Objectives Regardless of the local and international initiatives, excluding exempting services, demand satisfied for contraceptives remains low in Ethiopia. This circumstance is supposed to be attributed to different level factors; however, most were not well addressed in the previous studies. Therefore, this study aimed at assessing the magnitude and individual, household and community-level factors associated with demand satisfied for modern contraceptive (DSFMC) methods among married/in-union women of reproductive age. Design Cross-sectional study. Setting A community-based study across the country. Participants Randomly selected 9126 married/in-union women had participated using a structured questionnaire. Outcome DSFMC methods among married/in-union women of reproductive age. Results DSFMC methods in Ethiopia was 39.5% (95% CI 38.5% to 40.5%). Women aged 35-49 years (adjusted OR (AOR): 0.43, 95% CI 0.32 to 0.58), Muslim religion (AOR: 0.58, 95% CI0.43 to 0.78), husband lived elsewhere (AOR: 0.42, 95% CI 0.29 to 0.60), joint decision making to use (AOR: 1.30, 95% CI 1.04 to 1.62), good knowledge (AOR: 1.57, 95% CI 1.32 to 1.86) and wealth status of poorer (AOR: 1.56, 95% CI 1.17 to 2.06), middle (AOR: 1.77, 95% CI 1.33 to 2.35), richer (AOR: 1.96, 95% CI 1.49 to 2.59), and richest (AOR: 1.49, 95% CI 1.05 to 2.08), pastoralist regions (AOR: 0.28, 95% CI 0.18 to 0.42), and agrarian regions (AOR: 1.72, 95% CI 1.21 to 2.44) and rural residency (AOR: 0.56, 95% CI 0.37 to 0.82) were factors significantly associated. Conclusions Women’s age, religion, the current living place of husbands and women’s knowledge were individual-level factors. Household wealth status and mutual decision making to use were household-level factors. Region and residency were households and community-level factors associated with DSFMCs. Increasing the accessibility of modern contraceptive methods to women in rural areas and pastoralist regions, those living separately, engaging religious leaders and men in the programme, would increase their satisfying demand.
The study was conducted in Ethiopia using data from the 2016 EDHS. Ethiopia is found in the horn of Africa. Administratively, the country is subdivided into nine geographical regions: Tigray, Afar, Amhara, Oromia, Benishangul-Gumuz, Gambela, Harari, Somali and Southern Nations, Nationalities, and Peoples Region (SNNPR), and two administrative cities (Addis Ababa, the capital city of the country and Dire Dawa). Ethiopia is the second-most populous country in Africa with a population of 114 763 301, equivalent to 1.47% of the total world population ranked the second and twelfth populated country in Africa and the world, respectively. More than 40% of the population is below 15 and a fertility rate of over five children per woman.24 The EDHS is a nationally representative household data source gathered every 5 years with the ownership of the Central Statistical Agency.25 The survey was conducted from 18 January 2016 to 27 June 2016, by health professionals across all regions and administrative cities of the country. In EDHS 2016, a two-stage stratified cluster sampling was employed using the 2007 population and housing census as a frame. The census used a complete list of 84 915 enumeration areas (EAs) created for primary healthcare as a frame. In the first stage, 645 EAs were selected with probability proportional to the EA size. The regions were stratified into urban and rural areas. In the second stage, 28 households from each cluster were selected by systematic sampling. The data collectors interviewed only preselected households, and no replacements or changes of the preselected homes were allowed in the implementing stages to prevent bias.26 The 2016 EDHS maternal data sets across all regions and two administrative cities were used for analysis. All women aged 15–49 years who were the usual members of the selected households were eligible. The demand satisfied for this study was computed based on the demographic and health survey’s (DHS) revised definition of demand satisfied. Those who use any of the modern FP methods were considered as met needs and used in the calculation as nominator. Those who require modern methods for spacing or limiting but are unable to get and those who use traditional methods were considered as an unmet need. The met and the unmet needs were used as the denominator for the calculation (total demanded). Thus, demand satisfied=met need *100/met need +unmet need.27 Accordingly, 8734 women aged 15–49 years who are currently married/ in-union were identified as total demanded modern contraceptive methods (2900 met the need and 5834 unmet need) from 12 218 currently married reproductive-age women. To increase its representativeness, sample weighting was done. Thus, the met need was changed from 2900 to 3603, unmet need changed from 5834 to 5523. Consequently, total demand was changed from 8734 to 9126. The sampling procedure before sampling weight was done (figure 1). Schematic presentation of demand satisfied for modern contraceptives among currently married/in-union women of reproductive age (before sampling weight) (adapted from Bradley et al27) in Ethiopia, 2016. Individual, household and community-level independent variables were extracted, and further analyses of the selected variables were done. The study’s dependent variable was demand satisfaction for modern contraceptive methods among married or in-union women who were aged 15–49 years. It was measured using women who reported any of the following modern contraceptive methods: female sterilisation, male sterilisation, pill, intrauterine device (IUD), injectables, implants, male condom, female condom, emergency contraception or lactation amenorrhea method among the total demanded mothers. The independent variables were categorised into three levels: individual level, household level and community level. Participant’s age, religion, educational status, occupation, the presence of other wives, husband’s current residency, and knowledge of participants to modern methods and ovulatory cycles were the individual-level variables. The knowledge status of women for modern contraceptive methods: in the EDHS, the knowledge of women for contraceptives was recorded as ‘yes’ and ‘no’. After merging the results of all the selected modern contraceptive methods, the minimum and maximum values were determined, given that the minimum score is 0, maximum 10 and average 5.5. Then, taking the average as the cut point, the results were dichotomised. Thus, those who scored above 5 were levelled as having good knowledge, whereas those below 5 were levelled with poor knowledge. The sex household heads, family size, wealth status, number of living children and decision maker on FP use were household-level variables, whereas the community-level variables were residency, region, distance to a health facility, heard about FP on radio/at community event/conversation. In the EDHS, the wealth quantile was calculated as an index based on consumer goods and fixed assets, such as television, bicycle or car. Household characteristics were also considered in computing the wealth status. These scores were derived using principal component analysis, expressed in terms of quintiles of individuals in the population, and combined to produce a single asset index for all households. Finally, the wealth status was ranked into five (poorest, poorer, middle, richer and richest). Distance to the health facility in the EDHS was assessed using the respondent’s response and categorised as ‘big problem’ or ‘not a problem’.28 All independent variables were extracted from the data set considering their relevance to the identified research questions. An expert-based discussion was conducted along with the author, and other literature to determine relevant variables was reviewed. An extraction format specific to the study that comprised important variables was prepared, and face validity was done. Further, variables included in the previous studies were added, and critical appraisal for its relevance was employed. The extracted data were cleaned, recoded, and analysed using STATA V.14 (Stata Corp, College Station, Texas, USA). Finally, descriptive statistics were presented using tables and text. A multilevel analysis was conducted after checking the statistical assumptions. First, the model assumption was examined by calculating the intraclass correlation coefficient (ICC), and an ICC of more than 5% is deemed as eligible for multilevel analysis. The ICC was 38.97%. Since the EDHS data are hierarchical (individuals are nested within the household and household levels are also nested within the community), a three-level mixed-effects logistic regression model was fitted to estimate the individual-level, household-level and community-level variables (fixed and random-effects) on-demand satisfied for modern contraceptive methods. Bivariable and multivariable analyses were computed. First, in the bivariable logistic regression analysis, a value of p<0.2 was used to fit the four models (models for the individual level, household level, community level, and all individual, household and community levels together). In the final model (fixed-effect), for the individual, household and community levels, a value of p<0.05 was used to declare the presence of an association between individual-level, household-level and community-level factors with DSFMC methods. Next, the adjusted OR (AOR) with a 95% CI was used to estimate the strength and direction of the association. The measures of variation (random effects) were reported using ICC and proportional change in variance to measure the variation between clusters. The log-likelihood test was used to estimate the goodness of fit of the adjusted model compared with the preceding models. A model with the smallest log-likelihood value is better; accordingly, model 4 (a model for all the individual, household and community-level variables) was preferred. No patient or the public was directly involved in developing the research questions, the design, results and dissemination plan of the study. During EDHS data collection, the data collectors were trained in the data collection process and the handling of the participants without introducing biases. Participants were informed about the objective of EDHS data collection and their rights in the data collection process. Personal identifiers were omitted. Moreover, permission to access the data were obtained from the measure DHS on 07 September 2020, after submitting a brief study concept. However, since this study used secondary data from DHS, consent directly from the participants was not applicable. Further, this study adhered to the Declaration of Helsinki.
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