Who enrols in voluntary micro health insurance schemes in low-resource settings? Experience from a rural area in Bangladesh

Abstract

Background: Micro health insurance (MHI) has proved to be a potential health-financing tool for many developing countries. Bangladesh also included MHI in its current health-financing strategy which aims to achieve universal health coverage. However, low uptake, low renewal and high dropouts have historically challenged financial sustainability of these schemes. Objective: This study aims to identify factors influencing people from low-resource settings, particularly those from Bangladesh, to enrol in MHI schemes. Methods: The study analyses the ‘Amader Shasthya’ MHI scheme operating in Chakaria, a sub-district under Cox’s Bazar district, Bangladesh. A household survey was carried out during May–June 2016 among 2,000 households from the scheme coverage area. The Outreville’s insurance-demand framework was used to identify enrolment influencing factors. Multivariate logistic regression analysis was carried out to identify significant influencing factors of enrolment. Results: Enrolment influencing factors were identified in four dimensions: economic, socio-cultural, demographic and structural. Households with the main income earner having 10+ years of schooling (odds 1.9 [CI 1.2–2.9] compared to illiterate), having financial literacy (odds 1.5 [CI 1.2–1.8] compared to financially illiterate) and being a public/private service holder (odds 1.6 [CI 1.1–2.4] compared to menial labour) were more likely to enrol. Membership in development programmes of NGOs also influenced enrolment decision significantly (odds 1.3 [CI 1.0–1.5]). The presence of chronic illness in household encouraged enrolment (odds 1.5 [CI 1.2–1.8]). Households living closer to health centres were more likely to enrol (odds 2.1 [CI 1.6–2.7]) compared to those living further away. Conclusion: The findings are expected to have significant implications in terms of designing similar health insurance schemes, particularly in terms of designing demand-driven and context adapted schemes that have greater potential to attract a larger client pool, ensure effective risk pooling and eventually expedite the achievement of universal health coverage in low-resource settings.

Predictors of health insurance enrolment and wealth-related inequality in Nepal: evidence from Multiple Indicator Cluster Survey (MICS) 2019

“…Odds of getting enroled into the scheme increased with an increase in level of education. Our finding is consistent with similar studies conducted in Nepal, 28 42 Ghana, 7 29 43 Bangladesh, 30 Kenya 31 and Burkina Faso. 44 This result could be attributed to the fact that educated households head have a better understanding of financial risks associated with sudden health shock compared with not educated households head and they can make informed decision about getting enroled into the schemes.…”

“… 28 However, few studies have reported no significant association between the family size and odds of enrolment. 30 42 43 Our finding corroborates with the study from Bangladesh 30 that there is no significant association between households having under-five children and increased odds of enrolment. In contrast, Salari et al 29 based on the analysis of nationally representative household surveys of Ghana, have found significant association.…”

Education and Experience as Determinants of Micro Health Insurance Enrolment

“…After the household experienced illness and its drastic financial consequences measured by high OOPE and hardship financing in the form of usurious borrowing, newly insured families joined the risk pool of SSP. In support of this finding, 26,27,[31][32][33][34] research confirms a positive relationship between health expenditure risk aversion and participation in MHI. These households become risk-averse to avert negative financial consequences of future health shocks.…”

“…24,25 The households having high ratio of ill members and those reporting chronic illness enrol in MHI. [25][26][27] Most of these studies however, look at how insurance buying behaviour is associated with such socio-demographic variables. We try to go a step further and attempt to establish causality.…”

“…Outreville 53 groups the factors determining demand for life insurance under economic, demographic, socio-cultural and institutional categories. Akin to a study by Mahmood et al, 26 we adopted this framework by incorporating economic factors (income, types of borrowing for medical needs), social factors (education) and demographic factors (illness experience as a proxy for health status), but excluding structural factors such as non-government organization membership given that self-help group (SHG) membership is a prerequisite for buying MHI policy.…”