Abstract
BACKGROUND: Abortions performed unsafely, unintended births, and missed and unwanted pregnancies are linked to discontinuation of contraception for reasons other than wanting to become pregnant, and these situations raise the risk of maternal morbidity and death. However, a study on the determination of factors contributing to contraceptive discontinuation in Tanzania is limited. Therefore, we aimed to investigate the prevalence and determinants of contraceptive discontinuation among reproductive-age women in Tanzania using recent Tanzania Demographic Health Surveys. METHOD: A cross-sectional study was conducted using secondary data analysis from of 2022 Tanzania Demographic Health Survey (DHS). A total weighted sample of 6,467 reproductive-age women were included. To account for the clustering effects of DHS data and the binary nature of the outcome variable, a multilevel binary logistic regression model was applied. An adjusted odds ratio with a 95% confidence interval was reported to declare the statistical significance. In addition, the model that had the lowest deviance was the one that best fit the data. RESULT: The prevalence of discontinuation for all contraceptive methods among reproductive-age women was 34% (95% CI: 31.3%, 34.7%). Women who age group of 20-29 years (AOR = 4.45, 95% CI: 1.11, 17.78), women with no formal education (AOR = 1.94, 95% CI: 1.71, 2.93), women having no children (AOR = 4.25, 95% CI: 3.47, 8.06) women who want another child (AOR = 1.89, 95% CI: 1.76, 3.46), distance to the health facility as a big problem (AOR = 2.43, 95% CI: 1.38, 4.26), rural residence (AOR = 1.67, 95% CI: 1.48, 3.23) Were factors strongly associated with contraceptives discontinuation. CONCLUSION: Among women of reproductive age, the withdrawal of contraception was 34% and it was determined by several factors. Providing a variety of contraceptive techniques and spreading knowledge about family planning are the primary goals of contraceptive counseling. It is also suggested by prospective investigators to use primary data to address independent factors that were missing.