A simple filter model to guide the allocation of healthcare resources for improving the treatment of depression among cancer patients

Resource allocation for depression management in general practice: A simple data-based filter model

Background

This study aimed to illustrate the potential utility of a simple filter model in understanding the patient outcome and cost-effectiveness implications for depression interventions in primary care.

Methods

Modelling of hypothetical intervention scenarios during different stages of the treatment pathway was conducted.

Results

Three scenarios were developed for depression related to increasing detection, treatment response and treatment uptake. The incremental costs, incremental number of successes (i.e., depression remission) and the incremental costs-effectiveness ratio (ICER) were calculated. In the modelled scenarios, increasing provider treatment response resulted in the greatest number of incremental successes above baseline, however, it was also associated with the greatest ICER. Increasing detection rates was associated with the second greatest increase to incremental successes above baseline and had the lowest ICER.

Conclusions

The authors recommend utility of the filter model to guide the identification of areas where policy stakeholders and/or researchers should invest their efforts in depression management.

The impact of depressive symptoms on health value in patients with gynecologic cancer: A comparison with physical symptoms and performance status

OBJECTIVE

The importance of managing depressive symptoms is frequently underestimated in the clinic. In this study, the effects of depressive symptoms on health utility value (HUV) in gynecologic cancer patients are evaluated. In addition, the effects were compared with those of performance status and physical symptoms.

METHODS

Patient-reported outcome data from 274 gynecologic cancer patients were prospectively collected. HUV was measured using the 3-level version of the EuroQoL 5-dimension descriptive system (EQ-5D-3L) and the EuroQoL visual analog scale (EQ-VAS). The Patient Health Questionnaire-9 (PHQ-9) was used to measure the severity of depressive symptoms. The impact of depressive symptoms on HUV was analyzed using the generalized linear model.

RESULTS

Moderate-severe depressive symptoms were significantly associated with a decrease in HUV in gynecologic cancer patients (p < 0.0001 for the EQ-5D-3L and EQ-VAS). Severe fatigue and severe pain were also associated with a decrease in HUV (p = 0.018 and p < 0.0001 for the EQ-5D-3L and EQ-VAS; p < 0.0001 for the EQ-5D-3L, respectively), and the effect sizes were comparable to that of moderate-severe depressive symptoms. In addition to the patients with moderate-severe depressive symptoms, the patients with mild depressive symptoms also experienced a significant decrease in HUV (p < 0.0001 for the EQ-5D-3L and EQ-VAS). The effect size for mild depressive symptoms was comparable to those for mild fatigue or mild pain.

CONCLUSIONS

Even mild depressive symptoms may significantly compromise HUV in gynecologic cancer patients, and the effect is comparable to that of performance status or physical symptoms. Gynecologic oncologists should put more effort into properly preventing, detecting, and managing depressive symptoms.

Reducing alcohol-related harm in Australia: a simple data-based tool to assist prioritization of research and health care delivery in primary care

INTRODUCTION

The detection of harmful alcohol use and the delivery of brief advice in primary care are less than optimal. Given limited health care resources, deciding where best to allocate funding to optimize health outcomes is imperative. A simple data-based tool could be useful when access to specialist health economic advice is unavailable. This study aimed to examine the utility of a simple data-based calculator to facilitate priority setting in general practice for reducing alcohol-related harm.

METHODS

A simple algorithm was developed within Microsoft Excel to allow comparison of hypothetical intervention scenarios that aimed to increase detection and brief advice for harmful alcohol use in general practice. The calculator accommodated varying implementation costs, size of effect and reach for each scenario created. The incremental costs of the intervention scenarios, the incremental number of successes (i.e. abstinence or drinking at safe levels) and the incremental costs-effectiveness ratio (ICER) were calculated for each hypothetical scenario and compared with a usual care scenario.

RESULTS

In the hypothetical scenarios modelled, increasing both the detection of harmful alcohol consumption and the provision of brief advice produced the greatest number of incremental successes above baseline. Increasing detection alone produced fewer incremental successes but was the most cost-effective approach, as indicated by the lowest ICER.

DISCUSSION

The data-based calculator provides a simple method of exploring reach and cost-effectiveness outcomes without the need for any specific skills. Although this approach has limitations, the calculator can be used by decision makers to guide intervention planning.