The Importance of Psychosocial Variables in Predicting Low Birth Weight
Publication Date:March 2004
Objective: An analysis of a pre-existing data set of 606 inner city pregnant women collected by the Fetal Alcohol Research Center of Wayne State University School of Medicine in Detroit, Michigan, was conducted to determine if the inclusion of psychosocial variables would improve the prediction of low birth weight. Method: Along with the usual medical and demographic data typically collected by obstetricians interested in low birth weight, data collection had also included variables assessing maternal comfort, feelings of hopefulness, intimacy of relationship with the baby's father, and alcohol and drug use. The sample was divided into cases for developing the mathematical models and test cases for comparing two different mathematical approaches-discriminant function analysis (DFA) and a computer simulation modeling method derived from chaos theory (dynamic systems modeling or DSM). Unlike DFA, DSM required a priori specification of the relationships among the variables. Findings: Psychosocial variables were needed by both mathematical approaches to achieve the best predictions of which women would deliver low birth weight infants. Alcohol and drug use were also important. The DSM method correctly identified 74 of 78 women who did not deliver low birth weight infants compared to 69 of 78 for DFA. It correctly identified 16 of the 22 low birth weight (LBW) infants compared to 11 of 22 for DFA. Both models performed better than existing models in the literature which do not consider psychosocial measures or drug and alcohol consumption. DSM performed better than DFA. Its sensitivity was 80%; efficiency was 90%; and specificity, 92%. DSM's a prior specification of variables included both independent contributions of psychosocial factors to medical risk and modulating effects of psychosocial factors on medical risks. Conclusions: Psychosocial variables and alcohol and drug use measures permitted significant improvement in the ability to predict risk for low birth weight. Dynamic systems modeling was better than DFA and should be further explored and developed to create an explanatory theory for low birth weight which could be used to guide a clinical trial of psychosocial intervention its prevention.
KEY WORDS: Low birth weight, psychosocial variables, discriminant function analysis (DFA), dynamic systems modeling (DSM).
Bell, J. A., & Bell, J.F. (1980). Systems dynamics and the scientific method. In J. Randers (Ed). Elements of the Systems Dynamics Method. Cambridge, MA: MIT Press.
Committee to Study the Prevention of Low Birth Weight. (1985). Preventing Low Birth Weight. Washington, DC: National Academy of Sciences, Institute of Medicine.
Creasy, R.K., & Herron, M.A. (1981). Prevention of preterm birth. Seminars in Perinatology, 5, 295-302.
Creasy, U.K., & Merkatz, LR. (1990). Prevention of preterm birth: clinical opinion. Obstet Gynecol, 76(Suppl), 2S-4S.
Forrester, J.W. (1969). Market growth as influenced by capital investment. Industrial Management Review 9(Winter), 631-636.
Forrester, J.W. (1961). Industrial Dynamics. Cambridge, MA: MIT Press.
Forrester, J.W. (1975). World Dynamics. Cambridge, MA: MIT Press.
Giblin, P.T., Poland, M.L., & Ager, J.W. (1990). Effects of social supports on attitudes, health behaviors and obtaining prenatal care. Journal of Community Health, 15(6), 357-68.
Giblin, P.T., Poland, M.L., & Sachs, B.A. (1987). Effects of social supports on attitudes and health behaviors of pregnant adolescents. Journal of Adolescent Health Care, 8(3), 273-9.
Giblin, P.T., Poland, M.L., & Sachs, B.A. (1986). Pregnant adolescents' health information needs. Implications for health education and health seeking. Journal of Adolescent Health Care, 7(3), 168-72.
Giblin, P.T., Poland, M.L., Waller, J.B. Jr, & Ager, J.W. (1988). Correlates of neonatal morbidity: maternal characteristics and family resources. Journal of Genetic Psychology 149(4), 527-33.
Gjerdingen, D.K. (1992). Premature labor, part I: Risk Assessment, etiologic factors, and diagnosis. J. American Board of Family Practice, 5(5), 495-509.
High Performance Systems. (1991). Stella II: Manual for academic users. Hanover, NH. Mass, N.J., & Senge, P.M. (1980). Alternate tests for selecting model variables. In Renders, op cit.
McCormick, M. (1991). Trends in rates of low birthweight in the United States. In H. W. Berendes, S. Kessel, & S. Yaffe (Eds.), Advances in the Prevention of Low Birthweight. Washington, DC: National Center for Education in Maternal and Child Health, 1991, 3-11.
Main, D.M., Richardson, D., Gabbe, S.G., Strong, S., & Weller, S.C. (1991). Prospective evaluation of a risk scoring system for predicting preterm delivery in black inner city women. Obstet Gynecol, 165, 2-6.
Meadows, D.H. (1980). The unavoidable a prior. In Randers, op cit.
Mehl, L.E. (1990). Use of systems dynamics computer simulation modeling to predict risk in the medically low risk pregnant woman. Intl J. Perinatal Studies 1, 37-43.
Morrison, J.C. (1990). Preterm birth: a puzzle worth solving. Obstet Gynecol, 76(Suppl), 5S-12S.
Omer, H. (1986). Possible psychophysiologic mechanisms in premature labor. Psychosomatics, 27(8): 580-584.
Omer, H., Elizer, Y., Barnea, T., Friedlander, D., & Palti, Z. (1986). Psychological variables and premature labor: real connection or artificial findings? J. Psychosomatic Research, 30(5), 559-565.
Papiernik, E. (1969). Le coefficient de risque d'accouchement premautre. La Presse Medicale, 77, 780-93.
Poland, M.L., Ager, J.W., & Oison, K.L. (1987). Barriers to receiving adequate prenatal care. Am J Obstet Gynecol, 157(2): 297-303.
Poland, M.L., Ager, J.W., Oison, K.L., & Sokol R.J. (1990). Quality of prenatal care; selected social, behavioral, and biomedical factors; and birthweight. Obstetrics and Gynecology, 75(4), 607-12.
Poland, M.L., Ager, J.L., & Sokol, R.W. (1991). Prenatal care: a path (not taken) to improved perinatal outcome. Journal of Perinatal Medicine, 19(6), 427-33.
Preterm labor. (1989). Washington, DC: The American College of Obstetricians and Gynecologists, Technical Bulletin 133, October.
Lewis E. Mehl-Madrona, M.D., Ph.D.
Dr. Mehl-Madrona is the Coordinator, Integrative Psychiatry and Systems Medicine, with the Program in Integrative Medicine, University of Arizona College of Medicine. Correspondence can be addressed to Dr. Mehl-Madrona at the Arizona College of Medicine, 1650 E. Fort Lowell Rd. Suite 201, Tucson, AZ 85719 or E-mail: madrona@ email.arizona.edu
Supported in part by an NIAAA grant to Dr. Harold Holman at the University of California at Berkeley School of Public Health. Presented at the annual meeting on Complementary and Alternative Therapies of the Southern Medical Association, San Antonio, Texas, April 26, 2003.