Investigating the disparities in cervical cancer screening among Namibian women

Gynecol Oncol. 2015 Aug;138(2):411-6. doi: 10.1016/j.ygyno.2015.05.036. Epub 2015 May 30.

Abstract

Objectives: We examined the influence of knowledge and information, health care access and different socio-economic variables on women's decision to screen for cervical cancer using a nationally representative dataset.

Methods: We use hierarchical binary logit regression models to explore the determinants of screening for cervical cancer among women who reported hearing about cervical cancer. This enabled us to include the effect of unobserved heterogeneity at the cluster level that may affect screening behaviors.

Results: Among women who have heard about cervical cancer (N=6542), only 39% of them did undergo screening with a mean age of 33 years. The univariate results reveal that women who are educated, insured, can afford money needed for treatment and reported distance not a barrier to accessing healthcare were more likely to screen. Our multivariate results indicate that insured women (OR=1.89, p=0.001) and women who had access to information through education and contact with a health worker (OR=1.41, p=0.001) were more likely to undertake screening compared to uninsured women and those with no contact with a health personnel, after controlling for relevant variables.

Conclusions: The adoption of a universal health insurance scheme that ensures equity in access to health care and extension of public health information targeting women in rural communities especially within the Caprivi region may be needed for a large scale increase in cervical cancer screening in Namibia.

Keywords: Cervical cancer; Health insurance; Knowledge and information; Namibia; Screening.

MeSH terms

  • Adult
  • Early Detection of Cancer / economics
  • Early Detection of Cancer / methods
  • Early Detection of Cancer / statistics & numerical data
  • Female
  • Health Services Accessibility / economics
  • Health Services Accessibility / statistics & numerical data*
  • Healthcare Disparities / economics
  • Healthcare Disparities / statistics & numerical data*
  • Humans
  • Insurance, Health / statistics & numerical data
  • Logistic Models
  • Namibia / epidemiology
  • Uterine Cervical Neoplasms / diagnosis*
  • Uterine Cervical Neoplasms / epidemiology