ArticleBMC cancer2018
A simple filter model to guide the allocation of healthcare resources for improving the treatment of depression among cancer patients.
Article in BMC cancer, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
1 citing paper in PubMed.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
7 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundDepression is highly prevalent yet often poorly detected and treated among cancer patients. In light of the move towards evidence-based healthcare policy, we have developed a simple tool that can assist policy makers, organisations and researchers to logically think through the steps involved in improving patient outcomes, and to help guide decisions about where to allocate resources.
methodsThe model assumes that a series of filters operate to determine outcomes and cost-effectiveness associated with depression care for cancer patients, including: detection of depression, provider response to detection, patient acceptance of treatment, and effectiveness of treatment provided. To illustrate the utility of the model, hypothetical data for baseline and four scenarios in which filter outcomes were improved by 15% were entered into the model.
resultsThe model provides outcomes including: number of people successfully treated, total costs per scenario, and the incremental cost-effectiveness ratio per scenario compared to baseline. The hypothetical data entered into the model illustrate the relative effectiveness (in terms of the number of additional incremental successes) and relative cost-effectiveness (in terms of cost per successful outcome and total cost) of making changes at each step or filter.
conclusionsThe model provides a readily accessible tool to assist decision makers to think through the steps involved in improving depression outcomes for cancer patents. It provides transparent guidance about how to best allocate resources, and highlights areas where more reliable data are needed. The filter model presents an opportunity to improve on current practice by ensuring that a logical approach, which takes into account the available evidence, is applied to decision making.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.