Version history:This data are a new version of Geiger et al (2017, http:doi.org/10.5880/PIK.2017.003). Please use this updated version of this dataset which contains the following correction of errors in the original dataset: The linear interpolation in GDP per capita for Aruba (ABW) between observations in 2005 and SSP2 projections in 2010 was replaced by observed GDP per capita values for the years 2006-2009, as the SSP2 projection for Aruba turned out to be incorrect. As a result of this, the national GDP per capita and GDP timeseries for Aruba between 2006 and 2009 is different from the previous version. We here provide three different economic time series that amend or combine various existing time series for Gross Domestic Product (GDP), GDP per capita, and population to create consistent and continuous economic time series between 1850 and 2009 for up to 195 countries. All data, including the data description are included in a zip folder (2018-010_GDP_1850-2009_Data_v2.zip): (1) A continuous table of global income data (in 1990 Geary-Khamis $) based on the Maddison Project data base (MPD) for 160 individual countries and 3 groups of countries from 1850-2010: Maddison_Project_data_completed_1850-2010.csv. (2) A continuous table of global income data (in 2005 PPP $, PPP = purchasing power parity) for 195 countries based on a merged and harmonized dataset between MPD and Penn World Tables (PWT, version v8.1) from 1850-2009, and additionally extended using PWT v9.0 and World Development Indicators (WDI), that is consistent with future GDP per capita projections from the Shared Socioeconomic Pathways (SSPs): GDP-per-capita-national_PPP2005_SSP-harmonized_1850-2009_v2.csv. (3) A continuous table of global GDP data (in 2005 PPP $) for 195 countries from 1850-2009 based on the second income data set multiplied by country population data, again consistent with future SSP GDP projections: GDP-national_PPP2005_SSP-harmonized_1850-2009_v2.csv. These data are supplemented by a masking table indicating MPD original data and amended data based on current country definitions (Maddison_data_availability_masked_1850-2010.csv) and a file with PPP conversion factors used in this study (PPP_conversion_factors_PPP1990-PPP2005.csv). We use various interpolation and extrapolation methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set aims to provide valuable input, e.g., for climate impact research in order to consistently analyze economic impacts from pre-industrial times to the distant future. More information about data sources and data format description is given in the data description file (2018-010_Data-Description-GDP_1850-2009_v2.pdf).

We here provide three different economic time series that amend or combine various existing time series for Gross Domestic Product (GDP), GDP per capita, and population to create consistent and continuous economic time series between 1850 and 2009 for up to 195 countries:

(1) A continuous table of global income data (in 1990 Geary-Khamis $) based on the Maddison Project data base (MPD) for 160 individual countries and 3 groups of countries from 1850-2010: Maddison_Project_data_completed_1850-2010.csv.(2) A continuous table of global income data (in 2005 PPP $, PPP = purchasing power parity) for 195 countries based on a merged and harmonized dataset between MPD and Penn World Tables (PWT, version v8.1) from 1850-2009, and additionally extended using PWT v9.0 and World Development Indicators (WDI), that is consistent with future GDP per capita projections from the Shared Socioeconomic Pathways (SSPs): GDP-per-capita-national_PPP2005_SSP-harmonized_1850-2009.csv.(3) A continuous table of global GDP data (in 2005 PPP $) for 195 countries from 1850-2009 based on the second income data set multiplied by country population data, again consistent with future SSP GDP projections: GDP-national_PPP2005_SSP-harmonized_1850-2009.csv.

These data are supplemented by a masking table indicating MPD original data and amended data based on current country definitions (Maddison_data_availability_masked_1850-2010.csv) and a file with PPP conversion factors used in this study (PPP_conversion_factors_PPP1990-PPP2005.csv).

We use various interpolation and extrapolation methods to handle missing data and discuss the advantages and limitations of our methodology. Despite known shortcomings this data set aims to provide valuable input, e.g., for climate impact research in order to consistently analyze economic impacts from pre-industrial times to the distant future. More information about data sources and data format description is given in the data description file (Data-Description-GDP_1850-2009.pdf).

Version history: Please use the updated version of this dataset which contains correction of errors in the original dataset. For a detailed description of the changes please consult the CHANGELOG included in the data description document of the new version.

The PRIMAP-hist Socio-Eco dataset combines several published datasets to create a comprehensive set of population and Gross domestic product (GDP) pathways for every country covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data has no sector resolution. List of datasets included in this data publication: (1) PMHSOCIOECO21_GDP_26-Jul-2019.csv: contains the GDP data for all countries(2) PMHSOCIOECO21_Population_26-Jul-2019.csv: contains the population data for all countries(3) PRIMAP-hist_SocioEco_data_description.pdf: including CHANGELOG(all files are also included in the .zip folder) When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset. Please consider also citing the relevant original sources when using the PRIMAP-hist Socio-Eco dataset. See the full citations in the References section further below. A data description article is in preparation. Until it is published we refer to the description article of the PRIMAP-hist emissions time series for the methodology used. SOURCES: - UN World Population Prospects 2019 (UN2019)- World Bank World Development Indicators 2019 (July) (WDI2019B). We use the NY.GDP.MKTP.PP.KD variable for GDP.- Penn World Table version 9.1 (PWT91). We use the cgdpe variable for GDP (Robert and Feenstra, 2019; Feenstra et al., 2015)- Maddison Project Database 2018 (MPD2018). We use the cgdppc variable for GDP (Bolt et al,, 2018)- Anthropogenic land use estimates for the Holocene – HYDE 3.2 (HYDE32)(Klein Goldewijk, 2017)- Continuous national gross domestic product (GDP) time series for 195 countries: past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100) (Geiger2018, Geiger and Frieler, 2018)Full references are available in the data description document.

The average for 2022 based on 195 countries was 8.37 deaths per 1000 people. The highest value was in Ukraine: 21.4 deaths per 1000 people and the lowest value was in Qatar: 1.08 deaths per 1000 people. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

The data highlights the diverse approaches taken by countries worldwide regarding the minting of $0.01 coins. While some nations opt to mint specific coins denominated at $0.01, many others do not have a physical coin equivalent to $0.01. This disparity reflects varying economic contexts, where factors like inflation rates and the cost of producing low-denomination coins influence decision-making. However, for those countries that do mint $0.01 coins, they serve as essential components of everyday transactions, facilitating commerce and ensuring smooth monetary exchanges at the smallest denominational level.

As for the remaining countries utilizing $0.01 coins, they employ these small denominations as crucial components of their currency systems, facilitating transactions at the lowest monetary unit. These coins, though often overlooked due to their minimal value, play significant roles in daily commerce, particularly in transactions where exact change is necessary or customary. Whether it's the 1 centavo in Argentina, the 1 cent in Australia and Belize, or the 1 fen in China, these coins represent a fundamental aspect of each country's monetary infrastructure, contributing to the efficiency and functionality of their respective economies.

The PRIMAP-hist Socio-Eco dataset combines several published datasets to create a comprehensive set of population and Gross domestic product (GDP) pathways for every country covering the years 1850 to 2017, and all UNFCCC (United Nations Framework Convention on Climate Change) member states, as well as most non-UNFCCC territories. The data has no sector resolution. List of datasets included in this data publication: (1) PMHSOCIOECO21_GDP_26-Jul-2019.csv: contains the GDP data for all countries(2) PMHSOCIOECO21_Population_26-Jul-2019.csv: contains the population data for all countries(3) PRIMAP-hist_SocioEco_data_description.pdf: including CHANGELOG(all files are also included in the .zip folder) When using this dataset or one of its updates, please cite the DOI of the precise version of the dataset. Please consider also citing the relevant original sources when using the PRIMAP-hist Socio-Eco dataset. See the full citations in the References section further below. A data description article is in preparation. Until it is published we refer to the description article of the PRIMAP-hist emissions time series for the methodology used. SOURCES: - UN World Population Prospects 2019 (UN2019)- World Bank World Development Indicators 2019 (July) (WDI2019B). We use the NY.GDP.MKTP.PP.KD variable for GDP.- Penn World Table version 9.1 (PWT91). We use the cgdpe variable for GDP (Robert and Feenstra, 2019; Feenstra et al., 2015)- Maddison Project Database 2018 (MPD2018). We use the cgdppc variable for GDP (Bolt et al,, 2018)- Anthropogenic land use estimates for the Holocene – HYDE 3.2 (HYDE32)(Klein Goldewijk, 2017)- Continuous national gross domestic product (GDP) time series for 195 countries: past observations (1850–2005) harmonized with future projections according to the Shared Socio-economic Pathways (2006–2100) (Geiger2018, Geiger and Frieler, 2018)Full references are available in the data description document. Methods:Country resolved data is combined from different sources using the PRIMAP emissions module (Nabel et. al., 2011). It is supplemented with growth rates from regionally resolved sources and numerical extrapolations.

In 2023, Singapore dominated the ranking of the world's health and health systems, followed by Japan and South Korea. The health index score is calculated by evaluating various indicators that assess the health of the population, and access to the services required to sustain good health, including health outcomes, health systems, sickness and risk factors, and mortality rates. The health and health system index score of the top ten countries with the best healthcare system in the world ranged between 82 and 86.9, measured on a scale of zero to 100.

Global Health Security Index  Numerous health and health system indexes have been developed to assess various attributes and aspects of a nation's healthcare system. One such measure is the Global Health Security (GHS) index. This index evaluates the ability of 195 nations to identify, assess, and mitigate biological hazards in addition to political and socioeconomic concerns, the quality of their healthcare systems, and their compliance with international finance and standards. In 2021, the United States was ranked at the top of the GHS index, but due to multiple reasons, the U.S. government failed to effectively manage the COVID-19 pandemic. The GHS Index evaluates capability and identifies preparation gaps; nevertheless, it cannot predict a nation's resource allocation in case of a public health emergency.

Universal Health Coverage Index  Another health index that is used globally by the members of the United Nations (UN) is the universal health care (UHC) service coverage index. The UHC index monitors the country's progress related to the sustainable developmental goal (SDG) number three. The UHC service coverage index tracks 14 indicators related to reproductive, maternal, newborn, and child health, infectious diseases, non-communicable diseases, service capacity, and access to care. The main target of universal health coverage is to ensure that no one is denied access to essential medical services due to financial hardships. In 2021, the UHC index scores ranged from as low as 21 to a high score of 91 across 194 countries. 

This dataset presents the estimated number of live births in each country for the most recent reference year, based on the 2024 revision of the UN Population Division’s World Population Prospects. Live birth estimates are a key demographic indicator, used for planning health services, calculating health coverage indicators, and understanding population growth trends. These figures support maternal and newborn health monitoring and workforce planning at national and global levels.Data Source:UN Population Division World Population Prospects: https://population.un.org/wpp/Download/StandardData Dictionary: The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

The average for 2022 based on 195 countries was 18.38 births per 1000 people. The highest value was in Niger: 45.03 births per 1000 people and the lowest value was in Hong Kong: 4.4 births per 1000 people. The indicator is available from 1960 to 2022. Below is a chart for all countries where data are available.

This dataset reports the number of midwives per 10,000 population, based on data from the WHO National Health Workforce Accounts (NHWA) platform. It provides a standardised measure of workforce density, reflecting the availability of midwifery services in relation to population size. This indicator is essential for assessing health system capacity, identifying gaps in coverage, and informing policies aimed at equitable access to skilled midwifery care worldwide.Number of midwives (Midwifery Professionals + Midwifery Associate Professionals + Nurse-midwife professionals + Nurse-midwife associate professionals) per 10,000 population. Note: Data doesn't include nurse-midwives.Data Source: WHO national health workforce reporting systems: https://apps.who.int/nhwaportal/Data Dictionary:The data is collated with the following columns:Column headingContent of this columnPossible valuesRefNumerical counter for each row of data, for ease of identification1+CountryShort name for the country195 countries in total – all 194 WHO member states plus PalestineISO3Three-digit alphabetical codes International Standard ISO 3166-1 assigned by the International Organization for Standardization (ISO). e.g. AFG (Afghanistan)ISO22 letter identifier code for the countrye.g. AF (Afghanistan)ICM_regionICM Region for countryAFR (Africa), AMR (Americas), EMR (Eastern Mediterranean), EUR (Europe), SEAR (South east Asia) or WPR (Western Pacific)CodeUnique project code for each indicator:GGTXXnnnGG=data group e.g. OU for outcomeT = N for novice or E for ExpertXX = identifier number 00 to 30nnn = identifier name eg mmre.g. OUN01sbafor Outcome Novice Indicator 01 skilled birth attendance Short_nameIndicator namee.g. maternal mortality ratioDescriptionText description of the indicator to be used on websitee.g. Maternal mortality ratio (maternal deaths per 100,000 live births)Value_typeDescribes the indicator typeNumeric: decimal numberPercentage: value between 0 & 100Text: value from list of text optionsY/N: yes or noValue_categoryExpect this to be ‘total’ for all indicators for Phase 1, but this could allow future disaggregation, e.g. male/female; urban/ruraltotalYearThe year that the indicator value was reported. For most indicators, we will only report if 2014 or more recente.g. 2020Latest_Value‘LATEST’ if this is the most recent reported value for the indicator since 2014, otherwise ‘No’. Useful for indicators with time trend data.LATEST or NOValueIndicator valuee.g. 99.8. NB Some indicators are calculated to several decimal places. We present the value to the number of decimal places that should be displayed on the Hub.SourceFor Caesarean birth rate [OUN13cbr] ONLY, this column indicates the source of the data, either OECD when reported, or UNICEF otherwise.OECD or UNICEFTargetHow does the latest value compare with Global guidelines / targets?meets targetdoes not meet targetmeets global standarddoes not meet global standardRankGlobal rank for indicator, i.e. the country with the best global score for this indicator will have rank = 1, next = 2, etc. This ranking is only appropriate for a few indicators, others will show ‘na’1-195Rank out ofThe total number of countries who have reported a value for this indicator. Ranking scores will only go as high as this number.Up to 195TrendIf historic data is available, an indication of the change over time. If there is a global target, then the trend is either getting better, static or getting worse. For mmr [OUN04mmr] and nmr [OUN05nmr] the average annual rate of reduction (arr) between 2016 and latest value is used to determine the trend:arr <-1.0 = getting worsearr >=-1.0 AND <=1.0 = staticarr >1.0 = getting betterFor other indicators, the trend is estimated by comparing the average of the last three years with the average ten years ago:decreasing if now < 95% 10 yrs agoincreasing if now > 105% 10 yrs agostatic otherwiseincreasingdecreasing Or, if there is a global target: getting better,static,getting worseNotesClarification comments, when necessary LongitudeFor use with mapping LatitudeFor use with mapping DateDate data uploaded to the Hubthe following codes are also possible values:not reported does not apply don’t knowThis is one of many datasets featured on the Midwives’ Data Hub, a digital platform designed to strengthen midwifery and advocate for better maternal and newborn health services.

Background As mortality rates decline, life expectancy increases, and populations age, non-fatal outcomes of diseases and injuries are becoming a larger component of the global burden of disease. The Global Burden of Diseases, Injuries, and Risk Factors Study 2016 (GBD 2016) provides a comprehensive assessment of prevalence, incidence, and years lived with disability (YLDs) for 328 causes in 195 countries and territories from 1990 to 2016. Methods We estimated prevalence and incidence for 328 diseases and injuries and 2982 sequelae, their non-fatal consequences. We used DisMod-MR 2.1, a Bayesian meta-regression tool, as the main method of estimation, ensuring consistency between incidence, prevalence, remission, and cause of death rates for each condition. For some causes, we used alternative modelling strategies if incidence or prevalence needed to be derived from other data. YLDs were estimated as the product of prevalence and a disability weight for all mutually exclusive sequelae, corrected for comorbidity and aggregated to cause level. We updated the Socio-demographic Index (SDI), a summary indicator of income per capita, years of schooling, and total fertility rate. GBD 2016 complies with the Guidelines for Accurate and Transparent Health Estimates Reporting (GATHER). Findings Globally, low back pain, migraine, age-related and other hearing loss, iron-deficiency anaemia, and major depressive disorder were the five leading causes of YLDs in 2016, contributing 57.6 million (95% uncertainty interval [UI] 40.8-75.9 million [7.2%, 6.0-8.3]), 45.1 million (29.0-62.8 million [5.6%, 4.0-7.2]), 36.3 million (25.3-50.9 million [4.5%, 3.8-5.3]), 34.7 million (23.0-49.6 million [4.3%, 3.5-5.2]), and 34.1 million (23.5-46.0 million [4.2%, 3.2-5.3]) of total YLDs, respectively. Age-standardised rates of YLDs for all causes combined decreased between 1990 and 2016 by 2.7% (95% UI 2.3-3.1). Despite mostly stagnant age-standardised rates, the absolute number of YLDs from non-communicable diseases has been growing rapidly across all SDI quintiles, partly because of population growth, but also the ageing of populations. The largest absolute increases in total numbers of YLDs globally were between the ages of 40 and 69 years. Age-standardised YLD rates for all conditions combined were 10.4% (95% UI 9.0-11.8) higher in women than in men. Iron-deficiency anaemia, migraine, Alzheimer's disease and other dementias, major depressive disorder, anxiety, and all musculoskeletal disorders apart from gout were the main conditions contributing to higher YLD rates in women. Men had higher age-standardised rates of substance use disorders, diabetes, cardiovascular diseases, cancers, and all injuries apart from sexual violence. Globally, we noted much less geographical variation in disability than has been documented for premature mortality. In 2016, there was a less than two times difference in age-standardised YLD rates for all causes between the location with the lowest rate (China, 9201 YLDs per 100 000, 95% UI 6862-11943) and highest rate (Yemen, 14 774 YLDs per 100 000, 11 018-19 228). Interpretation The decrease in death rates since 1990 for most causes has not been matched by a similar decline in age-standardised YLD rates. For many large causes, YLD rates have either been stagnant or have increased for some causes, such as diabetes. As populations are ageing, and the prevalence of disabling disease generally increases steeply with age, health systems will face increasing demand for services that are generally costlier than the interventions that have led to declines in mortality in childhood or for the major causes of mortality in adults. Up-todate information about the trends of disease and how this varies between countries is essential to plan for an adequate health-system response. Copyright (C) The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license.

The forecast estimated that about **** percent of the mobile phone users were going to have a smartphone in 2019. As smartphones became the standard in 2019, the penetration rate compared to the population says more about the interest of the Indonesian population in smartphones. In 2019, the penetration rate of smartphones in Indonesia reached almost ** percent.

Smartphone usage in Indonesia

In 2014, an estimated ** percent of mobile phone users in Indonesia owned a smartphone and this figure was projected to almost double to ** percent by 2019. The number of mobile phone users in the country stood at around *** million in 2013 and was expected to rise to over *** million people by 2019. This would have meant that a projected ** million people in Indonesia owned a smartphone in 2019. Recent data shows that the actual number of smartphone users already reached over *** million in Indonesia.

Android was on the rise

As of December 2014, the most popular mobile operating system in Indonesia was Android, with the Google-backed OS holding a market share of just under ** percent. Its closest competitor was Series 40, used in Nokia phones, which held a share of around ** percent. Although Apple’s iOS was the second most popular smartphone operating system worldwide, it held just a **** percent share of the Indonesian mobile market at the end of 2014.

Chatting was one of the most popular activities on the mobile internet

An increasing number of people in Indonesia are using their mobile phones to access the internet. In 2015, mobile phone internet user penetration in the country was at ***** percent and this is expected to more than double to ***** percent by 2025. As of 2014, some of the most popular online services amongst mobile internet users in Indonesia were Gmail, Yahoo Mail, and BlackBerry Messenger (BBM). BBM was the preferred mobile messenger app of smartphone users in the country in 2014, with ** percent of users stating that they used the service to message friends.

As of July 2023, the number of prisoners in Italy was 57,749 inmates. Between 2000 and 2019, Italy's prison population increased, while it experienced a decrease in 2020 and 2021. In 2010, the population of prisoners in custody reached its peak at 68,000 inmates, whereas by the end of 2019, the number of inmates amounted to 60,800 individuals. During the coronavirus emergency, Italy adopted a decree for the supervised release of certain prisoners in order to reduce overcrowding. Thus, the number of detainees in the last year experienced a decrease.

Demography of incarcerated population

Among prisoners, two groups stand out for age and educational level. Data related to the age of people in jail show that individuals between 50 and 59 years old made up the largest group of the prison population (ten thousand inmates). Furthermore, a glance at their educational background reveals that 19 thousand individuals held a lower secondary school degree. Both groups represented a significant part of the incarcerated population.

Overcrowded prisons and tough conditions

Prison overcrowding is a worldwide phenomenon. Prison systems in more than one hundred countries operate at over double their capacities. Likewise, several prisons in Italy hold more prisoners than the facilities can accommodate. In 2019, a jail in the South Italian region Molise was rated the most overcrowded prison in the country, which was occupied at 195 percent of designed prison capacity. In addition to overcrowding, a further problem of the Italian prison system concerns the tough conditions for mafia prisoners. In October 2019, the European court of human rights (ECHR) ruled that the Italian prison system for mafia inmates must be reviewed, as the conditions in which they are serving their life term sentence violate their human rights.

Abstract

The 2009 MDHS was designed to provide data to monitor the population and health situation in Maldives. Specifically, the MDHS collected information on fertility levels and preferences, marriage, sexual activity, knowledge and use of family planning methods, breastfeeding practices, nutrition status of women and young children, childhood mortality, maternal and child health, and awareness and behaviour regarding AIDS and other sexually transmitted infections. At the household level, the survey collected information on domains of physical disability among those age 5 and older, developmental disability among young children, support for early learning, children at work, the impact of the tsunami of 2004, health expenditures, and care and support for physical activity of adults age 65 and older. At the individual level, the survey assessed additional features of blood pressure, diabetes, heart attack, and stroke.

Geographic coverage

National

Analysis unit

• Households
• Children age under 5
• Women age 15-49
• Men age 15-64

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN

The population of the republic of Maldives is distributed on 195 inhabited islands among a total of 202 inhabited islands; seven islands have no residents (MPND, 2008). Each inhabited island is an administrative unit with an island office that handles island-based affairs. The islands are regrouped to form atolls, a higher-level administrative unit with an atoll office and an atoll chief. There are 20 atolls in total in the republic. The capital city of Malé and the two surrounding islands, Villingili and Hulhumale, form a special atoll. The 21 atolls are regrouped to form six geographic regions according to their location. Malé atoll alone forms a region. In Maldives, there is no urbanrural designation for residential households within an atoll. All residential households in the 20 atolls outside of Malé are considered rural; all residential households in Malé are considered urban.

The 2009 Maldives DHS is based on a probability sample of 7,515 households. The sample was designed to produce representative data on households, women, and children for the country as a whole, for urban and rural areas, for the six geographical regions, and for each of the atolls of the country. The male and youth surveys were designed to produce representative results for the country as a whole, for urban and rural areas, and for each of the six geographical regions.

The 2006 Maldives Population and Housing Census provided the sampling frame for the 2009 MDHS. The MDHS sample was a stratified multistage sample selected in two stages from the census frame. In the first stage, 270 census blocks were selected using a systematic selection, with probability proportional to the number of residential households residing in the block. Stratification was achieved by treating each of the 21 atolls as a sampling stratum. Samples were selected independently in each stratum according to an appropriate allocation.

In the second stage of sampling, residential households were selected in each of the selected census blocks. Household selection involved an equal probability systematic selection of a fixed number of households: 28 households per block. Households were selected from the household listings created in the census, but to allow all households an opportunity to be included in the sample, listings were sent to island offices for updating prior to making household selections for the MDHS.

All ever-married women age 15-49 in the total sample of MDHS households, who were either usual residents of the household or visitors present in the household on the night before the survey, were eligible to be interviewed. In half of the households selected for the ever-married sample of women, all ever-married men age 15-64, who were either usual residents of the household or visitors present in the household on the night before the survey, were eligible to be interviewed. In the same half of households selected for the ever-married sample of men, never-married women and nevermarried men age 15-24, who were either usual residents of the household or visitors present in the household on the night before the survey, were also eligible to be interviewed. The MDHS was for the most part limited to Maldivian citizens; non-Maldivians were included in the survey only if they were the spouse, son, or daughter of a Maldivian.

Note: See detailed sample implementation information in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

Four questionnaires were used for the 2009 MDHS: the Household Questionnaire, the Women’s Questionnaire, the Men’s Questionnaire, and the Youth Questionnaire. The contents of the Household, Women’s, and Men’s questionnaires were based on model questionnaires developed by the MEASURE DHS programme. The DHS model questionnaires were modified to reflect concerns pertinent to the Maldives in the areas of population, women and children’s health, family planning, and others. Questionnaires were translated from English into Dhivehi.

The Household Questionnaire was used to list all the usual members and visitors in the selected households and to identify women and men who were eligible for the individual interview. Basic information was collected on the characteristics of each person listed, including their age, sex, education, and relationship to the head of the household. The Household Questionnaire was also designed to collect information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, water shortage, materials used for the floor and roof of the house, and ownership of various durable goods. In addition, height and weight measurements of ever-married women age 15-49 and children age 6-59 months were recorded in the Household Questionnaire to assess their nutritional status.

Topics added to the Household Questionnaire to reflect issues relevant in the Maldives include physical disability among those age 5 and older, developmental disability among young children, support for early learning, children at work, the tsunami of 2004, health expenditures, and care and support for physical activities of adults age 65 and older.

The Women’s Questionnaire was used to collect information from ever-married women age 15-49. These women were asked questions on the following topics: - Background characteristics (education, media exposure, etc.) - Reproductive history - Knowledge and use of family planning methods - Fertility preferences - Antenatal and delivery care - Breastfeeding and infant feeding practices - Vaccinations and childhood illnesses - Marriage and sexual activity - Woman’s work and husband’s background characteristics - Infant and child feeding practices - Childhood mortality - Awareness and behaviour about AIDS and other sexually transmitted infections (STIs) - Knowledge of blood pressure, diabetes, heart attack, and stroke

The Men’s Questionnaire was administered to all ever-married men age 15-64 living in every second household in the MDHS sample. The Men’s Questionnaire collected much of the same information as the Women’s Questionnaire, but it was shorter because it did not contain questions on reproduction, maternal and child health, and nutrition.

The Youth Questionnaire was administered to all never-married women and men age 15-24 living in every second household in the MDHS sample (the same one-half selected for the Men’s survey). The Youth Questionnaire focuses on priorities of the MOHF that pertain to young adults: reproductive health, knowledge and attitudes about HIV/AIDS, sexual activity, and tobacco, alcohol, and drug use.

Response rate

A total of 7,515 households were selected in the sample, of which 7,137 were found to be occupied at the time of data collection. The difference between the number of households selected and the number occupied usually occurs because some structures are found to be vacant or non-existent. The number of occupied households successfully interviewed was 6,443, yielding a household response rate of 90 percent.

In the households interviewed in the survey, a total of 8,362 ever-married women were identified as eligible for the individual interview; interviews were completed with 7,131 women, yielding a female response rate of 85 percent. In the one-half sub-sample of MDHS households, a total of 3,224 evermarried men age 15-64 were identified as eligible for the individual interview; interviews were completed with 1,727 men, yielding a male response rate of 54 percent. In the same sub-sample of households, a total of 3,205 never-married women and men age 15-24 (youth) were identified as eligible for individual interview; interviews were completed with 2,240 youth, yielding a youth response rate of 70 percent. The response rate was higher for female youth (80 percent) than male youth (61 percent).

The urban household response rate of 83 percent is lower than the 92 percent response rate among rural households. The same is true for individual interviews with ever-married respondents; response rates are somewhat lower among urban women (79 percent) and men (47 percent) than among their rural counterparts (87 percent and 55 percent, respectively). The difference in response rates between urban and rural youth is negligible.

Note: See summarized response rates by residence (urban/rural) in Table 1.1 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling

Israel: Death rate, per 1000 people: The latest value from 2022 is 5.4 deaths per 1000 people, unchanged from 5.4 deaths per 1000 people in 2021. In comparison, the world average is 8.37 deaths per 1000 people, based on data from 195 countries. Historically, the average for Israel from 1960 to 2022 is 6.15 deaths per 1000 people. The minimum value, 5 deaths per 1000 people, was reached in 2018 while the maximum of 7.2 deaths per 1000 people was recorded in 1972.

Afghanistan: Death rate, per 1000 people: The latest value from 2022 is 6.91 deaths per 1000 people, a decline from 7.34 deaths per 1000 people in 2021. In comparison, the world average is 8.37 deaths per 1000 people, based on data from 195 countries. Historically, the average for Afghanistan from 1960 to 2022 is 18.05 deaths per 1000 people. The minimum value, 6.79 deaths per 1000 people, was reached in 2019 while the maximum of 31.92 deaths per 1000 people was recorded in 1960.