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      Differential risk of death in older residents in nursing homes prescribed specific antipsychotic drugs: population based cohort study

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          Abstract

          Objective To assess risks of mortality associated with use of individual antipsychotic drugs in elderly residents in nursing homes.

          Design Population based cohort study with linked data from Medicaid, Medicare, the Minimum Data Set, the National Death Index, and a national assessment of nursing home quality.

          Setting Nursing homes in the United States.

          Participants 75 445 new users of antipsychotic drugs (haloperidol, aripiprazole, olanzapine, quetiapine, risperidone, ziprasidone). All participants were aged ≥65, were eligible for Medicaid, and lived in a nursing home in 2001-5.

          Main outcome measures Cox proportional hazards models were used to compare 180 day risks of all cause and cause specific mortality by individual drug, with propensity score adjustment to control for potential confounders.

          Results Compared with risperidone, users of haloperidol had an increased risk of mortality (hazard ratio 2.07, 95% confidence interval 1.89 to 2.26) and users of quetiapine a decreased risk (0.81, 0.75 to 0.88). The effects were strongest shortly after the start of treatment, remained after adjustment for dose, and were seen for all causes of death examined. No clinically meaningful differences were observed for the other drugs. There was no evidence that the effect measure modification in those with dementia or behavioural disturbances. There was a dose-response relation for all drugs except quetiapine.

          Conclusions Though these findings cannot prove causality, and we cannot rule out the possibility of residual confounding, they provide more evidence of the risk of using these drugs in older patients, reinforcing the concept that they should not be used in the absence of clear need. The data suggest that the risk of mortality with these drugs is generally increased with higher doses and seems to be highest for haloperidol and least for quetiapine.

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          Most cited references32

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          High-dimensional propensity score adjustment in studies of treatment effects using health care claims data.

          Adjusting for large numbers of covariates ascertained from patients' health care claims data may improve control of confounding, as these variables may collectively be proxies for unobserved factors. Here, we develop and test an algorithm that empirically identifies candidate covariates, prioritizes covariates, and integrates them into a propensity-score-based confounder adjustment model. We developed a multistep algorithm to implement high-dimensional proxy adjustment in claims data. Steps include (1) identifying data dimensions, eg, diagnoses, procedures, and medications; (2) empirically identifying candidate covariates; (3) assessing recurrence of codes; (4) prioritizing covariates; (5) selecting covariates for adjustment; (6) estimating the exposure propensity score; and (7) estimating an outcome model. This algorithm was tested in Medicare claims data, including a study on the effect of Cox-2 inhibitors on reduced gastric toxicity compared with nonselective nonsteroidal anti-inflammatory drugs (NSAIDs). In a population of 49,653 new users of Cox-2 inhibitors or nonselective NSAIDs, a crude relative risk (RR) for upper GI toxicity (RR = 1.09 [95% confidence interval = 0.91-1.30]) was initially observed. Adjusting for 15 predefined covariates resulted in a possible gastroprotective effect (0.94 [0.78-1.12]). A gastroprotective effect became stronger when adjusting for an additional 500 algorithm-derived covariates (0.88 [0.73-1.06]). Results of a study on the effect of statin on reduced mortality were similar. Using the algorithm adjustment confirmed a null finding between influenza vaccination and hip fracture (1.02 [0.85-1.21]). In typical pharmacoepidemiologic studies, the proposed high-dimensional propensity score resulted in improved effect estimates compared with adjustment limited to predefined covariates, when benchmarked against results expected from randomized trials.
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            Performance of comorbidity scores to control for confounding in epidemiologic studies using claims data.

            Comorbidity is an important confounder in epidemiologic studies. The authors compared the predictive performance of comorbidity scores for use in epidemiologic research with administrative databases. Study participants were British Columbia, Canada, residents aged >or=65 years who received angiotensin-converting enzyme inhibitors or calcium channel blockers at least once during the observation period. Six scores were computed for all 141,161 participants during the baseline year (1995-1996). Endpoints were death and health care utilization during a 12-month follow-up (1996-1997). Performance was measured by using the c statistic ranging from 0.5 for chance prediction of outcome to 1.0 for perfect prediction. In logistic regression models controlling for age and gender, four scores based on the International Classification of Diseases, Ninth Revision (ICD-9) generally performed better at predicting 1-year mortality (c = 0.771, c = 0.768, c = 0.745, c = 0.745) than medication-based Chronic Disease Score (CDS)-1 and CDS-2 (c = 0.738, c = 0.718). Number of distinct medications used was the best predictor of future physician visits (R(2) = 0.121) and expenditures (R(2) = 0.128) and a good predictor of mortality (c = 0.745). Combining ICD-9 and medication-based scores improved the c statistics (1.7% and 6.2%, respectively) for predicting mortality. Generalizability of results may be limited to an elderly, predominantly White population with equal access to state-funded health care.
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              Antipsychotic drug use and mortality in older adults with dementia.

              Antipsychotic drugs are widely used to manage behavioral and psychological symptoms in dementia despite concerns about their safety. To examine the association between treatment with antipsychotics (both conventional and atypical) and all-cause mortality. Population-based, retrospective cohort study. Ontario, Canada. Older adults with dementia who were followed between 1 April 1997 and 31 March 2003. The risk for death was determined at 30, 60, 120, and 180 days after the initial dispensing of antipsychotic medication. Two pairwise comparisons were made: atypical versus no antipsychotic use and conventional versus atypical antipsychotic use. Groups were stratified by place of residence (community or long-term care). Propensity score matching was used to adjust for differences in baseline health status. A total of 27,259 matched pairs were identified. New use of atypical antipsychotics was associated with a statistically significant increase in the risk for death at 30 days compared with nonuse in both the community-dwelling cohort (adjusted hazard ratio, 1.31 [95% CI, 1.02 to 1.70]; absolute risk difference, 0.2 percentage point) and the long-term care cohort (adjusted hazard ratio, 1.55 [CI, 1.15 to 2.07]; absolute risk difference, 1.2 percentage points). Excess risk seemed to persist to 180 days, but unequal rates of censoring over time may have affected these results. Relative to atypical antipsychotic use, conventional antipsychotic use was associated with a higher risk for death at all time points. Sensitivity analysis revealed that unmeasured confounders that increase the risk for death could diminish or eliminate the observed associations. Information on causes of death was not available. Many patients did not continue their initial treatments after 1 month of therapy. Unmeasured confounders could affect associations. Atypical antipsychotic use is associated with an increased risk for death compared with nonuse among older adults with dementia. The risk for death may be greater with conventional antipsychotics than with atypical antipsychotics.

                Author and article information

                Contributors
                Role: instructor in medicine
                Role: assistant professor
                Role: board of governors professor
                Role: professor of clinical psychiatry
                Role: professor of medicine
                Role: programmer
                Role: assistant research professor
                Role: associate professor of medicine
                Journal
                BMJ
                bmj
                BMJ : British Medical Journal
                BMJ Publishing Group Ltd.
                0959-8138
                1468-5833
                2012
                2012
                23 February 2012
                : 344
                : e977
                Affiliations
                [1 ]Division of Pharmacoepidemiology and Pharmacoeconomics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston 02120, MA, United States
                [2 ]Ernest Mario School of Pharmacy, Rutgers University, New Brunswick, NJ 08901
                [3 ]Columbia University, New York, NY 10032
                [4 ]Institute for Health, Health Care Policy and Aging Research, Rutgers University, New Brunswick, NJ 08901
                Author notes
                Correspondence to: K F Huybrechts khuybrechts@ 123456partners.org
                Article
                huyk000030
                10.1136/bmj.e977
                3285717
                22362541
                6f5cc17d-d771-412d-9923-2fc437314428
                © Huybrechts et al 2012

                This is an open-access article distributed under the terms of the Creative Commons Attribution Non-commercial License, which permits use, distribution, and reproduction in any medium, provided the original work is properly cited, the use is non commercial and is otherwise in compliance with the license. See: http://creativecommons.org/licenses/by-nc/2.0/ and http://creativecommons.org/licenses/by-nc/2.0/legalcode.

                History
                : 29 December 2011
                Categories
                Research
                Health Policy
                Epidemiologic Studies
                Memory Disorders (Neurology)
                Drugs: Psychiatry
                Memory Disorders (Psychiatry)
                Psychotic Disorders (Incl Schizophrenia)
                Health Service Research

                Medicine
                Medicine

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