Levels Of Severity Research Articles

Analyzing quality of life among people with opioid use disorder from the National Institute on Drug Abuse Data Share initiative: implications for decision making.

We aimed to estimate health state utility values (HSUVs) for the key health states found in opioid use disorder (OUD) cost-effectiveness models in the published literature. Data obtained from six trials representing 1,777 individuals with OUD. We implemented mapping algorithms to harmonize data from different measures of quality of life (the SF-12 Versions 1 and 2 and the EQ-5D-3L). We performed a regression analysis to quantify the relationship between HSUVs and the following variables: days of extra-medical opioid use in the past 30 days, injecting behaviors, treatment with medications for OUD, HIV status, and age. A secondary analysis explored the impact of opioid withdrawal symptoms. There were statistically significant reductions in HSUVs associated with extra-medical opioid use (-0.002 (95% CI [-0.003,-0.0001]) to -0.003 (95% CI [-0.005,-0.002]) per additional day of heroin or other opiate use, respectively), drug injecting compared to not injecting (-0.043 (95% CI [-0.079,-0.006])), HIV-positive diagnosis compared to no diagnosis (-0.074 (95% CI [-0.143,-0.005])), and age (-0.001 per year (95% CI [-0.003,-0.0002])). Parameters associated with medications for OUD treatment were not statistically significant after controlling for extra-medical opioid use (0.0131 (95% CI [-0.0479,0.0769])), in line with prior studies. The secondary analysis revealed that withdrawal symptoms are a fundamental driver of HSUVs, with predictions of 0.817 (95% CI [0.768, 0.858]), 0.705 (95% CI [0.607, 0.786]), and 0.367 (95% CI [0.180, 0.575]) for moderate, severe, and worst level of symptoms, respectively. We observed HSUVs for OUD that were higher than those from previous studies that had been conducted without input from people living with the condition.

Application and expansion of an algorithm predicting attention-deficit/hyperactivity disorder and impairment in a predominantly White sample.

Current assessment protocols for attention-deficit/hyperactivity disorder (ADHD) focus heavily on a set of highly overlapping symptoms, with well-validated factors like cognitive disengagement syndrome (CDS), executive function (EF), age, sex, and race and ethnicity generally being ignored. Using machine learning techniques, the current study aimed to validate recent findings proposing a subset of ADHD symptoms that, together, predict ADHD diagnosis, severity, and impairment level better than the full symptom list, while also testing whether the inclusion of the factors listed above could further increase accuracy. Parents of 1,922 children (50.1% male) aged 6-17 years completed rating scales of ADHD, CDS, EF, and impairment. Results suggested nine symptoms as most important in predicting outcomes: (a) has difficulty sustaining attention in tasks or play activities; (b) does not follow through on instructions and fails to finish work; (c) avoids tasks (e.g., schoolwork, homework) that require sustained mental effort; (d) is often easily distracted; (e) has difficulty organizing tasks and activities; (f) is often forgetful in daily activities; (g) fidgets with hands or feet or squirms in seat; (h) interrupts/intrudes on others; and (i) shifts around excessively or feels restless or hemmed in. The abbreviated algorithm achieved accuracy rates that did not significantly differ compared to an algorithm comprising all 18 symptoms in predicting impairment, while also demonstrating excellent discriminative ability in predicting ADHD diagnosis. Adding CDS and EF to the abbreviated algorithm further improved the prediction of global impairment. Continued refinement of screening tools will be key to ensuring access to clinical services for youth at risk for ADHD. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Autism Severity, Adverse Childhood Experiences, and Oral Health: A Comparative Study of Adolescents in the United States.

Dental health significantly influences overall child physical well-being, academic success, and psychosocial development. This paper explores the intersection of a range of Autism Spectrum Disorder (ASD) functionality, multiple types of Adverse Childhood Experiences (ACEs), and dental health in adolescents. The purpose is to investigate the independent and interactive effects of ASD severity and ACEs on dental outcomes. Data from the 2018 and 2019 National Surveys of Children's Health were analyzed, focusing on 28,263 adolescents (ages 11-17). Logistic regressions assessed associations between ASD severity (mild, moderate, severe), cumulative number of ACEs, and dental outcomes (teeth condition, cavities or bleeding gums, check-ups, cleanings). Covariates included demographic and socioeconomic factors. Controlling for covariates, severe autism independently increased the odds of poor teeth (odds ratio = 6.17), cavities or bleeding gums (OR = 3.76), no previous year check-up(OR = 2.94), and no previous year cleaning (OR = 4.20). Higher ACE scores also independently increased the odds of all dental outcomes. Interactions revealed a cumulative effect of multiple ACEs on poor dental outcomes only for adolescents with severe levels of autism. This study illuminates the heightened vulnerability of adolescents with severe autism and a history of ACEs, emphasizing the need for targeted interventions. Multidisciplinary strategies integrating dental care and mental health support within tailored interventions are crucial for improving dental health and access for older youth with low functioning autism. While these findings provide valuable insights, the cross-sectional design and reliance on parental reports necessitate cautious interpretation.

Supporting Mental Health Well-Being in the Most Vulnerable Future Nurses.

The aim of the study was to explore relationships between perceived faculty support and prevalence of severe/extremely severe depression, anxiety, and stress levels among undergraduate baccalaureate nursing students. Emerging research supports nursing students suffering from extreme psychological distress. Educational success and mental health wellness are influenced by external support. A mixed-methods design was used for this descriptive, cross-sectional study. A national convenience sample of 1,163 undergraduate baccalaureate nursing students completed the Depression, Anxiety and Stress Scale-21, Perceived Faculty Support Scale, and a demographic questionnaire. Emerging themes in the qualitative data were identified. Nearly 54 percent of nursing students suffered from severe/extremely severe depression, anxiety, or stress. Faculty support was critical to nursing student mental health well-being. This multisite study provides a better understanding of the enormity of psychological distress among undergraduate baccalaureate nursing students and emphasizes the immediate need for faculty to provide necessary support to this vulnerable student population.

A Cross-Sectional Study of Health-Related Quality of Life in Patients with Predominantly Antibody Deficiency.

Health-related quality of life (HRQoL) measures individual well-being across physical, psychological, and social domains. Patients with predominantly antibody deficiency (PAD) are at risk for morbidity and mortality, however, the effect of these complications on HRQoL requires additional study. Patients with PAD were asked to voluntarily complete the Centers for Disease Control (CDC) HRQoL-14 Healthy Days Measure questionnaire. These results were compared to data from the CDC-initiated Behavioral Risk Factor Surveillance System (BRFSS), a cross-sectional questionnaire including questions from CDC-HRQOL-14. Statistical analyses included two-proportion Z-test, t-tests, and analysis of variance. 83 patients with PAD completed the survey. Patients were sub-stratified into mild (23.7%), moderate (35.5%), severe (40.8%), and secondary (8.4%) PAD. "Fair or poor" health status was reported in 52.6% of PAD patients. Mental health challenges ≥ 14days/month occurred in 25% of patients. Physical health issues ≥ 14days/month was reported in 44.7% of patients. Activity limitations were noted by 80.3% of patients. There were no statistically significant differences by PAD severity. Patients with autoimmune and inflammatory disease co-morbidities reported more mental health challenges compared to those without (78% vs. 54.3%, p = 0.02). Compared to the CDC-BRFSS data, significantly more patients with PAD reported "fair or poor" health status (53% vs 12.0%; p < 0.0001), mental health challenges (24.1% vs 14.7%; p = 0.02), and poor physical health (44.6% vs 8.0%; p < 0.0001). Patients with PAD had significantly reduced HRQoL compared to CDC-BRFSS respondents from a similar geographical region. Decreased HRQoL was prevalent across all PAD severity levels. Additional research is needed to improve HRQoL for patients with PAD.

Automatic Methodology for Forest Fire Mapping with SuperDove Imagery

The global increase in wildfires due to climate change highlights the need for accurate wildfire mapping. This study performs a proof of concept on the usefulness of SuperDove imagery for wildfire mapping. To address this topic, we present an automatic methodology that combines the use of various vegetation indices with clustering algorithms (bisecting k-means and k-means) to analyze images before and after fires, with the aim of improving the precision of the burned area and severity assessments. The results demonstrate the potential of using this PlanetScope sensor, showing that the methodology effectively delineates burned areas and classifies them by severity level, in comparison with data from the Copernicus Emergency Management Service (CEMS). Thus, the potential of the SuperDove satellite sensor constellation for fire monitoring is highlighted, despite its limitations regarding radiometric distortion and the absence of Short-Wave Infrared (SWIR) bands, suggesting that the methodology could contribute to better fire management strategies.

An Interpretable System for Screening the Severity Level of Retinopathy in Premature Infants Using Deep Learning

Accurate evaluation of retinopathy of prematurity (ROP) severity is vital for screening and proper treatment. Current deep-learning-based automated AI systems for assessing ROP severity do not follow clinical guidelines and are opaque. The aim of this study is to develop an interpretable AI system by mimicking the clinical screening process to determine ROP severity level. A total of 6100 RetCam Ⅲ wide-field digital retinal images were collected from Guangdong Women and Children Hospital at Panyu (PY) and Zhongshan Ophthalmic Center (ZOC). A total of 3330 images of 520 pediatric patients from PY were annotated to train an object detection model to detect lesion type and location. A total of 2770 images of 81 pediatric patients from ZOC were annotated for stage, zone, and the presence of plus disease. Integrating stage, zone, and the presence of plus disease according to clinical guidelines yields ROP severity such that an interpretable AI system was developed to provide the stage from the lesion type, the zone from the lesion location, and the presence of plus disease from a plus disease classification model. The ROP severity was calculated accordingly and compared with the assessment of a human expert. Our method achieved an area under the curve (AUC) of 0.95 (95% confidence interval [CI] 0.90–0.98) in assessing the severity level of ROP. Compared with clinical doctors, our method achieved the highest F1 score value of 0.76 in assessing the severity level of ROP. In conclusion, we developed an interpretable AI system for assessing the severity level of ROP that shows significant potential for use in clinical practice for ROP severity level screening.

Severity and Risk Rating of Drug-Drug Interaction Potency in Chronic Kidney Disease in Public Health Hospital

Background: Chronic kidney disease has emerged as a prominent cause of mortality worldwide in the last two decades. Drug interactions are a significant problem in individuals with chronic kidney disease, as they often receive numerous concurrent treatments. The aim is to examine the frequency of drug-drug interactions in outpatient treatment for chronic renal disease, classified according to their severity and risk level. Furthermore, the study aims to demonstrate the combined effect of the most common drug-drug interactions potency and associated risk factors. Methods: This study is a retrospective cross-sectional analysis of outpatients diagnosed with chronic kidney disease at Ansari Saleh Public Hospital in Banjarmasin, Indonesia. Medical records from 2022 were obtained retrospectively from computerised sources. The evaluation of potential interactions between drugs was conducted using Lexicomp®. The categorical data were displayed as percentages, and the continuous variables were depicted as means. Results: The study found that most drug interactions fell into the moderate severity (77.1%) and category C (75.5%) for the risk assessment. The prevalence of drug-drug interactions was 83.6% and the average number of potential drug interactions per patient was 3.34 ± 3.481. The concurrent use of candesartan and furosemide was the most prevalent drug-drug interaction (5.1%). The number of prescribed medications was a notable determinant for probable drug-drug interactions (p &lt; 0.05). Conclusion: Identifying and averting potential drug-drug interactions is crucial to guarantee patient safety.

The severity level of malocclusion and orthodonthic treatment needs based on the index of orthodonthic treatment need (IOTN) on students aged 9-12 years old at candijati jember 1 state elementary school

Malocclusion is a condition of dental occlusion that is not suitable from normal conditions that affect oral function and facial aesthetics and has now become a great concern in the health sector. Malocclusion occurs mostly in children aged 9-12 years which is the second phase of the mixed teeth period. The severity’s level of malocclusion and orthodontic treatment needs can be measured using the malocclusion index, one of which is the Index of Orthodontic Treatment Need (IOTN). Dental Health Component (DHC) is one component of IOTN that is effective in overcoming malocclusion and has good potential for orthodontic treatment because it assesses objectively by examining and measuring the circumstances of the teeth. The purpose of this study was to determine the severity of malocclusion and orthodontic treatment needs based on the Index of Orthodontic Treatment Need (IOTN) in students aged 9-12 years at SDN 01 Candijati Jember. Sampling was determined using the total sampling method on subjects encountered inclusion criteria; 96 samples were obtained. This study was conducted by measuring the study model of the patient's dental printing results based on DHC from IOTN and analyzed descriptively. The results of the study found the severity of malocclusion and the need for orthodontic treatment based on the Index of Orthodontic Treatment Need (IOTN) in students aged 9-12 years SDN 01 Candijati Jember is most commonly found in grade 2, namely mild malocclusion with little need for treatment. Then the severity’s level of malocclusion and the need for orthodontic treatment is greater in women than men and great-est in students aged 12 years.

Bruton’s tyrosine kinase inhibition ameliorated neuroinflammation during chronic white matter ischemia

Chronic cerebral hypoperfusion (CCH), a disease afflicting numerous individuals worldwide, is a primary cause of cognitive deficits, the pathogenesis of which remains poorly understood. Bruton’s tyrosine kinase inhibition (BTKi) is considered a promising strategy to regulate inflammatory responses within the brain, a crucial process that is assumed to drive ischemic demyelination progression. However, the potential role of BTKi in CCH has not been investigated so far. In the present study, we elucidated potential therapeutic roles of BTK in both in vitro hypoxia and in vivo ischemic demyelination model. We found that cerebral hypoperfusion induced white matter injury, cognitive impairments, microglial BTK activation, along with a series of microglia responses associated with inflammation, oxidative stress, mitochondrial dysfunction, and ferroptosis. Tolebrutinib treatment suppressed both the activation of microglia and microglial BTK expression. Meanwhile, microglia-related inflammation and ferroptosis processes were attenuated evidently, contributing to lower levels of disease severity. Taken together, BTKi ameliorated white matter injury and cognitive impairments induced by CCH, possibly via skewing microglia polarization towards anti-inflammatory and homeostatic phenotypes, as well as decreasing microglial oxidative stress damage and ferroptosis, which exhibits promising therapeutic potential in chronic cerebral hypoperfusion-induced demyelination.

Ice-Ice Disease Prevalence and Intensity in Eucheumatoid Seaweed Farms: Seasonal Variability and Relationship with the Physicochemical and Meteorological Parameters

Since its initial report in the Philippines in 1981, ice-ice disease (IID) remains a substantial threat to the sustainability of eucheumatoid seaweed production. However, comprehensive investigations into the prevalence, intensity, and potential relationships with physicochemical and meteorological parameters are limited, particularly in open-sea farms. This is the first study to conduct a 12-month monitoring of IID prevalence and intensity in eucheumatoid seaweed farms in Tawi-Tawi, Philippines. The research aimed to elucidate seasonal variations and potential associations with physicochemical and meteorological parameters. The findings revealed significant seasonal variations in IID prevalence, with a higher incidence observed during the dry season compared to the wet season. Additionally, deep-water farms exhibited significantly higher prevalence and ice-ice spot numbers per bundle compared to shallow-water farms. Furthermore, Kappaphycus striatus displayed a significantly greater ice-ice spot length per bundle compared to K. alvarezii. Notably, no interaction effects were observed among season, species, and depth. Interestingly, while no correlations were found between ice-ice disease and most measured physicochemical and meteorological parameters (temperature, salinity, pH, water current velocity, wind speed) or nutrient concentrations (phosphate, nitrate, nitrite), an inverse correlation (p &lt; 0.05) emerged between ammonium levels and IID intensity parameters (number and length of ice-ice spot per bundle). Moreover, a positive correlation was observed between monthly rainfall and IID intensity. These findings offer valuable insights into the dynamics of IID in open-sea eucheumatoid seaweed (Kappaphycus) farming, highlighting the influence of seasonality, depth, and species susceptibility, as well as the relationship between IID severity and ammonium levels and rainfall.

Secondary Traumatic Stress in Nursing Students: An Integrative Review.

Nurses have frequent interactions with traumatized individuals and may develop secondary traumatic stress (STS). Similarly, nursing students are at risk for STS development while completing clinical training. To critique and synthesize available research on the prevalence, severity, and experience of STS and its associated factors in prelicensure nursing students. The Whittemore and Knafl integrative review methodology was used to perform structured searches of 6 electronic databases. Twenty-five studies met the inclusion criteria. Findings revealed a range of STS symptom severity levels and associated factors; however, measurement is hampered by researchers' use of other concepts, like compassion fatigue. Conceptual clarity is needed to improve STS measurement in nursing students; however, students experience STS and are at risk of developing its negative consequences. Educators should implement trauma-informed educational practices targeted at reducing factors associated with STS.

Systematic Assessment of Gambling Type Involvement: Reliability and Validity of the Gambling Disorder Identification Test (GDIT).

Gambling type involvement, both in terms of participation (engagement in specific gambling types) and diversity (how many gambling types an individual engages in), is a key feature to address in gambling self-report measures, but such systematic measurement procedures are scarce. The aim of this study was to test the psychometric performance of the gambling type assessment in the recently developed Gambling Disorder Identification Test (GDIT), in terms of test-retest reliability, convergent validity, and patterns of gambling diversity, among help-seeking and general population gambling samples (total n = 603). Overall, online gambling was more commonly reported as problematic than land-based gambling. Retest reliability varied for specific gambling types (ICC range 0.32-0.64, rtet range 0.66-0.85). In terms of gambling participation, online gambling showed stronger correlations with GDIT total score (i.e., symptom severity) than land-based gambling, where Slots showed the strongest correlation (r = 0.52), followed by Casino table games (r = 0.25), Sports and Horse betting (r = 0.16 and r = 0.14, respectively), and Poker (r = 0.14). Lotteries showed no correlation with GDIT total score (r=-0,01). For Slots gambling, all gambling diversity levels (including Slots as a single gambling type) were on average associated with the highest diagnostic severity level (GDIT total score > 30; severe gambling disorder). Finally, explorative configural frequency analysis identified typical and antitypical gambling diversity patterns. The result from the current study corroborates findings that engagement in specific gambling types matter, and that such features should be included in gambling measurement. We conclude that the GDIT is a reliable and valid measure for systematic assessment of gambling type involvement. The GDIT can be used to assess gambling participation and diversity, as part of a broad measurement setup for problem gambling and gambling disorder.

Dermatological conditions in the intensive care unit at a tertiary care hospital in Riyadh, Saudi Arabia.

To evaluate the various skin conditions diagnosed in intensive care unit (ICU) patients. This is a descriptive retrospective study of all adults, pediatric, and neonatal patients who were admitted to the ICU and had a dermatological manifestation during hospital stay or patients who had dermatological condition that requires ICU admission. All skin conditions were categorized and analyzed. A total of 344 ICU patients with 365 different dermatological conditions were included in the study. The age of patients ranged from less than 1-96 years, with a mean age of 43.6±30.1 years. Of the patients, 189 (54.9%) were males. The top 3 general disease categories observed were skin infections, inflammatory and autoimmune diseases, and drug reactions. The most commonly reported dermatological disorders included morbilliform drug eruption (6.8%), contact dermatitis (6.3%), vasculitis (5.5%), herpes zoster (4.6%), purpura due to thrombocytopenia (3.8%), dermatitis/eczema (3.8%), candidiasis (3.8%), infantile hemangioma (2.7%), unclassified drug reaction (2.5%), intertrigo (2.5%), and herpes simplex virus (2.5%). Dermatological disorders can occur at various levels of severity in the ICU. Skin infections, inflammatory and autoimmune diseases, and drug reactions were found to be the most prevalent conditions.

Interactive effects of elevation difference, slope variation, and terrain formation on road traffic crashes occurrences using triangular irregular network

AbstractNumerous factors contribute to traffic crashes, which result in fatalities and injuries. Road traffic crashes and their severity level are likely caused by variations in elevation, slope, and terrain formation. As far as our knowledge is concerned, the triangular irregular network (TIN) was not used to analyze dangerous zones of the road network with respect to traffic crashes' location. The main purpose of this study was to investigate a dangerous road network that was affected by traffic crashes using TIN. To address the goal, this study used elevation differences slope variation, and terrain between road traffic crash locations. The study used the intersection zone road network coordinates of traffic crashes in the city of Budapest that were recorded from 2017 to 2021. Using tools like ArcGIS and MS Excel, the study organized, modeled, and analyzed the data. Geometrical interval classification was used to classify the data in accordance with the characteristics of elevation deference and slope variation. Both inferential and descriptive statistics were engaged to explain, analyze, and summarize the findings. The results of this study demonstrated that the majority of traffic crashes occurred in locations with lower elevations and their differences, steeper/cliff slopes between points, and flat terrain. The finding was in contrast to what was observed in other studies. This study advocates that TIN is the optimal method and substitute for analyzing the risky zone of the road network at macroscopic level.

Structural and functional imaging of psoriasis for severity assessment and quantitative monitoring of treatment response using high-resolution optoacoustic imaging

Psoriasis is a chronic inflammatory skin disease, characterized by thick scaly plaques. It imposes a notable disease burden with varying levels of severity affecting the quality of life significantly. Current disease severity assessment relies on semi-objective visual inspection based on the Psoriasis Area and Severity index (PASI) score that might not be sensitive to sub-clinical changes. Histology of psoriasis skin lesions necessitate invasive skin biopsies. This indicates an unmet need for a non-invasive, objective and quantitative approach to assess disease severity serially. Herein, we employ multispectral Raster-Scanning Optoacoustic Mesoscopy (ms-RSOM) derived structural and microvascular functional imaging metrics to examine the lesional and non-lesional skin in psoriasis subjects across different severities and also evaluate the treatment outcome in a subject with topical steroids and biologics, such as adalimumab. ms-RSOM derived structural metrics like epidermal thickness and microvascular functional information such as oxygen saturation (sO2) and total blood volume (TBV) are evaluated by spectrally resolving the endogenous chromophores like melanin, oxy-, and deoxy-hemoglobin. Initial findings reveal an elevated sO2 and TBV with severity in lesional and non-lesional psoriasis skin, thus representing increasing inflammation. An increase in epidermal thickness is also noted with the degree of severity, corresponding to the inflammation and increased abnormal cell growth. As a marker to evaluate the treatment response, we observed a decrease in epidermal thickness, sO2, and TBV in a psoriasis patient post-treatment, which is consistent with the decrease in the PASI score from 4.1 to 1.9. We envision that ms-RSOM has a huge potential to be translated into routine clinical setting for the diagnosis of severity and assessment of treatment monitoring in psoriasis subjects.

If personality disorder is just maladaptive traits, there is no such thing as personality disorder.

The diagnosis of personality disorder (PD) is undergoing a transition from a categorical model that distinguishes types from one another to a model that characterizes patients with dimensional profiles. The DSM-5 (Diagnostic and Statistical Manual of Mental Disorders, 5th ed.) alternative model of personality disorder (AMPD) and the International Statistical Classification of Diseases and Related Health Problems (11th ed.) have two primary criteria: the first is a dimension that differentiates PD from both normal personality and other kinds of disorder, can be used to indicate the overall level of severity of a patient's functional difficulties, and is the basis for PD diagnosis. The second is a set of traits with structural connections to normal-range personality variables that characterize the type of problems the patient is likely to have. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Toward the Development of an Impact-Based Decision Support Tool for Surface-Transportation Hazards. Part II: An Hourly Winter Storm Severity Index

Abstract In line with the continued focus of the National Weather Service (NWS) to provide impact-based decision support services (IDSS) and effectively communicate potential impacts, a new IDSS forecasting tool for surface-transportation hazards is in development at the Weather Prediction Center: the hourly winter storm severity index (WSSI-H). This second part of the series outlines the current algorithms and thresholds for the components of the WSSI-H, which has been developed in line with the approach and considerations discussed in Part I of this series. These components—snow amount, ice accumulation, snow rate, liquid rate, and blowing and drifting snow—each address a specific hazard for motorists. The inclusion of metrics related to driving conditions for untreated road surfaces and time-of-day factoring for active precipitation types helps directly tie forecasted weather conditions to transportation impacts. Impact severity level thresholds are approximately in line with thresholds used by transportation agencies when considering various mitigation strategies (e.g., imposing speed restrictions or closing roadways). Whereas the product is not meant to forecast specific impacts (e.g., road closure or pileup), impact severity levels are designed to scale with increasingly poor travel conditions, which can prompt various mitigation efforts from motorists or transportation agencies to maintain safety. WSSI-H outputs for three winter events are discussed in depth to highlight the potential utility of the product. Overall, the WSSI-H is intended to provide high-resolution situational awareness of potential surface-transportation-related impacts and aid in enhanced collaborations between NWS forecasters and stakeholders like transportation agencies to improve motorist safety. Significance Statement A new impact-based forecast product designed to aid in situational awareness of potential impacts from surface-transportation-related hazards is in development. In this second part of the series, we outline the algorithms and thresholds for the various components of the product, where each component addresses a unique hazard. Product outputs for three winter events are presented to highlight the potential utility of the product in an operational forecast setting. Ultimately, enhanced collaboration between forecasters and transportation agencies alongside guidance from this product will bolster consistent messaging to motorists and improve safety and mobility on roads.

Toward the Development of an Impact-Based Decision Support Tool for Surface-Transportation Hazards. Part I: Tying Weather Variables to Road Hazards and Quantifying Impacts

Abstract Development of an impact-based decision support forecasting tool for surface-transportation hazards requires consideration for what impacts the product is intended to capture and how to scale forecast information to impacts to then categorize impact severity. In this first part of the series, we discuss the motivation and intent of such a product, in addition to outlining the approach we take to leverage existing and new research to develop the product. Traffic disruptions (e.g., crashes, increased travel times, roadway restrictions, or closures) are the intended impacts, where impact severity levels are intended to scale to reflect the increasing severity of adverse driving conditions that can correlate with a need for enhanced mitigation efforts by motorists and/or transportation agencies (e.g., slowing down, avoiding travel, and imposing roadway restrictions or closures). Previous research on how weather and road conditions impact transportation and novel research herein to create a metric for crash impact based on precipitation type and local hour of the day are both intended to help scale weather forecasts to impacts. Impact severity classifications can ultimately be determined through consideration of any thresholds used by transportation agencies, in conjunction with the scaling metrics. Significance Statement Weather can profoundly impact surface transportation and motorist safety. Because of this and because there are no explicit tools available to forecasters to identify and communicate potential impacts to surface transportation, there is a desire for the development of such a forecast product. However, doing so requires careful consideration for what impacts are intended to be included, how weather corresponds to impacts, and how thresholds for impact severity should be defined. In this first part of the paper series, we outline each of these aspects and present novel research and approaches for the development of an impact-based forecast product specifically tailored to surface-transportation hazards. The product is ultimately intended to improve motorist safety and mobility on roads.

THE ROLE OF DHAKA'S COMMUNITY-BASED PARKS IN IMPROVING CHILDREN’S MENTAL AND PHYSICAL HEALTH

This study focused on the significance of Community Park for enhancing children’s mental and physical health at the grassroots level of the community of Dhaka city. To assess the practical scenario both primary and secondary data was used in this study. Convenience sampling techniques have been applied to collect data with a pre-designed structured questionnaire. To reach the study goal, regression analyses have been applied to see the correlation between children’s health and community park activities. Furthermore,categorized the impact to understand the vulnerability. The evidence identified that such community park contributes significantly to children's physical and mental well-being, fostering a positive impact on their holistic development. The study findings demonstrated that ∼ 80% of children think community parks significantly contribute to maintaining their mental wellbeing. Similarly, 61% of children expressed that park activities reduce their stress and anxiety. In addition, regular activities impact on their physical growth which was expressed by 90% of respondents. The result also finds out a strong correlation between the physical and mental health of the children. In addition, it analyzed the correlation of impact between their education and health. Besides, the study also assesses the levels of severity of depression, anxiety, and sleeping disorders in children after covid-19 pandemic. Indeed, results also disclose numerous factors such as infrastructural conditions, greenery stock, safety measures, children's activities and communityparticipation in the community parks. The novelty of the study will help public health experts, psychologists, urban planners, environmentalists, policymakers, public representatives to take an ecofriendly sustainable development plan, thereby fostering a positive impact on children’s holistic development. Furthermore, this study provides some recommendations to increase the overall health conditions of the park’s children's zone.