The probability of dying between birth and the exact age of 1, expressed per 1,000 live births. The data is sorted by both sex and total and includes a range of values from 1900 to 2019. The calculation for infant mortality rates is derived from a standard period abridged life table using the age-specific deaths and mid-year population counts from civil registration data. This data is sourced from the UN Inter-Agency Group for Child Mortality Estimation. The UN IGME uses the same estimation method across all countries to arrive at a smooth trend curve of age-specific mortality rates. The estimates are based on high quality nationally representative data including statistics from civil registration systems, results from household surveys, and censuses. The child mortality estimates are produced in conjunction with national level agencies such as a country’s Ministry of Health, National Statistics Office, or other relevant agencies.

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and Zip codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential Zip code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality. For the Zip code-level life expectancy calculation, it is assumed that postal Zip codes share the same boundaries as Zip Code Census Tabulation Areas (ZCTAs). More information on the relationship between Zip codes and ZCTAs can be found at https://www.census.gov/geo/reference/zctas.html. Zip code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 Zip code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for Zip codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest Zip code with population. Zip code population for 2000 estimates comes from the Decennial Census. Zip code population for 2013 estimates are from the American Community Survey (5-Year Average). The ACS provides Zip code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to Zip codes based on majority land-area.

Zip codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, Zip codes with populations of less than 5,000 were aggregated with neighboring Zip codes until the merged areas had a population of more than 5,000. In this way, the original 305 Bay Area Zip codes were reduced to 218 Zip code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

This SAS macro generates childhood mortality estimates (neonatal, post-neonatal, infant (1q0), child (4q1) and under-five (5q0) mortality) and standard errors based on birth histories reported by women during a household survey. We have made the SAS macro flexible enough to accommodate a range of calculation specifications including multi-stage sampling frames, and simple random samples or censuses. Childhood mortality rates are the component death probabilities of dying before a specific age. This SAS macro is based on a macro built by Keith Purvis at MeasureDHS. His method is described in Estimating Sampling Errors of Means, Total Fertility, and Childhood Mortality Rates Using SAS (www.measuredhs.com/pubs/pdf/OD17/OD17.pdf, section 4). More information about Childhood Mortality Estimation can also be found in the Guide to DHS Statistics (www.measuredhs.com/pubs/pdf/DHSG1/Guide_DHS_Statistics.pdf, page 93). We allow the user to specify whether childhood mortality calculations should be based on 5 or 10 years of birth histories, when the birth history window ends, and how to handle age of death with it is reported in whole months (rather than days). The user can also calculate mortality rates within sub-populations, and take account of a complex survey design (unequal probability and cluster samples). Finally, this SAS program is designed to read data in a number of different formats.

ObjectivesUnder the prevailing conditions of imbalanced life table and historic gender discrimination in India, our study examines crossover between life expectancies at ages zero, one and five years for India and quantifies the relative share of infant and under-five mortality towards this crossover.MethodsWe estimate threshold levels of infant and under-five mortality required for crossover using age specific death rates during 1981–2009 for 16 Indian states by sex (comprising of India’s 90% population in 2011). Kitagawa decomposition equations were used to analyse relative share of infant and under-five mortality towards crossover.FindingsIndia experienced crossover between life expectancies at ages zero and five in 2004 for menand in 2009 for women; eleven and nine Indian states have experienced this crossover for men and women, respectively. Men usually experienced crossover four years earlier than the women. Improvements in mortality below ages five have mostly contributed towards this crossover. Life expectancy at age one exceeds that at age zero for both men and women in India except for Kerala (the only state to experience this crossover in 2000 for men and 1999 for women).ConclusionsFor India, using life expectancy at age zero and under-five mortality rate together may be more meaningful to measure overall health of its people until the crossover. Delayed crossover for women, despite higher life expectancy at birth than for men reiterates that Indian women are still disadvantaged and hence use of life expectancies at ages zero, one and five become important for India. Greater programmatic efforts to control leading causes of death during the first month and 1–59 months in high child mortality areas can help India to attain this crossover early.

In this data set, you can access the data from Localities of Bogotá, D.C., related to perinatal mortality, infant mortality, under-5 mortality, and maternal mortality. Also, you can access the calculations of the relative index of inequality by quintiles, quartiles, and tertiles of poverty between localities of Bogotá D.C.

Baby Food And Infant Formula Market Outlook

The global baby food and infant formula market size was valued at approximately USD 57 billion in 2023 and is projected to reach around USD 85 billion by 2032, growing at a compound annual growth rate (CAGR) of 4.5%. This growth trajectory is largely driven by increasing awareness among parents about the importance of nutrition during the early stages of a child's development. Furthermore, the rising number of working women worldwide has amplified the demand for convenient and nutritious food options for infants, as parents seek balanced diet solutions that can support the healthy growth of their babies without compromising on convenience. The evolving consumer preferences towards organic and natural baby food products also contribute significantly to market expansion.

In addition to lifestyle changes, the growth of the baby food and infant formula market is buoyed by technological advancements in food production and packaging. With innovations in food processing techniques, manufacturers are able to offer products with enhanced nutritional profiles while ensuring longer shelf lives without the use of artificial preservatives. This has not only helped meet the demands of consumers who are increasingly health-conscious but has also enabled companies to expand their product lines to cater to the diverse nutritional needs of infants at different developmental stages. The advent of fortified baby foods, which are enriched with essential vitamins and minerals, is another growth driver, as parents become increasingly concerned about filling potential nutritional gaps in their infants' diets.

Another significant factor contributing to the market's growth is the expanding middle-class population in developing regions. As disposable incomes rise, more families are able to afford high-quality baby food and infant formula products. This is particularly evident in countries like India and China, where urbanization and improved economic conditions have led to changes in dietary habits and increased spending on child health and nutrition. The demand for premium and organic baby food products is also growing in these regions, as consumers become more aware of the potential health benefits associated with such products. Moreover, government initiatives aimed at reducing infant mortality rates and improving child nutrition also play a crucial role in boosting market growth.

The growing awareness about the nutritional needs of infants has led to a significant rise in the demand for Infant Formula Foods. These products are meticulously formulated to provide essential nutrients that support the healthy development of infants, especially when breastfeeding is not an option. With advancements in nutritional science, infant formula foods now offer a closer approximation to the nutritional profile of breast milk, ensuring that infants receive the necessary vitamins and minerals for optimal growth. This has been particularly beneficial for working parents who require reliable and nutritious alternatives to meet their infants' dietary needs. As the market for infant formula foods expands, manufacturers are increasingly focusing on enhancing the quality and safety of these products to meet the stringent standards expected by health-conscious consumers.

Regionally, the market presents a diverse outlook with specific growth trends and opportunities. Asia Pacific holds a significant share of the market and is expected to continue its dominance over the forecast period, driven by the large populations in China and India, where increasing urbanization and a growing middle class are major factors. North America, with its high level of health consciousness and substantial expenditure on health-related products, shows a steady growth pattern. Conversely, Europe is experiencing growth driven by technological advancements and a strong inclination towards organic products, whereas Latin America and the Middle East & Africa are emerging markets with potential for expansion due to improving economic conditions and increasing birth rates.

Product Type Analysis

The baby food and infant formula market is segmented by product type, which includes milk formula, dried baby food, prepared baby food, and others. Milk formula remains the leading segment, accounting for a significant portion of the market share. The high demand for milk formula can be attributed to its convenience and nutritional benefits that closely mimic breast milk, making it a preferred choi

Abstract

The 1992 Namibia Demographic and Health Survey (NDHS) is a nationally representative survey conducted by the Ministry of Health and Social Services, assisted by the Central Statistical Office, with the aim of gathering reliable information on fertility, family planning, infant and child mortality, maternal mortality, maternal and child health and nutrition. Interviewers collected information on the reproductive histories of 5,421 women 15-49 years and on the health of 3,562 children under the age of five years.

The Namibia Demographic and Health Survey (NDHS) is a national sample survey of women of reproductive age designed to collect data on mortality and fertility, socioeconomic characteristics, marriage patterns, breastfeeding, use of contraception, immunisation of children, accessibility to health and family planning services, treatment of children during episodes of illness, and the nutritional status of women and children. More specifically, the objectives of NDHS are: - To collect data at the national level which will allow the calculation of demographic rates, particularly fertility rates and child mortality rates, and maternal mortality rates; To analyse the direct and indirect factors which determine levels and trends in fertility and childhood mortality, Indicators of fertility and mortality are important in planning for social and economic development; - To measure the level of contraceptive knowledge and practice by method, region, and urban/rural residence; - To collect reliable data on family health: immunisations, prevalence and treatment of diarrhoea and other diseases among children under five, antenatal visits, assistance at delivery and breastfeeding; - To measure the nutritional status of children under five and of their mothers using anthropometric measurements (principally height and weight).

MAIN RESULTS

According to the NDHS, fertility is high in Namibia; at current fertility levels, Namibian women will have an average of 5.4 children by the end of their reproductive years. This is lower than most countries in sub-Saharan Africa, but similar to results from DHS surveys in Botswana (4.9 children per woman) and Zimbabwe (5.4 children per woman). Fertility in the South and Central regions is considerably lower (4.1 children per woman) than in the Northeast (6.0) and Northwest regions (6.7).

About one in four women uses a contraceptive method: 29 percent of married women currently use a method (26 percent use a modem method), and 23 percent of all women are current users. The pill, injection and female sterilisation are the most popular methods among married couples: each is used by about 7 to 8 percent of currently married women. Knowledge of contraception is high, with almost 90 percent of all women age 15-49 knowing of any modem method.

Certain groups of women are much more likely to use contraception than others. For example, urban women are almost four times more likely to be using a modem contraceptive method (47 percent) than rural women (13 percent). Women in the South and Central regions, those with more education, and those living closer to family planning services are also more likely to be using contraception.

Levels of fertility and contraceptive use are not likely to change until there is a drop in desired family size and until the idea of reproductive choice is more widely accepted. At present, the average ideal family size (5.0 children) is only slightly lower than the total fertility rate (5.4 children). Thus, the vast majority of births are wanted.

On average, Namibian women have their first child when they are about 21 years of age. The median age at first marriage is, however, 25 years. This indicates that many women give birth before marriage. In fact, married women are a minority in Namibia: 51 percent of women 15-49 were not married, 27 percent were currently married, 15 percent were currently living with a man (informal union), and 7 percent were widowed, divorced or separated. Therefore, a large proportion of children in Namibia are born out of wedlock.

The NDHS also provides inlbrmation about maternal and child health. The data indicate that 1 in 12 children dies before the fifth birthday. However, infant and child mortality have been declining over the past decade. Infant mortality has fallen from 67 deaths per 1,000 live births for the period 1983-87 to 57 per 1,000 live births for the period 1988-92, a decline of about 15 percent. Mortality is higher in the Northeast region than elsewhere in Namibia.

The leading causes of death are diarrhoea, undemutrition, acute respiratory infection (pneumonia) and malaria: each of these conditions was associated with about one-fifth of under-five deaths. Among neonatal deaths low birth weight and birth problems were the leading causes of death. Neonatal tetanus and measles were not lbund to be major causes of death.

Maternal mortality was estimated from reports on the survival status of sisters of the respondent. Maternal mortality was 225 per 100,000 live births for the decade prior to the survey. NDHS data also show considerable excess male mortality at ages 15-49, which may in part be related to the war of independence during the 1980s.

Utilisation of maternal and child health services is high. Almost 90 percent of mothers received antenatal care, and two-thirds of children were bom in health facilities. Traditional birth attendants assisted only 6 percent of births in the five years preceding the survey. Child vaccination coverage has increased rapidly since independence. Ninety-five percent of children age 12-23 months have received at least one vaccination, while 76 percent have received a measles vaccination, and 70 percent three doses of DPT and polio vaccines.

Children with symptoms of possible acute respiratory infection (cough and rapid breathing) may have pneumonia and need to be seen by a health worker. Among children with such symptoms in the two weeks preceding the survey two-thirds were taken to a health facility. Only children of mothers who lived more than 30 km from a health facility were less likely to be taken to a facility.

About one in five children had diarrhoea in the two weeks prior to the survey. Diarrhoea prevalence was very high in the Northeast region, where almost half of children reportedly had diarrhoea. The dysentery epidemic contributed to this high figure: diarrhoea with blood was reported for 17 percent of children under five in the Northeast region. Among children with diarrhoea in the last two weeks 68 percent were taken to a health facility, and 64 percent received a solution prepared from ORS packets. NDHS data indicate that more emphasis needs to put on increasing fluids during diarrhoea, since only I 1 percent mothers of children with diarrhoea said they increased the amount of fluids given during the episode.

Nearly all babies are breastfed (95 percent), but only 52 percent are put on the breast immediately. Exclusive breastfeeding is practiced for a short period, but not for the recommended 4-6 months. Most babies are given water, formula, or other supplements within the first four months of life, which both jeopardises their nutritional status and increases the risk of infection. On average, children are breastfed for about 17 months, but large differences exist by region. In the South region children are breastfed lor less than a year, in the Northwest region for about one and a half years and in the Northeast region for almost two years.

Most babies are weighed at birth, but the actual birth weight could be recalled for only 44 percent of births. Using these data and data on reported size of the newborn, for all births in the last five years, it was estimated that the mean birth weight in Namibia is 3048 grams, and that 16 percent of babies were low birth weight (less than 2500 grams).

Stunting, an indication of chronic undemutrition, was observed for 28 percent of children under five. Stunting was more common in the Northeast region (42 percent) than elsewhere in Namibia. Almost 9 percent of children were wasted, which is an indication of acute undemutrition. Wasting is higher than expected for Namibia and may have been caused by the drought conditions during 1992.

Matemal height is an indicator of nutritional status over generations. Women in Namibia have an average height of 160 cm and there is little variation by region. The Body Mass Index (BM1), defined as weight divided by squared height, is a measure of current nutritional status and was lower among women in the Northwest and the Northeast regions than among women in the South and Central regions.

On average, women had a health facility available within 40 minutes travel time. Women in the Northwest region, however, had to travel more than one hour to reach the nearest health facility. At a distance of less than 10 km, 56 percent of women had access to antenatal services, 48 percent to maternity services, 72 percent to immunisation services, and 49 percent to family planning services. Within one hour of travel time, fifty-two percent of women had antenatal services, 48 percent delivery services, 64 percent immunisation services and 49 percent family planning services. Distance and travel time were greatest in the Northwest region.

Geographic coverage

The sample for the NDHS was designed to be nationally representative. The design involved a two- stage stratified sample which is self-weighting within each of the three health regions for which estimates of fertility and mortality were required--Northwest, Northeast, and the combined Central/South region. In order to have a sufficient number of cases for analysis, oversampling was necessary for the Northeast region, which has only 14.8 percent of the population. Therefore, the sample was not allocated proportionally across regions and is not completely

VITAL SIGNS INDICATOR Life Expectancy (EQ6)

FULL MEASURE NAME Life Expectancy

LAST UPDATED April 2017

DESCRIPTION Life expectancy refers to the average number of years a newborn is expected to live if mortality patterns remain the same. The measure reflects the mortality rate across a population for a point in time.

DATA SOURCE State of California, Department of Health: Death Records (1990-2013) No link

California Department of Finance: Population Estimates Annual Intercensal Population Estimates (1990-2010) Table P-2: County Population by Age (2010-2013) http://www.dof.ca.gov/Forecasting/Demographics/Estimates/

U.S. Census Bureau: Decennial Census ZCTA Population (2000-2010) http://factfinder.census.gov

U.S. Census Bureau: American Community Survey 5-Year Population Estimates (2013) http://factfinder.census.gov

CONTACT INFORMATION vitalsigns.info@mtc.ca.gov

METHODOLOGY NOTES (across all datasets for this indicator) Life expectancy is commonly used as a measure of the health of a population. Life expectancy does not reflect how long any given individual is expected to live; rather, it is an artificial measure that captures an aspect of the mortality rates across a population that can be compared across time and populations. More information about the determinants of life expectancy that may lead to differences in life expectancy between neighborhoods can be found in the Bay Area Regional Health Inequities Initiative (BARHII) Health Inequities in the Bay Area report at http://www.barhii.org/wp-content/uploads/2015/09/barhii_hiba.pdf. Vital Signs measures life expectancy at birth (as opposed to cohort life expectancy). A statistical model was used to estimate life expectancy for Bay Area counties and ZIP Codes based on current life tables which require both age and mortality data. A life table is a table which shows, for each age, the survivorship of a people from a certain population.

Current life tables were created using death records and population estimates by age. The California Department of Public Health provided death records based on the California death certificate information. Records include age at death and residential ZIP Code. Single-year age population estimates at the regional- and county-level comes from the California Department of Finance population estimates and projections for ages 0-100+. Population estimates for ages 100 and over are aggregated to a single age interval. Using this data, death rates in a population within age groups for a given year are computed to form unabridged life tables (as opposed to abridged life tables). To calculate life expectancy, the probability of dying between the jth and (j+1)st birthday is assumed uniform after age 1. Special consideration is taken to account for infant mortality.

For the ZIP Code-level life expectancy calculation, it is assumed that postal ZIP Codes share the same boundaries as ZIP Code Census Tabulation Areas (ZCTAs). More information on the relationship between ZIP Codes and ZCTAs can be found at http://www.census.gov/geo/reference/zctas.html. ZIP Code-level data uses three years of mortality data to make robust estimates due to small sample size. Year 2013 ZIP Code life expectancy estimates reflects death records from 2011 through 2013. 2013 is the last year with available mortality data. Death records for ZIP Codes with zero population (like those associated with P.O. Boxes) were assigned to the nearest ZIP Code with population. ZIP Code population for 2000 estimates comes from the Decennial Census. ZIP Code population for 2013 estimates are from the American Community Survey (5-Year Average). ACS estimates are adjusted using Decennial Census data for more accurate population estimates. An adjustment factor was calculated using the ratio between the 2010 Decennial Census population estimates and the 2012 ACS 5-Year (with middle year 2010) population estimates. This adjustment factor is particularly important for ZCTAs with high homeless population (not living in group quarters) where the ACS may underestimate the ZCTA population and therefore underestimate the life expectancy. The ACS provides ZIP Code population by age in five-year age intervals. Single-year age population estimates were calculated by distributing population within an age interval to single-year ages using the county distribution. Counties were assigned to ZIP Codes based on majority land-area.

ZIP Codes in the Bay Area vary in population from over 10,000 residents to less than 20 residents. Traditional life expectancy estimation (like the one used for the regional- and county-level Vital Signs estimates) cannot be used because they are highly inaccurate for small populations and may result in over/underestimation of life expectancy. To avoid inaccurate estimates, ZIP Codes with populations of less than 5,000 were aggregated with neighboring ZIP Codes until the merged areas had a population of more than 5,000. ZIP Code 94103, representing Treasure Island, was dropped from the dataset due to its small population and having no bordering ZIP Codes. In this way, the original 305 Bay Area ZIP Codes were reduced to 217 ZIP Code areas for 2013 estimates. Next, a form of Bayesian random-effects analysis was used which established a prior distribution of the probability of death at each age using the regional distribution. This prior is used to shore up the life expectancy calculations where data were sparse.

Abstract

The 1995 Uganda Demographic and Health Survey (UDHS-II) is a nationally-representative survey of 7,070 women age 15-49 and 1,996 men age 15-54. The UDHS was designed to provide information on levels and trends of fertility, family planning knowledge and use, infant and child mortality, and maternal and child health. Fieldwork for the UDHS took place from late-March to mid-August 1995. The survey was similar in scope and design to the 1988-89 UDHS. Survey data show that fertility levels may be declining, contraceptive use is increasing, and childhood mortality is declining; however, data also point to several remaining areas of challenge.

The 1995 UDHS was a follow-up to a similar survey conducted in 1988-89. In addition to including most of the same questions included in the 1988-89 UDHS, the 1995 UDHS added more detailed questions on AIDS and maternal mortality, as well as incorporating a survey of men. The general objectives of the 1995 UDHS are to: - provide national level data which will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; - analyse the direct and indirect factors which determine the level and trends of fertility; - measure the level of contraceptive knowledge and practice (of both women and men) by method, by urban-rural residence, and by region; - collect reliable data on maternal and child health indicators; immunisation, prevalence, and treatment of diarrhoea and other diseases among children under age four; antenatal visits; assistance at delivery; and breastfeeding; - assess the nutritional status of children under age four and their mothers by means of anthropometric measurements (weight and height), and also child feeding practices; and - assess among women and men the prevailing level of specific knowledge and attitudes regarding AIDS and to evaluate patterns of recent behaviour regarding condom use.

MAIN RESULTS

• Fertility:

Fertility Trends. UDHS data indicate that fertility in Uganda may be starting to decline. The total fertility rate has declined from the level of 7.1 births per woman that prevailed over the last 2 decades to 6.9 births for the period 1992-94. The crude birth rate for the period 1992-94 was 48 live births per I000 population, slightly lower than the level of 52 observed from the 1991 Population and Housing Census. For the roughly 80 percent of the country that was covered in the 1988-89 UDHS, fertility has declined from 7.3 to 6.8 births per woman, a drop of 7 percent over a six and a half year period.

Birth Intervals. The majority of Ugandan children (72 percent) are born after a "safe" birth interval (24 or more months apart), with 30 percent born at least 36 months after a prior birth. Nevertheless, 28 percent of non-first births occur less than 24 months after the preceding birth, with 10 percent occurring less than 18 months since the previous birth. The overall median birth interval is 29 months. Fertility Preferences. Survey data indicate that there is a strong desire for children and a preference for large families in Ugandan society. Among those with six or more children, 18 percent of married women want to have more children compared to 48 percent of married men. Both men and women desire large families.

• Family planning:

Knowledge of Contraceptive Methods. Knowledge of contraceptive methods is nearly universal with 92 percent of all women age 15-49 and 96 percent of all men age 15-54 knowing at least one method of family planning. Increasing Use of Contraception. The contraceptive prevalence rate in Uganda has tripled over a six-year period, rising from about 5 percent in approximately 80 percent of the country surveyed in 1988-89 to 15 percent in 1995.

Source of Contraception. Half of current users (47 percent) obtain their methods from public sources, while 42 percent use non-governmental medical sources, and other private sources account for the remaining 11 percent.

• Maternal and child health:

High Childhood Mortality. Although childhood mortality in Uganda is still quite high in absolute terms, there is evidence of a significant decline in recent years. Currently, the direct estimate of the infant mortality rate is 81 deaths per 1,000 births and under five mortality is 147 per 1,000 births, a considerable decline from the rates of 101 and 180, respectively, that were derived for the roughly 80 percent of the country that was covered by the 1988-89 UDHS.

Childhood Vaccination Coverage. One possible reason for the declining mortality is improvement in childhood vaccination coverage. The UDHS results show that 47 percent of children age 12-23 months are fully vaccinated, and only 14 percent have not received any vaccinations.

Childhood Nutritional Status. Overall, 38 percent of Ugandan children under age four are classified as stunted (low height-for-age) and 15 percent as severely stunted. About 5 percent of children under four in Uganda are wasted (low weight-for-height); 1 percent are severely wasted. Comparison with other data sources shows little change in these measures over time.

• AIDS:

Virtually all women and men in Uganda are aware of AIDS. About 60 percent of respondents say that limiting the number of sexual partners or having only one partner can prevent the spread of disease. However, knowledge of ways to avoid AIDS is related to respondents' education. Safe patterns of sexual behaviour are less commonly reported by respondents who have little or no education than those with more education. Results show that 65 percent of women and 84 percent of men believe that they have little or no chance of being infected.

Availability of Health Services. Roughly half of women in Uganda live within 5 km of a facility providing antenatal care, delivery care, and immunisation services. However, the data show that children whose mothers receive both antenatal and delivery care are more likely to live within 5 km of a facility providing maternal and child health (MCH) services (70 percent) than either those whose mothers received only one of these services (46 percent) or those whose mothers received neither antenatal nor delivery care (39 percent).

Geographic coverage

The 1995 Uganda Demographic and Health Survey (UDHS-II) is a nationally-representative survey. For the purpose of the 1995 UDHS, the following domains were utilised: Uganda as a whole; urban and rural areas separately; each of the four regions: Central, Eastern, Northern, and Western; areas in the USAID-funded DISH project to permit calculation of contraceptive prevalence rates.

Analysis unit

• Household
• Women age 15-49
• Men age 15-54
• Children under four

Universe

The population covered by the 1995 UDHS is defined as the universe of all women age 15-49 in Uganda. But because of insecurity, eight EAs could not be surveyed (six in Kitgum District, one in Apac District, and one in Moyo District). An additional two EAs (one in Arua and one in Moroto) could not be surveyed, but substitute EAs were selected in their place.

Kind of data

Sample survey data

Sampling procedure

A sample of 303 primary sampling units (PSU) consisting of enumeration areas (EAs) was selected from a sampling frame of the 1991 Population and Housing Census. For the purpose of the 1995 UDHS, the following domains were utilised: Uganda as a whole; urban and rural areas separately; each of the four regions: Central, Eastern, Northern, and Western; areas in the USAID-funded DISH project to permit calculation of contraceptive prevalence rates.

Districts in the DISH project area were grouped by proximity into the following five reporting domains: - Kasese and Mbarara Districts - Masaka and Rakai Districts - Luwero and Masindi Districts - Jinja and Kamuli Districts - Kampala District

The sample for the 1995 UDHS was selected in two stages. In the first stage, 303 EAs were selected with probability proportional to size. Then, within each selected EA, a complete household listing and mapping exercise was conducted in December 1994 forming the basis for the second-stage sampling. For the listing exercise, 11 listers from the Statistics Department were trained. Institutional populations (army barracks, hospitals, police camps, etc.) were not listed.

From these household lists, households to be included in the UDHS were selected with probability inversely proportional to size based on the household listing results. All women age 15-49 years in these households were eligible to be interviewed in the UDHS. In one-third of these selected households, all men age 15-54 years were eligible for individual interview as well. The overall target sample was 6,000 women and 2,000 men. Because of insecurity, eight EAs could not be surveyed (six in Kitgum District, one in Apac District, and one in Moyo District). An additional two EAs (one in Arua and one in Moroto) could not be surveyed, but substitute EAs were selected in their place.

Since one objective of the survey was to produce estimates of specific demographic and health indicators for the areas included in the DISH project, the sample design allowed for oversampling of households in these districts relative to their actual proportion in the population. Thus, the 1995 UDHS sample is not self-weighting at the national level; weights are required to estimate national-level indicators. Due to the weighting factor and rounding of estimates, figures may not add to totals. In addition, the percent total may not add to 100.0 due to rounding.

Mode of data collection

Face-to-face

Research instrument

Four questionnaires were used in the 1995 UDHS.

a) A Household Schedule was used to list the names and certain

The market value of the baby food industry in India, a country with one third of the world’s children, was around 240 million U.S. dollars in 2018. The population in the country was approximately 1.4 billion in mid-2018 and is well on course to beat China as the most populated country in the coming decades. To put things into perspective, one in every six people on the planet live in the south Asian country.

Advancement in technology and mindset

Technological advancements had a major impact on the mortality rates of infants in the country. However, it can be assumed that it was the change in mindset that has brought about the major change. With awareness campaigns like “Beti bachao, beti padhai” translating to “Save the daughter, educate the daughter” has reduced selective abortions and infanticide in recent years. The infant mortality rate was close to 30 deaths per thousand live births in 2017 which was the lowest recorded in the last decade.

Food up for grabs

With the reducing mortality rates, the baby food industry recorded huge profits. Baby food sales were more than 600 million U.S. dollars in 2016. With possibilities of delving into the organic baby food segment with milk formula and baby cereal, big players like Nestle India have promising prospects and opportunities to shape the future of the baby food industry.

Maternal Infant Care Product Market Outlook

The global maternal infant care product market size was valued at USD 24 billion in 2023 and is projected to reach USD 42 billion by 2032, growing at a compound annual growth rate (CAGR) of 6.5%. This growth can be attributed primarily to rising awareness about maternal and infant health, increasing disposable incomes, and technological advancements in healthcare products.

One of the key growth factors driving the maternal infant care product market is the increasing awareness and education regarding maternal and infant health. This trend is largely influenced by government initiatives, non-profit organizations, and healthcare providers who are focused on reducing infant mortality rates and enhancing maternal health outcomes. Educational campaigns and community health programs are playing a significant role in encouraging young mothers to adopt better prenatal and postnatal care practices, thereby boosting the demand for specialized maternal and infant care products.

The economic development in emerging markets has also contributed significantly to market growth. With the rise in disposable incomes, there is a growing willingness among consumers to invest in high-quality healthcare products, including maternal and infant care items. Furthermore, urbanization has brought about changes in lifestyle, leading to a greater demand for convenience products such as breast pumps and baby monitors that offer modern solutions for busy parents. This shift towards premium products is also evident in more developed markets, where consumers are increasingly opting for top-of-the-line brands that promise safety and efficacy.

Technological advancements in healthcare are another pivotal growth factor. Innovations such as smart baby monitors, which provide real-time data on a baby’s health, and advanced breast pumps designed for efficiency and comfort, are attracting a significant number of consumers. The integration of IoT and AI in these products is providing parents with enhanced capabilities for monitoring and ensuring the well-being of their infants, thereby fostering market growth. Additionally, advancements in the formulation of infant formula and prenatal vitamins are addressing specific nutritional needs, further driving the market.

From a regional perspective, North America currently dominates the maternal infant care product market, followed closely by Europe and Asia Pacific. North America’s leadership position is underpinned by high healthcare expenditure, robust healthcare infrastructure, and the presence of major market players. Europe’s growth is driven by similar factors, along with supportive government policies. The Asia Pacific region is expected to witness the fastest growth, attributed to its large population base, improving healthcare facilities, and increasing awareness about maternal and infant health.

Product Type Analysis

The prenatal vitamins segment holds a significant share within the maternal infant care product market. This segment's growth is primarily driven by heightened awareness about the importance of prenatal nutrition. Pregnant women are increasingly understanding the benefits of vitamins and minerals in ensuring a healthy pregnancy and fetal development. Health practitioners are also actively recommending these supplements, which has led to a surge in demand. Moreover, advancements in the formulation of prenatal vitamins, tailored to address specific nutritional deficiencies, are further propelling growth in this segment.

Breast pumps represent another vital segment in the maternal infant care product market. The increasing number of working mothers globally has significantly boosted the demand for breast pumps. These devices offer a convenient solution for nursing mothers to express and store milk, ensuring that their babies can be fed breast milk even when they are not around. Technological advancements in breast pump designs, aimed at enhancing comfort and efficiency, have further fueled market growth. Additionally, government initiatives in several countries to promote breastfeeding are positively impacting this segment.

Infant formula is yet another crucial segment, driven by its vital role in infant nutrition, especially for mothers who cannot breastfeed. The increasing number of working mothers and the rising awareness of the nutritional benefits of infant formula are key factors contributing to its growth. Manufacturers are focusing on developing advanced formulations that closely mimic breast milk, thereby attracting a larger consumer bas

Validation of predictive formulas and summary of cut-off points of the best models according to FNR (false negative rate).

ABSTRACT Life tables have been elaborated throughout much of human history. However, the first life table to use actuarial concepts was only constructed in 1815 by Milne for the city of Carlisle in England. Since then, numerous tables have been elaborated for different regions and countries, due to their crucial importance for analyzing various types of problems covering a vast range of possibilities, from actuarial studies to forecasting and evaluating demands in order to define public policies. The most common problem nowadays in an actuarial calculation is choosing a suitable table for a given population. Brazil has few specific tables for the pensions market and has been using imported tables that refer to other countries, with different cultures and different mortality experiences. Using data from the Integrated Human Resource Administration System, this table constructs life tables for Executive branch federal civil servants for the period from 1993 to 2014, disaggregated for sex, age, and educational level (high school and university). The international literature has recognized differences in mortality due to sex, socioeconomic differences, and occupation. The creation of the Complementary Pension Foundation for Federal Public Servants in 2013 requires specific mortality tables for this population to support actuarial studies, healthcare, and personnel policies. A mathematical equation is fitted to the data. This equation can be broken down into infant mortality (not present in the data), mortality from external causes, and mortality from senescence. Recent results acknowledging an upper limit for old age mortality are incorporated into the adjusted probabilities of death. Assuming a binomial distribution for deaths, the deviance was used as a figure of merit to evaluate the goodness of fit of the observed data both to a set of tables used by the insurance/pensions market and to the adjusted tables.

Abstract

The 2000 Albania Multiple Indicator Cluster Survey (MICS) is a nationally representative survey of households, women, and children. The main objectives of the survey are to provide for the first time information for assessing the situation of children and women in Albania at the end of the decade and to furnish data needed for monitoring progress toward goals established at the World Summit for Children and as a basis for future action.

Infant and Under Five Mortality · Infant mortality rate and under five estimates were obtained using United Nations QFIVE program. The information for calculating these ratios were provided by MICS survey carried out in June – July 2000. The infant mortality rate is 28 per 1000 and under five is 33 per 1000.

Education · Overall ninety percent of children of primary school age in Albania are attending primary school. In urban areas, 91 percent of children attend school while in rural areas 90 percent · 82 percent of children who enter the first grade of primary school reach grade five. · The vast majority (88 percent) of the population over age 15 years is literate. The percentage of literacy declines from 93 percent among those aged 15-34 to 65 percent among the population aged 65 and older.

Water and Sanitation · More than 45 percent of the population uses drinking water piped into their dwellings, 20 percent uses water piped into a yard or plot and 16,4 percent uses water from a public tap. However , it should be mentioned that these data do not estimate the real access of population to drinking water due to poor infrastructure and the lack of water.

Child Malnutrition · Four percent of children under age five in Albania are underweight or too thin for their age. 17 percent of children are stunted and 4 percent are wasted. · Children whose mothers have secondary or higher education are least likely to be underweight and stunted compared to children of mothers with less education.

Breastfeeding · Approximately 9 percent of children aged under four months are exclusively breastfed, a level considerably lower than recommended. At age 6-9 months, 24 percent of children are receiving breast milk and solid or semi-solid foods. By age 20-23 months, only 6 percent continue to breastfeed.

Salt Iodization · Seventy six percent of households in Albania have adequately iodized salt. The percentage of households with adequately iodized salt ranges from 70.9 percent in the urban areas to 47.8 percent in the rural areas.

Vitamin A Supplementation · Within six months prior to the MICS, 7.4 percent of children aged 6-59 months had received a high dose of Vitamin A supplement. Approximately 5 percent did not receive a supplement in the last 6 months but did receive one prior to that time. · The mother’s level of education effects the likelihood of Vitamin A supplementation. The percentage receiving a supplement in the last six months increases from 6.5 percent among children whose mothers have primary education to 11 percent among children of mothers with higher education. · Only about 3 percent of mothers who had given birth in the year preceding MICS received a Vitamin A supplement within eight weeks of giving birth

Low Birth weight · Approximately 3 percent of infants are estimated to weigh less than 2500 grams at birth. The prevalence of low birth weight does not vary much between urban and rural areas or by the mother’s education.

Immunization Coverage · Information on immunization coverage provided by MICS survey is based on vaccination cards. However, this information might not be periodically updated, due to the fact that many immunization campaigns are carried out during emergencies. Eighty percent of children aged 12 – 23 months received a BCG vaccination by the age of 12 months and the first dose of DPT was given to 71 percent. The percentage declines for subsequent doses of DPT to 61 percent for the second dose, and 52 percent for the third dose. Similarly, 57.3 percent of children received Polio 1 by age 12 months. This declines to 28.7 percent by the third dose. The coverage for measles vaccine by 12 months is at 61 percent. Male and female children are vaccinated at roughly the same rate. Vaccination coverage is highest among children whose mothers have secondary or higher education.

Diarrhea · Approximately 94 percent of children with diarrhea received one or more of the recommended home treatments (i.e., were treated with ORS or RHF). · Only 48.2 percent of children with diarrhea received increased fluids and continued eating as recommended.

Acute Respiratory Infection · Acute lower respiratory infections, particularly pneumonia. is one of the leading causes of child mortality in Albania. 83 percent of children with ARI were taken to an appropriate health provider.

IMCI Initiative · Among children under five who were reported to have had diarrhea or some other illness in the two weeks preceding the MICS, 47 percent received increased fluids and continued eating as recommended under the IMCI program. · In rural areas, mothers especially those without education recognized at least two of the signs that a child should be taken immediately to a health facility.

Malaria According to official data there are no malaria cases reported in Albania.

HIV/AIDS · 25 percent of women aged 15-49 know all three of the main ways to prevent HIV transmission, 55 percent believe that having only one uninfected sex partner can prevent HIV transmission, 42 percent believe that using a condom every time, and abstaining from sex can prevent HIV transmission. Less than two percent of women correctly stated that AIDS cannot be transmitted by supernatural means whereas 12,6 percent stated that AIDS cannot be spread by mosquito bites. More than 40 percent of women correctly believe that a healthy looking person can be infected. · Twenty three percent of women of reproductive age in Albania know where to get tested for AIDS. According to MICS results, only 0,7 percent of women have been tested for AIDS.

Contraception · Current use of contraception was reported by 58 percent of married or in union women. The most popular method is withdrawal which is used by 33 percent of married women.

Prenatal Care · Thirty percent of women with recent births in Albania are protected against neonatal tetanus. The vast majority of these women received two doses of tetanus toxoid within the last three years. · Virtually all women in Albania receive some type of prenatal care and 95 percent receive antenatal care from skilled personnel (doctor, nurse, midwife).

Assistance at Delivery · In the year prior to MICS survey, one in ten deliveries were assisted by a midwife. Doctors assisted the delivery of 57 percent of cases and nurses 37 percent. Less than one percent of deliveries did not have any assistance in the year prior to MICS survey.

Birth Registration · The births of 99 percent of children under five years in Albania have been registered. There are no significant variations in birth registration across sex, age, or education categories.

Orphaned children and Living Arrangements of Children · In Albania, 96,5 percent of children aged 0 – 14 are living with both parents. A very small percentage of children aged 0 – 14 years old have one or both parents dead, 0,2 percent are not living with a biological parent.

Child Labor · In Albania, the MICS survey estimates that less than one percent of children aged 5 – 14 years old engage in paid work. About 3 percent participate in unpaid work for someone other than a household member. Variations across urban and rural areas are greatest in the percentage of children who engage in less than four hours of domestic work a day. This percentage ranges from 49 percent in urban areas and 60 percent in rural areas.

Geographic coverage

The 2000 Albania Multiple Indicator Cluster Survey (MICS) is a nationally representative survey.

Analysis unit

Households, women, and children.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the Albania Multiple Indicator Cluster Survey (MICS) was designed to provide estimates various indicators at the national level, for urban and rural areas. The sample was selected in two stages. At the first stage, 376 primary Sampling Units (PSU) were systematically selected from 1665 PSU. At the second stage, households were selected systematically within each PSU. The total sample had 5182 households. Because the sample was stratified by urban and rural areas, it is not self-weighting. For reporting national level results, sample weights are used.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Albania MICS were based on the MICS Model Questionnaire with some modifications and additions. A household questionnaire was administered in each household, which collected various information on household members including sex, age, literacy, marital status, and orphaned children status. The household questionnaire also included education, child labor, water and sanitation, and salt iodization modules. In addition to a household questionnaire, questionnaires were administered in each household for women age 15-49 and children under age five. For children, the questionnaire was administered to the mother or caretaker of the child. The questionnaire for women contained the following modules: - Child mortality - Tetanus toxoid - Maternal and newborn health - Contraceptive use - HIV/AIDS.

The questionnaire for children under age five included modules on: - Birth registration and early learning - Vitamin A - Vitamin D - Breastfeeding - Care of Illness - Respiratory

The EDHS 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 main objective of the EDHS is to provide policymakers and programme formulators in population and health with adequate and reliable information. The EDHS collected information on demographic characteristics, fertility, infant and child mortality, maternal mortality, nuptiality, fertility preferences, family planning and health-related matters such as breastfeeding practices, antenatal care, children's immunization, childhood disease, nutritional status of mothers and young children and awareness and behaviour regarding sexually transmitted diseases including AIDS. The objectives of the EDHS are to: Collect data at the national level which will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Analyze the direct and indirect factors which determine levels and trends of fertility; Measure the level of contraceptive knowledge and practice (women and men) by urban-rural residence; Collect reliable data on maternal and child health indicators: immunizations, prevalence and treatment of diarrhea and diseases among children under age three, antenatal care visits, assistance at delivery, and breastfeeding; Assess the nutritional status of children under age three, and their mothers, by means of anthropometric measurements (height and weight ) and analysis of child feeding practices; and Assess the prevailing level of specific knowledge and attitudes regarding AIDS and to evaluate patterns of recent behavior regarding condom use, among women and men.

Abstract

The 1997 Jordan Population and Family Health Survey (JPFHS) is a national sample survey carried out by the Department of Statistics (DOS) as part of its National Household Surveys Program (NHSP). The JPFHS was specifically aimed at providing information on fertility, family planning, and infant and child mortality. Information was also gathered on breastfeeding, on maternal and child health care and nutritional status, and on the characteristics of households and household members. The survey will provide policymakers and planners with important information for use in formulating informed programs and policies on reproductive behavior and health.

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 1997 JPFHS sample was designed to produce reliable estimates of major survey variables for the country as a whole, for urban and rural areas, for the three regions (each composed of a group of governorates), and for the three major governorates, Amman, Irbid, and Zarqa.

The 1997 JPFHS sample is a subsample of the master sample that was designed using the frame obtained from the 1994 Population and Housing Census. A two-stage sampling procedure was employed. First, primary sampling units (PSUs) were selected with probability proportional to the number of housing units in the PSU. A total of 300 PSUs were selected at this stage. In the second stage, in each selected PSU, occupied housing units were selected with probability inversely proportional to the number of housing units in the PSU. This design maintains a self-weighted sampling fraction within each governorate.

UPDATING OF SAMPLING FRAME

Prior to the main fieldwork, mapping operations were carried out and the sample units/blocks were selected and then identified and located in the field. The selected blocks were delineated and the outer boundaries were demarcated with special signs. During this process, the numbers on buildings and housing units were updated, listed and documented, along with the name of the owner/tenant of the unit or household and the name of the household head. These activities took place between January 7 and February 28, 1997.

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

Mode of data collection

Face-to-face

Research instrument

The 1997 JPFHS used two questionnaires, one for the household interview and the other for eligible women. Both questionnaires were developed in English and then translated into Arabic. The household questionnaire was used to list all members of the sampled households, including usual residents as well as visitors. For each member of the household, basic demographic and social characteristics were recorded and women eligible for the individual interview were identified. The individual questionnaire was developed utilizing the experience gained from previous surveys, in particular the 1983 and 1990 Jordan Fertility and Family Health Surveys (JFFHS).

The 1997 JPFHS individual questionnaire consists of 10 sections: - Respondent’s background - Marriage - Reproduction (birth history) - Contraception - Pregnancy, breastfeeding, health and immunization - Fertility preferences - Husband’s background, woman’s work and residence - Knowledge of AIDS - Maternal mortality - Height and weight of children and mothers.

Cleaning operations

Fieldwork and data processing activities overlapped. After a week of data collection, and after field editing of questionnaires for completeness and consistency, the questionnaires for each cluster were packaged together and sent to the central office in Amman where they were registered and stored. Special teams were formed to carry out office editing and coding.

Data entry started after a week of office data processing. The process of data entry, editing, and cleaning was done by means of the ISSA (Integrated System for Survey Analysis) program DHS has developed especially for such surveys. The ISSA program allows data to be edited while being entered. Data entry was completed on November 14, 1997. A data processing specialist from Macro made a trip to Jordan in November and December 1997 to identify problems in data entry, editing, and cleaning, and to work on tabulations for both the preliminary and final report.

Response rate

A total of 7,924 occupied housing units were selected for the survey; from among those, 7,592 households were found. Of the occupied households, 7,335 (97 percent) were successfully interviewed. In those households, 5,765 eligible women were identified, and complete interviews were obtained with 5,548 of them (96 percent of all eligible women). Thus, the overall response rate of the 1997 JPFHS was 93 percent. The principal reason for nonresponse among the women was the failure of interviewers to find them at home despite repeated callbacks.

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

Sampling error estimates

The estimates from a sample survey are subject to two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the result of mistakes made in implementing data collection and data processing (such as failure to locate and interview the correct household, misunderstanding questions either by the interviewer or the respondent, and data entry errors). Although during the implementation of the 1997 JPFHS numerous efforts were made to minimize this type of error, nonsampling errors are not only impossible to avoid but also difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The respondents selected in the 1997 JPFHS constitute only one of many samples that could have been selected from the same population, given the same design and expected size. Each of those samples would have yielded results differing somewhat from the results of the sample actually selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

A sampling error is usually measured in terms of the standard error for 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 the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95 percent of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, since the 1997 JDHS-II sample resulted from a multistage stratified design, formulae of higher complexity had to be used. The computer software used to calculate sampling errors for the 1997 JDHS-II was the ISSA Sampling Error Module, which uses the Taylor linearization method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics, such as fertility and mortality rates.

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 years - Reporting of age at death in days - Reporting of age at death in months

Note: See detailed tables in APPENDIX C of the survey report.

Abstract

The authors combine data from 84 Demographic and Health Surveys from 46 countries to analyze trends and socioeconomic differences in adult mortality, calculating mortality based on the sibling mortality reports collected from female respondents aged 15-49.

The analysis yields four main findings. First, adult mortality is different from child mortality: while under-5 mortality shows a definite improving trend over time, adult mortality does not, especially in Sub-Saharan Africa. The second main finding is the increase in adult mortality in Sub-Saharan African countries. The increase is dramatic among those most affected by the HIV/AIDS pandemic. Mortality rates in the highest HIV-prevalence countries of southern Africa exceed those in countries that experienced episodes of civil war. Third, even in Sub-Saharan countries where HIV-prevalence is not as high, mortality rates appear to be at best stagnating, and even increasing in several cases. Finally, the main socioeconomic dimension along which mortality appears to differ in the aggregate is gender. Adult mortality rates in Sub-Saharan Africa have risen substantially higher for men than for women?especially so in the high HIV-prevalence countries. On the whole, the data do not show large gaps by urban/rural residence or by school attainment.

This paper is a product of the Human Development and Public Services Team, Development Research Group. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org.

Geographic coverage

We derive estimates of adult mortality from an analysis of Demographic and Health Survey (DHS) data from 46 countries, 33 of which are from Sub-Saharan Africa and 13 of which are from countries in other regions (Annex Table). Several of the countries have been surveyed more than once and we base our estimates on the total of 84 surveys that have been carried out (59 in Sub-Saharan Africa, 25 elsewhere).

The countries covered by DHS in Sub-Saharan Africa represent almost 90 percent of the region's population. Outside of Sub-Saharan Africa the DHS surveys we use cover a far smaller share of the population-even if this is restricted to countries whose GDP per capita never exceeds $10,000: overall about 14 percent of the population is covered by these countries, although this increases to 29 percent if China and India are excluded (countries for which we cannot calculate adult mortality using the DHS). It is therefore important to keep in mind that the sample of non-Sub-Saharan African countries we have cannot be thought of as "representative" of the rest of the world, or even the rest of the developing world.

Analysis unit

Country

Kind of data

Sample survey data [ssd]

Mode of data collection

Face-to-face [f2f]

Cleaning operations

In the course of carrying out this study, the authors created two databases of adult mortality estimates based on the original DHS datasets, both of which are publicly available for analysts who wish to carry out their own analysis of the data.

The naming conventions for the adult mortality-related are as follows. Variables are named:

GGG_MC_AAAA

GGG refers to the population subgroup. The values it can take, and the corresponding definitions are in the following table:

All - All Fem - Female Mal - Male Rur - Rural Urb - Urban Rurm - Rural/Male Urbm - Urban/Male Rurf - Rural/Female Urbf - Urban/Female Noed - No education Pri - Some or completed primary only Sec - At least some secondary education Noedm - No education/Male Prim - Some or completed primary only/Male Secm - At least some secondary education/Male Noedf - No education/Female Prif - Some or completed primary only/Female Secf - At least some secondary education/Female Rch - Rural as child Uch - Urban as child Rchm - Rural as child/Male Uchm - Urban as child/Male Rchf - Rural as child/Female Uchf - Urban as child/Female Edltp - Less than primary schooling Edpom - Primary or more schooling Edltpm - Less than primary schooling/Male Edpomm - Primary or more schooling/Male Edltpf - Less than primary schooling/Female Edpomf - Primary or more schooling/Female Edltpu - Less than primary schooling/Urban Edpomu - Primary or more schooling/Urban Edltpr - Less than primary schooling/Rural Edpomr - Primary or more schooling/Rural Edltpmu - Less than primary schooling/Male/Urban Edpommu - Primary or more schooling/Male/Urban Edltpmr - Less than primary schooling/Male/Rural Edpommr - Primary or more schooling/Male/Rural Edltpfu - Less than primary schooling/Female/Urban Edpomfu - Primary or more schooling/Female/Urban Edltpfr - Less than primary schooling/Female/Rural Edpomfr - Primary or more schooling/Female/Rural

M refers to whether the variable is the number of observations used to calculate the estimate (in which case M takes on the value "n") or whether it is a mortality estimate (in which case M takes on the value "m").

C refers to whether the variable is for the unadjusted mortality rate calculation (in which case C takes on the value "u") or whether it adjusts for the number of surviving female siblings (in which case C takes on the value "a").

AAAA refers to the age group that the mortality estimate is calculated for. It takes on the values: 1554 - Ages 15-54 1524 - Ages 15-24 2534 - Ages 25-34 3544 - Ages 35-44 4554 - Ages 45-54

Other variables that are in the databases are:

period - Period for which mortality rate is calculated (takes on the values 1975-79, 1980-84 … 2000-04) svycountry - Name of country for DHS countries ccode3 - Country code u5mr - Under-5 mortality (from World Development Indicators) cname - Country name gdppc - GDP per capita (constant 2000 US$) (from World Development Indicators) gdppcppp - GDP per capita PPP (constant 2005 intl $) (from World Development Indicators) pop - Population (from World Development Indicators) hivprev2001 - HIV prevalence in 2001 (from UNAIDS 2010) region - Region

Abstract

The 2009 Kiribati Demographic and Health Survey was the first survey in phase two of Pacific DHS Project with funding support from ADB. The primary objective of this survey was to provide up-to-date information for policy-makers, planners, researchers and programme managers, for use in planning, implementing, monitoring and evaluating population and health programmes within the country. The survey was intended to provide key estimates of Kiribati’s demographic and health situation.

The main objective of the 2009 Kiribati Demographic and Health Survey (2009 KDHS) is to provide current and reliable data on fertility and family planning behaviour, child mortality, adult and maternal mortality, children’s nutritional status, the use of maternal and child healthcare services, and knowledge of HIV and AIDS. Specific objectives are to:

• collect data (at the national level) that will allow the calculation of key demographic rates;
• analyse the direct and indirect factors that determine the level and trends of fertility;
• measure the level of contraceptive knowledge and practice among women and men by method, urban–rural residence and region;
• collect high-quality data on family health, including immunisation coverage among children, prevalence and treatment of diarrhoea and other diseases among children under age 5 years, and maternity care indicators (including antenatal visits, assistance at delivery, and postnatal care);
• collect data on infant and child mortality;
• obtain data on child feeding practices, including breastfeeding, and collect ‘observation’ information to use in assessing the nutritional status of women and children;
• collect data on knowledge and attitudes of women and men about sexually transmitted infections (STIs), HIV and AIDS, and evaluate patterns of recent behaviour regarding condom use; and
• collect data on knowledge and attitudes of women and men about tuberculosis.

Geographic coverage

National coverage.

Analysis unit

• Household,
• Individual.

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, and all men aged between 15-49 years.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary focus of the 2009 Kiribati Demographic Health Survey (DHS) was to provide estimates of key population and health indicators, including fertility and mortality rates, for the country as a whole, for the urban area and rural areas (separately) - urban is South Tarawa and urban settlement on Kiritimati Island while the rest of Kiribati is defined as rural areas. The survey used the sampling frame provided by the list of census enumeration areas, with population and household information coming from the 2005 Kiribati Population and Housing Census.

The survey was designed to obtain completed interviews of 2,193 women aged 15-49. In addition, males aged 15-59 in every second household were interviewed. To take non-response into account, 1,480 households countrywide were selected: 640 in the urban area and 840 in rural areas.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were administered during the 2009 Kiribati Demographic Health Survey (KDHS): a Household questionnaire, a Women’s questionnaire and a Men’s questionnaire. These were adapted to reflect population and health issues relevant to Kiribati, and were presented at a series of meetings with various stakeholders, including government ministries and agencies, NGOs and international donors. The final draft of each questionnaire was discussed at a questionnaire design workshop organised by Kiribati National Statistics Office (KNSO) in March 2009 in Tarawa. Survey questionnaires were then translated into the local language (I-Kiribati) and pretested from 7–19 August 2009.

The Household questionnaire was used to list all the usual members and visitors in selected households, and to identify women and men who were eligible for the individual interview. Some basic information was collected on the characteristics of each person listed, including age, sex, education and relationship to the head of the household. For children under age 18 years, the survival status of their parents was ascertained. The Household questionnaire also collected information on characteristics of each household’s dwelling unit, such as source of drinking water, type of toilet facility, material used for the floor, and ownership of various durable goods.

The Women’s questionnaire collected information from all women aged 15–49 about: - education, residential history and media exposure; - pregnancy history and childhood mortality; - knowledge and use of family planning methods; - fertility preferences; - antenatal, delivery and postnatal care; - breastfeeding and infant feeding practices; - immunisation and childhood illnesses; - marriage and sexual activity; - their own work and their husband’s background characteristics; and - awareness and behaviour regarding HIV and other STIs.

The Men’s questionnaire was administered to all men aged 15–49 living in every second household. It collected much of the same information as the women’s questionnaire, but was shorter because it did not contain questions about reproductive history or maternal and child health or nutrition.

Cleaning operations

Processing the 2009 Kiribati Demographic Health Survey (KDHS) results began three weeks after the start of fieldwork. Completed questionnaires were returned periodically from the field to the Kiribati National Statistics Office (KNSO) data processing center in South Tarawa, where the data were entered and edited by seven data processing personnel specially trained for this task. Data processing personnel were supervised by KNSO staff. Data entry and editing of questionnaires was completed by 30 March 30 2010. CSPRo was used for data processing.

Response rate

In total, 1,477 households were selected for the sample, of which 1,451 were found to be occupied during data collection. Of these existing households, 1,422 were successfully interviewed, giving a household response rate of 98%.

In households, 2,193 women were identified as being eligible for the individual interview. Interviews were completed with 1,978 women, yielding a response rate of 90%. Of the 1,337 eligible men identified in the selected sub-sample of households, 85% were successfully interviewed. Response rates were higher in rural areas than in the urban area, with the rural–urban difference in response rates being the greatest among eligible men.

Sampling error estimates

The sample of respondents selected in the 2009 Kiribati Demographic Health Survey (KDHS) is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling errors are the errors that result from taking a sample of the covered population through a particular sample design. Non-sampling errors are systematic errors that would be present even if the entire population was covered (e.g. response errors, coding and data entry errors, etc.).

For the entire covered population and for large subgroups, the KDHS sample is generally sufficiently large to provide reliable estimates. For such populations the sampling error is small and less important than the non-sampling error. However, for small subgroups, sampling errors become very important in providing an objective measure of reliability of the data.

Sampling errors will be displayed for total, urban and rural and each sample domain only. No other panels should be included in the sampling error table. The choice of variables for which sampling error computations will be done depends on the priority given to specific variables. However, it is recommended that sampling errors be calculated for at least the following variables, which was not case with Kiribati given the smallness of the sample compared to other countries in the Pacific.

Sampling errors are usually measured in terms of the standard error for 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 the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected by simple random sampling, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2009 KDHS sample was the result of a multistage stratified design, and, consequently, it is necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2009 KDHS is the Integrated Sample Survey Analysis (ISSA) Sampling Error Module. This module uses the Taylor linearisation method of variance estimation for survey estimates that are means or proportions. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

In addition to the standard error, ISSA

Abstract

The 1993 Turkish Demographic and Health Survey (TDHS) is a nationally representative survey of ever-married women less than 50 years old. The survey was designed to provide information on fertility levels and trends, infant and child mortality, family planning, and maternal and child health. The TDHS was conducted by the Hacettepe University Institute of Population Studies under a subcontract through an agreement between the General Directorate of Mother and Child Health and Family Planning, Ministry of Health and Macro International Inc. of Calverton, Maryland. Fieldwork was conducted from August to October 1993. Interviews were carried out in 8,619 households and with 6,519 women.

The Turkish Demographic and Health Survey (TDHS) is a national sample survey of ever-married women of reproductive ages, designed to collect data on fertility, marriage patterns, family planning, early age mortality, socioeconomic characteristics, breastfeeding, immunisation of children, treatment of children during episodes of illness, and nutritional status of women and children. The TDHS, as part of the international DHS project, is also the latest survey in a series of national-level population and health surveys in Turkey, which have been conducted by the Institute of Population Studies, Haeettepe University (HIPS).

More specifically, the objectives of the TDHS are to:

Collect data at the national level that will allow the calculation of demographic rates, particularly fertility and childhood mortality rates; Analyse the direct and indirect factors that determine levels and trends in fertility and childhood mortality; Measure the level of contraceptive knowledge and practice by method, region, and urban- rural residence; Collect data on mother and child health, including immunisations, prevalence and treatment of diarrhoea, acute respiratory infections among children under five, antenatal care, assistance at delivery, and breastfeeding; Measure the nutritional status of children under five and of their mothers using anthropometric measurements.

The TDHS information is intended to assist policy makers and administrators in evaluating existing programs and in designing new strategies for improving family planning and health services in Turkey.

MAIN RESULTS

Fertility in Turkey is continuing to decline. If Turkish women maintain current fertility rates during their reproductive years, they can expect to have all average of 2.7 children by the end of their reproductive years. The highest fertility rate is observed for the age group 20-24. There are marked regional differences in fertility rates, ranging from 4.4 children per woman in the East to 2.0 children per woman in the West. Fertility also varies widely by urban-rural residence and by education level. A woman living in rural areas will have almost one child more than a woman living in an urban area. Women who have no education have almost one child more than women who have a primary-level education and 2.5 children more than women with secondary-level education.

The first requirement of success ill family planning is the knowledge of family planning methods. Knowledge of any method is almost universal among Turkish women and almost all those who know a method also know the source of the method. Eighty percent of currently married women have used a method sometime in their life. One third of currently married women report ever using the IUD. Overall, 63 percent of currently married women are currently using a method. The majority of these women are modern method users (35 percent), but a very substantial proportion use traditional methods (28 percent). the IUD is the most commonly used modern method (I 9 percent), allowed by the condom (7 percent) and the pill (5 percent). Regional differences are substantial. The level of current use is 42 percent in tile East, 72 percent in tile West and more than 60 percent in tile other three regions. "File common complaints about tile methods are side effects and health concerns; these are especially prevalent for the pill and IUD.

One of the major child health indicators is immunisation coverage. Among children age 12-23 months, the coverage rates for BCG and the first two doses of DPT and polio were about 90 percent, with most of the children receiving those vaccines before age one. The results indicate that 65 percent of the children had received all vaccinations at some time before the survey. On a regional basis, coverage is significantly lower in the Eastern region (41 percent), followed by the Northern and Central regions (61 percent and 65 percent, respectively). Acute respiratory infections (ARI) and diarrhea are the two most prevalent diseases of children under age five in Turkey. In the two weeks preceding the survey, the prevalence of ARI was 12 percent and the prevalence of diarrhea was 25 percent for children under age five. Among children with diarrhea 56 percent were given more fluids than usual.

Breastfeeding in Turkey is widespread. Almost all Turkish children (95 percent) are breastfed for some period of time. The median duration of breastfeeding is 12 months, but supplementary foods and liquids are introduced at an early age. One-third of children are being given supplementary food as early as one month of age and by the age of 2-3 months, half of the children are already being given supplementary foods or liquids.

By age five, almost one-filth of children arc stunted (short for their age), compared to an international reference population. Stunting is more prevalent in rural areas, in the East, among children of mothers with little or no education, among children who are of higher birth order, and among those born less than 24 months after a prior birth. Overall, wasting is not a problem. Two percent of children are wasted (thin for their height), and I I percent of children under five are underweight for their age. The survey results show that obesity is d problem among mothers. According to Body Mass Index (BMI) calculations, 51 percent of mothers are overweight, of which 19 percent are obese.

Geographic coverage

The Turkish Demographic and Health Survey (TDHS) is a national sample survey.

Analysis unit

• Household
• Women age 12-49
• Children under five

Universe

The population covered by the 1993 DHS is defined as the universe of all ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Kind of data

Sample survey data

Sampling procedure

The sample for the TDHS was designed to provide estimates of population and health indicators, including fertility and mortality rates for the nation as a whole, fOr urban and rural areas, and for the five major regions of the country. A weighted, multistage, stratified cluster sampling approach was used in the selection of the TDHS sample.

Sample selection was undertaken in three stages. The sampling units at the first stage were settlements that differed in population size. The frame for the selection of the primary sampling units (PSUs) was prepared using the results of the 1990 Population Census. The urban frame included provinces and district centres and settlements with populations of more than 10,000; the rural frame included subdistricts and villages with populations of less than 10,000. Adjustments were made to consider the growth in some areas right up to survey time. In addition to the rural-urban and regional stratifications, settlements were classified in seven groups according to population size.

The second stage of selection involved the list of quarters (administrative divisions of varying size) for each urban settlement, provided by the State Institute of Statistics (SIS). Every selected quarter was subdivided according tothe number of divisions(approximately 100 households)assigned to it. In rural areas, a selected village was taken as a single quarter, and wherever necessary, it was divided into subdivisions of approximately 100 households. In cases where the number of households in a selected village was less than 100 households, the nearest village was selected to complete the 100 households during the listing activity, which is described below.

After the selection of the secondary sampling units (SSUs), a household listing was obtained for each by the TDHS listing teams. The listing activity was carried out in May and June. From the household lists, a systematic random sample of households was chosen for the TDHS. All ever-married women age 12-49 who were present in the household on the night before the interview were eligible for the survey.

Mode of data collection

Face-to-face

Research instrument

Two questionnaires were used in the main fieldwork for the TDHS: the Household Questionnaire and the Individual Questionnaire for ever-married women of reproductive age. The questionnaires were based on the model survey instruments developed in the DHS program and on the questionnaires that had been employed in previous Turkish population and health surveys. The questionnaires were adapted to obtain data needed for program planning in Turkey during consultations with population and health agencies. Both questionnaires were developed in English and translated into Turkish.

a) The Household Questionnaire was used to enumerate all usual members of and visitors to the selected households and to collect information relating to the socioeconomic position of the households. In the first part of the Household Questionnaire, basic information was collected on the age, sex, educational attainment, marital status and relationship to the head of household for each person listed as a household member

In 2020, the average age in Thailand is expected to reach 38.2 years, twenty years higher than in 1980, when it began to rise after a steady decrease in prior years. Previously, from 1950 to 1975, the average age hovered around 17 years. The increased average age corresponds with rising life expectancy globally, accelerating especially around the mid-twentieth century onward. In this century, the life expectancy in Thailand has increased by roughly 2.5 years since 2007, reaching 78.39 in 2017. The standard of living is increasing In Thailand, people ages 15 to 64 have consistently made up the majority of the population from 2007 to 2017. In this time, the older population grew increased by about three percent, while the younger population shrunk at roughly the same rate. This indicates that many people within the middle age category are reaching 65 or older, and that the birth rate is simultaneously declining. Birth rates are declining Every year, families are having fewer children in Thailand, with a fertility rate of less than 1.5 children per women of childbearing age in 2017. This is not necessarily a bad sign – it points towards increasing healthcare and living standards. Another indicator for this is the decreasing infant mortality in Thailand, meaning more of the children born each year survive. Lower infant mortality also contributes to the calculations of a higher life expectancy, and thus affects the country’s median age.