Osong Public Health and Research Perspectives最新文献

Background: The aim of this study was to investigate the relationship between the number of patient comorbidities and the delays in seeking treatment for coronary heart disease (CHD).

Methods: This longitudinal study utilized secondary data from the Non-Communicable Disease Risk Factor (NCDRF) cohort study conducted in Bogor City. Individuals who participated in the NCDRF cohort study and were diagnosed with CHD within the 6-year study period met the inclusion criteria. Respondents who were not continuously monitored up to the 6th year were excluded. The final sample included data from respondents with CHD who participated in the NCDRF cohort study and were monitored for the full 6-year duration. The final logistic regression analysis was conducted on data collected from 812 participants.

Results: Among the participants with CHD, 702 out of 812 exhibited a delay in seeking treatment. The risk of a delay in seeking treatment was significantly higher among individuals without comorbidities, with an odds ratio (OR) of 3.5 (95% confidence interval [CI], 1.735-7.036; p<0.001). Among those with a single comorbidity, the risk of delay in seeking treatment was still notable (OR, 2.6; 95% CI, 1.259-5.418; p=0.010) when compared to those with 2 or more comorbidities. These odds were adjusted for age, sex, education level, and health insurance status.

Conclusion: The proportion of patients with CHD who delayed seeking treatment was high, particularly among individuals with no comorbidities. Low levels of comorbidity also appeared to correlate with a greater tendency to delay in seeking treatment.

Background: Between July 2, 2021, and September 20, 2022, a Mycobacterium bovis outbreak occurred among exhibition animals at a zoo in the Republic of Korea. This study was conducted to assess the likelihood of M. bovis transmission to human contacts through a contact investigation and to implement preventive treatment for latent tuberculosis infection (LTBI).

Methods: In this descriptive study, the Korea Disease Control and Prevention Agency conducted a contact investigation, which included interviews, interferon-gamma release assay (IGRA) tests, and chest X-rays. Contacts underwent IGRA testing on 2 occasions: initial testing of 29 contacts (15 in the first cluster of infection and 14 in the second cluster) and follow-up testing of the 15 contacts in the first cluster.

Results: The study included 29 participants, 18 of whom were male (62.1%) and 11 female (37.9%). The mean participant age was 37.3 years (standard deviation, 9.6 years). In the initial IGRA tests, 6 of the 29 participants tested positive, indicating a prevalence of 20.7%. Following prolonged exposure, 1 additional positive case was detected in follow-up testing, raising the prevalence of LTBI to 24.1%. None of the contacts had active tuberculosis. Among the 7 individuals with positive results, 2 (28.6%) underwent treatment for LTBI.

Conclusion: This study faced challenges in confirming the transmission of M. bovis infection from infected animals to humans in the Republic of Korea. Nevertheless, adopting a One Health approach necessitates the implementation of surveillance systems and infection control protocols, particularly for occupational groups at high risk of exposure.

Background: The objective of this study was to examine the hypothesis that periodontal disease is associated with chronic non-communicable diseases.

Methods: In this cross-sectional study, we evaluated the periodontal health condition of the population, based on the community periodontal index, as well as the number of missing teeth and the presence of systemic health conditions. We quantified the association between oral health and the presence of chronic diseases using simple logistic regression, adjusting for confounding factors including age, smoking, and overweight.

Results: The study population consisted of 334 volunteers, aged between 19 and 81 years. In patients over 45 years old, periodontal disease was found to be significantly associated with hypertension and diabetes. Furthermore, in female patients, periodontal disease was significantly associated with hypertension, diabetes, and cancer.

Conclusion: Our findings indicate that periodontal disease is positively and significantly associated with both arterial hypertension and diabetes, independent of potential confounding factors.

Background: Post-coronavirus disease 2019 (COVID-19) symptoms were widely reported. However, data on post-COVID-19 conditions following infection with the Omicron variant remained scarce. This prospective study was conducted to understand the prevalence, patterns, and duration of symptoms in patients who had recovered from COVID-19.

Methods: A prospective study was conducted across 11 districts of Delhi, India, among individuals who had recovered from COVID-19. Study participants were enrolled, and then returned for post-recovery follow-up at 3 months and 6 months interval.

Results: The mean age of study participants was 42.07 years, with a standard deviation of 14.89 years. The majority of the participants (79.7%) reported experiencing post-COVID-19 symptoms. The most common symptoms included joint pain (36.0%), persistent dry cough (35.7%), anxiety (28.4%), and shortness of breath (27.1%). Other symptoms were persistent fatigue (21.6%), persistent headache (20.0%), forgetfulness (19.7%), and limb weakness (18.6%). The longest duration of symptom was observed to be anxiety (138.75±54.14 days), followed by fatigue (137.57±48.33 days), shortness of breath (131.89±60.21 days), and joint pain/swelling (131.59±58.76 days). At the first follow-up visit, 2.2% of participants presented with abnormal electrocardiogram readings, but no abnormalities were noticed during the second follow-up. Additionally, 4.06% of participants exhibited abnormal chest X-ray findings at the first followup, which decreased to 2.16% by the second visit.

Conclusion: The most frequently reported post-COVID-19 symptoms were joint pain, dry cough, anxiety and shortness of breath. These clinical symptoms persisted for up to 6 months, with evidence of multi-system involvement. Consequently, findings highlighted the need for long-term follow-up during the post-COVID-19 period.

This systematic review evaluated psychiatric adverse events (AEs) following vaccination against coronavirus disease 2019 (COVID-19). We included studies that reported or investigated psychiatric AEs in individuals who had received an approved COVID-19 vaccine in the Republic of Korea. Systematic electronic searches of Ovid-Medline, Embase, CENTRAL, and KoreaMed databases were conducted on March 22, 2023. Risk of bias was assessed using the Risk of Bias Assessment Tool for Non-randomized Studies 2.0. The study protocol was registered in the International Prospective Register of Systematic Reviews (CRD42023449422). Of the 301 articles initially selected, 7 were included in the final analysis. All studies reported on sleep disturbances, and 2 highlighted anxiety-related AEs. Sleep disorders like insomnia and narcolepsy were the most prevalent AEs, while depression was not reported. Our review suggests that these AEs may have been influenced by biological mechanisms as well as the broader psychosocial context of the COVID-19 pandemic. Although this study had limitations, such as a primary focus on the BNT162b2 vaccine and an observational study design, it offered a systematic, multi-vaccine analysis that fills a critical gap in the existing literature. This review underscores the need for continued surveillance of psychiatric AEs and guides future research to investigate underlying mechanisms, identify risk factors, and inform clinical management.

Rare diseases are predominantly genetic or inherited, and patients with these conditions frequently exhibit neurological symptoms. Diagnosing and treating many rare diseases is a complex challenge, and their low prevalence complicates the performance of research, which in turn hinders the advancement of therapeutic options. One strategy to address this issue is the creation of national or international registries for rare diseases, which can help researchers monitor and investigate their natural progression. In the Republic of Korea, we established a registry across 5 centers that focuses on 3 rare diseases, all of which are characterized by gait disturbances resulting from motor system dysfunction. The registry will collect clinical information and human bioresources from patients with amyotrophic lateral sclerosis, spinocerebellar ataxia, and hereditary spastic paraplegia. These resources will be stored at ICreaT and the National Biobank of Korea. Once the registry is complete, the data will be made publicly available for further research. Through this registry, our research team is dedicated to identifying genetic variants that are specific to Korean patients, uncovering biomarkers that show a strong correlation with clinical symptoms, and leveraging this information for early diagnosis and the development of treatments.

Background: The coronavirus disease 2019 (COVID-19) pandemic continues to pose significant challenges to the public health sector, including that of the United Arab Emirates (UAE). The objective of this study was to assess the efficiency and accuracy of various deep-learning models in forecasting COVID-19 cases within the UAE, thereby aiding the nation's public health authorities in informed decision-making.

Methods: This study utilized a comprehensive dataset encompassing confirmed COVID-19 cases, demographic statistics, and socioeconomic indicators. Several advanced deep learning models, including long short-term memory (LSTM), bidirectional LSTM, convolutional neural network (CNN), CNN-LSTM, multilayer perceptron, and recurrent neural network (RNN) models, were trained and evaluated. Bayesian optimization was also implemented to fine-tune these models.

Results: The evaluation framework revealed that each model exhibited different levels of predictive accuracy and precision. Specifically, the RNN model outperformed the other architectures even without optimization. Comprehensive predictive and perspective analytics were conducted to scrutinize the COVID-19 dataset.

Conclusion: This study transcends academic boundaries by offering critical insights that enable public health authorities in the UAE to deploy targeted data-driven interventions. The RNN model, which was identified as the most reliable and accurate for this specific context, can significantly influence public health decisions. Moreover, the broader implications of this research validate the capability of deep learning techniques in handling complex datasets, thus offering the transformative potential for predictive accuracy in the public health and healthcare sectors.