Determinants of barriers to accessing healthcare services among married women in Somalia: a multilevel analysis of nationwide survey data

Abstract

Objectives

To identify the individual and community-level factors associated with barriers to accessing healthcare services among currently married women in Somalia.

Design

A cross-sectional analysis using data from the 2020 Somalia Demographic and Health Survey.

Setting

Somalia.

Participants

A nationally representative sample of 30 311 currently married women aged 15–49 years with complete data on outcome and explanatory variables.

Primary outcome measures

The primary outcome was ‘reporting at least one barrier to accessing healthcare’, a composite binary variable based on four specific problems: obtaining permission to go for treatment, getting money for treatment, distance to the health facility and not wanting to go alone.

Results

A substantial majority (77.06%) of married women reported experiencing at least one barrier to accessing healthcare. Financial cost was the most common barrier (69.91%), followed by distance to health facilities (65.95%), reluctance to go alone (49.64%) and the requirement for permission (46.03%). Multilevel analysis confirmed that higher household wealth was strongly protective (richest vs poorest: adjusted OR (aOR)=0.27, 95% CI 0.24 to 0.32). Paradoxically, factors typically considered protective were associated with increased barriers: women with secondary education (aOR=1.19, 95% CI 1.00 to 1.41) and those with educated husbands (aOR=1.23, 95% CI 1.14 to 1.33) reported more obstacles. Similarly, urban residents faced higher odds of barriers than their nomadic counterparts (aOR=1.40, 95% CI 1.27 to 1.55). Significant regional disparities were evident, with community-level context explaining 26.30% of the total variance in reporting barriers.

Conclusion

Access to healthcare for married women in Somalia is predominantly hindered by economic, educational and community-level constraints. Targeted interventions addressing socioeconomic disparities, infrastructural deficits and specific community contexts are essential to alleviate these barriers.

STRENGTHS AND LIMITATIONS OF THIS STUDY.

• This study uses the most recent nationally representative data (Somalia Demographic and Health Survey 2020), offering a comprehensive view of healthcare access barriers across urban, rural and nomadic populations in Somalia.

• The use of multilevel modelling allows for the simultaneous examination of individual and community-level influences, accounting for the clustering of data.

• The analysis is limited by the cross-sectional nature of the data, which prevents the establishment of causal relationships between identified factors and access barriers.

• Data on barriers are self-reported, which may introduce recall bias or social desirability bias, particularly regarding sensitive topics like spousal permission.

• While the dataset is from 2020, it remains the most current national health survey available; however, the dynamic context of Somalia may have evolved since data collection.

Introduction

Globally, access to essential healthcare services is acknowledged as a fundamental human right and a vital element in achieving Sustainable Development Goal 3, which focuses on Good Health and Well-being.^{1 2} Nonetheless, considerable inequalities still exist, particularly among vulnerable groups, such as women in low- and middle-income countries (LMICs) and fragile states.^{3 4} Somalia’s healthcare system is facing significant difficulties due to prolonged conflict, political turmoil, climate-related disruptions and pervasive poverty.^{5 6} The health system is heavily privatised, unregulated and concentrated in urban areas, leaving vast rural and nomadic populations with limited coverage.^{6 7} As a result, the nation has some of the worst maternal, newborn and child health statistics worldwide.⁸ Consequently, the barriers to healthcare in Somalia are expected to be more severe than those observed in other low-income or fragile-state settings, warranting a specific investigation. The rate of maternal mortality is very high, and a substantial portion of the population has limited access to crucial services such as skilled birth attendance, antenatal care and family planning.^{6 9} Married women in Somalia often face a complex set of challenges that affect their ability to seek and use healthcare, including restrictive social norms, limited decision-making power, economic reliance and geographical obstacles.^{9 10}

Ensuring equitable access to healthcare for women is essential; however, married women of reproductive age face distinct sociocultural and economic constraints that differentiate them from women in general. In Somalia, marriage constitutes the primary social and legal framework for reproduction and healthcare decision-making, with women’s access to services often shaped by spousal consent, household resource allocation and childcare responsibilities. These barriers are less pronounced among unmarried women, whose healthcare decisions are more commonly influenced by parental or familial authority.^{11 12} Women aged 15–49 years are of particular importance because they represent the primary target group for maternal, reproductive and child health interventions and their healthcare utilisation has direct implications for maternal and child survival as well as broader population health outcomes.^{13 14} Focusing on married women within this age range allows for a more precise examination of marriage-specific and gender-based barriers, such as the need to obtain a husband’s permission, which would be obscured in analyses of women as a broader, more heterogeneous population.

Studies conducted in Somalia and similar regions have highlighted major obstacles to accessing healthcare, such as the distance to medical facilities, transportation expenses, lack of awareness about available services, a shortage of qualified healthcare workers (particularly female providers), perceived low quality of care and the requirement for a husband’s or family member’s permission to seek medical attention.15,17 Although these studies offer valuable insights, many concentrate on specific services (eg, maternal care) or take a single-level approach, often overlooking the combined impact of individual, household and community-level factors.¹⁵ By failing to account for this complexity, such studies may miss crucial insights into the mechanisms driving these barriers, limiting the development of effective, targeted interventions. Additionally, while understanding facilitators is important, this study focuses primarily on the determinants of reported barriers to provide actionable data for removing these obstacles¹⁸ . A multitiered perspective recognises that access to healthcare is shaped by factors at different interconnected levels, such as the individual level (eg, education, knowledge), household level (eg, wealth, decision-making dynamics) and community level (eg, norms, infrastructure, service availability).^{19 20} Examining data from this perspective can yield a more detailed understanding of the factors affecting healthcare access.

Although multilevel analyses have been used in neighbouring countries, such as Ethiopia, to investigate barriers,¹⁹ there is a lack of a similar in-depth analysis that specifically targets married women in the current Somali context using recent national data. Therefore, this study aimed to identify the factors associated with reporting barriers to accessing healthcare services among currently married women of reproductive age (15–49 years) in Somalia. It uses a multilevel analytical approach to data from the most recent 2020 Somalia Demographic and Health Survey (SDHS) to examine factors associated with reported barriers to accessing healthcare services. The findings are intended to provide robust, evidence-based insights to inform the development of targeted policies and interventions to improve access to healthcare and, ultimately, enhance the health and well-being of married women in Somalia.

Materials and methods

Study design and objective

This study employed a cross-sectional analytical design to investigate individual-level and community-level factors associated with barriers to healthcare services among married women of reproductive age (15–49 years) in Somalia. The analysis used data from the 2020 SDHS, which is the most recent nationally representative household survey designed to provide up-to-date health and population indicator estimates.²¹ The SDHS employed a multistage stratified sampling technique to ensure the representativeness of the urban, rural and nomadic populations. The survey was implemented by the Somali National Bureau of Statistics in collaboration with the Ministry of Health and Human Services. Data access was granted through the DHS programme, which anonymised the dataset to protect participant confidentiality.

Study population

The study focused on currently married women of reproductive age (15–49 years). This specific subpopulation was selected because the module on ‘Problems in Accessing Health Care’ includes questions regarding ‘obtaining permission to go for treatment’, which is intrinsically linked to spousal dynamics and household hierarchy in the Somali context. The inclusion criteria were as follows: currently married women aged 15–49 years and having complete responses for all four barrier indicators and all explanatory variables. Participants with missing data on the explanatory or outcome variables were excluded to ensure the integrity of the multilevel analysis. The final analytical sample included 30 311 women with complete outcome and explanatory variable data.

Measurement of variables

The dependent variable was ‘reporting a barrier to accessing healthcare’. This was a composite binary variable derived from women’s responses to the DHS module on problems in accessing healthcare. Women were asked: ‘Many different factors can prevent women from getting medical advice or treatment for themselves. When you are sick and want to get medical advice or treatment, is each of the following a big problem or not a big problem?’ The four specific problems assessed were: (1) getting permission to go for treatment, (2) getting money for advice or treatment, (3) distance to the health facility and (4) not wanting to go alone. Women who reported ‘big problem’ for at least one of these issues were coded as ‘1’ (having a barrier), while those who reported ‘not a big problem’ for all were coded as ‘0’. The independent variables were categorised into individual/household-level and community-level factors. Individual-level variables included age, education, employment, parity, household size, sex of the household head, husband’s education and employment, media exposure and household wealth index. Community-level factors included place of residence (urban, rural or nomadic) and the region.

Statistical analysis

All analyses accounted for the SDHS’s complex sampling design using the sample weight, cluster and strata to weight the data and ensure national representativeness. Descriptive statistics summarised the background characteristics. The Rao-Scott χ² test was used to test bivariate associations. A multilevel logistic regression modelling approach (using random intercepts at the cluster level) was used to determine the independent predictors.

• Model 0 (null model): contained no predictors, assessing baseline between-community variability.

• Model I (individual): adjusted for individual/household factors.

• Model II (community): adjusted for community factors.

• Model III (full): adjusted for both individual and community factors.

Fixed effects were presented as adjusted ORs (aORs) with 95% CIs. In contrast, random effects were assessed using the intraclass correlation coefficient (ICC), median OR (MOR) and proportional change in variance (PCV). Multicollinearity among independent variables was assessed using the variance inflation factor, and no significant collinearity was found. The model fit was evaluated using the log-likelihood, Akaike information criterion (AIC) and Bayesian information criterion (BIC). All analyses were conducted using Stata V.16.

Results

Socio-demographic characteristics and barrier prevalence

This study used data from 30 311 weighted women aged 15–49 years included in the descriptive analysis from the 2020 SDHS (table 1). The participants were distributed across age groups, with the most significant proportions being 35–39 years (23.5%), 30–34 years (22.0%) and 25–29 years (22.2%). Educational attainment was generally low, with 88.0% of the participants reporting no formal education. Regarding household composition, 50.2% of women had a parity of 4–7 children. Most households were male-headed (66.7%). Most husbands/partners (62.0%) had not worked in the last 12 months, and 85.5% had attended school at some point. Exposure to mass media (television or radio) was limited, with 86.8% of the participants reporting no exposure. The most common lifestyle was urban (61.1%), followed by rural (25.7%) and nomadic (13.2%). The sample covered all regions of Somalia, with Banadir (23.0%) having the highest representation.

Table 1. Socio-demographic characteristics.

Variables	Number (weighted %)	Barrier in access to healthcare		P value
		No barriers, number (weighted %)	Barriers, number (weighted %)
Age in years				0.5362
15–19	569 (1.9)	111 (19.5)	458 (80.5)
20–24	3040 (10.0)	733 (24.1)	2307 (75.9)
25–29	6732 (22.2)	1427 (21.2)	5305 (78.8)
30–34	6659 (22.0)	1598 (24.0)	5061 (76.0)
35–39	7124 (23.5)	1696 (23.8)	5428 (76.2)
40–44	4210 (13.9)	905 (21.5)	3305 (78.5)
45–49	1977 (6.5)	486 (24.6)	1491 (75.4)
Women’s educational level				0.0089**
No formal education	26 667 (88.0)	5947 (22.3)	20 720 (77.7)
Primary school	3058 (10.1)	804 (26.3)	2254 (73.7)
Secondary school	586 (1.9)	197 (33.7)	389 (66.3)
Parity				0.3252
0–3	5970 (19.7)	1475 (24.7)	4495 (75.3)
4–7 children	15 215 (50.2)	3454 (22.7)	11 761 (77.3)
8+ children	9126 (30.1)	2019 (22.1)	7107 (77.9)
Sex of household head				0.8865
Male	20 224 (66.7)	4636 (22.9)	15 588 (77.1)
Female	10 087 (33.3)	2331 (23.1)	7756 (76.9)
Husband working status				<0.0001***
Yes	11 526 (38.0)	3134 (27.2)	8392 (72.8)
No	18 785 (62.0)	3818 (20.3)	14 967 (79.7)
Husband educational level				0.0003***
No	4409 (14.6)	1289 (29.3)	3120 (70.7)
Yes	25 902 (85.4)	5663 (21.9)	20 239 (78.1)
Media exposure				<0.0001***
Weekly or more	2962 (9.8)	942 (31.7)	2020 (68.3)
Less than weekly	1034 (3.4)	403 (39.0)	631 (61.0)
Not at all	26 315 (86.8)	5607 (21.3)	20 708 (78.7)
Wealth quintiles				<0.0001***
Poorest	8115 (26.8)	1014 (12.5)	7100 (87.5)
Poorer	6435 (21.2)	1223 (19.0)	5212 (81.0)
Middle	5391 (17.8)	1370 (25.4)	4021 (74.6)
Richer	5793 (19.1)	1622 (28.0)	4171 (72.0)
Richest	4576 (15.1)	1728 (37.7)	2848 (62.3)
Place of residence				0.0311*
Rural	7781 (25.7)	2008 (25.8)	5773 (74.2)
Urban	18 517 (61.1)	3907 (21.1)	14 610 (78.9)
Nomadic	4013 (13.2)	1031 (25.7)	2982 (74.3)
Region				<0.0001***
Awdal	610 (2.0)	202 (33.1)	408 (66.9)
Woqooyi Galbeed	2826 (9.3)	492 (17.4)	2334 (82.6)
Togdheer	1736 (5.7)	422 (24.3)	1314 (75.7)
Sool	1054 (3.5)	208 (19.7)	846 (80.3)
Sanaag	1259 (4.2)	330 (26.2)	929 (73.8)
Bari	2162 (7.1)	482 (22.3)	1680 (77.7)
Nugaal	1025 (3.4)	178 (17.4)	847 (82.6)
Mudug	2181 (7.2)	432 (19.8)	1749 (80.2)
Galgaduud	1956 (6.5)	430 (22.0)	1526 (78.0)
Hiraan	1024 (3.4)	285 (27.8)	739 (72.2)
Middle Shabelle	1973 (6.5)	592 (30.0)	1381 (70.0)
Banadir	6975 (23.0)	1618 (23.2)	5357 (76.8)
Bay	1570 (5.2)	306 (19.5)	1264 (80.5)
Bakool	833 (2.8)	319 (38.3)	514 (61.7)
Gedo	1411 (4.7)	293 (20.8)	1118 (79.2)
Lower Juba	1716 (5.7)	366 (21.3)	1350 (78.7)

Note: Numbers under the ‘No Barriers’ and ‘Barriers’ columns represent weighted frequencies. Percentages are weighted row percentages (ie, the proportion within each variable category reporting no barriers or barriers). P values were obtained from survey-adjusted χ2 tests (design-based F-statistic). *p<0.05; **p<0.01; ***p<0.001.

Figure 1 shows the prevalence of the specific problems married women encounter when accessing healthcare. A substantial majority (77.06 %) reported facing at least one barrier. The most prominent specific barrier was obtaining money for advice or treatment (69.91%). This was followed by challenges related to ‘Distance’ to the health facility (66.95%), ‘Not wanting to go alone’ (49.64%) and needing ‘Permission’ to seek care (46.03%).

Figure 1. Prevalence of specific barriers to accessing healthcare services among married women in Somalia (Somalia Demographic and Health Survey 2020).

Table 1 presents the prevalence of reporting barriers to healthcare access across various socio-demographic subgroups, along with bivariate χ² test results. These unadjusted analyses revealed statistically significant associations (p<0.05) between reporting barriers and several factors. Higher barrier prevalence was noted among women without formal education (77.7% vs 66.3% for secondary education, p=0.0075). Barriers were more common among women whose husbands were not working (79.7% vs 72.8%, p<0.0001) and, unexpectedly, among those whose husbands had attended school (78.1% vs 70.7%, p=0.0003). Lack of media exposure was strongly linked to higher barrier reporting (no media exposure 78.7%, declining to 61.0% for those with less-than-weekly exposure and 68.3% for weekly exposure (p<0.001). A transparent socioeconomic gradient emerged, with barrier prevalence decreasing significantly from the poorest (87.5%) to the richest (62.3%) wealth quintiles (p<0.0001). Urban residents reported barriers more frequently (78.9%) than rural (74.2%) or nomadic (74.3%) residents (p=0.0231). Significant regional variations in barrier prevalence were also observed (p<0.0001). In these bivariate tests, women’s age (p=0.5362), parity (p=0.3252) and sex of the household head (p=0.8865) were not significantly associated with reporting healthcare access barriers.

Individual-level and community-level factors associated with barriers to accessing healthcare services

Random effects

Table 2 details the random effects (community-level variation) and the model comparison metrics. Without predictors, the null model (Model 0) confirmed significant between-community variation in reporting barriers (cluster variance=1.63). The ICC was 33.10%, indicating that approximately one-third of the total variance in reporting barriers stemmed from differences between communities. The median OR of 3.38 suggested substantial contextual influence, implying that if a woman moved to a community with a higher probability of barriers, her odds of reporting a barrier would increase by a median factor of 3.38. Adding individual-level factors (Model I) reduced the cluster variance to 1.31 (ICC=28.5%, MOR=2.99). These factors explained 19.4% of the initial community-level variance (PCV=19.4%). Conversely, adding only community-level factors (Model II) resulted in a variance of 1.528 (ICC=31.70%, MOR=3.25) and explained only 6.10% of the community variance (PCV=6.10%).

Table 2. Random effect and model comparison of individual-level and community-level factors.

Random effect/model fitness	Model 0 (null)	Model I (individual)	Model II (community)	Model III (full)
Cluster variance	1.63	1.31	1.53	1.19
ICC (%)	33.10	28.50	31.70	26.70
MOR	3.38	2.99	3.25	2.84
PCV (%)	Reference	19.40	6.10	26.30
Log-likelihood	−16 452.61	−16 188.07	−16 354.73	−16 055.07
AIC	32 909.23	32 418.14	32 747.46	32 186.13
BIC	32 925.98	32 594.07	32 906.63	32 504.47

AIC, Akaike information criterion; BIC, Bayesian information criterion; ICC, intraclass correlation coefficient; MOR, median OR; PCV, proportional change in variance relative to Model 0.

The final model (Model III), which included both individual and community factors, had the lowest cluster variance (1.19), ICC (26.70%) and MOR (2.84). All predictors combined explained 26.3% of the initial between-community variance (PCV=26.30). The persistence of significant cluster variance (p<0.0001) and an MOR substantially above one highlights the continued importance of the community context. Model fit assessment using log-likelihood, AIC and BIC showed progressive improvement across models. Model III yielded the lowest AIC (32 186.13) and BIC (32 504.47), indicating the best fit to the data while accounting for the model complexity. Consequently, Model III was used to interpret the fixed effects.

Fixed effects

Table 3 presents the final multilevel logistic regression model (Model III) (aOR with 95% CIs). At the individual level, older age was associated with lower odds of reporting barriers compared with women aged 15–19: specifically for ages 35–39 (aOR=0.64, 95% CI 0.51 to 0.82), 40–44 (aOR=0.68, 95% CI 0.53 to 0.86) and 45–49 (aOR=0.57, 95% CI 0.44 to 0.73). Women with secondary education had higher odds of reporting barriers (aOR=1.19, 95% CI 1.00 to 1.41) than those without education, while primary education showed no significant difference. Higher parity significantly increased the odds of facing barriers: women with 4–7 children (aOR=1.26, 95% CI 1.15 to 1.37) and those with 8+ children (aOR=1.42, 95% CI 1.28 to 1.57) had higher odds than women with 0–3 children.

Table 3. Multilevel multivariate logistic regression of the individual-level and community-level factors.

Variables	Model I (aOR, 95% CI)	Model II (aOR, 95% CI)	Model III (aOR, 95% CI)
Individual level factors
Maternal age (Ref: 15–19)
20–24	0.75 (0.59 to 0.94)**		0.73 (0.58 to 0.93)**
25–29	0.85 (0.68 to 1.08)		0.84 (0.67 to 1.06)
30–34	0.72 (0.57 to 0.91)**		0.71 (0.56 to 0.90)**
35–39	0.67 (0.53 to 0.84)***		0.64 (0.51 to 0.82)***
40–44	0.69 (0.55 to 0.89)**		0.68 (0.53 to 0.86)***
45–49	0.60 (0.47 to 0.77)***		0.57 (0.44 to 0.73)***
Women’s education (Ref: no education)
Primary	1.09 (0.99 to 1.19)		1.04 (0.95 to 1.14)
Secondary	1.16 (0.98 to 1.38)		1.19 (1.00 to 1.41)*
Parity (Ref: 0–3)
4–7 children	1.26 (1.16 to 1.37)***		1.26 (1.15 to 1.37)***
8+ children	1.45 (1.31 to 1.60)***		1.42 (1.28 to 1.57)***
Sex of household head (Ref: male)
Female	1.04 (0.98 to 1.11)		1.03 (0.97 to 1.10)
Husband working status (Ref: yes)
No	0.85 (0.80 to 0.90)***		0.83 (0.78 to 0.89)***
Husband education (Ref: no)
Yes	1.21 (1.11 to 1.30)***		1.23 (1.14 to 1.33)***
Media exposure (Ref: weekly or more)
Less than weekly	0.99 (0.84 to 1.17)		0.98 (0.83 to 1.15)
Not at all	1.22 (1.11 to 1.34)***		1.22 (1.11 to 1.34)***
Wealth quintiles (Ref: poorest)
Poorer	0.66 (0.60 to 0.74)***		0.69 (0.62 to 0.77)***
Middle	0.49 (0.44 to 0.55)***		0.47 (0.42 to 0.53)***
Richer	0.43 (0.38 to 0.49)***		0.39 (0.34 to 0.44)***
Richest	0.31 (0.27 to 0.35)***		0.27 (0.24 to 0.32)***
Community level factors
Place of residence (Ref: nomadic)		1.00 (reference)	1.00 (reference)
Rural		0.95 (0.86 to 1.05)	1.02 (0.92 to 1.12)
Urban		1.31 (1.19 to 1.45)***	1.40 (1.27 to 1.55)***
Region (Ref: Awdal)
Woqooyi Galbeed		1.38 (1.14 to 1.67)**	1.38 (1.14 to 1.68)**
Togdheer		1.54 (1.26 to 1.89)***	1.99 (1.62 to 2.45)***
Sool		2.11 (1.69 to 2.64)***	2.58 (2.05 to 3.23)***
Sanaag		1.79 (1.49 to 2.15)***	2.05 (1.70 to 2.48)***
Bari		1.96 (1.55 to 2.49)***	1.90 (1.50 to 2.41)***
Nugaal		2.16 (1.75 to 2.68)***	2.14 (1.72 to 2.65)***
Mudug		1.82 (1.49 to 2.23)***	2.04 (1.65 to 2.51)***
Galgaduud		1.69 (1.36 to 2.09)***	1.53 (1.23 to 1.90)***
Hiraan		1.06 (0.84 to 1.33)	1.13 (0.90 to 1.42)
Middle Shabelle		1.15 (0.93 to 1.42)	1.18 (0.96 to 1.47)
Banadir		1.11 (0.92 to 1.34)	1.32 (1.09 to 1.60)**
Bay		1.72 (1.27 to 2.34)**	1.71 (1.25 to 2.34)**
Bakool		1.20 (0.98 to 1.46)	1.04 (0.85 to 1.27)
Gedo		1.38 (1.11 to 1.73)**	1.12 (0.90 to 1.41)
Lower Juba		1.14 (0.94 to 1.37)	1.11 (0.92 to 1.35)

Model I: adjusted for individual-level factors only. Model II: adjusted for community-level factors only. Model III: adjusted for both individual and community-level factors. *p<0.05, **p<0.01, ***p<0.001.

aOR, adjusted OR; Ref, reference category.

Regarding partner factors, women whose husbands had attended school reported barriers more often (aOR=1.23, 95% CI 1.14 to 1.33) than those whose husbands had no formal education. Conversely, women whose husbands had not worked in the past year had lower odds of reporting barriers than those whose husbands had worked (aOR=0.83, 95% CI 0.78 to 0.89). Lack of media exposure significantly increased the odds of barriers (aOR=1.22, 95% CI 1.11 to 1.34) compared with weekly or more exposure frequency. Household wealth has a strong protective effect. Compared with the poorest quintile, odds of reporting barriers were significantly lower for women in the poorer (aOR=0.69, 95% CI 0.62 to 0.77), middle (aOR=0.47, 95% CI 0.42 to 0.53), wealthier (aOR=0.39, 95% CI 0.34 to 0.44) and richest (aOR=0.27, 95% CI 0.24 to 0.32) quintiles. The sex of the household head was not significantly associated with reporting barriers.

At the community level, urban residents had higher odds of reporting barriers (aOR=1.40, 95% CI 1.27 to 1.55) than nomadic residents (reference). Rural residence showed no significant difference from nomadic residences. Significant regional disparities persisted: compared with Awdal (reference), women in Woqooyi Galbeed (aOR=1.38, 95% CI 1.14 to 1.68), Togdheer (aOR=1.99, 95% CI 1.62 to 2.45), Sool (aOR=2.58, 95% CI 2.05 to 3.23), Sanaag (aOR=2.05, 95% CI 1.70 to 2.48), Bari (aOR=1.90, 95% CI 1.50 to 2.41), Nugaal (aOR=2.14, 95% CI 1.72 to 2.65), Mudug (aOR=2.04, 95% CI 1.65 to 2.51), Galgaduud (aOR=1.53, 95% CI 1.23 to 1.90), Banadir (aOR=1.32, 95% CI 1.09 to 1.60) and Bay (aOR=1.71, 95% CI 1.25 to 2.34) all had significantly higher odds of reporting healthcare access barriers. Other regions (Hiraan, Middle Shabelle, Bakool, Gedo and Lower Juba) were not significantly different from Awdal in the final model.

Discussion

This study used the 2020 SDHS data to conduct a multilevel analysis to identify individual-level and community-level factors associated with barriers to healthcare access among married women in Somalia. Our analysis indicates that a substantial majority (77.06%) of married women in Somalia face at least one significant barrier when attempting to access healthcare. This prevalence is markedly higher than that reported in several other studies across sub-Saharan Africa (SSA) that used similar DHS methodologies. For instance, studies reported prevalence rates of 51% in Ghana,²² 65% in Tanzania,²³ 64% in Rwanda, 69.9% in Ethiopia²⁴ and an average of 61.5% across 24 SSA countries.²⁵ A study in Bangladesh found a prevalence of 66.3%.²⁶ This stark difference likely reflects Somalia’s unique and challenging context, characterised by decades of conflict, political instability, widespread displacement, recurrent climatic shocks (droughts and floods) and a severely fragmented and under-resourced health system.²⁷ These factors collectively cripple infrastructure, limit service availability and quality, exacerbate poverty and create significant security concerns, all of which severely impede healthcare access, particularly for women in rural areas.

In our study, maternal age emerges as a significant factor. Compared with the youngest women (15–19), women aged 35–39, 40–44 and 45–49 had significantly lower odds of reporting barriers to healthcare access. This finding aligns with findings from Ghana and SSA more broadly.^{22 25} Possible explanations include increased life experience, greater autonomy or decision-making power acquired with age within the household or community, established social networks and perhaps different health needs or expectations compared with younger women.¹¹ Unexpectedly, our study found that women with secondary education had higher odds of reporting barriers than those without formal education. This contrasts with numerous studies where higher education is generally protective against healthcare barriers.^{19 22 26 28} This finding may be understood through the framework proposed by Levesque et al, which highlights ‘ability to perceive’ as a dimension of access. Educated women in Somalia may possess greater health literacy and higher expectations for quality care, making them more likely to perceive and report systemic deficiencies (such as distance or cost) as ‘barriers’, whereas less educated women might view these hardships as normal conditions or lack the knowledge to identify them as gaps in care.²⁹ Alternatively, educated women may be employed with limited flexibility.

Women with 4–7 children and those with eight or more children were significantly more likely to report barriers than those with 0–3 children. This finding is consistent with studies conducted in Benin and high maternal mortality settings.^{19 28} This is plausible, as higher parity increases demands on a woman’s time and household resources, potentially compounded by cumulative physical tolls from multiple pregnancies, making healthcare-seeking more challenging.^{30 31} Women whose husbands attended school were more likely to report barriers than those whose husbands had no education. This is highly counterintuitive, as partner education is typically protective.^{19 32} In the Somali context, this could potentially relate to power dynamics where educated husbands exert greater control over household decisions, including healthcare seeking, or perhaps reflect mismatched expectations or communication challenges between partners with differing educational levels.³³ Conversely, women whose husbands had not worked in the past year reported fewer obstacles. This could reflect situations where a non-working husband is more available to assist with transport, childcare or navigating health services, or perhaps households adapt their expectations and coping mechanisms when the primary earner is unemployed.^{33 34} Lack of media exposure significantly increased the odds of reporting barriers, a finding consistent with numerous studies conducted in LMICs.35,38 Mass media play a critical role in disseminating health information, raising awareness about available services and improving health literacy. It can also challenge restrictive norms that may prevent service use, thereby reducing perceived real access barriers.^{39 40}

Women in the poorer, middle, richer and richest quintiles had progressively lower odds of reporting barriers than those in the poorest quintile. This was supported by studies conducted in low and low-income countries.2841,43 Wealth directly impacts the ability to overcome financial barriers related to consultation fees, medication costs and transportation, which are often significant obstacles in low-income and out-of-pocket payment-heavy systems.⁴⁴ Urban residents had higher odds of reporting barriers than nomadic residents. This finding aligns with studies conducted in LMICs.^{43 45} This finding contrasts with many settings where rural residents typically face greater barriers due to distance and infrastructure limitations.²⁴ However, in the Somali context, rapid urbanisation, often driven by conflict and displacement, can lead to overcrowded informal settlements with inadequate infrastructure, strained public services, higher living costs and insecurity, potentially creating significant barriers for urban dwellers. This phenomenon, where urbanisation is driven by conflict and displacement rather than economic growth, helps explain why urban living in Somalia might not confer the same health access advantages seen in more stable settings. Significant regional variations persisted, highlighting deep geographical inequities in service availability, infrastructure, security and socioeconomic conditions across Somalia, consistent with findings in other diverse national contexts.^{36 43}

Study limitations

This study had several limitations. First, the cross-sectional design precludes causal inferences. Second, self-reported data may introduce recall and social desirability bias. Third, while the SDHS 2020 is the most recent national dataset, the 5-year gap means the current situation may have evolved. Fourth, the analysis was confined to married women to assess spousal permission, limiting generalisability to unmarried women. Finally, approximately 73.70% of the variance remained unexplained by the model. This suggests that unmeasured factors, such as localised security incidents, the perceived quality of clinical care at facilities and cultural nuances regarding traditional medicine, likely play a significant role in shaping healthcare access barriers in Somalia.

Conclusion

This study underscores the significant barriers to healthcare access among married women in Somalia, predominantly driven by economic limitations, educational paradoxes and distinct community-level contexts. These findings highlight that those interventions must move beyond simplistic assumptions and address the unique, intersecting challenges faced by Somali women. Economic constraints emerged as the strongest impediment, suggesting an urgent need for financial risk protection mechanisms and subsidised healthcare programmes targeting vulnerable populations. The counterintuitive findings regarding higher education levels potentially reflecting heightened awareness or employment constraints necessitate further qualitative exploration to fully understand the underlying causes. Additionally, stark regional and urban-nomadic disparities highlight the importance of tailored, context-specific interventions over uniform national policies. This underscores the critical need for decentralised, context-specific health policies that account for vast regional disparities, rather than a one-size-fits-all national approach. Future programmes should prioritise improving healthcare infrastructure, enhancing service availability and integrating comprehensive community-level initiatives, particularly in regions with significant barriers. The findings advocate for multisectoral strategies combining economic support, educational empowerment and infrastructural enhancement to systematically dismantle barriers, thus promoting equitable and sustainable healthcare access among Somali women.

Data availability statement

The data that support the findings of this study are available from the 2020 Somalia Health and Demographic Survey (SHDS). These data are publicly available upon registration from the Somali National Data Archive (SoNADA) and can be accessed at https://microdata.nbs.gov.so/index.php/catalog/50. The dataset reference number is SOM-SNBS-SHDS-2020-v01.