26 datasets found
  1. d

    COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE

    • catalog.data.gov
    • data.ct.gov
    • +2more
    Updated Aug 12, 2023
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    data.ct.gov (2023). COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE [Dataset]. https://catalog.data.gov/dataset/covid-19-cases-and-deaths-by-race-ethnicity
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    Dataset updated
    Aug 12, 2023
    Dataset provided by
    data.ct.gov
    Description

    Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

  2. Cumulative coronavirus cases in Africa 2022, by country

    • statista.com
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    Statista, Cumulative coronavirus cases in Africa 2022, by country [Dataset]. https://www.statista.com/statistics/1170463/coronavirus-cases-in-africa/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 18, 2022
    Area covered
    Africa
    Description

    As of November 18, 2022, the number of confirmed COVID-19 cases in Africa amounted to around 12.7 million, which represented around two percent of the infections around the world. By the same date, coronavirus cases globally were over 640 million, deaths were over six million, while approximately 620 million people recovered from the disease. On the African continent, South Africa was the most drastically affected country, with more than 3.6 million infections.

    The African continent fighting the pandemic  

    The African continent first came in contact with the coronavirus pandemic on February 14, 2020, in the northernmost part, particularly Egypt. Since then, the different governments took severe restrictive measures to try to curb the spread of the disease. Moreover, the official numbers of the African continent are significantly lower than those of Europe, North America, South America, and Asia. Nevertheless, the infectious disease still managed to have its effects on several countries. South Africa had the highest number of deaths. Morocco and Tunisia, the second and third most affected in Africa, recorded 16,002 and 27,824 deaths, respectively, while Egypt registered at 24,132 as of March 02, 2022.

    The light at the end of the tunnel  

    Although the African countries still have a long way to fully combat the virus, vaccination programs have been rolled out in the majority of Africa. Also, according to a survey, public opinion in several African countries shows a high willingness to be vaccinated, with Ethiopia having numbers as high as 94 percent. As of March 2022, Egypt was the country administering the highest number of vaccine doses, however, Seychelles had the highest per rate per 100 people .

  3. S1 Raw data -

    • figshare.com
    xlsx
    Updated Aug 31, 2023
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    The citation is currently not available for this dataset.
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    xlsxAvailable download formats
    Dataset updated
    Aug 31, 2023
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Max Jordan Nguemeni Tiako; Alyssa Browne
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundCOVID-19 has had a disproportionate impact on racial and ethnic minorities compared to White people. Studies have not sufficiently examined how sex and age interact with race/ethnicity, and potentially shape COVID-19 outcomes. We sought to examine disparities in COVID-19 outcomes by race, sex and age over time, leveraging data from Michigan, the only state whose Department of Health and Human Services (DHSS) publishes cross-sectional race, sex and age data on COVID-19.MethodsThis is an observational study using publicly available COVID-19 data (weekly cases, deaths, and vaccinations) from August 31 2020 to June 9 2021. Outcomes for descriptive analysis were age-standardized COVID-19 incidence and mortality rates, case-fatality rates by race, sex, and age, and within-gender and within-race incidence rate ratios and mortality rate ratios. We used descriptive statistics and linear regressions with age, race, and sex as independent variables.ResultsThe within-sex Black-White racial gap in COVID-19 incidence and mortality decreased at a similar rate among men and women but the remained wider among men. As of June 2021, compared to White people, incidence was lower among Asian American and Pacific Islander people by 2644 cases per 100,000 people and higher among Black people by 1464 cases per 100,000 people. Mortality was higher among those aged 60 or greater by 743.6 deaths per 100,000 people vs those 0–39. The interaction between race and age was significant between Black race and age 60 or greater, with an additional 708.5 deaths per 100,000 people vs White people aged 60 or greater. Black people had a higher case fatality rate than White people.ConclusionCOVID-19 incidence, mortality and vaccination patterns varied over time by race, age and sex. Black-White disparities decreased over time, with a larger effect on Black men, and Older Black people were particularly more vulnerable to COVID-19 in terms of mortality. Considering different individual characteristics such as age may further help elucidate the mechanisms behind racial and gender health disparities.

  4. f

    Summary of regression results.

    • plos.figshare.com
    xls
    Updated Aug 31, 2023
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    Max Jordan Nguemeni Tiako; Alyssa Browne (2023). Summary of regression results. [Dataset]. http://doi.org/10.1371/journal.pone.0288383.t003
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    xlsAvailable download formats
    Dataset updated
    Aug 31, 2023
    Dataset provided by
    PLOS ONE
    Authors
    Max Jordan Nguemeni Tiako; Alyssa Browne
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    BackgroundCOVID-19 has had a disproportionate impact on racial and ethnic minorities compared to White people. Studies have not sufficiently examined how sex and age interact with race/ethnicity, and potentially shape COVID-19 outcomes. We sought to examine disparities in COVID-19 outcomes by race, sex and age over time, leveraging data from Michigan, the only state whose Department of Health and Human Services (DHSS) publishes cross-sectional race, sex and age data on COVID-19.MethodsThis is an observational study using publicly available COVID-19 data (weekly cases, deaths, and vaccinations) from August 31 2020 to June 9 2021. Outcomes for descriptive analysis were age-standardized COVID-19 incidence and mortality rates, case-fatality rates by race, sex, and age, and within-gender and within-race incidence rate ratios and mortality rate ratios. We used descriptive statistics and linear regressions with age, race, and sex as independent variables.ResultsThe within-sex Black-White racial gap in COVID-19 incidence and mortality decreased at a similar rate among men and women but the remained wider among men. As of June 2021, compared to White people, incidence was lower among Asian American and Pacific Islander people by 2644 cases per 100,000 people and higher among Black people by 1464 cases per 100,000 people. Mortality was higher among those aged 60 or greater by 743.6 deaths per 100,000 people vs those 0–39. The interaction between race and age was significant between Black race and age 60 or greater, with an additional 708.5 deaths per 100,000 people vs White people aged 60 or greater. Black people had a higher case fatality rate than White people.ConclusionCOVID-19 incidence, mortality and vaccination patterns varied over time by race, age and sex. Black-White disparities decreased over time, with a larger effect on Black men, and Older Black people were particularly more vulnerable to COVID-19 in terms of mortality. Considering different individual characteristics such as age may further help elucidate the mechanisms behind racial and gender health disparities.

  5. COVID-19: socio-economic risk factors briefing - Dataset - data.gov.uk

    • ckan.publishing.service.gov.uk
    Updated Jun 4, 2020
    + more versions
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    ckan.publishing.service.gov.uk (2020). COVID-19: socio-economic risk factors briefing - Dataset - data.gov.uk [Dataset]. https://ckan.publishing.service.gov.uk/dataset/covid-19-socio-economic-risk-factors-briefing
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    Dataset updated
    Jun 4, 2020
    Dataset provided by
    CKANhttps://ckan.org/
    Description

    Coronavirus affects some members of the population more than others. Emerging evidence suggests that older people, men, people with health conditions such as respiratory and pulmonary conditions, and people of a Black, Asian Minority Ethnic (BAME) background are at particular risk. There are also a number of other wider public health risk factors that have been found to increase the likelihood of an individual contracting coronavirus. This briefing presents descriptive evidence on a range of these factors, seeking to understand at a London-wide level the proportion of the population affected by each.

  6. U.S. poverty rate 2024, by race and ethnicity

    • statista.com
    Updated Nov 5, 2025
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    Statista (2025). U.S. poverty rate 2024, by race and ethnicity [Dataset]. https://www.statista.com/statistics/200476/us-poverty-rate-by-ethnic-group/
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    Dataset updated
    Nov 5, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2024
    Area covered
    United States
    Description

    In 2024, **** percent of Black people living in the United States were living below the poverty line, compared to *** percent of white people. That year, the overall poverty rate in the U.S. across all races and ethnicities was **** percent. Poverty in the United States The poverty threshold for a single person in the United States was measured at an annual income of ****** U.S. dollars in 2023. Among families of four, the poverty line increases to ****** U.S. dollars a year. Women and children are more likely to suffer from poverty. This is due to the fact that women are more likely than men to stay at home, to care for children. Furthermore, the gender-based wage gap impacts women's earning potential. Poverty data Despite being one of the wealthiest nations in the world, the United States has some of the highest poverty rates among OECD countries. While, the United States poverty rate has fluctuated since 1990, it has trended downwards since 2014. Similarly, the average median household income in the U.S. has mostly increased over the past decade, except for the covid-19 pandemic period. Among U.S. states, Louisiana had the highest poverty rate, which stood at some ** percent in 2024.

  7. Data from: Inequality in the middle of a crisis: an analysis of health...

    • scielo.figshare.com
    xls
    Updated Jun 13, 2023
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    Giordano Magri; Michelle Fernandez; Gabriela Lotta (2023). Inequality in the middle of a crisis: an analysis of health workers during the COVID-19 pandemic from the profession, race, and gender perspectives [Dataset]. http://doi.org/10.6084/m9.figshare.21353195.v1
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    xlsAvailable download formats
    Dataset updated
    Jun 13, 2023
    Dataset provided by
    SciELOhttp://www.scielo.org/
    Authors
    Giordano Magri; Michelle Fernandez; Gabriela Lotta
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Abstract Studies show that people in vulnerable conditions and some social groups such as women and black people have suffered more intensely from the COVID-19 pandemic impacts. This expression of inequality also manifests itself among healthcare workers, with greater exposure of some specific groups. This paper analyzes the effect of COVID-19 on health care workers and the working conditions in the Brazilian public health system, analyzed from professional, gender, and race perspectives. Data were collected from an online survey of 1,829 health workers conducted in March 2021. Indeed, we identified inequalities in health workers’ experiences during the health crisis generated by COVID-19, which are marked by the profession of each worker and are traversed by their gender and race traits.

  8. f

    Datasheet1_Evaluating the impact of sickle cell disease on COVID-19...

    • datasetcatalog.nlm.nih.gov
    Updated Sep 12, 2023
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    Luo, Jiajun; Ahsan, Habibul; Johnson, Julie; Kim, Karen; Powell, Johnny; Ross, Sage; Olopade, Christopher O.; Pinto, Jayant; Aschebrook-Kilfoy, Briseis (2023). Datasheet1_Evaluating the impact of sickle cell disease on COVID-19 susceptibility and severity: a retrospective cohort study based on electronic health record.docx [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0000972862
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    Dataset updated
    Sep 12, 2023
    Authors
    Luo, Jiajun; Ahsan, Habibul; Johnson, Julie; Kim, Karen; Powell, Johnny; Ross, Sage; Olopade, Christopher O.; Pinto, Jayant; Aschebrook-Kilfoy, Briseis
    Description

    BackgroundSickle cell trait/disease (SCT/SCD) are enriched among Black people and associated with various comorbidities. The overrepresentation of these characteristics prevents traditional regression approach obtaining convincing evidence for the independent effect of SCT/SCD on other health outcomes. This study aims to investigate the association between SCT/SCD and COVID-19-related outcomes using causal inference approaches that balance the covariate.MethodsWe leveraged electronic health record (EHR) data from the University of Chicago Medicine between March 2020 and December 2021. Demographic characteristics were retrieved. Medical conditions were identified using ICD-10 codes. Five approaches, including two traditional regression approaches (unadjusted and adjusted) and three causal inference approaches [covariate balancing propensity score (CBPS) matching, CBPS weighting, and CBPS adjustment], were employed.ResultsA total of 112,334 patients were included in the study, among which 504 had SCT and 388 SCD. Patients with SCT/SCD were more likely to be non-Hispanic Black people, younger, female, non-smokers, and had a diagnosis of diabetes, heart failure, asthma, and cerebral infarction. Causal inference approaches achieved a balanced distribution of these covariates while traditional approaches failed. Across these approaches, SCD was consistently associated with COVID-19-related pneumonia (odds ratios (OR) estimates, 3.23 (95% CI: 2.13–4.89) to 2.57 (95% CI: 1.10–6.00)) and pain (OR estimates, 6.51 (95% CI: 4.68–9.06) to 2.47 (95% CI: 1.35–4.49)). While CBPS matching suggested an association between SCD and COVID-19-related acute respiratory distress syndrome (OR = 2.01, 95% CI: 0.97–4.17), this association was significant in other approaches (OR estimates, 2.96 (95% CI: 1.69–5.18) to 2.50 (95% CI: 1.43–4.37)). No association was observed between SCT and COVID-19-related outcomes in causal inference approaches.ConclusionUsing causal inference approaches, we provide comprehensive evidence for the link between SCT/SCD and COVID-19-related outcomes.

  9. Distribution of COVID-19 deaths in the U.S. as of June 14, 2023, by...

    • statista.com
    Updated Jun 21, 2023
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    Statista (2023). Distribution of COVID-19 deaths in the U.S. as of June 14, 2023, by race/ethnicity [Dataset]. https://www.statista.com/statistics/1122369/covid-deaths-distribution-by-race-us/
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    Dataset updated
    Jun 21, 2023
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    As of June 14, 2023, around 66 percent of all COVID-19 deaths in the United States have been among non-Hispanic whites, although non-Hispanic whites account for 60 percent of the total U.S. population. On the other hand, non-Hispanic Asians have accounted for just three percent of all deaths due to COVID-19 even though this group makes up almost six percent of the entire U.S. population. This statistic shows the distribution of COVID-19 (coronavirus disease) deaths in the United States, by race/ethnicity.

  10. u

    Co-POWeR: Consortium on Practices of Wellbeing and Resilience in Black,...

    • datacatalogue.ukdataservice.ac.uk
    Updated Jul 25, 2023
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    Solanke, I, University of Leeds; Bhattacharyya, G, University of East London; Gupta, A, Royal Holloway, University of London; Bernard, C, Goldsmiths, University of London; Lakhanpaul, M, UCL; Rai, S, University of Warwick; Stokes, M, University of Southampton; Ayisi, F, University of South Wales; Kaur, R, University of Sussex; Padmadas, S, University of Southampton (2023). Co-POWeR: Consortium on Practices of Wellbeing and Resilience in Black, Asian and Minority Ethnic Families and Communities, 2023 [Dataset]. http://doi.org/10.5255/UKDA-SN-856500
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    Dataset updated
    Jul 25, 2023
    Authors
    Solanke, I, University of Leeds; Bhattacharyya, G, University of East London; Gupta, A, Royal Holloway, University of London; Bernard, C, Goldsmiths, University of London; Lakhanpaul, M, UCL; Rai, S, University of Warwick; Stokes, M, University of Southampton; Ayisi, F, University of South Wales; Kaur, R, University of Sussex; Padmadas, S, University of Southampton
    License

    MIT Licensehttps://opensource.org/licenses/MIT
    License information was derived automatically

    Area covered
    United Kingdom
    Description

    The inequities of the COVID-19 pandemic were clear by April 2020 when data showed that despite being just 3.5% of the population in England, Black people comprised 5.8% of those who died from the virus; whereas White people, comprising 85.3% of the population, were 73.6% of those who died. The disproportionate impact continued with, for example, over-policing: 32% of stop and search in the year ending March 2021 were of Black, Asian and Minority Ethnic (BAME) males aged 15-34, despite them being just 2.6% of the population.

    The emergency measures introduced to govern the pandemic worked together to create a damaging cycle affecting Black, Asian and Minority Ethnic families and communities of all ages. Key-workers – often stopped by police on their way to provide essential services – could not furlough or work from home to avoid infection, nor support their children in home-schooling. Children in high-occupancy homes lacked adequate space and/ or equipment to learn; such homes also lacked leisure space for key workers to restore themselves after extended hours at work. Over-policing instilled fear across the generations and deterred BAME people – including the mobile elderly - from leaving crowded homes for legitimate exercise, and those that did faced the risk of receiving a Fixed Penalty Notice and a criminal record.

    These insights arose from research by Co-POWeR into the synergistic effects of emergency measures on policing, child welfare, caring, physical activity and nutrition. Using community engagement, a survey with 1000 participants and interviews, focus groups, participatory workshops and community testimony days with over 400 people in total, we explored the combined impact of COVID-19 and discrimination on wellbeing and resilience across BAME FC in the UK. This policy note crystallises our findings into a framework of recommendations relating to arts and media communications, systems and structures, community and individual well-being and resilience. We promote long term actions rather than short term reactions.

    In brief, we conclude that ignoring race, gender and class when tackling a pandemic can undermine not only wellbeing across Black, Asian and Minority Ethnic families and communities (BAME FC) but also their levels of trust in government. A framework to protect wellbeing and resilience in BAME FC during public health emergencies was developed by Co-POWeR to ensure that laws and guidance adopted are culturally competent.

    Two viruses - COVID-19 and discrimination - are currently killing in the UK (Solanke 2020), especially within BAMEFC who are hardest hit. Survivors face ongoing damage to wellbeing and resilience, in terms of physical and mental health as well as social, cultural and economic (non-medical) consequences. Psychosocial (ADCS 2020; The Children's Society 2020)/ physical trauma of those diseased and deceased, disproportionate job-loss (Hu 2020) multigenerational housing, disrupted care chains (Rai 2016) lack of access to culture, education and exercise, poor nutrition, 'over-policing' (BigBrotherWatch 2020) hit BAMEFC severely. Local 'lockdowns' illustrate how easily BAMEFC become subject to stigmatization and discrimination through 'mis-infodemics' (IOM 2020). The impact of these viruses cause long-term poor outcomes. While systemic deficiencies have stimulated BAMEFC agency, producing solidarity under emergency, BAMEFC vulnerability remains, requiring official support. The issues are complex thus we focus on the interlinked and 'intersectional nature of forms of exclusion and disadvantage', operationalised through the idea of a 'cycle of wellbeing and resilience' (CWAR) which recognises how COVID-19 places significant stress upon BAMEFC structures and the impact of COVID-19 and discrimination on different BAMEFC cohorts across the UK, in whose lives existing health inequalities are compounded by a myriad of structural inequalities. Given the prevalence of multi-generational households, BAMEFC are likely to experience these as a complex of jostling over-lapping stressors: over-policed unemployed young adults are more likely to live with keyworkers using public transport to attend jobs in the front line, serving elders as formal/informal carers, neglecting their health thus exacerbating co-morbidities and struggling to feed children who are unable to attend school, resulting in nutritional and digital deprivation. Historical research shows race/class dimensions to national emergencies (e.g. Hurricane Katrina) but most research focuses on the COVID-19 experience of white families/communities. Co-POWeR recommendations will emerge from culturally and racially sensitive social science research on wellbeing and resilience providing context as an essential strand for the success of biomedical and policy interventions (e.g. vaccines, mass testing). We will enhance official decision-making through strengthening cultural competence in ongoing responses to COVID-19 thereby maximizing success of national strategy. Evidenced recommendations will enable official mitigation of disproportionate damage to wellbeing and resilience in BAMEFC. Empowerment is a core consortium value. Supporting UKRI goals for an inclusive research culture, we promote co-design and co-production to create a multi-disciplinary BAME research community spanning multi-cultural UK to inform policy. CO-POWeR investigates the synergistic effect on different age groups of challenges including policing, child welfare, caring and physical activity and nutrition. WP1 Emergency Powers investigates these vague powers to understand their impact on practices of wellbeing and resilience across BAMEFC. WP2 Children, Young People and their Families investigates implications for children/young people in BAMEFC who experience COVID-19 negatively due to disproportionate socio-economic and psychosocial impacts on their families and communities. WP3 Care, Caring and Carers investigates the interaction of care, caring and carers within BAMEFC to identify how to increase the wellbeing and resilience of older people, and paid and unpaid carers. WP4 Physical Activity and Nutrition investigates improving resilience and wellbeing by tackling vulnerability to underlying health conditions in BAMEFC. WP5 Empowering BAMEFC through Positive Narratives channels research from WP1-4 to coproduce fiction and non-fiction materials tackling the vulnerability of BAMEFC to 'mis infodemics'.

  11. Coronavirus deaths in Africa 2022, by country

    • statista.com
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    Statista, Coronavirus deaths in Africa 2022, by country [Dataset]. https://www.statista.com/statistics/1170530/coronavirus-deaths-in-africa/
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    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Nov 23, 2022
    Area covered
    Africa
    Description

    As of November 18, 2022, the overall deaths due to coronavirus (COVID-19) in Africa reached 257,984. South Africa recorded the highest number of casualties. With over 100,000 deaths, the country accounted for roughly 40 percent of the total. Tunisia was the second most affected on the continent, as the virus made almost 30,000 victims in the nation, around 11 percent of the overall deaths in Africa. Egypt accounted for around 10 percent of the casualties on the continent, with 24,600 victims. By the same date, Africa had recorded more than 12 million cases of COVID-19.

  12. f

    Data_Sheet_2_Social determinants of health predict readmission following...

    • datasetcatalog.nlm.nih.gov
    • figshare.com
    Updated Mar 27, 2024
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    Boerwinkle, Eric; Ramphul, Ryan; Husain, Junaid; Mikhail, Jennifer L.; Sandoval, Micaela N.; Fink, Melyssa K.; Cao, Tru; Tortolero, Guillermo A. (2024). Data_Sheet_2_Social determinants of health predict readmission following COVID-19 hospitalization: a health information exchange-based retrospective cohort study.docx [Dataset]. https://datasetcatalog.nlm.nih.gov/dataset?q=0001398787
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    Dataset updated
    Mar 27, 2024
    Authors
    Boerwinkle, Eric; Ramphul, Ryan; Husain, Junaid; Mikhail, Jennifer L.; Sandoval, Micaela N.; Fink, Melyssa K.; Cao, Tru; Tortolero, Guillermo A.
    Description

    IntroductionSince February 2020, over 104 million people in the United States have been diagnosed with SARS-CoV-2 infection, or COVID-19, with over 8.5 million reported in the state of Texas. This study analyzed social determinants of health as predictors for readmission among COVID-19 patients in Southeast Texas, United States.MethodsA retrospective cohort study was conducted investigating demographic and clinical risk factors for 30, 60, and 90-day readmission outcomes among adult patients with a COVID-19-associated inpatient hospitalization encounter within a regional health information exchange between February 1, 2020, to December 1, 2022.Results and discussionIn this cohort of 91,007 adult patients with a COVID-19-associated hospitalization, over 21% were readmitted to the hospital within 90 days (n = 19,679), and 13% were readmitted within 30 days (n = 11,912). In logistic regression analyses, Hispanic and non-Hispanic Asian patients were less likely to be readmitted within 90 days (adjusted odds ratio [aOR]: 0.8, 95% confidence interval [CI]: 0.7–0.9, and aOR: 0.8, 95% CI: 0.8–0.8), while non-Hispanic Black patients were more likely to be readmitted (aOR: 1.1, 95% CI: 1.0–1.1, p = 0.002), compared to non-Hispanic White patients. Area deprivation index displayed a clear dose–response relationship to readmission: patients living in the most disadvantaged neighborhoods were more likely to be readmitted within 30 (aOR: 1.1, 95% CI: 1.0–1.2), 60 (aOR: 1.1, 95% CI: 1.2–1.2), and 90 days (aOR: 1.2, 95% CI: 1.1–1.2), compared to patients from the least disadvantaged neighborhoods. Our findings demonstrate the lasting impact of COVID-19, especially among members of marginalized communities, and the increasing burden of COVID-19 morbidity on the healthcare system.

  13. Summary statistics on study population.

    • plos.figshare.com
    • datasetcatalog.nlm.nih.gov
    xls
    Updated Jan 31, 2024
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    Vladimir Atanasov; Natalia Barreto; Lorenzo Franchi; Jeff Whittle; John Meurer; Benjamin W. Weston; Qian (Eric) Luo; Andy Ye Yuan; Ruohao Zhang; Bernard Black (2024). Summary statistics on study population. [Dataset]. http://doi.org/10.1371/journal.pone.0295936.t001
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    xlsAvailable download formats
    Dataset updated
    Jan 31, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Vladimir Atanasov; Natalia Barreto; Lorenzo Franchi; Jeff Whittle; John Meurer; Benjamin W. Weston; Qian (Eric) Luo; Andy Ye Yuan; Ruohao Zhang; Bernard Black
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    COVID-19 mortality rates increase rapidly with age, are higher among men than women, and vary across racial/ethnic groups, but this is also true for other natural causes of death. Prior research on COVID-19 mortality rates and racial/ethnic disparities in those rates has not considered to what extent disparities reflect COVID-19-specific factors, versus preexisting health differences. This study examines both questions. We study the COVID-19-related increase in mortality risk and racial/ethnic disparities in COVID-19 mortality, and how both vary with age, gender, and time period. We use a novel measure validated in prior work, the COVID Excess Mortality Percentage (CEMP), defined as the COVID-19 mortality rate (Covid-MR), divided by the non-COVID natural mortality rate during the same time period (non-Covid NMR), converted to a percentage. The CEMP denominator uses Non-COVID NMR to adjust COVID-19 mortality risk for underlying population health. The CEMP measure generates insights which differ from those using two common measures–the COVID-MR and the all-cause excess mortality rate. By studying both CEMP and COVID-MRMR, we can separate the effects of background health from Covid-specific factors affecting COVID-19 mortality. We study how CEMP and COVID-MR vary by age, gender, race/ethnicity, and time period, using data on all adult decedents from natural causes in Indiana and Wisconsin over April 2020-June 2022 and Illinois over April 2020-December 2021. CEMP levels for racial and ethnic minority groups can be very high relative to White levels, especially for Hispanics in 2020 and the first-half of 2021. For example, during 2020, CEMP for Hispanics aged 18–59 was 68.9% versus 7.2% for non-Hispanic Whites; a ratio of 9.57:1. CEMP disparities are substantial but less extreme for other demographic groups. Disparities were generally lower after age 60 and declined over our sample period. Differences in socio-economic status and education explain only a small part of these disparities.

  14. Leading causes of death among the white population in the United States...

    • statista.com
    Updated Sep 15, 2025
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    Statista (2025). Leading causes of death among the white population in the United States 2020-2023 [Dataset]. https://www.statista.com/statistics/233304/distribution-of-the-10-leading-causes-of-death-among-whites-in-2016/
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    Dataset updated
    Sep 15, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Area covered
    United States
    Description

    The leading causes of death among the white population of the United States are cardiovascular diseases and cancer. Cardiovascular diseases and cancer accounted for a combined **** percent of all deaths among this population in 2023. In 2020 and 2021, COVID-19 was the third leading cause of death among white people but was the eighth leading cause in 2023. Disparities in causes of death In the United States, there exist disparities in the leading causes of death based on race and ethnicity. For example, chronic liver disease and cirrhosis is the ***** leading cause of death among the white population and the ******* among the Hispanic population but is not among the ten leading causes for Black people. On the other hand, homicide is the ******leading cause of death among the Black population but is not among the 10 leading causes for whites or Hispanics. However, cardiovascular diseases and cancer by far account for the highest share of deaths for every race and ethnicity. Diseases of despair The American Indian and Alaska Native population in the United States has the highest rates of death from suicide, drug overdose, and alcohol. Together, these three behavior-related conditions are often referred to as diseases of despair. Asians have by far the lowest rates of death due to drug overdose and alcohol, as well as slightly lower rates of suicide.

  15. h

    Investigating Interactions between Mycobacterium Tuberculosis and SARS-CoV-2...

    • healthdatagateway.org
    unknown
    Updated Oct 8, 2024
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    This publication uses data from PIONEER, an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158) (2024). Investigating Interactions between Mycobacterium Tuberculosis and SARS-CoV-2 [Dataset]. https://healthdatagateway.org/en/dataset/161
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    unknownAvailable download formats
    Dataset updated
    Oct 8, 2024
    Dataset authored and provided by
    This publication uses data from PIONEER, an ethically approved database and analytical environment (East Midlands Derby Research Ethics 20/EM/0158)
    License

    https://www.pioneerdatahub.co.uk/data/data-request-process/https://www.pioneerdatahub.co.uk/data/data-request-process/

    Description

    Tuberculosis (TB) is caused by a bacterium called Mycobacterium tuberculosis.  TB remains a significant global health problem. The UK has one of the highest rates of TB in Europe, with almost 5000 new cases notified in 2019. Within the UK, Birmingham and the West Midlands are particular hotspots for TB, with over 300 cases of active disease and approximately 10 times that of new latent infections diagnosed each year.

    Birmingham and the West Midlands have experienced particularly high rates of COVID-19 during the pandemic and there is increasing evidence that individuals of Black, Asian and minority ethnicities (BAME) experience the most significant morbidity and highest mortality rates due to COVID-19. These groups also experience the highest burdens of TB, both in the UK and overseas.

    Epidemiological data suggests that current and previous tuberculosis (TB) increase the risk of COVID-19 mortality and severe disease. There is also evidence of immunopathogenic overlap between the two infections with in vitro studies finding that SARS-CoV-2 infection is increased in human macrophages cultured in the inflammatory milieu of TB-infected macrophages.

    This dataset would enable a deeper analysis of demography and clinical outcomes associated with COVID-19 in patients with concurrent TB.

    PIONEER geography: the West Midlands (WM) has a population of 5.9 million & includes a diverse ethnic & socio-economic mix.

    EHR. UHB is one of the largest NHS Trusts in England, providing direct acute services & specialist care across four hospital sites, with 2.2 million patient episodes per year, 2750 beds & an expanded 250 ITU bed capacity during COVID. UHB runs a fully electronic healthcare record (EHR) (PICS; Birmingham Systems), a shared primary & secondary care record (Your Care Connected) & a patient portal “My Health”.

    Scope: All hospitalised patients admitted to UHB during the COVID-19 pandemic, curated to focus on Mycobacterium tuberculosis and SARS-CoV-2. Longitudinal & individually linked, so that the preceding & subsequent health journey can be mapped & healthcare utilisation prior to & after admission understood. The dataset includes highly granular patient demographics & co-morbidities taken from ICD-10 & SNOMED-CT codes. Serial, structured data pertaining to acute care process (A&E, triage, IP, ITU admissions), presenting complaint, DNAR teal, all physiology readings (AVPU scale, Covid CFS, blood pressure, respiratory rate, oxygen saturations and others), all blood results, imaging reports, all prescribed & administered treatments, all outcomes.

    Available supplementary data: Matched controls; ambulance, OMOP data, synthetic data.

    Available supplementary support: Analytics, Model build, validation & refinement; A.I.; Data partner support for ETL (extract, transform & load) process, Clinical expertise, Patient & end-user access, Purchaser access, Regulatory requirements, Data-driven trials, “fast screen” services.

  16. Qualitative coding of strategies.

    • plos.figshare.com
    xls
    Updated Oct 15, 2025
    + more versions
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    Lester A. Mejia Gomez; David Menendez; Valerie Umscheid; Susan A. Gelman (2025). Qualitative coding of strategies. [Dataset]. http://doi.org/10.1371/journal.pone.0332140.t002
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    xlsAvailable download formats
    Dataset updated
    Oct 15, 2025
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Lester A. Mejia Gomez; David Menendez; Valerie Umscheid; Susan A. Gelman
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    The COVID-19 pandemic revealed substantial health disparities, disproportionately impacting Black individuals, individuals of lower socioeconomic status, and older adults in the US. Little is known as to whether and how adults discuss these disparities with their children, an essential first step toward determining when and how children come to understand these differences. To address these questions, we recruited parents with at least one child aged 5–12 (N = 443, 61% White) from CloudResearch Prime Panels. We asked participants to report their likelihood of discussing these disparities with their children, how they would explain them, their own beliefs regarding these disparities, and a series of group perception and attitudinal measures. An ordinal mixed-effects regression revealed that parents were significantly more likely to say they would discuss the age disparity than the race and class disparities, with no difference between the latter. Parents of older children reported being more likely to discuss race and age disparities than parents of younger children. Ordinal logistic regressions revealed that parents reported they would discuss the race disparity significantly more when they held stronger racial essentialist beliefs, held stronger racial social constructionist beliefs, and perceived Black people as warmer and less competent. Parents also reported that they would discuss the social class disparity significantly more when they held stronger essentialist beliefs about social class. Qualitative coding revealed that parents’ potential explanations for the disparity and reasons to discuss the disparities (or not) with their children differed by dimension. Finally, parents’ own beliefs about the existence, nature, and causes of these disparities predicted the likelihood that they would discuss them with their children -- though differently for the different dimensions. Overall, our findings suggest that parents’ likelihood of discussing health disparities reflects three key factors: their own beliefs about whether/how such disparities exist, their attitudes toward the affected groups, and their comfort in discussing social issues with their children.

  17. Players in the NFL in 2023, by ethnicity

    • statista.com
    Updated Nov 26, 2025
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    Statista (2025). Players in the NFL in 2023, by ethnicity [Dataset]. https://www.statista.com/statistics/1167935/racial-diversity-nfl-players/
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    Dataset updated
    Nov 26, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    2023
    Area covered
    United States
    Description

    In 2023, the greatest share of players by ethnic group in the National Football League (NFL) were black or African American athletes, constituting just over ** percent of players within the NFL. Despite the large population of Hispanic or Latino people within the United States, there is a substantial underrepresentation within the NFL, with only *** percent of players identifying as such. National Football League The National Football League (NFL) is a professional American football league that was established in 1920 and now consists of 32 clubs divided into two conferences, the National Football Conference (NFC) and the American Football Conference (AFC). The league culminates in the Super Bowl, the NFL's annual championship game. As the league’s championship game, the Super Bowl has grown into one of the world's largest single-day sporting events, attracting high television ratings and generating billions of dollars in consumer spending. NFL revenues The NFL is one of the most profitable sports leagues in the world, generating a staggering **** billion U.S. dollars in 2022. This total revenue of all ** NFL teams has constantly increased over the past 15 years and, although this figure dropped significantly in 2020, this was largely as a result of the impact of coronavirus (COVID-19) containment measures. This significant drop in revenue demonstrates one of the primary impacts of COVID-19 on professional sports leagues. NFL franchises As a result of this profitability in non-pandemic times, the franchises of the NFL are attributed extremely high market values. The Dallas Cowboys were by far the most valuable franchise in the NFL, with a market value of **** billion US dollars in 2023. The high value of NFL franchises can be seen clearly when compared to those of the NBA, MLB, and NHL. Franchises within the NFL had an average market value of approximately *** billion U.S. dollars in 2023.

  18. COVID-19 confirmed, recovered and deceased cumulative cases in India...

    • statista.com
    Updated Apr 29, 2021
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    Statista (2021). COVID-19 confirmed, recovered and deceased cumulative cases in India 2020-2023 [Dataset]. https://www.statista.com/statistics/1104054/india-coronavirus-covid-19-daily-confirmed-recovered-death-cases/
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    Dataset updated
    Apr 29, 2021
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Jan 29, 2020 - Oct 20, 2023
    Area covered
    India
    Description

    India reported almost 45 million cases of the coronavirus (COVID-19) as of October 20, 2023, with more than 44 million recoveries and about 532 thousand fatalities. The number of cases in the country had a decreasing trend in the past months.

    Burden on the healthcare system

    With the world's second largest population in addition to an even worse second wave of the coronavirus pandemic seems to be crushing an already inadequate healthcare system. Despite vast numbers being vaccinated, a new variant seemed to be affecting younger age groups this time around. The lack of ICU beds, black market sales of oxygen cylinders and drugs needed to treat COVID-19, as well as overworked crematoriums resorting to mass burials added to the woes of the country. Foreign aid was promised from various countries including the United States, France, Germany and the United Kingdom. Additionally, funding from the central government was expected to boost vaccine production.

    Situation overview
    Even though days in April 2021 saw record-breaking numbers compared to any other country worldwide, a nation-wide lockdown has not been implemented. The largest religious gathering - the Kumbh Mela, sacred to the Hindus, along with election rallies in certain states continue to be held. Some states and union territories including Maharashtra, Delhi, and Karnataka had issued curfews and lockdowns to try to curb the spread of infections.

  19. PASC symptoms by age group.

    • plos.figshare.com
    xls
    Updated Jul 25, 2024
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    Yiyi Wu; Eleni Mattas; Carey Brandenburg; Ethan Fusaris; Richard Overbey; Jerome Ernst; Mark Brennan-Ing (2024). PASC symptoms by age group. [Dataset]. http://doi.org/10.1371/journal.pone.0306322.t002
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    xlsAvailable download formats
    Dataset updated
    Jul 25, 2024
    Dataset provided by
    PLOShttp://plos.org/
    Authors
    Yiyi Wu; Eleni Mattas; Carey Brandenburg; Ethan Fusaris; Richard Overbey; Jerome Ernst; Mark Brennan-Ing
    License

    Attribution 4.0 (CC BY 4.0)https://creativecommons.org/licenses/by/4.0/
    License information was derived automatically

    Description

    Understanding how post-acute sequelae of SARS-CoV-2 infection (PASC) affects communities disproportionately affected by HIV is critically needed. This study aimed to identify the prevalence of PASC symptoms among Medicaid enrollees at risk for or living with HIV. Through a web survey, we received 138 valid responses from Medicaid-managed plan members who had received a COVID diagnosis. Participants’ mean age was 45.4 years (SD = 11.9) and most were non-Hispanic Black (43.5%) or Hispanic (39.1%). Almost thirty-two percent reported inadequate incomes and 77.5% were HIV-positive. In the overall population, the frequently reported symptoms included neck/back/low back pain, brain fog/difficulty concentrating, bone/joint pain, muscle aches, and fatigue. Findings indicate that there is no statistically significant difference in the prevalence and intensity of PASC symptoms lasting 6 months or more between individuals living with and without HIV. Multiple regression analysis found that the number of PASC symptoms 6 months or longer was independently associated with inadequate incomes and comorbidities (cardiac problems, cancer, fibromyalgia) (R2 = .34). Those with inadequate incomes and comorbidities have more numerous PASC symptoms. Implications for health care delivery and long-term COVID services will be discussed.

  20. COVID-19 impact on post-confinement shopping behavior in France 2020

    • statista.com
    Updated Jul 9, 2025
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    Statista (2025). COVID-19 impact on post-confinement shopping behavior in France 2020 [Dataset]. https://www.statista.com/statistics/1183544/covid-19-impact-forecast-shopping-behavior-france-post-confinement/
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    Dataset updated
    Jul 9, 2025
    Dataset authored and provided by
    Statistahttp://statista.com/
    Time period covered
    Apr 22, 2020 - Apr 27, 2020
    Area covered
    France
    Description

    The COVID-19 induced lockdown in France has boosted the e-commerce in a drastic way as consumers purchased more products online and made fewer trips to physical stores. Marketers have predicted that consumer behavior will likely not result in a pre-pandemic buying behavior. Thus, in April 2020, ** percent of internet users in France aged 16 to 64 said they expected to visit physical stores less frequently post-pandemic, and ** percent planned to spend less time in-store. Some ** percent of respondents said they would adopt click and collect (Drive), and ** percent would shop more online for home delivery.

    Second "light" lockdown and the effects on Black Friday sales

    The holy Black Friday dedicated to the madness of shopping will take place in France on Friday, November 27, 2020. Even if for deconfinement, all the retail brands had adopted the measures necessary for a return to a secure store, it seems that an important fringe of the population will shun these physical places. And with a second, "lighter" lockdown, marketers have asked themselves how the physical shopping sales loss will impact Black Friday or other sales seasons such as the French Days, as a significant revenue source for retail and the shopping economy in general.

    Online presence for retailers

    With a need for a plan to save physical stores, the development of online businesses has increased and so has the consumer adoption. While groceries are majoritarity carried out using Drive technologies and home delivery, the fashion retail sector has taken a toll for physical brands, which cannot be said to that extent about pure players such as Vinted.fr. But, as the sales for Black Friday had increased by *** percent compared to it's previous shopping weeks in 2019, the sales season and the French days awaited shopping peaks have only increased by a small amount compared to previous weeks of online shopping in 2020. Thus, for the fashion retail sector, which is one of the most sought after sector after high-tech products, virtual purchases are a hope for sellers to support the survival of their businesses.

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data.ct.gov (2023). COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE [Dataset]. https://catalog.data.gov/dataset/covid-19-cases-and-deaths-by-race-ethnicity

COVID-19 Cases and Deaths by Race/Ethnicity - ARCHIVE

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Dataset updated
Aug 12, 2023
Dataset provided by
data.ct.gov
Description

Note: DPH is updating and streamlining the COVID-19 cases, deaths, and testing data. As of 6/27/2022, the data will be published in four tables instead of twelve. The COVID-19 Cases, Deaths, and Tests by Day dataset contains cases and test data by date of sample submission. The death data are by date of death. This dataset is updated daily and contains information back to the beginning of the pandemic. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Cases-Deaths-and-Tests-by-Day/g9vi-2ahj. The COVID-19 State Metrics dataset contains over 93 columns of data. This dataset is updated daily and currently contains information starting June 21, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-State-Level-Data/qmgw-5kp6 . The COVID-19 County Metrics dataset contains 25 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-County-Level-Data/ujiq-dy22 . The COVID-19 Town Metrics dataset contains 16 columns of data. This dataset is updated daily and currently contains information starting June 16, 2022 to the present. The data can be found at https://data.ct.gov/Health-and-Human-Services/COVID-19-Town-Level-Data/icxw-cada . To protect confidentiality, if a town has fewer than 5 cases or positive NAAT tests over the past 7 days, those data will be suppressed. COVID-19 cases and associated deaths that have been reported among Connecticut residents, broken down by race and ethnicity. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. Deaths reported to the either the Office of the Chief Medical Examiner (OCME) or Department of Public Health (DPH) are included in the COVID-19 update. The following data show the number of COVID-19 cases and associated deaths per 100,000 population by race and ethnicity. Crude rates represent the total cases or deaths per 100,000 people. Age-adjusted rates consider the age of the person at diagnosis or death when estimating the rate and use a standardized population to provide a fair comparison between population groups with different age distributions. Age-adjustment is important in Connecticut as the median age of among the non-Hispanic white population is 47 years, whereas it is 34 years among non-Hispanic blacks, and 29 years among Hispanics. Because most non-Hispanic white residents who died were over 75 years of age, the age-adjusted rates are lower than the unadjusted rates. In contrast, Hispanic residents who died tend to be younger than 75 years of age which results in higher age-adjusted rates. The population data used to calculate rates is based on the CT DPH population statistics for 2019, which is available online here: https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Population-Statistics. Prior to 5/10/2021, the population estimates from 2018 were used. Rates are standardized to the 2000 US Millions Standard population (data available here: https://seer.cancer.gov/stdpopulations/). Standardization was done using 19 age groups (0, 1-4, 5-9, 10-14, ..., 80-84, 85 years and older). More information about direct standardization for age adjustment is available here: https://www.cdc.gov/nchs/data/statnt/statnt06rv.pdf Categories are mutually exclusive. The category “multiracial” includes people who answered ‘yes’ to more than one race category. Counts may not add up to total case counts as data on race and ethnicity may be missing. Age adjusted rates calculated only for groups with more than 20 deaths. Abbreviation: NH=Non-Hispanic. Data on Connecticut deaths were obtained from the Connecticut Deaths Registry maintained by the DPH Office of Vital Records. Cause of death was determined by a death certifier (e.g., physician, APRN, medical

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