Case-crossover studies of therapeutics: design approaches to addressing time-varying prognosis in elderly populations

Immeasurable Time Bias in Self-controlled Designs: Case-crossover, Case-time-control, and Case-case-time-control Analyses

BACKGROUND

Impact of immeasurable time bias (IMTB) is yet to be examined in self-controlled designs.

METHODS

We conducted case-crossover, case-time-control, and case-case-time-control analyses using Korea's healthcare database. Two empirical examples among elderly patients were used: 1) benzodiazepines-hip fracture; 2) benzodiazepines-mortality. For cases, the date of hip fracture diagnosis or death was defined as the index date, and the inherited date of their matched cases for controls or future cases. Exposure was assessed in the 1-30 day (hazard) and 61-90 day (control) windows preceding the index date. A non-missing exposure setting included in- and outpatient prescriptions and the pseudo-outpatient setting included only the outpatients. Conditional logistic regression was done to estimate odds ratios (ORs) with 95% confidence intervals (CIs), where the relative difference in OR among the two settings was calculated to quantify the IMTB.

RESULTS

The IMTB had negligible impacts in the hip fracture example in the case-crossover (non-missing exposure setting OR 1.27; 95% CI, 1.12-1.44; pseudo-outpatient setting OR 1.21; 95% CI, 1.06-1.39; magnitude 0.05), case-time-control (OR 1.18; 95% CI, 0.98-1.44; OR 1.13; 95% CI, 0.92-1.38; 0.04, respectively), and case-case-time-control analyses (OR 0.99; 95% CI, 0.80-1.23; OR 0.94; 95% CI, 0.75-1.18; 0.05, respectively). In the mortality example, IMTB had significant impacts in the case-crossover (non-missing exposure setting OR 1.44; 95% CI, 1.36-1.52; pseudo-outpatient setting OR 0.72; 95% CI, 0.67-0.78; magnitude 1.00), case-time-control (OR 1.38; 95% CI, 1.26-1.51; OR 0.68; 95% CI, 0.61-0.76; 1.03, respectively), and case-case-time-control analyses (OR 1.27; 95% CI, 1.15-1.40; OR 0.62; 95% CI, 0.55-0.69; 1.05, respectively).

CONCLUSION

Although IMTB had negligible impacts on the drug's effect on acute events, as these are unlikely to be accompanied with hospitalizations, it negatively biased the drug's effect on mortality, an outcome with prodromal phases, in the three self-controlled designs.

Systematic Review and Meta-Analysis on the Associations of Polypharmacy and Potentially Inappropriate Medication With Adverse Outcomes in Older Cancer Patients

BACKGROUND

Both polypharmacy and potentially inappropriate medication (PIM) intake are highly prevailing in older cancer patients. However, only studies on the association of polypharmacy and postoperative complications have been meta-analyzed previously.

METHODS

A systematic review and a meta-analysis of prospective/retrospective observational studies reporting associations of polypharmacy or PIM with at least one out of five predefined adverse health outcomes in a population of older cancer patients (≥60 years) were carried out. PubMed and Web of Science were used to search for relevant studies published between January 1991 and March 2020. Data were pooled by adopting a random-effects model.

RESULTS

Overall, 42 publications were included in the systematic review. Meta-analyses could be performed on 39 studies about polypharmacy and 13 studies about PIM. Polypharmacy was found to be statistically significantly associated with all-cause mortality (risk ratio [95% confidence interval]: 1.37 [1.25-1.50]), hospitalization (1.53 [1.37-1.71]), treatment-related toxicity (1.22 [1.01-1.47]), and postoperative complications (1.73 [1.36-2.20]). The association of polypharmacy with prolongation of hospitalization was not statistically significant at the p < .05 significance level (1.62 [0.98-2.66]). With respect to PIM, a statistically significant association with all-cause mortality (1.43 [1.08-1.88]) was observed but not with other adverse outcomes.

CONCLUSIONS

Polypharmacy was found to be associated with several adverse outcomes and PIM use with all-cause mortality in older cancer patients. However, these results should be interpreted with caution because about three-quarters of the studies identified did not adjust for comorbidity and are prone to confounding by indication.

Coprescription of Opioids With Other Medications and Risk of Opioid Overdose

Polypharmacy is common among patients taking prescription opioids long-term, and the codispensing of interacting medications may further increase opioid overdose risk. To identify nonopioid medications that may increase opioid overdose risk in this population, we conducted a case-crossover-based screening of electronic claims data from IBM MarketScan and Optum Clinformatics Data Mart spanning 2003 through 2019. Eligible patients were 18 years of age or older and had at least 180 days of continuous enrollment and 90 days of prescription opioid use immediately before an opioid overdose resulting in an emergency room visit or hospitalization. The main analysis quantified the odds ratio (OR) between opioid overdose and each nonopioid medication dispensed in the 90 days immediately before the opioid overdose date after adjustment for prescription opioid dosage and benzodiazepine codispensing. Additional analyses restricted to patients without cancer diagnoses and individuals who used only oxycodone for 90 days immediately before the opioid overdose date. The false discovery rate (FDR) was used to account for multiple testing. We identified 24,866 individuals who experienced opioid overdose. Baclofen (OR 1.56; FDR < 0.01; 95% confidence interval (CI), 1.29 to 1.89), lorazepam (OR 1.53; FDR < 0.01; 95% CI, 1.25 to 1.88), and gabapentin (OR 1.16; FDR = 0.09; 95% CI, 1.04 to 1.28), among other nonopioid medications, were associated with opioid overdose. Similar patterns were observed in noncancer patients and individuals who used only oxycodone. Interventions may focus on prescribing safer alternatives when a potential for interaction exists.

Drug-Drug Interaction Surveillance Study: Comparing Self-Controlled Designs in Five Empirical Examples in Real-World Data

Self-controlled designs, specifically the case-crossover (CCO) and the self-controlled case series (SCCS), are increasingly utilized to generate real-world evidence (RWE) on drug-drug interactions (DDIs). Although these designs share the advantages and limitations of within-individual comparison, they also have design-specific assumptions. It is not known to what extent the differences in assumptions lead to different results in RWE DDI analyses. Using a nationwide US commercial healthcare insurance database (2006-2016), we compared the CCO and SCCS designs, as they are implemented in DDI studies, within five DDI-outcome examples: (1) simvastatin + clarithromycin and muscle-related toxicity; (2) atorvastatin + valsartan, and muscle-related toxicity; and (3-5) dabigatran + P-glycoprotein inhibitor (clarithromycin, amiodarone, and verapamil) and bleeding. Analyses were conducted within person-time exposed to the object drug (statins and dabigatran) and adjusted for bias associated with the inhibiting drugs via control groups of individuals unexposed to the object drug. The designs yielded similar estimates in most examples, with SCCS displaying better statistical efficiency. With both designs, results varied across sensitivity analyses, particularly in CCO analyses with small number of exposed individuals. Analyses in controls revealed substantial bias that may be differential across DDI-exposed and control individuals. Thus, both designs showed no association between amiodarone or verapamil and bleeding in dabigatran-exposed but revealed strong positive associations in controls. Overall, bias adjustment via a control group had a larger impact on results than the choice of a design, highlighting the importance and challenges of appropriate control group selection for adequate bias control in self-controlled analyses of DDIs.

Control yourself: ISPE-endorsed guidance in the application of self-controlled study designs in pharmacoepidemiology

PURPOSE

Consensus is needed on conceptual foundations, terminology and relationships among the various self-controlled "trigger" study designs that control for time-invariant confounding factors and target the association between transient exposures (potential triggers) and abrupt outcomes. The International Society for Pharmacoepidemiology (ISPE) funded a working group of ISPE members to develop guidance material for the application and reporting of self-controlled study designs, similar to Standards of Reporting Observational Epidemiology (STROBE). This first paper focuses on navigation between the types of self-controlled designs to permit a foundational understanding with guiding principles.

METHODS

We leveraged a systematic review of applications of these designs, that we term Self-controlled Crossover Observational PharmacoEpidemiologic (SCOPE) studies. Starting from first principles and using case examples, we reviewed outcome-anchored (case-crossover [CCO], case-time control [CTC], case-case-time control [CCTC]) and exposure-anchored (self-controlled case-series [SCCS]) study designs.

RESULTS

Key methodological features related to exposure, outcome and time-related concerns were clarified, and a common language and worksheet to facilitate the design of SCOPE studies is introduced.

CONCLUSIONS

Consensus on conceptual foundations, terminology and relationships among SCOPE designs will facilitate understanding and critical appraisal of published studies, as well as help in the design, analysis and review of new SCOPE studies. This manuscript is endorsed by ISPE.

Study designs and statistical approaches to suicide and prevention research in real-world data

OBJECTIVE

To provide researchers, clinicians and policy makers with a primer to study designs, statistical approaches and graphical reporting methods for suicide research in real world data (RWD).

METHODS

Study designs, statistical method and graphical reporting standards are detailed with examples from the recently published literature.

RESULTS

Data sources and codes for identifying suicidal behavior are described. Study designs are described in detail for post-market surveillance, retrospective cohort studies, case control and nested case-control studies, and self-controlled (within-individual) studies including applications of marginal structural models. Graphical reporting of designs is described using an original research study.

CONCLUSIONS

Compared to RCTs, RWE studies offer larger sample sizes, greater generalizability, and real-world validity. However, these non-experimental data risk uncontrolled confounding and potential introduction of bias unless data, design and statistical approaches are rigorously aligned.

Association of Opioid Use Disorder Treatment With Alcohol-Related Acute Events

IMPORTANCE

Persons with opioid use disorder (OUD) and co-occurring alcohol use disorder (AUD) are understudied and undertreated. It is unknown whether the use of medications to treat OUD is associated with reduced risk of alcohol-related morbidity.

OBJECTIVE

To determine whether the use of OUD medications is associated with decreased risk for alcohol-related falls, injuries, and poisonings in persons with OUD with and without co-occurring AUD.

DESIGN, SETTING, AND PARTICIPANTS

This recurrent-event, case-control, cohort study used prescription claims from IBM MarketScan insurance databases from January 1, 2006, to December 31, 2016. The sample included persons aged 12 to 64 years in the US with an OUD diagnosis and taking OUD medication who had at least 1 alcohol-related admission. The unit of observation was person-day. Data analysis was performed from June 26 through September 28, 2020.

EXPOSURES

Days of active OUD medication prescriptions, with either agonist (ie, buprenorphine or methadone) or antagonist (ie, oral or extended-release naltrexone) treatments compared with days without OUD prescriptions.

MAIN OUTCOMES AND MEASURES

The primary outcome was admission for any acute alcohol-related event defined by International Classification of Diseases, Ninth Revision and International Statistical Classification of Diseases and Related Health Problems, Tenth Revision codes. Conditional logistic regression was used to compare OUD medication use between days with and without an alcohol-related event. Stratified analyses were conducted between patients with OUD with and without a recent AUD diagnostic code.

RESULTS

There were 8 424 214 person-days of observation time among 13 335 participants who received OUD medications and experienced an alcohol-related admission (mean [SD] age, 33.1 [13.1] years; 5884 female participants [44.1%]). Agonist treatments (buprenorphine and methadone) were associated with reductions in the odds of any alcohol-related acute event compared with nontreatment days, with a 43% reduction for buprenorphine (odds ratio [OR], 0.57; 95% CI, 0.52-0.61) and a 66% reduction for methadone (OR, 0.34; 95% CI, 0.26-0.45). The antagonist treatment naltrexone was associated with reductions in alcohol-related acute events compared with nonmedication days, with a 37% reduction for extended-release naltrexone (OR, 0.63; 95% CI, 0.52-0.76) and a 16% reduction for oral naltrexone (OR, 0.84; 95% CI, 0.76-0.93). Naltrexone use was more prevalent among patients with OUD with recent AUD claims than their peers without AUD claims.

CONCLUSIONS AND RELEVANCE

These findings suggest that OUD medication is associated with fewer admissions for alcohol-related acute events in patients with OUD with co-occurring AUD.

New methodological approaches were able to effectively reduce immeasurable time bias in case-only designs

OBJECTIVES

The objective of this study was to assess approaches to reduce immeasurable time bias in case-crossover (CCO), case-time-control (CTC), and case-case-time-control (CCTC) designs.

STUDY DESIGN AND SETTING

We used Korea's health care database that has inpatient and outpatient prescriptions and an empirical example of benzodiazepines and mortality among the elderly. We defined our unbiased exposure setting using all prescriptions and a pseudo-outpatient setting using outpatient records only. In the pseudo-outpatient setting, we assessed 10 approaches of restricting, adjusting, stratifying, or weighting on hospitalization-related factors. We conducted conditional logistic regression to estimate odds ratio (OR) with 95% confidence intervals (CI), where an approach was considered effective when its OR was within the unbiased exposure setting OR's 95% CI.

RESULTS

Immeasurable time bias negatively biased the unbiased exposure setting's OR in all three case-only designs, overestimating the protective effect of benzodiazepines on mortality. Of the 10 approaches examined, stratifying the proportion of hospitalized time in 0.01 intervals most effectively repaired the bias in the CCO (OR 1.25, 95% CI 1.10-1.43) and CTC analyses (1.11, 0.95-1.30); no approach was effective in the CCTC analysis.

CONCLUSION

Stratifying the proportion of hospitalized time in 0.01 intervals best approximated the unbiased exposure setting estimate by overcoming the significant impact of immeasurable time bias in CCO and CTC designs.

Comparison of Self-controlled Designs for Evaluating Outcomes of Drug-Drug Interactions: Simulation Study

BACKGROUND

Self-controlled designs, both case-crossover and self-controlled case series, are well suited for evaluating outcomes of drug-drug interactions in electronic healthcare data. Their comparative performance in this context, however, is unknown.

METHODS

We simulated cohorts of patients exposed to two drugs: a chronic drug (object) and a short-term drug (precipitant) with an associated interaction of 2.0 on the odds ratio scale. We analyzed cohorts using case-crossover and self-controlled case series designs evaluating exposure to the precipitant drug within person-time exposed to the object drug. Scenarios evaluated violations of key design assumptions: (1) time-varying, within-person confounding; (2) time trend in precipitant drug exposure prevalence; (3) nontransient precipitant exposure; and (4) event-dependent object drug discontinuation.

RESULTS

Case-crossover analysis produced biased estimates when 30% of patients persisted on the precipitant drug (estimated OR 2.85) and when the use of the precipitant drug was increasing in simulated cohorts (estimated OR 2.56). Self-controlled case series produced biased estimates when patients discontinued the object drug following the occurrence of an outcome (estimated incidence ratio [IR] of 2.09 [50% of patients stopping therapy] and 2.22 [90%]). Both designs yielded similarly biased estimates in the presence of time-varying, within-person confounding.

CONCLUSION

In settings with independent or rare outcomes and no substantial event-dependent censoring (<50%), self-controlled case series may be preferable to case-crossover design for evaluating outcomes of drug-drug interactions. With frequent event-dependent drug discontinuation, a case-crossover design may be preferable provided there are no time-related trends in drug exposure.

Comparison of Different Case-Crossover Variants in Handling Exposure-Time Trend or Persistent-User Bias: Using Dipeptidyl Peptidase-4 Inhibitors and the Risk of Heart Failure as an Example

OBJECTIVES

Inappropriate use of the case-crossover design, which is efficient for examining associations between brief exposure and abrupt outcomes, in evaluating the effects of medications in the presence of exposure-time trends or persistent drug use may generate spurious associations. We compared different approaches to adjusting for these sources of bias by examining the risk of heart failure hospitalization (HFH) associated with dipeptidyl peptidase-4 (DPP-4) inhibitors. Overall, existing evidence does not suggest a higher risk of HFH associated with DPP-4 inhibitors; however, case-crossover analyses of these medications may be susceptible to bias.

METHODS

We conducted case-crossover; age, sex, risk-set (ASR) matched case-time-control; disease risk score (DRS)-matched case-time-control; and case-case-time-control analyses to assess the association between DPP-4 inhibitors and HFH among patients with diabetes mellitus (DM) in a population-based Taiwanese database. We also examined metformin and sulfonylureas, both with assumed null associations.

RESULTS

Among 362 022 DM patients, 4105 (case-crossover), 4103 (ASR-matched case-time-control), 3957 (DRS-matched case-time-control), and 2812 (case-case-time-control) HFH cases were identified. The OR for DPP-4 inhibitors and HFH was elevated in the case-crossover analysis (1.52; 95% confidence interval [95% CI] 0.95-2.42). The ASR-matched case-time control, DRS-matched case-time-control, and case-case-time control analyses yielded near-null associations (0.90 [95% CI 0.45-1.83], 0.96 [95% CI 0.46-2.02], and 0.92 [95% CI 0.39-2.21], respectively). Null effects were observed for metformin across designs and for sulfonylureas in the case-case-time control analysis.

CONCLUSIONS

Our case-crossover analysis suggested DPP-4 inhibitors may be associated with HFH; however, each method for adjusting for exposure-time and persistent user bias attenuated the findings. The case-case-time-control analysis had the least precision.

The Case-Crossover Design for Drug-Drug Interactions: Considerations for Implementation

BACKGROUND

The case-crossover design may be useful for evaluating the clinical impact of drug-drug interactions in electronic healthcare data; however, experience with the design in this context is limited.

METHODS

Using US healthcare claims data (1994-2013), we evaluated two examples of interacting drugs with prior evidence of harm: (1) cytochrome P450 (CYP)3A4-metabolized statins + clarithromycin or erythromycin and rhabdomyolysis; and (2) clopidogrel + fluoxetine or fluvoxamine and ischemic events. We conducted case-crossover analyses with (1) a three-parameter model with a product term and a six-parameter saturated model that distinguished initiation order of the two drugs; and (2) with or without active comparators.

RESULTS

In the statin example, the three-parameter model produced estimates consistent with prior evidence with the active comparator (product term odds ratio [OR] = 2.05, 95% confidence interval [CI] = 1.00, 4.23) and without (OR = 1.99, 95% CI = 1.04, 3.81). In the clopidogrel example, this model produced results opposite of expectation (OR = 0.78, 95% = 0.68, 0.89), but closer to what was observed in prior studies when active comparator was used (OR = 1.03, 95% CI = 0.90, 1.19). The saturated model revealed heterogeneity of estimates across strata and considerable confounding; strata with concordant clopidogrel exposure likely produced the least biased estimates.

CONCLUSION

The three-parameter model assumes a common drug-drug interaction effect, whereas the saturated model is useful for identifying potential effect heterogeneity or differential confounding across strata. Restriction to certain strata or use of an active comparator may be necessary in the presence of within-person confounding.

Potentially Inappropriate Prescribing Among Older Persons: A Meta-Analysis of Observational Studies

PURPOSE

Potentially inappropriate prescribing (PIP) is a common yet preventable medical error among older persons in primary care. It is uncertain whether PIP produces adverse outcomes in this population, however. We conducted a systematic review with meta-analysis to pool the adverse outcomes of PIP specific to primary care.

METHOD

We searched PubMed, Embase, CINAHL, Web of Science, Scopus, PsycINFO, and previous review articles for studies related to "older persons," "primary care," and "inappropriate prescribing." Two reviewers selected eligible articles, extracted data, and evaluated the risk of bias. Meta-analysis was conducted to pool studies with similar PIP criteria and outcome measures.

RESULTS

Of the 2,804 articles identified, we included 8 articles with a total of 77,624 participants. All included studies had cohort design and low risk of bias. Although PIP did not affect mortality (risk ratio [RR] 0.98; 95% CI, 0.93-1.05), it was significantly associated with the other available outcomes, including emergency room visits (RR 1.63; 95% CI, 1.32-2.00), adverse drug events (RR 1.34; 95% CI, 1.09-1.66), functional decline (RR 1.53; 95% CI, 1.08-2.18), health-related quality of life (standardized mean difference -0.26; 95% CI, -0.36 to -0.16), and hospitalizations (RR 1.25; 95% CI, 1.09-1.44). A majority of the pooled estimates had negligible heterogeneity.

CONCLUSIONS

This meta-analysis provides consolidated evidence on the wide-ranging impact of PIP among older persons in primary care. It highlights the need to identify PIP in primary care, calls for further research on PIP interventions in primary care, and points to the need to consider potential implications when deciding on the operational criteria of PIP.

A Case-Crossover-Based Screening Approach to Identifying Clinically Relevant Drug-Drug Interactions in Electronic Healthcare Data

We sought to develop a semiautomated screening approach using electronic healthcare data to identify drug-drug interactions (DDIs) that result in clinical outcomes. Using a case-crossover design with 30-day hazard and referent windows, we evaluated codispensed drugs (potential precipitants) in 7,801 patients who experienced rhabdomyolysis while on cytochrome P450 (CYP)3A4-metabolized statins and in 15,147 who experienced bleeding while on dabigatran. Estimates of direct associations between precipitant drugs and outcomes were used to adjust for bias and precipitants' direct effects. The P values were adjusted for multiple testing using the false discovery rate (FDR). From among 460 drugs codispensed with statins, 1 drug (clarithromycin) generated an alert (adjusted odds ratio (OR) 5.83, FDR < 0.05). From among 485 drugs codispensed with dabigatran, 2 drugs (naproxen and enoxaparin, ORs 2.50 and 2.75; FDR < 0.05) generated an alert. All three signals reflected known pharmacologic interactions, confirming the potential of case-crossover-based approaches for DDI screening in electronic healthcare data.

Data Mining for Adverse Drug Events With a Propensity Score-matched Tree-based Scan Statistic

The tree-based scan statistic is a statistical data mining tool that has been used for signal detection with a self-controlled design in vaccine safety studies. This disproportionality statistic adjusts for multiple testing in evaluation of thousands of potential adverse events. However, many drug safety questions are not well suited for self-controlled analysis. We propose a method that combines tree-based scan statistics with propensity score-matched analysis of new initiator cohorts, a robust design for investigations of drug safety. We conducted plasmode simulations to evaluate performance. In multiple realistic scenarios, tree-based scan statistics in cohorts that were propensity score matched to adjust for confounding outperformed tree-based scan statistics in unmatched cohorts. In scenarios where confounding moved point estimates away from the null, adjusted analyses recovered the prespecified type 1 error while unadjusted analyses inflated type 1 error. In scenarios where confounding moved point estimates toward the null, adjusted analyses preserved power, whereas unadjusted analyses greatly reduced power. Although complete adjustment of true confounders had the best performance, matching on a moderately mis-specified propensity score substantially improved type 1 error and power compared with no adjustment. When there was true elevation in risk of an adverse event, there were often co-occurring signals for clinically related concepts. TreeScan with propensity score matching shows promise as a method for screening and prioritization of potential adverse events. It should be followed by clinical review and safety studies specifically designed to quantify the magnitude of effect, with confounding control targeted to the outcome of interest.

Risk of Hypersensitivity to Biologic Agents Among Medicare Patients With Rheumatoid Arthritis

BACKGROUND

Hypersensitivity reactions (HSRs) can occur with any of the available biologic agents used to treat rheumatoid arthritis (RA). We compared drug-specific risks for HSRs among RA patients enrolled in the US Medicare program.

METHODS

Using Medicare data, we identified new users of infused infliximab, abatacept, rituximab, tocilizumab, golimumab, and injected biologic agents. After identifying HSRs using validated algorithms, for each biologic agent, we calculated the cumulative incidence over 6 months and the incidence rates (IRs) in 0-1, 2-14, and 15-30 days of administration. For each biologic agent administration, followup started on the infusion/injection date and ended at HSR, subsequent biologic agent administration, death, coverage loss, 30-day followup, or December 31, 2013, whichever occurred first. Adjusted robust Poisson regression was used to compare the HSR risks across biologic agents. A sensitivity analysis was conducted using a nested case-crossover design.

RESULTS

We identified 725,591 biologic agent administrations and 248 HSRs among 80,587 new users of biologic agents. Of these, 26.9% occurred in users of intravenous abatacept, 4.6% in rituximab, 5.8% in intravenous tocilizumab, 22.9% in infliximab, and 39.7% in injectable anti-tumor necrosis factor inhibitors (anti-TNFi). The cumulative incidence of HSRs over 6 months for all biologic agents was low (<1%).The IRs for HSRs ranged from 2.4 (abatacept) to 239.5 (rituximab) per 10⁶ person-days. After adjustment, and using injectable anti-TNFi over 0-30 days as the referent, rituximab, infliximab, abatacept, and tocilizumab infusions were associated with a statistically significant higher risk of HSR. The sensitivity analysis yielded similar results.

CONCLUSION

Among RA patients taking biologic agents, rituximab and infliximab were most strongly associated with HSRs. The absolute IRs of HSR events for all biologic agent exposures were low.

The Association of Potentially Inappropriate Medication at Older Age With Cardiovascular Events and Overall Mortality: A Systematic Review and Meta-Analysis of Cohort Studies

OBJECTIVE

The aim of this systematic review was to identify, evaluate, and meta-analyze cohort studies reporting the association of potentially inappropriate medication (PIM) intake with mortality and cardiovascular events.

DESIGN

A systematic review and meta-analysis of prospective and retrospective cohort studies were conducted. Study appraisal included a thorough risk of bias assessment. Data synthesis followed a random-effects model.

DATA SOURCES

The included studies were retrieved from the databases MEDLINE and ISI Web of Knowledge. Additionally, the authors checked the references of the included studies for further relevant literature.

ELIGIBILITY CRITERIA FOR SELECTING STUDIES

For inclusion in a study, the population needed to be older than 60 years of age and not restricted to having one specific disease. The outcome had to address all-cause mortality or cardiovascular events. Studies that examined polypharmacy or specific drugs were excluded.

RESULTS

At first, 13 studies were included in a meta-analysis. The association of PIM with overall mortality was not statistically significant (risk ratio; 95% confidence interval, 1.13; 0.95-1.35). However, the majority of studies showed a high risk of specific forms of bias. These biases can be excluded by applying a new user design. It ascertains that adverse events occurring early in therapy are recorded. After restricting the meta-analysis to three studies with a new user design, the association of PIM use and mortality was statistically significant (risk ratio; 95% confidence interval, 1.59; 1.45-1.75). Only one study focused on cardiovascular events and found no statistically significant association. However, the study was not conducted with a new user design.

CONCLUSION

In studies with adequate methods (new user design), PIM use, defined by Beers criteria or the HEDIS-DAE list, was associated with a 1.6-fold increased mortality in older adults. Physicians should therefore avoid prescribing PIM for older adults whenever feasible. Further new user design studies are required for cardiovascular outcomes and to compare the predictive value of different PIM criteria for mortality.

Are metformin, statin and aspirin use still associated with overall mortality among colorectal cancer patients with diabetes if adjusted for one another?

BACKGROUND

Metformin, statin and aspirin use seem associated with decreased mortality in cancer patients, though, without adjusting for one another. Independent associations of these drugs with overall mortality after colorectal cancer (CRC) diagnosis within glucose-lowering drugs (GLDs) users were assessed.

METHODS

Patients starting GLDs before CRC diagnosis (1998-2011) were selected from the Eindhoven Cancer Registry linked with the PHARMO Database Network. The Cox regression model, with time since CRC diagnosis, included time-dependent variables of cumulative exposure to metformin, statins and aspirin after cancer diagnosis and time-dependent ever-never terms for drug exposure.

RESULTS

A total of 1043 patients used GLDs before CRC diagnosis; 666 (64%) used metformin, 639 (61%) used statins and 490 (47%) used aspirin after CRC diagnosis. Multivariable analyses revealed that longer cumulative exposure to metformin was not associated with overall mortality (HRCumulative exposure/6 months 1.02; 95% CI 0.97-1.07), whereas the favourable effect of statins increased with cumulative exposure (HRCumulative exposure/6 months 0.93; 95% CI 0.89-0.98). No association between aspirin use and overall mortality was seen (HRCumulative exposure/6 months 0.98; 95% CI 0.93-1.03).

CONCLUSIONS

No independent association between cumulative exposure to metformin, aspirin and overall mortality was found. Cumulative exposure to statins after CRC diagnosis was associated with lower overall mortality, supporting a drug effect of statins among GLDs users.

Association between potentially inappropriate medications from the Beers criteria and the risk of unplanned hospitalization in elderly patients

BACKGROUND

Predisposition to adverse drug events with advancing age has led to the development of lists of potentially inappropriate medications (PIMs) to be avoided in the elderly, such as the Beers Criteria. The prevalence of Beers medications has been studied widely, but it is still unclear whether PIM use is predictive of adverse events in older people.

OBJECTIVES

To examine potential associations between exposure to PIMs from the general Beers list and unplanned hospitalizations in elderly Western Australians.

METHODS

Using an enhanced case-time-control design and conditional logistic regression applied to the pharmaceutical claims and other linked health data of 251 305 Western Australians aged ≥65 years (1993-2005), odds ratios for unplanned hospitalization were obtained, from which attributable fractions, number and proportion of hospitalizations associated with drug exposure were derived.

RESULTS

Based on the health profiles of 383 150 hospitalized index subjects, overall PIM exposure was associated with an elevated risk of unplanned hospitalization (adjusted odds ratio = 1.18; 95% confidence interval = 1.15-1.21), this estimated risk increasing with the number of different PIMs and PIM quantity taken. Fifteen percent of unplanned hospitalizations in exposed index subjects (1980 per year) were attributed to PIM exposure. Patients taking meperidine (pethidine), nitrofurantoin, promethazine, indomethacin, and thioridazine appeared to be at particularly high risk of unplanned hospitalization, whereas temazepam, oxazepam, diazepam, digoxin, amiodarone, ferrous sulfate, and naproxen were attributed the greatest numbers of unplanned hospitalizations.

CONCLUSIONS

Due caution prescribing Beers medications in the elderly seems justified, paying particular attention to PIMs listed above and to the concurrent use of multiple PIMs. Our results also support the retention of specific medications on PIM lists in future developments.