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.

According to a survey conducted in 2023, 38 percent of people in Britain strongly supported social media checks being carried out by an employer as part of an application process when the job involved working with vulnerable people. Overall, 15 percent of respondents strongly opposed to this.

What is Social Vulnerability?Every community must prepare for and respond to hazardous events, whether a natural disaster like a tornado or a disease outbreak, or an anthropogenic event such as a harmful chemical spill. The degree to which a community exhibits certain social conditions, including high poverty, low percentage of vehicle access, or crowded households, among others, may affect that community’s ability to prevent human suffering and financial loss in the event of a disaster. These factors describe a community’s social vulnerability.What is the CDC/ATSDR Social Vulnerability Index?ATSDR’s Geospatial Research, Analysis, & Services Program (GRASP) created the Centers for Disease Control and Prevention and Agency for Toxic Substances and Disease Registry Social Vulnerability Index (hereafter, CDC/ATSDR SVI or SVI) to help public health officials and emergency response planners identify and map the communities that will most likely need support before, during, and after a hazardous event.SVI indicates the relative vulnerability of every U.S. census tract. Census tracts are subdivisions of counties for which the Census collects statistical data. SVI ranks the tracts on 16 social factors, such as unemployment, racial and ethnic minority status, and disability status. Then, SVI further groups the factors into four related themes. Thus, each tract receives a ranking for each Census variable and for each of the four themes as well as an overall ranking.Below, text that describes “tract” methods also refers to county methods.How can the SVI help communities be better prepared for hazardous events?SVI provides specific socially and spatially relevant information to help public health officials and local planners better prepare communities to respond to emergency events such as severe weather, floods, disease outbreaks, or chemical exposure.SVI can be used to:Assess community need during emergency preparedness planning.Estimate the type and quantity of needed supplies such as food, water, medicine, and bedding.Decide the number of emergency personnel required to assist people.Identify areas in need of emergency shelters.Create a plan to evacuate people, accounting for those who have special needs, such as those without vehicles, the elderly, or people who do not speak English well.Identify communities that will need continued support to recover following an emergency or natural disaster.For more detailed methodology and attribute details, please review this document.

The British Red Cross COVID-19 Vulnerability Index identifies areas in the UK where people might be more vulnerable to the effects of Covid-19. The Index looks at clinical vulnerability, wider health and wellbeing, and socioeconomic vulnerability.Click here for more details.The data sources for this application are as follows:British Red Cross Vulnerability Index by Local Authority DistrictBritish Red Cross COVID-19 Vulnerability Index by Middle Super Output Area (MSOA) in EnglandBritish Red Cross COVID-19 Vulnerability Index by Middle Super Output Area (MSOA) in WalesBritish Red Cross COVID-19 Vulnerability Index by Intermediate Zone in ScotlandBritish Red Cross COVID-19 Vulnerability Index by Super Output Area in Northern IrelandIndex of Multiple Deprivation 2015 (England)Index of Multiple Deprivation 2016 (Scotland)

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.

Persons experiencing homelessness (PEHs) have a higher risk of morbidity and mortality compared to the general population and are highly vulnerable during the coronavirus disease (COVID-19) pandemic. Understanding their experience of the pandemic is important for mitigating the effects of the pandemic. Accordingly, we conducted a qualitative study on their lived experiences during the COVID-19 pandemic. Semi-structured interviews were conducted in nine PEHs from Chennai, India, recruited at food stalls between September 14–25, 2020. Data were analyzed using interpretive phenomenological analysis. The participants shared their experiences of the COVID-19 pandemic, its impact on them, and their coping strategies. All the participants were migrant workers living alone, and were the sole breadwinners of their families. Five group experiential themes emerged relating to the experiences of the participants during the COVID-19 pandemic. Most participants reported significant psychosocial stress, but low suicide risk and robust coping mechanisms. They delayed seeking healthcare for non-COVID-19-related problems. Public hospitals were preferred over private hospitals due to cost constraints and prior experience of discrimination. Upward classism was observed as participants blamed the rich for the spread of COVID-19. Initial assumption that COVID-19 would only affect the rich was also reported. Free government testing and quarantine facilities assuaged their medico-psychosocial needs. Engaging in collective activities was a key stress mitigator. We highlight several important policy implications. Firstly, we underscore the importance of involving social workers to facilitate communication between healthcare providers and patients from vulnerable communities. This engagement can help minimize discrimination and promote equitable access to healthcare. Secondly, we emphasize the need for effective public health communication. Specifically, there is a need to address and alleviate concerns about the transmission of COVID-19 within hospital premises. Lastly, the research suggests that government initiatives aimed at fostering community participation should persist both during and after the pandemic.

Social vulnerability is defined as the disproportionate susceptibility of some social groups to the impacts of hazards, including death, injury, loss, or disruption of livelihood. In this dataset from Climate Ready Boston, groups identified as being more vulnerable are older adults, children, people of color, people with limited English proficiency, people with low or no incomes, people with disabilities, and people with medical illnesses. Source:The analysis and definitions used in Climate Ready Boston (2016) are based on "A framework to understand the relationship between social factors that reduce resilience in cities: Application to the City of Boston." Published 2015 in the International Journal of Disaster Risk Reduction by Atyia Martin, Northeastern University.Population Definitions:Older Adults:Older adults (those over age 65) have physical vulnerabilities in a climate event; they suffer from higher rates of medical illness than the rest of the population and can have some functional limitations in an evacuation scenario, as well as when preparing for and recovering from a disaster. Furthermore, older adults are physically more vulnerable to the impacts of extreme heat. Beyond the physical risk, older adults are more likely to be socially isolated. Without an appropriate support network, an initially small risk could be exacerbated if an older adult is not able to get help.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for population over 65 years of age.Attribute label: OlderAdultChildren: Families with children require additional resources in a climate event. When school is cancelled, parents need alternative childcare options, which can mean missing work. Children are especially vulnerable to extreme heat and stress following a natural disaster.Data source: 2010 American Community Survey 5-year Estimates (ACS) data by census tract for population under 5 years of age.Attribute label: TotChildPeople of Color: People of color make up a majority (53 percent) of Boston’s population. People of color are more likely to fall into multiple vulnerable groups aswell. People of color statistically have lower levels of income and higher levels of poverty than the population at large. People of color, many of whom also have limited English proficiency, may not have ready access in their primary language to information about the dangers of extreme heat or about cooling center resources. This risk to extreme heat can be compounded by the fact that people of color often live in more densely populated urban areas that are at higher risk for heat exposure due to the urban heat island effect.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract: Black, Native American, Asian, Island, Other, Multi, Non-white Hispanics.Attribute label: POC2Limited English Proficiency: Without adequate English skills, residents can miss crucial information on how to preparefor hazards. Cultural practices for information sharing, for example, may focus on word-of-mouth communication. In a flood event, residents can also face challenges communicating with emergency response personnel. If residents are more sociallyisolated, they may be less likely to hear about upcoming events. Finally, immigrants, especially ones who are undocumented, may be reluctant to use government services out of fear of deportation or general distrust of the government or emergency personnel.Data Source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract, defined as speaks English only or speaks English “very well”.Attribute label: LEPLow to no Income: A lack of financial resources impacts a household’s ability to prepare for a disaster event and to support friends and neighborhoods. For example, residents without televisions, computers, or data-driven mobile phones may face challenges getting news about hazards or recovery resources. Renters may have trouble finding and paying deposits for replacement housing if their residence is impacted by flooding. Homeowners may be less able to afford insurance that will cover flood damage. Having low or no income can create difficulty evacuating in a disaster event because of a higher reliance on public transportation. If unable to evacuate, residents may be more at risk without supplies to stay in their homes for an extended period of time. Low- and no-income residents can also be more vulnerable to hot weather if running air conditioning or fans puts utility costs out of reach.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for low-to- no income populations. The data represents a calculated field that combines people who were 100% below the poverty level and those who were 100–149% of the poverty level.Attribute label: Low_to_NoPeople with Disabilities: People with disabilities are among the most vulnerable in an emergency; they sustain disproportionate rates of illness, injury, and death in disaster events.46 People with disabilities can find it difficult to adequately prepare for a disaster event, including moving to a safer place. They are more likely to be left behind or abandoned during evacuations. Rescue and relief resources—like emergency transportation or shelters, for example— may not be universally accessible. Research has revealed a historic pattern of discrimination against people with disabilities in times of resource scarcity, like after a major storm and flood.Data source: 2008-2012 American Community Survey 5-year Estimates (ACS) data by census tract for total civilian non-institutionalized population, including: hearing difficulty, vision difficulty, cognitive difficulty, ambulatory difficulty, self-care difficulty, and independent living difficulty. Attribute label: TotDisMedical Illness: Symptoms of existing medical illnesses are often exacerbated by hot temperatures. For example, heat can trigger asthma attacks or increase already high blood pressure due to the stress of high temperatures put on the body. Climate events can interrupt access to normal sources of healthcare and even life-sustaining medication. Special planning is required for people experiencing medical illness. For example, people dependent on dialysis will have different evacuation and care needs than other Boston residents in a climate event.Data source: Medical illness is a proxy measure which is based on EASI data accessed through Simply Map. Health data at the local level in Massachusetts is not available beyond zip codes. EASI modeled the health statistics for the U.S. population based upon age, sex, and race probabilities using U.S. Census Bureau data. The probabilities are modeled against the census and current year and five year forecasts. Medical illness is the sum of asthma in children, asthma in adults, heart disease, emphysema, bronchitis, cancer, diabetes, kidney disease, and liver disease. A limitation is that these numbers may be over-counted as the result of people potentially having more than one medical illness. Therefore, the analysis may have greater numbers of people with medical illness within census tracts than actually present. Overall, the analysis was based on the relationship between social factors.Attribute label: MedIllnesOther attribute definitions:GEOID10: Geographic identifier: State Code (25), Country Code (025), 2010 Census TractAREA_SQFT: Tract area (in square feet)AREA_ACRES: Tract area (in acres)POP100_RE: Tract population countHU100_RE: Tract housing unit countName: Boston Neighborhood

IntroductionCaregivers of autistic persons often face “courtesy stigma,” a phenomenon by which caregivers experience stigma because of their association with a person whose disability may be stigmatized. Understanding the repercussions of this stigma is crucial not only for caregivers’ mental health but also for the quality of care provided to their dependent. This study aimed to explore courtesy stigma among caregivers of autistic persons in Quebec, examining its prevalence and impact in order to identify groups that are particularly susceptible to negative outcomes.MethodsThis study used a cross-sectional online survey methodology employing quota sampling to collect responses from 194 participants. Data were collected using a computer-assisted web interview (CAWI) platform. The impact of courtesy stigma was measured in terms of care burden, mental health, and overall well-being of caregivers.ResultsThe findings revealed that caregivers frequently experience rejection, isolation, and work-related challenges. Notably, caregivers’ health was below average with the lowest reported health outcomes in Quebec. The caregivers who are the most vulnerable to negative outcomes included female caregivers, those aged 45 or older, financially strained households, caregivers of children requiring elevated levels of support, caregivers who isolated due to their autistic dependents, and those who experienced stigmatization directed at themselves or their children in the form of rejection.Interestingly, 60% of respondents reported that the caregiving burden was “not at all” to “somewhat” difficult, raising questions about factors that may mitigate caregiving challenges over time.ConclusionNegative outcomes from courtesy stigma vary depending on certain risk factors and individual characteristic. This study underscores the need for targeted public policies and interventions, particularly for those at a higher risk of experiencing the negative effects of courtesy stigma on the burden of care, overall health, and mental health. By tailoring resources and support for these priority groups, we can better address the challenges faced by families of autistic persons.

Vulnerability, understood as the propensity to be adversely affected, has attained importance in the context of climate change by helping to understand what makes populations and territories predisposed to its impacts. Conditions of vulnerability may vary depending on the characteristics of each territory studied—social, environmental, infrastructural, public policies, among others. Thus, the present study aimed to evaluate what makes the municipalities of the state of Amazonas, Brazil, vulnerable to climate change in the context of the largest tropical forest in the world, and which regions of the State are the most susceptible. A Municipal Vulnerability Index was developed, which was used to associate current socio-environmental characteristics of municipalities with climate change scenarios in order to identify those that may be most affected by climate change. The results showed that poor adaptive capacity and poverty had the most influence on current vulnerability of the municipalities of Amazonas with the most vulnerable areas being the southern, northern, and eastern regions of the state. When current vulnerability was related to future climate change projections, the most vulnerable areas were the northern, northeastern, extreme southern, and southwestern regions. From a socio-environmental and climatic point of view, these regions should be a priority for public policy efforts to reduce their vulnerability and prepare them to cope with the adverse aspects of climate change.

Abstract copyright UK Data Service and data collection copyright owner. Siyakha Nentsha (SN) was a randomised experiment that targets young people in KwaZulu-Natal province, South Africa. The program addressed the real-life economic, social and health challenges young people encounter on a daily basis. The educational programme developed for the intervention was accredited by the South African Qualifications Authority (SAQA, the national government body that accredits education and training curricula) meaning that not only will young people who complete the program have received valuable skills, but that they have documentation of these skills that can be used in future job searches. Siyakha Nentsha was delivered in secondary schools during school hours. It was led by young adult mentors who were chosen from the local community and received extensive training. Sessions with students occurred 2-3 times per week and each was approximately one hour in length. The long-term objective of the programme is to improve lifelong functional capabilities and well-being of adolescent females and males who face high risks for HIV, teenage pregnancy, school dropout, and unemployment, coupled with the actual or potential loss of one or both parents. The skills are geared to help offer protective strategies against HIV and mechanisms for coping with and mitigating the impacts of AIDS, with the long-term goal of building economic, social and health assets. The study has three intervention arms: control, partial intervention and full intervention. These arms were randomised at the classroom level for 10th and 11th graders in Round 1 in seven secondary schools. One school that received a delayed intervention served as the control sample. The two versions of the intervention differ in that the full version includes HIV/AIDS education, social capital building, and financial capabilities, whereas the partial version omits the financial capabilities component. The study began in January 2008 and lasted for 36 months, with measures on individual students at baseline and post-intervention. The number of individuals who were part of at least Round 1 or Round 2 is 1,307. Individuals can be uniquely identified with the variables qnum (round 1) and IDNUM (round 2). Further information may be found on the ESRC Enhancing the economic, health and social capabilities of highly vulnerable youth award webpage. Main Topics: The surveys covered: demographic information; background and living conditions; household and personal assets; education, work and time use; life skills education; social capital; financial and economic skills; expectations and attitudes; HIV/AIDS and sexually-transmitted diseases (STDs); relationships, sexual experience and knowledge; reproductive health; maternity; paternity. Multi-stage stratified random sample Face-to-face interview

Objectives: To identify the perception of the availability of community support and the support needs of autistic people, people with disabilities and their caregivers at the time of the COVID-19 pandemic in Quebec and to assess the association between the available support and the perceived stress levels to evaluate the role of perceived social support as a potential buffer of this association. Methods: A total of 315 respondents participated in a 4 min online survey across the province of Quebec by snowball sampling. Community support was defined as availability of adapted healthcare, adapted information, adapted educational services and community services. Results: The community support and services during the COVID-19 pandemic, were not available or were not sufficiently adapted to their needs. About 40 % of autistic people or people with disabilities and 44 % of their caregivers perceived their days as being quite stressful or extremely stressful. This is twice the rate than that of the general population in non-pandemic time. Nevertheless, social supports can play a mediating role in attenuating the effects of the absence of adapted services on the stress level of this vulnerable population. Conclusion: The non-availability of adapted services was related to an increase of the stress level in this population. Our study adds that other than social support, adapted healthcare/tele-healthcare, and in-home support services could reduce the impact of the pandemic on the stress level of autistic people and people with disabilities. Adapted educational services and educational assistance for tele-education could reduce the impact on the stress level in caregivers. People with disabilities and their caregivers are one of the most vulnerable groups in our society. Public health measures of containment and mitigation need to consider more their specific needs.

Abstract

This dataset was collected as a complement to UN Global Pulse, UNHCR, Durham University, WHO and OCHA's study on simulation models to help with COVID-19 planning in world’s largest refugee settlement. The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. This survey collected data on individual's contact, interactions and time spent in public zones of refugees' camps in Cox's Bazar, in order to fill spreading matrices to inform this simulation of spread.

Geographic coverage

Cox's Bazar

Analysis unit

Individuals

Universe

All participants of Community Based Protection Groups

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample frame was obtained from lists of Community-Based Protection regular working groups. Each camp group was stratified by gender, age and disabilities, and members of each camp were randomly selected from the working groups of 20 camps in Cox's Bazar.

Mode of data collection

Telephone interview

As of June 13, 2023, there have been almost 768 million cases of coronavirus (COVID-19) worldwide. The disease has impacted almost every country and territory in the world, with the United States confirming around 16 percent of all global cases.

COVID-19: An unprecedented crisis Health systems around the world were initially overwhelmed by the number of coronavirus cases, and even the richest and most prepared countries struggled. In the most vulnerable countries, millions of people lacked access to critical life-saving supplies, such as test kits, face masks, and respirators. However, several vaccines have been approved for use, and more than 13 billion vaccine doses had already been administered worldwide as of March 2023.

The coronavirus in the United Kingdom Over 202 thousand people have died from COVID-19 in the UK, which is the highest number in Europe. The tireless work of the National Health Service (NHS) has been applauded, but the country’s response to the crisis has drawn criticism. The UK was slow to start widespread testing, and the launch of a COVID-19 contact tracing app was delayed by months. However, the UK’s rapid vaccine rollout has been a success story, and around 53.7 million people had received at least one vaccine dose as of July 13, 2022.

Abstract

UNHCR conducts post-distribution monitoring (PDM) on a regular basis for assistance activities in order to deepen its understanding of the impact these activities have on the persons the organization assists and provides protection to.

In Ukraine, UNHCR provides individual protection assistance in the following regions: Kyiv, Odesa, Zakarpattya (also covers Lviv) and Kharkiv regions. The UNHCR Cash-Based Interventions (CBI) support only vulnerable refugees and asylum-seekers. The type of assistance vary depending on the needs and vulnerability of persons of concern. the following types of CBI assistance that were provided to refugees and asylum-seekers by UNHCR and its Partners in Ukraine in 2020: 1. Supplementary assistance and newcomers' assistance - Modality: voucher (Metro cards, a supermarket chain that partners with UNHCR) - Available only in Kyiv and Odesa - Description: Distribution of vouchers (Metro cards) for food and non-food items to refugees and asylum-seekers who meet established vulnerability criteria (newcomers, PoCs in need of supplementary food or hygiene due to medical condition). In 2020, 121 families residing in Kyiv and Odesa received voucher assistance at least once.

In 2020 UNHCR used Metro Cash&Carry (big supermarket chain) cards in the value of 500 UAH. However, due to COVID-19 quarantine restrictions imposed by the government of Ukraine, UNHCR Ukraine has gradually shifted to provision of these types of assistance through other modalities. This PDM focused only on the cases processed through vouchers.

1. MSA (Monthly Subsistence Allowance)
2. Modality: cash. OTC (over the counter)
3. Available in Kyiv, Odesa, Kharkiv Description: MSA (monthly subsistence allowance) aims to support the most vulnerable persons of concern. It is given based on the strict vulnerability criteria and cases are reviewed every four months at the MSA committee meetings, composed of partner social counselors, SMS and UNHCR. In 2020, 105 vulnerable families were covered by this type of support.

The amount of MSA is calculated based on the family size. It is in line with the recommendations of the Cash Working Group on assistance provision at 60% of subsistence level (3 774.62 UAH as of February 2020), which corresponds to MSA amount. Assistance per single person provided by UNHCR amounted to 2 400 UAH per month, proportionally increased depending on the number of household members.

Geographic coverage

Kyiv, Odesa, Zakarpattya (also covers Lviv) and Kharkiv regions

Analysis unit

Households

Universe

All recepients of cash-based assistance.

Kind of data

Sample survey data [ssd]

Sampling procedure

Based on the experience with the low response rate during the phone surveys in 2018 and 2019, and in order to ensure representativity, a complete enumeration of all recepients was attempted.

Mode of data collection

Computer Assisted Telephone Interview [cati]

Breakdown of Non-Elderly Vulnerable Adult (VA) abuse cases by sex and type of abuse overseen by MSF Adult Protective Service (APS) from 2021 to 2023

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, 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.

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: This dataset takes the place of the original "COVID-19 Vaccinations by Town" dataset (https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town/pdqi-ds7f) , which will not be updated after 4/15/2021. A breakdown of vaccinations by town and by age group is also available here: https://data.ct.gov/Health-and-Human-Services/COVID-19-Vaccinations-by-Town-and-Age-Group/gngw-ukpw .

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.

COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: North Sumatera: North Labuhanbatu Regency data was reported at 165,223.000 Person in 22 Mar 2025. This stayed constant from the previous number of 165,223.000 Person for 20 Mar 2025. COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: North Sumatera: North Labuhanbatu Regency data is updated daily, averaging 165,223.000 Person from Nov 2021 (Median) to 22 Mar 2025, with 984 observations. The data reached an all-time high of 292,363.000 Person in 05 Nov 2022 and a record low of 69,792.000 Person in 25 Nov 2021. COVID-19: To-Date: Vaccination: Dose 1: General Public and Vulnerable: North Sumatera: North Labuhanbatu Regency data remains active status in CEIC and is reported by Ministry of Health. The data is categorized under Indonesia Premium Database’s Health Sector – Table ID.HLB012: Coronavirus Disease 2019 (Covid-19): Vaccination Status: by Regency and Municipality: General Public and Vulnerable.

IntroductionCaregivers of autistic persons often face “courtesy stigma,” a phenomenon by which caregivers experience stigma because of their association with a person whose disability may be stigmatized. Understanding the repercussions of this stigma is crucial not only for caregivers’ mental health but also for the quality of care provided to their dependent. This study aimed to explore courtesy stigma among caregivers of autistic persons in Quebec, examining its prevalence and impact in order to identify groups that are particularly susceptible to negative outcomes.MethodsThis study used a cross-sectional online survey methodology employing quota sampling to collect responses from 194 participants. Data were collected using a computer-assisted web interview (CAWI) platform. The impact of courtesy stigma was measured in terms of care burden, mental health, and overall well-being of caregivers.ResultsThe findings revealed that caregivers frequently experience rejection, isolation, and work-related challenges. Notably, caregivers’ health was below average with the lowest reported health outcomes in Quebec. The caregivers who are the most vulnerable to negative outcomes included female caregivers, those aged 45 or older, financially strained households, caregivers of children requiring elevated levels of support, caregivers who isolated due to their autistic dependents, and those who experienced stigmatization directed at themselves or their children in the form of rejection.Interestingly, 60% of respondents reported that the caregiving burden was “not at all” to “somewhat” difficult, raising questions about factors that may mitigate caregiving challenges over time.ConclusionNegative outcomes from courtesy stigma vary depending on certain risk factors and individual characteristic. This study underscores the need for targeted public policies and interventions, particularly for those at a higher risk of experiencing the negative effects of courtesy stigma on the burden of care, overall health, and mental health. By tailoring resources and support for these priority groups, we can better address the challenges faced by families of autistic persons.

Reporting of Aggregate Case and Death Count data was discontinued on May 11, 2023, with the expiration of the COVID-19 public health emergency declaration. Although these data will continue to be publicly available, this dataset will no longer be updated.

The surveillance case definition for COVID-19, a nationally notifiable disease, was first described in a position statement from the Council for State and Territorial Epidemiologists, which was later revised. However, there is some variation in how jurisdictions implemented these case definitions. More information on how CDC collects COVID-19 case surveillance data can be found at FAQ: COVID-19 Data and Surveillance.

Aggregate Data Collection Process Since the beginning of the COVID-19 pandemic, data were reported from state and local health departments through a robust process with the following steps:

• Aggregate county-level counts were obtained indirectly, via automated overnight web collection, or directly, via a data submission process.
• If more than one official county data source existed, CDC used a comprehensive data selection process comparing each official county data source to retrieve the highest case and death counts, unless otherwise specified by the state.
• A CDC data team reviewed counts for congruency prior to integration and set up alerts to monitor for discrepancies in the data.
• CDC routinely compiled these data and post the finalized information on COVID Data Tracker.
• County level data were aggregated to obtain state- and territory- specific totals.
• Counting of cases and deaths is based on date of report and not on the date of symptom onset. CDC calculates rates in these data by using population estimates provided by the US Census Bureau Population Estimates Program (2019 Vintage).
• COVID-19 aggregate case and death data are organized in a time series that includes cumulative number of cases and deaths as reported by a jurisdiction on a given date. New case and death counts are calculated as the week-to-week change in cumulative counts of cases and deaths reported (i.e., newly reported cases and deaths = cumulative number of cases/deaths reported this week minus the cumulative total reported the prior week.

This process was collaborative, with CDC and jurisdictions working together to ensure the accuracy of COVID-19 case and death numbers. County counts provided the most up-to-date numbers on cases and deaths by report date. Throughout data collection, CDC retrospectively updated counts to correct known data quality issues.

Description This archived public use dataset focuses on the cumulative and weekly case and death rates per 100,000 persons within various sociodemographic factors across all states and their counties. All resulting data are expressed as rates calculated as the number of cases or deaths per 100,000 persons in counties meeting various classification criteria using the US Census Bureau Population Estimates Program (2019 Vintage).

Each county within jurisdictions is classified into multiple categories for each factor. All rates in this dataset are based on classification of counties by the characteristics of their population, not individual-level factors. This applies to each of the available factors observed in this dataset. Specific factors and their corresponding categories are detailed below.

Population-level factors Each unique population factor is detailed below. Please note that the “Classification” column describes each of the 12 factors in the dataset, including a data dictionary describing what each numeric digit means within each classification. The “Category” column uses numeric digits (2-6, depending on the factor) defined in the “Classification” column.

Metro vs. Non-Metro – “Metro_Rural” Metro vs. Non-Metro classification type is an aggregation of the 6 National Center for Health Statistics (NCHS) Urban-Rural classifications, where “Metro” counties include Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro areas and “Non-Metro” counties include Micropolitan and Non-Core (Rural) areas. 1 – Metro, including “Large Central Metro, Large Fringe Metro, Medium Metro, and Small Metro” areas 2 – Non-Metro, including “Micropolitan, and Non-Core” areas

Urban/rural - “NCHS_Class” Urban/rural classification type is based on the 2013 National Center for Health Statistics Urban-Rural Classification Scheme for Counties. Levels consist of:

1 Large Central Metro
2 Large Fringe Metro 3 Medium Metro 4 Small Metro 5 Micropolitan 6 Non-Core (Rural)

American Community Survey (ACS) data were used to classify counties based on their age, race/ethnicity, household size, poverty level, and health insurance status distributions. Cut points were generated by using tertiles and categorized as High, Moderate, and Low percentages. The classification “Percent non-Hispanic, Native Hawaiian/Pacific Islander” is only available for “Hawaii” due to low numbers in this category for other available locations. This limitation also applies to other race/ethnicity categories within certain jurisdictions, where 0 counties fall into the certain category. The cut points for each ACS category are further detailed below:

Age 65 - “Age65”

1 Low (0-24.4%) 2 Moderate (>24.4%-28.6%) 3 High (>28.6%)

Non-Hispanic, Asian - “NHAA”

1 Low (<=5.7%) 2 Moderate (>5.7%-17.4%) 3 High (>17.4%)

Non-Hispanic, American Indian/Alaskan Native - “NHIA”

1 Low (<=0.7%) 2 Moderate (>0.7%-30.1%) 3 High (>30.1%)

Non-Hispanic, Black - “NHBA”

1 Low (<=2.5%) 2 Moderate (>2.5%-37%) 3 High (>37%)

Hispanic - “HISP”

1 Low (<=18.3%) 2 Moderate (>18.3%-45.5%) 3 High (>45.5%)

Population in Poverty - “Pov”

1 Low (0-12.3%) 2 Moderate (>12.3%-17.3%) 3 High (>17.3%)

Population Uninsured- “Unins”

1 Low (0-7.1%) 2 Moderate (>7.1%-11.4%) 3 High (>11.4%)

Average Household Size - “HH”

1 Low (1-2.4) 2 Moderate (>2.4-2.6) 3 High (>2.6)

Community Vulnerability Index Value - “CCVI” COVID-19 Community Vulnerability Index (CCVI) scores are from Surgo Ventures, which range from 0 to 1, were generated based on tertiles and categorized as:

1 Low Vulnerability (0.0-0.4) 2 Moderate Vulnerability (0.4-0.6) 3 High Vulnerability (0.6-1.0)

Social Vulnerability Index Value – “SVI" Social Vulnerability Index (SVI) scores (vintage 2020), which also range from 0 to 1, are from CDC/ASTDR’s Geospatial Research, Analysis & Service Program. Cut points for CCVI and SVI scores were generated based on tertiles and categorized as:

1 Low Vulnerability (0-0.333) 2 Moderate Vulnerability (0.334-0.666) 3 High Vulnerability (0.667-1)