Cervical Cancer Screening Outcomes Among a Sample of Low-Income Uninsured Women: A Program-Based Study - Samson Olowolaju; Morgan Kassabian; Marvellous A. Akinlotan; Anna Lichorad; Robert Pope; Brandon Williamson; Scott Horel; Jane N. Bolin (Mary Ann Liebert, 2022)
Background: Most studies examining cervical cancer screening outcomes have focused on either an age-specific diagnosis and outcomes of abnormal smears or frequency of abnormal outcomes among a sample of insured women. Thus, it is unclear what the distribution outcomes would be when other sociodemographic characteristics are considered. This study examines the variation in cervical cancer screening outcomes and sociodemographic characteristics (patients' age, marital status, race/ethnicity, rurality, and Papanicolaou [Pap] test screening history) within a sample of low-income and uninsured women. Materials and Methods: Our grant-funded program provided 751 Pap tests, 577 human papillomavirus (HPV) tests, and 262 colposcopies to 841 women between 2013 and 2019. Observed outcomes for each procedure type were cross-tabulated by patients' sociodemographic characteristics. Chi-squared and Fisher's exact tests were used to test the independence of screening outcomes and sociodemographic characteristics. Results: The overall positivity rate was 7.2% for Pap tests (n?=?54/751), 3.6% for HPV tests (n?=?21/577), and 44.7% for colposcopies (n?=?117/262). Significance tests suggested that the Pap test and colposcopy outcomes we observed were independent of sociodemographic characteristics in all but one instance?Pap test outcomes were not independent of patient age (p?=?0.009). Moreover, the Pap test positivity rate increased with patient age. Conclusions: Our findings support recommendations to discontinue screening for women older than 65 years at low risk for cervical cancer. Our ability to identify an association between cervical screening outcomes and other sociodemographic characteristics may have been limited by our small sample size. This highlights an important barrier to studying health outcomes within low-income and uninsured populations, which are often missing in larger research data sets (e.g., claims).