Understanding Seasonal Risks of Poor Health in Cross-Sectional Data Sources

Alice Wolfle , University of Southampton
Andrew Amos R. Channon, Univeristy of Southampton
Jim Wright, University of Southampton
Marije Schaafsma, Vrije Universiteit Amsterdam

Seasonal variation is observed in climatic conditions, infection risk, incomes and employment, food production and prices and health outcomes. This paper examines the potential of cross-sectional data to study seasonality by pooling multiple surveys, using Bangladesh as a case study. This study will introduce an innovative Seasonal Risk Index to capture seasonal variation in human behaviour and socio-economic activities that contribute to periods of poor health during the year. A theory-driven index, using z-scores to capture the relative risk of each risk factor was generated, to test the hypothesis that risk factors were worst during the monsoon season. The index captured seasonal risk factors from three pathways: disease environment, food and economic factors. The study found that the seasonal risk of poor health was highest between March and May (before the main monsoon season, when temperature and humidity are highest) and elevated between September and December (after the main monsoon season and when food insecurity is often highest). The Seasonal Risk Index (SRI) appeared to be broadly associated with seasonal health outcomes, particularly for children under-5 with an acute respiratory infection or diarrhoea. Understanding when the seasonal risk of poor health is highest will help facilitate the timely implementation of interventions to reduce variation in poor health outcomes throughout the year. Seasonal risk also appears to be similar in both urban and rural areas, highlighting that urban seasonality should not be overlooked.

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 Presented in Session P2. Health, Mortality, Ageing - Aperitivo