Validating a proxy for disease progression in metastatic cancer patients using prescribing and dispensing data

An evaluation of the healthcare costs of metastatic breast cancer: A retrospective matched cohort study

To determine the economic burden of metastatic breast cancer (MBC) in Taiwan, we conducted a national retrospective claim database analysis to evaluate the incremental healthcare costs and utilization of MBC patients as compared to their breast cancer (BC) and breast cancer free (BCF) counterparts.Data were obtained from the National Health Insurance Claim Database and the Taiwan Cancer Registry database between 2012 and 2015. All healthcare utilization and costs were calculated on a per-patient-per-month (PPPM) basis and were compared among groups using the generalized linear model adjusting for age group, residential area, and Charlson comorbidity index group.A total of 1,606 MBC patients were matched to 6,424 BC patients and 6,424 BCF patients. The majority of overall MBC healthcare costs were attributed to outpatient costs (75.1%), followed by inpatient (23.2%) and emergency room costs (1.7%). The PPPM total healthcare costs of the MBC, BC, and BCF groups were TWD 7,422, 14,425, and 2,114, respectively. The adjusted PPPM total healthcare cost ratio of MBC to BCF was 4.1. Compared to BCF patients, the patients receiving both human epidermal growth factor receptor 2-targeted therapy and endocrine therapy incurred 28.1 times PPPM total costs. The adjusted PPPM total healthcare cost ratio of recurrent MBC to BCF was 2.3, while the ratio was 12.2 in the de novo MBC group.Patients with MBC are associated with substantial economic burden, particularly in outpatient costs. The study findings could be useful for MBC-related economic evaluations and health resource allocation.

A Systematic Review of Estimating Breast Cancer Recurrence at the Population Level With Administrative Data

BACKGROUND

Exact numbers of breast cancer recurrences are currently unknown at the population level, because they are challenging to actively collect. Previously, real-world data such as administrative claims have been used within expert- or data-driven (machine learning) algorithms for estimating cancer recurrence. We present the first systematic review and meta-analysis, to our knowledge, of publications estimating breast cancer recurrence at the population level using algorithms based on administrative data.

METHODS

The systematic literature search followed Preferred Reporting Items for Systematic Reviews and Meta-Analysis guidelines. We evaluated and compared sensitivity, specificity, positive predictive value, negative predictive value, and overall accuracy of algorithms. A random-effects meta-analysis was performed using a generalized linear mixed model to obtain a pooled estimate of accuracy.

RESULTS

Seventeen articles met the inclusion criteria. Most articles used information from medical files as the gold standard, defined as any recurrence. Two studies included bone metastases only in the definition of recurrence. Fewer studies used a model-based approach (decision trees or logistic regression) (41.2%) compared with studies using detection rules without specified model (58.8%). The generalized linear mixed model for all recurrence types reported an accuracy of 92.2% (95% confidence interval = 88.4% to 94.8%).

CONCLUSIONS

Publications reporting algorithms for detecting breast cancer recurrence are limited in number and heterogeneous. A thorough analysis of the existing algorithms demonstrated the need for more standardization and validation. The meta-analysis reported a high accuracy overall, which indicates algorithms as promising tools to identify breast cancer recurrence at the population level. The rule-based approach combined with emerging machine learning algorithms could be interesting to explore in the future.

Identifying incident cancer cases in dispensing claims: A validation study using Australia's Repatriation Pharmaceutical Benefits Scheme (PBS) data

Introduction

Dispensing claims are used commonly as proxy measures in pharmacoepidemiological studies; however, their validity is often untested.

Objectives

To 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.

Methods

We 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.

Results

We 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.

Conclusions

Using dispensing of anticancer medicines to identify 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.

Keywords

cancer, diagnosis, proxy, dispensing records, validation study

Assessment of Medication Safety Using Only Dispensing Data

PURPOSE OF REVIEW

The purpose of this review is to provide an overview of the published studies that have been used to generate evidence on the safety of medicine use when only medication dispensing data are available.

RECENT FINDINGS

Medication dispensing databases are increasingly available for research on large populations, particularly in countries that provide universal coverage for medicines. These data are often used for drug utilisation studies to identify inappropriate medicine use at the population level that may be associated with known safety issues. Lack of coded diagnoses, to identify outcomes, and lack of data on confounders can limit use of these data in practice for medication safety assessment. To overcome these issues, studies have exploited the fact that symptoms of adverse effects of medications can be treated with other medications, for example antidepressants to treat depression or oxybutynin to treat urinary incontinence. The challenge of unmeasured confounding has been addressed by implementing self-controlled study designs that use within-person comparisons and provide inherent control for confounding. Prescription sequence symmetry analysis (SSA) is a within-person study design that has been demonstrated as a useful tool for safety signal generation in dispensing data.

SUMMARY

Using medicine initiation as a proxy for the development of adverse events can help to generate evidence of the safety of medicines when only medication dispensing data are available. Careful consideration, however, should be given to the sensitivity and specificity of the proxy medicine for the adverse event and potential for time-varying confounding due to trends in medicine utilisation. Data-mining approaches using dispensing data have the potential to improve safety assessments; however, the challenge of unmeasured confounding with these methods remains to be investigated.

Adherence to prescribing restrictions for HER2-positive metastatic breast cancer in Australia: A national population-based observational study (2001-2016)

BACKGROUND

Targeted cancer therapy is often complex, involving multiple agents and chemotherapeutic partners. In Australia, prescribing restrictions are put in place to reflect existing evidence of cost-effectiveness of these medicines. As therapeutic options continue to expand, these restrictions may not be perceived to align with best practice and it is not known if their use in the real-world clinic adheres to these restrictions. We examined the treatment of women receiving trastuzumab for HER2-positive metastatic breast cancer (HER2+MBC) to determine the extent to which treatment adhered to national prescribing restrictions.

PATIENTS AND METHODS

Our population-based, retrospective cohort study used dispensing records for every Australian woman initiating publicly-subsidised trastuzumab for HER2+MBC between 2001-2013, followed through 2016. We used group-based trajectory models (GBTMs) to cluster patients, first on their patterns of trastuzumab exposure, and then on their patterns of lapatinib and chemotherapy exposure. We described the characteristics of patients within each cluster, and examined their treatments and combinations of treatments to determine restriction adherence.

RESULTS

Of 5,052 patients initiating trastuzumab, 1,795 (36%) received at least one non-adherent HER2-targeted treatment. The most common non-adherent treatments were trastuzumab combinations involving vinorelbine (24% of non-adherent treatments); capecitabine (24%); and anthracyclines (10%). Non-adherent lapatinib use was observed in 4% of patients. GBTM identified three trastuzumab exposure clusters, each containing three further sub-clusters. The largest proportions of non-adherent treatments were in sub-clusters with longer trastuzumab exposure and more non-taxane chemotherapy. Patients in these sub-clusters were younger than those in sub-clusters with less non-adherent treatment.

CONCLUSIONS

Our study highlights that, even during the relatively simpler treatment era of our study period, a substantial amount of treatment did not adhere to prescribing restrictions. As more trials are conducted exploring pertuzumab and T-DM1 in combination with different chemotherapies and other HER2-targeted therapies, the regulation and funding of HER2-targeted treatment will become more challenging.