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 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.

In 2024, across all states in the United States, ********* was ranked first with a health index score of *****, followed by ************ and ************. The health index score was calculated by measuring 42 healthcare metrics relevant to health costs, access, and outcome.

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.

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

Luxembourg stands out as the European leader in quality of life for 2025, achieving a score of 220 on the Quality of Life Index. The Netherlands follows closely behind with 211 points, while Albania and Ukraine rank at the bottom with scores of 104 and 115 respectively. This index provides a thorough assessment of living conditions across Europe, reflecting various factors that shape the overall well-being of populations and extending beyond purely economic metrics. Understanding the quality of life index The quality of life index is a multifaceted measure that incorporates factors such as purchasing power, pollution levels, housing affordability, cost of living, safety, healthcare quality, traffic conditions, and climate, to measure the overall quality of life of a Country. Higher overall index scores indicate better living conditions. However, in subindexes such as pollution, cost of living, and traffic commute time, lower values correspond to improved quality of life. Challenges affecting life satisfaction Despite the fact that European countries register high levels of life quality by for example leading the ranking of happiest countries in the world, life satisfaction across the European Union has been on a downward trend since 2018. The EU's overall life satisfaction score dropped from 7.3 out of 10 in 2018 to 7.1 in 2022. This decline can be attributed to various factors, including the COVID-19 pandemic and economic challenges such as high inflation. Rising housing costs, in particular, have emerged as a critical concern, significantly affecting quality of life. This issue has played a central role in shaping voter priorities for the European Parliamentary Elections in 2024 and becoming one of the most pressing challenges for Europeans, profoundly influencing both daily experiences and long-term well-being.

The World Happiness Report is a landmark survey of the state of global happiness. The first report was published in 2012, the second in 2013, the third in 2015, and the fourth in the 2016 Update. The World Happiness 2017, which ranks 155 countries by their happiness levels, was released at the United Nations at an event celebrating International Day of Happiness on March 20th. The report continues to gain global recognition as governments, organizations and civil society increasingly use happiness indicators to inform their policy-making decisions. Leading experts across fields – economics, psychology, survey analysis, national statistics, health, public policy and more – describe how measurements of well-being can be used effectively to assess the progress of nations. The reports review the state of happiness in the world today and show how the new science of happiness explains personal and national variations in happiness.

The happiness scores and rankings use data from the Gallup World Poll. The scores are based on answers to the main life evaluation question asked in the poll. This question, known as the Cantril ladder, asks respondents to think of a ladder with the best possible life for them being a 10 and the worst possible life being a 0 and to rate their own current lives on that scale. The scores are from nationally representative samples for the years 2013-2016 and use the Gallup weights to make the estimates representative. The columns following the happiness score estimate the extent to which each of six factors – economic production, social support, life expectancy, freedom, absence of corruption, and generosity – contribute to making life evaluations higher in each country than they are in Dystopia, a hypothetical country that has values equal to the world’s lowest national averages for each of the six factors. They have no impact on the total score reported for each country, but they do explain why some countries rank higher than others.

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.

According to the survey, as of February 2023, four out of the six countries in the Gulf Cooperation Council ranked amongst the top ** in the world for expatriate quality of life. Qatar and the United Arab Emirates topped the list for quality of life, whereas Saudi Arabia and Kuwait came last in the region. Quality of life; an amalgamation of many metrics Since quality of life is dependent on many indicators, it can give us a good insight into many aspects of state welfare policies and services. Saudi Arabia, where the number of foreign workers in the private sector topped *** million, also ranked as having one of the region's lowest quality of life for expatriates. Qatar, which had the second-highest quality of life for expatriates living in the GCC, was ranked as one of the most challenging countries in the region for ease of settling in. The UAE and Qatar, both of which ranked the highest in the survey, also have the highest average salaries and living standards in the region. Foreign workers are a key pillar of the GCC economy Countries in the GCC all have sizable expatriate populations for which their economies are heavily reliant. Roughly ********** of the workforce in the GCC is foreign. Although the share of foreign workers in the GCC has slightly decreased in recent years, they still considerably outweigh the local workforce. Most of these workers comprise the unskilled portion of the occupational category in the GCC. However, with diversifying investments and programs such as Vision 2030, countries have seen a rise in the number of skilled foreign workers.

Estimated health-related quality of life (HRQoL) utilities by BMI, sex, and age, based on data from the Medical Expenditure Survey (MEPS) 2008-2016, adjusted for self-report bias using data from the National Health and Nutrition Examination Survey (NHANES). Estimated means and 95% CI.

The data is based on Economic Research Service (ERN) of USDA's dataset that shows where the creative people are in the U.S. Its an interpretation of Richard Florida's thesis that much of urban development is determined by people who work in the so called ideas and knowledge industry. The workers who are in ideas and knowledge industry are attracted to areas that offer jobs in these industries and also because of desirable traits such as quality of life indicators. For details see http://www.ers.usda.gov/data/creativeclasscodes/ and http://www.ers.usda.gov/Data/CreativeClassCodes/methods.htm

This data set provides information about the Air Quality Index (AQI). The AQI is an index for reporting how clean or polluted the air is and the associated health risks. Data in this set are given in number of days that the AQI fell within a certain range (good - hazardous). This data also provides information on which pollutants were in the air (O3, CO, SO2, etc). All data is given on the county level for the United States from 2001 to 2006. Data can be seen for earlier years and all of this data can be found at the EPAs website . This dataset can be used to see which areas have the poorest or best air quality, which can be useful for a number of industries, from real estate to health care and insurance.

The Russian capital Moscow had the highest quality of life in the country in 2022, according to the ranking, in which the city received ** points. The second best score was achieved at less than one point lower by Saint Petersburg.

Abstract

The objective of the survey was to produce baselines for 15 large urban centers in Kenya. The urban centers covered Nairobi, Mombasa, Naivasha, Nakuru, Malindi, Eldoret, Garissa, Embu, Kitui, Kericho, Thika, Kakamega, Kisumu, Machakos, and Nyeri. The survey covered the following issues: (a) household characteristics; (b) household economic profile; (c) housing, tenure, and rents; and (d) infrastructure services. The survey was undertaken to deepen understanding of the cities’ growth dynamics, and to identify specific challenges to quality of life for residents. The survey pays special attention to living conditions for residents of formal versus informal settlements, poor versus non-poor, and male and female headed households.

Analysis unit

Household Urban center

Kind of data

Sample survey data [ssd]

Sampling procedure

The Kenya State of the Cities Baseline Survey is aimed to produce reliable estimates of key indicators related to demographic profile, infrastructure access and economic profile for each of the 15 towns and cities based on representative samples, including representative samples of households (HHs) residing in slum and non-slum areas. For this baseline household survey, NORC used a two- or three-stage stratified cluster sampling design within each of the 15 urban centers. Our first-stage sampling frame was based on the 2009 census frame of enumeration areas. For each of the 15 towns and cities, NORC received the sampling frame of EAs from the Kenya National Bureau of Statistics (KNBS). In the first stage, NORC selected a sample of enumeration areas (PSUs). The second stage involved a random selection of households (SSUs) from each selected EA. In order to manage the field interviewing efficiently, we drew a fixed number of HHs from each selected EA, irrespective of EA size. The third stage arose in instances of very large EAs (EAs containing more than 200 households) in which EAs were divided into 2, 3 or 4 segments, from which one segment was selected randomly for household selection.

Stratification of Enumeration Areas: A few stratification factors were available for stratifying the EAs to help to achieve the survey objectives. As mentioned earlier, for this baseline survey we wanted to draw representative samples from slum and non-slum areas and also to include poor/non-poor households (HHs). For the 2009 census, depending on the location, KNBS divided the EAs into three categories: rural, urban, and peri-urban.

Although there is a clear distinction of EAs into slum and non-slum areas, it is hard to classify EAs into poor and non-poor categories. To guarantee enough representation of HHs living in slum and non-slum areas (also referred to as formal and informal areas) as well as HHs living below and above the poverty line, NORC stratified the first-stage sampling units (EAs) into strata, based on EA type (3 types) and settlement type (2 types). Given the resources available, we believe this stratification would serve our purpose as HHs living in slum and in rural areas tend to be poor. Table 1 in Appendix C of final Overview Report (provided under the Related Materials tab) presents the allocation of sampled EAs across the strata for each of the 15 cities in the baseline survey.

Sampling households is not as straightforward as the first-stage sampling of EAs, since the 2009 census frame of HHs does not exist. In the absence of a household sampling frame, NORC carried out a listing of HHs within each EA selected in the first stage. Trained listers, accompanied by local cluster guides (local residents with some form of authority in the EA), systematically listed all households in each selected EA, gathering the address, names of head of household and spouse, household description, latitude and longitude. To ensure completeness of listing data, avoid duplication and improve ease of locating households that were eventually selected for interview, listers enumerated households by chalking household identification number above the household doorway (an accepted practice for national surveys). The sampling frame of HHs produced from the listing activity was, therefore, up-to-date and included new formal and informal settlements that appeared after the 2009 census.

For adequate representativeness and to manage the interviewing task efficiently, NORC planned seven completed household interviews per EA. The final recommended sample size for the Kenya State of the Cities baseline survey is found in Table 2 in Appendix C of the final Overview Report.

Because the expected response rate was unknown prior to the start of the field period, the sampling team randomly selected ten households per enumeration area and distributed them to the interviewers working within the EA. Interviewing teams were instructed to complete at least seven interviews per EA from among the ten selected households. Interviewers were instructed to attempt at least three contacts with each selected household, approaching potential respondents on different days of the week and different times of day. Table 2 presents the final number of EAs listed per city and the final number of completed interviews per city. The table also presents the percent of planned EAs and interviews that were completed vs. planned. Please note that in several cities more interviews were completed than planned. As part of NORC's data quality plan, data collection teams were instructed to overshoot slightly the target of seven interviews per EA, if feasible, to mitigate any potential loss of cases due to poor quality or uncooperative respondents. Few cases were lost due to poor quality, therefore the target number of interviews remains over 100 percent in ten of the fifteen cities.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaire was developed by World Bank staff with input from stakeholders in the Kenya Municipal Program and NORC researchers and survey methodologists. The base questionnaire for the project was a 2004 World Bank survey of Nairobi slums. However, an extended iterative review process led to many changes in the questionnaire. The final version that was used for programming provided under the Related Materials tab, and in Volume II of the Overview.

The questionnaire’s topical coverage is indicated by the titles of its nine modules: 1. Demographics and household composition 2. Security of housing, land and tenure 3. Housing and settlement profile 4. Economic profile 5. Infrastructure services 6. Health 7. Household enterprises7 8. Civil participation and respondent tracking

Response rate

The completion rate is reported as the number of households that successfully completed an interview over the total number of households selected for the EA. These are shown by city in Table 5 in Appendix C of the final Overview Report, and have an average rate of 68.66 percent, with variation from 66 to 74 percent (aside from Nairobi at 61.47 percent and Machakos at 56 percent). As described earlier, ten households were selected per EA if the EA contained more than 10 households. For EAs where fewer than ten households were selected for interviews, all households were selected. In some EAs, more than ten households were selected due to a central office error.

This data explores Access to early childhood programs, by state: 2002 * In 2001, all preschool efforts in Florida were consolidated into a block grant administered by the Agency for Workforce Innovation. Funding is distributed to county-level early-childhood coalitions that make decisions on distribution. It is unclear yet how the new configuration will affect pre-K programs in the state. All data presented here are from before the consolidation. * In Alabama, pilot program is targeted based on need. However, any 4-year-old in pilot communities is eligible. In Minnesota, all 4-year-olds are eligible, but priority for services is given to children from low-income families that exceed Head Start income guidelines. * Risk factors are locally determined. In Nevada and West Virginia, all eligibility requirements for pre-K are locally determined. * Enrollment count for New Jersey is for Abbott districts only. In New Jersey, full-day kindergarten is mandated for 132 high-poverty districts. * Ohio serves an additional 18,705 children in its state-financed Head Start program. * Because pre-K funding is in the form of block grants and subject to district discretion, enrollment figures cannot be determined. NOTE: ES: Elementary School; MS: Middle School; HS: High School. SOURCE: Education Week, Quality Counts 2002, table Access to Early Childhood Programs. Data Source.

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.

ObjectiveDespite the significant advances in healthcare, people living with HIV still face challenges that affect their quality of life (QoL), both in terms of their physical state as represented by frailty and of their illness perceptions (IP). The aim of this study was to unravel the associations between these constructs (QoL, frailty, IP).MethodsThis multicenter, cross-sectional study included 477 people living with HIV (93% male; median age = 43 years, IQR = 51.7) from six HIV clinics in Greece. Frailty phenotype, QoL and IP were assessed using Fried’s criteria, EuroQoL (EQ-5D-5L) and Brief Illness Perception Questionnaire (BIPQ), respectively. Network analysis model was utilized.ResultsAmong frailty criteria, exhaustion had the highest expected influence, while the strongest correlation concerns exhaustion and weak grip strength (pr = 0.14). Regarding the QoL items, usual activities displayed the highest expected influence. The correlations of pain/discomfort with mobility (pr = 0.31), and usual activities with self-care (pr = 0.34) were the strongest. For the BIPQ items, the strongest correlation was found between illness concern and emotional response (pr = 0.45), whereas the latter item was the one that displayed the highest expected influence. Three communities were formed: 1) personal control, treatment control and coherence, 2) the frailty items with mobility, self-care, usual activities, and pain/discomfort, and 3) the rest BIPQ items with anxiety/depression. Identity displayed the highest bridge strength, followed by pain/discomfort, usual activities and consequences.ConclusionsThe interplay between QoL, frailty, and IP in people living with HIV requires clinical attention. Self-reported exhaustion, slow walking speed, and low physical activity affect the physical QoL dimensions, while anxiety/depression is strongly associated with illness-related concern and perceived emotional effects, leading to psychological distress. Symptom management can improve QoL, and information on the disease and treatment can enhance control over the disease. Developing interventions to address QoL, frailty, and IP is crucial.

This dataset explores Health-adjusted life expectancy by sex in Canada for 2001. 1. The estimates are based on two years of death data (2000 and 2001). 2. Life expectancy is the number of years a person would be expected to live, starting at birth, if the age- and sex-specific mortality rates for a given observation period (such as a calendar year) were held constant over the estimated life span. 3. Health-adjusted life expectancy is a more comprehensive indicator than that of life expectancy because it introduces the concept of quality of life. Health-adjusted life expectancy is the number of years in full health that an individual can expect to live given the current morbidity and mortality conditions. Health-adjusted life expectancy uses the Health Utility Index (HUI) to weigh years lived in good health higher than years lived in poor health. Thus, health-adjusted life expectancy is not only a measure of quantity of life but also a measure of quality of life. 4. Canada, excluding the territories. Source: Statistics Canada, CANSIM, table 102-0121 and Catalogue no. 82-221-X. Last modified: 2007-11-26.

Finland was ranked the happiest country in the world, according to the World Happiness Report from 2025. The Nordic country scored 7.74 on a scale from 0 to 10. Two other Nordic countries, Denmark and Iceland, followed in second and third place, respectively. The World Happiness Report is a landmark survey of the state of global happiness that ranks countries by how happy their citizens perceive themselves to be. Criticism The index has received criticism from different perspectives. Some argue that it is impossible to measure general happiness in a country. Others argue that the index places too much emphasis on material well-being as well as freedom from oppression. As a result, the Happy Planet Index was introduced, which takes life expectancy, experienced well-being, inequality of outcomes, and ecological footprint into account. Here, Costa Rica was ranked as the happiest country in the world. Afghanistan is the least happy country Nevertheless, most people agree that high levels of poverty, lack of access to food and water, as well as a prevalence of conflict are factors hindering public happiness. Hence, it comes as no surprise that Afghanistan was ranked as the least happy country in the world in 2024. The South Asian country is ridden by poverty and undernourishment, and topped the Global Terrorism Index in 2024.

In 2024, Switzerland led the ranking of countries with the highest average wealth per adult, with approximately ******* U.S. dollars per person. The United States was ranked second with an average wealth of around ******* U.S. dollars per adult, followed by Hong Kong SAR. However, the figures do not show the actual distribution of wealth. The Gini index shows wealth disparities in countries worldwide. Does wealth guarantee a longer life? As the adage goes, “money can’t buy you happiness,” yet wealth and income are continuously correlated to the quality of life of individuals in different countries around the world. While greater levels of wealth may not guarantee a higher quality of life, it certainly increases an individual’s chances of having a longer one. Although they do not show the whole picture, life expectancy at birth is higher in the wealthier world regions. Does money bring happiness? A number of the world’s happiest nations also feature in the list of those countries for which average income was highest. Finland, however, which was the happiest country worldwide in 2022, is missing from the list of the top twenty countries with the highest wealth per adult. As such, the explanation for this may be the fact that a larger proportion of the population has access to a high-income relative to global levels. Measures of quality of life Criticism of the use of income or wealth as a proxy for quality of life led to the creation of the United Nations’ Human Development Index. Although income is included within the index, it also has other factors taken into account, such as health and education. As such, the countries with the highest human development index can be correlated to those with the highest income levels. That said, none of the above measures seek to assess the physical and mental environmental impact of a high quality of life sourced through high incomes. The happy planet index demonstrates that the inclusion of experienced well-being and ecological footprint in place of income and other proxies for quality of life results in many of the world’s materially poorer nations being included in the happiest.