Social inequalities in healthcare-seeking are reflected in disparities in life expectancy and quality of care, shaped by income, education, gender, ethnicity, and housing. These inequalities follow a well-documented “social gradient”: the lower an individual’s social position, the poorer their health status tends to be, further compounding vulnerability [1][2].

In sub-Saharan Africa, 75% of the population has no access to any form of social protection [3], and where such systems do exist, they are typically characterized by limited coverage, marked regional disparities, and considerable heterogeneity in the benefits provided [3]. Many developing countries have implemented measures that have had a positive impact on reducing these inequalities, without succeeding in eliminating them entirely. The literature suggests that narrowing inequalities in access to care in developing countries depends on a combination of interventions. Fee-exemption policies and health insurance schemes act mainly on financial barriers [4][5], while strengthening primary care, community health workers, and outreach strategies help reduce geographic and organizational barriers [6]. Taken together, this body of evidence shows that improving average access to care does not necessarily translate into a reduction in social inequalities in access. Ethiopia’s experience illustrates this well: health insurance has been shown to increase service utilization, yet disparities persist by place of residence and educational level [7]. Reducing inequalities in access to care therefore appears to require combining financial interventions with measures that target the geographic, social, and environmental determinants of access.

Since 2009, the Government of Benin has pursued a series of measures aimed at strengthening social protection, including free cesarean delivery in public hospitals (Decree No. 2009-96 of March 30, 2009) [8], enhanced maternal care services, a stated commitment to Universal Health Coverage (UHC) under the National Health Development Plan (PNDS) [9], expansion of the primary healthcare network, and targeted support for low-income populations through the Single Social Registry (RSU) [10]. While these measures have had a demonstrable positive effect on healthcare-seeking, disparities persist: wealthier households remain free to consult the facility of their choice, whereas lower-income households are largely confined to basic care at first-line centers and often delay seeking care because of socioeconomic constraints [11].

Natitingou, a municipality in the Atacora department of northern Benin, spans 3045 km² and comprises nine arrondissements, with an estimated population exceeding 129,844 in 2025 [12]. Across its 65 villages and city neighborhoods, distributed over its 9 arrondissements, the municipality counts 17,772 registered households [13]. The local economy is dominated by agriculture [13], supplemented by small-scale trade, craft production (blacksmithing, weaving, sculpture), and tourism centered on Pendjari National Park [13]. These activities generate modest household incomes, sustaining a degree of financial precarity that shapes how families navigate illness. Higher-income households tend to seek care as soon as symptoms appear, whereas lower-income households more often turn first to self-medication or traditional healers [11]. This study set out to identify the sociodemographic, socioeconomic, environmental, and health-system factors associated with the use of health facilities in Natitingou municipality in 2025.

The dependent variable was healthcare-seeking, a dichotomous variable (yes/no) defined as consultation of a formal health facility (a health center, referral or departmental hospital, or private clinic) by at least one household member who had experienced an illness episode in the two weeks preceding the survey. The independent variables spanned four domains: i) sociodemographic characteristics (age, sex, ethnicity, religion, educational level, occupation); ii) socioeconomic characteristics (household type and size, monthly income, and composite socioeconomic status as measured by the EMICOV score); iii) physical environment and behaviors (household cleanliness, housing type, presence of latrines, access to safe drinking water, paved roads, recreational facilities, trust in the health system, forgone care, alcohol and tobacco use, and physical activity); and iv) factors related to the health system and the illness episode (travel time between the household and the nearest health facility, transportation cost, type of condition, perceived severity, cost of care, perceived quality of care, and perceived equity of resource distribution). During data collection, healthcare-seeking in the two weeks preceding the survey was assessed before respondents were asked about their trust in health facilities, perceived quality of care, and views on the equity of health-resource distribution.

Socioeconomic status was measured using the EMICOV score (Integrated Modular Survey on Household Living Conditions), a composite index built from 12 components: the primary construction material of the dwelling’s exterior walls, household size, the main source of lighting, the number of rooms used for sleeping, the primary cooking fuel, ownership of a motorcycle, moped, or car, the number of mobile phones owned, tenure status of a subdivided, built, or irrigated plot of land, ownership of a television or VCD/DVD player, radio ownership, educational level, and occupation. Each response category was assigned a point value between 0 and 50, and the sum across all 12 components yielded a total score with a theoretical maximum of 115 points. This score was then expressed as a percentage of the maximum: EMICOV score (%) = (score obtained/115) × 100. Based on the thresholds defined by the instrument, socioeconomic status was classified as high (≥85%), medium (50% - 85%), or low (≤50%).

Data were collected through face-to-face interviews using a structured questionnaire, administered by five interviewers who had received prior training on the study’s objectives, interview techniques, and questionnaire content. Analyses were performed in RStudio, version 4.6.1. The distribution of age was examined using descriptive statistics and graphical inspection; given its skewness, age is reported as a median with interquartile range (IQR), but was entered as a categorical variable in the association analyses. Qualitative variables are reported as frequencies and proportions. Associations between healthcare-seeking and categorical explanatory variables were assessed using the Rao-Scott χ² test, which accounts for the cluster sampling design. Binary logistic regression was then used to identify factors associated with healthcare-seeking. The adjustment set was defined a priori on conceptual grounds and comprised sociodemographic characteristics (age and sex), socioeconomic status, household size, geographic accessibility to care (road conditions and travel time), and select health-system-related perceptions, trust in health facilities and perceived equity in the distribution of health resources; no backward elimination procedure was applied. Associations are expressed as odds ratios (OR) with 95% confidence intervals (95% CI). Two multivariable models were fitted: a conventional model based on a simple analytic design, and a model accounting for the cluster structure of the sample, the latter serving as the primary model. In this model, the cluster was treated as the primary sampling unit, and standard errors were corrected to account for intra-cluster correlation. No missing data were observed among the variables included in the model, and no imputation was therefore necessary. Categories with sparse cell counts were reviewed prior to the multivariable analysis. Perceived quality of care was excluded from the main model because of a poorly represented category and a null cell for non-utilization. Given the small number of households that had not sought care within the “equitable” category of perceived equity in the distribution of health resources (n = 5), a sensitivity analysis was conducted to assess the robustness of the observed association. A Firth penalized-likelihood logistic regression was applied to limit the bias in estimates arising from small cell counts and rare events; this sensitivity model included the same adjustment variables as the main model, with results expressed as adjusted odds ratios (aOR) and 95% confidence intervals. Statistical significance was set at p < 0.05.

The study protocol was first submitted to the Biomedical Research Ethics Committee of the University of Abomey-Calavi and received approval (No. 223/2025/ FSS/DSP/SCOM). This approval was then presented to the administrative authorities of Natitingou municipality and to the heads of local health facilities to secure their agreement. Participation was voluntary and anonymous, and data collection was conducted under strict confidentiality.

In our study, 72.35% of households had consulted a health facility during the most recent illness episode. This figure is close to the 70.4% care-seeking rate reported among children under five with diarrhea in Niger by Page et al. [15], though somewhat lower than the 79.5% reported by Bapolisi et al. among a rural population in the Democratic Republic of the Congo [16]. That roughly seven in ten households sought care points to substantial reliance on health services in this rural setting (a pattern that may reflect limited access to preventive resources, insufficient health information regarding disease prevention, and a degree of precarity that leaves rural populations more exposed to disease vectors. It is worth noting that the survey was conducted in a locality where no household reached the threshold for high socioeconomic status and where the local economy rests largely on agriculture [12], small-scale trade, craft production (blacksmithing, weaving, sculpture), and tourism centered on Pendjari National Park [12]) activities that generate modest incomes and sustain a financial precarity that shapes how households seek care.

Equity in the distribution of health resources emerged as an important determinant of care-seeking in our study (aOR = 0.02; 95% CI: 0.005 - 0.089; p < 0.001). This finding is consistent with evidence from across Africa showing that regions where health resources are unevenly distributed tend to exhibit lower utilization of health services, particularly in rural areas [17]. Strengthening equity of access (through universal health coverage, financial-protection mechanisms, and a more balanced distribution of infrastructure) has been shown to help narrow such disparities in utilization [17].

The association between perceived equity in the distribution of health resources and care-seeking nonetheless warrants cautious interpretation. Among the households who perceived this distribution as equitable, five had not sought care, a sparse reference-category cell that can destabilize estimates from conventional logistic regression. The Firth penalized-likelihood sensitivity analysis produced a less extreme estimate (aOR = 0.025; 95% CI: 0.009 - 0.065), while preserving the same direction of association. This concordance indicates that the association is not driven solely by small-sample bias, although its precise magnitude remains uncertain.

Prior research has generally found that geographic accessibility (particularly the distance between the household and the nearest health facility) constitutes a major barrier to care-seeking [18]-[20]. Our findings, however, do not fully bear this out: the association between a short travel time and care-seeking was not significant at the 5% threshold, whereas a medium-to-long travel time was significantly associated with utilization (p = 0.003). This apparent paradox should be interpreted with caution given the cross-sectional design, which does not allow us to establish a causal mechanism. Rather than reflecting a straightforward distance effect, this pattern may capture unmeasured differences between households at different distances from care (for instance, in perceived illness severity, prior experience with local providers, or reliance on informal arrangements with health workers) that were not fully captured by the variables included in the model.

Regarding the association between trust in health facilities and care-seeking: after adjustment, households that reported distrust had higher odds of seeking care than those that reported trust (aOR = 9.13; 95% CI: 2.32 - 35.89; p = 0.003). Several mechanisms may underlie this association. First, the perceived severity or urgency of the illness episode, which was not fully captured in the final model, may act as a confounder: a household may seek care despite low trust when the health condition is perceived as serious. Second, a measurement or temporal bias is plausible: trust was assessed after care-seeking had already been evaluated, so a household’s recent experience of care may have shaped its reported level of trust. Reverse causality cannot therefore be ruled out using health services may itself expose households to positive or negative experiences that alter their subsequent trust. Unmeasured or imperfectly measured characteristics of the household or the illness episode may also contribute to this association. In this context, the elevated OR should be read as a statistical association rather than evidence that distrust causally increases care-seeking. Perceived illness severity is indeed a well-established determinant of care-seeking behavior [21][22]. In a cross-sectional study, reverse causality remains a plausible explanation: individuals who use health facilities more often are also more exposed to their shortcomings wait times, difficulties in obtaining care, or perceived staff attitudes and may consequently develop lower trust as a result [23][24].

In the bivariate analysis, care-seeking was less frequent among households of low socioeconomic status than among those of medium status (58.7% versus 76.6%), but this association was no longer statistically significant after adjustment (aOR = 1.78; 95% CI: 0.89 - 3.60; p = 0.102). The bivariate result therefore does not support an independent effect of socioeconomic status on care-seeking. The association observed may instead reflect the influence of factors correlated with both socioeconomic conditions and healthcare-seeking, such as household size, geographic accessibility, or characteristics of the illness episode. The EMICOV score is itself a composite measure incorporating educational level, occupation, housing characteristics, and asset ownership, so associations involving these related variables should be interpreted with this conceptual overlap in mind. The severity or urgency of an illness episode may also shape care-seeking behavior independently of a household’s available resources. The absence of a statistically significant association after adjustment therefore argues against interpreting socioeconomic status as an independent determinant of care-seeking in this study, a conclusion broadly consistent with the multicountry analysis by Bonfrer et al., conducted across 18 sub-Saharan African countries, which found that healthcare utilization was consistently higher among wealthier individuals in nearly all countries studied [25].

Care-seeking was likewise more frequent among households lacking latrines and among those living in earthen dwellings. These variables may serve as indirect markers of broader social, economic, residential, or territorial characteristics not fully captured elsewhere in the model. They may also correlate with differences in disease exposure or in the frequency of illness episodes, which could in turn affect the likelihood of consultation. The severity and urgency of an illness episode, in particular, could simultaneously influence both the emergence of a care need and the decision to seek treatment. Certain environmental variables were also based on household self-report or observation, which may be subject to misclassification. These associations should accordingly be regarded as patterns observed within this population rather than as causal relationships.

This study identifies associations between certain socioeconomic characteristics, perceived equity in the distribution of health resources, and the use of health facilities in Natitingou municipality. The finding for geographic accessibility, however, appears somewhat counterintuitive: only a travel time between the household and the health facility exceeding 60 minutes showed a statistically significant association with care-seeking. Improving the utilization of health services in this setting will therefore require more than simply expanding the supply of care: it will also depend on reducing social and territorial inequalities. Strengthening households’ financial protection, improving physical accessibility to services, ensuring a more equitable distribution of health resources, and investing in local infrastructure are essential levers for promoting more equitable healthcare-seeking.

Conceptualization, Yeni M’po Florent and Houeto David; methodology, Houeto David and Akpovi Enangnon Hilaire; software, Tokpanoude Coovi Nonwanou Ignace; validation, Badirou Aguemon, Géraud Padonou, and Georgia Damien Barikissou; formal analysis, Tokpanoude Coovi Nonwanou Ignace and Djossou Sègnon Eurydice Elvire; investigation, Yeni M’po Florent; resources, Yeni M’po Florent; data curation, Zinsalo Senan Lorens; writing—original draft preparation, Tokpanoude Coovi Nonwanou Ignace and Yeni M’po; writing—review and editing, Tokpanoude Coovi Nonwanou Ignace, Yeni M’po, and Djossou Sègnon Eurydice Elvire; visualization, Tokpanoude Coovi Nonwanou Ignace and Akpovi Enangnon Hilaire; supervision, Georgia Damien Barikissou; project administration, Badirou Aguemon; funding acquisition, Yeni M’po Florent. All authors have read and agreed to the published version of the manuscript.