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 IPUMS Contextual Determinants of Health (CDOH) data series provides access to measures of disparities, policies, and counts, by state or county, for historically marginalized populations in the United States including Black, Asian, Hispanic/Latina/o/e/x, and LGBTQ+ persons, and women. The IPUMS CDOH data are made available through ICPSR/DSDR for merging with the National Couples' Health and Time Study (NCHAT), United States, 2020-2021 (ICPSR 38417) by approved restricted data researchers. All other researchers can access the IPUMS CDOH data via the IPUMS CDOH website. Unlike other IPUMS products, the CDOH data are organized into multiple categories related to Race and Ethnicity, Sexual and Gender Minority, Gender, and Politics. The measures were created from a wide variety of data sources (e.g., IPUMS NHGIS, the Census Bureau, the Bureau of Labor Statistics, the Movement Advancement Project, and Myers Abortion Facility Database). Measures are currently available for states or counties from approximately 2015 to 2020. The Race and Ethnicity measure in this release is an indicator of income inequity which is measured using the index of concentration at the extremes (ICE). ICE is a measure of social polarization within a particular geographic unit. It shows whether people or households in a geographic unit are concentrated in privileged or deprived extremes. The privileged group in this study is the number of households with a householder identifying as White alone, not Hispanic or Latino, with an income equal to or greater than $100,000. The deprived group in this study is the number of households with a householder identifying as a different race/ethnic group (e.g., Black alone, Asian alone, Hispanic or Latino), with an income equal to or less than $25,000. To work with the IPUMS CDOH data, researchers will need to use the variable MATCH_ID to merge the data in DS1 with NCHAT surveys within the virtual data enclave (VDE).

The RAND Center for Population Health and Health Disparities (CPHHD) Data Core Series is composed of a wide selection of analytical measures, encompassing a variety of domains, all derived from a number of disparate data sources. The CPHHD Data Core's central focus is on geographic measures for census tracts, counties, and Metropolitan Statistical Areas (MSAs) from two distinct geo-reference points, 1990 and 2000. The current study, Segregation Indices, has cross-sectional and longitudinal data sets, containing a number of non-spatially sensitive segregation indices based on the main Decennial Census. These indices are considered non-spatial in that the indices did not take into account any spatial relationships of the geographical entities (i.e., distances apart, clustering within, spatial concentrations, etc.), only association of tracts with either County and/or MSA. In addition, the data are summarized at two different geographic levels: County (Geographic) and MSA (Geographic). The data consist of 11 different segregation indices, with several different binary indicators, and a 5-race indicator. Measures include: Normalized Simpson Interaction Diversity Index, Entropy Diversity Index, Dissimilarity Segregation Index, Gini Segregation Index, Information Theory Segregation Index, Squared Coefficient of Variation Segregation Index, Relative Diversity Segregation Index, N-group Normalized Exposure Segregation Index, Exposure Index, Isolation Index, and 2-group Normalized Exposure Index.

This is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Infant Mortality is defined as the number of deaths in infants under one year of age per 1,000 live births. Infant mortality is often used as an indicator to measure the health and well-being of a community, because factors affecting the health of entire populations can also impact the mortality rate of infants. Although California’s infant mortality rate is better than the national average, there are significant disparities, with African American babies dying at more than twice the rate of other groups. Data are from the Birth Cohort Files. The infant mortality indicator computed from the birth cohort file comprises birth certificate information on all births that occur in a calendar year (denominator) plus death certificate information linked to the birth certificate for those infants who were born in that year but subsequently died within 12 months of birth (numerator). Studies of infant mortality that are based on information from death certificates alone have been found to underestimate infant death rates for infants of all race/ethnic groups and especially for certain race/ethnic groups, due to problems such as confusion about event registration requirements, incomplete data, and transfers of newborns from one facility to another for medical care. Note there is a separate data table "Infant Mortality by Race/Ethnicity" which is based on death records only, which is more timely but less accurate than the Birth Cohort File. Single year shown to provide state-level data and county totals for the most recent year. Numerator: Infants deaths (under age 1 year). Denominator: Live births occurring to California state residents. Multiple years aggregated to allow for stratification at the county level. For this indicator, race/ethnicity is based on the birth certificate information, which records the race/ethnicity of the mother. The mother can “decline to state”; this is considered to be a valid response. These responses are not displayed on the indicator visualization.

Dataset, GDB, and Online Map created by Renee Haley, NMCDC, May 2023 DATA ACQUISITION PROCESS

Scope and purpose of project: New Mexico is struggling to maintain its healthcare workforce, particularly in Rural areas. This project was undertaken with the intent of looking at flows of healthcare workers into and out of New Mexico at the most granular geographic level possible. This dataset, in combination with others (such as housing cost and availability data) may help us understand where our healthcare workforce is relocating and why.

The most relevant and detailed data on workforce indicators in the United States is housed by the Census Bureau's Longitudinal Employer-Household Dynamics, LEHD, System. Information on this system is available here:

https://lehd.ces.census.gov/

The Job-to-Job flows explorer within this system was used to download the data. Information on the J2J explorer can ve found here:

https://j2jexplorer.ces.census.gov/explore.html#1432012

The dataset was built from data queried with the LED Extraction Tool, which allows for the query of more intersectional and detailed data than the explorer. This is a link to the LED extraction tool:

https://ledextract.ces.census.gov/

The geographies used are US Metro areas as determined by the Census, (N=389). The shapefile is named lehd_shp_gb.zip, and can be downloaded under this section of the following webpage: 5.5. Job-to-Job Flow Geographies, 5.5.1. Metropolitan (Complete). A link to the download site is available below:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_shapefiles.html

DATA CLEANING PROCESS

This dataset was built from 8 non intersectional datasets downloaded from the LED Extraction Tool.

Separate datasets were downloaded in order to obtain detailed information on the race, ethnicity, and educational attainment levels of healthcare workers and where they are migrating.

Datasets included information for the four separate quarters of 2021. It was not possible to download annual data, only quarterly. Quarterly data was summed in a later step to derive annual totals for 2021.

4 datasets for healthcare workers moving OUT OF New Mexico, with details on race, ethnicity, and educational attainment, were downloaded. 1 contained information on educational attainment, 2 contained information on 7 racial categories identifying as non- Hispanic, 3 contained information on those same 7 categories also identifying as Hispanic, and 4 contained information for workers identifying as white and Hispanic.

4 datasets for healthcare worker moving INTO New Mexico, with details on race, ethnicity, and educational attainment, were downloaded with the same details outlined above.

Each dataset was cleaned according to Data Template which kept key attributes and discarded excess information. Within each dataset, the J2J Indicators reflecting 6 different types of job migration were totaled in order to simplify analysis, as this information was not needed in detail.

After cleaning, each set of 4 datasets for workers moving INTO New Mexico were joined. The process was repeated for workers moving OUT OF New Mexico. This resulted 2 main datasets.

These 2 main datasets still listed all of the variables by each quarter of 2021. Because of this the data was split in JMP, so that attributes of educational attainment, race and ethnicity, of workers migrating by quarter were moved from rows to columns. After this, summary columns for the year of 2021 were derived. This resulted in totals columns for workers identifying as: 6 separate races and all ethnicities, all races and Hispanic, white-Hispanic, and workers of 6 different education levels, reflecting how many workers of each indicator migrated to and from metro areas in New Mexico in 2021.

The data split transposed duplicate rows reflecting differing worker attributes within the same metro area, resulting in one row for each metro area and reflecting the attributes in columns, thus resulting in a mappable dataset.

The 2 datasets were joined (on Metro Area) resulting in one master file containing information on healthcare workers entering and leaving New Mexico.

Rows (N=389) reflect all of the metro areas across the US, and each state. Rows include the 5 metro areas within New Mexico, and New Mexico State.

Columns (N=99) contain information on worker race, ethnicity and educational attainment, specific to each metro area in New Mexico.

78 of these rows reflect workers of specific attributes moving OUT OF the 5 specific Metro Areas in New Mexico and totals for NM State. This level of detail is intended for analyzing who is leaving what area of New Mexico, where they are going to, and why.

13 Columns reflect each worker attribute for healthcare workers moving INTO New Mexico by race, ethnicity and education level. Because all 5 metro areas and New Mexico state are contained in the rows, this information for incoming workers is available by metro area and at the state level - there is less possability for mapping these attributes since it was not realistic or possible to create a dataset reflecting all of these variables for every healthcare worker from every metro area in the US also coming into New Mexico (that dataset would have over 1,000 columns and be unmappable). Therefore this dataset is easier to utilize in looking at why workers are leaving the state but also includes detailed information on who is coming in.

The remaining 8 columns contain geographic information.

GIS AND MAPPING PROCESS

The master file was opened in Arc GIS Pro and the Shapefile of US Metro Areas was also imported

The excel file was joined to the shapefile by Metro Area Name as they matched exactly

The resulting layer was exported as a GDB in order to retain null values which would turn to zeros if exported as a shapefile.

This GDB was uploaded to Arc GIS Online, Aliases were inserted as column header names, and the layer was visualized as desired.

SYSTEMS USED

MS Excel was used for data cleaning, summing NM state totals, and summing quarterly to annual data.

JMP was used to transpose, join, and split data.

ARC GIS Desktop was used to create the shapefile uploaded to NMCDC's online platform.

VARIABLE AND RECODING NOTES

Summary of variables selected for datasets downloaded focused on educational attainment:

J2J Flows by Educational Attainment

Summary of variables selected for datasets downloaded focused on race and ethnicity:

J2J Flows by Race and Ethnicity

Note: Variables in Datasets 1 through 4 downloaded twice, once for workers coming into New Mexico and once for those leaving NM. VARIABLE: LEHD VARIABLE DEFINITION LEHD VARIABLE NOTES DETAILS OR URL FOR RAW DATA DOWNLOAD

Geography Type - State Origin and Destination State

Data downloaded for worker migration into and out of all US States

Geography Type - Metropolitan Areas Origin and Dest Metro Area

Data downloaded for worker migration into and out of all US Metro Areas

NAICS sectors North American Industry Classification System Under Firm Characteristics Only downloaded for Healthcare and Social Assistance Sectors

Other Firm Characteristics No Firm Age / Size Detail Under Firm Characteristics Downloaded data on all firm ages, sizes, and other details.

Worker Characteristics Education, Race, Ethnicity

Non Intersectional data aside from Race / Ethnicity data.

Sex Gender

0 - All Sexes Selected

Age Age

A00 All Ages (14-99)

Education Education Level E0, E1, E2, E3, 34, E5 E0 - All Education Categories, E1 - Less than high school, E2 - High school or equivalent, no college, E3 - Some college or Associate’s degree, E4 - Bachelor's degree or advanced degree, E5 - Educational attainment not available (workers aged 24 or younger)

Dataset 1 All Education Levels, E1, E2, E3, E4, and E5

RACE

A0, A1, A2, A3, A4, A5 OPTIONS: A0 All Races, A1 White Alone, A2 Black or African American Alone, A3 American Indian or Alaska Native Alone, A4 Asian Alone, A5 Native Hawaiian or Other Pacific Islander Alone, SDA7 Two or More Race Groups

ETHNICITY

A0, A1, A2 OPTIONS: A0 All Ethnicities, A1 Not Hispanic or Latino, A2 Hispanic or Latino

Dataset 2 All Races (A0) and All Ethnicities (A0)

Dataset 3 6 Races (A1 through A5) and All Ethnicities (A0)

Dataset 4 White (A1) and Hispanic or Latino (A1)

Quarter Quarter and Year

Data from all quarters of 2021 to sum into annual numbers; yearly data was not available

Employer type Sector: Private or Governmental

Query included all healthcare sector workflows from all employer types and firm sizes from every quarter of 2021

J2J indicator categories Detailed types of job migration

All options were selected for all datasets and totaled: AQHire, AQHireS, EE, EES, J2J, J2JS. Counts were selected vs. earnings, and data was not seasonally adjusted (unavailable).

NOTES AND RESOURCES

The following resources and documentation were used to navigate the LEHD and J2J Worker Flows system and to answer questions about variables:

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_public_use_schema.html

https://www.census.gov/history/www/programs/geography/metropolitan_areas.html

https://lehd.ces.census.gov/data/schema/j2j_latest/lehd_csv_naming.html

Statewide (New

The National Health and Nutrition Examination Survey I Epidemiologic Followup Study (NHEFS) originated as a joint project between the National Center for Health Statistics (NCHS) and the National Institute on Aging (NIA). The design of NHEFS, which contains follow-up data on the NHANES I cohort, consisted of five steps. The first step focused on tracing and locating all subjects in the cohort or their proxies and determining their vital status. The second step involved the obtaining of death certificates for subjects who were deceased. Interviews with the participants or their proxies constituted the third phase of the follow-up. The fourth phase of the follow-up included measurements of pulse, blood pressure, and weight for interviewed respondents, and the fifth step was the acquisition of relevant hospital and nursing home records, including pathology reports and electrocardiograms. The respondent interview was designed to gather information on selected aspects of the subject's health history since the time of the NHANES I exam. This information included a history of the occurrence or recurrence of selected medical conditions, an assessment of behavioral, social, nutritional, and medical risk factors believed to be associated with these conditions, and an assessment of various aspects of functional status. Whenever possible, the questionnaire was designed to retain item comparability between NHANES I and NHEFS in order to measure change over time. However, questionnaire items were modified, added, or deleted when necessary to take advantage of recent improvements in questionnaire methodology. The Vital and Tracing Status file is a master file containing tracing, vital status, and demographic data for all NHEFS respondents. In addition, it provides users with information on the availability of different survey components for each respondent. For example, variables have been created to indicate whether a death certificate was received for a deceased subject, hospital records were received, or a follow-up interview was completed. The Health Care Facility Record file offers data on respondents who had reported an overnight stay in a health care facility after 1970. Information on the name and address of the facility, the date of the stay, and the reason for the stay was recorded. The Mortality Data file contains death certificate information for 1,935 NHEFS decedents. The death certificate information is for deaths occurring from 1971 to 1983.

These indicators were designed to provide 2001 Census-based information about household residents providing various levels of unpaid care. For information on the definitions of what the indicator includes, please see the relevant specification. As of October 2018, please refer to Census data published by the Office for National Statistics for the following indicator: Provision of unpaid care : percent, all ages, P (P00890) The datasets can be accessed via the link in the ‘Resource links’ section.

Analysis of ‘Healthcare Cost and Utilization Project (HCUP) - National Inpatient Sample’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/5fd2d275-4019-407f-af21-58e453bc8caa on 27 January 2022.

--- Dataset description provided by original source is as follows ---

2001 forward. The National (Nationwide) Inpatient Sample (NIS) is part of a family of databases and software tools developed for the Healthcare Cost and Utilization Project (HCUP). The NIS is the largest publicly available all-payer inpatient health care database in the United States, yielding national estimates of hospital inpatient stays. Unweighted, it contains data from more than 7 million hospital stays each year. Weighted, it estimates more than 35 million hospitalizations nationally. Indicators from this data source have been computed by personnel in CDC's Division for Heart Disease and Stroke Prevention (DHDSP). This is one of the datasets provided by the National Cardiovascular Disease Surveillance System. The system is designed to integrate multiple indicators from many data sources to provide a comprehensive picture of the public health burden of CVDs and associated risk factors in the United States. The data are organized by indicator, and they include CVDs (e.g., heart failure). The data can be plotted as trends and stratified by age group, sex, and race/ethnicity.

--- Original source retains full ownership of the source dataset ---

The National Health and Nutrition Examination Survey I Epidemiologic Followup Study (NHEFS) is a longitudinal study that follows participants from the NHANES I who were aged 25-74 in 1971-1975. The NHEFS surveys were designed to investigate the association between factors measured at the baseline and the development of specific health conditions and functional limitations. Follow-up data were collected in 1982-1984 (ICPSR 8900), 1986 (ICPSR 9466), 1987 (ICPSR 9854), and 1992. The 1992 NHEFS collected information on changes in the health and functional status of the NHEFS cohort since the last contact period. The Vital and Tracing Status file (Part 1) provides summary information about the status of the NHEFS cohort. The Interview Data file (Part 2) covers selected aspects of the respondent's health history, including injuries, activities of daily living, vision and hearing, medical conditions, exercise, weight, family history of cancer, surgeries, smoking, alcohol use, and medical care utilization. The Health Care Facility Stay files (Parts 3 and 4) supply information about stays in hospitals, nursing homes, and mental health care facilities, as well as information abstracted from facility medical records. The Mortality Data file (Part 5) contains data abstracted from the death certificates for NHEFS decedents.

The Fairfax County COVID-19 Vulnerability Index shows five domains individually and as a composite index: Socioeconomic Status, Household Composition and Disability, Race/Ethnicity and Language, Housing and Transportation, and Health. Individual indicators were ranked into 5 classes using natural breaks and given a score of 1 to 5, with 5 being the most vulnerable (shown in dark blue on the maps). The individual indicators were combined, using equal weighting, to create the five sub-indices. The overall COVID-19 Vulnerability Index was created using the five sub-indices and applying equal weighting. Information on zip codes, Hispanic ethnicity, and race are provided for reference and are not included in the index or sub-index calculations. The indicators in Health are from the American Community Survey (ACS) 2014- 2018 and the Centers for Disease Control and Prevention (CDC) Behavioral Risk Factor Surveillance System.

Community Health Data

The Virginia Community Health Improvement Data Portal tracks key health and quality of life indicators across different topics, including health, economy, education, environment, public safety, social environment and transportation. It also allows users to create customized reports and maps.

With the dashboard, users can:

• Access social, health and demographic data by topic or geographic location within Loudoun County.
• Find data broken down by age, gender, and race/ethnicity, where available.
• View multiple health indicators at once, or a single indicator across multiple locations, including by zip code, county or census tract.
• Find similar data for other Virginia jurisdictions Build customized reports and maps on health topics of interest.

Patient experience measured by scoring the results of a selection of questions from the National Inpatient Survey looking at a range of elements of hospital care. This indicator aims to capture the experience of patients who have received medical treatment in hospital. Legacy unique identifier: P01774

The Healthy Communities Data and Indicators Project (HCI) provides a standardized set of statistical measures, data, and tools on the social determinants of health in California. The goal of the HCI is to provide datasets and tools that a broad array of sectors can use for planning healthy communities and evaluating the impact of plans, projects, policy, and environmental changes on community health.

Indicator datasets include data for California, as well as its counties, regions, communities, and census tracts when available. Datasets also include stratifications by race/ethnicity and other population characteristics. Main categories are: Income Security; Food Security and Nutrition; Child Development, Education, and Literacy; Housing; Environmental Quality; Accessible Built Environments; Health Care; Prevention Efforts; and Neighborhood Safety and Collective Efficacy.

Context

Heart Disease is among the most prevalent chronic diseases in the United States, impacting millions of Americans each year and exerting a significant financial burden on the economy. In the United States alone, heart disease claims roughly 647,000 lives each year — making it the leading cause of death. The buildup of plaques inside larger coronary arteries, molecular changes associated with aging, chronic inflammation, high blood pressure, and diabetes are all causes of and risk factors for heart disease.

While there are different types of coronary heart disease, the majority of individuals only learn they have the disease following symptoms such as chest pain, a heart attack, or sudden cardiac arrest. This fact highlights the importance of preventative measures and tests that can accurately predict heart disease in the population prior to negative outcomes like myocardial infarctions (heart attacks) taking place.

The Centers for Disease Control and Prevention has identified high blood pressure, high blood cholesterol, and smoking as three key risk factors for heart disease. Roughly half of Americans have at least one of these three risk factors. The National Heart, Lung, and Blood Institute highlights a wider array of factors such as Age, Environment and Occupation, Family History and Genetics, Lifestyle Habits, Other Medical Conditions, Race or Ethnicity, and Sex for clinicians to use in diagnosing coronary heart disease. Diagnosis tends to be driven by an initial survey of these common risk factors followed by bloodwork and other tests.

Content

The Behavioral Risk Factor Surveillance System (BRFSS) is a health-related telephone survey that is collected annually by the CDC. Each year, the survey collects responses from over 400,000 Americans on health-related risk behaviors, chronic health conditions, and the use of preventative services. It has been conducted every year since 1984. For this project, I downloaded a csv of the dataset available on Kaggle for the year 2015. This original dataset contains responses from 441,455 individuals and has 330 features. These features are either questions directly asked of participants, or calculated variables based on individual participant responses.

This dataset contains 253,680 survey responses from cleaned BRFSS 2015 to be used primarily for the binary classification of heart disease. Not that there is strong class imbalance in this dataset. 229,787 respondents do not have/have not had heart disease while 23,893 have had heart disease. The question to be explored is:

1. To what extend can survey responses from the BRFSS be used for predicting heart disease risk?

and

2. Can a subset of questions from the BRFSS be used for preventative health screening for diseases like heart disease?

Acknowledgements

It it important to reiterate that I did not create this dataset, it is just a cleaned and consolidated dataset created from the BRFSS 2015 dataset already on Kaggle. That dataset can be found here and the notebook I used for the data cleaning can be found here.

Inspiration

Let's build some predictive models for for heart disease.

Users can obtain descriptions, maps, profiles, and ranks of U.S. metropolitan areas pertaining to quality of life, diversity, and opportunities for racial and ethnic groups in the U.S. BackgroundThe Diversity Data project operates a website for users to explore how U.S. metropolitan areas perform on evidence-based social measures affecting quality of life, diversity and opportunity for racial and ethnic groups in the United States. These indicators capture a broad definition of quality of life and health, including opportunities for good schools, housing, jobs, wages, health and social services, and safe neighborhoods. This is a useful resource for people inter ested in advocating for policy and social change regarding neighborhood integration, residential mobility, anti-discrimination in housing, urban renewal, school quality and economic opportunities. The Diversity Data project is an ongoing project of the Harvard School of Public Health (Department of Society, Human Development and Health). User FunctionalityUsers can obtain a description, profile and rank of U.S. metropolitan areas and compare ranks across metropolitan areas. Users can also generate maps which demonstrate the distribution of these measures across the United States. Demographic information is available by race/ethnicity. Data NotesData are derived from multiple sources including: the U.S. Census Bureau; National Center for Health Statistics' Vital Statistics Natality Birth Data; Natio nal Center for Education Statistics; Union CPS Utilities Data CD; National Low Income Housing Coalition; Freddie Mac Conventional Mortgage Home Price Index; Neighborhood Change Database; Joint Center for Housing Studies of Harvard University; Federal Financial Institutions Examination Council Home Mortgage Disclosure Act (HMD); Dr. Russ Lopez, Boston University School of Public Health, Department of Environmental Health; HUD State of the Cities Data Systems; Agency for Healthcare Research and Quality; and Texas Transportation Institute. Years in which the data were collected are indicated with the measure. Information is available for metropolitan areas. The website does not indicate when the data are updated.

This study analyzed racial inequalities in health in 18,684 elderly Brazilians 65 years or older, interviewed in the National Household Sample Survey in 2008 (PNAD 2008), and who reported their color/race as white, brown, or black. Associations were estimated between self-rated health status, functional incapacity, and number of chronic conditions according to crude and adjusted regression analyses (α = 0.01). The majority of the elderly were white (56.2%). In the adjusted analysis, brown color/races was associated with worse self-rated health status (OR = 1.11; 95%CI: 1.03-1.18) and black color/race was associated with more chronic diseases (PR = 1.07; 95%CI: 1.02-1.13). Brown color/race appeared as a protective factor against functional incapacity. When brown and black elderly were combined in one category (“black”), “black” elderly continued to show worse self-rated health status (OR = 1.09; 95%CI: 1.02-1.16) and lower odds of functional incapacity (OR = 0.83; 95%CI: 0.76-0.92). “Black” color/race lost the association with number of chronic diseases. Color/race explained part of the health inequalities in elderly Brazilians, but other socioeconomic variables had a more striking effect.

Title: Indicators of Anxiety or Depression Based on Reported Frequency of Symptoms During Last 7 Days

Context:

This dataset contains information on the indicators of anxiety or depression based on the reported frequency of symptoms during the last 7 days. The data is collected through the Household Pulse Survey, launched by the U.S. Census Bureau in collaboration with five federal agencies. This survey aims to produce data on the social and economic impacts of Covid-19 on American households, including dimensions of physical and mental wellness.

Source: - Household Pulse Survey: Conducted by the U.S. Census Bureau. - Supporting Documentation: For more information, refer to the Household Pulse Survey.

Dataset Details and Key Features

This dataset provides weekly estimates of anxiety and depression indicators among different demographic groups in the United States. The data is collected using internet questionnaires, with invitations sent via email and text message. The sample frame is based on the Census Bureau Master Address File Data, and estimates are weighted to adjust for nonresponse and to match Census Bureau population estimates.

Key Features:

• Weekly Estimates: Provides timely and accurate weekly estimates of anxiety and depression indicators.
• Demographic Segmentation: Data segmented by age, sex, race/ethnicity, education, and state.
• Weighted Estimates: Adjusts for nonresponse and matches population estimates for more accurate representation.
• High Confidence Intervals: Estimates meet NCHS Data Presentation Standards for Proportions.

Usage

Research and Analysis:

• Mental Health Trends: Analyze trends in anxiety and depression during the Covid-19 pandemic.
• Public Health Response: Inform public health strategies to address mental health issues.
• Socioeconomic Analysis: Study the impact of socioeconomic factors on mental health.

Policy Making:

• Resource Allocation: Allocate resources effectively to support mental health services.
• Program Development: Develop programs aimed at reducing anxiety and depression.
• Crisis Management: Use data to manage and respond to mental health crises during pandemics.

Healthcare Planning:

• Preventive Measures: Design preventive measures and programs based on demographic data.
• Community Outreach: Plan community outreach programs targeting high-risk groups.
• Mental Health Services: Inform the creation and enhancement of mental health services.

Data Maintenance

• Maintainer: National Center for Health Statistics
• Publisher: Centers for Disease Control and Prevention
• Last Updated: June 27, 2024

Quality Assurance:

• Data Validation: Ensures data accuracy through rigorous validation processes.
• Consistency Checks: Regular consistency checks to maintain data integrity.

Additional Notes:

• Data is collected from the Household Pulse Survey, ensuring comprehensive coverage and reliability.
• For detailed definitions and explanations of measures, refer to the supporting documentation provided by the Household Pulse Survey.

Schema:

Patient experience measured by scoring the results of a selection of questions from the National Inpatient Survey looking at a range of elements of hospital care. This indicator aims to capture the experience of patients who have received medical treatment in hospital. Legacy unique identifier: P01774

Medical Service Study Areas (MSSAs)As defined by California's Office of Statewide Health Planning and Development (OSHPD) in 2013, "MSSAs are sub-city and sub-county geographical units used to organize and display population, demographic and physician data" (Source). Each census tract in CA is assigned to a given MSSA. The most recent MSSA dataset (2014) was used. Spatial data are available via OSHPD at the California Open Data Portal. This information may be useful in studying health equity.Definitions:Race/Ethnicity: Race/ethnicity is categorized as: All races/ethnicities, Non-Hispanic (NH) White, NH Black, Asian/Pacific Islander, or Hispanic. "All races" includes all of the above, as well as other and unknown race/ethnicity and American Indian/Alaska Native. The latter two groups are not reported separately due to small numbers for many cancer sites.Racial/Ethnic Composition: Distribution of residents' race/ethnicity (e.g., % Hispanic, % non-Hispanic White, % non-Hispanic Black, % non-Hispanic Asian/Pacific Islander). (Source: US Census, 2010.)Rural: Percent of residents who reside in blocks that are designated as rural. (Source: US Census, 2010.)Foreign Born: Percent of residents who were born outside the United States. (Source: American Community Survey, 2008-2012.)Socioeconomic Status (Neighborhood Level): A composite measure of seven indicator variables created by principal component analysis; indicators include: education, blue-collar job, unemployment, household income, poverty, rent, and house value. Quintiles based on state distribution, with quintile 1 being the lowest SES and 5 being the highest. (Source: American Community Survey, 2008-2012.)Spatial extent: CaliforniaSpatial Unit: MSSACreated: n/aUpdated: n/aSource: California Health MapsContact Email: gbacr@ucsf.eduSource Link: https://www.californiahealthmaps.org/?areatype=mssa&address=&sex=Both&site=AllSite&race=&year=05yr&overlays=none&choropleth=Obesity

This indicator measures the weighted percentage of people who report their overall experience of GP out-of-hours services as ‘fairly good’ or ‘very good’. This indicator aims to capture the experience of patients of GP out-of-hours services. There are no planned future updates for this indicator. The methodology for the indicator requires review, this is not actively being progressed at this time. Legacy unique identifier: P01772