The Importance of Time Metric Precision When Implementing Bivariate Latent Change Score Models.

The literature on latent change score models does not discuss the importance of using a precise time metric when structuring the data. This study examined the influence of time metric precision on model estimation, model interpretation, and parameter estimate accuracy in bivariate LCS (BLCS) models through simulation. Longitudinal data were generated with a panel study where assessments took place during a given time window with variation in start time and measurement lag. The data were analyzed using precise time metric, where variation in time was accounted for, and then analyzed using coarse time metric indicating only that the assessment took place during the time window. Results indicated that models estimated using the coarse time metric resulted in biased parameter estimates as well as larger standard errors and larger variances and covariances for intercept and slope. In particular, the coupling parameter estimates-which are unique to BLCS models-were biased with larger standard errors. An illustrative example of longitudinal bivariate relations between math and reading achievement in a nationally representative survey of children is then used to demonstrate how results and conclusions differ when using time metrics of varying precision. Implications and future directions are discussed.

Analyzing cross-lag effects: A comparison of different cross-lag modeling approaches.

Developmental researchers often have research questions about cross-lag effects-the effect of one variable predicting a second variable at a subsequent time point. The cross-lag panel model (CLPM) is often fit to longitudinal panel data to examine cross-lag effects; however, its utility has recently been called into question because of its inability to distinguish between-person effects from within-person effects. This has led to alternative forms of the CLPM to be proposed to address these limitations, including the random-intercept CLPM and the latent curve model with structured residuals. We describe these models focusing on the interpretation of their model parameters, and apply them to examine cross-lag associations between reading and mathematics. The results from the various models suggest reading and mathematics are reciprocally related; however, the strength of these lagged associations was model dependent. We highlight the strengths and limitations of each approach and make recommendations regarding modeling choice.

Adolescent tolerance for deviance, cigarette smoking trajectories, and premature mortality: A longitudinal study.

Cigarette smoking is a well-established cause of excess morbidity and mortality in the United States and globally. The current study builds on the existing literature by examining how smoking trajectories might be a mechanism through which adolescent tolerance for deviance predicts premature all-cause and tobacco-specific mortality. Participants were from a cohort-sequential study conducted in the Midwestern United States of the natural history of cigarette smoking from adolescence through midlife that collected nine waves of data from 1980 to 2011. For the current study, we selected participants who were measured at least once at age 18 or older and who did not die before age 24 (n = 7575). Participants' tolerance for deviance was assessed in adolescence, smoking trajectory group was based on self-reported smoking status during the first six waves of data collection, and cause of death for deceased participants (n = 222) was obtained from the National Death Index. Mediation analyses using the joint significance test were conducted separately for all-cause mortality and tobacco-specific mortality. Adolescent tolerance for deviance significantly predicted smoking trajectory group over and above the influence of covariates. Adolescents with higher tolerance for deviance were more likely to belong to any smoking trajectory group compared to abstainers, and membership in a smoking trajectory group characterized by early onset and heavy, persistent smoking was related to premature all-cause and tobacco-specific mortality. Finally, smoking trajectory group was a significant mediator of the relation between adolescent tolerance for deviance and all-cause mortality.

Statistical properties of four effect-size measures for mediation models.

This project examined the performance of classical and Bayesian estimators of four effect size measures for the indirect effect in a single-mediator model and a two-mediator model. Compared to the proportion and ratio mediation effect sizes, standardized mediation effect-size measures were relatively unbiased and efficient in the single-mediator model and the two-mediator model. Percentile and bias-corrected bootstrap interval estimates of ab/s Y , and ab(s X )/s Y in the single-mediator model outperformed interval estimates of the proportion and ratio effect sizes in terms of power, Type I error rate, coverage, imbalance, and interval width. For the two-mediator model, standardized effect-size measures were superior to the proportion and ratio effect-size measures. Furthermore, it was found that Bayesian point and interval summaries of posterior distributions of standardized effect-size measures reduced excessive relative bias for certain parameter combinations. The standardized effect-size measures are the best effect-size measures for quantifying mediated effects.

When the test of mediation is more powerful than the test of the total effect.

Although previous research has studied power in mediation models, the extent to which the inclusion of a mediator will increase power has not been investigated. To address this deficit, in a first study we compared the analytical power values of the mediated effect and the total effect in a single-mediator model, to identify the situations in which the inclusion of one mediator increased statistical power. The results from this first study indicated that including a mediator increased statistical power in small samples with large coefficients and in large samples with small coefficients, and when coefficients were nonzero and equal across models. Next, we identified conditions under which power was greater for the test of the total mediated effect than for the test of the total effect in the parallel two-mediator model. These results indicated that including two mediators increased power in small samples with large coefficients and in large samples with small coefficients, the same pattern of results that had been found in the first study. Finally, we assessed the analytical power for a sequential (three-path) two-mediator model and compared the power to detect the three-path mediated effect to the power to detect both the test of the total effect and the test of the mediated effect for the single-mediator model. The results indicated that the three-path mediated effect had more power than the mediated effect from the single-mediator model and the test of the total effect. Practical implications of these results for researchers are then discussed.

Dynamic change meets mechanisms of change: Examining mediators in the latent change score framework.

Researchers in behavioral sciences are often interested in longitudinal behavior change outcomes and the mechanisms that influence changes in these outcomes over time. The statistical models that are typically implemented to address these research questions do not allow for investigation of mechanisms of dynamic change over time. However, latent change score models allow for dynamic change (not just linear or exponential change) over time and have flexibility in parameter constraints that other longitudinal models do not have. Developmental researchers also frequently utilize mediation analyses to investigate mechanisms of influence in longitudinal research implemented in path analytic or latent growth curve models. In this article, we provide three examples of how mediation can be tested in the latent change score framework by combining aspects of traditional mediation models with latent change score models of repeated measures outcomes (and mediators and predictors) with more than two timepoints. We also provide the Mplus syntax to complete these analyses and practical considerations of latent change score mediation (LCSM) models.

Sexual and gender identity and note-leaving among adult suicide decedents in the USA.

BACKGROUND: Suicide is one of the leading causes of preventable death in the USA, representing a critical public health threat. Suicide risks differ for different populations. In particular, the sexual and gender minority (SGM) population remains at increased risk for suicide. One of the circumstances that may differ for SGM and non-SGM individuals is the propensity to leave a suicide note. Information regarding note-leaving may be helpful in informing suicide prevention and intervention. AIMS: This study documents the differences in note-leaving in SGM individuals compared with non-SGM individuals, using recent data from the National Violent Death Reporting System (N = 98 515) and accounting for important covariates. METHOD: We fit a logistic regression model with SGM status and covariates predicting note-leaving in suicide. RESULTS: SGM decedents were 1.508 times more likely to leave a note than their non-SGM counterparts, controlling for demographic, mental health and substance use covariates. CONCLUSIONS: These findings highlight the importance of tailoring suicide prevention and intervention efforts to meet the needs of SGM populations.

Are recreational cannabis laws associated with declining medical cannabis program enrollment in the U.S.? An analysis of cardholder enrollment and demographic characteristics from 2013 to 2020.

BACKGROUND: There is speculation that enrollment in U.S. state medical cannabis programs differs depending on whether adult recreational cannabis use is legal. If true, this could have implications for public health and policy. METHODS: Using medical cannabis registry data from U.S. states with a mandatory registry between 2013 and 2020 (N = 23 states), this study examined time-trends in medical cannabis cardholder enrollment and tested whether enrollment trends differed depending on adult recreational cannabis legalization. RESULTS: Multilevel models showed that time-trends for registered active medical cardholders differed during years when adult recreational cannabis use was legal versus not legal (time*recreational cannabis law interaction: b = -0.004, p < 0.01, 95% CI = -0.005, -0.003). The population prevalence of registered active medical cardholders increased over time in years when recreational cannabis was not legal (i.e., medical-only years; b = 0.004, p < 0.001, 95% CI = 0.003, 0.004, corresponding to an increase of 380 cardholders per 100,000 people per year), and decreased in years when recreational cannabis was legal (i.e., recreational years; b = -0.001, p < 0.001, 95% CI = -0.002, -0.001, corresponding to a decrease of 100 cardholders per 100,000 people per year). Time-trends were similar for each sex (male, female) and age group (18-30/35, 30/35+), with each cardholder group showing increases in medical-only years and decreases in recreational years. In medical-only years, there were no differences in enrollment time-trends across sex, but older cardholder (30/35+) enrollment increased at a faster rate than younger cardholder enrollment (18-30/35) (F = 16.199, p < 0.001). In recreational years, male cardholder enrollment decreased at a faster rate than female cardholder enrollment (F = 7.347, p < 0.01), but there was no difference in trends across age. Three states, all with medical-only years, provided data on ethnicity/race. Results showed significant increases from 2016 to 2020 in enrollment of White, African-American, and Hispanic individuals. CONCLUSIONS: Findings suggest that recreational cannabis legalization is associated with decreasing enrollment in medical cannabis programs, particularly for males.

Mediation analysis with zero-inflated substance use outcomes: Challenges and recommendations.

Mediating mechanisms are important components of substance use research, as many substance use interventions work by targeting mediating variables. One issue that is common in substance use research is the presence of many responses of zero in a count variable that is the primary outcome of interest, such as number of drinks per week or number of substances used in the past month. The goal of this paper is to highlight the unique challenges that substance use researchers face when conducting mediation analysis with a zero-inflated count outcome. In this paper, we first describe the models that are commonly used for zero-inflated count data, and when it is appropriate to use them. We then describe general mediation analysis and summarize the small body of work that has focused on mediation for count and zero-inflated count outcomes. We identify the main issue of computing the mediated effect when outcomes are zero-inflated, namely, that the path leading to the zero-inflated count outcome (or mediator) is modeled in two parts. We then provide two examples of mediation models with different conclusions that have zero-inflated count outcomes using adolescent substance use data and define the issues that arise when assessing mediation for each. Finally, we describe the directions in which we must target future methodological research to create accessible solutions for handling mediation with zero-inflated count data in substance use research.

Reasons for Testing Mediation in the Absence of an Intervention Effect: A Research Imperative in Prevention and Intervention Research.

OBJECTIVE: Mediation models are used in prevention and intervention research to assess the mechanisms by which interventions influence outcomes. However, researchers may not investigate mediators in the absence of intervention effects on the primary outcome variable. There is emerging evidence that in some situations, tests of mediated effects can be statistically significant when the total intervention effect is not statistically significant. In addition, there are important conceptual and practical reasons for investigating mediation when the intervention effect is nonsignificant. METHOD: This article discusses the conditions under which mediation may be present when an intervention effect does not have a statistically significant effect and why mediation should always be considered important. RESULTS: Mediation may be present in the following conditions: when the total and mediated effects are equal in value, when the mediated and direct effects have opposing signs, when mediated effects are equal across single and multiple-mediator models, and when specific mediated effects have opposing signs. Mediation should be conducted in every study because it provides the opportunity to test known and replicable mediators, to use mediators as an intervention manipulation check, and to address action and conceptual theory in intervention models. CONCLUSIONS: Mediators are central to intervention programs, and mediators should be investigated for the valuable information they provide about the success or failure of interventions.

Sex and post-menopause hormone therapy effects on hippocampal volume and verbal memory.

Many studies suggest sex differences in memory and hippocampal size, and that hormone therapy (HT) may positively affect these measures in women; however, the parameters of HT use that most likely confer benefits are debated. We evaluated the impact of sex and postmenopausal HT use on verbal learning and memory and hippocampal size in 94 cognitively intact women and 49 men. Using analysis of covariance that controlled for age and education, women had better total word learning and delayed verbal memory performance than men. HT analyses showed that non-HT users performed similarly to men, while HT users performed better than men in Delayed Memory regardless of whether use was current or in the past. Women had larger hippocampal volumes than men regardless of whether they were HT users. Using univariate linear models, we assessed group differences in the predictive value of hippocampal volumes for verbal learning and memory. Hippocampal size significantly predicted memory performance for men and non-HT users, but not for HT users. This lack of relationship between hippocampal size and verbal learning and memory performance in HT users suggests HT use may impact memory through extra-hippocampal neural systems.

The SHIELD (Safety & Health Improvement: Enhancing Law Enforcement Departments) Study: Mixed Methods Longitudinal Findings.

The SHIELD (Safety & Health Improvement: Enhancing Law Enforcement Departments) Study is a worksite wellness team-based intervention among police and sheriff departments assessing the program's effectiveness to reduce occupational risks and unhealthy lifestyle behaviors. The SHIELD program focused on improving diet, physical activity, body weight and sleep, and reducing the effects of unhealthy stress and behaviors, such as tobacco and substance abuse. The SHIELD team-based health promotion program was found to be feasible and effective at 6 months in improving diet, sleep, stress, and overall quality of life of law enforcement department personnel. Both intervention and control groups were followed for 24 months, and we report those durability findings, along with qualitative group interview results that provide insight into the changes of the long-term outcomes. Long-term effects were observed for consumption of fruits and vegetables, and there was some evidence for effects on tobacco and alcohol use. Assessment of dietary habits, physical activity behaviors, weight loss maintenance, and substance use is rare more than 1 year following an intervention, and in general, initial positive changes do not persist in prior research. The SHIELD program was feasible, effective, and durable for improving dietary changes.

Value-Expressive Volunteer Motivation and Volunteering by Older Adults: Relationships With Religiosity and Spirituality.

OBJECTIVES: This study investigates the interplay among religiosity, spirituality, value-expressive volunteer motivation, and volunteering. We examined religiosity and spirituality as predictors of value-expressive volunteer motivation and volunteering and whether religiosity moderated the relations between (a) spirituality and value-expressive volunteer motivation and (b) value-expressive volunteer motivation and volunteering. METHOD: After applying multiple imputation procedures to data from the Wisconsin Longitudinal Study among participants 64-67 years old who survived beyond 2004 (N = 8,148), we carried out regression analyses to predict value-expressive volunteer motivation and volunteering from religiosity and spirituality controlling for demographic variables, physical, emotional, and cognitive health, health risk behaviors, and personality traits. RESULTS: Both religiosity and spirituality were significant (p < .001) positive predictors of value-expressive volunteer motivation. Value-expressive volunteer motivation and religiosity were significant (p < .001) positive predictors, whereas spirituality was a significant (p < .001) negative predictor, of volunteering. Religiosity amplified the relation between value-expressive volunteer motivation and volunteering (p < .05) but did not moderate the relation between spirituality and value-expressive volunteer motivation (p > .45). DISCUSSION: Religiosity may provide the way, and value-expressive volunteer motivation the will, to volunteer. The implications of our findings for the forecasted shortage of older volunteers are discussed.

The safety and health improvement: enhancing law enforcement departments study: feasibility and findings.

This randomized prospective trial aimed to assess the feasibility and efficacy of a team-based worksite health and safety intervention for law enforcement personnel. Four-hundred and eight subjects were enrolled and half were randomized to meet for weekly, peer-led sessions delivered from a scripted team-based health and safety curriculum. Curriculum addressed: exercise, nutrition, stress, sleep, body weight, injury, and other unhealthy lifestyle behaviors such as smoking and heavy alcohol use. Health and safety questionnaires administered before and after the intervention found significant improvements for increased fruit and vegetable consumption, overall healthy eating, increased sleep quantity and sleep quality, and reduced personal stress.