Original Articles

Multiple Logistic Regression Model for Determinants of Injectable Contraceptive Uptake Among Women of Reproductive Age in Kenya

Abstract

Objective: The recent increase in the uptake of injectable contraceptives has occurred at the expense of the other modern contraceptive methods but the knowledge gap still exists on modeling dynamics and determinants associated with the use of the injectable. This study sought to model for injectable contraceptive usage to bridge the knowledge gap on the use of injectable contraceptives among women of childbearing age in Kenya.
Materials and methods: Analytical cross-sectional study design was adopted. Secondary data for women collected during the (Performance Monitoring for Action) PMA2020 survey was used. PMA2020 survey used multistage stratified sampling with urban-rural representation. To establish the factors associated with the uptake of injectable contraceptives, a multiple logistic regression model was fitted using Stata version 13 and R version 3.5.3 statistical software. Hosmer-Lemeshow Test statistic was used to evaluate the goodness of model fit in predicting injectable contraceptive usage.
Results: Multivariable analysis showed that women with post-primary/vocational levels of education were 54% less likely to use an injectable contraceptive compared to those who had no education at all. Hosmer-Lemeshow (HL) goodness of fit test statistic indicated that the model was a good fit for prediction. Education, marital status, wealth quintile, place of residence and number of births were significant predictors of the injectable contraceptive uptake among women of reproductive age in Kenya.
Conclusion: The findings of this study will inform the design of targeted interventions aimed at addressing the increasing demand for injectable devices among women of reproductive age in Kenya.

1. Izugbara C, Wekesah F, Tilahun T, Amo-adjei J, Dimbuene ZT, Family Planning in East Africa: Trends and Dynamics. African Population and Health 2018.
2. Ahmed S, Choi Y, Rimon JG, Alzouma S, Gichangi P, Guiella G, et al. Trends in contraceptive prevalence rates in sub-Saharan Africa since the 2012 London Summit on Family Planning: results from repeated cross-sectional surveys. Lancet Glob Heal 2019; 7: e904–11.
3. Gonsalves L, Wyss K, Cresswell JA, Waithaka M, Gichangi P, Hilber AM. Mixed methods study on pharmacies as contraception providers to Kenyan young people: who uses them and why? BMJ Open 2020; 10: e034769.
4. Tsui AO, Brown W, Li Q. Contraceptive Practice in Sub-Saharan Africa. Popul Dev Rev 2017; 43: 166–91.
5. Goldstone P, Mehta YH, McGeechan K, Francis K, Black KI. Factors predicting uptake of long-acting reversible methods of contraception among women presenting for abortion. Med J Aust 2014; 201: 412–6.
6. Anthony Makau, Anthony G. Waititu, Joseph K, Mung’atu JK. Multinomial Logistic Regression for Modeling Contraceptive Use Among Women of Reproductive Age in Kenya, American Journal of Theoretical and Applied Statistics 2016; 5: 242-51.
7. Ross JA, Agwanda AT. Increased use of injectable contraception in sub-Saharan Africa. Afr J Reprod Health 2012; 16: 68-80.
8. Sebuhoro D, Mung,atu JK, Ndengo M. Modelling Determinants of Choice of Contraceptive Methods. International Journal of Mathematics and Physical Sciences Research 2016; 4: 7-21.
9. Zimmerman L, Olson H, Tsui A, Radloff S. Rapid Turn-Around Survey Data to Monitor Family Planning Service and Practice in Ten Countries. Stud Fam Plann 2017; 48: 293–303.
10. Workie DL, Zike DT, Fenta HM, Mekonnen MA. A binary logistic regression model with complex sampling design of unmet need for family planning among all women aged (15-49) in Ethiopia. Afr Health Sci 2017; 17: 637-46.
11. Nattino G, Pennell ML, Lemeshow S. Assessing the Goodness of Fit of Logistic Regression Models in Large Samples: A Modification of the Hosmer-Lemeshow Test. Biometrics 2020; 76: 549-60.
12. Adetunji JA. Rising popularity of injectable contraceptives in sub-Saharan Africa. African Population Studies 2011; 25: 587-604.
13. Morroni C, Myer L, Moss M, Hoffman M. Preferences between injectable contraceptive methods among South African women. Contraception 2006; 73: 598–601.
14. Greiw BK, Nossier SA, Kharbiush IF. Decision-Making for Use of Injectable Contraceptives in Alexandria, Egypt: a Com-Parative Study. Misurata Medical Science Journal 2015; 1: 63–9.
15. 15. Baiden F, Mensah GP, Akoto NO, Delvaux T, Appiah PC. Covert contraceptive use among women attending a reproductive health clinic in a municipality in Ghana. BMC Womens Health 2016; 16: 31.
16. Prata N, Bell S, Fraser A, Carvalho A, Neves I, Nieto-Andrade B. Partner support for family planning and modern contraceptive use in Luanda, Angola. Afr
J Reprod Health 2017; 21:35–48.
17. Bakesiima R, Cleeve A, Larsson E, Tumwine JK, Ndeezi G, Danielsson KG, et al. Modern contraceptive use among female refugee adolescents in northern Uganda: Prevalence and associated factors. Reprod Health 2020; 17: 1–9.
18. Bursac Z, Gauss CH, Williams DK, Hosmer DW. Purposeful selection of variables in logistic regression. Source Code for Biology and Medicine 2008; 3:17.
19. Zhang Z. Model building strategy for logistic regression: Purposeful selection. Annals of Translational Medicine 2016; 4:111.
Files
IssueVol 15, No 2 (June 2021) QRcode
SectionOriginal Articles
DOI https://doi.org/10.18502/jfrh.v15i2.6448
Keywords
Contraceptive Devices Reproduction Kenya

Rights and permissions
Creative Commons License This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
How to Cite
1.
Kirui E, Mung’atu J, Gichangi P, Onguto N, Kamondo D. Multiple Logistic Regression Model for Determinants of Injectable Contraceptive Uptake Among Women of Reproductive Age in Kenya. J Family Reprod Health. 2021;15(2):82-90.