Conclusions

examined correlates explain less than 15 percent of the effort gap (table 4A.4, in annex 4A). The limited explanatory power of these rich sets of covariates thus highlights the difficulty in understanding the different drivers of idle capacity in LMIC contexts. This subsection has shown a broad-based presence of idle capacity in each of these contexts, including for actions that do not require physical equipment or supplies and for which health worker knowledge is high. However, what is driving those gaps is largely unknown.

This chapter built on the existing literature to provide an assessment of quality of care that decomposes constraints to quality into inadequate structural quality, that is, insufficient supplies or equipment; poor health worker knowledge; and underprovision of effort (health workers simply not doing the clinically necessary actions for which they have all needed supplies, equipment, and knowledge). The analysis focused on ANC as a key driver of the global burden of disease. Using rich data on ANC consultations, the chapter showed that poor quality, as benchmarked by the WHO protocol for ANC, is widespread. Across five Sub-Saharan African countries—Cameroon, the Central African Republic, the Democratic Republic of Congo, Nigeria, and the Republic of Congo—which are among the world’s leading contributors to maternal and neonatal mortality, health workers only perform about 50 to 60 percent of the WHO essential protocol for ANC.

The results show that an important share of quality deficits can be explained by a lack of effort/provider behavior, as illustrated using a decomposition of detailed data linking health facility infrastructure to health worker knowledge, and actual provision of care in patient-provider interactions. The decomposition shows that despite decades of infrastructure investments, structural capacity constraints continue to bind in most primary health care settings in these countries. Shortfalls in the availability of basic medical equipment and supplies are widespread, even for a widely provided service like ANC in facilities that are supposed to provide this service. Indeed, in every country examined, structural capacity constraints bind for at least some of the components of a complete ANC visit. Similarly, among health workers who are supposed to provide basic ANC, knowledge of basic ANC protocol is far from complete in all the countries examined.

However, the decomposition also shows that a third of all observed misadherence to international protocol is explained not by structural or

knowledge gaps but by idle capacity— when health workers have all the necessary structural capacity and knowledge but still fail to perform the necessary actions. Such idle capacity exists in all five countries studied in this chapter and for each component of the WHO essential protocol for ANC. Indeed, sizable know-do gaps exist even in actions like risk screening—which entails asking the patient about complications in prior pregnancies and does not require any supplies or equipment.

The WHO guidelines on the minimum number and required timing of ANC visits assume a certain minimum content of care—in other words, the implicit assumption is that a visit is inherently useful. ANC visits represent a cost to the household’s time—and there are, of course, out-ofpocket costs to households to avail themselves of such care. This reliance on service utilization is despite the evidence reviewed in the chapter suggesting a tenuous link between simple coverage and health outcomes. At the same time, ANC visits may be adopted as conditions for receiving cash transfers: households receive money from the government if they expend the time. However, as the chapter has shown, shortages of supplies or inadequate equipment are often not the only binding constraint; in many cases, effort is the lowest common denominator. This begs the question of whether policies should encourage ANC visits without ensuring that the quality of care delivered is sufficiently high for its benefits to offset the cost the visits represent to the households.

The results also provide evidence of significant variation across and within countries. The within-country variation stems from differences in the quality of care provided between facilities, but also within facilities. Idle capacity is not only pervasive across actions, it is also significant in terms of size. In the five countries studied in this chapter, even if all the structural and knowledge gaps were closed, fully a third of the shortfall in adherence to the WHO essential protocol for ANC would still remain as is. Further, the estimated know-do gaps presented in this chapter are likely underestimates of the actual know-do gaps because of the Hawthorne effect in direct clinical observations (Leonard and Masatu 2006).

In addition, the chapter showed that deviations from protocol can include overprescription. This topic is covered in depth in chapter 7, but the finding of overprescription in the context of ANC is striking because the measurement of preventive care is not geared toward picking up overprescription. Notably, the finding of overprescription includes actions that may be harmful to fetal development. Most of the evidence on overuse focuses on curative care and not preventive care. This finding thus motivates the concern surrounding overuse and a more careful assessment of it,

particularly in relationship to financial incentives aimed at improving quality of care, a topic to which this report will return later.

This chapter thus makes several contributions to the literature on the determinants of quality of care in primary health care settings in LMICs. First, it established that poor quality of care is pervasive in these settings. Second, it decomposed the observed level of care to show that it is driven by a combination of poor structural quality and poor health worker knowledge, but also by a lack of effort by health workers. Thus, the chapter suggested that physical and knowledge constraints are a limiting factor in many LMICs even for basic health service provision. However, the chapter also highlighted that policies must address the know-do gap. Previous literature has highlighted the important role played by financial incentives in determining provider effort (Das et al. 2016), and other studies have shown that extrinsic and intrinsic motivation are important drivers of health worker performance (Leonard and Masatu 2006, 2010; Leonard, Masatu, and Vialou 2007). Such evidence may underline the potential for tying performance to payments. Of course, interventions that bolster skills, such as hands-on training programs (Rowe et al. 2018), may be effective in improving performance and even idle capacity. This may be the case if skills and knowledge of protocol are not the same—for instance, health workers may know how to counsel a woman on proper nutrition in theory, but if they lack experience in actually counseling a patient, they may be unwilling to attempt it, particularly in front of a third-party enumerator. Thus, interventions that seek to increase effective coverage by improving the quality of care may need to leverage multiple entry points into the health system.

Finally, particularly rich data on potential explanatory factors, on facilities, health workers, and patients, allowed the chapter to explore what may explain such idle capacity. However, the decomposition of the differences in the overall performance and know-do gaps by facility and patient characteristics, with one exception, found little meaningful covariation in the measures with any set of correlates in the data. The “usual suspects,” like health worker training, grade, or gender, do not explain the idle capacity.

The one exception is that there may be some patient-driven differentials in quality of care: concentration curves show that wealthier women receive better care in some of these settings. Using an example from the Democratic Republic of Congo, the chapter illustrated that wealthier women appear to receive better quality of care than poorer women, even within the same facilities. This is not necessarily indicative of discrimination as the results could be explained by differences in education, ability