West Virginia, Mississippi, and Arkansas are the U.S. states with the highest percentage of their population who are obese. The states with the lowest percentage of their population who are obese include Colorado, Hawaii, and Massachusetts. Obesity in the United States Obesity is a growing problem in many countries around the world, but the United States has the highest rate of obesity among all OECD countries. The prevalence of obesity in the United States has risen steadily over the previous two decades, with no signs of declining. Obesity in the U.S. is more common among women than men, and overweight and obesity rates are higher among African Americans than any other race or ethnicity. Causes and health impacts Obesity is most commonly the result of a combination of poor diet, overeating, physical inactivity, and a genetic susceptibility. Obesity is associated with various negative health impacts, including an increased risk of cardiovascular diseases, certain types of cancer, and diabetes type 2. As of 2022, around 8.4 percent of the U.S. population had been diagnosed with diabetes. Diabetes is currently the eighth leading cause of death in the United States.

In 2021-2022, Mississippi topped the ranking of states with the highest share of children/adolescents between 6 and 17 years of age who were obese. This statistic illustrates the obesity rates among children/adolescents between 6 and 17 years of age in the United States in 2021-2022, by state.

The adult obesity rate, or the percentage of the county population (age 18 and older*) that is obese, or has a Body Mass Index (BMI) equal to or greater than 30 [kg/m2], is illustrative of a serious health problem, in Champaign County, statewide, and nationally.

The adult obesity rate data shown here spans from Reporting Years (RY) 2015 to 2024. Champaign County’s adult obesity rate fluctuated during this time, peaking in RY 2022. The adult obesity rates for Champaign County, Illinois, and the United States were all above 30% in RY 2024, but the Champaign County rate was lower than the state and national rates. All counties in Illinois had an adult obesity rate above 30% in RY 2024, but Champaign County's rate is one of the lowest among all Illinois counties.

Obesity is a health problem in and of itself, and is commonly known to exacerbate other health problems. It is included in our set of indicators because it can be easily measured and compared between Champaign County and other areas.

This data was sourced from the University of Wisconsin’s Population Health Institute’s and the Robert Wood Johnson Foundation’s County Health Rankings & Roadmaps. Each year’s County Health Rankings uses data from the most recent previous years that data is available. Therefore, the 2024 County Health Rankings (“Reporting Year” in the table) uses data from 2021 (“Data Year” in the table). The survey methodology changed in Reporting Year 2015 for Data Year 2011, which is why the historical data shown here begins at that time. No data is available for Data Year 2018. The County Health Rankings website notes to use caution if comparing RY 2024 data with prior years.

*The percentage of the county population measured for obesity was age 20 and older through Reporting Year 2021, but starting in Reporting Year 2022 the percentage of the county population measured for obesity was age 18 and older.

Source: University of Wisconsin Population Health Institute. County Health Rankings & Roadmaps 2024. www.countyhealthrankings.org.

United States Overweight or Obese Population was up 0.4points in 2019, compared to the previous year.

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.

In 2022, the U.S. states with the highest rates of obesity among women were Tennessee, Louisiana, and Mississippi. At that time, almost ** percent of women in Tennessee were considered obese. The states with the highest rates of obesity among men are West Virginia, Arkansas, and Oklahoma. Obesity: Women vs. men As of 2023, women in the United States had slightly higher rates of obesity than men. At that time, around **** percent of women were considered obese, compared to **** percent of men. Rates of obesity among both men and women are higher in the United States than any other OECD country, with high-calorie diets, often from fast food and sugary drinks, and large food portion sizes being partly to blame. In 2024, the mean self-reported weight among men in the United States was *** pounds, while women reported weighing an average of *** pounds. Which state is the most obese? As of 2023, West Virginia had the highest prevalence of adult obesity in the United States, with around ** percent of the population considered obese. Following West Virginia, Mississippi, Arkansas, and Louisiana, had some of the highest rates of obesity in the country. Colorado had the lowest share of adults who were obese at that time, but still, ********* of adults in the state were obese. West Virginia is also the state with the highest prevalence of obesity among high school students, with ** percent of high schoolers considered obese in 2021. Obesity in childhood is associated with obesity as adults, as well as mental health problems such as anxiety and depression.

In 2023, Black adults had the highest obesity rates of any race or ethnicity in the United States, followed by American Indians/Alaska Natives and Hispanics. As of that time, around ** percent of all Black adults were obese. Asians/Pacific Islanders had by far the lowest obesity rates. Obesity in the United States Obesity is a present and growing problem in the United States. An astonishing ** percent of the adult population in the U.S. is now considered obese. Obesity rates can vary substantially by state, with around ** percent of the adult population in West Virginia reportedly obese, compared to ** percent of adults in Colorado. The states with the highest rates of obesity include West Virginia, Mississippi, and Arkansas. Diabetes Being overweight and obese can lead to a number of health problems, including heart disease, cancer, and diabetes. Being overweight or obese is one of the most common causes of type 2 diabetes, a condition in which the body does not use insulin properly, causing blood sugar levels to rise. It is estimated that just over ***** percent of adults in the U.S. have been diagnosed with diabetes. Diabetes is now the seventh leading cause of death in the United States, accounting for ***** percent of all deaths.

Created for the 2023-2025 State of Black Los Angeles County (SBLA) interactive report. To learn more about this effort, please visit the report home page at https://ceo.lacounty.gov/ardi/sbla/. For more information about the purpose of this data, please contact CEO-ARDI. For more information about the configuration of this data, please contact ISD-Enterprise GIS. table_name indicator_name Universe source timeframe source_url

life_expectancy_countyhealthrankings_2020 Life Expectancy Total Population County Health Rankings 2018-2020 https://www.countyhealthrankings.org/app/california/2022/measure/outcomes/147/data

obese_est_adult_lachs_2018 Obese Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

obese_perc_adult_lachs_2018 Obese Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

overweight_est_adult_lachs_2018 Overweight Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

overweight_perc_adult_lachs_2018 Overweight Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

diabetes_est_adult_lachs_2018 Ever Diagnosed with Diabetes Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

diabetes_perc_adult_lachs_2018 Ever Diagnosed with Diabetes Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

regular_source_of_care_est_adult_lachs_2018 Reported Having a Regular Source of Health Care Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

regular_source_of_care_perc_adult_lachs_2018 Reported Having a Regular Source of Health Care Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

depression_est_adult_lachs_2018 Ever Diagnosed with Depression Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

depression_perc_adult_lachs_2018 Ever Diagnosed with Depression Percent (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

perceived_safe_est_adult_lachs_2018 Perceived Their Neighborhood to Be Safe from Crime Estimate (#) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

perceived_safe_perc_adult_lachs_2018 Perceived Their Neighborhood to Be Safe from Crime Estimate (%) Adults (Ages 18 Years and Older) LAC Health Survey 2018 www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

dental_care_est_child_lachs_2018 Had Dental Care within the past Year Estimate (#) Children (Ages 17 Years and Younger) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

dental_care_perc_child_lachs_2018 Had Dental Care within the past Year Percent (%) Children (Ages 17 Years and Younger) LAC Health Survey 2018www.publichealth.lacounty.gov/ha/LACHSDataTopics2018.htm

no_usual_source_est_chis_2020 No usual source of care Estimate (#) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

no_usual_source_perc_chis_2020 No usual source of care Percent (%) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

delayed_care_est_chis_2020 Delayed or didn't get medical care last year Estimate (#) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

delayed_care_est_chis_2020 Delayed or didn't get medical care last year Percent (%) Total Population California Health Interview Survey 2020 https://ask.chis.ucla.edu/AskCHIS/tools/_layouts/AskChisTool/home.aspx

covid_vax_one_or_more_est_2022 COVID-19 Vaccination 1+ Dose Estimate (#) Population 6 months and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_perc_2022 COVID-19 Vaccination 1+ Dose Percent (%) Population 6 months and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_full_est_2022 COVID-19 Fully Vaccinated Estimate (#) Population 6 months and older LAC DPH Sep-22publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_full_perc_2022 COVID-19 Fully Vaccinated Percent (%) Population 6 months and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_children_est_2022 COVID-19 Vaccination 1+ Dose - Children under 5 Estimate (#) Population older than 6 months and under 5 years LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_children_perc_2022 COVID-19 Vaccination 1+ Dose Children under 5 Percent (%) Population older than 6 months and under 5 years LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_youth_est_2022 COVID-19 Vaccination 1+ Dose - Youth 5-17 Estimate (#) Population 5-17 years LAC DPH Sep-22publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_youth_perc_2022 COVID-19 Vaccination 1+ Dose Youth 5-17 Percent (%) Population 5-17 years LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_adults_est_2022 COVID-19 Vaccination 1+ Dose - Adults Estimate (#) Population 18 and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

covid_vax_one_or_more_adults_perc_2022 COVID-19 Vaccination 1+ Dose Adults Percent (%) Population 18 and older LAC DPH Sep-22 publichealth.lacounty.gov/media/coronavirus/vaccine/vaccine-dashboard.htm

insured_pop_est_acs_2020 Insured population # Civilian noninstitutionalized population 2016-2020 ACS - S2701 https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S2701

insured_pop_perc_acs_2020 Insured population % Civilian noninstitutionalized population 2016-2020 ACS - S2701 https://data.census.gov/cedsci/table?g=0500000US06037&tid=ACSST5Y2020.S2701

mch_indicators_2019 Babies Born with Positive MCH Indicators Babies born in time frame Strong Start Index 2016-2019 https://infogram.com/1pj576jwy166z1s6ywvk32l5lkammrym3wy?live

current_asthma Percent of Adults (Ages 18 Years and Older) with Current Asthma Adults Los Angeles County Health Survey 2018 https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

no_med_insurance Percent of Insured Adults (Ages 18 Years and Older) Who Reported a Time Without Medical Insurance in the past 12 Months. Adults Los Angeles County Health Survey 2011 https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

transportation_problems Percent of Adults (Ages 18 Years and Older) Who Reported That Transportation Problems Kept Them from Obtaining Needed Medical Care in the past Year. Adults Los Angeles County Health Survey 2007 https://www.publichealth.lacounty.gov/ha/HA_DATA_TRENDS.htm

In Mississippi, over ***** out of ten adults were reported to be either overweight or obese in 2018, making it the leading U.S. state that year. Other prominent states, in terms of overweight and obesity, included Arkansas in ******, Oklahoma in *******, and Louisiana in ***** place.

Corpulence per state

When it comes to obesity, specifically, percentages were still very high for certain states. Almost forty percent of West Virginia’s population was obese in 2018. Colorado, Hawaii, and California were some of the healthier states that year, with obesity rates between ** and ** percent. The average for the country itself stood at just over ** percent.

Obesity-related health problems

Being obese can lead to various health-related complications, such as diabetes and diseases of the heart. In 2017, almost ** people per 100,000 died of diabetes mellitus in the United States. In the same year, roughly *** per 100,000 Americans died of heart disease. While the number of deaths caused by heart disease has decreased significantly over the past sixty to seventy years, it is still one of the leading causes of death in the country.

Sensitivity and specificity of self-selected silhouette ratings to detect overweight and obesity, or obese only in US, Seychelles, and Ghana.

Since 2016, the global edition of the Sustainable Development Report (SDR) has provided the most up-to-date data to track and rank the performance of all UN member states on the SDGs. This year’s edition was written by a group of independent experts at the SDG Transformation Center, an initiative of the SDSN. It focuses on the UN Summit of the Future, with an opening chapter endorsed by 100+ global scientists and practitioners. The report also includes two thematic chapters, related to SDG 17 (Strengthen the means of implementation and revitalize the Global Partnership for Sustainable Development) and SDG 2 (End hunger, achieve food security and improved nutrition and promote sustainable agriculture).This year’s SDR highlights five key findings:On average, globally, only 16% of the SDG targets are on track to be achieved by 2030, with the remaining 84% demonstrating limited or a reversal of progress. At the global level, SDG progress has been stagnant since 2020, with SDG 2 (Zero Hunger), SDG11 (Sustainable Cities and Communities), SDG14 (Life Below Water), SDG15 (Life on Land) and SDG16 (Peace, Justice, and Strong Institutions) particularly off-track. Globally, the five SDG targets on which the highest proportion of countries show a reversal of progress since 2015 include: obesity rate (under SDG 2), press freedom (under SDG 16), the red list index (under SDG 15), sustainable nitrogen management (under SDG 2), and – due in a large part to the COVID-19 pandemic and other factors that may vary across countries – life expectancy at birth (under SDG 3). Goals and targets related to basic access to infrastructure and services, including SDG9 (Industry, Innovation, and Infrastructure), show slightly more positive trends, although progress remains too slow and uneven across countries.The pace of SDG progress varies significantly across country groups. Nordic countries continue to lead on SDG achievement, with BRICS demonstrating strong progress and poor and vulnerable nations lagging far behind. Similar to past years, European countries – notably Nordic countries – top the 2024 SDG Index. Finland ranks number 1 on the SDG Index, followed by Sweden (#2), Denmark (#3), Germany (#4), and France (#5). Yet, even these countries face significant challenges in achieving several SDGs. Average SDG progress in BRICS (Brazil, the Russian Federation, India, China, and South Africa) and BRICS+ (Egypt, Ethiopia, Iran, Saudi Arabia, and the United Arab Emirates) since 2015 has been faster than the world average. In addition, East and South Asia has emerged as the region that has made the most SDG progress since 2015. By contrast, the gap between the world average SDG Index and the performance of the poorest and most vulnerable countries, including Small Island Developing States (SIDS), has widened since 2015.Sustainable development remains a long-term investment challenge. Reforming the Global Financial Architecture is more urgent than ever. The world requires many essential public goods that far transcend the nation-state. Low-income countries (LICs) and lower-middle-income countries (LMICs) urgently need to gain access to affordable long-term capital so that they can invest at scale to achieve their sustainable development objectives. Mobilizing the necessary levels of finance will require new institutions, new forms of global financing — including global taxation —, and new priorities for global financing, such as investing in quality education for all. The report presents five complementary strategies to reform the Global Financial Architecture.Global challenges require global cooperation. Barbados ranks the highest in its commitment to UN-based multilateralism; the United States ranks last. As with the challenge of SDGs, strengthening multilateralism requires metrics and monitoring. The report’s new Index of countries’ support to UN-based multilateralism (UN-Mi) ranks countries based on their engagement with the UN system including treaty ratification, votes at the UN General Assembly, membership in UN organizations, participation in conflicts and militarization, use of unilateral sanctions and financial contributions to the UN. The five countries most committed to UN-based multilateralism are: Barbados (#1), Antigua and Barbuda (#2), Uruguay (#3), Mauritius (#4), and the Maldives (#5). By contrast, the United States (#193), Somalia (#192), South Sudan (#191), Israel (#190), and the Democratic Republic of Korea (#189) rank the lowest on the UN-Mi.SDG targets related to food and land systems are particularly off-track. The SDR presents new FABLE pathways to support sustainable food and land systems. Globally, 600 million people will still suffer from hunger by 2030, obesity is increasing globally, and greenhouse gas emissions from Agriculture, Forestry, and Other Land Use (AFOLU) represent almost a quarter of annual global GHG emissions. The new FABLE pathways brought together more than 80 local researchers across 22 countries to assess how 16 targets related to food security, climate mitigation, biodiversity conservation, and water quality could be achieved by 2030 and 2050. The continuation of current trends widens the gap with targets related to climate mitigation, biodiversity, and water quality. Pursuing commitments that have been already taken by countries would improve the situation, but they are still largely insufficient. Significant progress is possible but requires several dramatic changes: 1) avoid overconsumption beyond recommended levels and limit animal-based protein consumption with dietary shifts compatible with cultural preferences; 2) invest to foster productivity, particularly for products and areas with strong demand growth; and 3) implement inclusive, robust, and transparent monitoring systems to halt deforestation. Our sustainable pathway avoids up to 100 million hectares of deforestation by 2030 and 100 Gt CO2 emissions by 2050. Additional measures would be needed to avoid trade-offs with on-farm employment and water pollution due to excessive fertilizer application and ensure that no one is left behind, particularly to end hunger.About the AuthorsProf. Jeffrey SachsDirector, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume LafortuneDirector, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: EmailGrayson FullerManager, SDG Index & Data team, SDSNGrayson Fuller is the manager of the SDG Index and of the team working on SDG data and statistics at SDSN. He is co-author of the Sustainable Development Report, for which he manages the data, coding, and statistical analyses. He also coordinates the production of regional and subnational editions of the SDG Index, in addition to other statistical reports, in collaboration with national governments, NGOs and international organizations such as the WHO, UNDP and the European Commission. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Romance Languages and Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese and Italian. He enjoys playing the violin, rock-climbing and taking care of his numerous plants in his free time.Contact: EmailAbout the PublishersDublin University PressDublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press

Malaysia tipped the ASEAN scale as having the highest share of the population being classified as obese in 2019, with over ** percent of its population classed as obese. Contrastingly, just over *** percent of Vietnam’s population was considered obese in 2019. Obesity in APAC The Asia Pacific region has the highest number of overweight and obese people in the world. Additionally, Australia and New Zealand had the highest share of overweight children globally. Diets across the region are rapidly changing as new food trends emerge. The development of economies across the region has made food more affordable and the transition from agriculture to industrialization has made employment for many citizens less physically demanding. These factors can be seen as having contributed to the rise of obesity across the region. Poor diets However, as the obesity rate increases, the levels of food insecurity across the Asia Pacific region have also risen. Food insecurity in South Asia has notably risen steadily since 2015. Malnutrition and undernourishment continue to be problems for the region, with many of its citizens not consuming enough essential vitamins and minerals in their diets. Furthermore, a low share of children in South and East Asia were eating from the minimum number of food groups in 2019.

Performance of self-reported silhouette ranking to predict overweight and obese BMI, elevated WC, and elevated WHtR in the US, Seychelles, and Ghana.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:

Breakdown by race/ethnicity in pop-up:

There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.

Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Breakdown by race/ethnicity in pop-up: (This map has been updated with new data, so figures may vary from those in this image.)There are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Proven strategies to improve life expectancy and health in general A database of dozens of strategies can be found at County Health Rankings' What Works for Health site, sorted by Health Behaviors, Clinical Care, Social & Economic Factors, and Physical Environment. Policies and Programs listed here have been evaluated as to their effectiveness. For example, consumer-directed health plans received an evidence rating of "mixed evidence" whereas cultural competence training for health care professionals received a rating of "scientifically supported." Data from County Health Rankings (layer referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World.

As of 2021, approximately ** percent of men and ** percent of women in the United Kingdom considered themselves overweight or obese. Across the European countries featured, overweight and obesity was more prevalent among males.

This multi-scale map shows life expectancy - a widely-used measure of health and mortality. From the 2020 County Health Rankings page about Life Expectancy:"Life Expectancy is an AverageLife Expectancy measures the average number of years from birth a person can expect to live, according to the current mortality experience (age-specific death rates) of the population. Life Expectancy takes into account the number of deaths in a given time period and the average number of people at risk of dying during that period, allowing us to compare data across counties with different population sizes.Life Expectancy is Age-AdjustedAge is a non-modifiable risk factor, and as age increases, poor health outcomes are more likely. Life Expectancy is age-adjusted in order to fairly compare counties with differing age structures.What Deaths Count Toward Life Expectancy?Deaths are counted in the county where the individual lived. So, even if an individual dies in a car crash on the other side of the state, that death is attributed to his/her home county.Some Data are SuppressedA missing value is reported for counties with fewer than 5,000 population-years-at-risk in the time frame.Measure LimitationsLife Expectancy includes mortality of all age groups in a population instead of focusing just on premature deaths and thus can be dominated by deaths of the elderly.[1] This could draw attention to areas with higher mortality rates among the oldest segment of the population, where there may be little that can be done to change chronic health problems that have developed over many years. However, this captures the burden of chronic disease in a population better than premature death measures.[2]Furthermore, the calculation of life expectancy is complex and not easy to communicate. Methodologically, it can produce misleading results caused by hidden differences in age structure, is sensitive to infant and child mortality, and tends to be overestimated in small populations."Click on the map to see a breakdown by race/ethnicity in the pop-up: Full details about this measureThere are many factors that play into life expectancy: rates of noncommunicable diseases such as cancer, diabetes, and obesity, prevalence of tobacco use, prevalence of domestic violence, and many more.Data from County Health Rankings 2020 (in this layer and referenced below), available for nation, state, and county, and available in ArcGIS Living Atlas of the World

California was the leading U.S. state in terms of the overall number of milk cows, with a total of over 1.7 million milk cows as of 2024. The total number of milk cows on farms in the United States shows that California holds a significant share of the total number of milk cows in the country. Unsurprisingly, California is also the leading milk producing state in the United States. Dairy industry in the U.S. According to the USDA, milk from U.S. farms is 90 percent water, with milk fat and skim solids making up the remaining 10 percent. Cow milk is a component of several dietary staples, such as cheese, butter, and yoghurt. Dairy is a very important industry in the United States, with this sector alone creating significant employment throughout the United States. The overall income of dairy farms in the U.S. amounted to about 51.3 billion U.S. dollars. Holtsein is the most popular breed of dairy cow farmed in the United States. Holstein have the highest milk production per cow in comparison to any other breed. Where is the U.S. positioned in the global dairy market? Topped only by the EU-27, the United States ranks as the second largest cow milk producer in the world, followed by India, Russia, and China. The United States also features among the top ten global milk exporters. The outlook for the future of the industry is also good, with milk production in the United States projected to steadily increase over the next years.

In 2022, the states with the highest death rates due to heart disease were Oklahoma, Mississippi, and Alabama. That year, there were around 257 deaths due to heart disease per 100,000 population in the state of Oklahoma. In comparison, the overall death rate from heart disease in the United States was 167 per 100,000 population. The leading cause of death in the United States Heart disease is the leading cause of death in the United States, accounting for 21 percent of all deaths in 2022. That year, cancer was the second leading cause of death, followed by unintentional injuries and COVID-19. In the United States, a person has a one in six chance of dying from heart disease. Death rates for heart disease are higher among men than women, but both have seen steady decreases in heart disease death rates since the 1950s. What are risk factors for heart disease? Although heart disease is the leading cause of death in the United States, the risk of heart disease can be decreased by avoiding known risk factors. Some of the leading preventable risk factors for heart disease include smoking, heavy alcohol use, physical inactivity, an unhealthy diet, and being overweight or obese. It is no surprise that the states with the highest rates of death from heart disease are also the states with the highest rates of heart disease risk factors. For example, Oklahoma, the state with the highest heart disease death rate, is also the state with the third-highest rate of obesity. Furthermore, Mississippi is the state with the highest levels of physical inactivity, and it has the second-highest heart disease death rate in the United States.

In 2021, around 16 percent of deaths in Bahrain were attributed to obesity, while around nine percent of deaths in the United States were attributed to obesity. This statistic shows the percentage of deaths in select countries worldwide that were attributed to obesity in 2021.