Abstract

The primary objective of the 2016 Nepal Demographic and Health Survey (NDHS) is to provide up-to-date estimates of basic demographic and health indicators. The NDHS provides a comprehensive overview of population, maternal, and child health issues in Nepal. Specifically, the 2016 NDHS: - Collected data that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and for the country’s seven provinces - Collected data that allowed for calculation of adult and maternal mortality rates at the national level - Explored the direct and indirect factors that determine levels and trends of fertility and child mortality - Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family health, including immunization coverage among children, prevalence and treatment of diarrhea and other diseases among children under age 5, maternity care indicators such as antenatal visits and assistance at delivery, and newborn care - Obtained data on child feeding practices, including breastfeeding - Collected anthropometric measures to assess the nutritional status of children under age 5 and women and men age 15-49 - Conducted hemoglobin testing on eligible children age 6-59 months and women age 15-49 to provide information on the prevalence of anemia in these groups - Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviors and condom use - Measured blood pressure among women and men age 15 and above - Obtained data on women’s experience of emotional, physical, and sexual violence

The information collected through the 2016 NDHS is intended to assist policymakers and program managers in the Ministry of Health and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population. The 2016 NDHS also provides data on indicators relevant to the Nepal Health Sector Strategy (NHSS) 2016-2021 and the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2016 NDHS is an updated version of the frame from the 2011 National Population and Housing Census (NPHC), conducted by the Central Bureau of Statistics (CBS).

The sampling frame contains information about ward location, type of residence (urban or rural), estimated number of residential households, and estimated population. In rural areas, the wards are small in size (average of 104 households) and serve as the primary sampling units (PSUs). In urban areas, the wards are large, with average of 800 households per ward. The CBS has a frame of enumeration areas (EAs) for each ward in the original 58 municipalities. However, for the 159 municipalities declared in 2014 and 2015, each municipality is composed of old wards, which are small in size and can serve as EAs.

The 2016 NDHS sample was stratified and selected in two stages in rural areas and three stages in urban areas. In rural areas, wards were selected as primary sampling units, and households were selected from the sample PSUs. In urban areas, wards were selected as PSUs, one EA was selected from each PSU, and then households were selected from the sample EAs.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were administered in the 2016 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, the Fieldworker Questionnaire, and the Verbal Autopsy Questionnaire (for neonatal deaths). The first five questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nepal. The Verbal Autopsy Questionnaire was based on the recent 2014 World Health Organization (WHO) verbal autopsy instruments (WHO 2015a).

Cleaning operations

The processing of the 2016 NDHS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the New ERA central office in Kathmandu. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The biomarker paper questionnaires were compared with the electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The secondary editing of the data was completed in the second week of February 2017. The final cleaning of the data set was carried out by The DHS Program data processing specialist and was completed by the end of February 2017.

Response rate

A total of 11,473 households were selected for the sample, of which 11,203 were occupied. Of the occupied households, 11,040 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 13,089 women age 15-49 were identified for individual interviews; interviews were completed with 12,862 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 4,235 men age 15-49 were identified and 4,063 were successfully interviewed, yielding a response rate of 96%.

Response rates were lower in urban areas than in rural areas. The difference was slightly more prominent for men than for women, as men in urban areas were often away from their households for work.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Non-sampling errors result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2016 Nepal DHS (NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2016 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2016 NDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Sibling size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

Abstract

The 2016 Timor-Leste Demographic and Health Survey (TLDHS) was implemented by the General Directorate of Statistics (GDS) of the Ministry of Finance in collaboration with the Ministry of Health (MOH). Data collection took place from 16 September to 22 December, 2016.

The primary objective of the 2016 TLDHS project is to provide up-to-date estimates of basic demographic and health indicators. The TLDHS provides a comprehensive overview of population, maternal, and child health issues in Timor-Leste. More specifically, the 2016 TLDHS: • Collected data at the national level, which allows the calculation of key demographic indicators, particularly fertility, and child, adult, and maternal mortality rates • Provided data to explore the direct and indirect factors that determine the levels and trends of fertility and child mortality • Measured the levels of contraceptive knowledge and practice • Obtained data on key aspects of maternal and child health, including immunization coverage, prevalence and treatment of diarrhea and other diseases among children under age 5, and maternity care, including antenatal visits and assistance at delivery • Obtained data on child feeding practices, including breastfeeding, and collected anthropometric measures to assess nutritional status in children, women, and men • Tested for anemia in children, women, and men • Collected data on the knowledge and attitudes of women and men about sexually-transmitted diseases and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviors and condom use), and coverage of HIV testing and counseling • Measured key education indicators, including school attendance ratios, level of educational attainment, and literacy levels • Collected information on the extent of disability • Collected information on non-communicable diseases • Collected information on early childhood development • Collected information on domestic violence • The information collected through the 2016 TLDHS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the TLDHS 2016 survey is the 2015 Timor-Leste Population and Housing Census (TLPHC 2015), provided by the General Directorate of Statistics. The sampling frame is a complete list of 2320 non-empty Enumeration Areas (EAs) created for the 2015 population census. An EA is a geographic area made up of a convenient number of dwelling units which served as counting units for the census, with an average size of 89 households per EA. The sampling frame contains information about the administrative unit, the type of residence, the number of residential households and the number of male and female population for each of the EAs. Among the 2320 EAs, 413 are urban residence and 1907 are rural residence.

There are five geographic regions in Timor-Leste, and these are subdivided into 12 municipalities and special administrative region (SAR) of Oecussi. The 2016 TLDHS sample was designed to produce reliable estimates of indicators for the country as a whole, for urban and rural areas, and for each of the 13 municipalities. A representative probability sample of approximately 12,000 households was drawn; the sample was stratified and selected in two stages. In the first stage, 455 EAs were selected with probability proportional to EA size from the 2015 TLPHC: 129 EAs in urban areas and 326 EAs in rural areas. In the second stage, 26 households were randomly selected within each of the 455 EAs; the sampling frame for this household selection was the 2015 TLPHC household listing available from the census database.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2016 TLDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Timor-Leste.

Cleaning operations

The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two staff who took part in the main fieldwork training. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in October 2016 and completed in February 2017.

Response rate

A total of 11,829 households were selected for the sample, of which 11,660 were occupied. Of the occupied households, 11,502 were successfully interviewed, which yielded a response rate of 99 percent.

In the interviewed households, 12,998 eligible women were identified for individual interviews. Interviews were completed with 12,607 women, yielding a response rate of 97 percent. In the subsample of households selected for the men’s interviews, 4,878 eligible men were identified and 4,622 were successfully interviewed, yielding a response rate of 95 percent. Response rates were higher in rural than in urban areas, with the difference being more pronounced among men (97 percent versus 90 percent, respectively) than among women (98 percent versus 94 percent, respectively). The lower response rates for men were likely due to their more frequent and longer absences from the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TLDHS 2016 to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TLDHS 2016 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the TLDHS 2016 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the TLDHS 2016 is a SAS program. This program used the Taylor linearization method of variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

{"en": "The 2018 global health financing report presents health spending data for all WHO Member States between 2000 and 2016 based on the SHA 2011 methodology. It shows a transformation trajectory for the global spending on health, with increasing domestic public funding and declining external financing. This report a so presents, for the first time, spending on primary health care and specific diseases and looks closely at the relationship between spending and service coverage.\r The report\u2019s key messages include:\r Global trends in health spending confirm the transformation of the world\u2019s funding of health services.\r Domestic spending on health is central to universal health coverage, but there is no clear trend of increased government priority for health.\r Primary health care is a priority for expenditure tracking.\r Allocations across disease and interventions differ between external and government sources and\r Performance of government spending on health can improve.", "lo": "", "km": "", "th": "", "vi": "", "my": ""}

ML: Incidence of Tuberculosis: per 100,000 People data was reported at 56.000 Ratio in 2016. This records a decrease from the previous number of 57.000 Ratio for 2015. ML: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 65.000 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 77.000 Ratio in 2000 and a record low of 56.000 Ratio in 2016. ML: Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mali – Table ML.World Bank: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

Dataset

This dataset was created by nambi

Contents

Context

The data here is from the Global Health Observatory (GHO) who provide data on malaria incidence, death and prevention from around the world. I have also included malaria net distribution data the Against Malaria Foundation (AMF). The AMF has consistently been ranked as the most cost effective charity by charity evaluators Give Well - http://www.givewell.org/charities/top-charities

Content

GHO data is all in narrow format, with variables for a country in a given year being found on different rows.

GHO data (there are a number or superfluous columns):

• GHO (CODE)
• GHO (DISPLAY) - this is the variable being measured
• GHO (URL)
• PUBLISHSTATE (CODE)
• PUBLISHSTATE (DISPLAY)
• PUBLISHSTATE (URL)
• YEAR (CODE)
• YEAR (DISPLAY)
• YEAR (URL)
• REGION (CODE)
• REGION (DISPLAY)
• REGION (URL)
• COUNTRY (CODE) - can be used to join this data with the AMF data
• COUNTRY (DISPLAY)
• COUNTRY (URL)
• Display Value - this is the measured value
• Low - lower confidence interval
• High - higher confidence interval
• Comments

AMF distribution data:

• #_llins - total number of malaria nets distributed
• location - the specific area that received the nets, within the target country
• country - the country in which the nets were distributed
• when - the period the distribution
• by_whom - the organisation(s) which partnered with the AMF to perform the distribution
• country_code - the country's GHO country code (this will allow joining with the GHO data)

For the current version all data was downloaded 20-08-17 The GHO data covers the years from 2000 to 2015 (not all files have data in all years) The AMF data runs from 2006 - the present.

The GHO data is taken as is from the csv (lists) available here: http://apps.who.int/gho/data/node.main.A1362?lang=en The source of the AMF's distribution data is here: https://www.againstmalaria.com/distributions.aspx - it was assembled into a single csv using Excel (mea culpa)

Inspiration

Malaria is one of the world's most devastating diseases, not least because it largely affects some of the poorest people. Over the past 15 years malaria rates and mortality have dropped (http://www.who.int/malaria/media/world-malaria-report-2016/en/), but there is still a long way to go. Understanding the data is generally one of the most important steps in solving any large problem. I'm excited to see what the Kaggle community can find out about the global trends in malaria over this period, and if we can find out anything about the impact of organisations such as the AMF.

Turkey TR: Incidence of Tuberculosis: per 100,000 People data was reported at 18.000 Ratio in 2016. This stayed constant from the previous number of 18.000 Ratio for 2015. Turkey TR: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 29.000 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 33.000 Ratio in 2006 and a record low of 18.000 Ratio in 2016. Turkey TR: Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkey – Table TR.World Bank: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

Dominican Republic DO: Incidence of Tuberculosis: per 100,000 People data was reported at 60.000 Ratio in 2016. This stayed constant from the previous number of 60.000 Ratio for 2015. Dominican Republic DO: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 73.000 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 100.000 Ratio in 2000 and a record low of 60.000 Ratio in 2016. Dominican Republic DO: Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Dominican Republic – Table DO.World Bank: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

The WHO have identified 20 neglected tropical diseases (NTDs), both communicable and non-communicable, that prevail in tropical and subtropical conditions in 149 countries.

The NTD portfolio currently includes: • Buruli ulcer • Chagas disease • Dengue and Chikungunya • Dracunculiasis (guinea-worm disease) • Echinococcosis • Foodborne trematodiases • Human African trypanosomiasis (sleeping sickness) • Leishmaniasis • Leprosy (Hansen's disease) • Lymphatic filariasis • Mycetoma, chromoblastomycosis and other deep mycoses • Onchocerciasis (river blindness) • Rabies • Scabies and other ectoparasites • Schistosomiasis • Soil-transmitted helminthiases • Snakebite envenoming • Taeniasis/Cysticercosis • Trachoma • Yaws (Endemic treponematoses)

Of the currently noted NTDs, only chikungunya, dengue, leprosy (Hansen’s disease) and rabies are nationally notifiable in Australia.

Chikungunya

There have been no reported cases of locally acquired chikungunya in Australia. Since 2013, there have been 575 cases of overseas acquired chikungunya diagnosed in Australia.

Dengue

Dengue is not endemic in Australia, but outbreaks associated with locally acquired cases do occur in coastal areas of mainland North Queensland, where Aedes aegypti is present in suitable environments near susceptible populations. The median number of cases associated with outbreaks in Australia since 2013 have been 4.5 cases each (range 1-146). Overseas acquired dengue in Australia is most frequently acquired in South East Asia, particularly Indonesia. Increases and trends are related to frequency of travel and local epidemiology in the source country. On average, over 90% of dengue cases reported annually in Australia are overseas acquired.

Leprosy

Leprosy is an uncommon disease in Australia with the majority of cases being diagnosed in migrants from leprosy endemic countries and occasionally in local Aboriginal and Torres Strait Islander populations.

In 2017, a total of nine cases of leprosy were notified, representing a rate of less than 0.1 case per 100,000 population. Between 2013 and 2017, annual notifications of leprosy in Australia have ranged from 9 to 21 cases per year.

Rabies

Australia is considered to be free of rabies with the last overseas acquired case being reported in 1987.

• Based on data extracted from the National Notifiable Diseases Surveillance System (NNDSS) on 19 June 2018. Due to the dynamic nature of the NNDSS, data on this extract is subject to retrospective revision and may vary from data reported in published NNDSS reports and reports of notification data by states and territories.

Trachoma

Australia is a signatory to the World Health Organisation (WHO) Alliance for the Global Elimination of Trachoma by 2020. Elimination of trachoma as a public health problem is defined by the WHO as ‘community prevalence of trachoma in children aged 1-9 years of less than 5%’.

As part of its WHO obligation to eliminate trachoma by 2020, Australia is required to regularly collect data on trachoma prevalence. The National Trachoma Surveillance and Reporting Unit, managed by the Kirby Institute, University of NSW, provides surveillance and annual reporting of trachoma prevalence, using State and Territory Government’s data.

Trachoma program activities, data collection and analysis are guided by the National Guidelines for the Public Health Management of Trachoma in Australia (revised in 2013 and published in 2014 – see link). The below information should be read in conjunction with the Guidelines.

In 2016, 150 communities were identified as being ‘at-risk’ of trachoma. A total of 11,671 people received antibiotic (azithromycin) treatment for trachoma (including people diagnosed with trachoma, their household contacts and community members as required by the Guidelines).

Further information can be found at: Australian Trachoma Surveillance Report 2016 - http://www.health.gov.au/internet/main/publishing.nsf/Content/1B9028E9FD71332ACA257BF00018CCD6/$File/2016%20Australian%20Trachhoma%20Surveillance%20report.pdf

Guidelines for the public health management of trachoma in Australia - http://www.health.gov.au/internet/main/publishing.nsf/Content/cda-cdna-pubs-trachoma.htm

United States US: Tuberculosis Case Detection Rate: All Forms data was reported at 87.000 % in 2016. This stayed constant from the previous number of 87.000 % for 2015. United States US: Tuberculosis Case Detection Rate: All Forms data is updated yearly, averaging 87.000 % from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 87.000 % in 2016 and a record low of 87.000 % in 2016. United States US: Tuberculosis Case Detection Rate: All Forms data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s USA – Table US.World Bank: Health Statistics. Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

BackgroundIn 2014, the Joint United Nations Program on HIV/AIDS (UNAIDS) issued treatment goals for human immunodeficiency virus (HIV). The 90-90-90 target specifies that by 2020, 90% of individuals living with HIV will know their HIV status, 90% of people with diagnosed HIV infection will receive antiretroviral treatment (ART), and 90% of those taking ART will be virally suppressed. Consistent methods and routine reporting in the public domain will be necessary for tracking progress towards the 90-90-90 target.Methods and findingsFor the period 2010–2016, we searched PubMed, UNAIDS country progress reports, World Health Organization (WHO), UNAIDS reports, national surveillance and program reports, United States President’s Emergency Plan for AIDS Relief (PEPFAR) Country Operational Plans, and conference presentations and/or abstracts for the latest available national HIV care continuum in the public domain. Continua of care included the number and proportion of people living with HIV (PLHIV) who are diagnosed, on ART, and virally suppressed out of the estimated number of PLHIV. We ranked the described methods for indicators to derive high-, medium-, and low-quality continuum. For 2010–2016, we identified 53 national care continua with viral suppression estimates representing 19.7 million (54%) of the 2015 global estimate of PLHIV. Of the 53, 6 (with 2% of global burden) were high quality, using standard surveillance methods to derive an overall denominator and program data from national cohorts for estimating steps in the continuum. Only nine countries in sub-Saharan Africa had care continua with viral suppression estimates. Of the 53 countries, the average proportion of the aggregate of PLHIV from all countries on ART was 48%, and the proportion of PLHIV who were virally suppressed was 40%. Seven countries (Sweden, Cambodia, United Kingdom, Switzerland, Denmark, Rwanda, and Namibia) were within 12% and 10% of achieving the 90-90-90 target for “on ART” and for “viral suppression,” respectively. The limitations to consider when interpreting the results include significant variation in methods used to determine national continua and the possibility that complete continua were not available through our comprehensive search of the public domain.ConclusionsRelatively few complete national continua of care are available in the public domain, and there is considerable variation in the methods for determining progress towards the 90-90-90 target. Despite bearing the highest HIV burden, national care continua from sub-Saharan Africa were less likely to be in the public domain. A standardized monitoring and evaluation approach could improve the use of scarce resources to achieve 90-90-90 through improved transparency, accountability, and efficiency.

Since 2016, the Sustainable Development Report (SDR) has provided the most up-to-date data available to track and rank the performance of all UN member states on the SDGs. Eighty years after the creation of the UN system, the report also provides improved and updated measures to track countries' efforts to support UN-based multilateralism. In total, more than 200,000 individual data points are used to produce 200+ country and regional SDG profiles. This year's edition was authored by a group of independent experts at the SDG Transformation Center, an initiative of the SDSN.This year's SDR emphasizes the following eight key message:Global commitment to the SDGs is strong: 190 out of 193 countries have presented national action plans for advancing sustainable development. A decade after the adoption of Agenda 30 and the SDGs, 190 of the 193 UN member states have participated in the Voluntary National Review (VNR) process, presenting their SDG implementation plans and sustainable development priorities to the international community. The European Union and State of Palestine have also presented VNRs. Most UN member states have presented two or more VNRs, and 39 countries volunteered to present one in 2025. Only three UN member states have not taken part in the VNR process: Haiti, Myanmar, and the United States. Additionally, a growing number of regional and local leaders have prepared Voluntary Local Reviews (VLRs) to report on SDG implementation at the subnational level. As of March 2025, 249 VLRs were listed on the dedicated UN websiteEast and South Asia has outperformed all other regions in SDG progress since 2015. This year's SDR introduces a streamlined SDG Index (SDGi), which uses 17 headline indicators to track overall SDG progress. On average, East and South Asia has shown the fastest progress on the SDGs since 2015, driven notably by rapid progress on the socioeconomic targetOther countries that have progressed more rapidly than their peers include the following: Benin (Sub-Saharan Africa), Nepal (East and South Asia), Peru (Latin America and the Caribbean), the United Arab Emirates (Middle East and North Africa), Uzbekistan (Eastern Europe and Central Asia), Costa Rica (OECD), and Saudi Arabia (G20)European countries continue to top the SDG Index. Finland ranks first this year and 19 of the top 20 countries are in Europe. Yet even these countries face significant challenges in achieving at least two goals, including those related to climate and biodiversity. In this year's SDG Index, China (#49) and India (#99) have entered the top 50 and top 100 performers respectivelyOn average globally, the SDGs are far off-track. At the global level, none of the 17 goals are currently on course to be achieved by 2030. Conflicts, structural vulnerabilities, and limited fiscal space impede SDG progress in many parts of the world. But while only 17 percent of the targets are on track to be achieved worldwide, most UN member states have made strong progress on targets related to access to basic services and infrastructure, including mobile broadband use (SDG 9), access to electricity (SDG 7), internet use (SDG 9), under-5 mortality rate (SDG 3), and neonatal mortality (SDG 3)Barbados ranks first and the United States ranks last in UN-based multilateralism. Barbados stands out as the country most committed to UN-based multilateralism, while the United States ranks last in this year's Index of countries' support for UN-based multilateralism (UN-Mi). In early 2025, the United States announced its withdrawal from the Paris Climate Agreement and the World Health Organization (WHO) and formally declared its opposition to the SDGs and the 2030 Agenda. Among G20 countries, Brazil is the most committed to UN-based multilateralism, with Chile leading among OECD countries For many developing countries, a lack of fiscal space is the major obstacle to SDG progress. Roughly half the world's population lives in countries that cannot invest adequately in sustainable development due to debt burdens and a lack of access to affordable, long-term capital. Global public goods are vastly under-financed. UN member states gathering at the 4th International Conference on Financing for Development (FfD4) in Seville, Spain (June 30 – July 3, 2025) have an enormous responsibility, not only to their own citizens but to all of humanitySustainable development offers high returns: capital should flow to the emerging and developing countries on more favourable terms. The Global Financial Architecture (GFA) is broken. Money flows readily to rich countries and not to the emerging and developing economies (EMDEs) that offer higher growth potential and rates of return. At the top of the agenda at FfD4 is the need to reform the GFA so that capital flows in far larger sums to the EMDEs. Part 1 of this report (also published online by the SDSN in May 2025) offers practical recommendations to scale up and align international financing flows to support global public goods and achieve sustainable development.About the AuthorsProf. Jeffrey Sachs, Director, SDSN; Project Director of the SDG IndexJeffrey D. Sachs is a world-renowned professor of economics, leader in sustainable development, senior UN advisor, bestselling author, and syndicated columnist whose monthly newspaper columns appear in more than 100 countries. He is the co-recipient of the 2015 Blue Planet Prize, the leading global prize for environmental leadership, and many other international awards and honors. He has twice been named among Time magazine’s 100 most influential world leaders. He was called by the New York Times, “probably the most important economist in the world,” and by Time magazine, “the world’s best known economist.” A survey by The Economist in 2011 ranked Professor Sachs as amongst the world’s three most influential living economists of the first decade of the 21st century.Professor Sachs serves as the Director of the Center for Sustainable Development at Columbia University. He is University Professor at Columbia University, the university’s highest academic rank. During 2002 to 2016 he served as the Director of the Earth Institute. Sachs is Special Advisor to United Nations Secretary-General António Guterres on the Sustainable Development Goals, and previously advised UN Secretary-General Ban Ki-moon on both the Sustainable Development Goals and Millennium Development Goals and UN Secretary-General Kofi Annan on the Millennium Development Goals.Guillaume Lafortune Director, SDSN Paris; Scientific Co-Director of the SDG IndexGuillaume Lafortune took up his duties as Director of SDSN Paris in January 2021. He joined SDSN in 2017 to coordinate the production of the Sustainable Development Report and other projects on SDG data and statistics.Previously, he has served as an economist at the Organisation for Economic Co-operation and Development (OECD) working on public governance reforms and statistics. He was one of the lead advisors for the production of the 2015 and 2017 flagship statistical report Government at a Glance. He also contributed to analytical work related to public sector efficiency, open government data and citizens’ satisfaction with public services. Earlier, Guillaume worked as an economist at the Ministry of Economic Development in the Government of Quebec (Canada). Guillaume holds a M.Sc in public administration from the National School of Public Administration (ENAP) in Montreal and a B.Sc in international economics from the University of Montreal.Contact: guillaume.lafortune@unsdsn.org Grayson Fuller Manager, SDG Index & Data team, SDSNGrayson Fuller is the lead statistician and senior manager for the SDG Index, and of the team working on SDG data and statistics at SDSN. He is co-author of the Sustainable Development Report, for which he manages the data, coding, and statistical analyses. He also coordinates the production of regional and subnational editions of the SDG Index, in addition to other statistical reports, in collaboration with national governments, NGOs and international organizations such as the WHO, UNDP and the European Commission. Grayson received his Masters degree in Economic Development at Sciences Po Paris. He holds a Bachelors in Romance Languages and Latin American Studies from Harvard University, where he graduated cum laude. Grayson has lived in several Latin American countries and speaks English, Spanish, French, Portuguese and Italian. He enjoys playing the violin, rock-climbing and taking care of his numerous plants in his free time.Contact: grayson.fuller@unsdsn.orgGuilherme Iablonovski GIS Specialist, SDG Index & Data team, SDSNGuilherme Iablonovski is a Geospatial Data Specialist at SDSN, where he conceptualizes and develops new geospatial indicators to measure important aspects of the Sustainable Development Goals. He holds a M.Sc in Urban and Environmental Planning from the Ecole d'Urbanisme de Paris, where his research focused on urban metabolism, environmental sustainability and universal scaling laws. Before joining SDSN, Guilherme worked as a solutions engineer for Esri and as geospatial data scientist for humanitarian organizations such as the World Bank, the Red Cross and UNEP. He also teaches GIS at the Peace Studies Master Programme at Université Paris-Dauphine PSL.Contact: guilherme.iablonovski@unsdsn.org---About the PublishersDublin University Press Dublin University Press is Ireland’s oldest printing and publishing house with its origins in Trinity College Dublin in 1734. The mission of Dublin University Press is to benefit society through scholarly communication, education, research and discourse. To further this goal, the Press operates as an open, innovative and inclusive channel for high quality scholarly publishing with an emphasis on equity, diversity and inclusion and with full support for author copyright retention, open access and open scholarship. As an independent, non-profit,

El Salvador SV: Tuberculosis Case Detection Rate: All Forms data was reported at 80.000 % in 2016. This stayed constant from the previous number of 80.000 % for 2015. El Salvador SV: Tuberculosis Case Detection Rate: All Forms data is updated yearly, averaging 80.000 % from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 80.000 % in 2016 and a record low of 80.000 % in 2016. El Salvador SV: Tuberculosis Case Detection Rate: All Forms data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s El Salvador – Table SV.World Bank: Health Statistics. Tuberculosis case detection rate (all forms) is the number of new and relapse tuberculosis cases notified to WHO in a given year, divided by WHO's estimate of the number of incident tuberculosis cases for the same year, expressed as a percentage. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

Abstract

The Service Delivery Indicators (SDI) are a set of health and education indicators that examine the effort and ability of staff and the availability of key inputs and resources that contribute to a functioning school or health facility. The indicators are standardized, allowing comparison between and within countries over time.

The Health SDIs include healthcare provider effort, knowledge and ability, and the availability of key inputs (for example, basic equipment, medicines and infrastructure, such as toilets and electricity). The indicators provide a snapshot of the health facility and assess the availability of key resources for providing high quality care.

The Tanzania SDI Health survey team visited a sample of 383 health facilities across Tanzania between August and October 2016. The survey team collected rosters covering 5,160 workers for absenteeism and assessed 498 health workers for competence using patient case simulations. The technical report and field manual are unavailable for Tanzania 2016. The questionnaire is the same as Tanzania 2014.

Geographic coverage

National

Analysis unit

Health facilities and healthcare providers

Universe

All health facilities providing primary-level care

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling strategy for SDI surveys is designed towards attaining indicators that are accurate and representative at the national level, as this allows for proper cross-country (i.e. international benchmarking) and across time comparisons, when applicable. In addition, other levels of representativeness are sought to allow for further disaggregation (rural/urban areas, public/private facilities, subregions, etc.) during the analysis stage.

The sampling strategy for SDI surveys follows a multistage sampling approach. The main units of analysis are facilities (schools and health centers) and providers (health and education workers: teachers, doctors, nurses, facility managers, etc.). The multi-stage sampling approach makes sampling procedures more practical by dividing the selection of large populations of sampling units in a step-by-step fashion. After defining the sampling frame and categorizing it by stratum, a first stage selection of sampling units is carried out independently within each stratum. Often, the primary sampling units (PSU) for this stage are cluster locations (e.g. districts, communities, counties, neighborhoods, etc.) which are randomly drawn within each stratum with a probability proportional to the size (PPS) of the cluster (measured by the location’s number of facilities, providers or pupils). Once locations are selected, a second stage takes place by randomly selecting facilities within location (either with equal probability or with PPS) as secondary sampling units. At a third stage, a fixed number of health and education workers and pupils are randomly selected within facilities to provide information for the different questionnaire modules.

The Tanzania 2016 survey is a repeated panel of the 2014 survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

The SDI Health Survey Questionnaire consists of four modules:

Module 1: General Information - Administered to the health facility manager to collect information on equipment, medicines, infrastructure and other facets of the health facility.

Module 2: Provider Absence - A roster of healthcare providers is collected and absence measured.

Module 3: Clinical Vignettes – A selection of providers are given clinical vignettes to measure knowledge of common medical conditions.

Module 4: Facility finances – Information on facility revenue and expenditures is collected from the health facility manager.

Weights: Weights for facilities, absentee-related analyses and clinical vignette analyses.

Cleaning operations

Quality control was performed in Stata.

"Health Index. Ukraine" is a large-scale, empirical, and representative study aimed at collecting quantitative data on the population's health-related knowledge and behaviors, as well as their evaluations of healthcare service quality based on personal experiences, and tracking changes in these indicators over time. Launched in 2016, the study has been conducted annually until 2020, with five waves completed during the 2016-2020 period, and resumed in 2023, offering the opportunity to reassess key health-related issues and access to medical services amid the challenges posed by the full-scale war. The study was initiated by the International Renaissance Foundation, with the Charitable Foundation "Health Solutions for Open Society" continuing its legacy as the successor to the Public Health program. The Kyiv International Institute of Sociology (KIIS) has served as the executing agency for the study across all years. Key partners and donors of the project also include the World Bank (2017), the Ministry of Health of Ukraine (2018, 2019, 2020, 2023), and the World Health Organization (2023). The data for this study come from sociological surveys of the adult population, with approximately 10,000 respondents participating in each wave. The sample for each wave is random and representative of the adult population (18 years and older) of Ukraine as a whole, as well as of each oblast covered by the study, including the city of Kyiv. The topics covered by the study include health and health-seeking behaviour, early disease detection, patient experiences with outpatient and inpatient care (including questions on official and unofficial expenses), general perceptions of vaccination and specific behaviours related to child immunization, medication availability, satisfaction with medical care, and perceptions of healthcare reforms. The data collection includes datasets, field questionnaires, and technical reports describing the methodology for all six survey waves. Each dataset is provided as a separate file in SAV format and labeled in Ukrainian. Additionally, the collection includes converted datasets in CSV format, accompanied by codebooks. The study results, including comprehensive analytical reports and selected infographics, are available on the project website: https://healthindex.com.ua/

Context

The World Happiness Ranking focuses on the social, urban, and natural environment. Specifically, the ranking relies on self-reports from residents of how they weigh the quality of life they are currently experiencing which englobes three main points: current life evaluation, expected future life evaluation, positive and negative affect (emotion). Half of the underlying data comes from multiple Gallup world polls which asked people to give their assessment of the previously mentioned points, and the other half of the data is comprised of six variables that could be used to try to explain the individuals’ perception in their answers.

Content

The data sources’ datasets were obtained in two different formats. The World Happiness Ranking Dataset is a Comma-separated Values (CSV) file with multiple columns (for the different variables and the score) and a row for each of the analyzed countries.

The rankings of national happiness are based on a Cantril ladder survey. Nationally representative samples of respondents are asked to think of a ladder, with the best possible life for them being a 10, and the worst possible life being a 0. They are then asked to rate their own current lives on that 0 to 10 scale. The report correlates the results with various life factors.

1. GDP per capita is in terms of Purchasing Power Parity (PPP) adjusted to constant 2011 international dollars, taken from the World Development Indicators (WDI) released by the World Bank on November 28, 2019. See Statistical Appendix 1 for more details. GDP data for 2019 are not yet available, so we extend the GDP time series from 2018 to 2019 using country-specific forecasts of real GDP growth from the OECD Economic Outlook No. 106 (Edition November 2019) and the World Bank’s Global Economic Prospects (Last Updated: 06/04/2019), after adjustment for population growth. The equation uses the natural log of GDP per capita, as this form fits the data significantly better than GDP per capita.
2. The time series of healthy life expectancy at birth are constructed based on data from the World Health Organization (WHO) Global Health Observatory data repository, with data available for 2005, 2010, 2015, and 2016. To match this report’s sample period, interpolation and extrapolation are used. See Statistical Appendix 1 for more details.
3. Social support is the national average of the binary responses (0=no, 1=yes) to the Gallup World Poll (GWP) question, “If you were in trouble, do you have relatives or friends you can count on to help you whenever you need them, or not?”
4. Freedom to make life choices is the national average of binary responses to the GWP question, “Are you satisfied or dissatisfied with your freedom to choose what you do with your life?”
5. Generosity is the residual of regressing the national average of GWP responses to the question, “Have you donated money to a charity in the past month?” on GDP per capita.
6. Perceptions of corruption are the average of binary answers to two GWP questions: “Is corruption widespread throughout the government or not?” and “Is corruption widespread within businesses or not?” Where data for government corruption are missing, the perception of business corruption is used as the overall corruption-perception measure.
7. Positive affect is defined as the average of previous-day affect measures for happiness, laughter, and enjoyment for GWP waves 3-7 (years 2008 to 2012, and some in 2013). It is defined as the average of laughter and enjoyment for other waves where the happiness question was not asked. The general form for the affect questions is: Did you experience the following feelings during a lot of the day yesterday? See Statistical Appendix 1 for more details.
8. Negative affect is defined as the average of previous-day affect measures for worry, sadness, and anger in all years.

Acknowledgements

The World Happiness Report is a publication of the Sustainable Development Solutions Network, powered by data from the Gallup World Poll, and supported by the Ernesto Illy Foundation, illycaffè, Davines Group, Blue Chip Foundation, the William, Jeff, and Jennifer Gross Family Foundation, and Unilever’s largest ice cream brand Wall’s.

Inspiration

Find the relationship between the ladder score and the other pieces of data.

The EOCHA Data Portal facilitates the access to heterogeneous datasets providing in a one-stop-shop the access services and basic data mining tools necessary to explore geospatial data i.e. in the context of the disease early warning systems. The Portal is based on the Multisensor Evolution Analysis (MEA) technology - an Earth Observation and geospatial data analysis tool empowered with OGC (Open Geospatial Consortium) standards and open source technologies to enable Big Data access and processing services. Key World Bank sectors can benefit from such climaterelated health risk assessment including Agriculture and Rural Development, Environment, and Health, Nutrition and Population in the regions that are afflicted by both high burdens of infectious disease and climate change - notably, Africa, South America, East Asia and the Pacific. This platform is produced under the 2016 World Bank (WBG) - European Space Agency (ESA) partnership, and is published in the partnership report: Earth Observation for Sustainable Development, June 2016

The Global Hunger Index (GHI) is a tool designed to comprehensively measure and track hunger globally, regionally, and by country. Each year, the International Food Policy Research Institute (IFPRI) calculates GHI scores in order to assess progress, or the lack thereof, in decreasing hunger. The GHI is designed to raise awareness and understanding of regional and country differences in the struggle against hunger. Since 2015, GHI scores have been calculated using a revised and improved formula. The revision replaces child underweight, previously the sole indicator of child undernutrition, with two indicators of child undernutrition—child wasting and child stunting—which are equally weighted in the GHI calculation. The revised formula also standardizes each of the component indicators to balance their contribution to the overall index and to changes in the GHI scores over time. The 2016 GHI has been calculated for 118 countries for which data on the four component indicators are available and where measuring hunger is considered most relevant. GHI scores are not calculated for some higher income countries where the prevalence of hunger is very low. The GHI is only as current as the data for its four component indicators. This year's GHI reflects the most recent available country-level data and projections available between 2011 and 2016. It therefore reflects the hunger levels during this period rather than solely capturing conditions in 2016. The 1992, 2000, 2008, and 2016 GHI scores reflect the latest revised data for the four component indicators of the GHI. Where original source data were not available, the estimates of the GHI component indicators were based on the most recent data available. The four component indicators used to calculate the GHI scores draw upon data from the following sources: 1. Undernourishment: Updated data from the Food and Agriculture Organization of the United Nations (FAO) were used for the 1992, 2000, 2008, and 2016 GHI scores. Undernourishment data and projections for the 2016 GHI are for 2014-2016. 2. Child wasting and stunting: The child undernutrition indicators of the GHI—child wasting and child stunting—include data from the joint database of United Nations Children's Fund (UNICEF), the World Health Organization (WHO), and the World Bank, and additional data from WHO's continuously updated Global Database on Child Growth and Malnutrition; the most recent Demographic and Health Survey (DHS) and Multiple Indicator Cluster Survey (MICS) reports; and statistical tables from UNICEF. For the 2016 GHI, data on child wasting and child stunting are for the latest year for which data are available in the period 2011-2015. 3. Child mortality: Updated data from the UN Inter-agency Group for Child Mortality Estimation were used for the 1992, 2000, 2008, and 2016 GHI scores. For the 2016 GHI, data on child mortality are from 2015. Resources related to 2016 Global Hunger Index 2016 Global Hunger Index Web App 2016 Global Hunger Index Linked Open Data (LOD) 2016 Global Hunger Index Report

San Marino Incidence of Tuberculosis: per 100,000 People data was reported at 0.000 Ratio in 2016. This stayed constant from the previous number of 0.000 Ratio for 2015. San Marino Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 0.000 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 4.200 Ratio in 2000 and a record low of 0.000 Ratio in 2016. San Marino Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s San Marino – Table SM.World Bank: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;

United States US: Incidence of Tuberculosis: per 100,000 People data was reported at 3.100 Ratio in 2016. This records a decrease from the previous number of 3.300 Ratio for 2015. United States US: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 4.900 Ratio from Dec 2000 (Median) to 2016, with 17 observations. The data reached an all-time high of 6.700 Ratio in 2000 and a record low of 3.100 Ratio in 2016. United States US: Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.; ; World Health Organization, Global Tuberculosis Report.; Weighted average;