Two decades of primary health care expansion in Latin America: evaluation and forecasting study for health-related SDGs

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The recent Astana Declaration emphasizes the importance of health systems based on a strong Primary Health Care (PHC), which should be part and coordinate multisectoral actions addressing economic and social determinants of health. PHC should be one of the main hubs of the network of interventions composing the welfare state, synergistically connecting the healthcare system with social and poverty-relief interventions. Several Latin American countries (LAC) have implemented and expanded over the last two decades PHC, healthcare system components, and social assistance interventions with different degrees of intensity and connection. However, recent economic crisis and austerity measures are threatening the consolidation of public healthcare systems and of the welfare state in the majority of LAC. Some evaluations of PHC in a limited number of countries have found positive effects on specific health outcomes, but no study has performed multi-country comparisons or adopted a comprehensive approach for the estimation of the effects -including long-term effects- of PHC programs on a broad range of health outcomes (notifiable diseases, hospitalizations and mortality - overall and for specific causes and age-groups), measuring its synergistic impact with the other healthcare system components and with social assistance, in particular conditional (CCT) and social pensions (SP). Similarly, no research has measured the influence of PHC coverage duration and of contextual factors such as local governance indexes or human development index. While the synergistic impact of PHC with CCT is expected because the health conditionalities should be attended in the PHC units, the interaction effects of SP with PHC are plausible because of the physiological effects of the relief from poverty, in particular in the pathways of prevention and cure. The aim of this project is to systematically address all these issues for a comprehensive evaluation of the effectiveness of PHC on the broadest possible range of morbidity and mortality outcomes in Brazil, Colombia, Ecuador and Mexico (BCEM). The comparative evaluation will be possible because BCEM have undertaken different PHC, healthcare and social assistance implementations during the last two decades. Aggregate and individual-level, longitudinal and cross-sectional, socioeconomic and health data from a wide range of sources in each BCEM will be merged and the most robust quasi-experimental designs will be used to evaluate these comprehensive PHC impacts on such a wide number of outcomes. With the large amount of data and parameters from all the retrospective impact evaluations, and using a microsimulation modelling approach -arguably one of the most accurate forecasting techniques up-to-date - we will forecast the effects of different PHC coverage reductions - due to budget constraints or austerity measures - in conjunction with the above mentioned welfare state components, versus scenarios of coverage stabilization or coverage expansion, evaluating differences in a broad range of health outcomes in BCEM up to 2030. Using all the granularity of data and effect estimates, we will evaluate the most effective PHC, CCT and SP expansion coverage dynamics -on specific subpopulations- in the next years. We will also calibrate and select the best policy scenarios for the reduction of health inequalities and the achievement of each health-related SDG in each BCME. This project will also consolidate a network of researchers in LAC and will develop algorithms and methods to integrate ex-post and ex-ante impact evaluations of public policies using ecologic and individual-level data. All the datasets created in the study will be made available in an open dynamic platform of comprehensive LAC data from healthcare systems, social interventions and health outcome, and the research network will use the platform and the developed algorithms to stimulate continuous cycles of monitoring, evaluation and forecasting.