Quality of Life, Symptom Burden, and Associated Factors Among Lung Cancer Patients: A Cross-Sectional Study at Two Teaching Hospitals Across Sub-Saharan Africa (Preprint)
Bibliographic record
Abstract
BACKGROUND: Lung cancer remains a major contributor to cancer mortality in sub-Saharan Africa (SSA), where late diagnosis, driven by low awareness, sociocultural barriers, and health system constraints, limits effective treatment. Despite the growing burden, evidence on patients' quality of life (QoL) and symptom experience in SSA is limited. OBJECTIVE: This study aimed to describe the common symptoms and QoL of patients with lung cancer treated at 2 hospitals in SSA, and to investigate the association of demographics, clinical characteristics, and symptom burden with QoL. METHODS: This was a cross-sectional study that consecutively recruited patients with lung cancer from 2 teaching hospitals in SSA: Bugando Medical Centre (BMC) in Tanzania and the University of the Witwatersrand Centre of Respiratory Excellence (WITS-CORE) in South Africa. Data collected included demographics, clinical information, and performance status using the Eastern Cooperative Oncological Group Performance Scale (ECOG-PS). Health-related QoL was assessed using the 30-item European Organization for Research and Treatment of Cancer Quality of Life Questionnaire Core 30 (EORTC QLQ-C30). The study followed all ethical procedures, and data were analyzed using both descriptive and inferential statistics in Stata 18. A P value of <.05 was considered statistically significant. RESULTS: A total of 174 patients with lung cancer were enrolled across the 2 sites. The score on the EORTC QLQ-C30 global health status/QoL subscale was low, with a median of 41.67 (IQR 33.33-41.67), and it varied by site. Patients from WITS-CORE demonstrated higher social functioning scores, while those from BMC reported greater financial difficulties. A low global health status/QoL score was independently associated with the BMC site (adjusted odds ratio [aOR] 3.5, 95% CI 1.3-9.3) and poor performance status (ECOG-PS 3-4; aOR 3.4, 95% CI 1.2-6.6). Furthermore, symptoms such as nausea and vomiting, pain, dyspnea, insomnia, appetite loss, diarrhea, and financial struggles were all associated with a low global health status/QoL score. CONCLUSIONS: QoL among patients with lung cancer in SSA is poor. Low QoL is strongly associated with the Multinational Lung Cancer Control Program study site, poor performance status, and a range of symptoms and financial difficulties. Addressing these factors may help to improve patient outcomes and well-being in SSA.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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".