Subsequent Regressions Research Articles

An energy transition for all: Investigating determinants of citizen support for energy community initiatives on the island of Mayotte

For an equitable transition towards a decentralized and clean energy future, the involvement and active participation of citizens is needed, exemplified by new organizational forms such as Energy Communities (ECs). While determinants of citizen engagement in renewable energy projects have received some attention in the literature, research mostly focuses on continental Europe, with limited understanding of the perceptions and motivations of citizens living in the Global South or in contexts of poverty and marginalization. Using novel survey data (n = 392) and insights from 16 key informant interviews from the French overseas territory Mayotte, we investigate citizens' perception of renewable energies and ECs and their willingness to participate in such initiatives, confirming high support of the local population for a community-based energy transition. Through exploratory factor analysis and subsequent multivariate regressions, we identify the factors Environmental Awareness, Access to Services and Social Inclusion as important determinants of citizen engagement, with Environmental Awareness exhibiting particularly strong and robust effects on both general support and the willingness to actively participate in an EC. Establishing the housing situation of respondents as a proxy for socio-economic status (SES), we find positive effects of SES on EC support, which is mediated by the identified factors and thereby represents a fourth, underlying determinant of support. Our research underscores the vital importance of considering the multifaceted, diverse perceptions and motivations of the local population to achieve the vision of an equitable transition. Inclusion of needs and priorities of various socio-economic groups, particularly the most vulnerable community members, is essential.

It's not just Black and White: Identifying the combined influence of multi-level determinants of tobacco use among Black adolescents.

Black individuals are disproportionately affected by tobacco-related illnesses. Although tobacco use is often initiated in adolescence and risk factors thereof originate from multiple domains of influence, investigations of tobacco use among Black adolescents seldom consider these multiple domains simultaneously. These examinations are needed to identify the influence of co-occurring risk factors on tobacco use and inform comprehensive tobacco prevention and treatment programs. Our study sought to identify the combined influence of factors across multiple domains on tobacco use among Black adolescents. Participants were Non-Hispanic Black (N = 1,801) adolescent (age 12-17 years) respondents from Wave 1 of the Population Assessment of Tobacco and Health study. Latent class analysis identified unique response patterns to items assessing risk of tobacco use across sociocultural, environmental, psychological, and behavioral (e.g., alcohol use) domains. Subsequent logistic regressions compared the odds of ever and current tobacco use between the classes. Four latent classes were identified. According to item response probabilities they were named: Low Risk (LR: 36.5%), Low Psychological (LP: 19.0 %), High Psychological (HP: 30.4%) and High Social, Psychological, and Behavioral (HSPB: 14.1%) risk. The odds of ever and current tobacco use were highest among adolescents in the HSPB latent class compared to the other latent classes (odds ratio: 6.5 to 42.1). Adolescents with the highest odds of tobacco use endorse multiple risks including feeling distress, perceiving tobacco as beneficial for handling stress, and using substances, and may prioritize the management of negative emotions over perceived health consequences from tobacco use. Multi-level interventions that incorporate the development of coping strategies for effectively handling negative affect may prove highly effective in preventing tobacco use among Black adolescents.

Neural Correlates of Obesity and Inflammation in Children and Adolescents with Congenital Adrenal Hyperplasia

Introduction: Patients with classical congenital adrenal hyperplasia (CAH) exhibit an increased prevalence of obesity from childhood including central adiposity and inflammation. There is also an emerging affected brain phenotype in CAH, with decreased cortico-limbic gray matter volumes and white matter abnormalities. We aimed to study the relationship between brain structure, obesity, and inflammation in children and adolescents with CAH compared to controls. Methods: 27 CAH (12.6 ± 3.4 y, 16 females) and 35 control (13.0 ± 2.8 y, 20 females) participants had MRI of gray matter regions of interest (prefrontal cortex [PFC], amygdala, hippocampus) and white matter microstructure (fornix, stria terminalis [ST]). Anthropometric measures and lab analytes were obtained. Relaimpo analyses (relative importance for linear regression; percent variance) identified which brain structures were most different between groups. Subsequent regressions further quantified the magnitude and direction of these relationships. Correlations analyzed relationships between brain structure, obesity, and inflammation in the context of CAH status. Results: PFC (13.3% variance) and its superior frontal (SF) subregion (14%) were most different between CAH and controls for gray matter; ST (16%) for white matter. Patients with CAH had lower caudal middle frontal (β = −0.56 [−0.96, −0.15]) and superior frontal (β = −0.58 [−0.92, −0.25]) subregion volumes, increased orientation dispersion index in the fornix (β = 0.56 [0.01, 1.10]) and ST (β = 0.85 [0.34, 1.36]), and decreased fractional anisotropy in the fornix (β = −0.91 [−1.42, −0.42]) and ST (β = −0.83 [−1.34, −0.33]) (all p’s r = −0.41), visceral adipose tissue (r = −0.25), and waist-to-height ratio (r = −0.27, all p’s r = 0.31) and TNF-α (r = 0.29, all p’s Conclusions: Relationships among key brain structures, body composition, and inflammatory markers in pediatric patients with CAH could be largely driven by having CAH, with implications for obesity and neuroinflammation in this high-risk population.

Clinical classification of memory and cognitive impairment with multimodal digital biomarkers.

Early detection of Alzheimer's disease and cognitive impairment is critical to improving the healthcare trajectories of aging adults, enabling early intervention and potential prevention of decline. To evaluate multi-modal feature sets for assessing memory and cognitive impairment, feature selection and subsequent logistic regressions were used to identify the most salient features in classifying Rey Auditory Verbal Learning Test-determined memory impairment. Multimodal models incorporating graphomotor, memory, and speech and voice features provided the stronger classification performance (area under the curve=0.83; sensitivity=0.81, specificity=0.80). Multimodal models were superior to all other single modality and demographics models. The current research contributes to the prevailing multimodal profile of those with cognitive impairment, suggesting that it is associated with slower speech with a particular effect on the duration, frequency, and percentage of pauses compared to normal healthy speech.

Potential factors associated with resilience among older adults in rural China: a multilevel analysis

BackgroundResilience is crucial for older adults who experience adversities, but research on the issue in rural China remains limited. This study aims to examine factors associated with resilience among older adults in rural China, as related to different types of resilience, and under different levels of adversity.MethodsData were taken from the eight-wave (2001–2021) Longitudinal Study of Older Adults in Anhui Province, China. We used data from the eighth wave (2021) for the outcome variables and lagged predictors (2018) to avoid reverse causal effects. The study sample included individuals 60 years and above, excluding new participants from 2021, those without any adverse events, and any respondents with incomplete analytic data. Resilience was operationalized as residuals of the regressions of life satisfaction (Life Satisfaction Scale) and depressive symptoms (CES-D) on adversity, referred to as Type-1 and Type-2 resilience respectively. These two types of resilience were then treated as the outcome variables in subsequent multilevel regressions, with the predictors focusing on individual, social, and environmental characteristics and resources. This study adheres to STROBE guidelines.Results43% of rural older adults exhibited both Type-1 and Type-2 resilience, whereas 18% exhibited only Type-1 resilience and 7% exhibited only Type-2 resilience. Common factors associated with both types of resilience included self-rated health, satisfaction with one’s own financial situation, and the prestigiousness of social networks. Predictors for higher levels of Type-1 resilience included higher levels of financial and emotional support and more options for places of leisure. Predictors for higher levels of Type-2 resilience included greater access to medical care. The prestigiousness of social networks, higher levels of emotional support and instrumental support, access to medical care, and more options of places of leisure were positively associated with resilience in the low-adversity group (first tertile of adversity), only satisfaction with financial situation was positively correlated with the resilience of the middle-adversity group (second tertile), while better self-rated health, satisfaction with financial situation, and financial support yielded greater resilience in the high-adversity group (third tertile).ConclusionsWe examined two types of resilience among older adults in rural China, and found that they have shared and unique associated factors. In addition, the potential factors influencing resilience varied with the level of adversity.

(Keynote) On Interpretation of Impedance Spectroscopy Data

Impedance spectroscopy represents a rich area of science that has been applied to many research disciplines, including those associated with corrosion and corrosion control, energy systems, and biological systems. In laboratory settings, impedance has been used to study the kinetics and mechanisms of electrochemical reactions. Impedance spectroscopy has also been applied in industrial applications to quality control and corrosion monitoring and constitutes the basis of a class of sensors [1].Impedance spectra are easily measured by used of modern instrumentation. The difficulty lies in interpretation of impedance spectra in terms of physically meaningful information. While graphical methods [2], distribution of relaxation times analyses [3], and measurement model analyses [4] can guide model development, interpretation is generally based on use of a deterministic model that describes the physics of the system under study [5]. Such models may be coupled with electrical circuits that account for the double layer formed at the electrified interface and for the electrolyte resistance. The deterministic model approach is often perceived as being much more complicated than use of electrical circuits alone because it requires mathematical expertise.In general, interpretation of the impedance response requires both a quantitative evaluation of the error structure of the impedance measurement and insight into the physics and chemistry of the system under study. Analysis of the error structure yields a standard deviation for the stochastic errors, which can be used to weight subsequent regressions. The error analysis also identifies the range of frequencies that are consistent with the Kramers–Kronig relations and are, therefore, suitable for regression analysis. The physical insight is used to guide the interpretation model development. It should be noted that interpretation models are not unique. The models may suggest other experiments which can be used to reject the proposed model. Greater confidence may be assigned to a model that explains a variety of experimental observations.The objective of this paper is to review our work on interpretation of impedance data, including the identification and quantification of the error structure associated with impedance spectroscopy measurements. The presentation makes use of a program that allows users to access the power of the measurement model concept and to fit custom process models [6].References S. Wang, J. Zhang, O. Gharbi, V. Vivier, M. Gao, and M. E. Orazem, “Electrochemical Impedance Spectroscopy,” Nat. Rev. Methods Primers, 1, 41 (2021), 1-21.M. E. Orazem, and N. Pébère, and B. Tribollet, “Enhanced Graphical Representation of Electrochemical Impedance Data,” J. Electrochem.Soc., 153 (2006), B129-B136.B. A. Boukamp, “Distribution (Function) of Relaxation Times, Successor to Complex Nonlinear Least Squares Analysis of Electrochemical Impedance Spectroscopy?” J. Phys. Energy, 4 (2020), 042001.H. Liao, W. Watson, A. Dizon, B. Tribollet, V. Vivier, and M. E. Orazem, “Physical Properties Obtained from Measurement Model Analysis of Impedance Measurements,” Electrochim. Acta, 354 (2020), 136747.V. Vivier and M. E. Orazem, “Impedance Analysis of Electrochemical Systems,” Chem. Rev., 122 (2022), 11131-11168.W. Watson and M. E. Orazem, EIS: Measurement Model Program, ECSArXiv, 2020, https://ecsarxiv.org/kze9x/.

Regional diversification and labour market upgrading: local access to skill-related high-income jobs helps workers escaping low-wage employment

Abstract This article investigates how the evolution of local labour market structure enables or constrains workers as regards escaping low-wage jobs. Drawing on the network-based approach of evolutionary economic geography, we employ a detailed individual-level panel dataset to construct skill-relatedness networks for 72 functional labour market regions in Sweden. Subsequent fixed-effect panel regressions indicate that increasing density of skill-related high-income jobs within a region is conducive to low-wage workers moving to better-paid jobs, hence facilitating labour market upgrading through diversification. While metropolitan regions offer a premium for this relationship, it also holds for smaller regions, and across various worker characteristics.

Heterogeneity and associated factors of patients with polycystic ovary syndrome health behaviors: a latent class analysis

ObjectiveUsing latent class to analyze whether there are subtypes of health behaviors in patients with PCOS can be addressed using targeted interventions.MethodsOctober 2021 to June 2022, 471 PCOS patients were surveyed using the Health Promoting Lifestyle Profile Questionnaire. Latent class analysis (LCA) was used to identify subgroups of PCOS patients. Subsequent multinomial latent variable regressions identified factors that were associated with health behaviors.ResultsA three-class subtypes was the optimum grouping classification: (1)High healthy behavior risk; (2)high healthy responsibility and physical activity risk; (3)low healthy behavior risk. The multinomial logistic regression analysis revealed that (1)Single (OR = 2.061,95% CI = 1.207–3.659), Education level is primary school or below (OR = 4.997,95%CI = 1.732–14.416), participants is student (OR = 0.362,95%=0.138–0.948), participants with pregnancy needs (OR = 1.869,95%=1.009–3.463) were significantly more likely to be in the high healthy behavior risk subtypes; (2)The older the age (OR = 0.953,95%=0.867–1.047) and the larger the WC (OR = 0.954,95%=0.916–0.993), participants is married (OR = 1.126,95%=0.725–1.961), participants is employed ( OR = 1.418,95%=0.667–3.012) were significantly more likely to be in the high health responsibility and physical activity risk subtypes.ConclusionPatients with PCOS are a heterogeneous population with potential subtypes that may be suitable for customized multi-level care and targeted interventions.

Social Determinants of COVID-19 Vaccination Rates: A Time-Constrained Multiple Mediation Analysis.

Objective To estimate the multiple direct/indirect effects of social, environmental, and economic factors on COVID-19 vaccination rates (series complete) in the 3109 continental counties in the United States (U.S.). Study design The dependent variable was the COVID-19 vaccination rates in the U.S. (April 15, 2022). Independent variables were collected from reliable secondary data sources, including the Census and CDC. Independent variables measured at two different time frames were utilized to predict vaccination rates. The number of vaccination sites in a given county was calculated using the geographic information system (GIS) packages as of April 9, 2022. The Internet Archive (Way Back Machine) was used to look up data for historical dates. Methods A chain of temporally-constrained least absolute shrinkage and selection operator (LASSO)regressions was used to identify direct and indirect effects on vaccination rates. The first regression identified direct predictors of vaccination rates. Next, the direct predictors were set as response variables in subsequent regressions and regressed on variables that occurred before them. These regressions identified additional indirect predictors of vaccination. Finally, both direct and indirect variables were included in a network model. Results Fifteen variables directly predicted vaccination rates and explained 43% of the variation in vaccination rates in April 2022. In addition, 11 variables indirectly affected vaccination rates, and their influence on vaccination was mediated by direct factors.For example, children in poverty rate mediated the effect of (a) median household income, (b) children in single-parent homes, and (c) income inequality. For another example, median household income mediated the effect of (a) the percentage of residents under the age of 18, (b) the percentage of residents who are Asian, (c) home ownership, and (d) traffic volume in the prior year. Our findings describe not only the direct but also the indirect effect of variables. Conclusions A diverse set of demographics, social determinants, public health status, and provider characteristics predicted vaccination rates. Vaccination rates change systematically and are affected by the demographic composition and social determinants of illness within the county.One of the merits of our study is thatit shows how the direct predictors of vaccination rates could be mediators of the effects of othervariables.

Description and prediction of copper contents in soils using different modeling approaches—Results of long‐term monitoring of soils of northern Germany

AbstractBackgroundDifferent regression approaches may be useful to predict dynamics of copper (Cu), an essential element for plants and microorganisms that becomes toxic at increased contents, in soils.AimOur objective was to explore the usefulness of mixed‐effects modeling and rule‐based models for a description and prediction of Cu contents in aqua regia (CuAR) in surface soils using site, pH, soil organic carbon (SOC), and the cation exchange capacity (CEC) as predictors.MethodsThree sites in northern Germany were intensively monitored with respect to CuAR and SOC contents, pH, and CEC. Data analysis consisted of calibrations using the entire data set and of calibration/validation approaches with and without spiking.ResultsThere was no consistent temporal trend, so data could be combined for the subsequent regressions. Calibration using the entire data set and calibration/validation after random splitting (i.e., pseudo‐independent validation) were successful for mixed‐effects and cubist models, with Spearman's rank correlation coefficients rs ranging from 0.83 to 0.91 and low root mean squared errors (RMSEs). Both algorithms included SOC, CEC, and pH as essential predictors, whereas site was important only in the mixed‐effects models. Three‐fold partitioning of the data according to site to create independent validations was again successful for the respective calibrations, but validation results were variable, with rs ranging from 0.04 to 0.76 and generally high RMSEs. Spiking the calibration samples resulted in generally marked improvements of the validations, with rs ranging from 0.45 to 0.67 and lower RMSEs.ConclusionsOverall, the information provided by SOC, pH, and CEC is beneficial for predicting CuAR contents in a closed population of sites using either mixed‐effects or cubist models. However, for a prediction of CuAR dynamics at new sites in the region, spiking is required.

Early oral language precursors of different types of reading difficulties in a consistent orthography

The present longitudinal study examined whether early oral language skills of Greek-speaking children assessed in grade 1 can predict the type of reading difficulties (RD) in grade 2. Sixty-six typically developing (TD) children and eighty-seven children with RD were assessed on phonological awareness (PA), morphological awareness (MA), rapid automatized naming (RAN), and vocabulary in the mid of grade 1. Children were classified in the two groups based on whether they scored consistently low (below the 25th percentile) or typically (above the 25th percentile) on standardized measures of text-reading fluency and reading comprehension at the end of grade 1 and the beginning of grade 2. Next, children with RD were assigned to two subgroups: the first group included children (N = 28) with predominantly reading fluency difficulties (RFD) and the second group included children (N = 59) with single reading comprehension difficulties (RCD). A series of binomial logistic regressions showed that children’s classification in an RD group than a TD group was predicted by PA, RAN, and vocabulary achievement. Subsequent multinomial logistic regressions indicated that vocabulary, PA, and MA predicted children’s classification in the RCD subgroup more than in the TD group. Furthermore, lower PA levels and higher RAN score predicted the classification of children in the RFD group than in the RCD or the TD group. These findings highlight the contribution of early oral language assessment to the identification of children with RD and specific types of RD. Theoretical implications for the role of oral language in reading will be discussed as well as practical implications for implementing customized interventions to match children’s educational needs on specific oral language deficits.

A diffusion tensor imaging analysis of white matter microstructures in non-operated craniosynostosis patients

PurposeIn 7 to 15-year-old operated syndromic craniosynostosis patients, we have shown the presence of microstructural anomalies in brain white matter by using DTI. To learn more about the cause of these anomalies, the aim of the study is to determine diffusivity values in white matter tracts in non-operated syndromic craniosynostosis patients aged 0–2 years compared to healthy controls.MethodsDTI datasets of 51 non-operated patients with syndromic craniosynostosis with a median [IQR] age of 0.40 [0.25] years were compared with 17 control subjects with a median of 1.20 [0.85] years. Major white matter tract pathways were reconstructed with ExploreDTI from MRI brain datasets acquired on a 1.5 T MRI system. Eigenvalues of these tract data were examined, with subsequent assessment of the affected tracts. Having syndromic craniosynostosis (versus control), gender, age, frontal occipital horn ratio (FOHR), and tract volume were treated as independent variables.Resultsʎ2 and ʎ3 of the tracts genu of the corpus callosum and the hippocampal segment of the cingulum bundle show a ƞ2 > 0.14 in the comparison of patients vs controls, which indicates a large effect on radial diffusivity. Subsequent linear regressions on radial diffusivity of these tracts show that age and FOHR are significantly associated interacting factors on radial diffusivity (p < 0.025).ConclusionSyndromic craniosynostosis shows not to be a significant factor influencing the major white matter tracts. Enlargement of the ventricles show to be a significant factor on radial diffusivity in the tracts corpus callosum genu and the hippocampal segment of the cingulate bundle.Clinical trial registration: MEC-2014-461

Public trust in citizens’ juries when the people decide on policies: evidence from Switzerland

ABSTRACT Facilitative political trust is the reduction in cognitive demand citizens experience when forming their opinions about political issues. This type of trust is important for generating legitimate democratic institutions in the eyes of politically uninvolved citizens. The article develops and validates an original direct measure of facilitative political trust among voters receiving a voting aid compiled by a Swiss municipal-level deliberative minipublic convening twenty citizens ahead of a federal popular initiative vote on expanding affordable housing policies. Based on perceiving the randomly selected group as competent and aligned with voters’ interests, we find a reliable and valid latent trust measure using confirmatory factor analyses among the same sample of voters within the municipality at two points during the campaign, ahead (N = 1159), and again around the time of the ballot (N = 472). In subsequent multiple regressions, increases in facilitative trust sores are the main driver of readers’ ratings of both the voting aid’s usefulness for deciding how to vote and of how important they judge its information for their peers’ vote decisions.

Parental Mediation and Adolescents' Internet Use: The Moderating Role of Parenting Style.

Internet use can be distinguished into different uses (e.g., leisure-related, learning-related), yet comprehensive studies on how different uses are associated with everyday parenting situations are still lacking. This study attempts to locate parental mediation within broader family contexts and simultaneously considers the relationships among general parenting style, media-specific parenting practices, and adolescents’ amount and types of Internet use. Building on survey data collected from 1284 middle school students in China (mean age = 13, SD = 0.79, 48.60% girls), the Latent Profile Analysis identified three child-perceived profiles of general parenting style: slight-engaged, supportive, and rejecting-controller. The subsequent regressions suggested that adolescents with supportive parents reported lower levels of time spent online as well as leisure-related use; more restrictive parental mediation was associated with reduced leisure-related use while more active mediation was associated with more learning-related use. Notably, associations between parental use of active mediation and youth’s amount of Internet use and leisure-related use varied based on parenting style profiles. Only for the supportive parenting profile, more use of active mediation was associated with decreased amount of Internet use as well as leisure-related use. These findings have implications on how parents can be more effective in guiding youth’s Internet use.

Neoantigen-Reactive T Cells: The Driving Force behind Successful Melanoma Immunotherapy.

Simple SummaryCancer immunotherapy is a revolutionary type of cancer therapy. It uses the patient’s own immune system to fight and potentially cure cancer. The first major breakthrough of immunotherapy came from successful clinical trials for melanoma treatments. Since then, researchers have focused on understanding the science behind immunotherapy, so that patients with other types of cancer may also benefit. One of the major findings is that the T cells in melanoma patients may recognize a specific type of tumor antigen, called neoantigens, and then kill tumor cells that present these neoantigens. The neoantigens mainly arise from the DNA mutations found in tumor cells. These mutations are translated into mutated proteins that are then distinguished by T cells. In this article, we discuss the critical role of T cells in immunotherapy, as well as the clinical trials that shaped the treatments for melanoma.Patients with metastatic cutaneous melanoma have experienced significant clinical responses after checkpoint blockade immunotherapy or adoptive cell therapy. Neoantigens are mutated proteins that arise from tumor-specific mutations. It is hypothesized that the neoantigen recognition by T cells is the critical step for T-cell-mediated anti-tumor responses and subsequent tumor regressions. In addition to describing neoantigens, we review the sentinel and ongoing clinical trials that are helping to shape the current treatments for patients with cutaneous melanoma. We also present the existing evidence that establishes the correlations between neoantigen-reactive T cells and clinical responses in melanoma immunotherapy.

Psychological and Contextual Predictors of Online Video Use: A Latent Profile Analysis

ABSTRACT The purpose of this exploratory study is to (i) empirically determine homogeneous classes of online video users based on variables involving three dimensions of video usage behaviors (viewing, interacting, and producing) by Latent Profile Analysis; (ii) examine how psychological characteristics and contextual factors predict different patterns of online video use. By analyzing the first-hand online survey data involving 598 valid responses, we distinguished four subgroups of online video users: Mild-users, Moderate-users, Moderate-bystanders, and Heavy-users. The subsequent multinomial regressions showed that neuroticism, extraversion, agreeableness, need for cognition, and personal education were associated with the distinct usage patterns of online streaming services.

Binge Drinking Relates to Worse Neurocognitive Functioning Among Adults Aging with HIV.

Given the aging population of people with HIV (PWH), along with increasing rates of binge drinking among both PWH and the general older adult population, this study examined the independent and interactive effects of HIV, binge drinking, and age on neurocognition. Participants were 146 drinkers stratified by HIV and binge drinking status (i.e., ≥4 drinks for women and ≥5 drinks for men within approximately 2 h): HIV+/Binge+ (n = 30), HIV-/Binge+ (n = 23), HIV+/Binge- (n = 55), HIV-/Binge- (n = 38). All participants completed a comprehensive neuropsychological battery measuring demographically-corrected global and domain-specific neurocognitive T scores. ANCOVA models examined independent and interactive effects of HIV and binge drinking on neurocognitive outcomes, adjusting for overall alcohol consumption, lifetime substance use, sex, and age. Subsequent multiple linear regressions examined whether HIV/Binge group moderated the relationship between age and neurocognition. HIV+/Binge+ participants had worse global neurocognition, processing speed, delayed recall, and working memory than HIV-/Binge- participants (p's < .05). While there were significant main effects of HIV and binge drinking, their interaction did not predict any of those neurocognitive outcomes (p's > .05). Significant interactions between age and HIV/Binge group showed that HIV+/Binge+ participants demonstrated steeper negative relationships between age and neurocognitive outcomes of learning, delayed recall, and motor skills compared to HIV-/Binge- participants (p's < .05). Results showed adverse additive effects of HIV and binge drinking on neurocognitive functioning, with older adults demonstrating the most vulnerability to these effects. Findings support the need for interventions to reduce binge drinking, especially among older PWH.

Clinical correlates of white matter lesions in Parkinson's disease using automated multi-modal segmentation measures

BackgroundAge-related white matter lesions (WML) are common, impact neuronal connectivity, and affect motor function and cognition. In addition to pathological nigrostriatal losses, WML are also common co-morbidities in Parkinson's disease (PD) that affect postural stability and gait. Automated brain volume measures are increasingly incorporated into the clinical reporting workflow to facilitate precision in medicine. Recently, multi-modal segmentation algorithms have been developed to overcome challenges with WML quantification based on single-modality input. ObjectiveWe evaluated WML volumes and their distribution in a case-control cohort of PD patients to predict the domain-specific clinical severity using a fully automated multi-modal segmentation algorithm. MethodsFifty-five subjects comprising of twenty PD patients and thirty-five age- and gender-matched control subjects underwent standardized motor/gait and cognitive assessments and brain MRI. Spatially differentiated WML obtained using automated segmentation algorithms on multi-modal MPRAGE and FLAIR images were used to predict domain-specific clinical severity. Preliminary statistical analysis focused on describing the relationship between WML and clinical scores, and the distribution of WML by brain regions. Subsequent stepwise regressions were performed to predict each clinical score using WML volumes in different brain regions, while controlling for age. ResultsWML volume strongly correlates with both motor and cognitive dysfunctions in PD patients (p < 0.05), with differential impact in the frontal lobe and periventricular regions on cognitive domains (p < 0.01) and severity of motor deficits (p < 0.01), respectively. ConclusionAutomated multi-modal segmentation algorithms may facilitate precision medicine through regional WML load quantification, which show potential as imaging biomarkers for predicting domain-specific disease severity in PD.

Patterns and associated factors of diabetes self-management: Results of a latent class analysis in a German population-based study.

Few studies on diabetes self-management considered the patterns and relationships of different self-management behaviours (SMB). The aims of the present study are 1) to identify patterns of SMB among persons with diabetes, 2) to identify sociodemographic and disease-related predictors of SMB among persons with diabetes. The present analysis includes data of 1,466 persons (age 18 to 99 years; 44.0% female; 56.0% male) with diabetes (type I and II) from the population-based study German Health Update 2014/2015 (GEDA 2014/2015-EHIS). We used latent class analysis in order to distinguish different patterns of self-management behaviours among persons with diabetes. The assessment of SMB was based on seven self-reported activities by respondents (dietary plan, diabetes-diary, diabetes health pass, self-assessment of blood glucose, self-examination of feet, retinopathy-screenings and assessment of HbA1c). Subsequent multinomial latent variable regressions identified factors that were associated with self-management behaviour. Latent class analysis suggested a distinction between three patterns of SMB. Based on modal posterior probabilities 42.8% of respondents showed an adherent pattern of diabetes self-management with above-average frequency in all seven indicators of SMB. 32.1% showed a nonadherent pattern with a below-average commitment in all seven forms of SMB. Another 25.1% were assigned to an ambivalent type, which showed to be adherent with regard to retinopathy screenings, foot examinations, and the assessment of HbA1c, yet nonadherent with regard to all other forms of SMB. In multivariable regression analyses, participation in Diabetes Self-Management Education programs (DSME) was the most important predictor of good self-management behaviour (marginal effect = 51.7 percentage points), followed by attentiveness towards one's personal health (31.0 percentage points). Respondents with a duration of illness of less than 10 years (19.5 percentage points), employed respondents (7.5 percentage points), as well as respondents with a high socioeconomic status (24.7 percentage points) were more likely to show suboptimal forms of diabetes self-management. In the present nationwide population-based study, a large proportion of persons with diabetes showed suboptimal self-management behaviour. Participation in a DSME program was the strongest predictor of good self-management. Results underline the need for continual and consistent health education for patients with diabetes.

Three Diagnoses for Problematic Hypersexuality; Which Criteria Predict Help-Seeking Behavior?

This study aimed to assess the best combination of indicators of problematic hypersexuality (PH), in a survey (n = 58,158) targeting individuals wondering if they were sex addicted. The survey allowed for testing of criteria from three theoretical models used to conceptualize PH. Factor analyses for women and men yielded an interpretable grouping of indicators consisting of four factors. In subsequent logistic regressions, these factors were used as predictors for experiencing the need for help for PH. The factors Negative Effects and Extreme positively predicted experiencing the need for help, with Negative Effects as the most important predictor for both women and men. This factor included, among others, withdrawal symptoms and loss of pleasure. The Sexual Desire factor negatively predicted the need for help, suggesting that for the targeted population more sexual desire leads to less PH. The Coping factor did not predict experiencing the need for help. Outcomes show that a combination of indicators from different theoretical models best indicates the presence of PH. Therefore, a measurement instrument to assess existence and severity of PH should consist of such a combination. Theoretically, this study suggests that a more comprehensive model for PH is needed, surpassing existing conceptualizations of PH.