Low Neighborhood Research Articles

Does socioeconomic and environmental burden affect vulnerability to extreme air pollution and heat? A case-crossover study of mortality in California.

Extreme heat and air pollution is associated with increased mortality. Recent evidence suggests the combined effects of both is greater than the effects of each individual exposure. Low neighborhood socioeconomic status ("socioeconomic burden") has also been associated with increased exposure and vulnerability to both heat and air pollution. We investigated if neighborhood socioeconomic burden or the combination of socioeconomic and environmental exposures ("socioenvironmental burden") modified the effect of combined exposure to extreme heat and particulate air pollution on mortality in California. We used a time-stratified case-crossover design to assess the impact of daily exposure to extreme particulate matter <2.5 μm (PM2.5) and heat on cardiovascular, respiratory, and all-cause mortality in California 2014-2019. Daily average PM2.5 and maximum temperatures based on decedent's residential census tract were dichotomized as extreme or not. Census tract-level socioenvironmental and socioeconomic burden was assessed with the CalEnviroScreen (CES) score and a social deprivation index (SDI), and individual educational attainment was derived from death certificates. Conditional logistic regression was used to estimate associations of heat and PM2.5 with mortality with a product term used to evaluate effect measure modification. During the study period 1,514,292 all-cause deaths could be assigned residential exposures. Extreme heat and air pollution alone and combined were associated with increased mortality, matching prior reports. Decedents in census tracts with higher socioenvironmental and socioeconomic burden experienced more days with extreme PM2.5 exposure. However, we found no consistent effect measure modification by CES or SDI on combined or separate extreme heat and PM2.5 exposure on odds of total, cardiovascular or respiratory mortality. No effect measure modification was observed for individual education attainment. We did not find evidence that neighborhood socioenvironmental- or socioeconomic burden significantly influenced the individual or combined impact of extreme exposures to heat and PM2.5 on mortality in California. We investigated the effect measure modification by socioeconomic and socioenvironmental of the co-occurrence of heat and PM2.5, which adds support to the limited previous literature on effect measure modification by socioeconomic and socioenvironmental burden of heat alone and PM2.5 alone. We found no consistent effect measure modification by neighborhood socioenvironmental and socioeconomic burden or individual level SES of the mortality association with extreme heat and PM2.5 co-exposure. However, we did find increased number of days with extreme PM2.5 exposure in neighborhoods with high socioenvironmental and socioeconomic burden. We evaluated multiple area-level and an individual-level SES and socioenvironmental burden metrics, each estimating socioenvironmental factors differently, making our conclusion more robust.

Trends in the Prevalence of Autism Spectrum Disorder in California: Disparities by Sociodemographic Factors and Region Between 1990-2018.

Autism Spectrum Disorders (ASD) prevalence has risen globally, with regional variation and sociodemographic disparities affecting diagnosis and intervention. This study examines ASD trends from 1990 to 2018 in California (CA), focusing on sociodemographic factors that may inform policy/interventions. Using CA Department of Public Health birth records (1990-2018) and Developmental Services ASD cases (1994-2022), we analyzed diagnosis incidence by age 4 or 8, stratified by sociodemographic and regional factors. From 1990 to 2018, for each birth year the cumulative incidence of ASD by 4 and 8 years of age in CA increased while the diagnosis age decreased. Distinct patterns emerged over these three decades. Children born to White and Asian and Pacific Islander (API) mothers, or to mothers with higher education or living in high socioeconomic status (SES) neighborhoods exhibited higher ASD cumulative incidences throughout the 1990s and early 2000s. However, in the mid-2000s, ASD incidence in children born to Black or Hispanic mothers, with low education, or living in low SES neighborhoods surpassed that of White/API children or those living in high SES neighborhoods. Black or Hispanic children now have the highest ASD cumulative incidence, even though age at first diagnosis remains lowest in high SES regions, for the highly educated, and for White/API children. ASD cumulative incidence in CA from 1990 to 2018 exhibited demographic reversals with higher rates in children born to Black or Hispanic mothers or lower SES neighborhoods. Black and Hispanic children still have delayed age at diagnosis compared to White/API children.

The influence of nativity/birthplace, neighborhood cohesion, and duration lived in the neighborhood on psychological distress

IntroductionNativity/birthplace and neighborhood cohesion are potential contributing factors to psychological distress. This study explores the impact of nativity/birthplace and neighborhood cohesion on moderate-severe psychological distress among United States (US) adults, considering the duration lived in a neighborhood. MethodsUsing the 2013–2018 National Health Interview Survey data, we conducted a stratified analysis based on years lived in the neighborhood (≤10 years [n = 96,175] and >10 years [n = 68,187]). Bivariate chi-square tests and multivariable logistic regression models were used to assess the statistical differences and associations between moderate-severe psychological distress and nativity/birthplace, and neighborhood cohesion, while adjusting for other covariates. ResultsIndividuals with 10 years or less of residence reported higher levels of moderate-severe psychological distress than those with more than 10 years (22.3 % vs. 18.1 %). Low or medium neighborhood cohesion, regardless of duration of residence, was associated with significantly higher odds of moderate-severe psychological distress compared to high cohesion. Foreign-born individuals had higher odds of psychological distress after more than 10 years in a neighborhood, although this difference was not statistically significant. However, they had lower odds of psychological distress after 10 years or less in the neighborhood compared to US-born individuals. Similarly, the interaction of foreign-born status and 10 years or less of residence in a neighborhood showed decreased odds of psychological distress. ConclusionsThese findings underscore the importance of strong social cohesion in neighborhoods for positive mental well-being. Establishing community initiatives to enhance neighborhood social cohesion is crucial.

Childhood maltreatment and alcohol and tobacco use trajectories in rural Chinese adolescents

BackgroundThere is a high prevalence of childhood maltreatment among Chinese children and adolescents, but little is known about its impact on alcohol and tobacco use trajectories and how positive school and neighborhood environments moderate the associations. The objective of this study was to assess the association between multiple forms of childhood maltreatment and longitudinal alcohol and tobacco use trajectories, and to assess the possibility that perceived connections to school and neighborhood moderate these associations.MethodsThis longitudinal cohort study included 2594 adolescents (9 to 13 years) from a low-income rural area in China. Childhood exposure to abuse and neglect was assessed using the Childhood Trauma Questionnaire. Participants reported past-month alcohol and tobacco use at three time points over 1 year.ResultsGrowth curve models revealed that childhood sexual abuse was associated with a higher risk of past-month drinking (OR = 1.53, 95% CI 1.19–2.03, p < 0.001) and smoking (OR = 1.82, 95% CI 1.30–2.55, p < 0.001). Neglect was associated with a higher risk of past-month drinking (OR = 1.52, 95% CI 1.06–1.90, p < 0.05) and smoking (OR = 2.02, 95% CI 1.34–3.02, p < 0.001). None of the maltreatment forms predicted a faster increase in either drinking or smoking. These associations were found independent of personal, family, and contextual characteristics. School and neighborhood connection moderated the association between physical abuse and past-month drinking, such that physical abuse was associated with a greater risk of drinking only for youth who perceived low school or neighborhood connections.ConclusionsFindings demonstrate the importance of early experiences of childhood maltreatment for adolescent alcohol and tobacco use. Enhancing school and neighborhood connectedness for physically abused youth may help protect them from alcohol use.

Incidence of Anxiety Determined by the Hamilton Anxiety Scale in Post-COVID-19 Patients After the Pandemic

Introduction: The coronavirus pandemic in 2019 represented a major global health emergency that brought with it multiple effects; both physically and mentally.1 Preceding studies in previous epidemics have revealed a deep and wide range of psychosocial consequences during outbreaks, with the human psyche being a commonly affected element. 2,3 Objective: To determine the incidence of anxiety in the sample population after the pandemic (SARS-CoV-2) by using the Hamilton Anxiety Scale (HAS) in post-COVID-19 patients. Methodology: The HAS4 was applied (June - August 2023) to a convenience sample of 250 subjects of both sex and inhabitants of the city of Puebla (Mexico) who suffered from COVID-19 infection (SARS-Cov-2) in two private clinics located in the colonies of “San Isidro Castillotla” and “Romero Vargas”. The inclusion criteria were: age range of 18-56 years, having presented mild or moderate symptoms during their infection and not having been diagnosed with anxiety prior to the pandemic. Results: The final sample was 250 post-COVID-19 patients. The average age in the sample was 33.76 years. With a sex distribution of 110 (44%) men and 140 (56%) women. 60% of the population presented moderate symptoms and the rest presented mild symptoms during their coronavirus infection. Furthermore, according to the HAS, the following levels and frequency of presentation were observed: 8% of the population did not present anxiety, 28% were determined as mild anxiety, 44% as moderate anxiety, 18% as severe and 2% as very severe. Also, insomnia was the most predominant symptom. Conclusion: Most of the research carried out worldwide in reference to the COVID-19 pandemic reveals that sociodemographic variables (in addition to the pathogenesis of the disease itself) influence the process of assimilating the situation and the response that the person develops to stress.5 In the present study, the entire population is residing in lower middle-class neighborhoods, a condition that evidently generated various forms of disadvantage in the face of the challenges posed by the health crisis. Studies point to the increase in anxiety diagnosis rates as a result of the COVID-19 (SARS-CoV-2) pandemic. Finally, in this research, one of the most frequent symptoms was insomnia (commonly linked to anxiety) and it was possible to identify that 92% presented some level of anxiety according to the HAS.

Persistent racial diversity in neighbourhoods across the United States: Where does it occur?

AbstractWhile there is a long history of racial change in the United States, and how this plays out within neighbourhoods, a key recurring question is whether some neighbourhoods are able to achieve and maintain racial diversity, or whether they simply transition to dominance by a new racial group. We test and find evidence of 1631 neighbourhoods across the United States from 1980 to 2020 that exhibit persistent racial diversity (PRD), and assess where this PRD occurs. Our analysis shows that PRD neighbourhoods (PRDNs) are likely to be present in counties with more economic opportunities–that is, counties with higher socioeconomic status (SES). PRDNs themselves, however, tend to be relatively lower SES neighbourhoods within relatively higher SES counties, suggesting that affordable locations surrounded by more economic opportunities may have served as an environment in which diversity can persist over a long period of time in the United States.

Variability of Social Inclusion Patterns Involving Personal, Family and Social Characteristics in Latino Migrant Families in Spain

ABSTRACTMigration entails important changes for families. Despite existing evidence on the process of adaptation, little is known about the degree of social inclusion in a context of high cultural affinity. We examine the variability of social inclusion patterns, indexed by personal, family and social conditions, in 263 Latin American families, with at least one child under 12 years, migrating to Spain. Three profiles of social inclusion were identified using cluster analysis: High inclusion (32%), characterized by a good financial and job situation, low perception of economic difficulty, good social support and medium scores in attraction to neighbourhood and community belonging and high scores in neighbourly relationship. Partial inclusion (35%) was characterized by ratings of extreme economic difficulties and lack of job, high scores in perceived economic hardship, high scores in all types of social support (instrumental, emotional and affectionate support and positive social interaction) and high scores in attraction to neighbourhood and community belonging, and medium scores in neighbourly relationship. Finally, Low inclusion cluster (33%) was characterized by moderate economic difficulties and lack of a job, high scores in perceived economic hardship, low scores in all types of social support and low neighbourhood cohesion in all the factors. Multinomial logistic regression analysis showed that a model comprising family composition, size of social networks and years of residence in the community increased the probability of belonging to the Partial or Low inclusion profiles compared with the High inclusion one. In conclusion, the heterogeneity in the patterns of social inclusion and their intersection with family conditions suggests the need for targeted intervention actions to reduce the risk of social exclusion.

Predictors of psychosocial adaptation in children with CHD.

Survival of CHD has significantly improved, but children with CHD remain susceptible to neurodevelopmental and psychosocial impairments. Our goal was to investigate the association between socio-demographic factors and psychosocial adaptation for future intervention. A retrospective cross-sectional study of an independent children's hospital's records was conducted. Psychosocial adaptation was measured by the Pediatric Cardiac Quality of Life Inventory Psychosocial Impact score (range 0-50, higher score indicates greater psychosocial adaptation). Bivariate and regression analyses were performed to estimate relationships between Psychosocial Impact score and socio-demographic variables including Child Opportunity Index, family support, financial support, academic support, and extracurricular activities. A total of 159 patients were included. Compared to patients in high opportunity neighbourhoods, patients in low opportunity neighbourhoods had a 9.27 (95% confidence interval [-17.15, -1.40], p = 0.021) point lower Psychosocial Impact score, whereas patients in moderate opportunity neighbourhoods had a 15.30 (95% confidence interval [-25.38, -5.22], p = 0.003) point lower Psychosocial Impact score. Compared to patients with adequate family support, those with limited support had a 6.23 point (95% confidence interval [-11.82, -0.643], p = 0.029) lower Psychosocial Impact score. Patients in moderate opportunity neighbourhoods had a higher Psychosocial Impact score by 11.80 (95% confidence interval [1.68, 21.91], p = 0.022) when they also had adequate family support compared to those with limited family support. Our findings indicate that among children with CHD, psychosocial adaptation is significantly impacted by neighbourhood resources and family support structures. These findings identify possible modifiable and protective factors to improve psychosocial adaptation in this vulnerable population.

Temporal trends in disparities in COVID-19 seropositivity among Canadian blood donors.

In Canada's largest COVID-19 serological study, SARS-CoV-2 antibodies in blood donors have been monitored since 2020. No study has analysed changes in the association between anti-N seropositivity (a marker of recent infection) and geographic and sociodemographic characteristics over the pandemic. Using Bayesian multi-level models with spatial effects at the census division level, we analysed changes in correlates of SARS-CoV-2 anti-N seropositivity across three periods in which different variants predominated (pre-Delta, Delta and Omicron). We analysed disparities by geographic area, individual traits (age, sex, race) and neighbourhood factors (urbanicity, material deprivation and social deprivation). Data were from 420319 blood donations across four regions (Ontario, British Columbia [BC], the Prairies and the Atlantic region) from December 2020 to November 2022. Seropositivity was higher for racialized minorities, males and individuals in more materially deprived neighbourhoods in the pre-Delta and Delta waves. These subgroup differences dissipated in the Omicron wave as large swaths of the population became infected. Across all waves, seropositivity was higher in younger individuals and those with lower neighbourhood social deprivation. Rural residents had high seropositivity in the Prairies, but not other regions. Compared to generalized linear models, multi-level models with spatial effects had better fit and lower error when predicting SARS-CoV-2 anti-N seropositivity by geographic region. Correlates of recent COVID-19 infection have evolved over the pandemic. Many disparities lessened during the Omicron wave, but public health intervention may be warranted to address persistently higher burden among young people and those with less social deprivation.

Assessing the influence of seasonal precipitation patterns on groundwater quality in the coal rich environment of Enugu, Nigeria

As the impacts of climate change continue to be felt around the world, understanding the effects on groundwater quality and quantity has become an important area of investigation. As a global source of water that contributes to preserving the environment, a better understanding of the effect of precipitation seasonal pattern on these systems is crucial; though studies connected to groundwater quality in this era of environmental crisis are at infancy. This study aims to evaluate the effect of precipitation seasonal pattern on groundwater quality in a coal enriched environment of developing city of west African sub-region with particular reference to Enugu, a coal city in Nigeria. Three residential areas (Abakpa, Achara, and Independence Layout) were randomly selected from high, medium, and low neighbourhood densities in the metropolis. Within the period spanning from April 2018 to March 2019, a physiochemical analysis was conducted on twelve deep wells utilizing weighted arithmetic index method. This technique was implemented in order to facilitate the assessment of the degree of water quality by translating a number of variables to just one metric value. The results of the investigation showed that the groundwater resources in the study region are mildly acidic, presumably as a result of the presence of pyrite, which is a byproduct of coal weathering, and chloride-ion-charged rains. Additionally, noticeable distinctions in the properties of water samples were observed between the dry and rainy seasons. Specifically, just 1% of the sampled water had excellent ratings, while 58.3% were considered good, 29.1% were deemed poor, and 8.3% of samples were categorized as very poor. The study concluded that coupled with climate crisis, seasonal precipitation patterns affect groundwater resources by reducing recharge, discharge, and the overall quality of water. These results have important implications for the management of groundwater resources in the region and highlight the need for continued monitoring and assessment of water quality in the face of ongoing environmental changes.

A Pivotal Time and Place: University Place Attachment, Childhood Neighborhood Affordances, and Internalizing Symptoms in Emerging Adulthood

People-place dynamics matter for well-being and for 69% of emerging adults in the US, the university is a pivotal place at a pivotal time. Research suggests that low childhood neighborhood affordances contribute to internalizing symptoms, but we know less about the impact of the university context. This study asked: how does university place attachment impact internalizing symptoms beyond the effects of childhood neighborhood affordances? Two-hundred and seventy-five university students self-reported on perceived childhood neighborhood affordances, university attachment, and current internalizing symptoms. Higher university attachment coincided with higher perceived neighborhood affordances and lower depression and social anxiety symptoms while controlling for demographic factors and parent-child relationship quality. Further, there was an indirect effect of neighborhood affordances via university attachment on social anxiety, but not depressive symptoms. Findings suggest that university place attachment contributes meaningfully to internalizing symptoms in emerging adulthood and that childhood neighborhood affordances may impact attachment to novel life-spaces.

Trajectories of maternal depressive symptoms from infancy through early childhood: The roles of perceived financial strain, social support, and intimate partner violence.

Although new mothers are at risk of heightened vulnerability for depressive symptoms, there is limited understanding regarding changes in maternal depressive symptoms over the course of the postpartum and early childhood of their child's life among rural, low-income mothers from diverse racial backgrounds. This study examined distinct trajectories of depressive symptoms among rural low-income mothers during the first five years of their child's life, at 6, 15, 24, and 58 months, using data from the Family Life Project (N = 1,292). Latent class growth analysis identified four distinct trajectories of maternal depressive symptoms, including Low-decreasing (50%; n = 622), Low-increasing (26%; n = 324), Moderate-decreasing (13%; n = 156), and Moderate-increasing (11%; n = 131) trajectories. Multinomial logistic regression demonstrated that higher perceived financial strain and intimate partner violence, and lower social support predicted higher-risk trajectories (Low-increasing, Moderate-decreasing, and Moderate-increasing) relative to the Low-decreasing trajectory. Compared to the Low-decreasing trajectory, lower neighborhood safety/quietness predicted to the Low-increasing trajectory. Moreover, lower social support predicted the Moderate-increasing trajectory, the highest-risk trajectory, compared to those in Moderate-decreasing. The current analyses underscore the heterogeneity on patterns of depressive symptoms among rural, low-income mothers, and that the role of both proximal and broader contexts contributing to distinct trajectories of maternal depressive symptoms over early childhood.

Are All Apartments Equal? An Investigation of Contemporary Apartment Design Quality by Neighbourhood-Level Socioeconomic Disadvantage

ABSTRACT Rapid increases in apartment construction have ignited concerns about design quality. Lower income populations could be disproportionately impacted by poor design as they are more likely to live in apartments and have fewer resources to mitigate design problems. We examined whether there was a socioeconomic gradient in contemporary apartment design by measuring the implementation of quantifiable policy-specific requirements in buildings (n = 172) sampled from relatively low, mid, and high socioeconomic neighbourhoods within Sydney, Melbourne, and Perth. Findings suggest the presence (or absence) of detailed policy instruments interacts with local market conditions to impact apartment design quality.

Association of factors with childhood asthma and allergic diseases using latent class analysis

We hypothesize that children characterized by deprived factors have poorer health outcomes. We aim to identify clustering of determinants and estimate risk of early childhood diseases. This 1993–2019 longitudinal cohort study combines three Canadian pediatric cohorts and their families. Mothers and children are clustered using latent class analysis (LCA) by 16 indicators in three domains (maternal and newborn; socioeconomic status [SES] and neighbourhood; environmental exposures). Hazard ratios (HR) of childhood asthma, allergic rhinitis (AR), and eczema are quantified with Cox proportional hazard (PH) regression. Rate ratios (RR) of children’s health services use (HSU) are estimated with Poisson regression. Here we report the inclusion of 15,724 mother–child pairs; our LCA identifies four mother-clusters. Classes 1 and 2 mothers are older (30–40 s), non-immigrants with university education, living in high SES neighbourhoods; Class 2 mothers have poorer air quality and less greenspace. Classes 3 and 4 mothers are younger (20–30 s), likely an immigrant/refugee, with high school-to-college education, living in lower SES neighborhoods with poorer air quality and less greenspace. Children’s outcomes differ by Class, in comparison to Class 1. Classes 3 and 4 children have higher risks of asthma (HR 1.24, 95% CI 1.11–1.37 and HR 1.39, 95% CI 1.22–1.59, respectively), and similar higher risks of AR and eczema. Children with AR in Class 3 have 20% higher all-cause physician visits (RR = 1.20, 95% CI 1.10–1.30) and those with eczema have 18% higher all-cause emergency department visits (RR = 1.18, 95% CI 1.09–1.28) and 14% higher all-cause physician visits (RR = 1.14, 95% CI 1.09–1.19). Multifactorial-LCA mother-clusters may characterize associations of children’s health outcomes and care, adjusting for interrelationships.

Abstract MP01: Life-Course Socioeconomic Status and the Gut Microbiome in the Hispanic Community Health Study/Study of Latinos (HCHS/SOL)

Introduction: Socioeconomic status (SES) in childhood and beyond may influence the gut microbiome (GMB), with implications for disease risk. Studies evaluating the relationship between life-course SES and the gut microbiome (GMB) are sparse, particularly among Hispanic/Latino individuals, who have a high prevalence of low SES. Hypothesis: Low life-course SES indicators are associated with less diversity and overall composition of the gut microbiome in United States (U.S.) Hispanic/Latino adults. Methods: The Hispanic Community Health Study/Study of Latinos (HCHS/SOL) is a multi-site population-based cohort study conducted in the U.S. Childhood sanitation conditions (plumbing, sewer/septic tank), childhood SES (parental education and childhood hardship), adulthood neighborhood SES, and adulthood SES (education, income, and economic hardship) were used as life-course SES indicators. Shotgun sequencing was performed on stool samples (n=1057). Alpha-diversity was measured by the Shannon diversity index. Permutational multivariate analysis of variance was used to assess the association of SES indicators with overall microbiome composition, as measured using the Jensen-Shannon Divergence distance. Analysis of Compositions of Microbiomes was used to identify associations of life-course SES indicators with GMB species, adjusting for age, sex, BMI, U.S. or foreign-born, study center, Hispanic/Latino background, type of stool, diet quality, smoking, alcohol consumption, depressive and anxiety symptoms, diabetes, hypertension, cardiovascular disease, dyslipidemia, and antibiotic use. Results: Low sanitation and low SES during childhood were reported by 32% (342 of 1053) and 37% (330 of 884) of participants, respectively. In adulthood, 56% (585 of 1053) lived in a low SES neighborhood and 45% (447 of 1002) had low SES. Life-course SES indicators were not associated with GMB diversity. However, overall microbiome composition differed significantly according to childhood SES (R2 =0.28%, P = 0.04). Low childhood sanitation was associated with higher abundance of Ruminococcus torques and Clostridium lactatifermentans , and depleted abundance of Prevotella species. Low childhood SES was associated with higher abundance of species from genus Streptococcus (S. salivarius, S. parasanguinis, and S. infantis), Veillonella parvula, Prevotella stercorea, and Roseburia intestinalis. Low adulthood SES was associated with depleted abundance of species from Bacteroides, and living in a low adulthood neighborhood SES was associated with depleted abundance of species from Rikenella . Conclusion: Low childhood SES was associated with an altered GMB composition in U.S. Hispanic/Latino adults. Early-life SES may have long-term effects on GMB composition, underscoring another biological mechanism linking early childhood factors to adulthood disease.

Machine learning approach using 18F-FDG-PET-radiomic features and the visibility of right ventricle 18F-FDG uptake for predicting clinical events in patients with cardiac sarcoidosis

ObjectivesTo investigate the usefulness of machine learning (ML) models using pretreatment 18F-FDG-PET-based radiomic features for predicting adverse clinical events (ACEs) in patients with cardiac sarcoidosis (CS).Materials and methodsThis retrospective study included 47 patients with CS who underwent 18F-FDG-PET/CT scan before treatment. The lesions were assigned to the training (n = 38) and testing (n = 9) cohorts. In total, 49 18F-FDG-PET-based radiomic features and the visibility of right ventricle 18F-FDG uptake were used to predict ACEs using seven different ML algorithms (namely, decision tree, random forest [RF], neural network, k-nearest neighbors, Naïve Bayes, logistic regression, and support vector machine [SVM]) with tenfold cross-validation and the synthetic minority over-sampling technique. The ML models were constructed using the top four features ranked by the decrease in Gini impurity. The AUCs and accuracies were used to compare predictive performances.ResultsPatients who developed ACEs presented with a significantly higher surface area and gray level run length matrix run length non-uniformity (GLRLM_RLNU), and lower neighborhood gray-tone difference matrix_coarseness and sphericity than those without ACEs (each, p < 0.05). In the training cohort, all seven ML algorithms had a good classification performance with AUC values of > 0.80 (range: 0.841–0.944). In the testing cohort, the RF algorithm had the highest AUC and accuracy (88.9% [8/9]) with a similar classification performance between training and testing cohorts (AUC: 0.945 vs 0.889). GLRLM_RLNU was the most important feature of the modeling process of this RF algorithm.ConclusionML analyses using 18F-FDG-PET-based radiomic features may be useful for predicting ACEs in patients with CS.

One size doesn't fit all: methodological reflections in conducting community-based behavioural science research to tailor COVID-19 vaccination initiatives for public health priority populations.

Promoting the uptake of vaccination for infectious diseases such as COVID-19 remains a global challenge, necessitating collaborative efforts between public health units (PHUs) and communities. Applied behavioural science can play a crucial role in supporting PHUs' response by providing insights into human behaviour and informing tailored strategies to enhance vaccination uptake. Community engagement can help broaden the reach of behavioural science research by involving a more diverse range of populations and ensuring that strategies better represent the needs of specific communities. We developed and applied an approach to conducting community-based behavioural science research with ethnically and socioeconomically diverse populations to guide PHUs in tailoring their strategies to promote COVID-19 vaccination. This paper presents the community engagement methodology and the lessons learned in applying the methodology. The community engagement methodology was developed based on integrated knowledge translation (iKT) and community-based participatory research (CBPR) principles. The study involved collaboration with PHUs and local communities in Ontario, Canada to identify priority groups for COVID-19 vaccination, understand factors influencing vaccine uptake and co-design strategies tailored to each community to promote vaccination. Community engagement was conducted across three large urban regions with individuals from Eastern European communities, African, Black, and Caribbean communities and low socioeconomic neighbourhoods. We developed and applied a seven-step methodology for conducting community-based behavioural science research: (1) aligning goals with system-level partners; (2) engaging with PHUs to understand priorities; (3) understanding community strengths and dynamics; (4) building relationships with each community; (5) establishing partnerships (community advisory groups); (6) involving community members in the research process; and (7) feeding back and interpreting research findings. Research partnerships were successfully established with members of prioritized communities, enabling recruitment of participants for theory-informed behavioural science interviews, interpretation of findings, and co-design of targeted recommendations for each PHU to improve COVID-19 vaccination uptake. Lessons learned include the importance of cultural sensitivity and awareness of sociopolitical context in tailoring community engagement, being agile to address the diverse and evolving priorities of PHUs, and building trust to achieve effective community engagement. Effective community engagement in behavioural science research can lead to more inclusive and representative research. The community engagement approach developed and applied in this study acknowledges the diversity of communities, recognizes the central role of PHUs, and can help in addressing complex public health challenges.

Gene–environment pathways to cognitive intelligence and psychotic-like experiences in children

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Gene-environment pathways to cognitive intelligence and psychotic-like experiences in children.

In children, psychotic-like experiences (PLEs) are related to risk of psychosis, schizophrenia, and other mental disorders. Maladaptive cognitive functioning, influenced by genetic and environmental factors, is hypothesized to mediate the relationship between these factors and childhood PLEs. Using large-scale longitudinal data, we tested the relationships of genetic and environmental factors (such as familial and neighborhood environment) with cognitive intelligence and their relationships with current and future PLEs in children. We leveraged large-scale multimodal data of 6,602 children from the Adolescent Brain and Cognitive Development Study. Linear mixed model and a novel structural equation modeling (SEM) method that allows estimation of both components and factors were used to estimate the joint effects of cognitive phenotypes polygenic scores (PGSs), familial and neighborhood socioeconomic status (SES), and supportive environment on NIH Toolbox cognitive intelligence and PLEs. We adjusted for ethnicity (genetically defined), schizophrenia PGS, and additionally unobserved confounders (using computational confound modeling). Our findings indicate that lower cognitive intelligence and higher PLEs are significantly associated with lower PGSs for cognitive phenotypes, lower familial SES, lower neighborhood SES, and less supportive environments. Specifically, cognitive intelligence mediates the effects of these factors on PLEs, with supportive parenting and positive school environments showing the strongest impact on reducing PLEs. This study underscores the influence of genetic and environmental factors on PLEs through their effects on cognitive intelligence. Our findings have policy implications in that improving school and family environments and promoting local economic development may enhance cognitive and mental health in children.

Lexical effects on talker discrimination in adult cochlear implant usersa).

The lexical and phonological content of an utterance impacts the processing of talker-specific details in normal-hearing (NH) listeners. Adult cochlear implant (CI) users demonstrate difficulties in talker discrimination, particularly for same-gender talker pairs, which may alter the reliance on lexical information in talker discrimination. The current study examined the effect of lexical content on talker discrimination in 24 adult CI users. In a remote AX talker discrimination task, word pairs-produced either by the same talker (ST) or different talkers with the same (DT-SG) or mixed genders (DT-MG)-were either lexically easy (high frequency, low neighborhood density) or lexically hard (low frequency, high neighborhood density). The task was completed in quiet and multi-talker babble (MTB). Results showed an effect of lexical difficulty on talker discrimination, for same-gender talker pairs in both quiet and MTB. CI users showed greater sensitivity in quiet as well as less response bias in both quiet and MTB for lexically easy words compared to lexically hard words. These results suggest that CI users make use of lexical content in same-gender talker discrimination, providing evidence for the contribution of linguistic information to the processing of degraded talker information by adult CI users.