The viral hepatitis B care cascade: A population-based comparison of immigrant groups.

BACKGROUND AND AIMS: The global burden of viral hepatitis B is substantial, and monitoring infections across the care cascade is important for elimination efforts. There is little information on care disparities by immigration status, and we aimed to quantify disease burden among immigrant subgroups. APPROACH AND RESULTS: In this population-based, retrospective cohort study, we used linked laboratory and health administrative records to describe the HBV care cascade in five distinct stages: (1) lifetime prevalence; (2) diagnosis; (3) engagement with care; (4) treatment initiation; and (5) treatment continuation. Infections were identified based on at least one reactive antigen or nucleic acid test, and lifetime prevalence was estimated as the sum of diagnosed and estimated undiagnosed cases. Care cascades were compared between long-term residents and immigrant groups, including subgroups born in hepatitis B endemic countries. Stratified analyses and multivariable Poisson regression were used to identify drivers for cascade progression. Between January 1997 and December 2014, 2,014,470 persons were included, 50,475 with infections, of whom 30,118 were engaged with care, 11,450 initiated treatment, and 6554 continued treatment >1 year. Lifetime prevalence was estimated as 163,309 (1.34%) overall, 115,722 (3.42%) among all immigrants, and 50,876 (9.37%) among those from highly endemic countries. Compared to long-term residents, immigrants were more likely to be diagnosed (adjusted rate ratio [aRR], 4.55; 95% CI, 4.46, 4.63), engaged with care (aRR, 1.07; 95% CI, 1.04, 1.09), and initiate treatment (aRR, 1.09; 95% CI, 1.03, 1.16). CONCLUSIONS: In conclusion, immigrants fared well compared to long-term residents along the care cascade, having higher rates of diagnosis and slightly better measures in subsequent cascade stages, although intensified screening efforts and better strategies to facilitate linkage to care are still needed.

Geographic concentration of SARS-CoV-2 cases by social determinants of health in metropolitan areas in Canada: a cross-sectional study.

BACKGROUND: Understanding inequalities in SARS-CoV-2 transmission associated with the social determinants of health could help the development of effective mitigation strategies that are responsive to local transmission dynamics. This study aims to quantify social determinants of geographic concentration of SARS-CoV-2 cases across 16 census metropolitan areas (hereafter, cities) in 4 Canadian provinces, British Columbia, Manitoba, Ontario and Quebec. METHODS: We used surveillance data on confirmed SARS-CoV-2 cases and census data for social determinants at the level of the dissemination area (DA). We calculated Gini coefficients to determine the overall geographic heterogeneity of confirmed cases of SARS-CoV-2 in each city, and calculated Gini covariance coefficients to determine each city's heterogeneity by each social determinant (income, education, housing density and proportions of visible minorities, recent immigrants and essential workers). We visualized heterogeneity using Lorenz (concentration) curves. RESULTS: We observed geographic concentration of SARS-CoV-2 cases in cities, as half of the cumulative cases were concentrated in DAs containing 21%-35% of their population, with the greatest geographic heterogeneity in Ontario cities (Gini coefficients 0.32-0.47), followed by British Columbia (0.23-0.36), Manitoba (0.32) and Quebec (0.28-0.37). Cases were disproportionately concentrated in areas with lower income and educational attainment, and in areas with a higher proportion of visible minorities, recent immigrants, high-density housing and essential workers. Although a consistent feature across cities was concentration by the proportion of visible minorities, the magnitude of concentration by social determinant varied across cities. INTERPRETATION: Geographic concentration of SARS-CoV-2 cases was observed in all of the included cities, but the pattern by social determinants varied. Geographically prioritized allocation of resources and services should be tailored to the local drivers of inequalities in transmission in response to the resurgence of SARS-CoV-2.

Viral hepatitis C cascade of care: A population-level comparison of immigrant and long-term residents.

BACKGROUND & AIMS: Viral hepatitis C represents a major global burden, particularly among immigrant-receiving countries such as Canada, where knowledge of disparities in hepatitis C virus among immigrant groups for micro-elimination efforts is lacking. We quantify the hepatitis C cascades of care among immigrants and long-term residents prior to the introduction of direct-acting antiviral medications. METHODS: Using laboratory and health administrative records, we described the hepatitis C virus cascades of care in terms of diagnosis, engagement with care, treatment initiation, and clearance in Ontario, Canada (1997-2014). We stratified the cascade by immigrant and long-term resident groups and identify drivers at each stage using multivariable Poisson regression. RESULTS: We included 940 245 individuals in the study with an estimated hepatitis C prevalence of 167 923 (1.4%) overall, 23 759 (0.7%) among all immigrants, and 6019 (1.1%) among immigrants from hepatitis C endemic countries. Overall there were 104 616 individuals with reactive antibody results, 73 861 tested for viral RNA, 52 388 with viral RNA detected, 50 805 genotyped, 13 159 on treatment and 3919 with evidence of viral clearance. Compared to long-term residents, immigrants showed increased nucleic-acid testing (aRR: 1.09 [95%CI: 1.08, 1.10]), treatment initiation (aRR: 1.46 [95%CI: 1.38, 1.54]), and higher clearance rates (aRR: 1.07 [95%CI: 1.03, 1.11]). CONCLUSIONS: Hepatitis C virus is more prevalent among long-term residents compared to immigrants overall, however, immigrants from endemic countries are an important subgroup to consider for future screening and linkage to care initiatives. These findings are prior to the introduction of newer medications and provide a population-based benchmark for follow-up studies and evaluation of treatment programs and surveillance activities.

Individual and social determinants of SARS-CoV-2 testing and positivity in Ontario, Canada: a population-wide study.

BACKGROUND: Optimizing the public health response to reduce the burden of COVID-19 necessitates characterizing population-level heterogeneity of risks for the disease. However, heterogeneity in SARS-CoV-2 testing may introduce biased estimates depending on analytic design. We aimed to explore the potential for collider bias in a large study of disease determinants, and evaluate individual, environmental and social determinants associated with SARS-CoV-2 testing and diagnosis among residents of Ontario, Canada. METHODS: We explored the potential for collider bias and characterized individual, environmental and social determinants of being tested and testing positive for SARS-CoV-2 infection using cross-sectional analyses among 14.7 million community-dwelling people in Ontario, Canada. Among those with a diagnosis, we used separate analytic designs to compare predictors of people testing positive versus negative; symptomatic people testing positive versus testing negative; and people testing positive versus people not testing positive (i.e., testing negative or not being tested). Our analyses included tests conducted between Mar. 1 and June 20, 2020. RESULTS: Of 14 695 579 people, we found that 758 691 were tested for SARS-CoV-2, of whom 25 030 (3.3%) had a positive test result. The further the odds of testing from the null, the more variability we generally observed in the odds of diagnosis across analytic design, particularly among individual factors. We found that there was less variability in testing by social determinants across analytic designs. Residing in areas with the highest household density (adjusted odds ratio [OR] 1.86, 95% confidence interval [CI] 1.75-1.98), highest proportion of essential workers (adjusted OR 1.58, 95% CI 1.48-1.69), lowest educational attainment (adjusted OR 1.33, 95% CI 1.26-1.41) and highest proportion of recent immigrants (adjusted OR 1.10, 95% CI 1.05-1.15) were consistently related to increased odds of SARS-CoV-2 diagnosis regardless of analytic design. INTERPRETATION: Where testing is limited, our results suggest that risk factors may be better estimated using population comparators rather than test-negative comparators. Optimizing COVID-19 responses necessitates investment in and sufficient coverage of structural interventions tailored to heterogeneity in social determinants of risk, including household crowding, occupation and structural racism.

CFH and ARMS2 genetic risk determines progression to neovascular age-related macular degeneration after antioxidant and zinc supplementation.

We evaluated the influence of an antioxidant and zinc nutritional supplement [the Age-Related Eye Disease Study (AREDS) formulation] on delaying or preventing progression to neovascular AMD (NV) in persons with age-related macular degeneration (AMD). AREDS subjects (n = 802) with category 3 or 4 AMD at baseline who had been treated with placebo or the AREDS formulation were evaluated for differences in the risk of progression to NV as a function of complement factor H (CFH) and age-related maculopathy susceptibility 2 (ARMS2) genotype groups. We used published genetic grouping: a two-SNP haplotype risk-calling algorithm to assess CFH, and either the single SNP rs10490924 or 372_815del443ins54 to mark ARMS2 risk. Progression risk was determined using the Cox proportional hazard model. Genetics-treatment interaction on NV risk was assessed using a multiiterative bootstrap validation analysis. We identified strong interaction of genetics with AREDS formulation treatment on the development of NV. Individuals with high CFH and no ARMS2 risk alleles and taking the AREDS formulation had increased progression to NV compared with placebo. Those with low CFH risk and high ARMS2 risk had decreased progression risk. Analysis of CFH and ARMS2 genotype groups from a validation dataset reinforces this conclusion. Bootstrapping analysis confirms the presence of a genetics-treatment interaction and suggests that individual treatment response to the AREDS formulation is largely determined by genetics. The AREDS formulation modifies the risk of progression to NV based on individual genetics. Its use should be based on patient-specific genotype.

Déterminants individuels et sociaux du test de dépistage du SRAS-CoV-2 et de l'obtention d'un résultat positif en Ontario, au Canada: une étude populationnelle.

CONTEXTE: Optimiser la réponse de la santé publique pour diminuer le fardeau de la COVID-19 nécessite la caractérisation de l'hétérogénéité du risque posé par la maladie à l'échelle de la population. Cependant, l'hétérogénéité du dépistage du SRAS-CoV-2 peut fausser les estimations selon le modèle d'étude analytique utilisé. Notre objectif était d'explorer les biais collisionneurs dans le cadre d'une vaste étude portant sur les déterminants de la maladie et d'évaluer les déterminants individuels, environnementaux et sociaux du dépistage et du diagnostic du SRAS-CoV-2 parmi les résidents de l'Ontario, au Canada. MÉTHODES: Nous avons exploré la présence potentielle de biais collisionneurs et caractérisé les déterminants individuels, environnementaux et sociaux de l'obtention d'un test de dépistage et d'un résultat positif à la présence de l'infection au SRAS-CoV-2 à l'aide d'analyses transversales parmi les 14,7 millions de personnes vivant dans la collectivité en Ontario, au Canada. Parmi les personnes ayant obtenu un diagnostic, nous avons utilisé des études analytiques distinctes afin de comparer les prédicteurs pour les personnes d'obtenir un résultat de test de dépistage positif plutôt que négatif, pour les personnes symptomatiques d'obtenir un résultat de test de dépistage positif plutôt que négatif et pour les personnes d'obtenir un résultat de test de dépistage positif plutôt que de ne pas obtenir un résultat positif (c.-à-d., obtenir un résultat de test de dépistage négatif ou ne pas obtenir de test de dépistage). Nos analyses comprennent des tests de dépistage réalisés entre le 1er mars et le 20 juin 2020. RÉSULTATS: Sur 14 695 579 personnes, nous avons constaté que 758 691 d'entre elles ont passé un test de dépistage du SRAS-CoV-2, parmi lesquelles 25 030 (3,3 %) ont obtenu un résultat positif. Plus la probabilité d'obtenir un test de dépistage s'éloignait de zéro, plus la variabilité généralement observée dans la probabilité d'un diagnostic était grande parmi les modèles d'études analytiques, particulièrement en ce qui a trait aux facteurs individuels. Nous avons constaté que la variabilité dans l'obtention d'un test de dépistage était moins importante en fonction des déterminants sociaux dans l'ensemble des études analytiques. Les facteurs tels que le fait d'habiter dans une région ayant une plus haute densité des ménages (rapport de cotes corrigé 1,86; intervalle de confiance [IC] à 95 % 1,75­1,98), une plus grande proportion de travailleurs essentiels (rapport de cotes corrigé 1,58; IC à 95 % 1,48­1,69), une population atteignant un plus faible niveau de scolarité (rapport de cotes corrigé 1,33; IC à 95 % 1,26­1,41) et une plus grande proportion d'immigrants récents (rapport de cotes corrigé 1,10; IC à 95 % 1,05­1,15), étaient systématiquement corrélés à une probabilité plus importante d'obtenir un diagnostic de SRAS-CoV-2, peu importe le modèle d'étude analytique employé. INTERPRÉTATION: Lorsque la capacité de dépister est limitée, nos résultats suggèrent que les facteurs de risque peuvent être estimés plus adéquatement en utilisant des comparateurs populationnels plutôt que des comparateurs de résultat négatif au test de dépistage. Optimiser la lutte contre la COVID-19 nécessite des investissements dans des interventions structurelles déployées de façon suffisante et adaptées à l'hétérogénéité des déterminants sociaux du risque, dont le surpeuplement des ménages, l'occupation professionnelle et le racisme structurel.

Genome-wide association scan identifies a colorectal cancer susceptibility locus on chromosome 8q24.

Using a multistage genetic association approach comprising 7,480 affected individuals and 7,779 controls, we identified markers in chromosomal region 8q24 associated with colorectal cancer. In stage 1, we genotyped 99,632 SNPs in 1,257 affected individuals and 1,336 controls from Ontario. In stages 2-4, we performed serial replication studies using 4,024 affected individuals and 4,042 controls from Seattle, Newfoundland and Scotland. We identified one locus on chromosome 8q24 and another on 9p24 having combined odds ratios (OR) for stages 1-4 of 1.18 (trend; P = 1.41 x 10(-8)) and 1.14 (trend; P = 1.32 x 10(-5)), respectively. Additional analyses in 2,199 affected individuals and 2,401 controls from France and Europe supported the association at the 8q24 locus (OR = 1.16, trend; 95% confidence interval (c.i.): 1.07-1.26; P = 5.05 x 10(-4)). A summary across all seven studies at the 8q24 locus was highly significant (OR = 1.17, c.i.: 1.12-1.23; P = 3.16 x 10(-11)). This locus has also been implicated in prostate cancer.

Predictors of all-cause mortality among patients hospitalized with influenza, respiratory syncytial virus, or SARS-CoV-2.

BACKGROUND: Shared and divergent predictors of clinical severity across respiratory viruses may support clinical and community responses in the context of a novel respiratory pathogen. METHODS: We conducted a retrospective cohort study to identify predictors of 30-day all-cause mortality following hospitalization with influenza (N = 45,749; 2010-09 to 2019-05), respiratory syncytial virus (RSV; N = 24 345; 2010-09 to 2019-04), or severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2; N = 8988; 2020-03 to 2020-12; pre-vaccine) using population-based health administrative data from Ontario, Canada. Multivariable modified Poisson regression was used to assess associations between potential predictors and mortality. We compared the direction, magnitude, and confidence intervals of risk ratios to identify shared and divergent predictors of mortality. RESULTS: A total of 3186 (7.0%), 697 (2.9%), and 1880 (20.9%) patients died within 30 days of hospital admission with influenza, RSV, and SARS-CoV-2, respectively. Shared predictors of increased mortality included older age, male sex, residence in a long-term care home, and chronic kidney disease. Positive associations between age and mortality were largest for patients with SARS-CoV-2. Few comorbidities were associated with mortality among patients with SARS-CoV-2 as compared with those with influenza or RSV. CONCLUSIONS: Our findings may help identify patients at greatest risk of illness secondary to a respiratory virus, anticipate hospital resource needs, and prioritize local prevention and therapeutic strategies to communities with higher prevalence of risk factors.

Increasing concentration of COVID-19 by socioeconomic determinants and geography in Toronto, Canada: an observational study.

BACKGROUND: Inequities in the burden of COVID-19 were observed early in Canada and around the world, suggesting economically marginalized communities faced disproportionate risks. However, there has been limited systematic assessment of how heterogeneity in risks has evolved in large urban centers over time. PURPOSE: To address this gap, we quantified the magnitude of risk heterogeneity in Toronto, Ontario from January to November 2020 using a retrospective, population-based observational study using surveillance data. METHODS: We generated epidemic curves by social determinants of health (SDOH) and crude Lorenz curves by neighbourhoods to visualize inequities in the distribution of COVID-19 and estimated Gini coefficients. We examined the correlation between SDOH using Pearson-correlation coefficients. RESULTS: Gini coefficient of cumulative cases by population size was 0.41 (95% confidence interval [CI]:0.36-0.47) and estimated for: household income (0.20, 95%CI: 0.14-0.28); visible minority (0.21, 95%CI:0.16-0.28); recent immigration (0.12, 95%CI:0.09-0.16); suitable housing (0.21, 95%CI:0.14-0.30); multigenerational households (0.19, 95%CI:0.15-0.23); and essential workers (0.28, 95%CI:0.23-0.34). CONCLUSIONS: There was rapid epidemiologic transition from higher- to lower-income neighborhoods with Lorenz curve transitioning from below to above the line of equality across SDOH. Moving forward necessitates integrating programs and policies addressing socioeconomic inequities and structural racism into COVID-19 prevention and vaccination programs.

A disproportionate epidemic: COVID-19 cases and deaths among essential workers in Toronto, Canada.

Shelter-in-place mandates and closure of nonessential businesses have been central to COVID19 response strategies including in Toronto, Canada. Approximately half of the working population in Canada are employed in occupations that do not allow for remote work suggesting potentially limited impact of some of the strategies proposed to mitigate COVID-19 acquisition and onward transmission risks and associated morbidity and mortality. We compared per-capita rates of COVID-19 cases and deaths from January 23, 2020 to January 24, 2021, across neighborhoods in Toronto by proportion of the population working in essential services. We used person-level data on laboratory-confirmed COVID-19 community cases and deaths, and census data for neighborhood-level attributes. Cumulative per-capita rates of COVID-19 cases and deaths were 3.3-fold and 2.5-fold higher, respectively, in neighborhoods with the highest versus lowest concentration of essential workers. Findings suggest that the population who continued to serve the essential needs of society throughout COVID-19 shouldered a disproportionate burden of transmission and deaths. Taken together, results signal the need for active intervention strategies to complement restrictive measures to optimize both the equity and effectiveness of COVID-19 responses.

Validating International Classification of Disease 10th Revision algorithms for identifying influenza and respiratory syncytial virus hospitalizations.

OBJECTIVE: Routinely collected health administrative data can be used to efficiently assess disease burden in large populations, but it is important to evaluate the validity of these data. The objective of this study was to develop and validate International Classification of Disease 10th revision (ICD -10) algorithms that identify laboratory-confirmed influenza or laboratory-confirmed respiratory syncytial virus (RSV) hospitalizations using population-based health administrative data from Ontario, Canada. STUDY DESIGN AND SETTING: Influenza and RSV laboratory data from the 2014-15, 2015-16, 2016-17 and 2017-18 respiratory virus seasons were obtained from the Ontario Laboratories Information System (OLIS) and were linked to hospital discharge abstract data to generate influenza and RSV reference cohorts. These reference cohorts were used to assess the sensitivity, specificity, positive predictive value (PPV) and negative predictive value (NPV) of the ICD-10 algorithms. To minimize misclassification in future studies, we prioritized specificity and PPV in selecting top-performing algorithms. RESULTS: 83,638 and 61,117 hospitalized patients were included in the influenza and RSV reference cohorts, respectively. The best influenza algorithm had a sensitivity of 73% (95% CI 72% to 74%), specificity of 99% (95% CI 99% to 99%), PPV of 94% (95% CI 94% to 95%), and NPV of 94% (95% CI 94% to 95%). The best RSV algorithm had a sensitivity of 69% (95% CI 68% to 70%), specificity of 99% (95% CI 99% to 99%), PPV of 91% (95% CI 90% to 91%) and NPV of 97% (95% CI 97% to 97%). CONCLUSION: We identified two highly specific algorithms that best ascertain patients hospitalized with influenza or RSV. These algorithms may be applied to hospitalized patients if data on laboratory tests are not available, and will thereby improve the power of future epidemiologic studies of influenza, RSV, and potentially other severe acute respiratory infections.

Validating viral hepatitis B and C diagnosis codes: a retrospective analysis using Ontario's health administrative data.

OBJECTIVE: We aimed to determine the criterion validity of using diagnosis codes for hepatitis B virus (HBV) and hepatitis C virus (HCV) to identify infections. METHODS: Using linked laboratory and administrative data in Ontario, Canada, from January 2004 to December 2014, we validated HBV/HCV diagnosis codes against laboratory-confirmed infections. Performance measures (sensitivity, specificity, and positive predictive value) were estimated via cross-validated logistic regression and we explored variations by varying time windows from 1 to 5 years before (i.e., prognostic prediction) and after (i.e., diagnostic prediction) the date of laboratory confirmation. Subgroup analyses were performed among immigrants, males, baby boomers, and females to examine the robustness of these measures. RESULTS: A total of 1,599,023 individuals were tested for HBV and 840,924 for HCV, with a resulting 41,714 (2.7%) and 58,563 (7.0%) infections identified, respectively. HBV/HCV diagnosis codes ± 3 years of laboratory confirmation showed high specificity (99.9% HBV; 99.8% HCV), moderate positive predictive value (70.3% HBV; 85.8% HCV), and low sensitivity (12.8% HBV; 30.8% HCV). Varying the time window resulted in limited changes to performance measures. Diagnostic models consistently outperformed prognostic models. No major differences were observed among subgroups. CONCLUSION: HBV/HCV codes should not be the only source used for monitoring the population burden of these infections, due to low sensitivity and moderate positive predictive values. These results underscore the importance of ongoing laboratory and reportable disease surveillance systems for monitoring viral hepatitis in Ontario.

RéSUMé: OBJECTIF: Nous avons cherché à déterminer le critère de validité de l'utilisation des codes de diagnostic du virus de l'hépatite B (VHB) et du virus de l'hépatite C (VHC) pour identifier les infections. MéTHODES: En utilisant des données de laboratoire et administratives couplées en Ontario, au Canada, de janvier 2004 à décembre 2014, nous avons validé les codes de diagnostic du VHB/VHC contre les infections confirmées en laboratoire. Les mesures du rendement (sensibilité, spécificité et valeur prédictive positive) ont été estimées par régression logistique croisée et nous avons exploré les variations en variant les fenêtres temporelles de 1 à 5 ans avant (c.-à-d. prédiction pronostique) et après (c.-à-d. prédiction diagnostique) la date de confirmation en laboratoire. Des analyses de sous-groupes ont été effectuées auprès d'immigrants, d'hommes, de baby-boomers et de femmes pour examiner la robustesse de ces mesures. RéSULTATS: 1 599 023 individus ont été testés pour le VHB et 840 924 pour le VHC, dont 41 714 (2,7 %) et 58 563 (7,0 %) infections ont été identifiées, respectivement. Les codes de diagnostic VHB/VHC ± 3 ans de confirmation en laboratoire ont montré une spécificité élevée (99,9 % VHB; 99,8 % VHC), une valeur prédictive positive modérée (70,3 % VHB; 85,8 % VHC) et une faible sensibilité (12,8 % VHB; 30,8 % VHC). La variation de la fenêtre temporelle a entraîné des changements limités aux mesures du rendement. Les modèles diagnostiques ont constamment surpassé les modèles pronostiques. Aucune différence majeure n'a été observée entre les sous-groupes. CONCLUSION: Les codes VHB/VHC ne devraient pas être la seule source utilisée pour surveiller la charge de population de ces infections, en raison de la faible sensibilité et des valeurs prédictives positives modérées. Ces résultats soulignent l'importance des systèmes continus de surveillance des maladies à déclaration obligatoire en laboratoire pour surveiller l'hépatite virale en Ontario.

Genetics and Age-Related Eye Disease Study Formulation Interaction in Neovascular Age-Related Macular Degeneration.

Purpose: This work aims to determine whether previously defined genotype risk groups interact with Age-Related Eye Disease Study formulation (AREDS-F) use in progression to neovascular age-related macular degeneration (nvAMD). Methods: We conducted a case-only study of 265 nvAMD patients. Patients were anonymously genotyped at the complement factor H and age-related maculopathy susceptibility 2 loci and segregated into genotype groups (GTGs) defined by specific combinations of risk alleles. Physicians, who were blind to patients' GTGs, obtained patients' AREDS-F use history. The facility performing genetic analysis was blind to the AREDS-F use history. We used logistic analysis to estimate the interaction coefficient between AREDS-F use and GTG 2 vs GTG 3 in a general-population model. Results: The odds ratio of numbers of patients reporting prior AREDS-F use to nonuse for GTG 2 vs GTG 3 was 4.18 (P = .001). Logistic regression, correcting for nongenetic risk factors, gave an estimate of the ß for interaction of AREDS-F with genotype of 1.57 (P = .001). This estimates a corrected odds ratio associated with the effect of interaction of 4.81 between those in GTG 2 compared with those in GTG 3. Conclusions: Our data indicate an interaction between GTGs and AREDS-F use that is consistent in size and direction with previously published reports, which had found that using AREDS-F supplements significantly increases the risk of nvAMD for some users and significantly protects other users.

The utility and predictive value of combinations of low penetrance genes for screening and risk prediction of colorectal cancer.

Despite the fact that colorectal cancer (CRC) is a highly treatable form of cancer if detected early, a very low proportion of the eligible population undergoes screening for this form of cancer. Integrating a genomic screening profile as a component of existing screening programs for CRC could potentially improve the effectiveness of population screening by allowing the assignment of individuals to different types and intensities of screening and also by potentially increasing the uptake of existing screening programs. We evaluated the utility and predictive value of genomic profiling as applied to CRC, and as a potential component of a population-based cancer screening program. We generated simulated data representing a typical North American population including a variety of genetic profiles, with a range of relative risks and prevalences for individual risk genes. We then used these data to estimate parameters characterizing the predictive value of a logistic regression model built on genetic markers for CRC. Meta-analyses of genetic associations with CRC were used in building science to inform the simulation work, and to select genetic variants to include in logistic regression model-building using data from the ARCTIC study in Ontario, which included 1,200 CRC cases and a similar number of cancer-free population-based controls. Our simulations demonstrate that for reasonable assumptions involving modest relative risks for individual genetic variants, that substantial predictive power can be achieved when risk variants are common (e.g., prevalence > 20%) and data for enough risk variants are available (e.g., approximately 140-160). Pilot work in population data shows modest, but statistically significant predictive utility for a small collection of risk variants, smaller in effect than age and gender alone in predicting an individual's CRC risk. Further genotyping and many more samples will be required, and indeed the discovery of many more risk loci associated with CRC before the question of the potential utility of germline genomic profiling can be definitively answered.

Heterogeneity in testing, diagnosis and outcome in SARS-CoV-2 infection across outbreak settings in the Greater Toronto Area, Canada: an observational study.

BACKGROUND: Congregate settings have been disproportionately affected by coronavirus disease 2019 (COVID-19). Our objective was to compare testing for, diagnosis of and death after severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection across 3 settings (residents of long-term care homes, people living in shelters and the rest of the population). METHODS: We conducted a population-based prospective cohort study involving individuals tested for SARS-CoV-2 in the Greater Toronto Area between Jan. 23, 2020, and May 20, 2020. We sourced person-level data from COVID-19 surveillance and reporting systems in Ontario. We calculated cumulatively diagnosed cases per capita, proportion tested, proportion tested positive and case-fatality proportion for each setting. We estimated the age- and sex-adjusted rate ratios associated with setting for test positivity and case fatality using quasi-Poisson regression. RESULTS: Over the study period, a total of 173 092 individuals were tested for and 16 490 individuals were diagnosed with SARS-CoV-2 infection. We observed a shift in the proportion of cumulative cases from all cases being related to travel to cases in residents of long-term care homes (20.4% [3368/16 490]), shelters (2.3% [372/16 490]), other congregate settings (20.9% [3446/16 490]) and community settings (35.4% [5834/16 490]), with cumulative travel-related cases at 4.1% (674/16490). Cumulatively, compared with the rest of the population, the diagnosed cases per capita was 64-fold and 19-fold higher among long-term care home and shelter residents, respectively. By May 20, 2020, 76.3% (21 617/28 316) of long-term care home residents and 2.2% (150 077/6 808 890) of the rest of the population had been tested. After adjusting for age and sex, residents of long-term care homes were 2.4 (95% confidence interval [CI] 2.2-2.7) times more likely to test positive, and those who received a diagnosis of COVID-19 were 1.4-fold (95% CI 1.1-1.8) more likely to die than the rest of the population. INTERPRETATION: Long-term care homes and shelters had disproportionate diagnosed cases per capita, and residents of long-term care homes diagnosed with COVID-19 had higher case fatality than the rest of the population. Heterogeneity across micro-epidemics among specific populations and settings may reflect underlying heterogeneity in transmission risks, necessitating setting-specific COVID-19 prevention and mitigation strategies.

Efficient p-value estimation in massively parallel testing problems.

We present a new method to efficiently estimate very large numbers of p-values using empirically constructed null distributions of a test statistic. The need to evaluate a very large number of p-values is increasingly common with modern genomic data, and when interaction effects are of interest, the number of tests can easily run into billions. When the asymptotic distribution is not easily available, permutations are typically used to obtain p-values but these can be computationally infeasible in large problems. Our method constructs a prediction model to obtain a first approximation to the p-values and uses Bayesian methods to choose a fraction of these to be refined by permutations. We apply and evaluate our method on the study of association between 2-way interactions of genetic markers and colorectal cancer using the data from the first phase of a large, genome-wide case-control study. The results show enormous computational savings as compared to evaluating a full set of permutations, with little decrease in accuracy.

EM-random forest and new measures of variable importance for multi-locus quantitative trait linkage analysis.

MOTIVATION: We developed an EM-random forest (EMRF) for Haseman-Elston quantitative trait linkage analysis that accounts for marker ambiguity and weighs each sib-pair according to the posterior identical by descent (IBD) distribution. The usual random forest (RF) variable importance (VI) index used to rank markers for variable selection is not optimal when applied to linkage data because of correlation between markers. We define new VI indices that borrow information from linked markers using the correlation structure inherent in IBD linkage data. RESULTS: Using simulations, we find that the new VI indices in EMRF performed better than the original RF VI index and performed similarly or better than EM-Haseman-Elston regression LOD score for various genetic models. Moreover, tree size and markers subset size evaluated at each node are important considerations in RFs. AVAILABILITY: The source code for EMRF written in C is available at www.infornomics.utoronto.ca/downloads/EMRF.

CFH and ARMS2 Polymorphisms Interact with Zinc Supplements in Cognitive Impairment in the Women's Health Initiative Hormone Trial.

BACKGROUND: An interaction between genetic variants in complement factor H (CFH) and age-related maculopathy susceptibility 2 (ARMS2) and high-dose zinc supplementation on progression to advanced age-related macular degeneration (AMD) exists. Because cognitive impairment (CI) is associated with AMD, we used data from the Women's Health Initiative (WHI) to search for a zinc/genetics interaction. OBJECTIVE: To study the interaction of chronic zinc supplementation with genetic variants in CFH and ARMS2 on the development of CI. BACKGROUND: Zinc dietary supplements, CFH and ARMS2 genotypes, and serial mental status was analyzed in participants with available genetic data (n = 7,483). Cognition was assessed using the Modified Mini-Mental State Examination. The development of CI over 5 years was analyzed by genotype and zinc intake using a repeated measures logistic regression model. RESULTS: Zinc supplementation of approximately 15 mg/day was associated with decreased development of CI in women with 1 or 2 CFH and no ARMS2 risk alleles (OR = 0.46: 1 CFH risk allele; 0.20: 2 CFH risk alleles; p = 0.002). CONCLUSION: Low-dose zinc (approximately 15 mg) is associated with reduced CI in women with 2 CFH and 0 ARMS2 AMD risk alleles. This interaction is opposite in direction to that observed in AMD, where patients with 2 CFH and 0 ARMS2 risk alleles had increased progression to neovascular AMD if treated with 80 mg/day of zinc. This may be due to a zinc dose-response or to a fundamental difference in the role of zinc in the progression of early CI versus advanced AMD.