{"doc_desc":{"title":"WLD_2011_TSGAM_v01_M","idno":"DDI_WLD_2011_TSGAM_v02_M_v01_WBDG","prod_date":"2011-07-21","version_statement":{"version":"DDI Document  - Version 02 - (04\/21\/21)\n This version is identical to DDI_WLD_2011_TSGAM_v01_M_v01_WBDG but country field has been updated to capture all the countries covered by survey."}},"study_desc":{"title_statement":{"idno":"WLD_2011_TSGAM_v01_M","title":"Trends and Socioeconomic Gradients in Adult Mortality Around the Developing World 1991-2009","alt_title":"TSGAM 1991-09"},"authoring_entity":[{"name":"Damien de Walque and Deon Filmer","affiliation":"World Bank"}],"production_statement":{"prod_date":"2011-07-21"},"distribution_statement":{"contact":[{"name":"Damien de Walque","affiliation":"World Bank","email":"research@worldbank.org","uri":"http:\/\/go.worldbank.org\/XLZAJITUW0"},{"name":"Deon Filmer","affiliation":"World Bank","email":"research@worldbank.org","uri":"http:\/\/go.worldbank.org\/WJF6HS00D0"}],"depositor":[{"name":"","abbreviation":"","affiliation":""}]},"version_statement":{"version_date":"2011-06"},"study_info":{"keywords":[{"keyword":"Demographics","vocab":"","uri":""},{"keyword":"Health Monitoring and Evaluation","vocab":"","uri":""},{"keyword":"Population Policies","vocab":"","uri":""},{"keyword":"Early Child and Children's Health","vocab":"","uri":""}],"abstract":"The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49. \n\nThe analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV\/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban\/rural residence or by school attainment.\n\nThis paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http:\/\/econ.worldbank.org.","coll_dates":[{"start":"1991","end":"2009","cycle":""}],"nation":[{"name":"Benin","abbreviation":"BEN"},{"name":"Burkina Faso","abbreviation":"BFA"},{"name":"Bolivia","abbreviation":"BOL"},{"name":"Brazil","abbreviation":"BRA"},{"name":"Cameroon","abbreviation":"CMR"},{"name":"Congo, Dem. Rep.","abbreviation":"COD"},{"name":"Dominican Republic","abbreviation":"DOM"},{"name":"Ethiopia","abbreviation":"ETH"},{"name":"Gabon","abbreviation":"GAB"},{"name":"Guinea","abbreviation":"GIN"},{"name":"Guatemala","abbreviation":"GTM"},{"name":"Haiti","abbreviation":"HTI"},{"name":"Indonesia","abbreviation":"IDN"},{"name":"Jordan","abbreviation":"JOR"},{"name":"Kenya","abbreviation":"KEN"},{"name":"Cambodia","abbreviation":"KHM"},{"name":"Liberia","abbreviation":"LBR"},{"name":"Lesotho","abbreviation":"LSO"},{"name":"Morocco","abbreviation":"MAR"},{"name":"Madagascar","abbreviation":"MDG"},{"name":"Mali","abbreviation":"MLI"},{"name":"Mozambique","abbreviation":"MOZ"},{"name":"Mauritania","abbreviation":"MRT"},{"name":"Malawi","abbreviation":"MWI"},{"name":"Namibia","abbreviation":"NAM"},{"name":"Niger","abbreviation":"NER"},{"name":"Nigeria","abbreviation":"NGA"},{"name":"Nepal","abbreviation":"NPL"},{"name":"Peru","abbreviation":"PER"},{"name":"Philippines","abbreviation":"PHL"},{"name":"Rwanda","abbreviation":"RWA"},{"name":"Senegal","abbreviation":"SEN"},{"name":"Sierra Leone","abbreviation":"SLE"},{"name":"Eswatini","abbreviation":"SWZ"},{"name":"Chad","abbreviation":"TCD"},{"name":"Togo","abbreviation":"TGO"},{"name":"Tanzania","abbreviation":"TZA"},{"name":"Uganda","abbreviation":"UGA"},{"name":"Yemen, Rep.","abbreviation":"YEM"},{"name":"South Africa","abbreviation":"ZAF"},{"name":"Zambia","abbreviation":"ZMB"},{"name":"Zimbabwe","abbreviation":"ZWE"}],"geog_coverage":"We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere). \n\nThe countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as \"representative\" of the rest of the world, or even the rest of the developing world.","analysis_unit":"Country","data_kind":"Sample survey data [ssd]"},"method":{"data_collection":{"coll_mode":"Face-to-face [f2f]","coll_situation":"The authors combine data from 84 Demographic and Health Surveys from 46 countries.","cleaning_operations":"In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.\n\nThe naming conventions for the adult mortality-related are as follows. Variables are named:\n\nGGG_MC_AAAA\n\nGGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:\n\nAll - All\nFem - Female\nMal - Male\nRur - Rural\nUrb - Urban\nRurm - Rural\/Male\nUrbm - Urban\/Male\nRurf - Rural\/Female\nUrbf - Urban\/Female\nNoed - No education\nPri - Some or completed primary only\nSec - At least some secondary education\nNoedm - No education\/Male\nPrim - Some or completed primary only\/Male\nSecm - At least some secondary education\/Male\nNoedf - No education\/Female\nPrif - Some or completed primary only\/Female\nSecf - At least some secondary education\/Female\nRch - Rural as child\nUch - Urban as child\nRchm - Rural as child\/Male\nUchm - Urban as child\/Male\nRchf - Rural as child\/Female\nUchf - Urban as child\/Female\nEdltp - Less than primary schooling\nEdpom - Primary or more schooling\nEdltpm - Less than primary schooling\/Male\nEdpomm - Primary or more schooling\/Male\nEdltpf - Less than primary schooling\/Female\nEdpomf - Primary or more schooling\/Female\nEdltpu - Less than primary schooling\/Urban\nEdpomu - Primary or more schooling\/Urban\nEdltpr - Less than primary schooling\/Rural\nEdpomr - Primary or more schooling\/Rural\nEdltpmu - Less than primary schooling\/Male\/Urban\nEdpommu - Primary or more schooling\/Male\/Urban\nEdltpmr - Less than primary schooling\/Male\/Rural\nEdpommr - Primary or more schooling\/Male\/Rural\nEdltpfu - Less than primary schooling\/Female\/Urban\nEdpomfu - Primary or more schooling\/Female\/Urban\nEdltpfr - Less than primary schooling\/Female\/Rural\nEdpomfr - Primary or more schooling\/Female\/Rural\n\nM refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value \"n\") or whether it is a mortality estimate (in which case M takes on the value \"m\").\n\nC refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value \"u\") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value \"a\").\n\nAAAA refers to the age group that the mortality estimate is calculated for. It takes on the values:\n1554 - Ages 15-54\n1524 - Ages 15-24\n2534 - Ages 25-34\n3544 - Ages 35-44\n4554 - Ages 45-54\n\nOther variables that are in the databases are:\n\nperiod - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 \u2026 2000-04)\nsvycountry - Name of country for DHS countries\nccode3 - Country code\nu5mr - Under-5 mortality (from World Development Indicators)\ncname - Country name\ngdppc - GDP per capita (constant 2000 US$) (from World Development Indicators)\ngdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators)\npop - Population (from World Development Indicators)\nhivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010)\nregion - Region"}},"data_access":{"dataset_use":{"cit_req":"\"Trends and Socioeconomic Gradients in Adult Mortality Around the Developing World\", Damien de Walque and Deon Filmer, World Bank Policy Research Working Paper 5716, June 2011. Electronic dataset Ref. WLD_2011_TSGAM_v01_M downloaded from [URL] on [date].","disclaimer":"The user of the data acknowledges that the original collector of the data, the authorized distributor of the data, and the relevant funding agency bear no responsibility for use of the data or for interpretations or inferences based upon such uses."}}},"schematype":"survey","tags":[{"tag":"DOI"},{"tag":"noDOI"}]}