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Open Access

Peer-reviewed

Research Article

Impact of community-based health insurance in low- and middle-income countries: A systematic review and meta-analysis

  • Paul Eze ,

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Project administration, Software, Supervision, Validation, Visualization, Writing – original draft, Writing – review & editing

    peze@psu.edu

    Affiliation Department of Health Policy and Administration, Penn State University, University Park, PA, United States of America

    ⨯
  • Stanley Ilechukwu,

    Roles Data curation, Formal analysis, Methodology, Writing – review & editing

    Affiliations Department of Global Health and Development, London School of Hygiene and Tropical Medicine, London, United Kingdom, Health Projects, South Saharan Social Development Organization (SSDO), Independence Layout, Enugu, Nigeria

    ⨯
  • Lucky Osaheni Lawani

    Roles Conceptualization, Data curation, Formal analysis, Investigation, Methodology, Validation, Writing – review & editing

    Affiliation Institute of Health Policy, Management and Evaluation, University of Toronto, Toronto, Canada

    ⨯

Abstract

Background

To systematically evaluate the empirical evidence on the impact of community-based health insurance (CBHI) on healthcare utilization and financial risk protection in low- and middle-income countries (LMIC).

Methods

We searched PubMed, CINAHL, Cochrane CENTRAL, CNKI, PsycINFO, Scopus, WHO Global Index Medicus, and Web of Science including grey literature, Google Scholar®, and citation tracking for randomized controlled trials (RCTs), non-RCTs, and quasi-experimental studies that evaluated the impact of CBHI schemes on healthcare utilization and financial risk protection in LMICs. We assessed the risk of bias using Cochrane’s Risk of Bias 2.0 and Risk of Bias in Non-randomized Studies of Interventions tools for RCTs and quasi/non-RCTs, respectively. We also performed a narrative synthesis of all included studies and meta-analyses of comparable studies using random-effects models. We pre-registered our study protocol on PROSPERO: CRD42022362796.

Results

We identified 61 articles: 49 peer-reviewed publications, 10 working papers, 1 preprint, and 1 graduate dissertation covering a total of 221,568 households (1,012,542 persons) across 20 LMICs. Overall, CBHI schemes in LMICs substantially improved healthcare utilization, especially outpatient services, and improved financial risk protection in 24 out of 43 studies. Pooled estimates showed that insured households had higher odds of healthcare utilization (AOR = 1.60, 95% CI: 1.04–2.47), use of outpatient health services (AOR = 1.58, 95% CI: 1.22–2.05), and health facility delivery (AOR = 2.21, 95% CI: 1.61–3.02), but insignificant increase in inpatient hospitalization (AOR = 1.53, 95% CI: 0.74–3.14). The insured households had lower out-of-pocket health expenditure (AOR = 0.94, 95% CI: 0.92–0.97), lower incidence of catastrophic health expenditure at 10% total household expenditure (AOR = 0.69, 95% CI: 0.54–0.88), and 40% non-food expenditure (AOR = 0.72, 95% CI: 0.54–0.96). The main limitations of our study are the limited data available for meta-analyses and high heterogeneity persisted in subgroup and sensitivity analyses.

Conclusions

Our study shows that CBHI generally improves healthcare utilization but inconsistently delivers financial protection from health expenditure shocks. With pragmatic context-specific policies and operational modifications, CBHI could be a promising mechanism for achieving universal health coverage (UHC) in LMICs.

Citation: Eze P, Ilechukwu S, Lawani LO (2023) Impact of community-based health insurance in low- and middle-income countries: A systematic review and meta-analysis. PLoS ONE 18(6): e0287600. https://doi.org/10.1371/journal.pone.0287600

Editor: Lara Vojnov, World Health Organization, SWITZERLAND

Received: November 28, 2022; Accepted: June 8, 2023; Published: June 27, 2023

Copyright: © 2023 Eze et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the paper and its Supporting Information files.

Funding: The author(s) received no specific funding for this work.

Competing interests: The authors have declared that no competing interests exist.

Introduction

Starting in the 2000s, low- and middle-income countries (LMIC) implemented health system reforms aimed at improving healthcare access and outcomes to achieve universal health coverage (UHC)–a situation where all people have access to the health services they need, when and where they need them, without financial hardship [1, 2]. To this end, there has been significant interest in expanding the breadth and depth of health insurance schemes including social health insurance (SHI), national health insurance, community-based health insurance (CBHI), and private health insurance (PHI) [3, 4]. Due to the widespread interest in expanding health insurance coverage, there has also been a contemporaneous interest in evaluating the impacts of health insurance programs on the following key UHC objectives: healthcare utilization, out-of-pocket spending, and health outcomes [5, 6].

CBHI–also known as micro health insurance or mutual health insurance–are voluntary schemes characterized by community members pooling funds to offset the cost of illness and improve access to quality health services for low-income rural households largely excluded from formal health insurance schemes [7, 8]. The following institutional design features generally characterize CBHI schemes: pooling of health risks and funds occurs within a community or a group of people who share common characteristics, such as occupation or geographical location; membership premiums are often offered at a flat rate and independent of individual health risks; entitlements to benefits are linked to contributions in most cases; affiliation is voluntary; and such schemes mostly operate on a non-profit basis [9]. China’s and Rwanda’s remarkable strides toward UHC through the roll-out of CBHI schemes exemplify CBHI’s potential for resource-limited countries seeking to achieve UHC [10].

Recent LMIC studies suggest financial barriers to healthcare, especially inpatient and specialist care and high catastrophic out-of-pocket (OOP) incidence, persists [11–14]. Previous reviews accessed the impact of CBHI schemes on healthcare utilization and/or financial protection in developing, low- and/or middle-income countries with inconsistent conclusions [15–20]. Since the publication of these reviews, several published studies that addressed these issues still inconsistently report on the impact of these schemes on UHC outcomes. Given the number of these studies, a meta-analysis would be more appropriate. We focused on CBHI’s impact on healthcare utilization and financial risk protection, as achieving these critical UHC goals is pivotal to improving overall health outcomes and continued participation in the schemes [20]. The objectives of this study, therefore, are two-fold. First, we update the literature on the impact of CBHI on these key UHC objectives and discuss their implications for achieving UHC in these countries. Second, we conduct a meta-analysis of similar studies to provide more robust estimates of the impacts of CBHI on healthcare utilization and financial risk protection in LMICs. Our main contribution is its comprehensive and rigorous evaluation of the causal evidence for CBHI’s impact in this setting.

Methods

The study protocol was prospectively published on PROSPERO: CRD42022362796; and the findings are reported according to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines [21].

Eligibility criteria

We defined CBHI as the application of the principles of insurance by a defined community in a way unique to their cultural and social context, as directed by a community’s choice and based on their arrangement and structures [9, 22]. Thus, we considered mutual health insurance, mutual health organizations, micro health insurance, rural health insurance, community health funds, and community health prepayment scheme as synonyms. To be included, a study must report the impact of CBHI on healthcare utilization and/or financial risk protection (S1 Table). However, the estimation of the impact of CBHI schemes using non-experimental data is complicated by endogeneity–heterogeneity in unobservable individual characteristics of the scheme enrollees and non-enrollees, which influences the decision to participate in the scheme and our study outcomes–pertaining to healthcare utilization and health expenditures [23, 24]. Hence, in addition to randomized control trials (RCTs) and non-RCTs, we only included studies that used statistical methods to simulate exogenous variation in the exposure to CBHI to identify and estimate causal effects [25] –S1 Table.

Search and identification strategy

We searched PubMed (MEDLINE), CINAHL, Cochrane CENTRAL, ECONLIT, Embase, CNKI, PsycINFO, Scopus, Web of Science, and Global Health Library indexes (African Index Medicus, Index Medicus for the Eastern Mediterranean Region, Index Medicus for the South-East Asia Region, Literatura Latino-Americana e do Caribe em Ciências da Saúde [LILACS], and Western Pacific Region Index Medicus). We also searched ELDIS (Institute of Development Studies), IDEAS/RePEc, and 3ie impact evaluation databases. We supplemented these with a search of (1) grey literature websites–New York Academy of Medicine Grey Literature and Open Grey; (2) preprints–Gates Open Research, medRxiv, PrePubMed, Research Square, SSRN, and Wellcome Open Research; (3) websites of the World Bank, World Health Organization WHOLIS database, USAID, Inter-American Development Bank, Global Development Network, National Bureau of Economic Research, and RAND Corporation; (4) ProQuest database for dissertations & theses; and (5) Google Scholar. Finally, we tracked included studies’ backward and forward citations.

We (PE and LOL) searched each database and website from its inception to 30 September 2022 using search relevant Medical Subject Headings (MeSH) terms–community-based health insurance, catastrophic health expenditure, financial risk protection, healthcare utilization, low- and middle-income countries, and developing countries from 04 to 17 October 2022 (S1 Text). We also used Boolean operators “AND” and “OR” to broaden the search. We sought evidence on the impact of CBHIs on healthcare utilization and financial risk protection derived from robust quantitative analysis of household or individual-level data. We considered studies published in any of the six United Nations (UN) languages–Arabic, Chinese, English, French, Russian and Spanish–and translated non-English publications using a translation service. Furthermore, we conducted a moderation exercise to ensure the eligibility criteria were uniformly applied to article selection before independently assessing the titles and abstracts. We retrieved and assessed the full texts of eligible studies against the inclusion criteria. At every stage, we resolved discrepancies through discussion. We used Mendeley Desktop® to identify and remove duplicates.

Data extraction

We (PE and SI) independently extracted data from the included studies using a template. We extracted the following data from each included study: authors’ names, publication status, publication year, study setting, study design, data source, study (data collection) period, sampling method, sample size, statistical analysis approach, and the effect estimate of CBHI on healthcare utilization and financial risk protection. We extracted the reported effect estimate with the 95% confidence interval or standard error at 5.0% statistical significance. In cases where two or more studies used the same secondary data to estimate the impact of the same CBHI scheme, we assessed the peer-review status of the studies, prioritizing peer-reviewed studies over non-peer-reviewed studies. In addition, we extracted outcome data for all thresholds where a study described outcome measures using more than one CHE definition. We extracted CBHI effect estimates on OOP payments or CHE incidence measured using incurred medical expenditure only [26].

While community involvement in the scheme’s management is common in all CBHI schemes, the degree of involvement varies from one scheme to the next. Therefore, based on the detailed description by Bennet et al. and Mebratie et al. [20, 27], we categorized CBHI schemes into community-driven and community-managed schemes where the community manages and administers the scheme even if the schemes was initiated by government, an NGO, or donors; provider-based health insurance schemes where provider, usually a hospital, plays a foremost role in mobilizing the community and community’s role is more supervisory; or government-supported community-involved schemes which is characterized by strong government supervision and involvement. We grouped study countries into six World Bank regions (East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean [LAC], Middle East and North Africa [MENA], South Asia, and Sub-Saharan Africa [SSA]) and three income categories (low, lower-middle, and upper-middle) based on the World Bank’s classification [22]. In the case of panel studies and repeated surveys that spanned multiple years, we assigned the study’s last year as the year of study. We prioritized outcome measures from intention-to-treat analysis for RCTs [28], and nearest-neighbor matching for non-randomized studies employing propensity score matching [29]. We contacted the study authors to request missing estimates and/or further analysis. In addition, we resolved discrepancies through discussion.

Risk of bias assessment

We (PE and SI) independently used Cochrane’s Risk of Bias 2.0 (RoB 2.0) tool to assess the risk of bias RCTs and their respective protocols and trial registry records [30] in five domains: (1) bias arising from the randomization process, (2) bias due to deviations from intended interventions, (3) bias due to missing outcome data, (4) bias in the measurement of the outcome, and (5) bias in the selection of the reported result. If any of the five domains were associated with some concerns of risk of bias or high risk of bias, then we rated the overall risk of bias as “some concern” or “high risk”, respectively. Otherwise, we rated RCTs as “low risk”. Likewise, we independently assessed the risk of bias in non-RCTs and quasi-experimental studies across seven domains using the Cochrane Risk Of Bias In Non-randomized Studies of Interventions (ROBINS-I) tool [31]. We rated the overall risk of bias as “low risk”, “moderate risk”, or “serious/critical risk”. We resolved discrepancies through discussion. We graphically presented the risk of bias assessment for RCTs and quasi/non-RCTs using the Risk-Of-Bias VISualization (ROBVIS) tool [32].

Data analysis

We performed narrative synthesis and meta-analysis following the Cochrane Handbook for Systematic Reviews of Interventions guidelines [33]. We used descriptive statistics to summarize the characteristics of included studies. We conducted a narrative synthesis of CBHI-impact data in included studies considering three possible effects: positive effect, statistically insignificant effect, and negative effect with relevant effect size. We performed pairwise meta-analyses using random-effects (DerSimonian-Laird) model to obtain pooled estimate of the impact of CBHI on healthcare utilization and financial protection for CBHI-insured households versus uninsured households. Multiplicity of empirical methods and outcome measures reported across included studies did not allow pooling financial risk protection outcome data in a global meta-analysis. Instead, we performed multiple meta-analyses using widely recognized measures of financial risk protection: OOP health expenditure, 10% of total expenditure, and 40% of non-food expenditures (defined also as ‘consumption expenditure’) [11, 26, 34–36]. For both healthcare utilization and financial protection meta-analyses; we performed “leave-one-study” sensitivity analysis to assess the impact of the different studies on the pooled estimate and sub-group analysis to assess the impact of intervention and study characteristics on pooled estimates. We assessed heterogeneity between studies using χ² test with Cochran’s Q statistic and quantified with I2. Our unit of analysis was the household. We conducted statistical analyses using Stata MP 17.0 (StataCorp LLC®) and considered α (alpha) of 0.05 as cut-off for statistical significance. Map was created using QGIS version 3.28 package. Finally, we assessed the quality of evidence using the Grading of Recommendations, Assessment, Development and Evaluation (GRADE) approach for pooled estimates [37].

Results

Identification of studies

The study selection process is illustrated in a PRISMA flow diagram (Fig 1). Our literature searches identified 16,039 studies, out of which 3,431 duplicates were removed, and 12,608 studies were screened for relevance. On applying the selection criteria, 12,459 studies were excluded. Finally, 149 full texts articles were assessed and further screened using the predesigned selection criteria. Sixty-one studies met the inclusion criteria for data extraction and were included in the review [38–98], whereas 88 studies were excluded for the following reasons: the study employed an ineligible identification strategy (n = 63) [99–161], reported data from a sample already included in the review (n = 11) [162–172], case studies, reviews (n = 7) [27, 173–178], the evaluated insurance scheme is not a CBHI (n = 6) [179–184], and could not isolate the impact of CBHI scheme (n = 1) [185] –S2 Table.

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Fig 1. PRISMA flow chart of the study identification process.

** Details are provided in S2 Text.

https://doi.org/10.1371/journal.pone.0287600.g001

Characteristics of included studies

Sixty-one studies, presented in Table 1, were included in this review: 11 RCTs, six non-RCTs, and 44 quasi-experimental studies which compromised 1,012,542 individuals in 221,568 households across 20 LMICs (Fig 2). The included studies consist of 49 peer-reviewed publications, 10 working papers, one preprint, and one graduate thesis–Table 2. Together, the studies evaluated 63 distinct CBHI schemes: 10 government-supported community-involved, 51 community-driven and community-managed, and two provider-based CBHI schemes. The primary studies were published between 1995 to 2022; and were undertaken in East Asia (n = 19), South Asia (n = 9), and sub-Saharan Africa (n = 33). Notably, we did not find any eligible studies in the Europe and Central Asia, LAC, and MENA regions. The median (IQR) period between the CBHI scheme’s launch and study assessment was 66 (30 to 114) months and the median (IQR) coverage was 49% (18.5% to 85.5%).

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Fig 2. Choropleth map of countries represented in the systematic review according to the number of studies in which each country is represented.

The base layer map is obtained in QGIS version 3.28 (Firenze) software, which imports the world map from Natural Earth, which is in the public domain and available from https://www.naturalearthdata.com; with terms of use available in http://www.naturalearthdata.com/about/terms-of-use/.

https://doi.org/10.1371/journal.pone.0287600.g002

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Table 1. Description of community-based health insurance (CBHI) schemes in LMICs reported in included studies.

https://doi.org/10.1371/journal.pone.0287600.t001

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Table 2. Summary of the reported impact of CBHI schemes on healthcare utilization and financial risk protection from included studies.

Adjusted effect estimates (with 95% confidence interval, where applicable) in bold.

https://doi.org/10.1371/journal.pone.0287600.t002

Of the 11 RCTs, most (n = 10, 91%) were rated as having low overall risk of bias, and one study was rated as having some concerns [54] (S1 Fig). Most of the included RCTs (n = 10; 91%) were rated as having a low risk of bias arising from the randomization process, whereas the remaining RCT was rated as having some concerns for this domain. Most RCTs (n = 8; 73%) had a low risk of bias due to deviation from the intended interventions, but three had some concerns in this domain [54, 79, 82]. However, for two of these three studies, we did not consider this deviation to substantially affect the overall risk of bias in the study [79, 82]. Based on weighted risk using trials’ sample size (in households), 95% of the included RCTs were rated as having a low risk of bias and approximately 5% high risk of bias (S2 Fig).

Of the 50 non-RCTs and quasi-experimental studies, most (n = 40; 80%) were rated as having a low overall risk of bias, eight were rated as having moderate overall risk, and two as having a serious or critical overall risk of bias (S3 Fig). Of note, most included non-RCTs and quasi-experimental studies were rated as having a low risk of bias for the classification of participants, deviation from intended interventions, measurement of outcomes, bias in the measurement of outcomes, and selection of reported results. Based on weighted risk using study sample size (in households), 80% of these studies were rated as having a low risk of bias, 16% as having a moderate risk of bias, and about 4% as serious/critical risk of bias (S4 Fig). The main causes of serious overall bias risk, according to ROBINS-I assessment for non-RCTs and quasi-experimental studies, were weaknesses in the confounding bias and selection of participants domains. Our assessment suggests these bias nudges the CBHI effects towards the null.

Healthcare utilization

Fig 3 summarizes the evidence of the impact of CBHI on the utilization of healthcare services. The evidence on utilization of healthcare services and outpatient services generally suggested a positive effect, with 14 out of 18 studies and 23 out of 30 studies reporting a statistically significant positive effect, respectively. However, the evidence on inpatient hospitalization is less clear, with 12 out of 23 studies reporting a positive effect, two studies finding a negative effect, and nine studies reporting statistically insignificant effects. Among the higher quality studies, that is, those with low overall risk of bias from RoB 2.0 and ROBINS-I assessments: 10 out of 13 studies, 20 out of 25 studies, and 10 out of 20 studies reported a positive relationship between CBHI enrolment and healthcare utilization, use of outpatient health services, and inpatient hospitalization, respectively. Two high-quality studies, however, reported CBHI enrolment decreased inpatient hospitalization [41, 77]. The existing evidence suggests that the government-supported community-involved model had the greatest impact on healthcare utilization–Table 3. However, the two studies showing a negative impact of CBHI on healthcare utilization were also a government-supported community-involved model in SSA, suggesting that government support in this region could have both favourable and unfavourable effects [41, 77]. In addition, the impact of CBHI on healthcare utilization generally improves as the scheme with older schemes–Table 3.

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Fig 3. Studies reporting the impact of CBHI schemes on healthcare utilization and financial risk protection in LMICs.

https://doi.org/10.1371/journal.pone.0287600.g003

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Table 3. CBHI impact on healthcare utilization and financial risk protection by CBHI mode, duration of scheme, and region.

https://doi.org/10.1371/journal.pone.0287600.t003

Meta-analysis of data from included studies showed that CBHI significantly improved overall healthcare utilization: AOR = 1.64 (95% CI = 1.12–2.39, I2 = 79.1%, n = 4 studies, sample = 5,122 households); use of outpatient medical services: AOR = 1.58 (95% CI = 1.22–2.05, I2 = 89.2%, n = 7 studies, sample = 42,210 households), and health facility delivery (maternity): AOR = 2.21 (95% CI = 1.61–3.02, I2 = 53.6%, n = 2 studies, sample = 7,140 households)–Fig 4, respectively. However, pooled data suggests CBHI had insignificant improved inpatient hospitalization: OR = 1.53 (95% CI = 0.74–3.14, I2 = 81.3%, sample = 2,886 households)–Fig 4. A CBHI scheme that’s exclusive for pregnant mothers decreased the Caesarean section delivery rate (AOR = 0.42, 95% CI = 0.22–0.78) [77]. Of note, restricting these analyses to higher quality studies yielded similar results. Likewise, restricting these analyses to non-China studies also yielded similar results.

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Fig 4.

Pooled estimate for impact of CBHI on (A) Healthcare utilization (non-specific), (B) Use of outpatient health services, (C) Health facility delivery, and (D) Inpatient hospitalization. CI: Confidence interval.

https://doi.org/10.1371/journal.pone.0287600.g004

Sensitivity analysis leaving individual studies, including all studies from China, did not yield significantly different impact results. In subgroup analysis, we did not find statistically significant differences in CBHI impact size for healthcare utilization based on a country’s income status (p = 0.61), region (p = 0.23), CBHI model (p = 0.20), and study quality (p = 0.20). However, the impact sizes were significantly different considering the study design. RCTs reported a slightly lower pooled estimate (OR = 1.12, 95% CI = 1.05–1.21) than non-RCTs and quasi-experiments (OR = 2.13, 95% CI = 1.62–2.80), p < 0.001 –S3 Table. For the utilization of outpatient medical services, however, there were no significant differences by income study CBHI model (p = 0.93) nor study quality (p = 0.95), but the impact estimates were statistically different by publication status (p = 0.01), region (p < 0.01), and stay design (p < 0.01). In this context, RCTs reported a substantially higher impact estimate (OR = 3.99, 95% CI = 2.53–6.27) than non-RCTs and quasi-experiments (OR = 1.55, 95% CI = 1.24–1.94)–S4 Table.

Financial risk protection

Overall, the evidence on the impact of CBHI on financial risk protection is less consistent than that for healthcare utilization–Fig 3. In total, 21 of the 61 studies reported the impact of CBHI on the level of OOP health expenditure. Among those 21 studies, 10 found a positive effect (that is, a reduction in OOP expenditure), eight studies found no statistically significant effect, and three studies–all from China [52, 53, 97]–reported a negative effect (that is, an increase in OOP expenditure). Another financial protection measure is the probability of incurring catastrophic health expenditure, defined as OOP payments exceeding a certain threshold percentage of total expenditure, income, non-food expenditure, or capacity-to-pay. Of the 14 studies reporting this measure, nine reported reductions in the risk of catastrophic expenditure and six found no statistically significant difference. Four of the five studies that used borrowing as a measure of financial protection reported a positive impact of CBHI [46, 56, 65, 93], whereas a single study reported no impact [54]. Finally, two high-quality studies evaluated the impact on financial protection by assessing the impact of CBHI on household assets and the probability of falling into poverty [75, 78]. Although CBHI had no significant impact on household assets [75], it significantly decreased the probability of falling into poverty [78].

Among high-quality studies, that is, those with low overall risk of bias from RoB 2.0 and ROBINS-I assessment, 8 out of 16 studies, 8 out of 13 studies, and 4 out of 4 studies reported a positive relationship between CBHI enrolment and decrease in the level in OOP health expenditure, decrease in the incidence of catastrophic expenditure, and decrease in the probability of borrowing. In addition, the impact of CBHI on financial risk protection generally improves over time–Table 3. The lone study that evaluated the impact of CBHI on financial risk protection exclusively in urban and semi-urban setting showed the positive impact of CBHI [42], but only half of studies conducted in rural settings showed a positive outcome. Likewise, the lone study that evaluated the impact of CBHI on financial protection for only women and children showed that CBHI provides significant financial risk protection [98]. Studies employing both RCT and non-RCT study designs largely reported similar results on the impact of CBHI on financial risk protection.

Meta-analyses of data from included studies showed that CBHI significantly decreased the level of OOP health expenditure: AOR = 0.94 (95% CI = 0.92–0.97, I2 = 0.0%, n = 4 studies, sample = 8,983 households); reduced the incidence of catastrophic health expenditure at 10% total household expenditure threshold: AOR = 0.69 (95% CI = 0.54–0.88, I2 = 59.6%, n = 4 studies, sample = 10,614 households) and 40% non-food expenditure threshold: AOR = 0.72 (95% CI = 0.54–0.96, I2 = 76.6%, n = 4 studies, sample = 22,543 households)–Figs 5 and 6. Restricting these meta-analyses to higher quality studies yielded similar results. Likewise, restricting these analyses to non-China studies also yielded similar results. Leave one sensitivity study also yielded largely analogous results. In subgroup analysis, there were no significant differences in CBHI impact size on the level of OOP expenditure by national income status (p = 0.29), region (p = 0.38), CBHI model (p = 0.63), and study’s quality (p = 0.21)–S5 Table. There was also no difference in the incidence of catastrophic health incidence at 10% total household expenditure threshold by national income (p = 0.38), region (p = 0.61), CBHI model (p = 0.31), and study design (p = 0.31); and at 40% non-food expenditure threshold by national income (p = 0.24), region (p = 0.09), publication status (p = 0.52), and study quality (p = 0.23)–S6 and S7 Tables.

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Fig 5. Pooled estimate for impact of CBHI on OOP expenditure.

CI: Confidence interval.

https://doi.org/10.1371/journal.pone.0287600.g005

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Fig 6.

Pooled estimate for impact of CBHI on catastrophic health expenditure at (A) 10% total household expenditure, (B) 40% Non-food expenditure. CI: Confidence interval.

https://doi.org/10.1371/journal.pone.0287600.g006

Quality of evidence

The GRADE assessments for the impact of CBHI schemes on overall healthcare utilization and financial risk protection in LMICs are outlined in S8 Table. The certainty of evidence varied. The evidence for overall healthcare utilization, use of outpatient services, and health facility delivery were graded as high, but the evidence for inpatient hospitalization was assessed as low. However, the evidence for the impact of CBHI on OOP health expenditure and catastrophic health expenditure were graded as high.

Discussion

This systematic review has summarized the best available evidence from 61 unique studies on the impact of CBHI schemes on healthcare utilization and financial risk protection in LMICs. The evidence suggests that, compared to uninsured households, CBHI-insured households had improved utilization of healthcare services but only marginally improved financial protection while accessing care. The findings from the meta-analyses support these findings, even though fewer reports were included in the meta-analyses with large heterogeneity in these outcomes. The body of evidence studying the effectiveness of CBHI schemes, determinants of enrolment in these schemes, and factors associated with renewal of subscription has increased, suggesting that health system researchers and policymakers find this topic relevant. This review has summarized and pooled available evidence and delivered strong conclusions on the CBHI impact for achieving financial risk protection and access to quality essential healthcare–key targets for UHC, while signalling an effective operational model that warrants further research.

Previous works have specifically focused on the impact of CBHI on financial risk protection [16, 17], healthcare utilization [18, 19], or both [4, 20]. However, none of these reviews included a quantitative meta-analysis of the data nor addressed the selection bias inherent in evaluating health insurance during study selection. Although Bhageerathy et al. narratively conclude that CBHI enrolment increased access to healthcare facilities and improved utilization of healthcare services [19]. Artignan and Bellanger’s review of CBHI schemes in SSA suggests this improvement was only evident for outpatient care but weakly evident for inpatient care [18]. Habib et al. review of the impact of micro health insurance on financial protection and narratively suggest a positive influence of MHI on OOP, catastrophic health expenditure, poverty, health expenditures, household consumption, borrowings, sale of assets, and household savings [16]. Previously, Ekman’s narrative review showed the same conclusion [17]. Spaan et al.’s and Mebratie et al.’s reviews reached the same conclusion: the impact of CBHI on improving healthcare utilization–especially fairly cheaper outpatient care services–and mitigating catastrophic healthcare spending [4, 20].

Healthcare utilization is a key performance indicator for measuring universal health coverage and our study indicates that CBHI immensely improved healthcare utilization especially outpatient healthcare services. By reducing the monetary cost of accessing healthcare services, CBHI schemes may induce higher utilization–a term known as moral hazard [186]. However, this increased utilization represents overcoming barriers to accessing healthcare rather than wasteful healthcare consumption per se [187]. The extent to which CBHI schemes overcome this financial barrier could depend on the benefits package, coverage, and co-payment policies [51, 188]. CBHI schemes, like the one in Burkina Faso, offer a comprehensive benefit package with minimum exclusions and no co-payments remove uncertainties at the time of illness and are likely to increase utilization [58]. Our review, however, provides weak evidence for inpatient hospitalization. First, CBHI schemes that demonstrated no/negative impact of CBHI on inpatient hospitalization did not cover inpatient admission [51]. In addition, even when inpatient hospitalization is covered, hospital admission is decided by physicians whose medical assessments moderate the impact of CBHI enrolment. Furthermore, CBHI enrolment should, in the long term, reduce the need for inpatient hospitalization, as chronic illnesses requiring hospital admission, are addressed early in outpatient clinics given the improved access to care [189].

CBHI is not only associated with higher healthcare utilization and better financial risk protection for enrolled households. The evidence regarding the protective effect of CBHI in LMICs, while not as strong as the evidence for healthcare utilization, is still positive. Increased volume and intensity of healthcare produces a smaller reduction in OOP expenditures than what it would have been otherwise [190], and in some instances erases the protective effects of insurance [52, 97]. This dovetails with the hypothesis that in LMIC, CBHI enrolment overcomes the financial barrier to access but fails to adequately protect the enrolled once inside the healthcare system [172]. The uneven success achieved in terms of providing financial risk protection is due to country-specific variations in the CBHI scheme implementation, benefit package, scheme coverage, and cost-sharing policy. However, delicately balancing affordable premiums, improving enrolment and coverage, providing generous benefits, and still remaining sustainable is often elusive for schemes without external funding and policy support. Schemes with fairly cheap premiums leave enrollees with high OOP expenses when they access care, especially for high-cost treatments [52, 188]. On the other hand, increasing premiums to reduce cost-sharing makes enrollment impossible for poor households–the main target of CBHI schemes. Nevertheless, our study findings establishes that CBHI provides significant financial risk protection for enrolled households.

Study limitations

To the best of our knowledge, our review is the most comprehensive analysis to date of the causal impact of CBHI on healthcare utilization and financial risk protection in LMICs. We also addressed the selection bias and heterogeneity issues–a common weakness in previous reviews, by including primary studies that addressed selection bias through randomization or appropriate statistical techniques. Hence, our conclusions are based on primary studies with causal inferences. Furthermore, we performed meta-analyses to provide more robust evidence that can help researchers and policymakers better understand the magnitude of the impact. Our study has several limitations. First, all systematic reviews are susceptible to publication and selection bias. Ours is not different, even though we minimized these biases by employing a comprehensive, pre-registered search strategy developed with the assistance of a university librarian, searching through multiple databases and grey literature, and utilizing two independent reviewers for study identification. Second, we had an unequal representation of countries that affects our findings’ generalizability–a quarter of included studies (14 out of 61 studies) were from China. The absence of eligible studies from LAC, MENA, and Europe and Central Asia regions exacerbates this limitation. However, sensitivity analyses excluding these studies did not yield different results. Third, as data from only a few studies were included in meta-analyses, we employed descriptive content analysis, which involves greater reliance on the original authors’ interpretations. Fourth, due to the limited number of studies included in the meta-analysis, we did not perform funnel plot tests to examine for heterogeneity, non-reporting bias, and chance in our pooled impact estimates [33]. However, to limit the chance that results from additional studies would be missing from our synthesis, we scrubbed through multiple specialized databases, searched grey literature, and considered studies in multiple languages.

Policy implications

Consistent with a growing body of literature, our review provides strong evidence of the causal impact of CBHI on healthcare utilization and financial risk protection in LMICs. Our study also provides compelling evidence that government-supported CBHI models improve healthcare utilization and financial protection. This is crucial given that most households in rural communities and in the informal sector, which are the key targets of CBHI schemes, cannot afford premiums that can sustain the schemes. If the schemes are to offer comprehensive benefit package with minimum exclusions and co-payments, the need for external funding and policy support is even greater [8, 188]. Equally important, nesting CBHI schemes within pre-existing social institutions (such as market women association, tricycle riders association, etc.) is necessary (but insufficient) for successful implementation. Enduring schemes such as the Self-Employed Women Association scheme in India and mutuelles de santé in Senegal typifies this [51, 60, 79], as these schemes provide strong social cohesion and managerial expertise required to achieve insurance objectives.

Conclusion

This systematic review and meta-analysis found evidence that CBHI improves healthcare utilization for enrolled households in LMICs. Although the evidence for financial risk protection is not as consistent as that for healthcare utilization, the evidence is still positive regardless of the health cost-induced catastrophe or impoverishment metric. This evidence, congruent with evidence from previous empirical studies, suggests that with a few pragmatic policy reforms and operational modifications, LMIC struggling to achieve UHC through publicly-funded health insurance schemes may consider CBHI for this purpose.

Supporting information

S1 Fig. Assessment plot of Cochrane RoB 2.0 risk of bias assessment and internal validity of included randomized controlled trials.

https://doi.org/10.1371/journal.pone.0287600.s001

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S2 Fig. Summary plot of Cochrane RoB 2.0 risk of bias assessment and internal validity of included randomized controlled trials.

https://doi.org/10.1371/journal.pone.0287600.s002

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S3 Fig. Assessment plot of Cochrane ROBINS-I risk of bias assessment and internal validity of included non-randomized controlled trials.

https://doi.org/10.1371/journal.pone.0287600.s003

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S4 Fig. Summary plot of Cochrane ROBINS-I risk of bias assessment and internal validity of included non-randomized controlled trials.

https://doi.org/10.1371/journal.pone.0287600.s004

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S1 Text. Search strategy.

https://doi.org/10.1371/journal.pone.0287600.s005

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S1 Table. Eligibility criteria. Inclusion criteria for studies reporting impact of community-based health insurance (CBHI) schemes on healthcare utilization and financial risk protection in low- and middle-income countries (LMICs).

https://doi.org/10.1371/journal.pone.0287600.s006

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S2 Table. Retrieved full text articles/studies excluded from the review and reasons for exclusion.

https://doi.org/10.1371/journal.pone.0287600.s007

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S3 Table. Sub-group analysis of the impact of CBHI on healthcare utilization (non-specific) in LMICs.

https://doi.org/10.1371/journal.pone.0287600.s008

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S4 Table. Sub-group analysis of the impact of CBHI on outpatient health services in LMICs.

https://doi.org/10.1371/journal.pone.0287600.s009

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S5 Table. Sub-group analysis of the impact of CBHI on OOP health expenditure in LMICs.

https://doi.org/10.1371/journal.pone.0287600.s010

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S6 Table. Sub-group analysis of the impact of CBHI on catastrophic health expenditure at 10% total household expenditure threshold in LMICs.

https://doi.org/10.1371/journal.pone.0287600.s011

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S7 Table. Sub-group analysis of the impact of CBHI on catastrophic health expenditure at 40% non-food expenditure threshold in LMICs.

https://doi.org/10.1371/journal.pone.0287600.s012

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S8 Table. GRADE assessment for quality of evidence.

https://doi.org/10.1371/journal.pone.0287600.s013

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S1 Checklist. PRISMA 2020 checklist.

https://doi.org/10.1371/journal.pone.0287600.s014

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References

  1. 1. World Health Organization. Universal Health Coverage (UHC) [Internet]. 2021 [cited 2022 Jun 30]. Available from: https://www.who.int/health-topics/universal-health-coverage#tab=tab_1
    • 2. Wagstaff A, Neelsen S. A comprehensive assessment of universal health coverage in 111 countries: a retrospective observational study. Lancet Glob Health. 2020;8(1):e39–49. pmid:31837954
    • 3. Ifeagwu SC, Yang JC, Parkes-Ratanshi R, Brayne C. Health financing for universal health coverage in Sub-Saharan Africa: a systematic review. Glob Health Res Policy. 2021;6(1). pmid:33641673
    • 4. Spaan E, Mathijssen J, Tromp N, McBain F, ten Have A, Baltussen R. The impact of health insurance in Africa and Asia: a systematic review. Bull World Health Organ. 2012;90(9):685–92. pmid:22984313
    • 5. Wagstaff A. Estimating health insurance impacts under unobserved heterogeneity: The case of Vietnam’s health care fund for the poor. Health Econ. 2010;19(2):189–208. pmid:19248053
    • 6. Degroote S, Ridde V, De Allegri M. Health Insurance in Sub-Saharan Africa: A Scoping Review of the Methods Used to Evaluate its Impact. Appl Health Econ Health Policy. 2020;18(6):825–40. pmid:31359270
    • 7. Umeh CA, Feeley FG. Inequitable access to health care by the poor in community-based health insurance programs: A review of studies from low-and middle-income countries. Glob Health Sci Pract. 2017;5(2):299–314. pmid:28655804
    • 8. De Allegri M, Sauerborn R, Kouyaté B, Flessa S. Community health insurance in sub-Saharan Africa: What operational difficulties hamper its successful development? Tropical Medicine and International Health. 2009;14(5):586–96. pmid:19389037
    • 9. World Health Organization. Community-based health insurance [Internet]. 2020 [cited 2022 Aug 6]. Available from: https://www.who.int/news-room/fact-sheets/detail/community-based-health-insurance-2020
      • 10. Chemouni B. The political path to universal health coverage: Power, ideas and community-based health insurance in Rwanda. World Dev. 2018;106(June):87–98.
      • 11. Eze P, Lawani O, Agu J, Acharya Y. Catastrophic health expenditure in sub-Saharan Africa: Systematic review and meta-analysis. Bull World Health Organ [Internet]. 2022;100(5):337–51. Available from: https://doi.org/10.2471/BLT.21.287673 pmid:35521041
      • 12. Wagstaff A, Flores G, Hsu J, Smitz MF, Chepynoga K, Buisman LR, et al. Progress on catastrophic health spending in 133 countries: a retrospective observational study. Lancet Glob Health. 2018;6(2):e169–79. pmid:29248367
      • 13. Pandey A, Ploubidis GB, Clarke L, Dandona L. Trends in catastrophic health expenditure in india: 1993 to 2014. Bull World Health Organ. 2018;96(1):18–28. pmid:29403097
      • 14. Mills A. Health Care Systems in Low- and Middle-Income Countries. New England Journal of Medicine. 2014;370(6):552–7. pmid:24499213
      • 15. Kent Ranson M. Community-based health insurance schemes in India: A review. National Medical Journal of India. 2003;16(2):79–89. pmid:12816186
      • 16. Habib SS, Perveen S, Khuwaja HMA. The role of micro health insurance in providing financial risk protection in developing countries- a systematic review. BMC Public Health. 2016;16(281). pmid:27004824
      • 17. Ekman B. Community-based health insurance in low-income countries: A systematic review of the evidence. Health Policy Plan. 2004;19(5):249–70. pmid:15310661
      • 18. Artignan J, Bellanger M. Does community-based health insurance improve access to care in sub-Saharan Africa? A rapid review. Health Policy Plan. 2021;36(4):572–84. pmid:33624113
      • 19. Bhageerathy R, Nair S, Bhaskaran U. A systematic review of community-based health insurance programs in South Asia. International Journal of Health Planning and Management. 2017;32(2):e218–31. pmid:27444739
      • 20. Mebratie AD, Sparrow R, Alemu G, Bedi AS. Community-based health insurance: A Systematic Review. The Hague, Netherlands; 2013. Report No.: 568.
      • 21. Page MJ, McKenzie JE, Bossuyt PM, Boutron I, Hoffmann TC, Mulrow CD, et al. The PRISMA 2020 statement: An updated guideline for reporting systematic reviews. The BMJ. 2021;372.
      • 22. Adebayo EF, Uthman OA, Wiysonge CS, Stern EA, Lamont KT, Ataguba JE. A systematic review of factors that affect uptake of community-based health insurance in low-income and middle-income countries. BMC Health Serv Res. 2015;15(543). pmid:26645355
      • 23. Alkenbrack S, Jacobs B, Lindelow M. Achieving universal health coverage through voluntary insurance: what can we learn from the experience of Lao PDR. BMC Health Serv Res. 2013;13(521). pmid:24344925
      • 24. Acharya A, Vellakkal S, Taylor F, Masset E, Satija A, Burke M, et al. The impact of health insurance schemes for the informal sector in low-and middle-income countries: A systematic review. World Bank Research Observer. 2013;28(2):236–66.
      • 25. Rockers PC, Røttingen JA, Shemilt I, Tugwell P, Bärnighausen T. Inclusion of quasi-experimental studies in systematic reviews of health systems research. Health Policy (New York). 2015;119(4):511–21. pmid:25776033
      • 26. Xu K. Distribution of health payments and catastrophic expenditures Methodology. FER/EIP discussion paper; 2005.
      • 27. Bennett S, Creese A, Monasch R. Health insurance schemes for people outside non-formal sector employment. Geneva: World Health Organization; 1998. 1–134 p.
        • 28. Hollis S, Campbell F. What is meant by intention to treat analysis? Survey of published randomised controlled trials. Br Med J. 1999;319(7211):670–4. pmid:10480822
        • 29. Stuart EA. Matching methods for causal inference: A review and a look forward. Statistical Science. 2010;25(1):1–21. pmid:20871802
        • 30. Sterne JAC, Savović J, Page MJ, Elbers RG, Blencowe NS, Boutron I, et al. RoB 2: A revised tool for assessing risk of bias in randomised trials. The BMJ. 2019;366:1–8. pmid:31462531
        • 31. Sterne JA, Hernán MA, Reeves BC, Savović J, Berkman ND, Viswanathan M, et al. ROBINS-I: A tool for assessing risk of bias in non-randomised studies of interventions. The BMJ. 2016;355:4–10. pmid:27733354
        • 32. McGuinness LA, Higgins JPT. Risk-of-bias VISualization (robvis): An R package and Shiny web app for visualizing risk-of-bias assessments. Res Synth Methods. 2021;12(1):55–61. pmid:32336025
        • 33. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al. Cochrane Handbook for Systematic Reviews of Interventions Version 6.1 [updated September 2020]. Higgins J, Thomas J, Chandler J, Cumpston M, Li T, Page M, et al., editors. Cochrane. Cochrane; 2020.
          • 34. Eze P, Lawani LO, Agu UJ, Amara LU, Okorie CA, Acharya Y. Factors associated with catastrophic health expenditure in sub-Saharan Africa: A systematic review. PLoS One [Internet]. 2022;17(10):e0276266. Available from: https://doi.org/10.1371/journal.pone.0276266 pmid:36264930
          • 35. Wagstaff A, van Doorslaer E. Catastrophe and impoverishment in paying for health care: With applications to Vietnam 1993–1998. Health Econ. 2003;12(11):921–33. pmid:14601155
          • 36. O’Donnell O, van Doorslaer E, Wagstaff A, Lindelow M. Analyzing Health Equity Using Household Survey Data: A Guide to Techniques and Their Implementation [Internet]. Washington, DC; 2008. Report No.: 42480. Available from: http://hdl.handle.net/10986/6896
            • 37. Guyatt G, Oxman AD, Akl EA, Kunz R, Vist G, Brozek J, et al. GRADE guidelines: 1. Introduction—GRADE evidence profiles and summary of findings tables. J Clin Epidemiol. 2011;64(4):383–94. pmid:21195583
            • 38. Adinma ED, Brian-D Adinma JI, Obionu CC, Asuzu MC. Effect of Government-Community Healthcare Co-Financing on Maternal and Child Healthcare in Nigeria. West Afr J Med. 2011;30(1):36–41.
            • 39. Aggarwal A. Impact evaluation of India’s “Yeshasvini” Community-Based Health Insurance Programme. Health Econ. 2010;19(1):5–35. pmid:20803629
            • 40. Ahmed S, Sarker AR, Sultana M, Chakrovorty S, Ahmed MW, Dorin F, et al. The impact of community-based health insurance on the utilization of medically trained healthcare providers among informal workers in Bangladesh. PLoS One. 2018;13(7):e0200265. pmid:29995899
            • 41. Alemayehu YK, Dessie E, Medhin G, Birhanu N, Hotchkiss DR, Teklu AM, et al. The impact of community-based health insurance on health service utilization and nancial risk protection in Ethiopia. Res Sq. 2022;
            • 42. Alkenbrack S, Lindelow M. The impact of community-based health insurance on utilization and out-of-pocket expenditures in Lao People’s Democratic Republic. Health Econ. 2015;24(3):379–99. pmid:25784572
            • 43. Ansah EK, Narh-Bana S, Asiamah S, Dzordzordzi V, Biantey K, Dickson K, et al. Effect of removing direct payment for health care on utilisation and health outcomes in Ghanaian children: A randomised controlled trial. PLoS Med. 2009;6(1):0048–58. pmid:19127975
            • 44. Asfaw DM, Shifaw SM, Belete AA, Aychiluhm SB. The Impact of Community-Based Health Insurance on Household’s Welfare in Chilga District, Amhara Regional State, Ethiopia. Front Public Health. 2022;10(6):868274. pmid:35719626
            • 45. Babatunde RO, Oyedeji O, Omoniwa E, Adenuga AH. Effect of Community Based Health Insurance on the Livelihood of Rural Households in Kwara State, Nigeria. Journal of Agricultural Faculty of Gaziosmanpasa University. 2016;33(2016–2):19–27.
            • 46. Babiarz KS, Miller G, Yi H, Zhang L, Rozelle S. New evidence on the impact of China’s New Rural Cooperative Medical Scheme and its implications for rural primary healthcare: Multivariate difference-in-difference analysis. BMJ. 2010;341:c5617. pmid:20966008
            • 47. Binagwaho A, Hartwig R, Ingeri D, Makaka A. Mutual health insurance and its contribution to improving child health in Rwanda. Volkswirtschaftliche Reihe. Passau, Germany; 2012. Report No.: V-66–12.
              • 48. Bonfrer I, Van de Poel E, Gustafsson-Wright E, van Doorslaer E. Voluntary health insurance in Nigeria: Effects on takers and non-takers. Soc Sci Med. 2018;205:55–63. pmid:29653298
              • 49. Brals D, Aderibigbe SA, Wit FW, Van Ophem JCM, Van Der List M, Osagbemi GK, et al. The effect of health insurance and health facility-upgrades on hospital deliveries in rural Nigeria: A controlled interrupted time-series study. Health Policy Plan. 2017;32(7):990–1001. pmid:28402420
              • 50. Brals D, Nelissen H, Van Der List M, Faye C, Juma C, Elbers C, et al. Impact of the community healthcare plan and the free maternity services programme on maternal and child healthcare utilisation in rural Kenya: A dairy farmer population-based study. Afr Health Sci. 2019;19(3):2600–14. pmid:32127833
              • 51. Chankova S, Sulzbach S, Diop F. Impact of mutual health organizations: Evidence from West Africa. Health Policy Plan. 2008;23(4):264–76. pmid:18480126
              • 52. Cheng L, Liu H, Zhang Y, Shen K, Zeng Y. The impact of health insurance on health outcomes and spending of the elderly: Evidence from China’s New Cooperative Medical Scheme. Health Econ. 2015;24(6):672–91. pmid:24777657
              • 53. Cheung D, Padieu Y. Heterogeneity of the effects of health insurance on household savings: Evidence from rural China. World Dev. 2015;66(2):84–103.
              • 54. Dercon S, Gunning JW, Zeitlin A, Lombardini S. The impact of a health insurance programme: evidence from a randomized controlled trial in Kenya. Geneva, Switzerland; 2012. Report No.: 24.
                • 55. Donato K, Rokicki S. Effects of China’s Rural Insurance Scheme on Objective Measures of Health. 2016.
                • 56. Dror DM, Chakraborty A, Majumdar A, Panda P, Koren R. Impact of community-based health insurance in rural India on self-medication & financial protection of the insured. Indian Journal of Medical Research. 2016;143(6):809–20.
                • 57. Fink G, Robyn PJ, Sié A, Sauerborn R. Does health insurance improve health? Evidence from a randomized community-based insurance rollout in rural Burkina Faso. J Health Econ. 2013;32(6):1043–56. pmid:24103498
                • 58. Gnawali DP, Pokhrel S, Sié A, Sanon M, De Allegri M, Souares A, et al. The effect of community-based health insurance on the utilization of modern health care services: Evidence from Burkina Faso. Health Policy (New York). 2009;90(2–3):214–22. pmid:19036467
                • 59. Jafree SR, Zakar R, Ahsan H, Mustafa M, Fischer F. Impact of microfinance health interventions on health-related outcomes among female informal workers in Pakistan: A retrospective quasi-experimental study. BMJ Open. 2021;11:e043544. pmid:33402411
                • 60. Jütting JP. Do community-based health insurance schemes improve poor people’s access to health care? Evidence from rural senegal. World Dev. 2004;32(2):273–88.
                • 61. Khan JAM, Ahmed S, Sultana M, Sarker AR, Chakrovorty S, Rahman MH, et al. The effect of a community-based health insurance on the out-of-pocket payments for utilizing medically trained providers in Bangladesh. Int Health. 2020;12(4):287–98. pmid:31782795
                • 62. Kihaule A. Impact of micro health insurance plans on protecting households against catastrophic health spending in Tanzania. GSTF Journal on Nursing & Health Care. 2015;2(2):71–9.
                • 63. Kihaule A, Ngasa C, Francis T, Kihuale A, Ngasa C, Francis T. The Impact Of The Elderly Coffee Farmers Membership In Micro- Health Insurance On Access And Utilization Of Health Services In Moshi District, Tanzania. J Public Health (Bangkok). 2019;5(10):1–17.
                • 64. Lei X, Lin W. The New Cooperative Medical Scheme in rural China: Does more coverage mean more service and better health? Health Econ. 2009;18(Suppl 2):S25–46. pmid:19551752
                • 65. Levine D, Polimeni R, Ramage I. Insuring health or insuring wealth? An experimental evaluation of health insurance in rural Cambodia. J Dev Econ. 2016;119(1):1–15.
                • 66. Li S, Zhang D, Chen Z. Impact of the New Cooperative Medical Scheme on Individual Healthcare Utilization and Expenditure. The Chinese Economy. 2019;52(6):488–504.
                • 67. Liu D, Tsegai D. The New Cooperative Medical Scheme (NCMS) and its implications for access to health care and medical expenditure: Evidence from rural China. Bonn, Germany; 2011. (ZEF-Discussion Papers on Development Policy). Report No.: 155.
                • 68. Ma X. New Rural Cooperative Medical Scheme and Its Effects on the Utilization of Healthcare Services. In: Ma X, editor. Public Medical Insurance Reforms in China. Singapore: Springer; 2022. p. 137–59.
                  • 69. Mebratie AD, Sparrow R, Yilma Z, Abebaw D, Alemu G, Bedi AS. The impact of Ethiopia ‘ s pilot community based health insurance scheme on healthcare utilization and cost of care. Vol. 220, Social Science and Medicine. The Hague, Netherlands: Elsevier; 2019. Report No.: 593.
                    • 70. Mekonen AM, Gebregziabher MG, Teferra AS. The effect of community based health insurance on catastrophic health expenditure in Northeast Ethiopia: A cross sectional study. PLoS One. 2018;13(10):e0205972. pmid:30335838
                    • 71. Nannini M, Biggeri M, Putoto G. Financial protection and coping strategies in rural Uganda: an impact evaluation of community-based zero-interest healthcare loans. Health Policy Plan. 2021;36(7):1090–1102. pmid:34159376
                    • 72. Noterman JP, Criel B, Kegels G, Isu K. A prepayment scheme for hospital care in the Masisi district in Zaire: A critical evaluation. Soc Sci Med. 1995;40(7):919–30. pmid:7792631
                    • 73. Nshakira-Rukundo E, Mussa EC, Nshakira N, Gerber N, von Braun J. Impact of community-based health insurance on utilisation of preventive health services in rural Uganda: a propensity score matching approach. Int J Health Econ Manag. 2021;21(2):203–27. pmid:33566252
                    • 74. Papoula E. Evaluating the Impact of the Mutual Health Insurance Scheme in Rwanda on Health Care Utilization, Out-of-pocket Expenses and Health Outcomes. Universiteit van Amsterdam; 2012.
                    • 75. Parmar D, Reinhold S, Souares A, Savadogo G, Sauerborn R. Does community-based health insurance protect household assets? Evidence from rural Africa. Health Serv Res. 2012;47(2):819–39. pmid:22091950
                    • 76. Pham TTT, Pham TL. Does Microinsurance Help the Poor? Evidence from the Targeted Health Microinsurance Program in Vietnam 2004–2008. Geneva, Switzerland; 2012. Report No.: 11.
                      • 77. Philibert A, Ravit M, Ridde V, Dossa I, Bonnet E, Bedecarrats F, et al. Maternal and neonatal health impact of obstetrical risk insurance scheme in Mauritania: A quasi experimental before-and-after study. Health Policy Plan. 2017;32(3):405–17. pmid:27935801
                      • 78. Qin L jian, Chen C ping, Li Y heng, Sun Y ming, Chen H. The impact of the New Rural Cooperative Medical Scheme on the “health poverty alleviation” of rural households in China. J Integr Agric. 2021;20(4):1068–79.
                      • 79. Ranson MK, Sinha T, Chatterjee M, Gandhi F, Jayswal R, Patel F, et al. Equitable utilisation of Indian community based health insurance scheme among its rural membership: Cluster randomised controlled trial. BMJ. 2007;334(7607):1309–12. pmid:17526594
                      • 80. Rao KD, Waters H, Steinhardt L, Alam S, Hansen P, Naeem AJ. An experiment with community health funds in Afghanistan. Health Policy Plan. 2009;24(4):301–11. pmid:19474086
                      • 81. Ravit M, Ravalihasy A, Audibert M, Ridde V, Bonnet E, Raffalli B, et al. The impact of the obstetrical risk insurance scheme in Mauritania on maternal healthcare utilization: A propensity score matching analysis. Health Policy Plan. 2020;35(4):388–98. pmid:32003810
                      • 82. Raza WA, Van de Poel E, Bedi A, Rutten F. Impact of Community-based health insurance on access and financial proctection: Evidence from three randomized controlled trials in rural India. Health Econ. 2016;25:675–87.
                      • 83. Robyn PJ, Fink G, Sié A, Sauerborn R. Health insurance and health-seeking behavior: Evidence from a randomized community-based insurance rollout in rural Burkina Faso. Soc Sci Med. 2012;75(4):595–603. pmid:22321392
                      • 84. Robyn PJ, Hill A, Liu Y, Souares A, Savadogo G, Sié A, et al. Econometric analysis to evaluate the effect of community-based health insurance on reducing informal self-care in Burkina Faso. Health Policy Plan. 2012;27(2):156–65. pmid:21414993
                      • 85. Sheth K. Delivering health insurance through informal financial groups: Evidence on moral hazard and adverse selection. Health Econ. 2021;30(9):2185–99. pmid:34114717
                      • 86. Shimeles A. Community based health insurance schemes in Africa: The case of Rwanda. Goteborg; 2010. (Working Paper in Economics). Report No.: 463.
                      • 87. Simieneh MM, Yitayal M, Gelagay AA. Effect of community-based health insurance on healthcare-seeking behavior for childhood illnesses among rural mothers in Aneded district, east Gojjam zone, Amhara region, northwest Ethiopia. Risk Manag Healthc Policy. 2021;14:1659–68. pmid:33911904
                      • 88. Sun X, Jackson S, Carmichael G, Sleigh AC. Catastrophic medical payment and financial protection in rural China: Evidence from the New Cooperative Medical Scheme in Shandong Province. Health Econ. 2009;18(2):103–19. pmid:18283715
                      • 89. Tilahun H, Atnafu DD, Asrade G, Minyihun A, Alemu YM. Factors for healthcare utilization and effect of mutual health insurance on healthcare utilization in rural communities of South Achefer Woreda, North West, Ethiopia. Health Econ Rev. 2018;8:15. pmid:30136052
                      • 90. Woldemichael A, Shimeles A. Measuring the impact of micro-health insurance on healthcare utilization: A Bayesian potential outcomes approach. Abidjan, Cote d’Ivoire; 2015. (Working Paper Series). Report No.: 225.
                      • 91. Woldemichael A, Gurara D, Shimeles A. The Impact of Community Based Health Insurance Schemes on Out-of-Pocket Healthcare Spending: Evidence from Rwanda. 2019. (IMF Working Paper). Report No.: WP/19/38.
                      • 92. Yang W. Catastrophic Outpatient Health Payments and Health Payment-induced Poverty under China’s New Rural Cooperative Medical Scheme. Agricultural & Applied Economics Association. 2015;37(1):64–85.
                      • 93. Yilma Z, Mebratie A, Sparrow R, Dekker M, Alemu G, Bedi AS. Health risk and insurance: Impact of Ethiopia’s community based health insurance on household economic welfare. World Bank Economic Review. 2015;29(Supplement):S164–73.
                      • 94. Yip W, Hsiao W. The Impact of a Social Experiment—Rural Mutual Health Care—on Health Care Use, Financial Risk Protection, and Health Status in Rural China. In: Escobar M-L, Griffin CC, Shaw PR, editors. The Impact of Health Insurance in Low- and Middle-Income Countries. Washington DC: Brookings Institution Press; 2010. p. 137–55.
                        • 95. Yip W, Wang H, Hsiao W. The impact of Rural Mutual Health Care on health status: Evaluation of a social experiment in rural China. Cambridge, MA; 2008.
                          • 96. Wagstaff A. Health insurance for the poor: initial impacts of Vietnam’s health care fund for the poor. World Bank Policy Research Working Paper. Washington DC; 2007. (WPS; vol. No.4134). Report No.: 4134.
                            • 97. Wagstaff A, Lindelow M, Jun G, Ling X, Juncheng Q. Extending health insurance to the rural population: An impact evaluation of China’s new cooperative medical scheme. J Health Econ. 2009;28(1):1–19. pmid:19058865
                            • 98. Lu C, Chin B, Lewandowski JL, Basinga P, Hirschhorn LR, Hill K, et al. Towards universal health coverage: An evaluation of Rwanda Mutuelles in its first eight years. PLoS One. 2012;7(6):e39282. pmid:22723985
                            • 99. Ahmed S, Hasan MZ, Ali N, Ahmed MW, Haq E, Shabnam S, et al. Effectiveness of health voucher scheme and micro-health insurance scheme to support the poor and extreme poor in selected urban areas of Bangladesh: An assessment using a mixed-method approach. Vol. 16, PLoS ONE. 2021. e0256067 p. pmid:34723992
                            • 100. Alatinga KA, Fielmua N. The Impact of Mutual Health Insurance Scheme on Access and Quality of Health Care in Northern Ghana: The Case of Kassena-Nankana East Scheme. J Sustain Dev. 2011;4(5):125–38.
                            • 101. Asfaw A, Braun J Von. Can community health insurance schemes shield the poor against the downside health effects of economic reforms? The case of rural Ethiopia. Health Policy (New York). 2004;70(1):97–108. pmid:15312712
                            • 102. Atim C, Sock M. An External Evalution of the Nkoranza Community Financing Health Insurance Scheme, Ghana. Vol. 50. Bethesda, Maryland, USA; 2000.
                              • 103. Atim C. Social movements and health insurance: A critical evaluation of voluntary, non-profit insurance schemes with case studies from Ghana and Cameroon. Soc Sci Med. 1999;48(7):881–96. pmid:10192556
                              • 104. Atnafu A, Gebremedhin T. Community-based health insurance enrollment and child health service utilization in Northwest Ethiopia: A cross-sectional case comparison study. ClinicoEconomics and Outcomes Research. 2020;12:435–44. pmid:32848434
                              • 105. Bhat R, Menezes L, Avila C. Annapurna Pariwar Community/Mutual Health Insurance Program: A Case study in Maharashtra, India. Bethesda, MD; 2017.
                              • 106. Blanchard-Horan C. Health microinsurance in Uganda: Affecting malaria treatment seeking behavior. International Journal of Public Administration. 2007;30(8–9):765–89.
                              • 107. Broomberg J, Price MR. The impact of the fee-for-service reimbursement system on the utilisation of health services. Part II. Comparison of utilisation patterns in medical aid schemes and a local health maintenance organisation. South African Medical Journal. 1990;78:133–6. pmid:2377943
                              • 108. Carrin G, Ron A, Hui Y, Hong W, Tuohong Z, Licheng Z, et al. The reform of the rural cooperative medical system in the People’s Republic of China: Interim experience in 14 pilot counties. Soc Sci Med. 1999;48(7):961–72. pmid:10192562
                              • 109. Chee G, Smith K, Kapinga A. Assessment of the Community Health Fund in Hanang District, Tanzania. Bethesda, MD; 2002.
                              • 110. Cheema AR, Zaidi S, Najmi R, Khan FA, Kori SA, Shah NA. Availability Does Not Mean Utilisation: Analysis of a Large Micro Health Insurance Programme in Pakistan. Glob J Health Sci. 2020;12(10):14–24.
                              • 111. Criel B, Kegels G. A health insurance scheme for hospital care in Bwamanda district, Zaire: Lessons and questions after 10 years of functioning. Tropical Medicine and International Health. 1997;2(7):654–72. pmid:9270733
                              • 112. Criel B, Van Der Stuyft P, Van Lerberghe W. The Bwamanda hospital insurance scheme: Effective for whom? A study of its impact on hospital utilization patterns. Soc Sci Med. 1999;48(7):897–911. pmid:10192557
                              • 113. Dekker M, Wilms A. Health Insurance and Other Risk-Coping Strategies in Uganda: The Case of Microcare Insurance Ltd. World Dev. 2010;38(3):369–78.
                              • 114. Desai S, Sinha T, Mahal A. Prevalence of hysterectomy among rural and urban women with and without health insurance in Gujarat, India. Reprod Health Matters. 2011;19(37):42–51. pmid:21555085
                              • 115. Devadasan N, Manoharan S, Menon N, Menon S, Thekaekara M, Thekaekara S, et al. Accord community health insurance—Increasing access to hospital care. Econ Polit Wkly. 2004;39(28):3189–94.
                              • 116. Devadasan N, Criel B, Van Damme W, Manoharan S, Sarma PS, Van Der Stuyft P. Community health insurance in Gudalur, India, increases access to hospital care. Health Policy Plan. 2010;25(2):145–54. pmid:19843637
                              • 117. Devadasan N, Criel B, Van Damme W, Ranson K, Van Der Stuyft P. Indian community health insurance schemes provide partial protection against catastrophic health expenditure. BMC Health Serv Res. 2007;7(43). pmid:17362506
                              • 118. Dror DM, Soriano ES, Lorenzo ME, Sarol JN, Azcuna RS, Koren R. Field based evidence of enhanced healthcare utilization among persons insured by micro health insurance units in Philippines. Health Policy (New York). 2005;73(3):263–71. pmid:16039345
                              • 119. Dror DM, Radermacher R, Khadilkar SB, Schout P, Hay FX, Singh A, et al. Microinsurance: Innovations in low-cost health insurance. Health Aff. 2009;28(6):1788–98. pmid:19887420
                              • 120. Ethiopian Health Insurance Agency. Evaluation of Community-Based Health Insurance Pilot Schemes in Ethiopia: Final Report. Addis Ababa, Ethiopia; 2015.
                                • 121. Ekman B. Catastrophic health payments and health insurance: Some counterintuitive evidence from one low-income country. Health Policy (New York). 2007;83(2–3):304–13. pmid:17379351
                                • 122. Fakunle B, Okunlola MA, Fajola A, Ottih U, Ilesanmi AO. Community health insurance as a catalyst for uptake of family planning and reproductive health services: The Obio Cottage Hospital experience. J Obstet Gynaecol (Lahore). 2014;34(6):501–3. pmid:24725223
                                • 123. Franco LM, Diop FP, Burgert CR, Kelley AG, Makinen M, Simparae CHT. Effects of mutual health organizations on use of priority health-care services in urban and rural Mali: A case-control study. Bull World Health Organ. 2008;86(11):830–8. pmid:19030688
                                • 124. Geng X, Janssens W, Kramer B, van der List M. Health insurance, a friend in need? Impacts of formal insurance and crowding out of informal insurance. World Dev. 2018;111:196–210.
                                • 125. Haddad S, Ridde V, Yacoubou I, Mák G, Gbetié M. An Evaluation of the Outcomes of Mutual Health Organizations in Benin. PLoS One. 2012;7(10):e47136. pmid:23077556
                                • 126. Hamid SA, Roberts J, Mosley P. Evaluating the Health Effects of Micro Health Insurance Placement: Evidence from Bangladesh. World Dev. 2011;39(3):399–411.
                                • 127. Hou Z, Van de Poel E, van Doorslaer E, Yu B, Meng Q. Effects of NCMS on access to care and financial protection in China. Health Econ. 2014;23:917–34. pmid:23983020
                                • 128. Hounton S, Byass P, Kouyate B. Assessing effectiveness of a community based health insurance in rural Burkina Faso. BMC Health Serv Res. 2012;12:363. pmid:23082967
                                • 129. Islam MT, Igarashi I, Kawabuchi K. The impact of Gonoshasthaya Kendra’s micro health insurance plan on antenatal care among poor women in rural Bangladesh. Biosci Trends. 2012;6(4):165–75. pmid:23006963
                                • 130. Jakab M, Preker AS, Krishnan C, Schneider P, Diop F, Jutting J, et al. Social Inclusion and Financial Protection Through Communiy Financing. Washington DC; 2001. Report No.: 28884.
                                  • 131. James KS, Prabhu S, Ahuja R, Narang A. Impact of CBHI Schemes on Access to Healthcare: Evidence from India 1. Indian Journal of Human Development. 2008;2(2):435–48.
                                  • 132. Jembere MY. The Role of Community Based Health Insurance Scheme on Financial Protection and Healthcare Seeking Behavior of Households in Tehuledere District, Northeast Ethiopia. International Journal of Health Economics and Policy. 2018;3(2):13–9.
                                  • 133. Jembere MY. Community Based Health Insurance Scheme as a New Healthcare Financing Approach in Rural Ethiopia: Role on Access, Use and Quality of Healthcare Services, the Case of Tehuledere District, South Wollo Zone, Northeast Ethiopia. Fam Med Med Sci Res. 2018;7(2):1000227.
                                  • 134. Joshi R, Pakhare A, Yelwatkar S, Bhan A, Kalantri SP, Jajoo UN. Impact of community-based health insurance and economic status on utilization of healthcare services: A household-level cross-sectional survey from rural central India. National Medical Journal of India. 2020;33(2):74–82. pmid:33753634
                                  • 135. Kassie G, Tefera B. of community-based health insurance on modern family planning utilization in Ethiopia [version 2; peer review: 1 approved with reservations ] Gates Open Research. Gates Open Res. 2019;3(1461):12960.
                                  • 136. Koch R, Nkurunziza T, Rudolfson N, Nkurunziza J, Bakorimana L, Irasubiza H, et al. Does community-based health insurance protect women from financial catastrophe after cesarean section? A prospective study from a rural hospital in Rwanda. BMC Health Serv Res. 2022;22:717. pmid:35642031
                                  • 137. Manzambi Kuwekita J, Gosset C, Guillaume M, Balula Semutsari MP, Tshiama Kabongo E, Bruyere O, et al. Le couplage microcrédit/microassurance santé/offre de soins peut améliorer l’accessibilité aux soins de santé en milieu urbain africain Résultats d’une expérience menée dans la zone de santé de Bandalungwa à Kinshasa, Congo / Combining microcredit, microi. Med Sante Trop. 2015;25(4):381–5.
                                  • 138. Lakshmi MR. Health care system in Hderabad public and private hospitals: A study on Rajiv Aarogyasri health community insurance scheme. International Journal of Social Science and Economic Research. 2019;4(7):5260–72.
                                  • 139. Liu K, Cook B, Lu C. Health inequality and community-based health insurance: a case study of rural Rwanda with repeated cross-sectional data. Int J Public Health. 2019;64(1):7–14. pmid:29947821
                                  • 140. Lu C, Liu K, Li L, Yang Y. Sensitivity of measuring the progress in financial risk protection to survey design and its socioeconomic and demographic determinants: A case study in Rwanda. Soc Sci Med. 2017;178:11–8. pmid:28189819
                                  • 141. Msuya JM, Jutting JP, Asfaw A. Impacts of Community Health Insurance Schemes on Health Care Provision in Rural Tanzania. Bonn, Germany; 2004. Report No.: 82.
                                    • 142. Msuya JM, Jütting JP, Asfaw A. Impact of community health funds on the access to health care: Empirical evidence from rural Tanzania. International Journal of Public Administration. 2007;30(8–9):813–33.
                                    • 143. Musango L, Dujardin B, Dramaix M, Criel B. Le profil des membres et des non membres des mutuelles de sante au Rwanda: la cas du district sanitaire de Kabutare. Tropical Medicine and International Health. 2004;9(11):1222–7.
                                    • 144. Mwaura JW, Pongpanich S. Access to health care: The role of a community based health insurance in Kenya. Pan African Medical Journal. 2012;12:35. pmid:22891093
                                    • 145. Ndongo JCA, Mahieu P-A, Nanfosso RT. Mutuelles de santé et État de santé des populations au Cameroun: une enquête conduite dans la région du Centre pour estimer l’effet de l’adhésion à une mutuelle sur l’état de santé déclaré. Journal de gestion et d’économie médicales. 2014;32(4):263–79.
                                    • 146. Parmar D, De Allegri M, Savadogo G, Sauerborn R. Do community-based health insurance schemes fulfil the promise of equity? A study from Burkina Faso. Health Policy Plan. 2014;29(1):76–84. pmid:23307908
                                    • 147. Peterson L, Comfort A, Omasanjuwa E, Ambrose K, Hatt L. Extending health insurance to informal sector workers in urban settings: findings from a micro insurance pilot in Lagos, Nigeria. Bethesda, MD; 2015.
                                    • 148. Peterson L, Comfort A, Hatt L, van Bastelaer T. Extending health insurance coverage to the informal sector: Lessons from a private micro health insurance scheme in Lagos, Nigeria. International Journal of Health Planning and Management. 2018;33:662–76.
                                    • 149. Ranson MK. The Impact of SEWA’s Medical Insurance on Hospital Utilization and Expenditure: A Household Survey. Washington DC; 2001. Report No.: 28895.
                                      • 150. Rao M, Ramachandra SS, Bandyopadhyay S, Chandran A, Shidhaye R, Tamisettynarayana S, et al. Addressing healthcare needs of people living below the poverty line: A rapid assessment of the Andhra Pradesh health insurance scheme. National Medical Journal of India. 2011;24(6):335–41. pmid:22680257
                                      • 151. Saksena P, Antunes AF, Xu K, Musango L, Carrin G. Mutual health insurance in Rwanda: Evidence on access to care and financial risk protection. Health Policy (New York). 2011;99(3):203–9. pmid:20965602
                                      • 152. Schneider P, Diop F. Synopsis of results on the impact of community-based health insurance on financial access to health care in Rwanda. Washington DC; 2001. Report No.: 28903.
                                        • 153. Schneider P, Hanson K. Horizontal equity in utilisation of care and fairness of health financing: A comparison of micro-health insurance and user fees in Rwanda. Health Econ. 2006;15(1):19–31. pmid:16145721
                                        • 154. Smith K V., Sulzbach S. Community-based health insurance and access to maternal health services: Evidence from three West African countries. Soc Sci Med. 2008;66(12):2460–73. pmid:18362047
                                        • 155. Stoermer M, Fuerst F, Rijal K, Bhandari R, Nogier C, Gautam GS, et al. Review of Community-based Health Insurance Initiatives in Nepal. ellars-Shrestha S, editor. Nepal: Health Sector Support Programme Department of Health Services Teku, Kathmandu, Nepal; 2012. 116 p.
                                        • 156. Tanko GI, Harun A, Gorondutse AH, Salihu AA. Investigating the Impact of Community Based Health Insurance Scheme Among the Rural Dwellers of Sokoto State Nigeria. Mediterr J Soc Sci. 2015;6(S1):145–55.
                                        • 157. Tesfay G. The impact of community based health insurance in health service utilization in Tigray; (Case of kilte Awlaelo woreda). Mekelle University; 2014.
                                          • 158. Yi H, Zhang L, Singer K, Rozelle S, Atlas S. Health insurance and catastrophic illness: A report on the New Cooperative Medical System in Rural China. Health Econ. 2009;18:S119–27. pmid:19551747
                                          • 159. Yip W, Hsiao WC. Non-evidence-based policy: How effective is China’s new cooperative medical scheme in reducing medical impoverishment? Soc Sci Med. 2009;68:201–9. pmid:19019519
                                          • 160. You X, Kobayashi Y. Determinants of Out-of-Pocket Health Expenditure in China. Appl Health Econ Health Policy. 2011;9(1):39–49.
                                          • 161. Zhou Z, Gao J, Xue Q, Yang X, Yan J. Effects of Rural Mutual Health Care on outpatient service utilization in Chinese Village medical institutions: Evidence from Panel Data. Health Econ. 2009;18:S129–36. pmid:19548324
                                          • 162. Adinma ED, Nwakoby BAN, Adinma B- DJI. Integrating Maternal Health Services into a Health Insurance Scheme: Effect on Healthcare Delivery. Nig Q J Hosp Med. 2010;20(2):86–93. pmid:21243858
                                          • 163. Alula MT, Alemayehu YK, Medhin G, Dessie E, Berhanu N. The Impact of Community-Based Health Insurance on Health Service Utilization, Out-of-pocket expenditure, Women’s empowerment, and Health Equity in Ethiopia: Final Report. Addis Ababa, Ethiopia; 2021.
                                          • 164. Diop FP, Sulzbach S, Chankova S. The Impact of Mutual Health Organizations on Social Inclusion, Access to Health Care, and Household Income Protection: Evidence from Ghana, Senegal, and Mali. Bethesda, MD; 2006.
                                          • 165. Jutting JP. The impact of Health Insurance on the access to health care and financial protection in rural developing countries: The example of Senegal. Washington DC; 2001. Report No.: 28886.
                                            • 166. Mebratie AD, Sparrow R, Yilma Z. The impact of Ethiopia ‘ s pilot community based health insurance scheme on healthcare utilization and cost of care. The Hague, Netherlands; 2014. Report No.: 593.
                                              • 167. Mebratie AD. Essays on Evaluating a Community Based Health Insurance Scheme in Rural Ethiopia. Erasmus University Rotterdam; 2015.
                                                • 168. Rukundo EN. Effects of community-based health insurance on child health outcomes and utilisation of preventive health services Evidence from rural south-western Uganda. Rheinischen Friedrich-Wilhelms-Universit¨at Bonn; 2018.
                                                • 169. Sheth K. Evaluating health-seeking behaviour, utilization of care, and health risk: Evidence from a community based insurance model in India. Geneva; 2014. Report No.: 36.
                                                  • 170. World Bank. Community-Based Health Insurance in Lao P.D.R. Understanding Enrollment and Impacts. Washington DC; 2010. Report No.: 58577.
                                                    • 171. Woldemichael A, Gurara DZ, Shimeles A. Community-Based Health Insurance and Out-of-Pocket Healthcare Spending in Africa: Evidence from Rwanda. Bonn, Germany; 2016. Report No.: 9922.
                                                      • 172. Wagstaff A, Lindelow M, Jun G, Ling X, Juncheng Q. Extending health insurance to the rural population: An impact evaluation of China’s new cooperative medical scheme. Washington DC; 2007. (Working Paper Series). Report No.: 4150.
                                                        • 173. Acharya A, Ranson KM. Health Care Financing for the Poor: Community-Based Health Insurance Schemes in Gujarat. Econ Polit Wkly. 2005;40(38):4141–50.
                                                        • 174. Mcfarlane GA, Sammon AM. A prepaid healthcare scheme in rural Africa. Trop Doct. 2000;30:151–4. pmid:10902473
                                                        • 175. Richard V. Financement communautaire de la sante en Afrique: Les mutuelles de sante. Medicine Tropicale. 2005;65(1):87–90.
                                                        • 176. Ron A. NGOs in community health insurance schemes: Examples from Guatemala and the Philippines. Soc Sci Med. 1999;48(7):939–50. pmid:10192560
                                                        • 177. Sudha venu M. Health Security for rural poor: study of community based health insurance. Munich Personal RePEc Archive. Ahmedabad, India; 2006. Report No.: 1649.
                                                          • 178. Werner WJ. Micro-insurance in Bangladesh: Risk Protection for the Poor? J Health Popul Nutr. 2009;27(4):563–73. pmid:19761089
                                                          • 179. Fischer T, Frölich M, Landmann A. Adverse Selection in Low-Income Health Insurance Markets: Evidence from a RCT in Pakistan. Bonn, Germany; 2018. Report No.: 11751.
                                                            • 180. Fitzpatrick A, Thornton R. The Effects of Health Insurance within Families: Experimental Evidence from Nicaragua. World Bank Econ Rev. 2019;33(3):736–49.
                                                            • 181. Haushofer J, Chemin M, Jang C, Abraham J. Economic and psychological effects of health insurance and cash transfers: Evidence from a randomized experiment in Kenya. J Dev Econ. 2020;144(December 2019):102416.
                                                            • 182. Idris H, Satriawan E, Trisnantoro L. The Impact of Health Insurance on Healthcare Utilization Among those working in the Informal Sector: Evidence from the National Socioeconomic Survey (Susenas) in Indonesia. Adv Sci Lett. 2017;23(5):4892–5.
                                                            • 183. Galárraga O, Sosa-Rubí SG, Salinas-Rodríguez A, Sesma-Vázquez S. Health insurance for the poor: Impact on catastrophic and out-of-pocket health expenditures in Mexico. European Journal of Health Economics. 2010;11(5):437–47. pmid:19756796
                                                            • 184. Mahmud KT, Hilton D. Does Microcredit Really Matter for Healthcare Expenditure of the Poor Fish-Farmers? Perspective from Rural Bangladesh. J Poverty. 2020;24(2):147–67.
                                                            • 185. Okonofua F, Ntoimo LF, Yaya S, Igboin B, Solanke O, Ekwo C, et al. Effect of a multifaceted intervention on the utilisation of primary health for maternal and child health care in rural Nigeria: A quasi-experimental study. BMJ Open. 2022;12:e049499. pmid:35135763
                                                            • 186. Pauly M V. The Economics of Moral Hazard: Comment. Am Econ Rev. 1968;58(3):531–7.
                                                            • 187. Nyman JA. The economics of moral hazard revisited. J Health Econ. 1999;18(6):811–24. pmid:10847936
                                                            • 188. Preker A, Carrin G. Health Financing for Poor People—Resource Mobilisation and Risk Sharing. Preker AS, Carrin G, editors. Health Financing for Poor People. Washington DC: The World Bank; 2004.
                                                              • 189. Carrin G, Waelkens MP, Criel B. Community-based health insurance in developing countries: A study of its contribution to the performance of health financing systems. Tropical Medicine and International Health. 2005;10(8):799–811. pmid:16045467
                                                              • 190. Wagstaff A, Lindelow M. Can insurance increase financial risk?. The curious case of health insurance in China. J Health Econ. 2008;27(4):990–1005.