Prevalence of Food Insecurity in Patients with Lymphoid Malignancies: A Systematic Review and Meta-Analysis
Notice bibliographique
Résumé
Introduction: Food insecurity (FI), a critical yet often overlooked issue in cancer care, remains poorly quantified despite its significant impact on treatment outcomes and quality of life (QoL). This systematic review and meta-analysis aims to estimate food insecurity prevalence among patients with lymphoid malignancies. By quantifying this hidden risk, we challenge the oncology community to confront an uncomfortable truth - our most advanced treatments may be compromised by our patients' most basic needs, demanding a recalibration of priorities in cancer care delivery. Methods: We systematically searched MEDLINE, CENTRAL, Embase, and Web of Science databases for studies reporting the prevalence of food insecurity among patients with hematological cancer. Prior to initiation, we registered the review protocol in PROSPERO. Two independent reviewers screened studies, extracted data, and assessed the risk of bias using the Newcastle-Ottawa Scale. Since the selected studies will be used to draw inferences from a broader universe of comparable studies, we employed a random-effects model for the meta-analysis, using a Generalized Linear Mixed Model for the prevalence estimation. Results: We identified seven studies (n=1359) that reported the prevalence of FI among patients with hematologic malignancies. However, only five studies (n=1,254 patients) stratified lymphoid malignancies by FI status and were included in the meta-analysis. Four studies were conducted in high-income countries (n=1029) and one in a low- and middle-income country (LMIC) (n=225). Most were cross-sectional studies (80%) with one cohort study. The most common FI measurement tool used was the USDA Food Security Survey Module. The systematic review revealed several important findings. Socioeconomic factors were consistently associated with higher FI rates. One study found that patients with incomes below the federal poverty level had the highest FI rates (33%). Food insecurity was also linked to treatment non-adherence, with one study reporting that 55% of patients with FI did not take prescribed medication due to cost, compared to 12.8% of food-secured patients and also noted the significantly lower quality of life scores, with a mean FACT-G score of 59.64±18.66 compared with 80.65±18.27 for food secure patients. Another study found significantly higher levels of depression among food insecure patients in the Brief Zung Self-Rating Depression Scale (mean±SD: 24.88 ±4.53 for FI vs 15.89 ±11.74 for food secure). For the meta-analysis of FI prevalence, we pooled data from the five included studies. The pooled prevalence of FI in lymphoid malignancies was 23% (95% CI: 11.56%-40.46%). There was significant heterogeneity among the studies (I² = 94%, p < 0.001), reflecting varied prevalence rates across different populations and settings. Individual study prevalence rates ranged from 9% (95% CI: 7.24%-11.05%) to 60% (95% CI: 38.67%-78.87%). Due to the limited available data, we were unable to conduct subgroup analyses. Conclusion: This meta-analysis provides the first pooled estimate of FI prevalence among patients with hematological malignancies revealing an often-overlooked aspect of cancer care with significant implications for treatment adherence, quality of life, and potentially survival outcomes. Our findings indicate that nearly one in four patients with lymphoid malignancies faces FI. However, the wide confidence interval and high heterogeneity reflect considerable variability across studies, likely due to differences in study populations, settings, and measurement tools. This variability is further emphasized by the dispersion of prevalence rates (9% - 60%) across individual studies, which underscores that FI in patients with cancer is highly context-dependent. Rather than a limitation, this highlights the need for the oncology community to move beyond one-size-fits-all approaches and develop targeted, population-specific interventions.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,017 | 0,042 |
| Méta-épidémiologie (sens strict) | 0,002 | 0,001 |
| Méta-épidémiologie (sens large) | 0,017 | 0,034 |
| Bibliométrie | 0,008 | 0,009 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,002 |
| Science ouverte | 0,002 | 0,002 |
| Intégrité de la recherche | 0,002 | 0,002 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».