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Living continuously in a high-poverty neighborhood is strongly linked to poor health at age 40.
PubMedNeighborhood Poverty and Physical Health at Midlife: The Role of Life-Course Exposure - PubMedStudies of the effect of neighborhood poverty on health are dominated by research designs that measure neighborhood poverty at a single point in time, ignoring the potential influence of exposure to neighborhood poverty over the life course. Applying latent class analysis to restricted residential h …
Social determinants of #health are real. #Poverty, low #SES, and other marginalisation characteristics of neighbourhoods significantly increase risks of disease, poor health outcomes, and quality of life in high- and low-income countries. And, one doesn't have to wait until adulthood to see outcomes.
We found higher likelihood of student #COVID infections in #Schools in deprived areas in #Ontario.
Our paper in BMJ Open: https://bmjopen.bmj.com/content/13/3/e065596.full
BMJ OpenEffects of school-level and area-level socio-economic factors on elementary school student COVID-19 infections: a population-based observational studyObjectives To estimate the variability of the cumulative incidence of SARS-CoV-2 infections among elementary school students attributable to individual schools and/or their geographic areas, and to ascertain whether socio-economic characteristics of school populations and/or geographic areas may be predictive of this variability.
Design Population-based observational study of SARS-CoV-2 infections among elementary school children.
Setting 3994 publicly funded elementary schools in 491 forward sortation areas (designated geographic unit based on first three characters of Canadian postal code), Ontario, Canada, September 2020 to April 2021.
Participants All students attending publicly funded elementary schools with a positive molecular test for SARS-CoV-2 reported by the Ontario Ministry of Education.
Main outcome measures Cumulative incidence of laboratory-confirmed elementary school student SARS-CoV-2 infections in Ontario, 2020–21 school year.
Results A multilevel modelling approach was used to estimate the effects of socio-economic factors at the school and area levels on the cumulative incidence of elementary school student SARS-CoV-2 infections. At the school level (level 1), the proportion of the student body from low-income households was positively associated with cumulative incidence (β=0.083, p<0.001). At the area level (level 2), all dimensions of marginalisation were significantly related to cumulative incidence. Ethnic concentration (β=0.454, p<0.001), residential instability (β=0.356, p<0.001) and material deprivation (β=0.212, p<0.001) were positively related, while dependency (β=-0.204, p<0.001) was negatively related. Area-related marginalisation variables explained 57.6% of area variability in cumulative incidence. School-related variables explained 1.2% of school variability in cumulative incidence.
Conclusions The socio-economic characteristics of the geographic area of schools were more important in accounting for the cumulative incidence of SARS-CoV-2 elementary school student infections than individual school characteristics. Schools in marginalised areas should be prioritised for infection prevention measures and education continuity and recovery plans.
Data are available in a public, open access repository. The statistical code and dataset for the reported analysis are available from the Borealis Dataverse Repository, ‘Analysis of Student COVID-19 School Infections Dataverse’. Lau NTT et al .25 26