About this dataset

Context

The dataset tabulates the Live Oak County population distribution across 18 age groups. It lists the population in each age group along with the percentage population relative of the total population for Live Oak County. The dataset can be utilized to understand the population distribution of Live Oak County by age. For example, using this dataset, we can identify the largest age group in Live Oak County.

Key observations

The largest age group in Live Oak County, TX was for the group of age 30 to 34 years years with a population of 1,494 (13.05%), according to the ACS 2019-2023 5-Year Estimates. At the same time, the smallest age group in Live Oak County, TX was the 85 years and over years with a population of 229 (2%). Source: U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates

Age groups:

• Under 5 years
• 5 to 9 years
• 10 to 14 years
• 15 to 19 years
• 20 to 24 years
• 25 to 29 years
• 30 to 34 years
• 35 to 39 years
• 40 to 44 years
• 45 to 49 years
• 50 to 54 years
• 55 to 59 years
• 60 to 64 years
• 65 to 69 years
• 70 to 74 years
• 75 to 79 years
• 80 to 84 years
• 85 years and over

Variables / Data Columns

• Age Group: This column displays the age group in consideration
• Population: The population for the specific age group in the Live Oak County is shown in this column.
• % of Total Population: This column displays the population of each age group as a proportion of Live Oak County total population. 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 Live Oak County Population by Age. You can refer the same here

This table contains 2394 series, with data for years 1991 - 1991 (not all combinations necessarily have data for all years). This table contains data described by the following dimensions (Not all combinations are available): Geography (1 items: Canada ...), Population group (19 items: Entire cohort; Income adequacy quintile 1 (lowest);Income adequacy quintile 2;Income adequacy quintile 3 ...), Age (14 items: At 25 years; At 30 years; At 40 years; At 35 years ...), Sex (3 items: Both sexes; Females; Males ...), Characteristics (3 items: Life expectancy; High 95% confidence interval; life expectancy; Low 95% confidence interval; life expectancy ...).

Analysis of ‘COVID-19 case rate per 100,000 population and percent test positivity in the last 7 days by town - ARCHIVE’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/ceb31b99-df28-4d47-bfc9-dd3ab1896172 on 26 January 2022.

--- Dataset description provided by original source is as follows ---

DPH note about change from 7-day to 14-day metrics: As of 10/15/2020, this dataset is no longer being updated. Starting on 10/15/2020, these metrics will be calculated using a 14-day average rather than a 7-day average. The new dataset using 14-day averages can be accessed here: https://data.ct.gov/Health-and-Human-Services/COVID-19-case-rate-per-100-000-population-and-perc/hree-nys2

As you know, we are learning more about COVID-19 all the time, including the best ways to measure COVID-19 activity in our communities. CT DPH has decided to shift to 14-day rates because these are more stable, particularly at the town level, as compared to 7-day rates. In addition, since the school indicators were initially published by DPH last summer, CDC has recommended 14-day rates and other states (e.g., Massachusetts) have started to implement 14-day metrics for monitoring COVID transmission as well.

With respect to geography, we also have learned that many people are looking at the town-level data to inform decision making, despite emphasis on the county-level metrics in the published addenda. This is understandable as there has been variation within counties in COVID-19 activity (for example, rates that are higher in one town than in most other towns in the county).

This dataset includes a weekly count and weekly rate per 100,000 population for COVID-19 cases, a weekly count of COVID-19 PCR diagnostic tests, and a weekly percent positivity rate for tests among people living in community settings. Dates are based on date of specimen collection (cases and positivity).

A person is considered a new case only upon their first COVID-19 testing result because a case is defined as an instance or bout of illness. If they are tested again subsequently and are still positive, it still counts toward the test positivity metric but they are not considered another case.

These case and test counts do not include cases or tests among people residing in congregate settings, such as nursing homes, assisted living facilities, or correctional facilities.

These data are updated weekly; the previous week period for each dataset is the previous Sunday-Saturday, known as an MMWR week (https://wwwn.cdc.gov/nndss/document/MMWR_week_overview.pdf). The date listed is the date the dataset was last updated and corresponds to a reporting period of the previous MMWR week. For instance, the data for 8/20/2020 corresponds to a reporting period of 8/9/2020-8/15/2020.

Notes: 9/25/2020: Data for Mansfield and Middletown for the week of Sept 13-19 were unavailable at the time of reporting due to delays in lab reporting.

--- Original source retains full ownership of the source dataset ---

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 born in the three years preceding NFHS-2 received at least one antenatal

Context

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This dataset collects characteristics of the population in each region (age distribution, unemployment rate, immigration percent and primary economic sector) and cross it with the votes per each political part.

It has 52 fields:

1) Code [String]: Region code of the different Spanish areas. There are 8126 different regions, but the dataset only contains 8119, because some sources were incomplete.

2) RegionName [String]: Name of the region.

3) Population [Int]: Amount of people living in that area (1st January 2015)

4) TotalCensus [Int]: Number of people over 18 years old, which means that can vote.

5) TotalVotes [Int]: Number of total votes.

6) AbstentionPtge [Float]: Percent of the people that have not votes in the election. (TotalCensus-TotalVotes)/TotalCensus*100 %

7) BlankVotesPtge [Float]: Percent of votes that were blank. Calculated as follows: BlankVotes/TotalVotes*100 %

8) NullVotesPtge [Float]: Percent of votes that were null. Calculated as follows: NullVotes/TotalVotes*100 %

9) PP_Ptge [Float]: Percent of the votes given to the political party called “Partido Popular”. (PP_Votes)/TotalVotes*100 %

10) PSOE_Ptge [Float]: Percent of the votes given to the political party called “Partido Socialista Obrero Español” (PSOE_Votes)/TotalVotes*100 %

11) Podemos_Ptge [Float]: Percent of the votes given to the political party called “Podemos” (Podemos_Votes)/TotalVotes*100 %

12) Ciudadanos_Ptge [Float]: Percent of the votes given to the political party called “Ciudadanos” (Ciudadanos_Votes)/TotalVotes*100 %

13) Others_Ptge [Float]: Percent of the votes given to the others political parties (∑▒MinoritaryVotes)/TotalVotes*100 %

14) Age_0-4_Ptge [Float]: Percent of the populations which age is between 0 and 4 years old. It is calculated as follows: (Number of people in (0-4))/TotalPopulation*100 %

15) Age_5-9_Ptge [Float]: Percent of the populations which age is between 5 and 9 year old.

16) Age_10-14_Ptge [Float]: Percent of the populations which age is between 10 and 14 years old

17) Age_15-19_Ptge [Float]: Percent of the populations which age is between 15 and 19 years old

18) Age_20-24_Ptge [Float]: Percent of the populations which age is between 20 and 24 years old

19) Age_25-29_Ptge [Float]: Percent of the populations which age is between 25 and 29 years old

20) Age_30-34_Ptge [Float]: Percent of the populations which age is between 30 and 34 years old

21) Age_35-39_Ptge [Float]: Percent of the populations which age is between 35 and 39 years old

22) Age_40-44_Ptge [Float]: Percent of the populations which age is between 40 and 44 years old

23) Age_45-49_Ptge [Float]: Percent of the populations which age is between 45 and 49 years old

24) Age_50-54_Ptge [Float]: Percent of the populations which age is between 50 and 54 years old

25) Age_55-59_Ptge [Float]: Percent of the populations which age is between 55 and 59 years old

26) Age_60-64_Ptge [Float]: Percent of the populations which age is between 60 and 64 years old

27) Age_65-69_Ptge [Float]: Percent of the populations which age is between 65 and 69 years old

28) Age_70-74_Ptge [Float]: Percent of the populations which age is between 70 and 74 years old

29) Age_75-79_Ptge [Float]: Percent of the populations which age is between 75 and 79 year old

30) Age_80-84_Ptge [Float]: Percent of the populations which age is between 80 and 84 years old

31) Age_85-89_Ptge [Float]: Percent of the populations which age is between 85 and 89 year old

32) Age_90-94_Ptge [Float]: Percent of the populations which age is between 90 and 94 years old

33) Age_95-99_Ptge [Float]: Percent of the populations which age is between 95 and 99 years old

34) Age_100+_Ptge [Float]: Percent of the populations which is older than 100 years old.

35) ManPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: ManPopulation/TotalPopulation*100

36) WomanPopulationPtge [Float]: Percentage of masculine population in a region. Calculated as follows: WomanPopulation/TotalPopulation*100

37) SpanishPtge [Float]: Percentage of people with spanish nationality in a region. Calculated as follows: NativeSpanishPopulation/TotalPopulation*100

38) ForeignersPtge [Float]: Percentage of foreign people in a region. Calculated as follows: ForeignPopulation/TotalPopulation*100

39) SameComAutonPtge [Float]: Percentage of people who live in the same autonomic community (same province) that was born. Calculated as follows: SameComAutonPopulation/TotalPopulation*100

40) SameComAutonDiffProvPtge [Float]: Percentage of people who live in the same autonomic community (different province) that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

41) DifComAutonPtge [Float]: Percentage of people who live in different autonomic community that was born. Calculated as follows: SameComAutonDiffProvPopulation/TotalPopulation*100

42) UnemployLess25_Ptge [Float]: Percent of unemployed people that are under 25 years and older than 18. It is calculated over the total amount of unemployment. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalUnemployment*100

43) Unemploy25_40_Ptge [Float]: Percent of unemployed people that are 25-40 years over the total amount of unemployment. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalUnemployment*100

44) UnemployMore40_Ptge [Float]: Percent of unemployed people that are older that 40 and younger than 69 years over the total amount of unemployment. (Unemployment(40-69)_Man+Unemployment(40-69)_Woman)/TotalUnemployment*100

45) UnemployLess25_population_Ptge [Float]: Percent of unemployed people younger than 25 and older than 18, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

46) Unemploy25_40_population_Ptge [Float]: Percent of unemployed people (25-40) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (Unemployment(25-40)_Man+ Unemployment(25-40)_Woman )/TotalPopulation*100

47) UnemployMore40_population_Ptge [Float]: Percent of unemployed people (40-69) years old, over the total population of the region. Note that the percent is calculated over the total population and not over the total active population. (UnemploymentLess25_Man+ UnemploymentLess25_Woman)/TotalPopulation*100

48) AgricultureUnemploymentPtge [Float]: Percent of unemployment in the agriculture sector relative to the total amount of unemployment. PeopleUnemployedInAgriculture/TotalUnemployment*100

49) IndustryUnemploymentPtge [Float]: Percent of unemployment in the industry sector relative to the total amount of unemployment. PeopleUnemployedInIndustry/TotalUnemployment*100

50) ConstructionUnemploymentPtge [Float]: Percent of unemployment in the construction sector relative to the total amount of unemployment. PeopleUnemployedInConstruction/TotalUnemployment*100

51) ServicesUnemploymentPtge [Float]: Percent of unemployment in the services sector relative to the total amount of unemployment. PeopleUnemployedInServices/TotalUnemployment*100

52) NotJobBeforeUnemploymentPtge [Float]: Percent of unemployment of people that didn’t have an employ before, over the total amount of unemployment. PeopleUnemployedWithoutEmployBefore/TotalUnemployment*100

References:

[1] Unemployment: www.datos.gob.es/es/catalogo/e00142804-paro-registrado-por-municipios

[2] Age distribution per region Relation between Spanish and foreigners Relation between woman and man Relation between people born in the same area or different areas of Spain http://www.ine.es/dynt3/inebase/index.htm?type=pcaxis&file=pcaxis&path=%2Ft20%2Fe245%2Fp05%2F%2Fa2015

[3] Congress elections result of Spanish election (June 2016) http://www.infoelectoral.interior.es/min/areaDescarga.html?method=inicio

Abstract

Round 1 of the Afrobarometer survey was conducted from July 1999 through June 2001 in 12 African countries, to solicit public opinion on democracy, governance, markets, and national identity. The full 12 country dataset released was pieced together out of different projects, Round 1 of the Afrobarometer survey,the old Southern African Democracy Barometer, and similar surveys done in West and East Africa.

The 7 country dataset is a subset of the Round 1 survey dataset, and consists of a combined dataset for the 7 Southern African countries surveyed with other African countries in Round 1, 1999-2000 (Botswana, Lesotho, Malawi, Namibia, South Africa, Zambia and Zimbabwe). It is a useful dataset because, in contrast to the full 12 country Round 1 dataset, all countries in this dataset were surveyed with the identical questionnaire

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

Basic units of analysis that the study investigates include: individuals and groups

Kind of data

Sample survey data [ssd]

Sampling procedure

A new sample has to be drawn for each round of Afrobarometer surveys. Whereas the standard sample size for Round 3 surveys will be 1200 cases, a larger sample size will be required in societies that are extremely heterogeneous (such as South Africa and Nigeria), where the sample size will be increased to 2400. Other adaptations may be necessary within some countries to account for the varying quality of the census data or the availability of census maps.

The sample is designed as a representative cross-section of all citizens of voting age in a given country. The goal is to give every adult citizen an equal and known chance of selection for interview. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible. A randomly selected sample of 1200 cases allows inferences to national adult populations with a margin of sampling error of no more than plus or minus 2.5 percent with a confidence level of 95 percent. If the sample size is increased to 2400, the confidence interval shrinks to plus or minus 2 percent.

Sample Universe

The sample universe for Afrobarometer surveys includes all citizens of voting age within the country. In other words, we exclude anyone who is not a citizen and anyone who has not attained this age (usually 18 years) on the day of the survey. Also excluded are areas determined to be either inaccessible or not relevant to the study, such as those experiencing armed conflict or natural disasters, as well as national parks and game reserves. As a matter of practice, we have also excluded people living in institutionalized settings, such as students in dormitories and persons in prisons or nursing homes.

What to do about areas experiencing political unrest? On the one hand we want to include them because they are politically important. On the other hand, we want to avoid stretching out the fieldwork over many months while we wait for the situation to settle down. It was agreed at the 2002 Cape Town Planning Workshop that it is difficult to come up with a general rule that will fit all imaginable circumstances. We will therefore make judgments on a case-by-case basis on whether or not to proceed with fieldwork or to exclude or substitute areas of conflict. National Partners are requested to consult Core Partners on any major delays, exclusions or substitutions of this sort.

Sample Design

The sample design is a clustered, stratified, multi-stage, area probability sample.

To repeat the main sampling principle, the objective of the design is to give every sample element (i.e. adult citizen) an equal and known chance of being chosen for inclusion in the sample. We strive to reach this objective by (a) strictly applying random selection methods at every stage of sampling and by (b) applying sampling with probability proportionate to population size wherever possible.

In a series of stages, geographically defined sampling units of decreasing size are selected. To ensure that the sample is representative, the probability of selection at various stages is adjusted as follows:

The sample is stratified by key social characteristics in the population such as sub-national area (e.g. region/province) and residential locality (urban or rural). The area stratification reduces the likelihood that distinctive ethnic or language groups are left out of the sample. And the urban/rural stratification is a means to make sure that these localities are represented in their correct proportions. Wherever possible, and always in the first stage of sampling, random sampling is conducted with probability proportionate to population size (PPPS). The purpose is to guarantee that larger (i.e., more populated) geographical units have a proportionally greater probability of being chosen into the sample. The sampling design has four stages

A first-stage to stratify and randomly select primary sampling units;

A second-stage to randomly select sampling start-points;

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

The primary sampling units (PSU's) are the smallest, well-defined geographic units for which reliable population data are available. In most countries, these will be Census Enumeration Areas (or EAs). Most national census data and maps are broken down to the EA level. In the text that follows we will use the acronyms PSU and EA interchangeably because, when census data are employed, they refer to the same unit.

We strongly recommend that NIs use official national census data as the sampling frame for Afrobarometer surveys. Where recent or reliable census data are not available, NIs are asked to inform the relevant Core Partner before they substitute any other demographic data. Where the census is out of date, NIs should consult a demographer to obtain the best possible estimates of population growth rates. These should be applied to the outdated census data in order to make projections of population figures for the year of the survey. It is important to bear in mind that population growth rates vary by area (region) and (especially) between rural and urban localities. Therefore, any projected census data should include adjustments to take such variations into account.

Indeed, we urge NIs to establish collegial working relationships within professionals in the national census bureau, not only to obtain the most recent census data, projections, and maps, but to gain access to sampling expertise. NIs may even commission a census statistician to draw the sample to Afrobarometer specifications, provided that provision for this service has been made in the survey budget.

Regardless of who draws the sample, the NIs should thoroughly acquaint themselves with the strengths and weaknesses of the available census data and the availability and quality of EA maps. The country and methodology reports should cite the exact census data used, its known shortcomings, if any, and any projections made from the data. At minimum, the NI must know the size of the population and the urban/rural population divide in each region in order to specify how to distribute population and PSU's in the first stage of sampling. National investigators should obtain this written data before they attempt to stratify the sample.

Once this data is obtained, the sample population (either 1200 or 2400) should be stratified, first by area (region/province) and then by residential locality (urban or rural). In each case, the proportion of the sample in each locality in each region should be the same as its proportion in the national population as indicated by the updated census figures.

Having stratified the sample, it is then possible to determine how many PSU's should be selected for the country as a whole, for each region, and for each urban or rural locality.

The total number of PSU's to be selected for the whole country is determined by calculating the maximum degree of clustering of interviews one can accept in any PSU. Because PSUs (which are usually geographically small EAs) tend to be socially homogenous we do not want to select too many people in any one place. Thus, the Afrobarometer has established a standard of no more than 8 interviews per PSU. For a sample size of 1200, the sample must therefore contain 150 PSUs/EAs (1200 divided by 8). For a sample size of 2400, there must be 300 PSUs/EAs.

These PSUs should then be allocated proportionally to the urban and rural localities within each regional stratum of the sample. Let's take a couple of examples from a country with a sample size of 1200. If the urban locality of Region X in this country constitutes 10 percent of the current national population, then the sample for this stratum should be 15 PSUs (calculated as 10 percent of 150 PSUs). If the rural population of Region Y constitutes 4 percent of the current national population, then the sample for this stratum should be 6 PSU's.

The next step is to select particular PSUs/EAs using random methods. Using the above example of the rural localities in Region Y, let us say that you need to pick 6 sample EAs out of a census list that contains a total of 240 rural EAs in Region Y. But which 6? If the EAs created by the national census bureau are of equal or roughly equal population size, then selection is relatively straightforward. Just number all EAs consecutively, then make six selections using a table of random numbers. This procedure, known as simple random sampling (SRS), will

In celebration of the “International year of elderly people” 1999 as well as by special request of the Federal Ministry for environment, youth and family this special survey from June 1998 is once more on the topic “elderly people”. It had already been the topic of several special programs of the years 1971, 1979 and 1987. With regard to the foreseeable superannuation of the population it is necessary to have up-to-date data on the life conditions of elderly people which can serve as a basis for political decisions. 20 percent of the Austrian population are over 60 years old. In 2030 it will be 32 percent. To handle these expected changes in the population structure it is necessary to take measures in the field of health care, sufficient social services, infrastructure and housing situation. To effectively control the socio-political superstructures, politicians need data on the so called “generation transfer”. Therefore, this survey also poses questions on in how far elderly people get help for free (certain expense allowances are acceptable) form their relatives and friends and how often elderly people themselves help their (grand)children or other persons. To gain more insight in the life situation of the population over 60, interviewees are also asked, whether they are part of a family network of whether they live socially isolated.

The 2006-07 Namibia Demographic and Health Survey (NDHS) is a nationally representative survey of 9,804 women age 15-49 and 3,915 men age 15-49. The 2006-07 NDHS is the third comprehensive survey conducted in Namibia as part of the Demographic and Health Surveys (DHS) programme. The data are intended to provide programme managers and policymakers with detailed information on levels and trends in fertility; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of mothers and young children; early childhood mortality, adult and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. The 2006-07 NDHS is the first NDHS survey to collect information on malaria prevention and treatment. The 2006-07 NDHS has been a large-scale research project. Twenty-eight field teams interviewed about 9,200 households, 9,800 women and 3,900 men age 15-49. The interviews were conducted between November 2006 and March 2007. The survey covered about 500 primary sampling units in all regions. The 2006-07 Namibia Demographic and Health Survey is designed to: Determine key demographic rates, particularly fertility, under-five mortality, and adult mortality rates; Investigate the direct and indirect factors that determine the level and trends of fertility; Measure the level of contraceptive knowledge and practice among women and men by method; Determine immunisation coverage and prevalence and treatment of diarrhoea and acute respiratory diseases among children under five; identify infant and young child feeding practices and assess the nutritional status of children age 6-59 months and women age 15-49 years; Assess knowledge and attitudes of women and men regarding sexually transmitted infections and HIV/AIDS, and evaluate patterns of recent behaviour regarding condom use; Identify behaviours that protect or predispose people to HIV infection and examine social, economic, and cultural determinants of HIV; Determine the proportion of households with orphans and vulnerable children (OVCs); and Determine the proportion of households with sick people taken care of at household level. The 2006-07 NDHS is part of the worldwide Demographic and Health Surveys (DHS) programme funded by the United States Agency for International Development (USAID). DHS surveys are designed to collect data on fertility, family planning, and maternal and child health; assist countries in conducting periodic surveys to monitor changes in population, health, and nutrition; and provide an international database that can be used by researchers investigating topics related to population, health, and nutrition. MAIN RESULTS Fertility : The survey results show that Namibia has experienced a decline in fertility of almost two births over the past 15 years, with the fertility rate falling from 5.4 births per woman in 19901992 to 3.6 births in 2005-07. Family planning : Knowledge of family planning in Namibia has been nearly universal since 1992. In the 2006-07 NDHS, 98 percent of all women reported knowing about a contraceptive method. Male condoms, injectables, and the pill are the most widely known methods. Child health : Data from the 2006-07 NDHS indicate that the under-five mortality rate in Namibia is 69 deaths per 1,000 live births (based on the five-year period preceding the survey). Maternal health : In Namibia, almost all women who had a live birth in the five years preceding the survey received antenatal care from health professionals (95 percent): 16 percent from a doctor and 79 percent from a nurse or midwife. Only 4 percent of mothers did not receive any antenatal care. Breastfeeding and nutrition : Breastfeeding is common in Namibia, with 94 percent of children breastfed at some point during childhood. The median breastfeeding duration in Namibia is 16.8 months. Malaria: One in four households interviewed in the survey has at least one mosquito net, and most of these households have a net that has been treated at some time with an insecticide (20 percent). HIV/AIDS and STIS : Knowledge of HIV and AIDS is universal in Namibia; 99 percent of women age 15-49 and 99 percent of men age 15-49 have heard of AIDS. Orphans and vulnerable children : One-quarter of Namibian children under age 18 in the households sampled for the 2006-07 NDHS live with both parents, while one in three does not live with either parent. Seventeen percent of children under age 18 are orphaned, that is, one or both parents is dead. Access to health facilities : Households interviewed in the 2006-07 NDHS were asked to name the nearest government health facility, the mode of transport they would use to visit the facility, and how long it takes to get to the facility using the transport of choice.

Number of deaths and mortality rates, by age group, sex, and place of residence, 1991 to most recent year.

On October 20, 2022, CDC began retrieving aggregate case and death data from jurisdictional and state partners weekly instead of daily. This dataset contains archived historical community transmission and related data elements by county. Although these data will continue to be publicly available, this dataset has not been updated since October 20, 2022. An archived dataset containing weekly historical community transmission data by county can also be found here: Weekly COVID-19 County Level of Community Transmission Historical Changes | Data | Centers for Disease Control and Prevention (cdc.gov).

Related data CDC has been providing the public with two versions of COVID-19 county-level community transmission level data: this historical dataset with the daily county-level transmission data from January 22, 2020, and a dataset with the daily values as originally posted on the COVID Data Tracker. Similar to this dataset, the original dataset with daily data as posted is archived on 10/20/2022. It will continue to be publicly available but will no longer be updated. A new dataset containing community transmission data by county as originally posted is now published weekly and can be found at: Weekly COVID-19 County Level of Community Transmission as Originally Posted | Data | Centers for Disease Control and Prevention (cdc.gov).

This public use dataset has 7 data elements reflecting historical data for community transmission levels for all available counties and jurisdictions. It contains historical data for the county level of community transmission and includes updated data submitted by states and jurisdictions. Each day, the dataset was updated to include the most recent days’ data and incorporate any historical changes made by jurisdictions. This dataset includes data since January 22, 2020. Transmission level is set to low, moderate, substantial, or high using the calculation rules below.

Methods for calculating county level of community transmission indicator The County Level of Community Transmission indicator uses two metrics: (1) total new COVID-19 cases per 100,000 persons in the last 7 days and (2) percentage of positive SARS-CoV-2 diagnostic nucleic acid amplification tests (NAAT) in the last 7 days. For each of these metrics, CDC classifies transmission values as low, moderate, substantial, or high (below and here). If the values for each of these two metrics differ (e.g., one indicates moderate and the other low), then the higher of the two should be used for decision-making.

CDC core metrics of and thresholds for community transmission levels of SARS-CoV-2

Total New Case Rate Metric: "New cases per 100,000 persons in the past 7 days" is calculated by adding the number of new cases in the county (or other administrative level) in the last 7 days divided by the population in the county (or other administrative level) and multiplying by 100,000. "New cases per 100,000 persons in the past 7 days" is considered to have transmission level of Low (0-9.99); Moderate (10.00-49.99); Substantial (50.00-99.99); and High (greater than or equal to 100.00).

Test Percent Positivity Metric: "Percentage of positive NAAT in the past 7 days" is calculated by dividing the number of positive tests in the county (or other administrative level) during the last 7 days by the total number of tests resulted over the last 7 days. "Percentage of positive NAAT in the past 7 days" is considered to have transmission level of Low (less than 5.00); Moderate (5.00-7.99); Substantial (8.00-9.99); and High (greater than or equal to 10.00).

If the two metrics suggest different transmission levels, the higher level is selected. If one metric is missing, the other metric is used for the indicator.

The reported transmission categories include:

Low Transmission Threshold: Counties with fewer than 10 total cases per 100,000 population in the past 7 days, and a NAAT percent test positivity in the past 7 days below 5%;

Moderate Transmission Threshold: Counties with 10-49 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 5.0-7.99%;

Substantial Transmission Threshold: Counties with 50-99 total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 8.0-9.99%;

High Transmission Threshold: Counties with 100 or more total cases per 100,000 population in the past 7 days or a NAAT test percent positivity in the past 7 days of 10.0% or greater.

Blank: total new cases in the past 7 days are not reported (county data known to be unavailable) and the percentage of positive NAATs tests during the past 7 days (blank) are not reported.

Data Suppression To prevent the release of data that could be used to identify people, data cells are suppressed for low frequency. When the case counts used to calculate the total new case rate metric ("cases_per_100K_7_day_count_change") is greater than zero and less than 10, this metric is set to "suppressed" to protect individual privacy. If the case count is 0, the total new case rate metric is still displayed.

The data in this dataset are considered provisional by CDC and are subject to change until the data are reconciled and verified with the state and territorial data providers. This datasets are created using CDC’s Policy on Public Health Research and Nonresearch Data Management and Access.

Duplicate Records Issue A bug was found on 12/28/2021 that caused many records in the dataset to be duplicated. This issue was resolved on 01/06/2022.

Estimated number of persons on July 1, by 5-year age groups and gender, and median age, for Canada, provinces and territories.

Over the past decade, Albania has been undergoing a transition toward a market economy and a more open society. It has faced severe internal and external challenges, such as lack of basic infrastructure, rapid collapse of output and inflation rise after the collapse of the communist regime, turmoil during the 1997 pyramid crisis, and social and economic instability because of the 1999 Kosovo crisis. Despite these shocks, Albanian economy has recovered from a very low income level through a sustained growth during the past few years, even though it remains one of the poorest countries in Europe, with GDP per capita at around 1,300$. Based on the Living Standard Measurement Study (LSMS) 2002 survey data (wave 1, henceforth), for the first time in Albania INSTAT has computed an absolute poverty line on a nationally representative poverty survey at household level. Based on this welfare measure, one quarter (25.4 percent) of the Albanian population, or close to 790,000 individuals, were defined as poor in 2002. The distribution of poverty is also disproportionately rural, as 68 percent of the poor are in rural areas, against 32 percent in urban areas (as compared to a total urban population well over 40 percent). These estimates are quite sensitive to the choice of the poverty line, as there are a large number of households clustered around the poverty line. Income related poverty is compounded by the severe lack of access to basic infrastructure, education and health services, clean water, etc., and the ability of the Government to address these issues is complicated by high levels of internal and external migration that are not well understood. The availability of a nationally representative survey is crucial as the paucity of household-level information has been a constraining factor in the design, implementation and evaluation of economic and social programs in Albania. Two recent surveys carried out by the Albanian Institute of Statistics (INSTAT) –the 1998 Living Conditions Survey (LCS) and the 2000 Household Budget Survey (HBS)– drew attention, once again, to the need for accurately measuring household welfare according to well-accepted standards, and for monitoring these trends on a regular basis. This target is well-achieved by drawing information over time on a panel component of LSMS 2002 households, namely the Albanian Panel Survey (APS), conducted in 2003 and 2004. An increasing attention to the policies aimed at achieving the Millennium Development Goals (MDGs) is paid by the National Parliament of Albania, recently witnessed by the resolution approved in July 2003, where it pushes “[...] the total commitment of both state structures and civil society to achieve the MDGs in Albania by 2015”. The path towards a sustained growth is constantly monitored through the National Reports on Progress toward Achieving the MDGs, which involves a close collaboration of the UN with the national institutions, led by the National Strategy for Social and Economic Development (NSSED) Department of the Ministry of Finance. Also, in the process leading to the Poverty Reduction Strategy Paper (PRSP; also known in Albania as Growth and Poverty Reduction Strategy, GPRS), the Government of Albania reinforced its commitment to strengthening its own capacity to collect and analyze on a regular basis information it needs to inform policy-makers. In its first phase (2001-2006), this monitoring system will include the following data collection instruments: (i) Population and Housing Census; (ii) Living Standards Measurement Surveys every 3 years, and (iii) annual panel surveys. The focus during this first phase of the monitoring system is on a periodic LSMS (in 2002 and 2005), followed by panel surveys on a sub-sample of LSMS households (APS 2003, 2004 and 2006), drawing heavily on the 2001 census information. Here our target is to illustrate the main characteristics of the APS 2004 data with reference to the LSMS. The survey work was undertaken by the Living Standards Unit of INSTAT, with the technical assistance of the World Bank.

As of July 2024, Nigeria's population was estimated at around 229.5 million. Between 1965 and 2024, the number of people living in Nigeria increased at an average rate of over two percent. In 2024, the population grew by 2.42 percent compared to the previous year. Nigeria is the most populous country in Africa. By extension, the African continent records the highest growth rate in the world. Africa's most populous country Nigeria was the most populous country in Africa as of 2023. As of 2022, Lagos held the distinction of being Nigeria's biggest urban center, a status it also retained as the largest city across all of sub-Saharan Africa. The city boasted an excess of 17.5 million residents. Notably, Lagos assumed the pivotal roles of the nation's primary financial hub, cultural epicenter, and educational nucleus. Furthermore, Lagos was one of the largest urban agglomerations in the world. Nigeria's youthful population In Nigeria, a significant 50 percent of the populace is under the age of 19. The most prominent age bracket is constituted by those up to four years old: comprising 8.3 percent of men and eight percent of women as of 2021. Nigeria boasts one of the world's most youthful populations. On a broader scale, both within Africa and internationally, Niger maintains the lowest median age record. Nigeria secures the 20th position in global rankings. Furthermore, the life expectancy in Nigeria is an average of 62 years old. However, this is different between men and women. The main causes of death have been neonatal disorders, malaria, and diarrheal diseases.

Victims of gang-related homicides (total number of homicide victims; number of homicide victims - unknown gang-relation; number of homicide victims - known gang relation; number of gang-related homicide victims; percentage of gang-related homicide victims; rate (per 100,000 population) of gang-related homicide victims), Canada and regions, 1999 to 2024.

This table contains mortality indicators by sex for Canada and all provinces except Prince Edward Island. These indicators are derived from three-year complete life tables. Mortality indicators derived from single-year life tables are also available (table 13-10-0837). For Prince Edward Island, Yukon, the Northwest Territories and Nunavut, mortality indicators derived from three-year abridged life tables are available (table 13-10-0140).

Unemployment Rate in South Korea decreased to 2.60 percent in June from 2.70 percent in May of 2025. This dataset provides - South Korea Unemployment Rate - actual values, historical data, forecast, chart, statistics, economic calendar and news.

This data presents national-level provisional maternal mortality rates based on a current flow of mortality and natality data in the National Vital Statistics System. Provisional rates which are an early estimate of the number of maternal deaths per 100,000 live births, are shown as of the date specified and may not include all deaths and births that occurred during a given time period (see Technical Notes).

A maternal death is the death of a woman while pregnant or within 42 days of termination of pregnancy irrespective of the duration and the site of the pregnancy, from any cause related to or aggravated by the pregnancy or its management, but not from accidental or incidental causes. In this data visualization, maternal deaths are those deaths with an underlying cause of death assigned to International Statistical Classification of Diseases, 10th Revision (ICD-10) code numbers A34, O00–O95, and O98–O99.

The provisional data include reported 12 month-ending provisional maternal mortality rates overall, by age, and by race and Hispanic origin. Provisional maternal mortality rates presented in this data visualization are for “12-month ending periods,” defined as the number of maternal deaths per 100,000 live births occurring in the 12-month period ending in the month indicated. For example, the 12-month ending period in June 2020 would include deaths and births occurring from July 1, 2019, through June 30, 2020. Evaluation of trends over time should compare estimates from year to year (June 2020 and June 2021), rather than month to month, to avoid overlapping time periods. In the visualization and in the accompanying data file, rates based on death counts less than 20 are suppressed in accordance with current NCHS standards of reliability for rates. Death counts between 1-9 in the data file are suppressed in accordance with National Center for Health Statistics (NCHS) confidentiality standards.

Provisional data presented on this page will be updated on a quarterly basis as additional records are received. Previously released estimates are revised to include data and record updates received since the previous release. As a result, the reliability of estimates for a 12-month period ending with a specific month will improve with each quarterly release and estimates for previous time periods may change as new data and updates are received.

About this dataset

Context

The dataset tabulates the Non-Hispanic population of Live Oak by race. It includes the distribution of the Non-Hispanic population of Live Oak across various race categories as identified by the Census Bureau. The dataset can be utilized to understand the Non-Hispanic population distribution of Live Oak across relevant racial categories.

Key observations

Of the Non-Hispanic population in Live Oak, the largest racial group is White alone with a population of 2,945 (55.42% of the total Non-Hispanic population).

Content

When available, the data consists of estimates from the U.S. Census Bureau American Community Survey (ACS) 2019-2023 5-Year Estimates.

Racial categories include:

• White
• Black or African American
• American Indian and Alaska Native
• Asian
• Native Hawaiian and Other Pacific Islander
• Some other race
• Two or more races (multiracial)

Variables / Data Columns

• Race: This column displays the racial categories (for Non-Hispanic) for the Live Oak
• Population: The population of the racial category (for Non-Hispanic) in the Live Oak is shown in this column.
• % of Total Population: This column displays the percentage distribution of each race as a proportion of Live Oak total Non-Hispanic population. 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 Live Oak Population by Race & Ethnicity. You can refer the same here

The 2006-07 Swaziland Demographic and Health Survey (SDHS) is a nationally representative survey of 4,843 households, 4,987 women age 15-49, and 4,156 men age 15-49. The SDHS also included individual interviews with boys and girls age 12-14 and older adults age 50 and over. The survey of persons age 12-14 and age 50 and over was carried out in every other household selected in the SDHS. Interviews were completed for 459 girls and 411 boys age 12-14, and 661 women and 456 men age 50 and over. The 2006-07 SDHS is the first national survey conducted in Swaziland as part of the Demographic and Health Surveys (DHS) programme. The data are intended to furnish programme managers and policymakers with detailed information on levels and trends in fertility; nuptiality; sexual activity; fertility preferences; awareness and use of family planning methods; breastfeeding practices; nutritional status of mothers and young children; early childhood mortality and maternal mortality; maternal and child health; and awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections. The survey also collected information on malaria prevention and treatment. The 2006-07 SDHS is the first nationwide survey in Swaziland to provide population-based prevalence estimates for anaemia and HIV. Children age 6 months and older as well as adults were tested for anaemia. Children age 2 years and older as well as adults were tested for HIV. The principal objective of the 2006-07 Swaziland Demographic and Health Survey (SDHS) was to provide up-to-date information on fertility, childhood mortality, marriage, fertility preferences, awareness, and use of family planning methods, infant feeding practices, maternal and child health, maternal mortality, HIV/AIDS-related knowledge and behaviour and prevalence of HIV and anaemia. More specifically the 2006-07 SDHS was aimed at achieving the following; Determine key demographic rates, particularly fertility, under-five mortality, and adult mortality rates Investigate the direct and indirect factors which determine the level and trends of fertility Measure the level of contraceptive knowledge and practice of women and men by method Determine immunization coverage and prevalence and treatment of diarrhoea and acute respiratory diseases among children under five Determine infant and young child feeding practices and assess the nutritional status of children 6-59 months, women age 15-49 years, and men aged 15-49 years Estimate prevalence of anaemia Assess knowledge and attitudes of women and men regarding sexually transmitted infections and HIV/AIDS, and evaluate patterns of recent behaviour regarding condom use Identify behaviours that protect or predispose the population to HIV infection Examine social, economic, and cultural determinants of HIV Determine the proportion of households with orphans and vulnerable children (OVCs) Determine the proportion of households with sick people taken care at household level Determine HIV prevalence among males and females age 2 years and older Determine the use of iodized salt in households Describe care and protection of children age 12-14 years, and their knowledge and attitudes about sex and HIV/AIDS. This information is intended to provide data to assist policymakers and programme implementers to monitor and evaluate existing programmes and to design new strategies for demographic, social and health policies in Swaziland. The survey also provides data to monitor the country's achievement towards the Millenium Development Goals. MAIN RESULTS Fertility in Swaziland has been declining rapidly, with the TFR falling from 6.4 births per woman in 1986 to 3.8 births at the time of the SDHS. As expected, fertility is higher in rural areas (4.2 births per woman) than in urban areas (3.0 births per woman). Fertility differentials by education and wealth are substantial. Women with no education have on average 4.9 children compared with 2.4 children for women with tertiary education. Fertility varies widely according to household wealth. Women in the highest wealth quintile have 2.9 children fewer than women in the lowest quintile (2.6 and 5.5 births per woman, respectively). Knowledge of family planning is universal in Swaziland. The most widely known method is the male condom (99 percent for both males and females). Among women, other widely known methods include injectables (96 percent), the pill (95 percent), and the female condom (91 percent). For men, the best known methods besides the male condom are the female condom (94 percent) and the pill and injectables (84 percent each). Children are considered fully vaccinated when they receive one dose of BCG vaccine, three doses each of DPT and polio vaccines, and one dose of measles vaccine. BCG coverage among children age 12-23 months is nearly universal (97 percent); coverage is also high for the first doses of DPT (96 percent) and polio (97 percent). The proportion of children receiving subsequent doses of DPT and polio vaccines drops slightly, with 92 percent of children receiving the third dose of DPT and 87 percent receiving the third dose of polio. Ninety-two percent of children had received a measles vaccination by the time of the SDHS. Overall, 82 percent of children age 12-23 months are fully immunised. In Swaziland, almost all women who had a live birth in the five years preceding the survey received antenatal care from health professionals (97 percent); 9 percent received care from a doctor, and 88 percent received care from a trained nurse or midwife. Only 3 percent of mothers did not receive any antenatal care Overall, 87 percent of children in Swaziland are breastfed for some period of time (ever breastfed). The median duration of any breast-feeding in Swaziland is almost 17 months. However, the median duration of exclusive breast-feeding is much shorter (0.7 months). In interpreting the malaria programme indicators in Swaziland, it is important to recognise that the disease affects an estimated 30 percent of the population where malaria is most prevalent (the Lubombo Plateau, the lowveld, and parts of the middleveld). Malaria is also seasonal, occurring mainly during or after the rainy season (from November to March). A substantial part of the SDHS fieldwork took place outside of this period. Results from the HIV testing component in the 2006-07 SDHS indicate that 26 percent of Swazi adults age 15-49 are infected with HIV. Among women, the HIV rate is 31 percent, compared with 20 percent among men. HIV prevalence peaks at 49 percent for women age 25-29, which is almost five times the rate among women age 15-19 and more than twice the rate observed among women age 45-49. HIV prevalence increases from 2 percent among men in the 15-19 age group to 45 percent in the age group 35-39 and then decreases to 28 percent among men age 45-49. HIV prevalence for women and men age 50 or over is 12 percent and 18 percent, respectively. Among the population age 2-14 years, 4 percent of girls and boys are infected.