Length of Maternal Schooling and Child's Risk of Malaria Infection in Uganda: Evidence from a Natural Experiment

Abstract

Background: An estimated 216 million cases of malaria occurred worldwide every year. Cross sectional studies have reported negative association between maternal education and child malaria risks, but no randomized trial has confirmed a causal relationship between these two factors. I utilized the free primary education reform in Uganda to assess the causal effects of maternal schooling on the child's risk of malaria infection. Methods: Malaria biomarkers of children aged < five years were collected from the 2009 and 2014 Uganda Malaria Indicator Surveys (N = 5,316). In 1997, the government eliminated tuition requirement in primary schools and this increased the educational attainment of the affected cohorts. Using exposure to the reform as an instrumental variable, I used a two-stage least squares approach to estimate the causal effects of maternal year of education on the probability that a child would contract malaria at the time of the survey. I also evaluated the cost-effectiveness of primary schooling as a malaria control intervention. Findings: One extra year of maternal education reduced child's risk of malaria infection by 7·5 percentage points (p=0·057), from a baseline 34·6% among the children of 1980 pre-reform cohort. The length of maternal education was also positively associated with insecticide-treated bednets usage. The results were robust to a variety of sensitivity tests. Primary schooling for women was a cost-effective intervention to reduce child's malaria infection. Interpretation: Improving access to primary education could be a cost-effective measure to reduce malaria prevalence among children aged < five years in malaria-endemic countries. Funding: None. Declaration of Interest: None exist. Ethical Approval: The present study was reviewed by the Hitotsubashi University Institutional Review Board and considered exempt from full ethics review because the analysis was based on a publicly available, anonymized dataset.