The COVID‑19 pandemic underscored the centrality of investments in human resources for health (HRH) to ensure the evolving health needs of the population are met without interruptions even in the context of health emergencies.

An emerging body of evidence from OECD countries demonstrates that countries with a track record of investing in the HRH capacity of their PHC systems reaped benefits in the course of the COVID‑19 pandemic. For instance, prior to the onset of the pandemic, Canada published a new pandemic preparedness plan, which tasked the PHC system with assessing and treating infected patients (Pan-Canadian Public Health Network, 2018[1]). In line with this pandemic plan, multi-disciplinary PHC teams played a key role in managing mild COVID‑19 cases within community settings throughout the course of the pandemic. Another example comes from Austria, where multi-disciplinary PHC teams were tasked with the triage and registration of suspected COVID‑19 cases, organised testing, and provided care for COVID‑19 patients (Pichler, Frühwald and Burgmann, 2020[2]). Other innovative practices at national and sub-national level were also implemented across LAC‑7 countries (see Chapters 4, 5, 6).

A key first step in informing efforts to scale up investments in strengthening HRH capacity is assessing current and future health workforce needs (Box 7.1). HRH assessments look at the interplay between demand for and supply of health workers. An oversupply of health workers can mean unemployment among qualified and trained stuff. It also means a waste of limited resources because training medical staff necessitates substantial investments. Conversely, an undersupply of health workers can result in unmet need for care, which is associated with adverse population health outcomes, declines in the quality of care and patient satisfaction. In recognition, efforts to assess HRH needs aim to understand actions needed to achieve a balance between the demand for and supply of health workers. These assessments can help inform policy decisions around the intake of students that will provide the basis for the future health workforce; examine the impact of retirement and immigration policies that influence the stock of available health workers; and assess the impact of HRH reforms.

The goal of this chapter is to review the landscape of health workforce in LAC‑7 countries. The chapter starts by documenting the recent trends in the density of physicians, general medical practitioners, nurses, and midwives. This analysis is based on data gathered from OECD databases (OECD, 2022[3]), World Health Organization (WHO) National Health Workforce Accounts Database (WHO, 2021[4]) and official data sources. Next, the chapter turns its attention to assessing the future HRH needs in LAC‑7 countries. Results are based on a novel OECD forecast of demand for and supply of health workers by 2030 using country-aggregated data extracted from publicly available datasets and government records. Motivated by the finding that most LAC‑7 countries are projected to face widening gaps between the demand for and supply of health workers, the Chapter, then, switches its attention to a comparative assessment of the key features of HRH assessments in LAC‑7 countries. Specifically, it looks at:

• • the level of decentralisation of tasks and responsibilities associated with HRH assessments;

• data infrastructure and needs to facilitate economic assessment of labour market dynamics in the health sector; and

• methodologies deployed in LAC‑7 countries to assess the demand for and supply of health workers.

Findings are generated based on semi-structured interviews with government officials and experts, as well as reviews of academic and grey literature including government reports and documents, and responses by experts to an OECD survey on HRH planning. While the reviews of the literature attempt to be as comprehensive as possible, there may be relevant studies or HRH assessments that may have been missed.

Over the last two decades, the density of physicians improved in all LAC‑7 countries, though in most LAC‑7 countries, the density of physicians remained below the OECD average (Figure 7.2). In Argentina, the density of physicians remained relatively stable, with a modest increase from 3.2 to 4.1 physicians per 1 000 inhabitants from 2001 to 2020. In this period, Brazil also experienced an expansion in the availability of physicians, though estimates vary depending on the data source (Scheffer, 2020[9]). In Brazil, the number of physicians per 1 000 inhabitants increased from 1.4 in 2000 to 2.3 in 2019. In Chile, physician density increased substantially between 2010 and 2020, rising from 1.6 to 2.8 per 1 000 inhabitants. Costa Rica also experienced an increase in physician density, with the number of physicians increasing from 2 to 2.9 per 1 000 inhabitants from 2006 to 2019. In Colombia, the density of doctors nearly doubled from 2000 to 2020, with the number of physicians increasing from 1.2 to 2.3 per 1 000 inhabitants. Physician density in Mexico also increased markedly and reached 2.4 per 1 000 inhabitants in 2019 from 1.6 in 2000. In Peru, according to the data reported by the Ministry of Health (MoH) and regional governments, the number of physicians per 1 000 inhabitants rose from about 1.1 in 2000 to 1.4 in 2020. Despite these improvements, in most LAC‑7 countries, the density of physicians remained below the OECD average of 2.79 per 1 000 inhabitants in 2000 and 3.64 per 1 000 inhabitants in 2020.

There has also been an expansion in the density of general medical practitioners in many LAC‑7 countries, though in many LAC‑7 countries the density of general medical practitioners remains below the OECD average (Figure 7.3). Standardised information on the availability of general medical practitioners that can facilitate cross-country comparisons is sparse. Despite this, the extant data suggests improvements in the density of general medical practitioners in LAC‑7 countries in the last two decades. From 2010 to 2020, Chile experienced an increase in the density of general medical practitioners from 0.9 to 1.3 per 1 000 inhabitants. Similarly, Colombia saw an expansion of its general medical practitioner capacity, with the number of practitioners increasing from 1.4 to 1.8 per 1 000 inhabitants from 2011 to 2020. While Mexico also experienced an increase in the density of general medical practitioners over the last two decades, this increase had a much smaller pace compared to Chile and Colombia. In Mexico, the density of general medical practitioners increased from 0.6 to 0.9 per 1 000 inhabitants from 2000 to 2019. Brazil also saw an increase in the number of practitioners that work at the PHC level, though these data are not directly comparable to those from Chile, Colombia and Mexico. According to data gathered from national data sources, in Brazil, the number of family and community doctors and physicians that work under the umbrella of Family Health Strategy programme and community physicians per 1 000 inhabitants increased from 0.08 in 2008 to 0.13 in 2020. For Costa Rica, data covers only primary health care physicians from 2007 to 2020. Costa Rica saw improvements in the density of PHC physicians. Between 2006 and 2020, the number of PHC physicians increased from 0.26 to 0.34 per 1 000 inhabitants. In Peru, the number of PHC physicians per 1 000 inhabitants increased from 0.2 to 0.3 per 1 000 inhabitants from 2009 to 2020. Combined, these trends suggest that the density of general medical practitioners remained below the OECD average over the last two decades, where the number of general practitioners increased from 0.9 to 1.2 per 1 000 inhabitants from 2000 to 2020.

In LAC‑7 countries, the density of nurses and midwives remained relatively stable over the last two decades though the density of nurses and midwives remained below the OECD average. (Figure 7.4). Information on the availability of nurses and midwives is sparser compared to data on physicians. Data gathered largely from national data sources in LAC‑7 countries suggest that the density of nurses and midwives improved modestly in most countries. Specifically, in Argentina, historical data that tracks the number of nurses and midwives over time is largely unavailable. The existing datasets suggests that in 2004, the density of nurses and midwives stood at 0.5 per 1 000 inhabitants compared to 0.4 in 2001. In Brazil, the density of nurses and midwives has been on the rise, with an increase from 0.5 to 1.4 per 1 000 inhabitants from 2008 to 2020. Chile also experienced improvements in the density of nurses and midwives who are licensed to practice from 1.3 to 4.3 per 1 000 inhabitants from 2010 to 2020. Colombia also saw enhancements in the availability of nurses and midwives, but these improvements occurred at a much slower pace compared to Brazil and Chile. In Colombia, the density of nurses and midwives increased from 0.9 in 2011 to 1.5 in 2020 per 1 000 inhabitants. Costa Rica is another LAC‑7 country that experienced steady improvements in the availability of nurses and midwives over time. From 2006 to 2020, the density of nurses and midwives increased from 1.8 to 2.6 per 1 000 inhabitants. Over the last two decades, Mexico achieved modest improvements, with the number of nurses and midwives increasing from 2.2 to 2.8 per 1 000 inhabitants from 2000 to 2019. Finally, in Peru, the density of nurses and midwives more than doubled in the last two decades, increasing from 0.9 to 2.3 from 2000 to 2020. While these trends generally point to improvements in the availability of nurses and midwives, it is important to note that the density of nurses and midwives remained below the OECD average in most LAC countries except Chile, where the OECD average increased from 7.8 to 10.9 per 1 000 inhabitants from 2000 to 2020.

Over the last two decades, the number of PHC teams expanded rapidly in Brazil and Peru whereas the number of PHC teams remained relatively stable in Costa Rica (Figure 7.5). Brazil experienced a rapid expansion of PHC teams over the last decade, with the number of PHC teams per 1 000 inhabitants rising from 0.17 in 2008 to 0.27 in 2019. Similarly, in Peru, the number of PHC teams grew by 38% over the past decade, reaching 0.30 PHC teams per 1 000 inhabitants in 2020. In comparison, the number of PHC teams remained relatively stable in Costa Rica, standing at around 0.21 per 1 000 inhabitants between 2006 and 2020.

Understanding the factors that influence the current and future HRH needs is a complex process. In recognition, as shown in Figure 7.6, the remainder of the chapter makes use of a general framework for a systematic assessment of the current approaches used by LAC‑7 countries to understand the current and future HRH needs (Ono, Lafortune and Schoenstein, 2013[17]). This framework is also used to inform the development of the OECD forecasting model on health workforce needs in LAC‑7 countries up to the year 2030 (Box 7.2).

This framework recognises that the current and future HRH needs are tightly linked with the organisation of the health system, and that policies to strengthen the resilience of the health workforce should not be made in isolation from the broader goals and objectives. In accordance with this framework, the supply of health workers refers to the total number of health workers with appropriate qualifications and skills who are willing to accept employment in the health sector and able to match with available vacancies. The current and future supply is expressed a function of inflow and outflow of health workers, as well as the activities of the current stock of health workers (e.g. working hours) (Ono, Lafortune and Schoenstein, 2013[17]). In comparison, demand for health workers is considered as a combination of willingness and ability of all of the health care purchasers or funders in the health system to have health workers in place to deliver health care services. The current and future demand for health services, and health workers, is expressed as a function of a wide host of factors including demography, epidemiology, health service utilisation and care delivery models, and gross domestic product (GDP) and growth in health spending, as well as other health financing arrangements (e.g. health insurance coverage) and technological advancements (Ono, Lafortune and Schoenstein, 2013[17]).

A number of challenges may hinder the application of this framework at the country level. In particular, the dearth of information on various drivers of demand for and supply of health workers makes it difficult to assess HRH needs accurately. On the supply side, data on inflow and outflow of health workers are not always readily available. Similarly, information on the current stock of health workers is often not collected. On the demand side, basic approaches that examine health worker to population ratios (e.g. physician-to-population ratios) do not typically impose heavy data requirements. Moreover, information on demographic variables like the age structure of the population is often available. However, other types of information such as health service utilisation by disease categories, as well as age and sex, is often not available in a systematic manner. Even when this information is available, it is not always straightforward to assess how current health care utilisation patterns will influence the current and future HRH needs.

The OECD analysis suggests that, in 2020, in all LAC‑7 countries, the demand for physicians, nurses and midwives was greater than their supply, resulting in a shortage for these health workers (Figure 7.7). The OECD analysis suggests that, in 2020, the shortage of physicians, nurses and midwives averaged at around 1.11 per 1 000 inhabitants across LAC‑7 countries. Specifically, in 2020, the deficit of health worker density is the highest in Argentina (2.42 per 1 000 inhabitants), and the lowest in Brazil (0.26 per 1 000 inhabitants). After Argentina, Colombia has the highest estimated shortage in health workers, as measured by density of physicians, nurses and midwives. In 2020, in Colombia, the estimated shortage is 1.88 per 1 000 inhabitants. Further, the OECD analysis suggests that, in 2020, other LAC‑7 countries also faced shortages in physicians, nurses and midwives. In Mexico, the estimated shortage in physician, nurse and midwife density is around 1.62 physicians, nurses, and midwives per 1 000 inhabitants, followed by Peru, Costa Rica and Chile where the estimated shortages in physicians, nurses and midwives averaged at 1.55, 1.21 and 1.13 per 1 000 inhabitants, respectively.

Figure 7.7 also suggests that, without effective policy action, the gaps between the demand for and supply of physicians, nurses and midwives is projected to persist in all LAC‑7 countries by 2030. The OECD analysis suggests that the shortage of physicians, nurses and midwives will average at around 1.03 per 1 000 inhabitants across LAC‑7 countries by 2030, a modest reduction compared to the estimated shortage in 2020.

Importantly, most LAC‑7 countries are projected to experience widening shortages in the density of physicians, nurses, and midwives. As shown in Figure 7.7, Brazil is estimated to remain the LAC‑7 country where the magnitude of the shortages in the density of physicians, nurses and midwives is the smallest. However, it is important to note that, without effective policy action, the estimated shortages in the density of health workers in Brazil is projected to grow by 2030, reaching to 0.40 per 1 000 inhabitants. Similarly, Chile is projected to have greater gaps in the demand for and supply of health workers compared to 2020, with the estimated shortage standing at 2.01 per 1 000 inhabitants. In Colombia and Costa Rica, the estimated shortages in the density of physicians, nurses and midwives are projected to reach 1.91 and 1.36 per 1 000 inhabitants, respectively.

In comparison, Argentina, Mexico and Peru are projected to narrow the gaps in the demand for and supply of physicians, nurses, and midwives by 2030, even though the estimated gaps will persist (Figure 7.7). Argentina is estimated to remain the LAC‑7 country with the greatest shortages in physicians, nurses, and midwives in 2030. In 2030, the shortage of health workers in Argentina estimated to stand at around 2.02 per 1 000 inhabitants. This finding suggests that, while the magnitude of the shortage in health worker density is estimated to be smaller in 2030 compared to 2020, the gaps between the demand for and supply of physicians, nurses and midwives will persist. Similarly, in Mexico, the projected shortage in the density of physicians, nurses, and midwives in 2030 is projected to stand around 1.17 per 1 000 inhabitants, whereas this figure is estimated to reach 1.10 per 1 000 inhabitants in Peru.

Motivated by the earlier findings that the gaps between the demand for and supply of health workers will widen in most LAC‑7 countries, the remainder of the chapter looks at the ways these countries assess their current and future HRH needs. Specifically, it looks at (1) level of decentralisation of tasks and responsibilities associated with HRH assessments; (2) data infrastructure and needs to facilitate economic assessment of labour market dynamics in the health sector; and (3) methodologies deployed in LAC‑7 countries to assess the demand for and supply of health workers.

Assessments of the current and future HRH needs do not occur in a vacuum. The degree to which the roles and responsibilities shared between the national and subnational authorities may have important implications for the evaluation of HRH needs, and development and implementation of policies to strengthen HRH capacity. Decentralisation of the roles and responsibilities for assessing HRH needs to subnational authorities can improve accountability to local populations; improve the efficiency of tracking HRH needs at the community level; strengthen the effectiveness of supervision in service provision; and incentivise local governments to design and implement HRH policies that can promote greater retention of health workers in their communities. Yet, decentralisation does not guarantee improvements in the assessment of HRH needs and formulating policy responses. Decentralisation may require stronger co‑ordination and co‑operation across different levels of the government with a clear delineation of roles and responsibilities associated with HRH assessments. Alternatively, communities that lack technical capacity may not be able to carry out robust assessments of their own HRH needs. The remainder of this section explores these issues in the context of LAC‑7 countries.

Most LAC‑7 countries have in place national and subnational authorities tasked with understanding HRH needs, but national-level HRH assessments are not always available (Figure 7.8). In Argentina, the Directorate of Human Resources and Knowledge (Dirección Nacional de Talento Humano y Conocimiento) within the national MoH plays a stewardship role in the development and implementation of HRH policies, including promoting knowledge to inform the development of HRH and strengthen quality of care. In Brazil, the Secretariat of Labour and Education Management in Health was created in 2003 as part of the MoH in order to develop policies and strategies planning for HRH, including training and education programs. To date, HRH assessments have been developed with specific agreements with research institutions across Brazil, though many of these assessments are not publicly available. In Chile, the Planning Department within the Division of People Management in the MoH is responsible for collecting HRH-relevant data on a regular basis and for assessing HRH needs in the public sector. In Colombia, the Directorate for the Development of Human Talent in Health of the Ministry of Health and Social Protection (MOHSP) is tasked with developing and implementing HRH policies in accordance with Law 1 438 and Article 97 dated 2011. In Costa Rica, a unit within the MoH was created in 2019 to assess HRH needs. Currently, the country is in the process of building a blueprint for national HRH policy. In addition, the Social Security Fund (Caja Costarricense de Seguro Social, CCSS) carries out periodic assessments of HRH needs for its own network of providers based on data collected from 105 health areas and 29 hospitals. In Peru, the General Directorate of Health Personnel develops HRH policies and carries out monitoring and evaluation of existing HRH measures based on the 2017 Supreme Decree 008‑2017‑SA.

In comparison, Mexico is the only LAC‑7 countries where the national MoH does not have a dedicated unit for assessing HRH needs. In lieu of a national department dedicated examining HRH needs, in Mexico, each network of health providers evaluate their own HRH needs through their own human resource departments including the Mexican Institute of Social Security (IMSS), Institute for Social Security and Services for State Workers, Medical services for oil industry workers, the Navy and the Army. The MoH also carries out HRH assessments, but these evaluations only include health facilities that service populations who are not affiliated with the IMSS.

In most LAC‑7 countries, subnational authorities also have the discretion to examine local HRH needs. In Argentina, the City of Buenos Aires and all 23 provincial-level MoHs except Salta, San Juan, Santa Cruz and Tucumán provinces carry out their own HRH planning and management within the local MoH departments. However, it is important to note that each province has their wage policies and contracting frameworks, which makes it more difficult to assess HRH needs using standardised approaches. Further, municipalities with large populations have human resource departments that are responsible for HRH planning. Similarly, in Brazil, 5 570 municipalities are tasked with examining their own HRH needs, along with the discretion to hire and fire health workers that work in their communities. Much like in Argentina, Brazilian municipalities offer different wage policies and service contracts, which complicates efforts to assess HRH needs using standardised methodologies. In Chile, municipalities are required to generate annual communal health plans that involve HRH assessments in accordance with Law 19.378 (Ministerio de Salud, 1995[23]). Upon completion, municipalities share their communal health plans with the directorates (Dirección de Servicio) within 29 geographic Health Services that combined comprise the National System of Health Services through a network of hospitals and primary health care centres. These directorates then communicate local HRH needs identified by municipalities to the MoH at the national level. In Peru, regional health directorates have departments dedicated to managing human resources at the local level, though these departments are not explicitly tasked with assessing HRH needs.

Several LAC‑7 countries publish their recent works focusing on HRH assessments (Table 7.1). In Argentina, one 2015 analysis carried out jointly by the MoH, WHO and Andalusian School of Public Health provided projections focusing on specialist physicians up to the year 2025, with the aim of assessing current HRH needs and informing the formulation of policies to train new specialists in accordance with the needs of different provinces (MoH, WHO, Andulusian School of Public Health, 2015[24]). Subsequently, several reports were published in 2019 to look at the state of health workforce in the county, as well as training and practice in obstetrics care and nursing though these publications did not attempt to examine future HRH needs (Government of Argentina, 2019[25]). In Brazil, one example of a publicly available HRH assessment is a 2018 study conducted by the Osvaldo Cruz Foundation and the Faculty of Medicine in the Federal University of Minas Gerais as part of the Brasil Saúde Amanhã Initiative that aims to support the MoH in national long-term health policy development for the Unified Health System (Girardi et al., 2018[26]). In this study, the authors provided projections for the supply of specialities for services considered as high complexity (Girardi et al., 2018[26]). In Chile, the MoH produces annual reports that look at existing deficits in health workers, which are subsequently used to inform the Parliament’s Budget Committee (Chilian Ministry of Health, 2018[27]). In addition, since 2010, three studies were commissioned to evaluate the deficit for several categories of health workers including medical specialists, GPs, and dentists. However, it is worth noting that these assessments did not involve private sector providers even though private providers play a crucial role in health service provision in the country. In Colombia, the Directorate for the Development of Human Talent in Health of the Ministry of Health and Social Protection and the Directorate of Epidemiology and Demography collaborated in a 2018 publication that provided a set of estimates on the supply of specialist physicians by different categories up to 2030, but this publication did no attempt to generate demand estimates (Government of Colombia, 2018[28]). In Peru, the MoH evaluated the supply of and demand for specialists in 2011 (Peruvian Ministry of Health, 2011[29]). Subsequently, in 2013, the MoH published an assessment of HRH regional goals from 2007 to 2015 (Peruvian Ministry of Health, 2013[30]). In Costa Rica, the MoH carried out a health workforce study jointly with the Pan American Health Organisation. In Mexico, one recent study published by the MoH examined the status of nursing in the country.

Clarifying the roles and responsibilities across different levels of government with a clear structure of accountability can help support efforts to assess HRH needs. For instance, in 2020, Colombia took steps to clarify the roles and responsibilities around HRH planning across different levels of government. To do that, the MoHSP indicated in its latest technical report that HRH planning responsibilities are to be transferred to the 37 health secretariats as part of their four‑year territorial plans in the health sector (MSPS, 2020[31]). Further, the MoH guidance indicated that the HRH planning should rest on data gathered through information management systems that track data on health worker availability and local health needs. Importantly, health secretariats were urged to examine their needs not only in the short term, but also make projections of needs within five‑ and ten‑year horizons (MSPS, 2020[31]).

Addressing the existing gaps in the co‑ordination and communication between different levels of government is also paramount to better understanding current and future HRH needs. One good practice example comes from Argentina. In Argentina, the Federal Observatory of Human Resources in Health (OFERHUS) was launched in 2016 in accordance with Resolution 1775/16 in order to foster co‑ordination and collaboration between the national government and provinces to co‑ordinate HRH policies (Government of Argentina, nd[32]). OFERHUS is meant to consolidate the network of provincial observatories that are responsible for evaluating and monitoring local trends in the health workforce (Government of Argentina, nd[32]). Importantly, one of the leading objectives of OFERHUS is to strengthen the local capacity to monitor and evaluate trends in health workforce in local communities, develop a baseline of information to assess HRH needs through assessments of the characteristics of health workers and their mobility (e.g. migratory trends).

Discrepancies in technical capacity can influence the ability of local communities to examine their own HRH needs and adopt relevant HRH policies. Previous studies point to notable differences across local communities within LAC‑7 countries in terms of technical capacity and financial resources. For example, in Argentina, an estimated six‑fold difference exists between the provinces with the highest and lowest per capita spending (Rubinstein et al., 2018[33]). To date, geographic areas with greater level of poverty experienced difficulties in attracting health workers. Similarly, in Brazil, previous studies documented substantial differences that persisted over time across geographic regions and municipalities in terms of their socio-economic development, the extent of poverty, level of health supplies, and availability of health personnel (Andrade et al., 2018[34]). Importantly, these factors have been shown to correlate with the pace with which local communities were able to adopt the implementation of large‑scale PHC programs (Andrade et al., 2018[35]).

In recognition of these discrepancies, it is vital to provide support to local communities that lack the sufficient scale, technical and financial capacity to assess their own HRH needs and formulate policy options. For instance, in Colombia, the recent MoH guidance noted that certain health secretariats may have limited technical capacity to carry out their own HRH assessments. Further, the MoH suggested that health secretariats utilise HRH management tools like the WHO Workload Indicators of Staffing Need (WISN) application as a way to examine the changing dynamics in the local health workforce, even though the MoH guidance did not lay out a consistent methodology to investigate HRH needs in local settings (MSPS, 2020[31]). Importantly, these guidelines suggested that results generated by WISN application can help determine the optimal strategies addressing HRH needs; examining priorities in terms of allocating new staff, transferring existing staff; identifying health facilities with the greatest needs, and therefore, narrowing inequities in the distribution of health workers across health facilities and geographic regions. However, it is important to note that the extent to which these guidelines are implemented by local authorities remains unknown. Another good practice example comes from Peru. Following the 2013 assessment of regional HRH goals up to 2015, the MoH published in 2014 a set of technical guidance notes were published to lay out methodological approaches that can be used to calculate HRH gaps at primary, secondary and tertiary care.

Addressing HRH needs is one area that requires collaboration and co‑ordination between different stakeholders at the national level. Typically, the number of trainees that are planned to be admitted to higher education institutions is under the purview of Ministries of Health (MoEs). HRH policies that entail an expansion of professional training and education capacity in the health sector necessitates close collaboration and co‑operation across several stakeholders. To date, several LAC‑7 countries put in place mechanisms to foster cross-sectoral collaboration. For instance, in Colombia, in accordance with Law 1 164 Article 4 dated 2007, HRH policies have ben guided by recommendations formulated through the National Council for Human Talent in Health (NCHT). As an advisory body, the NCHT is comprised of the MoE, MoH, public and private training institutions, representatives from providers and insurer associations, as well as student representatives in health training programs. Similarly, in Mexico, the Inter-institutional Commission on Training of Human Resources for Health (CIFRHS) for health workforce planning and supervision of the National Medical Residences Program. Currently, CIFRHS is a consulting, advising and technical support body that links the MoH and MoE to co‑ordinate on medical education.

Designing effective HRH policies requires high-quality and comprehensive data that can be used for monitoring the health labour market dynamics and evaluating the impact of HRH policies. Global experiences underscore the centrality of investments in information systems that provide reliable and up-to-date information on HRH that can help monitor the trends in the health workforce and the evolution of labour market dynamics. Through descriptive analyses of HRH trends over time and space in the variation of the characteristics of the health workforce, countries can identify the areas of concern like demand, compensation, market structure, interactions between demand and supply and distribution of health workforce and can help inform further investigations and policy development. Complementing descriptive analyses, data can also be used to examine the impact of specific HRH policies on outcomes.

A wide range of resources can be used to facilitate assessments of current and future HRH needs (Box 7.3). Typically, administrative datasets offer a range of useful information, including personnel records of employers, accreditation, age, gender, speciality, and geographic location. The main advantages of administrative data sources are that they typically include clear definitions for health worker types and capture the vast majority of health workers. They can also provide information on the type of health care level in which the health worker is working (e.g. hospitals, PHC clinics). However, administrative sources come with certain caveats. Administrative datasets may offer varying levels of data quality, reflecting the methodological used to collect and process data. Moreover, they typically provide information on a limited number of areas, because they are typically collected for administrative purposes rather than data analysis. Data collected through surveys are another crucial source of information. One of the biggest advantages of surveys is that they typically offer a richer range of variables (e.g. their jobs, their families, and the characteristics of where they live), which help enable controlling for other factors when assessing the impact of policies. Administrative and survey data are not complete on their own, however, it is useful to explore avenues that can help link information gathered through both administrative and survey data.

In recent years, all LAC‑7 countries made strides to strengthen their information infrastructure, which resulted in a notable expansion data that can facilitate HRH assessments (Table 7.2). In Argentina, the Sistema Integrado de Información en Salud Argentino is the main online platform that provides access to the main registry of health workers called the Federal Network of Health Professional (La Red Federal de Registros de Profesionales de la Salud – REFEPS). REFEPS collates information on health worker characteristics across professional registries in all provinces (MoH, 2022[37]). In accordance with Resolution 604/2005 of Mercosur, the Single Record of the Health Professionals (Ficha Única del Profesional), information on health worker characteristics is harmonised, including personal identification, training and registration records, specialisations and residency (MoH, 2022[37]). Experiences from OECD countries demonstrates how strengthening the existing data infrastructure on HRH can help improve assessments of current and future health workforce needs (Box 7.4).

In Brazil, municipalities report the number of health workers working in their communities in the National Registry of Health Establishments (CNES), which is processed by Informações de Saúde (TABNET) and other data sources each month. In Chile, the Superintendence of Health was established in 2004, which is responsible for maintaining a regularly updated registry of accredited institutional and individual health workers in accordance with their professions and specialisations. In Colombia, the Registro Único Nacional del Talento Humano en Salud (ReTHUS) database is a key source of information for calculation of workforce indicators (e.g. numbers, density, etc). In Costa Rica, the Sistema Nacional de Recursos Humanos en Salud (SINARHUS) was established in 2012 through the Health Services Directorate to collect information on health workers both in the public and the private sectors. This database is operated by the HRH unit within the MoH and serves a data platform to facilitate analyses of HRH needs and planning. Since 2019, the HRH unit within the MoH is making efforts to strengthen SINARHUS. In Mexico, the General Directorate of Health Information (Dirección General de Información en Salud, DGIS) within the MoH operates an HRH registry with the information of the number of health workers by category (e.g. doctors, nurses etc.) in the public sector. In addition, an HRH platform called the Sistema de Información Administrativa de los Recursos Humanos de Enfermería (SIARHE) was created in 2006. This digital platform focuses specifically on the nursing personnel through more than 30 variables collected across the different providers in the Mexican health system. This platform is estimated to include data for more than 90% of the nursing workforce. In Peru, the National Registry of Health Personnel (INFORHUS) was created in 2013 accordance with Legislative Decree 1 153, with the aim of gathering up-to-date information on HRH. In its first year, INFORUS reported data annually, and since 2015 data are available on a monthly basis.

Yet, LAC‑7 countries do not consistently use readily available data sources to assess their HRH needs. In Argentina, the databases that provide information on health workforce are mainly used for diagnostic purposes and policy design, but they are not used for systematic assessments of HRH needs. In Brazil, CNES data on health worker availability was utilised by the MoH to inform the development and implementation of several PHC national initiatives that aimed to improve the availability of health workers in geographic areas that are considered as underserved (Özçelik et al., 2021[39]).In Chile, the national registry of health workers is an essential source of information used in the HRH assessments. In Peru, INFORHUS is used both by the national MoH and regional authorities to assess HRH needs.

Data on health workers that are employed in the private sector are often unavailable due to the lack of reporting requirements for private providers. The dearth of data on the availability of health workers in the private sector undermines efforts to assess HRH needs particularly in countries where private providers play a crucial role in service provision. For instance, in Argentina, approximately 57% of health workers are employed in the private sector or in the National or Provincial Social Insurances. Despite this, there is limited capacity to gather information about health workers beyond the public system, coupled with the reluctance from the private providers in terms of sharing information. In Brazil, the current legislation requires that all health facilities are registered such that CNES database can provide data on all providers in the public and private sector. The accuracy of information provided in the CNES database is also validated. In Peru, HRH-relevant information systems have not yet fully integrated data on private providers.

These information systems can be further strengthened by minimising delays in data entry. For instance, in Argentina, provincial MoHs regularly upload information on health workers into their regional registries, which are, then, collated in the REFEPS. However, this process is undermined by delays in updating the databases by provinces and municipalities (MoH, 2022[37]). In Colombia, all health workers are obligated to register in ReTHUS in order to work in the health sector. However, given the diversity of employment modalities of the health workforce there are difficulties standardizing and collecting data on mobility, location by geographic area and type of facility, and income. Similar delays are reported in Mexico and Peru in terms of timely update of HRH relevant databases.

Figure 7.9 depicts the selected supply side factors included in the HRH assessments in LAC‑7 countries in accordance with the general framework used in this Chapter. This figure point to gaps in current attempts at evaluating supply side factors. In terms of inflow of health workers in the health labour market, all LAC‑7 countries incorporate data in their HRH assessments in terms of the inflow of graduates, though only Colombia, Chile and Brazil systematically monitor the inflow of foreign-trained health workers. With regards to outflow of health workers from the health labour market, information systems are available in Colombia and Argentina to track retired health workers but only Chile appears to track the out-migration of health workers. With respect to the understanding the stock of health workers, all LAC‑7 countries track head counts, but only Brazil, Peru and Argentina collect information on health workers’ workload. In OECD countries, data that tracks the supply of health worker were incorporated into models that aimed to predict the expected resource requirements to manage the COVID‑19 pandemic (Box 7.5).

In most LAC‑7 countries, the inflow of health workers into the health labour markets is monitored primarily through the inflow of new graduates. In Argentina, the MoH monitors the number of graduates but there is currently no mechanism to monitor the inflow of health professionals who were trained abroad. In Chile, the Planning Department in the MoH utilises data collated from the National Registry of the Superintendence of Health to monitor the number of health workers, which includes information gathered from universities on the number of annual admissions and graduates for each type of health worker. In Colombia, the MoHSP guidelines indicate that the inflow of health workers is accounted for by monitoring the number of graduates from training programs and workforce migration (MSPS, 2020[31]). In Peru, current efforts to assess supply of health workers do not consider the inflow of health workers.

Keeping up-to-date information on the number of foreign-trained health workers also helps improve the accuracy assessments to understand current and future HRH needs. The size of the foreign-trained health workforce is estimated to vary across LAC‑7 countries, though studies suggest that these countries increasingly rely on health workers who were trained abroad. One recent study estimated that foreign-trained health workers represent about 6% of the health workforce in Chile and around 5% in Argentina (de Vries, Steinmetz and Tijdens, 2016[41]). Similarly, Colombia is receiving a growing number of health workers from other countries (Carpio and Santiago Bench, 2015[42]). Costa Rica is another LAC‑7 country that is increasingly relying on foreign-trained health workers over the last three decades, particularly in provinces outside of the Great Metropolitan Area (Carpio and Santiago Bench, 2015[42]). Much like in many other countries, foreign-trained health workers are required to validate their degree through the University of Costa Rica – the main state university. One recent study showed that approximately 88% of all those who apply to practice medicine in Costa Rica are trained in Cuba, Venezuela, Nicaragua, and Mexico (Salazar Sánchez, Cordero Solis and López Dávila, 2021[43]).

It is crucial to assess the impact of reliance on foreign-trained health workers on health system performance over time. In most LAC‑7 countries, the impact of increased reliance on foreign-trained health workers on health system performance remains largely unknown. One exception comes from Brazil. In recent years, Brazil explicitly increased its reliance on foreign-trained doctors to narrow the short-term gaps in the availability of doctors that work in typically underserved communities. Specifically, Brazil rolled-out a large‑scale PHC programme in 2013, called the More Doctors Program, which relied heavily on the recruitment of foreign-trained doctors. At its height in 2015, Brazil recruited more than 15 000 physicians through this Program, primarily from Cuba (Silva et al., 2018[44]). A growing body of studies has evaluated the impact of this Program on health system performance. These studies suggested that this Program was associated with improvements in the number of PHC consultations and declines in hospitalisations due to health conditions considered sensitive to the availability of health care personnel (Hone et al., 2020[45]; Özçelik et al., 2020[46]), though results in terms of its impact on mortality remains mixed (Bexson et al., 2021[47]).

Better understanding the inflow of health workers in health labour markets can also help improve surge capacity in the context of health emergencies. For example, during the COVID‑19 pandemic, in Chile, students who were at the final stages of obtaining their medical qualifications were hired to help cope with the additional pressure on the health system (WHO, 2021[48]). In Colombia, more than 1 500 medical students were allowed to graduate ahead of their graduation schedule (WHO, 2021[48]). Similarly, in Peru, medical residency requirements in selected specialities were eliminated and degree procedures were temporarily halted for medical graduates (WHO, 2021[48]).

The OECD review suggests that LAC‑7 countries have in place mechanisms to monitor the flow of health workers outside of their health labour markets but more evidence is needed to better understand the main drivers of exit from the health workforce. Broadly, outflow of health workers occurs due to retirement, attrition from the workforce before reaching the retirement age (e.g. career shift) and emigration. In Argentina, the MoH routinely crosschecks information available in REFEPS with other information systems to identify the health workers who retired, left the health job market earlier than the retirement age, as well as deceased health workers. In Colombia, the most recent MoH technical guidance for national territories indicates that HRH assessments in the next five and ten years should consider the patterns in the outflow of health workers, including retirement, mortality, and migratory flows. In Mexico, HRH assessments carried out by local health secretariats consider retirement patterns and deaths, but not other types of outflows of health workers in the health labour markets. In Chile, in 2014, the MoH started to collect information on the migration of physicians and nurses.

The current efforts to monitor the outflow of health workers in health labour markets will be further strengthened by gathering new evidence that investigates the main drivers of attrition across different types of health workers in order to develop policies that can stem attrition rates. For instance, in Peru, where accurate and timely information on the outflow of health workers is not available, two recent studies pointed to considerable desire for out-migration among early career health workers. One 2008 study found that about 38% of medical interns at the San Marcos National University had intentions to migrate (Jimenez, Michelle et al., 2015[49]). In a subsequent study, in 2010, one University Census of students attending private and public universities in the country found that about 78% of medical students expressed their intention to migrate after completing their studies (Jimenez, Michelle et al., 2015[49]). Further evidence is needed to better assess the main drivers of attrition from the workforce across all LAC‑7 countries.

Better understanding the trends in the outflow of health workers can also facilitate the rapid development of HRH policies that can help scale up the availability of health workforce during health emergencies. During the COVID‑19 pandemic, many LAC‑7 countries relied on retired health workers to limit the disruption in service provision. For instance, in Colombia, retired health workers under the age of 60 were allowed to return to service as part of a broader strategy to address the deficits in health worker availability in the public sector during the COVID‑19 outbreak (WHO, 2021[48]). In Chile, approximately 1 500 additional health workers who had been previously released from duty were re‑deployed in the health workforce during the COVID‑19 outbreak (WHO, 2021[48]). Similar to Colombia, in Chile, re‑deployment of health workers who previously exited the health workforce was an explicit component of the broader strategy to address the health emergency. Further, in Chile, the assessment of HRH needs associated with the COVID‑19 pandemic relied on the periodic estimates of the HRH needs in the country and included both the public and private sectors (WHO, 2021[48]).

Broadly, HRH assessment aim to estimate the current stock of health workers either by tracking head counts, quantifying FTEs, or measuring both. FTEs refer to standardised measure of the workload of health workers with respect to the workload of full-time health workers. While calculating FTEs is considered a more accurate measure of the current and future supply of health workers compared to head count measures, the former requires more time and resources to collect the required data. Recently, in some OECD countries, FTEs were used in the assessment of HRH needs in the context of the COVID‑19 pandemic (Box 7.6).

LAC‑7 countries primarily collect information on the head count of different types of health workers with limited data that can help quantify FTEs. For instance, in Brazil and Colombia, the stock of health workers is tracked solely through head counts, whereas data on workload are not collected. In Argentina, REFEPS collates information on workload or hours worked, though provinces do not always update this information on a regular basis, and data do not always meet minimum quality standards. Further, considerable fragmentation across various information systems makes it difficult to estimate the current stock of health workers who are actively providing health care services. This is particularly worrisome in Argentina, because a considerable proportion of health workers engage in dual practice.

Compared to other LAC‑7 countries, Peru collects data on a wider range of variables that can help better understand time spent on various tasks and procedures by each occupational category in PHC and hospital settings. For instance, information is collected to track the number of hours spent on provision of services covered by the Essential Health Coverage Scheme (PEAS) (Peruvian Ministry of Health, 2014[51]). The MoH technical guidance for calculating human resource gaps at the PHC level indicates that information is gathered to calculate the number of hours each occupational category should allocate across medical assistance, administrative and training activities. For instance, this guidance indicates that at the first level of care, about 82% of the workload of surgeons should be allocated to PEAS-relevant care, compared to 74% for nurses, while the remainder of the time can be allocated to administrative and training activities (Peruvian Ministry of Health, 2014[51]).

Figure 7.10 depicts the demand supply side factors included in the HRH assessments in LAC‑7 countries in accordance with the general framework used in this Chapter. This figure suggests that there is substantial room for improving assessing demand-side factors. The good news is that all LAC‑7 countries incorporate some measure of population size into their assessments. However, epidemiological factors are only considered in assessments from Costa Rica, Colombia and Chile. Only three LAC‑7 countries consider health service utilisation patterns in their assessments of demand side factors. Moreover, only Mexico and Colombia consider economic factors. Finally, none of the LAC‑7 countries applies integrated approaches that go beyond HRH assessments focusing on single professions. The remainder of this section provides a more in-depth assessment of the selected demand side factors.

Understanding the demand for health workers can be a daunting task in comparison to assessing the supply of health workers (Ono, Lafortune and Schoenstein, 2013[17]). Approaches to estimating the demand for health workers vary. Relatively straightforward approaches include considering expected growth in population size. Whereas, more complicated methods may involve incorporating factors beyond demographic characteristics, such as morbidity and epidemiological characteristics, health care service utilisation patterns, organisation of care, economic growth, trends in health expenditures and salaries. The choices around estimating the demand for health workers come with important caveats. On one hand, straightforward methodologies can be applied with relative ease, because they do not impose substantial data requirements. On the other hand, these methods fall short of alluding to the optimal health worker density in a given area that can meet the needs of the population, nor do they consider factors other than population growth in determining the current and future demand.

The LAC‑7 countries primarily rely on estimated trends in the size of their populations to calculate the demand for health workers, while only a few countries consider epidemiological factors and health service utilisation in their assessments. For instance, in Argentina, the 2015 study noted that demand for health workers is a function of demographic, epidemiological factors, as well as health service utilisation, service delivery models and economic growth and growth in health spending (MoH, WHO, Andulusian School of Public Health, 2015[24]). However, this publication relied only on projections for population growth to assess demand in comparison to the base year 2014 (MoH, WHO, Andulusian School of Public Health, 2015[24]). The study, then, considered three scenarios for estimating demand based on different assumptions around population growth for 100 000 inhabitants. For each speciality, the first scenario considered a 1% decline in demand; the second scenario looked at unchanged demand, and the third scenario considered a 1% increase.

In Chile, demand for health workers is assessed regularly based on the density of health workers over time, measured as the ratio of different health worker types to the population. Health worker densities are also compared against international benchmarks set by the WHO, as well as cross-country comparisons against other OECD countries to assess the existing deficits in health worker availability. In Chile, one study focused on GPs and medical specialists in the public sector. This study analysed the supply of and demand for GPs and specialist physicians based on a model that relied on the utilisation rates of selected health care services. Supply was determined using the stock and flow method at the time of the study and future projections covering the next five and 15 years. The third study focusing on GPs and specialist physicians and dentists in the public system was recently commissioned to quantify the gap between demand and supply for health care providers in a period of 10 years.

In Brazil, the 2018 study notes that demand for and supply of health workers are influenced by a number of factors including changes in fertility and mortality, patterns of morbidity and disability, technological advancements and advent of new treatments, cultural and social changes, as well as HRH policy interventions (Girardi et al., 2018[26]). In Colombia, the MoHSP guidelines indicate that efforts to estimate demand for health workers should include department-level information on demographic factors (e.g. age and sex distribution of the service area), trends in morbidity, health service utilisation and delivery models (MSPS, 2020[31]). In Costa Rica, demand projections are carried out by the CCSS using data from the National Institute of Statistics and Censuses and the Actuarial Directorate. These projections consider the age distribution of the population and health service utilisation such as the use of hospital beds and operating rooms. In Mexico, the need for creating a new vacancy is decided separately by each health provider network based on the construction of new health facilities and budget availability.

Economic growth and spending allocated to health are important determinants of investments in the health workforce. As shown in Chapter 7, there are marked differences in financial resources allocated to health across LAC‑7 countries. Despite the importance of these factors, HRH assessments in most LAC‑7 countries do not always take into account prospects for economic growth and health expenditure. Whereas in some OECD countries, HRH assessments are conducted as part of economy-wide workforce planning efforts, which includes considerations around future economic growth (Box 7.7).

Remuneration is another crucial factor in understanding the dynamics in the health workforce. In the health sector, remuneration refers to the price of labour offered through formal payments (e.g. salary, capitation payment, pay-for-performance) in the public or private sector for services rendered (McPake, Anthony and Ijeoma, 2014[52]). For employers, the level of remuneration can influence decisions around how many and what mix of health professionals to hire. For health workers, the level of remuneration not only provides an incentive for the decision to become a health worker, retention and migration, but also further influences the type of speciality (e.g. physician, nurses and midwives), the location of medical practice, job satisfaction and choices around dual practices. Broadly, the literature points to a positive association between the level of remuneration and the availability of health workers. In addition, other forms of payment (e.g. housing allowance) have been shown to influence health worker decisions over their career path (McPake, Anthony and Ijeoma, 2014[52]).

The OECD analysis suggests that HRH assessments in LAC‑7 countries focus on single professions. Yet, integrated approaches that focus on examining the current and future needs for multi-disciplinary teams can help understand the influence of interactions across different health worker types on health system performance. Earlier chapters demonstrated that, in most LAC‑7 countries, PHC teams that are comprised of health workers across multiple disciplines provide primary care services (see Chapter 3). To date, a growing body of evidence demonstrated that the expansion of PHC teams in many LAC‑7 countries have been associated with enhanced health system performance, as measured by declines in mortality rates (Pesec et al., 2017[53]). Given the centrality of multi-disciplinary PHC teams in service provision, it is crucial to deploy more integrated approaches to assessing HRH needs that go beyond single professions. For instance, in Brazil, one recent micro-simulation study quantified that a decline in the coverage of Family Health Strategy teams due to several policies would yield, on average, a 13.2% increase in under‑5 mortality rates, and nearly 100 000 premature deaths by 2030 (Francesconi et al., 2020[54]).

One crucial barrier to adopting integrated approaches is the lack of reliable and real-time information on the availability of multi-disciplinary teams at the local level. As shown earlier in this Chapter, in many LAC‑7 countries, longitudinal information on the number of multi-disciplinary teams remains unavailable. One good practice example comes from Brazil. In Brazil, municipalities are provided financial incentives to report the number of PHC teams in their communities on a monthly basis. Information provided by municipalities are then used by the MoH to inform the design of large‑scale programs with HRH components. For example, recently, the More Doctors Programs made use of data on the number of PHC teams in order to identify the communities that were considered underserved for its programmatic purposes (Özçelik et al., 2021[39]).

Expenditure on human resources for health represent a considerable proportion of spending in the public sector, though there is substantial cross-country variation (Figure 7.11). In Peru, the share of health budget in the public sector allocated to the workforce grew steadily in recent years, increasing from 39.3% in 2015 to 45.2% in 2021. Similarly, in Mexico, the financial resources allocated to the health workforce averaged at 43% from 2015 in 2021. In comparison, in Chile, the public resources allocated to health workforce remained relatively stable between 2015 and 2020, averaging at around 32.8% of the total health budget in the public sector. Information on the level of subnational resources dedicated to the health workforce is more sparse but available evidence suggests that regional and socio-economic disparities exist across local communities in terms of their ability to allocate resources to the local health workforce. For instance, in Brazil where 5 570 municipalities have the discretion to directly hire and fire health workers, the share of expenditure from municipal governments increased from 25.5% in 2003 to 32.2% in 2016, whereas the federal government’s share in total public health expenditure declined from about 50% to 40.8% (Massuda et al., 2018[55]).

This chapter presented an overview of the current landscape of health workforce in LAC‑7 countries. First, it showed that the density of physicians, nurses and midwives in most LAC‑7 countries improved over the last two decades, though they continued to lag average health worker densities across OECD countries in the same period. Next, the chapter found that all LAC‑7 countries faced shortages in the density of physicians, nurses, and midwives at the outset of the COVID‑19 pandemic, measured as the difference in the supply of and demand for health workers. Results from a novel OECD forecast demonstrated that, without effective policy action, the estimated deficits in health worker availability in 2020 are expected to worsen in most LAC‑7 countries by 2030. Motivated by these findings, the chapter next examined the ways in which LAC‑7 countries assess their current and future health workforce needs. It found that most LAC‑7 countries do not assess their HRH needs systematically in regular intervals even though data sources that can facilitate these assessments are broadly available.

[34] Andrade, M. et al. (2018), “Brazil’s Family Health Strategy: factors associated with programme uptake and coverage expansion over 15 years (1998–2012)”, Health Policy and Planning, Vol. 33/3, pp. 368-380, https://doi.org/10.1093/heapol/czx189.

[47] Bexson, C. et al. (2021), “Brazil’s more doctors programme and infant health outcomes: a longitudinal analysis”, Human Resources for Health, Vol. 19/1, https://doi.org/10.1186/s12960-021-00639-3.

[42] Carpio, C. and N. Santiago Bench (2015), The Health Workforce in Latin America and the Caribbean: An Analysis of Colombia, Costa Rica, Jamaica, Panama, Peru, and Uruguay, The World Bank, https://doi.org/10.1596/978-1-4648-0594-3.

[27] Chilian Ministry of Health (2018), Informe sobre brechas del personal de salud en los servicios de salud y estado de situación de los recursos humanos de salud en Chile. Subsecretaría de Redes Asistenciales, https://www.senado.cl/site/presupuesto/2018/cumplimiento/Glosas%202018/tercera%20subcomision/16%20Salud/2135%20Salud.pdf (accessed on May 31 2022).

[40] CIHI (2021), CIHI’s Annual Report 2020-2021, https://www.cihi.ca/sites/default/files/document/cihi-annual-report-2020-2021-en.pdf (accessed on 11 July 2022).

[41] de Vries, D., S. Steinmetz and K. Tijdens (2016), “Does migration ‘pay off’ for foreign-born migrant health workers? An exploratory analysis using the global WageIndicator dataset”, Human Resources for Health, Vol. 14/1, https://doi.org/10.1186/s12960-016-0136-5.

[54] Francesconi, G. et al. (2020), “Mortality associated with alternative policy options for primary care and the Mais Médicos (More Doctors) Program in Brazil: forecasting future scenarios”, Revista Panamericana de Salud Pública, Vol. 44, p. 1, https://doi.org/10.26633/rpsp.2020.31.

[50] George Washington University (2021), State Hospital Workforce Deficit Estimator, https://www.gwhwi.org/estimator-statehospital.html (accessed on 11 July 2022).

[26] Girardi, S. et al. (2018), Study of supply and demand for medical specialities that are priority for conditions considered high complexity for SUS, https://saudeamanha.fiocruz.br/wp-content/uploads/2018/04/GIRARDI-et-al-DIMENSIONAMENTO-OFERTA-E-DEMANDA-ESPECIALIDADES-MEDICAS-PRIORITARIAS-PARA-SERVICOS-ALTA-COMPLEXIDADE.pdf (accessed on 22 May 2022).

[5] Gómez-Ochoa, S. et al. (2020), “COVID-19 in Health-Care Workers: A Living Systematic Review and Meta-Analysis of Prevalence, Risk Factors, Clinical Characteristics, and Outcomes”, American Journal of Epidemiology, Vol. 190/1, pp. 161-175, https://doi.org/10.1093/aje/kwaa191.

[25] Government of Argentina (2019), Obstétricas Formación y Ejercicio: Estado de situación en Argentina, https://www.argentina.gob.ar/sites/default/files/obstetricas._ejercicio_y_formacion_2019.pdf (accessed on 20 May 2022).

[32] Government of Argentina (nd), Observatorio Federal de Recursos Humanos en Salud, https://www.argentina.gob.ar/salud/oferhus/que-es-oferhus (accessed on 31 May 2022).

[28] Government of Colombia (2018), Estimación de oferta de médicos especialistas en Colombia 1950-2030, https://www.minsalud.gov.co/sites/rid/Lists/BibliotecaDigital/RIDE/VS/ED/GCFI/estimacion-cantidad-especialistas-medicas-septiembre-2018.pdf (accessed on 30 May 2022).

[45] Hone, T. et al. (2020), “Impact of the Programa Mais médicos (more doctors Programme) on primary care doctor supply and amenable mortality: quasi-experimental study of 5565 Brazilian municipalities”, BMC Health Services Research, Vol. 20/1, https://doi.org/10.1186/s12913-020-05716-2.

[49] Jimenez, Michelle et al. (2015), Analysis of the Health Care Labor Market in Peru, World Bank, https://openknowledge.worldbank.org/handle/10986/21625 (accessed on 29 May 2022).

[35] Kroneman, M. (ed.) (2018), “Transition to universal primary health care coverage in Brazil: Analysis of uptake and expansion patterns of Brazil’s Family Health Strategy (1998-2012)”, PLOS ONE, Vol. 13/8, p. e0201723, https://doi.org/10.1371/journal.pone.0201723.

[59] Kruk, M. et al. (2018), “High-quality health systems in the Sustainable Development Goals era: time for a revolution”, The Lancet Global Health, Vol. 6/11, pp. e1196-e1252, https://doi.org/10.1016/s2214-109x(18)30386-3.

[19] Liu, J. et al. (2017), “Global Health Workforce Labor Market Projections for 2030”, Human Resources for Health, Vol. 15/1, https://doi.org/10.1186/s12960-017-0187-2.

[36] Lopes, M., Á. Almeida and B. Almada-Lobo (2015), “Handling healthcare workforce planning with care: where do we stand?”, Human Resources for Health, Vol. 13/1, https://doi.org/10.1186/s12960-015-0028-0.

[13] Macinko, J. et al. (2016), “Gaps In Primary Care And Health System Performance In Six Latin American And Caribbean Countries”, Health Affairs, Vol. 35/8, pp. 1513-1521, https://doi.org/10.1377/hlthaff.2015.1366.

[55] Massuda, A. et al. (2018), “The Brazilian health system at crossroads: progress, crisis and resilience”, BMJ Global Health, Vol. 3/4, p. e000829, https://doi.org/10.1136/bmjgh-2018-000829.

[52] McPake, B., S. Anthony and E. Ijeoma (2014), Analyzing Markets for Health Workers: Insights from Labor and Health Economics, https://openknowledge.worldbank.org/handle/10986/18780 (accessed on 15 January 2022).

[58] Méndez Méndez, J. and A. Guerrero (2020), Presupuesto para salud 2021: Prioridad en la creación de plazas médicas, https://ciep.mx/presupuesto-para-salud-2021-prioridad-en-la-creacion-de-plazas-medicas (accessed on 15 December 2021).

[23] Ministerio de Salud (1995), LEY 19378 Establece Estatuto de Atencion Preimaria de Salud Municipal`, https://www.bcn.cl/leychile/navegar?idNorma=30745.

[56] Ministerio de Salud Costa Rica (2019), Health Accounts Report Period 2017-2019, https://www.ministeriodesalud.go.cr/index.php/noticias/noticias-2019/186-costa-rica-presenta-primer-informe-de-cuentas-de-salud (accessed on 15 December 2021).

[57] Ministry of Economy and Finance (2022), Budget, Public Sector, Health Function and Health Personnel 2015-2021, https://apps5.mineco.gob.pe/transparencia/Navegador/Default.aspx (accessed on 8 January 2022).

[37] MoH (2022), Red Federal de Registros de Profesionales de la Salud (REFEPS), https://sisa.msal.gov.ar/sisa/#sisa (accessed on 1 June 2022).

[24] MoH, WHO, Andulusian School of Public Health (2015), Análisis de la distribución geográfica de médicos especialistas en la República Argentina, https://docs.bvsalud.org/biblioref/2018/05/884869/2015_ops_ms_arg_eadp_demo_medica_argentina-1.pdf (accessed on 31 May 2022).

[31] MSPS (2020), Viceministerio de Salud Pública y Prestación de Servicios. Dirección de Desarrollo del Talento Humano en Salud. 2020. Planificación de Talento Humano en Salud en el Territorio. Orientaciones para el fortalecimiento del Talento Humano en Salud..

[16] National Registry of Health Establishments in Brazil (2022), Human Resources, http://tabnet.datasus.gov.br/cgi/tabcgi.exe?cnes/cnv/prid02br.def (accessed on 8 June 2022).

[11] OECD (2022), Gender Equality in Peru: Towards a Better Sharing of Paid and Unpaid Work, OECD Publishing, Paris, https://doi.org/10.1787/e53901b5-en.

[3] OECD (2022), OECD Data, https://data.oecd.org/ (accessed on 11 July 2022).

[10] OECD (2021), Gender Equality in Chile: Towards a Better Sharing of Paid and Unpaid Work, OECD Publishing, Paris, https://doi.org/10.1787/6cc8ea3e-en.

[12] OECD (2020), “Women at the core of the fight against COVID-19 crisis”, OECD Policy Responses to Coronavirus (COVID-19), OECD Publishing, Paris, https://doi.org/10.1787/553a8269-en.

[14] OECD (2016), Health Workforce Policies in OECD Countries: Right Jobs, Right Skills, Right Places, OECD Health Policy Studies, OECD Publishing, Paris, https://doi.org/10.1787/9789264239517-en.

[17] Ono, T., G. Lafortune and M. Schoenstein (2013), “Health Workforce Planning in OECD Countries: A Review of 26 Projection Models from 18 Countries”, OECD Health Working Papers, No. 62, OECD Publishing, Paris, https://doi.org/10.1787/5k44t787zcwb-en.

[39] Özçelik, E. et al. (2021), “Assessing the performance of beneficiary targeting in Brazil’s More Doctors Programme”, Health Policy and Planning, Vol. 36/2, pp. 149-161, https://doi.org/10.1093/heapol/czaa137.

[46] Özçelik, E. et al. (2020), “Impact of Brazil’s More Doctors Program on hospitalizations for primary care sensitive cardiovascular conditions”, SSM - Population Health, Vol. 12, p. 100695, https://doi.org/10.1016/j.ssmph.2020.100695.

[1] Pan-Canadian Public Health Network (2018), Canadian Pandemic Influenza Preparedness: Planning Guidance for the Health Sector, https://www.canada.ca/content/dam/phac-aspc/migration/phac-aspc/cpip-pclcpi/assets/pdf/report-rapport-02-2018-eng.pdf (accessed on 11 May 2022).

[15] Peruvian General Directorate of Health Personnel (2021), Information on Human Resources in the Health Sector, http://digep.minsa.gob.pe/publicaciones/bibliografica.html (accessed on 8 June 2022).

[51] Peruvian Ministry of Health (2014), Guía Técnica para la Metodología de Cálculo de las Brechas de Recursos Humanos en Salud para los Servicios Asistenciales del Primer Nivel de Atención, https://www.minsa.gob.pe/Recursos/OTRANS/08Proyectos/2021/PIM-SS-2021_norma-10.pdf (accessed on 7 June 2022).

[30] Peruvian Ministry of Health (2013), “Segunda Medición de las Metas Regionales de Recursos Humanos para la Salud Perú 2007 - 2015”, Online Report, https://docs.bvsalud.org/biblioref/2018/06/885008/i-15-segunda-medicion-de-las-metas-regionales-de-recursos-human_rBChN49.pdf (accessed on 31 May 2022).

[29] Peruvian Ministry of Health (2011), “Necesidad de Médicos Especialistas en Establecimientos de Salud del Ministerio de Salud y Gobiernos Regionales, año 2010, https://cdn.www.gob.pe/uploads/document/file/391079/Necesidad_de_m%C3%A9dicos_especialistas_en_establecimientos_de_salud_del_sector_salud20191017-26355-186rcta.pdf (accessed on 7 June 2022).

[53] Pesec, M. et al. (2017), “Primary Health Care That Works: The Costa Rican Experience”, Health Affairs, Vol. 36/3, pp. 531-538, https://doi.org/10.1377/hlthaff.2016.1319.

[2] Pichler, R., C. Frühwald and S. Burgmann (2020), New routine in primary care: Experiences from an Austrian PHC center during the COVID-19 crisis, https://improvingphc.org/blog/2020/06/11/new-routine-primary-care-experiences-austrian-phc-center-during-covid-19-crisis (accessed on 11 May 2022).

[33] Rubinstein, A. et al. (2018), “Making Universal Health Care Effective in Argentina: A Blueprint for Reform”, Health Systems & Reform, Vol. 4/3, pp. 203-213, https://doi.org/10.1080/23288604.2018.1477537.

[43] Salazar Sánchez, L., J. Cordero Solis and A. López Dávila (2021), “The process for recognizing of foreign medical degrees in Costa Rica: a statistical survey for the past 15 years”, GMS Journal for Medical Education, Vol. 38/7, pp. 1-14, https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8675375/.

[9] Scheffer, M. (2020), Demografia Médica no Brasil 2020.

[21] Scheffler, R. and D. Arnold (2018), “Projecting shortages and surpluses of doctors and nurses in the OECD: what looms ahead”, Health Economics, Policy and Law, Vol. 14/2, pp. 274-290, https://doi.org/10.1017/s174413311700055x.

[20] Scheffler, R. et al. (2016), Health Labor Market Analyses in Low- and Middle-Income Countries : An Evidence-Based Approach. Directions in Development--Human Development, http://hdl.handle.net/10986/25137 (accessed on 15 March 2022).

[18] Scheffler, R., R. Herbst and C. Lem (2016), Health Labour Market Analysis in Low- and Middle-Income Countries, https://openknowledge.worldbank.org/bitstream/handle/10986/25137/9781464809316.pdf?sequence=2&isAllowed=y (accessed on 15 January 2022).

[38] SEPEN (2021), Mapping of National Health Workforce Planning and Policies in the EU-28, https://healthworkforce.eu/wp-content/uploads/2021/02/D4_Final-study-report_EB-02-20-972-2A-N.pdf (accessed on 11 July  2022).

[60] Serje, J. et al. (2018), “Global health worker salary estimates: an econometric analysis of global earnings data”, Cost Effectiveness and Resource Allocation, Vol. 16/1, https://doi.org/10.1186/s12962-018-0093-z.

[44] Silva, E. et al. (2018), “Cost of providing doctors in remote and vulnerable areas: Programa Mais Médicos in Brazil”, Revista Panamericana de Salud Pública, pp. 1-7, https://doi.org/10.26633/rpsp.2018.11.

[22] USDA Economic Research Service (2022), International Macroeconomic Data Set, https://www.ers.usda.gov/data-products/international-macroeconomic-data-set/ (accessed on 15 May 2022).

[8] WHO (2022), Global Health Observatory: Healthy Life expectancy (HALE) at birth (years), https://www.who.int/data/gho/data/indicators/indicator-details/GHO/gho-ghe-hale-healthy-life-expectancy-at-birth (accessed on 12 May 2022).

[6] WHO (2022), The Impact of COVID-19 on Health and Care Workers, https://apps.who.int/iris/bitstream/handle/10665/345300/WHO-HWF-WorkingPaper-2021.1-eng.pdf?sequence=1&isAllowed=y (accessed on 12 May 2022).

[48] WHO (2021), Impact of COVID-19 on human resources: the case of Plurinational State of Bolivia, Chile, Colombia, Ecuador and Peru. Overview of findings from five Latin American countries, World Health Organisation, https://apps.who.int/iris/bitstream/handle/10665/350640/9789240039001-eng.pdf?sequence=1&isAllowed=y (accessed on 29 May 2022).

[4] WHO (2021), “National Health Workforce Accounts Data Portal”, WHO National Health Workforce Accounts Data Portal, https://apps.who.int/nhwaportal/ (accessed on 10 April 2021).

[7] World Bank (2022), World Development Indicators, http://databank.worldbank.org (accessed on 12 May 2022).