Life expectancy worldwide has seen significant improvements over the past three decades, with notable variations across regions. In 2021, a child born in the Americas could expect to live an average of **** years, compared to ** years in 1990. However, the COVID-19 pandemic caused a universal decline in life expectancy from 2019 to 2021, affecting all World Health Organization regions. Regional disparities and global trends While global life expectancy has generally increased over time, stark regional differences persist. ****** consistently reports the lowest life expectancy, with **** years in 2021. In fact, the twenty countries with the lowest life expectancy in the world are all found in ******, with **** and ******* reporting the lowest life expectancies at just ** years. In contrast, the *************** now has the highest life expectancy, reaching **** years in 2021. These disparities reflect broader socioeconomic factors, with low-income countries facing challenges such as limited healthcare access and higher rates of infectious diseases. Impact of health issues on life expectancy Various health issues contribute to differences in life expectancy across countries and regions. Mental health has emerged as a significant concern, with a survey of 31 countries identifying it as the biggest health problem facing people in these countries in 2024. The COVID-19 pandemic not only directly impacted life expectancy but also exacerbated mental health issues worldwide. Additionally, non-communicable diseases play a crucial role in determining life expectancy. In 2021, ********************** was the leading cause of death globally, highlighting the importance of addressing chronic health conditions to improve overall life expectancy.

Analysis of ‘Life Expectancy (WHO)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/kumarajarshi/life-expectancy-who on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

Although there have been lot of studies undertaken in the past on factors affecting life expectancy considering demographic variables, income composition and mortality rates. It was found that affect of immunization and human development index was not taken into account in the past. Also, some of the past research was done considering multiple linear regression based on data set of one year for all the countries. Hence, this gives motivation to resolve both the factors stated previously by formulating a regression model based on mixed effects model and multiple linear regression while considering data from a period of 2000 to 2015 for all the countries. Important immunization like Hepatitis B, Polio and Diphtheria will also be considered. In a nutshell, this study will focus on immunization factors, mortality factors, economic factors, social factors and other health related factors as well. Since the observations this dataset are based on different countries, it will be easier for a country to determine the predicting factor which is contributing to lower value of life expectancy. This will help in suggesting a country which area should be given importance in order to efficiently improve the life expectancy of its population.

Content

The project relies on accuracy of data. The Global Health Observatory (GHO) data repository under World Health Organization (WHO) keeps track of the health status as well as many other related factors for all countries The data-sets are made available to public for the purpose of health data analysis. The data-set related to life expectancy, health factors for 193 countries has been collected from the same WHO data repository website and its corresponding economic data was collected from United Nation website. Among all categories of health-related factors only those critical factors were chosen which are more representative. It has been observed that in the past 15 years , there has been a huge development in health sector resulting in improvement of human mortality rates especially in the developing nations in comparison to the past 30 years. Therefore, in this project we have considered data from year 2000-2015 for 193 countries for further analysis. The individual data files have been merged together into a single data-set. On initial visual inspection of the data showed some missing values. As the data-sets were from WHO, we found no evident errors. Missing data was handled in R software by using Missmap command. The result indicated that most of the missing data was for population, Hepatitis B and GDP. The missing data were from less known countries like Vanuatu, Tonga, Togo, Cabo Verde etc. Finding all data for these countries was difficult and hence, it was decided that we exclude these countries from the final model data-set. The final merged file(final dataset) consists of 22 Columns and 2938 rows which meant 20 predicting variables. All predicting variables was then divided into several broad categories:​Immunization related factors, Mortality factors, Economical factors and Social factors.

Acknowledgements

The data was collected from WHO and United Nations website with the help of Deeksha Russell and Duan Wang.

Inspiration

The data-set aims to answer the following key questions: 1. Does various predicting factors which has been chosen initially really affect the Life expectancy? What are the predicting variables actually affecting the life expectancy? 2. Should a country having a lower life expectancy value(<65) increase its healthcare expenditure in order to improve its average lifespan? 3. How does Infant and Adult mortality rates affect life expectancy? 4. Does Life Expectancy has positive or negative correlation with eating habits, lifestyle, exercise, smoking, drinking alcohol etc. 5. What is the impact of schooling on the lifespan of humans? 6. Does Life Expectancy have positive or negative relationship with drinking alcohol? 7. Do densely populated countries tend to have lower life expectancy? 8. What is the impact of Immunization coverage on life Expectancy?

--- Original source retains full ownership of the source dataset ---

Healthy life expectancy (HALE) at age 60 (years)

Analysis of ‘WHO national life expectancy ’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/mmattson/who-national-life-expectancy on 28 January 2022.

--- Dataset description provided by original source is as follows ---

Context

I am developing my data science skills in areas outside of my previous work. An interesting problem for me was to identify which factors influence life expectancy on a national level. There is an existing Kaggle data set that explored this, but that information was corrupted. Part of the problem solving process is to step back periodically and ask "does this make sense?" Without reasonable data, it is harder to notice mistakes in my analysis code (as opposed to unusual behavior due to the data itself). I wanted to make a similar data set, but with reliable information.

This is my first time exploring life expectancy, so I had to guess which features might be of interest when making the data set. Some were included for comparison with the other Kaggle data set. A number of potentially interesting features (like air pollution) were left off due to limited year or country coverage. Since the data was collected from more than one server, some features are present more than once, to explore the differences.

Content

A goal of the World Health Organization (WHO) is to ensure that a billion more people are protected from health emergencies, and provided better health and well-being. They provide public data collected from many sources to identify and monitor factors that are important to reach this goal. This set was primarily made using GHO (Global Health Observatory) and UNESCO (United Nations Educational Scientific and Culture Organization) information. The set covers the years 2000-2016 for 183 countries, in a single CSV file. Missing data is left in place, for the user to decide how to deal with it.

Three notebooks are provided for my cursory analysis, a comparison with the other Kaggle set, and a template for creating this data set.

Inspiration

There is a lot to explore, if the user is interested. The GHO server alone has over 2000 "indicators". - How are the GHO and UNESCO life expectancies calculated, and what is causing the difference? That could also be asked for Gross National Income (GNI) and mortality features. - How does the life expectancy after age 60 compare to the life expectancy at birth? Is the relationship with the features in this data set different for those two targets? - What other indicators on the servers might be interesting to use? Some of the GHO indicators are different studies with different coverage. Can they be combined to make a more useful and robust data feature? - Unraveling the correlations between the features would take significant work.

--- Original source retains full ownership of the source dataset ---

In the World Health Organization (WHO)-coordinated Cardiovascular Disease and Alimentary Comparison Study, isoflavones (I; biomarker for dietary soy) and taurine (T; biomarker for dietary fish) in 24-hour—urine (24U) were inversely related to coronary heart disease (CHD) mortality. High levels of these biomarkers are found in Japanese people, whose CHD mortality is lowest among developed countries. We analyzed the association of these biomarkers with cardiovascular disease risk in the Japanese to know their health effects within one ethnic population. First, to compare the Japanese intake of I and T with international intakes, the ratios of 24UI and 24UT to creatinine from the WHO Study were divided into quintiles for analysis. The ratio for the Japanese was the highest in the highest quintiles for both I and T, reaching 88.1%, far higher than the average ratio for the Japanese (26.3%) in the total study population. Second, 553 inhabitants of Hyogo Prefecture, Japan, aged 30 to 79 years underwent 24-U collection and blood analyses. The 24UT and 24UI were divided into tertiles and adjusted for age and sex. The highest T tertile, compared with the lowest tertile, showed significantly higher levels of high-density lipoprotein-cholesterol (HDL-C), total cholesterol, 24U sodium (Na) and potassium (K). The highest I tertile showed significantly higher folate, 24UNa and 24UK compared with the lowest tertile. The highest tertile of both T and I showed significantly higher HDL-C, folate, and 24UNa and 24UK compared with the lowest tertile. Thus, greater consumption of fish and soy were significantly associated with higher HDL-C and folate levels, possibly a contributor to Japan having the lowest CHD mortality and longest life expectancy among developed countries. As these intakes were also associated with a high intake of salt, a low-salt intake of fish and soy should be recommended for healthy life expectancy.

Healthy life expectancy (HALE) at birth (years)

Abstract

The Health Inequality Project uses big data to measure differences in life expectancy by income across areas and identify strategies to improve health outcomes for low-income Americans.

Section 7

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution. Both race-adjusted and unadjusted estimates are reported.

Source

Section 13

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each percentile of the national income distribution separately by year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 6

This dataset was created on 2020-01-10 18:53:00.508 by merging multiple datasets together. The source datasets for this version were:

Commuting Zone Life Expectancy Estimates by year: CZ-level by-year life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy: Commuting zone (CZ)-level life expectancy estimates for men and women, by income quartile

Commuting Zone Life Expectancy Trends: CZ-level estimates of trends in life expectancy for men and women, by income quartile

Commuting Zone Characteristics: CZ-level characteristics

Commuting Zone Life Expectancy for larger populations: CZ-level life expectancy estimates for men and women, by income ventile

Section 15

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by state of residence and year. Both race-adjusted and unadjusted estimates are reported.

Source

Section 11

This table reports US mortality rates by gender, age, year and household income percentile. Household incomes are measured two years prior to the mortality rate for mortality rates at ages 40-63, and at age 61 for mortality rates at ages 64-76. The “lag” variable indicates the number of years between measurement of income and mortality.

Observations with 1 or 2 deaths have been masked: all mortality rates that reflect only 1 or 2 deaths have been recoded to reflect 3 deaths

Source

Section 3

This table reports coefficients and standard errors from regressions of life expectancy estimates for men and women at age 40 for each quartile of the national income distribution on calendar year by commuting zone of residence. Only the slope coefficient, representing the average increase or decrease in life expectancy per year, is reported. Trend estimates for both race-adjusted and unadjusted life expectancies are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 9

This table reports life expectancy estimates at age 40 for Males and Females for all countries. Source: World Health Organization, accessed at: http://apps.who.int/gho/athena/

Source

Section 10

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by county of residence. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for counties with populations larger than 25,000 only

Source

Section 2

This table reports life expectancy point estimates and standard errors for men and women at age 40 for each quartile of the national income distribution by commuting zone of residence and year. Both race-adjusted and unadjusted estimates are reported. Estimates are reported for the 100 largest CZs (populations greater than 590,000) only.

Source

Section 8

This table reports US population and death counts by age, year, and sex from various sources. Counts labelled “dm1” are derived from the Social Security Administration Data Master 1 file. Counts labelled “irs” are derived from tax data. Counts labelled “cdc” are derived from NCHS life tables.

Source

Section 12

This table reports numerous county characteristics, compiled from various sources. These characteristics are described in the county life expectancy table.

Two variables constructed by the Cen

Spain ES: Life Expectancy at Birth: Total data was reported at 82.832 Year in 2016. This stayed constant from the previous number of 82.832 Year for 2015. Spain ES: Life Expectancy at Birth: Total data is updated yearly, averaging 76.747 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 83.229 Year in 2014 and a record low of 69.109 Year in 1960. Spain ES: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Spain – Table ES.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Progress in health outcomes across Africa has been uneven, marked by significant disparities among countries, which not only challenges the global health security but impede progress towards achieving the United Nations’ Sustainable Development Goals 3 and 10 (SDG 3 and SDG 10) and Universal Health Coverage (UHC). This paper examines the progress of African countries in reducing intra-country health outcome disparities between 2000 and 2019. In other words, the paper investigates the convergence hypothesis in health outcome using a panel data from 40 African countries. Data were sourced from the World Development Indicators, the World Governance Indicators, and the World Health Organization database. Employing a non-linear dynamic factor model, the study focused on three health outcomes: infant mortality rate, under-5 mortality rate, and life expectancy at birth. The findings indicate that while the hypothesis of convergence is not supported for the selected countries, evidence of convergence clubs is observed for the three health outcome variables. The paper further examine the factors contributing to club formation by using the marginal effects of the ordered logit regression model. The findings indicate that the overall impact of the control variables aligns with existing research. Moreover, governance quality and domestic government health expenditure emerge as significant determinants influencing the probability of membership in specific clubs for the child mortality rate models. In the life expectancy model, governance quality significantly drives club formation. The results suggest that there is a need for common health policies for the different convergence clubs, while country-specific policies should be implemented for the divergent countries. For instance, policies and strategies promoting health prioritization in national budget allocation and reallocation should be encouraged within each final club. Efforts to promote good governance policies by emphasizing anti-corruption measures and government effectiveness should also be encouraged. Moreover, there is a need to implement regional monitoring mechanisms to ensure progress in meeting health commitments, while prioritizing urbanization plans in countries with poorer health outcomes to enhance sanitation access.

Denmark DK: Life Expectancy at Birth: Male data was reported at 78.900 Year in 2016. This records an increase from the previous number of 78.800 Year for 2015. Denmark DK: Life Expectancy at Birth: Male data is updated yearly, averaging 71.990 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 78.900 Year in 2016 and a record low of 70.200 Year in 1965. Denmark DK: Life Expectancy at Birth: Male data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Denmark – Table DK.World Bank: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision. (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Mexico MX: Life Expectancy at Birth: Total data was reported at 77.305 Year in 2017. This records an increase from the previous number of 77.118 Year for 2016. Mexico MX: Life Expectancy at Birth: Total data is updated yearly, averaging 70.239 Year from Dec 1960 (Median) to 2017, with 58 observations. The data reached an all-time high of 77.305 Year in 2017 and a record low of 57.082 Year in 1960. Mexico MX: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Mexico – Table MX.World Bank.WDI: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

These datasets were collected to fulfil the requirement of University coursework.

The complete source code and paper are available on GitHub. Click here.

About Dataset

These datasets contain the information of the World Development Indicator (WDI) provided by the world bank, the non-communicable mortality rate, the suicide rate and the number of health workforce data by the World Health Organization (WHO).

Implementation

• Data Cleaning and Preparation
• Exploratory Data Analysis
• Machine Learning Prediction - Random Forest
• Deep Learning Prediction - Neural Network

United States US: Life Expectancy at Birth: Total data was reported at 78.690 Year in 2016. This stayed constant from the previous number of 78.690 Year for 2015. United States US: Life Expectancy at Birth: Total data is updated yearly, averaging 74.766 Year from Dec 1960 (Median) to 2016, with 57 observations. The data reached an all-time high of 78.841 Year in 2014 and a record low of 69.771 Year in 1960. United States US: Life Expectancy at Birth: Total 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. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.; ; (1) United Nations Population Division. World Population Prospects: 2017 Revision, or derived from male and female life expectancy at birth from sources such as: (2) Census reports and other statistical publications from national statistical offices, (3) Eurostat: Demographic Statistics, (4) United Nations Statistical Division. Population and Vital Statistics Reprot (various years), (5) U.S. Census Bureau: International Database, and (6) Secretariat of the Pacific Community: Statistics and Demography Programme.; Weighted average;

Progress in health outcomes across Africa has been uneven, marked by significant disparities among countries, which not only challenges the global health security but impede progress towards achieving the United Nations’ Sustainable Development Goals 3 and 10 (SDG 3 and SDG 10) and Universal Health Coverage (UHC). This paper examines the progress of African countries in reducing intra-country health outcome disparities between 2000 and 2019. In other words, the paper investigates the convergence hypothesis in health outcome using a panel data from 40 African countries. Data were sourced from the World Development Indicators, the World Governance Indicators, and the World Health Organization database. Employing a non-linear dynamic factor model, the study focused on three health outcomes: infant mortality rate, under-5 mortality rate, and life expectancy at birth. The findings indicate that while the hypothesis of convergence is not supported for the selected countries, evidence of convergence clubs is observed for the three health outcome variables. The paper further examine the factors contributing to club formation by using the marginal effects of the ordered logit regression model. The findings indicate that the overall impact of the control variables aligns with existing research. Moreover, governance quality and domestic government health expenditure emerge as significant determinants influencing the probability of membership in specific clubs for the child mortality rate models. In the life expectancy model, governance quality significantly drives club formation. The results suggest that there is a need for common health policies for the different convergence clubs, while country-specific policies should be implemented for the divergent countries. For instance, policies and strategies promoting health prioritization in national budget allocation and reallocation should be encouraged within each final club. Efforts to promote good governance policies by emphasizing anti-corruption measures and government effectiveness should also be encouraged. Moreover, there is a need to implement regional monitoring mechanisms to ensure progress in meeting health commitments, while prioritizing urbanization plans in countries with poorer health outcomes to enhance sanitation access.

This dataset provides a curated and comprehensive overview of global health, demographic, economic, and environmental metrics for 188 recognized countries over a period of 10 years (2012-2021). It was created by combining reliable data from the World Bank and the World Health Organization (WHO). Due to the absence of a single source containing all necessary indicators, over 60 datasets were analyzed, cleaned, and merged, prioritizing completeness and significance.

The dataset includes 29 key indicators, ranging from life expectancy, population metrics, and economic factors to environmental conditions and health-related behaviors. Missing values were carefully handled, and only the most relevant data with substantial coverage were retained.

This dataset is ideal for researchers, analysts, and policymakers interested in exploring relationships between economic development, health outcomes, and environmental factors at a global scale.

Abstract

The Afghanistan Mortality Survey (AMS) 2010 was designed to measure mortality levels and causes of death, with a special focus on maternal mortality. In addition, the data obtained in the survey can be used to derive mortality trends by age and sex as well as sub-national estimates. The study also provides current data on fertility and family planning behavior and on the utilization of maternal and child health services.

OBJECTIVES

The specific objectives of the survey include the following: - National estimates of maternal mortality; causes and determinants of mortality for adults, children, and infants by age, sex, and wealth status; and other key socioeconomic background variables; - Estimates of indicators for the country as a whole, for the urban and the rural areas separately, and for each of the three survey domains of North, Central, and South, which were created by regrouping the eight geographic regions; - Information on determinants of maternal health; - Other demographic indicators, including life expectancy, crude birth and death rates, and fertility rates.

ORGANIZATION OF THE SURVEY

The AMS 2010 was carried out by the Afghan Public Health Institute (APHI) of the Ministry of Public Health (MoPH) and the Central Statistics Organization (CSO) Afghanistan. Technical assistance for the survey was provided by ICF Macro, the Indian Institute of Health Management Research (IIHMR) and the World Health Organization Regional Office for the Eastern Mediterranean (WHO/EMRO). The AMS 2010 is part of the worldwide MEASURE DHS project that assists countries in the collection of data to monitor and evaluate population, health, and nutrition programs. Financial support for the survey was received from USAID, and the United Nations Children’s Fund (UNICEF). WHO/EMRO’s contribution to the survey was supported with funds from USAID and the UK Department for International Development and the Health Metrics Network (DFID/HMN). Ethical approval for the survey was obtained from the institutional review boards at the MoPH, ICF Macro, IIHMR, and the WHO.

A steering committee was formed to coordinate, oversee, advise, and make decisions on all major aspects of the survey. The steering committee comprised representatives from various ministries and key stakeholders, including MoPH, CSO, USAID, ICF Macro, IIHMR, UNICEF, UNFPA, WHO, and local and international NGOs. A technical advisory group (TAG) made up of experts in the field of mortality and health was also formed to provide technical guidance throughout the survey, including reviewing the questionnaires, the tabulation plan for this final report, the final report, and the results of the survey.

Geographic coverage

National

Kind of data

Sample survey data [ssd]

Sampling procedure

The AMS 2010 is the first nationwide survey of its kind. A nationally representative sample of 24,032 households was selected. All women age 12-49 who were usual residents of the selected households or who slept in the households the night before the survey were eligible for the survey. The survey was designed to produce representative estimates of indicators for the country as a whole, for the urban and the rural areas separately, and for each of the three survey domains, which are regroupings of the eight geographical regions. The compositions of the domains are given below: - The North, which combines the Northern region and the North Eastern region, consists of nine provinces: Badakhshan, Baghlan, Balkh, Faryab, Jawzjan, Kunduz, Samangan, Sari Pul, and Takhar. - The Central, which combines the Western region, the Central Highland region, and the Capital region, consists of 12 provinces: Badghis, Bamyan, Daykundi, Farah, Ghor, Hirat, Kabul, Kapisa, Logar, Panjsher, Parwan, and Maydan Wardak. - The South, which combines the Southern region, the South Eastern region, and the Eastern region, consists of 13 provinces: Ghazni, Hilmand, Kandahar, Khost, Kunar, Laghman, Nangarhar, Nimroz, Nuristan, Paktika, Paktya, Uruzgan, and Zabul.

The sample for the AMS 2010 is a stratified sample selected in two stages from the 2011 Population and Housing Census (PHC) preparatory frame obtained from the Central Statistics Organization (CSO). Stratification was achieved by separating each domain into urban and rural areas. Because of the low urban proportion for most of the provinces, the combined urban areas of each domain form a single sampling stratum, which is the urban stratum of the domain. On the other hand, the rural areas of each domain are further split into strata according to province; that is, the rural areas of each province form a sampling stratum. In total, 34 sampling strata have been created after excluding the rural areas of Hilmand, Kandahar, and Zabul from the domain of the south. Among the 34 sampling strata, 3 are urban strata, and the remaining 31 are rural strata, which correspond with the total number of provinces and their rural areas. Samples were selected independently in each sampling stratum by a twostage selection process. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels within a sampling stratum, by sorting the sampling frame according to administrative units at different levels within each stratum, and by using a probability proportional to size selection at the first stage of sampling.

The primary sampling unit was the enumeration area (EA). After selection of the EA and before the main fieldwork, a household listing operation was carried out in the selected EAs to provide the most updated sampling frame for the selection of households in the second stage. The household listing operation consisted of (1) visiting each of the 751 selected EAs, (2) drawing a location map and a detailed sketch, and (3) recording on the household listing forms all structures found in the EA and all households residing in the structure with the address and the name of the household head. The resulting lists of households serve as the sampling frame for the selection of households at the second stage of sampling. In the second stage of sampling, a fixed number of 32 households was selected randomly in each selected cluster by an equal probability systematic sampling technique. The household selection procedure was carried out at the IIHMR office in Kabul prior to the start of fieldwork. An Excel spreadsheet prepared by ICF Macro to facilitate the household selection was used. A level of non response, or refusals on the part of households and individuals, had already been taken into consideration in the sample design and sample calculation.

The survey interviewers interviewed only pre-selected households, and no replacements of pre-selected households were made during the fieldwork, thus maintaining the representativeness of the final results from the survey for the country. Interviewers were also trained to optimize their effort to identify selected households and to ensure that individuals cooperated to minimize non-response. It is important to note here that interviewers in the AMS were not remunerated according to the number of questionnaires completed but given a daily per diem for the number of days they spent in the field; in addition, it is also important to note that respondents were neither compensated in any way for agreeing to be interviewed nor coerced into completing an interview.

For security reasons, the rural areas of Kandahar, Hilmand, and Zabul, which constitute less than 9 percent of the population, were excluded during sample design from the sample selection; however, the urban areas of these provinces were included. Of the 751 EAs that were included in the sample, 34 EAs (5 urban and 29 rural) were not surveyed. Six of the selected EAs in Ghazni, 16 in Paktika, 1 in Uruzgan, 3 in Kandahar, 3 in Daykundi, and 2 in Faryab were not surveyed because of the security situation. In addition, two EAs from Badakshan and one from Takhar were not surveyed because base maps from the CSO were unavailable. The non-surveyed EAs-which were primarily in rural areas-represent 4 percent of the total population of the country,

Table 1.1 - Sample coverage (Percentage of the population represented by the sample surveyed in the Afghanistan Mortality Survey, Afghanistan 2010) Region / Urban / Rural / Total North / 97 / 98 / 98 Central / 100 / 98 / 99 South / 94 / 63 / 66 Total / 98 / 84 / 87

Overall, approximately 13 percent of the country was not surveyed; most of these areas were in the South zone. As shown in Table 1.1, the survey covered only 66 percent of the population in the South zone. Sample weights were adjusted accordingly to take into account those EAs that were selected but not completed for security or other reasons.

Overall, the AMS 2010 covered 87 percent of the population of the country, 98 percent of the urban population and 84 percent of the rural population. Nevertheless, the lack of total coverage and the disproportionate exclusion of areas in the South, and particularly the rural South, should be taken into consideration when interpreting national level estimates of key demographic indicators and estimates for the South zone and regions within. For this reason key indicators will be presented for all Afghanistan and Afghanistan excluding the South zone. Despite these exclusions, the AMS is the most comprehensive mortality survey conducted in Afghanistan in the last few decades in terms of geographic coverage of the country.

Throughout this report, numbers in the tables reflect weighted numbers unless indicated otherwise. In most cases, percentages based on 25-49 cases are shown in parentheses and percentages based on fewer than 25 unweighted cases are suppressed and replaced with an asterisk, to caution readers when interpreting data that a percentage may not

KM: Life Expectancy at Birth: Total data was reported at 66.777 Year in 2023. This records an increase from the previous number of 66.481 Year for 2022. KM: Life Expectancy at Birth: Total data is updated yearly, averaging 56.340 Year from Dec 1960 (Median) to 2023, with 64 observations. The data reached an all-time high of 66.777 Year in 2023 and a record low of 40.777 Year in 1960. KM: Life Expectancy at Birth: Total data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Comoros – Table KM.World Bank.WDI: Social: Health Statistics. Life expectancy at birth indicates the number of years a newborn infant would live if prevailing patterns of mortality at the time of its birth were to stay the same throughout its life.;(1) United Nations Population Division. World Population Prospects: 2024 Revision; or derived from male and female life expectancy at birth from sources such as: (2) Statistical databases and publications from national statistical offices; (3) Eurostat: Demographic Statistics.;Weighted average;

This dataset contains data from WHO's data portal covering the following categories:

Adolescent, Ageing, Air pollution, Assistive technology, Child, Child mortality, Cross-cutting, Dementia diagnosis, treatment and care, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, Global strategy, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and newborn, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics.

For links to individual indicator metadata, see resource descriptions.

BackgroundNeglected Tropical Diseases (NTDs) are important causes of morbidity, disability, and mortality among poor and vulnerable populations in several countries worldwide, including Brazil. We present the burden of NTDs in Brazil from 1990 to 2016 based on findings from the Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016).MethodologyWe extracted data from GBD 2016 to assess years of life lost (YLLs), years lived with disability (YLDs), and disability-adjusted life-years (DALYs) for NTDs by sex, age group, causes, and Brazilian states, from 1990 to 2016. We included all NTDs that were part of the priority list of the World Health Organization (WHO) in 2016 and that are endemic/autochthonous in Brazil. YLDs were calculated by multiplying the prevalence of sequelae multiplied by its disability weight. YLLs were estimated by multiplying each death by the reference life expectancy at each age. DALYs were computed as the sum of YLDs and YLLs.Principal findingsIn 2016, there were 475,410 DALYs (95% uncertainty interval [UI]: 337,334–679,482; age-standardized rate of 232.0 DALYs/100,000 population) from the 12 selected NTDs, accounting for 0.8% of national all-cause DALYs. Chagas disease was the leading cause of DALYs among all NTDs, followed by schistosomiasis and dengue. The sex-age-specific NTD burden was higher among males and in the youngest and eldest (children

This dataset contains data from WHO's data portal covering the following categories:

Adolescent, Ageing, Air pollution, Assistive technology, Child, Child mortality, Cross-cutting, Dementia diagnosis, treatment and care, Environment and health, Foodborne Diseases Estimates, Global Dementia Observatory (GDO), Global Health Estimates: Life expectancy and leading causes of death and disability, Global Information System on Alcohol and Health, Global Patient Safety Observatory, Global strategy, HIV, Health financing, Health systems, Health taxes, Health workforce, Hepatitis, Immunization coverage and vaccine-preventable diseases, Malaria, Maternal and newborn, Maternal and reproductive health, Mental health, Neglected tropical diseases, Noncommunicable diseases, Nutrition, Oral Health, Priority health technologies, Resources for Substance Use Disorders, Road Safety, SDG Target 3.8 | Achieve universal health coverage (UHC), Sexually Transmitted Infections, Tobacco control, Tuberculosis, Vaccine-preventable communicable diseases, Violence prevention, Water, sanitation and hygiene (WASH), World Health Statistics.

For links to individual indicator metadata, see resource descriptions.