metadata: authors: 0: author: Daniels,Benjamin B.
1: author: Das,Jishnu
2: author: Gatti,Roberta V.
3: author: Yi Chang,Andres
count: Africa
docna: 0: docna: Caseloads and Competence in Sub-Saharan
            Africa : A Fundamental Reassessment of the Human Resources
            Crisis in Primary Health Care
docty: Policy Research Working Paper
abstracts: cdata!: This paper presents a fundamental
            reassessment of the global human resources crisis in primary
            health care, using nationally representative survey data
            from 7,915 health facilities across 10 Sub-Saharan African
            countries. The reassessment consists of three main parts.
            First, in contrast to a literature that posits pervasive
            health workforce shortages, the paper estimates that the
            median primary health care provider sees 10.9 patients each
            day and spends under two hours doing so. However, variation
            in patient loads across facilities implies that most
            patients visit busier facilities, and therefore the median
            patient experiences long wait times. Second, by combining
            caseload data with measures of medical competence for 14,367
            individual providers, the paper demonstrates that provider
            caseload is very weakly correlated with medical competence.
            As a result, the most competent doctors in each system are
            nearly as likely to be underutilized as the least competent.
            Third, the paper assesses how much productivity is lost due
            to the low observed correlation between caseload and
            competence, by calculating potential quality improvements
            from matching the most competent providers to the busiest
            postings. Such transfers could increase the likelihood that
            a patient sees a provider who can correctly manage simple
            cases by 4.5 percentage points, or 12 percent, but with
            substantial variation across countries. The paper concludes
            that in half of the countries in the sample, there are
            substantial numbers of competent but underutilized
            providers; but in the other half, quality improvements will
            require a full overhaul of the training infrastructure and
            spatial distribution of facilities.
display_title: Caseloads and Competence in
            Sub-Saharan Africa : A Fundamental Reassessment of the Human
            Resources Crisis in Primary Health Care
listing_relative_url: /research/2025/01/34449038/caseloads-competence-sub-saharan-africa-fundamental-reassessment-human-resources-crisis-primary-health-care
topicv3: Health Service Delivery Markets m1582245 4619,Primary Health Care Markets m1583260 4620,Human Resources for Health m1586417 4556
publishtoextweb_dt: 2025-01-23T00:00:00Z
dois: 10.1596/1813-9450-11043
origu: Off of Sr VP Dev Econ/Chief Econ (DECVP)
no_of_pages: 32
author_keys: 0: author_key: 1163178
1: author_key: 439480
2: author_key: 321051
3: author_key: 1388215
model_extractions: Policy Research Working Paper 11043

# Caseloads and Competence in Sub-Saharan Africa

A Fundamental Reassessment of the Human Resources Crisis in Primary Health Care _Benjamin Daniels Jishnu Das Roberta Gatti Andres Yi Chang_ People Vertical & Middle East and North Africa Region January 2025
Policy Research Working Paper 11043

# **Abstract**

This paper presents a fundamental reassessment of the global - human resources crisis in primary health care, using nation ally representative survey data from 7,915 health facilities across 10 Sub-Saharan African countries. The reassessment consists of three main parts. First, in contrast to a literature that posits pervasive health workforce shortages, the paper estimates that the median primary health care provider sees 10.9 patients each day and spends under two hours doing so. However, variation in patient loads across facilities implies that most patients visit busier facilities, and therefore the median patient experiences long wait times. Second, by combining caseload data with measures of medical competence for 14,367 individual providers, the paper demonstrates that provider caseload is very weakly correlated with medical competence. As a result, the most competent doctors in each system are nearly as likely to be underutilized as the least competent. Third, the paper assesses how much productivity is lost due to the low observed correlation between caseload and competence, by calculating potential quality improvements from matching the most competent providers to the busiest postings. Such transfers could increase the likelihood that a patient sees a provider who can correctly manage simple cases by 4.5 percentage points, or 12 percent, but with substantial variation across countries. The paper concludes that in half of the countries in the sample, there are substantial numbers of competent but underutilized providers; but in the other half, quality improvements will require a full overhaul of the training infrastructure and spatial distribution of facilities.

This paper is a product the Office of the Chief Economist, the People Vertical and the Office of the Chief Economist, Middle East and North Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://www.worldbank.org/prwp. The authors may be contacted at ayichang@worldbank.org; rgatti@worldbank.org; Jishnu.Das@georgetown.edu; and bd581@georgetown.edu.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# **Caseloads and Competence in**

# **Sub-Saharan Africa: A Fundamental Reassessment of the Human Resources**

# **Crisis in Primary Health Care**

**Benjamin Daniels**<sup>1</sup> **, Jishnu Das**<sup>2,3,*</sup> **, Roberta Gatti**<sup>4</sup> **, and Andres Yi Chang**<sup>5</sup>

1Harvard T.H. Chan School of Public Health, 677 Huntington Avenue, Boston, MA 02115, United States 2McCourt School of Public Policy and the Walsh School of Foreign Service, Georgetown University, Washington, DC 20057, United States 3Centre for Policy Research, New Delhi 110021, India 4Office of the Chief Economist for Middle East and North Africa, The World Bank, Washington, DC 20433, United States 5Office of the Chief Economist for People, The World Bank, Washington, DC 20433, United States

*Correspondence to: jishnu.das@georgetown.edu

JEL classification: I1, I11, I15, I18 Keywords: Health Service Delivery, Primary Health Care, Sub-Saharan Africa, Human Resources
# **Highlights**

- We estimated outpatient caseloads and provider competence using nationally representative facility-level survey data from 10 Sub-Saharan African countries.

- The median primary health care provider sees 10.9 patients every day, but the median outpatient visits a provider who sees 25.7 patients daily.

- There is almost no correlation between caseloads and competence in most countries: low-competence providers are nearly as busy as the most competent.

- Hypothetical reallocations that match underutilized high-competence providers to larger caseloads could meaningfully improve the quality of care in half of the sampled countries.

# **1 Introduction**

A prominent consensus in the voluminous literature on “human resources for health” is that there is a global workforce crisis in primary health care (PHC) – a general shortage of providers, which is far worse in poorer countries and especially acute in Sub-Saharan Africa (SSA).<sup>1, 2</sup> The _prima facie_ case for this consensus is unassailable. Even in the OECD countries, doctors and nurses are extremely busy and visibly overworked, with frequent reports of provider burnout. There are 4.5 physicians per 1,000 people in Germany and 3.6 in the United States,<sup>3</sup> but only 1.55 physicians, nurses _and_ midwives for every 1,000 people across the WHO African Region.<sup>4</sup> Given that there are far fewer physicians per population in SSA than in OECD countries, it does not require a huge leap of faith to assume that the workforce crisis must be much worse in SSA compared to the United States or the European Union.

On closer consideration, however, these statistics present a puzzle. Assume each person in SSA seeks outpatient primary care 6 times per year, which is the OECD average for in-person physician consultations in 2021.<sup>5</sup> In the African Region, there are 1.55 providers per 1,000 people,<sup>4</sup> and if each provider worked 250 days a year, this would imply that the average provider would therefore have to see just 15.5 patients per day. Even at the highest LMIC observed PHC consultation length of 9 minutes,<sup>6</sup> the average provider would see patients for 140 minutes each day, or two hours and twenty minutes. After paperwork and administrative duties, the typical PHC provider in SSA must still spend substantial time not seeing patients, or “excess capacity”. Published statistics are therefore consistent with the idea that the workforce is in fact _under_ -utilized.

The first part of our study investigates this apparent contradiction: Academic consensus, personal observation, and patient reporting all indicate that SSA primary care facilities are highly overcrowded, but published national and regional statistics are instead consistent with substantial underutilized time for PHC providers.

Our investigation uses nationally representative PHC facility survey data covering 7,915 health facilities in 10 Sub-Saharan African countries, collected between 2014 and 2019 through the World Bank’s Service Delivery Initiative (SDI).<sup>7, 8</sup> We first show that,

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across the countries in our sample, the median outpatient caseload per facility is 25.9 patients per day. As many facilities have multiple providers, if we assume that the patient load in the facility is evenly distributed across available providers, the mean daily caseload per _provider_ was 15.7 and the median was 10.9 across our full sample, ranging from 1.2 in Nigeria to 20.7 in Malawi. This mean is remarkably close to the 15.5 patients per day that emerged from our crude calculation above, and it indicates that the caseloads of PHC providers are consistent with one hour and forty minutes of daily consultations across the countries in our sample.

To understand why patients still report long queues even though providers have substantial excess capacity, we then examine the _variation_ in caseloads across facilities. Across the countries in our sample, the 20% busiest providers saw between 40%–67% of all outpatients, which implies that most patients (by definition) visited facilities with relatively busy providers. The median _patient_ in our sample visited a provider who saw 25.7 patients each day, ranging from 6.6 in Nigeria to 64.6 in Malawi. These are substantially higher caseloads, and simulations using stochastic queuing theory show that even mild “clumping” of patients at any time of the day – or day of the week – would lead to long queues and waits at these higher caseloads. We conclude that official statistics and facility survey data are correct in indicating concurring patterns of excess capacity for providers. Simultaneously, the variation across facilities in the survey data implies that the characteristic patient experience is instead one of crowds and queues.

With this new perspective in hand, we turn to a second issue of human resource management for these PHC systems. We consider, as a starting point, that the unequal caseloads we observe could reflect efficient allocation mechanisms if the busier providers were also those of higher quality. Our second aim in this article is then to investigate whether caseloads were strongly positively correlated with provider competence.

In order to do so, we used measures of clinical competence for 14,367 health care providers that we linked to our estimates of utilization at the provider level,<sup>9</sup> again under the assumption that patients were equally divided among available providers in the facility. We uncover the striking result that the association between competence and caseloads is close to zero for most countries in our sample. It is negative so that more competent providers see _fewer_ patients in Madagascar and Mozambique, and positive but small in all other countries. Some examples are stunning: Nigeria has some of the most knowledgeable providers in the entire sample and is one of the countries with a statistically significant positive association between caseloads and competence. Even so, the correlation is so small that the average caseload among the 100 most knowledgeable Nigerian providers in the sample was only 5.2 patients per day.

The lack of a strong systematic association between caseloads and competence then motivates the third part of our study. We investigate the implications of this (mis)allocation, whereby highly competent providers are located in less busy facilities, by asking if overall quality could be improved by hypothetically matching underutilized high-competence providers to larger caseloads. An example demonstrates the reasoning behind this approach. Consider a health system with two providers: Doctor A, who scores 80 (out of 100) on a measure of competence and Doctor B, who scores 20. The average doctor

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therefore scores 50. However, from the patient point of view, the average quality of the system should be calculated accounting for each doctor’s share of total patients: If 1/4 of patients see Doctor A and 3/4 see Doctor B, the average interaction quality would be 35. If the doctors were to switch places, the average interaction quality would be 65 instead, with no change at all in the medical competence of either Doctor A or B, or the physical location of the patients.

Following this logic, we provide an estimate of how much the quality of the average patient consultation could improve in each country if providers were to be “optimally” reallocated. Specifically, we compute the competence of the provider at the average outpatient consultation in the observed data. Then, we calculate the same quality metric, but with the most knowledgeable health care providers hypothetically matched to the largest caseloads in their country and sector, holding constant the number of patients at each facility. We emphasize that this reallocation is not intended as a policy recommendation, but as a sufficient statistic to quantify the loss in quality due to the fact that some high-competence providers are underutilized, even while some low-competence providers manage very large patient loads.

For the average patient interaction, our hypothetical provider reallocations increased quality (defined as having the competence to correctly manage a basic case) by 4% to 30% relative to current performance. Two observations are noteworthy. First, even with the near-zero correlations between caseloads and competence in current systems, improved allocation offered substantial gains in only half of the countries. Second, in those countries where gains from reallocation were low, improvements were just as low in simulations of an alternate policy that “upskilled” every non-doctor health care provider to the level of a doctor from the same country, a major investment in training resources. In these countries, even though the health care provider population still has ample excess capacity, there is an insufficient number of high-competence providers to reach patients in any configuration of human resources. Systemic quality improvements in such cases may require a complete overhaul of training infrastructures.

Our paper makes two fundamental contributions to the literature. First and foremost, we contribute to the literature on human resources for health, particularly in Sub-Saharan Africa. In contrast to a common view that there is a general human resource shortage, we show that published aggregate statistics and the SDI facility survey data are both consistent with substantial excess capacity for the median provider. Our result rationalizes and explains why recent studies<sup>10–12</sup> find that the causal impact of patient load on the quality of medical care is close to zero in multiple SSA countries: Most providers have plenty of capacity to handle additional patients. We further contribute to this literature by using variation across the facility surveys to show that excess capacity among providers is _also_ consistent with long queues and waits for the typical patient, because a small share of providers handles a large share of the total caseload.

Second, we show how data on individual provider competence can be combined with data on caseloads to quantify lost productivity, by estimating the potential gains from improved allocation. While such allocative gains have been shown to be quite important for quality improvement in high-income countries,<sup>13, 14</sup> our approach to estimating potential

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quality improvements through hypothetical reallocation in the presence of excess capacity is new to the health care quality improvement literature in low-income countries. It is motivated by the fact that even though patients seeking care for common conditions are frequently misdiagnosed and incorrectly managed,<sup>11, 15–17</sup> current approaches of training existing providers have yet to generate substantive and consistent impacts across contexts. A comprehensive review in LMICs that documented 499 studies showed enormous variation in the effects of any given strategy, and found no “silver bullet” that reliably replicated across studies.<sup>18</sup> Our allocation-based analysis suggests that, at least in half the countries in our sample, the losses due to allocative inefficiency may be large, and that improved utilization of competent providers could lead to substantial quality gains. In the other half, because the fraction of providers who are highly competent is small, neither reallocation nor upskilling using the current infrastructure improves quality. In this case, a substantial rethinking of the entire medical training architecture may be required.

# **2 Methods**

## **2.1 Data Source**

Our analysis was based on data collected through the Service Delivery Indicators (SDI) surveys, conducted by the World Bank, in 10 Sub-Saharan African countries with a total population of 515 million, or 43% of the Sub-Saharan African population. Within each of these countries, facilities were first sampled from a list of facilities that provided primary care services, and survey data were collected on facility caseloads and rosters of clinical staff. Then, in a second stage at an unannounced visit, present providers were interviewed, with clinical vignettes administered for up to seven common medical conditions, assessing their medical competence (knowledge). The final dataset that was fully matched with caseload data included vignette data from 14,367 health care providers practicing at 7,915 health facilities. A complete description of the vignette sample selection and administration process is presented in Daniels _et al_ . (2024).<sup>9</sup>

## **2.2 Primary Outcomes**

Using the SDI data, we constructed two primary outcome measures. The first was the perprovider “outpatients per working day,” a measure of each provider’s caseload calculated from the facility survey and staff survey. The second was the “vignettes competence,” a measure of the quality of each health care provider in terms of their medical knowledge. We considered each provider’s ability to correctly diagnose and treat the conditions presented in the vignettes based on the presentations and definitions in **Supplemental Appendix Table 1** , and we present the vignette competence here as the predicted share of vignette cases each provider could have correctly managed.

Using the reported number of outpatients seen at the facility in the last three months as recorded by the SDI, we computed the mean daily outpatient caseload for each provider in the sample following Kovacs and Lagarde (2022) as _PLf_ = _Sta f f Consultations f ×_ 20 _×_ 3 _<u>f</u>_ . _PLf_ , the estimated average daily patient caseload per provider at the facility level, is calculated as the total number of monthly consultations at the facility level divided by the number of staff serving

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outpatients times 20 expected working days per month for each provider.<sup>12</sup> We winsorized the final number of daily outpatients per provider for the top 2.5% of facilities within each country.

SDI surveys measured absenteeism in health facilities via unannounced visits where enumerators compared the actual presence of providers to the roster of listed providers. We provide an upper-bound estimate of the per-provider caseload by counting only those providers who were present at the facility on the day of the unannounced provider survey and we assumed that this count was representative across an estimated 20 working days at the facility. This implicitly assumed that providers who were observed on the survey day across the sample of facilities were representative of the availability of providers at each facility on any given day and representative of the workload of others not observed. If, for instance, the providers who were not observed were at home and not seeing patients, we would over-estimate the average patient load in the system. We were not able to match the identity of providers who worked at multiple facilities, either inside or outside the sample. For our purposes, the representative “provider-day” workload at the sampled facilities is accurately estimated and is the quantity of interest: It is the number of patients a typical provider sees during a typical working day.

We caution that the differences under other assumptions can be large, as the mean facility reported 4.3 providers on staff, of whom only 2.6 were present during the unannounced provider survey visit. If we spread patients across all providers on the roster (include those not present), or assumed that each facility would be open more days each month, the number of patients per average provider-day would decrease proportionally. For the 5% of facilities with over 10 staff on the roster, we did not have a true sample of provider availability from the vignette survey. In these cases, we estimated the number of providers present. To do so, we calculated the average share of providers present across all facilities with 2–10 providers (60%), and multiplied that by the total number of providers listed on the roster. To have the providers observed in the vignettes represent the present providers not observed in the vignettes, we weighted those observations by the factor appropriate to each facility’s staff roster count. The **Supplemental Appendix** describes these computations and the robustness of our results to the exclusion of those large facilities where we estimated the true number of present staff.

Following Daniels _et al._ (2024),<sup>9</sup> we used item response theory (IRT) to estimate a continuously-distributed provider competence index based on adherence to the predetermined diagnostic checklists in the clinical vignettes. We used a regression model to predict the share of vignette cases each provider would have correctly managed, which increases from 14% at the 10th percentile to 55% at the 90th percentile. We report either the predicted or actual correct management shares as our measure of quality as statistically appropriate.

# **2.3 Simulation: Potential Gains from Provider Reallocation**

We characterize the potential quality gains from improving the allocation of providers across facilities in the following way. We define a “posting” as the number of daily outpatients seen by each provider as calculated above. We then define an “optimal

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allocation” as one in which the provider with the highest competence is matched to the posting with the largest outpatient caseload; the provider with the second-highest competence to the posting with the second-largest outpatient caseload; and so on. We restrict hypothetical reallocations to postings within the same health facility level and rural/urban setting (together, a “sector”) in each country, while holding the caseload constant at each posting.

To calculate the potential gains from provider reallocation, we first computed the share of vignette conditions that the average provider could correctly treat, weighted by the observed outpatient caseload at each posting. Then, we reallocated providers to the “optimal allocation” taking the constraints into account till all existing providers were assigned to all existing postings. This process produced the highest possible proportions of vignettes correctly treated when weighted by the observed outpatient caseloads. Following the reallocation simulations, we re-computed the predicted correct management shares for the average patient and calculated the realized gains from the reallocation. Regardless of the practicability of any such reallocation, the resulting estimate is a valid measure of the quality implication of provider misallocation. The **Supplemental Appendix** discusses the process in further detail.

# **3 Results: Provider Caseloads and Patient Crowding**

## **3.1 Caseload Patterns for PHC Providers**

The first part of our results establishes the overall patterns in daily outpatient caseloads across the countries in our sample. The top panel of **Table 1** presents summary statistics for each country as well as for all countries in our sample (as a country-level average of medians). The median facility in the average country sample saw on average 25.9 patients per day, ranging from 2.1 in Nigeria to 87.2 patients per day in Malawi (Column 4, Table 1). The median provider in the average country saw on average 10.9 patients per day, with country-level medians ranging from 1.2 (Nigeria) to 20.7 patients per day (Malawi). A provider who saw 10.9 patients per day would face a one hour and forty minute workday in addition to other duties if providers were spending nine minutes per patient, which is the _highest_ mean consultation time observed in LMIC settings.<sup>6</sup> Given that the mean consultation time across LMIC settings is usually closer to five minutes, it could well be the case that the median SSA provider spends no more than one hour a day seeing outpatients.

In the bottom panel of Table 1, we compared these estimates to WHO statistics, using the combined number of physicians, nurses, midwives, and CHWs reported per 1,000 population.<sup>19</sup> We assumed 6 visits per person per year and 250 working days for each provider. These assumptions indicated that the mean provider (physician, nurse or midwife) would be responsible for 30.6 outpatients per day, ranging from 12.4 to 96.0 across countries, as these countries had generally lower availability of PHC providers than the African Region as a whole. Across all countries, the mean provider outpatient caseload we estimated from the SDI data tracked these estimates in a range from 9% (in Niger) to 114% (in Kenya) of the WHO-based calculation, with an average 49% lower

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caseload using the SDI data than the WHO data at the country level. The comparison confirms that our computations based on crude averages are likely an overestimate of the true caseload in primary care. The most plausible reason for this overestimate is that the number of PHC visits per person per year is likely much lower than the OECD average of 6, but we cannot rule out that there are other systematic differences in the way that health care workers are counted in WHO statistics versus the on-the-ground surveys used here.

We also investigated if patient loads could be much higher for doctors under the assumption that in facilities where they were present, all patients would be allocated to that doctor rather than the nurse. The estimated outpatient caseload per doctor increased from 9.5 to 19.3 patients per day ( **Supplemental Appendix Figure 4** ). However, 63% of clinics were exclusively staffed by health care professionals who were _not_ doctors; as a result, average caseloads per provider remained low for the majority of providers. Overall, the facility-level surveys supported the conclusion that PHC providers across these 10 countries experienced substantial excess capacity and underutilization during the workday.

# **3.2 Variation in Caseloads across Facilities**

A key feature of these data is that even though the mean and median caseloads are low, there is enormous variation across providers, as shown in **Figure 1** . At the 75th percentile of the caseload distribution, for example, providers see on average 2.5 times as many patients as the median provider does; and 5 times as many at the 90th percentile of the caseload distribution. One question is whether this variation largely reflects differences in facility levels and/or differences across urban and rural areas.

We therefore examined the variation in caseloads and providers across facilities using multiple regression analysis in a specification with country-level fixed effects ( **Table 2** ). Column 1 shows that at the facility level, indeed urban hospitals carried the largest caseload, followed by rural hospitals followed by urban and rural clinics, all compared to rural health posts. These results remain similar, but attenuated once we include the number of providers at the facility. Note however, that the estimated coefficient on the number of providers implies that facilities with one additional provider see 6.98 additional patients. Since this is below the mean in our sample, facilities with more providers should see fewer providers per patient, and this is what we show next. In Column 3, we use as the dependent variable the caseload _per provider_ , accounting for the number of providers at the facility, and as expected, facilities with more providers are also those that have fewer patients per provider.

We then investigated these patterns at the provider level, as opposed to the facility level. Column 4 confirms that the association between staffing levels and caseloads is sufficiently strong that urban hospitals do not have significantly more patients per provider than health posts. In fact, providers at hospitals are less busy than providers at urban and rural clinics, which have the highest per-provider caseloads. Rural hospitals, by contrast, have even lower per-provider caseloads than rural and urban health posts. There are no significant differences in caseloads between urban and rural hospitals; nor between

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urban and rural clinics.

Finally, Column 5 includes additional covariates at the provider level, showing that doctors and other para-professionals have slightly higher caseloads compared to nurses. Because we assume that caseloads are evenly distributed within facilities, this implies that doctors are, on average, located in busier facilities. A final result is that at the provider level, we could not explain more than 25.6% of the variation in caseloads, of which only 5.6% is explained by facility location and provider characteristics (the remaining 20% is explained by country). This results from the fact that facilities with any given combination of characteristics and staffing had a very wide range of per-provider caseloads in each country.

A first implication of this wide unexplained variation in caseloads is that the experience of the average patient differs from that of the average provider. To see this in our data, we weighted each provider by the number of patients they saw, and we report the “crowd” faced by the median patient in **Table 1** , Column 6. The median patient was seen by a provider who sees 25.7 patents each day, with country-level medians ranging from 6.6 patients a day in Nigeria to 64.6 a day in Malawi.

Using queuing theory simulations, we find that substantial queues can develop at these crowd sizes if patients arrivals and consultation lengths vary randomly (as opposed to uniformly). **Figure 2** demonstrates the method, showing demonstrative queuing and service patterns over a single simulated working day for a single provider facing various average caseloads, using randomly distributed patient arrivals and departures and evaluating the queue flow minute-by-minute over an assumed six hour outpatient shift (see the **Supplemental Appendix** for details). In these examples, the provider works 2.3 hours out of the 6-hour day when there are an average of 15 patients, and patients wait under a minute each; there is nearly never a queue. But when the provider faces 40 patients, they are serving patients the entire day, and patient wait times and queue lengths increase dramatically.

**Figure 3** generalizes this approach to show the wide variation in utilization and queue lengths that can occur given any level of “average” utilization. With 20 or fewer patients per day, providers are frequently underutilized over the course of a six-hour workday. With 30 or more patients per day, average wait times become long on average, and excess capacity quickly declines. At 40 patients per day, provider working time ranged from 60% to 100%, and patient wait times ranged from five minutes to over one hour. Any “clumping” of arrival times onto specific days of the week or times of the day substantially lengthened the queues, driving wait times to “infinity” in some cases. This means that not all patients who showed up would be seen by closing time, since a facility working at or above capacity would have a constantly-growing queue. Thus, the caseloads we estimated in many countries would be consistent with long waiting times for the typical patient under conditions of even moderate clumping.

Under these conditions of variation, caseloads were observed to be substantial at the busiest facilities in each country, but only the very busiest providers appeared to have caseloads that would commit them to consistently serving patients throughout a full workday. Combining these observations and simulation results with the facility survey

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data, we can conclude that most patients were seen by providers whose workloads are consistent with long queues in our simulated data, even while most providers appeared to have substantial excess capacity.

# **4 Results: Quality Consequences of Uneven Patient Allocations**

## **4.1 Correlations between Caseloads and Competence**

We now turn to the question of whether such unequal variation in caseloads has important implications for the quality of care. One hypothesis is that high-quality providers have high caseloads and low-quality providers would often have excess capacity, such that the system approximates an efficient utilization of resources. To examine this possibility, **Figure 4** introduces the interplay of caseloads and provider competence. The left panel illustrates the share of patients treated by the busiest providers, while the right panel illustrates the share of patients treated by the highest-competence providers. From the left panel, the busiest 20% of providers in each country see on average over half the total patients, ranging from a low of 40% in Togo to a high of 67% in Nigeria. By contrast, the least-busy 60% of providers see between 14% (Nigeria) and 36% (Togo) of all patients.

If the most competent providers were also the busiest providers, we would expect similar proportions to hold when we examined caseloads at different levels of competence. However, as the second panel of **Figure 4** suggests, caseloads were not systematically aligned with the competence of providers. Instead, the 20% _most competent_ providers saw only very slightly more than 20% of patients; the next most competent 20% generally saw a slightly smaller share; and the least competent 60% of providers saw 49%–65% of the patients. While the overall correlation was not quite zero, these top-line comparisons indicated that the most competent providers were not systematically managing substantially greater caseloads.

**Figure 5** extends this analysis by directly comparing caseloads for providers at various levels of competence in the same country and sector. In most countries, the relationship was observed to be at or near zero over the entire range of possible competence levels. Across all countries – with the exceptions of Kenya and Tanzania, where the most competent providers could see 12–17 more patients per day than the least competent – providers with higher competence were not systematically located in busier facilities.

Indeed, even over the pooled sample, the correlation between provider competence and caseload was such that the most competent providers in the sample saw only slightly more patients per day more than the least competent: We estimated an increase of 0.05 patients per day for each one-percent improvement in a provider’s vignettes performance, accounting for all other characteristics. This implies that moving from a provider who is at the 10th percentile of the competence distribution (and manages no conditions correctly) to one who is at the 90th percentile (managing two-thirds of conditions correctly) is associated with an increase of only three patients per day.

This lack of correlation between caseloads and competence indicates that large caseloads are not efficiently allocated to more competent providers. Instead, we observed patterns that more closely resembled random allocation, with high-competence providers

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nearly as likely to be underutilized as low-competence providers. This evidence of substantial “misallocation” suggests that there is room to improve the quality of care even without aggregate improvements in provider competence. We turn to this next.

# **4.2 Quality Implications of Provider Misallocation**

To quantify the consequences of this misallocation in terms of the quality of care that patients could receive under a hypothetical “optimal” allocation, we first calculate the likelihood that a provider in an average patient interaction could manage one of the vignette cases correctly. Then, we simulate the optimal allocation of the most competent providers to the busiest facilities, and calculate the same likelihood under that configuration.

**Table 3** reports the simulated improvements in average patient-weighted interaction quality by country. Following the reallocation, the average quality of care improved from 36.8% to 41.2% of vignettes correctly managed. However, there were wide variations in potential improvement across countries, from a low of 0.9 percentage points (p.p.) in Togo and Niger to a high of 9.9p.p. in Malawi. The variation in potential improvement across countries reflects the starting distribution of patient caseloads and provider competence in each country.

**Table 3** demonstrates this dynamic by reporting the 25th and 75th percentiles for both caseloads and competence in each country. In Malawi, the 75th percentile provider in terms of caseload saw 4.8 times as many patients as the 25th percentile provider while the 75th percentile provider in terms of competence managed 14.5% more conditions correctly than the 25th percentile provider. Consequently, reallocating the most competent providers to the busiest postings, where they would treat a large number of patients, resulted in large aggregate gains. By contrast, in Togo and Niger, there was much less variation in the competence _and_ caseloads of providers across postings. In these countries, the 75th percentile provider could not correctly manage many more of the vignette conditions than the 25th percentile provider; nor were there opportunities for the more-competent providers to see many more patients. Therefore, there were very limited potential gains from reallocation.

**Figure 6** illustrates the observed and optimal caseloads by competence for each country. Under current configurations (dashed lines), more-competent providers in each

country (horizontal axis; histogram) typically see similar numbers of patients as lesscompetent providers. Under “optimal” allocations, more-competent providers would see many more patients (solid black line). Across countries, the actual numerical differences ranged from as little as 10 patients per day (Togo) to nearly 300 patients per day (Malawi). Under those optimal allocations, low-competence providers would often be assigned to postings with nearly no patients. Since we never alter the patient allocation across postings, these smaller and larger caseloads reflect the status quo variation in the data.

The competence of top-performing providers also ranged widely across countries. In some countries, the most-competent providers could correctly manage around 30% of vignettes (Niger, Togo) versus nearly 100% in others (Malawi, Tanzania). Similar to **Table 3** , **Figure 6** shows how the potential improvements from reallocation depend on both the existence of busy postings and on the availability of high-competence providers.

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Substantial quality improvements were only possible from reallocation in systems where sufficiently high-competence providers were available _and_ sufficiently busy postings existed.

In a benchmarking exercise ( **Supplemental Appendix Figure 6** ), we contextualize the size of the potential quality improvements from reallocation by using the SDI data to simulate instead the impact of training existing nurses and para-professionals in each country to the same level of medical knowledge as doctors in the same country. Specifically, we replaced the competence of those providers with a random draw from the competence distribution of doctors, again without changing any caseloads or postings, and then calculated the potential improvement in quality of care.

In countries where reallocation offered substantial improvements, we estimated that “upskilling” would typically be about half as effective as reallocation – either because doctors already made up a substantial share of observed providers, or because doctors were not reliably more competent than other provider cadres.<sup>9</sup> In other countries, reallocation was relatively ineffective because the availability of high-competence providers was very low. In these countries – namely, Madagascar, Mozambique, Niger, Sierra Leone, and Togo – increasing workforce training to the level of local doctors was equally ineffective, because even the most competent providers still had low rates of correct management in the vignettes. In such contexts, where systems of medical education and retention have failed to produce a minimally competent workforce, there appear to be few options within the existing system that would offer viable avenues to quality improvement.

# **5 Discussion: Unequal Burdens, Unequal Outcomes**

Organizations such as the World Health Organization have repeatedly argued that SubSaharan African countries face severe health worker shortages and that, as a consequence, health facilities are overcrowded and providers are routinely overburdened. A new literature on quality and availability suggests that the picture is more complicated. Studies that count the number of actual patients and medical staff at health care facilities show that health care providers, on average, are not overloaded and in fact have substantial excess capacity.<sup>20, 21</sup> Consistent with these summary measures, three recent studies that estimated the causal impact of additional patient load on the quality of medical care all found near-zero relationships between caseload and quality outcomes.<sup>10–12</sup> Our study represents a fundamental reassessment of the human resources situation in SSA. First, across a nationally representative sample of health facilities in 10 countries, we confirm that most health care providers operate under conditions of substantial excess capacity; the median _provider_ likely spends at most one hour and forty minutes each day seeing patients. Using facility-level survey data, we provide a new perspective on this fact. In these data, aggregate excess capacity across the provider population masks the fact that a minority of facilities and providers provide the majority of care in each system. The number of patients reported at busy facilities was such that the median _patient_ experience was indeed consistent with long wait times and queues, especially if most patients arrive at specific times of the day.

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Second, we introduce the concept of allocative efficiencies to the PHC quality improvement literature and use it to quantify losses due to provider misallocation. In our case, the misallocation takes the form of a very low correlation between a provider’s caseload and their competence: More- and less-competent providers were nearly equally likely to be underutilized. In combination with the variation in caseloads we observed, this means it was often true that large patient populations were handled by low-competence providers while facilities with high-competence providers reported few or no patients.

Combining caseloads and competence measures allowed us to determine that half of the countries in this study are experiencing substantial productivity losses from underutilization among high-competence providers, and could improve the quality of care by reallocating better providers to busier facilities (or making other similar allocative improvements). Such improvements were generally comparable to or greater than the impact of training additional providers to the level of local doctors, suggesting the availability of lower-cost and shorter-term paths to quality improvement.

At the same time, the other half of countries had severe shortages of high-competence providers, and therefore had little to no potential gains from reallocation. These countries also demonstrated substantial excess capacity across their workforce, but typically had fewer high-volume facilities where their few high-competence providers could attend to large numbers of patients. Furthermore, due to the very low overall competence of their workforce, these countries would also be unable to improve the quality of care by producing more providers under business-as-usual medical education, suggesting very serious underlying problems in the development of human capital.

Our findings therefore support a revision in perspective regarding the “human resources crisis in health”, at least at the primary case level. We find that this “crisis” is not consistent with a _general_ shortage of health care providers, as aggregate statistics and facility surveys both indicate that many providers in every context have substantial excess capacity. Instead, there are two different workforce crises at play, whose interaction offers a promising new approach to system-wide quality improvement.

The first crisis arises from substantial variation in utilization. When combined with the temporal clumping of patients, the few facilities where utilization is high can quickly develop long queues. If we take patient demand as fixed, such queues could be better managed by applying stochastic queuing theory as a standard tool in facility personnel management. This would require detailed time and motion studies as well as the development of appropriate administrative tools and protocols, but it does not require a fundamental reassessment of the health system’s human resources.

The harder question is why there is such variation in caseloads to begin with. Our results show that the variation in caseloads is not just a question of rural versus urban clinics, large versus small clinics, or clinics with more and less competent providers. It could be that caseloads reflect historical spatial patterns while circumstances have changed – for instance, clinics may represent relatively fixed investments while changes in population distributions or improvements in transportation networks have occurred rapidly. We do not have the scope here to pursue this further, although we note that contemporary research using data such as the geolocations of clinics, population density estimates, and

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transport networks offers a promising line of inquiry.<sup>22</sup> Further research could use these and other inputs to understand the drivers of variation in caseloads. As an alternative, health system policy could move towards allocating more resources to locations where caseloads are already higher, rather than applying rules that do not account for spatial density (such as “one health facility for every 3,000 population”).

The second crisis is the misallocation that arises because of the weak correlation between provider competence and caseloads. Put simply, some countries are spending millions of dollars training competent providers, but at the caseload levels we observed, simulations using queuing theory suggest they are often spending as much as 75% of their time not seeing patients. We provide a novel measure of the quality implications of this misallocation, and we find that in some countries the magnitude of these inefficiencies exceeds the potential gains from training nearly every existing PHC provider to the competence level of a local doctor.

Whether the kind of hypothetical reallocations we presented here are actually desirable or feasible is a harder question. To begin with, understanding where to place the most competent providers requires an open debate about health system priorities: Is it preferable to place a highly competent provider in a slum, where they will see many more poor people, or in a sparsely populated rural region where there may not be any other options? To add to this challenge, as we have seen in previous work, the competence of PHC providers is not well-predicted by their qualifications, so even among doctors and nurses there is wide variation in proficiency. Administrators cannot make efficient allocation decisions unless there is a concerted effort to measure and document medical competence at the individual level using direct assessments that go beyond formal qualifications.

In summary, we provide evidence that the current consensus of a _general_ human resources crisis in health is misleading. Under conditions of general scarcity, policy options are limited to increasing capacity. Our data suggest that general scarcity is almost never a binding constraint at the system level. Instead, we argue that the problem is a more nuanced one of variation in caseloads and misallocation of competent providers. There is excess capacity in every health system already, even among high-competence providers: We find that human resources are simply not in the same places as patients are.

In part because human capital investments are already underutilized, we find that increasing capacity through upskilling is generally a _less_ effective approach in contexts where improved allocation could produce quality improvements. Finally, we show that in very low-performing countries, there are much more serious underlying issues. These countries seem to have extremely limited capacity to consistently train competent medical providers, so neither allocative improvements nor increased training levels would produce meaningful quality improvements with the status quo institutions. Our hope is that the results presented here will open up the conversation and allow us to more precisely benchmark and suggest policy options for countries that are more tailored to their specific needs.

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# **6 Limitations and Robustness**

That data that we have used here has several limitations.

First, there may be measurement error in utilization data due to poor recall or inaccurate records. To benchmark this possibility, the SDI survey data from Kenya was compared to the Kenya DHIS2 survey for the same period ( **Supplemental Appendix Figure 1** ) and found that caseloads were correlated in both reports, with a correlation of 0.79.<sup>8</sup> In addition, time and motion studies have documented similar caseloads in India and Viet Nam; and other studies that measured caseloads report similar averages to ours.<sup>20, 21, 23, 24</sup> Second, SDI data collected caseloads at the level of the facility, not the provider. This is less of a problem in small facilities, which are the majority, but a substantive portion of our results reflect the experience of patients at large, crowded facilities with multiple providers. In these cases, since no data exists on the actual work patterns of individual providers, we split caseloads equally among all providers present, assuming that the present providers were representative of staffing at the facility on a random day. Because the SDI randomly observed in-person providers at the place of practice, our analysis is robust to flexible daily staffing patterns _on average_ , but cannot fully account for changes such as providers who work part-time, providers who move between multiple facilities, and staffing patterns that are responsive to diurnal variation in demand. Ideally, future research would observe detailed work patterns for all clinic staff including precise patient assignments to each provider over a series of work days. In large facilities where we had to estimate the number of providers actually present, there are additional complications; we present a full description of this process and results robust to the exclusion of these facilities in **Supplemental Appendix Tables 2–4** .

Third, in terms of the implications for quality, multiple studies have found a large gap between medical competence and clinical practice.<sup>21, 25–29</sup> Any gains that we compute here (either from reallocation or upskilling) are based on provider competence rather than clinical practice. The data used here are also based on a sample of the health facilities and providers in each country. While previous studies have documented similar variation in caseloads and quality even at the level of the village,<sup>30, 31</sup> we would require a substantial increase in the scale of data collection to accurately compute the gains to reallocation within local areas. These data limitations arguably imply that our results are a lower bound of the gains that could actually ensue from allocative strategies. Larger samples and data on clinical quality (as opposed to knowledge) may further increase the gains from such approaches if the variation in clinical quality is larger than that in knowledge, and larger samples could increase this variation even further.

In contrast, measurement error, either in caseloads or in competence, will overstate the gains from better allocative efficiency. The SDI surveys do not take repeat measurements, which are required to establish the reliability of these measures. For caseloads, the measures track well with other official statistics, but for facilities like central hospitals, where caseloads are very high, small errors can lead to large changes as the reassignment of just a few providers can lead to disproportionately large gains. In both these

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cases, further validation either using physical patient counts or repeat measurements can be used to improve accuracy in the estimated variation in the underlying distributions.

# **7 Conclusion**

The World Health Organization argues that 90% of LMICs suffer from critical health worker shortages.<sup>32, 33</sup> Using insights from country-level estimates for the WHO African Region combined with nationally representative facility-level survey data in 10 countries, we submit that the consensus of a human resource crisis in low-income countries needs a fundamental reassessment. The problem appears not to be so much a general “shortage” of providers as it is a complex issue of unequal caseloads across unequal providers, which results in under-utilization of high-competence providers while low-competence providers manage large caseloads.

Combining novel data on these two dynamics, we estimate that, in half of the countries in this study, the productivity losses due to misallocation are quite large. We emphasize that these calculations are intended as a sufficient statistic to estimate productivity losses in primary health care quality due to systematic excess capacity among highcompetence providers, not as a policy prescription. A policy of provider reallocation is potentially realistic only to the extent that (public) health systems can post or transfer individual providers to different facilities. In practice, our results suggest that a wide variety of allocative strategies, such as triage and patient-transfer, may be worth further investigation at the local and subnational levels, in addition to centralized provider reassignment.

In the other half of the countries, the potential gains from reallocations are much lower. The inconsistent quality of LMIC medical education appears to be a significant constraint as there are so few high-competence providers that reallocating them does very little. In these countries, there is no easy way out of the quality crisis. Their problem is still not a general shortage of health care providers – as in other systems, there is plenty of excess capacity among the workforce. Instead, the inability of these countries to train and retain highly competent providers leads us to believe that a total overhaul of their health care systems is required.

# **Ethical Approvals**

This study used only de-identified secondary data provided by the SDI program and therefore was not classified as human subjects research. Each of the SDI surveys was carried out by the SDI program in collaboration with the Ministry of Health in each country. All survey participants consented to participate in the survey. Details regarding data collection ethical approvals are available in each country survey report. The authors therefore declare that no further ethical approval was necessary to conduct this study.

# **Disclaimer**

The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the World Bank and its

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affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

# **Conflicts of Interest**

All authors declare they have no conflicts of interest.

# **Role of the Funding Sources**

This research did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.

# **Data and Code Availability Statement**

Constructed data and analytical code for this study is available at `https://doi.org/ 10.5281/zenodo.11130652` .

# **CRediT Author Statement**

**Daniels** – Conceptualization, Methodology, Software, Formal Analysis, Writing – Original Draft, Visualization. **Das** – Conceptualization, Methodology, Resources, Investigation, Writing – Original Draft, Supervision. **Gatti** – Data Curation, Investigation, Writing – Review and Editing, Project Administration. **Yi Chang** – Data Curation, Investigation, Writing – Review and Editing, Project Administration.

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