The data-tables contain water and sanitation technology cost, infrastructure, flow rates, population socio-economics and water access, water use, and sewage needs, for fifteen districts in the Greater Accra Metropolitan Area in Ghana The tables are were used in the DFID funded Future Cities Africa project, in specific the resilience.io modelling component as carried out by Imperial College London, IIER, and The Ecological Sequestration Trust, with support from Cities Alliance. For more details see: http://resilience.io/

This statistic shows the total population of Ghana from 2013 to 2023 by gender. In 2023, Ghana's female population amounted to approximately 16.91 million, while the male population amounted to approximately 16.88 million inhabitants.

Historical dataset of population level and growth rate for the Accra, Ghana metro area from 1950 to 2025.

Male population with no education, % of Greater Accra sank by 17.14% from 3.5 % in 2008 to 2.9 % in 2014.

Population density per pixel at 100 metre resolution. WorldPop provides estimates of numbers of people residing in each 100x100m grid cell for every low and middle income country. Through ingegrating cencus, survey, satellite and GIS datasets in a flexible machine-learning framework, high resolution maps of population counts and densities for 2000-2020 are produced, along with accompanying metadata. DATASET: Alpha version 2010 and 2015 estimates of numbers of people per grid square, with national totals adjusted to match UN population division estimates (http://esa.un.org/wpp/) and remaining unadjusted. REGION: Africa SPATIAL RESOLUTION: 0.000833333 decimal degrees (approx 100m at the equator) PROJECTION: Geographic, WGS84 UNITS: Estimated persons per grid square MAPPING APPROACH: Land cover based, as described in: Linard, C., Gilbert, M., Snow, R.W., Noor, A.M. and Tatem, A.J., 2012, Population distribution, settlement patterns and accessibility across Africa in 2010, PLoS ONE, 7(2): e31743. FORMAT: Geotiff (zipped using 7-zip (open access tool): www.7-zip.org) FILENAMES: Example - AGO10adjv4.tif = Angola (AGO) population count map for 2010 (10) adjusted to match UN national estimates (adj), version 4 (v4). Population maps are updated to new versions when improved census or other input data become available. Ghana data available from WorldPop here.

Female population with no education, % of Greater Accra leapt by 7.79% from 7.7 % in 2008 to 8.3 % in 2014. Since the 36.73% drop in 2003, female population with no education, % sank by 33.06% in 2014.

Abstract

The population and housing census (PHC) is the unique source of reliable and comprehensive data about the size of population and also on major socio-economic & socio-demographic characteristics of the country. It provides data on geographic and administrative distribution of population and household in addition to the demographic and socio-economic characteristics of all the people in the country. Generally, it provides for comparing and projecting demographic data, social and economic characteristics, as well as household and housing conditions at all levels of the country’s administrative units and dimensions: national, regional, districts and localities. The data from the census is classified, tabulated and disseminated so that researchers, administrators, policy makers and development partners can use the information in formulating and implementing various multi-sectorial development programs at the national and community levels. Data on all key variables namely area, household, population, economic activity, literacy and education, fertility and child survival, housing conditions and sanitation are collected and available in the census data. The 2021 PHC in Ghana had an overarching goal of generating updated demographic, social and economic data, housing characteristics and dwelling conditions to support national development planning activities.

Geographic coverage

National Coverage , Region , District

Analysis unit

• Individuals
• Households
• Emigrants
• Absentee population
• Mortality
• Type of residence (households and non household)

Universe

All persons who spent census night (midnight of 27th June 2021) in Ghana

Kind of data

Census/enumeration data [cen]

Sampling procedure

This 10% sample data for the 2021 PHC is representative at the district/subdistrict level and also by the urban rural classification.

Mode of data collection

Computer Assisted Personal Interview [capi]

Research instrument

GSS developed two categories of instruments for the 2021 PHC: the listing form and the enumeration instruments. The listing form was only one, while the enumeration instruments comprised six questionnaires, designated as PHC 1A, PHC 1B, PHC 1C, PHC 1D, PHC 1E and PHC 1F. The PHC 1A was the most comprehensive with the others being its subsets.

1. Listing Form: The listing form was developed to collect data on type of structures, level of completion, whether occupied or vacant and use(s) of the structures. It was also used to collect information about the availability, number and types of toilet facilities in the structures. It was also used to capture the number of households in a structure, number of persons in households and the sex of the persons residing in the households if occupied. Finally, the listing form was used to capture data on non-household populations such as the population in institutions, floating population and sex of the non-household populations.

2. PHC 1A: The PHC 1A questionnaire was used to collect data from all households in the country. Primarily, it was used to capture household members and visitors who spent the Census Night in the dwelling of the household, and their relationship with the head of the household. It was also used to collect data on homeless households. Members of the households who were absent were enumerated at the place where they had spent the Census Night. The questionnaire was also used to collect the following household information: emigration; socio-demographic characteristics (sex, age, place of birth and enumeration, survival status of parents, literacy and education; economic activities; difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

3. PHC 1B: The PHC 1B questionnaire was used to collect data from persons in stable institutions comprising boarding houses, hostels and prisons who were present on Census Night. Other information that was captured with this instrument are socio-demographic characteristics, literacy and education, economic activities, difficulty in performing activities; ownership and usage of information, technology and communication facilities; fertility; mortality; housing characteristics and conditions and sanitation.

4. PHC 1C: The PHC 1C questionnaire was used to collect data from persons in “unstable” institutions such as hospitals and prayer camps who were present at these places on Census Night. The instrument was used to capture only the socio-demographic characteristics of individuals.

5. PHC 1D: The PHC 1D questionnaire was used to collect data from the floating population. This constitutes persons who were found at airports, seaports, lorry stations and similar locations waiting for or embarking on long-distance travel, as well as outdoor sleepers on Census Night. The instrument captured the socio-demographic information of individuals.

6. PHC 1E: All persons who spent the Census Night at hotels, motels and guest houses were enumerated using the PHC 1E. The content of the questionnaire was similar to that of the PHC 1D.

7. PHC 1F: The PHC 1F questionnaire was administered to diplomats in the country.

Cleaning operations

The Census data editing was implemented at three levels: 1. data editing by enumerators and supervisors during data collection 2. data editing was done at the regional level by the regional data quality monitors during data collection 3. Final data editing was done at the national level using the batch edits in CSPro and STATA Data editing and cleaning was mainly digital.

Response rate

100 percent

Data appraisal

A post Enumeration Survey (PES) was conducted to assess the extent of coverage and content error.

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

This statistic shows the biggest cities in Ghana, as of 2010. In September 2010, approximately **** million people lived in Accra, making it the biggest city in Ghana.

The population of Ghana

Accra and Kumasi, are by far the biggest cities in Ghana. Both metropolises boast populations of around two million inhabitants, while the majority of the other cities in Ghana have populations below *******. with Accra being the capital, both cities are located on the Gulf of Guinea on the Atlantic Ocean; Accra stretches along the coast, while Kumasi is located in the rain forest region inland and to the north of Accra.

Ghana is in the middle of an ever-growing urbanization, and its economy has experienced rapid growth over the past few years. While growth has now slowed somewhat, it is expected to pick up again in the future.

Alongside an increasing urbanization, Ghana is rapidly shifting from agriculture as its main source of GDP to an increasingly dominant services sector, alongside growth in industry. While the majority of employment is still largely agriculturally based, this will change, and with increasing urbanization and increasing economic growth, Ghana will need to cope with the transition; as of 2010, for example, ** percent of the urban population in Ghana was still living in slums. Ghana will have to deal with these changes, reduce the negative side effects and increase the positive ones. The provision and accessibility to urban services and infrastructure will improve the quality of life for an increasingly urban population, but it will need to be properly planned.

As of 2023, children aged 0-14 years in Ghana added up to around **** million, registering an increment of *** thousand people compared to 2022. Furthermore, the number of children belonging to this age group has increased progressively over the years. Compared to the elderly people, children at the age of ** and younger represented a greater percentage of the population in Ghana in the period examined.

Since December 2019 when COVID-19 was detected, it took the world by surprise in terms of spread and morbidity/mortality. The high rate of spread and casualties recorded from COVID-19 called for research in all directions to find ways to contain and reverse the incidences. It is against this background that this paper sought to measure the association of the socio-demographic factors in the hard-hit districts in Greater Accra and Ashanti to analyze its relationship with the novel COVID-19 virus. Data on COVID-19 cases from 35 Districts in both Greater Accra and Ashanti Regions were collected from the Ghana Health Service and population data from Ghana Statistical Service. Descriptive statistics and regression analysis were generated using R. We found that some socio-demographic variables have an association with COVID-19 infections. For example, age and religion especially Christianity and Islam pose risk to COVID-19. The population aged 15–64 was particularly at high risk of infections due to the high level of movement of this age group. We, therefore, recommend that places of congregation such as Churches and Mosques be targeted for vigorous sensitization on COVID-19 protocols and prevention. Also, districts with a high population between the ages of 15–64 should step sensitization efforts to educate their inhabitants on the need to reduce travel and related activities to curb the spread of the virus.

Abstract

The Ghana-Accra Multiple Indicator Cluster Survey (MICS4) was conducted in 2010-2011 by the Institute of Statistical, Social and Economic Research (ISSER) at the University of Ghana. The survey was carried out in 5 high densely populated localities of Accra with the primary objective of providing up-to-date information for assessing the situation of children and women in these areas. Financial and technical support was provided by the United Nations Children's Fund (UNICEF). The Ghana Urban MICS was conducted as part of the fourth global round of MICS surveys (MICS4). MICS is an international household survey programme developed by UNICEF to provide up-to-date information on the situation of children and women and measures key indicators that allow countries to monitor progress towards the Millennium Development Goals (MDGs) and other internationally agreed upon commitments.

Geographic coverage

Five high population density localities, namely Nima, New Town, James Town, La and Bubuashie.

Analysis unit

• individuals
• households

Universe

The survey covered all de jure household members (usual residents), all women aged between 15-49 years, all men aged between 15-59 years and all children under 5 living in the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

The primary objective of the sample design for the Ghana-Accra Multiple Indicator Cluster Survey was to produce statistically reliable estimates of most indicators for these high density population suburbs of Accra.

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

The target sample size for the Accra MICS was calculated as 1,500 households. For the calculation of the sample size, the key indicator used was full immunization among children aged 12-23 months using the results from the 2006 national MICS. This indicator yielded the largest sample size among 5 potential indicators considered, the others being BCG, Polio 3, MMR and DPT coverage.

The resulting number of households from this calculation was 1,266 households, which was the minimum sample size required to achieve the desired level of accuracy. Since the calculated sample size is not too far from the previous proposed size that is 1500, the decision is made to maintain this first proposal. Primary allocation of the total sample size to the five localities was done using probability proportional to size with the EAs serving as the primary sampling units. The national practice is to select 15 households from each EA for such household surveys and therefore, 100 EAs were required to meet the computed sample size of 1,500.

The sampling procedures are more fully described in "Ghana-Accra Multiple Indicator Cluster Survey 2010 - Report" pp.134.-138.

Mode of data collection

Face-to-face [f2f]

Research instrument

The questionnaires for the Generic MICS were structured questionnaires based on the MICS4 model questionnaire with some modifications and additions. Household questionnaires were administered to a knowledgeable adult living in the household. The household questionnaire includes Household Listing Form, Education, Water and Sanitation, Household Characteristics, Insecticide Treated Nets, Indoor Residual Spraying, Child Labour, Child Discipline, Handwashing and Salt Iodization.

In addition to a household questionnaire, the Questionnaire for Individual Women was administered to all women aged 15-49 years living in the households. The women's questionnaire includes Women's Background, Access to Mass Media and Use of Information/Communication Technology, Child Mortality (however, given the small sample size, indicators for child mortality have not been included in this report), Desire for Last Birth, Maternal and Newborn Health, Post-Natal Health Checks, Illness Symptoms, Contraception, Unmet Need, Female Genital Mutilation/Cutting, Attitudes Towards Domestic Violence, Marriage/Union, Sexual Behaviour, National Health Insurance and HIV/AIDS.

The Questionnaire for Children Under-Five was administered to mothers or caretakers of children under 5 years of age1 living in the households. The children's questionnaire includes Age, Birth Registration, Early Childhood Development, Breastfeeding, Care of Illness, Malaria, Immunization and Anthropometry.

The Questionnaire for Individual Men was administered to each third man among all men aged 15-59 living in the households. The men's questionnaire includes Men's Background, Access to Mass Media and Use of Information/Communication Technology, Marriage/Union, Attitudes Towards Contraception, Attitudes Towards Domestic Violence, Sexual Behaviour, National Health Insurance and HIV/AIDS.

Cleaning operations

Data were entered using the CSPro software. The data were entered on 14 microcomputers and carried out by 14 data entry operators under the supervision of 4 data entry supervisors. In order to ensure quality control, all questionnaires were double entered and internal consistency checks were performed. Procedures and standard programs developed under the global MICS4 programme and adapted to the final questionnaires were used throughout. Data processing began two weeks after data collection in December 2010 and was completed in February 2011. Data were analysed using the Statistical Package for Social Sciences (SPSS) software program, Version 18, and the model syntax and tabulation plans developed by UNICEF were used for this purpose, after amending to take into account the changes/additions to the Questionnaires.

Response rate

Of the 1,500 households selected for the sample, 1,453 were found to be occupied. Of these, 1,409 were successfully interviewed for a household response rate of 97 percent. In the interviewed households, 1,427 women (age 15-49 years) were identified. Of these, 1,294 were successfully interviewed, yielding a response rate of 91 percent within interviewed households. In addition, 472 children under age five were listed in the household questionnaire. Questionnaires were completed for 453 of these children, which corresponds to a response rate of 96 percent within interviewed households. Similarly, out of the 688 eligible men identified, 607 were successfully interviewed, giving a response rate of 88 percent. Overall response rates of 88 percent, 93 percent and 86 percent are calculated for the women’s, under-5’s and men’s interviews respectively.

Sampling error estimates

Sampling errors are a measure of the variability between the estimates from all possible samples. The extent of variability is not known exactly, but can be estimated statistically from the survey data.

The following sampling error measures are presented in this appendix for each of the selected indicators: - Standard error (se): Sampling errors are usually measured in terms of standard errors for particular indicators (means, proportions etc). Standard error is the square root of the variance of the estimate. The Taylor linearization method is used for the estimation of standard errors. - Coefficient of variation (se/r) is the ratio of the standard error to the value of the indicator, and is a measure of the relative sampling error. - Design effect (deff) is the ratio of the actual variance of an indicator, under the sampling method used in the survey, to the variance calculated under the assumption of simple random sampling. The square root of the design effect (deft) is used to show the efficiency of the sample design in relation to the precision. A deft value of 1.0 indicates that the sample design is as efficient as a simple random sample, while a deft value above 1.0 indicates the increase in the standard error due to the use of a more complex sample design. - Confidence limits are calculated to show the interval within which the true value for the population can be reasonably assumed to fall, with a specified level of confidence. For any given statistic calculated from the survey, the value of that statistic will fall within a range of plus or minus two times the standard error (r + 2.se or r – 2.se) of the statistic in 95 percent of all possible samples of identical size and design.

For the calculation of sampling errors from MICS data, SPSS Version 18 Complex Samples module has been used. The results are shown in the tables that follow. In addition to the sampling error measures described above, the tables also include weighted and unweighted counts of denominators for each indicator.

Sampling errors are calculated for indicators of primary interest, for the national level, for the regions, and for urban and rural areas. Three of the selected indicators are based on households, 8 are based on household members, 13 are based on women, and 15 are based on children under 5. All indicators presented here are in the form of proportions.

Data appraisal

A series of data quality tables are available to review the quality of the data and include the following:

• Age distribution of the household population
• Age distribution of eligible and interviewed women
• Age distribution of children under 5 in household and children under 5 questionnaires
• Women's completion rates by socio-economic characteristics of households
• Completion rates for under-five questionnaires by socio-economic characteristics of households
• Completeness of reporting
• Completeness of information for anthropometric indicators
• Heaping in anthropometric measurements
• Observation of bednets and places for hand washing
• Observation of women's health cards
• Observation of children under 5 birth

Nurse population ratio of Greater Accra declined by 3.01% from 764 per 100000 population in 2014 to 741 per 100000 population in 2015. Since the 56.88% jump in 2011, nurse population ratio plummeted by 40.96% in 2015.

BackgroundIn case-control studies, population controls can help ensure generalizability; however, the selection of population controls can be challenging in environments that lack population registries. We developed a population enumeration and sampling strategy to facilitate use of population controls in a breast cancer case-control study conducted in Ghana.MethodsHousehold enumeration was conducted in 110 census-defined geographic areas within Ghana’s Ashanti, Central, Eastern, and Greater Accra Regions. A pool of potential controls (women aged 18 to 74 years, never diagnosed with breast cancer) was selected from the enumeration using systematic random sampling and frequency-matched to the anticipated distributions of age and residence among cases. Multiple attempts were made to contact potential controls to assess eligibility and arrange for study participation. To increase participation, we implemented a refusal conversion protocol in which initial non-participants were re-approached after several months.Results2,528 women were sampled from the enumeration listing, 2,261 (89%) were successfully contacted, and 2,106 were enrolled (overall recruitment of 83%). 170 women were enrolled through refusal conversion. Compared with women enrolled after being first approached, refusal conversion enrollees were younger and less likely to complete the study interview in the study hospital (13% vs. 23%). The most common reasons for non-participation were lack of interest and lack of time.ConclusionsUsing household enumeration and repeated contacts, we were able to recruit population controls with a high participation rate. Our approach may provide a blue-print for others undertaking epidemiologic studies in populations that lack accessible population registries.

Doctor population ratio of Greater Accra shot up by 16.11% from 2,744 per 100000 population in 2014 to 3,186 per 100000 population in 2015. Since the 16.59% slump in 2013, doctor population ratio went up by 0.25% in 2015.

The urbanization of Sub-Saharan Africa is occurring more rapidly than in any other region in the world, at a historically unprecedented absolute rate of increase, within an institutional framework desperately lacking in resources. In step with its Sub-Saharan location, Ghana is experiencing unprecedented urbanization with currently 50% of its 23 million people living in urban areas; that share is expected to become 65% by 2030. The lion’s share of this growth is taking place in the administrative and commercial center, Accra, which has a population of more than three million people. It is exhibiting a growth rate in excess of 4% per year and is expected to double its population within 16 years.

As of 2021, ****** Asians resided in Ghana. The majority of them, some *****, lived in the Greater Accra region, which hosts the nation's capital. Moreover, a greater number of people of Asian origin lived in the Ashanti, Western, and Central regions. Those amounted to *****, ***, and *** people, respectively. Overall in Ghana, foreign residents are spread across regions.

Abstract

The Ghana Demographic and Health Survey (GDHS) is a national sample survey designed to provide information on fertility, family planning and health in Ghana. The survey, which was conducted by the Statistical Service of Ghana, is part of a worldwide programme coordinated by the Institute for Resource Development/Macro Systems, Inc., in more than 40 countries in Africa, Asia and Latin America.

The short-term objectives of the Ghana Demographic and Health Survey (GDHS) are to provide policymakers and those implementing policy with current data on fertility levels, knowledge and use of contraception, reproductive intentions of women 15-49, and health indicators. The information will also serve as the basis for monitoring and evaluating programmes initiated by the government such as the extended programme on immunization, child nutrition, and the family planning programme. The long-term objectives are to enhance the country's ability to undertake surveys of excellent technical quality that seek to measure changes in fertility levels, health status (particularly of children), and the extent of contraceptive knowledge and use. Finally, the results of the survey will form part of an international data base for researchers investigating topics related to the above issues.

Geographic coverage

National

Analysis unit

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

Kind of data

Sample survey data

Sampling procedure

The 150 clusters from which a representative sample of women aged 15-49 was selected from a subsample of the 200 clusters used for the Ghana Living Standards Survey (GLSS). All census Enumeration Areas (EAs) were first stratified by ecological zones into 3 strata, namely Coastal Savanna, Forest, and Northern Savanna. These were further stratified into urban, semi-urban, and rural EAs. The EAs (in some cases, segments of EAs) were then selected with probability proportional to the number of households. All households in the selected EAs were subsequently listed.

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

Mode of data collection

Face-to-face

Research instrument

Three different types of questionnaires were used for the GDHS. These were the household, individual and the husband questionnaires. The household and the individual questionnaires were adapted from the Model "B" Questionnaire for the DHS program. The GDHS is one of the few surveys in which special effort was made to collect information from husbands of interviewed women on such topics as fertility preferences, knowledge and use of contraception, and environmental and health related issues.

All usual members and visitors in the selected households were listed on the household questionnaire. Recorded in the household questionnaire were data on the age and sex of all listed persons in addition to information on fostering for children aged 0-14. Eligible women and eligible husbands were also identified in the household questionnaire.

The individual questionnaire was used to collect data on eligible women. Eligible women were definedas those aged 15-49 years who spent the night prior to the household interview in the selected household, irrespective of whether they were usual members of the household or not. Items of information collected in this questionnaire are as follows: 1) Respondent's Background 2) Reproductive Behavior 3) Knowledge and Use of Contraception 4) Health and Breastfeeding 5) Marriage 6) Fertility Preferences 7) Husband's Background and Women's Work 8) Weight and Height of Children Aged 3-36 Months.

In half of the selected clusters a husband's questionnaire was used to collect data on eligible husbands. Eligible husbands were defined as those who were co-resident with their wives and whose wives had been successfully interviewed. Data on the husband's background, contraceptive knowledge and use, as well as fertility preferences were collected.

All three questionnaires were translated into seven local languages, namely, Twi, Fante, Nzema, Ga, Ewe, Hausa and Dagbani. All the GDHS interviewers were able to conduct interviews in English and at least one local language. The questionnaires were pretested from mid-October to early November 1987. Five teams were used for the pretest fieldwork. These included 19 persons who were trained for 11 days.

Cleaning operations

Completed questionnaires were collected weekly from the regions by the field coordinators. Coding, data entry and machine editing went on concurrently at the Ghana Statistical Service in Accra as the fieldwork progressed. Coding and data entry were started in March 1988 and were completed by the end of June 1988. Preliminary tabulations were produced by mid-July 1988, and by August 1988 preliminary results of the survey were published.

Response rate

Of the 4966 households selected, 4406 were successfully interviewed. Excluding 9 percent of households that were vacant, absent, etc., the household response rate is 98 percent.

Out of 4574 eligible women in the household schedule, 4488 were interviewed successfully. The response rate at the individual level is 98 percent. Of the 997 eligible husbands, 943 were successfully interviewed, representing a response rate of 95 percent.

Sampling error estimates

The results from sample surveys are affected by two types of errors: non-sampling error and sampling error. The former is due to mistakes in implementing the field activities, such as failing to locate and interview the correct household, errors in asking questions, data entry errors, etc. While numerous steps were taken to minimize this sort of error in the GDHS, non-sampling errors are impossible to avoid entirely, and are difficult to evaluate statistically.

Sampling errors, on the other hand, can be evaluated statistically. The sample of women selected in the GDHS is only one of many samples of the same size that could have been drawn from the population using the same design. Each sample would have yielded slightly different results from the sample actually selected. The variability observed among all possible samples constitutes sampling error, which can be estimated from survey results (though not measured exactly).

Sampling error is usually measured in terms of the "standard error" (SE) of a particular statistic (mean, percentage, etc.), which is the square root of the variance of the statistic across all possible samples of equal size and design. The standard error can be used to calculate confidence intervals within which one can be reasonably sure 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 of identical size and design will fall within a range of plus or minus two times the standard error of that statistic.

If simple random sampling had been used to select women for the GDHS, it would have been possible to use straightforward formulas for calculating sampling errors. However, the GDHS sample design used three stages and clusters of households, and it was necessary to use more complex formulas. Therefore, the computer package CLUSTERS, developed for the World Fertility Survey, and was used to compute sampling errors.

Note: See detailed estimate of sampling error calculation in APPENDIX C of the survey report.

In Sub-Saharan Africa and other developing regions, there has been very little systematic attempt to document the uses and perceived health benefits of urban green spaces in cities and the factors influencing usage. We therefore sought to establish the availability, accessibility and use of urban green spaces, and the perceived health benefits in an African population. We also ascertained the factors influencing use and development of green spaces at home. A population-based survey was conducted in Accra, the capital city of Ghana, spanning 11 Municipal and 3 Sub-Metropolitan areas. Multivariable binary logistic regression adjusting for potential confounders was used to establish the association between green space use and development at home, and socio-demographic, neighbourhood and health factors. Odds ratios and their corresponding 95% confidence intervals were estimated from the models. Several socio-demographic (gender, age, marital status, occupation, ethnicity, religion) and district-level (population density, income level, neighbourhood greenness) factors were associated with use of green spaces and development of green spaces at home in Accra. Residents who were worried about depletion of green spaces in their community were more likely to develop green spaces at home. In neighbourhoods with moderate and high level of greenness, residents were less likely to develop green spaces at home. Five-percent and 47% of green space users in Accra reported witnessing an improvement in their physical and mental health, respectively, from use of green spaces. The study findings can inform policy action for promoting use and development of green spaces in African cities and for mitigating depletion and degradation of the limited urban greenery.

Demographic characteristics of participants, Ghana, 2020.