The case of young people who use e-cigarettes infrequently: Who is this population? What becomes of them?

BACKGROUND: Emerging data indicate that many adolescents and young adults ("youth") engage in infrequent, or occasional, e-cigarette use. However, little is known about this population as they are often subsumed into the broader "any past-30-day use" category used to define youth "current use." This study aimed to focus on infrequent e-cigarette use by youth, examining its correlates and transitional outcomes. METHODS: Participants were from a prospective cohort study of youth (aged 15-24 at baseline). Among youth who had used e-cigarettes, we classified "infrequent use" as using e-cigarettes ≤5 days in the last 30 days (n=273) and "frequent use" as using e-cigarettes ≥6 days in the last 30 days (n=278). Descriptive statistics, Markov modeling, and logistic regression were utilized. RESULTS: By the 12-month follow-up, 76.8% of those using infrequently at baseline remained in the "infrequent use" category, 6.3% reported no recent use, and 16.8% had escalated to the "frequent use" category. Among the youth using infrequently at baseline, those who did (vs. did not) escalate to frequent use by follow-up had higher baseline nicotine dependence and were more likely to have family members who used tobacco. CONCLUSIONS: Infrequent e-cigarette use is extremely common, and often fairly stable, among young people. Prevention efforts must certainly attempt to reduce escalation and attend to both individual and interpersonal factors (e.g., nicotine dependence, family use). Yet prevention efforts must additionally attend to the case of continued infrequent use, given the high prevalence of people in this category and their regular exposure to e-cigarette harms.

Spatial Pattern Analysis using Closest Events (SPACE)-A Nearest Neighbor Point Pattern Analysis Framework for Assessing Spatial Relationships from Digital Images.

The quantitative description of biological structures is a valuable yet difficult task in the life sciences. This is commonly accomplished by imaging samples using fluorescence microscopy and analyzing resulting images using Pearson's correlation or Manders' co-occurrence intensity-based colocalization paradigms. Though conceptually and computationally simple, these approaches are critically flawed due to their reliance on signal overlap, sensitivity to cursory signal qualities, and inability to differentiate true and incidental colocalization. Point pattern analysis provides a framework for quantitative characterization of spatial relationships between spatial patterns using the distances between observations rather than their overlap, thus overcoming these issues. Here we introduce an image analysis tool called Spatial Pattern Analysis using Closest Events (SPACE) that leverages nearest neighbor-based point pattern analysis to characterize the spatial relationship of fluorescence microscopy signals from image data. The utility of SPACE is demonstrated by assessing the spatial association between mRNA and cell nuclei from confocal images of cardiac myocytes. Additionally, we use synthetic and empirical images to characterize the sensitivity of SPACE to image segmentation parameters and cursory image qualities such as signal abundance and image resolution. Ultimately, SPACE delivers performance superior to traditional colocalization methods and offers a valuable addition to the microscopist's toolbox.

Variations in Tobacco Retailer Type Across Community Characteristics: Place Matters.

INTRODUCTION: The density of tobacco retailers varies by community characteristics such as poverty levels or racial and ethnic composition. However, few studies have investigated how specific types of tobacco retailers vary by community characteristics. Our objective was to assess how the types of tobacco retailers in Ohio varied by the characteristics of the communities in which they were located. RESULTS: For all US Census tracts, convenience stores were the most common type of retailer selling tobacco. Yet, the prevalence of convenience stores was higher in high-poverty urban tracts than in low-poverty urban tracts. Discount stores were the second-most common type of tobacco retailer and were most prevalent in rural tracts and high-racial and ethnic minority urban tracts. Grocery stores, pharmacies, and vape or hookah shops typically had the highest prevalence in more advantaged tracts. CONCLUSION: Our findings demonstrate that the distribution of specific retailer types varies by community characteristics. The distribution of these retailer types has implications for product availability and price, which may subsequently affect tobacco use and cessation. To create equitable outcomes, policies should focus on retailers such as convenience and discount stores, which are heavily located in communities experiencing tobacco-related health disparities.

Associations between disparities in tobacco retailer density and disparities in tobacco use.

Research has separately established that there are disparities in tobacco use, that greater tobacco retailer density (TRD) is positively associated with tobacco use, and that TRD is greater in high poverty and high racial/ethnic minority neighborhoods. Connecting these topics, this study examined the association between disparities in TRD and disparities in the prevalence of tobacco use among adults and youth. We obtained Ohio data on tobacco use from two statewide adult surveys and two sub-state regional youth surveys (2017-2019). Licensed tobacco retailers in Ohio were geocoded within census tracts. Disparity in TRD within regions across the state was defined as the ratio of TRD in high vs. low poverty (and in high vs. low racial/ethnic minority) census tracts per region. Disparity in cigarette smoking (adults) and any tobacco use (youth) was defined as the ratio of use prevalence among socioeconomically disadvantaged vs. non-disadvantaged (and racial/ethnic minority vs. non-minority) individuals. We estimated Pearson correlation coefficients to assess the linear relationship between the TRD disparity ratios and tobacco use disparity ratios. Poverty-based and race/ethnicity-based TRD disparities were positively associated with smoking disparities among adults. Negative associations between TRD disparities and tobacco use disparities were found among youth. To our knowledge, this is the first analysis directly linking TRD disparities and tobacco use disparities. Different adult and youth findings may be due to trends by age and product preferences. For adults in particular, this analysis suggests a detrimental effect of the tobacco retail environment on disadvantaged populations.

The legacy of redlining: Associations between historical neighborhood mapping and contemporary tobacco retailer density in Ohio.

In the 1930s United States, urban neighborhoods were graded on their desirability for investment (often based on race), a process known as "redlining." This study examined how historical redlining relates to current disparities in an important health determinant: tobacco retailer density. Analyses were conducted for thirteen Ohio cities using negative binomial models that accounted for retailer spatial dependence and controlled for present-day sociodemographic characteristics. Findings indicated that as grades increased from "Best" to "Still Desirable" to "Definitely Declining" and "Hazardous," retailer density increased monotonically. These results highlight the persisting impacts of redlining and how disparities, once intentionally created, can be perpetuated over time.

Evaluating how licensing-law strategies will impact disparities in tobacco retailer density: a simulation in Ohio.

OBJECTIVES: To assess tobacco licensing-law strategies (eg, restricting the sale of tobacco near schools, banning the sale of tobacco in pharmacies) in terms of the equity of their impact and ability to correct existing disparities in tobacco retailer density. METHODS: We geocoded all 11 392 tobacco retailers in Ohio, categorised neighbourhoods based on their demographic characteristics and calculated current disparities in tobacco retailer density. We next simulated the four main types of licensing-law strategies (capping-based, declustering-based, school-based and pharmacy-based), as well as strategy combinations. Finally, using statistical methods that account for residual spatial dependence, we evaluated how each strategy would impact density disparities. FINDINGS: The most impactful licensing-law strategy depended on the type of community. School-based reductions were equitable for low-income, African-American and urban neighbourhoods (eg, eliminating retailers from 1000 feet of all schools produced a 9.2% reduction in the log retailer rate for neighbourhoods with a low prevalence of African-Americans and a 17.7% reduction for neighbourhoods with a high prevalence of African-Americans). Conversely, capping-based reductions were equitable for rural neighbourhoods. Pharmacy-based reductions demonstrated inequitable impacts. CONCLUSION: Licensing-law strategies could be a central tobacco control effort that benefits both the overall population and vulnerable communities. Policymakers will need to consider their community's characteristics when selecting licensing-law strategies to correct (rather than inadvertently widen) density disparities. But when matched with the appropriate strategy, high-risk communities could remove over 20% of their tobacco retailers.

A class of generalized linear mixed models adjusted for marginal interpretability.

Two popular approaches for relating correlated measurements of a non-Gaussian response variable to a set of predictors are to fit a marginal model using generalized estimating equations and to fit a generalized linear mixed model (GLMM) by introducing latent random variables. The first approach is effective for parameter estimation, but leaves one without a formal model for the data with which to assess quality of fit or make individual-level predictions for future observations. The second approach overcomes these deficiencies, but leads to parameter estimates that must be interpreted conditional on the latent variables. To obtain marginal summaries, one needs to evaluate an analytically intractable integral or use attenuation factors as an approximation. Further, we note an unpalatable implication of the standard GLMM. To resolve these issues, we turn to a class of marginally interpretable GLMMs that lead to parameter estimates with a marginal interpretation while maintaining the desirable statistical properties of a conditionally specified model and avoiding problematic implications. We establish the form of these models under the most commonly used link functions and address computational issues. For logistic mixed effects models, we introduce an accurate and efficient method for evaluating the logistic-normal integral.

Estimation of long-range dependence in gappy Gaussian time series.

Knowledge of the long range dependence (LRD) parameter is critical to studies of self-similar behavior. However, statistical estimation of the LRD parameter becomes difficult when the observed data are masked by short range dependence and other noise, or are gappy in nature (i.e., some values are missing in an otherwise regular sampling). Currently there is a lack of theory for spectral- and wavelet-based estimators of the LRD parameter for gappy data. To address this, we estimate the LRD parameter for gappy Gaussian semiparametric time series based upon undecimated wavelet variances. We develop estimation methods by using novel estimators of the wavelet variances, providing asymptotic theory for the joint distribution of the wavelet variances and our estimator of the LRD parameter. We introduce sandwich estimators to compute standard errors for our estimates. We demonstrate the efficacy of our methods using Monte Carlo simulations, and provide guidance on practical issues such as how to select the range of wavelet scales. We demonstrate the methodology using two applications: one for gappy Arctic sea-ice draft data, and another for gap free and gappy daily average temperature data collected at 17 locations in south central Sweden.

Exposures to the tobacco retail environment among adolescent boys in urban and rural environments.

BACKGROUND: The tobacco industry spends billions on retail marketing and such marketing is associated with tobacco use. Previous research has not examined actual and potential exposures that adolescents have on a daily basis. OBJECTIVE: The objective of this study was to determine whether both self-reported and geographically estimated tobacco retailer exposures differ by participant or neighborhood characteristics among urban and rural adolescents. METHODS: The data for this study were part of a cohort study of 1220 adolescent males residing in urban and rural (Appalachian) regions in Ohio. The baseline survey asked participants how often they visited stores that typically sell tobacco in the past week (self-reported exposures). The number of tobacco retailers between home and school was determined using ArcGIS software (potential exposures). Adjusted regression models were fit to determine the characteristics that were associated with self-reported or potential exposures to retailers. RESULTS: Adolescents who were non-Hispanic black or other racial/ethnic minority, had used tobacco in the past, and lived in rural areas had higher self-reported exposures. Urban adolescents, non-Hispanic black or other racial/ethnic minority, and those living in neighborhoods with a higher percentage of poverty had more potential exposures to tobacco retailers in their path between home and school. CONCLUSIONS: Rural adolescents had more self-reported marketing exposures than urban adolescents. However, urban adolescents had more potential tobacco exposures between home and school. Thus, point of sale marketing limitations might be a more effective policy intervention in rural areas whereas limits on tobacco retailers might be more effective for urban areas.

The Relationship between Tobacco Retailer Density and Neighborhood Demographics in Ohio.

INTRODUCTION: Studies from various parts of the country suggest that tobacco-related health disparities are exacerbated by disparities in the distribution of tobacco retailers (convenience stores, tobacco shops, etc.). The purpose of the present study was to use advanced spatial modeling techniques for count data to estimate current disparities in tobacco retailer density in Ohio. METHODS: We identified and geocoded 11,392 tobacco retailers in Ohio. Next, we obtained census tract-level information on race/ethnicity, poverty, and age and obtained county-level information on whether an area was Urban, Suburban, or Rural. Finally, we used negative binomial generalized linear models, adapted for residual spatial dependence, to determine the association between per capita tobacco retailer density and demographic characteristics-summarized by adjusted rate ratios. RESULTS: There were more (from 1.4-1.9 times as many) retailers per capita in high-poverty, vs. low-poverty tracts. Poverty also interacted with age: the association between high poverty and high retailer density was stronger for tracts with a low youth population. Density was also greater in tracts with a high (vs. low) prevalence of African Americans (1.1 times as many) and Hispanics (1.2 times as many). Finally, density was generally greater in rural (vs. suburban or urban) tracts, although the effect was modified by a three-way interaction: density was particularly high for rural tracts that also had both a high prevalence of poverty and a low youth population. DISCUSSION: Overall, our findings indicate that Ohio's vulnerable populations are exposed to a greater per capita density of tobacco retailers. PUBLIC HEALTH IMPLICATIONS: There is a need for state and local-level tobacco control policies that will improve equity and reduce health disparities.

Spectral Estimation Using Multitaper Whittle Methods with a Lasso Penalty.

Spectral estimation provides key insights into the frequency domain characteristics of a time series. Naive non-parametric estimates of the spectral density, such as the periodogram, are inconsistent, and the more advanced lag window or multitaper estimators are often still too noisy. We propose an L 1 penalized quasi-likelihood Whittle framework based on multitaper spectral estimates which performs semiparametric spectral estimation for regularly sampled univariate stationary time series. Our new approach circumvents the problematic Gaussianity assumption required by least square approaches and achieves sparsity for a wide variety of basis functions. We present an alternating direction method of multipliers (ADMM) algorithm to efficiently solve the optimization problem, and develop universal threshold and generalized information criterion (GIC) strategies for efficient tuning parameter selection that outperform cross-validation methods. Theoretically, a fast convergence rate for the proposed spectral estimator is established. We demonstrate the utility of our methodology on simulated series and to the spectral analysis of electroencephalogram (EEG) data.

Demographic and behavioral modifiers of arsenic exposure pathways: a Bayesian hierarchical analysis of NHEXAS data.

We introduce a Bayesian hierarchical statistical model that describes subpopulation-specific pathways of exposure to arsenic. Our model is fitted to data collected as part of the National Human Exposure Assessment Survey (NHEXAS) and builds on the structural-equation-based analysis of the same data by Clayton et al. (Journal of Exposure Analysis and Environmental Epidemiology, 2002, 12, 29-43). Using demographic information (e.g., gender or age) and surrogates for environmental exposure (e.g., tobacco usage or the average number of minutes spent in an enclosed workshop), we identify subgroup differences in exposure routes. Missing and censored data, as well as uncertainty due to measurement error, are handled systematically in the Bayesian framework. Our analysis indicates that household size, amount of time spent at home, use of tapwater for drinking and cooking, number of glasses of water drunk, use of central air conditioning, and use of gas equipment significantly modify the arsenic exposure pathways.

Modeling and detection of respiratory-related outbreak signatures.

BACKGROUND: Time series methods are commonly used to detect disease outbreak signatures (e.g., signals due to influenza outbreaks and anthrax attacks) from varying respiratory-related diagnostic or syndromic data sources. Typically this involves two components: (i) Using time series methods to model the baseline background distribution (the time series process that is assumed to contain no outbreak signatures), (ii) Detecting outbreak signatures using filter-based time series methods. METHODS: We consider time series models for chest radiograph data obtained from Midwest children's emergency departments. These models incorporate available covariate information such as patient visit counts and smoothed ambient temperature series, as well as time series dependencies on daily and weekly seasonal scales. Respiratory-related outbreak signature detection is based on filtering the one-step-ahead prediction errors obtained from the time series models for the respiratory-complaint background. RESULTS: Using simulation experiments based on a stochastic model for an anthrax attack, we illustrate the effect of the choice of filter and the statistical models upon radiograph-attributed outbreak signature detection. CONCLUSION: We demonstrate the importance of using seasonal autoregressive integrated average time series models (SARIMA) with covariates in the modeling of respiratory-related time series data. We find some homogeneity in the time series models for the respiratory-complaint backgrounds across the Midwest emergency departments studied. Our simulations show that the balance between specificity, sensitivity, and timeliness to detect an outbreak signature differs by the emergency department and the choice of filter. The linear and exponential filters provide a good balance.

Periodogram based tests for distortion product otoacoustic emissions.

Distortion product otoacoustic emissions (DPOAEs) are an important nonbehavioral measure of cochlear function, which provides a close analogue of the behavioral pure-tone audiogram. DPOAEs are sinusoidal distortion products (DPs) produced by nonlinearities in the healthy cochlea. Detection of DPs is accomplished in the Fourier domain with a periodogram based test. The test compares the power in the DP periodogram bin to a noise estimate derived from a certain number of the surrounding bins. Statistical properties of this test to date have only been examined by constructing receiver operator characteristics curves derived from DPOAE measurements in normal and hearing impaired individuals. In this paper the null distribution of this order-statistic based test is explicitly derived, and via simulations intended to mimic the nonwhite features of real-ear noise measurements, the power of the test is demonstrated. These simulations demonstrate that a local F test is more powerful than this DPOAE test, with critical values that are easier to calculate. Although the power of both tests increase with an increasing number of bins, the improvement is negligible at around four bins. Since the power of both tests decrease at lower DP frequencies, it is not recommended to use a large number of bins.