sources

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nChildren aged 6-59 months who received vitamin A supplementation (%)\n\nName abbreviated\n\n    \n\nData Type Representation\nPercent\n\nTopic\nHealth service coverage\n\nISO Health Indicators Framework\n\n    \n\nRationale\nSupplementation with vitamin A is considered to be an important intervention for child survival owing to the strong evidence that exists for its impact on reducing child mortality among populations where vitamin A deficiency is prevalent. Therefore, measuring the proportion of children who have received vitamin A within the last 6 months is crucial for monitoring coverage of interventions towards the child survival-related Millennium Development Goals and Strategies.\n\nDefinition\nProportion of children aged 6\u201359 months who received a high-dose vitamin A supplement within the last 6 months.\n\u00a0\nHigh dose vitamin A, according to the International Vitamin A Consultative Group (IVACG) definition, refers to \"doses equal or greater than 25 000 IU\".\n\nAssociated terms\n\n    \n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\n\n    \n\nMethod of measurement\n\n    \n\nMethod of estimation\nWHO\u00a0compiles\u00a0empirical data from nationally-representative household surveys.\n\u00a0\nPredominant type of statistics: adjusted\n\nM&E Framework\nOutcome\n\nMethod of estimation of global and regional aggregates\n\n    \n\nDisaggregation\nAge\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nEducation level : Maternal education\n\nDisaggregation\nWealth : Wealth quintile\n\nDisaggregation\nBoundaries : Administrative regions\n\nDisaggregation\nBoundaries : Health regions\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\n\n    \n\nExpected frequency of data collection\n\n    \n\nLimitations\nThese indicators are usually collected in DHS and MICS surveys; however the accuracy of reporting in household surveys varies and is likely to include recall bias. The comparability of results across countries and over time may therefore be affected. There are also significant discrepancies between data obtained through household surveys and those obtained from National Immunization Days and routine service statistics for this indicator, which are currently under investigation.\n\nLinks\nHow many child deaths can we prevent this year? (Jones et al, 2003)\n\nLinks\nVitamin A deficiency. In: Comparative quantification of health risks: global and regional burden of disease attributable to selected major risk factors. (Rice et al, 2003)\n\nLinks\nDemographic and Health Surveys\n\nLinks\nMultiple Indicator Cluster Surveys\n\nLinks\nThe State of the World's Children (UNICEF)\n\nComments\nThe framework for the discussion and review of child health indicators in the UNICEF/WHO Meeting on Child Survival Survey-based Indicators was the set of prevention and treatment interventions outlined in the Lancet series on child survival.\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDensity of community health workers (per 1 000 population)\n\nName abbreviated\nDensity of community health workers\n\nData Type Representation\nRatio\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\nPreparing the health workforce to work towards the attainment of a country's health objectives represents one of the most important challenges for its health system. Methodologically, there are no gold standards for assessing the sufficiency of the health workforce to address the health care needs of a given population. It has been estimated however, in the World Health Report 2006, that countries with fewer than 23 physicians, nurses and midwives per 10 000 population generally fail to achieve adequate coverage rates for selected primary health care interventions as prioritized by the Millennium Development Goals framework.\n\nDefinition\nNumber of community health workers per 1 000 population.\n\nAssociated terms\nClassification of health workers : The WHO framework for classifying health workers draws on the latest revisions of international classifications for social and economic statistics, including the International Standard Classification of Occupations (2008 revision), the International Standard Classification of Education (1997 revision) and the International Standard Industrial Classification of All Economic Activities (fourth revision).\n\nPreferred data sources\nAdministrative reporting system\n\nPreferred data sources\nHousehold surveys\n\nPreferred data sources\nPopulation census\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nThe method of estimation for number of community health workers (including community health officers, community health-education workers, community health aides, family health workers and associated occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting 'community health worker' as their current occupation (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, staffing records, payroll records, training records, or tallies from other types of routine administrative records on human resources. Ideally, information on the stock of health workers should be assessed through administrative records compiled, updated and reported at least annually, and periodically validated and adjusted against data from a population census or other nationally representative source.\n\nMethod of estimation\nWHO compiles data on health workforce from four major sources: population censuses, labour force and employment surveys, health facility assessments and routine administrative information systems (including reports on public expenditure, staffing and payroll as well as professional training, registration and licensure). Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices\nIn general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database.\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\n\n    \n\nDisaggregation\nAge\n\nDisaggregation\nSex\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nOccupational specialization\n\nDisaggregation\nMain work activity\n\nDisaggregation\nProvider type (public/private)\n\nUnit of Measure\nPersons per 1000 population\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\n\n    \n\nLimitations\nThe classification of health workers is based on criteria for vocational education and training, regulation of health occupations, and the activities and tasks involved in carrying out a job, i.e. a framework for categorizing key workforce variables according to shared characteristics. The roles and activities of community health workers are enormously diverse throughout their history, within and across countries and across programmes.\nWhile much effort has been made to harmonize the data to enhance comparability, the diversity of health worker roles and information sources means that considerable variability remains across countries and over time in the coverage and quality of the original data.\nSome figures may be underestimated or overestimated when it is not possible to distinguish whether the data include health workers in the private sector (for-profit or not-for-profit), double counts of health workers holding two or more jobs at different locations, workers who are unpaid or unregulated but performing health care tasks (e.g. volunteer community health workers), or people with training in services provision working outside the health care sector (e.g. at a teaching institution) or who are not currently engaged in the national health labour market (e.g. unemployed, migrated, retired or withdrawn from the labour force for personal reasons).\n\nLinks\nWHO Global  Health Workforce Statistics database\n\nLinks\nThe world health report 2006 \u2013 working together for health (WHO, 2006)\n\nComments\n\n    \n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDensity of physicians (per 1 000 population)\n\nName abbreviated\nDensity of physicians\n\nData Type Representation\nRatio\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\n\u00a0The WHO Global Strategy on Human Resources for Health: Workforce 2030 sets out the policy agenda to ensure a workforce that is fit for purpose to attain the targets of the Sustainable Development Goals (SDGs). One of its objectives is primarily linked to strengthening data on human resources for health\n\nDefinition\nNumber of medical doctors (physicians), including generalist and specialist medical practitioners, per 1 000 population.\n\nAssociated terms\nClassification of health workers : The WHO framework for classifying health workers draws on the latest revisions of international classifications for social and economic statistics, including the International Standard Classification of Occupations (2008 revision), the International Standard Classification of Education (1997 revision) and the International Standard Industrial Classification of All Economic Activities (fourth revision).\n\nPreferred data sources\nAdministrative reporting system\n\nPreferred data sources\nHousehold surveys\n\nPreferred data sources\nPopulation census\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nThe method of estimation for density of physicians depends on the nature of the original data source. Estimating the number of physicians using population census data is a count of the number of people reporting 'physician' as their current occupation (as classified according to the tasks and duties of their job). A similar method is used for counting physicians from labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, duty rosters, staffing records, payroll records, registries of health professional regulatory bodies, or tallies from other types of routine administrative records on human resources. Ideally, information on the stock of health workers should be assessed through administrative records compiled, updated and reported at least annually, and periodically validated and adjusted against data from a population census or other nationally representative source.\n\nMethod of estimation\n\u00a0WHO compiles data on health workforce from routine administrative information systems (including reports on public expenditure, staffing and payroll as well as professional training, registration and licensure), population censuses, labour force and employment surveys and health facility assessments. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database.\n\u00a0\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\nRegional and global aggregates are based on population-weighted averages weighted by the total number of population. They are presented only if available data cover at least 50% of total population in the regional or global groupings.\n\nDisaggregation\nAge\n\nDisaggregation\nSex\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nOccupational specialization\n\nDisaggregation\nMain work activity\n\nDisaggregation\nProvider type (public/private)\n\nUnit of Measure\nPersons per 1000 population\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\n\n    \n\nLimitations\nThe classification of health workers used here is based on criteria for vocational education and training, regulation of health professions, and the activities and tasks involved in carrying out a job, i.e. a framework for categorizing key workforce variables according to shared characteristics. The WHO framework draws on the latest revisions to the internationally standardized classification systems of the International Labour Organization (International Standard Classification of Occupations), the United Nations Educational, Scientific and Cultural Organization (International Standard Classification of Education) and the United Nations Statistics Division (International Standard Industrial Classification of All Economic Activities). While much effort has been made to harmonize the data to enhance cross-national comparability, the diversity of sources means that considerable variability remains across countries in the coverage, quality and reference year of the original data. In particular, for some countries the available information from official sources does not make it clear whether both the public and private sectors are included. Data derived from population censuses, and on physicians and nursing and midwifery personnel, are generally the most complete and comparable information on human resources in health systems; data on health management and support workers tend to be the least complete. Some figures may be underestimated or overestimated when it is not possible to distinguish whether the data include health workers in the private sector, double counts of health workers holding two or more jobs at different locations, health service providers working outside the health care sector (e.g. nurses working in a school or large private company), workers who are unpaid or unregulated but performing health care tasks (e.g. volunteer community health workers) or people with health vocational training who are not currently engaged in the national health labour market (e.g. unemployed, migrated, retired or withdrawn from the labour force for personal reasons)\n\nLinks\nWHO Global  Health Workforce Statistics database\n\nLinks\nThe world health report 2006 - working together for health (WHO, 2006)\n\nComments\n\n    \n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nPopulation aged over 60 years (%)\n\nName abbreviated\nPopulation > 60 (%)\n\nData Type Representation\nPercent\n\nTopic\nDemographics\n\nISO Health Indicators Framework\n\n    \n\nRationale\n\n    \n\nDefinition\nThe percentage of de facto population aged 60 years and older in a country, area or region as of 1 July of the year indicated.\n\nAssociated terms\n\n    \n\nPreferred data sources\nCivil registration\n\nPreferred data sources\nPopulation census\n\nOther possible data sources\n\n    \n\nMethod of measurement\n\n    \n\nMethod of estimation\nPopulation data are taken from the most recent UN Population Division's \"World Population Prospects\".\n\nM&E Framework\nDeterminant\n\nMethod of estimation of global and regional aggregates\n\n    \n\nDisaggregation\n\n    \n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\n\n    \n\nExpected frequency of data collection\n\n    \n\nLimitations\n\n    \n\nLinks\nWorld Population Prospects (UN Population Division)\n\nComments\n\n    \n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nNumber of environment and public health workers\n\nName abbreviated\nNumber of environment and public health workers\n\nData Type Representation\nCount\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\n\u00a0The WHO Global Strategy on Human Resources for Health: Workforce 2030 sets out the policy agenda to ensure a workforce that is fit for purpose to attain the targets of the Sustainable Development Goals (SDGs). One of its objectives is primarily linked to strengthening data on human resources for health\n\u00a0\n\nDefinition\nTotal number of environment and public health workers in the country\n\nAssociated terms\nClassification of health workers : The WHO framework for classifying health workers draws on the latest revisions of international classifications for social and economic statistics, including the International Standard Classification of Occupations (2008 revision), the International Standard Classification of Education (1997 revision) and the International Standard Industrial Classification of All Economic Activities (fourth revision).\n\nPreferred data sources\nAdministrative reporting system\n\nPreferred data sources\nPopulation census\n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nThe method of estimation for number of environment and public health workers (including environmental and public health officers, environmental and public health technicians, sanitarians, hygienists and related occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting their current occupation in dentistry (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, duty rosters, staffing records, payroll records, registries of health professional regulatory bodies, or tallies from other types of routine administrative records on human resources. Ideally, information on the stock of health workers should be assessed through administrative records compiled, updated and reported at least annually, and periodically validated and adjusted against data from a population census or other nationally representative source.\n\nMethod of estimation\n\u00a0WHO compiles data on health workforce from routine administrative information systems (including reports on public expenditure, staffing and payroll as well as professional training, registration and licensure), population censuses, labour force and employment surveys and health facility assessments. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database.\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\n\n    \n\nDisaggregation\nAge\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nMain work activity\n\nDisaggregation\nOccupational specialization\n\nDisaggregation\nProvider type (public/private)\n\nUnit of Measure\nPersons\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\n\n    \n\nLimitations\nThe classification of health workers is based on criteria for vocational education and training, regulation of health professions, and the activities and tasks involved in carrying out a job, i.e. a framework for categorizing key workforce variables according to shared characteristics.  While much effort has been made to harmonize the data to enhance comparability, the diversity of sources means that considerable variability remains across countries and over time in the coverage and quality of the original data. Some figures may be underestimated or overestimated when it is not possible to distinguish whether the data include health workers in the private sector, double counts of health workers holding two or more jobs at different locations, workers who are unpaid or unregulated but performing health care tasks, or people with education in dental studies working outside the health care sector (e.g. at a research or teaching institution) or who are not currently engaged in the national health labour market (e.g. unemployed, migrated, retired or withdrawn from the labour force for personal reasons).\n\u00a0\n\nLinks\nWHO Global  Health Workforce Statistics database\n\nLinks\n Global Strategy on Human Resources for Health: Workforce 2030 \n\nLinks\nGlobal Strategy on Human Resources for Health: Workforce 2030 \n\nComments\n\n    \n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDensity of environment and public health workers (per 1 000 population)\n\nName abbreviated\nDensity of environment and public health workers\n\nData Type Representation\nRatio\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\nThe WHO Global Strategy on Human Resources for Health: Workforce 2030 sets out the policy agenda to ensure a workforce that is fit for purpose to attain the targets of the Sustainable Development Goals (SDGs). One of its objectives is primarily linked to strengthening data on human resources for health\n\nDefinition\nNumber of environment and public health workers per 1 000 population.\n\nAssociated terms\n\n    \n\nPreferred data sources\nAdministrative reporting system\n\nPreferred data sources\nPopulation census\n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nThe method of estimation for number of environment and public health workers (environmental and public health officers, environmental and public health technicians, sanitarians, hygienists and related occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting their current occupation in dentistry (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, duty rosters, staffing records, payroll records, registries of health professional regulatory bodies, or tallies from other types of routine administrative records on human resources. Ideally, information on the stock of health workers should be assessed through administrative records compiled, updated and reported at least annually, and periodically validated and adjusted against data from a population census or other nationally representative source.\n\nMethod of estimation\n\u00a0WHO compiles data on health workforce from routine administrative information systems (including reports on public expenditure, staffing and payroll as well as professional training, registration and licensure), population censuses, labour force and employment surveys and health facility assessments. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database.\n\u00a0\n\u00a0\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\n\n    \n\nDisaggregation\nAge\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nMain work activity\n\nDisaggregation\nOccupational specialization\n\nDisaggregation\nProvider type (public/private)\n\nUnit of Measure\nPersons per 1000 population\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\n\n    \n\nLimitations\nThe classification of health workers is based on criteria for vocational education and training, regulation of health professions, and the activities and tasks involved in carrying out a job, i.e. a framework for categorizing key workforce variables according to shared characteristics.  While much effort has been made to harmonize the data to enhance comparability, the diversity of sources means that considerable variability remains across countries and over time in the coverage and quality of the original data.  Some figures may be underestimated or overestimated when it is not possible to distinguish whether the data include health workers in the private sector, double counts of health workers holding two or more jobs at different locations, workers who are unpaid or unregulated but performing health care tasks, or people with education in dental studies working outside the health care sector (e.g. at a research or teaching institution) or who are not currently engaged in the national health labour market (e.g. unemployed, migrated, retired or withdrawn from the labour force for personal reasons).\n\nLinks\nWHO Global  Health Workforce Statistics database\n\nComments\n\n    \n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nChildren with suspected pneumonia taken to an appropriate health provider (%)\n\nName abbreviated\nCareseeking ARI\n\nData Type Representation\nPercent\n\nTopic\nHealth service coverage\n\nISO Health Indicators Framework\n\n    \n\nRationale\nAcute respiratory infections (ARI) are a leading cause of deaths of children aged less than 5 years worldwide. Appropriate care of the sick child is defined as providers that can correctly diagnose and treat pneumonia. The proportion of under-fives with ARI that are taken to an appropriate health-care provider is therefore a key indicator for coverage of intervention and care-seeking, and provides critical inputs to the monitoring of progress towards child survival-related Sustainable Development Goals and related strategies.\n\nDefinition\nPercentage of children under 5 years of age with symptoms of pneumonia (cough and difficult breathing NOT due to a problem in the chest and a blocked nose) in the two weeks preceding the survey taken to an appropriate health facility or provider.\n\nAssociated terms\n\n    \n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\nFacility reporting system\n\nMethod of measurement\nMothers or caregivers of children under five years of age are asked if the child had symptoms of acute respiratory infection (ARI),  and if so, whether treatment was sought and where it was sought.   During the UNICEF/WHO Meeting on Child Survival Survey-based Indicators, held in New York, USA, on 17\u201318 June 2004, it was recommended that suspected ARI be described as \u201cpresumed pneumonia\u201d to better reflect the probable cause and the recommended interventions. The definition of ARI used in the DHS and MICS was chosen by the group and is based on the mother\u2019s perceptions of a child who has a cough, is breathing faster than usual with short, quick breaths or is having difficulty breathing, excluding children who had only a blocked nose.\n\nMethod of estimation\nData are taken from UNICEF database (see link below), which compiled data from household surveys such as Demographic and Health Surveys (DHS) and Multiple Indicator Cluster Surveys (MICS).\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\nPopulation-weighted average.\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nEducation level : Maternal education\n\nDisaggregation\nWealth : Wealth quintile\n\nDisaggregation\nSex\n\nUnit of Measure\nN/A\n\nUnit Multiplier\nNot Applicable\n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nEvery 3-5 years\n\nLimitations\nThis indicator is usually collected in DHS and MICS surveys; however, the accuracy of reporting in household surveys varies and is likely to be prone to recall bias. Seasonality related to the prevalence of ARI may also affect the results and their comparability between and within countries.\n\nLinks\nUnicef data: pneumonia\n\nLinks\nCare Seeking Behaviour for Children with Suspected Pneumonia in Countries in Sub-Saharan Africa with High Pneumonia Mortality (Noordam et al, PLOS Med, 2015)\n\nComments\nThe framework for the review of child survival indicators during the UNICEF/WHO Meeting on Child Survival Survey-based Indicators was the set of prevention and treatment interventions outlined in the Lancet series on child survival.\n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nPreterm birth (<37 weeks of gestation) rate per 100 live births\n\nName abbreviated\nPreterm birth rate\n\nData Type Representation\nRate\n\nTopic\nRisk factors\n\nISO Health Indicators Framework\n\n    \n\nRationale\nGlobally, prematurity is the leading cause of newborn deaths and the second leading cause of death after pneumonia in children under the age of five.\n\nDefinition\nThe number of babies born alive before 37 weeks of pregnancy are completed, per 100 live births per year.\n\nAssociated terms\n\n    \n\nPreferred data sources\nCivil registration\n\nPreferred data sources\nPopulation-based surveys\n\nOther possible data sources\nAdministrative reporting system\n\nOther possible data sources\nSpecial studies\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nData from civil registration, surveys, administrative reporting systems/registries: the number of preterm births divided by the total number of live births.\nData from health facilities: the number of preterm births divided by the total number of documented live births in the facility.\n\nMethod of estimation\nFor 13 countries classified as having good vital registration for maternal deaths, using the standard definition for preterm birth, and with data for more than 50% of the years 1990\u20132010 including at least one year before 1995 and one year after 2005, country-level loess regression was used to estimate preterm birth rates.\n\u00a0\nFor all other countries, preterm birth rates were modelled using preterm birth data from the country itself, when available, along with other countries preterm birth data. Where data for continuous predictors were not available for all years 1990-2010 for all the countries, the missing years were interpolated using loess regression or linear interpolation.\n\nM&E Framework\n\n    \n\nMethod of estimation of global and regional aggregates\n\n    \n\nDisaggregation\n\n    \n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nEvery 3-5 years\n\nExpected frequency of data collection\nContinuous\n\nLimitations\nThe reliability of estimates of preterm births depends on the accuracy and completeness of reporting and recording of preterm births. \n\nLinks\n\n    \n\nComments\n\n    \n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDensity of nursing and midwifery personnel (per 1 000 population)\n\nName abbreviated\nDensity of nursing and midwifery personnel \n\nData Type Representation\nRatio\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\n\u00a0The WHO Global Strategy on Human Resources for Health: Workforce 2030 sets out the policy agenda to ensure a workforce that is fit for purpose to attain the targets of the Sustainable Development Goals (SDGs). One of its objectives is primarily linked to strengthening data on human resources for health\n\nDefinition\nNumber of nursing and midwifery personnel per 1 000 population.\n\nAssociated terms\nClassification of health workers : The WHO framework for classifying health workers draws on the latest revisions of international classifications for social and economic statistics, including the International Standard Classification of Occupations (2008 revision), the International Standard Classification of Education (1997 revision) and the International Standard Industrial Classification of All Economic Activities (fourth revision).\n\nPreferred data sources\nAdministrative reporting system\n\nPreferred data sources\nHousehold surveys\n\nPreferred data sources\nPopulation census\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nThe method of estimation for number of nursing and midwifery personnel (including professional nurses, professional midwives, auxiliary nurses, auxiliary midwives, enrolled nurses, enrolled midwives and related occupations such as dental nurses and primary care nurses) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting their current occupation in nursing or midwifery (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, duty rosters, staffing records, payroll records, registries of health professional regulatory bodies, or tallies from other types of routine administrative records on human resources. Ideally, information on the stock of health workers should be assessed through administrative records compiled, updated and reported at least annually, and periodically validated and adjusted against data from a population census or other nationally representative sourc\n\nMethod of estimation\n\u00a0WHO compiles data on health workforce from routine administrative information systems (including reports on public expenditure, staffing and payroll as well as professional training, registration and licensure), population censuses, labour force and employment surveys and health facility assessments. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database.\n\u00a0\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\nRegional and global aggregates are based on population-weighted averages weighted by the total number of population. They are presented only if available data cover at least 50% of total population in the regional or global groupings.\n\nDisaggregation\nAge\n\nDisaggregation\nSex\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nOccupational specialization\n\nDisaggregation\nMain work activity\n\nDisaggregation\nProvider type (public/private)\n\nUnit of Measure\nPersons per 1000 population\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\n\n    \n\nLimitations\nThe classification of health workers is based on criteria for vocational education and training, regulation of health professions, and the activities and tasks involved in carrying out a job, i.e. a framework for categorizing key workforce variables according to shared characteristics. While much effort has been made to harmonize the data to enhance comparability, the diversity of sources means that considerable variability remains across countries and over time in the coverage and quality of the original data.  Some figures may be underestimated or overestimated when it is not possible to distinguish whether the data include health workers in the private sector, double counts of health workers holding two or more jobs at different locations, workers who are unpaid or unregulated but performing health care tasks, or people with training in nursing and midwifery working outside the health care sector (e.g. at a research or teaching institution) or who are not currently engaged in the national health labour market (e.g. unemployed, migrated, retired or withdrawn from the labour force for personal reasons).\n\nLinks\nWHO Global  Health Workforce Statistics database\n\nLinks\nThe world health report 2006 \u2013 working together for health (WHO, 2006)\n\nComments\n\n    \n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDensity of dentistry personnel (per 1 000 population)\n\nName abbreviated\nDensity of dentistry personnel\n\nData Type Representation\nRatio\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\nThe WHO Global Strategy on Human Resources for Health: Workforce 2030 sets out the policy agenda to ensure a workforce that is fit for purpose to attain the targets of the Sustainable Development Goals (SDGs). One of its objectives is primarily linked to strengthening data on human resources for health\n\nDefinition\nNumber of dentistry personnel per 1 000 population.\n\nAssociated terms\nClassification of health workers : The WHO framework for classifying health workers draws on the latest revisions of international classifications for social and economic statistics, including the International Standard Classification of Occupations (2008 revision), the International Standard Classification of Education (1997 revision) and the International Standard Industrial Classification of All Economic Activities (fourth revision).\n\nPreferred data sources\nAdministrative reporting system\n\nPreferred data sources\nHousehold surveys\n\nPreferred data sources\nPopulation census\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nThe method of estimation for number of dentistry personnel (including dentists, dental assistants, dental therapists and related occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting their current occupation in dentistry (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, duty rosters, staffing records, payroll records, registries of health professional regulatory bodies, or tallies from other types of routine administrative records on human resources. Ideally, information on the stock of health workers should be assessed through administrative records compiled, updated and reported at least annually, and periodically validated and adjusted against data from a population census or other nationally representative source.\n\nMethod of estimation\nWHO compiles data on health workforce from routine administrative information systems (including reports on public expenditure, staffing and payroll as well as professional training, registration and licensure), population censuses, labour force and employment surveys and health facility assessments. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database.\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\nRegional and global aggregates are based on population-weighted averages weighted by the total number of population. They are presented only if available data cover at least 50% of total population in the regional or global groupings.\n\nDisaggregation\nAge\n\nDisaggregation\nSex\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nOccupational specialization\n\nDisaggregation\nMain work activity\n\nDisaggregation\nProvider type (public/private)\n\nUnit of Measure\nPersons per 1000 population\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\n\n    \n\nLimitations\nThe classification of health workers is based on criteria for vocational education and training, regulation of health professions, and the activities and tasks involved in carrying out a job, i.e. a framework for categorizing key workforce variables according to shared characteristics.  While much effort has been made to harmonize the data to enhance comparability, the diversity of sources means that considerable variability remains across countries and over time in the coverage and quality of the original data.  Some figures may be underestimated or overestimated when it is not possible to distinguish whether the data include health workers in the private sector, double counts of health workers holding two or more jobs at different locations, workers who are unpaid or unregulated but performing health care tasks, or people with education in dental studies working outside the health care sector (e.g. at a research or teaching institution) or who are not currently engaged in the national health labour market (e.g. unemployed, migrated, retired or withdrawn from the labour force for personal reasons).\n\nLinks\nWHO Global  Health Workforce Statistics database\n\nLinks\nThe world health report 2006 \u2013 working together for health (WHO, 2006)\n\nLinks\nGlobal Strategy on Human Resources for Health: Workforce 2030 \n\nComments\n\n    \n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nChildren aged <5 years with ARI symptoms receiving antibiotics (%)\n\nName abbreviated\n\n    \n\nData Type Representation\nPercent\n\nTopic\nHealth service coverage\n\nISO Health Indicators Framework\n\n    \n\nRationale\nPneumonia accounts for an estimated 15% of deaths among children under five. Appropriate care of the sick child is defined as providers that can correctly diagnose and treat pneumonia. Antibiotics have an essential role in reducing deaths due to pneumonia. Pneumonia prevention and treatment is therefore essential to the achievement of MDG4.\u00a0 \n\u00a0\n\nDefinition\nPercentage of children ages 0-59 months with suspected pneumonia receiving\nantibiotics.\n\nAssociated terms\n\n    \n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\n\n    \n\nMethod of measurement\n\n    \n\nMethod of estimation\nWHO compiles empirical data from household surveys.\n\u00a0\nPredominant type of statistics: adjusted\n\nM&E Framework\nOutcome\n\nMethod of estimation of global and regional aggregates\n\na.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The WHO regional, income-group and global aggregates are population and prevalence weighted from available survey data and may differ from previously reported aggregates.\n\n\nDisaggregation\n\n    \n\nUnit of Measure\n\n    \n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\n\n    \n\nExpected frequency of data collection\n\n    \n\nLimitations\nThis indicator is usually collected in DHS and MICS surveys. It is subject to variation as the denominator \u2013 children with suspected pneumonia in the two weeks preceding the survey \u2013 will vary by season and caretaker reporting and does not always reflect true pneumonia cases.\nIn terms of the numerator, this indicator does not measure timing or dosage of treatment, or the type of antibiotic used.\n\u00a0\nNotably, the responses on antibiotic use will be dependent upon the mother or caretaker\u2019s knowledge about the drugs used to treat the illness and compliance to the treatment.\n\nLinks\nDemographic and Health Surveys\n\nLinks\nMultiple Indicator Cluster Surveys\n\nLinks\nMeasuring coverage in MNCH: Challenges in monitoring the proportion of young children with pneumonia who receive antibiotic treatment. (Campbell H et al; 2013)\n\nLinks\nMeasuring coverage in MNCH: A prospective validation study in Pakistan and Bangladesh on measuring correct treatment of childhood pneumonia. (Tabish H et al; 2013)\n\nComments\nThis indicator constitutes one of the 11 indicators selected to monitor the status of women's and children's health by the Commission on Information and Accountability (CoIA).\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDeaths due to HIV/AIDS (per 100 000 population)\n\nName abbreviated\nDeaths due to HIV/AIDS (per 100 000 population)\n\nData Type Representation\nRate\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nThe HIV/AIDS mortality rates of adults and of children aged less than 15 years are leading indicators of the level of impact of the HIV/AIDS epidemic and of the impact of interventions, particularly the scaling-up of treatment and prevention of mother-to-child transmission in countries with generalized HIV epidemics.\n\nDefinition\nThe estimated number of adults and children that have died due to HIV/AIDS in a specific year, expressed per 100 000 population.\n\nAssociated terms\n\n    \n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSurveillance systems\n\nOther possible data sources\nHousehold surveys\n\nMethod of measurement\n\n    \n\nMethod of estimation\nEmpirical data from different HIV surveillance sources are consolidated to obtain estimates of the level and trend\u00a0 on HIV infection and of mortality in adults and children. Standard methods and tools for HIV estimates that are appropriate to the pattern of the HIV epidemic are used . However, to obtain the best possible estimates, judgement needs to be used as to the quality of the data and how representative it is of the population. UNAIDS and WHO produce country-specific estimates of mortality due to HIV/AIDS every two years. The most recent estimates are presented in the 2008 Report on the Global AIDS epidemics (UNAIDS, 2008).\n\nTo calculate mortality rates, the total population are derived from the World Population Prospects: The 2006 Revision (UN Population Division, 2007).\n\nPredominant type of statistics: predicted\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\n\n    \n\nDisaggregation\nAge\n\nDisaggregation\nSex\n\nUnit of Measure\nDeaths per 100 000 population\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nBiennial (Two years)\n\nExpected frequency of data collection\n\n    \n\nLimitations\nAlthough many countries have collected information on mortality in adults and children in recent years, underreporting is a feature of systems in many countries, partly owing to stigma and lack of diagnosis. It is crucial that civil registration systems (completeness of registration) and survey data-collection are of high quality. WHO does estimate the level of underestimation of civil registration systems and there clearly is substantial variation in data quality and consistency between countries.\n\nLinks\nHIV/AIDS Data and Statistics (WHO)\n\nLinks\nImproved data, methods and tools for the 2007 HIV and AIDS estimates and projections (Sex Transm Infect, August 2008, Volume 84, Issue Suppl 1 )\n\nLinks\nReport on the global AIDS epidemic\n\nComments\n\n    \n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nBirths by caesarean section (%)\n\nName abbreviated\nBirths by caesarean section\n\nData Type Representation\nPercent\n\nTopic\nHealth service coverage\n\nISO Health Indicators Framework\n\n    \n\nRationale\nThe percentage of births by caesarean section is an indicator of access to and use of health care during childbirth.\n\nDefinition\nPercentage of births by caesarean section among all live births in a given time period.\n\nAssociated terms\nLive birth : The complete expulsion or extraction from its mother of a product of conception, irrespective of the duration of the pregnancy, which, after such separation, breathes or shows any other evidence of life such as beating of the heart, pulsation of the umbilical cord, or definite movement of voluntary muscles, whether or not the umbilical cord has been cut or the placenta is attached. (ICD-10)\n\nPreferred data sources\nFacility reporting system\n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\n\n    \n\nMethod of measurement\nHousehold surveys: birth history\u2014detailed questions on the last-born child or all children a woman has given birth to during a given period preceding the survey (usually 3 to 5 years), including characteristics of the birth(s). The number of live births to women surveyed provides the denominator.\n\u00a0\nService or facility records: the number of women having given birth by caesarean section (numerator). Census projections or, in some cases, vital registration data can be used to provide the denominator (numbers of live births).\n\nMethod of estimation\nWHO compiles\u00a0empirical data from household surveys and facility reporting systems for this indicator.\n\u00a0\nPredominant type of statistics: adjusted\n\nM&E Framework\nOutcome\n\nMethod of estimation of global and regional aggregates\nRegional estimates are weighted averages of the country data, using the number of live births for the reference year in each country as the weight. No figures are reported if less than 50 per cent of live births in the region are covered.\n\nDisaggregation\n\n    \n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\nThis indicator does not provide information on the reason for undergoing caesarean section, and includes caesarean sections that were performed without a clinical indication as well as those that were medically indicated. The extent to which caesarean sections are performed according to clinical need, therefore, is not possible to determine.\n\nLinks\nThe world health report 2005\u2014make every mother and child count (WHO, 2005)\n\nLinks\nDemographic and Health Surveys\n\nLinks\nReproductive health indicators: Guidelines for their generation, interpretation and analysis for global monitoring (WHO, 2006)\n\nLinks\nReproductive Health Monitoring and Evaluation (WHO)\n\nComments\nValues lower than 5% may indicate that an insufficient number of caesarean sections are being conducted, and that there may be some women who need a caesarean section but do not receive it.\n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nHospital beds (per 10 000 population)\n\nName abbreviated\nHospital beds (per 10 000 population)\n\nData Type Representation\nRatio\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\n\n    \n\nDefinition\nThe number of hospital beds available per every 10 000 inhabitants in a population.\n\nAssociated terms\n\n    \n\nPreferred data sources\n\n    \n\nOther possible data sources\n\n    \n\nMethod of measurement\n\n    \n\nMethod of estimation\nData were compiled from the WHO Regional offices and modified to standardize the unit of measure of per 10 000 population.\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\nRegional and global estimates are based on population-weighted averages weighted by the total population. These estimates are presented only if available data cover at least 50% of total population in the regional or global groupings.\n\nDisaggregation\n\n    \n\nUnit of Measure\n\n    \n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\n\n    \n\nExpected frequency of data collection\n\n    \n\nLimitations\nStatistics on hospital bed density are generally drawn from routine administrative records but in some settings only public sector beds are included.\n\nLinks\nEuropean Health for All Database (WHO Regional Office for Europe)\n\nLinks\nCountry Health Information Profiles (WHO Regional Office for Western Pacific)\n\nLinks\nCore Health Indicators and MDGs (WHO Regional Office for South-East Asia)\n\nLinks\nRegional Core Health Data Initiative (PAHO)\n\nComments\nHospital beds are used to indicate the availability of inpatinet services. There is no global norm for the density of hospital beds in relation to total population.\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nChildren aged <5 years with diarrhoea receiving oral rehydration therapy (%)\n\nName abbreviated\n\n    \n\nData Type Representation\nPercent\n\nTopic\nHealth service coverage\n\nISO Health Indicators Framework\n\n    \n\nRationale\nDiarrhoeal diseases remain one of the major causes of mortality among under-fives, accounting for more than 600 000 child deaths worldwide, despite all the progress in its management and the undeniable success of the oral rehydration therapy (ORT). Therefore monitoring of the coverage of this very cost-effective intervention is crucial for the monitoring of progress towards the child survival-related Millennium Development Goals and Strategies.\n\nDefinition\nProportion of children aged 0\u201359 months who had diarrhoea in the\u00a0previous 2 weeks and were treated with oral rehydration salts or an appropriate household solution (ORT).\nAccording to DHS, the term(s) used for diarrhoea should encompass the expressions used for all forms of diarrhoea, including bloody stools (consistent with dysentery), watery stools, etc. It encompasses the mother`s definition as well as the \u2018local term(s)\u2019.\nThe definition of \"appropriate household solution\" may vary between countries.\n\u00a0\n\nAssociated terms\n\n    \n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\n\n    \n\nMethod of measurement\n\n    \n\nMethod of estimation\nWHO compiles empirical data from household surveys.\n\u00a0\nPredominant type of statistics: adjusted\n\nM&E Framework\nOutcome\n\nMethod of estimation of global and regional aggregates\n\na.\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0\u00a0 The WHO regional, income-group and global aggregates are population and prevalence weighted from available survey data and may differ from previously reported aggregates.\n\n\nDisaggregation\nAge\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nEducation level : Maternal education\n\nDisaggregation\nWealth : Wealth quintile\n\nDisaggregation\nBoundaries : Administrative regions\n\nDisaggregation\nBoundaries : Health regions\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\n\n    \n\nExpected frequency of data collection\n\n    \n\nLimitations\nThese indicators are usually collected in DHS and MICS surveys; however, the accuracy of reporting in household surveys varies and is likely to be prone to recall bias. Also, seasonal influences related to the prevalence of diarrhoeal disease may affect the results of data collection for this indicator. The comparability of results across countries and over time may therefore be affected. Frequent changes in the definition of this indicator have seriously compromised the ability to reliably assess trends over time.\n\u00a0\nThere are two specific limitations with some of the associated terms of this indicator: \n1.\u00a0Discussions have been held on whether treated should be considered when the electrolyte solution was \u2018given\u2019, \u2018received\u2019, \u2018ingested\u2019, or \u2018offered\u2019 to the child; and \n2.\u00a0Comparability of data on appropriate household solution.\n\nLinks\nHow many child deaths can we prevent this year? (Jones et al, 2003)\n\nLinks\nFactors associated with trends in infant and child mortality in developing countries during the 1990s (Rutstein, 2000)\n\nLinks\nReducing deaths from diarrhoea through oral rehydration therapy (Victora et al, 2000)\n\nLinks\nUse of oral rehydration therapy in acute watery diarrhoea (Sack, 1991)\n\nLinks\nChild Morbidity and Treatment Patterns (DHS, 1991)\n\nLinks\nDemographic and Health Surveys\n\nLinks\nMultiple Indicator Cluster Surveys\n\nLinks\nThe State of the World's Children (UNICEF)\n\nComments\nThe framework for the discussion and review of child health indicators in the UNICEF/WHO Meeting on Child Survival Survey-based Indicators was the set of prevention and treatment interventions outlined in the Lancet series on child survival.\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDensity of pharmaceutical personnel (per 1 000 population)\n\nName abbreviated\nDensity of pharmaceutical personnel\n\nData Type Representation\nRatio\n\nTopic\nHealth systems resources\n\nISO Health Indicators Framework\n\n    \n\nRationale\n\u00a0The WHO Global Strategy on Human Resources for Health: Workforce 2030 sets out the policy agenda to ensure a workforce that is fit for purpose to attain the targets of the Sustainable Development Goals (SDGs). One of its objectives is primarily linked to strengthening data on human resources for health\n\nDefinition\nNumber of pharmaceutical personnel per 1 000 population\n\nAssociated terms\n\n    \n\nPreferred data sources\nAdministrative reporting system\n\nPreferred data sources\nPopulation census\n\nPreferred data sources\nHousehold surveys\n\nOther possible data sources\nHealth facility assessments\n\nMethod of measurement\nThe method of estimation for number of pharmaceutical personnel (including pharmacists, pharmaceutical assistants, pharmaceutical technicians\u00a0and related occupations) depends on the nature of the original data source. Enumeration based on population census data is a count of the number of people reporting their current occupation in dentistry (as classified according to the tasks and duties of their job). A similar method is used for estimates based on labour force survey data, with the additional application of a sampling weight to calibrate for national representation. Data from health facility assessments and administrative reporting systems may be based on head counts of employees, duty rosters, staffing records, payroll records, registries of health professional regulatory bodies, or tallies from other types of routine administrative records on human resources. Ideally, information on the stock of health workers should be assessed through administrative records compiled, updated and reported at least annually, and periodically validated and adjusted against data from a population census or other nationally representative source.\n\nMethod of estimation\n\u00a0WHO compiles data on health workforce from routine administrative information systems (including reports on public expenditure, staffing and payroll as well as professional training, registration and licensure), population censuses, labour force and employment surveys and health facility assessments. Most of the data from administrative sources are derived from published national health sector reviews and/or official country reports to WHO offices In general, the denominator data for workforce density (i.e. national population estimates) are obtained from the United Nations Population Division's World Population Prospects database.\n\nM&E Framework\nOutput\n\nMethod of estimation of global and regional aggregates\nRegional and global aggregates are based on population-weighted averages weighted by the total number of population. They are presented only if available data cover at least 50% of total population in the regional or global groupings.\n\nDisaggregation\nAge\n\nDisaggregation\nLocation (urban/rural)\n\nDisaggregation\nMain work activity\n\nDisaggregation\nOccupational specialization\n\nDisaggregation\nProvider type (public/private)\n\nUnit of Measure\nPersons per 1000 population\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\n\n    \n\nLimitations\nThe classification of health workers is based on criteria for vocational education and training, regulation of health professions, and the activities and tasks involved in carrying out a job, i.e. a framework for categorizing key workforce variables according to shared characteristics.  While much effort has been made to harmonize the data to enhance comparability, the diversity of sources means that considerable variability remains across countries and over time in the coverage and quality of the original data.  Some figures may be underestimated or overestimated when it is not possible to distinguish whether the data include health workers in the private sector, double counts of health workers holding two or more jobs at different locations, workers who are unpaid or unregulated but performing health care tasks, or people with education in dental studies working outside the health care sector (e.g. at a research or teaching institution) or who are not currently engaged in the national health labour market (e.g. unemployed, migrated, retired or withdrawn from the labour force for personal reasons).\n\nLinks\nWHO Global  Health Workforce Statistics database\n\nComments\n\n    \n\nContact Person\n\n      \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}

{
    "link": "http://apps.who.int/gho/data/node.home",
    "retrievedDate": "23-March-18",
    "additionalInfo": "Indicator name\nDistribution of causes of death among children aged <5 years (%)\n\nName abbreviated\nDistribution of causes of death among children aged <5 years (%)\n\nData Type Representation\nPercent\n\nTopic\nMortality\n\nISO Health Indicators Framework\n\n    \n\nRationale\nEfforts to improve child survival can be effective only if they are based on reasonably accurate information about the causes of childhood deaths. Cause-of-death information is needed to prioritize interventions and plan for their delivery, to determine the effectiveness of disease-specific interventions, and to assess trends in disease burden in relation to national and international goals.\n\nDefinition\nDistribution of main causes of death among children aged < 5 years, expressed as percentage of total deaths.\n\nThe causes of death refers to the concept of the 'underlying cause of death' as defined by ICD-10 (WHO, 1992).\n\nAssociated terms\nUnderlying cause of death : a) the disease or injury which initiated the train of morbid events leading directly to death, or (b) the circumstances of the accident or violence which produced the fatal injury (ICD-10)\u00a0\n\nPreferred data sources\nCivil registration with complete coverage and medical certification of cause of death\n\nOther possible data sources\nSpecial studies\n\nMethod of measurement\nData from civil registration with complete coverage (80% or over) and medical certification of cause of death, or nationally representative epidemiological studies of causes of child death (special studies analysing causes of death based on verbal autopsy studies or other sources for countries without civil registration data).\n\n\u00a0\n\nMethod of estimation\nEstimates of child causes of death were prepared by WHO and the Maternal Child Epidemiology Estimation group (MCEE).\n\u00a0\nWHO regularly receives mortality-by-cause data from Member States, as recorded in national civil registration systems. These statistics are evaluated for their completeness and quality. Complete and nationally-representative data were then grouped by ICD codes into the cause categories, and the proportions of these causes with regard to the total number of deaths of children aged less than 5 years were then computed.\n\u00a0\nFor low mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to death registration data. For high mortality countries without adequate vital registration data, the cause distribution was estimated using a multinomial model applied to (largely) verbal autopsy (VA) data from research studies. Cause-specific under-five mortality estimates from the WHO technical programmes and UNAIDS were taken into account in assigning the distribution of deaths to specific causes. A variety of methods were used by MCEE and WHO to develop country- and regional-level cause-specific mortality estimates.\n\u00a0\nFor more details on MCEE/WHO methodology to estimate child causes of death, please click here.\n\u00a0\nPredominant type of statistics: predicted and adjusted.\n\u00a0\n\nM&E Framework\nImpact\n\nMethod of estimation of global and regional aggregates\nAggregation of estimates for WHO Member States\n\nDisaggregation\nAge\n\nUnit of Measure\nN/A\n\nUnit Multiplier\n\n    \n\nExpected frequency of data dissemination\nAnnual\n\nExpected frequency of data collection\nAnnual\n\nLimitations\n\n    \n\nLinks\nGlobal Health Estimates (WHO website)\n\nComments\nA\u00a0better understanding of the indirect contributions of diseases to child deaths is needed in order to assess disease control priorities and evaluate interventions.\n\u00a0\n\nContact Person\n\n    \n\n",
    "dataPublishedBy": "World Health Organization Global Health Observatory (GHO)",
    "dataPublisherSource": null
}