According to a survey in January 2023, nearly 60 percent of adults in the United States stated that they believe that climate change is currently harming the health of Americans either a great deal or a moderate amount. This statistic illustrates the percentage of adults in the United States who believe that climate change is causing adverse health effects.

According to a March 2024 survey conducted in the United States, 32 percent of adults reported feeling that social media had neither a positive nor negative effect on their own mental health. Only seven percent of social media users said that online platforms had a very positive effect on their mental health, while 12 percent of users said it had a very negative impact. Furthermore, 22 percent of respondents said social media had a somewhat negative effect on their mental health. Is social media addictive? A 2023 survey of individuals between 11 and 59 years old in the United States found that over 73 percent of TikTok users agreed that the platform was addictive. Furthermore, nearly 27 percent of those surveyed reported experiencing negative psychological effects related to TikTok use. Users belonging to Generation Z were the most likely to say that TikTok is addictive, yet millennials felt the negative effects of using the app more so than Gen Z. In the U.S., it is also not uncommon for social media users to take breaks from using online platforms, and as of March 2024, over a third of adults in the country had done so. Following mental health-related content Although online users may be aware of the negative and addictive aspects of social media, it is also a useful tool for finding supportive content. In a global survey conducted in 2023, 32 percent of social media users followed therapists and mental health professionals on social media. Overall, 24 percent of respondents said that they followed people on social media if they had the same condition as they did. Between January 2020 and March 2023, British actress and model Cara Delevingne was the celebrity mental health activist with the highest growth in searches tying her name to the topic.

Despite a growing body of epidemiological evidence in recent years documenting the health impacts of racism, the cumulative evidence base has yet to be synthesized in a comprehensive meta-analysis focused specifically on racism as a determinant of health. This meta-analysis reviewed the literature focusing on the relationship between reported racism and mental and physical health outcomes. Data from 293 studies reported in 333 articles published between 1983 and 2013, and conducted predominately in the U.S., were analysed using random effects models and mean weighted effect sizes. Racism was associated with poorer mental health (negative mental health: r = -.23, 95% CI [-.24,-.21], k = 227; positive mental health: r = -.13, 95% CI [-.16,-.10], k = 113), including depression, anxiety, psychological stress and various other outcomes. Racism was also associated with poorer general health (r = -.13 (95% CI [-.18,-.09], k = 30), and poorer physical health (r = -.09, 95% CI [-.12,-.06], k = 50). Moderation effects were found for some outcomes with regard to study and exposure characteristics. Effect sizes of racism on mental health were stronger in cross-sectional compared with longitudinal data and in non-representative samples compared with representative samples. Age, sex, birthplace and education level did not moderate the effects of racism on health. Ethnicity significantly moderated the effect of racism on negative mental health and physical health: the association between racism and negative mental health was significantly stronger for Asian American and Latino(a) American participants compared with African American participants, and the association between racism and physical health was significantly stronger for Latino(a) American participants compared with African American participants. Protocol PROSPERO registration number: CRD42013005464.

When there is no treatment available for a life-threatening disease, providing personal health information could lead to despair or fatalistic behaviors resulting in negative health outcomes. We document this possibility utilizing an experiment in Malawi that randomized incentives to learn HIV testing results in a context where anti-retroviral treatment was not yet available. Six years after the experiment, among HIV+s, those who learned their status were 23 percentage points less likely to survive than those who did not, with effects persisting after 15 years. Receiving an HIV+ diagnosis resulted in riskier health behaviors, greater anxiety, and higher discount rates.

This layer contains 2010-2014 American Community Survey (ACS) 5-year data, and contains estimates and margins of error. The layer shows health insurance coverage sex and race by age group. This is shown by tract, county, and state boundaries. There are also additional calculated attributes related to this topic, which can be mapped or used within analysis. Sums may add to more than the total, as people can be in multiple race groups (for example, Hispanic and Black). Later vintages of this layer have a different age group for children that includes age 18. This layer is symbolized to show the percent of population with no health insurance coverage. To see the full list of attributes available in this service, go to the "Data" tab, and choose "Fields" at the top right. Vintage: 2010-2014ACS Table(s): B27010, C27001B, C27001C, C27001D, C27001E, C27001F, C27001G, C27001H, C27001I (Not all lines of these tables are available in this layer.)Data downloaded from: Census Bureau's API for American Community Survey Date of API call: November 28, 2020National Figures: data.census.govThe United States Census Bureau's American Community Survey (ACS):About the SurveyGeography & ACSTechnical DocumentationNews & UpdatesThis ready-to-use layer can be used within ArcGIS Pro, ArcGIS Online, its configurable apps, dashboards, Story Maps, custom apps, and mobile apps. Data can also be exported for offline workflows. For more information about ACS layers, visit the FAQ. Please cite the Census and ACS when using this data.Data Note from the Census:Data are based on a sample and are subject to sampling variability. The degree of uncertainty for an estimate arising from sampling variability is represented through the use of a margin of error. The value shown here is the 90 percent margin of error. The margin of error can be interpreted as providing a 90 percent probability that the interval defined by the estimate minus the margin of error and the estimate plus the margin of error (the lower and upper confidence bounds) contains the true value. In addition to sampling variability, the ACS estimates are subject to nonsampling error (for a discussion of nonsampling variability, see Accuracy of the Data). The effect of nonsampling error is not represented in these tables.Data Processing Notes:This layer has associated layers containing the most recent ACS data available by the U.S. Census Bureau. Click here to learn more about ACS data releases and click here for the associated boundaries layer. The reason this data is 5+ years different from the most recent vintage is due to the overlapping of survey years. It is recommended by the U.S. Census Bureau to compare non-overlapping datasets.Boundaries come from the US Census TIGER geodatabases. Boundary vintage (2014) appropriately matches the data vintage as specified by the Census. These are Census boundaries with water and/or coastlines clipped for cartographic purposes. For census tracts, the water cutouts are derived from a subset of the 2010 AWATER (Area Water) boundaries offered by TIGER. For state and county boundaries, the water and coastlines are derived from the coastlines of the 500k TIGER Cartographic Boundary Shapefiles. The original AWATER and ALAND fields are still available as attributes within the data table (units are square meters). The States layer contains 52 records - all US states, Washington D.C., and Puerto RicoCensus tracts with no population that occur in areas of water, such as oceans, are removed from this data service (Census Tracts beginning with 99).Percentages and derived counts, and associated margins of error, are calculated values (that can be identified by the "_calc_" stub in the field name), and abide by the specifications defined by the American Community Survey.Field alias names were created based on the Table Shells file available from the American Community Survey Summary File Documentation page.Negative values (e.g., -4444...) have been set to null, with the exception of -5555... which has been set to zero. These negative values exist in the raw API data to indicate the following situations:The margin of error column indicates that either no sample observations or too few sample observations were available to compute a standard error and thus the margin of error. A statistical test is not appropriate.Either no sample observations or too few sample observations were available to compute an estimate, or a ratio of medians cannot be calculated because one or both of the median estimates falls in the lowest interval or upper interval of an open-ended distribution.The median falls in the lowest interval of an open-ended distribution, or in the upper interval of an open-ended distribution. A statistical test is not appropriate.The estimate is controlled. A statistical test for sampling variability is not appropriate.The data for this geographic area cannot be displayed because the number of sample cases is too small.

A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value i

Introduction

Social Media Mental Health Statistics: Social media has many uses, but it often causes the most harm to younger users. Teens face significant mental health issues due to social media, and the COVID-19 pandemic made things worse by increasing screen time and social media use. This created more opportunities for teens to encounter online problems, worsening the situation.

Teens also use social media to find communities and interest groups, watch live streams, and support good causes. It’s important to US teens that they feel welcome and safe online. Despite some problems, social media offers many chances for connection and entertainment. We shall shed more light on the Social Media Mental Health Statistics through this article.

Partisan polarization is a significant driver of stress and anxiety for the American public, and recent aggregate-level studies suggest polarization may be shaping Americans’ health. This individual-level study uses a new representative dataset of 2752 US residents surveyed between December 2019 and January 2020, testing why some Americans report more days of poor physical and mental health per month than others. Using negative binomial models, zero inflated models, and descriptive visualizations, we find evidence that polarization is detrimental to an individual's physical and mental health; the more distant they feel politically from the average voter in their state, the worse health outcomes they report. By uncovering the individual-level political drivers of health, this study aims to encourage further study and attention to the broader consequences of political polarization on American communities.

ObjectiveReal-world data characterizing differences between African American (AA) and White women with metastatic triple-negative breast cancer (mTNBC) are limited. Using 9 years of data collected from community practices throughout the United States, we assessed racial differences in the proportion of patients with mTNBC, and their characteristics, treatment, and overall survival (OS).MethodsThis retrospective study analyzed de-identified data from 2,116 patients with mTNBC in the Flatiron Health database (January 2011 to March 2020). Characteristics and treatment patterns between AA and White patients with mTNBC were compared using descriptive statistics. OS was examined using Kaplan-Meier analysis and a multivariate Cox proportional hazards regression model.ResultsAmong patients with metastatic breast cancer, more AA patients (23%) had mTNBC than White patients (12%). This difference was particularly pronounced in patients who lived in the Northeast, were aged 45–65, had commercial insurance, and had initial diagnosis at stage II. AA patients were younger and more likely to have Medicaid. Clinical characteristics and first-line treatments were similar between AA and White patients. Unadjusted median OS (months) was shorter in AA (10.3; 95% confidence interval [CI]: 9.1, 11.7) vs. White patients (11.9; 95% CI: 10.9, 12.8) but not significantly different. After adjusting for potential confounders, the hazard ratio for OS was 1.09 (95% CI: 0.95, 1.25) for AA vs. White patients.ConclusionsThe proportion of patients with mTNBC was higher in AA than White mBC patients treated in community practices. Race did not show an association with OS. Both AA and White patients with mTNBC received similar treatments. OS was similarly poor in both groups, particularly in patients who had not received any documented anti-cancer treatment. Effective treatment remains a substantial unmet need for all patients with mTNBC.

The New York Times is releasing a series of data files with cumulative counts of coronavirus cases in the United States, at the state and county level, over time. We are compiling this time series data from state and local governments and health departments in an attempt to provide a complete record of the ongoing outbreak.

Since the first reported coronavirus case in Washington State on Jan. 21, 2020, The Times has tracked cases of coronavirus in real time as they were identified after testing. Because of the widespread shortage of testing, however, the data is necessarily limited in the picture it presents of the outbreak.

We have used this data to power our maps and reporting tracking the outbreak, and it is now being made available to the public in response to requests from researchers, scientists and government officials who would like access to the data to better understand the outbreak.

The data begins with the first reported coronavirus case in Washington State on Jan. 21, 2020. We will publish regular updates to the data in this repository.

According to a 2019 survey, 86 percent of patients were of the opinion that cost negatively impacted care, while 30 percent believed that electronic health records had an adverse effect on patient care.

This dataset contains the first part of the National Practitioner Data Bank (NPDB) Public Use Data File, which contains reports from 1990 to 2014 of sequence number 1 to 1357832. The NPDB, according to the Health Care Quality Improvement Act of 1986 collects and discloses de-identified professional competence and conduct of professionals reports about practitioners, providers, suppliers and entities figuring malpractice payments, adverse reactions and other actions or decisions.

Asian Americans and Pacific Islanders (AAPI) make up approximately 4-5% of the total U.S. Armed Forces (U.S. Department of Defense, 2017); however, little is known about their experiences and general well-being. Recent studies that have explored the well-being of service members suggest a lack of adequate representation of AAPIs in research and also identify AAPIs as having the highest rates of suicide within the military (Eisen et al., 2012; Foynes et al., 2015; Pietrzak et al., 2015; Schoenbaum et al., 2014). Social interactions and teamwork are essential to efficient operations and functioning in the military. Positive social experiences, such as strong unit cohesion, may serve as a protective factor against negative life events, whereas instances of discrimination may place chronic strain on an individual’s well-being (Adams et al., 2017; Adler & Castro, 2013; Williams et al., 2016). To address a significant gap in the current literature and develop a better understanding of AAPIs in the military, this study examined how AAPIs’ social experiences in the U.S. military influence their health and well-being. Significant relationships were identified between perceived workplace discrimination and unit cohesion, perceived discrimination and health outcomes, and unit cohesion and health outcomes. Implications for policy, practice, and theory, limitations of the current study, and future directions for research are discussed.

Surgo Ventures' Maternal Vulnerability Index (MVI) ranks the U.S. states and counties on indicators measuring drivers of poor maternal health outcomes. Maternal vulnerability is assessed based on 43 drivers grouped into six themes: (1) Reproductive Healthcare, (2) Physical Health, (3) Mental Health and Substance Abuse, (4) General Healthcare, (5) Socioeconomic Determinants, and (6) Physical Environment. The MVI captures factors that put birthing people at increased risk of adverse outcomes during pregnancy that may lead to morbidities and/or death.

Data gaps on both maternal outcomes and their determinants constrain crafting evidence-based policies and interventions to address the maternal health crisis in the US. A growing body of evidence has found that the environment in which mothers live, work, deliver and raise their children can increase their risk of adverse health outcomes during and beyond pregnancy. Although data on these environmental and structural factors exist, the US MVI is the first nation-wide index to measure how contextual factors systematically expose populations of mothers to environments of lower or higher risk. State and local decision makers can use the tool to better understand where and why mothers are at increased risk for poor outcomes, to develop tailored, localized interventions and/or policies. Researchers can use the MVI, either alone or linked with other datasets, to provide a richer and more nuanced understanding of the drivers of poor maternal health outcomes in the US.

Data files:

1. mvi_data_dictionary.csv: Names and definitions of the variables in the data files.

2. mvi_county.csv: US MVI and six thematic scores for 3141 counties in the continental US and District of Columbia.

3. mvi_state.csv: US MVI and six thematic scores for 50 states and District of Columbia.

4. mvi_architecture_and_data_sources.csv: Index architecture and data sources by indicator used to construct the Maternal Vulnerability Index.

Data format and access

The database consists of full-text patient reviews, reflecting their dissatisfaction with healthcare quality. Materials in Russian have been posted in the «Review list» of the site infodoctor.ru. Publication period: July 2012 to August 2023. The database consists of 18,492 reviews covering 16 Russian cities with population of over one million. Data format: .xlsx.

Data access: 10.5281/zenodo.15257447

Data collection methodology

Based on the fact that negative reviews may be more reliable than positive ones, the authors carried out negative reviews from 16 Russian cities with a population of over one million, for which it was possible to collect representative samples (at least 1000 reviews for each city). We have extracted reviews from the one-star section of this site's guestbook, as they are reliably identified as negative. Duplicates were removed from the database. Personal data in comment texts have been replaced with "##########". The author's gender was determined manually based on his/her name or gender endings in the texts of reviews. Otherwise, we indicated "0" - gender cannot be determined.

For Moscow reviews, classification was carried out using manual markup methods - based on the majority of votes for the review class from 3 annotators (if at least one annotator indicated that it was impossible to determine, the review was classified as #N/A - impossible to clearly determine). For reviews from other cities, classification was made into 3 classes using machine learning methods based on logistic regression. The classification accuracy was 88%.

The medical specialties were distributed into large groups for the convenience of further analysis. The correspondence of medical specialties to large groups is presented in detail in Appendix 1.

Sample structure and description of variables

· CITY – the name of a city with a population of over a million (on a separate sheet – Moscow), the other 15 are Volgograd, Voronezh, Yekaterinburg, Kazan, Krasnodar, Krasnoyarsk, Nizhny Novgorod, Novosibirsk, Omsk, Perm, Rostov-on-Don, Samara, St. Petersburg, Ufa, Chelyabinsk

· TEXT – review text

· GENDER – gender of the review author (2 – female, 1 – male, 0 – cannot be determined)

· CLASS_1 – group of reasons for dissatisfaction with medical care (M – issues of medical content, O – issues of organizational support and economic aspect, C – mixed (combined) class, #N/A – cannot be clearly determined)[1]

· CLASS_2 – group of reasons for dissatisfaction with medical care (0 – issues of medical content, 1 – issues of organizational support and economic aspect, 2 – mixed (combined) class, #N/A – cannot be clearly determined)

· DAY – day of the month the review was posted

· MONTH – month the review was posted

· YEAR – year the review was posted

· DOCTOR_OR_CLINIC – what or who is the review dedicated to – the doctor or the clinic

· SPEC – physician specialty (for observations where the review is dedicated to the physician)

· GROUP_SPEC – a large group of a physician’s specialty

· ID – observation identifier

Database application

The data are suitable for analyzing patient dissatisfaction trends with medical services in Russia over the period from July 2012 to August 2023. This dataset could be particularly useful for healthcare providers, policymakers, and researchers interested in understanding patient experiences and identifying areas for quality improvement in Russian healthcare. Some potential applications include:

• Analyzing geographic patterns of patient complaints across different cities in Russia
• Examining trends in patient dissatisfaction over time
• Identifying common reasons for dissatisfaction with medical care
• Comparing dissatisfaction levels between different medical specialties
• Assessing gender differences in patient complaints

The database provides rich qualitative data through full-text review texts, allowing for in-depth analysis of patient experiences. The structured variables like city, date, doctor/clinic information, etc. enable quantitative analysis as well. This combination of qualitative and quantitative data makes it possible to gain a comprehensive understanding of patient dissatisfaction patterns in Russia's healthcare system over more than a decade.

For researchers specifically interested in healthcare quality issues, this dataset could serve as an important resource for studying patient experiences and outcomes in Russia's medical system. The longitudinal nature of the data (2012-2023) also allows for analysis of changes over time in patient satisfaction.

Overall, this database provides valuable insights into patient perceptions of healthcare quality that could inform policy decisions, quality improvement

This table contains data on income inequality. The primary measure is the Gini index – a measure of the extent to which the distribution of income among families/households within a community deviates from a perfectly equal distribution. The index ranges from 0.0, when all families (households) have equal shares of income (implies perfect equality), to 1.0 when one family (household) has all the income and the rest have none (implies perfect inequality). Index data is provided for California and its counties, regions, and large cities/towns. The data is from the U.S. Census Bureau, American Community Survey. The table is part of a series of indicators in the Healthy Communities Data and Indicators Project of the Office of Health Equity. Income is linked to acquiring resources for healthy living. Both household income and the distribution of income across a society independently contribute to the overall health status of a community. On average Western industrialized nations with large disparities in income distribution tend to have poorer health status than similarly advanced nations with a more equitable distribution of income. Approximately 119,200 (5%) of the 2.4 million U.S. deaths in 2000 are attributable to income inequality. The pathways by which income inequality act to increase adverse health outcomes are not known with certainty, but policies that provide for a strong safety net of health and social services have been identified as potential buffers. More information about the data table and a data dictionary can be found in the About/Attachments section.

This is data and code for "Hospital Network Competition and Adverse Selection" to be published in the American Economic Review. Here is the paper's abstract:Health insurers increasingly compete on their networks of medical providers. Using data from Massachusetts’ insurance exchange, I find substantial adverse selection against plans covering the most prestigious and expensive “star” hospitals. I highlight a theoretically distinct selection channel: consumers loyal to star hospitals incur high spending, conditional on their medical state, because they use these hospitals' expensive care. This implies heterogeneity in consumers' incremental costs of gaining access to star hospitals, posing a challenge for standard selection policies. Along with selection on unobserved sickness, I find this creates strong incentives to exclude star hospitals, even with risk adjustment in place.

Fiscal year 2013.

Fiscal year 2013.

BackgroundThe relationship between dietary niacin and atherosclerotic cardiovascular disease (ASCVD) is still not fully understood. Our objective was to assess the association between dietary niacin intake and the prevalence of ASCVD.MethodsIn this cross-sectional study, we examined a cohort of 15,685 adult individuals in the United States, aged 20 years and older, who participated in the National Health and Nutrition Examination Survey (NHANES) carried out between 2007 and 2014. Dietary Niacin consumption was assessed using a 24-h dietary recall method. The assessment of the presence of ASCVD was conducted through the Patient Medical Conditions Questionnaire. To assess the reliability of the results, restricted cubic spline models and logistic regression analyses were employed, along with conducting subgroup analyses.ResultsThe analysis included 15,685 participants who were 20 years or older, drawn from the NHANES data for the cycles spanning 2007 to 2014. Of which 10.4% (1,638/15,685) were diagnosed with ASCVD. The probability of ASCVD diminishes by 9% with each 10 mg/day increment in dietary niacin intake (OR = 0.91, 95% CI: 0.87–0.96). This association held true when niacin consumption was assessed as a categorical variable. Compared to individuals with the lowest dietary niacin intake, defined as T1 (≤17.4 mg/day), the adjusted odds ratios for ASCVD in those with higher niacin intakes, T2 (17.5–27.2 mg/day) and T3 (≥27.3 mg/day), were 0.87 (95% CI: 0.76–0.99, p = 0.037) and 0.75 (95% CI: 0.64–0.87, p