PROBLEM/CONDITION: Since 1971, CDC and the U.S. Environmental Protection Agency have maintained a collaborative surveillance system for collecting and periodically reporting data that relate to occurrences and causes of waterborne-disease outbreaks (WBDOs). REPORTING PERIOD COVERED: This summary includes data for January 1993 through December 1994 and for previously unreported outbreaks in 1992. DESCRIPTION OF THE SYSTEM: The surveillance system includes data about outbreaks associated with water intended for drinking (i.e., drinking water) and those associated with recreational water. State, territorial, and local public health departments are primarily responsible for detecting and investigating WBDOs and voluntarily reporting them to CDC on a standard form. RESULTS: For the 2-year period 1993-1994, 17 states and one territory reported a total of 30 outbreaks associated with drinking water. These outbreaks caused an estimated 405,366 persons to become ill, including 403,000 from an outbreak of cryptosporidiosis in Milwaukee, the largest WBDO ever documented in the United States, and 2,366 from the other 29 outbreaks. No etiologic agent was identified for five (16.7%) of the 30 outbreaks. The protozoan parasites Giardia lamblia and Cryptosporidium parvum caused 10 (40.0%) of the 25 outbreaks for which the etiologic agent was identified. Two outbreaks of cryptosporidiosis occurred in large metropolitan areas (i.e., Milwaukee and Las Vegas/Clark County) and were associated with deaths among immunocompromised persons. The waterborne nature of these two outbreaks was not recognized until at least 2 weeks after the onset of the Milwaukee outbreak and until after the end of the Las Vegas outbreak. Campylobacter jejuni was implicated for three outbreaks and the following pathogens for one outbreak each: Shigella sonnei, Shigella flexneri, non-O1 Vibrio cholerae (in a U.S. territory; the vehicle was commercially bottled water), and Salmonella serotype Typhimurium (the outbreak was associated with seven deaths). Eight outbreaks of chemical poisoning were reported: three were caused by lead (one case each), two by fluoride, two by nitrate and one by copper. Twenty (66.7%) of the 30 outbreaks were associated with a well-water source. Fourteen states reported a total of 26 outbreaks associated with recreational water, in which an estimated 1,714 persons became ill. Fourteen (53.8%) of these 26 were outbreaks of gastroenteritis. The etiologic agent in each of these 14 outbreaks was identified; 10 (71.4%) were caused by G. lamblia or C. parvum. Six of these 10 were associated with chlorinated, filtered pool water, and three with lake water. One of the latter was the first reported outbreak of cryptosporidiosis associated with the recreational use of lake water. Four outbreaks of lake water-associated bacterial gastroenteritis were reported, two caused by S. sonnei, one by S. flexneri, and one by Escherichia coli O157:H7. Nine outbreaks of hot tub- whirlpool-, or swimming pool-associated pseudomonas dermatitis were reported. Two outbreaks of swimming pool-associated dermatitis had a suspected chemical etiology. The child who had the one reported case of primary amebic meningoencephalitis, caused by infection with Naegleria fowleri, died. INTERPRETATION: The number of WBDOs reported annually has been similar for each year during 1987-1994, except for an increase in 1992. Protozoan parasites, especially C. parvum and G. lamblia, remain important etiologic agents of WBDOs. The outbreaks of cryptosporidiosis in Milwaukee and Las Vegas demonstrate that WBDOs can occur in large metropolitan areas. Surveillance methods are needed that expedite the detection of WBDOs and the institution of preventive measures (e.g., boil-water advisories). ACTIONS TAKEN: Surveillance data that identify the types of water systems, their deficiencies, and the etiologic agents associated with outbreaks are used to evaluate the adequacy of current technologies for prov
Traditional disease surveillance, such as manual case investigation, was the primary method for identifying disease clusters during the COVID-19 pandemic. However, the pandemic also provides an opportunity to explore how genomic data can be used to improve cluster detection and response. While genomic data can complement traditional methods, guidelines are needed to integrate genomic data into real-time outbreak response. Using binomial and multinomial logistic regression, we compared two methods of disease surveillance in Utah: genomic sequencing of COVID-19 cases and manual case investigation. We evaluated whether these two methods reached the same populations geographically and demographically. Next, we performed genomic clustering using SNP distance thresholds and a logit regression model to identify potential transmission clusters. We compared genomic clusters with epi-identified clusters, defined by manual case investigation, using cluster validation metrics (Adjusted Rand Index, VI), and by assessing biological plausibility (monophyly). The odds of a case being sequenced varied significantly by jurisdiction and race/ethnicity, with patients in several non-White groups being less likely to undergo sequencing. The genomic clustering methods produced clusters that were notably different from epi-identified clusters. Genomic methods, particularly the logit model, resulted in strong clusters based on metrics of cluster validation and biological plausibility. Analysis of specific epi-defined clusters revealed significant discordance with genomic data. Many large clusters were likely composed of multiple distinct genomic introductions, or contained cases that were not genomically linked. Genomic data provides an advanced level of resolution for defining disease clusters compared to traditional epidemiological data. The disparities in sequencing coverage necessitate demographically and geographically diverse sampling strategies. Furthermore, it is essential to prioritize sequencing cases in a suspected cluster to maximize the impact of genomic surveillance. Integrating genomic data into epidemiologic investigation enables more precise cluster definitions, strengthening outbreak investigation and public health mitigation.
GII.4 noroviruses have been the leading cause of acute gastroenteritis outbreaks in the United States (U.S.) for the past decade. Recently, GII.17 viruses have emerged globally, raising concerns about changes in disease burden and potential replacement of GII.4 as the predominant strain. We characterized molecular and epidemiological features of U.S. GII.17 norovirus outbreaks from September 2021-August 2025 submitted to CaliciNet. Norovirus-positive outbreak samples were sequenced followed by RdRp and VP1 phylogenetic analyses. CaliciNet and National Outbreak Reporting System data were linked to compare GII.17 and GII.4 epidemiologic and clinical characteristics. Virus-like particles binding to gastric mucin and antibody blockade assays were conducted to assess antigenic variation among GII.17 variants. Among 1,412 outbreaks, GII.17 prevalence increased from 5.0% (2021-2022) to 74.8% (2024-2025). Compared with GII.4, GII.17 was more frequently reported with foodborne exposure (16.4% vs 9.6%), affected persons aged 10-49 years compared with other groups (31.1% vs 23.1%), and associated with slightly higher rates of vomiting (78.2% vs 72.2%), abdominal cramps (34.2% vs 30.1%), and fever (15.9% vs 10.9%). Phylogenetic analysis detected GII.17[P17] Romania-like (93.3%) followed by GII.17[P17] Kawasaki308-like strains (4.6%) and two novel tentative variants: Santa Clara (1.3%) and London (0.1%). Despite substitutions in key P2 antigenic sites, surrogate antibody neutralization remained conserved. These findings indicate a recent shift in norovirus genotype dominance in the U.S., likely driven by enhanced transmissibility or population susceptibility rather than increased virulence. Continued molecular surveillance is essential to monitor viral evolution and public health impact of norovirus.
A rise in exclusionary nationalism can escalate conflicts; however, there are limited quantitative analyses of the timing and magnitudes of such shifts. This study proposes a quantitative approach for tracking changes in exclusionary nationalism by focusing on language, specifically the acceptance/rejection of foreign loanwords. Using 300,110 Japanese newspaper articles spanning 1912-1943-the period surrounding the outbreak of the 1941 conflict with the United States and the United Kingdom-we employed the singular spectrum transformation method to detect change points in exclusionary nationalism. We found that exclusionary nationalism emerged in Japan in 1936 and subsequently exhibited substantial oscillations rather than continuous intensification in the years leading up to the outbreak of the Pacific War. Moreover, attitudes toward potential adversaries and allies were evident from the 1920s. This study deepens our understanding of the role of exclusionary nationalism in conflicts, highlighting its nuanced nature in shaping friend-enemy distinctions during wartime.
Yersinia pestis, the causative agent of plague, poses a persistent global health threat due to its rapid transmission and high mortality. To enable rapid, field-deployable surveillance, we developed a high-resolution, culture-independent genotyping assay for precise identification of Y. pestis lineages and sublineages. We curated and validated 25 canonical single nucleotide polymorphisms (canSNPs) to resolve 24 global lineages, along with 12 region-specific canSNPs distinguishing 9 predominant sublineages circulating in Inner Mongolia, China. By integrating ARMS-HANDS PCR with multicolor melting curve analysis, we established a hierarchical multiplex assay capable of simultaneously genotyping 37 canSNPs in three real-time PCR reactions. The system exhibited high sensitivity (detection limit: 50 genome copies per reaction) and achieved 100% accuracy in a double-blind evaluation of 166 Y. pestis strains. Incorporated into a portable "sample-in, result-out" device, the assay enabled direct lineage identification from infected gerbil liver samples within 120 min, accurately detecting the epidemiologically critical sublineage 2.MED3.1.4 in field-collected specimens. This robust and deployable genotyping platform addresses key limitations in current plague surveillance efforts, offering a transformative solution for real-time outbreak investigation and epidemiological tracking in resource-constrained settings.
The most recent assessment of antibodies against measles, mumps, rubella, and varicella-zoster viruses (MeV, MuV, RuV, VZV) in the United States was conducted in 2009-2010. Regular assessments are essential to measure the impact of immunization programs, identify immunity gaps, and inform vaccination strategies. We report updated estimates of national seroprevalence. We used a CDC-developed immunoglobulin G multiplex bead assay to quantitatively detect antibodies against MeV, MuV, RuV, and VZV among participants aged 6-59 years in the 2017-March 2020 National Health and Nutrition Examination Survey. We estimated seroprevalence overall and by sociodemographic characteristics and assessed independent predictors of seropositivity using logistic regression models with weighted procedures for complex survey design. Overall seroprevalence was 95.2% (95% CI, 94.0%-96.3%) against MeV, 87.8% (95% CI, 86.5%-89.2%) against MuV, 95.6% (95% CI, 94.5%-96.5%) against RuV, and 97.2% (95% CI, 96.4%-98.0%) against VZV, and remained high across advancing age groups. Younger age and non-Hispanic Black race and ethnicity were independent predictors for MeV, MuV, and RuV seropositivity; sex, birthplace, and health insurance status were independent predictors for seropositivity for at least one of the viruses. Nationally, seroprevalence for MeV, MuV, RuV, and VZV antibodies remains high among persons aged 6-59 years and our findings indicate robust antibody responses are maintained decades after vaccination or natural infection. Maintaining high population immunity through vaccination is critical to prevent cases and outbreaks of all four vaccine-preventable diseases.
Lyme disease is the most common vector-borne illness in the United States. The limitations of traditional surveillance strategies for Lyme disease affect the ability to reliably track its burden and evaluate interventions. The US Centers for Disease Control and Prevention (CDC) established the Surveillance Based Lyme Disease Network (SubLyme) in September 2023 to strengthen Lyme disease surveillance and research using electronic health record (EHR) data. SubLyme has three primary objectives: (1) to establish and evaluate criteria for identifying Lyme disease cases in EHR data (ie, create computable phenotypes [CPs]) that can be scaled across diverse health systems, (2) to estimate Lyme disease incidence, and (3) to describe Lyme disease incidence by key demographics. Secondary objectives are to develop CPs that distinguish between acute and disseminated Lyme disease, identify clinical manifestations, and support future research efforts. This paper describes SubLyme, its structure, and its methods. SubLyme includes 5 health systems in 3 US regions with a high risk of Lyme disease: Geisinger, in Pennsylvania; Marshfield Clinic Health System, in Wisconsin; and Mass General Brigham, Tufts Medical Center, and MaineHealth in New England. The network is administered by a coordinating center (Westat) and the US CDC. SubLyme is evaluating the validity of EHR-based CP definitions for Lyme disease. CP performance is assessed by measuring sensitivity, specificity, positive predictive value, and negative predictive value against manually abstracted medical charts. Each site identified a cohort of patients with any Lyme disease element in their EHR (Lyme disease diagnosis code, Lyme disease laboratory test order, and Lyme-appropriate antibiotic order) during 2022 to 2023 and selected 500 charts for manual review as the gold standard against which CP performance was evaluated. SubLyme will use the Lyme disease CPs to generate incidence rates for Lyme disease overall and for various subgroups. SubLyme identified 332,256 patients with at least 1 Lyme disease element in their record from more than 4.6 million patients. Of these patients, 55.6% (n=184,734) were female, 87.9% (n=292,053) were White, and 90.8% (n=301,688) were non-Hispanic. More than half of the patients only had a Lyme-appropriate medication order (n=177,425, 53.4%) and 35.8% (n=118,948) only had a Lyme disease test order. The most common combination was a medication order with a laboratory test order (n=22,926, 6.9%), followed by a combination of a diagnosis, test, and medication order (n=5316, 1.6%). SubLyme is well positioned to advance Lyme disease surveillance using EHR data across multiple health systems. The exploration of new surveillance methods in Lyme disease is critical as disease frequency increases and the geography expands. An EHR-based approach to surveillance has the potential to overcome challenges of current surveillance strategies and to accelerate Lyme disease research. DERR1-10.2196/94921.
Invasive lobular carcinoma (ILC) represents the second most common histologic subtype of breast cancer (BC), yet its genomic landscape and clinical implications remain less well defined compared with invasive ductal carcinoma. Understanding genetic predisposition in ILC may improve risk assessment and guide tailored clinical management. To investigate the prevalence and clinical outcomes of germline pathogenic or likely pathogenic variants (PVs) in BC predisposition genes among women with ILC and to assess the prognostic utility of polygenic risk scores (PRSs) in this population. This prospective, longitudinal cohort study was conducted at the European Institute of Oncology, Milan, Italy, from May 16, 2022, to January 31, 2025. Women diagnosed with primary ILC were enrolled and underwent multigene panel testing of 113 genes using next-generation sequencing. Follow-up data were collected until January 31, 2023. Statistical analysis was performed in January 2026. The primary outcome was BC-free survival, defined as the time from surgery to ipsilateral recurrence, contralateral disease, distant metastasis, or BC-related death. Secondary outcomes included overall survival and PRS distribution across genetic subgroups. A total of 414 White women (mean [SD] age, 53.7 [9.7] years; 211 [51.0%] with postmenopausal status) with ILC were tested. No significant associations were found between germline variant subgroups and patients' characteristics. PVs were identified in 46 patients (11.1%), with 20 (4.8%) carrying variants in moderate- to high-risk BC genes (ATM, BARD1, BRCA1, BRCA2, CDH1, CHEK2, NF1, FANCM, PALB2, RAD51C, RAD51D, STK11, TP53, and PTEN). The group of women carrying PVs in moderate- to high-risk BC genes had significantly reduced 5-year BC-free survival compared with the rest of cohort (62.2% [95% CI, 32.3%-82.0%] vs 92.1% [95% CI, 87.6%-95.0%]; hazard ratio, 3.91; 95% CI, 1.99-7.67; P < .001). PRS analysis did not reveal statistically significant differences in relapse risk across quartiles of PRS, and no association was found between PRSs and germline variant status. This cohort study of women with primary ILC identified a clinically relevant subset of patients carrying moderate- to high-risk germline PVs who exhibited an increased risk of early relapse. Although PRSs did not show prognostic value in this setting, multigene panel testing findings may refine genetic counseling and inform surveillance and therapeutic strategies in lobular breast tumors.
Academic health systems - particularly those serving underserved states - carry a unique responsibility to advance patient care, education, and research while operating under significant financial, workforce, and regulatory constraints. This article describes the transformation of OU Health and the University of Oklahoma College of Medicine, Oklahoma's only academic health system, emerging from a period of clinical, financial, and cultural distress as a more unified, mission-driven enterprise demonstrating early signs of recovery and performance improvement. Following a costly divestiture from a for-profit management structure and the disruption of the coronavirus disease 2019 pandemic, OU Health undertook a comprehensive turnaround strategy anchored in three interdependent pillars: creating a shared vision, leading with safety as a foundational priority, and aligning leadership and accountability across the enterprise. Through structured change management, a systemwide learning map engagement of nearly 10,000 employees, redefined safety governance grounded in learning health system principles, and explicit expectations for leadership behavior, the organization achieved measurable early improvements, including reductions in hospital-acquired infections and mortality alongside stabilization of financial performance. The authors present a replicable framework for academic and nonacademic health systems navigating large-scale transformation, emphasizing culture and safety as prerequisites for sustainable operational and financial recovery.
Studies assessing the anterior tibiofibular gap (ATFG) using ultrasonography (US) under various weight-bearing conditions and ankle angles in young athletes are lacking. This study aimed to examine the within-session repeatability of US-measured ATFG under different weight-bearing conditions and ankle angles in young athletes. A total of 49 young athletes aged 9-17 years (32 males, 17 females; 98 ankles) participated in this study. ATFG was assessed at the level of the anterior inferior tibiofibular ligament using US in both sitting and standing postures. The ankle was positioned under three conditions: neutral (0°), 20° dorsiflexion, and 20° dorsiflexion with 30° external rotation. The within-session repeatability of the US-based ATFG measurements was assessed for each weight-bearing condition and ankle angle according to maturation stage. Additionally, ATFG was compared between maturation stages, and sex-related and side-to-side differences were analyzed. The within-session repeatability was excellent for all measurement conditions, with intraclass correlation coefficient values exceeding 0.90 and standard error of measurement ranging from 0.05 to 0.21 mm. ATFG was greater in more mature athletes. No sex-related or side-to-side differences were observed in the ATFG or in the widening ratio. US-based assessment of the ATFG demonstrated excellent within-session repeatability under standardized measurement conditions in young athletes.
Anaplastic lymphoma kinase (ALK) inhibitors have emerged as promising agents for patients with resectable ALK-positive non-small-cell lung cancer (NSCLC). Whether ensartinib, a second-generation ALK inhibitor, is safe and effective in such patients is unknown. In this phase 3, double-blind, randomized trial involving patients with completely resected, ALK-positive stage IB to IIIB NSCLC after adjuvant chemotherapy, we randomly assigned patients in a 1:1 ratio to receive ensartinib at a dose of 225 mg once daily or placebo for 24 months. The primary end point was disease-free survival in patients with stage II to IIIB NSCLC. The key secondary end point was disease-free survival in the overall patient population. A total of 274 patients were randomly assigned to receive ensartinib or placebo (137 patients in each group). At 24 months, the percentage of patients with stage II to IIIB disease who were alive and disease-free was 86.4% in the ensartinib group and 53.5% in the placebo group (hazard ratio for disease recurrence or death, 0.20; 95% confidence interval [CI], 0.11 to 0.38; P<0.001). In the overall patient population, the percentage of patients who were alive and disease-free was 87.3% in the ensartinib group and 57.2% in the placebo group (hazard ratio, 0.20; 95% CI, 0.10 to 0.37; P<0.001). Overall survival data were immature. Adverse events of grade 3 or higher occurred in 35.8% of the patients who received ensartinib (most commonly rash) and in 18.2% of those who received placebo. Among patients with completely resected stage IB to IIIB ALK-positive NSCLC, the percentage of patients who were alive and disease-free at 24 months was significantly higher with ensartinib than with placebo. (Funded by Betta Pharmaceuticals; ELEVATE ClinicalTrials.gov number, NCT05341583.).
Diagnostic testing was key in preventing transmission of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) in the pre-vaccination era of the coronavirus disease 2019 pandemic. This study compared SARS-CoV-2 polymerase chain reaction (PCR) testing uptake and its determinants across six ethnic groups in Amsterdam, the Netherlands. We analyzed data from the population-based Healthy Life in an Urban Setting cohort linked to SARS-CoV-2 testing registry data from the Public Health Service of Amsterdam. Testing uptake was defined as completing at least one SARS-CoV-2 PCR test before 6 September 2021. We examined the association between ethnicity and testing uptake, and assessed determinants of testing uptake per ethnic group, using logistic regression while correcting for ethnic-specific age and sex distributions in Amsterdam. We included 19 006 participants (median age 53 years; 57% female). Testing uptake ranged from 25.3% (95% confidence interval (CI) = 23.1%-27.5%) in the Ghanaian to 52.2% (95% CI = 50.3%-54.1%) in the Turkish group. Individuals of Turkish origin [adjusted odds ratio (aOR) = 1.12, 95% CI = 1.01-1.23] were more likely, while those of African Surinamese (aOR = 0.88, 95% CI = 0.79-0.97) and particularly Ghanaian (aOR = 0.35, 95% CI = 0.30-0.40) origin less likely to be tested compared to those of Dutch origin. Younger age and perceived work or home-related stress were associated with testing uptake across most ethnic groups. Other determinants were specific to certain groups. SARS-CoV-2 testing uptake varied slightly across most ethnic groups in Amsterdam with the highest uptake among individuals of Turkish origin but was remarkably lower among individuals of Ghanaian origin. Given the diversity of identified determinants, testing strategies should be tailored to the needs of specific groups.
Fleas are ectoparasites of wild and domestic animals and can transmit various pathogens, including Rickettsia species, while also harboring endosymbiotic Wolbachia species. Although some studies have morphologically identified certain fleas along with their hosts, no reports have documented the spatiotemporal occurrence of fleas and/or molecular screening of flea-associated microorganisms in Pakistan. A total of 617 hosts were examined, including dogs (253, 41.0%), goats (282, 45.7%), humans (44, 7.1%), rats (19, 3.1%), and porcupines (19, 3.1%). The collected fleas were morphologically identified as Ctenocephalides canis (347/534, 65.0%), Ctenocephalides felis (120/534, 22.5%), Xenopsylla cheopis (16/534, 3.0%), and Pulex irritans (51/534, 9.6%). Overall, 259 hosts (42.0%) were found to be infested, with the highest prevalence in dogs (152/253, 60.1%), followed by porcupines (8/19, 42.1%), goats (87/282, 30.9%), rats (5/19, 26.3%), and humans (7/44, 15.9%). Ctenocephalides canis and C. felis were collected on both dogs and goats, while X. cheopis was collected from rats, and P. irritans on both humans and porcupines. In the collected samples, more fleas were observed during summer than winter. The obtained Rickettsiales gltA sequence from C. canis showed 99.7% identity with Rickettsia asembonensis previously reported from C. canis and C. felis from Brazil, Colombia, and Peru, whereas amplification of ompA and ompB was unsuccessful. The obtained 16S rDNA sequence of Rickettsiales detected in C. canis and C. felis showed 99.8% and 99.7% identity, respectively, with siphonaptern-associated Wolbachia spp. reported from Malaysia and Brazil. Similarly, the obtained coxA sequence of Rickettsiales from C. canis and C. felis showed 100% and 99.7% identity, respectively, with siphonaptern-associated Wolbachia spp. reported from Malaysia and the United States. Out of 80 screened fleas, 35/80 (43.8%) were positive for Wolbachia spp., and 16/80 (20%) for R. asembonensis, while 7/80 (8.8%) fleas were co-infected with Wolbachia spp. and R. asembonensis. Phylogenetically, gltA sequence of R. asembonensis was clustered with the same species reported from Peru, Brazil, and Colombia. Similarly, 16S rDNA sequences of Wolbachia spp. from C. canis and C. felis clustered with the Wolbachia spp. previously detected in C. canis from China and Turkey and Ctenocephalides orientis from Malaysia, and in C. felis from Brazil and the United States, respectively. The coxA sequences of Wolbachia spp. from C. canis and C. felis clustered with Wolbachia spp. previously detected in C. orientis in Malaysia and in C. felis from United States, respectively. Besides flea diversity, this preliminary study, for the first time in Pakistan, investigated spatiotemporal distribution and host associations of fleas, as well as molecular assessment of flea-associated microorganisms.
Type 2 diabetes (T2D) is an increasingly important public health concern in Indonesia and is increasingly affecting adolescents at a growing rate due to lifestyle transitions and limited awareness. Evidence regarding adolescents' knowledge, attitudes, and practices (KAP) toward T2D in rural settings remains limited. This study assessed KAP levels related to T2D, identified sociodemographic factors associated with KAP outcomes, and examined interrelationships among KAP domains among rural Indonesian adolescents. A cross-sectional study was conducted from September 2024 to February 2025 among 1,546 senior high school students in Kampar Regency, Riau Province. Participants were selected using multistage cluster sampling. Data were collected using a validated KAP questionnaire (Cronbach's α = 0.725). Descriptive and inferential statistics, including Mann-Whitney U tests, Kruskal-Wallis tests, Spearman's rank correlations, and multiple linear regression analyses, were used to examine KAP outcomes and associated factors. Participants demonstrated limited knowledge regarding T2D, particularly related to risk factors, symptoms, and complications. Although attitudes toward diabetes prevention were generally positive, preventive practices remained suboptimal, especially regarding physical activity. Female gender, peri-urban school location, higher academic rank, extracurricular participation, and prior exposure to diabetes information were associated with higher KAP scores. Knowledge was moderately associated with attitude but only weakly associated with preventive practices. Rural adolescents demonstrated limited diabetes-related knowledge and suboptimal preventive behaviors despite generally positive attitudes toward T2D prevention. Several sociodemographic and school-related factors were associated with KAP outcomes. However, the weak relationship between knowledge and practice highlights a persistent gap between awareness and preventive behavior. School-based, peer-led, and after-school interventions that integrate education with behavioral reinforcement may strengthen diabetes prevention efforts among rural adolescents.
The prevalence of depressive symptoms and balance disorders may be higher in high-altitude Andean regions due to chronic hypoxia, neurochemical alterations, and limited access to health services. Since both conditions can coexist and contribute to functional decline and falls, their association is relevant. We aimed to estimate the association between depressive symptoms and balance disorders in older adults living in 12 high Andean communities. We carried out a secondary analysis of data from a cross-sectional analytical study in older adults residing in 12 Peruvian high Andean communities during the period 2013-2020. The exposure variable was depressive symptoms (defined as a score greater than or equal to two on the five-item geriatric depression scale), while the outcome variable was balance disorders (defined by a functional reach test less than or equal to 20.32 cm). We constructed generalized linear models from Poisson family with link log and robust variances. We estimated crude (cPR) and adjusted (aPR) prevalence ratios with their respective 95% confidence intervals (95%CI). We analyzed 417 older adults; 61.1% (n = 255) were women, with a mean age of 73.2 ± 6.9 years. Additionally, 52.8% (n = 220) presented depressive symptoms, while 48.9% (n = 204) presented balance disorders. In the adjusted regression model, depressive symptoms were associated with a higher prevalence of balance disorders in older adults (aPR = 1.66; 95%CI: 1.28-2.15; p < 0.001). Depressive symptoms were associated with a higher prevalence of balance disorders in older adults residing in the 12 high Andean communities. Future epidemiological studies with a larger sample size are needed to evaluate depressive symptoms and balance disorders to develop early screening programs in older adults to improve their quality of life and access to primary health care.
Psychiatric emergency department (ED) visits following discharge from inpatient psychiatric care remain a significant challenge for mental health systems and may reflect gaps in continuity of care and community support. While sociodemographic and clinical predictors of recurrent ED utilization have been widely studied, the role of inpatient satisfaction and transitional care interventions in predicting post-discharge psychiatric ED visits remains less clear. This study aimed to examine predictors of psychiatric ED visits within 12 months following discharge from acute psychiatric inpatient units, with particular focus on inpatient satisfaction, prior ED utilization, intervention exposure, sociodemographic characteristics, and clinical factors. This observational cohort analysis used data from participants recruited through a pragmatic stepped-wedge transitional care study in Alberta, Canada. A multivariable logistic regression model was conducted to identify predictors of psychiatric ED visits within 12 months post-discharge. Predictor variables included intervention group [treatment as usual (TAU), supportive text messaging (SMS), and supportive text messaging combined with peer support (SMS + PS)], prior ED visits within 6 months before index admission, inpatient satisfaction, sociodemographic variables, and clinical characteristics. The study included 1,070 participants. Prior psychiatric ED visits within 6 months preceding the index admission emerged as the strongest predictor of post-discharge psychiatric ED utilization. Unemployment and housing instability were also significantly associated with increased likelihood of ED visits within 12 months following discharge. In contrast, inpatient satisfaction, intervention group, gender, ethnicity, relationship status, resilience, wellbeing, depression, and anxiety measures were not independently associated with post-discharge psychiatric ED visits. Psychiatric ED visits following discharge were primarily associated with prior ED utilization and socioeconomic factors, particularly unemployment and housing instability. Although inpatient satisfaction represents an important component of patient-centered psychiatric care, it was not independently associated with subsequent psychiatric ED visits in this cohort. These findings highlight the importance of addressing structural and social determinants alongside transitional care planning to reduce recurrent psychiatric ED utilization.
Accurate estimation of prognosis and life expectancy is essential in patients with advanced cancer, as it guides clinical decision-making and helps avoid unnecessary interventions while facilitating timely integration of palliative and supportive care. Palliative radiotherapy plays a key role within multidisciplinary management, offering effective and well-tolerated symptom relief for complications such as pain, bleeding, and obstruction, with treatment strategies closely tailored to expected survival. Although recent advances in machine learning have improved prognostic accuracy by modeling complex variable interactions, their application in palliative care settings remains limited. To aid clinical decision-making, we developed a decision tree multi-classifier to predict the mortality at 3, 24, and 52 weeks following palliative radiotherapy for bone metastases. Data from 573 adults diagnosed with metastatic cancer were analyzed. The primary endpoint was the overall survival (OS) defined as the number of months from treatment to death event. Four clinically relevant classes were defined: Class 0 (OS: ≤ 3 weeks), Class 1 (OS: 3-24 weeks), Class 2 (OS: 24-52 weeks) and Class 3 (OS ≥ 52 weeks). Candidate covariate predictors consisted of 65 clinical, dosimetric and laboratory variables. Two supervised decision tree machine-learning models were trained and validated using the Python package. A SHapley Additive exPlanations (SHAP) explanaibility analysis was performed to infer the global and local feature importance. The SHAP analysis selected three laboratory variables, the interleukin8, haemoglobin and lymphocytes count as the first three ranked variables representing the major impact on OS in each of the four classes and accounting for more than 80% of contribution. In all classes, higher chance of OS was associated with low values of interleukin8 (IL8) and higher values of haemoglobin (HEM) and lymphocytes count (LYMPH). Pre-treatment values of IL8 > 36.7 relocated more than 50% of patients with survival < 3 weeks and only 1.5% of patient with survival > 52 weeks. On the other hand, pre-treatment values of IL8 < 19 relocated about 92% of patients with survival > 52 weeks. Patients are then additionally separated based on the lymphocytes count (LYMPH). LYMPH values higher than 7.5 will drive the probability of survival > 52 weeks still over 90% while it drops down to 2.1% for LYMPH < 7.5. An explainable machine learning approach based on decision trees is able to predict the survival at different timing after radiotherapy in patients with advanced cancer. This approach provides an intelligible explanation of individualized risk prediction, helping clinicians to identify the best strategy for patient stratification and treatment selection.
Depression in people who are obese, and smoke cigarettes is often complicated by the possibility of using smoking as a tool for coping with stress. This study sought to determine the mediating role of depression medication on smoking among obese, overweight, normal weight and underweight adults. Data from the 2023 National Health Interview Survey, an annual public health survey of adults from 18-65 years of age in the United States of America, was analyzed using a Generalized Structural Equation Model. Underweight participants on depression medication were more likely to smoke compared to obese participants (reference group: obese; aOR = 0.49, 95% CI: [0.38, 0.63]). There was an indirect association between obesity and depression medication on smoking and obese participants on depression medication had 1.32 times higher odds of smoking when using depression medication compared to underweight individuals (aOR = 1.32, 95% CI: [1.09, 1.56]). Use of depression medication had the highest mediating role on smoking among underweight and the lowest role among obese participants (aOR = 1.64, 95% CI: [1.05, 2.22]). The findings suggest that body mass index should be considered in planning smoking cessation interventions in health care settings.
To estimate risk of concussion, risk functions based on injuries occurring in sports are often used. A range of datasets have been used to develop injury risk functions for concussion based on either global kinematics or tissue-level predictors. Two such datasets are one from American football, and another one from Australian football and rugby. These two datasets constitute the largest published collections of video-verified concussive cases in sports with known kinematics suitable for constructing risk functions. The objective of this study was to analyze the differences between two datasets of concussion for injury predictions to better understand the influence on injury risk functions. The kinematics were applied to the KTH head model and risk functions for different kinematic- and tissue-based predictors were developed and compared. The accuracy, sensitivity, specificity, and AUC were also compared. The two datasets evaluated in this study generated different risk curves. The datasets had some similarities such as having no significant difference in resultant linear acceleration, but also some differences, for example having a significant difference in resultant angular velocity. The Australian cases had relatively equally distributed major x-, y-, and z-components for angular velocity while the majority (59%) of the NFL cases had a major x-component (coronal plane rotation) representing more than 50% of the resultant. The y-component of the linear acceleration (lateral direction) was the major component in 64% of the Australian cases and 72% of the NFL cases. The two datasets, from Australian football/rugby, and American football, generated different injury risk curves with a lower 50% risk of concussion for the Australian dataset. This indicates that the choice of data as input for the development of injury risk functions is important. Therefore, it is necessary to improve methodology with focus on sampling methods and reliable/valid data collection.
Missed outpatient appointments (or no-shows) pose a significant challenge to health systems by reducing access to care, wasting resources, and negatively impacting patient outcomes. Traditional interventions - such as overbooking, transportation services, phone calls, and financial incentives - have yielded mixed results and face limitations in terms of scalability. Predictive modeling offers a promising tool for identifying patients at high risk of missing their appointment, allowing for targeted outreach. However, targeted outreach typically needs to be paired with manual outreach efforts to ensure patients make it to their appointments. To address these challenges, Penn Medicine's Patient Access team partnered with the Center for Health Care Transformation and Innovation (CHTI) to better understand the reasons for appointment no-shows and design and implement a scalable intervention. A text message survey of 186 patients who missed appointments revealed the top reason was "I did not know I had an appointment" (22% of patients). The team developed an automated targeted outreach program for high-risk patients using an interactive voice response (IVR) system that complemented the existing text message appointment reminder program. Leveraging the system's electronic health record no-show predictive model and CHTI's telecommunications platform, an automated IVR outreach campaign was developed to remind these patients of their upcoming appointments. The IVR call offered patients options to confirm, cancel, or reschedule their appointments, and represented a way to reduce no-show rates with minimal effort. Using these methods jointly to reduce no-show rates - while not overburdening patients at low risk of no-show - became the center of the design and intention. A rapid randomized trial (ClinicalTrials.gov number NCT06767423) was conducted with 59,994 patients at high risk of missing their appointment over a 4-week period in 2024. The no-show rate was 1.7 percentage points lower (down to 9.6% from 11.3%), and the net number of appointment completions was 1.9 percentage points higher (up to 77.8% from 75.9%) in the group of patients who got an IVR call in addition to text message reminders than in the group of patients who got text message reminders alone. The intervention had the greatest effect on reducing no-shows among patients in the highest risk quartile. It also increased appointment completion rates the most among Black patients, helping to reduce a preexisting equity gap. Overall, the results imply that 19,000 additional appointments could be completed per million new appointment slots. Based on the results of the trial, the intervention was immediately implemented at scale. Follow-up data from over 244,000 high-risk patients over a 6-month period demonstrate that the improved appointment completion rates were sustained. This case study demonstrates that integrating predictive modeling with IVR outreach can serve as a low-cost, scalable solution for improving appointment completion, increasing clinic efficiency, enhancing health equity, and generating revenue for health systems.