Determining Factors that Influence Unmet Need For Family Planning Using Geographically Weighted Logistic Regression With LASSO: - Dian Ayu Permata Sari Rusdy; Sri Astuti Thamrin; Anna Islamiyati (Department of Mathematics, Hasanuddin University, 2025)
Binary logistic regression is a regression used for categorical response variables with two possibilities: success or failure. This regression is a global model, making it inappropriate for spatial data. Binary logistic regression was then developed into geographically weighted logistic regression (GWLR). GWLR considers location factors into the model through a weight function. Nevertheless, GWLR is unable to overcome multicollinearity issue. Multicollinearity can cause the estimated parameters to be insignificant, thus it needs to be solved. A method to deal with multicollinearity is least absolute shrinkage and selection operator (LASSO). LASSO is applicable to various areas, including health, namely in the case of unmet need for family planning (FP). Unmet need for FP refers to productive-age women who do not wish to have more children or wish to postpone having children without using contraceptive methods. This study aims to obtain GWLR model with LASSO and influential factors, and acquire the performance of GWLR model with LASSO on unmet need for FP in South Sulawesi. The AIC value of the GWLR with LASSO model, which is 31,918, is less than the AIC value of the GWLR without LASSO, which is 38,879. This implies that GWLR with LASSO method is able to model unmet need for FP better than GWLR model. In addition, it was obtained that the status of unmet need for FP in 22 districts/cities was affected by the percentage of women with junior high school education or equivalent or lower, number of high-fertility women, percentage of husbands/families who refuse family planning, and number of KB staffs, while there were 2 districts/cities where the status of unmet need for KB was determined by the number of high-fertility women, percentage of husbands/families who refuse family planning, and number of FP staffs.