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Medicine and Public Policy |

A Decision Analysis of Mandatory Compared with Voluntary HIV Testing in Pregnant Women

Inaam A. Nakchbandi, MD; J. Craig Longenecker, MD, MPH; M. Ann Ricksecker, MPH; Richard A. Latta, MD; Cheryl Healton, DrPH; and David G. Smith, MD
[+] Article and Author Information

From Abington Memorial Hospital, Abington, Pennsylvania; Allegheny University of the Health Sciences, Philadelphia, Pennsylvania; and Columbia University School of Public Health, New York, New York. Acknowledgments: The authors thank Patricia Bankes and Helen Nuskey for assistance in preparation of the manuscript. Requests for Reprints: David G. Smith, MD, Department of Medicine, 2B Elkins, Abington Memorial Hospital, 1200 Old York Road, Abington, PA 19001. Current Author Addresses: Dr. Nakchbandi: Department of Medicine/Endocrinology, Yale University School of Medicine, 333 Cedar Street, Fitkin 1, New Haven, CT 06520-8020.


Copyright ©2004 by the American College of Physicians


Ann Intern Med. 1998;128(9):760-767. doi:10.7326/0003-4819-128-9-199805010-00010
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Background: The benefit of antiretroviral therapy in reducing maternal-fetal transmission of HIV during pregnancy has caused a public policy debate about the relative benefits of mandatory HIV screening and voluntary HIV screening in pregnant women.

Objective: To evaluate the benefits and risks of mandatory compared with voluntary HIV testing of pregnant women to help guide research and policy.

Design: A decision analysis that incorporated the following variables: acceptance and benefit of prenatal care, acceptance and benefit of zidovudine therapy in HIV-infected women, prevalence of HIV infection, and mandatory compared with voluntary HIV testing.

Measurements: The threshold deterrence rate (defined as the percentage of women who, if deterred from seeking prenatal care because of a mandatory HIV testing policy, would offset the benefit of zidovudine in reducing vertical HIV transmission) and the difference between a policy of mandatory testing and a policy of voluntary testing in the absolute number of HIV-infected infants or dead infants.

Results: Voluntary HIV testing was preferred over a broad range of values in the model. At baseline, the threshold deterrence rate was 0.4%. At a deterrence rate of 0.5%, the number of infants (n = 167) spared HIV infection annually in the United States under a mandatory HIV testing policy would be lower than the number of perinatal deaths (n = 189) caused by lack of prenatal care.

Conclusions: The most important variables in the model were voluntary HIV testing, the deterrence rate associated with mandatory testing compared with voluntary testing, and the prevalence of HIV infection in women of childbearing age. At high levels of acceptance of voluntary HIV testing, the benefits of a policy of mandatory testing are minimal and may create the potential harms of avoiding prenatal care to avoid mandatory testing.

Figures

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Figure 1.
Decision tree showing testing and treatment patterns under a mandatory and a voluntary policy of testing for HIV in pregnant women.2112

Deterrence rate is defined as P − P , the percentage of women deterred from seeking prenatal care (PNC) because of a mandatory policy. The decision tree is divided into a mandatory arm and a voluntary arm. The decision tree continues to branches A and B, which illustrate infant outcomes after maternal HIV testing (branch A) or no maternal HIV testing (branch B). Note that the last-level probability of infant death in branch B depends on whether the mother receives prenatal care. 1 −P = percentage of women who are currently seeking prenatal care; 1 −P = percentage of women who would seek prenatal care under a policy of mandatory testing.

Grahic Jump Location
Grahic Jump Location
Figure 2.
Sensitivity analysis of the decision model for mandatory compared with voluntary HIV testing in pregnant women.

Threshold deterrence rate is the deterrence rate above which a voluntary HIV testing policy is preferred. The horizontal bars illustrate the effects on the deterrence rate of varying each component in the model through its specified range of values (shown at the ends of each bar) while holding the values of the other components at baseline.

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