Assessing the performance of beneficiary targeting in Brazil's More Doctors Programme.

Abstract

Many countries employ strategies that rest on the use of an explicitly defined set of criteria to identify underserved communities. Yet, we know relatively little about the performance of community-level targeting in large-scale health programmes. To address this gap, we examine the performance of community targeting in the More Doctors Programme (MDP). Our analysis covers all 5570 municipalities in the period between 2013 and 2017 using publicly available data. We first calculate the rate at which vulnerable municipalities enrolled in the MDP. Next, we consider two types of mistargeting: (1) proportion of vulnerable municipalities that did not have any MDP physicians (i.e. under-coverage municipalities) and (2) proportion of MDP enrolees that did not fit the vulnerability criteria (i.e. non-target municipalities). We found that almost 70% of vulnerable municipalities received at least one MDP physician between 2013 and 2017; whereas non-target municipalities constituted 33% of beneficiaries. Targeting performance improved over time. Non-target municipalities had the highest levels of socioeconomic development and greater physician availability. The poverty rate among under-coverage municipalities was almost six times that in non-target municipalities. Under-coverage municipalities had the lowest primary care physician availability. They were also smaller and more sparsely populated. We also found small differences in the political party alignments of mayors and the President between under-coverage and non-target municipalities. Our results suggest that using community-level targeting approaches in large-scale health programmes is a complex process. Programmes using these approaches may face substantial challenges in beneficiary targeting. Our results highlight that policymakers who consider using these approaches should carefully study various municipal characteristics that may influence the implementation process, including the level of socioeconomic development, health supply factors, population characteristics and political party alignments.