Cancer prevention in primary care: Predictors of patient counseling across four risk behaviors over 24 months

Objective: Rates of preventive counseling remain below national guidelines. We explored physician and patient predictors of preventive counseling across multiple cancer risk behaviors in at-risk primary care patients. Methods: We surveyed 3557 patients, with at least one of four cancer risk behaviors: smoking, diet, sun exposure, and/or mammography screening, at baseline and 24 months. Patients reported receipt of 4A\u27s (Ask, Advise, Assist, Arrange follow-up); responses were weighted and combined to reflect more thorough counseling (Ask = 1, Advise = 2, Assist = 3, Arrange = 4, score range 0–10) for each target behavior. A series of linear-regression models, controlling for office clustering, examined patient, physician and other situational predictors at 24 months. Results: Risk behavior topics were brought up more often for mammography (90%) and smoking (79%) than diet (56%) and sun protection (30%). Assisting and Arranging follow-up were reported at low frequencies across all behaviors. More thorough counseling for all behaviors was associated with multiple visits and higher satisfaction with care. Prior counseling predicted further counseling on all behaviors except smoking, which was already at high levels. Other predictors varied by risk behavior. Conclusions: More thorough risk behavior counseling can be delivered opportunistically across multiple visits; doing so is associated with more satisfaction with care

Randomized Controlled Trial of a Computer-Based, Tailored Intervention to Increase Smoking Cessation Counseling by Primary Care Physicians

OBJECTIVE: The primary care visit represents an important venue for intervening with a large population of smokers. However, physician adherence to the Smoking Cessation Clinical Guideline (5As) remains low. We evaluated the effectiveness of a computer-tailored intervention designed to increase smoking cessation counseling by primary care physicians. METHODS: Physicians and their patients were randomized to either intervention or control conditions. In addition to brief smoking cessation training, intervention physicians and patients received a one-page report that characterized the patients’ smoking habit and history and offered tailored recommendations. Physician performance of the 5As was assessed via patient exit interviews. Quit rates and smoking behaviors were assessed 6 months postintervention via patient phone interviews. Intervention effects were tested in a sample of 70 physicians and 518 of their patients. Results were analyzed via generalized and mixed linear modeling controlling for clustering. MEASUREMENTS AND MAIN RESULTS: Intervention physicians exceeded controls on “Assess” (OR 5.06; 95% CI 3.22, 7.95), “Advise” (OR 2.79; 95% CI 1.70, 4.59), “Assist–set goals” (OR 4.31; 95% CI 2.59, 7.16), “Assist–provide written materials” (OR 5.14; 95% CI 2.60, 10.14), “Assist–provide referral” (OR 6.48; 95% CI 3.11, 13.49), “Assist–discuss medication” (OR 4.72;95% CI 2.90, 7.68), and “Arrange” (OR 8.14; 95% CI 3.98, 16.68), all p values being < 0.0001. Intervention patients were 1.77 (CI 0.94, 3.34,p = 0.078) times more likely than controls to be abstinent (12 versus 8%), a difference that approached, but did not reach statistical significance, and surpassed controls on number of days quit (18.4 versus 12.2, p < .05) but not on number of quit attempts. CONCLUSIONS: The use of a brief computer-tailored report improved physicians’ implementation of the 5As and had a modest effect on patients’ smoking behaviors 6 months postintervention

Assessing the transtheoretical model of change constructs for physicians counseling smokers

Baseline data from a population-based sample of 259 primary care physicians were used to examine the interrelations of 3 central constructs of the Transtheoretical Model of Change (TTM; stages of change, self-efficacy, and decisional balance) in regard to smoking cessation counseling behavior. In this article we explore the potential use of the TTM for future interventions to help understand and guide physicians\u27 behavior change toward increasing adoption of smoking cessation interventions with their patients. It was hypothesized that self-efficacy and the decisional balance of counseling would be significantly related to physicians\u27 stages of change, which in turn would be related to self-reported physician counseling behavior. Principal components analyses were conducted to examine the self-efficacy and decisional balance constructs. Coefficient alphas were .90 for self-efficacy and .84 and .78 for the pros and cons scales, respectively. Consistent with the TTM, analyses of variance revealed that later stages of physicians\u27 readiness to provide smoking cessation counseling were associated with higher self-efficacy scores. Earlier stages showed significantly higher cons and lower pros of smoking cessation counseling. Structural equation modeling procedures supported the hypothesized path analysis model in which 3 constructs related to stage of readiness, which in turn related to reported physicians\u27 counseling behavior

Stage-based expert systems to guide a population of primary care patients to quit smoking, eat healthier, prevent skin cancer, and receive regular mammograms

Background. Treating multiple health behavior risks on a population basis is one of the most promising approaches to enhancing health and reducing health care costs. Previous research demonstrated the efficacy of expert system interventions for three behaviors in a population of parents. The interventions provide individualized feedback that guides participants through the stages of change for each of their risk behaviors. This study extended that research to a more representative population of patients from primary care practice and to targeting of four rather than three behaviors. Methods. Stage-based expert systems were applied to reduce smoking, improve diet, decrease sun exposure, and prevent relapse from regular mammography. A randomized clinical controlled trial recruited 69.2% of primary care patients (N = 5407) at home via telephone. Three intervention contacts were delivered for each risk factor at 0, 6, and 12 months. The primary outcome measures were the percentages of at-risk patients at baseline who progressed to the action or maintenance stages at 24-month follow-up for each of the risk behaviors. Results. Significant treatment effects were found for each of the four behaviors, with 25.4% of intervention patients in action or maintenance for smoking, 28.8% for diet, and 23.4% for sun exposure. The treatment group had less relapse from regular mammography than the control group (6% vs. 10%). Conclusion. Proactive, home-based, and stage-matched expert systems can produce relatively high population impacts on multiple behavior risks for cancer and other chronic diseases. © 2005 Elsevier Inc. All rights reserved