Psychological distress in patients with cancer at the Kenyatta National Hospital in Nairobi, Kenya, during the COVID-19 pandemic
Bibliographic record
Abstract
Abstract Background: Psychosocial care for oncology patients is now recognized as a critical aspect of care because it has a positive impact on patient outcomes. Various screening tools have been validated to objectively measure the levels of distress, such as the National Comprehensive Cancer Network distress thermometer. However, there is little evidence of its use in sub-Saharan Africa, where the cancer burden continues to increase. This study sought to evaluate the levels of psychological distress in patients with cancer and the impact of the COVID-19 pandemic. Methods: This was a single-center cross-sectional study among patients with a histological diagnosis of cancer attending the hemato-oncology and radio-oncology units at the Kenyatta National Hospital, a referral tertiary center. We used the National Comprehensive Cancer Network Distress Thermometer and Problem Checklist to define psychological distress, fear of COVID-19 scale, and Corona Anxiety Score to determine the level of fear and anxiety caused by COVID-19 given the study happened during the pandemic, and the Eastern Cooperative Oncology Group (ECOG) to assess the performance status. Results: Of the 361 patients, the median age was 54 years (interquartile range, 43–63), and most were female (70%). The leading cancer diagnosis was breast cancer (26%), followed by cervical cancer (24%), with most of the patients having advanced disease and 28% having ECOG 3. Most (80%) patients were able to continue with their treatment despite the COVID-19 pandemic; however, 71% had a high level of fear of COVID-19 but minimal anxiety symptoms based on Corona Anxiety Score. The mean distress thermometer score was 2.7 (SD, 2.6), with 30% having a high level of distress (4 or above). ECOG status was the only variable significantly associated with high levels of distress, with the strongest association observed in the highest ECOG status (ECOG 4: OR, 6.8 [95% CI, 2.8–16.6] P < .001). Transportation was the main problem in the practical domain (62%) while fears and worries in the emotional domain (46% and 49%, respectively), and pain (65%) were the main physical problems. Conclusions: One-third of patients experienced high levels of distress. These patients reported significant concerns, such as transportation, fears, worry, and pain, in the problem checklist. There is a need to incorporate screening for distress into our patient population to help identify these patients and institute appropriate interventions.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 source (direct Gemma or distilled Codex), 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".