Abstract

The Tanzania Demographic and Health Survey (TDHS) is a national sample survey of women of reproductive ages (15-49) and men aged 15 to 60. The survey was designed to collect data on socioeconomic characteristics, marriage patterns, birth history, breastfeeding, use of contraception, immunisation of children, accessibility to health and family planning services, treatment of children during times of illness, and the nutritional status of children and their mothers.

The primary objectives of the TDHS were to: - Collect data for the evaluation of family planning and health programmes, - Determine the contraceptive prevalence rate, which will help in the design of future national family planning programmes, and - Assess the demographic situation of the country.

Geographic coverage

The Tanzania Demographic and Health Survey (TDHS) is a national sample survey. This sample should allow for separate analyses in urban and rural areas, and for estimation of contraceptive use in each of the 20 regions located on the mainland and in Zanzibar.

Analysis unit

• Households
• Children under five years
• Women age 15-49
• Men age 15-60

Kind of data

Sample survey data

Sampling procedure

The principal objective of the Tanzania Demographic and Health Survey (TDHS) was to collect data on fertility, family planning, and health of the people. This survey involved randomly selected women aged 15-49 and men aged 15-60 in selected households.

Before the sampling frame was developed, two possibilities for the TDHS sample design were considered: - The 1988 Population census list of Enumeration Areas (EAs) - The National Master Sample for Tanzania created in 1986 (NMS).

The NMS was intended mainly for agricultural purposes and, at that time, only for rural areas. The NMS was based on the 1978 Census information while the urban frame was still being worked upon. Therefore, it was decided that the TDHS sample design would use the 1988 Census information as the basic sampling frame. Since the TDHS sample was to be clustered, it was necessary to have sampling units of manageable and fairly uniform size and with very well defined boundaries. The 1988 Census frame provided the list of enumeration area units (EAs) that had well defined boundaries and manageable uniform size. Therefore, EAs were used as primary sampling units (PSUs).

The target of the TDHS sample was about 7850 women age 15-49 with completed interviews. This sample should allow for separate analyses in urban and rural areas, and for estimation of contraceptive use in each of the 20 regions located on the mainland and in Zanzibar. Estimates for large domains (by combination of a group of regions) were also taken into consideration.

The TDHS used a three-stage sample. The frame was stratified by urban and rural areas. The primary sampling units in the TDHS survey were the wards/branches. The design involved the target of 350 completed interviews for each of 19 regions on the mainland and 500 in each of Dar es Salaam and Zanzibar.

In the first stage, the wards/branches were systematically selected with probability proportional to size (according to 1988 census information). In a second sampling stage, two EAs per selected rural ward/branch and one EA per selected urban ward/branch were chosen with probability proportional to size (also according to 1988 census information). In total, 357 EAs were selected for the TDHS, 95 in the urban area and 262 in the rural. A new listing of households was made shortly before the TDHS fieldwork by special teams including a total of 14 field workers. These teams visited the selected EAs all over the country to list the names of the heads of the households and obtain the population composition of each household (total number of persons in the household). In urban areas, the address of the dwelling was also recorded in order to make it easy to identify the household during the main survey. A fixed number of 30 households in each rural EA and 20 in each urban EA were selected.

About 9560 households were needed to achieve the required sample size, assuming 80 percent overall household completion rate.

See detailed sampling information in the APPENDIX B of the final 1991-1992 Tanzania Demographic and Health Survey report.

Mode of data collection

Face-to-face

Research instrument

The household, female, and male questionnaires were designed by following the Model Questionnaire "B" which is for low contraceptive prevalence countries. Some adaptations were made to suit the Tanzania situation, but the core questions were not changed. The original questionnaire was prepared in English and later translated into Kiswahili, the language that is widely spoken in the country. There are parts in the country where people are not very conversant with Kiswahili and would find it difficult to respond in Kiswahili but would understand when they are asked anything. The translated document was given to another translator to translate it back into English and comparisons were made to determine the differences.

PRETEST

A pretest to assess the viability of the survey instruments, particularly the questionnaires and the field organization, was carried out in Iringa Rural District, Iringa Region. It covered 16 enumeration areas with a total of 320 households. The pretest, which took a month to complete, was carded out in November/December, 1990, and covered both rural and urban EAs.

The pretest training took two weeks and consisted of classroom training and field practice in neighborhood areas. In all, 14 newly recruited interviewers and the Census staff were involved. The Census staffs who were to be transformed into the TDHS team handled the training for both the fieldwork management and the questionnaire. During the later fieldwork, they supervised the field exercise.

During the fieldwork, the administrative structure of the CCM Party, which involved the Party Branch Offices and the ten-cell leadership, were utilized in an effort to secure the maximum confidence and cooperation of the people in the areas where the team was working. At the end of the fieldwork, the interviewers and the supervisory team returned to the head office in Dares Salaam for debriefing and discussion of their field experiences, particularly those related to the questionnaires and the logistic problems that were encountered. All these experiences were used to improve upon the final version of the questionnaires and the overall logistic arrangements.

Response rate

Out of the 9282 households selected for interview, 8561 households could be located and 8327 were actually interviewed. The shortfall between selected and interviewed households was largely due to the fact that many dwellings were either vacant or destroyed or no competent respondents were present at the time of the interview. A total of 9647 eligible women (i.e., women age 15-49 who spent the night before the interview in a sampled household) were identified for interview, and 9238 women were actually interviewed (96 percent response rate). The main reason for non-interview was absence from the home or incapacitation.

The Tanzania DHS male survey covered men aged between 15 and 60 years who were living in selected households (every fourth household of the female survey). The results of the survey show that 2392 eligible men were identified and 2114 men were interviewed (88 percent response rate). Men were generally not interviewed because they were either incapacitated or not at home during the time of the survey.

Sampling error estimates

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

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

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

If the sample of women had been selected as a simple random sample, it would have been possible to use straightforward formulas for calculating sampling errors. However, the Tanzania DHS sample designs depended on stratification, stages, and clusters. Consequently, it was necessary to utilize more complex formulas. The computer package CLUSTERS, developed by the International Statistical

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Data on enterprise births, deaths, active enterprises and survival rates across boroughs.

Data includes:

1. the most recent annual figures for enterprise births and deaths
2. a time series of the number of births and deaths of entrprises together with a percentage of births and deaths to active enterprises in a given year
3. a time series of the number of active enterprises.
4. survival rates of enterprises for up to 5 years after birth

Notes and definitions:

• The starting point for business demography is the concept of a population of active businesses in a reference year (t). These are defined as businesses that had either turnover or employment at any time during the reference period.
• A birth is identified as a business that was present in year t, but did not exist in year t-1 or t-2. Births are identified by making comparison of annual active population files and identifying those present in the latest file, but not the two previous ones.
• A death is defined as a business that was on the active file in year t, but was no longer present in the active file in t+1 and t+2. In order to provide an early estimate of deaths, an adjustment has been made to the 2007 and 2008 deaths to allow for reactivations. These figures are provisional and subject to revision.

Data on size of firms (micro-business, SME, large) for business and employees in London by industry can be found on the ONS website.

More Business Demographics data on the ONS website

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

Note: all tests corrected for heteroscedacity; HC, healthy controls; ROP, Recent Onset Psychosis patients; NROP, ROP no schizophrenia; SROP, ROP with schizophrenia; Ns, p>.05; IQ, Intelligence Quotient; PANSS Pos, PANSS Positive scale; PANSS Neg, PANSS Negative scale; PANSS Gen, PANSS General scale; Dis-P2, score on PANSS P2 item Conceptual Disorganization; Dis-CogSyn, Disorganization score on Cognitive Syndrome factor; Dis-5Fact, Disorganization factor score; AP, Antipsychotic; CPZ eq dose/d, Chlorpromazine equivalent dose per day; Str, Stroop test; Trl, Trailmaking test; CVLT, California Verbal Learning Test; CPT d', Continuous Performance Test d-prime; FingerTp, Fingertapping test; RT, Reaction Time; W-Obj, within-object; B-Obj, between-object; BORT – WORT, between-object RT minus within-object RT;

How high is the brand awareness of Stop & Shop in the United States?When it comes to grocery store customers, brand awareness of Stop & Shop is at *** in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Stop & Shop in the United States?In total, ** of U.S. grocery store customers say they like Stop & Shop. However, in actuality, among the *** of U.S. respondents who know Stop & Shop, *** of people like the brand.What is the usage share of Stop & Shop in the United States?All in all, ** of grocery store customers in the United States use Stop & Shop. That means, of the *** who know the brand, *** use them.How loyal are the customers of Stop & Shop?Around ** of grocery store customers in the United States say they are likely to use Stop & Shop again. Set in relation to the ** usage share of the brand, this means that *** of their customers show loyalty to the brand.What's the buzz around Stop & Shop in the United States?In August 2022, about ** of U.S. grocery store customers had heard about Stop & Shop in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's ***, meaning at the time of the survey there's little buzz around Stop & Shop in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Although South Africa is the global epicenter of the HIV epidemic, the uptake of HIV testing and treatment among young people remains low. Concerns about confidentiality impede the utilization of HIV prevention services, which signals the need for discrete HIV prevention measures that leverage youth-friendly platforms. This paper describes the process of developing a youth-friendly internet-enabled HIV risk calculator in collaboration with young people, including young key populations aged between 18 and 24 years old. Using qualitative research, we conducted an exploratory study with 40 young people including young key population (lesbian, gay, bisexual, transgender (LGBT) individuals, men who have sex with men (MSM), and female sex workers). Eligible participants were young people aged between 18–24 years old and living in Soweto. Data was collected through two peer group discussions with young people aged 18–24 years, a once-off group discussion with the [Name of clinic removed for confidentiality] adolescent community advisory board members and once off face-to-face in-depth interviews with young key population groups: LGBT individuals, MSM, and female sex workers. LGBT individuals are identified as key populations because they face increased vulnerability to HIV/AIDS and other health risks due to societal stigma, discrimination, and obstacles in accessing healthcare and support services. The measures used to collect data included a socio-demographic questionnaire, a questionnaire on mobile phone usage, an HIV and STI risk assessment questionnaire, and a semi-structured interview guide. Framework analysis was used to analyse qualitative data through a qualitative data analysis software called NVivo. Descriptive statistics were summarized using SPSS for participant socio-demographics and mobile phone usage. Of the 40 enrolled participants, 58% were male, the median age was 20 (interquartile range 19–22.75), and 86% had access to the internet. Participants’ recommendations were considered in developing the HIV risk calculator. They indicated a preference for an easy-to-use, interactive, real-time assessment offering discrete and private means to self-assess HIV risk. In addition to providing feedback on the language and wording of the risk assessment tool, participants recommended creating a colorful, interactive and informational app. A collaborative and user-driven process is crucial for designing and developing HIV prevention tools for targeted groups. Participants emphasized that privacy, confidentiality, and ease of use contribute to the acceptability and willingness to use internet-enabled HIV prevention methods.

How high is the brand awareness of Nordstrom Rack in the United States?When it comes to fashion online shop users, brand awareness of Nordstrom Rack is at **% in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Nordstrom Rack in the United States?In total, **% of U.S. fashion online shop users say they like Nordstrom Rack. However, in actuality, among the **% of U.S. respondents who know Nordstrom Rack, **% of people like the brand.What is the usage share of Nordstrom Rack in the United States?All in all, **% of fashion online shop users in the United States use Nordstrom Rack. That means, of the **% who know the brand, **% use them.How loyal are the customers of Nordstrom Rack?Around *% of fashion online shop users in the United States say they are likely to use Nordstrom Rack again. Set in relation to the **% usage share of the brand, this means that **% of their customers show loyalty to the brand.What's the buzz around Nordstrom Rack in the United States?In June 2022, about *% of U.S. fashion online shop users had heard about Nordstrom Rack in the media, on social media, or in advertising over the past three months. Of the **% who know the brand, that's **%, meaning at the time of the survey there's little buzz around Nordstrom Rack in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Participant demographic data presented as means and standard deviations.

How high is the brand awareness of Blue Moon in the United States?When it comes to beer drinkers, brand awareness of Blue Moon is at **% in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Blue Moon in the United States?In total, **% of U.S. beer drinkers say they like Blue Moon. However, in actuality, among the **% of U.S. respondents who know Blue Moon, **% of people like the brand.What is the usage share of Blue Moon in the United States?All in all, **% of beer drinkers in the United States use Blue Moon. That means, of the **% who know the brand, **% use them.How loyal are the drinkers of Blue Moon?Around **% of beer drinkers in the United States say they are likely to use Blue Moon again. Set in relation to the **% usage share of the brand, this means that **% of their drinkers show loyalty to the brand.What's the buzz around Blue Moon in the United States?In August 2022, about **% of U.S. beer drinkers had heard about Blue Moon in the media, on social media, or in advertising over the past three months. Of the **% who know the brand, that's **%, meaning at the time of the survey there's some buzz around Blue Moon in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Current population at a parcel level within Fairfax County as of the VALID_TO date in the attribute table.

For methodology and a data dictionary please view the IPLS data dictionary

30-year population forecasts at a parcel level within Fairfax County developed on the VALID_TO date in the attribute table. For methodology and a data dictionary please view the IPLS data dictionary

The detailed definition, data source, and years of data extracted for each county-level variable.

How high is the brand awareness of Hennessy in the United States?When it comes to spirits drinkers, brand awareness of Hennessy is at *** in the United States. The survey was conducted using the concept of aided brand recognition, showing respondents both the brand's logo and the written brand name.How popular is Hennessy in the United States?In total, *** of U.S. spirits drinkers say they like Hennessy. However, in actuality, among the *** of U.S. respondents who know Hennessy, *** of people like the brand.What is the usage share of Hennessy in the United States?All in all, *** of spirits drinkers in the United States use Hennessy. That means, of the *** who know the brand, *** use them.How loyal are the drinkers of Hennessy?Around *** of spirits drinkers in the United States say they are likely to use Hennessy again. Set in relation to the *** usage share of the brand, this means that *** of their drinkers show loyalty to the brand.What's the buzz around Hennessy in the United States?In October 2022, about *** of U.S. spirits drinkers had heard about Hennessy in the media, on social media, or in advertising over the past three months. Of the *** who know the brand, that's ***, meaning at the time of the survey there's little buzz around Hennessy in the United States.If you want to compare brands, do deep-dives by survey items of your choice, filter by total online population or users of a certain brand, or drill down on your very own hand-tailored target groups, our Consumer Insights Brand KPI survey has you covered.

Heading in football (soccer) is a complex skill involving deliberate head-to-ball contact, which may pose short-, medium-, and long-term risk to player brain health. However, understanding header exposure during matches and training sessions, as well as comparing header incidence between studies is currently challenging given the lack of standardisation in descriptors, definitions, and reporting methods. This Delphi study aimed to establish a consensus on the descriptors, definitions, and reporting methods for heading in football research to improve consistency and quality. The study involved 167 participants from diverse football-related backgrounds including coaches, players, medical personnel, and researchers, with consensus achieved to include 27 descriptors in minimum reporting criteria for heading in football research. An additional 27 descriptors were also defined for inclusion in an expanded framework. The operational definition of a header was standardised as “a head-to-ball contact where the player makes a deliberate movement to redirect the trajectory of the ball using their head.” The consensus framework provides a standardised approach to heading in football research to enhance data quality and comparability across studies. Improved header incidence data quality has the potential to contribute significantly to our understanding of the risks associated with heading in football to inform future research and practice guidelines.

Participants’ means and standard error of means (S.E.M. in brackets) of demographic variables and self-report questionnaire scores.

BackgroundWhile COVID-19 has had a wide-ranging impact on individuals and societies, persons with disabilities are uniquely affected largely due to secondary health conditions and challenges in adhering to protective measures. However, research on COVID-19 and vaccine acceptance has primarily focused on the general population and healthcare workers but has specifically not targeted PwDs, who are more vulnerable within societies. Hence, this study assessed PwDs knowledge of COVID-19 and factors associated with COVID-19 vaccine acceptance.MethodsA cross-sectional survey was conducted among PwDs in the Atwima Mponua District in the Ashanti Region of Ghana. Respondents were sampled systematically and data was collected using a structured questionnaire. The data were analyzed with STATA version 16.0. Descriptive analysis was done using means and proportions. The chi-square test and Logistic regression were used to assess Covid-19 vaccine acceptance among the respondents.Results250 PwDs were recruited for the study. A higher proportion of the respondents were females, physically impaired, and between 30–50 years. The majority (74%) of the PwDs had average knowledge about Covid-19. Factors such as age, educational level and type of disability were significantly associated with PwDs’ knowledge of COVID-19. The acceptance rate for COVID-19 among PwDs was 71.2%. Age, religion, knowledge of COVID-19, and educational level were significantly associated with Covid-19 vaccine acceptance. Persons with disabilities with low and average knowledge of COVID-19 were 95% and 65%, respectively, less likely to accept the vaccine compared to those with high knowledge of COVID-19 (AOR = 0.05, 95%CI: 0.01, 0.21; AOR = 0.35, 95%CI: 0.12, 1.03). Older people and those with higher education were more likely to accept the vaccine compared to younger people and those with no or less education.ConclusionPersons with disabilities have average knowledge of COVID-19 and a greater percentage of them were willing to accept the vaccine. The study identified age, religion, knowledge of COVID-19, and educational level as contributing factors to their willingness to accept the COVID-19 vaccine. This suggest that PwDs will lean positive toward COVID-19 vaccine programs and as such, vaccination programs should target them.

Socio-demographic characteristics of the people under the National Influenza Sentinel Surveillance System in Tanzania, 2019.

Demographic, clinical, and epidemiological characteristics of all participants enrolled in the clinical validation study (N = 868).