# Predictors for Emergency Admission Among Homeless Metastatic Cancer Patients and Association of Social Determinants of Health with Negative Health Outcomes

## TL;DR

Homeless cancer patients with poor social conditions face worse health outcomes and emergency admissions, which can be predicted using machine learning.

## Contribution

This study identifies key predictors of emergency admissions and highlights the impact of social determinants on cancer patient outcomes.

## Key findings

- Homeless metastatic cancer patients showed higher rates of anxiety, depression, and longer hospital stays.
- Machine learning models like Lasso and random forest effectively predicted emergency admissions.
- Homelessness was significantly linked to worse clinical outcomes across multiple cancer types.

## Abstract

Cancer patients with adverse social determinants of health (SDOHs), specifically homelessness and living alone, in the prostate, breast, and lung categories, exhibited the most unfavorable clinical outcomes. These outcomes included heightened anxiety, depression, and extended hospital stays. SDOHs have a significant impact on the likelihood of emergency admissions among cancer patients. Machine learning algorithms are the most appropriate method for predicting the specific services needed by these patients.

Background/Objectives: Social determinants of health (SDOHs) are especially impactful with respect to emergency reliance among patients with cancer. Methods: To better predict the extent to which SDOHs affect emergency admissions in homeless patients with metastatic disease, we employed machine learning models, Lasso, ridge, random forest (RF), and elastic net (EN) regression. We also examined prostate cancer (PC), breast cancer (BC), lung (LC) cancer, and cancers of the lip, oral cavity, and pharynx (CLOP) for association between key SDOH variables—homelessness and living alone—and clinical outcomes. For this, we utilized generalized linear models to assess the association while controlling for patient and clinical characteristics. We used the United States National Inpatient Sample database for this study. Results: There were 2635 (weighted) metastatic cancer patients with homelessness. Transfer from another facility or not, elective admission or not, deficiency anemia, alcohol dependence, weekend admission or not, and blood loss anemia were the important predictors of emergency admission. C-statistics were associated with Lasso (train AUC-0.85; test AUC—0.86), ridge (85, 88), RF (0.96, 0.85), and EN (0.83, 0.80), respectively. In the adjusted analysis, PC homelessness was significantly associated with anxiety and depression (5.15, 95% CI: 3.17–8.35) and a longer LOS (1.96; 95% CI: 1.03–3.74). Findings were comparable in the BC, LC, and CLOP cohorts. Cancer patients with poor SDOHs presented with the worst clinical outcomes. Conclusions: Cancer patients with poor SDOH presented with worst clinical outcomes. The findings of this study highlight a vacuum in the cancer literature, and the recommendations stress the value of social support in achieving a better prognosis and Quality of life.