Time to Follow-up After Colorectal Cancer Screening by Health Insurance Type.

INTRODUCTION: The purpose of this study was to test the hypothesis that patients with Medicaid insurance or Medicaid-like coverage would have longer times to follow-up and be less likely to complete colonoscopy compared with patients with commercial insurance within the same healthcare systems. METHODS: A total of 35,009 patients aged 50-64years with a positive fecal immunochemical test were evaluated in Northern and Southern California Kaiser Permanente systems and in a North Texas safety-net system between 2011 and 2012. Kaplan-Meier estimation was used between 2016 and 2017 to calculate the probability of having follow-up colonoscopy by coverage type. Among Kaiser Permanente patients, Cox regression was used to estimate hazard ratios and 95% CIs for the association between coverage type and receipt of follow-up, adjusting for sociodemographics and health status. RESULTS: Even within the same integrated system with organized follow-up, patients with Medicaid were 24% less likely to complete follow-up as those with commercial insurance. Percentage receiving colonoscopy within 3 months after a positive fecal immunochemical test was 74.6% for commercial insurance, 63.10% for Medicaid only, and 37.5% for patients served by the integrated safety-net system. CONCLUSIONS: This study found that patients with Medicaid were less likely than those with commercial insurance to complete follow-up colonoscopy after a positive fecal immunochemical test and had longer average times to follow-up. With the future of coverage mechanisms uncertain, it is important and timely to assess influences of health insurance coverage on likelihood of follow-up colonoscopy and identify potential disparities in screening completion.

Assessment of biomarkers for risk prediction with nested case-control studies.

BACKGROUND: Accurate risk prediction plays a key role in disease prevention and disease management; emergence of new biomarkers may lead to an important question about how much improvement in prediction accuracy it would achieve by adding the new markers into the existing risk prediction tools. PURPOSE: In large prospective cohort studies, the standard full-cohort design, requiring marker measurement on the entire cohort, may be infeasible due to cost and low rate of the clinical condition of interest. To overcome such difficulties, nested case-control (NCC) studies provide cost-effective alternatives but bring about challenges in statistical analyses due to complex data sets generated. METHODS: To evaluate prognostic accuracy of a risk model, Cai and Zheng proposed a class of nonparametric inverse probability weighting (IPW) estimators for accuracy measures in the time-dependent receiver operating characteristic curve analysis. To accommodate a three-phase NCC design in Nurses' Health Study, we extend the double IPW estimators of Cai and Zheng to develop risk prediction models under time-dependent generalized linear models and evaluate the incremental values of new biomarkers and genetic markers. RESULTS: Our results suggest that aggregating the information from both the genetic markers and biomarkers substantially improves the accuracy for predicting 5-year and 10-year risks of rheumatoid arthritis. CONCLUSIONS: Our method provided robust procedures to evaluate the incremental value of new biomarkers allowing for complex sampling designs.