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.

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.

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.

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.

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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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...

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.

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.

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*...

Real-time city rankings with political preference weighting for Ignore Politics

The Department of Justice launched Operation Weed and Seed in 1991 as a means of mobilizing a large and varied array of resources in a comprehensive, coordinated effort to control crime and drug problems and improve the quality of life in targeted high-crime neighborhoods. In the long term, Weed and Seed programs are intended to reduce levels of crime, violence, drug trafficking, and fear of crime, and to create new jobs, improve housing, enhance the quality of neighborhood life, and reduce alcohol and drug use. This baseline data collection effort is the initial step toward assessing the achievement of the long-term objectives. The evaluation was conducted using a quasi-experimental design, matching households in comparison neighborhoods with the Weed and Seed target neighborhoods. Comparison neighborhoods were chosen to match Weed and Seed target neighborhoods on the basis of crime rates, population demographics, housing characteristics, and size and density. Neighborhoods in eight sites were selected: Akron, OH, Bradenton (North Manatee), FL, Hartford, CT, Las Vegas, NV, Pittsburgh, PA, Salt Lake City, UT, Seattle, WA, and Shreveport, LA. The "neighborhood" in Hartford, CT, was actually a public housing development, which is part of the reason for the smaller number of interviews at this site. Baseline data collection tasks included the completion of in-person surveys with residents in the target and matched comparison neighborhoods, and the provision of guidance to the sites in the collection of important process data on a routine uniform basis. The survey questions can be broadly divided into these areas: (1) respondent demographics, (2) household size and income, (3) perceptions of the neighborhood, and (4) perceptions of city services. Questions addressed in the course of gathering the baseline data include: Are the target and comparison areas sufficiently well-matched that analytic contrasts between the areas over time are valid? Is there evidence that the survey measures are accurate and valid measures of the dependent variables of interest -- fear of crime, victimization, etc.? Are the sample sizes and response rates sufficient to provide ample statistical power for later analyses? Variables cover respondents' perceptions of the neighborhood, safety and observed security measures, police effectiveness, and city services, as well as their ratings of neighborhood crime, disorder, and other problems. Other items included respondents' experiences with victimization, calls/contacts with police and satisfaction with police response, and involvement in community meetings and events. Demographic information on respondents includes year of birth, gender, ethnicity, household income, and employment status.

Live city rankings with ignore politics political preference weighting applied. Showing 1-50 of 386 cities.

The United States senior living market, valued at $112.93 billion in 2025, is experiencing robust growth, projected to expand at a Compound Annual Growth Rate (CAGR) of 5.86% from 2025 to 2033. This expansion is fueled by several key drivers. The aging population, particularly the baby boomer generation, is a significant factor, creating an increasing demand for assisted living, independent living, memory care, and nursing care facilities. Furthermore, rising disposable incomes and increasing awareness of the benefits of senior living communities contribute to market growth. Technological advancements in senior care, such as telehealth and remote monitoring, are also enhancing the quality of life for residents and boosting market appeal. However, the market faces some restraints, including the rising costs of healthcare and senior care services, potentially limiting accessibility for some segments of the population. Furthermore, staffing shortages within the industry represent a significant challenge. The market is segmented by property type, with assisted living, independent living, and memory care facilities representing the largest segments. Key states driving market growth include New York, Illinois, California, North Carolina, and Washington, reflecting higher concentrations of the senior population and higher disposable incomes. Major players in the market such as Ensign Group Inc, Sunrise Senior Living, Brookdale Senior Living Inc, and Atria Senior Living Inc, compete fiercely, driving innovation and service improvements. The forecast period (2025-2033) anticipates continued growth, driven by the ongoing demographic shifts and increased demand for high-quality senior care options. Strategic partnerships, acquisitions, and investments in technology are likely to shape the competitive landscape in the coming years. The industry will continue to adapt to meet the evolving needs of the aging population, focusing on personalized care, innovative technologies, and cost-effective solutions. This comprehensive report provides an in-depth analysis of the booming United States senior living market, covering the period from 2019 to 2033. With a base year of 2025 and a forecast period spanning 2025-2033, this report is an invaluable resource for investors, industry professionals, and anyone seeking to understand the dynamics of this rapidly evolving sector. The report leverages extensive data analysis to provide insightful projections and uncover key trends shaping the future of senior care in the US. Expect detailed breakdowns of key segments, including assisted living, independent living, memory care, and nursing care, across major states like California, New York, Illinois, North Carolina, and Washington. Recent developments include: July 2023: Spring Cypress senior living site expansion is set to open at the end of 2024 and will consist of three phases. The first phase of the expansion will include 19 independent-living, two-bedroom cottages. The second phase will include 24 townhomes. The third phase will feature 95 apartments. The final phase will feature a resort with several luxury amenities., Apr 2023: For seniors looking for innovative, high-quality care, Avista Senior Living is transitioning away from its SafelyYou partnership to empower safer, more personalized dementia care with real-time, AI video and remote clinical experts 24/7.. Key drivers for this market are: 4., Increase in Aging Population Driving the Market4.; Healthcare and Long-term Care Needs Driving the Market. Potential restraints include: 4., High Affordability and Cost of Care Affecting the Market4.; Staffing and Workforce Challenges Affecting the Market. Notable trends are: Senior Housing Witnessing Increased Demand.

Of the most populous cities in the U.S., San Jose, California had the highest annual income requirement at ******* U.S. dollars annually for homeowners to have an affordable and comfortable life in 2024. This can be compared to Houston, Texas, where homeowners needed an annual income of ****** U.S. dollars in 2024.

This study was designed to collect comprehensive data on the types of "crime prevention through environmental design" (CPTED) methods used by cities of 30,000 population and larger, the extent to which these methods were used, and their perceived effectiveness. A related goal was to discern trends, variations, and expansion of CPTED principles traditionally employed in crime prevention and deterrence. "Security by design" stems from the theory that proper design and effective use of the built environment can lead to a reduction in the incidence and fear of crime and an improvement in quality of life. Examples are improving street lighting in high-crime locations, traffic re-routing and control to hamper drug trafficking and other crimes, inclusion of security provisions in city building codes, and comprehensive review of planned development to ensure careful consideration of security. To gather these data, the United States Conference of Mayors (USCM), which had previously studied a variety of issues including the fear of crime, mailed a survey to the mayors of 1,060 cities in 1994. Follow-up surveys were sent in 1995 and 1996. The surveys gathered information about the role of CPTED in a variety of local government policies and procedures, local ordinances, and regulations relating to building, local development, and zoning. Information was also collected on processes that offered opportunities for integrating CPTED principles into local development or redevelopment and the incorporation of CPTED into decisions about the location, design, and management of public facilities. Questions focused on whether the city used CPTED principles, which CPTED techniques were used (architectural features, landscaping and landscape materials, land-use planning, physical security devices, traffic circulation systems, or other), the city department with primary responsibility for ensuring compliance with CPTED zoning ordinances/building codes and other departments that actively participated in that enforcement (mayor's office, fire department, public works department, planning department, city manager, economic development office, police department, building department, parks and recreation, zoning department, city attorney, community development office, or other), the review process for proposed development, security measures for public facilities, traffic diversion and control, and urban beautification programs. Respondents were also asked about other security-by-design features being used, including whether they were mandatory or optional, if optional, how they were instituted (legislation, regulation, state building code, or other), and if applicable, how they were legislated (city ordinance, city resolution, or state law). Information was also collected on the perceived effectiveness of each technique, if local development regulations existed regarding convenience stores, if joint code enforcement was in place, if banks, neighborhood groups, private security agencies, or other groups were involved in the traffic diversion and control program, and the responding city's population, per capita income, and form of government.

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.

BackgroundBecause of a lack of preference-based health-related quality of life (HRQoL) instruments suitable for infants aged 0–12 months, we previously developed the Infant QoL Instrument (IQI). The present study aimed to generate an algorithm to estimate utilities for the IQI.MethodsVia an online survey, respondents from the general population and primary caregivers from China-Hong Kong, the UK, and the USA were presented 10 discrete choice scenarios based on the IQI classification system. An additional sample of respondents from the general population were also asked if they considered the examined health states to be worse than death. Coefficients for the IQI item levels were obtained with a conditional logit model based on the responses of the primary caregivers for IQI states only. These coefficients were then normalized using the rank-ordered logit model based on the responses from the general population who assessed “death” as a choice option. In this way, the values were rescaled from full health (1.0) to death (0.0), and consequently, they became suitable for the computation of quality-adjusted life years.ResultsThe total sample consisted of 1409 members of the general population and 1229 primary caregivers. Results indicated that, out of the 7 IQI items (“sleeping,” “feeding,” “breathing,” “stooling/poo,” “mood,” “skin,” and “interaction”), “breathing” had the highest impact on the HRQoL of infants. Moreover, except for “stooling,” all item levels were statistically significant. The general population sample considered none of the health states as worse than death. The utility value for the worst health state was 0.015 (State 4444444).ConclusionsThe IQI is the first generic instrument to assess overall HRQoL in 0–1-year-old infants by providing values and utilities. Using discrete choice experiments, we demonstrated that it is possible to derive utilities of infant health states. The next step will be to collect IQI values in a clinical population of infants and to compare these values with those of other instruments.

US State Transportation & Health

Modes, Fatalities, and Physical Activity

By Department of Transportation [source]

About this dataset

This dataset provides a comprehensive look at the transportation and health of each US state. Included are important indicators such as commute mode share (auto, transit, bicycle and walk), complete streets policies, person miles of travel by private vehicle and walking, physical activity from transportation sources, road traffic fatalities exposure rates (auto, bicycle and pedestrian), seat belt use, transit trips per capita, use of federal funds for bicycle/pedestrian efforts, vehicle miles traveled per capita and proximity to major roadways. All these parameters allow for a comprehensive evaluation of the health state in regards to transportation. Thus allowing users to gain insights into the way different states go about their fundamental transport practices that may have implications on their overall health. This tool will allow you to compare different states across these variables in order to make correlations between policy choices and public health outcomes over time – equipping decision makers with crucial information that could help make data-driven decisions in the future

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This dataset contains transportation and health information for every state in the US. This data can be used to gain a better understanding of how transportation affects our health and quality of life.

To use this dataset, you first need to understand what each column means. The columns are: State, Commute Mode Share - Auto, Commute Mode Share - Transit, Commute Mode Share - Bicycle, Commute Mode Share - Walk , Complete Streets Policies, Person Miles of Travel by Private Vehicle , Person Miles of Travel by Walking , Physical Activity from Transportation , Road Traffic Fatalities Exposure Rate- Auto , Road Traffic Fatalities Exposure Rate- Bicycle , Road Traffic Fatalities Exposure Rate-Pedestrian , Seat Belt Use Transit Trips per Capita Use of Federal Funds for Bicycle and Pedestrian Efforts Vehicle Miles Traveled per Capita Proximity to Major Roadways . Each column describes a different aspect related to transportation and health in the US states such as the number commuters who drive their own car or those who use the public transit system.

Once you understand what each column represents you can start exploring different states’ data on that particular feature with statistics such as mean value or maximum/minimum value or visualize it in charts/graphs. Additionally, you can look at correlations between different features across multiple states and try to see if they have any relationship or not. You may also want to combine multiple columns together in order create new metrics (or score) that can be compared across all the states (e.g., calculate a “Commuting Score” based on commute mode share for private vehicle/transit/bicycle). Once your analysis is complete you should have an idea about which state has better (or worse) conditions concerning transportation & health indicators and draw conclusions from there!

Research Ideas

• Creating an interactive map of the US illustrating transportation and health data from each state.
• Developing predictive models to forecast the impact of different transportation policies on health outcomes in various states.
• Identifying correlations between changes in transit mode share and road traffic fatalities/injuries based on locations/states within the US over a particular period of time

Acknowledgements

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

License

Unknown License - Please check the dataset description for more information.

Columns

File: THT_Data_508.csv | Column name | Description | |:----------------------------------------------|:------------------------------------------------------------------------------| | State | The name of the US state. (String) | | Commute Mode Share - Auto | The score assigned to the commute mode share for auto. (Number) | | **Commute Mode Share ** | Score | | Commute Mode Share - Transit | The score assigned to the commute mode share for transit. (Number) ...

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 survey of minority groups was part of a larger project to investigate the patterns, predictors, and consequences of midlife development in the areas of physical health, psychological well-being, and social responsibility. Conducted in Chicago and New York City, the survey was designed to assess the well-being of middle-aged, urban, ethnic minority adults living in both hyper-segregated neighborhoods and in areas with lower concentrations of minorities. Respondents' views were sought on issues relevant to quality of life, including health, childhood and family background, religion, race and ethnicity, personal beliefs, work experiences, marital and close relationships, financial situation, children, community involvement, and neighborhood characteristics. Questions on health explored the respondents' physical and emotional well-being, past and future attitudes toward health, physical limitations, energy level and appetite, amount of time spent worrying about health, and physical reactions to those worries. Questions about childhood and family background elicited information on family structure, the role of the parents with regard to child rearing, parental education, employment status, and supervisory responsibilities at work, the family financial situation including experiences with the welfare system, relationships with siblings, and whether as a child the respondent slept in the same bed as a parent or adult relative. Questions on religion covered religious preference, whether it is good to explore different religious teachings, and the role of religion in daily decision-making. Questions about race and ethnicity investigated respondents' backgrounds and experiences as minorities, including whether respondents preferred to be with people of the same racial group, how important they thought it was to marry within one's racial or ethnic group, citizenship, reasons for moving to the United States and the challenges faced since their arrival, their native language, how they would rate the work ethic of certain ethnic groups, their views on race relations, and their experiences with discrimination. Questions on personal beliefs probed for respondents' satisfaction with life and confidence in their opinions. Respondents were asked whether they had control over changing their life or their personality, and what age they viewed as the ideal age. They also rated people in their late 20s in the areas of physical health, contribution to the welfare and well-being of others, marriage and close relationships, relationships with their children, work situation, and financial situation. Questions on work experiences covered respondents' employment status, employment history, future employment goals, number of hours worked weekly, number of nights away from home due to work, exposure to the risk of accident or injury, relationships with coworkers and supervisors, work-related stress, and experience with discrimination in the workplace. A series of questions was posed on marriage and close relationships, including marital status, quality and length of relationships, whether the respondent had control over his or her relationships, and spouse/partner's education, physical and mental health, employment status, and work schedule. Questions on finance explored respondents' financial situation, financial planning, household income, retirement plans, insurance coverage, and whether the household had enough money. Questions on children included the number of children in the household, quality of respondents' relationships with their children, prospects for their children's future, child care coverage, and whether respondents had changed their work schedules to accommodate a child's illness. Additional topics focused on children's identification with their culture, their relationships with friends of different backgrounds, and their experiences with racism. Community involvement was another area of investigation, with items on respondents' role in child-rearing, participation on a jury, voting behavior, involvement in charitable organizations, volunteer experiences, whether they made monetary or clothing donations, and experiences living in an institutional setting or being homeless. Respondents were also queried about their neighborhoods, with items on neighborhood problems including racism, vandalism, crime, drugs, poor schools, teenag

This submission includes publicly available data extracted in its original form. Please reference the Related Publication listed here for source and citation information "The United States Cancer Statistics (USCS) are the official federal statistics on cancer incidence from registries having high-quality data and cancer mortality statistics for 50 states and the District of Columbia. USCS are produced by the Centers for Disease Control and Prevention (CDC) and the National Cancer Institute (NCI)." [Quote from: https://wonder.cdc.gov/cancer.htm]>