Data on clinically extremely vulnerable people in England during the coronavirus (COVID-19) pandemic from the Shielding Behavioural Survey. Includes information on their behaviours and well-being since receiving shielding guidance.

While diseases can make anyone sick, some Canadians are more at risk of getting an infection and developing severe complications due to their health, social and economic circumstances. Organizations, staff and volunteers play an important role in helping to prevent these populations from getting or spreading the COVID-19 virus. Start by sharing simple things they can do to help keep themselves and others healthy, guide them to help if they develop any signs and symptoms and learn ways help care for sick clients recovering from COVID-19.

Analysis of people previously considered to be clinically extremely vulnerable (CEV) in England during the coronavirus (COVID-19) pandemic, including their behaviours and mental and physical well-being.

In the emerging post-pandemic era (the ‘wavelet’ era), humans must coexist with viruses for the foreseeable future, and personal protective behaviors will largely replace national-level preventive measures. In this new normal, encouraging the public to implement proper personal protective behaviors against the coronavirus disease (COVID-19) is vital to the sustainable development of cities and communities. This knowledge–attitude–practice (KAP) survey conducted in Chengdu (N = 900) narrowed the knowledge gap regarding post-pandemic public practices of protective behavior. Findings show that:(1) approximately 1/3 of the respondents are currently not concerned about COVID-19 at all; (2) respondents with different demographics and individual COVID-19-related factors showed significant differences in practice behaviors indoors and outdoors; (3) vulnerable groups performed better in practice behavior indoors/outdoors; (4) because the public may relax their vigilance outdoors, public places may become a transmission threat in the next outbreak; (5) attitudes are important, but limited incentives for practice; and (6) when knowledge increases beyond a threshold (68.75–75% in this study), protective behaviors decrease. Our results suggest that authorities must continue to educate and motivate the public, extending measures to cover personal protective practices, and have targeted policies for specific demographics to ensure equity in healthcare in the event of another pandemic (COVID-19 and alike crisis). Besides, comparing the results of the current study with similar studies conducted in other parts of the world can provide insights into how different populations respond to and adopt COVID-19 protective behaviors. The epidemiologists can use the data collected by this and other KAP surveys to refine epidemiologic models, which can help predict the spread of the virus and the impact of interventions in different settings.

NOTE: As of 2/16/2023, this page is not being updated. This tables shows the number and percent of people that have initiated COVID-19 vaccination, are fully vaccinated and had additional dose 1 grouped by whether they live in an SVI Priority Zip Code. People with an out-of-state zip code are excluded from this analysis. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. A person who has received at least one dose of any COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if they have completed a primary vaccine series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional dose of COVID-19 vaccine is considered to have had additional dose 1. The additional monovalent dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. SVI scores were calculated for each zip code in CT. The zip codes in the top 20% were designated as SVI Priority Zip Codes. Percentages are based on 2018 zip code population data supplied by ESRI corporation. The percent with at least one dose many be over-estimated and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported. Connecticut COVID-19 Vaccine Program providers are required to report information on all COVID-19 vaccine doses administered to CT WiZ, the Connecticut Immunization Information System. Data on doses administered to CT residents out-of-state are being added to CT WiZ jurisdiction-by-jurisdiction. Doses administered by some Federal entities (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) are not yet reported to CT WiZ.  Data reported here reflect the vaccination records currently reported to CT WiZ. Note: As part of continuous data quality improvement efforts, duplicate records were removed from the COVID-19 vaccination data during the weeks of 4/19/2021 and 4/26/2021.

NOTE: As of 4/15/2021, this dataset will no longer be updated and will be replaced by two new datasets: 1) "COVID-19 Vaccinations by Town" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town/x7by-h8k4) and "COVID-19 Vaccinations by Town and Age Group" (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town-and-Age-Group/gngw-ukpw). A summary of COVID-19 vaccination coverage in Connecticut by town. Records without an address could not be included in town vaccine coverage estimates. Total population estimates are based on 2019 data. A person who has received one dose of any vaccine is considered to have received at least one dose. A person is considered fully vaccinated if they have received 2 doses of the Pfizer or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the number who have received at least one dose. The number with At Least One Dose and the number Fully Vaccinated add up to more than the total number of doses because people who received the Johnson & Johnson vaccine fit into both categories. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. Towns with a "yes" in the "Has SVI tract >0.75" field are those that have at least one census tract that is in the top quartile of vulnerability (e.g., a high-need area). 34 towns in Connecticut have at least one census tract in the top quartile for vulnerability. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.

In the context of COVID-19, RAND and the Robert Wood Johnson Foundation partnered again to build from the National Survey of Health Attitudes to implement a longitudinal survey to understand how these health views and values have been affected by the experience of the pandemic, with particular focus on populations deemed vulnerable or underserved, including people of color and those from low- to moderate-income backgrounds. The questions in this COVID-19 survey focused specifically on experiences related to the pandemic (e.g., financial, physical, emotional), how respondents viewed the disproportionate impacts of the pandemic, whether and how respondents' views and priorities regarding health actions and investments are changing (including the roles of government and the private sector), and how general values about such issues as freedom and racism may be related to pandemic views and response expectations. This study includes the results for Wave 4 for the general population. Demographic information includes sex, marital status, household size, race and ethnicity, family income, employment status, age, and census region.

Abstract The scope of the study was to investigate the relationship between COVID-19 and social vulnerability variables in Fortaleza, as well as to verify the influence of violence on the work of the Community Health Worker (ACS) and the implications for tackling COVID-19. Primary quantitative data (e.g., questionnaires with the ACS) and secondary data (e.g., HDI, homicide, illiteracy, Bolsa Família [PBF] coverage and Family Health Strategy [ESF] coverage) were used. There was a relationship between COVID-19 and indicators of social vulnerability, where cases had a negative relationship with HDI and illiteracy; and deaths had a positive relationship with the homicide rate and PBF coverage. Regions with high death rates due to COVID-19 and social vulnerability have greater FHS coverage and less home visits by the CHA. The latter’s role in tackling COVID-19 is limited by the violence in the territory. The thorough performance of the ACS in confronting this pandemic is dependent on intersectoral policies. Thus, the creation of a policy to tackle COVID-19 with the participation of the ACS needs to consider intersectoral actions for curtailing and preventing violence in the territory, in addition to adequate training in the prevention and detection of COVID-19.

The novel coronavirus infectious disease (COVID-19) pandemic has negatively impacted not only our physical health but also mental health, including increasing depressive and anxiety symptoms. In particular, socially and physically vulnerable populations, such as people experiencing homelessness (PEH), may be more likely to have their mental health worsened by the pandemic due to having more difficulty meeting basic human needs. Therefore, this study aims to assess the impact of COVID-19 on mental health of the homeless in Japan by evaluating depressive and anxiety symptoms and identifying the associated factors particularly, sociodemographic variables as age, employment status and the fear and perceived risk of COVID-19 infection. A cross-sectional interview survey among 158 PEH in Osaka Prefecture was conducted from April to May 2022. The survey included sociodemographic questions and history and perceived risk of infection with COVID-19. Depressive symptoms were measured using the nine-item Patient Health Questionnaire (PHQ-9) and anxiety symptoms using the seven-item Generalized Anxiety Disorder Scale (GAD-7), and the fear of COVID-19 using the seven-item Fear of New Coronavirus Scale (FCV-19S). In this study, the prevalence of depression (PHQ-9≥10) was 38.6%, anxiety disorder (GAD≥10) was 19.0%, and high fear of COVID-19 (FCV-19S≥19) was 28.5%. Univariate logistic regression analysis revealed that PEH in younger age groups (18–34 years), and with joblessness, higher perceived infection risk, and higher fear of COVID-19 were more likely to suffer from depression and anxiety (p

BackgroundPeople with structural vulnerabilities (including immigrants, people who use drugs, and those who are unhoused or uninsured) are more likely to experience COVID-19 testing disparities relative to other groups. We documented barriers and facilitators to COVID-19 testing and explored how structural vulnerabilities created and/or exacerbated COVID-19 testing barriers.MethodsBetween 2021 and 2022, we conducted semi-structured interviews with 34 members of structurally vulnerable populations and 27 key informants who provide health and social services to them. Our abductive analysis was iterative, utilizing both inductive and deductive coding processes. Recognizing that adequate and appropriate testing for COVID-19 is a complex health behavior that involves both decision-making and issues related to access, we developed a hybrid model of COVID-19 testing behavior to organize reported barriers. We then used that model for more in-depth analysis of structural vulnerabilities in the context of testing.ResultsOur model of testing behaviors provides a framework for understanding the many barriers and facilitators relevant to COVID-19 testing. After identifying locally-reported barriers, we found that specific conditions—economic precarity, legal precarity, the confusing U.S. healthcare landscape, English-exclusive environments, and stigmatizing medical encounters—make adequate and appropriate testing less likely by making COVID-19 testing feel riskier (entailing legal, financial, and psycho-social risks) and making healthcare, and thus vicariously testing, more difficult to access.ConclusionThe COVID-19 pandemic exposed disparities in health care delivery. To avoid under-testing and its associated health consequences during the next pandemic, public health efforts should address structural conditions to ameliorate risks and bolster testing infrastructure to improve access.

IntroductionCOVID-19 vaccine inequities have been widespread across California, the United States, and globally. As COVID-19 vaccine inequities have not been fully understood in the youth population, it is vital to determine possible factors that drive inequities to enable actionable change that promotes vaccine equity among vulnerable minor populations.MethodsThe present study used the social vulnerability index (SVI) and daily vaccination numbers within the age groups of 12–17, 5–11, and under 5 years old across all 58 California counties to model the growth velocity and the anticipated maximum proportion of population vaccinated.ResultsOverall, highly vulnerable counties, when compared to low and moderately vulnerable counties, experienced a lower vaccination rate in the 12–17 and 5–11 year-old age groups. For age groups 5–11 and under 5 years old, highly vulnerable counties are expected to achieve a lower overall total proportion of residents vaccinated. In highly vulnerable counties in terms of socioeconomic status and household composition and disability, the 12–17 and 5–11 year-old age groups experienced lower vaccination rates. Additionally, in the 12–17 age group, high vulnerability counties are expected to achieve a higher proportion of residents vaccinated compared to less vulnerable counterparts.DiscussionThese findings elucidate shortcomings in vaccine uptake in certain pediatric populations across California and may help guide health policies and future allocation of vaccines, with special emphasis placed on vulnerable populations, especially with respect to socioeconomic status and household composition and disability.

The COVID-19 Vulnerability and Recovery Index uses Tract and ZIP Code-level data* to identify California communities most in need of immediate and long-term pandemic and economic relief. Specifically, the Index is comprised of three components — Risk, Severity, and Recovery Need with the last scoring the ability to recover from the health, economic, and social costs of the pandemic. Communities with higher Index scores face a higher risk of COVID-19 infection and death and a longer uphill economic recovery. Conversely, those with lower scores are less vulnerable.

The Index includes one overarching Index score as well as a score for each of the individual components. Each component includes a set of indicators we found to be associated with COVID-19 risk, severity, or recovery in our review of existing indices and independent analysis. The Risk component includes indicators related to the risk of COVID-19 infection. The Severity component includes indicators designed to measure the risk of severe illness or death from COVID-19. The Recovery Need component includes indicators that measure community needs related to economic and social recovery. The overarching Index score is designed to show level of need from Highest to Lowest with ZIP Codes in the Highest or High need categories, or top 20th or 40th percentiles of the Index, having the greatest need for support.

The Index was originally developed as a statewide tool but has been adapted to LA County for the purposes of the Board motion. To distinguish between the LA County Index and the original Statewide Index, we refer to the revised Index for LA County as the LA County ARPA Index.

*Zip Code data has been crosswalked to Census Tract using HUD methodology

Indicators within each component of the LA County ARPA Index are:Risk: Individuals without U.S. citizenship; Population Below 200% of the Federal Poverty Level (FPL); Overcrowded Housing Units; Essential Workers Severity: Asthma Hospitalizations (per 10,000); Population Below 200% FPL; Seniors 75 and over in Poverty; Uninsured Population; Heart Disease Hospitalizations (per 10,000); Diabetes Hospitalizations (per 10,000)Recovery Need: Single-Parent Households; Gun Injuries (per 10,000); Population Below 200% FPL; Essential Workers; Unemployment; Uninsured PopulationData are sourced from US Census American Communities Survey (ACS) and the OSHPD Patient Discharge Database. For ACS indicators, the tables and variables used are as follows:

Indicator

ACS Table/Years

Numerator

Denominator

Non-US Citizen

B05001, 2019-2023

b05001_006e

b05001_001e

Below 200% FPL

S1701, 2019-2023

s1701_c01_042e

s1701_c01_001e

Overcrowded Housing Units

B25014, 2019-2023

b25014_006e + b25014_007e + b25014_012e + b25014_013e

b25014_001e

Essential Workers

S2401, 2019-2023

s2401_c01_005e + s2401_c01_011e + s2401_c01_013e + s2401_c01_015e + s2401_c01_019e + s2401_c01_020e + s2401_c01_023e + s2401_c01_024e + s2401_c01_029e + s2401_c01_033e

s2401_c01_001

Seniors 75+ in Poverty

B17020, 2019-2023

b17020_008e + b17020_009e

b17020_008e + b17020_009e + b17020_016e + b17020_017e

Uninsured

S2701, 2019-2023

s2701_c05_001e

NA, rate published in source table

Single-Parent Households

S1101, 2019-2023

s1101_c03_005e + s1101_c04_005e

s1101_c01_001e

Unemployment

S2301, 2019-2023

s2301_c04_001e

NA, rate published in source table

The remaining indicators are based data requested and received by Advancement Project CA from the OSHPD Patient Discharge database. Data are based on records aggregated at the ZIP Code level:

Indicator

Years

Definition

Denominator

Asthma Hospitalizations

2017-2019

All ICD 10 codes under J45 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Gun Injuries

2017-2019

Principal/Other External Cause Code "Gun Injury" with a Disposition not "Died/Expired". ICD 10 Code Y38.4 and all codes under X94, W32, W33, W34, X72, X73, X74, X93, X95, Y22, Y23, Y35 [All listed codes with 7th digit "A" for initial encounter]

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Heart Disease Hospitalizations

2017-2019

ICD 10 Code I46.2 and all ICD 10 codes under I21, I22, I24, I25, I42, I50 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

Diabetes (Type 2) Hospitalizations

2017-2019

All ICD 10 codes under E11 (under Principal Diagnosis)

American Community Survey, 2015-2019, 5-Year Estimates, Table DP05

For more information about this dataset, please contact egis@isd.lacounty.gov.

As of July 2020, the death rate due to COVID-19 was 4.5x higher in areas experiencing severe housing problems. This statistic shows the multiplied ratio of COVID-19 deaths per 100,000 population in areas with select socioeconomic vulnerabilities in the United States.

NOTE: As of 2/16/2023, this table is not being updated. For data on COVID-19 updated (bivalent) booster coverage by town please to go to https://data.ct.gov/Health-and-Human-Services/COVID-19-Updated-Bivalent-Booster-Coverage-By-Town/bqd5-4jgh. This table shows the number and percent of residents of each CT town that have initiated COVID-19 vaccination, are fully vaccinated and who have received additional dose 1. All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected. In the data shown here, a person who has received at least one dose of COVID-19 vaccine is considered to have initiated vaccination. A person is considered fully vaccinated if he/she has completed a primary vaccination series by receiving 2 doses of the Pfizer, Novavax or Moderna vaccines or 1 dose of the Johnson & Johnson vaccine. The fully vaccinated are a subset of the people who have received at least one dose. A person who completed a Pfizer, Moderna, Novavax or Johnson & Johnson primary series (as defined above) and then had an additional monovalent dose of COVID-19 vaccine is considered to have had additional dose 1. The additional dose may be Pfizer, Moderna, Novavax or Johnson & Johnson and may be a different type from the primary series. For people who had a primary Pfizer or Moderna series, additional dose 1 was counted starting August 18th, 2021. For people with a Johnson & Johnson primary series additional dose 1 was counted starting October 22nd, 2021. For most people, additional dose 1 is a booster. However, additional dose 1 may represent a supplement to the primary series for a people who is moderately or severely immunosuppressed. Bivalent booster administrations are not included in the additional dose 1 calculations. The percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated because of vaccine administration records for individuals that cannot be linked because of differences in how names or date of birth are reported. Percentages are calculated using 2019 census data (https://portal.ct.gov/DPH/Health-Information-Systems--Reporting/Population/Annual-Town-and-County-Population-for-Connecticut). Town of residence is verified by geocoding the reported address and then mapping it to a town using municipal boundaries. If an address cannot be geocoded, the reported town is used, if available. People for whom an address is not currently available are shown in this table as “Address pending validation”. Out-of-state residents vaccinated by CT providers are excluded from the table. Town-level coverage estimates have been capped at 100%. Observed coverage may be greater than 100% for multiple reasons, including census denominator data not including all individuals that currently reside in the town (e.g., part time residents, change in population size since the census), errors in address data or other reporting errors. Also, the percent with at least one dose many be over-estimated, and the percent fully vaccinated and with additional dose 1 may be under-estimated when records for an individual cannot be linked because of differences in how names or date of birth are reported. Caution should be used when interpreting coverage estimates for towns with large college/university populations since coverage may be underestimated. In the census, college/university students who live on or just off campus would be counted in the college/university town. However, if a student was vaccinated while studying remotely in his/her hometown, the student may be counted as a vaccine recipient in that town. SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, ho

Frailty is a syndrome of increased vulnerability to incomplete resolution of homeostasis (healing or return to baseline function) following a stressor event (such as an infection or fall) and it is associated with poor outcomes including increased mortality and reduced quality of life. The pathophysiology of frailty is poorly understood. Age and frailty have been proven to be independently predictive of outcomes in patients admitted with an acute illness. In COVID-19, routine frailty identification informed decision making about treatment.

This dataset from 01-03-2020 to 01-04-2022 of 327,346 patients admitted during all waves of the COVID pandemic both with and without COVID-19. The dataset includes granular demographics, frailty scores, physiology and vital signs, all care contacts and investigations (including imaging), all medications including dose and routes, care outcomes, length of stay and outcomes including readmission and mortality.

Geography: The West Midlands (WM) has a population of 6 million & includes a diverse ethnic & socio-economic mix. 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 & > 120 ITU bed capacity. 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”. 

Data set availability:  Data access is available via the PIONEER Hub for projects which will benefit the public or patients.  This can be by developing a new understanding of disease, by providing insights into how to improve care, or by developing new models, tools, treatments, or care processes.  Data access can be provided to NHS, academic, commercial, policy and third sector organisations.  Applications from SMEs are welcome.  There is a single data access process, with public oversight provided by our public review committee, the Data Trust Committee.  Contact pioneer@uhb.nhs.uk or visit www.pioneerdatahub.co.uk for more details. 

Available supplementary data: Matched controls; ambulance and community data. Unstructured data (images).  We can provide the dataset in OMOP and other common data models and can build synthetic data to meet bespoke requirements. 

Available supplementary support: Analytics, model build, validation & refinement; A.I. support.  Data partner support for ETL (extract, transform & load) processes.  Bespoke and “off the shelf” Trusted Research Environment (TRE) build and run.  Consultancy with clinical, patient & end-user and purchaser access/ support.  Support for regulatory requirements.  Cohort discovery. Data-driven trials and “fast screen” services to assess population size.

Background: COVID-19 prevention and mitigation efforts were abrupt and challenging for most countries with the protracted lockdown straining socioeconomic activities. Marginalized groups and individuals are particularly vulnerable to adverse effects of the pandemic such as human rights abuses and violations which can lead to psychological distress. In this review, we focus on mental distress and disturbances that have emanated due to human rights restrictions and violations amidst the pandemic. We underscore how mental health is both directly impacted by the force of pandemic and by prevention and mitigation structures put in place to combat the disease.Methods: We conducted a review of relevant studies examining human rights violations in COVID-19 response, with a focus on vulnerable populations, and its association with mental health and psychological well-being. We searched PubMed and Embase databases for studies between December 2019 to July 2020. Three reviewers evaluated the eligibility criteria and extracted data.Results: Twenty-four studies were included in the systematic inquiry reporting on distress due to human rights violations. Unanimously, the studies found vulnerable populations to be at a high risk for mental distress. Limited mobility rights disproportionately harmed psychiatric patients, low-income individuals, and minorities who were at higher risk for self-harm and worsening mental health. Healthcare workers suffered negative mental health consequences due to stigma and lack of personal protective equipment and stigma. Other vulnerable groups such as the elderly, children, and refugees also experienced negative consequences.Conclusions: This review emphasizes the need to uphold human rights and address long term mental health needs of populations that have suffered disproportionately during the pandemic. Countries can embed a proactive psychosocial response to medical management as well as in existing prevention strategies. International human rights guidelines are useful in this direction but an emphasis should be placed on strengthening rights informed psychosocial response with specific strategies to enhance mental health in the long-term. We underscore that various fundamental human rights are interdependent and therefore undermining one leads to a poor impact on the others. We strongly recommend global efforts toward focusing both on minimizing fatalities, protecting human rights, and promoting long term mental well-being.

As of 1/19/2022, this dataset is no longer being updated. For more data on COVID-19 in Connecticut, visit data.ct.gov/coronavirus.

This tables shows the percent of people who have received at least one dose of COVID-19 vaccine who live in a Priority SVI Zip Code. About a third of people in CT live in a Priority SVI zip code.

SVI refers to the CDC's Social Vulnerability Index - a measure that combines 15 demographic variables to identify communities most vulnerable to negative health impacts from disasters and public health crises. Measures of social vulnerability include socioeconomic status, household composition, disability, race, ethnicity, language, and transportation limitations - among others. SVI scores were calculated for each zip code in CT. The zip codes in the top 20% were designated as Priority SVI zip codes. Percentages are based on 2018 zip code population data supplied by ESRI corporation.

All data in this report are preliminary; data for previous dates will be updated as new reports are received and data errors are corrected.

The data are presented cumulatively and by week of first dose of vaccine. Percentages are reported for all providers combined and for pharmacies, FQHCs (Federally Qualified Health Centers), local public health departments / districts and hospitals. The table excludes people with a missing or out-of-state zip code and doses administered by the Federal government (including Department of Defense, Department of Correction, Department of Veteran’s Affairs, Indian Health Service) or out-of-state providers.

COVID-19 and COVID-related decisions are having significant impacts on children and adults vulnerable to, and already experiencing, the crime of forced marriage. This mixed-methods project aimed to chart and understand this impact, inform evaluation of the UK's response to COVID-19, and shape on-going policy regarding the UK's pandemic response. This data includes the questions for and responses to a survey of staff at a national helpline for victims of forced marriage. It also includes visualisations of the data made for the published report.

COVID-19 and COVID-related decisions are having significant impacts on children and adults vulnerable to, and already experiencing, the crime of forced marriage. Our mixed-methods project will chart and understand this impact, inform evaluation of the UK's response to COVID-19, and shape on-going policy regarding the UK's pandemic response. We consider the uneven economic and social impact of the pandemic, and the ethical dimensions of unequal impacts of COVID-related decision-making, on this vulnerable group, and seek to impact how civil society and the voluntary sector support vulnerable people.

The government's Forced Marriage Unit (FMU) and the charity Karma Nirvana (KN) (which provides a national forced marriage helpline) have warned about the significant impact of the pandemic on forced marriage in the UK. We designed this project with both organisations, and will work with them to analyse quantitative and qualitative data about the impact of COVID-19 on those at risk of, or experiencing, forced marriage; and to record and analyse the challenges faced in the pandemic, evaluate the efficacy of mitigation strategies, and formulate new policies and practises for protection and response.

Within the first 6 months, we will have co-created an accurate account of the economic and social impact of COVID-19 and COVID-related decision-making on victims of forced marriage, and the ethical implications of unequal impacts. We will then continue to chart the changing risk environment, while co-developing policy reports and recommendations for the UK government (including FMU), NGO practice responses (including KN), and other stakeholders, to improve the on-going response to COVID-19 and build community resilience.