Childhood attention-deficit/hyperactivity disorder (ADHD) is a known risk factor for later alcohol-related outcomes, such as drinking at young ages or developing alcohol use disorder by adulthood. However, research has yet to determine whether common ADHD-related impairments (e.g., lower educational attainment) in early adulthood play a role in this outcome above and beyond ADHD symptom persistence. Individuals with (n = 316) and without (n = 223) ADHD in childhood participated in a longitudinal study (Mage = 29). Childhood diagnoses were based on comprehensive, standardized assessments, and follow-up data were self-report and parent report. Mediating pathways through key impairments and ADHD symptom persistence in early adulthood were simultaneously tested, from childhood ADHD (absent/present) to later adulthood (Mage = 29) alcohol outcomes (alcohol-related problems and heavy drinking frequency), using Mplus 8.2. Support was found for the mediating roles of greater social impairment, lower educational attainment, and ADHD symptom persistence in the association between childhood ADHD and alcohol-related problems. Mediation by early adulthood delinquency for alcohol problems was not supported. No mediating pathways to heavy drinking frequency were supported. These findings illustrate the importance of social and academic functioning, in addition to ADHD symptom persistence, in risk for alcohol-related problems as individuals with a history of ADHD in childhood enter a phase of life requiring substantial adulthood responsibility. These results suggest the critical importance of focusing prevention and treatment efforts on major domains of functioning in addition to ADHD symptom reduction for prevention and treatment of harmful alcohol use. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

Approaches to multilevel measurement modeling are often distinguished as aggregated or disaggregated. While past research has established the benefits and limitations of these modeling frameworks, none have critically evaluated the reliability of factor score predictions representing level-1 and level-2 processes. We utilize simulation methodology to explore factor-scoring techniques within both modeling frameworks. Results suggest that factor scores from the aggregated approach are at least as reliable as those from the disaggregated approach after simple numeric transformations, contingent on accurate knowledge of the level-2 factor structure in the presence of cross-level configural non-invariance. Further, given differences in the range of reliability across approaches when fewer clusters are available for analysis, the aggregated approach may be preferable within any given sample.

During elementary school, children demonstrate significant growth in an array of cognitive skills, including their ability to use deliberate strategies for remembering. Despite a rich literature documenting age-related changes in these skills (Schneider & Ornstein, 2019), much remains to be learned about contextual factors that support the development of strategic memory. Data from a longitudinal investigation were used to examine the role of kindergarten teachers' instructional language in the growth of children's abilities related to the use of meaning-based sorting in the service of memory goals. A sample of 76 kindergarteners from 10 classrooms was followed across 2 school years. Kindergarten teachers were observed for their use of cognitive processing language (CPL; Ornstein & Coffman, 2020) while they taught mathematics and language arts lessons. CPL is thought to help children process information deeply, reflect on their own cognition, and acquire strategies for remembering. The participating teachers were characterized as being higher or lower in the use of CPL, and multilevel models were used to examine children's growth in sorting across kindergarten and first grade. Despite similar baseline performance, children exposed to higher levels of CPL engaged in more strategic sorting at the end of first grade than peers exposed to less CPL in kindergarten. Moreover, children in high-CPL classrooms demonstrated faster rates of change in sorting than children in low-CPL classrooms, controlling for working memory skills and parental education. These findings highlight links between the instructional language to which children are exposed in kindergarten and their growth in organizational sorting. (PsycInfo Database Record (c) 2025 APA, all rights reserved).

A currently overlooked application of the latent curve model (LCM) is its use in assessing the consequences of development patterns of change-that is as a predictor of distal outcomes. However, there are additional complications for appropriately specifying and interpreting the distal outcome LCM. Here, we develop a general framework for understanding the sensitivity of the distal outcome LCM to the choice of time coding, focusing on the regressions of the distal outcome on the latent growth factors. Using artificial and real-data examples, we highlight the unexpected changes in the regression of the slope factor which stand in contrast to prior work on time coding effects, and develop a framework for estimating the distal outcome LCM at a point in the trajectory-known as the aperture-which maximizes the interpretability of the effects. We also outline a prioritization approach developed for assessing incremental validity to obtain consistently interpretable estimates of the effect of the slope. Throughout, we emphasize practical steps for understanding these changing predictive effects, including graphical approaches for assessing regions of significance similar to those used to probe interaction effects. We conclude by providing recommendations for applied research using these models and outline an agenda for future work in this area.

OBJECTIVE The aim of this naturalistic process study was to investigate the relationship between self-compassion, fear of compassion from others, and depressive symptoms over the course of psychotherapy in patients with chronic depression. METHOD A sample of 226 patients with chronic depression who received inpatient short-term psychodynamic psychotherapy (STPP) provided weekly self-report measures of self-compassion, fear of compassion, and depressive symptoms (Patient Health Questionnaire-9). Trivariate latent curve modeling with structured residuals was applied to investigate the between- and within-patient relationships among the variables. RESULTS At the between-patient level, a significant positive correlation was found between slope of depression and the slope of fear of compassion. At the within-patient level, a lower than expected level of fear of compassion predicted a subsequent lower than expected level of depression (mean weekly effect size = 0.12), with a smaller reciprocal relationship (mean weekly effect size = 0.08). There was no significant within-patient effect of self-compassion predicting subsequent depression, but a significant effect of a lower than expected level of depression predicting a subsequent higher than expected level of self-compassion (mean weekly effect size = -0.13). No within-patient effect between self-compassion and fear of compassion was found. CONCLUSIONS In the context of this study, it appears that fear of compassion may be a putative mechanism of change involved in alleviating depressive symptoms in patients with chronic depression treated with STPP. On the other hand, self-compassion appears to be an outcome of psychotherapy. (PsycInfo Database Record (c) 2023 APA, all rights reserved).

OBJECTIVE:The aim of this naturalistic process study was to investigate the relationship between emotional clarity and tolerance of emotional distress and depressive symptoms over the course of short-term psychodynamic psychotherapy for chronically depressed patients.METHOD:Weekly self-reports of emotional clarity, tolerance of emotional distress, and depressive symptoms (PHQ-9) were provided by 252 patients with chronic depression who were admitted to a 13-week inpatient treatment program. Latent curve modeling with structured residuals (LCM-SR) was applied to investigate the between- and within-person effects of week-to-week change in emotional clarity and tolerance of emotional distress as predictors of subsequent depression. The relationship between emotional clarity and tolerance of emotional distress was also investigated.RESULTS:At the within-person level, higher level of emotional clarity and tolerance of emotional distress predicted subsequent lower level of depression. A reciprocal relationship was found for tolerance of emotional distress (lower level of depression predicted subsequent level of tolerance emotional distress) but not for emotional clarity. No within-person effect between emotional clarity and tolerance of emotional distress was found.DISCUSSION:The results indicate that emotional clarity and tolerance of emotional distress may be mechanisms of change in short-term psychodynamic psychotherapy for chronic depression. The results are consistent with previous findings of the importance of emotional clarity and tolerance of emotional distress in psychotherapy. This study demonstrated the utility of LCM-SR as a method to identity mechanisms of change in psychotherapy. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

SUMMARY Goal: Perceived organizational support (POS) may promote healthcare worker mental health, but organizational factors that foster POS during the COVID-19 pandemic are unknown. The goals of this study were to identify actions and policies regarding COVID-19 that healthcare organizations can implement to promote POS and to evaluate the impact of POS on physicians’ mental health, burnout, and intention to leave patient care. Methods: We conducted a cross-sectional national survey with an online panel of internal medicine physicians from the American College of Physicians in September and October of 2020. POS was measured with a 4-item scale, based on items from Eisenberger’s Perceived Organizational Support Scale that were adapted for the pandemic. Mental health outcomes and burnout were measured with short screening scales. Principal Findings: The response rate was 37.8% (N = 810). Three healthcare organization actions and policies were independently associated with higher levels of POS in a multiple linear regression model that included all actions and policies as well as potential confounding factors: opportunities to discuss ethical issues related to COVID-19 (β (regression coefficient) = 0.74, p = .001), adequate access to personal protective equipment (β = 1.00, p = .005), and leadership that listens to healthcare worker concerns regarding COVID-19 (β = 3.58, p < .001). Sanctioning workers who speak out on COVID-19 safety issues or refuse pandemic deployment was associated with lower POS (β = –2.06, p < .001). In multivariable logistic regression models, high POS was associated with approximately half the odds of screening positive for generalized anxiety, depression, post-traumatic stress disorder, burnout, and intention to leave patient care within 5 years. Applications to Practice: Our results suggest that healthcare organizations may be able to increase POS among physicians during the COVID-19 pandemic by guaranteeing adequate personal protective equipment, making sure that leaders listen to concerns about COVID-19, and offering opportunities to discuss ethical concerns related to caring for patients with COVID-19. Other policies and actions such as rapid COVID-19 tests may be implemented for the safety of staff and patients, but the policies and actions associated with POS in multivariable models in this study are likely to have the largest positive impact on POS. Warning or sanctioning workers who refuse pandemic deployment or speak up about worker and patient safety is associated with lower POS and should be avoided. We also found that high degrees of POS are associated with lower rates of adverse outcomes. So, by implementing the tangible support policies positively associated with POS and avoiding punitive ones, healthcare organizations may be able to reduce adverse mental health outcomes and attrition among their physicians.

Combining datasets in an integrative data analysis (IDA) requires researchers to make a number of decisions about how best to harmonize item responses across datasets. This entails two sets of steps: logical harmonization, which involves combining items which appear similar across datasets, and analytic harmonization, which involves using psychometric models to find and account for cross-study differences in measurement. Embedded in logical and analytic harmonization are many decisions, from deciding whether items can be combined prima facie to how best to find covariate effects on specific items. Researchers may not have specific hypotheses about these decisions, and each individual choice may seem arbitrary, but the cumulative effects of these decisions are unknown. In the current study, we conducted an IDA of the relationship between alcohol use and delinquency using three datasets (total N = 2245). For analytic harmonization, we used moderated nonlinear factor analysis (MNLFA) to generate factor scores for delinquency. We conducted both logical and analytic harmonization 72 times, each time making a different set of decisions. We assessed the cumulative influence of these decisions on MNLFA parameter estimates, factor scores, and estimates of the relationship between delinquency and alcohol use. There were differences across paths in MNLFA parameter estimates, but fewer differences in estimates of factor scores and regression parameters linking delinquency to alcohol use. These results suggest that factor scores may be relatively robust to subtly different decisions in data harmonization, and measurement model parameters are less so.

Chromosome 15q11.2-q13.1 duplication syndrome (Dup15q syndrome) is a severe neurodevelopmental disorder characterized by intellectual disability, impaired motor coordination, and autism spectrum disorder. Chromosomal multiplication of the UBE3A gene is presumed to be the primary driver of Dup15q pathophysiology, given that UBE3A exhibits maternal monoallelic expression in neurons and that maternal duplications typically yield far more severe neurodevelopmental outcomes than paternal duplications. However, studies into the pathogenic effects of UBE3A overexpression in mice have yielded conflicting results. Here, we investigated the neurodevelopmental impact of Ube3a gene overdosage using bacterial artificial chromosome-based transgenic mouse models (Ube3aOE) that recapitulate the increases in Ube3a copy number most often observed in Dup15q. In contrast to previously published Ube3a overexpression models, Ube3aOE mice were indistinguishable from wild-type controls on a number of molecular and behavioral measures, despite suffering increased mortality when challenged with seizures, a phenotype reminiscent of sudden unexpected death in epilepsy. Collectively, our data support a model wherein pathogenic synergy between UBE3A and other overexpressed 15q11.2-q13.1 genes is required for full penetrance of Dup15q syndrome phenotypes.

Conducting valid and reliable empirical research in the prevention sciences is an inherently difficult and challenging task. Chief among these is the need to obtain numerical scores of underlying theoretical constructs for use in subsequent analysis. This challenge is further exacerbated by the increasingly common need to consider multiple reporter assessments, particularly when using integrative data analysis to fit models to data that have been pooled across two or more independent samples. The current article uses both simulated and real data to examine the utility of a recently proposed psychometric model for multiple reporter data called the trifactor model (TFM) in settings that might be commonly found in prevention research. Results suggest that numerical scores obtained using the TFM are superior to more traditional methods, particularly when pooling samples that contribute different reporter perspectives.

One of the most vexing challenges facing developmental researchers today is the statistical modeling of two or more behaviors as they unfold jointly over time. Although quantitative methodologists have studied these issues for more than half a century, no widely agreed-upon principled strategy exists to empirically analyze codevelopmental processes. Indeed, the plethora of available options makes selecting a specific analytic approach both confusing and overwhelming. In this article, we argue that a key step in adjudicating among alternative modeling strategies is to embrace the concept of within- and between-person components of change over time. First, we define the disaggregation of effects in grouped data, and then we extend these concepts to repeated measures. Then we review several available modeling strategies that capture these effects to varying degrees and raise three issues that can help to guide practice.

BackgroundThe burden of COVID-19 in low-income and conflict-affected countries remains unclear, largely reflecting low testing rates. In parts of Yemen, reports indicated a peak in hospital admissions and burials during May–June 2020. To estimate excess mortality during the epidemic period, we quantified activity across all identifiable cemeteries within Aden governorate (population approximately 1 million) by analysing very high-resolution satellite imagery and compared estimates to Civil Registry office records.MethodsAfter identifying active cemeteries through remote and ground information, we applied geospatial analysis techniques to manually identify new grave plots and measure changes in burial surface area over a period from July 2016 to September 2020. After imputing missing grave counts using surface area data, we used alternative approaches, including simple interpolation and a generalised additive mixed growth model, to predict both actual and counterfactual (no epidemic) burial rates by cemetery and across the governorate during the most likely period of COVID-19 excess mortality (from 1 April 2020) and thereby compute excess burials. We also analysed death notifications to the Civil Registry office over the same period.ResultsWe collected 78 observations from 11 cemeteries. In all but one, a peak in daily burial rates was evident from April to July 2020. Interpolation and mixed model methods estimated ≈1500 excess burials up to 6 July, and 2120 up to 19 September, corresponding to a peak weekly increase of 230% from the counterfactual. Satellite imagery estimates were generally lower than Civil Registry data, which indicated a peak 1823 deaths in May alone. However, both sources suggested the epidemic had waned by September 2020.DiscussionTo our knowledge, this is the first instance of satellite imagery being used for population mortality estimation. Findings suggest a substantial, under-ascertained impact of COVID-19 in this urban Yemeni governorate and are broadly in line with previous mathematical modelling predictions, though our method cannot distinguish direct from indirect virus deaths. Satellite imagery burial analysis appears a promising novel approach for monitoring epidemics and other crisis impacts, particularly where ground data are difficult to collect.

No research exists on how body mass index (BMI) changes with age over the full life span and social disparities therein. This study aims to fill the gap using an innovative life-course research design and analytic methods to model BMI trajectories from early adolescence to old age across 20th-century birth cohorts and test sociodemographic variation in such trajectories. We conducted the pooled integrative data analysis (IDA) to combine data from four national population-based NIH longitudinal cohort studies that collectively cover multiple stages of the life course (Add Health, MIDUS, ACL, and HRS) and estimate mixed-effects models of age trajectories of BMI for men and women. We examined associations of BMI trajectories with birth cohort, race/ethnicity, parental education, and adult educational attainment. We found higher mean levels of and larger increases in BMI with age across more recent birth cohorts as compared with earlier-born cohorts. Black and Hispanic excesses in BMI compared with Whites were present early in life and persisted at all ages, and, in the case of Black-White disparities, were of larger magnitude for more recent cohorts. Higher parental and adulthood educational attainment were associated with lower levels of BMI at all ages. Women with college-educated parents also experienced less cohort increase in mean BMI. Both race and education disparities in BMI trajectories were larger for women compared with men.

Although there is empirical evidence supporting associations between exposure to violence and engaging in physically aggressive behavior during adolescence, there is limited longitudinal research to determine the extent to which exposure to violence is a cause or a consequence of physical aggression, and most studies have not addressed the influence of other negative life events experienced by adolescents. This study examined bidirectional relations between physical aggression, two forms of exposure to violence-witnessing violence and victimization, and other negative life events. Participants were a sample of 2568 adolescents attending three urban public middle schools who completed measures of each construct every 3 months during middle school. Their mean age was 12.76 (SD = 0.98); 52% were female. The majority were African American (89%); 17% were Hispanic or Latino/a. Cross-lagged regression analyses across four waves of data collected within the same grade revealed bidirectional relations between witnessing violence and physical aggression, and between witnessing violence and negative life events. Although physical aggression predicted subsequent changes in victimization, victimization predicted changes in physical aggression only when witnessing violence was not taken into account. Findings were consistent across sex and grades. Overall, these findings highlight the need for interventions that break the connection between exposure to violence and aggression during adolescence.

Nested data arise frequently in clinical research. The nesting might be hierarchical, such as patients nested within clinicians, or it might be longitudinal, such as repeated assessments over time nested within individuals. As articulated in this chapter, whenever and however nesting occurs, it is necessary to account for the statistical dependence of observations within units when analyzing the data. Further, it is important to determine the level(s) of the data at which predictors exert their effects. Multilevel models are a particularly popular and useful approach for addressing these issues. We thus describe these models in detail, illustrating the application of multilevel models in clinical research via two examples. The first example considers nesting of siblings within families and demonstrates the importance of separating within- versus between-family effects. The second example focuses on the application of multilevel models with repeated measures to evaluate within-person change over time. Additionally, we provide a brief survey of other approaches to the analysis of nested data (e.g., cluster-robust standard errors, generalized estimating equations, fixed-effects models).

In this chapter, we introduce Integrative Data Analysis (IDA) for use in the field of Global Health. IDA is a novel framework for simultaneous analysis of individual-level data pooled from multiple studies. This framework has been applied to address questions about substance use, cancer, HIV, and rare diseases from studies around the world. Advantages of this approach include efficiency (i.e., reuse of extant data), statistical power (i.e., large combined sample sizes), the potential to address questions not answerable by a single contributing study (e.g., combining studies with overlapping ethnicities to examine cross-cultural differences or age periods to examine longer periods of development), and the opportunity to test replicability of effects across studies in the pooled analysis. We describe the IDA methodological framework, emphasizing unique issues in measurement harmonization and hypothesis testing. We illustrate the application of the method using examples. We also describe emerging tools to handle specific harmonization challenges. Finally, we consider the potential utility of IDA in Global Health and epidemiological research.

In the current study, we used an analogue integrative data analysis (IDA) design to test optimal scoring strategies for harmonizing alcohol- and drug-use consequence measures with varying degrees of alteration across four study conditions. We evaluated performance of mean, confirmatory factor analysis (CFA), and moderated nonlinear factor analysis (MNLFA) scores based on traditional indices of reliability (test–retest, internal, and score recovery or parallel forms) and validity. Participants in the analogue study included 854 college students (46% male; 21% African American, 5% Hispanic/Latino, 56% European American) who completed two versions of the altered measures at two sessions, separated by 2 weeks. As expected, mean, CFA, and MNLFA scores all resulted in scales with lower reliability given increasing scale alteration (with less fidelity to formerly developed scales) and shorter scale length. MNLFA and CFA scores, however, showed greater validity than mean scores, demonstrating stronger relationships with external correlates. Implications for measurement harmonization in the context of IDA are discussed.