Faculty Bibliography

BACKGROUND: There is paucity of information on the pattern of bacterial colonization of a new neonatal intensive care unit. OBJECTIVE: To study the pattern of bacterial colonization on the environmental surfaces in a new neonatal intensive care unit (NICU) and correlate it with infections in the infants. METHODS: Environmental cultures from the faucets and computer keyboards in the NICU were obtained prospectively every 2 weeks for 1 year. Positive blood, cerebrospinal fluid, and respiratory cultures from the infants in the NICU were also obtained. RESULTS: A total of 175 swab cultures was collected, which were sterile for initial 6-week period. Subsequently, 31 cultures grew microbes: 26 (83.8%) from the faucets and 5 (16.2%) from the computers keyboard (P < .001). Of the 48 positive blood cultures in NICU patients, 6 (12.5%) matched the organism growing from the surveillance sites, but the correlation was not significant (P = .076). None of the 31 positive respiratory cultures and 1 positive cerebrospinal fluid culture correlated to the organisms grown from the NICU environment. CONCLUSION: The environment was colonized after an initial period of sterile cultures in a new NICU. Once colonized, they can persist, increasing the risk of developing resistance to antibiotics. They did not correlate with the positive cultures from the infants admitted to the NICU during the study period.

Most medical organizations recommend informed decision making before undergoing prostate cancer screening. The authors conducted a detailed evaluation of men's use of an interactive, Web-based prostate cancer screening decision aid. Participants (N = 531) were 57 years old (SD = 6.8), 37% were African American, and 92% had Internet access. Men completed 2 telephone interviews, pre- and 1-month post-Web site availability. Half of the sample (n = 256) accessed the Web site. Multivariate analysis revealed that users were more likely than nonusers to be White (OR = 2.37, CI 1.6-3.6), previously screened (OR = 2.13, CI 1.07-4.26), have Internet access (OR = 3.66, CI 1.15-11.58), and to report daily Internet use (OR = 2.58, CI 1.47-4.55). Agreement between self-reported and actual Web site use was moderate (kappa = .67). Tracking software revealed a mean of 1.3 (SD = 0.5) log-ons and a median of 38 min per log-on. Of participants, 84% used the values clarification tool, and more than 50% viewed each video testimonial. Baseline screening preference was associated with values clarification tool responses and Web site feedback. This study revealed that, beyond the digital divide, Web site use depended on more than Internet access. Further, electronic tracking of Web site use demonstrated overestimation of self-reported use, high use of interactive features, and effect of baseline screening preference on men's response to the Web site.

ABSTRACT: BACKGROUND: Hospitalization may be a particularly important time to promote smoking cessation, especially in the immediate post-discharge period. However, there are few studies to date that shed light on the most effective or cost-effective methods to provide post-discharge cessation treatment, especially among low-income populations and those with a heavy burden of mental illness and substance use disorders. METHODS/DESIGN: This randomized trial will compare the effectiveness and cost-effectiveness of two approaches to smoking cessation treatment among patients discharged from two urban public hospitals in New York City. During hospitalization, staff will be prompted to ask about smoking and to offer nicotine replacement therapy (NRT) on admission and at discharge. Subjects will be randomized on discharge to one of two arms: one arm will be proactive multi-session telephone counseling with motivational enhancement delivered by study staff, and the other will be a faxed or online referral to the New York State Quitline. The primary outcome is 30-day point-prevalence abstinence from smoking at 6-month follow-up post-discharge. We will also examine cost-effectiveness from a societal and a payer perspective, as well as explore subgroup analyses related to patient location of hospitalization, race/ethnicity, immigrant status, and inpatient diagnosis. DISCUSSION: This study will explore issues of implementation feasibility in a post-hospitalization patient population, as well as add information about the effectiveness and cost-effectiveness of different strategies for designing smoking cessation programs for hospitalized patients. TRIAL REGISTRATION: Clinicaltrials.gov ID# NCT01363245.

OBJECTIVES: Predicting the price change percentages and timings of drugs is important to policy makers, pharmaceutical companies, and even investment firms. As a case study, we utilize a set of oncology drugs in the US and apply hazard models to perform the predictions. METHODS: Using data from First DataBank (2003-2012), we have a panel of ex-factory drug prices for drug packs for 18 brand names. We convert the data into survival time data by calculating the time duration between each price change, which results in a total of 200 price increases and 38 censored outcomes. In our hazard models, we include the FDA approval date for each drug as an exogenous variable to answer the following questions: 1) how is the percentage change in price related to the time since the last price change and the time since FDA approval, and 2) does the probability of a price change depend on the time since FDA approval? We use Cox Proportional Hazard models for prediction. RESULTS: The average "event" is a price increase of 5%. For percentage changes in price, we find that for each additional month of constant price, the subsequent price increase drops by 0.08%. For a second order effect, we find that the negative effect of time since last price change is decreasing. Also, time since FDA approval has a large and significant effect: for each additional month since FDA approval, the subsequent price increase drops by 0.03%. The average duration between events is 8.8 months. The Cox model shows that for each additional month since FDA approval, the "risk" of a price increase increases by 0.7%. Similarly, there is a second order effect showing this risk diminishing over time. CONCLUSIONS: Hazard models can predict the timing and percentage of price changes in oncology drugs in the United States