A population-based analysis of the effect of marital status on overall and cancer-specific mortality in patients with squamous cell carcinoma of the penis

Cancer Causes Control. 2013 Jan;24(1):71-9. doi: 10.1007/s10552-012-0091-y. Epub 2012 Oct 30.

Abstract

Purpose: The association between marital status and tumor stage and grade, as well as overall mortality (OM) and cancer-specific mortality (CSM) received little attention in patients with squamous cell carcinoma of the penis (SCCP).

Methods: We relied on the surveillance, epidemiology, and end results (SEER) 17 database to identify patients diagnosed with primary SCCP. Logistic and Cox regression models, respectively, addressed the effect of marital status on the rate of locally advanced disease and its effect on OM and CSM. Covariates consisted of age, race, socioeconomic status, year of surgery, and SEER registries.

Results: Between 1988 and 2006, 1,884 patients with SCCP were identified. At surgery, 1,192 (63.3 %) were married and 966 (51.3 %) had locally advanced disease. In multivariable logistic regression models predicting locally advanced disease at surgery, unmarried men had a 1.5-fold higher (p < 0.001) risk than others. In multivariable Cox models predicting CSM, marital status had no effect [hazard ratio (HR) = 1.3, p = 0.1]. Finally, in multivariable Cox models predicting OM, unmarried men had a 1.3-fold higher (p = 0.001) risk than others.

Conclusion: Unmarried men tend to present with less favorable disease stage at SCCP. Moreover, unmarried men tend to live less long than their married counterparts. However, marital status has no effect on CSM.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Aged
  • Carcinoma, Squamous Cell / epidemiology*
  • Carcinoma, Squamous Cell / mortality*
  • Cause of Death
  • Humans
  • Male
  • Marital Status / statistics & numerical data*
  • Middle Aged
  • Penile Neoplasms / epidemiology*
  • Penile Neoplasms / mortality*
  • Penis / pathology
  • Population
  • SEER Program / statistics & numerical data
  • Socioeconomic Factors
  • Survival Analysis
  • United States / epidemiology