A. SUMMARY This dataset includes San Francisco COVID-19 tests by race/ethnicity and by date. This dataset represents the daily count of tests collected, and the breakdown of test results (positive, negative, or indeterminate). Tests in this dataset include all those collected from persons who listed San Francisco as their home address at the time of testing. It also includes tests that were collected by San Francisco providers for persons who were missing a locating address. This dataset does not include tests for residents listing a locating address outside of San Francisco, even if they were tested in San Francisco.

The data were de-duplicated by individual and date, so if a person gets tested multiple times on different dates, all tests will be included in this dataset (on the day each test was collected). If a person tested multiple times on the same date, only one test is included from that date. When there are multiple tests on the same date, a positive result, if one exists, will always be selected as the record for the person. If a PCR and antigen test are taken on the same day, the PCR test will supersede. If a person tests multiple times on the same day and the results are all the same (e.g. all negative or all positive) then the first test done is selected as the record for the person.

The total number of positive test results is not equal to the total number of COVID-19 cases in San Francisco.

When a person gets tested for COVID-19, they may be asked to report information about themselves. One piece of information that might be requested is a person's race and ethnicity. These data are often incomplete in the laboratory and provider reports of the test results sent to the health department. The data can be missing or incomplete for several possible reasons:

• The person was not asked about their race and ethnicity.
• The person was asked, but refused to answer.
• The person answered, but the testing provider did not include the person's answers in the reports.
• The testing provider reported the person's answers in a format that could not be used by the health department.

For any of these reasons, a person's race/ethnicity will be recorded in the dataset as “Unknown.”

B. NOTE ON RACE/ETHNICITY The different values for Race/Ethnicity in this dataset are "Asian;" "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" "White;" "Multi-racial;" "Other;" and “Unknown."

The Race/Ethnicity categorization increases data clarity by emulating the methodology used by the U.S. Census in the American Community Survey. Specifically, persons who identify as "Asian," "Black or African American," "American Indian or Alaska Native," "Native Hawaiian or Other Pacific Islander," "White," "Multi-racial," or "Other" do NOT include any person who identified as Hispanic/Latino at any time in their testing reports that either (1) identified them as SF residents or (2) as someone who tested without a locating address by an SF provider. All persons across all races who identify as Hispanic/Latino are recorded as “"Hispanic or Latino/a, all races." This categorization increases data accuracy by correcting the way “Other” persons were counted. Previously, when a person reported “Other” for Race/Ethnicity, they would be recorded “Unknown.” Under the new categorization, they are counted as “Other” and are distinct from “Unknown.”

If a person records their race/ethnicity as “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other” for their first COVID-19 test, then this data will not change—even if a different race/ethnicity is reported for this person for any future COVID-19 test. There are two exceptions to this rule. The first exception is if a person’s race/ethnicity value is reported as “Unknown” on their first test and then on a subsequent test they report “Asian;” "Black or African American;" "Hispanic or Latino/a, all races;" "American Indian or Alaska Native;" "Native Hawaiian or Other Pacific Islander;" or "White”, then this subsequent reported race/ethnicity will overwrite the previous recording of “Unknown”. If a person has only ever selected “Unknown” as their race/ethnicity, then it will be recorded as “Unknown.” This change provides more specific and actionable data on who is tested in San Francisco.

The second exception is if a person ever marks “Hispanic or Latino/a, all races” for race/ethnicity then this choice will always overwrite any previous or future response. This is because it is an overarching category that can include any and all other races and is mutually exclusive with the other responses.

A person's race/ethnicity will be recorded as “Multi-racial” if they select two or more values among the following choices: “Asian,” “Black or African American,” “American Indian or Alaska Native,” “Native Hawaiian or Other Pacific Islander,” “White,” or “Other.” If a person selects a combination of two or more race/ethnicity answers that includes “Hispanic or Latino/a, all races” then they will still be recorded as “Hispanic or Latino/a, all races”—not as “Multi-racial.”

C. HOW THE DATASET IS CREATED COVID-19 laboratory test data is based on electronic laboratory test reports. Deduplication, quality assurance measures and other data verification processes maximize accuracy of laboratory test information.

D. UPDATE PROCESS Updates automatically at 5:00AM Pacific Time each day. Redundant runs are scheduled at 7:00AM and 9:00AM in case of pipeline failure.

E. HOW TO USE THIS DATASET San Francisco population estimates for race/ethnicity can be found in a view based on the San Francisco Population and Demographic Census dataset. These population estimates are from the 2016-2020 5-year American Community Survey (ACS).

Due to the high degree of variation in the time needed to complete tests by different labs there is a delay in this reporting. On March 24, 2020 the Health Officer ordered all labs in the City to report complete COVID-19 testing information to the local and state health departments.

In order to track trends over time, a user can analyze this data by sorting or filtering by the "specimen_collection_date" field.

Calculating Percent Positivity: The positivity rate is the percentage of tests that return a positive result for COVID-19 (positive tests divided by the sum of positive and negative tests). Indeterminate results, which could not conclusively determine whether COVID-19 virus was present, are not included in the calculation of percent positive. When there are fewer than 20 positives tests for a given race/ethnicity and time period, the positivity rate is not calculated for the public tracker because rates of small test counts are less reliable.

Calculating Testing Rates: To calculate the testing rate per 10,000 residents, divide the total number of tests collected (positive, negative, and indeterminate results) for the specified race/ethnicity by the total number of residents who identify as that race/ethnicity (according to the 2016-2020 American Community Survey (ACS) population estimate), then multiply by 10,000. When there are fewer than 20 total tests for a given race/ethnicity and time period, the testing rate is not calculated for the public tracker because rates of small test counts are less reliable.

Read more about how this data is updated and validated daily: https://sf.gov/information/covid-19-data-questions

F. CHANGE LOG

• 1/12/2024 - This dataset will stop updating as of 1/12/2024
• 6/21/2023 - A small number of additional COVID-19 testing records were released as part of our ongoing data cleaning efforts. An update to the race or ethnicity designation among a subset of testing records was simultaneously released.
• 1/31/2023 - updated “population_estimate” column to reflect the 2020 Census Bureau American Community Survey (ACS) San Francisco Population estimates.
• 1/31/2023 - renamed column “last_updated_at” to “data_as_of”.
• 3/23/2022 - ‘Native American’ changed to ‘American Indian or Alaska Native’ to align with the census.
• 2/10/2022 - race/ethnicity categorization was changed. See section NOTE ON RACE/ETHNICITY for additional information.
• 4/16/2021 - dataset updated to refresh with a five-day data lag.

Abstract

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

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

Geographic coverage

Botswana Lesotho Malawi Namibia South Africa Zambia Zimbabwe

Analysis unit

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

Kind of data

Sample survey data [ssd]

Sampling procedure

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

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

Sample Universe

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

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

Sample Design

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

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

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

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

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

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

A third stage to randomly choose households;

A final-stage involving the random selection of individual respondents

We shall deal with each of these stages in turn.

STAGE ONE: Selection of Primary Sampling Units (PSUs)

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

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

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

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

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

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

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

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

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

Abstract

The primary objective of the 2018 NDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the NDHS collected information on fertility, awareness and use of family planning methods, breastfeeding practices, nutritional status of women and children, maternal and child health, adult and childhood mortality, women’s empowerment, domestic violence, female genital cutting, prevalence of malaria, awareness and behaviour regarding HIV/AIDS and other sexually transmitted infections (STIs), disability, and other health-related issues such as smoking.

The information collected through the 2018 NDHS is intended to assist policymakers and programme managers in evaluating and designing programmes and strategies for improving the health of the country’s population. The 2018 NDHS also provides indicators relevant to the Sustainable Development Goals (SDGs) for Nigeria.

Geographic coverage

National coverage

Analysis unit

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

Universe

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

Kind of data

Sample survey data [ssd]

Sampling procedure

The sampling frame used for the 2018 NDHS is the Population and Housing Census of the Federal Republic of Nigeria (NPHC), which was conducted in 2006 by the National Population Commission. Administratively, Nigeria is divided into states. Each state is subdivided into local government areas (LGAs), and each LGA is divided into wards. In addition to these administrative units, during the 2006 NPHC each locality was subdivided into convenient areas called census enumeration areas (EAs). The primary sampling unit (PSU), referred to as a cluster for the 2018 NDHS, is defined on the basis of EAs from the 2006 EA census frame. Although the 2006 NPHC did not provide the number of households and population for each EA, population estimates were published for 774 LGAs. A combination of information from cartographic material demarcating each EA and the LGA population estimates from the census was used to identify the list of EAs, estimate the number of households, and distinguish EAs as urban or rural for the survey sample frame. Before sample selection, all localities were classified separately into urban and rural areas based on predetermined minimum sizes of urban areas (cut-off points); consistent with the official definition in 2017, any locality with more than a minimum population size of 20,000 was classified as urban.

The sample for the 2018 NDHS was a stratified sample selected in two stages. Stratification was achieved by separating each of the 36 states and the Federal Capital Territory into urban and rural areas. In total, 74 sampling strata were identified. Samples were selected independently in every stratum via a two-stage selection. Implicit stratifications were achieved at each of the lower administrative levels by sorting the sampling frame before sample selection according to administrative order and by using a probability proportional to size selection during the first sampling stage.

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

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

Four questionnaires were used for the 2018 NDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The 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 Nigeria. Comments were solicited from various stakeholders representing government ministries and agencies, nongovernmental organisations, and international donors. In addition, information about the fieldworkers for the survey was collected through a self-administered Fieldworker Questionnaire.

Cleaning operations

The processing of the 2018 NDHS data began almost immediately after the fieldwork started. As data collection was completed in each cluster, all electronic data files were transferred via the IFSS to the NPC central office in Abuja. These data files were registered and checked for inconsistencies, incompleteness, and outliers. The field teams were alerted to any inconsistencies and errors. Secondary editing, carried out in the central office, involved resolving inconsistencies and coding the open-ended questions. The NPC data processor coordinated the exercise at the central office. The biomarker paper questionnaires were compared with electronic data files to check for any inconsistencies in data entry. Data entry and editing were carried out using the CSPro software package. The concurrent processing of the data offered a distinct advantage because it maximised the likelihood of the data being error-free and accurate. Timely generation of field check tables allowed for effective monitoring. The secondary editing of the data was completed in the second week of April 2019.

Response rate

A total of 41,668 households were selected for the sample, of which 40,666 were occupied. Of the occupied households, 40,427 were successfully interviewed, yielding a response rate of 99%. In the households interviewed, 42,121 women age 15-49 were identified for individual interviews; interviews were completed with 41,821 women, yielding a response rate of 99%. In the subsample of households selected for the male survey, 13,422 men age 15-59 were identified and 13,311 were successfully interviewed, yielding a response rate of 99%.

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 2018 Nigeria Demographic and Health Survey (NDHS) to minimise this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2018 NDHS is only one of many samples that could have been selected from the same population, using the same design and expected size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability 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 2018 NDHS sample is the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. Sampling errors are computed in SAS, using programs developed by ICF. These programs use the Taylor linearisation method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data appraisal

Data Quality Tables - Household age distribution - Age distribution of eligible and interviewed women - Age distribution of eligible and interviewed men - Completeness of reporting - Births by calendar years - Reporting of age at death in days - Reporting of age at death in months - Standardisation exercise results from anthropometry training - Height and weight data completeness and quality for children - Height measurements from random subsample of measured children - Sibship size and sex ratio of siblings - Pregnancy-related mortality trends - Data collection period - Malaria prevalence according to rapid diagnostic test (RDT)

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

Abstract

The 2022 Ghana Demographic and Health Survey (2022 GDHS) is the seventh in the series of DHS surveys conducted by the Ghana Statistical Service (GSS) in collaboration with the Ministry of Health/Ghana Health Service (MoH/GHS) and other stakeholders, with funding from the United States Agency for International Development (USAID) and other partners.

The primary objective of the 2022 GDHS is to provide up-to-date estimates of basic demographic and health indicators. Specifically, the GDHS collected information on: - Fertility levels and preferences, contraceptive use, antenatal and delivery care, maternal and child health, childhood mortality, childhood immunisation, breastfeeding and young child feeding practices, women’s dietary diversity, violence against women, gender, nutritional status of adults and children, awareness regarding HIV/AIDS and other sexually transmitted infections, tobacco use, and other indicators relevant for the Sustainable Development Goals - Haemoglobin levels of women and children - Prevalence of malaria parasitaemia (rapid diagnostic testing and thick slides for malaria parasitaemia in the field and microscopy in the lab) among children age 6–59 months - Use of treated mosquito nets - Use of antimalarial drugs for treatment of fever among children under age 5

The information collected through the 2022 GDHS is intended to assist policymakers and programme managers in designing and evaluating programmes 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
• Man age 15-59

Universe

The survey covered all de jure household members (usual residents), all women aged 15-49, men aged 15-59, and all children aged 0-4 resident in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

To achieve the objectives of the 2022 GDHS, a stratified representative sample of 18,450 households was selected in 618 clusters, which resulted in 15,014 interviewed women age 15–49 and 7,044 interviewed men age 15–59 (in one of every two households selected).

The sampling frame used for the 2022 GDHS is the updated frame prepared by the GSS based on the 2021 Population and Housing Census.1 The sampling procedure used in the 2022 GDHS was stratified two-stage cluster sampling, designed to yield representative results at the national level, for urban and rural areas, and for each of the country’s 16 regions for most DHS indicators. In the first stage, 618 target clusters were selected from the sampling frame using a probability proportional to size strategy for urban and rural areas in each region. Then the number of targeted clusters were selected with equal probability systematic random sampling of the clusters selected in the first phase for urban and rural areas. In the second stage, after selection of the clusters, a household listing and map updating operation was carried out in all of the selected clusters to develop a list of households for each cluster. This list served as a sampling frame for selection of the household sample. The GSS organized a 5-day training course on listing procedures for listers and mappers with support from ICF. The listers and mappers were organized into 25 teams consisting of one lister and one mapper per team. The teams spent 2 months completing the listing operation. In addition to listing the households, the listers collected the geographical coordinates of each household using GPS dongles provided by ICF and in accordance with the instructions in the DHS listing manual. The household listing was carried out using tablet computers, with software provided by The DHS Program. A fixed number of 30 households in each cluster were randomly selected from the list for interviews.

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

Mode of data collection

Face-to-face computer-assisted interviews [capi]

Research instrument

Four questionnaires were used in the 2022 GDHS: the Household Questionnaire, the Woman’s Questionnaire, the Man’s Questionnaire, and the Biomarker Questionnaire. The questionnaires, based on The DHS Program’s model questionnaires, were adapted to reflect the population and health issues relevant to Ghana. In addition, a self-administered Fieldworker Questionnaire collected information about the survey’s fieldworkers.

The GSS organized a questionnaire design workshop with support from ICF and obtained input from government and development partners expected to use the resulting data. The DHS Program optional modules on domestic violence, malaria, and social and behavior change communication were incorporated into the Woman’s Questionnaire. ICF provided technical assistance in adapting the modules to the questionnaires.

Cleaning operations

DHS staff installed all central office programmes, data structure checks, secondary editing, and field check tables from 17–20 October 2022. Central office training was implemented using the practice data to test the central office system and field check tables. Seven GSS staff members (four male and three female) were trained on the functionality of the central office menu, including accepting clusters from the field, data editing procedures, and producing reports to monitor fieldwork.

From 27 February to 17 March, DHS staff visited the Ghana Statistical Service office in Accra to work with the GSS central office staff on finishing the secondary editing and to clean and finalize all data received from the 618 clusters.

Response rate

A total of 18,540 households were selected for the GDHS sample, of which 18,065 were found to be occupied. Of the occupied households, 17,933 were successfully interviewed, yielding a response rate of 99%. In the interviewed households, 15,317 women age 15–49 were identified as eligible for individual interviews. Interviews were completed with 15,014 women, yielding a response rate of 98%. In the subsample of households selected for the male survey, 7,263 men age 15–59 were identified as eligible for individual interviews and 7,044 were successfully interviewed.

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 2022 Ghana Demographic and Health Survey (2022 GDHS) to minimize this type of error, nonsampling errors are impossible to avoid and difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of respondents selected in the 2022 GDHS is only one of many samples that could have been selected from the same population, using the same design and identical size. Each of these samples would yield results that differ somewhat from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. Although the degree of variability is not known exactly, it can be estimated from the survey results. A sampling error is usually measured in terms of the standard error for a particular statistic (mean, percentage, etc.), which is the square root of the variance. The standard error can be used to calculate confidence intervals within which the true value for the population can reasonably be assumed to fall. For example, for any given statistic calculated from a sample survey, the value of that statistic will fall within a range of plus or minus two times the standard error of that statistic in 95% of all possible samples of identical size and design.

If the sample of respondents had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the 2022 GDHS sample was the result of a multistage stratified design, and, consequently, it was necessary to use more complex formulas. The computer software used to calculate sampling errors for the GDHS 2022 is an SAS program. This program used the Taylor linearization method to estimate variances for survey estimates that are means, proportions, or ratios. The Jackknife repeated replication method is used for variance estimation of more complex statistics such as fertility and mortality rates.

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

Data appraisal

Data Quality Tables

• Age distribution of eligible and interviewed women
• Age distribution of eligible and interviewed men
• Age displacement at age 14/15
• Age displacement at age 49/50
• Pregnancy outcomes by years preceding the survey
• Completeness of reporting
• Standardisation exercise results from anthropometry training
• Height and weight data completeness and quality for children
• Height measurements from random subsample of measured children
• Interference in height and weight measurements of children
• Interference in height and weight measurements of women and men
• Heaping in anthropometric measurements for children (digit preference)
• Observation of mosquito nets
• Observation of handwashing facility
• School attendance by single year of age
• Vaccination cards photographed
• Number of