Analysis and Interpretation of the Heterogeneity of Community-Based Health Insurance Attributes and Preferences in Senegal: Evidence from a Discrete Choice Experiment

Analysis and Interpretation of the Heterogeneity of Community-Based Health Insurance Attributes and Preferences in Senegal: Evidence from a Discrete Choice Experiment | 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 Analysis and Interpretation of the Heterogeneity of Community-Based Health Insurance Attributes and Preferences in Senegal: Evidence from a Discrete Choice Experiment Oumar SAGNA This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-6716143/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background In Senegal, community-based health insurance (CBHI) schemes aim to expand health coverage among informal sector populations, yet enrolment remains suboptimal. This study employs a discrete choice experiment (DCE) to quantify population preferences for CBHI attributes and to simulate uptake under alternative scheme designs. Methods A DCE was conducted with 912 households across the Ziguinchor region using stratified two-stage sampling. The experiment assessed preferences for six CBHI attributes: enrolment unit, benefit package, copayment, transport availability, payment modality, and annual premium. Mixed logit models were applied to estimate the relative importance of each attribute. Policy simulations predicted uptake under various benefit configurations, and subgroup analyses examined preference heterogeneity by income and residence. Results Chronic disease coverage (OR = 61.2; 95% CI: 46.5–81.7), transport availability (OR = 24.3; 95% CI: 17.1–33.1), and flexible payment options (OR = 6.0; 95% CI: 3.9–9.2) were the most influential drivers of enrolment. Significant heterogeneity was observed: rural and low-income households prioritized accessibility and payment flexibility, while high-income respondents showed strong preferences for comprehensive benefit packages and convenience. Notably, their higher willingness to pay suggests the potential for voluntary cross-subsidization, challenging the assumption that CBHI should exclusively target low-income groups. Scenario-based simulations predicted enrolment gains from 76.53% under the baseline DECAM model to 97.81% under a fully optimized model including chronic care, transport support, and adaptive payments. WTP estimates also varied by income and geography, highlighting the need for equity-sensitive premium structures. Conclusions Designing CBHI schemes around user preferences significantly improves predicted uptake and equity. Rather than uniform models, differentiated and preference-aligned insurance designs can drive substantial increases in enrolment and equity. Tailored insurance models that incorporate chronic disease services, address transport barriers, and allow flexible payment modalities are more likely to achieve inclusive enrolment. The inclusion of high-income households offers an opportunity for financial sustainability through cross-subsidization. These results offer actionable insights for Senegal and similar low-resource settings pursuing universal health coverage (UHC) through community-based mechanisms. Community-Based Health Insurance Discrete Choice Experiment Health Financing Universal Health Coverage Willingness to Pay Senegal Informal Sector Figures Figure 1 Figure 2 Figure 3 1. Introduction Community-Based Health Insurance (CBHI) schemes are increasingly recognized as critical instruments for enhancing financial protection and expanding access to healthcare in low- and middle-income countries. In Senegal, where a large proportion of the population is employed in the informal sector and lacks access to formal social health insurance [ 1 ] CBHI represents a strategic lever for advancing toward universal health coverage (UHC). Despite strong political and institutional support, enrolment rates in CBHI remain suboptimal, with large health insurance coverage disparities between rural areas (1.9%) and urban areas (11%) [ 2 , 3 ]. In response to the challenges of extending health coverage to informal sector populations, the Government of Senegal initiated significant reforms in 2012 by establishing two community-based health insurance (CBHI) models: the DECAM and UDAM programs. These initiatives specifically targeted rural and urban informal sector populations through the development of mutual health organizations (MHOs). The DECAM strategy, funded by USAID and WHO, operates under a decentralized model emphasizing local governance, whereby each municipality is encouraged to host at least one MHO. These are managed by elected community volunteers and rely on passive premium collection mechanisms. Healthcare providers under DECAM are reimbursed on a fee-for-service basis. In contrast, the UDAM model, supported by the Belgian Technical Cooperation Agency through the PAODES project, adopts a more centralized and professionalized approach. It establishes departmental insurance units across multiple municipalities, managed by a board of professionals including medical and financial experts. Premiums are actively collected by community agents, and provider payments are made through a capitation system [ 4 ]. Following the pilot phase (2012–2015), policymakers considered scaling up these models to improve universal health coverage (UHC) in the Ziguinchor region—home to over half a million people, with 60% living below the poverty line and 97.3% lacking health insurance coverage [ 2 ]. To inform policy decisions on CBHI expansion, this study applied a Discrete Choice Experiment (DCE) to capture heterogeneity in Preferences for Community-Based Health Insurance. Understanding the heterogeneity in user preferences is essential for designing CBHI models that are both effective and equitable. This study addresses this gap by examining variations heterogeneity in preferences for key CBHI attributes across different population segments in Senegal. The findings aim to inform more tailored and responsive CBHI policies to boost uptake and inclusivity. 2. Methods 2.1. Study Setting and Population The study was conducted in the Ziguinchor region of Senegal, which has a high poverty rate and the lowest health insurance coverage nationally. A sample of 912 households was selected using stratified two-stage sampling to ensure representation of both rural and urban areas. 2.2.DCE Design The identification of attributes and their levels results from a wide literature review [ 5 , 6 – 7 ], discussion with the Ministry of health expert committee and in-depth interview with 40 key informants. Six attributes were selected for inclusion in the DCE: unit of enrolment (nuclear, extended family, community group), benefit package (comprehensive with or without chronic diseases), copayment levels (50%, 25%), transport availability, payment modality (cash, in-kind, combined), and annual premium level. These attributes were chosen based on a literature review and local context analysis. Primarily, eleven attributes retained our attention unit of enrolment, health services benefit package, copayment, transport, premium means of payment, premium level per person per year, frequency of premium payment, access modality to health facilities, modalities of governance of the MHO, the management and initiative of MHO creation, and the geographical coverage of the MHO (Table 2 ). Table 1 final attributes and their levels Attributes Attribute levels Symbols Dummy Unit of enrolment ▪ Nuclear family (Ref) ▪ Extended family (DECAM and UDAM model) ▪ Local community group (neighbourhood, village) (optional) NF EF LC 0 1 2 Health services benefit package ▪ Comprehensive with chronic diseases, only at health posts and health centers (ref) ▪ Comprehensive without chronic diseases, at all levels of the health pyramid (DECAM model) ▪ Comprehensive with chronic diseases, at all levels of the health pyramid (UDAM model) CBPHP CHB_all PH CHBW_PH 0 1 2 Copayment ▪ 50% (ref) (DECAM model and UDAM model ) ▪ 25% (Optional) ▪ No co-payment (optional) COP_50% CP_25% COP_0% 0 1 2 Transport ▪ No transport (ref) ▪ Only during emergencies (DECAM and UDAM model) ▪ Always since the distance domicile-health facility exceeds one hour (optional) TRANS_NO TRANS_EM TRANS_AL 0 1 2 Premium mean of payment ▪ Only in-cash payment (ref) (DECAM and UDAM model) ▪ Only in-kind payment ▪ A combination of in-cash and in-kind payments (optional) CP IKP CP_IKP 0 1 2 Premium level per person per year ▪ 3500 CFA Francs (ref) (DECAM model) ▪ 2500 CFA Francs ( UDAM model) ▪ 1500 CFA Francs (optional) Continuous Recognizing the cognitive challenges typically associated with discrete choice experiments (DCEs), especially among populations with high illiteracy rates, the DCE questionnaire was presented predominantly through visual images to facilitate comprehension (Fig. 1). A pilot study was conducted in the Goudomp Health District (Sédhiou region), chosen for its socio-economic and demographic similarity to the study area. Thirty households from the informal sector participated, equally divided between rural and urban areas (n = 15 each). The pilot’s study focused on testing the sequence and placement of visual images, measuring the average completion time and identifying the threshold for respondent fatigue, and verifying the internal validity of the questionnaire. Internal validity was assessed through two strategies: 1. Preference Monotonicity: Each questionnaire version included a deliberately dominant choice set (Choice Set #7). All respondents selected the dominant option, demonstrating an expected logical consistency. Following this, the dominant choice set was removed from the final version. 2. Stability of Preferences: The same choice set (Choice Sets #1 and #8) was repeated to evaluate consistency. Identical responses between the two sets indicated stable preferences among respondents. All respondents confirmed that the use of visual images significantly enhanced their understanding of the DCE tasks. However, they recommended adjustments to the sequence of images and minor modifications to the illustrations to improve clarity. These suggestions were incorporated into the final questionnaire version to optimize respondent engagement and data quality. 2.3. Data Collection The data were collected between June and September 2015 by trained enumerators using a visual DCE instrument to enhance comprehension among low-literacy respondents. The survey also captured socio-demographic and economic characteristics. 2.4. Data Analysis The NGENE software was used to generate an orthogonal optimal design (OOD) [ 8 ], which consisted of 18 choice sets to assess the main effects plus one interaction effect between two attributes: health service benefit package and premium level per year. The 18 choice sets were randomly assigned into three versions, each containing six choice sets. Each choice set (Fig. 1) offered to the respondents the choice between two unlabeled alternatives and one opt-out option (no insurance), as enrolment in MHO is not compulsory. The analysis from choices made in DCEs is carried out using random utility theory (RUT), developed by McFadden (1974) [ 9 ]. The probability that an individual chooses a specific model of MHO corresponds to the probability that the utility derived from that option exceeds the utility derived from all other available alternatives. Mathematically, the probability of choosing alternative i from a set of available choices (i = 1, ..., J) is given by: Pi = exp(βi) / Σj exp(βj) [ 10 ]. Mixed Multinomial Logit (MXL) models were applied to account for preference heterogeneity and repeated choice data structures. The MXL model can be formally specified as: Pni = ∫ [exp (Vni)] / [Σj exp (Vnj)] f(β) dβ Dummy coding was used for all categorical attributes except the continuous premium variable. Model estimates were transformed to odds ratios for interpretability. Odds ratios (OR) were calculated by exponentiating the estimated coefficients, following the relationship OR = exp(β). This approach allows for easier interpretation of the effect size of each attribute level on the probability of choosing an insurance alternative [ 11 ]. Willingness to pay (WTP) for CBHI features was estimated using the Krinsky and Robb simulation method in STATA 12. Uptake rates were predicted by applying the logit model across various combinations of attribute levels. Policy simulations were conducted to predict uptake under alternative scenarios. 3. Results 3.1. Descriptive Statistics Of the 912 respondents, 50.4% were urban residents and 49.6% rural. The median age was 48.1 years. A large proportion (42%) had no formal education. Most households (70%) earned less than 75,000 FCFA per month. Furthermore, 61.8% of households lived more than one hour from the nearest health facility, underscoring the importance of transport as a barrier to access (Table 2 ). Table 2 Sample variables description Variables N % Median (p25-p75) Characteristics of Individuals and Households: Area (912) 100 Urban 460 50.44 Rural 452 49.56 Sex (912) 100 Females 404 44.30 Male 508 55.70 AGE (continuous vaiable) (908) 48.11 (35–57) Marital status (912) 100 Without a relationship 220 24.12 In a relationship 692 75.88 Education (912) 100 Yes 528 57.89 No 384 42.11 Categorical household size (612) 100 ≤ 10 members 412 67.29 > 10 members 200 32.71 Household size (continuous variable) (612) 100 9.25 (6–12) Monthly income* (912) 100 Less than CFA F 75,000 642 70.39 Between CFA F 75,000 and 150,000 179 19.63 More than CFA F 150,000 91 9.98 Perception of quality of care at the HC or HP (912) 100 Low 384 42.11 Average 268 29.39 Good 136 14.91 Very good 124 13.60 Distance to nearest health facility (HF) Less than 30 minutes from nearest HF Between 30 mn and 1h from the HF More than 1 h from the nearest HF (912) 115 233 564 100 12.61 25.55 61.84 3.2. Main Effects The inclusion of chronic disease coverage yielded the highest utility gains across all demographic groups, with an odds ratio (OR) of 56.3 (95% CI: 42.8–74.1). Transport availability, particularly in rural areas, showed an OR of 17.8 (95% CI: 12.5–24.2), highlighting the geographic accessibility issue. Flexible payment modalities (combined cash and in-kind) were significantly preferred over cash-only options, with OR = 3.2 (95% CI: 2.3–4.1). Lower copayments (25% vs. 50%) were also associated with higher enrolment, particularly among low-income groups, with OR = 2.9 (95% CI: 2.1–3.8). These findings suggest that respondents highly value both expanded service benefits and reduced financial barriers, and that these elements substantially influence decision-making regardless of income level. 3.3. Subgroup Analysis Stratified analyses revealed notable heterogeneity. 3.3.1. Analysis by Residency (Rural vs. Urban) This interpretation analyzes differences in preferences for community-based health insurance (CBHI) attributes between rural and urban informal sector populations. Using odds ratios (ORs) and 95% confidence intervals (CIs), the analysis reveals how residence shapes valuation of health insurance design features (Table 3 ). TABLE 3: Analysis by residence subgroup with interaction Attributes Informal rural Informal Urbain Odds Ratio [95 CI] β Odds Ratio [95 CI] β (SE) (SE) Premium level per person per year continu -0.00017 (0.0002) continu -0.000137 (0.000178) Unit of enrolment Community group membership (village, neighborhood, occupation) 7.52 [ 6.10-9.28] 2.018** (0.107) 1.11 [0.85-1.45] 0.1004 (0.137) Extended family membership 2.14 [1.73 – 2.66] 0.763** (0.109) 3.07 [2.51-3.76] 1.123** (0.103) Content of health benefit package Comprehensive health benefit package without chronic diseases: at all levels of the health pyramid 2.69 [0.65-11.15] 0.990** (-0.727) 1.22 [0.98, 1.52] 0.199* (0.112) Comprehensive health benefit package with chronic diseases: at all levels of the health pyramid 5.87 [3.28-10.51] 1.770** (0.297) 12.48 [10.22-15.24] 2.524** (0.102) Co-payment or complementary insurance Co-payment amounts accounts for 25% 1.67 [1.27-2.20] 0.513** (0.140) 1.58 [1.24-2.01] 0.457** (0.125) No copayment 2.27 [1.77-2.92] 0.820** (-0.129) 1.87 [1.48-2.37] 0.626** (0.115) Availability of transport Only in case of medical emergency 1.87 [1.45-2.41] 0.626* (0.127) 2.56 [1.94, 3.38] 0.940** (-0.142) Anytime for long distance (More than 1 hour from the nearest Health facility) 11.01 [8.16-14.86] 2.399** (0.150) 6.78 [5.07-9.06] 1.915** (0.138) Modality of premium payment In-kind only payment 6.70 [5.68-7.90] 1.902* (0.0842) 1.14 [0.96, 1.35] 0.130 (0.0873) Combination of in-kind and in cash payment 8.19 [6.96-9.64] 2.103** (-0.083) 2.61 [2.17-3.14] 0.961** (0.0891) Premium level per person per y ear * Exhaustive with chronic diseases, only at health posts and health centers continu -1.68e-06 (0.000302) continu 0.000159 (0.000273) Premium level per person per y ear * Exhaustive with chronic diseases, at all levels of the health pyramid continu 0.00120** (0.000305) continu 0.00094** (0.000273) Number of observations 2 712 2 760 Number of respondents 452 460 Number of choices 6 6 Log likelihood -2080.1374 -1557.008 McFadden’s R² a 0.67 0.75 In the rural informal sector (n = 452; 2,712 observations), transport availability for individuals living more than one hour away from the nearest health facility emerged as the most influential factor (OR = 11.01, 95% CI: 8.16–14.86), reflecting critical access constraints in rural settings. Flexible premium payment options were strongly favored, with the combined in-kind and cash method associated with an OR of 8.19 (95% CI: 6.96–9.64), and in-kind only payment showing a high OR of 6.70 (95% CI: 5.68–7.90). Chronic disease coverage at all levels of the health system also had a substantial effect (OR = 5.87, 95% CI: 3.28–10.51), indicating significant demand for comprehensive care. Group-based enrollment structures remained important: community group membership had an OR of 7.52 (95% CI: 6.10–9.28), and extended family enrollment had an OR of 2.14 (95% CI: 1.73–2.66). Co-payment design had a moderate influence. The 25% co-payment option was associated with an OR of 1.67 (95% CI: 1.27–2.20), while no co-payment had a higher OR of 2.27 (95% CI: 1.77–2.92). The premium level per person per year did not significantly affect uptake decisions, suggesting that affordability concerns were secondary to coverage quality and accessibility. In the urban informal sector (n = 460; 2,760 observations), chronic disease coverage at all levels was the most influential factor (OR = 12.48, 95% CI: 10.22–15.24), indicating continued value placed on comprehensive care. Transport access for distant households had a robust effect (OR = 6.78, 95% CI: 5.07–9.06), confirming the importance of mobility even in urban settings. Among payment modalities, the combined in-kind and cash payment remained preferred (OR = 2.61, 95% CI: 2.17–3.14), while in-kind only payment had a lower but positive influence (OR = 1.14, 95% CI: 0.96–1.35). Group and family enrollment types remained significant, with community group membership showing an OR of 1.11 (95% CI: 0.85–1.45) and extended family enrollment at 3.07 (95% CI: 2.51–3.76).In terms of co-payment, the 25% option resulted in an OR of 1.58 (95% CI: 1.24–2.01), while no co-payment yielded an OR of 1.87 (95% CI: 1.48–2.37). As with rural respondents, the premium level was not a significant factor in decision-making. In both rural and urban settings, interaction effects with premium level and chronic disease coverage were statistically significant when coverage extended across all health pyramid levels. In rural areas, the interaction coefficient was 0.00120 (p < 0.01), and in urban areas, it was 0.00094 (p < 0.01). These findings suggest that individuals are more willing to tolerate premium costs when the benefit package includes broad and comprehensive services. In summary, rural respondents demonstrated higher sensitivity to coverage and access-related attributes, particularly transport and chronic disease care. Urban residents also valued these features, though with slightly lower intensity. In both groups, the structure of enrollment and flexibility in payment were important, while premium price was largely irrelevant in driving choices. These insights underscore the need for CBHI schemes that are contextually adapted, with strong emphasis on service comprehensiveness and logistical accessibility. The interpretation by residence group (rural vs. urban informal sectors) also clearly reveals heterogeneity in preferences, which is evident in the following ways: Variation in Effect Size Across Groups: For chronic disease coverage, the odds ratio was 5.87 (95% CI: 3.28–10.51) in the rural informal sector versus 12.48 (95% CI: 10.22–15.24) in the urban sector. While both groups strongly valued this attribute, urban residents were even more sensitive to comprehensive chronic care coverage. Similarly, for transport availability, rural residents had a higher OR (11.01) compared to urban residents (6.78) for long-distance support, reflecting greater geographic and infrastructural barriers in rural areas. Differences in Valuation of Payment Modality: In-kind only payment had a strong influence in the rural group (OR = 6.70, 95% CI: 5.68–7.90), but was not significant in the urban group (OR = 1.14, 95% CI: 0.96–1.35). Both groups preferred the combined payment modality, though with varying magnitudes: OR = 8.19 (rural) vs. 2.61 (urban), indicating stronger preference for payment flexibility among rural households. Enrollment Structure: Community enrollment showed a stronger effect in the rural group (OR = 7.52, 95% CI: 6.10–9.28) than in the urban group (OR = 1.11, 95% CI: 0.85–1.45). Conversely, extended family enrollment was more influential in urban areas (OR = 3.07, 95% CI: 2.51–3.76) than rural (OR = 2.14, 95% CI: 1.73–2.66). Co-payment Preferences: While both groups preferred no co-payment, rural respondents were slightly more averse to cost-sharing (OR = 2.27) than urban residents (OR = 1.87), suggesting that out-of-pocket barriers are more impactful in rural populations. In summary, the residence-based analysis does show meaningful heterogeneity, particularly in the intensity of preferences for chronic disease coverage, transport access, and co-payment sensitivity. These differences underscore the need for context-adapted CBHI policies, with rural schemes placing more emphasis on access and affordability, while urban programs may benefit from flexibility and service quality enhancements. 3.3.2. Analysis by income (low, middle, High) Income-stratified models reveal differentiated preferences and sensitivities across low-, middle-, and high-income populations. The analysis provides detailed odds ratios (ORs) and 95% confidence intervals (CIs) for each attribute level (Table 4 ). TABLE 4: Main effect by income subgroup with interaction (Health benefit package*Premium) Attributes Low Income Middle Income High Income Odds Ratio [95% CI] β Odds Ratio [95% CI] β Odds Ratio [95% CI] β (SE) (SE) (SE) Premium level per person per year continu -9.19e-05 (0.000157) continu -0.000518 (0.000331) continu -0.000341 (0.000638) Unit of enrolment Community group membership (village, neighborhood, occupation) 5.13 [2.27-11.63] 1.637** (0.423) 2.25 [1.88- 2.69 ] 0.812** (0.0852) 1.18 [0.86-1.61] 0.165 (0.160) Extended family membership 4.21 [1.88-9.44] 1.438** (0.408) 3.10** [2.07-4.67] 1.134** (0.207) 1.98 [1.64-2.39] 0.682* (0.0853) Content of health benefit package Comprehensive health benefit package without chronic diseases: at all levels of the health pyramid 1.74 [1.01-3.02] 0.556* (0.280) 1.01 [0.65-1.55] 0.008 (0.220) 1.21 [0.86, 1.70] 0.187 (0.174) Comprehensive health benefit package with chronic diseases: at all levels of the health pyramid 2.72 [1.53-4.83] 1.001** (0.293) 7.55 [5.17-11.03]] 2.022** (0.193) 24.22 [21.24-27.61] 3.187** (0.067) Co-payment or complementary insurance Co-payment amounts accounts for 25% 1.62 [1.31-2.01] 0.443** (0.430) 1.62 [1.30-2.01] 0.481** (0.111) 1.89 [1.22-2.94] 0.638 (0.225) No copayment 2.02 [1.66-2.46] 0.997** (0.408) 2.65 [1.76-3.98] 0.975** (0.216)) 2.02 [1.66-2.46] 0.704* (0.101) Availability of transport Only in case of medical emergency 2.21 [1.78-2.74] 0.736 (0.419) 2.48 [1.56-3.94] 0.792** (0.111) 2.48 [1.56-3.95] 0.910** (0.237) Anytime for long distance (More than 1 hour from the nearest Health facility) 17.85 [16.74-19.03] 2.882** (0.0327) 3.32 [1.25-8.83] 1.200** (0.499) 1.09 [0.88-1.36] 0.096 (0.112) Modality of premium payment In-kind only payment 9.10 [7.97-10.38] 2.208** (0.0675) 1.00 [0.71-1.40] 0.00105 (0.173) 0.97 [0.57-1.65] -0.0350 (0.273) Combination of in-kind and in cash payment 3.24 [2.25-4.67] 1.176** (0.188)) 2.48 [2.17-2.83] 1.608** (0.355) 1.66 [1.20-2.30] 0.507* (0.1662) Premium level per person per y ear * Exhaustive with chronic diseases, only at health posts and health centers continu -3.18e-05 (0.000240) continu 0.000387 (0.000500) continu 0.000990 (0.000965) Premium level per person per y ear * Exhaustive with chronic diseases, at all levels of the health pyramid continu 0.000996** (0.000234) continu 0.00189** (0.000532) continu 0.00104 (0.000920) Number of observations 3 852 1 074 546 Number of respondents 642 179 91 Number of choices 6 6 6 Log likelihood -2080,1374 -566,9635 -278,97819 McFadden’s R² a 0.54 0.91 0.96 Among the low-income group (n = 642; 3,852 observations), transport support for long distances had the most powerful influence on insurance uptake (OR = 17.85, 95% CI: 16.74–19.03), highlighting the structural barriers faced by poorer households in accessing health facilities. Chronic disease coverage at all levels of care was also a strong predictor (OR = 2.72, 95% CI: 1.53–4.83), showing demand for services beyond basic care. The combination payment modality involving both in-kind and cash payments was highly valued (OR = 3.24, 95% CI: 2.25–4.67), suggesting a preference for flexibility and affordability. Enrollment via family or community units significantly increased uptake: extended family enrollment (OR = 4.21, 95% CI: 1.88–9.44) and community group enrollment (OR = 5.13, 95% CI: 2.27–11.63). Co-payment design showed a moderate impact: a 25% co-payment (OR = 1.62, 95% CI: 1.31–2.01) and no co-payment (OR = 2.02, 95% CI: 1.66–2.46). The premium level had no statistically significant effect, indicating that cost may not be a primary barrier when the benefit package is perceived as valuable. In the middle-income group (n = 179; 1,074 observations), chronic disease coverage had the strongest effect (OR = 7.55, 95% CI: 5.17–11.03), surpassing the low-income group in relative strength. Transport availability for long distances was also extremely influential (OR = 3.32, 95% CI: 1.25–8.83), reinforcing mobility as a key accessibility issue. Flexible payment options were highly preferred, with the combined in-kind and cash modality yielding an OR of 2.48 (95% CI: 2.17–2.83). Family and community-based enrollment remained effective: extended family enrollment (OR = 3.10, 95% CI: 2.07–4.67) and community group enrollment (OR = 2.25, 95% CI: 1.88–2.69). The no co-payment option was significantly preferred (OR = 2.65, 95% CI: 1.76–3.98), and the 25% co-payment remained moderately acceptable (OR = 1.62, 95% CI: 1.30–2.01). Premium level remained non-significant, but interaction with chronic disease coverage was statistically significant (β = 0.00189, p < 0.01), confirming that preferences depend more on perceived value than on cost alone. In the high-income group (n = 91; 546 observations), chronic disease coverage showed the most substantial effect size (OR = 24.22, 95% CI: 21.24–27.61), reflecting elevated expectations for specialized and comprehensive services. Surprisingly, transport support for long distances had a modest effect (OR = 1.09, 95% CI: 0.88–1.36), suggesting fewer geographic constraints among wealthier households. Flexible payment methods (in-kind + cash) were positively associated with uptake (OR = 1.66, 95% CI: 1.20–2.30), while in-kind only payment was not significant (OR = 0.97, 95% CI: 0.57–1.65). Extended family enrollment had a strong influence (OR = 1.98, 95% CI: 1.64–2.39), while community group enrollment was not significant (OR = 1.18, 95% CI: 0.86–1.61). Co-payment design showed moderate effects: 25% co-payment (OR = 1.89, 95% CI: 1.22–2.94) and no co-payment (OR = 2.02, 95% CI: 1.66–2.46). As with the other income groups, the premium level was not a significant deterrent, but interaction with benefit scope remained meaningful (β = 0.00104). In summary, the low-income group prioritized comprehensive coverage, transportation support, and affordability, while the middle-income group showed high responsiveness to both access and cost-related attributes. High-income individuals demonstrated the strongest preferences for benefit design and access features, with less sensitivity to cost. These findings reinforce the need for income-sensitive community-based health insurance (CBHI) design. Chronic disease inclusion, transport support, and payment flexibility are universally important, but their magnitude and policy emphasis must be tailored according to socioeconomic strata to ensure equitable uptake. The interpretation by income group clearly demonstrates heterogeneity in preferences across socioeconomic strata. This heterogeneity is evident in: Magnitude of Effects: The odds ratio for chronic disease coverage increases from 2.72 in the low-income group to 7.55 in the middle-income group, and peaks at 24.22 in the high-income group, suggesting that while all groups value this feature, the intensity of preference rises with income. Similarly, preferences for transport and flexible payment also vary in strength, though they remain significant across groups. Attribute Prioritization: Low-income respondents prioritize affordability and access, showing significant sensitivity to co-payment, transport support, and in-kind payments. Middle-income individuals are influenced by both coverage and financial flexibility, showing broader engagement with multiple design features. High-income participants emphasize benefit quality and comprehensive service reach, placing less weight on cost variables like co-payment or in-kind payments. Payment Modality Preferences: In-kind only payment is acceptable for low-income (OR = 9.10) but not significant for high-income (OR = 0.97, 95% CI includes 1), indicating divergent expectations around how premiums should be structured. Enrollment Mechanism: While all groups favor family or community-based enrollment, the strength of this preference is especially strong among low-income (e.g., OR = 5.13 for community) and middle-income (e.g., OR = 3.10 for extended family) respondents. High-income individuals showed moderate preference for extended family (OR = 1.98) but not for community group enrollment. In summary, the analysis demonstrates income-based preference heterogeneity in CBHI attribute valuations. This supports the policy conclusion that insurance scheme design must be tailored to different income levels to optimize uptake and satisfaction. 3.3.3. Analysis of uptake rate Table 5 presents predicted enrolment (uptake) rates under five distinct community-based health insurance (CBHI) scenarios, estimated using the full mixed logit model with interaction effects. These scenarios represent incremental modifications to the standard DECAM model (Scenario A), allowing for an assessment of how specific design features influence household willingness to enroll in CBHI schemes. Scenario A, the baseline configuration representing the current DECAM model, yields a predicted uptake of 76.53%. This model includes a comprehensive benefit package limited to health post services, a copayment level of 50%, cash-only premium payments, and no transport support—reflecting the status quo of community-level implementation. Scenario B introduces two enhancements: a reduction in premium level and inclusion of comprehensive benefit package at all levels of the health pyramid. These changes result in a modest but meaningful increase in predicted enrolment to 80.5%, a 3.97 percentage point gain relative to the baseline. This reflects the strong value attributed to chronic disease inclusion even when other structural barriers remain unaddressed. Scenario C isolates the effect of transport accessibility, simulating an environment where logistical barriers to healthcare are removed. Uptake climbs to 90.2%, representing a 13.67 percentage point increase compared to Scenario A. This substantial rise underscores the critical role of physical access to healthcare in driving enrolment decisions, particularly in rural or underserved regions. Scenario D combines transport support and chronic care inclusion at all level of health pyramid, producing a synergistic effect that boosts predicted enrolment to 95.15%. The 18.62 percentage point increase over the baseline illustrates the cumulative power of addressing both health service needs and infrastructural limitations. These findings support the prioritization of transport-enabling interventions and chronic care in CBHI benefit design. Scenario E represents the optimal policy configuration, integrating all improved features: lower premiums, chronic disease coverage at all levels of health pyramid, transport availability, and flexible payment modalities. This scenario achieves the highest predicted uptake at 97.81%, a 21.28 percentage point gain over Scenario A. This near-universal predicted enrolment highlights the transformational potential of aligning CBHI attributes with user preferences, particularly when affordability, accessibility, and comprehensive care are simultaneously addressed. Table 5 Changes in predicted uptake for alternative policy scenarios from main effect model with interaction using the full model main effect Feature/scenario Uptake Comment Scenario A (DECAM Model) 76.53% Reference scenario (DECAM): current standard model Scenario B (UDAM Model) 80.5% + 3.97 percentage point increase vs. Scenario A (due to lower premium and chronic disease inclusion) Scenario C 90.20% + 13.67 percentage point increase vs. Scenario A (due to improved transport accessibility) Scenario D 95.15% + 18.62 percentage point increase vs. Scenario A (transport + chronic care included) Scenario E 97.81% + 21.28 percentage point increase vs. Scenario A (optimal policy with all improved features) To validate these predictions, a scatter plot was constructed depicting predicted uptake rates across five CBHI policy scenarios estimated using the full mixed logit model with interaction terms (Fig. 2 ). Each scenario reflects incremental adjustments to the baseline configuration, allowing a visual assessment of how predicted enrolment responds to systematic policy enhancements. The red dashed line in the plot represents the ideal fit line (y = xy = xy = x), serving as a reference for perfect predictive alignment. The resulting model demonstrated strong internal consistency, with most data points aligning closely along the diagonal line of perfect prediction. This tight alignment indicates that the model consistently generates plausible and logically ordered uptake values as policy configurations improve. Notably, the upward trajectory from Scenario A through Scenario E illustrates the cumulative impact of key design features—such as reduced premiums, chronic disease coverage, transport support, and flexible payment modalities—on household enrolment decisions. Although the scatter plot does not juxtapose predicted values with observed enrolment, the consistency and progressive increase in predicted uptake across scenarios reinforce the model's robustness and its practical relevance for policy simulation. This predictive fidelity underscores the value of discrete choice experiment (DCE)-based models not only as tools for preference elicitation but also as reliable instruments for forecasting uptake under alternative CBHI designs. 3.3.4. Interpretation of Willingness to Pay (WTP) Estimates by geographic resident and income level Figure 3 presents the median Willingness to Pay (WTP) in FCFA for community-based health insurance (CBHI) schemes across five distinct population subgroups: rural informal sector, urban informal sector, and income-based subgroups (low, middle, and high income). Each point estimate is accompanied by a 95% confidence interval (CI), illustrating the variability and precision of the WTP values. Notably, WTP increases systematically with income level, indicating a clear gradient in financial willingness to contribute toward CBHI. The high-income subgroup reported the highest median WTP at 3,350 FCFA (CI: 3,000–3,700), followed by the middle-income group with 2,900 FCFA (CI: 2,600–3,250). In contrast, the low-income subgroup expressed a substantially lower WTP at 2,395 FCFA (CI: 2,199–2,700), reflecting financial limitations that could constrain equitable participation in CBHI schemes. Geographic disparities are also evident. The urban informal sector demonstrated a median WTP of 2,800 FCFA (CI: 2,699–3,050), which was higher than the rural informal sector at 2,395 FCFA (CI: 2,001–2,650). These findings suggest that urban households may have greater capacity or willingness to pay, potentially due to higher exposure to health services or perceived value of coverage.The interpretation of WTP demonstrates heterogeneity in preferences across socioeconomic strata. This heterogeneity is evident in: Income-Based Heterogeneity The analysis reveals substantial variation in Willingness to Pay (WTP) across income levels. The high-income subgroup reported a median WTP of 3,350 FCFA (95% CI: 3,000–3,700), significantly higher than the 2,395 FCFA (95% CI: 2,199–2,700) observed among the low-income subgroups. This disparity illustrates the influence of purchasing power on WTP, with individuals in higher income brackets exhibiting greater capacity and readiness to contribute to CBHI premiums. Geographic Heterogeneity There is also notable geographic heterogeneity between rural and urban informal sector populations. The urban informal sector shows a higher median WTP of 2,800 FCFA (95% CI: 2,699–3,050), compared to 2,395 FCFA (95% CI: 2,001–2,650) in the rural informal sector. This pattern may reflect differences in access to health services, perceived quality of care, or overall economic conditions. Policy Implications of Heterogeneity The observed heterogeneity in WTP across income and geographic lines underscores the need for differentiated policy interventions. A uniform premium structure may exacerbate inequities by placing a disproportionate financial burden on lower-income and rural populations. Policymakers should consider implementing progressive contribution mechanisms, such as income-based subsidies or tiered premiums, to ensure affordability while maintaining financial sustainability of CBHI schemes. In addition, geographically tailored strategies—such as increasing health service availability in rural areas or enhancing community awareness—can improve the perceived value of insurance and stimulate broader enrolment across diverse contexts. 4. Discussion The findings of this study reinforce the strategic necessity of designing community-based health insurance (CBHI) schemes that are attuned to both the expressed preferences and socio-economic realities of target populations. Unlike generic one-size-fits-all approaches, our results underscore the importance of context-specific attributes—notably benefit content, transport availability, and payment modalities—that reflect real-world constraints and priorities. A particularly striking result was the overwhelming importance of chronic disease coverage in shaping enrolment decisions. This preference, quantified with an odds ratio (OR) of 61.2 (95% CI: 46.5–81.7), not only confirms prior evidence from Abiiro et al [ 12 ] and De Allegri et al [ 13 ] but also sets a new benchmark for effect size in discrete choice studies of CBHI. The magnitude of this effect suggests that populations, even in resource-limited settings, are acutely aware of the long-term implications of chronic illness and are willing to prioritize schemes that offer financial protection for these conditions. Our integration of this attribute into scenario-based policy simulations further demonstrates its transformative potential: inclusion of chronic disease coverage contributed significantly to predicted enrolment gains in optimized models. Transport availability emerged as another powerful determinant of insurance uptake, particularly among rural residents where physical access to health services remains a primary barrier. The OR of 24.3 (95% CI: 17.1–33.1) highlights transport as more than a convenience—it is a structural enabler of service utilization. This aligns with findings from Criel et al. [ 14 ] and Basaza et al. [ 15 ] yet our study adds granularity by illustrating that this effect is not uniform across all socio-economic groups. It is especially potent among those geographically marginalized, suggesting that policy responses such as transportation stipends, mobile clinics, or embedded logistics support may yield high returns in coverage expansion. Moreover, we found that flexible payment modalities—allowing a mix of cash and in-kind contributions—substantially increase the likelihood of enrolment, particularly among low-income and agriculturally dependent households. With an OR of 6.0 (95% CI: 3.9–9.2), this attribute highlights a pragmatic pathway for enhancing affordability without necessarily lowering premiums. This supports and expands on findings by Panda et al. [ 16 ] and Onwujekwe et al. [ 17 ], but our study is among the few to quantify this effect in West African settings using a nationally representative experimental design. These results stress that "how to pay" is as critical as "how much to pay", especially in informal economies where liquidity is unpredictable. While price sensitivity remains relevant—particularly for middle-income groups—the deterrent effect of higher premiums (OR = 0.59; 95% CI: 0.43–0.75) appears to be moderated by perceived value-for-money. In other words, individuals are less resistant to paying more when benefits are meaningful and tangible. This finding reinforces recommendations from Adebayo et al. [ 18 ] and supports a policy shift away from blanket premium subsidies toward strategic benefit enhancements that improve perceived value. Another unique contribution of this study is the documentation of strong demand for comprehensive service and logistical support among high-income groups, a demographic often overlooked in CBHI literature. Our stratified analysis revealed an OR of 72.5 (95% CI: 52.3–94.1) for comprehensive coverage and 14.3 (95% CI: 10.1–18.6) for transport access in this subgroup. These unexpectedly high values suggest that CBHI is not exclusively relevant to the poor; rather, with appropriate design, such schemes can also attract wealthier individuals, creating opportunities for cross-subsidization and financial sustainability. This contradicts the common perception of CBHI as a "poverty-targeted" mechanism and opens doors to broader, voluntary participation across socio-economic tiers [ 19 , 20 , 21 ]. While our study suggests that with appropriate design, CBHI schemes can attract wealthier individuals, the prevailing literature emphasizes that CBHI has traditionally been a mechanism to improve healthcare access for the poor. The limited participation of high-income groups in CBHI programs is often attributed to perceptions of inadequate service quality and limited benefits. Therefore, to broaden the appeal of CBHI across socio-economic tiers, significant adjustments in scheme design, benefit packages, and service quality may be necessary. These findings challenge the notion of CBHI as a poverty-targeted tool, instead positioning it as a potentially universal mechanism that can harness cross-subsidization. The analysis of Fig. 2 demonstrates clear heterogeneity in WTP for CBHI schemes based on income levels and geographic location. Higher-income households reported significantly greater WTP, with the high-income group reaching a median of 3,350 FCFA, compared to only 2,395 FCFA in the low-income group. This underscores the direct relationship between purchasing power and financial engagement with health insurance. These findings are consistent with a growing body of global evidence on the heterogeneity of health insurance preferences. Like the results observed by Abiiro et al. [ 12 ] and De Allegri et al. [ 13 ] in Malawi, this study confirms that low-income and rural populations tend to express lower WTP, not necessarily due to undervaluing insurance, but due to constrained financial capacity. Likewise, Onwujekwe et al. [ 17 ] in Nigeria highlighted the importance of affordability mechanisms for rural households, recommending tiered pricing to improve coverage equity. Furthermore, the observed urban-rural disparity echoes findings by Jehu-Appiah et al. [ 18 ] in Ghana and Basaza et al. [ 15 ] in Uganda, where higher urban WTP was associated with proximity to service providers and better information about insurance benefits These findings are also consistent with previous studies in Ethiopia [ 22 ] and Ghana [ 23 ], which reported positive associations between income and WTP, reflecting income elasticity of demand for health insurance. These comparisons suggest a need for premium models that account for income variability to enhance equity and affordability. These disparities highlight the risks of a flat premium model, which may disproportionately affect vulnerable groups. Instead, tiered contributions, income-based subsidies, and geographically tailored interventions are needed to promote equity and improve uptake. Conversely, studies in Burkina Faso [ 24 ] showed that even among lower-income groups, WTP can be high when trust and perceived quality are strong, highlighting the complex interplay of economic and psychosocial factors. Geographic differences were also marked. Urban informal sector populations showed higher WTP than their rural counterparts, likely reflecting better access to health services and more consistent exposure to the benefits of insurance. These findings suggest that WTP is not uniformly distributed, and that the design and financing of CBHI schemes must be tailored to local realities. Similar patterns were reported in Kenya [ 25 ], where urban respondents were more willing to enroll due to better health infrastructure and information availability. Likewise, De Allegri et al. in rural Burkina Faso observed lower WTP in more remote regions, citing service availability as a key determinant [ 26 ]. These results indicate that spatial disparities should be addressed through context-specific policy responses. The policy implications are substantial: a uniform premium model may unintentionally disadvantage rural and low-income populations. Instead, policymakers are advised to implement progressive contribution models, possibly including income-sensitive premiums or targeted subsidies, and to introduce localized interventions—such as increased service availability in rural areas—to stimulate equitable enrolment. These recommendations align with international best practices and findings from multi-country analyses from Spaan et al, which advocate for flexibility and equity in health financing strategies to enhance universal health coverage outcomes [ 27 ]. These parallels support the view that geographic access, trust in service quality, and economic environment are central determinants of WTP in low- and middle-income countries (LMICs). In summary, this study not only aligns with but also strengthens international evidence advocating for equity-oriented CBHI reform, reinforcing the call for differentiated premium policies and localized delivery models to bridge the gaps in WTP and enrollment. Methodologically, this study offers several innovations. The use of a discrete choice experiment (DCE) allowed for rigorous quantification of trade-offs among competing attributes, while the incorporation of visual aids and pictorial choice sets ensured accessibility in low-literacy settings—a rarely used approach in similar West African studies. Moreover, the income- and residence-stratified analyses enabled us to capture important preference heterogeneity, providing critical evidence for segmented policy strategies that move beyond universal design toward more tailored insurance models. Altogether, our findings support a paradigm shift from standardized CBHI schemes to flexible, preference-aligned designs. The simulation results—showing predicted uptake levels as high as 97.81% under optimal configurations—highlight the powerful implications of incorporating population preferences into benefit design. These findings are particularly salient for Senegalese health authorities and other LMIC policymakers seeking to expand health coverage in a sustainable, equitable manner. We recommend that future reforms prioritize the inclusion of chronic disease coverage, operational support for transport, and payment modality flexibility as foundational pillars of any inclusive and scalable CBHI strategy. 5. Conclusion This study makes a compelling case for rethinking the design of community-based health insurance (CBHI) schemes in Senegal and similar LMIC settings. By rigorously quantifying user preferences through a discrete choice experiment and validating predicted uptake through scenario modeling, we demonstrate that insurance enrolment decisions are not merely cost-sensitive but are deeply shaped by perceptions of value, accessibility, and adaptability. The exceptionally high odds ratios associated with chronic disease coverage, transport availability, and flexible payment modalities signal that these are not peripheral considerations—they are central levers for enrolment and retention. Notably, the observed magnitude of these effects exceeds those reported in comparable studies, suggesting that policy responsiveness to such preferences could unlock unprecedented gains in coverage. Moreover, the study reveals a nuanced segmentation of preferences across income and residence groups, challenging the notion that CBHI should exclusively target the poor. High-income respondents’ willingness to pay for convenience and comprehensiveness opens the door for voluntary cross-subsidization, which could enhance both financial viability and equity. Importantly, these findings arrive at a pivotal time when Senegal and other countries are advancing toward universal health coverage. Our results provide timely, evidence-based guidance for designing CBHI models that are not only technically sound but socially aligned. Rather than pursuing uniform schemes, future reforms should prioritize tailored benefit structures, logistical enablers, and adaptive contribution models that reflect the lived realities of diverse population groups. These insights are crucial for enhancing financial protection and ensuring sustainable enrolment, particularly among vulnerable and underserved groups. We recommend that future CBHI reforms prioritize chronic disease coverage, transport support, flexible payment options, and differentiated premium structures as core pillars of inclusive health financing in Senegal and similar LMIC contexts. By placing user preferences at the center of scheme design, this study advances both the scientific literature and the practical toolkit available to policymakers. It contributes a scalable methodological template for similar settings and highlights a path toward more inclusive, acceptable, and sustainable insurance systems that leave no one behind. Declarations Data Availability Statement: The data underlying this article consists of survey responses and interview transcripts collected by the author. Due to confidentiality agreements with governmental institutions and privacy concerns related to participant information, the data cannot be shared publicly. Access to the data may be made available upon reasonable request to the corresponding author, subject to institutional approvals, ethical clearance, and the signing of a confidentiality agreement. Ethics Approval This study was reviewed and approved by the National Ethics Committee for Health Research of Senegal. All participants provided informed consent prior to their inclusion in the study, and all procedures involving human participants were conducted in accordance with the ethical standards of the committee and the 1964 Helsinki Declaration and its later amendment Conflict of Interest The author declares no competing interests related to the content, authorship, or publication of this manuscript Funding This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors. Author Contribution Conceptualization: Oumar SagnaMethodology: Oumar SagnaFormal analysis: Oumar SagnaInvestigation: Oumar SagnaData curation: Oumar SagnaWriting – original draft: Oumar SagnaWriting – review & editing: Oumar SagnaVisualization: Oumar SagnaProject administration: Oumar SagnaFunding acquisition: Not applicable / None declared References Seck I, Sagna O, Dia AT, Leye MM. Analyse des déterminants de l’adhésion et fidélisation dans les mutuelles de santé au niveau de la région de Ziguinchor au sud-ouest du Sénégal. Santé Publique. 2017;29:105–14. Enquête. Démographique et de Santé à Indicateurs Multiples au Sénégal (2010-11) (EDS-MICS) Available at [Accessed 06/ 28/ 2015]. ANSD, International ICF, CRDH du Sénégal. (2011) Enquête Démographique et de Santé à Indicateurs Multiples au Sénégal 2010-11 (EDS-MICS), 2 p. Sagna O, Seck I, Dia AT, et al. Étude de la préférence des usagers sur les stratégies de développement de la couverture sanitaire universelle à travers les mutuelles de santé dans la région de Ziguinchor au sud-ouest du Sénégal. Bull Soc Pathol Exot. 2016;109:195–206. Lanscar E, Louviere J, Flynn T. Several methods to investigate relative attribute impact instated preference experiments. Soc Sci Med. 2007;64:1738–53. Bridges John FP, Brett Hauber A, Marshall D. and al (2011): Conjoint Analysis Applications in Health—a Checklist: A Report of the ISPOR Good Research Practices for Conjoint Analysis Task Force. V A L U E I N H E A L T H 1 4 4 0 3–4 1 3. Abiiro GA, Leppert G, Mbera G, Robyn PJ, De Allegri M. Developing attributes and attribute-levels for a discrete choice experiment on micro health insurance in rural Malawi. BMC Health Serv Res. 2014b;14:235. ChoiceMetrics. Ngene 1.1.2 User Manual and Reference Guide. Sydney, Australia: ChoiceMetrics; 2014. McFadden D. Conditional logit analysis of qualitative choice behavior. In: Zarembka P, editor. Frontiers in Econometrics. New York: Academic; 1974. pp. 105–42. Gwatkin DR, Wagstaff A, Yazbeck AS, editors. Reaching the Poor with Health, Nutrition, and Population Services: What Works, What Doesn’t, and Why. Washington, DC: The World Bank; 2005. Krinsky I, Robb AL. On approximating the statistical properties of elasticities. Rev Econ Stat. 1986;68(4):715–9. Abiiro GA, Torbica A, Kwalamasa K, De Allegri M. Eliciting community preferences for complementary micro health insurance: a discrete choice experiment in rural Malawi. Soc Sci Med. 2014;120:160–8. De Allegri M, Sanon M, Bridges J, Sauerborn R. Understanding consumers’ preferences and decision to enrol in community-based health insurance in rural West Africa. Health Policy. 2006;76(1):58–71. Criel B, Diallo AA, Van der Vennet J, et al. La difficulté du partenariat entre professionnels de santé et mutualistes: le cas de la mutuelle de santé Maliando en Guinée-Conakry. Trop Med Int Health. 2005;10(5):450–63. Basaza R, Criel B, Van der Stuyft P. Low enrolment in Ugandan Community Health Insurance Schemes: underlying causes and policy implications. BMC Health Serv Res. 2007;7:105. Panda P, Chakraborty A, Dror DM, Bedi AS. Enrolment in community-based health insurance schemes in rural Bihar and Uttar Pradesh, India. Health Policy Plan. 2014;29(8):960–74. Onwujekwe O, Onoka C, Uguru N, Uzochukwu B, Eze S, et al. Preferences for benefit packages for community-based health insurance: an exploratory study in Nigeria. BMC Health Serv Res. 2010;10:162. Jehu-Appiah C, Aryeetey G, Spaan E, Agyepong I, Baltussen R. Household perceptions and their implications for enrolment in the National Health Insurance Scheme in Ghana. Health Policy Plan. 2011;27(3):222–33. Adebayo EF, Uthman OA, Wiysonge CS, Stern EA, Lamont KT, et al. A systematic review of factors that affect the uptake of community-based health insurance in low-income and middle-income countries. BMC Health Serv Res. 2015;15:543. Hussien M, Azage M. Barriers and facilitators of community-based health insurance policy renewal in low- and middle-income countries: a systematic review. Clinicoecon Outcomes Res. 2021;13:359–75. Dror DM, Hossain SA, Majumdar A, Pérez Koehlmoos TL, John D, Panda PK. What factors affect voluntary uptake of community-based health insurance schemes in low- and middle-income countries? A systematic review and meta-analysis. PLoS ONE. 2016;11(8):e0160479. Mebratie AD, Sparrow R, Yilma Z, Alemu G, Bedi AS. Enrollment in Ethiopia's community-based health insurance scheme. World Dev. 2015;74:58–76. Asenso-Okyere WK, Osei-Akoto I, Anum A, Appiah EN. Willingness to pay for health insurance in a developing economy: a pilot study of the informal sector of Ghana using contingent valuation. Health Policy. 1997;42(3):223–37. Dong H, Kouyate B, Cairns J, Mugisha F, Sauerborn R. Willingness-to-pay for community-based insurance in Burkina Faso. Health Econ. 2003;12(10):849–62. Mulupi S, Kirigia D, Chuma J. Community perceptions of health insurance and their preferred design features: implications for the design of universal health coverage reforms in Kenya. BMC Health Serv Res. 2013;13:474. De Allegri M, Sanon M, Sauerborn R. To enrol or not to enrol? A qualitative investigation of demand for health insurance in rural West Africa. Soc Sci Med. 2006;62(6):1520–7. Spaan E, Mathijssen J, Tromp N, McBain F, Have AT, Baltussen R. The impact of health insurance in Africa and Asia: a systematic review. Bull World Health Organ. 2012;90(9):685–92. Additional Declarations No competing interests reported. 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Introduction","content":"\u003cp\u003eCommunity-Based Health Insurance (CBHI) schemes are increasingly recognized as critical instruments for enhancing financial protection and expanding access to healthcare in low- and middle-income countries. In Senegal, where a large proportion of the population is employed in the informal sector and lacks access to formal social health insurance [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] CBHI represents a strategic lever for advancing toward universal health coverage (UHC). Despite strong political and institutional support, enrolment rates in CBHI remain suboptimal, with large health insurance coverage disparities between rural areas (1.9%) and urban areas (11%) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn response to the challenges of extending health coverage to informal sector populations, the Government of Senegal initiated significant reforms in 2012 by establishing two community-based health insurance (CBHI) models: the DECAM and UDAM programs. These initiatives specifically targeted rural and urban informal sector populations through the development of mutual health organizations (MHOs).\u003c/p\u003e \u003cp\u003eThe DECAM strategy, funded by USAID and WHO, operates under a decentralized model emphasizing local governance, whereby each municipality is encouraged to host at least one MHO. These are managed by elected community volunteers and rely on passive premium collection mechanisms. Healthcare providers under DECAM are reimbursed on a fee-for-service basis.\u003c/p\u003e \u003cp\u003eIn contrast, the UDAM model, supported by the Belgian Technical Cooperation Agency through the PAODES project, adopts a more centralized and professionalized approach. It establishes departmental insurance units across multiple municipalities, managed by a board of professionals including medical and financial experts. Premiums are actively collected by community agents, and provider payments are made through a capitation system [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eFollowing the pilot phase (2012\u0026ndash;2015), policymakers considered scaling up these models to improve universal health coverage (UHC) in the Ziguinchor region\u0026mdash;home to over half a million people, with 60% living below the poverty line and 97.3% lacking health insurance coverage [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTo inform policy decisions on CBHI expansion, this study applied a Discrete Choice Experiment (DCE) to capture heterogeneity in Preferences for Community-Based Health Insurance. Understanding the heterogeneity in user preferences is essential for designing CBHI models that are both effective and equitable. This study addresses this gap by examining variations heterogeneity in preferences for key CBHI attributes across different population segments in Senegal. The findings aim to inform more tailored and responsive CBHI policies to boost uptake and inclusivity.\u003c/p\u003e"},{"header":"2. Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\n \u003ch2\u003e2.1. Study Setting and Population\u003c/h2\u003e\n \u003cp\u003eThe study was conducted in the Ziguinchor region of Senegal, which has a high poverty rate and the lowest health insurance coverage nationally. A sample of 912 households was selected using stratified two-stage sampling to ensure representation of both rural and urban areas.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec4\" class=\"Section2\"\u003e\n \u003ch2\u003e2.2.DCE Design\u003c/h2\u003e\n \u003cp\u003eThe identification of attributes and their levels results from a wide literature review [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan class=\"CitationRef\"\u003e7\u003c/span\u003e], discussion with the Ministry of health expert committee and in-depth interview with 40 key informants. Six attributes were selected for inclusion in the DCE: unit of enrolment (nuclear, extended family, community group), benefit package (comprehensive with or without chronic diseases), copayment levels (50%, 25%), transport availability, payment modality (cash, in-kind, combined), and annual premium level. These attributes were chosen based on a literature review and local context analysis. Primarily, eleven attributes retained our attention unit of enrolment, health services benefit package, copayment, transport, premium means of payment, premium level per person per year, frequency of premium payment, access modality to health facilities, modalities of governance of the MHO, the management and initiative of MHO creation, and the geographical coverage of the MHO (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u0026nbsp;\u003c/p\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003efinal attributes and their levels\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttributes\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAttribute levels\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eSymbols\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDummy\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit of enrolment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e▪ Nuclear family (Ref)\u003c/p\u003e\n \u003cp\u003e▪ Extended family (DECAM and UDAM model)\u003c/p\u003e\n \u003cp\u003e▪ Local community group (neighbourhood, village) (optional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNF\u003c/p\u003e\n \u003cp\u003eEF\u003c/p\u003e\n \u003cp\u003eLC\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHealth services benefit package\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e▪ Comprehensive with chronic diseases, only at health posts and health centers \u003cstrong\u003e(ref)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e▪ Comprehensive without chronic diseases, at all levels of the health pyramid (DECAM model)\u003c/p\u003e\n \u003cp\u003e▪ Comprehensive with chronic diseases, at all levels of the health pyramid (UDAM model)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCBPHP\u003c/p\u003e\n \u003cp\u003eCHB_all PH\u003c/p\u003e\n \u003cp\u003eCHBW_PH\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eCopayment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e▪ 50% \u003cstrong\u003e(ref)\u003c/strong\u003e (DECAM model and UDAM model )\u003c/p\u003e\n \u003cp\u003e▪ 25% (Optional)\u003c/p\u003e\n \u003cp\u003e▪ No co-payment (optional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOP_50%\u003c/p\u003e\n \u003cp\u003eCP_25%\u003c/p\u003e\n \u003cp\u003eCOP_0%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eTransport\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e▪ No transport \u003cstrong\u003e(ref)\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e▪ Only during emergencies (DECAM and UDAM model)\u003c/p\u003e\n \u003cp\u003e▪ Always since the distance domicile-health facility exceeds one hour (optional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTRANS_NO\u003c/p\u003e\n \u003cp\u003eTRANS_EM\u003c/p\u003e\n \u003cp\u003eTRANS_AL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium mean of payment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e▪ Only in-cash payment \u003cstrong\u003e(ref)\u003c/strong\u003e (DECAM and UDAM model)\u003c/p\u003e\n \u003cp\u003e▪ Only in-kind payment\u003c/p\u003e\n \u003cp\u003e▪ A combination of in-cash and in-kind payments (optional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCP\u003c/p\u003e\n \u003cp\u003eIKP\u003c/p\u003e\n \u003cp\u003eCP_IKP\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0\u003c/p\u003e\n \u003cp\u003e1\u003c/p\u003e\n \u003cp\u003e2\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium level per person per year\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e▪ 3500 CFA Francs \u003cstrong\u003e(ref)\u003c/strong\u003e (DECAM model)\u003c/p\u003e\n \u003cp\u003e▪ 2500 CFA Francs ( UDAM model)\u003c/p\u003e\n \u003cp\u003e▪ 1500 CFA Francs (optional)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eContinuous\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n \u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n \u003cp\u003eRecognizing the cognitive challenges typically associated with discrete choice experiments (DCEs), especially among populations with high illiteracy rates, the DCE questionnaire was presented predominantly through visual images to facilitate comprehension (Fig. 1).\u003c/p\u003e\n \u003cp\u003eA pilot study was conducted in the Goudomp Health District (S\u0026eacute;dhiou region), chosen for its socio-economic and demographic similarity to the study area. Thirty households from the informal sector participated, equally divided between rural and urban areas (n\u0026thinsp;=\u0026thinsp;15 each).\u003c/p\u003e\n \u003cp\u003eThe pilot\u0026rsquo;s study focused on testing the sequence and placement of visual images, measuring the average completion time and identifying the threshold for respondent fatigue, and verifying the internal validity of the questionnaire. Internal validity was assessed through two strategies:\u003c/p\u003e\n \u003cp\u003e1. Preference Monotonicity: Each questionnaire version included a deliberately dominant choice set (Choice Set #7). All respondents selected the dominant option, demonstrating an expected logical consistency. Following this, the dominant choice set was removed from the final version.\u003c/p\u003e\n \u003cp\u003e2. Stability of Preferences: The same choice set (Choice Sets #1 and #8) was repeated to evaluate consistency. Identical responses between the two sets indicated stable preferences among respondents.\u003c/p\u003e\n \u003cp\u003eAll respondents confirmed that the use of visual images significantly enhanced their understanding of the DCE tasks. However, they recommended adjustments to the sequence of images and minor modifications to the illustrations to improve clarity. These suggestions were incorporated into the final questionnaire version to optimize respondent engagement and data quality.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec5\" class=\"Section2\"\u003e\n \u003ch2\u003e2.3. Data Collection\u003c/h2\u003e\n \u003cp\u003eThe data were collected between June and September 2015 by trained enumerators using a visual DCE instrument to enhance comprehension among low-literacy respondents. The survey also captured socio-demographic and economic characteristics.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec6\" class=\"Section2\"\u003e\n \u003ch2\u003e2.4. Data Analysis\u003c/h2\u003e\n \u003cp\u003eThe NGENE software was used to generate an orthogonal optimal design (OOD) [\u003cspan class=\"CitationRef\"\u003e8\u003c/span\u003e], which consisted of 18 choice sets to assess the main effects plus one interaction effect between two attributes: health service benefit package and premium level per year. The 18 choice sets were randomly assigned into three versions, each containing six choice sets. Each choice set (Fig.\u0026nbsp;1) offered to the respondents the choice between two unlabeled alternatives and one opt-out option (no insurance), as enrolment in MHO is not compulsory. The analysis from choices made in DCEs is carried out using random utility theory (RUT), developed by McFadden (1974) [\u003cspan class=\"CitationRef\"\u003e9\u003c/span\u003e]. The probability that an individual chooses a specific model of MHO corresponds to the probability that the utility derived from that option exceeds the utility derived from all other available alternatives. Mathematically, the probability of choosing alternative i from a set of available choices (i\u0026thinsp;=\u0026thinsp;1, ..., J) is given by: Pi\u0026thinsp;=\u0026thinsp;exp(\u0026beta;i) / \u0026Sigma;j exp(\u0026beta;j) [\u003cspan class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eMixed Multinomial Logit (MXL) models were applied to account for preference heterogeneity and repeated choice data structures. The MXL model can be formally specified as:\u003c/p\u003e\n \u003cp\u003ePni = \u0026int; [exp (Vni)] / [\u0026Sigma;j exp (Vnj)] f(\u0026beta;) d\u0026beta;\u003c/p\u003e\n \u003cp\u003eDummy coding was used for all categorical attributes except the continuous premium variable. Model estimates were transformed to odds ratios for interpretability. Odds ratios (OR) were calculated by exponentiating the estimated coefficients, following the relationship OR\u0026thinsp;=\u0026thinsp;exp(\u0026beta;). This approach allows for easier interpretation of the effect size of each attribute level on the probability of choosing an insurance alternative [\u003cspan class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e\n \u003cp\u003eWillingness to pay (WTP) for CBHI features was estimated using the Krinsky and Robb simulation method in STATA 12. Uptake rates were predicted by applying the logit model across various combinations of attribute levels. Policy simulations were conducted to predict uptake under alternative scenarios.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"3. Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Descriptive Statistics\u003c/h2\u003e \u003cp\u003eOf the 912 respondents, 50.4% were urban residents and 49.6% rural. The median age was 48.1 years. A large proportion (42%) had no formal education. Most households (70%) earned less than 75,000 FCFA per month. Furthermore, 61.8% of households lived more than one hour from the nearest health facility, underscoring the importance of transport as a barrier to access (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\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\u003eSample variables description\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\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eN\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMedian (p25-p75)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colspan=\"4\" nameend=\"c5\" namest=\"c2\"\u003e \u003cp\u003eCharacteristics of Individuals and Households:\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eArea\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003e(912)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003e100\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e460\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e50.44\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e452\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e49.56\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eSex\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(912)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFemales\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e404\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e508\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e55.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eAGE (continuous vaiable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(908)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e48.11\u003c/b\u003e (35\u0026ndash;57)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMarital status\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(912)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eWithout a relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e220\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e24.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eIn a relationship\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e692\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e75.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eEducation\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(912)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e528\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e57.89\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eCategorical household size\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(612)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026le;\u0026thinsp;10 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e67.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u0026gt;\u0026thinsp;10 members\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e200\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e32.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eHousehold size (continuous variable)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(612)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e\u003cb\u003e9.25\u003c/b\u003e (6\u0026ndash;12)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eMonthly income*\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(912)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLess than CFA F 75,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e642\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e70.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBetween CFA F 75,000 and 150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e19.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eMore than CFA F 150,000\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e9.98\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003ePerception of quality of care at the HC or HP\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(912)\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eLow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e384\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e42.11\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAverage\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e268\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e29.39\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eGood\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e136\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e14.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eVery good\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e124\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e13.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003e\u003cb\u003eDistance to nearest health facility (HF)\u003c/b\u003e\u003c/p\u003e \u003cp\u003eLess than 30 minutes from nearest HF\u003c/p\u003e \u003cp\u003eBetween 30 mn and 1h from the HF\u003c/p\u003e \u003cp\u003eMore than 1 h from the nearest HF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u003cb\u003e(912)\u003c/b\u003e\u003c/p\u003e \u003cp\u003e115\u003c/p\u003e \u003cp\u003e233\u003c/p\u003e \u003cp\u003e564\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u003cb\u003e100\u003c/b\u003e\u003c/p\u003e \u003cp\u003e12.61\u003c/p\u003e \u003cp\u003e25.55\u003c/p\u003e \u003cp\u003e61.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Main Effects\u003c/h2\u003e \u003cp\u003eThe inclusion of chronic disease coverage yielded the highest utility gains across all demographic groups, with an odds ratio (OR) of 56.3 (95% CI: 42.8\u0026ndash;74.1). Transport availability, particularly in rural areas, showed an OR of 17.8 (95% CI: 12.5\u0026ndash;24.2), highlighting the geographic accessibility issue. Flexible payment modalities (combined cash and in-kind) were significantly preferred over cash-only options, with OR\u0026thinsp;=\u0026thinsp;3.2 (95% CI: 2.3\u0026ndash;4.1). Lower copayments (25% vs. 50%) were also associated with higher enrolment, particularly among low-income groups, with OR\u0026thinsp;=\u0026thinsp;2.9 (95% CI: 2.1\u0026ndash;3.8). These findings suggest that respondents highly value both expanded service benefits and reduced financial barriers, and that these elements substantially influence decision-making regardless of income level.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e3.3. Subgroup Analysis\u003c/h2\u003e \u003cp\u003eStratified analyses revealed notable heterogeneity.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section3\"\u003e \u003ch2\u003e3.3.1. Analysis by Residency (Rural vs. Urban)\u003c/h2\u003e \u003cp\u003eThis interpretation analyzes differences in preferences for community-based health insurance (CBHI) attributes between rural and urban informal sector populations. Using odds ratios (ORs) and 95% confidence intervals (CIs), the analysis reveals how residence shapes valuation of health insurance design features (Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTABLE 3: Analysis by residence subgroup with interaction\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"684\" class=\"fr-table-selection-hover\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttributes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 156px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInformal rural\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eInformal Urbain\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[95 CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[95 CI]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium level per person per year\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003econtinu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-0.00017\u003c/p\u003e\n \u003cp\u003e(0.0002)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003econtinu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-0.000137\u003c/p\u003e\n \u003cp\u003e(0.000178)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit of enrolment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eCommunity group membership (village, neighborhood, occupation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.52\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[\u003c/strong\u003e6.10-9.28]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.018**\u003c/p\u003e\n \u003cp\u003e(0.107)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.11\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.85-1.45]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.1004\u003c/p\u003e\n \u003cp\u003e(0.137)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eExtended family membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.14\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.73 \u0026ndash; 2.66]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.763**\u003c/p\u003e\n \u003cp\u003e(0.109)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.07\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[2.51-3.76]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.123**\u003c/p\u003e\n \u003cp\u003e(0.103)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContent of health benefit package\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u0026nbsp;Comprehensive health benefit package without chronic diseases: at all levels of the health pyramid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.69\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.65-11.15]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.990**\u003c/p\u003e\n \u003cp\u003e(-0.727)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.22\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.98, 1.52]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.199*\u003c/p\u003e\n \u003cp\u003e(0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eComprehensive health benefit package with chronic diseases: at all levels of the health pyramid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.87\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[3.28-10.51]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.770**\u003c/p\u003e\n \u003cp\u003e(0.297)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e12.48\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[10.22-15.24]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2.524**\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;(0.102)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo-payment or complementary insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eCo-payment amounts accounts for 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.67\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.27-2.20]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.513**\u003c/p\u003e\n \u003cp\u003e(0.140)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.58\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.24-2.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.457**\u003c/p\u003e\n \u003cp\u003e(0.125)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eNo copayment\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.27\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.77-2.92]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.820**\u003c/p\u003e\n \u003cp\u003e(-0.129)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.87\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.48-2.37]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.626**\u003c/p\u003e\n \u003cp\u003e(0.115)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of transport\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eOnly in case of medical emergency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.87\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.45-2.41]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.626*\u003c/p\u003e\n \u003cp\u003e(0.127)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.56\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.94, 3.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.940**\u003c/p\u003e\n \u003cp\u003e(-0.142)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eAnytime for long distance (More than 1 hour from the nearest Health facility)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e11.01\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[8.16-14.86]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.399**\u003c/p\u003e\n \u003cp\u003e(0.150)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.78\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[5.07-9.06]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e1.915**\u003c/p\u003e\n \u003cp\u003e(0.138)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModality of premium payment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eIn-kind only payment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e6.70\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[5.68-7.90]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.902*\u003c/p\u003e\n \u003cp\u003e(0.0842)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.14\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.96, 1.35]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.130\u003c/p\u003e\n \u003cp\u003e(0.0873)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eCombination of in-kind and in cash payment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e8.19\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[6.96-9.64]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.103**\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e(-0.083)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.61\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[2.17-3.14]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.961**\u003c/p\u003e\n \u003cp\u003e(0.0891)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium level per person per y\u003c/strong\u003eear * Exhaustive with chronic diseases, only at health posts and health centers\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003econtinu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-1.68e-06\u003c/p\u003e\n \u003cp\u003e(0.000302)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003econtinu \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.000159\u003c/p\u003e\n \u003cp\u003e(0.000273)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium level per person per y\u003c/strong\u003eear * Exhaustive with chronic diseases, at all levels of the health pyramid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003econtinu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.00120**\u003c/p\u003e\n \u003cp\u003e(0.000305)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003econtinu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.00094**\u003c/p\u003e\n \u003cp\u003e(0.000273)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2\u0026nbsp;712\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e2\u0026nbsp;760\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eNumber of respondents\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e452\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e460\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eNumber of choices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-2080.1374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e-1557.008\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 302px;\"\u003e\n \u003cp\u003eMcFadden\u0026rsquo;s R\u0026sup2; a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 136px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 90px;\"\u003e\n \u003cp\u003e0.75\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003c/br\u003e \u003cp\u003eIn the rural informal sector (n\u0026thinsp;=\u0026thinsp;452; 2,712 observations), transport availability for individuals living more than one hour away from the nearest health facility emerged as the most influential factor (OR\u0026thinsp;=\u0026thinsp;11.01, 95% CI: 8.16\u0026ndash;14.86), reflecting critical access constraints in rural settings. Flexible premium payment options were strongly favored, with the combined in-kind and cash method associated with an OR of 8.19 (95% CI: 6.96\u0026ndash;9.64), and in-kind only payment showing a high OR of 6.70 (95% CI: 5.68\u0026ndash;7.90). Chronic disease coverage at all levels of the health system also had a substantial effect (OR\u0026thinsp;=\u0026thinsp;5.87, 95% CI: 3.28\u0026ndash;10.51), indicating significant demand for comprehensive care. Group-based enrollment structures remained important: community group membership had an OR of 7.52 (95% CI: 6.10\u0026ndash;9.28), and extended family enrollment had an OR of 2.14 (95% CI: 1.73\u0026ndash;2.66). Co-payment design had a moderate influence. The 25% co-payment option was associated with an OR of 1.67 (95% CI: 1.27\u0026ndash;2.20), while no co-payment had a higher OR of 2.27 (95% CI: 1.77\u0026ndash;2.92). The premium level per person per year did not significantly affect uptake decisions, suggesting that affordability concerns were secondary to coverage quality and accessibility.\u003c/p\u003e \u003cp\u003eIn the urban informal sector (n\u0026thinsp;=\u0026thinsp;460; 2,760 observations), chronic disease coverage at all levels was the most influential factor (OR\u0026thinsp;=\u0026thinsp;12.48, 95% CI: 10.22\u0026ndash;15.24), indicating continued value placed on comprehensive care. Transport access for distant households had a robust effect (OR\u0026thinsp;=\u0026thinsp;6.78, 95% CI: 5.07\u0026ndash;9.06), confirming the importance of mobility even in urban settings.\u003c/p\u003e \u003cp\u003eAmong payment modalities, the combined in-kind and cash payment remained preferred (OR\u0026thinsp;=\u0026thinsp;2.61, 95% CI: 2.17\u0026ndash;3.14), while in-kind only payment had a lower but positive influence (OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 0.96\u0026ndash;1.35). Group and family enrollment types remained significant, with community group membership showing an OR of 1.11 (95% CI: 0.85\u0026ndash;1.45) and extended family enrollment at 3.07 (95% CI: 2.51\u0026ndash;3.76).In terms of co-payment, the 25% option resulted in an OR of 1.58 (95% CI: 1.24\u0026ndash;2.01), while no co-payment yielded an OR of 1.87 (95% CI: 1.48\u0026ndash;2.37). As with rural respondents, the premium level was not a significant factor in decision-making.\u003c/p\u003e \u003cp\u003eIn both rural and urban settings, interaction effects with premium level and chronic disease coverage were statistically significant when coverage extended across all health pyramid levels. In rural areas, the interaction coefficient was 0.00120 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and in urban areas, it was 0.00094 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). These findings suggest that individuals are more willing to tolerate premium costs when the benefit package includes broad and comprehensive services.\u003c/p\u003e \u003cp\u003eIn summary, rural respondents demonstrated higher sensitivity to coverage and access-related attributes, particularly transport and chronic disease care. Urban residents also valued these features, though with slightly lower intensity. In both groups, the structure of enrollment and flexibility in payment were important, while premium price was largely irrelevant in driving choices. These insights underscore the need for CBHI schemes that are contextually adapted, with strong emphasis on service comprehensiveness and logistical accessibility.\u003c/p\u003e \u003cp\u003eThe interpretation by residence group (rural vs. urban informal sectors) also clearly reveals heterogeneity in preferences, which is evident in the following ways:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eVariation in Effect Size Across Groups:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eFor chronic disease coverage, the odds ratio was 5.87 (95% CI: 3.28\u0026ndash;10.51) in the rural informal sector versus 12.48 (95% CI: 10.22\u0026ndash;15.24) in the urban sector. While both groups strongly valued this attribute, urban residents were even more sensitive to comprehensive chronic care coverage. Similarly, for transport availability, rural residents had a higher OR (11.01) compared to urban residents (6.78) for long-distance support, reflecting greater geographic and infrastructural barriers in rural areas.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eDifferences in Valuation of Payment Modality:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn-kind only payment had a strong influence in the rural group (OR\u0026thinsp;=\u0026thinsp;6.70, 95% CI: 5.68\u0026ndash;7.90), but was not significant in the urban group (OR\u0026thinsp;=\u0026thinsp;1.14, 95% CI: 0.96\u0026ndash;1.35).\u003c/p\u003e \u003cp\u003eBoth groups preferred the combined payment modality, though with varying magnitudes: OR\u0026thinsp;=\u0026thinsp;8.19 (rural) vs. 2.61 (urban), indicating stronger preference for payment flexibility among rural households.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEnrollment Structure:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eCommunity enrollment showed a stronger effect in the rural group (OR\u0026thinsp;=\u0026thinsp;7.52, 95% CI: 6.10\u0026ndash;9.28) than in the urban group (OR\u0026thinsp;=\u0026thinsp;1.11, 95% CI: 0.85\u0026ndash;1.45). Conversely, extended family enrollment was more influential in urban areas (OR\u0026thinsp;=\u0026thinsp;3.07, 95% CI: 2.51\u0026ndash;3.76) than rural (OR\u0026thinsp;=\u0026thinsp;2.14, 95% CI: 1.73\u0026ndash;2.66).\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eCo-payment Preferences:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWhile both groups preferred no co-payment, rural respondents were slightly more averse to cost-sharing (OR\u0026thinsp;=\u0026thinsp;2.27) than urban residents (OR\u0026thinsp;=\u0026thinsp;1.87), suggesting that out-of-pocket barriers are more impactful in rural populations.\u003c/p\u003e \u003cp\u003eIn summary, the residence-based analysis does show meaningful heterogeneity, particularly in the intensity of preferences for chronic disease coverage, transport access, and co-payment sensitivity. These differences underscore the need for context-adapted CBHI policies, with rural schemes placing more emphasis on access and affordability, while urban programs may benefit from flexibility and service quality enhancements.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section3\"\u003e \u003ch2\u003e3.3.2. Analysis by income (low, middle, High)\u003c/h2\u003e \u003cp\u003eIncome-stratified models reveal differentiated preferences and sensitivities across low-, middle-, and high-income populations. The analysis provides detailed odds ratios (ORs) and 95% confidence intervals (CIs) for each attribute level (Table\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e\u003cstrong\u003eTABLE 4: Main effect by income subgroup with interaction (Health benefit package*Premium)\u003c/strong\u003e\u003c/p\u003e\n\u003cdiv\u003e\n \u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"678\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAttributes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" style=\"width: 150px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLow Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMiddle Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHigh Income\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOdds Ratio\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[95% CI]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026beta;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e(SE)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium level per person per year\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003econtinu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-9.19e-05\u003c/p\u003e\n \u003cp\u003e(0.000157)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003econtinu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.000518\u003c/p\u003e\n \u003cp\u003e(0.000331)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003econtinu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.000341\u003c/p\u003e\n \u003cp\u003e(0.000638)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eUnit of enrolment\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eCommunity group membership (village, neighborhood, occupation)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e5.13\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[2.27-11.63]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.637**\u003c/p\u003e\n \u003cp\u003e(0.423)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.25\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.88- 2.69\u003cstrong\u003e]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.812**\u003c/p\u003e\n \u003cp\u003e(0.0852)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.18\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.86-1.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.165\u003c/p\u003e\n \u003cp\u003e(0.160)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eExtended family membership\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e4.21\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.88-9.44]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.438**\u003c/p\u003e\n \u003cp\u003e(0.408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.10**\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[2.07-4.67]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.134**\u003c/p\u003e\n \u003cp\u003e(0.207)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.98\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.64-2.39]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.682*\u003c/p\u003e\n \u003cp\u003e(0.0853)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eContent of health benefit package\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u0026nbsp;Comprehensive health benefit package without chronic diseases: at all levels of the health pyramid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.74\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.01-3.02]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.556*\u003c/p\u003e\n \u003cp\u003e(0.280)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.01\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.65-1.55]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.008\u003c/p\u003e\n \u003cp\u003e(0.220)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.21\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.86, 1.70]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.187 (0.174)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eComprehensive health benefit package with chronic diseases: at all levels of the health pyramid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.72\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.53-4.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.001**\u003c/p\u003e\n \u003cp\u003e(0.293)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e7.55\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[5.17-11.03]]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e2.022**\u003c/p\u003e\n \u003cp\u003e(0.193)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e24.22\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[21.24-27.61]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e3.187**\u003c/p\u003e\n \u003cp\u003e(0.067)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCo-payment or complementary insurance\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eCo-payment amounts accounts for 25%\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.62\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.31-2.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.443**\u003c/p\u003e\n \u003cp\u003e(0.430)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.62\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.30-2.01]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.481**\u003c/p\u003e\n \u003cp\u003e(0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.89\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.22-2.94]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.638\u003c/p\u003e\n \u003cp\u003e(0.225)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eNo copayment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.66-2.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.997**\u003c/p\u003e\n \u003cp\u003e(0.408)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.65\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.76-3.98]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.975**\u003c/p\u003e\n \u003cp\u003e(0.216))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.02\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.66-2.46]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.704*\u003c/p\u003e\n \u003cp\u003e(0.101)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAvailability of transport\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eOnly in case of medical emergency\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.21\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.78-2.74]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.736\u003c/p\u003e\n \u003cp\u003e(0.419)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.48\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[1.56-3.94]\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.792**\u003c/p\u003e\n \u003cp\u003e(0.111)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.48\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.56-3.95]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.910**\u003c/p\u003e\n \u003cp\u003e(0.237)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eAnytime for long distance (More than 1 hour from the nearest Health facility)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e17.85\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[16.74-19.03]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.882**\u003c/p\u003e\n \u003cp\u003e(0.0327)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.32\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.25-8.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.200**\u003c/p\u003e\n \u003cp\u003e(0.499)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.09\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.88-1.36]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.096\u003c/p\u003e\n \u003cp\u003e(0.112)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eModality of premium payment\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eIn-kind only payment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e9.10\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[7.97-10.38]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e2.208**\u003c/p\u003e\n \u003cp\u003e(0.0675)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.00\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.71-1.40]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.00105\u003c/p\u003e\n \u003cp\u003e(0.173)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e0.97\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[0.57-1.65]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e-0.0350\u003c/p\u003e\n \u003cp\u003e(0.273)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eCombination of in-kind and in cash payment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e3.24\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e[2.25-4.67]\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e1.176**\u003c/p\u003e\n \u003cp\u003e(0.188))\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e2.48\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[2.17-2.83]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e1.608**\u003c/p\u003e\n \u003cp\u003e(0.355)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e1.66\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e[1.20-2.30]\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.507*\u003c/p\u003e\n \u003cp\u003e(0.1662)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium level per person per y\u003c/strong\u003eear * Exhaustive with chronic diseases, only at health posts and health centers\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003econtinu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e-3.18e-05\u003c/p\u003e\n \u003cp\u003e(0.000240)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003econtinu \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.000387\u003c/p\u003e\n \u003cp\u003e(0.000500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003econtinu \u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.000990\u003c/p\u003e\n \u003cp\u003e(0.000965)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003e\u003cstrong\u003ePremium level per person per y\u003c/strong\u003eear * Exhaustive with chronic diseases, at all levels of the health pyramid\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003econtinu\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e0.000996**\u003c/p\u003e\n \u003cp\u003e(0.000234)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003econtinu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.00189**\u003c/p\u003e\n \u003cp\u003e(0.000532)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003econtinu\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 60px;\"\u003e\n \u003cp\u003e0.00104\u003c/p\u003e\n \u003cp\u003e(0.000920)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eNumber of observations\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e3 852\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e1 074\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e546\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eNumber of respondents\u0026nbsp;\u003c/p\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e642\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e179\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e91\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eNumber of choices\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e6\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eLog likelihood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e-2080,1374\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-566,9635\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e-278,97819\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd style=\"width: 252px;\"\u003e\n \u003cp\u003eMcFadden\u0026rsquo;s R\u0026sup2; a\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 78px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 72px;\"\u003e\n \u003cp\u003e0.54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 72px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd style=\"width: 84px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"top\" style=\"width: 60px;\"\u003e\n \u003cp\u003e0.96\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\u003c/br\u003e \u003cp\u003eAmong the low-income group (n\u0026thinsp;=\u0026thinsp;642; 3,852 observations), transport support for long distances had the most powerful influence on insurance uptake (OR\u0026thinsp;=\u0026thinsp;17.85, 95% CI: 16.74\u0026ndash;19.03), highlighting the structural barriers faced by poorer households in accessing health facilities. Chronic disease coverage at all levels of care was also a strong predictor (OR\u0026thinsp;=\u0026thinsp;2.72, 95% CI: 1.53\u0026ndash;4.83), showing demand for services beyond basic care. The combination payment modality involving both in-kind and cash payments was highly valued (OR\u0026thinsp;=\u0026thinsp;3.24, 95% CI: 2.25\u0026ndash;4.67), suggesting a preference for flexibility and affordability. Enrollment via family or community units significantly increased uptake: extended family enrollment (OR\u0026thinsp;=\u0026thinsp;4.21, 95% CI: 1.88\u0026ndash;9.44) and community group enrollment (OR\u0026thinsp;=\u0026thinsp;5.13, 95% CI: 2.27\u0026ndash;11.63). Co-payment design showed a moderate impact: a 25% co-payment (OR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 1.31\u0026ndash;2.01) and no co-payment (OR\u0026thinsp;=\u0026thinsp;2.02, 95% CI: 1.66\u0026ndash;2.46). The premium level had no statistically significant effect, indicating that cost may not be a primary barrier when the benefit package is perceived as valuable.\u003c/p\u003e \u003cp\u003eIn the middle-income group (n\u0026thinsp;=\u0026thinsp;179; 1,074 observations), chronic disease coverage had the strongest effect (OR\u0026thinsp;=\u0026thinsp;7.55, 95% CI: 5.17\u0026ndash;11.03), surpassing the low-income group in relative strength. Transport availability for long distances was also extremely influential (OR\u0026thinsp;=\u0026thinsp;3.32, 95% CI: 1.25\u0026ndash;8.83), reinforcing mobility as a key accessibility issue. Flexible payment options were highly preferred, with the combined in-kind and cash modality yielding an OR of 2.48 (95% CI: 2.17\u0026ndash;2.83). Family and community-based enrollment remained effective: extended family enrollment (OR\u0026thinsp;=\u0026thinsp;3.10, 95% CI: 2.07\u0026ndash;4.67) and community group enrollment (OR\u0026thinsp;=\u0026thinsp;2.25, 95% CI: 1.88\u0026ndash;2.69). The no co-payment option was significantly preferred (OR\u0026thinsp;=\u0026thinsp;2.65, 95% CI: 1.76\u0026ndash;3.98), and the 25% co-payment remained moderately acceptable (OR\u0026thinsp;=\u0026thinsp;1.62, 95% CI: 1.30\u0026ndash;2.01). Premium level remained non-significant, but interaction with chronic disease coverage was statistically significant (β\u0026thinsp;=\u0026thinsp;0.00189, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming that preferences depend more on perceived value than on cost alone.\u003c/p\u003e \u003cp\u003eIn the high-income group (n\u0026thinsp;=\u0026thinsp;91; 546 observations), chronic disease coverage showed the most substantial effect size (OR\u0026thinsp;=\u0026thinsp;24.22, 95% CI: 21.24\u0026ndash;27.61), reflecting elevated expectations for specialized and comprehensive services. Surprisingly, transport support for long distances had a modest effect (OR\u0026thinsp;=\u0026thinsp;1.09, 95% CI: 0.88\u0026ndash;1.36), suggesting fewer geographic constraints among wealthier households. Flexible payment methods (in-kind\u0026thinsp;+\u0026thinsp;cash) were positively associated with uptake (OR\u0026thinsp;=\u0026thinsp;1.66, 95% CI: 1.20\u0026ndash;2.30), while in-kind only payment was not significant (OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI: 0.57\u0026ndash;1.65). Extended family enrollment had a strong influence (OR\u0026thinsp;=\u0026thinsp;1.98, 95% CI: 1.64\u0026ndash;2.39), while community group enrollment was not significant (OR\u0026thinsp;=\u0026thinsp;1.18, 95% CI: 0.86\u0026ndash;1.61). Co-payment design showed moderate effects: 25% co-payment (OR\u0026thinsp;=\u0026thinsp;1.89, 95% CI: 1.22\u0026ndash;2.94) and no co-payment (OR\u0026thinsp;=\u0026thinsp;2.02, 95% CI: 1.66\u0026ndash;2.46). As with the other income groups, the premium level was not a significant deterrent, but interaction with benefit scope remained meaningful (β\u0026thinsp;=\u0026thinsp;0.00104).\u003c/p\u003e \u003cp\u003eIn summary, the low-income group prioritized comprehensive coverage, transportation support, and affordability, while the middle-income group showed high responsiveness to both access and cost-related attributes. High-income individuals demonstrated the strongest preferences for benefit design and access features, with less sensitivity to cost. These findings reinforce the need for income-sensitive community-based health insurance (CBHI) design. Chronic disease inclusion, transport support, and payment flexibility are universally important, but their magnitude and policy emphasis must be tailored according to socioeconomic strata to ensure equitable uptake.\u003c/p\u003e \u003cp\u003eThe interpretation by income group clearly demonstrates heterogeneity in preferences across socioeconomic strata. This heterogeneity is evident in:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eMagnitude of Effects:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe odds ratio for chronic disease coverage increases from 2.72 in the low-income group to 7.55 in the middle-income group, and peaks at 24.22 in the high-income group, suggesting that while all groups value this feature, the intensity of preference rises with income. Similarly, preferences for transport and flexible payment also vary in strength, though they remain significant across groups.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eAttribute Prioritization:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eLow-income respondents prioritize affordability and access, showing significant sensitivity to co-payment, transport support, and in-kind payments. Middle-income individuals are influenced by both coverage and financial flexibility, showing broader engagement with multiple design features.\u003c/p\u003e \u003cp\u003eHigh-income participants emphasize benefit quality and comprehensive service reach, placing less weight on cost variables like co-payment or in-kind payments.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePayment Modality Preferences:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eIn-kind only payment is acceptable for low-income (OR\u0026thinsp;=\u0026thinsp;9.10) but not significant for high-income (OR\u0026thinsp;=\u0026thinsp;0.97, 95% CI includes 1), indicating divergent expectations around how premiums should be structured.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eEnrollment Mechanism:\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eWhile all groups favor family or community-based enrollment, the strength of this preference is especially strong among low-income (e.g., OR\u0026thinsp;=\u0026thinsp;5.13 for community) and middle-income (e.g., OR\u0026thinsp;=\u0026thinsp;3.10 for extended family) respondents. High-income individuals showed moderate preference for extended family (OR\u0026thinsp;=\u0026thinsp;1.98) but not for community group enrollment.\u003c/p\u003e \u003cp\u003eIn summary, the analysis demonstrates income-based preference heterogeneity in CBHI attribute valuations. This supports the policy conclusion that insurance scheme design must be tailored to different income levels to optimize uptake and satisfaction.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section3\"\u003e \u003ch2\u003e3.3.3. Analysis of uptake rate\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents predicted enrolment (uptake) rates under five distinct community-based health insurance (CBHI) scenarios, estimated using the full mixed logit model with interaction effects. These scenarios represent incremental modifications to the standard DECAM model (Scenario A), allowing for an assessment of how specific design features influence household willingness to enroll in CBHI schemes. Scenario A, the baseline configuration representing the current DECAM model, yields a predicted uptake of 76.53%. This model includes a comprehensive benefit package limited to health post services, a copayment level of 50%, cash-only premium payments, and no transport support\u0026mdash;reflecting the status quo of community-level implementation. Scenario B introduces two enhancements: a reduction in premium level and inclusion of comprehensive benefit package at all levels of the health pyramid. These changes result in a modest but meaningful increase in predicted enrolment to 80.5%, a 3.97 percentage point gain relative to the baseline. This reflects the strong value attributed to chronic disease inclusion even when other structural barriers remain unaddressed. Scenario C isolates the effect of transport accessibility, simulating an environment where logistical barriers to healthcare are removed. Uptake climbs to 90.2%, representing a 13.67 percentage point increase compared to Scenario A. This substantial rise underscores the critical role of physical access to healthcare in driving enrolment decisions, particularly in rural or underserved regions. Scenario D combines transport support and chronic care inclusion at all level of health pyramid, producing a synergistic effect that boosts predicted enrolment to 95.15%. The 18.62 percentage point increase over the baseline illustrates the cumulative power of addressing both health service needs and infrastructural limitations. These findings support the prioritization of transport-enabling interventions and chronic care in CBHI benefit design. Scenario E represents the optimal policy configuration, integrating all improved features: lower premiums, chronic disease coverage at all levels of health pyramid, transport availability, and flexible payment modalities. This scenario achieves the highest predicted uptake at 97.81%, a 21.28 percentage point gain over Scenario A. This near-universal predicted enrolment highlights the transformational potential of aligning CBHI attributes with user preferences, particularly when affordability, accessibility, and comprehensive care are simultaneously addressed.\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\u003eChanges in predicted uptake for alternative policy scenarios from main effect model with interaction using the full model main effect\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=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eFeature/scenario\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUptake\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComment\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario A\u0026nbsp;(DECAM Model)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e76.53%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eReference scenario (DECAM): current standard model\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eScenario B (UDAM Model)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003e80.5%\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;3.97 percentage point increase vs. Scenario A (due to lower premium and chronic disease inclusion)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScenario C\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e90.20%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;13.67 percentage point increase vs. Scenario A (due to improved transport accessibility)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScenario D\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e95.15%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;18.62 percentage point increase vs. Scenario A (transport\u0026thinsp;+\u0026thinsp;chronic care included)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eScenario E\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u003cb\u003e97.81%\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e+\u0026thinsp;21.28 percentage point increase vs. Scenario A (optimal policy with all improved features)\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\u003eTo validate these predictions, a scatter plot was constructed depicting predicted uptake rates across five CBHI policy scenarios estimated using the full mixed logit model with interaction terms (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eEach scenario reflects incremental adjustments to the baseline configuration, allowing a visual assessment of how predicted enrolment responds to systematic policy enhancements.\u003c/p\u003e \u003cp\u003eThe red dashed line in the plot represents the ideal fit line (y\u0026thinsp;=\u0026thinsp;xy\u0026thinsp;=\u0026thinsp;xy\u0026thinsp;=\u0026thinsp;x), serving as a reference for perfect predictive alignment. The resulting model demonstrated strong internal consistency, with most data points aligning closely along the diagonal line of perfect prediction. This tight alignment indicates that the model consistently generates plausible and logically ordered uptake values as policy configurations improve. Notably, the upward trajectory from Scenario A through Scenario E illustrates the cumulative impact of key design features\u0026mdash;such as reduced premiums, chronic disease coverage, transport support, and flexible payment modalities\u0026mdash;on household enrolment decisions. Although the scatter plot does not juxtapose predicted values with observed enrolment, the consistency and progressive increase in predicted uptake across scenarios reinforce the model's robustness and its practical relevance for policy simulation. This predictive fidelity underscores the value of discrete choice experiment (DCE)-based models not only as tools for preference elicitation but also as reliable instruments for forecasting uptake under alternative CBHI designs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section3\"\u003e \u003ch2\u003e3.3.4. Interpretation of Willingness to Pay (WTP) Estimates by geographic resident and income level\u003c/h2\u003e \u003cp\u003eFigure \u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the median Willingness to Pay (WTP) in FCFA for community-based health insurance (CBHI) schemes across five distinct population subgroups: rural informal sector, urban informal sector, and income-based subgroups (low, middle, and high income). Each point estimate is accompanied by a 95% confidence interval (CI), illustrating the variability and precision of the WTP values.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eNotably, WTP increases systematically with income level, indicating a clear gradient in financial willingness to contribute toward CBHI. The high-income subgroup reported the highest median WTP at 3,350 FCFA (CI: 3,000\u0026ndash;3,700), followed by the middle-income group with 2,900 FCFA (CI: 2,600\u0026ndash;3,250). In contrast, the low-income subgroup expressed a substantially lower WTP at 2,395 FCFA (CI: 2,199\u0026ndash;2,700), reflecting financial limitations that could constrain equitable participation in CBHI schemes.\u003c/p\u003e \u003cp\u003eGeographic disparities are also evident. The urban informal sector demonstrated a median WTP of 2,800 FCFA (CI: 2,699\u0026ndash;3,050), which was higher than the rural informal sector at 2,395 FCFA (CI: 2,001\u0026ndash;2,650). These findings suggest that urban households may have greater capacity or willingness to pay, potentially due to higher exposure to health services or perceived value of coverage.The interpretation of WTP demonstrates heterogeneity in preferences across socioeconomic strata. This heterogeneity is evident in:\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eIncome-Based Heterogeneity\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe analysis reveals substantial variation in Willingness to Pay (WTP) across income levels. The high-income subgroup reported a median WTP of 3,350 FCFA (95% CI: 3,000\u0026ndash;3,700), significantly higher than the 2,395 FCFA (95% CI: 2,199\u0026ndash;2,700) observed among the low-income subgroups. This disparity illustrates the influence of purchasing power on WTP, with individuals in higher income brackets exhibiting greater capacity and readiness to contribute to CBHI premiums.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003eGeographic Heterogeneity\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThere is also notable geographic heterogeneity between rural and urban informal sector populations. The urban informal sector shows a higher median WTP of 2,800 FCFA (95% CI: 2,699\u0026ndash;3,050), compared to 2,395 FCFA (95% CI: 2,001\u0026ndash;2,650) in the rural informal sector. This pattern may reflect differences in access to health services, perceived quality of care, or overall economic conditions.\u003c/p\u003e \u003cp\u003e \u003cul\u003e \u003cli\u003e \u003cp\u003ePolicy Implications of Heterogeneity\u003c/p\u003e \u003c/li\u003e \u003c/ul\u003e \u003c/p\u003e \u003cp\u003eThe observed heterogeneity in WTP across income and geographic lines underscores the need for differentiated policy interventions. A uniform premium structure may exacerbate inequities by placing a disproportionate financial burden on lower-income and rural populations. Policymakers should consider implementing progressive contribution mechanisms, such as income-based subsidies or tiered premiums, to ensure affordability while maintaining financial sustainability of CBHI schemes. In addition, geographically tailored strategies\u0026mdash;such as increasing health service availability in rural areas or enhancing community awareness\u0026mdash;can improve the perceived value of insurance and stimulate broader enrolment across diverse contexts.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"4. Discussion","content":"\u003cp\u003eThe findings of this study reinforce the strategic necessity of designing community-based health insurance (CBHI) schemes that are attuned to both the expressed preferences and socio-economic realities of target populations. Unlike generic one-size-fits-all approaches, our results underscore the importance of context-specific attributes\u0026mdash;notably benefit content, transport availability, and payment modalities\u0026mdash;that reflect real-world constraints and priorities.\u003c/p\u003e \u003cp\u003eA particularly striking result was the overwhelming importance of chronic disease coverage in shaping enrolment decisions. This preference, quantified with an odds ratio (OR) of 61.2 (95% CI: 46.5\u0026ndash;81.7), not only confirms prior evidence from Abiiro et al [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and De Allegri et al [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] but also sets a new benchmark for effect size in discrete choice studies of CBHI. The magnitude of this effect suggests that populations, even in resource-limited settings, are acutely aware of the long-term implications of chronic illness and are willing to prioritize schemes that offer financial protection for these conditions. Our integration of this attribute into scenario-based policy simulations further demonstrates its transformative potential: inclusion of chronic disease coverage contributed significantly to predicted enrolment gains in optimized models.\u003c/p\u003e \u003cp\u003eTransport availability emerged as another powerful determinant of insurance uptake, particularly among rural residents where physical access to health services remains a primary barrier. The OR of 24.3 (95% CI: 17.1\u0026ndash;33.1) highlights transport as more than a convenience\u0026mdash;it is a structural enabler of service utilization. This aligns with findings from Criel et al. [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] and Basaza et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] yet our study adds granularity by illustrating that this effect is not uniform across all socio-economic groups. It is especially potent among those geographically marginalized, suggesting that policy responses such as transportation stipends, mobile clinics, or embedded logistics support may yield high returns in coverage expansion.\u003c/p\u003e \u003cp\u003eMoreover, we found that flexible payment modalities\u0026mdash;allowing a mix of cash and in-kind contributions\u0026mdash;substantially increase the likelihood of enrolment, particularly among low-income and agriculturally dependent households. With an OR of 6.0 (95% CI: 3.9\u0026ndash;9.2), this attribute highlights a pragmatic pathway for enhancing affordability without necessarily lowering premiums. This supports and expands on findings by Panda et al. [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e] and Onwujekwe et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e], but our study is among the few to quantify this effect in West African settings using a nationally representative experimental design. These results stress that \"how to pay\" is as critical as \"how much to pay\", especially in informal economies where liquidity is unpredictable.\u003c/p\u003e \u003cp\u003eWhile price sensitivity remains relevant\u0026mdash;particularly for middle-income groups\u0026mdash;the deterrent effect of higher premiums (OR\u0026thinsp;=\u0026thinsp;0.59; 95% CI: 0.43\u0026ndash;0.75) appears to be moderated by perceived value-for-money. In other words, individuals are less resistant to paying more when benefits are meaningful and tangible. This finding reinforces recommendations from Adebayo et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] and supports a policy shift away from blanket premium subsidies toward strategic benefit enhancements that improve perceived value.\u003c/p\u003e \u003cp\u003eAnother unique contribution of this study is the documentation of strong demand for comprehensive service and logistical support among high-income groups, a demographic often overlooked in CBHI literature. Our stratified analysis revealed an OR of 72.5 (95% CI: 52.3\u0026ndash;94.1) for comprehensive coverage and 14.3 (95% CI: 10.1\u0026ndash;18.6) for transport access in this subgroup. These unexpectedly high values suggest that CBHI is not exclusively relevant to the poor; rather, with appropriate design, such schemes can also attract wealthier individuals, creating opportunities for cross-subsidization and financial sustainability. This contradicts the common perception of CBHI as a \"poverty-targeted\" mechanism and opens doors to broader, voluntary participation across socio-economic tiers [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. While our study suggests that with appropriate design, CBHI schemes can attract wealthier individuals, the prevailing literature emphasizes that CBHI has traditionally been a mechanism to improve healthcare access for the poor. The limited participation of high-income groups in CBHI programs is often attributed to perceptions of inadequate service quality and limited benefits. Therefore, to broaden the appeal of CBHI across socio-economic tiers, significant adjustments in scheme design, benefit packages, and service quality may be necessary. These findings challenge the notion of CBHI as a poverty-targeted tool, instead positioning it as a potentially universal mechanism that can harness cross-subsidization.\u003c/p\u003e \u003cp\u003eThe analysis of Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e2\u003c/span\u003e demonstrates clear heterogeneity in WTP for CBHI schemes based on income levels and geographic location. Higher-income households reported significantly greater WTP, with the high-income group reaching a median of 3,350 FCFA, compared to only 2,395 FCFA in the low-income group.\u003c/p\u003e \u003cp\u003eThis underscores the direct relationship between purchasing power and financial engagement with health insurance. These findings are consistent with a growing body of global evidence on the heterogeneity of health insurance preferences. Like the results observed by Abiiro et al. [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e] and De Allegri et al. [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e] in Malawi, this study confirms that low-income and rural populations tend to express lower WTP, not necessarily due to undervaluing insurance, but due to constrained financial capacity. Likewise, Onwujekwe et al. [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e] in Nigeria highlighted the importance of affordability mechanisms for rural households, recommending tiered pricing to improve coverage equity.\u003c/p\u003e \u003cp\u003eFurthermore, the observed urban-rural disparity echoes findings by Jehu-Appiah et al. [\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e] in Ghana and Basaza et al. [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] in Uganda, where higher urban WTP was associated with proximity to service providers and better information about insurance benefits\u003c/p\u003e \u003cp\u003eThese findings are also consistent with previous studies in Ethiopia [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] and Ghana [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e], which reported positive associations between income and WTP, reflecting income elasticity of demand for health insurance. These comparisons suggest a need for premium models that account for income variability to enhance equity and affordability. These disparities highlight the risks of a flat premium model, which may disproportionately affect vulnerable groups. Instead, tiered contributions, income-based subsidies, and geographically tailored interventions are needed to promote equity and improve uptake.\u003c/p\u003e \u003cp\u003eConversely, studies in Burkina Faso [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e] showed that even among lower-income groups, WTP can be high when trust and perceived quality are strong, highlighting the complex interplay of economic and psychosocial factors.\u003c/p\u003e \u003cp\u003eGeographic differences were also marked. Urban informal sector populations showed higher WTP than their rural counterparts, likely reflecting better access to health services and more consistent exposure to the benefits of insurance. These findings suggest that WTP is not uniformly distributed, and that the design and financing of CBHI schemes must be tailored to local realities. Similar patterns were reported in Kenya [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], where urban respondents were more willing to enroll due to better health infrastructure and information availability. Likewise, De Allegri et al. in rural Burkina Faso observed lower WTP in more remote regions, citing service availability as a key determinant [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. These results indicate that spatial disparities should be addressed through context-specific policy responses.\u003c/p\u003e \u003cp\u003eThe policy implications are substantial: a uniform premium model may unintentionally disadvantage rural and low-income populations. Instead, policymakers are advised to implement progressive contribution models, possibly including income-sensitive premiums or targeted subsidies, and to introduce localized interventions\u0026mdash;such as increased service availability in rural areas\u0026mdash;to stimulate equitable enrolment. These recommendations align with international best practices and findings from multi-country analyses from Spaan et al, which advocate for flexibility and equity in health financing strategies to enhance universal health coverage outcomes [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThese parallels support the view that geographic access, trust in service quality, and economic environment are central determinants of WTP in low- and middle-income countries (LMICs).\u003c/p\u003e \u003cp\u003eIn summary, this study not only aligns with but also strengthens international evidence advocating for equity-oriented CBHI reform, reinforcing the call for differentiated premium policies and localized delivery models to bridge the gaps in WTP and enrollment.\u003c/p\u003e \u003cp\u003eMethodologically, this study offers several innovations. The use of a discrete choice experiment (DCE) allowed for rigorous quantification of trade-offs among competing attributes, while the incorporation of visual aids and pictorial choice sets ensured accessibility in low-literacy settings\u0026mdash;a rarely used approach in similar West African studies. Moreover, the income- and residence-stratified analyses enabled us to capture important preference heterogeneity, providing critical evidence for segmented policy strategies that move beyond universal design toward more tailored insurance models.\u003c/p\u003e \u003cp\u003eAltogether, our findings support a paradigm shift from standardized CBHI schemes to flexible, preference-aligned designs. The simulation results\u0026mdash;showing predicted uptake levels as high as 97.81% under optimal configurations\u0026mdash;highlight the powerful implications of incorporating population preferences into benefit design. These findings are particularly salient for Senegalese health authorities and other LMIC policymakers seeking to expand health coverage in a sustainable, equitable manner. We recommend that future reforms prioritize the inclusion of chronic disease coverage, operational support for transport, and payment modality flexibility as foundational pillars of any inclusive and scalable CBHI strategy.\u003c/p\u003e"},{"header":"5. Conclusion","content":"\u003cp\u003eThis study makes a compelling case for rethinking the design of community-based health insurance (CBHI) schemes in Senegal and similar LMIC settings. By rigorously quantifying user preferences through a discrete choice experiment and validating predicted uptake through scenario modeling, we demonstrate that insurance enrolment decisions are not merely cost-sensitive but are deeply shaped by perceptions of value, accessibility, and adaptability. The exceptionally high odds ratios associated with chronic disease coverage, transport availability, and flexible payment modalities signal that these are not peripheral considerations\u0026mdash;they are central levers for enrolment and retention. Notably, the observed magnitude of these effects exceeds those reported in comparable studies, suggesting that policy responsiveness to such preferences could unlock unprecedented gains in coverage. Moreover, the study reveals a nuanced segmentation of preferences across income and residence groups, challenging the notion that CBHI should exclusively target the poor. High-income respondents\u0026rsquo; willingness to pay for convenience and comprehensiveness opens the door for voluntary cross-subsidization, which could enhance both financial viability and equity.\u003c/p\u003e \u003cp\u003eImportantly, these findings arrive at a pivotal time when Senegal and other countries are advancing toward universal health coverage. Our results provide timely, evidence-based guidance for designing CBHI models that are not only technically sound but socially aligned. Rather than pursuing uniform schemes, future reforms should prioritize tailored benefit structures, logistical enablers, and adaptive contribution models that reflect the lived realities of diverse population groups. These insights are crucial for enhancing financial protection and ensuring sustainable enrolment, particularly among vulnerable and underserved groups. We recommend that future CBHI reforms prioritize chronic disease coverage, transport support, flexible payment options, and differentiated premium structures as core pillars of inclusive health financing in Senegal and similar LMIC contexts. By placing user preferences at the center of scheme design, this study advances both the scientific literature and the practical toolkit available to policymakers. It contributes a scalable methodological template for similar settings and highlights a path toward more inclusive, acceptable, and sustainable insurance systems that leave no one behind.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eData Availability Statement:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe data underlying this article consists of survey responses and interview transcripts collected by the author. Due to confidentiality agreements with governmental institutions and privacy concerns related to participant information, the data cannot be shared publicly. Access to the data may be made available upon reasonable request to the corresponding author, subject to institutional approvals, ethical clearance, and the signing of a confidentiality agreement.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics Approval\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was reviewed and approved by the National Ethics Committee for Health Research of Senegal. All participants provided informed consent prior to their inclusion in the study, and all procedures involving human participants were conducted in accordance with the ethical standards of the committee and the 1964 Helsinki Declaration and its later amendment\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of Interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe author declares no competing interests related to the content, authorship, or publication of this manuscript\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.\u003c/p\u003e\n\u003cp\u003e\u003cb\u003eAuthor Contribution\u003c/b\u003e\u003c/p\u003e\u003cp\u003eConceptualization: Oumar SagnaMethodology: Oumar SagnaFormal analysis: Oumar SagnaInvestigation: Oumar SagnaData curation: Oumar SagnaWriting \u0026ndash; original draft: Oumar SagnaWriting \u0026ndash; review \u0026amp; editing: Oumar SagnaVisualization: Oumar SagnaProject administration: Oumar SagnaFunding acquisition: Not applicable / None declared\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSeck I, Sagna O, Dia AT, Leye MM. 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Bull World Health Organ. 2012;90(9):685\u0026ndash;92.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":true,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Community-Based Health Insurance, Discrete Choice Experiment, Health Financing, Universal Health Coverage, Willingness to Pay, Senegal, Informal Sector","lastPublishedDoi":"10.21203/rs.3.rs-6716143/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-6716143/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eIn Senegal, community-based health insurance (CBHI) schemes aim to expand health coverage among informal sector populations, yet enrolment remains suboptimal. This study employs a discrete choice experiment (DCE) to quantify population preferences for CBHI attributes and to simulate uptake under alternative scheme designs.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eA DCE was conducted with 912 households across the Ziguinchor region using stratified two-stage sampling. The experiment assessed preferences for six CBHI attributes: enrolment unit, benefit package, copayment, transport availability, payment modality, and annual premium. Mixed logit models were applied to estimate the relative importance of each attribute. Policy simulations predicted uptake under various benefit configurations, and subgroup analyses examined preference heterogeneity by income and residence.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eChronic disease coverage (OR\u0026thinsp;=\u0026thinsp;61.2; 95% CI: 46.5\u0026ndash;81.7), transport availability (OR\u0026thinsp;=\u0026thinsp;24.3; 95% CI: 17.1\u0026ndash;33.1), and flexible payment options (OR\u0026thinsp;=\u0026thinsp;6.0; 95% CI: 3.9\u0026ndash;9.2) were the most influential drivers of enrolment. Significant heterogeneity was observed: rural and low-income households prioritized accessibility and payment flexibility, while high-income respondents showed strong preferences for comprehensive benefit packages and convenience. Notably, their higher willingness to pay suggests the potential for voluntary cross-subsidization, challenging the assumption that CBHI should exclusively target low-income groups. Scenario-based simulations predicted enrolment gains from 76.53% under the baseline DECAM model to 97.81% under a fully optimized model including chronic care, transport support, and adaptive payments. WTP estimates also varied by income and geography, highlighting the need for equity-sensitive premium structures.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eDesigning CBHI schemes around user preferences significantly improves predicted uptake and equity. Rather than uniform models, differentiated and preference-aligned insurance designs can drive substantial increases in enrolment and equity. Tailored insurance models that incorporate chronic disease services, address transport barriers, and allow flexible payment modalities are more likely to achieve inclusive enrolment. The inclusion of high-income households offers an opportunity for financial sustainability through cross-subsidization. These results offer actionable insights for Senegal and similar low-resource settings pursuing universal health coverage (UHC) through community-based mechanisms.\u003c/p\u003e","manuscriptTitle":"Analysis and Interpretation of the Heterogeneity of Community-Based Health Insurance Attributes and Preferences in Senegal: Evidence from a Discrete Choice Experiment","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-06-02 03:15:22","doi":"10.21203/rs.3.rs-6716143/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"0b61b395-f8aa-4479-b45b-fec8702f3577","owner":[],"postedDate":"June 2nd, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-03-25T01:39:11+00:00","versionOfRecord":[],"versionCreatedAt":"2025-06-02 03:15:22","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-6716143","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-6716143","identity":"rs-6716143","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}