دورية أكاديمية
Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi.
العنوان: | Comparison of univariate and bivariate Poisson regression methods in the analysis of determinants of female schooling and fertility in Malawi. |
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المؤلفون: | Mponda E; Department of Mathematical Sciences, School of Natural and Applied Sciences, University of Malawi, Zomba, Malawi. enelessmponda@gmail.com., Kaombe TM; Department of Mathematical Sciences, School of Natural and Applied Sciences, University of Malawi, Zomba, Malawi. |
المصدر: | BMC public health [BMC Public Health] 2024 Aug 22; Vol. 24 (1), pp. 2285. Date of Electronic Publication: 2024 Aug 22. |
نوع المنشور: | Journal Article; Comparative Study |
اللغة: | English |
بيانات الدورية: | Publisher: BioMed Central Country of Publication: England NLM ID: 100968562 Publication Model: Electronic Cited Medium: Internet ISSN: 1471-2458 (Electronic) Linking ISSN: 14712458 NLM ISO Abbreviation: BMC Public Health Subsets: MEDLINE |
أسماء مطبوعة: | Original Publication: London : BioMed Central, [2001- |
مواضيع طبية MeSH: | Educational Status*, Humans ; Malawi ; Female ; Adult ; Poisson Distribution ; Young Adult ; Adolescent ; Middle Aged ; Fertility ; Health Surveys ; Regression Analysis |
مستخلص: | Recent research has established existence of a correlation between women's education and fertility, suggesting that they share similar risk factors. However, in many studies, the two variables were analysed separately, which could bias the conclusions by undermining the apparent correlations of such paired outcomes. In this article, the univariate and bivariate Poisson regression models were applied to nationally representative sample of 24,562 women from the 2015-16 Malawi demographic and health survey to examine the risk factors of women's education levels and fertility. The R software version 4.1.2 was used for the analyses. The results showed that estimates from the bivariate Poisson model were consistent with those obtained from the separate univariate Poisson models. The sizes of estimates of coefficients, their standard errors, p-values, and directions were comparable in both bivariate and univariate Poisson models. Using either the univariate or bivariate Poisson model, it was found that the age of a woman at first sexual experience, her current age, household wealth index, and contraceptive usage were significantly associated with both the woman's schooling and fertility. The study further revealed that ethnicity, religion, and region of residence impacted education level only and not fertility. Similarly, marital status and occupation impacted fertility only and not education. The study also found that higher education levels were linked to a lower number of children, with a strong negative correlation of -0.62 between the two variables. The study recommends using bivariate Poisson regression for analysing paired count response data, when there is an apparent covariance between the outcome variables. The results suggest that efforts by policymakers to achieve the desired women's sexual and reproductive health in sub-Saharan Africa should be intertwined with improving women's and girls' education attainment in the region. (© 2024. The Author(s).) |
References: | Forty J, Navaneetham K, Letamo G. Determinants of fertility in Malawi: Does women autonomy dimension matter? BMC Womens Health. 2022;22(1):1–16. (PMID: 10.1186/s12905-022-01926-4) Berlie AB, Alamerew YT. Determinants of fertility rate among reproductive age women (15–49) in Gonji-Kollela District of the Amhara National Regional State. Ethiopia Ethiop J Health Dev. 2018;32(3):144–55. Bongaarts J. Trends in fertility and fertility preferences in sub-Saharan Africa: the roles of education and family planning programs. Genus. 2020;76(1):1–15. (PMID: 10.1186/s41118-020-00098-z) Skakkebaek NE, Lindahl-Jacobsen R, Levine H, Andersson AM, Jørgensen N, Main KM, et al. Environmental factors in declining human fertility. Nat Rev Endocrinol. 2022;18(3):139–57. (PMID: 3491207810.1038/s41574-021-00598-8) Tesfa D, Tiruneh SA, Gebremariam AD, Azanaw MM, Engidaw MT, Kefale B, et al. The pooled estimate of the total fertility rate in sub-Saharan Africa using recent (2010–2018) Demographic and Health Survey data. Front Public Health. 2023;10:1053302. (PMID: 36777768990940210.3389/fpubh.2022.1053302) Al-Balushi MS, Ahmed M, Islam MM, Khan MHR. Multilevel poisson regression modeling to identify factors influencing the number of children ever born to married women in Oman. J Stat Manag Syst. 2020;23(8):1357–73. Boateng D, Oppong FB, Senkyire EK, Logo DD. Socioeconomic factors associated with the number of children ever born by married Ghanaian females: a cross-sectional analysis. BMJ Open. 2023;13(2):e067348. (PMID: 36754565992326310.1136/bmjopen-2022-067348) Dwivedi VK, Sediadie T, Ama N. Factors affecting children ever born (CEB) in Botswana: application of Poisson regression model. Res J Math Stat Sci. 2016;4(10):1–9. Haque A, Hossain T, Nasser M. Predicting the number of children ever born using logistic regression model. Biom Biostat Int J. 2015;2(4):00034. Munakampe MN, Fwemba I, Zulu JM, Michelo C. Association between socioeconomic status and fertility among adolescents aged 15 to 19: an analysis of the 2013/2014 Zambia Demographic Health Survey (ZDHS). Reprod Health. 2021;18:1–11. (PMID: 10.1186/s12978-021-01230-8) McTavish S, Moore S, Harper S, Lynch J. National female literacy, individual socio-economic status, and maternal health care use in sub-Saharan Africa. Soc Sci Med. 2010;71(11):1958–63. (PMID: 2098008910.1016/j.socscimed.2010.09.007) Assari S. Understanding America: Unequal economic returns of years of schooling in whites and blacks. World J Educ Res (Los Angeles, Calif). 2020;7(2):78. (PMID: 731438410.22158/wjer.v7n2p78) Ahsan H, Haque ME. Threshold effects of human capital: Schooling and economic growth. Econ Lett. 2017;156:48–52. (PMID: 10.1016/j.econlet.2017.04.014) McAlister C, Baskett TF. Female education and maternal mortality: a worldwide survey. J Obstet Gynaecol Canada. 2006;28(11):983–90. (PMID: 10.1016/S1701-2163(16)32294-0) Ahmed S. Socioeconomic determinants of female education in a Muslim family: an econometric analysis. Indian Econ J. 2007;54(4):140–52. (PMID: 10.1177/0019466220070409) Hashmi N, Zafar MI, Ahmad M. Cultural determinants of female educational attainment in rural Jhang, Punjab, Pakistan. Pak J Agric Sci. 2008;45(1):45–51. Kaombe TM. A bivariate Poisson regression to analyse impact of outlier women on correlation between female schooling and fertility in Malawi. BMC Womens Health. 2024;24(1):55. (PMID: 382457361079944810.1186/s12905-024-02891-w) Akeju KF, et al. Fertility Preference in Older Women: Effect of Place of Residence and Use of Contraceptives in Nigeria. Int J Soc Sci Perspect. 2021;8(1):25–30. Kiser H, Hossain MA. Estimation of number of ever born children using zero truncated count model: evidence from Bangladesh Demographic and Health Survey. Health Inf Sci Syst. 2018;7(1):3. (PMID: 30588293628153910.1007/s13755-018-0064-y) Machira K, Palamuleni M. Fertility differentials in Malawi: any lesson learnt from regional socio-economic and demographic variations? Fertility differences in Malawi. J Hum Ecol. 2017;58(1–2):88–97. (PMID: 10.1080/09709274.2017.1305607) Ntoimo LFC. Family size preferences among women in a union in Nigeria and associated factors. Int J Popul Stud. 2022;7(1):51–65. (PMID: 10.18063/ijps.v7i1.1318) Ali FRM, Gurmu S. The impact of female education on fertility: A natural experiment from Egypt. Rev Econ Househ. 2018;16:681–712. (PMID: 10.1007/s11150-016-9357-6) Cheng H, Luo W, Si S, Xin X, Peng Z, Zhou H, et al. Global trends in total fertility rate and its relation to national wealth, life expectancy and female education. BMC Public Health. 2022;22(1):1–13. (PMID: 10.1186/s12889-022-13656-1) Islam MA, Chowdhury RI. A generalized right truncated bivariate Poisson regression model with applications to health data. PLoS ONE. 2017;12(6):e0178153. (PMID: 28586344546082310.1371/journal.pone.0178153) Zamani H, Faroughi P, Ismail N. Bivariate generalized Poisson regression model: applications on health care data. Empir Econ. 2016;51:1607–21. (PMID: 10.1007/s00181-015-1051-7) National Statistical Office (NSO) [Malawi] and ICF. 2015-16 Malawi Demographic and Health Survey: Key Findings. Zomba, Malawi, and Rockville, Maryland, USA NSO and ICF: Author. 2017. Al-Riyami AA, Afifi M. Determinants of women’s fertility in Oman. Saudi Med J. 2003;24(7):748–53. (PMID: 12883607) Karlis D, Ntzoufras I. Bivariate Poisson and diagonal inflated bivariate Poisson regression models in R. J Stat Softw. 2005;14:1–36. (PMID: 10.18637/jss.v014.i10) Kaombe TM, Banda JC, Hamuza GA, Muula AS. Bivariate logistic regression model diagnostics applied to analysis of outlier cancer patients with comorbid diabetes and hypertension in Malawi. Sci Rep. 2023;13(1):8340. (PMID: 372213051020609810.1038/s41598-023-35475-z) Yee TW, Yee MT, VGAMdata S. Package ‘VGAM’. 2023. Sakate D, Kashid D. A deviance-based criterion for model selection in GLM. Statistics. 2014;48(1):34–48. (PMID: 10.1080/02331888.2012.708035) Famoye F. On the bivariate negative binomial regression model. J Appl Stat. 2010;37(6):969–81. (PMID: 10.1080/02664760902984618) Cherie N, Getacher L, Belay A, Gultie T, Mekuria A, Sileshi S, et al. Modeling on number of children ever born and its determinants among married women of reproductive age in Ethiopia: A Poisson regression analysis. Heliyon. 2023;9(3). Hindin MJ. Women’s autonomy, women’s status and fertility-related behavior in Zimbabwe. Popul Res Policy Rev. 2000;19:255–82. (PMID: 10.1023/A:1026590717779) Saha UR, Bairagi R. Inconsistencies in the relationship between contraceptive use and fertility in Bangladesh. Int Fam Plan Perspect. 2007;1:31–7. (PMID: 10.1363/3303107) Iyanda AE, Dinkins BJ, Osayomi T, Adeusi TJ, Lu Y, Oppong JR. Fertility knowledge, contraceptive use and unintentional pregnancy in 29 African countries: a cross-sectional study. Int J Public Health. 2020;65:445–55. (PMID: 3227023410.1007/s00038-020-01356-9) Bongaarts J. The effect of contraception on fertility: Is sub-Saharan Africa different? Demogr Res. 2017;37:129–46. (PMID: 10.4054/DemRes.2017.37.6) Nayupe SF, Touray A, Tembo D, Msiska MT, Mputeni J, Muhome AH. Addressing the rights and well-being of domestic workers in Africa: Status in Malawi. Public Health Challenges. 2023;2(3):e123. (PMID: 10.1002/puh2.123) Reichenheim ME, Coutinho ES. Measures and models for causal inference in cross-sectional studies: arguments for the appropriateness of the prevalence odds ratio and related logistic regression. BMC Med Res Methodol. 2010;10:1–12. (PMID: 10.1186/1471-2288-10-66) Spector PE. Do not cross me: Optimizing the use of cross-sectional designs. J Bus Psychol. 2019;34(2):125–37. (PMID: 10.1007/s10869-018-09613-8) Ndahindwa V, Kamanzi C, Semakula M, Abalikumwe F, Hedt-Gauthier B, Thomson DR. Determinants of fertility in Rwanda in the context of a fertility transition: a secondary analysis of the 2010 Demographic and Health Survey. Reprod Health. 2014;11:1–9. (PMID: 10.1186/1742-4755-11-87) Baruwa OJ, Amoateng AY, Biney E. Socio-demographic changes in age at first marriage in Malawi: evidence from Malawi Demographic and Health Survey data, 1992–2016. J Biosoc Sci. 2020;52(6):832–45. (PMID: 3185254910.1017/S0021932019000816) Kaombe TM, Manda SO. Identifying Outlying and Influential Clusters in Multivariate Survival Data Models. In: Modern Biostatistical Methods for Evidence-Based Global Health Research. Cham: Springer; 2022. p. 377–410. Kaombe TM, Manda SO. A novel outlier statistic in multivariate survival models and its application to identify unusual under-five mortality sub-districts in Malawi. J Appl Stat. 2023;50(8):1836–52. (PMID: 3726047110.1080/02664763.2022.2043255) Kaombe TM, Manda SO. Detecting influential data in multivariate survival models. Commun Stat Theory Methods. 2023;52(11):3910–26. (PMID: 10.1080/03610926.2021.1982983) Dean C, Nielsen JD. Generalized linear mixed models: a review and some extensions. Lifetime Data Anal. 2007;13:497–512. (PMID: 1800075510.1007/s10985-007-9065-x) Nakagawa S, Schielzeth H. A general and simple method for obtaining R2 from generalized linear mixed-effects models. Methods Ecol Evol. 2013;4(2):133–42. (PMID: 10.1111/j.2041-210x.2012.00261.x) |
فهرسة مساهمة: | Keywords: Bivariate Poisson regression; Determinants; Female education; Fertility; Univariate Poisson regression |
تواريخ الأحداث: | Date Created: 20240822 Date Completed: 20240823 Latest Revision: 20240822 |
رمز التحديث: | 20240823 |
DOI: | 10.1186/s12889-024-19816-9 |
PMID: | 39174971 |
قاعدة البيانات: | MEDLINE |
تدمد: | 1471-2458 |
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DOI: | 10.1186/s12889-024-19816-9 |