Literature DB >> 11679797

Paradoxical relations of drug treatment with mortality in older persons.

R J Glynn1, E L Knight, R Levin, J Avorn.   

Abstract

Medication use patterns provide popular surrogate measures of disease, yet selective under-use of drugs by elderly patients with potentially unmeasured comorbidity may lead to artifactual "protective" associations between use of specific drugs and mortality. We examined the relation between use of 20 common classes of drugs and mortality among the 129,111 residents of New Jersey 65-99 years of age who had at least one hospitalization during the years 1991-1994 and filled prescriptions through either Medicaid or that state's Pharmacy Assistance for the Aged and Disabled program. Each study drug class was used by more than 5,000 subjects during the 120 days before hospitalization; 41,930 subjects died in the hospital or during the year after discharge. Users of drugs from each of seven therapeutic classes had reduced age- and sex-adjusted rates of death relative to non-users: lipid-lowering agents, nonsteroidal anti-inflammatory agents, beta blockers, thiazides, glaucoma drugs, calcium channel blockers, and anti-anxiety drugs. Adjustment for comorbidity and polypharmacy had little effect on these results. We found similar results in a separate nonhospitalized cohort of 132,071 elderly persons. Much of this observed association appears to be nonetiologic. These findings raise concerns about using observational studies in high-risk populations to infer associations between drug use and outcomes.

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Year:  2001        PMID: 11679797     DOI: 10.1097/00001648-200111000-00017

Source DB:  PubMed          Journal:  Epidemiology        ISSN: 1044-3983            Impact factor:   4.822


  79 in total

1.  Improved comorbidity adjustment for predicting mortality in Medicare populations.

Authors:  Sebastian Schneeweiss; Philip S Wang; Jerry Avorn; Robert J Glynn
Journal:  Health Serv Res       Date:  2003-08       Impact factor: 3.402

Review 2.  Healthy user and related biases in observational studies of preventive interventions: a primer for physicians.

Authors:  William H Shrank; Amanda R Patrick; M Alan Brookhart
Journal:  J Gen Intern Med       Date:  2011-01-04       Impact factor: 5.128

3.  Propensity score methods for confounding control in nonexperimental research.

Authors:  M Alan Brookhart; Richard Wyss; J Bradley Layton; Til Stürmer
Journal:  Circ Cardiovasc Qual Outcomes       Date:  2013-09-10

4.  Analytic strategies to adjust confounding using exposure propensity scores and disease risk scores: nonsteroidal antiinflammatory drugs and short-term mortality in the elderly.

Authors:  Til Stürmer; Sebastian Schneeweiss; M Alan Brookhart; Kenneth J Rothman; Jerry Avorn; Robert J Glynn
Journal:  Am J Epidemiol       Date:  2005-05-01       Impact factor: 4.897

5.  Adjusting effect estimates for unmeasured confounding with validation data using propensity score calibration.

Authors:  Til Stürmer; Sebastian Schneeweiss; Jerry Avorn; Robert J Glynn
Journal:  Am J Epidemiol       Date:  2005-06-29       Impact factor: 4.897

Review 6.  Indications for propensity scores and review of their use in pharmacoepidemiology.

Authors:  Robert J Glynn; Sebastian Schneeweiss; Til Stürmer
Journal:  Basic Clin Pharmacol Toxicol       Date:  2006-03       Impact factor: 4.080

Review 7.  Developments in post-marketing comparative effectiveness research.

Authors:  S Schneeweiss
Journal:  Clin Pharmacol Ther       Date:  2007-06-06       Impact factor: 6.875

8.  Increasing levels of restriction in pharmacoepidemiologic database studies of elderly and comparison with randomized trial results.

Authors:  Sebastian Schneeweiss; Amanda R Patrick; Til Stürmer; M Alan Brookhart; Jerry Avorn; Malcolm Maclure; Kenneth J Rothman; Robert J Glynn
Journal:  Med Care       Date:  2007-10       Impact factor: 2.983

9.  Immortal time bias in pharmacoepidemiological studies on cancer patient survival: empirical illustration for beta-blocker use in four cancers with different prognosis.

Authors:  Janick Weberpals; Lina Jansen; Myrthe P P van Herk-Sukel; Josephina G Kuiper; Mieke J Aarts; Pauline A J Vissers; Hermann Brenner
Journal:  Eur J Epidemiol       Date:  2017-09-01       Impact factor: 8.082

10.  Propensity scores for confounder adjustment when assessing the effects of medical interventions using nonexperimental study designs.

Authors:  T Stürmer; R Wyss; R J Glynn; M A Brookhart
Journal:  J Intern Med       Date:  2014-02-13       Impact factor: 8.989

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