Assessing local health outcomes using spatially-resolved health surveillance data

Complete and accurate health information systems are necessary inputs for effective health policy. Although many countries maintain civil registration and infectious disease surveillance systems, variation in data completeness often impedes analysis of aggregated health records at the spatial level where policymaking occurs, discouraging greater investment in these systems. This thesis aims to integrate health information systems into local health decision-making using spatial modelling approaches. In four case studies, I introduce a class of spatial statistical models based on incomplete vital and health surveillance records that offer insights into the health of a country that would be impossible to derive from other sources. I demonstrate how civil registration and vital statistics data (CRVS) can be synthesised with supplementary spatial data sources to estimate both neonatal mortality and CRVS completeness by municipality across Mexico. This spatial modelling strategy can be applied to a wide array of health outcomes, including infectious diseases. I demonstrate how an analogous model can combine data from a tuberculosis (TB) prevalence survey and TB case notifications to estimate TB prevalence across Uganda. Complete registration of births and deaths ensures that all citizens receive the same legal and health protections. I take a holistic approach to analyse India's three health surveillance systems in relation to the Indian National Health Plan's child survival goals. The COVID-19 pandemic has highlighted gaps in health surveillance capacity worldwide, including in high-income countries. In Italy, I develop a small-area excess mortality model to estimate the number of misdiagnosed COVID-19 deaths during the first six months of the pandemic. This analysis reveals important information about the mortality dynamics of the pandemic across sub-populations of Italy. The results, limitations, and conclusions of these case studies are discussed with recommendations for how these findings influence our understanding of health information systems and implications for greater integration between health surveillance data and policy.