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Assessing the Economic Impact of Paternal Involvement: A Comparison of the Generalized Linear Model Versus Decision Analysis Trees

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Abstract

Lack of paternal involvement has been shown to be associated with adverse pregnancy outcomes, including infant morbidity and mortality, but the impact on health care costs is unknown. Various methodological approaches have been used in cost minimization and cost effectiveness analyses and it remains unclear how cost estimates vary according to the analytic strategy adopted. We illustrate a methodological comparison of decision analysis modeling and generalized linear modeling (GLM) techniques using a case study that assesses the cost-effectiveness of potential father involvement interventions. We conducted a 12-year retrospective cohort study using a statewide enhanced maternal-infant database that contains both clinical and nonclinical information. A missing name for the father on the infant’s birth certificate was used as a proxy for lack of paternal involvement, the main exposure of this study. Using decision analysis modeling and GLM, we compared all infant inpatient hospitalization costs over the first year of life. Costs were calculated from hospital charges using department-level cost-to-charge ratios and were adjusted for inflation. In our cohort of 2,243,891 infants, 9.2 % had a father uninvolved during pregnancy. Lack of paternal involvement was associated with higher rates of preterm birth, small-for-gestational age, and infant morbidity and mortality. Both analytic approaches estimate significantly higher per-infant costs for father uninvolved pregnancies (decision analysis model: $1,827, GLM: $1,139). This paper provides sufficient evidence that healthcare costs could be significantly reduced through enhanced father involvement during pregnancy, and buttresses the call for a national program to involve fathers in antenatal care.

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References

  1. Murray, C. J., & Frenk, J. (2010). Ranking 37th–measuring the performance of the US health care system. New England Journal of Medicine, 362(2), 98–99. doi:10.1056/NEJMp0910064.

    Article  CAS  PubMed  Google Scholar 

  2. US Department of Health and Human Services. (2009). Federal coordinating council for comparative effectiveness research: Report to the president and the congress.

  3. Henderson, J., Hockley, C., Petrou, S., et al. (2004). Economic implications of multiple births: Inpatient hospital costs in the first 5 years of life. Archives of Disease in Childhood Fetal and Neonatal Edition, 89(6), F542–F545. doi:10.1136/adc.2003.043851.

    Article  CAS  PubMed Central  PubMed  Google Scholar 

  4. Petrou, S., Mehta, Z., Hockley, C., et al. (2003). The impact of preterm birth on hospital inpatient admissions and costs during the first 5 years of life. Pediatrics, 112(6 Pt 1), 1290–1297.

    Article  PubMed  Google Scholar 

  5. Behrman, R., & Butler, A. (2007). Preterm birth: Causes, consequences, and prevention (1st ed.). Washington, DC: National Academies Press.

  6. Black, M. M., Dubowitz, H., & Starr, R. H, Jr. (1999). African American fathers in low income, urban families: Development, behavior, and home environment of their three-year-old children. Child Development, 70(4), 967–978.

    Article  CAS  PubMed  Google Scholar 

  7. Coley, R. L. (1998). Children’s socialization experiences and functioning in single-mother households: The importance of fathers and other men. Child Development, 69(1), 219–230.

    Article  CAS  PubMed  Google Scholar 

  8. Yogman, M. W., Kindlon, D., & Earls, F. (1995). Father involvement and cognitive/behavioral outcomes of preterm infants. Journal of the American Academy of Child and Adolescent Psychiatry, 34(1), 58–66. doi:10.1097/00004583-199501000-00015.

    Article  CAS  PubMed  Google Scholar 

  9. Ngui, E., Cortright, A., & Blair, K. (2009). An investigation of paternity status and other factors associated with racial and ethnic disparities in birth outcomes in Milwaukee, Wisconsin. Maternal and Child Health Journal, 13(4), 467–478. doi:10.1007/s10995-008-0383-8.

    Article  PubMed  Google Scholar 

  10. Luo, Z. C., Wilkins, R., & Kramer, M. S. (2004). Disparities in pregnancy outcomes according to marital and cohabitation status. Obstetrics and Gynecology, 103(6), 1300–1307. doi:10.1097/01.AOG.0000128070.44805.1f.

    Article  PubMed  Google Scholar 

  11. Alio, A. P., Kornosky, J. L., Mbah, A. K., et al. (2010). The impact of paternal involvement on feto-infant morbidity among Whites, Blacks and Hispanics. Maternal and Child Health Journal, 14(5), 735–741. doi:10.1007/s10995-009-0482-1.

    Article  PubMed  Google Scholar 

  12. Alio, A. P., Mbah, A. K., Kornosky, J. L., et al. (2011). Assessing the impact of paternal involvement on racial/ethnic disparities in infant mortality rates. Journal of Community Health, 36(1), 63–68. doi:10.1007/s10900-010-9280-3.

    Article  PubMed  Google Scholar 

  13. Alio, A. P., Salihu, H. M., Kornosky, J. L., et al. (2010). Feto-infant health and survival: Does paternal involvement matter? Maternal and Child Health Journal, 14(6), 931–937. doi:10.1007/s10995-009-0531-9.

    Article  PubMed  Google Scholar 

  14. MacDonald, L. D., Peacock, J. L., & Anderson, H. R. (1992). Marital status: Association with social and economic circumstances, psychological state and outcomes of pregnancy. Journal of Public Health Medicine, 14(1), 26–34.

    CAS  PubMed  Google Scholar 

  15. Ventura, S. J. (1995). Births to unmarried mothers: United States 1980–92. Vital and Health Statistics Series 21, 21(53), 1–55.

    Google Scholar 

  16. Ventura, S. J. (2009). Changing patterns of nonmarital childbearing in the United States. NCHS Data Brief, 18, 1–8.

    PubMed  Google Scholar 

  17. Salemi, J. L., Tanner, J. P., Bailey, M., et al. (2013). Creation and evaluation of a multi-layered maternal and child health database for comparative effectiveness research. Journal of Registry Management, 40(1), 14–28.

    PubMed  Google Scholar 

  18. Teitler, J. (2001). Father involvement, child health and maternal health behavior. Children and Youth Services Review, 23, 403–425.

    Article  Google Scholar 

  19. Alexander, G. R., Kogan, M., Martin, J., et al. (1998). What are the fetal growth patterns of singletons, twins, and triplets in the United States? Clinical Obstetrics and Gynecology, 41(1), 114–125.

    Article  CAS  PubMed  Google Scholar 

  20. Song, X., & Friedman, B. (2008). Calculate cost adjustment factors by APR-DRG and CCS using selected states with detailed charges. HCUP methods series report # 2008-04. Online October 8, 2008. US agency for healthcare research and quality. Available: http://www.hcup-us.ahrq.gov/reports/methods.jsp.2008.

  21. Sun, Y., & Friedman B. (2011). Tools for more accurate inpatient cost estimates with HCUP databases, 2009. HCUP Methods Series Report # 2011-04. Online October 17, 2011. US agency for healthcare research and quality. Available: http://www.hcup-us.ahrq.gov/reports/methods/methods.jsp.2011.

  22. Centers for Medicare and Medicaid Services. (2012). Cost reports by fiscal year. http://www.cms.gov/Research-Statistics-Data-and-Systems/Files-for-Order/CostReports/Cost-Reports-by-Fiscal-Year.html. Accessed September 12, 2012.

  23. Salemi, J. L., Comins, M. M., Chandler, K., et al. (2013). A practical approach for calculating reliable cost estimates from observational data: application to cost analyses in maternal and child health. Applied Health Economics and Health Policy, 11(4), 343–357. doi:10.1007/s40258-013-0040-2.

    Article  PubMed  Google Scholar 

  24. United States Department of Labor: Bureau of Labor Statistics. (2012). Consumer Price Index: All Urban Consumers—(CPI-U). http://www.bls.gov/cpi/. Accessed November 16, 2012.

  25. Shah, B. R., Laupacis, A., Hux, J. E., et al. (2005). Propensity score methods gave similar results to traditional regression modeling in observational studies: a systematic review. Journal of Clinical Epidemiology, 58(6), 550–559. doi:10.1016/j.jclinepi.2004.10.016.

    Article  PubMed  Google Scholar 

  26. Akaike, H. (1973). Information theory and an extension of the maximum likelihood principle. In B. N. Petrov & F. Csaki (Eds.), Proceedings of the second international symposium on information theory. Budapest: Akademiai Kiado.

    Google Scholar 

  27. Copper, R. L., Goldenberg, R. L., Das, A., et al. (1996). The preterm prediction study: maternal stress is associated with spontaneous preterm birth at less than thirty-five weeks’ gestation. National Institute of Child Health and Human Development Maternal-Fetal Medicine Units Network. American Journal of Obstetrics and Gynecology, 175(5), 1286–1292.

    Article  CAS  PubMed  Google Scholar 

  28. Giscombe, C. L., & Lobel, M. (2005). Explaining disproportionately high rates of adverse birth outcomes among African Americans: The impact of stress, racism, and related factors in pregnancy. Psychological Bulletin, 131(5), 662–683. doi:10.1037/0033-2909.131.5.662.

    Article  PubMed  Google Scholar 

  29. Hobel, C. J., Goldstein, A., & Barrett, E. S. (2008). Psychosocial stress and pregnancy outcome. Clinical Obstetrics and Gynecology, 51(2), 333–348. doi:10.1097/GRF.0b013e31816f2709.

    Article  PubMed  Google Scholar 

  30. Sable, M. R., & Wilkinson, D. S. (2000). Impact of perceived stress, major life events and pregnancy attitudes on low birth weight. Family Planning Perspectives, 32(6), 288–294.

    Article  CAS  PubMed  Google Scholar 

  31. Kramer, M. S., Goulet, L., Lydon, J., et al. (2001). Socio-economic disparities in preterm birth: Causal pathways and mechanisms. Paediatric and Perinatal Epidemiology, 15(Suppl 2), 104–123.

    Article  PubMed  Google Scholar 

  32. Wadhwa, P. D., Culhane, J. F., Rauh, V., et al. (2001). Stress, infection and preterm birth: A biobehavioral perspective. Paediatric and Perinatal Epidemiology, 15(Suppl 2), 17–29.

    Article  PubMed  Google Scholar 

  33. Fagan, J., & Iglesias, A. (1999). Father involvement program effects on fathers, father figures, and their head start children: A quasi-experimental study. Early Childhood Research Quarterly, 14(2), 243–269.

    Article  Google Scholar 

  34. Salihu, H. M., Mbah, A. K., Jeffers, D., et al. (2009). Healthy start program and feto-infant morbidity outcomes: Evaluation of program effectiveness. Maternal and Child Health Journal, 13(1), 56–65. doi:10.1007/s10995-008-0400-y.

    Article  PubMed  Google Scholar 

  35. Bond, M. J., Heidelbaugh, J. J., Robertson, A., et al. (2010). Improving research, policy and practice to promote paternal involvement in pregnancy outcomes: the roles of obstetricians–gynecologists. Current Opinion in Obstetrics and Gynecology, 22(6), 525–529. doi:10.1097/GCO.0b013e3283404e1e.

    Article  PubMed  Google Scholar 

  36. Alio, A. P., Lewis, C. A., Scarborough, K., et al. (2013). A community perspective on the role of fathers during pregnancy: A qualitative study. BMC Pregnancy and Childbirth, 13, 60. doi:10.1186/1471-2393-13-60.

    Article  PubMed Central  PubMed  Google Scholar 

  37. Gaudino, J. A, Jr, Jenkins, B., & Rochat, R. W. (1999). No fathers’ names: A risk factor for infant mortality in the State of Georgia, USA. Social Science and Medicine, 48(2), 253–265.

    Article  PubMed  Google Scholar 

  38. Zhang, S., Cardarelli, K., Shim, R., et al. (2012). Racial disparities in economic and clinical outcomes of pregnancy among medicaid recipients. Maternal and Child Health Journal,. doi:10.1007/s10995-012-1162-0.

    PubMed Central  Google Scholar 

  39. Rogowski, J. (1999). Measuring the cost of neonatal and perinatal care. Pediatrics, 103(1 Suppl E), 329–335.

    CAS  PubMed  Google Scholar 

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Acknowledgments

The authors acknowledge the following organizations and individuals for contributing to this project: the Agency for Healthcare Research and Quality for promoting the enhancement of statewide, hospital-based, encounter-level databases (Grant Number R01HS019997); Social and Scientific Systems, Inc. for their coordination and direction of enhanced state data grants; and staff of the Florida Department of Health and the Agency for Health Care Administration for providing access to these data.

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Authors and Affiliations

  1. The MCH Comparative Effectiveness Research Group, Department of Epidemiology and Biostatistics, College of Public Health, University of South Florida, 13201 Bruce B Downs, MDC56, Tampa, FL, 33612, USA

    Hamisu M. Salihu, Jason L. Salemi, Michelle C. Nash, Kristen Chandler & Alfred K. Mbah

  2. Department of Public Health Sciences, University of Rochester, Rochester, NY, USA

    Amina P. Alio

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  1. Hamisu M. Salihu
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  2. Jason L. Salemi
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Correspondence to Hamisu M. Salihu.

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Disclaimer The content is solely the responsibility of the authors and does not necessarily represent the official views of the Agency for Healthcare Research and Quality, or the University of South Florida.

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Salihu, H.M., Salemi, J.L., Nash, M.C. et al. Assessing the Economic Impact of Paternal Involvement: A Comparison of the Generalized Linear Model Versus Decision Analysis Trees. Matern Child Health J 18, 1380–1390 (2014). https://doi.org/10.1007/s10995-013-1372-0

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