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Background: Community-based health insurance (CBHI) schemes have been implemented in developing countries to facilitate modern medical care access. However, studies conducted on the effect of CBHI on healthcare-seeking behavior (HSB) have been limited and revealed inconsistent results. Therefore, this study aimed to assess the effect of CBHI on mothers’ HSB for common under-five childhood illnesses. Methods: A community-based comparative cross-sectional study was conducted among 410 rural mothers (205 insured and 205 non-insured), and a multistage random sampling technique was used to select the study participants. Binary logistic regression and propensity score matching were used to identify factors associated with the mothers’ HSB, and estimate the effect of CBHI on mothers’ HSB, respectively. Results: The overall mother’s HSB for childhood illnesses was 48.8% (200/410). From those mothers who visited healthcare, 92.0% were married, 86.0% were unable to read and write, 94.5% were farmers, and 54.5% were from low wealth status, 58.50% had a family size of ≤5, 54.0% had children less than 24 months of age. Besides, 63.0% were members of CBHI, 37.0% perceived their child’s illness as severe, 78.0% made a shared decision to visit a health facility, and 67.5% lived within less than five Kms from the nearby health facilities. Being a member of CBHI, the child’s age, decision to visit a health facility, and perceived disease severity were predictors of HSB. The CBHI had a significant effect on the HSB for childhood illnesses with ATT of 28.7% (t = 3.959). Conclusion: The overall mothers’ HSB for common childhood illnesses was low though the CBHI has a significant effect. CBHI should be strengthened to improve the mothers’ HSB. It is also crucial to strengthen awareness creation regarding joint decision-making and educate mothers to visit the health facilities regardless of children’s age and disease severity.
A community-based cross-sectional study was conducted among 410 rural mothers (205 insured and 205 non-insured) in Aneded district, East Gojjam zone of Amhara region, from February to March 2016. Aneded district is located 283 km away from Addis Ababa, Ethiopia’s capital city, and 305 kms from Bahir Dar city, the capital city of Amhara region, and 40 Kms from Debre Markos, the capital city of East Gojjam zone. The district’s total population was estimated to be 104,053 (50,991 males and 53,062 females), and the total number of under-five children was 12,351. The district has 19 rural and one urban kebeles (the lowest administrative units in the country), five health centers, 20 health posts, three private clinics, and two drug stores. The CBHI was introduced in the district as a pilot scheme in 2011. At the time of this study, 8507 (41%) households were members of the CBHI scheme. The source population was all rural mothers living in the Aneded district with at least one under-five child. The study population was all mothers who had children under-five years of age with a history of illness like diarrhea, fever, and/or acute respiratory infection (ARI) three months preceding the survey. Mothers working in the formal sectors and who were seriously sick were excluded from the study. The sample size was determined by using the two-population proportion formula and calculated using Open Epi software. The assumptions taken to determine the sample size were the proportion of mothers’ HSB who had media exposure (69%) and those with no media exposure (47.7%),23 1:1 ratio, design effect of 2, and 10% non-response rate. The final sample size was 410 mothers, ie, 205 from households with CBHI coverage and 205 from households with no CBHI coverage. A multistage sampling technique was used. Among the 19 rural Kebeles in the district, five Kebeles (25% of the study area), namely Gudalem, Amberzura, Daget, Yewobie, and Nefasam were selected first using a simple random sampling technique (lottery method). A minimum of 50% of “Ketenas” (a smaller division of kebele) from the selected Kebeles was selected using a simple random sampling method. The sample size was proportionally allocated to each kebele by considering the total population in each kebele. Households were visited to assess the presence of under-five children who were sick within the past three months. Each household was visited until the sample size was reached. Outcome variable: The outcome variable of the study was HSB. We categorized the HSB into Yes or No response: Yes, if mothers undertook any response for signs and symptoms of illnesses to reduce severity and complication after recognizing the child’s disease, and visited health facilities, private and/or public health facilities; and No, if otherwise. Independent variables: Socio-demographic factors or predisposing factors (maternal age, educational status, occupation, marital status, previous use of service, sex of the child, age of the child); enabling factors (distance from the health facility, cost to transportation, insurance status, income, waiting time at health facility); need factors (awareness about the disease, media exposure, perceptions on the use of timely health care seeking); and the CBHI membership status were the independent variables of the study. The study participants’ wealth status was assessed based on housing conditions and durable assets and categorized into poor and rich. The term “rich” was used to describe those in the fourth or fifth quintile, whereas the term “poor” was used to explain those in the first three quintiles. Perceived illness severity was categorized into “mild,” “moderate,” and “severe” based on the perception of the mothers regarding the sickness of their child. A structured questionnaire was prepared by adapting from the Anderson model and reviewing relevant literature.33 The questionnaire was designed in English and translated to the local language, Amharic, by a professional language expert for simplicity of understanding on the part of respondents and then translated it back to English. Five data collectors, who completed 12th grade to collect data, and two nurses, who have a diploma for the supervision of the data collection process were selected. The one-day training was given to the data collectors and supervisors about the research’s whole purposes and procedures to ensure the data quality. A pre-test was conducted on 5% of the sample in one of the district’s kebeles, which we did not include in the actual data collection, and modification was made, based on the pre-test findings, before the beginning of the actual data collection. We conducted the data collection process at each household level. The investigator and supervisors checked the accuracy of the data collection process each day, and if problems were encountered, they communicated them to data collectors for immediate action. The principal investigator supervised the overall activity of the data collection. The data were entered into Epi-Info™ 3.5.1 (Centers for Disease Control and Prevention, Atlanta, GA, USA), and transferred to SPSS 20 (SPSS Inc., Chicago, IL, USA) for binary logistic regression analysis and STATA 12 (StataCorp LP, College Station, TX, USA) for propensity score analysis. Descriptive statistics such as mean and standard deviation were used to describe the study participants’ socio-demographic and economic characteristics. A binary logistic regression was run to see the association of HSB of mothers or caregivers for childhood illnesses with predisposing, enabling, and needs factors. Multivariable logistic regression was also run to control the confounding factors and get less biased estimates of the association between explanatory and HSB. A level of significance of p-value ≤0.2 in the bivariable logistic regression was used as a cut-off point to select candidate variables for the final model, the multivariable logistic regression model. A propensity score analysis was run to determine the effect of the CBHI on the HSB of mothers or caregivers for childhood illnesses. A propensity score is a probability of being exposed to the intervention (in this case, being a member of CBHI) given a set of observed covariates, X. It was estimated using the logistic regression model. It constructs a statistical comparison group based on a model of the probability of participating in the treatment (CBHI), using observed characteristics. The nearest neighborhood matching was used in the analysis, which matches a given insured mother to non-insured members whose propensity score was closest to that of the treated subject or vice versa. The method could balance the insured and non-insured mothers so that a direct comparison would be possible for evaluating the effects of being the members of CBHI on the HSB. The insured mothers were matched based on this probability, or propensity score, to non-insured mothers. Then, the program’s average treatment effect (ATT) was calculated as the mean difference in outcomes across these two groups.
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