Quality of Life by State 2024

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This dataset provides insights into the quality of life across different states in the United States for the year 2024. Quality of life, encompassing aspects like comfort, health, and happiness, is evaluated through various metrics including affordability, economy, education, and safety. Dive into this dataset to understand how different states fare in terms of overall quality of life and its individual components.

Columns Description

• State: The name of the U.S. state.
• QualityOfLifeTotalScore: The total score representing the overall quality of life for the respective state. This score is calculated based on various quality of life metrics.
• QualityOfLifeQualityOfLife: The score representing the quality of life aspect for the respective state. This aspect may include subjective factors related to happiness, satisfaction, and overall well-being. Higher scores may indicate a higher level of subjective well-being, happiness, or overall satisfaction among residents. Lower scores could suggest lower levels of subjective well-being.
• QualityOfLifeAffordability: The score representing the affordability aspect of the quality of life for the respective state. This aspect evaluates factors such as cost of living, housing affordability, and income levels. Higher scores typically indicate greater affordability of housing, cost of living, and basic necessities. Lower scores may suggest that these essentials are less accessible or more expensive for residents.
• QualityOfLifeEconomy: The score representing the economic aspect of the quality of life for the respective state. This aspect assesses factors such as employment opportunities, economic growth, and income distribution. Higher scores may reflect a stronger economy with more job opportunities, higher incomes, and lower levels of poverty. Lower scores might indicate economic challenges such as unemployment or income inequality.
• QualityOfLifeEducationAndHealth: The score representing the education and health aspect of the quality of life for the respective state. This aspect considers factors such as access to quality education, healthcare facilities, and overall public health indicators. Higher scores generally signify better access to quality education, healthcare services, and overall public health. Lower scores may indicate deficiencies in these areas, such as limited access to healthcare or lower educational attainment levels.
• QualityOfLifeSafety: The score representing the safety aspect of the quality of life for the respective state. This aspect evaluates factors such as crime rates, public safety measures, and community well-being initiatives. Higher scores suggest lower crime rates, better community safety, and a higher sense of security among residents. Lower scores may indicate higher crime rates or concerns about safety.

These descriptions provide an overview of what each column represents and the specific aspects of quality of life they assess for each U.S. state.

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.

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 2023, Uruguay and Chile had the highest Digital Quality of Life index in Latin America and the Caribbean region, at **** and **** 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.

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.

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.

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.

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 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).

Behavioral Risk Factors: HRQOL

Analyzing Health-Related Quality of Life in the United States

By Health [source]

About this dataset

The Behavioral Risk Factor Surveillance System (BRFSS) is an annual state-based, telephone survey of adults in the United States. It collects a variety of health-related data, including Health Related Quality of Life (HRQOL). This dataset contains results from the HRQOL survey within a range of locations across the US for the year indicated.

This dataset includes 14 columns which summarize and quantify different aspects concerning HRQOL topics. The year, location abbreviation, description and geo-location provide background contextual information which help define each row. The question column indicates the response provided to by respondents, while category classifies it into overarching groupings. Additionally there are columns covering sample size and data value attributes such as standard error, unit and type all evidence chipping away at informative insights into how Americans’ quality of life is changing over time — all cleverly presented in this one concise dataset!

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Featured Notebooks

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How to use the dataset

In order to analyze this dataset, it is important have a good understanding of the columns included in it. The columns provide various pieces of information about the data such as year collected, location abbreviation, location name and type of data value collected. Furthermore, understanding what each column means is essential for proper interpretation and analysis; for example knowing that ‘Data_Value %’ indicates what percentage responded a certain way or that ‘Sample_Size’ shows how many people were surveyed can help you make better decisions when looking at patterns within the data set.

Once you understand the general structure behind this dataset one should also familiarize themselves with some basic statistical analysis tools such as mean/median/mode calculations comparative/correlative analysis so they can really gain insights into how health-related quality of life affects different populations across countries or regions.. To get even more meaningful results you might also want to consider adding other variables or datasets into your report that correlate with HRQOL - like poverty rate or average income level - so you can make clearer conclusions about potential contributing factors towards certain insights you uncover while using this dataset alone.

Research Ideas

• Identifying trends between geolocation and health-related quality of life indicators to better understand how environmental factors may impact specific communities.
• Visualizing the correlations between health-related quality of life variables across different locations over time to gain insights on potential driving developmental or environmental issues.
• Monitoring the effects of public health initiatives dealing with qualitative health data such as those conducted by CDC, Department of Health and Human Services, and other organizations by tracking changes in different aspects of HRQOL measures over time across multiple locations

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 when the data was collected. (Integer) | | LocationAbbr | Abbreviations of various locations where data was recorded. (String) | | LocationDesc | Full names of states whose records are included in this survey. (String) | | Category | Particular topic chosen for research such as “Healthy People 2010 Topics” or “Older Adults Issues”. (String) | | Question | Each question corresponds to metrics tracked within each topic. (String) | | DataSource | Source from which survey responses were collected. (String) | | Data_Value_Unit | Units taken for recording survey types...

The social environment represents the external conditions under which people engage in social activity within their community. It includes aspects of social opportunity, leisure and recreation, education, access to health services, health status and participation in democratic processes. Fourteen indicators have been used to assess aspects of quality of the social environment.

The dataset contains US counties ranking data based on measures of health outcomes and health determinants. The measures used to establish counties ranks are related to length and quality of life for health outcomes and to health behavior, clinical care, socioeconomic and physical environment factors for health determinants. US counties are described along with their FIPS (Federal Information Processing Standard) code and the US state they belong.

Americans are questioned indepth about their quality of life, consumerism, and ways their standard of living affects the environment and economic patterns.Questions focus on means of reducing consumption, health hazards, health foods, personal attitudes toward buying, and value system. Respondents are also asked about vacation travels, current political events, and status of women.

This statistic shows the results of a survey among religious groups in the United States regarding the quality of their life now and in five years from now. They were asked to rate thie life quality for both points in time on a scale from 1 (worst possible life) to 10 (best possible life). On this scale, the surveyed muslims rated their current life with 7 points and their life in five years with 8.4 points.

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 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”.

The data package provides county-level data and rankings for measures that define the population health status and for a set factors with great influence on population health. The measures used to establish counties ranks are related to length and quality of life and to health behavior, clinical care, socioeconomic and environmental factors.

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.

The interests of economists, psychologists, social scientists and others on the relations of income, demographics, religion and subjective well-being, have generated a vast global literature. It is apparent that biomedical research has focused on white with men. The Women’s Health Initiative and Observational Study (WHI OS) was initiated in 1992. The OS represents the scientific need for social priorities to improve the health and welfare of women; it includes 93.676 relatively healthy postmenopausal women, 49 to 79, from diverse backgrounds. The objective of this study is to examine how lifestyle and other factors influence women’s health. Data from the WHI OS questionnaire were analyzed. Statistical methods included descriptive statistics square, correlations, linear regression and analyses of covariance (GLM). New findings and insights relate primarily to general health, religion, club attendance, and likelihood of depression. The most important predictor of excellent or very good health is quality of life and general health is a major predictor of quality of life. A great deal of strength and comfort from religion was reported by 62.98% of the women, with little variation by denomination. More from religion related to poorer health, and less likelihood of depression. Religion and lower income are in accord with of across country studies. Attendance at clubs was associated with religion and with all factors associated with religion, except income. Though general health and likelihood of depression are highly correlated, better health is associated with higher income; however, likelihood of depression is not associated with income—contrary to conventional wisdom about socioeconomic disparities and mental health. Subjective well-being variables, with the exception of quality of life, were not associated with income. Social networks—religion and clubs—among a diverse population, warrant further attention from economists, psychologists, sociologists, and others.

A dataset of a longitudinal study of over 3,000 Mexican-Americans aged 65 or over living in five southwestern states. The objective is to describe the physical and mental health of the study group and link them to key social variables (e.g., social support, health behavior, acculturation, migration). To the extent possible, the study was modeled after the existing EPESE studies, especially the Duke EPESE, which included a large sample if African-Americans. Unlike the other EPESE studies that were restricted to small geographic areas, the Hispanic EPESE aimed at obtaining a representative sample of community-dwelling Mexican-American elderly residing in Texas, New Mexico, Arizona, Colorado, and California. Approximately 85% of Mexican-American elderly reside in these states and data were obtained that are generalizable to roughly 500,000 older people. The final sample of 3,050 subjects at baseline is comparable to those of the other EPESE studies. Data Availability: Waves I to IV are available through the National Archive of Computerized Data on Aging (NACDA), ICPSR. Also available through NACDA is the ����??Resource Book of the Hispanic Established Populations for the Epidemiologic Studies of the Elderly����?? which offers a thorough review of the data and its applications. All subjects aged 75 or older were interviewed for Wave V and 902 new subjects were added. Hemoglobin A1c test kits were provided to subjects who self-reported diabetes. Approximately 270 of the kits were returned for analyses. Wave V data are being validated and reviewed. A tentative timeline for the archiving of Wave V data is November 2006. Wave VI interviewing and data collection is scheduled to begin in Fall 2006. * Dates of Study: 1993-2006 * Study Features: Longitudinal, Minority oversamples, Anthropometric Measures * Sample Size: ** 1993-4: 3,050 (Wave I) ** 1995-6: 2,438 (Wave II) ** 1998-9: 1,980 (Wave III) ** 2000-1: 1,682 (Wave IV) ** 2004-5: 2,073 (Wave V) ** 2006-7: (Wave VI) Links: * ICPSR Wave 1: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/2851 * ICPSR Wave 2: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/3385 * ICPSR Wave 3: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4102 * ICPSR Wave 4: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/4314 * ICPSR Wave 5: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/25041 * ICPSR Wave 6: http://www.icpsr.umich.edu/icpsrweb/ICPSR/studies/29654