Journal of Epidemiology and Global Health最新文献

Purpose: Previous research shows conflicting views on the relationship between obesity and osteoporosis, partly due to variations in obesity classification and the nonlinear nature of these relationships. This study investigated the association between adiposity indices and osteoporosis, diagnosed using dual-energy X-ray absorptiometry (DXA), employing nonlinear models and offering optimal thresholds to prevent further bone mineral density decline.

Methods: In 2019, a prospective study enrolled males over 50 years and postmenopausal women. Anthropometric measurements, blood biochemistry, and osteoporosis measured by DXA were collected. Associations between adiposity indices and osteoporosis were analyzed using a generalized additive model and segmented regression model.

Results: The study included 872 women and 1321 men. Indices such as abdominal volume index (AVI), visceral adiposity index (VAI), waist circumference (WC), hip circumference, body mass index (BMI), waist-to-hip ratio, and waist-to-height ratio (WHtR) were inversely associated with osteoporosis. In women, the relationship between the risk of osteoporosis and the adiposity indices was U-shaped, with thresholds of WC = 94 cm, AVI = 17.67 cm², BMI = 25.74 kg/m², VAI = 4.29, and WHtR = 0.61, considering changes in bone mineral density. Conversely, men exhibited a linear patterns for the inverse association.

Conclusion: The impact of obesity and adiposity on osteoporosis varies significantly between women and men. In postmenopausal women, the relationship is nonlinear (U-shaped), with both very low and very high adiposity linked to higher osteoporosis risk. In men over 50, the relationship is linear, with higher adiposity associated with lower osteoporosis risk. The study suggests that maintaining specific levels of adiposity could help prevent osteoporosis in postmenopausal women.

Background: The second most common cause of death for children under five is diarrhea. Early Predicting diarrhea disease and identify its determinants (factors) using an advanced machine learning model is the most effective way to save the lives of children. Hence, this study aimed to predict diarrheal diseases, identify their determinants, and generate some rules using machine learning models.

Methods: The study used secondary data from the 12 east African countries for DHS dataset analysis using Python. Machine learning techniques such as Random Forest, Decision Tree (DT), K-Nearest Neighbor, Logistic Regression (LR), wrapper feature selection and SHAP values are used for identify determinants.

Result: The final experimentation results indicated the random forest model performed the best to predict diarrhea disease with an accuracy of 86.5%, precision of 89%, F-measure of 86%, AUC curve of 92%, and recall of 82%. Important predictors' identified age, countries, wealth status, mother's educational status, mother's age, source of drinking water, number of under-five children immunization status, media exposure, timing of breast feeding, mother's working status, types of toilet, and twin status were associated with a higher predicted probability of diarrhea disease.

Conclusion: According to this study, child caregivers are fully aware of sanitation and feeding their children, and moms are educated, which can reduce child mortality by diarrhea in children in east Africa. This leads to a recommendation for policy direction to reduce infant mortality in East Africa.

Introduction: Nonalcoholic fatty liver disease (NAFLD) is associated with increased risk of cardiovascular disease (CVD). We investigated the primary preventive effect of statins on CVD according to the level of fatty liver index (FLI), which is a marker of NAFLD.

Methods: We conducted a nested case-control study on the basis of a nationwide health screening cohort in Korea. The participants were divided into tertiles (T1, T2, and T3) according to their FLI score. Cases were defined as individuals who developed CVD (composite of myocardial infarction and stroke). Three controls were matched to each case and multivariable conditional logistic regression analysis was performed.

Results: Within a cohort of 206,263 participants without prior CVD, 7044 individuals suffered the primary outcome. For the nested case-control study, we selected these 7044 cases along with their corresponding 20,641 matched controls. Individuals in the T3 tertiles of FLI had a higher risk of CVD than those in the T1 tertile [adjusted odds ratio (OR) 1.30; 95% confidence interval (CI) 1.20-1.40, P < 0.001]. In sub-analyses based on FLI tertiles, statin therapy was associated with a lower risk of CVD (adjusted OR 0.72; 95% CI 0.61-0.85, P < 0.001) in the T3 tertile but not in the T1 and T2 tertiles.

Conclusions: Statin therapy was associated with a reduced risk of CVD in individuals with high FLI but not in those with low FLI. Further research is needed to determine the pathophysiologic mechanism between statin and NAFLD.

Whether in the field of medical care, or in people's daily life and health protection, the importance of masks has been paid more and more attention. Acne, the most common complication after wearing masks, which is also called maskne, has been successfully introduced into the common language as a common topic of dermatologist consultations. This study aims to study the changes of microflora in maskne patients and healthy controls before and after wearing masks. In the summer of 2023, we collected a total of 50 samples from 15 maskne patients and 10 healthy controls before and after wearing surgical masks for a long time. 16 S ribosomal DNA sequencing and identification technology with V3-V4 variable region were adopted to explore the microbiome changes caused by mask wearing, analyze the changes in microbial diversity, and make interaction network. LDA effect size analysis was used to identify which bacteria showed significant changes in their relative abundance from phylum to genus. After wearing a mask, the microbiome of the maskne patients changed significantly more than that of the healthy controls, with both α diversity and β diversity lower than those of maskne patients before wearing masks and those of healthy controls after wearing masks. Co-occurrence network analysis showed that compared with other groups, the network of maskne patients after wearing masks for a long time had the lowest connectivity and complexity, but the highest clustering property, while the opposite was true for healthy controls. Many microbes that are potentially beneficial to the skin decreased significantly after wearing a mask. There was almost no difference in healthy controls before and after wearing a mask.

Background: Enhanced adherence counseling refers to the counseling intervention for Human Immunodeficiency Virus (HIV) patients with an elevated viral load result, a viral load of > 1000 copies/ml, on a routine or need-based viral load test. The Federal Ministry of Health, Ethiopia, has launched routine viral load testing and enhanced adherence counseling since 2016 for high-viral load people living with HIV, which is applicable throughout the country for all health facilities providing HIV care and treatment. Our study aimed to assess viral load suppression after enhanced adherence counseling and its predictors among high viral load people living with HIV who were on antiretroviral therapy.

Method: We conducted a health facility-based retrospective follow-up study among 352 HIV-infected high-viral load people enrolled in enhanced adherence counseling from July 2018 to June 2021 in Nekemte town public health facilities. Cox proportional hazard analysis was used to identify independent predictors.

Results: The overall 65.1% of 352 persons on antiretroviral treatment achieved HIV viral load suppression after enhanced adherence counseling, (15.01 per 100 person months (95% CI13.02-16.99)). The median time to viral load suppression was 5 months. Age ≥ 15 years (AHR = 1.99, 95% CI: 1.11-3.57), no history of opportunistic infection (AHR = 2.01, 95% CI: 1.18-3.41), and not using substances (AHR = 2.48, 95% CI: 1.19-5.14) were more likely to have viral load suppressed, while having an initial viral load count greater than 50,000 RNA copies/ml (AHR = 0.56, 95% CI: 0.37-0.85) were less likely to have viral load suppressed after enhanced adherence counseling.

Conclusion: Age, history of opportunistic infections, substance use, and an initial viral load count > 50,000 RNA copies/mL were significant predictors of viral load suppression. Enrolling all high-viral-load patients in enhanced adherence counseling is recommended for viral load suppression.

Background: Botulism has not been previously reported in the Kingdom of Saudi Arabia. This rare and sometimes fatal foodborne illness is caused by neurotoxins and primarily results from consuming home-canned fruits, vegetables, dairy, and seafood products & it can lead to paralysis.

Objective: The purpose of this study was to evaluate the clinical features of patients who developed botulism in Riyadh in 2024 after consuming mayonnaise from a well-known local chain of restaurants in Riyadh, Saudi Arabia.

Methods: We conducted a retrospective analysis of medical records and interviewed patients or their attendants for all hospitalized cases of foodborne botulism at Riyadh First Health Cluster. For each patient, a standard case report form was completed, containing information on demographics, clinical aspects, botulinum test results, and type of exposure. Descriptive statistics were applied to assess the data. During the outbreak, nineteen patients with foodborne diseases were admitted to Riyadh First Health Cluster Hospitals. Following thorough physical examinations, botulism was suspected in each case.

Results: Eight of the 19 suspected foodborne illness patients fully satisfied the botulism case definition requirements set forth by the Saudi Arabian Public Health Authority (Weqaya). Among these eight patients, 2 (25%) were male and 6 (75%) were female, with a mean age of 23.25 ± 9.29 years (range: 12-38 years). The incubation period for our patients was 36.25 ± 26.26 h. Notable symptoms included dysphagia in all eight patients (100%), dysarthria, generalized weakness, nausea and vomiting in seven patients (88%), diplopia in four patients (50%), and stomach discomfort in three patients (38%). Of the eight cases, six required intubation, one mimicked brain death, and two were stable. The presence of Clostridium botulinum spores as the cause of the outbreak was confirmed by detecting botulinum spores in contaminated food.

Conclusion: Diplopia and dysarthria were the most common early sign of botulism. Early manifestations may include respiratory symptoms without any musculoskeletal symptoms. or nausea, vomiting and disorientation.

Background: Although many health problems, including sleep disorders, have been associated with video gaming, further studies are required to establish the validity of these connections. This study aimed to determine the prevalence of gaming addiction among medical students and its association with poor sleep quality, which may be reflected in academic performance.

Method: A cross-sectional survey was conducted between January and June 2023 among medical students at the institution under study. An online survey was conducted and was divided into three sections. The first section included the demographic data, the second section included the 7-item Gaming Addiction Scale (GAS), and the third section included the Pittsburgh Sleep Quality Index. Using the GAS, and based on the total score, gamers were classified as addicted, problematic, engaged, or normal. Hence, abnormal gamers include engaged, problematic, and addicted gamers.

Result: There were 356 participants with a mean age of 22.5 -/+ 1.8 years, and 75.3% were males. The data showed that 38.8% of the study population were abnormal gamers: 40 (11.2%) engaged gamers, 81 (22.8%) problematic gamers, and 17 (4.8%) addicted gamers. Furthermore, abnormal gaming was linked to poor sleep quality when comparing abnormal gamers with normal gamers (92% vs. 80.3%, p = 0.002). Further comparison between the types of abnormal gamers revealed that addicted gamers were found to rely on sleep medication to help them sleep at night and took longer time to fall asleep (p = 0.050 and p = 0.045, respectively).

Conclusion: Abnormal gamers are common among medical students and strongly associated with poor sleep quality compared to normal gamers.

Background: Asia's elderly Baby Boomer demographic (born between 1946 and 1964) faced a huge problem during the COVID-19 pandemic due to increased all-cause mortality. We aimed to provide a unique Taiwan situation regarding the impact of Baby Boomers on excess mortalities from all causes relative to non-Baby Boomers throughout distinct times of SARS-CoV-2 mutations during the COVID-19 pandemic.

Methods: We used the Poisson time series design with a Bayesian directed acyclic graphic approach to build the background mortality prior to the COVID-19 pandemic between 2015 and 2019. It was then used for predicting the expected all-cause deaths compared to the reported figures during the COVID-19 pandemic period based on Taiwan residents, an Omicron-naïve cohort.

Results: Baby Boomers experienced a 2% negative excess mortality in 2020 (Wuhan/D614G) and a 4% excess mortality in 2021 (Alpha/Delta) with a rising background mortality trend whereas non-Baby Boomers showed the corresponding figures of 4% negative excess and 1% excess with a stable trend. Baby Boomer and non-Baby Boomer excess mortality soared to 9% (95% CI: 7-10%) and 10% (95% CI: 9-11%), respectively, during the epidemic Omicron period from January to June 2022. Surprisingly, Baby Boomers aged 58-76 experienced the same 9% excess mortality as non-Baby Boomers aged 77 and beyond. Non-COVID-19 deaths were more prevalent among Baby Boomers than non-Baby Boomers (33% vs. 29%).

Conclusion: Baby Boomers were more likely to die from COVID-19 in early pandemic and had more non-COVID-19 deaths in late pandemic than older non-Baby Boomers demonstrated in Taiwan Omicron-naïve cohort. For this vulnerable population, adequate access to medical care and medical capacity require more consideration.

Since the first autochthonous transmission of West Nile Virus was detected in Germany (WNV) in 2018, it has become endemic in several parts of the country and is continuing to spread due to the attainment of a suitable environment for vector occurrence and pathogen transmission. Increasing temperature associated with a changing climate has been identified as a potential driver of mosquito-borne disease in temperate regions. This scenario justifies the need for the development of a spatially and temporarily explicit model that describes the dynamics of WNV transmission in Germany. In this study, we developed a process-based mechanistic epidemic model driven by environmental and epidemiological data. Functional traits of mosquitoes and birds of interest were used to parameterize our compartmental model appropriately. Air temperature, precipitation, and relative humidity were the key climatic forcings used to replicate the fundamental niche responsible for supporting mosquito population and infection transmission risks in the study area. An inverse calibration method was used to optimize our parameter selection. Our model was able to generate spatially and temporally explicit basic reproductive number (R₀) maps showing dynamics of the WNV occurrences across Germany, which was strongly associated with the deviation from daily means of climatic forcings, signaling the impact of a changing climate in vector-borne disease dynamics. Epidemiological data for human infections sourced from Robert Koch Institute and animal cases collected from the Animal Diseases Information System (TSIS) of the Friedrich-Loeffler-Institute were used to validate model-simulated transmission rates. From our results, it was evident that West Nile Virus is likely to spread towards the western parts of Germany with the rapid attainment of environmental suitability for vector mosquitoes and amplifying host birds, especially short-distance migratory birds. Locations with high risk of WNV outbreak (Baden-Württemberg, Bavaria, Berlin, Brandenburg, Hamburg, North Rhine-Westphalia, Rhineland-Palatinate, Saarland, Saxony-Anhalt and Saxony) were shown on R₀ maps. This study presents a path for developing an early warning system for vector-borne diseases driven by climate change.

Background: During the COVID-19 pandemic, the Chinese government implemented nationwide public health interventions to control its spread. However, the impact of these measures on other infectious diseases remains unclear.

Methods: The incidence of three types of notifiable infectious diseases in China were analyzed between 2013 and 2021. The seasonal Mann-Kendall test and Mann-Kendall mutation test were employed to examine trends and mutations in the time series. Based on the counterfactual inference, historical incidence rates were employed to construct SARIMA models and predict incidence between January 2020 and December 2021. Differences between reported and predicted incidences during the pandemic were compared using the Mann-Whitney U test.

Results: Between 2013 and 2019, the incidence rate of three types of notifiable infectious diseases fluctuated between 494.05/100,000 and 550.62/100,000. No discernible trend was observed for types A and B infectious diseases (Z = -1.344, P = 0.18). A significant upward trend was observed for type C infectious diseases (Z = 2.56, P = 0.01). In 2020, the overall incidence rate of three types of notifiable infectious diseases decreased to 367.08/100,000. Compared to predicted values, the reported incidence of three types of infectious diseases was, on average, 30.05% lower in 2020 and 16.58% lower in 2021.

Conclusion: The public health interventions implemented during the pandemic had a positive consequence on the prevention and control of other infectious diseases, with a particularly notable effect on type C infectious diseases. Among the diseases with different transmission routes, respiratory diseases and gastrointestinal or enteroviral diseases decreased significantly.