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ABSTRACT
ObjectivesIn 2001 the Australian government circumvented traditional drug subsidy listing processes to create the Herceptin Program (HP) for HER2 metastatic breast cancer (MBC) patients; in 2006 trastuzumab was subsidised for early breast cancer (EBC) on the Pharmaceutical Benefits Scheme (PBS) creating opportunities for data linkage and observing patients across both treatment settings. Clinical trials established the efficacy of trastuzumab for MBC in trastuzumab-naïve patients but little is known about patients initiating trastuzumab in the adjuvant setting and subsequently receiving trastuzumab for MBC. We characterise patterns of treatment and outcomes for these patients in Australia. We estimate time from adjuvant trastuzumab initiation to metastatic trastuzumab initiation and overall survival (OS).
ApproachWe identified a cohort of patients with a trastuzumab claim in both PBS and HP records. We calculated duration of EBC treatment as date of first trastuzumab dispensing until 21 days after the last PBS dispensing for trastuzumab or observation of a trastuzumab dispensing record in HP claims, whichever came first. We used Kaplan-Meier methods to estimate time-to-MBC treatment and OS.
ResultsOf 11,477 EBC patients treated with trastuzumab between December 2006 and December 2012, 637 were also treated with trastuzumab for MBC. Median age at EBC treatment initiation was 53 (range 17 – 87). 94% of EBC patients received a taxane, anthracycline, cyclophosphamide and/or carboplatin as part of their treatment protocol. 61% of MBC patients received trastuzumab with a taxane, 18% as monotherapy and 7% with capecitabine. Median duration of trastuzumab therapy in EBC and MBC therapy was 11.3 (95% CI 10.7-11.6) and 9.3 (8.6-10.4) months, respectively. Median time-to-MBC therapy was 27.3 (25.5-28.2) months. Median OS from initiation of EBC and MBC treatment was 58.1 (52.9-65.7) and 23.2 (20.5-25.4) months, respectively.
ConclusionHER2BC patients initiating trastuzumab for EBC therapy and progressing to MBC treatment are little examined in the clinical trial and observational literature. Australia's unique funding arrangements allowed us to clearly distinguish between treatment for EBC and MBC which is not always possible in dispensing claims alone. We demonstrated: most EBC patients are treated according to guideline recommendations; that this patient cohort receive approximately 20 months of trastuzumab therapy across both settings; and median OS from initiation of MBC trastuzumab is approximately 2 months shorter than in the pivotal clinical trial (25.1 months).

AbstractIn this research, we assess whether the number of public comments filed in response to proposed agency rules has dramatically increased as a result of the automation of the submission process. Specifically, we compare the volume of comment activity across two large sets of rules issued by the Department of Transportation, one that occurred before the launch of an agency‐wide electronic docket system and another that occurred after this launch in 1998. Our analysis shows that, contrary to expectations held by many researchers and practitioners, the overall levels and patterns of stakeholder behavior showed a remarkable degree of similarity across the two periods. This finding implies that public involvement in rulemaking is not likely to become vastly more prevalent in the information age, confounding both hopes of democratization of the process and fears of costly and harmful mass participation.

IntroductionDispensing claims are used commonly as proxy measures in pharmacoepidemiological studies, however, their validity is often untested.
Objectives and ApproachWe quantified the level of ascertainment and potential biases arising when using dispensing claims to identify incident cancer cases in cohort studies. We used Department of Veterans' Affairs client data linked with the New South Wales (NSW) Cancer Registry and Repatriation and Pharmaceutical Benefits Scheme data. We included clients aged ≥65 residing in NSW between July 2004 and December 2012. We matched clients with a cancer diagnosis to clients without a diagnosis based on demographic characteristics and available observation time. We used dispensing claims for anticancer medicines dispensed between July 2004 and December 2013 as a proxy for cancer diagnosis and calculated sensitivity, specificity, positive predictive values and negative predictive values compared with cancer registry data (gold standard), overall and by cancer site. We illustrated the potential for misclassification by the proxy in a cohort of people initiating opioid therapy.
ResultsWe identified 15,679 incident cancer diagnoses in 14,112 clients from the cancer registry and 62,663 clients without a diagnosis. The proxy's sensitivity was 30% for all cancers and ranged from 10-67% for specific cancers. Specificity was >90% for all cancers. The proxy correctly identified 26% of people with a cancer diagnosis who initiated opioid therapy, failed to identify 74% those with a cancer diagnosis, and was most robust for clients with breast cancer (61% were correctly identified).
Conclusion / ImplicationsUse of anticancer medicine dispensings for identifying people with incident cancer diagnosis is a poor proxy. Excluding people with evidence of anticancer medicine dispensing from cohort studies may remove a disproportionate number of women with breast cancer. Researchers excluding or otherwise using anticancer medicine dispensing to identify people with cancer in pharmacoepidemiological studies should acknowledge the potential biases introduced to their findings.

IntroductionDispensing claims are used commonly as proxy measures in pharmacoepidemiological studies; however, their validity is often untested.
ObjectivesTo assess the performance of a proxy for identifying cancer cases based on the dispensing of anticancer medicines and estimate the misclassification of cancer status and potential for bias researchers may encounter when using this proxy.
MethodsWe conducted our validation study using Department of Veterans' Affairs (DVA) client data linked with the New South Wales (NSW) Cancer Registry and Repatriation Pharmaceutical Benefits Scheme data. We included DVA clients aged ≥65 years residing in NSW between July 2004 and December 2012. We matched clients with a cancer diagnosis to clients without a diagnosis based on demographic characteristics and available observation time. We used dispensing claims for anticancer medicines dispensed between July 2004 and December 2013 as a proxy to identify clients with cancer and calculated sensitivity, specificity, positive predictive values and negative predictive values compared with cancer registrations (gold standard), overall and by cancer site. We illustrated misclassification by the proxy in a cohort of people initiating opioid therapy. Using the proxy, we excluded people with cancer from the cohort, in an attempt to delineate people potentially using opioids for cancer rather than chronic non-cancer pain.
ResultsWe identified 15,679 new cancer diagnoses in 14,112 DVA clients from the cancer registry and 62,663 clients without a diagnosis. Sensitivity of the proxy based on dispensing claims was 30% for all cancers and around 20% for specific cancers (range: 10-67%). Specificity was above 90% for all cancers. The dispensing proxy correctly identified 26% of people with a cancer diagnosis who initiated opioid therapy and failed to identify 74% those with a cancer diagnosis; the proxy was most robust for clients with breast cancer where 61% were correctly identified by proxy.
ConclusionsDispensings of anticancer medicines as a proxy for people with a cancer diagnosis performed poorly. Excluding patients with evidence of anticancer medicine use from cohort studies may result removal of a disproportionate number of women with breast cancer. Researchers excluding or otherwise using anticancer medicine dispensing to identify people with cancer in pharmacoepidemiological studies should acknowledge the potential biases introduced to their findings.

The Australian Asthma Handbook does not recommend use of fixed dose combination (FDC) controller medicines for asthma in children aged ≤5 years. FDCs are only recommended in children and adolescents (aged 6–18 years) not responding to initial inhaled corticosteroid (ICS) therapy. Using Pharmaceutical Benefits Scheme dispensing claims from 2013–2018, we examined the annual incident FDC dispensing and the incident FDC dispensing without prior ICS up to 365 days. We also determined cost of FDCs to government and patients. During 2013–2018, there were 35,635 FDC initiations and 31,368 (88%) did not have a preceding ICS dispensing. The annual incidence of FDC dispensing declined from 14.7 to 7.2/1000 children. Incidence of FDC dispensing/1000 children without a preceding ICS declined from 2.1 to 0.5 in children aged 1–2 years, 7.2 to 1.7 in 3–5 years, 14.8 to 5.1 in 6–11 years, and 18.6 to 11.9 in ≥12years. The cost of FDCs was 7.8 million Australian dollars (AUD); of which 4.4 million AUD was to government and 3.3 million AUD was to patient. Despite inappropriate dispensing of FDCs in children aged ≤5 years, incidence of FDC dispensing and more importantly incidence without a preceding ICS is declining in Australia.

IntroductionMedicineInsight is a database containing de-identified electronic health records (EHRs) from over 700 Australian general practices. Previous research validated algorithms used to derive medical condition flags in MedicineInsight, but the accuracy of data fields following EHR extractions from clinical practices and data warehouse transformation processes have not been formally validated.
ObjectivesTo examine the accuracy of the extraction and transformation of EHR fields for selected demographics, observations, diagnoses, prescriptions, and tests into MedicineInsight.
MethodsWe benchmarked MedicineInsight values against those recorded in original EHRs. Forty-six general practices contributing data to MedicineInsight met our eligibility criteria, eight were randomly selected, and four agreed to participate. We randomly selected 200 patients ≥ 18 years of age within each participating practice from MedicineInsight. Trained staff reviewed the original EHRs for the selected patients and recorded data from the relevant fields. We calculated the percentage of agreement (POA) between MedicineInsight and EHR data for all fields; Cohen's Kappa for categorical and intra-class correlation (ICC) for continuous measures; and sensitivity, specificity, and positive and negative predictive values (PPV/NPV) for diagnoses.
ResultsA total of 796 patients were included in our analysis. All demographic characteristics, observations, diagnoses, prescriptions and random pathology test results had excellent (> 90%) POA, Kappa, and ICC. POA for most recent pathology/imaging test was moderate (81%, [95% CI: 78% to 84%]). Sensitivity, specificity, PPV, and NPV were excellent (> 90%) for all but one of the examined diagnoses which had a poor PPV.
ConclusionsOverall, our study shows good agreement between the majority of MedicineInsight data and those from original EHRs, suggesting MedicineInsight data extraction and warehousing procedures accurately conserve the data in these key fields. Discrepancies between test data may have arisen due to how data from pathology, radiology and other imaging providers are stored in EHRs and MedicineInsight and this requires further investigation.

This study was sponsored by Allergan (prior to its acquisition by AbbVie). This research was further supported by the National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Medicines Intelligence (ID: 1196900). HZ is supported by a UNSW Scientia Programme Award. CB is supported by an Australian Government Research Training Programme PhD scholarship. Publisher Copyright: © ; Objective Medical therapy can halt or significantly slow the progression of glaucoma if medicines are used in accordance with the guidelines. We used dispensing claims for a 10% sample of all Australians dispensed publicly subsidised glaucoma medicines to determine the prevalence and incidence of glaucoma medicine treatment and to examine treatment persistence between July 2012 and June 2019. Methods We estimated incidence and prevalence per 10 000 population for Australian financial years (1 July to 30 June). We defined prevalence as at least one dispensing of any glaucoma medicine and incidence as a dispensing of any glaucoma medicine with no previous dispensing during the preceding 12 months. We estimated duration of treatment for a cohort initiating glaucoma medicines and used Kaplan-Meier methods to estimate the proportion of people persisting on treatment at 6, 12, 18 and 36 months after initiation. We stratified analyses by the number of repeats prescribed at initiation, age, sex and medicine class. Results Prevalence remained stable over the study period at around 180/10 000 people/year; incidence was also stable around 36/10 000/year. Among 34 900 people initiating glaucoma medicines, 37.0% remained on treatment at 6 months from initiation, 29.8% at 12 months and 19.2% at 36 months. Median duration of treatment was 13.2 months (IQR: 2.5 - not reached) for people initiating prostaglandin analogues and less than 3 months for those initiating other medicine classes. Conclusion Prevalence and incidence of glaucoma treatment have not changed in Australia over the past decade. Persistence to treatment increased with age but remained poor throughout the study period. ; Peer reviewed

Front Cover -- Contents -- Foreword -- Acknowledgments -- About the Authors -- Abbreviations -- Introduction -- How to read this book -- The DIME Wiki: A complementary resource -- Standardizing data work -- Standardizing coding practices -- The team behind this book -- Looking ahead -- References -- Chapter 1 Conducting reproducible, transparent, and credible research -- Developing a credible research project -- Conducting research transparently -- Analyzing data reproducibly and preparing a reproducibility package -- Looking ahead -- References -- Chapter 2 Setting the stage for effective and efficient collaboration -- Preparing a collaborative work environment -- Organizing code and data for replicable research -- Preparing to handle confidential data ethically -- Looking ahead -- References -- Chapter 3 Establishing a measurement framework -- Documenting data needs -- Translating research design to data needs -- Creating research design variables by randomization -- Looking ahead -- References -- Chapter 4 Acquiring development data -- Acquiring data ethically and reproducibly -- Collecting high-quality data using electronic surveys -- Handling data securely -- Looking ahead -- References -- Chapter 5 Cleaning and processing research data -- Making data "tidy" -- Implementing data quality checks -- Processing confidential data -- Preparing data for analysis -- Looking ahead -- References -- Chapter 6 Constructing and analyzing research data -- Creating analysis data sets -- Writing analysis code -- Creating reproducible tables and graphs -- Increasing efficiency of analysis with dynamic documents -- Looking ahead -- References -- Chapter 7 Publishing reproducible research outputs -- Publishing research papers and reports -- Preparing research data for publication -- Publishing a reproducible research package -- Looking ahead -- References.

Publisher's version (útgefin grein) ; Background Proton pump inhibitor (PPI) use is widespread. There have been increasing concerns about overuse of high-dose PPIs for durations longer than clinically necessary. Objective To evaluate the impact of national education initiatives on reducing PPI use in Australia. Design Population-based, controlled interrupted time series analysis of PPI dispensing claims data for Australian adults from July 2012 to June 2018; we used statin dispensing as a control. Interventions A year-long educational initiative led by NPS MedicineWise (previously the National Prescribing Service) from April 2015. Simultaneously, Choosing Wisely released recommendations in April 2015 and May 2016. Both promoted review of prolonged PPI use and encouraged stepping down or ceasing treatment, where appropriate. Measurements We examined monthly changes in PPI (and statin) dispensing (stratified by high, standard and low tablet strength), rates of switching from higher to lower strength PPIs and rates of PPI (and statin) discontinuation. Results We observed 12 040 021 PPI dispensings to 579 594 people. We observed a sustained -1.7% (95% CI: -2.7 to -0.7%) decline in monthly dispensing of standard strength PPIs following the initiatives until the end of the study period. There were no significant changes in high or low strength PPI (or statin) dispensings, switching to lower strength PPIs, or PPI (and statin) treatment discontinuation. Conclusion Our findings suggest that these educational initiatives alone were insufficient in curbing overuse of PPIs on a national level. Concerted efforts with policy levers such as imposing tighter restrictions on subsidised use of PPIs may be more effective. Noting low strength esomeprazole is not publicly subsidised in Australia, availability of these preparations may also facilitate more appropriate practice ; This research is supported by the National Health and Medical Research Council (NHMRC) Centre of Research Excellence in Medicines and Ageing (ID: 1060407) and a Cooperative Research Centre Project (CRC-P) Grant from the Australian Government Department of Industry, Innovation and Science (ID: CRC-P-439). Dr Zoega was supported by a Scientia Fellowship from UNSW Sydney. Dr Schaffer was supported by a NHMRC Early Career Fellowship (#1158763). ; Peer Reviewed

IntroductionLinked data are increasingly used in pharmacoepidemiology studies to enhance value beyond that which can be achieve from stand-alone pharmaceutical data. The complexity of pharmaceutical data can make any linked data analysis challenging and it is imperative that this is matched by the human capacity to perform this work.
Objectives and ApproachResearch is needed to understand the state of the current pharmacoepidemiology workforce and to prioritise its capacity building needs. We aim to profile the Australian pharmacoepidemiology workforce to explore views, needs, priority areas and perspectives relevant to capacity building. Participants are the regular pharmacoepidemiology workforce (Group 1) and senior medicines stakeholders (Group 2). Following a literature review and consultation with a group of key informants, we developed survey and interview instruments for each group. We piloted the instruments in February 2018 and study data collection is planned for March 2018. We will use a mixed-methods approach to analyse the data.
ResultsWe conducted a review of existing literature and identified workforce views, needs and priorities at four levels: personal, team, organisation and wider community. During the consultative process, the informants highlighted the multidisciplinary nature of the pharmacoepidemiology workforce including many with non-health related backgrounds. They also raised concerns about attracting applicants with suitable skills and experience, job satisfaction, career progression and workforce retention. We developed instruments to (i) further explore these issues, (ii) ascertain their experience with linked health data, (iii) determine their training needs, and, (iv) learn about their future intentions. We will present findings on issues pertinent to the Australian pharmacoepidemiology landscape and suggest priorities for building workforce capacity.
Conclusion/ImplicationsThis study will provide empirical evidence to support and prioritise capacity building in the Australian pharmacoepidemiology workforce to improve their ability to work with linked data. The instruments that we developed and findings may be relevant to phamacoepidemiology workforce in other countries and other emerging fields that use linked data.

IntroductionHCTZ is first-line treatment for hypertension and among the most commonly used medicines in Australia. Recent evidence suggests increased risks of lip and skin cancers in association with HCTZ use.
Objectives and ApproachTo determine the risk of SCC of the lip and melanoma among people prescribed HCTZ in Australia we conducted a case-control study nested within a cohort of Department of Veterans' Affairs clients 65 years and older in 2004-2015. We identified incident cases of SCC of the lip (lip cancer) and of cutaneous melanoma (malignant melanoma), each matched by sex and age with up to 20 controls through risk-set sampling. We ascertained HCTZ use from dispensing data and classified use according to ever/never use and cumulative use. We estimated odds ratios (ORs) associating HCTZ use with lip cancer and malignant melanoma using conditional logistic regression, adjusting for predefined confounders obtained from dispensing and hospitalisation data.
ResultsFor lip cancer (45 cases) ever-use of HCTZ yielded an OR of 2.6 (95%CI: 1.4–5.0) and high HCTZ use (>25,000mg) an OR of 4.7 (1.61–13.7). For malignant melanoma (659 cases) ever-use of HCTZ resulted in an OR of 1.2 (1.0–1.5) and high HCTZ use in an OR of 1.2 (0.8–1.8).
Conclusion / ImplicationsOur study provides further evidence that the photosensitising properties of HCTZ may promote SCC carcinogenesis, and possibly melanoma, in susceptible sun-exposed tissues. Our findings are the first from the Australian population—already at elevated risk of developing skin cancer—and add to the growing body of data supporting the need for skin cancer prevention advice and behaviours, and potentially heightened surveillance, for individuals prescribed this medication.