Suicide disparities across metropolitan areas in the US: A comparative assessment of socio-environmental factors using a data-driven predictive approach.

Disparity in suicide rates across various metropolitan areas in the US is growing. Besides personal genomics and pre-existing mental health conditions affecting individual-level suicidal behaviors, contextual factors are also instrumental in determining region-/community-level suicide risk. However, there is a lack of quantitative approach to model the complex associations and interplays of the socio-environmental factors with the regional suicide rates. In this paper, we propose a holistic data-driven framework to model the associations of socio-environmental factors (demographic, socio-economic, and climate) with the suicide rates, and compare the key socio-environmental determinants of suicides across the large and medium/small metros of the vulnerable US states, leveraging a suite of advanced statistical learning algorithms. We found that random forest outperforms all the other models in terms of both in-sample goodness-of-fit and out-of-sample predictive accuracy, which is then used for statistical inferencing. Overall, our findings show that there is a significant difference in the relationships of socio-environmental factors with the suicide rates across the large and medium/small metropolitan areas of the vulnerable US states. Particularly, suicides in medium/small metros are more sensitive to socio-economic and demographic factors, while that in large metros are more sensitive to climatic factors. Our results also indicate that non-Hispanics, native Hawaiian or Pacific islanders, and adolescents aged 15-29 years, residing in the large metropolitan areas, are more vulnerable to suicides compared to those living in the medium/small metropolitan areas. We also observe that higher temperatures are positively associated with higher suicide rates, with large metros being more sensitive to such association compared to that of the medium/small metros. Our proposed data-driven framework underscores the future opportunities of using big data analytics in analyzing the complex associations of socio-environmental factors and inform policy actions accordingly.

A multilevel scenario based predictive analytics framework to model the community mental health and built environment nexus.

The built environment affects mental health outcomes, but this relationship is less studied and understood. This article proposes a novel multi-level scenario-based predictive analytics framework (MSPAF) to explore the complex relationships between community mental health outcomes and the built environment conditions. The MSPAF combines rigorously validated interpretable machine learning algorithms and scenario-based sensitivity analysis to test various hypotheses on how the built environment impacts community mental health outcomes across the largest metropolitan areas in the US. Among other findings, our results suggest that declining socio-economic conditions of the built environment (e.g., poverty, low income, unemployment, decreased access to public health insurance) are significantly associated with increased reported mental health disorders. Similarly, physical conditions of the built environment (e.g., increased housing vacancies and increased travel costs) are significantly associated with increased reported mental health disorders. However, this positive relationship between the physical conditions of the built environment and mental health outcomes does not hold across all the metropolitan areas, suggesting a mixed effect of the built environment's physical conditions on community mental health. We conclude by highlighting future opportunities of incorporating other variables and datasets into the MSPAF framework to test additional hypotheses on how the built environment impacts community mental health.

A Two-Stage Data-Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms.

Risk-informed asset management is key to maintaining optimal performance and efficiency of urban sewer systems. Although sewer system failures are spatiotemporal in nature, previous studies analyzed failure risk from a unidimensional aspect (either spatial or temporal), not accounting for bidimensional spatiotemporal complexities. This is owing to the insufficiency of good-quality data, which ultimately leads to under-/overestimation of failure risk. Here, we propose a generalized methodology/framework to facilitate a robust spatiotemporal analysis of urban sewer system failure risk, overcoming the intrinsic challenges of data imperfections-e.g., missing data, outliers, and imbalanced information. The framework includes a two-stage data-driven modeling technique that efficiently models the highly right-skewed sewer system failure data to predict the failure risk, leveraging a bidimensional space-time approach. We implemented our analysis for Bogotá, the capital city of Colombia. We train, test, and validate a battery of machine learning algorithms-logistic regression, decision trees, random forests, and XGBoost-and select the best model in terms of goodness-of-fit and predictive accuracy. Finally, we illustrate the applicability of the framework in planning/scheduling sewer system maintenance operations using state-of-the-art optimization techniques. Our proposed framework can help stakeholders to analyze the failure-risk models' performance under different discrimination thresholds, and provide managerial insights on the model's adequate spatial resolution and appropriateness of decentralized management for sewer system maintenance.

Health-Behaviors Associated With the Growing Risk of Adolescent Suicide Attempts: A Data-Driven Cross-Sectional Study.

PURPOSE: Identify and examine the associations between health behaviors and increased risk of adolescent suicide attempts, while controlling for socio-economic and demographic differences. DESIGN: A data-driven analysis using cross-sectional data. SETTING: Communities in the state of Montana from 1999 to 2017. Selected Montana as it persistently ranks among the top 3 vulnerable states in the U.S. over the past years. SUBJECTS: Selected 22,447 adolescents of whom 1,631 adolescents attempted suicide at least once. MEASURES: Overall 29 variables (predictors) accounting for psychological behaviors, illegal substances consumption, daily activities at schools and demographic backgrounds were considered. ANALYSIS: A library of machine learning algorithms along with the traditionally-used logistic regression were used to model and predict suicide attempt risk. Model performances-goodness-of-fit and predictive accuracy-were measured using accuracy, precision, recall and F-score metrics. Additionally, χ2 analysis was used to evaluate the statistical significance of each variable. RESULTS: The non-parametric Bayesian tree ensemble model outperformed all other models, with 80.0% accuracy in goodness-of-fit (F-score: 0.802) and 78.2% in predictive accuracy (F-score: 0.785). Key health-behaviors identified include: being sad/hopeless (p < 0.0001), followed by safety concerns at school (p < 0.0001), physical fighting (p < 0.0001), inhalant usage (p < 0.0001), illegal drugs consumption at school (p < 0.0001), current cigarette usage (p < 0.0001), and having first sex at an early age (below 15 years of age). Additionally, the minority groups (American Indian/Alaska Natives, Hispanics/Latinos) (p < 0.0001), and females (p < 0.0001) are also found to be highly vulnerable to attempting suicides. CONCLUSION: Significant contribution of this work is understanding the key health-behaviors and health disparities that lead to higher frequency of suicide attempts among adolescents, while accounting for the non-linearity and complex interactions among the outcome and the exposure variables. Findings provide insights on key health-behaviors that can be viewed as early warning signs/precursors of suicide attempts among adolescents.

A Data-Driven Approach to Assessing Supply Inadequacy Risks Due to Climate-Induced Shifts in Electricity Demand.

The U.S. electric power system is increasingly vulnerable to the adverse impacts of extreme climate events. Supply inadequacy risk can result from climate-induced shifts in electricity demand and/or damaged physical assets due to hydro-meteorological hazards and climate change. In this article, we focus on the risks associated with the unanticipated climate-induced demand shifts and propose a data-driven approach to identify risk factors that render the electricity sector vulnerable in the face of future climate variability and change. More specifically, we have leveraged advanced supervised learning theory to identify the key predictors of climate-sensitive demand in the residential, commercial, and industrial sectors. Our analysis indicates that variations in mean dew point temperature is the common major risk factor across all the three sectors. We have also conducted a statistical sensitivity analysis to assess the variability in the projected demand as a function of the key climate risk factor. We then propose the use of scenario-based heat maps as a tool to communicate the inadequacy risks to stakeholders and decisionmakers. While we use the state of Ohio as a case study, our proposed approach is equally applicable to all other states.

Data on major power outage events in the continental U.S.

This paper presents the data that is used in the article entitled "A Multi-Hazard Approach to Assess Severe Weather-Induced Major Power Outage Risks in the U.S." (Mukherjee et al., 2018) [1]. The data described in this article pertains to the major outages witnessed by different states in the continental U.S. during January 2000-July 2016. As defined by the Department of Energy, the major outages refer to those that impacted atleast 50,000 customers or caused an unplanned firm load loss of atleast 300 MW. Besides major outage data, this article also presents data on geographical location of the outages, date and time of the outages, regional climatic information, land-use characteristics, electricity consumption patterns and economic characteristics of the states affected by the outages. This dataset can be used to identify and analyze the historical trends and patterns of the major outages and identify and assess the risk predictors associated with sustained power outages in the continental U.S. as described in Mukherjee et al. [1].

A multi-paradigm framework to assess the impacts of climate change on end-use energy demand.

Projecting the long-term trends in energy demand is an increasingly complex endeavor due to the uncertain emerging changes in factors such as climate and policy. The existing energy-economy paradigms used to characterize the long-term trends in the energy sector do not adequately account for climate variability and change. In this paper, we propose a multi-paradigm framework for estimating the climate sensitivity of end-use energy demand that can easily be integrated with the existing energy-economy models. To illustrate the applicability of our proposed framework, we used the energy demand and climate data in the state of Indiana to train a Bayesian predictive model. We then leveraged the end-use demand trends as well as downscaled future climate scenarios to generate probabilistic estimates of the future end-use demand for space cooling, space heating and water heating, at the individual household and building level, in the residential and commercial sectors. Our results indicated that the residential load is much more sensitive to climate variability and change than the commercial load. Moreover, since the largest fraction of the residential energy demand in Indiana is attributed to heating, future warming scenarios could lead to reduced end-use demand due to lower space heating and water heating needs. In the commercial sector, the overall energy demand is expected to increase under the future warming scenarios. This is because the increased cooling load during hotter summer months will likely outpace the reduced heating load during the more temperate winter months.

A comparative analysis of in vitro and in vivo efficacies of the enantiomers of thioridazine and its racemate.

A long list of chemotherapeutical drugs used in the treatment of the peripheral and the central nervous systems possess anti-microbial activity. Some of these neurotropic compounds are chiral, with the one stereo isomeric form exaggerating reduced neurotropism. This is the case for the levorotatory form of thioridazine. The phenothiazine thioridazine is an interesting compound, characterized by exhibiting a significant growth inhibiting activity on a wide array of micro-organisms. Thioridazine is characterized by another challenging feature, because the compound is concentrated in certain human tissue cells. The present study describes a comparative study of the two enantiomers as well as the racemic form of thioridazine. The study exploits the stereochemical aspect and the in vitro and in vivo potential of these compounds, with a focus on the effects on gram negative organism Salmonella enterica serover Typhimurium. In summary, the results of this study yielded a significant antibacterial activity of all forms of thioridazine, indicating the levorotatory (-)-form to be superior in terms of both its in vitro and in vivo efficacies.

Degradation of bacterial DNA by a natural antimicrobial agent with the help of biomimetic membrane system.

The antimicrobial efficacy of methylglyoxal (MG) against several gram-negative bacteria including Escherichia coli has been reported. To determine the mechanism of action of MG, molecular interactions between lipid and MG within the liposomal membrane were also investigated. Multilamellar and unilamellar vesicles were prepared from 1, 2-dipalmitoyl-snglycero-3-phosphocholine (DPPC). The effect of MG on DPPC liposomal membrane was studied by fluorescence spectroscopy and differential scanning calorimetry. The results indicate that MG interacts mainly with the DPPC head group that produces a significant increase in the fluidity of liposomal vesicles, which could be the cause of a fusion/aggregation effect in microbial cells. The agarose gel electrophoresis study with the genomic DNA extracted from E. coli ATCC 25922 revealed that addition of MG could completely degrade this DNA within 1 h, pointing out to their distinctly high degree of sensitivity towards MG. Further, the drug was able to cross the cell membranes, penetrating into the interior of the cell and interacting with DNA for demonstrating antibacterial activity of MG.

Distinct synergistic action of piperacillin and methylglyoxal against Pseudomonas aeruginosa.

The dicarbonyl compound methylglyoxal is a natural constituent of Manuka honey produced from Manuka flowers in New Zealand. It is known to possess both anticancer and antibacterial activity. Such observations prompted to investigate the ability of methylglyoxal as a potent drug against multidrug resistant Pseudomonas aeruginosa. A total of 12 test P. aeruginosa strains isolated from various hospitals were tested for their resistances against many antibiotics, most of which are applied in the treatment of P. aeruginosa infections. Results revealed that the strains were resistant to many drugs at high levels, only piperacillin, carbenicillin, amikacin and ciprofloxacin showed resistances at comparatively lower levels. Following multiple experimentations it was observed that methylglyoxal was also antimicrobic against all the strains at comparable levels. Distinct and statistically significant synergism was observed between methylglyoxal and piperacillin by disc diffusion tests when compared with their individual effects. The fractional inhibitory concentration index of this combination evaluated by checkerboard analysis, was 0.5, which confirmed synergism between the pair. Synergism was also noted when methylglyoxal was combined with carbenicillin and amikacin.

Thioridazine protects the mouse from a virulent infection by Salmonella enterica serovar Typhimurium 74.

When administered to mice at doses of 100microg/mouse and 200microg/mouse, thioridazine (TDZ) significantly protected animals from the lethality produced by a virulent strain of Salmonella enterica serovar Typhimurium and reduced the number of bacteria retrieved from the spleen, liver and heart blood. The protection conferred by TDZ against a virulent Salmonella infection is hypothesised to be due to a reduction in the 55kDa virulence protein of the outer membrane of the organism, as this protein is almost totally absent when the organism is exposed to the phenothiazine. It is further hypothesised that the reduction in the 55kDa virulence factor renders the organism susceptible to the action of hydrolytic enzymes of the neutrophil phagolysosome, whereas in the absence of exposure to TDZ intracellular ingestion and localisation of the phagocytosed bacterium does not result in killing owing to rapid induction of the two-step PmrA/B regulon that results in the eventual synthesis and insertion of lipid A into the nascent lipopolysaccharide layer of the outer membrane.