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Research Article

Factors Associated with Women Fertility in Bangladesh: Application on Count Regression Models

Author(s): Iqramul Haq, Md. Ismail Hossain, Ahmed Abdus Saleh Saleheen, Md. Iqbal Hossain Nayan, Tanjina Afrin and Ashis Talukder*

Volume 19, Issue 2, 2023

Published on: 13 July, 2022

Article ID: e210322202477 Pages: 12

DOI: 10.2174/1573404818666220321143010

Price: $65

Abstract

Background: The current total fertility rate in Bangladesh is now 2.3 births per woman, which is still above the replacement level of 2.1.

Objectives: The main objective of this study was to identify potential factors associated with fertility transition in Bangladesh.

Methods: This study applied several regression models to find the best-fitted model to determine factors associated with the number of children ever-born in Bangladesh and utilize data from the 2019 Bangladesh Multiple Indicator Cluster Survey.

Results: Based on the principles of the AIC, BIC, and Vuong tests, the best-fit model was the Hurdle- Poisson regression model compared to other models. Findings based on the Hurdle Poisson regression result revealed that the number of children increases with the increase of women’s age, but the number of children declines if the education status of women as well as their delayed marriage increases. Women who had secondary or higher education were less likely to have children than illiterate women. Similarly, division, residential area, wealth index, women’s functional difficulties, prenatal care, and migration have significantly influenced the number of children ever born.

Conclusion: Based on the findings, the study suggests that fertility can be decreased by improving female education, minimizing early marriage, and eliminating poverty for all ever-married women who were particularly live in rural areas of the Chattogram and Sylhet divisions in Bangladesh. Such steps would be the largest contribution to a future reduction in fertility rates in Bangladesh.

Keywords: Fertility, children ever-born, count regression, disability, early marriage, Bangladesh.

Graphical Abstract

[1]
Salvati, L.; Benassi, F.; Miccoli, S.; Rabiei-Dastjerdi, H.; Mat-thews, S.A. Spatial variability of total fertility rate and crude birth rate in a low-fertility country: Patterns and trends in re-gional and local scale heterogeneity across Italy, 2002-2018. Appl. Geogr., 2020, 124, 102321.
[http://dx.doi.org/10.1016/j.apgeog.2020.102321]
[2]
Haq, I. Relationship between age at marriage, education and fertility among residence of Bangladesh. Am. J. Soc. Sci. Res., 2018, 4(2), 33-39.
[3]
United Nations Department of Economic and Social Affairs World Fertility and Family Planning 2020; United Nations, 2020.
[4]
Gietel-Basten, S.; Scherbov, S. Exploring the ‘true value’of replacement rate fertility. Popul. Res. Policy Rev., 2020, 39(4), 763-772.
[http://dx.doi.org/10.1007/s11113-019-09561-y]
[5]
Islam, R.; Islam, N.; Rahman, M.; Hossain, G.; Islam, R. Fer-tility situation in Bangladesh: Application of revised Bon-gaarts model. Sci Technol., 2015, 5(2), 33-38.
[http://dx.doi.org/10.5923/j.scit.20150502.03]
[6]
Haq, I.; Alam, M.; Methun, I.H. Contributions of proximate determinants to fertility transition in Bangladesh: An analysis of Bongaarts’ fertility model. Int. J. Travel Med. Glob. Health, 2019, 7(1), 23-32.
[http://dx.doi.org/10.15171/ijtmgh.2019.06]
[7]
National Institute of Population Research and Training (NIPORT), Mitra and Associates, ICF International. In: Bangladesh Demographic and Health Survey 2014. Dhaka, Bangladesh, and Rockville; NIPORT, Mitra and Associates, and ICF International: Maryland, USA , 2016.
[8]
Islam, M.M. Rapid fertility decline in Oman: Understanding the role of proximate determinants. Middle East Fertil. Soc. J., 2017, 22(4), 275-284.
[http://dx.doi.org/10.1016/j.mefs.2017.04.007]
[9]
Lai, S.L.; Tey, N.P. Socio-economic and proximate determi-nants of fertility in the Philippines. World Appl. Sci. J., 2014, 31(10), 1828-1836.
[http://dx.doi.org/10.5829/idosi.wasj.2014.31.10.591]
[10]
Dyson, T. The role of the demographic transition in the pro-cess of urbanization. Popul. Dev. Rev., 2011, 37(Suppl. 1), 34-54.
[http://dx.doi.org/10.1111/j.1728-4457.2011.00377.x] [PMID: 21280364]
[11]
Bilsborrow, R.E. Migration, Urbanization, and Development: New Directions and Issues. New York; United Nations Popula-tion Fund: Norwell, Mass., USA, 1998.
[http://dx.doi.org/10.1007/978-94-011-4852-8]
[12]
Jedwab, R.; Christiaensen, L.; Gindelsky, M. Demography, urbanization and development: Rural push, urban pull and urban push? J. Urban Econ., 2017, 98, 6-16.
[http://dx.doi.org/10.1016/j.jue.2015.09.002]
[13]
Jiang, L.; O’Neill, B.C. Determinants of urban growth during demographic and mobility transitions: Evidence from India, Mexico, and the US. Popul. Dev. Rev., 2018, 44(2), 363-389.
[http://dx.doi.org/10.1111/padr.12150]
[14]
Hasan, M.M.; Islam, M.; Sakib, S.; Haq, I. Influences of proximate determinates on fertility among urban and rural women in Bangladesh. Dhaka Univ. J. Sci., 2018, 66(1), 49-54.
[http://dx.doi.org/10.3329/dujs.v66i1.54544]
[15]
Uddin, M.S.; Azad, M.A.; Kibria, M.G. Factors divide fertility between east and west areas of Bangladesh: Implications for further strengthening the family planning program. Bangladesh J. Sci. Res., 2013, 26(1-2), 37-46.
[http://dx.doi.org/10.3329/bjsr.v26i1-2.20229]
[16]
Majumder, N.; Ram, F. Explaining the role of proximate de-terminants on fertility decline among poor and non-poor in Asian countries. PLoS One, 2015, 10(2), e0115441.
[http://dx.doi.org/10.1371/journal.pone.0115441] [PMID: 25689843]
[17]
Flückiger, M.; Ludwig, M. Urbanization, fertility and child education in Sub-Saharan Africa. Econ. Lett., 2017, 157, 97-102.
[http://dx.doi.org/10.1016/j.econlet.2017.05.024]
[18]
Islam, M.M.; Al Mamun, A.; Bairagi, R. Fertility and its prox-imate determinants in Bangladesh: Evidence from the 1993/94 demographic and health survey. Asia Pac. Popul. J., 1998, 13(3), 3-22.
[PMID: 12321906]
[19]
Chola, M.; Michelo, C. Proximate determinants of fertility in Zambia: Analysis of the 2007 Zambia demographic and health survey. Int. J. Popul. Res., 2016, 2016, 1-7.
[http://dx.doi.org/10.1155/2016/5236351]
[20]
Laelago, T.; Habtu, Y.; Yohannes, S. Proximate determinants of fertility in Ethiopia; an application of revised Bongaarts model. Reprod. Health, 2019, 16(1), 13.
[http://dx.doi.org/10.1186/s12978-019-0677-x] [PMID: 30717804]
[21]
Rutaremwa, G.; Galande, J.; Nviiri, H.L.; Akiror, E.; Jhamba, T. The contribution of contraception, marriage and postpar-tum insusceptibility to fertility levels in Uganda: An applica-tion of the aggregate fertility model. Fertil. Res. Pract., 2015, 1(1), 16.
[http://dx.doi.org/10.1186/s40738-015-0009-y] [PMID: 28620521]
[22]
Eloundou-Enyegue, P.; Giroux, S.; Tenikue, M. African tran-sitions and fertility inequality: A demographic Kuznets hy-pothesis. Popul. Dev. Rev., 2017, 43, 59-83.
[http://dx.doi.org/10.1111/padr.12034]
[23]
Bangladesh Bureau of Statistics (BBS) and UNICEF Bangladesh. progotir pathey, Bangladesh multiple indicator cluster survey 2019, survey findings report. Dhaka, Bangladesh: Bangladesh bureau of statistics (BBS); 2019.
[24]
Cameron, A.C.; Trivedi, P.K. Regression analysis of count data; Cambridge University press, 2013.
[http://dx.doi.org/10.1017/CBO9781139013567]
[25]
Scott Long, J. Regression models for categorical and limited dependent variables; Sage Publications: Thousand Oaks; Ca-lif, 2011.
[26]
Haq, I.; Uddin, S.G.; Haq Methun, I.; Islam, A.; Bethe, S.; Latif, A.; Alam, M. Impact of proximate determinants on fer-tility transition behind the socio-demographic factors in Bang-ladesh: A hierarchical approach from the national survey. Int. J. Travel Med. Glob. Health, 2019, 7(2), 62-68.
[http://dx.doi.org/10.15171/ijtmgh.2019.14]
[27]
Haque, A.; Hossain, M.T.; Nasser, M. Predicting the number of children ever born using logistic regression model. Biom. Biostat. Int. J., 2015, 2(4), 96-99.
[http://dx.doi.org/10.15406/bbij.2015.02.00034]
[28]
Adhikari, R. Demographic, socio-economic, and cultural fac-tors affecting fertility differentials in Nepal. BMC Pregnancy Childbirth, 2010, 10(1), 19.
[http://dx.doi.org/10.1186/1471-2393-10-19] [PMID: 20426863]
[29]
Dwivedi, V.; Sediadie, T.; Ama, N.O. International science community association factors affecting Children Ever Born (CEB) in Botswana: Application of poisson regression model. Res. J. Mathematical and Statistical Sci., 2016, 4, 1-9.
[30]
Saadati, M. Factors Affecting Children Ever Born for 15–49 year-old women in Semnan using poisson regression. J. Health Syst. Rev., 2015, 11(3), 627-637.
[31]
Mekonnen, W.; Worku, A. Determinants of fertility in rural Ethiopia: The case of butajira demographic surveillance sys-tem (DSS). BMC Public Health, 2011, 11(1), 782.
[http://dx.doi.org/10.1186/1471-2458-11-782] [PMID: 21985493]
[32]
Pandey, R.; Kaur, C. Modelling fertility: An application of count regression models. Zhongguo Renkou Ziyuan Yu Huanjing, 2015, 13(4), 349-357.
[http://dx.doi.org/10.1080/10042857.2015.1111573]
[33]
Ariho, P.; Nzabona, A. Determinants of change in fertility among women in rural areas of Uganda. J. Pregnancy, 2019, 2019, 6429171.
[http://dx.doi.org/10.1155/2019/6429171] [PMID: 31929908]
[34]
Ibeji, J.U.; Zewotir, T.; North, D.; Amusa, L. Modelling fertili-ty levels in Nigeria using generalized poisson regression-based approach. Sci. Am., 2020, 9, e00494.
[http://dx.doi.org/10.1016/j.sciaf.2020.e00494]
[35]
Ahmmed, F.; Nasser, M. Modeling and predicting of children ever born in Bangladesh. International conference on statistical data mining for bioinformatics health agriculture and environment, 2012. Dec 22-24 University of Rajshahi, Bangladesh
[36]
Islam, M.R.; Islam, M.R.; Alam, M.R.; Hossain, M.M. Affect-ing socio-demographic factors on children ever born for women who have experienced domestic violence and women who have not experienced domestic violence in Bangladesh. Am. J. Sociol. Res., 2012, 2(5), 113-119.
[http://dx.doi.org/10.5923/j.sociology.20120205.04]
[37]
Kiser, H.; Hossain, M.A. 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.
[http://dx.doi.org/10.1007/s13755-018-0064-y] [PMID: 30588293]
[38]
Kleiber, C.; Zeileis, A.; Jackman, S. Regression models for count data in R. J. Stat. Softw., 2008, 27(8), 1-25.
[http://dx.doi.org/10.18637/jss.v027.i08]
[39]
Fenta, S.M.; Fenta, H.M. Risk factors of child mortality in Ethiopia: Application of multilevel two-part model. PLoS One, 2020, 15(8), e0237640.
[http://dx.doi.org/10.1371/journal.pone.0237640] [PMID: 32804942]
[40]
Yang, S.; Harlow, L.L.; Puggioni, G.; Redding, C.A. A com-parison of different methods of zero-inflated data analysis and an application in health surveys. J. Mod. Appl. Stat. Methods, 2017, 16(1), 518-543.
[http://dx.doi.org/10.22237/jmasm/1493598600]
[41]
Akaike, H. Akaike’s Information Criterion; International En-cyclopedia of Statistical Science, 2011, pp. 25-5.
[http://dx.doi.org/10.1007/978-3-642-04898-2_110]
[42]
Schwarz, G. Estimating the dimension of a model. Ann. Stat., 1978, 6(2), 461-464.
[http://dx.doi.org/10.1214/aos/1176344136]
[43]
Vuong, Q.H. Likelihood ratio tests for model selection and non-nested hypotheses. Econometrica, 1989, 57(2), 307.
[http://dx.doi.org/10.2307/1912557]
[44]
Desmarais, B.A.; Harden, J.J. Testing for zero inflation in count models: Bias correction for the vuong test. Stata J., 2013, 13(4), 810-835.
[http://dx.doi.org/10.1177/1536867X1301300408]
[45]
Hossain, M.M.; Majumder, A.K. Determinants of the age of mother at first birth in Bangladesh: Quantile regression ap-proach. J. Public Health (Bangkok), 2019, 27(4), 419-424.
[http://dx.doi.org/10.1007/s10389-018-0977-6]
[46]
Ghasemi, A.; Zahediasl, S. Normality tests for statistical anal-ysis: A guide for non-statisticians. Int. J. Endocrinol. Metab., 2012, 10(2), 486-489.
[http://dx.doi.org/10.5812/ijem.3505] [PMID: 23843808]
[47]
Oppong, F.B.; Agbedra, S.Y. Assessing univariate and multi-variate normality. A guide for non-statisticians. Math. Theory Modeling., 2016, 6(2), 26-33.
[48]
Bozdogan, H. Akaike’s information criterion and recent de-velopments in information complexity. J. Math. Psychol., 2000, 44(1), 62-91.
[http://dx.doi.org/10.1006/jmps.1999.1277] [PMID: 10733858]
[49]
Saffar, S.E.; Adnan, R.; Greene, W. Parameter estimation on hurdle Poisson regression model with censored data. J. Teknol., 2012, 57(1), 189-198.
[50]
Rahman, A.; Islam, A.; Yeasmin, S. Influencing factors of fertility in developing countries: Evidence from 16 DHS data. J. Int. Womens Stud., 2020, 21(6), 419-429.
[51]
Chowdhury, M.; Moloy, D.J. A statistical analysis to identify potential factors of fertility in Bangladesh. J. Sci. Technol., 2014, 4(1), 65-78.
[52]
Mandiwa, C.; Namondwe, B.; Makwinja, A.; Zamawe, C. Factors associated with contraceptive use among young wom-en in Malawi: Analysis of the 2015-16 Malawi demographic and health survey data. Contracept. Reprod. Med., 2018, 3(1), 12.
[http://dx.doi.org/10.1186/s40834-018-0065-x] [PMID: 30250748]
[53]
Nahar, M.Z.; Zahangir, M.S. Determinants of fertility in Bang-ladesh: Evidence from the 2014 demographic and health sur-vey. Int. Q. Community Health Educ., 2019, 40(1), 29-38.
[http://dx.doi.org/10.1177/0272684X19857426] [PMID: 31216258]
[54]
Kassa, G.M.; Arowojolu, A.O.; Odukogbe, A.A.; Yalew, A.W. Prevalence and determinants of adolescent pregnancy in Afri-ca: A systematic review and meta-analysis. Reprod. Health, 2018, 15(1), 195.
[http://dx.doi.org/10.1186/s12978-018-0640-2] [PMID: 30497509]
[55]
Mekonnen, W.; Worku, A. Levels and proximate determi-nants of fertility in Butajira district, South Central Ethiopia. Ethiop. J. Health Dev., 2011, 25(3), 184-191.
[56]
Ewemooje, O.S.; Biney, E.; Amoateng, A.Y. Determinants of fertility intentions among women of reproductive age in South Africa: Evidence from the 2016 demographic and health sur-vey. J. Popul. Res. (Canberra), 2020, 37(3), 265-289.
[http://dx.doi.org/10.1007/s12546-020-09246-w]
[57]
Muche, S.M.; Gebremichael, S.G. Determinants of high fertili-ty rate among married women in Ethiopia; Res. Sq, 2020.
[http://dx.doi.org/10.21203/rs.2.21834/v1]
[58]
Asamoah, B.O.; Agardh, A.; Östergren, P.O. Inequality in fertility rate and modern contraceptive use among Ghanaian women from 1988-2008. Int. J. Equity Health, 2013, 12(1), 37.
[http://dx.doi.org/10.1186/1475-9276-12-37] [PMID: 23718745]
[59]
Sayem, A.M.; Nury, A.T. Factors associated with teenage marital pregnancy among Bangladeshi women. Reprod. Health, 2011, 8(1), 16.
[http://dx.doi.org/10.1186/1742-4755-8-16] [PMID: 21599904]
[60]
Kim, J. Female education and its impact on fertility. IZA World Labor, 2016, 228.
[http://dx.doi.org/10.15185/izawol.228]
[61]
Girma, S.; Paton, D. Is education the best contraception: The case of teenage pregnancy in England? Soc. Sci. Med., 2015, 131, 1-9.
[http://dx.doi.org/10.1016/j.socscimed.2015.02.040] [PMID: 25748109]
[62]
Urale, P.W.; O’Brien, M.A.; Fouché, C.B. The relationship between ethnicity and fertility in New Zealand. Kotuitui, 2019, 14(1), 80-94.
[http://dx.doi.org/10.1080/1177083X.2018.1534746]
[63]
Wusu, O.; Isiugo-Abanihe, U.C. Consistency of the effects of female education on fertility across the North-South demo-graphic divide in Nigeria, 2003-2013. J. Biosoc. Sci. Suppl., 2019, 51(1), 138-153.
[http://dx.doi.org/10.1017/S0021932018000111] [PMID: 29633675]
[64]
Matovu, J.K.; Makumbi, F.; Wanyenze, R.K.; Serwadda, D. Determinants of fertility desire among married or cohabiting individuals in Rakai, Uganda: A cross-sectional study. Reprod. Health, 2017, 14(1), 2.
[http://dx.doi.org/10.1186/s12978-016-0272-3] [PMID: 28069056]
[65]
Wolde, T.F.; Ayele, A.D.; Takele, W.W. Prelacteal feeding and associated factors among mothers having children less than 24 months of age, in Mettu district, Southwest Ethiopia: A community based cross-sectional study. BMC Res. Notes, 2019, 12(1), 9.
[http://dx.doi.org/10.1186/s13104-019-4044-3] [PMID: 30616665]
[66]
Flacking, R.; Dykes, F. Perceptions and experiences of using a nipple shield among parents and staff - an ethnographic study in neonatal units. BMC Pregnancy Childbirth, 2017, 17(1), 1-8.
[http://dx.doi.org/10.1186/s12884-016-1183-6] [PMID: 28049520]
[67]
O’Connor, M.; McGowan, K.; Jolivet, R.R. An awareness-raising framework for global health networks: Lessons learned from a qualitative case study in respectful maternity care. Reprod. Health, 2019, 16(1), 1-13.
[http://dx.doi.org/10.1186/s12978-018-0662-9] [PMID: 30621726]
[68]
Khanam, M.; Shimul, S.N.; Sarker, A.R. Individual-, house-hold-, and community-level determinants of childhood un-dernutrition in Bangladesh. Health Serv. Res. Manag. Epidemiol., 2019, 6, 2333392819876555.
[http://dx.doi.org/10.1177/2333392819876555] [PMID: 31555719]
[69]
Kim, J. Women’s education and fertility: An analysis of the relationship between education and birth spacing in Indone-sia. Econ. Dev. Cult. Change, 2010, 58(4), 739-774.
[http://dx.doi.org/10.1086/649638]
[70]
Adebowale, S.A.; Palamuleni, M.E. Childbearing dynamics among married women of reproductive age in Nigeria: Re-affirming the role of education. Afr. Popul. Stud., 2014, 27(2), 301-318.
[http://dx.doi.org/10.11564/27-2-476]
[71]
Acharya, D.R.; Bell, J.S.; Simkhada, P.; van Teijlingen, E.R.; Regmi, P.R. Women’s autonomy in household decision-making: A demographic study in Nepal. Reprod. Health, 2010, 7(1), 15.
[http://dx.doi.org/10.1186/1742-4755-7-15] [PMID: 20630107]
[72]
Talukder, A.; Hasan, M.M.; Razu, S.R.; Hossain, Z. Early marriage in Bangladesh: A cross-sectional study exploring the associated factors. J. Int. Womens Stud., 2020, 21(1), 68-78.
[73]
Bezie, M.; Addisu, D. Determinants of early marriage among married women in Injibara town, north West Ethiopia: Com-munity-based cross-sectional study. BMC Womens Health, 2019, 19(1), 134.
[http://dx.doi.org/10.1186/s12905-019-0832-0] [PMID: 31703577]
[74]
Colleran, H.; Snopkowski, K. Variation in wealth and educa-tional drivers of fertility decline across 45 countries. Popul. Ecol., 2018, 60(1), 155-169.
[http://dx.doi.org/10.1007/s10144-018-0626-5]
[75]
Rabbi, A.M. Factors influencing fertility preference of a de-veloping country during demographic transition: Evidence from Bangladesh. South East Asia J. Public Health, 2014, 4(2), 23-30.
[http://dx.doi.org/10.3329/seajph.v4i2.23691]
[76]
Adebowale, S.A.; Adedini, S.A.; Ibisomi, L.D.; Palamuleni, M.E. Differential effect of wealth quintile on modern contra-ceptive use and fertility: Evidence from Malawian women. BMC Womens Health, 2014, 14(1), 40.
[http://dx.doi.org/10.1186/1472-6874-14-40] [PMID: 24602452]
[77]
Palamuleni, M.E. Determinants of fertility decline in Namibia: An analysis of the proximate determinants. Bangladesh E-J. Sociol., 2017, 14, 41-63.
[78]
Fazle Rabbi, A.M. Mass media exposure and its impact on fertility: Current scenario of Bangladesh. J. Sci. Res., 2012, 4(2), 383.
[http://dx.doi.org/10.3329/jsr.v4i2.8917]
[79]
Islam, M.; Sultana, N. Risk factors for pregnancy related complications among urban slum and non-slum women in Bangladesh. BMC Pregnancy Childbirth, 2019, 19(1), 235.
[http://dx.doi.org/10.1186/s12884-019-2392-6] [PMID: 31286898]
[80]
Oberg, A.S.; VanderWeele, T.J.; Almqvist, C.; Hernandez-Diaz, S. Pregnancy complications following fertility treatment-disentangling the role of multiple gestation. Int. J. Epidemiol., 2018, 47(4), 1333-1342.
[http://dx.doi.org/10.1093/ije/dyy103] [PMID: 29939263]
[81]
Shi, R.; Hu, B.; Ning, W. Assessing the impact of C-section at first birth on the second birth. China Popul Dev Stud., 2020, 3(3), 252-268.
[http://dx.doi.org/10.1007/s42379-020-00043-9]
[82]
Vilanova, C.S.; Hirakata, V.N.; de Souza Buriol, V.C.; Nunes, M.; Goldani, M.Z.; da Silva, C.H. The relationship between the different low birth weight strata of newborns with infant mortality and the influence of the main health determinants in the extreme south of Brazil. Popul. Health Metr., 2019, 17(1), 15.
[http://dx.doi.org/10.1186/s12963-019-0195-7] [PMID: 31775758]
[83]
Banougnin, B.H.; Adekunle, A.O.; Oladokun, A.; Sanni, M.A. Impact of internal migration on fertility in Cotonou, Benin Republic. Afr. Popul. Stud., 2018, 32(2)
[http://dx.doi.org/10.11564/32-2-1212]

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