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1.
BMJ Open ; 11(12): e052019, 2021 12 17.
Article in English | MEDLINE | ID: covidwho-1583101

ABSTRACT

OBJECTIVE: The objective of this study was to estimate background rates of selected thromboembolic and coagulation disorders in Ontario, Canada. DESIGN: Population-based retrospective observational study using linked health administrative databases. Records of hospitalisations and emergency department visits were searched to identify cases using International Statistical Classification of Diseases and Related Health Problems, 10th Revision, Canada diagnostic codes. PARTICIPANTS: All Ontario residents. PRIMARY OUTCOME MEASURES: Incidence rates of ischaemic stroke, intracerebral haemorrhage, subarachnoid haemorrhage, deep vein thrombosis, pulmonary embolism, idiopathic thrombocytopaenia, disseminated intravascular coagulation and cerebral venous thrombosis during five prepandemic years (2015-2019) and 2020. RESULTS: The average annual population was 14 million with 51% female. The mean annual rates per 100 000 population during 2015-2019 were 127.1 (95% CI 126.2 to 127.9) for ischaemic stroke, 22.0 (95% CI 21.6 to 22.3) for intracerebral haemorrhage, 9.4 (95% CI 9.2 to 9.7) for subarachnoid haemorrhage, 86.8 (95% CI 86.1 to 87.5) for deep vein thrombosis, 63.7 (95% CI 63.1 to 64.3) for pulmonary embolism, 6.1 (95% CI 5.9 to 6.3) for idiopathic thrombocytopaenia, 1.6 (95% CI 1.5 to 1.7) for disseminated intravascular coagulation, and 1.5 (95% CI 1.4 to 1.6) for cerebral venous thrombosis. Rates were lower in 2020 than during the prepandemic years for ischaemic stroke, deep vein thrombosis and idiopathic thrombocytopaenia. Rates were generally consistent over time, except for pulmonary embolism, which increased from 57.1 to 68.5 per 100 000 between 2015 and 2019. Rates were higher for females than males for subarachnoid haemorrhage, pulmonary embolism and cerebral venous thrombosis, and vice versa for ischaemic stroke and intracerebral haemorrhage. Rates increased with age for most of these conditions, but idiopathic thrombocytopaenia demonstrated a bimodal distribution with incidence peaks at 0-19 years and ≥60 years. CONCLUSIONS: Our estimated background rates help contextualise observed events of these potential adverse events of special interest and to detect potential safety signals related to COVID-19 vaccines.


Subject(s)
Brain Ischemia , COVID-19 , Disseminated Intravascular Coagulation , Pulmonary Embolism , Stroke , Adolescent , Adult , COVID-19 Vaccines , Child , Child, Preschool , Emergency Service, Hospital , Female , Hospitalization , Humans , Incidence , Infant , Infant, Newborn , Male , Ontario/epidemiology , Pulmonary Embolism/epidemiology , SARS-CoV-2 , Stroke/epidemiology , Young Adult
3.
Non-conventional in English | [Unspecified Source], Grey literature | ID: grc-750456

ABSTRACT

OBJECTIVE: To assess the associations between COVID-19 mortality and immigrant and farm worker population at the county level. METHODS: We used publicly accessible datasets to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) Percentage of Non-English speaking households, (2) percentage of individuals engaged in hired farm work, (3) percentage of uninsured individuals under the age of 65, and (3) percentage of individuals living at or below the poverty line. RESULTS: In urban counties (n=114), only population density was significantly associated with COVID19 mortality (b = 0.21, p <0.001). In non-urban counties (n=2,629), all hypothesized social determinants were significantly associated with higher levels of mortality. Percentage of uninsured individuals was associated with lower reported COVID-19 mortality (b = -0.36, p = 0.001). CONCLUSIONS: Individuals who do not speak English, individuals engaged in farm work, and individuals living in poverty may be at heightened risk for COVID-19 mortality in non-urban counties. Mortality among the uninsured may be being systematically undercounted in county and national level surveillance.

4.
BMJ ; 374: n1943, 2021 08 20.
Article in English | MEDLINE | ID: covidwho-1367424

ABSTRACT

OBJECTIVE: To estimate the effectiveness of mRNA covid-19 vaccines against symptomatic infection and severe outcomes (hospital admission or death). DESIGN: Test negative design study. SETTING: Ontario, Canada between 14 December 2020 and 19 April 2021. PARTICIPANTS: 324 033 community dwelling people aged ≥16 years who had symptoms of covid-19 and were tested for SARS-CoV-2. INTERVENTIONS: BNT162b2 (Pfizer-BioNTech) or mRNA-1273 (Moderna) vaccine. MAIN OUTCOME MEASURES: Laboratory confirmed SARS-CoV-2 by reverse transcription polymerase chain reaction (RT-PCR) and hospital admissions and deaths associated with SARS-CoV-2 infection. Multivariable logistic regression was adjusted for personal and clinical characteristics associated with SARS-CoV-2 and vaccine receipt to estimate vaccine effectiveness against symptomatic infection and severe outcomes. RESULTS: Of 324 033 people with symptoms, 53 270 (16.4%) were positive for SARS-CoV-2 and 21 272 (6.6%) received at least one dose of vaccine. Among participants who tested positive, 2479 (4.7%) were admitted to hospital or died. Vaccine effectiveness against symptomatic infection observed ≥14 days after one dose was 60% (95% confidence interval 57% to 64%), increasing from 48% (41% to 54%) at 14-20 days after one dose to 71% (63% to 78%) at 35-41 days. Vaccine effectiveness observed ≥7 days after two doses was 91% (89% to 93%). Vaccine effectiveness against hospital admission or death observed ≥14 days after one dose was 70% (60% to 77%), increasing from 62% (44% to 75%) at 14-20 days to 91% (73% to 97%) at ≥35 days, whereas vaccine effectiveness observed ≥7 days after two doses was 98% (88% to 100%). For adults aged ≥70 years, vaccine effectiveness estimates were observed to be lower for intervals shortly after one dose but were comparable to those for younger people for all intervals after 28 days. After two doses, high vaccine effectiveness was observed against variants with the E484K mutation. CONCLUSIONS: Two doses of mRNA covid-19 vaccines were observed to be highly effective against symptomatic infection and severe outcomes. Vaccine effectiveness of one dose was observed to be lower, particularly for older adults shortly after the first dose.


Subject(s)
COVID-19 Nucleic Acid Testing/statistics & numerical data , COVID-19 Vaccines/therapeutic use , COVID-19/mortality , Patient Admission/statistics & numerical data , Adolescent , Adult , Aged , COVID-19/diagnosis , COVID-19/prevention & control , Female , Humans , Male , Middle Aged , Ontario/epidemiology , SARS-CoV-2 , Treatment Outcome , Young Adult
6.
CMAJ ; 193(20): E723-E734, 2021 05 17.
Article in English | MEDLINE | ID: covidwho-1238783

ABSTRACT

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.


Subject(s)
COVID-19 Testing/methods , COVID-19/epidemiology , Pandemics , Population Surveillance , RNA, Viral/analysis , SARS-CoV-2/genetics , Social Determinants of Health/statistics & numerical data , Adolescent , Adult , COVID-19/diagnosis , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Ontario/epidemiology , Young Adult
7.
Nat Med ; 27(7): 1178-1186, 2021 07.
Article in English | MEDLINE | ID: covidwho-1217708

ABSTRACT

Recent studies have provided insights into innate and adaptive immune dynamics in coronavirus disease 2019 (COVID-19). However, the exact features of antibody responses that govern COVID-19 disease outcomes remain unclear. In this study, we analyzed humoral immune responses in 229 patients with asymptomatic, mild, moderate and severe COVID-19 over time to probe the nature of antibody responses in disease severity and mortality. We observed a correlation between anti-spike (S) immunoglobulin G (IgG) levels, length of hospitalization and clinical parameters associated with worse clinical progression. Although high anti-S IgG levels correlated with worse disease severity, such correlation was time dependent. Deceased patients did not have higher overall humoral response than discharged patients. However, they mounted a robust, yet delayed, response, measured by anti-S, anti-receptor-binding domain IgG and neutralizing antibody (NAb) levels compared to survivors. Delayed seroconversion kinetics correlated with impaired viral control in deceased patients. Finally, although sera from 85% of patients displayed some neutralization capacity during their disease course, NAb generation before 14 d of disease onset emerged as a key factor for recovery. These data indicate that COVID-19 mortality does not correlate with the cross-sectional antiviral antibody levels per se but, rather, with the delayed kinetics of NAb production.


Subject(s)
Antibodies, Neutralizing/immunology , Antibodies, Viral/immunology , COVID-19/immunology , Immunoglobulin G/immunology , Spike Glycoprotein, Coronavirus/immunology , Aged , Aged, 80 and over , COVID-19/mortality , COVID-19/prevention & control , COVID-19 Vaccines/therapeutic use , Carrier State/immunology , Female , Humans , Immunity, Humoral , Kinetics , Length of Stay/statistics & numerical data , Male , Middle Aged , SARS-CoV-2/immunology , Severity of Illness Index , Time Factors
8.
CMAJ ; 193(20): E723-E734, 2021 05 17.
Article in English | MEDLINE | ID: covidwho-1206209

ABSTRACT

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.


Subject(s)
COVID-19 Testing/methods , COVID-19/epidemiology , Pandemics , Population Surveillance , RNA, Viral/analysis , SARS-CoV-2/genetics , Social Determinants of Health/statistics & numerical data , Adolescent , Adult , COVID-19/diagnosis , Cross-Sectional Studies , Female , Humans , Male , Middle Aged , Ontario/epidemiology , Young Adult
9.
PLoS One ; 15(10): e0240151, 2020.
Article in English | MEDLINE | ID: covidwho-868672

ABSTRACT

As of August 2020, the United States is the global epicenter of the COVID-19 pandemic. Emerging data suggests that "essential" workers, who are disproportionately more likely to be racial/ethnic minorities and immigrants, bear a disproportionate degree of risk. We used publicly available data to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) percentage of individuals engaged in farm work, (2) percentage of households without a fluent, adult English-speaker, (3) percentage of uninsured individuals under the age of 65, and (4) percentage of individuals living at or below the federal poverty line. We further adjusted these models for total population, population density, and number of days since the first reported case in a given county. We found that across all counties that had reported a case of COVID-19 as of July 12, 2020 (n = 3024), a higher percentage of farmworkers, a higher percentage of residents living in poverty, higher density, higher population, and a higher percentage of residents over the age of 65 were all independently and significantly associated with a higher number of deaths in a county. In urban counties (n = 115), a higher percentage of farmworkers, higher density, and larger population were all associated with a higher number of deaths, while lower rates of insurance coverage in a county was independently associated with fewer deaths. In non-urban counties (n = 2909), these same patterns held true, with higher percentages of residents living in poverty and senior residents also significantly associated with more deaths. Taken together, our findings suggest that farm workers may face unique risks of contracting and dying from COVID-19, and that these risks are independent of poverty, insurance, or linguistic accessibility of COVID-19 health campaigns.


Subject(s)
Coronavirus Infections/epidemiology , Pneumonia, Viral/epidemiology , Socioeconomic Factors , COVID-19 , Coronavirus Infections/mortality , Demography/statistics & numerical data , Emigrants and Immigrants/statistics & numerical data , Farmers/statistics & numerical data , Humans , Insurance Coverage/statistics & numerical data , Pandemics , Pneumonia, Viral/mortality , United States
10.
medRxiv ; 2020 Jul 01.
Article in English | MEDLINE | ID: covidwho-637921

ABSTRACT

OBJECTIVE: To assess the associations between COVID-19 mortality and immigrant and farm worker population at the county level. METHODS: We used publicly accessible datasets to build a series of spatial autoregressive models assessing county level associations between COVID-19 mortality and (1) Percentage of Non-English speaking households, (2) percentage of individuals engaged in hired farm work, (3) percentage of uninsured individuals under the age of 65, and (3) percentage of individuals living at or below the poverty line. RESULTS: In urban counties (n=114), only population density was significantly associated with COVID19 mortality (b = 0.21, p <0.001). In non-urban counties (n=2,629), all hypothesized social determinants were significantly associated with higher levels of mortality. Percentage of uninsured individuals was associated with lower reported COVID-19 mortality (b = -0.36, p = 0.001). CONCLUSIONS: Individuals who do not speak English, individuals engaged in farm work, and individuals living in poverty may be at heightened risk for COVID-19 mortality in non-urban counties. Mortality among the uninsured may be being systematically undercounted in county and national level surveillance.

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