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Background: The Inter-Mountain Project on Antibiotic Resistance and Therapy (IMPART) is an intervention that addresses emerging antimicrobial resistance and the reduction of unnecessary antimicrobial use. Purpose: This study assesses the design and implementation of the community intervention component of IMPART. Methods: The study was conducted in 12 rural Utah and Idaho communities. Following initial implementation, the intervention was evaluated and redesigned based on health behavior theory. Community penetration and intercept interview data were analyzed using multi-level logistic regression. Results: Over 10% of interview respondents were familiar with IMPART. Those exposed to intervention materials tended to be younger, female, and more likely to have had a family member with recent respiratory illness. Of those who had seen the project Self-Care Guide, 62% reported having a copy and 32% had talked to others about the information it contained. Correct responses to antibiotic knowledge questions were higher among those with high (OR = 2.02) and low exposure (OR = 1.27) to the intervention versus no exposure. Discussion: Theoretically-based community interventions such as IMPART can be used to promote appropriate, positively framed health behaviors. Translation to Health Education Practice: IMPART serves as an example of how health interventions can benefit from health behavior theory. (Contains 3 tables and 1 figure.)

Eleven second-year college students, representing various characteristics of the student population, were interviewed on issues related to school persistence. Major factors relating to attrition or persistence for second-year students included: (1) adjustment to stress; (2) grade satisfaction; (3) time management; (4) financial management; and (5) stabilization of career choice. (EV)

Several cohort analyses are conducted to determine the effectiveness of the automated safety surveillance which is used to detect the postprocedure safety signals of approved cardiovascular devices. The surveillance is shown to be extremely feasible and accurate.

A study is conducted to measure the added value of clinical decision support systems (CDSS) when coupled with a community intervention to reduce inappropriate prescribing of antimicrobial drugs for acute respiratory tract infections. CDSS implemented in rural primary care settings reduced overall antimicrobial use and improved appropriateness of antimicrobial selection for acute respiratory tract infections.

Abstract Background Automated adverse outcome surveillance tools and methods have potential utility in quality improvement and medical product surveillance activities. Their use for assessing hospital performance on the basis of patient outcomes has received little attention. We compared risk-adjusted sequential probability ratio testing (RA-SPRT) implemented in an automated tool to Massachusetts public reports of 30-day mortality after isolated coronary artery bypass graft surgery. Methods A total of 23,020 isolated adult coronary artery bypass surgery admissions performed in Massachusetts hospitals between January 1, 2002 and September 30, 2007 were retrospectively re-evaluated. The RA-SPRT method was implemented within an automated surveillance tool to identify hospital outliers in yearly increments. We used an overall type I error rate of 0.05, an overall type II error rate of 0.10, and a threshold that signaled if the odds of dying 30-days after surgery was at least twice than expected. Annual hospital outlier status, based on the state-reported classification, was considered the gold standard. An event was defined as at least one occurrence of a higher-than-expected hospital mortality rate during a given year. Results We examined a total of 83 hospital-year observations. The RA-SPRT method alerted 6 events among three hospitals for 30-day mortality compared with 5 events among two hospitals using the state public reports, yielding a sensitivity of 100% (5/5) and specificity of 98.8% (79/80). Conclusions The automated RA-SPRT method performed well, detecting all of the true institutional outliers with a small false positive alerting rate. Such a system could provide confidential automated notification to local institutions in advance of public reporting providing opportunities for earlier quality improvement interventions.