The bidirectional temporal relationship between parenting stress and child maltreatment: A cross-lagged study based on intervention and cohort data

BACKGROUND

Parenting stress has long been proposed as a major risk factor for child maltreatment. However, there is a lack of evidence from existing studies on the temporal sequence to establish a causal relationship. This study aims to examine bidirectional temporal relationships between parenting stress and child maltreatment.

METHODS

Longitudinal data from two different sources were analysed: a pre-post study of an online parenting programme conducted across six countries - the ePLH Evaluation Study, and a prospective cohort study in the United States - LONGSCAN. Cross-lagged panel model on parenting stress and child maltreatment was used in each dataset.

RESULTS

Based on repeatedly measured data of 484 caregivers in the ePLH study across five time points (every two weeks), we found that parenting stress at an earlier time point predicted later child maltreatment (IRR = 1.14, 95 % CI: 1.10,1.18). In addition, the occurrence of child maltreatment was associated with higher subsequent short-term parenting stress (IRR = 1.04, 95 % CI: 1.01,1.08) and thus could form a vicious circle. In the LONGSCAN analysis with 772 caregivers who were followed up from child age of 6 to child age of 16, we also found parenting stress at an earlier time point predicted later child maltreatment (β = 0.11, 95 % CI: 0.01,0.20), but did not observe an association between child maltreatment and subsequent long-term parenting stress.

LIMITATIONS

Potential information bias on the measurements.

CONCLUSIONS

This study provides evidence for a bidirectional temporal relationship between parenting stress and child maltreatment, which should be considered in parenting intervention programmes.

Evolution of Sleep Duration and Screen Time Between 2018 and 2022 Among Canadian Adolescents: Evidence of Drifts Accompanying the COVID-19 Pandemic

PURPOSE

We quantified the joint evolution of sleep duration and screen time between 2018 and 2022 in a large sample of adolescents from Quebec, Canada, to ascertain changes that occurred during the COVID-19 pandemic.

METHODS

A natural experiment design was used to compare variations from year to year and in association with the pandemic outbreak. Using structural equation modeling on data collected between 2018 and 2022 among adolescents attending 63 high schools, we analyzed the joint evolution of sleep duration and screen time while adjusting for previous year values, concurrent flourishing score, sex, age, and family level of material deprivation.

RESULTS

A total of 28,307 adolescents, aged on average 14.9 years, were included in the analyses. Between 2019 and 2022, sleep duration increased by 9.6 (5.7, 13.5) minutes and screen time by 129.2 (120.5, 138.0) minutes on average. In 2022, the adolescents spent almost equal amounts of time sleeping and using screens. Lower flourishing scores were associated with shorter sleep duration and lengthier screen time. Girls' screen time became similar to boys' over time.

DISCUSSION

Adolescents now spend almost equal amounts of time sleeping and using screens, a situation that calls for urgent public health actions. These findings highlight the importance of tracking changes in adolescents' behaviours over time, to design and implement interventions adapted to the changing health needs of different groups.

A nested grouped random parameter negative binomial model for modeling segment-level crash counts

In this study, a nested grouped random parameter negative binomial framework is proposed to model crash counts at the segment level, a three-level longitudinal framework. The proposed model accounts for correlations along county routes and over time and thus includes a time variable, the year index, to analyze crash counts. The model is applied to crashes on undivided two-lane arterial roads in Ohio from 2012 to 2017. The results present two variants of the model: one with varying intercepts and fixed slopes and the other with varying intercepts and slopes. Both variants have comparable interpretations concerning the fixed parameters, but the latter variant exhibits a significantly improved fit and provides additional information on the interpretations. The results show a significant quadratic relationship between the time variable and the crash count, indicating that, on average, the crash count of segments increases with a decreasing rate as time variable increases. Regarding random parameters, the findings show that 17% of segments within routes and 2% of routes exhibit crash counts that decrease at accelerating downward trend as time variable increases. The effect of the natural logarithm of the segment length varies significantly across different routes, with an increase in this value primarily leading to an increase in crashes. On the other hand, the effect of the total shoulder width also varies across routes, but unlike the former, an increase in this value generally results in a decrease in crashes. The proposed model shows high forecast accuracy for crash count prediction, making it a valuable tool for informed decision-making in safety improvement.

Network analysis of social anxiety and problematic mobile phone use in Chinese adolescents: A longitudinal study

Social anxiety is a common comorbid factor of problematic mobile phone use, but the relationship between them from the inside structure has been underexplored. This two-wave longitudinal study aims to identify the central symptoms of problematic mobile phone use and social anxiety, respectively, as well as the bridge symptoms between the two disorders. Furthermore, this study examined the time and gender differences. A total of 3,294 Chinese adolescents were recruited during two waves (T1: January 2023; T2: August 2023), and completed the questionnaires of social anxiety and problematic mobile phone use. Through network analysis, the results showed that 1) the central symptom of problematic mobile phone use was "loss of control" both at T1 and T2, 2) the central symptom of social anxiety was "fear of negative evaluation" both at T1 and T2, and 3) the bridge symptom between social anxiety and problematic mobile phone use was "withdrawal" both at T1 and T2. Moreover, the network comparison test indicated that the nodes of social anxiety were more strongly associated with "craving", "loss of control", and "negative life consequences" of problematic mobile phone use at T2 than at T1. The nodes of social anxiety were more strongly associated with "negative life consequences" of problematic mobile phone use for boys than for girls, while the "fear of negative evaluation" was more strongly associated with "loss of control" for girls than for boys. These findings provide new scientific basis for interventions targeting adolescents who are susceptible to social anxiety and problematic mobile phone use.

Longitudinal Associations Between Interpersonal Distrust and Social Aggression During College: Disentangling the Within-Person Process from Stable Between-Person Differences

There is a dearth of information on the relationship between interpersonal distrust and social aggression in the youth, although both may lead to negative interpersonal relationships. Furthermore, scholars have not explored whether interpersonal distrust influences later social aggression over time at the within-person level. This study used five wave longitudinal data to investigate the longitudinal association between interpersonal distrust and social aggression and the role of hostile attribution bias in this relationship; notably, it used a relatively rigorous approach-the random intercept cross-lagged panel model-to disentangle within-person processes from stable between-person differences. The final number of participants included 1053 undergraduate students (677 female students and 376 male students), and 64.3% were female students, with a mean age of 18.45 years (SD = 0.95) at first measurement. Participants completed assessments for interpersonal distrust, hostile attribution bias, and social aggression at five time points across 6-month intervals. At the within-person level, the results revealed that interpersonal distrust was a predictor of later social aggression and that hostile attribution bias acted as a longitudinal mediator in this relationship. This result indicates that to enhance interpersonal harmony and reduce individual hostility and aggression toward others, intervention programs should aim to reduce interpersonal distrust.

Wordform variability in infants' language environment and its effects on early word learning

Most research regarding early word learning in English tends to make the simplifying assumption that there exists a one-to-one mapping between concrete objects and their labels. In the current work, we provide evidence that runs counter to this assumption, aligning English with more morphologically-rich languages. We suggest that even in a morphologically-poor language like English, real world language input to infants does not provide tidy 1-to-1 mappings. Instead, infants encounter many variant wordforms for familiar nouns (e.g. dog∼doggy∼dogs). We explore this wordform variability in 44 English-learning infants' naturalistic environments using a longitudinal corpus of infant-available speech. We look at both the frequency and composition of wordform variability. We find two broad categories of variability: referent-changing alterations, where words were pluralized or compounded (e.g. coat∼raincoats); and wordplay, where words changed form without a notable change in referent (e.g. bird∼birdie). We further find that wordplay occurs with a limited number of lemmas that are usually early-learned, high-frequency, and shorter. When looking at all wordform variability, we find that individual words with higher levels of wordform variability are learned earlier than words with fewer wordforms, over and above the effect of frequency.

Symptomatic remission and recovery in major psychosis: Is there a role for BDNF? A secondary analysis of the LABSP cohort data

Remission, relapse prevention, and clinical recovery are crucial areas of interest in schizophrenia (SCZ) research. Although SCZ is a chronic disorder with poor overall outcomes, years of research demonstrated that recovery is possible. There are considerable data linking brain-derived neurotrophic factor (BDNF) to SCZ, however, evidence on the role of BDNF in remission in SCZ is scarce. This secondary analysis of the Longitudinal Assessment of BDNF in Sardinian patients (LABSP) data aimed to investigate the relationship between serum BDNF levels and symptomatic remission, simultaneous clinical and functional remission, and recovery in patients with SCZ. A total of 105 patients with SCZ or schizoaffective disorder were recruited for a longitudinal assessment of BDNF levels over 24 months. Longitudinal data were analyzed using mixed-effects linear regression models. The study found significant associations between use of long acting injectables (χ2 = 7.075, df = 1, p = 0.008), baseline serum BDNF levels (U = 701, z = -2.543, p = 0.011), and "childhood" (U = 475, z = -2.124, p = 0.034) and "general" (U = 55, z = -2.014, p = 0.044) subscales of the Premorbid Adjustment Scale (PAS) with patients maintaining remission and recovery. The diagnosis of SCZ was significantly associated with lower BDNF levels for patients with simultaneous clinical and functional remission (Z = 2.035, p = 0.0419) and recovery (Z = 2.009, p = 0.0445) compared to those without. There were no significant associations between remission in the entire sample and longitudinal serum BDNF levels or genetic variants within the BDNF gene. These findings provide further insight into the complex relationship between BDNF and SCZ.

The chronically ill in the labour market - are they hierarchically sorted by education?

BACKGROUND

The chronically ill as a group has on average lower probability of employment compared to the general population, a situation that has persisted over time in many countries. Previous studies have shown that the prevalence of chronic diseases is higher among those with lower levels of education. We aim to quantify the double burden of low education and chronic illness comparing the differential probabilities of employment between the chronically ill with lower, medium, and high levels of education and how their employment rates develop over time.

METHODS

Using merged Norwegian administrative data over a 11-year period (2008-2018), our estimations are based on multivariable regression with labour market and time fixed effects. To reduce bias due to patients' heterogeneity, we included a series of covariates that may influence the association between labour market participation and level of education. To explicitly explore the 'shielding effect' of education over time, the models include the interaction effects between chronic illness and level of education and year.

RESULTS

The employment probabilities are highest for the high educated and lowest for chronically ill individuals with lower education, as expected. The differences between educational groups are changing over time, though, driven by a revealing development among the lower-educated chronically ill. That group has a significant reduction in employment probabilities both in absolute terms and relative to the other groups. The mean predicted employment probabilities for the high educated chronic patient is not changing over time indicating that the high educated as a group is able to maintain labour market participation over time. Additionally, we find remarkable differences in employment probabilities depending on diagnoses.

CONCLUSION

For the chronically ill as a group, a high level of education seems to "shield" against labour market consequences. The magnitude of the shielding effect is increasing over time leaving chronically ill individuals with lower education behind. However, the shielding effect varies in size between types of chronic diseases. While musculoskeletal, cardiovascular and partly cancer patients are "sorted" hierarchically according to level of education, diabetes, respiratory and mental patients are not.

Longitudinal metabolite profiling of Streptococcus pneumoniae-associated community-acquired pneumonia

INTRODUCTION

Longitudinal biomarkers in patients with community-acquired pneumonia (CAP) may help in monitoring of disease progression and treatment response. The metabolic host response could be a potential source of such biomarkers since it closely associates with the current health status of the patient.

OBJECTIVES

In this study we performed longitudinal metabolite profiling in patients with CAP for a comprehensive range of metabolites to identify potential host response biomarkers.

METHODS

Previously collected serum samples from CAP patients with confirmed Streptococcus pneumoniae infection (n = 25) were used. Samples were collected at multiple time points, up to 30 days after admission. A wide range of metabolites was measured, including amines, acylcarnitines, organic acids, and lipids. The associations between metabolites and C-reactive protein (CRP), procalcitonin, CURB disease severity score at admission, and total length of stay were evaluated.

RESULTS

Distinct longitudinal profiles of metabolite profiles were identified, including cholesteryl esters, diacyl-phosphatidylethanolamine, diacylglycerols, lysophosphatidylcholines, sphingomyelin, and triglycerides. Positive correlations were found between CRP and phosphatidylcholine (34:1) (cor = 0.63) and negative correlations were found for CRP and nine lysophosphocholines (cor = - 0.57 to - 0.74). The CURB disease severity score was negatively associated with six metabolites, including acylcarnitines (tau = - 0.64 to - 0.58). Negative correlations were found between the length of stay and six triglycerides (TGs), especially TGs (60:3) and (58:2) (cor = - 0.63 and - 0.61).

CONCLUSION

The identified metabolites may provide insight into biological mechanisms underlying disease severity and may be of interest for exploration as potential treatment response monitoring biomarker.

Modeling clinical malaria episodes in different ecological settings in Mali, 2018-2022

Objectives

Following the scaling-up of malaria control strategies in Mali, understanding the changes in age-specific prevalence of infection and risk factors associated with remains necessary to determine new priorities to progress toward disease elimination. This study aimed to estimate the risk of clinical malaria using longitudinal data across three different transmission settings in Mali.

Methods

Cohort-based longitudinal studies were performed from April 2018 to December 2022. Incidence of malaria was measured through community health center-based passive case detection. Generalized estimation equation model was used to assess risk factors for clinical malaria.

Results

A total of 21,453 clinical presentations were reported from 4500 participants, mainly from July to November. Data shows a significant association between malaria episodes, sex, age group, season, and year. Women had lower risk, the risk of clinical episode increased with age up to 14 years then declined, and in both sites, the dry-season risk of clinical episode was significantly lower compared to the rainy season.

Conclusion

Determining factors associated with the occurrence of clinical malaria across different ecological settings across the country could help in the development of new strategies aiming to accelerate malaria elimination in an area where malaria transmission remains intense.

A Longitudinal Analysis of Trajectories and Predictors of Fidelity Using the SafeCare Parenting Model

Evidence-based practice (EBP) fidelity, understood as the extent to which a program is implemented as the developers intended, is a key implementation variable which likely relates to consumer outcomes. However, studies that track fidelity longitudinally and at large scale are uncommon, and finding reliable predictors of fidelity has proven to be a complex challenge. Further, attitudes toward EBP are a potentially important predictor of fidelity, but results across the literature have been mixed. The purpose of the present study is to use data from the ongoing implementation and dissemination of the SafeCare model to better understand (1) the characteristics of SafeCare implementation fidelity trajectories, and (2) whether individual level factors predict differences in fidelity and fidelity trajectories, especially provider attitudes toward EBP. The analyses reported here include 14,778 observed fidelity sessions by 868 providers in 172 agencies. We use multilevel modeling to examine fidelity, fidelity trajectories over time, and several potential individual-level predictors of fidelity, including demographics, work history, and attitudes toward EBP. We found: (1) that SafeCare fidelity begins high at baseline (93.85% on average); (2) that SafeCare fidelity displays a statistically significant trend of positive linear growth, even among those with less positive attitudes; and (3) that positive attitudes are associated with slightly higher fidelity on average and at baseline, while negative attitudes are associated with slightly lower fidelity on average and at baseline. To our knowledge, this is the largest longitudinal analysis of EBP fidelity in a child welfare program to date, and our findings support the notion that intensive coaching supports which are titrated over time can be sufficient to ensure sustained high fidelity, at least in some cases. Further, these findings indicate that robust training and coaching processes can ensure high fidelity and fidelity growth even among providers with less positive attitudes toward EBP.

Behavioral interventions and symptom cluster change in adults with chronic obstructive pulmonary disease and insomnia

BACKGROUND

People with chronic obstructive pulmonary disease (COPD) and insomnia experience multiple co-occurring symptoms, but no studies have examined symptom cluster change over time in this population.

OBJECTIVES

This study explored longitudinal patterns of symptom cluster profiles for adults with COPD and insomnia and evaluated whether behavioral interventions were associated with changes in symptom cluster profiles.

METHODS

This study included 91 adults with COPD and insomnia who participated in a randomized trial of cognitive behavioral therapy for insomnia (CBT-I) and COPD education. The pre-specified symptom cluster included insomnia, dyspnea, fatigue, anxiety, and depression. Latent profile analysis identified participant groups with distinct symptom cluster profiles at baseline, immediately post-intervention, and at 3-month follow-up; latent transition analysis then estimated the probability of group membership change over time. Multinomial logistic regression was used to determine whether the interventions were associated with changes in symptom cluster profiles.

RESULTS

Three groups were identified at each of three time-points: Class 1 (low symptom burden), Class 2 (intermediate), and Class 3 (high). Classes 1 and 2 showed less movement to other classes (16 % and 38 %, respectively), whereas Class 3 showed greater transition (64 %). The CBT-I intervention was significantly associated with movement to a lower symptom burden group (Class 3 to 2 or 2 to 1).

CONCLUSIONS

CBT-I, with or without COPD education, shows promise as a tailored intervention to reduce symptom burden in the study population. Study findings will facilitate development of interventions to reduce the severity of multiple co-occurring symptoms in people with COPD and insomnia.

CLINICAL TRIAL REGISTRATION

Registry: ClinicalTrials.gov; Name: A Behavioral Therapy for Insomnia Co-existing with COPD; Identifier: NCT01973647.

Flexible Bayesian semiparametric mixed-effects model for skewed longitudinal data

BACKGROUND

In clinical trials and epidemiological research, mixed-effects models are commonly used to examine population-level and subject-specific trajectories of biomarkers over time. Despite their increasing popularity and application, the specification of these models necessitates a great deal of care when analysing longitudinal data with non-linear patterns and asymmetry. Parametric (linear) mixed-effect models may not capture these complexities flexibly and adequately. Additionally, assuming a Gaussian distribution for random effects and/or model errors may be overly restrictive, as it lacks robustness against deviations from symmetry.

METHODS

This paper presents a semiparametric mixed-effects model with flexible distributions for complex longitudinal data in the Bayesian paradigm. The non-linear time effect on the longitudinal response was modelled using a spline approach. The multivariate skew-t distribution, which is a more flexible distribution, is utilized to relax the normality assumptions associated with both random-effects and model errors.

RESULTS

To assess the effectiveness of the proposed methods in various model settings, simulation studies were conducted. We then applied these models on chronic kidney disease (CKD) data and assessed the relationship between covariates and estimated glomerular filtration rate (eGFR). First, we compared the proposed semiparametric partially linear mixed-effect (SPPLM) model with the fully parametric one (FPLM), and the results indicated that the SPPLM model outperformed the FPLM model. We then further compared four different SPPLM models, each assuming different distributions for the random effects and model errors. The model with a skew-t distribution exhibited a superior fit to the CKD data compared to the Gaussian model. The findings from the application revealed that hypertension, diabetes, and follow-up time had a substantial association with kidney function, specifically leading to a decrease in GFR estimates.

CONCLUSIONS

The application and simulation studies have demonstrated that our work has made a significant contribution towards a more robust and adaptable methodology for modeling intricate longitudinal data. We achieved this by proposing a semiparametric Bayesian modeling approach with a spline smoothing function and a skew-t distribution.

Response shift in coronary artery disease

PURPOSE

Patients with coronary artery disease (CAD) experience significant angina symptoms and lifestyle changes. Revascularization procedures can result in better patient-reported outcomes (PROs) than optimal medical therapy (OMT) alone. This study evaluates the impact of response shift (RS) on changes in PROs of patients with CAD across treatment strategies.

METHODS

Data were from patients with CAD in the Alberta Provincial Project on Outcome Assessment in Coronary Heart Disease (APPROACH) registry who completed the 16-item Canadian version of the Seattle Angina Questionnaire at 2 weeks and 1 year following a coronary angiogram. Multi-group confirmatory factor analysis (MG-CFA) was used to assess measurement invariance across treatment groups at week 2. Longitudinal MG-CFA was used to test for RS according to receipt of coronary artery bypass grafting (CABG), percutaneous coronary intervention (PCI), or optimal medical therapy (OMT) alone.

RESULTS

Of the 3116 patients included in the analysis, 443 (14.2%) received CABG, 2049(65.8%) PCI, and the remainder OMT alone. The MG-CFA revealed a partial-strong invariance across the treatment groups at 2 weeks (CFI = 0.98, RMSEA [90% CI] = 0.05 [0.03, 0.06]). Recalibration RS was detected on the Angina Symptoms and Burden subscale and its magnitude in the OMT, PCI, and CABG groups were 0.32, 0.28, and 0.53, respectively. After adjusting for RS effects, the estimated target changes were largest in the CABG group and negligible in the OMT group.

CONCLUSION

Adjusting for RS is recommended in studies that use SAQ-CAN to assess changes in patients with CAD who have received revascularization versus OMT alone.

White matter and literacy: A dynamic system in flux

Cross-sectional studies have linked differences in white matter tissue properties to reading skills. However, past studies have reported a range of, sometimes conflicting, results. Some studies suggest that white matter properties act as individual-level traits predictive of reading skill, whereas others suggest that reading skill and white matter develop as a function of an individual's educational experience. In the present study, we tested two hypotheses: a) that diffusion properties of the white matter reflect stable brain characteristics that relate to stable individual differences in reading ability or b) that white matter is a dynamic system, linked with learning over time. To answer these questions, we examined the relationship between white matter and reading in a five-year longitudinal dataset and a series of large-scale, single-observation, cross-sectional datasets (N = 14,249 total participants). We find that gains in reading skill correspond to longitudinal changes in the white matter. However, in the cross-sectional datasets, we find no evidence for the hypothesis that individual differences in white matter predict reading skill. These findings highlight the link between dynamic processes in the white matter and learning.

Novel linkage approach to join community-acquired and national data

BACKGROUND

Community optometrists in Scotland have performed regular free-at-point-of-care eye examinations for all, for over 15 years. Eye examinations include retinal imaging but image storage is fragmented and they are not used for research. The Scottish Collaborative Optometry-Ophthalmology Network e-research project aimed to collect these images and create a repository linked to routinely collected healthcare data, supporting the development of pre-symptomatic diagnostic tools.

METHODS

As the image record was usually separate from the patient record and contained minimal patient information, we developed an efficient matching algorithm using a combination of deterministic and probabilistic steps which minimised the risk of false positives, to facilitate national health record linkage. We visited two practices and assessed the data contained in their image device and Practice Management Systems. Practice activities were explored to understand the context of data collection processes. Iteratively, we tested a series of matching rules which captured a high proportion of true positive records compared to manual matches. The approach was validated by testing manual matching against automated steps in three further practices.

RESULTS

A sequence of deterministic rules successfully matched 95% of records in the three test practices compared to manual matching. Adding two probabilistic rules to the algorithm successfully matched 99% of records.

CONCLUSIONS

The potential value of community-acquired retinal images can be harnessed only if they are linked to centrally-held healthcare care data. Despite the lack of interoperability between systems within optometry practices and inconsistent use of unique identifiers, data linkage is possible using robust, almost entirely automated processes.

Magnitude and determinants of early initiation and exclusive breastfeeding at six weeks postpartum: evidence from the PMA Ethiopia longitudinal survey

BACKGROUND

Early initiation and exclusive breastfeeding are crucial in preventing child morbidity and mortality. Despite the importance of these practices, rates of timely initiation of breastfeeding and exclusive breastfeeding remain suboptimal in many sub-Saharan countries, including Ethiopia. This paper aimed to estimate the prevalence and identify determinants of breastfeeding initiation within the first hour after birth and exclusive breastfeeding in Ethiopia.

METHODS

Data from the Performance Monitoring for Action Ethiopia project, a national survey conducted from August 2019 to September 2020, were used. The analytical sample comprises 2564 postpartum women and their children; data reported at baseline during enrollment and six weeks postpartum were used in this analysis. A multi-level binary logistic regression model was employed to identify determinant factors linked with initiation breastfeeding and exclusive breastfeeding.

RESULTS

Of the 2564 mothers, 77.8% of infants breastfed within the first hour of birth and 68.4% of women practiced exclusive breastfeeding at six weeks postpartum with significant variation across regions. In the multivariate analysis, mothers who had cesarean delivery were less likely to initiate early breastfeeding as compared to mothers with vaginal delivery (AOR 0.27; 95% CI 0.17, 0.41). The odds of early initiation of breastfeeding were higher for mothers whose baby cried immediately after birth (AOR 3.31; 95% CI 1.95, 5.62) and who had skilled assisted delivery (AOR 2.13; 95% CI 1.01, 4.48). Other factors that were significantly associated with early initiation of breastfeeding were obstetric complication(s), parity, immediate mother-to-baby skin-to-skin contact, immediate postnatal care and the region. Similarly, mothers whose babies had a good neonatal birth status (AOR 1.81; 95% CI 1.09, 2.99) were more likely to exclusively breastfeed their child at six weeks postpartum.

CONCLUSIONS

Early initiation of breastfeeding and exclusive breastfeeding is sub-optimal in Ethiopia. Nutrition programmers should consider regional variations in designing intervention programs to enhance breastfeeding practices. Healthcare providers should give special attention to women at risk such as those giving birth through cesarean section and having obstetric complications during delivery.

Hypergraph convolutional network for longitudinal data analysis in Alzheimer's disease

Alzheimer's disease (AD) is an irreversible and progressive neurodegenerative disease. Longitudinal structural magnetic resonance imaging (sMRI) data have been widely used for tracking AD pathogenesis and diagnosis. However, existing methods tend to treat each time point equally without considering the temporal characteristics of longitudinal data. In this paper, we propose a weighted hypergraph convolution network (WHGCN) to use the internal correlations among different time points and leverage high-order relationships between subjects for AD detection. Specifically, we construct hypergraphs for sMRI data at each time point using the K-nearest neighbor (KNN) method to represent relationships between subjects, and then fuse the hypergraphs according to the importance of the data at each time point to obtain the final hypergraph. Subsequently, we use hypergraph convolution to learn high-order information between subjects while performing feature dimensionality reduction. Finally, we conduct experiments on 518 subjects selected from the Alzheimer's disease neuroimaging initiative (ADNI) database, and the results show that the WHGCN can get higher AD detection performance and has the potential to improve our understanding of the pathogenesis of AD.

A new computational workflow to guide personalized drug therapy

OBJECTIVE

Computational models are at the forefront of the pursuit of personalized medicine thanks to their descriptive and predictive abilities. In the presence of complex and heterogeneous data, patient stratification is a prerequisite for effective precision medicine, since disease development is often driven by individual variability and unpredictable environmental events. Herein, we present GreatNectorworkflow as a valuable tool for (i) the analysis and clustering of patient-derived longitudinal data, and (ii) the simulation of the resulting model of patient-specific disease dynamics.

METHODS

GreatNectoris designed by combining an analytic strategy composed of CONNECTOR, a data-driven framework for the inspection of longitudinal data, and an unsupervised methodology to stratify the subjects with GreatMod, a quantitative modeling framework based on the Petri Net formalism and its generalizations.

RESULTS

To illustrate GreatNectorcapabilities, we exploited longitudinal data of four immune cell populations collected from Multiple Sclerosis patients. Our main results report that the T-cell dynamics after alemtuzumab treatment separate non-responders versus responders patients, and the patients in the non-responders group are characterized by an increase of the Th17 concentration around 36 months.

CONCLUSION

GreatNectoranalysis was able to stratify individual patients into three model meta-patients whose dynamics suggested insight into patient-tailored interventions.

Associations Between Internalized and Anticipated HIV Stigma and Depression Symptoms Among People Living with HIV in China: A four-wave Longitudinal Model

Previous research has documented that HIV-related stigma (e.g., internalized and anticipated stigma) is detrimental to the mental health of people living with HIV (PLWH). However, longitudinal data on the bidirectional relationship between HIV-related stigma and depression symptoms are limited. The purpose of this study was to examine the bidirectional association among internalized and anticipated HIV stigma and depression symptoms among Chinese PLWH. A four-wave longitudinal design (6 months intervals) was employed among 1,111 Chinese PLWH (Mage = 38.58, SD = 9.16, age range: 18-60 years; 64.1% men). The bidirectional model was examined using a random-intercept cross-lagged panel model (RI-CLPM), which evaluated the within- and between-person effects of study variables. At the within-person level, results indicated that depression symptoms at T2 mediated the linkage between internalized HIV stigma at T1 and anticipated HIV stigma at T3, and that anticipated HIV stigma at both T2 and T3 mediated the relationship between depression symptoms at the previous time point and internalized HIV stigma at the subsequent time point. Furthermore, a bidirectional association was found between anticipated HIV stigma and depression symptoms across four waves. At the between-person level, internalized and anticipated HIV stigma were significantly associated with depression symptoms. This study highlights the complex interplay between different forms of HIV-related stigma and mental health problems among PLWH and underscores the importance of considering the bidirectional relationship between the development of psychopathology and stigmatization process in clinical practice.

How has the strict lockdown during the SARS-COV-2 outbreak changed the diet of Spaniards?

Purpose

We investigate the impact of a specific macroeconomic shock that occurred as a response to the SARS-COV-2 outbreak, namely the strict lockdown imposed in Spain on the March 14, 2020.

Methods

We use fortnightly purchase data relating to over 50,000 households from a supermarket chain in Catalonia from March to June in 2019 and 2020. Using a panel data approach, we analyse the impact of the lockdown on the caloric content, sugar composition, and alcohol content in beverages and food purchases bought before and after lockdown. We corrected our results to take into account the likelihood of stockpiling.

Results

The lockdown is related to an increase in unhealthy beverage and food purchases. We find heterogeneous effects across groups of the sample based on cardholder characteristics. Families with children or babies and those in the upper two income quintiles had the unhealthiest changes. As the lockdown went through phases of relaxation, households made better food decisions but maintained unhealthy beverage choices.

Conclusions

The very restrictive lockdown negatively impacted the characteristics of food and beverage purchases made by Spaniards. However, we are unsure whether there was substitution to restaurant and bar visits. Additional work to find out whether there were permanent changes in purchasing behaviour after lockdown ended is needed in the future.

The health of mothers caring for a child with a disability: a longitudinal study

BACKGROUND

Raising a child with disabilities requires a significant parental investment that is greater than that required by typically developing children. Previous studies have shown that parents caring for a child with a disability experience a range of health problems, particularly the mothers. However, few of these studies have controlled for maternal health prior to birth.

METHODS

This study used a sample from the Norwegian administrative register that comprised all children born between 2009 and 2015. We followed the mothers and their children for 11 years, between 2009 and 2019. The outcome variable was the mothers' physical and mental health, which was assessed using specific ICD-10 diagnoses recorded in the Norwegian Patient Register (NPR). The data included information on the mothers' health before and after the birth of their first child, enabling us to control for maternal health prior to birth in our analysis, in addition to socio-demographic characteristics. The analyses of maternal health were performed using multiple logistic regression, and the results are presented on both a relative scale (odds ratio [OR]) and an absolute scale (average marginal effect [AME]), both with 95% confidence intervals.

RESULTS

Mothers caring for a child with a disability have higher odds of having a diagnosis of a musculoskeletal disorder, depression, anxiety, sleeping disorder or migraines than mothers of children without a disability. The differences between the two groups of mothers decrease after adjusting for the characteristics of the children, mothers and families, but remain significant for musculoskeletal disorder, depression, anxiety and sleeping disorder, although the absolute differences are modest.

CONCLUSION

The findings suggest that mothers caring for a child with a disability are more likely to have health problems than mothers of children without a disability after controlling for maternal health prior to birth. Providing more support for mothers of children with a disability might help to improve their health.

Development and evaluation of a method to assess breast cancer risk using a longitudinal history of mammographic density: a cohort study

BACKGROUND

Women with dense breasts have an increased risk of breast cancer. However, breast density is measured with variability, which may reduce the reliability and accuracy of its association with breast cancer risk. This is particularly relevant when visually assessing breast density due to variation in inter- and intra-reader assessments. To address this issue, we developed a longitudinal breast density measure which uses an individual woman's entire history of mammographic density, and we evaluated its association with breast cancer risk as well as its predictive ability.

METHODS

In total, 132,439 women, aged 40-73 yr, who were enrolled in Kaiser Permanente Washington and had multiple screening mammograms taken between 1996 and 2013 were followed up for invasive breast cancer through 2014. Breast Imaging Reporting and Data System (BI-RADS) density was assessed at each screen. Continuous and derived categorical longitudinal density measures were developed using a linear mixed model that allowed for longitudinal density to be updated at each screen. Predictive ability was assessed using (1) age and body mass index-adjusted hazard ratios (HR) for breast density (time-varying covariate), (2) likelihood-ratio statistics (ΔLR-χ2) and (3) concordance indices.

RESULTS

In total, 2704 invasive breast cancers were diagnosed during follow-up (median = 5.2 yr; median mammograms per woman = 3). When compared with an age- and body mass index-only model, the gain in statistical information provided by the continuous longitudinal density measure was 23% greater than that provided by BI-RADS density (follow-up after baseline mammogram: ΔLR-χ2 = 379.6 (degrees of freedom (df) = 2) vs. 307.7 (df = 3)), which increased to 35% (ΔLR-χ2 = 251.2 vs. 186.7) for follow-up after three mammograms (n = 76,313, 2169 cancers). There was a sixfold difference in observed risk between densest and fattiest eight-category longitudinal density (HR = 6.3, 95% CI 4.7-8.7), versus a fourfold difference with BI-RADS density (HR = 4.3, 95% CI 3.4-5.5). Discriminatory accuracy was marginally greater for longitudinal versus BI-RADS density (c-index = 0.64 vs. 0.63, mean difference = 0.008, 95% CI 0.003-0.012).

CONCLUSIONS

Estimating mammographic density using a woman's history of breast density is likely to be more reliable than using the most recent observation only, which may lead to more reliable and accurate estimates of individual breast cancer risk. Longitudinal breast density has the potential to improve personal breast cancer risk estimation in women attending mammography screening.

Longitudinal plasmode algorithms to evaluate statistical methods in realistic scenarios: an illustration applied to occupational epidemiology

INTRODUCTION

Plasmode simulations are a type of simulations that use real data to determine the synthetic data-generating equations. Such simulations thus allow evaluating statistical methods under realistic conditions. As far as we know, no plasmode algorithm has been proposed for simulating longitudinal data. In this paper, we propose a longitudinal plasmode framework to generate realistic data with both a time-varying exposure and time-varying covariates. This work was motivated by the objective of comparing different methods for estimating the causal effect of a cumulative exposure to psychosocial stressors at work over time.

METHODS

We developed two longitudinal plasmode algorithms: a parametric and a nonparametric algorithms. Data from the PROspective Québec (PROQ) Study on Work and Health were used as an input to generate data with the proposed plasmode algorithms. We evaluated the performance of multiple estimators of the parameters of marginal structural models (MSMs): inverse probability of treatment weighting, g-computation and targeted maximum likelihood estimation. These estimators were also compared to standard regression approaches with either adjustment for baseline covariates only or with adjustment for both baseline and time-varying covariates.

RESULTS

Standard regression methods were susceptible to yield biased estimates with confidence intervals having coverage probability lower than their nominal level. The bias was much lower and coverage of confidence intervals was much closer to the nominal level when considering MSMs. Among MSM estimators, g-computation overall produced the best results relative to bias, root mean squared error and coverage of confidence intervals. No method produced unbiased estimates with adequate coverage for all parameters in the more realistic nonparametric plasmode simulation.

CONCLUSION

The proposed longitudinal plasmode algorithms can be important methodological tools for evaluating and comparing analytical methods in realistic simulation scenarios. To facilitate the use of these algorithms, we provide R functions on GitHub. We also recommend using MSMs when estimating the effect of cumulative exposure to psychosocial stressors at work.

Differential discontinuation by covert use status in Kenya

Objectives

Qualitative research suggests that covert users may be more likely to discontinue contraception due to the logistics of discretion and fear of disclosure. This study sought to quantify whether covert users are more likely to discontinue contraception than overt users.

Study design

We used a national longitudinal survey from Kenya conducted from November 2019/February 2020 to November 2020/April 2021 to test whether the time to discontinuation between covert and overt users still in need of contraception differed using survival analyses over a period of 5 years since method initiation.

Results

Multivariate Cox regression results showed there was an interaction with time and covert use on the risk of discontinuation; for every additional month of use, there was an increased risk of discontinuation of covert users compared to overt users (3% increased hazard, p = 0.02). At 1 and 2 years, there were no differences in the hazard of discontinuation (adjusted hazard ratio [aHR]1 year 0.95, 95% CI 0.54-1.65 and aHR2 years 1.37, 95% CI 0.85-2.21), yet at 3, 4, and 5 years, the hazard of discontinuation was higher for covert compared to overt users (aHR3 years 1.99, 95% 1.11-3.56; aHR4 years 2.89, 95% CI 2.0-6.40; aHR5 years 4.18, 95% CI 1.45-12.0).

Conclusions

These results suggest efforts are needed to support covert users in managing their contraceptive use and for improving contraceptive counseling surrounding covert use. Our findings shed light on the increasing challenge covert users face after approximately the first 2 years of use; covert users require additional follow-up in both research and care provision.

Implications

Covert users are at a higher risk of discontinuation of contraception while still trying to avoid pregnancy, particularly after the first 2 years of use. Family planning providers and programs must protect access to and maintain the privacy of reproductive services to this population, focusing on follow-up care provision and counseling.

Utilizing Longitudinal Chest X-Rays and Reports to Pre-Fill Radiology Reports

Despite the reduction in turn-around times in radiology reporting with the use of speech recognition software, persistent communication errors can significantly impact the interpretation of radiology reports. Pre-filling a radiology report holds promise in mitigating reporting errors, and despite multiple efforts in literature to generate comprehensive medical reports, there lacks approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset. To address this gap, we propose to use longitudinal multi-modal data, i.e., previous patient visit CXR, current visit CXR, and the previous visit report, to pre-fill the "findings" section of the patient's current visit. We first gathered the longitudinal visit information for 26,625 patients from the MIMIC-CXR dataset, and created a new dataset called Longitudinal-MIMIC. With this new dataset, a transformer-based model was trained to capture the multi-modal longitudinal information from patient visit records (CXR images + reports) via a cross-attention-based multi-modal fusion module and a hierarchical memory-driven decoder. In contrast to previous works that only uses current visit data as input to train a model, our work exploits the longitudinal information available to pre-fill the "findings" section of radiology reports. Experiments show that our approach outperforms several recent approaches. Code will be published at https://github.com/CelestialShine/Longitudinal-Chest-X-Ray.

Evidence of the cooperative response of Blattella germanica gut microbiota to antibiotic treatment

Gut microbiota plays a key role in host health under normal conditions. However, bacterial composition can be altered by external factors such as antibiotic (AB) intake. While there are many descriptive publications about the effects of AB on gut microbiota composition after treatment, the dynamics and interactions among the bacterial taxa are still poorly understood. In this work, we performed a longitudinal study of gut microbiome dynamics in B. germanica treated with kanamycin. The AB was supplied in three separate periods, giving the microbiota time to recover between each antibiotic intake. We applied two new statistical models, not focusing on pair-wise interactions, to more realistically study the interactions between groups of bacterial taxa and how some groups affect a single taxon. The first model provides information on the importance of a given genus, and the rest of the community, to define the abundance of that genus. The second model, on the other hand, provides details about the relationship between groups of bacteria, focusing on which community groups affect the taxa. These models help us to identify which bacteria are community-dependent in stress conditions, which taxa might be better adapted than the rest of the community, and which bacteria might be working together within the community to overcome the antibiotic. In addition, these models enable us to identify different bacterial groups that were separated in control conditions but were found together in treated conditions, suggesting that when the environment is more hostile (as it is under antibiotic treatment), the whole community tends to work together.

Bayesian semiparametric joint model of multivariate longitudinal and survival data with dependent censoring

We consider a novel class of semiparametric joint models for multivariate longitudinal and survival data with dependent censoring. In these models, unknown-fashion cumulative baseline hazard functions are fitted by a novel class of penalized-splines (P-splines) with linear constraints. The dependence between the failure time of interest and censoring time is accommodated by a normal transformation model, where both nonparametric marginal survival function and censoring function are transformed to standard normal random variables with bivariate normal joint distribution. Based on a hybrid algorithm together with the Metropolis-Hastings algorithm within the Gibbs sampler, we propose a feasible Bayesian method to simultaneously estimate unknown parameters of interest, and to fit baseline survival and censoring functions. Intensive simulation studies are conducted to assess the performance of the proposed method. The use of the proposed method is also illustrated in the analysis of a data set from the International Breast Cancer Study Group.

Utilizing Longitudinal Chest X-Rays and Reports to Pre-fill Radiology Reports

Despite the reduction in turn-around times in radiology reporting with the use of speech recognition software, persistent communication errors can significantly impact the interpretation of radiology reports. Pre-filling a radiology report holds promise in mitigating reporting errors, and despite multiple efforts in literature to generate comprehensive medical reports, there lacks approaches that exploit the longitudinal nature of patient visit records in the MIMIC-CXR dataset. To address this gap, we propose to use longitudinal multi-modal data, i.e., previous patient visit CXR, current visit CXR, and the previous visit report, to pre-fill the "findings" section of the patient's current visit. We first gathered the longitudinal visit information for 26,625 patients from the MIMIC-CXR dataset, and created a new dataset called Longitudinal-MIMIC. With this new dataset, a transformer-based model was trained to capture the multi-modal longitudinal information from patient visit records (CXR images + reports) via a cross-attention-based multi-modal fusion module and a hierarchical memory-driven decoder. In contrast to previous works that only uses current visit data as input to train a model, our work exploits the longitudinal information available to pre-fill the "findings" section of radiology reports. Experiments show that our approach outperforms several recent approaches by ≥3% on F1 score, and ≥2% for BLEU-4, METEOR and ROUGE-L respectively. Code will be published at https://github.com/CelestialShine/Longitudinal-Chest-X-Ray.

Phenotype similarities in automatically grouped T2D patients by variation-based clustering of IL-1β gene expression

Background

Analyzing longitudinal gene expression data is extremely challenging due to limited prior information, high dimensionality, and heterogeneity. Similar difficulties arise in research of multifactorial diseases such as Type 2 Diabetes. Clustering methods can be applied to automatically group similar observations. Common clinical values within the resulting groups suggest potential associations. However, applying traditional clustering methods to gene expression over time fails to capture variations in the response. Therefore, shape-based clustering could be applied to identify patient groups by gene expression variation in a large time metabolic compensatory intervention.

Objectives

To search for clinical grouping patterns between subjects that showed similar structure in the variation of IL-1β gene expression over time.

Methods

A new approach for shape-based clustering by IL-1β expression behavior was applied to a real longitudinal database of Type 2 Diabetes patients. In order to capture correctly variations in the response, we applied traditional clustering methods to slopes between measurements.

Results

In this setting, the application of K-Medoids using the Manhattan distance yielded the best results for the corresponding database. Among the resulting groups, one of the clusters presented significant differences in many key clinical values regarding the metabolic syndrome in comparison to the rest of the data.

Conclusions

The proposed method can be used to group patients according to variation patterns in gene expression (or other applications) and thus, provide clinical insights even when there is no previous knowledge on the subject clinical profile and few timepoints for each individual.

Dynamic household structure and composition: a manual for longitudinal analysis of living arrangements

OBJECTIVE

This research note introduces a set of tools to conduct analysis of household structure and composition with either limited or comprehensive longitudinal data. The data used here are from Health and Demographic Surveillance Systems in Africa, but the methods can be adapted and applied to other longitudinal micro-data such as register data. A training manual describing the procedures for creating time-varying household measures step-by-step is supplied as supplementary material to this note. Code is provided in STATA but can easily be translated for other statistical software, and the logic for each step remains the same.

RESULTS

The analysis of household structure demonstrates how with limited data (such as a household identifier), it is possible to construct time-varying measures of household membership, including household size or the number of members in specific age and sex groups. The analysis of household composition demonstrates how with expanded data (including links to parents in addition to residence status in the same household), it is possible to construct time-varying measures of household membership of specific kin, i.e. mother, sibling or grandparent. The results illustrated in this research note are a taste of what can be achieved by following the training manual in the supplementary material.

Variable selection for joint models of multivariate skew-normal longitudinal and survival data

Many joint models of multivariate skew-normal longitudinal and survival data have been presented to accommodate for the non-normality of longitudinal outcomes in recent years. But existing work did not consider variable selection. This article investigates simultaneous parameter estimation and variable selection in joint modeling of longitudinal and survival data. The penalized splines technique is used to estimate unknown log baseline hazard function, the rectangle integral method is adopted to approximate conditional survival function. Monte Carlo expectation-maximization algorithm is developed to estimate model parameters. Based on local linear approximations to conditional expectation of likelihood function and penalty function, a one-step sparse estimation procedure is proposed to circumvent the computationally challenge in optimizing the penalized conditional expectation of likelihood function, which is utilized to select significant covariates and trajectory functions, and identify the departure from normality of longitudinal data. The conditional expectation of likelihood function-based Bayesian information criterion is developed to select the optimal tuning parameter. Simulation studies and a real example from the clinical trial are used to illustrate the proposed methodologies.

Contrasting socio-economic influences on colorectal cancer incidence and survival in England and Wales

Colorectal cancer (CRC) is the third most commonly diagnosed cancer in the world and second most common cause of cancer death. The relationship between socio-economic deprivation and CRC incidence is unclear and previous findings have been inconsistent. There is stronger evidence of an association between area-level deprivation and CRC survival; however, few studies have investigated the association between individual-level socio-economic status (SES) and CRC survival. Data from the Office for National Statistics Longitudinal Study (LS) in England and Wales was used. LS members aged 50+ were stratified by individual-level educational attainment, social class, housing tenure and area deprivation quintile, measured at the 2001 Census. Time-to-event analysis examined associations between indicators of SES and CRC incidence and survival (all-cause and CRC death), over a 15-year follow-up period. Among 178116 LS members, incidence of CRC was lower among those with a degree, compared to those with no degree and higher among those employed in manual occupations compared to non-manual occupations. No clear relationship was observed between CRC incidence and the area-based measure of deprivation. Disparities were greater for survival. Among 5016 patients diagnosed with CRC aged 50+, probability of death from all-causes was lower among those with a degree, compared to no degree and higher among those employed in manual occupations, compared to non-manual occupations and among those living in social-rented housing, compared to owner-occupiers. Individual indicators of SES were also associated with probability of death from CRC. Those living in the most deprived areas had a higher probability of death (from all-causes and CRC) compared to those in the least deprived areas. Both individual and area-based indicators of SES were associated with CRC survival, and the relationships were stronger than those observed for CRC incidence. These findings could help inform more effective targeting of public health interventions for CRC.

Rural-urban differentials in child body mass index over time

BACKGROUND

The body mass index is a simple index based on weight and height that can be used to screen children and adults for potential weight problems. The objective of this study was to investigate urban-rural variations in child BMI and its distribution from 2006 to 2016 in four low and middle-income countries.

METHODS

This study used data from the Young Lives prospective cohort study conducted in Ethiopia, India, Peru, and Vietnam to assess the BMI change for children aged 5 to 15 between 2006 and 2016. We adopted a mixed-effect model to analyze the data.

RESULTS

The study revealed substantial changes and rises in BMI in Vietnam, Peru, India, and Ethiopia between 2006 and 2016. Peru had the highest BMI changes in both urban-rural areas. A low BMI was observed in Ethiopia and India. Urban-rural differences had a significant role in determining BMI variation. In urban Ethiopia, the mean BMI increased from 14.56 kg/m2 to 17.52 kg/m2, and in rural areas, it increased from 14.57 kg/m2 to 16.67 kg/m2. Similarly, in urban Vietnam, the BMI increased from 16 kg/m2 to 20.3 kg/m2, and in rural areas, it increased from 14.69 kg/m2 to 18.93 kg/m2.

CONCLUSIONS

The findings showed an increase in BMI changes in Ethiopia, India, Peru, and Vietnam from 2006 to 2016. Urban-rural differences have a significant contribution to determining BMI variation.

Cohort profile: Evaluation of the Methods and Management of Acute Coronary Events (EMMACE) Longitudinal Cohort

AIMS

The Evaluation of the Methods and Management of Acute Coronary Events (EMMACE) longitudinal cohort study aims to investigate health trajectories of individuals following hospitalization for myocardial infarction (MI).

METHODS AND RESULTS

EMMACE is a linked multicentre prospective cohort study of 14 899 patients with MI admitted to 77 hospitals in England who participated in the EMMACE-3 and -4 studies between 1st November 2011 and 24th June 2015. Long-term follow-up of the EMMACE cohorts was conducted through the EMMACE-XL (27th September 2020 to 31st March 2022) and EMMACE-XXL (1st July 2021 to 1st July 2023) studies. EMMACE collected individual participant data for health-related quality of life (HRQoL) measured by three-level EuroQol five-dimension and visual analogy scale at admission, 1 month, 6 months, 12 months, and 10 years follow-up, as well as medications, medication adherence, beliefs about medicines, Satisfaction with Information about Medicines Scale, and illness perceptions. Participant data were deterministically linked to the Myocardial Infarction National Audit Project (MINAP) for information on baseline treatments and comorbidities, Hospital Episode Statistics Admitted Patient Care (for cause-specific hospitalization data), and the Office for National Statistics (for mortality data) up to 2020.

CONCLUSION

EMMACE is a nationwide prospective cohort that will provide unique insights into fatal and non-fatal outcomes, medication adherence, and HRQoL following MI.Trial registration: ClinicalTrials.gov NCT01808027 and NCT01819103.

Trajectories of drug treatment and illicit opioid use in the AIDS Linked to the IntraVenous Experience cohort, 2014-2019

BACKGROUND

Medication for opioid use disorder (MOUD) is an effective intervention to combat opioid use disorder and overdose, yet there is limited understanding of engagement in treatment over time in the community, contextualized by ongoing substance use. We aimed to identify concurrent trajectories of methadone prescriptions, buprenorphine prescriptions, and illicit opioid use among older adults with a history of injection drug use.

METHODS

We used data on 887 participants from the AIDS Linked to the IntraVenous Experience cohort, who were engaged in the study in 2013 and attended ≥1 visit during follow-up (2014-2019). Outcomes were self-reported MOUD prescription and illicit opioid use in the last 6 months. To identify concurrent trajectories in all 3 outcomes, we used group-based multi-trajectory modeling. We examined participant characteristics, including sociodemographics, HIV status, and other substance use, overall and by cluster.

RESULTS

We identified 4 trajectory clusters: (1) no MOUD and no illicit opioid use (43%); (2) buprenorphine and some illicit opioid use (11%); (3) methadone and no illicit opioid use (28%); and (4) some methadone and illicit opioid use (18%). While prevalence of each outcome was stable across time, transitions on/off treatment or on/off illicit opioid use occurred, with the rate of transition varying by cluster. The rate of transition was highest in Cluster 3 (0.74/person-year) and lowest in Cluster 1 (0.18/person-year). We saw differences in participant characteristics by cluster, including that the buprenorphine cluster had the highest proportion of people with HIV and participants who identified as non-Hispanic Black.

CONCLUSIONS

Most participants had discontinued illicit opioid use and were also not accessing MOUD. Trajectories defined by engagement with buprenorphine or methadone had distinct sociodemographic and behavioral characteristics, indicating that tailored interventions to expand access to both types of treatment are likely needed to reduce harms associated with untreated opioid use disorder.

A taxonomy of multiple regression methods for immunologists

Multiple regression is a powerful tool in the immunologist's toolbox. This paper defines multiple regression, discusses availability and accessibility, provides some additional helpful definitions, treats the topics of transformation and extreme value screening, and establishes the paper's scope and philosophy. Then eleven methods of multiple regression are detailed, giving strengths and limitations. Throughout an emphasis is placed on application to immunological assays. A flowchart to guide selection of multiple regression methods is provided.

A hybrid CNN-RNN approach for survival analysis in a Lung Cancer Screening study

In this study, we present a hybrid CNN-RNN approach to investigate long-term survival of subjects in a lung cancer screening study. Subjects who died of cardiovascular and respiratory causes were identified whereby the CNN model was used to capture imaging features in the CT scans and the RNN model was used to investigate time series and thus global information. To account for heterogeneity in patients' follow-up times, two different variants of LSTM models were evaluated, each incorporating different strategies to address irregularities in follow-up time. The models were trained on subjects who underwent cardiovascular and respiratory deaths and a control cohort matched to participant age, gender, and smoking history. The combined model can achieve an AUC of 0.76 which outperforms humans at cardiovascular mortality prediction. The corresponding F1 and Matthews Correlation Coefficient are 0.63 and 0.42 respectively. The generalisability of the model is further validated on an 'external' cohort. The same models were applied to survival analysis with the Cox Proportional Hazard model. It was demonstrated that incorporating the follow-up history can lead to improvement in survival prediction. The Cox neural network can achieve an IPCW C-index of 0.75 on the internal dataset and 0.69 on an external dataset. Delineating subjects at increased risk of cardiorespiratory mortality can alert clinicians to request further more detailed functional or imaging studies to improve the assessment of cardiorespiratory disease burden. Such strategies may uncover unsuspected and under-recognised pathologies thereby potentially reducing patient morbidity.

Semiparametric predictive inference for failure data using first-hitting-time threshold regression

The progression of disease for an individual can be described mathematically as a stochastic process. The individual experiences a failure event when the disease path first reaches or crosses a critical disease level. This happening defines a failure event and a first hitting time or time-to-event, both of which are important in medical contexts. When the context involves explanatory variables then there is usually an interest in incorporating regression structures into the analysis and the methodology known as threshold regression comes into play. To date, most applications of threshold regression have been based on parametric families of stochastic processes. This paper presents a semiparametric form of threshold regression that requires the stochastic process to have only one key property, namely, stationary independent increments. As this property is frequently encountered in real applications, this model has potential for use in many fields. The mathematical underpinnings of this semiparametric approach for estimation and prediction are described. The basic data element required by the model is a pair of readings representing the observed change in time and the observed change in disease level, arising from either a failure event or survival of the individual to the end of the data record. An extension is presented for applications where the underlying disease process is unobservable but component covariate processes are available to construct a surrogate disease process. Threshold regression, used in combination with a data technique called Markov decomposition, allows the methods to handle longitudinal time-to-event data by uncoupling a longitudinal record into a sequence of single records. Computational aspects of the methods are straightforward. An array of simulation experiments that verify computational feasibility and statistical inference are reported in an online supplement. Case applications based on longitudinal observational data from The Osteoarthritis Initiative (OAI) study are presented to demonstrate the methodology and its practical use.

Does a longer sexual resume affect marriage rates?

Sociologists have proposed numerous theories for declining marriage rates in the United States, often highlighting demographic, economic, and cultural factors. One controversial theory contends that having multiple non-marital sex partners reduces traditional incentives for men to get married and simultaneously undermines their prospects in the marriage market. For women, multiple partners purportedly reduces their desirability as spouses by evoking a gendered double-standard about promiscuity. Though previous studies have shown that having multiple premarital sex partners is negatively associated with marital quality and stability, to date no research has examined whether having multiple non-marital sex partners affects marriage rates. Data from four waves of the National Survey of Family Growth reveal that American women who report more sex partners are less likely to get married by the time of the survey (though so too were virgins). Yet this finding is potentially misleading given the retrospective and cross-sectional nature of the data. Seventeen waves of prospective data from the National Longitudinal Survey of Youth's 1997 mixed-gender cohort that extend through 2015 show the association between non-marital sex partners and marriage rates is temporary: recent sex partners predict lower odds of marriage, but not lifetime non-marital sex partners. Seemingly unrelated bivariate probit models suggest the short-term association likely reflects a causal effect. Our findings ultimately cast doubt on recent scholarship that has implicated the ready availability of casual sex in the retreat from marriage. Rather, the effect of multiple sex partners on marriage rates is "seasonal" for most Americans.

Estimating Student Attrition in School-Based Prevention Studies: Guidance from State Longitudinal Data in Maryland

Attrition is a critical concern for evaluating the rigor of prevention studies, and the current study provides rates of attrition for subgroups of students and schools who are often sampled for prevention science. This is the first study to provide practical guidance for expected rates of attrition using population-level statewide data; findings indicated that researchers using K-12 school-based samples should plan for attrition rates as high as 27% during middle school and 54% during elementary school. However, researchers should consider the grade levels initially sampled, the length of follow-up, and the specific student characteristics and schools available for sampling. Postsecondary attrition ranged from 45% for bachelor's degree seekers to 73% for associate degree seekers. This practical guidance can help researchers to proactively plan for attrition in the study design phase, limiting bias and increasing the validity of prevention studies.

Longitudinal machine learning model for predicting systolic blood pressure in patients with heart failure

Objective

Systolic blood pressure (SBP) strongly indicates the prognosis of heart failure (HF) patients, as it is closely linked to the risk of death and readmission. Hence, maintaining control over blood pressure is a vital factor in the management of these patients. In order to determine significant variables associated with changes in SBP over time and assess the effectiveness of classical and machine learning models in predicting SBP, this study aimed to conduct a comparative analysis between the two.

Methods

This retrospective cohort study involved the analysis of data from 483 patients with HF who were admitted to Farshchian Heart Center located in Hamadan in the west of Iran, and hospitalized at least two times between October 2015 and July 2019. To predict SBP, we utilized a linear mixed-effects model (LMM) and mixed-effects least-square support vector regression (MLS-SVR). The effectiveness of both models was evaluated based on the mean absolute error and root mean squared error.

Results

The LMM analysis revealed that changes in SBP over time were significantly associated with sex, body mass index (BMI), sodium, time, and history of hypertension (P-value < 0.05). Furthermore, according to the MLS-SVR analysis, the four most important variables in predicting SBP were identified as history of hypertension, sodium, BMI, and triglyceride. In both the training and testing datasets, MLS-SVR outperformed LMM in terms of performance.

Conclusions

Based on our results, it appears that MLS-SVR has the potential to serve as a viable alternative to classical longitudinal models for predicting SBP in patients with HF.

Using a Recurrent Neural Network To Inform the Use of Prostate-specific Antigen (PSA) and PSA Density for Dynamic Monitoring of the Risk of Prostate Cancer Progression on Active Surveillance

The global uptake of prostate cancer (PCa) active surveillance (AS) is steadily increasing. While prostate-specific antigen density (PSAD) is an important baseline predictor of PCa progression on AS, there is a scarcity of recommendations on its use in follow-up. In particular, the best way of measuring PSAD is unclear. One approach would be to use the baseline gland volume (BGV) as a denominator in all calculations throughout AS (nonadaptive PSAD, PSAD_NA), while another would be to remeasure gland volume at each new magnetic resonance imaging scan (adaptive PSAD, PSAD_A). In addition, little is known about the predictive value of serial PSAD in comparison to PSA. We applied a long short-term memory recurrent neural network to an AS cohort of 332 patients and found that serial PSAD_NA significantly outperformed both PSAD_A and PSA for follow-up prediction of PCa progression because of its high sensitivity. Importantly, while PSAD_NA was superior in patients with smaller glands (BGV ≤55 ml), serial PSA was better in men with larger prostates of >55 ml.

Patient summary

Repeat measurements of prostate-specific antigen (PSA) and PSA density (PSAD) are the mainstay of active surveillance in prostate cancer. Our study suggests that in patients with a prostate gland of 55 ml or smaller, PSAD measurements are a better predictor of tumour progression, whereas men with a larger gland may benefit more from PSA monitoring.

Globally Adaptive Longitudinal Quantile Regression with High Dimensional Compositional Covariates

In this work, we propose a longitudinal quantile regression framework that enables a robust characterization of heterogeneous covariate-response associations in the presence of high-dimensional compositional covariates and repeated measurements of both response and covariates. We develop a globally adaptive penalization procedure, which can consistently identify covariate sparsity patterns across a continuum set of quantile levels. The proposed estimation procedure properly aggregates longitudinal observations over time, and ensures the satisfaction of the sum-zero coefficient constraint that is needed for proper interpretation of the effects of compositional covariates. We establish the oracle rate of uniform convergence and weak convergence of the resulting estimators, and further justify the proposed uniform selector of the tuning parameter in terms of achieving global model selection consistency. We derive an efficient algorithm by incorporating existing R packages to facilitate stable and fast computation. Our extensive simulation studies confirm the theoretical findings. We apply the proposed method to a longitudinal study of cystic fibrosis children where the association between gut microbiome and other diet-related biomarkers is of interest.

Patterns of Personality Development and Psychosocial Functioning in Japanese Adolescents: A Four-Wave Longitudinal Study

While patterns of adolescent personality development are country-specific, previous studies that have examined them have been limited to the Netherlands and Finland. This study aimed to identify the patterns of personality development and examine the relationship between these patterns and psychosocial functioning among Japanese adolescents. Overall, 618 Japanese adolescents (49.5% girls; 16 years) participated in the annual longitudinal survey from 2013 to 2016. Using latent class growth analysis, the following four patterns of personality development were identified: resilient, over-controlled, vulnerable, and moderate. Although the mean-level changes in the Big Five domains were generally insignificant among the four patterns, the vulnerable pattern showed a progressive increase in conscientiousness, and the moderate pattern showed a decrease in neuroticism and an increase in conscientiousness. Furthermore, multivariate analysis of variance tests indicated that the resilient pattern showed higher subjective well-being and lower psychosocial problems than the other personality patterns; the over-controlled pattern showed higher internalizing problems than the resilient pattern; the vulnerable pattern showed lower subjective well-being and higher internalizing problems than the other patterns; and the moderate pattern scored between the resilient, over-controlled, and vulnerable patterns in both subjective well-being and psychosocial problems. These findings suggest that the vulnerable and moderate patterns, which are immature patterns compared to the resilient and over-controlled ones, showed positive changes to the direction of maturity from middle to late adolescence in Japan.

The association between online learning, parents' marital status, and internet addiction among adolescents during the COVID-19 pandemic period: A cross-lagged panel network approach

INTRODUCTION

Previous research has identified the association between online learning and Internet addiction (IA) and the role of family factors in it. However, few studies have treated IA as a multidimensional mechanism and explored the underlying linkage of online learning, IA, and parental marital status with a cross-lagged network approach. The study aimed to examine the relationship between online learning during the COVID-19 pandemic, Internet addiction (IA), and parental marital status among Chinese adolescents.

METHODS

The sample consisted of 2356 adolescents who completed the Internet Addiction Test twice over a four-month period. Four symptom networks and two cross-lagged panel networks were performed.

RESULTS

The results showed that adolescents from divorced families had a higher prevalence of IA (27 %) compared to those from non-divorced families (17 %). The strongest cross-lagged association was found between "spending more time online" and "preferring the excitement online". In the divorced group, "school grades suffering" had the highest influence, while in the non-divorced group, "anticipation" had the highest influence.

CONCLUSION

This study highlights the relationship between online learning, IA, and parental divorce and suggests that long-term online learning may contribute to IA, and parental divorce may exacerbate problematic Internet use and increase IA levels.

Differences in the longitudinal change of behaviours related to dementia in long-term care: a growth mixture modelling approach

BACKGROUND

There is still a need for more information about the different trajectories of responsive behaviours that people living with dementia present in long-term care homes (LTC).

OBJECTIVE

This study identified subgroups of individuals with similar trajectories of responsive behaviours related to dementia in LTC and evaluated the role of demographic variables, depressive symptomatology, social engagement, cognitive functioning, and activities of daily living (ADL) on class membership.

METHODS

Growth mixture models were run using data from the Continuing Care Reporting System.

RESULTS

Results suggest that change in responsive behaviours is best represented by seven classes of trajectories. The largest class was composed of individuals who presented the lowest frequency of behaviours upon entry in LTC that increased at a slow linear rate. The other classes were composed of individuals who presented different frequencies of behaviours upon entry in LTC and varying rates of change (e.g., individuals who presented a low frequency of behaviours upon entry in LTC that increased at a linear rate followed by a decrease in the later months, individuals who presented a high frequency of responsive behaviours upon entry in LTC and that remained stable). Cognitive functioning, social engagement, depressive symptomatology, and ADL were markers of class membership.

CONCLUSIONS

These findings can help identify individuals at increased risk of presenting a high frequency of responsive behaviours and highlight interventions that could decrease behaviours in LTC.

Longitudinal trends and determinants of stunting among children aged 1-15 years

BACKGROUND

Stunting increases morbidity and mortality, hindering mental development and influencing cognitive capacity of children. This study aimed to examine the trends and determinants of stunting from infancy to middle adolescence in four countries: Ethiopia, India, Peru, and Vietnam.

METHODS

A 15-year longitudinal data on the trends of stunting were obtained from the Young Lives cohort study. The study includes 38,361 observations from 4 countries. A generalized mixed-effects model was adopted to estimate the determinant of stunting.

RESULTS

The patterns of stunting in children from aged 1 to 15 years have declined from an estimated 30% in 2002 to 20% in 2016. Stunting prevalence varied among four low- and middle-income countries with children in Ethiopia, India, and Peru being more stunted compared to children in Vietnam. The highest stunted was recorded in India and the lowest was recorded in Vietnam. In all four countries, the highest prevalence of severe stunting was observed in 2002 and moderate stunting was observed in 2006. Parents' education level played a significance role in determining a child stunting. Children of uneducated parents were shown to be at a higher risk of stunting.

CONCLUSION

Disparities of stunting were observed between- and within-country of four low- and middle-income with the highest prevalence recorded in low-income country. Child stunting is caused by factors related to child's age, household wealth, household size, the mother's and father's education level, residence area and access to save drinking water.

Longitudinal associations between specific types/amounts social contact and cognitive function among middle-aged and elderly Chinese: A causal inference and longitudinal targeted maximum likelihood estimation analysis

BACKGROUND

Social contact has demonstrated associations with cognitive function, while the literature on the effect of specific social relationship subdomains on cognitive function is limited. This study aimed to examine the causal effects of specific types/amounts of social contact on cognitive function among middle-aged and elderly Chinese.

METHODS

A total of 38,883 middle-aged and elderly adults from the China Health and Retirement Longitudinal Study were involved. Social contact in this study included interaction with families, taking care of grandchildren, interaction with friends, and participation in three types of social activities. We performed the linear mixed-effects model analysis with propensity score approach and the longitudinal targeted maximum likelihood-based estimation analysis after adjusting for potential covariates and confounders.

RESULTS

Interaction with families, caring for grandchildren, interaction with friends and participation in social activities were all associated with cognitive z-scores. Participants who interacted with families "2-3 times a week" and "once a week" versus "almost every day" had higher cognitive z-scores. Those who interacted with friends and participated in social activities "almost every week" versus "almost daily" had higher cognitive z-scores.

LIMITATIONS

The assessment of cognition was biased against people with poor education due to elements of language and mathematical testing, and against those with visual impairment.

CONCLUSIONS

Social contact was associated with better cognitive function among middle-aged and elderly Chinese. Social contact "1-3 times a week" was optimal for cognitive function. More social contact in middle-aged and elderly Chinese led to less cognitive decline in later life than in their inactive peers.