Systematic Literature Review to Identify Cost and Resource Use Data in Patients with Early-Stage Non-small Cell Lung Cancer (NSCLC)
Bibliographic record
Abstract
BACKGROUND: Approximately 2 million new cases and 1.76 million deaths occur annually due to lung cancer, with the main histological subtype being non-small cell lung cancer (NSCLC). The costs and resource use associated with NSCLC are important considerations to understand the economic impact imposed by the disease on patients, caregivers and healthcare services. OBJECTIVE: The objective of this systematic literature review (SLR) is to provide a comprehensive overview of the available direct medical costs, direct non-medical costs, indirect costs, cost drivers and resource use data available for patients with early-stage NSCLC. METHODS: Electronic searches were conducted via the Ovid platform in March 2021 and June 2022 and were supplemented by grey literature searches. Eligible patients had early-stage (stage I-III) resectable NSCLC and received treatment in the neoadjuvant or adjuvant setting. There was no restriction on intervention or comparators. Publication date was restricted to 2011 onwards, and English language publications or non-English language publications with an English abstract were of primary interest. Due to the anticipation of many studies meeting the inclusion criteria, analyses were restricted to full publications from countries of primary interest (Australia, Brazil, Canada, China, France, Germany, Italy, Japan, South Korea, Spain, UK and the US) and those with > 200 patients. The Molinier checklist was applied to conduct quality assessment. RESULTS: Forty-two full publications met the eligibility criteria and were included in this SLR. Early-stage NSCLC was associated with significant direct medical costs and healthcare utilisation, and the economic burden of the disease increased with its progression. Surgery was the primary cost driver in stage I patients, but as patients progressed to stage II and III, treatments such as chemotherapy and radiotherapy, and inpatient care became the main cost drivers. There was no significant difference in resource use between patients with early-stage disease. However, these data were heavily US-centric and there was a paucity of data relating to direct non-medical and indirect costs associated with early-stage NSCLC. CONCLUSIONS: Preventing disease progression for patients with NSCLC could reduce the economic burden of NSCLC on patients, caregivers and healthcare systems. This review provides a comprehensive overview of the available cost and resource use data in this indication, which is important in guiding the decisions of policy makers regarding the allocation of resources. However, it also indicates a need for more studies comparing the economic impact of NSCLC in markets in addition to the US.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".