Cross-sectional Model Research Articles

How do natural resources, digitalization, and institutional governance contribute to ecological sustainability through load capacity factors in highly resource-consuming economies?

Amid rising environmental concerns, resource consumption has become a critical concern for all. Swift economic growth primarily relies on high resource consumption, which is significantly accountable for the imbalance between biodiversity and ecological footprint, resulting in minifying load capacity factor (LCF). Therefore, researchers and policymakers seek ways to enhance the LCF in this contemporary era without affecting economic growth. In the same pursuits, this study investigates the role of natural resources, digitalization, governance, and economic growth in improving the LCF of high-resource-consuming economies from 1996 to 2019. This study applies the Cross-Sectional Augmented ARDL model to address cross-sectional dependency and slope heterogeneity. The long-run results reveal that reliance on natural resources and economic growth reduced LCF, while digitalization and good governance improved LCF. Likewise, the short-run results offer consistent results but lower in magnitude. Similar results are endorsed using alternative estimators and offer relevant policy implications.

FRESHWATER POTENTIAL BASED ON IDENTIFICATION OF AQUIFER LAYER IN BALANGAN COUNTY, INDONESIA

Groundwater is a reservoir of fresh water located beneath the ground surface in soil pore spaces and the fractures of lithology formations. Groundwater contained in the aquifer layer has a certain depth. Aquifer layer measurement was conducted with five tracks using the AGI Super Sting R1 IP tool with a spacing of 25 m and a track length of 675 m in Balangan County. This study aims to determine the 2D cross-sectional model, analyze lithology types, and determine the depth, thickness, and aquifer type in the study area based on the resistivity value. The results showed that the study area includes two rock formations – the Dahor and Warukin formations. The Dahor formation is clay with a resistivity value of 1–28.284 m, sandy clay 28.284–99.055 m, and sand 99.055–800 m. The Warukin formation is clay with a resistivity value of 1–10 m, sandy clay 10–31.623 m, and sand 31.623–100 m. Aquifer layer estimation shows that track 1 has a free aquifer and contains aquitard rock. Next, track 2 has a confined aquifer and contains rocks that are both aquifer and aquitard. Then, track 3 has a free aquifer type and contains rocks that are aquifer and aquitard. Track 4 has two measurements with a depressed aquifer and contains rocks that are aquifer. Finally, Track 5 has a depressed aquifer and contains aquitard rock. This study shows the existence of groundwater deposits in the thickness of the aquifer layer in Balangan County, Indonesia.

Testing a psychosocial model of sexual communication and sexual risk-taking: A cross-sectional, online survey study of Australian University students

ObjectiveSexually transmitted infection rates are higher among young people relative to other age groups. Despite much investigation, there is not enough research about in-the-moment factors associated with risky sexual behaviour, such as relational and situational variables. The present study sought to test a comprehensive psychosocial model of sexual risk-taking that included a range of factors: individual (emotion dysregulation, impulsivity), relational (attachment, communication), societal (norms, gender), and situational (dating application use, alcohol use). Methods1244 young people aged 18 to 25 years (82.9 % women; 59.2 % single, 36.0 % in a monogamous relationship) participated in an online survey. The cross-sectional model was tested using Structural Equation Modelling. ResultsSexual communication mediated the relationship between attachment anxiety and sexual risk. However, contrary to predictions, greater sexual communication was related to higher risk. Post-hoc analysis showed that this unexpected relationship may be due to the way sexual risk was measured, as an alternative model specifying casual unprotected sex as the outcome showed no positive relationship between sexual communication and risk. ConclusionFindings indicate that young people with anxious attachment are less confident communicating about sexual health. Measures of sexual risk focusing on specific risk activities in context yield different findings to those assessing general sexual behaviours. Findings support interventions focusing on addressing perceived sexual risk norms and the dating application context to reduce risk.

Angiogenic factors assessment in pre-eclampsia high-risk population for the prediction of small-for-gestational age neonates: A prospective longitudinal study.

The authors aimed to compare cross-sectional versus longitudinal models for prediction of small-for-gestational age (SGA) neonates among pregnancies with high risk of early pre-eclampsia (PE). A prospective longitudinal study was performed in Hospital Universitari Dexeus, Barcelona. The study population included 390 pregnancies with a high risk of early PE according to the first trimester algorithm. Cross-sectional models combining first trimester risk plus placental growth factor and FMS-like tyrosine kinase 1/placental growth factor ratio, respectively, were created at 19-22, 24-26, and 27-30 weeks and compared with a model assessing longitudinal changes of these parameters. Models adding mean uterine artery pulsatility index and abdominal circumference were evaluated. SGA neonates were defined as having a birth weight less than the tenth centile. The predictive performance of a model assessing longitudinal changes of angiogenic factors was similar to that of single evaluations at the second and early third trimesters. The performance of the models combining angiogenic factors with mean uterine artery pulsatility index and abdominal circumference was better than those using only biochemical markers. However, the longitudinal evaluation of biochemical and biophysical parameters did not perform better than cross-sectional evaluations. Evaluation of angiogenic factors are useful for prediction of SGA neonates in a high-risk population for early PE. However, longitudinal models do not increase their predictive capacity.

Dam deformation early warning model based on cluster analysis and spatiotemporal data fusion

The deformation behavior has a vitally spatial correlation to hydraulic concrete structures. The majority of traditional monitoring model focuses on the displacement law of local monitoring points, in which the single objective method is employed, and results in the ignorance of temporal and spatial diversity of structural behaviors. Thus, the state of the whole structure cannot be truly reflected. In this paper, the correlation between the field data obtained from different measuring points was considered, and a dam deformation early warning model based on spatiotemporal data fusion was proposed. The model was established with respect to temporal and spatial dimensions, and the correlation of monitoring points was explored by utilizing cluster analysis. The cross-sectional series fusion model was used to establish the mapping relationship between environmental factors and cross-sectional clusters for extracting the variation regulation of overall horizontal displacements or the structural deflection curve for dams during long-term operation and identifying the abnormal deformation position. Meanwhile, the time series fusion model was established based on the insight of the Stacked Single-Target strategy, in which the correlation between monitoring points was fully utilized to improve the prediction accuracy. Then, it was used to judge whether the monitoring points conform with the operation regulation in temporal dimension. Based on the early warning model in spatiotemporal dimension, a comprehensive diagnosis method of dam deformation was proposed. In addition, this method can be used for further analyzing the critical environmental factors affecting the local and overall deformation of dams. Finally, a project was selected to verify the effectiveness of the proposed model through cluster analysis and spatiotemporal data fusion.

Survival analysis of localized prostate cancer with deep learning

In recent years, data-driven, deep-learning-based models have shown great promise in medical risk prediction. By utilizing the large-scale Electronic Health Record data found in the U.S. Department of Veterans Affairs, the largest integrated healthcare system in the United States, we have developed an automated, personalized risk prediction model to support the clinical decision-making process for localized prostate cancer patients. This method combines the representative power of deep learning and the analytical interpretability of parametric regression models and can implement both time-dependent and static input data. To collect a comprehensive evaluation of model performances, we calculate time-dependent C-statistics C_{text {td}} over 2-, 5-, and 10-year time horizons using either a composite outcome or prostate cancer mortality as the target event. The composite outcome combines the Prostate-Specific Antigen (PSA) test, metastasis, and prostate cancer mortality. Our longitudinal model Recurrent Deep Survival Machine (RDSM) achieved C_{text {td}} 0.85 (0.83), 0.80 (0.83), and 0.76 (0.81), while the cross-sectional model Deep Survival Machine (DSM) attained C_{text {td}} 0.85 (0.82), 0.80 (0.82), and 0.76 (0.79) for the 2-, 5-, and 10-year composite (mortality) outcomes, respectively. In addition to estimating the survival probability, our method can quantify the uncertainty associated with the prediction. The uncertainty scores show a consistent correlation with the prediction accuracy. We find PSA and prostate cancer stage information are the most important indicators in risk prediction. Our work demonstrates the utility of the data-driven machine learning model in prostate cancer risk prediction, which can play a critical role in the clinical decision system.

Econometric Approaches That Consider Farmers’ Adaptation in Estimating the Impacts of Climate Change on Agriculture: A Review

The question of whether and to what extent farmers can adapt to climate change has recently gained academic interest. This paper reviews contemporary econometric approaches that assess the impacts of climate change on agriculture and consider farmer adaptation, complementing previous methodological reviews with this distinctive adaptation perspective. The value of adaptation can be measured by comparing the differences between the long-term climate change effect and the short-term weather shock effect. However, this theoretical model has not yet been well supported by empirical evidence, as it is difficult to identify true adaptation, incorporating adaptation cost, and estimated adaptation rate. Quasi-natural experiments, cost-benefit analysis, and Bayesian models are effective tools to address these methodological drawbacks. Two methods dominate in the estimation of climate effects, but each has its own advantages. A good estimate provides a trade-off between the incorporation of farmers’ adaptive behavior and the reduction in omitted variables bias. Cross-sectional data models based on climate variability can capture farmers’ long-term adaptations but are prone to bias due to omitted variables. Panel data models are more effective at mitigating omitted variable bias by applying fixed effects, but do not consider farmers’ adaptative behavior to long-term climate change. To address this dilemma, several cutting-edge approaches have been developed, including integration with the weather and climate model, the long differences approach, and the long- and short-term hybrid approach. We found three key challenges, namely: (1) exploring adaptation mechanisms, (2) the CO2 fertilization effect, and (3) estimating the distributional effects of climate impacts. We also recommend future empirical studies to incorporate satellite remote sensing data, examine the relationship between different adaptation measures, model farmers’ future climate expectations, and include adaptation costs.

The Effects of Electricity Consumption on Electrification Access on Economic Growth in Papua Province, Indonesia

The current research aims to analyze the influence of electricity and infrastructure on regional economy. The study empirically tested the impact of the independent constructs on electrification access and economic growth in Papua, Indonesia by using mediating role of electricity consumption. Independent variables examined in this study are electrical installations and electricity capital, while the dependent variables are measured by using labor absorption. This study specifically investigates the relationship between these variables with a cross-sectional study model, conducted at Papua Province, Indonesia with data from 2012 to 2016. The results show that electricity consumption in Papua Province is significantly influenced by electrical installation, household electricity capital and industrial electricity capital. Furthermore, electricity consumption in general affects employment. The test of the mediating variable shows the role of the consumption variable in the ratio of electrification and employment. Theoretical implication posed from the findings is about the relationship between economic growth and energy infrastructure which is more likely to attract both domestic and foreign investment in a region. The novelty of this research is to reveal the role of electrification in industrialization and employment.

Identifying the Subjects in Which Sixth-Grade Students Have Difficulty Learning in Science Lessons, as well as the Causes of the Problems and Possible Solutions

This researched aimed to determine the subjects that 6th grade students have learning problems, the reasons for the problems and the suggestions for the solution of the problems. Cross-sectional scanning model has been adopted in the study. In the research, in the 2016-2017 academic year, 1959 6th grade students in Erzurum and 141 science teachers who teach at this grade level were studied. The data were collected through the Learning Problems Identification Questionnaire and the Teacher Learning Problems Identification Survey. Descriptive statistics and content analysis were used in analyzing the data. It was determined that the students experienced the problems in Curriculum System and Force and Motion subjects, learning difficulties, low motivation, increased anxiety and non-persistent knowledge. This situation arises from the reasons that the subject is difficult, orienting to memorization, confusing the concepts, the lesson / subject is boring, they are stressful, they are not given the opportunity for their own learning, the teachers tell the subject themselves, and there is not enough activity. It can be suggested that the lessons aimed at solving the learning problems experienced by students are taught with games, creating fun learning environments, simplifying the subject, and having more communication between students and teachers.

Do climate technology, financialization, and sustainable finance impede environmental challenges? Evidence from G10 economies

Climate technologies and sustainable finance are imperative to ensure green growth agenda. Though there exists literature, however, little is known regarding the distinct influence of financial development and green finance in the framework of climate tech. This study examines the relevance of climate technologies, financialization (financial market efficiency and financial institutions' access), and sustainable finance in G10 countries. The initial findings confirm a long-run cointegrating relationship between model variables and highlight the issues of cross-sectional dependence and slope heterogeneity. Therefore, we employ the Cross-Sectional Augmented ARDL model and find that climate technologies, financial institutions' access, and green bonds (financial market efficiency) significantly decrease (increase) carbon emissions. Manifestly, the marginal contribution of climate technologies is higher towards emissions reduction, followed by green financing. Similar outcomes are observed in the short run; nevertheless, the coefficient magnitudes are relatively lower. The stability of the model is also confirmed through significant and negative error correction terms, implying a substantial convergence towards steady-state equilibrium in case of any deviation. These results are also validated by alternative estimators and offer valuable policy recommendations.

Spanish Pre-Service EFL Teachers’ Perceptions of Emergency Remote Language Teaching

This study examines the perceptions of pre-service English as a foreign language (EFL) teachers of their skills to face emergency remote language teaching (ERLT) situations, and to determine whether these vary according to gender, age, university, or course. A cross-sectional survey model was used with 332 pre-service EFL teachers studying in two Spanish universities, an onsite institution, and a distance university. After analyzing the results, it can be said that participants’ ERLT skills are moderate, and they vary significantly according to age, course, and type of university, but no statistically significant differences are found regarding gender. The results can guide teacher trainers and researchers, as they reveal the needs of pre-service EFL teachers to teach effectively in ERLT situations.

Quantification of long-term fate and health-risk of remobilized radiocesium through porous and fractured aquifers below a dam

Understanding the long-term fate and transport of radiocesium (137Cs) through porous and fractured aquifers is critical for the risk assessment of nuclear accidents. In particular, characterizing 137Cs transport below a dam storing potable water is critical for assessing the risk of 137Cs migration. In this study, a 2D cross-sectional aquifer model was developed based on Paldang Reservoir in South Korea, and transport of desorbed 137Cs beneath a dam was investigated systematically. Various scenarios investigated 137Cs transport within the reported ranges of distribution coefficient (Kd) and local temperature conditions (basal heat flow and reservoir-bottom temperature) affecting the 137Cs desorption rate and hydrogeologic heterogeneity (hydraulic conductivity and fracture networks). The characteristics of the 137Cs plume represented by the average plume concentration and the migration rate of the mass center were assessed, and the health risk of chronic exposure for humans was predicted. In base-case (K=8.64×10−2 m/day and Kd=10 mL/g), approximately 0.06 % of initially released 137Cs was transported downstream over 300 years; the maximum migration rate was 5.1×10−4 m/day, and a maximum annual radioactive dose was 4.5 mSv/y after 230 years. The notable effect of Kd on the migration rate (5.1×10−5 ∼ 9.9×10−4 m/day) and annual dose (9.1×10−11 ∼ 130 mSv/y) indicates that the Kd is a critical parameter that controls the transport of 137Cs in the subsurface aquifer. The effect of temperature on 137Cs transport was relatively insignificant, but it still had a noticeable effect, especially on the desorption rate. In addition, 50 realizations of heterogeneous K and Kd were generated to evaluate the influence of physical and chemical heterogeneity on 137Cs transport. The migration rate and annual dose of 137Cs plume showed large variability when K and Kd were correlated. Finally, 137Cs transport was evaluated in fractured aquifers and assessed the importance of fracture orientation and connectivity.

Investigating the Link Between IADL and Depressive Symptoms in Older Adults: A Cross-Sectional Serial Mediation Model

ABSTRACT Objectives There is a dearth of research on the psychological processes that underlie the negative relation between impaired instrumental activities of daily living (IADL) and depressive symptoms in older adults. Drawing on the stress process model and the resilience framework, we investigated whether purpose in life and resilience serially mediate the relationship between impaired IADL and depressive symptoms. Methods We recruited 111 cognitively healthy community-dwelling older adults (ages 54–85; M = 66.5) who scored a minimum of 25 points on the Mini-Mental State Examination. Results We found that purpose in life and resilience serially mediated the relationship between IADL and depressive symptomatology in older adults. This association held true when we controlled for covariates. Additional sensitivity analyses also supported these findings. Conclusions This study extends our understanding of how IADL limitations contribute to depressive symptoms. Using a community-dwelling, cognitively healthy sample, we demonstrate that functional limitations indirectly influence older adults’ depressive symptoms through a decreased sense of purpose in life and decreased resilience. Clinical implications Our findings have implications for intervention programs that aim to alleviate IADL limitations and mental health issues in an aging population and promote healthy aging by improving psychosocial resources (i.e., purpose in life and resilience).

What predicts climate change activism?: An examination of how depressive symptoms, climate change distress, and social norms are associated with climate change activism.

The current and future harms caused by climate change are highly distressing. Different theoretical models suggest diverse impacts of distress on behavior. We examined how psychological distress, climate change distress, and social norms may foster or impede climate change activism. As part of an ongoing online longitudinal study in the US beginning in March 2020, respondents were assessed on their depressive symptoms (CES-D 10), climate change distress, climate change mitigation social norms, and six outcomes of the climate change activism behaviors of writing letters, e-mailing, or phoning government officials; voting for candidates who support measures to reduce climate change; signing petitions; volunteering with organizations; donating money to organizations; and attending protests. Of the 775 respondents, 53% were female, 72% white, 12% Black, 7% Hispanic, and 6% Asian. Climate change social norms predicted all six climate change actions in the bivariate and multivariable cross-sectional logistic regression models. A similar finding was observed with the brief climate change distress scale (BCCDS), except it was not associated with volunteering in the multivariable model. Depressive symptoms were associated with greater odds of contacting government officials and signing petitions in the bivariate models but did not retain significance in the multivariable models. Longitudinal models indicated a weak association between depressive symptoms and climate change activism. Climate change distress and social norms are positively associated with climate change activism. Although climate change distress may not usually impede climate change activism, organizations addressing climate change should consider providing social support to members and assisting those with high levels of psychological and climate change distress. Social norms around climate change activism should be fostered.

Investigating the Asymmetry of Magnetic Field Profiles of “Simple” Magnetic Ejecta through an Expansion-modified Flux Rope Model

Magnetic clouds (MCs) are most often fitted with flux rope models that are static and have symmetric magnetic field profiles. However, spacecraft measurements near 1 au show that MCs usually expand when propagating away from the Sun and that their magnetic field profiles are asymmetric. Both effects are expected to be related, since expansion has been shown to result in a shift of the peak of the magnetic field toward the front of the MC. In this study, we investigate the effects of expansion on the asymmetry of the total magnetic field strength profile of MCs. We restrict our study to the simplest events, i.e., those that are crossed close to the nose of the MC. From a list of 25 such “simple” events, we compare the fitting results of a specific expanding Lundquist model with those of a classical force-free circular cross-sectional static Lundquist model. We quantify the goodness of the fits by the χ 2 of the total magnetic field and identify three types of MCs: (i) those with little expansion, which are well fitted by both models; (ii) those with moderate expansion, which are well fitted by the expanding model, but not by the static model; and (iii) those with expansion, whose asymmetry of the magnetic field cannot be explained. We find that the assumption of self-similar expansion cannot explain the measured asymmetry in the magnetic field profiles of some of these magnetic ejecta (MEs). We discuss our results in terms of our understanding of the magnetic fields of the MEs and their evolution from the Sun to Earth.

Production impacts of climate change on agricultural livelihoods among smallholder panel households across Ethiopia

This study analyzes the production impacts of climate change on smallholder agricultural households across Ethiopia. Hypothesizing that climate change affects agricultural livelihoods mainly through productive and distributive effects, this article examines an integrated farm [crop, livestock, mixed] production impacts overtime. Methodically, the comprehensive Ethiopian socioeconomic survey (ESS] panel data, and nation-wide observatory 60-years climate data- precipitation and temperature [1960-2019] were merged to form a joint panel database; then analyzed using Ricardian panel model with random effects regression. Objectively, factor productivity, the rate of convergence, the historical, real, and seasonal climate impacts were investigated against net-agricultural return overtime. The applied panel model augments both the temporal, spatial, and individual effects and yields more efficient and consistent estimates than the cross-sectional and time series models. The results revealed that CC poses net-negative, increasing and significant impacts on factor elasticity, percapita farm output, and net-farm revenue [NFR] due to diminishing marginal returns; the progressive temporal impacts; regressive duration impacts; divergence effects on the growth of net-farm return; and mixed regional, farm, and HH impacts. Therefore, introducing institutionalized sustainable livelihoods framework [green institutions, finance, education, training, research, inputs, subsidy, insurance, market] in agricultural production system would enhance sustainable production and improved welfare among smallholder households even under changing climate.

Global Association between Traditional Japanese Diet Score and All-Cause, Cardiovascular Disease, and Total Cancer Mortality: A Cross-Sectional and Longitudinal Ecological Study

Objective Studies conducted on Japanese people have suggested that a traditional Japanese diet contributes to good health, longevity, and protection against several non-communicable diseases. However, it is unknown whether traditional Japanese dietary patterns are associated with all-cause mortality, cardiovascular disease, and cancer mortality globally. The purpose of this cross-sectional and longitudinal ecological study is to clarify the global association between the traditional Japanese diet score (TJDS) and all-cause, cardiovascular disease, and total cancer mortality. Methods Data on food supply and all-cause mortality, cardiovascular disease mortality, total cancer mortality, and covariables by country were obtained from a relevant internationally available database. TJDS by country was calculated from eight food groups and the total score ranged from −8 to 8, with higher scores indicating greater adherence to a traditional Japanese diet. We evaluated the cross-sectional and 10-year longitudinal association between TJDS and all-cause, cardiovascular disease, and total cancer mortality using 2009 as the baseline in 142 countries with populations of more than one million. A cross-sectional analysis and a longitudinal analysis were performed using three general linear models or three linear mixed models with different covariables. Results In cross-sectional models controlled for fully-adjusted covariables, TJDS was negatively associated with all-cause mortality (β ± standard error; −43.819 ± 11.741, p < 0.001), cardiovascular disease mortality (−22.395 ± 4.638, p < 0.001), and total cancer mortality (−3.893 ± 1.048, p < 0.001). In 10-year longitudinal models controlled for fully-adjusted covariables, TJDS was significantly negatively associated with all-cause mortality (−31.563 ± 7.695, p < 0.001), cardiovascular disease mortality (−16.249 ± 4.054, p < 0.001), and total cancer mortality (−3.499 ± 0.867, p < 0.001). Conclusions This cross-sectional and longitudinal ecological study suggests that the traditional Japanese diet is associated with lower all-cause mortality, cardiovascular disease mortality, and total cancer mortality, worldwide.

Social Determinants of Health and Body Mass Index in American Indian/Alaska Native Children.

Objective: To examine the associations between social determinants of health (SDOH) and prevalent overweight/obesity status and change in adiposity status among American Indian and Alaska Native (AI/AN) children. Methods: The study sample includes 23,950 AI/AN children 2-11 years of age, who used Indian Health Service (IHS) from 2010 to 2014. Multivariate generalized linear mixed models were used to examine the following: (1) cross-sectional associations between SDOH and prevalent overweight/obesity status and (2) longitudinal associations between SDOH and change in adiposity status over time. Results: Approximately 49% of children had prevalent overweight/obesity status; 18% had overweight status and 31% had obesity status. Prevalent severe obesity status was 20% in 6-11-year olds. In adjusted cross-sectional models, children living in counties with higher levels of poverty had 28% higher odds of prevalent overweight/obesity status. In adjusted longitudinal models, children 2-5 years old living in counties with more children eligible for free or reduced-priced lunch had 15% lower odds for transitioning from normal-weight status to overweight/obesity status. Conclusions: This work contributes to accumulating knowledge that economic instability, especially poverty, appears to play a large role in overweight/obesity status in AI/AN children. Research, clinical practice, and policy decisions should aim to address and eliminate economic instability in childhood.

Food insecurity as a risk factor for obesity: A review.

Obesity is considered a 21st-century epidemic and it is a metabolic risk factor for Non-Communicable Diseases such as cardiovascular diseases, type 2 diabetes, metabolic syndrome, hypertension, some types of cancer, among others. Thus, its prevention and treatment are important public health concerns. Obesity within the context of food insecurity adds an additional layer of complexity to the current obesity epidemic. Efficient policies and interventions ought to take into consideration the effects of food insecurity on the risks of developing obesity among food insecure households. This review aims to analyze the recent available evidence around the obesity – food insecurity paradox. Most of the literature has consistently shown that there is a significant association between food insecurity and obesity, specifically in women of high-income countries. However, mechanisms explaining the paradox are still lacking. Even though researchers have tried to analyze the issue using different individual and societal variables, these studies have failed to explain the mediatory mechanisms of the food insecurity–obesity relationship since the proposed mechanisms usually lack strength or are purely theoretical. The research focus should shift from cross-sectional models to other research designs that allow the exploration of pathways and mechanisms underlying the food insecurity and obesity relationship, such as longitudinal studies, which will hopefully lead to consecutive research testing the effectiveness of different approaches and scale up such interventions into diverse contexts among those affected by obesity and the different degrees of food insecurity.

Families following pediatric traumatic medical events: identifying psychosocial risk profiles using latent profile analysis

ABSTRACT Background: Post-traumatic stress symptoms (PTSS) are often experienced by children and family members after pediatric traumatic medical events (PTMEs). Assessing families’ psychosocial risk factors is a crucial part of trauma-informed practice as it helps identify risk for PTSS in the aftermath of PTME. Objectives: Using the Psychosocial Assessment Tool 2.0 (PAT2.0), this study describes the psychosocial risk of families following PTMEs in two ways: 1. Describing the psychosocial risk defined by the PAT2.0 based on three-tiered risk levels; 2. Using latent profile analysis (LPA); identifying psychosocial risk profiles and examining how child- and injury-related factors can affect profile membership. Methods: Caregivers of 374 children following PTMEs admitted to a pediatric rehabilitation department in Israel completed the PAT2.0. Total PAT2.0 score and the seven PAT2.0 subscales (family structure/resources, social support, child problems, sibling problems, family problems, caregiver stress reactions, and family beliefs) were included in the first analysis. Mean PAT2.0 scores of three risk categories (universal, targeted, clinical) were calculated; LPA, which allows for cross-sectional latent variable mixture models to identify heterogeneity within a population, and multinomial logistic regressions using six out of the seven PAT2.0 subscales, were used to determine distinct profile differences and predictors of profile membership. Results: The three-tiered risk levels revealed were relatively high, as compared to levels in families of children with other clinical diagnoses. LPA yielded a three-profile solution: low family risk (63.53%); high caregiver stress, above-average levels of family risk (22.5%); and sibling problems, above-average levels of family risk (13.94%). Ethnicity and type of injury predicted group membership. Conclusion: Families of children following PTMEs are at increased psychosocial risk. A clinically useful approach to identifying and preventing PTSS may be to evaluate specific domain patterns rather than just the total PAT2.0 risk level alone, based on the PAT2.0 subscales.