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 These projections, which are based on 1990 data, continue to be widely quoted, although they are substantially outdated; in particular, they substantially underestimated the spread of HIV/AIDS.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.

Based on a comparison of coronavirus deaths in 210 countries relative to their population, Peru had the most losses to COVID-19 up until July 13, 2022. As of the same date, the virus had infected over 557.8 million people worldwide, and the number of deaths had totaled more than 6.3 million. Note, however, that COVID-19 test rates can vary per country. Additionally, big differences show up between countries when combining the number of deaths against confirmed COVID-19 cases. 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.

The difficulties of death figures

This table aims to provide a complete picture on the topic, but it very much relies on data that has become more difficult to compare. As the coronavirus pandemic developed across the world, countries already used different methods to count fatalities, and they sometimes changed them during the course of the pandemic. On April 16, for example, the Chinese city of Wuhan added a 50 percent increase in their death figures to account for community deaths. These deaths occurred outside of hospitals and went unaccounted for so far. The state of New York did something similar two days before, revising their figures with 3,700 new deaths as they started to include “assumed” coronavirus victims. The United Kingdom started counting deaths in care homes and private households on April 29, adjusting their number with about 5,000 new deaths (which were corrected lowered again by the same amount on August 18). This makes an already difficult comparison even more difficult. Belgium, for example, counts suspected coronavirus deaths in their figures, whereas other countries have not done that (yet). This means two things. First, it could have a big impact on both current as well as future figures. On April 16 already, UK health experts stated that if their numbers were corrected for community deaths like in Wuhan, the UK number would change from 205 to “above 300”. This is exactly what happened two weeks later. Second, it is difficult to pinpoint exactly which countries already have “revised” numbers (like Belgium, Wuhan or New York) and which ones do not. One work-around could be to look at (freely accessible) timelines that track the reported daily increase of deaths in certain countries. Several of these are available on our platform, such as for Belgium, Italy and Sweden. A sudden large increase might be an indicator that the domestic sources changed their methodology.

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. For more information or other freely accessible content, please visit our dedicated Facts and Figures page.

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.

JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data was reported at 0.300 % in 2010. JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data is updated yearly, averaging 0.300 % from Dec 2010 (Median) to 2010, with 1 observations. JP: Prevalence of Severe Wasting: Weight for Height: Male: % of Children under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Japan – Table JP.World Bank: Health Statistics. Prevalence of severe wasting, male, is the proportion of boys under age 5 whose weight for height is more than three standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Rheumatic heart disease (RHD), the principal long-term sequel of acute rheumatic fever (ARF), has been a major contributor to cardiac-related mortality in general population, especially in developing countries. With improvement in health and sanitation facilities across the globe, there has been almost a 50% reduction in mortality rate due to RHD over the last 25 years. However, recent estimates suggest that RHD still results in more than 300,000 deaths annually. In India alone, more than 100,000 deaths occur due to RHD every year (Watkins DA et al., N Engl J Med, 2017). Children and adolescents (aged below 15 years) constitute at least one-fourth of the total population in India. Besides, ARF is, for the most part, a pediatric disorder. The pediatric population, therefore, requires special consideration in developing countries to reduce the burden of RHD. In the developed world, Kawasaki disease (KD) has emerged as the most important cause of acquired heart disease in children. Mirroring global trends over the past two decades, India also has witnessed a surge in the number of cases of KD. Similarly, many regions across the globe classified as “high-risk” for ARF have witnessed an increasing trend in the incidence of KD. This translates to a double challenge faced by pediatric health care providers in improving cardiac outcomes of children affected with ARF or KD. We highlight this predicament by reviewing the incidence trends of ARF and KD over the last 50 years in ARF “high-risk” regions.

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.

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

Flood mortality is still a serious concern in both developed and developing countries, requiring a deeper understanding to identify hazardous factors and mitigate the life losses. with this database, we compared the flood fatalities occurred in the period 1990-2022 in two Mediterranean regions characterized by different natural and anthropogenic frameworks and located in western Algeria and southern Italy, respectively. The main goal is to detect, either common features controlling flood mortality or typical factors causing local differences among the two areas, in order to identify the drivers of flood mortality and suggest how alleviate their impact applying mitigation strategies customized to the detected failures. With these purposes we created the database containing information 242 flood fatalities occurred in the two regions in the 33-year study period, including time and place of fatal accidents, age and gender of the victims, death circumstances and victim’s behavior.

Background - We have previously estimated that respiratory syncytial virus (RSV) was associated with 22% of all episodes of (severe) acute lower respiratory infection (ALRI) resulting in 55000 to 199000 deaths in children younger than 5 years in 2005. In the past 5 years, major research activity on RSV has yielded substantial new data from developing countries. With a considerably expanded dataset from a large international collaboration, we aimed to estimate the global incidence, hospital admission rate, and mortality from RSV-ALRI episodes in young children in 2015. Methods - We estimated the incidence and hospital admission rate of RSV-associated ALRI (RSV-ALRI) in children younger than 5 years stratified by age and World Bank income regions from a systematic review of studies published between Jan 1, 1995, and Dec 31, 2016, and unpublished data from 76 high quality population-based studies. We estimated the RSV-ALRI incidence for 132 developing countries using a risk factor-based model and 2015 population estimates. We estimated the in-hospital RSV-ALRI mortality by combining in-hospital case fatality ratios with hospital admission estimates from hospital-based (published and unpublished) studies. We also estimated overall RSV-ALRI mortality by identifying studies reporting monthly data for ALRI mortality in the community and RSV activity. Findings - We estimated that globally in 2015, 33*1 million (uncertainty range [UR] 21*6-50*3) episodes of RSV-ALRI, resulted in about 3*2 million (2*7-3*8) hospital admissions, and 59 600 (48 000-74 500) in-hospital deaths in children younger than 5 years. In children younger than 6 months, 1*4 million (UR 1*2-1*7) hospital admissions, and 27 300 (UR 20 700-36 200) in-hospital deaths were due to RSV-ALRI. We also estimated that the overall RSV-ALRI mortality could be as high as 118 200 (UR 94 600-149 400). Incidence and mortality varied substantially from year to year in any given population. Interpretation Globally, RSV is a common cause of childhood ALRI and a major cause of hospital admissions in young children, resulting in a substantial burden on health-care services. About 45% of hospital admissions and in- hospital deaths due to RSV-ALRI occur in children younger than 6 months. An effective maternal RSV vaccine or monoclonal antibody could have a substantial effect on disease burden in this age group.

Venezuela VE: Prevalence of Stunting: Height for Age: % of Children Under 5 data was reported at 13.400 % in 2009. This records a decrease from the previous number of 14.600 % for 2008. Venezuela VE: Prevalence of Stunting: Height for Age: % of Children Under 5 data is updated yearly, averaging 17.600 % from Dec 1987 (Median) to 2009, with 21 observations. The data reached an all-time high of 19.900 % in 1997 and a record low of 7.000 % in 1987. Venezuela VE: Prevalence of Stunting: Height for Age: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Venezuela – Table VE.World Bank.WDI: Health Statistics. Prevalence of stunting is the percentage of children under age 5 whose height for age is more than two standard deviations below the median for the international reference population ages 0-59 months. For children up to two years old height is measured by recumbent length. For older children height is measured by stature while standing. The data are based on the WHO's new child growth standards released in 2006.; ; UNICEF, WHO, World Bank: Joint child malnutrition estimates (JME). Aggregation is based on UNICEF, WHO, and the World Bank harmonized dataset (adjusted, comparable data) and methodology.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

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

The Sudan Demographic and Health Survey (SDHS) was conducted in two phases between November 15, 1989 and May 21, 1990 by the Department of Statistics of the Ministry of Economic and National Planning. The survey collected information on fertility levels, marriage patterns, reproductive intentions, knowledge and use of contraception, maternal and child health, maternal mortality, and female circumcision. The survey findings provide the National Population Committee and the Ministry of Health with valuable information for use in evaluating population policy and planning public health programmes. A total of 5860 ever-married women age 15-49 were interviewed in six regions in northern Sudan; three regions in southern Sudan could not be included in the survey because of civil unrest in that part of the country. The SDHS provides data on fertility and mortality comparable to the 1978-79 Sudan Fertility Survey (SFS) and complements the information collected in the 1983 census. The primary objective of the SDHS was to provide data on fertility, nuptiality, family planning, fertility preferences, childhood mortality, indicators of maternal health care, and utilization of child health services. Additional information was coUected on educational level, literacy, source of household water, and other housing conditions. The SDHS is intended to serve as a source of demographic data for comparison with the 1983 census and the Sudan Fertility Survey (SFS) 1978-79, and to provide population and health data for policymakers and researchers. The objectives of the survey are to: assess the overall demographic situation in Sudan, assist in the evaluation of population and health programmes, assist the Department of Statistics in strengthening and improving its technical skills for conducting demographic and health surveys, enable the National Population Committee (NPC) to develop a population policy for the country, and measure changes in fertility and contraceptive prevalence, and study the factors which affect these changes, and examine the basic indicators of maternal and child health in Sudan. MAIN RESULTS Fertility levels and trends Fertility has declined sharply in Sudan, from an average of six children per women in the Sudan Fertility Survey (TFR 6.0) to five children in the Sudan DHS survey flTR 5.0). Women living in urban areas have lower fertility (TFR 4.1) than those in rural areas (5.6), and fertility is lower in the Khartoum and Northern regions than in other regions. The difference in fertility by education is particularly striking; at current rates, women who have attained secondary school education will have an average of 3.3 children compared with 5.9 children for women with no education, a difference of almost three children. Although fertility in Sudan is low compared with most sub-Saharan countries, the desire for children is strong. One in three currently married women wants to have another child within two years and the same proportion want another child in two or more years; only one in four married women wants to stop childbearing. The proportion of women who want no more children increases with family size and age. The average ideal family size, 5.9 children, exceeds the total fertility rate (5.0) by approximately one child. Older women are more likely to want large families than younger women, and women just beginning their families say they want to have about five children. Marriage Almost all Sudanese women marry during their lifetime. At the time of the survey, 55 percent of women 15-49 were currently married and 5 percent were widowed or divorced. Nearly one in five currently married women lives in a polygynous union (i.e., is married to a man who has more than one wife). The prevalence of polygyny is about the same in the SDHS as it was in the Sudan Fertility Survey. Marriage occurs at a fairly young age, although there is a trend toward later marriage among younger women (especially those with junior secondary or higher level of schooling). The proportion of women 15-49 who have never married is 12 percentage points higher in the SDHS than in the Sudan Fertiliy Survey. There has been a substantial increase in the average age at first marriage in Sudan. Among SDHS. Since age at first marriage is closely associated with fertility, it is likely that fertility will decrease in the future. With marriages occurring later, women am having their first birth at a later age. While one in three women age 45-49 had her first birth before age 18, only one in six women age 20-24 began childbearing prior to age 18. The women most likely to postpone marriage and childbearing are those who live in urban areas ur in the Khartoum and Northern regions, and women with pest-primary education. Breastfeeding and postpartum abstinence Breastfeeding and postpartum abstinence provide substantial protection from pregnancy after the birth uf a child. In addition to the health benefits to the child, breastfeeding prolongs the length of postpartum amenorrhea. In Sudan, almost all women breastfeed their children; 93 percent of children are still being breastfed 10-11 months after birth, and 41 percent continue breastfeeding for 20-21 months. Postpartum abstinence is traditional in Sudan and in the first two months following the birth of a child 90 percent of women were abstaining; this decreases to 32 percent after two months, and to 5 percent at~er one year. The survey results indicate that the combined effects of breastfeeding and postpartum abstinence protect women from pregnancy for an average of 15 months after the birth of a child. Knowledge and use of contraception Most currently married women (71 percent) know at least one method of family planning, and 59 percent know a source for a method. The pill (70 percent) is the most widely known method, followed by injection, female sterilisation, and the IUD. Only 39 percent of women knew a traditional method of family planning. Despite widespread knowledge of family planning, only about one-fourth of ever-married women have ever used a contraceptive method, and among currently married women, only 9 percent were using a method at the time of the survey (6 percent modem methods and 3 percent traditional methods). The level of contraceptive use while still low, has increased from less than 5 percent reported in the Sudan Fertility Survey. Use of family planning varies by age, residence, and level of education. Current use is less than 4 percent among women 15-19, increases to 10 percent for women 30-44, then decreases to 6 percent for women 45-49. Seventeen percent of urban women practice family planning compared with only 4 percent of rural women; and women with senior secondary education are more likely to practice family planning (26 percent) than women with no education (3 percent). There is widespread approval of family planning in Sudan. Almost two-thirds of currently married women who know a family planning method approve of the use of contraception. Husbands generally share their wives's views on family planning. Three-fourths of married women who were not using a contraceptive method at the time of the survey said they did not intend to use a method in the future. Communication between husbands and wives is important for successful family planning. Less than half of currently married women who know a contraceptive method said they had talked about family planning with their husbands in the year before the survey; one in four women discussed it once or twice; and one in five discussed it more than twice. Younger women and older women were less likely to discuss family planning than those age 20 to 39. Mortality among children The neonatal mortality rate in Sudan remained virtually unchanged in the decade between the SDHS and the SFS (44 deaths per 1000 births), but under-five mortality decreased by 14 percent (from 143 deaths per 1000 births to 123 per thousand). Under-five mortality is 19 percent lower in urban areas (117 per 1000 births) than in rural areas (144 per 10(30 births). The level of mother's education and the length of the preceding birth interval play important roles in child survival. Children of mothers with no education experience nearly twice the level of under-five mortality as children whose mother had attained senior secondary or nigher education. Mortality among children under five is 2.7 times higher among children born after an interval of less than 24 months than among children born after interval of 48 months or more. Maternal mortality The maternal mortality rate (maternal deaths per 1000 women years of exposure) has remained nearly constant over the twenty years preceding the survey, while the maternal mortality ratio (number of maternal deaths per 100,000 births), has increased (despite declining fertility). Using the direct method of estimation, the maternal mortality ratio is 352 maternal deaths per 100,000 births for the period 1976-82, and 552 per 100,000 births for the period 1983-89. The indirect estimate for the maternal mortality ratio is 537. The latter estimate is an average of women's experience over an extended period before the survey centred on 1977. Maternal health care The health care mothers receive during pregnancy and delivery is important to the survival and well-being of both children and mothers. The SDHS results indicate that most women in Sudan made at least one antenatal visit to a doctor or trained health worker/midwife. Eighty-seven percent of births benefitted from professional antenatal care in urban areas compared with 62 percent in rural areas. Although the proportion of pregnant mothers seen by trained health workers/midwives are similar in urban and rural areas, doctors provided antenatal care for 42 percent and 19 percent of births in urban and rural areas, respectively. Neonatal tetanus, a major cause of infant deaths in developing countries, can be prevented if mothers receive tetanus toxoid vaccinations.

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 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

United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data was reported at 0.700 % in 2012. This records an increase from the previous number of 0.500 % for 2009. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data is updated yearly, averaging 0.550 % from Dec 1991 (Median) to 2012, with 6 observations. The data reached an all-time high of 0.800 % in 2005 and a record low of 0.100 % in 2001. United States US: Prevalence of Wasting: Weight for Height: Female: % of Children Under 5 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s United States – Table US.World Bank.WDI: Health Statistics. Prevalence of wasting, female, is the proportion of girls under age 5 whose weight for height is more than two standard deviations below the median for the international reference population ages 0-59.; ; World Health Organization, Global Database on Child Growth and Malnutrition. Country-level data are unadjusted data from national surveys, and thus may not be comparable across countries.; Linear mixed-effect model estimates; Undernourished children have lower resistance to infection and are more likely to die from common childhood ailments such as diarrheal diseases and respiratory infections. Frequent illness saps the nutritional status of those who survive, locking them into a vicious cycle of recurring sickness and faltering growth (UNICEF, www.childinfo.org). Estimates of child malnutrition, based on prevalence of underweight and stunting, are from national survey data. The proportion of underweight children is the most common malnutrition indicator. Being even mildly underweight increases the risk of death and inhibits cognitive development in children. And it perpetuates the problem across generations, as malnourished women are more likely to have low-birth-weight babies. Stunting, or being below median height for age, is often used as a proxy for multifaceted deprivation and as an indicator of long-term changes in malnutrition.

Abstract

The 1993 National Demographic Survey (NDS) is a nationally representative sample survey of women age 15-49 designed to collect information on fertility; family planning; infant, child and maternal mortality; and maternal and child health. The survey was conducted between April and June 1993. The 1993 NDS was carried out by the National Statistics Office in collaboration with the Department of Health, the University of the Philippines Population Institute, and other agencies concerned with population, health and family planning issues. Funding for the 1993 NDS was provided by the U.S. Agency for International Development through the Demographic and Health Surveys Program.

Close to 13,000 households throughout the country were visited during the survey and more than 15,000 women age 15-49 were interviewed. The results show that fertility in the Philippines continues its gradual decline. At current levels, Filipino women will give birth on average to 4.1 children during their reproductive years, 0.2 children less than that recorded in 1988. However, the total fertility rate in the Philippines remains high in comparison to the level achieved in the neighboring Southeast Asian countries.

The primary objective of the 1993 NDS is to provide up-to-date inform ation on fertility and mortality levels; nuptiality; fertility preferences; awareness, approval, and use of family planning methods; breastfeeding practices; and maternal and child health. This information is intended to assist policymakers and administrators in evaluating and designing programs and strategies for improving health and family planning services in 'the country.

MAIN RESULTS

Fertility varies significantly by region and socioeconomic characteristics. Urban women have on average 1.3 children less than rural women, and uneducated women have one child more than women with college education. Women in Bicol have on average 3 more children than women living in Metropolitan Manila.

Virtually all women know of a family planning method; the pill, female sterilization, IUD and condom are known to over 90 percent of women. Four in 10 married women are currently using contraception. The most popular method is female sterilization ( 12 percent), followed by the piU (9 percent), and natural family planning and withdrawal, both used by 7 percent of married women.

Contraceptive use is highest in Northern Mindanao, Central Visayas and Southern Mindanao, in urban areas, and among women with higher than secondary education. The contraceptive prevalence rate in the Philippines is markedly lower than in the neighboring Southeast Asian countries; the percentage of married women who were using family planning in Thailand was 66 percent in 1987, and 50 percent in Indonesia in 199l.

The majority of contraceptive users obtain their methods from a public service provider (70 percent). Government health facilities mainly provide permanent methods, while barangay health stations or health centers are the main sources for the pill, IUD and condom.

Although Filipino women already marry at a relatively higher age, they continue to delay the age at which they first married. Half of Filipino women marry at age 21.6. Most women have their first sexual intercourse after marriage.

Half of married women say that they want no more children, and 12 percent have been sterilized. An additional 19 percent want to wait at least two years before having another child. Almost two thirds of women in the Philippines express a preference for having 3 or less children. Results from the survey indicate that if all unwanted births were avoided, the total fertility rate would be 2.9 children, which is almost 30 percent less than the observed rate,

More than one quarter of married women in the Philippines are not using any contraceptive method, but want to delay their next birth for two years or more (12 percent), or want to stop childbearing (14 percent). If the potential demand for family planning is satisfied, the contraceptive prevalence rate could increase to 69 percent. The demand for stopping childbearing is about twice the level for spacing (45 and 23 percent, respectively).

Information on various aspects of maternal and child health---antenatal care, vaccination, breastfeeding and food supplementation, and illness was collected in the 1993 NDS on births in the five years preceding the survey. The findings show that 8 in 10 children under five were bom to mothers who received antenatal care from either midwives or nurses (45 percent) or doctors (38 percent). Delivery by a medical personnel is received by more than half of children born in the five years preceding the survey. However, the majority of deliveries occurred at home.

Tetanus, a leading cause of infant deaths, can be prevented by immunization of the mother during pregnancy. In the Philippines, two thirds of bitlhs in the five years preceding the survey were to mothers who received a tetanus toxoid injection during pregnancy.

Based on reports of mothers and information obtained from health cards, 90 percent of children aged 12-23 months have received shots of the BCG as well as the first doses of DPT and polio, and 81 percent have received immunization from measles. Immunization coverage declines with doses; the drop out rate is 3 to 5 percent for children receiving the full dose series of DPT and polio. Overall, 7 in 10 children age 12-23 months have received immunization against the six principal childhood diseases---polio, diphtheria, ~rtussis, tetanus, measles and tuberculosis.

During the two weeks preceding the survey, 1 in 10 children under 5 had diarrhea. Four in ten of these children were not treated. Among those who were treated, 27 percent were given oral rehydration salts, 36 percent were given recommended home solution or increased fluids.

Breasffeeding is less common in the Philippines than in many other developing countries. Overall, a total of 13 percent of children born in the 5 years preceding the survey were not breastfed at all. On the other hand, bottle feeding, a widely discouraged practice, is relatively common in the Philippines. Children are weaned at an early age; one in four children age 2-3 months were exclusively breastfed, and the mean duration of breastfeeding is less than 3 months.

Infant and child mortality in the Philippines have declined significantly in the past two decades. For every 1,000 live births, 34 infants died before their first birthday. Childhood mortality varies significantly by mother's residence and education. The mortality of urban infants is about 40 percent lower than that of rural infants. The probability of dying among infants whose mother had no formal schooling is twice as high as infants whose mother have secondary or higher education. Children of mothers who are too young or too old when they give birth, have too many prior births, or give birth at short intervals have an elevated mortality risk. Mortality risk is highest for children born to mothers under age 19.

The 1993 NDS also collected information necessary for the calculation of adult and maternal mortality using the sisterhood method. For both males and females, at all ages, male mortality is higher than that of females. Matemal mortality ratio for the 1980-1986 is estimated at 213 per 100,000 births, and for the 1987-1993 period 209 per 100,000 births. However, due to the small number of sibling deaths reported in the survey, age-specific rates should be used with caution.

Information on health and family planning services available to the residents of the 1993 NDS barangay was collected from a group of respondents in each location. Distance and time to reach a family planning service provider has insignificant association with whether a woman uses contraception or the choice of contraception being used. On the other hand, being close to a hospital increases the likelihood that antenatal care and births are to respondents who receive ANC and are delivered by a medical personnel or delivered in a health facility.

Geographic coverage

National. The main objective of the 1993 NDS sample is to allow analysis to be carried out for urban and rural areas separately, for 14 of the 15 regions in the country. Due to the recent formation of the 15th region, Autonomous Region in Muslim Mindanao (ARMM), the sample did not allow for a separate estimate for this region.

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1993 Phillipines NDS is defined as the universe of all females age 15-49 years, who are members of the sample household or visitors present at the time of interview and had slept in the sample households the night prior to the time of interview, regardless of marital status.

Kind of data

Sample survey data

Sampling procedure

The main objective of the 1993 National Demographic Survey (NDS) sample is to provide estimates with an acceptable precision for sociodemographics characteristics, like fertility, family planning, health and mortality variables and to allow analysis to be carried out for urban and rural areas separately, for 14 of the 15 regions in the country. Due to the recent formation of the 15th region, Autonomous Region in Muslim Mindanao (ARMM), the sample did not allow for a separate estimate for this region.

The sample is nationally representative with a total size of about 15,000 women aged 15 to 49. The Integrated Survey of Households (ISH) was used as a frame. The ISH was developed in 1980, and was comprised of samples of primary sampling units (PSUs) systematically selected and with a probability proportional to size in each of the 14 regions. The PSUs were reselected in 1991, using the 1990 Population Census data on

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.

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.

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