Evaluating the health effect of a Social Housing programme, Minha Casa Minha Vida, using the 100 million Brazilian Cohort: a natural experiment study protocol

Abstract

IntroductionSocial housing programmes have been shown to influence health, but their effects on cardiovascular mortality and incidence of infectious diseases, such as leprosy and tuberculosis, are unknown. We will use individual administrative data to evaluate the effect of the Brazilian housing programme Minha Casa Minha Vida (MCMV) on cardiovascular disease (CVD) mortality and incidence of leprosy and tuberculosis.Methods and analysisWe will link the baseline of the 100 Million Brazilian Cohort (2001–2015), which includes information on socioeconomic and demographic variables, to the MCMV (2009–2015), CVD mortality (2007–2015), leprosy (2007–2015) and tuberculosis (2007–2015) registries. We will define our exposed population as individuals who signed the contract to receive a house from MCMV, and our non-exposed group will be comparable individuals within the cohort who have not signed a contract for a house at that time. We will estimate the effect of MCMV on health outcomes using different propensity score approaches to control for observed confounders. Follow-up time of individuals will begin at the date of exposure ascertainment and will end at the time a specific outcome occurs, date of death or end of follow-up (31 December 2015). In addition, we will conduct stratified analyses by the follow-up time, age group, race/ethnicity, gender and socioeconomic position.Ethics and disseminationThe study was approved by the ethic committees from Instituto Gonçalo Muniz-Oswaldo Cruz Foundation and University of Glasgow Medical, Veterinary and Life Sciences College. Data analysis will be carried out using an anonymised dataset, accessed by researchers in a secure computational environment according to the Centre for Integration of Data and Health Knowledge procedures. Study findings will be published in high quality peer-reviewed research journals and will also be disseminated to policy makers through stakeholder events and policy briefs.

The use of machine learning in paediatric nutrition

“…Delving deeper in the field of diet planning, a Korean group used generative adversarial networks and reinforcement learning to generate novel diet plans for children attending daycare facilities [27]. In Brazil, a large study is being planned to analyse the growth of millions of children to identify growth patterns and use machine learning to predict obesity [28 ▪ ]. A Saudi group has focused on guiding the vital dietary management of maple syrup urine disease [29,30 ▪ ].…”

Cohort Profile: The 100 Million Brazilian Cohort

“… 33 Several other studies are in progress involving the social determinants or impact of social protection on child and maternal health, suicide, homicides, leprosy, tuberculosis, HIV, cancer and cardiovascular diseases. 34 , 35 The ongoing linkage of hospitalization data will enhance the previous analyses and open possibilities for multi-morbidity studies. In addition, the coverage of long periods comprising before and after major economic changes and crisis in the past decade, allows study of the impact of those changes on health and on the economic fragility (e.g.…”