Data source and sampling techniques
The study participants chosen using a stratified, two stage cluster design, and enumeration areas were the sampling units for the first stage. In the first stage, 645 enumeration areas were randomly selected: 202 in urban areas and 443 in rural areas. In the second stage, a fixed number of 28 households per cluster were selected randomly for each enumeration areas. The 18,060 households were randomly selected and 16,650 households were eligible and interviewed. Additional information about the methodology of EDHS 2016 can be accessed in the published report of the main findings of the survey .
Every selected reproductive age women was included and data were collected on various socioeconomic, obstetric and nutrition variables. As our focus in this study was 15-49 years aged women, we extracted the EDHS 2016 data set. We found in the data set 70 women with experienced obstetric fistula (Cases) and 210 (Controls) selected from the data set using random number table and 280 women were included in the final analysis showed sampling techniques (Figure :1).
A community-based unmatched case-control study was conducted among reproductive age women.
Variables and measurements:
The outcome variable was fistula, which is defined as reproductive aged women experiencing lifelong obstetric fistula.
The selection of the independent variables was guided by the literature and availability of the variables in the data set. Some of the independent variables for obstetric fistula among reproductive age women 15-49 years.
Maternal characteristics: maternal age, maternal educational status, maternal antenatal care follow up, whether the mother is currently living with her husband or not, whether the mother is engaged in income generating work or not.
Household characteristics: number of household members, residence, wealth index ranked in to five (poorest, poorer, middle, richer and richest) , sex of household head.
Obstetric characteristics: Place of delivery, ANC follow up, size of child at birth, postnatal check up, Preceding birth interval, Height(Cm) and ever had a terminated pregnancy.
The nutritional category of women was measured by use of height and body mass index (BMI). To calculate BMI, during EDHS measured the height and weight of women age 15–49 years. BMI is used to measure thinness or obesity. BMI is defined as weight in kilograms divided by height in meters squared (kg/m2). A BMI below 18.5 kg/m2 shows thinness. A BMI below 12- 17 kg/m2 indicates severe undernutrition BMI of 25.0 kg/m2 or above shows overweight or obesity. Height was also categorized in a single cut off point < 145 cm as short stature.
A wealth index in the EDHS survey was measure based on household asset data to classify individuals into 5 wealth indeces (poorest, poorer, medium, richer and richest). Variables incorporated in the wealth index were ownership of chosen household assets (television, bicycle or car), size of agricultural land, number of livestock and materials used for house construction .
EDHS have developed recode files in order to facilitate data analysis. All data tartan for its completeness and reliability. Preliminary analysis was done to check the first round finding. In all analysis, sample weights have done due to two stage cluster sampling design in the EDHS data set to adjust for the imbalance probability selection among the strata . All the analyses were performed using STATA version 14.0 Categorical type of data was analyzed by descriptive statistics (frequency and percentage).
Logistic regression analysis was used to identify factors associated with obstetric fistula. Bivariate analysis was carried out to see the crude association of each independent variable with the outcome variable (Obstetric fistula). Those independent variable variables with 𝑃-value ≤ 0.05 in the bivariate analysis were included in the final multivariable logistic regression analysis to adjust for confounding and to identify the final factors associated with obstetric fistula. Logistic regression method was used during the multivariable logistic regression analysis. Before inclusion of predictors to the final logistic regression model, the multi-collinearity was checked using VIF<10/Tolerance >0.1 for continuous independent variables. The goodness of fit of the final logistic model was tested using Hosmer and lemeshow test at p value of >0.05. The strength of association of the predictors and outcome variable have been indicated by Adjusted odds ratio at 95% confidence interval. The significant association was declared at p≤ 0.05 for the final logistic regression model
The study proposal got ethical approval from Tigray health research institute and formal letter of permission was obtained from measure DHS project website to access the dataset (http://www.measuredhs.com).