Study area
This study is an ecological time series study across 29 districts in the Greater Accra Region (GAR) of Ghana between 2018 and 2021. Greater Accra is the capital region where Ghana’s national capital Accra is located. The region has health facilities including teaching hospitals, regional hospitals, university hospitals, district hospitals, psychiatric hospitals, polyclinics, clinics, maternity homes, health centers, private health facilities, and community health post service. In this ecological study, the unit of observation is the population of children and women using health services across the 29 administrative districts. The GAR has a population of 5.5 million persons according to the 2021 population and housing census (10). The population of Ghana has not only grown but has also experienced rapid urbanization in the past several decades in Greater Accra and Ashanti regions. Today, more than half of the country’s population resides in urban areas (11). According to the United Nations Habitat report, the proportion of the urban population living in slum households in Ghana is 30.4% as of 2018. Using 2010 National Population Census data and the UN-Habitat's definition of slums, the Accra Metropolitan Assembly identified 78 informal settlements and hotspots in Accra (12). According to the World Bank, approximately 37.4% of people who live in Ghana’s urban regions live in slums (13).
Data source
The unit of analysis is the district where the facilities are located because of the difficulty of identifying catchment populations (unstable denominators) for individual facilities in urban areas. In addition, there was no data on population size at the sub-district level. We used the data from RHMIS, which routinely collects data on MNCH health outcomes and service utilization as clients interact with health services at health facilities or during outreach services. RHMIS is a comprehensive health management information system solution for the reporting and analysis needs of district health administrations and health facilities at every level. Healthcare workers at facilities collect the data and input aggregated reports via the District Health Information System (DHIS) database. The system receives data from Government and private health facilities and has been operational since 2012 but slum data from Ghana Statistical Service were available from 2018. We obtained monthly data on the indicators from RHMIS between 2018–2021 from the office of Policy, Planning, Monitoring and Evaluation Division of the Ghana Health Service and merged the data with slum information at the district level. The data from RHMIS can be summed from the health facility level to higher administrative areas like sub-districts, districts, regions up to the national level. The data on service utilization of antenatal care, skilled birth attendants, and postnatal care are monthly aggregates of women receiving maternal health services at different health facilities. The vaccination coverage indicators were aggregates of services provided at health facilities and through outreach programs provided by community health nurses.
Slum areas were identified by triangulating field survey, spatial and census data. The data on slum locations across the districts in Greater Accra were obtained from the synthesis of data obtained from the field in 2021. Also, Ghana Statistical Service slum delineated data for 2021, and a literature review of similar slum identification in Greater Accra using census, survey and remotely sensed and GPS located data were used (14, 15). The data on the population size and population projections for districts and sub-districts were obtained from the Ghana Statistical Service.
Health service coverage indicators
The primary service coverage measures include antenatal care (ANC) attendance, skilled delivery, postnatal care (PNC) attendance, and vaccination coverage for Bacillus Calmette-Guérin (BCG), Oral Polio, Measles, and Pentavalent 1 vaccine at the district level (Table 1).
Table 1
Health service coverage indicator definitions
Indicator
|
Definition
|
Numerator
|
Denominator
|
Source of denominator
|
BCG Coverage
|
The proportion of children under 1 year receiving BCG vaccine
|
Number of children under 1 year receiving the BCG vaccine in the period
|
Number of children under 1 year (estimated as 4% of the population)
|
Ghana Statistical Service from the 2010 Population and Housing Census that adjust for monthly population growth rate
|
Oral Polio vaccination
|
The proportion of children under 1-year receiving oral polio (OPV1) vaccine
|
Number of children under 1 year receiving the OPV1 vaccine in the period
|
Number of children under 1 year (estimated as 4% of the population)
|
Ghana Statistical Service from the 2010 Population and Housing Census that adjust for monthly population growth rate
|
Pentavalent vaccination
|
The proportion of children under 1 year receiving Penta1 vaccine
|
Number of children under 1 year receiving the Penta 1 vaccine in the period
|
Number of children under 1 year (estimated as 4% of the population)
|
Ghana Statistical Service from the 2010 Population and Housing Census that adjust for monthly population growth rate
|
Measles-Rubella Coverage
|
The proportion of children under 1 year receiving Measles-Rubella Vaccine
|
Number of children under 1 year receiving the Measles-Rubella vaccine in the period
|
Number of children under 1 year (estimated as 4% of the population)
|
Ghana Statistical Service from the 2010 Population and Housing Census that adjust for monthly population growth rate
|
Antenatal Care Coverage
|
The proportion of pregnant women receiving antenatal care during pregnancy (at least once).
|
Total number of antenatal registrants in a specified period
|
Total number of expected pregnancies of the catchment area within the specified period
|
Ghana Statistical Service from the 2010 Population and Housing Census that adjust for monthly population growth rate
|
Skilled delivery
|
Percentage of deliveries conducted by skilled attendants (nurses and doctors).
|
The number of deliveries supervised by doctors or nurses in the specified period.
|
Number of expected pregnancies (estimated as 4% of the population)
|
Ghana Statistical Service from the 2010 Population and Housing Census that adjust for monthly population growth rate
|
Postnatal coverage
|
The proportion of PNC registrants seen after delivery
|
Number of PNC registrants (within 48 hours)
|
Number of expected pregnancies (estimated as 4% of the population)
|
Ghana Statistical Service from the 2010 Population and Housing Census that adjust for monthly population growth rate
|
Measuring slums in Greater Accra Region
The primary exposure of interest was the number of slums in a district. The UN-Habitat 2004 defines a slum household as a group of individuals living under the same roof in an urban area who lack one or more of the following: 1. Durable housing of a permanent nature that protects against extreme climate conditions. 2. Sufficient living space which means not more than three people sharing the same room. 3. Easy access to safe water in sufficient amounts at an affordable price. 4. Access to adequate sanitation in the form of a private or public toilet shared by a reasonable number of people. 5. Security of tenure that prevents forced evictions (1).
In this study, we based the determination of slums in Greater Accra on the UN-Habitat definition of a slum. The slum areas were identified by triangulating three data sources: 1) evidence from literature based on the UN Habitat definition in the last two decades; 2) a listing of slums in Greater Accra from the Ghana Statistical Service (GSS); and 3) a field survey. From the literature, we extracted maps of Accra slums from two published manuscripts (14, 15). These Accra slum maps were digitized, georeferenced, and compared to establish the location of the slums in Accra. The list of slum locations (towns) obtained from the GSS was geocoded and mapped. Geocoding is the process of transforming place names or addresses to spatial data. These two data sources were overlaid to be sure that the borders matched and further validated the slum map based on this overlay through site visits. That is, the research team validated the existence of slums in the locations identified in the literature and the list of slum locations obtained from the GSS through field visits.
The final judgment of the slum locations was decided by the team based on the UN-Habitat definition and took into consideration that not all slums are homogeneous and not all slum dwellers suffer from the same degree of deprivation. The degree of deprivation depends on how many of the five conditions that define slums are prevalent within a slum household. The final list of slums identified includes households that suffer from at least two shelter deprivations.
Districts were used as the unit of analysis in this study. The districts in Greater Accra were categorized into slum and non-slum districts. A district was designated as a slum if it intersects considerably (i.e., at least one-quarter or more of the households in the district) with the slum areas derived from the literature and UN-habitat definition. That is, the district must satisfy the intersect condition and or contain at least one town from the GSS list of slum towns. A district was designated a slum if it met one or both of the following conditions: 1) sufficient intersection with the slum areas derived from the literature and UN-habitat definition (i.e., at least one quarter or more of the households in the district, and 2) at least one town from the GSS list of towns. A total of 22 out of 29 districts in the Greater Accra region were classified as slum-districts and the remaining seven were considered non-slum districts (Fig. 1).
Statistical analysis
Descriptive summary measures such as median, 25th and 75th percentile, mean, standard deviation (SD), and the range were used to describe the service coverage measures of interest. In addition, time series tools were used to explore the distribution of the coverage measures and identified the underlying coverage trends between the urban slum and non-urban slum districts, seasonal patterns, and outliers.
Initial data exploration showed the coverage measures were heavily skewed. Therefore, we quantified the impact of living in urban slum districts on MNCH outcomes and service utilization using the quantile regression (least-absolute-value models, median absolute deviation and minimum \(L1\)-norm) models with a robust standard error. Furthermore, we adjusted for seasonality deviation, and linear time trends. The quantile regression is a natural extension of the ordinary least square (OLS) regression model that is used when the conditions of OLS regression are not met (i.e., linearity, homoscedasticity, independence, or normality). The quantile regression model equation for the \(\tau\)th quantile is given as follows:
Where \({y}_{ij}\) is the \(i\)th month observation for the \(j\)th districts All the multivariable models adjusted for seasonality in month and year fixed effect, the impact of COVID-19 (a binary indicator indicating observations before and after the onset of COVID-19), total OPD attendance, number of health facilities in the district (a proxy for access), geographical location (urban or rural), and the monthly population size of the district where appropriate.
Assessment of equity
The WHO defines health equity as the absence of unfair and avoidable or remediable differences in health among population groups defined socially, economically, demographically, or geographically. In this study, inequalities in the coverage of MNCH services were measured and monitored and served as an indirect means of evaluating health inequity. We assessed the inequality of MNCH indicators between slum and non-slum districts using the Gini Index with bootstrapped standard errors and the Generalized Lorenz curve.
All statistical analyses were conducted using Stata MP version 17 (StataCorp LP, College Station, TX, USA) and a p-value less than 0.05 was considered statistically significant.