Environmental Exposure Variables Research Articles

Examining factors associated with cocaine use in late adolescence and early adulthood

Abstract Background In 2022, cocaine was the leading problem drug for which individuals sought treatment in Ireland, overtaking opioids for the first time. Despite increasing concerns about cocaine, there is limited longitudinal research examining factors associated with cocaine use in the general youth population. We describe levels of use and associated factors at 17 and 20 years old Methods Past-year cocaine use was studied at age 17 (N = 5965) and 20 (N = 4679) in a nationally representative cohort. Individual, family, social and environmental exposure variables were selected a priori. Weighted logistic regression models were fitted using Generalised Estimating Equations to multiply imputed and complete case data. Results Cocaine use was reported by 220 (4%) at age 17, and 1072 (23%) at age 20. Both individual and community factors predict use. Earlier alcohol initiation and having friends who use cannabis were strongly associated with increased odds of cocaine use at both ages. Those with parents who reported that neighbourhood intoxication or drug-taking was common had elevated odds of cocaine use at age 17. Conclusions Past-year cocaine use at 20 years old in this cohort is substantially higher than other international cohort studies. Levels of cocaine use increase significantly between 17 and 20 years old. Acknowledgement: Growing Up in Ireland (GUI) is funded by the Department of Children, Equality, Disability, Integration and Youth (DCEDIY). It is managed by DCEDIY in association with the Central Statistics Office (CSO). Results in this report are based on analyses of data from Research Microdata Files provided by the CSO. Neither the CSO nor DCEDIY take any responsibility for the views expressed or the outputs generated from these analyses. Key messages • Our study underscores significant cocaine use rates and correlations with early substance initiation and neighbourhood factors. • This emphasizes the necessity of a comprehensive, multilevel strategy for preventing cocaine use.

Variable selection for single-index varying-coefficients models with applications to synergistic G × E interactions

Epidemiological evidence suggests that simultaneous exposures to multiple environmental risk factors (Es) can increase disease risk larger than the additive effect of individual exposure acting alone. The interaction between a gene and multiple Es on a disease risk is termed as synergistic gene-environment interactions (synG × E). Single-index varying-coefficients models (SIVCM) have been a promising tool to model synergistic G × E effect and to understand how multiple Es jointly influence genetic risks on a disease outcome. In this work, we proposed a unified variable selection approach for SIVCM to estimate different effects of gene variables: varying, non-zero constant and zero effects which respectively correspond to nonlinear synG × E, no synG × E and no genetic effect. For multiple environmental exposure variables, we also estimated and selected important environmental variables that contribute to the synergistic interaction effect. We theoretically evaluated the oracle property of the proposed variable selection approach. Extensive simulation studies were conducted to evaluate the finite sample performance of the method, considering both continuous and discrete gene variables. Application to a real dataset further demonstrated the utility of the method. Our method has broad applications in areas where the purpose is to identify synergistic interaction effect.

33576 How lifestyle-based environmental exposures impact skin health through microbiome

Precise skin care receives increasing attention to provide targeted solutions and communication for consumers with different lifestyles and unique skin needs. However, systematic research on their relationships is limited. Here we aim to create a knowledge network through systematically analyzing the correlations of environmental exposure (UV, pollution, temperature, humidity), skin conditions and microbiome. In an 8-week clinical trial, healthy women with dry skin used a portable device (AirRadio) to track personal real-life environment (temperature, humidity, air pollutants), and daily recorded lifestyle-based environment exposures. Skin microbiome was collected and quantified based on 16S sequencing, and skin signs were measured by non-invasive instruments and dermatologist grading. Subjects were clustered into C1-C4 based on their distinct skin conditions of acne, tone, texture. Environmental exposure variables and microbiome characteristics of each cluster were identified and correlated with skin conditions. Bayesian network was then applied to construct regulatory networks, which were validated by literature scraping using data collected from publications. 3682 relationships across 5 domains were identified, and 499 relationships were validated, which reveals skin microbiome mediated lifestyle-based environmental aggressors’ impact on skin health. For example, subjects with dull, rough, and acne-free skin (C3) expose more sunshine and automobile exhaust, whereas subjects with dull, rough, and acne skin (C4) have the highest kitchen fumes exposure. The microbiome in C3 consisted of Polycyclic Aromatic Hydrocarbon degraders which may contribute to less acne. This knowledge network can provide novel insights into the next-generation skin care development to consumers with different lifestyle exposures and skin conditions.

Abstract 13960: Socioeconomic and Environmental Exposures (Socio-Enviromics) and Cardiometabolic Health in the United States

Introduction: Environmental exposures and socioeconomic status may have complex interactions at the neighborhood level with respect to contributionto cardiovascular disease. We sought to understand the association between integreated social/environmentalexposures (socio-enviromics) with geospatial variation in various chronic cardiometabolic and pulmonary conditions in the United States. Methods: We mapped 27 census tract (CT)-level multi-source environmental exposure and socioeconomic variables throughout more >70,000 census tracts in the US. We trained a regression-based machine learning model using 80% training and tested it on 20% hold-out test to predict CT-level estimated prevalence of various risk factors and chronic diseases. We additionally tested the association between the predicted prevalence of coronary artery disease (socio-enviromic score) with major adverse cardiovascular events (MACE) in a prospective cohort of 45,188 individuals who underwent coronary artery calcium (CAC) score testing using cox regression model. Results: Enviromics/SocioEnviromics explained significant proportion of variation in prevalence of health conditions (figure). In the CAC cohort, socio-enviromic score was associated with MACE independently of age, sex, race and CAC (HR 1.12 [1.08-1.17], P<0.001). The association between socioenviromic score and MACE was stronger among individuals with lower CAC (socioenviromic score * CAC P interaction =0.01). Conclusions: A significant proportion of the census-tract level prevalence of cardiometabolic conditions can be explained by geographic variation in socio-enviromics, and an integrated socio-enviromic score is associated with MACE at the individual level, independently of CAC. The impact of integrated socio-enviromics in individual cardiovascular risk prediction should be studied in prospective cohorts.

Metabolomics, Microbiota, and In Vivo and In Vitro Biomarkers in Type 2 Severe Asthma: A Perspective Review

Precision medicine refers to the tailoring of therapeutic strategies to the individual characteristics of each patient; thus, it could be a new approach for the management of severe asthma that considers individual variability in genes, environmental exposure, and lifestyle. Precision medicine would also assist physicians in choosing the right treatment, the best timing of administration, consequently trying to maximize drug efficacy, and, possibly, reducing adverse events. Metabolomics is the systematic study of low molecular weight (bio)chemicals in a given biological system and offers a powerful approach to biomarker discovery and elucidating disease mechanisms. In this point of view, metabolomics could play a key role in targeting precision medicine.

Clinical and sociodemographic factors associated with lupus nephritis in Colombian patients: A cross-sectional study

IntroductionOver the past decades, incidence of SLE (Systemic Lupus Erythematosus) has increased due to early case detection and improved survival of patients. SLE presents at an earlier age and has a more severe presentation in African-American, Native American, Asian, and Hispanic populations. Worldwide, lupus nephritis (LN) is observed in 29–60% of SLE patients, it has a negative impact in renal survival and patient mortality. Several cohorts have established potential risk factors associated with lupus nephritis, such as male sex, serological markers, and some extra-renal manifestations. ObjectivesTo describe sociodemographic, clinical, immunological, and environmental risk factors in Colombian SLE patients and to compare the population with and without nephritis, in order to establish risk factors and possible associations. Materials and methodsA total of 1175 SLE patients participated in this study. During medical care, an interview and structured survey was conducted and later registered in a database. Sociodemographic, clinical, immunological, and environmental exposure variables were analyzed. Bivariate and multivariate analyses were performed using presence of LN as an outcome. ResultsPrevalence of LN was 38.7%. Variables significantly associated with LN included being male (OR 1.98), a duration of SLE>10 years (OR 1.48), positive anti-DNA (OR 1.34), positive anti-Sm (OR 1.45), and smoking (OR 1.66). Being non-smoker was a protective factor (OR 0.52). ConclusionThis study describes potential factors associated with lupus nephritis in a Latin American population. Smoking status could be a target for intervention as it is a modifiable risk factor. The association between being male and LN is observed in Latin-American populations such as presented here. Further research in other large-scale population studies and more efforts are needed to gain better insights to explicate these relationships.

Impact of Environmental Exposure Conditions on the Maintenance of Facades’ Claddings

Façades, as the most external building envelope component, are subject to different ex-ternal environmental loads, such as: Temperature, precipitation, damp, and wind. Therefore, the contribution of environmental actions to the occurrence of defects in façades claddings is an important subject of study since these actions strongly affect the degradation process and natural ageing of these components during their service life. In this study, a methodology to support decision-makers in the process of selecting a façade cladding system and the maintenance strategy to implement is presented and discussed. This methodology covers the performance of four façade claddings (ceramic tiling systems (CTS), natural stone claddings (NSC), external thermal insulation composite systems (ETICS), and architectural concrete façades (ACF)) over time, according to three environmental exposure variables (exposure to damp, distance from the sea, and orientation). The databases were established based on the diagnosis of the degradation condition of these claddings in-service conditions, in Portugal. The results reveal that the environmental exposure variables have a significant impact on maintenance requirements and costs. For all the categories of the environmental exposure condition variables, under all scenarios, ETICS is the least favorable constructive solution while CTS is the most advantageous solution. Furthermore, the results show that properly implemented maintenance activities enhance the performance level of building components, which positively affects their degradation behavior over time.

The growing role of precision medicine for the treatment of autoimmune diseases; results of a systematic review of literature and Experts’ Consensus

Autoimmune diseases (AIDs) share similar serological, clinical, and radiological findings, but, behind these common features, there are different pathogenic mechanisms, immune cells dysfunctions, and targeted organs. In this context, multiple lines of evidence suggest the application of precision medicine principles to AIDs to reduce the treatment failure. Precision medicine refers to the tailoring of therapeutic strategies to the individual characteristics of each patient, thus it could be a new approach for management of AIDS which considers individual variability in genes, environmental exposure, and lifestyle. Precision medicine would also assist physicians in choosing the right treatment, the best timing of administration, consequently trying to maximize drug efficacy, and, possibly, reducing adverse events. In this work, the growing body of evidence is summarized regarding the predictive factors for drug response in patients with AIDs, applying the precision medicine principles to provide high-quality evidence for therapeutic opportunities in improving the management of these patients.

Is Environmental and Occupational Particulate Air Pollution Exposure Related to Type-2 Diabetes and Dementia? A Cross-Sectional Analysis of the UK Biobank.

Human exposure to particulate air pollution (e.g., PM2.5) can lead to adverse health effects, with compelling evidence that it can increase morbidity and mortality from respiratory and cardiovascular disease. More recently, there has also been evidence that long-term environmental exposure to particulate air pollution is associated with type-2 diabetes mellitus (T2DM) and dementia. There are many occupations that may expose workers to airborne particles and that some exposures in the workplace are very similar to environmental particulate pollution. We conducted a cross-sectional analysis of the UK Biobank cohort to verify the association between environmental particulate air pollution (PM2.5) exposure and T2DM and dementia, and to investigate if occupational exposure to particulates that are similar to those found in environmental air pollution could increase the odds of developing these diseases. The UK Biobank dataset comprises of over 500,000 participants from all over the UK. Environmental exposure variables were used from the UK Biobank. To estimate occupational exposure both the UK Biobank’s data and information from a job exposure matrix, specifically developed for UK Biobank (Airborne Chemical Exposure–Job Exposure Matrix (ACE JEM)), were used. The outcome measures were participants with T2DM and dementia. In appropriately adjusted models, environmental exposure to PM2.5 was associated with an odds ratio (OR) of 1.02 (95% CI 1.00 to 1.03) per unit exposure for developing T2DM, while PM2.5 was associated with an odds ratio of 1.06 (95% CI 0.96 to 1.16) per unit exposure for developing dementia. These environmental results align with existing findings in the published literature. Five occupational exposures (dust, fumes, diesel, mineral, and biological dust in the most recent job estimated with the ACE JEM) were investigated and the risks for most exposures for T2DM and for all the exposures for dementia were not significantly increased in the adjusted models. This was confirmed in a subgroup of participants where a full occupational history was available allowed an estimate of workplace exposures. However, when not adjusting for gender, some of the associations become significant, which suggests that there might be a bias between the occupational assessments for men and women. The results of the present study do not provide clear evidence of an association between occupational exposure to particulate matter and T2DM or dementia.

Unraveling racial disparities in asthma emergency department visits using electronic healthcare records and machine learning

Objective Hospital emergency department (ED) visits by asthmatics differ based on race and season. The objectives of this study were to investigate season- and race-specific disparities for asthma risk, and to identify environmental exposure variables associated with ED visits among more than 42,000 individuals of African American (AA) and European American (EA) descent identified through electronic health records (EHRs). Methods We examined data from 42,375 individuals (AAs = 14,491, EAs = 27,884) identified in EHRs. We considered associated demographic (race, age, gender, insurance), clinical (smoking status, ED visits, FEV1%), and environmental exposures data (mold, pollen, and pollutants). Machine learning techniques, including random forest (RF), extreme gradient boosting (XGB), and decision tree (DT) were used to build and identify race- and -season-specific predictive models for asthma ED visits. Results Significant differences in ED visits and FEV1% among AAs and EAs were identified. ED visits by AAs was 32.0% higher than EAs and AAs had 6.4% lower FEV1% value than EAs. XGB model was used to accurately classify asthma patients visiting ED into AAs and EAs. Pollen factor and pollution (PM2.5, PM10) were the key variables for asthma in AAs and EAs, respectively. Age and cigarette smoking increase asthma risk independent of seasons. Conclusions In this study, we observed racial and season-specific disparities between AAs and EAs asthmatics for ED visit and FEV1% severity, suggesting the need to address asthma disparities through key predictors including socio-economic status, particulate matter, and mold.

Asthma in farm children is more determined by genetic polymorphisms and in non-farm children by environmental factors.

The asthma syndrome is influenced by hereditary and environmental factors. With the example of farm exposure, we study whether genetic and environmental factors interact for asthma. Statistical learning approaches based on penalized regression and decision trees were used to predict asthma in the GABRIELA study with 850 cases (9% farm children) and 857 controls (14% farm children). Single-nucleotide polymorphisms (SNPs) were selected from a genome-wide dataset based on a literature search or by statistical selection techniques. Prediction was assessed by receiver operating characteristics (ROC) curves and validated in the PASTURE cohort. Prediction by family history of asthma and atopy yielded an area under the ROC curve (AUC) of 0.62 [0.57-0.66] in the random forest machine learning approach. By adding information on demographics (sex and age) and 26 environmental exposure variables, the quality of prediction significantly improved (AUC=0.65 [0.61-0.70]). In farm children, however, environmental variables did not improve prediction quality. Rather SNPs related to IL33 and RAD50 contributed significantly to the prediction of asthma (AUC=0.70 [0.62-0.78]). Asthma in farm children is more likely predicted by other factors as compared to non-farm children though in both forms, family history may integrate environmental exposure, genotype and degree of penetrance.

Respiratory health effects of residential individual and cumulative risk factors in children living in two cities of the Pearl River Delta Region, China.

BackgroundIndoor environment is complex, with many factors potentially interacting with each other to affect health. However, previous studies have usually focused on effect of a single factor. Assessment of the combined effects of multiple factors can help with understanding the overall health risk.MethodsA cross-sectional study was conducted among 2,306 school children in Guangzhou and Shenzhen. Questionnaire data on respiratory symptoms and diseases were collected along with sociodemographic and residential environmental information. A subset of children (N=987) were measured for their lung function. A random forest algorithm was applied to screen the top-ranked indoor environmental exposure variables and to form a composite index for cumulative risk of indoor pollution (CRIP). Logistic regressions were conducted to analyze the independent effect of single indoor environmental risk factors and the combined effect of CRIP on children’s respiratory health. Multiple linear regressions were used to examine the independent and combined effects of indoor environmental exposure on lung function.ResultsWe found that home dampness and molds as well as environmental tobacco smoke (ETS) were significantly and independently associated with increased prevalence of children’s respiratory symptoms and diseases and with reduced lung function. A higher CRIP level was significantly associated with increased risk of cough with cold (OR =1.37, 95% CI: 1.05–1.79) and wheeze (OR =2.71, 95% CI: 1.16–6.34). A higher CRIP level was also associated with reduced lung function measured as FVC, FEV1, PEF, FEF25%, FEF25–75% and VC.ConclusionsIn children living in the subtropical region of the Pearl River Delta, home dampness and the presence of mold as well as ETS were individual risk factors for children’s respiratory health. The composite CRIP index was associated with respiratory symptoms and lung function, suggesting the utility of this index for predicting the combined effects of multiple risk factors.

Leukocyte mitochondrial DNA copy number and built environment in Mexican Americans: a cross-sectional study

Mitochondrial DNA (mtDNA) copy number in leukocytes has been regarded as a biomarker for various environmental exposures and chronic diseases. Our previous study showed that certain demographic factors (e.g. age, gender, BMI, etc.) significantly affect levels of leukocyte mtDNA copy number in Mexican Americans. However, the effect of the built environment on leukocyte mtDNA copy number has not been studied previously. In this cross-sectional study, we examined the association between multiple components of the built environment with leukocyte mtDNA copy number among 5,502 Mexican American adults enrolled in Mano-A-Mano, the Mexican American Cohort Study (MACS). Based on the median levels of mtDNA copy number, the study population was stratified into low mtDNA copy number group (< median) and high mtDNA copy number group (≥ median). Among all built environment exposure variables, household density and road/intersection ratio were found to be statistically significant between groups with low and high mtDNA copy number (P < 0.001 and 0.002, respectively). In the multivariate logistic regression analysis, individuals living in areas with elevated levels of household density had 1.24-fold increased odds of having high levels of mtDNA copy number [Odds ratio (OR) = 1.24, 95% confidence interval (CIs) 1.08, 1.36]. Similarly, those living in areas with elevated levels of road/intersection ratio had 1.12-fold increased odds of having high levels of mtDNA copy number (OR = 1.12, 95% CI 1.01, 1.27). In further analysis, when both variables were analyzed together in a multivariate logistic regression model, the significant associations remained. In summary, our results suggest that selected built environment variables (e.g. population density and road/intersection ratio) may influence levels of mtDNA copy number in leukocytes in Mexican Americans.

Adults\u2019 leisure-time physical activity and the neighborhood built environment: a contextual perspective

BackgroundContext-free outcome measures, such as overall leisure-time physical activity (LTPA), are habitually applied to study the neighborhood built environment correlates of physical activity. This cross sectional study identifies and empirically tests potential methodological limitations related to the use of context-free measures and discusses how these may help in the interpretation of inconsistent associations between participation in moderate-to-vigorous LTPA and objectively measured neighborhood-level built environment attributes.MethodsWe employ a public participation geographic information system (PPGIS), an advanced participatory mapping method, to study the spatial distribution of moderate-to-vigorous LTPA among adult urban Finnish residents (n 1322). Secondary sources of GIS land-use and sport facility data were used to disaggregate respondent-mapped LTPA by the behavioral context, such as indoor and outdoor sport facilities, green spaces, and other public open spaces. Associations between the use of the identified LTPA settings and a range of objectively measured neighborhood built environment attributes were studied with multilevel logistic regression models.ResultsDisaggregated by behavioral context, we observed varied and partly opposite built environment correlates for LTPA. The use of indoor and outdoor sport facilities showed no significant associations with their neighborhood availability, but were significantly associated with personal-level attributes. By contrast, participation in LTPA in green and built public open space shared significant associations with access to and availability of neighborhood green space that persisted after controlling for personal-level covariates. Moreover, neighborhood distances up to 1600 m poorly captured participation in moderate-to-vigorous LTPA, as, on average, 40% of visits were located further from home. However, we found the immediate home environment to be an important LTPA setting for the least active participants.ConclusionsThis study demonstrates that LTPA can be a highly heterogeneous measure regarding both the spatial distribution and the environmental correlates of behavioral contexts. The results show that context-free LTPA outcome measures yield inconsistent associations with built environment exposure variables, challenging the applicability of such measures in designing neighborhood-level built environment interventions.

Scale effects in remotely sensed greenspace metrics and how to mitigate them for environmental health exposure assessment

Metrics representing exposure to the natural environment are widely used in environmental health-related studies. They are calculated using a variety of different data sources representing greenspace and a range of buffer sizes representing human interaction with the environment. Previous studies have identified issues relating to buffer distance and scaling effects on greenspace exposure assessments when using satellite image-derived metrics. We evaluate the spatial scale sensitivity of three common greenspace metrics (i.e., Normalised Difference Vegetation Index- NDVI, Leaf Area Index- LAI, and Land Use and Land Cover-LULC), using lacunarity analysis, as a scale-dependent measure of heterogeneity based on the principles of fractals. By producing a ‘lacunarity curve’ across multiple spatial scales, we defined the scale-variances for specific greenspace metrics, including the upper scale limit at which the metrics become invariant, approximately 640 m for Sentinel-2 and 480 m for Landsat-8. Each of the greenspace metrics we considered exhibited scale sensitivities, meaning that each is expected to have a different influence on the strength and significance of the statistical associations found between greenspace exposure and health depending on the spatial scale of analysis (e.g., buffer distance). Using lacunarity curves, we produced a novel composite, multi-scale greenspace ‘exposure index’ in which each input scale is weighted according to its relative scale sensitivity. We also created a multi-scale, multi-metric map combining the different vegetation measures while accounting for scale. We found that cumulative exposure gradients across a large urban conurbation are even more marked when using our multi-scale ‘exposure index’ maps compared to traditional approaches. Our multi-scale, composite greenspace ‘exposure index’ mapping techniques are not as vulnerable to scale effects as traditional approaches and can be readily transferred to the analysis of other environmental exposure variables such as air pollution.

Clinical and sociodemographic factors associated with lupus nephritis in Colombian patients: A cross-sectional study.

Over the past decades, incidence of SLE (Systemic Lupus Erythematosus) has increased due to early case detection and improved survival of patients. SLE presents at an earlier age and has a more severe presentation in African-American, Native American, Asian, and Hispanic populations. Worldwide, lupus nephritis (LN) is observed in 29-60% of SLE patients, it has a negative impact in renal survival and patient mortality. Several cohorts have established potential risk factors associated with lupus nephritis, such as male sex, serological markers, and some extra-renal manifestations. To describe sociodemographic, clinical, immunological, and environmental risk factors in Colombian SLE patients and to compare the population with and without nephritis, in order to establish risk factors and possible associations. A total of 1175 SLE patients participated in this study. During medical care, an interview and structured survey was conducted and later registered in a database. Sociodemographic, clinical, immunological, and environmental exposure variables were analyzed. Bivariate and multivariate analyses were performed using presence of LN as an outcome. Prevalence of LN was 38.7%. Variables significantly associated with LN included being male (OR 1.98), a duration of SLE>10 years (OR 1.48), positive anti-DNA (OR 1.34), positive anti-Sm (OR 1.45), and smoking (OR 1.66). Being non-smoker was a protective factor (OR 0.52). This study describes potential factors associated with lupus nephritis in a Latin American population. Smoking status could be a target for intervention as it is a modifiable risk factor. The association between being male and LN is observed in Latin-American populations such as presented here. Further research in other large-scale population studies and more efforts are needed to gain better insights to explicate these relationships.

Assessment of environmental exposure factors on child diarrhea and systemic inflammation in the Eastern Cape

Poor environmental technologies and gastrointestinal illnesses have been hypothesized to be a primary cause to the lack of impact of child health programs on child stunting rates (low height-for-age) in South Africa. This study assessed correlations between environmental exposures (water source, water treatment, sanitation, refuse), diarrheal occurrences, and systemic inflammation proxies among female and male children under five years of age in the Eastern Cape. A conceptual model was hypothesized using structural equation (SE) modeling and two sex-specific (female and male) datasets were subsequently generated from the data and applied to the hypothesized SE model. Results suggested that environmental exposure variables associated with diarrhea and systemic inflammation proxies were different between females and males. For diarrheal occurrences among females, an increase in local authority management of refuse (compared to household management) (0.161, p-value (p) = 0.007), sharing sanitation facilities (0.060, p = 0.023), and a decrease in the frequency of the treatment of drinking water (−0.043, p = 0.025) were correlated with an increase in diarrhea. For males, an increase in household use of flush toilets (as compared to ventilated pit latrines) was correlated with an increase in diarrhea (0.113, p = 0.027). For systemic inflammation among both sexes, an increase in household use of water pumped into the premises (as compared to a public water tap) and an increase in diarrheal occurrences were correlated with an increase in systemic inflammation. The data support an increased focus on sex and gender specific factors among field practitioners and policy makers working in the environmental health field in South Africa.

223 Prevalence of Depression Among People with Diabetes; Comparative Analysis of Older Adults Across Three Health Systems Using Nationally Representative Data

Abstract Background The co-occurrence of depression and diabetes is an emerging global challenge but any association is influenced by variations in prevalence, study design and measure used. We compared depression prevalence in older adults with and without diabetes across three health systems using a validated measure to examine the causal association between diabetes and depression. Methods We used data of adults aged over 49 years, from three nationally representative ageing datasets; The Irish Longitudinal Study on Ageing (TILDA), the English Longitudinal Study on Ageing (ELSA) and the Health and Retirement Study (HRS). Data were collected during 2009–2011 (TILDA), and 2010 (ELSA, HRS). The 8-item CESD scale categorised depression using recommended cut-off scores of &lt; 3 for ELSA and HRS and &lt; 8 for TILDA. Self-reported individual, environmental, behavioural risk, and health system exposure variables were selected a-priori using literature and a directed acyclic graph. Prevalence was presented as a percentage with corresponding 95% confidence intervals. Binomial logistic regression examined the odds of depression by diabetes status. Data were analysed in Stata v15 using the ‘svy’ function. Results Diabetes prevalence (Ireland;8%[(95%CI:7.5–8.6), England;11%(95%CI:10.6–12.0) and USA;19%[(95%CI:18.8-20.1);p&lt;0.001]) and depression prevalence (Ireland;10%(95%CI:9.0–10.6), England;16%(95%CI:15.3-16.9), USA;14%[(95%CI:13.5-14.7);p&lt;0.001] varied across countries. Depression was consistently higher among people with versus without diabetes (Ireland;14%(95%CI:10.9-16.6)vs.9%(95%CI:8.7–10.3),p=0.001], England;25%(95%CI:21.7-27.7)vs.15%[(95%CI:14.2-15.9),p≤0.001] and USA;19%(95%CI:16.9-20.0)vs.13%[(95%CI:12.4-13.8);p≤0.001]. The odds of depression were significantly higher in people with versus without diabetes (Ireland;OR=1.5(95%CI:1.2-1.9);p=0.001, England;OR=1.8(95%CI:1.5–2.2);p&lt;0.000, USA;OR=1.5(95%1.3-1.7);p&lt;0.000), but only remained significant in the English sample after adjustment; (Ireland;OR=1.2(0.8-1.6);p=0.356, England;OR=1.3(1.0–1.6);p=0.045, USA;OR=1.0(0.9-1.1);p=0.898). Conclusion Among older adults in different health systems, depression was consistently higher among people with versus without diabetes. The findings support incorporation of holistic approaches to diabetes management across health systems.

Premorbid and concurrent predictors of TMD onset and persistence.

Multiple risk factors predict temporomandibular disorders (TMD) onset, but temporal changes in risk factors and their contribution to risk of TMD have not been evaluated. The study aims were to (a) describe changes occurring in premorbid TMD risk factors when re-measured at TMD onset and 6months later, and (b) determine if measures of change improve accuracy in predicting TMD incidence compared to premorbid measures alone. In this observational prospective cohort study at four university research clinics, 3,258 community-based, 18- to 44-year-olds without TMD were enrolled. During the 3-year median follow-up, 260 incident cases of first-onset TMD were identified, and 196 TMD-free subjects were selected as matched controls. Six-months later, 147 of 260 incident cases (56.6%) were re-examined revealing 72 (49%) with 'persistent TMD' and 75 (51%) whose condition had resolved ('transient TMD'). Virtually all (126) of the 127 re-examined controls remained without TMD. Questionnaires and clinical measurements evaluated risk factors from clinical, health, psychological and behavioural and neurosensory domains. Most risk factors across all four domains increased with TMD onset, remained elevated in the persistent group and declined in the transient group (i.e., significant ANOVA interactions, p<.05). Accuracy in predicting first-onset TMD, quantified as area under the receiver operating characteristic curve was 0.71 (95% CL 0.68, 0.73) using only premorbid measures of risk factors, which increased to 0.91 (95% CL 0.89, 0.94) after addition of change measures. TMD pain onset and persistence appear to be determined by enduring characteristics of the person as well as mutually interactive with temporally evolving variables. TMD is known to be a complex disorder, in which onset and persistence are associated with disease-related variables in multiple domains, including environmental exposure, clinical, psychological, health status, and pain processing variables. Using a more dynamic approach in order to capture change across time, many aspects of those domains were found to worsen prior to the reporting of pain, with bidirectional influences between domains and pain emergence likely. TMD onset appears to represent the cumulative effect of multiple system dysregulation.

Environmental Performance Versus Corporate Financial Performance (Environemntal Media Exposure di Indonesia)

The purpose of this study is examine the effect of environmental performance, type of industry and environmental media exposure on corporate financial performance in public companies listed on the Indonesia Stock Exchange during the period 2015-2017. The population used in this study are public companies or issuers other than financial service institutions or the banking and financial sectors listed on the Indonesia Stock Exchange. The data obtained is secondary data using library research. The sampling method used purposive sampling technique, obtained as many as 24 companies as a study sample for four years. The analysis in this study uses Statistical Product and Service Solutions (SPSS) software program version 24.0, where the statistical model used is multiple regression analysis. The results in this study are that environmental performance is good without and with the existence of moderating environmental media exposure variables having a significant and positive influence on corporate financial performance. While typr of industry is good without being moderated or moderating the variable environmental media exposure, it does not have a significant effect on corporate financial performance. For further research, it is recommended to enlarge the research sample not only limited to public companies or issuers other than financial service institutions, but also include public companies or issuers of the banking and financial sectors as research samples.