- Research article
- Open Access
- Open Peer Review
Residential characteristics as correlates of occupants’ health in the greater Accra region, Ghana
- Emilia Asuquo Udofia†1, 2Email author,
- Alfred E Yawson†2,
- Kwesi Adu Aduful†3 and
- Francis Mulekya Bwambale†4
© Udofia et al.; licensee BioMed Central Ltd. 2014
- Received: 1 June 2013
- Accepted: 4 March 2014
- Published: 11 March 2014
Housing has been a relatively neglected site for public health action. However, it remains a place where human beings spend the most part of their day. As a result, the quality of housing has consequences for human health. We investigate residential characteristics associated with self-rated occupant health in five neighbourhoods in the Greater Accra Region, Ghana.
A cross sectional study using a semi-structured questionnaire was conducted among 500 informed adults aged 18 years and above to investigate residential characteristics associated with self-rated occupant health in five neighbourhoods in the Greater Accra Region, Ghana. Correlates of occupant rated health were determined using Pearson chi-square test and binary logistic regression.
Forty-two per cent of houses were rented, 44% required repair and 46% shared sanitation facilities. One in twenty occupants reported poor health. Gender, employment status, income, ventilation, house wall material, odours, stale air, privacy, shared facilities, hand washing facility, type of house and house repair status were associated with poor health in the bivariate analysis. Only two variables were independently associated with poor self-rated health: occupants who lacked privacy were eight times more likely to report poor self-rated health when compared to peers who did not lack privacy [OR = 8.16, 95% CI 2.86-23.26] and women were three times more likely than men to report poor health [OR = 2.98, 95% CI 1.06-8.35].
The results provide further evidence of housing as a determinant of occupants’ health, and identify housing characteristics and living conditions as issues for public health action in Ghana.
- Built environment
“We give shape to our buildings, and they in turn shape us.”
(Winston Churchill in a 1943 speech to the House of Commons) .
The World Health Organization (WHO) states that the housing characteristics, community and neighbourhood environment have the potential to affect human health, through physical, mental and social mechanisms . Adequate housing should provide shelter from climatic conditions, intrusions by vectors and rodents as well as environmental nuisances such as noise. It should also offer security and privacy . Access to safe water and basic sanitation are critical to maintaining a healthy residential and neighbourhood environment. Studies on housing from developing countries have suggested that the provision of basic amenities may result in reduced illness . Substandard housing has been associated with a diversity of health conditions including asthma, tuberculosis, lead poisoning, injuries and poor mental health . It is in recognition of growing evidence regarding the association between housing and health that the United States Center for Disease Control and Prevention stresses the improvement of housing and living conditions as a strategy to promote health . Most studies investigating the relationship between housing and health have been conducted in developed countries [6–11] while fewer studies have been reported in African countries [12–15], especially Ghana [13, 16].
Although efforts are increasingly being made to improve housing standards and housing stock; population growth, urbanization, migration, natural disasters, conflicts and unemployment seemingly slow progress made in most developing countries. Standard housing can hardly be afforded by the urban poor who often resort to makeshift housing in insecure neighbourhoods where social amenities are scarce and environmental nuisances are commonplace. In Ghana, as in other developing countries such as Tanzania, Kenya and Nigeria, population growth and internal rural urban migration have contributed to the sprawl of unplanned informal settlements [13, 17, 18]. These settlements often have substandard housing characterized by poor structural quality, inadequate access to social amenities such as water and basic sanitation, insecurity of tenure and overcrowding .
It has been estimated that urban areas in Ghana will require nearly 2 million dwellings by 2020 if built as self-contained dwellings, one for each household. Currently about 90% of urban housing in Ghana is classified as informal due to their construction without local authority control and almost 60% of households occupy single rooms . With the growth of cities, new rooms are added to existing houses in central areas. District hospital records indicate that respiratory illnesses and diarrhoea are among the top ten causes of outpatient hospital visits in Ghana, after malaria (unpublished: district hospital records compiled at the Department of Community Health, University of Ghana Medical School). Crowded housing is associated with higher rates of infectious disease transmission such as in respiratory infections and tuberculosis [5, 13]. Lack of safe drinking water, ineffective waste disposal and inadequate food storage contribute to the transmission of diarrhoeal diseases.
The Urban Multiple Indicator Cluster Survey (MICS 2011) conducted among residents of Accra living in five high density urban neighbourhoods showed that only 11% of households were using an improved sanitation facility or toilet . Fifty two per cent of households use public facilities and nearly 12% of households share the toilet facility among 5 or more households . Studies indicate that health problems from lack of sanitation facilities are greater among residents of informal settlements and deprived poor communities compared to towns and cities in Ghana .
The house is where most individuals spend the most part of their day. For the employed working an eight hour job, this would translate to a maximum of 16 hours. For individuals who are at extremes of age (pre-schoolers and elderly), homemakers and pregnant women for instance, the time spent at home could be much longer. Previous studies have shown that the quality of the house has the potential to affect the health of its occupants through various exposures and the time spent within the exposures, although host immunity and body surface area are other important factors. This is well expressed in the “rule of 1000” which states that a pollutant released indoors is 1000 times more likely to reach people’s lungs than a pollutant released outdoors . This present survey was conceived to obtain local evidence linking housing and health in five neighbourhoods with varying characteristics in the Greater Accra Region (GAR), as well as determine if the results would corroborate evidence from previous studies [13, 14]. The results provide evidence of association with occupant health for some characteristics suggesting that improving housing quality has the potential to promote health in Ghana.
Study design and setting
Labone is situated in La Dade Kotopon Sub-metropolis and has a population of 183, 528 . Houses in this middle to high income, urban neighbourhood are a mix of detached flats and storey buildings. Lartebiokorshie is a medium density urban settlement in the Ablekuma Central District. It has a mix of affluent residents in the Radio Gold and Bishop Bouwers divisions, while those in the lower socioeconomic class live along the Town Council line. All four locations described are under the Accra Metropolitan Assembly. Ashiyie is a rural town situated within Adenta Municipal Assembly, known as the Koose electoral area. It has an estimated population of about two thousand people (personal communication with Unit Secretary, Mr. Enoch). Estate development is ongoing in this area and engages most youth in construction activities.
Ethical approval for this research was obtained from the Ethical and Protocol Review Committee of the University of Ghana Medical School. Permission to conduct the survey was given by the Assembly men and women of the respective study sites directly or conveyed through the Unit Secretaries. Permission to interview a respondent in the household was obtained from the household head or a responsible adult acting on his or her behalf. Individual informed consent was obtained from the respondents who were interviewed in their houses for convenience and to facilitate inspection where this was permitted by the occupant. The inspection was not conducted if the respondent declined. Anonymity was ensured by the use of codes and access to data was restricted to the researcher and interviewers only.
Study population and sampling
The study population comprised of adults aged 18 years or older, living in a housing unit in any of five purposively selected neighbourhoods. A total sample of 500 informed (capable of providing information about the house and household) adults aged 18 years and older, living in a housing unit in the selected neighbourhoods, was enlisted for the study comparable to a previous survey by Arku et al. .
Cluster sampling method was used to reach eligible respondents. Given that some of the study sites were informal settlements (Chorkor, Old Fadama) and most of the study sites did not have numbered dwellings, preliminary visits were made by the researcher and interviewers to map out the study sites in clusters divided mainly by streets and specific landmarks. The clusters were assigned serial numbers and a ballot drawn by the research team to pick one cluster in each site. A total enumeration of housing units was conducted in the selected cluster. A housing unit was defined as the regular accommodation of the respondent where household activities take place and possessions are kept. In each study site, we aimed at reaching 100 respondents in order to capture a variety of living conditions and attain the final sample size. Where this number was not attained in any cluster, a ballot was drawn to pick another cluster to continue enumeration until at least 100 housing units were covered. Where more than one eligible adult was present in a house, a simple ballot was performed by the interviewers to select the respondent.
A 62-item, semi-structured questionnaire was administered to respondents by a team of four trained interviewers. The questionnaire had six major themes namely: socio-demographics, housing characteristics, amenities, hygiene, sanitation and refuse disposal, health and physical complaints. All questionnaires were administered in the language of preference to the respondents during weekends and in the afternoon and evenings during weekdays. Where permitted, an inspection of the house was performed to observe the presence of uncovered or spilled refuse, animal dander, and drains and observations were noted on a checklist.
The independent variables were: socio-demographic variables namely age, sex, marital status, religion, educational attainment, employment status and income; housing variables namely type of house, structural materials (house walls), occupancy, ventilation (number of windows per bedroom), daylight penetration, tenure, length of residency, and repair status; housing facilities namely electricity, water supply, availability of water supply, sanitary and hand washing facilities in premises, and refuse disposal system; and housing conditions namely presence of specific problems (dampness, dryness, dust, odours, indoor smoke, stale air, outdoor smoke), presence of pests, presence of animals and sanitation status. Privacy and fear of ejection were ascertained as specific questions. Both variables were used in a previous study as indicators of housing demand and control that could affect self-rated and mental health [13, 14, 16]. A question was also asked about common illnesses in the neighbourhood (malaria, typhoid, acute respiratory infection, skin infection, gastroenteritis and others). Under ‘others’, respondents were allowed suggest other diseases not included in the foregoing list. Respondents were asked if any member of the household had been ill with any of the diseases listed in the last six months. The recall period of six months was allowed to accommodate the possibility of occurrence of any of the illnesses listed. Although a shorter period may have been more effective for recall, such as two weeks, it is likely that some illnesses may not have occurred in that time frame. Ten specific physical complaints experienced at least once a month (headache, dizziness, watery eyes, itchy skin, cough, difficulty hearing, chest pain or tightness, watery stools, abdominal pain and spiking temperature) were also listed and respondents asked if they had experienced any of these. While the first seven physical complaints are consistent with those reported in literature associated with poor housing, the latter three non-specific complaints were added as dummy options.
For inferential analysis, all explanatory variables were dichotomized such that the risk status was represented by ‘1’ and the health promoting status was represented by ‘0’.
The dependent variable was self–rated (occupant health) which was measured by asking the following questions: “Compared to other people your age, how would you assess your general health?” The options provided were in ranked order: (1) excellent, (2) very good, (3) good, (4) fair and (5) poor. In the binary logistic analysis, the categories (1), (2) and (3) were merged as good health, while (4) and (5) were merged as poor health as consistent with a previous study . Therefore the final outcome variable was dichotomous with two levels namely ‘good health’ and ‘poor health’.
The data were entered into an electronic database and analysed using Statistical Package for Social Sciences (SPSS) version 17. Descriptive statistics for categorical and continuous variables were summarised using frequency distributions and percentages for categorical variables; and the mean, standard deviation, mode, maximum and minimum values for continuous variables. Pearson chi square test was used to test for the association between independent variables and occupants’ health. Fisher’s exact test was used for any cell with smaller than 10 counts. A p-value of 0.05 or less was deemed statistically significant. Only variables statistically significant in the bivariate analysis at the p-value of 0.5 or less were evaluated with binary logistic regression using the backward elimination method. The binary logistic regression analysis was used as the dependent and explanatory variables were dichotomous. The variables in the final model included: type of house, house wall material, shared toilet, hand washing facility, number of windows per bedroom, sex, employment status, household income, stale air present, odour present, house needs repair, has privacy in the house, and location. These were regressed against self-reported ill health. The strength of association was determined by the adjusted Odd’s ratio (OR) and the 95% confidence limits were constructed around the estimates.
Socio-demographic characteristics of study participants, Greater Accra Region, Ghana, 2012
Age in years (n = 499)
Gender (n = 500)
Marital status (n = 500)
Educational status (n = 500)
No formal education
Occupation (n = 500)
Household income (n = 498)
Housing characteristics and living conditions of the study participants, Greater Accra Region, Ghana, 2012
Good health Frequency (%)
Poor health Frequency (%)
Physical structure (n = 500)
Roofing material (n = 500)
Ventilation (n = 500)
House type (n = 500)
Single family detached
House age (n = 498)
Duration of residence (n = 494)
Occupancy at 2ppr** (n = 500)
Repair required (n = 496)
House tenure (n = 499)
Affordability of rent (n = 497)
Privacy (n = 500)
Electricity (n = 500)
Primary water source (n = 500)
Refuse disposal system (n = 499)
House to house by service provider
Traditional methods/communal collection
Type of sanitation facility (n = 496)
Flush to septic tank
Pit latrine/open defaecation
Sanitary facility in the house (n = 500)
Sanitation facility shared (n = 500)
Descriptive analysis of numerical parameters in the housing survey, Greater Accra Region, Ghana, 2012
Rooms per house*
Persons per house*
Problems of the indoor environment in homes of study participants, Greater Accra Region, Ghana, 2012 (n = 500)
Animals (pets, poultry)
Poor air exchange
Two hundred and sixty nine (53.8%) respondents reported they had a constant supply of electricity and 336 (67.2%) respondents obtained water supplies from piped systems within their premises/yards and boreholes within the neighbourhood. Among those who treated their water supply, the main forms of treatment were: filtration only 102 (20.4%); filtration and boiling 80 (16.0%) and boiling only 42 (8.4%). The main methods of refuse disposal were collection in household refuse bins, 204 (40.8) and burial in a backyard pit with eventual burning, 102 (20.4%). House to house collection of refuse by private service providers was available to 210 (42.1%) houses and refuse disposal was done weekly (42.5%; n = 212). Pests were reported in 359 (71.8%) houses. Those commonly mentioned were: mosquitoes 359 (71.8%), flies 277 (55.5%), ants 196 (39.3%) and rodents 158 (31.7%). Common pest control methods used were insecticides, 282 (56.5%), mosquito coils, 194 (39.0), insecticide treated nets, 173 (34.7%) and rat traps, 102 (20.4%). Animals were kept by 195 (39.1%) respondents and they came in contact with food in 73 (14.6%) houses.
Based on a sanitation checklist, 335 (68.2%) dwellings were found to be in satisfactory condition. The checklist was not applied in 19 (3.8%) homes due to lack of consent.
Four hundred and fourteen (82.8%) occupants acknowledged their houses offered privacy and 284 (57.3%) were satisfied with the state of their houses. On the other hand, 195 (63.3%) respondents feared ejection from their houses.
Physical complaints reported by study participants, Greater Accra Region, Ghana, 2012 (n = 500)
Chest pain or tightness
Bivariate and multivariable analyses of sample and housing characteristics and occupants’ self-reported poor health, Greater Accra Region, Ghana, 2012
Self-rated occupant’s health
Unadjusted OR, 95% CI
P = Value
Multivariable analysis (R 2 = 33.8) Adjusted ORs (95% CI)
Good health n(%)
Poor heath n(%)
# of windows per bedroom
2 or more
< 2 windows
House wall material
Stale air present
Has privacy 1 in the house
Shares toilet facility
Hand washing facility
Type of house
House needs repair
Results from the present survey of five hundred respondents aged 18 years or older in five neighbourhoods (four urban and one rural), situated in the Greater Accra Region of Ghana, lend support to earlier studies in Ghana [13, 14] and a recent survey reported in the Ghana Housing profile . These surveys draw attention to housing quality as a major determinant of health.
The present study indicates that most residents in the Greater Accra region are living within the threshold occupancy limit. Similar to the Ghana Living Standards Survey (GLSS5) which reports mean room occupancy of 2.3 persons per room (ppr) in urban Ghana, 2.1 ppr for Accra and 2.4 ppr for other urban areas, the present survey found a mean room occupancy of 2.43 ppr with three in five persons living within this limit. However, it is worrisome that two in five persons live outside this threshold in crowded houses. Crowding has been associated with increased rates of infectious transmission, poor mental health, short stature and stomach cancer .
The effect of gender remained significant in the study and may be explained by the fact that generally women tend to spend more time in the domestic environment than males by sheer reason of their housekeeping roles. A similar finding was reported in the study by Arku et al., . The implications for health deserve emphasis as women are primary caregivers in the family and they need to be healthy to play their role effectively.
The government of Ghana has demonstrated commitment to ensure an improved housing stock in the near future. One such example was the proposed public private partnership between the Government of Ghana and STX Engineering and Construction, Ghana Limited, for the development of 200,000 units of affordable housing for the low and middle income group known as the Ghana Housing Project, with additional 300 units for senior state officers over five years. This was later aborted due to unresolved internal conflicts. Otherwise this may have been a laudable project as the gated communities offered by private developers serves the high end of the housing market demand. Notwithstanding, the Government of Ghana continues to encourage private developers to provide low income housing by offering incentives such as land and removal of duties from imported construction materials . However, the pace of estate development hardly matches the demand for housing. Rather up to 90% of all housing is provided by individual householders who contract private builders to build for them at a pace dictated mainly by availability of funds and cost of building materials which ultimately takes many years. The estimated need for about two million self-contained dwellings (at one per household) by 2020 in Ghana remains a challenge under the circumstances. The rights to privacy and health through adequate housing remain issues for public health action.
● The association between self-reported occupant health and residential characteristics was found to be evident among females and in occupants of houses characterized by lack of privacy.
● Despite the prevalence of common neighbourhood illnesses and physical complaints, occupants in the surveyed neighbourhoods tended to report good self-rated health.
● Housing improvement which enhances privacy has the potential to promote self-rated health and should be advanced as an issue for public health action.
Acknowledgement is made to the University of Michigan African Presidential Scholars (UMAPS) 2012–2013 cohort and staff of the African Studies Center, International Institute, University of Michigan and academic mentors for their constructive comments. Our sincere gratitude goes to the following for their relentless efforts and support during the field work: Joseph, Stanley, George, Jennifer, Joan and K. Danso. Mrs. Rosalind Quartey of Ghana Statistical Services is acknowledged for the digital map of the study sites. Finally, we are grateful to the Assemblies, Unit secretaries and neighbourhoods who made the study possible. This work is dedicated to you.
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