Araştırma Makalesi
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SPATIAL INTERACTION OF UNDER-FIVE MORTALITY RATE AMONG PROVINCES IN TURKEY: SPATIAL PROBIT MODELS

Yıl 2023, Sayı: 58, 327 - 342, 08.09.2023
https://doi.org/10.30794/pausbed.1259311

Öz

Despite the rapid development shown in recent years, Turkey still lags behind many countries in terms of under-five mortality rate. As a matter of fact, it is known that there are still great inequalities in child mortality among provinces across the country. It is critical to determine the factors affecting the death rate in order to prevent the related problem. The studies to be carried out in this direction will pave the way for the development of strategies and policies that can be effective in reducing the mortality rate. In this direction, the aim of the study is to analyze the spatial interaction between provinces and the determinants of the under-five mortality rate in Turkey in 2019 by using spatial probit models. After determining the spatial dependence structure in the classical probit model, probit models with spatial delay (Spatial Autoregressive Model, SAR) and spatial error dependence (Spatial Error Model, SEM) have been estimated using the Approximate Likelihood Estimation method. As a result of the estimations, it has been concluded that per capita income, central government health expenditures and primary and secondary education graduation rates have a decreasing effect on under-five mortality rate.

Kaynakça

  • Ağır. H., & Tıraş, H. H., (2018). Türkiye’de sağlık harcama türlerinin değerlendirilmesi. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 15(2), 643-670.
  • Amaral, P. V., Anselin, L., & Arribas-Bel, D. (2013). Testing for spatial error dependence in probit models. Letters in Spatial and Resource Sciences, 6, 91-101.
  • Anselin, L. (1988). Spatial econometrics: methods and models. Springer Science & Business Media.
  • Anyanwu, J. C., & Erhijakpor, A. E. (2009). Health expenditures and health outcomes in Africa. African Development Review, 21(2), 400-433.
  • Atasever, M. (2014). Türkiye sağlık hizmetlerinin finansmanı ve sağlık harcamalarının analizi 2002-2013 dönemi.
  • Baker, D., & Fugh-Berman, A. (2009). Do new drugs increase life expectancy? A critique of a Manhattan Institute paper. Journal of general internal medicine, 24(5), 678-682.
  • Balaj, M., York, H. W., Sripada, K., Besnier, E., Vonen, H. D., Aravkin, A., & Eikemo, T. A. (2021). Parental education and inequalities in child mortality: a global systematic review and meta-analysis. The Lancet, 398(10300), 608-620.
  • Boehmer, U., & Williamson, J. B. (1996). The impact of women’s status on infant mortality rate: A cross-national analysis. Social Indicators Research, 37(3), 333-360.
  • Çevik, S., & Taşar, O., (2013). “Public Spending on Health Care and Health Outcomes: Cross Country Comparison”, Journal of Business, Economics and Finance, Cilt 2(4), 82-100.
  • Darmofal, D. (2015). Spatial analysis for the social sciences. Cambridge University Press.
  • Deaton, A. (2006). Global patterns of income and health: facts, interpretations, and policies.
  • Elhorst, J. P. (2012). Dynamic spatial panels: models, methods, and inferences. Journal of geographical systems, 14(1), 5-28.
  • Eryurt, M. A., & Koç, İ. (2009). Yoksulluk ve çocuk ölümlülüğü: Hanehalkı refah düzeyinin çocuk ölümlülüğü üzerindeki etkisi. Cocuk Sagligi ve Hastaliklari Dergisi, 52(3).
  • Fleming, M. M. (2004). Techniques for estimating spatially dependent discrete choice models. Advances in spatial econometrics: methodology, tools and applications, 145-168.
  • Gordon, R. M. (2009). Socio-economic determinants of infant and child mortality in Haiti. Journal of Eastern Caribbean Studies, 34(1).
  • Iram, U., & Butt, M. S. (2008). Socioeconomic determinants of child mortality in Pakistan: Evidence from sequential probit model. International Journal of Social Economics.
  • Jelamschi, L., & De Ver Dye, T. (2009). Decline in under-5 mortality rate (U5MR) in Turkey: a case study. Ankara: UNICEF Turkey.
  • Kazembe, L. N., Appleton, C. C., & Kleinschmidt, I. (2007). Spatial analysis of the relationship between early childhood mortality and malaria endemicity in Malawi. Geospatial Health, 2(1), 41-50.
  • Kelejian, H. H., & Prucha, I. R. (2001). On the asymptotic distribution of the Moran I test statistic with applications. Journal of Econometrics, 104(2), 219-257.
  • Khan, G. R., Baten, A., & Azad, M. A. K. (2023). Influence of contraceptive use and other socio-demographic factors on under-five child mortality in Bangladesh: semi-parametric and parametric approaches. Contraception and Reproductive Medicine, 8(1), 22.
  • Lesage, J. P., Kelley Pace, R., Lam, N., Campanella, R., & Liu, X. (2011). New Orleans business recovery in the aftermath of Hurricane Katrina. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 1007-1027.
  • Levels & trends in child mortality report, (2021). United Nations Inter-Agency Group for Child Mortality Estimation (UN IGME).
  • Levels & trends in child mortality report, (2022). United Nations Inter-Agency Group for Child Mortality Estimation (UN IGME). Erişim Tarihi: 22.02.2023, https://reliefweb.int/report/world/levels-trends-child-mortality-report-2022.
  • Martinetti, D., & Geniaux, G. (2017). Approximate likelihood estimation of spatial probit models. Regional Science and Urban Economics, 64, 30-45.
  • Martinetti, D., & Geniaux, G. (2022). R Package “ProbitSpatial”.
  • Mendell, N. R., & Elston, R. C. (1974). Multifactorial qualitative traits: genetic analysis and prediction of recurrence risks. Biometrics, 41-57.
  • Morey, E. R., Rowe, R. D., & Watson, M. (1993). A repeated nested‐logit model of Atlantic salmon fishing. American journal of agricultural economics, 75(3), 578-592.
  • Nyamuranga, C., & Shin, J. (2019). Public health expenditure and child mortality in Southern Africa. International Journal of Social Economics.
  • OECD, (2008). OECD Sağlık Sistemi İncelemeleri: Türkiye, OECD and the World Bank.
  • O’Hare, B., Makuta, I., Chiwaula, L., & Bar-Zeev, N. (2013). Income and child mortality in developing countries: a systematic review and meta-analysis. Journal of the Royal Society of Medicine, 106(10), 408-414.
  • Pamuk, E. R., Fuchs, R., & Lutz, W. (2011). Comparing relative effects of education and economic resources on infant mortality in developing countries. Population and development review, 37(4), 637-664.
  • Pinkse, J. (1999). Asymptotics of the Moran test and a test for spatial correlation in Probit models, Working paper, Department of Economics, University of British Columbia, Vancouver.
  • Pinkse, J. (2004). Moran-flavored tests with nuisance parameters: examples. In Advances in Spatial Econometrics (pp. 67-77). Springer, Berlin, Heidelberg.
  • Pinkse, J., & Slade, M. E. (1998). Contracting in space: An application of spatial statistics to discrete-choice models. Journal of Econometrics, 85(1), 125-154.
  • Preston S. (1975). The changing relation between mortality and level of economic development. Population Studies. 29: 231–248.
  • Pritchett, L., & Summers, L. H. (1996). Wealthier is healthier. J Human Resources, 31(4), 841-868.
  • Qu, X., & Lee, L. F. (2012). LM tests for spatial correlation in spatial models with limited dependent variables. Regional Science and Urban Economics, 42(3), 430-445.
  • Smirnov, O. A. (2010). Modeling spatial discrete choice. Regional science and urban economics, 40(5), 292-298.
  • Studenmund, A. H. (2014). Using econometrics a practical guide. Pearson Education Limited.
  • Subaşı Ertekin, M., Yüce Dural, B., & Kırca, M. (2016). Türkiye’de Ekonomik Büyüme ve İşsizliğin Bebek Ölümlerine Etkisi. Gümüshane University Electronic Journal of the Institute of Social Science/Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 7(17).
  • Tibebu, N. S., Emiru, T. D., Tiruneh, C. M., Nigat, A. B., Getu, B. D., & Mekonnen, A. B. (2022). Potential determinant factors of under-five mortality in the Amhara region of Ethiopia. BMC pediatrics, 22(1), 1-7.
  • Todaro, M. P. (1992). “Human Development Report 1992”, Population and Development Review. 359-363.
  • TÜİK. Bölgesel İstatistikler Veri Tabanı. Erişim Tarihi: 25.01.2023, https://biruni.tuik.gov.tr/bolgeselistatistik/sorguSayfa.do?target=degisken.
  • TÜİK. (2020). “İstatistiklerle Çocuk”, www.tuik.gov.tr.
  • UNDP. (1998). İnsani Gelişme Raporu: Türkiye, Ankara. Erişim Tarihi: 20.02.2023, https://www.undp.org/tr/turkiye/publications/1998-ulusal-insani-gelisme-raporu-insani-gelisme-ve-haklara-dayali-bir-kalkinma-yaklasimina-dogru.
  • Vakili, R., Moghadam, Z. E., Khademi, G., Vakili, S., & Saeidi, M. (2015). Child mortality at different world regions: a comparison review.
  • Warren, J. L., Mwanza, J. C., Tanna, A. P., & Budenz, D. L. (2016). A statistical model to analyze clinician expert consensus on glaucoma progression using spatially correlated visual field data. Translational Vision Science & Technology, 5(4), 1-11.
  • Wheatley, L. (2015). Factors affecting child mortality. Chattanooga: University of Tennessee. Erişim Tarihi: 20.02.2023, https://scholar.utc.edu/honors-theses/39/.
  • Yetim, B., Demirci, Ş., Konca, M., İlgün, G., & Çilhoroz, Y. (2021). Türkiye’de Bebek Ölüm Hızının Sosyoekonomik Belirleyicileri. Sosyoekonomi, 29(47).

TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ

Yıl 2023, Sayı: 58, 327 - 342, 08.09.2023
https://doi.org/10.30794/pausbed.1259311

Öz

Beş yaş altı çocuk ölüm hızı konusunda, Türkiye özellikle son yıllarda gösterdiği hızlı gelişmeye rağmen hala birçok ülkenin gerisinde bulunmaktadır. Nitekim, ülke genelinde iller arasında çocuk ölümleri hususunda büyük eşitsizliklerin mevcut olduğu bilinmektedir. İlgili sorunun önüne geçilmesinde, ölüm hızını etkileyen faktörlerin belirlenmesi oldukça önem arz etmektedir. Bu yönde yapılacak çalışmalar ile, ölüm hızının düşüşünde etkili olabilecek strateji ve politikaların geliştirilmesinin yolu açılacaktır. Bu doğrultuda çalışmanın amacını, 2019 yılında Türkiye’de beş yaş altı çocuk ölüm hızının iller arasındaki mekânsal etkileşiminin ve belirleyicilerinin mekânsal probit modelleri kullanarak analiz edilmesi oluşturmaktadır. Klasik probit modelinde mekânsal bağımlılık yapısının tespit edilmesinin ardından, Yaklaşık Olabilirlik tahmin yöntemi (Approximate Likelihood Estimation) kullanılarak, mekânsal gecikme (Spatial Autoregressive Model, SAR) ve mekânsal hata bağımlığına (Spatial Error Model, SEM) sahip probit modelleri tahmin edilmiştir. Tahminler neticesinde, kişi başında düşen gelir, merkezi devlet sağlık harcamaları ile ilköğretim ve ortaöğretim mezun oranlarının, beş yaş altı çocuk ölüm hızının üzerinde etkili olduğu ve ölüm hızını azalttığı belirlenmiştir.

Kaynakça

  • Ağır. H., & Tıraş, H. H., (2018). Türkiye’de sağlık harcama türlerinin değerlendirilmesi. Kahramanmaraş Sütçü İmam Üniversitesi Sosyal Bilimler Dergisi, 15(2), 643-670.
  • Amaral, P. V., Anselin, L., & Arribas-Bel, D. (2013). Testing for spatial error dependence in probit models. Letters in Spatial and Resource Sciences, 6, 91-101.
  • Anselin, L. (1988). Spatial econometrics: methods and models. Springer Science & Business Media.
  • Anyanwu, J. C., & Erhijakpor, A. E. (2009). Health expenditures and health outcomes in Africa. African Development Review, 21(2), 400-433.
  • Atasever, M. (2014). Türkiye sağlık hizmetlerinin finansmanı ve sağlık harcamalarının analizi 2002-2013 dönemi.
  • Baker, D., & Fugh-Berman, A. (2009). Do new drugs increase life expectancy? A critique of a Manhattan Institute paper. Journal of general internal medicine, 24(5), 678-682.
  • Balaj, M., York, H. W., Sripada, K., Besnier, E., Vonen, H. D., Aravkin, A., & Eikemo, T. A. (2021). Parental education and inequalities in child mortality: a global systematic review and meta-analysis. The Lancet, 398(10300), 608-620.
  • Boehmer, U., & Williamson, J. B. (1996). The impact of women’s status on infant mortality rate: A cross-national analysis. Social Indicators Research, 37(3), 333-360.
  • Çevik, S., & Taşar, O., (2013). “Public Spending on Health Care and Health Outcomes: Cross Country Comparison”, Journal of Business, Economics and Finance, Cilt 2(4), 82-100.
  • Darmofal, D. (2015). Spatial analysis for the social sciences. Cambridge University Press.
  • Deaton, A. (2006). Global patterns of income and health: facts, interpretations, and policies.
  • Elhorst, J. P. (2012). Dynamic spatial panels: models, methods, and inferences. Journal of geographical systems, 14(1), 5-28.
  • Eryurt, M. A., & Koç, İ. (2009). Yoksulluk ve çocuk ölümlülüğü: Hanehalkı refah düzeyinin çocuk ölümlülüğü üzerindeki etkisi. Cocuk Sagligi ve Hastaliklari Dergisi, 52(3).
  • Fleming, M. M. (2004). Techniques for estimating spatially dependent discrete choice models. Advances in spatial econometrics: methodology, tools and applications, 145-168.
  • Gordon, R. M. (2009). Socio-economic determinants of infant and child mortality in Haiti. Journal of Eastern Caribbean Studies, 34(1).
  • Iram, U., & Butt, M. S. (2008). Socioeconomic determinants of child mortality in Pakistan: Evidence from sequential probit model. International Journal of Social Economics.
  • Jelamschi, L., & De Ver Dye, T. (2009). Decline in under-5 mortality rate (U5MR) in Turkey: a case study. Ankara: UNICEF Turkey.
  • Kazembe, L. N., Appleton, C. C., & Kleinschmidt, I. (2007). Spatial analysis of the relationship between early childhood mortality and malaria endemicity in Malawi. Geospatial Health, 2(1), 41-50.
  • Kelejian, H. H., & Prucha, I. R. (2001). On the asymptotic distribution of the Moran I test statistic with applications. Journal of Econometrics, 104(2), 219-257.
  • Khan, G. R., Baten, A., & Azad, M. A. K. (2023). Influence of contraceptive use and other socio-demographic factors on under-five child mortality in Bangladesh: semi-parametric and parametric approaches. Contraception and Reproductive Medicine, 8(1), 22.
  • Lesage, J. P., Kelley Pace, R., Lam, N., Campanella, R., & Liu, X. (2011). New Orleans business recovery in the aftermath of Hurricane Katrina. Journal of the Royal Statistical Society: Series A (Statistics in Society), 174(4), 1007-1027.
  • Levels & trends in child mortality report, (2021). United Nations Inter-Agency Group for Child Mortality Estimation (UN IGME).
  • Levels & trends in child mortality report, (2022). United Nations Inter-Agency Group for Child Mortality Estimation (UN IGME). Erişim Tarihi: 22.02.2023, https://reliefweb.int/report/world/levels-trends-child-mortality-report-2022.
  • Martinetti, D., & Geniaux, G. (2017). Approximate likelihood estimation of spatial probit models. Regional Science and Urban Economics, 64, 30-45.
  • Martinetti, D., & Geniaux, G. (2022). R Package “ProbitSpatial”.
  • Mendell, N. R., & Elston, R. C. (1974). Multifactorial qualitative traits: genetic analysis and prediction of recurrence risks. Biometrics, 41-57.
  • Morey, E. R., Rowe, R. D., & Watson, M. (1993). A repeated nested‐logit model of Atlantic salmon fishing. American journal of agricultural economics, 75(3), 578-592.
  • Nyamuranga, C., & Shin, J. (2019). Public health expenditure and child mortality in Southern Africa. International Journal of Social Economics.
  • OECD, (2008). OECD Sağlık Sistemi İncelemeleri: Türkiye, OECD and the World Bank.
  • O’Hare, B., Makuta, I., Chiwaula, L., & Bar-Zeev, N. (2013). Income and child mortality in developing countries: a systematic review and meta-analysis. Journal of the Royal Society of Medicine, 106(10), 408-414.
  • Pamuk, E. R., Fuchs, R., & Lutz, W. (2011). Comparing relative effects of education and economic resources on infant mortality in developing countries. Population and development review, 37(4), 637-664.
  • Pinkse, J. (1999). Asymptotics of the Moran test and a test for spatial correlation in Probit models, Working paper, Department of Economics, University of British Columbia, Vancouver.
  • Pinkse, J. (2004). Moran-flavored tests with nuisance parameters: examples. In Advances in Spatial Econometrics (pp. 67-77). Springer, Berlin, Heidelberg.
  • Pinkse, J., & Slade, M. E. (1998). Contracting in space: An application of spatial statistics to discrete-choice models. Journal of Econometrics, 85(1), 125-154.
  • Preston S. (1975). The changing relation between mortality and level of economic development. Population Studies. 29: 231–248.
  • Pritchett, L., & Summers, L. H. (1996). Wealthier is healthier. J Human Resources, 31(4), 841-868.
  • Qu, X., & Lee, L. F. (2012). LM tests for spatial correlation in spatial models with limited dependent variables. Regional Science and Urban Economics, 42(3), 430-445.
  • Smirnov, O. A. (2010). Modeling spatial discrete choice. Regional science and urban economics, 40(5), 292-298.
  • Studenmund, A. H. (2014). Using econometrics a practical guide. Pearson Education Limited.
  • Subaşı Ertekin, M., Yüce Dural, B., & Kırca, M. (2016). Türkiye’de Ekonomik Büyüme ve İşsizliğin Bebek Ölümlerine Etkisi. Gümüshane University Electronic Journal of the Institute of Social Science/Gümüshane Üniversitesi Sosyal Bilimler Enstitüsü Elektronik Dergisi, 7(17).
  • Tibebu, N. S., Emiru, T. D., Tiruneh, C. M., Nigat, A. B., Getu, B. D., & Mekonnen, A. B. (2022). Potential determinant factors of under-five mortality in the Amhara region of Ethiopia. BMC pediatrics, 22(1), 1-7.
  • Todaro, M. P. (1992). “Human Development Report 1992”, Population and Development Review. 359-363.
  • TÜİK. Bölgesel İstatistikler Veri Tabanı. Erişim Tarihi: 25.01.2023, https://biruni.tuik.gov.tr/bolgeselistatistik/sorguSayfa.do?target=degisken.
  • TÜİK. (2020). “İstatistiklerle Çocuk”, www.tuik.gov.tr.
  • UNDP. (1998). İnsani Gelişme Raporu: Türkiye, Ankara. Erişim Tarihi: 20.02.2023, https://www.undp.org/tr/turkiye/publications/1998-ulusal-insani-gelisme-raporu-insani-gelisme-ve-haklara-dayali-bir-kalkinma-yaklasimina-dogru.
  • Vakili, R., Moghadam, Z. E., Khademi, G., Vakili, S., & Saeidi, M. (2015). Child mortality at different world regions: a comparison review.
  • Warren, J. L., Mwanza, J. C., Tanna, A. P., & Budenz, D. L. (2016). A statistical model to analyze clinician expert consensus on glaucoma progression using spatially correlated visual field data. Translational Vision Science & Technology, 5(4), 1-11.
  • Wheatley, L. (2015). Factors affecting child mortality. Chattanooga: University of Tennessee. Erişim Tarihi: 20.02.2023, https://scholar.utc.edu/honors-theses/39/.
  • Yetim, B., Demirci, Ş., Konca, M., İlgün, G., & Çilhoroz, Y. (2021). Türkiye’de Bebek Ölüm Hızının Sosyoekonomik Belirleyicileri. Sosyoekonomi, 29(47).
Toplam 49 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Ekonomi
Bölüm Araştırma Makalesi
Yazarlar

Nazife Zeynep Çakır 0000-0002-3207-4528

Ebru Çağlayan 0000-0002-9998-5334

Erken Görünüm Tarihi 28 Ağustos 2023
Yayımlanma Tarihi 8 Eylül 2023
Kabul Tarihi 19 Haziran 2023
Yayımlandığı Sayı Yıl 2023 Sayı: 58

Kaynak Göster

APA Çakır, N. Z., & Çağlayan, E. (2023). TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi(58), 327-342. https://doi.org/10.30794/pausbed.1259311
AMA Çakır NZ, Çağlayan E. TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ. PAUSBED. Eylül 2023;(58):327-342. doi:10.30794/pausbed.1259311
Chicago Çakır, Nazife Zeynep, ve Ebru Çağlayan. “TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 58 (Eylül 2023): 327-42. https://doi.org/10.30794/pausbed.1259311.
EndNote Çakır NZ, Çağlayan E (01 Eylül 2023) TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 58 327–342.
IEEE N. Z. Çakır ve E. Çağlayan, “TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ”, PAUSBED, sy. 58, ss. 327–342, Eylül 2023, doi: 10.30794/pausbed.1259311.
ISNAD Çakır, Nazife Zeynep - Çağlayan, Ebru. “TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi 58 (Eylül 2023), 327-342. https://doi.org/10.30794/pausbed.1259311.
JAMA Çakır NZ, Çağlayan E. TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ. PAUSBED. 2023;:327–342.
MLA Çakır, Nazife Zeynep ve Ebru Çağlayan. “TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ”. Pamukkale Üniversitesi Sosyal Bilimler Enstitüsü Dergisi, sy. 58, 2023, ss. 327-42, doi:10.30794/pausbed.1259311.
Vancouver Çakır NZ, Çağlayan E. TÜRKİYE’DE İLLER ARASINDA BEŞ YAŞ ALTI ÇOCUK ÖLÜM HIZININ MEKÂNSAL ETKİLEŞİMİ: MEKÂNSAL PROBİT MODELLERİ. PAUSBED. 2023(58):327-42.