The study addresses one of the most persistent problems in global health: the deeply unequal distribution of human resources for health, often abbreviated HRH. Doctors, nurses, midwives, and other trained personnel are the backbone of any health system, yet their density per capita varies enormously between wealthy and poor nations. Previous work has documented this inequity in detail, but far less attention has been paid to how disastrous emergencies, particularly epidemics, actively reshape the workforce over time. The new analysis set out to quantify that relationship on a global, regional, and national scale between 1990 and 2019.

To do so, the team combined two large data sources. The first was the Emergency Events Database, an international repository of disaster records, from which they extracted country-specific counts of epidemic events for each year across 194 countries and territories. The second was the Global Burden of Disease study, which provides statistics on the density of health workers, defined as the number of personnel per 10,000 population, broken down by professional cadre. Overlaying these datasets produced a thirty-year ecological picture of where and when epidemics occurred and how health workforce levels moved in their aftermath.

The statistical core of the study is a Generalized Estimating Equation model, a technique well suited to repeated measures data of this kind. Because the same countries contribute observations year after year, standard regression would violate assumptions of independence; the GEE framework accounts for that clustering while still estimating an average population-level effect. The researchers adjusted for geographic and socioeconomic status to reduce the chance that the observed association simply reflects poverty, geography, or other confounding factors that influence both epidemic frequency and workforce levels.

The headline finding is striking in its specificity. Across the full study period, 1,185 epidemic events were recorded in the 194 countries and regions analyzed. Globally, an increase of one epidemic event in the preceding year was associated with an average decrease of 0.0575 health workers per 10,000 population in the following year, with a 95 percent confidence interval spanning 0.0185 to 0.0964 and a p-value of 0.004. In other words, the association was statistically robust, though the effect size for any single country and single outbreak is modest, which is precisely why the authors aggregated their estimates to reveal the cumulative global toll.

Translating that statistical association into absolute numbers, the team estimated that, if the observed relationship were causal, epidemics would be attributable to an average loss of 17,549 health workers per year worldwide, with a 95 percent confidence interval of 5,661 to 29,437. Expressed relative to workforce size, that amounts to 2.57 health workers lost per 10,000 health workers annually. The authors are careful to frame these figures as estimates conditional on a causal interpretation of their ecological data, an important caveat, since ecological studies link population-level trends rather than tracking individuals, and unmeasured confounders or reverse causation could bias the results.

The regional pattern is where the study’s equity message becomes unmistakable. South Asia recorded the highest average attributable fraction over the observation period, at 20.52 health workers lost per 10,000, followed closely by sub-Saharan Africa at 18.03 per 10,000. When countries were grouped by income, the gradient was even sharper: low-income countries showed an average attributable fraction of 15.89 per 10,000 and lower-middle-income countries 12.76 per 10,000, compared with just 0.96 per 10,000 in upper-middle-income countries and a negligible 0.12 per 10,000 in high-income countries. The countries with the ten highest attributable fractions were concentrated in sub-Saharan Africa, including Niger at 57.06 per 10,000, Somalia at 44.19 per 10,000, and Ethiopia at 37.12 per 10,000.

These numbers describe a self-reinforcing cycle with profound implications. Epidemics strike hardest in regions that already have the fewest health workers, and by depleting that workforce they weaken the very systems needed to detect, contain, and treat the next outbreak. A country that loses a meaningful fraction of its nurses and physicians after one epidemic enters the next emergency with diminished surge capacity, longer waiting times, and higher baseline mortality from everyday conditions. The study’s findings suggest that epidemic disasters are not merely temporary shocks to health systems but contributors to a structural, widening gap in workforce equality between rich and poor nations.

The mechanisms behind the association are not directly measured in this study, but several plausible pathways are consistent with the evidence. Epidemics can drive health worker mortality through occupational infection, as seen historically with Ebola and other hemorrhagic fevers. They can accelerate emigration of clinicians from fragile systems, prompt early retirement or career exit due to burnout and trauma, and reduce the pipeline of new graduates as training institutions close or divert staff to emergency response. Disentangling these channels would require individual-level and longitudinal workforce data, which the authors identify as a priority for future research.

The study’s limitations deserve emphasis alongside its findings. As an ecological and modeling analysis, it cannot prove that epidemics caused the workforce losses; uncontrolled confounders and reverse causation remain possible, and countries with weaker disaster-surveillance systems may underreport epidemic events, potentially biasing estimates downward precisely where the burden is thought to be greatest. Even so, the consistency of the pattern across regions and income levels, and its alignment with documented experiences during outbreaks such as Ebola and COVID-19, lend weight to the conclusion that epidemics of infectious diseases are associated with health workforce losses globally, particularly in low- and middle-income countries, further aggravating the global inequity in the health workforce. For policymakers, the message is that investing in epidemic preparedness is also an investment in protecting the people who make health systems function at all.

Subject of Research: The impact of epidemic disasters on global health workforce density and equality from 1990 to 2019

Article Title: Global, regional, and national impact of epidemic disasters on health workforce equality between 1990 and 2019: An ecological and modeling study

Article References: Chen, M., Wang, W., Sha, F., Ye, Z., Zhao, Y., Yang, C., Liang, Z., Jiang, Y., Wang, X., & Tang, J. (2026). Global, regional, and national impact of epidemic disasters on health workforce equality between 1990 and 2019: An ecological and modeling study. PLOS Medicine, 23(9), e1005134. https://doi.org/10.1371/journal.pmed.1005134

Image Credits: AI Generated

DOI: 10.1371/journal.pmed.1005134

Keywords: health workforce, epidemics, global health equity, human resources for health, PLOS Medicine, ecological study, sub-Saharan Africa, South Asia, disaster modeling, low-income countries, Global Burden of Disease, health systems

The research was commissioned to inform the World Health Organization’s efforts to develop a practical toolkit for human resources for health planning, often abbreviated as HRH planning. The stakes are considerable. Effective planning ensures a balance between the workforce and patient needs, optimizing both the quality of care and the job satisfaction of health and care workers. A diverse mix of approaches and methodologies tailored to different health-system contexts has been developed over the years, but the authors note that many health systems have simply not implemented them effectively. The gap between what the planning literature offers and what ministries of health actually use has persisted through pandemics, economic shocks and sweeping health reforms, and closing it requires knowing precisely what tools exist, what they demand of their users and where their blind spots lie.

To map that landscape, the team employed a rapid review methodology, a streamlined form of systematic review designed to deliver rigorous evidence under time constraints. Working in consultation with experienced library scientists, the researchers developed both a database search strategy and a targeted grey literature search strategy, the latter capturing reports, manuals and working papers that never appear in peer-reviewed journals but often document the real-world use of planning models. Only documents written in English or French were included, and each source was carefully screened against explicit inclusion criteria. Relevant articles were then extracted, and the resulting data were used to classify the tools into distinct planning approaches, such as needs-based models, and to rate them along five domains: accessibility, capacity, adaptability, usability and applicability.

The scale of the resulting inventory is striking. From 123 articles that passed screening, the team identified a total of 96 distinct planning tools that have been applied across 184 countries. That figure alone tells a story: workforce planning is not a niche technical exercise but a genuinely global undertaking, with tools deployed in high-income countries and low- and middle-income countries alike. The tools were classified into eight planning approaches and assessed against ten criteria spanning the five assessment domains. Among the well-known models captured in the review are the Workload Indicators for Staffing Need method, widely used to translate service workload into staffing requirements, and Health Labour Market Analysis, which examines the supply of and demand for health workers as an economic system rather than a static headcount.

The headline finding, however, is a negative one: no tool received a high score across all assessment criteria. Every instrument in the review, no matter how sophisticated or widely adopted, carries trade-offs. Some models that excel at projecting future requirements demand granular, disaggregated data that many countries cannot readily produce. Others are easy to use but too rigid to adapt to different epidemiological profiles, service delivery models or professional mixes. The review also found no consensus, either within the review itself or among the broader HRH community, on a single best planning tool. This absence of a clear winner is not a failure of the field so much as a reflection of the diversity of contexts in which planning takes place; a tool calibrated for a data-rich national health service may be poorly matched to a decentralized system with fragmented information systems.

Still, the analysis did identify a small group of standouts. Four tools received high ratings on five key criteria: capacity for multi-professional and scenario-based planning, opportunity to engage stakeholders, adaptability across settings, and utility of results. These are exactly the qualities planners prize. Multi-professional capacity means a model can account for the full team, from physicians to community health aides, rather than optimizing one cadre in isolation. Scenario-based planning allows users to test how demographic change, task-shifting or new treatment guidelines might alter future requirements. Stakeholder engagement ensures that the people who must implement the resulting plans, including professional associations, educators and service managers, have a voice in the assumptions that drive the numbers. And adaptability determines whether a tool built in one country can be meaningfully applied in another with a very different disease burden and resource base.

Yet even these top performers come with caveats that matter enormously for real-world uptake. The four leading models require moderate user capacity and substantial data, necessitating disaggregate data for model operation and some prior knowledge of HRH planning from the user. In practice, this means the tools most likely to produce useful, credible results are also the tools most likely to stall in settings where workforce information systems are weak or where planning expertise is concentrated in a handful of national officials. The review’s authors are candid about this tension, and it shapes their central recommendation: the value of the project depends greatly on the WHO’s complementary capacity-building initiatives, which they describe as essential for real-world uptake by countries. A toolkit, in other words, cannot simply be published and downloaded; it must be accompanied by training, data-system investment and sustained technical support.

The technical details of the assessment framework deserve attention from anyone who plans or evaluates planning tools. By rating each instrument on ten criteria across the five domains of accessibility, capacity, adaptability, usability and applicability, the review creates a common vocabulary for comparing tools that were previously described in incompatible ways. Accessibility speaks to how easily a country can obtain and license the tool. Capacity captures the skills and data the user must bring. Adaptability measures how well the model can be reconfigured for new contexts. Usability reflects the learning curve and the clarity of documentation. Applicability, perhaps the most consequential criterion, asks whether the outputs are actually useful for decision-making. This structure allows the forthcoming WHO toolkit to guide users toward tools matched to their own capacity and data realities rather than toward a one-size-fits-all recommendation that the evidence does not support.

The timing of the review is significant. Health workforces worldwide emerged from the COVID-19 pandemic depleted and demoralized, with the WHO and its member states confronting projected shortfalls of millions of health and care workers concentrated disproportionately in low- and middle-income countries. Planning tools are among the few levers that can turn anxiety about shortages into concrete strategies for education, recruitment, retention and deployment. By cataloguing 96 tools and their applications across 184 countries, the review provides an evidence base for countries seeking to expand beyond their traditional planning processes and work towards sustained improvements in their health systems, as the authors put it. It also signals a shift in emphasis at the global level: from producing yet another planning model to curating, assessing and supporting the use of the many models that already exist.

For health planners, the practical message is to start from an honest appraisal of local data and capacity before selecting a tool, to favour models that support multi-professional, scenario-based planning and stakeholder engagement where capacity permits, and to pair any tool with the training and data investments it requires. For researchers, the review exposes persistent gaps, including the lack of consensus on best practice and the untested assumption that high-scoring tools translate into better workforce outcomes on the ground. For the WHO, the findings set a clear specification for the guidance toolkit now under development: it must help countries match tools to context, build the user capacity those tools demand, and integrate planning into the routine machinery of health-system governance. The full open-access article, published on 6 October 2026 in BMC Health Services Research, offers the detailed assessments that will underpin that work.

Subject of Research: Systematic review of human resources for health planning tools to inform WHO guidance for health workforce planning

Article Title: A comprehensive review and analysis of human resources for health planning tools to inform the development of a guidance toolkit for HRH planning

Article References: Tomblin Murphy, G., Sampalli, T., Diallo, K., Chamberland-Rowe, C., Nair, T. S., Lawrence, L., Murdoch, J., Kunjumen, T., Boniol, M., Rigby, J., MacInnis, M., & Murphy-Boyle, K. (2026). A comprehensive review and analysis of human resources for health planning tools to inform the development of a guidance toolkit for HRH planning. BMC Health Services Research. https://doi.org/10.1186/s12913-026-14326-3

Image Credits: AI Generated

DOI: 10.1186/s12913-026-14326-3

Keywords: health workforce, human resources for health, HRH planning, systematic review, World Health Organization, planning tools, health systems, workforce planning, rapid review, health policy, capacity building, BMC Health Services Research