From June 24 to June 30, 2020, around 52.1 percent of Hispanic adults aged 18 years and older in the U.S. reported having one or more adverse mental or behavioral health symptoms during the COVID-19 pandemic. This statistic illustrates the percentage of U.S. adults who reported adverse mental health symptoms, increased substance use, and suicidal ideation during COVID-19 pandemic from June 24 to 30, 2020, by race.

In 2020, around 24 percent of non-Hispanic white adults 18 years and older in the United States received some mental health treatment in the past 12 months, compared to less than 15 percent of non-Hispanic black adults. This statistic illustrates the percentage of U.S. adults aged 18 years and older who had received any mental health treatment or medication in the past 12 months in 2020, by race.

This data set includes annual counts and percentages of Medicaid and Children’s Health Insurance Program (CHIP) enrollees who received mental health (MH) or substance use disorder (SUD) services, overall and by six subpopulation topics: age group, sex or gender identity, race and ethnicity, urban or rural residence, eligibility category, and primary language. These results were generated using Transformed Medicaid Statistical Information System (T-MSIS) Analytic Files (TAF) Release 1 data and the Race/Ethnicity Imputation Companion File. This data set includes Medicaid and CHIP enrollees in all 50 states, the District of Columbia, Puerto Rico, and the U.S. Virgin Islands, ages 12 to 64 at the end of the calendar year, who were not dually eligible for Medicare and were continuously enrolled with comprehensive benefits for 12 months, with no more than one gap in enrollment exceeding 45 days. Enrollees who received services for both an MH condition and SUD in the year are counted toward both condition categories. Enrollees in Guam, American Samoa, the Northern Mariana Islands, and select states with TAF data quality issues are not included. Results shown for the race and ethnicity subpopulation topic exclude enrollees in the U.S. Virgin Islands. Results shown for the primary language subpopulation topic exclude select states with data quality issues with the primary language variable in TAF. Some rows in the data set have a value of "DS," which indicates that data were suppressed according to the Centers for Medicare & Medicaid Services’ Cell Suppression Policy for values between 1 and 10. This data set is based on the brief: "Medicaid and CHIP enrollees who received mental health or SUD services in 2020." Enrollees are assigned to an age group subpopulation using age as of December 31st of the calendar year. Enrollees are assigned to a sex or gender identity subpopulation using their latest reported sex in the calendar year. Enrollees are assigned to a race and ethnicity subpopulation using the state-reported race and ethnicity information in TAF when it is available and of good quality; if it is missing or unreliable, race and ethnicity is indirectly estimated using an enhanced version of Bayesian Improved Surname Geocoding (BISG) (Race and ethnicity of the national Medicaid and CHIP population in 2020). Enrollees are assigned to an urban or rural subpopulation based on the 2010 Rural-Urban Commuting Area (RUCA) code associated with their home or mailing address ZIP code in TAF (Rural Medicaid and CHIP enrollees in 2020). Enrollees are assigned to an eligibility category subpopulation using their latest reported eligibility group code, CHIP code, and age in the calendar year. Enrollees are assigned to a primary language subpopulation based on their reported ISO language code in TAF (English/missing, Spanish, and all other language codes) (Primary Language). Please refer to the full brief for additional context about the methodology and detailed findings. Future updates to this data set will include more recent data years as the TAF data become available.

Of the individuals who reported anxiety (GAD-7) and depression (PHQ-9) in the United States, some ** percent were white (non-Hispanic) during 2020, while the percentage was significantly higher in 2019 with almost ** percent. This statistic shows the percentage of Americans who reported anxiety (GAD-7) and depression (PHQ-9) before the pandemic in 2019 and during the pandemic in 2020, by race/ethnicity.

Data Source: Substance Abuse and Mental Health Services Administration (SAMHSA) 2020, U.S. Department of Health and Human Services (HHS).

This is the dataset used for my first project for mental health analysis with Ann Bertram and Tiffany McBride at Purdue Fort Wayne. It has been cleaned and divided into datasets based on the states. Each dataset will include demographic information such as age, education level, ethnicity, race, genders, mental illness flags, etc. For more information, please refer to the codebook.

This is the second (wave 2) in a series of follow up reports to the Mental Health and Young People Survey (MHCYP) 2017, exploring the mental health of children and young people in February/March 2021, during the Coronavirus (COVID-19) pandemic and changes since 2017. Experiences of family life, education, and services during the COVID-19 pandemic are also examined. The sample for the Mental Health Survey for Children and Young People, 2021 (MHCYP 2021), wave 2 follow up was based on 3,667 children and young people who took part in the MHCYP 2017 survey, with both surveys also drawing on information collected from parents. Cross-sectional analyses are presented, addressing three primary aims: Aim 1: Comparing mental health between 2017 and 2021 – the likelihood of a mental disorder has been assessed against completion of the Strengths and Difficulties Questionnaire (SDQ) in both years in Topic 1 by various demographics. Aim 2: Describing life during the COVID-19 pandemic - Topic 2 examines the circumstances and experiences of children and young people in February/March 2021 and the preceding months, covering: COVID-19 infection and symptoms. Feelings about social media use. Family connectedness. Family functioning. Education, including missed days of schooling, access to resources, and support for those with Special Educational Needs and Disabilities (SEND). Changes in circumstances. How lockdown and restrictions have affected children and young people’s lives. Seeking help for mental health concerns. Aim 3: Present more detailed data on the mental health, circumstances and experiences of children and young people by ethnic group during the coronavirus pandemic (where sample sizes allow). The data is broken down by gender and age bands of 6 to 10 year olds and 11 to 16 year olds for all categories, and 17 to 22 years old for certain categories where a time series is available, as well as by whether a child is unlikely to have a mental health disorder, possibly has a mental health disorder and probably has a mental health disorder. This study was funded by the Department of Health and Social Care, commissioned by NHS Digital, and carried out by the Office for National Statistics, the National Centre for Social Research, University of Cambridge and University of Exeter.

This report presents findings from the third (wave 3) in a series of follow up reports to the 2017 Mental Health of Children and Young People (MHCYP) survey, conducted in 2022. The sample includes 2,866 of the children and young people who took part in the MHCYP 2017 survey. The mental health of children and young people aged 7 to 24 years living in England in 2022 is examined, as well as their household circumstances, and their experiences of education, employment and services and of life in their families and communities. Comparisons are made with 2017, 2020 (wave 1) and 2021 (wave 2), where possible, to monitor changes over time.

All racial/ethnic groups surveyed showed a decrease in moderate to severe anxiety from 2020 to 2021, however they continue to be higher than the pre-pandemic levels. This statistic shows the percentage of U.S. adults showing moderate to severe anxiety during voluntary mental health screenings from 2019 to 2021, by ethnicity.

When the COVID-19 pandemic began, U.S. college students reported increased anxiety and depression. This study examines mental health among U.S college students during the subsequent 2020–2021 academic year by surveying students at the end of the fall 2020 and the spring 2021 semesters. Our data provide cross-sectional snapshots and longitudinal changes. Both surveys included the PSS, GAD-7, PHQ-8, questions about students’ academic experiences and sense of belonging in online, in-person, and hybrid classes, and additional questions regarding behaviors, living circumstances, and demographics. The spring 2021 study included a larger, stratified sample of eight demographic groups, and we added scales to examine relationships between mental health and students’ perceptions of their universities’ COVID-19 policies. Our results show higher-than-normal frequencies of mental health struggles throughout the 2020–2021 academic year, and these were substantially higher for female college students, but by spring 2021, the levels did not vary substantially by race/ethnicity, living circumstances, vaccination status, or perceptions of university COVID-19 policies. Mental health struggles inversely correlated with scales of academic and non-academic experiences, but the struggles positively correlated with time on social media. In both semesters, students reported more positive experiences with in-person classes, though all class types were rated higher in the spring semester, indicating improvements in college students’ course experiences as the pandemic continued. Furthermore, our longitudinal data indicate the persistence of mental health struggles across semesters. Overall, these studies show factors that contributed to mental health challenges among college students as the pandemic continued.

Context

In October 2021 the World Health Organization (WHO) published an article about how Mental Health services have been pushed to the limit during the COVID-19 pandemic while stating that the next pandemic will be on mental health encouraging governments to increase their expenditure on Mental Health.

While searching for Mental Health service usage and expenditure in New Zealand we found that the information is spread in several excel spreadsheets from 2002 to 2020 with different sources, formats, and accessibility.

We then proceeded to gather some of the information provided by the New Zealand Ministry of health into 3 datasets that summarise the usage of these services in the last 20 years.

Inspiration

The main inspiration for this dataset was to find a way of building a continuous pipeline for future reference of Mental Health Service usage in New Zealand.

Our first approach was to match Government Expenditure and usage of Mental Health Services in NZ over the last years but finding information about specific government expenditure is hard to come by, So we decided to focus mostly in creating a solid dataframe about mental health service usage over the years.

Data Sources

Ministry of Health New Zealand

This data source was chosen for its ease of access and ability to web scrape.

Datasets were available from three sources: 1) Datasets from 2002 to 2008 2) Dataset of 2010 3) Dataset from 2011 to 2020

3 different crawlers were developed in order to maintain consistency over sources. Datasets from 2011 onwards are displayed in the ministry of health new Aggregated Data Site..

Datasets from 2002 were gathered from legacy sources on the list of reports by the ministry of health

The data provided in this data sets can be classified into 3 groups:

1) NZ Mental Health services usage by gender, age and ethnicity. 2) NZ Mental Health service usage by DHB's (District Health boards) 3) NZ Metal Health service usage by Service provided.

"Data is sourced from the Programme for the Integration of Mental Health Data (PRIMHD). PRIMHD contains Ministry of Health funded mental health and addiction service activity and outcomes data. The data is collected from district health boards (DHBs) and non-governmental organisations (NGOs).

PRIMHD data is used to report on what services are being provided, who is providing the services, and what outcomes are being achieved for health consumers across New Zealand's mental health sector. These reports enable better quality service planning and decision making by mental health and addiction service providers, at local, regional and national levels."

We have combined the data in the excel files provided by the MOH into a single data frame.

Intention of usage.

The idea behind the project is to have an incremental dataset for past and future reference, allow ease of access to timeseries information and better visibility.

Problems while gathering data.

We will divide the problems while gathering information into 2 categories: Downloading and Wrangling.

Downloading

The ministry of health releases an anual report on Mental Health since 2002, this reports are uploaded to the stats page of the Ministry of Health's website.

Reports from 2002 to 2007 have an aggregated site where they can be downlaoded programmatically. Reports from 2008 and 2010 have their individual site and had to be added manually to the download process. Reports from 2011 onwards have their own Mental Health page where they get uploaded every year. This website allows us to make incremental updates to the current dataset.

This 3 types of published papers required individual processes to download programmatically, 2 of them were scrapped from lists, and 1 of them manually added to the dataset.

While developing the download process for reports from 2011 we also find that some links were broken or required manual intervention, this had to be solved with exceptions for different years.

We developed parsers for this matter and expect changes in the future that can be solved by adding simple exceptions to new years given that they change, which at this stage is uncertain.

For the scraping part of the project we used R's rvest library.

Wrangling

The reports published by the Ministry of Health are given in excel format. R's tidiyverse and readxl libraries were used.

These reports are given in multi sheet excel files that have changed considerably over the years and had to be solved with individual parsers.

For this we...

Black people were 3.5 times more likely to be detained than white people under the Mental Health Act in the year to March 2023.

Data by medical encounter for the following conditions by age, race/ethnicity, and sex (gender):

Alcohol Poisoning Alcohol Related Disorders All Drug Overdoses All Opioid Overdoses Anxiety and Fear Related Disorders Cannabis Use/Abuse/Dependency Depression Impulse and Conduct Disorders Miscellaneous Mental Health Disorders Mood Disorders Neurodevelopmental Disorders Opioid Use/Abuse/Dependency Personality Disorders Schizophrenia Sedative Use/Abuse/Dependency Stimulant Use/Abuse/Dependency Substance Use/Abuse/Dependency Suicide Trauma and Stressor Related Disorders

Rates per 100,000 population. Age-adjusted rates per 100,000 2000 US standard population. Blank Cells: Events less than 11 are suppressed. Starting with data year 2022, geographies with less than 20,000 population contain no age-adjusted rates and all rates based on events <20 are suppressed due to statistical instability. Rates not calculated in cases where zip code is unknown. SES: Is the median household income by Subregional Area (SRA) community. Data for SRA only.

Data sources: California Department of Public Health, Center for Health Statistics, Office of Health Information and Research, Vital Records Business Intelligence System (VRBIS), 2022. California Department of Health Care Access and Information (HCAI), Emergency Department Discharge Database and Patient Discharge Database, 2022. SANDAG Population Estimates, 2022 (v11/23). 2022 population estimates were derived from the 2020 decennial census. Comparison of rates to prior years may not be appropriate. Prepared by: County of San Diego, Health and Human Services Agency, Public Health Services, Community Health Statistics Unit, May 2024.

2022 Community Profile Data Guide and Data Dictionary Dashboard: https://public.tableau.com/app/profile/chsu/viz/2022COREDataGuideandDataDictionary/Home

This dataset includes Medicaid Managed Care, Commercial HMO, and Commercial PPO performance data from the Quality Assurance Reporting Requirements (QARR) by member demographic characteristics. QARR is largely based on measures of quality developed and published by the National Committee for Quality Assurance (NCQA) Healthcare Effectiveness Data and Information Set (HEDIS®). Plans are required to submit quality performance data each year. Demographic information analyzed in this report includes members’ sex, age, race/ethnicity, Medicaid aid category, cash assistance status, behavioral health conditions including serious mental illness (SMI) and substance use disorder (SUD), payer status, and region of residence. Measuring the quality of care, and the ability to measure disparities in care is an important first step to a better understanding of the underlying factors that drive differences in care among certain populations within Medicaid Managed Care, Commercial HMO, and Commercial PPO.

These data are published annually for Medicaid Managed Care in the Health Care Disparities in New York State Report and on the NYSDOH website: http://www.health.ny.gov/health_care/managed_care/reports/

These data were reported to the NYC DOHMH by March 31, 2021

This dataset includes data on new diagnoses of HIV and AIDS in NYC for the calendar years 2016 through 2020. Reported cases and case rates (per 100,000 population) are stratified by United Hospital Fund (UHF) neighborhood, age group, and race/ethnicity.

All racial/ethnic groups surveyed showed an increase in moderate to severe anxiety from May to July in 2020, coinciding with the COVID-19 pandemic. This statistic shows the percentage of U.S. respondents who were reported to have anxiety (PHQ-9) after a voluntary mental health screening, from April to August 2020.

This publication brings together the Learning Disabilities and Autism (LDA) data from the Assuring Transformation (AT) collection and the LDA service specific statistics from the Mental Health Statistics Data Set (MHSDS). There are differences in the inpatient figures between the MHSDS and AT data sets and work is underway to better understand these. The MHSDS LDA data are currently labelled experimental as they are undergoing evaluation. Further information on the quality of these statistics is available in the Data Quality section of the main report. There is a slight difference in scope between the two data collections. The MHSDS data is from providers based in England and includes care provided in England but may be commissioned outside England. Whereas the Assuring Transformation data are provided by English commissioners and healthcare will typically be provided in England but also includes data on care commissioned in England and provided elsewhere in the UK. The release comprises: Assuring Transformation Publication. MHSDS LDA Publication: These statistics are derived from submissions made using version 4.1 of the Mental Health Services Dataset (MHSDS). Prior to May 2018 the LDA service specific statistics were included in the main MHSDS publication. MHSDS Multiple Submission Window Model (MSWM) The MHSDS v4.1 data model allows providers to retrospectively submit data for any monthly reporting period until the end of year cut-off as part of the Multiple Submission Window Model (MSWM). So, for 2020-21, providers are able to resubmit data for any previous months until the end of March 2021. (This was possible for the first time in MHSDS v4.0 but just for the end of year submission for March 2020 data). This model allows providers to improve the quality of previous submissions. Historical comparison with previous years should therefore be reviewed in that context. Additional information on the MSWM for MHSDS is available via the link at the bottom of this page (related links). We hope this information is helpful and would be grateful if you could spare a couple of minutes to complete a short customer satisfaction survey. Please use the link to the form at the bottom of this page to provide us with any feedback or suggestions for improving the report.

Originally, the dataset come from the CDC and is a major part of the Behavioral Risk Factor Surveillance System (BRFSS), which conducts annual telephone surveys to gather data on the health status of U.S. residents. As the CDC describes: "Established in 1984 with 15 states, BRFSS now collects data in all 50 states as well as the District of Columbia and three U.S. territories. BRFSS completes more than 400,000 adult interviews each year, making it the largest continuously conducted health survey system in the world.". The most recent dataset (as of February 15, 2022) includes data from 2020. It consists of 401,958 rows and 279 columns. The vast majority of columns are questions asked to respondents about their health status, such as "Do you have serious difficulty walking or climbing stairs?" or "Have you smoked at least 100 cigarettes in your entire life? [Note: 5 packs = 100 cigarettes]".

To improve the efficiency and relevance of our analysis, we removed certain attributes from the original BRFSS dataset. Many of the 279 original attributes included administrative codes, metadata, or survey-specific variables that do not contribute meaningfully to heart disease prediction—such as respondent IDs, timestamps, state-level identifiers, and detailed lifestyle questions unrelated to cardiovascular health. By focusing on a carefully selected subset of 18 attributes directly linked to medical, behavioral, and demographic factors known to influence heart health, we streamlined the dataset. This not only reduced computational complexity but also improved model interpretability and performance by eliminating noise and irrelevant information. All predicting variables could be divided into 4 broad categories:

1. Demographic factors: sex, age category (14 levels), race, BMI (Body Mass Index)

2. Diseases: weather respondent ever had such diseases as asthma, skin cancer, diabetes, stroke or kidney disease (not including kidney stones, bladder infection or incontinence)

3. Unhealthy habits:

   • Smoking - respondents that smoked at least 100 cigarettes in their entire life (5 packs = 100 cigarettes)
   • Alcohol Drinking - heavy drinkers (adult men having more than 14 drinks per week and adult women having more than 7 drinks per week

4. General Health:

   • Difficulty Walking - weather respondent have serious difficulty walking or climbing stairs
   • Physical Activity - adults who reported doing physical activity or exercise during the past 30 days other than their regular job
   • Sleep Time - respondent’s reported average hours of sleep in a 24-hour period
   • Physical Health - number of days being physically ill or injured (0-30 days)
   • Mental Health - number of days having bad mental health (0-30 days)
   • General Health - respondents declared their health as ’Excellent’, ’Very good’, ’Good’ ,’Fair’ or ’Poor’

Below is a description of the features collected for each patient:

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.

In 2023, around 60 million adults in the United States received treatment or counseling for their mental health within the past year. Such treatment included inpatient or outpatient treatment or counseling, or the use of prescription medication. Anxiety and depression are two common reasons for seeking mental health treatment. Who most often receives mental health treatment? In the United States, women are almost twice as likely than men to have received mental health treatment in the past year, with around 21 percent of adult women receiving some form of mental health treatment in the past year, as of 2021. Considering age, those between 18 and 44 years are more likely to receive counseling or therapy than older adults, however older adults are more likely to take medication to treat their mental health issues. Furthermore, mental health treatment in general is far more common among white adults in the U.S. than among other races or ethnicities. In 2020, around 24.4 percent of white adults received some form of mental health treatment in the past year compared to 15.3 percent of black adults and 12.6 percent of Hispanics. Reasons for not receiving mental health treatment Although stigma surrounding mental health treatment has declined over the last few decades and access to such services has greatly improved, many people in the United States who want or need treatment for mental health issues still do not get it. For example, it is estimated that almost half of women with some form of mental illness did not receive any treatment in the past year, as of 2022. Sadly, the most common reason for U.S. adults to not receive mental health treatment is that they thought they could handle the problem without treatment. Other common reasons for not receiving mental health treatment include not knowing where to go for services or could not afford the costs.