the baby’s breathing, drying the baby, keeping the baby warm and initiating resuscitation with a bag/mask) and five actions for post-partum hemorrhage (determining the cause, providing bimanual uterine massage, placing a foley catheter, running an IV and providing oxytocin or similar drugs). Providers are scored on the number of actions that they propose. Further information on the clinical vignettes is available in **Appendix Table A2** and **Appendix Table A3.** Finally, the SDI survey also collects information on inappropriate antibiotic use, defined as providers that prescribe an antibiotic during the tuberculosis vignettes (aside from the antibiotics recommended as part of the tuberculosis regimen) or any antibiotics for the diarrhea vignettes (for which antibiotics are not indicated given the patient examination). Inappropriate antibiotic usage is calculated as the percentage of health care providers that inappropriately prescribe antibiotics among all health care providers to whom the clinical vignettes are administered.

# 3.3 Methods

Summary statistics and estimates are reported using facility-level weights, which are created based on the inverse probability of being sampled. Facility-level sample weights are unavailable for Mozambique so the unweighted results are reported. Provider-level weights are also incorporated for health care provider absence and all vignette-based variables. More details on the sampling and weights are provided in **Appendix Table A4** . The sample includes a mix of facilities in terms of location (urban/rural), ownership (public and private) and facility levels (hospitals, health clinics and health posts). In all figures, countries are ordered by increasing GDP per capita to demonstrate how performance on health service indicators may correlate with country income. The estimates of GDP per capita (based on purchasing power parity in current international dollars) are from World Bank Open Data and the GDP value corresponds to the year of the survey in each country.<sup>21</sup> While much can be gleaned already from cross tabulations, multivariate regressions are used to describe the relationship between each variable and the characteristics of the facilities or health care providers, controlling for potential confounders. For example, private facilities are more frequently located in urban areas, whereas public facilities are more frequently located in rural areas. Regression analyses are able to help tease out the location versus ownership effects and show which is most strongly correlated with the dependent variable. For facilitylevel indicators, such as equipment, medicine and infrastructure availability, the independent variables are public/private, rural/urban, facility level (hospital/health clinic/health post) and country-level fixed effects. For provider-level indicators, such as diagnostic accuracy, treatment accuracy and unauthorized absence, the independent variables are the same as above, plus provider cadre, education, age and sex. The regression results are shown in **Appendix Table A5** and **Appendix Table A6** .

To measure performance across all the variables, a composite indicator is created using a principal components analysis (PCA) on six variables: diagnostic accuracy, treatment accuracy, staff presence, equipment availability, medicine availability<sup>22</sup> and infrastructure availability. Since the variables are correlated with one another at the facility level, PCA is useful as a dimension reduction technique, taking into account the correlation between variables and producing a single metric that preserves as much variation as possible. This composite indicator is intended as a proxy for overall facility performance and allows for the identification of high and low performers within and across countries. In turn, identifying these high and low performers allows for exploration of their characteristics, with the aim of distilling generalizable evidence on the correlates of high-performing facilities.

> 21 World Bank (2020b).

> 22 One of the variables, medicine availability (based on 14 common medicines) is unavailable for Kenya, Uganda and Nigeria. For the purposes of creating the PCA index, medicine availability is calculated for these three countries based on their individual listings of medicines.

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