United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: Weight for Height: 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 United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; 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.

United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data was reported at 6.900 % in 2012. This records an increase from the previous number of 6.400 % for 2009. United States US: Prevalence of Overweight: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 6.900 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 8.700 % in 2005 and a record low of 5.100 % in 1991. United States US: Prevalence of Overweight: Weight for Height: 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 overweight, female, is the percentage of girls 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.; ; 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; 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

In 2022, the average height of South Korean women in their thirties was ****** centimeters, with women in their twenties having a very similar average height. On average, South Korean women were ****** centimeters tall, and older women tended to be shorter. Average height increases slowly The average height of women in South Korea has seen a noticeable increase over the past decade. In 2012, women in their twenties were ****** centimeters on average. However, the average height grew by around **** centimeters in the past ten years. The change could be attributed to improvements in overall nutrition, healthcare, and living conditions in the country. The South Korean beauty standard In a survey, South Korean women were asked about their preferred height range, and their answers fell between *** to *** centimeters. Women not only have a preferred height for themselves but also for their future spouses. Single women’s ideal height for their potential husbands was at around ***** centimeters, while single men wished for their potential wives to be *** centimeters tall.

This statistic depicts the average body weight of U.S. females aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average female body weight for those that identified as non-Hispanic white has increased from ***** in ********* to ***** in *********.

United States US: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data was reported at 0.000 % in 2012. This stayed constant from the previous number of 0.000 % for 2009. United States US: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data is updated yearly, averaging 0.050 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.100 % in 2005 and a record low of 0.000 % in 2012. United States US: Prevalence of Severe Wasting: Weight for Height: Male: % 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 severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59.; ; 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.

United States US: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data was reported at 1.200 % in 2012. This records a decrease from the previous number of 2.200 % for 2009. United States US: Prevalence of Stunting: Height for Age: Female: % of Children Under 5 data is updated yearly, averaging 2.400 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 3.200 % in 2002 and a record low of 1.200 % in 2012. United States US: Prevalence of Stunting: Height 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 stunting, female, is the percentage of girls under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. 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.

This statistic depicts the average body weight of U.S. adults aged 20 years and over from 1999 to 2016. According to the data, the average male body weight has increased from 189.4 in 1999-2000 to 197.9 in 2015-2016.

United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data was reported at 3.000 % in 2012. This records a decrease from the previous number of 3.200 % for 2009. United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data is updated yearly, averaging 3.600 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 4.500 % in 2002 and a record low of 3.000 % in 2012. United States US: Prevalence of Stunting: Height for Age: Male: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of stunting, male, is the percentage of boys under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. 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.

Graph and download economic data for Unemployment Rate - 20 Yrs. & over, Black or African American Women (LNU04000032) from Jan 1972 to May 2025 about 20 years +, females, African-American, household survey, unemployment, rate, and USA.

United States US: Prevalence of Wasting: Weight for Height: % of Children Under 5 data was reported at 0.500 % in 2012. This stayed constant from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: % of Children Under 5 data is updated yearly, averaging 0.500 % from Dec 1991 (Median) to 2012, with 5 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.400 % in 2001. United States US: Prevalence of Wasting: 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 USA – Table US.World Bank: Health Statistics. Prevalence of wasting is the proportion of children under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; 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; 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.

United States US: Prevalence of Wasting: Weight for Height: Male: % of Children Under 5 data was reported at 0.400 % in 2012. This records a decrease from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Male: % of Children Under 5 data is updated yearly, averaging 0.650 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.400 % in 2012. United States US: Prevalence of Wasting: Weight for Height: Male: % 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 wasting, male,is the proportion of boys under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; 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.

This statistic depicts the average body weight of U.S. men aged 20 years and over from 1999 to 2016, by ethnicity. According to the data, the average male body weight for those that identified as non-Hispanic white has increased from 192.3 in 1999-2000 to 202.2 in 2015-2016.

United States US: Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data was reported at 4.200 % in 2024. This records an increase from the previous number of 4.000 % for 2023. United States US: Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data is updated yearly, averaging 2.800 % from Dec 2000 (Median) to 2024, with 25 observations. The data reached an all-time high of 4.200 % in 2024 and a record low of 2.500 % in 2012. United States US: Prevalence of Stunting: Height for Age: % of Children Under 5, Modeled Estimate data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Social: Health Statistics. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's 2006 Child Growth Standards.;UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).;Weighted average;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). 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. Estimates are modeled estimates produced by the JME. Primary data sources of the anthropometric measurements are national surveys. These surveys are administered sporadically, resulting in sparse data for many countries. Furthermore, the trend of the indicators over time is usually not a straight line and varies by country. Tracking the current level and progress of indicators helps determine if countries are on track to meet certain thresholds, such as those indicated in the SDGs. Thus the JME developed statistical models and produced the modeled estimates.

Abstract

The 2022 Ghana Demographic and Health Survey (2022 GDHS) is the seventh in the series of DHS surveys conducted by the Ghana Statistical Service (GSS) in collaboration with the Ministry of Health/Ghana Health Service (MoH/GHS) and other stakeholders, with funding from the United States Agency for International Development (USAID) and other partners.

The primary objective of the 2022 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the GDHS collected information on: - Fertility levels and preferences, contraceptive use, antenatal and delivery care, maternal and child health, childhood mortality, childhood immunisation, breastfeeding and young child feeding practices, women’s dietary diversity, violence against women, gender, nutritional status of adults and children, awareness regarding HIV/AIDS and other sexually transmitted infections, tobacco use, and other indicators relevant for the Sustainable Development Goals - Haemoglobin levels of women and children - Prevalence of malaria parasitaemia (rapid diagnostic testing and thick slides for malaria parasitaemia in the field and microscopy in the lab) among children age 6–59 months - Use of treated mosquito nets - Use of antimalarial drugs for treatment of fever among children under age 5

The information collected through the 2022 GDHS is intended to assist policymakers and programme managers in designing and evaluating programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the objectives of the 2022 GDHS, a stratified representative sample of 18,450 households was selected in 618 clusters, which resulted in 15,014 interviewed women age 15–49 and 7,044 interviewed men age 15–59 (in one of every two households selected).

The sampling frame used for the 2022 GDHS is the updated frame prepared by the GSS based on the 2021 Population and Housing Census.1 The sampling procedure used in the 2022 GDHS was stratified two-stage cluster sampling, designed to yield representative results at the national level, for urban and rural areas, and for each of the country’s 16 regions for most DHS indicators. In the first stage, 618 target clusters were selected from the sampling frame using a probability proportional to size strategy for urban and rural areas in each region. Then the number of targeted clusters were selected with equal probability systematic random sampling of the clusters selected in the first phase for urban and rural areas. In the second stage, after selection of the clusters, a household listing and map updating operation was carried out in all of the selected clusters to develop a list of households for each cluster. This list served as a sampling frame for selection of the household sample. The GSS organized a 5-day training course on listing procedures for listers and mappers with support from ICF. The listers and mappers were organized into 25 teams consisting of one lister and one mapper per team. The teams spent 2 months completing the listing operation. In addition to listing the households, the listers collected the geographical coordinates of each household using GPS dongles provided by ICF and in accordance with the instructions in the DHS listing manual. The household listing was carried out using tablet computers, with software provided by The DHS Program. A fixed number of 30 households in each cluster were randomly selected from the list for interviews.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Face-to-face computer-assisted interviews [capi]

Research instrument

Four questionnaires were used in the 2022 GDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Ghana. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

The GSS organized a questionnaire design workshop with support from ICF and obtained input from government and development partners expected to use the resulting data. The DHS Program optional modules on domestic violence, malaria, and social and behavior change communication were incorporated into the Woman’s Questionnaire. ICF provided technical assistance in adapting the modules to the questionnaires.

Cleaning operations

DHS staff installed all central office programmes, data structure checks, secondary editing, and field check tables from 17–20 October 2022. Central office training was implemented using the practice data to test the central office system and field check tables. Seven GSS staff members (four male and three female) were trained on the functionality of the central office menu, including accepting clusters from the field, data editing procedures, and producing reports to monitor fieldwork.

From 27 February to 17 March, DHS staff visited the Ghana Statistical Service office in Accra to work with the GSS central office staff on finishing the secondary editing and to clean and finalize all data received from the 618 clusters.

Response rate

A total of 18,540 households were selected for the GDHS sample, of which 18,065 were found to be occupied. Of the occupied households, 17,933 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,317 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,014 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 7,263 men age 15–59 were identified as eligible for individual interviews and 7,044 were successfully interviewed.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Ghana Demographic and Health Survey (2022 GDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 GDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 GDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the GDHS 2022 is an SAS program. This program used the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women and men
• Heaping in anthropometric measurements for children (digit preference)
• Observation of mosquito nets
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Number of

In 2022, the average height of South Korean men in their thirties lay at ****** centimeters. Men in older age groups tended to be shorter. On average, South Korean men were ****** centimeters tall that year. Diet and healthcare in South Korea It has been observed that improvements in nutrition and healthcare lead to increased average height over time. With the rapid industrialization in South Korea came improvements in healthcare and nutritional intake. South Korea ranks among the leading countries in the health index, which measures a population’s health and a country’s healthcare system. Even with an excellent healthcare system, South Koreans have increasingly been concerned about their diet and nutrition, exemplified by the share of people trying to consume certain nutrients every day. Height preferences in South Korea  According to a 2019 survey, for most respondents the preferred height for South Korean men was higher than the current average. This discrepancy was similar for the preferred height for women, showing how preferences for taller people stretched across genders. Not only are South Koreans preferring taller partners, but they are also getting taller over time. Another survey found that the ideal height for a spouse in the country came closer to the average height of younger generations.

According to April 2020 survey data of adults in the United States, 10 percent of male respondents were currently using Bumble. Only 6 percent of female survey respondents confirmed being users of the social dating app.

Abstract

The 2000-2001 Uganda Demographic and Health Survey (UDHS) is a nationally representative survey of 7,246 women age 15-49 and 1,962 men age 15-54. The main purpose of the 2000-2001 UDHS is to provide policy-makers and programme managers with detailed information on fertility; family planning; childhood and adult mortality; maternal and child health and nutrition; and knowledge of, attitudes about, and practices related to HIV/AIDS. The 2000-2001 UDHS is the third national sample survey of its kind to be undertaken in Uganda. The first survey was implemented in 1988-1989 and was followed by the 1995 UDHS. Caution needs to be exercised when analysing trends using the three UDHS data sets because of some differences in geographic coverage.

The 2000-2001 Uganda Demographic and Health Survey (UDHS) was designed to provide information on demographic, health, and family planning status and trends in the country. Specifically, the UDHS collected information on fertility levels, marriage, sexual activity, fertility preferences, awareness and use of family planning methods, and breastfeeding practices. In addition, data were collected on the nutritional status of mothers and young children; infant, child, adult, and maternal mortality; maternal and child health; awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections; and levels of haemoglobin and vitamin A in the blood.

The 2000-2001 UDHS is a follow-up to the 1988-1989 and 1995 UDHS surveys, which were also implemented by the Uganda Bureau of Statistics (UBOS, previously the Department of Statistics). The 2000-2001 UDHS is significantly expanded in scope but also provides updated estimates of basic demographic and health indicators covered in the earlier surveys.

The specific objectives of the 2000-2001 UDHS are as follows: - To collect data at the national level that will allow the calculation of demographic rates, particularly the fertility and infant mortality rates - To analyse the direct and indirect factors that determine the level and trends in fertility and mortality - To measure the level of contraceptive knowledge and practice of women and men by method, by urban-rural residence, and by region - To collect data on knowledge and attitudes of women and men about sexually transmitted infections and HIV/AIDS, and to evaluate patterns of recent behaviour regarding condom use - To assess the nutritional status of children under age five and women by means of anthropometric measurements (weight and height), and to assess child feeding practices - To collect data on family health, including immunisations, prevalence and treatment of diarrhoea and other diseases among children under five, antenatal visits, assistance at delivery, and breastfeeding - To measure levels of haemoglobin and vitamin A in the blood of women and children - To collect information on the extent of child labour.

Geographic coverage

The 2000-2001 Uganda Demographic and Health Survey (UDHS) is a nationally representative survey. But it was not possible to cover all 45 districts in the country because of security problems in a few areas. The survey was hence limited to 41 out of the then 45 districts in the country, excluding the districts of Kasese and Bundibugyo in the Western Region and Gulu and Kitgum in the Northern Region. These districts cover approximately 5 percent of the total population.

Analysis unit

• Household
• Women age 15-49
• Men age 15-54
• Children under five

Universe

The population covered by the 2000 UDHS is defined as the universe of all women age 15-49 in Uganda and all men age 15-54.

Kind of data

Sample survey data

Sampling procedure

The sample was drawn through a two-stage design. The first-stage sample frame for this survey is the list of enumeration areas (EAs) compiled from the 1991 Population Census. In this frame, the EAs are grouped by parish within a subcounty, by subcounty within a county, and by county within a district. A total of 298 EAs (102 in urban areas and 196 in rural areas) were selected. Urban areas and districts included in the Delivery of Improved Services for Health (DISH) project and the Community Reproductive Health Project (CREHP) were oversampled in order to produce estimates for these segments of the population.

Within each selected EA, a complete household listing was done to provide the basis for the second-stage sampling. The number of households to be selected in each sampled EA was allocated proportionally to the number of households in the EA.

It was not possible to cover all districts in the country because of security problems in a few areas. The survey was hence limited to 41 out of the then 45 districts in the country,1 excluding the districts of Kasese and Bundibugyo in the Western Region and Gulu and Kitgum in the Northern Region. These districts cover approximately 5 percent of the total population.

The sample for the 2000-2001 UDHS was aimed at providing reliable estimates of important indicators for the population of Uganda at the national level (less the excluded districts), for urban and rural areas, and for each of the four regions in Uganda defined as:

• Central: Kalangala, Kampala, Kiboga, Luwero, Masaka, Mpigi, Mubende, Mukono, Sembabule, Nakasongola, and Rakai
• Eastern: Bugiri, Busia, Iganga, Jinja, Kamuli, Kapchorwa, Katakwi, Kumi, Mbale, Pallisa, Soroti, and Tororo
• Northern: Adjumani, Apac, Arua, Kotido, Lira, Moyo, Moroto, and Nebbi
• Western: Bushenyi, Hoima, Kabale, Kabarole, Kibaale, Kisoro, Masindi, Mbarara, Ntungamo, and Rukungiri.

The sample was also designed to generate estimates of contraceptive prevalence rates for the districts in the DISH project funded by the United States Agency for International Development (USAID) and districts in the CREHP project. These districts are grouped in six subdomains, namely, the following: - Group I: Mbarara and Ntungamo
- Group II: Masaka, Rakai, and Sembabule
- Group III: Luwero, Masindi, and Nakasongola
- Group IV: Jinja and Kamuli
- Group V: Kampala
CREHP districts:
DISH districts: Kabale, Kisoro, and Rukungiri.

In each group, a minimum of 500 completed interviews with women was targeted to allow for separate estimates. Consequently, data for Kampala District can be presented separately because it has more than the specified minimum number of completed interviews.

The 2000-2001 UDHS covered the same EAs as were covered by the 1995 UDHS. However, a new list of households within the EA was compiled and the sample households were not necessarily the same as those selected in 1995. In the case of the CREHP districts (Kabale, Kisoro and Rukungiri), five extra EAs were selected to generate a sample size sufficient to allow independent estimates. Because the 1995 and 2000-2001 UDHS did not cover the same geographical areas, the two surveys are not exactly comparable.

Details of the UDHS sample design are provided in Appendix A and estimations of sampling errors are included in Appendix B of the Final report.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were used for the 2000-2001 UDHS, namely, a) the Household Questionnaire, b) the Women's Questionnaire, and c) the Men's Questionnaire. The contents of these questionnaires were based on the MEASURE DHS+ Model “B” Questionnaire, which was developed for use in countries with a low level of contraceptive use. In consultation with technical institutions and local organisations, UBOS modified these questionnaires to reflect relevant issues in population, family planning, and other health issues in Uganda. The revised questionnaires were translated from English into six major languages, namely, Ateso, Luganda, Lugbara, Luo, Runyankole/Rukiga, and Runyoro/Rutoro.

The questionnaires were pretested prior to their finalisation. The pretest training took place from June 14 to July 8, 2000. For this exercise, seven women and seven men were trained to be interviewers, forming seven teams of one woman and one man each. Each team was assigned to test the questionnaires in one of the seven language groups (including English) into which the questionnaires had been translated. Three nurses were recruited to participate in the anemia testing exercise as health technicians. The pretest fieldwork was conducted during a one-week period (July 10-16, 2000).

a) The Household Questionnaire was used to list all the usual members and visitors in selected households. Some basic information was collected on the characteristics of each person listed, including his or her age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. In addition, the Household Questionnaire collected information on characteristics of the household's dwelling unit, such as the source of water, type of toilet facilities, materials used for the floor of the house, and ownership of various durable goods. It also included questions that were designed to assess the extent of child labour and that were used to record the height and weight and the hemoglobin level of women 15-49 and children under the age of five. In households selected for the male survey, the hemoglobin level of men eligible for the individual interview was also recorded.

b) The Women's Questionnaire was used to collect information from all women age 15-49. These women were asked questions on topics related to their background, childbearing experience and preferences, marriage and sexual activity, employment, maternal and child care, and awareness and behaviour

美国：肥胖的普遍性：高度重量：女性：5岁以下儿童百分比在12-01-2012达6.900%，相较于12-01-2009的6.400%有所增长。美国：肥胖的普遍性：高度重量：女性：5岁以下儿童百分比数据按年更新，12-01-1991至12-01-2012期间平均值为6.900%，共6份观测结果。该数据的历史最高值出现于12-01-2005，达8.700%，而历史最低值则出现于12-01-1991，为5.100%。CEIC提供的美国：肥胖的普遍性：高度重量：女性：5岁以下儿童百分比数据处于定期更新的状态，数据来源于World Bank，数据归类于Global Database的美国 – 表 US.世界银行：卫生统计。

美国：严重消瘦的发病率：高度重量：5岁以下儿童百分比：女性在12-01-2012达0.000%，相较于12-01-2009的0.000%保持不变。美国：严重消瘦的发病率：高度重量：5岁以下儿童百分比：女性数据按年更新，12-01-1991至12-01-2012期间平均值为0.000%，共6份观测结果。该数据的历史最高值出现于12-01-2002，达0.100%，而历史最低值则出现于12-01-2012，为0.000%。CEIC提供的美国：严重消瘦的发病率：高度重量：5岁以下儿童百分比：女性数据处于定期更新的状态，数据来源于World Bank，数据归类于全球数据库的美国 – Table US.World Bank.WDI：卫生统计。

Of the forty* men who have been elected to the office of U.S. president, the average weight of U.S. presidents has been approximately 189lbs (86kg). The weight range has been between 122lbs (55kg) and 332lbs (151kg), meaning that the heaviest president, William Howard Taft, was almost three times as heavy as the lightest president, James Madison (who was also the shortest president). Although Taft weighed over 300lbs during his presidency in 1909, he did implement a fitness and dietary regimen in the 1920s, that helped him lose almost 100lbs (45kg) before his death due to cardiovascular disease in 1930. Increase over time The tallest ever president, Abraham Lincoln (who was 6'4"), actually weighed less than the presidential average, and also less than the average adult male in the U.S. in 2018. It is important to note that the average weight of U.S. males has gradually increased in the past two decades, with some studies suggesting that it may have even increased by 15lbs (7kg) since the 1980s. The presidential averages have also increased over time, as the first ten elected presidents had an average weight of 171lbs (78kg), while the average weight of the ten most recent is 194lbs (88kg). Recent presidents In recent years, the heaviest president has been Donald Trump, who weighed 237lbs (108kg) during his first term in office; however medical reports published in June 2020 show that he gained 7lbs (3kg) during this term. There was also controversy in 2018, when it appeared that Trump's official height had been increased from 6'2" to 6'3", which many speculated was done to prevent him from being categorized as "obese" (according to his BMI). In the past half century, George H. W. Bush and Bill Clinton were the only other presidents to have weighed more than the presidential average, although both men were also 6'2" (188cm) tall. President Joe Biden weighs below the presidential average, at 177lbs (81kg).