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April 3, 2026PLOS Global Public Health2 citationsOpen Access

Depression and cancer outcomes in resource-limited settings: A cross-sectional analysis of treatment uptake and survival among metastatic breast cancer patients in Lagos, Nigeria

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EWEmma WisniewskiMilken InstituteAAAdewumi AlabiALAbdulRazzaq Oluwagbemiga LawalUniversity of Lagos

Key Points

  • This study investigates the relationship between clinically significant depression and treatment uptake and survival in metastatic breast cancer patients in Nigeria.
  • Conducted a cross-sectional analysis of 313 metastatic breast cancer patients
  • Assessed depression using the Beck Depression Inventory-II (BDI-II)
  • Evaluated associations using multivariable logistic regression
  • Analyzed survival patterns with Kaplan-Meier and Cox proportional hazards models
  • 52.6% of patients experienced any depression, with 17.9% classified as clinically significant
  • Overall treatment uptake was 64.6%
  • CSD showed no significant association with treatment receipt or mortality
  • Clinical factors like lung metastasis and ulceration strongly predicted treatment outcomes
  • Multimodal therapy significantly reduced mortality risk

Abstract

The intersection of depression and cancer outcomes represents a critical yet understudied domain in global oncology, particularly in low- and middle-income countries (LMICs) where both conditions impose substantial disease burdens. While evidence from high-income countries demonstrates associations between depression and adverse cancer outcomes, the generalizability of these findings to resource-limited settings remains uncertain. Theoretical frameworks including Beck's cognitive model and the biopsychosocial model suggest multiple pathways through which depression may influence cancer outcomes; however, these pathways may operate differently in resource-constrained healthcare environments. This study examines the relationship between clinically significant depression (CSD) and treatment uptake and survival among metastatic breast cancer patients in Nigeria, providing novel insights into psycho-oncology within the sub-Saharan African context. We conducted a cross-sectional analysis of 313 metastatic breast cancer patients presenting to NSIA-LUTH Cancer Centre, Lagos, Nigeria (September 2020-February 2022). Depression was assessed using the Beck Depression Inventory-II (BDI-II), with CSD defined as BDI-II ≥ 20, consistent with oncology-validated cutoffs and African psychometric validation studies. Associations between CSD and treatment receipt were evaluated using multivariable logistic regression. Survival patterns were examined using Kaplan-Meier analysis and Cox proportional hazards models, adjusting for clinicopathological confounders. The cross-sectional design precludes causal inference regarding temporal relationships between depression and outcomes. Among 274 patients with complete data (mean age 54.0 years, 52.1% triple-negative), depression was prevalent (52.6% any depression; 17.9% CSD). Treatment uptake was 64.6% overall. Contrary to findings from high-income settings, CSD showed no significant association with treatment receipt (adjusted OR 0.95, 95% CI 0.36-2.52, p = 0.92) or mortality (adjusted HR 1.12, 95% CI 0.48-2.61, p = 0.80). Clinical factors dominated outcome prediction: lung metastasis (OR 3.63), ulceration (OR 20.39), and lymph node status significantly predicted treatment receipt, while multimodal therapy was strongly protective against mortality (HR 0.25, 95% CI 0.10-0.65). Findings should be interpreted in the context of 12.5% missing depression data and potential selection and misclassification biases. In this Nigerian cohort, CSD was common but not associated with treatment uptake or survival. These findings suggest that in resource-constrained settings where clinical triage predominates, tumor biology and treatment access may supersede psychosocial factors as outcome determinants. The substantial depression prevalence nonetheless warrants integrated mental health services within cancer care, with implications for comprehensive oncology models in LMICs.

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Cite This Study

Wisniewski et al. (2026) studied this question.

synapsesocial.com/papers/69cf5e5f5a333a821460ca97https://doi.org/10.1371/journal.pgph.0005969
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