BackgroundGlobal and regional projections of mortality and burden of disease by cause for the years 2000, 2010, and 2030 were published by Murray and Lopez in 1996 as part of the Global Burden of Disease project. These projections, which are based on 1990 data, continue to be widely quoted, although they are substantially outdated; in particular, they substantially underestimated the spread of HIV/AIDS. To address the widespread demand for information on likely future trends in global health, and thereby to support international health policy and priority setting, we have prepared new projections of mortality and burden of disease to 2030 starting from World Health Organization estimates of mortality and burden of disease for 2002. This paper describes the methods, assumptions, input data, and results. Methods and FindingsRelatively simple models were used to project future health trends under three scenarios—baseline, optimistic, and pessimistic—based largely on projections of economic and social development, and using the historically observed relationships of these with cause-specific mortality rates. Data inputs have been updated to take account of the greater availability of death registration data and the latest available projections for HIV/AIDS, income, human capital, tobacco smoking, body mass index, and other inputs. In all three scenarios there is a dramatic shift in the distribution of deaths from younger to older ages and from communicable, maternal, perinatal, and nutritional causes to noncommunicable disease causes. The risk of death for children younger than 5 y is projected to fall by nearly 50% in the baseline scenario between 2002 and 2030. The proportion of deaths due to noncommunicable disease is projected to rise from 59% in 2002 to 69% in 2030. Global HIV/AIDS deaths are projected to rise from 2.8 million in 2002 to 6.5 million in 2030 under the baseline scenario, which assumes coverage with antiretroviral drugs reaches 80% by 2012. Under the optimistic scenario, which also assumes increased prevention activity, HIV/AIDS deaths are projected to drop to 3.7 million in 2030. Total tobacco-attributable deaths are projected to rise from 5.4 million in 2005 to 6.4 million in 2015 and 8.3 million in 2030 under our baseline scenario. Tobacco is projected to kill 50% more people in 2015 than HIV/AIDS, and to be responsible for 10% of all deaths globally. The three leading causes of burden of disease in 2030 are projected to include HIV/AIDS, unipolar depressive disorders, and ischaemic heart disease in the baseline and pessimistic scenarios. Road traffic accidents are the fourth leading cause in the baseline scenario, and the third leading cause ahead of ischaemic heart disease in the optimistic scenario. Under the baseline scenario, HIV/AIDS becomes the leading cause of burden of disease in middle- and low-income countries by 2015. ConclusionsThese projections represent a set of three visions of the future for population health, based on certain explicit assumptions. Despite the wide uncertainty ranges around future projections, they enable us to appreciate better the implications for health and health policy of currently observed trends, and the likely impact of fairly certain future trends, such as the ageing of the population, the continued spread of HIV/AIDS in many regions, and the continuation of the epidemiological transition in developing countries. The results depend strongly on the assumption that future mortality trends in poor countries will have a relationship to economic and social development similar to those that have occurred in the higher-income countries.

This study aimed to analyze changing trends in child injury deaths from 2006 to 2016 and to provide basic data for initiatives to help prevent child injury deaths through improvements in social systems and education. Specific causes of death were analyzed using micro-data of the death statistics of Korea from 2006 to 2016, which were made available by Statistics Korea. Types and place of death were classified according to the KCD-7 (Korean Standard Classification of Diseases and Causes of Death). The data were compared to those of other Organization for Economic Co-operation and Development countries. Changing trends were presented. The number of child deaths by injury was 270 in 2016. The death rate was 8.1 per 100,000 population in 2006, while it was 3.9 in 2016. The death rate of boys was 1.7 times greater than that of girls. Unintentional injury deaths comprised 72.6% of all child injury deaths in 2016, while intentional injury deaths comprised 27.4%. The first leading cause of unintentional injury deaths in infants (less than 1-year-old) was suffocation, while that of children aged 1-14 years was transport accidents. The second leading cause of death in infants was transport accidents, that of children aged 1-4 was falling, and that of children aged 5-14 was drowning. Pedestrian accidents comprised 43.7% of the transport accidents from 2014 to 2016. To prevent child injury deaths by both unintentional and intentional causes, nation-wide policy measures and more specific interventions according to cause are required.

BackgroundChina's rapid economic and social development since the early 2000s has caused significant shifts in its epidemiological transition, potentially leading to health disparities across regions.ObjectivesThis study employs Life Expectancy (LE) to assess health disparities and trends among China's eastern, central, and western regions. It also examines the pace of LE gains relative to empirical trends and investigates age and causes of death mortality improvement contributing to regional LE gaps.Data and methodsUsing a log-quadratic model, the study estimates LE in China and its regions from 2004 to 2020, using census and death cause surveillance data. It also utilizes the Human Mortality Database (HMD) and the LE gains by LE level approach to analyze China and its regions' LE gains in comparison to empirical trend of developed countries. The study investigates changes in LE gaps due to age and causes of death mortality improvements during two periods, 2004–2012 and 2012–2020, through the LE factor decomposition method.ResultsFrom 2000 to 2020, China's LE exhibited faster pace of gains compared to developed countries. While men's LE growth gradually aligns with empirical trends, women experience slightly higher growth rates. Regional LE disparities significantly reduced from 2004 to 2012, with a marginal reduction from 2012 to 2020. In the latter period, the changing LE gap aligns with expected trends in developed countries, with all Chinese regions surpassing empirical estimates. Cardiovascular diseases and malignant neoplasms emerged as the primary contributors to expanding regional LE gaps, with neurological disorders and diabetes playing an increasingly negative role.ConclusionLE disparities in China have consistently decreased, although at a slower pace in recent years, mirroring empirical trends. To further reduce regional LE disparities, targeted efforts should focus on improving mortality rates related to cardiovascular diseases, neoplasms, neurological disorders and diabetes, especially in the western region. Effective health interventions should prioritize equalizing basic public health services nationwide.

Abstract

The JPFHS is part of the worldwide Demographic and Health Surveys (DHS) program, which is designed to collect data on fertility, family planning, and maternal and child health.

The 1990 Jordan Population and Family Health Survey (JPFHS) was carried out as part of the Demographic and Health Survey (DHS) program. The Demographic and Health Surveys is assisting governments and private agencies in the implementation of household surveys in developing countries.

The JPFIS was designed to provide information on levels and trends of fertility, infant and child mortality, and family planning. The survey also gathered information on breastfeeding, matemal and child health cam, the nutritional status of children under five, as well as the characteristics of households and household members.

The main objectives of the project include: a) Providing decision makers with a data base and analyses useful for informed policy choices, b) Expanding the international population and health data base, c) Advancing survey methodology, and d) Developing skills and resources necessary to conduct high quality demographic and health surveys in the participating countries.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

The sample for the JPFHS survey was selected to be representative of the major geographical regions, as well as the nation as a whole. The survey adopted a stratified, multi-stage sampling design. In each governorate, localities were classified into 9 strata according to the estimated population size in 1989. The sampling design also allowed for the survey results to be presented according to major cities (Amman, Irbid and Zarqa), other urban localities, and the rural areas. Localities with fewer than 5,000 people were considered rural.

For this survey, 349 sample units were drawn, containing 10,708 housing units for the individual interview. Since the survey used a separate household questionnaire, the Department of Statistics doubled the household sample size and added a few questions on labor force, while keeping the original individual sample intact. This yielded 21,172 housing units. During fieldwork for the household interview, it was found that 4,359 household units were ineligible either because the dwelling was vacant or destroyed, the household was absent during the team visit, or some other reason. There were 16,296 completed household interviews out of 16,813 eligible households, producing a response rate of 96.9 percent.

The completed household interviews yielded 7,246 women eligible for the individual interview, of which 6,461 were successfully interviewed, producing a response rate of 89.2 percent.

Note: See detailed description of sample design in APPENDIX A of the survey report.

Mode of data collection

Face-to-face

Research instrument

The 1990 JPFIS utilized two questionnaires, one for the household interview and the other for individual women. Both questionnaires were developed first in English and then translated into Arabic. The household questionnaire was used to list all members of the sample households, including usual residents as well as visitors. For each member of the household, basic demographic and socioeconomic characteristics were recorded and women eligible for the individual interview were identified. To be eligible for individual interview, a woman had to be a usual member of the household (part of the de jure population), ever-married, and between 15 and 49 years of age. The household questionnaire was expanded from the standard DHS-II model questionnaire to facilitate the estimation of adult mortality using the orphanhood and widowhood techniques. In addition, the questionnaire obtained information on polygamy, economic activity of persons 15 years of age and over, family type, type of insurance covering the household members, country of work in the summer of 1990 which coincided with the Gulf crisis, and basic data for the calculation of the crude birth rate and the crude death rate. Additional questions were asked about deceased women if they were ever-married and age 15-49, in order to obtain information for the calculation of materoal mortality indices.

The individual questionnaire is a modified version of the standard DHS-II model "A" questionnaire. Experience gained from previous surveys, in particular the 1983 Jordan Fertility and Family Health Survey, and the questionnaire developed by the Pan Arab Project for Child Development (PAPCHILD), were useful in the discussions on the content of the JPFHS questionnaire. A major change from the DHS-II model questionnaire was the rearrangement of the sections so that the marriage section came before reproduction; this allowed the interview to flow more smoothly. Questions on children's cause of death based on verbal autopsy were added to the section on health, which, due to its size, was split into two parts. The first part focused on antenatal care and breastfeeding; the second part examined measures for prevention of childhood diseases and information on the morbidity and mortality of children loom since January 1985. As questions on sexual relations were considered too sensitive, they were replaced by questions about the husband's presence in the household during the specified time period; this served as a proxy for recent sexual activity.

The JPFHS individual questionnaire consists of nine sections: - Respondent's background and household characteristics - Marriage - Reproduction - Contraception - Breastfeeding and health - Immunization, morbidity, and child mortality - Fertility preferences - Husband's background, residence, and woman's work - Height and weight of children

Response rate

For the individual interview, the number of eligible women found in the selected households and the number of women successfully interviewed are presented. The data indicate a high response rate for the household interview (96.9 percent), and a lower rate for the individual interview (89.2 percent). Women in large cities have a slightly lower response rate (88.6 percent) than those in other areas. Most of the non-response for the individual interview was due to the absence of respondents and the postponement of interviews which were incomplete.

Note: See summarized response rates by place of residence in Table 1.1 of the survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the JPFHS 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 measured statistically. The sample of women selected in the JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of standard error of 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 one can reasonably assured that, apart from nonsampling errors, the true value of the variable for the whole population falls. For example, for any given statistic calculated from a sample survey, the value of that same statistic as measured in 95 percent of all possible samples with the same design (and expected size) will fall within a range of plus or minus two times the standard error of that statistic.

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the JPFI-IS sample design depended on stratification, stages and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical Institute for the World Fertility Survey, was used to assist in computing the sampling errors with the proper statistical methodology.

Note: See detailed estimate of sampling error calculation in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Completeness of reporting - Births by calendar year since birth - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the report which is presented in this documentation.

Abstract

The Yemen Demographic and Maternal and Child Health Survey (YDMCHS) is the first national survey conducted in Yemen since unification of the country. It was designed to collect data on households, ever-married women of reproductive age, and children under age five. The subjects covered in the household survey were: characteristics of households, housing and living conditions, school enrollment, labor force participation, general mortality, disability, fertility, and child survival. The areas covered in the survey of women of reproductive age were: demographic and socioeconomic characteristics, marriage and reproductive history, fertility regulation and preferences, antenatal care, breastfeeding, and child care. For children under five in the survey, the topics included diarrheal and other morbidity, nutritional supplementation, accidents, vaccination, and nutritional status.

The survey was carried out as a part of the DHS program and also the PAPCHILD program. The DHS program is assisting governments and private agencies in the implementation of household surveys in developing countries; PAPCHILD has similar goals for developing countries in the Arab League. The main objectives of the DHS project are to: (a) provide decision makers with a data base and analyses useful for informed policy choices, (b) expand the international population and health data base, (c) advance survey methodology, and (d) develop skills and resources necessary to conduct high quality demographic and health surveys in the participating countries.

The YDMCHS was specifically aimed at furnishing information on basic population and household characteristics, maternal and child health, fertility, family planning, and infant and child mortality in Yemen. The survey also presents information on breastfeeding practices and the nutritional status of children under age five. The survey will provide policymakers and planners with important information for use in formulating programs and policies regarding maternal and child health, child mortality, and reproductive behavior.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men

Kind of data

Sample survey data

Sampling procedure

SAMPLE DESIGN AND IMPLEMENTATION

The YDMCHS sample was designed to enable data analysis for Yemen as a whole, and separately for urban and rural areas, and for two regions: (1) the Northern and Western governorates, and (2) the Southern and Eastern governorates. The target sample was set at completed interviews for about 12,000 households with about 6,000 eligible women. No target number was fixed for children under five, for whom information was to be collected for all children in each household that was selected for the women's interview. In half of the selected households, only the Household Questionnaire was administered; in the other half, in addition to administering the Household Questionnaire, all eligible women were interviewed and information on eligible children was collected.

The YDMCHS covered the entire country, except for nomadic peoples and those living on hard-to-reach Yemeni islands. The survey adopted a stratified, multi-stage sampling design. The sample was stratified by urban and rural areas in the two regions. In this report, the Northern and Western governorates region includes: Sana'a City and the governorates of Sana'a, Taiz, Hodeidah, lbb, Dhamar, Hajjah, A1-Beida, Sa'adah, AI-Mahweet, Ma'areb, and AI-Jawf. The Southern and Eastern governorates region consists of Aden, Laheg, Abyen, Shabwah, Hadramout, and AI-Mahrah govemoratcs. In the first stage, sampling units or clusters were selected; the second stage involved selection of households. The initial objective of having a self-weighted sample was compromised in order to have reliable estimates for urban and rural areas within each region. Sana'a City, the urban (not rural) areas of Aden, and the rural areas of Laheg were oversampled.

For the survey, 258 sampling units were selected, which contained 13,712 households. In half of the selected households, only the Household and Housing Characteristics Questionnaires were administered. In the other half, the Women's and Child's Questionnaires were also administered to all eligible women and children.

Note: See detailed description of sample design in APPENDIX B of the final survey report.

Mode of data collection

Face-to-face

Research instrument

Design, Preparation and Revision of Questionnaires

The YDMCHS survey includes the following questionnaires: - Household Questionnaire - Housing Characteristics Questionnaire - Reproductive Health Questionnaire (also called the Women's Questionnaire - Child Health Questionnaire (also called the Children's Questionnaire) - Community Questionnaire

The items included in these questionnaires were selected after reviewing similar surveys such as those carried out by the Pan Arab Project for Child Development (PAPCHILD), which was sponsored by the Arab League Organization, and the model questionnaires of the Demographic and Health Surveys (DHS) in Calverton, Maryland, USA. The final YDMCHS questionnaires were mainly based on PAPCHILD's model questionnaires. The questionnaires were modified to suit the conditions of Yemen society and to meet the information requirements of the country. A large number of questions were included in the YDMCHS questionnaires in order to obtain as much information as possible on demographic and population dynamics, health and environmental issues, other indicators of standards of living, housing conditions, maternal and child health, and characteristics of local communities regarding provision of health services. English versions of the questionnaires (except the Community Questionnaire) are reproduced in Appendix E.

The Household Questionnaire consists of a household roster, including questions on orphan hood, education level and economic activity of household members. It also collects information on general mortality, disability and, for ever-married women under age 55, information on fertility and child survival.

The Housing Characteristics Questionnaire was administered as pan of the household survey. It includes eight sections: housing, cooking, water, lighting, sanitation, and waste disposal, ownership of objects and assets, and drainage.

The YDMCHS Women's Questionnaire or Reproductive Health Questionnaire consists of nine sections: - Respondent's background - Marriage and co-residence - Reproduction and child survival - Antenatal care: current pregnancy - Maternal care: the last five years - Child feeding - Cause of death for children who died - Family planning and childbearing attitudes - Husband's background

The Child Health Questionnaire, which is also referred to as Children's Questionnaire, consists of six sections: - General child care - Morbidity: diarrhea - Morbidity: other illnesses - Immunization - Weight and height

Cleaning operations

Editing and Coding

Data preparation began one week after the start of fieldwork and continued simultaneously with the fieldwork activities. Field editors checked the questionnaires for completeness and consistency. Field supervisors also checked completed questionnaires on a sample basis. Completed questionnaires were then sent to the central office in Sana'a or brought by staff when they returned after visiting the teams. In the central office in Sana'a the questionnaires were edited again, and open-ended and other questions requiring coding were coded. This stage started on 22 November 1991 and was completed by the end of January 1992.

Response rate

Of the 13,712 households selected for inclusion in the survey, 13,206 were found and 12,836, or 97 percent, were successfully interviewed. In all, 6,150 ever-married women age 15-49 years were identified in the households selected for individual interviews. Of these, 5,687 women were successfully interviewed and information was collected for 6,715 of 7,022 eligible children under five. The response rates for eligible women and children are 93 and 96 percent, respectively. The response rates for urban and rural areas are almost the same. The main reason for not completing some household interviews was that the dwellings were vacant at the time of fieldwork, although they were occupied when the household listing was carried out. The principal reason for non-response in the case of eligible women was that respondents were not at home despite repeated visits by interviewers to the selected households.

Note: See summarized response rates by place of residence in Table 1.1 of the final survey report.

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Non-sampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the YDMCHS 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 measured statistically. The sample of women selected in the YDMCHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all

Coronary artery disease (CAD) is the leading cause of death in both developed and developing nations. The objective of this study was to identify risk factors for coronary artery disease through machine-learning and assess this methodology. A retrospective, cross-sectional cohort study using the publicly available National Health and Nutrition Examination Survey (NHANES) was conducted in patients who completed the demographic, dietary, exercise, and mental health questionnaire and had laboratory and physical exam data. Univariate logistic models, with CAD as the outcome, were used to identify covariates that were associated with CAD. Covariates that had a p

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding. The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine. The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators. The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well. The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables. MAIN RESULTS Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level. Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time. Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion. Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively). HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing. Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis. Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care. Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women. Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

IntroductionHypertension is the common disorder encountered during pregnancy, complicating 5% to 10% of all pregnancies. Hypertensive disorders in pregnancy (HDP) are also a leading cause of maternal and perinatal morbidity and mortality. The majority of feto-maternal complications due to HPD have occurred in the low- and middle-income countries. However, few studies have been done to assess the feto-maternal outcomes and the predictors of adverse perinatal outcome among women with HDP in these countries.MethodsA prospective cohort study was conducted on women with HDP who were delivered at National Hospital of Obstetrics and Gynecology, Vietnam from March 2023 to July 2023. Socio-demographic and obstetrics characteristics, and feto-maternal outcomes were obtained by trained study staff from interviews and medical records. Statistical analysis was performed using SPSS version 26.0. Bivariate and multiple logistic regressions were done to determine factors associated with adverse perinatal outcome. A 95% confidence interval not including 1 was considered statically significant.ResultsA total of 255 women with HDP were enrolled. Regarding adverse maternal outcomes, HELLP syndrome (3.9%), placental abruption (1.6%), and eclampsia (1.2%) were three most common complications. There was no maternal death associated with HDP. The most common perinatal complication was preterm delivery developed in 160 (62.7%) of neonates. Eight stillbirths (3.1%) were recorded whereas the perinatal mortality was 6.3%. On bivariate logistic regression, variables such as residence, type of HDP, highest systolic BP, highest diastolic BP, platelet count, severity symptoms, and birth weight were found to be associated with adverse perinatal outcome. On multiple logistic regression, highest diastolic BP, severity symptoms, and birth weight were found to be independent predictors of adverse perinatal outcome.ConclusionOur study showed lower prevalence of stillbirth, perinatal mortality, and maternal complication compared to some previous studies. Regular antenatal care and early detection of abnormal signs during pregnancy help to devise an appropriate monitoring and treatment strategies for each women with HDP.

Abstract

The Kagera Health and Development Survey was conducted for the research project on “The Economic Impact of Fatal Adult Illness due to AIDS and Other Causes”, Mead Over (Principal Investigator, World Bank), Martha Ainsworth (Co-investigator, World Bank), and Godlike Koda, George Lwihula, Phare Mujinja, and Innocent Semali (Co-investigators, University of Dar es Salaam).

The primary objective of the Kagera Health and Development Survey (KHDS) was to estimate the economic impact of the death of prime-age adults on surviving household members. This impact was primarily measured as the difference in well-being between households with and without the death of a prime-age adult (15-50), over time. An additional hypothesis was that households in communities with high mortality rates might be less successful in coping with a prime-age adult death. Thus, the research design called for collecting extensive socioeconomic information from households with and without adult deaths in communities with high and low adult mortality rates. Data collected by the KHDS can be used to estimate the "direct costs” of illness and mortality in terms of out-of-pocket expenditures, the "indirect costs" in terms of foregone earnings of the patient, and the "coping costs” in terms of changes in the well-being of other household members and in the allocation on of time and resources within the household as these events unfold.

The KHDS was an economic survey. It did not attempt to measure knowledge, attitudes, behaviors or practices related to HIV infection or AIDS in households or communities. It also did not collect blood samples or attempt to measure HIV seroprevalence; this would have substantially affected the costs and complexity of the research and possibly the willingness of households to participate. Information on the cause of death in the KHDS household survey is based on the reports of surviving household members; the researchers maintained that household coping will respond to the perceived cause of death, irrespective of whether the deceased actually died of AIDS. Lastly, the KHDS did not attempt to measure the psycho-social impact of HIV infection or AIDS deaths.

OVERVIEW OF THE RESEARCH DESIGN

The research design called for a longitudinal survey of a sample of households, some of which would experience an adult death and some of which would not, some of them drawn from communities with high adult mortality rates, and some drawn from low-mortality communities.

The sampling frame for the survey was based on the 1988 Tanzania Census, which also provided information on adult death rates by ward within Kagera region. While it was possible to determine which communities had relatively high and low adult death rates from the census data, two additional problems arose that led to the decision for a stratified sample of households based on multiple criteria:

• First, despite the high rates of HIV infection in Kagera and the large number of deaths over time due to AIDS, the death of a prime-age adult is still a relatively rare event over a short time period. This meant that a very large sample would have had to be selected in order to ensure that the survey could interview enough families suffering our about to suffer the death of a prime-age adult.

• Second, HIV prevalence and adult mortality rates in Kagera were geographically concentrated and thus strongly correlated with different climates and cropping patterns. The highest rural HIV infection rates were in the northeast (10% in Bukoba Rural and Muleba districts and 24% in the town of Bukoba), where tree crops (bananas, coffee) were predominant, while the lowest rates were in the south and west (0.4% in Ngara and Biharamulo districts), where perennial crops and livestock are more common (Killewo and others 1990). A survey design stratified only on mortality rates might confound the effects of high mortality with different agricultural, soil, and rainfall patterns. Thus, the sample of households was selected from a stratified random sample of communities from the 1988 census (stratified on agroclimatic zone and adult mortality rate). Within communities, the household sample was stratified according to the anticipated risk of each household of suffering a prime-age adult death. Households were classified as “high-risk” or “low-risk”, based on information obtained from a house-to-house enumeration of all selected communities.

One additional concern was that the high mortality of households might lead to attrition from the sample that is systematically related to household coping. For example, if out-migration is an important coping behavior, then the most severely affected households might leave the sample and the analysis of the remaining households would understate the economic impact of adult deaths. For this reason, at the conclusion of the fieldwork, interviewers attempted to locate and interview all of the individuals who were members of households that dropped out of the longitudinal survey between the first and last interviews, and who were still resident in the region. Individuals were given a specially designed “follow-up questionnaire” that included much of the individual information collected in the household questionnaire, plus information on the reason for leaving the sample and the characteristics of the household were they were now residing.

The final longitudinal household survey followed 816 households at 6-7 month intervals, over a 24-month period from 1991-94. The 816 households were selected from 51 “clusters” of 16 households each located in 49 villages or urban areas representing four economic zones of all districts in Kagera region and, within each zone, representing areas with both high and low adult mortality.

Because household coping behavior is conditioned on local prices, services, and available programs, the KHDS also collected data from the communities from which households were drawn, local markets, the nearest source of modern medical care, and all of the primary schools in the community. This information was collected longitudinally, with the exception of a questionnaire for traditional healers, which was administered only once. While households were drawn from a stratified random sample of households, the health facilities, schools, markets and healers interviewed represent those closest to each community and thus are not random samples that are statistically representative of Kagera facilities.

The panel survey was conducted in a total of five waves.

• Wave 1 September 1991 - May 1992
• Wave 2 April 1992 - November 1992
• Wave 3 November 1992 - May 1993
• Wave 4 June 1993 - January 1994
• Wave 5 - 2004

Geographic coverage

Kagera region of Tanzania

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

SAMPLE DESIGN AND SELECTION

Qualitative studies of small samples of households can point to hypotheses about the ways in which fatal adult illness affects households. However, policymakers need to know which households are suffering the most, the size of the impact, the extent to which they suffer more than other households in a poor country, and the potential costs and effects of assistance programs. For this purpose, the sample of households must be representative of the population, a random sample for which the probability of selecting each household from the whole population is known.

The KHDS used a random sample that was stratified geographically and according to several measures of adult mortality risk. This strategy allowed the team to ensure an adequate number of households with an adult death in the sample while retaining the ability to extrapolate the results to the entire population. The results from the household survey show that stratification of the sample on mortality risk at both the community and household level proved to be worthwhile. Among the 816 households in the original sample that began the survey in the first passage, 91 had an adult death in the course of the survey—more than three times the expected number (25) had the households been drawn at random with no stratification. The 816 households that began the survey in the first passage were observed, on average, for 1.6 years, generating a total of 1,322.7 years of observation. The average probability of an adult death per household per year, according to the 1988 Tanzania Census, is 0.0188. Thus, the expected number of deaths from a random sample of 816 households observed for 1.6 years is 25. Because households were added to the sample to compensate for attrition, a total of 918 households were eventually interviewed at least once. Between the first and last interview, 102 of these households had an adult death, compared to 27 households that would have been expected to have a death from from a non-stratified sample.

A. THE TWO-STAGE STRATIFIED RANDOM SAMPLING PROCEDURE

The KHDS household sample was drawn in two stages, with stratification based on geography in the first stage and mortality risk in both stages.

1. First Stage: Selection of communities and clusters

In the first stage of selecting the sample, the 550 primary sampling units (PSUs) in Kagera region were classified according to eight strata defined over four agronomic zones and, within each zone, the level of adult mortality (high and low). A PSU is a geographical area delineated by the 1988 Tanzanian Census that usually corresponds to a community or, in the case of a town, to a neighborhood. Clusters of households were drawn randomly from the PSUs in each stratum, with a probability of selection proportional to the size of the PSU.

a) Classification of communities by sampling stratum

The four agronomic zones are: - Tree Crop

Abstract

The 2017 Ghana Maternal Health Survey (GMHS) was implemented by the Ghana Statistical Service (GSS). Data collection took place from 15 June to 12 October 2017. ICF provided technical assistance through The DHS Program, which is funded by the United States Agency for International Development (USAID) and offers financial support and technical assistance for population and health surveys in countries worldwide. Financial support for the 2017 GMHS was provided by the Government of Ghana through the Ministry of Health (MOH) and by USAID, the European Union (EU) delegation to Ghana, and the United Nations Population Fund (UNFPA).

SURVEY OBJECTIVES The primary objectives of the 2017 GMHS were as follows: - To collect data at the national level that will allow an assessment of the level of maternal mortality in Ghana for the country as a whole and for three zones: Coastal (Western, Central, Greater Accra, and Volta regions), Middle (Eastern, Ashanti, and Brong Ahafo regions), and Northern (Northern, Upper East, and Upper West regions) - To identify specific causes of maternal and non-maternal deaths, in particular deaths due to abortionrelated causes, among adult women - To collect data on women’s perceptions of and experiences with antenatal, maternity, and emergency obstetrical care, especially with regard to care received before, during, and following the termination or abortion of a pregnancy - To measure indicators of the utilisation of maternal health services, especially post-abortion care services - To allow follow-on studies and surveys that will be used to observe possible reductions in maternal mortality as well as abortion-related mortality

The information collected through the 2017 GMHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

Household Woman

Universe

the survey covered all household members, all women aged 15-49 and for autopsy questionnaire women aged 12-49.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the 2017 GMHS was designed to provide estimates of key reproductive health indicators for the country as a whole, for urban and rural areas separately, for three zonal levels (Coastal, Middle, and Northern), and for each of the 10 administrative regions in Ghana (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, and Upper West).

The sampling frame used for the 2017 GMHS is the frame of the 2010 Population and Housing Census (PHC) conducted in Ghana. The 2010 PHC frame is maintained by GSS and updated periodically as new information is received from various surveys. The frame is a complete list of all census enumeration areas (EAs) created for the PHC. An EA is a geographic area that covers an average of 161 households (per updates to the PHC frame from the 2014 Ghana Demographic and Health Survey [GDHS]). Individual EA size is the number of residential households in the EA according to the 2010 PHC. The average size of urban EAs (185 households) is slightly larger than the average size of rural EAs (114 households). The sampling frame contains information about the EA’s location, type of residence (urban or rural), and estimated number of residential households.

The 2017 GMHS sample was stratified and selected from the sampling frame in two stages. Each region was separated into urban and rural areas; this yielded 20 sampling strata. Samples of EAs were selected independently in each stratum in two stages. Implicit stratification and proportional allocation were achieved at each of the lower administrative levels by sorting the sampling frame within each sampling stratum before the sample selection, according to administrative units at different levels, and by using a probability proportional to size selection at the first stage of sampling.

In the first stage, 900 EAs (466 EAs in urban areas and 434 EAs in rural areas) were selected with probability proportional to EA size and with independent selection in each sampling stratum. A household listing operation was implemented from 25 January to 9 April 2017 in all of the selected EAs. The resulting lists of households then served as a sampling frame for the selection of households in the second stage. The household listing operation included inquiring of each household if there had been any deaths in that household since January 2012 and, if so, the name, sex, and age at time of death of the deceased person(s).

Some of the selected EAs were very large. To minimise the task of household listing, each large EA selected for the 2017 GMHS was segmented. Only one segment was selected for the survey with probability proportional to segment size. Household listing was conducted only in the selected segment. Thus, in the GMHS, a cluster is either an EA or a segment of an EA. As part of the listing, the field teams updated the necessary maps and recorded the geographic coordinates of each cluster. The listing was conducted by 20 teams that included a supervisor, three listers/mappers, and a driver.

The second stage of selection provided two outputs: the list of households selected for the main survey (Household Questionnaire and Woman’s Questionnaire) and the list of households selected for the verbal autopsy survey (Verbal Autopsy Questionnaire).

Selection for Main Survey In the second stage of selection for the main survey, a fixed number of 30 households were selected from each cluster, resulting in a total sample size of 27,000 households. Replacement of nonresponding households was not allowed. Due to the non-proportional allocation of the sample to the different regions and the possible differences in response rates, sampling weights are required for any analysis that uses the 2017 GMHS data. This ensures the representativeness of the survey results at the national and regional levels. Results shown in this report have been weighted to account for the complex sample design.

All women age 15-49 who were either permanent residents of the selected households or visitors who stayed in the household the night before the survey were eligible to be interviewed.

Selection for Verbal Autopsy Survey In the second stage of selection for the verbal autopsy survey, all households in which a female resident age 10-54 died in 2012 or later were selected to be visited by an interviewer. However, only the deaths of female residents who were age 12-49 at the time of death were eligible to be included in the survey. A wider age range was used for the initial selection in case of minor inaccuracies on the part of the person who provided information during the household listing operation; the first questions in the Verbal Autopsy Questionnaire established true eligibility, and interviews ended if the deceased woman was discovered to have died before age 12, after age 49, or before 2012.

There is a chance that some households were both purposively selected for the verbal autopsy survey and randomly selected for the main survey.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2017 GMHS: the Household Questionnaire, the Woman’s Questionnaire, and the Verbal Autopsy Questionnaire. The survey protocol was reviewed and approved by the ICF Institutional Review Board.

The Household Questionnaire and the Woman’s Questionnaire were adapted from The DHS Program’s standard Demographic and Health Survey questionnaires and the questionnaires used in the 2007 GMHS to reflect the specific interests and data needs of this survey. The Verbal Autopsy Questionnaire was adapted from the recent 2016 World Health Organization (WHO) verbal autopsy instrument.

For all questionnaires, input was solicited from stakeholders representing government ministries and development partners. After the finalization of the questionnaires in English, they were translated into three major languages: Akan, Ga, and Ewe. The Household and Woman’s Questionnaires were programmed into tablet computers to facilitate computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the four languages for either of the questionnaires.

The Verbal Autopsy Questionnaire was filled out on paper during data collection and entered into the CAPI system afterwards. The tablet computers were equipped with Bluetooth® technology to enable remote electronic transfer of files, such as assignments from the team supervisor to the interviewers, individual questionnaires among survey team members, and completed questionnaires from interviewers to team supervisors. The CAPI data collection system employed in the 2017 GMHS was developed by The DHS Program using the mobile version of CSPro. The CSPro software was developed jointly by the U.S. Census Bureau, The DHS Program, and Serpro S.A.

Household Questionnaire The Household Questionnaire was used to list all members of and visitors to selected households. Basic demographic information was collected on the characteristics of each person listed, including his or her age, sex, marital status, education, and relationship to the head of the household. The data on age and sex of household members obtained in the Household Questionnaire were used to identify women who were eligible for individual interviews. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as source of water, type of toilet facilities, materials used for the floor of the dwelling unit, and ownership of various

The percentage of patients with stroke or transient ischaemic attack (TIA), as recorded on practice disease registers (proportion of total list size).

Rationale Stroke is the third most common cause of death in the developed world. One quarter of stroke deaths occur under the age of 65 years. There is evidence that appropriate diagnosis and management can improve outcomes.

Definition of numerator Patients with stroke or transient ischaemic attack (TIA), as recorded on practice disease registers.

Definition of denominator Total practice list size.

UNICEF's country profile for South Africa, including under-five mortality rates, child health, education and sanitation data.

Abstract

The Kagera Health and Development Survey 2004 (KHDS 2004) took place in 2004 as a fifth round following on the four rounds of the baseline Kagera Health and Development Survey 1991-1994 (KHDS 91-94). The KHDS 2004 was designed to provide data to understand economic mobility and changes in living standards of the sample of individuals interviewed 10-13 years ago. The KHDS 2004 attempted to reinterview all respondents ever interviewed in the KHDS 91-94. This entailed attempting to track these individuals, even if they had moved out of the village, region or country.

Geographic coverage

Kagera region of Tanzania Domains: Agronomic zone (Tree Crop, Riverine, Annual Crop, Urban)

Analysis unit

• Households
• Individuals
• Community

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample size is 900 households

KHDS 91-94 Household Sample: First Stage

The KHDS 91-94 household sample was drawn in two stages, with stratification based on geography in the first stage and mortality risk in both stages. A more detailed overview of the sampling procedures is outlined in "User’s Guide to the Kagera Health and Development Survey Datasets." (World Bank, 2004).

In the first stage of selecting the sample, the 550 primary sampling units (PSUs) in Kagera region were classified according to eight strata defined over four agronomic zones and, within each zone, the level of adult mortality (high and low). A PSU is a geographical area delineated by the 1988 Tanzanian Census that usually corresponds to a community or, in the case of a town, to a neighborhood. Enumeration areas of households were drawn randomly from the PSUs in each stratum, with a probability of selection proportional to the size of the PSU.

Within each agronomic zone, PSUs were classified according to the level of adult mortality. The 1988 Tanzanian Census asked a 15 percent sample of households about recent adult deaths. Those answers were aggregated at the level of the "ward", which is an administrative area that is smaller than a district. The adult mortality rate (ages 15-50) was calculated for each ward and each PSU was assigned the mortality rate of its ward. Because the adult mortality rates were much higher in some zones than others and the distribution was quite different within zones, "high" and "low" mortality PSUs were defined relative to other PSUs within the same zone. A PSU was allocated to the "high" mortality category if its ward adult mortality rate was at the 90th percentile or higher of the ward adult mortality rates within a given agronomic zone.

The KHDS 1991-1994 selected 51 communities as primary sampling units (also referred to as enumeration areas or clusters). In actuality, 2 pairs of enumeration areas were within the same community (in the sense of collecting community data on infrastructure, prices or schools). This, for community-level surveys, there are 49 areas to interview.

KHDS 91-94 Household Sample: Second Stage

The household selection at the second stage (with enumeration areas) was a stratified random sample. That is, households expected to experience an adult death were over-sampled. In order to stratify the population, an enumeration of all households was undertaken. Between March 15 and June 13, 1991, 29,602 households were enumerated in the 51 areas. In addition to recording the name of the head of each household, the number of adults in the household (15 and older), and the number of children, the enumeration form asked: "Are any adults in this household ill at this moment and unable to work? If so, the age of the sick adult and the number of weeks he/she has been too sick to work were also noted." "Has any adult 15-50 in this household died in the past 12 months? If so, the age of each adult and the cause of death (illness, accident, childbirth, other) were also noted. The enumeration form asked explicitly about illness and death of adults between the ages of 15-50 because this is the age group disproportionately affected by the HIV/AIDS epidemic; it is the impact of these deaths that was of research interest. Out of over 29,000 households enumerated, only 3.7 percent, or 1,101, had experienced the death of an adult aged 15-50 caused by illness during the twelve months before the interview and only 3.9 percent, or 1,145, contained a primeage adult too sick to work at the time of the interview. Only 77 households had both an adult death due to illness and a sick adult. This supports the point that, even with some stratification based on community mortality rates and in an area with very high adult mortality caused by an AIDS epidemic, a very large sample would have had to have been selected to ensure a sufficient number of households that would experience an adult death during the two-year survey.

Using data from the enumeration survey, households were stratified according to the extent of adult illness and mortality. It was assumed that in communities suffering from an HIV epidemic, a history of prior adult death or illness in a household might predict future adult deaths in the same household. The households in each enumeration area were classified into two groups, based on their response to the enumeration: - "Sick" huseholds: Those that had either an adult death (aged 15-50) due to illness in the past 12 months, an adult too sick to work at the time of the survey, or both (n=2,169). - "well" households: Those that had neither an adult death (aged 15-50) due to illness nor an adult (aged 15-50) too sick to work (n=27,433). In selecting the sixteen households to be interviewed in each enumeration area from which a enumeration area was drawn, fourteen were selected at random from the "sick" households in that enumeration area and two were selected at random from the "well" households. In one enumeration area, where the number of "sick" households available was less than fourteen, all available sick households were included in the sample; the numbers were balanced using well households. The final sample drawn for the first passage consisted of 816 households in 51 enumeration areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

The KHDS 2004 mainly consists of a household survey covering a wide range of topics. The KHDS 2004 also includes three community questionnaires to accompany the household survey (community, price, and primary school questionnaires).

The KHDS 2004 project used the original questionnaires from the KHDS 91-94 as the foundation of the survey instruments. The household questionnaire collects information on a wide range of topics, including: housing amenities, consumption, income, assets, time allocation of individuals, business activities, remittances, support from organizations, education, and health, including anthropometric measures. The community questionnaire collects data on the physical, economic and social infrastructure of the baseline communities. The primary school questionnaire collects information on the amenities at schools, composition of the student body, and assistance to schools. Finally, up to three price observations are collected in each community from local markets/stalls on a list of commonly purchased food and non-food items.

Where possible, comparability is maintained with the KHDS 91-94 survey instruments. However, the questionnaires for the KHDS 2004 were revised to reflect changes in the region since 1994. Further, the household questionnaire was redesigned in an effort to capture key transitions that have occurred since the previous survey. These revisions included: - Inclusion of a module on the incidence of economic shocks from the last 10 years (both positive and negative) for all panel respondents. - Inclusion of a module on migration for respondents who relocated since the KHDS 91-94. - Inclusion of a module on informal insurance groups. - Expansion of questions on the circumstances of deaths. - Inclusion of information on the remittances, loans, bride price payments, social communication and labor transfers between previous members of the KHDS 91-94. This section of the Basic Information Document reviews the 4 surveys of the KHDS 2004. For each survey, substantial differences are highlighted between the survey instrument used in the 1991-1994 rounds and in 2004.

Users are encouraged to use this as a general guide to understand the questionnaires; however, this should not substitute for looking at the actual questionnaires directly. Users are encouraged to look directly at the survey instruments for literal question wording and to identify differences between survey instruments. The household questionnaires are available in Swahili (as used in the field) and English (a translated version of the Swahili field questionnaire); the community surveys were produced only in English.

Household Questionnaire: Review of Sections

The household questionnaire is divided into numerous sections, each of which covers a fairly distinct aspect of household activities. Anthropometric measurements and the questionnaire on mortality of household members are administered in separate forms attached to the household questionnaire.

Each section of the household questionnaire has four types of respondents selected according to the content of the section: the interviewer, household head, most knowledgeable person in the household and individual household members. The only section for which household members are not respondents is the first section covering basic survey information (household location, GPS Coordinates, interviewing language, completion status of section, etc…).

Household Questionnaire: Highlights of Substantial Differences

Many changes were made in the KHDS 2004 household questionnaire

Abstract

The Ukraine Demographic and Health Survey (UDHS) is a nationally representative survey of 6,841 women age 15-49 and 3,178 men age 15-49. Survey fieldwork was conducted during the period July through November 2007. The UDHS was conducted by the Ukrainian Center for Social Reforms in close collaboration with the State Statistical Committee of Ukraine. The MEASURE DHS Project provided technical support for the survey. The U.S. Agency for International Development/Kyiv Regional Mission to Ukraine, Moldova, and Belarus provided funding.

The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The primary goal of the survey was to develop a single integrated set of demographic and health data for the population of the Ukraine.

The UDHS was conducted from July to November 2007 by the Ukrainian Center for Social Reforms (UCSR) in close collaboration with the State Statistical Committee (SSC) of Ukraine, which provided organizational and methodological support. Macro International Inc. provided technical assistance for the survey through the MEASURE DHS project. USAID/Kyiv Regional Mission to Ukraine, Moldova and Belarus provided funding for the survey through the MEASURE DHS project. MEASURE DHS is sponsored by the United States Agency for International Development (USAID) to assist countries worldwide in obtaining information on key population and health indicators.

The 2007 UDHS collected national- and regional-level data on fertility and contraceptive use, maternal health, adult health and life style, infant and child mortality, tuberculosis, and HIV/AIDS and other sexually transmitted diseases. The survey obtained detailed information on these issues from women of reproductive age and, on certain topics, from men as well.

The results of the 2007 UDHS are intended to provide the information needed to evaluate existing social programs and to design new strategies for improving the health of Ukrainians and health services for the people of Ukraine. The 2007 UDHS also contributes to the growing international database on demographic and health-related variables.

MAIN RESULTS

• Fertility rates. A useful index of the level of fertility is the total fertility rate (TFR), which indicates the number of children a woman would have if she passed through the childbearing ages at the current age-specific fertility rates (ASFR). The TFR, estimated for the three-year period preceding the survey, is 1.2 children per woman. This is below replacement level.

• Contraception : Knowledge and ever use. Knowledge of contraception is widespread in Ukraine. Among married women, knowledge of at least one method is universal (99 percent). On average, married women reported knowledge of seven methods of contraception. Eighty-nine percent of married women have used a method of contraception at some time.

• Abortion rates. The use of abortion can be measured by the total abortion rate (TAR), which indicates the number of abortions a woman would have in her lifetime if she passed through her childbearing years at the current age-specific abortion rates. The UDHS estimate of the TAR indicates that a woman in Ukraine will have an average of 0.4 abortions during her lifetime. This rate is considerably lower than the comparable rate in the 1999 Ukraine Reproductive Health Survey (URHS) of 1.6. Despite this decline, among pregnancies ending in the three years preceding the survey, one in four pregnancies (25 percent) ended in an induced abortion.

• Antenatal care. Ukraine has a well-developed health system with an extensive infrastructure of facilities that provide maternal care services. Overall, the levels of antenatal care and delivery assistance are high. Virtually all mothers receive antenatal care from professional health providers (doctors, nurses, and midwives) with negligible differences between urban and rural areas. Seventy-five percent of pregnant women have six or more antenatal care visits; 27 percent have 15 or more ANC visits. The percentage is slightly higher in rural areas than in urban areas (78 percent compared with 73 percent). However, a smaller proportion of rural women than urban women have 15 or more antenatal care visits (23 percent and 29 percent, respectively).

• HIV/AIDS and other sexually transmitted infections : The currently low level of HIV infection in Ukraine provides a unique window of opportunity for early targeted interventions to prevent further spread of the disease. However, the increases in the cumulative incidence of HIV infection suggest that this window of opportunity is rapidly closing.

• Adult Health : The major causes of death in Ukraine are similar to those in industrialized countries (cardiovascular diseases, cancer, and accidents), but there is also a rising incidence of certain infectious diseases, such as multidrug-resistant tuberculosis.

• Women's status : Sixty-four percent of married women make decisions on their own about their own health care, 33 percent decide jointly with their husband/partner, and 1 percent say that their husband or someone else is the primary decisionmaker about the woman's own health care.

• Domestic Violence : Overall, 17 percent of women age 15-49 experienced some type of physical violence between age 15 and the time of the survey. Nine percent of all women experienced at least one episode of violence in the 12 months preceding the survey. One percent of the women said they had often been subjected to violent physical acts during the past year. Overall, the data indicate that husbands are the main perpetrators of physical violence against women.

• Human Trafficking : The UDHS collected information on respondents' awareness of human trafficking in Ukraine and, if applicable, knowledge about any household members who had been the victim of human trafficking during the three years preceding the survey. More than half (52 percent) of respondents to the household questionnaire reported that they had heard of a person experiencing this problem and 10 percent reported that they knew personally someone who had experienced human trafficking.

Geographic coverage

The survey is a nationally representative sample survey designed to provide information on population and health issues in Ukraine. The 27 administrative regions were grouped for this survey into five geographic regions: North, Central, East, South and West. The five geographic regions are the five study domains of the survey. The estimates obtained from the 2007 UDHS are presented for the country as a whole, for urban and rural areas, and for each of the five geographic regions.

Analysis unit

• Household
• Women age 15-49
• Men age 15-49

Universe

The population covered by the 2007 UDHS is defined as the universe of all women and men age 15-49 in Ukraine.

Kind of data

Sample survey data

Sampling procedure

The 2007 Ukraine Demographic and Health Survey (UDHS) was the first survey of its kind carried out in Ukraine. The survey was a nationally representative sample survey of 15,000 households, with an expected yield of about 7,900 completed interviews of women age 15-49. It was designed to provide estimates on fertility, infant and child mortality, use of contraception and family planning, knowledge and attitudes toward HIV/AIDS and other sexually transmitted infections (STI), and other family welfare and health indicators. Ukraine is made up of 24 oblasts, the Autonomous Republic of Crimea, and two special cities (Kyiv and Sevastopol), which together make up 27 administrative regions, each subdivided into lower-level administrative units. The 27 administrative regions were grouped for this survey into five geographic regions: North, Central, East, South and West. The five geographic regions are the five study domains of the survey. The estimates obtained from the 2007 UDHS are presented for the country as a whole, for urban and rural areas, and for each of the five geographic regions.

A men's survey was conducted at the same time as the women's survey, in a subsample consisting of one household in every two selected for the female survey. All men age 15-49 living in the selected households were eligible for the men's survey. The survey collected information on men's use of contraception and family planning and their knowledge and attitudes toward HIV/AIDS and other sexually transmitted infections (STI).

SAMPLING FRAME

The sampling frame used for the 2007 UDHS was the Ukraine Population Census conducted in 2001 (SSC, 2003a), provided by the State Statistical Committee (SSC) of Ukraine. The sampling frame consisted of about 38 thousand enumeration areas (EAs) with an average of 400-500 households per EA. Each EA is subdivided into 4-5 enumeration units (EUs) with an average of 100 households per EU. An EA is a city block in urban areas; in rural areas, an EA is either a village or part of a large village, or a group of small villages (possibly plus a part of a large village). An EU is a list of addresses (in a neighborhood) that was used as a convenient counting unit for the census. Both EAs and EUs include information about the location, type of residence, address of each structure in it, and the number of households in each structure.

Census maps were available for most of the EAs with marked boundaries. In urban areas, the census maps have marked boundaries/locations of the EUs. In rural areas, the EUs are defined by detailed descriptions available at the SSC local office. Therefore, either the EA or the EU could be used as the primary sampling unit (PSU) for the 2007 UDHS. Because the EAs in urban areas are large (an average of 500 households), using

Abstract

The KHDS 2010 was designed to provide data to understand changes in living standards of the sample of individuals originally interviewed 16-19 years ago. The KHDS 2010 attempted to re-interview all respondents ever interviewed in the KHDS 91-94 – irrespective of whether the respondent had moved out of the original village, region, or country, or was residing in a new household.

Geographic coverage

Kagera region of Tanzania

Analysis unit

Households and individuals

Universe

The KHDS attempts to re-interview all respondents interviewed in the original KHDS 1991-1994, irrespective of whether the respondent had moved out of the original village, region or country or was residing in a new household.

Kind of data

Sample survey data [ssd]

Sampling procedure

KHDS 1991-1994 Household Sample: First Stage

The KHDS 91-94 household sample was drawn in two stages, with stratification based on geography in the first stage and mortality risk in both stages. A more detailed overview of the sampling procedures is outlined in "User's Guide to the Kagera Health and Development Survey Datasets." (World Bank, 2004).

In the first stage of selecting the sample, the 550 primary sampling units (PSUs) in Kagera region were classified according to eight strata defined over four agronomic zones and, within each zone, the level of adult mortality (high and low). A PSU is a geographical area delineated by the 1988 Tanzanian Census that usually corresponds to a community or, in the case of a town, to a neighbourhood. Enumeration areas of households were drawn randomly from the PSUs in each stratum, with a probability of selection proportional to the size of the PSU.

Within each agronomic zone, PSUs were classified according to the level of adult mortality. The 1988 Tanzanian Census asked a 15 percent sample of households about recent adult deaths. Those answers were aggregated at the level of the "ward", which is an administrative area that is smaller than a district. The adult mortality rate (ages 15-50) was calculated for each ward and each PSU was assigned the mortality rate of its ward.

Because the adult mortality rates were much higher in some zones than others and the distribution was quite different within zones, "high" and "low" mortality PSUs were defined relative to other PSUs within the same zone. A PSU was allocated to the "high" mortality category if its ward adult mortality rate was at the 90th percentile or higher of the ward adult mortality rates within a given agronomic zone.

The KHDS 91-94 selected 51 communities as primary sampling units (also referred to as enumeration areas or clusters). In actuality, two pairs of enumeration areas were within the same community (in the sense of collecting community data on infrastructure, prices or schools). Thus, for community-level surveys, there are 49 areas to interview.

KHDS 1991-1994 Household Sample: Second Stage

The household selection at the second stage (with enumeration areas) was a stratified random sample, where households which were expected to experience an adult death were oversampled. In order to stratify the population, an enumeration of all households was undertaken.

Between March 15 and June 13, 1991, 29,602 households were enumerated in the 51 areas. In addition to recording the name of the head of each household, the number of adults in the household (15 and older), and the number of children, the enumeration form asked:

1. Are any adults in this household ill at this moment and unable to work? If so, the age of the sick adult and the number of weeks he/she has been too sick to work were also noted.
2. Has any adult 15-50 in this household died in the past 12 months? If so, the age of each adult and the cause of death (illness, accident, childbirth, other) were also noted.

The enumeration form asked explicitly about illness and death of adults between the ages of 15-50 because this is the age group disproportionately affected by the HIV/AIDS epidemic; it is the impact of these deaths that was of research interest. Out of over 29,000 households enumerated, only 3.7 percent, or 1,101, had experienced the death of an adult aged 15-50 caused by illness during the 12 months before the interview and only 3.9 percent, or 1,145, contained a prime-age adult too sick to work at the time of the interview. Only 77 households had both an adult death due to illness and a sick adult. This supports the point that, even with some stratification based on community mortality rates and in an area with very high adult mortality caused by an AIDS epidemic, a very large sample would have had to have been selected to ensure a sufficient number of households that would experience an adult death during the two-year survey.

Using data from the enumeration survey, households were stratified according to the extent of adult illness and mortality. It was assumed that in communities suffering from an HIV epidemic, a history of prior adult death or illness in a household might predict future adult deaths in the same household. The households in each enumeration area were classified into two groups, based on their response to the enumeration:

1. Sick" households: Those that had either an adult death (aged 15-50) due to illness in the past 12 months, an adult too sick to work at the time of the survey, or both (n=2,169).
2. "Well" households: Those that had neither an adult death (aged 15-50) due to illness nor an adult (aged 15-50) too sick to work (n=27,433).

In selecting the sixteen households to be interviewed in each enumeration area, fourteen were selected at random from the "sick" households in that enumeration area and two were selected at random from the "well" households. In one enumeration area, where the number of "sick" households available was less than fourteen, all available sick households were included in the sample; the numbers were balanced using well households. The final sample drawn for the first passage consisted of 816 households in 51 enumeration areas.

KHDS 2004 and 2010 Household Samples

The sampling strategy in KHDS 2004 and KHDS 2010 was to re-interview all individuals who were household members in any wave of the KHDS 91-94, a total of 6,353 people. The Household Questionnaire was administered in the household in which these PHHMs lived. If a household member was alive during the last interview in 1991-1994, but found to be deceased by the time of the fieldwork in 2004 and 2010 then the information about the deceased was collected in the Mortality Questionnaire. The next sections provide statistics of the KHDS 2004 and 2010 households.

KHDS 2004 Households

Although the KHDS is a panel of individuals and the concept of a household after 10-19 years is a vague notion, it is common in panel surveys to consider re-contact rates in terms of households. Table 4 shows the rate of re-contact of the baseline households in KHDS 2004, where a re-contact is defined as having interviewed at least one person from the household. In this case, the term household is defined by the baseline KHDS survey which spans a period of 2.5 years. Due to movements in and out of the household, some household members may have not, in fact, lived together in the household at the same time in the 1991-1994 waves (for example, consider one sibling of the household head moving into the household for one year and then moving out, followed by another sibling moving into the household).

Excluding households in which all previous members are deceased (17 households and 27 respondents), the KHDS 2004 field team managed to re-contact 93 percent of the baseline households. Not all 915 households received four interviews. Unsurprisingly, households that were in the baseline survey for all four waves had the highest probability of being reinterviewed. Of these 746 households, 96 percent were re-interviewed.

Turning to re-contact rates of the sample of 6,353 respondents, Table 5 shows the status of the respondents by age group (based on their age at first interview in the 1991-1994 waves). Reinterview rates are monotonically decreasing with age, although the reasons (deceased or not located) vary by age group. The older respondents were much more likely to be located if alive. Among the youngest respondents, over three-quarter were successfully re-interviewed. Excluding people who died, 82 percent of all respondents were re-interviewed.

KHDS 2010 Households

The re-contact rates in the KHDS 2010 are in line with the ones achieved in KHDS 2004. Table 4 of the Basic Information Document shows the KHDS 2010 re-contacting rates in terms of the baseline households. Excluding the households in which all PHHMs were deceased, 92 percent of the households were recontacted.

As in KHDS 2004, households that were interviewed four times at the baseline were more likely to be found in 2010. Excluding the households in which all members had died, 95 percent of these households were re-interviewed in 2010.

The KHDS 2010 re-contact rates in terms of panel respondents are provided in Table 5 of the Basic Information Document. As in 2004, the older respondents, if alive, were much more likely to be re-contacted than younger respondents. In the oldest age category, 60 years and older at the baseline, the interview teams managed to re-contact almost 98 percent of all survivors. The length of the KHDS survey starts to be seen in this age category however, as almost three quarters of the respondents had passed away by 2010.

Table 6 of the Basic Information Document provides the KHDS 2010 re-contact rates by location. More than 50 percent of the reinterviewed panel respondents were located in the same community as in KHDS 91-94. Nearly 14 percent of the re-contacted respondents were found from

COVID-19 Trends MethodologyOur goal is to analyze and present daily updates in the form of recent trends within countries, states, or counties during the COVID-19 global pandemic. The data we are analyzing is taken directly from the Johns Hopkins University Coronavirus COVID-19 Global Cases Dashboard, though we expect to be one day behind the dashboard’s live feeds to allow for quality assurance of the data.Revisions added on 4/23/2020 are highlighted.Revisions added on 4/30/2020 are highlighted.Discussion of our assertion of an abundance of caution in assigning trends in rural counties added 5/7/2020. Correction on 6/1/2020Methodology update on 6/2/2020: This sets the length of the tail of new cases to 6 to a maximum of 14 days, rather than 21 days as determined by the last 1/3 of cases. This was done to align trends and criteria for them with U.S. CDC guidance. The impact is areas transition into Controlled trend sooner for not bearing the burden of new case 15-21 days earlier.Reasons for undertaking this work:The popular online maps and dashboards show counts of confirmed cases, deaths, and recoveries by country or administrative sub-region. Comparing the counts of one country to another can only provide a basis for comparison during the initial stages of the outbreak when counts were low and the number of local outbreaks in each country was low. By late March 2020, countries with small populations were being left out of the mainstream news because it was not easy to recognize they had high per capita rates of cases (Switzerland, Luxembourg, Iceland, etc.). Additionally, comparing countries that have had confirmed COVID-19 cases for high numbers of days to countries where the outbreak occurred recently is also a poor basis for comparison.The graphs of confirmed cases and daily increases in cases were fit into a standard size rectangle, though the Y-axis for one country had a maximum value of 50, and for another country 100,000, which potentially misled people interpreting the slope of the curve. Such misleading circumstances affected comparing large population countries to small population counties or countries with low numbers of cases to China which had a large count of cases in the early part of the outbreak. These challenges for interpreting and comparing these graphs represent work each reader must do based on their experience and ability. Thus, we felt it would be a service to attempt to automate the thought process experts would use when visually analyzing these graphs, particularly the most recent tail of the graph, and provide readers with an a resulting synthesis to characterize the state of the pandemic in that country, state, or county.The lack of reliable data for confirmed recoveries and therefore active cases. Merely subtracting deaths from total cases to arrive at this figure progressively loses accuracy after two weeks. The reason is 81% of cases recover after experiencing mild symptoms in 10 to 14 days. Severe cases are 14% and last 15-30 days (based on average days with symptoms of 11 when admitted to hospital plus 12 days median stay, and plus of one week to include a full range of severely affected people who recover). Critical cases are 5% and last 31-56 days. Sources:U.S. CDC. April 3, 2020 Interim Clinical Guidance for Management of Patients with Confirmed Coronavirus Disease (COVID-19). Accessed online. Initial older guidance was also obtained online. Additionally, many people who recover may not be tested, and many who are, may not be tracked due to privacy laws. Thus, the formula used to compute an estimate of active cases is: Active Cases = 100% of new cases in past 14 days + 19% from past 15-30 days + 5% from past 31-56 days - total deaths.We’ve never been inside a pandemic with the ability to learn of new cases as they are confirmed anywhere in the world. After reviewing epidemiological and pandemic scientific literature, three needs arose. We need to specify which portions of the pandemic lifecycle this map cover. The World Health Organization (WHO) specifies six phases. The source data for this map begins just after the beginning of Phase 5: human to human spread and encompasses Phase 6: pandemic phase. Phase six is only characterized in terms of pre- and post-peak. However, these two phases are after-the-fact analyses and cannot ascertained during the event. Instead, we describe (below) a series of five trends for Phase 6 of the COVID-19 pandemic.Choosing terms to describe the five trends was informed by the scientific literature, particularly the use of epidemic, which signifies uncontrolled spread. The five trends are: Emergent, Spreading, Epidemic, Controlled, and End Stage. Not every locale will experience all five, but all will experience at least three: emergent, controlled, and end stage.This layer presents the current trends for the COVID-19 pandemic by country (or appropriate level). There are five trends:Emergent: Early stages of outbreak. Spreading: Early stages and depending on an administrative area’s capacity, this may represent a manageable rate of spread. Epidemic: Uncontrolled spread. Controlled: Very low levels of new casesEnd Stage: No New cases These trends can be applied at several levels of administration: Local: Ex., City, District or County – a.k.a. Admin level 2State: Ex., State or Province – a.k.a. Admin level 1National: Country – a.k.a. Admin level 0Recommend that at least 100,000 persons be represented by a unit; granted this may not be possible, and then the case rate per 100,000 will become more important.Key Concepts and Basis for Methodology: 10 Total Cases minimum threshold: Empirically, there must be enough cases to constitute an outbreak. Ideally, this would be 5.0 per 100,000, but not every area has a population of 100,000 or more. Ten, or fewer, cases are also relatively less difficult to track and trace to sources. 21 Days of Cases minimum threshold: Empirically based on COVID-19 and would need to be adjusted for any other event. 21 days is also the minimum threshold for analyzing the “tail” of the new cases curve, providing seven cases as the basis for a likely trend (note that 21 days in the tail is preferred). This is the minimum needed to encompass the onset and duration of a normal case (5-7 days plus 10-14 days). Specifically, a median of 5.1 days incubation time, and 11.2 days for 97.5% of cases to incubate. This is also driven by pressure to understand trends and could easily be adjusted to 28 days. Source used as basis:Stephen A. Lauer, MS, PhD *; Kyra H. Grantz, BA *; Qifang Bi, MHS; Forrest K. Jones, MPH; Qulu Zheng, MHS; Hannah R. Meredith, PhD; Andrew S. Azman, PhD; Nicholas G. Reich, PhD; Justin Lessler, PhD. 2020. The Incubation Period of Coronavirus Disease 2019 (COVID-19) From Publicly Reported Confirmed Cases: Estimation and Application. Annals of Internal Medicine DOI: 10.7326/M20-0504.New Cases per Day (NCD) = Measures the daily spread of COVID-19. This is the basis for all rates. Back-casting revisions: In the Johns Hopkins’ data, the structure is to provide the cumulative number of cases per day, which presumes an ever-increasing sequence of numbers, e.g., 0,0,1,1,2,5,7,7,7, etc. However, revisions do occur and would look like, 0,0,1,1,2,5,7,7,6. To accommodate this, we revised the lists to eliminate decreases, which make this list look like, 0,0,1,1,2,5,6,6,6.Reporting Interval: In the early weeks, Johns Hopkins' data provided reporting every day regardless of change. In late April, this changed allowing for days to be skipped if no new data was available. The day was still included, but the value of total cases was set to Null. The processing therefore was updated to include tracking of the spacing between intervals with valid values.100 News Cases in a day as a spike threshold: Empirically, this is based on COVID-19’s rate of spread, or r0 of ~2.5, which indicates each case will infect between two and three other people. There is a point at which each administrative area’s capacity will not have the resources to trace and account for all contacts of each patient. Thus, this is an indicator of uncontrolled or epidemic trend. Spiking activity in combination with the rate of new cases is the basis for determining whether an area has a spreading or epidemic trend (see below). Source used as basis:World Health Organization (WHO). 16-24 Feb 2020. Report of the WHO-China Joint Mission on Coronavirus Disease 2019 (COVID-19). Obtained online.Mean of Recent Tail of NCD = Empirical, and a COVID-19-specific basis for establishing a recent trend. The recent mean of NCD is taken from the most recent fourteen days. A minimum of 21 days of cases is required for analysis but cannot be considered reliable. Thus, a preference of 42 days of cases ensures much higher reliability. This analysis is not explanatory and thus, merely represents a likely trend. The tail is analyzed for the following:Most recent 2 days: In terms of likelihood, this does not mean much, but can indicate a reason for hope and a basis to share positive change that is not yet a trend. There are two worthwhile indicators:Last 2 days count of new cases is less than any in either the past five or 14 days. Past 2 days has only one or fewer new cases – this is an extremely positive outcome if the rate of testing has continued at the same rate as the previous 5 days or 14 days. Most recent 5 days: In terms of likelihood, this is more meaningful, as it does represent at short-term trend. There are five worthwhile indicators:Past five days is greater than past 2 days and past 14 days indicates the potential of the past 2 days being an aberration. Past five days is greater than past 14 days and less than past 2 days indicates slight positive trend, but likely still within peak trend time frame.Past five days is less than the past 14 days. This means a downward trend. This would be an

COVID-19 rate of death, or the known deaths divided by confirmed cases, was over ten percent in Yemen, the only country that has 1,000 or more cases. This according to a calculation that combines coronavirus stats on both deaths and registered cases for 221 different countries. Note that death rates are not the same as the chance of dying from an infection or the number of deaths based on an at-risk population. By April 26, 2022, the virus had infected over 510.2 million people worldwide, and led to a loss of 6.2 million. The source seemingly does not differentiate between "the Wuhan strain" (2019-nCOV) of COVID-19, "the Kent mutation" (B.1.1.7) that appeared in the UK in late 2020, the 2021 Delta variant (B.1.617.2) from India or the Omicron variant (B.1.1.529) from South Africa.

Where are these numbers coming from?

The numbers shown here were collected by Johns Hopkins University, a source that manually checks the data with domestic health authorities. For the majority of countries, this is from national authorities. In some cases, like China, the United States, Canada or Australia, city reports or other various state authorities were consulted. In this statistic, these separately reported numbers were put together. Note that Statista aims to also provide domestic source material for a more complete picture, and not to just look at one particular source. Examples are these statistics on the confirmed coronavirus cases in Russia or the COVID-19 cases in Italy, both of which are from domestic sources. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

A word on the flaws of numbers like this

People are right to ask whether these numbers are at all representative or not for several reasons. First, countries worldwide decide differently on who gets tested for the virus, meaning that comparing case numbers or death rates could to some extent be misleading. Germany, for example, started testing relatively early once the country’s first case was confirmed in Bavaria in January 2020, whereas Italy tests for the coronavirus postmortem. Second, not all people go to see (or can see, due to testing capacity) a doctor when they have mild symptoms. Countries like Norway and the Netherlands, for example, recommend people with non-severe symptoms to just stay at home. This means not all cases are known all the time, which could significantly alter the death rate as it is presented here. Third and finally, numbers like this change very frequently depending on how the pandemic spreads or the national healthcare capacity. It is therefore recommended to look at other (freely accessible) content that dives more into specifics, such as the coronavirus testing capacity in India or the number of hospital beds in the UK. Only with additional pieces of information can you get the full picture, something that this statistic in its current state simply cannot provide.

BackgroundCause-specific mortality statistics remain scarce for the majority of low-income countries, where the highest disease burdens are experienced. Neither facility-based information systems nor vital registration provide adequate or representative data. The expansion of sample vital registration with verbal autopsy procedures represents the most promising interim solution for this problem. The development and validation of core verbal autopsy forms and suitable coding and tabulation procedures are an essential first step to extending the benefits of this method. Methods and FindingsCore forms for peri- and neonatal, child, and adult deaths were developed and revised over 12 y through a project of the Tanzanian Ministry of Health and were applied to over 50,000 deaths. The contents of the core forms draw upon and are generally comparable with previously proposed verbal autopsy procedures. The core forms and coding procedures based on the International Statistical Classification of Diseases (ICD) were further adapted for use in China. These forms, the ICD tabulation list, the summary validation protocol, and the summary validation results from Tanzania and China are presented here. ConclusionsThe procedures are capable of providing reasonable mortality estimates as adjudged against stated performance criteria for several common causes of death in two countries with radically different cause structures of mortality. However, the specific causes for which the procedures perform well varied between the two settings because of differences in the underlying prevalence of the main causes of death. These differences serve to emphasize the need to undertake validation studies of verbal autopsy procedures when they are applied in new epidemiological settings.

Maternal and child health, and health care service characteristics of participants in public hospitals of Dire Dawa Administrative, Eastern Ethiopia, 2021.

Non-communicable diseases (NCDs) and their risk factors are the leading cause of death worldwide and contribute to 74.3% of deaths globally in 2019. The burden of NCDs is escalating in Afghanistan. Currently, every seconds, people in Afghanistan are dying of NCDs. Addressing this challenge in Afghanistan needs effective and practical interventions. This study aimed to identify the strategies developed and implemented in countries with low non-communicable premature death. To conduct a scoping review, we followed the six-step Arksey and O'Malley protocol and searched for eligible articles on eight international databases and the gray literature. The study followed the Preferred Reporting Items for Systematic review and Meta-Analysis (PRISMA) guidelines. The inclusion criteria were English documents and evidence produced up to 30 November 2021 for the control of NCDs. We excluded incomplete texts, duplicates, and dissertations due to lack of access. We used EndNote X9 and MaxQDA software for data management and analysis. We conducted content analysis for this study. A total of 122 documents developed between 1984 and 2021 met the inclusion criteria. We identified 35 strategies from which the most used strategies were related to unhealthy diets and smoking cessation programs. Canada (26.4%), Korea (19.8%), and the United Kingdom (19%) have the most publications on the control and prevention of NCDs among the countries included in the study. Most strategies were implemented over 2 years (41%). This study recommends specific interventions to control and prevent NCDs for the main risk factors of tobacco use, unhealthy diet, physical inactivity, and the main non-communicable diseases such as heart diseases, cancers, diabetes, and chronic obstructive pulmonary diseases. Afghanistan Ministry of Public Health, the WHO country office, and other involved stakeholders can use the findings of this review to design and implement strategies for controlling and preventing NCDs in Afghanistan. International organizations such as the World Health Organization, United Nations Agencies, the World Bank, and other involving communities should invest in strengthening good health governance in Afghanistan. The Afghan Government should focus on promoting and funding health literacy among the public and self-care to control and prevent NCDs.