Prevention Management Research Articles (Page 1)

Public health increasingly relies on digital infrastructures, yet data remains fragmented across clinical, behavioral, and social domains. Customer Data Platforms (CDPs), originally created in marketing to unify diverse information into dynamic individual profiles, could provide a new approach for person-centered public health. This article explores the strategic potential of applying CDP principles, such as data unification, identity resolution, segmentation, and timely intervention, to enhance surveillance, prevention, and chronic disease management. A conceptual framework is presented and demonstrated through a breast cancer screening scenario, illustrating how CDPs could enable personalized outreach and integration with artificial intelligence (AI). Although promising, there are significant challenges related to privacy, interoperability, fairness, and governance. Responsible deployment requires socio-technical strategies that emphasize transparency, ethical oversight, and person involvement.

The primary objective of this study was to explore type 2 diabetes (T2D) awareness, knowledge, attitudes and risk factors among youth in Auckland. We undertook convenience sampling of participants aged 16-25 years of Pacific and Māori descent recruited from South, Central and East Auckland through multiple community outreach events organised by the Tongan Health Society from 25 May to 31 July 2024. An additional three participants aged 26-31 years were assessed opportunistically to enhance our study power. Data were collected through a structured survey, an HbA1c point-of-care test, body composition assessments (using the TANITA RD-545 InnerScan PRO body composition scale) and height measurements. In a sample of 138 participants (aged 16-31 years; 58% female; 62% Tongan, 18% Samoan and 3% Māori), 51.9% were classified as obese, and one new case of diabetes was identified. Approximately 60.1% of participants reported awareness of T2D. Of these, 40% were made aware primarily through familial sources. High consumption of sugary drinks was common. Non-dietary risk factors included a first-degree family history of T2D (36%), smoking (39%) and alcohol consumption (45%). Most participants reportedly engaged in regular physical activity (41% males and 59% females). Participants suggested a multifaceted, youth-focussed care model, primarily lifestyle management for T2D prevention and management. A significant proportion of young people aged 16-31 years were identified as obese and had a higher proportion of dietary and non-dietary risks for T2D. The results underscore the necessity for tailored prevention strategies, mainly aimed at Pacific and Māori youth, to mitigate the risk of future T2D development.

The Dutch guideline "Oral care for children: prevention and treatment of caries" describes how to prevent, stabilise, or treat caries lesions using minimally invasive methods in children. The objectives of the present study were to 1) evaluate to what extent oral healthcare professionals (OHCPs) in the Netherlands apply preventive and minimally invasive caries management according to the guideline, 2) evaluate their attitudes towards these treatments, and 3) assess differences between OHCPs in general oral health practice and paediatric dentistry. In this cross-sectional survey study, OHCPs completed questions on guideline knowledge, and practice regarding primary (5 items), secondary and tertiary (9 items) caries prevention was asked using a 5-point Likert scale (1 = never, 5 = always). Attitude towards minimally invasive caries treatments was evaluated as sum score over six statements (range 6 [negative] to 30 [positive]). Amongst 187 participants (36.7% paediatric dentists; 79.1% female), 134 (71.7%) OHCPs reported being familiar with the guideline content. OHCPs working in paediatric dentistry applied statistically significantly more primary and (partially) secondary and tertiary prevention than OHCPs in general oral health practice. Median score of attitude towards minimally invasive caries treatments was neutral at 17.2 (SD = 2.6). The majority of OHCPs in the present study reported being familiar with the guideline. However there is room for improvement in its implementation in practice. OHCPs working in paediatric dentistry reported greater familiarity with the guideline. Generally, OHCPs had a neutral attitude towards preventive caries management.

Subjects with chronic kidney disease (CKD) are at higher risk for various neurological disorders, including dementia, depression, and sleep disturbances, all of which can influence disease progression and clinical outcomes. Cognitive impairment is often linked to renal decline, with the severity of cognitive deficits increasing in parallel with a reduction in glomerular filtration rate (GFR). For patients undergoing dialysis, the risk of moderate to severe cognitive impairment is significantly elevated. A central, unifying hypothesis proposes that the brain and kidneys share a similar microvascular architecture, making both organs highly susceptible to vascular injury. In this review, we make explicit two complementary mechanisms within this framework: (i) a common-cause model in which shared systemic risk factors (such as hypertension, diabetes, inflammation) concurrently damage renal and cerebral small vessels, and (ii) a CKD-mediated causal pathway whereby kidney dysfunction and its treatments propagate downstream neurovascular injury (uremic toxins, endothelial dysfunction and blood-brain barrier changes, anemia/hypoxia, metabolic and mineral disturbances, and dialysis-related hemodynamic stress), increasing the risk of cognitive impairment. We synthesize the current evidence on these mechanisms, outline the epidemiological patterns and clinical manifestations of cognitive decline in CKD, and discuss prevention, early diagnosis, and multidisciplinary management strategies aimed at improving outcomes in this vulnerable population.

Global prevalence of HIV and Mycobacterium tuberculosis co-infection: A systematic review and meta-analysis of 371 included articles.

The rising global incidence of Type 2 Diabetes Mellitus (T2DM) is a major contributor to increased morbidity and mortality. Research consistently links type 2 diabetes mellitus (T2DM) with various hematological and lipid abnormalities. However, studies in Libya that examine the relationship between these factors in T2DM patients and correlate them with geographical distribution are scarce. This study aims to evaluate the variations in hematological and lipid profiles among Libyan individuals with type 2 diabetes mellitus (T2DM) and to investigate potential correlations between these parameters. Additionally, it seeks to conduct T2DM surveillance in densely populated regions. A cross-sectional study was conducted at laboratories located in three different districts of Tripoli: Ghout Al Shaal Specialized Hospital, Abu Salim Hospital, and Al Sarai Laboratory in Hay Al Andalus. A total of 261 Libyan participants were divided into 170 patients with type 2 diabetes and 91 non-diabetic (controls), aged 50-85 years. Anthropometric data (weight, height, and body mass index (BMI)) were measured using standard protocols. Complete blood count (CBC) and lipid profile analysis were measured using the Sysmex XP-300 automated hematology analyzer and the Roche Cobas Integra 400 Plus system, respectively. Data were analyzed statistically using GraphPad Prism and SPSS version 27. A p-value of <0.05 was considered statistically significant. -value of <0.05 was considered statistically significant. T2DM subjects exhibited a higher body mass index (BMI) across study groups, genders, and districts (p<0.001) and demonstrated a statistically significant increase in red blood cell (RBC) counts (p=0.018) when compared to controls. Gender analysis indicated that diabetic women had a slightly higher RBC count (p=0.02), while men showed elevated neutrophil percentages (p=0.046). District-specific analyses revealed distinct trends. In Abu Salim, both hemoglobin (Hb) concentrations and RBC counts were high (p=0.036 and p=0.012, respectively). In Hay Al Andalus, mean cell volume (MCV) was significantly lower, and white blood cell (WBC) counts were higher (p=0.031 and p=0.022, respectively). In Ghout Al Shaal, significantly higher neutrophil percentages (p<0.001) and lower lymphocyte percentages (p=0.005) were found among diabetics. Additionally, the diabetic group exhibited substantially high levels of HbA1c, fasting blood sugar (FBS), and triglycerides (TG) (p<0.001). While diabetic women aligned with these trends, the male subgroup displayed similar glycaemic levels but exhibited less pronounced lipid differences. Notably, lipid variances were only observed in Hay Al Andalus; diabetic participants had lower high-density lipoprotein cholesterol (HDL-C) and higher TG (p=0.02 and p=0.001, respectively). T2DM Libyan patients suffer from increased adiposity and poor glycemic control. Distinct lipid abnormalities were also observed among patients, but these are commonly seen in women. Patients in the Hay Al Andalus region had the most devastating dyslipidaemia compared to other districts. Moreover, the observed variations in blood indices may be attributed to chronic inflammation, disease severity, and potential hypoxia, influenced by gender and geographic factors. Our findings may offer valuable guidance for prevention and clinical management strategies for T2DM patients in Tripoli.

The integration of Computer Science and Software Technology has revolutionized the global sports ecosystem, particularly in the context of the Olympic Games, where data-driven insights and artificial intelligence (AI) have become central to performance optimization, injury prevention, and event management. This research explores the transformative role of AI-powered performance analytics in enhancing athletic preparation, competition fairness, and strategic decision-making at the Olympic level. With advancements in machine learning, computer vision, and biomechanical modeling, coaches and analysts now rely on algorithmic interpretations of real-time data to assess movement efficiency, predict fatigue levels, and identify injury risks. The study highlights how wearable technologies and Internet of Things (IoT) devices collect and transmit continuous physiological data—such as heart rate variability, muscle activity, and motion trajectory—to cloud-based analytical platforms for predictive modeling. Furthermore, it investigates how these intelligent systems contribute to evidence-based training programs, equitable talent identification, and precision coaching. Beyond athletic enhancement, AI also influences officiating accuracy, event scheduling, and environmental sustainability by optimizing energy use in Olympic venues through smart systems. This paper discusses both the ethical and technical implications of AI in Olympic sports, including concerns regarding data privacy, algorithmic bias, and the potential over-reliance on automated systems in human-centered athletic domains. A comprehensive review of existing Olympic applications, such as AI-driven video analysis in gymnastics, real-time tracking in swimming, and computer-assisted officiating in athletics, illustrates how AI tools are redefining performance assessment and transparency. The research concludes that the convergence of AI, big data analytics, and sports technology represents not only a scientific evolution but also a philosophical shift in how athletic excellence is understood and measured. The findings emphasize that future Olympic events will increasingly depend on interdisciplinary collaboration between computer scientists, sports technologists, and performance analysts to ensure ethical, efficient, and inclusive technological integration.

Background/Objectives: Secondary hyperparathyroidism (SHPT) affects 30–50% of end-stage renal disease patients. Parathyroidectomy (PTX), while effective for medication-refractory SHPT, carries 20–70% risk of hungry bone syndrome (HBS)—severe sustained hypocalcemia requiring intensive care and prolonged hospitalization. Accurate preoperative risk stratification using biochemical markers and validated prediction tools is critical for optimal preventive management. Methods: We conducted a comprehensive narrative review synthesizing evidence on HBS predictors after PTX in SHPT, evaluating traditional and novel bone turnover markers, clinical risk factors, and multivariate prediction models, through a structured literature search and analysis. Results: Preoperative bone turnover status represents the strongest contributor to HBS risk. Traditional biomarkers—particularly parathyroid hormone (PTH > 1000–2400 pg/mL) and alkaline phosphatase (ALP > 150–300 U/L)—demonstrate moderate-to-strong individual predictive power. Novel bone turnover markers (bone-specific ALP, P1NP, TRAP-5b) offer incremental value, especially in CKD populations where renal clearance affects traditional markers. Combined risk prediction models substantially outperform single biomarkers, achieving area under curve values of 0.87–0.95. The simple NYU 2-point score (ALP > 150 U/L + PTH > 1000 pg/mL) showed 96.8% accuracy, with 100% negative predictive value. More complex tools like nomograms (C-index 0.92–0.94) and machine-learning algorithms (AUC 0.88) provide enhanced discrimination by integrating multiple continuous parameters. Additional clinical factors—younger age (<48 years), prolonged dialysis (≥5 years), low preoperative calcium, high gland weight, and absence of autotransplantation—further refine risk assessment. Postoperative calcium typically reaches nadir at 48–72 h, defining the critical monitoring window. Conclusions: High-turnover bone biomarkers and combined risk models effectively identify high-risk SHPT patients. Risk-stratified protocols (i.e., prophylactic supplementation, intensive monitoring, and selective ICU admission) can substantially reduce HBS-related morbidity. Ongoing efforts should focus on validating these predictive tools across diverse populations and integrating them into clinical practice, thereby facilitating real-time HBS risk assessment and protocol-driven care.

Dengue fever is a mosquito-borne disease representing significant public health challenges worldwide. Management of dengue prevention and control faces numerous challenges, including rapid urbanization, climate change, and socioeconomic inequalities. This systematic review aims to determine the challenges of the dengue control and prevention strategies. This review conducted a comprehensive search of articles published from 2013 to 2024, utilizing databases such as Scopus, ScienceDirect, and PubMed. The Preferred Reporting Items for Systematic Reviews (PRISMA) are used to improve transparency and completeness of reporting, and the Joanna Briggs Institute (JBI) is used for critical appraisal. The findings from the risk of bias assessment revealed moderate to high quality across the studies, with scores ranging from 5 to 9 out of 10. The populations studied included community members, health officers, healthcare workers, caregivers, and health surveillance experts representing diverse geographic locations like Malaysia, Thailand, the Dominican Republic, and Brazil. The types of studies included were qualitative, with one case report. Major challenges identified include weak community participation, inconsistent stakeholder coordination, socioeconomic constraints, and environmental factors affecting vector control sustainability. Integrated strategies combining traditional and innovative approaches, with strong community involvement and inter-sectoral collaboration, are essential for sustainable dengue control. In conclusion, the management of dengue needs integrated strategies that combine traditional methods with innovative approaches. In addition, it is crucial to involve the community and coordinate with stakeholders to enhance the effectiveness of dengue control measures by adapting to the local context. Future research should focus on developing sustainable and community-driven interventions, as well as enhancing inter-sectoral collaboration to address the multifaceted challenges of dengue prevention and control.

KEY MESSAGES • Obesity arises from a complex interplay of genetic, biological, behavioural, psychosocial and environmental factors.• Obesity has a strong genetic component, with twin studies indicating a 50 - 80% concordance in body mass index (BMI) and regional fat distribution. A Swedish study on identical twins raised apart found no correlation between BMI and their adoptive families but a strong correlation with their biological twin, despite being raised in separate households.• The regulation of appetite, body weight and energy balance is highly complex, governed by a network of hormonal signals from the gut, adipose tissue and other organs, as well as neural signals that shape eating behaviours. Many of these signalling pathways are disrupted in people living with obesity.• Since body weight is homeostatically regulated, weight loss triggers physiological adaptations that promote weight regain. These include a decrease in energy expenditure, and hormonal changes that enhance appetite while reducing satiety.• Adipose tissue influences the central regulation of energy homeostasis, and excess adiposity can become dysfunctional, with production of proinflammatory cytokines and associated metabolic health complications.• Individual variations in body composition, fat distribution and function result in a highly variable threshold at which excess adiposity begins to negatively affect health.• Emerging research in obesity science has widened to include brown fat, the gut microbiome, immune system regulation, and the intricate mechanisms that regulate body weight.• Obesity can be classified as primary, secondary and genetic obesity.• In the current management of primary obesity, prevention (the path in) and treatment (the path out) need to be distinctly separated.• Effective primary obesity treatment requires an integrated approach that addresses the non-modifiable cause (increased appetite) together with modifiable contributors (poor diet quality, increased stress, poor sleep, reduced physical activity and increased sedentary behaviour). Behavioural modification and psychological support provide additional benefit.• Effective treatment in genetic and secondary obesity requires treatment of the underlying causes along with modification of the contributors.

Background: The cardiovascular impact of diabetes and prediabetes, especially in the context of behavioral risk factors and comorbidities, remains an urgent public health issue. We evaluated cardiometabolic burden and predictors of cardiovascular disease (CVD) among adults with diabetes or prediabetes in the U.S. Methods: We analyzed National Health and Nutrition Examination Survey (NHANES) 2021–2022 data on adults aged ≥18 years, using non survey-weighted methods. Diabetes status was defined by self-reported diabetes or prediabetes. Cardiometabolic burden was computed as a composite of self-reported hypertension, obesity, hyperlipidemia, and smoking. CVD was defined as any self-reported congestive heart failure, coronary heart disease, myocardial infarction, stroke, or angina. Logistic regression identified independent predictors of CVD adjusting for sociodemographics, comorbidities, and behaviors. Results: A total of 9,693 were included in final analysis. Among these, 14.7% had diabetes and 9.5% had prediabetes. Adults with diabetes had higher cardiometabolic burden (mean 2.74; 95% CI: 2.65–2.83) than those with prediabetes (mean 2.37; 95% CI: 2.25–2.50; Figure 1). Current smoking and physical inactivity were common in both groups (Figure 2). Independent predictors of increased CVD included older age (OR: 1.05 per year), hypertension (OR: 2.38), and non-Hispanic Black race (OR: 2.94). Female sex (OR: 0.64) and higher income (OR: 0.87 per unit of PIR) were associated with lower CVD risk. In adjusted models (Figure 3), prediabetes did not have a positive association with CVD (OR: 0.58; 95% CI: 0.36–0.95). Conclusions: Diabetes and prediabetes are highly prevalent and linked to elevated cardiometabolic burden. However, prediabetes alone may not independently increase CVD risk. Findings highlight the need for aggressive risk factor management and equitable care strategies in cardiometabolic disease prevention.

Background: The COVID-19 pandemic disrupted healthcare systems and significantly impacted individuals with chronic conditions. Research Question: What was the impact of the pandemic on diabetes mellitus (DM) as a contributing condition on cardiovascular disease (CVD) mortality trends and place of death? Methods: Using the CDC WONDER database (2018–2023), we identified deaths due to CVD (ICD-10 codes: I00–I99) in individuals with DM (ICD-10 codes: E10-E14) in the United States. The study was divided into pre-COVID (2018-2019), during COVID (2020-2021), and post-COVID (2022-2023) periods. Age-adjusted mortality rates (AAMRs) per 100,000 population were stratified by age, sex, race/ethnicity, and regions. Places of death were classified as inpatient, outpatient/ER, home, hospice, or nursing home/long-term care. Results: There were a total of 617,414 deaths due to CVD in patients with DM and the AAMR increased from 22.18 per 100,000 pre-COVID to 26.24 during COVID and remained high at 25.84 post-COVID. AAMR increased across all age groups during the pandemic, with the most increase among adults aged 25-44 (+29.8%), followed by 45-64 (+20.8%) and ≥65 (+17.3%) which decreased by 4.68%, 4.02% and 0.73% post-COVID respectively (Table 1). Home deaths increased considerably from 40.6% (69,344 of 170,777) pre-COVID to 46.2% (94,512 of 204,566) during COVID-19, with a slight decline to 45.1% (94,020 of 208,388) post-COVID. In contrast, deaths in nursing homes (−2.67%), outpatient/ER settings (−1.07%), and inpatient (−1.68%) declined during COVID. Regionally, the South had greatest increase in AAMR (+21.3%) which increased to (+23.2%) post-COVID, followed by the West (+16.2% to +13.3%), Midwest (+16.6% to 12.6%), and Northeast (+15.8% to +9.4). Males and females had a comparable mortality increase during the pandemic (+18% and 17.9%) while non-Hispanic individuals had a higher increase in AAMR (+17.1%) as compared to Hispanics (+10.6%). Among races, Black individuals had the highest AAMR increase (+23.6%), followed by Asian (+21%), White (+17%), and American Indian or Alaska Native (+6.2%). Conclusion: The COVID-19 pandemic was associated with considerable rise in CVD mortality in patients with DM, especially younger adults. This trend continued post-COVID and a considerable and lasting shift in place of death toward home settings was observed. These disparities underscore systemic gaps in prevention and chronic disease management that were magnified during the pandemic.

The agricultural sector remains one of the most hazardous working environments, with viticulture posing particularly high risks due to repetitive manual tasks, pesticide exposure, and machinery operation. This study explores the potential of vision-based Artificial Intelligence (AI) systems to enhance occupational health and safety by evaluating their coherence with human expert assessments. A dataset of 203 annotated images, collected from 50 vineyards in Northern Italy, was analyzed across three domains: manual work activities, workplace environments, and agricultural machinery. Each image was independently assessed by safety professionals and an AI pipeline integrating convolutional neural networks, regulatory contextualization, and risk matrix evaluation. Agreement between AI and experts was quantified using weighted Cohen’s Kappa, achieving values of 0.94–0.96, with overall classification error rates below 14%. Errors were primarily false negatives in machinery images, reflecting visual complexity and operational variability. Statistical analyses, including McNemar and Wilcoxon signed-rank tests, revealed no significant differences between AI and expert classifications. These findings suggest that AI can provide reliable, standardized risk detection while highlighting limitations such as reduced sensitivity in complex scenarios and the need for explainable models. Overall, integrating AI with complementary sensors and regulatory frameworks offers a credible path toward proactive, transparent, and preventive safety management in viticulture and potentially other high-risk agricultural sectors. Furthermore, vision-based AI systems inherently act as optical sensors capable of capturing and interpreting occupational risk conditions. Their integration with complementary sensor technologies—such as inertial, environmental, and proximity sensors—can enhance the precision and contextual awareness of automated safety assessments in viticulture.

The present study investigated the drivers and ecological consequences of forest fires and proposes a community-based management framework, with a particular focus on Odisha state, India. A structured literature review on fire drivers, impacts, and governance was integrated with a policy analysis of national and international fire management approaches. Secondary analyses of satellite-based fire records from ISFR (MODIS/VIIRS; 2020–2024) were conducted alongside field surveys and photographic documentation in the Bonai Forest Division, Odisha (2021–2024), to assess burn severity, fuel loads, vegetation response, and ignition sources. Results indicate that India’s Central Forest Cluster accounts for the largest burned area, especially within dry deciduous forests. Anthropogenic ignitions predominate, with increasingly hot and dry conditions exacerbating fire severity. High-severity fires were found to reduce seedling recruitment, facilitate invasive species proliferation, and degrade soil structure, moisture, and microbial activity. Habitat simplification and prey scarcity further intensify human-wildlife conflicts near settlements. Evidence highlights that prevention-first strategies such as prescribed patch burns, participatory fire lines, integrated early-warning systems, tiered response teams, and post-fire invasive control serve as the most effective interventions. Overall, the findings emphasize the need for transition from suppression-oriented responses to community-centered, technology-enabled prevention systems that can mitigate destructive fires while preserving the ecological functions of low-intensity burns.

Introduction: Patients with underlying cardiovascular (CV) disease are vulnerable to adverse health events related to extreme weather. Limited data exist in those with heart failure (HF). In the western U.S., where heat waves and wildfire exposure are common, understanding the impact of heat on HF–related outcomes is critical for prevention and clinical management. Research Question/Hypothesis: We examined the risk of hospitalizations and emergency room (ER) visits associated with short-term exposure to extreme heat and poor air quality in HF patients stratified by HF type. Methods: Using 2021 Medicare data, we identified patients with HF before January 1, 2021, residing in 11 western U.S. states (Arizona, California, Colorado, Idaho, Montana, New Mexico, Nevada, Oregon, Utah, Washington, and Wyoming). The primary outcomes included all-cause, CV, and HF-related hospitalizations and ER visits. A case-crossover design was used, with case days defined as the event date and control days matched by day of the week within the same month. Patients were stratified based on HF type and identified on inpatient or outpatient diagnoses prior to the case day. Conditional logistic regression was applied to estimate odds ratios (ORs) for outcomes associated with extreme heat exposure, defined as daily maximum temperature above the 85th percentile of the local decadal average, adjusting for airborne particulate matter (PM 2.5. ) levels and public holidays. Results: We identified 43,447 HF patients (mean age 73 ±16 years) of whom 9,676 had HF with reduced ejection fraction (HFrEF) and 13,093 HF with preserved ejection fraction (HFpEF). Only extreme heat exposure was associated with a 6% increased odds for all-cause hospitalization (OR 1.06; 95% CI: 1.02,1.10); 10% increase in all-cause ER visits (OR:1.10; 95% CI 1.06-1.15); and 8% increase CV-related ER visits (OR 1.08; 95% CI: 1.00–1.17). These effects were demonstrated primarily in HFpEF and not HFrEF (Table). Conclusion: Short-term extreme heat exposure but not poor air quality was associated with an increased risk of hospitalization and ER visits in HF patients residing in the Western US, especially in those with HFpEF. This heightened vulnerability may be due to impaired thermoregulation and reduced cardiovascular adaptability in those with HFpEF

Respiratory infections and allergic reactions caused by airborne fungi have received considerable public attention; however, fungal communities remain poorly investigated. This research performed the first year-long investigation of airborne mycobiome in the world's largest subway system. We found that the fungal diversity peaks in autumn and at stations with higher PM2.5 levels. Intercity hubs exhibit the highest diversity of pathogenic fungi and the least seasonal fluctuation. Suburban stations revealed a reduced diversity of human pathogens but an elevated presence of plant pathogens. Core fungal taxa in subways include both common soil fungi (e.g., Alternaria and Cladosporium) and potential plant and human pathogens (e.g., Phoma, Fusarium, and Rhinocladiella) that pose potential health risks. These results are crucial for infection prevention and public health management in city transit systems.

Introduction: Atherosclerotic cardiovascular disease (ASCVD) has many well-established risk factors; clinical risk tools (CRTs) aggregate these to estimate future disease risk. In 2023 the American Heart Association endorsed a new PREVENT CRT to guide preventive management. Previous work has shown that combining CRTs with a polygenic risk score (PRS), summarising the contribution of thousands of common variants, improves risk prediction, and that statin efficacy is increased in high PRS individuals. But while genetics is known to be a major risk factor, it is not currently incorporated into PREVENT or other CRTs. Research question: Does an integrated risk tool (IRT), combining a PRS with PREVENT 10yr risk for ASCVD, improve clinical utility? Methods: We assessed IRT performance on ASCVD outcomes within the Kaiser-Permanente Research Biobank, a large, US-based health system research cohort (n~450,000, n=59,823 used for testing, including 2,543 10-year incident ASCVD cases - defined as acute myocardial infarction, ischemic stroke or fatal CHD). Results: Individuals often experienced large changes in risk: 13.6% had an IRT risk more than double or less than half their PREVENT risk; and 23.1% had an IRT risk at least 2 percentage points greater or smaller, which the HEART study showed could positively impact management decisions. These changes significantly improved prediction accuracy at a 7.5% actionable risk threshold (overall Net Reclassification Improvement 6.0% (95% CI 4.7-7.4%)). Similar results were observed across age and sex subgroups. Individuals with borderline PREVENT scores (5.0-7.5%) especially benefitted from the addition of PRS: the observed 10y ASCVD rate moved from 4.8% (4.0-5.7%) to 8.8% (7.7-10.0%) in low (bottom 20%) versus high (top 20%) PRS individuals, an odds ratio of 1.9. A primary use case for ASCVD risk prediction is to prioritise individuals for statins. Restricting to a statin-naive subset, the addition of PRS to PREVENT up-classified 337 cases per 100,000 individuals to above the 7.5% risk threshold. Statin treatment of this group would prevent up to 151 major ASCVD events per 100,000 individuals over 10 years, assuming a higher efficacy in high-PRS individuals. Conclusion: Combining a PRS with PREVENT results in substantial and beneficial changes in individual risk of ASCVD. Predictive performance is improved, both overall and especially for those at borderline risk, presenting opportunities to prevent future ASCVD events.

This study aimed to evaluate the prevalence, characteristics, and management of post-operative infections (POI) following dentoalveolar surgery in patients admitted to a tertiary oral and maxillofacial surgery (OMS) unit in South Australia over a five-year period. A retrospective cross-sectional audit was conducted at a tertiary hospital OMS Unit from January 2019 to December 2023. A post-operative infection was defined as an infection occurring at the surgical site after an initial surgical procedure. Data were collected from 107 patients admitted with POI following surgery. Data on demographics, clinical management, antibiotic use, and microbiological findings were analyzed using descriptive and quantitative statistical methods. The mean age of patients was 43.5 years, with a slight female predominance (57.9%). Mandibular molars were the most common source of infection (88.8%), with the submandibular space being the most frequently involved (42.1%). Microbiological analysis revealed a 35% antibiotic resistance rate, with clindamycin resistance being particularly high (22.5%). Patients with resistant infections had significantly longer hospital stays (mean 8.9 vs. 4.9 days, t-test, p = 0.012). POI following dentoalveolar surgery represents a significant clinical and economic burden, particularly in cases involving antibiotic-resistant organisms. The findings underscore the need for improved antibiotic stewardship, enhanced preventive strategies and definitive early management to optimize patient outcomes and reduce healthcare costs.

National survey on aspiration pneumonia in elderly hospitalized or institutionalized patients in France in 2023.

Use of Risk Assessment to Guide Decision-Making for Blood Pressure Management in the Primary Prevention of Cardiovascular Disease: A Scientific Statement From the American Heart Association and American College of Cardiology.