Background

This study illustrates alternative statistical methods for estimating cumulative risk of screening mammography outcomes in longitudinal studies.

Methods

Data from the US Breast Cancer Surveillance Consortium (BCSC) and the Nijmegen Breast Cancer Screening Program in the Netherlands were used to compare four statistical approaches to estimating cumulative risk. We estimated cumulative risk of false-positive recall and screen-detected cancer after 10 screening rounds using data from 242,835 women ages 40 to 74 years screened at the BCSC facilities in 1993-2012 and from 17,297 women ages 50 to 74 years screened in Nijmegen in 1990-2012.

Results

In the BCSC cohort, a censoring bias model estimated bounds of 53.8% to 59.3% for false-positive recall and 2.4% to 7.6% for screen-detected cancer, assuming 10% increased or decreased risk among women screened for one additional round. In the Nijmegen cohort, false-positive recall appeared to be associated with subsequent discontinuation of screening leading to overestimation of risk of a false-positive recall based on adjusted discrete-time survival models. Bounds estimated by the censoring bias model were 11.0% to 19.9% for false-positive recall and 4.2% to 9.7% for screen-detected cancer.

Conclusion

Choice of statistical methodology can substantially affect cumulative risk estimates. The censoring bias model is appropriate under a variety of censoring mechanisms and provides bounds for cumulative risk estimates under varying degrees of dependent censoring.

Impact

This article illustrates statistical methods for estimating cumulative risks of cancer screening outcomes, which will be increasingly important as screening test recommendations proliferate.

Objective

The patient-centered medical home (PCMH) includes comprehensive chronic illness and preventive services, including identifying patients who are overdue for colorectal cancer screening (CRCS). The association between PCMH implementation and CRCS during the Systems of Support to Increase Colorectal Cancer Screening Trial (SOS) is described.

Methods

The SOS enrolled 4664 patients from 21 clinics from August 2008 to November 2009. Patients were randomized to usual care, mailed fecal kits, kits plus brief assistance, or kits plus assistance and navigation. A PCMH model that included a workflow for facilitating CRCS was implemented at all study clinics in late 2009. Patients enrolled early had little exposure to the PCMH, whereas patients enrolled later were exposed during most of their first year in the trial. Logistic regression models were used to assess the association between PCMH exposure and CRCS.

Results

Usual care patients with ≥8 months in the PCMH had higher CRCS rates than those with ≤4 months in the PCMH (adjusted difference, 10.1%; 95% confidence interval, 5.7-14.6). SOS interventions led to significant increases in CRCS, but the magnitude of effect was attenuated by exposure to the PCMH (P for interaction = .01).

Conclusion

Exposure to a PCMH was associated with higher CRCS rates. Automated mailed and centrally delivered stepped interventions increased CRCS rates, even in the presence of a PCMH.

Purpose: Conceptual models provide frameworks to illustrate relationships among patient-, provider-, system-, and community-level factors that inform care delivery and research. Existing models of cancer survivorship care focus largely on pediatric or adult populations whose needs differ from adolescents and young adults (AYAs). We developed a patient-centered conceptual model of AYA survivorship care. Methods: We conducted a narrative literature review of current conceptual and theoretical models of care. We engaged AYA cancer survivors (n = 25) in semi-structured one-hour telephone interviews. Most participants were in their 20s and 30s, and the majority (84%) were women. Recruitment was stratified by age and time since cancer diagnosis. We conducted a thematic analysis of interview transcripts to identify themes that exemplified patient-centered care. Results: Most participants identified as white and female. Leukemia and breast cancer were the most common cancer types. Main themes included the need for (1) care coordination, (2) ongoing mental health support, (3) connection to AYA peer support, (4) support during fertility preservation efforts, (5) support with financial burden, (6) support for quality of life, (7) information about and support with side effects and late effects, and (8) attention to the unique needs of young adults. Conclusions: We present a patient-centered conceptual model of AYA survivorship care needs that can inform future cancer care delivery and research.

There are >1.9 million survivors of adolescent and young adult cancers (AYA, diagnosed at ages 15-39) living in the U.S. today. Epidemiologic studies to address the cancer burden in this group have been a relatively recent focus of the research community. In this article, we discuss approaches and data resources for cancer epidemiology and health services research in the AYA population. We consider research that uses data from cancer registries, vital records, healthcare utilization, and surveys, and the accompanying challenges and opportunities of each. To illustrate the strengths of each data source, we present example research questions or areas that are aligned with these data sources and salient to AYAs. Integrating the respective strengths of cancer registry, vital records, healthcare data, and survey-based studies sets the foundation for innovative and impactful research on AYA cancer treatment and survivorship to inform a comprehensive understanding of diverse AYA needs and experiences.