This dataset presents the preliminary estimates of the resident population by age and sex as at 30 June 2017. The data is aggregated to Greater Capital City Statistical Areas (GCCSA), according to the 2016 edition of the Australian Statistical Geography Standard (ASGS). Estimated resident population (ERP) is the official estimate of the Australian population, which links people to a place of usual residence within Australia. Usual residence within Australia refers to that address at which the person has lived or intends to live for six months or more in a given reference year. For the 30 June reference date, this refers to the calendar year around it. Estimates of the resident population are based on Census counts by place of usual residence (excluding short-term overseas visitors in Australia), with an allowance for Census net undercount, to which are added the estimated number of Australian residents temporarily overseas at the time of the Census. A person is regarded as a usual resident if they have been (or expected to be) residing in Australia for a period of 12 months or more over a 16-month period. This data is ABS data (catalogue number: 3235.0) available from the Australian Bureau of Statistics. For more information please visit the Explanatory Notes.

Household Travel Survey (HTS) is the most comprehensive source of personal travel data for the Sydney Greater Metropolitan Area (GMA). This data explores average weekday travel patterns for residents in Sydney GMA.\r \r The Household Travel Survey (HTS) collects information on personal travel behaviour. The study area for the survey is the Sydney Greater Metropolitan Area (GMA) which includes Sydney Greater Capital City Statistical Area (GCCSA), parts of Illawarra and Hunter regions. All residents of occupied private dwellings within the Sydney GMA are considered within scope of the survey and are randomly selected to participate.\r The HTS has been running continuously since 1997/981 and collects data for all days through the year – including during school and public holidays.\r \r Typically, approximately 2,000-3,000 households participate in the survey annually. Data is collected on all trips made over a 24-hour period by all members of the participating households.\r \r Annual estimates from the HTS are usually produced on a rolling basis using multiple years of pooled data for each reporting year2. All estimates are weighted to the Australian Bureau of Statistics’ Estimated Resident Population, corresponding to the year of collection3. Unless otherwise stated, all reported estimates are for an average weekday.\r \r \r \r Due to disruptions in data collection resulting from the lockdowns during the COVID-19 pandemic, post-COVID releases of HTS data are based on a lower sample size than previous HTS releases. To ensure integrity of the results and mitigate risk of sampling errors some post-COVID results have been reported differently to previous years. Please see below for more information on changes to HTS post-COVID (2020/21 onwards).\r \r 1. Data collection for the HTS was suspended during lock-down periods announced by the NSW Government due to COVID-19.\r \r 2. Exceptions apply to the estimates for 2020/21 which are based on a single year of sample as it was decided not to pool the sample with data collected pre-COVID-19. \r \r 3. HTS population estimates are also slightly lower than those reported in the ABS census as the survey excludes overseas visitors and those in non-private dwellings.\r \r Changes to HTS post-COVID (2020/21 onwards)\r \r HTS was suspended from late March 2020 to early October 2020 due to the impact and restrictions of COVID-19, and again from July 2021 to October 2021 following the Delta wave of COVID-19. Consequently, both the 2020/21 and 2021/22 releases are based on a reduced data collection period and smaller samples.\r \r Due to the impact of changed travel behaviours resulting from COVID-19 breaking previous trends, HTS releases since 2020/21 have been separated from pre-COVID-19 samples when pooled. As a result, HTS 2020/21 was based on a single wave of data collection which limited the breadth of geography available for release. Subsequent releases are based on pooled post-COVID samples to expand the geographies included with reliable estimates.\r \r Disruption to the data collection during, and post-COVID has led to some adjustments being made to the HTS estimates released post-COVID:\r \r SA3 level data has not been released for 2020/21 and 2021/22 due to low sample collection.\r LGA level data for 2021/22 has been released for selected LGAs when robust Relative Standard Error (RSE) for total trips are achieved\r Mode categories for all geographies are aggregated differently to the pre-COVID categories\r Purpose categories for some geographies are aggregated differently across 2020/21 and 2021/22.\r A new data release – for six cities as defined by the Greater Sydney Commission - is included since 2021/22.\r Please refer to the Data Document for 2022/23 (PDF, 262.54 KB) for further details.\r \r \r RELEASE NOTE\r \r The latest release of HTS data is 15 May 2025. This release includes Region, LGA, SA3 and Six Cities data for 2023/24. Please see 2023/24 Data Document for details.\r \r A revised dataset for LGAs and Six Cities for HTS 2022/23 data has also been included in this release on 15 May 2025. If you have downloaded HTS 2022/23 data by LGA and/or Six Cities from this link prior to 15/05/2025, we advise you replace it with the revised tables. If you have been supplied bespoke data tables for 2022/23 LGAs and/or Six Cities, please request updated tables.\r \r Revisions to HTS data may be made on previously published data as new sample data is appended to improve reliability of results. Please check this page for release dates to ensure you are using the most current version or create a subscription (https://opendata.transport.nsw.gov.au/subscriptions) to be notified of revisions and future releases.\r

Abstract

Obtaining reliable size estimates for key populations is crucial for the Rwanda Biomedical Center/Institute of HIV/AIDS, Disease Prevention and Control (RBC/IHDPC) and their partners to design an effective HIV response in line with the national HIV strategy. Estimating the size of key populations at higher risk for HIV not only allows for an understanding of the magnitude of the response that is needed, but also helps in more accurately projecting the future of the epidemic in Rwanda. To be effective, it is important to produce consistent and comparable estimates over time. The following study utilized a single household survey to estimate the size of several key populations, including sex workers, men who have sex with men (MSM), injecting drug users (IDU), and clients of sex workers. These populations include several groups outlined in the National Strategic Plan for HIV and AIDS as most at risk for HIV infection, specifically sex workers and MSM.

Geographic coverage

National

Analysis unit

• Household
• Individuals

Kind of data

Sample survey data [ssd]

Sampling procedure

The ESPHS used a two-stage sample design, implemented in a representative sample of 2,125 households selected nationwide in which all women and men age 15 years and above where eligible for an individual interview. Each of these households was visited to obtain information using the Household Questionnaire. All women and all men age 15 years and above were eligible to be individually interviewed, if they were either usual residents of the household or visitors present in the household on the night before the survey. A total of 4,669 women and men were successfully interviewed.

The sampling frame used was the preparatory frame for the Rwanda Population and Housing Census (RPHC) 2012, provided by the National Institute of Statistics of Rwanda (NISR). The sampling frame is a complete list of natural villages covering the whole country (14,837 villages). Two strata were defined: the city of Kigali and the rest of the country. One hundred and thirty Primary Sampling Units (PSU) were selected from the sampling frame (35 in Kigali and 95 in the other stratum). To reduce clustering effect, only 20 households were selected per cluster in Kigali and 15 in the other clusters. As a result, 33 percent of the households in the sample were located in Kigali.

The list of households in each cluster was updated upon arrival of the survey team in the cluster. Once the listing had been updated, a number was assigned to each existing household in the cluster. The supervisor then identified the households to be interviewed in the survey by using a table in which the households were randomly pre-selected. This table also provided the list of households pre-selected for each of the two different definitions of what it means “to know” someone.

Mode of data collection

Face-to-face [f2f]

Research instrument

The survey used two types of questionnaires: a household questionnaire and an individual questionnaire. The same individual questionnaire was used to interview both women and men. In addition, two versions of the individual questionnaire were developed, using two different definitions of what it means “to know” someone. Each version of the individual questionnaire was used in half of the selected households.

Household questionnaire: The Household Questionnaire was a short version of the 2011 Rwanda DHS questionnaire. It was primarily used to list all the usual members and visitors in the selected households and to collect some basic information on the characteristics of each person listed, including age, sex, status of residence, and marital status. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of water, type of toilet facilities, and ownership of various durable goods. This information was used to create an index representing the wealth of the households. The wealth index is a proxy for long-term standard of living of the households and is used in the following analysis as a background characteristic of the respondents who are members of these households.

Individual questionnaire: The individual questionnaire was organized accordingly and included six sections: - Respondent’s background; - Known population; - Summation; - Target population; - Proxy respondent; and - Stigma.

Cleaning operations

The processing of the ESPHS data began shortly after the fieldwork commenced. Completed questionnaires were returned periodically from the field to the SPH office in Kigali, where they were entered and checked for consistency by data processing personnel who were specially trained for this task. Data were entered using CSPro, a programme specially developed for use in DHS surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, because the School of Public Health had the opportunity to advise field teams of problems detected during data entry. The data entry and editing phase of the survey was completed in late August 2011.

Response rate

The number of occupied households successfully interviewed was 2,102, yielding a household response rate of 99%. From the households interviewed, 2,629 women were found to be eligible and 2,567 were interviewed, giving a response rate of 98%. Interviews with men covered 2,102 of the eligible 2,149 men, yielding a response rate of 98%. The response rates do not significantly vary by type of questionnaire or residence.

Dataset for the maps accompanying the Housing in Aotearoa New Zealand: 2025 report. This dataset contains data for severe housing deprivation from the 2018 and 2023 Censuses.

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Data is available by health district.

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Severe housing deprivation has data for the census usually resident population from the 2018 and 2023 Censuses, including:

• estimated prevalence rate of severe housing deprivation (per 10,000 people)
• estimated rate for those; without shelter, in temporary accommodation, sharing someone else’s private dwelling, in uninhabitable housing, for whom it could not be determined whether they were severely housing deprived or not.

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Map shows the estimated prevalence rate of severe housing deprivation (per 10,000 people) for the census usually resident population for the 2023 Census.

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Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

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Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city. 

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Population counts

Stats NZ publishes a number of different population counts, each using a different definition and methodology. Population statistics – user guide has more information about different counts. 

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Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

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Severe housing deprivation time series

The 2018 estimates of severe housing deprivation have been updated using the 2023 methodology for estimating severe housing deprivation. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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Severe housing deprivation

Figures in this map and geospatial file exclude Women’s refuge data, as well as estimates for children living in non-private dwellings. Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information.

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About the 2023 Census dataset

For information on the 2023 Census dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

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Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

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Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Census usually resident population count concept quality rating

The census usually resident population count is rated as very high quality.

Census usually resident population count – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Quality of severe housing deprivation data

Severe housing deprivation (homelessness) estimates – updated methodology: 2023 Census has more information on the data quality of this variable.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

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Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

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Inconsistencies in definitions

Please note that there may be differences in definitions between census classifications and those used for other data collections.

Dataset contains life-cycle age group census usually resident population counts from the 2013, 2018, and 2023 Censuses, as well as the percentage change in the age group population counts between the 2013 and 2018 Censuses, and between the 2018 and 2023 Censuses. Data is available by regional council.

The life-cycle age groups are:

• under 15 years
• 15 to 29 years
• 30 to 64 years
• 65 years and over.

Map shows the percentage change in the census usually resident population count for life-cycle age groups between the 2018 and 2023 Censuses.

Download lookup file from Stats NZ ArcGIS Online or embedded attachment in Stats NZ geographic data service. Download data table (excluding the geometry column for CSV files) using the instructions in the Koordinates help guide.

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Footnotes

Geographical boundaries

Statistical standard for geographic areas 2023 (updated December 2023) has information about geographic boundaries as of 1 January 2023. Address data from 2013 and 2018 Censuses was updated to be consistent with the 2023 areas. Due to the changes in area boundaries and coding methodologies, 2013 and 2018 counts published in 2023 may be slightly different to those published in 2013 or 2018.

​

Subnational census usually resident population

The census usually resident population count of an area (subnational count) is a count of all people who usually live in that area and were present in New Zealand on census night. It excludes visitors from overseas, visitors from elsewhere in New Zealand, and residents temporarily overseas on census night. For example, a person who usually lives in Christchurch city and is visiting Wellington city on census night will be included in the census usually resident population count of Christchurch city.

​

Caution using time series

Time series data should be interpreted with care due to changes in census methodology and differences in response rates between censuses. The 2023 and 2018 Censuses used a combined census methodology (using census responses and administrative data), while the 2013 Census used a full-field enumeration methodology (with no use of administrative data).

​

About the 2023 Census dataset

For information on the 2023 dataset see Using a combined census model for the 2023 Census. We combined data from the census forms with administrative data to create the 2023 Census dataset, which meets Stats NZ's quality criteria for population structure information. We added real data about real people to the dataset where we were confident the people who hadn’t completed a census form (which is known as admin enumeration) will be counted. We also used data from the 2018 and 2013 Censuses, administrative data sources, and statistical imputation methods to fill in some missing characteristics of people and dwellings.

​

Data quality

The quality of data in the 2023 Census is assessed using the quality rating scale and the quality assurance framework to determine whether data is fit for purpose and suitable for release. Data quality assurance in the 2023 Census has more information.

​

Quality rating of a variable

The quality rating of a variable provides an overall evaluation of data quality for that variable, usually at the highest levels of classification. The quality ratings shown are for the 2023 Census unless stated. There is variability in the quality of data at smaller geographies. Data quality may also vary between censuses, for subpopulations, or when cross tabulated with other variables or at lower levels of the classification. Data quality ratings for 2023 Census variables has more information on quality ratings by variable.

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Age concept quality rating

Age is rated as very high quality.

Age – 2023 Census: Information by concept has more information, for example, definitions and data quality.

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Using data for good

Stats NZ expects that, when working with census data, it is done so with a positive purpose, as outlined in the Māori Data Governance Model (Data Iwi Leaders Group, 2023). This model states that "data should support transformative outcomes and should uplift and strengthen our relationships with each other and with our environments. The avoidance of harm is the minimum expectation for data use. Māori data should also contribute to iwi and hapū tino rangatiratanga”.

​

Confidentiality

The 2023 Census confidentiality rules have been applied to 2013, 2018, and 2023 data. These rules protect the confidentiality of individuals, families, households, dwellings, and undertakings in 2023 Census data. Counts are calculated using fixed random rounding to base 3 (FRR3) and suppression of ‘sensitive’ counts less than six, where tables report multiple geographic variables and/or small populations. Individual figures may not always sum to stated totals. Applying confidentiality rules to 2023 Census data and summary of changes since 2018 and 2013 Censuses has more information about 2023 Census confidentiality rules.

Frequency: OccasionalTable: 98-10-0003-01Release date: 2022-02-09Geography: Census subdivision, Census metropolitan area, Census agglomerationUniverse: All persons, 2021 and 2016 censuses – 100% dataVariable List: Population and dwelling counts (13)List of abbreviations and acronyms found within various Census products.(https://www12.statcan.gc.ca/census-recensement/2021/ref/symb-ab-acr-eng.cfm)Footnotes appear in brackets in text.1) Content considerationsThe 2021 Census population counts for a particular geographic area represent the number of Canadians whose usual place of residence is in that area, regardless of where they happened to be on Census Day. Also included are any Canadians who were staying in that area on Census Day and who had no usual place of residence elsewhere in Canada, as well as those considered to be non-permanent residents. For most areas, there is little difference between the number of usual residents and the number of people staying in the area on Census Day. For certain places, however, such as tourist or vacation areas, or those including large work camps, the number of people staying in that area at any particular time could significantly exceed the number of usual residents shown here. The population counts include Canadians living in other countries, but do not include foreign residents living in Canada. Given these differences, users are advised not to interpret population counts as being the number of people living in the reported dwellings.The dwelling counts refer to total private dwellings and private dwellings occupied by usual residents in Canada. The census dwelling counts do not include collective dwellings, which are dwellings of a commercial, institutional or communal nature. The usual residents in collective dwellings are, however, included in the population counts.Changes occur to the names, boundaries and other characteristics of geographic areas (e.g., census subdivisions may amalgamate, or there may be an annexation or a change of name or status). Since the geographic framework is used for census data collection, the geographic reference date must be set several months before the date of the census in order to have these changes made in time. For the 2021 Census, the geographic reference date was January 1, 2021.Land area is the area in square kilometres of the land-based portions of standard geographic areas. The data are unofficial, and are provided for the sole purpose of calculating population density. Land area data for the standard geographic areas reflect the boundaries in effect on January 1, 2021 (the geographic reference date for the 2021 Census of Canada).DefinitionsThe Census Dictionary is a reference document which contains detailed definitions of Census of Population concepts, universes, variables, and geographic terms, as well as historical informationIncompletely enumerated reserves and settlementsIn 2021, a total of 63 census subdivisions defined as reserves and settlements were incompletely enumerated. For these reserves and settlements, dwelling enumeration was either not permitted or could not be completed because of the various reasons below.This represents an increase compared with the 14 census subdivisions defined as reserves and settlements that were incompletely enumerated in the 2016 Census. Health and safety restrictions put in place to slow the spread of COVID-19 and natural events (including evacuations because of forest fires) contributed to the incomplete enumeration of many reserves and settlements.The 2021 Census population and dwelling counts are not available for the 63 incompletely enumerated reserves and settlements, and are not included in 2021 Census tabulations. Data for geographic areas containing one or more of these reserves and settlements are noted accordingly. Because of the missing data, users are cautioned that—for the affected geographic areas—comparisons (e.g., percentage change) between 2016 and 2021 may not be precise. The impact of the missing data for higher-level geographic areas (Canada, provinces and territories, census metropolitan areas and census agglomerations) is usually very/ small. However, the impact can be significant for lower-level geographic areas (e.g., census divisions), where incompletely enumerated reserves and settlements account for a higher proportion of the population. This is especially true for lower-level geographic areas where a particular reserve or settlement was incompletely enumerated for the 2021 Census but enumerated for the 2016 Census and, vice versa.Adjustment of population countsStatistics Canada is committed to protect the privacy of all Canadians and the confidentiality of the data they provide to us. As part of this commitment, some population counts of geographic areas are adjusted in order to ensure confidentiality.Counts of the total population are rounded to a base of 5 for any dissemination block having a population of less than 15. Population counts for all standard geographic areas above the dissemination block level are derived by summing the adjusted dissemination block counts. The adjustment of dissemination block counts is controlled to ensure that the population counts for dissemination areas will always be within 5 of the actual values. The adjustment has no impact on the population counts of census divisions and large census subdivisions. Dwelling counts are not adjusted.Difference between census counts and population estimates.The Census of Population is designed to conduct a complete count of the population. Inevitably, however, some individuals will not be enumerated (undercoverage), while others, usually less numerous, will be enumerated more than once (overcoverage).To determine the number of people who were missed or counted more than once, Statistics Canada conducts postcensal studies of the coverage of the census population, using representative samples of the population. Results of these studies are usually available two years after Census Day. They are used, in combination with census figures and other sources, to develop the population estimates produced by Statistics Canada on a regular basis. Population estimates are used for equalization payments, to follow trends in the Canadian population on a quarterly basis and to understand the underlying components of population change (for example, births, deaths, immigrants, emigrants and non-permanent residents). Population estimates differ from census counts and are usually higher, because census counts are not adjusted for under-coverage or over-coverage.Cite: Statistics Canada. Table 98-10-0003-01 Population and dwelling counts: Census metropolitan areas, census agglomerations and census subdivisions (municipalities)https://www150.statcan.gc.ca/t1/tbl1/en/tv.action?pid=9810000301

Abstract

The 2005 Vietnam Population and AIDS Indicator Survey (VPAIS) was designed with the objective of obtaining national and sub-national information about program indicators of knowledge, attitudes and sexual behavior related to HIV/AIDS. Data collection took place from 17 September 2005 until mid-December 2005.

The VPAIS was implemented by the General Statistical Office (GSO) in collaboration with the National Institute of Hygiene and Epidemiology (NIHE). ORC Macro provided financial and technical assistance for the survey through the USAID-funded MEASURE DHS program. Financial support was provided by the Government of Vietnam, the United States President’s Emergency Plan for AIDS Relief, the United States Agency for International Development (USAID), and the United States Centers for Disease Control and Prevention/Global AIDS Program (CDC/GAP).

The survey obtained information on sexual behavior, and knowledge, attitudes, and behavior regarding HIV/AIDS. In addition, in Hai Phong province, the survey also collected blood samples from survey respondents in order to estimate the prevalence of HIV. The overall goal of the survey was to provide program managers and policymakers involved in HIV/AIDS programs with strategic information needed to effectively plan, implement and evaluate future interventions.

The information is also intended to assist policymakers and program implementers to monitor and evaluate existing programs and to design new strategies for combating the HIV/AIDS epidemic in Vietnam. The survey data will also be used to calculate indicators developed by the United Nations General Assembly Special Session on HIV/AIDS (UNGASS), UNAIDS, WHO, USAID, the United States President’s Emergency Plan for AIDS Relief, and the HIV/AIDS National Response.

The specific objectives of the 2005 VPAIS were: • to obtain information on sexual behavior. • to obtain accurate information on behavioral indicators related to HIV/AIDS and other sexually transmitted infections. • to obtain accurate information on HIV/AIDS program indicators. • to obtain accurate estimates of the magnitude and variation in HIV prevalence in Hai Phong Province.

Geographic coverage

National coverage

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame for the 2005 Vietnam Population and AIDS Indicator Survey (VPAIS) was the master sample used by the General Statistical Office (GSO) for its annual Population Change Survey (PCS 2005). The master sample itself was constructed in 2004 from the 1999 Population and Housing Census. As was true for the VNDHS 1997 and the VNDHS 2002 the VPAIS 2005 is a nationally representative sample of the entire population of Vietnam.

The survey utilized a two-stage sample design. In the first stage, 251 clusters were selected from the master sample. In the second stage, a fixed number of households were systematically selected within each cluster, 22 households in urban areas and 28 in rural areas.

The total sample of 251 clusters is comprised of 97 urban and 154 rural clusters. HIV/AIDS programs have focused efforts in the four provinces of Hai Phong, Ha Noi, Quang Ninh and Ho Chi Minh City; therefore, it was determined that the sample should be selected to allow for representative estimates of these four provinces in addition to the national estimates. The selected clusters were allocated as follows: 35 clusters in Hai Phong province where blood samples were collected to estimate HIV prevalence; 22 clusters in each of the other three targeted provinces of Ha Noi, Quang Ninh and Ho Chi Minh City; and the remaining 150 clusters from the other 60 provinces throughout the country.

Prior to the VPAIS fieldwork, GSO conducted a listing operation in each of the selected clusters. All households residing in the sample points were systematically listed by teams of enumerators, using listing forms specially designed for this activity, and also drew sketch maps of each cluster. A total of 6,446 households were selected. The VPAIS collected data representative of: • the entire country, at the national level • for urban and rural areas • for three regions (North, Central and South), see Appendix for classification of regions. • for four target provinces: Ha Noi, Hai Phong, Quang Ninh and Ho Chi Minh City.

All women and men aged 15-49 years who were either permanent residents of the sampled households or visitors present in the household during the night before the survey were eligible to be interviewed in the survey. All women and men in the sample points of Hai Phong who were interviewed were asked to voluntarily give a blood sample for HIV testing. For youths aged 15-17, blood samples were drawn only after first obtaining consent from their parents or guardians.

(Refer Appendix A of the final survey report for details of sample implementation)

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used in the survey, the Household Questionnaire and the Individual Questionnaire for women and men aged 15-49. The content of these questionnaires was based on the model AIDS Indicator Survey (AIS) questionnaires developed by the MEASURE DHS program implemented by ORC Macro.

In consultation with government agencies and local and international organizations, the GSO and NIHE modified the model questionnaires to reflect issues in HIV/AIDS relevant to Vietnam. These questionnaires were then translated from English into Vietnamese. The questionnaires were further refined after the pretest.

The Household Questionnaire was used to list all the usual members and visitors in the selected households. Some basic information was collected on the characteristics of each person listed, including age, sex, relationship to the head of the household, education, basic material needs, survivorship and residence of biological parents of children under the age of 18 years and birth registration of children under the age of 5 years. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit, such as the source of drinking water, type of toilet facilities, type of material used in the flooring of the house, and ownership of various durable goods, in order to allow for the calculation of a wealth index. The Household Questionnaire also collected information regarding ownership and use of mosquito nets.

The Individual Questionnaire was used to collect information from all women and men aged 15-49 years.

All questionnaires were administered in a face-to-face interview. Because cultural norms in Vietnam restrict open discussion of sexual behavior, there is concern that this technique may contribute to potential under-reporting of sexual activity, especially outside of marriage.

All aspects of VPAIS data collection were pre-tested in July 2005. In total, 24 interviewers (12 men and 12 women) were involved in this task. They were trained for thirteen days (including three days of fieldwork practice) and then proceeded to conduct the survey in the various urban and rural districts of Ha Noi. In total, 240 individual interviews were completed during the pretest. The lessons learnt from the pretest were used to finalize the survey instruments and logistical arrangements for the survey and blood collection.

Cleaning operations

The data processing of the VPAIS questionnaire began shortly after the fieldwork commenced. The first stage of data editing was done by the field editors, who checked the questionnaires for completeness and consistency. Supervisors also reviewed the questionnaires in the field. The completed questionnaires were then sent periodically to the GSO in Ha Noi by mail for data processing.

The office editing staff first checked that questionnaires of all households and eligible respondents had been received from the field. The data were then entered and edited using CSPro, a software package developed collaboratively between the U.S. Census Bureau, ORC Macro, and SerPRO to process complex surveys. All data were entered twice (100 percent verification). The concurrent processing of the data was a distinct advantage for data quality, as VPAIS staff was able to advise field teams of errors detected during data entry. The data entry and editing phases of the survey were completed by the end of December 2005.

Response rate

A total of 6,446 households were selected in the sample, of which 6,346 (98 percent) were found to be occupied at the time of the fieldwork. Occupied households include dwellings in which the household was present but no competent respondent was home, the household was present but refused the interview, and dwellings that were not found. Of occupied households, 6,337 were interviewed, yielding a household response rate close to 100 percent.

All women and men aged 15-49 years who were either permanent residents of the sampled households or visitors present in the household during the night before the survey were eligible to be interviewed in the survey. Within interviewed households, a total of 7,369 women aged 15-49 were identified as eligible for interview, of whom 7,289 were interviewed, yielding a response rate to the Individual interview of 99 percent among women. The high response rate was also achieved in male interviews. Among the 6,788 men aged 15-49 identified as eligible for interview, 6,707 were successfully interviewed, yielding a response rate of 99 percent.

Sampling error

Abstract

Rwanda Interim Demographic and Health Survey (RIDHS) follows the Demographic and Health Surveys (RDHS) that were successfully conducted in 1992, 2000, and 2005, and is part of a broad, worldwide program of socio-demographic and health surveys conducted in developing countries since the mid-1980s. RIDHS collected the indicators on fertility, family planning and maternal and child health which the survey normally provides. In addition, RIDHS integrated a malaria module and tests for the prevalence of malaria and anemia among women and children, thus determining the prevalence of malaria and anemia for women and children at the national level.

The main objectives of the RIDHS were: • At the national level, gather data to determine demographic rates, particularly fertility and infant and child mortality rates, and analyze the direct and indirect factors that determine fertility and child mortality rates and trends. • Evaluate the level of knowledge and use of contraceptives among women and men. • Gather data concerning family health: vaccinations; prevalence and treatment of diarrhea, acute respiratory infections (ARI), and fever in children under the age of five; antenatal care visits; and assistance during childbirth. • Gather data concerning the prevention and treatment of malaria, particularly the possession and use of mosquito nets, and the prevention of malaria in pregnant women. • Gather data concerning child feeding practices, including breastfeeding. • Gather data concerning circumcision among men between the ages of 15 and 59. • Collect blood samples in all of the households surveyed for anemia testing of women age 15-49, pregnant women and children under age five. • Collect blood samples in all of the households surveyed for hemoglobin and malaria diagnostic testing of women age 15 to 49, pregnant women and children under age five.

Geographic coverage

National coverage

Analysis unit

Household Individual Woman age 15-49 Man age 15-59

Kind of data

Sample survey data [ssd]

Sampling procedure

The sample for the RIDHS is a two-stage stratified area sample. Clusters are the primary sampling units and are constituted from enumeration areas (EA). The EA were defined in the 2002 General Population and Housing Census (RGPH) (SNR, 2005).

These enumeration areas provided the master frame for the drawing of 250 clusters (187 rural and 63 urban), selected with a representative probability proportional to their size. Only 249 of these clusters were surveyed, because one cluster located in a refugee camp had to be eliminated from the sample. A strictly proportional sample allocation would have resulted in a very low number of urban households in certain provinces. It was therefore necessary to slightly oversample urban areas in order to survey a sufficient number of households to produce reliable estimates for urban areas. The second stage involved selecting a sample of households in these enumeration areas. In order to adequately guarantee the accuracy of the indicators, the total number drawn was limited to 30 households per cluster. Because of the nonproportional distribution of the sample among the different strata and the fact that the number of households was set for each cluster, weighting was used to ensure the validity of the sample at both national and provincial levels.

All women age 15-49 years who were either usual residents of the selected household or visitors present in the household on the night before the survey were eligible to be interviewed (7,528 women). In addition, a sample of men age 15-59 who were either usual residents of the selected household or visitors present in the household on the night before the survey were eligible for the survey (7,168 men). Finally, all women age 15-49 and all children under the age of five were eligible for the anemia and malaria diagnostic tests.

The sample for the 2007-08 RIDHS covered the population residing in ordinary households across the country. A national sample of 7,469 households (1,863 in urban areas and 5,606 in rural areas) was selected. The sample was first stratified to provide adequate representation from urban and rural areas as well as all the four provinces and the city of Kigali, the nation’s capital.

Sampling deviation

One cluster located in a refugee camp had to be eliminated from the sample.

Mode of data collection

Face-to-face [f2f]

Research instrument

Three questionnaires were used in the 2007-08 RIDHS: the Household Questionnaire, the Women’s Questionnaire, and the Men’s Questionnaire. The content of these questionnaires was based on model questionnaires developed by the MEASURE DHS project.

Initial technical meetings that were held beginning in September 2007 allowed a wide range of government agencies as well as local and international organizations to contribute to the development of the questionnaires. Based on these discussions, the DHS model questionnaires were modified to reflect the needs of users and relevant issues in population, family planning, anemia, malaria and other health concerns in Rwanda. The questionnaires were then translated from French into Kinyarwanda. These questionnaires were finalized in December 2007 before the training of male and female interviewers.

The Household Questionnaire was used to list all of the usual members and visitors in the selected households. In addition, some basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of the household. The main purpose of the Household Questionnaire was to identify women and men who were eligible for the individual interview. The Household Questionnaire also collected information on characteristics of the household’s dwelling unit such as the main source of drinking water, type of toilet facilities, materials used for the floor of the house, the main energy source used for cooking and ownership of various durable goods. Finally, the Household Questionnaire was also used to identify women and children eligible for the hemoglobin (anemia) and malaria diagnostic tests.

The Women’s Questionnaire was used to collect information on women of reproductive age (15-49 years) and covered questions on the following topics: • Background characteristics • Marital status • Birth history • Knowledge and use of family planning methods • Fertility preferences • Antenatal and delivery care • Breastfeeding practices • Vaccinations and childhood illnesses

The Men’s Questionnaire was administered to all men age 15-59 years living in the selected households. The Men’s Questionnaire collected information similar to that of the Women’s Questionnaire, with the only difference being that it did not include birth history or questions on maternal and child health or nutrition. In addition, the Men’s Questionnaire also collected information on circumcision.

Cleaning operations

Data entry began on January 7, 2008, three weeks after the beginning of data collection activities in the field. Data were entered by a team of five data processing personnel recruited and trained by staff from ICF Macro. The data entry team was reinforced during this work with an additional staffer. Completed questionnaires were periodically brought in from the field to the National Institute of Statistics in Kigali, where assigned staff checked them and coded the open-ended questions. Next, the questionnaires were sent to the data entry staff. Data were entered using CSPro, a program developed jointly by the United States Census Bureau, the ICF Macro MEASURE DHS program, and Serpro S.A. All questionnaires were entered twice to eliminate as many data entry errors as possible from the files. In addition, a quality control program was used to detect data collection errors for each team. This information was shared with field teams during supervisory visits to improve data quality. The data entry and internal consistency verification phase of the survey was completed on May 14, 2008.

Response rate

The response rate was high for both men (95.4 percent) and women (97.5 percent).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: (1) nonsampling errors, and (2) sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the 2007-08 RIDHS 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 2007-08 RIDHS 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

This table provides quarterly estimates of the number of non-permanent residents by type for Canada, provinces and territories.

Abstract

The 1997 Viemam Demographic and Health Survey (VNDHS-II) is a nationally representative survey of 5,664 ever-married women age 15-49 selected from 205 sampling clusters throughout Vietnam. The VNDHS-II was designed to provide information on levels of fertility, family planning knowledge and use, infant and child mortality, and indicators of maternal and child health. The survey included a Community/Health Facility Questionnaire that was implemented in each of the sample clusters included in the women's survey. Fieldwork for the survey took place from July to October 1997. All provinces were separated into "project" and "non-project" groups to permit separate estimates for about one-third of provinces where the health infrastructure is being upgraded.

The primary objectives of the second Vietnam National Demographic and Health Survey (VNDHS-II) in 1997 were to provide up-to-date information on fertility levels, fertility preferences, awareness and use of family planning methods, breastfeeding practices, early childhood mortality, child health and knowledge of AIDS.

VNDHS-II data confirm the patterns of declining fertility and increasing use of contraception that were observed between the 1988 VNDHS-I and the 1994 lntercensal Demographic Survey (ICDS-94).

Geographic coverage

The 1997 Viemam Demographic and Health Survey (VNDHS-II) is a nationally representative survey. Itwas designed to provide separate estimates for the whole country, for urban and rural areas, for 18 project provinces, and for the remaining non-project provinces as well. Project provinces refer to 18 focus provinces targeted for the strengthening of their primary health care systems by the Government's Population and Family Health Project to be implemented over a period of seven years, from 1996 to 2002 (At the outset of this project there were 15 focus provinces, which became 18 by the creation of 3 new provinces from the initial set of 15). These provinces were selected according to criteria based on relatively low health and family planning status, no substantial family planning donor presence, and regional spread. These criteria resulted in the selection of the country's poorer provinces. Nine of these provinces have significant proportions of ethnic minorities among their population.

Analysis unit

• Household
• Women age 15-49

Universe

The population covered by the 1997 VNDHS is defined as the universe of all women age 15-49 in Vietnam.

Kind of data

Sample survey data

Sampling procedure

The Second Vietnam Demographic and Health Survey (VNDHS-1I) covers the population residing in private households in the country. The design for the VNDHS-II calls for a representative probability sample of approximately 5,500 completed individual interviews of ever-married 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, for urban and the rural areas separately, and for the group of 18 project provinces. These 18 provinces are in the following geographic regions:

Six of the 18 project provinces are new provinces that will, in the near future, be formed out of three old provinces: Bac Can and Thai, Nguyen from Bac Thai; Hai Duong and Hung Yen from Hal Hung; Nam Dinh and Ha Nam from Nam Ha.

Northern Uplands: Tuyen Quang, Lai Chau, Lao Cai, Bac Can and Thai Nguyen.
Red River Delta: Hai Phong, Hai Duong, Hung Yen, Nam Dinh and Ha Nam.
North Central: Thanh Hoa and Thua Thien-Hue.
Central Highlands: Dac Lac and Lam Dong.
Mekong River Delta: Dong Thap, Vinh Long, Tra Vinh and Kien Giang.

Since the formation of the new provinces has not been formalized and no population data exist for them, this report will only refer to 15 project provinces out of 53 provinces in Vietnam (instead of 18 project provinces out of 61 provinces).

SAMPLING FRAME

The sampling frame for the VNDHS-II was the sample of the 1996 Vietnam Multi-Round Survey (VNMRS), conducted bi-annually by the General Statistical Office (GSO). A thorough evaluation of this sample was necessary to ensure that the sample was representative of the country, before it was used for the VNDHS-II.

The sample design for the VNMRS was developed by GSO with technical assistance provided by Mr. Anthony Turney, sampling specialist of the United Nations Statistics Division. The VNMRS sample was stratified and selected in two stages. Within each province, stratification was geographic by urban- rural residence. Sample selection was done independently for each province.

In the first stage, primary sampling units (PSUs) corresponding to communes (rural areas) and blocks (urban areas) were selected using equal probability systematic random selection (EPSEM), since no recent population data on communes and blocks existed that could be used for selection with probability proportional to size. The assumption underlying the decision to use EPSEM was that, within each province, the majority of communes and blocks vary little in population size, with the exception of a few communes; i.e., within each province, most communes and blocks have a population size that is close to the average for the province. In each province, the number of selected communes/blocks was proportional to the urban-rural population in the province. The total number of communes/blocks selected for the VNMRS was 1,662 with tbe number of communes/blocks in each province varying from 26 to 43 according to the size of the province. After the communes/blocks were selected, a field operation was mounted by GSO to create enumeration areas (EAs) in each selected commune/block. The number of EAs that was created in each commune/block was based on the number of households in the commune/block divided by the standard EA size which was set at 150 households. The list of EAs for the whole province was then ordered geographically by commune/block and used for the second stage selection. Thirty EAs were selected in each province with equal probability from a random start, for a total of 1,590 EAs. Because of this method of systematic random selection, communes/blocks that were large in size had one or rnore EAs selected into the sample while communes/blocks that were very small in size were excluded from the sample. In each selected EA, all households were interviewed for the VNMRS.

To evaluate the representativity of the VNMRS, EA weights were calculated based on the selection probability at tile various sampling stages of the VNMRS: also, the percent distribution of households in the VNMRS across urban/rural strata and provinces was estimated and compared with the percent distribution of the 1996 population across the same strata. The distribution obtaiued from the VNMRS agrees closely with that of the 1996 population

CHARACTERISTICS OF THE VNDHS-II SAMPLE

The sample for the VNDHS-II was stratified and selected in two stages. There were two principal sampling domains: the group of 15 project provinces and the group of other provinces. In the group of project provinces, all 15 provinces were included in the salnple. At the first stage. 70 PSUs corresponding to the EAs as defined in the VNMRS were selected from the VNMRS with equal probability, the size of the EA in the VNMRS being very uniform. and hence sampling with probability proportional to size (PPS) was not necessary. The list of households interviewed for the VNMRS (updated when necessary) were used as the frame for the second-stage sampling, in which households were selected for interview during the main survey fieldwork. Ever-married women between the ages of 15 and 49 were identified in these households and interviewed.

In the group of other provinces, an additional stage was added in order to reduce field costs although this might increase sampling errors. In the first stage, 20 provinces, serving as PSUs. were selected with PPS. the size being the population of the provinces estimated in 1997. In the second stage, 135 secondary sampling units corresponding to the EAs were selected in the same manner as for the project provinces.

Mode of data collection

Face-to-face

Research instrument

Three types of questionnaires were used in the VNDHS-II: the Household Questionnaire, the Individual Questionnaire, and the Community/Health Facility Questionnaire. A draft of the first two questionnaires was prepared using the DHS Model A Questionnaire. A user workshop was organized to discuss the contents of the questionnaires. Additions and modifications to the draft of the questionnaires were made after the user workshop and in consultation with staff from Macro International Inc., and with members of the Technical Working Group, who were convened for the purpose of providing technical assistance to the GSO in planning and conducting the survey. The questionnaires were developed in English and translated into and printed in Vietnamese. The draft questionnaires were pretested in two clusters in Hanoi City (one urban and one rural cluster).

a) The Household Questionnaire was used to enumerate all usual members and visitors in selected households and to collect information on age, sex, education, marital status, and relationship to the head of household. The main purpose of the Household Questionnaire was to identify women eligible for the individual interview (ever-married women age 15-49). In addition, the Household Questionnaire collected information on characteristics of the household such as the source of water, type of toilet facilities, material used for the floor and roof,

Abstract

The 2010 Rwanda Demographic and Health Survey (RDHS) is designed to provide data for monitoring the population and health situation in Rwanda. The 2010 RDHS is the fifth Demographic and Health Survey to be conducted in Rwanda. The objective of the survey is to provide up-to-date information on fertility, family planning, childhood mortality, nutrition, maternal and child health, domestic violence, malaria, maternal mortality, awareness and behavior regarding HIV/AIDS, HIV prevalence, malaria prevalence, and anemia prevalence. A nationally representative sample of 13,671 women, age 15–49 from 12,540 surveyed households, and 6,329 men, age 15–59 from half of these households, were interviewed. This represents a response rate of 99 percent for women and 99 percent for men. The sample provides estimates at the national and provincial levels.

The main objectives of the 2010 RDHS were to: - Collect data at the national level to facilitate calculation of essential demographic rates, especially rates for fertility and infant and child mortality, and to analyze the direct and indirect factors that determine levels and trends in fertility and child mortality - Measure the levels of knowledge of contraceptive practices among women - Collect data on family health, including immunization practices; prevalence and treatment of diarrhea, acute upper respiratory infections, fever and/or convulsions among children under age 5; antenatal visits; and assistance at delivery - Collect data on the prevention and treatment of malaria, in particular the possession and use of bed nets among children under 5 and among women and pregnant women - Collect data on nutritional practices of children, including breastfeeding - Collect data on the knowledge and attitudes of men and women concerning sexually transmitted infections (STIs) and acquired immune deficiency syndrome (AIDS) and evaluate recent behavioral changes with regard to condom use - Collect data for the estimation of adult mortality and maternal mortality at the national level - Take anthropometric measurements in half of surveyed households in order to evaluate the nutritional status of children, men, and women - Conduct confidential testing for malaria parasitemia using Rapid Diagnostic Testing in half of the surveyed households and anonymous blood smear testing at the National Reference Laboratory - Collect dried blood spots (from finger pricks) for anonymous HIV testing at the National Reference Laboratory in half of surveyed households - Measure hemoglobin level (by finger prick) for anemia of surveyed respondents in half of surveyed households.

Geographic coverage

National. The sample provides estimates at the national and provincial levels.

Analysis unit

Household, adult woman, adult man

Kind of data

Sample survey data

Sampling procedure

The sample for the 2010 RDHS was designed to provide population and health indicator estimates for the country as a whole and for urban and rural areas in particular. Survey estimates are also reported for the provinces (South, West, North, and East) and for the City of Kigali. The results presented in this report show key indicators that correspond to these provinces and the City of Kigali.

A representative sample of 12,792 households was selected for the 2010 RDHS. The sample was selected in two stages. In the first stage, 492 villages (also known as clusters or enumeration areas) were selected with probability proportional to the village size. The village size is the number of households residing in the village. Then, a complete mapping and listing of all households existing in the selected villages was conducted. The resulting lists of households served as the sampling frame for the second stage of sample selection. Households were systematically selected from those lists for participation in the survey.

All women age 15-49 who were either permanent residents of the household or visitors present in the household on the night before the survey were eligible to be interviewed. In addition, in a subsample of half of all households selected for the survey, all men age 15-59 were eligible to be interviewed if they were either permanent residents or visitors present in the household on the night before the survey.

SAMPLING FRAME

The sampling frame used for the 2010 RDHS is the preparatory frame for the Rwanda General Population and Housing Census (RGPH), which will be conducted in 2012. Provided by the National Institute of Statistics of Rwanda (NISR), the sampling frame is a complete list of natural villages covering the entire country. Though it is preferable to work with a frame consisting of enumeration areas (EAs) because the natural villages are too variable in size, an EA frame is not available at the time of sampling design. The sampling frame that was available is the list of 14,837 natural villages, which contains the administrative characteristics for each village and village population. The village population comes from the national ID card project carried out in 2007-08, which may be under estimated compared with the population projection conducted in 2009 by NISR.

Rwanda's administrative units were reformed in 2006, so the country is currently divided into 5 provinces; 30 districts, 417 sectors, and 14,837 villages.The average village size is 610 residents, which is equivalent to 133 households. The sizes of the districts are quite homogeneous, varying from 2.7 percent to 4.4 percent. There is no urban-rural specification in the sampling frame because the urban-rural definition has not been released by the Ministry of Local Administration (MINALOC). It was expected that the urban-rural definition of the sampled villages will be determined during the data collection or in the office once the MINALOC releases the definition.

Mode of data collection

Face-to-face

Research instrument

Three questionnaires were used for the 2010 RDHS: the Household Questionnaire, the Woman’s Questionnaire, and the Man’s Questionnaire. They are based on questionnaires developed by the worldwide Demographic and Health Surveys (DHS) program and on questionnaires used during the 2005 RDHS and 2007-08 RIDHS surveys. To reflect relevant issues in population and health in Rwanda, the questionnaires were adapted during a series of technical meetings with various stakeholders from government ministries and agencies, nongovernmental organizations, and international donors. The questionnaires were translated from English and French into Kinyarwanda.

The Household Questionnaire was used to list all the usual members and visitors in the selected households as well as to identify women and men eligible for individual interviews. Basic information was collected on the characteristics of each person listed, including age, sex, education, and relationship to the head of household. For children under 18, survival status of the parents was determined. The Household Questionnaire also collected information on the following: - Dwelling characteristics - Utilization of health services and health expenditures for recent illness and injury - Possession of iodized salt - Possession and utilization of mosquito nets - Height and weight of women and children - Hemoglobin measurement of women and children - Blood collection from women and children for rapid test and laboratory testing of malaria - Blood collection from women and men for laboratory testing for HIV

The Woman’s Questionnaire was used to collect information from all women age 15-49 and was organized by the following sections: - Respondent background characteristics - Reproduction, including a complete birth and death history of respondents’ children and information on abortion - Contraception - Pregnancy and postnatal care - Child’s immunization, health, and nutrition - Marriage and sexual activity - Fertility preferences - Husband’s background and woman’s work - HIV/AIDS and other sexually transmitted infections - Other health issues - Adult mortality - Relationship in the household

The Man’s Questionnaire was administered to all men age 15-59 living in every other household in the RDHS sample. The Man’s Questionnaire collected much of the same information as the Woman’s Questionnaire but was shorter because it did not contain a detailed reproductive history or questions on maternal and child health or nutrition.

An instruction manual was also developed to support standardized data collection. All data collection instruments were pretested in June-July 2010. The observations and experiences gathered from the pretest were used to improve the instruments for the main survey data collection.

Cleaning operations

Data entry began on November 1, 2010, almost one month after the survey was launched in the field. Data were entered by a team of 15 data processing personnel recruited and trained for this task. They were assisted during these operations by 4 data verification and codification officers and 2 receptionists. Completed questionnaires were periodically brought in from the field to the National Institute of Statistics headquarters, where assigned agents checked them and coded the open-ended questions. Next, the questionnaires were sent to the data entry facility and the blood samples (DBS and malaria slides) were sent to the NRL to be screened for HIV. Data were entered using CSPro, a program developed jointly by the United States Census Bureau, the ORC Macro MEASURE DHS+ program, and Serpro S.A. Processing the data concurrently with data collection allowed for regular monitoring of teams’ performance and data quality. Field check tables were regularly generated during data processing to check

Abstract

The Multiple Indicator Cluster Survey (MICS) is a household survey programme developed by UNICEF to assist countries in filling data gaps for monitoring human development in general and the situation of children and women in particular. MICS is capable of producing statistically sound, internationally comparable estimates of social indicators. The current round of MICS is focused on providing a monitoring tool for the Millennium Development Goals (MDGs), the World Fit for Children (WFFC), as well as for other major international commitments, such as the United Nations General Assembly Special Session (UNGASS) on HIV/AIDS and the Abuja targets for malaria.

Survey Objectives The 2006 Bangladesh Multiple Indicator Cluster Survey has the following objectives: - To provide up-to-date information for assessing the situation of children and women in Bangladesh; - To furnish data needed for monitoring progress toward goals established by the Millennium Development Goals, the goals of A World Fit For Children (WFFC), and other internationally agreed upon goals, as a basis for future action; - To contribute to the improvement of data and monitoring systems in Bangladesh and to strengthen technical expertise in the design, implementation, and analysis of such systems.

Survey Content MICS questionnaires are designed in a modular fashion that can be easily customized to the needs of a country. They consist of a household questionnaire, a questionnaire for women aged 15-49 and a questionnaire for children under the age of five (to be administered to the mother or caretaker). Other than a set of core modules, countries can select which modules they want to include in each questionnaire.

Survey Implementation The survey was implemented by the Bangladesh Bureau of Statistics , with the support and assistance of UNICEF and other partners. Technical assistance and training for the surveys is provided through a series of regional workshops, covering questionnaire content, sampling and survey implementation; data processing; data quality and data analysis; report writing and dissemination.

Geographic coverage

The survey is nationally representative and covers the whole of Bangladesh.

Analysis unit

Households (defined as a group of persons who usually live and eat together)

De jure household members (defined as memers of the household who usually live in the household, which may include people who did not sleep in the household the previous night, but does not include visitors who slept in the household the previous night but do not usually live in the household)

Women aged 15-49

Children aged 0-4

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49 years resident in the household, and all children aged 0-4 years (under age 5) resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Bangladesh Multiple Indicator Cluster Survey was to produce statistically reliable estimates of most indicators, at the national level, for urban and rural areas, and for the six divisions of the country, municipal areas, city corporation's slum areas of two big cities and tribal areas. Rural areas, municipal areas, city corporation areas, slum areas and tribal areas were defined as the sampling domain.

A multi-stage, stratified cluster sampling approach was used for the selection of the survey sample.

Sample Size and Sample Allocation The target sample size for the Bangladesh MICS was calculated as 68247 households. For the calculation of the sample size, the key indicator used was the DPT immunization (3+doses) prevalence among children aged 12-23 months. The following formula was used to estimate the required sample size for these indicators: n = [ 4 (r) (1-r) (f) (1.1) ] [ (0.12r)2 (p) (nh) ]

where n is the required sample size, expressed as number of households 4 is a factor to achieve the 95 per cent level of confidence r is the predicted or anticipated prevalence (coverage rate) of the indicator 1.1 is the factor necessary to raise the sample size by 10 per cent for non-response f is the shortened symbol for deff (design effect) 0.12r is the margin of error to be tolerated at the 95 per cent level of confidence, defined as 12 per cent of r (relative sampling error of r) p is the proportion of the total population upon which the indicator, r, is based nh is the average household size.

For the calculation, r (DPT immunization 3+doses prevalence) was assumed to be 39.7 percent in the Rangamati districts. The value of deff (design effect) was taken as 1.5 based on estimates from previous surveys, p (percentage of children aged 12-23 months in the total population) was taken as 2.3 percent, and nh (average household size) was taken as 4.9 households.

For the sub national level, the margin of error should be high which was also acknowledged in the MICS manual. Therefore, for sub national estimates the margin of error need to be relaxed considerably. If a rate of 30% of r is used this would give a margin of error ± 0.06 for prevalence rates of 0.20, ± 0.12 for prevalence rates of 0.40, and so on. Considering this phenomenon, in case of Rangamati 30% of r has been used.

The resulting number of households from this exercise was about 900 households which is the sample size needed in each district - thus yielding about 68250 in total. The average cluster size in the Bangladesh MICS was determined as 35 households, based on a number of considerations, including the budget available, and the time that would be needed per team to complete one cluster. Dividing the total number of households by the number of households per cluster, it was calculated that the selection of a total number of 26 clusters would be needed in each district.

Equal allocation of the total sample size to the 75 domains was targeted. Therefore, 26 clusters were allocated to each district with the final sample size calculated at 68250 households (1950 cluster X 35 households per cluster). In each stratum, the clusters (primary sampling units) were distributed to rural, municipal, city corporations, slum and tribal areas on PPS method.

Sampling Frame and Selection of Clusters The 2001 census frame was used for the selection of clusters. Census enumeration areas were defined as primary sampling units (PSUs), and were selected from each of the sampling domains by using systematic pps (probability proportional to size) sampling procedures, based on the estimated sizes of the enumeration areas from the 2001 Population Census. The first stage of sampling was thus completed by selecting the required number of enumeration areas from each of the 5 strata namely rural, municipal, city corporations, slum and tribal areas.

Listing Activities Since the sample frame of the 2001 Population Census was not up to date, household lists in all selected enumeration areas were updated prior to the selection of households. For this purpose, listing teams were formed, who visited each enumeration area, and listed the occupied households. The BBS officials working in the upazila were responsible for the listing of all households in the respective PSUs.

Selection of Households Lists of households were prepared by the Upazila officials of BBS. The households were sequentially numbered from 1 to 100 (or more) households in each enumeration area at the where selection of 35 households in each enumeration area was carried out using systematic selection procedures.

(Information extracted from the final report: BBS and UNICEF. 2007. Bangladesh Multiple Indicator Cluster Survey 2006, Final Report. Dhaka, Bangladesh: BBS and UNICEF)

Sampling deviation

No major deviations from the original sample design were made. All sample enumeration areas were accessed and successfully interviewed with good response rates.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires of MICS 2006 are based on the global format of MICS3 model questionnaire. From the MICS3 model English version, the questionnaires were translated into Bangla and were pre-tested in four sample areas of which two were in rural areas, one in City Corporation and one in the slum area during May 2006. Based on the results of the pre-test, modifications were made to the wording and translation of the questionnaires.

The questionnaire for under-five children was administered to mothers or caretakers of under-five children living in the households. Normally, the questionnaire was administered to mothers of under-five children; in cases when the mother was not listed in the household roster, a primary caretaker for the child was identified and interviewed.

Cleaning operations

Data editing took place at a number of stages throughout the processing (see Other processing), including: a) Office editing and coding b) During data entry c) Structure checking and completeness d) Secondary editing e) Structural checking of SPSS data files

Detailed documentation of the editing of data can be found in the data processing guidelines

Response rate

Of the 68,247 of households selected for the sample, 67,540 were found to be occupied. Of these, 62,463 households were successfully interviewed for a household response rate of 92.5 percent. In the interviewed households, 78,260 of eligible women (age 15-49) were identified. Of these, 69,860 of women were successfully interviewed, yielding a response rate of 89.3 percent. In addition, 34,710 of children under 5 were listed in HH questionnaire.

Abstract

The 2017 Philippines National Demographic and Health Survey (NDHS 2017) is a nationwide survey with a nationally representative sample of approximately 30,832 housing units. The primary objective of the survey is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS 2017 collected information on marriage, fertility levels, fertility preferences, awareness and use of family planning methods, breastfeeding, maternal and child health, child mortality, awareness and behavior regarding HIV/AIDS, women’s empowerment, domestic violence, and other health-related issues such as smoking.

The information collected through the NDHS 2017 is intended to assist policymakers and program managers in the Department of Health (DOH) and other organizations in designing and evaluating programs and strategies for improving the health of the country’s population.

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling scheme provides data representative of the country as a whole, for urban and rural areas separately, and for each of the country’s administrative regions. The sample selection methodology for the NDHS 2017 is based on a two-stage stratified sample design using the Master Sample Frame (MSF), designed and compiled by the PSA. The MSF is constructed based on the results of the 2010 Census of Population and Housing and updated based on the 2015 Census of Population. The first stage involved a systematic selection of 1,250 primary sampling units (PSUs) distributed by province or HUC. A PSU can be a barangay, a portion of a large barangay, or two or more adjacent small barangays.

In the second stage, an equal take of either 20 or 26 sample housing units were selected from each sampled PSU using systematic random sampling. In situations where a housing unit contained one to three households, all households were interviewed. In the rare situation where a housing unit contained more than three households, no more than three households were interviewed. The survey interviewers were instructed to interview only the pre-selected housing units. No replacements and no changes of the preselected housing units were allowed in the implementing stage in order to prevent bias. Survey weights were calculated, added to the data file, and applied so that weighted results are representative estimates of indicators at the regional and national levels.

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

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

Mode of data collection

Face-to-face [f2f]

Research instrument

Two questionnaires were used for the NDHS 2017: the Household Questionnaire and the Woman’s Questionnaire. Both questionnaires, based on The DHS Program’s standard Demographic and Health Survey (DHS-7) questionnaires, were adapted to reflect the population and health issues relevant to the Philippines. Input was solicited from various stakeholders representing government agencies, universities, and international agencies.

Cleaning operations

The processing of the NDHS 2017 data began almost as soon as fieldwork started. As data collection was completed in each PSU, all electronic data files were transferred via an Internet file streaming system (IFSS) to the PSA central office in Quezon City. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors while still in the PSU. Secondary editing involved resolving inconsistencies and the coding of openended questions; the former was carried out in the central office by a senior data processor, while the latter was taken on by regional coordinators and central office staff during a 5-day workshop following the completion of the fieldwork. Data editing was carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage, because it maximized the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for more effective monitoring. The secondary editing of the data was completed by November 2017. The final cleaning of the data set was carried out by data processing specialists from The DHS Program by the end of December 2017.

Response rate

A total of 31,791 households were selected for the sample, of which 27,855 were occupied. Of the occupied households, 27,496 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 25,690 women age 15-49 were identified for individual interviews; interviews were completed with 25,074 women, yielding a response rate of 98%.

The household response rate is slightly lower in urban areas than in rural areas (98% and 99%, respectively); however, there is no difference by urban-rural residence in response rates among women (98% for each).

Sampling error estimates

The estimates from a sample survey are affected by two types of errors: nonsampling errors and sampling errors. Nonsampling errors are the results of mistakes made in implementing data collection and data processing, such as failure to locate and interview the correct household, misunderstanding of the questions on the part of either the interviewer or the respondent, and data entry errors. Although numerous efforts were made during the implementation of the Philippines National Demographic and Health Survey (NDHS) 2017 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 NDHS 2017 is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability among all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results.

Sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the NDHS 2017 sample is the result of a multi-stage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data appraisal

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

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

Abstract

The Demographic and Health Survey (DHS) is an important link in a chain of surveys carried out in Sri Lanka in the past decade or so. Having been designed as part of an international survey program and modelled on the lines of the well renowned World Fertility Survey (WFS) program, the DHS provides an exceptionally valuable source of data for the estimation of trends over time within Sri Lanka as well as for cross national comparison.

The survey focussed primarily on fertility, contraception and child mortality as did WFS but. also measured several indicators of child health, particularly immunization coverage and nutrition status. The inclusion of health sector information has been welcome and fruitful, for improve- ment of nutrition status is a subject to which the Government of Sri Lanka has accorded high priority.

The Sri Lanka Demographic and Health Survey has the following objectives: 1. To provide policymakers and administrators with current and accurate data on fertility, morbidity, family planning and selected indicators of health status which could be used for planning new strategies for the wellbeing of the population; etc. 2. To provide data which can be used to analyze trends over time. The SLDHS examines many of the same fertility, mortality, and health issues that were addressed in earlier surveys, most notably the SLWFS and the more recent SLCPS; and 3. To add to the international body of data which can be used for comparative studies.

Geographic coverage

National

Analysis unit

• Household
• Women age 15-49

Universe

In principle, the sample was designed to cover private households in the areas sampled. The population residing in institutions and institutional households was excluded. For the detailed individual interview, the eligibility criteria were: ever-married women aged 15 through 49 who slept in the household the previous night.

Kind of data

Sample survey data

Sampling procedure

SURVEY SAMPLE DESIGN

On the basis of socio-economic and ecological criteria, and the experience of the SLWFS, nine zones were created. It was felt that some of the six SLWFS zones were too heterogeneous and should be redrawn as shown in Figure i.i and described below:

Zone 1 - Colombo Metropolitan area consisting of SLWFS zone 1 and parts of zone 2. Zone 2 - Colombo feeder areas and Northern part of SLWFS zone 2. Zone 3 - South Western coastal low lands corresponding to Southern part of SLWFS zone 2. Zone 4 - Lower South Central hill country corresponding to Western and Southern part of SLWFS zone 6, excluding districts with a concentration of estates. Zone 5 - South Central hill country corresponding to part of SLWFS zone 5 with a concentration of estates. Zone 6 - Irrigated Dry Zone corresponding to SLWFS zone 3, with major or minor irrigation schemes. Zone 7 - Rain fed Dry Zone covering the rest of SLWFS zone 3. Zone 8 - Eastern Coastal Belt, corresponding to SLWFS zone 4 (not included in SLDNS). Zone 9 - Northern Province corresponding to SLWFS zone 5 (not included in SLDHS).

The changes SLDHS made to the SLWFS zones were designed: a) to separate the Colombo urban feeder areas from rural hinterlands; b) to separate rural areas with predominantly estate populations from other rural areas; and c) to distinguish between irrigated dry zone areas which are new settlements under development projects from those areas which rely primarily on rains for cultivation.

Although the survey originally planned to conduct interviews in all nine zones, Civil disturbances in zones 9 and 8 (the Northern and Eastern provinces) prevented interviews from being conducted there. These zones, which contain approximately 14 percent of the 1986 estimated population of Sri Lanka, have been excluded from the SLDHS.

With the exception of zone 5, the sample was allocated equally between zones with an estimated target 900 completed individual interviews per zone. Zone 5 was given a larger target sample size of 1,350 to permit over sampling of the estate plantation workers.

In principle, the sample was designed to cover private households in the areas sampled. The population residing in institutions and institutional households was excluded. For the detailed individual interview, the eligibility criteria were: ever-married women aged 15 through 49 who slept in the household the previous night.

For the selection of area units, the sample frame was based on block statistics from the 1981 Census of Population and Housing. However, these figures were updated where possible on the basis of the work done in connection with a 1985-86 labour force survey. This applied in particular to newly settled areas with the development of irrigation schemes in the dry zone. For the final selection of housing units within ultimate area units, a special operation was undertaken before the survey to update household lists within selected census blocks.

The zones created by the SLDHS, which were designed to capture relatively homogeneous subgroups of the population, served as the primary strata. Each zone was further stratified into (up to) three strata: urban, rural, and estate areas. Further implicit stratification was achieved by ordering the sampling areas according to administrative and geographical location. Similar systematic sampling procedures were followed at all stages up to and including the selection of housing units.

The sampling of housing units was undertaken in two or three stages depending upon the stratum. In densely populated zones i, 2, and 3, and in urban strata of all zones a three stage design was used:

At the first stage, a stratified sample of Gram Savaka or equivalent areas (waras or estates) with probability proportional to size (PPS) was selected. The number of primary sampling units (PSIs) selected was 54 in zones 5 and 36 in each of the other zones. Within a given zone, the number to be selected in a stratum was allocated proportionately to the strata populations.

1.Within each PSU, two census blocks were selected with PPS, systematically without replacement. 2.The final stage consisted of the selection of the housing units in selected blocks with inverse PPS so as to yield a self weighting sample within each stratum.

For the main survey, there was no further sampling as all eligible women in each selected housing unit were taken into the sample. Also, for the anthropometric measurements, all children 3 through 36 months of eligible women were taken.

In the non-urban strata in zones 3 through 7, the only difference in procedures was that generally only one block was selected per PSU. This procedure effectively reduced the number of stages to two: blocks as the first stage and housing units as the second stage.

Since zones were allocated generally uniform sample sizes, the overall sampling fractions varied in inverse proportion to the zone population.

Sampling deviation

It is important to note once again that the districts in the northern and eastern portions of the country were not covered by the SLDHS because of civil disturbances. Whenever comparisons are made between the SLDHS and the earlier SLWFS and SLCPS, the differences in areas covered by the surveys should be kept in mind.

Mode of data collection

Face-to-face

Research instrument

The Sri Lanka Demographic and Health Survey used two questionnaires each of which was pretested.

a) The first, called the Household Questionnaire, was used to list all usual household members and any visitors who slept in the household the preceding night. For each person listed, information on age, sex, and marital status and whether or not he/she slept in the household the previous night was recorded. From this list eligible respondents were selected for interview. An eligible respondent is defined as a woman currently married, divorced, separated, or widowed between the ages of 15 and 49 who slept in the household the previous night.

b) The second or Individual Questionnaire was administered to each eligible respondent. On the average, an individual interview took approximately 35 to 40 minutes. The Individual Questionnaire consisted of nine sections: 1. Respondents background 2. Birth history-dates of all live births and infant and child deaths 3. Contraception-knowledge, ever use, current use and a detailed history of inter birth use in the last 5 years 4. Child health -immunization status, episodes of diarrhea, breastfeeding, the use of supplementary foods, prenatal care, and assistance at delivery 5. Marriage and migration 6. Fertility preferences 7. Husband's background and respondent's work 8. Socio-economic indicators 9. Length and weight-measurements of all children 3 through 36 months.

More than in similar fertility and family planning surveys conducted in the past, the SLDHS devoted considerable time and attention to obtaining information on the health status of mothers and children. In addition to many health related questions, anthropometric length and weight measurements were taken on all children 3 months through 36 months.

Cleaning operations

Data were entered onto microcomputers starting just two weeks after the commencement of field work. The ISSA (Integrated System for Survey Analysis) software package of programs developed by IRD/Westinghouse was used for data entry, machine editing, and tabulation. An especially effective procedure for correcting errors and inconsistencies detected during office editing and data entry was to relay information about problems in a questionnaire to the interviewers while they were still in the field. In most cases the problem could be

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