Spatial distribution and barriers to access to health care among women in Ghana

doi:10.21203/rs.3.rs-4247885/v1

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Spatial distribution and barriers to access to health care among women in Ghana

https://doi.org/10.21203/rs.3.rs-4247885/v1

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Background: This study aims to assess the spatial distribution of barriers to healthcare access among Ghana women. Despite government efforts to reduce barriers such as cost and distance, a significant proportion of women still experience barriers in accessing healthcare. Understanding the spatial distribution is crucial for targeted interventions aimed at addressing the existing barriers that are likely to hinder Ghana from attaining SDG target 3.8.

Methods: The study used a cross-sectional study based on a sample of 20,620 women from the 2017 Ghana Maternal Health Survey. Spatial autocorrelation and hotspot assessment were conducted in the geospatial analysis to determine the spatial distribution of barriers to access to healthcare in Ghana. At the same time, bivariate and multivariate logistic regression models were used to estimate associated factors of barriers to accessing healthcare.

Results: This study assessed the spatial distribution of barriers to healthcare access among women in Ghana. Over half of women (55.4%) experienced at least one barrier. The Northern zone emerged as a hotspot, while the Southern zone had cold spots. Wealth, health insurance coverage, education, TV watching, being in a union, and parity were associated with barriers to healthcare access. Targeted policies should be designed to address the spatial disparities, improve healthcare infrastructure, promote education, enhance financial support, and empower women to overcome barriers to healthcare access in Ghana.

Conclusion: We conclude that over half of Ghanaian women encounter barriers in accessing healthcare, with Northern Ghana being a hotspot and Southern Ghana a cold spot. The Government of Ghana and health agencies should prioritise improving healthcare accessibility, particularly in Northern Ghana. Targeted interventions should focus on vulnerable sub-populations such as unmarried women, those with low education, individuals with poor wealth status, and those lacking health insurance coverage. Addressing these barriers will help reduce disparities and ensure equitable healthcare access for all women in Ghana.

Accessibility

Barriers

Healthcare services

Spatial disparities

Public health

Since the ratification of the 17 Sustainable Development Goals (SDGs) in 2015, there has been a remarkable global interest in achieving universal health coverage (UHC) [1]. This interest is seen in the SDG target 3.8, which enjoins signatories to the SDG to work towards achieving UHC by 2030 [2]. Achieving UHC requires an optimal coverage of essential health services, as well as non-catastrophic health expenditure to households [1]. This implies that the existence of any barrier to accessing essential healthcare services is a potential threat to achieving UHC. Hence, it is imperative to understand the barriers to accessing healthcare, particularly in resource-constrained settings such as countries in sub-Saharan Africa.

Available evidence suggests that multiple factors serve as barriers to access to healthcare. A qualitative study conducted in Romania [3] revealed that difficulty in getting money was a common barrier to accessing healthcare. Similar findings have been reported in a systematic review by Matin et al. [4]. Other studies have identified distance [5], service fragmentation [6], and lack of autonomy [7] as major barriers to accessing healthcare, especially among women.

In Ghana, successive governments have introduced policies and programmes to improve healthcare access by reducing barriers such as cost and distance. For instance, the introduction of Community-based Health Planning and Services (CHPS), free maternal healthcare policy, and the national health insurance scheme (NHIS) have contributed significantly to improving accessibility and utilisation of maternal and child healthcare services in Ghana [8–10]. Despite these policies and programmes, a significant proportion of women in Ghana still experience barriers to accessing healthcare. A study by Seidu et al. [11] revealed that 51% of Ghanaian women experienced at least one barrier to accessing healthcare. Their study further indicates that the likelihood of experiencing at least one barrier to accessing healthcare was lower among women aged 45–49 years, those with higher education, health insurance coverage, having been exposed to the media, and being in the richest wealth index while being widowed and residing in the Volta region as associated with higher odds [11].

Although there is evidence of the determinants of experiencing at least one barrier to accessing healthcare in Ghana [11], no study has investigated the spatial distribution of these barriers. Hence, it is unclear from the current body of literature, the hotspots of barriers to accessing healthcare in Ghana. Identifying the spatial distribution is significant to developing targeted interventions to address the existing barriers that might militate against the attainment of SDG target 3.8. The study aims to assess the spatial distribution of the barriers to accessing healthcare among women in Ghana.

Data Source

The 2017 Ghana Maternal Health Survey (GMHS) used a standard model questionnaire developed by the Measure DHS Programme. This study used the most recent GMHS data and a cross-sectional study design. The 2017 GMHS is the second national representative household survey to collect comprehensive information on maternal health issues- antenatal care, delivery, postnatal care, maternal mortality and child mortality. The 2017 GMHS interviewed 25,062 women from 26,324 households within 898 clusters in 1900 enumeration areas. A sample of 20,620 women with complete data required for our analysis participated in this study. The MEASURE DHS permitted to use of the data set following the assessment of our concept note. The datasets are freely available to the public at www.measuredhs.com.

Definition of variables and Measurement

Outcome Variable.

The outcome variable was barriers to access to health care. This was categorised as getting medical help for self: getting permission to go (not a big problem – 0, Big problem – 1); getting money needed for treatment (not a big problem – 0, Big problem – 1); Distance to health facility (not a big problem – 0, Big problem – 1); Not wanting to go alone (not a big problem – 0, Big problem – 1).

Explanatory variables

Twelve explanatory variables were used in the study based on evidence in the literature [7, 11, 12, 23]. These variables included age (15–19, 20–24, 25–29, 30–34, 35–39, 40–44, 45–49), wealth status was derived from household ownership of a diversity of assets and categorised as poorest, poorer, middle, richer, richest, region of residence (Western, Central, Greater Accra, Volta, Eastern, Ashanti, Brong Ahafo, Northern, Upper East, Upper West), residence (urban, rural), religion (Christian, Islam, Traditional/spiritual, no religion), marital status (never in a union, married, cohabitation, widowed, divorced, separated), parity (0, 1, 2, 3, 4, 5, 6+), frequency of reading newspapers and magazine (not at all, less than once a week, at least once a week), frequency of listening to the radio (not at all, less than once a week, at least once a week), frequency of watching television (not at all, less than once a week, at least once a week), health insurance (no, yes) and educational status (no education, primary, secondary, higher).

Spatial analyses

Spatial analyses in this study are techniques for visualising barriers to access to healthcare at the district level. For the analyses, the coordinates of respondents were obtained from the Measure DHS website. The coordinates were linked to the district shapefile of Ghana (216 districts) obtained from the Department of Geography and Regional Planning, University of Cape Coast. The district name was merged with surveyed coordinates. This process was done to tie the district information to the respondent surveyed in the study. The required variable for the study was then extracted from the 2017 GMHS. The extracted non-spatial data (GMHS data) were merged with the coordinates using the DHS cluster values using SPSS version 25. As part of the data preparation for the spatial analyses, respondents with barriers to healthcare were assigned one (1), whereas those without barriers to healthcare were assigned zero (0). A spatial join was performed to transfer the extracted data to the 216-district boundary layer using ArcMap version 10.8. This activity allowed us to readily identify and pinpoint the location of each case within a district. During the process, it was observed that some districts had more than one cluster, and the data from the clusters were aggregated at the district level. Therefore, the counts of respondents with barriers to healthcare served as the dependent variable for the spatial analyses.

The spatial Autocorrelation (Global Moran’s I) tool in ArcMap version 10.5 was used to determine the spatial distribution of access to healthcare in Ghana. This was based on the hypothesis that barriers to access to healthcare are randomly distributed across various districts in Ghana. The null hypothesis is rejected when the z-score is greater than ± 1.65, implying that the observed spatial pattern is unlikely to result from random events. Hotspot analysis (Getis-Ord G) was further carried out to visualise the statistically significant spatial variations in barriers to access to healthcare in Ghana. The hotspot analysis will help determine districts with high and low barriers to access to health in Ghana. In addition, Cluster and Outlier (Anselin Local Moran’s I) analysis was conducted to identify districts that appeared as outliers in relation to their neighbouring districts. The results of the spatial analyses are presented in figures and maps.

Statistical analysis

Descriptive and inferential statistics were conducted. Percentages were used to report descriptive data. The relationship between the explanatory and outcome variables was examined using binary and multivariate logistic regression models. The survey command in Stata was used to correct the complex sample structure of the data in the regression analysis, while all frequency distributions were weighted. A multicollinearity test with a mean-variance inflation factor (VIF) of 2.71 was observed for the analysis, indicating the absence of multicollinearity. The odds ratios (ORs) with 95 percent confidence intervals (CIs) were used to present the results of the logistic analyses.

Table 1 highlights the multifaceted nature of barriers to accessing healthcare. Among the weighted sample of 20,620 women, financial constraints appear to be the most prevalent issue affecting almost half (45%) of the participants. Other significant barriers include distance to health facilities (22.6%) and the reluctance to access healthcare alone (13.5%). The least mentioned barrier to accessing healthcare was the challenge of seeking permission (6.1%). Generally, more than half (55.4%) of the respondents encountered at least one of the challenges mentioned above in their pursuit of healthcare.

Table 1

Barriers to accessing healthcare.
Variables	Frequency (n 20,620)	Percent
Getting permission to go
Big problem	1,264	6.1
Getting money needed for treatment
Big problem	9,279	45.0
Distance to a health facility
Big problem	4,654	22.6
Not wanting to go alone
Big problem	2,782	13.5
At least one barrier
No	9,190	44.6
Yes	11,430	55.4

Table 2 shows the distribution of the frequency and proportions of women experiencing barriers to healthcare access in Ghana, categorised by different variables. Regarding age distribution, the analysis reveals that the proportion of individuals facing at least one barrier to accessing healthcare increases with young age and older age. The highest proportion was observed among individuals aged 15–19 (58.4%) and 45–49 (62.9%), while the lowest proportion was fairly distributed among those aged 20–24 to 34–39. The results indicate a clear association between wealth status and barriers to healthcare access. As wealth status increases, the proportion of respondents facing barriers decreases. The poorest women had the highest proportion (79.2%), while the richest had the lowest proportion (39.6%) facing at least one barrier to accessing healthcare.

Concerning region of residence, there were regional disparities in the proportions of women facing barriers to healthcare access. The regions with the highest proportions were Upper West (68.3%), Upper East (67.8%), and the Northern region (68.6), while the region with the lowest proportion is Central (49.9%). Women who reside in rural areas had a higher proportion of facing barriers to healthcare access (64.3%) compared to urban areas (48.4%). We observed some differences in the proportions of respondents facing barriers to healthcare based on religious affiliation. Thus traditional/spiritual practitioners had the highest proportion (78.5%), while the lowest proportion of women facing at least one barrier to healthcare was affiliated with Christianity (55.5%).

Women who were widowed (71.1%) or separated (65.9%) reported the highest proportion of barriers to healthcare access, while married individuals reported the lowest (52.2%). Moreover, the proportion of barriers to healthcare access increases with higher parity, with respondents having six or more children reporting the highest proportion (68.2%). With regards to media exposure (newspaper, radio, and television), most women who reported not reading the newspaper at all (58.2%), not listening to the radio at all (62.8%), and not watching the television at all (73.0%) faced higher barriers to healthcare compared those who were exposed at least once a week.

With respect to health insurance coverage, respondents without health insurance reported slightly higher barriers to healthcare access (59.6%) compared to those with coverage (52.5%). Finally, the results indicate that women with no education (68.5%) or primary education (62.8%) have higher proportions of barriers o healthcare access compared to those with secondary education (53.0%) or higher education (32.8%).

Tale 2: Proportions with Barriers to Access to Healthcare

Variables	Frequency (n 25,059)	Proportions with Barriers to Access to Healthcare
Variables	Frequency (n 25,059)	Getting permission to go	Getting money needed for treatment	Distance to health facility	Not wanting to go alone	At least one barrier
Age
15–19	3,606	8.3	41.3	23.0	25.5	58.4
20–24	3,468	5.9	42.3	22.3	13.8	53.6
25–29	3,599	5.8	42.8	22.2	11.6	52.5
30–34	3,238	4.9	43.6	20.5	10.1	52.3
35–39	2,807	5.6	47.5	21.8	8.7	55.0
40–44	2,060	5.9	49.7	22.4	8.9	57.1
45–49	1,842	6.3	55.0	27.8	11.4	62.9
Wealth status
Poorest	3,139	9.9	68.5	51.4	24.3	79.2
Poorer	3,765	6.1	55.8	27.6	13.5	65.5
Middle	4,155	5.5	47.1	17.2	11.2	56.4
Richer	4,618	4.7	39.7	15.2	10.2	48.2
Richest	4,943	5.7	25.0	11.9	11.7	39.6
Region of residence
Western	2,552	4.7	46.5	22.4	8.7	56.6
Central	1,711	4.7	42.2	14.2	7.9	49.9
Greater Accra	3,543	5.9	34.5	13.2	11.5	46.0
Volta	1,430	3.9	58.2	28.4	12.1	65.5
Eastern	2,203	9.8	47.4	30.7	15.5	61.0
Ashanti	4,097	4.0	43.4	16.7	11.8	53.3
Brong Ahafo	2,187	6.1	38.2	19.1	13.6	48.4
Northern	1,495	6.9	56.9	44.5	22.8	68.6
Upper East	813	13.0	58.5	35.9	26.6	67.8
Upper West	589	13.1	56.8	38.9	28.3	68.3
Place of residence
Urban	11,433	5.5	38.4	14.1	11.4	48.4
Rural	9,187	7.0	53.3	33.1	16.1	64.3
Religion
Christian	16,550	5.9	44.2	21.2	12.7	55.5
Islam	3,377	7.0	44.9	26.4	16.7	56.4
Traditional/Spiritual	299	7.4	68.4	51.8	21.1	78.5
No religion	394	6.8	60.1	27.4	14.7	67.1
Marital status
Never in union	6,305	7.3	40.6	20.9	19.7	54.8
Married	8,212	5.8	42.8	23.9	11.1	52.2
Cohabitation	4,323	5.2	49.5	23.0	10.4	58.6
Widowed	456	7.3	68.5	28.0	14.7	71.1
Divorced	481	4.3	49.7	20.1	8.0	57.5
Separated	843	5.9	60.8	18.6	9.0	65.9
Parity
0	6,012	7.0	37.4	19.7	20.8	52.7
1	3,431	5.7	41.1	21.0	9.8	50.4
2	3,002	4.7	44.9	21.7	10.0	53.4
3	2,673	5.4	45.7	20.3	9.7	53.1
4	1,993	6.1	52.0	23.3	10.5	60.3
5	1,464	6.1	54.4	28.9	9.8	62.4
6+	2045	7.4	59.6	32.8	13.9	68.2
Frequency of reading newspaper or magazine
Not at all	16,737	6.2	48.6	24.4	13.4	58.2
Less than once a week	2,326	5.9	32.6	15.4	14.7	46.4
At least once a week	1,557	5.6	25.1	13.4	13.0	39.7
Frequency of Listening to radio
Not at all	4,615	7.8	51.9	27.6	16.7	62.8
Less than once a week	5,413	6.6	45.5	23.3	14.3	56.0
At least once a week	10,592	5.1	41.8	20.0	11.7	52.0
Frequency of watching television
Not at all	4,169	8.8	63.0	40.6	18.6	73.0
Less than once a week	3,970	6.9	46.4	23.7	15.6	58.2
At least once a week	12,481	5.0	38.5	16.2	11.1	48.7
Coverage of health insurance
No	8,607	6.5	40.5	21.3	13.1	59.6
Yes	12,013	5.9	41.0	23.5	13.7	52.5
Level of education
No education	3,591	8.5	59.6	35.8	16.5	68.5
Primary	3.040	6.7	53.8	25.7	14.7	62.8
Secondary	12,240	5.5	42.3	19.2	12.8	53.0
Higher	1,749	5.0	18.6	13.7	10.1	32.8
Total	20,620	6.1	45.0	22.6	13.5	55.4

Spatial analysis results

Spatial distribution of access to healthcare

Results from Fig. 1, Moran’s I spatial autocorrelation analysis, revealed that the z-score value was greater than 2.5, implying that the incidence of barriers to healthcare in Ghana was not random but clustered in some parts of the country at a 99% confidence level. This suggests that respondents’ experience of barriers to healthcare in Ghana was clustered among some districts in the country. One limitation of Moran’s I spatial autocorrelation tool is its inability to show specific areas where the clustering can be observed. The study, therefore, used the Getis-Ord Gi hotspot analysis to visualise the distribution of barriers to access to healthcare in Ghana.

Hotspot of barriers to access to healthcare

The hotpot analysis shows areas of statistically significant high and low intensity of the distribution of phenomena under study. From the hotspot analysis, areas in red indicate a high incidence of barriers to access to healthcare. In contrast, areas in blue have a low incidence of barriers to healthcare due to one or more of the listed barriers. The result from Fig. 2a shows a statistically significant (99% confidence level) high clustering of barriers to accessing healthcare in the northern part of Ghana. Thus, most of the districts in northern Ghana had a high incidence of barriers to access to health. From the result, over thirty (30) districts in northern Ghana had barriers to accessing healthcare. Some of the districts include Garu, Lambussie-Karni, Kasena Nankana West, Jirapa, Nadowli-Kaleo, Sissala East, Sissala West, Gushegu, Wa East, Wa Municipal Wa West, Bolgatanga Municipal, Bongo, Sawla-Tuna-Kalba, Kasena Nankana East, Bunkpurugu Nakpanduri, East Mamprusi, Bawku West, Lawra, Nandom, Nabdam, Builsa South, Builsa North. This suggests that individuals living in these districts have difficulty accessing health care.

On the contrary, areas in the blue shades in the southern part were found to be the cold spots in barriers to accessing healthcare in Ghana. This implies that individuals from these areas (districts) had low barriers to accessing healthcare compared to districts in the hotspot zone. Districts such As Shai Osudoku, Ningo/Prampram, Akwapem South, Akwapem North, Ayensuano, Agona East, Okaikwei North Municipal, Ga North Municipal, Ga West Municipal, Awutu Senya East, Gomoa East, Weija Gbawe Municipal, Gomoa Central among other districts had 99% confidence level of having access to health care in Ghana. Thus, these districts had little or no barriers to accessing healthcare in Ghana.

Although the hotspot analysis gives a spatial visualisation of the areas with high and low incidence barriers to accessing healthcare, the cluster and outlier analysis (Fig. 2b) revealed some unique findings that were overly generalised by the hotspot analysis. The cluster and outlier results (Fig. 2b) showed that some districts with low access to healthcare and vice versa surrounded some districts with high barriers to access to healthcare. In Fig. 2b, districts with a low incidence of barriers to accessing healthcare surrounded by districts with a high incidence of barriers to accessing healthcare are represented as blue and the opposite as red. For instance, districts such as Bolga East, Mamprugu Moagduri, Kumbungu, and North East Gonja were found to have a low incidence of healthcare barriers but are surrounded by neighbours with high incidences of barriers to access to healthcare. On the other hand, the cluster and outlier results (Fig. 2b) also revealed that some districts within the southeastern were also identified as having a high incidence of barriers to health but were surrounded by districts with a low incidence of barriers to healthcare. Districts such as La Dade-Kotopon, Ga South Municipal, Ga Central Municipal, Ho Municipal, Okere, Yilo Krobo, Asuogyaman, Suhum Municipal¸ West Akim, as shown in red were found to have a high incidence of barriers to access to healthcare but were surrounded by districts with low barriers access to healthcare.

Multivariate logistic regression on Barriers to Access Healthcare

To determine factors that statistically influence access to healthcare, we identified and segregated four main barriers such as difficulty in getting permission to visit the health facility, getting the money needed for treatment, distance to health facilities, not wanting to go alone, and the difficulty in accessing healthcare due to at least one of the segregated barriers. Using multivariate logistic regression, separate Models were built for the four main identified barriers, and the final Model (Model 5) was built on the difficulty in accessing healthcare resulting from at least one of the segregated barriers. The findings show that age, wealth status, region of residence, and health insurance coverage were statistically significantly associated with all the identified barriers to healthcare access under each model.

Model 1 focused on the difficulty in obtaining permission to access healthcare. The findings showed that age, wealth status, region of residence, marital status, health insurance coverage, and level of education were statistically significantly associated with difficulty in getting permission as a barrier to healthcare access. For instance, compared to young women aged 15–19, older women (40–44) were less likely to face difficulty getting permission as a barrier to healthcare access. Compared to women of the poorest wealth status, those who were of the middle (OR = 0.72, CI = 0.58–0.90) and richer (OR = 0.65, CI = 0.51–0.84) wealth status had lower odds of facing difficulty in getting permission to access healthcare. This inverse relationship between wealth and the outcome was similar across all models (Model 1–5). Compared to the reference category (western region), women in the Upper West and Upper East were 2. 44 times and 2.11 times more likely to face difficulty seeking healthcare permission. The study revealed that women in union (married: OR = 0.76, CI = 0.60–0.95 and cohabitation: OR = 0.74, CI = 0.58–0.93) had lower odds of facing difficulty in seeking permission as a barrier to healthcare access compared to their counterparts who were never married. Relatedly, women without health insurance subscriptions were 1.18 times more likely to have trouble in seeking permission to access healthcare than those who were not on subscriptions. Similarly, women without any formal education and those with primary education were 63% and 47%, respectively, more likely to face difficulty in seeking permission to access healthcare than those with higher levels of education. Except for Model 3 which education was not statistically significant, the results revealed similar observations in Models 2, 4 and 5 with an inverse relationship between educational levels and the likelihood of facing the segregated barriers identified in the models.

Regarding Model 2, we observed an increased odds of facing difficulty in getting the money needed for treatment with increasing age. Thus, older women (45–49 years old) had higher odds of financial constraints as a barrier to healthcare access than young women (15–19 years old). As expected, we found that increased household wealth status was associated with a lower likelihood of facing difficulty in getting the money needed for treatment as a barrier to healthcare utilisation. Regionally, women in the Central (OR = 0.79, CI = 0.67–0.92) and Brong Ahafo (OR = 0.54, CI = 0.47–0.62) compared to the respondents from the Western were less likely to be confronted with difficulty in getting the money needed for treatment as a barrier to healthcare utilisation. In contrast, women in Volta (OR = 1.19, CI = 1.01–1.40) compared to those from the Western were more likely to be challenged with difficulty in getting the money needed for treatment as a barrier to healthcare. The difficulty in getting the money needed for treatment was significantly lower among women in rural areas than those in urban settings. Compared to women with no religious affiliation, Islamic women were less likely to be challenged with difficulty getting the money needed for treatment as a barrier to healthcare access. There is an indication that married, and cohabitation women were less likely challenged with difficulty in getting the money needed for treatment than their counterparts who were never married. However, widowed and separated women had 1.29 times and 1.32 times more likely to face difficulty getting treatment money than those who had never married. The model further demonstrates increased odds of difficulty in getting the money needed for treatment with the rising parity of a woman. Also, the effect of media (newspaper, radio, and television) exposure was positive: women who were exposed to the newspaper/magazine (OR = 0.78, CI = 0.67, 0.90), radio (OR = 0.92, CI = 0.85–0.99), and television (OR = 0.80, CI = 0.74–0.88) at least once a week had a lower likelihood of facing difficulty in getting money for treatment as a barrier to healthcare utilisation. Twenty-three percent of women without health insurance coverage were more likely to face financial challenges as a barrier to healthcare access than those who were subscribed to health insurance. Likewise, compared to women with higher education levels, those without formal education were 2.04 times more likely to face financial challenges as a barrier to healthcare utilisation.

Model 3 assessed the difficulty of distance to a health facility as a barrier to healthcare access. Older women (44–49 years old) were 1.26 times more likely to face problems with distance to health facilities as a barrier to healthcare access than young women (15–19). Women in the Northern and Eastern regions were significantly more likely to face problems associated with distance as a barrier to healthcare access than their counterparts in the Western region. As expected, women residing in rural areas were 72% more likely to face distance as a barrier to healthcare access than those in rural settings. Regarding media exposure, women exposed to television at least once a week had a lower likelihood of experiencing distance as a barrier to healthcare access compared to those who were not exposed at all. Women without health insurance subscriptions were less likely to face distance-related barriers to healthcare utilisation compared to those who were subscribed to health insurance.

Model 4 focused on reluctance to visit health facilities alone. As expected, increasing one’s age leads to a decline in reluctance to visit the health facility alone as a barrier to accessing healthcare. Thus, compared to young women aged 15–19 years old, older women aged 45–49 years older had lower odds (OR = 0.45, CI = 0.35–0.56) of being reluctant to visit health facilities alone as a barrier to healthcare access. Apart from the Central and Volta regions, which did not show a significant relationship with the outcome variable, women in all the other regions had significantly higher odds of being reluctant to visit the health facilities alone as a barrier to accessing healthcare than women in the Western region. Women in rural areas were 28% more likely to be reluctant to visit health facilities alone as a barrier to utilising healthcare than those in urban areas. Compared to nulliparous women, those with 1–5 parity were significantly less likely to be reluctant to visit the health facility alone as a barrier to healthcare access. Related to women with health insurance coverage, those without health insurance subscriptions were more likely to be reluctant to visit health facilities, which serve as a barrier to healthcare utilisation.

Finally, Model 5 assessed the overall difficulty in accessing healthcare due to any of the identified barriers. Wealth status, region and place of residence, religion, marital status, parity, exposure to the media (radio and television), health insurance coverage, and level of education were significant factors associated with at least one of the four segregated barriers to accessing healthcare among the sampled population. The findings demonstrate that an improvement in wealth status reduces the risk of facing at least one of the barriers to healthcare utilisation. Thus, compared to women in the poorest wealth index, those with richer and richest wealth status were 70% and 77%, respectively, less likely to face at least one of the barriers to healthcare utilisation. Likewise, women in the Central (OR = O.73, CI = 0.63–0.86), Brong Ahafo (OR = 0.57, CI = 0.50–0.65), and Upper East (OR = 0.85, CI = 0.73–0.98) had lower odds of facing at least one of the barriers to accessing healthcare. However, women residing in the Eastern region were more likely to face at least one barrier to accessing healthcare. Regarding religious influence on the barriers to healthcare access, we found that women with Islamic affiliation were less likely to face at least one of the barriers to healthcare utilisation compared to their Christian counterparts. The finding highlights the potential impact of marital status on healthcare access. Thus, married (OR = 0.72, CI = 0.64–0.81) and cohabited (OR = 0.85, CI = 0.75–0.96) individuals had lower odds of facing at least one of the identified barriers to accessing healthcare compared to the reference group (never married). But separated women had higher odds of facing at least one of the identified barriers to healthcare access compared to their counterparts who had never married. Women with high parity (5, 6 and above) were 22% and 33%, respectively, more likely to face at least one of the challenges associated with accessing healthcare than nulliparous women. Women without any formal education were 74% more likely to face at least one of the identified barriers to healthcare access; however, this risk reduces with an increasing level of education (see Table 3).

Table 3

Binary Logistic regression of barriers to access to healthcare.
Variables	Model 1	Model 2	Model 3	Model 4	Model 5
	Getting permission to go	Getting money needed for treatment	Distance to health facility	Not wanting to go alone	At least one barrier
	Odds Ratio (95% CI)	Odds Ratio (95% CI)	Odds Ratio (95% CI)	Odds Ratio (95% CI)	Odds Ratio (95% CI)
Age
15–19	Ref	Ref	Ref	Ref	Ref
20–24	0.85(0.70, 1.03)	1.21**(1.08, 1.35)	1.16**(1.02, 1.32)	0.69***(0.61, 0.79)	1.02(0.92, 1.14)
25–29	0.82(0.65, 1.03)	1.33***(1.17, 1.52)	1.19**(1.03, 1.39)	0.61***(0.52, 0.72)	1.03(0.91, 1.17)
30–34	0.77(0.59, 1.00)	1.31***(1.13, 1.51)	1.10(0.93, 1.31)	0.53***(0.44, 0.64)	0.99(0.86, 1.15)
35–39	0.72 *(0.54, 0.96)	1.34***(1.15, 1.57)	1.08(0.90, 1.29)	0.46***(0.37, 0.57)	0.97(0.83, 1.13)
40–44	0.68*(0.50, 0.93)	1.52***(1.28, 1.80)	1.11(0.91, 1.35)	0.44***(0.35, 0.55)	1.08(0.91, 1.28)
45–49	0.85(0.62, 1.16)	1.52***(1.30, 1.81)	1.26*(1.03, 1.54)	0.45***(0.35, 0.56)	1.13(0.95, 1.35)
Wealth status
Poorest	Ref	Ref	Ref	Ref	Ref
Poorer	0.78**(0.66, 0.93)	0.62***(0.56, 0.68)	0.53***(0.48, 0.59)	0.70***(0.62, 0.79)	0.56***(0.50, 0.62)
Middle	0.72**(0.58, 0.90)	0.44***(0.39, 0.49)	0.36***(0.31, 0.41)	0.61***(0.53, 0.72)	0.39***(0.35, 0.44)
Richer	0.65**(0.51, 0.84)	0.33***(0.29, 0.37)	0.33***(0.28, 0.38)	0.63***(0.53, 0.74)	0.30***(0.26, 0.34)
Richest	0.87(0.66, 1.14)	0.22***(0.19, 0.25)	0.26***(0.22, 0.31)	0.77**(0.64, 0.94)	0.23***(0.20, 0.27)
Region of residence
Western	Ref	Ref	Ref	Ref	Ref
Central	1.13(0.66, 0.93)	0.79**(0.67, 0.92)	0.63***(0.52, 0.78)	0.88(0.68, 1.15)	0.73***(0.63, 0.86)
Greater Accra	1.77***(1.30, 2.40)	1.02(0.89, 1.18)	0.97(0.80, 1.18)	1.36**(1.08, 1.70)	1.03(0.89, 1.19)
Volta	0.78(0.53, 1.17)	1.19*(1.01, 1.40)	0.89(0.74, 1.08)	1.09(0.85, 1.41)	1.06(0.90, 1.25)
Eastern	2.21***(1.67, 2.93)	1.00(0.87, 1.14)	1.56***(1.33, 1.83)	1.64***(1.33, 2.02)	1.15*(1.00, 1.32)
Ashanti	0.97(0.71, 1.31)	1.02(0.89, 1.16)	0.91(0.78, 1.07)	1.46***(1.19, 1.78)	1.04(0.92, 1.18)
Brong Ahafo	1.30(0.96, 1.74)	0.54***(0.47, 0.62)	0.70***(0.59, 0.83)	1.40**(1.14, 1.72)	0.57***(0.50, 0.65)
Northern	1.14(0.85, 1.53)	0.93(0.81, 1.06)	1.52***(1.30, 1.78)	2.04***(1.68, 2.48)	1.01(0.88, 1.16)
Upper East	2.44***(1.85, 3.22)	0.87(0.76, 1.01)	0.93(0.79, 1.09)	2.31***(1.90, 2.80)	0.85*(0.73, 0.98)
Upper West	2.11***(1.59, 2.78)	0.90(0.78, 1.04)	0.98(0.84, 1.15)	2.33***(1.91, 2.83)	0.92(0.79, 1.06)
Place of residence
Urban	Ref	Ref	Ref	Ref	Ref
Rural	1.08(0.92, 1.24)	0.91*(0.84, 0.98)	1.72***(1.58, 1.88)	1.28***(1.16, 1.43)	1.02(0.95, 1.11)
Religion
Christian	Ref	Ref	Ref	Ref	Ref
Islam	0.99(0.86, 1.13)	0.80***(0.74, 0.87)	0.98(0.90, 1.08)	1.03(0.93, 1.13)	0.84***(0.77, 0.91)
Traditional/spiritual	0.88(0.62, 1.26)	1.15(0.92, 1.44)	1.14(0.92, 1.40)	1.10(0.87, 1.38)	1.20(0.93, 1.54)
No religion	0.95(0.68, 1.34)	1.12(0.90, 1.38)	0.94(0.76, 1.16)	1.09(0.85, 1.39)	1.08(0.86, 1.36)
Marital status
Never in union	Ref	Ref	Ref	Ref	Ref
Married	0.76*(0.60, 0.95)	0.67***(0.59, 0.76)	0.88(0.76, 1.01)	0.87(0.74, 1.02)	0.72***(0.64, 0.81)
Cohabitation	0.74*(0.58, 0.93)	0.78***(0.69, 0.88)	0.86(0.75, 1.00)	0.89(0.76, 1.05)	0.85**(0.75, 0.96)
Widowed	0.79(0.54, 1.16)	1.29*(1.02, 1.63)	1.03(0.81, 1.32)	1.36*(1.03, 1.79)	1.16(0.91, 1.47)
Divorced	0.70(0.43, 1.16)	0.89(0.70, 1.12)	1.00(0.75, 1.32)	0.89(0.61, 1.28)	0.96(0.76, 1.22)
Separated	0.86(0.60, 1.23)	1.32**(1.09, 1.60)	0.81(0.65, 1.02)	0.88(0.67, 1.17)	1.27**(1.04, 1.54)
Parity
0	Ref	Ref	Ref	Ref	Ref
1	1.05(0.84, 1.31)	1.11(0.98, 1.24)	1.00(0.87, 1.14)	0.66***(0.56, 0.77)	0.97 (0.87, 1.09)
2	1.06(0.81, 1.37)	1.31***(1.14, 1.50)	1.03(0.89, 1.22)	0.77**(0.64, 0.92)	1.14 (1.00, 1.31)
3	1.03(0.77, 1.38)	1.24**(1.07, 1.44)	0.90(0.76, 1.07)	0.73**(0.60, 0.90)	1.06(0.92, 1.23)
4	1.11(0.81, 1,51)	1.34***(1.40, 1.57)	0.94(0.78, 1.13)	0.75*(0.60, 0.94)	1.16(0.99, 1.36)
5	1.10(0.79, 1.53)	1.40***(1.17, 1.66)	1.01(0.83, 1.23)	0.75*(0.59, 0.95)	1.22*(1.03, 1.46)
6+	1.35(0.98, 1.87)	1.42***(1.19, 1.69)	1.09(0.90, 1.33)	1.03(0.82, 1.30)	1.33**(1.12, 1.59)
Frequency of reading newspaper or magazine
Not at all	Ref	Ref	Ref	Ref	Ref
Less than once a week	0.92(0.74, 1.14)	0.85**(0.76, 0.95)	0.93(0.81, 1.06)	1.09(0.94, 1.25)	0.93(0.84, 1.03)
At least once a week	0.90(0.78, 1.02)	0.78**(0.67, 0.90)	1.08(0,91, 1.29)	1.08(0.90, 1.30)	0.92(0.81, 1.06)
Frequency of Listening to radio
Not at all	Ref	Ref	Ref	Ref	Ref
Less than once a week	1.05(0.91, 1.22)	0.98(0.90, 1.06)	1.01(0.92, 1.11)	1.16**(1.04, 1.29)	0.99(0.90, 1.07)
At least once a week	0.90(0.78, 1.02)	0.92*(0.85, 0.99)	1.01(0.92, 1.09)	1.12**(1.02, 1.24)	0.95(0.88, 1.03)
Frequency of watching television
Not at all	Ref	Ref	Ref	Ref	Ref
Less than once a week	1.01(0.86, 1.19)	0.86**(0.78, 0.95)	0.83***(0.75, 0.92)	1.03(0.92, 1.16)	0.89*(0.81, 0.99)
At least once a week	0.85(0.73, 1.00)	0.80***(0.74, 0.88)	0.72***(0.66, 0.79)	0.83**(0.74, 0.92)	0.79***(0.72, 0.86)
Coverage of health insurance
No	1.18**(1.06, 1,32)	1.23***(1.16, 1.31)	0.91**(0.85, 0.97)	1.10**(1.01, 1.19)	1.20***(1.13, 1.28)
Yes	Ref	Ref	Ref	Ref	Ref
Level of education
No education	1.63**(1.19, 2.23)	2.04***(1.73, 2.40)	1.12(0.92, 1.35)	1.50***(1.21, 1.87)	1.74***(1.49, 2.03)
Primary	1.47**(1.07, 2.01)	1.97***(1.67, 2.33)	1.06(0.87, 1.28)	1.33**(1.07, 1.65)	1.66***(1.42, 1.93)
Secondary	1.11(0.84, 1.46)	1.77***(1.53, 2.04)	1.01(0.85, 1.20)	1.12(0.92, 1.35)	1.44***(1.27, 1.64)
Higher	Ref	Ref	Ref	Ref	Ref
p < 0.05, p < 0.01, **p < 0.001 Ref, Reference category CI, Confidence interval

Understanding the spatial distribution of barriers to accessing healthcare is crucial for implementing targeted interventions and advancing Ghana's progress toward achieving SDG target 3.8. This study assessed the spatial distribution barriers to healthcare access among women in Ghana. Our study revealed that more than half of women (55.4%) of reproductive age in Ghana experienced at least one barrier in accessing healthcare. This is consistent with Seidu et al. [11] study, which also found a proportion of 51%. It is, however, important to note that the present study’s estimated proportion is slightly higher than that of Seidu et al.’s [11] study. A plausible explanation for this difference may be due to the different datasets used. While our study used the 2017 GMHS, Seidu et al.’s [11] study relied on the 2014 GDHS. The GMHS had a higher sample size compared to the GDHS, thus, explaining the differences.

The spatial analyses indicate evidence of clustering of the barriers to accessing healthcare in particular districts. Moreover, the results indicate that the Northern zone of Ghana emerges as a hotspot for barriers to accessing healthcare. This implies that within this region, a higher concentration or intensity of barriers hinders individuals from obtaining necessary healthcare services. Conversely, the Southern zone of Ghana appears to be characterised by cold spots in terms of barriers to accessing healthcare. The observed spatial distribution is consistent with prior evidence that opines that Northern Ghana is deprived of adequate healthcare resources, thereby creating more barriers to the accessibility of healthcare compared to Southern Ghana [12]. A plausible explanation for this spatial variation could be the unavailability or limited healthcare resources in Northern Ghana compared to Southern Ghana. For instance, the Ghana Health Service [13] reports a doctor-patient ratio of 1:3136 and 1:18,380 in Greater Accra (Southern Ghana) and the Northern region (Northern Ghana), respectively. Such a high doctor-patient ratio in Northern Ghana implies that many women must cover a significant distance to access healthcare. This creates a situation where getting money for healthcare and distance become paramount barriers to accessing healthcare. Another perspective on this spatial variation could be the existing cultural inclinations. There is evidence suggesting that Northern Ghana is primarily patriarchal; hence, there is a significant dominance of male partners in all decisions, including healthcare decisions, compared to the situation in the South of Ghana [14–16]. Furthermore, the spatial disparities highlight the country’s over-concentration in improving healthcare accessibility in the South compared to Northern Ghana.

Consistent with previous studies [17, 18], we found a higher wealth index to be significantly associated with lower odds of facing at least one healthcare barrier. This is explained by the fact that women from affluent households are more likely to have the necessary financial resources that enables them to afford both direct and indirect health-related expenditure. Related to this finding was the observation that there was a higher likelihood of facing at least one barrier to healthcare accessibility among women who had no health insurance coverage compared to those with insurance coverage. The result is corroborated by Seidu et al.’s [11] study found lower odds of facing at least one barrier among women with health insurance coverage when they are accessing healthcare. An explanation is that health insurance coverage reduces out-of-pocket payments for direct healthcare costs. Hence, limiting the possibility of having difficulty getting money for treatment can be a barrier to healthcare accessibility.

In line with Seidu et al. [11], we observed that women with lower levels or no formal education were more likely to face at least one barrier when accessing healthcare than those with higher educational attainment. The reason could be that highly educated women are more likely to get high-paying or stable jobs that offer them the financial power to afford healthcare regardless of location or cost. Moreover, higher education is linked to empowerment [19, 20]. This means that highly educated women are most likely to be empowered and would therefore be autonomous in making healthcare decisions. Also, the study shows that watching TV significantly reduced the likelihood of experiencing at least one barrier in terms of access to healthcare. A possible justification for this result is that individuals who regularly watch TV may also have higher levels of media literacy, enabling them to critically evaluate health-related information, identify reputable sources, and make informed decisions regarding their healthcare.

Our study also shows that women in union (i.e., currently married and cohabiting) were less likely to experience at least one barrier in accessing healthcare compared to those never married. This aligns with studies conducted in Ghana [11], Ethiopia [21], and Montenegro [22]. Women in a union may have shared financial resources with their partners, which can contribute to their ability to afford healthcare expenses. Since financial barriers, such as the cost of treatment or medication, have shown to be significant deterrents to accessing healthcare, being in a union may provide a more stable financial situation, enabling women to overcome these barriers and seek necessary healthcare services without excessive financial strain.

We found that grand multiparous women were more likely to experience at least one barrier in terms of accessing healthcare. This finding is consistent with a study conducted in Ghana [11]. The financial burden associated with raising and supporting a larger family may contribute to the higher likelihood of experiencing barriers to healthcare access among grand multiparous women. Additional children can increase household expenses, such as education, food, and other necessities. This may result in a trade-off between meeting the family's needs and allocating resources for individual healthcare. Limited financial resources can make it difficult to afford transportation costs, medical fees, or medications, creating barriers to accessing necessary healthcare services [23]. Lower odds of facing at least one barrier in accessing healthcare were observed among those who professed Islam compared to Christians. Further studies are needed to comprehend this association fully.

Implications for Policy and Practice

The findings of this research provide insights into policy and practice. This study strengthens the idea that Ghana’s policymakers and program implementers must consider spatial variation when implementing healthcare accessibility programs and policies such as the CHPS initiative, free maternal healthcare policy, and the NHIS. The spatial disparities in healthcare accessibility between Ghana's Northern and Southern zones indicate an over-concentration of resources and improvements in healthcare accessibility in the South. Therefore, policymakers must consider redistributing healthcare resources to ensure equitable access across different regions of the country. This could involve increasing the availability of healthcare facilities, healthcare professionals, and health services in underserved areas, particularly in the Northern zone. The results also highlight a need to expand the NHIS to facilitate a reduction in the incidence of barriers to healthcare accessibility. The study highlights the need for further research to better understand the association between religious affiliation (Islam vs. Christianity) and barriers to healthcare access.

Strengths and limitations

Our study is to perform spatial analysis in understanding the barriers to accessing healthcare in Ghana. This contributes significantly to the scant body of literature. Also, the large dataset used guarantees the extrapolation of the study findings to the larger population of Ghanaian women of reproductive age. Nevertheless, we are unable to establish causality due to the cross-sectional nature of the design that informed the GMHS. As we relied on a secondary dataset, healthcare variables and cultural factors could not be accounted for due to their unavailability in the dataset.

This study set out to investigate the spatial distribution of the barriers to accessing healthcare among women in Ghana. We conclude that more than half of Ghanaian women face at least one barrier in terms of accessing healthcare. Northern Ghana is a hotspot for the barriers in accessing healthcare, while Southern Ghana is a cold spot area. Taken together, the findings from this study underscore a need for the Government of Ghana, through its health agencies, to improve healthcare accessibility in Northern Ghana. Interventions to reduce barriers to healthcare accessibility, especially in Northern Ghana, must target key sub-populations, including women not in marital union, those with no formal education, those in poor wealth index, and those without health insurance coverage.

SGDs: Sustainable Development Goals

UHC: Universal Health Coverage

CHPS: Community-based Health Planning and Services

NHIS: National Health Insurance Scheme

OR: Odds Ratios

GMHS: Ghana Maternal Health Survey

DHS- Demographic and Health Survey

VIF: Variance Inflation Factor

Acknowledgment

We are grateful to the DHS Program for providing us with access to the dataset.

Authors’ Contribution

KSD: conceived the study and all authors designed the study. ENKB, KSD, and BA: contributed to the acquisition of data and the analysis. KSD, JO, CA, BA, and ENKB: contributed to drafting the various sections of the manuscript. All authors read, edited the content of the manuscript, and approved the manuscript for submission. EKMD supervised the entire process.

Funding

No funding was received.

Availability of data and materials

Data for the current study are accessible at the DHS data repository: www.measuredhs.com.

Ethical Approval and Consent to Participate

We confirm that all methods were carried out in compliance with the relevant norms and regulations in existence at the time, including regulatory approvals from NHS organisations, for research involving human subjects. All participants signed an informed consent form. The Institutional Review Board of the ICF International and Institutional Review Boards in the various host countries have approved the survey protocols.

Consent for publication

Not applicable

Competing Interest

None.

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No competing interests reported.

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Spatial distribution and barriers to access to health care among women in Ghana

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Abstract

Figures

Introduction

Methods

Data Source

Definition of variables and Measurement

Explanatory variables

Spatial analyses

Statistical analysis

Results

Spatial analysis results

Spatial distribution of access to healthcare

Hotspot of barriers to access to healthcare

Multivariate logistic regression on Barriers to Access Healthcare

Discussion

Implications for Policy and Practice

Strengths and limitations

Conclusion

Abbreviations

Declarations

References

Additional Declarations

Status:

Version 1