Crime Survey for England and Wales (CSEW) estimates, by each combination of offence group, age, sex, and important demographic characteristics.

2011 to present. BRFSS SMART MMSA Prevalence combined land line and cell phone data. The Selected Metropolitan Area Risk Trends (SMART) project uses the Behavioral Risk Factor Surveillance System (BRFSS) to analyze the data of selected metropolitan statistical areas (MMSAs) with 500 or more respondents. BRFSS data can be used to identify emerging health problems, establish and track health objectives, and develop and evaluate public health policies and programs. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://data.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data was reported at 6.000 % in 2015. This records a decrease from the previous number of 8.700 % for 2014. Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data is updated yearly, averaging 8.400 % from Dec 1996 (Median) to 2015, with 16 observations. The data reached an all-time high of 11.000 % in 2001 and a record low of 6.000 % in 2015. Kuwait KW: Prevalence of Overweight: Weight for Height: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kuwait – Table KW.World Bank: Health Statistics. Prevalence of overweight children is the percentage of children under age 5 whose weight for height is more than two standard deviations above the median for the international reference population of the corresponding age as established by the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Estimates of overweight children are also from national survey data. Once considered only a high-income economy problem, overweight children have become a growing concern in developing countries. Research shows an association between childhood obesity and a high prevalence of diabetes, respiratory disease, high blood pressure, and psychosocial and orthopedic disorders (de Onis and Blössner 2003). Childhood obesity is associated with a higher chance of obesity, premature death, and disability in adulthood. In addition to increased future risks, obese children experience breathing difficulties and increased risk of fractures, hypertension, early markers of cardiovascular disease, insulin resistance, and psychological effects. Children in low- and middle-income countries are more vulnerable to inadequate nutrition before birth and in infancy and early childhood. Many of these children are exposed to high-fat, high-sugar, high-salt, calorie-dense, micronutrient-poor foods, which tend be lower in cost than more nutritious foods. These dietary patterns, in conjunction with low levels of physical activity, result in sharp increases in childhood obesity, while under-nutrition continues

Nearly 39 percent of adults (18 years and older) worldwide were overweight as of 2019. The statistic illustrates the percentage of people worldwide who were overweight as of 2019, by age.

AM: Prevalence of Undernourishment: % of Population data was reported at 2.500 % in 2022. This stayed constant from the previous number of 2.500 % for 2021. AM: Prevalence of Undernourishment: % of Population data is updated yearly, averaging 2.500 % from Dec 2001 (Median) to 2022, with 22 observations. The data reached an all-time high of 25.900 % in 2001 and a record low of 2.500 % in 2022. AM: Prevalence of Undernourishment: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Health Statistics. Prevalence of undernourishments is the percentage of the population whose habitual food consumption is insufficient to provide the dietary energy levels that are required to maintain a normal active and healthy life. Data showing as 2.5 may signify a prevalence of undernourishment below 2.5%.;Food and Agriculture Organization (http://www.fao.org/faostat/en/#home).;Weighted average;This is the Sustainable Development Goal indicator 2.1.1[https://unstats.un.org/sdgs/metadata/].

United States US: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data was reported at 0.400 % in 2012. This records a decrease from the previous number of 0.700 % for 2009. United States US: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data is updated yearly, averaging 0.800 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 1.200 % in 1991 and a record low of 0.400 % in 2012. United States US: Prevalence of Underweight: Weight for Age: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Prevalence of underweight, female, is the percentage of girls under age 5 whose weight for age is more than two standard deviations below the median for the international reference population ages 0-59 months. The data are based on the WHO's new child growth standards released in 2006.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

1995-2010. BRFSS land line only prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct/data

Estimates of the prevalence of self-reported long COVID and associated activity limitation, using UK Coronavirus (COVID-19) Infection Survey data. Experimental Statistics.

The German Centre for Cancer Registry Data (ZfKD) provides the topical cancer statistics for Germany. In an interactive database query you will get information on incidence and mortality rates as well as for prevalence and survival rates for different types of cancer.

Percentages are weighted to population characteristics. Data are not available if it did not meet BRFSS stability requirements.For more information on these requirements, as well as risk factors and calculated variables, see the Technical Documents and Survey Data for a specific year - http://www.cdc.gov/brfss/annual_data/annual_data.htm.Recommended citation: Centers for Disease Control and Prevention (CDC). Behavioral Risk Factor Surveillance System. Atlanta, Georgia: U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, [appropriate year].

2011 to present. BRFSS combined land line and cell phone prevalence data. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://data.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

Armenia AM: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 5.600 % in 2021. This records a decrease from the previous number of 8.500 % for 2011. Armenia AM: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 7.050 % from Dec 2011 (Median) to 2021, with 2 observations. The data reached an all-time high of 8.500 % in 2011 and a record low of 5.600 % in 2021. Armenia AM: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Armenia – Table AM.World Bank.WDI: Social: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes. It is calculated by adjusting to a standard population age-structure.;International Diabetes Federation, Diabetes Atlas.;Weighted average;

This data table provides a collection of information from peer-reviewed autism prevalence studies. Information reported from each study includes the autism prevalence estimate and additional study characteristics (e.g., case ascertainment and criteria). A PubMed search was conducted to identify studies published at any time through September 2020 using the search terms: autism (title/abstract) OR autistic (title/abstract) AND prevalence (title/abstract). Data were abstracted and included if the study fulfilled the following criteria: • The study was published in English; • The study produced at least one autism prevalence estimate; and • The study was population-based (any age range) within a defined geographic area.

IT: Diabetes Prevalence: % of Population Aged 20-79 data was reported at 4.780 % in 2017. IT: Diabetes Prevalence: % of Population Aged 20-79 data is updated yearly, averaging 4.780 % from Dec 2017 (Median) to 2017, with 1 observations. IT: Diabetes Prevalence: % of Population Aged 20-79 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Italy – Table IT.World Bank.WDI: Health Statistics. Diabetes prevalence refers to the percentage of people ages 20-79 who have type 1 or type 2 diabetes.; ; International Diabetes Federation, Diabetes Atlas.; Weighted average;

2011 to 2012. BRFSS SMART County Prevalence combined land line and cell phone data. The Selected Metropolitan Area Risk Trends (SMART) project uses the Behavioral Risk Factor Surveillance System (BRFSS) to analyze the data of selected counties with 500 or more respondents. BRFSS data can be used to identify emerging health problems, establish and track health objectives, and develop and evaluate public health policies and programs. BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available. Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://chronicdata.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

This table provides county-level prevalence for 2018 for seven US states using linked statewide health and education data. For full methods see: Shaw KA, Williams S, Hughes MM, Warren Z, Bakian AV, Durkin MS, et al. Statewide county-level autism spectrum disorder prevalence estimates — seven U.S. states, 2018. Annals of Epidemiology. 2023 Jan 18; Available from: https://www.sciencedirect.com/science/article/pii/S1047279723000182

Cancer Prevalence Statistics from the National Disease Registration Service publishes data on the number of people living with and beyond a cancer diagnosis on a given date (index date). The index date for the present publication is 31 December 2022. Understanding the size of the population living with and beyond a cancer diagnosis at a point in time can support planning for the delivery of local health and social care services, quantify the ‘burden’ of disease in an area or population and could determine the number of people who may have unmet health needs that could potentially benefit from new treatment interventions. The publication includes an interactive dashboard and data workbook for download. Detailed methodology notes are included at the Cancer Prevalence Statistics home page. The data are anonymous and available in an open format for anyone to access and use. Cancer prevalence Statistics are provided for all cancers combined as well as by cancer sites for England and sub-national geographies as follows: Cancer Alliance; Integrated Care Board; Local Authority. If you have feedback or any other queries about Cancer Prevalence, please email us at NDRSenquires@nhs.net and mention 'Cancer Prevalence Statistics' in your email.

2011 to present. BRFSS combined land line and cell phone age-adjusted prevalence data. The BRFSS is a continuous, state-based surveillance system that collects information about modifiable risk factors for chronic diseases and other leading causes of death. Data will be updated annually as it becomes available.

Detailed information on sampling methodology and quality assurance can be found on the BRFSS website (http://www.cdc.gov/brfss). Methodology: http://www.cdc.gov/brfss/factsheets/pdf/DBS_BRFSS_survey.pdf Glossary: https://data.cdc.gov/Behavioral-Risk-Factors/Behavioral-Risk-Factor-Surveillance-System-BRFSS-H/iuq5-y9ct

Mali ML: Prevalence of Undernourishment: % of Population data was reported at 4.000 % in 2015. This records a decrease from the previous number of 4.900 % for 2014. Mali ML: Prevalence of Undernourishment: % of Population data is updated yearly, averaging 8.350 % from Dec 2000 (Median) to 2015, with 16 observations. The data reached an all-time high of 14.600 % in 2000 and a record low of 4.000 % in 2015. Mali ML: Prevalence of Undernourishment: % of Population data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Population below minimum level of dietary energy consumption (also referred to as prevalence of undernourishment) shows the percentage of the population whose food intake is insufficient to meet dietary energy requirements continuously. Data showing as 5 may signify a prevalence of undernourishment below 5%.; ; Food and Agriculture Organization (http://www.fao.org/publications/en/).; Weighted average;