Determinants of Household Participation in Healthcare Prepayment Schemes in the City of Goma, in the Eastern Democratic Republic of the Congo: A Cross-Sectional Study

Determinants of Household Participation in Healthcare Prepayment Schemes in the City of Goma, in the Eastern Democratic Republic of the Congo: A Cross-Sectional Study | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Determinants of Household Participation in Healthcare Prepayment Schemes in the City of Goma, in the Eastern Democratic Republic of the Congo: A Cross-Sectional Study Justin Murhabazi Ntabiruba¹, Célestin Kimanuka Ruriho², Zacharie Tsongo Kibendelwa³, and 8 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8147855/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Out-of-pocket health expenditures are often difficult for households to manage and can lead to financial hardship and poverty. Prepayment mechanisms, including community-based health insurance, offer a pathway toward financial protection, yet uptake remains low in many low-income settings. This study aimed to identify the determinants of household participation in healthcare prepayment schemes in Goma, Democratic Republic of Congo. Methods We conducted an analytical cross-sectional study using multistage cluster random sampling among 807 households in Goma in November 2023. Data were collected using a structured questionnaire, encoded, and analyzed in SPSS version 23. Logistic regression models were used to assess factors associated with participation in healthcare prepayment. Results Overall, 13.1% of households participated in a prepayment scheme. Factors negatively associated with participation included: male household head (OR = 0.5), being unmarried (OR = 0.3), no prior membership in associations (OR = 0.09), primary-level or lower education (OR = 0.3), poor knowledge of mutual health schemes (OR = 0.04), unemployment (OR = 0.4), low socioeconomic status (OR = 0.3), poor reception at health facilities (OR = 0.9), perceived incompetence of health staff (OR = 0.6), unavailability of medicines (OR = 0.5), care-related adverse events (OR = 0.3), lack of explanations during care (OR = 0.5), high contribution fees (OR = 0.9), incomplete benefit package (OR = 0.2), and lack of trust in scheme management (OR = 0.2). Age below 30 years among household heads was positively associated with participation (OR = 1.8). Conclusion Household enrollment in healthcare prepayment schemes in Goma remains low. Multiple socioeconomic, cultural, and healthcare quality-related factors negatively influence participation. Targeted strategies are needed to strengthen trust, accessibility, and the perceived value of community-based health insurance. Community-based health insurance Healthcare prepayment Determinants Household enrollment Goma Democratic Republic of Congo Background Prepayment for healthcare is a financing mechanism in which individuals pay in advance for part or all of the cost of health services before they are used. Payments are made by individuals through taxes or contributions to a health insurance scheme prior to using health services, and the prepaid contributions are pooled[ 1 ]. Over the past decade, the World Health Organization, in collaboration with the World Bank, has reaffirmed its commitment to universal health coverage. The Political Declaration adopted at the UN Summit in September 2023 reiterates the need for States to mobilize robust policies and financing to accelerate the achievement of UHC by 2030, with a particular focus on investing in primary care, equitable access, and financial risk protection [ 2 ]. Most developing countries have faced challenges in sustaining the financing of their health systems over the past two decades. Out-of-pocket payments accounted for a significant share of health expenditures in these countries, whereas developed countries moved toward establishing prepayment mechanisms [ 3 ]. Since the introduction of direct payments for healthcare in the 1980s in low- and middle-income countries, the discourse among global health actors has increasingly shifted against this mode of health financing [ 4 ]. According to the Tracking Universal Health Coverage: 2023 Global Monitoring Report, jointly published by the World Bank and the World Health Organization (WHO) on 18 September 2023, more than half of the world’s population approximately 4.5 billion people still does not have full access to essential health services. In addition, 2 billion people face severe financial hardship due to direct health expenditures, and about 1.3 billion people have been pushed, or further pushed, into poverty because of these expenses [ 2 ]. At the regional level in Africa, the share of general public expenditure allocated to health generally does not exceed 7–8%, which remains far below the 15% target set by the Abuja Declaration[ 5 ]. In most low-income countries, the majority of health expenditures about 35% on average, and often up to 60% is paid directly by patients in the form of out-of-pocket payments. In some countries, this rate reaches 40–43% of total health expenditures. These direct payments are identified by the WHO as the leading cause of financial hardship among households[ 5 ]. In Africa, the proportion of households that had to borrow money or sell assets to cover their healthcare expenses ranged from 23% in Zambia to 68% in Burkina Faso [ 6 ]. This situation in Africa also affects the Democratic Republic of Congo, where the health financing system is clearly too fragile to protect households from catastrophic health expenditures. Out-of-pocket payments remain the dominant mode of health financing in the DRC, with over 90% of households relying on them[ 7 ]. In 2023, in South Kivu province, more than 50% of households had to sell assets to cover medical expenses (compared to 65.5% in 2011). In Kenge, about 35% of households sold assets to pay for healthcare, whereas in Goma, only 2.8% of households had to sell or pawn assets [ 8 ]. In the Democratic Republic of Congo (DRC), the government and Parliament have adopted a strategy based on health mutuals (MUSA) and health insurance as pillars of the national health financing policy[ 9 ]. These reforms appear to resonate to some extent with the population: various community-based health mutual initiatives have emerged in several provinces, such as North Kivu and Bukavu. In the city of Bukavu, local health mutuals (MUSA) have been studied and identified as promoting better utilization of health services among their members. Members of these mutuals seek care more frequently and spend less on healthcare than non-members[ 10 ]. Among the major challenges faced by health mutuals in sub-Saharan Africa, population enrollment remains the primary difficulty. Despite the apparent growth of the mutualist movement, coverage rates remain very low: in most countries, less than 10% of the population is covered by a mutual, with only a few exceptions exceeding 20%[ 11 ]. Despite intensive awareness campaigns on health mutuals, enrollment is progressing very slowly in the Democratic Republic of Congo (DRC). According to national health accounts, the national coverage rate reached 8% in 2020, while the 2018 MICS 3 survey reported coverage below 5% (4.3% among women aged 15–49 years and 4.1% among men aged 15–59 years)[ 12 ]. In the current context of the DRC’s commitment to universal health coverage (UHC), with North Kivu province also engaged in the UHC implementation process, a key question is what factors limit household enrollment in health mutuals in the city of Goma, eastern DRC. Studying this phenomenon in the urban setting of Goma could help refine strategies to develop an evidence-based health insurance system that considers the specificities of the urban Kivu context. Within this framework, this study aims to identify the determinants of household participation in healthcare prepayment schemes in Goma, eastern DRC. Materials and Methods Study Setting The study site, the city of Goma, is the capital of North Kivu province in eastern Democratic Republic of Congo (DRC). North Kivu province faces recurrent violence due to the ongoing conflict between the Armed Forces of the Democratic Republic of Congo (FARDC) and the armed group March 23 Movement (M23). This conflict has exacerbated an already critical humanitarian situation and has led to large-scale displacement within North Kivu province [ 13 ]. Located between Virunga National Park to the northwest, Lake Kivu to the south, and Rwanda to the east, the city of Goma had an estimated population of around 2 million in 2023, showing substantial growth from 1 million in 2017. A large proportion of the population (over 80%) lives in precarious conditions, particularly in the informal sector. The economic situation is also characterized by significant challenges, exacerbated by political instability and armed conflicts [ 14 ]. In terms of healthcare, the city of Goma is served by two urban health zones, Goma and Karisimbi, which continue to face multiple challenges, including limited access to health services due to insecurity and high population density [ 14 ]. Study Design, Period, Population, and Sampling This descriptive and analytical study was conducted from 1 to 30 January 2024 among households in the city of Goma, eastern DRC. The study population comprised households in Goma, estimated at 160,310 in 2023. Households were eligible if they were permanent residents of Goma during the study period and had an adult respondent (≥ 18 years) available to provide the required information. Participation required informed consent. Only households listed in the sampling frame and consenting to the survey were included. The sample size was calculated using Cochran’s formula: $$\:n=\frac{{Z}^{2}\cdot\:p(1-p)}{{e}^{2}}$$ Where: \(\:n\) = required sample size \(\:Z\) = 1.96 for 95% confidence level \(\:p\) = estimated proportion of healthcare prepayment, set at 50% \(\:e\) = margin of error, set at 5% (0.05) The calculated sample size was 384 households. To account for cluster sampling and non-respondents, the final sample size was set at 807 households. Considering the population weight of the two health zones in Goma, the sample was distributed as follows: 266 households for Goma zone and 541 households for Karisimbi zone. A multistage probabilistic sampling method was used: First stage: Each health zone served as a stratum. Second stage: Clusters were determined within each stratum using ENA software. Third stage: Households were selected by simple random sampling. With the assistance of community relays and street leaders, enumerators located randomly selected households using a random number generator. The sampling frame was based on lists prepared by the Goma and Karisimbi health zone teams in collaboration with community relays. Data Collection Data were collected using a structured questionnaire, pre-tested in Mugunga, 10 km west of Goma. The questionnaire used in this study was specifically developed for the purpose of this research, based on determinants identified in the literature and contextual factors relevant to healthcare prepayment schemes in Goma. An English version of the questionnaire is provided as Additional file 1. The survey was conducted in January 2024 by 10 trained enumerators from the University of Goma School of Public Health. Training lasted three days and included translation of the questionnaire into Swahili, the most widely spoken language in Goma. The primary respondents were household heads. In their absence, the oldest adult present provided the information. Data were collected via direct administration of the questionnaire using KoboToolbox. Study Variables The questionnaire covered seven main domains: Household participation in healthcare prepayment(adherent vs. non-adherent) Sociodemographic determinants (sex, age, household size, marital status) Sociocultural determinants (religion, prior association membership, education level, ethnicity, mutual health knowledge) Socioeconomic determinants (wealth level, occupation) Healthcare service-related determinants (reception, waiting time, therapeutic use, household–facility distance, staff efficiency, drug availability and quality, explanation of care, care-related adverse events) Mutual health scheme-related determinants (benefit package, co-payment, affiliation method, internal regulations, contribution amount, membership fees, trust in management) Data Analysis Data collected via KoboToolbox were cleaned and analyzed using SPSS. Statistical analyses included: Univariate analyses: frequencies, proportions, means Bivariate analyses: Chi-square and Fisher’s exact tests Multivariate analyses: binary logistic regression, with estimation of odds ratios (OR) and 95% confidence intervals (CI) Model quality was assessed using the ROC curve. Test assumptions were verified, confirming validity. The logistic regression model showed excellent performance, while the discriminant model demonstrated good performance. Statistical significance was set at \(\:p<0.05\) (α = 5%) Results Sociodemographic and Cultural Characteristics Among the 807 household heads surveyed, 54.2% were aged 30 years or older, and 57.9% were women. The majority were married or in union (67.4%), and households with fewer than six members predominated (71.3%). Christianity was the predominant religion (92.1%), and 51.1% of participants had prior association membership. More than half had attained secondary education (56.8%), and 64.6% belonged to southern tribes. Finally, 64.2% of households demonstrated insufficient knowledge of health mutuals. Table 1 Sociodemographic and Cultural Characteristics of Surveyed Participants Variables Fréquency(n = 807) Pourcentage Age (Year) < 30 370 45,8 ≥ 30 437 54,2 Sexe Female 467 57,9 Male 340 42,1 Marital Status In union 544 67,4 Not in union 263 32,6 Size ≥ 6 232 28,7 < 6 575 71,3 Religion Christian 743 92,1 Not Christian 64 7,9 Associated past Yes 412 51,1 No 395 48,9 Education Secondary or higher 458 56,8 Primary or lower 349 43,2 Tribe Northern tribe 286 35,4 Southern tribe 521 64,6 Knowledge level Good 36 4,4 Average 253 31,4 low 518 64,2 Socioeconomic Characteristics and Participation in Healthcare Prepayment Among the 807 participants, nearly six in ten (59.4%) lived in poverty, and more than half (54.9%) were employed in the informal sector. The vast majority (86.9%) were not enrolled in any healthcare prepayment system. Table 2 Socioeconomic Characteristics and Participation in Healthcare Prepayment among Surveyed Participants Variables Frequency (n = 807) Pourcentage Wealth status wealthy 141 17,5 Middle 187 23,2 Poor 479 59,4 Ocupation Formal 260 32,2 Informal 443 54,9 Unemployed 104 12,9 Participation in Prepayment Schemes No 701 86,9 Yes 106 13,1 Explanatory Factors of Participation in Healthcare Prepayment Tables 3 , 4 , 5 , and 6 show that household participation in healthcare prepayment schemes in Goma was influenced by several factors. Regarding sociodemographic and cultural factors, young household heads (< 30 years) were more likely to participate (OR = 1.8), while male gender (OR = 0.3), unmarried status (OR = 0.3), low education level (OR = 0.3), lack of prior association membership (OR = 0.09), and insufficient knowledge of mutual health schemes (OR = 0.04) reduced participation. In terms of socioeconomic factors, unemployment (OR = 0.4) and poor wealth status (OR = 0.3) also decreased the likelihood of participation. Among healthcare service-related factors, poor reception (OR = 0.9), incompetent staff (OR = 0.6), care-related accidents (OR = 0.3), unavailability of medicines (OR = 0.5), and lack of explanations during care (OR = 0.5) were unfavorable. Finally, regarding the mutual health schemes themselves, an incomplete benefit package (OR = 0.2), high contribution fees (OR = 0.9), and lack of trust in scheme management (OR = 0.2) reduced participation. Table 3 Prediction of Household Participation in Healthcare Prepayment Schemes in Goma City (Socioeconomic Factors) Variables A Sig. Exp(B) IC for Exp(B) 95% low high Ocupation Formal 1 Informal -,698 ,019 ,498 ,277 ,893 unemployed -,761 ,009 ,467 ,121 ,798 Wealth status wealthy 1 Rich -,463 ,007 ,629 ,321 ,933 Poor -,986 ,002 ,373 ,107 ,802 Table 4 Prediction of Household Participation in Healthcare Prepayment Schemes in Goma City (Sociodemographic and Cultural Factors) Variables A Sig. Exp(B) IC for Exp(B) 95% low High Age ( year) ≥ 30 1 < 30 ans ,598 ,047 1,819 1,109 3,279 Sexe Female 1 Male -,677 ,031 ,508 ,275 ,941 Marital status In union 1 Not in union -1,057 ,029 ,347 ,134 ,899 Associated past Yes 1 No -2,383 ,000 ,092 ,038 ,225 Education Secondary and higher 1 Primary and lower -1,106 ,003 ,331 ,159 ,689 Knowledge level Good ,000 1 Average -1,140 ,001 ,320 ,166 ,617 Poor -3,063 ,000 ,047 ,013 ,174 Table 5 Prediction of Household Participation in Healthcare Prepayment Schemes in Goma City (Factors Related to Healthcare Service Provision) Variables A Sig. Exp(B) IC pour Exp(B) 95% Low High Reception in the health facility Very Good 1 Good -,380 ,005 ,684 ,487 ,996 Poor -,037 ,005 ,464 ,242 ,720 Staff effectiveness Competent 1 Not competent -,469 ,005 ,626 ,251 ,962 Availability and quality of medicines Medicine available and of good quality 1 Medicine available but of poor quality -1,466 ,000 ,231 ,145 ,367 Medicine not available -,672 ,035 ,511 ,238 ,997 Care-related accidents No accident 1 With accident -1,049 ,034 ,350 ,133 ,922 Explenation care With explanation 1 No explanation -,536 ,020 ,585 ,373 ,917 Table 6 Prediction of Household Enrollment in Prepayment Schemes in Goma City (Factors Related to Health Insurance Schemes) Variables A Sig. Exp(B) IC pour Exp(B) 95% low high Benefit package offered Completed 1 Incompleted -,910 ,002 ,202 ,102 ,898 Contribution amount Less expensive 1 Expensive -,071 ,003 ,432 ,289 ,777 Trust in mutual management Yes 1 No -,606 ,006 ,276 ,180 ,971 Table 7 shows excellent performance for the logistic regression model, with an area under the curve (AUC) of 0.9, and good performance for the discriminant model, with an AUC of 0.8. Table 7 Area Under the ROC Curve for the Logistic and Discriminant Models in Multivariate Analysis Outcome variable(s) tested Area under the curve (AUC) Error Std. a Asymptotic significance b 95% Asymptotic Confidence Interva Lower bound Higer bound Predicted probability (logistic)” “Predicted probability (discriminant) ,918 ,015 ,000 ,888 ,948 ,837 ,020 ,000 ,798 ,877 Discussion This study aimed to identify the determinants of household participation in healthcare prepayment schemes in an urban African setting, specifically in the city of Goma, eastern Democratic Republic of Congo. The results highlighted several predictors of participation in prepayment schemes. Regarding overall participation, our study found that 13.1% of households were enrolled in healthcare prepayment schemes. Although this rate is relatively low, it should be considered in the context of other recent studies conducted in similar or comparable settings. For instance, a study conducted in the Democratic Republic of Congo in 2015 reported a health mutual enrollment rate of approximately 6.3%. In comparison, our finding of 13.1% represents a notable improvement, possibly reflecting a slightly more favorable socioeconomic or institutional context, or more advanced efforts to promote prepayment schemes[ 1 ]. Furthermore, considering a more developed context such as France, a national study conducted between 2015 and 2018 found that 25.4% of individuals reported forgoing care for financial reasons, particularly due to upfront costs or high out-of-pocket expenses. Although this figure is indirect, it highlights that even in systems where prepayment mechanisms are well established (e.g., Social Security and mutual health insurance), financial access to care is not always guaranteed. This underscores that enrollment in prepayment schemes alone is insufficient to ensure effective coverage, especially when reimbursement mechanisms are complex or partial [ 15 ]. Thus, our observed rate of 13.1% falls between two extremes: it is higher than that seen in nascent or fragile systems such as in the DRC, yet remains far below the desired levels of enrollment, and well below those observed in countries with more developed universal health coverage systems. This highlights a concerning reality: a large majority of the population (nearly 87%) is still not covered by any prepayment mechanism, which may lead to forgoing care, delays in treatment, or worsening health conditions. This comparison underscores the need to strengthen awareness efforts, improve trust in prepayment schemes, and consider targeted subsidies or inclusion mechanisms to expand enrollment, particularly among the most vulnerable populations. Our study identified several sociodemographic and sociocultural factors influencing household participation in healthcare prepayment schemes in Goma. These factors include age, gender, marital status, education level, prior association membership, and knowledge of mutual health schemes. A recent study on health insurance in Rwanda identified several determinants of enrollment: being female increases the likelihood of enrollment (aOR ≈ 1.20), higher education levels (secondary or tertiary) strongly increase the chances of being enrolled, and age is also influential, with younger individuals aged 14–24 much less likely to be insured (aOR ≈ 0.30). Marital status matters as well: single individuals are less likely to be covered, while widowed individuals are more often insured. Additionally, income or socioeconomic status plays a major role, as does living in the capital city[ 16 ]. A study in Burkina Faso revealed that the key determinants of willingness to pay for health insurance were household income, type of occupation, and, once again, the education level of the household head. Association experience and perceptions of service quality also played a role, with greater willingness to pay observed among individuals who were better informed, involved in associations, or engaged in stable employment [ 17 ]. Our results align with the general pattern observed in several African countries: enrollment in prepayment systems is strongly influenced by sociodemographic variables such as education, age, gender, marital status, and economic situation. Additionally, prior association membership and knowledge of the system emerge as essential factors, as confirmed by the Burkina Faso study. This suggests that our findings are robust and broadly consistent with observations elsewhere in sub-Saharan Africa. To promote participation in prepayment schemes, it will be important not only to strengthen educational and informational campaigns but also to integrate associations and potentially community leaders into mobilization strategies. Our study identified occupation and income level as key socioeconomic factors influencing household enrollment in healthcare prepayment schemes in Goma. Specifically, unemployment decreased the likelihood of enrollment in health mutuals among Goma households (OR = 0.4), and poor income status also reduced the likelihood of enrollment (OR = 0.3). In Nigeria and South Africa, cross-sectional analyses based on Demographic and Health Surveys showed that employed individuals are significantly more likely to have health insurance. Unemployed individuals in these countries are less likely to be insured, with a high proportion reporting no coverage (30% in Nigeria, 66% in South Africa). These findings clearly demonstrate that employment status, in particular being employed, is a crucial determinant of enrollment in prepayment systems [ 18 ]. A study in Rwanda highlighted that socioeconomic status, measured notably by income or the Ubudehe classification, strongly influences enrollment in health insurance. Non-poor households were two to seven times more likely to be covered than poor households. In contrast, the most economically vulnerable (poor) are much less likely to have insurance due to their inability to pay premiums [ 16 ]. Our findings align with observations from Nigeria and South Africa, where the unemployed are much less likely to be insured, and with Rwanda, where poor households have a high likelihood of being uninsured. This implies that, to increase enrollment rates in Goma, targeted subsidy mechanisms for poor or unemployed households should be considered, as well as alternatives adapted for people without employment, such as contributions scaled to actual capacity or flexible contributory models. Pro-employment and financial inclusion policies could also enhance access to health insurance through stable jobs or income-generating activities. Our study also identified several healthcare service-related factors that influence household enrollment in mutual health schemes in Goma. These factors include reception, staff effectiveness, availability and quality of medicines, care-related accidents, and clarity of care explanations. A study assessing beneficiary satisfaction with the NHIS in Ibadan, Nigeria, revealed that perceived service quality particularly staff effectiveness and information available prior to enrolment significantly influenced both enrollment and satisfaction. Private-sector staff and individuals who sought information about service quality were more satisfied (OR ≈ 1.8 for private-sector workers). Although the impact of incompetent staff was not precisely quantified in this study, professional competence and reception clearly emerge as key determinants of satisfaction, and therefore of enrollment or renewal[ 19 ]. In a survey conducted in 2022 in Gondar Zuria, Northwest Ethiopia, although the enrollment rate was high (65%), a large proportion of households reported dissatisfaction with the services. The main reasons included long waiting times, unavailability of medicines and laboratory tests (up to 88.7% dissatisfaction), and slow service delivery [ 20 ]. Our data show strong similarities with findings from Nigeria (Ibadan) and Ethiopia (Gondar): the quality of healthcare services, staff competence and behavior, availability of medicines, and waiting times are major determinants of enrollment in mutual health schemes. The barriers identified in Goma illustrate how degraded services can hinder participation. This suggests the need to strengthen staff training in patient relations and clear communication, improve logistical management to ensure the consistent availability of essential medicines, optimize service organization, and establish feedback systems allowing households to report accidents or malpractice, in order to restore trust and encourage long-term enrollment. Our study also shows that several factors related to the health mutuals themselves explain household enrollment in Goma: the benefit package offered, contribution amount, and trust in the mutual’s management. A survey of informal sector workers in Lusaka, Zambia, highlighted that trust in the private health system’s ability to provide effective care was strongly associated with health insurance enrollment (OR ≈ 3.4). In contrast, trust in public management or government institutions was not significantly related to enrollment[ 21 ] . A study conducted in a peri-urban area of Gondar, Ethiopia, identified that trust in the management of the Community-Based Health Insurance (CBHI) was a major determinant of enrollment (OR ≈ 0.40, with the scale inverted depending on the variable). The researchers also emphasized the importance of households’ understanding of the system’s modalities [ 22 ]. In Goma, a limited benefit package drastically reduces enrollment (OR = 0.2). Although this specific variable is not quantified in the cited studies, meta-analyses from Ethiopia indicate that the perception of healthcare service quality including the range of services provided and their effectiveness strongly promotes enrollment [ 23 ]. The observations made in Goma align closely with findings from other African countries. This implies that, to encourage enrollment in health mutuals, it is essential to strengthen transparency and accountability to build trust, design more attractive benefit packages that include clearly communicated essential services, adjust contributions based on perceived service value, and offer subsidized or tiered rates when necessary. Conclusion Enrollment in healthcare prepayment schemes remains low, hindered by multiple sociodemographic, economic, cultural, service quality, and mutual organization related factors. This situation can lead to forgone care, delays in treatment, or worsening health conditions. These findings highlight the need for targeted actions aimed at improving the quality of care, making services more financially accessible, and strengthening users’ trust in mutual health systems. Combined, these efforts could positively impact household enrollment in prepayment schemes in Goma and contribute to advancing financial protection for healthcare users. Declarations Acknowledgements The authors extend sincere appreciation to all who supported the study. Authors’ Contributions J.M.N. and C.K.R. conceived and designed the study. Z.T.K., A.K.T., and J.I.B. coordinated field implementation, data collection, and analysis. W.B.B., Z.M.M., and L.B.N. contributed to questionnaire development and data validation. T.P. drafted the initial manuscript, while J.M.N. provided methodological supervision and critical review. A.M.N. and S.W.O. supported community engagement and participant recruitment. All authors contributed to data interpretation, revised the manuscript critically, and approved the final version for submission. Funding This study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors. Availability of Data and Materials The datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. To protect participant confidentiality, only de-identified data will be shared. Ethics Approval and Consent to Participate The study protocol was approved by the Ethics Committee of the University of Goma (Reference: UNIGOM/CEM/008/2023). Informed consent was obtained from all participants, and confidentiality and anonymity were strictly maintained throughout data collection and analysis. The research was conducted in accordance with the ethical principles outlined in the Declaration of Helsinki (2013 revision) . Participation was voluntary, anonymous, and confidential, and participants were free to withdraw at any time without consequence. Consent for Publication Not applicable. Competing Interests The authors declare that they have no competing interests. References Kabinda JM, Mitashi PM, Chenge FM. Analysis of financing arrangements for health care in the Democratic Republic of Congo: a systematic review. Ann Africaines Médecine Artic Orig Ann Afr Med. 2019;12(2):3203. 10.13140/RG.2.2.35834.52169 . Tracking Universal Health Coverage. 2023 Global Monitoring Report . 2023. 10.1596/40348 Ndiaye I, et al. Étude des facteurs associés à l’adhésion des populations aux mutuelles de santé au Sénégal en 2019. Rev Epidemiol Sante Publique. 2023;71:102043. 10.1016/j.respe.2023.102043 . Robert E, Ridde V. Les paiements directs des soins dans les pays à faible et moyen revenu ne font plus l’unanimité au sein de la communauté internationale. Une analyse documentaire, Les Cah. du CIRDIS - Collect. Rech. , no. 2012–03, p. 33 pages, 2012, [Online]. Available: http://www.ieim.uqam.ca/IMG//pdf/Cahier_CIRDIS_-_no_2012_-_03.pdf World Health Organization. Global spending on health 2020. Weathering the storm. Geneva: OMS. 2020. ISBN 978-92-4-001778-8 (électronique), 978-92-4-001779-5 (papier). Disponible sur: https://iris.who.int/handle/10665/337859 Timothy PO. Financial Inclusion and Health Shocks. A Panel Data Analysis of 36 African Countries. Asian J Econ Empir Res. 2019;6(1):45–51. 10.20448/journal.501.2019.61.45.51 . Nyamugira AB, Flessa S. Richter.Health insurance uptake, poverty and financial inclusion in the Democratic Republic of Congo. Sustain Dev. 2024;32(4):3293–312. 10.1002/sd.2841 . Mugisa A, et al. Trends in socio-economic level and accessibility to healthcare of households in Eastern Democratic Republic of Congo from 2011 to 2023: Documentary review of South Kivu. no January. 2025. 10.21203/rs.3.rs-5866175/v1 . Criel B, Waelkens MP, Nappa FK, Coppieters Y, Laokri S. Can mutual health organisations influence the quality and the affordability of healthcare provision? The case of the Democratic Republic of Congo. PLoS ONE. 2020;15(4):1–21. 10.1371/journal.pone.0231660 . Bashi J, Sia D, Tchouaket E, Balegamire SJ, Karemere H. Mutual health insurance in bukavu in the democratic republic of the congo: Factors favouring the utilization of health services by adherents. Pan Afr Med J. 2020;35:1–11. 10.11604/pamj.2020.35.100.20441 . Barasa E, Kazungu J, Nguhiu P, Ravishankar N. Examining the level and inequality in health insurance coverage in 36 sub-Saharan African countries. BMJ Glob Heal. 2021;6(4). 10.1136/bmjgh-2020-004712 . Kahindo Mbeva J-B, Ndeba PM, Nguemeleu T, Nzanzu E, Nyavanda M, L. K., Syayipuma Kambere J-R. (2023). Enjeux et défis de couverture santé universelle en République Démocratique du Congo: synthèse critique interprétative de la littérature . International Journal of Innovation and Scientific Research , 66(1), 42–56. ISSN 2351–8014. Disponible à l’adresse: https://www.ulb-cooperation.org/wp-content/uploads/2023/04/ijisr-22-359-10-kahindo-et-al-enjeux-defis-csu-rdc.pdf D. T. M. (DTM) de R. — N.-K.: R. d’Enregistrement — G. (Juillet 2023) L’OIM, RDC — Nord-Kivu : Rapport d’Enregistrement — Goma (Juillet 2023), 2023, [Online]. Available: https://dtm.iom.int/reports/rdc-nord-kivu-rapport-denregistrement-goma-juillet-2023 M. Nzanzu, M. N. Prudence, E. Tchouaket, E. T. Musubao, and K. K. Aminata, la République Démocratique du Congo [ Household income and health care expenditure in Goma city,easter … Revenu et dépenses de soins des ménages en milieu urbain de Goma, à l ’ est de la République Démocratique du Congo. vol. 57, no. November, pp. 65–79, 2021. Daabek N, et al. Why People Forgo Healthcare in France: A National Survey of 164 092 Individuals to Inform Healthcare Policy-Makers. Int J Heal Policy Manag. 2022;11(12):2972–81. 10.34172/ijhpm.2022.6310 . Muremyi R, et al. Barriers to health insurance uptake in Rwanda: a nationwide cross-sectional survey. Pan Afr Med J. 2025;51. 10.11604/pamj.2025.51.8.45920 . Topan GJ, Thiombiano N, Sarambe I. Determinants of households’ willingness to pay for health insurance in Burkina Faso. Health Econ Rev. 2024;14(1). 10.1186/s13561-024-00576-6 . Akokuwebe ME, Idemudia ES. A Comparative Cross-Sectional Study of the Prevalence and Determinants of Health Insurance Coverage in Nigeria and South Africa: A Multi-Country Analysis of Demographic Health Surveys. Int J Environ Res Public Health. 2022;19(3). 10.3390/ijerph19031766 . Adewole DA, Reid S, Oni T, Adebowale AS. Factors Influencing Satisfaction with Service Delivery Among National Health Insurance Scheme Enrollees in Ibadan, Southwest Nigeria. J Patient Exp. 2022;9. 10.1177/23743735221074186 . Sendekie AK, Gebremichael AH, Tadesse MW. Enrollment and clients’ satisfaction with a community-based health insurance scheme: a community-based survey in Northwest Ethiopia. BMC Health Serv Res. 2024;24(1):1–11. 10.1186/s12913-024-10570-7 . Osei Afriyie D, Masiye F, Tediosi F, Fink G. November. Confidence in the health system and health insurance enrollment among the informal sector population in Lusaka, Zambia. Soc. Sci. Med. , vol. 321, no. 2022, p. 115750, 2023. 10.1016/j.socscimed.2023.115750 Taddesse G, Atnafu DD, Ketemaw A, Alemu Y. Determinants of enrollment decision in the community-based health insurance, North West Ethiopia: A case-control study. Global Health. 2020;16(1):1–9. 10.1186/s12992-019-0535-1 . Kebede MM. Exploring Factors Influencing Family’s Enrollment in Community-Based Health Insurance in the City of Gondar Peri-Urban Community, Northwest Ethiopia: A Health Belief Model Approach. Risk Manag. Healthc. Policy , vol. 17, no. March, pp. 603–622, 2024, 10.2147/RMHP.S454683 Additional Declarations No competing interests reported. Supplementary Files Englishversionofthestudyquestionnaire.pdf “Additional file 1: English version of the study questionnaire.” Cite Share Download PDF Status: Under Review Version 1 posted Reviews received at journal 10 Jan, 2026 Reviewers agreed at journal 02 Jan, 2026 Reviewers agreed at journal 28 Dec, 2025 Reviewers agreed at journal 21 Dec, 2025 Reviewers invited by journal 19 Dec, 2025 Editor invited by journal 24 Nov, 2025 Editor assigned by journal 24 Nov, 2025 Submission checks completed at journal 24 Nov, 2025 First submitted to journal 24 Nov, 2025 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-8147855","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":563630475,"identity":"9f135738-2391-40e4-b668-fd8e4c4d75e3","order_by":0,"name":"Justin Murhabazi Ntabiruba¹","email":"data:image/png;base64,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","orcid":"","institution":"University of Goma","correspondingAuthor":true,"prefix":"","firstName":"Justin","middleName":"Murhabazi","lastName":"Ntabiruba¹","suffix":""},{"id":563630476,"identity":"50c770e0-3b8d-40c4-bad6-fbdd7231d056","order_by":1,"name":"Célestin Kimanuka Ruriho²","email":"","orcid":"","institution":"National Institute of Statistics","correspondingAuthor":false,"prefix":"","firstName":"Célestin","middleName":"Kimanuka","lastName":"Ruriho²","suffix":""},{"id":563630478,"identity":"0dcb1573-62c5-4af2-86b0-c7a56632c6de","order_by":2,"name":"Zacharie Tsongo Kibendelwa³","email":"","orcid":"","institution":"University of Kisangani","correspondingAuthor":false,"prefix":"","firstName":"Zacharie","middleName":"Tsongo","lastName":"Kibendelwa³","suffix":""},{"id":563630481,"identity":"43e02d8f-1ecb-4ee5-b0f4-5312a71f0963","order_by":3,"name":"Amani Kabesha Théophile⁴","email":"","orcid":"","institution":"Official University of Bukavu","correspondingAuthor":false,"prefix":"","firstName":"Amani","middleName":"Kabesha","lastName":"Théophile⁴","suffix":""},{"id":563630484,"identity":"e938d6b6-61bb-4010-8871-45c9330e538c","order_by":4,"name":"John Inipavudu Baelani⁵","email":"","orcid":"","institution":"University of Goma, Democratic Republic of the Congo","correspondingAuthor":false,"prefix":"","firstName":"John","middleName":"Inipavudu","lastName":"Baelani⁵","suffix":""},{"id":563630488,"identity":"730aa2a7-71a7-4455-bb99-74e3d64bbafb","order_by":5,"name":"Wenceslas Barijoro Birikunjira⁶","email":"","orcid":"","institution":"Higher Institute of Medical Techniques of Goma","correspondingAuthor":false,"prefix":"","firstName":"Wenceslas","middleName":"Barijoro","lastName":"Birikunjira⁶","suffix":""},{"id":563630491,"identity":"02dbec7b-4bd5-411f-a238-71edf74da18e","order_by":6,"name":"Zita Masika Mumbere⁷","email":"","orcid":"","institution":"Notre Vie Mutual Health Organization","correspondingAuthor":false,"prefix":"","firstName":"Zita","middleName":"Masika","lastName":"Mumbere⁷","suffix":""},{"id":563630493,"identity":"eb6b5709-bc5e-4f42-810e-ce307e6ddfa6","order_by":7,"name":"Léon Barigereka Nsengiyumva⁸","email":"","orcid":"","institution":"Higher Institute of Medical Techniques of Rutshuru","correspondingAuthor":false,"prefix":"","firstName":"Léon","middleName":"Barigereka","lastName":"Nsengiyumva⁸","suffix":""},{"id":563630501,"identity":"aba35f0c-a2d2-4ab2-a84e-2a0457a31b8f","order_by":8,"name":"Tambwe patrick","email":"","orcid":"","institution":"Mpox Incident Management System","correspondingAuthor":false,"prefix":"","firstName":"Tambwe","middleName":"","lastName":"patrick","suffix":""},{"id":563630502,"identity":"f82b9b0b-ba3e-4b95-a4ff-46d6ba3219fa","order_by":9,"name":"Arsene Murhambo Ntabiruba","email":"","orcid":"","institution":"University of Goma, Democratic Republic of the Congo","correspondingAuthor":false,"prefix":"","firstName":"Arsene","middleName":"Murhambo","lastName":"Ntabiruba","suffix":""},{"id":563630503,"identity":"6d913fbd-3f28-4119-8ebe-c931478b331e","order_by":10,"name":"Stanis Wembonyama Okitosho","email":"","orcid":"","institution":"University of Lubumbashi","correspondingAuthor":false,"prefix":"","firstName":"Stanis","middleName":"Wembonyama","lastName":"Okitosho","suffix":""}],"badges":[],"createdAt":"2025-11-18 17:08:07","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8147855/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8147855/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":98823520,"identity":"53725f51-af68-442e-8464-e1a9df83de76","added_by":"auto","created_at":"2025-12-22 17:54:06","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":80092,"visible":true,"origin":"","legend":"","description":"","filename":"Revisedmanuscriptcleanversionadhesionor.docx","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/62cbd1af42a0238da2741f61.docx"},{"id":98823519,"identity":"926a20b5-2adf-4528-a4ea-ba885f64a983","added_by":"auto","created_at":"2025-12-22 17:54:06","extension":"json","order_by":1,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":10934,"visible":true,"origin":"","legend":"","description":"","filename":"1f19d663e3594a4fa50f51bba629a3f1.json","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/b1a103a9d4510bc362da8005.json"},{"id":99308170,"identity":"262ccb87-03f9-4708-ad8a-2512328f67e6","added_by":"auto","created_at":"2025-12-31 16:07:54","extension":"pdf","order_by":2,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":232066,"visible":true,"origin":"","legend":"","description":"","filename":"Englishversionofthestudyquestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/2d4d2c2b2c93a4a9aed04af2.pdf"},{"id":98823465,"identity":"343d6e82-e42a-4b5e-b927-96d99cc3b98f","added_by":"auto","created_at":"2025-12-22 17:53:59","extension":"xml","order_by":3,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":115443,"visible":true,"origin":"","legend":"","description":"","filename":"1f19d663e3594a4fa50f51bba629a3f11enriched.xml","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/126af0540a104734ca99ee7d.xml"},{"id":98823523,"identity":"86595fbb-8216-4e5f-8d82-575c102a7f04","added_by":"auto","created_at":"2025-12-22 17:54:07","extension":"xml","order_by":4,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":111913,"visible":true,"origin":"","legend":"","description":"","filename":"1f19d663e3594a4fa50f51bba629a3f11structuring.xml","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/859857af5281ecc66f6f90a9.xml"},{"id":99307620,"identity":"eb095c9e-680c-42bb-9fe1-c268db184750","added_by":"auto","created_at":"2025-12-31 16:06:27","extension":"html","order_by":5,"title":"","display":"","copyAsset":false,"role":"acdc-reference","size":127432,"visible":true,"origin":"","legend":"","description":"","filename":"earlyproof.html","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/1a243c9a2a4bc33f38401ebd.html"},{"id":99322178,"identity":"b7e689c7-cac2-42e7-a4b4-f97aaf512605","added_by":"auto","created_at":"2025-12-31 16:43:05","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1392027,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/416c63e1-87df-4c74-8d2b-0c751227da7a.pdf"},{"id":98823522,"identity":"b920248a-0736-4a65-86d9-970b2f1acb03","added_by":"auto","created_at":"2025-12-22 17:54:06","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":232066,"visible":true,"origin":"","legend":"\u003cp\u003e“Additional file 1: English version of the study questionnaire.”\u003c/p\u003e","description":"","filename":"Englishversionofthestudyquestionnaire.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8147855/v1/998d6874b728dd380ed7b95a.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eDeterminants of Household Participation in Healthcare Prepayment Schemes in the City of Goma, in the Eastern Democratic Republic of the Congo: A Cross-Sectional Study\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003ePrepayment for healthcare is a financing mechanism in which individuals pay in advance for part or all of the cost of health services before they are used. Payments are made by individuals through taxes or contributions to a health insurance scheme prior to using health services, and the prepaid contributions are pooled[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. Over the past decade, the World Health Organization, in collaboration with the World Bank, has reaffirmed its commitment to universal health coverage.\u003c/p\u003e \u003cp\u003eThe Political Declaration adopted at the UN Summit in September 2023 reiterates the need for States to mobilize robust policies and financing to accelerate the achievement of UHC by 2030, with a particular focus on investing in primary care, equitable access, and financial risk protection [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Most developing countries have faced challenges in sustaining the financing of their health systems over the past two decades. Out-of-pocket payments accounted for a significant share of health expenditures in these countries, whereas developed countries moved toward establishing prepayment mechanisms [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eSince the introduction of direct payments for healthcare in the 1980s in low- and middle-income countries, the discourse among global health actors has increasingly shifted against this mode of health financing [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. According to the Tracking Universal Health Coverage: 2023 Global Monitoring Report, jointly published by the World Bank and the World Health Organization (WHO) on 18 September 2023, more than half of the world\u0026rsquo;s population approximately 4.5\u0026nbsp;billion people still does not have full access to essential health services. In addition, 2\u0026nbsp;billion people face severe financial hardship due to direct health expenditures, and about 1.3\u0026nbsp;billion people have been pushed, or further pushed, into poverty because of these expenses [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the regional level in Africa, the share of general public expenditure allocated to health generally does not exceed 7\u0026ndash;8%, which remains far below the 15% target set by the Abuja Declaration[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In most low-income countries, the majority of health expenditures about 35% on average, and often up to 60% is paid directly by patients in the form of out-of-pocket payments. In some countries, this rate reaches 40\u0026ndash;43% of total health expenditures. These direct payments are identified by the WHO as the leading cause of financial hardship among households[\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. In Africa, the proportion of households that had to borrow money or sell assets to cover their healthcare expenses ranged from 23% in Zambia to 68% in Burkina Faso [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e]. This situation in Africa also affects the Democratic Republic of Congo, where the health financing system is clearly too fragile to protect households from catastrophic health expenditures. Out-of-pocket payments remain the dominant mode of health financing in the DRC, with over 90% of households relying on them[\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. In 2023, in South Kivu province, more than 50% of households had to sell assets to cover medical expenses (compared to 65.5% in 2011). In Kenge, about 35% of households sold assets to pay for healthcare, whereas in Goma, only 2.8% of households had to sell or pawn assets [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. In the Democratic Republic of Congo (DRC), the government and Parliament have adopted a strategy based on health mutuals (MUSA) and health insurance as pillars of the national health financing policy[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese reforms appear to resonate to some extent with the population: various community-based health mutual initiatives have emerged in several provinces, such as North Kivu and Bukavu. In the city of Bukavu, local health mutuals (MUSA) have been studied and identified as promoting better utilization of health services among their members. Members of these mutuals seek care more frequently and spend less on healthcare than non-members[\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAmong the major challenges faced by health mutuals in sub-Saharan Africa, population enrollment remains the primary difficulty. Despite the apparent growth of the mutualist movement, coverage rates remain very low: in most countries, less than 10% of the population is covered by a mutual, with only a few exceptions exceeding 20%[\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Despite intensive awareness campaigns on health mutuals, enrollment is progressing very slowly in the Democratic Republic of Congo (DRC). According to national health accounts, the national coverage rate reached 8% in 2020, while the 2018 MICS 3 survey reported coverage below 5% (4.3% among women aged 15\u0026ndash;49 years and 4.1% among men aged 15\u0026ndash;59 years)[\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. In the current context of the DRC\u0026rsquo;s commitment to universal health coverage (UHC), with North Kivu province also engaged in the UHC implementation process, a key question is what factors limit household enrollment in health mutuals in the city of Goma, eastern DRC. Studying this phenomenon in the urban setting of Goma could help refine strategies to develop an evidence-based health insurance system that considers the specificities of the urban Kivu context. Within this framework, this study aims to identify the determinants of household participation in healthcare prepayment schemes in Goma, eastern DRC.\u003c/p\u003e"},{"header":"Materials and Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy Setting\u003c/h2\u003e \u003cp\u003eThe study site, the city of Goma, is the capital of North Kivu province in eastern Democratic Republic of Congo (DRC). North Kivu province faces recurrent violence due to the ongoing conflict between the Armed Forces of the Democratic Republic of Congo (FARDC) and the armed group March 23 Movement (M23). This conflict has exacerbated an already critical humanitarian situation and has led to large-scale displacement within North Kivu province [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eLocated between Virunga National Park to the northwest, Lake Kivu to the south, and Rwanda to the east, the city of Goma had an estimated population of around 2\u0026nbsp;million in 2023, showing substantial growth from 1\u0026nbsp;million in 2017. A large proportion of the population (over 80%) lives in precarious conditions, particularly in the informal sector. The economic situation is also characterized by significant challenges, exacerbated by political instability and armed conflicts [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. In terms of healthcare, the city of Goma is served by two urban health zones, Goma and Karisimbi, which continue to face multiple challenges, including limited access to health services due to insecurity and high population density [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eStudy Design, Period, Population, and Sampling\u003c/h3\u003e\n\u003cp\u003eThis descriptive and analytical study was conducted from 1 to 30 January 2024 among households in the city of Goma, eastern DRC. The study population comprised households in Goma, estimated at 160,310 in 2023. Households were eligible if they were permanent residents of Goma during the study period and had an adult respondent (\u0026ge;\u0026thinsp;18 years) available to provide the required information. Participation required informed consent. Only households listed in the sampling frame and consenting to the survey were included.\u003c/p\u003e \u003cp\u003eThe sample size was calculated using Cochran\u0026rsquo;s formula:\u003cdiv id=\"Equa\" class=\"Equation\"\u003e\u003cdiv format=\"TEX\" class=\"mathdisplay\" id=\"FileID_Equa\" name=\"EquationSource\"\u003e\n$$\\:n=\\frac{{Z}^{2}\\cdot\\:p(1-p)}{{e}^{2}}$$\u003c/div\u003e\u003c/div\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:n\\)\u003c/span\u003e \u003c/span\u003e= required sample size\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Z\\)\u003c/span\u003e \u003c/span\u003e= 1.96 for 95% confidence level\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:p\\)\u003c/span\u003e \u003c/span\u003e= estimated proportion of healthcare prepayment, set at 50%\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:e\\)\u003c/span\u003e \u003c/span\u003e= margin of error, set at 5% (0.05)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe calculated sample size was 384 households. To account for cluster sampling and non-respondents, the final sample size was set at 807 households. Considering the population weight of the two health zones in Goma, the sample was distributed as follows: 266 households for Goma zone and 541 households for Karisimbi zone.\u003c/p\u003e \u003cp\u003eA multistage probabilistic sampling method was used:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eFirst stage: Each health zone served as a stratum.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSecond stage: Clusters were determined within each stratum using ENA software.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eThird stage: Households were selected by simple random sampling. With the assistance of community relays and street leaders, enumerators located randomly selected households using a random number generator.\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cp\u003eThe sampling frame was based on lists prepared by the Goma and Karisimbi health zone teams in collaboration with community relays.\u003c/p\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected using a structured questionnaire, pre-tested in Mugunga, 10 km west of Goma.\u003c/p\u003e \u003cp\u003eThe questionnaire used in this study was specifically developed for the purpose of this research, based on determinants identified in the literature and contextual factors relevant to healthcare prepayment schemes in Goma. An English version of the questionnaire is provided as Additional file 1.\u003c/p\u003e \u003cp\u003eThe survey was conducted in January 2024 by 10 trained enumerators from the University of Goma School of Public Health. Training lasted three days and included translation of the questionnaire into Swahili, the most widely spoken language in Goma.\u003c/p\u003e \u003cp\u003eThe primary respondents were household heads. In their absence, the oldest adult present provided the information. Data were collected via direct administration of the questionnaire using KoboToolbox.\u003c/p\u003e\n\u003ch3\u003eStudy Variables\u003c/h3\u003e\n\u003cp\u003eThe questionnaire covered seven main domains:\u003c/p\u003e \u003cp\u003e \u003col\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHousehold participation in healthcare prepayment(adherent vs. non-adherent)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSociodemographic determinants (sex, age, household size, marital status)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSociocultural determinants (religion, prior association membership, education level, ethnicity, mutual health knowledge)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eSocioeconomic determinants (wealth level, occupation)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eHealthcare service-related determinants (reception, waiting time, therapeutic use, household\u0026ndash;facility distance, staff efficiency, drug availability and quality, explanation of care, care-related adverse events)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003cspan\u003e \u003cli\u003e \u003cp\u003eMutual health scheme-related determinants (benefit package, co-payment, affiliation method, internal regulations, contribution amount, membership fees, trust in management)\u003c/p\u003e \u003c/li\u003e \u003c/span\u003e \u003c/ol\u003e \u003c/p\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eData Analysis\u003c/h2\u003e \u003cp\u003eData collected via KoboToolbox were cleaned and analyzed using SPSS. Statistical analyses included:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eUnivariate analyses: frequencies, proportions, means\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eBivariate analyses: Chi-square and Fisher\u0026rsquo;s exact tests\u003c/p\u003e \u003c/li\u003e \u003cli\u003e \u003cp\u003eMultivariate analyses: binary logistic regression, with estimation of odds ratios (OR) and 95% confidence intervals (CI)\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eModel quality was assessed using the ROC curve. Test assumptions were verified, confirming validity. The logistic regression model showed excellent performance, while the discriminant model demonstrated good performance. Statistical significance was set at \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:p\u0026lt;0.05\\)\u003c/span\u003e\u003c/span\u003e(α\u0026thinsp;=\u0026thinsp;5%)\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eSociodemographic and Cultural Characteristics\u003c/h2\u003e \u003cp\u003eAmong the 807 household heads surveyed, 54.2% were aged 30 years or older, and 57.9% were women. The majority were married or in union (67.4%), and households with fewer than six members predominated (71.3%). Christianity was the predominant religion (92.1%), and 51.1% of participants had prior association membership. More than half had attained secondary education (56.8%), and 64.6% belonged to southern tribes. Finally, 64.2% of households demonstrated insufficient knowledge of health mutuals.\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\u003eSociodemographic and Cultural Characteristics of Surveyed Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFr\u0026eacute;quency(n\u0026thinsp;=\u0026thinsp;807)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePourcentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAge (Year)\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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e370\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e45,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e437\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexe\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e57,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e340\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e42,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e544\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e67,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot in union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e263\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSize\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e232\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e28,7\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;6\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e575\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e71,3\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReligion\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eChristian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e743\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e92,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot Christian\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e64\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociated past\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e51,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e395\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e48,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary or higher\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e458\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e56,8\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary or lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e349\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e43,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTribe\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNorthern tribe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e286\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e35,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSouthern tribe\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e521\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64,6\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnowledge level\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e4,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e253\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e31,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e518\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e64,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eSocioeconomic Characteristics and Participation in Healthcare Prepayment\u003c/h3\u003e\n\u003cp\u003eAmong the 807 participants, nearly six in ten (59.4%) lived in poverty, and more than half (54.9%) were employed in the informal sector. The vast majority (86.9%) were not enrolled in any healthcare prepayment system.\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\u003eSocioeconomic Characteristics and Participation in Healthcare Prepayment among Surveyed Participants\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\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 \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFrequency (n\u0026thinsp;=\u0026thinsp;807)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePourcentage\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWealth 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 \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewealthy\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e141\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17,5\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMiddle\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e23,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e479\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e59,4\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOcupation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e260\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e32,2\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e443\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e54,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eUnemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e104\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e12,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eParticipation in Prepayment Schemes\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e701\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e86,9\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e13,1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eExplanatory Factors of Participation in Healthcare Prepayment\u003c/h2\u003e \u003cp\u003eTables\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e, \u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, and \u003cspan refid=\"Tab6\" class=\"InternalRef\"\u003e6\u003c/span\u003e show that household participation in healthcare prepayment schemes in Goma was influenced by several factors. Regarding sociodemographic and cultural factors, young household heads (\u0026lt;\u0026thinsp;30 years) were more likely to participate (OR\u0026thinsp;=\u0026thinsp;1.8), while male gender (OR\u0026thinsp;=\u0026thinsp;0.3), unmarried status (OR\u0026thinsp;=\u0026thinsp;0.3), low education level (OR\u0026thinsp;=\u0026thinsp;0.3), lack of prior association membership (OR\u0026thinsp;=\u0026thinsp;0.09), and insufficient knowledge of mutual health schemes (OR\u0026thinsp;=\u0026thinsp;0.04) reduced participation.\u003c/p\u003e \u003cp\u003eIn terms of socioeconomic factors, unemployment (OR\u0026thinsp;=\u0026thinsp;0.4) and poor wealth status (OR\u0026thinsp;=\u0026thinsp;0.3) also decreased the likelihood of participation. Among healthcare service-related factors, poor reception (OR\u0026thinsp;=\u0026thinsp;0.9), incompetent staff (OR\u0026thinsp;=\u0026thinsp;0.6), care-related accidents (OR\u0026thinsp;=\u0026thinsp;0.3), unavailability of medicines (OR\u0026thinsp;=\u0026thinsp;0.5), and lack of explanations during care (OR\u0026thinsp;=\u0026thinsp;0.5) were unfavorable.\u003c/p\u003e \u003cp\u003eFinally, regarding the mutual health schemes themselves, an incomplete benefit package (OR\u0026thinsp;=\u0026thinsp;0.2), high contribution fees (OR\u0026thinsp;=\u0026thinsp;0.9), and lack of trust in scheme management (OR\u0026thinsp;=\u0026thinsp;0.2) reduced participation.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction of Household Participation in Healthcare Prepayment Schemes in Goma City (Socioeconomic Factors)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eIC for Exp(B) 95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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 \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eOcupation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFormal\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eInformal\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,698\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,019\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,498\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,277\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,893\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eunemployed\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,761\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,009\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,467\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,121\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,798\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eWealth 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ewealthy\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRich\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,463\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,629\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,321\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,933\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,986\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,107\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,802\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction of Household Participation in Healthcare Prepayment Schemes in Goma City (Sociodemographic and Cultural Factors)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eIC for Exp(B) 95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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 \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAge ( year)\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;30\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u0026lt;\u0026thinsp;30 ans\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e,598\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1,819\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1,109\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3,279\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSexe\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFemale\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,677\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,275\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital 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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIn union\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot in union\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,057\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,347\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,134\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,899\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAssociated past\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-2,383\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,092\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,038\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,225\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSecondary and higher\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePrimary and lower\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,106\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,331\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,159\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,689\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eKnowledge level\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,140\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,001\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,320\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,166\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,617\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-3,063\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,047\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,174\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction of Household Participation in Healthcare Prepayment Schemes in Goma City (Factors Related to Healthcare Service Provision)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eIC pour Exp(B) 95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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 \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eReception in the health facility\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVery Good\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,380\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,684\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,487\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,996\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePoor\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,037\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,464\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,242\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,720\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eStaff effectiveness\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompetent\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNot competent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,469\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,005\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,626\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,251\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,962\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAvailability and quality of medicines\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine available and of good quality\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine available but of poor quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,466\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,231\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,145\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,367\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMedicine not available\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,672\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,035\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,997\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCare-related accidents\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo accident\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith accident\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-1,049\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,034\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,350\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,133\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,922\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eExplenation care\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWith explanation\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo explanation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,536\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,585\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,373\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,917\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab6\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 6\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003ePrediction of Household Enrollment in Prepayment Schemes in Goma City (Factors Related to Health Insurance Schemes)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eA\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSig.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eExp(B)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eIC pour Exp(B) 95%\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\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 \u003cp\u003elow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003ehigh\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eBenefit package offered\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCompleted\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIncompleted\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,910\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,202\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,102\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,898\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eContribution amount\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLess expensive\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eExpensive\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,071\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,003\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,432\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,777\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eTrust in mutual management\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 \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eYes\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 \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-,606\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,006\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,180\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,971\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab7\" class=\"InternalRef\"\u003e7\u003c/span\u003e shows excellent performance for the logistic regression model, with an area under the curve (AUC) of 0.9, and good performance for the discriminant model, with an AUC of 0.8.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab7\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 7\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eArea Under the ROC Curve for the Logistic and Discriminant Models in Multivariate Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\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 \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOutcome variable(s) tested\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eArea under the curve (AUC)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eError Std.\u003csup\u003ea\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u003csup\u003eAsymptotic significance\u003c/sup\u003e\u003c/p\u003e \u003cp\u003e\u003csup\u003eb\u003c/sup\u003e\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003e95% Asymptotic Confidence Interva\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eLower bound\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eHiger bound\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003ePredicted probability (logistic)\u0026rdquo;\u003c/p\u003e \u003cp\u003e\u0026ldquo;Predicted probability (discriminant)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e,918\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,015\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,888\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,948\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e,837\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e,020\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e,798\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e,877\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study aimed to identify the determinants of household participation in healthcare prepayment schemes in an urban African setting, specifically in the city of Goma, eastern Democratic Republic of Congo.\u003c/p\u003e \u003cp\u003eThe results highlighted several predictors of participation in prepayment schemes. Regarding overall participation, our study found that 13.1% of households were enrolled in healthcare prepayment schemes. Although this rate is relatively low, it should be considered in the context of other recent studies conducted in similar or comparable settings.\u003c/p\u003e \u003cp\u003eFor instance, a study conducted in the Democratic Republic of Congo in 2015 reported a health mutual enrollment rate of approximately 6.3%. In comparison, our finding of 13.1% represents a notable improvement, possibly reflecting a slightly more favorable socioeconomic or institutional context, or more advanced efforts to promote prepayment schemes[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFurthermore, considering a more developed context such as France, a national study conducted between 2015 and 2018 found that 25.4% of individuals reported forgoing care for financial reasons, particularly due to upfront costs or high out-of-pocket expenses. Although this figure is indirect, it highlights that even in systems where prepayment mechanisms are well established (e.g., Social Security and mutual health insurance), financial access to care is not always guaranteed. This underscores that enrollment in prepayment schemes alone is insufficient to ensure effective coverage, especially when reimbursement mechanisms are complex or partial [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThus, our observed rate of 13.1% falls between two extremes: it is higher than that seen in nascent or fragile systems such as in the DRC, yet remains far below the desired levels of enrollment, and well below those observed in countries with more developed universal health coverage systems. This highlights a concerning reality: a large majority of the population (nearly 87%) is still not covered by any prepayment mechanism, which may lead to forgoing care, delays in treatment, or worsening health conditions. This comparison underscores the need to strengthen awareness efforts, improve trust in prepayment schemes, and consider targeted subsidies or inclusion mechanisms to expand enrollment, particularly among the most vulnerable populations.\u003c/p\u003e \u003cp\u003eOur study identified several sociodemographic and sociocultural factors influencing household participation in healthcare prepayment schemes in Goma. These factors include age, gender, marital status, education level, prior association membership, and knowledge of mutual health schemes.\u003c/p\u003e \u003cp\u003eA recent study on health insurance in Rwanda identified several determinants of enrollment: being female increases the likelihood of enrollment (aOR\u0026thinsp;\u0026asymp;\u0026thinsp;1.20), higher education levels (secondary or tertiary) strongly increase the chances of being enrolled, and age is also influential, with younger individuals aged 14\u0026ndash;24 much less likely to be insured (aOR\u0026thinsp;\u0026asymp;\u0026thinsp;0.30). Marital status matters as well: single individuals are less likely to be covered, while widowed individuals are more often insured. Additionally, income or socioeconomic status plays a major role, as does living in the capital city[\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. A study in Burkina Faso revealed that the key determinants of willingness to pay for health insurance were household income, type of occupation, and, once again, the education level of the household head. Association experience and perceptions of service quality also played a role, with greater willingness to pay observed among individuals who were better informed, involved in associations, or engaged in stable employment [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur results align with the general pattern observed in several African countries: enrollment in prepayment systems is strongly influenced by sociodemographic variables such as education, age, gender, marital status, and economic situation. Additionally, prior association membership and knowledge of the system emerge as essential factors, as confirmed by the Burkina Faso study. This suggests that our findings are robust and broadly consistent with observations elsewhere in sub-Saharan Africa. To promote participation in prepayment schemes, it will be important not only to strengthen educational and informational campaigns but also to integrate associations and potentially community leaders into mobilization strategies.\u003c/p\u003e \u003cp\u003eOur study identified occupation and income level as key socioeconomic factors influencing household enrollment in healthcare prepayment schemes in Goma. Specifically, unemployment decreased the likelihood of enrollment in health mutuals among Goma households (OR\u0026thinsp;=\u0026thinsp;0.4), and poor income status also reduced the likelihood of enrollment (OR\u0026thinsp;=\u0026thinsp;0.3). In Nigeria and South Africa, cross-sectional analyses based on Demographic and Health Surveys showed that employed individuals are significantly more likely to have health insurance. Unemployed individuals in these countries are less likely to be insured, with a high proportion reporting no coverage (30% in Nigeria, 66% in South Africa). These findings clearly demonstrate that employment status, in particular being employed, is a crucial determinant of enrollment in prepayment systems [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA study in Rwanda highlighted that socioeconomic status, measured notably by income or the Ubudehe classification, strongly influences enrollment in health insurance. Non-poor households were two to seven times more likely to be covered than poor households. In contrast, the most economically vulnerable (poor) are much less likely to have insurance due to their inability to pay premiums [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur findings align with observations from Nigeria and South Africa, where the unemployed are much less likely to be insured, and with Rwanda, where poor households have a high likelihood of being uninsured. This implies that, to increase enrollment rates in Goma, targeted subsidy mechanisms for poor or unemployed households should be considered, as well as alternatives adapted for people without employment, such as contributions scaled to actual capacity or flexible contributory models. Pro-employment and financial inclusion policies could also enhance access to health insurance through stable jobs or income-generating activities.\u003c/p\u003e \u003cp\u003eOur study also identified several healthcare service-related factors that influence household enrollment in mutual health schemes in Goma. These factors include reception, staff effectiveness, availability and quality of medicines, care-related accidents, and clarity of care explanations.\u003c/p\u003e \u003cp\u003eA study assessing beneficiary satisfaction with the NHIS in Ibadan, Nigeria, revealed that perceived service quality particularly staff effectiveness and information available prior to enrolment significantly influenced both enrollment and satisfaction. Private-sector staff and individuals who sought information about service quality were more satisfied (OR\u0026thinsp;\u0026asymp;\u0026thinsp;1.8 for private-sector workers). Although the impact of incompetent staff was not precisely quantified in this study, professional competence and reception clearly emerge as key determinants of satisfaction, and therefore of enrollment or renewal[\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn a survey conducted in 2022 in Gondar Zuria, Northwest Ethiopia, although the enrollment rate was high (65%), a large proportion of households reported dissatisfaction with the services. The main reasons included long waiting times, unavailability of medicines and laboratory tests (up to 88.7% dissatisfaction), and slow service delivery [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Our data show strong similarities with findings from Nigeria (Ibadan) and Ethiopia (Gondar): the quality of healthcare services, staff competence and behavior, availability of medicines, and waiting times are major determinants of enrollment in mutual health schemes. The barriers identified in Goma illustrate how degraded services can hinder participation. This suggests the need to strengthen staff training in patient relations and clear communication, improve logistical management to ensure the consistent availability of essential medicines, optimize service organization, and establish feedback systems allowing households to report accidents or malpractice, in order to restore trust and encourage long-term enrollment. Our study also shows that several factors related to the health mutuals themselves explain household enrollment in Goma: the benefit package offered, contribution amount, and trust in the mutual\u0026rsquo;s management. A survey of informal sector workers in Lusaka, Zambia, highlighted that trust in the private health system\u0026rsquo;s ability to provide effective care was strongly associated with health insurance enrollment (OR\u0026thinsp;\u0026asymp;\u0026thinsp;3.4). In contrast, trust in public management or government institutions was not significantly related to enrollment[\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] .\u003c/p\u003e \u003cp\u003eA study conducted in a peri-urban area of Gondar, Ethiopia, identified that trust in the management of the Community-Based Health Insurance (CBHI) was a major determinant of enrollment (OR\u0026thinsp;\u0026asymp;\u0026thinsp;0.40, with the scale inverted depending on the variable). The researchers also emphasized the importance of households\u0026rsquo; understanding of the system\u0026rsquo;s modalities [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e]. In Goma, a limited benefit package drastically reduces enrollment (OR\u0026thinsp;=\u0026thinsp;0.2). Although this specific variable is not quantified in the cited studies, meta-analyses from Ethiopia indicate that the perception of healthcare service quality including the range of services provided and their effectiveness strongly promotes enrollment [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. The observations made in Goma align closely with findings from other African countries. This implies that, to encourage enrollment in health mutuals, it is essential to strengthen transparency and accountability to build trust, design more attractive benefit packages that include clearly communicated essential services, adjust contributions based on perceived service value, and offer subsidized or tiered rates when necessary.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eEnrollment in healthcare prepayment schemes remains low, hindered by multiple sociodemographic, economic, cultural, service quality, and mutual organization related factors. This situation can lead to forgone care, delays in treatment, or worsening health conditions. These findings highlight the need for targeted actions aimed at improving the quality of care, making services more financially accessible, and strengthening users\u0026rsquo; trust in mutual health systems. Combined, these efforts could positively impact household enrollment in prepayment schemes in Goma and contribute to advancing financial protection for healthcare users.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors extend sincere appreciation to all who supported the study.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; Contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eJ.M.N. and C.K.R. conceived and designed the study.\u003c/p\u003e\n\u003cp\u003eZ.T.K., A.K.T., and J.I.B. coordinated field implementation, data collection, and analysis.\u003c/p\u003e\n\u003cp\u003eW.B.B., Z.M.M., and L.B.N. contributed to questionnaire development and data validation.\u003c/p\u003e\n\u003cp\u003eT.P. drafted the initial manuscript, while J.M.N. provided methodological supervision and critical review.\u003c/p\u003e\n\u003cp\u003eA.M.N. and S.W.O. supported community engagement and participant recruitment.\u003c/p\u003e\n\u003cp\u003eAll authors contributed to data interpretation, revised the manuscript critically, and approved the final version for submission.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study did not receive any specific grant from funding agencies in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of Data and Materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets generated and analyzed during this study are available from the corresponding author upon reasonable request. To protect participant confidentiality, only de-identified data will be shared.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval and Consent to Participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study protocol was approved by the Ethics Committee of the University of Goma (Reference: UNIGOM/CEM/008/2023). Informed consent was obtained from all participants, and confidentiality and anonymity were strictly maintained throughout data collection and analysis. The research was conducted \u003cstrong\u003ein accordance with the ethical principles outlined in the Declaration of Helsinki (2013 revision)\u003c/strong\u003e\u003cstrong\u003e.\u003c/strong\u003e Participation was voluntary, anonymous, and confidential, and participants were free to withdraw at any time without consequence.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for Publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting Interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eKabinda JM, Mitashi PM, Chenge FM. Analysis of financing arrangements for health care in the Democratic Republic of Congo: a systematic review. Ann Africaines M\u0026eacute;decine Artic Orig Ann Afr Med. 2019;12(2):3203. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.13140/RG.2.2.35834.52169\u003c/span\u003e\u003cspan address=\"10.13140/RG.2.2.35834.52169\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003e\u003cem\u003eTracking Universal Health Coverage. 2023 Global Monitoring Report\u003c/em\u003e. 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1596/40348\u003c/span\u003e\u003cspan address=\"10.1596/40348\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNdiaye I, et al. \u0026Eacute;tude des facteurs associ\u0026eacute;s \u0026agrave; l\u0026rsquo;adh\u0026eacute;sion des populations aux mutuelles de sant\u0026eacute; au S\u0026eacute;n\u0026eacute;gal en 2019. Rev Epidemiol Sante Publique. 2023;71:102043. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.respe.2023.102043\u003c/span\u003e\u003cspan address=\"10.1016/j.respe.2023.102043\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRobert E, Ridde V. Les paiements directs des soins dans les pays \u0026agrave; faible et moyen revenu ne font plus l\u0026rsquo;unanimit\u0026eacute; au sein de la communaut\u0026eacute; internationale. Une analyse documentaire, \u003cem\u003eLes Cah. du CIRDIS - Collect. Rech.\u003c/em\u003e, no. 2012\u0026ndash;03, p. 33 pages, 2012, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.ieim.uqam.ca/IMG//pdf/Cahier_CIRDIS_-_no_2012_-_03.pdf\u003c/span\u003e\u003cspan address=\"http://www.ieim.uqam.ca/IMG//pdf/Cahier_CIRDIS_-_no_2012_-_03.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Health Organization. Global spending on health 2020. Weathering the storm. Geneva: OMS. 2020. ISBN 978-92-4-001778-8 (\u0026eacute;lectronique), 978-92-4-001779-5 (papier). Disponible sur: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://iris.who.int/handle/10665/337859\u003c/span\u003e\u003cspan address=\"https://iris.who.int/handle/10665/337859\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTimothy PO. Financial Inclusion and Health Shocks. A Panel Data Analysis of 36 African Countries. Asian J Econ Empir Res. 2019;6(1):45\u0026ndash;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.20448/journal.501.2019.61.45.51\u003c/span\u003e\u003cspan address=\"10.20448/journal.501.2019.61.45.51\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNyamugira AB, Flessa S. Richter.Health insurance uptake, poverty and financial inclusion in the Democratic Republic of Congo. Sustain Dev. 2024;32(4):3293\u0026ndash;312. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1002/sd.2841\u003c/span\u003e\u003cspan address=\"10.1002/sd.2841\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMugisa A, et al. Trends in socio-economic level and accessibility to healthcare of households in Eastern Democratic Republic of Congo from 2011 to 2023: Documentary review of South Kivu. no January. 2025. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.21203/rs.3.rs-5866175/v1\u003c/span\u003e\u003cspan address=\"10.21203/rs.3.rs-5866175/v1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eCriel B, Waelkens MP, Nappa FK, Coppieters Y, Laokri S. Can mutual health organisations influence the quality and the affordability of healthcare provision? The case of the Democratic Republic of Congo. PLoS ONE. 2020;15(4):1\u0026ndash;21. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1371/journal.pone.0231660\u003c/span\u003e\u003cspan address=\"10.1371/journal.pone.0231660\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBashi J, Sia D, Tchouaket E, Balegamire SJ, Karemere H. Mutual health insurance in bukavu in the democratic republic of the congo: Factors favouring the utilization of health services by adherents. Pan Afr Med J. 2020;35:1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11604/pamj.2020.35.100.20441\u003c/span\u003e\u003cspan address=\"10.11604/pamj.2020.35.100.20441\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBarasa E, Kazungu J, Nguhiu P, Ravishankar N. Examining the level and inequality in health insurance coverage in 36 sub-Saharan African countries. BMJ Glob Heal. 2021;6(4). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1136/bmjgh-2020-004712\u003c/span\u003e\u003cspan address=\"10.1136/bmjgh-2020-004712\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKahindo Mbeva J-B, Ndeba PM, Nguemeleu T, Nzanzu E, Nyavanda M, L. K., Syayipuma Kambere J-R. (2023). \u003cem\u003eEnjeux et d\u0026eacute;fis de couverture sant\u0026eacute; universelle en R\u0026eacute;publique D\u0026eacute;mocratique du Congo: synth\u0026egrave;se critique interpr\u0026eacute;tative de la litt\u0026eacute;rature\u003c/em\u003e. \u003cem\u003eInternational Journal of Innovation and Scientific Research\u003c/em\u003e, 66(1), 42\u0026ndash;56. ISSN 2351\u0026ndash;8014. Disponible \u0026agrave; l\u0026rsquo;adresse: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.ulb-cooperation.org/wp-content/uploads/2023/04/ijisr-22-359-10-kahindo-et-al-enjeux-defis-csu-rdc.pdf\u003c/span\u003e\u003cspan address=\"https://www.ulb-cooperation.org/wp-content/uploads/2023/04/ijisr-22-359-10-kahindo-et-al-enjeux-defis-csu-rdc.pdf\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eD. T. M. (DTM) de R. \u0026mdash; N.-K.: R. d\u0026rsquo;Enregistrement \u0026mdash; G. (Juillet 2023) L\u0026rsquo;OIM, RDC \u0026mdash; Nord-Kivu : Rapport d\u0026rsquo;Enregistrement \u0026mdash; Goma (Juillet 2023), 2023, [Online]. Available: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://dtm.iom.int/reports/rdc-nord-kivu-rapport-denregistrement-goma-juillet-2023\u003c/span\u003e\u003cspan address=\"https://dtm.iom.int/reports/rdc-nord-kivu-rapport-denregistrement-goma-juillet-2023\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eM. Nzanzu, M. N. Prudence, E. Tchouaket, E. T. Musubao, and K. K. Aminata, la R\u0026eacute;publique D\u0026eacute;mocratique du Congo [ Household income and health care expenditure in Goma city,easter \u0026hellip; Revenu et d\u0026eacute;penses de soins des m\u0026eacute;nages en milieu urbain de Goma, \u0026agrave; l \u0026rsquo; est de la R\u0026eacute;publique D\u0026eacute;mocratique du Congo. vol. 57, no. November, pp. 65\u0026ndash;79, 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDaabek N, et al. Why People Forgo Healthcare in France: A National Survey of 164 092 Individuals to Inform Healthcare Policy-Makers. Int J Heal Policy Manag. 2022;11(12):2972\u0026ndash;81. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.34172/ijhpm.2022.6310\u003c/span\u003e\u003cspan address=\"10.34172/ijhpm.2022.6310\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMuremyi R, et al. Barriers to health insurance uptake in Rwanda: a nationwide cross-sectional survey. Pan Afr Med J. 2025;51. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.11604/pamj.2025.51.8.45920\u003c/span\u003e\u003cspan address=\"10.11604/pamj.2025.51.8.45920\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTopan GJ, Thiombiano N, Sarambe I. Determinants of households\u0026rsquo; willingness to pay for health insurance in Burkina Faso. Health Econ Rev. 2024;14(1). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s13561-024-00576-6\u003c/span\u003e\u003cspan address=\"10.1186/s13561-024-00576-6\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAkokuwebe ME, Idemudia ES. A Comparative Cross-Sectional Study of the Prevalence and Determinants of Health Insurance Coverage in Nigeria and South Africa: A Multi-Country Analysis of Demographic Health Surveys. Int J Environ Res Public Health. 2022;19(3). \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.3390/ijerph19031766\u003c/span\u003e\u003cspan address=\"10.3390/ijerph19031766\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAdewole DA, Reid S, Oni T, Adebowale AS. Factors Influencing Satisfaction with Service Delivery Among National Health Insurance Scheme Enrollees in Ibadan, Southwest Nigeria. J Patient Exp. 2022;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1177/23743735221074186\u003c/span\u003e\u003cspan address=\"10.1177/23743735221074186\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSendekie AK, Gebremichael AH, Tadesse MW. Enrollment and clients\u0026rsquo; satisfaction with a community-based health insurance scheme: a community-based survey in Northwest Ethiopia. BMC Health Serv Res. 2024;24(1):1\u0026ndash;11. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12913-024-10570-7\u003c/span\u003e\u003cspan address=\"10.1186/s12913-024-10570-7\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eOsei Afriyie D, Masiye F, Tediosi F, Fink G. November. Confidence in the health system and health insurance enrollment among the informal sector population in Lusaka, Zambia. \u003cem\u003eSoc. Sci. Med.\u003c/em\u003e, vol. 321, no. 2022, p. 115750, 2023. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1016/j.socscimed.2023.115750\u003c/span\u003e\u003cspan address=\"10.1016/j.socscimed.2023.115750\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTaddesse G, Atnafu DD, Ketemaw A, Alemu Y. Determinants of enrollment decision in the community-based health insurance, North West Ethiopia: A case-control study. Global Health. 2020;16(1):1\u0026ndash;9. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1186/s12992-019-0535-1\u003c/span\u003e\u003cspan address=\"10.1186/s12992-019-0535-1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKebede MM. Exploring Factors Influencing Family\u0026rsquo;s Enrollment in Community-Based Health Insurance in the City of Gondar Peri-Urban Community, Northwest Ethiopia: A Health Belief Model Approach. \u003cem\u003eRisk Manag. Healthc. Policy\u003c/em\u003e, vol. 17, no. March, pp. 603\u0026ndash;622, 2024, \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.2147/RMHP.S454683\u003c/span\u003e\u003cspan address=\"10.2147/RMHP.S454683\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\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":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Community-based health insurance, Healthcare prepayment, Determinants, Household enrollment, Goma, Democratic Republic of Congo","lastPublishedDoi":"10.21203/rs.3.rs-8147855/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8147855/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cb\u003eBackground\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOut-of-pocket health expenditures are often difficult for households to manage and can lead to financial hardship and poverty. Prepayment mechanisms, including community-based health insurance, offer a pathway toward financial protection, yet uptake remains low in many low-income settings. This study aimed to identify the determinants of household participation in healthcare prepayment schemes in Goma, Democratic Republic of Congo.\u003c/p\u003e\u003cp\u003e\u003cb\u003eMethods\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWe conducted an analytical cross-sectional study using multistage cluster random sampling among 807 households in Goma in November 2023. Data were collected using a structured questionnaire, encoded, and analyzed in SPSS version 23. Logistic regression models were used to assess factors associated with participation in healthcare prepayment.\u003c/p\u003e\u003cp\u003e\u003cb\u003eResults\u003c/b\u003e\u003c/p\u003e \u003cp\u003eOverall, 13.1% of households participated in a prepayment scheme. Factors negatively associated with participation included: male household head (OR\u0026thinsp;=\u0026thinsp;0.5), being unmarried (OR\u0026thinsp;=\u0026thinsp;0.3), no prior membership in associations (OR\u0026thinsp;=\u0026thinsp;0.09), primary-level or lower education (OR\u0026thinsp;=\u0026thinsp;0.3), poor knowledge of mutual health schemes (OR\u0026thinsp;=\u0026thinsp;0.04), unemployment (OR\u0026thinsp;=\u0026thinsp;0.4), low socioeconomic status (OR\u0026thinsp;=\u0026thinsp;0.3), poor reception at health facilities (OR\u0026thinsp;=\u0026thinsp;0.9), perceived incompetence of health staff (OR\u0026thinsp;=\u0026thinsp;0.6), unavailability of medicines (OR\u0026thinsp;=\u0026thinsp;0.5), care-related adverse events (OR\u0026thinsp;=\u0026thinsp;0.3), lack of explanations during care (OR\u0026thinsp;=\u0026thinsp;0.5), high contribution fees (OR\u0026thinsp;=\u0026thinsp;0.9), incomplete benefit package (OR\u0026thinsp;=\u0026thinsp;0.2), and lack of trust in scheme management (OR\u0026thinsp;=\u0026thinsp;0.2). Age below 30 years among household heads was positively associated with participation (OR\u0026thinsp;=\u0026thinsp;1.8).\u003c/p\u003e\u003cp\u003e\u003cb\u003eConclusion\u003c/b\u003e\u003c/p\u003e \u003cp\u003eHousehold enrollment in healthcare prepayment schemes in Goma remains low. Multiple socioeconomic, cultural, and healthcare quality-related factors negatively influence participation. Targeted strategies are needed to strengthen trust, accessibility, and the perceived value of community-based health insurance.\u003c/p\u003e","manuscriptTitle":"Determinants of Household Participation in Healthcare Prepayment Schemes in the City of Goma, in the Eastern Democratic Republic of the Congo: A Cross-Sectional Study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-12-22 17:53:30","doi":"10.21203/rs.3.rs-8147855/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"editorInvitedReview","content":"","date":"2026-01-10T10:37:46+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"95905037232622151223529657661101282209","date":"2026-01-02T09:41:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"104760439123486797967543823465166018233","date":"2025-12-28T06:47:03+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"242329639645880799936989080875475938951","date":"2025-12-21T12:11:20+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-12-19T11:18:51+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-11-24T08:59:08+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-11-24T08:56:16+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-11-24T08:43:00+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2025-11-24T08:39:21+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5f59e6ba-7d2e-45a9-8f03-fa44d906b356","owner":[],"postedDate":"December 22nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-12-22T17:53:30+00:00","versionOfRecord":[],"versionCreatedAt":"2025-12-22 17:53:30","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8147855","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8147855","identity":"rs-8147855","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}