Osong Public Health and Research Perspectives最新文献

Objectives: Non-communicable disease (NCD) risk among adolescents represents a growing concern due to modifiable, lifestyle-related behavioral risk factors. Early identification and control of these factors are essential for prevention. This study assessed the correlates and co-occurrence of NCD-related lifestyle risk factors among school-going adolescents in Karnataka, India, aiming to inform intervention development.

Methods: Screening was conducted among 1,100 school adolescents aged 13-16 years from 8 randomly selected urban and rural schools in Karnataka. Data were collected using a validated self-administered questionnaire covering sociodemographic characteristics and social cognitive theory predictors of lifestyle practices. Descriptive statistics, chi-square tests, and logistic regression were employed.

Results: Of the 1,100 adolescents surveyed, 552 and 548 were from urban and rural areas, respectively. Both groups reported high rates of insufficient fruit and vegetable (FV) intake (96.7% and 67.7%, respectively), inadequate physical activity (96.7% and 68.6%), tobacco use (5.6% and 11.5%), and alcohol consumption (5.6% and 10.8%). On logistic regression, urban adolescents were significantly more likely than rural peers to exhibit multiple behavioral risk factors, with 19-fold higher odds of having ≥1 factor (adjusted odds ratio [AOR], 19.04; p<0.001) and 4-fold higher odds of having ≥2 (AOR, 4.06; p<0.001). Parental (particularly maternal) education was associated with NCD risk (AOR, 1.82; p=0.001). Physical inactivity significantly co-occurred with low FV intake (71.7%) and junk food consumption (72.8%).

Conclusion: Unhealthy lifestyle behaviors among adolescents displayed significant co-occurrence, underscoring the critical need for comprehensive, theory-based school interventions to address multiple interconnected risk factors and mitigate the burden of NCDs.

Objectives: In May 2025, clusters of salmonellosis were identified in seven cities in the Republic of Korea, all associated with consumption of identical bakery products. This investigation aimed to characterize the outbreak, identify potential contributing factors, and inform strategies for preventing similar multi-facility foodborne outbreaks.

Methods: A case series study was conducted among individuals who consumed Manufacturer H's Product I and Product II on May 15-16, 2025 at seven facilities (n=1,235). Clinical specimens from symptomatic individuals, retained food samples, and environmental samples were collected and tested. Food-exposure histories were assessed, and active case finding was implemented across all supplied facilities. Traceback investigations were conducted at the manufacturer, distributor, and egg farms. Human and food isolates underwent pulsed-field gel electrophoresis (PFGE) and whole-genome sequencing (WGS).

Results: A total of 323 cases met the outbreak case definition (attack rate, 26.2%), of which 48 were laboratory-confirmed. Salmonella Enteritidis was isolated from both clinical specimens and retained bakery products. PFGE patterns were indistinguishable between human and food isolates, and WGS demonstrated high genetic relatedness. These findings confirmed a common-source outbreak linked to the implicated bakery products.

Conclusion: This outbreak underscores the value of integrating epidemiological investigation, active case finding, and molecular typing to identify common food vehicles in outbreaks involving widely distributed manufactured foods. Coordinated collaboration between public health and food safety authorities is essential for the effective detection, response, and prevention of multi-facility foodborne outbreaks.

Objectives: Smartphone overdependence (SOD) and anxiety are major concerns in adolescent mental health; however, few studies have examined their bidirectional relationship. This study aimed to examine reciprocal associations between SOD and anxiety among adolescents.

Methods: A secondary analysis was conducted with data from 50,975 adolescents in the 19th Korea Youth Risk Behavior Survey. SOD was measured using the SOD scale, and anxiety was assessed using the generalized anxiety disorder 7-item scale. Multivariable logistic regression analyses were conducted to examine reciprocal associations, adjusting for sociodemographic factors, perceived stress, loneliness, and depressive symptoms.

Results: Moderate to severe anxiety was found in 12.6% of participants, and 3.3% were classified as being at high risk for SOD. In adjusted models, the model with anxiety as the outcome demonstrated higher predictive performance (concordance rate, 86.5%) than the model with SOD as the outcome (77.3%). Adolescents at high risk for SOD had higher odds of reporting anxiety, and those with severe anxiety had higher odds of being classified as at high risk for SOD. Stress, loneliness, and smartphone use time were also identified as significant predictors.

Conclusion: SOD and anxiety were strongly associated with each other among adolescents. Integrated approaches addressing both digital behavior and mental health may help inform strategies to reduce psychological distress. Public health strategies may benefit from considering both aspects when screening for problematic smartphone use and anxiety.

The coronavirus disease 2019 (COVID-19) pandemic had an unprecedented global impact, resulting in both positive and negative consequences. The virus not only affected millions of lives worldwide but also caused long-term harm to multiple organ systems in many survivors, thereby substantially impairing quality of life. This persistent condition is now referred to as long COVID (LC). The aim of this study is to raise awareness of LC-related organ system impacts and to highlight the key role of artificial intelligence (AI) in mitigating these effects. The present research conducts a narrative review focusing on LC-related impacts. In this context, unstructured searches were conducted to identify a total of 69 relevant studies indexed in Embase, PubMed, Web of Science, or Scopus, each of which was reviewed by at least 2 experts with sufficient domain knowledge in health sciences. Based on the authors' perspectives and insights, the review narratively examines damage to human organ systems attributable to LC and explores the role of AI in addressing LC-related challenges. Significant ethical, practical, and societal concerns arising from the extensive use of AI, particularly major issues such as data privacy and algorithmic bias, are also discussed. LC has caused lasting impacts on human organ systems, while AI is offering substantial potential for LC-related care.

This review examines how biotechnology advances (CRISPR/Cas9, next-generation targeted therapies, nanotechnology-based drug delivery, and immunotherapies) can be applied to address cancer drug resistance worldwide. It also considers the economic burden of resistance, inequities in access to biotechnology solutions, and ethical concerns surrounding rapid innovation, particularly in low-resource settings. A narrative review synthesized evidence from basic science studies, clinical trials, translational research, and policy analyses. Evidence was prioritized for 2015-2025 publications. The synthesis highlights resistance biology and evaluates how precision medicine, biomarker-guided treatment, and high-throughput drug screening can inform individualized regimens and rational combinations. Breakthroughs in gene editing, targeted inhibitors, nanocarriers, and immune engineering can counter key resistance mechanisms, including resistance-conferring mutations, altered drug transport, immune evasion, and tumor microenvironment-mediated protection. Despite progress, implementation barriers remain substantial: high drug and development costs, limited molecular diagnostics and manufacturing capacity, and regulatory and governance challenges that can delay adoption and widen disparities, particularly in low- and middle-income countries. Integrating biotechnology innovations within precision medicine frameworks may improve treatment selection and patient outcomes. Maximizing public health impact requires affordability and financing strategies, robust ethical oversight, timely regulatory pathways, and coordinated global collaboration to ensure access to effective therapies across health systems worldwide.

Objectives: This study evaluated the effectiveness of government epidemic control policies centered on diagnostic testing and examined their impact on the in vitro diagnostics (IVD) industry. It also analyzed the complex interplay among policy interventions, epidemic dynamics, and the IVD industry's value chain to identify key leverage points for managing future public health crises.

Methods: A system dynamics (SD) model calibrated using national data from the Republic of Korea simulated the interactions between epidemic progression and the IVD value chain. We conducted a scenario analysis encompassing 6 policy interventions: research and development (R&D) investment, public-private collaboration, regulatory easing, diagnostic test performance, testing intensity, and social distancing.

Results: Policies promoting investment, public-private collaboration, and regulatory easing accelerated the market entry of diagnostics, thereby reducing infections and deaths. However, these interventions were associated with lower overall industry revenue, attributable to increased market competition and a reduced patient population. A critical trade-off was noted: although regulatory speed is advantageous, using low-sensitivity diagnostics substantially worsened public health outcomes. Aggressive testing strategies and stringent social distancing were also confirmed to be effective in reducing both infections and mortality.

Conclusion: This study provides a strategic framework for understanding interactions between pandemic control policies and the IVD industry. Sustained pre-crisis investment in R&D, public-private networks, and public health infrastructure is essential for effective pandemic preparedness. During a crisis, policymakers must carefully manage the critical trade-off between regulatory speed and diagnostic quality to ensure that rapid responses do not compromise public health outcomes.

Objectives: This study aimed to examine the temporal dynamics of dengue cases in Malaysia from 2022 to 2024 using seasonal-trend decomposition and time-series modeling.

Methods: Weekly dengue case counts from the national registry were analyzed across all states using seasonal-trend decomposition using LOESS (STL) to separate trend, seasonal, and irregular components. Autoregressive integrated moving average (ARIMA) and seasonal ARIMA (SARIMA) models were fitted to validate temporal structures, with model selection based on the Akaike information criterion (AIC), corrected AIC, and Bayesian information criterion. Diagnostic checks, including residual analysis and Ljung-Box testing, were performed to ensure model adequacy.

Results: Dengue incidence showed marked heterogeneity across states. STL decomposition indicated that long-term trends contributed more strongly to case dynamics than seasonality in most states, although seasonal influences were significant in the states of Kedah and Kelantan. Seasonal peak timing varied between states, highlighting differences in epidemic cycles. ARIMA and SARIMA modeling confirmed that no single temporal structure could adequately represent all states; while some series were well fitted by simple ARIMA models, others required seasonal adjustments. Residual diagnostics demonstrated that the selected models were statistically adequate.

Conclusion: Dengue dynamics in Malaysia are shaped by both trend and seasonal components, with considerable variation across states. Combining STL decomposition with ARIMA/SARIMA modeling strengthens the evidence base for state-specific forecasting and proactive vector control. Tailoring surveillance systems and interventions to local temporal patterns may improve early warning capacity and optimize resource allocation for dengue prevention.

Background: This study evaluated the effectiveness of rapid diagnostic testing (RDT) for the early detection of imported dengue cases at Gimhae International Airport in the Republic of Korea, and analyzed patient characteristics and response processes following positive results.

Methods: From 2022 to 2024, 334 individuals underwent RDT at the airport. Testing was performed for travelers presenting with dengue-like symptoms or recent mosquito bites. Two dengue RDT kits (NS1 and immunoglobulin G/M) were used, and confirmatory tests-including real-time reverse transcription polymerase chain reaction and enzyme-linked immunosorbent assays-were performed for RDT-positive cases. Time intervals between sample collection and diagnostic confirmation were compared by institution type and day of the week.

Results: Of the 334 cases tested, 12 yielded positive RDT results, and 3 were confirmed as dengue. No confirmed cases were identified among asymptomatic travelers or those with travel durations shorter than 5 days. All 3 confirmed cases showed moderate or higher RDT intensity. The confirmatory results were negative for all 7 marginally positive cases. The average turnaround time for diagnostic confirmation was 4.00 days in hospitals versus 2.71 days in public health centers. Samples collected on weekdays produced faster results (2.33 days) than those collected across weekends (5.00 days). One individual with a strong RDT-positive result declined confirmatory testing.

Conclusion: RDT is a valuable tool for detecting dengue at ports of entry. However, timely confirmatory diagnosis requires improved inter-agency coordination and logistical systems, particularly for weekend operations. These findings offer practical insights for strengthening quarantine-based infectious disease control.

Background: This study aimed to examine the effectiveness of resistance training on hemoglobin A1c (HbA1c) levels and body mass index in patients with diabetes mellitus, categorized by age.

Methods: A comprehensive search of English-language literature published between 1997 and 2025 was performed across 6 databases (Embase, CINAHL, Medline, Cochrane, PubMed, and PEDro). Standardized mean differences and 95% confidence intervals were calculated, and publication bias was assessed using funnel plots and Egger's test. The Joanna Briggs Institute checklist was applied to evaluate study quality.

Results: Thirty randomized controlled trials met the inclusion criteria, comprising 620 participants in the older (<60 years of age) subgroup and 1,389 in the middle-aged (40-59 years of age) subgroup. In both subgroups, resistance training significantly reduced HbA1c levels and body mass index, while increasing muscle strength (primary outcome). It also significantly increased high-density lipoprotein, improved VO₂ peak, and reduced low-density lipoprotein (secondary outcomes). However, the effects of resistance training were significant only in the older-adult subgroup for total cholesterol and only in the middle-aged subgroup for triglycerides.

Conclusion: Resistance training is a recommended rehabilitation exercise for patients with diabetes mellitus. Routine resistance training has been shown to help maintain optimal HbA1c and body mass index levels and improve muscle strength. In addition, it does not pose a risk of adverse events in either middle-aged or older patients. Nonetheless, patients are advised to monitor blood glucose levels and adhere to a proper diet to achieve optimal outcomes.

Background: Although family members of persons with disabilities face elevated suicide risk, predictive models remain underdeveloped in Korean contexts. This study aimed to develop machine learning-based predictive models for suicidal ideation among family members of persons with disabilities and examine differential risk patterns by disability onset type.

Methods: This cross-sectional study analyzed 124,783 adult family members (59.9% spouses, 20.3% parents/ascendants, 14.6% adult children, 5.2% extended family) from the 2018 Korean Disability and Life Dynamics Panel using survey weights. Four predictive models, including machine learning approaches, were compared using 31 variables. The dataset was divided into training (70%) and test (30%) sets, with stratified analyses comparing congenital and acquired disability groups.

Results: Among the 124,783 family members analyzed, least absolute shrinkage and selection operator (LASSO) with cross-validation achieved optimal performance (area under the receiver operating characteristic curve, 0.875 training; 0.853 test). LASSO selected 16 of 31 variables for the total sample, with family members' depression as the strongest predictor (β=0.554), followed by disabled persons' suicidal ideation (β=0.425). Stratified LASSO analyses revealed that national basic livelihood beneficiary status was the strongest predictor for families with congenital disability (β=0.541), while family members' depression was the strongest predictor for families with acquired disability (β=0.562), demonstrating distinct predictive patterns by disability onset.

Conclusion: These findings show that predictive factors differ substantially by disability onset type, indicating the need for tailored intervention approaches and offering an evidence-based foundation for targeted suicide prevention strategies.