SDG Metadata/3-8-1 — English @ Hosted Weblate

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None Resource updated SDG Metadata 3-8-1 English	The “`translations-metadata/en/3-8-1.yml`” file was changed. a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p><strong>URL: </strong><a href="https://www.who.int/health-topics/universal-health-coverage">https://www.who.int/health-topics/universal-health-coverage</a></p> <p> </p> <p><strong>References: </strong><a href="https://www.who.int/publications/i/item/tracking-universal-health-coverage">https://www.who.int/publications/i/item/tracking-universal-health-coverage</a> </p> <p><a href="http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf" target="_blank"><u>http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf</u></a> </p> <p><a href="https://www.who.int/health-topics/universal-health-coverage">https://www.who.int/health-topics/universal-health-coverage</a></p> <p>For historical development of methods, see: </p> <p><a href="https://www.who.int/publications/i/item/9789241565264">https://www.who.int/publications/i/item/9789241565264</a> </p> <p><a href="https://www.who.int/publications/i/item/monitoring-progress-towards-universal-health-coverage-at-country-and-global-levels-framework-measures-and-targets">https://www.who.int/publications/i/item/monitoring-progress-towards-universal-health-coverage-at-country-and-global-levels-framework-measures-and-targets</a> </p> <p><a href="http://collections.plos.org/uhc2014" target="_blank"><u>http://collections.plos.org/uhc2014</u></a> </p> <p> </p> <p>Annex 1: Metadata for tracer indicators used to measure the coverage of essential health services for monitoring SDG indicator 3.8.1.</p> <p>Please send any comments or queries to: uhc_stats@who.int</p> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Family planning</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of women of reproductive age (15−49 years) who are married or in-union who have their need for family planning satisfied with modern methods.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of women aged 15-49 who are married or in-union who are currently using, or whose partner is currently using a modern method of contraception </p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of women aged 15-49 who are married or in-union with a need for family planning </p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based health surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Household surveys include a series of questions to measure the modern contraceptive prevalence rate and need for family planning. The number of women with a need for family planning is defined as the sum of the number of women of reproductive age (15–49 years) who are married or in a union and who are currently using, or whose sexual partner is currently using, at least one contraceptive method (modern or traditional), and the number of women of reproductive age with an unmet need for family planning. Unmet need for family planning is the proportion of women of reproductive age (15–49 years) either married or in a consensual union, who are fecund and sexually active but who are not using any method of contraception (modern or traditional), and report not wanting any more children or wanting to delay the birth of their next child for at least two years. Included are:</p> <ol> <li>all pregnant women (married or in a consensual union) whose pregnancies were unwanted or mistimed at the time of conception;</li> <li>all postpartum amenorrhoeic women (married or in consensual union) who are not using family planning and whose last birth was unwanted or mistimed;</li> <li>all fecund women (married or in consensual union) who are neither pregnant nor postpartum amenorrhoeic, and who either do not want any more children (want to limit family size), or who wish to postpone the birth of a child for at least two years or do not know when or if they want another child (want to space births), but are not using any contraceptive method.</li> </ol> <p>Modern methods include female and male sterilization, the intra-uterine device (IUD), the implant, injectables, oral contraceptive pills, male and female condoms, vaginal barrier methods (including the diaphragm, cervical cap and spermicidal foam, jelly, cream and sponge), lactational amenorrhea method (LAM), emergency contraception and other modern methods not reported separately.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>The United Nations Population Division produces a systematic and comprehensive series of annual estimates and projections of the proportion of need for family planning among women of reproductive age (15-49) satisfied with modern methods. A Bayesian hierarchical model is applied to a comprehensive global dataset of a country-specific data to generate the estimates and projections. The model accounts for differences by data source, sample population, and survey questions. </p> <p>See here for details: </p> <p><a href="https://www.un.org/development/desa/pd/data/family-planning-indicators">https://www.un.org/development/desa/pd/data/family-planning-indicators</a> </p> <p>Data compilation of country-specific survey data in World Contraceptive Use: </p> <p><a href="https://www.un.org/development/desa/pd/node/3285">https://www.un.org/development/desa/pd/node/3285</a> </p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td></td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Pregnancy care</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times </p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of women aged 15−49 years with a live birth in a given time period who received antenatal care four or more times</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of women aged 15−49 years with a live birth in the same period.</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys and routine facility information systems.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Data on four or more antenatal care visits is based on questions that ask if and how many times the health of the woman was checked during pregnancy. Household surveys that can generate this indicator include DHS, MICS, RHS and other surveys based on similar methodologies. Service/facility reporting systems can be used where the coverage is high, usually in higher income countries.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>WHO maintains a data base on coverage of antenatal care: <a href="http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4"><u>http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4</u></a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Ideally this indicator would be replaced with a more comprehensive measure of pregnancy care, for example the proportion of women who have a skilled provider attend the birth or an institutional delivery. A challenge in measuring skilled attendance at birth is determining which providers are “skilled”. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Child immunization</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Children 1 year of age who have received three doses of diphtheria-tetanus-pertussis containing vaccine</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>All children 1 year of age</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys and facility information systems.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>For survey data, the vaccination status of children aged 12–23 months is collected from child health cards or, if there is no card, from recall by the care-taker. For administrative data, the total number of doses administered to the target population is extracted.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Together, WHO and UNICEF derive estimates of DTP3 coverage based on data officially reported to WHO and UNICEF by Member States, as well as data reported in the published and grey literature. They also consult with local experts - primarily national EPI managers and WHO regional office staff - for additional information regarding the performance of specific local immunization services. Based on the available data, consideration of potential biases, and contributions from local experts, WHO/UNICEF determine the most likely true level of immunization coverage. </p> <p>For details, see here: </p> <p> <a href="https://www.who.int/teams/immunization-vaccines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimates-of-national-immunization-coverage">https://www.who.int/teams/immunization-vaccines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimates-of-national-immunization-coverage</a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>There is variability in national vaccine schedules across countries. Given this, one option for monitoring full child immunization is to monitor the fraction of children receiving vaccines included in their country’s national schedule. A second option, which may be more comparable across countries and time, is to monitor DTP3 coverage as a proxy for full child immunization. Diphtheria-tetanus-pertussis containing vaccine often includes other vaccines, e.g., against Hepatitis B and Haemophilus influenza type B, and is a reasonable measure of the extent to which there is a robust vaccine delivery platform within a country. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Child treatment </p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of children younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey </p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>The indicator is captured by household surveys including DHS, MICS and other national population-based surveys and is intended for use in high under-5 mortality settings to monitor efforts to reduce mortality from acute respiratory infections (including pneumonia) which are a leading cause of death for children under the age of 5 years. The Child Health Accountability Tracking Technical Advisory Group (CHAT TAG), convened by WHO and UNICEF, has ratified this indicator and is working to standardize its use across household surveys.</p> <p>WHO/UNICEF maintains a database of country-level observations from household surveys that can be accessed here: <a href="https://data.unicef.org/topic/child-health/pneumonia/">https://data.unicef.org/topic/child-health/pneumonia/</a> </p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>UNICEF and WHO maintain a data base on this indicator and work on ensuring that values presented are comparable, using the same indicator definition.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>This indicator is not typically measured in higher income countries with well-established health systems. </p> <p>For countries without observed data, coverage was estimated from a regression that predicts coverage of care-seeking for symptoms of acute respiratory infection (on the logit scale), obtained from the WHO data base described above, as a function of the log of the estimated under-five all-causes mortality rate, which can be found here: <a href="https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates">https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates</a></p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Tuberculosis treatment</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of incidence TB cases that are detected and treated in a given year</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of new and relapse cases detected and treated in a given year </p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of new and relapse cases in the same year</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Facility information systems, surveillance systems, population-based health surveys with TB diagnostic testing, TB register and related quarterly reporting system (or electronic TB registers)</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>This indicator requires two main inputs:</p> <p>(1) The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO in a given year.</p> <p>(2) The number of incident TB cases for the same year, typically estimated by WHO.</p> <p>The final indicator = (1)/(2) </p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and information from death (vital) registration systems. Estimates of incidence for each country are derived, using one or more of the following approaches depending on available data:</p> <p>1. incidence = case notifications/estimated proportion of cases detected;</p> <p>2. incidence = prevalence/duration of condition;</p> <p>3. incidence = deaths/proportion of incident cases that die. </p> <p>Dynamic and statistical models were introduced to produce estimates for 2020 and 2021 that account for the major disruptions to the provision of and access to TB diagnostic and treatment services that have occurred in the context of the coronavirus (COVID-19) pandemic.</p> <p>These estimates of TB incidence are combined with country-reported data on the number of cases detected and treated, and the percentage of cases successfully treated, as described above.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>To compute the indicator using WHO estimates, one can access necessary files here: <a href="http://www.who.int/tb/country/data/download/en/"><u>http://www.who.int/tb/country/data/download/en/</u></a>, and compute the indicator as = c_cdr </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>HIV treatment</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of adults and children living with HIV currently receiving antiretroviral therapy (ART)</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of adults and children who are currently receiving ART at the end of the reporting period</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of adults and children living with HIV during the same period</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Facility reporting systems, sentinel surveillance sites, population-based surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Numerator: The numerator is generated by counting the number of adults and children who received ART at the end of the reporting period. Data can be collected from facility-based ART registers or drug supply management systems. These are then tallied and transferred to cross sectional monthly or quarterly reports which will then be aggregated for national totals. Patients receiving ART in the private sector and public sector should be included in the numerator.</p> <p>Denominator: Data on the number of people with HIV infection may come from epidemic models and population-based surveys or, as is common in sub-Saharan Africa, surveillance systems based on antenatal care clinics.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Estimates of antiretroviral treatment coverage among people living with HIV for 2000-2018 are derived as part of the 2019 UNAIDS' estimation round. </p> <p>To estimate the number of people living with HIV across time in high burden countries, UNAIDS in collaboration with countries use an epidemic model (Spectrum) that combines surveillance data on prevalence with the current number of patients receiving ART and assumptions about the natural history of HIV disease progression. </p> <p>Since ART is now recommended for all individuals living with HIV, monitoring ART coverage is less complicated than before, when only those with a certain level of disease severity were eligible to receive ART.</p> <p>Estimates of ART coverage can be found here: <a href="https://www.who.int/data/gho/data/indicators/indicator-details/GHO/estimated-antiretroviral-therapy-coverage-among-people-living-with-hiv-(-)">https://www.who.int/data/gho/data/indicators/indicator-details/GHO/estimated-antiretroviral-therapy-coverage-among-people-living-with-hiv-(-)</a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Comparable estimates of ART coverage in high income countries, in particular time trends, are not always available.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Malaria prevention</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of population in malaria-endemic areas who slept under an ITN the previous night.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of people in malaria-endemic areas who slept under an ITN.</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of people in malaria endemic areas.</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Data on household access and use of ITNs come from nationally representative household surveys such as Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and Malaria Indicator Surveys. Data on the number of ITNs delivered by manufacturers to countries are compiled by Milliner Global Associates, and data on the number of ITNs distributed within countries are reported by National Malaria Control Programs.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Many recent national surveys report the number of ITNs observed in each respondent household. Ownership rates can be converted to the proportion of people sleeping under an ITN using a linear relationship between access and use that has been derived from 62 surveys that collect information on both indicators. </p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Mathematical models can be used to combine data from household surveys on access and use with information on ITN deliveries from manufacturers and ITN distribution by national malaria programmes to produce annual estimates of ITN coverage. WHO uses this approach in collaboration with the Malaria Atlas Project. Methodological details can be found in pages 122-123 of the World Malaria Report 2021: <a href="https://www.who.int/publications/i/item/9789240040496">https://www.who.int/publications/i/item/9789240040496</a>.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>WHO produces comparable ITN coverage estimates for 40 of the 47 malaria endemic countries or areas of sub-Saharan Africa. The islands of </p> <p>Mayotte (for which no ITN delivery or distribution data </p> <p>were available) and Cabo Verde (which does not distribute </p> <p>ITNs) were excluded, as were the low transmission </p> <p>countries of Eswatini, Namibia, Sao Tome and Principe, </p> <p>and South Africa, for which ITNs comprise a small </p> <p>proportion of vector control. Analyses were limited to </p> <p>populations categorized by NMPs as being at risk. For other countries, ITN coverage is not included in the UHC service coverage index due to data limitations. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Water, sanitation and hygiene</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of population using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush toilets connected to piped sewer systems, septic tanks or pit latrines; pit latrines with slabs (including ventilated pit latrines), and composting toilets</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total population</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based household surveys and censuses</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Data on improved sanitation facilities are routinely collected in household surveys and censuses. These data sources may also collect information on sharing of sanitation facilities are shared among two or more households, and on emptying of on-site sanitation facilities. Household-level responses, weighted by household size, are used to compute population coverage.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>The WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) is responsible for SDG reporting on drinking water, sanitation and hygiene (WASH) and has produced regular estimates of coverage of the population using at least basic sanitation services since 2000. The JMP assembles, reviews and assesses national data collected by statistics offices and other relevant institutions including sectoral authorities. Linear regression is used to provide estimates of the population using improved sanitation facilities, as well as the proportion practising open defecation. Regressions are also made to estimate the population using improved sanitation facilities connected to sewers and septic tanks; these are constrained to not exceed the estimates for total improved facilities. The proportion of the population sharing sewered and non-sewered sanitation facilities is estimated by making a linear regression on all available data on sharing from household surveys and censuses. Basic sanitation services are calculated by multiplying the proportion of the population using improved sanitation facilities by the proportion of improved sanitation facilities which are not shared among two or more households. Separate estimates are made for urban and rural areas, and national estimates are generated as weighted averages of the two, using population data from the most recent report of the United Nations Population Division. The most recent household survey or census available for most countries was typically conducted two to six years ago. The JMP extrapolates regressions for two years beyond the last available data point. Beyond this point the estimates remain unchanged for up to four years unless coverage is below 0.5 per cent or above 99.5 per cent, in which case the line is extended indefinitely. For more information see <a href="https://washdata.org/monitoring/methods/estimation-methods">https://washdata.org/monitoring/methods/estimation-methods</a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>The SDG global indicator of “proportion of population using safely managed sanitation services” (SDG 6.2.1a) is an expanded version of the MDG indicator, which additionally considers safe management of excreta along the entire sanitation chain, including treatment and disposal This indicator is not used for UHC monitoring due to lower data availability. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Prevention of cardiovascular disease</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Prevalence of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of adults aged 30-79 years who took medication for hypertension</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of adults aged 30-79 years with hypertension (defined as having systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or taking medication for hypertension)</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based surveys and surveillance systems</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Data sources recording measured blood pressure are used (self-reported data are excluded). If multiple blood pressure readings are taken per participant, the first reading is dropped and the remaining readings are averaged. Whether medication is taken for hypertension may be assessed using questions worded as variations of “Are you currently taking any medicines, tablets, or pills for high blood pressure?” or “In the past 2 weeks, have you taken any drugs (medication) for raised blood pressure prescribed by a doctor or other health worker?” In studies that gather information on prescribed medicines, survey information may be used to establish that the purpose of taking a blood pressure-lowering drug was specifically to treat hypertension.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Full details of input and data methods are available at: NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet S0140-6736(21)01330-1 (https://www.thelancet.com/article/S0140-6736(21)01330-1/fulltext). A total of 1,201 population-based studies that included measured blood pressure and data on blood pressure treatment in 104 million individuals aged 30–79 years were used to estimate trends in hypertension and hypertension diagnosis, treatment and control from 1990 to 2019. Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Management of diabetes</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Age-standardized mean fasting plasma glucose for adults aged 18 years and older</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based surveys and surveillance systems</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Fasting plasma glucose (FPG) levels are determined by taking a blood sample from participants who have fasted for at least 8 hours. Other related biomarkers, such as hemoglobin A1c (HbA1c), were used to help calculate estimates (see below).</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>For producing comparable national estimates, data observations based on mean FPG, oral glucose tolerance test (OGTT), HbA1c, or combinations therein, are all converted to mean FPG. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country specific prevalences, which accounts for national vs. subnational data sources, urban vs. rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population. Methodological details can be found here: <a href="https://www.who.int/diabetes/global-report/en/"><u>https://www.who.int/diabetes/global-report/en/</u></a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>An individual’s FPG may be low because of effective treatment with glucose-lowering medication, or because the individual is not diabetic as a result of health promotion activities or other factors such as genetics. Mean FPG is thus a proxy for both effective promotion of healthy diets and behaviors and effective treatment of diabetes. </p> <p>The above estimates are done separately for men and women; for the UHC tracer indicator a simple average of values for men and women is computed. The indicator, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.41 mmol/L). </p> <p>rescaled value = (7.41 - original value) / (7.41-5.1) * 100</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Tobacco control</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Age-standardized percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Estimated number of adults 15 years and older who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of adults 15 years and older</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps will have large uncertainty ranges, and such results should be interpreted with caution.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Prevalence of tobacco non-use is computed as 1 minus the prevalence of tobacco use. The indicator is then rescaled based on a non-zero minimum to obtain finer resolution : rescaled tobacco non-use = (X-30)/(100-30)100.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Hospital access</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Hospital beds per capita, relative to a maximum threshold of 18 per 10,000 population</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of hospital beds (should exclude labor and delivery beds)</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total population</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Administrative systems / Health facility reporting system</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Country administrative systems are used to total the number of hospital beds, which are divided by the total estimated population, and multiplied by 10,000. </p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Using available data, the indicator is computed relative to a threshold value of 18 hospital beds per 10,000 population. This threshold is below the observed OECD high income country minimum (since year 2000) of 20 per 10,000 (OECD Health Statistics database, 2015 edition) and tends to correspond to an inpatient hospital admission rate of around 5 per 100 per year. This indicator is designed to capture low levels of hospital capacity; the maximum threshold is used because very high hospital bed densities are not necessary an efficient use of resources. The indicator is computed as follows, using country data on hospital bed density (<em>x</em>), which results in values ranging from 0 to 100:</p> <ul> <li>Country with a hospital bed density <em>x</em> < 18 per 10,000 per year, the indicator = <em>x</em> /18100. </li> <li>Country with a hospital bed density <em>x</em> >= 18 per 10,000 per year, the indicator = 100.</li> </ul> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>This indicator is used as proxy for the full coverage of inpatient care services. An alternative indicator could be hospital in-patient admission rate, relative to a maximum threshold. However, that indicator is currently not reported widely across regions, in particular the African Region. In countries where both hospital beds per capita and in-patient admission rates are available, they are highly correlated.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Health workforce</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of physicians, psychiatrists and surgeons</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total population</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>National Health Workforce Accounts. This includes reported data from Member States based on national registry of health workers, ideally coupled with regular assessment of completeness using census data, labour force surveys, professional association registers, or facility censuses.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>The classification of health workers is based on criteria for vocational education and training, regulation of health professions, and activities and tasks of jobs, i.e., a framework for categorizing key workforce variables according to shared characteristics. The WHO framework largely draws on the latest revisions to the internationally standardized classification systems of the International Labour Organization (International Standard Classification of Occupations), 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). Methodological details can be found here: <a href="https://www.who.int/activities/improving-health-workforce-data-and-evidence">https://www.who.int/activities/improving-health-workforce-data-and-evidence</a></p> <p>Health workforce data can be accessed on the NHWA data portal: <a href="https://apps.who.int/nhwaportal/">https://apps.who.int/nhwaportal/</a></p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Using available data, the indicator is computed by first rescaling, separately, health worker density ratios for each of the three cadres (physicians, psychiatrists and surgeons) relative to the minimum observed values across OECD countries since 2000 (OECD Health Statistics database, 2015 edition), which are as follows: physicians = 0.9 per 1000, psychiatrists = 1 per 100,000, and surgeons = 14 per 100,000. This rescaling is done in the same way as that for the hospital bed density indicator described above, resulting in indicator values that range from 0 to 100 for each of the three cadres. For example, using country data on physicians per 1000 population (<em>x</em>), the cadre-specific indicator would be computed as:</p> <ul> <li>Country with <em>x</em> < 0.9 per 1000 per year, the cadre-specific indicator = <em>x</em> /0.9100. </li> <li>Country with <em>x</em> >= 0.9 per 1000 per year, the cadre-specific indicator = 100.</li> </ul> <p>As a final step, the geometric mean of the three cadre-specific indicator values is computed to obtain the final indicator of health workforce density.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Due to major challenges measuring coverage in all health areas, which leaves major gaps for important areas such as routine medical exams, treatment for mental illnesses, emergency care and surgical procedure, proxies are used. Physician, psychiatrist and surgeon densities are used as proxies for the full coverage of outpatient care, mental health care and emergency/surgical care services, respectively. It should be noted that those measures are difficult to interpret because the optimal level for those indicators is unknown and they do not relate to a specific need for services. Despite this fact, low levels for these indicators are indicative of poor access to and use of essential health services.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Health security</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of all core capacities that have been attained at a specific point in time. </p> <p>The second edition SPAR tool has been expanded from 13 to 15 capacities. The 15 core capacities are (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies. </p> <p>The 13 core capacities of the first edition of the IHR State Parties Annual Assessment and Reporting Tool are (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3) Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies. </p> <p>Both SPAR questionnaires (1st and 2nd editions) use a five-level scoring with indicators based on five cumulative levels to measure the implementation status for each capacity. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party's current status. To move to the next level, all capacities described in previous levels should be in place for each indicator.</p> <p>For the years 2010 to 2017, Member States used the IHR monitoring questionnaire. The questionnaire is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards. Individual questions are grouped by components and indicators in the questionnaires. States Parties can provide additional information on the questions in the comment boxes. Responses to the questions include marking one appropriate value (Yes, No, or Not Known) or the appropriate percentages. For statistical purposes, the "Not Known" value will be computed as a "No" value. The IHR monitoring questionnaire includes the following: IHR01. National legislation, policy and financing; IHR02. Coordination and National Focal Point communications; IHR03. Surveillance; IHR04. Response; IHR05. Preparedness; IHR06. Risk communication; IHR07. Human resources; IHR08. Laboratory; IHR09. Points of entry; IHR10. Zoonotic events; IHR11. Food safety; IHR12. Chemical events; IHR13. Radio nuclear emergencies.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of attributes attained</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of attributes</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Key informant survey</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Key informants report on attainment of a set of attributes for each of the core capacities using a standard WHO instrument. This instrument is based on a self-assessment and self-reporting by the State Party. There are three datasets based on the different tools to collect data for SPAR. For the period 2010 to 2017, the questionnaire, known as the IHR monitoring questionnaire, is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards and information on the status of implementation for each capacity. The IHR monitoring questionnaire ( 2010 to 2017) was replaced by the IHR State Parties Self-Assessment Tool – SPAR, published in July 2018 also known as SPAR 1st edition. The States Parties used the questionnaire from the 2018 – 2020 SPAR reporting cycle. The current questionnaire replaced the SPAR 1st edition and was used by the Member States for 2021. Under each capacity, the indicators were either retained, replaced or added. Historical trends based on the data for similar capacity titles may be taken with caution.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>The score of each indicator level is classified as a percentage of performance along the “1 to 5” scale. e.g. for a country selecting level 3 for indicator 2.1, the indicator level will be expressed as: 3/5100=60% CAPACITY LEVEL The level of the capacity is expressed as the average of all indicators. e.g. for a country selecting level 3 for indicator 2.1 and level 4 for indicator 2.2. Indicator level for 2.1 will be expressed as: 3/5100=60%, indicator level for 2.2 will be expressed as: 4/5100=80% and capacity level for 2 will be expressed as: (60+80)/2=70%</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Countries began reporting IHR core capacity attainment to WHO for the year 2010. The earliest available IHR score for each country is used for all years 2000-2009.</p> </td> </tr> </tbody> </table> English 11631 characters edited Current translation Read only <p><strong>URL: </strong><a href="https://www.who.int/health~~info~~-topics/universal_-health_-coverage~~/en/" target="_blank"><u~~">https://www.who.int/health~~info~~-topics/universal_-health_-coverage~~/en/</u>~~</a> </p> <p> </p> <p><strong>References: </strong><a href="https://www.who.int/~~healthinfo/~~publications/i/item/tracking-universal_-health_-coverage~~/report/2017/en/" target="_blank"><u~~">https://www.who.int/~~healthinfo/~~publications/i/item/tracking-universal_-health_-coverage~~/report/2017/en/</u>~~</a> </p> <p><a href="http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf" target="_blank"><u>http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf</u></a> </p> <p><a href="https://www.who.int/health~~info~~-topics/universal_-health_-coverage~~/en/" target="_blank"><u~~">https://www.who.int/health~~info~~-topics/universal_-health_-coverage~~/en/</u>~~</a> </p> <p>For historical development of methods, see: </p> <p><a href="https://www.who.int/~~healthinfo/universal_health_coverage/UHC_WHS2016_TechnicalNote_May2016.pdf?ua=1" target="_blank"><u~~publications/i/item/9789241565264">https://www.who.int/~~healthinfo/universal_health_coverage/UHC_WHS2016_TechnicalNote_May2016.pdf?ua=1</u></a> (superseded by this document)~~publications/i/item/9789241565264</a> </p> <p><a href="https://www.who.int/~~healthinfo/universal_health_coverage/report/2015/en/" target="_blank"><u>http://www.who.int/healthinfo/~~publications/i/item/monitoring-progress-towards-universal_-health_-coverage/report/2015/en/</u></a> </p> <p><a href="http://www.who.int/healthinfo/universal_health_coverage/report/2014/en/" target="_blank"><u-at-country-and-global-levels-framework-measures-and-targets">https://www.who.int/~~healthinfo/~~publications/i/item/monitoring-progress-towards-universal_-health_-coverage~~/report/2014/en/</u>~~-at-country-and-global-levels-framework-measures-and-targets</a> </p> <p><a href="http://collections.plos.org/uhc2014" target="_blank"><u>http://collections.plos.org/uhc2014</u></a> </p> <p> </p> <p>Annex 1: Metadata for tracer indicators used to measure the coverage of essential health services for monitoring SDG indicator 3.8.1.</p> <p>Please send any comments or queries to: uhc_stats@who.int</p> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Family planning</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of women of reproductive age (15−49 years) who are married or in-union who have their need for family planning satisfied with modern methods.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of women aged 15-49 who are married or in-union who are currently using, or whose partner is currently using a modern method of contraception </p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of women aged 15-49 who are married or in-union with a need for family planning </p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based health surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Household surveys include a series of questions to measure the modern contraceptive prevalence rate and need for family planning. The number of women with a need for family planning is defined as the sum of the number of women of reproductive age (15–49 years) who are married or in a union and who are currently using, or whose sexual partner is currently using, at least one contraceptive method (modern or traditional), and the number of women of reproductive age with an unmet need for family planning. Unmet need for family planning is the proportion of women of reproductive age (15–49 years) either married or in a consensual union, who are fecund and sexually active but who are not using any method of contraception (modern or traditional), and report not wanting any more children or wanting to delay the birth of their next child for at least two years. Included are:</p> <ol> <li>all pregnant women (married or in a consensual union) whose pregnancies were unwanted or mistimed at the time of conception;</li> <li>all postpartum amenorrhoeic women (married or in consensual union) who are not using family planning and whose last birth was unwanted or mistimed;</li> <li>all fecund women (married or in consensual union) who are neither pregnant nor postpartum amenorrhoeic, and who either do not want any more children (want to limit family size), or who wish to postpone the birth of a child for at least two years or do not know when or if they want another child (want to space births), but are not using any contraceptive method.</li> </ol> <p>Modern methods include female and male sterilization, the intra-uterine device (IUD), the implant, injectables, oral contraceptive pills, male and female condoms, vaginal barrier methods (including the diaphragm, cervical cap and spermicidal foam, jelly, cream and sponge), lactational amenorrhea method (LAM), emergency contraception and other modern methods not reported separately.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>The United Nations Population Division produces a systematic and comprehensive series of annual estimates and projections of the proportion of need for family planning among women of reproductive age (15-49) satisfied with modern methods. A Bayesian hierarchical model is applied to a comprehensive global dataset of a country-specific data to generate the estimates and projections. The model accounts for differences by data source, sample population, and survey questions. </p> <p>See here for details: </p> <p><a href="https://www.un.org/development/desa/pd/data/family-planning-indicators">https://www.un.org/development/desa/pd/data/family-planning-indicators</a> </p> <p>Data compilation of country-specific survey data in World Contraceptive Use: </p> <p><a href="https://www.un.org/development/desa/pd/node/3285">https://www.un.org/development/desa/pd/node/3285</a> </p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td></td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Pregnancy ~~and delivery~~ care</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of women aged 15-49 years with a live birth in a given time period who received antenatal care four or more times </p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of women aged 15−49 years with a live birth in a given time period who received antenatal care four or more times</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of women aged 15−49 years with a live birth in the same period.</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys and routine facility information systems.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Data on four or more antenatal care visits is based on questions that ask if and how many times the health of the woman was checked during pregnancy. Household surveys that can generate this indicator include DHS, MICS, RHS and other surveys based on similar methodologies. Service/facility reporting systems can be used where the coverage is high, usually in higher income countries.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>WHO maintains a data base on coverage of antenatal care: <a href="http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4"><u>http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4</u></a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Ideally this indicator would be replaced with a more comprehensive measure of pregnancy ~~and delivery~~ care, for example the proportion of women who have a skilled provider attend the birth or an institutional delivery. A challenge in measuring skilled attendance at birth is determining which providers are “skilled”. </p> </td> </tr> </tbody> </table> ~~<p><br></p>~~ <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Child immunization</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of infants receiving three doses of diphtheria-tetanus-pertussis containing vaccine</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Children 1 year of age who have received three doses of diphtheria-tetanus-pertussis containing vaccine</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>All children 1 year of age</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys and facility information systems.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>For survey data, the vaccination status of children aged 12–23 months is collected from child health cards or, if there is no card, from recall by the care-taker. For administrative data, the total number of doses administered to the target population is extracted.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Together, WHO and UNICEF derive estimates of DTP3 coverage based on data officially reported to WHO and UNICEF by Member States, as well as data reported in the published and grey literature. They also consult with local experts - primarily national EPI managers and WHO regional office staff - for additional information regarding the performance of specific local immunization services. Based on the available data, consideration of potential biases, and contributions from local experts, WHO/UNICEF determine the most likely true level of immunization coverage. </p> <p>For details, see here: </p> <p> <a href="https://www.who.int/bulletin/volumes/87/7/08-053819/en/</p> <pteams/immunization-vaccines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimates-of-national-immunization-coverage">https://www.who.int/teams/immunization~~/monitoring_surveillance/routine/coverage/en/index4.html~~-vaccines-and-biologicals/immunization-analysis-and-insights/global-monitoring/immunization-coverage/who-unicef-estimates-of-national-immunization-coverage</a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>There is variability in national vaccine schedules across countries. Given this, one option for monitoring full child immunization is to monitor the fraction of children receiving vaccines included in their country’s national schedule. A second option, which may be more comparable across countries and time, is to monitor DTP3 coverage as a proxy for full child immunization. Diphtheria-tetanus-pertussis containing vaccine often includes other vaccines, e.g., against Hepatitis B and Haemophilus influenza type B, and is a reasonable measure of the extent to which there is a robust vaccine delivery platform within a country. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Child treatment ~~(care-seeking for symptoms of pneumonia)~~</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of children youndger than 5 years ~~of age with suspected pneumonia~~with symptoms of acute respiratory infection (cough and fast or difficult breathing ~~NOT~~ <strong>due to a problem in the chest ~~and~~</strong> <strong>and not due to a blocked nose only</strong>) in the ~~two~~2 weeks preceding the survey ~~taken to an appropriate~~for whom advice or treatment was sought from a health facility or provider.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of children ~~with suspected pneumonia in the two weeks preceding the survey taken to an appropriate health~~younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey for whom advice or treatment was sought from a health facility or provider.</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of children ~~with suspected pneumonia in the two~~younger than 5 years with symptoms of acute respiratory infection (cough and fast or difficult breathing <strong>due to a problem in the chest</strong> <strong>and not due to a blocked nose only</strong>) in the 2 weeks preceding the survey. </p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>During the UNICEF/WHO Meeting on Child Survival Survey-based Indicators, held in New York, 17–18 June 2004, it was recommended that acute respiratory infections (ARI) be described as “presumed pneumonia” to better reflect probable cause and the recommended interventions. The definition of presumed pneumonia used in the Demographic and Health Surveys (DHS) and in the Multiple Indicator Cluster Surveys (MICS) was chosen by the group and is based on mothers’ perceptions of a child who has a cough, is breathing faster than usual with short, quick breaths or is having difficulty breathing, excluding children that had only a blocked nose. The definition of “appropriate” care provider varies between countrieThe indicator is captured by household surveys including DHS, MICS and other national population-based surveys and is intended for use in high under-5 mortality settings to monitor efforts to reduce mortality from acute respiratory infections (including pneumonia) which are a leading cause of death for children under the age of 5 years. The Child Health Accountability Tracking Technical Advisory Group (CHAT TAG), convened by WHO and UNICEF, has ratified this indicator and is working to standardize its use across household surveys.</p> <p>WHO/UNICEF maintains a data base of country-level observations from household surveys that can be accessed here: <a href="https://~~apps.who.int/gho/data/node.main.38?lang=en"><u~~data.unicef.org/topic/child-health/pneumonia/">https://~~apps.who.int/gho/data/node.main.38?lang=en</u>~~data.unicef.org/topic/child-health/pneumonia/</a> </p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>~~There are currently no internationally comparable estimates for this indicator~~UNICEF and WHO maintain a data base on this indicator and work on ensuring that values presented are comparable, using the same indicator definition.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>This indicator is not typically measured in higher income countries with well-established health systems. </p> <p>For countries without observed data, coverage was estimated from a regression that predicts coverage of care-seeking for symptoms of ~~pneumonia~~acute respiratory infection (on the logit scale), obtained from the WHO data base described above, as a function of the log of the estimated under-five ~~pneumonia~~all-causes mortality rate, which can be found here: <a href="https://www.who.int/~~healthinfo/global_burden_disease/estimates/en/index2.html"><u>https://www.who.int/healthinfo/global_burden_disease/estimates/en/index2.html</u>~~data/gho/data/themes/mortality-and-global-health-estimates">https://www.who.int/data/gho/data/themes/mortality-and-global-health-estimates</a></p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Tuberculosis treatment</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of incidence TB cases that are detected and treated in a given year</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of new and relapse cases detected and treated in a given year </p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of new and relapse cases in the same year</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Facility information systems, surveillance systems, population-based health surveys with TB diagnostic testing, TB register and related quarterly reporting system (or electronic TB registers)</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>This indicator requires two main inputs:</p> <p>(1) The number of new and relapse TB cases diagnosed and treated in national TB control programmes and notified to WHO in a given year.</p> <p>(2) The number of incident TB cases for the same year, typically estimated by WHO.</p> <p>The final indicator = (1)/(2) </p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Estimates of TB incidence are produced through a consultative and analytical process led by WHO and are published annually. These estimates are based on annual case notifications, assessments of the quality and coverage of TB notification data, national surveys of the prevalence of TB disease and information from death (vital) registration systems. Estimates of incidence for each country are derived, using one or more of the following approaches depending on available data:</p> <p>1. incidence = case notifications/estimated proportion of cases detected;</p> <p>2. incidence = prevalence/duration of condition;</p> <p>3. incidence = deaths/proportion of incident cases that die. </p> <p>Dynamic and statistical models were introduced to produce estimates for 2020 and 2021 that account for the major disruptions to the provision of and access to TB diagnostic and treatment services that have occurred in the context of the coronavirus (COVID-19) pandemic.</p> <p>These estimates of TB incidence are combined with country-reported data on the number of cases detected and treated, and the percentage of cases successfully treated, as described above.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>To compute the indicator using WHO estimates, one can access necessary files here: <a href="http://www.who.int/tb/country/data/download/en/"><u>http://www.who.int/tb/country/data/download/en/</u></a>, and compute the indicator as = c_cdr </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>HIV treatment</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of ~~people~~adults and children living with HIV currently receiving antiretroviral therapy (ART)</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of adults and children who are currently receiving ART at the end of the reporting period</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of adults and children living with HIV during the same period</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Facility reporting systems, sentinel surveillance sites, population-based surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Numerator: The numerator ~~can be~~is generated by counting the number of adults and children who received ~~antiretroviral combination therapy~~ART at the end of the reporting period. Data can be collected from facility-based ART registers or drug supply management systems. These are then tallied and transferred to cross sectional monthly or quarterly reports which ~~can~~will then be aggregated for national totals. Patients receiving ART in the private sector and public sector should be included in the numerator.</p> <p>Denominator: Data on the number of people with HIV infection may come from epidemic models and population-based surveys or, as is common in sub-Saharan Africa, surveillance systems based on antenatal care clinics.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Estimates of antiretroviral treatment coverage among people living with HIV for 2000-2018 are derived as part of the 2019 UNAIDS' estimation round. </p> <p>To estimate the number of people living with HIV across time in high burden countries, UNAIDS in collaboration with countries uses an epidemic model (Spectrum) that combines surveillance data on prevalence with the current number of patients receiving ART and assumptions about the natural history of HIV disease progression. </p> <p>Since ART is now recommended for all individuals living with HIV, monitoring ART coverage is less complicated than before, when only those with a certain level of disease severity were eligible to receive ART.</p> <p>Estimates of ART coverage can be found here: <a href="https://~~aidsinfo.unaids.org/~~www.who.int/data/gho/data/indicators/indicator-details/GHO/estimated-antiretroviral-therapy-coverage-among-people-living-with-hiv-(-)">https://www.who.int/data/gho/data/indicators/indicator-details/GHO/estimated-antiretroviral-therapy-coverage-among-people-living-with-hiv-(-)</a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Comparable estimates of ART coverage in high income countries, in particular time trends, are not always available.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Malaria prevention</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of population in malaria-endemic areas who slept under an ITN the previous night.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of people in malaria-endemic areas who slept under an ITN.</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of people in malaria endemic areas.</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Data on household access and use of ITNs come from nationally representative household surveys such as Demographic and Health Surveys, Multiple Indicator Cluster Surveys, and Malaria Indicator Surveys. Data on the number of ITNs delivered by manufacturers to countries are compiled by Milliner Global Associates, and data on the number of ITNs distributed within countries are reported by National Malaria Control Programs.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Many recent national surveys report the number of ITNs observed in each respondent household. Ownership rates can be converted to the proportion of people sleeping under an ITN using a linear relationship between access and use that has been derived from 62 surveys that collect information on both indicators. </p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Mathematical models can be used to combine data from household surveys on access and use with information on ITN deliveries from manufacturers and ITN distribution by national malaria programmes to produce annual estimates of ITN coverage. WHO uses this approach in collaboration with the Malaria Atlas Project. Methodological details can be found in ~~the Annex~~pages 122-123 of the World Malaria Report 20~~15:<u>https://apps.who.int/iris/handle/10665/200018</u~~21: <a href="https://www.who.int/publications/i/item/9789240040496">https://www.who.int/publications/i/item/9789240040496</a>.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>WHO produces comparable ITN coverage estimates for 40 ~~high burden countries~~of the 47 malaria endemic countries or areas of sub-Saharan Africa. The islands of </p> <p>Mayotte (for which no ITN delivery or distribution data </p> <p>were available) and Cabo Verde (which does not distribute </p> <p>ITNs) were excluded, as were the low transmission </p> <p>countries of Eswatini, Namibia, Sao Tome and Principe, </p> <p>and South Africa, for which ITNs comprise a small </p> <p>proportion of vector control. Analyses were limited to </p> <p>populations categorized by NMPs as being at risk. For other countries, ITN coverage is not included in the UHC service coverage index due to data limitations. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Water ~~and~~, sanitation and hygiene</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of population using at least basic sanitation services, that is, improved sanitation facilities that are not shared with other households</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of people using basic sanitation services as well as those using safely managed sanitation services. Improved sanitation facilities include flush/pour flush toilets connected to piped sewer systems, septic tanks or pit latrines; pit latrines with slabs (including ventilated pit latrines), and composting toilets</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total population</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based household surveys and censuses</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Data on improved sanitation facilities are routinely collected in household surveys and censuses. These data sources may also collect information on sharing of sanitation facilities are shared among two or more households, and on emptying of on-site sanitation facilities. Household-level responses, weighted by household size, are used to compute population coverage.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>The JMP assembles, reviews and assesses national data collected by statistics offices and other relevant institutions including sectoral authorities. Linear regression is used to provide estimates of the population using improved sanitation facilities, as well as the proportion practising open defecation. Regressions are also made to estimate the population using improved sanitation facilities connected to sewers and septic tanks; these are constrained to not exceed the estimates for total improved facilities. The proportion of the population sharing sewered and non-sewered sanitation facilities is estimated by taking an average of all available data on sharing from household surveys and censuses. Basic sanitation services are calculated by multiplying the proportion of the population using improved sanitation facilities by the proportion of improved sanitation facilities which are not shared among two or more households. Separate estimates are made for urban and rural areas, and national estimates are generated as weighted averages of the two, using population data from the most recent report of the United Nations Population Division. The most recent household survey or census available for most countries was typically conducted two to six years ago. The JMP extrapolates regressions for two years beyond the last available data point. Beyond this point the estimates remain unchanged for up to four years unless coverage is below 0.5 per cent or above 99.5 per cent, in which case the line is extended indefinitely. For more information see https://washdata.org/monitoring/methods/estimation-methods</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>The SDG indicator for sanitation (SDG 6.2.1) is an expanded version of the MDG indicator, incorporating the quality of sanitation facilities. It is not for UHC monitoring due to lower data availability. A joint indicator that identifies the proportion of households with access to both safe water and sanitation could also be considered.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Prevention of cardiovascular disease</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Age-standardized prevalence of non-raised blood pressure among adults aged 18+, regardless of treatment status</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of adults aged 18 or older with systolic blood pressure <140 mm Hg and diastolic blood pressure <90 mm Hg (regardless of treatment status)</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of adults aged 18 or older</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based surveys and surveillance systems</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Data sources recording measured blood pressure are used (self-reported data are excluded). If multiple blood pressure readings are taken per participant, the first reading is dropped and the remaining readings are averaged.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>For producing comparable national estimates, data observations of prevalence defined in terms of alternate SBP and/or DBP cut-offs are converted into prevalence of raised blood pressure, defined as systolic blood pressure >=140 mm Hg or diastolic blood pressure >=90 mm Hg using regression equations. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country specific prevalences, which accounts for national vs. subnational data sources, urban vs. rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population. Details on the statistical methods are here: https://www.thelancet.com/journals/lancet/article/piiS0140-6736(21)01330-1/fulltext</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Prevalence estimates are converted to the prevalence of normal blood pressure for incorporation into the UHC index, so that a value of 100% is the optimal target. This is computed as: normal blood pressure prevalence = 1 – raised blood pressure prevalence. The above estimates are done separately for men and women; for the UHC tracer indicator a simple average of values for men and women is computed.</p> <p>Non raised blood pressure is the sum of the percentage of individuals who do not have hypertension, and the percentage of individuals whose hypertension is controlled by medication. The absence of hypertension is a result of prevention efforts via promotion of physical activity and healthy diets, as well as other factors. Hypertension controlled with medication is a result of effective treatment. This indicator is thus a proxy for both effective health promotion and effective medical services.WHO/UNICEF Joint Monitoring Programme for Water Supply, Sanitation and Hygiene (JMP) is responsible for SDG reporting on drinking water, sanitation and hygiene (WASH) and has produced regular estimates of coverage of the population using at least basic sanitation services since 2000. The JMP assembles, reviews and assesses national data collected by statistics offices and other relevant institutions including sectoral authorities. Linear regression is used to provide estimates of the population using improved sanitation facilities, as well as the proportion practising open defecation. Regressions are also made to estimate the population using improved sanitation facilities connected to sewers and septic tanks; these are constrained to not exceed the estimates for total improved facilities. The proportion of the population sharing sewered and non-sewered sanitation facilities is estimated by making a linear regression on all available data on sharing from household surveys and censuses. Basic sanitation services are calculated by multiplying the proportion of the population using improved sanitation facilities by the proportion of improved sanitation facilities which are not shared among two or more households. Separate estimates are made for urban and rural areas, and national estimates are generated as weighted averages of the two, using population data from the most recent report of the United Nations Population Division. The most recent household survey or census available for most countries was typically conducted two to six years ago. The JMP extrapolates regressions for two years beyond the last available data point. Beyond this point the estimates remain unchanged for up to four years unless coverage is below 0.5 per cent or above 99.5 per cent, in which case the line is extended indefinitely. For more information see <a href="https://washdata.org/monitoring/methods/estimation-methods">https://washdata.org/monitoring/methods/estimation-methods</a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>The SDG global indicator of “proportion of population using safely managed sanitation services” (SDG 6.2.1a) is an expanded version of the MDG indicator, which additionally considers safe management of excreta along the entire sanitation chain, including treatment and disposal This indicator is not used for UHC monitoring due to lower data availability. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Prevention of cardiovascular disease</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Prevalence of treatment (taking medicine) for hypertension among adults aged 30-79 years with hypertension (age-standardized estimate) (%)</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of adults aged 30-79 years who took medication for hypertension</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of adults aged 30-79 years with hypertension (defined as having systolic blood pressure ≥ 140 mmHg, diastolic blood pressure ≥ 90 mmHg, or taking medication for hypertension)</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based surveys and surveillance systems</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Data sources recording measured blood pressure are used (self-reported data are excluded). If multiple blood pressure readings are taken per participant, the first reading is dropped and the remaining readings are averaged. Whether medication is taken for hypertension may be assessed using questions worded as variations of “Are you currently taking any medicines, tablets, or pills for high blood pressure?” or “In the past 2 weeks, have you taken any drugs (medication) for raised blood pressure prescribed by a doctor or other health worker?” In studies that gather information on prescribed medicines, survey information may be used to establish that the purpose of taking a blood pressure-lowering drug was specifically to treat hypertension.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Full details of input and data methods are available at: NCD Risk Factor Collaboration (NCD-RisC). Worldwide trends in hypertension prevalence and progress in treatment and control from 1990 to 2019: a pooled analysis of 1201 population-representative studies with 104 million participants. The Lancet S0140-6736(21)01330-1 (https://www.thelancet.com/article/S0140-6736(21)01330-1/fulltext). A total of 1,201 population-based studies that included measured blood pressure and data on blood pressure treatment in 104 million individuals aged 30–79 years were used to estimate trends in hypertension and hypertension diagnosis, treatment and control from 1990 to 2019. Age-standardized estimates are produced by applying the crude estimates to the WHO Standard Population.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p> </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Management of diabetes</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Age-standardized mean fasting plasma glucose for adults aged 18 years and older</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Population-based surveys and surveillance systems</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Fasting plasma glucose (FPG) levels are determined by taking a blood sample from participants who have fasted for at least 8 hours. Other related biomarkers, such as hemoglobin A1c (HbA1c), were used to help calculate estimates (see below).</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>For producing comparable national estimates, data observations based on mean FPG, oral glucose tolerance test (OGTT), HbA1c, or combinations therein, are all converted to mean FPG. A Bayesian hierarchical model is then fitted to these data to calculate age-sex-year-country specific prevalences, which accounts for national vs. subnational data sources, urban vs. rural data sources, and allows for variation in prevalence across age and sex. Age-standardized estimates are then produced by applying the crude estimates to the WHO Standard Population. Methodological details can be found here: <a href="https://www.who.int/diabetes/global-report/en/"><u>https://www.who.int/diabetes/global-report/en/</u></a></p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>An individual’s FPG may be low because of effective treatment with glucose-lowering medication, or because the individual is not diabetic as a result of health promotion activities or other factors such as genetics. Mean FPG is thus a proxy for both effective promotion of healthy diets and behaviors and effective treatment of diabetes. </p> <p>The above estimates are done separately for men and women; for the UHC tracer indicator a simple average of values for men and women is computed.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Tobacco control</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Age-standardized percentage of the population aged 15 years and over who do not currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Adults 15 years and older who do not The indicator, which is a continuous measure (units of mmol/L), is converted to a scale of 0 to 100 using the minimum theoretical biological risk (5.1 mmol/L) and observed maximum across countries (7.41 mmol/L). </p> <p>rescaled value = (7.41 - original value) / (7.41-5.1) * 100</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Tobacco control</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Age-standardized percentage of the population aged 15 years and over who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Estimated number of adults 15 years and older who currently use any tobacco product (smoked and/or smokeless tobacco) on a daily or non-daily basis</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>ATotal number of adults 15 years and older</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Household surveys</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Tobacco products include cigarettes, pipes, cigars, cigarillos, waterpipes (hookah, shisha), bidis, kretek, heated tobacco products, and all forms of smokeless (oral and nasal) tobacco. Tobacco products exclude e-cigarettes (which do not contain tobacco), “e-cigars”, “e-hookahs”, JUUL and “e-pipes”.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>A statistical model based on a Bayesian negative binomial meta-regression is used to model prevalence of current tobacco use for each country, separately for men and women. A full description of the method is available as a peer-reviewed article in The Lancet, volume 385, No. 9972, p966–976 (2015). Once the age-and-sex-specific prevalence rates from national surveys were compiled into a dataset, the model was fit to calculate trend estimates from the year 2000 to 2025. The model has two main components: (a) adjusting for missing indicators and age groups, and (b) generating an estimate of trends over time as well as the 95% credible interval around the estimate. Depending on the completeness/comprehensiveness of survey data from a particular country, the model at times makes use of data from other countries to fill information gaps. When a country has fewer than two nationally representative population-based surveys in different years, no attempt is made to fill data gaps and no estimates are calculated. To fill data gaps, information is “borrowed” from countries in the same UN subregion. The resulting trend lines are used to derive estimates for single years, so that a number can be reported even if the country did not run a survey in that year. In order to make the results comparable between countries, the prevalence rates are age-standardized to the WHO Standard Population. Estimates for countries with irregular surveys or many data gaps will have large uncertainty ranges, and such results should be interpreted with caution.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Prevalence of tobacco non-use is computed as 1 minus the prevalence of tobacco use. The indicator is then rescaled based on a non-zero minimum to obtain finer resolution : rescaled tobacco non-use = (X-30)/(100-30)100.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Hospital access</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Hospital beds per capita, relative to a maximum threshold of 18 per 10,000 population</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of hospital beds (should exclude labor and delivery beds)</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total population</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Administrative systems / Health facility reporting system</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Country administrative systems are used to total the number of hospital beds, which are divided by the total estimated population, and multiplied by 10,000. </p> <p>WHO regional offices and other groups collect information on national hospital bed density, including the following online resources:</p> <p>WHO EMRO regional observatory: https://rho.emro.who.int/rhodata/node.main.A36</p> <p>WHO AFRO regional observatory: http://www.aho.afro.who.int/en/data-statistics/hospital-beds-10-000-population</p> <p>WHO EURO European Health for All Database: https://gateway.euro.who.int/en/datasets/european-health-for-all-database/</p> <p>OECD: https://data.oecd.org/healtheqt/hospital-beds.htm</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Using available data, the indicator is computed relative to a threshold value of 18 hospital beds per 10,000 population. This threshold is below the observed OECD high income country minimum (since year 2000) of 20 per 10,000 and tends to correspond to an inpatient hospital admission rate of around 5 per 100 per year. This indicator is designed to capture low levels of hospital capacity; the maximum threshold is used because very high hospital bed densities are not necessary an efficient use of resources. The indicator is computed as follows, using country data on hospital bed density (<em>x</em>), which results in values ranging from 0 to 100:</p> <ul> <li>Country with a hospital bed density <em>x</em> < 18 per 10,000 per year, the indicator = <em>x</em> /18100. </li> <li>Country with a hospital bed density <em>x</em> >= 18 per 10,000 per year, the indicator = 100.</li> </ul> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>An alternative indicator could be hospital in-patient admission rate, relative to a maximum threshold. However, that indicator is currently not reported widely across regions, in particular the African Region. In countries where both hospital beds per capita and in-patient admission rates are available, they are highly correlated.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Health workforce</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of physicians, psychiatrists and surgeons</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total population</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>National database or registry of health workers, ideally coupled with regular assessment of completeness using census data, professional association registers, or facility censuses.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>The classification of health workers is based on criteria for vocational education and training, regulation of health professions, and activities and tasks of jobs, i.e., a framework for categorizing key workforce variables according to shared characteristics. The WHO framework largely draws on the latest revisions to the internationally standardized classification systems of the International Labour Organization (International Standard Classification of Occupations), 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). Methodological details and data can be found here: <a href="http://www.who.int/hrh/statistics/hwfstats/en/">http://www.who.int/hrh/statistics/hwfstats/en/</a></p> <p>Data are from the following sources:</p> <p>Physicians: <a href="http://apps.who.int/gho/data/node.main.HWFGRP_0020?lang=en">http://apps.who.int/gho/data/node.main.HWFGRP_0020?lang=en</a></p> <p>Psychiatrists: <a href="https://www.who.int/healthinfo/universal_health_coverage/report/2017/en/">https://www.who.int/healthinfo/universal_health_coverage/report/2017/en/</a></p> <p>Surgeons: http://apps.who.int/gho/data/node.main.HWF9?lang=en (data here were supplemented by prior editions of the database)</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Using available data, the indicator is computed by first rescaling, separately, health worker density ratios for each of the three cadres (physicians, psychiatrists and surgeons) relative to the minimum observed values across OECD countries since 2000, which are as follows: physicians = 0.9 per 1000, psychiatrists = 1 per 100,000, and surgeons = 14 per 100,000. This rescaling is done in the same way as that for the hospital bed density indicator described above, resulting in indicator values that range from 0 to 100 for each of the three cadres. For example, using country data on physicians per 1000 population (<em>x</em>), the cadre-specific indicator would be computed as:</p> <ul> <li>Country with <em>x</em> < 0.9 per 1000 per year, the cadre-specific indicator = <em>x</em> /0.9100. </li> <li>Country with <em>x</em> >= 0.9 per 1000 per year, the cadre-specific indicator = 100.</li> </ul> <p>As a final step, the geometric mean of the three cadre-specific indicator values is computed to obtain the final indicator of health workforce density.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>The “physicians” category would ideally be expanded to include all “core health professionals”, such as nurses and midwives. However, no internationally comparable data base exists that uses consistent definitions of non-physician core health professionals to allow for fully accurate cross-country comparisons. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Health security</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of 13 core capacities that have been attained at a specific point in time. </p> <p>The 13 core capacities are: (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3) Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of attributes attained</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of attributes</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Key informant survey</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Key informants report on attainment of a set of attributes for each of 13 core capacities using a standard WHO instrument. This instrument is based on a self-assessment and self-reporting by the State Party. The questionnaire was revised in 2018 and been used for reporting in 2018 and 2019 with same format, different from the questionnaire used during period from 2010-2017, thus there is limitation for comparison of scores from reports between 2010-2017 period with reports after 2018</td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Using available data, the indicator is computed relative to a threshold value of 18 hospital beds per 10,000 population. This threshold is below the observed OECD high income country minimum (since year 2000) of 20 per 10,000 (OECD Health Statistics database, 2015 edition) and tends to correspond to an inpatient hospital admission rate of around 5 per 100 per year. This indicator is designed to capture low levels of hospital capacity; the maximum threshold is used because very high hospital bed densities are not necessary an efficient use of resources. The indicator is computed as follows, using country data on hospital bed density (<em>x</em>), which results in values ranging from 0 to 100:</p> <ul> <li>Country with a hospital bed density <em>x</em> < 18 per 10,000 per year, the indicator = <em>x</em> /18100. </li> <li>Country with a hospital bed density <em>x</em> >= 18 per 10,000 per year, the indicator = 100.</li> </ul> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>This indicator is used as proxy for the full coverage of inpatient care services. An alternative indicator could be hospital in-patient admission rate, relative to a maximum threshold. However, that indicator is currently not reported widely across regions, in particular the African Region. In countries where both hospital beds per capita and in-patient admission rates are available, they are highly correlated.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Health workforce</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Health professionals (physicians, psychiatrists, and surgeons) per capita, relative to maximum thresholds for each cadre</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of physicians, psychiatrists and surgeons</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total population</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>National Health Workforce Accounts. This includes reported data from Member States based on national registry of health workers, ideally coupled with regular assessment of completeness using census data, labour force surveys, professional association registers, or facility censuses.</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>The classification of health workers is based on criteria for vocational education and training, regulation of health professions, and activities and tasks of jobs, i.e., a framework for categorizing key workforce variables according to shared characteristics. The WHO framework largely draws on the latest revisions to the internationally standardized classification systems of the International Labour Organization (International Standard Classification of Occupations), 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). Methodological details can be found here: <a href="https://www.who.int/activities/improving-health-workforce-data-and-evidence">https://www.who.int/activities/improving-health-workforce-data-and-evidence</a></p> <p>Health workforce data can be accessed on the NHWA data portal: <a href="https://apps.who.int/nhwaportal/">https://apps.who.int/nhwaportal/</a></p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>Using available data, the indicator is computed by first rescaling, separately, health worker density ratios for each of the three cadres (physicians, psychiatrists and surgeons) relative to the minimum observed values across OECD countries since 2000 (OECD Health Statistics database, 2015 edition), which are as follows: physicians = 0.9 per 1000, psychiatrists = 1 per 100,000, and surgeons = 14 per 100,000. This rescaling is done in the same way as that for the hospital bed density indicator described above, resulting in indicator values that range from 0 to 100 for each of the three cadres. For example, using country data on physicians per 1000 population (<em>x</em>), the cadre-specific indicator would be computed as:</p> <ul> <li>Country with <em>x</em> < 0.9 per 1000 per year, the cadre-specific indicator = <em>x</em> /0.9100. </li> <li>Country with <em>x</em> >= 0.9 per 1000 per year, the cadre-specific indicator = 100.</li> </ul> <p>As a final step, the geometric mean of the three cadre-specific indicator values is computed to obtain the final indicator of health workforce density.</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Due to major challenges measuring coverage in all health areas, which leaves major gaps for important areas such as routine medical exams, treatment for mental illnesses, emergency care and surgical procedure, proxies are used. Physician, psychiatrist and surgeon densities are used as proxies for the full coverage of outpatient care, mental health care and emergency/surgical care services, respectively. It should be noted that those measures are difficult to interpret because the optimal level for those indicators is unknown and they do not relate to a specific need for services. Despite this fact, low levels for these indicators are indicative of poor access to and use of essential health services.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Tracer area</p> </td> <td> <p>Health security</p> </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>International Health Regulations (IHR) core capacity index, which is the average percentage of attributes of all core capacities that have been attained at a specific point in time. </p> <p>The second edition SPAR tool has been expanded from 13 to 15 capacities. The 15 core capacities are (1) Policy, legal and normative instruments to implement IHR; (2) IHR Coordination and National Focal Point Functions; (3) Financing; (4) Laboratory; (5) Surveillance; (6) Human resources; (7) Health emergency management (8) Health Service Provision; (9) Infection Prevention and Control; (10) Risk communication and community engagement; (11) Points of entry and border health; (12) Zoonotic diseases; (13) Food safety; (14) Chemical events; (15) Radiation emergencies. </p> <p>The 13 core capacities of the first edition of the IHR State Parties Annual Assessment and Reporting Tool are (1) Legislation and financing; (2) IHR Coordination and National Focal Point Functions; (3) Zoonotic events and the Human-Animal Health Interface; (4) Food safety; (5) Laboratory; (6) Surveillance; (7) Human resources; (8) National Health Emergency Framework; (9) Health Service Provision; (10) Risk communication; (11) Points of entry; (12) Chemical events; (13) Radiation emergencies. </p> <p>Both SPAR questionnaires (1st and 2nd editions) use a five-level scoring with indicators based on five cumulative levels to measure the implementation status for each capacity. For each indicator, the reporting State Party is asked to select which of the five levels best describes the State Party's current status. To move to the next level, all capacities described in previous levels should be in place for each indicator.</p> <p>For the years 2010 to 2017, Member States used the IHR monitoring questionnaire. The questionnaire is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards. Individual questions are grouped by components and indicators in the questionnaires. States Parties can provide additional information on the questions in the comment boxes. Responses to the questions include marking one appropriate value (Yes, No, or Not Known) or the appropriate percentages. For statistical purposes, the "Not Known" value will be computed as a "No" value. The IHR monitoring questionnaire includes the following: IHR01. National legislation, policy and financing; IHR02. Coordination and National Focal Point communications; IHR03. Surveillance; IHR04. Response; IHR05. Preparedness; IHR06. Risk communication; IHR07. Human resources; IHR08. Laboratory; IHR09. Points of entry; IHR10. Zoonotic events; IHR11. Food safety; IHR12. Chemical events; IHR13. Radio nuclear emergencies.</p> </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of attributes attained</p> </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Total number of attributes</p> </td> </tr> <tr> <td> <p>Main data sources</p> </td> <td> <p>Key informant survey</p> </td> </tr> <tr> <td> <p>Method of measurement</p> </td> <td> <p>Key informants report on attainment of a set of attributes for each of the core capacities using a standard WHO instrument. This instrument is based on a self-assessment and self-reporting by the State Party. There are three datasets based on the different tools to collect data for SPAR. For the period 2010 to 2017, the questionnaire, known as the IHR monitoring questionnaire, is divided into thirteen sections, one for each of the eight core capacities, PoE and four hazards and information on the status of implementation for each capacity. The IHR monitoring questionnaire ( 2010 to 2017) was replaced by the IHR State Parties Self-Assessment Tool – SPAR, published in July 2018 also known as SPAR 1st edition. The States Parties used the questionnaire from the 2018 – 2020 SPAR reporting cycle. The current questionnaire replaced the SPAR 1st edition and was used by the Member States for 2021. Under each capacity, the indicators were either retained, replaced or added. Historical trends based on the data for similar capacity titles may be taken with caution.</p> </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <p>The score of each indicator level is classified as a percentage of performance along the “1 to 5” scale. e.g. for a country selecting level 3 for indicator 2.1, the indicator level will be expressed as: 3/5100=60% CAPACITY LEVEL The level of the capacity is expressed as the average of all indicators. e.g. for a country selecting level 3 for indicator 2.1 and level 4 for indicator 2.2. Indicator level for 2.1 will be expressed as: 3/5100=60%, indicator level for 2.2 will be expressed as: 4/5100=80% and capacity level for 2 will be expressed as: (60+80)/2=70%</p> </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>Countries began reporting IHR core capacity attainment to WHO for the year 2010. The earliest available IHR score for each country is used for all years 2000-2009.</p> </td> </tr> </tbody> </table> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p><strong>Data availability:</strong></p> <p>Summarizing data availability for the universal health coverage (UHC) service coverage index is not straightforward, as different data sources are used across the 14 tracer indicators. Additionally, for many indicators comparable estimates have been produced, in many cases drawing on different types of underlying data sources to inform the estimates while also using projections to impute missing values. </p> <p><strong>Time series:</strong></p> <p>A baseline value for the UHC service coverage index for 2015 across 183 countries was published in late 2017. As part of this process, data sources going back to 2000 were assembled. In 2019, UHC service coverage index were estimates for the years: 2000, 2005, 2010, 2015 and 2017. From 2021, the index is estimated every two years for all countries (i.e. 194 WHO member states).</p> <p><strong>Disaggregation:</strong></p> <p>Equity is central to the definition of UHC, and therefore the UHC service coverage index should be used to communicate information about inequalities in service coverage within countries. This can be done by presenting the index separately for the national population vs disadvantaged populations to highlight differences between them. </p> <p> </p> <p>For countries, geographic location is likely the most feasible dimension for sub-national disaggregation based on average coverage levels measured with existing data sources. To do this, the UHC index can be computed separately by, e.g., province or urban vs rural residence, which would allow for subnational comparisons of service coverage. Currently, the most readily available data for disaggregation on other dimensions of inequality, such as household wealth, is for indicators of coverage within the reproductive, maternal, newborn and child health services category. Inequality observed in this dimension can be used as a proxy to understand differences in service coverage across key inequality dimensions. This approach should be replaced with full disaggregation of all 14 tracer indicators once data are available to do so. </p> English 75 characters edited Current translation Read only <p><strong>Data availability:</strong></p> <p>Summarizing data availability for the universal health coverage (UHC) service coverage index is not straightforward, as different data sources are used across the 14 tracer indicators. Additionally, for many indicators comparable estimates have been produced, in many cases drawing on different types of underlying data sources to inform the estimates while also using projections to impute missing values. </p> <p><strong>Time series:</strong></p> <p>A baseline value for the UHC service coverage index for 2015 across 183 countries was published in late 2017. As part of this process, data sources going back to 2000 were assembled. In 2019, UHC service coverage index were estimates for the years: 2000, 2005, 2010, 2015 and 2017. InFrom 2021, the index is estimated ~~for all years from 2000 to 2019 and for all countries~~every two years for all countries (i.e. 194 WHO member states).</p> <p><strong>Disaggregation:</strong></p> <p>Equity is central to the definition of UHC, and therefore the UHC service coverage index should be used to communicate information about inequalities in service coverage within countries. This can be done by presenting the index separately for the national population vs disadvantaged populations to highlight differences between them. </p> <p> </p> <p>For countries, geographic location is likely the most feasible dimension for sub-national disaggregation based on average coverage levels measured with existing data sources. To do this, the UHC index can be computed separately by, e.g., province or urban vs rural residence, which would allow for subnational comparisons of service coverage. Currently, the most readily available data for disaggregation on other dimensions of inequality, such as household wealth, is for indicators of coverage within the reproductive, maternal, newborn and child health services category. Inequality observed in this dimension can be used as a proxy to understand differences in service coverage across key inequality dimensions. This approach should be replaced with full disaggregation of all 14 tracer indicators once data are available to do so. </p> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p>See 4.d Validation</p> English 25 characters edited Current translation Read only <p>See 4.d Validation</p> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p>Not applicable</p> English 21 characters edited Current translation Read only <p>Not applicable</p> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p>Not applicable</p> English 21 characters edited Current translation Read only <p>Not applicable</p> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p>Not applicable</p> English 21 characters edited Current translation Read only <p>Not applicable</p> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p>Regional and global aggregates use United Nations population estimates at the country level to compute a weighted average of country values for the index. This is justified because universal health coverage (UHC) is a property of countries, and the index of essential services is a summary measure of access to essential services for each country’s population. United Nations population estimates at country level are used to ensure consistency and comparability of estimates within countries and between countries over time. </p> English 28 characters edited Current translation Read only <p>Regional and global aggregates use United Nations population estimates at the country level to compute a weighted average of country values for the index. This is justified because universal health coverage (UHC) is a property of countries, and the index of essential services is a summary measure of access to essential services for each country’s population. United Nations population estimates at country level are used to ensure consistency and comparability of estimates within countries and between countries over time. </p> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <ul> <li><strong>At country level </strong></li> </ul> <p>The starting point for computing the index is to assemble existing information for each tracer indicator. In many cases, this involves using country time series that have been produced or collated by UN agencies in consultation with country governments (e.g., immunization coverage, access to sanitation, HIV treatment coverage, etc). Some of these published time series involve mathematical modelling to reconcile multiple data sources or impute missing values, and these details are summarized in Annex 1. </p> <p> </p> <p>After assembling these inputs, there are still missing values for some country-years for some indicators. Calculating the universal health coverage (UHC) service coverage index requires values for each tracer indicator for a country, so some imputation is necessary to fill these data gaps. The current approach involves a simple imputation algorithm. For each indicator: </p> <ul> <li>If a country has missing values between two years with values, linear interpolation is used to fill missing values for the intervening years </li> <li>If a country has historical years with values, but no current value, constant extrapolation is used to fill missing values to the current year </li> <li>If a country has no values, a value is imputed. For pneumonia care-seeking and density of surgeons, a regression is fit to impute missing values (see Annex 1 for details). For all other indicators, a regional median is calculated to impute missing values. By default, regions are based on UN SDG subregions. However, when there are not enough countries within UN SDG subregions with available data, other groupings can be used.</li> </ul> <p> </p> <p>Given the timing and distribution of various health surveys and other data collection mechanisms, countries do not collect and report on all 14 tracer indicators of health service coverage on an annual basis. In addition, monitoring at country level is most suitably done at broader time intervals, e.g., every 5 years, to allow for new data collection across indicators. Therefore, the extent to which imputation has been used to fill missing information should be communicated along with the index value. </p> <p> </p> <ul> <li><strong>At regional and global levels </strong></li> </ul> <p>Any needed imputation is done at country level. These country values can then be used to compute regional and global values. </p> English 153 characters edited Current translation Read only <ul> <li><strong>At country level </strong></li> </ul> <p>The starting point for computing the index is to assemble existing information for each tracer indicator. In many cases, this involves using country time series that have been produced or collated by UN agencies in consultation with country governments (e.g., immunization coverage, access to sanitation, HIV treatment coverage, etc). Some of these published time series involve mathematical modelling to reconcile multiple data sources or impute missing values, and these details are summarized in Annex 1. </p> <p> </p> <p>After assembling these inputs, there are still missing values for some country-years for some indicators. Calculating the universal health coverage (UHC) service coverage index requires values for each tracer indicator for a country, so some imputation is necessary to fill these data gaps. The current approach involves a simple imputation algorithm. For each indicator: </p> <ul> <li>If a country has missing values between two years with values, linear interpolation is used to fill missing values for the intervening years </li> <li>If a country has historical years with values, but no current value, constant extrapolation is used to fill missing values to the current year </li> <li>If a country has no values, a value is imputed. For pneumonia care-seeking and density of surgeons, a regression is fit to impute missing values (see Annex 1 for details). For all other indicators, a regional median is calculated to impute missing values. RBy default, regions are based on ~~World Bank geographic~~ UN SDG subregions. However, when there are not enough countries within UN SDG subregions, with a ~~separate grouping of traditional high-income countries<sup>1</sup>~~ vailable data, other groupings can be used.</li> </ul> <p> </p> <p>Given the timing and distribution of various health surveys and other data collection mechanisms, countries do not collect and report on all 14 tracer indicators of health service coverage on an annual basis. In addition, monitoring at country level is most suitably done at broader time intervals, e.g., every 5 years, to allow for new data collection across indicators. Therefore, the extent to which imputation has been used to fill missing information should be communicated along with the index value. </p> <p> </p> <ul> <li><strong>At regional and global levels </strong></li> </ul> <p>Any needed imputation is done at country level. These country values can then be used to compute regional and global onvalues. </p> a year ago
None String updated in the repository SDG Metadata 3-8-1 English	English <p>Not applicable</p> English 1 character edited Current translation Read only <p>Not applicable.</p> a year ago

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