A comparison of multiple imputation methods for missing data in longitudinal studies

Trauma-focused and personality disorder treatment for posttraumatic stress disorder and comorbid cluster C personality disorder: a randomized clinical trial

Background: Posttraumatic stress disorder (PTSD) is associated with high rates of cluster C personality disorders (PD), which may negatively affect PTSD treatment. It is unknown whether concurrent treatment for PTSD and comorbid PD leads to superior treatment effects, compared to standard trauma-focused treatment.Objective: The objective was to test the efficacy of adding personality disorder treatment (group schema therapy; GST) to individual trauma-focused treatment (imagery rescripting; ImRs).Method: A two-arm randomized clinical trial (1:1 allocation ratio) was conducted between 2018 and 2023 at two sites of a mental health institution in the Netherlands. Raters were blind to treatment allocation. Adult outpatients with PTSD and comorbid cluster C personality disorders were randomized to receive either ImRs (12-18 sessions) or ImRs + GST (12-18 ImRs + 52-58 GST). The main outcome was PTSD severity one year after start of treatment measured with the Clinician-Administered PTSD Scale for DSM-5.Results: Of 130 patients (mean [SD] age = 40.6 [11.2], 110 [85%] females), 66 were assigned to ImRs and 64 to ImRs + GST. At 12 months, there were large decreases in PTSD severity (dImRs = 2.42, 95%CI = 1.97-2.87; dImRs + GST = 2.44, 95%CI = 1.99-2.90), but there was no significant difference between conditions (d = 0.02, 95%CI = -0.33-0.36, p = .944). Reductions in personality disorder symptoms and all other secondary outcomes were observed in both conditions. There were no significant differences between conditions on any of the secondary outcomes at 12 months.Conclusion: The more intensive concurrent trauma-focused and personality disorder treatment (ImRs + GST) was not superior to trauma-focused treatment alone (ImRs) for patients with PTSD and comorbid CPD. This suggests that trauma-focused treatment is the preferred primary treatment in patients presenting with both internalizing personality disorder and PTSD, reserving the stepping up to more intensive psychotherapy aimed at the personality disorder as a second line of treatment.Trial registration: ClinicalTrials.gov identifier: NCT03833531.

Six Years of Quality Improvement in Pectus Excavatum Repair: Implementation of Intercostal Nerve Cryoablation and ERAS Protocols for Patients Undergoing Nuss Procedure

BACKGROUND

The Nuss procedure for pectus excavatum is associated with prolonged hospitalizations due to pain. We evaluated implementation of intercostal nerve cryoablation and enhanced recovery after surgery (ERAS) protocols on outcomes of Nuss procedures performed over six years at a single institution.

METHODS

This retrospective cohort study included patients who underwent Nuss procedure from 10/2017 to 09/2023. Patients received epidurals prior to 06/2019, cryoablation from 06/2019 to 07/2021, and ERAS with cryoablation and intraoperative methadone administration after 07/2021. We used multivariable linear regression to evaluate length of stay (LOS), inpatient morphine milligram equivalents (MMEs), and discharge opioids. We assessed the balancing measures of operative time, postoperative pain scores, and complications.

RESULTS

We identified 62 patients; 15 who received epidurals, 18 cryoablation, and 29 cryoablation with ERAS. Cryoablation was associated with a 62.3% (p < 0.001) decrease in length of stay, an 86.6% (p < 0.001) decrease in inpatient MMEs, and a 72.9% (p < 0.001) decrease in discharge opioids. Cryoablation was additionally associated with 24.5% (p = 0.02) longer operative times and 46.4% (p = 0.04) higher postoperative day one pain scores. Subsequent implementation of an ERAS protocol was associated with a further 82.8% (p = 0.04) decrease in discharge opioids and a 25.0% (p = 0.04) decrease in postoperative day one pain scores.

CONCLUSIONS

Over six years of quality improvement efforts, we found the implementation of cryoablation and ERAS protocols to be associated with a significant decrease in length of stay and opioid exposures. Protocolized pain management and cryoablation may work synergistically to improve outcomes without compromising patient experience.

LEVEL OF EVIDENCE

Level III - Retrospective comparative study.

Randomized Trial of the Effectiveness of Videoconferencing-Based Versus Message-Based Psychotherapy on Depression

OBJECTIVE

The authors compared the engagement, clinical outcomes, and adverse events of text or voice message-based psychotherapy (MBP) versus videoconferencing-based psychotherapy (VCP) among adults with depression.

METHODS

The study used a sequential multiple-assignment randomized trial design with data drawn from phase 1 of a two-phase small business innovation research study. In total, 215 adults (ages ≥18 years) with depression received care from Talkspace, a digital mental health care company. Participants were initially randomly assigned to receive either asynchronous MBP or weekly VCP. All therapists provided evidence-based treatments such as cognitive-behavioral therapy. After 6 weeks of treatment, participants whose condition did not show a response on the Patient Health Questionnaire-9 or was rated as having not improved on the Clinical Global Impressions scale were randomly reassigned to receive either weekly VCP plus MBP or monthly VCP plus MBP. Longitudinal mixed-effects models with piecewise linear time trends applied to multiple imputed data sets were used to address missingness of data.

RESULTS

Participants who were initially assigned to the MBP condition engaged with their therapists over more weeks than did participants in the VCP condition (7.8 weeks for MBP vs. 4.9 weeks for VCP; p<0.001). No meaningful differences were observed between the two groups in rates of change by 6 or 12 weeks for depression, anxiety, disability, or global ratings of improvement. Neither treatment resulted in any adverse events.

CONCLUSIONS

MBP appears to be a viable alternative to VCP for treating adults with depression.

Dietary patterns and intrinsic capacity in older adults: a 6-year prospective cohort study

OBJECTIVES

Only a few studies have investigated dietary patterns and intrinsic capacity (IC). This study examined the prospective associations between dietary patterns, IC, and IC sub-domains over 6 years in community-dwelling Korean older adults.

DESIGN

A prospective cohort study.

SETTING AND PARTICIPANTS

Data were obtained from participants aged 70-84 years in the Korean Frailty and Aging Cohort Study (2016-2022). The study population included 665 enrollees at baseline who completed IC and dietary data.

METHODS

Dietary data were obtained from baseline surveys of the nutritional sub-cohort using two nonconsecutive 24-hour dietary recalls, and dietary patterns were derived using cluster analysis. IC was constructed by measuring cognitive, locomotor, vitality, sensory, and psychological domains. A generalized estimating equation was used to analyze the longitudinal associations between dietary patterns, IC, and IC sub-domain scores.

RESULTS

In total, 665 enrollees were included in the analysis. After adjusting for confounders, in older men, the dietary pattern of cluster 1 (variety of healthy foods and alcohols) compared to that of cluster 2 (rice and kimchi) was positively associated with changes in the IC score (ß = 0.41, 95% confidence interval [CI] = 0.04-0.78). In older women, the dietary pattern of cluster 1 (variety of healthy foods) was positively associated with changes in the IC score (ß = 0.30, 95% CI = 0.02-0.58), IC score group (ß = 0.11, 95% CI = 0.02-0.20), and psychological domain (ß = 0.25, 95% CI = 0.11-0.38) compared to that of cluster 3 (rice, vegetables, and kimchi).

CONCLUSIONS

Dietary patterns (variety of healthy foods) were positively associated with changes in IC scores and their sub-domains in older adults.

Radiologic Outcomes after Operative Management of Traumatic Spine Fractures: Stand-Alone Posterior Stabilization versus Combined Anteroposterior Approach

BACKGROUND

 Previous research emphasizes correcting deformities resulting from spine fractures by restoring sagittal alignment and vertebral height. This study aims to compare radiologic outcomes, including sagittal index (SI) and loss of vertebral body height (LVBH), between stand-alone posterior stabilization (group I) and the posteroanterior/combined approach (group II) in the operative management of traumatic thoracic or lumbar spine fractures.

METHODS

 In this retrospective single-center study, all patients with traumatic spine fractures (T1-L5) undergoing surgical stabilization between January 1, 2015, and May 31, 2021, were included. Two spine surgeons independently assessed imaging, recording the SI and LVBH values at baseline, after each surgical intervention, and during follow-up (at least 3 months posttreatment). The mean SI and LVBH values between the assessing surgeons were utilized. Linear mixed-effects regression models, adjusted to baseline values, compared the SI and the LVBH values between the two groups.

RESULTS

 In all, 71 patients (42 men), with the median age of 38 years (interquartile range [IQR]: 28-54) and median follow-up of 4 months (IQR: 3-17), were included. Thirty-two patients were in group I and 39 patients were in group II. Forty fractures included the thoracolumbar junction (T12 or L1), 15 affected the thoracic spine, and 14 the lumbar spine. The regression model revealed superior sagittal alignment in group II, with an adjusted mean difference for SI of -4.24 (95% confidence interval [CI]: -7.13 to -1.36; p = 0.004), and enhanced restoration of vertebral body height with an adjusted mean difference for LVBH of 0.11 in the combined approach (95% CI: 0.02-0.20; p = 0.02). Nine postoperative complications occurred in the entire cohort (4 in group I and 5 in group II).

CONCLUSIONS

 Combined posteroanterior stabilization for spine fractures improves deformities by enhancing sagittal alignment and increasing vertebral body height, with acceptable morbidity compared with the stand-alone posterior approach.

Characterizing Sensory Phenotypes of Subgroups with a Known Genetic Etiology Pertaining to Diagnoses of Autism Spectrum Disorder and Intellectual Disability

We aimed to identify unique constellations of sensory phenotypes for genetic etiologies associated with diagnoses of autism spectrum disorder (ASD) and intellectual disability (ID). Caregivers reported on sensory behaviors via the Sensory Profile for 290 participants (younger than 25 years of age) with ASD and/or ID diagnoses, of which ~ 70% have a known pathogenic genetic etiology. Caregivers endorsed poor registration (i.e., high sensory threshold, passive behaviors) for all genetic subgroups relative to an "idiopathic" comparison group with an ASD diagnosis and without a known genetic etiology. Genetic profiles indicated prominent sensory seeking in ADNP, CHD8, and DYRK1A, prominent sensory sensitivities in SCN2A, and fewer sensation avoidance behaviors in GRIN2B (relative to the idiopathic ASD comparison group).

Maintenance of Normal Blood Pressure From Middle to Older Age: Results From the Atherosclerosis Risk in Communities Study

BACKGROUND

It is unknown whether maintaining normal blood pressure (BP) from middle to older age is associated with improved health outcomes.

METHODS

We estimated the proportion of Atherosclerosis Risk in Communities study participants who maintained normal BP from 1987 to 1989 (visit 1) through 1996 to 1998 and 2011 to 2013 (over 4 and 5 visits, respectively). Normal BP was defined as systolic BP <120 mm Hg and diastolic BP <80 mm Hg, without antihypertensive medication. We estimated the risk of cardiovascular disease, dementia, and poor physical functioning after visit 5. In exploratory analyses, we examined participant characteristics associated with maintaining normal BP.

RESULTS

Among 2699 participants with normal BP at baseline (mean age 51.3 years), 47.1% and 15.0% maintained normal BP through visits 4 and 5, respectively. The hazard ratios comparing participants who maintained normal BP through visit 4 but not visit 5 and through visit 5 versus those who did not maintain normal BP through visit 4 were 0.80 (95% CI, 0.63-1.03) and 0.60 (95% CI, 0.42-0.86), respectively, for cardiovascular disease, and 0.85 (95% CI, 0.71-1.01) and 0.69 (95% CI, 0.54-0.90), respectively, for poor physical functioning. Maintaining normal BP through visit 5 was more common among participants with normal body mass index versus obesity at visit 1, those with normal body mass index at visits 1 and 5, and those with overweight at visit 1 and overweight or normal body mass index at visit 5, compared with those with obesity at visits 1 and 5.

CONCLUSIONS

Maintaining normal BP was associated with a lower risk of cardiovascular disease and poor physical functioning.

Malignant tumor increases the risk of all causes in-hospital mortality in critically ill patients with ischemic stroke: analysis of the MIMIC-IV database

BACKGROUND

Ischemic stroke (IS) and malignant tumor (MT) have high morbidity and mortality rates worldwide, and several associations exist between them. This study aimed to determine the effect of MT on hospital mortality in patients with IS.

METHODS

Based on their MT status, participants with IS in the Medical Information Mart for Intensive Care IV (MIMIC-IV) were divided into two groups. The primary outcome was in-hospital all causes mortality. Kaplan-Meier survival analysis was performed to evaluate the intergroup in-hospital mortality, and three Cox regression models were used to determine the association between MT and in-hospital mortality.

RESULTS

A total of 1605 participants (749 males and 856 females) were included in the study. The mean age was 72.030 ± 15.463 years. Of these, 257 (16%) patients died in the hospital. Kaplan-Meier analysis showed that the MT group had a significantly lower possibility of in-hospital survival than the non-MT group. In the unadjusted model, in-hospital mortality among MT patients had a higher odds ratio (OR) of 1.905 (95% CI, 1.320-2.748; P < 0.001) than the non-MT group. After adjusting for basic information, vital signs, and laboratory data, MT was also associated with increased in-hospital mortality (OR = 1.844, 95% CI: 1.255-2.708; P = 0.002).

CONCLUSIONS

Among the patients with IS, the risk of all causes in-hospital mortality was higher for MT than for patients non-MT. This finding can assist clinicians in more accurately assessing prognosis and making informed treatment decisions.

Positive effects of the COVID-19 pandemic on depression and anxiety in Chinese adolescents

The potential impact on mental health of home schooling and social isolation due to COVID-19 lockdowns has led to widespread concern, particularly for adolescents. However, studies including pre-pandemic data from longitudinal cohorts with an assessment of the longer-term impact of the Covid-19 pandemic beyond the first months of 2020 are scarce. This longitudinal study of 1534 adolescents attending a secondary school in Hunan province investigated self-reported symptoms of anxiety and depression using two validated scales (Screen for Child Anxiety Related Disorders, Child Mood and Feelings Questionnaire) at six time points before, during, and after the 2020 national lockdown restrictions in China. Perceived COVID-related stress was assessed by an author-developed scale at two timepoints during the lockdown. We investigated trends in symptoms over time with a fixed effects model and multiple imputations of missing data. Counter to our expectations, depressive and anxiety symptoms were reduced during the 2020 lockdown relative to pre-lockdown (depression: b = - 3.37, SE = 0.345, Cohen's d = - 0.25, p < 0.0001; anxiety: b = - 4.55, SE = 0.382, Cohen's d = - 0.30, p < 0.0001). Symptoms remained significantly reduced even after lockdown restrictions eased. Higher symptom levels during lockdown were associated with greater self-reported COVID-related stress (depression: b = 0.11, SE = 0.026, p < 0.0001; anxiety: b = 0.11, SE = 0.036, p < 0.0001). Although COVID-related stresses correlated with higher levels of anxiety and depression, the lockdown period was associated with improved symptom levels in the adolescents taking part in our study. School closures may have improved the mental health of adolescents in China. We speculate this beneficial effect of lockdown can be explained by the adverse effects of attending school itself such as exposure to bullying and achievement pressures.

Protocol of the study: Multilevel community-based mental health intervention to address structural inequities and adverse disparate consequences of COVID-19 pandemic on Latinx Immigrants and African refugees

The NIMH-funded Multilevel Community-Based Mental Health Intervention to Address Structural Inequities and Adverse Disparate Consequences of COVID-19 Pandemic on Latinx Immigrants and African Refugees study aims to advance the science of multilevel interventions to reduce the disparate, adverse mental health, behavioral, and socioeconomic consequences of the COVID-19 pandemic that are a result of complex interactions between underlying structural inequities and barriers to health care. The study tests three nested levels of intervention: 1) an efficacious 4-month advocacy and mutual learning model (Refugee and Immigrant Well-being Project, RIWP); 2) engagement with community-based organizations (CBOs); and 3) structural policy changes enacted in response to the pandemic. This community-based participatory research (CBPR) study builds on long-standing collaboration with five CBOs. By including 240 Latinx immigrants and 60 African refugees recruited from CBO partners who are randomly assigned to treatment-as-usual CBO involvement or the RIWP intervention and a comparison group comprised of a random sample of 300 Latinx immigrants, this mixed methods longitudinal waitlist control group design study with seven time points over 36 months tests the effectiveness of the RIWP intervention and engagement with CBOs to reduce psychological distress, daily stressors, and economic precarity and increase protective factors (social support, access to resources, English proficiency, cultural connectedness). The study also tests the ability of the RIWP intervention and engagement with CBOs to increase access to the direct benefits of structural interventions. This paper reports on the theoretical basis, design, qualitative and quantitative analysis plan, and power for the study.

Cost-Effectiveness of an Intensive Upper Limb Rehabilitation Therapy for Children With Unilateral Cerebral Palsy: An Economic Evaluation of a Randomized Controlled Trial

Background: Unilateral cerebral palsy is a major cause of childhood disability and a substantial economic burden. Intensive group-based therapy, consisting of hybrid constraint-induced movement and bimanual therapies, has been shown to be effective in improving specific quality-of-life domains in children with this disability. Our objective in this study was to assess if this intervention was cost-effective compared with standard care. Methods: An open-label, parallel, randomized controlled trial with an embedded economic evaluation of the intervention was conducted. A total of 47 children were randomized to either the intervention group (n = 27) or the standard care (n = 20) group. The effectiveness of the intervention was assessed using the Cerebral Palsy Quality of Life (Child) questionnaire across several domains. Nonparametric bootstrapping was used to quantify uncertainty intervals (UIs) for incremental cost-effectiveness ratios. Results: The incremental cost-effectiveness ratios for the intervention were 273 ( 95 107 to 945 ) f o r P a i n a n d I m p a c t o f D i s a b i l i t y , 1071 (95% UI: -5718 t o 4606) for Family Health and 1732 ( 95 6448 to 8775 ) f o r A c c e s s t o S e r v i c e s . F o r t h e 4 r e m a i n i n g d o m a i n s , t h e i n t e r v e n t i o n w a s d o m i n a t e d b y s t a n d a r d c a r e . A t a w i l l i n g n e s s - t o - p a y t h r e s h o l d o f 1000, only for the Pain and Impact of Disability domain was the intervention likely to have a probability of being cost-effective exceeding 0.75. Conclusions: Other than the Pain and Impact of Disability domain, there was insufficient evidence demonstrating the intervention to be cost-effective over a 13-week time horizon.

Assessing the daily association of sleep hygiene behaviours with sleep: A between and within persons approach

Sleep hygiene behaviours are recommendations given to both clinical and non-clinical populations with a focus on modifying behaviours to maximise sleep outcomes. However, methodological issues present in sleep hygiene research make it difficult to conclusively determine the impact of each behaviour. This study aimed to address these issues by adopting a two-week, repeated measures design which incorporated objective sleep measures and used linear mixed effect modelling to assess the daily association of a wide range of sleep hygiene behaviours on sleep in a non-clinical, university sample. Between-persons effects revealed that bedtime and frequency of daytime napping, alcohol use, and social media use were negatively related to sleep duration while waketime and frequency of too much water consumption were positively related to sleep duration. Within-person effects revealed that later than usual bedtime, earlier than usual waketime, no sunlight exposure, poor ventilation, having an unpleasant conversation before bed were negatively associated with sleep duration whereas using alcohol to deliberately help full asleep was positively related to sleep duration. In contrast, disproportionately more behaviours were not significantly related to either sleep outcome, only some of which could be explained by individual differences, which suggests that more research is needed to determine the conditions under which these behaviours affect sleep, if at all.

Multiple imputation of systematically missing data on gait speed in the Swedish National Study on Aging and Care

BACKGROUND

There is insufficient investigation of multiple imputation for systematically missing discrete variables in individual participant data meta-analysis (IPDMA) with a small number of included studies. Therefore, this study aims to evaluate the performance of three multiple imputation strategies - fully conditional specification (FCS), multivariate normal (MVN), conditional quantile imputation (CQI) - on systematically missing data on gait speed in the Swedish National Study on Aging and Care (SNAC).

METHODS

In total, 1 000 IPDMA were simulated with four prospective cohort studies based on the characteristics of the SNAC. The three multiple imputation strategies were analysed with a two-stage common-effect multivariable logistic model targeting the effect of three levels of gait speed (100% missing in one study) on 5-years mortality with common odds ratios set to OR1 = 0.55 (0.8-1.2 vs ≤0.8 m/s), and OR2 = 0.29 (>1.2 vs ≤0.8 m/s).

RESULTS

The average combined estimate for the mortality odds ratio OR1 (relative bias %) were 0.58 (8.2%), 0.58 (7.5%), and 0.55 (0.7%) for the FCS, MVN, and CQI, respectively. The average combined estimate for the mortality odds ratio OR2 (relative bias %) were 0.30 (2.5%), 0.33 (10.0%), and 0.29 (0.9%) for the FCS, MVN, and CQI respectively.

CONCLUSIONS

In our simulations of an IPDMA based on the SNAC where gait speed data was systematically missing in one study, all three imputation methods performed relatively well. The smallest bias was found for the CQI approach.

How did COVID-19 restrictions impact alcohol consumption in Australia? A longitudinal study

INTRODUCTION

The state of Victoria experienced more stringent public health measures than other Australian states during the COVID-19 pandemic. This study investigated how these public health measures impacted: (i) total alcohol consumption; (ii) location-specific consumption; and (iii) consumption among different pre-pandemic drinking groups, in Victoria compared to the rest of Australia during the first year of the pandemic.

METHOD

A longitudinal study with six survey waves was conducted between April and December 2020. A total of 775 adults completed data on alcohol use, including detailed consumption location information. Based on their 2019 consumption, participants were classified into low, moderate or high-risk groups. Data were analysed descriptively.

RESULTS

There was no difference in total alcohol consumption from 2019 levels among Victorians and those from the other Australian states when Victoria was the only state in lockdown. Location-specific consumption was relatively similar for Victoria and the rest of Australia, with an increase in home drinking, and a decrease in consumption in someone else's home, licensed premises and public spaces during lockdown compared with 2019. Participants in the high-risk group reported a reduction of two standard drinks per day in November 2020 compared with 2019. In contrast, consumption remained relatively stable for participants in the low and moderate-risk groups once accounting for regression to the mean.

DISCUSSION AND CONCLUSION

Contrary to expectations, restrictions on licensed premises appeared to impact high-risk drinkers more than low and moderate-risk drinkers. Reducing availability of on-premise alcohol may be an effective way to reduce consumption in heavier drinkers.

Handling of outcome missing data dependent on measured or unmeasured background factors in micro-randomized trial: Simulation and application study

Background

Micro-randomized trials (MRTs) enhance the effects of mHealth by determining the optimal components, timings, and frequency of interventions. Appropriate handling of missing values is crucial in clinical research; however, it remains insufficiently explored in the context of MRTs. Our study aimed to investigate appropriate methods for missing data in simple MRTs with uniform intervention randomization and no time-dependent covariates. We focused on outcome missing data depending on the participants' background factors.

Methods

We evaluated the performance of the available data analysis (AD) and the multiple imputation in generalized estimating equations (GEE) and random effects model (RE) through simulations. The scenarios were examined based on the presence of unmeasured background factors and the presence of interaction effects. We conducted the regression and propensity score methods as multiple imputation. These missing data handling methods were also applied to actual MRT data.

Results

Without the interaction effect, AD was biased for GEE, but there was almost no bias for RE. With the interaction effect, estimates were biased for both. For multiple imputation, regression methods estimated without bias when the imputation models were correct, but bias occurred when the models were incorrect. However, this bias was reduced by including the random effects in the imputation model. In the propensity score method, bias occurred even when the missing probability model was correct.

Conclusions

Without the interaction effect, AD of RE was preferable. When employing GEE or anticipating interactions, we recommend the multiple imputation, especially with regression methods, including individual-level random effects.

Lung function tracking in children with perinatally acquired HIV following early antiretroviral therapy initiation

INTRODUCTION

Lung disease remains a frequent complication in children with perinatal HIV infection (CHIV) and exposure without infection (CHEU), resulting in diminished lung function. In CHIV, early antiretroviral therapy (ART) initiation improves survival and extrapulmonary outcomes. However, it is unknown if there is benefit to lung function.

METHODS

Cohorts of CHIV (ART initiated at median 4.0 months), CHEU and HIV-unexposed children (CHU) prospectively performed pulmonary function testing (PFT) consisting of spirometry, plethysmography and diffusing capacity from 2013 to 2020. We determined lung function trajectories for PFT outcomes comparing CHIV to CHU and CHEU to CHU, using linear mixed effects models with multiple imputation. Potential confounders included sex, age, height, weight, body mass index z-score, urine cotinine and Tanner stage.

RESULTS

328 participants (122 CHIV, 126 CHEU, 80 CHU) performed PFT (ages 6.6-15.6 years). Spirometry (forced expiratory volume in 1 s, FEV1, forced vital capacity (FVC), FEV1/FVC) outcomes were similar between groups. In plethysmography, the mean residual volume (RV) z-score was 17% greater in CHIV than CHU (95% CI 1% to 33%, p=0.042). There was no difference in total lung capacity (TLC) or RV/TLC z-scores between groups. Diffusing capacity for carbon monoxide was similar in all groups, while alveolar volume (VA) differed between HIV groups by sex.

CONCLUSION

Our study indicates that early ART initiation can mitigate the loss of lung function in CHIV with lasting benefit through childhood; however, there remains concern of small airway disease. CHEU does not appear to disrupt childhood lung function trajectory.

Development and validation of nomogram prognostic model for predicting OS in patients with diffuse large B-cell lymphoma: a cohort study in China

This study comprehensively incorporates pathological parameters and novel clinical prognostic factors from the international prognostic index (IPI) to develop a nomogram prognostic model for overall survival in patients with diffuse large B-cell lymphoma (DLBCL). The aim is to facilitate personalized treatment and management strategies. This study enrolled a total of 783 cases for analysis. LASSO regression and stepwise multivariate COX regression were employed to identify significant variables and build a nomogram model. The calibration curve, receiver operating characteristic (ROC) curve, and decision curve analysis (DCA) curve were utilized to assess the model's performance and effectiveness. Additionally, the time-dependent concordance index (C-index) and time-dependent area under the ROC curve (AUC) were computed to validate the model's stability across different time points. The study utilized 8 selected clinical features as predictors to develop a nomogram model for predicting the overall survival of DLBCL patients. The model exhibited robust generalization ability with an AUC exceeding 0.7 at 1, 3, and 5 years. The calibration curve displayed evenly distributed points on both sides of the diagonal, and the slopes of the three calibration curves were close to 1 and statistically significant, indicating high prediction accuracy of the model. Furthermore, the model demonstrated valuable clinical significance and holds the potential for widespread adoption in clinical practice. The novel prognostic model developed for DLBCL patients incorporates readily accessible clinical parameters, resulting in significantly enhanced prediction accuracy and performance. Moreover, the study's use of a continuous general cohort, as opposed to clinical trials, makes it more representative of the broader lymphoma patient population, thus increasing its applicability in routine clinical care.

Comparison of Imputation Strategies for Incomplete Longitudinal Data in Life-Course Epidemiology

Incomplete longitudinal data are common in life-course epidemiology and may induce bias leading to incorrect inference. Multiple imputation (MI) is increasingly preferred for handling missing data, but few studies explore MI-method performance and feasibility in real-data settings. We compared 3 MI methods using real data under 9 missing-data scenarios, representing combinations of 10%, 20%, and 30% missingness and missing completely at random, at random, and not at random. Using data from Health and Retirement Study (HRS) participants, we introduced record-level missingness to a sample of participants with complete data on depressive symptoms (1998-2008), mortality (2008-2018), and relevant covariates. We then imputed missing data using 3 MI methods (normal linear regression, predictive mean matching, variable-tailored specification), and fitted Cox proportional hazards models to estimate effects of 4 operationalizations of longitudinal depressive symptoms on mortality. We compared bias in hazard ratios, root mean square error, and computation time for each method. Bias was similar across MI methods, and results were consistent across operationalizations of the longitudinal exposure variable. However, our results suggest that predictive mean matching may be an appealing strategy for imputing life-course exposure data, given consistently low root mean square error, competitive computation times, and few implementation challenges.

Multiple imputation of missing data in multilevel ecological momentary assessments: an example using smoking cessation study data

Advances in digital technology have greatly increased the ease of collecting intensive longitudinal data (ILD) such as ecological momentary assessments (EMAs) in studies of behavior changes. Such data are typically multilevel (e.g., with repeated measures nested within individuals), and are inevitably characterized by some degrees of missingness. Previous studies have validated the utility of multiple imputation as a way to handle missing observations in ILD when the imputation model is properly specified to reflect time dependencies. In this study, we illustrate the importance of proper accommodation of multilevel ILD structures in performing multiple imputations, and compare the performance of a multilevel multiple imputation (multilevel MI) approach relative to other approaches that do not account for such structures in a Monte Carlo simulation study. Empirical EMA data from a tobacco cessation study are used to demonstrate the utility of the multilevel MI approach, and the implications of separating participant- and study-initiated EMAs in evaluating individuals' affective dynamics and urge.

The impact of imputation quality on machine learning classifiers for datasets with missing values

BACKGROUND

Classifying samples in incomplete datasets is a common aim for machine learning practitioners, but is non-trivial. Missing data is found in most real-world datasets and these missing values are typically imputed using established methods, followed by classification of the now complete samples. The focus of the machine learning researcher is to optimise the classifier's performance.

METHODS

We utilise three simulated and three real-world clinical datasets with different feature types and missingness patterns. Initially, we evaluate how the downstream classifier performance depends on the choice of classifier and imputation methods. We employ ANOVA to quantitatively evaluate how the choice of missingness rate, imputation method, and classifier method influences the performance. Additionally, we compare commonly used methods for assessing imputation quality and introduce a class of discrepancy scores based on the sliced Wasserstein distance. We also assess the stability of the imputations and the interpretability of model built on the imputed data.

RESULTS

The performance of the classifier is most affected by the percentage of missingness in the test data, with a considerable performance decline observed as the test missingness rate increases. We also show that the commonly used measures for assessing imputation quality tend to lead to imputed data which poorly matches the underlying data distribution, whereas our new class of discrepancy scores performs much better on this measure. Furthermore, we show that the interpretability of classifier models trained using poorly imputed data is compromised.

CONCLUSIONS

It is imperative to consider the quality of the imputation when performing downstream classification as the effects on the classifier can be considerable.

Individual-level changes in religious/spiritual beliefs and behaviors over three decades in the parental generation of the ALSPAC cohort, UK

Longitudinal data on religious/spiritual beliefs and behaviors (RSBB) are essential for understanding both how religion shapes our lives and the factors determining religiosity. Despite this importance, there are few longitudinal studies with detailed and repeated RSBB data. Using data spanning nearly 30 years from the parental generation of the Avon Longitudinal Study of Parents and Children (ALSPAC) based in the Southwest of England, we describe individual-level changes in various aspects of self-reported RSBB (religious belief, affiliation, and attendance, among others) measured on four occasions (pregnancy, plus 5, 9, and 28 years post-partum; approx. 3600 mothers and 1200 partners have data at all four time-points). Although RSBBs were generally consistent over time, a shift towards non-religiosity was observed; exceptions included Roman Catholic affiliation, which was remarkably stable over three decades, and religious attendance, which increased from pregnancy to 5 years, before declining at 28 years. Most changes in RSBB were minor, e.g., between "yes" and "not sure" regarding religious belief, rather than between "yes" and "no." We also provide a simple illustrative example of how these longitudinal data can be analyzed. In addition to describing these longitudinal patterns, this paper will help inform future research using ALSPAC's longitudinal RSBB data.

Machine learning can accurately predict risk factors for all-cause reoperation after ACLR: creating a clinical tool to improve patient counseling and outcomes

PURPOSE

Identifying predictive factors for all-cause reoperation after anterior cruciate ligament reconstruction could inform clinical decision making and improve risk mitigation. The primary purposes of this study are to (1) determine the incidence of all-cause reoperation after anterior cruciate ligament reconstruction, (2) identify predictors of reoperation after anterior cruciate ligament reconstruction using machine learning methodology, and (3) compare the predictive capacity of the machine learning methods to that of traditional logistic regression.

METHODS

A longitudinal geographical database was utilized to identify patients with a diagnosis of new anterior cruciate ligament injury. Eight machine learning models were appraised on their ability to predict all-cause reoperation after anterior cruciate ligament reconstruction. Model performance was evaluated via area under the receiver operating characteristics curve. To explore modeling interpretability and radiomic feature influence on the predictions, we utilized a game-theory-based method through SHapley Additive exPlanations.

RESULTS

A total of 1400 patients underwent anterior cruciate ligament reconstruction with a mean postoperative follow-up of 9 years. Two-hundred and eighteen (16%) patients experienced a reoperation after anterior cruciate ligament reconstruction, of which 6% of these were revision ACL reconstruction. SHapley Additive exPlanations plots identified the following risk factors as predictive for all-cause reoperation: diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. XGBoost was the best-performing model (area under the receiver operating characteristics curve of 0.77) and outperformed logistic regression in this regard.

CONCLUSIONS

All-cause reoperation after anterior cruciate ligament reconstruction occurred at a rate of 16%. Machine learning models outperformed traditional statistics and identified diagnosis of systemic inflammatory disease, distal tear location, concomitant medial collateral ligament repair, higher visual analog scale pain score prior to surgery, hamstring autograft, tibial fixation via radial expansion device, younger age at initial injury, and concomitant meniscal repair as predictive risk factors for reoperation. Pertinent negatives, when compared to previous studies, included sex and timing of surgery. These models will allow surgeons to tabulate individualized risk for future reoperation for patients undergoing anterior cruciate ligament reconstruction.

LEVEL OF EVIDENCE

III.

Identifying Modifiable Cost Drivers of Outpatient Unicompartmental Knee Arthroplasty With Machine Learning

BACKGROUND

Implementing tools that identify cost-saving opportunities for ambulatory orthopaedic surgeries can improve access to value-based care. We developed and internally validated a machine learning (ML) algorithm to predict cost drivers of total charges after ambulatory unicompartmental knee arthroplasty (UKA).

METHODS

We queried the New York State Ambulatory Surgery and Services database to identify patients who underwent ambulatory, defined as <24 hours of care before discharge, elective UKA between 2014 and 2016. A total of 1,311 patients were included. The median costs after ambulatory UKA were $14,710. Patient demographics and intraoperative parameters were entered into 4 candidate ML algorithms. The most predictive model was selected following internal validation of candidate models, with conventional linear regression as a benchmark. Global variable importance and partial dependence curves were constructed to determine the impact of each input parameter on total charges.

RESULTS

The gradient-boosted ensemble model outperformed all candidate algorithms and conventional linear regression. The major differential cost drivers of UKA identified (in decreasing order of magnitude) were increased operating room time, length of stay, use of regional and adjunctive periarticular analgesia, utilization of computer-assisted navigation, and routinely sending resected tissue to pathology.

CONCLUSION

We developed and internally validated a supervised ML algorithm that identified operating room time, length of stay, use of computer-assisted navigation, regional primary anesthesia, adjunct periarticular analgesia, and routine surgical pathology as essential cost drivers of UKA. Following external validation, this tool may enable surgeons and health insurance providers optimize the delivery of value-based care to patients receiving outpatient UKA.

LEVEL OF EVIDENCE

III.

Defining Clinically Meaningful Subgroups for Risk Stratification in Patients Undergoing Revision Total Hip Arthroplasty: A Combined Unsupervised and Supervised Machine Learning Approach

BACKGROUND

Studies developing predictive models from large datasets to risk-stratify patients under going revision total hip arthroplasties (rTHAs) are limited. We used machine learning (ML) to stratify patients undergoing rTHA into risk-based subgroups.

METHODS

We retrospectively identified 7,425 patients who underwent rTHA from a national database. An unsupervised random forest algorithm was used to partition patients into high-risk and low-risk strata based on similarities in rates of mortality, reoperation, and 25 other postoperative complications. A risk calculator was produced using a supervised ML algorithm to identify high-risk patients based on preoperative parameters.

RESULTS

There were 3,135 and 4,290 patients identified in the high-risk and low-risk subgroups, respectively. Each group significantly differed by rate of 30-day mortalities, unplanned reoperations/readmissions, routine discharges, and hospital lengths of stay (P < .05). An Extreme Gradient Boosting algorithm identified preoperative platelets < 200, hematocrit > 35 or < 20, increasing age, albumin < 3, international normalized ratio > 2, body mass index > 35, American Society of Anesthesia class ≥ 3, blood urea nitrogen > 50 or < 30, creatinine > 1.5, diagnosis of hypertension or coagulopathy, and revision for periprosthetic fracture and infection as predictors of high risk.

CONCLUSION

Clinically meaningful risk strata in patients undergoing rTHA were identified using an ML clustering approach. Preoperative labs, demographics, and surgical indications have the greatest impact on differentiating high versus low risk.

LEVEL OF EVIDENCE

III.

Effectiveness of wheelchair skills training for improving manual wheelchair mobility in children and adolescents: protocol for a multicenter randomized waitlist-controlled trial

BACKGROUND

Self-directed mobility during childhood can influence development, social participation, and independent living later in life. For children who experience challenges with walking, manual wheelchairs (MWCs) provide a means for self-directed mobility. An effective MWC skills training program exists for adults, but controlled trials have not yet been documented in children and adolescents. This paper outlines the protocol for a multi-centre randomized wait-list controlled trial. The primary objective is to test the hypothesis that children and adolescents who receive MWC skills training will have higher MWC skills capacity compared to children and adolescents in the control group who receive usual care. The secondary objectives are to explore the influence of MWC skills training in children and adolescents (MWC use self-efficacy and satisfaction with participation in meaningful activities), and parents (perceived MWC skills); and to measure retention three months later.

METHODS

A multi-centre, parallel-group, single-blind randomized wait-list controlled trial will be conducted. A sample of 60 children and adolescents who use MWCs will be recruited in rehabilitation centres, specialized schools, and the communities of three Canadian cities. Participants will be randomized (1:1) to the experimental (Wheelchair Skills Training Program [WSTP]) or wait-list control group (usual care). Performance-based and self-report measures will be completed at baseline (T1), three months (post-intervention, T2), and three months post-intervention (T3). The primary outcome will be MWC skills capacity post-intervention. Secondary outcomes will be MWC use self-efficacy and satisfaction with participation of the child/adolescent, and parent-perceived MWC skills. The WSTP will consist of 12 sessions, 45-60 min each, delivered 1-2 times per week by trained personnel with health professions education. Training will be customized according to the child's baseline skills and participation goals that require the use of the MWC. The wait-list control group will receive usual care for 3 months and then receive the WSTP after completing T2 evaluations. Data will be analysed using ANCOVA (controlling for baseline scores).

DISCUSSION

MWC skills training may be one way to improve self-directed mobility and related outcomes for children and adolescents. The results of this multi-centre randomized wait-list controlled trial will allow for the effectiveness of the intervention to be evaluated in a variety of clinical contexts and geographical regions.

TRIAL REGISTRATION

ClinicalTrials.gov: NCT05564247, Version October 3, 2022.

Study protocol for development and validation of a single tool to assess risks of stroke, diabetes mellitus, myocardial infarction and dementia: DemNCD-Risk

INTRODUCTION

Current efforts to reduce dementia focus on prevention and risk reduction by targeting modifiable risk factors. As dementia and cardiometabolic non-communicable diseases (NCDs) share risk factors, a single risk-estimating tool for dementia and multiple NCDs could be cost-effective and facilitate concurrent assessments as compared with a conventional single approach. The aim of this study is to develop and validate a new risk tool that estimates an individual's risk of developing dementia and other NCDs including diabetes mellitus, stroke and myocardial infarction. Once validated, it could be used by the public and general practitioners.

METHODS AND ANALYSIS

Ten high-quality cohort studies from multiple countries were identified, which met eligibility criteria, including large representative samples, long-term follow-up, data on clinical diagnoses of dementia and NCDs, recognised modifiable risk factors for the four NCDs and mortality data. Pooled harmonised data from the cohorts will be used, with 65% randomly allocated for development of the predictive model and 35% for testing. Predictors include sociodemographic characteristics, general health risk factors and lifestyle/behavioural risk factors. A subdistribution hazard model will assess the risk factors' contribution to the outcome, adjusting for competing mortality risks. Point-based scoring algorithms will be built using predictor weights, internally validated and the discriminative ability and calibration of the model will be assessed for the outcomes. Sensitivity analyses will include recalculating risk scores using logistic regression.

ETHICS AND DISSEMINATION

Ethics approval is provided by the University of New South Wales Human Research Ethics Committee (UNSW HREC; protocol numbers HC200515, HC3413). All data are deidentified and securely stored on servers at Neuroscience Research Australia. Study findings will be presented at conferences and published in peer-reviewed journals. The tool will be accessible as a public health resource. Knowledge translation and implementation work will explore strategies to apply the tool in clinical practice.

Maternal and child nutrition programme of investigation within the 100 Million Brazilian Cohort: study protocol

INTRODUCTION

There is a limited understanding of the early nutrition and pregnancy determinants of short-term and long-term maternal and child health in ethnically diverse and socioeconomically vulnerable populations within low-income and middle-income countries. This investigation programme aims to: (1) describe maternal weight trajectories throughout the life course; (2) describe child weight, height and body mass index (BMI) trajectories; (3) create and validate models to predict childhood obesity at 5 years of age; (4) estimate the effects of prepregnancy BMI, gestational weight gain (GWG) and maternal weight trajectories on adverse maternal and neonatal outcomes and child growth trajectories; (5) estimate the effects of prepregnancy BMI, GWG, maternal weight and interpregnancy BMI changes on maternal and child outcomes in the subsequent pregnancy; and (6) estimate the effects of maternal food consumption and infant feeding practices on child nutritional status and growth trajectories.

METHODS AND ANALYSIS

Linked data from four different Brazilian databases will be used: the 100 Million Brazilian Cohort, the Live Births Information System, the Mortality Information System and the Food and Nutrition Surveillance System. To analyse trajectories, latent-growth, superimposition by translation and rotation and broken stick models will be used. To create prediction models for childhood obesity, machine learning techniques will be applied. For the association between the selected exposure and outcomes variables, generalised linear models will be considered. Directed acyclic graphs will be constructed to identify potential confounders for each analysis investigating potential causal relationships.

ETHICS AND DISSEMINATION

This protocol was approved by the Research Ethics Committees of the authors' institutions. The linkage will be carried out in a secure environment. After the linkage, the data will be de-identified, and pre-authorised researchers will access the data set via a virtual private network connection. Results will be reported in open-access journals and disseminated to policymakers and the broader public.

Exploring assessment of balance using virtual reality in patients at risk of chemotherapy-induced peripheral neuropathy

BACKGROUND

Chemotherapy-induced peripheral neuropathy (CIPN) is a common dose-limiting toxicity for people treated for cancer. Impaired balance and falls are functional consequences of CIPN. Virtual reality (VR) technology may be able to assess balance and identify patients at risk of falls.

AIMS

To assess the impact of potentially neurotoxic chemotherapy on balance using VR, and explore associations between VR balance assessment, falls and CIPN.

METHODS

This prospective, repeated measures longitudinal study was conducted at two Australian cancer centres. Eligible participants were commencing adjuvant chemotherapy containing a taxane for breast cancer, or oxaliplatin for colorectal cancer (CRC), per institutional guidelines. Balance assessments using VR were conducted at baseline, end of chemotherapy and 3 and 6 months after completion of chemotherapy. Participants also completed a comprehensive CIPN assessment comprising clinical and patient-reported outcomes, and recorded falls or near falls.

RESULTS

Out of 34 participants consented, 24 (71%) had breast cancer and 10 (29%) had CRC. Compared to baseline, balance threshold was reduced in 10/28 (36%) evaluable participants assessed at the end of chemotherapy, and persistent in 7/22 (32%) at 6 months. CIPN was identified in 86% at end of chemotherapy and persisted to 6 months after chemotherapy completion in 73%. Falls or near falls were reported by 12/34 (35%) participants, and were associated with impaired VR balance threshold (P = 0.002).

CONCLUSIONS

While VR balance assessment was no better at identifying CIPN than existing measures, it is a potential surrogate method to assess patients at risk of falls from CIPN.

A wide range of missing imputation approaches in longitudinal data: a simulation study and real data analysis

BACKGROUND

Missing data is a pervasive problem in longitudinal data analysis. Several single-imputation (SI) and multiple-imputation (MI) approaches have been proposed to address this issue. In this study, for the first time, the function of the longitudinal regression tree algorithm as a non-parametric method after imputing missing data using SI and MI was investigated using simulated and real data.

METHOD

Using different simulation scenarios derived from a real data set, we compared the performance of cross, trajectory mean, interpolation, copy-mean, and MI methods (27 approaches) to impute missing longitudinal data using parametric and non-parametric longitudinal models and the performance of the methods was assessed in real data. The real data included 3,645 participants older than 18 years within six waves obtained from the longitudinal Tehran cardiometabolic genetic study (TCGS). The data modeling was conducted using systolic and diastolic blood pressure (SBP/DBP) as the outcome variables and included predictor variables such as age, gender, and BMI. The efficiency of imputation approaches was compared using mean squared error (MSE), root-mean-squared error (RMSE), median absolute deviation (MAD), deviance, and Akaike information criteria (AIC).

RESULTS

The longitudinal regression tree algorithm outperformed based on the criteria such as MSE, RMSE, and MAD than the linear mixed-effects model (LMM) for analyzing the TCGS and simulated data using the missing at random (MAR) mechanism. Overall, based on fitting the non-parametric model, the performance of the 27 imputation approaches was nearly similar. However, the SI traj-mean method improved performance compared with other imputation approaches.

CONCLUSION

Both SI and MI approaches performed better using the longitudinal regression tree algorithm compared with the parametric longitudinal models. Based on the results from both the real and simulated data, we recommend that researchers use the traj-mean method for imputing missing values of longitudinal data. Choosing the imputation method with the best performance is widely dependent on the models of interest and the data structure.

Addressing health disparities using multiply imputed injury surveillance data

BACKGROUND

Assessing disparities in injury is crucial for injury prevention and for evaluating injury prevention strategies, but efforts have been hampered by missing data. This study aimed to show the utility and reliability of the injury surveillance system as a trustworthy resource for examining disparities by generating multiple imputed companion datasets.

METHODS

We employed data from the National Electronic Injury Surveillance System-All Injury Program (NEISS-AIP) for the period 2014-2018. A comprehensive simulation study was conducted to identify the appropriate strategy for addressing missing data limitations in NEISS-AIP. To evaluate the imputation performance more quantitatively, a new method based on Brier Skill Score (BSS) was developed to assess the accuracy of predictions by different approaches. We selected the multiple imputations by fully conditional specification (FCS MI) to generate the imputed companion data to NEISS-AIP 2014-2018. We further assessed health disparities systematically in nonfatal assault injuries treated in U.S. hospital emergency departments (EDs) by race and ethnicity, location of injury and sex.

RESULTS

We found for the first time that significantly higher age-adjusted nonfatal assault injury rates for ED visits per 100,000 population occurred among non-Hispanic Black persons (1306.8, 95% Confidence Interval [CI]: 660.1 - 1953.5), in public settings (286.3, 95% CI: 183.2 - 389.4) and for males (603.5, 95% CI: 409.4 - 797.5). We also observed similar trends in age-adjusted rates (AARs) by different subgroups for non-Hispanic Black persons, injuries occurring in public settings, and for males: AARs of nonfatal assault injury increased significantly from 2014 through 2017, then declined significantly in 2018.

CONCLUSIONS

Nonfatal assault injury imposes significant health care costs and productivity losses for millions of people each year. This study is the first to specifically look at health disparities in nonfatal assault injuries using multiply imputed companion data. Understanding how disparities differ by various groups may lead to the development of more effective initiatives to prevent such injury.

Methods for comparative effectiveness based on time to confirmed disability progression with irregular observations in multiple sclerosis

Real-world data sources offer opportunities to compare the effectiveness of treatments in practical clinical settings. However, relevant outcomes are often recorded selectively and collected at irregular measurement times. It is therefore common to convert the available visits to a standardized schedule with equally spaced visits. Although more advanced imputation methods exist, they are not designed to recover longitudinal outcome trajectories and typically assume that missingness is non-informative. We, therefore, propose an extension of multilevel multiple imputation methods to facilitate the analysis of real-world outcome data that is collected at irregular observation times. We illustrate multilevel multiple imputation in a case study evaluating two disease-modifying therapies for multiple sclerosis in terms of time to confirmed disability progression. This survival outcome is derived from repeated measurements of the Expanded Disability Status Scale, which is collected when patients come to the healthcare center for a clinical visit and for which longitudinal trajectories can be estimated. Subsequently, we perform a simulation study to compare the performance of multilevel multiple imputation to commonly used single imputation methods. Results indicate that multilevel multiple imputation leads to less biased treatment effect estimates and improves the coverage of confidence intervals, even when outcomes are missing not at random.

Remote Monitoring of Positive Airway Pressure Data: Challenges, Pitfalls, and Strategies to Consider for Optimal Data Science Applications

Over recent years, positive airway pressure (PAP) remote monitoring has transformed the management of OSA and produced a large amount of data. Accumulated PAP data provide valuable and objective information regarding patient treatment adherence and efficiency. However, the majority of studies that have analyzed longitudinal PAP remote monitoring have summarized data trajectories in static and simplistic metrics for PAP adherence and the residual apnea-hypopnea index by the use of mean or median values. The aims of this article are to suggest directions for improving data cleaning and processing and to address major concerns for the following data science applications: (1) conditions for residual apnea-hypopnea index reliability, (2) lack of standardization of indicators provided by different PAP models, (3) missing values, and (4) consideration of treatment interruptions. To allow fair comparison among studies and to avoid biases in computation, PAP data processing and management should be conducted rigorously with these points in mind. PAP remote monitoring data contain a wealth of information that currently is underused in the field of sleep research. Improving the quality and standardizing data handling could facilitate data sharing among specialists worldwide and enable artificial intelligence strategies to be applied in the field of sleep apnea.

The impact of preservation and recovery of renal function on survival after veno-arterial extracorporeal life support: A retrospective cohort study

BACKGROUND

Veno-arterial extracorporeal life support (V-A ECLS) has become a cornerstone in the management of critical cardiogenic shock, but it can also precipitate organ injury, e.g., acute kidney injury (AKI). Available studies highlight the effect of non-cardiac organ injury on patient outcomes. Only very little is known about the impact of non-cardiac organ recovery on patient survival. AKI occurs frequently during cardiogenic shock and carries a poor prognosis. We have developed descriptive models to hypothesize on the role of AKI severity versus that of recovery of renal function for patient survival.

METHODS

Retrospective, observational study including 175 patients who were successfully decannulated from V-A ECLS. We assessed AKI severity using the "Kidney Disease: Improving Global Outcomes" (KDIGO) criteria. We defined recovered or preserved renal function (RPRF) prior to decannulation from V-A ECLS as 0 (AKI with no improvement) or 1 (no AKI or AKI with improvement). We classified patient outcomes as alive or dead at hospital discharge.

RESULTS

78% (n = 138) of all patients survived hospital discharge of which 38% (n = 67) never developed AKI. After adjusting for shock severity and non-renal organ injury, RPRF emerged as an independent predictor of survival in both the overall cohort [OR (95% CI) - 4.11 (1.72-9.79)] and the AKI-only sub-cohort [OR (95% CI) - 5.18 (1.8-14.92)]. Neither maximum KDIGO stage nor KDIGO stage at the end of V-A ECLS was independently associated with survival.

CONCLUSIONS

Our model identifies RPRF, but not AKI severity, as an independent predictor of hospital survival in patients undergoing V-A ECLS for cardiogenic shock. We hypothesize that recovered or preserved non-cardiac organ function during V-A ECLS is crucial for patient survival.

COVID-Dynamic: A large-scale longitudinal study of socioemotional and behavioral change across the pandemic

The COVID-19 pandemic has caused enormous societal upheaval globally. In the US, beyond the devastating toll on life and health, it triggered an economic shock unseen since the great depression and laid bare preexisting societal inequities. The full impacts of these personal, social, economic, and public-health challenges will not be known for years. To minimize societal costs and ensure future preparedness, it is critical to record the psychological and social experiences of individuals during such periods of high societal volatility. Here, we introduce, describe, and assess the COVID-Dynamic dataset, a within-participant longitudinal study conducted from April 2020 through January 2021, that captures the COVID-19 pandemic experiences of >1000 US residents. Each of 16 timepoints combines standard psychological assessments with novel surveys of emotion, social/political/moral attitudes, COVID-19-related behaviors, tasks assessing implicit attitudes and social decision-making, and external data to contextualize participants' responses. This dataset is a resource for researchers interested in COVID-19-specific questions and basic psychological phenomena, as well as clinicians and policy-makers looking to mitigate the effects of future calamities.

Risk factors for secondary meniscus tears can be accurately predicted through machine learning, creating a resource for patient education and intervention

PURPOSE

This study sought to develop and internally validate a machine learning model to identify risk factors and quantify overall risk of secondary meniscus injury in a longitudinal cohort after primary ACL reconstruction (ACLR).

METHODS

Patients with new ACL injury between 1990 and 2016 with minimum 2-year follow-up were identified. Records were extensively reviewed to extract demographic, treatment, and diagnosis of new meniscus injury following ACLR. Four candidate machine learning algorithms were evaluated to predict secondary meniscus tears. Performance was assessed through discrimination using area under the receiver operating characteristics curve (AUROC), calibration, and decision curve analysis; interpretability was enhanced utilizing global variable importance plots and partial dependence curves.

RESULTS

A total of 1187 patients underwent ACLR; 139 (11.7%) experienced a secondary meniscus tear at a mean time of 65 months post-op. The best performing model for predicting secondary meniscus tear was the random forest (AUROC = 0.790, 95% CI: 0.785-0.795; calibration intercept = 0.006, 95% CI: 0.005-0.007, calibration slope = 0.961 95% CI: 0.956-0.965, Brier's score = 0.10 95% CI: 0.09-0.12), and all four machine learning algorithms outperformed traditional logistic regression. The following risk factors were identified: shorter time to return to sport (RTS), lower VAS at injury, increased time from injury to surgery, older age at injury, and proximal ACL tear.

CONCLUSION

Machine learning models outperformed traditional prediction models and identified multiple risk factors for secondary meniscus tears after ACLR. Following careful external validation, these models can be deployed to provide real-time quantifiable risk for counseling and timely intervention to help guide patient expectations and possibly improve clinical outcomes.

LEVEL OF EVIDENCE

III.

Quality of life and mood disorders of mild to moderate stroke survivors in the early post-hospital discharge phase: a cross-sectional survey study

BACKGROUND

Exploring sociodemographic and disease characteristics that contribute to patient-reported outcomes can inform targeting of strategies to support recovery and adaptation to stroke. This study aimed to examine among a sample of stroke survivors at three months post-hospital discharge: (1) self-reported physical and mental health quality of life scores; (2) self-reported depressive and anxiety symptoms; and (3) sociodemographic and clinical predictors of these outcomes.

METHODS

This cross-sectional survey study recruited stroke survivors from eight hospitals in one Australian state. Adult survivors recently discharged from hospital stroke wards (within 3 months) were mailed a study information package and invited to complete a pen-and paper survey. Survey items assessed: quality of life (SF12v2), depression (PHQ-9), anxiety (GAD-7) and sociodemographic and clinical characteristics. Predictors were examined using multiple linear regression analysis.

RESULTS

Of the 1161 eligible patients who were posted a recruitment pack, 401 (35%) returned a completed survey. Participants reported a mean SF-12v2 Physical Composite Score (PCS) quality of life score of 44.09 (SD = 9.57); and a mean SF-12v2 Mental Composite Score (MCS) quality of life score of 46.84 (SD = 10.0). Approximately one third of participants (34%; n = 132) were classified as depressed (PHQ-9 ≥ 10); and 27% (n = 104) were classified as anxious (GAD-7 ≥ 8). Lower PCS was associated with being female, not employed and having a comorbid diagnosis of diabetes and atrial fibrillation. Lower MCS was associated with a history of transient ischemic attack (TIA). Males and those with higher levels of education, had greater odds of having lower depression severity; those with a history of TIA or diabetes had lower odds of having lower depression severity. Males had greater odds of having lower anxiety severity; those with a history of TIA had lower odds of having lower anxiety severity.

CONCLUSION

Sub-groups of stroke survivors may be at-risk of poorer quality of life and psychological morbidity in the early post-discharge phase. These findings support the role of early identification and prioritisation of at-risk survivors at discharge, as they may require modifications to standard hospital discharge processes tailored to their level of risk.

Empirical Comparison of Imputation Methods for Multivariate Missing Data in Public Health

Sample estimates derived from data with missing values may be unreliable and may negatively impact the inferences that researchers make about the underlying population due to nonresponse bias. As a result, imputation is often preferred to listwise deletion in handling multivariate missing data. In this study, we compared three popular imputation methods: sequential multiple imputation, fractional hot-deck imputation, and generalized efficient regression-based imputation with latent processes for handling multivariate missingness under different missing patterns by conducting descriptive and regression analyses on the imputed data and seeing how the estimates differ from those generated from the full sample. Limited Monte Carlo simulation results by using the National Health Nutrition and Examination Survey and Behavioral Risk Factor Surveillance System are presented to demonstrate the effect of each imputation method on reducing bias and increasing efficiency for the parameter estimate of interest for that particular incomplete variable. Although these three methods did not always outperform listwise deletion in our simulated missing patterns, they improved many descriptive and regression estimates when used to impute all incomplete variables at once.

Data processing pipeline for cardiogenic shock prediction using machine learning

Introduction

Recent advances in machine learning provide new possibilities to process and analyse observational patient data to predict patient outcomes. In this paper, we introduce a data processing pipeline for cardiogenic shock (CS) prediction from the MIMIC III database of intensive cardiac care unit patients with acute coronary syndrome. The ability to identify high-risk patients could possibly allow taking pre-emptive measures and thus prevent the development of CS.

Methods

We mainly focus on techniques for the imputation of missing data by generating a pipeline for imputation and comparing the performance of various multivariate imputation algorithms, including k-nearest neighbours, two singular value decomposition (SVD)-based methods, and Multiple Imputation by Chained Equations. After imputation, we select the final subjects and variables from the imputed dataset and showcase the performance of the gradient-boosted framework that uses a tree-based classifier for cardiogenic shock prediction.

Results

We achieved good classification performance thanks to data cleaning and imputation (cross-validated mean area under the curve 0.805) without hyperparameter optimization.

Conclusion

We believe our pre-processing pipeline would prove helpful also for other classification and regression experiments.

Validation of the CogDrisk Instrument as Predictive of Dementia in Four General Community-Dwelling Populations

BACKGROUND

Lack of external validation of dementia risk tools is a major limitation for generalizability and translatability of prediction scores in clinical practice and research.

OBJECTIVES

We aimed to validate a new dementia prediction risk tool called CogDrisk and a version, CogDrisk-AD for predicting Alzheimer's disease (AD) using cohort studies.

DESIGN, SETTING, PARTICIPANTS AND MEASUREMENTS

Four cohort studies were identified that included majority of the dementia risk factors from the CogDrisk tool. Participants who were free of dementia at baseline were included. The predictors were component variables in the CogDrisk tool that include self-reported demographics, medical risk factors and lifestyle habits. Risk scores for Any Dementia and AD were computed and Area Under the Curve (AUC) was assessed. To examine modifiable risk factors for dementia, the CogDrisk tool was tested by excluding age and sex estimates from the model.

RESULTS

The performance of the tool varied between studies. The overall AUC and 95% CI for predicting dementia was 0.77 (0.57, 0.97) for the Swedish National study on Aging and Care in Kungsholmen, 0.76 (0.70, 0.83) for the Health and Retirement Study - Aging, Demographics and Memory Study, 0.70 (0.67,0.72) for the Cardiovascular Health Study Cognition Study, and 0.66 (0.62,0.70) for the Rush Memory and Aging Project.

CONCLUSIONS

The CogDrisk and CogDrisk-AD performed well in the four studies. Overall, this tool can be used to assess individualized risk factors of dementia and AD in various population settings.

Review and evaluation of imputation methods for multivariate longitudinal data with mixed-type incomplete variables

Estimating relationships between multiple incomplete patient measurements requires methods to cope with missing values. Multiple imputation is one approach to address missing data by filling in plausible values for those that are missing. Multiple imputation procedures can be classified into two broad types: joint modeling (JM) and fully conditional specification (FCS). JM fits a multivariate distribution for the entire set of variables, but it may be complex to define and implement. FCS imputes missing data variable-by-variable from a set of conditional distributions. In many studies, FCS is easier to define and implement than JM, but it may be based on incompatible conditional models. Imputation methods based on multilevel modeling show improved operating characteristics when imputing longitudinal data, but they can be computationally intensive, especially when imputing multiple variables simultaneously. We review current MI methods for incomplete longitudinal data and their implementation on widely accessible software. Using simulated data from the National Health and Aging Trends Study, we compare their performance for monotone and intermittent missing data patterns. Our simulations demonstrate that in a longitudinal study with a limited number of repeated observations and time-varying variables, FCS-Standard is a computationally efficient imputation method that is accurate and precise for univariate single-level and multilevel regression models. When the analyses comprise multivariate multilevel models, FCS-LMM-latent is a statistically valid procedure with overall more accurate estimates, but it requires more intensive computations. Imputation methods based on generalized linear multilevel models can lead to biased subject-level variance estimates when the statistical analyses involve hierarchical models.

Beyond ANOVA and MANOVA for repeated measures: Advantages of generalized estimated equations and generalized linear mixed models and its use in neuroscience research

In neuroscience research, longitudinal data are often analysed using analysis of variance (ANOVA) and multivariate analysis of variance (MANOVA) for repeated measures (rmANOVA/rmMANOVA). However, these analyses have special requirements: The variances of the differences between all possible pairs of within-subject conditions (i.e., levels of the independent variable) must be equal. They are also limited to fixed repeated time intervals and are sensitive to missing data. In contrast, other models, such as the generalized estimating equations (GEE) and the generalized linear mixed models (GLMM), suggest another way to think about the data and the studied phenomenon. Instead of forcing the data into the ANOVAs assumptions, it is possible to design a flexible/personalized model according to the nature of the dependent variable. We discuss some advantages of GEE and GLMM as alternatives to rmANOVA and rmMANOVA in neuroscience research, including the possibility of using different distributions for the parameters of the dependent variable, a better approach for different time length points, and better adjustment to missing data. We illustrate these advantages by showing a comparison between rmANOVA and GEE in a real example and providing the data and a tutorial code to reproduce these analyses in R. We conclude that GEE and GLMM may provide more reliable results when compared to rmANOVA and rmMANOVA in neuroscience research, especially in small sample sizes with unbalanced longitudinal designs with or without missing data.

Methods for handling missing data in serially sampled sputum specimens for mycobacterial culture conversion calculation

BACKGROUND

The occurrence and timing of mycobacterial culture conversion is used as a proxy for tuberculosis treatment response. When researchers serially sample sputum during tuberculosis studies, contamination or missed visits leads to missing data points. Traditionally, this is managed by ignoring missing data or simple carry-forward techniques. Statistically advanced multiple imputation methods potentially decrease bias and retain sample size and statistical power.

METHODS

We analyzed data from 261 participants who provided weekly sputa for the first 12 weeks of tuberculosis treatment. We compared methods for handling missing data points in a longitudinal study with a time-to-event outcome. Our primary outcome was time to culture conversion, defined as two consecutive weeks with no Mycobacterium tuberculosis growth. Methods used to address missing data included: 1) available case analysis, 2) last observation carried forward, and 3) multiple imputation by fully conditional specification. For each method, we calculated the proportion culture converted and used survival analysis to estimate Kaplan-Meier curves, hazard ratios, and restricted mean survival times. We compared methods based on point estimates, confidence intervals, and conclusions to specific research questions.

RESULTS

The three missing data methods lead to differences in the number of participants achieving conversion; 78 (32.8%) participants converted with available case analysis, 154 (64.7%) converted with last observation carried forward, and 184 (77.1%) converted with multiple imputation. Multiple imputation resulted in smaller point estimates than simple approaches with narrower confidence intervals. The adjusted hazard ratio for smear negative participants was 3.4 (95% CI 2.3, 5.1) using multiple imputation compared to 5.2 (95% CI 3.1, 8.7) using last observation carried forward and 5.0 (95% CI 2.4, 10.6) using available case analysis.

CONCLUSION

We showed that accounting for missing sputum data through multiple imputation, a statistically valid approach under certain conditions, can lead to different conclusions than naïve methods. Careful consideration for how to handle missing data must be taken and be pre-specified prior to analysis. We used data from a TB study to demonstrate these concepts, however, the methods we described are broadly applicable to longitudinal missing data. We provide valuable statistical guidance and code for researchers to appropriately handle missing data in longitudinal studies.

Early ACLR and Risk and Timing of Secondary Meniscal Injury Compared With Delayed ACLR or Nonoperative Treatment: A Time-to-Event Analysis Using Machine Learning

BACKGROUND

Surgical and nonoperative management of anterior cruciate ligament (ACL) injuries seek to mitigate the risk of knee instability and secondary meniscal injury. However, the associated risk and timing of secondary meniscal tears have not been completely elucidated.

PURPOSE

To compare risk and timing of secondary meniscal injury between patients receiving nonoperative management, delayed ACL reconstruction (ACLR), and early ACLR using a machine learning survival analysis.

STUDY DESIGN

Cohort study; Level of evidence, 3.

METHODS

A geographic database was used to identify and review records of patients with a diagnosis of ACL rupture between 1990 and 2016 with minimum 2-year follow-up. Patients undergoing ACLR were matched 1:1 with nonoperatively treated controls. Rate and time to secondary meniscal tear were compared using random survival forest algorithms; independent models were developed and internally validated for predicting injury-free duration in both cohorts. Performance was measured using out-of-bag c-statistic, calibration, and Brier score. Model interpretability was enhanced using global variable importance and partial dependence curves.

RESULTS

The study included 1369 patients who underwent ACLR and 294 patients who had nonoperative treatment. After matching, no significant differences in rates of secondary meniscal tear were found (P = .09); subgroup analysis revealed the shortest periods of meniscal survival in patients undergoing delayed ACLR. The random survival forest algorithm achieved excellent predictive performance for the ACLR cohort, with an out-of-bag c-statistic of 0.80 and a Brier score of 0.11. Significant variables for risk of meniscal tear for the ACLR cohort included time to return to sports or activity ≤350 days, time to surgery ≥50 days, age at injury ≤40 years, and high-impact or rotational landing sports, whereas those in the nonoperative cohort model included time to RTS ≤200 days, visual analog scale pain score >3 at consultation, hypermobility, and noncontact sports.

CONCLUSION

Delayed ACLR demonstrated the greatest long-term risk of meniscal injury compared with nonoperative treatment or early ACLR. Risk factors for decreased meniscal survival after ACLR included increased time to surgery, shorter time to return to sports or activity, older age at injury, and involvement in high-impact or rotational landing sports. Pending careful external validation, these models may be deployed in the clinical space to provide real-time insights and enhance decision making.

The role of financial stress in mental health changes during COVID-19

Using longitudinal data before and during the first six months of the COVID-19 pandemic for a representative sample of Dutch households, we examined the role of financial stress, defined as the subjective experience of lacking financial resources to cope with demands, in mental health changes. Also, we examined financial stress and mental health relations with households' income, savings, and debts. The data revealed that average mental health did not change during the first six months of the pandemic but showed considerable underlying heterogeneity. Results showed that financial stress changes significantly explained this heterogeneity. Increases in financial stress predicted decreases in mental health, whereas decreases in financial stress predicted increases in mental health. While income did not explain financial stress changes, fewer savings and more debts were related to increased financial stress, which was, in turn, negatively related to mental health. We discuss the implications of our findings for mental health care and financial security policy and provide suggestions for future research.

Identifying Chronic Pain Subgroups in Pediatric Sickle Cell Disease: A Cluster-Analytic Approach

OBJECTIVES

Youth with sickle cell disease (SCD) and chronic pain, defined in this study as pain on most days for 3 months, experience variability in daily pain and physical and psychosocial functioning. This study aimed to (1) empirically derive chronic pain subgroups based on pain characteristics among youth with chronic SCD pain; and (2) investigate derived subgroups for differences in sociodemographics, clinical characteristics, and psychosocial and functional outcomes.

MATERIALS AND METHODS

Youth with chronic SCD pain (n=62, Mage =13.9, SD=2.5, 10 to 18 y; 58% female, 60% HbSS) completed a battery of questionnaires. Clinical characteristics (eg, medications, treatments) and health care utilization were abstracted from electronic medical records. Hierarchical cluster analysis informed the number of clusters at the patient level. k-means cluster analysis used multidimensional pain assessment to identify and assign patients to clusters.

RESULTS

Cluster 1 (n=35; Moderate Frequency, Moderate Pain) demonstrated significantly lower worst pain intensity, number of pain days per month, number of body sites affected by pain, and pain quality ratings. Cluster 2 (n=27; Almost Daily, High Pain) reported high ratings of worst pain intensity, almost daily to daily pain, greater number of body sites affected by pain, and higher ratings of pain quality (all P 's <0.05). There were no differences between subgroups by sociodemographics, clinical characteristics, or health care utilization. The Almost Daily, High Pain subgroup reported significantly higher pain interference, depressive symptoms, and pain catastrophizing than the Moderate Frequency, Moderate Pain subgroup.

DISCUSSION

Identifying chronic SCD pain subgroups may inform tailored assessment and intervention to mitigate poor pain and functional outcomes.

The handling of missing data in trial-based economic evaluations: should data be multiply imputed prior to longitudinal linear mixed-model analyses?

INTRODUCTION

For the analysis of clinical effects, multiple imputation (MI) of missing data were shown to be unnecessary when using longitudinal linear mixed-models (LLM). It remains unclear whether this also applies to trial-based economic evaluations. Therefore, this study aimed to assess whether MI is required prior to LLM when analyzing longitudinal cost and effect data.

METHODS

Two-thousand complete datasets were simulated containing five time points. Incomplete datasets were generated with 10, 25, and 50% missing data in follow-up costs and effects, assuming a Missing At Random (MAR) mechanism. Six different strategies were compared using empirical bias (EB), root-mean-squared error (RMSE), and coverage rate (CR). These strategies were: LLM alone (LLM) and MI with LLM (MI-LLM), and, as reference strategies, mean imputation with LLM (M-LLM), seemingly unrelated regression alone (SUR-CCA), MI with SUR (MI-SUR), and mean imputation with SUR (M-SUR).

RESULTS

For costs and effects, LLM, MI-LLM, and MI-SUR performed better than M-LLM, SUR-CCA, and M-SUR, with smaller EBs and RMSEs as well as CRs closers to nominal levels. However, even though LLM, MI-LLM and MI-SUR performed equally well for effects, MI-LLM and MI-SUR were found to perform better than LLM for costs at 10 and 25% missing data. At 50% missing data, all strategies resulted in relatively high EBs and RMSEs for costs.

CONCLUSION

LLM should be combined with MI when analyzing trial-based economic evaluation data. MI-SUR is more efficient and can also be used, but then an average intervention effect over time cannot be estimated.

Explaining gaps by parental education in children’s early language and social outcomes at age 3–4 years: evidence from harmonised data from three countries

Child outcomes vary by family’s socioeconomic status (SES). Research on explanatory factors underlying early SES-related disparities has mainly focused on specific child outcomes (e.g., language skills) and selected influencing factors in single countries often with a focus on individual differences but not explicitly on early SES-related gaps. This study uses harmonised data from longitudinal large-scale studies conducted in the United Kingdom, United States, and Germany to examine parental education-related gaps in early child language and social skills. Twelve theoretically proposed family-, child-, and childcare-related factors were systematically evaluated as explanatory factors. In all countries, parental education-related gaps were particularly pronounced for early child language compared to social skills. In the decomposition analyses, the home learning environment was the only measure that significantly explained gaps in all child outcomes across all countries. Early centre-based care attendance, family income, and maternal age at childbirth contributed to gaps in child outcomes with the specific pattern of results varying across outcomes and countries. Maternal depressive feelings significantly contributed only to explaining gaps in children’s social skills. Thus, while some mechanisms found to underpin early parental education-related gaps can be generalized from single-country, single-domain studies, others are outcome- and context-specific.

Development of a long-term time-weighted exposure metric that accounts for missing data in the Seychelles Child Development Study

In many studies of the health effects of toxicants, exposure is measured once even though exposure may be continuous. However, some studies collect repeated measurements on participants over an extended time with the goal of determining a long-term metric that captures the average or cumulative exposure. This can be challenging, especially when exposure is measured at irregular intervals and has some missing values. Here we describe a method for determining a measure of long-term exposure using data on postnatal mercury (Hg) from the Seychelles Child Development Study (SCDS) Main Cohort as a model. In this cohort (n = 779), we incorporate postnatal Hg values that were measured on most study participants at seven ages, three between 6 months and 5.5 years ("childhood"), and an additional four between 17 and 24 years ("early adulthood"). We develop time-weighted measures of average exposure during the childhood and the early adulthood periods and compare the strengths and weaknesses of our metric to two standard measures: overall average and cumulative exposure. We account for missing values through an imputation method that uses information about age- and sex-specific Hg means and the participant's Hg values at similar ages to estimate subject-specific missing Hg values. We compare our method to the implicit imputation assumed by these two standard methods, and to Fully Conditional Specification (FCS), an alternative method of imputing missing data. To determine the accuracy of our imputation method we use data from participants with no missing Hg values in the relevant time window. The imputed values from our proposed method are substantially closer to the observed values on average than the average or cumulative exposure, while also performing slightly better than FCS. In conclusion, time-weighted long-term exposure appears to offer advantages over cumulative exposure in longitudinal studies with repeated measures where the follow-up period for a toxicant is similar for all participants. Additionally, our method to impute missing values maximizes the number of participants for whom the overall exposure metric can be calculated and should provide a more accurate long-term exposure metric than standard methods when exposure has missing values. Our method is applicable to any study of long-term toxicant effects when longitudinal exposure measurements are available but have missing values.

Examining protective factors for substance use problems and self-harm behavior during adolescence: A longitudinal co-twin control study

Sports participation, physical activity, and friendship quality are theorized to have protective effects on the developmental emergence of substance use and self-harm behavior in adolescence, but existing research has been mixed. This ambiguity could reflect, in part, the potential for confounding of observed associations by genetic and environmental factors, which previous research has been unable to rigorously rule out. We used data from the prospective, population-based Child and Adolescent Twin Study in Sweden (n = 18,234 born 1994-2001) and applied a co-twin control design to account for potential genetic and environmental confounding of sports participation, physical activity, and friendship quality (assessed at age 15) as presumed protective factors for adolescent substance use and self-harm behavior (assessed at age 18). While confidence intervals widened to include the null in numerous co-twin control analyses adjusting for childhood psychopathology, parent-reported sports participation and twin-reported positive friendship quality were associated with increased odds of alcohol problems and nicotine use. However, parent-reported sports participation, twin-reported physical activity, and twin-reported friendship quality were associated with decreased odds of self-harm behavior. The findings provide a more nuanced understanding of the risks and benefits of putative protective factors for risky behaviors that emerge during adolescence.

Creating a developmental scale to chart the development of psychopathology with different informants and measures across time

Research Domain Criteria (RDoC) aims to advance a dimensional, multilevel understanding of psychopathology across the life span. Two key challenges exist in applying a developmental perspective to RDoC: First, the most accurate informants for assessing a person's psychopathology often differ across development (e.g., parents and teachers may be better informants of a person's externalizing problems in early childhood, whereas peer- and self-report may also be important to assess in adolescence). Second, many constructs change in their behavioral manifestation across development (i.e., heterotypic continuity). Thus, different informants and measures across time may be necessary to account for the construct's changing manifestation. The challenge of using different informants and measures of a construct across time is ensuring that the same construct is assessed in a comparable way across development. Vertical scaling creates a developmental scale to link scores from changing informants and measures to account for heterotypic continuity and study people's development of psychopathology across the life span. This is the first study that created a developmental scale to assess people's development by putting different informants and measures on the same scale. We examined the development of externalizing problems from ages 2 to 15 years (N = 1,364) using annual ratings by mothers, fathers, teachers, other caregivers, and self-report. The developmental scale linked different informants and measures on the same scale. This allowed us to chart people's growth trajectories and to identify multilevel risk factors, including poor verbal comprehension. Creating a developmental scale may be crucial to advance RDoC's goal of studying the development of psychopathology across the life span. (PsycInfo Database Record (c) 2022 APA, all rights reserved).