Study setting and period
The study was conducted in Shebedino District, Southern Ethiopia, from February to June 2019. The study District is known for producing coffee as an important cash crop globally. It is located in Southern Nation Nationalities People Region Ethiopia, at a distance of 300 kilo meter from Addis Ababa, capital city of Ethiopia and 24 km from Hawassa, capital city of SNNPR.
The District is one of the 36 Districts & 4 City administrations of Sidama Zone with 21 rural Kebeles (kebele is the smallest administration unit in Ethiopia).
According to the 2007 projected Population and Housing Census, Shebedino District has a total population of 181‚460 accounting for about 8% of the Sidama Zone’s population in the year 2018. Married Women among reproductive age groups (15-49) years make up close to 37‚017 (20.4%). Among the total population 85% are living in rural areas. It has a total of 21 health posts, six functional health centers and one primary hospital providing preventive, promotive, curative and rehabilitative services [41].
Study Design and Population
Community based cross-sectional study was employed. The source population was all reproductive age group women in the district and the study population were sampled married women found in their reproductive age groups.
Sample size and Sampling procedures
The sample size is determined using single population proportion formula with the following assumptions and the second objective with G-power software version 3.1.9.1. Therefore, the values for assumption are 95% confidence level (1.96), 5% margin of error (d), proportion (P) of unmet need in family planning is taken as 21% (0.21%) from the 2016 EDHS (14), design effect n= 1.5 and non-response rate is 5%. Then, using the following formal the total sample is determined to be 447.
From Shebedino District a total of 6 Kebeles were selected using simple random sampling technique. Households with married reproductive age women were taken from family folder of health extension workers (HEW) using simple random sampling technique. Study participants were allocated proportionally to each kebele based on number of married reproductive age women in selected kebeles. Lottery method was used to select one reproductive age women when there is more than one woman in the selected household.
Variables
Dependent variable (outcome variable) is unmet need for family planning (1=Yes, 0=No) The independent variables /Exposure variables/include, Socio economic and demographic factors: age of the women, marital status, ethnicity, religion, educational status of the women and the partner, women occupation, residence, husbands occupation, household’s monthly income, family size, Sex of child, number of live children. Reproductive History and health factors: age at the first marriage, age at the first pregnancy, history of pregnancy, parity, desired number of children, side effect of contraception, knowledge and practices of family planning use, exposure to family planning messages via the media.
Operational Definition
Unmet need for Family planning: is referring to those women who prefer to space or limit childbearing but is not using any effective modern contraceptive to fulfill its desire.
Women are defined as having an unmet need if they are fecund, married or living in union, but not using any contraception or have a mistimed or unwanted current pregnancy, or are postpartum amenorrhoeic and their last birth in the last 2 years was mistimed or unwanted [14].
Data collection tool and Procedure
Data were collected using pretested, structured questionnaire and semi-structured questionnaire. The questionnaire is initially prepared in English and translated to local language (Sidaamu afoo) and then the Sidaamu afoo questionnaire was back translated to English to maintain the consistency in the meaning of the words and/or concepts. The Sidamigna versions were used for data collection after pre-testing on five percent of the actual sample size in other similar settings to ensure that respondents understand the questions and to check the wording, logic and skip order of the questions in a sensible way to the respondents. An amendment was made accordingly after the pre-tested.
The data collection was done by 12 females’ degree holder with 6 teams. Each team comprising two data collectors and six supervisors were supervised the overall data collection process. One day training was given to all data collectors and supervisors to have common understanding on the data collection tools and process.
After data collection, questionnaires were reviewed and checked every day for completeness by the supervisors. The investigator was also given the necessary feedback for the data collectors immediately. Finally, the data was cleaned and coded before entering in to computer.
Data processing and analysis
Data were checked, coded and entered to Epi-data 3.1 versions and SPSS version 20 statistical package software and analysis was made. Before analysis of the data, missing observations were cleaned and presence of cells of each category under each variable with zero values was checked by cross tabulation of each independent variable against the dependent variable. Categories containing cells with zero values are going to be merged with the other category within the variable to have better validity on its result.
The results of the analysis were presented in the form of tables, figures and summary statistics. For categorical variables, frequencies, percentages and figures are going to be used in the socio-demographic part and for continuous variables after checking their normality using figures like box plot or scatter plot, mean and standard deviation for normal distributions and for those that are not normally distributed median and inter quartile range was used.
Analysis of the data involved descriptive statistics of the demographic profile of the participants and testing and identifying potential predictors of unmet need using the simple and multiple binary logistic regression techniques. Simple binary logistic regression analysis for each independent variable was performed against the dependent variable to see the impact of each factor on the pattern of unmet need for family planning, the dependent variable in the sampled observations, without adjusting for the effect of other variables.
Goodness of the models was also be tested by diagnosing correctness of formulation of the models by using Hosmer-Lemeshow and Omnibus test of model coefficients that are the values (p value >0.05 and <0.05 respectively.