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Strings Words Characters
32 4,147 31,004
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32 4,147 31,004
Read-only strings Browse Translate Zen
22 3,122 23,596
Strings with any failing checks Browse Translate Zen
6 1,057 6,939
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1 340 2,588
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Overview

Project website github.com/worldbank/sdg-metadata
Instructions for translators

This project is limited to Russian translation only, for now.

More detailed instructions to come.
Project maintainers User avatar brockfanning
Translation license MIT License
Translation process
  • Translations can be made directly.
  • Translation suggestions are turned off.
  • Any authenticated user can contribute.
  • The translation uses monolingual files.
  • The translation base language can not be edited.
Source code repository https://github.com/worldbank/sdg-metadata
Repository branch master
Last remote commit Merge pull request #542 from weblate/weblate-sdg-metadata-1-1-1a 85d59a4a6e9
User avatar brockfanning authored 2 weeks ago
Last commit in Weblate Translated using Weblate (French) feda7753c00
User avatar deblsyl authored a year ago
Weblate repository https://hosted.weblate.org/git/sdg-metadata/1-1-1a/
File mask translations-metadata/*/2-2-2a.yml
Monolingual base language file translations-metadata/en/2-2-2a.yml
Translation file Download translations-metadata/en/2-2-2a.yml
Last change None
Last author None
11 days ago

String statistics

Strings percent Hosted strings Words percent Hosted words Characters percent Hosted characters
Total 32 4,147 31,004
Translated 100% 32 100% 4,147 100% 31,004
Needs editing 0% 0 0% 0 0% 0
Read-only 100% 32 100% 4,147 100% 31,004
Failing checks 68% 22 75% 3,122 76% 23,596
Strings with suggestions 0% 0 0% 0 0% 0
Untranslated strings 0% 0 0% 0 0% 0
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Resource updated

  1. SDG Metadata
  2. 2-2-2a
  3. English
The “translations-metadata/en/2-2-2a.yml” file was changed. 8 months ago
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String updated in the repository

  1. SDG Metadata
  2. 2-2-2a
  3. English
<p><strong>URL:</strong></p>
<p>data.unicef.org/nutrition/malnutrition.html;</p>
<p>https://www.who.int/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;</p>
<p><strong>References:</strong></p>
<p>de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/15542535/">https://pubmed.ncbi.nlm.nih.gov/15542535/</a>&gt;</p>
<p>de Onis, M., Onyango, A., Borghi, E., Garza, C., and Yang, H. (2006). Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: Implications for child health pro&#xAD;grammes. Public Health Nutrition, 9(7), 942-947. doi:10.1017/PHN20062005 &lt;<a href="https://www.who.int/childgrowth/publications/Comparison_implications.pdf">https://www.who.int/childgrowth/publications/Comparison_implications.pdf</a>&gt;</p>
<p>McLain A, Frongillo E, Feng J, Borghi E (2018). Prediction intervals for penalized longitudinal models with multi-source summary measures: an application to childhood malnutrition. Stat Med; 38(6):1002-1012; doi: 10.1002/sim.8024. Epub 2018 Nov 14. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">https://pubmed.ncbi.nlm.nih.gov/30430613/</a>&gt;</p>
<p>United Nations Children&#x2019;s Fund (UNICEF), World Health Organization, International Bank for Reconstruction and Development/The World Bank (2019). Meeting report on Technical Consultation on a Country-level model for SDG2.2. December 2019</p>
<p>UNICEF-WHO-World Bank (2020). Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight &lt;<a href="https://data.unicef.org/resources/jme-2021-country-consultations/">https://data.unicef.org/resources/jme-2021-country-consultations/</a>&gt;</p>
<p>WHO (2006). WHO Multicentre Growth Reference Study (MGRS) &lt;https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study&gt;</p>
<p>World Health Organization and United Nations Children&#x2019;s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children&#x2019;s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. &lt;<a href="https://www.who.int/nutrition/publications/anthropometry-data-quality-report">https://www.who.int/nutrition/publications/anthropometry-data-quality-report</a>&gt;</p>
<p>WHO. WHO Anthro Survey Analyser (2019). Available at <a href="https://www.who.int/tools/child-growth-standards/software">https://www.who.int/tools/child-growth-standards/software</a>. </p>
<p>Yang H and de Onis M (2008). <a href="http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a>. BMC Pediatrics 2008, 8:19 (05 May 2008) &lt;http://www.biomedcentral.com/1471-2431/8/19&gt;.</p>
<p><strong>URL:</strong></p>
<p>data.unicef.org/nutrition/malnutrition.html; </p>
<p>https://www.who.int/nutgrowthdb/estimates2014/en/data/gho/data/themes/topics/joint-child-malnutrition-estimates-unicef-who-wb; http://datatopics.worldbank.org/child-malnutrition;</p>
<p><strong>References:</strong></p>
<p>United Nations Children&#x2019;s Fund, World Health Organization, The World Bank (2012). UNICEFWHO-World Bank Joint Child Malnutrition Estimates.de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/15542535/">https://pubmed.ncbi.nlm.nih.gov/15542535/</a>&gt;</p>
<p>de Onis, M., Onyango, A., Borghi, E., Garza, C., and Yang, H. (2006). Comparison of the World Health Organization (WHO) Child Growth Standards and the National Center for Health Statistics/WHO international growth reference: Implications for child health pro&#xAD;grammes. Public Health Nutrition, 9(7), 942-947. doi:10.1017/PHN20062005 &lt;<a href="https://www.who.int/childgrowth/publications/Comparison_implications.pdf">https://www.who.int/childgrowth/publications/Comparison_implications.pdf</a>&gt;</p>
<p>McLain A, Frongillo E, Feng J, Borghi E (2018). Prediction intervals for penalized longitudinal models with multi-source summary measures: an application to childhood malnutrition. Stat Med; 38(6):1002-1012; doi: 10.1002/sim.8024. Epub 2018 Nov 14. &lt;<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">https://pubmed.ncbi.nlm.nih.gov/30430613/</a>&gt;</p>
<p>United Nations Children&#x2019;s Fund (UNICEF), New York; WHO, Geneva; The World Bank, Washington, DC; 2012). </p>
<p>de Onis M, Bl&#xF6;ssner M, Borghi E, et al. (2004), Methodology for estimating regional and global trends of childhood malnutrition. Int J Epidemiol, 33(6):1260-70.World Health Organization, International Bank for Reconstruction and Development/The World Bank (2019). Meeting report on Technical Consultation on a Country-level model for SDG2.2. December 2019</p>
<p>UNICEF-WHO-World Bank (2020). Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight &lt;<a href="https://data.unicef.org/resources/jme-2021-country-consultations/">https://data.unicef.org/resources/jme-2021-country-consultations/</a>&gt;</p>
<p>WHO (2006). WHO Multicentre Growth Reference Study (MGRS) &lt;https://www.who.int/tools/child-growth-standards/who-multicentre-growth-reference-study&gt;</p>
<p>World Health Organization and United Nations Children&#x2019;s Fund (2019). Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old. Geneva: World Health Organization and the United Nations Children&#x2019;s Fund (UNICEF), 2019. Licence: CC BY-NC-SA 3.0 IGO. &lt;<a href="https://www.who.int/nutrition/publications/anthropometry-data-quality-report">https://www.who.int/nutrition/publications/anthropometry-data-quality-report</a>&gt;</p>
<p>WHO. WHO Anthro Survey Analyser (2019). Available at <a href="https://www.who.int/tools/child-growth-standards/software">https://www.who.int/tools/child-growth-standards/software</a>. </p>
<p>Yang H and de Onis M (2008). <a href="http://www.who.int/entity/nutgrowthdb/publications/algorithms/en/index.html">Algorithms for converting estimates of child malnutrition based on the NCHS reference into estimates based on the WHO Child Growth Standards</a>. <br>BMC Pediatrics 2008, 8:19 (05 May 2008) <br>(&lt;http://www.biomedcentral.com/1471-2431/8/19).</p>
<p>World Health Organization (2008). Training Course on Child Growth Assessment. Geneva, WHO, 2008.</p>
<p>International Journal of Epidemiology 2004;33:1260-70</p>
<p>International Journal of Epidemiology 2003;32:518-26</p>
<p>http://www.biomedcentral.com/1471-2431/8/19&gt;.</p>
8 months ago
User avatar None

String updated in the repository

  1. SDG Metadata
  2. 2-2-2a
  3. English
<p><strong>Sources of discrepancies:</strong></p>
<p>For the survey estimates included in the JME joint database of primary sources, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (<a href="https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) is applied whenever microdata are available to enhance comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) to undertake survey analysis and harmonize with the global standard analysis methods. </p>
<p>For the inclusion of survey estimates into the JME database, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. Discrepancies between results from standard methodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (<a href="http://www.biomedcentral.com/1471-2431/8/19">Yang and de Onis, 2008</a>). In addition, when surveys do not cover the age interval 0-59 months, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.</p>
<p>The JME country modelled estimates, which are based on smoothing techniques and covariates, as described elsewhere (<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">McLain et al. 2018</a>), vary from estimates from primary data sources such as household surveys, but in most cases the 95 per cent confidence bounds of the country modelled estimates for a given country in a given year fall within the 95 per cent confidence bounds of the estimate from the primary source for the corresponding country and year(s).</p>
<p><strong>Sources of discrepancies:</strong></p>
<p>The standard analysis approach to construct the joint data set aims for a maximum comparability of country estimates. For the survey estimates included in the JME joint database of primary sources, re-analysis based on standardized methodology using the WHO Child Growth Standards as described in Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years old (<a href="https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/">WHO/UNICEF 2019</a>) and WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) is applied whenever microdata are available to enhance comparability across the time series. Country teams are encouraged to use the WHO Anthro Survey Analyser (<a href="https://www.who.int/tools/child-growth-standards/software">WHO, 2019</a>) to undertake survey analysis and harmonize with the global standard analysis methods. </p>
<p>For the inclusion of survey estimates into the JME databaset, the inter-agency group applies a set of survey quality assessment criteria. When there is insufficient documentation, the survey is not included until information becomes available. When raw data are available, and there is a question about the analysis approach, data re-analysis is performed following the standard methodology. Discrepancies between results from standardised approach methodology and those reported may occur for various reasons, for example, the use of different standards for z-score calculations, imputation of the day of birth when missing, the use of rounded age in months, the use of different flagging systems for data exclusion. For surveys based on the previous NCHS/WHO references, and for which raw data are not available, a method for converting the z-scores to be based on the WHO Child Growth Standards is applied (<a href="http://www.biomedcentral.com/1471-2431/8/19">Yang and de Onis, 2008</a>). In addition, when surveys do not cover the age interval 0-&lt;5 year59 months, or are only representative of the rural areas, an adjustment based on other surveys for the same country, is performed. Any adjustment or conversion is transparently stated in the annotated joint data set.</p>
<p>The JME country modelled estimates, which are based on smoothing techniques and covariates, as described elsewhere (<a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">McLain et al. 2018</a>), vary from estimates from primary data sources such as household surveys, but in most cases the 95 per cent confidence bounds of the country modelled estimates for a given country in a given year fall within the 95 per cent confidence bounds of the estimate from the primary source for the corresponding country and year(s).</p>
8 months ago
User avatar None

String updated in the repository

  1. SDG Metadata
  2. 2-2-2a
  3. English
<p><strong>Data availability:</strong></p>
<p>The JME modelled country estimates from 2000 to 2022 for overweight were released for 161 countries that had at least one primary data source (e.g., from household survey) included in the 2023 JME country database.</p>
<p><strong>Time series:</strong></p>
<p>At country level, JME country modelled estimates from 2000 to the year before the JME release ) are presented for countries with at least one data point (e.g., from survey/surveillance included in the joint database of primary data sources. Survey years range from 1983 to the year before the JME release. Worldwide and regional annual estimates are available from 2000 to the year before the JME release. </p>
<p><strong>Disaggregation:</strong></p>
<p>Country, regional and worldwide JME estimates refer to the age group of children under 5 years, sexes combined. Disaggregations are currently not available for the JME modelled estimates. However, a disaggregated dataset of national primary sources with sub national and stratified estimates (e.g., sex, age groups, wealth, mothers&apos; education, residence) is available.</p>
<p><strong>Data availability:</strong></p>
<p>More than 150 countries.</p>
<p><strong>Time series:</strong></p>
<p>At country level, dataThe JME modelled country estimates from 2000 to 2022 for overweight were released for 161 countries that had at least one primary data source (e.g., from household survey) included in the 2023 JME country database.</p>
<p><strong>Time series:</strong></p>
<p>At country level, JME country modelled estimates from 2000 to the year before the JME release ) are providesented for the years where surveys are included in the joint datasetcountries with at least one data point (e.g., from survey/surveillance included in the joint database of primary data sources. Survey years range from 1983 to 2016. Fthe year before the globalJME release. Worldwide and regional levels,annual estimates for the years 1990 to the latest available estimate upon release each year (i.e. in Sept 2016 edition of the Joint Malnutrition Estimates the latest available estimate was for 2015)are available from 2000 to the year before the JME release. </p>
<p><strong>Disaggregation:</strong></p>
<p>Global and regionalCountry, regional and worldwide JME estimates refer to the age group of children under 5 years, sexes combined. Disaggregated country data areions are currently not available in a majority of household surveys and UNICEF - WHO- The World Bank Group are expanding the jointfor the JME modelled estimates. However, a disaggregated data set to includeof national primary sources with sub national and stratified estimates (e.g., sex, age groups, wealth, mothers&apos; education, residence) in 2017s available.</p>
8 months ago
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String updated in the repository

  1. SDG Metadata
  2. 2-2-2a
  3. English
<p>Data consistency and quality checks described above are conducted for each potential primary data source (e.g., household survey) before inclusion in the JME country database of primary sources that are used to generate the JME modelled estimates. Cross-validation exercises are performed for the modelled estimates to ensure the method generates estimates that are aligned to national data points. Country consultations with SDG 2.2 focal points an held every other year also provide opportunity to ensure the estimates include all recent and relevant country data. </p>
<p>Data consistency and quality checks described above are conducted for each potential primary data source (e.g., household survey) before inclusion in the JME country database of primary sources that are used to generate the JME modelled estimates. Cross-validation exercises are performed for the modelled estimates to ensure the method generates estimates that are aligned to national data points. Country consultations with SDG 2.2 focal points an held every other year also provide opportunity to ensure the estimates include all recent and relevant country data. </p>
8 months ago
User avatar None

String updated in the repository

  1. SDG Metadata
  2. 2-2-2a
  3. English
<p>The quality criteria established in the 2019 UNICEF/WHO guidance (<a href="https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/">WHO/UNICEF, 2019</a>) were used to update the JME primary data source review form . The JME review form is used to abstract key information including methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., response rates, weight and height distributions, percentage of cases that were flagged as having implausible anthropometry outcomes according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from each primary data source (e.g., household survey) under review. One JME working group member fills in the review form for each data source and when information is missing or further details are required, the country teams are contacted. Once all information is available and the JME primary data source review form is completed, each data source is reviewed by the three agencies (UNICEF, WHO, WB) which form the JME working group. This allows for a thorough and efficient standard joint review of each data source by the three agencies prior to inclusion in the JME country database of primary sources (e.g., household surveys) that are used to generate the JME country modelled estimates. </p>
<p>The quality criteria established in the 2019 UNICEF/WHO guidance (<a href="https://data.unicef.org/resources/data-collection-analysis-reporting-on-anthropometric-indicators-in-children-under-5/">WHO/UNICEF, 2019</a>) were used to update the JME primary data source review form . The JME review form is used to abstract key information including methodological details (e.g., sampling procedures, description of anthropometrical equipment), data quality outputs (e.g., response rates, weight and height distributions, percentage of cases that were flagged as having implausible anthropometry outcomes according to the WHO Child Growth Standards) and the malnutrition prevalence estimates from each primary data source (e.g., household survey) under review. One JME working group member fills in the review form for each data source and when information is missing or further details are required, the country teams are contacted. Once all information is available and the JME primary data source review form is completed, each data source is reviewed by the three agencies (UNICEF, WHO, WB) which form the JME working group. This allows for a thorough and efficient standard joint review of each data source by the three agencies prior to inclusion in the JME country database of primary sources (e.g., household surveys) that are used to generate the JME country modelled estimates. </p>
8 months ago
User avatar None

String updated in the repository

  1. SDG Metadata
  2. 2-2-2a
  3. English
<p>The JME working group, which was formed in 2011 with representatives from UNICEF, WHO and the World Bank, is responsible for management of the processes used to develop regular updates of the JME estimates. This includes the regular update of the country database of surveys used to generate the JME country modelled estimates, for which regular communication with regional and country teams allows the JME working group to secure microdata for re-analysis according to the standard method. The JME working group also continuously review methods and considers and tests different methodologies to improve the estimates as necessary. Additionally, a Technical Expert Advisory Group on Nutrition Monitoring (TEAM), jointly established by UNICEF and WHO, provides advice on nutrition monitoring methods and processes, including on the JME.</p>
<p>The JME working group, which was formed in 2011 with representatives from UNICEF, WHO and the World Bank, is responsible for management of the processes used to develop regular updates of the JME estimates. This includes the regular update of the country database of surveys used to generate the JME country modelled estimates, for which regular communication with regional and country teams allows the JME working group to secure microdata for re-analysis according to the standard method. The JME working group also continuously review methods and considers and tests different methodologies to improve the estimates as necessary. Additionally, a Technical Expert Advisory Group on Nutrition Monitoring (TEAM), jointly established by UNICEF and WHO, provides advice on nutrition monitoring methods and processes, including on the JME.</p>
8 months ago
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String updated in the repository

  1. SDG Metadata
  2. 2-2-2a
  3. English
<p>Methods and guidance: </p>
<p><a href="https://www.who.int/publications/i/item/9789241515559">Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years of age (WHO/UNICEF, 2019) </a></p>
<p>Analysis tool: <a href="https://worldhealthorg.shinyapps.io/anthro/">WHO Anthro Survey Analyser (shinyapps.io)</a></p>
<p><a href="https://data.unicef.org/resources/jme-2021-country-consultations/">UNICEF-WHO-World Bank 2020. Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight</a></p>
<p>Methods and guidance: </p>
<p><a href="https://www.who.int/publications/i/item/9789241515559">Recommendations for data collection, analysis and reporting on anthropometric indicators in children under 5 years of age (WHO/UNICEF, 2019) </a></p>
<p>Analysis tool: <a href="https://worldhealthorg.shinyapps.io/anthro/">WHO Anthro Survey Analyser (shinyapps.io)</a></p>
<p><a href="https://data.unicef.org/resources/jme-2021-country-consultations/">UNICEF-WHO-World Bank 2020. Technical notes from the country consultation on SDG Indicators 2.2.1 on stunting, 2.2.2a on wasting and 2.2.2b on overweight</a></p>
8 months ago
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  1. SDG Metadata
  2. 2-2-2a
  3. English
<p>Regional aggregates are available for the following classifications: UN, SDG, UNICEF, WHO, The World Bank regions and income groups. </p>
<p>Regional aggregates are available for the following classifications: UN, MSDG, UNICEF, WHO, The World Bank regions and income groups. </p>
8 months ago
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  1. SDG Metadata
  2. 2-2-2a
  3. English
<ul>
<li><strong>At country level</strong></li>
</ul>
<p>Missing values are derived as part of the methods used to generate the JME country modelled estimates by closely fitting the estimates from country data primary sources, with due attention to unwarranted variability. Please refer to <a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">McLain et al. 2018</a> for technical details of the methods applied. Based on these methods, the JME country modelled estimates are produced from 2000 until the year before the year of publication <u>(e.g., until 2022 for the JME 2023 edition)</u> and used to generate regional and worldwide aggregates. For <s>49 of these </s>countries<u> without any primary input data meeting inclusion criteria</u>, the JME country modelled estimates were produced solely for generation of regional and worldwide aggregates, and were not released to the public </p>
<ul>
<li><strong>At regional and worldwide levels</strong></li>
</ul>
<p>There are no missing data for the generation of worldwide and regional estimates as modelled estimates are <u>produced for all countries, those with and those without primary data in the JME country database, even though the country estimates are not released to the public for those countries without primary data</u>. </p>
<ul>
<li><strong>At country level</strong></li>
</ul>
<p>No imputation methodology is applied to derive estimates for countries or years where no data is avaialble.Missing values are derived as part of the methods used to generate the JME country modelled estimates by closely fitting the estimates from country data primary sources, with due attention to unwarranted variability. Please refer to <a href="https://pubmed.ncbi.nlm.nih.gov/30430613/">McLain et al. 2018</a> for technical details of the methods applied. Based on these methods, the JME country modelled estimates are produced from 2000 until the year before the year of publication <u>(e.g., until 2022 for the JME 2023 edition)</u> and used to generate regional and worldwide aggregates. For <s>49 of these </s>countries<u> without any primary input data meeting inclusion criteria</u>, the JME country modelled estimates were produced solely for generation of regional and worldwide aggregates, and were not released to the public </p>
<ul>
<li><strong>At regional and globalworldwide levels</strong></li>
</ul>
<p>Countries and years are treated as missing randomly following a multilevel modeling approach (International Journal of Epidemiology 2004;33:1260-70).There are no missing data for the generation of worldwide and regional estimates as modelled estimates are <u>produced for all countries, those with and those without primary data in the JME country database, even though the country estimates are not released to the public for those countries without primary data</u>. </p>
8 months ago
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