Do racial or socioeconomic disparities exist in lung cancer treatment?
Open accessAbstract
BACKGROUND: Determine the effects of race, socioeconomic status, and treatment on outcomes for patients diagnosed with lung cancer. METHODS: The Florida cancer registry and inpatient and ambulatory data were queried for patients diagnosed from 1998‐2002. RESULTS: A total 76,086 of lung cancer patients were identified. Overall, 55.6% were male and 44.4% were female. The demographic distribution of patients was 92.7% Caucasian, 6.7% African American, and 5.7% Hispanic. The mean age of diagnosis was 70 years old. African American patients presented at a younger age, with more advanced disease, and were less likely to undergo surgical therapy than their Caucasian counterparts. Median survival time (MST) for the entire cohort was 8.7 months, while MST for African American patients was 7.5 months. Patients who received surgery, chemotherapy, or radiation therapy demonstrated significantly improved outcomes. Stepwise multivariate analysis revealed that African American race was no longer a statistically significant predictor of worse outcomes once corrections were made for demographics and comorbid conditions, suggesting that the originally reported disparities in lung cancer outcomes and race may be in part because of poor pretreatment performance status. In contrast, patients of the lowest socioeconomic status continue to have a slightly worse overall prognosis than their affluent counterparts (hazard ratio = 1.05, P = .001). CONCLUSIONS: Lung cancer continues to carry a poor prognosis for all patients. Once comorbidities are corrected for, African American patients carry equivalently poor outcomes. Nonetheless, emphasis must be placed on improving pretreatment performance status among African American patients and efforts for earlier diagnosis among the impoverished patients must be made. Cancer 2010. © 2010 American Cancer Society.
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Disparities in race, sex, socioeconomic status, level of education, residence, and insurance status, among other factors, continue to be associated with outcomes such as mortality, incidence, stage at diagnosis, and timely receipt of recommended treatment for patients with NSCLC.
“The results show that disparities in race,128–131 sex,132 socioeconomic status,132–135 level of education,136 residence,128,136 and insurance status,137–139 among other factors, continue to be associated with outcomes such as mortality,128,140 incidence,137,140 stage at diagnosis, and timely receipt of recommended treatment129,131 for patients with NSCLC.”
From Systemic Therapy for Stage IV Non–Small-Cell Lung Cancer: American Society of Clinical Oncology Clinical Practice Guideline Update (2015) · cites this paper as [131]
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- Authors
- Yang, R., Cheung, M. C., Byrne, M. M., Huang, Y., Nguyen, D., Lally, B. E., & Koniaris, L. G.
- Affiliations
- University of Miami (US)
- Florida Department of Health (US)
- Corresponding author
- Koniaris, Leonidas G.
- Venue
- Cancer
- Publisher
- Wiley
- Published
- 2010-04-28
- Volume
- 116
- Issue
- 10
- Pages
- 2437-2447
- ISSN
- 0008-543X (ISSN-L)
- 1097-0142
- OpenAlex
- W2023254395
- Cited by
- 113
- References
- 26
- Citations by year
- 2026: 4 · 2025: 7 · 2024: 5 · 2023: 6 · 2022: 10 · 2021: 5 · 2020: 9 · 2019: 11 · 2018: 5 · 2017: 3 · 2016: 12 · 2015: 8 · 2014: 10 · 2013: 9 · 2012: 2
- FWCI
- 4.79
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Cancer (publishedVersion)
- PubMed (publishedVersion)
- Topics
- Health Sciences › Medicine › Pulmonary and Respiratory Medicine › Lung Cancer Diagnosis and Treatment
- Health Sciences › Medicine › Oncology › Global Cancer Incidence and Screening
- Social Sciences › Health › Health disparities and outcomes
- Keywords
- lung cancer, racial disparities, socioeconomic status, African American patients, survival, surgical treatment, chemotherapy, radiotherapy, performance status, comorbidities, early diagnosis, health disparities
- MeSH terms
- Aged, Ethnicity, Female, Florida, Humans, Lung Neoplasms, Male, Middle Aged, Poverty, Social Class, Comorbidity, Databases as Topic, Racial Groups, Healthcare Disparities
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