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.

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

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

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

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

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

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

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

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

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

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

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

Mode of data collection

Face-to-face

Research instrument

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

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

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

Response rate

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

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

Sampling error estimates

The results from sample surveys are affected by two types of errors, non-sampling error and sampling error. Nonsampling error is due to mistakes made in carrying out field activities, such as failure to locate and interview the correct household, errors in the way the questions are asked, misunderstanding on the part of either the interviewer or the respondent, data entry errors, etc. Although efforts were made during the design and implementation of the JPFHS to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically

Sampling errors, on the other hand, can be measured statistically. The sample of women selected in the JPFHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each one would have yielded results that differed somewhat from the actual sample selected. The sampling error is a measure of the variability between all possible samples; although it is not known exactly, it can be estimated from the survey results.

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

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

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

Data appraisal

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

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

The number of babies born with no sign of life at 28 weeks or more of gestation, per 1,000 total births. The data includes a range of values from 2000 to 2019 for the toal number of babies born. This data is sourced from the UN Inter-Agency Group for Child Mortality Estimation. When calculating the stillbirth rate, the UN IGME uses a model to identify global and national trends and compare between countries. In cases where counts of stillbirths and live births are available, the stillbirth rate is calculated as the number of stillbirths divided by the sum of the number of live births and number of stillbirths. For household surveys, stillbirth data are obtained from full pregnancy histories (PH) or reproductive calendars (RC). In the PH, women are asked to provide information on the duration of all lifetime pregnancies, the outcome of the pregnancy (e.g. miscarriage, stillbirth or live birth) and the date of birth or end of pregnancy. In the RC, women are asked about the duration and month of pregnancy end for pregnancies that did not end in a live birth in the last 60 months.The estimates are based on high quality nationally representative data including statistics from civil registration systems, medical birth and death registries, results from household surveys, population studies and censuses.The stillbirth rate estimates are produced in conjunction with national level agencies such as a country’s Ministry of Health, National Statistics Office, or other relevant agencies.

Abstract

The 2016 Timor-Leste Demographic and Health Survey (TLDHS) was implemented by the General Directorate of Statistics (GDS) of the Ministry of Finance in collaboration with the Ministry of Health (MOH). Data collection took place from 16 September to 22 December, 2016.

The primary objective of the 2016 TLDHS project is to provide up-to-date estimates of basic demographic and health indicators. The TLDHS provides a comprehensive overview of population, maternal, and child health issues in Timor-Leste. More specifically, the 2016 TLDHS: • Collected data at the national level, which allows the calculation of key demographic indicators, particularly fertility, and child, adult, and maternal mortality rates • Provided data to explore the direct and indirect factors that determine the levels and trends of fertility and child mortality • Measured the levels of contraceptive knowledge and practice • Obtained data on key aspects of maternal and child health, including immunization coverage, prevalence and treatment of diarrhea and other diseases among children under age 5, and maternity care, including antenatal visits and assistance at delivery • Obtained data on child feeding practices, including breastfeeding, and collected anthropometric measures to assess nutritional status in children, women, and men • Tested for anemia in children, women, and men • Collected data on the knowledge and attitudes of women and men about sexually-transmitted diseases and HIV/AIDS, potential exposure to the risk of HIV infection (risk behaviors and condom use), and coverage of HIV testing and counseling • Measured key education indicators, including school attendance ratios, level of educational attainment, and literacy levels • Collected information on the extent of disability • Collected information on non-communicable diseases • Collected information on early childhood development • Collected information on domestic violence • The information collected through the 2016 TLDHS is intended to assist policy makers and program managers in evaluating and designing programs and strategies for improving the health of the country’s population.

Geographic coverage

National

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-59 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the TLDHS 2016 survey is the 2015 Timor-Leste Population and Housing Census (TLPHC 2015), provided by the General Directorate of Statistics. The sampling frame is a complete list of 2320 non-empty Enumeration Areas (EAs) created for the 2015 population census. An EA is a geographic area made up of a convenient number of dwelling units which served as counting units for the census, with an average size of 89 households per EA. The sampling frame contains information about the administrative unit, the type of residence, the number of residential households and the number of male and female population for each of the EAs. Among the 2320 EAs, 413 are urban residence and 1907 are rural residence.

There are five geographic regions in Timor-Leste, and these are subdivided into 12 municipalities and special administrative region (SAR) of Oecussi. The 2016 TLDHS sample was designed to produce reliable estimates of indicators for the country as a whole, for urban and rural areas, and for each of the 13 municipalities. A representative probability sample of approximately 12,000 households was drawn; the sample was stratified and selected in two stages. In the first stage, 455 EAs were selected with probability proportional to EA size from the 2015 TLPHC: 129 EAs in urban areas and 326 EAs in rural areas. In the second stage, 26 households were randomly selected within each of the 455 EAs; the sampling frame for this household selection was the 2015 TLPHC household listing available from the census database.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Four questionnaires were used for the 2016 TLDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. These questionnaires, based on The DHS Program’s standard Demographic and Health Survey questionnaires, were adapted to reflect the population and health issues relevant to Timor-Leste.

Cleaning operations

The data processing operation included registering and checking for inconsistencies, incompleteness, and outliers. Data editing and cleaning included structure and consistency checks to ensure completeness of work in the field. The central office also conducted secondary editing, which required resolution of computer-identified inconsistencies and coding of open-ended questions. The data were processed by two staff who took part in the main fieldwork training. Data editing was accomplished with CSPro software. Secondary editing and data processing were initiated in October 2016 and completed in February 2017.

Response rate

A total of 11,829 households were selected for the sample, of which 11,660 were occupied. Of the occupied households, 11,502 were successfully interviewed, which yielded a response rate of 99 percent.

In the interviewed households, 12,998 eligible women were identified for individual interviews. Interviews were completed with 12,607 women, yielding a response rate of 97 percent. In the subsample of households selected for the men’s interviews, 4,878 eligible men were identified and 4,622 were successfully interviewed, yielding a response rate of 95 percent. Response rates were higher in rural than in urban areas, with the difference being more pronounced among men (97 percent versus 90 percent, respectively) than among women (98 percent versus 94 percent, respectively). The lower response rates for men were likely due to their more frequent and longer absences from the household.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: non-sampling errors and sampling errors. Non-sampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the TLDHS 2016 to minimize this type of error, non-sampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the TLDHS 2016 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.

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, the TLDHS 2016 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the TLDHS 2016 is a SAS program. This program used the Taylor linearization method of variance estimation for survey estimates that are means, proportions or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Height and weight data completeness and quality for children - Completeness of information on siblings - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

Monaco is the country with the highest median age in the world. The population has a median age of around 57 years, which is around six years more than in Japan and Saint Pierre and Miquelon – the other countries that make up the top three. Southern European countries make up a large part of the top 20, with Italy, Slovenia, Greece, San Marino, Andorra, and Croatia all making the list. Low infant mortality means higher life expectancy Monaco and Japan also have the lowest infant mortality rates in the world, which contributes to the calculation of a higher life expectancy because fewer people are dying in the first years of life. Indeed, many of the nations with a high median age also feature on the list of countries with the highest average life expectancy, such as San Marino, Japan, Italy, and Lichtenstein. Demographics of islands and small countries Many smaller countries and island nations have populations with a high median age, such as Guernsey and the Isle of Man, which are both island territories within the British Isles. An explanation for this could be that younger people leave to seek work or education opportunities, while others choose to relocate there for retirement.

Abstract

The 2019-20 Gambia Demographic and Health Survey (2019-20 GDHS) is a nationwide survey with a nationally representative sample of residential households. The survey was implemented by The Gambia Bureau of Statistics (GBoS) in collaboration with the Ministry of Health (MoH).

The primary objective of the 2019-20 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the 2019-20 GDHS: ▪ collected data on fertility levels and preferences; contraceptive use; maternal and child health; infant, child, and neonatal mortality levels; maternal mortality; gender; nutrition; awareness about HIV/AIDS; self-reported sexually transmitted infections (STIs); and other health issues relevant to the achievement of the Sustainable Development Goals (SDGs) ▪ obtained information on the availability of, access to, and use of mosquito nets as part of the National Malaria Control Programme ▪ gathered information on other health issues such as injections, tobacco use, hypertension, diabetes, and health insurance ▪ collected data on women’s empowerment, domestic violence, fistula, and female genital mutilation/cutting ▪ tested household salt for the presence of iodine ▪ obtained data on child feeding practices, including breastfeeding, and conducted anthropometric measurements to assess the nutritional status of children under age 5 and women age 15-49 ▪ conducted anaemia testing of women age 15-49 and children age 6-59 months ▪ conducted malaria testing of children age 6-59 months

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15 to 59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, all men age 15-59, and all children aged 0-5 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2019-20 GDHS was based on an updated version of the 2013 Gambia Population and Housing Census (2013 GPHC) conducted by GBoS. The census counts were updated in 2015-16 based on district-level projected counts from the 2015-16 Integrated Household Survey (IHS). Administratively, The Gambia is divided into eight Local Government Areas (LGAs). Each LGA is subdivided into districts and each district is subdivided into settlements. A settlement, a group of small settlements, or a part of a large settlement can form an enumeration area (EA). These units allow the country to be easily separated into small geographical area units, each with an urban or rural designation. There are 48 districts, 120 wards, and 4,098 EAs in The Gambia; the EAs have an average size of 68 households.

The sample for the 2019-20 GDHS was a stratified sample selected in two stages. In the first stage, EAs were selected with a probability proportional to their size within each sampling stratum. A total of 281 EAs were selected.

In the second stage, the households were systematically sampled. A household listing operation was undertaken in all of the selected clusters. The resulting lists of households served as the sampling frame from which a fixed number of 25 households were systematically selected per cluster, resulting in a total sample size of 7,025 selected households. Results from this sample are representative at the national, urban, and rural levels and at the LGA levels.

For further details on sample selection, see Appendix A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Five questionnaires were used for the 2019-20 GDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, and the Fieldworker Questionnaire. These questionnaires, based on The DHS Program’s standard questionnaires, were adapted to reflect the population and health issues relevant to The Gambia. Suggestions were solicited from various stakeholders representing government ministries, departments, and agencies; nongovernmental organisations; and international donors. All questionnaires were written in English, and interviewers translated the questions into the appropriate local language to carry out the interview.

Cleaning operations

All electronic data files were transferred via the Internet File Streaming System (IFSS) to the GBoS central office. The IFSS automatically encrypts the data and sends the data to a server, and the server in turn downloads the data to the data processing supervisor’s password-protected computer in the central office. The data processing operation included secondary editing, which required resolution of computeridentified inconsistencies and coding of open-ended questions. The data were processed by two IT specialists and three secondary editors who took part in the main fieldwork training; they were supervised remotely by staff from The DHS Program. Data editing was accomplished using CSPro software. During the fieldwork, field-check tables were generated to check various data quality parameters, and specific feedback was given to the teams to improve performance. Secondary editing and data processing were initiated in November 2019 and completed in May 2020.

Response rate

All 6,985 households in the selected housing units were eligible for the survey, of which 6,736 were occupied. Of the occupied households, 6,549 were successfully interviewed, yielding a response rate of 97%. Among the households successfully interviewed, 1,948 interviews were completed in 2019 and 4,601 in 2020.

In the interviewed households, 12,481 women age 15-49 were identified for individual interviews; interviews were completed with 11,865 women, yielding a response rate of 95%, a 4 percentage point increase from the 2013 GDHS. Among men, 5,337 were eligible for individual interviews, and 4,636 completed an interview; this yielded a response rate of 87%, a 5 percentage point increase from the previous survey.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2019-20 Gambia Demographic and Health Survey (GDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2019-20 GDHS 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 among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

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% 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, the 2019-20 GDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

Note: A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Completeness of reporting
• Births by calendar years
• Reporting of age at death in days
• Reporting of age at death in months
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Number of enumeration areas completed by month, according to Local Government Area, The Gambia DHS 2019-20
• Percentage of children age 6-59 months classified as having malaria according to RDT, by month and Local Government Area, The Gambia DHS 2019-20
• Completeness of information on siblings
• Sibship size and sex ratio of siblings

See details of the data quality tables in Appendix C of the final report.

Abstract

The 2022 Philippines National Demographic and Health Survey (NDHS) was implemented by the Philippine Statistics Authority (PSA). Data collection took place from May 2 to June 22, 2022.

The primary objective of the 2022 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, fertility preferences, family planning practices, childhood mortality, maternal and child health, nutrition, knowledge and attitudes regarding HIV/AIDS, violence against women, child discipline, early childhood development, and other health issues.

The information collected through the NDHS is intended to assist policymakers and program managers in designing and evaluating programs and strategies for improving the health of the country’s population. The 2022 NDHS also provides indicators anchored to the attainment of the Sustainable Development Goals (SDGs) and the new Philippine Development Plan for 2023 to 2028.

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the 2022 NDHS was based on a two-stage stratified sample design using the Master Sample Frame (MSF) designed and compiled by the PSA. The MSF was constructed based on the listing of households from the 2010 Census of Population and Housing and updated based on the listing of households from the 2015 Census of Population. The first stage involved a systematic selection of 1,247 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 22 or 29 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the preselected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

All women age 15–49 who were either usual residents of the selected households or visitors who stayed in the households the night before the survey were eligible to be interviewed. Among women eligible for an individual interview, one woman per household was selected for a module on women’s safety.

For further details on sample design, see APPENDIX A of the final report.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Two questionnaires were used for the 2022 NDHS: the Household Questionnaire and the Woman’s Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, academe, and international agencies. The survey protocol was reviewed by the ICF Institutional Review Board.

After all questionnaires were finalized in English, they were translated into six major languages: Tagalog, Cebuano, Ilocano, Bikol, Hiligaynon, and Waray. The Household and Woman’s Questionnaires were programmed into tablet computers to allow for computer-assisted personal interviewing (CAPI) for data collection purposes, with the capability to choose any of the languages for each questionnaire.

Cleaning operations

Processing the 2022 NDHS data began almost as soon as fieldwork started, and data security procedures were in place in accordance with confidentiality of information as provided by Philippine laws. As data collection was completed in each PSU or cluster, all electronic data files were transferred securely via SyncCloud to a server maintained by the PSA Central Office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the area of assignment. Timely generation of field check tables allowed for effective monitoring of fieldwork, including tracking questionnaire completion rates. Only the field teams, project managers, and NDHS supervisors in the provincial, regional, and central offices were given access to the CAPI system and the SyncCloud server.

A team of secondary editors in the PSA Central Office carried out secondary editing, which involved resolving inconsistencies and recoding “other” responses; the former was conducted during data collection, and the latter was conducted following the completion of the fieldwork. Data editing was performed using the CSPro software package. The secondary editing of the data was completed in August 2022. The final cleaning of the data set was carried out by data processing specialists from The DHS Program in September 2022.

Response rate

A total of 35,470 households were selected for the 2022 NDHS sample, of which 30,621 were found to be occupied. Of the occupied households, 30,372 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 28,379 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 27,821 women, yielding a response rate of 98%.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and in data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2022 Philippines National Demographic and Health Survey (2022 NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 NDHS is only one of many samples that could have been selected from the same population, using the same design and identical 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.

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% 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, the 2022 NDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in APPENDIX B of the survey report.

Data appraisal

Data Quality Tables

• Household age distribution
• Age distribution of eligible and interviewed women
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Population pyramid
• Five-year mortality rates

See details of the data quality tables in Appendix C of the final report.

Abstract

The primary objective of the 2016 Nepal Demographic and Health Survey (NDHS) is to provide up-to-date estimates of basic demographic and health indicators. The NDHS provides a comprehensive overview of population, maternal, and child health issues in Nepal. Specifically, the 2016 NDHS: - Collected data that allowed calculation of key demographic indicators, particularly fertility and under-5 mortality rates, at the national level, for urban and rural areas, and for the country’s seven provinces - Collected data that allowed for calculation of adult and maternal mortality rates at the national level - Explored the direct and indirect factors that determine levels and trends of fertility and child mortality - Measured levels of contraceptive knowledge and practice - Collected data on key aspects of family health, including immunization coverage among children, prevalence and treatment of diarrhea and other diseases among children under age 5, maternity care indicators such as antenatal visits and assistance at delivery, and newborn care - Obtained data on child feeding practices, including breastfeeding - Collected anthropometric measures to assess the nutritional status of children under age 5 and women and men age 15-49 - Conducted hemoglobin testing on eligible children age 6-59 months and women age 15-49 to provide information on the prevalence of anemia in these groups - Collected data on knowledge and attitudes of women and men about sexually transmitted diseases and HIV/AIDS and evaluated potential exposure to the risk of HIV infection by exploring high-risk behaviors and condom use - Measured blood pressure among women and men age 15 and above - Obtained data on women’s experience of emotional, physical, and sexual violence

The information collected through the 2016 NDHS is intended to assist policymakers and program managers in the Ministry of Health and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population. The 2016 NDHS also provides data on indicators relevant to the Nepal Health Sector Strategy (NHSS) 2016-2021 and the Sustainable Development Goals (SDGs).

Geographic coverage

National coverage

Analysis unit

• Household
• Individual
• Children age 0-5
• Woman age 15-49
• Man age 15-49

Universe

The survey covered all de jure household members (usual residents), women age 15-49 years and men age 15-49 years resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2016 NDHS is an updated version of the frame from the 2011 National Population and Housing Census (NPHC), conducted by the Central Bureau of Statistics (CBS).

The sampling frame contains information about ward location, type of residence (urban or rural), estimated number of residential households, and estimated population. In rural areas, the wards are small in size (average of 104 households) and serve as the primary sampling units (PSUs). In urban areas, the wards are large, with average of 800 households per ward. The CBS has a frame of enumeration areas (EAs) for each ward in the original 58 municipalities. However, for the 159 municipalities declared in 2014 and 2015, each municipality is composed of old wards, which are small in size and can serve as EAs.

The 2016 NDHS sample was stratified and selected in two stages in rural areas and three stages in urban areas. In rural areas, wards were selected as primary sampling units, and households were selected from the sample PSUs. In urban areas, wards were selected as PSUs, one EA was selected from each PSU, and then households were selected from the sample EAs.

For further details on sample design, see Appendix A of the final report.

Mode of data collection

Face-to-face [f2f]

Research instrument

Six questionnaires were administered in the 2016 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, the Biomarker Questionnaire, the Fieldworker Questionnaire, and the Verbal Autopsy Questionnaire (for neonatal deaths). The first five questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to Nepal. The Verbal Autopsy Questionnaire was based on the recent 2014 World Health Organization (WHO) verbal autopsy instruments (WHO 2015a).

Cleaning operations

The processing of the 2016 NDHS data began simultaneously with the fieldwork. As soon as data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the New ERA central office in Kathmandu. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The biomarker paper questionnaires were compared with the electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The secondary editing of the data was completed in the second week of February 2017. The final cleaning of the data set was carried out by The DHS Program data processing specialist and was completed by the end of February 2017.

Response rate

A total of 11,473 households were selected for the sample, of which 11,203 were occupied. Of the occupied households, 11,040 were successfully interviewed, yielding a response rate of 99%.

In the interviewed households, 13,089 women age 15-49 were identified for individual interviews; interviews were completed with 12,862 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 4,235 men age 15-49 were identified and 4,063 were successfully interviewed, yielding a response rate of 96%.

Response rates were lower in urban areas than in rural areas. The difference was slightly more prominent for men than for women, as men in urban areas were often away from their households for work.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Non-sampling errors result from mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2016 Nepal DHS (NDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2016 NDHS 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 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, the 2016 NDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in either ISSA or SAS, using programs developed by ICF. These programs use the Taylor linearization method of variance estimation for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

A more detailed description of estimates of sampling errors are presented in Appendix B of the survey final report.

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Sibling size and sex ratio of siblings - Pregnancy-related mortality trends

See details of the data quality tables in Appendix C of the survey final report.

Abstract

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 major aim of the 2002 EDHS was to provide up-to-date information on: fertility and childhood mortality levels, fertility preferences, awareness and use of family planning methods, use of maternal and child health services, breastfeeding practices, nutritional status of mothers and young children, and awareness and behavior regarding HIV/AIDS and other sexually transmitted infections. It was designed as a follow-on to the 1995 EDHS survey. However, compared with the 1995 survey, the 2002 EDHS is significantly expanded in scope and coverage.

More specifically, the 2002 EDHS survey was designed to: - Collect data at the national level that allow the calculation of demographic rates, particularly fertility and childhood mortality rates; - Assess the health status of mothers and children under age five in Eritrea, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, use of immunization services, and malaria prevention activities; - Measure the levels and patterns of knowledge and behavior of women about sexually transmitted infections, HIV/AIDS, and female circumcision; - Provide information on changes in fertility and contraceptive prevalence and the factors that have contributed to these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding practices, and other important socioeconomic factors; and - Assess gender issues.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The sample for the 2002 EDHS survey is a nationally representative sample of households and is self-weighted in each of the six zobas but not proportionally distributed among the zobas. The sample was designed using information provided by the Ministry of Local Government on the total number of households in various administrative units, mainly villages (in rural areas) and towns (in urban areas). It is a two-stage cluster design in rural areas and a three-stage cluster design in urban areas.

A national sample of 368 clusters was selected, with 249 in rural areas and 119 in urban areas. A complete household listing operation was carried out in all the selected clusters to provide a frame for the final systematic selection of households. Twenty-five households were selected from each cluster in urban and rural areas in all zobas except one. In zoba Debubawi Keih Bahri, 40 households were selected in each cluster because this zoba contains less than 4 percent of the national population, and has transportation problems, so it was decided to select fewer, larger clusters in this zoba.

Around 9,800 households were selected from the 368 clusters to provide an expected sample of 8,500 eligible women.

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

Mode of data collection

Face-to-face

Research instrument

Two kinds of questionnaires were used in the 2002 EDHS survey: the Household Questionnaire and the Women’s Questionnaire. The contents of the questionnaires were based on the MEASURE DHS+ Model “B”, which was developed for countries with low levels of contraceptive use. The NSEO held several meetings with experts and professionals from partner ministries, most importantly the Ministry of Health, to discuss the questionnaires. The MOH, the Ministry of Education, the Ministry of Labor and Human Welfare, and other concerned institutions in Eritrea actively participated in reviewing and modifying the questionnaires to address Eritrean concerns. Both questionnaires, which were originally prepared in English, were translated into and printed in seven local languages: Tigrigna, Tigre, Bilen, Saho, Afar, Kunama, and Nara. A pretest of the questionnaires was conducted in December 2002.

The Household Questionnaire was used to list all of the usual members and visitors who spent the night before the interview in the selected households. Basic background information on each listed person was collected, including age, sex, marital status, educational level attained, occupation, and relationship to the head of the household. The information on age was used to identify women eligible for the individual interview and children less than five years of age whose height and weight would be measured. The Household Questionnaire also obtained information on selected socioeconomic indicators such as number of rooms in the dwelling, type of floor material, source of drinking water, type of toilet facilities, and ownership of various durable goods. Information on the household’s possession of mosquito nets was collected, and a test was conducted by interviewers to assess whether the household used cooking salt fortified with iodine.

The Women’s Questionnaire was used to collect information from all women age 15-49. Respondents were asked questions on the following topics: background characteristics; reproductive history; contraceptive knowledge and use; antenatal, delivery and postnatal care; infant feeding practices; child immunization, health and nutrition; marriage and sexual activity; and fertility preferences. In addition, respondents were asked questions about their husband’s background characteristics. Data on female circumcision and on knowledge, attitudes and behavior related to HIV/AIDS and other sexually transmitted infections were collected.

Response rate

A total of 9,824 households were selected in the sample, of which 9,512 households were occupied. Of the total occupied households, 9,389 were interviewed successfully, giving a household response rate of 99 percent. In general, response rates for households were not influenced by urban-rural residence.

From the interviewed households, 9,096 women eligible were identified for the individual interview, of whom 8,754 were successfully interviewed. The women’s response rate for the 2002 EDHS was 96 percent. Nonresponse among women was mainly due to the absence of women at home at the time of interview, despite repeated visits to the household. The women’s response rate is higher in rural areas than in urban areas.

Note: See summarized response rates by place of residence in Table 1.1 and Table 1.2 of the survey report.

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2002 EDHS to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2002 EDHS 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.

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, the 2002 EDHS sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 2002 EDHS is the ISSA Sampling Error Module (ISSAS). This module used 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.