Publications

BACKGROUND

Cigarette smoking presents a salient risk for HIV-positive populations. This study is among the first to examine smoking prevalence, nicotine dependence, and associated factors in a large sample of HIV-positive patients receiving antiretroviral therapy (ART) in Vietnam.

METHODS

A cross-sectional study of 1133 HIV-positive people was conducted from January to September 2013 at 8 ART clinics in Hanoi (the capital) and Nam Dinh (a rural area). Smoking history and nicotine dependence (Fagerstrom Test of Nicotine Dependence-FTND) were assessed by participant self-report. Logistic regression and Tobit linear regression were performed to identify factors significantly associated with smoking outcomes.

RESULTS

Prevalence of current, former, and never smokers in the sample was 36.1%, 9.5%, and 54.4%, respectively. The current smoking proportion was higher in males (59.7%) than females (2.6%). The mean FTND score was 3.6 (SD = 2.1). Males were more likely to currently smoke than females (OR = 23.4, 95% CI = 11.6-47.3). Individuals with problem drinking (OR = 1.8, 95% CI = 1.1-2.9) and ever drug use (OR = 3.7, 95%CI = 2.5-5.7) were more likely to be current smokers. Older age and currently feeling pain were associated with lower nicotine dependence. Conversely, receiving care in Nam Dinh, greater alcohol consumption, ever drug use, and a longer smoking duration were associated with greater nicotine dependence.

CONCLUSIONS

Given the high prevalence of smoking among HIV-positive patients, smoking screening and cessation support should be offered at ART clinics in Vietnam. Risk factors (i.e., substance use) linked with smoking behavior should be considered in prevention programs.

BACKGROUND

Chronic pain is prevalent, costly, and clinically vexatious. Clinicians typically use a trial-and-error approach to treatment selection. Repeated crossover trials in a single patient (n-of-1 trials) may provide greater therapeutic precision. N-of-1 trials are the most direct way to estimate individual treatment effects and are useful in comparing the effectiveness and toxicity of different analgesic regimens. The goal of the PREEMPT study is to test the 'Trialist' mobile health smartphone app, which has been developed to make n-of-1 trials easier to accomplish, and to provide patients and clinicians with tools for individualizing treatments for chronic pain.

METHODS/DESIGN

A randomized controlled trial is being conducted to test the feasibility and effectiveness of the Trialist app. A total of 244 participants will be randomized to either the Trialist app intervention group (122 patients) or a usual care control group (122 patients). Patients assigned to the Trialist app will work with their clinicians to set up an n-of-1 trial comparing two pain regimens, selected from a menu of flexible options. The Trialist app provides treatment reminders and collects data entered daily by the patient on pain levels and treatment side effects. Upon completion of the n-of-1 trial, patients review results with their clinicians and develop a long-term treatment plan. The primary study outcome (comparing Trialist to usual care patients) is pain-related interference with daily functioning at 26 weeks.

DISCUSSION

Trialist will allow patients and clinicians to conduct personalized n-of-1 trials. In prior studies, n-of-1 trials have been shown to encourage greater patient involvement with care, which has in turn been associated with better health outcomes. mHealth technology implemented using smartphones may offer an efficient means of facilitating n-of-1 trials so that more patients can benefit from this approach.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT02116621 , first registered 15 April 2014.

Estimating population exposure to particulate matter during wildfires can be difficult because of insufficient monitoring data to capture the spatiotemporal variability of smoke plumes. Chemical transport models (CTMs) and satellite retrievals provide spatiotemporal data that may be useful in predicting PM2.5 during wildfires. We estimated PM2.5 concentrations during the 2008 northern California wildfires using 10-fold cross-validation (CV) to select an optimal prediction model from a set of 11 statistical algorithms and 29 predictor variables. The variables included CTM output, three measures of satellite aerosol optical depth, distance to the nearest fires, meteorological data, and land use, traffic, spatial location, and temporal characteristics. The generalized boosting model (GBM) with 29 predictor variables had the lowest CV root mean squared error and a CV-R2 of 0.803. The most important predictor variable was the Geostationary Operational Environmental Satellite Aerosol/Smoke Product (GASP) Aerosol Optical Depth (AOD), followed by the CTM output and distance to the nearest fire cluster. Parsimonious models with various combinations of fewer variables also predicted PM2.5 well. Using machine learning algorithms to combine spatiotemporal data from satellites and CTMs can reliably predict PM2.5 concentrations during a major wildfire event.

Long-lasting Ab responses rely on the germinal center (GC), where B cells bearing high-affinity Ag receptors are selected from a randomly mutated pool to populate the memory and plasma cell compartments. Signaling downstream of the BCR is dampened in GC B cells, raising the possibility that Ag presentation and competition for T cell help, rather than Ag-dependent signaling per se, drive these critical selection events. In this study we use an in vivo reporter of BCR signaling, Nur77-eGFP, to demonstrate that although BCR signaling is reduced among GC B cells, a small population of cells exhibiting GC light zone phenotype (site of Ag and follicular helper T cell encounter) express much higher levels of GFP. We show that these cells exhibit somatic hypermutation, gene expression characteristic of signaling and selection, and undergo BCR signaling in vivo.

OBJECTIVE

This study estimates how making oral contraceptive pills (OCPs) available without a prescription may affect contraceptive use, unintended pregnancies and associated contraceptive and pregnancy costs among low-income women.

STUDY DESIGN

Based on published figures, we estimate two scenarios [low over-the-counter (OTC) use and high OTC use] of the proportion of low-income women likely to switch to an OTC pill and predict adoption of OCPs according to the out-of-pocket costs per pill pack. We then estimate cost-savings of each scenario by comparing the total public sector cost of providing OCPs OTC and medical care for unintended pregnancy.

RESULTS

Twenty-one percent of low-income women at risk for unintended pregnancy are very likely to use OCPs if they were available without a prescription. Women's use of OTC OCPs varies widely by the out-of-pocket pill pack cost. In a scenario assuming no out-of-pocket costs for the over-the counter pill, an additional 11-21% of low-income women will use the pill, resulting in a 20-36% decrease in the number of women using no method or a method less effective than the pill, and a 7-25% decrease in the number of unintended pregnancies, depending on the level of use and any effect on contraceptive failure rates.

CONCLUSIONS

If out-of-pocket costs for such pills are low, OTC access could have a significant effect on use of effective contraceptives and unintended pregnancy. Public health plans may reduce expenditures on pregnancy and contraceptive healthcare services by covering oral contraceptives as an OTC product.

IMPLICATIONS

Interest in OTC access to oral contraceptives is high. Removing the prescription barrier, particularly if pill packs are available at low or zero out-of-pocket cost, could increase the use of effective methods of contraception and reduce unintended pregnancy and healthcare costs for contraceptive and pregnancy care.

While modernization has dramatically increased lifespan, it has also witnessed the increasing prevalence of diseases such as obesity, hypertension, and type 2 diabetes. Such chronic, acquired diseases result when normal physiologic control goes awry and may thus be viewed as failures of homeostasis. However, while nearly every process in human physiology relies on homeostatic mechanisms for stability, only some have demonstrated vulnerability to dysregulation. Additionally, chronic inflammation is a common accomplice of the diseases of homeostasis, yet the basis for this connection is not fully understood. Here we review the design of homeostatic systems and discuss universal features of control circuits that operate at the cellular, tissue, and organismal levels. We suggest a framework for classification of homeostatic signals that is based on different classes of homeostatic variables they report on. Finally, we discuss how adaptability of homeostatic systems with adjustable set points creates vulnerability to dysregulation and disease. This framework highlights the fundamental parallels between homeostatic and inflammatory control mechanisms and provides a new perspective on the physiological origin of inflammation.