Physicians, Clinics, and Neighborhoods: Multiple Levels of Influence on Colorectal Cancer Screening

Abstract

Background We 1) Described variability in colorectal cancer (CRC) test use across multiple levels, including physician, clinic, and neighborhood; and 2) Compared the performance of novel cross-classified vs. traditional hierarchical models. Methods We examined multilevel variation in CRC test use among patients not up-to-date with screening in a large, urban safety net health system (2011–2012). Outcomes included: 1) fecal occult blood test (FOBT) or 2) colonoscopy and were ascertained using claims data during a 1-year follow-up. We compared Bayesian 1) Cross-classified 4-level logistic models nesting patients within separate, non-overlapping “levels” (physicians, clinics, and census tracts) vs. 2) Three hierarchical 2-level models using deviance information criterion. Models adjusted for covariates (patient sociodemographic factors, driving time to clinic, and census tract poverty rate). Results Of 3,195 patients, 157 (4.9%) completed FOBT and 292 (9.1%) completed colonoscopy during the study year. Patients attended 19 clinics, saw 177 physicians, and resided in 332 census tracts. Significant variability was observed across all levels in both hierarchical and cross-classified models that was unexplained by measured covariates. For colonoscopy, variance was similar across all levels. For FOBT, physicians, followed by clinics, demonstrated the largest variability. Model fit using cross-classified models was superior or similar to 2-level hierarchical models. Conclusions Significant and substantial variability was observed across neighborhood, physician, and clinic levels in CRC test use, suggesting the importance of factors at each of these levels on CRC testing. Impact Future multilevel research and intervention should consider the simultaneous influences of multiple levels, including clinic, physician, and neighborhood.

Missed opportunities: racial and neighborhood socioeconomic disparities in emergency colorectal cancer diagnosis and surgery

“…Notably, the neighborhood variability observed in our study persisted even after controlling for all covariates, including neighborhood poverty rate, indicating significant unexplained neighborhood variation in both outcomes. These results confirm a growing body of research suggesting that CRC outcomes, including screening, incidence, stage, and mortality, vary across geography [ 17 , 19 , 51 – 53 ]. For example, a prior study identified significant census-tract level variation in CRC survival that remained unexplained after accounting for individual covariates and neighborhood SES [ 19 ].…”

Geospatial Approaches to Cancer Control and Population Sciences

“…Studies could also include data about other contextual locations, including where individuals work and receive medical care. Multilevel research should consider the simultaneous influences of multiple levels, including clinic, hospital, physician, family, and neighborhood (26,27). Because neighborhoods also change over time, difference-in-difference models may allow for a better understanding of the impact of their dynamic nature on cancer etiology and outcomes (28).…”

GIScience and cancer: State of the art and trends for cancer surveillance and epidemiology

“…Statistical dimension reduction methods, such as principle component analysis and factor analysis, often are used to develop a single index or a set of factors to capture complex social environmental risk factors that are multidimensional in nature . Multilevel regression methods can be used to assess the impact of cancer risk factors that operate at different spatial scales . Because many cancer risk factors operate over an extended period, spatial‐temporal analysis methods are needed to assess exposure as individuals progress through daily travel and residential mobility …”

“…76 Multilevel regression methods can be used to assess the impact of cancer risk factors that operate at different spatial scales. 77,78 Because many cancer risk factors operate over an extended period, spatial-temporal analysis methods are needed to assess exposure as individuals progress through daily travel and residential mobility. 79 Types of spatial risk-factor data There are several important types of spatial data available for characterizing behavioral risk factors.…”