ABSTRACT
PurposeThere is limited information on the role of individual- and neighbourhood-level characteristics in explaining the geographic variation in the novel coronavirus 2019 (COVID-19) between regions. This study quantified the magnitude of the variation in COVID-19 rates between neighbourhoods in Ontario, Canada, and examined the extent to which neighbourhood-level differences are explained by census-based neighbourhood measures, after adjusting for individual-level covariates (i.e., age, sex, and chronic conditions). MethodsWe conducted a multilevel population-based study of individuals nested within neighbourhoods. COVID-19 laboratory testing data were obtained from a centralized laboratory database and linked to health-administrative data. The median rate ratio and the variance partition coefficient were used to quantify the magnitude of the neighbourhood-level characteristics on the variation of COVID-19 rates. ResultsThe unadjusted median rate ratio for the between-neighbourhood variation in COVID-19 was 2.22. In the fully adjusted regression models, the individual- and neighbourhood-level covariates accounted for about 44% of the variation in COVID-19 between neighbourhoods, with 43% attributable to neighbourhood-level census-based characteristics. ConclusionNeighbourhood-level characteristics could explain almost half of the observed geographic variation in COVID-19. Understanding how neighbourhood-level characteristics influence COVID-19 rates can support jurisdictions in creating effective and equitable intervention strategies.
ABSTRACT
ImportancePopulation stratification of the adult population in Ontario, Canada by their risk of COVID-19 complications can support rapid pandemic response, resource allocation, and decision making. ObjectiveTo develop and validate a multivariable model to predict risk of hospitalization due to COVID-19 severity from routinely collected health records of the entire adult population of Ontario, Canada. Design, Setting, and ParticipantsThis cohort study included 36,323 adult patients (age [≥] 18 years) from the province of Ontario, Canada, who tested positive for SARS-CoV-2 nucleic acid by polymerase chain reaction between February 2 and October 5, 2020, and followed up through November 5, 2020. Patients living in long-term care facilities were excluded from the analysis. Main Outcomes and MeasuresRisk of hospitalization within 30 days of COVID-19 diagnosis was estimated via Gradient Boosting Decision Trees, and risk factor importance was examined via Shapley values. ResultsThe study cohort included 36,323 patients with majority female sex (18,895 [52.02%]) and median (IQR) age of 45 (31-58) years. The cohort had a hospitalization rate of 7.11% (2,583 hospitalizations) with median (IQR) time to hospitalization of 1 (0-5) days, and a mortality rate of 2.49% (906 deaths) with median (IQR) time to death of 12 (6-27) days. In contrast to patients who were not hospitalized, those who were hospitalized had a higher median age (64 years vs 43 years, p-value < 0.001), majority male (56.25% vs 47.35%, p-value<0.001), and had a higher median [IQR] number of comorbidities (3 [2-6] vs 1 [0-3], p-value<0.001). Patients were randomly split into development (n=29,058, 80%) and held-out validation (n=7,265, 20%) cohorts. The final Gradient Boosting model was built using the XGBoost algorithm and achieved high discrimination (development cohort: mean area under the receiver operating characteristic curve across the five folds of 0.852; held-out validation cohort: 0.8475) as well as excellent calibration (R2=0.998, slope=1.01, intercept=-0.01). The patients who scored at the top 10% in the validation cohort captured 47.41% of the actual hospitalizations, whereas those scored at the top 30% captured 80.56%. Patients in the held-out validation cohort (n=7,265) with a score of at least 0.5 (n=2,149, 29.58%) had a 20.29% hospitalization rate (positive predictive value 20.29%) compared with 2.2% hospitalization rate for those with a score less than 0.5 (n=5,116, 70.42%; negative predictive value 97.8%). Aside from age, gender and number of comorbidities, the features that most contribute to model predictions were: history of abnormal blood levels of creatinine, neutrophils and leukocytes, geography and chronic kidney disease. ConclusionsA risk stratification model has been developed and validated using unique, de-identified, and linked routinely collected health administrative data available in Ontario, Canada. The final XGBoost model showed a high discrimination rate, with the potential utility to stratify patients at risk of serious COVID-19 outcomes. This model demonstrates that routinely collected health system data can be successfully leveraged as a proxy for the potential risk of severe COVID-19 complications. Specifically, past laboratory results and demographic factors provide a strong signal for identifying patients who are susceptible to complications. The model can support population risk stratification that informs patients protection most at risk for severe COVID-19 complications.
ABSTRACT
BackgroundOptimizing the public health response to reduce coronavirus disease 2019 (COVID-19) burden necessitates characterizing population-level heterogeneity of COVID-19 risks. However, heterogeneity in severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) testing may introduce biased estimates depending on analytic design. MethodsWe explored the potential for collider bias and characterized individual, environmental, and social determinants of testing and diagnosis using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those diagnosed, we used separate analytic designs to compare predictors of: 1) individuals testing positive versus negative; 2) symptomatic individuals only testing positive versus testing negative; and 3) individuals testing positive versus individuals not testing positive (i.e., testing negative or not being tested). Analyses included tests conducted between March 1 and June 20, 2020. ResultsOf a total of 14,695,579 individuals, 758,691 were tested for SARS-CoV-2, of whom 25,030 (3.3%) tested positive. The further the odds of testing from the null, the more variability observed in the odds of diagnosis across analytic design, particularly among individual factors. There was less variability in testing by social determinants across analytic designs. Residing in areas with highest household density (adjusted odds ratio [aOR]: 1.86; 95%CI: 1.75-1.98), highest proportion of essential workers (aOR: 1.58; 95%CI: 1.48-1.69), lowest educational attainment (aOR: 1.33; 95%CI: 1.26-1.41), and highest proportion of recent immigrants (aOR: 1.10; 95%CI: 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. InterpretationWhere testing is limited, risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation, and structural racism.
