 2013; Campbell et al., 2013) despite its centrality to the success of global campaigns such as the 3 According to World Bank designations. 4 Included countries: Australia, Austria, Belgium, Canada, Chile, the Czech Millennium Development Goals (Campbell et al., 2014). Recent efforts Republic, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, by the World Health Organization (WHO), the Global Health Workforce Iceland, Ireland, Israel, Italy, Japan, Luxembourg, the Netherlands, New Zealand, Norway, Poland, Portugal, Republic of Korea, Slovakia, Slovenia, Spain, Alliance (GHWA) and partner organizations to facilitate the development of Sweden, Switzerland, the United Kingdom and the United States of America.
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3. Identify key themes and trends in these analyses that may be from various approaches used in the past to develop similar emerging over time. estimates.
4. Identify and report gaps in the knowledge base formed by these Development of simulations of future supply and requirements analyses to inform the development of a global HRH strategy. 6. Provide simulations of the future supply of and needs-based 5. Identify a methodology to project future HRH requirements in requirements for midwives, nurses and physicians in the included OECD countries. This may be an aggregate method taking into countries to the year 2030 using a methodology as consistent as account economic, demographic and epidemiologic factors possible with the criteria identified through the synthesis of evidence.
10 2. Methods
2.1 Synthesis of evidence The database and website searches yielded over 1000 documents deemed to be potentially relevant. The titles, abstracts and/or executive Multiple steps were taken to obtain all possible relevant analyses of HRH summaries of these documents were then reviewed to ensure that requirements and labour market dynamics used in the last decade for they were published in English between March 2005 and March 2015 the target countries. These included a systematic search of published and included primary analysis of HRH requirements or labour market peer-reviewed literature, targeted searches of key HRH websites, and dynamics in one or more included countries. Based on these criteria, multi-stage mining of the references of documents obtained through 181 documents were selected for full-text review. Although it was these means. This analysis was undertaken in collaboration with not possible within the allotted time to mine the reference lists of all colleagues in the WHO’s Health Workforce unit, who contributed to the 181 documents, a selection of 30 that cited multiple other potentially development of the search parameters, helped identify potential relevant relevant works were mined for additional documents. This initial list of websites and invited prospective members to join an international documents was then circulated to the AG with the request that they Advisory Group (AG) of key HRH researchers. identify any relevant works they felt had not yet been included.
The search of the peer-reviewed literature targeted several electronic Despite these efforts to assemble a complete set of HRH analyses, the databases, including Pubmed, Informa HealthCare, Web of Science, limited resources available to perform the review meant that relevant EconLit, ABI/Inform and CINAHL. These were searched for articles documents may have been missed, for any of the following reasons: whose titles or abstracts contained terms from each of two groups: • It was not possible to include documents written in languages other 1. Any of (health human resources, HHR, human resources for health, than English. This is a particularly important limitation given that HRH, health workforce, health workers, health manpower, doctors, English is not an official language for some countries included in the physicians, nurses, midwives, pharmacists, dentists); and review.
2. Any of (planning, forecasting, modelling, requirements, needs, • Not all potentially relevant websites could be included in the review, demand, gaps, shortage, supply, oversupply, labour market, and those that were included could not be searched exhaustively. dynamics, horizon scan, Australia, Austria, Belgium, Canada, Chile, Czech Republic, Denmark, Estonia, Finland, France, Germany, • It was not possible to mine the references of every relevant Greece, Hungary, Iceland, Ireland, Israel, Italy, Japan, South Korea, document. Luxembourg, the Netherlands, New Zealand, Norway, Poland, Portugal, Slovakia, Slovenia, Spain, Sweden, Switzerland, UK, USA, • Some relevant peer-reviewed publications may not have been part OECD, high-income). of the databases searched and/or may not have included any of the search terms used in their titles or abstracts. This may be due to Websites of multiple national organizations such departments of health, variations in the use of headings and subcategories in the various bureaux of statistics and HRH-specific planning bodies were searched, databases or in the language used to describe HRH research and as well as those of international organizations such as the WHO, the planning (see results section for examples). GHWA, the OECD and the World Bank. When search functions were available for website content, the relevant search terms presented In total, 224 documents were included in the review. A data extraction above were used. If search functions were not available, sections of tool was created to facilitate summarization of key features of each of these websites titled “Publications”, “Reports”, or similar, were searched these documents in terms of their scopes, methods, conclusions, and for documents pertaining to HRH analyses. A full list of the websites any major issues or themes identified. A copy of this tool is presented in searched is included as Annex 1. Annex 2.
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2.2 Development of simulations of To inform the process of estimating HRH requirements, and in keeping future HRH supply and requirements with criterion 1, a review of the objectives of each included country’s health-care system, as described in documents obtained for the first The criteria identified as part of the first phase were as follows: report, was conducted. The primary source for this information was the set of health system reviews published by the European Observatory on 1. The approach is consistent with the objectives of the health system. Health Systems and Policies (2016) in their Health Systems in Transition a. HRH requirements are derived from service requirements; series, which provide detailed descriptions of the health-care systems b. Those service requirements are aligned with system objectives in most of the included countries. Where possible this information was (e.g. addressing population needs for care arising from various supplemented by cross-references with original source documents diseases or other health problems). (such as strategic plans, legislation or referenced journal articles) or searches on individual national health ministry websites. A table 2. The approach considers HRH requirements in the context of summarizing the objectives of each of the included countries’ health- production functions for health services (i.e. dependent upon the care systems, as described in these documents, is provided in Annex 3. availability or use of facilities and other non-human inputs to service production and on models of care to be used). Ensuring equitable access to health-care services and maintaining and/or promoting the health of their respective populations were the 3. The approach explicitly considers the role and determinants of objectives shared across most of the included countries. As such, productivity (i.e. units of service per hour of work). service requirements were simulated according to different levels of health within countries and existing levels of service provision by those 4. HRH supply is measured in terms of time devoted to service delivery levels of health. This means that this approach differentiates between, (i.e. flow generated by a stock of HRH) as opposed to focussing only for example, the number of physician visits a 75-year-old woman in on the HRH stock (numbers of HRH). poor health would require as opposed to a 75-year-old woman with good health. Under more common utilization-based approaches, all 5. The approach considers the determinants of flow (e.g. hours 75-year-old women would be assumed to require the same number worked) and stock (entries/exits) as policy variables. of physician visits, regardless of their level of health. These service requirements were then converted to simulated HRH requirements (to 6. The approach considers: meet criterion 2) based on estimates of the productivity of different a. The cost implications of HRH plans; and types of HRH (criterion 4). This approach is consistent with that b. The extent to which HRH plans are aligned with health system described in several previous studies (e.g. Birch et al., 2007; Segal et financial planning. al., 2008; New Zealand Ministry of Health, 2011a).
Based on these criteria, a simulation of future HRH supply in terms of In this way, the supply of and requirements for midwives, nurses and head counts was produced using a stock-and-flow approach, which physicians were simulated for each included country in 2030. The bulk entails adjusting current HRH stocks according to expected flows in (e.g. of the data used to populate the models for each profession and country new graduates, inward migration) and out (e.g. retirements, attrition were obtained from the OECD’s indicator database (OECD, 2015a). to other sectors, outward migration etc.) of each country’s stock. (e.g. Other data were obtained from other existing public online sources such Barber & González López-Valcárcel, 2010; Buchan & Seccombe, 2013; as the WHO’s indicator database (WHO, 2015a) or through documents Dall et al., 2015). In line with criteria 5 and 6, these head counts are then gathered as part of the first paper. In the time available it was not adjusted according to levels of participation (providing direct patient care) possible to engage with the various HRH stakeholder groups in the and activity (proportion of full-time hours spent providing direct patient included countries to obtain additional information or to do targeted care) for different types of HRH. Several documents reviewed as part of internet searches for information on each individual country and the first paper used such adjustments in estimating HRH supply (e.g. profession. HRH planners and other relevant stakeholder groups within Artoisenet & Deliège, 2006; Health Workforce Australia, 2014a; Centre the included countries would likely be better positioned to obtain this for Workforce Intelligence, 2015c). Costs of producing and maintaining information. these stocks can then be simulated based on average training costs and wages (criterion 7); although several of the documents reviewed in the Table 1.1 provides an overview of the data that were sought – and found first paper considered HRH remuneration in estimating HRH supply and – to inform the projections. The values in each cell indicate the number associated costs (e.g. Aiken & Cheung, 2008; WHO, 2010; Marvasti, of countries, out of the 31 included in these simulations, for which input 2014), none included analyses that also considered training costs. data were found for a given parameter, profession, and country.
12 In aggregate, 35% of the data elements required to implement the This analysis was focused only on three types of HRH – midwives, estimation approach were found. This should not be interpreted to mean nurses and physicians – due to data limitations. A more comprehensive that the data required to estimate HRH supply and requirements in a HRH analysis would need to also take into account the role of and needs manner consistent with the above criteria do not exist. For example, for all other types of HRH. there is information on acute care provision in Canada maintained by the Canadian Institute for Health Information (2015a) that can be adjusted In the absence of reliable data on the future values of various for acuity and the intensity of care provided, but which could not be model variables, all except the size and age-sex distribution of the obtained within the timelines of this project. Planners within individual population were held constant for the production of the baseline countries would be more familiar with potential local sources of the simulations. However, univariate and multivariate sensitivity analyses information required to provide a stronger base for the simulations. were conducted modulating the assumptions on each individual model parameter, as well as on several parameters simultaneously, In the absence of “gold standards” defining appropriate levels of to explore and illustrate how the results produced can change health-care service provision by age, sex and health status, the values according to different scenarios. These analyses were based on included in the model are based on current values. This is done for the actual variation in parameter values found within one of the included purposes of demonstrating the model’s application and does not imply countries. Thus, the baseline simulations should not be interpreted that these levels are optimal. Planners within individual countries can as predictions of what will happen; instead they are meant to serve update these data (and any others they desire) to reflect planned levels as compass bearings, showing the directions in which the HRH of service provision within their respective jurisdictions. To illustrate situations in the included countries are heading, and what policy this functionality, the impact of different values for the level of service levers may be used to move towards a better matching of HRH parameter is shown in the results section. supply with population needs.
Table 1.1 Overview of data availability by profession
Planning parameter Number of countries for which data were found Population characteristics Population size and projections by age and sex 32 Health status by age1 and sex 31 Health-care system characteristics Midwives model Nurses model Physicians model Health-care provision by age, sex and health status 0 2 2 Service provision per FTE provider per year 32 29 29 Average total annual wages per FTE 0 28 27 Average training cost per graduate 0 0 0 Average replacement cost per FTE 0 1 0 Number of new graduates per year 31 32 32 Age distribution of new graduates 0 1 1 Number of in-migrants per year 3 22 24 Age distribution of in-migrants 0 0 0 Exits per year by age 1 1 1 Total yearly enrolment in training programmes 1 1 1 % of students completing training 0 1 1 % of graduates remaining in jurisdiction 0 1 1 Head count of current supply 31 32 32 Age distribution of current supply 3/31 3 29 % of licensed workforce providing any patient care 24 19 1 Average hours worked/week by participating workforce 0 1 1 Note: FTE – full-time equivalent.
Series No 20 13 Human Resources for Health Observer 3. Results – synthesis of available evidence
3.1 Jurisdictional focus (14%) focused on multiple countries, most commonly the European Union and the OECD with seven documents (3%) each. Most of the The documents reviewed covered the 32 included countries specifically, documents focusing on individual countries were those where English with others looking at groups of countries (such as the European Union is an official language – not surprising given the search parameters or WHO regions) or globally (Figure 3.1). – although it is noteworthy that Belgium, Japan and the Netherlands were each the focus of at least five documents. Other countries for The United States of America was the jurisdiction covered by the which more than one document was found included Finland (7), most documents, with 53 (24% of all documents) specific to that Germany (3), Iceland (2), Israel (2), Italy (2), Norway (3), Portugal (2), country, followed by Canada with 27 (12%). Some 32 documents Scotland (4) and Wales (2).
Figure 3.1 Number of documents by jurisdiction(s) of focus
60 53 50
40
30 27 24
20 17 17 18 15
10 7 7 8 7 7 7 6 5
0 EU UK USA OECD Japan Ireland Canada England Belgium Australia Netherlands Other single Non-specific New Zealand Other multiple
Figure 3.2 Number of documents by profession of focus 3.2 Professions included
A diverse range of health professions were covered in the documents (Figure 3.2). 29 Nurses 6 75 Pharmacists Some 75 documents were not specific to any particular health Physicians profession, instead analysing health-care system and labour market issues that affect all health professions to varying degrees. Among 52 Other single profession documents focusing on a single profession, 52 (23%) documents Multiple professions covered physicians, 29 (13%) covered nurses and 6 (3%) pharmacists. Non-specific 13 The only other professions to be the sole focus of more than one 49 document were dentists (4), midwives (2) and physiotherapists (2).
Although 49 documents included analyses for more than one profession, the majority of these (38) considered those professions
14 in isolation of each other. Only 11 of these included analyses that they covered, with 27 each documents each including projections one considered the interdependence between different professions. to five years ahead and 11–15 years ahead. Five documents included projections more than 30 years into the future, while eight used multiple 3.3 Date of publication projection periods in their analyses.
Although the review spanned the years 2005–2015, 143 documents 3.5 Types of models used (64% of those included) were published in 2010 or later (Figure 3.3). The main types of models used for HRH planning have been described The number of documents published increased annually from 2005 to repeatedly and thoroughly elsewhere, and differ chiefly in the methods 2009, with the maximum of 32 published in 2012. they use for estimating HRH requirements (as opposed to HRH supply). Briefly, these fall into three categories: provider-based (sometimes 3.4 Analytical timeframes referred to as supply-based); utilization-based (sometimes referred to as demand-based); and needs-based approaches. In utilization- The documents that provided quantitative analyses of HRH supplies or based approaches, current or target utilization rates are multiplied requirements did so over varying time periods (Figure 3.4). by estimates of future population size, which are then converted to HRH requirements using productivity estimates. Under provider-based Sixteen analyses were cross-sectional, providing HRH analyses for one approaches, HRH requirements are estimated primarily by multiplying particular point in time. The others varied widely in the length of time current or target provider-population ratios to the estimated size of the future population, sometimes adjusting for basic demographic factors Figure 3.3 Number of documents by year of publication like age and sex. Under needs-based approaches, HRH requirements are determined by applying estimated future distribution of health in 35 the population to best practices (or current policy) for service provision 31 in response to different levels of health. Provider requirements are then 30 28 27 26 estimated from best-practice ways (or current productivity norms) of 25 25 delivering those health-care needs. 21 20 18 5 16 A number of different types of models were used to perform the HRH 15 13 analyses in the included documents (Figure 3.5). 10 10 Figure 3.5 Number of documents by type of planning 6 model 5 2
0 8 3 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 29 No Date Supply only 18 Figure 3.4 Number of documents by duration of analyses Provider-based (years) Utilization-based Needs-based 35 27 27 23 Other 30 22 Not clear 25 18 49 20 16
15 9 8 10 5 5 3 5 It should be noted that the models have been categorized here according 0 to their characteristics and the typology referred to above opposed to how 0 documents’ authors may have characterized them. See the results section for 1-5 31+ 6-10
11-15 16-20 21-25 26-30 more detail on the different use of terms such as “need” and “demand” across
Variable documents.
Series No 20 15 Human Resources for Health Observer
Of the 224 documents reviewed, 130 described quantitative analytical demonstrate the sensitivity of their results to different parameter methods and/or analyses of HRH supply or requirements (as opposed values such as HRH retirements, population growth or productivity. to, for example, focusing on qualitative discussion of HRH labour market Each of these demonstrated that projections of future HRH supply and or policy issues). Of these, 29 (22%) analysed HRH supply only – i.e. requirements are highly sensitive to different assumptions about how without contrasting it with HRH requirements. Among those documents various determinants would change over time. analysing requirements, 49 (38%) used utilization-based approaches. Twenty-three (18%) used provider-based approaches while 18 (14%) 3.7 Trends and issues identified used needs-based approaches. We were not able to determine which type of approach was used in eight documents (6%). A number of trends and issues were raised repeatedly in these documents, many of which have been identified by other recent reviews The approaches described in three documents (2%) did not fit any of HRH studies (e.g. Dussault et al., 2010; Ono et al., 2013; Kuhlmann of these categories. The primary determinants of HRH requirements et al., 2013; Fellows et al., 2014). An exhaustive list of citations for described in these documents included: documents in which each issue is raised would be prohibitively long, but for each of these recurring topics a few relevant documents are cited as • “Local health board plans” (Scottish Executive, 2006). examples. These recurring topics are listed below.
• Health needs, willingness/ability to pay, regulatory structures Ageing populations (McPake et al., 2013). The most frequently raised issue across these documents was the ageing of populations. For example, the population aged 65 and older • Demography, health needs, service levels, productivity, “clinical across all OECD countries is expected to increase by over 70% from care microsystems”, “policy/governance/power”, “funds and 208 million in 2015 to 355 million in 2050, while the number of those support”, “individual response: behaviour biology” – several of these aged 80 and over is expected to increase from 56 million to 133 million are identified as influencing both HRH supply and requirements over the same period (OECD, 2015b). This growth in older populations is (Masnick & McDonnell, 2010). expected to significantly increase requirements for health care in these countries, because older people tend to use more health care. However, 3.6 Assumptions described this view conflates the relationships between ageing, population health and service requirements. While the probability of sickness increases A detailed analysis of the various assumptions used in each of these with age, population ageing is partially reflective of improvements planning models is difficult because such assumptions are often implicit. the average level of health within each age group. As such, failure to However, the assumption most commonly articulated in these documents account for this distinction between demographic and epidemiological was that “the status quo will continue” – generally that the only model shifts results in overestimates of HRH requirements (Tomblin Murphy et parameters whose values will change over time are those pertaining al., 2009; Birch et al., 2013; Mason et al., 2015). to population demographics, while other parameters such as service delivery models, practice patterns and productivity, and prevalence rates Chronic disease and comorbidities of various health conditions are assumed to remain constant. Three other As people age, they are more likely to develop various chronic illnesses, explicit assumptions noted in multiple documents were: each of which can exacerbate the other but which can often be treated with increasingly complex health-care interventions. Multiple documents • There are no unmet HRH requirements in the base (or first) year of identified a trend of increasing prevalence of multiple chronic conditions as the analysis (see for example United States Human Resources & likely to contribute to increased future requirements for HRH (e.g. Buske, Services Administration (HRSA), 2015b). 2007; Helsedirektoratet et al., 2013). Only a small minority of these, however, included empirical analyses of such trends and their potential • Utilization of services is a proxy for demand (see for example Centre impacts on HRH requirements (e.g. Birch et al., 2007; Singh et al., 2010; for Workforce Intelligence, 2012). Tomblin Murphy et al., 2012). Others emphasize that this change in the complexity of health-care needs will require changes in the skill mix of • The current ratio of HRH to population is adequate (see for example available HRH (e.g. OECD, 2008) and/or the models of care delivery they Tennant & Kruger, 2014). use (e.g. Dussault et al., 2010; New Zealand Ministry of Health, 2011b; Ono et al., 2013), however, we found no documents that modelled the Although the assumptions used in these analyses were often not impacts of changes in needs and potential associated changes in skill mix stated explicitly, 13 documents (6%) included multiple scenarios to or care models in terms of their impacts on HRH requirements.
16 Ageing workforces In addition, the term “shortage” is used to describe different HRH The stock of available HRH is aging. For example, an average of one in situations in different documents. For example, HRH shortages are three physicians in OECD countries is aged 55 or older (OECD, 2013). defined in terms of vacant HRH positions (e.g. Crescent Management This has prompted concerns about large future HRH shortages, and a Consulting, 2009; Barber & González López-Valcárcel, 2010) or number of documents included specific analyses of the potential impacts measured as the degree to which jurisdictional HRH requirements of different retirement scenarios on HRH supply in their respective exceed supply (e.g. Oakley et al., 2008; Gallagher et al., 2010; HENSE, jurisdictions. In several documents it is suggested that strategies to 2012; Health Workforce Australia, 2014a; Centre for Workforce encourage HRH to delay retirement be considered as a means of Intelligence, 2013a). Similarly, the presence of any unemployment addressing HRH shortages (e.g. Centre for Workforce Intelligence, 2012). among HRH is deemed by some to reflect a surplus (e.g. Chaloff, 2008), whereas in others a surplus is considered to mean that the jurisdictional Migration supply is greater than requirements (e.g. Takata et al., 2011; Bloom HRH migration across countries is frequently raised as an issue in these et al., 2012; Juraschek et al., 2012). In either case, it is often noted documents, and in several cases is the primary focus of the analysis that estimated surpluses (or shortages) at one jurisdictional level (e.g. (e.g. Buchan, 2009; Ognyanova & Busse, 2011; Longley et al., 2012). national) may mask shortages (or surpluses) at other (e.g. state or Perspectives on migration differ across documents; for example, municipal) levels (e.g. United States Health Resources and Services migration is variously identified as a leading cause of (e.g. Pantelli Administration (HRSA), 2015a). et al., 2011; Sigurgeirsdóttir et al., 2014) or an important solution to (e.g. Cooper, 2008; Health Workforce Australia, 2014a) existing and/ Distribution of resources or projected future shortages. Others, in contrast, examine the potential The planning models included in the review focus on optimizing the impacts of reducing in-migration from other countries, for example number of HRH at a particular jurisdictional level; ensuring those due to concerns about self-sufficiency and the ethics of international resources are appropriately distributed within the jurisdiction is a recruitment of HRH (e.g. Tomblin Murphy et al., 2012; Royal Australian separate but important issue. Several types of HRH imbalances are College of Surgeons, 2011), or in response to perceived HRH surpluses discussed. Most often the imbalance noted is between urban and (e.g. Aiken, 2007). rural areas (e.g. Hawthorne, 2009; Chevreul et al., 2010; Toyokawa & Kobayashi, 2010; Pantelli et al., 2011; Campbell et al., 2013; New Inconsistent use of terms York Health Workforce Data System, Center for Health Care Workforce Terminology differs across documents, with terms such as “need”, Studies, 2014a). Other types of imbalances described are those that “demand” and “utilization” used almost interchangeably by some occur across professions, sectors, and/or specialties, such as between authors. Crettenden and colleagues (2014), for example, use utilization the acute and primary care sectors (e.g.; Ide et al., 2009; Olejaz et as a measure of demand, suggesting that their authors consider these al., 2012; Anell et al. 2012; Centre for Health Care Workforce Studies, to be equivalent. In fact, utilization represents the intersection of supply 2014; Tennant & Kruger, 2014). However, some of the possible and demand and, unlike in most markets, demand for health care is not strategies identified to address these issues – for example providing independent of supply thus creating market failure (Hall, 1978; Evans incentives for HRH to practise in underserved areas, scarce professions 1984; Birch, 1985; Markham and Birch, 1997; Birch et al., 2007; or specialties or underserved sectors – lack any clear evidence base McPake et al., 2014). Similarly, the Centre for Workforce Intelligence (Simoens & Hurst, 2006; McPake et al., 2014). (2012) describes its model as estimating demand but uses utilization to measure demand, and identifies a needs-based approach (Birch et Interprofessional education and practice al., 2007) as the basis for the model. Other documents, in contrast, As the complexity of health care services grows, it becomes increasingly emphasize that these are three distinct constructs, none of which can likely that the services involved will require the competencies of more be considered a measure of the other (e.g. Birch et al., 2007; Holmes, than one profession, so that systems must begin to rely increasingly 2012; McPake et al., 2014). on multi-professional teams of health-care providers (Dubois & Singh, 2009; Dussault et al., 2010; Scott et al., 2011; European Commission, Similarly, the term productivity is used in some documents to describe 2012). A number of documents included in this review have identified the number of hours worked by various types of HRH (e.g. Polgreen that this growth in the use of multi-professional teams requires HRH et al., 2011; Yuji et al., 2012; Longley et al., 2012), despite this being planning models with the capacity for team-based planning as opposed strictly a measure of activity. Other analyses, in contrast, use the term to single professions planning in isolation (e.g. Dussault et al., 2010; to refer to the volume of services produced during those hours of work Scott et al., 2011; Anell et al., 2012; Ono et al., 2013; Tennant & (e.g. Segal et al., 2008; Segal and Leach, 2011; Singh et al., 2010; Kruger, 2014; New York Health Workforce Data System, Center for Centre for Workforce Intelligence, 2014b, 2015c). Health Care Workforce Studies, 2014b). However, only a few such
Series No 20 17 Human Resources for Health Observer
models – from Australia (Andrews & Titov, 2007; Segal et al., 2008; substantially increase demand for nurses (Spetz, 2014). Other examples Segal & Leach, 2011; Segal et al., 2013), Canada (Tomblin Murphy of regulatory developments likely to impact HRH labour markets are et al., 2013a&b), and New Zealand (New Zealand Ministry of Health, changes to retirement age in countries such as France (Ono et al., 2011a-c, 2014) were found through this review. 2013), and policies aimed at regulating dual practice by physicians, which some argue reduce public sector HRH supply as physicians shift Changing care delivery models their practices entirely to the private sector (e.g. McPake et al., 2014). Many of the approaches reviewed assume that current care delivery More broadly, it is becoming increasingly recognized that existing models will continue in the future. In contrast, a number of documents national regulatory structures require strengthening to ensure the (e.g. New Zealand Ministry of Health, 2006; Andrews & Titov, 2007; adoption and implementation of effective HRH policies (e.g. McPake et OECD, 2008; Segal et al., 2008; Segal & Leach, 2011; Health al., 2013; Campbell et al., 2013; Stange, 2014). Education England, 2013; Tomblin Murphy et al., 2013a & b; Ireland Health Services Executive, 2014; New Zealand Ministry of Health, Stakeholder engagement 2014) identify a need for HRH planning models that can incorporate or To ensure the relevance, validity and uptake of HRH policies, effective accommodate changes in care delivery models which may be required engagement of stakeholder groups is essential. The planning to address changes in health-care needs and/or more effective or approaches identified through this review are a mix of theoretical and efficient ways of addressing those needs. applied methods. Among the latter, the importance of stakeholder engagement is acknowledged to varying degrees. Perhaps the most Changing practice patterns and scopes of practice thoroughly documented examples of stakeholder engagement in HRH Changing practice patterns or other provider behaviours are identified planning methods are the elicitation and horizon scanning efforts in some documents as determinants of HRH requirements. A frequently described by the United Kingdom’s Centre for Workforce Intelligence cited example of such a change is a reduction in the working hours (i.e. (2014a; 2015b), wherein multiple groups of stakeholders are engaged levels of activity, not levels of productivity) of physicians. Other things in a variety of activities at multiple stages of the analytical process being equal, such reductions reduce the service flow per physician to help planners identify current and potential HRH planning issues, and hence the supply of the services they provide. Although decreases articulate potential policy scenarios, supplement administrative data in physician working hours are frequently attributed to the growing with professional opinions, and interpret and validate results based on proportion of female physicians (e.g. Buske, 2007; Goodyear & Janes, their respective experiences. 2008; Koike et al., 2009; Chevreul et al., 2010; Health Workforce Australia, 2012; Johannessen & Hagen, 2012), other evidence indicates Incentives that this reduction in activity is occurring among all physicians, Some of the reviewed documents found (e.g. Baltagi et al., 2005; regardless of gender or age (Crossley et al., 2008; Stordeur & Léonard, Dubois et al., 2006; Cox et al., 2008; Dubois & Singh, 2009; Jeon & 2010). Hurley, 2010; Buchan & Black, 2011; Spetz, 2014) acknowledge the importance of provider financial and other incentives as determinants The issue of scopes of practice is addressed in several documents, of provider behaviour, but do not include any quantitative analyses of specifically in terms of expanding scopes of different health professions the relationships between these incentives and HRH supply (including over time (e.g. Dubois et al., 2006; Dussault et al., 2010; Holmes, levels of participation and activity) or requirements (as influenced by 2012; Centre for Workforce Intelligence, 2013a-d; Dall et al., 2015). productivity rates). Some documents factor provider rates of pay into Although some approaches include the capacity to adjust for changes in future HRH supply and requirements estimates (e.g. Spetz et al., 2006; scopes of practice (e.g. New Zealand Ministry of Health, 2011a; Segal WHO Western Pacific Regional Office, 2008; WHO, 2010; Gallagher et et al., 2008; Segal & Leach, 2011; Tomblin Murphy et al., 2013a&b), al., 2010); others explicitly investigate the relationship between different similar to another recent review of HRH planning approaches (Ono incentives and HRH outcomes such as labour force participation and et al., 2013), we found no examples of such changes actually being activity (e.g. Aiken & Cheung, 2008; Buchan & Seccombe, 2013; accounted for in modelling exercises. In none of the included documents Marvasti, 2014). were potentially contracting scopes of practice discussed. Technological changes Evolving regulatory structures Many of the reviewed documents identified technological change as Changes to legislation and the other structures that govern the a factor likely to have a significant impact on HRH balance in their regulation, management and delivery of health care are identified as an respective jurisdictions (e.g. New Zealand Ministry of Health, 2006; issue in several documents. An example is the Affordable Care Act (ACA) OECD, 2008; Dussault et al., 2010; Centre for Workforce Intelligence, in the United States of America; one estimate suggests that the ACA will 2013a; Health Education England, 2013; McPake et al., 2014). These
18 include changes in the technology used in health care – such as future changes in factors such as productivity. It is better to base plans those allowing for less invasive surgeries (Matrix Insight, 2012) – as on appropriate concepts imperfectly measured than on inappropriate well as those used by health-care consumers, such as mobile phone concepts that can be easily measured. If the available data are deemed apps for management of chronic conditions (e.g. Arnhold & Quade, to require some improvement to fully inform planning then investments 2014). The impacts of such changes in terms of HRH, however, are should be made in improving the quality of the available data rather not clear from the analyses included in this review. Comparatively few than in further entrenching the use of intrinsically flawed models. To that documents directly incorporate consideration of technology changes end, the identification and assessment of the data required to inform into quantitative analyses. Among those that do, the exact nature of the HRH planning should be based on the question of how many of what changes in question is not clear (e.g. New Zealand Ministry of Health, type of HRH are required to perform what services, for whom, and under 2006; Baumann & Kolotylo, 2010; Health Workforce New Zealand, what circumstances (Birch et al., 2007). 2010). Iterative planning Balancing the private and public sectors Effective HRH planning requires appropriate methods and adequate The health-care systems in included countries incorporate a mix of data. But no matter how rigorous and comprehensive the methods public and private service providers, and the relative size of these or accurate the data, it is not possible to fully anticipate future sectors varies considerably across and within countries. The public developments in health care. Even demographic projections, which are sector is the focus of the vast majority of the included analyses, the result of their own dedicated field of scientific study, are subject although several documents emphasize the need for better data on, and to significant inaccuracies, which have had profound implications for coordination by national planners with, the private health care sectors HRH planning decades after their publication (Evans, 2009). Many (e.g. Oakley et al., 2008; Gallagher et al., 2010; Ono et al., 2013; documents (e.g. Birch et al., 2007; Dubois et al., 2006; Dussault et al., Centre for Workforce Intelligence, 2013a). An example of the challenges 2010; Health Education England, 2013; Ono et al., 2013) emphasized in effectively managing both the public and private health-care systems the importance of HRH planning being conducted on an iterative, is raised by McPake and colleagues (2014) who discuss the issue of ongoing basis. This is not only so that planning models can be updated dual-practising physicians – i.e. those who provide services in both the with the most current available data, but also so that the relevant public and private sectors – noting that policies intended to dissuade stakeholders can be engaged, the validity of any necessary assumptions physicians from devoting time to private practice may instead drive can be tested, and the effectiveness of implemented strategies can be them to leave the public sector entirely. regularly assessed.
Data quality Although many documents have been published describing various The validity of any HRH planning exercise is contingent upon the aspects of the HRH market in included countries, they do not collectively availability, relevance and accuracy of the data to which planners have present a clear or consistent picture of what the HRH situation is access. Needs-based approaches in particular require comprehensive expected to be across – or even within – these jurisdictions in the data on not only the supply of HRH but also on the health of the future. There are several reasons for this: population, planned levels of service provision for different health problems, and HRH productivity, which may not be available in some • The HRH research and/or policy question(s) to be answered by the cases. Despite significant improvements in HRH data collection in various analyses are often not clear; different approaches may be several high-income OECD countries and advancements in population required to answer different questions. health survey methods (McDowell & Newell, 2006, p. 12), almost every document included in this review articulated concerns about • As shown in Figure 3.2, the majority of the available studies focus inadequacies in the data available to inform HRH planning (e.g. Dubois et on physicians and nurses; there is little evidence on supply of – let al., 2006; Cameron Health Strategies Group, 2009; Matrix Insight, 2012; alone the requirements for – the other HRH that make up each Gallagher et al., 2010; Campbell et al., 2013; Campbell et al., 2014). country’s workforce.
Concerns about data are as long-standing as the study of HRH • For most of the jurisdictions included in this review, no quantitative planning itself (WHO, 1971). As has been noted elsewhere, however analyses of national-level HRH gaps – i.e. the difference between (e.g. Birch et al., 2007), problems with data are not avoided by relying HRH supply and HRH requirements – were found. For others, only on conceptually invalid models that, for example, ignore fundamental analyses of HRH supply (not requirements) were found, so it is health-care system objectives such as meeting population health not possible to present a picture of the HRH situation across the needs by failing to incorporate these, or cannot account for potential included countries here.
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• Even among those analyses that project HRH shortages or on specific specialty areas (e.g. general practice, obstetrics, surgery) as surpluses, these are measured in different units – for example, opposed to the physician supply as whole. As Figure 3.2 suggests, such head counts as opposed to full-time equivalents (FTEs) in different a table for other professions would be even more sparsely populated. studies – they span differing time periods, and are specific to Virtually all documents reviewed seemed to suggest that HRH supply is a variety of different sectors (e.g. primary care, mental health expected to increase. In contrast, as exemplified by the United States services, long-term care etc.). of America studies cited above, there are differing views about whether the requirements for different types of HRH are expected to increase • As outlined above, there are important differences in the various or decrease in the future in different jurisdictions. Although most of the approaches used to conceptualize and measure HRH supply, included analyses for nurses and physicians suggest that national-level requirements, shortages and surpluses across these countries. Some of shortages are likely in the next decade, several analyses for other non- these approaches, for example, describe their approach as measuring physician health professions such as pharmacists and dentists suggest HRH requirements in terms of demand, even though demand in the substantial surpluses are likely over this time period (e.g. United States market for health care is not independent of supply (Evans, 1984), Health Resources and Services Administration (HRSA), 2014c, 2015a). while others do so in terms of need, which is independent of supply. Should these projections come to pass, the potential for substitution of Given these two fundamentally different concepts, estimates of different types of HRH would become an increasingly important policy HRH “demand” cannot be meaningfully combined with estimates issue; however, as noted above most of the approaches identified through of HRH needs, since they are likely to address significantly different this review lack the capacity to account for such substitution. policy questions and hence generate different conclusions about the adequacy of HRH supply relative to requirements. There has been a considerable amount of study devoted to the current and potential future states of the supply of and requirements for HRH in • Estimates of future HRH supply or requirements, however high-income OECD countries. Despite these efforts, however, no clear sophisticated, require numerous assumptions. As a number of the or consistent picture of the status of these countries’ HRH appears to reviewed documents show, the estimated future HRH supplies and exist. Some of the gaps in information are likely due to limitations in requirements are sensitive to differences in these assumptions. the various strategies used here to collect HRH analyses for this review. Even a few modest changes to these assumptions can mean the However, other recent studies, which did not have the same limitations difference between a large estimated future shortage and a large (e.g. Ono et al., 2013), describe similar gaps in the information estimated future surplus of HRH for a given jurisdiction and time available. This is in partly the result of significantly different approaches period (e.g. Tomblin Murphy et al., 2012; Centre for Workforce to HRH planning being used across and within these countries. The Intelligence, 2013b). appropriateness of different HRH planning approaches for given jurisdictions depends on the objectives of the health-care systems, the To illustrate the implications of the above complexities, the substantial precise policy questions being asked for which they are planning, as gaps in the available evidence, and the large qualitatively and well as the context in which that planning takes place. quantitatively different conclusions reached by different HRH analyses, Table 3.1 summarizes the results of the reviewed documents pertaining to 3.8 A methodology for projecting HRH nurses. Although several of these documents included multiple scenarios requirements in high-income OECD in their estimates, in the interests of a minimal level of comparability the countries “status quo” scenario is cited below unless otherwise stated. The final objective of this review was to identify a methodology for As shown in Table 3.1, for the majority of included countries, no estimates projecting HRH requirements in high-income OECD countries, with of the size of any future nurse shortage or surplus was found during the proviso that this may be a combination of existing approaches. To this review. For the few jurisdictions for which such estimates were guide the selection of such a methodology, we developed a draft set of found, studies using different methods reached different conclusions. evaluation criteria which were circulated to the Advisory Group for their For example, Juraschek and colleagues (2012) projected a shortage input, which was considered for the development of the list below: of over 900 000 registered nurses (RNs) in the United States by 2030; meanwhile the United States Health Resources and Services 1. The approach is consistent with the objectives of the health Administration (HRSA) (2014b), using different methods and assumptions, system estimated a surplus of over 300 000 RNs for 2025. A similar table for physicians would contain more information but no more This means, for example, that a system whose objective involves clarity, since most analyses of physician supply and requirements focus addressing the health-care needs of its population must use an HRH
20 Table 3.1 Projected national nurse shortages or surpluses by country for 2025
Country Document Projected 2025 shortage (-) or surplus (+) Australia Health Workforce Australia, 2012 -109 400 (includes RNs and ENs) Health Workforce Australia, 2012 -80 142 Health Workforce Australia, 2014b -85 000 (includes RNs and ENs) Austria No gap analyses found Belgium No gap analyses found Canada Tomblin Murphy et al., 2012 -60 000 Chile No gap analyses found Czech Republic No gap analyses found Denmark No gap analyses found Estonia No gap analyses found Finland No gap analyses found France No gap analyses found Germany Maier & Afentakis, 2013 -195 000 (includes RNs and ENs; estimated from graph) Greece No gap analyses found Hungary No gap analyses found Iceland No gap analyses found Ireland Behan et al., 2009 -836 (for year 2020) Israel No gap analyses found Italy No gap analyses found Japan No gap analyses found Luxembourg No gap analyses found Netherlands No gap analyses found New Zealand No gap analyses found Norway No gap analyses found Poland No gap analyses found Portugal No gap analyses found Republic of Korea No gap analyses found Slovakia No gap analyses found Slovenia No gap analyses found Spain No gap analyses found Sweden No gap analyses found Switzerland No gap analyses found United Kingdom Centre for Workforce Intelligence, 2013d -50 000 (for England only; estimated from graph; taken as midpoint of the range of demand and supply projection scenarios) United States of America Aiken & Cheung, 2008 -1 016 900 (for year 2020; analysis from another study) Juraschek et al., 2012 -918 232 (for year 2030) United States Health Resources and Services +340 000 Administration (HRSA), 2014b EN – enrolled nurse; RN – registered nurse planning method that estimates HRH requirements as a function of in accordance with increases or decreases in those needs, while population health measures, so that resources can be planned in allowing for changes in the way needs are to be met (e.g. using accordance with needs for health care. This means that resources new technologies) and changes in the ways services are delivered are allocated between populations based on differences in needs (e.g. changes in health-care teams). Although meeting population between those populations and increased or decreased over time health-care needs is a goal shared by many health care systems, the
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findings of this review indicate that few countries appear to be using major limitations of this approach have been extensively documented needs-based methods for HRH planning. Instead, those HRH analyses for decades. For example, the WHO noted over 40 years ago that such we found appear to be using utilization- or supply-based approaches. approaches may be appropriate in, For countries such as the United States of America, where the ability to pay for health-care services has traditionally been an important “…a country, whatever its stage of development, where element of access to care, utilization-based approaches may be professional judgement, backed by utilization studies, suggests more relevant in addressing the policy questions facing decision- that the current patterns of the organizations and delivery of makers. Although some of these approaches (e.g. United States health services [through] existing ratios do not present any Health Resources and Services Administration (HRSA), 2014a; Dall et anomalies or problems. Unfortunately, such situations are rare, al., 2015) incorporate consideration of how population health needs and in any case this static view of society implies that the affect service use, this is done within the context of the prevailing present situation cannot be significantly improved upon in the organization of services in the United States of America and hence foreseeable future.” are unlikely to represent how needs for care would be served in the absence of payment at the point of delivery, or via private insurance “The exclusive use of health manpower: population ratios for plans as a means of using care. For countries where meeting the estimation of future health manpower requirements is population health-care needs is a primary objective, however, increasingly difficult to justify in the developed countries where, failure to plan for HRH according to those needs means planning depending on the specific characteristics of the health system, not to meet that system objective. Moreover, the use of supply- or other and more refined criteria can be used to improve the utilization-based approaches will perpetuate and exacerbate existing country’s capacity to provide health care.” (WHO, 1971). efficiencies and inequalities in these systems (Birch et al., 2007; Evans, 2009). Other analyses found in this review use methods based on historical population-utilization ratios without any consideration of whether those Needs-based approaches to HRH planning are not new. Over 40 ratios are consistent with the objectives of the health-care system in years ago the WHO (1971), for example, outlined a history of them question. The use of these methods for planning future services, and dating back to at least the 1930s in the United States of America, hence HRH, perpetuates any current patterns of inappropriate use also noting their widespread use in what are now former Soviet such as over-reliance on emergency departments instead of primary states. A more detailed description was provided by Hall (1978), and health care, or inadequate access to (and therefore use of) services subsequently such approaches were also described in the 1980s by disadvantaged populations. The needs-based approaches from and 1990s in the United Kingdom (Birch, 1985; Birch & Maynard, Australia, Canada and New Zealand do not have this limitation. In 1985) and Canada (Lavis & Birch, 1997; Markham & Birch, 1997). addition, some approaches from the United States of America such as Several more recent examples from Australia (Andrews & Titov, 2007; the one described by Dall and colleagues (2015), although utilization- as Segal et al., 2008; Segal & Leach, 2011), Canada (Birch et al., 2007; opposed to needs-based, are designed to explicitly consider changes to Tomblin Murphy et al., 2009, 2012, 2013a&b) and New Zealand service provision as a consequence, for example, of changes in health- (New Zealand Ministry of Health, 2011a; Naccarella et al., 2013) are care legislation or practice models. included in this review. 3. The approach considers HRH requirements in the context 2a. HRH requirements are derived from service requirements of production functions for health services (i.e. dependent upon the availability or use of other inputs to service 2b. Those service requirements are aligned with system production) objectives Although the availability of HRH is important to the delivery of health- Requirements for HRH are a manifestation of requirements for the care services, other types of human and non-human resources, such services they provide. Hence estimates of HRH requirements must be as facilities, equipment and medications, are also necessary. Effective derived from estimates of the requirements for those services. This health systems planning approaches must recognize this dependency makes it possible to consider and plan for potential future changes by considering how the availability (or lack thereof) of: a) other HRH; in the way services are delivered resulting from new technologies, and b) non-human resources may affect their collective production of changes in scopes of practice and so on. The results of this review health-care services, including the potential for substitution of one type show, however, that HRH planning approaches which cannot account of resource for another. For example, the availability of operating theatre for such changes remain prevalent. This is unfortunate given that the nurses or operating theatres may impact on the volume of surgeries
22 that surgeons can perform, even if the number of surgeons and the education and payment models. Most of the analyses of HRH supply hours they work remain the same. The review found several examples found through the review reflected this situation; in some cases, such of approaches that explicitly incorporated this potential for different factors were the primary focus of the analyses (e.g. Crossley et al., types of HRH (e.g. Andrews & Titov, 2007; Segal et al., 2008; Segal 2008; Jeon & Hurley, 2010; Buchan & Black, 2011; McPake et al., & Leach, 2011; Scottish Government, 2010; New Zealand Ministry of 2014). Health, 2011a; Tomblin Murphy et al., 2013a & b). However, although documents sometimes acknowledged the influence of the availability 7. The approach considers: a. The cost implications of HRH of non-human resources on HRH requirements, the review found no plans; and b. The extent to which HRH plans are aligned with analyses that directly incorporated this relationship. health system financial planning
4. The approach explicitly considers the role and determinants Essential to determining the relative appropriateness of any potential of productivity (i.e. units of service per hour of work) HRH policy is an understanding of its financial implications in the broader context of the jurisdictional fiscal situation. Although many In order to translate health-care service requirements into HRH of the documents included in the review acknowledged this point, requirements, HRH planners must consider the rate at which different comparatively few (e.g. Andrews & Titov, 2007; WHO Western Pacific types of HRH are able to provide those services per unit time – i.e. their Regional Office, 2008; Gallagher et al., 2010; WHO, 2010; Polgreen productivity – under a given set of circumstances. In addition to the et al., 2011; Marvasti, 2014; Tennant & Kruger, 2014) explicitly needs-based approaches referred to above, numerous other analyses incorporated financial considerations into their analyses. found by the review (e.g. Artoisenet & Deliege, 2006; Oakley et al., 2008; Dill & Salsberg, 2008; Singh et al., 2010; Centre for Workforce Although over 200 documents discussing the HRH situations in included Intelligence, 2015a-c) explicitly included productivity as part of their countries were reviewed as part of this study, it appears that none of calculations. Although the contexts in which productivity was considered these approaches to HRH planning meets all seven of the above criteria. varied widely across these documents, they generally showed that However, several of the needs-based approaches described above projections regarding the future HRH situation are highly sensitive to meet all but one. The needs-based approaches described in Australia even small changes to HRH productivity. (Andrews & Titov, 2007; Segal et al., 2008; Segal & Leach, 2011; Segal et al., 2013), Canada (Tomblin Murphy et al., 2013a&b), and New 5. HRH supply is measured in terms of time devoted to service Zealand (New Zealand Ministry of Health, 2011a-c, 2014), each identify delivery (i.e. flow generated by a stock of HRH) as opposed specific service requirements based on population health needs and to focusing only on the HRH stock (numbers of HRH) translate these into HRH requirements based on information on scopes of practice and standards of care delivery. However, the Australian and The availability of health-care services is determined by a number of New Zealand approaches do not appear to explicitly consider the role factors in addition to the raw “stock” or head count of different types of and determinants of productivity, while the Canadian approach does HRH available to provide them. Analyses in many countries have shown not include considerations of cost implications. Further, as noted above, how changes or differences in these factors can have profound effects none of the identified approaches appears to account for the impact of on the effective supply of HRH (e.g. Crossley et al., 2008; Koike et non-human resources on HRH requirements. al., 2009; Johannessen & Hagen, 2012), and most of the HRH supply analyses found through the review considered at least one of these; An example of a methodology for projecting HRH requirements in the most frequently hours worked. Several analyses (e.g. Landry et al., included countries, then, could build on these needs-based approaches. 2007; Barber & González López-Valcárcel, 2010; Capiciteits Orgaan, This would require augmenting them in two ways. First, the Canadian 2013), however, did not take any of these factors into account, and approach would need additional features to include consideration of instead estimated HRH supply based solely on head counts. cost implications, and the Australian and New Zealand approaches would need to be further developed to include consideration of the role 6. The approach considers the determinants of flow (e.g. hours and determinants of productivity. Second, both approaches would need worked) and stock (entries/exits) as policy variables further enhancements to include consideration of the impacts of the availability of non-human resources. The factors that determine the stock and flow of HRH supply, such as the amount of time spent providing patient care (activity levels), and The second of these two improvements is evidently the more difficult the proportion of licenced HRH who are actively practising (participation to achieve, as no HRH planning approaches found through this review levels), are sensitive – to varying degrees – to HRH policies such as included consideration of the impacts of non-human resources. The
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first of these improvements, however, has already begun. An analytical sustainability. This framework has not yet been applied, and was not framework for such an approach has been developed by a team from included in the review because it was published after its specified Canada, Australia and New Zealand (Birch et al., 2015) that extends timeframe. Similar international collaborations may be one way of the needs-based methods used by Tomblin Murphy and colleagues encouraging the continued advancement of HRH planning methods (2013a&b) to include consideration of the implications of HRH and so that they are also able to account for the impacts of non-human other health system policies on the system’s fiscal and sociopolitical resources in the future.
24 4. Results – development of simulations on future HRH supply and requirements
On the basis of the methodology and the criteria outlined above, the with the surplus or shortfall implied by these results. The simulated supply of and requirements for midwives, nurses and physicians in surpluses or shortfalls for each profession and country are provided in each included country were simulated for the period to 2030, together Table 4.1.
Table 4.1 Simulated6 HRH shortfalls or surpluses7 by profession and country for 2030 – based on demographic change alone8
Projected Projected Projected shortfall (-) or shortfall (-) or shortfall (-) or Country Profession surplus (+) Country Profession surplus (+) Country Profession surplus (+) Australia Midwives -9068 Greece Midwives Poland Midwives +2042 Nurses -144 654 Nurses Nurses -48 036 Physicians -23 393 Physicians -33 761 Physicians -16 524 Austria Midwives -238 Hungary Midwives -510 Portugal Midwives -752 Nurses +13 505 Nurses +11 027 Nurses -8538 Physicians -12 584 Physicians -4398 Physicians -13 285 Belgium Midwives -1428 Iceland Midwives -174 Republic of Midwives -474 Nurses -65 590 Nurses -1294 Korea Nurses +104 127 Physicians -5365 Physicians -499 Physicians -27 861 Canada Midwives -437 Ireland Midwives -5618 Slovakia Midwives -126 Nurses -84 719 Nurses -32 498 Nurses +8304 Physicians +8108 Physicians +7026 Physicians -5238 Chile Midwives -4735 Israel Midwives -781 Slovenia Midwives +180 Nurses Nurses -24 181 Nurses +13 626 Physicians Physicians -23,789 Physicians -82 Czech Midwives -544 Italy Midwives -1779 Spain Midwives +54 Republic Nurses -31 975 Nurses -153 147 Nurses -85 858 Physicians -9195 Physicians -89 467 Physicians -27 341 Denmark Midwives -202 Japan Midwives -7086 Sweden Midwives -3576 Nurses -46 540 Nurses +58 188 Nurses -57 574 Physicians -475 Physicians -7579 Physicians -9352 Estonia Midwives +70 Luxembourg Midwives -112 Switzerland Midwives -262 Nurses -6319 Nurses -5156 Nurses -4690 Physicians -3375 Physicians -1252 Physicians -14 581 Finland Midwives +622 Netherlands Midwives -773 United Midwives -2225 Nurses -1361 Nurses -31 068 Kingdom Nurses -36 240 Physicians -956 Physicians -8369 Physicians +33 318 France Midwives -3485 New Midwives +1271 United Midwives -2630 Nurses -177 497 Zealand Nurses -10 035 States of Nurses -143 302 Physicians -83 950 Physicians +5297 America Physicians -303 760 Germany Midwives -2058 Norway Midwives -1192 Nurses -135 236 Nurses -47 886 Physicians -73 130 Physicians -13 057
6 These simulations are not to be taken as predictions about the future. Should large shortfalls or surpluses appear on the planning horizon, it is likely that substantial changes to these variables – such as increases or reductions in training capacity or levels of service provision – will be made; indeed the purpose of performing these simulations is to inform such planning. 7 The shortfalls or surpluses shown here are measured relative to any shortfalls or surpluses that may already exist for these professions in these countries. 8 In these simulations all other planning parameters are held constant. Analyses demonstrating the sensitivity of these simulations to changes in other planning parameters are provided elsewhere in this report.
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These simulation results suggest that, if the current HRH situations in Although providing detailed results in terms of the supply of and the included countries continue to 2030, most of the included countries requirements for each of the three professions and country would could face shortfalls of one or more types of HRH; that is, holding require a prohibitively lengthy report, results for physicians in Canada constant all the parameters included in the model except population, are shown in Figures 4.1 and 4.2 as an illustration. Figure 4.1 shows they would not have enough HRH available to continue to provide their the simulated supply of and requirements for physicians through 2030, current levels of health-care services to their respective populations. In measured in FTEs. contrast, some countries may experience surpluses of some types of HRH; that is, they would have more than the number needed to continue 4.1 HRH supply vs. requirements to provide current levels of health-care services to their respective populations. In total, these simulations in the baseline scenarios In this scenario, in which all planning variables except population are sum to shortfalls of nearly 45 000 midwives, 1.1 million nurses, and held constant at current levels, both the supply of and requirements 754 000 physicians across the 32 included countries for 2030. These for physicians in Canada increase to 2030, with supply increasing at a simulated shortfalls are the collective result of simulated future supplies more rapid rate, resulting in a growing surplus (net of any existing gap) of 157 000 midwives, 6.8 million nurses, and 2.4 million physicians of just under 9000 FTEs by 2030. The difference or “gap” between the against simulated requirements of 202 000 midwives, 7.9 million simulated supply of and requirements for physicians in Canada is shown nurses, and 3.2 million physicians. in more detail in Figure 4.2.
Figure 4.1 Example simulated physician supply and requirements to 2030 (Canada)
120 000
100 000
80 000
60 000 FTEs
40 000
20 000
0 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Supply Requirements
Figure 4.2 Example simulated physician gap to 2030 (Canada)
9 000 8 000 7 000 6 000 5 000 4 000 3 000 2 000
Simulated surplus (FTEs) 1 000 0 -1 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
26 As also noted above, many of these simulations are based on three curves begins with a productivity value set at the average number incomplete information regarding variables in the model. To demonstrate of consultations per FTE physician across the country as captured the sensitivity of these simulations to different variable values, several in administrative data collected by the Canadian Institute for Health different scenarios are presented below. These begin with scenarios Information (2015a&b). The middle or “baseline” curve shows how pertaining to the four factors used to determine HRH requirements – the the simulated physician gap would change if this level of productivity productivity of the different types of HRH, the levels of service provision remained at the national average level through 2030. The lower of the according to different levels of health status, the distribution of health three curves represents the simulated gap should productivity decrease status in the population by age and sex groups, and the size and age- to the lowest currently found among Canadian provinces, while the sex distribution of the population. upper curve shows how the estimates would change should productivity increase to the level found in the province with the highest average In this way the potential impacts of each of these determinants of numbers of consultations per physician in the country.9 HRH requirements is demonstrated separately. Canada is used as the example because that was the country for which the most complete Figure 4.3 demonstrates the sensitivity of HRH estimates to different data were found, and as such allowed for the most complete application levels of productivity: if all Canadian physicians became, on average, of the model as well as the best empirical evidence to guide sensitivity as productive as those in the province with the highest reported analyses. productivity, this would result in a growing physician surplus by 2030, other things being equal. Alternatively, should Canadian physicians 4.2 Impacts of changes in HRH become, on average, as productive as those in the province with the productivity lowest reported productivity levels, this would result in a growing physician shortfall of even larger magnitude over the same time period, In the context of HRH planning, the term productivity refers to the other things being equal. The difference between these scenarios is number and type of services FTE HRH on average can be reasonably approximately 55 000 FTEs, which would represent 70% the country’s expected to provide at some basic standard of quality. For the current physician supply. purposes of these simulations, HRH productivity is measured in terms of numbers of consultations for physicians, hospital patient days for 4.3 Impacts of changes in levels of nurses, and births for midwives. There are substantial differences in service the values of these measures across and even within countries and jurisdictions. Figure 4.3 shows the impact of different assumed values Figure 4.4 shows the impact of different assumed future levels of for productivity on the estimated physician gap for Canada. Each of the service provision on the simulated physician gap in Canada. Levels
Figure 4.3 Examples of simulated physician gap in different productivity scenarios (Canada)
30 000
20 000
10 000
0
-10 000
-20 000
-30 000 Simulated shortage (-) or surplus (+)
-40 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Productivity remains constant Productivity decreases Productivity increases
9 Because the administration of health care is largely the responsibility of provincial and territorial (e.g. as opposed to the federal) governments in Canada, there are substantial differences in HRH remuneration and practice models across the country, which likely affect the numbers of consultations per physician.
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of service, in this context, refers to the average number of physician As Figure 4.4 shows, should the level of physician services in 2030 be consultations to be provided per person, given the person’s age, lower than it is currently – that is, should fewer physician consultations be sex and health status. The higher the level of service – i.e. the required for patients of a given age, sex and health status – the surplus greater the number of physician consultations to be provided – the of physicians would be higher than it would otherwise be. Similarly, more physicians are required to deliver those services, other things should more physician consultations be required for a given level of being equal. In the “average service levels” scenario, the required health status by 2030, more physicians would be required to provide number of physicians is based on the average number of physician those consultations, and the surplus would be lower than otherwise. The consultations by patient age, sex and health status across the country. difference in the assumed future levels of service provision equates to a In the “low service levels” scenario, physician requirements are difference in the simulated physician gap of over 1000 FTEs by 2030. simulated based on the average numbers of consultations reported by patients in the Canadian province whose residents report receiving 4.4 Impacts of changes in health status the fewest physician consultations per person given their age, sex and health status in the country. In the “high service levels” scenario, The health status of the population to be served can also affect the requirements are based on the average numbers of consultations requirements for HRH, since sicker people require more health-care per patient in the province where the average number of reported services, other things being equal. Figure 4.5 illustrates examples of physician consultations given their age, sex and health status is the how changes in population health over time can impact the simulated highest in the country. “gap” in physicians.
Figure 4.4 Examples of simulated physician gap in different level of service scenarios (Canada)
9 000 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000
Simulated shortage (-) or surplus (+) 0 -1 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Average service levels Low service levels High service levels
Figure 4.5 Examples of simulated physician gap in different health status scenarios (Canada)
10 000
8 000
6 000
4 000
2 000
0 Simulated shortage (-) or surplus (+) -2 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Age-specific levels of Age-specific levels Age-specific levels health remain constant of health worsen of health improve
28 The middle of the three curves shows the simulated physician gap – each of which are is based on different projections developed by (again, in this case a surplus) for Canada if the current average level of Statistics Canada – results in a difference of over 10 000 FTEs in the health across the country remains the same through 2030. The other simulated physician gap by 2030. two curves show the impact on the gap of worsening or improving health status. More specifically, the latter scenario shows the trajectory 4.6 Impacts of changes in HRH activity of the estimated physician gap should the average age- and sex- specific self-reported health status of Canadians improve to match that The effective supply of HRH is dependent not only on the number of of province with the best self-reported health status.10 practising members of various personnel, but also on the time they collectively spend providing patient care. Figure 4.7 shows the impact of Similarly, the former shows the trajectory of the estimated physician gap different assumed levels of physician activity on the simulated surplus. should the average age- and sex-specific health status of Canadians worsen to match that of the province with the worst self-reported health The more time the average physician spends providing direct care to status. The difference between assumed future levels of health status patients, the fewer physicians will be required, other things being equal. in these scenarios translates into a difference in the projected physician In Canada, there is substantial variation in the hours that physicians gap of over 5000 FTEs by 2030. report spending on direct patient care. The middle curve in Figure 4.7 shows the trajectory of the projected physician surplus assuming 4.5 Impacts of demographic changes the average Canadian physician spends the same number of hours on direct patient care in 2030 as reported by the average Canadian An additional factor affecting simulations of requirements for health- physician in 2014 – 39.6 hours per week, including direct care provided care services – and by extension the requirements for HRH – is the during on-call hours, according to self-reported data from the National size and age-sex distribution of the population. Although considerable Physician Survey (2015), which was developed by the Canadian Medical effort is put into developing estimates of the future population size Association in partnership with several other national health-care and characteristics in many countries, predicting these features stakeholder organizations. The upper curve shows how the estimated with accuracy remains difficult. Figure 4.6 shows the impact on the surplus would be greater if the average Canadian physician were to simulated Canadian physician gap of different assumed changes to work as many hours as the average physician in the province with the population size. As Figure 4.6 demonstrates, the trajectory of the highest reported number of hours spent providing direct patient care simulated physician gap is substantially affected by the assumed – 43.5 per week. Similarly, the lower curve shows how the estimated demographic trajectory of the population those physicians are to serve. surplus would be reduced if the average Canadian physician were to The difference between the high and low population growth scenarios work as many hours as the average physician in the province with the
Figure 4.6 Examples of simulated physician gap in different population growth scenarios (Canada)
16 000
14 000
12 000
10 000
8 000
6 000
4 000
2 000 Simulated shortage (-) or surplus (+) 0
-2 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Medium population growth High population growth Low population growth
10 There are substantial differences across Canadian provinces and territories in various measures of health status (Statistics Canada, 2015).
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lowest reported number of hours spent providing direct patient care Figure 4.8 illustrates the sensitivity of the simulated physician gap – 38.0 per week. The difference between these scenarios equates to to different numbers of physician graduates. Although the changes a difference in the projected physician surplus of over 1000 FTEs by take at least four years to have any impact (because of the duration 2030. of the training programmes), these substantial differences in medical school graduates result in substantially different simulated future 4.7 Impacts of changes in HRH training physician gap, with a simulated difference between the “low” and “high” graduation scenarios of roughly 7000 FTEs by 2030. In 2014 there were 2804 graduates of Canadian medical schools. This was the highest number of graduates in the country’s history and 4.8 Impacts of changes in HRH represents a 15% increase from the number of graduates in 2010 participation (2448). Figure 4.8 shows the impacts on the simulated physician gap of three different levels of annual physician graduation. In one, the level According to the most recent results of the annual National Physician from 2014 is maintained through 2030. In the others, the number of Survey, an estimated 95% of physicians in Canada report providing at graduates is decreased to the value from 2010 and increased another least some direct patient care, while the remaining 5% work exclusively 15%. in other roles such as administration or education. This is a higher
Figure 4.7 Examples of simulated physician gap in different physician activity scenarios (Canada)
9 000
8 000
7 000
6 000
5 000
4 000
3 000
2 000
1 000 Simulated shortage (-) or surplus (+) 0
-2 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Medium activity Low activity High activity
Figure 4.8 Examples of simulated physician gap in different physician graduation scenarios (Canada)
11 000
9 000
7 000
5 000
3 000
1 000 Simulated shortage (-) or surplus (+)
-1 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Graduate numbers maintained Low Graduate numbers reduced 15% Graduate numbers increased 15%
30 level of participation than nurses in Canada, of whom 81% are actively due to migration, retirement or death (Canadian Collaborative Centre practising according to the OECD indicator database. Figure 4.9 shows for Physician Resources, 2013). Canada is a net recipient of migrating the impact of two different levels of participation on the simulated future physicians, and has been for the past decade (Canadian Medical physician gap in Canada. In one, the current level of 95% is maintained Association, 2015), and the most recent specific quantitative analysis through 2030. In the other it is decreased to 90%. of attrition among practising physicians reported that less than 1% of physicians in Canada either die or retire before age 65 (Pong, 2011). As Figure 4.9 illustrates, the simulated physician gap is directly affected After age 65, the combined proportion of physicians retiring or dying by different assumed values of future participation levels, with the each year increases with age; in recent years this proportion has varied modest difference between 90% and 95% participation resulting in a between 2% and 12% per year (Pong, 2011). Figure 4.10 shows the difference of roughly 500 FTEs in the simulated gap by 2015. impacts of three different exit rate scenarios on the simulated physician gap in Canada. In these, the proportion of physicians aged 65 and over 4.9 Impacts of changes in HRH retention exiting the Canadian supply each year is set at 2%, 7% and 12%.
There is very high retention among physicians in Canada (Government of The simulated physician gap is highly sensitive to different assumptions Canada, 2014); when physicians do exit the Canadian supply it is usually about the annual rates of exit from the supply. As Figure 4.10 indicates,
Figure 4.9 Examples of simulated physician gap in different physician participation scenarios (Canada)
9 000 8 000 7 000 6 000 5 000 4 000 3 000 2 000 1 000 Simulated shortage (-) or surplus (+) 0 -1 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
95% participation 90% participation
Figure 4.10 Examples of simulated physician gap in different exit rate scenarios (Canada)
14 000
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the difference between 2% and 12% of older physicians leaving per situation, the population grows and ages according to Statistics year is roughly 6000 FTEs by 2030. Canada’s “low growth” projections, the average age-specific health of Canadians improves to that of its healthiest province, physician service 4.10 Impacts of changes in multiple levels are reduced to the lowest found in its provinces, exit rates parameters among older physicians occur at the lowest they have been in recent years, physician activity and productivity increase to the highest levels The scenarios above show the potential impacts of changes in in its provinces and graduation is increased 15%. individual planning variables over time. In reality, it is likely that more than one – and perhaps all – of the variables that affect estimated Figure 4.11 demonstrates that, depending on which assumed values HRH supply and requirements will change to some degree between are assigned to the various variables that determine HRH supply and now and 2030. Figure 4.11 shows the potential cumulative impacts requirements over time, the simulated physician gap in Canada for of each of the individual changes described in the earlier scenarios 2030 varies considerably. In these examples it may range anywhere on the simulated physician gap in Canada in three scenarios. In the from a shortfall of nearly 39 000 FTEs (47% less than the simulated “status quo” scenario, no variables change except the population supply) to a surplus of over 35 000 FTEs (43% greater than the grows and ages according to Statistics Canada’s medium growth simulated supply) – a difference of nearly 75 000 FTEs – depending on projections. In the “high requirements, low supply” scenario, the the assumptions made about the various factors that determine HRH population grows and ages according to Statistics Canada’s “high supply and requierments. growth” projections, the average age-specific health of Canadians worsens to that of its least healthy province, physician service levels If the same level of variability in the size of Canada’s physician gap increase to the highest in its provinces, exit rates among older relative to supply is applied to the aggregate gaps simulated for all physicians occur at the highest they have been in recent years, 31 included countries, these could range from shortfalls of 74 000 physician activity and productivity decrease to the lowest levels midwives, 3.2 million nurses, and 1.2 million physicians (47% less than reported in its provinces, graduation is reduced to 2010 levels and supply) to surpluses of 67 000 midwives, 2.9 million nurses, and 1.0 participation reduced to 90%. In the “low requirements, high supply” million physicians (43% greater than supply) by 2030.
Figure 4.11 Cumulative impact of multiple parameter changes on simulated physician gap (Canada)
40 000
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0
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-20 000
-30 000
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-50 000 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024 2025 2026 2027 2028 2029 2030
Status quo continues Low requirements, high supply High requirements, low supply
32 5. Discussion
5.1 Synthesis of evidence have been adopted with policy “interests” (as distinct from questions) made to fit these methods. This review sought to synthesize the findings of the past decade of published research on HRH requirements and labour market dynamics In an attempt to inform improvements to this situation, seven criteria for in high-income OECD countries. Although over 200 documents identifying an HRH planning approach appropriate to a given jurisdiction pertaining to these topics were reviewed in detail, collectively they have been presented. Although none of the approaches found through do not include sufficient information to provide a clear picture of the the review met all of these criteria, several – from Australia, Canada and expected future HRH situation in these countries. At best it can be New Zealand – met all but one. Examples of other approaches which said that the HRH supply in these countries is generally expected to met each individual criterion have also been identified so that planners grow; because different analyses reach different conclusions about can explore different options depending on the relative importance of future HRH requirements in these countries, it is not clear whether that these criteria given their respective circumstances and health system growth will be adequate to meet health system objectives in the future. objectives. Although most analyses suggest that the numbers of physicians and nurses required in the included countries are likely to increase in the 5.2 Simulations of future HRH supply future, this view varies across analyses depending on the methods and and requirements assumptions used. Further, most analyses for professions other than nurses and physicians suggest that the numbers required are likely to Limitations decrease in the future. The implications of these projected respective Although online resources such as the OECD’s indicator database surpluses and shortages in terms of meeting health system objectives provide a great deal of data relevant to HRH planning in member are not clear. countries, a more comprehensive approach consistent with the criteria outlined in the methodology section would require additional information Several recurring themes regarding factors of importance in HRH as well as direct engagement with the relevant stakeholder groups in the planning were evident across the documents reviewed. These included: respective countries. As such, the methods used have several important ageing populations and health workforces; changes in disease patterns, limitations that must be considered when interpreting the results: models of care delivery, scopes of practice, regulatory structures and technologies in health care; migration; incentives; data quality; the • The need to rely on secondary descriptions of the included distribution of resources; interprofessional education and practice; countries’ health systems means that a full understanding of their stakeholder engagement; and balancing the public and private sectors. respective objectives may not have been achieved – in other words, They also included important inconsistencies in the use of key HRH criterion 1 may not have been fully met in all cases. planning terms, which in turn affected the choice of methods in different documents for HRH planning. • During the review of existing analyses of HRH requirements conducted as part of Phase 1, no existing methods were found Different approaches to HRH planning will be appropriate for different that explicitly incorporated determinants of productivity – i.e. that jurisdictions depending on their respective contexts and the objectives met criterion 3. Although such models have been described (e.g. of their health-care systems and hence the policy questions being Masnick & McDonnell, 2010; Birch et al., 2015), it was not possible faced. Based on these objectives, methods need to be adopted that to obtain the data necessary to incorporate this feature into the produce relevant answers for the precise HRH questions facing policy- simulations. Instead, the sensitivity of the projections to different makers in their particular contexts. The results of this review suggest, levels of productivity is demonstrated. however, that rarely have explicit policy questions been identified to guide HRH research methods and analyses; instead available methods • Although the OECD indicator database includes information on average remuneration paid to general and specialist physicians
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as well as hospital nurses for many member countries, no multi- §§ As information on the proportion of pregnancies and births country source of information on training or recruitment costs was attended by midwives (as opposed to physicians, for example) found. As a result, cost considerations are not incorporated into was available across countries, it was assumed that these these simulations. proportions for each country – whatever they may currently be – would be maintained throughout the simulation period. • As shown in Table 1.1, the information needed to perform §§ The measures of service provision found for most countries simulations in accordance with the other criteria was not readily – physician consultations, nights in hospital and numbers of available for many countries. As such a variety of assumptions were births – were not presented by level of acuity, nor do they made in order to produce the simulations: fully capture the wide range of services provided by midwives, §§ For most countries, no information on how health care service nurses or physicians. However, they were the only measures provision is organized according to the objectives of their of service provision found for most countries. As such, these various health-care systems – beyond improving the health relatively crude measures of service provision – and hence the of their respective populations – was found. In fact, only for productivity of the different professions – were used as proxies Canada and Australia was information on service provision of overall service provision to simulate requirements. according to different levels of health found. For the other §§ As information on unmet need for health care was not found countries, current Canadian levels of service provision according for most countries, the estimates are initialized using an initial to different levels of health were used to estimate requirements. HRH “gap” of zero. Hence the surpluses or shortfalls simulated §§ The measure of health status available by age and sex for represent changes to any existing imbalance between supply most included countries was self-assessed health status. This and requirements in each country. For cases where an existing measure was used to simulate the requirements for nursing and shortfall or surplus has been documented and quantified, the physician services in each country. model can be initialized at any value desired. This measure was available for all included countries except §§ Both the WHO and OECD indicator databases provide relatively Chile. As such, simulations of the requirements for nurses recent (usually from 2011 or later for the WHO database and and physicians in Chile could not be performed. 2013 or later for the OECD database) head counts of midwives, A more comprehensive approach to population needs- nurses and physicians for most member countries with the based planning would identify requirements by types of notable exception of Greece, for which no information on need/condition (e.g. Segal et al., 2008; Health Workforce the supply of midwives or nurses since 2005 was found. In New Zealand, 2011a; Tomblin Murphy et al., 2013a&b) and the absence of any recent information on the current supply then aggregate over all needs/conditions. Although other of these professionals, simulations of the future supply of measures such as the incidence or prevalence of specific midwives or nurses in Greece could not be performed. health problems (e.g. HIV/AIDS, diabetes) are available §§ The OECD indicator database also provides breakdowns of for virtually every country, these are seldom presented by countries’ physician supplies by age. However, age breakdowns age and sex of patient. More complex measures such as were not available for midwives or nurses from any multi-country disability-adjusted life years (DALYs) lost to poor health source. For some countries age distributions of midwives and are also widely available at the country level, and could nurses were found in individual documents obtained as part of potentially be used to prioritize population health issues so Phase 1. For other countries, in the absence of such information, that health-care resources (including HRH) can be allocated the age distribution observed for those providers in Canada was accordingly. used to illustrate the application of the model. §§ The OECD indicator database provides historical data on §§ Data on the incidence of low birth weight in the included annual numbers of graduates for various health professions countries are available from the OECD indicator database. in most of the included countries. Where no information on However, these data are not available by the age of mothers; as numbers of midwifery graduates from Portugal was found, it such, it was assumed that the incidence of low birth weight was was assumed that no new graduates from that profession in equal across mother age groups within countries. For countries that country would join its supply during the simulation period. whose female populations are ageing, this would likely result This assumption would likely bias the simulations toward in underestimates of the numbers of babies born underweight, underestimating future HRH supply in this case. while for countries whose female populations are becoming No multi-country source of information on the retention of younger, this would likely result in overestimates of the numbers new HRH graduates was found. As such, it was assumed of babies born underweight. that all graduates of different professions in individual
34 countries would enter their respective countries’ supplies. different. With access to more accurate and comprehensive country- This assumption would likely bias the simulations toward specific information, planners in individual countries can easily replace over-estimating future HRH supply. these assumptions with relevant data to better reflect their actual HRH situations. Further, despite the above limitations, considerable §§ The OECD indicator database also includes historical data on effort was put into estimating the current or “baseline” value of the physician and nurse migration for most member countries. It various parameters used to simulate HRH supply and requirements for does not include information on migration of midwives. For physicians, nurses and midwives for the included countries. However, it countries where no information on in-migrants of a particular is not possible to accurately predict what values these parameters will profession was found it was assumed that no new members take on in the future. of that profession would join the respective stocks of these provider groups during the study period. This assumption would The simulations of the supply of and requirements for midwives, nurses likely bias the simulations toward under-estimating future HRH and physicians suggest that the trajectory of HRH situations varies supply in these cases. considerably by profession and by country. Rather than serving as §§ No multi-country source of information on retirements or other predictions of the future HRH situations in these countries, the results exits from the respective national supplies was found. For some illustrate the opportunities and the policy levers that decision-makers countries this information was provided in documents obtained as have to influence the supply of and requirements for HRH and the part of Phase 1, but for countries and professions for which this potential consequences of failing to take timely action. They assume information was not found, the simulations were run based on an that current service provision practices and population health levels will assumption that 5% of the existing stock would exit each year. not change before 2030. However, should large shortfalls or surpluses §§ For most member countries, the OECD indicator database appear on the planning horizon, it is likely that substantial changes to differentiates between the numbers of midwives and nurses these variables – such as increases or reductions in training capacity licenced to practise and the numbers actively practising; or levels of service provision – will be made; indeed, the purpose of for these countries this information provided the basis for performing these simulations is to inform such planning. The additional estimating levels of participation by these professions. For the scenarios presented demonstrate the high degree of sensitivity of few countries where this information was not available, the such simulations to differences in assumed values of the various average participation level of the other countries was used. determinants of HRH supply and requirements. It is important to note The OECD indicator database does not provide comparable that these scenarios are not based on hypothetical values of variables, participation information for physicians, nor does any other but on actual values that represent real variations within a single multi-country source found. For some countries information country. These results underscore the importance of several factors on physician participation levels was found in individual critical to effective HRH planning, each of which have been repeatedly documents obtained as part of Phase 1. In the absence of identified by earlier analyses of HRH planning worldwide. this information, it was assumed that all licenced physicians are active in providing at least some direct patient care. First, HRH planning must be conducted iteratively, recognizing the This assumption would likely bias the simulations toward constantly evolving nature of population health needs, health-care overestimating the future HRH supply in these cases. practices and regulatory structures, and health labour markets, and incorporating ongoing monitoring and evaluation. Long-term simulations §§ No multi-country data source found includes information on such as those included here are useful for showing the direction in levels of activity (e.g. average hours worked per week) by HRH. which HRH situations may be heading, but as the results presented here For some countries this information was found in individual demonstrate, they cannot be used to predict the future. As such, HRH documents obtained as part of Phase 1. For countries where plans must be regularly updated to accommodate changes in planning such information was not found, it was assumed that all variables over time (e.g. WHO, 1971; Mejía, 1978; Dussault & Dubois, members of these professions work full-time hours. This 2003; Stordeur & Léonard, 2010; Tomblin Murphy et al., 2012). assumption would likely bias the simulations toward over- estimating the future HRH supply in these cases. Second, HRH planning must be conducted by engaging with the relevant stakeholders (e.g. WHO, 1971; Mejía & Fülöp, 1978; Dubois et al., Interpretation 2006; Stordeur & Léonard, 2010). This is necessary to ensure that It is important to note that these scenarios have been presented for the approach being used is relevant, that the information necessary illustrative purposes. For some countries the assumptions described to utilize a valid approach is made available, and that planning gives above may be very close to reality; for others they may be quite appropriate consideration to the perspectives of different stakeholder
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groups. Planners in individual countries would have direct access to and supply under different future scenarios” (WHO, 2015b). Consistent these stakeholders, which was not possible for the analyses reported with such calls, the strength of the workforce planning approach here. presented here lies in its use of methods that are linked directly to service planning based on the needs of populations, but that can at Third, HRH planning must be based on appropriate data, and this the same time accommodate the uncertainty – and the opportunity includes measures of both the health levels of the population as well for policy intervention – in relation to a range of health systems and as the health-care services planned to respond to those health levels, health workforce parameters. The results of such approaches cannot together with characteristics of the health workforce and the broader be meaningfully compared with those generated by more commonly economic, sociopolitical, environmental and technological contexts used workforce planning models which cannot account for differences in which the planning is being done (e.g. WHO, 1971; Mejía & Fülöp, in populations’ needs for care or the levels of services required to 1978; Lavis & Birch, 1997; O’Brien-Pallas et al., 2007; Vujicic & Zurn, meet those needs. The approaches may produce very different results 2006; Kuhlmann et al., 2013). The relatively crude measures used for because they are addressing very different research or health policy the simulations in this report were chosen because they were either the questions implicit in the planning models being used. only measures available or the only ones provided for multiple countries from a single source. Comprehensive analyses of HRH supply and The review and synthesis of recent analyses of HRH requirements in requirements can be conducted to inform national level HRH policies the included countries conducted as the first phase of this work found, and strategies (e.g. Segal et al., 2008; Health Workforce New Zealand, however, that most of these focused primarily on measures of HRH 2011a; Tomblin Murphy et al., 2013a&b). It is important to note that supply and utilization. This is perhaps because these are more readily the more comprehensive data required to support such analyses do in available; there were numerous data-related challenges encountered general exist in high-income countries; for example, the data collected through this study that hindered efforts to simulate future HRH supply for Canada, most of which is publicly available online, were much more and requirements. However, as noted in the previous report, problems complete than for other countries. with data are not avoided by relying on more conceptually limited models. If the information available is inadequate to fully inform Other, more commonly used approaches to health workforce planning planning, then investments should be made in improving the quality of have been widely and repeatedly recognized as ineffective. Over 40 that information rather than in further entrenching the use of intrinsically years ago the WHO (1971) noted that “The exclusive use of health narrower models. To that end, the identification and assessment of manpower: population ratios for the estimation of future health the data required to inform HRH planning should be based on the manpower requirements is increasingly difficult to justify in the question of how many of what type of HRH are required to perform developed countries where, depending on the specific characteristics what services, for whom, and under what circumstances (e.g. Mejía & of the health system, other and more refined criteria can be used to Fülöp, 1978; Birch et al., 2007; de Savigny & Adam, 2009). The HRH improve the country’s capacity to provide health care”. Along similar planning approach described in this report and its companion paper are lines, the UK House of Commons Health Committee (2007) commented designed to address this question and to overcome the limitations of on the “disastrous failure of (health) workforce planning” which it more traditional approaches which have resulted in the numerous HRH attributes to the fact that “little if any thought has been given to long challenges being experienced worldwide. term or strategic planning”. A report commissioned in response (UK Department of Health, 2008) called for workforce planning to “be While the focus of this analysis was on high-income OECD countries, based on service planning and ... reflect how health and social care the analytical approach described can theoretically be applied in any will meet the needs of the local population planning”. More recently jurisdiction where the required data are available. Similar approaches the Global Strategy on Human Resources for Health recommended have already been applied, for example, in Guinea (Jansen et al., 2014), that all countries “Build planning capacity to develop or improve HRH Jamaica (Tomblin Murphy et al., 2014), Thailand (Tianviwat et al., policy and strategies that quantify health workforce needs, demands 2009), and Zambia (Goma et al., 2014).
36 6. Conclusions
The objective of this analysis was to review the evidence on and both domestic and international perspectives; for example, domestic develop a methodology for producing simulations of the supply of and underproduction and/or maldistribution of health workers can requirements for midwives, nurses and physicians in high-income OECD contribute to a disproportionate reliance on recruitment of foreign- countries until 2030, to inform global policy dialogue and strategic trained health personnel. Historically, the included countries as a group planning at both international and national levels, such as that occurring have been the beneficiaries of the bulk of international migration of as part of the development of the global strategy on HRH. This was health workers from low- and middle-income countries, worsening carried out using publicly available data and using assumptions about deficits in these contexts, and that these mobility trends are likely to the values of the various determinants of supply and requirements, continue and even increase in future (OECD, 2015b). In this context, aside from population projections, remaining constant throughout the analysis reported here provides evidence that can also inform a that time period. Despite the limitations resulting from challenges global policy dialogue on international imbalances and mobility patterns obtaining some of the necessary data in the time available, the results across countries. The type of HRH planning approach described here demonstrate the potential to apply an approach to simulating HRH also allows the identification of the policy levers with the greatest supply and requirements in a manner more comprehensive than most of potential to affect trends and prevent the emergence of future gaps, those currently being used in many countries. HRH planners in individual thereby informing policy options that may alleviate or avoid these gaps, countries, who have more direct access to data on the relevant planning such as changes to models of care delivery or to HRH training and/or parameters, are best placed to implement such an approach working regulatory structures. with their respective stakeholder groups. The sensitivity analyses demonstrate that these simulations are highly The results of these simulations suggest that the trajectories of dependent on the assumptions on current and future values of the HRH supplies and requirements vary widely across countries and various variables that determine HRH supply and requirements. These professions, with some potentially headed for large surpluses, while findings highlight the importance of HRH planning that is iterative, others may be moving toward large shortfalls. These findings reinforce based on a valid methodology, and engages the relevant stakeholder the concern highlighted by the WHO Global Strategy on Human groups. Further, it is imperative that these stakeholders collaborate to Resources for Health: Workforce 2030 (2015b) that many high-income ensure that the types of data necessary for such methodologies are countries are challenged with matching supply and requirements regularly collected and analysed to inform HRH planning. Evidence- for HRH under existing and future affordability and sustainability based analyses of HRH situations requires reliable and regularly updated constraints, and often experience periodic swings between perceived information on population health status, labour market dynamics, and shortfalls and oversupply. These can be particularly problematic from health service configurations.
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Cox A, Grimshaw D, Carroll M, McBride A (2008). Reshaping internal Retrieved from http://ec.europa.eu/chafea/projects/database. labour markets in the National Health Service: new prospects for html?prjno=20122201. pay and training for lower skilled service workers. Human Resource Management Journal, 18(4): 347-365. European Observatory on Health Systems and Policies, “Health system reviews (HiT series),” 2016. [Online]. Available: http://www.euro. Crescent Management Consulting (2009). New Brunswick Health who.int/en/about-us/partners/observatory/publications/health- Human Resources Supply and Demand Update 2008-2015. system-reviews-hits. Fredericton: New Brunswick Department of Health. Retrieved from http://hhr-rhs.ca. Evans RG (1984). Strained Mercy: The Economics of Canadian Health Care. Toronto: Butterworths. Crettenden IF, McCarty M, Fenech BJ, Heywood T, Taitz MC, Tudman S (2014). How evidence-based workforce planning in Australia is Evans RG (2009). There’s no reason for it, it’s just our policy. Healthcare informing policy development in the retention and distribution of the Policy, 5(2): 14-24. health workforce. Human Resources for Health, 12(7). Fellows J, Lyscom T, Burge C, Edwards M (2014). Horizon 2035: Crossley TF, Hurley J, Jeon SH (2009). Physician labour supply in International responses to big picture challenges. London: Centre for Canada: a cohort analysis. Health Economics, 18: 437-456. Workforce Intelligence.
Dal Poz M, Dreesch N, Fletcher S et al. (2010). Models and tools for Gallagher JE, Kleinman ER, Harper PR (2010). Modelling workforce health workforce planning and projections. Human Resources for skill-mix: How can dental professionals meet the needs and Health Observer No. 3. Geneva: WHO. demands of older people in England? British Dental Journal, 208: E6. Dall T, West T, Chakrabati R, Iobucci W (2015). The complexities of physician supply and demand: projections from 2013 to 2015. Goma FM, Tomblin Murphy G, Libetwa M, MacKenzie A, Nzala SH, Washington, DC: Association of American Medical Colleges. Mbwili-Muleya C et al. (2014). Pilot-testing service-based planning for health care in rural Zambia. BMC Health Services Research, de Savigny D, Adam T (2009). Systems thinking for health system 14(Supplement 1): S7. strengthening. Geneva: WHO. Retrieved from http://apps.who.int/ iris/bitstream/10665/44204/1/9789241563895_eng.pdf. Goodyear-Smith F, Janes R. (2008). New Zealand rural primary health care workforce in 2005: more than just a doctor shortage. Dill MJ, Salsberg E (2008). The complexities of physician supply and Australian Journal of Rural Health, 16: 40-46. demand: projections through 2025. Washington, DC: Association of American Medical Colleges. Government of Canada (2014). General Practitioners and Family Physicians. Ottawa: Author. Retrieved from http://www. Dubois CA, McKee M, Nolte E (eds.) (2006). Human resources for health servicecanada.gc.ca/eng/qc/job_futures/statistics/3112.shtml. in Europe. Berkshire: Open University Press. Hall T (1978) Demand. In Hall T, Mejía A (eds.) (1978). Health Manpower Dubois CA, Singh D (2009). From staff-mix to skill-mix and beyond: Planning: Principles, Methods, Issues. Geneva: WHO. towards a systemic approach to health workforce management. Human Resources for Health, 7(87): 1-19. Hawthorne N, Anderson C (2009). The global pharmacy workforce: a systematic review of the literature. Human Resources for Health, Dussault G, Buchan G, Sermeus W, Padaiga Z (2010). Assessing future 7(48). health workforce needs. Geneva: WHO. Health and Education National Strategic Exchange (HENSE) (2012). Dussault G, Dubois C-A (2003). Human resources for health policies: A Review of Medical and Dental School Intakes in England. Leeds: critical component in health policies. Human Resources for Health, 14:1. Author.
European Commission (2012). Commission Staff Working Document Health Education England (2013). Workforce Plan for England: Proposed on an Action Plan for the EU Health Workforce. Strasbourg: Author. Education and Training Commissions for 2014/15. London: Author.
40 Health Workforce Australia (2012). Health Workforce 2025 – Doctors, Jeon SH, Hurley J (2010). Physician resource planning in Canada: the Nurses and Midwives – Volume 1. Canberra: Australia Department need for a stronger behavioural foundation. Canadian Public Policy, of Health. Retrieved from http://www.hwa.gov.au. 36(3): 359-375.
Health Workforce Australia (2014a). Australia’s future health workforce Juraschek SP, Zhang X, Ranganathan VK, Lin VW (2012). United States – doctors. Canberra: Australia Department of Health. Retrieved from registered nurse workforce report card and shortage forecast. http://www.health.gov.au/internet/main/publishing.nsf/Content/ American Journal of Medical Quality, 27(3): 241-249 australias-future-health-workforce-doctors. Koike S, Matusmoto S, Kodama T, Ide H, Yasunaga HIT (2009). Health Workforce Australia (2014b). Australia’s future health Estimation of physician supply by specialty and the distribution workforce – nurses. Canberra: Australia Department of Health. impact of increasing female physicians in Japan. BMC Health Retrieved from http://www.health.gov.au/internet/main/ Services Research, 9(180). publishing.nsf/Content/australias-future-health-workforce- nurses. Kuhlmann E, Batenburg R, Groenewegen PP, Larsen C (2013). Bringing a European perspective to the health human resources debate: A Health Workforce New Zealand (2010). Health Workforce Projections scoping study. Health Policy, 110(1): 6-13. Modelling 2010: Primary Health Care (PHC) Nursing Workforce. Wellington: Health Workforce New Zealand. Landry MD, Ricketts TC, Verrier MC (2007). The precarious supply of physical therapists across Canada: exploring national trends in Helsedirektoratet, World Health Organization, Global Health Workforce health human resources (1991 to 2005). Human Resources for Alliance (2013). Consultation on Human Resources for Health for Health, 5: 23. High Income Countries. Preparatory Meeting for the Third Global Forum on HRH: Meeting Report. Geneva. Lavis J, Birch S (1997). The answer is …, now what was the question? Applying alternative approaches to estimating nurse requirements. Holmes M (2012). Developing an open source simulation model of Canadian Journal of Nursing Administration, 10(1): 24-44. physician supply and healthcare utilization. Presented at AAMC Workforce Conference, Washington, DC. Available at: http://www. Longley M, Riley N, Davies P, Hernández-Quevedo C (2012). United shepscenter.unc.edu/wp-content/uploads/2014/09/AAMC_ Kingdom (Wales): Health system review. Health Systems in Holmes_May2012.pdf. Transition, 14(11). Retrieved from http://www.euro.who.int/en/ about-us/partners/observatory/publications. Ide H, Yasunaga HKT, Koike S, Taketani Y, Imamura T (2009). The dynamics of obstetricians and gynecologists in Japan: Maier T, Afentakis A (2013). Forecasting supply and demand in nursing a retrospective cohort model using the nationwide survey of profession: Impacts of occupational flexibility and employment physicians data. Journal of Obstetrics & Gynaecology Research, structure in Germany. Human Resources for Health, 11:24. 35(4): 761-766. Markham B, Birch S (1997). Back to the future: a framework for Ireland Health Service Executive (2014). Medical workforce planning: estimating health-care human resource requirements. Canadian Second interim project report. Dublin: Author. Retrieved from http:// Journal of Nursing Administration, 10(1): 7-23. www.hse.ie/eng/services/Publications/. Marvasti A (2014). An estimation of the demand and supply for physician Jansen C, Codjia L, Cometto G, Yasané ML, Dieleman M (2014). services using panel data. Economic Modelling, 43: 279-285. Realizing universal health coverage for maternal health services in the Republic of Guinea: the use of workforce projections to design Masnick K, McDonnell G (2010). A model linking clinical workforce skill health labor market interventions. Risk Management and Healthcare mix planning to health and health care dynamics. Human Resources Policy, 7: 219-232. for Health, 8(11).
Johannessen KA, Hagen TP (2012). Variations in labor supply between Mason T, Sutton M, Whittaker W, Birch S (2015). Exploring the female and male hospital physicians: results from a modern welfare limitations of age-based models for health care planning. Social state. Health Policy, 107: 74-82. Science & Medicine, 132: 11-19.
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Matrix Insight (2012). EU level Collaboration on Forecasting Health New Zealand Ministry of Health (2011b). Diabetes workforce service Workforce Needs, Workforce Planning and Health Workforce Trends review. Wellington: New Zealand Ministry of Health. – A Feasibility Study. Revised Final Report. Retrieved from http:// www.hspm.org/mainpage.aspx. New Zealand Ministry of Health (2011c). Mental health and addictions workforce service forecast. Wellington: New Zealand Ministry of Health. McDowell I, Newell C (2006). Measuring Health: A Guide to Rating Scales and Questionnaires, 3rd edition. Oxford: Oxford University Press. New Zealand Ministry of Health (2014). Dermatology workforce service forecast. Wellington: New Zealand Ministry of Health. McPake B, Maeda A, Araujo EC, Lemeire C, El Maghraby A, Cometto G (2013). Why do health labour market forces matter? Bulletin of the Oakley P, Ngo S, Vinten M, Budjanovcanin (2008). NHS Wales: Midwifery World Health Organization, 91: 841-846. Workforce Planning Project – Developing a Workforce Planning Model. London: Perfect Ltd. McPake B, Scott A, Edoka I (2014). Analyzing Markets for Health Workers. Washington, DC: The World Bank. O’Brien-Pallas L, Tomblin Murphy G. Birch S (2007). “A Framework for Collaborative Pan-Canadian Health Human Resources Planning,” Mejía A (1978). The health manpower planning process. In Hall T, Mejía Health Canada, Ottawa. A (eds.) (1978). Health Manpower Planning: Principles, Methods, Issues. Geneva: WHO. Retrieved from http://apps.who.int/iris/ OECD (Organisation for Economic Co-operation and Development). handle/10665/40341. (2008). The Looming Crisis in the Health Workforce: How Can OECD Countries Respond? Paris: OECD Publishing. Mejía A, Fülöp T (1978). Health manpower planning: An overview. In Hall T, Mejía A (eds.) (1978). Health Manpower Planning: Principles, OECD (2013). Health at a Glance, 2013: OECD Indicators. Paris: OECD Methods, Issues. Geneva: WHO. Publishing.
Naccarella L, Buchan J, Newton B, Brooks P (2011). Role of Australian OECD (Organisation for Economic Co-operation and Development) primary healthcare organisations (PHCOs) in primary healthcare (2015a). “OECD Statistics,” 2015. [Online]. Available: http://stats. (PHC) workforce planning: lessons from abroad. Australian Health oecd.org/index.aspx. Review, 35: 262-266. OECD (2015b). Population Age Structure. Retrieved from http://stats. National Physician Survey (2015). Survey Results. Mississauga: Author. oecd.org/. Retrieved from http://nationalphysiciansurvey.ca/surveys/2014- survey/survey-results-2/. Ognyanova D, Busse R (2011). A destination and a source: Germany manages regional health workforce disparities with foreign New York Health Workforce Data System, Center for Health Care medical doctors. In: Wismar M, Meier C, Glinos I et al. (eds). Health Workforce Studies (2014a). The Health Care Workforce in New York: Professional Mobility and Health Systems: Evidence from 17 Trends in the Supply and Demand for Health Workers. Resselaer, European countries. Copenhagen: World Health Organization on USA: Author. Retrieved from http://chws.albany.edu. behalf of the European Observatory on Health Systems and Policies, 211-242. New York Health Workforce Data System, Center for Health Care Workforce Studies (2014b). Trends in Demand for New Physicians, 2009-2013: Olejaz M, Nielsen AJ, Rudkjøbing A et al. (2012). Denmark: Health A Summary of Demand Indicators for 35 Physician Specialties. system review. Health Systems in Transition, 14(2). Retrieved Resselaer, USA: Author. Retrieved from http://chws.albany.edu. from http://www.euro.who.int/en/about-us/partners/observatory/ publications. New Zealand Ministry of Health (2006). Health Workforce Development: An Overview. Wellington: Author. Retrieved from http:// Ono T, Lafortune G, Schoenstein M (2013). Health Workforce Planning hrhresourcecenter.org/. in OECD Countries: A Review of 26 Projection Models from 18 Countries. OECD Health Working Papers No. 62. Paris: OECD. New Zealand Ministry of Health (2011a). Aged Care Workforce Service Retrieved from http://www.oecd-ilibrary.org/social-issues-migration- Review. Wellington: New Zealand Ministry of Health. health/oecd-health-working-papers_18152015.
42 Pantelli D, Sagan A, Busse R (2011). Poland health system review. Retrieved from http://www.oecd-ilibrary.org/social-issues-migration- Health Systems in Transition, 13(8). health/oecd-health-working-papers_18152015.
Polgreen LA, Mott DA, Doucette WR (2011). An examination of Singh D, Lalani H, Kralj B et al. (2010). Ontario population needs- pharmacists’ labor supply and wages. Research in Social & based physician simulation model. Retrieved from https://www. Administrative Pharmacy, 7: 406-414. healthforceontario.ca/UserFiles/file/PolicymakersResearchers/ needs-based-model-report-oct-2010-en.pdf. Pong R (2011). Putting Away the Stethoscope for Good? Toward a New Perspective on Physician Retirement. Ottawa: CIHI. Retrieved Spetz J, Dyer WT, Chapman S, Seago JA (2006). Hospital demand for from https://secure.cihi.ca/free_products/HHR%20Physician%20 licensed practical nurses. Western Journal of Nursing Research, Report_En_Web.pdf. 28(6): 726-739.
Royal Australasian College of Surgeons (2011). Surgical Workforce Spetz J (2014). How will health reform affect demand for RNs? Nursing Projections to 2025 (for Australia). Melbourne: Author. Economics, 32(1): 42-44.
Scheffler R, Bruckner T, Spetz J (2012). The labour market for human Stange K (2014). How does provider supply and regulation influence resources for health in low- and middle-income countries. Human health care markets? Evidence from nurse practitioners and Resources for Health Observer No. 11. Geneva: WHO. physician assistants. Journal of Health Economics, 33: 1-27.
Scott A, Sivey P, Joyce C, Schofield D, Davies P (2011). Alternative Statistics Canada (2015). CANSIM Table 105-0592: Health indicator approaches to health workforce planning. Adelaide: National Health profile, two-year period estimates, by age group and sex, Canada, Workforce Planning and Research Collaboration. provinces, territories, census metropolitan areas and influence zones. Ottawa: Author. Retrieved from http://www5.statcan.gc.ca/ Scottish Executive (2006). National Workforce Plan 2006. Edinburgh: cansim/a05?lang=eng&id=1050592. Author. Retrieved from http://hrhresourcecenter.org/. Stordeur S, Léonard C (2010). Challenges in physician supply planning: Scottish Government (2010). An Analysis of the Dental Workforce in The case of Belgium. Human Resources for Health, 8:28. Scotland. Edinburgh: Author. Takata H, Nagata H, Nogawa H, Tanaka H (2011). The current shortage Segal L, Dalziel K, Bolton T (2008). A work force model to support the and future surplus of doctors: a projection of the future growth of adoption of best practice care in chronic diseases – a missing the Japanese medical workforce. Human Resources for Health, 9: piece in clinical guideline implementation. Implementation Science, 14. 3(35). Tennant M, Kruger E (2014). Turning Australia into a ‘flat-land’: what Segal L, Leach MJ (2011). An evidence-based health workforce model are the implications for workforce supply of addressing the disparity for primary and community care. Implementation Science, 6(93). in rural-city dentist distribution. International Dental Journal, 64: 29-33. Segal L, Leach MJ, May E, Turnbull C (2013). Regional primary care team to deliver best-practice diabetes care: a needs-driven health Tianviwat S, Chongsuvivatwong V, Birch S (2009). Optimizing the mix workforce model reflecting a biopsychosocial construct of health. of basic dental services for Southern Thai schoolchildren based Diabetes Care, 36(7): 1898-1907. on resource consumption, service needs and parental preference. Community Dentistry and Oral Epidemiology, 37(4): 372-380. Sigurgeirsdóttir S, Waagfjörð J, Maresso A (2014). Iceland: Health system review. Health Systems in Transition, 16(6). Retrieved Tomblin Murphy G, Kephart G, Lethbridge L, O’Brien-Pallas L, Birch S from http://www.euro.who.int/en/about-us/partners/observatory/ (2009). Planning for what? Challenging the assumptions of health publications. human resources planning. Health Policy, 92(2-3): 225-233.
Simoens S, Hurst J (2006). The Supply of Physician Services in OECD Tomblin Murphy G, Birch S, MacKenzie A, Alder R, Lethbridge L, Little Countries. OECD Health Working Papers No. 21. Paris: OECD. L (2012). Eliminating the shortage of registered nurses in Canada:
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an exercise in applied needs-based planning. Health Policy, 105: United States Health Resources and Services Administration (HRSA) 192-202. (2014c). Health Workforce Projections: Pharmacists. Washington, DC: US Department of Health and Human Services. Retrieved from Tomblin Murphy G, MacKenzie A, Alder R, Langley J, Hickey M, Cook A http://hrsa.gov/. (2013a). Pilot-testing an applied competency-based approach to health human resources planning. Health Policy and Planning, 28(7): United States Health Resources and Services Administration (HRSA) 739-749. (2015a). National and State-Level Projections of Dentists and Dental Hygienists in the US, 2012-2025. Washington, DC: US Department Tomblin Murphy G, MacKenzie A, Rigby J, Rockwood K, Gough A, of Health and Human Services. Retrieved from http://hrsa.gov/. Greeley A et al. (2013b). Service-based health human resources planning for older adults. Journal of the American Medical Directors United States Health Resources and Services Administration (HRSA) Association, 14: 611-615. (2015b). Health Workforce Projections: Respiratory Therapists. Washington, DC: US Department of Health and Human Services. Tomblin Murphy G, MacKenzie A, Walker C, Guy-Walker J (2014). Retrieved from http://hrsa.gov/. Needs-based human resources for health planning in Jamaica: using simulation modelling to inform policy options for pharmacists Vujicic M, Zurn P (2006). The dynamics of the health labour market. in the public sector. Human Resources for Health, 12:67. International Journal of Health Planning and Management, 21(2): 101-115. Toyokawa S, Kobayashi Y (2010). Increasing supply of dentists induces their geographic diffusion in contrast with physicians in Japan. WHO (1971). The Development of Studies in Health Manpower: Report Social Science & Medicine, 71: 2014-2019. of a WHO Scientific Group. Geneva: WHO. Retrieved from http:// apps.who.int/iris/handle/10665/40928?locale=fr. UK Department of Health (2008). High Quality Care for All: NHS Next Stage Review Final Report. London: Author. Retrieved from https:// WHO (2010). Workload Indicators of Staffing Need (WISN) Software www.gov.uk/government/uploads/system/uploads/attachment_data/ Manual. Geneva: WHO. Retrieved from http://www.who.int/hrh/ file/228836/7432.pdf#page=3&zoom=auto,-73,144. resources/wisn_software_manual/en/.
UK House of Commons Health Committee (2007). Workforce Planning: WHO (2015a). Global Health Observatory Data Repository. Geneva: Fourth Report of Session 2006-7, Volume 1. London: The Stationery Author. Retrieved from http://apps.who.int/gho/data/?theme=home. Office Limited. Retrieved from http://www.publications.parliament. uk/pa/cm200607/cmselect/cmhealth/171/171i.pdf. WHO (2015b). Global Strategy on Human Resources for Health: Workforce 2030. Geneva: WHO. Retrieved from http://who.int/hrh/ United States Health Resources and Services Administration (HRSA) resources/WHO_GSHRH_DRAFT_05Jan16.pdf. (2014a). Technical Documentation for HRSA’s Health Workforce Simulation Model. Washington, DC: US Department of Health and WHO Western Pacific Office (2008). WPRO Workforce Projection Tool Human Services. Retrieved from http://hrsa.gov/. Version 1.0 User’s Manual. Manila: WHO.
United States Health Resources and Services Administration (HRSA) Yuji K, Imoto S, Yamaguchi R, Matsumura T, Murashige N, et al. (2012) (2014b). The Future of the Nursing Workforce: National- and State- Forecasting Japan’s Physician Shortage in 2035 as the First Full- Level Projections, 2012-2025. Washington, DC: US Department of Fledged Aged Society. PLoS ONE, 7(11): e50410. doi:10.1371/ Health and Human Services. Retrieved from http://hrsa.gov/. journal.pone.0050410.
44 Annex 1: List of websites consulted
International Australia Institute of Health and Welfare http://www.aihw.gov.au/
Global Health Workforce Alliance http://www.who.int/workforcealliance/ Austria WHO Health Workforce http://www.who.int/hrh/resources/en/ Ministry of Health http://bmg.gv.at/home/Schwerpunkte/ OECD http://www.oecd.org/ Gesundheitssystem_Qualitaetssicherung/ World Bank http://www.worldbank.org/ Public Health Portal https://www.gesundheit.gv.at/Portal.Node/ghp/ Andean Network of Observatories for Human Resources for Health public http://www.observatoriorh.org/andino/?q=taxonomy/term/23 European Observatory on Health Systems and Policies http://www.euro. Belgium who.int/en/about-us/partners/observatory Federal Ministry of Health (planning commission) http://www. WHO EURO Health Evidence Network (HEN) health.belgium.be/eportal/Healthcare/Consultativebodies/ http://www.euro.who.int/en/data-and-evidence/evidence-informed- Planningcommission/index.htm#.VT5tVq3BzGc policy-making/health-evidence-network-hen The Health Systems and Policy Monitor http://www.hspm.org/mainpage.aspx Canada European Commission on Public Health, Health Workforce http:// Health Canada (human resources strategy) http://www.hc-sc.gc.ca/hcs- ec.europa.eu/health/workforce/policy/index_en.htm sss/hhr-rhs/strateg/index-eng.php Joint Action on Health Workforce Planning and Forecasting http:// Canadian Health Human Resources Network (CHHRN) http://www.hhr- healthworkforce.eu/ rhs.ca/ HRH Global Resource Centre http://www.hrhresourcecenter.org/ KIT https://www.kit.nl/ Chile GIZ http://www.giz.de/en/html/index.html Ministry of Health http://desal.minsal.cl/ Health Cluster EU http://healthclusternet.eu WHO collaborating centres focusing on HRH Czech Republic • University of Western Cape http://www.uwc.ac.za/Faculties/CHS/ Ministry of Health http://www.mzcr.cz/Cizinci/ soph/Pages/WHO-Collaborating-Center-.aspx • University of Illinois at Rockford http://ncrhp.uic.edu/index. Denmark cfm?id=1031&b=1003&page=World%20Health%20 Health and Medicines Authority http://sundhedsstyrelsen.dk/en Organization%20%28WHO%29%20Collaborating%20Centre • McMaster University http://nursing.mcmaster.ca/WHO_ Estonia collaborating_centre.html National Institute for Health Development http://www.tai.ee/en/ • WHO Collaborating Centre on Health Workforce Policy and Planning publications/publications http://whoccworkforce.ihmt.unl.pt/ Ministry of Social Affairs http://www.sm.ee/et
Country-specific Finland Government VATT Institute for Economic Research http://www.vatt.fi/en/ Australia National Supervisory Authority for Welfare and Health http://www.valvira. Department of Health http://www.health.gov.au/internet/main/publishing. fi/en/ nsf/Content/Health+Workforce-2 National Institute for Health and Welfare https://www.thl.fi/en/web/thlfi- Health Workforce Australia http://www.health.gov.au/internet/main/ en/publications publishing.nsf/Content/hwa-archived-publications
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France New Zealand Ministry of Health, Social Affairs and Women’s Rights http://www.sante. Health Workforce New Zealand http://healthworkforce.health.govt.nz/ gouv.fr/les-reseaux-de-sante.html Ministry of Health http://www.health.govt.nz/our-work/health-workforce/ Institute for Research and Information in Health Economics http://www. workforce-service-forecasts irdes.fr/english/home.html Norway Germany Government of Norway https://www.regjeringen.no/en/id4/ Federal Ministry of Health http://www.bmg.bund.de/en.html Directorate of Health https://helsedirektoratet.no/English National Statistics Bureau https://www.destatis.de/DE/Startseite.html Statistics Norway http://www.ssb.no/en/
Greece Poland Ministry of Health and Social Solidarity http://www.moh.gov.gr/ Ministry of Health http://www.mz.gov.pl/en/
Hungary Portugal Health Services Management Training Centre, Semmelweis University Ministry of Health [http://www.portugal.gov.pt/en/the-ministries/ http://semmelweis.hu/emk/main/ ministry-of-health.aspx - link not working as of March 2017] Health Information Portal http://www.min-saude.pt/portal [link not Iceland working, March 2017] Directorate of Health http://www.landlaeknir.is/english/ Republic of Korea Ireland Ministry of Health and Social Welfare http://www.mohw.go.kr/eng/ Expert Group on Future Skills Need http://www.skillsireland.ie/ Department of Health (Health Services Executive) http://www.hse.ie/ Slovakia eng/ Ministry of Health http://www.health.gov.sk/Titulka
Israel Slovenia Taub Center for Social Policy Studies in Israel http://taubcenter.org.il/ Ministry of Health http://www.mz.gov.si/en/ Central Bureau of Statistics http://www.cbs.gov.il/reader/cw_usr_view_ Folder?ID=141 Spain Ministry of Health http://www.health.gov.il/Services/Publications/Pages/ Ministry of Health http://www.msssi.gob.es/en/home.htm PublicationsSearch.aspx Sweden Italy Ministry of Health and Social Affairs http://www.government.se/sb/d/2061 Ministry of Health http://www.salute.gov.it/ National Board of Health and Welfare http://www.socialstyrelsen.se/english
Japan Switzerland Ministry of Health, Labour and Welfare http://www.mhlw.go.jp/english/ Swiss Health Observatory http://www.obsan.admin.ch/bfs/obsan/en/ index/01.html Luxembourg Ministry of Health (health information portal) http://www.sante.public.lu/ United Kingdom fr/index.html Centre for Workforce Intelligence http://www.cfwi.org.uk/ Department of Health https://www.gov.uk/government/publications?dep Netherlands artments[]=department-of-health Netherlands Institute for Health Services Research (NIVEL) http://www. Health Education England https://hee.nhs.uk/ nivel.nl/en Northern Ireland Department of Health, Social Services and Public Advisory Committee on Medical Manpower Planning http://www. Safety (workforce planning unity) https://www.health-ni.gov.uk/ capaciteitsorgaan.nl/Publicaties/tabid/68/language/en-US/Default. topics/health-workforce-policy-and-management aspx The Scottish Government http://www.gov.scot/Publications/ NHS Wales http://www.wales.nhs.uk/
46 Wales National Leadership and Innovation Agency http://www.nliah. Center for Health Workforce Studies http://depts.washington.edu/ wales.nhs.uk/ uwchws/chws-publications.php Association of School and Programs of Public Health http://www.aspph. United States of America org/ HRSA National Center for Health Workforce Analysis https://bhw.hrsa. Association of American Medical Colleges https://www.aamc.org/data/ gov/health-workforce-analysis workforce/reports/
Series No 20 47 Human Resources for Health Observer Annex 2: Data extraction tool for peer and non-peer reviewed literature
Explicitly Analysis considers: Au thors T itle n Y ear of p u blicatio ( s ) or ju risdictio n( C o un try i n cl u ded year S tart En d year P rofessio n s i cl u ded D eter m i n a ts of s u pply D eter m i n a ts of re qu ire e n eeds n health P op u latio P la nn ed ser v ice le els by differe n t le v els of eed P ro v ider prod u cti ity A ss um ptio n s described T re n ds / iss u es ide tified C o n cl u sio reached u res O ther re m arkable feat C o mm e n ts
48 Annex 3: Stated objectives of included countries’ health-care systems - ce n tred n t choice atie n t atie C o un try Equ ity A ccess n a d / or Mai n tai i m pro v e health S er v ice accordi n g to n eeds Q u ality E fficie n cy n ability Su stai C o m prehe n si v e U n i v ersal P P ortability Pu blic f un di n g PHC n cy T ra n spare P Australia • • • • • • • • • Austria • • • • • • • Belgium • • • Canada • • • • • Chile • • • Czech Republic • • • • Denmark • • • • • • • • Estonia • • • • • Finland • • • • France • • • • • Germany • • • • • Greece • • • • Hungary • • • • • Iceland • • • • • • Ireland • • • Israel • • Italy • • • • • • • Japan • Luxembourg • • • • • Netherlands • • New Zealand • • • • • • • Norway • • • • • Poland • • • • • Portugal • • • • Republic of Korea • • • • • • Slovakia • • • Slovenia • • Spain • • • • Sweden • • • • • Switzerland • • • • United Kingdom • • • • • • • • United States of America • •
Series No 20 49 Human Resources for Health Observer
Sources for health system overview Czech Republic Vuorenkoski L, Mladovsky P and Mossialos E. Finland: Health system Australia review. Health Systems in Transition. 2008; 10(4):1-168. Healy J, Sharman E, Lokuge B. Australia: Health system review. Health Systems in Transition 2006; 8(5):1-158. Denmark Olejaz M, Juul Nielsen A, Rudkjøbing A, Okkels Birk H, Krasnik A, Health Workforce Australia 2011: National Health Workforce Innovation Hernández-Quevedo C. Denmark: Health system review. Health Systems and Reform Strategic Framework for Action 2011–2015. Available in Transition, 2012, 14(2):1-192. online at https://www.hwa.gov.au/sites/uploads/hwa-wir-strategic- framework-for-action-201110.pdf (accessed 13 October 2015). Estonia Lai T, Habicht T, Kahur K, Reinap M, Kiivet R, van Ginneken E. Estonia: Department of Health (Australia). Department of Health Corporate health system review. Health Systems in Transition, 2013; 15(6):1-196. Plan 2015-2016. Canberra: DoH. 2015. Available online at http://www.health.gov.au/internet/main/publishing.nsf/Content/ Finland C849A8AF6220D3BACA257D2E001A6987/$File/Corporate-Plan.pdf Vuorenkoski L, Mladovsky P and Mossialos E. Finland: Health system (accessed 13 October 2015). review. Health Systems in Transition. 2008; 10(4):1-168.
Austria France Hofmarcher M, Quentin W. Austria: Health system review. Health Chevreul K, Durand-Zaleski I, Bahrami S, Hernández-Quevedo C Systems in Transition, 2013; 15(7): 1-291. and Mladovsky P. France: Health system review. Health Systems in Transition,2010; 12(6):1-291. Gesundheit Österreich. The Austrian Health Care System – Key Facts. Updated version 2013. Available online at http://www.goeg.at/en/GOEG- Germany Current/Brochure-on-the-Austrian-Health-Care-System.html (accessed Busse R, Blümel M. Germany: health system review. Health Systems in 20 October 2015). Transition, 2014, 16(2):1-296.
Belgium Greece Gerkens S, Merkur S. Belgium: Health system review. Health Systems in Economou C. Greece: Health system review. Health Systems in Transition, 2010, 12(5):1-266. Transition, 2010, 12(7):1-180.
Vrijens F, Renard F, Jonckheer P, Van den Heede K, Desomer A, Van Iceland de Voorde C, Walckiers D, Dubois C, Camberlin C, Vlayen J, Van Oyen Sigurgeirsdóttir S, Waagfjörð J, Maresso A. Iceland: Health system H, Léonard C, Meeus P. Performance of the Belgian Health System. review. Health Systems in Transition, 2014; 16(6):1-182. Report 2012. Health Services Research (HSR). Brussels: Belgian Health Care Knowledge Centre (KCE). 2012. KCE Report 196Cs. Ireland D/2012/10.273/118. McDaid D, Wiley M, Maresso A and Mossialos E. Ireland: Health system review. Health Systems in Transition, 2009; 11(4):1-268. Canada Gregory P. Marchildon. Canada: Health system review. Health Systems in Israel Transition, 2013; 15(1): 1 – 179. Rosen B and Merkur S Israel: Health system review. Health Systems in Transition 2009 11(2):1-226. Chile Rosenberg E, Grotto I, Dweck T, Horev T, Cohen M, Lev B. Healthy Israel PAHO. Health in the Americas, 2012 Edition: Country Volume: CHILE. 2020: Israel’s blueprint for health promotion and disease prevention. Washington, DC: Pan American Health Organization. Public Health Reviews. 2014;35: epub ahead of print.
Missoni E, Solimano G. Towards Universal Health Coverage: the Chilean Italy Experience. World Health Reports (2010), Background Paper, 4. Geneva: Ferré F, de Belvis AG, Valerio L, Longhi S, Lazzari A, Fattore G, Ricciardi WHO. W, Maresso A. Italy: Health System Review. Health Systems in Transition, 2014, 16(4):1-168.
50 Japan Slovakia WHO, Ministry of Health, Labour and Welfare, Japan. Health Services Szalay T, Pažitný P, Szalayová A, Frisová S, Morvay K, Petrovič M and Delivery Profile: Japan. Geneva: WHO, 2012. van Ginneken E. Slovakia: Health system review. Health Systems in Transition, 2011; 13(2):1-200. Luxembourg Kerr, E. Health Systems in Transition: Luxembourg. Copenhagen: Slovenia European Observatory on Health Care Systems: 1999. Albreht T, Turk E, Toth M, Ceglar J, Marn S, Pribaković Brinovec R, Schäfer M, Avdeeva O and van Ginneken E. Slovenia: Health system Netherlands review. Health Systems in Transition. 2009; 11(3):1-168. Schäfer W, Kroneman M, Boerma W, van den Berg M, Westert G, Devillé W and van Ginneken E. The Netherlands: Health system review. Health Spain Systems in Transition, 2010; 12(1):1-229. García-Armesto S, Abadía-Taira MB, Durán A, Hernández-Quevedo C, Bernal-Delgado E. Spain: Health system review. Health Systems in van den Berg M, Kringos D, Marks L, Klazinga N. The Dutch health Transition, 2010, 12(4):1-295. care performance report: seven years of health care performance assessment in the Netherlands. Health Research Policy and Systems Sweden 2014; 12:1. Anell A, Glenngård AH, Merkur S. Sweden: Health system review. Health Systems in Transition, 2012, 14(5):1-159. New Zealand Cumming J, McDonald J, Barr C, Martin G, Gerring Z, Daubé J. New Switzerland Zealand Health System Review (Health Systems in Transition, 2010; Federal Department of Home Affairs (FDHA). The Federal Council’s 4(2). Geneva: WHO. health-policy priorities: HEALTH2020. Berne, Switzerland: Federal Office of Public Health (FOPH). 2013. Ministry of Health. 2014. Statement of Intent 2014 to 2018: Ministry of Health. Wellington: Ministry of Health. Available online at www.health. OECD/WHO (2011), OECD Reviews of Health Systems: Switzerland govt.nz (accessed 31 August 2015). 2011, OECD Publishing. http://dx.doi.org/10.1787/9789264120914-en
Norway United Kingdom Ringard Å, Sagan A, Sperre Saunes I, Lindahl AK. Norway: Health system Department of Health. Guide to the Healthcare System in England: For review. Health Systems in Transition, 2013; 15(8):1-162. England. London: Department of Health. May 2013. Accessed online at https://www.gov.uk/government/publications Poland Sagan A, Panteli D, Borkowski W, Dmowski M, Domański F, Czyżewski Seán Boyle: United Kingdom (England): Health system review. Health M, Goryński P, Karpacka D, Kiersztyn E, Kowalska I, Księżak M, Systems in Transition, 2011; 13(1):1-486. Kuszewski K, Leśniewska A, Lipska I, Maciąg R, Madowicz J, Mądra A, Marek M, Mokrzycka A, Poznański D, Sobczak A, Sowada C, United States of America Świderek M, Terka A, Trzeciak P, Wiktorzak K, Włodarczyk C, Wojtyniak Rice T, Rosenau P, Unruh LY, Barnes AJ, Saltman RB, van Ginneken B, Wrześniewska-Wal I, Zelwiańska D, Busse R. Poland: Health system E. United States of America: Health system review. Health Systems in review. Health Systems in Transition, 2011, 13(8):1-193. Transition, 2013; 15(3):1-431.
Portugal Other Barros P, Machado S, Simões J. Portugal: Health system review. Health Thomson S, Osborn R, Squires D, Jun M (Eds). International Profiles Systems in Transition, 2011, 13(4):1-156. of Health Care Systems, 2012. New York: The Commonwealth Fund. November 2012.. Republic of Korea Chun C-B, Kim S-Y, Lee J-Y, Lee S-Y. Republic of Korea: Health system review. Health Systems in Transition, 2009; 11(7):1-184.
Series No 20 51
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