Local Data Research Articles

Assessment of the Impact of LID Techniques on Water Cycle Recovery in Industrial Complexes

Urbanization and industrialization have significantly disrupted the natural water cycle, leading to increased runoff, flooding, and groundwater depletion. Research on LID (Low Impact Development) techniques is ongoing in order to address these challenges. This study has analyzed the improvement in the water cycle system by applying LID techniques to the Daejeongcheon watershed in Ulsan. Using SWMM, this study has assessed the long-term effects of LID on runoff reduction and water cycle recovery rate improvement. Based on local climate data, topographical information, and regional characteristics, LID elements such as permeable pavements, green roofs, bioretention, and vegetative swales were applied. The results showed that the LID techniques reduced runoff by 8.44% and also improved the water cycle recovery rate by 13.52%. Additionally, the study confirmed that LID techniques effectively contributed to managing runoff from the 25.5 mm rainfall management target. LID techniques are expected to serve as sustainable water management strategies in urbanized and industrialized areas, offering effective restoration and management solutions.

Rethinking Food and Nutrition Security: Why Income Alone Isn’t Enough in Coffee-Producing Areas of Ethiopia.

Background Coffee-growing communities in Yayu, Southwestern Ethiopia, are perceived to be food secure due to income from coffee production. However, while income ensures caloric sufficiency, it does not necessarily translate into complete food security, especially in terms of nutrient intake. Our study assessed the food and nutrition security of smallholder farm households in Yayu, with a particular focus on dietary diversity and hidden hunger. We found significant gaps in nutritional intake, especially for vulnerable groups such as women and children under five. Policy and Implications Ethiopia’s Productive Safety Net Program (PSNP) targets food-insecure areas based on economic status, assuming that income ensures food security. However, our findings suggest that this approach may be flawed, as households in coffee-growing regions are often labeled food secure despite significant deficiencies in essential nutrients such as heme iron. This calls for a re-evaluation of food security policies in cash-crop-dominated areas to address not only income but also access to diverse and nutrient-rich foods. Recommendations 1. Enhance Nutritional Education: Implement targeted educational programs focusing on the importance of dietary diversity, particularly in coffee-producing areas. 2. Diversify Agriculture: Encourage smallholder farmers to diversify crops, integrating nutrient-dense foods alongside coffee cultivation. 3. Improve Market Access: Strengthen local markets to ensure that nutrient-rich foods are available and affordable during both surplus and shortage seasons. 4. Revise PSNP Criteria: Use localized food and nutrition data rather than income alone to identify food insecurity in cash-crop regions. Conclusion Income from coffee production in Yayu improves caloric sufficiency but does not guarantee nutritional adequacy. To combat hidden hunger, policies must move beyond income-based indicators of food security and prioritize access to diverse, nutrient-rich foods. This will ensure a more comprehensive approach to food and nutrition security in Ethiopia’s coffee-growing regions.

Improving Behavioral Healthcare Access Disparities by Training Providers in Disadvantaged Communities - Evidence of Strategy Effectiveness.

Inadequate access to behavioral health services disproportionately impacts marginalized populations who live in disadvantaged areas. To reduce this gap, programs dedicated to optimizing behavioral health education and training must focus their efforts to enroll providers who practice in these disadvantaged areas. The Train New Trainers (TNT) fellowship program aims to enhance behavioral health knowledge, skills, and attitudes of primary care providers (PCPs) who deliver care in disadvantaged communities. We evaluate the effectiveness of the TNT recruitment strategy and the use of scholarships for targeting and recruiting PCPs who practice in disadvantaged communities. Observational study. TNT fellows from 2016 to 2023. State/federal classifications of medically underserved counties were used to establish scholarship criteria. Area Deprivation Index (ADI) was utilized to provide criterion validity for the use of state/federal criteria in the recruitment strategy, and to evaluate the effectiveness of the program in successfully recruiting PCPs practicing in disadvantaged communities. Practice location data were available for 347 fellows, 88.8% of whom received scholarships. Of the 347 practices, 300 (86.5%) primarily served communities meeting at least one state or federal criterion for medical shortage areas and/or underserved areas. According to ADI scores, 32.3% of practices served areas classified in the highest ADI (ADI decile 9 or 10), with a progressive increase in the proportion of fellows practicing in underserved areas each year; in 2023, 89.9% of practices met federal shortage criteria and 40.5% served areas with the highest deciles of ADI. The TNT program strategy for recruiting PCPs from high medical need geographical areas is associated with bringing primary care psychiatry education to areas considered underserved and disadvantaged. Equipping PCPs practicing in underserved areas with enhanced knowledge and skills in behavioral medicine has the potential to significantly improve the existing access gap in disadvantaged communities.

Is Delta-8-THC use associated with state-level cannabis policies? A content analysis from different jurisdictions in the United States.

The United States has seen a rapid increase in the use of Δ-8-tetrahydrocannabinol (Δ8-THC), a psychoactive compound similar to Δ-9-tetrahydrocannabinol (Δ9-THC). This study aimed to explore themes in Δ8-THC related tweets from U.S. jurisdictions with varying cannabis regulations from 2020 to 2022. Tweets mentioning Δ8-THC were collected using search terms: D8, Delta-8, and Delta8 THC. The tweets were date-restricted from January 21, 2020, to May 31, 2022, resulting in 139,843 tweets. The final dataset included 61,800 U.S. tweets with complete location data. A subset of 1,000 tweets (n = 250 for each legal jurisdiction) was selected for content analysis. Three main themes related to Δ8-THC emerged across all jurisdictions: 'Sales and Advertising', 'Policy', and 'Personal Use'. In states where cannabis was legal for adult use, sales-themed tweets were most prevalent, promoting product sales and discounts. 'Policy' tweets were more common in medical CBD-only (34.5%) and fully illegal (24.8%) jurisdictions than in those with legalized medical (20.4%) and adult use (20.2%). Tweets about personal use appeared at similar rates in fully illegal (27.7%), medical (25.0%), medical CBD-only (24.5%), and fully legal jurisdictions (22.8%). Reports of adverse effects were frequent in tweets from fully illegal (28.9%), fully legal (27.7%), and medical (27.7%) jurisdictions, with the lowest frequency in medicinal CBD-only (15.6%) jurisdictions. The current study on the public discourse on Twitter indicates that discussions about the use and marketing of Δ8-THC are present in all U.S. jurisdictions, not just those without legal recreational cannabis markets.

Automating dairy farm grazing records using GPS technology

Long-term grazing records can be used to assess paddock productivity (e.g., grazings per year), yet inconsistent record-keeping among New Zealand farmers hampers accurate analysis. Recently, GPS ear tags have emerged as an automated solution for identifying and recording paddocks grazed, providing data that could be used to underpin on-farm decisions and aid sector scale tracking of pasture productivity. Two GPS-enabled ear tags (GSatSolar and Ceres Trace) powered by solar energy and reporting direct-to-satellite were evaluated on two Waikato dairy farms for recording grazing events. Five cows were fitted with GSatSolar ear tags on each farm, and a further four were fitted with Ceres Trace ear tags on the second farm. Comparing the GPS location data with manual grazing records revealed varying levels of accuracy in identifying the grazed paddock. The grazing records inferred using the GSatSolar ear tags correctly matched the manual grazing records on 58% of occasions on the first farm and 85% on the second farm, with the difference being attributed to the number of working devices between farms. By comparison, the Ceres Trace ear tags identified the grazed paddock on 91% of occasions. These findings suggest that it is feasible to automate the recording of on-farm grazing events using GPS-enabled devices. However, a greater number of devices should be deployed, and/or the location reporting frequency must be increased to identify the grazed paddock accurately. The considerations when choosing devices for these purposes are also discussed, including reporting frequency and tag attachment methods.

Evaluating the budget impact of Empagliflozin in managing heart failure with reduced ejection fraction: Proposing strategic policies for Malaysian public healthcare.

Sodium-glucose co-transporter-2 (SGLT2) inhibitors such as Empagliflozin, are increasingly recommended as part of guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) in many developed nations. This recommendation is based on robust clinical evidence showing that adding Empagliflozin to GDMT improves heart failure symptoms, clinical outcomes, functional status, and overall quality of life. In Malaysia, where healthcare is predominantly public and heavily subsidized, the introduction of new treatments can significantly impact costs, requiring detailed economic assessments. This study evaluates the budget impact of incorporating Empagliflozin into GDMT for HFrEF from the perspective of the public healthcare system. A five-year budget impact model was developed, integrating local data such as population, drug use, costs, clinical outcomes, and healthcare expenses. In the current scenario (GDMT alone), the projected five-year expenditure is MYR 6.12 billion (USD 3.92 billion). With Empagliflozin, the total cost rises by 0.71% to MYR 6.16 billion (USD 3.95 billion), driven by drug acquisition costs of MYR 160.12 million (USD 102.64 million) and adverse event costs of MYR 211,543 (USD 135,604). However, these costs are offset by savings from reduced HF hospitalizations, fewer cardiovascular deaths, and improved renal outcomes. Sensitivity analysis identified hospitalization costs, the price of Empagliflozin, and cardiovascular deaths in diabetic patients as key factors influencing the budget impact. Policymakers can improve the affordability of Empagliflozin through strategies like price negotiations, cost-sharing, and focusing on high-risk groups to optimize healthcare expenditure while ensuring effective treatment access.

Spectrum and Antifungal Drug Resistance among Fungal Pathogens Isolated from Prison Inmates in Nairobi, Kenya

Background The emergence of antifungal resistance in fungal pathogens highlights the need for local epidemiological data to guide empirical therapy in clinical settings. Fungal research and anti-fungal drug resistance studies are limited in developing countries; hence, there is a need for burden estimation in low- and middle-income countries. This study aimed to determine the spectrum of fungal pathogens and the anti-fungal resistance profile of fungal pathogens isolated from the respiratory and urinary tracts of prison inmates in Nairobi, Kenya. Methods A cross-sectional study was conducted in which sputum and urine samples were obtained from inmates presenting with symptoms of respiratory and urogenital infections at a prison outpatient clinic. One hundred and sixty-two samples were collected and subjected to fungal investigation using standard protocols. Susceptibility to fluconazole, itraconazole, and voriconazole was assessed using standard broth microdilution. Clinical and sociodemographic data were obtained using a structured questionnaire. Results From the 162, 94 samples were positive for fungal pathogens, with an overall prevalence of 58%. Seventeen (18%) of the isolated fungi were Aspergillus fumigatus, Aspergillus flavus and Histoplasma. There was a statistically significant difference between fungal pathogens isolated from the respiratory and urogenital tracts in both sexes (p<0.05). Antifungal susceptibility testing against itraconazole showed 2 of Aspergillus flavus and Aspergillus fumigatus were resistant. Conclusion Mycological agents are significant causes of respiratory and UTI infections among prison inmates, which could be attributed to prison conditions and misdiagnosis as bacterial infections. This highlights the need for specific control measures to reduce exposure to fungal infections in prisons and in the general population.

At-scale Model Output Statistics in mountain environments (AtsMOS v1.0)

Abstract. This paper introduces the AtsMOS (At-scale Model Output Statistics) workflow, designed to enhance mountain meteorology predictions through the downscaling of coarse numerical weather predictions using local observational data. AtsMOS provides a modular, open-source toolkit for local and large-scale forecasting of various meteorological variables through modified model output statistics – and may be applied to data from a single station or an entire network. We demonstrate its effectiveness through an example application at the summit of Mt. Everest, where it improves the prediction of both meteorological variables (e.g. wind speed, temperature) and derivative variables (e.g. facial frostbite time) critical for mountaineering safety. As a bridge between numerical weather prediction models and ground observations, AtsMOS contributes to hazard mitigation, water resource management, and other weather-dependent issues in mountainous regions and beyond.

The Basque Impact Weather Catalogue

Abstract. This paper introduces the Basque Country impact weather catalogue, a versatile tool designed, developed, and maintained by the Basque Meteorology Agency (Euskalmet), with the primary objective of systematically recording detailed information regarding adverse weather events that generate some degree of impact in the Basque Autonomous Community (BAC). The catalogue serves as a repository where information is gathered for the comprehensive characterisation and evaluation of extreme weather events, focusing on both environmental conditions and their associated impacts. For this purpose, three key aspects are included: context, hazard and risk and impact. In the context section, we include information about the date, duration, spatial extent, measurement statistics data, synoptic/mesoscale aspects, and other metadata. The hazard and risk section includes information about hazard and risk typology according to Euskalmet severe weather criteria and warnings procedure. Regarding the impact, a range of metrics and indicators are defined to characterise critical aspects, encompassing the severity of impact including economic damages, human fatalities, and disruptions to normal life. The creation of the Catalogue involves utilising various data sources and repositories, not only from Euskalmet but also, particularly in terms of impact, from emergency interventions, newspapers, local media websites, social media, and data from the Spanish Insurance Consortium. While the volume of available material from each event description may vary from case to case, a standardised information structure and minimum content are imperative for all registered events. This ensures that qualitative analyses based on extensive reports, and quantitative analyses based on recorded or estimated metrics and indicators, are possible for events included. This paper outlines into the design and structuring of the catalogue, and the fundamental steps undertaken during its implementation.

A Comparison of Different Strategies for Optimizing the Selection of Empiric Antibiotic Therapy for Pneumonia Caused by Gram-Negative Bacteria in Intensive Care Units (ICUs): Unit-Specific Combination Antibiograms Versus Patient-Specific Risk Factors

Abstract Background Guidelines suggest dual anti-pseudomonal therapy for empiric treatment of pneumonia caused by Gram-negative bacteria in ICU patients. Additionally, consideration of local susceptibility data and patient-specific risk factors for resistance is recommended for selecting optimal empiric regimens. However, data assessing how to best do this are lacking, and it is unclear whether a local susceptibility data-based or a patient specific risk factor-based approach will better drive appropriate empiric treatment. This study aims to compare these two strategies. Methods This retrospective study was divided into two periods. In period I, Gram-negative respiratory cultures from ICU patients were used to develop unit-specific combination antibiograms, and individual patient charts were reviewed to assess the impact of risk factors on antimicrobial susceptibility to develop a risk-factor based treatment algorithm. Optimal empiric regimens based on these two strategies were then defined. In period II, these regimens were hypothetically applied to patients to compare rates of appropriate empiric therapy and overuse by the two methods. Results Risk factor-based regimens had a higher appropriateness rate compared to regimens derived from antibiograms (89.9% vs 83.7%). Additionally, applying antibiogram based regimens resulted in a higher prevalence of antibiotic overuse than a patient-specific risk factor based approach (69.8% vs. 40.3%), with excess overuse driven by a higher frequency of unnecessary use of combination therapy. Conclusion Both strategies provided high rates of appropriateness in empiric antibiotic selection. However, the patient-specific risk factor-based approach demonstrated a higher rate of appropriate therapy and offered advantages in reducing rates of unnecessary combination therapy.

Deep Learning-Based Time Series Analysis for Environment Changes

The effects of weather alteration are generally cited as one of the most significant challenges facing conservation efforts. Recent research indicates that it is feasible to identify the consequences of a changing climate on biological systems. Environment change is a worldwide problem that requires quick attention. The topic of environmental change and how to adapt to it has been the topic of several studies. More efficient and effective variation and extenuation measures are needed, but only if new approaches are developed to investigate the intricacies of environmental change. In recent years, Deep Learning (DL) approaches have become more popular across a variety of industries, environmental change included. It is to examine the most widely used DL techniques for combating and familiarising to environmental change. The second goal is to classify the greatest widely-studied mitigation and adaptation measures across all locations, but especially in urban regions using DL techniques. According to the results, the most widely used DL approach is also the most effective in mitigating and adapting to environmental changes. Furthermore, geo-engineering and land surface temperature studies have employed DL algorithms more than any others in the field of environment change mitigation and reworking. The weather, including its temperature, humidity, clouds, and wind speed, are all potential contributors. This work analyses the major impacts of the local environment and climate on these characteristics. The study uses Indian state Haryana's local weather data (from January 1, 2012, to December 31, 2022). In the result, shows that this section’s highest winds speed are around 9 Km/h in the month of June.

Using vessels of opportunity for determining important habitats of bottlenose dolphins in Port Phillip Bay, south-eastern Australia.

Understanding species' critical habitat requirements is crucial for effective conservation and management. However, such information can be challenging to obtain, particularly for highly mobile, wide-ranging species such as cetaceans. In the absence of systematic surveys, alternative economically viable methods are needed, such as the use of data collected from platforms of opportunity, and modelling techniques to predict species distribution in un-surveyed areas. The present study used data collected by ecotourism and other vessels of opportunity to investigate important habitats of a small, poorly studied population of bottlenose dolphins in Port Phillip Bay, south-eastern Australia. Using 16 years of dolphin sighting location data, an ensemble habitat suitability model was built from which physical factors influencing dolphin distribution were identified. Results indicated that important habitats were those areas close to shipping channels and coastlines with these factors primarily influencing the variation in the likelihood of dolphin presence. The relatively good performance of the ensemble model suggests that simple presence-background data may be sufficient for predicting the species distribution where sighting data are limited. However, additional data from the center of Port Phillip Bay is required to further support this contention. Important habitat features identified in the study are likely to relate to favorable foraging conditions for dolphins as they are known to provide feeding, breeding, and spawning habitat for a diverse range of fish and cephalopod prey species. The results of the present study highlight the importance of affordable community-based data collection, such as ecotourism vessels, for obtaining information critical for effective management.

Evaluation of ground motion models for Northern Vietnam based on ground motion records of the 2020 Mw5.0 Moc-Chau earthquake sequence

It is challenging to select ground motion models (GMMs) for seismic hazard assessments for a region with sparse recorded data. In this study, data on the 2020 Mw5 Moc-Chau earthquake and its aftershocks were used to select an appropriate GMM for northern Vietnam (NVN). The 204 strong motion records were collected from 32 seismic stations and then used to compare eleven non-Vietnamese and two simplified Vietnamese local GMMs to assess their model prediction efficiencies. Among all the candidates, the global NGA-West2 GMMs performed the best fit with the data. Our analyses revealed the possibility of damage resulting from shaking in the Hanoi metropolitan area caused by recognized earthquake sources in NVN. In our examination of total residuals of differences between the GMM predictions and observed data, the average standard deviation from ASK14 was slightly higher than the limit accepted for modern seismic hazard assessments. ASK14 was further adjusted by the spatially varying coefficients that were derived from observations ground motion of this event. The adjusted ASK14 was used to evaluate seismic risk scenarios of large earthquakes in NVN and compared with the structures’ design spectra of the Hanoi area. To increase the prediction efficiency, additional local data are required to develop a region-specific GMM for NVN. We suggest that GMM be developed in the near future by regionalizing the ASK14 GMM according to additional local data further collected from existing broadband seismic observations and new accumulating continuous recording data from Vietnam’s broadband seismic networks.

Fungal disease incidence and prevalence in Iraq – Preliminary estimates

BackgroundThe surveillance of serious and superficial skin fungal infections in Iraq has not been conducted. Limited information exists on their incidence and prevalence. ObjectivesThis study aimed to analyze, compute and estimate the prevalence and burden of fungal infections, as no previous data is available and no studies has been attempted in Iraq. MethodsIn the present study the data were collected and reviewed from published data on epidemiology of fungal infections nationally, internationally, from unpublished postgraduate master and PhD theses, hospital records and private clinic records. External sources of data from other countries were used for diseases which lacked sufficient local data. ResultsWe estimated 985,628 annual serious infections comprising of 2.26 % of the total population. When including superficial fungal infections group of the skin, this rises to 2,075,113 infections at 4.76 % of the total population. The most common serious and skin infections were recurrent Candida vaginitis, fungal rhinosinusitis and tinea capitis comprising 61.5 %, 21.8 %, and 22.4 % of all infections although the total incidence of superficial fungal infections was also high at 1,071,485. Respiratory fungal disease is also common comprising 14.0 % of infections. We predicted the following annual burden per 100,000; oral candidiasis at 247.9, esophageal candidiasis at 6.04, candidemia at 5.0, Candida peritonitis at 0.75, recurrent Candida vaginitis at 5461, allergic bronchopulmonary aspergillosis at 35, severe asthma with fungal sensitisation at 46, invasive aspergillosis at 7.9, chronic pulmonary aspergillosis at 11.7, chronic fungal rhinosinusitis at 496, mucormycosis at 0.99, fungal keratitis at 14.0, and total dermatophytosis at 1631, the most severe being tinea capitis at 366. Many of these estimates were made with data sourced from other nations, so additional data from Iraq is required to validate or modify these estimates. ConclusionRecurrent Candida vaginitis, fungal rhinosinusitis, and tinea capitis are considered to be the most frequent fungal diseases present in Iraq.

Climate-sensitive training for sustainable business in Dickson County with global applications

Purpose This paper aims to examine the implementation and impact of climate-sensitive training programs tailored to the specific climate risks and business needs of Dickson County, Tennessee. It aims to emphasize the importance of integrating climate adaptation strategies into business training and to propose a replicable model that can be adapted to regions facing similar challenges, contributing to global sustainability efforts. Design/methodology/approach The research uses Dickson County’s climate profile as a case study, analyzing its impact on local businesses in sectors such as agriculture, manufacturing and tourism. This paper integrates theoretical frameworks, including organizational learning and adaptive capacity, to provide practical strategies for climate-sensitive training. It further incorporates insights from local institutions, industry partnerships and public data, using a qualitative approach to analyze key themes related to training effectiveness and sustainability. Findings The findings demonstrate that climate-sensitive training significantly enhances the resilience and sustainability of businesses by preparing them to address local climate challenges. Critical factors for successful implementation include stakeholder engagement, industry-specific customization and the continuous evaluation of training outcomes. The model presented shows promise for broader applicability in other regions, especially when localized climate data and collaborative efforts are integrated. Originality/value This paper provides a novel, region-specific approach to climate-sensitive training, distinguishing itself by combining theoretical insights with practical strategies for businesses. By focusing on localized adaptation and stakeholder collaboration, the model addresses an existing gap in the literature on climate adaptation training for smaller, climate-vulnerable regions. This work offers a replicable and scalable framework that contributes to the global discourse on sustainable development and climate resilience in business practices.

Five new species and numerous locality records of Spirobolida millipedes from Madagascar (Diplopoda, Spirobolida, Pachybolidae)

Based mainly on the results of generalized American biodiversity inventory programmes, several samples of Malagasy Spirobolida millipedes became available to study. Based on this material, five new species of Spirobolida are described from Madagascar: four potential microendemic species, Aphistogoniulus amberivery sp. nov. from the Amberivery forest, A. manombo sp. nov., from the lowland rainforest of Manombo, Spiromimus endemicus sp. nov. from the Montagne des Français, and a species from the littoral forest of Tampolo, tentatively placed in the genus Eucarlia Brölemann, 1913, E. tampolo sp. nov.. A fifth species appears to be more widespread and is a morphologically unusual species of the genus Alluviobolus Wesener, 2009, A. omega sp. nov.. Aphistogoniulus manombo sp. nov. was previously described as a population of A. jeekeli Decker & Wesener, 2011. One of the most enigmatic Malagasy millipede species, Spirobolus olympiacus Karsch, 1881, is redescribed based on topotypic material as Colossobolus olympiacus (Karsch, 1881) new combination. Additional locality data is provided for 14 other Spirobolida species, of which 11 are listed on the IUCN Red List: Aphistogoniulus aridus Wesener, 2009, A. diabolicus Wesener, 2009, A. erythrocephalus (Pocock, 1893), A. hova (de Saussure & Zehntner, 1897), Colossobolus semicyclus Wesener, 2009, Spiromimus albipes Wesener & Enghoff, 2009, S. litoralis Wesener & Enghoff, 2009, S. simplex Wesener & Enghoff, 2009, S. triaureus Wesener & Enghoff, 2009, S. univirgatus deSaussure & Zehntner, 1901, Flagellobolus pauliani Wesener, 2009, Dactylobolus bivirgatus (Karsch, 1881), Madabolus maximus Wesener & Enghoff, 2008 and Hylekobolus rufus Wesener, 2009. Fourteen new genetic barcoding COI sequences are provided for nine species: Hylekobolus rufus, Colossobolus semicyclus, Aphistogoniulus amberivery sp. nov., A. aridus, A. diabolicus, A. erythrocephalus, A. hova, Spiromimus univirgatus and S. scapularis Wesener & Enghoff, 2009.

A Novel Neuro-Probabilistic Framework for Energy Demand Forecasting in Electric Vehicle Integration

This paper presents a novel grid-to-vehicle modeling framework that leverages probabilistic methods and neural networks to accurately forecast electric vehicle (EV) charging demand and overall energy consumption. The proposed methodology, tailored to the specific context of Medellin, Colombia, provides valuable insights for optimizing charging infrastructure and grid operations. Based on collected local data, mathematical models are developed and coded to accurately reflect the characteristics of EV charging. Through a rigorous analysis of criteria, indices, and mathematical relationships, the most suitable model for the city is selected. By combining probabilistic modeling with neural networks, this study offers a comprehensive approach to predicting future energy demand as EV penetration increases. The EV charging model effectively captures the charging behavior of various EV types, while the neural network accurately forecasts energy demand. The findings can inform decision-making regarding charging infrastructure planning, investment strategies, and policy development to support the sustainable integration of electric vehicles into the power grid.

Icelandic Prevention Model in Rural Appalachian Communities: Gauging Stakeholder Experience with the Core Processes Three Years into County-Level Implementation

Background: This study assessed stakeholder experience with the core processes of The Integrated Community Engagement (ICE) Collaborative, a primary prevention approach addressing adolescent substance use in rural West Virginia, after three years of county-level implementation. Objectives: Guided by the Icelandic Prevention Model (IPM), the ICE Collaborative aims to enhance cooperation between researchers, policy makers, local practitioners, and community members and facilitate a paradigm shift in youth and community substance use prevention. This shift involves moving away from strategies focused on the repeated allocation of short-term grants that fund time-limited programs to a long-term, holistic, and sustainable approach overseen by local practitioners and coalitions. Results: We conducted qualitative interviews and focus groups with 33 stakeholders during the fall of 2022. Data analyses generated six major themes: 1) It Takes a Village to Prevent Adolescent Substance Use, 2) Improving Understanding and Commitment to Prevention Through Outreach, 3) Enhancing Student Engagement and Program Accessibility, 4) Addressing the Chronic Underfunding of Prevention and Youth Development Programs, 5) Acknowledging Family Contexts and Family Member Substance Use as Risk Factors, and 6) ICE/IPM Inspiring Solution-based Conversations, Goal Setting, and Strategy Selection. Conclusions: Stakeholders reported multiple positive characteristics of ICE for their communities and applauded the long-term focus and access to local data. Several suggestions for improved strategies were also reported. Results are discussed in line with the theoretical underpinnings of the IPM and current discourse around community health promotion in rural areas.

Establishing disease models for the Belgian national burden of disease study: challenges and perspectives

Abstract The Belgian national burden of disease study (BeBOD) was launched in 2022, and currently provides disease burden estimates for 38 key diseases. The calculation of the non-fatal component of the disease burden, the Years Lived with Disability (YLD), follows a stepwise approach that aims to establish priority outcomes; quantify prevalence “best estimates”; establish disease models; and perform expert evaluation of methods and results. The disease models visualize the relationship between the different health states associated with a given outcome. Health states include the different acute and chronic stages of the outcome (including complications), which may be stratified in different severity levels (e.g., mild, moderate, severe). Disease models used in burden of disease studies primarily aim to document the considered health states, and do not aim at a representation of the complete clinical picture of the condition. The disease models instead help in understanding how the number of cases for each health state is calculated. Models typically start with one “parent node”, which contains all cases. This parent node then gives rise to multiple “child nodes”, with the terminal child nodes representing the individual health states. In BeBOD, disease models and severity distributions are adapted from the Global Burden of Disease (GBD) study, and complemented with local data where possible. This process ensures consistency with available disability weights, while still allowing for local data to feed into the calculations. In this presentation, we will demonstrate the BeBOD process for establishing disease models based on a number of archetypical examples, which represent varying challenges linked to disease model complexity, clarity and completeness of the GBD disease model documentation, and availability of local data. Future perspectives will be discussed, including the improved leveraging of local clinical data, and the implementation of tailored active data collection.

Policies and programs on children’s health promotion in the EU in the context of the JA Health4EUKids

Abstract Background Overweight and obesity prevalence are a global public health challenge. The prevalence is rapidly increasing, with significant health risks such as cardiovascular disease, type 2 diabetes, and cancer. Objectives To describe the state of the art of policies and programs (PPs) on children’s health promotion and responsive parenthood (children 0-12 years) in the EU, and to identify their gaps. Methods A cross-sectional study was carried out through an online survey, as part of the Health4EUKids Joint Action (JA) (HADEA n.101082462), between October and December 2023, targeting all EU member states (MSs) involved. Results The survey collected information about 74 PPs from 11 EU MSs of the JA. The PPs are primarily focused on healthcare (62.1%), education (24.1%), and social sectors (10.3%), with a focus on children aged 6-11 years. The settings for PPs include primary schools, communities, secondary schools, and healthcare services. The PPs are characterized by different degrees of innovation, controversy, equity, transferability, and sustainability, although the structural or systemic impact and public visibility appear to be lower among other characteristics. The formulation and design of PPs include problem analysis (73%), based on national or local data and on stakeholder consultation. As for implementation, most PPs are implemented upon release, but recommended action and allocated human resources are not fully adequate. Regular monitoring and evaluation (47.3%) are based on defined outcomes, standards, and indicators (63.5%). The results are disseminated to decision-makers and stakeholders (37.8%), through media, and to the general population (32.4%). Conclusions Most PPs are variable and context-dependent. It is necessary to align the policies with the implementation, in all its aspects, and to strengthen monitoring, evaluation, and communication with the relevant stakeholders, which currently appear to be the most lacking aspects. Key messages • Most European policies and programs are context-dependent, mainly in healthcare. Yet, a cross-sectoral trend involving health, education, and social sectors emerges. • The study stresses the need for better alignment between policy formulation and implementation to enhance the effectiveness of EU children’s health programs.