| Language | Approved | Translated | Unfinished | Unfinished words | Unfinished characters | Untranslated | Checks | Suggestions | Comments | |
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| English MIT | 0 | 0 | 0 | 0 | 22 | 0 | 0 | |||
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| Arabic MIT | 0% | 0% | 32 | 9,014 | 141,394 | 31 | 5 | 0 | 0 | |
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| French MIT | 0% | 40% | 19 | 1,511 | 10,999 | 7 | 6 | 0 | 0 | |
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| Portuguese MIT | 0% | 0% | 32 | 9,014 | 141,394 | 32 | 0 | 0 | 0 | |
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| Spanish MIT | 0% | 0% | 32 | 9,014 | 141,394 | 11 | 21 | 0 | 0 | |
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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 #542 from weblate/weblate-sdg-metadata-1-1-1a
85d59a4a6e9
brockfanning authored 7 months ago |
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| Last commit in Weblate |
Translated using Weblate (Portuguese)
5afc8b49daa
brockfanning authored a month 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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13 hours ago
String statistics
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| Total | 192 | 54,084 | 848,364 | |||
| Source | 32 | 9,014 | 141,394 | |||
| Approved | 0% | 0 | 0% | 0 | 0% | 0 |
| Waiting for review | 6% | 13 | 13% | 7,503 | 15% | 130,395 |
| Translated | 23% | 45 | 30% | 16,517 | 32% | 271,789 |
| Needs editing | 28% | 55 | 35% | 19,013 | 34% | 289,596 |
| Read-only | 16% | 32 | 16% | 9,014 | 16% | 141,394 |
| Failing checks | 38% | 73 | 63% | 34,075 | 64% | 551,052 |
| Strings with suggestions | 0% | 0 | 0% | 0 | 0% | 0 |
| Untranslated strings | 47% | 92 | 34% | 18,554 | 33% | 286,979 |
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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>Ссылки</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>Ссылки: </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>По истории развития методов см.:</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> (заменено этим документом)</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>Приложение 1. Метаданные для контрольных показателей, используемых для оценки охвата основными медицинскими услугами для мониторинга показателя 3.8.1 ЦУР. </h1> <p> Любые комментарии или запросы направляйте по адресу: <a href="mailto:uhc_stats@who.int">uhc_stats@who.int</a></p> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Планирование семьи</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля женщин репродуктивного возраста (15 - 49 лет), состоящих в браке или в консенсуальном союзе, которые удовлетворяют свои потребности в планировании семьи с помощью современных методов</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество замужних или состоящих в консенсуальном союзе женщин в возрасте 15-49 лет, пользующихся современными методами</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Общее количество замужних или состоящих в консенсуальном союзе женщин в возрасте 15-49 лет, нуждающихся в планировании семьи</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования состояния здоровья населения</p> </td> </tr> <tr> <td> <p>Метод измерения</td> <td> <p>Обследования домашних хозяйств включают ряд вопросов для оценки уровня использования современных противозачаточных средств и спроса на услуги по планированию семьи. Общая потребность в планировании семьи определяется как сумма количества женщин репродуктивного возраста (15 - 49 лет), которые состоят в браке или в союзе и которые в настоящее время используют или чей сексуальный партнер в настоящее время использует хотя бы один метод контрацепции, и неудовлетворенных потребностей в планировании семьи. Неудовлетворенная потребность в планировании семьи - это доля женщин репродуктивного возраста (15 - 49 лет), состоящих в браке или консенсуальном союзе, имеющих способность к оплодотворению и сексуально активных, но не использующих какие-либо методы контрацепции (современные или традиционные), и заявляющих, что не хотят больше детей или хотят отложить рождение следующего ребенка как минимум на два года. Сюда включены:</p> все беременные женщины (замужние или состоящие в консенсуальном союзе), чья беременность была нежелательной или несвоевременной на момент зачатия; </p> <p> все женщины с послеродовой аменореей (замужние или состоящие консенсуальном союзе), которые не используют планирование семьи и чьи последние роды были нежелательными или несвоевременными; </p> <p> все плодовитые женщины (замужние или состоящие в консенсуальном союзе), которые не беременны и не страдают послеродовой аменореей, и которые либо не хотят больше детей (хотят ограничить размер семьи), либо хотят отложить рождение ребенка на, по крайней мере, два года или не знают, когда захотят и захотят ли они еще одного ребенка (хотят иметь промежутки между родами), но не используют никаких методов контрацепции. </p> <p> Современные методы включают женскую и мужскую стерилизацию, внутриматочные средства (ВМС), имплант, инъекции, оральные противозачаточные таблетки, мужские и женские презервативы, методы влагалищного барьера (включая диафрагму, цервикальный колпачок и спермицидную пену, желе, крем и губку), метод лактационной аменореи (МЛА), экстренную контрацепцию и другие современные методы, которые отдельно не включаются в отчет.</p> </td> </tr> <tr> <td> <p>Метод оценки</td> <td> <p>Отдел народонаселения Организации Объединенных Наций составляет систематические и всеобъемлющие серии ежегодных оценок и прогнозов доли замужних женщин или женщин, состоящих в консенсуальном союзе, чьи потребности по планированию семьи удовлетворяются. Байесовская иерархическая модель в сочетании с данными по конкретной стране используется для создания оценок, прогнозов и оценок неопределенности на основе данных обследований. Модель учитывает различия в источниках данных, выборочной совокупности и методах контрацепции. </p> <p> Подробнее см. здесь: <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>Примечания, относящиеся к ВОУЗ</p> </td> <td></td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Беременность и помощь при родах</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля женщин в возрасте 15-49 лет, родивших живого ребенка в определенный период времени, которые получали дородовую помощь четыре или более раз </p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество женщин в возрасте 15-49 лет, родивших живого ребенка в определенный период времени, которые получали дородовую помощь четыре или более раз</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Общее количество женщин в возрасте 15-49 лет, родивших живого ребенка в тот же период времени.</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования домашних хозяйств и стандартные информационные системы учреждений</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Данные по четырем или более посещениям с целью дородового наблюдения основаны на вопросах, в которых спрашивают, проверялось ли и сколько раз здоровье женщины во время беременности. Обследования домашних хозяйств, на основе которых может рассчитываться этот показатель, включают DHS, MICS, RHS и другие обследования, основанные на аналогичных методологиях. Системы отчетности об услугах / учреждений могут использоваться там, где охват высок, обычно в странах с более высоким уровнем дохода.</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>ВОЗ ведет базу данных по охвату дородовой помощью: <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>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>В идеале этот показатель следует заменить более полным показателем ухода во время беременности и родов, например, долей женщин, у которых квалифицированный медработник присутствует при родах или в родильных домах. Проблема оценки квалифицированного родовспоможения состоит в том, чтобы определить, какие медицинские работники являются «квалифицированными».</p> </td> </tr> </tbody> </table> <p><br></p> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Иммунизация детей</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля младенцев, получивших три дозы вакцины против дифтерии, столбняка и коклюша</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Дети до 1 года, получившие три дозы вакцины против дифтерии, столбняка и коклюша.</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Все дети в возрасте до 1 года</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования домашних хозяйств и информационные системы учреждений</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Что касается данных обследования, прививочный статус детей в возрасте от 12 до 23 месяцев собирается из детских медицинских карт или, если карт нет, из отзыва их опекуна. Из административных данных извлекается информация об общем количестве доз, введенных целевой группе населения.</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Совместно ВОЗ и ЮНИСЕФ получают оценки охвата введенных доз вакцины против дифтерии, столбняка и коклюша (АКДС3) на основе данных, официально представленных в ВОЗ и ЮНИСЕФ государствами-членами, а также данных, представленных в опубликованных изданиях и "серой" литературе. Они также консультируются с местными экспертами - в первую очередь с национальными управляющими РПИ и сотрудниками региональных бюро ВОЗ - для получения дополнительной информации о деятельности конкретных местных служб иммунизации. Основываясь на имеющихся данных, учете возможных предубеждений и вкладе местных экспертов, ВОЗ / ЮНИСЕФ определяют наиболее вероятный истинный уровень охвата иммунизацией. </p> <p> Подробнее см. здесь: <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>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Национальные графики вакцинации в разных странах неодинаковы. Учитывая это, одним из вариантов мониторинга полной иммунизации детей является мониторинг доли детей, получающих вакцины, включенных в национальный календарь их страны. Второй вариант, который может быть более сопоставимым по странам и по времени, - это мониторинг охвата АКДС3 в качестве показателя полной иммунизации детей. Вакцина против дифтерии, столбняка и коклюша часто включает другие вакцины, например, против гепатита B и гемофильной инфекции типа B, и является разумным отражением того, до какой степени создана надежная платформа вакцинирования в стране. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Лечение детей (обращение за помощью при симптомах пневмонии)</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля детей в возрасте до 5 лет с подозрением на пневмонию (кашель и затрудненное дыхание, НЕ из-за проблем в грудной клетке и заложенного носа) в течение двух недель, предшествующих обследованию, доставленных в соответствующее медицинское учреждение или к медработнику.</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество детей с подозрением на пневмонию в течение двух недель, предшествующих обследованию, доставленных к соответствующему медработнику.</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Количество детей с подозрением на пневмонию за две недели, предшествующие обследованию.</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования домохозяйств</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>На совещании ЮНИСЕФ / ВОЗ по показателям выживаемости детей, состоявшемся в Нью-Йорке 17-18 июня 2004 года, было рекомендовано описывать острые респираторные инфекции (ОРИ) как «предполагаемую пневмонию», чтобы лучше отразить вероятную причину и рекомендуемые методы лечения. Определение предполагаемой пневмонии, используемое в обследованиях демографических характеристик и состояния здоровья (DHS) и в кластерных обследованиях по многим показателям (MICS), было выбрано группой и основано на восприятии матерями ребенка, который кашляет, дышит чаще, чем обычно, с короткими, частыми вдохами или затрудненным дыханием, за исключением детей, у которых был только заложен нос. Определение "соответствующего" медицинского работника варьируется в зависимости от страны. </p> <p> ВОЗ ведет базу данных наблюдений на страновом уровне по результатам обследований домашних хозяйств, доступ к которой можно получить здесь: <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>Метод оценки</p> </td> <td> <p>В настоящее время нет сопоставимых на международном уровне оценок по этому показателю.</p> </td> </tr> <tr> <td> <p>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Этот показатель обычно не рассчитывается в странах с более высокими доходами и хорошо развитыми системами здравоохранения. </p> <p> Для стран, в которых нет данных наблюдений, охват оценивался с помощью метода регрессии, который прогнозирует охват обращений за помощью по поводу симптомов пневмонии (по логит-шкале), полученной из базы данных ВОЗ, описанной выше, как функции логарифма расчетного коэффициент смертности детей от пневмонии в возрасте до пяти лет, который можно найти здесь: <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>Область контроля</p> </td> <td> <p>Лечение туберкулеза</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля случаев заболевания туберкулезом, которые были выявлены и успешно вылечены в конкретном году</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество новых случаев и рецидивов, выявленных за год и успешно вылеченных</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Количество новых случаев и рецидивов в том же году</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Информационные системы учреждений, системы санэпиднадзора, обследования состояния здоровья населения с диагностическим тестированием на ТБ, регистр ТБ и соответствующая система ежеквартальной отчетности (или электронные регистры ТБ)</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Этот показатель требует трех основных входных параметров: </p> <p> (1) Число новых и рецидивных случаев ТБ, диагностированных и вылеченных в рамках национальных программ борьбы с ТБ и сообщенных в ВОЗ в течение данного года. </p> <p> (2) Число случаев туберкулеза за тот же год обычно оценивается ВОЗ. </p> <p> (3) Доля случаев ТБ, успешно вылеченных (вылеченных плюс лечение завершено ) среди случаев ТБ, о которых было сообщено национальным органам здравоохранения.</p> <p>Заключительный показатель = (1)/(2) x (3)</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Оценки заболеваемости ТБ производятся в рамках консультативно-аналитического процесса под руководством ВОЗ и публикуются ежегодно. Эти оценки основаны на ежегодных уведомлениях о случаях заболевания, оценке качества и охвата данных уведомлений о ТБ, национальных обследованиях распространенности ТБ и информации из систем регистрации смертей (естественного движения населения). Оценки заболеваемости для каждой страны производятся с использованием одного или нескольких из следующих подходов в зависимости от имеющихся данных: </p> <p> 1. заболеваемость = уведомления о случаях / приблизительная доля выявленных случаев; </p> <p> 2. заболеваемость = распространенность / продолжительность состояния; </p> <p> 3. заболеваемость = количество смертей / доля умерших от впервые выявленных случаев. </p> <p> Эти оценки заболеваемости туберкулезом сочетаются с данными по стране по количеству выявленных и вылеченных случаев, а также по доле успешно вылеченных случаев, как описано выше.</p> </td> </tr> <tr> <td> <p>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Чтобы рассчитать показатель с использованием оценок ВОЗ, можно получить доступ к необходимым файлам здесь: <a href="http://www.who.int/tb/country/data/download/en/">http://www.who.int/tb/country/data/download/en/</a>, and compute the indicator as = c_cdr x c_new_tsr</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Лечение ВИЧ</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля людей, живущих с ВИЧ, в настоящее время получающих антиретровирусную терапию (АРТ)</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество взрослых и детей, получающих АРТ на конец отчетного периода</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Количество взрослых и детей, живущих с ВИЧ, за тот же период</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Системы отчетности учреждений, участки дозорного санэпиднадзора, обследования населения</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Числитель: числитель можно получить, подсчитав количество взрослых и детей, получавших комбинированную антиретровирусную терапию в конце отчетного периода. Данные могут быть собраны из регистров АРТ на базе учреждений или систем управления поставками лекарств. Затем они подсчитываются и передаются в ежемесячные или квартальные отчеты, которые затем могут быть агрегированы для получения общенациональных итогов. Пациенты, получающие АРТ в частном и государственном секторах, должны быть включены в числитель. </p> <p> Знаменатель: данные по количеству людей с ВИЧ-инфекцией могут поступать из обследований населения или, как это принято в странах Африки к югу от Сахары, из систем санэпиднадзора, основанных при женских консультациях.</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Оценки охвата антиретровирусным лечением людей, живущих с ВИЧ в 2000-2018 годах, получены в рамках оценочного раунда исследования ЮНЭЙДС в 2019 году.</p> <p> Для оценки количества людей, живущих с ВИЧ, в динамике по времени в странах с высоким уровнем заболеваемости ВИЧ, ЮНЭЙДС в сотрудничестве со странами использует эпидемическую модель (Spectrum), которая объединяет данные санэпиднадзора по распространенности с текущим количеством пациентов, получающих АРТ, и предположениями о естественной истории прогрессирования ВИЧ-инфекции. </p> <p> Поскольку теперь АРТ рекомендуется всем людям, живущим с ВИЧ, мониторинг охвата АРТ менее сложен, чем раньше, когда право на АРТ имели только люди с определенным уровнем тяжести заболевания. </p> <p>Оценки охвата АРТ можно найти здесь: <a href="http://aidsinfo.unaids.org/">http://aidsinfo.org</a></p> </td> </tr> <tr> <td> <p>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Сопоставимые оценки охвата АРТ в странах с высоким уровнем доходов, в частности временные тенденции, не всегда доступны.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Профилактика малярии</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля населения в эндемичных по малярии районах, которые прошлой ночью спали под обработанными инсектицидами противомоскитными сетками.</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество населения в эндемичных по малярии районах, которые спят под обработанными инсектицидами противомоскитными сетками</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Общее количество населения в эндемичных по малярии районах.</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Данные по доступу домашних хозяйств к обработанным инсектицидами противомоскитным сеткам (ОИС) и их использовании поступают из национальных репрезентативных обследований домашних хозяйств, таких как обследования демографических характеристик и состояния здоровья, кластерные обследования по многим показателям и обследования показателей малярии. Данные по количеству ОИС, доставленных производителями в страны, собираются Milliner Global Associates, а данные по количеству ОИС, распределенных внутри стран, представляются Национальными программами борьбы с малярией.</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Многие недавние национальные обследования отчитываются о количестве ОИС, наблюдаемых в каждом домохозяйстве-респонденте. Коэффициенты владения могут быть преобразованы в долю людей, спящих под ОИС, с использованием линейной зависимости между доступом и использованием, которая была получена в результате 62 обследований, в которых собирается информация по обоим показателям. </p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Математические модели могут использоваться для объединения данных обследований домашних хозяйств по доступу и использованию с информацией о доставке ОИС от производителей и о распределении ОИС национальными программами по борьбе с малярией для получения ежегодных оценок охвата ОИС. ВОЗ использует этот подход в сотрудничестве с проектом «Атлас малярии». Подробные методологические данные можно найти в Приложении к Всемирному докладу по малярии за 2015 год:<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>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>ВОЗ производит сопоставимые оценки охвата ОИС для 40 стран с высоким уровнем заболеваемости. Для других стран охват ОИС не включается в индекс охвата услугами ВОУЗ из-за ограничений данных. </p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Водоснабжение и санитария</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Доля домохозяйств, пользующихся хотя бы основными средствами санитарии</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Население, проживающее в домохозяйстве, где имеется: смыв или туалет с ручным смывом, септическая яма или уборная с выгребной ямой; вентилируемая уборная с выгребной ямой; выгребная яма с плитой; или компостный туалет.</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Общая численность населения</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования домашних хозяйств и переписи населения</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Ответы на уровне домохозяйства, взвешенные на размер домохозяйства, используются для расчета охвата населения.</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Совместная программа мониторинга ВОЗ / ЮНИСЕФ регулярно производит оценки охвата, по крайней мере, базовых средств санитарии для мониторинга ЦРТ. После создания базы данных доступных источников данных для каждой страны простые линейные регрессии подгоняются к рядам страновых данных, чтобы получить внутривыборочную оценку, а также произвести двухлетнюю экстраполяцию вне последнего доступного элемента данных, после чего охват сохраняется постоянным в течение 4 лет, а затем считается отсутствующим. Это делается отдельно для городских и сельских регионов, а затем они объединяются для получения оценок национального охвата. Подробную информацию о методологии и самые последние оценки можно найти здесь.: <a href="http://www.wssinfo.org/">http://www.wssinfo.org</a></p> </td> </tr> <tr> <td> <p>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Показатель ЦУР в области санитарии (ЦУР 6.2.1) представляет собой расширенную версию показателя ЦРТ, включающую качество санитарного оборудования. Это не используется для мониторинга ВОУЗ из-за более низкой доступности данных. Можно также рассмотреть совместный показатель, определяющий долю домохозяйств, имеющих доступ как к безопасной воде, так и к санитарным услугам.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Профилактика сердечно-сосудистых заболеваний</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Стандартизованная по возрасту распространенность нормального артериального давления среди взрослых в возрасте 18+, независимо от статуса лечения</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество взрослых в возрасте 18 лет и старше с систолическим артериальным давлением <140 мм рт. ст. и диастолическим артериальным давлением <90 мм рт. ст. (независимо от статуса лечения)</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Количество взрослых в возрасте 18 лет и старше</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования населения и системы санэпиднадзора</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Используются источники данных, фиксирующие измеренное артериальное давление (данные самостоятельных измерений исключены). Если у каждого участника измеряется несколько значений артериального давления, первое показание отбрасывается, а остальные значения усредняются.</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Для получения сопоставимых национальных оценок данные наблюдений за распространенностью, определенные в виде альтернативных пороговых значений САД и / или ДАД, преобразуются в распространенность повышенного артериального давления, определяемого как систолическое артериальное давление > = 140 мм рт. ст. или диастолическое артериальное давление > = 90 мм. рт. ст. с использованием уравнений регрессии. Затем для этих данных подбирается Байесовская иерархическая модель для расчета распространенности по возрасту, полу, году и стране, которая учитывает национальные и субнациональные источники данных, городские и сельские источники данных и учитывает различия в распространенности по возрасту и полу. Стандартизированные по возрасту оценки затем производятся путем применения приблизительных оценок к Стандарту населения ВОЗ. Подробная информация о статистических методах находится здесь: <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>ВОЗ и Сотрудничество по факторам риска неинфекционных заболеваний - НИЗ (NCD-RisC) подготовили сопоставимые оценки для этого показателя вплоть до 2015 года, которые доступны здесь: </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>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Оценки распространенности преобразуются в распространенность нормального артериального давления для включения в индекс ВОУЗ, так что значение 100% является оптимальной целью. Это вычисляется как: распространенность нормального артериального давления = 1 - распространенность повышенного артериального давления. Вышеуказанные оценки сделаны отдельно для мужчин и женщин; для показателя ВОУЗ вычисляется простое среднее значение для мужчин и женщин </p> <p> Нормальное артериальное давление - это сумма доли лиц, не страдающих гипертонией, и доли лиц, у которых гипертония контролируется лекарствами. Отсутствие гипертонии является результатом профилактических мер, направленных на пропаганду физической активности и здорового питания, а также других факторов. Гипертония, контролируемая лекарствами, - это результат эффективного лечения. Таким образом, этот показатель является показателем как эффективного укрепления здоровья, так и эффективных медицинских услуг.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Лечение диабета</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Стандартизированный по возрасту средний уровень глюкозы в плазме натощак для взрослых в возрасте 18 лет и старше</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования населения и системы санэпиднадзора</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Уровни глюкозы в плазме натощак (ГПН) определяют путем взятия пробы крови участников, голодавших не менее 8 часов. Другие связанные биомаркеры, такие как гемоглобин A1c (HbA1c), использовались для расчета оценок (см. ниже).</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Для получения сопоставимых национальных оценок все данные наблюдений, основанные на среднем значении ГПН, пероральном тесте на толерантность к глюкозе (OGTT), HbA1c или ее комбинациям, преобразуются в среднее значение ГПН. Затем к этим данным подбирается Байесовская иерархическая модель для расчета распространенности по возрасту, полу, году и стране, которая учитывает национальные и субнациональные источники данных, городские и сельские источники данных и учитывает различия в распространенности по возрасту и полу. Стандартизированные по возрасту оценки затем производятся путем применения приблизительных оценок к Стандарту населения ВОЗ. Подробности методологии можно найти здесь: <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>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>ГПН человека может быть низким из-за эффективного лечения сахароснижающими препаратами или из-за того, что человек не страдает диабетом в результате мероприятий по укреплению здоровья или других факторов, таких как генетика. Таким образом, среднее значение ГПН является показателем как эффективной пропаганды здорового питания и поведения, так и эффективного лечения диабета. </p> <p> Вышеуказанные оценки сделаны отдельно для мужчин и женщин; для показателя ВОУЗ вычисляется простое среднее значение для мужчин и женщин.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Борьба против табакокурения</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Стандартизованный по возрасту показатель распространенности среди взрослых > = 15 лет, не куривших табак в течение последних 30 дней </p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Взрослые от 15 лет и старше, не курившие табак в течение последних 30 дней</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Взрослые от 15 лет и старше</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследования домохозяйств</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>"Текущее табакокурение" включает сигареты, сигары, трубки или любые другие курительные табачные изделия, использованные за последние 30 дней. Данные собираются посредством самоотчетов в обследованиях.</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>ВОЗ оценивает распространенность нынешнего табакокурения (некурящих) с помощью модели отрицательной биномиальной мета-регрессии, которая создает сопоставимые оценки путем корректировки различий в возрастных группах и определения показателей в национальных обследованиях, включенных в анализ. Эти оценки делаются отдельно для мужчин и женщин. Подробности методологии можно найти здесь:<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>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Распространенность отказа от табакокурения рассчитывается как 1 минус распространенность табакокурения</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Доступ к больнице</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Количество больничных коек на душу населения относительно максимального порогового значения 18 на 10 000 населения</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество больничных коек (не включая кровати для рожениц и родов)</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Общая численность населения</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Административные системы / Система отчетности медицинских учреждений</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Страновые административные системы используются для суммирования количества больничных коек, которое делится на общую предполагаемую численность населения и умножается на 10 000. </p> <p> Региональные бюро ВОЗ и другие группы собирают информацию о плотности койко-мест в национальных больницах, включая следующие онлайн-ресурсы:</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>Региональная обсерватория ВОЗ AFRO: <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>Европейская база данных ВОЗ "Здоровье для всех": <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>ОЭСР: <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>Метод оценки</p> </td> <td> <p>На основании имеющихся данных показатель рассчитывается относительно порогового значения 18 больничных коек на 10 000 населения. Этот порог ниже наблюдаемого минимума для стран ОЭСР с высоким уровнем дохода (с 2000 года), составляющего 20 на 10 000, и, как правило, соответствует уровню госпитализации около 5 на 100 в год. Этот показатель предназначен для определения низкого уровня пропускной способности больниц; используется максимальный порог, поскольку очень высокая плотность больничных коек не является необходимой для эффективного использования ресурсов. Показатель рассчитывается следующим образом с использованием данных по стране по плотности больничных коек (x), что дает значения от 0 до 100:</p> <p>Страна с плотностью больничных коек x <18 на 10 000 в год, показатель = </p> <p><em>x</em> /18*100. </p> <p>Страна с плотностью больничных коек x> = 18 на 10 000 в год, показатель = 100.</p> </td> </tr> <tr> <td> <p>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Альтернативным показателем может быть частота госпитализаций по отношению к максимальному порогу. Однако этот показатель в настоящее время широко не представляется по регионам, особенно по африканскому региону. В странах, где доступны как больничные койки на душу населения, так и показатели госпитализации, они сильно взаимосвязаны.</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Кадры здравоохранения</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Количество специалистов здравоохранения (врачей, психиатров и хирургов) на душу населения относительно максимальных пороговых значений для каждой группы специалистов</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество врачей, психиатров и хирургов</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Общая численность населения</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Национальная база данных или регистр медицинских работников, в идеале в сочетании с регулярной оценкой полноты с использованием данных переписи, регистров профессиональных ассоциаций или переписей учреждений.</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Классификация медицинских работников основана на критериях профессионального образования и обучения, упорядочении медицинских профессий, а также деятельности и задачах рабочих мест, то есть на основе классификации ключевых переменных кадровых ресурсов в соответствии с общими характеристиками. Структура ВОЗ в значительной степени основана на последних пересмотрах международно стандартизированных систем классификации Международной организации труда (Международная стандартная классификация занятий), Организации Объединенных Наций по вопросам образования, науки и культуры (Международная стандартная классификация образования) и Статистического отдела Организации Объединенных Наций (Международная стандартная отраслевая классификация всех видов экономической деятельности). </p> <p>Подробную методологическую информацию и данные можно найти здесь: <a href="http://www.who.int/hrh/statistics/hwfstats/en/">http://www.who.int/hrh/statistics/hwfstats/en/</a></p> <p>Данные взяты из следующих источников:</p> <p>Врачи: <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>Психиатры: <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>Хирурги: <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>(данные здесь были дополнены из предыдущих редакций баз данных)</p> </td> </tr> <tr> <td> <p>Метод оценки</p> </td> <td> <p>Используя имеющиеся данные, показатель рассчитывается путем первого отдельного пересчета коэффициентов плотности медицинских работников для каждой из трех групп специальностей (врачи, психиатры и хирурги) относительно минимальных наблюдаемых значений в странах ОЭСР с 2000 года, которые выглядят следующим образом: врачи = 0,9 на 1000, психиатры = 1 на 100000, хирурги = 14 на 100000. Это изменение масштаба выполняется так же, как и для показателя плотности больничных коек, описанного выше, в результате чего значения показателя находятся в диапазоне от 0 до 100 для каждой из трех групп специальностей. Например, используя страновые данные по врачам на 1000 человек населения (<em> x </em>), показатель для конкретных групп будет рассчитан как:</p> <p>Страна с x <0,9 на 1000 в год, групповой показатель = x / 0,9 * 100 </p> <p>Страна с x> = 0,9 на 1000 в год, групповой показатель = 100</p> <p>На заключительном этапе вычисляется среднее геометрическое значение трех показателей для конкретных групп специальнотей, чтобы получить окончательный показатель плотности работников здравоохранения.</p> </td> </tr> <tr> <td> <p>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>"Врачи" В идеале категорию можно было бы расширить, включив в нее всех "основных медицинских работников", таких как медсестры и акушерки. Однако не существует сопоставимой на международном уровне базы данных, в которой использовались бы последовательные определения основных медицинских работников, не являющихся врачами, для проведения полностью совпадающих сравнений между странами. </p> <p> Для стран, у которых нет данных наблюдений, плотность хирургов была оценена с помощью регрессии, которая прогнозирует логарифм количества хирургов на 100000 человек, полученный из базы данных ВОЗ, описанной выше, как функцию логарифма значения ВВП на душу населения по оценке Всемирного банка</p> </td> </tr> </tbody> </table> <table> <tbody> <tr> <td> <p>Область контроля</p> </td> <td> <p>Безопасность здоровья</p> </td> </tr> <tr> <td> <p>Определение показателя</p> </td> <td> <p>Индекс основных возможностей Международных медико-санитарных правил (ММСП), который представляет собой средний процент атрибутов 13 основных возможностей, которые были достигнуты в определенный момент времени </p> <p> К 13 основным возможностям относятся: (1) Национальное законодательство, политика и финансирование; (2) Координация и общение с национальными координаторами; (3) Санэпиднадзор; (4) Ответ; (5) Готовность; (6) Информирование о рисках; (7) Человеческие ресурсы; (8) Лаборатория; (9) Пункты въезда; (10) Зоонозные явления; (11) Пищевая безопасность; (12) Чрезвычайные происшествия с отравляющими веществами; (13) Радиоизотопные аварийные ситуации.</p> </td> </tr> <tr> <td> <p>Числитель</p> </td> <td> <p>Количество достигнутых атрибутов</p> </td> </tr> <tr> <td> <p>Знаменатель</p> </td> <td> <p>Общее количество атрибутов</p> </td> </tr> <tr> <td> <p>Основные источники данных</p> </td> <td> <p>Обследование ключевых информаторов</p> </td> </tr> <tr> <td> <p>Метод измерения</p> </td> <td> <p>Ключевые информаторы отчитываются о достижении набора атрибутов для каждой из 13 основных возможностей с использованием стандартного инструмента ВОЗ, как описано здесь: <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>Значения показателя уровня возможности можно найти здесь: </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>Метод оценки</p> </td> <td> <p>Показатель рассчитывается путем усреднения по 13 основным возможностям, доля атрибутов для каждой достигнутой возможности.</p> </td> </tr> <tr> <td> <p>Примечания, относящиеся к ВОУЗ</p> </td> <td> <p>Страны начали отчитываться в ВОЗ о достижении основных возможностей по Международным медико-санитарным правилам (ММСП) за 2010 год. Для 2000-2009 годов используются самые ранние из имеющихся баллов по ММСП для каждой страны.</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> Источники расхождений: </h2>
<p> Индекс охвата услугами основан на существующих общедоступных данных и оценках контрольных показателей. Эти цифры уже прошли процесс страновых консультаций (например, по охвату иммунизацией) или взяты непосредственно из данных, представленных странами. </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> Доступность данных </h1>
<h2> Описание: </h2> <p> Обобщение доступности данных для индекса охвата услугами ВОУЗ произвести непросто, поскольку для 14 контрольных показателей используются разные источники данных. Кроме того, по многим показателям были произведены сопоставимые оценки, во многих случаях с использованием различных типов источников базовых данных для формирования оценок, а также с использованием прогнозов для вменения недостающих значений. Основываясь на источниках базовых данных для каждого контрольного показателя (т.е. без учета оценок и прогнозов), средняя доля показателей, используемых для расчета индекса с базовыми данными, доступными с 2010 года, составляет около 70% по странам во всем мире.</p> <h2> Временные ряды: </h2> <p> В конце 2017 года было опубликовано базовое значение индекса охвата услугами ВОУЗ за 2015 год по 183 странам. В рамках этого процесса были собраны исходные данные, начиная с 2000 года. В 2019 году ожидается публикация временных рядов с 2000 по 2017 годы. </p> <h2> Дезагрегирование: </h2> <p>Равенство занимает центральное место в определении ВОУЗ, и поэтому индекс охвата услугами ВОУЗ следует использовать для обмена информацией о неравенстве в охвате услугами в странах. Это можно сделать, представив индекс отдельно для населения страны и малообеспеченных слоев населения, чтобы выявить различия между ними. </p> <p> Для стран географическое положение, вероятно, является наиболее подходящим параметром для субнациональной дезагрегации на основе средних уровней охвата, рассчитанных с использованием существующих источников данных. Для этого индекс ВОУЗ может быть рассчитан отдельно, например, по провинциям, городам и сельской местности, что позволит проводить сравнения охвата услугами на субнациональном уровне. В настоящее время наиболее доступные данные для дезагрегирования по другим параметрам неравенства, таким как благосостояние домохозяйств, относятся к показателям охвата в категории услуг в области репродуктивного здоровья, здоровья матерей, новорожденных и детей. Неравенство, наблюдаемое в этой сфере, можно использовать в качестве косвенного показателя для понимания различий в охвате услугами по ключевым параметрам неравенства. Этот подход следует заменить полным дезагрегированием всех 14 контрольных показателей, как только для этого станут доступны данные.</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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