Frederico C Guanais1. 1. División de Protección Social y Salud, Banco Interamericano de Desarrollo, Washington, D.C, Estados Unidos de América, fredericog@iadb.org.
Abstract
OBJECTIVES: I examined the combined effects of access to primary care through the Family Health Program (FHP) and conditional cash transfers from the Bolsa Familia Program (BFP) on postneonatal infant mortality (PNIM) in Brazil. METHODS: I employed longitudinal ecological analysis using panel data from 4 583 Brazilian municipalities from 1998 to 2010, totaling 54 253 observations. I estimated fixed-effects ordinary least squares regressions models with PNIM rate as the dependent variable and FHP, BFP, and their interactions as the main independent variables of interest. RESULTS: The association of higher FHP coverage with lower PNIM became stronger as BFP coverage increased. At the means of all other variables, when BFP coverage was 25%, predicted PNIM was 5.24 (95% confidence interval [CI] = 4.95, 5.53) for FHP coverage = 0% and 3.54 (95% CI = 2.77, 4.31) for FHP coverage = 100%. When BFP coverage was 60%, predicted PNIM was 4.65 (95% CI = 4.36, 4.94) when FHP coverage = 0% and 1.38 (95% CI = 0.88, 1.89) when FHP coverage = 100%. CONCLUSIONS: The effect of the FHP depends on the expansion of the BFP. For impoverished, underserved populations, combining supply- and demand-side interventions may be necessary to improve health outcomes.
OBJECTIVES: I examined the combined effects of access to primary care through the Family Health Program (FHP) and conditional cash transfers from the Bolsa Familia Program (BFP) on postneonatal infant mortality (PNIM) in Brazil. METHODS: I employed longitudinal ecological analysis using panel data from 4 583 Brazilian municipalities from 1998 to 2010, totaling 54 253 observations. I estimated fixed-effects ordinary least squares regressions models with PNIM rate as the dependent variable and FHP, BFP, and their interactions as the main independent variables of interest. RESULTS: The association of higher FHP coverage with lower PNIM became stronger as BFP coverage increased. At the means of all other variables, when BFP coverage was 25%, predicted PNIM was 5.24 (95% confidence interval [CI] = 4.95, 5.53) for FHP coverage = 0% and 3.54 (95% CI = 2.77, 4.31) for FHP coverage = 100%. When BFP coverage was 60%, predicted PNIM was 4.65 (95% CI = 4.36, 4.94) when FHP coverage = 0% and 1.38 (95% CI = 0.88, 1.89) when FHP coverage = 100%. CONCLUSIONS: The effect of the FHP depends on the expansion of the BFP. For impoverished, underserved populations, combining supply- and demand-side interventions may be necessary to improve health outcomes.