Osong Public Health and Research Perspectives最新文献

Background: Subacute sclerosing panencephalitis (SSPE) is a rare but fatal neurodegenerative disease caused by persistent measles virus infection. After a significant measles outbreak in 2000-2001, the Republic of Korea implemented a nationwide measles elimination program, which led to a dramatic reduction in measles incidence. This study aimed to evaluate the impact of these measles elimination efforts on the incidence of SSPE in the Republic of Korea.

Methods: This nationwide, population-based retrospective cohort study identified patients newly diagnosed with measles and SSPE between 2007 and 2022, registered in the Health Insurance Review and Assessment Service (HIRA) and Korea Disease Control and Prevention Agency (KDCA) databases. Population-based incidence rates of measles and SSPE were calculated and compared annually.

Results: A total of 236 measles cases (HIRA data) and 1,168 measles cases (KDCA data), along with 2,736 SSPE cases, were diagnosed during the study period. Measles incidence significantly declined, reaching zero cases in 2021, while SSPE incidence displayed an upward trend, peaking in 2014. The mean age at SSPE onset was 21.2 years, with a marked male-to-female ratio of 13.0:1.

Conclusion: SSPE incidence was remarkably low in the post-outbreak period, likely attributable to successful measles control. This study underscores the critical importance of maintaining low measles incidence through sustained vaccination efforts, preventing SSPE and other measles-related complications.

Background: This review and meta-analysis examined the effectiveness of non-pharmacological therapies delivered through school-based interventions for smoking cessation among adolescents in South and Southeast Asian countries.

Methods: A systematic search was conducted across PubMed, Scopus, Science Direct, BioMed Central, the Cochrane Library, and ProQuest Dissertations & Theses Global from inception to October 2024. Eligible studies comprised randomized controlled trials and quasi-experimental studies that compared non-pharmacological smoking cessation interventions delivered in schools or other educational institutions. Data on smoking abstinence outcomes were extracted from published studies, and odds ratios (ORs) with 95% confidence intervals (CIs) were pooled using a random-effects model via the Mantel-Haenszel estimator.

Results: Seven studies involving 1,260 participants were included. The meta-analysis demonstrated that non-pharmacological school-based therapies significantly increased smoking abstinence compared to controls (OR, 2.83; 95% CI, 1.83-4.40; p<0.001. Subgroup analyzes revealed benefits across both randomized controlled trials and quasi-experimental studies with varying abstinence rates. Studies utilizing biochemical verification showed significant positive effects despite substantial heterogeneity, and short-term (<3 months) abstinence was significantly higher in intervention groups compared to controls. Overall, no differences were found between subgroups regarding intervention effectiveness.

Conclusion: This meta-analysis indicates that non-pharmacological school-based interventions positively impact smoking abstinence rates, although effectiveness may vary based on study design, follow-up duration, and use of biochemical verification. The findings underscore the need for further research with larger sample sizes, extended follow-up periods, and improved methodological rigor in these regions.

Background: To develop and evaluate forecasting models using the Holt-Winters statistical approach and the long short-term memory (LSTM) deep learning method for weekly seasonal influenza-like illness (ILI) incidences in Saudi Arabia. The study compares model performance and assesses the predictive value added by incorporating region-specific exogenous variables within Middle Eastern epidemiological modeling.

Methods: This study compared the performance of Holt-Winters and LSTM models in forecasting weekly ILI cases in Saudi Arabia, using data collected from 2017 to 2022. Time series analysis integrated exogenous variables including climatic conditions and population mobility trends. The Holt-Winters model employed both additive and multiplicative seasonal components. Model performance was evaluated using root mean squared error (RMSE), mean absolute percentage error, and R2.

Results: The best-performing model, LSTM with exogenous variables, achieved an RMSE of 28.55, mean absolute error (MAE) of 0.14, R2 of 0.96, and percent bias (PBIAS) of +2.1%, indicating negligible systematic error. The LSTM model without exogenous variables demonstrated slightly lower accuracy (RMSE of 34.07, MAE of 0.18, R2 of 0.93, PBIAS of +5.8%), indicating strong predictive capability but less precision in determining peak ILI cases. The Holt-Winters model effectively captured seasonal and long-term trends, but showed a moderate performance with an RMSE of 82.57, MAE of 0.38, R2 of 0.58, and a high PBIAS of +14.2%, revealing significant unexplained variability during periods of high incidence fluctuation.

Conclusion: This study highlights the respective strengths and limitations of statistical and machine learning approaches for ILI forecasting.

Background: This study investigated the relationship between metabolic factors (blood lipids and glucose) and inflammatory indicators (tumor necrosis factor-alpha [TNF-α] and high-sensitivity C-reactive protein [hs-CRP]), disease activity, and the rheumatoid arthritis (RA) risk.

Methods: Serum fasting blood glucose (FBG) and lipid profiles-including total cholesterol (Chol), triglycerides (TG), high-density lipoprotein (HDL), and low-density lipoprotein-were measured in 100 RA patients and 100 healthy individuals. Disease severity was assessed using the disease activity score 28. Inflammatory indicators (TNF-α and hs-CRP) were measured using the enzyme-linked immunosorbent assay method.

Results: In RA patients, serum FBG, TG, Chol/HDL, and TG/HDL were significantly elevated, whereas HDL levels reduced compared to healthy individuals. Multivariate analysis indicated that each unit increase in serum FBG, HDL, Chol/HDL, and TG/HDL was associated with a 64% increase (p<0.001), a 7% reduction (p=0.001), a 52% increase (p=0.007), and a 54% increase (p=0.001) in the odds of RA, respectively. Disease activity showed no correlation with metabolic factors (p>0.05). Among all metabolic factors studied, FBG had the largest area under the curve (0.981) (p<0.0001) for predicting RA. Across the total participant group, FBG, TG, and TG/HDL were positively associated with hs-CRP and TNF-α (p<0.05). HDL showed an inverse association with hs-CRP (p=0.008). Among RA patients specifically, TNF-α positively correlated with TG and TG/HDL, while hs-CRP correlated only with TG/HDL.

Conclusion: These findings indicate that increased FBG and Chol/HDL and decreased HDL may elevate RA risk by promoting systemic inflammation. Among these, elevated FBG may serve as the strongest predictor of RA risk.

Background: Klebsiella pneumoniae is known as one of the most common causes of hospitalacquired infections. Its prevalence poses substantial challenges to both hospital and public health systems, particularly due to the rise of multidrug-resistant strains. Understanding the epidemiology and resistance properties of K. pneumoniae can inform antimicrobial stewardship and infection control programs. A cross-sectional study was employed from November 2021 to November 2023.

Methods: A total of 24 isolates underwent antimicrobial susceptibility testing using the disk diffusion method, an extended-spectrum beta-lactamase (ESBL) production test, and molecular gene detection.

Results: The study found that 95.8% of clinical isolates were classified as multidrug-resistant. All isolates were resistant to ampicillin (100%). A high percentage of isolates were resistant to cefazolin (91.7%), ceftriaxone (87.5%), cefotaxime (87.5%), cefepime (87.5%), ciprofloxacin (83.3%), and sulfamethoxazole-trimethoprim (83.3%). Of the 24 isolates, 87.5% harbored ESBL genes, while the frequencies for GES, NDM, SIM, and OXA-48 were 16.7%, 20.8%, 8.3%, and 41.7%, respectively. Notably, the OXA-23 and OXA-51 genes, which are typically associated with Acinetobacter baumannii, were detected in 16.7% and 20.8% of isolates, respectively. Moreover, the prevalence of virulence genes rmpA, acrAB, and tolC was 0%, 95.8%, and 87.5%, respectively.

Conclusion: This study demonstrated a high level of antibiotic resistance and a significant presence of virulence genes among K. pneumoniae isolates. Consequently, these findings represent a critical public health issue that requires heightened awareness among all stakeholders, including health workers.

Background: This study aimed to explore factors influencing satisfaction with medical services among medically underserved populations at the free medical clinic, providing data to improve free medical services for these populations.

Methods: We employed a descriptive correlational study design involving 112 individuals (aged 19 years and older) from medically underserved populations who visited the clinic. Data were collected through face-to-face surveys from September to October 2023, and statistical analyses (t-tests, analysis of variance, Pearson correlation, and hierarchical multiple regression) were used to identify key predictors of satisfaction.

Results: Perceived support from healthcare providers emerged as the strongest predictor of satisfaction with medical services, demonstrating a significant positive association. While social support was positively correlated with perceived support from healthcare providers, it did not independently predict satisfaction.

Conclusion: These findings underscore the importance of healthcare provider and social support in increasing satisfaction with medical services among medically underserved populations. Developing tailored healthcare programs and specialized healthcare provider training are essential strategies to improve healthcare access and outcomes for these vulnerable groups.

Background: The study aimed to analyze the core capacities to implement World Health Organization International Health Regulations (IHR) in 6 countries: Indonesia, Cambodia, Vietnam, the Dominican Republic, Ghana, and the Republic of Korea.

Methods: Secondary data from relevant databases and reports, including the electronic State Party Self-Assessment Annual Reporting mechanism and global health security index, were used to assess health security in these countries. Descriptive statistics summarized the basic features of the scores, and a strengths, weaknesses, opportunities, and threats (SWOT) analysis was subsequently performed to identify factors affecting health security scores while highlighting key similarities and differences between countries.

Results: Early warning and event management emerged as the primary strength in most countries. Common opportunities included international commitments and immunization programs. In contrast, many countries shared weaknesses related to the policy, legal, and normative frameworks for IHR implementation, as well as challenges in human resources, chemical event management, and radiation emergency preparedness. Recurring threats involved issues such as biosafety, biosecurity, dual-use research and the culture of responsible science, infection control practices, coordination between public health and security authorities, laboratory supply chain vulnerabilities, and communication with healthcare workers during public health emergencies.

Conclusion: In order to counter future global health threats, countries should prioritize enhancing surveillance capacity (early warning and event management) as well as the immunization indicator (vaccination rates for human and animal diseases, including the national vaccine delivery system).

Background: This study aimed to describe and compare health-related quality of life (QoL) as measured by the World Health Organization Quality of Life-BREF (WHOQoL-BREF) and the EuroQol-5 Dimensions (EQ-5D) among the Malaysian population, examining differences by sociodemographic characteristics including age, income, sex, ethnicity, educational level, and occupation.

Methods: This cross-sectional study used data from 19,402 individuals collected as part of a health and demographic surveillance system survey conducted in the Segamat district of Malaysia in 2018-2019. Descriptive statistics and measures of central tendency were produced. Differences in QoL among demographic sub-groups were examined using the t-test and analysis of variance, while the correlations between the WHOQoL-BREF and EQ-5D were evaluated using Pearson correlation coefficients.

Results: Based on complete case analysis (n=19,129), the average scores for the 4 WHOQoLBREF domains were 28.2 (physical), 24.1 (psychological), 12.0 (social relationships), and 30.4 (environment). The percentages of participants not in full health for each EQ-5D dimension were 12.8% (mobility), 3.1% (self-care), 6.9% (usual activities), 20.9% (pain/discomfort), and 6.8% (anxiety/depression). Correlations between the 4 WHOQoL-BREF domains and the 5 EQ-5D dimensions were relatively weak, ranging from -0.06 (social relationships with self-care and pain/discomfort; p<0.001) to -0.42 (physical with mobility; p<0.001).

Conclusion: Although health-related QoL as measured by the WHOQoL-BREF and the EQ-5D are correlated, these 2 measures should not be considered interchangeable. The choice between them should be guided by the specific research questions and the intended use of the data.

Background: This study systematically reviewed and analyzed epidemiological evidence regarding the association between dietary total antioxidant capacity (DTAC) and both the risk of developing diabetes and glycemic biomarker levels.

Methods: We searched the PubMed, Scopus, ScienceDirect, and Google Scholar databases through July 2024 without imposing any date restrictions. Original studies that examined the relationship between DTAC and either the risk of developing diabetes or glycemic biomarker levels-specifically fasting blood glucose (FBG), hemoglobin A1C (HbA1C), insulin, and the homeostatic model assessment for insulin resistance (HOMA-IR)-were eligible for inclusion. After eliminating duplicates and irrelevant records, relevant studies were selected, and data were extracted through rigorous critical analysis.

Results: A total of 32 articles were included in the review. Of the 19 studies that evaluated diabetes risk, 15 reported a lower risk among subjects with higher DTAC values. All 4 studies examining prediabetes risk found lower risk in participants with high DTAC scores. Additionally, significant inverse relationships were observed between DTAC values and FBG (9/15 studies), HbA1C (1/6 studies), insulin (5/6 studies), and HOMA-IR (8/9 studies).

Conclusion: The majority of evidence indicates that high adherence to an antioxidant-rich diet may reduce diabetes risk and improve glycemic biomarkers, including FBG, insulin, and HOMA-IR.

Background: This study aimed to investigate the relationship between blood microbiota, specifically bacterial DNA, and cognitive decline in individuals with subjective cognitive decline (SCD) and amnestic mild cognitive impairment (aMCI). The objective was to identify potential microbial signatures that could serve as biomarkers for cognitive deterioration.

Methods: Forty-seven participants were recruited, including 13 with aMCI, 20 with SCD, and 14 normal cognition (NC). Blood samples were collected, and microbial DNA was analyzed using 16S rRNA sequencing on the Illumina MiSeq platform. Bioinformatics analyses-including α- and β-diversity measures and differential abundance testing (using edgeR)-were employed to assess microbial diversity and differences in bacterial composition among groups. Logistic regression models were used to evaluate the predictive impact of the microbiota on cognitive decline.

Results: Microbial diversity differed significantly between groups, with NC exhibiting the highest α-diversity. Both the aMCI and SCD groups showed reduced diversity. Taxa such as Bacteroidia, Alphaproteobacteria, and Clostridia were significantly decreased in the aMCI group compared to NC (p< 0.05). In contrast, Gammaproteobacteria increased significantly in the aMCI group compared to both NC and SCD, indicating progressive microbial changes from SCD to aMCI. No significant differences were found between the NC and SCD groups.

Conclusion: Distinct bacterial taxa-particularly the increase in Gammaproteobacteria along with decreases in Bacteroidia, Alphaproteobacteria, and Clostridia-are associated with the progression of cognitive decline. These findings suggest that blood microbiota could serve as potential biomarkers for the early detection of aMCI. However, the small sample size and the lack of control for confounding factors such as diet and medication limit the findings. Larger studies are needed to validate these results and further explore the role of microbiota in neurodegeneration.