Mary K. Kowal

Antidepressant Use in Pregnancy and the Risk of Cardiac Defects

BACKGROUND Whether the use of selective serotonin-reuptake inhibitors (SSRIs) and other antidepressants during pregnancy is associated with an increased risk of congenital cardiac defects is uncertain. In particular, there are concerns about a possible association between paroxetine use and right ventricular outflow tract obstruction and between sertraline use and ventricular septal defects. METHODS We performed a cohort study nested in the nationwide Medicaid Analytic eXtract for the period 2000 through 2007. The study included 949,504 pregnant women who were enrolled in Medicaid during the period from 3 months before the last menstrual period through 1 month after delivery and their liveborn infants. We compared the risk of major cardiac defects among infants born to women who took antidepressants during the first trimester with the risk among infants born to women who did not use antidepressants, with an unadjusted analysis and analyses that restricted the cohort to women with depression and that used propensity-score adjustment to control for depression severity and other potential confounders. RESULTS A total of 64,389 women (6.8%) used antidepressants during the first trimester. Overall, 6403 infants who were not exposed to antidepressants were born with a cardiac defect (72.3 infants with a cardiac defect per 10,000 infants), as compared with 580 infants with exposure (90.1 per 10,000 infants). Associations between antidepressant use and cardiac defects were attenuated with increasing levels of adjustment for confounding. The relative risks of any cardiac defect with the use of SSRIs were 1.25 (95% confidence interval [CI], 1.13 to 1.38) in the unadjusted analysis, 1.12 (95% CI, 1.00 to 1.26) in the analysis restricted to women with depression, and 1.06 (95% CI, 0.93 to 1.22) in the fully adjusted analysis restricted to women with depression. We found no significant association between the use of paroxetine and right ventricular outflow tract obstruction (relative risk, 1.07; 95% CI, 0.59 to 1.93) or between the use of sertraline and ventricular septal defects (relative risk, 1.04; 95% CI, 0.76 to 1.41). CONCLUSIONS The results of this large, population-based cohort study suggested no substantial increase in the risk of cardiac malformations attributable to antidepressant use during the first trimester. (Funded by the Agency for Healthcare Research and Quality and the National Institutes of Health.).

National trends in antidepressant medication treatment among publicly insured pregnant women

OBJECTIVE The risk of depression in women is greatest at childbearing age. We sought to examine and explain national trends in antidepressant use in pregnant women. METHODS This was a cohort study including pregnant women aged 12-55 who were enrolled in Medicaid during 2000-2007. We examined the proportion of women taking antidepressants during pregnancy by patient characteristics (descriptive), by region (mixed-effects model) and over time (interrupted time series). RESULTS We identified 1,106,757 pregnancies in 47 states; mean age was 23 years, and 60% were nonwhite. Nearly 1 in 12 used an antidepressant during pregnancy. Use was higher for older (11.2% for age ≥30 vs. 7.6% for <30) and white (14.4% vs. 4.0% for nonwhite) women. There was a four- to fivefold difference in rate of antidepressant use among states. Of the 5.3% of women taking antidepressants at conception, 33% and 17% were still on treatment 90 and 180 days, respectively, into pregnancy; an additional 4% began use during pregnancy. Labeled pregnancy-related health advisories did not appear to affect antidepressant use. CONCLUSIONS Antidepressant use during pregnancy remains high in this population; treatment patterns vary substantially by patient characteristics and region. Comparative safety and effectiveness data to help inform treatment choices are needed in this setting.

Harnessing the Medicaid Analytic eXtract (MAX) to Evaluate Medications in Pregnancy: Design Considerations

Background In the absence of clinical trial data, large post-marketing observational studies are essential to evaluate the safety and effectiveness of medications during pregnancy. We identified a cohort of pregnancies ending in live birth within the 2000–2007 Medicaid Analytic eXtract (MAX). Herein, we provide a blueprint to guide investigators who wish to create similar cohorts from healthcare utilization data and we describe the limitations in detail. Methods Among females ages 12–55, we identified pregnancies using delivery-related codes from healthcare utilization claims. We linked women with pregnancies to their offspring by state, Medicaid Case Number (family identifier) and delivery/birth dates. Then we removed inaccurate linkages and duplicate records and implemented cohort eligibility criteria (i.e., continuous and appropriate enrollment type, no private insurance, no restricted benefits) for claim information completeness. Results From 13,460,273 deliveries and 22,408,810 child observations, 6,107,572 pregnancies ending in live birth were available after linkage, cleaning, and removal of duplicate records. The percentage of linked deliveries varied greatly by state, from 0 to 96%. The cohort size was reduced to 1,248,875 pregnancies after requiring maternal eligibility criteria throughout pregnancy and to 1,173,280 pregnancies after further applying infant eligibility criteria. Ninety-one percent of women were dispensed at least one medication during pregnancy. Conclusions Mother-infant linkage is feasible and yields a large pregnancy cohort, although the size decreases with increasing eligibility requirements. MAX is a useful resource for studying medications in pregnancy and a spectrum of maternal and infant outcomes within the indigent population of women and their infants enrolled in Medicaid. It may also be used to study maternal characteristics, the impact of Medicaid policy, and healthcare utilization during pregnancy. However, careful attention to the limitations of these data is necessary to reduce biases.

Validity of maternal and infant outcomes within nationwide Medicaid data

The aim of this study is to assess the validity of preeclampsia, congenital cardiac malformations, and persistent pulmonary hypertension of the newborn (PPHN) diagnoses in the US Medicaid Analytic eXtract (MAX), a nationwide health care utilization database that may be useful for perinatal research.

Prescription Drug Insurance Coverage and Patient Health Outcomes: A Systematic Review

Previous reviews have shown that changes in prescription drug insurance benefits can affect medication use and adherence. We conducted a systematic review of the literature to identify studies addressing the association between prescription drug coverage and health outcomes. Studies were included if they collected empirical data on expansions or restrictions of prescription drug coverage and if they reported clinical outcomes. We found 23 studies demonstrating that broader prescription drug insurance reduces use of other health care services and has a positive impact on patient outcomes. Coverage gaps or caps on drug insurance generally led to worse outcomes. States should consider implementing the Affordable Care Act expansions in drug coverage to improve the health of low-income patients receiving state-based health insurance.

Automatable algorithms to identify nonmedical opioid use using electronic data: a systematic review

Objective Improved methods to identify nonmedical opioid use can help direct health care resources to individuals who need them. Automated algorithms that use large databases of electronic health care claims or records for surveillance are a potential means to achieve this goal. In this systematic review, we reviewed the utility, attempts at validation, and application of such algorithms to detect nonmedical opioid use. Materials and Methods We searched PubMed and Embase for articles describing automatable algorithms that used electronic health care claims or records to identify patients or prescribers with likely nonmedical opioid use. We assessed algorithm development, validation, and performance characteristics and the settings where they were applied. Study variability precluded a meta-analysis. Results Of 15 included algorithms, 10 targeted patients, 2 targeted providers, 2 targeted both, and 1 identified medications with high abuse potential. Most patient-focused algorithms (67%) used prescription drug claims and/or medical claims, with diagnosis codes of substance abuse and/or dependence as the reference standard. Eleven algorithms were developed via regression modeling. Four used natural language processing, data mining, audit analysis, or factor analysis. Discussion Automated algorithms can facilitate population-level surveillance. However, there is no true gold standard for determining nonmedical opioid use. Users must recognize the implications of identifying false positives and, conversely, false negatives. Few algorithms have been applied in real-world settings. Conclusion Automated algorithms may facilitate identification of patients and/or providers most likely to need more intensive screening and/or intervention for nonmedical opioid use. Additional implementation research in real-world settings would clarify their utility.

Erratum to: Changing Interactions Between Physician Trainees and the Pharmaceutical Industry: A National Survey

reported receiving gifts when they attended medical schools that received higher levels of NIH funding (odds ratio (OR) 0.51, 95 % confidence interval (CI) 0.38-0.67, p<0.001). However, there was also a non-significant negative correlation between receiving gifts and a school’s AMSA score (OR 0.83, 95 % CI 0.61-1.12, p=0.21). Since medical students’ exposure to pharmaceutical marketing is more strongly related to the school’s NIH funding level, policymakers seeking to further insulate students from industry marketing could focus their resources on less research-intensive medical schools. Students in more research-intensive schools (OR 1.36, 95 % CI 1.00-1.85, p=0.052) and in schools with high AMSA scores (OR 1.29, 95 % CI 0.92-1.80, p=0.14) were more likely to report that receiving gifts would affect their prescribing practices, although neither association was statistically significant. Neither NIH funding level (OR 0.87, 95 % CI 0.51-1.48, p=0.60) nor AMSA score (OR 1.14, 95 % CI 0.70-1.85, p=0.24) was correlated with students’ report of “adequate separation” between school faculty and the pharmaceutical industry. We apologize for the erroneous rows in the Figure.