Background:
Health outcomes and causality are usually assessed with individual level sociodemographic variables. Studies that consider only individual-level variables can suffer from residual confounding. This can result in individual variables that are unrelated to risk behaving as proxies for uncaptured information. There is a scarcity of literature on risk factors for snakebite. In this study, we evaluate the individual-level risk factors of snakebite in Sri Lanka and highlight the impact of spatial confounding on determining the individual-level risk effects.
Methods:
Data was obtained from the National Snakebite Survey of Sri Lanka. This was an Island-wide community-based survey. The survey sampled 165,665 individuals from all 25 districts of the country. We used generalized linear models to identify individual-level factors that contribute to an individual's risk of experiencing a snakebite event. We fitted separate models to assess risk factors with and without considering spatial variation in snakebite incidence in the country.
Results:
Both spatially adjusted and non-adjusted models revealed that middle-aged people, males, field workers and individuals with low level of education have high risk of snakebites. The model without spatial adjustment showed an interaction between ethnicity and income levels. When the model included a spatial adjustment for the overall snakebite incidence, this interaction disappeared and income level appeared as an independent risk factor. Both models showed similar effect sizes for gender and age. HEmployment and education showed lower effect sizes in the spatially adjusted model.
Conclusions:
Both individual-level characteristics and local snakebite incidence are important to determine snakebite risk at a given location. Individual level variables could act as proxies for underling residual spatial variation when environmental information is not considered. This can lead to misinterpretation of risk factors and biased estimates of effect sizes. Both individual-level and environmental variables are important in assessing causality in epidemiological studies.