Schistosomiasis is a significant public health threat, and Oncomelania hupensis is the only intermediate host for schistosoma japonicum. We conducted 12-year monthly repeated surveys to explore the interactive and lag effects of environmental factors on snail density and to monitor their long-term and seasonal trends in a bottomland around the Dongting Lake region in China. Relevant environmental data were obtained from multiple sources. A Bayesian kernel machine regression model and a Bayesian temporal model combined with a distributed lag model were constructed to analyze interactive and lag effects of environmental factors on snail density. The results indicated the average annual snail density in the study site exhibited an increasing and then decreasing trend, peaking in 2013. Snail densities were the highest in October and the lowest in January in a year. Normalized Difference Vegetation Index (NDVI) and water level were the most effective predictors of snail density, with potential interactions among temperature, precipitation, and NDVI. The mean minimum temperature in January, water level, precipitation and NDVI were positively correlated with snail density at lags ranging from 1 to 4 months. These findings could serve as references for relevant authorities to monitor the changing trend of snail density and implement control measures, thereby reducing the occurrence of schistosomiasis.

Fine particulate matter (PM2.5) is a risk factor of cardiovascular disease. Associations between PM2.5 compositions and cardiovascular disease are a point of special interest but inconsistent. This study aimed to explore the cardiovascular effects of heavy metal(loid) compositions in PM2.5. Data for mortality, air pollutants and meteorological factors in Anyang, China from 2017 to 2021 were collected. Heavy metal(loid) in PM2.5 were monitored and examined monthly. A Case-crossover design was applied to the estimated data set. The interquartile range increase in cadmium (Cd), antimony (Sb) and arsenic (As) at lag 1 was associated with increment of 8.1% (95% CI: 3.3, 13.2), 4.8% (95% CI: 0.2, 9.5) and 3.5% (95% CI: 1.1, 6.0) cardiovascular mortality. Selenium in lag 2 was inversely associated with cerebrovascular mortality (RR = 0.920 95% CI: 0.862, 0.983). Current-day exposure of aluminum was positively associated with mortality from ischemic heart disease (RR = 1.083 95% CI: 1.001, 1.172). Stratified analysis indicated sex, age and season modified the cardiovascular effects of As (P < 0.05). Our study reveals that heavy metal(loid) play key roles in adverse effects of PM2.5. Cd, Sb and As were significant risk factors of cardiovascular mortality. These findings have potential implications for accurate air pollutants control and management to improve public health benefits.

UNASSIGNED: Temperature changes unfavorably impact on cardiovascular disease. However, the association between temperature changes and coronary artery disease (CAD) is not well documented. This study aimed to explore the association between daily mean temperature and daily CAD hospital admissions on the southeast coast of China (Fuzhou City).
UNASSIGNED: A total of 1883 CAD patients who underwent percutaneous coronary intervention between 2017 and 2019 were obtained. The severity of CAD was evaluated by the Gensini score. Distributed lag non-linear model (DLNM) combined with a quasi-Poisson regression model was used to examine the delayed effect between daily mean temperature and daily CAD hospital admissions. Stratified analyses were performed by Gensini score and severity of lesions. The relative risk (RR) with a 95% confidence interval (CI) was used to assess the relationship.
UNASSIGNED: Extreme cold (8°C) (RR=0.49, 95% CI: 0.25-0.99) and moderate cold (10°C) (RR=0.56, 95% CI: 0.31-0.99) daily mean temperature with a lag of 0-20 days were correlated with lower risk of daily CAD hospital admissions. Moderate heat (30°C) (RR=1.80, 95% CI: 1.01-3.20) and extreme heat (32°C) (RR=2.02, 95% CI: 1.01-4.04) daily mean temperature with a lag of 0-20 days related to a higher risk of daily CAD hospital admissions. Similar results were observed for daily mean temperature with a lag of 0-25 days. Stratified analysis showed the lagged effect of daily mean temperature (lag 0, 0-5, 0-15, 0-20, and 0-25 days) on the daily CAD hospital admissions was observed only in patients with a Gensini score ≤39 (tertile 1).
UNASSIGNED: Cold temperatures may have a protective effect on daily CAD hospital admissions in the Fuzhou area, whereas hot temperatures can have an adverse effect.

Ghost forests consisting of dead trees adjacent to marshes are striking indicators of climate change, and marsh migration into retreating coastal forests is a primary mechanism for marsh survival in the face of global sea-level rise. Models of coastal transgression typically assume inundation of a static topography and instantaneous conversion of forest to marsh with rising seas. In contrast, here we use four decades of satellite observations to show that many low-elevation forests along the US mid-Atlantic coast have survived despite undergoing relative sea-level rise rates (RSLRR) that are among the fastest on Earth. Lateral forest retreat rates were strongly mediated by topography and seawater salinity, but not directly explained by spatial variability in RSLRR, climate, or disturbance. The elevation of coastal tree lines shifted upslope at rates correlated with, but far less than, contemporary RSLRR. Together, these findings suggest a multi-decadal lag between RSLRR and land conversion that implies coastal ecosystem resistance. Predictions based on instantaneous conversion of uplands to wetlands may therefore overestimate future land conversion in ways that challenge the timing of greenhouse gas fluxes and marsh creation, but also imply that the full effects of historical sea-level rise have yet to be realized.

BACKGROUND: Some studies have found hot temperatures to be associated with exacerbations of schizophrenia, namely psychoses. As climate changes faster in Northern countries, our understanding of the association between temperature and hospital admissions (HA) for psychosis needs to be deepened.
OBJECTIVE: 1) Among adults diagnosed with schizophrenia, measure the relationship between mean temperatures and HAs for psychosis during summer. 2) Determine the influence of individual and ecological characteristics on this relationship.
METHODS: A cohort of adults diagnosed with schizophrenia (n = 30,649) was assembled using Quebec\'s Integrated Chronic Disease Surveillance System (QICDSS). The follow-up spanned summers from 2001 to 2019, using hospital data from the QICDSS and meteorological data from the National Aeronautics and Space Administration\'s (NASA) Daymet database. In four geographic regions of the province of Quebec, a conditional logistic regression was used for the case-crossover analysis of the relationship between mean temperatures (at lags up to 6 days) and HAs for psychosis using a distributed lag non-linear model (DLNM). The analyses were adjusted for relative humidity, stratified according to individual (age, sex, and comorbidities) and ecological (material and social deprivation index and exposure to green space) factors, and then pooled through a meta-regression.
RESULTS: The statistical analyses revealed a statistically significant increase in HAs three days (lag 3) after elevated mean temperatures corresponding to the 90th percentile relative to a minimum morbidity temperature (MMT) (OR 1.040; 95% CI 1.008-1.074), while the cumulative effect over six days was not statistically significant (OR 1.052; 95% IC 0.993-1.114). Stratified analyses revealed non statistically significant gradients of increasing HAs relative to increasing material deprivation and decreasing green space levels.
CONCLUSIONS: The statistical analyses conducted in this project showed the pattern of admissions for psychosis after hot days. This finding could be useful to better plan health services in a rapidly changing climate.

Dry eye disease (DED) is a common disorder of tear secretion on the ocular surface caused by multiple factors with dry eyes as the main symptom, but until now studies focusing on relationship between local meteorological factors and ocular surface diseases in Urumqi are very limited. Besides, the effects of long-term and extreme meteorological factors on DED and the lag effect have not been fully evaluated. Electronic case information of 9970 DED outpatients from the Ophthalmology Department of the First Affiliated Hospital of Xinjiang Medical University (Urumqi, Xinjiang, China) between January 1, 2013, and December 31, 2020, was screened and analyzed. We used a time-series analysis design and a quasi-Poisson generalized linear regression model combined with a distributed lagged nonlinear model (DLNM) to fit the effects of exposure to different meteorological factors and extreme weather on DED outpatient visits. Subgroup analyses were further performed for gender, age, and season. The results showed that exposure to extremely low mean temperature (P1:RR = 1.18), atmospheric pressure (P1:RR = 1.11), and extremely high relative humidity (P99:RR = 1.35) were the risk factors, while extremely high atmospheric pressure (P90:RR = 0.883) and extremely low humidity (P10:RR = 0.856) appeared to have a positive effect on reduced risk of DED. Relative humidity exhibited a 1-day lag effect (RR = 1.06). Increased mean temperature positively affected female DED patients (RR = 0.761) with similar effects in the cold season (RR = 0.926). However, elevated relative humidity had a negative effect on female patients (RR = 1.14). We conducted the first large sample size time-series analysis study in this major city at the farthest distance from the ocean in the world and in northwest China, confirming the association of DED outpatient visits with the remaining three meteorological factors except wind speed in Urumqi, and a larger sample size multi-center epidemiological study with a longer duration is still needed.

Fragile karst areas of Guangxi are the key areas of vegetation protection and restoration work of the government. It is of great significance to study the effects of meteorological disasters on vegetation change for disaster prevention and reduction and ecological protection in the karst areas. The Normalized Differential Vegetation Index (NDVI), as a tool for monitoring vegetation growth, has a strong responsiveness to vegetation and can qualitatively and quantitatively evaluate the vegetation cover and its growth vitality. Therefore, in this study, we analyzed the trend of vegetation change and the impacts of multiple meteorological hazards, such as drought, torrential rainfall, high temperature, and low temperature, on the vegetation of the karst region in Guangxi by using the linear trend method, GIS spatial analysis, and correlation analysis, using the MODIS NDVI and temperature and precipitation information from 2000 to 2020 in the karst region of Guangxi. The results show that: (1) NDVI increased significantly in the karst areas, and 32.2% of the areas had significant improvement in vegetation. The improvement was the most obvious mainly in the central part of the study area, while the vegetation degradation trend was obvious in partial scattered areas in northeast and southwest. (2) On the interannual scale, NDVI was negatively correlated with some meteorological disaster indexes, such as relative humidity index, the number of drought days, the amount of extremely heavy rainstorm, the amount of heavy rainstorm, the number of days with high temperature of ≥35 °C, the number of days with high temperature of 35-37 °C, the number of days with high temperature of ≥37 °C, the minimum temperature, and the effective accumulated temperature of ≤0 °C. The obvious negative effect area of drought on vegetation was mainly concentrated in the middle of the study area, and that of rainfall was mainly distributed in the southwest, northeast and northwest; that of high temperature was mainly distributed in the northwest and northeast, and that of low temperature was mainly concentrated in the southwest and north. (3) On the multi-year monthly scale, the responses of NDVI to drought, high temperature and low temperature disaster indexes had a lag effect, but had no lag effect on rainfall disaster indexes. The lag time of vegetation to drought was 1 month, and the lag time to high temperature and low temperature was 3 months.

COVID-19 has ravaged Brazil, and its spread showed spatial heterogeneity. Changes in the environment have been implicated as potential factors involved in COVID-19 transmission. However, considerable research efforts have not elucidated the risk of environmental factors on COVID-19 transmission from the perspective of infectious disease dynamics. The aim of this study is to model the influence of the environment on COVID-19 transmission and to analyze how the socio-ecological factors affecting the probability of virus transmission in 10 states dramatically shifted during the early stages of the epidemic in Brazil. First, this study used a Pearson correlation to analyze the interconnection between COVID-19 morbidity and socio-ecological factors and identified factors with significant correlations as the dominant factors affecting COVID-19 transmission. Then, the time-lag effect of dominant factors on the morbidity of COVID-19 was investigated by constructing a distributed lag nonlinear model and standard two-stage meta-analytic model, and the results were considered in the improved SEIR model. Lastly, a machine learning method was introduced to explore the nonlinear relationship between the environmental propagation probability and socio-ecological factors. By analyzing the impact of environmental factors on virus transmission, it can be found that population mobility directly caused by human activities had a greater impact on virus transmission than temperature and humidity. The heterogeneity of meteorological factors can be accounted for by the diverse climate patterns in Brazil. The improved SEIR model was adopted to explore the interconnection of COVID-19 transmission and the environment, which revealed a new strategy to probe the causal links between them.

In an arid or semi-arid environment, precipitation plays a vital role in vegetation growth. Recent researches reveal that the response of vegetation growth to precipitation has a lag effect. To explore the mechanism behind the lag phenomenon, we propose and investigate a water-vegetation model with spatiotemporal nonlocal effects. It is shown that the temporal kernel function does not affect Turing bifurcation. For better understanding the influences of lag effect and nonlocal competition on the vegetation pattern formation, we choose some special kernel functions and obtain some insightful results: (i) Time delay does not trigger the vegetation pattern formation, but can postpone the evolution of vegetation. In addition, in the absence of diffusion, time delay can induce the occurrence of stability switches, while in the presence of diffusion, spatially nonhomogeneous time-periodic solutions may emerge, but there are no stability switches; (ii) The spatial nonlocal interaction may trigger the pattern onset for small diffusion ratio of water and vegetation, and can change the number and size of isolated vegetation patches for large diffusion ratio. (iii) The interaction between time delay and spatial nonlocal competition may induce the emergence of traveling wave patterns, so that the vegetation remains periodic in space, but is oscillating in time. These results demonstrate that precipitation can significantly affect the growth and spatial distribution of vegetation.

Accumulating evidence suggested that the risk of preterm births (PTBs) following prenatal exposure to air pollution was inconclusive. The aim of this study is to investigate the relationship between air pollution exposure in the days before delivery and PTB and assess the threshold effect of short-term prenatal exposure to air pollution on PTB. This study collected data including meteorological factors, air pollutants, and information in Birth Certificate System from 9 districts during 2015-2020 in Chongqing, China. Generalized additive models (GAMs) with the distributed lag non-linear models were conducted to assess the acute impact of air pollutants on the daily counts of PTB, after controlling for potential confounding factors. We observed that PM2.5 was related to increased occurrence of PTB on lag 0-3 and lag 10-21 days, with the strongest on the first day (RR = 1.017, 95%CI: 1.000-1.034) and then decreasing. The thresholds of PM2.5 for lag 1-7 and 1-30 days were 100 μg/m3 and 50 μg/m3, respectively. The lag effect of PM10 on PTB was very similar to that of PM2.5. In addition, the lagged and cumulative exposure of SO2 and NO2 was also associated with the increased risk of PTB. The lag relative risk and cumulative relative risk of CO exposure were the strongest, with a maximum RR at lag 0 (RR = 1.044, 95%CI: 1.018, 1.069). Importantly, the exposure-response curve of CO showed that RR increased rapidly when the concentration exceeded 1000 μg/m3. This study indicated significant associations between air pollution and PTB. The relative risk decreases with day lag, while the cumulative effect increases. Thus, pregnant women should understand the risk of air pollution and try to avoid high concentration exposure.