Quality of life is a measure of comfort, health, and happiness by a person or a group of people. Quality of life is determined by both material factors, such as income and housing, and broader considerations like health, education, and freedom. Each year, US & World News releases its “Best States to Live in” report, which ranks states on the quality of life each state provides its residents. In order to determine rankings, U.S. News & World Report considers a wide range of factors, including healthcare, education, economy, infrastructure, opportunity, fiscal stability, crime and corrections, and the natural environment. More information on these categories and what is measured in each can be found below:

Healthcare includes access, quality, and affordability of healthcare, as well as health measurements, such as obesity rates and rates of smoking. Education measures how well public schools perform in terms of testing and graduation rates, as well as tuition costs associated with higher education and college debt load. Economy looks at GDP growth, migration to the state, and new business. Infrastructure includes transportation availability, road quality, communications, and internet access. Opportunity includes poverty rates, cost of living, housing costs and gender and racial equality. Fiscal Stability considers the health of the government's finances, including how well the state balances its budget. Crime and Corrections ranks a state’s public safety and measures prison systems and their populations. Natural Environment looks at the quality of air and water and exposure to pollution.

This survey, conducted by Gallup across the United States in January 2014, shows the extent of satisfaction among the U.S. population with various aspects regarding American life. 32 percent of respondents were satisfied with the income and wealth distribution, whereas 74 percent were satisfied in the overall quality of life in the United States.

This statistic shows a ranking of the best U.S. federal states to live in, according to selected metrics and based on a survey among more than 530,000 Americans. The survey was conducted between January 2011 and June 2012. The findings are presented as index scores composed of the scores regarding various parameters*. According to this index, Utah is the city with the highest liveability and life quality, as it scored 7.5 points.

This dataset is a continuation of one created seven years earlier, QUALITY OF AMERICAN LIFE, 1971 (ICPSR 3508). In the 1978 study, a national sample was drawn that included many respondents from the 1971 study. The purpose of the study was to survey Americans about their perceived quality of life by measuring their perceptions of their socio-psychological condition, their needs and expectations from life, and the degree to which those needs were satisfied. The data, similar in scope and content of that in the 1971 survey, were collected via personal interviews from a nationwide probability sample of 3,692 persons 18 years of age and older during the summer of 1978. Closed and open-ended questions were used to probe respondents' satisfactions, dissatisfactions, aspirations, and disappointments in a variety of life domains, such as dwelling/neighborhood, local services (e.g., police, roads, and schools), public transportation, present personal life, life in the United States, education, occupation, job history/expectation, work life, housework, leisure activities, organizational affiliations, religious affiliation, health problems, financial situation, marriage (including widowhood, divorce, and separation), children/family life, and relationships with family and friends. In addition to broad questions about satisfaction with each of these domains and their importance to the respondents, specific sources of gratification and frustration were explored. Other questions focused on life as a whole and about the extent to which respondents felt they had control over their lives (e.g., rating of various aspects of life, (dis)satisfaction with life, personal efficacy, and social desirability measures). A major difference between this study and the earlier study is that the 1978 respondents were asked more detailed questions concerning their perceived financial status relative to their family, friends, and past personal financial status. Personal data include sex, age, race, ethnic background, childhood family stability, military service, and father's occupation and education. Observational data are included on housing and neighborhood characteristics as well as respondents' appearance, intelligence, and sincerity.

The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

This EnviroAtlas dataset portrays the percentage of population within different household income ranges for each Census Block Group (CBG), a threshold estimated to be an optimal household income for quality of life, and the percentage of households with income below this threshold. Data were compiled from the Census ACS (American Community Survey) 5-year Summary Data (2008-2012). This dataset was produced by the US EPA to support research and online mapping activities related to EnviroAtlas. EnviroAtlas (https://www.epa.gov/enviroatlas) allows the user to interact with a web-based, easy-to-use, mapping application to view and analyze multiple ecosystem services for the contiguous United States. The dataset is available as downloadable data (https://edg.epa.gov/data/Public/ORD/EnviroAtlas) or as an EnviroAtlas map service. Additional descriptive information about each attribute in this dataset can be found in its associated EnviroAtlas Fact Sheet (https://www.epa.gov/enviroatlas/enviroatlas-fact-sheets).

The U.S. Census defines Asian Americans as individuals having origins in any of the original peoples of the Far East, Southeast Asia, or the Indian subcontinent (U.S. Office of Management and Budget, 1997). As a broad racial category, Asian Americans are the fastest-growing minority group in the United States (U.S. Census Bureau, 2012). The growth rate of 42.9% in Asian Americans between 2000 and 2010 is phenomenal given that the corresponding figure for the U.S. total population is only 9.3% (see Figure 1). Currently, Asian Americans make up 5.6% of the total U.S. population and are projected to reach 10% by 2050. It is particularly notable that Asians have recently overtaken Hispanics as the largest group of new immigrants to the U.S. (Pew Research Center, 2015). The rapid growth rate and unique challenges as a new immigrant group call for a better understanding of the social and health needs of the Asian American population.

In an April 2024 online survey, an overwhelming majority of respondents in the United States said that **** U.S. dollars per hour is not enough for the average American worker to have a decent quality of life. The U.S. federal minimum wage has not been raised since 2009. Since then, many states have raised the wage, with a number of states having more than doubled the federal minimum.

In 2023, Uruguay and Chile had the highest Digital Quality of Life index in Latin America and the Caribbean region, at 0.57 and 0.56 points on a scale from zero to one, respectively. In comparison, Venezuela and Honduras scored the lowest index among the presented countries. The index ranks the quality of digital wellbeing in a country.

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 map shows the percent of adults 18+ who report 14 or more days during the past 30 days during which their physical health was not good.As stated by the CDC in the methodology:Physical health is an important component of Health-related quality of life (HRQOL), a multi-dimensional concept that focuses on the impact of health status on quality of life.Who is included in this survey?Resident adults aged ≥18 years. Respondents aged ≥18 years who report or do not report the number of days during the past 30 days during which their physical health was not good (excluding those who refused to answer, had a missing answer, or answered “don’t know/not sure”).Data SourceCDC's 2017 500 Cities ProjectArcGIS Living Atlas of the World contains multiple years of 500 Cities CDC layers, which can be found here. For more information about the methodology, visit https://www.cdc.gov/500cities or contact 500Cities@cdc.gov.

Population Health (BRFSS: HRQOL)

Examining Trends, Disparities and Determinants of Health in the US Population

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) offers an expansive collection of data on the health-related quality of life (HRQOL) from 1993 to 2010. Over this time period, the Health-Related Quality of Life dataset consists of a comprehensive survey reflecting the health and well-being of non-institutionalized US adults aged 18 years or older. The data collected can help track and identify unmet population health needs, recognize trends, identify disparities in healthcare, determine determinants of public health, inform decision making and policy development, as well as evaluate programs within public healthcare services.

The HRQOL surveillance system has developed a compact set of HRQOL measures such as a summary measure indicating unhealthy days which have been validated for population health surveillance purposes and have been widely implemented in practice since 1993. Within this study's dataset you will be able to access information such as year recorded, location abbreviations & descriptions, category & topic overviews, questions asked in surveys and much more detailed information including types & units regarding data values retrieved from respondents along with their sample sizes & geographical locations involved!

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How to use the dataset

This dataset tracks the Health-Related Quality of Life (HRQOL) from 1993 to 2010 using data from the Behavioral Risk Factor Surveillance System (BRFSS). This dataset includes information on the year, location abbreviation, location description, type and unit of data value, sample size, category and topic of survey questions.

Using this dataset on BRFSS: HRQOL data between 1993-2010 will allow for a variety of analyses related to population health needs. The compact set of HRQOL measures can be used to identify trends in population health needs as well as determine disparities among various locations. Additionally, responses to survey questions can be used to inform decision making and program and policy development in public health initiatives.

Research Ideas

• Analyzing trends in HRQOL over the years by location to identify disparities in health outcomes between different populations and develop targeted policy interventions.
• Developing new models for predicting HRQOL indicators at a regional level, and using this information to inform medical practice and public health implementation efforts.
• Using the data to understand differences between states in terms of their HRQOL scores and establish best practices for healthcare provision based on that understanding, including areas such as access to care, preventative care services availability, etc

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: rows.csv | Column name | Description | |:-------------------------------|:----------------------------------------------------------| | Year | Year of survey. (Integer) | | LocationAbbr | Abbreviation of location. (String) | | LocationDesc | Description of location. (String) | | Category | Category of survey. (String) | | Topic | Topic of survey. (String) | | Question | Question asked in survey. (String) | | DataSource | Source of data. (String) | | Data_Value_Unit | Unit of data value. (String) | | Data_Value_Type | Type of data value. (String) | | Data_Value_Footnote_Symbol | Footnote symbol for data value. (String) | | Data_Value_Std_Err | Standard error of the data value. (Float) | | Sample_Size | Sample size used in sample. (Integer) | | Break_Out | Break out categories used. (String) | | Break_Out_Category | Type break out assessed. (String) | | **GeoLocation*...

This statistic shows the results of a survey in 22 states asking respondents who they think has a better life in their country - men or women. The survey was conducted in 2010. 39 percent of respondents from the United States thought that men had a better life in their country, while 23 percent thought that a woman's life in the United States is better than a man's. 24 percent of American respondents thought the life quality of men and women in the United States is the same.

Abstract (en): Nearly 9 million Americans live in extreme-poverty neighborhoods, places that also tend to be racially segregated and dangerous. Yet, the effects on the well-being of residents of moving out of such communities into less distressed areas remain uncertain. Moving to Opportunity (MTO) is a randomized housing experiment administered by the United States Department of Housing and Urban Development that gave low-income families living in high-poverty areas in five cities the chance to move to lower-poverty areas. Families were randomly assigned to one of three groups: (1) the low-poverty voucher (LPV) group (also called the experimental group) received Section 8 rental assistance certificates or vouchers that they could use only in census tracts with 1990 poverty rates below 10 percent. The families received mobility counseling and help in leasing a new unit. One year after relocating, families could use their voucher to move again if they wished, without any special constraints on location; (2) the traditional voucher (TRV) group (also called the Section 8 group) received regular Section 8 certificates or vouchers that they could use anywhere; these families received no special mobility counseling; (3) the control group received no certificates or vouchers through MTO, but continued to be eligible for project-based housing assistance and whatever other social programs and services to which they would otherwise be entitled. Families were tracked from baseline (1994-1998) through the long-term evaluation survey fielding period (2008-2010) with the purpose of determining the effects of "neighborhood" on participating families. This data collection includes data from the 3,273 adult interviews completed as part of the MTO long-term evaluation. Using data from the long-term evaluation, the associated article reports that moving from a high-poverty to lower-poverty neighborhood was associated in the long-term (10 to 15 years) with modest, but potentially important, reductions in the prevalence of extreme obesity and diabetes. The data contain all outcomes and mediators analyzed for the associated article (with the exception of a few mediator variables from the interim MTO evaluation) as well as a variety of demographic and other baseline measures that were controlled for in the analysis. All analysis of the data should be weighted using the total survey weight. The cell-level file includes a separate weight for each outcome and mediator measure that is the sum of weights for all observations in the cell with valid data for the measure (for example, wt_f_db_hba1c_diab_final is the weight for the glycated hemoglobin measure, mn_f_db_hba1c_diab_final). In the pseudo-individual file, mn_f_wt_totsvy is the average of the total survey weight variable for all observations in the cell. In the original individual-level file, the total survey weight (f_wt_totsvy) is calculated as the product of three component weights: (1) Randomization ratio weight -- At the start of the MTO program, random assignment (RA) ratios were set to produce equal numbers of leased-up families in the low-poverty and traditional voucher groups based on expected leased-up rates. The initial ratios were "8 to 3 to 5": eight low-poverty voucher group families to three traditional voucher families to five control families. During the demonstration program, these RA ratios were adjusted to accommodate higher than anticipated leased-up rates among low-poverty voucher group families. This weight ensures that the proportion of families in a given site is the same across all three treatment groups. This component weight value ranges from 0.59 to 2.09. (2) Survey sample selection weight -- For budgetary reasons, adults from only a random two-thirds of traditional voucher group households were selected for the long-term survey interview sample (while adults from all low-poverty voucher and control group families were selected), so this component weights up the selected traditional voucher group adults so that they are representative of all traditional voucher group adults. This weight component is equal to the inverse probability of selection into the subsample (~1.52). (3) Phase 2 subsample weight -- The long-term survey data collection was completed as a two-phase process. In the first phase, we sought to interview all selected respondents. Phase 2 of fielding was triggered when the response rate reached approximately 74 percent. In the second phase, we su...

In 2022, the United States' E-infrastructure index amounted to 0.1944. By contrast, the Internet affordability index was only 0.0326.

In an April 2024 online survey, an overwhelming majority of respondents in the United States, regardless of which political party they identified with, said that **** U.S. dollars per hour is not enough for the average American worker to have a decent quality of life. The U.S. federal minimum wage has not been raised since 2009. Since then, many states have raised the wage, with a number of states having more than doubled the federal minimum.

This statistic shows the percentage of adults in the U.S. who agreed that with aging quality of life decreases as of 2018. It was found that 42 percent of adults stated they somewhat agreed that with aging quality of life decreases.

An Internet-based survey was administered to a national sample of individuals with recent nursing home experience. The survey elicited preferences using both contingent evaluation (CV) experiments as well as the assessment of quality of the nursing home. The CV experiments ask the respondent if they or their family member would be willing to move to a higher quality nursing home with a greater travel time. Information about the health status, demographic status, and economic status of the respondent and/or family member was also collected. The goals of the study were (1) To develop two alternative composite measures to the CMS 5 Star rating system that includes consumer preferences. (2) Measure variation in consumer preferences based on socio-demographics and health conditions.

This dataset contains the data and figures associated with the publication “Aging in Latin America and the Caribbean: Social Protection and Quality of Life of Older Person”.

Key quality of life indicators - housing costs, arts.