| Abstract: | Community Based Health insurance is an emerging social security instrument for the poor, for whom chronic health problems, arising due to prevalence of diseases and inaccessibility to an affordable health care system, is a major threat to their income earning capacity which leads all people aspire to receive quality and affordable health care. The main purpose of this study was to identify factors affecting the adoption of community based health insurance among households in Adama City, Ethiopia. The study used a mixed cross sectional survey research design. Structured interview with 353 sample respondents were held, who were selected using probability sampling technique supplemented by key informant interview. Descriptive statistics and binary logistic regression model are used to identify factors that determine the adoption of community based health insurance in the study area. The result show that most of (56.37%) the household heads adopted community based health insurance. The resulting distribution on perception of CBHI further, show that (35.69%) and (41.64%) of the respondents in the study area perceived CBHI as very good and good respectively. On the other hand (21.53%) of the respondents have bad perception on community based health insurance. The rest (1.13%) of the respondents are neutral to respond their perception on community based health insurance. Variables such as income, legal framework, occupation, chronic ill family member and facility preference are significantly and negatively associated with the adoption of community based health insurance in the study area. On the other hand, higher education and drug availability significantly and positively affects the adoption of community based health insurance in the study area. Thus, in the process of implementing community based health insurance, these variables should be considered by the government decision makers, health sector donor agencies at different level and individual household heads. For future studies, considering time serious data is recommended to understand the observed differences. |
