Environmental Health and Preventive Medicine最新文献

Background: Hemorrhagic stroke (HS) is associated with significant disability and mortality. However, the relationship between meteorological factors and hemorrhagic stroke, as well as the potential moderating role of these factors, remains unclear.

Methods: Daily data on HS, air pollution, and meteorological conditions were collected from January 2015 to December 2021 in Ganzhou to analyze the relationship between meteorological factors and HS admissions. This analysis employed a time-stratified case-crossover design in conjunction with a distributional lag nonlinear model. Additionally, a bivariate response surface modelling was utilized to further investigate the interaction between meteorological factors and particulate matter. The study also stratified the analyses by gender and age. To investigate the potential impact of extreme weather conditions on HS, this study defined the 97.5th percentile as representing extremely high weather conditions, while the 2.5th percentile was classified as extremely low.

Results: In single-day lags, the risk of admissions for HS was significantly associated with extremely low temperature (lag 1-2 and lag 13-14), extremely low humidity (lag 1 and lag 9-12), and extremely high precipitation (lag 2-7). Females exhibited greater susceptibility to extremely low temperature than males within the single-day lag pattern in the subcomponent layer, with a maximum relative risk (RR) that was 7% higher. In the cumulative lag analysis, the risk of HS admissions was significantly associated with extremely high temperature (lag 0-8∼lag 0-14), extremely low humidity (lag 0-2∼lag 0-14), and extremely high precipitation (lag 0-4∼lag 0-14). Within the cumulative lag day structure of the subcomponent layer, both extremely low and extremely high temperature had a more pronounced effect on females and aged ≥65 years. The risk of HS admissions was positively associated with extremely high barometric pressure in the female subgroups (lag 0-1 and lag 0-2). The highest number of HS admissions occurred when high PM_2.5 concentrations coexisted with low precipitation.

Conclusions: Meteorological factors were significantly associated with the risk of hospital admissions for HS. Individuals who were female and aged ≥65 years were found to be more susceptible to these meteorological influences. Additionally, an interaction was observed between airborne particulate matter and meteorological factors. These findings contributed new evidence to the association between meteorological factors and HS.

Background: To explore the associations of green tea, coffee, black tea, and oolong tea consumption with mortality from chronic kidney disease (CKD) as the underlying cause among Japanese adults.

Methods: We conducted a prospective cohort study of 110,585 men and women aged 40-79 years at recruitment from 1986 to 1990. Baseline information on the consumption of tea and coffee, lifestyles, and medical histories was obtained via self-administered questionnaires. We used multivariable Cox regression models to estimate sex-specific hazard ratios and 95% CIs of mortality from CKD associated with the consumption of green tea, coffee, black tea, or oolong tea.

Results: After a median 19-year follow-up, the hazard ratios of mortality from CKD in women were 0.49 (95% CI, 0.22-1.06) for 1-2 cups of green tea per day, 0.56 (0.31-0.99) for 3-4 cups per day, and 0.55 (0.32-0.93) for ≥5 cups per day, compared with <1 cup per day. No such association was found in men. Coffee, black tea, and oolong tea consumption were not associated with CKD risk in either sex.

Conclusions: Daily consumption of green tea was associated with a lower risk of mortality from CKD in women.

Background: Cancer is a major public health concern, particularly among middle-aged and elderly populations, who are disproportionately affected by rising cancer incidence. Environmental pollution has been identified as a significant risk factor for cancer development. China's Carbon Emission Trading Policy (CETP), implemented in pilot regions since 2013, aims to reduce carbon emissions and improve air quality. This study evaluates the impact of CETP on pan-cancer incidence, with a focus on its effects on specific cancer types and vulnerable populations.

Methods: This quasi-natural experiment utilized data from the China Health and Retirement Longitudinal Study (CHARLS) and environmental data from the China National Environmental Monitoring Center (2011-2018). A staggered difference-in-differences (DID) model was employed to estimate the impact of CETP on cancer incidence. Robustness tests, including parallel trend tests, placebo analysis, and entropy balancing, validated the findings. Subgroup analyses were performed to assess the policy's heterogeneous effects based on gender, Body Mass Index (BMI), and smoking status.

Results: CETP implementation significantly reduced the incidence of six cancer types: endometrial, cervical, gastric, esophageal, breast, and lung cancers. Overall, pan-cancer incidence significantly declined post-policy implementation (CETP × POST: -47.200, 95% CI: [-61.103, -33.296], p < 0.001). The policy demonstrated stronger effects in highly polluted areas and among individuals with poorer mental health. Subgroup analysis revealed that females, individuals with lower BMI, and non-smokers experienced more substantial benefits.

Conclusions: CETP significantly reduces cancer incidence by improving environmental quality and influencing mental health, with particularly strong effects observed among high-risk populations. This study highlights the important role of environmental economic policies in mitigating cancer burden and promoting public health. Future research should further explore the long-term impacts of this policy and its applicability across different national and regional contexts.

Background: Neglected tropical diseases (NTDs) and malaria pose a major health challenge, especially in low- and middle-income countries.

Methods: Initially, we performed a descriptive analysis of the Global Burden of Disease (GBD) 2021 database, categorizing data by subtypes. Next, linear regression models were employed to analyze temporal trends. We then utilized four predictive models to forecast the future burden. Additionally, we explored the relationship between estimated annual percentage change (EAPCs) and age-standardized rates (ASRs), as well as Human Development Index (HDI) scores for 2021. Furthermore, decomposition analysis was applied to assess the influence of aging, population dynamics, and epidemiological changes. Lastly, frontier analysis was conducted to examine the connection between disease burden and sociodemographic development.

Results: In 2021, NTDs and malaria contributed significantly to the global disease burden, with considerable disparities across genders, age groups, Socio-demographic Index (SDI) regions, GBD regions, and individual countries. From 1990 to 2021, both the number of cases and the associated ASRs have shown a recent downward trend. The EAPCs are positively correlated with ASRs and HDI scores. Projections indicate a continued decline in disease burden through 2046. Additionally, our decomposition analysis highlighted the positive impact of aging and epidemiological shifts on the reduction of the disease burden. Finally, frontier analysis revealed that countries and regions with higher SDI scores have greater potential for further reducing their health burden.

Conclusion: While the global burden of NTDs and malaria has improved overall, significant disparities remain across regions and countries. Our findings highlight the importance of implementing targeted intervention strategies and maintaining sustained investments to tackle the ongoing challenges.

Background: Older adults in Japan have the highest drowning mortality rate globally due to frequent bathing practices. Low outdoor temperatures have been linked to bath-related deaths; however, previous studies employed limited statistical models and focused on a single prefecture. Given Japan's aging population, preventing bath-related deaths is a public health priority. This study aimed to analyze the association between outdoor temperature and bath-related drowning deaths across Japan from 1995 to 2020 (n = 110,938), examining regional variations and identifying contributing prefectural characteristics.

Methods: Daily counts of bath-related drowning deaths per prefecture were matched with daily mean temperature data from the Japan Meteorological Agency. Prefecture-level demographic and environmental data were obtained from Japan's Official Statistics. We applied a generalized additive mixed model to examine the association between daily mean temperature and bath-related drowning death risk. Meta-regression was used to identify prefecture-level modifiers.

Results: Bath-related drowning death risk peaked at a daily mean temperature of 1.8 °C (relative risk [RR] 9.7, 95% confidence interval [CI]: 9.5-9.9), compared with the lowest risk at 30.3 °C. The association was stronger at mid-range temperatures, particularly among males and individuals aged ≥65 years. Among prefectures, Kagoshima-the southernmost prefecture on Japan's main islands-had the highest maximum RR at 19.6 (95% CI: 16.2-23.6), while Hokkaido-the northernmost prefecture-had the lowest at 3.8 (95% CI: 3.4-4.3). Prefecture-level factors that strengthened this relationship included a lower prevalence of double-pane windows as a proxy of housing insulation and higher annual mean temperatures with ratio of RR change per one standard deviation increase of 0.76 (95% CI: 0.69-0.83) and 1.27 (95% CI: 1.18-1.37), respectively.

Conclusions: Warmer prefectures in southern regions exhibited greater maximum-to-minimum risk ratios compared to cooler northern prefectures. This paradoxical finding underscores the importance of region-specific interventions to reduce bath-related deaths.

Background: Our previous observational cohort study, the Kashima Scan Study (KSS), identified associations between lifestyle, cerebral small vessel disease (SVD) as detected by magnetic resonance imaging of the brain, and disease outcomes including cognitive impairment and vascular diseases. However, established modifiers of the outcomes such as genetic background, drinking and exercise habits, and socioeconomic status were not considered. Regarding genetic factors in particular, the ALDH2 rs671 variant, East Asian-specific diversity, and APOE status are expected to have strong effects. The aim of KSS-2 is to examine the interactions of genetic background, lifestyle factors including drinking habit, socioeconomic status, and/or SVD markers for cognitive impairment, vascular disease, and death.

Method: The KSS-2 is a prospective regional observational study of a healthy Japanese cohort that will clarify lifestyle habits to better maintain brain health from midlife by genotype. Japanese adults who underwent brain health checkups at their own expense are enrolled and will be followed-up for 10 years. We will extend the protocol of the KSS to include genetic background and potential confounding factors, including lifestyle (including drinking and exercise habit) and socioeconomic status, and perform survival analyses. The study outcomes are cognitive impairment, vascular events, and death.

Results: We enrolled 908 healthy adults (mean age 64.2 years; range 35 to 84 years; 41% male) from September 1, 2018 until December 31, 2024.

Conclusion: This study will provide important insights into the development of individualized health intervention strategies.

Background: Methylmercury (MeHg) causes damage specifically in cerebrocortical neurons, but not in hippocampal neurons. In our previous studies using cultured neurons, we found that brain-derived neurotrophic factor (BDNF), which is prominently present in hippocampal neurons, plays a key role in resistance to MeHg neurotoxicity. Our findings, combined with recent findings that moderate exercise increases BDNF in the brain, led us to hypothesize that moderate exercise protects against MeHg-induced neurotoxicity by inducing BDNF expression.

Methods: C57 black 6NJcl (C57BL/6NJcl) male mice were used to evaluate the effects of treadmill exercise (a moderate exercise) on the neurotoxicity of MeHg exposure at 1.5 mg/kg/day. The effects of treadmill exercise on MeHg neurotoxicity were evaluated through neurobehavioral, neuropathological, and biochemical analyses using brain tissue, blood, and muscle tissue.

Results: Treadmill exercise had a significant inhibitory effect on the neurological symptoms associated with apoptotic neuronal death and subsequent cerebrocortical neuron loss induced by MeHg exposure. In the cerebral cortex, treadmill exercise significantly increased BDNF levels and activated the neuroprotective-related BDNF-tropomyosin receptor kinase (Trk) B and p44/42 mitogen-activated protein kinase (MAPK) pathways along with significantly suppressing the neuronal cell death-associated p38 MAPK pathway. Furthermore, treadmill exercise significantly increased fibronectin type III domain containing 5 (FNDC5) expression in the muscle tissue and elevated ed the concentration of its metabolite, irisin, in the blood.

Conclusions: These results suggest that treadmill exercise increases BDNF in the brain and suppresses neurotoxic pathways, ultimately protecting against MeHg neurotoxicity. Moreover, the increase of BDNF in the brain may be attributed to the exercise-induced increased expression of FNDC5 in muscle tissue from where it is released into the blood as irisin and finally transferred into the brain and promoted BDNF production.

Background: Hypertension is a serious chronic disease that can significantly lead to various cardiovascular diseases, affecting vital organs such as the heart, brain, and kidneys. Our goal is to predict the risk of new onset hypertension using machine learning algorithms and identify the characteristics of patients with new onset hypertension.

Methods: We analyzed data from the 2011 China Health and Nutrition Survey cohort of individuals who were not hypertensive at baseline and had follow-up results available for prediction by 2015. We tested and evaluated the performance of four traditional machine learning algorithms commonly used in epidemiological studies: Logistic Regression, Support Vector Machine, XGBoost, LightGBM, and two deep learning algorithms: TabNet and AMFormer model. We modeled using 16 and 29 features, respectively. SHAP values were applied to select key features associated with new onset hypertension.

Results: A total of 4,982 participants were included in the analysis, of whom 1,017 developed hypertension during the 4-year follow-up. Among the 16-feature models, Logistic Regression had the highest AUC of 0.784(0.775∼0.806). In the 29-feature prediction models, AMFormer performed the best with an AUC of 0.802(0.795∼0.820), and also scored the highest in MCC (0.417, 95%CI: 0.400∼0.434) and F1 (0.503, 95%CI: 0.484∼0.505) metrics, demonstrating superior overall performance compared to the other models. Additionally, key features selected based on the AMFormer, such as age, province, waist circumference, urban or rural location, education level, employment status, weight, WHR, and BMI, played significant roles.

Conclusion: We used the AMFormer model for the first time in predicting new onset hypertension and achieved the best results among the six algorithms tested. Key features associated with new onset hypertension can be determined through this algorithm. The practice of machine learning algorithms can further enhance the predictive efficacy of diseases and identify risk factors for diseases.

Background: Evidence on the combined effects of air pollutants and greenspace exposure on pulmonary tuberculosis (PTB) treatment is limited, particularly in developing countries with high levels of air pollution.

Objective: We aimed to examine the individual and combined effects of long-term exposure to air pollutants on PTB treatment outcomes while also investigating the potential modifying effect of greenspace.

Methods: This population-based study included 82,784 PTB cases notified in Zhejiang Province, China, from 2015 to 2019. The 24-month average concentrations of particulate matter with an aerodynamic diameter ≤2.5 µm (PM_2.5), ozone (O₃), nitrogen dioxide (NO₂), and sulfur dioxide (SO₂) before PTB diagnosis were estimated using a dataset derived from satellite-based machine learning models and monitoring stations. Greenspace exposure was assessed using the annual China Land Cover Dataset. We conducted analyses using time-varying Cox proportional hazards models and cumulative risk indices.

Results: In individual effect models, each 10 µg/m³ increase in PM_2.5, NO₂, O₃, and SO₂ concentrations was associated with hazard ratios for PTB treatment success of 0.95 (95% confidence interval (CI): 0.93-0.97), 0.92 (95% CI: 0.91-0.94), 0.98 (95% CI: 0.97-0.99), and 1.52 (95% CI: 1.49-1.56), respectively. In combined effect models, long-term exposure to the combination of air pollutants was negatively associated with PTB treatment success, with a joint hazard ratio (JHR) of 0.79 (95% CI: 0.63-0.96). Among the pollutants examined, O₃ contributed the most to the increased risks, followed by PM_2.5 and NO₂. Additionally, areas with moderate levels of greenspace showed a reduced risk (JHR = 0.81, 95% CI: 0.62-0.98) compared with the estimate from the third quantile model (JHR = 0.68, 95% CI: 0.52-0.83).

Conclusions: Combined air pollutants significantly impede successful PTB treatment outcomes, with O₃ and PM_2.5 accounting for nearly 75% of this detrimental effect. Moderate levels of greenspace can mitigate the adverse effects associated with combined air pollutants, leading to improved treatment success for patients with PTB.

Background: The mechanisms distinguishing metabolically healthy from unhealthy phenotypes within the same BMI categories remain unclear. This study aimed to investigate the associations between regional fat distribution and metabolically unhealthy phenotypes in Chinese adults across different BMI categories.

Methods: This cross-sectional study involving 11833 Chinese adults aged 20 years and older. Covariance analysis, adjusted for age, compared the percentage of regional fat (trunk, leg, or arm fat divided by whole-body fat) between metabolically healthy and unhealthy participants. Trends in regional fat percentage with the number of metabolic abnormalities were assessed by the Jonckheere-Terpstra test. Odds ratios (ORs) and their 95% confidence intervals (CIs) were estimated by logistic regression models. All analyses were performed separately by sex.

Results: In non-obese individuals, metabolically unhealthy participants exhibited higher percent trunk fat and lower percent leg fat compared to healthy participants. Additionally, percent trunk fat increased and percent leg fat decreased with the number of metabolic abnormalities. After adjustment for demographic and lifestyle factors, as well as BMI, higher percent trunk fat was associated with increased odds of being metabolically unhealthy [highest vs. lowest quartile: ORs (95%CI) of 1.64 (1.35, 2.00) for men and 2.00 (1.63, 2.46) for women]. Conversely, compared with the lowest quartile, the ORs (95%CI) of metabolically unhealthy phenotype in the highest quartile for percent arm and leg fat were 0.64 (0.53, 0.78) and 0.60 (0.49, 0.74) for men, and 0.72 (0.56, 0.93) and 0.46 (0.36, 0.59) for women, respectively. Significant interactions between BMI and percentage of trunk and leg fat were observed in both sexes, with stronger associations found in individuals with normal weight and overweight.

Conclusions: Trunk fat is associated with a higher risk of metabolically unhealthy phenotype, while leg and arm fat are protective factors. Regional fat distribution assessments are crucial for identifying metabolically unhealthy phenotypes, particularly in non-obese individuals.