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SIR—The most common type of medication-related adverse events in older adults is Type A (‘augmented’) adverse drug reactions (ADRs) [1–3]. Type A reactions are an exaggeration of the expected pharmacologic effect of a drug. These ADRs are more predictable, dose dependent and potentially preventable than Type B (‘bizarre’) ADRs (i.e. allergic reactions) [3, 4].
The relationship of different elements of suboptimal prescribing to ADRs in older outpatients has not been adequately explored. Recently, Chrischilles et al.  examined the association between multiple aspects of potentially inappropriate prescribing (defined by explicit criteria for drugs-to-avoid, drug–disease interactions, drug–drug interactions and therapeutic duplication) with self-reported adverse drug events (ADEs). A recent study used a modified weighting system for the medication appropriateness index (MAI), a validated measure that employs a standardised implicit approach to determining prescribing appropriateness, to examine the association of potentially inappropriate prescribing with self-reported ADEs [6, 7]. Neither of the above studies, however, had a specific focus on Type A ADRs.
Given this background, the objective of this study was to determine whether incorrect dosage, incorrect directions, drug–drug interactions and drug–disease interactions, as measured by the MAI, are associated with the Type A ADRs among frail older veterans transitioning from the hospital to the community.
This retrospective cohort study included a random sample of 400 patients from the Geriatric Evaluation and Management (GEM) Drug Study, which examined the impact of GEM care on drug-related problems in 1,388 older veterans from 11 Veterans Affairs Medical Centers (VAMC) . Details about inclusion and exclusion criteria can be found elsewhere . We further restricted the sample to those 359 patients taking one or more high-risk medications (see Supplementary data available in Age and Ageing online; http://www.ageing.oupjournals.org/) [3, 9, 10]. The study was approved by the VAMC Research and Human Subjects Committees at each study site and the Institutional Review Boards of Duke University and the University of Pittsburgh.
Detailed information about data collection and screening for potential drug-related adverse events has been previously published [8, 11]. Briefly a trained research assistant at each site prepared an abstract of each patients VAMC inpatient and outpatient medical chart. A trained research nurse reviewed the abstracted charts and screened for potential drug-related adverse events using a standardised approach. In addition, at the 12 month closeout a trained research clinical pharmacist queried patients for self-reports of potential drug-related adverse events using previously validated methods . For each potential drug-related adverse event identified by chart review and/or patient interview, a trained clinical pharmacist created a detailed narrative based on reporting information required by the Food and Drug Administration MEDWatch program .
The primary outcome measure was any Type A ADR with a causality rating of at least ‘possible’ . Blinded geriatrician and geropharmacist pairs evaluated ADR causality using the narrative and the validated Naranjo ADR causality algorithm . These ADRs were also assessed for type of ADR (i.e. Type A or not) [3, 4]. Any discordances among evaluators regarding the presence or type of ADR were resolved by clinical consensus conference.
The primary independent variables were inappropriate dosage, directions, drug–drug and drug–disease interactions. Physician–pharmacist pairs evaluated each patient's medication regimen for these potential problems using the MAI . Any discordances among evaluators were resolved by clinical consensus conference.
Several factors may confound any relationship between potentially inappropriate prescribing and ADRs and were controlled for in multivariable analyses [5, 7, 9]. Demographic factors included categorical variables for age, race and marital status. Health status factors included continuous measures for the number of high-risk medications, chronic disease status (Charlson Comorbidity Index) and for basic activities of daily living, and a categorical variable for self-rated health [14, 15].
Baseline patient characteristics are presented as either means and standard deviations or frequencies and percents of the respective totals. We used backward selection (alpha = 0.15) multivariate logistic regression to determine covariates to be added along with all four MAI variables in the final model . Hosmer and Lemeshow  testing for goodness of fit was conducted. We also conducted collinearity diagnostic testing. Post hoc we reran the final multivariate logistic regression model replacing the two individual variables for drug–drug interactions and drug–disease interactions with one composite variable that summarised the occurrence of either type of drug interaction. SAS 9.1 software (SAS Institute Inc., Cary, NC, USA) was used to perform all analyses.
Table 1 displays the characteristics of the study sample and Supplementary data available in Age and Ageing online (http://www.ageing.oupjournals.org/) display the rate of high-risk medication use.
Overall, 31.8% of patients experienced one or more Type A ADRs during the follow-up period (median = 1; range 1–7). Only 14% of those with ADRs had more than two.
Table 2 provides information about the frequency of the four MAI prescribing problems and the univariate and multivariate results. Neither dosage nor directions problems were significantly associated with Type A ADRs (P > 0.05). However, there was some evidence (P < 0.10) that both drug–drug interactions (adjusted odds ratio [AOR] 2.37, 95% confidence interval [CI] 0.91–6.11) and drug–disease interactions (AOR 1.93, 95% CI 1.00–3.72) separately were associated with Type A ADRs. Moreover, post hoc analyses revealed that having either type of drug interaction problem increased the risk of Type A ADRs nearly 2-fold (AOR 1.83, 95% CI 1.03–3.25).
To the best of our knowledge this is one of the first studies using standardised implicit methods to examine types of inappropriate prescribing and their association with Type A ADRs as determined by a structured causality algorithm. Our findings provide some evidence of an association of the occurrence of Type A ADRs with both drug–drug and drug–disease interactions. Chrischilles et al.  reported that the use of drugs to avoid, and the occurrence of drug–disease interactions both independently and significantly increased the risk of the occurrence of one or more self-reported ADE. They also reported a trend towards increased risk of ADEs with drug–drug interactions and therapeutic duplication. When these four explicit measures of inappropriate prescribing were combined, a statistically significant relationship with ADEs was demonstrated . Lund et al. , found that a modified summated MAI score, with the highest weights applied to drug–drug and drug–disease interactions, increased the risk of self-reported ADEs.
We did not find a significant relationship between dosage and direction problems and Type A ADRs. One possible explanation is that these two items measured by the MAI only assess whether they are incorrect. Therefore some of these incorrect ratings were due to dosage being too low or directions for use too infrequent which in both cases could lead to decreased drug concentrations and be more likely to be associated with therapeutic failure than with ADRs. This is consistent with the results from the Lund et al. , who assigned these two items a weight of zero and found they were not associated with ADRs.
This study has a number of limitations. First, the association did not achieve statistical significance. Second, the study relied primarily on chart review of information to assess prescribing and ADR causality. We may have underestimated problems if the information was not recorded or was erroneously recorded in the medical chart.
Third, we utilised a modification of the MAI incorporating only four of the original 10 items. This modification has not been independently validated. Moreover, our rate of ADRs discovered only by self-reported potential ADEs may be an underestimate especially in those with cognitive impairment and those who died (nearly 8% of subjects) before the 12 month follow-up period. It is reassuring that a previous study by our group found that nearly 60% of self-reported ADEs were also found during chart screening . It is also possible that we overestimated the use of high-risk drugs as only prescribing and not adherence was assessed. Also the generalisability of our findings is unknown as it involved mostly male frail older veteran outpatients recently discharged from hospital and thus may differ from other ADR studies of older outpatients who were not hospitalised.
Despite these limitations, our results confirm that Type A ADRs are common in frail older outpatients, and provide evidence of an association with drug interactions. Further studies, possibly with larger cohorts, are required to test the reproducibility of these findings. In the meantime, quality improvement activities to reduce ADRs by improving prescribing appropriateness should continue to include a focus on the identification of potential drug–drug and drug–disease interactions.
This study was not funded by outside sources. The original GEM Drug Study  was supported by the National Institutes of Health [R01-AG-15432] and the Veterans Affairs Cooperative Study Program 006. J.T.H. was supported by the following: National Institute of Aging grants (P30AG024827, T32 AG021885, 3U01 AG012553 K07AG033174, R01AG034056, 2R56AG027017), a National Institute of Mental Health grant (R34 MH082682), a National Institute of Nursing Research grant (R01 NR010135), an Agency for Healthcare Research and Quality grants (R01 HS017695 and R01HS018721) and a VA Health Services Research grant (IIR-06-062).