Trends and inequalities in distance-related barriers to healthcare access among women of reproductive age in Ghana, 2003–2022

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In Ghana, distance to health facilities remains a commonly reported barrier to care, yet evidence on long term trends and socioeconomic, residential, and regional inequalities is limited. This study examined trends and inequalities in distance related barriers to healthcare access among women aged 15–49 years in Ghana between 2003 and 2022. Methods We analysed four rounds of the Ghana Demographic and Health Surveys (2003, 2008, 2014, and 2022). The outcome was self-reported distance to a health facility as a major barrier to seeking care. Inequalities were assessed across wealth quintile, place of residence, and subnational region using the WHO Health Equity Assessment Toolkit. Summary measures included difference (D), ratio (R), population attributable risk (PAR), and population attributable fraction (PAF), with 95% confidence intervals (CIs), accounting for the complex survey design. Results Nationally, the prevalence of women reporting distance as a major barrier declined over the study period. However, inequalities persisted across all dimensions. Rural women consistently reported higher prevalence than urban women, although the rural–urban gap narrowed over time (D: 30.9 percentage points in 2003 to 17.3 in 2022). Wealth-related inequalities were substantial: after narrowing between 2003 and 2014, absolute inequality widened again by 2022 (D = 38.4), with relative inequality remaining high (R = 4.7). The wealth-related PAF remained large across survey years (− 58.5% in 2003; −53.0% in 2022). Regional disparities were pronounced but declined over time, with the highest prevalence shifting from Upper East in 2003 to Northern region in 2022. Conclusions Although distance-related barriers declined nationally, national improvements in reported access may mask persistent and substantial subpopulation disadvantage. The findings provide policy relevant evidence supporting equity focused and geographically targeted health infrastructure and service delivery strategies to reduce distance related barriers to reproductive healthcare. distance barrier Ghana healthcare access inequality women of reproductive age Figures Figure 1 Figure 2 1.0 Introduction 1.1 Background Access to healthcare is a well established determinant of population health outcomes that shapes patterns of morbidity, mortality, equity and inequality across global contexts [ 1 , 2 ]. Despite international commitments to universal health coverage [ 3 ], it has been stated that more than 400 million people worldwide lack access to essential healthcare services, contributing to approximately eight million deaths annually from conditions that are largely treatable or preventable [ 4 ]. Access to healthcare is closely linked to the wider determinants of health and extends beyond service availability to encompass geographical access, referring to the physical proximity of health facilities, cultural access, relating to acceptability and appropriateness of care, and economic access, reflecting affordability and cost barriers [ 5 – 8 ]. Women of reproductive age, defined as those aged 15 to 45 years [ 9 ], are disproportionately affected by interacting barriers to healthcare access in low and middle income settings. Rather than biomedical causes alone, structural barriers related to access play a significant role in shaping adverse maternal health outcomes, including miscarriage, unsafe abortion, stillbirth, and maternal mortality, many of which are preventable [ 10 – 12 ]. These barriers often intersect with social and economic disadvantage to reinforce gendered vulnerabilities across the life course. In Africa, inequalities in healthcare access remain pronounced, with earlier estimates suggesting that only about half of the population had access to modern healthcare facilities [ 13 ]. Among women of reproductive age, geographic inaccessibility, particularly distance to healthcare facilities, alongside economic constraints and restrictive cultural norms, has been consistently identified as a major barrier to care, contributing to persistent health inequalities, increased morbidity and avoidable mortality [ 14 – 15 ]. In Ghana, distance related barriers to healthcare access among women are recognised in both national monitoring frameworks and peer reviewed research. The Demographic and Health Survey (DHS) programme routinely captures women’s self reported problems in accessing care when sick, including among Ghanaian women [ 16 ]. This includes whether distance to a healthcare facility constitutes a serious barrier, and this indicator is widely used as a population level proxy for health accessibility constraints [ 16 ]. Emerging evidence using the Ghana Maternal Health Survey further demonstrates that women reporting distance as a major problem have significantly longer modelled travel times to facilities providing birthing services, with rural and poorer women bearing a disproportionate burden [ 17 ]. While distance to health facilities is therefore a well documented barrier in Ghana, particularly among rural and socioeconomically disadvantaged populations, evidence on long term trends and equity patterns remains limited. However, despite a growing body of work on geographic accessibility in Ghana, which is valuable for local service planning and identifying geographical inequalities in provision, the literature remains dominated by cross sectional and geographically bounded analyses [ 17 – 21 ]. This limits inference about whether observed improvements in access have been equitable and sustained over time at the national level. In addition, most Ghana Demographic and Health Survey (GDHS) based studies focus on modelling associations between perceived distance barriers and healthcare service utilisation outcomes, with limited application of absolute, relative, or population attributable inequality measures required to quantify the magnitude, distribution, and avoidable burden of inequities [ 17 , 22 – 23 ]. To respond to these evidence gaps, this study aimed to examine long term trends and socioeconomic, residential, and regional inequalities in distance related barriers to healthcare access among women aged 15 to 49 years in Ghana between 2003 and 2022 using four rounds of nationally representative GDHS data. By applying standardised inequality metrics and equity monitoring methods, the study goes beyond national averages and assesses how progress is distributed across wealth, place of residence, and subnational regions. This approach quantifies both absolute and relative inequalities as well as the avoidable burden attributable to disadvantage. The study provides equity focused and policy relevant evidence on whether reductions in geographic access barriers have been equitable and sustained, and identifies the population groups and regions where disadvantage persists and where geographically targeted public health and service delivery interventions are most needed. 2.0 Methods 2.1 Study design and data sources This study used a repeated cross-sectional design based on nationally representative data from the 2003, 2008, 2014 and 2022 GDHS [ 25 – 28 ]. For each survey round, the women’s individual recode file served as the primary data source. The GDHS employs a two-stage stratified cluster sampling strategy based on the Ghana Population and Housing Census sampling frame, with stratification by region and urban–rural residence to ensure representativeness at national, urban–rural and regional levels [ 24 ]. Detailed sampling procedures are available in the technical reports for each survey year [ 25 – 28 ]. 2.2 Study population The study population consisted of women aged 15–49 years who were either usual residents or de facto members of selected households and who completed the women’s questionnaire. Women with missing data on the main outcome (distance as a barrier to seeking care) or on any of the key equity stratifiers (wealth quintile, place of residence or region) were excluded from the analytic sample. All analyses incorporated the DHS individual sampling weights so that estimates are representative of women aged 15–49 years in Ghana in each survey year. 2.3 Outcome variable The primary outcome was self-reported distance to a health facility as a major barrier to obtaining care when ill. In every survey round, women were asked: “When you are sick and want to get medical advice or treatment, is distance to a health facility a big problem?” Responses were recorded as “yes” or “no.” A binary indicator was created, taking the value 1 for women who reported distance as a “big problem” and 0 otherwise. To support comparability across time, the wording of the question and the response categories were checked and confirmed to be consistent in all four GDHS rounds [ 17 , 25 – 28 ]. 2.4 Equity stratifiers Inequalities were assessed across three dimensions: economic status, place of residence and subnational region. Economic status was captured using the DHS wealth index, a composite measure derived through principal components analysis of household assets, housing characteristics and access to basic services. Households are ranked and grouped into five wealth quintiles (Q1 = poorest to Q5 = richest); the standard DHS wealth quintile variable was used and harmonised across survey years following DHS coding conventions. Place of residence was classified as urban or rural, based on DHS definitions grounded in national census criteria for each survey. Subnational geography was represented using the administrative regions recorded in each GDHS but reclassified to the pre-2018 10-region structure (Ashanti, Brong Ahafo, Central, Eastern, Greater Accra, Northern, Upper East, Upper West, Volta, Western) to enable temporal comparisons. For 2022, districts belonging to newly created regions were reassigned to their corresponding pre-2018 regions. 2.5 Statistical analyses 2.5.1 Descriptive analysis Analyses began with a description of the study population by survey year and equity strata, including wealth quintile, urban–rural residence and region. For each survey, the weighted prevalence of women reporting distance to a health facility as a major barrier to care was estimated overall and within each subgroup. Ninety-five percent confidence intervals were calculated while accounting for the complex survey design, including clustering, stratification and sampling weights. 2.5.2 Analytical technique Inequality analysis was conducted using the WHO Health Equity Assessment Toolkit (HEAT), version 6.1, which offers a standardized platform for generating disaggregated health indicators and summary measures of inequality from population-based surveys [ 29 , 30 ]. In this study, harmonised DHS datasets already embedded within the HEAT software were used. Within HEAT, the binary indicator “distance as a major barrier” was specified as the health outcome. For each survey year and for each equity dimension, place of residence, economic status, and region, we estimated four summary measures of inequality: Difference (D), Ratio (R), Population Attributable Risk (PAR), and Population Attributable Fraction (PAF). D captured the absolute gap in prevalence (percentage points) between the most disadvantaged and least disadvantaged subgroups (for example, rural versus urban residents, poorest versus richest wealth quintiles, and regions with the highest versus lowest prevalence). R expressed relative inequality as the ratio of prevalence in the most disadvantaged subgroup to that in the least disadvantaged subgroup. PAR was defined as the absolute difference in percentage points between the national prevalence and the prevalence in the best-performing (reference) subgroup, representing the potential absolute reduction in the national prevalence if all subgroups attained the same level as this reference group. PAF was derived by expressing PAR as a percentage of the national prevalence, yielding values between − 100% and + 100%, where negative values indicate that the reference group has a lower prevalence than the national average [ 31 , 32 ]. All estimates were generated using sampling weights, and 95% confidence intervals were computed in HEAT via Taylor linearisation, taking into account the complex survey design [ 33 ]. 2.5.3 Trend analysis Temporal patterns were examined for both the overall prevalence of distance-related barriers and the magnitude of inequalities between 2003 and 2022. Trends were summarised in figures and tables displaying the outcome and inequality measures across survey years. Visual inspection of trajectories for D, R, PAR and PAF was used to assess whether inequalities appeared to narrow, widen or remain stable over time for each equity dimension, considering sampling variability as reflected by overlapping confidence intervals (CIs) and considering contemporaneous changes in the health policy environment. 2.5.4 Geospatial analysis and mapping Spatial variation in distance-related barriers was explored using regional prevalence mapping. For each survey year, regional prevalence estimates and their 95% confidence intervals were exported from HEAT and merged with a harmonised Ghana regional shapefile corresponding to the pre-2018 10-region configuration. All geospatial mapping and figure production were conducted in Python (version 3.13.3; tags/v3.13.3:6280bb5, Apr 8 2025, 14:47:33) [MSC v.1943 64 bit (AMD64)], using the geopandas, pandas and matplotlib libraries. For each round, regional prevalence data were joined to the shapefile using region identifiers, and choropleth maps were produced to depict the percentage of women who reported distance as a major barrier to care in each region. A consistent colour scale and classification scheme were applied across years to facilitate visual comparison of spatial patterns over time. 2.6 Handling of survey design In this study, all core analyses were performed within the WHO HEAT, which automatically accounts for the complex survey design of the DHS, including sampling weights, clustering and stratification. As a result, all prevalence estimates, confidence intervals and inequality measures used in the paper were already adjusted for the survey design. External software was used only to present these HEAT-derived results. Weighted prevalence estimates (and their 95% CIs) were exported from HEAT and then organized into descriptive tables and graphs in Excel. For geospatial visualization, these same estimates were merged with a Ghana regional shapefile and mapped using Python (pandas, geopandas, matplotlib). No additional modelling, re-weighting or variance estimation was performed outside HEAT. 2.7 Ethical considerations All GDHS protocols received ethical approval from the Ghana Health Service Ethical Review Committee and the ICF Institutional Review Board. Written informed consent was obtained from all participants at the time of primary data collection. The present study involved secondary analysis of anonymised, publicly available datasets and did not require further contact with participants; consequently, additional ethics approval was not sought. Permission to use the datasets was granted by The DHS Program following submission of a brief research proposal. 3.0 Results 3.1 Trends in the Prevalence of Women Aged 15–49 Reporting Distance to a Health Facility as a Major Access Barrier, Ghana (2003–2022) The analysis included a pooled sample of 102,640 women aged 15–49 years drawn from four rounds of the Ghana Demographic and Health Surveys (GDHS) conducted in 2003, 2008, 2014, and 2022. The distribution of the pooled sample across survey years was 16.3% in 2003, 14.0% in 2008, 26.8% in 2014, and 42.9% in 2022, indicating the proportion of the total analytic sample contributed by each survey round. The year-specific prevalence of women reporting distance to a health facility as a major barrier was 32.7% in 2003, 25.9% in 2008, 25.4% in 2014, and 22.3% in 2022. Overall, the prevalence declined across survey rounds, with the largest reduction observed between 2003 and 2008, followed by smaller reductions between subsequent survey years. The trend in prevalence across survey years is presented in Fig. 1 3.2 Trends in the Prevalence of Women Aged 15–49 Reporting Distance to a Health Facility as a Major Access Barrier, Ghana, by different inequality dimensions, (2003–2022) Table 1 presents trends in the prevalence of women aged 15–49 years reporting distance to a health facility as a major barrier to healthcare access in Ghana, highlighting persistent rural–urban, socioeconomic, and regional inequalities over time. With respect to place of residence, distance-related barriers declined over the study period in both rural and urban settings, although substantial disparities persisted. Among rural women, prevalence declined from 47.7% (95% CI: 45.9–49.5) in 2003 to 34.8% in 2008, remained relatively stable in 2014 (34.9%), and declined further to 32.2% (95% CI: 31.1–33.3) in 2022. In contrast, prevalence among urban women remained consistently lower and relatively stable, changing modestly from 16.8% (95% CI: 15.4–18.2) in 2003 to 14.9% (95% CI: 14.1–15.7) by 2022. Consequently, the rural–urban gap narrowed slightly over time but remained evident across all survey years. Similarly, socioeconomic differentials showed pronounced and persistent gradients over time. In 2003, distance-related barriers were most prevalent among women in the poorest wealth quintile, at 60.1% (95% CI: 57.0–63.2), declining progressively across quintiles to 13.6% (95% CI: 11.8–15.4) among the richest. Between 2003 and 2014, prevalence declined across all wealth groups; however, by 2022, divergent trends emerged. While prevalence among the richest continued to decline to 10.5% (95% CI: 9.5–11.5), prevalence among the poorest increased slightly from 45.1% in 2014 to 48.9% (95% CI: 46.9–50.9) in 2022, reinforcing the persistence of large wealth-related inequalities over time. By contrast, regional trends revealed substantial temporal and spatial heterogeneity. In 2003, the prevalence ranged from 12.4% (95% CI: 10.3–14.5) in Greater Accra to 63.8% (95% CI: 58.5–69.1) in the Upper East, with the Northern region also reporting a high prevalence of 55.0% (95% CI: 50.6–59.4). By 2008, Upper East recorded a further increase to 69.4% (95% CI: 63.7–75.1), while several southern regions remained below 25%. The 2014 survey marked notable shifts, with sharp declines in Upper East to 20.0% (95% CI: 15.9–24.1), alongside increases in Upper West to 53.8% (95% CI: 47.1–60.5) and Northern to 49.8% (95% CI: 46.3–53.3). By 2022, regional disparities persisted despite overall declines. Northern 40.2% (95% CI: 37.4–43.0) and Upper West 30.3% (95% CI: 25.8–34.8) continued to record the highest prevalence, while Greater Accra remained the lowest 11.7% (95% CI: 10.4–13.0), alongside relatively low prevalence in Eastern 13.4%95% CI: 11.5–15.3) and Volta 15.1% (95% CI: 12.5–17.7). These patterns underscore persistent geographic inequalities in distance-related access to healthcare across survey years. Spatial patterns across survey years are illustrated in Fig. 2 . Table 1 Trends in the Prevalence of Women Aged 15–49 Reporting Distance to a Health Facility as a Major Access Barrier, Ghana by different inequality dimensions, (2003–2022) Dimensions of inequality 2003 2008 2014 2022 n % (95% CI) n % (95% CI) n % (95% CI) n % (95% CI) Place of residence Rural 2937 47.7(45.9, 49.5) 2533 34.8(32.9, 36.7) 4345 34.9(33.5, 36.3) 6457 32.2(31.1, 33.3) Urban 2755 16.8(15.4, 18.2) 2383 16.4(14.9, 17.9) 5051 17.2(16.2, 18.2) 8557 14.9(14.1, 15.7) Economic status Quintile 1 (Poorest) 970 60.1(57.0, 63.2) 783 50.9(47.4, 54.4) 1511 45.1(42.6, 47.6) 2447 48.9(46.9, 50.9) Quintile 2 949 51.7(48.5, 54.9) 900 30.2(27.2, 33.2) 1636 35.2(32.9, 37.5) 2712 29.7(28.0, 31.4) Quintile 3 1071 31.7(28.9, 34.5) 979 22.5(19.9, 25.1) 1938 23.7(21.8, 25.6) 3121 18.0(16.7, 19.3) Quintile 4 1245 20.3(18.1, 22.5) 1119 18.6(16.3, 20.9) 2117 18.4(16.7, 20.1) 3379 13.0(11.9, 14.1) Quintile 5 (Richest) 1457 13.6(11.8, 15.4) 1135 15.2(13.1, 17.3) 2194 12.6(11.2, 14.0) 3355 10.5(9.5, 11.5) Region Ashanti 1142 23.2(20.8, 25.6) 1011 20.3(17.8, 22.8) 1798 26.6(24.6, 28.6) 2928 23.1(21.6, 24.6) Brong Ahafo* 569 39.4(35.4, 43.4) 425 24.8(20.7, 28.9) 769 21.3(18.4, 24.2) 1560 23.0(19.5, 26.5) Central 431 44.4(39.7, 49.1) 424 22.2(18.2, 26.2) 936 18.2(15.7, 20.7) 1703 17.4(15.6, 19.2) Eastern 601 30.4(26.7, 34.1) 483 22.8(19.1, 26.5) 878 36.2(33.0, 39.4) 1220 13.4(11.5, 15.3) Greater Accra 942 12.4(10.3, 14.5) 853 15.7(13.3, 18.1) 1898 12.6(11.1, 14.1) 2326 11.7(10.4, 13.0) Northern* 499 55.0(50.6, 59.4) 467 35.1(30.8, 39.4) 786 49.8(46.3, 53.3) 1758 40.2(37.4, 43.0) Upper East 310 63.8(58.5, 69.1) 253 69.4(63.7, 75.1) 358 20.0(15.9, 24.1) 640 24.6(21.3, 27.9) Upper West 153 46.9(39.0, 54.8) 122 35.5(27.0, 44.0) 215 53.8(47.1, 60.5) 398 30.3(25.8, 34.8) Volta* 492 32.4(28.3, 36.5) 431 30.6(26.2, 35.0) 720 32.2(28.8, 35.6) 1115 15.1(12.5, 17.7) Western* 553 32.8(28.9, 36.7) 447 24.1(20.1, 28.1) 1038 19.7(17.3, 22.1) 1366 17.3(14.9, 19.7) *Indicates the administrative regions of Ghana that were subdivided in 2018, n: sample size, CI: confidence interval 3.3 Summary measures of economic, residential, and regional inequalities in distance-related barriers to health facility access Table 2 summarises trends in socioeconomic, residential, and regional inequalities in distance-related barriers to health facility access among women aged 15–49 years in Ghana between 2003 and 2022, using four summary inequality measures: absolute difference (D), population attributable fraction (PAF), population attributable risk (PAR), and ratio (R). Across all survey years, these measures consistently indicated persistent inequalities, although the magnitude and direction of change varied over time. By economic status, wealth-related inequalities remained substantial throughout the study period, with evidence of both decline and resurgence. The absolute difference between women in the poorest and richest wealth quintiles was largest in 2003 (46.5 percentage points), declined steadily through 2008 and 2014, and then widened again by 2022 (38.4 percentage points). A similar pattern was reflected in the PAF, which indicated that over half of distance-related barriers in 2003 were attributable to wealth-related inequality (PAF: −58.5%), declining in 2008 before increasing again in 2014 and 2022. Correspondingly, the PAR showed that the potential reduction in overall prevalence if all women experienced the conditions of the most advantaged group decreased over time, from − 19.1 percentage points in 2003 to − 11.8 percentage points in 2022. The ratio measure further indicated persistent relative inequality, with the poorest women consistently reporting several times higher prevalence than the richest across all surveys. Similarly, residential inequalities showed a clear declining but persistent trend over time. The absolute difference between rural and urban women decreased markedly from 30.9 percentage points in 2003 to 17.3 percentage points in 2022, indicating a narrowing of the rural–urban gap. This decline was mirrored by reductions in both the PAF and PAR, suggesting that the proportion and absolute burden of distance-related barriers attributable to place of residence decreased over time. However, relative inequality remained evident, as reflected by ratio estimates consistently above two across survey years. By contrast, regional inequalities exhibited greater fluctuation over time. The absolute difference between the most- and least-advantaged regions was high in 2003 (51.4 percentage points), increased slightly in 2008, and then declined substantially by 2022 (29.7 percentage points). Despite this reduction, regional PAF values indicated that a large share of distance-related barriers remained attributable to regional disparities throughout the study period, decreasing from − 62.2% in 2003 to − 47.6% in 2022. Similarly, the PAR declined over time but remained sizeable in 2022 (− 10.6 percentage points), indicating that regional inequalities continued to contribute meaningfully to the overall burden. Relative inequality, as measured by the ratio, also declined but remained elevated, highlighting persistent geographic disparities Table 2 Summary measures of economic, residential and regional inequalities in distance-related barriers to health facility access among women aged 15–49 years, Ghana DHS 2003–2022 Subgroups/Summary measures 2003% (95% CI) 2008% (95% CI) 2014% (95% CI) 2022% (95% CI) Economic status Difference (D) 46.5 (NA) 35.7 (NA) 32.5 (NA) 38.4 (NA) Population attributable fraction (PAF) -58.5 (-58.5, -58.4) -41.2 (-41.3, -41.1) -50.3 (-50.4, -50.3) -53 (-53.1, -53) Population attributable risk (PAR) -19.1 (-20.8, -17.5) -10.7 (-12.6, -8.7) -12.8 (-14.1, -11.5) -11.8 (-12.8, -10.9) Ratio (R) 4.4 (NA) 3.3 (NA) 3.6 (NA) 4.7 (NA) Place of residence Difference (D) 30.9 (NA) 18.4 (NA) 17.7 (NA) 17.3 (NA) Population attributable fraction (PAF) -48.7 (-48.7, -48.7) -36.6 (-36.7, -36.6) -32.2 (-32.3, -32.2) -33.3 (-33.3, -33.3) Population attributable risk (PAR) -15.9 (-17.1, -14.8) -9.5 (-10.7, -8.3) -8.2 (-9, -7.4) -7.4 (-8, -6.9) Ratio (R) 2.8 (NA) 2.1 (NA) 2 (NA) 2.2 (NA) Subnational region Difference (D) 51.4 (NA) 53.7 (NA) 41.2 (NA) 29.7 (NA) Population attributable fraction (PAF) -62.2 (-62.2, -62.1) -39.3 (-39.4, -39.2) -50.3 (-50.4, -50.3) -47.6 (-47.7, -47.6) Population attributable risk (PAR) -20.4 (-22.4, -18.3) -10.2 (-12.5, -7.9) -12.8 (-14.2, -11.4) -10.6 (-11.9, -9.4) Ratio (R) 5.1 (NA) 4.4 (NA) 4.3 (NA) 3.5 (NA) Notes: D = absolute difference between the most-and least-advantaged subgroups; R = ratio of the most- to least-advantaged subgroup; PAR = population attributable risk (percentage points); PAF = population attributable fraction (%), bounded between − 100% and + 100%; reference = best-performing subgroup. NA = Not available. All estimates are weighted, and 95% Confidence Intervals (Cis) account for the complex survey design. 4.0 Discussion This study provides robust national evidence on long-term trends and inequalities in distance-related barriers to healthcare access among women aged 15–49 years in Ghana between 2003 and 2022. Overall, the proportion of women reporting distance to a health facility as a major barrier declined from 32.7% in 2003 to 22.3% in 2022, an absolute reduction of 10.4 percentage points over nearly two decades. The steepest decline occurred between 2003 and 2008, after which progress became more modest and gradual, suggesting early gains followed by a period of slower improvement. Despite these overall gains, reductions in prevalence have not translated into equitable access. Across all survey years, rural women consistently reported substantially higher distance barriers than urban women. In 2022, nearly one-third of rural women (32.2%) continued to report distance as a major barrier, compared with 14.9% of urban women. Although the rural–urban absolute gap narrowed from 30.9 percentage points in 2003 to 17.3 percentage points in 2022, its persistence indicates that geographic disadvantage remains structurally embedded. The pronounced decline observed between 2003 and 2008 may be partly attributable to Ghana’s Community-based Health Planning and Services (CHPS) strategy, which aimed to bring essential health services closer to hard-to-reach populations, particularly in rural and deprived areas, through community-based compounds and outreach services [ 34 ]. This period also coincided with the implementation of Ghana’s Health Sector Five-Year Programme of Work (2002–2006), which explicitly targeted inequality reduction and included investments in health centres, district hospitals, and logistical support, plausibly improving physical access to care [ 35 ]. However, the subsequent slowdown may reflect well-documented bottlenecks in CHPS and related reforms, including difficulties sustaining scale-up, persistent human resource and logistics constraints, and uneven implementation between rural and urban areas [ 34 ]. Similar rural–urban gradients have been reported across sub-Saharan Africa, where facility density, transport infrastructure, and emergency referral systems remain weaker in rural settings despite national primary healthcare expansion [ 36 , 37 ]. This pattern aligns with broader regional evidence showing that while early investments in primary healthcare infrastructure can generate rapid improvements in geographic access, sustaining these gains and achieving equity over time remains challenging, particularly in resource-constrained settings [ 38 , 39 ]. Socioeconomic inequalities in distance-related barriers were pronounced and showed evidence of stagnation and partial reversal over time. In 2003, 60.1% of women in the poorest wealth quintile reported distance to a health facility as a major barrier, compared with 13.6% among the richest. Although prevalence among the poorest declined to 45.1% by 2014, it subsequently increased to 48.9% in 2022, while prevalence among the richest continued to decline steadily to 10.5%. As a result, the absolute wealth gap narrowed to 32.5 percentage points in 2014 but widened again to 38.4 percentage points in 2022, alongside an increase in the relative inequality ratio to 4.7. This indicates that women in the poorest quintile were nearly five times as likely as those in the richest quintile to experience distance-related access barriers, consistent with earlier published evidence showing strong socioeconomic gradients in reported distance barriers [ 17 ]. The PAF further underscores the structural nature of these inequalities. In 2022, an estimated 53.0% of distance-related barriers were attributable to wealth-related inequality, while 33.3% were attributable to residential inequality and 47.6% to regional inequality. In practical terms, this suggests that more than half of the burden of distance barriers could be eliminated if all women experienced the same level of access as those in the most advantaged groups. These patterns indicate that early gains in reducing socioeconomic disparities were not sustained and that improvements in geographic access have accrued disproportionately to wealthier women. Similar patterns have been reported in Ghana and multi-country sub-Saharan African analyses using DHS and other population-representative data, which show that women with greater economic resources are better able to overcome geographic constraints by affording transport and bypassing nearer but lower-quality facilities [ 40 – 43 ]. This reinforces that distance is not merely a geographical constraint but a social one. Notably, much of this comparative evidence draws on DHS data collected from 2010 onwards, whereas the present study extends the temporal lens back to 2003, highlighting the persistence and long-term resilience of wealth-based inequalities in distance-related access in sub-Saharan Africa which Ghana is included. Marked and persistent regional inequalities were also evident. Northern Ghana consistently recorded high prevalence, with 40.2% of women reporting distance barriers in 2022, compared with 11.7% in Greater Accra. Reports from Ghana’s Universal Health Coverage roadmap [ 23 ], and the Ghana Statistical Service [ 44 ], show that northern regions consistently perform worse on indicators related to physical access to services, road infrastructure, and poverty, all of which directly shape women’s ability to reach healthcare facilities. Although the absolute regional difference declined from 51.4 percentage points in 2003 to 29.7 percentage points in 2022, these disparities remain substantial. The volatility observed in regions such as Upper East and Upper West, where prevalence declined sharply between 2008 and 2014 but increased again by 2022, suggests that gains in geographic access may be fragile and vulnerable to health system shocks, population growth, and infrastructure decay. For instance, studies from sub-Saharan and low-income countries report that women in rural and poorer regions faced greater transport barriers and longer travel times during and after COVID-19 [ 45 , 46 ], which likely explains why distance barriers increased again by 2022, even where earlier gains had been achieved. Implications for policy and practice Collectively, these findings advance the literature in three key ways. First, they demonstrate that national improvements in reported distance barriers hide persistent and, in some cases, widening inequalities by residence, wealth, and region. Second, they show that wealth-based inequalities contribute more to the overall burden of distance-related barriers than residence alone, highlighting the interaction between poverty and geography and reinforcing that distance is not merely a physical barrier but a socially shaped one. Importantly, the DHS measure of distance reflects perceived difficulty rather than physical kilometres alone, capturing transport availability, cost, travel time, and road quality, and thereby emphasising the multidimensional or interconnected nature of access barriers. Third, by applying absolute, relative, and population-attributable measures across multiple survey waves, this study provides equity-sensitive, policy-relevant evidence aligned with international commitments to universal health coverage and Ghana’s UHC Roadmap, which explicitly calls for monitoring progress beyond national averages 3,23 , Without targeted, place-based and pro-poor interventions that address both geographic and socioeconomic constraints, distance-related barriers are likely to remain a key mechanism through which broader social inequalities continue to translate into unequal health outcomes for women. 4.1 Strengths and limitations This study draws strength from the use of nationally representative Ghana DHS data spanning nearly two decades, enabling assessment of long-term trends in women’s reported distance-related barriers to healthcare access. The standardised DHS sampling design and questionnaire structure across survey rounds support comparability over time. In addition, applying multiple summary inequality measures (D, R, PAR, and PAF) provides a comprehensive picture of both absolute and relative inequalities across socioeconomic, residential, and regional dimensions, generating policy-relevant evidence on equity in geographic access to care. Several limitations should be considered. First, the repeated cross-sectional design limits causal inference; the results describe population-level dynamics over time rather than the effects of specific policies or individual-level changes [ 47 , 48 ]. Second, the outcome is based on a self-reported, perception-based measure of distance, which may capture broader access constraints (e.g., transport availability, cost, road conditions, safety, and opportunity costs) and may be subject to reporting bias, rather than reflecting objective distance or travel time alone. Third, the DHS does not include detailed measures of facility quality or service readiness, limiting the ability to distinguish distance-related barriers from quality-driven bypassing of nearer facilities. Fourth, small-area spatial interpretation is constrained by the intentional displacement of DHS cluster coordinates for confidentiality [ 49 ]. In addition, subnational comparability may be affected by changes in administrative boundaries over time and potential differences in fieldwork timing across survey rounds. Finally, the 2022 survey period overlapped with the COVID-19 pandemic, during which mobility restrictions and transport disruptions may have disproportionately affected rural and poorer women, potentially contributing to short-term deviations from longer-term trends. 5.0 Conclusion The findings from this study showed an overall decline in the proportion of women aged 15 to 49 years in Ghana reporting distance to a health facility as a major barrier to healthcare access between 2003 and 2022, with the largest reduction occurring between 2003 and 2008. However, the results also showed persistent and, in some dimensions, widening inequalities, with rural women, women in the poorest wealth quintiles, and women in northern regions consistently experiencing substantially higher distance-related barriers. The partial reversal of improvements among poorer women and the volatility observed in some northern regions suggest that gains in geographic access can be fragile and vulnerable to health system shocks and broader structural constraints. Based on these findings, it is recommended that policy makers and implementers strengthen equity-focused strategies to reduce distance-related barriers through targeted, place-based and pro poor action, including improved rural transport and referral arrangements, prioritised investment in underserved northern regions, and sustained resourcing of community-level primary healthcare delivery. The government should ensure that essential services and supplies remain accessible during periods of disruption, while health managers should strengthen monitoring systems that track inequalities beyond national averages. Further research is needed to link household reported barriers with objective measures of travel time and facility readiness, and to examine how shocks such as COVID 19 and USAID cut shape both short and long-term trends in geographic access. Declarations Acknowledgments The authors wish to acknowledge the World Health Organization (WHO) for providing access to the Health Equity Assessment Toolkit (HEAT) and the Health Equity Monitor database, which made this analysis possible. We also thank the USAID-Ghana Demographic and Health Surveys (DHS) Program, for generating and making the data publicly available. Authors’ contributions AI and MN conceived the study. AI and MN wrote the methods section and performed the data analysis. AI and MN, were responsible for the initial draft of the manuscript. AS and SZ review the initial draft. All the authors reviewed and approved the final version of the manuscript. Funding This study received no funding. Conflicts of Interest: The authors declare no conflicts of interest. Ethic s Approval This study involved secondary analysis of publicly available, anonymized data from the Ghana Demographic and Health Surveys (2003, 2008, 2014, 2022). Ethical approval and informed consent were obtained by the original data collectors. No additional ethical approval was required for this analysis. Clinical trial number : not applicable. Patient Consent for Publication Not applicable. This study used anonymized, publicly available survey data, and no individual-level identifiable information is reported. Data availability The datasets analysed are publicly available from the WHO Health Equity Monitor and the DHS Program repositories respectively, and can be accessed from: https://www.who.int/data/inequality-monitor/data, https://dhsprogram.com/publications/publication-fr152-dhs-final-reports.cfm, https://dhsprogram.com/publications/publication-FR221-DHS-Final-Reports.cfm, https://dhsprogram.com/publications/publication-FR307-DHS-Final-Reports.cfm, https://dhsprogram.com/publications/publication-FR380-DHS-Final-Reports.cfm. Declaration on the Use of Artificial Intelligence (AI) Tools Artificial intelligence (AI) tools were used solely for grammatical and language refinement of the manuscript. The AI tool did not contribute to the study design, data collection, data analysis, interpretation of findings, or the generation of scientific content. All intellectual contributions and responsibility for the manuscript rest entirely with the authors. Author details Abdul-Wahab Inusah, MPH¹ ORCID: 0000-0002-8184-7355 ¹Department of Global and International Health, School of Public Health, University for Development Studies, Box TL1350, Tamale, Ghana Email: [email protected] Moses Nwuzoh, MPH 2 ,3 2 Department of Food Science and Technology, Chukwuemeka Odumegwu Ojukwu University, Igbariam Campus, PMB 6059, Anambra State Nigeria. 3 Department of Public Health, Sheffield Hallam University, Sheffield, United Kingdom. Email: [email protected] ✉ (Corresponding Author) Abdul‑Aziz Seidu, PhD 4 4 Public Health and Tropical Medicine, James Cook University, Townsville, QLD 4811, Australia. Email: [email protected] Shamsu-Deen Ziblim, PhD 5 5 Department of Population and Reproductive Health, School of Public Health, University for Development Studies, Box TL1350, Tamale, Ghana Email: [email protected] References Commission on Social Determinants of Health. Closing the gap in a generation: health equity through action on the social determinants of health. World Health Organization. 2008. https://iris.who.int/server/api/core/bitstreams/cb08095c-55c8-484e-bff6-0e9c78fd38dd/content Chen AM. Why access matters in value based healthcare: a systematic review. J Healthc Qual. 2025;47:e0471. https://doi.org/10.1097/JHQ.0000000000000471 . World Health Organization, World Bank. Tracking universal health coverage: 2025 global monitoring report. World Health Organization; 2025. https://www.who.int/news-room/fact-sheets/detail/universal-health-coverage-(uhc . Harvard Medical School. Preventable deaths from lack of high quality medical care cost trillions. ScienceDaily. 2018. https://www.sciencedaily.com/releases/2018/06/180604160447.htm Anderson LM, Scrimshaw SC, Fullilove MT, Fielding JE, Normand J. Culturally competent healthcare systems: a systematic review. Am J Prev Med. 2003;24:68–79. https://doi.org/10.1016/S0749-3797(02)00657-8 . Gruen RL, Weeramanthri TS, Knight SSE, Bailie RS. Specialist outreach clinics in primary care and rural hospital settings. Cochrane Database Syst Rev. 2010;1:CD003798. https://doi.org/10.1002/14651858.CD003798.pub2 . Peters DH, Garg A, Bloom G, Walker DG, Brieger WR, Rahman MH. Poverty and access to health care in developing countries. Ann N Y Acad Sci. 2008;1136:161–71. https://doi.org/10.1196/annals.1425.011 . Bambra C, Gibson M, Sowden A, Wright K, Whitehead M, Petticrew M. Tackling the wider social determinants of health and health inequalities: evidence from systematic reviews. J Epidemiol Community Health. 2010;64:284–91. https://doi.org/10.1136/jech.2008.082743 . United Nations Statistics Division. SDG indicator metadata: indicator 3.7.1. United Nations. 2025. https://unstats.un.org/sdgs/metadata/files/Metadata-03-07-01.pdf Souza JP, Day LT, Rezende-Gomes AC, Zhang J, Mori R, Baguiya A, et al. 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Open Public Health J. 2019;12:504–14. https://doi.org/10.2174/1874944501912010504 . Zegeye B, El-Khatib Z, Ameyaw EK, Yaya S, et al. Breaking barriers to healthcare access in Benin. Int J Environ Res Public Health. 2021;18:725. https://doi.org/10.3390/ijerph18020750 . The DHS Program. Guide to DHS statistics: problems in accessing health care (DHS-8). ICF. 2023. https://dhsprogram.com/data/Guide-to-DHS-Statistics/Problems_in_Accessing_Health_Care.htm Dotse-Gborgbortsi W, Nilsen K, Ofosu A, Matthews Z, Tejedor-Garavito N, Wright J, et al. Distance is a big problem. BMC Pregnancy Childbirth. 2022;22:672. https://doi.org/10.1186/s12884-022-04998-0 . Gething PW, Johnson FA, Frempong-Ainguah F, Nyarko P, Baschieri A, Aboagye P, et al. Geographical access to care at birth in Ghana. BMC Public Health. 2012;12:991. https://doi.org/10.1186/1471-2458-12-991 . Masters SH, Burstein R, Amofah G, Abaogye P, Kumar S, Hanlon M. Travel time to maternity care and utilisation in rural Ghana. Soc Sci Med. 2013;93:147–54. https://doi.org/10.1016/j.socscimed.2013.06.01 . Riley PL, Cowley P, Bradbury-Jones C, Irvine F, Noyes J. Understanding how and why women’s groups improve maternal outcomes. BMC Pregnancy Childbirth. 2016;16:309. Agbenyo F, Nunbogu AM, Dongzagla A. Accessibility mapping of health facilities in rural Ghana. J Transp Health. 2017;6:73–83. https://doi.org/10.1016/j.jth.2017.04.010 . Nesbitt RC, Lohela TJ, Soremekun S, Vesel L, Manu A, Okyere E, et al. Influence of distance and quality of care on place of delivery. Sci Rep. 2016;6:30291. https://doi.org/10.1038/srep30291 . Ministry of Health Ghana. Ghana’s roadmap for attaining universal health coverage 2020–2030. Government of Ghana; 2020. Alhassan A, Doe PF, Amoadu M, Osborne A. Trends and inequalities in insecticide-treated net use in Ghana. Trop Med Health. 2025;53. https://doi.org/10.1186/s41182-025-00869-4 . Ghana Statistical Service, Macro ICF. Ghana Demographic and Health Survey 2003: final report. Ghana Statistical Service; 2004. Available from: https://dhsprogram.com/publications/publication-FR152-DHS-final-reports.cfm Ghana Statistical Service, Service GH, Macro ICF. Ghana Demographic and Health Survey 2008: final report. Ghana Statistical Service; 2009. Available from: https://dhsprogram.com/publications/publication-FR221-DHS-Final-Reports.cfm Ghana Statistical Service, Service GH, International ICF. Ghana Demographic and Health Survey 2014: final report. Ghana Statistical Service; 2015. Available from: https://dhsprogram.com/publications/publication-FR307-DHS-Final-Reports.cfm Ghana Statistical Service, Service GH. ICF. Ghana Demographic and Health Survey 2022: final report. Ghana Statistical Service; 2023. Available from: https://dhsprogram.com/publications/publication-FR380-DHS-Final-Reports.cfm Hosseinpoor AR, Nambiar D, Schlotheuber A, Reidpath D, Ross Z. Health Equity Assessment Toolkit (HEAT). BMC Med Res Methodol. 2016;16:141. https://doi.org/10.1186/s12874-016-0229-9 . Kirkby K, Bergen N, Baptista A, Schlotheuber A, Hosseinpoor AR, WHO Health Inequality Data Repository. Int J Epidemiol. 2023;52:e253–62. https://doi.org/10.1093/ije/dyad078 . Hosseinpoor AR, Bergen N, Kirkby K, Schlotheuber A. Strengthening health inequality monitoring. Int J Equity Health. 2023;22:49. https://doi.org/10.1186/s12939-022-01811-4 . Hosseinpoor AR, Bergen N, Kirkby K, Schlotheuber A, Antiporta D, MacFeely S. WHO’s health inequality data repository. Bull World Health Organ. 2023;101:298–A298. https://doi.org/10.2471/BLT.23.290004 . Verma V, Betti G. Taylor linearization sampling errors. J Appl Stat. 2011;38:1549–76. https://doi.org/10.1080/02664763.2010.515674 . Nyonator FK, Awoonor-Williams JK, Phillips JF, Jones TC, Miller RA. Ghana CHPS initiative. Health Policy Plan. 2005;20:25–34. https://doi.org/10.1093/heapol/czi003 . Ministry of Health Ghana. Review of Ghana health sector 2003 programme of work. Government of Ghana; 2004. Ihantamalala FA, Bonds MH, Cordier LF, Rakotonanahary RJL, Evans MV, et al. Geographic barriers in rural Madagascar. BMJ Glob Health. 2021;6:e007145. https://doi.org/10.1136/bmjgh-2021-007145 . Anand S, Ayodele V, Ashraf A, Shilleh MA, Rahim FO, Mmbaga BT, Rugakingira A. Expanding healthcare access in rural sub-Saharan Africa. Perspect Public Health. 2024;144:333–5. https://doi.org/10.1177/17579139241263707 . World Health Organization. Universal health coverage (UHC). World Health Organization; 2025. World Health Organization. Billions left behind on the path to universal health coverage. World Health Organization; 2023. Kruk ME, Mbaruku G, McCord C, et al. Bypassing primary care facilities. Health Policy Plan. 2009;24:279–88. https://doi.org/10.1093/heapol/czp011 . Bell G, Macarayan EK, Ratcliffe H, Kim JH, Otupiri E, Lipsitz S, et al. Bypass of nearest primary health care facility in Ghana. JAMA Netw Open. 2020;3:e2012552. https://doi.org/10.1001/jamanetworkopen.2020.12552 . Seidu AA. Barriers to accessing healthcare among women in sub-Saharan Africa. PLoS ONE. 2020;15:e0241409. https://doi.org/10.1371/journal.pone.0241409 . Alamneh TS, Teshale AB, Yeshaw Y, Alem AZ, Ayalew HG, Liyew AM, et al. Socioeconomic inequality in healthcare barriers. BMC Womens Health. 2022;22:130. https://doi.org/10.1186/s12905-022-01716-y . Ghana Statistical Service. 2021 Population and Housing Census: multidimensional poverty index report. Ghana Statistical Service; 2023. Kassa ZY, Scarf V, Turkmani S, Fox D. COVID-19 and maternal health service uptake. Int J Environ Res Public Health. 2024;21:1188. https://doi.org/10.3390/ijerph21091188 . Arsenault C, Roder-DeWan S, Kruk ME, et al. Disruptions to health service access during COVID-19. BMC Health Serv Res. 2025;25:13162. https://doi.org/10.1186/s12913-025-13162-1 . Capili B. Overview: cross-sectional studies. 2021. https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9536510 UK Data Service. Analysing change over time: repeated cross-sectional and longitudinal survey data. UK Data Service. The DHS Program. Linking DHS household and SPA facility surveys. ICF; 2014. Additional Declarations No competing interests reported. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8759067","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":605810156,"identity":"298bd9ff-932e-4fd2-9bd7-641472a6dcfb","order_by":0,"name":"Abdul-Wahab Inusah","email":"","orcid":"","institution":"University for Development Studies","correspondingAuthor":false,"prefix":"","firstName":"Abdul-Wahab","middleName":"","lastName":"Inusah","suffix":""},{"id":605810183,"identity":"41565501-edc2-4845-8762-f90ec66a8e14","order_by":1,"name":"Moses Nwuzoh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1ElEQVRIiWNgGAWjYHADHsYHDAwHSNPCbECyFjYJorTotrc/fPhzx+HE+e29x6p5au7I8TMwP3x0A48WszNnjI15zxxObOw5l3ab59gzY8kGNmPjHHxabuSwSTO23U5slsgxu83DdjhxwwEeNmn8WtKf//wJ1NIG1FLM848oLQlmDLxALT1ALcy8bcRoAfpFmrftv/EMnjPGknP7DhtLNhPyy/H2hx9/tqXJzm/vMfzw5tthOX725oeP8WlBAUw8IJKZWOUgwPiDFNWjYBSMglEwYgAAAe5QUyPzjPwAAAAASUVORK5CYII=","orcid":"","institution":"Chukwuemeka Odumegwu Ojukwu University","correspondingAuthor":true,"prefix":"","firstName":"Moses","middleName":"","lastName":"Nwuzoh","suffix":""},{"id":605810185,"identity":"09fa92a8-a654-4b5d-ab0c-d6d6e37feb94","order_by":2,"name":"Abdul‑Aziz Seidu","email":"","orcid":"","institution":"James Cook University","correspondingAuthor":false,"prefix":"","firstName":"Abdul‑Aziz","middleName":"","lastName":"Seidu","suffix":""},{"id":605810199,"identity":"d60d917a-715c-49bd-b09e-da176657266d","order_by":3,"name":"hamsu-Deen Ziblim","email":"","orcid":"","institution":"University for Development Studies","correspondingAuthor":false,"prefix":"","firstName":"hamsu-Deen","middleName":"","lastName":"Ziblim","suffix":""}],"badges":[],"createdAt":"2026-02-01 22:53:39","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8759067/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8759067/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":104748921,"identity":"211b835a-0128-4aa5-bc71-dd595331bb74","added_by":"auto","created_at":"2026-03-16 18:50:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":1188630,"visible":true,"origin":"","legend":"\u003cp\u003eTrends in the Prevalence (%) of Women Aged 15–49 Reporting Distance to a Health Facility as a Major Access Barrier, Ghana (2003–2022)\u003c/p\u003e","description":"","filename":"Figure1.png","url":"https://assets-eu.researchsquare.com/files/rs-8759067/v1/ee547e0c0d321c89d80a41b3.png"},{"id":104748920,"identity":"986d608c-121c-4cc0-bab9-251fcf3ed777","added_by":"auto","created_at":"2026-03-16 18:50:47","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":3399323,"visible":true,"origin":"","legend":"\u003cp\u003eRegional prevalence (%) of women aged 15–49 years reporting distance to a health facility as a major barrier to healthcare access in Ghana, by survey year. Regions are harmonised to the pre-2018 administrative structure.\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-8759067/v1/f6fa3e63ea9034eb76dcbbad.png"},{"id":104783438,"identity":"beaea0fd-429b-4f31-b214-f33f5c7cf7b9","added_by":"auto","created_at":"2026-03-17 07:58:58","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1294012,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8759067/v1/20f33a65-d8f8-427c-95de-2fd996bfdf0a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Trends and inequalities in distance-related barriers to healthcare access among women of reproductive age in Ghana, 2003–2022","fulltext":[{"header":"1.0 Introduction","content":"\u003cdiv id=\"Sec2\" class=\"Section2\"\u003e \u003ch2\u003e1.1 Background\u003c/h2\u003e \u003cp\u003eAccess to healthcare is a well established determinant of population health outcomes that shapes patterns of morbidity, mortality, equity and inequality across global contexts [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Despite international commitments to universal health coverage [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e], it has been stated that more than 400\u0026nbsp;million people worldwide lack access to essential healthcare services, contributing to approximately eight million deaths annually from conditions that are largely treatable or preventable [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Access to healthcare is closely linked to the wider determinants of health and extends beyond service availability to encompass geographical access, referring to the physical proximity of health facilities, cultural access, relating to acceptability and appropriateness of care, and economic access, reflecting affordability and cost barriers [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eWomen of reproductive age, defined as those aged 15 to 45 years [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e], are disproportionately affected by interacting barriers to healthcare access in low and middle income settings. Rather than biomedical causes alone, structural barriers related to access play a significant role in shaping adverse maternal health outcomes, including miscarriage, unsafe abortion, stillbirth, and maternal mortality, many of which are preventable [\u003cspan additionalcitationids=\"CR11\" citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. These barriers often intersect with social and economic disadvantage to reinforce gendered vulnerabilities across the life course.\u003c/p\u003e \u003cp\u003eIn Africa, inequalities in healthcare access remain pronounced, with earlier estimates suggesting that only about half of the population had access to modern healthcare facilities [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Among women of reproductive age, geographic inaccessibility, particularly distance to healthcare facilities, alongside economic constraints and restrictive cultural norms, has been consistently identified as a major barrier to care, contributing to persistent health inequalities, increased morbidity and avoidable mortality [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn Ghana, distance related barriers to healthcare access among women are recognised in both national monitoring frameworks and peer reviewed research. The Demographic and Health Survey (DHS) programme routinely captures women\u0026rsquo;s self reported problems in accessing care when sick, including among Ghanaian women [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This includes whether distance to a healthcare facility constitutes a serious barrier, and this indicator is widely used as a population level proxy for health accessibility constraints [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Emerging evidence using the Ghana Maternal Health Survey further demonstrates that women reporting distance as a major problem have significantly longer modelled travel times to facilities providing birthing services, with rural and poorer women bearing a disproportionate burden [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. While distance to health facilities is therefore a well documented barrier in Ghana, particularly among rural and socioeconomically disadvantaged populations, evidence on long term trends and equity patterns remains limited.\u003c/p\u003e \u003cp\u003eHowever, despite a growing body of work on geographic accessibility in Ghana, which is valuable for local service planning and identifying geographical inequalities in provision, the literature remains dominated by cross sectional and geographically bounded analyses [\u003cspan additionalcitationids=\"CR18 CR19 CR20\" citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. This limits inference about whether observed improvements in access have been equitable and sustained over time at the national level. In addition, most Ghana Demographic and Health Survey (GDHS) based studies focus on modelling associations between perceived distance barriers and healthcare service utilisation outcomes, with limited application of absolute, relative, or population attributable inequality measures required to quantify the magnitude, distribution, and avoidable burden of inequities [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo respond to these evidence gaps, this study aimed to examine long term trends and socioeconomic, residential, and regional inequalities in distance related barriers to healthcare access among women aged 15 to 49 years in Ghana between 2003 and 2022 using four rounds of nationally representative GDHS data. By applying standardised inequality metrics and equity monitoring methods, the study goes beyond national averages and assesses how progress is distributed across wealth, place of residence, and subnational regions. This approach quantifies both absolute and relative inequalities as well as the avoidable burden attributable to disadvantage. The study provides equity focused and policy relevant evidence on whether reductions in geographic access barriers have been equitable and sustained, and identifies the population groups and regions where disadvantage persists and where geographically targeted public health and service delivery interventions are most needed.\u003c/p\u003e \u003c/div\u003e"},{"header":"2.0 Methods","content":"\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Study design and data sources\u003c/h2\u003e \u003cp\u003eThis study used a repeated cross-sectional design based on nationally representative data from the 2003, 2008, 2014 and 2022 GDHS [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]. For each survey round, the women\u0026rsquo;s individual recode file served as the primary data source. The GDHS employs a two-stage stratified cluster sampling strategy based on the Ghana Population and Housing Census sampling frame, with stratification by region and urban\u0026ndash;rural residence to ensure representativeness at national, urban\u0026ndash;rural and regional levels [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Detailed sampling procedures are available in the technical reports for each survey year [\u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Study population\u003c/h2\u003e \u003cp\u003eThe study population consisted of women aged 15\u0026ndash;49 years who were either usual residents or de facto members of selected households and who completed the women\u0026rsquo;s questionnaire. Women with missing data on the main outcome (distance as a barrier to seeking care) or on any of the key equity stratifiers (wealth quintile, place of residence or region) were excluded from the analytic sample. All analyses incorporated the DHS individual sampling weights so that estimates are representative of women aged 15\u0026ndash;49 years in Ghana in each survey year.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Outcome variable\u003c/h2\u003e \u003cp\u003eThe primary outcome was self-reported distance to a health facility as a major barrier to obtaining care when ill. In every survey round, women were asked: \u0026ldquo;When you are sick and want to get medical advice or treatment, is distance to a health facility a big problem?\u0026rdquo; Responses were recorded as \u0026ldquo;yes\u0026rdquo; or \u0026ldquo;no.\u0026rdquo; A binary indicator was created, taking the value 1 for women who reported distance as a \u0026ldquo;big problem\u0026rdquo; and 0 otherwise. To support comparability across time, the wording of the question and the response categories were checked and confirmed to be consistent in all four GDHS rounds [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan additionalcitationids=\"CR26 CR27\" citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e2.4 Equity stratifiers\u003c/h2\u003e \u003cp\u003eInequalities were assessed across three dimensions: economic status, place of residence and subnational region. Economic status was captured using the DHS wealth index, a composite measure derived through principal components analysis of household assets, housing characteristics and access to basic services. Households are ranked and grouped into five wealth quintiles (Q1\u0026thinsp;=\u0026thinsp;poorest to Q5\u0026thinsp;=\u0026thinsp;richest); the standard DHS wealth quintile variable was used and harmonised across survey years following DHS coding conventions. Place of residence was classified as urban or rural, based on DHS definitions grounded in national census criteria for each survey. Subnational geography was represented using the administrative regions recorded in each GDHS but reclassified to the pre-2018 10-region structure (Ashanti, Brong Ahafo, Central, Eastern, Greater Accra, Northern, Upper East, Upper West, Volta, Western) to enable temporal comparisons. For 2022, districts belonging to newly created regions were reassigned to their corresponding pre-2018 regions.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e2.5 Statistical analyses\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003e2.5.1 Descriptive analysis\u003c/h2\u003e \u003cp\u003eAnalyses began with a description of the study population by survey year and equity strata, including wealth quintile, urban\u0026ndash;rural residence and region. For each survey, the weighted prevalence of women reporting distance to a health facility as a major barrier to care was estimated overall and within each subgroup. Ninety-five percent confidence intervals were calculated while accounting for the complex survey design, including clustering, stratification and sampling weights.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section3\"\u003e \u003ch2\u003e2.5.2 Analytical technique\u003c/h2\u003e \u003cp\u003eInequality analysis was conducted using the WHO Health Equity Assessment Toolkit (HEAT), version 6.1, which offers a standardized platform for generating disaggregated health indicators and summary measures of inequality from population-based surveys [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e]. In this study, harmonised DHS datasets already embedded within the HEAT software were used. Within HEAT, the binary indicator \u0026ldquo;distance as a major barrier\u0026rdquo; was specified as the health outcome. For each survey year and for each equity dimension, place of residence, economic status, and region, we estimated four summary measures of inequality: Difference (D), Ratio (R), Population Attributable Risk (PAR), and Population Attributable Fraction (PAF). D captured the absolute gap in prevalence (percentage points) between the most disadvantaged and least disadvantaged subgroups (for example, rural versus urban residents, poorest versus richest wealth quintiles, and regions with the highest versus lowest prevalence). R expressed relative inequality as the ratio of prevalence in the most disadvantaged subgroup to that in the least disadvantaged subgroup. PAR was defined as the absolute difference in percentage points between the national prevalence and the prevalence in the best-performing (reference) subgroup, representing the potential absolute reduction in the national prevalence if all subgroups attained the same level as this reference group. PAF was derived by expressing PAR as a percentage of the national prevalence, yielding values between \u0026minus;\u0026thinsp;100% and +\u0026thinsp;100%, where negative values indicate that the reference group has a lower prevalence than the national average [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. All estimates were generated using sampling weights, and 95% confidence intervals were computed in HEAT via Taylor linearisation, taking into account the complex survey design [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e2.5.3 Trend analysis\u003c/h2\u003e \u003cp\u003eTemporal patterns were examined for both the overall prevalence of distance-related barriers and the magnitude of inequalities between 2003 and 2022. Trends were summarised in figures and tables displaying the outcome and inequality measures across survey years. Visual inspection of trajectories for D, R, PAR and PAF was used to assess whether inequalities appeared to narrow, widen or remain stable over time for each equity dimension, considering sampling variability as reflected by overlapping confidence intervals (CIs) and considering contemporaneous changes in the health policy environment.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e2.5.4 Geospatial analysis and mapping\u003c/h2\u003e \u003cp\u003eSpatial variation in distance-related barriers was explored using regional prevalence mapping. For each survey year, regional prevalence estimates and their 95% confidence intervals were exported from HEAT and merged with a harmonised Ghana regional shapefile corresponding to the pre-2018 10-region configuration. All geospatial mapping and figure production were conducted in Python (version 3.13.3; tags/v3.13.3:6280bb5, Apr 8 2025, 14:47:33) [MSC v.1943 64 bit (AMD64)], using the geopandas, pandas and matplotlib libraries. For each round, regional prevalence data were joined to the shapefile using region identifiers, and choropleth maps were produced to depict the percentage of women who reported distance as a major barrier to care in each region. A consistent colour scale and classification scheme were applied across years to facilitate visual comparison of spatial patterns over time.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e2.6 Handling of survey design\u003c/h2\u003e \u003cp\u003eIn this study, all core analyses were performed within the WHO HEAT, which automatically accounts for the complex survey design of the DHS, including sampling weights, clustering and stratification. As a result, all prevalence estimates, confidence intervals and inequality measures used in the paper were already adjusted for the survey design.\u003c/p\u003e \u003cp\u003eExternal software was used only to present these HEAT-derived results. Weighted prevalence estimates (and their 95% CIs) were exported from HEAT and then organized into descriptive tables and graphs in Excel. For geospatial visualization, these same estimates were merged with a Ghana regional shapefile and mapped using Python (pandas, geopandas, matplotlib). No additional modelling, re-weighting or variance estimation was performed outside HEAT.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e2.7 Ethical considerations\u003c/h2\u003e \u003cp\u003eAll GDHS protocols received ethical approval from the Ghana Health Service Ethical Review Committee and the ICF Institutional Review Board. Written informed consent was obtained from all participants at the time of primary data collection. The present study involved secondary analysis of anonymised, publicly available datasets and did not require further contact with participants; consequently, additional ethics approval was not sought. Permission to use the datasets was granted by The DHS Program following submission of a brief research proposal.\u003c/p\u003e \u003c/div\u003e"},{"header":"3.0 Results","content":"\u003cp\u003e \u003cb\u003e3.1 Trends in the Prevalence of Women Aged 15\u0026ndash;49 Reporting Distance to a Health Facility as a Major Access Barrier, Ghana (2003\u0026ndash;2022)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe analysis included a pooled sample of 102,640 women aged 15\u0026ndash;49 years drawn from four rounds of the Ghana Demographic and Health Surveys (GDHS) conducted in 2003, 2008, 2014, and 2022. The distribution of the pooled sample across survey years was 16.3% in 2003, 14.0% in 2008, 26.8% in 2014, and 42.9% in 2022, indicating the proportion of the total analytic sample contributed by each survey round. The year-specific prevalence of women reporting distance to a health facility as a major barrier was 32.7% in 2003, 25.9% in 2008, 25.4% in 2014, and 22.3% in 2022. Overall, the prevalence declined across survey rounds, with the largest reduction observed between 2003 and 2008, followed by smaller reductions between subsequent survey years. The trend in prevalence across survey years is presented in Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e \u003cb\u003e3.2 Trends in the Prevalence of Women Aged 15\u0026ndash;49 Reporting Distance to a Health Facility as a Major Access Barrier, Ghana, by different inequality dimensions, (2003\u0026ndash;2022)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e presents trends in the prevalence of women aged 15\u0026ndash;49 years reporting distance to a health facility as a major barrier to healthcare access in Ghana, highlighting persistent rural\u0026ndash;urban, socioeconomic, and regional inequalities over time.\u003c/p\u003e \u003cp\u003eWith respect to place of residence, distance-related barriers declined over the study period in both rural and urban settings, although substantial disparities persisted. Among rural women, prevalence declined from 47.7% (95% CI: 45.9\u0026ndash;49.5) in 2003 to 34.8% in 2008, remained relatively stable in 2014 (34.9%), and declined further to 32.2% (95% CI: 31.1\u0026ndash;33.3) in 2022. In contrast, prevalence among urban women remained consistently lower and relatively stable, changing modestly from 16.8% (95% CI: 15.4\u0026ndash;18.2) in 2003 to 14.9% (95% CI: 14.1\u0026ndash;15.7) by 2022. Consequently, the rural\u0026ndash;urban gap narrowed slightly over time but remained evident across all survey years.\u003c/p\u003e \u003cp\u003eSimilarly, socioeconomic differentials showed pronounced and persistent gradients over time. In 2003, distance-related barriers were most prevalent among women in the poorest wealth quintile, at 60.1% (95% CI: 57.0\u0026ndash;63.2), declining progressively across quintiles to 13.6% (95% CI: 11.8\u0026ndash;15.4) among the richest. Between 2003 and 2014, prevalence declined across all wealth groups; however, by 2022, divergent trends emerged. While prevalence among the richest continued to decline to 10.5% (95% CI: 9.5\u0026ndash;11.5), prevalence among the poorest increased slightly from 45.1% in 2014 to 48.9% (95% CI: 46.9\u0026ndash;50.9) in 2022, reinforcing the persistence of large wealth-related inequalities over time.\u003c/p\u003e \u003cp\u003eBy contrast, regional trends revealed substantial temporal and spatial heterogeneity. In 2003, the prevalence ranged from 12.4% (95% CI: 10.3\u0026ndash;14.5) in Greater Accra to 63.8% (95% CI: 58.5\u0026ndash;69.1) in the Upper East, with the Northern region also reporting a high prevalence of 55.0% (95% CI: 50.6\u0026ndash;59.4). By 2008, Upper East recorded a further increase to 69.4% (95% CI: 63.7\u0026ndash;75.1), while several southern regions remained below 25%. The 2014 survey marked notable shifts, with sharp declines in Upper East to 20.0% (95% CI: 15.9\u0026ndash;24.1), alongside increases in Upper West to 53.8% (95% CI: 47.1\u0026ndash;60.5) and Northern to 49.8% (95% CI: 46.3\u0026ndash;53.3).\u003c/p\u003e \u003cp\u003eBy 2022, regional disparities persisted despite overall declines. Northern 40.2% (95% CI: 37.4\u0026ndash;43.0) and Upper West 30.3% (95% CI: 25.8\u0026ndash;34.8) continued to record the highest prevalence, while Greater Accra remained the lowest 11.7% (95% CI: 10.4\u0026ndash;13.0), alongside relatively low prevalence in Eastern 13.4%95% CI: 11.5\u0026ndash;15.3) and Volta 15.1% (95% CI: 12.5\u0026ndash;17.7). These patterns underscore persistent geographic inequalities in distance-related access to healthcare across survey years. Spatial patterns across survey years are illustrated in Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eTrends in the Prevalence of Women Aged 15\u0026ndash;49 Reporting Distance to a Health Facility as a Major Access Barrier, Ghana by different inequality dimensions, (2003\u0026ndash;2022)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"9\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDimensions of inequality\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c3\" namest=\"c2\"\u003e \u003cp\u003e2003\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c5\" namest=\"c4\"\u003e \u003cp\u003e2008\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c7\" namest=\"c6\"\u003e \u003cp\u003e2014\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c9\" namest=\"c8\"\u003e \u003cp\u003e2022\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e \u003cp\u003en\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e \u003cp\u003e% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePlace of residence\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2937\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e47.7(45.9, 49.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2533\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e34.8(32.9, 36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e4345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e34.9(33.5, 36.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e6457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e32.2(31.1, 33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e2755\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.8(15.4, 18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e16.4(14.9, 17.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e5051\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e17.2(16.2, 18.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e8557\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e14.9(14.1, 15.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEconomic status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 1 (Poorest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e970\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e60.1(57.0, 63.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e783\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e50.9(47.4, 54.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e45.1(42.6, 47.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e48.9(46.9, 50.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e949\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51.7(48.5, 54.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e900\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.2(27.2, 33.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1636\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e35.2(32.9, 37.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2712\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e29.7(28.0, 31.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 3\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31.7(28.9, 34.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e979\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.5(19.9, 25.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1938\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e23.7(21.8, 25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e18.0(16.7, 19.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 4\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1245\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.3(18.1, 22.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1119\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e18.6(16.3, 20.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2117\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.4(16.7, 20.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3379\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.0(11.9, 14.1)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eQuintile 5 (Richest)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1457\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13.6(11.8, 15.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1135\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.2(13.1, 17.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e2194\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.6(11.2, 14.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e3355\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e10.5(9.5, 11.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eRegion\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAshanti\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23.2(20.8, 25.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1011\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e20.3(17.8, 22.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e26.6(24.6, 28.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2928\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.1(21.6, 24.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBrong Ahafo*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e569\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e39.4(35.4, 43.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e425\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.8(20.7, 28.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e769\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e21.3(18.4, 24.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1560\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e23.0(19.5, 26.5)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCentral\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e44.4(39.7, 49.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e424\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.2(18.2, 26.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e936\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e18.2(15.7, 20.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1703\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.4(15.6, 19.2)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEastern\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e30.4(26.7, 34.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e483\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e22.8(19.1, 26.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e878\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e36.2(33.0, 39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e13.4(11.5, 15.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGreater Accra\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e942\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12.4(10.3, 14.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e853\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.7(13.3, 18.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1898\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e12.6(11.1, 14.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e2326\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e11.7(10.4, 13.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e55.0(50.6, 59.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.1(30.8, 39.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e786\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e49.8(46.3, 53.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1758\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e40.2(37.4, 43.0)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper East\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e63.8(58.5, 69.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e69.4(63.7, 75.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e358\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e20.0(15.9, 24.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e640\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e24.6(21.3, 27.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUpper West\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e153\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e46.9(39.0, 54.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e122\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e35.5(27.0, 44.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e215\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e53.8(47.1, 60.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e398\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e30.3(25.8, 34.8)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVolta*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e492\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.4(28.3, 36.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e431\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e30.6(26.2, 35.0)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e720\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e32.2(28.8, 35.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1115\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e15.1(12.5, 17.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWestern*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e553\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32.8(28.9, 36.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e447\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e24.1(20.1, 28.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e1038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c7\"\u003e \u003cp\u003e19.7(17.3, 22.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e \u003cp\u003e1366\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c9\"\u003e \u003cp\u003e17.3(14.9, 19.7)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"9\"\u003e*Indicates the administrative regions of Ghana that were subdivided in 2018, n: sample size, CI: confidence interval\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e3.3 Summary measures of economic, residential, and regional inequalities in distance-related barriers to health facility access\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e summarises trends in socioeconomic, residential, and regional inequalities in distance-related barriers to health facility access among women aged 15\u0026ndash;49 years in Ghana between 2003 and 2022, using four summary inequality measures: absolute difference (D), population attributable fraction (PAF), population attributable risk (PAR), and ratio (R). Across all survey years, these measures consistently indicated persistent inequalities, although the magnitude and direction of change varied over time.\u003c/p\u003e \u003cp\u003eBy economic status, wealth-related inequalities remained substantial throughout the study period, with evidence of both decline and resurgence. The absolute difference between women in the poorest and richest wealth quintiles was largest in 2003 (46.5 percentage points), declined steadily through 2008 and 2014, and then widened again by 2022 (38.4 percentage points). A similar pattern was reflected in the PAF, which indicated that over half of distance-related barriers in 2003 were attributable to wealth-related inequality (PAF: \u0026minus;58.5%), declining in 2008 before increasing again in 2014 and 2022. Correspondingly, the PAR showed that the potential reduction in overall prevalence if all women experienced the conditions of the most advantaged group decreased over time, from \u0026minus;\u0026thinsp;19.1 percentage points in 2003 to \u0026minus;\u0026thinsp;11.8 percentage points in 2022. The ratio measure further indicated persistent relative inequality, with the poorest women consistently reporting several times higher prevalence than the richest across all surveys.\u003c/p\u003e \u003cp\u003eSimilarly, residential inequalities showed a clear declining but persistent trend over time. The absolute difference between rural and urban women decreased markedly from 30.9 percentage points in 2003 to 17.3 percentage points in 2022, indicating a narrowing of the rural\u0026ndash;urban gap. This decline was mirrored by reductions in both the PAF and PAR, suggesting that the proportion and absolute burden of distance-related barriers attributable to place of residence decreased over time. However, relative inequality remained evident, as reflected by ratio estimates consistently above two across survey years.\u003c/p\u003e \u003cp\u003eBy contrast, regional inequalities exhibited greater fluctuation over time. The absolute difference between the most- and least-advantaged regions was high in 2003 (51.4 percentage points), increased slightly in 2008, and then declined substantially by 2022 (29.7 percentage points). Despite this reduction, regional PAF values indicated that a large share of distance-related barriers remained attributable to regional disparities throughout the study period, decreasing from \u0026minus;\u0026thinsp;62.2% in 2003 to \u0026minus;\u0026thinsp;47.6% in 2022. Similarly, the PAR declined over time but remained sizeable in 2022 (\u0026minus;\u0026thinsp;10.6 percentage points), indicating that regional inequalities continued to contribute meaningfully to the overall burden. Relative inequality, as measured by the ratio, also declined but remained elevated, highlighting persistent geographic disparities\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eSummary measures of economic, residential and regional inequalities in distance-related barriers to health facility access among women aged 15\u0026ndash;49 years, Ghana DHS 2003\u0026ndash;2022\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSubgroups/Summary measures\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2003% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2008% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2014% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2022% (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEconomic status\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e46.5 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e35.7 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32.5 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e38.4 (NA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation attributable fraction (PAF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-58.5 (-58.5, -58.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-41.2 (-41.3, -41.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50.3 (-50.4, -50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-53 (-53.1, -53)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation attributable risk (PAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-19.1 (-20.8, -17.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.7 (-12.6, -8.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.8 (-14.1, -11.5)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-11.8 (-12.8, -10.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.4 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3.3 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e3.6 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e4.7 (NA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePlace of residence\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e30.9 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e18.4 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17.7 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e17.3 (NA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation attributable fraction (PAF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-48.7 (-48.7, -48.7)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-36.6 (-36.7, -36.6)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-32.2 (-32.3, -32.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-33.3 (-33.3, -33.3)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation attributable risk (PAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-15.9 (-17.1, -14.8)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-9.5 (-10.7, -8.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-8.2 (-9, -7.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-7.4 (-8, -6.9)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.8 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.1 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.2 (NA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSubnational region\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDifference (D)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e51.4 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e53.7 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41.2 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.7 (NA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation attributable fraction (PAF)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-62.2 (-62.2, -62.1)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-39.3 (-39.4, -39.2)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-50.3 (-50.4, -50.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-47.6 (-47.7, -47.6)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePopulation attributable risk (PAR)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-20.4 (-22.4, -18.3)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-10.2 (-12.5, -7.9)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-12.8 (-14.2, -11.4)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-10.6 (-11.9, -9.4)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRatio (R)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e5.1 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.4 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.3 (NA)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e3.5 (NA)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNotes: D\u0026thinsp;=\u0026thinsp;absolute difference between the most-and least-advantaged subgroups; R\u0026thinsp;=\u0026thinsp;ratio of the most- to least-advantaged subgroup; PAR\u0026thinsp;=\u0026thinsp;population attributable risk (percentage points); PAF\u0026thinsp;=\u0026thinsp;population attributable fraction (%), bounded between \u0026minus;\u0026thinsp;100% and +\u0026thinsp;100%; reference\u0026thinsp;=\u0026thinsp;best-performing subgroup. NA\u0026thinsp;=\u0026thinsp;Not available. All estimates are weighted, and 95% Confidence Intervals (Cis) account for the complex survey design.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"4.0 Discussion","content":"\u003cp\u003eThis study provides robust national evidence on long-term trends and inequalities in distance-related barriers to healthcare access among women aged 15\u0026ndash;49 years in Ghana between 2003 and 2022. Overall, the proportion of women reporting distance to a health facility as a major barrier declined from 32.7% in 2003 to 22.3% in 2022, an absolute reduction of 10.4 percentage points over nearly two decades. The steepest decline occurred between 2003 and 2008, after which progress became more modest and gradual, suggesting early gains followed by a period of slower improvement.\u003c/p\u003e \u003cp\u003eDespite these overall gains, reductions in prevalence have not translated into equitable access. Across all survey years, rural women consistently reported substantially higher distance barriers than urban women. In 2022, nearly one-third of rural women (32.2%) continued to report distance as a major barrier, compared with 14.9% of urban women. Although the rural\u0026ndash;urban absolute gap narrowed from 30.9 percentage points in 2003 to 17.3 percentage points in 2022, its persistence indicates that geographic disadvantage remains structurally embedded.\u003c/p\u003e \u003cp\u003eThe pronounced decline observed between 2003 and 2008 may be partly attributable to Ghana\u0026rsquo;s Community-based Health Planning and Services (CHPS) strategy, which aimed to bring essential health services closer to hard-to-reach populations, particularly in rural and deprived areas, through community-based compounds and outreach services [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. This period also coincided with the implementation of Ghana\u0026rsquo;s Health Sector Five-Year Programme of Work (2002\u0026ndash;2006), which explicitly targeted inequality reduction and included investments in health centres, district hospitals, and logistical support, plausibly improving physical access to care [\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. However, the subsequent slowdown may reflect well-documented bottlenecks in CHPS and related reforms, including difficulties sustaining scale-up, persistent human resource and logistics constraints, and uneven implementation between rural and urban areas [\u003cspan citationid=\"CR34\" class=\"CitationRef\"\u003e34\u003c/span\u003e]. Similar rural\u0026ndash;urban gradients have been reported across sub-Saharan Africa, where facility density, transport infrastructure, and emergency referral systems remain weaker in rural settings despite national primary healthcare expansion [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. This pattern aligns with broader regional evidence showing that while early investments in primary healthcare infrastructure can generate rapid improvements in geographic access, sustaining these gains and achieving equity over time remains challenging, particularly in resource-constrained settings [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e, \u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSocioeconomic inequalities in distance-related barriers were pronounced and showed evidence of stagnation and partial reversal over time. In 2003, 60.1% of women in the poorest wealth quintile reported distance to a health facility as a major barrier, compared with 13.6% among the richest. Although prevalence among the poorest declined to 45.1% by 2014, it subsequently increased to 48.9% in 2022, while prevalence among the richest continued to decline steadily to 10.5%. As a result, the absolute wealth gap narrowed to 32.5 percentage points in 2014 but widened again to 38.4 percentage points in 2022, alongside an increase in the relative inequality ratio to 4.7. This indicates that women in the poorest quintile were nearly five times as likely as those in the richest quintile to experience distance-related access barriers, consistent with earlier published evidence showing strong socioeconomic gradients in reported distance barriers [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe PAF further underscores the structural nature of these inequalities. In 2022, an estimated 53.0% of distance-related barriers were attributable to wealth-related inequality, while 33.3% were attributable to residential inequality and 47.6% to regional inequality. In practical terms, this suggests that more than half of the burden of distance barriers could be eliminated if all women experienced the same level of access as those in the most advantaged groups. These patterns indicate that early gains in reducing socioeconomic disparities were not sustained and that improvements in geographic access have accrued disproportionately to wealthier women.\u003c/p\u003e \u003cp\u003eSimilar patterns have been reported in Ghana and multi-country sub-Saharan African analyses using DHS and other population-representative data, which show that women with greater economic resources are better able to overcome geographic constraints by affording transport and bypassing nearer but lower-quality facilities [\u003cspan additionalcitationids=\"CR41 CR42\" citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. This reinforces that distance is not merely a geographical constraint but a social one. Notably, much of this comparative evidence draws on DHS data collected from 2010 onwards, whereas the present study extends the temporal lens back to 2003, highlighting the persistence and long-term resilience of wealth-based inequalities in distance-related access in sub-Saharan Africa which Ghana is included.\u003c/p\u003e \u003cp\u003eMarked and persistent regional inequalities were also evident. Northern Ghana consistently recorded high prevalence, with 40.2% of women reporting distance barriers in 2022, compared with 11.7% in Greater Accra. Reports from Ghana\u0026rsquo;s Universal Health Coverage roadmap [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], and the Ghana Statistical Service [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], show that northern regions consistently perform worse on indicators related to physical access to services, road infrastructure, and poverty, all of which directly shape women\u0026rsquo;s ability to reach healthcare facilities. Although the absolute regional difference declined from 51.4 percentage points in 2003 to 29.7 percentage points in 2022, these disparities remain substantial.\u003c/p\u003e \u003cp\u003eThe volatility observed in regions such as Upper East and Upper West, where prevalence declined sharply between 2008 and 2014 but increased again by 2022, suggests that gains in geographic access may be fragile and vulnerable to health system shocks, population growth, and infrastructure decay. For instance, studies from sub-Saharan and low-income countries report that women in rural and poorer regions faced greater transport barriers and longer travel times during and after COVID-19 [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e, \u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], which likely explains why distance barriers increased again by 2022, even where earlier gains had been achieved.\u003c/p\u003e \u003cp\u003e \u003cb\u003eImplications for policy and practice\u003c/b\u003e \u003c/p\u003e \u003cp\u003eCollectively, these findings advance the literature in three key ways. First, they demonstrate that national improvements in reported distance barriers hide persistent and, in some cases, widening inequalities by residence, wealth, and region. Second, they show that wealth-based inequalities contribute more to the overall burden of distance-related barriers than residence alone, highlighting the interaction between poverty and geography and reinforcing that distance is not merely a physical barrier but a socially shaped one. Importantly, the DHS measure of distance reflects perceived difficulty rather than physical kilometres alone, capturing transport availability, cost, travel time, and road quality, and thereby emphasising the multidimensional or interconnected nature of access barriers. Third, by applying absolute, relative, and population-attributable measures across multiple survey waves, this study provides equity-sensitive, policy-relevant evidence aligned with international commitments to universal health coverage and Ghana\u0026rsquo;s UHC Roadmap, which explicitly calls for monitoring progress beyond national averages\u003csup\u003e3,23\u003c/sup\u003e, Without targeted, place-based and pro-poor interventions that address both geographic and socioeconomic constraints, distance-related barriers are likely to remain a key mechanism through which broader social inequalities continue to translate into unequal health outcomes for women.\u003c/p\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Strengths and limitations\u003c/h2\u003e \u003cp\u003eThis study draws strength from the use of nationally representative Ghana DHS data spanning nearly two decades, enabling assessment of long-term trends in women\u0026rsquo;s reported distance-related barriers to healthcare access. The standardised DHS sampling design and questionnaire structure across survey rounds support comparability over time. In addition, applying multiple summary inequality measures (D, R, PAR, and PAF) provides a comprehensive picture of both absolute and relative inequalities across socioeconomic, residential, and regional dimensions, generating policy-relevant evidence on equity in geographic access to care.\u003c/p\u003e \u003cp\u003eSeveral limitations should be considered. First, the repeated cross-sectional design limits causal inference; the results describe population-level dynamics over time rather than the effects of specific policies or individual-level changes [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e, \u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e]. Second, the outcome is based on a self-reported, perception-based measure of distance, which may capture broader access constraints (e.g., transport availability, cost, road conditions, safety, and opportunity costs) and may be subject to reporting bias, rather than reflecting objective distance or travel time alone. Third, the DHS does not include detailed measures of facility quality or service readiness, limiting the ability to distinguish distance-related barriers from quality-driven bypassing of nearer facilities. Fourth, small-area spatial interpretation is constrained by the intentional displacement of DHS cluster coordinates for confidentiality [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. In addition, subnational comparability may be affected by changes in administrative boundaries over time and potential differences in fieldwork timing across survey rounds. Finally, the 2022 survey period overlapped with the COVID-19 pandemic, during which mobility restrictions and transport disruptions may have disproportionately affected rural and poorer women, potentially contributing to short-term deviations from longer-term trends.\u003c/p\u003e \u003c/div\u003e"},{"header":"5.0 Conclusion","content":"\u003cp\u003eThe findings from this study showed an overall decline in the proportion of women aged 15 to 49 years in Ghana reporting distance to a health facility as a major barrier to healthcare access between 2003 and 2022, with the largest reduction occurring between 2003 and 2008. However, the results also showed persistent and, in some dimensions, widening inequalities, with rural women, women in the poorest wealth quintiles, and women in northern regions consistently experiencing substantially higher distance-related barriers. The partial reversal of improvements among poorer women and the volatility observed in some northern regions suggest that gains in geographic access can be fragile and vulnerable to health system shocks and broader structural constraints.\u003c/p\u003e \u003cp\u003eBased on these findings, it is recommended that policy makers and implementers strengthen equity-focused strategies to reduce distance-related barriers through targeted, place-based and pro poor action, including improved rural transport and referral arrangements, prioritised investment in underserved northern regions, and sustained resourcing of community-level primary healthcare delivery. The government should ensure that essential services and supplies remain accessible during periods of disruption, while health managers should strengthen monitoring systems that track inequalities beyond national averages. Further research is needed to link household reported barriers with objective measures of travel time and facility readiness, and to examine how shocks such as COVID 19 and USAID cut shape both short and long-term trends in geographic access.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors wish to acknowledge the World Health Organization (WHO) for providing access to the Health Equity Assessment Toolkit (HEAT) and the Health Equity Monitor database, which made this analysis possible. We also thank the USAID-Ghana Demographic and Health Surveys (DHS) Program, for generating and making the data publicly available.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors’ contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAI and MN conceived the study. AI and MN wrote the methods section and performed the data analysis. AI and MN, were responsible for the initial draft of the manuscript. AS and SZ review the initial draft. \u0026nbsp;All the authors reviewed and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study received no funding.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflicts of Interest:\u0026nbsp;\u003c/strong\u003eThe authors declare no conflicts of interest.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthic\u003c/strong\u003e\u003cstrong\u003es\u003c/strong\u003e\u003cstrong\u003e\u0026nbsp;Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study involved secondary analysis of publicly available, anonymized data from the Ghana Demographic and Health Surveys (2003, 2008, 2014, 2022). Ethical approval and informed consent were obtained by the original data collectors. No additional ethical approval was required for this analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e: not applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePatient Consent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable. This study used anonymized, publicly available survey data, and no individual-level identifiable information is reported.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData availability\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets analysed are publicly available from the WHO Health Equity Monitor and the DHS Program repositories respectively, and can be accessed from:\u0026nbsp;https://www.who.int/data/inequality-monitor/data, https://dhsprogram.com/publications/publication-fr152-dhs-final-reports.cfm,\u0026nbsp;https://dhsprogram.com/publications/publication-FR221-DHS-Final-Reports.cfm,\u0026nbsp;https://dhsprogram.com/publications/publication-FR307-DHS-Final-Reports.cfm,\u0026nbsp;https://dhsprogram.com/publications/publication-FR380-DHS-Final-Reports.cfm.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeclaration on the Use of Artificial Intelligence (AI) Tools\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eArtificial intelligence (AI) tools were used solely for grammatical and language refinement of the manuscript. The AI tool did not contribute to the study design, data collection, data analysis, interpretation of findings, or the generation of scientific content. All intellectual contributions and responsibility for the manuscript rest entirely with the authors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor details\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAbdul-Wahab Inusah, MPH¹\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eORCID: 0000-0002-8184-7355\u003c/p\u003e\n\u003cp\u003e¹Department of Global and International Health, School of Public Health, University for Development Studies, Box TL1350, Tamale, Ghana\u003c/p\u003e\n\u003cp\u003eEmail: \u003cu\[email protected]\u003c/u\u003e\u003c/p\u003e\n\u003cp\u003eMoses Nwuzoh, MPH\u003csup\u003e2\u003c/sup\u003e\u003csup\u003e,3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e2\u003c/sup\u003eDepartment of Food Science and Technology, Chukwuemeka Odumegwu Ojukwu University, Igbariam Campus, PMB 6059, Anambra State Nigeria.\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e3\u003c/sup\u003eDepartment of Public Health, Sheffield Hallam University, Sheffield, United Kingdom.\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u0026nbsp;✉\u0026nbsp;(Corresponding Author)\u003c/p\u003e\n\u003cp\u003eAbdul‑Aziz Seidu, PhD\u003csup\u003e4\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e4\u003c/sup\u003ePublic Health and Tropical Medicine, James Cook University,\u003c/p\u003e\n\u003cp\u003eTownsville, QLD 4811, Australia.\u003c/p\u003e\n\u003cp\u003eEmail:[email protected]\u003c/p\u003e\n\u003cp\u003eShamsu-Deen Ziblim, PhD\u003csup\u003e5\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003e\u003csup\u003e5\u003c/sup\u003eDepartment of Population and Reproductive Health, School of Public Health, University for Development Studies, Box TL1350, Tamale, Ghana\u003c/p\u003e\n\u003cp\u003eEmail: [email protected]\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCommission on Social Determinants of Health. 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ICF; 2014.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"distance barrier, Ghana, healthcare access, inequality, women of reproductive age","lastPublishedDoi":"10.21203/rs.3.rs-8759067/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8759067/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eGeographic access to healthcare is a key determinant of women\u0026rsquo;s reproductive health outcomes. In Ghana, distance to health facilities remains a commonly reported barrier to care, yet evidence on long term trends and socioeconomic, residential, and regional inequalities is limited. This study examined trends and inequalities in distance related barriers to healthcare access among women aged 15\u0026ndash;49 years in Ghana between 2003 and 2022.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe analysed four rounds of the Ghana Demographic and Health Surveys (2003, 2008, 2014, and 2022). The outcome was self-reported distance to a health facility as a major barrier to seeking care. Inequalities were assessed across wealth quintile, place of residence, and subnational region using the WHO Health Equity Assessment Toolkit. Summary measures included difference (D), ratio (R), population attributable risk (PAR), and population attributable fraction (PAF), with 95% confidence intervals (CIs), accounting for the complex survey design.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eNationally, the prevalence of women reporting distance as a major barrier declined over the study period. However, inequalities persisted across all dimensions. Rural women consistently reported higher prevalence than urban women, although the rural\u0026ndash;urban gap narrowed over time (D: 30.9 percentage points in 2003 to 17.3 in 2022). Wealth-related inequalities were substantial: after narrowing between 2003 and 2014, absolute inequality widened again by 2022 (D\u0026thinsp;=\u0026thinsp;38.4), with relative inequality remaining high (R\u0026thinsp;=\u0026thinsp;4.7). The wealth-related PAF remained large across survey years (\u0026minus;\u0026thinsp;58.5% in 2003; \u0026minus;53.0% in 2022). Regional disparities were pronounced but declined over time, with the highest prevalence shifting from Upper East in 2003 to Northern region in 2022.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eAlthough distance-related barriers declined nationally, national improvements in reported access may mask persistent and substantial subpopulation disadvantage. The findings provide policy relevant evidence supporting equity focused and geographically targeted health infrastructure and service delivery strategies to reduce distance related barriers to reproductive healthcare.\u003c/p\u003e","manuscriptTitle":"Trends and inequalities in distance-related barriers to healthcare access among women of reproductive age in Ghana, 2003–2022","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-16 18:50:42","doi":"10.21203/rs.3.rs-8759067/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2026-03-27T21:09:30+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-27T07:50:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-25T00:31:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"169000937391601755325496819706789106321","date":"2026-03-23T07:35:07+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"320444928312236957684929503719189270346","date":"2026-03-21T16:35:33+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-19T12:23:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"151051375474157634469330844982676900306","date":"2026-03-18T08:32:25+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"248984563888669702560933200418898714764","date":"2026-03-17T16:58:44+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-03-13T07:42:17+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-24T07:58:13+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-19T04:11:29+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-18T10:36:41+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Public Health","date":"2026-02-18T10:31:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Public Health](https://link.springer.com/journal/12982)","snPcode":"12982","submissionUrl":"https://submission.springernature.com/new-submission/12982/3","title":"Discover Public Health","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"32faf84e-4a5d-4285-99d9-dfb92b09a979","owner":[],"postedDate":"March 16th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-05-08T10:09:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-16 18:50:42","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8759067","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8759067","identity":"rs-8759067","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}