Does pay-for-performance design matter? Evidence from Brazil.

Pay-for-performance (P4P) schemes have been shown to have mixed effects on health care outcomes. A challenge in interpreting this evidence is that P4P is often considered a homogenous intervention, when in practice schemes vary widely in their design. Our study contributes to this literature by providing a detailed depiction of incentive design across municipalities within a national P4P scheme in Brazil [Primary Care Access and Quality (PMAQ)] and exploring the association of alternative design typologies with the performance of primary health care providers. We carried out a nation-wide survey of municipal health managers to characterize the scheme design, based on the size of the bonus, the providers incentivized and the frequency of payment. Using OLS regressions and controlling for municipality characteristics, we examined whether each design feature was associated with better family health team (FHT) performance. To capture potential interactions between design features, we used cluster analysis to group municipalities into five design typologies and then examined associations with quality of care. A majority of the municipalities included in our study used some of the PMAQ funds to provide bonuses to FHT workers, while the remaining municipalities spent the funds in the traditional way using input-based budgets. Frequent bonus payments (monthly) and higher size bonus allocations (share of 20-80%) were strongly associated with better team performance, while who within a team was eligible to receive bonuses did not in isolation appear to influence performance. The cluster analysis showed what combinations of design features were associated with better performance. The PMAQ score in the 'large bonus/many workers/high-frequency' cluster was 8.44 points higher than the 'no bonus' cluster, equivalent to a difference of 21.7% in the mean PMAQ score. Evidence from our study shows how design features can potentially influence health provider performance, informing the design of more effective P4P schemes.