This statistic shows the number of people aged 100 and over (centenarians) in the United States from 2016 to 2060. In 2016, there were 82,000 centenarians in the United States. This figure is expected to increase to 589,000 in the year 2060.

As of 2023, 98 percent of the Americans between 30-49 years and 97 percent of 18-29 years used the internet at least occasionally. The United States have made large gains in online adoption over the last decade - in 2009, only 76 percent of U.S. adults were online users. This share increased to 95 percent of the adult population in 2023.

Abstract

The second National Family Health Survey (NFHS-2), conducted in 1998-99, provides information on fertility, mortality, family planning, and important aspects of nutrition, health, and health care. The International Institute for Population Sciences (IIPS) coordinated the survey, which collected information from a nationally representative sample of more than 90,000 ever-married women age 15-49. The NFHS-2 sample covers 99 percent of India's population living in all 26 states. This report is based on the survey data for 25 of the 26 states, however, since data collection in Tripura was delayed due to local problems in the state.

IIPS also coordinated the first National Family Health Survey (NFHS-1) in 1992-93. Most of the types of information collected in NFHS-2 were also collected in the earlier survey, making it possible to identify trends over the intervening period of six and one-half years. In addition, the NFHS-2 questionnaire covered a number of new or expanded topics with important policy implications, such as reproductive health, women's autonomy, domestic violence, women's nutrition, anaemia, and salt iodization.

The NFHS-2 survey was carried out in two phases. Ten states were surveyed in the first phase which began in November 1998 and the remaining states (except Tripura) were surveyed in the second phase which began in March 1999. The field staff collected information from 91,196 households in these 25 states and interviewed 89,199 eligible women in these households. In addition, the survey collected information on 32,393 children born in the three years preceding the survey. One health investigator on each survey team measured the height and weight of eligible women and children and took blood samples to assess the prevalence of anaemia.

SUMMARY OF FINDINGS

POPULATION CHARACTERISTICS

Three-quarters (73 percent) of the population lives in rural areas. The age distribution is typical of populations that have recently experienced a fertility decline, with relatively low proportions in the younger and older age groups. Thirty-six percent of the population is below age 15, and 5 percent is age 65 and above. The sex ratio is 957 females for every 1,000 males in rural areas but only 928 females for every 1,000 males in urban areas, suggesting that more men than women have migrated to urban areas.

The survey provides a variety of demographic and socioeconomic background information. In the country as a whole, 82 percent of household heads are Hindu, 12 percent are Muslim, 3 percent are Christian, and 2 percent are Sikh. Muslims live disproportionately in urban areas, where they comprise 15 percent of household heads. Nineteen percent of household heads belong to scheduled castes, 9 percent belong to scheduled tribes, and 32 percent belong to other backward classes (OBCs). Two-fifths of household heads do not belong to any of these groups.

Questions about housing conditions and the standard of living of households indicate some improvements since the time of NFHS-1. Sixty percent of households in India now have electricity and 39 percent have piped drinking water compared with 51 percent and 33 percent, respectively, at the time of NFHS-1. Sixty-four percent of households have no toilet facility compared with 70 percent at the time of NFHS-1.

About three-fourths (75 percent) of males and half (51 percent) of females age six and above are literate, an increase of 6-8 percentage points from literacy rates at the time of NFHS-1. The percentage of illiterate males varies from 6-7 percent in Mizoram and Kerala to 37 percent in Bihar and the percentage of illiterate females varies from 11 percent in Mizoram and 15 percent in Kerala to 65 percent in Bihar. Seventy-nine percent of children age 6-14 are attending school, up from 68 percent in NFHS-1. The proportion of children attending school has increased for all ages, particularly for girls, but girls continue to lag behind boys in school attendance. Moreover, the disparity in school attendance by sex grows with increasing age of children. At age 6-10, 85 percent of boys attend school compared with 78 percent of girls. By age 15-17, 58 percent of boys attend school compared with 40 percent of girls. The percentage of girls 6-17 attending school varies from 51 percent in Bihar and 56 percent in Rajasthan to over 90 percent in Himachal Pradesh and Kerala.

Women in India tend to marry at an early age. Thirty-four percent of women age 15-19 are already married including 4 percent who are married but gauna has yet to be performed. These proportions are even higher in the rural areas. Older women are more likely than younger women to have married at an early age: 39 percent of women currently age 45-49 married before age 15 compared with 14 percent of women currently age 15-19. Although this indicates that the proportion of women who marry young is declining rapidly, half the women even in the age group 20-24 have married before reaching the legal minimum age of 18 years. On average, women are five years younger than the men they marry. The median age at marriage varies from about 15 years in Madhya Pradesh, Bihar, Uttar Pradesh, Rajasthan, and Andhra Pradesh to 23 years in Goa.

As part of an increasing emphasis on gender issues, NFHS-2 asked women about their participation in household decisionmaking. In India, 91 percent of women are involved in decision-making on at least one of four selected topics. A much lower proportion (52 percent), however, are involved in making decisions about their own health care. There are large variations among states in India with regard to women's involvement in household decisionmaking. More than three out of four women are involved in decisions about their own health care in Himachal Pradesh, Meghalaya, and Punjab compared with about two out of five or less in Madhya Pradesh, Orissa, and Rajasthan. Thirty-nine percent of women do work other than housework, and more than two-thirds of these women work for cash. Only 41 percent of women who earn cash can decide independently how to spend the money that they earn. Forty-three percent of working women report that their earnings constitute at least half of total family earnings, including 18 percent who report that the family is entirely dependent on their earnings. Women's work-participation rates vary from 9 percent in Punjab and 13 percent in Haryana to 60-70 percent in Manipur, Nagaland, and Arunachal Pradesh.

FERTILITY AND FAMILY PLANNING

Fertility continues to decline in India. At current fertility levels, women will have an average of 2.9 children each throughout their childbearing years. The total fertility rate (TFR) is down from 3.4 children per woman at the time of NFHS-1, but is still well above the replacement level of just over two children per woman. There are large variations in fertility among the states in India. Goa and Kerala have attained below replacement level fertility and Karnataka, Himachal Pradesh, Tamil Nadu, and Punjab are at or close to replacement level fertility. By contrast, fertility is 3.3 or more children per woman in Meghalaya, Uttar Pradesh, Rajasthan, Nagaland, Bihar, and Madhya Pradesh. More than one-third to less than half of all births in these latter states are fourth or higher-order births compared with 7-9 percent of births in Kerala, Goa, and Tamil Nadu.

Efforts to encourage the trend towards lower fertility might usefully focus on groups within the population that have higher fertility than average. In India, rural women and women from scheduled tribes and scheduled castes have somewhat higher fertility than other women, but fertility is particularly high for illiterate women, poor women, and Muslim women. Another striking feature is the high level of childbearing among young women. More than half of women age 20-49 had their first birth before reaching age 20, and women age 15-19 account for almost one-fifth of total fertility. Studies in India and elsewhere have shown that health and mortality risks increase when women give birth at such young ages?both for the women themselves and for their children. Family planning programmes focusing on women in this age group could make a significant impact on maternal and child health and help to reduce fertility.

INFANT AND CHILD MORTALITY

NFHS-2 provides estimates of infant and child mortality and examines factors associated with the survival of young children. During the five years preceding the survey, the infant mortality rate was 68 deaths at age 0-11 months per 1,000 live births, substantially lower than 79 per 1,000 in the five years preceding the NFHS-1 survey. The child mortality rate, 29 deaths at age 1-4 years per 1,000 children reaching age one, also declined from the corresponding rate of 33 per 1,000 in NFHS-1. Ninety-five children out of 1,000 born do not live to age five years. Expressed differently, 1 in 15 children die in the first year of life, and 1 in 11 die before reaching age five. Child-survival programmes might usefully focus on specific groups of children with particularly high infant and child mortality rates, such as children who live in rural areas, children whose mothers are illiterate, children belonging to scheduled castes or scheduled tribes, and children from poor households. Infant mortality rates are more than two and one-half times as high for women who did not receive any of the recommended types of maternity related medical care than for mothers who did receive all recommended types of care.

HEALTH, HEALTH CARE, AND NUTRITION

Promotion of maternal and child health has been one of the most important components of the Family Welfare Programme of the Government of India. One goal is for each pregnant woman to receive at least three antenatal check-ups plus two tetanus toxoid injections and a full course of iron and folic acid supplementation. In India, mothers of 65 percent of the children

In 2021, the elderly population represented 7.42 percent of the total population of Morocco, the highest ever registered in the country since 1960. From that year onwards, the percentage of people aged 65 years and older in Morocco increased gradually.

This dataset presents information on high birth weight percentage, that is the percentage of live births in a given year that are 4,000 grams or more at birth.

Abstract

The 1998 Ghana Demographic and Health Survey (GDHS) is the latest in a series of national-level population and health surveys conducted in Ghana and it is part of the worldwide MEASURE DHS+ Project, designed to collect data on fertility, family planning, and maternal and child health.

The primary objective of the 1998 GDHS is to provide current and reliable data on fertility and family planning behaviour, child mortality, children’s nutritional status, and the utilisation of maternal and child health services in Ghana. Additional data on knowledge of HIV/AIDS are also provided. This information is essential for informed policy decisions, planning and monitoring and evaluation of programmes at both the national and local government levels.

The long-term objectives of the survey include strengthening the technical capacity of the Ghana Statistical Service (GSS) to plan, conduct, process, and analyse the results of complex national sample surveys. Moreover, the 1998 GDHS provides comparable data for long-term trend analyses within Ghana, since it is the third in a series of demographic and health surveys implemented by the same organisation, using similar data collection procedures. The GDHS also contributes to the ever-growing international database on demographic and health-related variables.

Geographic coverage

National

Analysis unit

• Household
• Children under five years
• Women age 15-49
• Men age 15-59

Kind of data

Sample survey data

Sampling procedure

The major focus of the 1998 GDHS was to provide updated estimates of important population and health indicators including fertility and mortality rates for the country as a whole and for urban and rural areas separately. In addition, the sample was designed to provide estimates of key variables for the ten regions in the country.

The list of Enumeration Areas (EAs) with population and household information from the 1984 Population Census was used as the sampling frame for the survey. The 1998 GDHS is based on a two-stage stratified nationally representative sample of households. At the first stage of sampling, 400 EAs were selected using systematic sampling with probability proportional to size (PPS-Method). The selected EAs comprised 138 in the urban areas and 262 in the rural areas. A complete household listing operation was then carried out in all the selected EAs to provide a sampling frame for the second stage selection of households. At the second stage of sampling, a systematic sample of 15 households per EA was selected in all regions, except in the Northern, Upper West and Upper East Regions. In order to obtain adequate numbers of households to provide reliable estimates of key demographic and health variables in these three regions, the number of households in each selected EA in the Northern, Upper West and Upper East regions was increased to 20. The sample was weighted to adjust for over sampling in the three northern regions (Northern, Upper East and Upper West), in relation to the other regions. Sample weights were used to compensate for the unequal probability of selection between geographically defined strata.

The survey was designed to obtain completed interviews of 4,500 women age 15-49. In addition, all males age 15-59 in every third selected household were interviewed, to obtain a target of 1,500 men. In order to take cognisance of non-response, a total of 6,375 households nation-wide were selected.

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

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the GDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. These questionnaires were based on model survey instruments developed for the international MEASURE DHS+ programme and were designed to provide information needed by health and family planning programme managers and policy makers. The questionnaires were adapted to the situation in Ghana and a number of questions pertaining to on-going health and family planning programmes were added. These questionnaires were developed in English and translated into five major local languages (Akan, Ga, Ewe, Hausa, and Dagbani).

The Household Questionnaire was used to enumerate all usual members and visitors in a selected household and to collect information on the socio-economic status of the household. The first part of the Household Questionnaire collected information on the relationship to the household head, residence, sex, age, marital status, and education of each usual resident or visitor. This information was used to identify women and men who were eligible for the individual interview. For this purpose, all women age 15-49, and all men age 15-59 in every third household, whether usual residents of a selected household or visitors who slept in a selected household the night before the interview, were deemed eligible and interviewed. The Household Questionnaire also provides basic demographic data for Ghanaian households. The second part of the Household Questionnaire contained questions on the dwelling unit, such as the number of rooms, the flooring material, the source of water and the type of toilet facilities, and on the ownership of a variety of consumer goods.

The Women’s Questionnaire was used to collect information on the following topics: respondent’s background characteristics, reproductive history, contraceptive knowledge and use, antenatal, delivery and postnatal care, infant feeding practices, child immunisation and health, marriage, fertility preferences and attitudes about family planning, husband’s background characteristics, women’s work, knowledge of HIV/AIDS and STDs, as well as anthropometric measurements of children and mothers.

The Men’s Questionnaire collected information on respondent’s background characteristics, reproduction, contraceptive knowledge and use, marriage, fertility preferences and attitudes about family planning, as well as knowledge of HIV/AIDS and STDs.

Response rate

A total of 6,375 households were selected for the GDHS sample. Of these, 6,055 were occupied. Interviews were completed for 6,003 households, which represent 99 percent of the occupied households. A total of 4,970 eligible women from these households and 1,596 eligible men from every third household were identified for the individual interviews. Interviews were successfully completed for 4,843 women or 97 percent and 1,546 men or 97 percent. The principal reason for nonresponse among individual women and men 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 affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of shortfalls 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 1998 GDHS 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 1998 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 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 1998 GDHS sample is the result of a two-stage stratified design, and, consequently, it was necessary to use more complex formulae. The computer software used to calculate sampling errors for the 1998 GDHS is the ISSA Sampling Error Module. This module 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.

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

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

About this dataset

Context

The dataset tabulates the Blountsville population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Blountsville across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Blountsville was 98, a 0.00% decrease year-by-year from 2021. Previously, in 2021, Blountsville population was 98, a decline of 0.00% compared to a population of 98 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Blountsville decreased by 73. In this period, the peak population was 172 in the year 2001. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Blountsville is shown in this column.
• Year on Year Change: This column displays the change in Blountsville population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Blountsville Population by Year. You can refer the same here

About this dataset

Context

The dataset tabulates the Prairie View population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Prairie View. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 98 (65.33% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Prairie View population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Prairie View is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Prairie View is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Prairie View Population by Age. You can refer the same here

In 2023, around 19.2 percent of all children lived in households that were classified as food insecure in the United States. This is a slight increase from the previous year, when 18.5 percent of children were in food-insecure households.

About this dataset

Context

The dataset tabulates the Slick population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Slick. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 98 (48.51% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Slick population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Slick is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Slick is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Slick Population by Age. You can refer the same here

About this dataset

Context

The dataset tabulates the Stark City population by age cohorts (Children: Under 18 years; Working population: 18-64 years; Senior population: 65 years or more). It lists the population in each age cohort group along with its percentage relative to the total population of Stark City. The dataset can be utilized to understand the population distribution across children, working population and senior population for dependency ratio, housing requirements, ageing, migration patterns etc.

Key observations

The largest age group was 18 - 64 years with a poulation of 98 (64.47% of the total population). Source: U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2017-2021 5-Year Estimates.

Age cohorts:

• Under 18 years
• 18 to 64 years
• 65 years and over

Variables / Data Columns

• Age Group: This column displays the age cohort for the Stark City population analysis. Total expected values are 3 groups ( Children, Working Population and Senior Population).
• Population: The population for the age cohort in Stark City is shown in the following column.
• Percent of Total Population: The population as a percent of total population of the Stark City is shown in the following column.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Stark City Population by Age. You can refer the same here

This table contains 6930 series, with data for years 1996 - 1996 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (11 items: Canada; Newfoundland and Labrador; Prince Edward Island; Nova Scotia ...), Age group (10 items: Total; 12 years and over;12-14 years;15-19 years;20-24 years ...), Sex (3 items: Both sexes; Males; Females ...), Characteristics (21 items: Number of smokers in 1996/97;Number of smokers in 1996/97 who quit by 1998/99;Number of smokers in 1996/97 who did not state their smoking status by 1998/99;Number of smokers in 1996/97 who did not quit by 1998/99 ...).

This dataset of U.S. mortality trends since 1900 highlights the differences in age-adjusted death rates and life expectancy at birth by race and sex.

Age-adjusted death rates (deaths per 100,000) after 1998 are calculated based on the 2000 U.S. standard population. Populations used for computing death rates for 2011–2017 are postcensal estimates based on the 2010 census, estimated as of July 1, 2010. Rates for census years are based on populations enumerated in the corresponding censuses. Rates for noncensus years between 2000 and 2010 are revised using updated intercensal population estimates and may differ from rates previously published. Data on age-adjusted death rates prior to 1999 are taken from historical data (see References below).

Life expectancy data are available up to 2017. Due to changes in categories of race used in publications, data are not available for the black population consistently before 1968, and not at all before 1960. More information on historical data on age-adjusted death rates is available at https://www.cdc.gov/nchs/nvss/mortality/hist293.htm.

SOURCES

CDC/NCHS, National Vital Statistics System, historical data, 1900-1998 (see https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm); CDC/NCHS, National Vital Statistics System, mortality data (see http://www.cdc.gov/nchs/deaths.htm); and CDC WONDER (see http://wonder.cdc.gov).

REFERENCES

1. National Center for Health Statistics, Data Warehouse. Comparability of cause-of-death between ICD revisions. 2008. Available from: http://www.cdc.gov/nchs/nvss/mortality/comparability_icd.htm.

2. National Center for Health Statistics. Vital statistics data available. Mortality multiple cause files. Hyattsville, MD: National Center for Health Statistics. Available from: https://www.cdc.gov/nchs/data_access/vitalstatsonline.htm.

3. Kochanek KD, Murphy SL, Xu JQ, Arias E. Deaths: Final data for 2017. National Vital Statistics Reports; vol 68 no 9. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_09-508.pdf.

4. Arias E, Xu JQ. United States life tables, 2017. National Vital Statistics Reports; vol 68 no 7. Hyattsville, MD: National Center for Health Statistics. 2019. Available from: https://www.cdc.gov/nchs/data/nvsr/nvsr68/nvsr68_07-508.pdf.

5. National Center for Health Statistics. Historical Data, 1900-1998. 2009. Available from: https://www.cdc.gov/nchs/nvss/mortality_historical_data.htm.

Abstract

Registration of vital events commenced in 1867 with the enactment of civil registration laws which conferred the legal sanction for the registration of events namely, live births, deaths, still births and marriages. According to the law every live birth has to be registered within 42 days from the date of occurrence.

Although birth and death registrations are compulsory by law, few events are missed and not registered for various reasons.

By the survey conducted in 1980, to assess the completeness of birth and death registrations, it was found that about 98.8 per cent of births and 94.0 per cent of deaths are being registered at any given time.

Births are registered at the place of occurrence and not in the area of residence of the mother.

Geographic coverage

National coverage.

Analysis unit

Individual Birth

Universe

Records pertaining to Live births of Sri Lankan Nationals whose birth occurs in Sri Lanka

Kind of data

Administrative records data [adm]

Mode of data collection

Other [oth]

Abstract

The 1998 Kenya Demographic and Health Survey (KDHS) is a nationally representative survey of 7,881 women age 15-49 and 3,407 men age 15-54. The KDHS was implemented by the National Council for Population and Development (NCPD) and the Central Bureau of Statistics (CBS), with significant technical and logistical support provided by the Ministry of Health and various other governmental and nongovernmental organizations in Kenya. Macro International Inc. of Calverton, Maryland (U.S.A.) provided technical assistance throughout the course of the project in the context of the worldwide Demographic and Health Surveys (DHS) programme, while financial assistance was provided by the U.S. Agency for International Development (USAID/Nairobi) and the Department for International Development (DFID/U.K.). Data collection for the KDHS was conducted from February to July 1998.

Like the previous KDHS surveys conducted in 1989 and 1993, the 1998 KDHS was designed to provide information on levels and trends in fertility, family planning knowledge and use, infant and child mortality, and other maternal and child health indicators. However, the 1998 KDHS went further to collect more in-depth data on knowledge and behaviours related to AIDS and other sexually transmitted diseases (STDs), detailed “calendar” data that allows estimation of contraceptive discontinuation rates, and information related to the practice of female circumcision. Further, unlike earlier surveys, the 1998 KDHS provides a national estimate of the level of maternal mortality (i.e. related to pregnancy and childbearing). The KDHS data are intended for use by programme managers and policymakers to evaluate and improve health and family planning programmes in Kenya.

OBJECTIVES OF THE SURVEY

The principal aim of the 1998 KDHS project is to provide up-to-date information on fertility and childhood mortality levels, nuptiality, fertility preferences, awareness and use of family planning methods, use of maternal and child health services, and knowledge and behaviours related to HIV/AIDS and other sexually-transmitted diseases. It was designed as a follow-on to the 1989 KDHS and 1993 KDHS, national-level surveys of similar size and scope. Ultimately, the 1998 KDHS project seeks to:

• Assess the overall demographic situation in Kenya;
• Assist in the evaluation of the population and reproductive health programmes in Kenya;
• Advance survey methodology; and
• Assist the NCPD to strengthen its capacity to conduct demographic and health surveys.

The 1998 KDHS was specifically designed to: - Provide data on the family planning and fertility behaviour of the Kenyan population, and to thereby enable the NCPD to evaluate and enhance the national family planning programme; - Measure changes in fertility and contraceptive prevalence and at the same time study the factors which affect these changes, such as marriage patterns, desire for children, availability of contraception, breastfeeding habits, and important social and economic factors; - Examine the basic indicators of maternal and child health in Kenya, including nutritional status, use of antenatal and maternity services, treatment of recent episodes of childhood illness, and use of immunisation services; - Describe levels and patterns of knowledge and behaviour related to the prevention of AIDS and other sexually transmitted infection; - Measure adult and maternal mortality at the national level; and - Ascertain the extent and pattern of female circumcision in the country.

MAIN RESULTS

Fertility. The survey results demonstrate a continuation of the fertility transition in Kenya. Marriage. The age at which women and men first marry has risen slowly over the past 20 years. Fertility Preferences. Fifty-three percent of women and 46 percent of men in Kenya do not want to have any more children. Family Planning. Knowledge and use of family planning in Kenya has continued to rise over the last several years. Early Childhood Mortality. Results from the 1998 KDHS data make clear that childhood mortality conditions have worsened in the early-mid 1990s;Maternal Health. Utilisation of antenatal services is high in Kenya; in the three years before the survey, mothers received antenatal care for 92 percent of births (Note: These data do not speak to the quality of those antenatal services). Childhood Immunisation. The KDHS found that 65 percent of children age 12-23 months are fully vaccinated: this includes BCG and measles vaccine, and at least 3 doses of both DPT and polio vaccines. Infant Feeding. Almost all children (98 percent) are breastfed for some period of time; however, only 58 percent are breastfed within the first hour of life, and 86 percent within the first day after birth. Nutritional Status The results indicate that one-third of children in Kenya are stunted (i.e., too short for their age), a condition reflecting chronic malnutrition; and 1 in 16 children are wasted (i.e., very thin), a problem indicating acute or short-term food deficit.

Knowledge, Attitudes and Behaviour regarding HIV/AIDS and Other Sexually Transmitted Infections. As a measure of the increasing toll taken by AIDS on Kenyan society, the percentage of respondents who reported “personally knowing someone who has AIDS or has died from AIDS” has risen from about 40 percent of men and women in the 1993 KDHS to nearly three-quarters of men and women in 1998. Female Circumcision. The results indicate that 38 percent of women age 15-49 in Kenya have been circumcised. The prevalence of FC has however declined significantly over the last 2 decades from about one-half of women in the oldest age cohorts to about one-quarter of women in the youngest cohorts (including daughters age 15+).

Geographic coverage

The 1998 KDHS sample is national in scope, with the exclusion of all three districts in North Eastern Province and four other northern districts (Samburu and Turkana in Rift Valley Province and Isiolo and 4 Marsabit in Eastern Province). Together the excluded areas account for less than 4 percent of Kenya's population

Analysis unit

• Household
• Women age 15-49
• Men age 20-54
• Children under five

Universe

The population covered by the 1998 KDHS is defined as the universe of all women age 15-49 in Kenya and all husband age 20-54 living in the household.

Kind of data

Sample survey data

Sampling procedure

The 1998 Kenya Demographic and Health Survey (KDHS) covered the population residing in private households1 throughout the country, with the exception of sparsely-populated areas in the north of the country that together comprise about 4 percent of the national population. Like the 1993 KDHS, the 1998 KDHS was designed to produce reliable national estimates as well as urban and rural estimates of fertility and childhood mortality rates, contraceptive prevalence, and various other health and population indicators. The design also allows for estimates of selected variables for the rural parts of 15 oversampled districts. Because of the relative rarity of maternal death, the maternal mortality ratio is estimated only at the national level.

In addition to the KDHS principal sample of women, a sub-sample of men age 15-54 were also interviewed to allow for the study of HIV/AIDS, family planning, and other selected topics.

SAMPLING FRAME AND FIRST-STAGE SELECTION

The KDHS utilised a two-stage, stratified sampling approach. The first step involved selecting sample points or "clusters"; the second stage involved selecting households within sample points from a list compiled during a special KDHS household listing exercise.

The 1998 KDHS sample points were the same as those used in the 1993 KDHS, and were selected from a national master sample (i.e., sampling frame) maintained by the Central Bureau of Statistics. From this master sample, called NASSEP-3,3 were drawn 536 sample points: 444 rural and 92 urban.

Selected districts were oversampled in the 1998 KDHS in order to produce reliable estimates for certain variables at the district level. Fifteen districts were thus targeted in both the 1993 and 1998 KDHS: Bungoma, Kakamega, Kericho, Kilifi, Kisii, Machakos, Meru, Murang'a, Nakuru, Nandi, Nyeri, Siaya, South Nyanza, Taita-Taveta, and Uasin Gishu. In addition, Nairobi and Mombasa were targeted. Due to this oversampling, the 1998 KDHS is not self-weighting (i.e., sample weights are needed to produce national estimates). Within each of the 15 oversampled (rural) districts, about 400 households were selected. In all other rural areas combined, about 1,400 households were selected, and 2,000 households were selected in urban areas. The total number of households targeted for selection was thus approximately 9,400 households. Within each sampling stratum, implicit stratification was introduced by ordering the sample points geographically within the hierarchy of administrative units (i.e., sublocation, location, and district within province).

SELECTION OF HOUSEHOLDS AND INDIVIDUALS

The Central Bureau of Statistics began a complete listing of households in all sample points during November 1997 and finished the exercise in February 1998. In the end, listing in 6 of 536 sample points4 could not be completed (and were thus not included in the survey) due to problems of inaccesibility. From these 530 household lists, a systematic sample of households was drawn, with a "take" of 22 households in urban clusters and 17 households in the rural clusters for a total of 9,465 households. All women age 15-49 were targeted for interview in the selected households. Every second household was identified for inclusion in the male survey; in those households, all men age 15-54 were identified and considered

Abstract

The 1998 South Africa Demographic and Health Survey (SADHS) is the first study of its kind to be conducted in South Africa and heralds a new era of reliable and relevant information in South Africa. The SADHS, a nation-wide survey has collected information on key maternal and child health indicators, and in a first for international demographic and health surveys, the South African survey contains data on the health and disease patterns in adults.

Plans to conduct the South Africa Demographic and Health Survey go as far back as 1995, when the Department of Health National Health Information Systems of South Africa (NHIS/SA) committee, recognised serious gaps in information required for health service planning and monitoring.

Fieldwork was conducted between late January and September 1998, during which time 12,247 households were visited, 17,500 people throughout nine provinces were interviewed and 175 interviewers were trained to interview in 11 languages.

The aim of the 1998 South Africa Demographic and Health Survey (SADHS) was to collect data as part of the National Health Information System of South Africa (NHIS/SA). The survey results are intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving health services in the country. A variety of demographic and health indicators were collected in order to achieve the following general objectives:

(i) To contribute to the information base for health and population development programme management through accurate and timely data on a range of demographic and health indicators. (ii) To provide baseline data for monitoring programmes and future planning. (iii) To build research and research management capacity in large-scale national demographic and health surveys.

The primary objective of the SADHS is to provide up-to-date information on: - basic demographic rates, particularly fertility and childhood mortality levels, - awareness and use of contraceptive methods, - breastfeeding practices, - maternal and child health, - awareness of HIV/AIDS, - chronic health conditions among adults, - lifestyles that affect the health status of adults, and - anthropometric indicators.

Geographic coverage

It was designed principally to produce reliable estimates of demographic rates (particularly fertility and childhood mortality rates), of maternal and child health indicators, and of contraceptive knowledge and use for the country as a whole, the urban and the non-urban areas separately, and for the nine provinces.

Analysis unit

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

Universe

The 1998 South African Demographic and Health Survey (SADHS) covered the population living in private households in the country.

Kind of data

Sample survey data

Sampling procedure

The 1998 South African Demographic and Health Survey (SADHS) covered the population living in private households in the country. The design for the SADHS called for a representative probability sample of approximately 12,000 completed individual interviews with women between the ages of 15 and 49. It was designed principally to produce reliable estimates of demographic rates (particularly fertility and childhood mortality rates), of maternal and child health indicators, and of contraceptive knowledge and use for the country as a whole, the urban and the non-urban areas separately, and for the nine provinces. As far as possible, estimates were to be produced for the four South African population groups. Also, in the Eastern Cape province, estimates of selected indicators were required for each of the five health regions.

In addition to the main survey of households and women 15-49 that followed the DHS model, an adult health module was administered to a sample of adults aged 15 and over in half of the households selected for the main survey. The adult health module collected information on oral health, occupational hazard and chronic diseases of lifestyle.

SAMPLING FRAME

The sampling frame for the SADHS was the list of approximately 86,000 enumeration areas (EAs) created by Central Statistics (now Statistics South Africa, SSA) for the Census conducted in October 1996. The EAs, ranged from about 100 to 250 households, and were stratified by province, urban and non-urban residence and by EA type. The number of households in the EA served as a measure of size of the EA.

CHARACTERISTICS OF THE SADHS SAMPLE

The sample for the SADHS was selected in two stages. Due to confidentiality of the census data, the sampling was carried out by experts at the CSS according to specifications developed by members of the SADHS team. Within each stratum a two stage sample was selected. The primary sampling units (PSUs), corresponded to the EAs and will be selected with probability proportional to size (PPS), the size being the number of households residing in the EA, or where this was not available, the number of census visiting points in the EA. This led to 972 PSUs being selected for the SADHS (690 in urban areas and 282 in non-urban areas. Where provided by SSA, the lists of visiting points together with the households found in these visiting points, or alternatively a map of the EA which showed the households, was used as the frame for second-stage sampling to select the households to be visited by the SADHS interviewing teams during the main survey fieldwork. This sampling was carried out by the MRC behalf of the SADHS working group. If a list of visiting points or a map was not available from SSA, then the survey team took a systematic sample of visiting points in the field. In an urban EA ten visiting points were sampled, while in a non-urban EA twenty visiting points were sampled. The survey team then interviewed the household in the selected visiting point. If there were two households in the selected visiting point, both households were interviewed. If there were three or more households, then the team randomly selected one household for interview. In each selected household, a household questionnaire was administered; all women between the ages of 15 and 49 were identified and interviewed with a woman questionnaire. In half of the selected households (identified by the SADHS working group), all adults over 15 years of age were also identified and interviewed with an adult health questionnaire.

SAMPLE ALLOCATION

Except for Eastern Cape, the provinces were stratified by urban and non-urban areas, for a total of 16 sampling strata. Eastern Cape was stratified by the five health regions and urban and non-urban within each region, for a total of 10 sampling strata. There were thus 26 strata in total.

Originally, it was decided that a sample of 9,000 women 15-49 with complete interviews allocated equally to the nine provinces would be adequate to provide estimates for each province separately; results of other demographic and health surveys have shown that a minimum sample of 1,000 women is required in order to obtain estimates of fertility and childhood mortality rates at an acceptable level of sampling errors. Since one of the objectives of the SADHS was to also provide separate estimates for each of the four population groups, this allocation of 1,000 women per province would not provide enough cases for the Asian population group since they represent only 2.6 percent of the population (according to the results of the 1994 October Household Survey conducted by SSA). The decision was taken to add an additional sample of 1,000 women to the urban areas of KwaZulu-Natal and Gauteng to try to capture as many Asian women as possible as Asians are found mostly in these areas. A more specific sampling scheme to obtain an exact number of Asian women was not possible for two reasons: the population distribution by population group was not yet available from the 1996 census and the sampling frame of EAs cannot be stratified by population group according to SSA as the old system of identifying EAs by population group has been abolished.

An additional sample of 2,000 women was added to Eastern Cape at the request of the Eastern Cape province who funded this additional sample. In Eastern Cape, results by urban and non-urban areas can be given. Results of selected indicators such as contraceptive knowledge and use can also be produced separately for each of the five health regions but not for urban/non-urban within health region.

Result shows the allocation of the target sample of 12,000 women by province and by urban/nonurban residence. Within each province, the sample is allocated proportionately to the urban/non-urban areas.

In the above allocation, the urban areas of KwaZulu-Natal have been oversampled by about 57 percent while those of Gauteng have been oversampled by less than 1 percent. For comparison purposes, it shows a proportional allocation of the 12,000 women to the nine provinces that would result in a completely self-weighting sample but does not allow for reliable estimates for at least four provinces (Northern Cape, Free State, Mpumalanga and North-West).

The number of households to be selected for each stratum was calculated as follows:

• According to the 1994 October Household Survey, the estimated number of women 15-49 per households is 1.2. The overall response rate was assumed to be 80 percent, i.e., of the households selected for the survey only 90 percent would be successfully interviewed, and of the women identified in the households with completed interviews, only 90 percent would have a complete woman questionnaire. Using these two parameters in the above equation, we would expect to select approximately 12,500 households in order to yield the target sample of women.

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The life expectancy for men aged 65 years in the U.S. has gradually increased since the 1960s. Now men in the United States aged 65 can expect to live 17 more years on average. Women aged 65 years can expect to live around 19.7 more years on average.

Life expectancy in the U.S.

As of 2021, the average life expectancy at birth in the United States was 76.33 years. Life expectancy in the U.S. had steadily increased for many years but has recently dropped slightly. Women consistently have a higher life expectancy than men but have also seen a slight decrease. As of 2019, a woman in the U.S. could be expected to live up to 79.3 years.

Leading causes of death

The leading causes of death in the United States include heart disease, cancer, unintentional injuries, chronic lower respiratory diseases and cerebrovascular diseases. However, heart disease and cancer account for around 38 percent of all deaths. Although heart disease and cancer are the leading causes of death for both men and women, there are slight variations in the leading causes of death. For example, unintentional injury and suicide account for a larger portion of deaths among men than they do among women.

How high is the brand awareness of PayPal in Germany?When it comes to digital payment users, brand awareness of PayPal is at 98 percent in Germany. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is PayPal in Germany?In total, 81 percent of German digital payment users say they like PayPal. However, in actuality, among the 98 percent of German respondents who know PayPal, 83 percent of people like the brand.What is the usage share of PayPal in Germany?All in all, 82 percent of digital payment users in Germany use PayPal. That means, of the 98 percent who know the brand, 84 percent use them.How loyal are the users of PayPal?Around 79 percent of digital payment users in Germany say they are likely to use PayPal again. Set in relation to the 82 percent usage share of the brand, this means that 96 percent of their users show loyalty to the brand.What's the buzz around PayPal in Germany?In November 2023, about 37 percent of German digital payment users had heard about PayPal in the media, on social media, or in advertising over the past three months. Of the 98 percent who know the brand, that's 38 percent, meaning at the time of the survey there's some buzz around PayPal in Germany.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Libya: Muslims as percent of the total population: The latest value from 2013 is 98 percent, unchanged from 98 percent in 2012. In comparison, the world average is 34.3 percent, based on data from 128 countries. Historically, the average for Libya from 1960 to 2013 is 96.8 percent. The minimum value, 95 percent, was reached in 1960 while the maximum of 98 percent was recorded in 1996.

About this dataset

Context

The dataset tabulates the Alpha population over the last 20 plus years. It lists the population for each year, along with the year on year change in population, as well as the change in percentage terms for each year. The dataset can be utilized to understand the population change of Alpha across the last two decades. For example, using this dataset, we can identify if the population is declining or increasing. If there is a change, when the population peaked, or if it is still growing and has not reached its peak. We can also compare the trend with the overall trend of United States population over the same period of time.

Key observations

In 2022, the population of Alpha was 97, a 1.02% decrease year-by-year from 2021. Previously, in 2021, Alpha population was 98, a decline of 0.00% compared to a population of 98 in 2020. Over the last 20 plus years, between 2000 and 2022, population of Alpha decreased by 33. In this period, the peak population was 130 in the year 2000. The numbers suggest that the population has already reached its peak and is showing a trend of decline. Source: U.S. Census Bureau Population Estimates Program (PEP).

Content

When available, the data consists of estimates from the U.S. Census Bureau Population Estimates Program (PEP).

Data Coverage:

• From 2000 to 2022

Variables / Data Columns

• Year: This column displays the data year (Measured annually and for years 2000 to 2022)
• Population: The population for the specific year for the Alpha is shown in this column.
• Year on Year Change: This column displays the change in Alpha population for each year compared to the previous year.
• Change in Percent: This column displays the year on year change as a percentage. Please note that the sum of all percentages may not equal one due to rounding of values.

Good to know

Margin of Error

Data in the dataset are based on the estimates and are subject to sampling variability and thus a margin of error. Neilsberg Research recommends using caution when presening these estimates in your research.

Custom data

If you do need custom data for any of your research project, report or presentation, you can contact our research staff at research@neilsberg.com for a feasibility of a custom tabulation on a fee-for-service basis.

Inspiration

Neilsberg Research Team curates, analyze and publishes demographics and economic data from a variety of public and proprietary sources, each of which often includes multiple surveys and programs. The large majority of Neilsberg Research aggregated datasets and insights is made available for free download at https://www.neilsberg.com/research/.

Recommended for further research

This dataset is a part of the main dataset for Alpha Population by Year. You can refer the same here