Abstract

Background: Barriers to healthcare access among people with reduced mobility contribute to complex and suboptimal care pathways. In Benin, the factors influencing these pathways remain insufficiently documented. Objective: The objective was to identify the factors contributing to suboptimal healthcare pathways for these individuals. Study Framework: The study was conducted in public hospitals in Cotonou, Porto-Novo and Parakou in Benin. Methods: A cross-sectional analytical study was conducted with 497 participants in Cotonou, Porto-Novo, and Parakou. Data were collected via questionnaire and then analyzed using Stata 11.0. Associations were investigated using bivariate and multivariate logistic regression at a significance level of 5%. Results: Suboptimal healthcare pathways accounted for 86.72% of cases. Five factors remained significantly associated: illness-related cultural beliefs and practices (p < 0.001), patient education (p = 0.04), type of disease (p = 0.04), chronicity of the illness (p < 0.001), and the patient’s social position (p = 0.04). Sociocultural, informational, clinical, and socioeconomic factors strongly influence treatment choices and continuity of care. Conclusion and Contribution: Optimizing care pathways for people with reduced mobility requires strengthening patient education, reducing social and financial inequalities in access to care, and better consideration of sociocultural realities.

Keywords

Pathway, Illness, Care, Customs, Education

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1. Introduction

A healthcare pathway refers to the sequence of healthcare services used by an individual from the onset of symptoms until the resolution or stabilization of their health problem. It results from complex interactions between the patient’s individual characteristics, sociocultural, economic, and environmental factors, and the organization of the health system. The World Health Organization (WHO) emphasizes that access to continuous, coordinated, and patient-centered care is an essential element of universal health coverage and improved population health [1].

In many low- and middle-income countries, particularly in sub-Saharan Africa, healthcare pathways are often characterized by the coexistence of pluralistic healthcare systems. Patients frequently resort to self-medication, traditional practices, spiritual or religious resources, and conventional health services, often sequentially or simultaneously [2] [3]. This medical pluralism can lead to diagnostic delays, treatment interruptions and increased costs borne by patients and their families [4].

People with reduced mobility represent a population particularly vulnerable to difficulties accessing healthcare. According to the World Health Organization, people living with a functional limitation or disability face more geographical, financial, organizational, and informational barriers than the general population, compromising the continuity and effectiveness of care [5]. The World Disability Report indicates that these individuals have greater health needs while facing persistent inequalities in access to health services [6]. More recently, the WHO Global Report on Health Equity for Persons with Disabilities highlighted that barriers encountered throughout the healthcare journey contribute significantly to excess mortality and a decline in the quality of life of this population [7]. The determinants of healthcare pathways have been the subject of much investigation. Andersen demonstrated that healthcare utilization depends on predisposing factors, facilitating factors, and perceived or actual health needs [8]. Other studies have shown that cultural beliefs, social norms, education level, economic resources and the availability of services strongly influence individuals’ therapeutic choices [9] [10]. In the Democratic Republic of the Congo, Mwana-Wabene et al. observed that patients with hypertension and diabetes frequently resorted to unconventional therapeutic alternatives before consulting modern healthcare facilities [3]. In Guinea, Diakite et al. reported a high frequency of self-medication in the treatment pathway of sickle cell patients [11]. Furthermore, several African studies have highlighted the influence of religious beliefs, cultural representations of illness, and financial constraints on healthcare-seeking behaviors [9] [12] [13].

In Benin, reforms undertaken in the health sector aim to improve the accessibility and quality of care. However, little scientific data documents the determinants of healthcare pathways for people with reduced mobility, even though this population is particularly vulnerable to barriers to accessing healthcare services. A better understanding of the factors associated with suboptimal care pathways is essential to guide public policy, strengthen equitable access to care, and improve the management of these patients. This study aims to identify the sociocultural, informational, economic, and clinical factors associated with the care pathways of people with reduced mobility in hospitals in cities with special status in Benin.

2. Materials and Methods

2.1. Study Setting

The study was conducted in public hospitals in the special-status cities of Porto-Novo, Cotonou, and Parakou in Benin.

2.2. Study Type

This was a cross-sectional analytical study.

2.3. Primary Targets

The primary target group consisted of individuals with reduced mobility recorded in hospital admission registers from July 1 to December 31, 2024, at the participating health centers. For the central level, the study was conducted at the Hubert Koutoukou MAGA National University Hospital Center (CNHU-HKM) in Cotonou, and the University Clinic of Internal Medicine was also included. For the intermediate level, the study was conducted in the Medical Departments of the Ouémé Departmental University Hospital Center (CHUD-O) in Porto-Novo and the Borgou Departmental University Hospital Center (CHUD-B) in Parakou. For the peripheral level, we conducted the study in the Medical Services of the Suru-Léré University Zone Hospital Center (CHUZ-SL) and the Menontin Zone Hospital Center (CHZ-M) in Cotonou.

2.4. Secondary Targets

These were the caregivers of people with reduced mobility who stayed at the health center with the primary targets during their hospitalization. Their responses were processed in the same way as those of the identified primary targets.

2.5. Inclusion Criteria

The study included people with reduced mobility and/or their caregivers who were interviewed during data collection and who gave their free and informed consent to participate.

2.6. Exclusion Criteria

The study excluded any people with reduced mobility and caregivers interviewed who did not give their free and informed consent to participate.

2.7. Sampling Size, Methods, and Techniques

The sample was constructed using a simple random sampling method. The sample size was deduced from probabilistic calculations using Swartz’s formula. Our literature search did not find any reported prevalence figures for this phenomenon. In the absence of prior data on the prevalence of the phenomenon under study, a p-value of 50% was used, corresponding to the maximum prevalence hypothesis, in order to obtain a conservative sample size. Let (p) be the prevalence rate; q = 1 – p, the accepted risk of error; εα² = 1.962, the power i = 0.05. The SWHARTZ formula being:

n = εα²∙p∙q/i²

After entering the data and performing the calculations, we arrived at a result of 385 individuals to be surveyed at a minimum.

For sample construction, we used simple random selection without replacement to determine the target groups. In each health center, we considered the number of people with reduced mobility admitted for hospitalization from July 1 to December 31, 2024, to create our sampling frame and performed simple random selection without replacement. The sample size for each center was weighted by the minimum sample size and the weight of the center’s sampling frame. From this, we derived the following calculation formula: Let Pb be the weight of each center’s sampling frame, n be the minimum sample size required, and Eb be the number of individuals to be surveyed per center: $Eb=Pb/\left({\displaystyle \sum Eb/n}\right)$ .

2.8. Data Collection Techniques and Tools

Data were collected through a questionnaire survey administered to patients or their caregivers. To identify the target groups, we reviewed hospital admission records. After selecting participants, we contacted each group, visited them, and administered the survey questionnaire.

2.9. Dependent Variable

The dependent variable in our study was the care pathway of individuals who had developed reduced mobility, within hospital centers in Benin’s cities with special status. This referred to the subject’s therapeutic journey based on their condition. It was a nominal variable with several categories:

-Traditional or folk treatment: In this case, treatment involved care provided by a traditional healer, herbalist, marabout, diviner, Imam, priest, pastor, or other religious figure, or was based on known or acquired indigenous therapeutic knowledge and properties.

-Self-medication using pharmaceutical products.

-Purely medical pathway: A pathway involving only medical care, without any admixture of non-conventional or traditional therapies.

-Complex medical pathway: A pathway involving up to three physicians (specialists or generalists) and one or more paramedical professionals, preceded or accompanied by non-conventional or traditional therapeutic practices.

-Labyrinthine medical pathway: A pathway involving four or more physicians (specialists or generalists) and one or more paramedical professionals, preceded or accompanied by non-conventional or traditional therapeutic practices.

However, during statistical analysis, this variable was dichotomized into two categories:

-The “complex pathways” and “labyrinthine pathways” categories were grouped together as “suboptimal pathways”.

-The remaining categories were grouped into an “other pathways” category.

2.10. Independent Variables

These are:

-Sociocultural accessibility, including: popular aspects of the pathology, endogenous aspects of the pathology, customs and traditions related to the pathology, beliefs and values related to the pathology;

-Informational accessibility, including: educational concepts related to the pathology, the frequency or rarity of the pathology in the general population;

-Geographical accessibility, including: the distance between home and health center, accessibility to available means of transport, spatial inequality in the provision of care;

-Organizational accessibility, including: the presence of medical specialties useful to the patient, the presence of paramedical specialties useful to the patient, the practice or not of Therapeutic Patient Education (TPE) sessions in the center, the outpatient pathway, the conventional hospitalization pathway, the regulations and laws governing the pathology, the technological and scientific context of the country, the institutional aspects of care and pathways;

-Economic accessibility, including: the economic context of the region, the patient’s social position, the overall cost of the various procedures and treatments already received (modern, endogenous, traditional, spiritual care, etc.), the direct costs of care, the indirect costs of care, the cost-effectiveness of care according to the patient, the patient’s perception of the cost of services in a hospital setting, the promptness in settling healthcare bills, direct payment of healthcare costs, coverage by health insurance or social security, the inability to pay for healthcare costs;

-Patient-related factors include: the subject’s age, the subject’s sex, the type of pathology, the patient’s clinical condition, the chronicity of the disease, the patient’s beliefs and values about the disease, and the time between the first symptoms of the pathology and the first medical consultation.

2.11. Data Processing

The completeness, accuracy, and consistency of the collected data were verified daily during data collection. The data collected for analysis were entered using Epi Data software version 7.2.3.0 and analyzed with Stata software version 11.0. Tables for organizing the data were created using Microsoft Excel 2010, and the report was typed using Microsoft Word 2010. The entered data were cleaned and refined before analysis.

2.12. Data Description

In this section, we will describe the routes taken and the variables surveyed. For quantitative variables, the results were presented as mean and standard deviation when the distribution was normal, or as median and quartiles when the distribution was skewed. Qualitative variables were presented as proportions.

2.13. Data Analysis

We performed a bivariate analysis using logistic regression. This allowed us to determine the associations between the types of care pathways encountered and the various factors through the raw odds ratios (OR) and their 95% confidence intervals (95% CI). In multivariate analysis, variables with a p-value less than 5% in the bivariate analysis were included in an initial multivariate logistic regression model. A backward stepwise logistic regression procedure was then applied to identify factors influencing suboptimal care pathways. The variables retained in the final model were those with a p-value less than 5%. The goodness of fit of the final model was assessed using the Hosmer-Lemeshow test. According to this test, the model is considered adequate when the p-value is greater than 5%.

2.14. Ethics Committee Declaration

At the outset, this research was conducted in accordance with basic ethical and professional principles governing research involving human subjects. The agreement of the administrative authorities of the selected hospitals was requested and obtained before data collection began. In accordance with the principle of professional secrecy, the confidentiality of the collected data was strictly maintained throughout the study. The free and informed consent of the participants and the anonymity of the information collected were guaranteed throughout the study and beyond.

This study was submitted to the Local Ethics Committee for Biomedical Research of the University of Parakou (CLERB-UP) in Benin, and received a favorable opinion with the reference: REF 1023/2024/CLERB-UP/P/SPμ/R/SA dated April 18, 2025, Parakou, Benin.

3. Results

3.1. Data Description

We set out to survey a minimum of 385 individuals, having selected 512 after reviewing the centers’ hospitalization registers. Ultimately, a total of 497 individuals were surveyed, while 15 could not be reached during the survey period.

3.2. Dependent Variables

This concerns the healthcare pathways of people with reduced mobility in Benin. Highly fragmented care pathways were the most frequent at 70.82%, followed by complex care pathways at 15.90%. Therefore, 86.72% of care pathways were found to be suboptimal.

3.3. Independent Variables

The distribution was normal. The description of the independent variables is presented in Table 1 and Table 2.

Table 1. Description of sociocultural and informational accessibility data and related factors for individuals throughout their healthcare journey in hospitals in cities with special status in Benin.

Table 2. Description of data on the economic accessibility and personalized aspects of care pathways for people with reduced mobility in hospitals in cities with special status in Benin.

Also:

-Analysis of the economic context reveals that the economic context allows for the management of 100% of the pathologies encountered. However, significant disparities exist between individuals;

-Analysis of the country’s technological and scientific context reveals that the technology exists to manage 100% of the various pathologies. The fundamental problem is the difficulty of accessing this technology economically when it is indicated;

-The analysis of knowledge on the availability of medical and paramedical specialties useful for the pathology reveals that 100% of individuals declare having knowledge of the existence of these specialties on site, able to treat and manage their health problems on site in the hospital centers investigated.

3.4. Bivariate Analysis

Nineteen variables were significantly associated with the non-optimization of healthcare pathways at the 5% level (p < 0.05). These variables were: popular aspects of the condition (OR = 17; 95% CI [2.81; 102.81]; p < 0.001); endogenous aspects of the condition (OR = 2.32; 95% CI [1.48; 3.64]; p < 0.001); customs and practices related to the condition (OR = 3.62; 95% CI [2.25; 5.81]; p < 0.001); beliefs and values regarding the condition (OR = 26.53; 95% CI [7.47; 94.22]; p < 0.001); educational concepts related to the condition (OR = 4.55; 95% CI [1.05; 19.71]; p = 0.04); therapeutic patient education sessions (OR = 0.12; 95% CI [0.04; 0.38]; p < 0.001); perception of the outpatient care pathway (OR = 5.27; 95% CI [3.39; 8.16]; p < 0.001); perception regarding conventional hospitalization (OR = 5.65; 95% CI [3.68; 8.67]; p < 0.001); type of condition (OR = 5; 95% CI [1.80; 13.86]; p < 0.001); patient’s clinical status (OR = 2.29; 95% CI [1.28; 4.11]; p < 0.001); chronicity of the disease (OR = 18.70; 95% CI [4.19; 83.41]; p < 0.001); patient’s own beliefs and values (OR = 2.94; 95% CI [1.35; 6.39]; p = 0.01); time elapsed between first symptoms and first medical consultation (OR = 2.49; 95% CI [1.49; 4.17]; p < 0.001); patient’s social standing (OR = 5.67; 95% CI [1.68; 19.08]; p < 0.001); total cost of procedures and care already received (OR = 0.56; 95% CI [0.36; 0.88]; p = 0.01); patient’s view of the cost-effectiveness of care (OR = 5.65; 95% CI [3.40; 9.38]; p < 0.001); patient’s perception of hospital service costs (OR = 2.64; 95% CI [1.35; 5.16]; p < 0.001); health insurance or social security coverage (OR = 1.82; 95% CI [1.13; 2.93]; p = 0.01); and inability to pay for care (OR = 1.95; 95% CI [1.31; 2.88]; p < 0.001).

Similarly:

-The chi-square analysis comparing popular aspects of treating the condition with endogenous aspects and interpretations of the disease reveals a statistically significant link (p < 0.001);

-The chi-square analysis comparing popular aspects of treating the condition with popular beliefs regarding the outcome of the condition reveals a statistically significant link (p < 0.001);

-The chi-square analysis comparing popular aspects of treating the condition with the failure to attend at least one patient therapeutic education session regarding their own condition reveals a statistically significant link (p < 0.001);

-The chi-square analysis comparing the type of condition with endogenous aspects and interpretations of the disease reveals a statistically significant link (p < 0.001);

-The chi-square analysis comparing the type of condition with the patient’s own beliefs and values regarding their condition reveals a statistically significant link (p = 0.00);

-The analysis comparing the patient’s social status with the overall cost of the condition reveals a statistically significant link (p < 0.001). Social status determines the overall cost of the disease;

-The analysis comparing the patient’s social status with the direct cost of the condition reveals a statistically significant link (p < 0.001). The patient’s social status determines the overall cost of the disease.

It can be concluded that there are no confounding factors among the variables significantly associated with the suboptimal healthcare pathways of patients with reduced mobility.

3.5. Multivariate Analysis

We initially included in the multiple logistic regression model the 19 variables whose p-values were significant in the bivariate analysis. After progressively removing non-significant variables, we retained 5 factors significantly associated at the 5% threshold with the suboptimal care pathways of people with reduced mobility in hospitals in cities with special status in Benin, with a p-value less than 0.05. We then verified the goodness of fit of the regression model using the Osmer-Lemshow test, which yielded a p-value of 0.47 (greater than 0.05). It is concluded that the model is adequate. The findings indicate that the lack of optimization of care pathways for people with reduced mobility in hospitals within Benin’s special-status cities was associated with the following variables: customs and practices related to the condition (OR = 10.58; 95% CI [2.45; 45.66]; p < 0.001), concepts of patient therapeutic education (OR = 1.28; 95% CI [1.15; 3.22]; p = 0.04), the type of condition (OR = 0.29; 95% CI [0.09; 0.97]; p = 0.04), the chronicity of the disease (OR = 25.02; 95% CI [3.28; 191.06]; p < 0.001), and the patient’s social status (OR = 4.58; 95% CI [2.05; 19.97]; p = 0.04).

4. Discussion

4.1. Predominance of Suboptimal Care Pathways among People with Reduced Mobility

This study highlights a high proportion of suboptimal care pathways among people with reduced mobility in cities with special status in Benin. More than eight out of ten patients experienced complex treatment pathways involving multiple forms of care before accessing appropriate hospital care. This finding demonstrates the existence of gaps in continuity of care and persistent barriers to early access to health services. This observation aligns with research conducted in several African countries where patients frequently alternate between conventional medicine, self-medication, recourse to traditional healers, and religious practices before entering the modern healthcare system [3] [11] [14]. In Burkina Faso, Ilboudo showed that the perceived severity of illness strongly influences the use of healthcare facilities, with symptoms considered mild often being managed outside the formal medical system [2]. Comparable results have been reported in Guinea in sickle cell patients and in the Democratic Republic of the Congo in people suffering from chronic diseases [4] [11].

4.2. Sociocultural Determinants of Healthcare Pathways

One of the main contributions of this study is highlighting the role of customs and traditions in shaping healthcare pathways. Patients exposed to certain sociocultural representations of illness experienced more suboptimal pathways.

Collective beliefs contribute to the social definition of illness and influence how individuals interpret symptoms, identify their causes, and choose which therapeutic resources to use. When illness is associated with supernatural, spiritual, or mystical causes, recourse to traditional healers, religious leaders, or other community actors may precede medical consultation [15]. These results are consistent with those observed by Manya et al. in the Democratic Republic of the Congo, who showed that cultural and religious norms significantly influence the use of health services [9]. Similarly, Kane et al. highlighted the crucial role of cultural beliefs and practices in health behaviors observed during the Ebola epidemic in West Africa [12]. Several international studies also indicate that social representations of illness remain major determinants of healthcare utilization in resource-limited countries [16].

4.3. The Role of Therapeutic Patient Education in Care Pathway Optimization

The concept of patient therapeutic education also appears to be an independent determinant of care pathways. Individuals with low levels of information about their illness or who have not benefited from an appropriate educational approach experienced more complex care pathways. Therapeutic education promotes understanding of the illness, adherence to treatments, decision-making autonomy, and the appropriate use of health services. It also helps reduce consultation delays and disruptions in continuity of care [17]. The observed results are consistent with those reported by Achouri et al. in North Africa, who showed that the absence of therapeutic education was associated with poorer treatment adherence in diabetic patients [18]. Internationally, several systematic reviews have demonstrated that therapeutic education programs improve clinical outcomes and enhance the efficiency of health systems [19].

4.4. Influence of Disease Type and Chronicity

The study reveals that pathology type and disease chronicity significantly influence care pathways. Patients with chronic diseases or complex conditions were more likely to experience suboptimal pathways. Chronic diseases generally require prolonged, multidisciplinary, and coordinated care. They often involve multiple healthcare providers, repeated consultations, and continuous adaptation of treatment strategies. In contexts where healthcare systems remain fragmented, this complexity can lead to increased healthcare utilization and longer treatment pathways [20]. These results are consistent with the behavioral model of Andersen, according to which healthcare needs are one of the main determinants of healthcare utilization [8]. They also corroborate the observations of Ilboudo in Burkina Faso, who showed that persistent symptoms and disease progression gradually lead patients to seek more treatment [2].

4.5. Social Position and Inequalities in Access to Care

The patient’s social position was the last factor independently associated with care pathways in our study. Individuals from the least advantaged social categories were more likely to follow suboptimal pathways. This relationship is well-documented in the scientific literature. Economic resources influence access to consultations, additional tests, medications, and the means of transport necessary to reach healthcare facilities [21]. In contexts where direct financing of care remains significant, financial constraints can lead to postponed consultations or the use of less expensive therapeutic alternatives [1]. Our results are consistent with those of Bloy et al., who highlighted the influence of the social gradient on healthcare-seeking behaviors [10]. They also align with the work of Samba et al. in Senegal and with the literature review by Fomba et al. on treatment pathways in sub-Saharan Africa [13] [22].

4.6. Implications for Public Health

The results of this study suggest that optimizing care pathways for people with reduced mobility requires multisectoral interventions. Strengthening therapeutic education, reducing financial inequalities in access to care, improving social protection, and taking sociocultural realities into account in health promotion strategies appear to be priority levers. Better coordination of health services could also help reduce fragmentation of care pathways and improve continuity of care.

4.7. Strengths and Limitations of the Study

This study is valuable because it simultaneously explores several dimensions of the care pathways of people with reduced mobility at different levels of the Beninese hospital system. It provides original data on a topic that remains poorly documented in the national context.

However, certain limitations must be considered. The cross-sectional nature of the study does not allow for establishing a causal relationship between the identified factors and the observed care pathways. Furthermore, the information collected was partly based on reports from patients or their caregivers, exposing the study to a risk of recall or reporting bias. Finally, the results primarily concern hospitals in cities with special status, and their generalization to the entire country should be undertaken with caution.

5. Conclusions

This study highlighted a high prevalence of suboptimal care pathways among people with reduced mobility receiving care in hospitals in cities with special status in Benin. The results show that these pathways are influenced by a combination of sociocultural, informational, clinical, and socioeconomic factors.

Among the identified factors, some appear modifiable, notably knowledge related to the illness, patient education, and certain sociocultural influences likely to affect treatment choices. Others, such as the type of disease or its chronic nature, require more adaptation of the organization of care and strengthened coordination among the various stakeholders involved in patient care.

These results highlight the importance of developing integrated strategies to improve the accessibility, continuity, and coordination of care for people with reduced mobility. Strengthening therapeutic education programs, improving financial protection against healthcare expenses, and taking sociocultural realities into account in public health interventions could contribute to optimizing care pathways for this vulnerable population. Further studies, including those in other regions of Benin, as well as qualitative approaches to explore the mechanisms underlying therapeutic choices, would be useful for deepening our understanding of the determinants of care pathways and guiding health policies toward more equitable and efficient care.

Funding

This research received no funding.

Acknowledgements

We thank all those who contributed to this research, including the research team at the Comlan Alfred Quenum Regional Institute of Public Health in Ouidah, Benin.

Author Contributions

Conceptualization, T.N. and C.P.M.; methodology, T.N.; software, T.N.; validation, C.P.M., C.D., M.A., and C.J.S.; formal analysis, T.N.; investigation, T.N.; resources, T.N.; data curation, T.N.; writing—original draft preparation, T.N.; writing review and editing, T.N.; visualization, T.N.; supervision, C.P.M.; project administration, C.P.M.; funding acquisition, T.N. All authors have read and agreed to the published version of the manuscript.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

References