Overview

The Associated Press is sharing data from the COVID Impact Survey, which provides statistics about physical health, mental health, economic security and social dynamics related to the coronavirus pandemic in the United States.

Conducted by NORC at the University of Chicago for the Data Foundation, the probability-based survey provides estimates for the United States as a whole, as well as in 10 states (California, Colorado, Florida, Louisiana, Minnesota, Missouri, Montana, New York, Oregon and Texas) and eight metropolitan areas (Atlanta, Baltimore, Birmingham, Chicago, Cleveland, Columbus, Phoenix and Pittsburgh).

The survey is designed to allow for an ongoing gauge of public perception, health and economic status to see what is shifting during the pandemic. When multiple sets of data are available, it will allow for the tracking of how issues ranging from COVID-19 symptoms to economic status change over time.

The survey is focused on three core areas of research:

• Physical Health: Symptoms related to COVID-19, relevant existing conditions and health insurance coverage.
• Economic and Financial Health: Employment, food security, and government cash assistance.
• Social and Mental Health: Communication with friends and family, anxiety and volunteerism. (Questions based on those used on the U.S. Census Bureau’s Current Population Survey.) ## Using this Data - IMPORTANT This is survey data and must be properly weighted during analysis: DO NOT REPORT THIS DATA AS RAW OR AGGREGATE NUMBERS!!

Instead, use our queries linked below or statistical software such as R or SPSS to weight the data.

Queries

If you'd like to create a table to see how people nationally or in your state or city feel about a topic in the survey, use the survey questionnaire and codebook to match a question (the variable label) to a variable name. For instance, "How often have you felt lonely in the past 7 days?" is variable "soc5c".

Nationally: Go to this query and enter soc5c as the variable. Hit the blue Run Query button in the upper right hand corner.

Local or State: To find figures for that response in a specific state, go to this query and type in a state name and soc5c as the variable, and then hit the blue Run Query button in the upper right hand corner.

The resulting sentence you could write out of these queries is: "People in some states are less likely to report loneliness than others. For example, 66% of Louisianans report feeling lonely on none of the last seven days, compared with 52% of Californians. Nationally, 60% of people said they hadn't felt lonely."

Margin of Error

The margin of error for the national and regional surveys is found in the attached methods statement. You will need the margin of error to determine if the comparisons are statistically significant. If the difference is:

• At least twice the margin of error, you can report there is a clear difference.
• At least as large as the margin of error, you can report there is a slight or apparent difference.
• Less than or equal to the margin of error, you can report that the respondents are divided or there is no difference. ## A Note on Timing Survey results will generally be posted under embargo on Tuesday evenings. The data is available for release at 1 p.m. ET Thursdays.

About the Data

The survey data will be provided under embargo in both comma-delimited and statistical formats.

Each set of survey data will be numbered and have the date the embargo lifts in front of it in the format of: 01_April_30_covid_impact_survey. The survey has been organized by the Data Foundation, a non-profit non-partisan think tank, and is sponsored by the Federal Reserve Bank of Minneapolis and the Packard Foundation. It is conducted by NORC at the University of Chicago, a non-partisan research organization. (NORC is not an abbreviation, it part of the organization's formal name.)

Data for the national estimates are collected using the AmeriSpeak Panel, NORC’s probability-based panel designed to be representative of the U.S. household population. Interviews are conducted with adults age 18 and over representing the 50 states and the District of Columbia. Panel members are randomly drawn from AmeriSpeak with a target of achieving 2,000 interviews in each survey. Invited panel members may complete the survey online or by telephone with an NORC telephone interviewer.

Once all the study data have been made final, an iterative raking process is used to adjust for any survey nonresponse as well as any noncoverage or under and oversampling resulting from the study specific sample design. Raking variables include age, gender, census division, race/ethnicity, education, and county groupings based on county level counts of the number of COVID-19 deaths. Demographic weighting variables were obtained from the 2020 Current Population Survey. The count of COVID-19 deaths by county was obtained from USA Facts. The weighted data reflect the U.S. population of adults age 18 and over.

Data for the regional estimates are collected using a multi-mode address-based (ABS) approach that allows residents of each area to complete the interview via web or with an NORC telephone interviewer. All sampled households are mailed a postcard inviting them to complete the survey either online using a unique PIN or via telephone by calling a toll-free number. Interviews are conducted with adults age 18 and over with a target of achieving 400 interviews in each region in each survey.Additional details on the survey methodology and the survey questionnaire are attached below or can be found at https://www.covid-impact.org.

Attribution

Results should be credited to the COVID Impact Survey, conducted by NORC at the University of Chicago for the Data Foundation.

AP Data Distributions

​To learn more about AP's data journalism capabilities for publishers, corporations and financial institutions, go here or email kromano@ap.org.

The U.S. Census Bureau, in collaboration with five federal agencies, launched the Household Pulse Survey to produce data on the social and economic impacts of Covid-19 on American households. The Household Pulse Survey was designed to gauge the impact of the pandemic on employment status, consumer spending, food security, housing, education disruptions, and dimensions of physical and mental wellness. The survey was designed to meet the goal of accurate and timely weekly estimates. It was conducted by an internet questionnaire, with invitations to participate sent by email and text message. The sample frame is the Census Bureau Master Address File Data. Housing units linked to one or more email addresses or cell phone numbers were randomly selected to participate, and one respondent from each housing unit was selected to respond for him or herself. Estimates are weighted to adjust for nonresponse and to match Census Bureau estimates of the population by age, sex, race and ethnicity, and educational attainment. All estimates shown meet the NCHS Data Presentation Standards for Proportions.

The ongoing coronavirus pandemic has strongly impacted the shopping behavior of consumers in the United States. A May 2020 survey revealed that 37 percent of respondents had used contactless delivery more than usual. Buying items online and picking them up in store has also gained in popularity, as 29 percent of responding U.S. consumers stated that they had done so more often than usual.

The United States COVID-19 Trends and Impact Survey (CTIS) was a voluntary survey of Facebook users in the United States conducted from April 2020 to June 2022. CTIS was intended to aid in pandemic forecasting and response at fine spatiotemporal detail. Through collaboration with Meta, it randomly sampled Facebook active users at a rate sufficient to provide roughly 35,000 responses per day, on average. Survey questions covered topics including COVID-like symptoms, behavior (such as social distancing), COVID testing, mental health, health-related beliefs, trust in officials and information sources, schooling, vaccination acceptance and hesitancy, and related subjects. Respondents provided their ZIP code. Demographic variables include age, gender, education, race/ethnicity, and occupation. Meta generated survey weights to correct for non-response and to match the US adult population age and gender distribution. The 27 datasets make up the microdata. Users should see the Microdata User Guide for documentation on the use and interpretation of the microdata files. Two zip files are available for public download: a monthly data zip file and a weekly data zip file. These include the aggregate data. To access these files, go to the "Download" tab and select "Other." Ensure you have enough storage space before proceeding, as the files are large.

In summer 2020, SARS-CoV-2 was detected on mink farms in Utah. An interagency One Health response was initiated to assess the extent of the outbreak and included sampling animals from or near affected mink farms and testing them for SARS-CoV-2 and non-SARS coronaviruses. Among the 365 animals sampled, including domestic cats, mink, rodents, raccoons, and skunks, 261 (72%) of the animals harbored at least one coronavirus at the time. Among the samples which could be further characterized, 126 alphacoronaviruses and 88 betacoronaviruses (including 74 detections of SARS-CoV-2) were identified. Moreover, at least 10% (n=27) of the corona-virus-positive animals were found to be co-infected with more than one coronavirus. Our findings indicate an unexpectedly high prevalence of coronavirus among the domestic and wild animals tested on mink farms and raise the possibility that commercial animal husbandry operations could be potential hot spots for future trans-species viral spillover and the emergence of new pandemic coronaviruses. Figure 1. Phylogenetic relationships of the identified coronaviruses from mink and other animals from mink farms in Utah. The four genera of coronaviruses are highlighted in different colors. AlphaCoV, alkphacoronavirus; BetaCoV, betacoronavirus; DeltaCoV, deltacoronaviruses; and GammaCoV, gammacoronavirus. Type species for the currently recognized subgenera are annotated according to the nomenclature scheme used in this manuscript with the addition of the ICTV subgenus. Additional viruses, including the closest GenBank entry as identified by the BLAST tool, were included to help delineate relationship. Red circles are viruses identified in this study. Panel A. Full phylogenetic tree (A full-size image is included in Supplementary Figure 1). Red arrows designate the group of nearly identical Utah mink coronavirus strains collapsed into the colored triangle in Panel B. Table 1. Coronavirus distribution among species tested. The species are listed by their common names; Total, the total number of animals of each species tested; Negative, number of each species with no coronavirus detected among the tissues tested; Positive, number of animals positive for coronavirus in at least one tissue; % Pos, percentage of coronavirus positives in each species. Table 2. Detailed tissue panel tested for SARS-CoV-2. The distribution of SARS-CoV-2 RNA detection in the first 96 animals is listed. Tissue, tissue or tissue pools received; Total, total number tested in each category; Negative, number of N1 RT-PCR negatives; Posi-tives, number of N1 RT-PCR positives; % Pos, percentage of tissues positive for corona-virus. Table 3. Summary of coronaviruses identified. The distribution of coronaviruses detected and characterized according to their host is listed. Species, common name of animal species tested; AlphaCoV, number of alphacoronaviruses identified; BetaCoV, number of betacoronaviruses identified; Sequenced, number of viruses identified by sequencing, Unchar, number of coronavirus-positive samples not further characterized. Table 4. SARS-CoV-2 coinfections identified in Utah mammals. The individual animals that are both SARS-CoV-2 positive and infected with a second coronavirus are listed. Animal ID, Unique animal identification number; Common name, common name of animal; Scientific name, scientific name of animal; Sex, F, female, M, male. Unk, un-known; Age, A adult, J juvenile, Unk, unknown; SARS-CoV-2, Neg-N1 RT-PCR nega-tive, Pos-N1 RT-PCR positive, Second strain, genus and common name of the coronavirus, Pan-CoV RT-PCR Equivocal, sample is PCR positive but not further characterized. Supplementary Figure 1. Phylogenetic relationships of the identified coronaviruses from mink farms in Utah. The four genera of coronaviruses are highlighted in different colors. AlphaCoV, alkphacoronavirus; BetaCoV, betacoronavirus; DeltaCoV, deltacoronaviruses; and GammaCoV, gammacoronavirus. Type species for the currently recognized subgenera are annotated according to the nomenclature scheme used in this manuscript with the addition of the ICTV subgenus. Additional viruses, including the closest GenBank entry as identified by the BLAST tool were included to help delineate relationship. Red circles are viruses identified in this study. Supplementary Table 1. List of animals and tissues sampled and RT-PCR test results. Animal ID, unique identifier for each animal; Specimen ID, unique identifier for each tissue; Common name, common name of the animal species; Scientific name, scientific name of the animal species, Sex, F-female, M-male, UNK-unknown; Age, J-juvenile, A-adult, UNK-unknown; Tissue, organ or organ pools tested; Tissue study, X denotes the animals and tissues used in the tissue distribution sub-study; N1 PCR, Ct values from the CDC N1 assay; Pan-CoV PCR, Neg, negative, Pos, positive, Equiv, equivocal; * wild mink. Supplementary Table 2. Summary of coronavirus test results. Animal ID, unique identifier for each animal; Common name, common name of the animal species; Scientific name, scientific name of the animal species, Sex, F-female, M-male, UNK-unknown; Age, J-juvenile, A-adult, UNK-unknown; CoV, Neg-negative, Pos-positive on either one or both RT-PCR tests; SARS-CoV-2, animals positive in the CDC N1 test; AlphaCoV, the tissues positive for alphacoronavirus for each animal is listed; BetaCoV, the tissues positive for betacoronavirus for each animal is listed; C-colon, C/R-colon/rectum pool, H-heart, L-lung, L/S-live/spleen pool, S int-small intestine; Co-infections, Y-yes; PCR only, Y-yes; Virus identified by sequencing, brief name of virus identified.

The COVID-19 Trends and Impact Survey (CTIS) was conducted by the Delphi Group at Carnegie Mellon University (CMU) in the United States (US) and by the University of Maryland (UMD) Social Data Science Center (SoDa) globally, in partnership with Meta. CTIS was a daily repeated cross-sectional survey that ran continuously starting April 6, 2020 in the US and starting April 23, 2020 globally. Both surveys concluded data collection on June 25, 2022. CTIS collected data in 200+ countries and territories, including 114 where Meta provided survey weights. The sampling frame was Facebook users aged 18 years or older who have been active on the platform in the last month. Sampled Facebook users saw the invitation at the top of their Feed, but the surveys were collected by the universities using Qualtrics. Meta neither collected nor received survey responses. The sample was stratified by subnational regions. Respondents were sampled as frequently as every month and as infrequently as every six months, depending on the population density of the subnational region in which they lived. Due to the minimum sampling frequency, pooled analyses should not combine more than a month of data. There were 12 versions of the survey questionnaires. The Delphi US CTIS was translated into 8 languages. The UMD Global CTIS was translated into 66 languages. This collection is comprised of three categories of data: a. Individual-level microdata files, which will be available to eligible academic and nonprofit researchers with fully executed Data Use Agreements (DUAs). b. Daily aggregate estimates at the country and subnational region levels disseminated via public APIs at CMU and UMD. c. Weekly and monthly aggregate estimates broken out by respondent characteristics (e.g., age, gender, vaccination status) at the country and subnational administrative level-1 region-level disseminated via publicly available CSV-formatted contingency tables. This collection currently only contains the aggregate data, contingency tables and associated documentation. The microdata are forthcoming.

"The COVID-19 web survey began fielding on March 13, 2020 with daily random samples of U.S. adults, aged 18 and older who are members of the Gallup Panel. Approximately 1,200 daily completes were collected from March 13 through April 26, 2020. From April 27 to August 16, 2020 approximately 500 daily completes are being collected. Starting August 17, 2020, the survey moved from daily surveying to a survey conducted one time per month over a two week field period (typically the last two weeks of the month). Beginning in 2022, the COVID survey moved to quarterly data collection." - from the 'Methodology and Codebook' document updated May 31, 2022. The dataset is available in Stata and SPSS formats; both are bundled with the 'Methodology and Codebook' in PDF format as a single ZIP file available for downloading here. Also included are the Wellbeing Panel Survey data in SPSS and Stata formats. Please note: There is a delay between the latest survey round being concluded and AU Library receiving the files - for example, the Aug. 2021 update included the dataset for surveys conducted from March 2020-June 2021.

U.S. respondents in a recent multi-country poll have the highest confidence in local health services, to be prepared and effectively deal with the coronavirus (COVID-19). This statistic shows the percentage of U.S. survey respondents who, based on what they have seen, read or heard, are very or somewhat confident in the services provided by the following, as of February 9, 2020: U.S. healthcare services, community doctors and healthcare professionals, the WHO, local hospitals, local and national government, U.S. airlines and airports, and friends and neighbors.

The COVID-19 pandemic has dramatically altered family life in the United States. Over the long duration of the pandemic, parents had to adapt to shifting work conditions, virtual schooling, the closure of daycare facilities, and the stress of not only managing households without domestic and care supports but also worrying that family members may contract the novel coronavirus. Reports early in the pandemic suggest that these burdens have fallen disproportionately on mothers, creating concerns about the long-term implications of the pandemic for gender inequality and mothers’ well-being. Nevertheless, less is known about how parents’ engagement in domestic labor and paid work has changed throughout the pandemic, what factors may be driving these changes, and what the long-term consequences of the pandemic may be for the gendered division of labor and gender inequality more generally. The Study on U.S. Parents’ Divisions of Labor During COVID-19 (SPDLC) collects longitudinal survey data from partnered U.S. parents that can be used to assess changes in parents’ divisions of domestic labor, divisions of paid labor, and well-being throughout and after the COVID-19 pandemic. The goal of SPDLC is to understand both the short- and long-term impacts of the pandemic for the gendered division of labor, work-family issues, and broader patterns of gender inequality. Survey data for this study is collected using Prolifc (www.prolific.co), an opt-in online platform designed to facilitate scientific research. The sample is comprised U.S. adults who were residing with a romantic partner and at least one biological child (at the time of entry into the study). In each survey, parents answer questions about both themselves and their partners. Wave 1 of SPDLC was conducted in April 2020, and parents who participated in Wave 1 were asked about their division of labor both prior to (i.e., early March 2020) and one month after the pandemic began. Wave 2 of SPDLC was collected in November 2020. Parents who participated in Wave 1 were invited to participate again in Wave 2, and a new cohort of parents was also recruited to participate in the Wave 2 survey. Wave 3 of SPDLC was collected in November 2021. Parents who participated in either of the first two waves were invited to participate again in Wave 3, and another new cohort of parents was also recruited to participate in the Wave 3 survey. This research design (follow-up survey of panelists and new cross-section of parents at each wave) will continue through 2024, culminating in six waves of data spanning the period from March 2020 through September 2024. An estimated total of approximately 6,500 parents will be surveyed at least once throughout the duration of the study. SPDLC data will be released to the public two years after data is collected; Wave 1 will be publicly available in April 2022, Wave 2 will be publicly available in November 2022, Wave 3 will be publicly available in November 2023, etc. Data will be available to download in both SPSS (.sav) and Stata (.dta) formats, and the following data files will be available: (1) a data file for each individual wave, which contains responses from all participants in that wave of data collection, (2) a longitudinal panel data file, which contains longitudinal follow-up data from all available waves, and (3) a repeated cross-section data file, which contains the repeated cross-section data (from new respondents at each wave) from all available waves. Codebooks for each survey wave and a detailed user guide describing the data are also available.

Decision-makers need clear information about the prevalence of coronavirus as well as its impacts on the American people and our society. The COVID Impact Survey will provide national and regional statistics about physical health, mental health, economic security, and social dynamics in the United States.

HHS COVID-19 Small Area Estimations Survey - Monovalent Booster Audience - Wave 19

  Description

The goal of the Monthly Outcome Survey (MOS) Small Area Estimations (SAE) are to generate estimates of the proportions of adults, by county and month, who were in the population of interest for the U.S. Department of Health and Human Services’ (HHS) We Can Do This COVID-19 Public Education Campaign. These data are designed to be used by practitioners and researchers to… See the full description on the dataset page: https://huggingface.co/datasets/HHS-Official/hhs-covid-19-small-area-estimations-survey-monoval.

The Monthly Outcome Survey (MOS) was designed to assess COVID-19 vaccine uptake as well as beliefs, intentions, and behaviors relevant to COVID-19 vaccination at a point in time. The survey fielded on a monthly basis from January 2021 to April 2023. When the MOS first launched, it focused on the primary series of COVID-19 vaccines; in later waves, it was expanded to assess parents’ intentions to get their children vaccinated or boosted and to track booster and updated vaccine uptake and readiness. The MOS fielded as part of an online omnibus survey, conducted with a cross-sectional sample of approximately 5,000 U.S. adults each month.

The Household Pulse Survey is designed to deploy quickly and efficiently, collecting data to measure household experiences during the coronavirus pandemic and recovery.

This dataset is no longer being updated as of 5/11/2023. It is being retained on the Open Data Portal for its potential historical interest.

This table displays the number of COVID-19 deaths among Cambridge residents by race and ethnicity. The count reflects total deaths among Cambridge COVID-19 cases.

The rate column shows the rate of COVID-19 deaths among Cambridge residents by race and ethnicity. The rates in this chart were calculated by dividing the total number of deaths among Cambridge COVID-19 cases for each racial or ethnic category by the total number of Cambridge residents in that racial or ethnic category, and multiplying by 10,000. The rates are considered “crude rates” because they are not age-adjusted. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts.

Of note:

This chart reflects the time period of March 25 (first known Cambridge death) through present.

It is important to note that race and ethnicity data are collected and reported by multiple entities and may or may not reflect self-reporting by the individual case. The Cambridge Public Health Department (CPHD) is actively reaching out to cases to collect this information. Due to these efforts, race and ethnicity information have been confirmed for over 80% of Cambridge cases, as of June 2020.

Race/Ethnicity Category Definitions: “White” indicates “White, not of Hispanic origin.” “Black” indicates “Black, not of Hispanic origin.” “Hispanic” refers to a person having Hispanic origin. A person having Hispanic origin may be of any race. “Asian” indicates “Asian, not of Hispanic origin.” To protect individual privacy, a category is suppressed when it has one to four people. Categories with zero cases are reported as zero. "Other" indicates multiple races, another race that is not listed above, and cases who have reported nationality in lieu of a race category recognized by the US Census. Population data are from the U.S. Census Bureau’s 2014–2018 American Community Survey estimates and may differ from actual population counts. "Other" also includes a small number of people who identify as Native American or Native Hawaiian/Pacific islander. Because the count for Native Americans or Native Hawaiian/Pacific Islanders is currently < 5 people, these categories have been combined with “Other” to protect individual privacy.

Household Pulse Survey (HPS): HPS is a rapid-response survey of adults ages ≥18 years led by the U.S. Census Bureau, in partnership with seven other federal statistical agencies, to measure household experiences during the COVID-19 pandemic. Detailed information on probability sampling using the U.S. Census Bureau’s Master Address File, questionnaires, response rates, and bias assessment is available on the Census Bureau website (https://www.census.gov/data/experimental-data-products/household-pulse-survey.html). Data from adults ages ≥18 years and older are collected by a 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). Data are weighted to represent total persons ages 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Data from adults ages ≥18 years and older are collected by 20-minute online survey from randomly sampled households stratified by state and the top 15 metropolitan statistical areas (MSAs). For more information on this survey, see https://www.census.gov/programs-surveys/household-pulse-survey.html. Data are weighted to represent total persons ages 18 and older living within households and to mitigate possible bias that can result from non-responses and incomplete survey frame. Responses in the Household Pulse Survey (https://www.census.gov/programs-surveys/household-pulse-survey.html) are self-reported. Estimates of vaccination coverage may differ from vaccine administration data reported at COVID-19 Vaccinations in the United States (https://covid.cdc.gov/covid-data-tracker/#vaccinations).

A. SUMMARY Medical provider confirmed COVID-19 cases and confirmed COVID-19 related deaths in San Francisco, CA aggregated by several different geographic areas and normalized by 2016-2020 American Community Survey (ACS) 5-year estimates for population data to calculate rate per 10,000 residents. On September 12, 2021, a new case definition of COVID-19 was introduced that includes criteria for enumerating new infections after previous probable or confirmed infections (also known as reinfections). A reinfection is defined as a confirmed positive PCR lab test more than 90 days after a positive PCR or antigen test. The first reinfection case was identified on December 7, 2021. Cases and deaths are both mapped to the residence of the individual, not to where they were infected or died. For example, if one was infected in San Francisco at work but lives in the East Bay, those are not counted as SF Cases or if one dies in Zuckerberg San Francisco General but is from another county, that is also not counted in this dataset. Dataset is cumulative and covers cases going back to 3/2/2020 when testing began. Geographic areas summarized are: 1. Analysis Neighborhoods 2. Census Tracts 3. Census Zip Code Tabulation Areas B. HOW THE DATASET IS CREATED Addresses from medical data are geocoded by the San Francisco Department of Public Health (SFDPH). Those addresses are spatially joined to the geographic areas. Counts are generated based on the number of address points that match each geographic area. The 2016-2020 American Community Survey (ACS) population estimates provided by the Census are used to create a rate which is equal to ([count] / [acs_population]) * 10000) representing the number of cases per 10,000 residents. C. UPDATE PROCESS Geographic analysis is scripted by SFDPH staff and synced to this dataset daily at 7:30 Pacific Time. D. HOW TO USE THIS DATASET San Francisco population estimates for geographic regions can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS). Privacy rules in effect To protect privacy, certain rules are in effect: 1. Case counts greater than 0 and less than 10 are dropped - these will be null (blank) values 2. Death counts greater than 0 and less than 10 are dropped - these will be null (blank) values 3. Cases and deaths dropped altogether for areas where acs_population < 1000 Rate suppression in effect where counts lower than 20 Rates are not calculated unless the case count is greater than or equal to 20. Rates are generally unstable at small numbers, so we avoid calculating them directly. We advise you to apply the same approach as this is best practice in epidemiology. A note on Census ZIP Code Tabulation Areas (ZCTAs) ZIP Code Tabulation Areas are special boundaries created by the U.S. Census based on ZIP Codes developed by the USPS. They are not, however, the same thing. ZCTAs are areal representations of routes. Read how the Census develops ZCTAs on their website. Row included for Citywide case counts, incidence rate, and deaths A single row is included that has the Citywide case counts and incidence rate. This can be used for comparisons. Citywide will capture all cases regardless of address quality. While some cases cannot be mapped to sub-areas like Census Tracts, ongo

The Research and Development Survey (RANDS) is a platform designed for conducting survey question evaluation and statistical research. RANDS is an ongoing series of surveys from probability-sampled commercial survey panels used for methodological research at the National Center for Health Statistics (NCHS). RANDS estimates are generated using an experimental approach that differs from the survey design approaches generally used by NCHS, including possible biases from different response patterns and sampling frames as well as increased variability from lower sample sizes. Use of the RANDS platform allows NCHS to produce more timely data than would be possible using traditional data collection methods. RANDS is not designed to replace NCHS’ higher quality, core data collections. Below are experimental estimates of reduced access to healthcare for three rounds of RANDS during COVID-19. Data collection for the three rounds of RANDS during COVID-19 occurred between June 9, 2020 and July 6, 2020, August 3, 2020 and August 20, 2020, and May 17, 2021 and June 30, 2021. Information needed to interpret these estimates can be found in the Technical Notes. RANDS during COVID-19 included questions about unmet care in the last 2 months during the coronavirus pandemic. Unmet needs for health care are often the result of cost-related barriers. The National Health Interview Survey, conducted by NCHS, is the source for high-quality data to monitor cost-related health care access problems in the United States. For example, in 2018, 7.3% of persons of all ages reported delaying medical care due to cost and 4.8% reported needing medical care but not getting it due to cost in the past year. However, cost is not the only reason someone might delay or not receive needed medical care. As a result of the coronavirus pandemic, people also may not get needed medical care due to cancelled appointments, cutbacks in transportation options, fear of going to the emergency room, or an altruistic desire to not be a burden on the health care system, among other reasons. The Household Pulse Survey (https://www.cdc.gov/nchs/covid19/pulse/reduced-access-to-care.htm), an online survey conducted in response to the COVID-19 pandemic by the Census Bureau in partnership with other federal agencies including NCHS, also reports estimates of reduced access to care during the pandemic (beginning in Phase 1, which started on April 23, 2020). The Household Pulse Survey reports the percentage of adults who delayed medical care in the last 4 weeks or who needed medical care at any time in the last 4 weeks for something other than coronavirus but did not get it because of the pandemic. The experimental estimates on this page are derived from RANDS during COVID-19 and show the percentage of U.S. adults who were unable to receive medical care (including urgent care, surgery, screening tests, ongoing treatment, regular checkups, prescriptions, dental care, vision care, and hearing care) in the last 2 months. Technical Notes: https://www.cdc.gov/nchs/covid19/rands/reduced-access-to-care.htm#limitations

UAS COVID sample demographic composition (N = 6,932) vs ACS estimates.

As part of an ongoing partnership with the Census Bureau, the National Center for Health Statistics (NCHS) recently added questions to assess the prevalence of post-COVID-19 conditions (long COVID), on the experimental Household Pulse Survey. This 20-minute online survey was designed to complement the ability of the federal statistical system to rapidly respond and provide relevant information about the impact of the coronavirus pandemic in the U.S. Data collection began on April 23, 2020. Beginning in Phase 3.5 (on June 1, 2022), NCHS included questions about the presence of symptoms of COVID that lasted three months or longer. Phase 3.5 will continue with a two-weeks on, two-weeks off collection and dissemination approach. Estimates on this page are derived from the Household Pulse Survey and show the percentage of adults aged 18 and over who a) as a proportion of the U.S. population, the percentage of adults who EVER experienced post-COVID conditions (long COVID). These adults had COVID and had some symptoms that lasted three months or longer; b) as a proportion of adults who said they ever had COVID, the percentage who EVER experienced post-COVID conditions; c) as a proportion of the U.S. population, the percentage of adults who are CURRENTLY experiencing post-COVID conditions. These adults had COVID, had long-term symptoms, and are still experiencing symptoms; d) as a proportion of adults who said they ever had COVID, the percentage who are CURRENTLY experiencing post-COVID conditions; and e) as a proportion of the U.S. population, the percentage of adults who said they ever had COVID.

A recent survey found that although around half of Americans wanted to keep up with news concerning the coronavirus epidemic, 51 percent of adults surveyed said that they were seeking out news that is unrelated to the virus. As of May 2020, news fatigue surrounding the COVID-19 outbreak is apparent, with a significant proportion of the population tired of news concerning coronavirus, and the majority searching for information on unrelated topics.