A Predictive Model for Determinants of Reproductive Behaviour in Şanlıurfa

Öz

Aim: This study aims to predict the determinants of current and future reproductive behaviour in Şanlıurfa. Material and Method: A cross-sectional study was conducted with 385 married women to examine the determinants of reproductive behavior. Data were collected with a survey form and evaluated with descriptive statistics and Structural Equation Model. Results: The mean age was 29.91 ± 7.41, the mean pregnancies number was 4.04 ± 2.35, the mean number children was 3.38 ± 1.87, and the mean ideal children number was 3.59 ± 1.64. 39.2% of the women did not want another child. Women's age (β=0.42), education status of women (β=-0.15), marriage age (β=-0.19), contraceptive failure (β=0.16), and the number of ideal children (β=0.34) were found to be the predictors of the current fertility. The number of ideal children (β=0.59), contraceptive failure (β=-0.14), and the number of children (β=-0.70) are the most important predictors of future fertility (p<0.05). Conclusion: As the number of women age and the ideal children number increases, the education level and marriage age decrease, and the children number increases. As the children number increases, the desire for future fertility decreases. The predictive model can guide the design, implementation, and evaluation of policies and programs for women's health professionals. More research is needed to examine how different factors affect current and future fertility.

Anahtar Kelimeler

• Fertility
• reproductive behavior
• structural equation modelling
• Türkiye

Şanlıurfa’da Üreme Davranışının Belirleyicileri İçin Bir Öngörü Modeli

Öz

Amaç: Bu çalışma Şanlıurfa'da mevcut ve gelecekteki üreme davranışının belirleyicilerini tahmin etmeyi amaçlamaktadır. Gereç ve Yöntem: Üreme davranışının belirleyicilerini incelemek amacıyla 385 evli kadınla kesitsel bir çalışma yapıldı. Veriler anket formu yardımıyla toplanmış, tanımlayıcı istatistikler ve Yapısal Eşitlik Modeli ile değerlendirilmiştir. Bulgular: Ortalama yaş 29.91 ± 7.41, ortalama gebelik sayısı 4.04±2.35, ortalama çocuk sayısı 3.38±1.87 ve ortalama ideal çocuk sayısı 3.59±1.64 olarak belirlendi. Kadınların %39.2'si başka çocuk istemiyordu. Kadınların yaşı (β=0.42), kadınların eğitim durumu (β=-0.15), evlenme yaşı (β=-0.19), kontraseptif başarısızlık (β=0.16) ve ideal çocuk sayısı (β=0.34) mevcut doğurganlığın yordayıcıları olduğu görülürken, ideal çocuk sayısı (β=0.59), kontraseptif başarısızlık (β=-0.14) ve çocuk sayısı (β=-0.70) gelecekteki doğurganlığın en önemli yordayıcılarıdır (p<0.05). Sonuç: Kadın yaşı ve ideal çocuk sayısı arttıkça eğitim düzeyi ve evlenme yaşı azalmakta, çocuk sayısı ise artmaktadır. Çocuk sayısı arttıkça gelecekte doğurgan olma isteği azalmaktadır. Tahmin modeli, kadın sağlığı profesyonellerine yönelik politika ve programların tasarlanmasına, uygulanmasına ve değerlendirilmesine rehberlik edebilir. Farklı faktörlerin mevcut ve gelecekteki doğurganlığı nasıl etkilediğini incelemek için daha fazla araştırmaya ihtiyaç vardır.

Anahtar Kelimeler

• Doğurganlık
• üreme davranışı
• yapısal eşitlik modellemesi
• türkiye

Kaynakça

1. Abdelghany, A., El Abbbasy, A. M., & Shabrawy, A. El. (2020). Structural equations modeling to quantify the effect of direct and intermediate factors on fertility changes in Egypt during 2000–2014. Eastern Mediterranean Health Journal, 26(10), 1210–1217. https://doi.org/10.26719/emhj.20.098
2. Akintayo, A. O., Sulaimon, M. A., Akinwale, L. O., & Statistics, M. (2021). Structural equation modeling of factors influencing fertility among married women of reproductive age in Kaduna State, Nigeria. International Journal of Innovative Science and Research Technology, 6(5), 146–153.
3. Anser, M. K., Yousaf, Z., Khan, M. A., Voo, X. H., Nassani, A. A., Alotaibi, S. M., … & Zaman, K. (2020). The impacts of COVID-19 measures on global environment and fertility rate: double coincidence. Air Quality, Atmosphere, & Health, 13(9), 1083–1092. https://doi.org/10.1007/S11869-020-00865-Z
4. Bashir, S., & Guzzo, K. (2021). Women’s education, spousal agreement on future fertility intentions, and contraceptive use in Pakistan. Studies in Family Planning, 52(3), 281–298. https://doi.org/10.1111/SIFP.12167
5. Bayram, N. (2013). Introduction to Structural Equation Modeling AMOS Applications (2. ed.). Ezgi Kitapevi. Beaujouan, E., & Berghammer, C. (2019). The Gap between lifetime fertility intentions and completed fertility in Europe and the United States: A Cohort Approach. Population Research and Policy Review, 38(4), 507–535. https://doi.org/10.1007/s11113-019-09516-3
6. Bongaarts, J., & Casterline, J. B. (2018). From fertility preferences to reproductive outcomes in the developing world. Population and Development Review, 44(4), 793–809. https://doi.org/10.1111/padr.12197
7. Eroğlu, K., Koruk, F., Koruk, İ., Çelik, K., Güner, P., & Kiliçli, A. (2021). Women’s reproductive behaviour and perspectives on fertility, and their modifying factors, in a Turkish province with a high fertility rate. The European Journal of Contraception & Reproductive Health Care, 26(2), 139–147. https://doi.org/10.1080/13625187.2020.1857355
8. Eser, A., Kaygusuz, İ., Namli Kalem, M., & Canbal, M. (2016). Vücut kitle indeksinin doğurganlık, gebelik ve doğum üzerine etkileri. Jinekoloji-Obstetrik ve Neonatoloji Tıp Dergisi, 13(4), 170–175. Eurostat. (2021). Total fertility rate. https://ec.europa.eu/eurostat/databrowser/view/tps00199/default/table?lang=en

9. Götmark, F., & Andersson, M. (2020). Human fertility in relation to education, economy, religion, contraception, and family planning programs. BMC Public Health, 20(1), 1–17. https://doi.org/10.1186/s12889-020-8331-7
10. Gürbüz, S. (2019). Structural equation modeling with AMOS. Seçkin Publishing.
11. Hassneen, E., El-Abbasi, A. H., Khalifa, M., & Shoaeb, F. (2019). Using a two-level structural equation model to study the determinants of reproductive behaviour in Giza Governorate. Egyptian Informatics Journal, 20(2), 143–150. https://doi.org/10.1016/j.eij.2019.02.001
12. HUIPS. (2019). 2018 Turkey Demographic and Health Survey. In Hacettepe University Institute of Population Studies. Elma Technical Printing Ltd.
13. Islam, A., Hossain, T., Sarwar, G., Alahi Kawsar, L., Akter Smrity, L., Ul Alam, A., … & Bhuia, M. R. (2016). Structural Equation modeling to assess the impact of socio-demographic variables on fertility of ethnic Manipuri women. Biometrics & Biostatistics International Journal, 3(5), 167–172. https://doi.org/10.15406/bbij.2016.03.00078
14. Kline, R. R. B. (2011). Principles and practice of structural equation modeling (3rd ed, Vol. 1, Issue). The Guilford Press. https://doi.org/10.1017/CBO9781107415324.004
15. Lai, S. L. (2021). Fertility differentials in Bangladesh and Pakistan: evidence from demographic and health surveys. Asian Population Studies. https://doi.org/10.1080/17441730.2021.1986254
16. Lee, A. S. D., & Burke, A. E. (2019). Integration of a comprehensive contraception education program into clinical practice in a family planning clinic. Nursing for Women’s Health, 23(5), 414–423. https://doi.org/10.1016/J.NWH.2019.07.007
17. Målqvist, M., Hultstrand, J., Larsson, M., & KC, A. (2018). High levels of unmet need for family planning in Nepal. Sexual & Reproductive Healthcare, 17, 1–6. https://doi.org/10.1016/J.SRHC.2018.04.012
18. Matysiak, A., Sobotka, T., & Vignoli, D. (2021). The great recession and fertility in Europe: a sub-national analysis. European Journal of Population, 37(1), 29–64. https://doi.org/10.1007/s10680-020-09556-y
19. Nitsche, N. & Hayford, S. R. (2020). Preferences, partners, and parenthood: linking early fertility desires, marriage timing, and achieved fertility. Demography, 57(6), 1975–2001. https://doi.org/10.1007/s13524-020-00927-y Özdamar, K. (2017). Ölçek ve test geliştirme yapısal eşitlik modellemesi IBM SPSS, IBM SPSS AMOS ve MINTAB uygulamalı. Eskişehir: Nisan Kitabevi, 78-79.
20. The World Bank. (2019). Fertility rate, total (births per woman) - United States, United Kingdom. https://data.worldbank.org/indicator/SP.DYN.TFRT.IN?locations=US-GB. Erişim Tarihi:20.06.21.
21. TUIK. (2020a). Birth Statistics, 2019. https://data.tuik.gov.tr/Bulten/Index?p=Dogum-Istatistikleri-2019-33706. Erişim Tarihi:10.05.21.
22. TUIK. (2020b). World Population Day, 2020. https://data.tuik.gov.tr/Bulten/Index?p=Dunya-Nufus-Gunu-2020-33707. Erişim Tarihi:21.06.21.
23. TUIK. (2021). Birth Statistics, 2020. https://data.tuik.gov.tr/Bulten/Index?p=Dogum-Istatistikleri-2020-37229&dil=1 Erişim Tarihi:20.06.21.
24. UNDESA. (2021). The impact of the COVID-19 pandemic on fertility Ten key messages. https://www.un.org/development/desa/pd/. Erişim Tarihi:05.07.21.
25. United Nations Department of Economic and Social Affairs. (2020). World Fertility and Family Planning 2020. www.unpopulation.org. Erişim Tarihi:20.06.21.
26. Vander Borght, M., & Wyns, C. (2018). Fertility and infertility: Definition and epidemiology. Clinical Biochemistry, 62, 2–10. https://doi.org/10.1016/j.clinbiochem.2018.03.012
27. Woldegiorgis, M. A., Meyer, D., Hiller, J., Mekonnen, W., & Bhowmik, J. (2018). interrelationships among key reproductive health indicators in Sub-Saharan Africa. BioRxiv, 430207. https://doi.org/10.1101/430207