[version 2; peer review: 1 approved with reservations, 1 not approved]
No competing interests were disclosed.
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.
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 administrations
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 years
CBHI contributes to the improvement of health seeking behaviour, health care utilization and service quality
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 formula
Data collection was conducted from October 1 to December 31, 2017 and 3313 women of reproductive age (15–49) were interviewed. A household questionnaire
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).
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.
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 (
| Variables | Frequency (% |
|---|---|
| age in Years <25 | 902 (27.2) |
| 25–30 | 1337 (40.4) |
| 31–36 | 580 (17.5) |
| >36 | 494 (14.9) |
|
|
29.5 ±8.34 |
|
|
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
|
1280 (45.1) |
HH; Households
| Method Mix | Prevalence rate of women with
|
|
|---|---|---|
| 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.
| 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) |
|
|
| Believe CBHI is important (Y/N) | 1.75(1.27-2.41) | 1.42(0.94-2.14) |
| Exposed/Received FP related information
|
|
|
HH; Households, AOR; adjusted odds ratio, COR; crude odds ratio
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 tools
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
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.
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.
Open Science Framework: Effects of community-based health insurance on modern family planning utilization in Ethiopia.
This project contains the raw responses to the survey for each participant in file HH 2017.sav.
Open Science Framework: Effects of community-based health insurance on modern family planning utilization in Ethiopia.
This project contains the survey questionnaire in file HH FUV Checklist.pdf.
Data are available under the terms of the
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.
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.
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."
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 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.
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
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
Is the study design appropriate and is the work technically sound?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Yes
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.
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
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.
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)
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.
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. 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.
The manuscript strength would be improved, if the following would be done;
Is the work clearly and accurately presented and does it cite the current literature?
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
Is the study design appropriate and is the work technically sound?
Partly
Are the conclusions drawn adequately supported by the results?
Partly
Are sufficient details of methods and analysis provided to allow replication by others?
Partly
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.
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
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.
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)
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