Corneal inflammatory events with daily silicone hydrogel lens wear.

PURPOSE: This study aimed to determine the probability and risk factors for developing a corneal inflammatory event (CIE) during daily wear of lotrafilcon A silicone hydrogel contact lenses. METHODS: Eligible participants (n = 218) were fit with lotrafilcon A lenses for daily wear and followed up for 12 months. Participants were randomized to either a polyhexamethylene biguanide-preserved multipurpose solution or a one-step peroxide disinfection system. The main exposures of interest were bacterial contamination of lenses, cases, lid margins, and ocular surface. Kaplan-Meier (KM) plots were used to estimate the cumulative unadjusted probability of remaining free from a CIE, and multivariate Cox proportional hazards regression was used to model the hazard of experiencing a CIE. RESULTS: The KM unadjusted cumulative probability of remaining free from a CIE for both lens care groups combined was 92.3% (95% confidence interval [CI], 88.1 to 96.5%). There was one participant with microbial keratitis, five participants with asymptomatic infiltrates, and seven participants with contact lens peripheral ulcers, providing KM survival estimates of 92.8% (95% CI, 88.6 to 96.9%) and 98.1% (95% CI, 95.8 to 100.0%) for remaining free from noninfectious and symptomatic CIEs, respectively. The presence of substantial (>100 colony-forming units) coagulase-negative staphylococci bioburden on lid margins was associated with about a five-fold increased risk for the development of a CIE (p = 0.04). CONCLUSIONS: The probability of experiencing a CIE during daily wear of lotrafilcon A contact lenses is low, and symptomatic CIEs are rare. Patient factors, such as high levels of bacterial bioburden on lid margins, contribute to the development of noninfectious CIEs during daily wear of silicone hydrogel lenses.

Risk factors for microbial bioburden during daily wear of silicone hydrogel contact lenses.

PURPOSE: To assess risk factors associated with substantial microbial bioburden of lids, conjunctivae, contact lenses, and storage cases during daily wear of silicone hydrogel contact lenses. METHODS: Two hundred eighteen patients were fit to lotrafilcon A lenses, randomized to use either a multipurpose solution or a hydrogen peroxide care system, and followed up for 1 year. Lenses, lens transport saline, lids, conjunctivae, and storage cases were cultured and considered to have substantial microbial bioburden when they harbored high levels of commensal or pathogenic organisms. Univariate and multivariate logistic regression analyses were conducted to examine which demographic covariates were associated with significant bioburden at each location while controlling for solution use. RESULTS: In multivariate analyses, smoking trended toward an association with lens bioburden (odds ratio [OR]=2.15, 95% confidence interval [CI]: 0.95-4.88). Clerical occupations were found to be associated with more frequent overall storage case contamination (OR=3.51, 95% CI: 1.15-10.70) and, specifically, higher gram-positive storage case contamination (OR=5.57, 95% CI: 1.82-17.06). The peroxide system was associated with more frequent storage case contamination (OR=7.6, 95% CI: 3.79-15.19). Coagulase-negative staphylococci (CNS) were the most frequently cultured organisms within storage cases, and in multivariate analyses, CNS were more frequently found in storage cases of peroxide users (OR=6.12, 95% CI: 2.91-13.09). CONCLUSIONS: Clerical occupations were associated with increased microbial bioburden of storage cases during daily wear of silicone hydrogel lenses. Smoking may increase the risk of lens contamination. Storage cases are most frequently contaminated with normal skin flora, and peroxide cases were associated with more frequent contamination. However, the solution type was not associated with lid or lens contamination nor with corneal infiltrative events in this study.

Alzheimer's disease and smoking: bias in cohort studies.

The discrepancy between cohort and case-control studies regarding the association between smoking and Alzheimer's disease (AD) has been attributed to the competing risk of early mortality of smokers. A simulation study was conducted to show that the bias favoring smokers acts also on cohort studies. In the model, individuals {grow older} and have smoking habits according to published year-age-gender-specific patterns, with morbidity and mortality according to their demographic and smoking profiles. Those individuals dying of smoking-related causes ("phantoms") remain at risk of AD and of death from other causes. Three scenarios were considered: no association of AD and smoking, increased risk for smokers, and decreased risk for smokers. For each simulation of a cohort study, two incidence density ratios (IDR) were computed: one including the phantoms that developed AD (thus ignoring smoking-related deaths) and another excluding them (thus mimicking real-life studies). For all scenarios, the simulations show that smoking-related death creates a bias, resulting in smokers having an understated risk of AD compared to non-smokers. The speculation that the conflicting results of case-control and cohort studies are solely due to the increased mortality in smokers thus appears unjustified. Other factors must also be considered to explain the discrepancy in results.

Health impact and cost-effectiveness of a domestically-produced rotavirus vaccine in India: A model based analysis.

BACKGROUND: Currently, Indian officials are incorporating a domestically manufactured rotavirus vaccine (based on the 116E rotavirus strain) into the country's universal immunization program; this vaccine will cost significantly less than western rotavirus vaccines. Here, we examine the public health impact, cost, and cost-effectiveness of universal vaccination in India using the 116E vaccine. This work will allow comparison of universal 116E vaccination with other approaches to child mortality reduction, shed light on the future burden of rotavirus disease in India, and help stakeholders understand future resource needs. METHODS: Using information from published literature, we developed a dynamic simulation model of rotavirus transmission, natural history, and related utilization among Indian infants followed until age five. Infection risk depended on the degree of viral shedding in the population. Infection risk and severity were influenced by age, number of previous infections, and vaccination history. Probabilities of inpatient and outpatient health services utilization depended on symptom severity. With the model, we compared a strategy of nationwide 116E vaccination to one of no vaccination. Costs were considered from the perspective of all payers (including families) and from the societal perspective. RESULTS: We estimated that an established 116E vaccination program would reduce symptomatic rotavirus infection by 13.0%, while reducing population-wide rotavirus mortality by 34.6% (over 34,000 lives annually). Rotavirus outpatient visits would decline by 21.3%, and hospitalization would decline by 28.1%. The cost per disability-adjusted life year (DALY) averted was estimated at 3,429 Rupees (approximately $56). Predicted mortality reduction in children born during the first five years of vaccination implementation was nearly identical to that in children born in later years (34.4% versus 34.6%). CONCLUSIONS: 116E vaccination of Indian infants would likely substantially reduce rotavirus-related morbidity, mortality, and utilization at a cost considered highly cost-effective by standard criteria. Nearly the entire mortality reduction benefit of vaccination was attributable to direct protection of those vaccinated, as opposed to indirect "herd immunity" effects.

Identification of neurodevelopmental disabilities in underserved children using telehealth (INvesT): Clinical trial study design.

BACKGROUND: Children living in poverty are at high risk for delays in development of language and behavior and they experience a discrepancy in diagnosis and access to intervention services. This gap is partially caused by barriers in access as well as traits that are specific to each child and family. The Identification of Neurodevelopmental Disabilities in Underserved Children using Telehealth (INvesT) trial is a novel intervention approach that was specifically designed to address these barriers. AIMS: The INvesT trial has three primary aims: 1) to reduce the age of identification of neurodevelopmental disability for high-risk, low-income children. 2) To validate the INvesT protocol as a service delivery model that will decrease age of identifications of neurodevelopmental disability for high-risk, low-income children; and 3) to identify important child-specific factors, family-specific factors, and environmental factors that impact feasibility and success of the INvesT trial for high-risk, low-income children. METHODS: The INvesT trial is an open-label, double-blinded, placebo-controlled multi-level study that includes telehealth risk assessment, telehealth screening, traditional full assessment, and follow through to enrollment in early intervention. The trial is conducted in partnership with an urban community health clinic that largely serves a low-income patient population. CONCLUSIONS: The results of the INvesT trial will provide evidence for the use of a telehealth service delivery model to improve access to care for neurodevelopmental disabilities for high-risk, low-income children.

CLINICAL/MEDICAL OUTCOME PREDICTION BY NEURAL NETWORKS WITH STATISTICAL ENHANCEMENT.

Neural networks offer a powerful new approach to information processing through their ability to generalize from a specific training data set. The success of this approach has raised interesting new possibilities of incorporating statistical methodology in order to enhance their predictive ability. This paper reports on two complementary methods of prediction. one using neural networks and the other using traditional statistical methods. The two methods are compared on the basis of their prediction applied to standardized developmental infant outcome measures using preselected infant and maternal variables measured at birth. Three neural network algorithms were employed. In our study, no one network outperformed the other two consistently. The neural networks provided significantly better results than the regression model in terms of variation and prediction of extreme outcomes. Finally we demonstrated that selection of relevant input variables through statistical means can produce a reduced network structure with no loss in predictive ability.