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Introduction and aims: Mental health disorders and substance abuse are risk factors that both precede and follow chronic opioid use. We predicted that incident opioid users would have lower rates of mental health comorbidities than chronic opioid users, but that incident chronic opioid users would have lower rates of mental health comorbidities than prevalent chronic users.Design and methods: We used administrative health claims data to evaluate differences in lifetime mental health and substance abuse comorbidity profiles of people who were prevalent and incident chronic opioid users, as well as those who used opioids acutely. Results were stratified by age.Results: Over 5,188 people were prevalent chronic opioid users at study entry. Of the 10,079 people who initiated opioids, 10.2 percent had a subsequent chronic episode (incident chronic) and the remainder stopped within 90 days (incident acute). In prevalent chronic users compared to incident chronic users, rates of depression and anxiety were higher across all age groups (odds ratio (OR) across age groups range from = 1.60, 95 percent confidence interval (CI) = 1.35,1.89, to OR = 6.66, 95 percent CI = 3.02, 14.69) and prevalence of alcohol abuse was higher in those aged 55 to 74 years (OR = 5.11, 95 percent CI = 1.83, 14.24, p = 0.002). Acute users were less likely than incident chronic users to have depression and anxiety in those aged over 74 years (depression OR = 0.82, 95 percent CI = 0.70, 0.95; anxiety OR = 0.82, 95% CI 0.70, 0.98).Conclusions: Mental health morbidities commonly associated with chronic opioid use increase in prevalence as chronic use continues. Refereed/Peer-reviewed

Background The case-crossover design is suited to medication safety studies but is vulnerable to exposure misclassification. Using the example of tricyclic antidepressants and the risk of hip fracture, we present a data visualisation tool for observing exposure misclassification in case-crossover studies. Methods A case-crossover study was conducted using Australian Government Department of Veterans’ Affairs claims data. Beneficiaries aged over 65 years who were hospitalised for hip fracture between 2009 and 2012 were included. The case window was defined as 1–50 days pre fracture. Control window one and control window two were defined as 101–150 and 151–200 days pre fracture, respectively. Patients were stratified by whether exposure status changed when control window two was specified instead of control window one. To visualise potential misclassification, each subject’s tricyclic antidepressant dispensings were plotted over the 200 days pre fracture. Results The study population comprised 8828 patients with a median age of 88 years. Of these subjects, 348 contributed data to the analyses with either control window. The data visualisation suggested that 14% of subjects were potentially misclassified with control window one while 45% were misclassified with control window two. The odds ratio for the association between tricyclic antidepressants and hip fracture was 1.18 (95% confidence interval = 0.91–1.52) using control window one, whereas risk was significantly increased (odds ratio = 1.43, 95% confidence interval = 1.11–1.83) using control window two. Conclusions Exposure misclassification was less likely to be present with control window one than with an earlier control window, control window two. When specifying different control windows in a case-crossover study, data visualisation can help to assess the extent to which exposure misclassification may contribute to variable results.

Background Antidepressants are associated with adverse effects such as sedation and hypotension, which can result in falls and fractures. Few studies have assessed the risk of hip fracture due to mirtazapine, and no known studies have assessed whether the risk of hip fracture is higher in patients taking other antidepressant medicines in combination with mirtazapine. Objectives This study aimed to examine the risk of hip fracture in older people due to mirtazapine use as well as switching between or concurrently using mirtazapine and other antidepressants. Method A matched case–control study was conducted. Cases were people aged over 65 years who were eligible for Australian Government Department of Veterans’ Affairs (DVA) benefits and who sustained a hip fracture between 2009 and 2012. Each case was matched with up to four randomly selected controls of the same gender and age (± 2 years). Multivariable conditional logistic regression was used to estimate associations between antidepressant use and hip fracture. In order to assess whether combined antidepressant effects differed from the sum of individual effects, the relative excess risk due to interaction (RERI) was calculated. Results The study population comprised 8828 cases and 35,310 controls. The median age of these participants was 88 years and 63% were women. The risk of hip fracture was increased for mirtazapine (continuous use: odds ratio [OR] 1.27, 95% confidence interval [CI] 1.12–1.44). The combinations associated with increased odds of hip fracture were addition of selective serotonin reuptake inhibitors (SSRIs) to mirtazapine (OR 11, 95% CI 2.2–51; RERI 7.7, 95% CI −9.0 to 24), addition of tricyclic antidepressants (TCAs) to mirtazapine (OR 14, 95% CI 1.4–132; RERI 12, 95% CI −19 to 43) and continuous use of both SSRIs and mirtazapine (OR 2.4, 95% CI 1.4–4.2; RERI 0.4, 95% CI −0.9 to 1.7). RERIs indicated that the effect of each antidepressant pair equalled the sum of the effects of individual antidepressant use. There was no evidence of dispensing of lower strength mirtazapine upon introducing TCAs and SSRIs. Conclusions Our results show elevated risk of hip fracture following use of mirtazapine alone and in combination with other antidepressants. The overlapping use of antidepressants may reflect the treatment of comorbidities (e.g. anxiety), switching from mirtazapine to other antidepressants, or add-on therapy. Our results highlight the risks of employing add-on therapy or switching antidepressants in older people, providing further evidence to support cautious cross-tapering where switching between antidepressants is required. Electronic supplementary material The online version of this article (doi:10.1007/s40801-017-0120-y) contains supplementary material, which is available to authorized users.

Background Few studies have assessed the risk of hip fracture following concurrent use of psychoactive medicines, and none has investigated combinations with selective serotonin reuptake inhibitors. Objectives To assess the risk of hip fracture in older people as a result of concurrent use of selective serotonin reuptake inhibitors and other psychoactive medicines. Methods A matched case–control design was employed. Cases were Australian Government Department of Veterans’ Affairs beneficiaries aged over 65 years who experienced a hip fracture between 2009 and 2012. Each case was matched with up to four randomly selected controls of the same age (±2 years) and sex. Medicine-hip fracture associations were estimated via conditional logistic regression. The relative excess risk due to interaction (RERI) was calculated to determine whether combined effects differed from the sum of individual effects. Results There were 8828 cases and 35,310 controls. The median age of subjects was 88 years and 63% were women. The risk of hip fracture was elevated for all medicines assessed individually, most notably selective serotonin reuptake inhibitors (initiation: odds ratio [OR] = 2.7, 95% confidence interval [CI] 2.1, 3.6) and opioids (initiation: OR = 2.3, 95% CI 1.9, 2.9). Combinations associated with an increased odds of hip fracture included addition of benzodiazepines to selective serotonin reuptake inhibitor therapy (OR = 3.0, 95% CI 1.9, 4.8; RERI = 0.9, 95% CI −0.5, 2.3), concurrent use of both opioids and selective serotonin reuptake inhibitors (OR = 2.2, 95% CI 1.9, 2.6; RERI = 0.1, 95% CI −0.3, 0.5), addition of opioids to selective serotonin reuptake inhibitor therapy (OR = 3.2, 95% CI 1.8, 5.5; RERI = −0.1, 95% CI −2.0, 1.7), and initiation of both benzodiazepines and selective serotonin reuptake inhibitors (OR = 4.7, 95% CI 1.7, 13; RERI = 1.3, 95% CI −3.8, 6.3). The RERI results suggested that the effect of each of these medicine combinations equalled the sum of the effects of individual medicine use. Conclusions In older people, the concurrent use of selective serotonin reuptake inhibitors and other psychoactive medicines increased the risk of hip fracture as much as the sum of the risks owing to individual medicine use. Our results highlight the need for prescribers to consider the sedative burden of medicines in each older patient as well as the potential for an additive risk of hip fracture when initiating additional psychoactive therapy.

Objective: To identify factors that contribute to older Australians admitted to hospital with diabetes being re-hospitalised within 30 days of discharge. Design, setting and participants: A retrospective cohort study of Department of Veterans’ Affairs administrative data for all patients hospitalised for diabetes and discharged alive during the period 1 January – 31 December 2012. Main outcome measures: Causes of re-hospitalisation and prevalence of clinical factors associated with re-hospitalisation within 30 days of discharge. Methods: Multivariate logistic regression analysis (backward stepwise) was used to identify characteristics predictive of 30-day re-hospitalisation. Results: 848 people were hospitalised for diabetes; their median age was 87 years (interquartile range, 77–89 years) and 60% were men. 209 patients (24.6%) were re-hospitalised within 30 days of discharge, of whom 77.5% were re-admitted within 14 days of discharge. 51 re-hospitalisations (24%) were for diabetes-related conditions; 41% of those re-admitted within 14 days had not seen their general practitioner between discharge and re-admission. Factors predictive of rehospitalisation included comorbid heart failure (adjusted odds ratio [aOR], 1.49; 95% confidence interval [CI], 1.03–2.17; P = 0.036), numbers of prescribers in previous year (aOR [for each additional prescriber], 1.06; 95% CI, 1.01–1.08; P = 0.031), and two or more hospitalisations in the 6 months before the index admission (aOR, 1.79; 95% CI 1.15–2.78; P = 0.009). Conclusion: Older people hospitalised for diabetes who have comorbid heart failure, multiple recent hospitalisations, and multiple prescribers involved in their care are at greatest risk of being re-admitted to hospital within 30 days. Targeted follow-up during the initial 14 days after discharge may facilitate appropriate interventions that avert re-admission of these at-risk patients. Refereed/Peer-reviewed

This study examined the use of potentially inappropriate medicines that may affect cognition (PIMcog) in people with dementia and its associated factors. Medical records of all outpatients with dementia attending a tertiary hospital in Vietnam between January 1, 2015, and December 31, 2016, were examined. Medicine use was assessed against a list of PIMcog. Variables associated with having a PIMcog were assessed using a multiple logistic regression. Of the 128 patients, 41% used a PIMcog, 39.1% used cholinesterase inhibitors (CEIs) concomitantly with anticholinergics, and 18% used antipsychotics. The number of hospital visits (adjusted odds ratio [OR]: 1.08; 95% confidence interval [CI]: 1.02-1.16) and number of treating specialists (adjusted OR: 0.61; 95% CI: 0.45-0.83) were associated with PIMcog use. This study highlights a high-level use of medicines that can further impair cognition or reduce the effectiveness of CEIs in people with dementia. Efforts to improve quality use of medicines for this population are warranted. Refereed/Peer-reviewed

Background Prescription sequence symmetry analysis (PSSA) is a signal detection method for adverse drug events. Its capacity to consistently detect adverse drug events across different settings has not been tested. We aimed to determine the consistency of PSSA results for detecting positive and negative control adverse drug events across different settings. Methods Using a distributed network model, we analyzed prescription dispensing data using PSSA in Australia, Hong Kong, Japan, Korea, and Taiwan. Positive control was amiodarone and thyroxine, as a marker of amiodarone-induced hypothyroidism, a known adverse event with a clear temporal relationship to amiodarone initiation. Negative controls were amiodarone and allopurinol, as a marker of amiodarone-induced gout and thyroxine and allopurinol, as a marker of thyroxine-induced gout. Gout is not recorded as an adverse event in product information for either medicine. Adjusted sequence ratios (ASR) were calculated for each country. Pooled estimates were obtained by using the generic inverse variance method. Results A positive association was identified between amiodarone and thyroxine in all settings with a pooled ASR 2.63 (95% confidence interval (CI) 1.47–4.72). Temporal analysis showed the effect occurred within the first few weeks of treatment. No significant associations were found for the negative controls in any setting; pooled ASR were 0.76 (95%CI 0.62–0.93) and 0.98 (95%CI 0.85–1.12) for amiodaroneallopurinol and thyroxine-allopurinol, respectively. Conclusion Despite different health settings, different populations, and different patterns of medicine utilization, PSSA gave consistent estimates across countries for a well-known positive association and two negative control adverse events. © 2015 The Authors Pharmacoepidemiology and Drug Safety Published by John Wiley & Sons Ltd. key words—prescription symmetry analysis; pharmacovigilance; amiodarone; hyperthyroidism; pharmacoepidemiology

Background: There are several different frailty measures available for identifying the frail elderly. However, their predictive performance in an Australian population has not been examined. Objective: To examine the predictive performance of four internationally validated frailty measures in an older Australian population. Methods: A retrospective study in the Australian Longitudinal Study of Ageing (ALSA) with 2,087 participants. Frailty was measured at baseline using frailty phenotype (FP), simplified frailty phenotype (SFP), frailty index (FI) and prognostic frailty score (PFS). Odds ratios (OR) were calculated to measure the association between frailty and outcomes at Wave 3 including mortality, hospitalisation, nursing home admission, fall and a combination of all outcomes. Predictive performance was measured by assessing sensitivity, specificity, positive and negative predictive values (PPV and NPV) and likelihood ratio (LR). Area under the curve (AUC) of dichotomised and the multilevel or continuous model of the measures was examined. Results: Prevalence of frailty varied from 2% up to 49% between the measures. Frailty was significantly associated with an increased risk of any outcome, OR (95% confidence interval) for FP: 1.9 (1.4-2.8), SFP: 3.6 (1.5-8.8), FI: 3.4 (2.7-4.3) and PFS: 2.3 (1.8-2.8). PFS had high sensitivity across all outcomes (sensitivity: 55.2-77.1%). The PPV for any outcome was highest for SFP and FI (70.8 and 69.7%, respectively). Only FI had acceptable accuracy in predicting outcomes, AUC: 0.59-0.70. Conclusions: Being identified as frail by any of the four measures was associated with an increased risk of outcomes; however, their predictive accuracy varied. Refereed/Peer-reviewed

Conclusion : This case study highlights that a post-marketing quantitative method utilising administrative claims data can be a complementary tool to traditional quantitative methods employed in spontaneous reports that may help to verify safety signals detected in spontaneous reporting data. Introduction : The objective of post-marketing surveillance of medicines is to rapidly detect adverse drug reactions (ADRs). Early ADR detection will enable policy makers and health professionals to recognise adverse events that may not have been identified in pre-marketing clinical trials. Multiple methods exist for ADR signal detection. Traditional quantitative methods employed in spontaneous reports data have include reporting odds ratio (ROR), proportional reporting ratio (PRR) and Bayesian techniques. With the development of administrative health claims databases, additional methods such as sequence symmetry analysis (SSA) may be able to be employed routinely to confirm ADR signals. Objective & Method : We tested the time to signal detection of quantitative ADR signalling methods in a health claims database (SSA) and in a spontaneous reporting database (ROR, PRR, Bayesian confidence propagation neural network) for rofecoxib-induced myocardial infarction and rosiglitazone-induced heart failure. Results : This study demonstrated that all four signalling methods detected safety signals within 1-3 years of market entry or subsidisation of the medicines, and for both cases the signals were detected before post-marketing clinical trial results. By contrast, the trial results and subsequent warning or withdrawal were published 5-7 years after first marketing of these medicines.

Background: Pain is an indication for total hip arthroplasty (THA) and it should be resolved post-surgery. Because patients' pain is typically treated pharmacologically we tested whether opioid use can be used as a surrogate for patient-reported pain and as an indicator for early surgical failure. Specifically, we evaluated whether the amount of opioids taken within the year after THA was associated with one and five years risk of revision surgery. Methods: A cohort of 9943 THAs (01/2001-12/2012) was evaluated. Post-operative opioid use was the exposure of interest and cumulative daily oral morphine equivalent (OME) amounts were calculated. Total OMEs/90-day periods were categorised into quartiles. Revisions within one and five years were the outcomes of interest. Results: Of the THAs, 2.0 % (N = 200) were revised within one year and 4.2 % (N = 413) within five years. After adjustments for gender, age, surgical indication, co-morbidities, and other analgesics, revision was associated with amount of OMEs in the second quarter after THA (days 91-180 after discharge). Patients on medium-high amounts of OME (400-1119 mg) had higher risk of one (hazard ratio (HR) = 2.22, 95 % CI 1.08-4.56) and five year (HR = 1.66, 95 % CI 1.08-2.56) revision than a patient not taking opioids. During the same period, patients taking the highest amounts of OMEs (≥1120 mg) had a 2.64 (95 % CI 1.03-6.74) times higher risk of one year and a 2.11 (95 % CI 1.13-3.96) times higher risk of five year revision. Conclusions: Opioid use 91-180 days post-surgery is associated with higher risk of revision surgery and therefore is an early and useful indicator for surgical failure. Refereed/Peer-reviewed