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Overview
| Project website | github.com/worldbank/sdg-metadata | |
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| Instructions for translators | This project is limited to Russian translation only, for now. More detailed instructions to come. |
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| Project maintainers |
 brockfanning
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| Translation license | MIT License | |
| Translation process |
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| Source code repository |
https://github.com/worldbank/sdg-metadata
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| Repository branch | master | |
| Last remote commit |
Merge pull request #553 from weblate/weblate-sdg-metadata-1-1-1a
d3a3ca3e2bb
brockfanning authored 21 hours ago |
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| Last commit in Weblate |
Merge pull request #553 from weblate/weblate-sdg-metadata-1-1-1a
d3a3ca3e2bb
brockfanning authored 21 hours ago |
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| Weblate repository |
https://hosted.weblate.org/git/sdg-metadata/1-1-1a/
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| File mask | translations-metadata/*/3-8-1.yml |
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| Monolingual base language file | translations-metadata/en/3-8-1.yml |
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| Translation file |
Download
translations-metadata/es/3-8-1.yml
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| Last change | Feb. 7, 2023, 8:09 p.m. | |
| Last change made by | None | |
| Language | Spanish | |
| Language code | es | |
| Text direction | Left to right | |
| Number of speakers | 506,540,366 | |
| Number of plurals | 2 | |
| Plural type | One/other | |
| Plurals | Singular | 1 | Plural | 0, 2, 3, 4, 5, 6, 7, 8, 9, 10, … |
| Plural formula | n != 1 |
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| Strings percent | Hosted strings | Words percent | Hosted words | Characters percent | Hosted characters | |
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| Total | 32 | 9,014 | 141,394 | |||
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| Needs editing | 65% | 21 | 97% | 8,830 | 98% | 139,939 |
| Read-only | 0% | 0 | 0% | 0 | 0% | 0 |
| Failing checks | 65% | 21 | 97% | 8,804 | 98% | 139,723 |
| Strings with suggestions | 0% | 0 | 0% | 0 | 0% | 0 |
| Untranslated strings | 34% | 11 | 2% | 184 | 1% | 1,455 |
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<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> /18*100. </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> /0.9*100. </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: 3/5*100=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: 3/5*100=60%, indicator level for 2.2 will be expressed as: 4/5*100=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>
<h1>Referencias</h1>
<h2><strong>URL: </strong></h2> <p><a href="http://www.who.int/healthinfo/universal_health_coverage/en/">http://www.who.int/healthinfo/universal_health_coverage/en/</a></p> <h2><strong>Referencias: </strong></h2> <p><a href="http://www.who.int/healthinfo/universal_health_coverage/report/2017/en/">http://www.who.int/healthinfo/universal_health_coverage/report/2017/en/</a></p> <p><a href="http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf">http://www.thelancet.com/pdfs/journals/langlo/PIIS2214-109X(17)30472-2.pdf</a></p> <p><a href="http://www.who.int/healthinfo/universal_health_coverage/en/">http://www.who.int/healthinfo/universal_health_coverage/en/</a></p> <p>Para la evolución histórica de los métodos, véase:</p> <p><a href="http://www.who.int/healthinfo/universal_health_coverage/UHC_WHS2016_TechnicalNote_May2016.pdf?ua=1">http://www.who.int/healthinfo/universal_health_coverage/UHC_WHS2016_TechnicalNote_May2016.pdf?ua=1</a> (sustituido por este documento)</p> <p><a href="http://www.who.int/healthinfo/universal_health_coverage/report/2015/en/">http://www.who.int/healthinfo/universal_health_coverage/report/2015/en/</a></p> <p><a href="http://www.who.int/healthinfo/universal_health_coverage/report/2014/en/">http://www.who.int/healthinfo/universal_health_coverage/report/2014/en/</a></p> <p><a href="http://collections.plos.org/uhc2014">http://collections.plos.org/uhc2014</a></p> <h1>Anexo 1: Metadatos de los indicadores trazadores utilizados para medir la cobertura de los servicios sanitarios esenciales para el seguimiento del indicador 3.8.1 de los ODS.</h1> <p>Por favor, envíe cualquier comentario o consulta a: <a href="mailto:uhc_stats@who.int">uhc_stats@who.int</a></p> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Planificación familiar</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de mujeres en edad reproductiva (15−49 años) casadas o en unión libre que tienen su necesidad de planificación familiar satisfecha con métodos modernos</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Número de mujeres de 15 a 49 años casadas o unidas que utilizan métodos modernos</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número total de mujeres de 15 a 49 años casadas o unidas que necesitan planificación familiar</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Encuestas de salud basadas en la población</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Las encuestas de hogares incluyen una serie de preguntas para medir la tasa de prevalencia de anticonceptivos modernos y la demanda de planificación familiar. La demanda total de planificación familiar se define como la suma del número de mujeres en edad reproductiva (15–49 años) que están casadas o en pareja y que actualmente utilizan, o cuya pareja sexual utiliza actualmente, al menos un método anticonceptivo, y la necesidad insatisfecha de planificación familiar. La necesidad insatisfecha de planificación familiar es la proporción de mujeres en edad reproductiva (15–49 años) casadas o en una unión consensuada, que son fecundas y sexualmente activas pero que no utilizan ningún método anticonceptivo (moderno o tradicional), y que declaran no querer tener más hijos o querer retrasar el nacimiento de su siguiente hijo durante al menos dos años. Se incluyen:</p> <p>todas las mujeres embarazadas (casadas o en una unión consensuada) cuyos embarazos fueron no deseados o inoportunos en el momento de la concepción;</p> <p>todas las mujeres amenorreicas posparto (casadas o en unión consensuada) que no utilizan planificación familiar y cuyo último parto fue no deseado o inoportuno;</p> <p>Todas las mujeres fecundas (casadas o en unión consensuada) que no están embarazadas ni son amenorreicas posparto, y que o bien no quieren tener más hijos (quieren limitar el tamaño de la familia), o bien desean posponer el nacimiento de un hijo durante al menos dos años o no saben cuándo o si quieren tener otro hijo (quieren espaciar los nacimientos), pero no están utilizando ningún método anticonceptivo.</p> <p>Los métodos modernos incluyen la esterilización femenina y masculina, el dispositivo intrauterino (DIU), el implante, los inyectables, las píldoras anticonceptivas orales, los preservativos masculinos y femeninos, los métodos vaginales de barrera (incluidos el diafragma, el capuchón cervical y la espuma, la jalea, la crema y la esponja espermicida), el método de amenorrea de la lactancia (LAM, por su sigla en inglés), la anticoncepción de emergencia y otros métodos modernos no informados por separado.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>La División de Población de las Naciones Unidas produce una serie sistemática y completa de estimaciones y proyecciones anuales del porcentaje de demanda de planificación familiar que se satisface entre las mujeres casadas o unidas. Se utiliza un modelo jerárquico bayesiano combinado con datos específicos de cada país para generar las estimaciones, proyecciones y evaluaciones de incertidumbre a partir de los datos de las encuestas. El modelo tiene en cuenta las diferencias según la fuente de datos, la población de la muestra y los métodos anticonceptivos.</p> <p>Vea aquí los detalles: <a href="http://www.un.org/en/development/desa/population/theme/family-planning/cp_model.shtml">http://www.un.org/en/development/desa/population/theme/family-planning/cp_model.shtml</a></p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Atención al embarazo y al parto</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de mujeres de entre 15 y 49 años con un nacimiento vivo en un periodo de tiempo determinado que recibieron atención prenatal cuatro o más veces</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Número de mujeres de 15−49 años con un nacimiento vivo en un período de tiempo determinado que recibieron atención prenatal cuatro o más veces</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número total de mujeres de 15−49 años con un nacimiento vivo en el mismo período</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Encuestas a hogares y sistemas de información rutinarios de los establecimientos</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Los datos sobre cuatro o más visitas de atención prenatal se basan en preguntas que consultan si se controló la salud de la mujer durante el embarazo y cuántas veces. Las encuestas a hogares que pueden generar este indicador incluyen DHS, MICS, RHS (por sus siglas en inglés) y otras encuestas basadas en metodologías similares. Los sistemas de notificación de servicios/centros pueden utilizarse cuando la cobertura es alta, normalmente en los países de ingresos más altos.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>La OMS mantiene una base de datos sobre la cobertura de la atención prenatal: <a href="http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4">http://apps.who.int/gho/data/node.main.ANTENATALCARECOVERAGE4</a>.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>En principio, este indicador se sustituiría por una medida más exhaustiva de la atención al embarazo y al parto, por ejemplo, la proporción de mujeres que tienen un proveedor cualificado que asiste al parto o un parto institucional. Un reto a la hora de medir la asistencia cualificada al parto es determinar qué proveedores son “calificados”.</p> </td> </tr> </tbody> </table> <p><br></p> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Inmunización infantil</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de lactantes que reciben tres dosis de la vacuna que contiene difteria-tétanos-tos ferina.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Niños de 1 año de edad que han recibido tres dosis de la vacuna que contiene difteria-tétanos-tos ferina.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Todos los niños de 1 año de edad.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos.</p> </td> <td> <p>Encuestas a hogares y sistemas de información de instalaciones.</p> </td> </tr> <tr> <td> <p>Método de medición.</p> </td> <td> <p>Para los datos de la encuesta, el estado de vacunación de los niños de 12–23 meses se recopila de las tarjetas de salud infantil o, si no hay tarjeta, del recuerdo del cuidador. Para los datos administrativos, se extrae el número total de dosis administradas a la población objetivo.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Juntos, la OMS y el UNICEF obtienen estimaciones de la cobertura de la DTP3 basadas en los datos comunicados oficialmente a la OMS y al UNICEF por los Estados Miembros, así como en los datos comunicados en la literatura publicada y gris. También consultan a expertos locales -principalmente a los gestores nacionales del PAI y al personal de las oficinas regionales de la OMS- para obtener información adicional sobre el funcionamiento de servicios de inmunización locales específicos. Sobre la base de los datos disponibles, la consideración de posibles sesgos y las contribuciones de los expertos locales, la OMS y el UNICEF determinan el nivel real más probable de cobertura de inmunización </p> <p>Para más detalles, véase aquí:</p> <p><a href="http://www.who.int/bulletin/volumes/87/7/08-053819/en/">http://www.who.int/bulletin/volumes/87/7/08-053819/en/</a></p> <p><a href="http://www.who.int/immunization/monitoring_surveillance/routine/coverage/en/index4.html">http://www.who.int/immunization/monitoring_surveillance/routine/coverage/en/index4.html</a></p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>Existe una variabilidad en los calendarios nacionales de vacunación entre los países. Teniendo en cuenta esto, una opción para supervisar la inmunización completa de los niños es supervisar la fracción de niños que reciben las vacunas incluidas en el calendario nacional de su país. Una segunda opción, que puede ser más comparable entre países y en el tiempo, es vigilar la cobertura de la DTP3 como indicador de la inmunización infantil completa. La vacuna que contiene difteria-tétanos-tos ferina suele incluir otras vacunas, por ejemplo, contra la hepatitis B y el Haemophilus influenza tipo B, y es una medida razonable del grado de solidez de la plataforma de vacunación de un país.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Tratamiento de niños (búsqueda de atención por síntomas de neumonía).</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de niños menores de 5 años con sospecha de neumonía (tos y dificultad para respirar NO debidas a un problema en el pecho y a una nariz tapada) en las dos semanas anteriores a la encuesta llevados a un centro o proveedor de salud adecuado.</p> </td> </tr> <tr> <td>. <p>Numerador</p> </td> <td> <p>Número de niños con sospecha de neumonía en las dos semanas anteriores a la encuesta llevados a un proveedor de salud adecuado.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número de niños con sospecha de neumonía en las dos semanas anteriores a la encuesta.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Encuestas de hogares.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Durante la Reunión de UNICEF/OMS sobre indicadores basados en encuestas de supervivencia infantil, celebrada en Nueva York el 17–18 de junio de 2004, se recomendó que las infecciones respiratorias agudas (IRA) se describieran como “presunta neumonía” para reflejar mejor la causa probable y las intervenciones recomendadas. La definición de presunta neumonía utilizada en las Encuestas Demográficas y de Salud (EDS) y en las Encuestas de Indicadores Múltiples por Conglomerados (MICS, por su sigla en inglés) fue elegida por el grupo y se basa en la percepción de las madres de que un niño tiene tos, respira más rápido de lo habitual con respiraciones cortas y rápidas o tiene dificultades para respirar, excluyendo a los niños que sólo tenían la nariz tapada. La definición de "proveedor de atención adecuada" varía según los países.</p> <p>La OMS mantiene una base de datos de observaciones a nivel de país de las encuestas de hogares a la que se puede acceder aquí: <a href="http://apps.who.int/gho/data/node.main.38?lang=en">http://apps.who.int/gho/data/node.main.38?lang=en</a>.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Actualmente no existen estimaciones comparables a nivel internacional para este indicador.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>Este indicador no suele medirse en los países de mayor renta con sistemas sanitarios bien establecidos.</p> <p>Para los países sin datos observados, la cobertura se estimó a partir de una regresión que predice la cobertura de la búsqueda de atención para los síntomas de la neumonía (en la escala logit), obtenida de la base de datos de la OMS descrita anteriormente, en función del logaritmo de la tasa estimada de mortalidad por neumonía en menores de cinco años, que se puede encontrar aquí: <a href="https://www.who.int/healthinfo/global_burden_disease/estimates/en/index2.html">https://www.who.int/healthinfo/global_burden_disease/estimates/en/index2.html</a>.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Tratamiento de la tuberculosis.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de casos de incidencia de tuberculosis que se detectan y tratan con éxito en un año determinado.</p> </td> </tr> <tr>. <td> <p>Numerador</p> </td> <td> <p>Número de casos nuevos y de recaída detectados en un año determinado y tratados con éxito.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número de casos nuevos y de recaídas en el mismo año.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Sistemas de información de los centros, sistemas de vigilancia, encuestas de salud basadas en la población con pruebas de diagnóstico de la tuberculosis, registro de la tuberculosis y sistema de notificación trimestral relacionado (o registros electrónicos de la tuberculosis)</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Este indicador requiere tres insumos principales:</p> <p>(1) El número de casos de tuberculosis nuevos y de recaída diagnosticados y tratados en los programas nacionales de control de la tuberculosis y notificados a la OMS en un año determinado.</p> <p>(2) El número de casos incidentes de TB para el mismo año, normalmente estimado por la OMS.</p> <p>(3) Porcentaje de casos de tuberculosis tratados con éxito (curados más tratamiento completado) entre los casos de tuberculosis notificados a las autoridades sanitarias nacionales.</p> <p>El indicador final = (1)/(2) x (3).</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Las estimaciones de la incidencia de la tuberculosis se elaboran mediante un proceso consultivo y analítico dirigido por la OMS y se publican anualmente. Estas estimaciones se basan en las notificaciones anuales de casos, las evaluaciones de la calidad y la cobertura de los datos de notificación de la tuberculosis, las encuestas nacionales de prevalencia de la enfermedad de la tuberculosis y la información de los sistemas de registro de defunciones (vitales). Las estimaciones de incidencia para cada país se derivan, utilizando uno o más de los siguientes enfoques, dependiendo de los datos disponibles:</p> <p>1. incidencia = notificaciones de casos/proporción estimada de casos detectados;</p> <p>2. incidencia = prevalencia/duración de la enfermedad;</p> <p>3. incidencia = muertes/proporción de casos incidentes que fallecen.</p> <p>Estas estimaciones de la incidencia de la tuberculosis se combinan con los datos notificados por los países sobre el número de casos detectados y tratados, y el porcentaje de casos tratados con éxito, como se ha descrito anteriormente.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>Para calcular el indicador utilizando las estimaciones de la OMS, se puede acceder a los archivos necesarios aquí: <a href="http://www.who.int/tb/country/data/download/en/">http://www.who.int/tb/country/data/download/en/</a>, y calcular el indicador como = c_cdr x c_new_tsr.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Tratamiento del VIH.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de personas que viven con el VIH y que actualmente reciben terapia antirretroviral (ART).</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td>. <p>Número de adultos y niños que están recibiendo actualmente tratamiento antirretroviral al final del período de notificación.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número de adultos y niños que viven con el VIH durante el mismo periodo.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Sistemas de notificación de los centros, sitios de vigilancia centinela, encuestas basadas en la población.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Numerador: El numerador puede generarse contando el número de adultos y niños que recibieron terapia combinada antirretroviral al final del periodo de notificación. Los datos pueden recopilarse a partir de los registros de tratamiento antirretroviral de los centros o de los sistemas de gestión de suministros de medicamentos. A continuación, se contabilizan y se transfieren a informes transversales mensuales o trimestrales que pueden agregarse a los totales nacionales. Los pacientes que reciben terapia antirretroviral en el sector privado y en el sector público deben incluirse en el numerador.</p> <p>Denominador: Los datos sobre el número de personas infectadas por el VIH pueden proceder de encuestas de población o, como es habitual en el África subsahariana, de sistemas de vigilancia basados en las clínicas de atención prenatal.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Las estimaciones de la cobertura del tratamiento antirretroviral entre las personas que viven con el VIH para el período 2000-2018 se derivan como parte de la ronda de estimación de ONUSIDA de 2019.</p> <p>Para estimar el número de personas que viven con el VIH a lo largo del tiempo en los países con alta carga, ONU/SIDA, en colaboración con los países, utiliza un modelo epidémico (Spectrum) que combina los datos de vigilancia sobre la prevalencia con el número actual de pacientes que reciben tratamiento antirretroviral y las hipótesis sobre la historia natural de la progresión de la enfermedad del VIH.</p> <p>Dado que la terapia antirretrovírica se recomienda ahora a todas las personas que viven con el VIH, el seguimiento de la cobertura de la terapia antirretrovírica es menos complicado que antes, cuando sólo podían recibirla las personas con un determinado nivel de gravedad de la enfermedad.</p> <p>Las estimaciones de la cobertura del TAR se pueden encontrar aquí: http://aidsinfo.unaids.org/.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>No siempre se dispone de estimaciones comparables de la cobertura de la terapia antirretroviral en los países de renta alta, en particular de las tendencias temporales.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Prevención de la malaria.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de la población de las zonas donde la malaria es endémica que durmió con un mosquitero impregnado de insecticida la noche anterior.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Número de personas en zonas donde la malaria es endémica que durmieron bajo un MTI.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número total de personas en zonas donde la malaria es endémica.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Los datos sobre el acceso y el uso de los mosquiteros por parte de los hogares provienen de encuestas a hogares representativas a nivel nacional, como las Encuestas Demográficas y de Salud, las Encuestas de Indicadores Múltiples por Conglomerados y las Encuestas de Indicadores de Malaria. Los datos sobre el número de MTI entregados por los fabricantes a los países son recopilados por Milliner Global Associates, y los datos sobre el número de MTI distribuidos dentro de los países son comunicados por los Programas Nacionales de Control de la Malaria.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Muchas encuestas nacionales recientes informan del número de MTI observados en cada hogar encuestado. Las tasas de propiedad pueden convertirse en la proporción de personas que duermen bajo un MTI utilizando una relación lineal entre el acceso y el uso que se ha derivado de 62 encuestas que recopilan información sobre ambos indicadores.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Se pueden utilizar modelos matemáticos para combinar los datos de las encuestas a hogares sobre el acceso y el uso con la información sobre la entrega de MTI por parte de los fabricantes y la distribución de MTI por parte de los programas nacionales de malaria para producir estimaciones anuales de la cobertura de MTI. La OMS utiliza este enfoque en colaboración con el Proyecto Atlas del Paludismo. Los detalles metodológicos se encuentran en el Anexo del Informe Mundial sobre el Paludismo 2015: <a href="http://www.who.int/malaria/publications/world-malaria-report-2015/report/en/">http://www.who.int/malaria/publications/world-malaria-report-2015/report/en/</a>.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>La OMS elabora estimaciones comparables de la cobertura de los MTI para 40 países con alta carga. Para otros países, la cobertura de los mosquiteros no se incluye en el índice de cobertura de servicios de CSU debido a las limitaciones de los datos.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Agua y saneamiento.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Porcentaje de hogares que utilizan al menos instalaciones de saneamiento básico.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Población que vive en un hogar con: cisterna o vaciado al sistema de alcantarillado por tuberías, fosa séptica o letrina de pozo; letrina de pozo mejorada y ventilada; letrina de pozo con losa; o inodoro de compostaje.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Población total.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Encuestas a hogares y censos basados en la población.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Las respuestas a nivel de hogar, ponderadas por el tamaño del mismo, se utilizan para calcular la cobertura de la población.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>El Programa Conjunto de Monitoreo de la OMS y el UNICEF ha elaborado estimaciones periódicas de la cobertura de al menos el saneamiento básico para el monitoreo de los ODM. Después de compilar una base de datos de fuentes de datos disponibles, para cada país, se ajustan regresiones lineales simples a las series de datos del país para obtener una estimación dentro de la muestra, así como para producir una extrapolación de 2 años más allá del último punto de datos disponible, después de lo cual la cobertura se mantiene constante durante 4 años y luego se asume que falta. Esto se hace por separado para las regiones urbanas y rurales, y luego se combina para obtener estimaciones de cobertura nacional. Los detalles de la metodología y las estimaciones más recientes pueden encontrarse aquí: http://www.wssinfo.org/.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>El indicador de los ODS para el saneamiento (ODS 6.2.1) es una versión ampliada del indicador de los ODM, que incorpora la calidad de las instalaciones de saneamiento. No se utiliza para el seguimiento de la cobertura sanitaria universal debido a la menor disponibilidad de datos. También podría considerarse un indicador conjunto que identifique la proporción de hogares con acceso tanto a agua potable como a saneamiento.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Prevención de enfermedades cardiovasculares.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Prevalencia estandarizada por edad de presión arterial normal entre los adultos mayores de 18 años, independientemente del estado de tratamiento.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Número de adultos de 18 años o más con presión arterial sistólica <140 mm Hg y presión arterial diastólica <90 mm Hg (independientemente del estado del tratamiento).</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número de adultos de 18 años o más.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Estudios basados en la población y sistemas de vigilancia.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Se utilizan las fuentes de datos que registran la presión arterial medida (se excluyen los datos autodeclarados). Si se toman varias lecturas de presión arterial por participante, se descarta la primera lectura y se promedian las restantes.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Para producir estimaciones nacionales comparables, las observaciones de datos de prevalencia definidas en términos de cortes alternativos de PAS y/o PAD se convierten en prevalencia de presión arterial elevada, definida como presión arterial sistólica >=140 mm Hg o presión arterial diastólica >=90 mm Hg utilizando ecuaciones de regresión. A continuación, se ajusta un modelo jerárquico bayesiano a estos datos para calcular las prevalencias específicas por edad, sexo y país, que tiene en cuenta las fuentes de datos nacionales frente a las sub-nacionales, las fuentes de datos urbanas frente a las rurales, y permite la variación de la prevalencia según la edad y el sexo. Las estimaciones estandarizadas por edad se obtienen aplicando las estimaciones brutas a la población estándar de la OMS. Los detalles de los métodos estadísticos se encuentran aquí: <a href="http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)31919-5/fulltext">http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(16)31919-5/fulltext</a>.</p> <p>La OMS y la NCD Risk Factor Collaboration (NCD-RisC, por su sigla en inglés) han elaborado estimaciones comparables para este indicador hasta el año 2015, que están disponibles aquí. </p> <p><a href="http://apps.who.int/gho/data/node.main.A875STANDARD?lang=en">http://apps.who.int/gho/data/node.main.A875STANDARD?lang=en</a>.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>Las estimaciones de prevalencia se convierten en la prevalencia de la presión arterial normal para su incorporación al índice CSU, de modo que un valor del 100% es el objetivo óptimo. Esto se calcula como: prevalencia de presión arterial normal = 1 – prevalencia de presión arterial elevada. Las estimaciones anteriores se realizan por separado para hombres y mujeres; para el indicador de seguimiento de la CSU se calcula una media simple de los valores de hombres y mujeres.</p> <p>La presión arterial normal es la suma del porcentaje de individuos que no tienen hipertensión y el porcentaje de individuos cuya hipertensión está controlada por la medicación. La ausencia de hipertensión es el resultado de los esfuerzos de prevención a través de la promoción de la actividad física y las dietas saludables, así como de otros factores. La hipertensión controlada con medicación es el resultado de un tratamiento eficaz. Este indicador es, por tanto, un indicador indirecto tanto de la promoción de la salud como de los servicios médicos eficaces.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Manejo de la diabetes</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Glucosa plasmática media en ayunas estandarizada por edad para adultos de 18 años o más.</p> </td> </tr> <tr> <td> <p>Fuentes de datos principales</p> </td> <td> <p>Estudios basados en la población y sistemas de vigilancia.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Los niveles de glucosa plasmática en ayunas (FPG, según su sigla en inglés) se determinan tomando una muestra de sangre de los participantes que han ayunado durante al menos 8 horas. Se utilizaron otros biomarcadores relacionados, como la hemoglobina A1c (HbA1c), para ayudar a calcular las estimaciones (véase más adelante).<p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Para producir estimaciones nacionales comparables, las observaciones de datos basadas en la FPG media, la prueba de tolerancia a la glucosa oral (OGTT, según su sigla en inglés), la HbA1c, o sus combinaciones, se convierten todas en FPG media. A continuación, se ajusta un modelo jerárquico bayesiano a estos datos para calcular las prevalencias específicas por edad, sexo y país, que tiene en cuenta las fuentes de datos nacionales frente a las sub-nacionales, las fuentes de datos urbanas frente a las rurales, y permite la variación de la prevalencia según la edad y el sexo. Las estimaciones estandarizadas por edad se obtienen aplicando las estimaciones brutas a la población estándar de la OMS. Los detalles metodológicos se pueden encontrar aquí: <a href="https://www.who.int/diabetes/global-report/en/">https://www.who.int/diabetes/global-report/en/</a>.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>La FPG de un individuo puede ser baja debido a un tratamiento eficaz con medicación reductora de la glucosa, o porque el individuo no es diabético como resultado de actividades de promoción de la salud u otros factores como la genética. La FPG media es, por tanto, un indicador de la promoción efectiva de dietas y comportamientos saludables y del tratamiento efectivo de la diabetes.</p> <p>Las estimaciones anteriores se realizan por separado para hombres y mujeres; para el indicador de seguimiento de la CSU se calcula una media simple de los valores para hombres y mujeres.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Control del tabaco.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Prevalencia estandarizada por edad de adultos >=15 años que no han fumado tabaco en los últimos 30 días.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Adultos de 15 años o más que no han fumado tabaco en los últimos 30 días.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Adultos de 15 años o más.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Encuestas a hogares.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>“El consumo actual de tabaco" incluye cigarrillos, puros, pipas o cualquier otro producto de tabaco fumado consumido en los últimos 30 días. Los datos se recopilan mediante autoinforme en las encuestas.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>La OMS estima la prevalencia del tabaquismo actual (no) con un modelo de metarregresión binomial negativa, que genera estimaciones comparables ajustando las diferencias en los grupos de edad y la definición de los indicadores entre las encuestas nacionales incluidas en el análisis. Estas estimaciones se realizan por separado para hombres y mujeres. Los detalles metodológicos se pueden encontrar aquí: <a href="http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)60264-1/supplemental">http://www.thelancet.com/journals/lancet/article/PIIS0140-6736(15)60264-1/supplemental</a>.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>La prevalencia de no fumar tabaco se calcula como 1 menos la prevalencia de fumar tabaco.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Acceso al hospital.</p> </td> </tr> <tr>. <td> <p>Definición del indicador</p> </td> <td> <p>Camas hospitalarias per cápita, en relación con un umbral máximo de 18 por cada 10.000 habitantes.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Número de camas de hospital (debe excluir las camas de parto).</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td>. <p>Población total.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Sistemas administrativos / Sistema de notificación de los centros de salud.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Se utilizan los sistemas administrativos de los países para totalizar el número de camas de hospital, que se dividen por la población total estimada, y se multiplican por 10.000.</p> <p>Las oficinas regionales de la OMS y otros grupos recopilan información sobre la densidad nacional de camas hospitalarias, incluyendo los siguientes recursos en línea:</p> <p><a href="https://rho.emro.who.int/rhodata/node.main.A36">https://rho.emro.who.int/rhodata/node.main.A36</a>.</p> <p>Observatorio regional AFRO de la OMS: <a href="http://www.aho.afro.who.int/en/data-statistics/hospital-beds-10-000-population">http://www.aho.afro.who.int/en/data-statistics/hospital-beds-10-000-population</a></p> <p>Base de datos europea de salud para todos EURO de la OMS: <a href="https://gateway.euro.who.int/en/datasets%20european-health-for-all-database/">https://gateway.euro.who.int/en/datasets european-health-for-all-database/</a>.</p> <p>OCDE: <a href="https://data.oecd.org/healtheqt/hospital-beds.htm">https://data.oecd.org/healtheqt/hospital-beds.htm</a>.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Utilizando los datos disponibles, el indicador se calcula en relación con un valor umbral de 18 camas de hospital por cada 10.000 habitantes. Este umbral está por debajo del mínimo observado en los países de renta alta de la OCDE (desde el año 2000) de 20 por 10.000 y suele corresponder a una tasa de ingresos hospitalarios de alrededor de 5 por 100 al año. Este indicador está diseñado para captar niveles bajos de capacidad hospitalaria; el umbral máximo se utiliza porque las densidades de camas hospitalarias muy altas no son necesariamente un uso eficiente de los recursos. El indicador se calcula de la siguiente manera, utilizando los datos del país sobre la densidad de camas hospitalarias (<em>x</em>), lo que da como resultado valores que van de 0 a 100:</p> <p>País con una densidad de camas hospitalarias <em>x</em> < 18 por 10.000 al año, el indicador = </p> <p><em>x</em> /18*100.</p> <p>País con una densidad de camas hospitalarias <em>x</em> >= 18 por 10.000 al año, el indicador = 100.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>Un indicador alternativo podría ser la tasa de ingresos hospitalarios, en relación con un umbral máximo. Sin embargo, ese indicador no se comunica actualmente de forma generalizada en todas las regiones, en particular en la Región de África. En los países en los que se dispone tanto de camas de hospital per cápita como de tasas de admisión de pacientes internos, están muy correlacionados.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Personal sanitario.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Profesionales de la salud (médicos, psiquiatras y cirujanos) per cápita, en relación con los umbrales máximos de cada cuadro.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Número de médicos, psiquiatras y cirujanos.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Población total.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Base de datos o registro nacional de personal sanitario, idealmente acoplado a una evaluación periódica de la exhaustividad mediante datos censales, registros de asociaciones profesionales o censos de centros.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>La clasificación de los trabajadores sanitarios se basa en criterios de formación profesional, regulación de las profesiones sanitarias y actividades y tareas de los puestos de trabajo, es decir, en un marco de categorización de las variables clave de la fuerza de trabajo según características compartidas. El marco de la OMS se basa en gran medida en las últimas revisiones de los sistemas de clasificación normalizados internacionalmente de la Organización Internacional del Trabajo (Clasificación Internacional Uniforme de Ocupaciones), la Organización de las Naciones Unidas para la Educación, la Ciencia y la Cultura (Clasificación Internacional Uniforme de la Enseñanza) y la División de Estadísticas de las Naciones Unidas (Clasificación Industrial Internacional Uniforme de Todas las Actividades Económicas). </p> <p>Los detalles metodológicos y los datos pueden encontrarse aquí: <a href="http://www.who.int/hrh/statistics/hwfstats/en/">http://www.who.int/hrh/statistics/hwfstats/en/</a>.</p> <p>Los datos proceden de las siguientes fuentes:</p> <p>Médicos: <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>Psiquiatras: <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>Cirujanos: <a href="http://apps.who.int/gho/data/node.main.HWF9?lang=en%20">http://apps.who.int/gho/data/node.main.HWF9?lang=en</a>.<p> <p>(los datos aquí fueron complementados por ediciones anteriores de la base de datos).</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>Utilizando los datos disponibles, el indicador se calcula reescalando primero, por separado, los ratios de densidad de personal sanitario para cada uno de los tres cuadros (médicos, psiquiatras y cirujanos) en relación con los valores mínimos observados en todos los países de la OCDE desde el año 2000, que son los siguientes: médicos = 0,9 por 1000, psiquiatras = 1 por 100.000 y cirujanos = 14 por 100.000. Este reajuste se realiza de la misma manera que el del indicador de densidad de camas hospitalarias descrito anteriormente, lo que da como resultado valores del indicador que van de 0 a 100 para cada uno de los tres cuadros. Por ejemplo, utilizando los datos del país sobre los médicos por cada 1000 habitantes (<em>x</em>), el indicador específico de los cuadros se calcularía como:</p> <p>País con <em>x</em> < 0,9 por 1000 al año, el indicador específico de cuadro = <em>x</em> /0,9*100.</p> <p>País con <em>x</em> >= 0,9 por 1000 al año, el indicador específico de cuadro = 100.</p> <p>Como paso final, se calcula la media geométrica de los tres valores del indicador específico de cuadro para obtener el indicador final de densidad de personal sanitario.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>La categoría de “médicos” se ampliaría idealmente para incluir a todos los “profesionales sanitarios básicos”, como las enfermeras y las comadronas. Sin embargo, no existe ninguna base de datos comparable a nivel internacional que utilice definiciones coherentes de los profesionales sanitarios básicos no médicos para poder realizar comparaciones totalmente precisas entre países.</p> <p>Para los países sin datos observados, la densidad de cirujanos se estimó a partir de una regresión que predice el logaritmo de cirujanos por cada 100.000, obtenida de la base de datos de la OMS descrita anteriormente, en función del logaritmo del PIB per cápita, estimado por el Banco Mundial.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Área del trazador</p> </td> <td> <p>Seguridad sanitaria.</p> </td> </tr> <tr> <td> <p>Definición del indicador</p> </td> <td> <p>Índice de capacidad básica del Reglamento Sanitario Internacional (RSI), que es el porcentaje medio de atributos de 13 capacidades básicas que se han alcanzado en un momento determinado.</p> <p>Las 13 capacidades básicas son: (1) Legislación, política y financiación nacional; (2) Coordinación y comunicaciones del Punto Focal Nacional; (3) Vigilancia; (4) Respuesta; (5) Preparación; (6) Comunicación de riesgos; (7) Recursos humanos; (8) Laboratorio; (9) Puntos de entrada; (10) Eventos zoonóticos; (11) Seguridad alimentaria; (12) Eventos químicos; (13) Emergencias radionucleares.</p> </td> </tr> <tr> <td> <p>Numerador</p> </td> <td> <p>Número de atributos alcanzados.</p> </td> </tr> <tr> <td> <p>Denominador</p> </td> <td> <p>Número total de atributos.</p> </td> </tr> <tr> <td> <p>Principales fuentes de datos</p> </td> <td> <p>Encuesta a informantes clave.</p> </td> </tr> <tr> <td> <p>Método de medición</p> </td> <td> <p>Los informantes clave informan sobre el logro de un conjunto de atributos para cada una de las 13 capacidades básicas utilizando un instrumento estándar de la OMS, como se describe aquí: <a href="http://apps.who.int/iris/bitstream/10665/84933/1/WHO_HSE_GCR_2013.2_eng.pdf">http://apps.who.int/iris/bitstream/10665/84933/1/WHO_HSE_GCR_2013.2_eng.pdf</a>.</p> <p>Los valores de los indicadores a nivel de capacidad se pueden encontrar aquí:</p> <p><a href="http://apps.who.int/gho/data/node.main.IHRSPARALL?lang=en">http://apps.who.int/gho/data/node.main.IHRSPARALL?lang=en</a>.</p> </td> </tr> <tr> <td> <p>Método de estimación</p> </td> <td> <p>El indicador se calcula promediando, entre las 13 capacidades básicas, el porcentaje de atributos de cada capacidad que se han alcanzado.</p> </td> </tr> <tr> <td> <p>Notas relacionadas con la CSU</p> </td> <td> <p>Los países comenzaron a informar a la OMS sobre el logro de la capacidad básica del RSI para el año 2010. Se utiliza la puntuación del RSI más antigua disponible para cada país para todos los años 2000-2009.</p> </td> </tr> </tbody> </table> |
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<p><strong>URL: </strong><a href="https://www.who.int/health
<p> </p> <p><strong>References: </strong><a href="https://www.who.int/ <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 <p>For historical development of methods, see: </p> <p><a href="https://www.who.int/ <p><a href="https://www.who.int/ <p><a href="http://www.who.int/healthinfo/universal_health_coverage/report/2014/en/" target="_blank"><u <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 </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 </td> </tr> </tbody> </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/ <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 </td> </tr> <tr> <td> <p>Indicator definition</p> </td> <td> <p>Percentage of children youn </td> </tr> <tr> <td> <p>Numerator</p> </td> <td> <p>Number of children </td> </tr> <tr> <td> <p>Denominator</p> </td> <td> <p>Number of children </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> <p>WHO/UNICEF maintains a data </td> </tr> <tr> <td> <p>Method of estimation</p> </td> <td> <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 </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 </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 <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 <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:// </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 </td> </tr> <tr> <td> <p>UHC-related notes</p> </td> <td> <p>WHO produces comparable ITN coverage estimates for 40 <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 </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 </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. </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. </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 <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> </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 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> /18*100. </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> /0.9*100. </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 </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> /18*100. </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> /0.9*100. </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: 3/5*100=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: 3/5*100=60%, indicator level for 2.2 will be expressed as: 4/5*100=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> |
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<p><strong>Sources of discrepancies:</strong></p>
<p>The service coverage index draws on existing, publicly available data and estimates for tracer indicators. These numbers have already been through a country consultation process (e.g., for immunization coverage), or are taken directly from country reported data.</p>
<h2>Fuentes de discrepancias:</h2>
<p>El índice de cobertura de servicios se basa en datos y estimaciones existentes y disponibles públicamente para los indicadores de seguimiento. Estas cifras ya han pasado por un proceso de consulta con el país (por ejemplo, para la cobertura de inmunización), o se toman directamente de los datos notificados por el país.</p> |
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<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>
<h1> Disponibilidad de los datos</h1>
<h2> Descripción:</h2> <p>Resumir la disponibilidad de datos para el índice de cobertura del servicio CSU no es sencillo, ya que se utilizan diferentes fuentes de datos en los 14 indicadores de seguimiento. Además, para muchos indicadores se han producido estimaciones comparables, en muchos casos recurriendo a diferentes tipos de fuentes de datos subyacentes para informar las estimaciones y al mismo tiempo utilizar proyecciones para imputar los valores faltantes. Sobre la base de las fuentes de datos subyacentes para cada uno de los indicadores de seguimiento (es decir, ignorando las estimaciones y proyecciones), la proporción promedio de indicadores utilizados para calcular el índice con los datos subyacentes disponibles desde 2010 es de alrededor del 70% en todos los países del mundo.</p> <h2>Serie temporal:</h2> <p>A finales de 2017 se publicó un valor de referencia para el índice de cobertura de los servicios de CSU para 2015 en 183 países. Como parte de este proceso, se reunieron fuentes de datos que se remontan a 2000. En 2019, se prevé publicar una serie temporal de 2000 a 2017.</p> <h2>Desagregación:</h2> <p>La equidad es fundamental para la definición de la CSU y, por lo tanto, el índice de cobertura de los servicios de CSU debe utilizarse para comunicar información sobre las desigualdades en la cobertura de los servicios dentro de los países. Esto se puede hacer presentando el índice por separado para la población nacional frente a las poblaciones desfavorecidas para resaltar las diferencias entre ellas.</p> <p>Para los países, la ubicación geográfica es probablemente la dimensión más factible para la desagregación sub-nacional basada en los niveles medios de cobertura medidos con las fuentes de datos existentes. Para ello, el índice de CSU puede calcularse por separado, por ejemplo, por la provincia o la residencia urbana frente a la rural, lo que permitiría realizar comparaciones sub-nacionales de la cobertura de los servicios. En la actualidad, los datos más fácilmente disponibles para desagregar otras dimensiones de la desigualdad, como la riqueza de los hogares, se refieren a los indicadores de cobertura dentro de la categoría de servicios de salud reproductiva, materna, neonatal e infantil. La desigualdad observada en esta dimensión se puede utilizar como un proxy para comprender las diferencias en la cobertura de servicios entre las dimensiones clave de desigualdad. Este enfoque debería sustituirse por la desagregación completa de los 14 indicadores de trazado una vez que se disponga de datos para hacerlo.</p> |
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<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. <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> |
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<p>See 4.d Validation</p>
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<p>Not applicable</p>
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<p>Not applicable</p>
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<p>Not applicable</p>
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| 32 | All strings, converted files enriched with comments; suitable for offline translation | Android String Resource | CSV | JSON | JSON nested structure file | gettext PO | iOS strings | TBX | TMX | XLIFF 1.1 with gettext extensions | XLIFF 1.1 | XLSX |
| 32 | Unfinished strings, converted files enriched with comments; suitable for offline translation | Android String Resource | CSV | JSON | JSON nested structure file | gettext PO | iOS strings | TBX | TMX | XLIFF 1.1 with gettext extensions | XLIFF 1.1 | XLSX |
translations-metadata/en/3-8-1.yml” file was changed. a year ago