fails to diagnose a disease correctly, the data do not allow further investigation on why providers are unable to correctly diagnose the disease or propose a correct treatment option. To address this, the future SDIs will record the incorrect diagnosis or treatment proposed by the respondent. Furthermore, to better adapt to the evolving burden of disease, future SDI surveys will expand to include vignettes of other non-communicable diseases and chronic conditions including hypertension and depression. At the same time, they will expand beyond clinical interaction to include an evaluation of interpersonal aspects of care delivery, and patient education and counseling. Another key aspect of the SDI survey is the measurement of provider absence. A careful review of the data across nine surveys has revealed a need for redesigning the questions informing this indicator. For example, in our data, a large fraction of absent workers are classified as “other excused” which does not provide adequate information on the nature of the absence. In response, it will be important for future SDI surveys to provide better clarity and granularity for correctly classifying provider absence from the facility during their expected hours of clinical duty.

There are some limitations of the SDI survey’s approach to measuring key inputs to care, including infrastructure and availability of medicine and supplies. As the analysis here has indicated, the data do not allow an investigation of the quantity or stock of medicine, equipment or other medical supplies. Likewise, there is limited information on service readiness as it pertains to the growing burden of non-communicable diseases globally and recently on important aspects of pandemic preparedness, such as infection control or surge capacity. The SDI is not intended for rapid measurement of facility readiness for pandemics but it does provide some insight on hospitalization capacity, WaSH measures and effective communication and coordination, summarized in a previous paper.<sup>47</sup> These measures of infrastructure do not adequately capture the basic requirements for access for differently abled individuals, or gender dimensions of accessing basic services and amenities at health facilities.<sup>48</sup> A revised SDI instrument for measuring these foundations for delivering quality care can include more and better questions that allow an investigation of readiness, considering both sustainability and equity goals.

Finally, it is worth noting that facility performance is also influenced to a large extent by factors outside the facility, including the economic, epidemiological and demographic characteristics of the catchment population and community, public sector governance, and other demand-side elements, including patient preferences and utilization patterns. These, along with the expected differences in survey implementation across different settings, can limit spatial and temporal comparability of the survey results. Some of these differences can be mitigated analytically, for example by utilizing survey weights, where available, to correct for differences in samples across countries. However, certain differences, for example the inclusion of more community health workers in Nigeria compared to the rest of the sample, cannot be corrected analytically. Similarly, as mentioned previously, the SDI in Nigeria is not representative (as it includes only 12 of the country’s 36 states) and inferences drawn from this limited sample should be interpreted with caution. Overall, the differences between countries should be treated carefully, including as a reflection of differences in survey design and implementation.

Collectively, a careful review of the methodological choices in conducting SDI surveys across the nine countries indicates the strength of this large-scale survey in measuring health system performance and identifying priorities for future investments in building a resilient health system that better responds to the population’s needs. The limitations documented here are also helpful in ensuring that the next phase of health measurement is more responsive to global guidance on measuring health system quality and in producing actionable evidence to ensure quality and efficiency in health service delivery. COVID-19 has catalyzed a long overdue health system redesign and strengthening effort. The analysis in this paper documented substantial shortfalls in the primary health care system capacity and performance in the pre-COVID period, which have likely been compounded by the current pandemic. The evidence presented here can help to guide and optimize ongoing and future investments in improving service

> 47 Sharma et al. (2020).

> 48 WHO & UNICEF (2018).

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