How do New Zealand youth perceive the smoke-free generation policy? A qualitative analysis

INTRODUCTION

Aotearoa New Zealand (NZ) plans to introduce a smoke-free generation (SFG) policy, alongside denicotinisation and reducing the availability of tobacco products. The SFG has a clear rationale, yet we know little about how young people, those the policy targets, perceive it. To inform policy design, communication and implementation, we explored how NZ youth perceived the SFG.

METHODS

We undertook in-depth interviews with a sample of 20 youth aged 17 or 18 and explored their knowledge of the SFG, and how they perceived its individual and societal implications. We interpreted the data using a reflexive thematic analysis approach.

RESULTS

We identified two overarching themes. The first theme, 'societal good and protection from harm', reflected benefits participants associated with the SFG, which outweighed perceptions of lost freedoms. The second theme, 'privileging personal choice', corresponded to two small groups within the sample. The first preferred measures they considered less restrictive, such as increasing the purchase age, and some came to support the SFG as they rationalised their views. The second subgroup expressed more entrenched opposition and felt the SFG deprived them of a choice.

CONCLUSIONS

Young people's deep reflection on the SFG led most to view it as liberating rather than restrictive. Communications that avoid prompting heuristic-based responses could encourage youth to reflect on the policy and elicit strong support from the group the SFG aims to benefit.

Retirement behavior of cancer survivors: role of health insurance

PURPOSE

Workers who rely on employment for health insurance may be unable to reduce work during and following treatment for a serious health condition, potentially harming their health in retirement. In this study, we examine the influence of retiree and employment-contingent insurance on the retirement and health of workers diagnosed with cancer.

METHODS

This longitudinal cohort study used 2000-2018 Health and Retirement Study data to examine changes in employment, weekly hours worked, and health status measures following a cancer diagnosis. We selected respondents who reported a new cancer diagnosis (n = 354) and a matched, non-cancer sample (n = 1770), restricting both samples to those employed and younger than age 63.

RESULTS

Following a cancer diagnosis, women with retiree health insurance were 18.6 percentage points less likely to work (95% CI: - 36.3 to - 1.0; p < 0.05) relative to women with employer health insurance, but no retiree insurance. Employed women with cancer but without employment-contingent health insurance increased weekly hours worked by 34% relative to similar non-cancer controls. Men and women with a cancer diagnosis and without employment-contingent health or retiree insurance were also less likely to work (p < 0.05). Among those who stopped working, respondents with cancer and employment-contingent health insurance reported better health status than respondents without employment-contingent health insurance.

CONCLUSIONS

Cancer survivors with employer and retiree health insurance leave the workforce earlier and report better health status when they stop working than those without equivalent insurance.

IMPLICATIONS FOR CANCER SURVIVORS

Policies to support health insurance outside of employment may allow cancer survivors to retire earlier and may have positive health benefits.

Cigarette prices and smoking among adults in eight sub-Saharan African countries: evidence from the Global Adult Tobacco Survey

BACKGROUND

Despite growing concern over tobacco use in sub-Saharan Africa, evidence on the association between cigarette prices and adult smoking behaviour in the region is limited.

OBJECTIVES

To provide new evidence on the association between cigarette prices and adult smoking in eight sub-Saharan African countries.

METHODS

The analysis uses data from 51 270 individuals taken from the Global Adult Tobacco Survey, which was conducted in eight African countries during 2012-2018. The relationship between prices and smoking is estimated using probit models for smoking participation and generalised linear models for conditional cigarette demand.

RESULTS

Higher prices are significantly associated with lower cigarette demand across African countries. The estimated price elasticity of participation is -0.362 (95% CI -0.547 to -0.177). The price elasticity of conditional cigarette demand is -0.133 (95% CI -0.194 to -0.072) for people who have just started smoking. The estimated total price elasticity of cigarette demand by new adult smokers is -0.495. The absolute value of the conditional demand elasticity becomes smaller by 0.004 units for each additional year that a person smokes. For the average smoker in the sample, with a smoking duration of 18.07 years, the total elasticity estimate is -0.422.

CONCLUSIONS

Higher cigarette prices significantly decrease the likelihood of smoking and decrease the intensity of cigarette consumption among African adults. Increases in the excise tax that increase the retail price of cigarettes will play an important role in reducing adult tobacco use on the continent. Governments are encouraged to increase excise taxes to improve public health.

A Stable EEG Epilepsy Network Spans From Infraslow to Ripple and From Interictal to Ictus

PURPOSE

To characterize the epilepsy network as reflected in intracranial electroencephalography (iEEG) across the full spectrum of iEEG frequencies and different phases of epilepsy, using a single, conceptually straightforward mathematical measure.

METHODS

The authors applied the spectral Granger causality techniques to intracranial electroencephalography recordings and computed contact-by-contact inward, outward, and total causal flow across frequencies and seizure phases in a selected group of three patients with well-defined, nonlesional seizure foci and prolonged responses to invasive procedures. One seizure and one interictal sample were analyzed per subject.

RESULTS

A prominent intracranial electroencephalography network was identified by Granger causality at both high and low frequencies. This network persists during the preictal and interictal phases of epilepsy and closely matches the visible seizure onset. The causal inflow network corresponded to seizure onset electrode contacts in 8 of 12 conditions, including ripple, infraslow, preictal, and interictal phases of epilepsy. Its most striking feature is the consistent dominance of causal inflow rather than outflow in the vicinity of the seizure onset zone.

CONCLUSIONS

Findings of this study indicate that a stable intracranial electroencephalography epilepsy network persists, and it can be characterized by a single Granger causality measure from infraslow to ripple frequencies and from the interictal to the immediate preictal phases of epilepsy.

Heterogeneous donor circles for fair liver transplant allocation

The United States (U.S.) Department of Health and Human Services is interested in increasing geographical equity in access to liver transplant. The geographical disparity in the U.S. is fundamentally an outcome of variation in the organ supply to patient demand (s/d) ratios across the country (which cannot be treated as a single unit due to its size). To design a fairer system, we develop a nonlinear integer programming model that allocates the organ supply in order to maximize the minimum s/d ratios across all transplant centers. We design circular donation regions that are able to address the issues raised in legal challenges to earlier organ distribution frameworks. This allows us to reformulate our model as a set-partitioning problem. Our policy can be viewed as a heterogeneous donor circle policy, where the integer program optimizes the radius of the circle around each donation location. Compared to the current policy, which has fixed radius circles around donation locations, the heterogeneous donor circle policy greatly improves both the worst s/d ratio and the range between the maximum and minimum s/d ratios. We found that with the fixed radius policy of 500 nautical miles (NM), the s/d ratio ranges from 0.37 to 0.84 at transplant centers, while with the heterogeneous circle policy capped at a maximum radius of 500 NM, the s/d ratio ranges from 0.55 to 0.60, closely matching the national s/d ratio average of 0.5983. Our model matches the supply and demand in a more equitable fashion than existing policies and has a significant potential to improve the liver transplantation landscape.

The smoking population is not hardening in South Korea: a study using the Korea Community Health Survey from 2010 to 2018

INTRODUCTION

The hardening hypothesis proposes that the proportion of hardcore smokers increases when smoking prevalence declines. To evaluate whether such hardening occurs in South Korea, we examined the association between quitting behaviours, the number of cigarettes smoked per day and the proportion of hardcore smokers and smoking prevalence among local districts in South Korea.

METHODS

This study used the cross-sectional data from the Korea Community Health Survey (2010-2018) to examine local district-level associations between smoking prevalence and quit attempts, quit plans, quit ratios, cigarettes smoked per day and the proportion of hardcore smokers. Panel regression analysis was performed using the indicators of hardcore smoking (quit attempts, quit plans, quit ratios, cigarettes smoked per day and proportion of hardcore smokers) as the outcome variables, and prevalence of smoking, local districts, age and sex as predictor variables.

RESULTS

When the smoking prevalence of the districts decreased by 1%, quit attempts, quit plans and quit ratios increased by 0.24% (95% CI 0.11 to 0.37), 0.37% (95% CI 0.26 to 0.47) and 1.71% (95% CI 1.65 to 1.76), respectively. Cigarette consumption decreased by 0.17 cigarettes per day (95% 0.15 to 0.19), and the prevalence of hardcore smokers decreased by 0.88% (95% CI 0.78 to 0.98) when smoking prevalence decreased by 1%.

CONCLUSION

Hardening of smoking did not occur in South Korea when smoking prevalence declined, which suggests tobacco control policies in South Korea have been effective in reducing smoking prevalence without increasing the proportion of hardcore smokers.

The many foibles of Likert scales challenge claims that self-report measures of self-control are better than performance-based measures

Self-control and executive functioning are often treated as highly related psychological constructs. However, measures of each rarely correlate with one another. This reflects some combination of true separability between the constructs and measurement differences. Traditionally, executive functioning is objectively measured as performance on computer-controlled tasks in the laboratory, whereas self-control is subjectively measured with self-report scales of predispositions and behaviors in everyday life. Self-report measures tend to better predict outcomes that should be affected by individual differences in control. Our two studies show that the original version of Tangney, Baumeister, and Boone's brief self-control scale (consisting of four positive and nine negative items) strongly correlates with self-esteem, mental health, fluid intelligence, but only weakly with satisfaction with life and happiness. Four variants of the original scale were created by reverse-wording the 13 original items and recombining them to form, for example, versions with all positive or all negative items. As the proportion of items with positive valence increased: (1) the outcomes with strong correlations in the original scale weakened and the weak correlations strengthened and (2) the mean overall scores increased. Both studies replicated a common finding that the original scale yields two factors in an exploratory factor analysis. However, the second factor is generated by method differences, namely, having items with both positive and negative valence. The second factor is induced by the common practice of reverse-coding the items with negative valence and the faulty assumption that Likert scales are equal-interval scales with a neutral-point at midscale.

Racial, Ethnic, and Regional Disparities in Cocaine-Involved Overdose Deaths in the US, 1999-2020

BACKGROUND

Social inequalities among underrepresented communities may lead to higher overdose mortality involving cocaine use. We assessed the temporal trends in cocaine-involved overdose mortality rate in the US by race, ethnicity, and geographic region from 1999 to 2020.

METHODS

We conducted a cross-sectional study among adults in the US using data from the Centers for Disease Control and Prevention Wide-Ranging Online Data for Epidemiologic Research database (1999 to 2020). To identify cocaine-involved overdose decedents, we used the International Classification of Diseases Code, 10th Revision-T40.5. We used Joinpoint regression to examine the trends in age-adjusted cocaine-involved overdose mortality rates (AAMR) by race, ethnicity, and geographic region and estimated annual percentage changes (APC).

RESULTS

Overall, cocaine-involved overdose mortality trends increased (APC, 11.3%; 95% CI, 0.6, 23.2) from 2017 to 2020. The latest trends have remained stable among Non-Hispanic Whites since 2017 (APC, 4.3%; 95% CI, -5.7%, 15.4%) but have significantly increased among Non-Hispanic Blacks (APC, 27.2%; 95% CI, 22.1%, 32.5%), Hispanics (APC, 26.9%; 95% CI, 20.6%, 33.5%), and American Indians/Alaska Natives (APC, 24.1%; 95% CI, 16.5%, 32.2%).

CONCLUSION

Cocaine-related overdose deaths in the US significantly increased between 2017 and 2020, but the increase was among racial and ethnic minorities and not among Non-Hispanic Whites. These findings suggest a need to address the US' longstanding racial and ethnic healthcare inequities.

Taking stock of the past: A psychometric evaluation of the Autobiographical Interview

Autobiographical memory (AM) involves a rich phenomenological re-experiencing of a spatio-temporal event from the past, which is challenging to objectively quantify. The Autobiographical Interview (AI; Levine et al. Psychology and Aging, 17(4), 677-689, 2002) is a manualized performance-based assessment designed to quantify episodic (internal) and semantic (external) features of recalled and verbally conveyed prior experiences. The AI has been widely adopted, yet has not undergone a comprehensive psychometric validation. We investigated the reliability, validity, association to individual differences measures, and factor structure in healthy younger and older adults (N = 352). Evidence for the AI's reliability was strong: the subjective scoring protocol showed high inter-rater reliability and previously identified age effects were replicated. Internal consistency across timepoints was robust, suggesting stability in recollection. Central to our validation, internal AI scores were positively correlated with standard, performance-based measures of episodic memory, demonstrating convergent validity. The two-factor structure for the AI was not well supported by confirmatory factor analysis. Adjusting internal and external detail scores for the number of words spoken (detail density) improved trait estimation of AM performance. Overall, the AI demonstrated sound psychometric properties for inquiry into the qualities of autobiographical remembering.

Developmental cascade models linking contextual risks, parenting, and internalizing symptoms: A 17-year longitudinal study from early childhood to emerging adulthood

Although internalizing problems are the most common forms of psychological distress among adolescents and young adults, they have precursors in multiple risk domains established during childhood. This study examined cascading risk pathways leading to depression and anxiety symptoms in emerging adulthood by integrating broad contextual (i.e., multiple contextual risks), parental (i.e., negative parenting), and child (i.e., internalizing behaviors) characteristics in early and middle childhood. We also compared common and differential pathways to depression and anxiety symptoms depending on the conceptualization of symptom outcomes (traditional symptom dimension vs. bifactor dimensional model). Participants were 235 children (109 girls) and their families. Data were collected at 3, 6, 10, and 19 years of child age, using multiple informants and contexts. Results from a symptom dimension approach indicated mediation pathways from early childhood risk factors to depression and anxiety symptoms in emerging adulthood, suggesting common and distinct risk processes between the two disorders. Results from a bifactor modeling approach indicated several indirect pathways leading to a general internalizing latent factor, but not to symptom-specific (i.e., depression, anxiety) latent factors. Our findings highlighted comparative analytic approaches to examining transactional processes associated with later internalizing symptoms and shed light on issues of early identification and prevention.

Oral health promotion and labour market prospects of socially disadvantaged and unemployed people - a randomised controlled trial

BACKGROUND

Previous studies indicate that poor oral health may constitute a barrier for labour market success. This study examines whether an oral health promotion intervention has an effect on economic self-support, and proximity to the labour market, among socially disadvantaged unemployed people.

METHODS

From April-June 2018, we enrolled 273 vulnerable people on welfare benefits into a randomised controlled trial. Participants were allocated to either control (n=159), or intervention (n=114). Intervention consisted of individual support to improve individuals' oral health by reducing significant barriers to dental care. Ten participants were excluded, leaving 263 participants (intervention n=110, control n=153) for analyses. A national register was used to assess economic self-support and proximity to the labour market within 1.5 years post-intervention. Items of information on health and socio-demographics were obtained from a questionnaire and from national registers. Logistic and multiple linear regression modelling was performed.

RESULTS

Overall, higher frequencies of economic self-support and proximity to the labour market were found in the intervention group, although no significant results were seen for economic self-support alone. Adjusted models revealed significantly higher odds (odds ratio=1.85, 95% confidence interval 1.08-3.17) among the intervention group of achieving proximity to the labour market after 12 months. After 18 months, the intervention group had on average obtained 5 more weeks (95% confidence interval 0.02-9.99) with proximity to the labour market.

CONCLUSIONS

Overall, the oral health promotion intervention had a positive effect on proximity to the labour market in the intervention group, especially within the first year of the study. Our findings suggest that interventions promoting oral health among socially disadvantaged and unemployed groups may contribute to social rehabilitation.

Tracking COVID-19 vaccination expectancies and vaccination refusal in the United States

To identify factors that predict COVID-19 vaccination refusal and show how expectancies affect vaccination acceptance for non-vaccinated adults, we used a monthly repeated cross-sectional sample from June/2021 to October/2021 to collect data on vaccination behaviors and predictor variables for 2,116 US adults over 50 years of age. Selection bias modeling - which is required when data availability is a result of behavioral choice - predicts two outcomes: (1) no vaccination vs. vaccination for the entire sample and (2) the effects of expectancy indices predicting vaccination Refuser vs. vaccination Accepters for the unvaccinated group. Vaccine refusers were younger and less educated, endorsed common misconceptions about the COVID-19 epidemic, and were Black. Vaccination expectancies were related to vaccination refusal in the unvaccinated eligible group: negative expectancies increased vaccine refusal, while positive expectancies decreased it. We conclude that behavior-related expectancies (as opposed to more stable psychological traits) are important to identify because they are often modifiable and provide a point of intervention, not just for COVID-19 vaccination acceptance but also for other positive health behaviors.

Measuring self-regulation in everyday life: Reliability and validity of smartphone-based experiments in alcohol use disorder

Self-regulation, the ability to guide behavior according to one's goals, plays an integral role in understanding loss of control over unwanted behaviors, for example in alcohol use disorder (AUD). Yet, experimental tasks that measure processes underlying self-regulation are not easy to deploy in contexts where such behaviors usually occur, namely outside the laboratory, and in clinical populations such as people with AUD. Moreover, lab-based tasks have been criticized for poor test-retest reliability and lack of construct validity. Smartphones can be used to deploy tasks in the field, but often require shorter versions of tasks, which may further decrease reliability. Here, we show that combining smartphone-based tasks with joint hierarchical modeling of longitudinal data can overcome at least some of these shortcomings. We test four short smartphone-based tasks outside the laboratory in a large sample (N = 488) of participants with AUD. Although task measures indeed have low reliability when data are analyzed traditionally by modeling each session separately, joint modeling of longitudinal data increases reliability to good and oftentimes excellent levels. We next test the measures' construct validity and show that extracted latent factors are indeed in line with theoretical accounts of cognitive control and decision-making. Finally, we demonstrate that a resulting cognitive control factor relates to a real-life measure of drinking behavior and yields stronger correlations than single measures based on traditional analyses. Our findings demonstrate how short, smartphone-based task measures, when analyzed with joint hierarchical modeling and latent factor analysis, can overcome frequently reported shortcomings of experimental tasks.

Combining observational and experimental datasets using shrinkage estimators

We consider the problem of combining data from observational and experimental sources to draw causal conclusions. To derive combined estimators with desirable properties, we extend results from the Stein shrinkage literature. Our contributions are threefold. First, we propose a generic procedure for deriving shrinkage estimators in this setting, making use of a generalized unbiased risk estimate. Second, we develop two new estimators, prove finite sample conditions under which they have lower risk than an estimator using only experimental data, and show that each achieves a notion of asymptotic optimality. Third, we draw connections between our approach and results in sensitivity analysis, including proposing a method for evaluating the feasibility of our estimators.

Prostate volume, baseline urinary function, and their association with treatment choice and post-treatment urinary function in men treated for localized prostate cancer

BACKGROUND

Benign prostatic hyperplasia, lower urinary tract symptoms, and prostate cancer often co-occur. Their effect on urinary function is an important consideration regarding prostate cancer treatment choices. While prostate volume (PV) and urinary symptoms are commonly used in treatment choice decision making, their association with post-treatment urinary function is unknown. We evaluated the associations between PV and baseline urinary function with treatment choice and post-treatment urinary function among men with localized prostate cancer.

METHODS

We identified 1647 patients from CEASAR, a multicenter population-based, prospective cohort study of men with localized prostate cancer, for analysis. Primary outcomes were treatment choice and health-related quality of life (HRQOL) assessed by the 26-item Expanded Prostate Index Composite (EPIC-26) at pre-specified intervals up to 5 years. Multivariable analysis was performed, controlling for demographic and clinicopathologic features.

RESULTS

Median baseline PV was 36 mL (IQR 27-48), and baseline urinary irritative/obstructive domain score was 87 (IQR 75-100). There was no observed clinically meaningful association between PV and treatment choice or post-treatment urinary function. Among patients with poor baseline urinary function, treatment with radiation or surgery was associated with statistically and clinically significant improvement in urinary function at 6 months which was durable through 5 years (improvement from baseline at 5 years: radiation 20.4 points, surgery 24.5 points).

CONCLUSIONS

PV was not found to be associated with treatment modality or post-treatment urinary irritative/obstructive function among men treated for localized prostate cancer. Men with poor baseline urinary irritative/obstructive function improve after treatment with surgery or radiation therapy.

Cross-direct effects in settings with two mediators

When multiple mediators are present, there are additional effects that may be of interest beyond the well-known natural (NDE) and controlled direct effects (CDE). These effects cross the type of control on the mediators, setting one to a constant level and one to its natural level, which differs across subjects. We introduce five such estimands for the cross-CDE and -NDE when two mediators are measured. We consider both the scenario where one mediator is influenced by the other, referred to as sequential mediators, and the scenario where the mediators do not influence each other. Such estimands may be of interest in immunology, as we discuss in relation to measured immunological responses to SARS-CoV-2 vaccination. We provide identifying expressions for the estimands in observational settings where there is no residual confounding, and where intervention, outcome, and mediators are of arbitrary type. We further provide tight symbolic bounds for the estimands in randomized settings where there may be residual confounding of the outcome and mediator relationship and all measured variables are binary.

A flexible approach for predictive biomarker discovery

An endeavor central to precision medicine is predictive biomarker discovery; they define patient subpopulations which stand to benefit most, or least, from a given treatment. The identification of these biomarkers is often the byproduct of the related but fundamentally different task of treatment rule estimation. Using treatment rule estimation methods to identify predictive biomarkers in clinical trials where the number of covariates exceeds the number of participants often results in high false discovery rates. The higher than expected number of false positives translates to wasted resources when conducting follow-up experiments for drug target identification and diagnostic assay development. Patient outcomes are in turn negatively affected. We propose a variable importance parameter for directly assessing the importance of potentially predictive biomarkers and develop a flexible nonparametric inference procedure for this estimand. We prove that our estimator is double robust and asymptotically linear under loose conditions in the data-generating process, permitting valid inference about the importance metric. The statistical guarantees of the method are verified in a thorough simulation study representative of randomized control trials with moderate and high-dimensional covariate vectors. Our procedure is then used to discover predictive biomarkers from among the tumor gene expression data of metastatic renal cell carcinoma patients enrolled in recently completed clinical trials. We find that our approach more readily discerns predictive from nonpredictive biomarkers than procedures whose primary purpose is treatment rule estimation. An open-source software implementation of the methodology, the uniCATE R package, is briefly introduced.

Factors associated with postsurgical muscle weakness in patients who undergo thoracic aortic surgery: a retrospective cohort study

OBJECTIVE

Aortic surgery is often performed in elderly patients, and these patients have a high risk of postsurgical muscle weakness. To reinforce purposeful postsurgical rehabilitation, we aimed to investigate the factors associated with postsurgical muscle weakness in patients who underwent thoracic aortic surgery.

METHODS

This retrospective cohort study analyzed data of consecutive patients who underwent elective thoracic aortic surgery with cardiopulmonary bypass, and whose knee extensor isometric muscle strength (KEIS) were measured pre- and postoperatively at University Hospital between January 2012 and December 2018. The primary outcome was percent change in KEIS (% change in KEIS). Multivariate linear regression analysis was used to identify independent risk factors for % change in KEIS.

RESULTS

Overall, 218 patients were included. Multivariate linear regression analysis showed that mechanical ventilation time, days from initial sitting to 100 m walking, and the number of exercises in the rehabilitation room were associated with % change in KEIS.

CONCLUSIONS

This study may serve as a reference to stratify patients at risk of postsurgical muscle weakness. The preventive or alternative interventions in patients undergoing thoracic aortic surgery will be assessed in future studies.

The effects of sound in the Balloon Analogue Risk Task

In this study, we examined the effects of pairing sounds with positive and negative outcomes in the Balloon Analogue Risk Task (BART). A number of published studies using the BART incorporate sounds into the task, where a slot machine or cash register sound is produced when rewards are collected and a popping sound is produced when balloons pop. However, some studies do not use sound, and other studies do not specify whether sound was used. Given that sensory information contributes to the intensity of experiences, it is possible that outcome-related sounds in the BART influence risk-taking behaviors, and inconsistent use of sounds across the many BART variations may affect how results are interpreted. Therefore, the purpose of this study was to investigate the effects of sounds paired with outcomes in the BART, and whether the presence or valence of a sound would systematically alter participants' risk-taking. Across two experiments using Bayesian censored regressions, we show that sounds, regardless of the outcomes they were paired with or their valence, did not affect risk-taking in an adult, non-clinical sample. We consider the implications of these results within methodological and theoretical contexts and encourage researchers to continue dissociating the role of auditory stimuli in feedback processing and subsequent responding.

Estimating individual treatment effects on COPD exacerbations by causal machine learning on randomised controlled trials

RATIONALE

Estimating the causal effect of an intervention at individual level, also called individual treatment effect (ITE), may help in identifying response prior to the intervention.

OBJECTIVES

We aimed to develop machine learning (ML) models which estimate ITE of an intervention using data from randomised controlled trials and illustrate this approach with prediction of ITE on annual chronic obstructive pulmonary disease (COPD) exacerbation rates.

METHODS

We used data from 8151 patients with COPD of the Study to Understand Mortality and MorbidITy in COPD (SUMMIT) trial (NCT01313676) to address the ITE of fluticasone furoate/vilanterol (FF/VI) versus control (placebo) on exacerbation rate and developed a novel metric, Q-score, for assessing the power of causal inference models. We then validated the methodology on 5990 subjects from the InforMing the PAthway of COPD Treatment (IMPACT) trial (NCT02164513) to estimate the ITE of FF/umeclidinium/VI (FF/UMEC/VI) versus UMEC/VI on exacerbation rate. We used Causal Forest as causal inference model.

RESULTS

In SUMMIT, Causal Forest was optimised on the training set (n=5705) and tested on 2446 subjects (Q-score 0.61). In IMPACT, Causal Forest was optimised on 4193 subjects in the training set and tested on 1797 individuals (Q-score 0.21). In both trials, the quantiles of patients with the strongest ITE consistently demonstrated the largest reductions in observed exacerbations rates (0.54 and 0.53, p<0.001). Poor lung function and blood eosinophils, respectively, were the strongest predictors of ITE.

CONCLUSIONS

This study shows that ML models for causal inference can be used to identify individual response to different COPD treatments and highlight treatment traits. Such models could become clinically useful tools for individual treatment decisions in COPD.

Comparison of a multidomain frailty index from routine health data with the hospital frailty risk score in older patients in an Australian hospital

BACKGROUND

Frailty is an important determinant of health-care needs and outcomes for people in hospital.

OBJECTIVES

To compare characteristics and predictive ability of a multidomain frailty index derived from routine health data (electronic frailty index-acute hospital; eFI-AH) with the hospital frailty risk score (HFRS).

METHODS

This retrospective study included 6771 patients aged ≥75 years admitted to an Australian metropolitan tertiary referral hospital between October 2019 and September 2020. The eFI-AH and the HFRS were calculated for each patient and compared with respect to characteristics, agreement, association with age and ability to predict outcomes.

RESULTS

Median eFI-AH was 0.17 (range 0-0.66) whilst median HFRS was 3.2 (range 0-42.9). Moderate agreement was shown between the tools (Pearson's r 0.61). After adjusting for age and gender, both models had associations with long hospital stay, in-hospital mortality, unplanned all-cause readmission and fall-related readmission. Specifically, the eFI-AH had the strongest association with in-hospital mortality (adjusted odds ratio (aOR) 2.81, 95% confidence intervals (CI) 2.49-3.17), whilst the HFRS was most strongly associated with long hospital stay (aOR 1.20, 95% CI 1.18-1.21). Both tools predicted hospital stay >10 days with good discrimination and calibration.

CONCLUSIONS

Although the eFI-AH and the HFRS did not consistently identify the same inpatients as frail, both were associated with adverse outcomes and they had comparable predictive ability for prolonged hospitalisation. These two constructs of frailty may have different implications for clinical practice and health service provision and planning.

Understanding guilt-related interpersonal dysfunction in obsessive-compulsive personality disorder through computational modeling of two social interaction tasks

BACKGROUND

Obsessive-compulsive personality disorder (OCPD) is a high-prevalence personality disorder characterized by subtle but stable interpersonal dysfunction. There have been only limited studies addressing the behavioral patterns and cognitive features of OCPD in interpersonal contexts. The purpose of this study was to investigate how behaviors differ between OCPD individuals and healthy controls (HCs) in the context of guilt-related interpersonal responses.

METHOD

A total of 113 participants were recruited, including 46 who were identified as having OCPD and 67 HCs. Guilt-related interpersonal responses were manipulated and measured with two social interactive tasks: the Guilt Aversion Task, to assess how anticipatory guilt motivates cooperation; and the Guilt Compensation Task, to assess how experienced guilt induces compensation behaviors. The guilt aversion model and Fehr-Schmidt inequity aversion model were adopted to analyze decision-making in the Guilt Aversion Task and the Guilt Compensation Task, respectively.

RESULTS

Computational model-based results demonstrated that, compared with HCs, the OCPD group exhibited less guilt aversion when making cooperative decisions as well as less guilt-induced compensation after harming others.

CONCLUSION

Our findings indicate that individuals with OCPD tend to be less affected by guilt than HCs. These impairments in guilt-related responses may prevent adjustments in behaviors toward compliance with social norms and thus result in interpersonal dysfunctions.

Double reduction estimation and equilibrium tests in natural autopolyploid populations

Many bioinformatics pipelines include tests for equilibrium. Tests for diploids are well studied and widely available, but extending these approaches to autopolyploids is hampered by the presence of double reduction, the comigration of sister chromatid segments into the same gamete during meiosis. Though a hindrance for equilibrium tests, double reduction rates are quantities of interest in their own right, as they provide insights about the meiotic behavior of autopolyploid organisms. Here, we develop procedures to (i) test for equilibrium while accounting for double reduction, and (ii) estimate the double reduction rate given equilibrium. To do so, we take two approaches: a likelihood approach, and a novel U-statistic minimization approach that we show generalizes the classical equilibrium χ2 test in diploids. For small sample sizes and uncertain genotypes, we further develop a bootstrap procedure based on our U-statistic to test for equilibrium. We validate our methods on both simulated and real data.

Nonparametric scanning tests of homogeneity for hierarchical models with continuous covariates

In many applications of hierarchical models, there is often interest in evaluating the inherent heterogeneity in view of observed data. When the underlying hypothesis involves parameters resting on the boundary of their support space such as variances and mixture proportions, it is a usual practice to entertain testing procedures that rely on common heterogeneity assumptions. Such procedures, albeit omnibus for general alternatives, may entail a substantial loss of power for specific alternatives such as heterogeneity varying with covariates. We introduce a novel and flexible approach that uses covariate information to improve the power to detect heterogeneity, without imposing unnecessary restrictions. With continuous covariates, the approach does not impose a regression model relating heterogeneity parameters to covariates or rely on arbitrary discretizations. Instead, a scanning approach requiring continuous dichotomizations of the covariates is proposed. Empirical processes resulting from these dichotomizations are then used to construct the test statistics, with limiting null distributions shown to be functionals of tight random processes. We illustrate our proposals and results on a popular class of two-component mixture models, followed by simulation studies and applications to two real datasets in cancer and caries research.

Grouped generalized estimating equations for longitudinal data analysis

Generalized estimating equation (GEE) is widely adopted for regression modeling for longitudinal data, taking account of potential correlations within the same subjects. Although the standard GEE assumes common regression coefficients among all the subjects, such an assumption may not be realistic when there is potential heterogeneity in regression coefficients among subjects. In this paper, we develop a flexible and interpretable approach, called grouped GEE analysis, to modeling longitudinal data with allowing heterogeneity in regression coefficients. The proposed method assumes that the subjects are divided into a finite number of groups and subjects within the same group share the same regression coefficient. We provide a simple algorithm for grouping subjects and estimating the regression coefficients simultaneously, and show the asymptotic properties of the proposed estimator. The number of groups can be determined by the cross validation with averaging method. We demonstrate the proposed method through simulation studies and an application to a real data set.

Bayesian hierarchical quantile regression with application to characterizing the immune architecture of lung cancer

The successful development and implementation of precision immuno-oncology therapies requires a deeper understanding of the immune architecture at a patient level. T-cell receptor (TCR) repertoire sequencing is a relatively new technology that enables monitoring of T-cells, a subset of immune cells that play a central role in modulating immune response. These immunologic relationships are complex and are governed by various distributional aspects of an individual patient's tumor profile. We propose Bayesian QUANTIle regression for hierarchical COvariates (QUANTICO) that allows simultaneous modeling of hierarchical relationships between multilevel covariates, conducts explicit variable selection, estimates quantile and patient-specific coefficient effects, to induce individualized inference. We show QUANTICO outperforms existing approaches in multiple simulation scenarios. We demonstrate the utility of QUANTICO to investigate the effect of TCR variables on immune response in a cohort of lung cancer patients. At population level, our analyses reveal the mechanistic role of T-cell proportion on the immune cell abundance, with tumor mutation burden as an important factor modulating this relationship. At a patient level, we find several outlier patients based on their quantile-specific coefficient functions, who have higher mutational rates and different smoking history.

Mendelian randomization mixed-scale treatment effect robust identification and estimation for causal inference

Standard Mendelian randomization (MR) analysis can produce biased results if the genetic variant defining an instrumental variable (IV) is confounded and/or has a horizontal pleiotropic effect on the outcome of interest not mediated by the treatment variable. We provide novel identification conditions for the causal effect of a treatment in the presence of unmeasured confounding by leveraging a possibly invalid IV for which both the IV independence and exclusion restriction assumptions may be violated. The proposed Mendelian randomization mixed-scale treatment effect robust identification (MR MiSTERI) approach relies on (i) an assumption that the treatment effect does not vary with the possibly invalid IV on the additive scale; (ii) that the confounding bias does not vary with the possibly invalid IV on the odds ratio scale; and (iii) that the residual variance for the outcome is heteroskedastic with respect to the possibly invalid IV. Although assumptions (i) and (ii) have, respectively, appeared in the IV literature, assumption (iii) has not; we formally establish that their conjunction can identify a causal effect even with an invalid IV. MR MiSTERI is shown to be particularly advantageous in the presence of pervasive heterogeneity of pleiotropic effects on the additive scale. We propose a simple and consistent three-stage estimator that can be used as a preliminary estimator to a carefully constructed efficient one-step-update estimator. In order to incorporate multiple, possibly correlated, and weak invalid IVs, a common challenge in MR studies, we develop a MAny Weak Invalid Instruments (MR MaWII MiSTERI) approach for strengthened identification and improved estimation accuracy. Both simulation studies and UK Biobank data analysis results demonstrate the robustness of the proposed methods.

Semiparametric distributed lag quantile regression for modeling time-dependent exposure mixtures

Studying time-dependent exposure mixtures has gained increasing attentions in environmental health research. When a scalar outcome is of interest, distributed lag (DL) models have been employed to characterize the exposures effects distributed over time on the mean of final outcome. However, there is a methodological gap on investigating time-dependent exposure mixtures with different quantiles of outcome. In this paper, we introduce semiparametric partial-linear single-index (PLSI) DL quantile regression, which can describe the DL effects of time-dependent exposure mixtures on different quantiles of outcome and identify susceptible periods of exposures. We consider two time-dependent exposure settings: discrete and functional, when exposures are measured in a small number of time points and at dense time grids, respectively. Spline techniques are used to approximate the nonparametric DL function and single-index link function, and a profile estimation algorithm is proposed. Through extensive simulations, we demonstrate the performance and value of our proposed models and inference procedures. We further apply the proposed methods to study the effects of maternal exposures to ambient air pollutants of fine particulate and nitrogen dioxide on birth weight in New York University Children's Health and Environment Study (NYU CHES).

An Empirical Study of the Pathway Model Link Between Cognitive Distortions and Gambling Problems

This research empirically tests the relationship between gambling-related cognitive distortions and the development of gambling problems. In two separate studies using methodologies designed to support non-experimental causal inference, we demonstrate that holding false beliefs about gambling experiences is related to current and future risk of developing problems with gambling. In our first study, we use an instrumental variable estimation strategy on an internet sample (n = 184) and observe a statistically significant relationship between Gamblers' Belief Questionnaire scores and measures of loss chasing, overspending, and gambling problems. These findings were robust to linear and ordinal estimation strategies and multiple model specifications. In our second study, we examine five-year prospective longitudinal data (n = 1,431) to validate our initial findings and test whether irrational thoughts are also related to future problems with gambling. While controlling for current fallacies, we find that past Gambling Fallacies Measure scores are related to present gambling problems across two survey waves. The effect size of each of the past fallacy levels is roughly half of the effect size of present levels, suggesting meaningful impacts. Our findings support the Pathways Model of Problem and Pathological Gambling.

Neighbourhood cohesion, loneliness and perceived social isolation prior and during the COVID-19 pandemic. Longitudinal evidence from the German Ageing Survey

PURPOSE

To investigate the longitudinal association between neighbourhood cohesion and loneliness as well as perceived social isolation prior and during the COVID-19 pandemic (stratified by sex).

METHODS

Longitudinal data were taken from a nationally representative sample (German Ageing Survey) of inhabitants aged 40 years and over in Germany prior (wave 6: year 2017) and during the COVID-19 pandemic (wave 8: November 2020 until February 2021; n = 6688 observations, mean age was 67.4 years). The De Jong Gierveld tool was used to measure loneliness and the Bude and Lantermann tool was used to measure perceived social isolation. Neighbourhood cohesion was assessed based on different items.

RESULTS

FE regressions showed that decreases in closeness of contact with neighbours were associated with increases in loneliness and perceived social isolation levels among men, but not women. In contrast, decreases in different indicators of involvement in neighbourhood activities were associated with increases in loneliness and perceived social isolation levels among women, but not men.

CONCLUSION

Changes in neighbourhood factors are differently associated with loneliness and perceived social isolation among middle-aged and older women and men. Gender-specific efforts to avoid loneliness and social isolation are, therefore, needed.

An information ratio-based goodness-of-fit test for copula models on censored data

Copula is a popular method for modeling the dependence among marginal distributions in multivariate censored data. As many copula models are available, it is essential to check if the chosen copula model fits the data well for analysis. Existing approaches to testing the fitness of copula models are mainly for complete or right-censored data. No formal goodness-of-fit (GOF) test exists for interval-censored or recurrent events data. We develop a general GOF test for copula-based survival models using the information ratio (IR) to address this research gap. It can be applied to any copula family with a parametric form, such as the frequently used Archimedean, Gaussian, and D-vine families. The test statistic is easy to calculate, and the test procedure is straightforward to implement. We establish the asymptotic properties of the test statistic. The simulation results show that the proposed test controls the type-I error well and achieves adequate power when the dependence strength is moderate to high. Finally, we apply our method to test various copula models in analyzing multiple real datasets. Our method consistently separates different copula models for all these datasets in terms of model fitness.

Change-plane analysis for subgroup detection with a continuous treatment

Detecting and characterizing subgroups with differential effects of a binary treatment has been widely studied and led to improvements in patient outcomes and population risk management. Under the setting of a continuous treatment, however, such investigations remain scarce. We propose a semiparametric change-plane model and consequently a doubly robust test statistic for assessing the existence of two subgroups with differential treatment effects under a continuous treatment. The proposed testing procedure is valid when either the baseline function for the covariate effects or the generalized propensity score function for the continuous treatment is correctly specified. The asymptotic distributions of the test statistic under the null and local alternative hypotheses are established. When the null hypothesis of no subgroup is rejected, the change-plane parameters that define the subgroups can be estimated. This paper provides a unified framework of the change-plane method to handle various types of outcomes, including the exponential family of distributions and time-to-event outcomes. Additional extensions with nonparametric estimation approaches are also provided. We evaluate the performance of our proposed methods through extensive simulation studies under various scenarios. An application to the Health Effects of Arsenic Longitudinal Study with a continuous environmental exposure of arsenic is presented.

Quantile regression for nonignorable missing data with its application of analyzing electronic medical records

Over the past decade, there has been growing enthusiasm for using electronic medical records (EMRs) for biomedical research. Quantile regression estimates distributional associations, providing unique insights into the intricacies and heterogeneity of the EMR data. However, the widespread nonignorable missing observations in EMR often obscure the true associations and challenge its potential for robust biomedical discoveries. We propose a novel method to estimate the covariate effects in the presence of nonignorable missing responses under quantile regression. This method imposes no parametric specifications on response distributions, which subtly uses implicit distributions induced by the corresponding quantile regression models. We show that the proposed estimator is consistent and asymptotically normal. We also provide an efficient algorithm to obtain the proposed estimate and a randomly weighted bootstrap approach for statistical inferences. Numerical studies, including an empirical analysis of real-world EMR data, are used to assess the proposed method's finite-sample performance compared to existing literature.

Reward advantage over punishment for incentivizing visual working memory

The prospects of gaining reward and avoiding punishment widely influence human behavior. Despite of numerous attempts to investigate the influence of motivational signals on working memory (WM), whether the valence and the magnitude of motivational signals interactively influence WM performance remains unclear. To investigate this, the present study used a free-recall working memory task with EEG recording to compare the effect of incentive valence (reward or punishment), as well as the magnitude of incentives on visual WM. Behavioral results revealed that the presence of incentive signals improved WM precision when compared with no-incentive condition, and compared with punishing cues, rewarding cues led to greater facilitation in WM precision, as well as confidence ratings afterward. Moreover, event related potential (ERP) results suggested that compared with punishment, reward led to an earlier latency of late positive component (LPC), a larger amplitude of contingent negative variation (CNV) during the expectation period, and a larger P300 amplitude during the sample and delay periods. Furthermore, reward advantage over punishment in behavioral and neural results were correlated, such that individuals with larger CNV difference between reward and punishment conditions also report greater distinction in confidence ratings between the two conditions. In sum, our results demonstrate what and how rewarding cues cause more beneficial effects than punishing cues when incentivizing visual WM.

How Can Framed Mammography Screening Messages Be Optimally Persuasive for Foreign-Born Chinese American Women?

BACKGROUND

Foreign-born Chinese American women (FBCAW) have the lowest mammography rates compared with other racial groups despite the overwhelming evidence of the benefits of screening. Message framing based on the prospect theory has shown significant but inconsistent effects on mammogram screening among ethnic minority groups.

OBJECTIVE

Using data from a randomized controlled trial, this secondary analysis aims to identify factors that interact with message framing to improve mammography screening in FBCAW.

METHODS

In the parent study, participants were randomized to receive either a gain- or loss-framed brochure that encouraged mammography screening. Data were collected at baseline and 2 months using validated questionnaires. For this secondary analysis, ordinal logistic regression was performed to identify moderation effects using both per-protocol and intention-to-treat principles.

RESULTS

Participants predominantly had low income, a low level of English proficiency, and no insurance. Lack of access to mammography services, knowledge of making a mammogram appointment, knowledge of insurance coverage for breast cancer treatment, education levels, and mammogram history were significant moderators of the framing effects. Overall, the moderation effects were larger when using per-protocol analysis. Some moderation results under intention-to-treat analysis were different from those using per-protocol analysis.

CONCLUSIONS

The persuasive effects of the loss- or gain-framed messages depend on the characteristics of FBCAW. Future studies can identify algorithms to select tailored messages that match individual FBCAW's characteristics to optimize the effects of framed messages.

IMPLICATIONS FOR PRACTICE

Findings of this study can guide healthcare providers, especially nurses, to choose different wording when communicating with their clients.

Number 19: Another Victim of the COVID-19 Pandemic?

Conscious selection is the mental process by which lottery players select numbers nonrandomly. In this paper, we show that the number 19, which has been heard, read, seen, and googled countless times since March 2020, has become significantly less popular among Belgian lottery players after the World Health Organization named the disease caused by the coronavirus SARS-CoV-2 "COVID-19". We argue that the reduced popularity of the number 19 is due to its negative association with the COVID-19 pandemic. Our study triangulates evidence from field data from the Belgian National Lottery and survey data from a nationally representative sample of 500 Belgian individuals. The field data indicate that the number 19 has been played significantly less frequently since March 2020. However, a potential limitation of the field data is that an unknown proportion of players selects numbers randomly through the "Quick Pick" computer system. The survey data do not suffer from this limitation and reinforce our previous findings by showing that priming an increase in the salience of COVID-19 prior to the players' selection of lottery numbers reduces their preference for the number 19. The effect of priming is concentrated amongst those with high superstitious beliefs, further supporting our explanation for the reduced popularity of the number 19 during the COVID-19 pandemic.

Segmented correspondence curve regression for quantifying covariate effects on the reproducibility of high-throughput experiments

High-throughput biological experiments are essential tools for identifying biologically interesting candidates in large-scale omics studies. The results of a high-throughput biological experiment rely heavily on the operational factors chosen in its experimental and data-analytic procedures. Understanding how these operational factors influence the reproducibility of the experimental outcome is critical for selecting the optimal parameter settings and designing reliable high-throughput workflows. However, the influence of an operational factor may differ between strong and weak candidates in a high-throughput experiment, complicating the selection of parameter settings. To address this issue, we propose a novel segmented regression model, called segmented correspondence curve regression, to assess the influence of operational factors on the reproducibility of high-throughput experiments. Our model dissects the heterogeneous effects of operational factors on strong and weak candidates, providing a principled way to select operational parameters. Based on this framework, we also develop a sup-likelihood ratio test for the existence of heterogeneity. Simulation studies show that our estimation and testing procedures yield well-calibrated type I errors and are substantially more powerful in detecting and locating the differences in reproducibility across workflows than the existing method. Using this model, we investigated an important design question for ChIP-seq experiments: How many reads should one sequence to obtain reliable results in a cost-effective way? Our results reveal new insights into the impact of sequencing depth on the binding-site identification reproducibility, helping biologists determine the most cost-effective sequencing depth to achieve sufficient reproducibility for their study goals.

Efficient targeted learning of heterogeneous treatment effects for multiple subgroups

In biomedical science, analyzing treatment effect heterogeneity plays an essential role in assisting personalized medicine. The main goals of analyzing treatment effect heterogeneity include estimating treatment effects in clinically relevant subgroups and predicting whether a patient subpopulation might benefit from a particular treatment. Conventional approaches often evaluate the subgroup treatment effects via parametric modeling and can thus be susceptible to model mis-specifications. In this paper, we take a model-free semiparametric perspective and aim to efficiently evaluate the heterogeneous treatment effects of multiple subgroups simultaneously under the one-step targeted maximum-likelihood estimation (TMLE) framework. When the number of subgroups is large, we further expand this path of research by looking at a variation of the one-step TMLE that is robust to the presence of small estimated propensity scores in finite samples. From our simulations, our method demonstrates substantial finite sample improvements compared to conventional methods. In a case study, our method unveils the potential treatment effect heterogeneity of rs12916-T allele (a proxy for statin usage) in decreasing Alzheimer's disease risk.

Linear and Nonlinear Effect of Years of Schooling, Sex, and Age on the CERAD-MX and Complementary Tasks in a Mexican Sample: A Cross-Sectional Study

OBJECTIVE

Since evidence of adults' cognition decline is based on standardized testing, we developed regression-based continuous norms by linear regression (LR) and nonlinear quantile regression (NQR) with years of schooling (YoS), age, and sex as covariates on the Mexican adaptation of the Consortium to Establish a Registry for Alzheimer's Disease (CERAD-MX) and complementary tasks.

METHODS

392 healthy, Spanish-speaking Mexican adults (50.25% women) aged 18-59 completed the 15 CERAD-MX cognitive tasks and complementary tasks. We used raw scores and examined YoS-related effects considering sex and age as covariates. For the NQR, we used calibrated scores for sex and age. While LR represents one line across the performance, NQR differentiated several nonlinear performance bands by quantiles.

RESULTS

LR showed positive relationships between YoS and cognitive performance with a funnel variance pattern. Therefore, this relationship is better represented with NQR than LR. A small, but significant, negative effect of age was found for this age range (18-59 years). The band with fewer years of schooling (1-6) showed greater variability in the cognitive measures than those with more years of schooling (16-22).

CONCLUSION

This study shows that NQR is useful for accurately positioning participants' performance relative to their peers. NQR accounts more than LR for the inconsistent variability of cognitive performance as a function of YoS by identifying the variability according to YoS (low, medium, high). Thus, NQR represents an appropriate way to construct norms for the cognitive performance of adults.

Decision-making and frontoparietal resting-state functional connectivity among impulsive-compulsive diagnoses. Insights from a Bayesian approach

The Iowa Gambling Task (IGT) is one of the most widely used paradigms for assessing decision-making. An impairment in this process may be linked to several psychopathological disorders, such as obsessive-compulsive disorder (OCD), substance abuse disorder (SUD) or attention-deficit/hyperactivity disorder (ADHD), which could make it a good candidate for being consider a transdiagnostic domain. Resting-state functional connectivity (rsFC) has been proposed as a promising biomarker of decision-making. In this study, we aimed to identify idiosyncratic decision-making profiles among healthy people and impulsive-compulsive spectrum patients during the IGT, and to investigate the role of frontoparietal network (FPN) rsFC as a possible biomarker of different decision-making patterns. Using functional near-infrared spectroscopy (fNIRS), rsFC of 114 adults (34 controls; 25 OCD; 41 SUD; 14 ADHD) was obtained. Then, they completed the IGT. Hybrid clustering methods based on individual deck choices yielded three decision-makers subgroups. Cluster 1 (n = 27) showed a long-term advantageous strategy. Cluster 2 (n = 25) presented a maladaptive decision-making strategy. Cluster 3 (n = 62) did not develop a preference for any deck during the task. Interestingly, the proportion of participants in each cluster was not different between diagnostic groups. A Bayesian general linear model showed no credible differences in the IGT performance between diagnostic groups nor credible evidence to support the role of FPN rsFC as a biomarker of decision-making under the IGT context. This study highlights the importance of exploring in depth the behavioral and neurophysiological variables that may drive decision-making in clinical and healthy populations.

COVID-19 stressor reduces risk taking: the role of trait interoception

The Framing Effect (FE) demonstrated that the way two alternatives are displayed affects people's inclination to make a specific choice, showing a risk aversion when alternatives are displayed on positive frames and risk seeking in negative frames. Risk seeking in negative frames is closely linked to loss aversion. Moreover, classical research and the salience-of-losses hypothesis argues that stress may enhance the FE and loss aversion. Recent studies also suggest that the trait interoception and alexithymia could interact and moderate the framing susceptibility. However, experimental paradigms on stress could ignore variables such as threat perception. In this sense, COVID-19 pandemic has become a powerful real-life stressor in many countries. We aimed to study how real-life stressors influence decision-making under risk. A total of 97 participants were divided into a control (n = 48) and an experimental group (n = 49). The experimental group were exposed to a stressor manipulation, a 5 min COVID-19 lockdown documentary. Our results show that COVID-19-related stressors significantly decreased bet acceptance regardless of the frame, also reducing loss aversion. Moreover, interoception was a significant predictor of loss aversion under stress conditions. Our results do not support classical research on stress and FE.

The effect of perceptual organization on numerical and preference-based decisions shows inter-subject correlation

Individual differences in cognitive processing have been the subject of intensive research. One important type of such individual differences is the tendency for global versus local processing, which was shown to correlate with a wide range of processing differences in fields such as decision making, social judgments and creativity. Yet, whether these global/local processing tendencies are correlated within a subject across different domains is still an open question. To address this question, we develop and test a novel method to quantify global/local processing tendencies, in which we directly set in opposition the local and global information instead of instructing subjects to specifically attend to one processing level. We apply our novel method to two different domains: (1) a numerical cognition task, and (2) a preference task. Using computational modeling, we accounted for classical effects in choice and numerical-cognition. Global/local tendencies in both tasks were quantified using a salience parameter. Critically, the salience parameters extracted from the numerical cognition and preference tasks were highly correlated, providing support for robust perceptual organization tendencies within an individual.

Decision-making under risk and ambiguity in adults with Tourette syndrome

BACKGROUND

Tourette syndrome (TS) as well as its most common comorbidities are associated with a higher propensity for risky behaviour in everyday life. However, it is unclear whether this increased risk propensity in real-life contexts translates into a generally increased attitude towards risk. We aimed to assess decision-making under risk and ambiguity based on prospect theory by considering the effects of comorbidities and medication.

METHODS

Fifty-four individuals with TS and 32 healthy controls performed risk and ambiguity decision-making tasks under both gains and losses conditions. Behavioural and computational parameters were evaluated using (i) univariate analysis to determine parameters difference taking independently; (ii) supervised multivariate analysis to evaluate whether our parameters could jointly account for between-group differences (iii) unsupervised multivariate analysis to explore the potential presence of sub-groups.

RESULTS

Except for general 'noisier' (less consistent) decisions in TS, we showed no specific risk-taking behaviour in TS or any relation with tics severity or antipsychotic medication. However, the presence of comorbidities was associated with distortion of decision-making. Specifically, TS with obsessive-compulsive disorder comorbidity was associated with a higher risk-taking profile to increase gain and a higher risk-averse profile to decrease loss. TS with attention-deficit hyperactivity disorder comorbidity was associated with risk-seeking in the ambiguity context to reduce a potential loss.

CONCLUSIONS

Impaired valuation of risk and ambiguity was not related to TS per se. Our findings are important for clinical practice: the involvement of individuals with TS in real-life risky situations may actually rather result from other factors such as psychiatric comorbidities.

Military service and risk of subsequent drug use disorders among Swedish men

PURPOSE

Environmental factors contribute substantially to risk for drug use disorders (DUD). The current study applies multiple methods to empirically test whether military service is associated with subsequent DUD, as previous findings are inconsistent.

METHODS

Longitudinal Swedish national registry data on a cohort of male conscripts born 1972-1987 (maximum N = 485,900) were used to test the association between military service and subsequent registration for DUD. Cox proportional hazard models were used in preliminary analyses, followed by three methods that enable causal inference: propensity score models, co-relative models, and instrumental variable analysis.

RESULTS

Across all methods, military service was causally associated with lower risk of DUD. Hazard ratios ranged from HR = 0.43 (95% confidence intervals [CI] 0.37; 0.50) in the instrumental variable analysis to 0.77 (0.75; 0.79) in the multivariate propensity score matching analysis. This effect diminished across time. In the model including a propensity score, HRs remained below 1 across the observation period, while confidence intervals included 1 after ~ 11 years in the co-relative analysis and after ~ 21 years in the instrumental variable analysis.

CONCLUSIONS

In this cohort of Swedish men, complementary methods indicate that military service conferred substantial but time-limited protection against subsequent DUD. The observed effect could be due to reduced opportunity for substance use during service, social cohesion experienced during and after service, and/or socioeconomic advantages among veterans. Additional research is necessary to clarify these protective mechanisms and determine how other environmental contexts can provide similar benefits.