Ryn, Michelle van

BACKGROUNDShared decision‐making is an important component of patient‐centered care and is associated with improved outcomes. To the authors' knowledge, little is known concerning the extent and predictors of the involvement of a patient's family in decisions regarding cancer treatments.METHODSThe Cancer Care Outcomes Research and Surveillance (CanCORS) Consortium is a large, multiregional, prospective cohort study of the cancer care and outcomes of patients with lung and colorectal cancer. Participants reported the roles of their families in decision‐making regarding treatment. Multinomial logistic regression was used to assess patient factors associated with family roles in decisions.RESULTSAmong 5284 patients, 80 (1.5%) reported family‐controlled decisions, with the highest adjusted rates (12.8%) noted among non‐English‐speaking Asians. Among the 5204 remaining patients, 49.4% reported equally sharing decisions with family, 22.1% reported some family input, and 28.5% reported little family input. In adjusted analyses, patients who were married, female, older, and insured more often reported equally shared decisions with family (all P <.001). Adjusted family involvement varied by race/ethnicity and language, with Chinese‐speaking Asian (59.8%) and Spanish‐speaking Hispanic (54.8%) patients equally sharing decisions with family more often than white individuals (47.6%). Veterans Affairs patients were least likely to report sharing decisions with family, even after adjustment for marital status and social support (P <.001).CONCLUSIONSThe majority of patients with newly diagnosed lung or colorectal cancer involve family members in treatment decisions. Non‐English‐speaking Asians and Hispanics rely significantly on family. Further studies are needed to determine the impact of family involvement in treatment decisions on outcomes; until then, physicians should consider eliciting patients' preferences for family involvement. Cancer 2015;121:1079–1087. © 2015 American Cancer Society.

Objectives. We examined whether a proactive care smoking cessation intervention designed to overcome barriers to treatment would be especially effective at increasing cessation among African Americans receiving care in the Veterans Health Administration. Methods. We analyzed data from a randomized controlled trial, the Veterans Victory over Tobacco study, involving a population-based electronic registry of current smokers (702 African Americans, 1569 Whites) and assessed 6-month prolonged smoking abstinence at 1 year via a follow-up survey of all current smokers. We also examined candidate risk adjustors for the race effect on smoking abstinence. Results. The interaction between patient race and intervention condition (proactive care vs usual care) was not significant. Overall, African Americans had higher quit rates than Whites (13% vs 9%; P < .006) regardless of condition. Conclusions. African Americans quit at higher rates than Whites. These findings may be a result of the large number of veterans receiving smoking cessation services and the lack of racial differences in receipt of these services as well as racial differences in smoking history, self-efficacy, and motivation to quit that favor African Americans.

Purpose High-quality supportive care is an essential component of comprehensive cancer care. We implemented a patient-centered quality of cancer care survey to examine and identify predictors of quality of supportive care for bowel problems, pain, fatigue, depression, and other symptoms among 1,109 patients with colorectal cancer. Patients and Methods Patients with new diagnosis of colorectal cancer at any Veterans Health Administration medical center nationwide in 2008 were ascertained through the Veterans Affairs Central Cancer Registry and sent questionnaires assessing a variety of aspects of patient-centered cancer care. We received questionnaires from 63% of eligible patients (N = 1,109). Descriptive analyses characterizing patient experiences with supportive care and binary logistic regression models were used to examine predictors of receipt of help wanted for each of the five symptom categories. Results There were significant gaps in patient-centered quality of supportive care, beginning with symptom assessment. In multivariable modeling, the impact of clinical factors and patient race on odds of receiving wanted help varied by symptom. Coordination of care quality predicted receipt of wanted help for all symptoms, independent of patient demographic or clinical characteristics. Conclusion This study revealed substantial gaps in patient-centered quality of care, difficult to characterize through quality measurement relying on medical record review alone. It established the feasibility of collecting patient-reported quality measures. Improving quality measurement of supportive care and implementing patient-reported outcomes in quality-measurement systems are high priorities for improving the processes and outcomes of care for patients with cancer.

ObjectiveTo examine the magnitude of explicit and implicit weight biases compared to biases against other groups; and identify student factors predicting bias in a large national sample of medical students.MethodsA web‐based survey was completed by 4,732 1st year medical students from 49 medical schools as part of a longitudinal study of medical education. The survey included a validated measure of implicit weight bias, the implicit association test, and 2 measures of explicit bias: a feeling thermometer and the anti‐fat attitudes test.ResultsA majority of students exhibited implicit (74%) and explicit (67%) weight bias. Implicit weight bias scores were comparable to reported bias against racial minorities. Explicit attitudes were more negative toward obese people than toward racial minorities, gays, lesbians, and poor people. In multivariate regression models, implicit and explicit weight bias was predicted by lower BMI, male sex, and non‐Black race. Either implicit or explicit bias was also predicted by age, SES, country of birth, and specialty choice.ConclusionsImplicit and explicit weight bias is common among 1st year medical students, and varies across student factors. Future research should assess implications of biases and test interventions to reduce their impact.