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The design of complex health systems interventions, such as pay for performance (P4P), can be critical to determining such programmes' success. In P4P programmes, the design of financial incentives is crucial in shaping how these programmes work. However, the design of such schemes is usually homogenous across providers within a given scheme. Consequently, there is a limited understanding of the strengths and weaknesses of P4P design elements from the implementers' perspective. This study takes advantage of the unique context of Brazil, where municipalities adapted the federal incentive design, resulting in variations in incentive design across municipalities. The study aims to understand why municipalities in Brazil chose certain P4P design features, the associated challenges and the local adaptations made to address problems in scheme design. This study was a multiple case study design relying on qualitative data from 20 municipalities from two states in Northeastern Brazil. We conducted two key informant interviews with municipal-level stakeholders and focus group discussions with primary care providers. We also reviewed municipal Primary Care Access and Quality laws in each municipality. We found substantial variation in the design choices made by municipalities regarding 'who was incentivized', the 'payment size' and 'frequency'. Design choices affected relationships within municipalities and within teams. Challenges were chiefly associated with fairness relating to 'who received the incentive', 'what is incentivized' and the 'incentive size'. Adaptations were made to improve fairness, mostly in response to pressure from the healthcare workers. The significant variation in design choices across municipalities and providers' response to them highlights the importance of considering local context in the design and implementation of P4P schemes and ensuring flexibility to accommodate local preferences and emerging needs. Attention is needed to ensure that the choice of 'who is incentivized' and the 'size of incentives' are inclusive and fair and the allocation and 'use of funds' are transparent. [ABSTRACT FROM AUTHOR]

Effective policies for adaptation to climate change require understanding how impacts are related to exposures and vulnerability, the dimensions of the climate system that will change most and where human impacts will be most draconian, and the institutions best suited to respond. Here, we propose a simple method for more credibly pairing empirical statistical damage estimates derived from recent weather and outcome observations with projected future climate changes and proposed responses. We first analyze agricultural production and loan repayment data from Brazil to understand vulnerability to historical variation in the more predictable components of temperature and rainfall (trend and seasonality) as well as to shocks (both local and over larger spatial scales). This decomposed weather variation over the past two decades explains over 50% of the yield variation in major Brazilian crops and, critically, can be constructed in the same way for future climate projections. Combining our estimates with bias-corrected downscaled climate simulations for Brazil, we find increased variation in yields and revenues (including more bad years and worse outcomes) and higher agricultural loan default at midcentury. Results in this context point to two particularly acute dimensions of vulnerability: Intensified seasonality and local idiosyncratic shocks both contribute to worsening outcomes, along with a reduced capacity for spatially correlated ("covariate") shocks to ameliorate these effects through prices. These findings suggest that resilience strategies should focus on institutions such as water storage, financial services, and reinsurance. [ABSTRACT FROM AUTHOR]