Effects of community-based health insurance on modern family planning utilization in Ethiopia

Girma Kassie; Bekele Tefera

doi:10.12688/gatesopenres.12960.2

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Research Article

Revised

Effects of community-based health insurance on modern family planning utilization in Ethiopia

[version 2; peer review: 1 approved with reservations, 1 not approved]

Girma Kassie¹, Bekele Tefera ¹

PUBLISHED 19 Sep 2019

Author details Author details

¹ Department of Monitoring, Evaluation and Learning, pathfinder international, Addis Ababa, 12655, Ethiopia

Girma Kassie
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

Bekele Tefera
Roles: Conceptualization, Data Curation, Formal Analysis, Funding Acquisition, Investigation, Methodology, Project Administration, Resources, Software, Supervision, Validation, Visualization, Writing – Original Draft Preparation, Writing – Review & Editing

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REVIEWER STATUS

This article is included in the International Conference on Family Planning gateway.

Abstract

Background: Community-based health insurance (CBHI) has been established in a number of developing countries to expand access to modern health care service. However, few studies have focused on health care utilization of CBHI members in Ethiopia. Accordingly, the aim of this study was to assess the effect of CBHI on modern family planning (FP) utilization as part of its routine outcome monitoring activities.
Methods: The USAID Transform: Primary Health Care project, conducted a continuous monitoring follow up visit using a multistage sampling technique in its four major targeted regions. A total of 3433 households were selected and 3313 women of reproductive age (15-49 years) were interviewed. The questionnaire captured the CBHI status of each household and FP use data from randomly selected women. Microsoft Access database was used to enter the data, which was then transferred to SPSS Version 20 for further analysis.
Results: 49% of women (aged 15-49 years) were found to be enrolled in CBHI scheme. Half of the women (50.2%) use any family planning method, of which 49% of them used modern family planning method in project-supported areas. Over half of women (57%) who are exposed to CBHI schemes utilized family planning method which is statistically significant
Conclusions: Modern FP utilization among insured women was higher compared with uninsured women. While FP methods are provided for free, CBHI enrollment improves FP use among women of child-bearing age. Women who have access to CBHI may frequently visit health facilities seeking services for themselves and their families, during which they may be introduced to FP services. This in turn may improve their awareness and attitude towards FP. The results will increase awareness for program implementer's of the benefits of CBHI schemes in FP programming, particularly in rural settings, and provide an opportunity to increase lifelong returns in Ethiopia.

Keywords

Family planning, utilization, CBHI, Ethiopia

Corresponding authors: Girma Kassie, Bekele Tefera

Competing interests: No competing interests were disclosed.

Grant information: This study was funded by the United States Agency for International Development under the terms of Award No. AID-663-A-17-00002 (Transform: Primary Health Care). The opinions expressed herein are those of the author(s) and do not necessarily reflect the views of Pathfinder International or the U.S. Agency for International Development. The research publication was sponsored by The Bill and Melinda Gates Foundation (OPP1181398).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Copyright: © 2019 Kassie G and Tefera B. 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 work is properly cited.

How to cite: Kassie G and Tefera B. Effects of community-based health insurance on modern family planning utilization in Ethiopia [version 2; peer review: 1 approved with reservations, 1 not approved]. Gates Open Res 2019, 3:1461 (https://doi.org/10.12688/gatesopenres.12960.2) First published: 09 May 2019, 3:1461 (https://doi.org/10.12688/gatesopenres.12960.1) Latest published: 19 Sep 2019, 3:1461 (https://doi.org/10.12688/gatesopenres.12960.2)

Revised Amendments from Version 1

Based on the reviewers comment and suggestion the manuscript revised and all revised sentence highlighted in red color.
Abstract section: The reviewers provide important comment in the abstract section: The authors of this article revised the abstract specifically result section.
Introduction section: the reviewer heighted some points in the introduction section related to FP status in Ethiopia. We tried to address the issues in the introduction section related to FP status in Ethiopia.
Method section: The reviewer highlighted issues related to analysis method. The revised manuscript contain detailed about the statistical analysis using regression method.
Result section: based on the reviewer comment all result section including tables and result interpretation revised.
Discussion section: The reviewers address some issue related to brief discussion of the section because of the few factors. The authors of this manuscript briefly described based on the revised result and add detailed additional information. The limitation of the study described separately.
The reference section: the revised manuscript adds some new reference list, because the result and discussion section revised accordingly to the reviewer comment.

See the authors' detailed response to the review by Joseph Obure and Carl Mhina

Introduction

In 2011, the Government of Ethiopia introduced a community-based health insurance (CBHI) program and implemented it using a phased-in approach. The program was first piloted in 2011–2013, within 13 woredas (districts) of the four most populous regions: Oromia, Amhara, SNNP and Tigray. Prompted by its success, lessons learned, and viability of implementing the scheme as concluded in the evaluation carried out of the 13 pilot CBHI schemes in 2014/15, the Government decided to scale-up the CBHI program to other woredas. Currently, CBHI is being implemented in a total of 512 woredas in six regions and two city administrations¹. CBHI is a health insurance scheme that pools resources from citizens (members) in the informal sector in the form of contributions into a collective fund, which is managed by a scheme management body, generally functioning under the woreda health bureau, and governed by board members drawn from CBHI scheme members and other key stakeholders as appropriate^2,3. USAID has been supporting the Government of Ethiopia in health sector financing reforms in Ethiopia, including CBHI implementation since their inception through its consecutive previous projects. In continuation of this assistance, USAID recently launched a new 5-year activity, the Health Financing Improvement Program, 2018–2023 to support health sector financing. Additionally, the USAID Transform: Primary Health Care project provides technical support in its focus woredas towards the initiation, launching and operationalization of CBHI schemes⁴.

CBHI is believed to considerably increase the demand for health care services because the financial burden on households and/or individual beneficiaries at the time of seeking health care will be removed as CBHI schemes cover these costs. The design of Ethiopian CBHI learned from the experience of other countries, and has recorded notable progress in several community health outcomes over the past 10 years⁵. Family planning (FP) is a proven strategy to reduce maternal and child mortality and morbidity among women of reproductive age⁶. In low income countries, including Ethiopia, use of modern family planning method is low but is showing an increase in recent years. In 2019, 41 percent of currently married Ethiopian women were using a modern method of contraception, a significant improvement from 6 percent in 2000⁷. These achievements occurred as a result of the Ethiopian government’s and development partners work in expanding family planning through various strategies. The low increase in the rate of contraceptive use over the decade suggests sluggish progress in the family planning program. This suggests that the challenge for Ethiopian family planning program is to promote family planning by providing better information and services about family planning as well as reproductive health, especially in rural areas.

CBHI contributes to the improvement of health seeking behaviour, health care utilization and service quality⁸. Different evidence suggests that CBHI membership has positive effects on health care utilization^8–10. Despite this positive pattern, the overall health risk profile of those who have enrolled in the scheme does not seem to be very different from those who have not yet enrolled¹¹. There is no specific evidence of the role of CBHI in improving modern FP utilization. The main aim of this study was to evaluate the effect of CBHI on modern FP utilization.

Methods

Study background

The USAID Transform: Primary Health Care project conduct continuous monitoring follow up visits using a multistage sampling technique in its four major targeted regions. The project had been supporting 360 woredas in its four major target regions and established 29 clusters-level offices (CLOs) and considers all CLOs. The sample technique was a combination of simple random and random-walk technique. A sampling frame was prepared by listing the woreda health offices (WorHOs) under each CLO, health centers (HCs) under each WorHOs and Health Posts (HPs) under each HC. A total of 164 WorHOs, 328 HCs and 694 HPs were selected randomly in proportion to the size of the region. A HP is found in a kebele (village), which is the lowest administrative area in Ethiopia. If the HP was randomly selected for the project continuous household survey, then the kebele associated with the selected HP was chosen for the next stage of sampling.

After getting the associated list of kebeles, prepared the list of gotts from the HP associated kebeles in consultation with the health extension worker (HEW) and selected one gott randomly from each associated kebeles. A gott is a geographic area under the kebele covering on average 250 households. The survey team utilized a list of households under the selected gotts from the health post registry book. The list of the households served as a sampling frame for the selection of the households. The survey team selected five households from each got using a random-walk technique in person at their household. If there was more than one eligible respondent found in a household, simple random sampling was used to select one eligible respondent. Of the total planned 3433 households, data were collected and analyzed from 3313 women of reproductive age in the households.

The sample size requirements were based on estimates for the proportion of modern methods of FP utilization on the day of the monitoring follow-up visit at 95% confidence. The sample size determines used in double population proportion formula¹². For the purpose of this analysis, our definition of modern FP included oral contraceptives, emergency contraceptives, injectable contraceptives, contraceptive implants, and IUDs.

Data collection was conducted from October 1 to December 31, 2017 and 3313 women of reproductive age (15–49) were interviewed. A household questionnaire¹³ was developed in consultation with program technical advisors. Regional and CLO staff members received training to use the survey questionnaire and were responsible for data collection in their respective catchment.

Data processing and analysis

The data management process was managed by the monitoring, evaluation and learning teams at regional and country office levels. Microsoft Access database was used to enter the data, which was then transferred to SPSS Version 20 for analysis. A total of 10% of the questionnaire was selected and re-entered and cross-checked for data consistency and completeness. It was observed that about 99% of the questionnaires were entered accurately. Data cleaning was performed to check for frequencies, accuracy, consistencies and missing values. Frequencies, proportion, and summary statistics were used to describe the study population in relation to the study variables. Significance tests performed using the cut-off values set is p<0.05 with 95% confidence interval (CI). Further, logistic regression analysis was employed to predict the effect of CBHI on contraceptive use. Significance tests performed using the cut-off values set is p<0.05 with 95% confidence interval (CI).

Ethical consideration

This report used project data that has been collected as part of the annual random follow-up monitoring visit to households in project areas and the result was not linked to individual identifiers. The results of the study did not distinguish respondents’ race, age, health information, religion, sex and sexual orientation or any other social groups. Therefore, the study did not require ethical clearance by a human research ethics committee. However, the project obtained permission to implement and assess progress from the regional health bureaus of Amhara, Oromia, SNNPR and Tigray.

The assessment questionnaires of CBHI effect on modern family planning directly related to their program monitoring and meant to inform the program. The interviews were not intended to develop or contribute to generalizable ‘knowledge. Participants were informed about the purpose of the survey, project approaches to enabling the responsive, iterative implementation that has taken place project life cycle. After discussion and understanding of the purpose the project obtained oral consent from each participant, since participants were fearful of being identified.

Results

Family planning usage

A total of 3313 reproductive age women found in 3433 households were included in this analysis. The mean age of the women was 29 years ±8.3 standard deviations (SDs). Approximately 40% of women were in the 25–30 years age bracket, while 15% of the respondents were above 36 years. Almost all of the respondents (96%) were currently married. Among the study participants, 64% of reproductive age women were found under CBHI woredas, of which 49% of the women were enrolled in CBHI. Most of the respondents (87%) believed CBHI is important, of which 67% of them enrolled CBHI scheme. From the total respondent, 45% of them reported exposure to family planning information through different media (Table 1). (Table 1). De-identified raw data are available on Open Science Framework¹³.

Table 1. Respondents characteristics and enrolment in community-based health insurance (CBHI), USAID Transform: Primary Health Care project Follow up visits, 2017.

Variables	Frequency (%
age in Years <25	902 (27.2)
25–30	1337 (40.4)
31–36	580 (17.5)
>36	494 (14.9)
Mean age	29.5 ±8.34
Marital status (Married/not married)	3250 (95.5)
HHs found under CBHI in a woreda	2009 (63.9)
CBHI Enrollment status	1011 (48.8)
Believe CBHI is important	1308 (86.7)
Believe CBHI is important and enrolled	979 (76.6)
Believe CBHI is not important but enrolled	22 (11.9)
Exposed/Received FP related information (at least three FP method (Y/N)	1280 (45.1)

HH; Households

Table 2 depicts the prevalence rate of modern contraceptives method mix among reproductive age women. The table shows that 50.2% of the respondents utilized any method of FP, 49% used modern methods and 0.5% of the respondents utilized permanent methods. The table shows that 53.5% of young women are currently using any FP method and 16.4% of respondents are using long-acting reversible contraceptives

Table 2. Contraceptive method mix among reproductive age women (15–49 years), USAID Transform: Primary Health Care project Follow up visits, 2017.

Method Mix	Prevalence rate of women with age group (95% CI)
Method Mix	15–49 years	15–24 years
Any method	50.2(48.4-51.9)	53.5 (50.1-56.8)
Modern method	49.0(47.2-50.7)	51.9 (48.6-55.3)
Long-acting reversible contraceptive	15.1(13.9-16.4)	16.4 (13.9-18.9)
Short acting contraceptive	33.3 (31.7-35)	35.6(32.3-38.8)
Permanent methods	0.5(0.2-0.7)

CI; confidence interval.

Effect of CBHI on FP usage

Table 3. The result of logistic regression revealed that women who were 36 years and above, who are enrolled CBHI scheme and received FP information (at least three methods) were significantly associated with use of FP methods. Women who fall between the age of 36 and 49 years were 2.3 times more likely to use contraceptive method than who age 15–24 years. Exposure/received to FP information showed that women exposed family planning (at least three FP methods) information through different media were 1.65 times more likely to use contraceptive than those exposed less than three FP method related information. Over half of the women (57%) who are enrolled in CBHI scheme utilized modern family planning methods. Women enrolled CBHI scheme, the results show that women who are exposed to CBHI scheme (enrolled CBHI) 1.43 times more likely to use contraceptive method than those that never enrolled CBHI. The effect of CBHI on family planning utilization is statistically significantly (p=0.002). While family planning methods are provided for free, CBHI enrolment improves family planning use among women of child bearing age. Women who have access to CBHI may frequently visit health facilities seeking services for themselves and their families, during which they may be introduced to family planning services. This in turn may improve their awareness and attitude towards family planning. As a result, women who are part of CBHI may have better FP utilization rate compared to women who are not.

Table 3. logistic regression model with crude and adjusted odds ratio using the 2017 USAID Transform: Primary Health Care project Follow up visits.

Variables	COR (95%)	AOR (95%)
Age in Years <25	1	1
25–30	1.08 (0.90-1.3)	1.23(0.91-1.68)
31–36	1.19(0.96-1.49)	1.39(0.97-1.99)
>36	1.94(1.53-2.46)	2.3(1.56-3.41)
HHs found under CBHI in a woreda (Y/N)	0.87(0.74-1.01)	1.46 (0.66-3.24)
HHs enrolled in CBHI scheme (Y/N)	1.34 (1.12-1.62)	1.43 (1.08-1.90)
Believe CBHI is important (Y/N)	1.75(1.27-2.41)	1.42(0.94-2.14)
Exposed/Received FP related information (at least three FP method (Y/N)	1.56(1.36-1.83)	1.65(1.31-2.08)

HH; Households, AOR; adjusted odds ratio, COR; crude odds ratio

Discussion

This study revealed that CBHI impacted modern family planning utilization among insured and uninsured women. Modern family planning utilization among insured women was higher compared with uninsured women. The positive results of the CBHI schemes in encouraging health care utilization suggests that CBHI can be effective instrument for achieving universal health coverage, together with other policy tools¹⁴. According to the findings of Shimelis, CBHI membership has a potential to increase healthcare utilization¹⁵. Similarly, Giedion et al.¹⁶ reviewed 23 studies and found differences in how CBHI affected service access and utilization among different population groups. Nevertheless, the evidence indicates that, overall, the CBHI universal health coverage scheme improves access and utilization of services¹⁶. Similar evidence showed that the trend of outpatient service care increased for insured households, while it declined for the uninsured^8,9. The finding of this study showed that women with CBHI improved modern family planning utilization among uninsured women of child-bearing age. The government of Ethiopia is rolling out CBHI to improve community financial protection when receiving health facility service and utilization as well as of health care service improvements^10,11,17,18. Although FP methods are provided for free, women who have access to CBHI may frequently visit health facilities seeking services for themselves and their families, during which health care provider might be introduced to FP services and their importance. Studies show that the number of outpatient visits per insured household member increased, while for uninsured households in the CBHI districts the corresponding members are reduced their visits^9,11. This in turn may improve their awareness and attitude towards family planning.

The finding of this study confirmed that, women who have exposure/received more than three family planning method information more likely utilized family planning methods. Providing family planning information and or service to women during their other health service visits may be an effective way to reach women with high unmet need for family planning. Pervious evidence also showed that integration of family planning information and or service with others health service significantly contributed to family planning utilization^19–21. Offer integrated family planning service and or information with maternal and childbirth services an ideal platform to reach women and their partners with family planning information and service²².

Limitation

This study report highlighted important findings to support the integration of family planning and CBHI program in Ethiopia, but not without limitations, which could affect conclusions based on some of the findings. During the analysis, the study not included important contributed variables to the change of family planning utilization due to lack of individual and social cultural variables in the follow up visit tool. The analysis not allowed the casual effect of individual factors that contribute their association, except characteristics such as perception about CBHI and age; this study did not allow a link with other potential causes that may affect utilization of modern FP methods. An important limitation of this study is that strong conclusions could not be drawn with respect to the casual effect of changes of family planning use. This is due to the cross-sectional design of the survey; hence causality could not be established.

Conclusion and recommendation

The present study showed that more than half of the respondents are currently using modern FP methods. CBHI enrolment was significantly associated with use of family planning method. Child-bearing women who were not exposed to the CBHI scheme should be the target audience. Moreover, it needs communication strategy that will provide information about the contribution of CBHI on FP utilization. The results of this study will help program implementers be aware of the benefits of the CBHI scheme in FP programming particularly in rural settings, and the opportunity to achieve family planning program.

Data availability

Underlying data

Open Science Framework: Effects of community-based health insurance on modern family planning utilization in Ethiopia. https://doi.org/10.17605/OSF.IO/ZS2T3¹².

This project contains the raw responses to the survey for each participant in file HH 2017.sav.

Extended data

Open Science Framework: Effects of community-based health insurance on modern family planning utilization in Ethiopia. https://doi.org/10.17605/OSF.IO/ZS2T3¹².

This project contains the survey questionnaire in file HH FUV Checklist.pdf.

Data are available under the terms of the Creative Commons Zero “No rights reserved” data waiver (CC0 1.0 Public domain dedication).

Acknowledgements

USAID Transform: Primary Health Care project is a USAID-funded health programme implemented by Pathfinder International and John Snow, Inc. (JSI) in partnership with the health sector at various levels (FMOH, RHBs of Amhara, Oromia, SNNP and Tigray, woreda health offices, health centers, health posts and kebeles) and due acknowledgement is forwarded to the above institutions. Our deep gratitude also goes to data collectors who showed their commitment to collect data during the survey.

F1000 recommended

References

1. HFSR & HFG: TECHNICAL BRIEF: Community-Based Health Insurance: Achievements and Recommendations for Sustaining Gains in Ethiopia. 2018. Reference Source
2. USAID, HSFR & HFG: Health Care Financing Reform in Ethiopia: Improving Quality and Equity. 2015. Reference Source
3. Mebratie AD, Sparrow R, Yilma Z, et al.: Dropping out of Ethiopia's community-based health insurance scheme. Health Policy Plan. 2015; 30(10): 1296–306. PubMed Abstract | Publisher Full Text
4. USAID Transform: primary health care project document, 2017. Addis Ababa, Ethiopia.
5. Central Statistical Agency: ETHIOPIA Demographic and Health Survey 2016; Key Indicators Report. Addis Ababa, Ethiopia. Reference Source
6. WHO: Improving Maternal and New-born Health: The Role of Family Planning Report of a Regional Consultation. Chiang Mai Thailand. 2004. Reference Source
7. Central Statistical Agency [Ethiopia]: Ethiopia Mini Demographic and Health Survey 2019. Addis Ababa, Ethiopia. Central Statistical Agency; 2019. Reference Source
8. Modern health care use includes utilization of health care services from health posts, health centers, private/NGO clinics, and public/private/NGO hospitals.
9. Anagaw DM, Robert S, Zelalem Y, et al.: The impact of Ethiopia’s pilot community-based health insurance scheme on healthcare utilization and cost of care. 2013. Reference Source
10. 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. PubMed Abstract | Publisher Full Text | Free Full Text
11. Ethiopian Health Insurance Agency: Evaluation of Community-Based Health Insurance Pilot Schemes in Ethiopia: Final Report. Addis Ababa, Ethiopia. 2015. Reference Source
12. Magnani R: Sampling Guide: Food and Nutrition Technical Assistance Project (FANTA). Academy for Educational Development. Published December 1997. Reference Source
13. Tefera BB, Kassie G: Effects of community-based health insurance on modern family planning utilization in Ethiopia. 2019. http://www.doi.org/10.17605/OSF.IO/ZS2T3
14. Lu C, Chin B, Lewandowski JL, et al.: Towards universal health coverage: an evaluation of Rwanda Mutuelles in its first eight years. PLoS One. 2012; 7(6): e39282. PubMed Abstract | Publisher Full Text | Free Full Text
15. Shimeles A: Working Paper 120 - Community Based Health Insurance Schemes in Africa: The Case of Rwanda. Working Paper Series 257, African Development Bank. 2010. Reference Source
16. Giedion U, Andrés Alfonso E, Díaz Y: The Impact of Universal Coverage Schemes in the Developing World: A Review of the Existing Evidence. UNICO Studies Series 25. World Bank. 2013. Reference Source
17. Health financing and governance: Ethiopia Scales Up Community-based Health Insurance. Accessed in October 2018. Reference Source
18. Health Sector Financing Reform Project: Brief Note on the Design of Community Based Health Insurance (CBHI): technical briefing Number 1. Bethesda, MD: Abt Associates. 2008.
19. Integration of Family Planning with Immunization Services: A Promising Approach to Improving Maternal and Child Health. Technical briefing Fhi360, 2010. Reference Source
20. Vance G, Janowitz B, Chen M, et al.: Integrating family planning messages into immunization services: a cluster-randomized trial in Ghana and Zambia. Health Policy Plan. 2014; 29(3): 359–366. PubMed Abstract | Publisher Full Text
21. World Health Organization (WHO): Programming Strategies for Postpartum Family Planning. Geneva: WHO; 2013; Accessed August 8, 2019. Reference Source
22. High Impact Practices in Family Planning (HIPs): Immediate postpartum family planning: A key component of childbirth care. Washington, DC USAID; 2017. Reference Source

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Version 2

VERSION 2 PUBLISHED 09 May 2019

Author details Author details

¹ Department of Monitoring, Evaluation and Learning, pathfinder international, Addis Ababa, 12655, Ethiopia

Competing interests

No competing interests were disclosed.

Grant information

This study was funded by the United States Agency for International Development under the terms of Award No. AID-663-A-17-00002 (Transform: Primary Health Care). The opinions expressed herein are those of the author(s) and do not necessarily reflect the views of Pathfinder International or the U.S. Agency for International Development. The research publication was sponsored by The Bill and Melinda Gates Foundation (OPP1181398).
The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.

Article Versions (2)

version 2

Revised

Published: 19 Sep 2019, 3:1461

https://doi.org/10.12688/gatesopenres.12960.2

version 1

Published: 09 May 2019, 3:1461

https://doi.org/10.12688/gatesopenres.12960.1

© 2019 Kassie G and Tefera B. 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 work is properly cited.

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Kassie G and Tefera B. Effects of community-based health insurance on modern family planning utilization in Ethiopia [version 2; peer review: 1 approved with reservations, 1 not approved]. Gates Open Res 2019, 3:1461 (https://doi.org/10.12688/gatesopenres.12960.2)

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Open Peer Review

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PUBLISHED 19 Sep 2019

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Reviewer Report 02 Mar 2021

Nirali M. Chakraborty, Metrics for Management, Oakland, CA, USA

Not Approved

https://doi.org/10.21956/gatesopenres.14199.r30276

This article looks at the relationship between enrollment in a community-based health insurance scheme (CBHI) and use of family planning in Ethiopia. The data appear to be drawn from what is described as household data collection for program monitoring purposes.

Review of language:
Prior to indexing, this manuscript requires significant grammatical editing. In some cases, the sentences were difficult to follow. For example: "The sample size determines used in double population proportion formula" or "The survey team selected five households from each got using a random- walk technique in person at their household."

Methods:

Please indicate what the sample size assumptions were.
Ethical Considerations: I am unclear as to why this study did not require any ethical approval. Data was collected at the household level, with the clear intention to analyze for monitoring and research. If the interviews were not intended to "develop or contribute to generalizable knowledge", then why is it being submitted for publication? While it is understandable that participants may have only given oral consent, it seems reasonable that this determination would be made from an ethical review board. Please elaborate on the ethical obligations of the research team.

Analytic methods:
The study data is clustered - woreda, kebele, health center. The resulting analyses should take this into account - using survey adjusted regression techniques, or robust errors to account for the clustering. Please indicate if this was done, or adjust analyses to reflect the sampling design.

Results:
The variable "households found under CBHI in woreda" is confusing. In order to participate in a CBHI scheme, doesn't the household need to be within a woreda which offers CBHI? In the results section describing table 3 (adjusted logistic regression), please specify the following:

The only variables adjusted for were the ones presented, of which Age is the only demographic variable. This analysis did not adjust for parity, marital status, or wealth. Each of these other variables has a likely relationship to the use of FP and/or enrollment in a CBHI scheme.
The likelihood of a woman using FP is 1.43 times higher for HH enrolled in a CBHI scheme, among women of the same age, and receiving the same amount of FP information. Similarly for the other odds ratios presented.

Finally, have the authors considered exploring the relationship between exposure to FP information AND CBHI enrollment on the use of modern FP? Given that both are significant, and that the authors hypothesize that women with insurance may visit facilities more often, thereby receiving more information on FP, it seems like this should be tested with the data.

This paper requires some additional analyses.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: No competing interests were disclosed.

Reviewer Expertise: Family planning, Health systems

I confirm that I have read this submission and believe that I have an appropriate level of expertise to state that I do not consider it to be of an acceptable scientific standard, for reasons outlined above.

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Reviewer Report 30 Aug 2019

Joseph Obure, Department of Global Health, Save the Children US, Washington, DC, USA

Carl Mhina, Duke University Global Health Institute, Durham, NC, USA

Approved with Reservations

https://doi.org/10.21956/gatesopenres.14066.r27626

The paper aims at evaluate the effect of introduction and enrollment to CBHI on modern FP utilization in Ethiopia. Overall the paper is interesting as it picks a relevant area of exploration which cuts across two relevant concepts ‘having access to CBHI’ a key tool for universal health coverage and ‘utilisation of modern FP’ which is an intervention that addresses a synergy of multiple sustainable development goals. The authors made a great introduction of the context (Ethiopia) as it relates to the introduction and scale up of the CBHI program reaching 512 woreda from initial 13 pilot woreda. Unfortunately, the authors did not describe the status of or trend of FP utilization in Ethiopia, neither describing the the socio-economical concepts and factors that affect both utilisation and access to FP services as well as CBHI, quiet an important dimension when it comes to the discussion of CBHI and utilisation of health care. The study has also used the routine CBHI monitoring data, which were limited to very few variables to inform more about the effectiveness or association between CBHI use and FP use. This led to a narrow result and the discussion section as there was lack of key elements tying/ or explaining the effect of CBHI to utilisation of modern FP. However, the authors have shown reasonable knowledge and understanding of the relevant points with good explanations.

Specific Comments:

Methodology:
The data from this study is a result of a cross section survey conducted in well-sampled population of women in areas where CBHI program was implemented using the routinely collected monitoring checklist. The sampling and study recruitment is well described. However, the tool used to collect monitoring data was limited to the CBHI purposes and did not include other variables that may have an effect on the use of FP services beyond the CBHI. Furthermore, the lack of comparison data (intervention vs control or pre vs post intervention) limited the ability to establish any causal relationship between the CBHI and FP use. The statistical analysis is descriptive and is limited to married women excluding unmarried women. Other factors that may have an effect on the FP use were not accounted for in the analysis.

Results:
The results section was brief, reflecting on less variables/factors the authors chose to explore. Few misrepresentations including a mismatch of the contents in the table and the results explanation, the use of FP methods reported to be 47.5% is not shown in Table 1 as outlined. It would also make sense for me to present tables with; A. include the descriptive statistic table for participants with their demographic, social, economic and reproductive health related information comparing between FP use status (users vs non users), B). Combine table 2 and 3, C. Ensure the data presented include both CBHI users and non users, D) Account for other variables that may have an effect on the use of FP beyond the CBHI status, and E) use regression analysis to establish independent factors for the use of FP methods.

It will be clearer to the readers if the authors outline the following terms clearly, women of reproductive age, young women, married women and unmarried women it gets confusing with which group within the participants are we explaining. The fact that married women were first described rather than all participants it deviates the scope (maybe a systematic description of the whole sample then focus on age groups the married vs unmarried, these factors themselves do affect the use of FP regardless of being in an insurance scheme)

Discussion & Conclusion
The brief discussion on this section reflects on the few variables/factors explored to answer the study question and hence difficult to draw a meaningful conclusion. The study limitations should be taken into account as the conclusion statements are presented.

The use of the economic term “life returns” should be clearly elaborated rather than just using it (it masks a lot of exactly what are these returns which could be a stronger talking point, also it risks a questionable thought of returns to scale in demand for health care in insured people)

The conclusion is addressing the overview provided by the study, this could be widened as the paper limits itself on its initial role of just monitoring and inform its project (method section). Few grammatical errors are also of note and should be counter checked in the final paragraphs of both the discussion and conclusion sections.

Summary

The paper has many strengths in;

It’s an important public health issue and the current global focus of universal health coverage (CBHI) and sustainable development goals (family planning)
Its large sample size selected from multiple regions and the household survey gives weight to the sample and ability to infer it to the population.
The paper has enough information to demonstrate the likelihood of the association rather than the causal-effect relationship.

The manuscript strength would be improved, if the following would be done;

Clearly define the study population
Include other variables that may have an influence on the use of FP services
Think of using conceptual framework that demonstrate the factors and path to FP use.
Present data in a more scientific way (tables)
Use relevant statistical methods
Account for other variables during statistical analysis
Use conclusion statements that reflects on the study strength and limitation.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests: Joseph John Obure worked for Pathfinder International in Tanzania office between April 2015 to January 2017. However, I did not and do not have any affiliation with this project. Neither the research team who are currently Pathfinder employees in Ethiopia.

Reviewer Expertise: Maternal, reproductive and Newborn Health

We confirm that we have read this submission and believe that we have an appropriate level of expertise to confirm that it is of an acceptable scientific standard, however we have significant reservations, as outlined above.

CITE

Report a concern

Author Response 19 Sep 2019

Bekele Tefera, Department of Monitoring, Evaluation and Learning, pathfinder international, Addis Ababa, 12655, Ethiopia

19 Sep 2019

Author Response

Dear Joseph Obure and Carl Mhina
Thank you for your detailed review and attention dated 30 August, 2019. We have carefully checked the comments and revised the manuscript accordingly. Our responses ... Continue reading Dear Joseph Obure and Carl Mhina
Thank you for your detailed review and attention dated 30 August, 2019. We have carefully checked the comments and revised the manuscript accordingly. Our responses are given in a point by point manner below.

Sincerely,
Author Team

Methodology:
Reviewer comment (RC) 1: The data from this study is a result of a cross section survey conducted in well-sampled population of Women in areas where CBHI program was implemented using the routinely collected monitoring checklist. The sampling and study recruitment is well described. However, the tool used to collect monitoring data was limited to the CBHI purposes and did not include other variables that may have an effect on the use of FP services beyond the CBHI. Furthermore, the lack of comparison data (intervention vs control or pre vs post intervention) limited the ability to establish any causal relationship between the CBHI and FP use. The statistical analysis is descriptive and is limited to married women excluding unmarried women. Other factors that may have an effect on the FP use were not accounted for in the analysis.

Response (R) 1:
Thank you for providing the opportunity to clarify. We agree with your point, but it is important to understand the nature of the data. USAID Transform: Primary health care project implemented different thematic areas jointly (12 different thematic areas together). The primary mechanism for outcome monitoring and evaluation relied on the baseline and end line data conducted by external evaluator. In 2017, following an external evaluation, program implementer and USAID mission in Ethiopia agreed that if TRANSFORM: PHC relied on facility and community level surveys to measure the program outcomes; it would have to wait until mid- or end-of-project evaluations to understand how to optimize the contributions made to reproductive health in Ethiopia. By then, the opportunity to adapt in response to the results would have passed. For this program, meaningful monitoring would require engaging a range of actors with the power to turn this evidence into learning and action in real time. The follow up visit focused on progress outcome, not focused with comparison groups (interventions vs control). The checklist allows the project to gain insight into progress in service delivery (e.g., FP by method, CBHI related, maternal child health, gender related, quality improvement related, malaria related major indicators etc. for decision-making) and change in health outcomes in target areas at the household level (e.g., modern contraceptive prevalence rates, percent of women with children between 0 and 11 months old who had at least four or more antenatal care visits at a health facility, percent of women with children between 0 and 11 months old who had delivered with the assistance of skilled birth attendants and the percent of children between 12 and 23 months old that are fully vaccinated). The checklist is integrated into routine program activities, and contains indicators that can be measured objectively. The checklist has focused program related indictors but no individual characteristic because of the objective of the follow up visit. We have mentioned in the method section about the primary role of the checklist for monitoring and inform its project. In this analysis we extracted only FP and CBHI related indictors. The project has remained three years in implementation and in the future we will compare yearly progress. In the method of analysis because of the nature of the variable, we have limited our analysis in descriptive statistics because we don’t have individual characteristics to do analytical or regression analysis. We have mentioned all this as a limitation of the study. But know I have tried to do simple traditional logistic regression analysis based on your recommendation, but not that comprehensive to see the causal effect of CBHI on family planning utilization.

Results:
Reviewer comment (RC) 2: The results section was brief, reflecting on less variables/factors the authors chose to explore. Few misrepresentations including a mismatch of the contents in the table and the results explanation, the use of FP methods reported to be 47.5% is not shown in Table 1 as outlined. It would also make sense for me to present tables with;
A). include the descriptive statistic table for participants with their demographic, social, economic and reproductive health related information comparing between FP use status (users vs non users),
B). Combine table 2 and 3,
C). Ensure the data presented include both CBHI users and non-users,
D). Account for other variables that may have an effect on the use of FP beyond the CBHI status, and
E). use regression analysis to establish independent factors for the use of FP methods.
It will be clearer to the readers if the authors outline the following terms clearly, women of reproductive age, young women, married women and unmarried women it gets confusing with which group within the participants are we explaining. The fact that married women were first described rather than all participants it deviates the scope (maybe a systematic description of the whole sample then focus on age groups the married vs unmarried, these factors themselves do affect the use of FP regardless of being in an insurance scheme)

Response (R) 2: Thank you for pointing this out. Yes, we observe fewer variables addressed in this analysis because of the limited number of variables in the follow up checklist and also have mismatched the content of the table, this mismatch was corrected accordingly.
A). Yes, now corrected with limited variables. Table 1 explains the characteristics of the respondents.
B). Corrected, table 2 and 3 merged together for reader clarity.
C-E). Table 3 present logistic regression analysis to see the difference of CBHI users vs non-users and their FP utilization. For clarity about the study participants, the reviewer comment is totally accepted and corrections have been made accordingly. The study participants are all reproductive age group women (15-49 years), they might be married or unmarried. Our analysis focused on 3313 reproductive age women; we have mentioned in the method section and also the first paragraph of the results section. The details of the point you highlighted have been addressed accordingly. But the survey check list only focused on merged indicators rather than individual characteristics, as we have mentioned above, so this analysis has a shortage of important factors to analyse and mention as a limitation of the survey checklist.

Discussion & Conclusion:
Reviewer comment (RC) 3: The brief discussion on this section reflects on the few variables/factors explored to answer the study question and hence difficult to draw a meaningful conclusion. The study limitations should be taken into account as the conclusion statements are presented. The use of the economic term “life returns” should be clearly elaborated rather than just using it (it masks a lot of exactly what are these returns which could be a stronger talking point, also it risks a questionable thought of returns to scale in demand for health care in insured people). The conclusion is addressing the overview provided by the study, this could be widened as the paper limits itself on its initial role of just monitoring and inform its project (method section). Few grammatical errors are also of note and should be counter checked in the final paragraphs of both the discussion and conclusion sections.

Response (R) 3: Thank you for pointing this out and highlighting the importance of the inclusion of this information. This detail has been added in the revised version of the manuscript.
Dear Joseph Obure and Carl Mhina
Thank you for your detailed review and attention dated 30 August, 2019. We have carefully checked the comments and revised the manuscript accordingly. Our responses are given in a point by point manner below.

Sincerely,
Author Team

Methodology:
Reviewer comment (RC) 1: The data from this study is a result of a cross section survey conducted in well-sampled population of Women in areas where CBHI program was implemented using the routinely collected monitoring checklist. The sampling and study recruitment is well described. However, the tool used to collect monitoring data was limited to the CBHI purposes and did not include other variables that may have an effect on the use of FP services beyond the CBHI. Furthermore, the lack of comparison data (intervention vs control or pre vs post intervention) limited the ability to establish any causal relationship between the CBHI and FP use. The statistical analysis is descriptive and is limited to married women excluding unmarried women. Other factors that may have an effect on the FP use were not accounted for in the analysis.

Response (R) 1:
Thank you for providing the opportunity to clarify. We agree with your point, but it is important to understand the nature of the data. USAID Transform: Primary health care project implemented different thematic areas jointly (12 different thematic areas together). The primary mechanism for outcome monitoring and evaluation relied on the baseline and end line data conducted by external evaluator. In 2017, following an external evaluation, program implementer and USAID mission in Ethiopia agreed that if TRANSFORM: PHC relied on facility and community level surveys to measure the program outcomes; it would have to wait until mid- or end-of-project evaluations to understand how to optimize the contributions made to reproductive health in Ethiopia. By then, the opportunity to adapt in response to the results would have passed. For this program, meaningful monitoring would require engaging a range of actors with the power to turn this evidence into learning and action in real time. The follow up visit focused on progress outcome, not focused with comparison groups (interventions vs control). The checklist allows the project to gain insight into progress in service delivery (e.g., FP by method, CBHI related, maternal child health, gender related, quality improvement related, malaria related major indicators etc. for decision-making) and change in health outcomes in target areas at the household level (e.g., modern contraceptive prevalence rates, percent of women with children between 0 and 11 months old who had at least four or more antenatal care visits at a health facility, percent of women with children between 0 and 11 months old who had delivered with the assistance of skilled birth attendants and the percent of children between 12 and 23 months old that are fully vaccinated). The checklist is integrated into routine program activities, and contains indicators that can be measured objectively. The checklist has focused program related indictors but no individual characteristic because of the objective of the follow up visit. We have mentioned in the method section about the primary role of the checklist for monitoring and inform its project. In this analysis we extracted only FP and CBHI related indictors. The project has remained three years in implementation and in the future we will compare yearly progress. In the method of analysis because of the nature of the variable, we have limited our analysis in descriptive statistics because we don’t have individual characteristics to do analytical or regression analysis. We have mentioned all this as a limitation of the study. But know I have tried to do simple traditional logistic regression analysis based on your recommendation, but not that comprehensive to see the causal effect of CBHI on family planning utilization.

Results:
Reviewer comment (RC) 2: The results section was brief, reflecting on less variables/factors the authors chose to explore. Few misrepresentations including a mismatch of the contents in the table and the results explanation, the use of FP methods reported to be 47.5% is not shown in Table 1 as outlined. It would also make sense for me to present tables with;
A). include the descriptive statistic table for participants with their demographic, social, economic and reproductive health related information comparing between FP use status (users vs non users),
B). Combine table 2 and 3,
C). Ensure the data presented include both CBHI users and non-users,
D). Account for other variables that may have an effect on the use of FP beyond the CBHI status, and
E). use regression analysis to establish independent factors for the use of FP methods.
It will be clearer to the readers if the authors outline the following terms clearly, women of reproductive age, young women, married women and unmarried women it gets confusing with which group within the participants are we explaining. The fact that married women were first described rather than all participants it deviates the scope (maybe a systematic description of the whole sample then focus on age groups the married vs unmarried, these factors themselves do affect the use of FP regardless of being in an insurance scheme)

Response (R) 2: Thank you for pointing this out. Yes, we observe fewer variables addressed in this analysis because of the limited number of variables in the follow up checklist and also have mismatched the content of the table, this mismatch was corrected accordingly.
A). Yes, now corrected with limited variables. Table 1 explains the characteristics of the respondents.
B). Corrected, table 2 and 3 merged together for reader clarity.
C-E). Table 3 present logistic regression analysis to see the difference of CBHI users vs non-users and their FP utilization. For clarity about the study participants, the reviewer comment is totally accepted and corrections have been made accordingly. The study participants are all reproductive age group women (15-49 years), they might be married or unmarried. Our analysis focused on 3313 reproductive age women; we have mentioned in the method section and also the first paragraph of the results section. The details of the point you highlighted have been addressed accordingly. But the survey check list only focused on merged indicators rather than individual characteristics, as we have mentioned above, so this analysis has a shortage of important factors to analyse and mention as a limitation of the survey checklist.

Discussion & Conclusion:
Reviewer comment (RC) 3: The brief discussion on this section reflects on the few variables/factors explored to answer the study question and hence difficult to draw a meaningful conclusion. The study limitations should be taken into account as the conclusion statements are presented. The use of the economic term “life returns” should be clearly elaborated rather than just using it (it masks a lot of exactly what are these returns which could be a stronger talking point, also it risks a questionable thought of returns to scale in demand for health care in insured people). The conclusion is addressing the overview provided by the study, this could be widened as the paper limits itself on its initial role of just monitoring and inform its project (method section). Few grammatical errors are also of note and should be counter checked in the final paragraphs of both the discussion and conclusion sections.

Response (R) 3: Thank you for pointing this out and highlighting the importance of the inclusion of this information. This detail has been added in the revised version of the manuscript.
Competing Interests: NA Close
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Respond or Comment

COMMENTS ON THIS REPORT

Author Response 19 Sep 2019

Bekele Tefera, Department of Monitoring, Evaluation and Learning, pathfinder international, Addis Ababa, 12655, Ethiopia

19 Sep 2019

Author Response

Dear Joseph Obure and Carl Mhina
Thank you for your detailed review and attention dated 30 August, 2019. We have carefully checked the comments and revised the manuscript accordingly. Our responses ... Continue reading Dear Joseph Obure and Carl Mhina
Thank you for your detailed review and attention dated 30 August, 2019. We have carefully checked the comments and revised the manuscript accordingly. Our responses are given in a point by point manner below.

Sincerely,
Author Team

Methodology:
Reviewer comment (RC) 1: The data from this study is a result of a cross section survey conducted in well-sampled population of Women in areas where CBHI program was implemented using the routinely collected monitoring checklist. The sampling and study recruitment is well described. However, the tool used to collect monitoring data was limited to the CBHI purposes and did not include other variables that may have an effect on the use of FP services beyond the CBHI. Furthermore, the lack of comparison data (intervention vs control or pre vs post intervention) limited the ability to establish any causal relationship between the CBHI and FP use. The statistical analysis is descriptive and is limited to married women excluding unmarried women. Other factors that may have an effect on the FP use were not accounted for in the analysis.

Response (R) 1:
Thank you for providing the opportunity to clarify. We agree with your point, but it is important to understand the nature of the data. USAID Transform: Primary health care project implemented different thematic areas jointly (12 different thematic areas together). The primary mechanism for outcome monitoring and evaluation relied on the baseline and end line data conducted by external evaluator. In 2017, following an external evaluation, program implementer and USAID mission in Ethiopia agreed that if TRANSFORM: PHC relied on facility and community level surveys to measure the program outcomes; it would have to wait until mid- or end-of-project evaluations to understand how to optimize the contributions made to reproductive health in Ethiopia. By then, the opportunity to adapt in response to the results would have passed. For this program, meaningful monitoring would require engaging a range of actors with the power to turn this evidence into learning and action in real time. The follow up visit focused on progress outcome, not focused with comparison groups (interventions vs control). The checklist allows the project to gain insight into progress in service delivery (e.g., FP by method, CBHI related, maternal child health, gender related, quality improvement related, malaria related major indicators etc. for decision-making) and change in health outcomes in target areas at the household level (e.g., modern contraceptive prevalence rates, percent of women with children between 0 and 11 months old who had at least four or more antenatal care visits at a health facility, percent of women with children between 0 and 11 months old who had delivered with the assistance of skilled birth attendants and the percent of children between 12 and 23 months old that are fully vaccinated). The checklist is integrated into routine program activities, and contains indicators that can be measured objectively. The checklist has focused program related indictors but no individual characteristic because of the objective of the follow up visit. We have mentioned in the method section about the primary role of the checklist for monitoring and inform its project. In this analysis we extracted only FP and CBHI related indictors. The project has remained three years in implementation and in the future we will compare yearly progress. In the method of analysis because of the nature of the variable, we have limited our analysis in descriptive statistics because we don’t have individual characteristics to do analytical or regression analysis. We have mentioned all this as a limitation of the study. But know I have tried to do simple traditional logistic regression analysis based on your recommendation, but not that comprehensive to see the causal effect of CBHI on family planning utilization.

Results:
Reviewer comment (RC) 2: The results section was brief, reflecting on less variables/factors the authors chose to explore. Few misrepresentations including a mismatch of the contents in the table and the results explanation, the use of FP methods reported to be 47.5% is not shown in Table 1 as outlined. It would also make sense for me to present tables with;
A). include the descriptive statistic table for participants with their demographic, social, economic and reproductive health related information comparing between FP use status (users vs non users),
B). Combine table 2 and 3,
C). Ensure the data presented include both CBHI users and non-users,
D). Account for other variables that may have an effect on the use of FP beyond the CBHI status, and
E). use regression analysis to establish independent factors for the use of FP methods.
It will be clearer to the readers if the authors outline the following terms clearly, women of reproductive age, young women, married women and unmarried women it gets confusing with which group within the participants are we explaining. The fact that married women were first described rather than all participants it deviates the scope (maybe a systematic description of the whole sample then focus on age groups the married vs unmarried, these factors themselves do affect the use of FP regardless of being in an insurance scheme)

Response (R) 2: Thank you for pointing this out. Yes, we observe fewer variables addressed in this analysis because of the limited number of variables in the follow up checklist and also have mismatched the content of the table, this mismatch was corrected accordingly.
A). Yes, now corrected with limited variables. Table 1 explains the characteristics of the respondents.
B). Corrected, table 2 and 3 merged together for reader clarity.
C-E). Table 3 present logistic regression analysis to see the difference of CBHI users vs non-users and their FP utilization. For clarity about the study participants, the reviewer comment is totally accepted and corrections have been made accordingly. The study participants are all reproductive age group women (15-49 years), they might be married or unmarried. Our analysis focused on 3313 reproductive age women; we have mentioned in the method section and also the first paragraph of the results section. The details of the point you highlighted have been addressed accordingly. But the survey check list only focused on merged indicators rather than individual characteristics, as we have mentioned above, so this analysis has a shortage of important factors to analyse and mention as a limitation of the survey checklist.

Discussion & Conclusion:
Reviewer comment (RC) 3: The brief discussion on this section reflects on the few variables/factors explored to answer the study question and hence difficult to draw a meaningful conclusion. The study limitations should be taken into account as the conclusion statements are presented. The use of the economic term “life returns” should be clearly elaborated rather than just using it (it masks a lot of exactly what are these returns which could be a stronger talking point, also it risks a questionable thought of returns to scale in demand for health care in insured people). The conclusion is addressing the overview provided by the study, this could be widened as the paper limits itself on its initial role of just monitoring and inform its project (method section). Few grammatical errors are also of note and should be counter checked in the final paragraphs of both the discussion and conclusion sections.

Response (R) 3: Thank you for pointing this out and highlighting the importance of the inclusion of this information. This detail has been added in the revised version of the manuscript.
Dear Joseph Obure and Carl Mhina
Thank you for your detailed review and attention dated 30 August, 2019. We have carefully checked the comments and revised the manuscript accordingly. Our responses are given in a point by point manner below.

Sincerely,
Author Team

Methodology:
Reviewer comment (RC) 1: The data from this study is a result of a cross section survey conducted in well-sampled population of Women in areas where CBHI program was implemented using the routinely collected monitoring checklist. The sampling and study recruitment is well described. However, the tool used to collect monitoring data was limited to the CBHI purposes and did not include other variables that may have an effect on the use of FP services beyond the CBHI. Furthermore, the lack of comparison data (intervention vs control or pre vs post intervention) limited the ability to establish any causal relationship between the CBHI and FP use. The statistical analysis is descriptive and is limited to married women excluding unmarried women. Other factors that may have an effect on the FP use were not accounted for in the analysis.

Response (R) 1:
Thank you for providing the opportunity to clarify. We agree with your point, but it is important to understand the nature of the data. USAID Transform: Primary health care project implemented different thematic areas jointly (12 different thematic areas together). The primary mechanism for outcome monitoring and evaluation relied on the baseline and end line data conducted by external evaluator. In 2017, following an external evaluation, program implementer and USAID mission in Ethiopia agreed that if TRANSFORM: PHC relied on facility and community level surveys to measure the program outcomes; it would have to wait until mid- or end-of-project evaluations to understand how to optimize the contributions made to reproductive health in Ethiopia. By then, the opportunity to adapt in response to the results would have passed. For this program, meaningful monitoring would require engaging a range of actors with the power to turn this evidence into learning and action in real time. The follow up visit focused on progress outcome, not focused with comparison groups (interventions vs control). The checklist allows the project to gain insight into progress in service delivery (e.g., FP by method, CBHI related, maternal child health, gender related, quality improvement related, malaria related major indicators etc. for decision-making) and change in health outcomes in target areas at the household level (e.g., modern contraceptive prevalence rates, percent of women with children between 0 and 11 months old who had at least four or more antenatal care visits at a health facility, percent of women with children between 0 and 11 months old who had delivered with the assistance of skilled birth attendants and the percent of children between 12 and 23 months old that are fully vaccinated). The checklist is integrated into routine program activities, and contains indicators that can be measured objectively. The checklist has focused program related indictors but no individual characteristic because of the objective of the follow up visit. We have mentioned in the method section about the primary role of the checklist for monitoring and inform its project. In this analysis we extracted only FP and CBHI related indictors. The project has remained three years in implementation and in the future we will compare yearly progress. In the method of analysis because of the nature of the variable, we have limited our analysis in descriptive statistics because we don’t have individual characteristics to do analytical or regression analysis. We have mentioned all this as a limitation of the study. But know I have tried to do simple traditional logistic regression analysis based on your recommendation, but not that comprehensive to see the causal effect of CBHI on family planning utilization.

Results:
Reviewer comment (RC) 2: The results section was brief, reflecting on less variables/factors the authors chose to explore. Few misrepresentations including a mismatch of the contents in the table and the results explanation, the use of FP methods reported to be 47.5% is not shown in Table 1 as outlined. It would also make sense for me to present tables with;
A). include the descriptive statistic table for participants with their demographic, social, economic and reproductive health related information comparing between FP use status (users vs non users),
B). Combine table 2 and 3,
C). Ensure the data presented include both CBHI users and non-users,
D). Account for other variables that may have an effect on the use of FP beyond the CBHI status, and
E). use regression analysis to establish independent factors for the use of FP methods.
It will be clearer to the readers if the authors outline the following terms clearly, women of reproductive age, young women, married women and unmarried women it gets confusing with which group within the participants are we explaining. The fact that married women were first described rather than all participants it deviates the scope (maybe a systematic description of the whole sample then focus on age groups the married vs unmarried, these factors themselves do affect the use of FP regardless of being in an insurance scheme)

Response (R) 2: Thank you for pointing this out. Yes, we observe fewer variables addressed in this analysis because of the limited number of variables in the follow up checklist and also have mismatched the content of the table, this mismatch was corrected accordingly.
A). Yes, now corrected with limited variables. Table 1 explains the characteristics of the respondents.
B). Corrected, table 2 and 3 merged together for reader clarity.
C-E). Table 3 present logistic regression analysis to see the difference of CBHI users vs non-users and their FP utilization. For clarity about the study participants, the reviewer comment is totally accepted and corrections have been made accordingly. The study participants are all reproductive age group women (15-49 years), they might be married or unmarried. Our analysis focused on 3313 reproductive age women; we have mentioned in the method section and also the first paragraph of the results section. The details of the point you highlighted have been addressed accordingly. But the survey check list only focused on merged indicators rather than individual characteristics, as we have mentioned above, so this analysis has a shortage of important factors to analyse and mention as a limitation of the survey checklist.

Discussion & Conclusion:
Reviewer comment (RC) 3: The brief discussion on this section reflects on the few variables/factors explored to answer the study question and hence difficult to draw a meaningful conclusion. The study limitations should be taken into account as the conclusion statements are presented. The use of the economic term “life returns” should be clearly elaborated rather than just using it (it masks a lot of exactly what are these returns which could be a stronger talking point, also it risks a questionable thought of returns to scale in demand for health care in insured people). The conclusion is addressing the overview provided by the study, this could be widened as the paper limits itself on its initial role of just monitoring and inform its project (method section). Few grammatical errors are also of note and should be counter checked in the final paragraphs of both the discussion and conclusion sections.

Response (R) 3: Thank you for pointing this out and highlighting the importance of the inclusion of this information. This detail has been added in the revised version of the manuscript.
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Version 2

VERSION 2 PUBLISHED 09 May 2019

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	1	2
Version 2 (revision) 19 Sep 19		read
Version 1 09 May 19	read

Joseph Obure, Save the Children US, Washington, USA

Carl Mhina, Duke University Global Health Institute, Durham, USA
Nirali M. Chakraborty, Metrics for Management, Oakland, USA

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02 Mar 2021 | for Version 2

Nirali M. Chakraborty, Metrics for Management, Oakland, CA, USA

11 Views Cite this report Responses(0)

Not Approved

Please indicate what the sample size assumptions were.
Ethical Considerations: I am unclear as to why this study did not require any ethical approval. Data was collected at the household level, with the clear intention to analyze for monitoring and research. If the interviews were not intended to "develop or contribute to generalizable knowledge", then why is it being submitted for publication? While it is understandable that participants may have only given oral consent, it seems reasonable that this determination would be made from an ethical review board. Please elaborate on the ethical obligations of the research team.

The only variables adjusted for were the ones presented, of which Age is the only demographic variable. This analysis did not adjust for parity, marital status, or wealth. Each of these other variables has a likely relationship to the use of FP and/or enrollment in a CBHI scheme.
The likelihood of a woman using FP is 1.43 times higher for HH enrolled in a CBHI scheme, among women of the same age, and receiving the same amount of FP information. Similarly for the other odds ratios presented.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Yes
If applicable, is the statistical analysis and its interpretation appropriate?

No
Are all the source data underlying the results available to ensure full reproducibility?

Yes
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

No competing interests were disclosed.

Reviewer Expertise

Family planning, Health systems

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13 Views

30 Aug 2019 | for Version 1

Joseph Obure, Department of Global Health, Save the Children US, Washington, DC, USA

Carl Mhina, Duke University Global Health Institute, Durham, NC, USA

13 Views Cite this report Responses(1)

Approved With Reservations

It’s an important public health issue and the current global focus of universal health coverage (CBHI) and sustainable development goals (family planning)
Its large sample size selected from multiple regions and the household survey gives weight to the sample and ability to infer it to the population.
The paper has enough information to demonstrate the likelihood of the association rather than the causal-effect relationship.

The manuscript strength would be improved, if the following would be done;

Clearly define the study population
Include other variables that may have an influence on the use of FP services
Think of using conceptual framework that demonstrate the factors and path to FP use.
Present data in a more scientific way (tables)
Use relevant statistical methods
Account for other variables during statistical analysis
Use conclusion statements that reflects on the study strength and limitation.

Is the work clearly and accurately presented and does it cite the current literature?

Partly
Is the study design appropriate and is the work technically sound?

Partly
Are sufficient details of methods and analysis provided to allow replication by others?

Partly
If applicable, is the statistical analysis and its interpretation appropriate?

Partly
Are all the source data underlying the results available to ensure full reproducibility?

Partly
Are the conclusions drawn adequately supported by the results?

Partly

Competing Interests

Joseph John Obure worked for Pathfinder International in Tanzania office between April 2015 to January 2017. However, I did not and do not have any affiliation with this project. Neither the research team who are currently Pathfinder employees in Ethiopia.

Reviewer Expertise

Maternal, reproductive and Newborn Health

Respond to this report

Responses (1)

Author Response

19 Sep 2019

Bekele Tefera, Department of Monitoring, Evaluation and Learning, pathfinder international, Addis Ababa, 12655, Ethiopia

Dear Joseph Obure and Carl Mhina
Thank you for your detailed review and attention dated 30 August, 2019. We have carefully checked the comments and revised the manuscript accordingly. Our responses are given in a point by point manner below.

Sincerely,
Author Team

Methodology:
Reviewer comment (RC) 1: The data from this study is a result of a cross section survey conducted in well-sampled population of Women in areas where CBHI program was implemented using the routinely collected monitoring checklist. The sampling and study recruitment is well described. However, the tool used to collect monitoring data was limited to the CBHI purposes and did not include other variables that may have an effect on the use of FP services beyond the CBHI. Furthermore, the lack of comparison data (intervention vs control or pre vs post intervention) limited the ability to establish any causal relationship between the CBHI and FP use. The statistical analysis is descriptive and is limited to married women excluding unmarried women. Other factors that may have an effect on the FP use were not accounted for in the analysis.

Response (R) 1:
Thank you for providing the opportunity to clarify. We agree with your point, but it is important to understand the nature of the data. USAID Transform: Primary health care project implemented different thematic areas jointly (12 different thematic areas together). The primary mechanism for outcome monitoring and evaluation relied on the baseline and end line data conducted by external evaluator. In 2017, following an external evaluation, program implementer and USAID mission in Ethiopia agreed that if TRANSFORM: PHC relied on facility and community level surveys to measure the program outcomes; it would have to wait until mid- or end-of-project evaluations to understand how to optimize the contributions made to reproductive health in Ethiopia. By then, the opportunity to adapt in response to the results would have passed. For this program, meaningful monitoring would require engaging a range of actors with the power to turn this evidence into learning and action in real time. The follow up visit focused on progress outcome, not focused with comparison groups (interventions vs control). The checklist allows the project to gain insight into progress in service delivery (e.g., FP by method, CBHI related, maternal child health, gender related, quality improvement related, malaria related major indicators etc. for decision-making) and change in health outcomes in target areas at the household level (e.g., modern contraceptive prevalence rates, percent of women with children between 0 and 11 months old who had at least four or more antenatal care visits at a health facility, percent of women with children between 0 and 11 months old who had delivered with the assistance of skilled birth attendants and the percent of children between 12 and 23 months old that are fully vaccinated). The checklist is integrated into routine program activities, and contains indicators that can be measured objectively. The checklist has focused program related indictors but no individual characteristic because of the objective of the follow up visit. We have mentioned in the method section about the primary role of the checklist for monitoring and inform its project. In this analysis we extracted only FP and CBHI related indictors. The project has remained three years in implementation and in the future we will compare yearly progress. In the method of analysis because of the nature of the variable, we have limited our analysis in descriptive statistics because we don’t have individual characteristics to do analytical or regression analysis. We have mentioned all this as a limitation of the study. But know I have tried to do simple traditional logistic regression analysis based on your recommendation, but not that comprehensive to see the causal effect of CBHI on family planning utilization.

Results:
Reviewer comment (RC) 2: The results section was brief, reflecting on less variables/factors the authors chose to explore. Few misrepresentations including a mismatch of the contents in the table and the results explanation, the use of FP methods reported to be 47.5% is not shown in Table 1 as outlined. It would also make sense for me to present tables with;
A). include the descriptive statistic table for participants with their demographic, social, economic and reproductive health related information comparing between FP use status (users vs non users),
B). Combine table 2 and 3,
C). Ensure the data presented include both CBHI users and non-users,
D). Account for other variables that may have an effect on the use of FP beyond the CBHI status, and
E). use regression analysis to establish independent factors for the use of FP methods.
It will be clearer to the readers if the authors outline the following terms clearly, women of reproductive age, young women, married women and unmarried women it gets confusing with which group within the participants are we explaining. The fact that married women were first described rather than all participants it deviates the scope (maybe a systematic description of the whole sample then focus on age groups the married vs unmarried, these factors themselves do affect the use of FP regardless of being in an insurance scheme)

Response (R) 2: Thank you for pointing this out. Yes, we observe fewer variables addressed in this analysis because of the limited number of variables in the follow up checklist and also have mismatched the content of the table, this mismatch was corrected accordingly.
A). Yes, now corrected with limited variables. Table 1 explains the characteristics of the respondents.
B). Corrected, table 2 and 3 merged together for reader clarity.
C-E). Table 3 present logistic regression analysis to see the difference of CBHI users vs non-users and their FP utilization. For clarity about the study participants, the reviewer comment is totally accepted and corrections have been made accordingly. The study participants are all reproductive age group women (15-49 years), they might be married or unmarried. Our analysis focused on 3313 reproductive age women; we have mentioned in the method section and also the first paragraph of the results section. The details of the point you highlighted have been addressed accordingly. But the survey check list only focused on merged indicators rather than individual characteristics, as we have mentioned above, so this analysis has a shortage of important factors to analyse and mention as a limitation of the survey checklist.

Discussion & Conclusion:
Reviewer comment (RC) 3: The brief discussion on this section reflects on the few variables/factors explored to answer the study question and hence difficult to draw a meaningful conclusion. The study limitations should be taken into account as the conclusion statements are presented. The use of the economic term “life returns” should be clearly elaborated rather than just using it (it masks a lot of exactly what are these returns which could be a stronger talking point, also it risks a questionable thought of returns to scale in demand for health care in insured people). The conclusion is addressing the overview provided by the study, this could be widened as the paper limits itself on its initial role of just monitoring and inform its project (method section). Few grammatical errors are also of note and should be counter checked in the final paragraphs of both the discussion and conclusion sections.

Response (R) 3: Thank you for pointing this out and highlighting the importance of the inclusion of this information. This detail has been added in the revised version of the manuscript.

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Competing Interests

Alongside their report, reviewers assign a status to the article:

Approved - the paper is scientifically sound in its current form and only minor, if any, improvements are suggested

Approved with reservations - A number of small changes, sometimes more significant revisions are required to address specific details and improve the papers academic merit.

Not approved - fundamental flaws in the paper seriously undermine the findings and conclusions

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Effects of community-based health insurance on modern family planning utilization in Ethiopia

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Methods

Study background

Data processing and analysis

Ethical consideration

Results

Family planning usage

Table 1. Respondents characteristics and enrolment in community-based health insurance (CBHI), USAID Transform: Primary Health Care project Follow up visits, 2017.

Table 2. Contraceptive method mix among reproductive age women (15–49 years), USAID Transform: Primary Health Care project Follow up visits, 2017.

Effect of CBHI on FP usage

Table 3. logistic regression model with crude and adjusted odds ratio using the 2017 USAID Transform: Primary Health Care project Follow up visits.

Discussion

Limitation

Conclusion and recommendation

Data availability

Underlying data

Extended data

Acknowledgements

References

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Effects of community-based health insurance on modern family planning utilization in Ethiopia

Abstract

Keywords

Revised Amendments from Version 1

Introduction

Methods

Study background

Data processing and analysis

Ethical consideration

Results

Family planning usage

Table 1. Respondents characteristics and enrolment in community-based health insurance (CBHI), USAID Transform: Primary Health Care project Follow up visits, 2017.

Table 2. Contraceptive method mix among reproductive age women (15–49 years), USAID Transform: Primary Health Care project Follow up visits, 2017.

Effect of CBHI on FP usage

Table 3. logistic regression model with crude and adjusted odds ratio using the 2017 USAID Transform: Primary Health Care project Follow up visits.

Discussion

Limitation

Conclusion and recommendation

Data availability

Underlying data

Extended data

Acknowledgements

References

Comments on this article Comments (0)

Open Peer Review

Comments on this article Comments (0)

Open Peer Review

Reviewer Status

Reviewer Reports

Comments on this article

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Are you a Gates-funded researcher?

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