Sonia Hernández-Díaz1, Brian T Bateman2, Kristin Palmsten3, Sebastian Schneeweiss2, Krista F Huybrechts2. 1. Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, Massachusetts. 2. Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women's Hospital and Harvard Medical School, Boston, Massachusetts. 3. HealthPartners Institute, Minneapolis, Minnesota.
Abstract
PURPOSE: To evaluate the use of data from population-based surveys such as the National Health and Nutrition Examination Survey (NHANES) for external adjustment for confounders imperfectly measured in health care databases in the United States. METHODS: Our example study used Medicaid Analytic eXtract (MAX) data to estimate the relative risk (RR) for prenatal serotonin-norepinephrine reuptake inhibitors (SNRIs) exposure and cardiac defects. Smoking and obesity are known confounders poorly captured in databases. NHANES collects information on lifestyle factors, depression, and prescription medications. External adjustment requires information on the prevalence of confounders and their association with SNRI use; which was obtained from the NHANES. It also requires estimates of their association with the outcome, which were based on the literature and allowed us to correct the RR using sensitivity analyses. RESULTS: In MAX, the RR for the association between prenatal SNRI exposure and cardiac defects was 1.51 unadjusted and 1.20 adjusted for measured confounders and restricted to women with depression. In NHANES, among women of childbearing age with depression, the prevalence of smoking was 60.2% (95% Confidence Interval 43.2, 74.3) for SNRI users and 44.1% (39.6, 48.8) for nonusers of antidepressants. The corresponding estimates for obesity were 59.2% (43.2, 74.3) and 40.5% (35.9, 45.0), respectively. If the associations between smoking and obesity with cardiac defects are independent from each other and from other measured confounders, additional adjustment for smoking and obesity would move the RR from 1.20 to around 1.10. CONCLUSION: National surveys like NHANES are readily available sources of information on potential confounders and they can be used to assess and improve the validity of RR estimates from observational studies missing data on known risk factors.
PURPOSE: To evaluate the use of data from population-based surveys such as the National Health and Nutrition Examination Survey (NHANES) for external adjustment for confounders imperfectly measured in health care databases in the United States. METHODS: Our example study used Medicaid Analytic eXtract (MAX) data to estimate the relative risk (RR) for prenatal serotonin-norepinephrine reuptake inhibitors (SNRIs) exposure and cardiac defects. Smoking and obesity are known confounders poorly captured in databases. NHANES collects information on lifestyle factors, depression, and prescription medications. External adjustment requires information on the prevalence of confounders and their association with SNRI use; which was obtained from the NHANES. It also requires estimates of their association with the outcome, which were based on the literature and allowed us to correct the RR using sensitivity analyses. RESULTS: In MAX, the RR for the association between prenatal SNRI exposure and cardiac defects was 1.51 unadjusted and 1.20 adjusted for measured confounders and restricted to women with depression. In NHANES, among women of childbearing age with depression, the prevalence of smoking was 60.2% (95% Confidence Interval 43.2, 74.3) for SNRI users and 44.1% (39.6, 48.8) for nonusers of antidepressants. The corresponding estimates for obesity were 59.2% (43.2, 74.3) and 40.5% (35.9, 45.0), respectively. If the associations between smoking and obesity with cardiac defects are independent from each other and from other measured confounders, additional adjustment for smoking and obesity would move the RR from 1.20 to around 1.10. CONCLUSION: National surveys like NHANES are readily available sources of information on potential confounders and they can be used to assess and improve the validity of RR estimates from observational studies missing data on known risk factors.
Authors: Krista F Huybrechts; Kristin Palmsten; Helen Mogun; Mary Kowal; Jerry Avorn; Soko Setoguchi-Iwata; Sonia Hernández-Díaz Journal: Gen Hosp Psychiatry Date: 2013-01-30 Impact factor: 3.238
Authors: Krista F Huybrechts; Kristin Palmsten; Jerry Avorn; Lee S Cohen; Lewis B Holmes; Jessica M Franklin; Helen Mogun; Raisa Levin; Mary Kowal; Soko Setoguchi; Sonia Hernández-Díaz Journal: N Engl J Med Date: 2014-06-19 Impact factor: 91.245