Developmentrelevance:
Low birth-weight, which is associated with maternal malnutrition, raises the risk of infant mortality and stunts growth in infancy and childhood. There is also emerging evidence that low-birth-weight babies are more prone to non-communicable diseases such as diabetes and cardiovascular diseases. Low birth-weight can arise as a result of a baby being born too soon or too small for gestational age. Babies born prematurely, who are also small for their gestational age, have the worst prognosis.

In low- and middle-income countries low birth-weight stems primarily from poor maternal health and nutrition. Three factors have the most impact: poor maternal nutritional status before conception, mother's short stature (due mostly to under-nutrition and infections during childhood), and poor nutrition during pregnancy (UNICEF Data, https://data.unicef.org/).

IndicatorName: Low-birthweight babies (% of births)

Longdefinition: Low-birthweight babies are newborns weighing less than 2,500 grams, with the measurement taken within the first hour of life, before significant postnatal weight loss has occurred.

Source: UNICEF-WHO Low birthweight estimates [data.unicef.org]


Aggregationmethod: Weighted average

Developmentrelevance: Diabetes, an important cause of ill health and a risk factor for other diseases in developed countries, is spreading rapidly in developing countries. Highest among the elderly, prevalence rates are rising among younger and productive populations in developing countries. Economic development has led to the spread of Western lifestyles and diet to developing countries, resulting in a substantial increase in diabetes. Without effective prevention and control programs, diabetes will likely continue to increase.

IndicatorName: Diabetes prevalence (% of population ages 20 to 79)

License_Type: CC BY-4.0

Limitationsandexceptions: The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information.

Longdefinition: 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.

Periodicity: Annual

Source: International Diabetes Federation, Diabetes Atlas.

Topic: Health: Risk factors


Generalcomments: This is the Sustainable Development Goal indicator 5.3.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Female genital mutilation prevalence (%)

Longdefinition: Percentage of women aged 15–49 who have gone through partial or total removal of the female external genitalia or other injury to the female genital organs for cultural or other non-therapeutic reasons.

Source: UNICEF DATA (http://www.data.unicef.org/);  Demographic and Health Surveys (DHS); Multiple Indicator Cluster Surveys (MICS), and other surveys.


Developmentrelevance: Hygiene is closely correlated with human health.   Target 6.2 of the Sustainable Development Goals recognizes that access to facilities allowing good hygiene and sanitation should be universal, and especially important to women and girls, and those in vulnerable situations.   Of the range of hygiene behaviors considered important for health, hand washing with soap and water is a top priority in all settings, and is considered one of the most cost-effective interventions to prevent diarrheal diseases. The availability of a basic handwashing facility is a prerequisite for basic hygiene facilities on premises,  and is a useful proxy for hygienic behavior.

Generalcomments: This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People with basic handwashing facilities including soap and water, rural (% of rural population)

Limitationsandexceptions: Presence of a handwashing station with soap and water does not guarantee that household members consistently wash hands at key times, but is accepted as the most suitable proxy.  Data on handwashing facilities are available for a growing number of low- and middle-income countries after hygiene questions were standardized in international surveys. However, this type of information is not available from most high-income countries, where access to basic handwashing facilities is assumed to be nearly universal.

Longdefinition: The percentage of people living in households that have a handwashing facility with soap and water available on the premises. Handwashing facilities may be fixed or mobile and include a sink with tap water, buckets with taps, tippy-taps, and jugs or basins designated for handwashing. Soap includes bar soap, liquid soap, powder detergent, and soapy water but does not include ash, soil, sand or other handwashing agents.

Source: WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene (washdata.org).

Statisticalconceptandmethodology: Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys.  WHO/UNICEF defines a basic handwashing facility as a device to contain, transport or regulate the flow of water to facilitate handwashing with soap and water in the household.

Topic: Health: Disease prevention


IndicatorName: People with basic handwashing facilities including soap and water, urban (% of urban population)


Generalcomments: This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People with basic handwashing facilities including soap and water (% of population)


DataQuality: UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source included in the joint database. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitation. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME dataset, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available.

Derivationmethod: Survey estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old and WHO Anthro Survey Analyser.

Generalcomments: 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). Estimates are from national survey data. 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.

IndicatorName: Prevalence of underweight, weight for age, female (% of children under 5)

Limitationsandexceptions: Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels.

Longdefinition: 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 2006 Child Growth Standards.

Otherweblinks:
Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO.
https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/
https://www.who.int/publications/i/item/9789241515559

WHO Anthro Survey Analyzer - https://www.who.int/tools/child-growth-standards/software

Source: 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.

Topic: Health: Nutrition


IndicatorName: Prevalence of underweight, weight for age, male (% of children under 5)

Longdefinition: Prevalence of underweight, male, is the percentage of boys 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 2006 Child Growth Standards.


Aggregationmethod: Linear mixed-effect model estimates

IndicatorName: Prevalence of underweight, weight for age (% of children under 5)

Longdefinition: Prevalence of underweight children is the percentage of children 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 2006 Child Growth Standards.


Generalcomments: This indicator represents the risk associated with each pregnancy and is also a Sustainable Development Goal Indicator (3.1.1) for monitoring maternal health.

IndicatorName: Maternal mortality ratio (modeled estimate, per 100,000 live births)

Limitationsandexceptions: The methodology differs from that used for previous estimates, so data should not be compared historically. Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality.

Longdefinition: Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births. The data are estimated with a regression model using information on the proportion of maternal deaths among non-AIDS deaths in women ages 15-49, fertility, birth attendants, and GDP measured using purchasing power parities (PPPs).

Notesfromoriginalsource: Estimates of maternal mortality are presented along with upper and lower  limits of intervals (see footnote) designed to depict the uncertainty of estimates. The intervals are the product of a detailed probabilistic evaluation of the uncertainty attributable to the various components of the estimation process. For estimates derived from the multilevel regression model, the components of uncertainty were divided into two groups: those reflected within the regression model (internal sources), and those due to assumptions or calculations that occur outside the model (external sources). Estimates of the total uncertainty reflect a combination of these various sources.

Source: WHO, UNICEF, UNFPA, World Bank Group, and UNDESA/Population Division. Trends in Maternal Mortality 2000 to 2020. Geneva, World Health Organization, 2023

Statisticalconceptandmethodology:
Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.

Maternal mortality is generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.

The estimates are based on an exercise by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) which consists of World Health Organization (WHO), United Nations Children's Fund (UNICEF), World Bank, and United Nations Population Fund (UNFPA), and include country-level time series data. For countries without complete registration data but with other types of data and for countries with no data, maternal mortality is estimated with a regression model using available national maternal mortality data and socioeconomic information.

Topic: Health: Reproductive health


IndicatorName: Maternal mortality ratio (national estimate, per 100,000 live births)

Limitationsandexceptions: Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. The ratios cannot be assumed to provide an exact estimate of maternal mortality. Some of the figures shown were adjusted by the Maternal Mortality Estimation Inter-Agency Group (MMEIG) to enhance reliability, so that they may differ from the published figures from sources such as household surveys.

Longdefinition: Maternal mortality ratio is the number of women who die from pregnancy-related causes while pregnant or within 42 days of pregnancy termination per 100,000 live births.

Source: The country data compiled, adjusted and used in the estimation model by the Maternal Mortality Estimation Inter-Agency Group (MMEIG). The country data were compiled from the following sources:  civil registration and vital statistics; specialized studies on maternal mortality; population based surveys and censuses; other available data sources including data from surveillance sites.

Statisticalconceptandmethodology:
Reproductive health is a state of physical and mental well-being in relation to the reproductive system and its functions and processes. Means of achieving reproductive health include education and services during pregnancy and childbirth, safe and effective contraception, and prevention and treatment of sexually transmitted diseases. Complications of pregnancy and childbirth are the leading cause of death and disability among women of reproductive age in developing countries.

Maternal mortality ratios are generally of unknown reliability, as are many other cause-specific mortality indicators. Household surveys such as Demographic and Health Surveys attempt to measure maternal mortality by asking respondents about survivorship of sisters. The main disadvantage of this method is that the estimates of maternal mortality that it produces pertain to any time within the past few years before the survey, making them unsuitable for monitoring recent changes or observing the impact of interventions. In addition, measurement of maternal mortality is subject to many types of errors. Even in high-income countries with reliable vital registration systems, misclassification of maternal deaths has been found to lead to serious underestimation.

The national estimates of maternal mortality ratios are based on national surveys, vital registration records, and surveillance data or are derived from community and hospital records.


IndicatorName: People practicing open defecation, rural (% of rural population)

Longdefinition: People practicing open defecation refers to the percentage of the population defecating in the open, such as in fields, forest, bushes, open bodies of water, on beaches, in other open spaces or disposed of with solid waste.


IndicatorName: People practicing open defecation, urban (% of urban population)


IndicatorName: People practicing open defecation (% of population)


Developmentrelevance: Most diarrhea-related deaths are due to dehydration, and many of these deaths can be prevented with the use of oral rehydration salts at home.

IndicatorName: Diarrhea treatment (% of children under 5 receiving oral rehydration and continued feeding)

Limitationsandexceptions: Recommendations for the use of oral rehydration therapy have changed over time based on scientific progress, so it is difficult to accurately compare use rates across countries. Until the current recommended method for home management of diarrhea is adopted and applied in all countries, the data should be used with caution. Also, the prevalence of diarrhea may vary by season. Since country surveys are administered at different times, data comparability is further affected.

Longdefinition: Children with diarrhea who received oral rehydration and continued feeding refer to the percentage of children under age five with diarrhea in the two weeks prior to the survey who received either oral rehydration therapy or increased fluids, with continued feeding.

Source: UNICEF, State of the World's Children, Childinfo, and Demographic and Health Surveys.


IndicatorName: Diarrhea treatment (% of children under 5 who received ORS packet)

Longdefinition: Percentage of children under age 5 with diarrhea in the two weeks preceding the survey who received oral rehydration salts (ORS packets or pre-packaged ORS fluids).


Generalcomments: Estimates of overweight children are 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.

IndicatorName: Prevalence of overweight, weight for height, female (% of children under 5)

Longdefinition: 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 2006 Child Growth Standards.


IndicatorName: Prevalence of overweight, weight for height, male (% of children under 5)

Longdefinition: Prevalence of overweight, male, is the percentage of boys 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 2006 Child Growth Standards.


DataQuality: UNICEF, WHO and the World Bank undertake a joint review for each potential primary data source used as to generate the JME global estimates. The group conducts a review when (at minimum) a final report with full methodological details and results are available, as well as (ideally) a data quality assessment flagging potential limitations. When the raw data are available, they are analysed using the Anthro Survey Analyzer software to produce a standard set of results and data quality outputs against which the review is conducted. Comments are documented in a standard review template extracting methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., weight and height distributions, percentage of cases that were flagged as implausible according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from the data source under review generated based on the standard recommended methodology. These estimates are compared against the reported values, as well as against those from other data sources already included in the JME dataset, to assess the plausibility of the trend before including the new point. Reports that are preliminary, or that lack key details on methodology or results, cannot be reviewed and are left pending until full information is available. The methods used to generate the JME global estimates for stunting and overweight were cross validated to ensure estimates produced by the method are closely aligned to national data points.

Derivationmethod:
National estimates from primary sources (e.g., from household surveys) used to generate the JME global estimates are based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old and WHO Anthro Survey Analyser (WHO, 2019). The JME global estimates are generated using smoothing techniques and covariates applied to quality-assured national data to derive trends and up-to-date estimates. Worldwide and regional estimates are derived as the respective country averages weighted by the countries’ under-five population estimates (UNPD-WPP latest available edition) using annual JME global estimates for 205
Countries and areas.

Generalcomments:
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.

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.

IndicatorName: Prevalence of overweight (modeled estimate, % of children under 5)

Limitationsandexceptions: Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). The JME global estimates for overweight take into account estimates of sampling error around survey estimates. While non-sampling error cannot be accounted for or reviewed in full, when available, a data quality review of weight, height and age measurements from household surveys supports compilation of a time series that is comparable across countries and over time.

Longdefinition: 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 2006 Child Growth Standards.

Otherweblinks:
McLain AC, Frongillo EA, Feng J, Borghi E. Prediction intervals for penalized longitudinal models with multisource summary measures: An application to childhood malnutrition. Stat Med. 2019 Mar 15;38(6):1002-1012. doi: 10.1002/sim.8024. Epub 2018 Nov 14. PMID: 30430613.

Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children’s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO.
https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/
https://www.who.int/publications/i/item/9789241515559

WHO Anthro Survey Analyzer - https://www.who.int/tools/child-growth-standards/software

Periodicity: Every two years

Source: UNICEF, WHO, World Bank: Joint child Malnutrition Estimates (JME).


Aggregationmethod: See SH.STA.OWGH.ME.ZS for aggregation

IndicatorName: Prevalence of overweight, weight for height (% of children under 5)


Developmentrelevance: Mortality rates due to unintentional poisoning remains relatively high in low income countries.  This indicator implicates inadequate management of hazardous chemicals and pollution, and of the effectiveness of a country’s health system.

Generalcomments: This is the Sustainable Development Goal indicator 3.9.3[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to unintentional poisoning (per 100,000 population)

Limitationsandexceptions: Some countries do not have death registration data or sample registration systems.  The estimates on this indicator need to be completed with other type of information for these countries.

Longdefinition:
Mortality rate attributed to unintentional poisonings is the number of deaths from unintentional poisonings in a year per 100,000 population.  Unintentional poisoning can
be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.

Source: World Health Organization, Global Health Observatory Data Repository (http://apps.who.int/ghodata/).

Topic: Health: Mortality


Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.9.3[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to unintentional poisoning, female (per 100,000 female population)

Longdefinition: Mortality rate attributed to unintentional poisonings is the number of female deaths from unintentional poisonings in a year per 100,000 female population.  Unintentional poisoning can be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.


IndicatorName: Mortality rate attributed to unintentional poisoning, male (per 100,000 male population)

Longdefinition:
Mortality rate attributed to unintentional poisonings is the number of male deaths from unintentional poisonings in a year per 100,000 male population. Unintentional poisoning can
be caused by household chemicals, pesticides, kerosene, carbon monoxide and medicines, or can be the result of environmental contamination or occupational chemical exposure.


Developmentrelevance:
Sanitation is fundamental to human development. Many international organizations use hygienic sanitation facilities as a measure for progress in the fight against poverty, disease, and death. Access to proper sanitation is also considered to be a human right, not a privilege, for every man, woman, and child.

Sanitation generally refers to the provision of facilities and services for the safe disposal of human urine and feces. Inadequate sanitation is a major cause of disease world-wide and improving sanitation is known to have a significant beneficial impact on people's health. Basic and safely managed sanitation services can reduce diarrheal disease, and can significantly lessen the adverse health impacts of other disorders responsible for death and disease among millions of children. Diarrhea and worm infections weaken children and make them more susceptible to malnutrition and opportunistic infections like pneumonia, measles and malaria.

The combined effects of inadequate sanitation, unsafe water supply and poor personal hygiene are responsible for many of childhood deaths. Every year, the failure to tackle these deficits results in severe welfare losses - wasted time, reduced productivity, ill health, impaired learning, environmental degradation and lost opportunities. Fundamental behavior changes are required before the use of improved facilities and services can be integrated into daily life. Many hygiene behaviors and habits are formed in childhood and, therefore, school health and hygiene education programs are an important part of water and sanitation improvements.

Most basic sanitation technologies are not expensive to implement. However, those facing the problems of inadequate sanitation may not be aware of either the origin of their ills, or the true costs of poor sanitation and hygiene. As a result, in most of the developing countries those without sanitation are hard to convince of the need to invest scarce resources in sanitation facilities, or of the critical importance of changing long-held habits and unhygienic behaviors. Consequently, the people's representatives - governments and elected political leaders - rarely give sanitation or hygiene improvements the priority that is needed in order to tackle the massive sanitation deficit faced by the developing world.

Children bear the brunt of sanitation-related impacts - their health, nutrition, growth, education, self-respect, and life opportunities suffer as a result of inadequate sanitation. Without improved sanitation, many of the current generation of children in developing countries are unlikely to develop to their full potential. Countries that don't take urgent action to redress sanitation deficiencies will find their future development and prosperity impaired.

Generalcomments:
Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.

This is a disaggregated indicator for Sustainable Development Goal 6.2.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People using safely managed sanitation services, rural (% of rural population)

Limitationsandexceptions: There are three main ways to meet the criteria for having a safely managed sanitation service (People should use improved sanitation facilities that are not shared with other households, and the excreta produced should either be: treated and disposed of in situ; stored temporality and then emptied, transported and treated off-site, or transported through a sewer with wastewater and then treated off-site).  Many countries lack information on either wastewater treatment or the management of on-site sanitation. A national estimate is produced if information is available for the dominant type of sanitation system.  If no information is available, it is assumed that 50 percent is safely managed.  Regional and income group estimates are made when data are available for at least 30 percent of the population.

Longdefinition: The percentage of people using improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite. Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs.

Statisticalconceptandmethodology: Data on drinking water, sanitation and hygiene are produced by the Joint Monitoring Programme of the World Health Organization (WHO) and United Nations Children's Fund (UNICEF) based on administrative sources, national censuses and nationally representative household surveys.  WHO/UNICEF defines safely managed sanitation facilities as improved sanitation facilities that are not shared with other households and where excreta are safely disposed of in situ or transported and treated offsite.  Improved sanitation facilities include flush/pour flush to piped sewer systems, septic tanks or pit latrines: ventilated improved pit latrines, compositing toilets or pit latrines with slabs.


IndicatorName: People using safely managed sanitation services, urban (% of urban population)


Generalcomments:
Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the WHO/UNICEF Joint Monitoring Programme (JMP) for Water Supply, Sanitation and Hygiene.

This is the Sustainable Development Goal indicator 6.2.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: People using safely managed sanitation services (% of population)


IndicatorName: Prevalence of stunting, height for age, female (% of children under 5)

Longdefinition: 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 2006 Child Growth Standards.


IndicatorName: Prevalence of stunting, height for age, male (% of children under 5)

Longdefinition: 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 2006 Child Growth Standards.


Generalcomments:
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.

IndicatorName: Prevalence of stunting, height for age (modeled estimate, % of children under 5)

Longdefinition: 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.


Aggregationmethod: See SH.STA.STNT.ME.ZS for aggregation

IndicatorName: Prevalence of stunting, height for age (% of children under 5)


Generalcomments: This is a sex-disaggregated indicator for Sustainable Development Goal 3.4.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Suicide mortality rate, female (per 100,000 female population)

Longdefinition: Suicide mortality rate is the number of suicide deaths in a year per 100,000 population. Crude suicide rate (not age-adjusted).


IndicatorName: Suicide mortality rate, male (per 100,000 male population)


Generalcomments: This is the Sustainable Development Goal indicator 3.4.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Suicide mortality rate (per 100,000 population)


Developmentrelevance: Road traffic injuries and deaths is a major global public health problem. Road traffic crashes are currently the leading cause of death for children and young adults in the world.  There is a strong association between the risk of road traffic death and the income level of countries.  The burden of road traffic deaths is disproportionately high among low- and middle-income countries.

Generalcomments: This is the Sustainable Development Goal indicator 3.6.1 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality caused by road traffic injury (per 100,000 population)

Longdefinition: Mortality caused by road traffic injury is estimated road traffic fatal injury deaths per 100,000 population.


Developmentrelevance: Unsafe drinking water, unsafe sanitation and lack of hygiene are important causes of death.  Most diarrheal deaths in the world are caused by unsafe water, sanitation or hygiene.  According to the World Health Organization, in addition to diarrea, the following diseases could be prevented if adequate WASH services are provided: malnutrition, intestinal nematode infections, lymphatic filariasis, trachoma, schistosomiasis and malaria.

Generalcomments: This is the Sustainable Development Goal indicator 3.9.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene (per 100,000 population)

Longdefinition: Mortality rate attributed to unsafe water, unsafe sanitation and lack of hygiene is deaths attributable to unsafe water, sanitation and hygiene focusing on inadequate WASH services per 100,000 population. Death rates are calculated by dividing the number of deaths by the total population. In this estimate, only the impact of diarrhoeal diseases, intestinal nematode infections, and protein-energy malnutrition are taken into account.


IndicatorName: Prevalence of wasting, weight for height, female (% of children under 5)

Limitationsandexceptions: Survey estimates come with levels of uncertainty due to both sampling error and non-sampling error (e.g., measurement technical error, recording error etc.,). None of the two sources of errors have been fully taken into account for deriving estimates neither at country nor at regional or worldwide levels. Surveys are carried out in a specific period of the year, usually over a few months. However, this indicator can be affected by seasonality, factors related to food availability (e.g., pre-harvest periods), disease (e.g.,rainy season and diarrhoea, malaria, etc.), and natural disasters and conflicts. Hence, country-year estimates may not necessarily be comparable over time. Consequently, only latest estimates are provided.

Longdefinition: 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 months.


IndicatorName: Prevalence of wasting, weight for height, male (% of children under 5)

Longdefinition: 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 months.


IndicatorName: Prevalence of wasting, weight for height (% of children under 5)

Longdefinition: 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 months.


IndicatorName: Prevalence of severe wasting, weight for height, female (% of children under 5)

Longdefinition: Prevalence of severe wasting, female, is the proportion of girls under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.


IndicatorName: Prevalence of severe wasting, weight for height, male (% of children under 5)

Longdefinition: 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 months.


IndicatorName: Prevalence of severe wasting, weight for height (% of children under 5)

Longdefinition: Prevalence of severe wasting is the proportion of children under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59 months.


Generalcomments: Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.

IndicatorName: Tuberculosis treatment success rate (% of new cases)

Longdefinition: Tuberculosis treatment success rate is the percentage of all new tuberculosis cases (or new and relapse cases for some countries) registered under a national tuberculosis control programme in a given year that successfully completed treatment, with or without bacteriological evidence of success ("cured" and "treatment completed" respectively).

Source: World Health Organization, Global Tuberculosis Report.

Statisticalconceptandmethodology: Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. Data on the success rate of tuberculosis treatment are provided for countries that have submitted data to the WHO. The treatment success rate for tuberculosis provides a useful indicator of the quality of health services. A low rate suggests that infectious patients may not be receiving adequate treatment. An important complement to the tuberculosis treatment success rate is the case detection rate, which indicates whether there is adequate coverage by the recommended case detection and treatment strategy.


IndicatorName: Tuberculosis case detection rate (%, all forms)

Longdefinition: Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.

Notesfromoriginalsource: Estimates are presented with uncertainty intervals (see footnote). When ranges are presented, the lower and higher numbers correspond to the 2.5th and 97.5th centiles of the outcome distributions (generally produced by simulations). For more detailed information, see the original source.

Statisticalconceptandmethodology: Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. This indicator shows the tuberculosis detection rate for all detection methods. Editions before 2010 included the tuberculosis detection rates by DOTS, the internationally recommended strategy for tuberculosis control. Thus data on the case detection rate from 2010 onward cannot be compared with data in previous editions.


Generalcomments:
Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization.

This is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Incidence of tuberculosis (per 100,000 people)

Limitationsandexceptions:
The limited availability of data on health status is a major constraint in assessing the health situation in developing countries. Surveillance data are lacking for many major public health concerns. Estimates of prevalence and incidence are available for some diseases but are often unreliable and incomplete. National health authorities differ widely in capacity and willingness to collect or report information. To compensate for this and improve reliability and international comparability, the World Health Organization (WHO) prepares estimates in accordance with epidemiological models and statistical standards.

Uncertainty bounds for the incidence are available at http://data.worldbank.org

Longdefinition: Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.

Statisticalconceptandmethodology: Tuberculosis is one of the main causes of adult deaths from a single infectious agent in developing countries. In developed countries tuberculosis has reemerged largely as a result of cases among immigrants. Since tuberculosis incidence cannot be directly measured, estimates are obtained by eliciting expert opinion or are derived from measurements of prevalence or mortality.


Developmentrelevance: Financial protection in health is one of two dimensions of Universal Health Coverage (UHC) which is defined as all people being able to access the health services they need without facing financial hardship. As Sustainable Development Goal (SDG) indicator 3.8.2, financial protection forms part of SDG Target 3.8 (UHC) and directly relates to SDG 3 (Ensure healthy lives and promote well-being for all at all ages) and SDG 1 (End poverty in all its forms everywhere). As a component of UHC, it is key to improving the well-being of a country’s population, an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development.

Generalcomments: This indicator is related to Sustainable Development Goal 3.8.2 [https://unstats.un.org/sdgs/metadata/].

IndicatorName: Proportion of population pushed further below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)

Longdefinition: This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Notesfromoriginalsource:
1. The “Pushed further below the poverty line by out-of-pocket health payments” indicators were newly introduced in the 2021 edition of the Global Monitoring Report on Financial Protection in Health (GMR). They represent the number and population shares of people who live in households which have both (a) total consumption (including out-of-pocket health payments) beneath the poverty line and (b) any out-of-pocket payments. Because the households are already poor, any out-of-pocket payments are considered financial hardship. The new indicators replace the “Change in poverty-gap due to out-of-pocket health payments”-indicators used to measure the poverty deepening effect of out-of-pocket payments in previous GMR editions (e.g., SH.UHC.NOP1.ZG and SH.UHC.NOP1.CG). For the measurement of overall medical impoverishment, the new indicators are complementary to the “Pushed below the poverty line by out-of-pocket health payments” indicators reported in all GMRs (e.g., SH.UHC.NOP1.TO and SH.UHC.NOP1.ZS), which represent the number and population share of people who live in households which lie above the poverty line when out-of-pocket health payments are included in consumption, but fall below the poverty line when out-of-pocket health payments are subtracted from consumption.

2. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used.  For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.

3. For details of the definition for out-of-pocket health spending, please see the following publication:   World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.

Shortdefinition: Further impoverishing health spending, 2.15$ (%)

Source: Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/financial-protection)

Statisticalconceptandmethodology: Out-of-pocket payments are those made by people at the time of getting any type of service (preventive, curative, rehabilitative, palliative or long-term care) provided by any type of provider. They include cost-sharing (the part not covered by a third party like an insurer) and informal payments, but they exclude insurance premiums. Out-of-pocket payments exclude any reimbursement by a third party, such as the government, a health insurance fund or a private insurance company. Out-of-pocket payments are defined as catastrophic at the 10% (25%) threshold when they represent 10% (25%) or more of household total consumption or income. They are defined as impoverishing if they push household consumption or income below the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption, or if they are incurred by households already living under the $2.15 or $3.65 ($ 2017 PPP) per day poverty lines or the relative poverty line of 60% of median consumption.

Topic: Health: Universal Health Coverage


IndicatorName: Proportion of population pushed further below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)

Longdefinition: This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Further impoverishing health spending, 3.65$ (%)


IndicatorName: Proportion of population pushed further below the 60% median consumption poverty line by out-of-pocket health care expenditure (%)

Longdefinition:
This indicator shows the fraction of a country’s population living in households whose non-health expenditures are already below the 60% median consumption poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending.
Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Further impoverishing health spending, 60% of median (%)


IndicatorName: Proportion of population pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)

Longdefinition: This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $ 2.15 poverty line, but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Notesfromoriginalsource:
1. For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used.  For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.

2. For details of the definition for out-of-pocket health spending, please see the following publication:   World Health Organization and World Bank. 2021. Global Monitoring Report on Financial Protection in Health 2021.

Shortdefinition: Impoverishing health spending, 2.15$ (%)


IndicatorName: Proportion of population pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)

Longdefinition: This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the $3.65 poverty line, but because of the expenditures is below the poverty line.  Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Impoverishing health spending, 3.65$ (%)


IndicatorName: Proportion of population pushed below the 60% median consumption poverty line by out-of-pocket health expenditure (%)

Longdefinition: This indicator shows the fraction of a country’s population experiencing out-of-pocket health impoverishing expenditures, defined as expenditures without which the household they live in would have been above the 60% median consumption but because of the expenditures is below the poverty line. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Impoverishing health spending, 60% of median (%)


Generalcomments: This is the Sustainable Development Goal indicator 3.8.2[https://unstats.un.org/sdgs/metadata/].

IndicatorName: Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure (%)

Longdefinition: Proportion of population spending more than 10% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Catastrophic Health Expenditure, 10% of total expenditure/income (%)


IndicatorName: Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure (%)

Longdefinition: Proportion of population spending more than 25% of household consumption or income on out-of-pocket health care expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Catastrophic Health Expenditure, 25% of total expenditure/income (%)


Developmentrelevance: Universal Health Coverage (UHC) is about ensuring that all people can access the health services they need without facing financial hardship. It is key to improving the well-being of a country’s population. UHC is also an investment in human capital and a foundational driver of inclusive and sustainable economic growth and development. UHC is a target associated with the Sustainable Development Goals (target 3.8), and it relates directly to Goal 3 (Ensure healthy lives and promote well-being for all at all ages) and to Goal 1 (End poverty in all its forms everywhere).

Generalcomments: This is the Sustainable Development Goal indicator 3.8.1[https://unstats.un.org/sdgs/metadata/].

IndicatorName: UHC service coverage index

Longdefinition: Coverage index for essential health services (based on tracer interventions that include reproductive, maternal, newborn and child health, infectious diseases, noncommunicable diseases and service capacity and access). It is presented on a scale of 0 to 100.

Notesfromoriginalsource: For the aggregated data, World Bank’s historical income classification that was based on data from each respective year was used.  For example, the FY2002 income classification based on the 2000 GNI per capita was used for the 2000 aggregates, the FY2007 income classification based on the 2005 GNI per capita was used for the 2005 aggregates, and so on.

Shortdefinition: UHC service coverage index

Source: Global Health Observatory. Geneva: World Health Organization; 2023. (https://www.who.int/data/gho/data/themes/topics/service-coverage)

Statisticalconceptandmethodology: The Service Coverage Index used to track SDG 3.8.1 includes four indicator categories, namely (1) reproductive, manternal and newborn and child health, (2) infectious diseases, (3) non-communicable diseases and (4) service capacity and access. Each category contains several tracers. The index is constructed from geometric means of the tracer indicators; first, within each of the four categories, and then across the four category-specific means to obtain the final summary index. See Source for details about methodology.


IndicatorName: Proportion of population pushed or further pushed below the $2.15 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)

Longdefinition: This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $2.15 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $2.15 poverty line but fall below the $2.15 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Impoverishing or further impoverishing health spending, 2.15$ (%)


IndicatorName: Proportion of population pushed or further pushed below the $3.65 ($ 2017 PPP) poverty line by out-of-pocket health care expenditure (%)

Longdefinition: This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the $3.65 poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the $3.65 poverty line but fall below the $3.65 poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Impoverishing or further impoverishing health spending, 3.65$ (%)


IndicatorName: Proportion of population pushed or further pushed below the 60% median consumption poverty line by out-of-pocket health expenditure (%)

Longdefinition: This indicator shows the fraction of a country's population who is either (1) living in households whose non-health expenditures are already below the relative poverty line and who as a result are pushed further into poverty by their out-of-pocket health spending or (2) live in households whose total expenditures are above the relative poverty line but fall below the relative poverty line when out-of-pocket health spending is subtracted from total expenditure. Out-of-pocket health expenditure is defined as any spending incurred by a household when any member uses a health good or service to receive any type of care (preventive, curative, rehabilitative, long-term or palliative care); provided by any type of provider; for any type of disease, illness or health condition; in any type of setting (outpatient, inpatient, at home).

Shortdefinition: Impoverishing or further impoverishing health spending, 60% of median (%)


IndicatorName: Newborns protected against tetanus (%)

Longdefinition: Newborns protected against tetanus are the percentage of births by women of child-bearing age who are immunized against tetanus.

Source: WHO and UNICEF (http://www.who.int/immunization/monitoring_surveillance/en/).


Developmentrelevance: Strengthening health financing is one objective of Sustainable Development Goal 3 (SDG target 3.c).  The levels and trends of health expenditure data identify key issues such as weaknesses and strengths and areas that need investment, for instance additional health facilities, better health information systems, or better trained human resources.  Health financing is also critical for reaching universal health coverage (UHC) defined as all people obtaining the quality health services they need without suffering financial hardship (SDG 3.8).  The data on out-of-pocket spending is a key indicator with regard to financial protection and hence of progress towards UHC.

IndicatorName: Current health expenditure (% of GDP)

Longdefinition: Level of current health expenditure expressed as a percentage of GDP.  Estimates of current health expenditures include healthcare goods and services consumed during each year. This indicator does not include capital health expenditures such as buildings, machinery, IT and stocks of vaccines for emergency or outbreaks.

Notesfromoriginalsource: The World Health Organization (WHO) has revised health expenditure data using the new international classification for health expenditures in the revised System of Health Accounts (SHA 2011).  WHO’s Global Health Expenditure Database in this new version is the reference source for health expenditure for international comparison imbedded in a standardized framework.  The SHA 2011 clarifies the financing mechanisms and introduces new dimensions which improve the comparability of health expenditures in the perspective of universal health coverage.

Source: World Health Organization Global Health Expenditure database (http://apps.who.int/nha/database). The data was retrieved on April 15, 2024.

Statisticalconceptandmethodology: The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).  The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.

Topic: Health: Health systems


IndicatorName: Current health expenditure per capita (current US$)

Longdefinition: Current expenditures on health per capita in current US dollars. Estimates of current health expenditures include healthcare goods and services consumed during each year.


IndicatorName: Current health expenditure per capita, PPP (current international $)

Longdefinition: Current expenditures on health per capita expressed in international dollars at purchasing power parity.

Statisticalconceptandmethodology: The health expenditure estimates have been prepared by the World Health Organization (WHO) under the framework of the System of Health Accounts 2011 (SHA 2011). The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making. WHO converted the expenditure data using PPP time series extracted from WDI (based on ICP 2017) and OECD data. Where WDI/OECD data were not available, IMF or WHO estimates were utilized. Detailed metadata are available at <https://apps.who.int/nha/database/Select/Indicators/en>.


IndicatorName: External health expenditure (% of current health expenditure)

Longdefinition: Share of current health expenditures funded from external sources. External sources compose of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country. External sources either flow through the government scheme or are channeled through non-governmental organizations or other schemes.


IndicatorName: External health expenditure per capita (current US$)

Longdefinition: Current external expenditures on health per capita expressed in current US dollars. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country.


IndicatorName: External health expenditure per capita, PPP (current international $)

Longdefinition: Current external expenditures on health per capita expressed in international dollars at purchasing power parity. External sources are composed of direct foreign transfers and foreign transfers distributed by government encompassing all financial inflows into the national health system from outside the country.


IndicatorName: Domestic general government health expenditure (% of current health expenditure)

Longdefinition: Share of current health expenditures funded from domestic public sources for health.  Domestic public sources include domestic revenue as internal transfers and grants, transfers, subsidies to voluntary health insurance beneficiaries, non-profit institutions serving households (NPISH) or enterprise financing schemes as well as compulsory prepayment and social health insurance contributions. They do not include external resources spent by governments on health.


IndicatorName: Domestic general government health expenditure (% of GDP)

Longdefinition: Public expenditure on health from domestic sources as a share of the economy as measured by GDP.


IndicatorName: Domestic general government health expenditure (% of general government expenditure)

Longdefinition: Public expenditure on health from domestic sources as a share of total public expenditure.  It indicates the priority of the government to spend on health from own domestic public resources.


IndicatorName: Domestic general government health expenditure per capita (current US$)

Longdefinition: Public expenditure on health from domestic sources per capita expressed in current US dollars.


IndicatorName: Domestic general government health expenditure per capita, PPP (current international $)

Longdefinition: Public expenditure on health from domestic sources per capita expressed in international dollars at purchasing power parity.


IndicatorName: Out-of-pocket expenditure (% of current health expenditure)

Longdefinition: Share of out-of-pocket payments of total current health expenditures.  Out-of-pocket payments are spending on health directly out-of-pocket by households.

Statisticalconceptandmethodology:
The health expenditure estimates have been prepared by the World Health Organization under the framework of the System of Health Accounts 2011 (SHA 2011).  The Health SHA 2011 tracks all health spending in a given country over a defined period of time regardless of the entity or institution that financed and managed that spending. It generates consistent and comprehensive data on health spending in a country, which in turn can contribute to evidence-based policy-making.

Aggregations are weighted by total current health expenditure (not by population).


IndicatorName: Out-of-pocket expenditure per capita (current US$)

Longdefinition: Health expenditure through out-of-pocket payments per capita in USD.  Out of pocket payments are spending on health directly out of pocket by households in each country.


IndicatorName: Out-of-pocket expenditure per capita, PPP (current international $)

Longdefinition: Health expenditure through out-of-pocket payments per capita in international dollars at purchasing power parity.


IndicatorName: Domestic private health expenditure (% of current health expenditure)

Longdefinition: Share of current health expenditures funded from domestic private sources.  Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.


IndicatorName: Domestic private health expenditure per capita (current US$)

Longdefinition: Current private expenditures on health per capita expressed in current US dollars. Domestic private sources include funds from households, corporations and non-profit organizations. Such expenditures can be either prepaid to voluntary health insurance or paid directly to healthcare providers.


IndicatorName: Domestic private health expenditure per capita, PPP (current international $)

Longdefinition: Current private expenditures on health per capita expressed in international dollars at purchasing power parity.


Developmentrelevance: The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country. Income distribution measures are important background indicators for shared prosperity.

Generalcomments: The World Bank’s internationally comparable poverty monitoring database now draws on income or detailed consumption data from more than 2000 household surveys across 169 countries. See the Poverty and Inequality Platform (PIP) for details (www.pip.worldbank.org).

IndicatorName: Income share held by second 20%

Limitationsandexceptions:
Despite progress in the last decade, the challenges of measuring poverty remain. The timeliness, frequency, quality, and comparability of household surveys need to increase substantially, particularly in the poorest countries. The availability and quality of poverty monitoring data remains low in small states, countries with fragile situations, and low-income countries and even some middle-income countries. The low frequency and lack of comparability of the data available in some countries create uncertainty over the magnitude of poverty reduction.

Besides the frequency and timeliness of survey data, other data quality issues arise in measuring household living standards. The surveys ask detailed questions on sources of income and how it was spent, which must be carefully recorded by trained personnel. Income is generally more difficult to measure accurately, and consumption comes closer to the notion of living standards. And income can vary over time even if living standards do not. But consumption data are not always available: the latest estimates reported here use consumption data for about two-thirds of countries.

However, even similar surveys may not be strictly comparable because of differences in timing or in the quality and training of enumerators. Comparisons of countries at different levels of development also pose a potential problem because of differences in the relative importance of the consumption of nonmarket goods. The local market value of all consumption in kind (including own production, particularly important in underdeveloped rural economies) should be included in total consumption expenditure but may not be. Most survey data now include valuations for consumption or income from own production, but valuation methods vary.

Longdefinition: Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles. Percentage shares by quintile may not sum to 100 because of rounding.

Relatedsourcelinks: World Bank, Poverty and Inequality Platform: https://pip.worldbank.org/

Shortdefinition: Percentage share of income or consumption that accrues to the second quintile.

Source: World Bank, Poverty and Inequality Platform. Data are based on primary household survey data obtained from government statistical agencies and World Bank country departments. Data for high-income economies are mostly from the Luxembourg Income Study database. For more information and methodology, please see http://pip.worldbank.org.

Statisticalconceptandmethodology:
Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.

Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.

The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.

The year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.

Percentage shares by quintile may not sum to 100 because of rounding.

Topic: Poverty: Income distribution


IndicatorName: Income share held by third 20%

Shortdefinition: Percentage share of income or consumption that accrues to the third quintile.


IndicatorName: Income share held by fourth 20%

Shortdefinition: Percentage share of income or consumption that accrues to the fourth quintile.


IndicatorName: Income share held by highest 20%

Shortdefinition: Percentage share of income or consumption that accrues to the fifth (wealthiest) quintile.


IndicatorName: Income share held by highest 10%

Longdefinition: Percentage share of income or consumption is the share that accrues to subgroups of population indicated by deciles or quintiles.

Shortdefinition: Percentage share of income or consumption that accrues to the 10th (wealthiest) decile.

Statisticalconceptandmethodology:
Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.

Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.

The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.

The year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.


Developmentrelevance: The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country. Income distribution measures are important background indicators for shared prosperity. The share living below half the median income is Sustainable Development Goal indicator 10.2.1.

IndicatorName: Proportion of people living below 50 percent of median income (%)

Longdefinition: The percentage of people in the population who live in households whose per capita income or consumption is below half of the median income or consumption per capita. The median is measured at 2017 Purchasing Power Parity (PPP) using the Poverty and Inequality Platform (http://www.pip.worldbank.org). For some countries, medians are not reported due to grouped and/or confidential data. The reference year is the year in which the underlying household survey data was collected. In cases for which the data collection period bridged two calendar years, the first year in which data were collected is reported.


IndicatorName: Income share held by lowest 10%

Shortdefinition: Percentage share of income or consumption that accrues to the first (poorest) decile.

Statisticalconceptandmethodology:
Inequality in the distribution of income is reflected in the share of income or consumption accruing to a portion of the population ranked by income or consumption levels. The portions ranked lowest by personal income receive the smallest shares of total income.

Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to directly calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.

The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption. No adjustment has been made for spatial differences in cost of living within countries, because the data needed for such calculations are generally unavailable. For further details on the estimation method for low- and middle-income economies, see Ravallion and Chen (1996).

Survey year is the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year in which most of the data were collected.


IndicatorName: Income share held by lowest 20%

Shortdefinition: Percentage share of income or consumption that accrues to the first (poorest) quintile.


Developmentrelevance: The World Bank Group is committed to reducing extreme poverty to 3 percent or less, globally, by 2030. Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The World Bank produced its first global poverty estimates for developing countries for World Development Report 1990: Poverty (World Bank 1990) using household survey data for 22 countries (Ravallion, Datt, and van de Walle 1991). Since then there has been considerable expansion in the number of countries that field household income and expenditure surveys.

IndicatorName: Poverty headcount ratio at $2.15 a day (2017 PPP) (% of population)

Longdefinition: Poverty headcount ratio at $2.15 a day is the percentage of the population living on less than $2.15 a day at 2017 purchasing power adjusted prices. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

Shortdefinition: Poverty headcount ratio at $2.15 a day is the percentage of the population living on less than $2.15 a day at 2017 international prices.

Statisticalconceptandmethodology:
International comparisons of poverty estimates entail both conceptual and practical problems. Countries have different definitions of poverty, and consistent comparisons across countries can be difficult. Local poverty lines tend to have higher purchasing power in rich countries, where more generous standards are used, than in poor countries.

Since World Development Report 1990, the World Bank has aimed to apply a common standard in measuring extreme poverty, anchored to what poverty means in the world's poorest countries. The welfare of people living in different countries can be measured on a common scale by adjusting for differences in the purchasing power of currencies. The commonly used $1 a day standard, measured in 1985 international prices and adjusted to local currency using purchasing power parities (PPPs), was chosen for World Development Report 1990 because it was typical of the poverty lines in low-income countries at the time. As differences in the cost of living across the world evolve, the international poverty line has to be periodically updated using new PPP price data to reflect these changes. The last change was in September 2022, when we adopted $2.15 as the international poverty line using the 2017 PPP. Poverty measures based on international poverty lines attempt to hold the real value of the poverty line constant across countries, as is done when making comparisons over time. The $3.65 poverty line is derived from typical national poverty lines in countries classified as Lower Middle Income. The $6.85 poverty line is derived from typical national poverty lines in countries classified as Upper Middle Income.

Early editions of World Development Indicators used PPPs from the Penn World Tables to convert values in local currency to equivalent purchasing power measured in U.S dollars. Later editions used 1993, 2005, and 2017 consumption PPP estimates produced by the World Bank. The current extreme poverty line is set at $2.15 a day in 2017 PPP terms, which represents the mean of the poverty lines found in 15 of the poorest countries ranked by per capita consumption. The new poverty line maintains the same standard for extreme poverty - the poverty line typical of the poorest countries in the world - but updates it using the latest information on the cost of living in developing countries. As a result of revisions in PPP exchange rates, poverty rates for individual countries cannot be compared with poverty rates reported in earlier editions.

The statistics reported here are based on consumption data or, when unavailable, on income surveys.

Topic: Poverty: Poverty rates


IndicatorName: Poverty gap at $2.15 a day (2017 PPP) (%)

Longdefinition: Poverty gap at $2.15 a day (2017 PPP) is the mean shortfall in income or consumption from the poverty line $2.15 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.


Developmentrelevance: The World Bank Group's goal of promoting shared prosperity has been defined as fostering income growth of the bottom 40 per cent of the welfare distribution in every country. Gini coefficients are important background information for shared prosperity.

IndicatorName: Gini index

Limitationsandexceptions:
Gini coefficients are not unique. It is possible for two different Lorenz curves to give rise to the same Gini coefficient. Furthermore it is possible for the Gini coefficient of a developing country to rise (due to increasing inequality of income) while the number of people in absolute poverty decreases. This is because the Gini coefficient measures relative, not absolute, wealth.

Another limitation of the Gini coefficient is that it is not additive across groups, i.e. the total Gini of a society is not equal to the sum of the Gini's for its sub-groups. Thus, country-level Gini coefficients cannot be aggregated into regional or global Gini's, although a Gini coefficient can be computed for the aggregate.

Because the underlying household surveys differ in methods and types of welfare measures collected, data are not strictly comparable across countries or even across years within a country. Two sources of non-comparability should be noted for distributions of income in particular. First, the surveys can differ in many respects, including whether they use income or consumption expenditure as the living standard indicator. The distribution of income is typically more unequal than the distribution of consumption. In addition, the definitions of income used differ more often among surveys. Consumption is usually a much better welfare indicator, particularly in developing countries. Second, households differ in size (number of members) and in the extent of income sharing among members. And individuals differ in age and consumption needs. Differences among countries in these respects may bias comparisons of distribution.

World Bank staff have made an effort to ensure that the data are as comparable as possible. Wherever possible, consumption has been used rather than income. Income distribution and Gini indexes for high-income economies are calculated directly from the Luxembourg Income Study database, using an estimation method consistent with that applied for developing countries.

Longdefinition: Gini index measures the extent to which the distribution of income (or, in some cases, consumption expenditure) among individuals or households within an economy deviates from a perfectly equal distribution. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual or household. The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Shortdefinition: The Gini index measures the extent to which the distribution of income or consumption among individuals or households within an economy deviates from a perfectly equal distribution. A Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

Statisticalconceptandmethodology:
The Gini index measures the area between the Lorenz curve and a hypothetical line of absolute equality, expressed as a percentage of the maximum area under the line. A Lorenz curve plots the cumulative percentages of total income received against the cumulative number of recipients, starting with the poorest individual. Thus a Gini index of 0 represents perfect equality, while an index of 100 implies perfect inequality.

The Gini index provides a convenient summary measure of the degree of inequality. Data on the distribution of income or consumption come from nationally representative household surveys. Where the original data from the household survey were available, they have been used to calculate the income or consumption shares by quintile. Otherwise, shares have been estimated from the best available grouped data.

The distribution data have been adjusted for household size, providing a more consistent measure of per capita income or consumption.

The year reflects the year in which the underlying household survey data were collected or, when the data collection period bridged two calendar years, the year data collection started.


IndicatorName: Poverty headcount ratio at $3.65 a day (2017 PPP) (% of population)

Longdefinition: Poverty headcount ratio at $3.65 a day is the percentage of the population living on less than $3.65 a day at 2017 international prices.


IndicatorName: Poverty gap at $3.65 a day (2017 PPP) (%)

Longdefinition: Poverty gap at $3.65 a day (2017 PPP) is the mean shortfall in income or consumption from the poverty line $3.65 a day (counting the nonpoor as having zero shortfall), expressed as a percentage of the poverty line. This measure reflects the depth of poverty as well as its incidence.


Derivationmethod: The methodology is extensively outlined in the ‘Source’ and the Stata do-files used for each country are published on Oxford Poverty and Human Development Initiative (OPHI) (https://cloud-ophi.qeh.ox.ac.uk/index.php/s/mmkGGBZXimCWkwP)and Human Development Report Office (HDRO)(https://hdr.undp.org/mpi-statistical-programmes) websites.

Developmentrelevance: The global MPI was developed Sabina Alkire and Maria Emma Santos (2014), in collaboration with the Human Development Report Office (HDRO) at UNDP as an internationally comparable measure of acute poverty. Technically, the global MPI relies on the Alkire-Foster method (2011). In 2018, five out of the ten indicators have been revised to better align with the SDGs (see Alkire, Kanagaratnam, Nogales and Suppa 2022; Alkire and Kanagaratnam 2020; Alkire and Jahan 2018).

IndicatorName: Multidimensional poverty headcount ratio (UNDP) (% of population)

Limitationsandexceptions: Besides the frequency and timeliness of survey data, other limitations include if the household survey data being used is missing any of the 10 indicators, that indicator is dropped from the calculation. The weights are then adjusted so that each dimension continues to be given a weight of one-third. MPI poverty estimates are only calculated if at least one indicator in health and education dimensions is available, and if at least four indicators in the living standards dimension are available.

Longdefinition: The multidimensional poverty headcount ratio (UNDP) is the percentage of a population living in poverty according to UNDPs multidimensional poverty index. The index includes three dimensions -- health, education, and living standards.

Othernotes: The global MPI is estimated using comprehensive household survey data, which is largely provided by the Multiple Indicator Cluster Surveys programme (MICS) and the Demographic and Health Surveys (DHS) Program. For a few countries, data from national household surveys that are comparable to DHS and MICS is used.

Referenceperiod: Datasets are incorporated each year from a moving window starting 12 years before the current year (e.g. 2011 for the global MPI in 2023)

Shortdefinition: The multidimensional poverty headcount ratio (UNDP) represents the proportion of a population living in poverty according to UNDPs multidimensional poverty index.

Source: Alkire, S., Kanagaratnam, U., and Suppa, N. (2023). ‘The global Multidimensional Poverty Index (MPI) 2023 country results and methodological note’, OPHI MPI Methodological Note 55, Oxford Poverty and Human Development Initiative (OPHI), University of Oxford. (https://ophi.org.uk/mpi-methodological-note-55-2/)

Statisticalconceptandmethodology:
The global MPI, published annually since 2010, captures acute multidimensional poverty in developing regions of the world (Alkire and Santos, 2014). This measure is based on the dual-cutoff counting methodology developed by Alkire and Foster (2011). The global MPI is composed of three dimensions (health, education, and living standards) and 10 indicators (nutrition, child mortality, school attendance, years of schooling, electricity, drinking water, sanitation, cooking fuel, housing, and assets).

The global MPI begins by constructing a deprivation profile for each household and person in it that tracks deprivations in each of the 10 indicators. For example, a household and all people living in it are deprived if any child is stunted or any child or adult for whom data are available is underweight; if any child died in the past five years; if any school-aged child is not attending school up to the age at which he or she would complete class 8 or no household member has completed six years of schooling; or if the household lacks access to electricity, an improved source of drinking water within a 30 minute walk round trip, an improved sanitation facility that is not shared, nonsolid cooking fuel, durable housing materials, and basic assets such as a radio, animal cart, phone, television, computer, refrigerator, bicycle or motorcycle.

All indicators are equally weighted within each dimension, so the health and education indicators are weighted 1/6 each, and the standard of living indicators are weighted 1/18 each. A person’s deprivation score is the sum of the weighted deprivations she or he experiences. The global MPI identifies people as multidimensionally poor if their deprivation score is 1/3 or higher.

MPI values are the product of the incidence (H, or the proportion of population who live in multidimensional poverty) and intensity of poverty (A, or the average deprivation score among multidimensionally poor people). MPI = H × A.

The MPI ranges from 0 to 1, and higher values imply higher poverty. MPI values decline when fewer people are poor or when poor people have fewer deprivations.


Developmentrelevance: Monitoring poverty is important on the global development agenda as well as on the national development agenda of many countries. The first Sustainable Development Goal calls for ending poverty in all forms by 2030. Poverty is multidimensional, containing both monetary and non-monetary dimensions. The World Bank's multidimensional poverty measure captures both elements, allowing for a more complete tracking of poverty in all forms

IndicatorName: Multidimensional poverty headcount ratio (World Bank) (% of population)

Longdefinition: The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's Multidimensional Poverty Measure. The Multidimensional Poverty Measure includes three dimensions – monetary poverty, education, and basic infrastructure services – to capture a more complete picture of poverty.

Shortdefinition: The multidimensional poverty headcount ratio (World Bank) is the percentage of a population living in poverty according to the World Bank's multidimensional poverty measure.

Statisticalconceptandmethodology:
The World Bank's Multidimensional Poverty Measure (MPM) seeks to understand poverty beyond monetary deprivations by including access to education and basic infrastructure along with the monetary headcount ratio at the $2.15 international poverty line.
The measure takes inspiration and guidance from other prominent global multidimensional measures, particularly the Multidimensional Poverty Index (MPI) developed by the United Nations Development Programme (UNDP) and Oxford University but differs from them in one important aspect: it includes monetary poverty less than $2.15 per day, the new International Poverty Line at 2017 PPP (Purchasing Power Parity), as one of the dimensions.
The MPM is composed of six indicators: consumption or income, educational attainment, educational enrollment, drinking water, sanitation, and electricity. These are mapped into three dimensions of well-being: monetary, education, and basic infrastructure services.
The three MPM dimensions are weighted equally, and within each dimension each indicator is also weighted equally. Individuals are considered multidimensionally deprived if they fall short of the threshold in at least one dimension or in a combination of indicators equivalent in weight to a full dimension. In other words, households will be considered poor if they are deprived in indicators whose weight adds up to 1/3 or more. Because the monetary dimension is measured using only one indicator, anyone who is income poor is automatically also poor under the multidimensional poverty measure.
Summarizing the information on the different deprivations into a single index proves useful in making comparisons across populations and across time. However, any aggregation of indicators into a single index always involves a decision on how each indicator is to be weighted.