In June 2022, members from the Population Health Analytics Lab were excited to return to the Society for Epidemiologic Research (SER) Annual Meeting to share some of the lab’s emerging research. Congratulations to Vin Harish, Lief Pagalan, and Emma Buajitti who presented their work at SER 2022!
Vin Harish: The Development of a Social Vulnerability Index for Infectious Disease Emergencies in Canada
Vulnerability to infectious disease emergencies arises at the intersection of various social determinants of health. Therefore, we developed a custom-made social vulnerability index (SVI) for infectious disease emergencies in Canada. We created a social vulnerability index for 1,620 Forward Sortation Areas across all Canadian provinces using thirteen variables from the 2016 Census across five domains: socioeconomic status, household composition, minority status and language, housing and transportation, and employment.
Our results showed that social vulnerability was highly concentrated in cities and was spatially associated with COVID-19 cases, hospitalizations, and deaths in Ontario between March 2020 and March 2021. Our custom-made social vulnerability index may more fully capture infectious diseases–related vulnerability better than pre-existing scores of marginalization. This social vulnerability index can also support immediate response operations and pandemic preparedness and, for researchers, provide a tool to characterize the impact of emergencies across vulnerable populations.
Lief Pagalan: Autism Spectrum Disorder, Prenatal Greenspace Exposure, and Mediation by Reduced Air Pollution: A Population-Based Birth Cohort Study in Vancouver, Canada
Autism spectrum disorder (ASD) incidence has increased in past decades, and the etiology of ASD remains inconclusive. Research suggests genetic, epigenetic, and environmental contributing factors. But few studies have examined modifiable environmental risk factors for ASD, and far fewer have examined protective exposures. Greenspace has been associated with positive child development, so we measured the impact of prenatal greenspace exposure on reducing ASD risk and how much of that protective effect was due to greenspace reduction of air pollution, a suspected ASD risk factor. This research is one of the first population-based studies to evaluate this association.
Our study showed that prenatal greenspace exposure was associated with a small reduction in the odds of ASD. However, at the population level, there were no significant risk differences in the additive scale nor mediation through air pollution reduction. Our study took place in Vancouver, which has a high amount of greenspace and relatively low air pollution levels, so the effect of greenspace on ASD may have been too small to detect in our study setting. Further research is needed to evaluate the association between greenspace and ASD in different geographical, social, and climate contexts and using greenspace metrics of higher variability and spatial resolution.
Emma Buajitti: Health predictors of neighbourhood selection: a prospective cohort study of residential mobility in Ontario, Canada
Health selection into neighbourhoods occurs when health status influences movement into areas of low- or high-socioeconomic status (SES), and it may bias observed neighbourhood effects on health. We used a representative survey population with longitudinal postal code information from health administrative data to quantify health selection into neighbourhoods among community-dwelling adults in Ontario, Canada.
To quantify health status before neighbourhood mobility, we used objective and subjective measures, which were assessed separately at baseline. Subjective health status was assessed during the interview as self-rated health, while objective health status (number of chronic conditions) was measured using a lookback of medical billings since 1992 and established algorithms for 17 chronic conditions. Our results showed that subjective and objective health measures predict future neighbourhood SES, whereby less healthy individuals are more likely to move into low-SES areas and less likely to move to high-SES areas. Future studies of neighbourhood-level effects on health should consider the potential for bias due to health selection into neighbourhoods.