Frederico C Guanais1. 1. Inter-American Development Bank, Washington, District of Columbia, United States of America, fredericog@iadb.org.
Abstract
OBJECTIVE: To propose a method for the interpolation of yearly local-level covariates of health status that is suitable for panel data analysis of the effect of health services. METHODS: The proposed method distributes the yearly rate of growth of covariates at the regional level (e.g., state) from household survey data, and applies it to interpolate yearly data at the local level (e.g., municipality) between two consecutive census surveys. The method was applied to municipal-level socioeconomic covariates of health status in Brazil for every year between 2001 and 2009. The data was tested on a previously validated analysis of the effects of the Family Health Program on post-neonatal infant mortality in Brazil. RESULTS: A total of 895 628 values were generated for 20 socioeconomic predictors of health status. Valid data were obtained for 5 057 municipalities in the Northeast, Southeast, South, and Center-West regions of Brazil, from 2001 to 2009, covering 98.89% of the municipalities in these regions and 90.87% of municipalities in the country. A supplemental annex includes the interpolated data from 2001 to 2009, plus the 2000 and 2010 census data, for all 5 057 municipalities. An application on a fixed-effect regression model suggested that, compared to linear interpolation, the proposed method reduced multi-collinearity and improved the precision of the estimates of the effects of health services. CONCLUSIONS: The advantages of the proposed interpolation method suggest that it is a feasible solution for panel data analysis of health services at the local level in Brazil and other countries.
OBJECTIVE: To propose a method for the interpolation of yearly local-level covariates of health status that is suitable for panel data analysis of the effect of health services. METHODS: The proposed method distributes the yearly rate of growth of covariates at the regional level (e.g., state) from household survey data, and applies it to interpolate yearly data at the local level (e.g., municipality) between two consecutive census surveys. The method was applied to municipal-level socioeconomic covariates of health status in Brazil for every year between 2001 and 2009. The data was tested on a previously validated analysis of the effects of the Family Health Program on post-neonatal infant mortality in Brazil. RESULTS: A total of 895 628 values were generated for 20 socioeconomic predictors of health status. Valid data were obtained for 5 057 municipalities in the Northeast, Southeast, South, and Center-West regions of Brazil, from 2001 to 2009, covering 98.89% of the municipalities in these regions and 90.87% of municipalities in the country. A supplemental annex includes the interpolated data from 2001 to 2009, plus the 2000 and 2010 census data, for all 5 057 municipalities. An application on a fixed-effect regression model suggested that, compared to linear interpolation, the proposed method reduced multi-collinearity and improved the precision of the estimates of the effects of health services. CONCLUSIONS: The advantages of the proposed interpolation method suggest that it is a feasible solution for panel data analysis of health services at the local level in Brazil and other countries.
Authors: Philipp Hessel; María José González Jaramillo; Davide Rasella; Ana Clara Duran; Olga L Sarmiento Journal: Health Aff (Millwood) Date: 2020-07 Impact factor: 6.301