Do magnetic resonance imaging abnormalities of the non-dominant wrist correlate with ulnar-sided wrist pain in elite tennis players?

OBJECTIVE: Ulnar-sided injuries of the non-dominant wrist are common in elite tennis players that use the double-handed backhand technique. This study aimed to define the relationship between ulnar-sided wrist pain in symptomatic and asymptomatic elite tennis players, and the presence of abnormalities on magnetic resonance imaging (MRI). MATERIALS AND METHODS: Fourteen symptomatic tennis players, 14 asymptomatic tennis players, and 12 healthy controls who did not play tennis, were analyzed prospectively, after undergoing MRI of their non-dominant wrist. Five anatomical regions were analyzed, thought to relate to ulnar-sided wrist pain. These consisted of the triangular fibrocartilage complex (TFCC), ulnar collateral ligament (UCL), extensor carpi ulnaris tendon (ECU), osseous-articular structures, and ganglia. Images were independently reviewed by two blinded musculoskeletal radiologists. RESULTS: Non-dominant, ulnar-sided, wrist pain in elite tennis players was not statistically significantly associated with an increased number of MRI abnormalities when compared with asymptomatic tennis players (p > 0.05). However, some evidence of statistical association was seen with an increased prevalence of ECU tendon abnormalities (OR = 8.0, 95% CI = (0.74, 20.00), p = 0.07). A statistically significant increase in MRI abnormalities of osseous structures (OR = 15.1, 95% CI = (1.56, 656.05), p = 0.02) and the dorsal radioulnar ligament (DRUL) (OR = 12.5, 95% CI = (2.15, 111.11), p = 0.03), was observed in symptomatic players compared with controls. CONCLUSIONS: Non-dominant, ulnar-sided, wrist pain in a subgroup of elite tennis players using a double-handed backhand technique is not associated with a statistically significant increased prevalence of MRI abnormalities when compared with asymptomatic tennis players, other than some evidence of statistical association with ECU tendon abnormalities. Therefore, significance of MRI abnormalities should be interpreted in the context of clinical findings.

Implications of Nine Risk Prediction Models for Selecting Ever-Smokers for Computed Tomography Lung Cancer Screening.

Background: Lung cancer screening guidelines recommend using individualized risk models to refer ever-smokers for screening. However, different models select different screening populations. The performance of each model in selecting ever-smokers for screening is unknown. Objective: To compare the U.S. screening populations selected by 9 lung cancer risk models (the Bach model; the Spitz model; the Liverpool Lung Project [LLP] model; the LLP Incidence Risk Model [LLPi]; the Hoggart model; the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial Model 2012 [PLCOM2012]; the Pittsburgh Predictor; the Lung Cancer Risk Assessment Tool [LCRAT]; and the Lung Cancer Death Risk Assessment Tool [LCDRAT]) and to examine their predictive performance in 2 cohorts. Design: Population-based prospective studies. Setting: United States. Participants: Models selected U.S. screening populations by using data from the National Health Interview Survey from 2010 to 2012. Model performance was evaluated using data from 337 388 ever-smokers in the National Institutes of Health-AARP Diet and Health Study and 72 338 ever-smokers in the CPS-II (Cancer Prevention Study II) Nutrition Survey cohort. Measurements: Model calibration (ratio of model-predicted to observed cases [expected-observed ratio]) and discrimination (area under the curve [AUC]). Results: At a 5-year risk threshold of 2.0%, the models chose U.S. screening populations ranging from 7.6 million to 26 million ever-smokers. These disagreements occurred because, in both validation cohorts, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) were well-calibrated (expected-observed ratio range, 0.92 to 1.12) and had higher AUCs (range, 0.75 to 0.79) than 5 models that generally overestimated risk (expected-observed ratio range, 0.83 to 3.69) and had lower AUCs (range, 0.62 to 0.75). The 4 best-performing models also had the highest sensitivity at a fixed specificity (and vice versa) and similar discrimination at a fixed risk threshold. These models showed better agreement on size of the screening population (7.6 million to 10.9 million) and achieved consensus on 73% of persons chosen. Limitation: No consensus on risk thresholds for screening. Conclusion: The 9 lung cancer risk models chose widely differing U.S. screening populations. However, 4 models (the Bach model, PLCOM2012, LCRAT, and LCDRAT) most accurately predicted risk and performed best in selecting ever-smokers for screening. Primary Funding Source: Intramural Research Program of the National Institutes of Health/National Cancer Institute.

Getting to the top: an analysis of 25 years of career rankings trajectories for professional women's tennis.

Official rankings are the most common measure of success in professional women's tennis. Despite their importance for earning potential and tournament seeding, little is known about ranking trajectories of female players and their influence on career success. Our objective was to conduct a comprehensive study of the career progression of elite female tennis talent. The study examined the ranking trajectories of the top 250 female professionals between 1990 and 2015. Using regression modelling of yearly peak rankings, we found a strong association between the shape of the ranking trajectory and the highest career ranking earned. Players with the highest career peak ranking were the youngest when first ranked. For example, top 10 players were first ranked at age 15.5 years (99% CI = 14.8-15.9), 1.2 years (99% CI = 0.8-1.5) earlier than top 51-100 players. Top 10 players were also ranked in the top 100 longer than other players, holding a top 100 ranking until a mean age of 29.0 years (99% CI = 27.8-30.3) compared with age 24.4 years (99% CI = 23.7-25.2) for top 51-100 players. Ranking trajectories were more distinct with respect to player age than years from first ranking. The present study's findings will be instructive for players, coaches, and administrators in setting goals and assessing athlete development in women's tennis.

Comparing Matchplay Characteristics and Physical Demands of Junior and Professional Tennis Athletes in the Era of Big Data.

Differences in the competitive performance characteristics of junior and professional tennis players are not well understood. The present study provides a comprehensive comparative analysis of junior and professional matchplay. The study utilized multiple large-scale datasets covering match, point, and shot outcomes over multiple years of competition. Regression analysis was used to identify differences between junior and professional matchplay. Top professional men and women were found to play significantly more matches, sets, and games compared to junior players of an equivalent ranking. Professional players had a greater serve advantage, men winning 4 and women winning 2 additional percentage points on serve compared to juniors. Clutch ability in break point conversion was 6 to 8 percentage points greater for junior players. In general, shots were more powerful and more accurate at the professional level with the largest differences observed for male players on serve. Serving to the center of the court was more than two times more common for junior players on first serve. While male professionals performed 50% more total work in a Grand Slam match than juniors, junior girls performed 50% more work than professional women. Understanding how competitiveness, play demands, and the physical characteristics of shots differ between junior and professional tennis players can help set realistic expectations and developmentally appropriate training for transitioning players.

Targeting of low-dose CT screening according to the risk of lung-cancer death.

BACKGROUND: In the National Lung Screening Trial (NLST), screening with low-dose computed tomography (CT) resulted in a 20% reduction in lung-cancer mortality among participants between the ages of 55 and 74 years with a minimum of 30 pack-years of smoking and no more than 15 years since quitting. It is not known whether the benefits and potential harms of such screening vary according to lung-cancer risk. METHODS: We assessed the variation in efficacy, the number of false positive results, and the number of lung-cancer deaths prevented among 26,604 participants in the NLST who underwent low-dose CT screening, as compared with the 26,554 participants who underwent chest radiography, according to the quintile of 5-year risk of lung-cancer death (ranging from 0.15 to 0.55% in the lowest-risk group [quintile 1] to more than 2.00% in the highest-risk group [quintile 5]). RESULTS: The number of lung-cancer deaths per 10,000 person-years that were prevented in the CT-screening group, as compared with the radiography group, increased according to risk quintile (0.2 in quintile 1, 3.5 in quintile 2, 5.1 in quintile 3, 11.0 in quintile 4, and 12.0 in quintile 5; P=0.01 for trend). Across risk quintiles, there were significant decreasing trends in the number of participants with false positive results per screening-prevented lung-cancer death (1648 in quintile 1, 181 in quintile 2, 147 in quintile 3, 64 in quintile 4, and 65 in quintile 5). The 60% of participants at highest risk for lung-cancer death (quintiles 3 through 5) accounted for 88% of the screening-prevented lung-cancer deaths and for 64% of participants with false positive results. The 20% of participants at lowest risk (quintile 1) accounted for only 1% of prevented lung-cancer deaths. CONCLUSIONS: Screening with low-dose CT prevented the greatest number of deaths from lung cancer among participants who were at highest risk and prevented very few deaths among those at lowest risk. These findings provide empirical support for risk-based targeting of smokers for such screening. (Funded by the National Cancer Institute.).

Monoclonal gammopathy of undetermined significance and risk of lymphoid and myeloid malignancies: 728 cases followed up to 30 years in Sweden.

In 728 Swedish cases of monoclonal gammopathy of undetermined significance (MGUS), followed up to 30 years (median, 10 years), we estimated the cumulative risk of hematologic disorders originating from lymphoid and myeloid lineages. Using Cox regression models, we examined associations of demographic and laboratory factors with progression and determined the discriminatory power of 3 prediction models for progression. Eighty-four MGUS cases developed a lymphoid disorder, representing a cumulative risk of 15.4%. Multiple myeloma (MM) occurred in 53 patients, and the 30-year cumulative risk was 10.6%; an ∼0.5% annual risk. Three factors were significantly associated with progression: abnormal free light-chain (FLC) ratio (<0.26 or >1.65), M-protein concentration (≥1.5 g/dL), and reduction of 1 or 2 noninvolved immunoglobulin isotype levels (immunoparesis). A prediction model with separate effects for these 3 factors and the M-protein isotype had higher discriminatory power than other models, although the differences were not statistically significant. The 30-year cumulative risk for myeloid malignancies was <2%. Our study confirms that abnormal FLC ratio and M-protein concentration >1.5 g/dL, factors previously considered by Mayo Clinic researchers, are predictors for MM progression and suggests that separate consideration of immunoparesis and the Mayo Clinic risk factors could improve identification of MGUS patients at high risk for progression.

Development and Validation of Risk Models to Select Ever-Smokers for CT Lung Cancer Screening.

IMPORTANCE: The US Preventive Services Task Force (USPSTF) recommends computed tomography (CT) lung cancer screening for ever-smokers aged 55 to 80 years who have smoked at least 30 pack-years with no more than 15 years since quitting. However, selecting ever-smokers for screening using individualized lung cancer risk calculations may be more effective and efficient than current USPSTF recommendations. OBJECTIVE: Comparison of modeled outcomes from risk-based CT lung-screening strategies vs USPSTF recommendations. DESIGN, SETTING, AND PARTICIPANTS: Empirical risk models for lung cancer incidence and death in the absence of CT screening using data on ever-smokers from the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial (PLCO; 1993-2009) control group. Covariates included age; education; sex; race; smoking intensity, duration, and quit-years; body mass index; family history of lung cancer; and self-reported emphysema. Model validation in the chest radiography groups of the PLCO and the National Lung Screening Trial (NLST; 2002-2009), with additional validation of the death model in the National Health Interview Survey (NHIS; 1997-2001), a representative sample of the United States. Models were applied to US ever-smokers aged 50 to 80 years (NHIS 2010-2012) to estimate outcomes of risk-based selection for CT lung screening, assuming screening for all ever-smokers, yield the percent changes in lung cancer detection and death observed in the NLST. EXPOSURES: Annual CT lung screening for 3 years beginning at age 50 years. MAIN OUTCOMES AND MEASURES: For model validity: calibration (number of model-predicted cases divided by number of observed cases [estimated/observed]) and discrimination (area under curve [AUC]). For modeled screening outcomes: estimated number of screen-avertable lung cancer deaths and estimated screening effectiveness (number needed to screen [NNS] to prevent 1 lung cancer death). RESULTS: Lung cancer incidence and death risk models were well calibrated in PLCO and NLST. The lung cancer death model calibrated and discriminated well for US ever-smokers aged 50 to 80 years (NHIS 1997-2001: estimated/observed = 0.94 [95%CI, 0.84-1.05]; AUC, 0.78 [95%CI, 0.76-0.80]). Under USPSTF recommendations, the models estimated 9.0 million US ever-smokers would qualify for lung cancer screening and 46,488 (95% CI, 43,924-49,053) lung cancer deaths were estimated as screen-avertable over 5 years (estimated NNS, 194 [95% CI, 187-201]). In contrast, risk-based selection screening of the same number of ever-smokers (9.0 million) at highest 5-year lung cancer risk (≥1.9%) was estimated to avert 20% more deaths (55,717 [95% CI, 53,033-58,400]) and was estimated to reduce the estimated NNS by 17% (NNS, 162 [95% CI, 157-166]). CONCLUSIONS AND RELEVANCE: Among a cohort of US ever-smokers aged 50 to 80 years, application of a risk-based model for CT screening for lung cancer compared with a model based on USPSTF recommendations was estimated to be associated with a greater number of lung cancer deaths prevented over 5 years, along with a lower NNS to prevent 1 lung cancer death.

Aggregate-data estimation of an individual patient data linear random effects meta-analysis with a patient covariate-treatment interaction term.

Individual patient-data meta-analysis of randomized controlled trials is the gold standard for investigating how patient factors modify the effectiveness of treatment. Because participant data from primary studies might not be available, reliable alternatives using published data are needed. In this paper, I show that the maximum likelihood estimates of a participant-level linear random effects meta-analysis with a patient covariate-treatment interaction can be determined exactly from aggregate data when the model's variance components are known. I provide an equivalent aggregate-data EM algorithm and supporting software with the R package ipdmeta for the estimation of the "interaction meta-analysis" when the variance components are unknown. The properties of the methodology are assessed with simulation studies. The usefulness of the methods is illustrated with analyses of the effect modification of cholesterol and age on pravastatin in the multicenter placebo-controlled regression growth evaluation statin study. When a participant-level meta-analysis cannot be performed, aggregate-data interaction meta-analysis is a useful alternative for exploring individual-level sources of treatment effect heterogeneity.

Population-based absolute risk estimation with survey data.

Absolute risk is the probability that a cause-specific event occurs in a given time interval in the presence of competing events. We present methods to estimate population-based absolute risk from a complex survey cohort that can accommodate multiple exposure-specific competing risks. The hazard function for each event type consists of an individualized relative risk multiplied by a baseline hazard function, which is modeled nonparametrically or parametrically with a piecewise exponential model. An influence method is used to derive a Taylor-linearized variance estimate for the absolute risk estimates. We introduce novel measures of the cause-specific influences that can guide modeling choices for the competing event components of the model. To illustrate our methodology, we build and validate cause-specific absolute risk models for cardiovascular and cancer deaths using data from the National Health and Nutrition Examination Survey. Our applications demonstrate the usefulness of survey-based risk prediction models for predicting health outcomes and quantifying the potential impact of disease prevention programs at the population level.

Assessing heterogeneity of treatment effect in a clinical trial with the proportional interactions model.

Understanding how individuals vary in their response to treatment is an important task of clinical research. For standard regression models, a proportional interactions model first described by Follmann and Proschan (1999) offers a powerful approach for identifying effect modification in a randomized clinical trial when multiple variables influence treatment response. In this paper, we present a framework for using the proportional interactions model in the context of a parallel-arm clinical trial with multiple prespecified candidate effect modifiers. To protect against model misspecification, we propose a selection strategy that considers all possible proportional interactions models. We develop a modified Bonferroni correction for multiple testing that accounts for the positive correlation among candidate models. We describe methods for constructing a confidence interval for the proportionality parameter. In simulation studies, we show that our modified Bonferroni adjustment controls familywise error and has greater power to detect proportional interactions compared with multiplcity-corrected subgroup analyses. We demonstrate our methodology by using the Studies of Left Ventricular Dysfunction Treatment trial, a placebo-controlled randomized clinical trial of the efficacy of enalapril to reduce the risk of death or hospitalization in chronic heart failure patients. An R package called anoint is available for implementing the proportional interactions methodology.

A general binomial regression model to estimate standardized risk differences from binary response data.

Estimates of absolute risks and risk differences are necessary for evaluating the clinical and population impact of biomedical research findings. We have developed a linear-expit regression model (LEXPIT) to incorporate linear and nonlinear risk effects to estimate absolute risk from studies of a binary outcome. The LEXPIT is a generalization of both the binomial linear and logistic regression models. The coefficients of the LEXPIT linear terms estimate adjusted risk differences, whereas the exponentiated nonlinear terms estimate residual odds ratios. The LEXPIT could be particularly useful for epidemiological studies of risk association, where adjustment for multiple confounding variables is common. We present a constrained maximum likelihood estimation algorithm that ensures the feasibility of risk estimates of the LEXPIT model and describe procedures for defining the feasible region of the parameter space, judging convergence, and evaluating boundary cases. Simulations demonstrate that the methodology is computationally robust and yields feasible, consistent estimators. We applied the LEXPIT model to estimate the absolute 5-year risk of cervical precancer or cancer associated with different Pap and human papillomavirus test results in 167,171 women undergoing screening at Kaiser Permanente Northern California. The LEXPIT model found an increased risk due to abnormal Pap test in human papillomavirus-negative that was not detected with logistic regression. Our R package blm provides free and easy-to-use software for fitting the LEXPIT model.

A regression model for risk difference estimation in population-based case-control studies clarifies gender differences in lung cancer risk of smokers and never smokers.

BACKGROUND: Additive risk models are necessary for understanding the joint effects of exposures on individual and population disease risk. Yet technical challenges have limited the consideration of additive risk models in case-control studies. METHODS: Using a flexible risk regression model that allows additive and multiplicative components to estimate absolute risks and risk differences, we report a new analysis of data from the population-based case-control Environment And Genetics in Lung cancer Etiology study, conducted in Northern Italy between 2002-2005. The analysis provides estimates of the gender-specific absolute risk (cumulative risk) for non-smoking- and smoking-associated lung cancer, adjusted for demographic, occupational, and smoking history variables. RESULTS: In the multiple-variable lexpit regression, the adjusted 3-year absolute risk of lung cancer in never smokers was 4.6 per 100,000 persons higher in women than men. However, the absolute increase in 3-year risk of lung cancer for every 10 additional pack-years smoked was less for women than men, 13.6 versus 52.9 per 100,000 persons. CONCLUSIONS: In a Northern Italian population, the absolute risk of lung cancer among never smokers is higher in women than men but among smokers is lower in women than men. Lexpit regression is a novel approach to additive-multiplicative risk modeling that can contribute to clearer interpretation of population-based case-control studies.

Mother's CD4+ count moderates the risk associated with higher parity for late postnatal HIV-free survival of breastfed children: an individual patient data meta-analysis of randomized controlled trials.

Risk association studies of late postnatal outcomes for children breastfed by HIV-1 positive mothers have had inconsistent findings and have not explored interactions among risk factors. This study addresses these limitations through an individual patient data (IPD) meta-analysis of HIV-free survival outcomes of nine randomized controlled trials to prevent early mother-to-child transmission of HIV-1. The pooled sample consisted of 3,324 African children in resource-limited settings who survived to age 28 days and were at-risk of acquiring HIV through breast milk. Based on a proportional hazards mixed effects meta-analysis, the composite endpoint of HIV-1 infection and all-cause mortality was found to be significantly associated with maternal immune status (CD4(+) ≥350 cells/mm(3), HR 0.59 95% CI (0.39, 0.87)), infant preterm delivery (gestational age <37 weeks, 1.40 (1.03, 1.89)), infant oral candidiasis infection (1.87, (1.53, 2.29)), and occurrence of breast abnormality before breastfeeding cessation (2.56 (1.90, 3.46)). A significant interaction between mother's parity (any previous pregnancy) and CD4(+) count ≥350 (HR 0.63 (0.40, 0.99), P-value = 0.045) suggested that higher CD4(+) count offsets the risk associated with higher parity. Further research is needed to elucidate the moderating effect of immune status on the risk associated with high parity and adverse late postnatal outcomes for infants breastfed by HIV-infected mothers in the absence of antiretroviral treatment.

An association study of disease activity score components and patient satisfaction with overall health for early RA patients on non-biologic DMARD therapy.

Patient overall satisfaction with health (PSH) was measured by a subset of questions from the Arthritis Impact Measurement Scales II. Based on longitudinal observations for 267 early rheumatoid arthritis (RA) patients of the United States Western Consortium (WC) cohort receiving first non-biologic DMARD treatment, we estimated the 1-year change in PSH (Δ PSH). Logistic regression analysis was used to estimate the association of improvement in Δ PSH with the core set of clinical and patient-reported components of disease activity scores (DAS). Most patients were more satisfied with health after 1 year of treatment (80%); few achieved DAS28-ESR minimal disease activity (27%) or remission (7%). Laboratory and joint count measures were not associated with improved 12-month PSH. Patients with greater HAQ-DI (P = 0.0473) and self-reported stiffness (P = 0.0669) were more likely to have a perceived overall health benefit from treatment. Regardless of objective disease status, patients are generally satisfied with first-line treatment, which could present a challenge to implementing DAS-guided treatment change. Patients with greater self-reported functional limitations might have lower expectations for treatment benefit and be less willing to modify their current therapy; subjective assessments of function and stiffness could be particularly useful in identifying these patients.

Using aggregate data to estimate the standard error of a treatment-covariate interaction in an individual patient data meta-analysis.

Subgroup analyses are important to medical research because they shed light on the heterogeneity of treatment effectts. A treatment-covariate interaction in an individual patient data (IPD) meta-analysis is the most reliable means to estimate how a subgroup factor modifies a treatment's effectiveness. However, owing to the challenges in collecting participant data, an approach based on aggregate data might be the only option. In these circumstances, it would be useful to assess the relative efficiency and power loss of a subgroup analysis without patient-level data. We present methods that use aggregate data to estimate the standard error of an IPD meta-analysis' treatment-covariate interaction for regression models of a continuous or dichotomous patient outcome. Numerical studies indicate that the estimators have good accuracy. An application to a previously published meta-regression illustrates the practical utility of the methodology.

'In search of lost time': Identifying the causative role of cumulative competition load and competition time-loss in professional tennis using a structural nested mean model.

Injury prevention is critical to the achievement of peak performance in elite sport. For professional tennis players, the topic of injury prevention has gained even greater importance in recent years as multiple of the best male players have been sidelined owing to injury. Identifying potential causative factors of injury is essential for the development of effective prevention strategies, yet such research is hampered by incomplete data, the complexity of injury etiology, and observational study biases. The present study attempts to address these challenges by focusing on competition load and time-loss to competition-a completely observable risk factor and outcome-and using a structural nested mean model (SNMM) to identify the potential causal role of cumulative competition load on the risk of time-loss. Using inverse probability of treatment weights to balance exposure histories with respect to player ability, past injury, and consecutive competition weeks at each time point; the SNMM analysis of 389 professional male players and 55,773 weeks of competition found that total load significantly increases the risk of time-loss (HR = 1.05 per 1,000 games of additional load 95% CI 1.01-1.10) and this effect becomes magnified with age. Standard regression showed a protective effect of load, highlighting the value of more robust causal methods in the study of dynamic exposures and injury in sport and the need for further applications of these methods for understanding how time-loss and injuries of elite athletes might be prevented in the future.

Exposure to alcohol advertising and adolescents' drinking beliefs: Role of message interpretation.

OBJECTIVE: Recent research revealed momentary associations between exposure to alcohol advertising and positive beliefs about alcohol among adolescents (Martino et al., 2016). We reanalyzed those data to determine whether associations depend on adolescents' appraisal of ads. METHOD: Over a 10-month period in 2013, 589 youth, ages 11-14, in the Los Angeles, CA, area, participated in a 14-day ecological momentary assessment, logging all exposures to alcohol advertisements as they occurred and completing brief assessments of their skepticism toward, liking of, and identification with any people in each ad, as well as their alcohol-related beliefs at the moment. Participants also completed measures of their alcohol- related beliefs at random moments of nonexposure throughout each day. Mixed-effects regression models compared beliefs about alcohol at moments of exposure to alcohol advertising that was appraised in a particular way (e.g., with liking, without liking) to beliefs at random moments. RESULTS: When youth encountered ads they appraised positively, their beliefs about alcohol were significantly more positive than when they were queried at random moments. Beliefs in the presence of ads that were not positively appraised were generally similar to beliefs at random moments. CONCLUSION: Youth are active participants in the advertising process. How they respond to and process alcohol advertising strongly moderates the association between exposure and alcohol-related beliefs. More effort is needed to identify attributes of alcohol advertisements, and of youth, that determine how youth process alcohol ads. This information can be used to either limit exposure to problematic ads or make youth more resilient to such exposure. (PsycINFO Database Record

Facilitators and Barriers of Drop-In Center Use Among Homeless Youth.

Drop-in centers for homeless youth address basic needs for food, hygiene, and clothing but can also provide critical services that address youth's "higher level" needs (e.g., substance use treatment, mental health care, HIV-related programs). Unlike other services that have restrictive rules, drop-in centers typically try to break down barriers and take a "come as you are" approach to engaging youth in services. Given their popularity, drop-in centers represent a promising location to deliver higher level services to youth that may not seek services elsewhere. A better understanding of the individual-level factors (e.g., characteristics of homeless youth) and agency-level factors (e.g., characteristics of staff and environment) that facilitate and impede youth engagement in drop-in centers will help inform research and outreach efforts designed to engage these at-risk youth in services. Thus, the goal of this review was to develop a preliminary conceptual model of drop-in center use by homeless youth. Toward this goal, we reviewed 20 available peer-reviewed articles and reports on the facilitators and barriers of drop-in center usage and consulted broader models of service utilization from both youth and adult studies to inform model development.

Ecological Momentary Assessment of the Association Between Exposure to Alcohol Advertising and Early Adolescents' Beliefs About Alcohol.

PURPOSE: To evaluate the momentary association between exposure to alcohol advertising and middle-school students' beliefs about alcohol in real-world settings and to explore racial/ethnic differences in this association. METHODS: Middle-school students (N = 588) carried handheld data collection devices for 14 days, recording their exposures to all forms of alcohol advertising during the assessment period. Students also responded to three investigator-initiated control prompts (programmed to occur randomly) on each day of the assessment period. After each exposure to advertising and at each control prompt, students reported their beliefs about alcohol. Mixed-effects regression models compared students' beliefs about alcohol between moments of exposure to alcohol advertising and control prompts. RESULTS: Students perceived the typical person their age who drinks alcohol (prototype perceptions) more favorably and perceived alcohol use as more normative at times of exposure to alcohol advertising than at times of nonexposure (i.e., at control prompts). Exposure to alcohol advertising was not associated with shifts in the perceived norms of black and Hispanic students, however, and the association between exposure and prototype perceptions was stronger among non-Hispanic students than among Hispanic students. CONCLUSIONS: Exposure to alcohol advertising is associated with acute shifts in adolescents' perceptions of the typical person that drinks alcohol and the normativeness of drinking. These associations are both statistically and substantively meaningful.

Alcohol Advertising Exposure Among Middle School-Age Youth: An Assessment Across All Media and Venues.

OBJECTIVE: The purpose of this study was to quantify middle school youth's exposure to alcohol advertisements across media and venues, determine venues of greatest exposure, and identify characteristics of youth who are most exposed. METHOD: Over a 10-month period in 2013, 589 Los Angeles-area youth ages 11-14 from diverse racial/ethnic backgrounds completed a short paper-and-pencil survey assessing background characteristics and then participated in a 14-day ecological momentary assessment, logging all exposures to alcohol advertisements on handheld computers as they occurred. RESULTS: African American and Hispanic youth were exposed to an average of 4.1 and 3.4 advertisements per day, respectively, nearly two times as many as non-Hispanic White youth, who were exposed to 2.0 advertisements per day. Girls were exposed to 30% more advertisements than boys. Most exposures were to outdoor advertisements, with television advertisements a close second. CONCLUSIONS: Exposure to alcohol advertising is frequent among middle school-age youth and may put them at risk for earlier or more frequent underage drinking. Greater restrictions on alcohol advertising outdoors and on television should be considered by regulators and by the alcohol industry and should focus particularly on reducing exposure among minority youth.