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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