Cohort bias in predictive risk assessments of future criminal justice system involvement.

Risk assessment instruments (RAIs) are widely used to aid high-stakes decision-making in criminal justice settings and other areas such as health care and child welfare. These tools, whether using machine learning or simpler algorithms, typically assume a time-invariant relationship between predictors and outcome. Because societies are themselves changing and not just individuals, this assumption may be violated in many behavioral settings, generating what we call cohort bias. Analyzing criminal histories in a cohort-sequential longitudinal study of children, we demonstrate that regardless of model type or predictor sets, a tool trained to predict the likelihood of arrest between the ages of 17 and 24 y on older birth cohorts systematically overpredicts the likelihood of arrest for younger birth cohorts over the period 1995 to 2020. Cohort bias is found for both relative and absolute risks, and it persists for all racial groups and within groups at highest risk for arrest. The results imply that cohort bias is an underappreciated mechanism generating inequality in contacts with the criminal legal system that is distinct from racial bias. Cohort bias is a challenge not only for predictive instruments with respect to crime and justice, but also for RAIs more broadly.

Recent Advances in Group-Based Trajectory Modeling for Clinical Research.

Group-based trajectory modeling (GBTM) identifies groups of individuals following similar trajectories of one or more repeated measures. The categorical nature of GBTM is particularly well suited to clinical psychology and medicine, where patients are often classified into discrete diagnostic categories. This review highlights recent advances in GBTM and key capabilities that remain underappreciated in clinical research. These include accounting for nonrandom subject attrition, joint trajectory and multitrajectory modeling, the addition of the beta distribution to modeling options, associating trajectories with future outcomes, and estimating the probability of future outcomes. Also discussed is an approach to selecting the number of trajectory groups.

Social change and cohort differences in group-based arrest trajectories over the last quarter-century.

This article draws on official criminal histories for multiple birth cohorts spanning a 17-y difference in birth year to study how social change can alter our understanding of influential theories and policies about criminal offender groups. Arrest histories are linked to comprehensive longitudinal measurement on over 1,000 individuals originally from Chicago. Using group-based trajectory modeling, we investigated the magnitude and type of cohort differences in trajectories of arrest over the period 1995 to 2020. Our results show that trajectory group membership varies strongly by birth cohort. Membership in the nonoffender group is nearly 15 percentage points higher for cohorts born in the mid-1990s as compared to those born in the 1980s; conversely, older cohorts are more likely to be members of adolescent-limited and chronic-offender groups. Large cohort differences in trajectory group membership persist after controlling for a wide-ranging set of demographic characteristics and early-life risk factors that vary by cohort and that prior research has identified as important influences on crime. Not only does the effect of social change on cohort differentiation persist, but its magnitude is comparable to-indeed larger than-differences in trajectory group membership associated with varying levels of self-control or by whether individuals grew up in high-poverty households. These results suggest that changes in the broader social environment shared by members of the same birth cohort are as powerful in shaping their trajectory group membership as classic predictors identified in prior research, a finding that carries implications for crime-control policies that rely on prediction.

Mortality Risk in Pediatric Sepsis Based on C-reactive Protein and Ferritin Levels.

OBJECTIVES: Interest in using bedside C-reactive protein (CRP) and ferritin levels to identify patients with hyperinflammatory sepsis who might benefit from anti-inflammatory therapies has piqued with the COVID-19 pandemic experience. Our first objective was to identify patterns in CRP and ferritin trajectory among critically ill pediatric sepsis patients. We then examined the association between these different groups of patients in their inflammatory cytokine responses, systemic inflammation, and mortality risks. DATA SOURCES: A prospective, observational cohort study. STUDY SELECTION: Children with sepsis and organ failure in nine pediatric intensive care units in the United States. DATA EXTRACTION: Two hundred and fifty-five children were enrolled. Five distinct clinical multi-trajectory groups were identified. Plasma CRP (mg/dL), ferritin (ng/mL), and 31 cytokine levels were measured at two timepoints during sepsis (median Day 2 and Day 5). Group-based multi-trajectory models (GBMTM) identified groups of children with distinct patterns of CRP and ferritin. DATA SYNTHESIS: Group 1 had normal CRP and ferritin levels ( n = 8; 0% mortality); Group 2 had high CRP levels that became normal, with normal ferritin levels throughout ( n = 80; 5% mortality); Group 3 had high ferritin levels alone ( n = 16; 6% mortality); Group 4 had very high CRP levels, and high ferritin levels ( n = 121; 11% mortality); and Group 5 had very high CRP and very high ferritin levels ( n = 30; 40% mortality). Cytokine responses differed across the five groups, with ferritin levels correlated with macrophage inflammatory protein 1α levels and CRP levels reflective of many cytokines. CONCLUSIONS: Bedside CRP and ferritin levels can be used together to distinguish groups of children with sepsis who have different systemic inflammation cytokine responses and mortality risks. These data suggest future potential value in personalized clinical trials with specific targets for anti-inflammatory therapies.

Assessing benefits, costs, and disparate racial impacts of confrontational proactive policing.

Effective policing in a democratic society must balance the sometime conflicting objectives of public safety and community trust. This paper uses a formal model of optimal policing to explore how society might reasonably resolve the tension between these two objectives as well as evaluate disparate racial impacts. We do so by considering the social benefits and costs of confrontational types of proactive policing, such as stop, question, and frisk. Three features of the optimum that are particularly relevant to policy choices are explored: (i) the cost of enforcement against the innocent, (ii) the baseline level of crime rate without confrontational enforcement, and (iii) differences across demographic groups in the optimal rate of enforcement.

Using the Beta distribution in group-based trajectory models.

BACKGROUND: We demonstrate an application of Group-Based Trajectory Modeling (GBTM) based on the beta distribution. It is offered as an alternative to the normal distribution for modeling continuous longitudinal data that are poorly fit by the normal distribution even with censoring. The primary advantage of the beta distribution is the flexibility of the shape of the density function. METHODS: GBTM is a specialized application of finite mixture modeling designed to identify clusters of individuals who follow similar trajectories. Like all finite mixture models, GBTM requires that the distribution of the data composing the mixture be specified. To our knowledge this is the first demonstration of the use of the beta distribution in GBTM. A case study of a beta-based GBTM analyzes data on the neurological activity of comatose cardiac arrest patients. RESULTS: The case study shows that the summary measure of neurological activity, the suppression ratio, is not well fit by the normal distribution but due to the flexibility of the shape of the beta density function, the distribution of the suppression ratio by trajectory appears to be well matched by the estimated beta distribution by group. CONCLUSIONS: The addition of the beta distribution to the already available distributional alternatives in software for estimating GBTM is a valuable augmentation to extant distributional alternatives.

Group-based trajectory modeling: an overview.

This article provides an overview of a group-based statistical methodology for analyzing developmental trajectories - the evolution of an outcome over age or time. Across all application domains, this group-based statistical method lends itself to the presentation of findings in the form of easily understood graphical and tabular data summaries. In so doing, the method provides statistical researchers with a tool for figuratively painting a statistical portrait of the predictors and consequences of distinct trajectories of development. Data summaries of this form have the great advantage of being accessible to nontechnical audiences and quickly comprehensible to audiences that are technically sophisticated. Examples of the application of the method are provided. A detailed account of the statistical underpinnings of the method and a full range of applications are provided by the author in a previous study.

Bayesian Outcome Prediction After Resuscitation From Cardiac Arrest.

BACKGROUND AND OBJECTIVES: Postarrest prognostication research does not typically account for the sequential nature of real-life data acquisition and interpretation and reports nonintuitive estimates of uncertainty. Bayesian approaches offer advantages well suited to prognostication. We used Bayesian regression to explore the usefulness of sequential prognostic indicators in the context of prior knowledge and compared this with a guideline-concordant algorithm. METHODS: We included patients hospitalized at a single center after cardiac arrest. We extracted prospective data and assumed these data accrued over time as in routine practice. We considered predictors demographic and arrest characteristics, initial and daily neurologic examination, laboratory results, therapeutic interventions, brain imaging, and EEG. We fit Bayesian hierarchical generalized linear multivariate models predicting discharge Cerebral Performance Category (CPC) 4 or 5 (poor outcomes) vs 1-3 including sequential clinical and prognostic data. We explored outcome posterior probability distributions (PPDs) for individual patients and overall. As a comparator, we applied the 2021 European Resuscitation Council and European Society of Intensive Care Medicine (ERC/ESICM) guidelines. RESULTS: We included 2,692 patients of whom 864 (35%) were discharged with a CPC 1-3. Patients' outcome PPDs became narrow and shifted toward 0 or 1 as sequentially acquired information was added to models. These changes were largest after arrest characteristics and initial neurologic examination were included. Using information typically available at or before intensive care unit admission, sensitivity predicting poor outcome was 51% with a 0.6% false-positive rate. In our most comprehensive model, sensitivity for poor outcome prediction was 76% with 0.6% false-positive rate (FPR). The ERC/ESICM algorithm applied to 547 of 2,692 patients and yielded 36% sensitivity with 0% FPR. DISCUSSION: Bayesian models offer advantages well suited to prognostication research. On balance, our findings support the view that in expert hands, accurate neurologic prognostication is possible in many cases before 72 hours postarrest. Although we caution against early withdrawal of life-sustaining therapies, rapid outcome prediction can inform clinical decision making and future clinical trials.

The neurocognition of conduct disorder behaviors: specificity to physical aggression and theft after controlling for ADHD symptoms.

There is growing evidence that among the different conduct disorder (CD) behaviors, physical aggression, but not theft, links to low neurocognitive abilities. Specifically, physical aggression has consistently been found to be negatively related to neurocognitive abilities, whereas theft has been shown to be either positively or not related to neurocognition. The specificity of these links needs further examination because attention deficit hyperactivity disorder (ADHD) links to both physical aggression and neurocognitive variation. The development of self-reported physical aggression and theft, from age 11 to 17 years, was studied in a prospective at-risk male cohort via a dual process latent growth curve model. Seven neurocognitive tests at age 20 were regressed on the growth parameters of physical aggression and theft. The links between neurocognition and the growth parameters of physical aggression and theft were adjusted for ADHD symptoms at ages 11 and 15 (parent, child and teacher reports). Results indicated that verbal abilities were negatively related to physical aggression while they were positively associated with theft. However, inductive reasoning was negatively associated with increases in theft across adolescence. Symptoms of ADHD accounted for part of the neurocognitive test links with physical aggression but did not account for the associations with theft. These differences emphasize the importance of examining specific CD behaviors to better understand their neurodevelopmental mechanisms. They also suggest that youth who engage in different levels of physical aggression or theft behaviors may require different preventive and corrective interventions.

Group-based trajectory modeling in clinical research.

Group-based trajectory models are increasingly being applied in clinical research to map the developmental course of symptoms and assess heterogeneity in response to clinical interventions. In this review, we provide a nontechnical overview of group-based trajectory and growth mixture modeling alongside a sampling of how these models have been applied in clinical research. We discuss the challenges associated with the application of both types of group-based models and propose a set of preliminary guidelines for applied researchers to follow when reporting model results. Future directions in group-based modeling applications are discussed, including the use of trajectory models to facilitate causal inference when random assignment to treatment condition is not possible.

Trajectories of childhood sexual abuse and early adolescent HIV/AIDS risk behaviors: the role of other maltreatment, witnessed violence, and child gender.

Childhood sexual abuse (CSA) has been associated with HIV/AIDS risk behavior; however, much of this work is retrospective and focuses on women. The current study used semi-parametric mixture modeling with youth (n = 844; 48.8% boys) from the Longitudinal Studies of Child Abuse and Neglect (LONGSCAN) to examine the link between trajectories of CSA (2 to 12 years old) and HIV/AIDS risk behavior at age 14 (i.e., sexual intercourse & alcohol use). Trajectory analyses revealed a link between a history of CSA and the development of risky behavior. In addition, trajectories for physical and emotional abuse, but not neglect or witnessed violence, contributed to risky behavior over and above the role of CSA. Child gender did not moderate the findings. Findings highlight the significance of CSA histories, as well as the broader context of maltreatment, for better understanding the development of risk behaviors in both girls and boys.

Comparison of parametric and nonparametric methods for outcome prediction using longitudinal data after cardiac arrest.

INTRODUCTION: Predicting outcome after cardiac arrest is challenging. We previously tested group-based trajectory modeling (GBTM) for prognostication based on baseline characteristics and quantitative electroencephalographic (EEG) trajectories. Here, we describe implementation of this method in a freely available software package and test its performance against alternative options. METHODS: We included comatose patients admitted to a single center after resuscitation from cardiac arrest from April 2010 to April 2019 who underwent ≥6 h of EEG monitoring. We abstracted clinical information from our prospective registry and summarized suppression ratio in 48 hourly epochs. We tested three classes of longitudinal models: frequentist, statistically based GBTMs; non-parametric (i.e. machine learning) k-means models; and Bayesian regression. Our primary outcome of interest was discharge CPC 1-3 (vs unconsciousness or death). We compared sensitivity for detecting poor outcome at a false positive rate (FPR) <1%. RESULTS: Of 1,010 included subjects, 250 (25%) were awake and alive at hospital discharge. GBTM and k-means derived trajectories, group sizes and group-specific outcomes were comparable. Conditional on an FPR < 1%, GBTMs yielded optimal sensitivity (38%) over 48 h. More sensitive methods had 2-3 % FPRs. CONCLUSION: We explored fundamentally different tools for patient-level predictions based on longitudinal and time-invariant patient data. Of the evaluated methods, GBTM resulted in optimal sensitivity while maintaining a false positive rate <1%. The provided code and software of this method provides an easy-to-use implementation for outcome prediction based on GBTMs.

Depression and anxiety symptoms: onset, developmental course and risk factors during early childhood.

BACKGROUND: Depressive and anxiety disorders are among the top ten leading causes of disabilities. We know little, however, about the onset, developmental course and early risk factors for depressive and anxiety symptoms (DAS). OBJECTIVE: Model the developmental trajectories of DAS during early childhood and to identify risk factors for atypically high DAS. METHOD: Group-based developmental trajectories of DAS conditional on risk factors were estimated from annual maternal ratings (1(1/2) to 5 years) in a large population sample (n = 1759). RESULTS: DAS increased substantially in two of the three distinct trajectory groups identified: High-Rising (14.7%); Moderate-Rising (55.4%); and Low (29.9%). Two factors distinguished the High-Rising group from the other two: Difficult temperament at 5 months (High-Rising vs Moderate-Rising: OR = 1.32; 95% CI = 1.13-1.55; High-Rising vs Low: OR = 1.31, CI = 1.12-1.54) and maternal lifetime major depression (High-Rising vs Moderate-Rising: OR = 1.10; CI = 1.01-1.20; High-Rising vs Low: OR = 1.19; CI = 1.08-1.31). Two factors distinguished the High-Rising group from the Low group: High family dysfunction (OR = 1.24; CI = 1.03-1.5) and Low parental self-efficacy (OR = .71; CI = .54-.94). CONCLUSIONS: DAS tend to increase in frequency over the first 5 years of life. Atypically high level can be predicted from mother and child characteristics present before 6 months of age. Preventive interventions should be experimented with at risk infants and parents.

Transactional processes in child disruptive behavior and maternal depression: a longitudinal study from early childhood to adolescence.

Although much has been written about the utility of applying Sameroff and Chandler's transactional perspective to the study of child psychopathology, relatively few researchers have used such an approach to trace the emergence of child problem behavior from infancy to adolescence. Using a sample of 289 male toddlers from predominantly low-income families, the current study examined associations between various forms of early child disruptive behavior, subsequent trajectories of maternal depressive symptoms over the course of 8 years, and adolescent problem behavior. Results indicated that early child noncompliance was the most robust predictor of more chronic and elevated trajectories of maternal depression, which in turn discriminated teacher and youth reports of adolescent antisocial behavior but not internalizing symptoms. The findings were consistent with transactional perspectives of developmental psychopathology that have emphasized the dynamic interplay between child and parent characteristics.

Is it important to prevent early exposure to drugs and alcohol among adolescents?

Exposure to alcohol and illicit drugs during early adolescence has been associated with poor outcomes in adulthood. However, many adolescents with exposure to these substances also have a history of conduct problems, which raises the question of whether early exposure to alcohol and drugs leads to poor outcomes only for those adolescents who are already at risk. In a 30-year prospective study, we tested whether there was evidence that early substance exposure can be a causal factor for adolescents' future lives. After propensity-score matching, early-exposed adolescents remained at an increased risk for a number of poor outcomes. Approximately 50% of adolescents exposed to alcohol and illicit drugs prior to age 15 had no conduct-problem history, yet were still at an increased risk for adult substance dependence, herpes infection, early pregnancy, and crime. Efforts to reduce or delay early substance exposure may prevent a wide range of adult health problems and should not be restricted to adolescents who are already at risk.

Combining group-based trajectory modeling and propensity score matching for causal inferences in nonexperimental longitudinal data.

A central theme of research on human development and psychopathology is whether a therapeutic intervention or a turning-point event, such as a family break-up, alters the trajectory of the behavior under study. This article describes and applies a method for using observational longitudinal data to make more transparent causal inferences about the impact of such events on developmental trajectories. The method combines 2 distinct lines of research: work on the use of finite mixture modeling to analyze developmental trajectories and work on propensity score matching. The propensity scores are used to balance observed covariates and the trajectory groups are used to control pretreatment measures of response. The trajectory groups also aid in characterizing classes of subjects for which no good matches are available. The approach is demonstrated with an analysis of the impact of gang membership on violent delinquency based on data from a large longitudinal study conducted in Montréal, Canada.

Group-based multi-trajectory modeling.

Identifying and monitoring multiple disease biomarkers and other clinically important factors affecting the course of a disease, behavior or health status is of great clinical relevance. Yet conventional statistical practice generally falls far short of taking full advantage of the information available in multivariate longitudinal data for tracking the course of the outcome of interest. We demonstrate a method called multi-trajectory modeling that is designed to overcome this limitation. The method is a generalization of group-based trajectory modeling. Group-based trajectory modeling is designed to identify clusters of individuals who are following similar trajectories of a single indicator of interest such as post-operative fever or body mass index. Multi-trajectory modeling identifies latent clusters of individuals following similar trajectories across multiple indicators of an outcome of interest (e.g., the health status of chronic kidney disease patients as measured by their eGFR, hemoglobin, blood CO2 levels). Multi-trajectory modeling is an application of finite mixture modeling. We lay out the underlying likelihood function of the multi-trajectory model and demonstrate its use with two examples.

Risk Factors Associated With Boys' and Girls' Developmental Trajectories of Physical Aggression From Early Childhood Through Early Adolescence.

Importance: This study used multitrajectory modeling to identify distinct trajectories of physical aggression from ages 1.5 to 13 years for boys and girls. Objectives: To trace the development of boys' and girls' physical aggression problems from infancy to adolescence using mother ratings, teacher ratings, and self-ratings and to identify early family predictors of children on the high physical aggression trajectories. Design, Setting, and Participants: This cohort study used data from the Quebec Longitudinal Study of Child Development (QLSCD), a study of a representative, population-based sample of 2223 infants born in 1997 and 1998 in the Canadian province of Quebec. The dates of analysis were January 2017 to January 2018. Main Outcomes and Measures: Trained research assistants conducted 7 interviews (at child ages 1.5, 2.5, 3.5, 4.5, 5, 6, and 8 years) with the person most knowledgeable about the child (mothers in 99.6% [2214 of 2223] of cases). Teachers assessed the child's behavior at ages 6, 7, 8, 10, 12, and 13 years. Self-reports of behavior problems were obtained from the child at ages 10, 12, and 13 years. Results: The sample included 2223 participants, 51.2% of whom were boys and 91.2% of whom were of white race/ethnicity. The mean response rate for mother ratings of physical aggression during the first 8 years of life was 80.9% (range, 65.1%-91.7%). For teacher ratings of physical aggression from ages 6 to 13 years, the mean response rate was 45.7% (range, 35.4%-56.9%), while the mean response rate of physical aggression assessment from self-ratings between ages 10 and 13 years was 57.9% (range, 55.2%-60.5%). Attrition was higher among families with low socioeconomic status and single-parent families, as well as among young mothers and mothers who were not fluent in French or English. A statistical analysis to examine the consequences of attrition was included. For boys and girls, the frequency of physical aggressions increased from age 1.5 years (2039 [91.7%]) to age 3.5 years (1941 [87.3%]) and then substantially decreased until age 13 years (1228 [55.2%]). Three distinct developmental trajectories of physical aggression were observed for girls and 5 for boys. Most family characteristics measured at 5 months after the child's birth were associated with a high physical aggression trajectory for boys and girls. Conclusions and Relevance: Family characteristics at 5 months after the child's birth could be used to target preschool interventions aimed at preventing the development of boys' and girls' chronic physical aggression problems.

Prediction of early-onset deviant peer group affiliation: a 12-year longitudinal study.

CONTEXT: Deviant peer group involvement is strongly related to onset, aggravation, and persistence of conduct problems during adolescence. OBJECTIVE: To identify early childhood behavioral profiles that predict early-onset deviant peer group involvement. DESIGN: A 12-year longitudinal study of behavioral development. SETTING: Fifty-three inner-city elementary schools in a large Canadian city. PARTICIPANTS: A total of 1037 boys in kindergarten from low socioeconomic neighborhoods. MAIN OUTCOME MEASURES: Annual self-reported deviant peer group involvement from 11 to 17 years of age. RESULTS: Kindergarten boys were at highest risk of following an early adolescence trajectory of deviant peer group affiliation if they were hyperactive, fearless, and low on prosocial behaviors but much less at risk if they scored high on only 2 of these dimensions. Family adversity had no main effect but substantially increased the risk of following an early adolescence trajectory of deviant peer group affiliation for boys with a profile of hyperactivity, fearlessness, and low prosocial behaviors. CONCLUSIONS: Kindergarten boys from low socioeconomic areas who are hyperactive, fearless, infrequently prosocial, and raised in adverse family environments are at much heightened risk of engaging in deviant peer groups early in their development. Boys at high risk can be identified as early as kindergarten and should be targeted for preventive intervention.

Combining propensity score matching and group-based trajectory analysis in an observational study.

In a nonrandomized or observational study, propensity scores may be used to balance observed covariates and trajectory groups may be used to control baseline or pretreatment measures of outcome. The trajectory groups also aid in characterizing classes of subjects for whom no good matches are available and to define substantively interesting groups between which treatment effects may vary. These and related methods are illustrated using data from a Montreal-based study. The effects on subsequent violence of gang joining at age 14 are studied while controlling for measured characteristics of boys prior to age 14. The boys are divided into trajectory groups based on violence from ages 11 to 13. Within trajectory group, joiners are optimally matched to a variable number of controls using propensity scores, Mahalanobis distances, and a combinatorial optimization algorithm. Use of variable ratio matching results in greater efficiency than pair matching and also greater bias reduction than matching at a fixed ratio. The possible impact of failing to adjust for an important but unmeasured covariate is examined using sensitivity analysis.