A tailored approach to horizon scanning for cancer medicines

BACKGROUND

Horizon scanning (HS) is the systematic identification of emerging therapies to inform policy and decision-makers. We developed an agile and tailored HS methodology that combined multi-criteria decision analysis weighting and Delphi rounds. As secondary objectives, we aimed to identify new medicines in melanoma, non-small cell lung cancer and colorectal cancer most likely to impact the Australian government's pharmaceutical budget by 2025 and to compare clinician and consumer priorities in cancer medicine reimbursement.

METHOD

Three cancer-specific clinician panels (total n = 27) and a consumer panel (n = 7) were formed. Six prioritisation criteria were developed with consumer input. Criteria weightings were elicited using the Analytic Hierarchy Process (AHP). Candidate medicines were identified and filtered from a primary database and validated against secondary and tertiary sources. Clinician panels participated in a three-round Delphi survey to identify and score the top five medicines in each cancer type.

RESULTS

The AHP and Delphi process was completed in eight weeks. Prioritisation criteria focused on toxicity, quality of life (QoL), cost savings, strength of evidence, survival, and unmet need. In both curative and non-curative settings, consumers prioritised toxicity and QoL over survival gains, whereas clinicians prioritised survival. HS results project the ongoing prevalence of high-cost medicines. Since completion in October 2021, the HS has identified 70 % of relevant medicines submitted for Pharmaceutical Benefit Advisory Committee assessment and 60% of the medicines that received a positive recommendation.

CONCLUSION

Tested in the Australian context, our method appears to be an efficient and flexible approach to HS that can be tailored to address specific disease types by using elicited weights to prioritise according to incremental value from both a consumer and clinical perspective.

POLICY SUMMARY

Since HS is of global interest, our example provides a reproducible blueprint for adaptation to other healthcare settings that integrates consumer input and priorities.

Quantifying the Travel Undertaken by Cancer Patients Receiving Radiotherapy in Victoria, Australia

PURPOSE/OBJECTIVE(S)

This study aimed to investigate the travel distance for cancer patients treated with radiotherapy in Victoria, Australia, during a 9-year period relative to their home address. Additionally, the study aimed to quantify 'excess' travel distance where patients travelled to treatment centers that were not their closest.

MATERIALS/METHODS

The PRedicting the health economic IMPact of new and current CAncer Treatments (PRIMCAT) dataset was used for the study. PRIMCAT is a multi-institution research initiative taking a data-driven modelling approach to understand and forecast cancer treatment utilization in Australia. The PRIMCAT dataset is a comprehensive linked dataset including a retrospective cohort of patients 18 years and over, diagnosed with cancer included in the Victorian Cancer Registry between January 2010 to December 2019. For each patient in the cohort, data linkage includes a range of datasets managed by state and federal health departments including the Victorian Radiotherapy Minimum Dataset. A distance matrix was constructed using the Google Distance Matrix API that included driving distance between postcodes of selected patients and the postcodes of radiotherapy facilities. The centroid of each post-code was used for driving distance measurements. We first analyzed the realized travel distances of patients followed by the excess travel by patients. The excess travel was further quantified separately for public and private radiotherapy facilities.

RESULTS

There were 86,408 unique patient-radiotherapy courses available and of sufficient data quality to analyze. Patients travelled an average of 42.5km (s.d. = 72.2km) one-way, with a median travel distance of 16.5km. The largest distance travelled was 723km with 95% of patients travelling between 0km and 257km. Of the patients who received radiotherapy in a public facility, 44.3% travelled to a facility that was not their closest public facility. The average excess travel of these patients was 26.8km, with the median excess travel being 13.6km. Additionally, of patients who received care in a public facility, 47.4% travelled past a closer private facility. The average excess travel distance of these patients was 23.1km and the median excess travel was 11.5km. In the case of patients who received radiotherapy at a private facility, 92.8% travelled to a clinic that was not their closest. The average excess of these patients was 36.6km with a median excess travel distance of 15.6km.

CONCLUSION

Access to radiotherapy facilities remains unequal in Victoria with some patients having to travel significantly greater distances than average. This significant travel may require patients to take time off work or live away from home presenting additional financial challenges. The reasons for the large travel distances and any excess travel have not yet been identified but will be the subject of further study.

Efficacy of online lifestyle interventions targeting lifestyle behaviour change in depressed populations: A systematic review

BACKGROUND

It is well established that depression and non-communicable diseases are highly co-morbid and bi-directional in nature. 'Lifestyle medicine' has recently gained traction in the field of psychiatry, aimed at improvement of both physical and mental health. Online interventions can be an effective and inexpensive alternative or supplement to therapy that is delivered using more traditional modes, overcoming barriers that may prohibit people from accessing treatment by promoting flexibility and accessibility.

METHODS

This systematic review evaluates the existing evidence for the efficacy or effectiveness of lifestyle interventions for (1) individuals with depressive symptoms, (2) clinically depressed populations or discussing the outcomes of depression within a subset of a larger cohort that are delivered online or via smart phone. Included studies were randomised controlled trials, with active comparator conditions, in adult populations and with reported lifestyle and depression-related outcomes. The analysis examined attrition, engagement, adherence and behaviour change techniques employed to achieve the target behaviours.

RESULTS

Seven studies were included in the review and targeted behaviour change in five domains: alcohol reduction, improved sleep quality/insomnia reduction, increased physical activity, reduced/cessation of substance abuse and smoking cessation. Four of the studies achieved significant improvements in the targeted behaviour; of these three also reported significant improvements in depressive symptoms. No studies reported significant improvements in depressive symptoms without a change in the target lifestyle behaviour.

CONCLUSION

The results of this review highlight the potential of online lifestyle interventions as adjunctive treatments for depression, and the possibility of achieving significant improvements in depressive symptoms when targeting lifestyle behaviour change.