Analysis of Infant Congenital Malformation Data using the Bayesian Count Regression

IRCMJ logo
PDF
HTML

Keywords

Congenital malformation
Count regression
Hurdle
Zero-inflation

How to Cite

Ali Akbari KhoeiR., KazemnejadA., EskandariF., & HeidarzadehM. (2021). Analysis of Infant Congenital Malformation Data using the Bayesian Count Regression . Iranian Red Crescent Medical Journal, 23(2). https://doi.org/10.32592/ircmj.2021.23.2.180

Abstract

Background: Congenital malformations are one of the most important and common types of anomalies in infants, which are one of the main causes of disability and mortality in children.      

Objectives: This study aimed to investigate the risk factors affecting the incidence of congenital malformations, as well as the number of different infant anomalies recorded in neonatal health data in Khoy, Iran, during 2017.

Methods: In this study, all neonates born in the maternity wards of hospitals in Khoy, Iran, during 2017 were evaluated in terms of gender, weight, and parental consanguinity. Hurdle and Zero-inflation approaches were utilized for the double Poisson model. Moreover, the data were collected using some checklists, and the analyses were performed in R-3-6-1 software.

Results: According to the results of the present study, the Hurdle approach was better than Zero-inflation. The birth weight and parental consanguinity affected the incidence of congenital malformations in infants.

Conclusion: Given that a significant proportion of infants are born without any congenital malformations, it is important to use count regression models based on excess zero approaches to assess congenital malformations. It is also necessary to take steps to reduce consanguineous marriages and the number of infants with low-birth-weight to prevent congenital malformations.

https://doi.org/10.32592/ircmj.2021.23.2.180
PDF
HTML

References

  1. Centers for Disease Control and Prevention (CDC). Update on overall prevalence of major birth defects - Atlanta, Georgia, 1978-2005. Morb Mortal Wkly Rep. 2008;57(1):1-5. doi: 10.1001/jama.299.7.756. [PubMed: 18185492].
  2. Kehr PH, Karger C. John Herring (ed): Tachdjian’s pediatric orthopaedics, 4th edn. Eur J Orthop Surg Traumatol. 2009;19(8):605. doi: 10.1007/s00590-009-0508-9.
  3. Fama EF, French KR. Dissecting anomalies with a five-factor model. Rev Finan Stud. 2016;29(1):69-103. Doi: 10.1093/rfs/hhv043.
  4. Garry VF, Harkins ME, Erickson LL, Long-Simpson LK, Holland SE, Burroughs BL. Birth defects, season of conception, and sex of children born to pesticide applicators living in the Red River Valley of Minnesota, USA. Environ Health Perspect. 2002;110(Suppl 3):441-9. doi: 10.1289/ehp.02110s3441. [PubMed: 12060842].
  5. Shawky RM, Sadik DI. Congenital malformations prevalent among Egyptian children and associated risk factors. Egypt J Med Hum Genet. 2011;12(1):69-78. doi: 10.1016/j.ejmhg.2011.02.016.
  6. Baxter R, Hastings N, Law A, Glass EJ. A rapid and robust sequence‐based genotyping method for BoLA‐DRB3 alleles in large numbers of heterozygous cattle. Anim Genet. 2008;39(5):561-3. doi: 10.1111/j.1365-2052.2008.01757.x. [PubMed: 18637877].
  7. Karbasi SA, Golestan M, Fallah R, Mirnaseri F, Barkhordari K, Bafghee MS. Prevalence of congenital malformations. Acta Med Iran. 2009;47(2):149-53.
  8. Dastgiri S, Stone DH, Le-Ha C, Gilmour WH. Prevalence and secular trend of congenital anomalies in Glasgow, UK. Arch Dis Child. 2002;86:257-63. doi: 10.1136/adc.86.4.257. [PubMed: 11919098].
  9. Corsello G, Giuffrè M. Congenital malformations. J Matern Neonatal Med. 2012;25(Suppl 1):25-9. doi: 10.3109/14767058.2012.664943. [PubMed: 22356564].
  10. Cameron A, Trivedi P. Microeconometrics: methods and applications. Cambridge: Cambridge University Press; 2005.
  11. Dalrymple ML, Hudson IL, Ford RP. Finite mixture, zero-inflated poisson and hurdle models with application to SIDS. Comput Stat Data Anal. 2003;41(3-4):491-504. doi: 10.1016/S0167-9473(02)00187-1.
  12. Mullahy J. Specification and testing of some modified count data models. J Econ. 1986;33(3):341-65. doi: 10.1016/0304-4076(86)90002-3.
  13. Lambert D. Zero-inflated poisson regression, with an application to defects in manufacturing. Technometrics. 1992;34(1):1-14. doi: 10.1080/00401706.1992.10485228.
  14. Hilbe JM. Negative binomial regression. 2nd ed. Cambridge: Cambridge University Press; 2011.
  15. Guikema SD, Goffelt JP. A flexible count data regression model for risk analysis. Risk Anal. 2008;28(1):213-23. doi: 10.1111/j.1539-6924.2008.01014.x. [PubMed: 18304118].
  16. Giles DE. Hermite regression analysis of multi-modal count data. Econ Bull. 2010;30(4):2936-45.
  17. Consul PC, Famoye F. Generalized poisson regression model. Commun Stat Theory Methods. 1992;21(1):89-109. doi: 10.1080/03610929208830766.
  18. Gurmu S, Trivedi PK. Excess zeros in count models for recreational trips. J Bus Econ Stat. 1996;14(4):469-77. doi: 10.1080/07350015.1996.10524676.
  19. Barriga GD, Louzada F. The zero-inflated Conway–Maxwell–Poisson distribution: Bayesian inference, regression modeling and influence diagnostic. Stat Methodol. 2014;21:23-34. doi: 10.1016/j.stamet.2013.11.003.
  20. Satheesh Kumar C, Ramachandran R. On some aspects of a zero-inflated overdispersed model and its applications. J Appl Stat. 2020;47(3):506-23. doi: 10.1080/02664763.2019.1645098.
  21. Gupta PL, Gupta RC, Tripathi RC. Score test for zero inflated generalized poisson regression model. Commun Stat Theory Methods. 2005;33(1):47-64. doi: 10.1081/STA-120026576.
  22. Efron B. Double exponential families and their use in generalized linear regression. J Am Stat Assoc. 1986;81(395):709-21. doi: 10.1080/01621459.1986.10478327.
  23. Gurmu S. Generalized hurdle count data regression models. Econ Lett. 1998;58(3):263-8. doi: 10.1016/S0165-1765(97)00295-4.
  24. Yip KC, Yau KK. On modeling claim frequency data in general insurance with extra zeros. Insur Math Econ. 2005;36(2):153-63. doi: 10.1016/j.insmatheco.2004.11.002.
  25. Aragon DC, Achcar JA, Martinez EZ. Maximum likelihood and Bayesian estimators for the double Poisson distribution. J Stat Theory Pract. 2018;12(4):886-911. doi: 10.1080/15598608.2018.1489919.
  26. Madigan D, Genkin A, Lewis DD, Fradkin D. Bayesian multinomial logistic regression for author identification. AIP Conference Proceedings. Ame Instit Physics. 2005;803(1):509-16. doi: 10.1063/1.2149832.
  27. Eskandari F, Meshkani MR. Bayesian logistic regression model choice via laplace-metropolis algorithm. J Iran Stat Soc. 2006;5(1):9-24.
  28. Spiegelhalter DJ, Best NG, Carlin BP, Van Der Linde A. Bayesian measures of model complexity and fit. J R Stat Soc Ser B Stat Methodol. 2002;64(4):583-616. doi: 10.1111/1467-9868.00353.
  29. Geweke J. Statistics, probability and chaos: comment: inference and prediction in the presence of uncertainty and determinism. Stat Sci. 1992;7:94-101. doi: 10.2307/2245993.
  30. Carlo M. Comment: one long run with diagnostics: implementation strategies for markov chain. Stat Sci. 1992;7(4):493-7.
  31. Rittler M, Liascovich R, López-Camelo J, Castilla EE. Parental consanguinity in specific types of congenital anomalies. Am J Med Genet. 2001;102(1):36-43. doi: 10.1002/1096-8628(20010722)102:1<36::AID-AJMG1394>3.0.CO;2-M. [PubMed: 11471170].
  32. Mosayebi Z, Movahedian AH. Pattern of congenital malformations in consanguineous versus nonconsanguineous marriages in Kashan, Islamic Republic of Iran. EMHJ East Mediterr Health J. 2007;13(4):868-75.
  33. Tulandi T, Martin J, Al-Fadhli R, Kabli N, Forman R, Hitkari J, et al. Congenital malformations among 911 newborns conceived after infertility treatment with letrozole or clomiphene citrate. Fertil Steril. 2006;85(6):1761-5. doi: 10.1016/j.fertnstert.2006.03.014. [PubMed: 16650422].
  34. Taksande A, Vilhekar K, Chaturvedi P, Jain M. Congenital malformations at birth in central India: a rural medical college hospital based data. Indian J Hum Genet. 2010;16(3):159-63. doi: 10.4103/0971-6866.73412. [PubMed: 21206705].