Quantifying demographic and socioeconomic transitions for computational epidemiology: an open-source modeling approach applied to India.

Sanjay Basu,Jeremy D Goldhaber-Fiebert

doi:10.1186/s12963-015-0053-1

Abstract

BackgroundDemographic and socioeconomic changes such as increasing urbanization, migration, and female education shape population health in many low- and middle-income countries. These changes are rarely reflected in computational epidemiological models, which are commonly used to understand population health trends and evaluate policy interventions. Our goal was to create a “backbone” simulation modeling approach to allow computational epidemiologists to explicitly reflect changing demographic and socioeconomic conditions in population health models.MethodsWe developed, evaluated, and “open-sourced” a generalized approach to incorporate longitudinal, commonly available demographic and socioeconomic data into epidemiological simulations, illustrating the feasibility and utility of our approach with data from India. We constructed a series of nested microsimulations of increasing complexity, calibrating each model to longitudinal sociodemographic and vital registration data. We then selected the model that was most consistent with the data (i.e., greater accuracy) while containing the fewest parameters (i.e., greater parsimony). We validated the selected model against additional data sources not used for calibration.ResultsWe found that standard computational epidemiology models that do not incorporate demographic and socioeconomic trends quickly diverged from past mortality and population size estimates, while our approach remained consistent with observed data over decadal time courses. Our approach additionally enabled the examination of complex relations between demographic, socioeconomic and health parameters, such as the relationship between changes in educational attainment or urbanization and changes in fertility, mortality, and migration rates.ConclusionsIncorporating demographic and socioeconomic trends in computational epidemiology is feasible through the “open source” approach, and could critically alter population health projections and model-based evaluations of health policy interventions in unintuitive ways.Electronic supplementary materialThe online version of this article (doi:10.1186/s12963-015-0053-1) contains supplementary material, which is available to authorized users.

Highlights

Demographic and socioeconomic changes such as increasing urbanization, migration, and female education shape population health in many low- and middle-income countries
Model structures We modeled historic and future cohorts of Indian females, focusing on fertility, mortality, educational attainment, and urban/rural migration given the strong evidence linking these indicators to population health in India [2, 20, 21]
Calibration, and validation The Stanford Project for Open Knowledge in Epidemiology (SPOKE)-I model – the model including fertility, mortality, educational attainment, migration, and trends in each of these variables – fit the data listed in Table 1 better than the two simpler models, even after being penalized for increased complexity (Table 2)

Summary

Introduction

Demographic and socioeconomic changes such as increasing urbanization, migration, and female education shape population health in many low- and middle-income countries. These changes are rarely reflected in computational epidemiological models, which are commonly used to understand population health trends and evaluate policy interventions. Demographic and socioeconomic conditions that shape population health are changing rapidly in many low- and middle-income countries. These changes are challenging to incorporate into models, as they affect population health in complex ways. Large developing countries are shifting from being majority rural to mostly urban by 2050, highlighting the pressing importance of understanding the health effects of complex socioeconomic transitions [5]. Educational attainment and literacy levels are increasing [8, 9], and accompany lower fertility, higher female labor force participation and associated complex changes in maternal and child health outcomes [10]

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