Search

Background Real-time surveillance of search behavior on the internet has achieved accessibility in measuring disease activity. In this study, we systematically assessed the associations between internet search trends of gastrointestinal (GI) symptom terms and daily newly confirmed COVID-19 cases at both global and regional levels. Methods Relative search volumes (RSVs) of GI symptom terms were derived from internet search engines. Time-series analyses with autoregressive integrated moving average models were conducted to fit and forecast the RSV trends of each GI symptom term before and after the COVID-19 outbreak. Generalized additive models were used to quantify the effects of RSVs of GI symptom terms on the incidence of COVID-19. In addition, dose-response analyses were applied to estimate the shape of the associations. Results The RSVs of GI symptom terms could be characterized by seasonal variation and a high correlation with symptoms of “fever” and “cough” at both global and regional levels; in particular, “diarrhea” and “loss of taste” were abnormally increased during the outbreak period of COVID-19, with elevated point changes of 1.31 and 8 times, respectively. In addition, these symptom terms could effectively predict a COVID-19 outbreak in advance, underlying the lag correlation at 12 and 5 days, respectively, and with mutual independence. The dose-response curves showed a consistent increase in daily COVID-19 risk with increasing search volumes of “diarrhea” and “loss of taste”. Conclusion This is the first and largest epidemiologic study that comprehensively revealed the advanced prediction of COVID-19 outbreaks at both global and regional levels via GI symptom indicators.

Background: Active commuting is inversely related with cardiovascular disease (CVD) risk factors yet associations with CVD prevalence in the US population are unknown. Methods: Aggregate data from national surveys conducted in 2017 provided state-level percentages of adults who have/had coronary heart disease, myocardial infarction, and stroke, and who actively commuted to work. Associations between active commuting and CVD prevalence rates were assessed using Pearson correlations and generalized additive models controlling for covariates. Results: Significant correlations were observed between active commuting and all CVD rates (r range = −.31 to −.47; P Conclusion: Significant negative correlations were observed between active commuting and prevalence rates of coronary heart disease, myocardial infarction, and stroke. Controlling for covariates influenced these associations and highlights the need for future research to explore the potential of active commuting modes to reduce CVD in the United States.

Objectives. To determine whether there have been shifts in nonmedical ketamine use, poisonings (“exposures”), and seizures. Methods. We used generalized additive models to detect trends in past-year use (2006–2019), exposures (1991–2019), and seizures (2000–2019) involving ketamine in the United States. Results. There was a quarterly increase in self-reported past-year nonmedical ketamine use in 2006 to 2014 (Β = 0.21; P = .030) and an increase in 2015 to 2019 (Β = 0.29; P = .036), reaching a peak of 0.9% in late 2019. The rate of exposures increased from 1991 through 2019 (Β = 0.87; P = .006), and there was an increase to 1.1 exposures per 1 000 000 population in 2014, with rates remaining stable through 2019. The rate of ketamine seizures increased from 2000 through 2019 (Β = 2.27; P Conclusions. Indicators suggest that ketamine use and availability has increased, including before increased medical indications, but nonmedical use is still currently uncommon despite increased acceptance and media coverage. (Am J Public Health. 2021;111(11):2046–2049. https://doi.org/10.2105/AJPH.2021.306486 )

In estuaries, fluctuating environmental conditions exact strenuous physiological demands on the fishes that inhabit these oft-impacted areas, including the Critically Endangered delta smeltHypomesus transpacificusin California (USA). Using an archive of otoliths spanning 2011-2019, we examined how growth rates of wild subadult delta smelt vary ontogenetically, regionally, and in relation to variation in the physical environment during late-summer and fall in the upper San Francisco Estuary. Recent growth rates were quantified using otolith increment analysis and modeled as functions of both intrinsic (age) and extrinsic (temperature, salinity, clarity, and region) factors using a suite of generalized additive models. Age explained 60% of the variation in log10-transformed growth rates, which peaked at 50-80 d post hatch. Overall, age-adjusted growth rates declined at temperatures >20°C, increased with practical salinity values of 0-4, and exhibited interactive patterns with water clarity. Growth rates appeared highest in the West and Central Delta, and lowest in the North Delta, also corresponding with patterns in environmental conditions. Here, we provide new evidence for how vital rates of wild delta smelt vary spatially and in relation to abiotic environmental variation. Such otolith-based growth reconstructions often provide the first direct look at how the vital rates of wild fish respond to environmental variationin situ, and how future changes are likely to affect the dynamics of wild populations.

This study aimed to explore the short-term health effects of ambient air pollutants PM2.5, PM10, SO2, NO2, O3, and CO on COVID-19 daily new cases and COVID-19 daily new deaths. A time-series design used in this study. Data were obtained from 1 April 2020 to 31 December 2020 in the National Capital Territory (NCT) of Delhi, India. The generalized additive models (GAMs) were applied to explore the associations of six air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. The GAMs revealed statistically significant associations of ambient air pollutants with COVID-19 daily new cases and COVID-19 daily new deaths. These findings suggest that governments need to give greater considerations to regions with higher concentrations of PM2.5, PM10, SO2, NO2, O3, and CO, since these areas may experience a more serious COVID-19 pandemic or, in general, any respiratory disease.

Air pollution and its negative effects on health of people have been a global concern. Many studies had found a strong association between air pollutants and risk of death, but few had focused on the effects of six pollutants and rural areas. Our study aimed to investigate the effects of six air pollutants (CO, NO2, O3, PM2.5, PM10, and SO2) on non-accidental and respiratory deaths in rural areas of Anhui Province by adjusting for confounding factors, and to further clarify which populations were susceptible to death associated with air pollution. In the first phase of the analysis, the generalized additive models were combined with the distributed lag non-linear models to evaluate the individual effects of air pollution on death in each area. In the second stage, random-effects models were used to aggregate the associations between air pollutants and mortality risk in nine areas. Overall, six pollutants had the strongest effects on the risk of death on the lag 07 days. The associations between PM2.5 and NO2 and daily non-accidental deaths were strongest, with maximum RR (lag 07): 1.63 (1.37-1.88) and 1.67 (1.37-1.96). The maximum pooled effects of association between six air pollutants and RD were PM2.5, with RR (lag 07): 1.89 (1.45-2.34). PM2.5 and PM10 had significant differences between the elderly and the non-elderly with respectively, RRR: 1.22 (1.04-1.41) and 1.26 (1.11-1.42). In general, we found that six air pollutants were the important risk factors for deaths (deaths from respiratory disease and non-accidental) in rural areas of Anhui Province. PM10 and PM2.5 had a considerable impact on the elderly.

Particulate matters with an aerodynamic diameter ≤1 μm (PM1) significantly increased mortality risk, and the effect of PM1 was even greater than that of PM2.5 (aerodynamic diameter ≤2.5 μm). But the quantitative impact of PM1 on life expectancy was unknown. We aim to examine the extent to which that people’s life expectancy was shortened by PM1 and PM2.5. We obtained daily data on deaths, PM1 and PM2.5 records, and weather variables during 2016–2017 in Nanjing, China. Years of life lost (YLLs) were calculated by matching each decedent’s age and sex to the Chinese life table. The fitted nonlinear dose-response associations of YLLs with PM1 and PM2.5 were estimated by utilizing a generalized additive model with a Gaussian link that controlled for confounding factors including meteorological variables, day of week, and long-term trend and seasonality. The effect estimates were presented as the YLLs when PM1 and PM2.5 concentrations fell in different ranges. Life expectancy losses attributable to PM1 and PM2.5 were calculated. Stratified analyses were also performed by age, sex, and death causes. Significant PM-YLL associations were observed, with greater increases in YLLs associated with PM1 (68.9 thousand). PM1 was estimated to reduce life expectancy, which was greater than PM2.5 (PM1: 1.67 years; PM2.5: 1.55 years). For PM1, greater years of loss in PM-related life expectancy were found in the female group, ≥65 years group, and cardiovascular disease group. Exposure to PM1 had a greater impact on life expectancy loss than did PM2.5. Constant efforts are urgently needed to control PM1 air pollution to improve people’s longevity.

There is a rising concern that air pollution plays an important role in the COVID-19 pandemic. However, the results were not consistent on the association between air pollution and the spread of COVID-19. In the study, air pollution data and the confirmed cases of COVID-19 were both gathered from five severe cities across three countries in South America. Daily real-time population regeneration (Rt) was calculated to assess the spread of COVID-19. Two frequently used models, generalized additive models (GAM) and multiple linear regression, were both used to explore the impact of environmental pollutants on the epidemic. Wide ranges of all six air pollutants were detected across the five cities. Spearman’s correlation analysis confirmed the positive correlation within six pollutants. Rt value showed a gradual decline in all the five cities. Further analysis showed that the association between air pollution and COVID-19 varied across five cities. According to our research results, even for the same region, varied models gave inconsistent results. For example, in Sao Paulo, both models show SO2 and O3 are significant independent variables, however, the GAM model shows that PM10 has a nonlinear negative correlation with Rt, while PM10 has no significant correlation in the multiple linear model. Moreover, in the case of multiple regions, currently used models should be selected according to local conditions. Our results indicate that there is a significant relationship between air pollution and COVID-19 infection, which will help states, health practitioners, and policy makers in combating the COVID-19 pandemic in South America. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-15508-8.

Descriptions of regional climate expression require data from multiple lakes, yet little is known of how variation in records within morphometrically complex lakes may affect interpretations. In northeast Ontario (Canada), this issue was addressed using records of pollen, pigments, and diatoms in three sediment cores from two small boreal lakes spanning the last ~6000 years. Pollen analysis suggested warm conditions between ~6000 and ~4000 cal yr BP, coherent with previous assessments from boreal eastern Ontario and western Quebec. Analysis of phototrophic communities from fossil pigments and diatom valves suggested relatively eutrophic conditions with lower lake-levels during this interval. Generalized additive model trends identified significant regional changes in pollen assemblages and declines in pigment concentrations after ~4000 cal yr BP consistent with cooler and wetter climate conditions that resulted in regional lake oligotrophication and increased lake levels during the late-Holocene. Despite contemporaneous changes in pollen and pigment biomarkers across lakes, cores collected from adjacent basins of the same lake (Green Lake) did not show similar trends in fossil pigments likely reflecting preferential deposition of clay-rich allochthonous material in the deeper central basin and suggesting that regional signals in climate may be complicated by lake- or basin-specific catchment processes.

Some studies have demonstrated that precipitation is an important risk factor of dengue epidemics. However, current studies mostly focused on a single precipitation variable, and few studies focused on the impact of precipitation patterns on dengue epidemics. This study aims to explore optimal precipitation patterns for dengue epidemics. Weekly dengue case counts and meteorological data from 2006 to 2018 in Guangzhou of China were collected. A generalized additive model with Poisson distribution was used to investigate the association between precipitation patterns and dengue. Precipitation patterns were defined as the combinations of three weekly precipitation variables: accumulative precipitation (Pre_A), the number of days with light or moderate precipitation (Pre_LMD), and the coefficient of precipitation variation (Pre_CV). We explored to identify optimal precipitation patterns for dengue epidemics. With a lead time of 10 weeks, minimum temperature, relative humidity, Pre_A, and Pre_LMD were positively associated with dengue, while Pre_CV was negatively associated with dengue. A precipitation pattern with Pre_A of 20.67–55.50 mm per week, Pre_LMD of 3–4 days per week, and Pre_CV less than 1.41 per week might be an optimal precipitation pattern for dengue epidemics in Guangzhou. The finding may be used for climate-smart early warning and decision-making of dengue prevention and control.

PURPOSE This study aimed to present age- and sex-specific percentiles for daily wrist-worn movement metrics in US youth and adults. This metric represents a summary of all recorded movement, regardless of the purpose, context, or intensity. METHODS Wrist-worn accelerometer data from the combined 2011-2014 National Health and Nutrition Examination Survey cycles and the 2012 National Health and Nutrition Examination Survey National Youth Fitness Survey were used for this analysis. Monitor-Independent Movement Summary units (MIMS-units) from raw triaxial accelerometer data were used. We removed the partial first and last assessment days and days with ≥5% nonwear time. Participants with ≥1 valid day were included. Mean MIMS-units were calculated across all valid days. Percentile tables and smoothed curves of daily MIMS-units were calculated for each age and sex using the Generalized Additive Models for Location Shape and Scale. RESULTS The analytical sample included 14,705 participants age ≥3 yr. The MIMS-unit activity among youth was similar for both sexes, whereas adult females generally had higher MIMS-unit activity than did males. Median daily MIMS-units peaked at age 6 yr for both sexes (males, 20,613; females, 20,706). Lowest activity was observed for males and females 80+ yr of age: 8799 and 9503, respectively. CONCLUSIONS Population referenced MIMS-unit percentiles for US youth and adults are a novel means of characterizing total activity volume. By using MIMS-units, we provide a standardized reference that can be applied across various wrist-worn accelerometer devices. Further work is needed to link these metrics to activity intensity categories and health outcomes.

HIV / AIDS is a contagious disease that can attack all age groups of the population and is a health challenge in almost all over the world including Indonesia. Therefore, it is necessary to model HIV / AIDS cases for the factors that are suspected to influence them. One suitable method for estimating factors that influence HIV / AIDS is the Generalized Additive Model for Location, Scale, and Shape (GAMLSS). The GAMLSS method is flexible because it includes expansion of a good exponential family distribution to handle overdispersion data, continuous data, and discrete data. This research will apply GAMLSS semiparametric modeling with LOESS smoothing to find out the characteristics and models of HIV / AIDS cases in East Java in 2017. Based on the analysis, it was found that the variables that significantly affected were the number of homeless people, number of victims of drug abuse, population poor, and the number of fertile age couples using condom contraception with AIC value of 437,404, degree = 1 and span = 0.3, and the distribution used is Negative Binomial I.

Natural hazards like floods and droughts affect many aspects of life. The study in particular examined the impacts of droughts on under-five mortality rate in Southern Africa, adjusting for gross domestic product (GDP) and literacy rate. Despite drought and child mortality being key public health concerns in Southern Africa over the past few decades, there have hardly been any studies examining the relationships between them. The study used publicly available data from 1980 to 2012. The Standard Precipitation Index (SPI) was calculated for 3-, 6-, 9-, and 12-monthly time scales for ten southern African countries. The wetter and drier states are represented by positive and negative SPI values, respectively. SPI, GDP, and literacy rate were considered for predicting child mortality rate using both Multiple Linear Regression techniques and nonlinear methods (Generalized Additive Model), on a leave-one-year-out cross validation approach for model evaluation. Child mortality increased as the drought worsened for five countries in this region, namely Angola, Malawi, Mozambique, Namibia, and Zambia. We found that child mortality can be predicted with a high degree of accuracy using three predictor variables—drought index, GDP and literacy rate. Statistical modelling based on early warning system can complement regional capacities for drought response systems to increase child survival rate in drought-prone areas

The survival of COVID-19 in different environments may be affected by a variety of weather, pollution, and seasonal parameters. Therefore, the present study aims to conduct an ecological investigation on COVID-19 average growth rate of daily cases and deaths influenced by environmental factors (temperature, humidity, and air pollution) using a sample size of adjusted cumulative incidence of daily cases and deaths based on five 60-day periods. Research data was gathered on official websites, including information on COVID-19, meteorological data, and air pollution indicators from December 31, 2019, to October 12, 2020, from 210 countries. Spearman correlation and generalized additive model (GAM) were used to analyze the data. During the observed period, the COVID-19 average growth rate of daily cases (r = −0.08, P =0.151) and deaths (r= −0.09, P = 0.207) were not correlated with humidity. Also, there was a negative relationship between the COVID-19 average growth rate of new cases and deaths with the Air Quality Index (AQI) and wind (new cases and wind: r=−0.25, P= 0.04). Furthermore, the data related to the first and second 60 day of the adjusted cumulative incidence of COVID-19 daily cases and deaths were not associated with humidity and Air Quality Index (AQI). The result of GAM showed the effect of AQI on the average growth rate of COVID-19 new cases and deaths. This study provides evidence for a positive relationship between COVID-19 daily cases, deaths, and AQI. Supplementary Information The online version contains supplementary material available at 10.1007/s11356-021-14322-6.

Previous studies have found that particulate matter (PM) pollution is a risk factor for respiratory disease by affecting body's immunity and carrying microorganisms. This study aimed to explore the association between PM and the incidence of mumps in 31 provinces from China. Monthly mumps cases, air pollution concentration, and meteorological factors in each province were obtained between January 2014 to December 2017. We used a generalized additive model (GAM) to investigate the associations of PM2.5 and PM10 with monthly mumps cases. We also tested the statistical significance of the differences between effect estimates in the warm season (April to September) and cold season (October to March) to explore potential effect modification. We found that a 10-μg/m3 increase (lag0) in PM2.5, and PM10 was associated with a 2.34% (95% CI: 1.32 to 3.36) and 1.90% (95% CI: 1.19 to 2.62) increase in the monthly counts of mumps cases, respectively. We also observed significant positive associations of PM2.5 and PM10 with mumps cases at lag0-1. These results were robust in our sensitivity analyses. No significant differences were found between the season-specific effects. Our results indicate that there is a positive relationship between PM and the incidence of mumps, which provides important implications for the prevention and control of mumps.

Objective To predict the future incidence trend of pneumoconiosis in China, and to evaluate three predictive models. Methods We selected pneumoconiosis cases (2000-2019) to fit Generalized Additive Model (GAM), Curve Fitting Method, and GM (1,1) Model, chosen average fitting relative error, relative error of prediction, and coefficient of determination to evaluate models. Results Chinese incidence trend of pneumoconiosis would decrease in the future. Predicted value of GAM (14,566) and Curve Fitting Method (15,781) in 2019 was close to the actual value (15,898). Relative error of prediction of GAM and Curve Fitting Method was -8.38% and -0.73%, respectively. Conclusions The government needs to strengthen prevention and control since pneumoconiosis cases might remain huge in the future. Besides, we advise that GAM and Curve Fitting Method can be used to predict Chinese incidence trend of pneumoconiosis.

Background The effects of extreme temperature on infectious diseases are complex and far-reaching. There are few studies to access the relationship of pulmonary tuberculosis (PTB) with extreme temperature. The study aimed to identify whether there was association between extreme temperature and the reported morbidity of PTB in Shandong Province, China, from 2005 to 2016. Methods A generalized additive model (GAM) was firstly conducted to evaluate the relationship between daily reported incidence rate of PTB and extreme temperature events in the prefecture-level cities. Then, the effect estimates were pooled using meta-analysis at the provincial level. The fixed-effect model or random-effect model was selected based on the result of heterogeneity test. Results Among the 446,016 PTB reported cases, the majority of reported cases occurred in spring. The higher reported incidence rate areas were located in Liaocheng, Taian, Linyi and Heze. Extreme low temperature had an impact on the reported incidence of PTB in only one prefecture-level city, i.e., Binzhou (RR = 0.903, 95% CI: 0.817–0.999). While, extreme high temperature was found to have a positive effect on reported morbidity of PTB in Binzhou (RR = 0.924, 95% CI: 0.856–0.997) and Weihai (RR = 0.910, 95% CI: 0.843–0.982). Meta-analysis showed that extreme high temperature was associated with a decreased risk of PTB (RR = 0.982, 95% CI: 0.966–0.998). However, extreme low temperature was no relationship with the reported incidence of PTB. Conclusion Our findings are suggested that extreme high temperature has significantly decreased the risk of PTB at the provincial levels. The findings have implications for developing strategies to response to climate change.

The outbreak of COVID-19, caused by SARS-CoV-2 has spread across many countries globally. Greatly limited study concerned the effect of airborne pollutants on COVID-19 infection, while exposure to airborne pollutants may harm human health. This paper aimed to examine the associations of acute exposure to ambient atmospheric pollutants to daily newly COVID-19 confirmed cases in 41 Chinese cities. Using a generalized additive model with Poisson distribution controlling for temperature and relative humidity, we evaluated the association between pollutant concentrations and daily COVID-19 confirmation at single-city level and multi-city level. We observed a 10 μg/m3 rise in levels of PM2.5 (lag 0−14), O3 (lag 0−1), SO2 (lag 0) and NO2 (lag 0−14) were positively associated with relative risks of 1.050 (95% CI: 1.028, 1.073), 1.011 (1.007, 1.015), 1.052 (1.022, 1.083) and 1.094 (1.028, 1.164) of daily newly confirmed cases, respectively. Further adjustment for other pollutants did not change the associations materially (excepting in the model for SO2). Our results indicated that COVID-19 incidence may be susceptible to airborne pollutants such as PM2.5, O3, SO2 and NO2, and mitigation strategies of environmental factors are required to prevent spreading.

Distribution models are needed to understand spatiotemporal patterns in cetacean occurrence and to mitigate anthropogenic impacts. Shipboard line-transect visual surveys are the standard method for estimating abundance and describing the distributions of cetacean populations. Ship-board surveys provide high spatial resolution but lack temporal resolution and seasonal coverage. Stationary passive acoustic monitoring (PAM) employs acoustic sensors to sample point locations nearly continuously, providing high temporal resolution in local habitats across days, seasons and years. To evaluate whether cross-platform data synthesis can improve distribution predictions, models were developed for Cuvier’s beaked whales, sperm whales, and Risso’s dolphins in the oceanic Gulf of Mexico using two different methods: generalized additive models and neural networks. Neural networks were able to learn unspecified interactions between drivers. Models that incorporated PAM datasets out-performed models trained on visual data alone, and joint models performed best in two out of three cases. The modeling results suggest that, when taken together, multiple species distribution models using a variety of data types may support conservation and management of Gulf of Mexico cetacean populations by improving the understanding of temporal and spatial species distribution trends.

Qing Sun,1,2 Liang Tang,2 Qiurong Zeng,2 Mingjun Gu2 1Department of Clinical Nutrition, Fuling Center Hospital of Chongqing City, Chongqing, 408000, People’s Republic of China; 2Department of Endocrinology, Fuling Center Hospital of Chongqing City, Chongqing, 408000, People’s Republic of ChinaCorrespondence: Mingjun GuDepartment of Endocrinology, Fuling Center Hospital of Chongqing City, No. 2 Fuling District, Gaosuntang Road, Chongqing, 408000, People’s Republic of ChinaTel +86 13996860971Email mingjungudr@163.comPurpose: In this study, we explored the correlation between diabetic retinopathy (DR) and metabolic syndrome (MetS) among diabetes mellitus (DM) patients.Methods: Logistic regression analysis was utilized to test the effects of MetS and its indicators on the incidence of DR and vision-related functional burden. The spline smoothing functions of continuous indicators of MetS were used to establish the logistic generalized additive model (GAM). The effective degree of freedom (EDF) =1 was served as a sign of linear relationship. EDF> 1 was a sign of a more complex association between MetS and DR.Results: The proportion of difficulties of looking for objects on the crowded shelves in the DR group was higher than that in the non-DR group (19.40 vs 12.10, P< 0.05). Elevated fasting glucose (Glu) and blood pressure levels were related to the vision-related functional burden. The risk of DR development increased by 6% [95%confidence interval (CI): 1.03– 1.09, P< 0.001] and 1% (95% CI: 1.01– 1.02, P=0.004) per 1 unit increase in Glu and systolic blood pressure (SBP) of DM patients, respectively. In the univariate GAM, Glu had a linear effect on DR (EDF=1, P< 0.001) with a positive correlation after controlling SBP. And there was a nonlinear correlation between SBP and DR after controlling Glu (EDF=2.44, P=0.024).Conclusion: Both Glu and blood pressure were associated with the occurrence of DR and vision-related functional burden. Controlling the levels of Glu and blood pressure may reduce the risk of DR and vision loss among DM patients.Keywords: diabetic retinopathy, generalized additive model, glucose, blood pressure

Summary Background & aim Several studies consider vitamin D deficiency as a modifiable risk factor for adverse pregnancy outcomes. However, there is no specific cut-off point for the serum level of this prohormone to identify high-risk pregnant women. This study aimed to determine the thresholds for the circulating levels of 25-hydroxyvitamin D (25(OH)D), associated with adverse pregnancy outcomes, including preterm labor, preeclampsia (PE), and gestational diabetes mellitus (GDM), using a generalized additive model. Methods This is a descriptive and analytical cross-sectional study carried out on the data collected from the Khuzestan Vitamin D Deficiency Screening Program in Pregnancy. Of 1800 pregnant women, referred to the health centers of Masjed-Soleyman and Shushtar (Khuzestan Province, Iran), we used the data of 1763 pregnant women, whose serum vitamin D status during the third trimester of pregnancy was available. The datasets were randomly divided into training (70%) and validation (30%) subsets. The cut-off levels of 25(OH)D were identified for the low, moderate, and high risk of adverse pregnancy outcomes, according to generalized additive models (GAM) with smooth functions in the training data set. Then Generalized Linear Model (GLM), with logit link function was applied in the validation dataset to explore the relationships between the optimal vitamin D classification and adverse pregnancy outcomes after adjusting for the potential confounders. Results The optimal cut-off levels of 25(OH)D for the high, moderate, and low risk of GDM were ≤16, 16–26, and >26 ng/mL, respectively. Also, the optimal cut-off points of 25(OH)D for the high, moderate, and low risk of preterm delivery were ≤15, 15–21, and >21 ng/mL, respectively. Finally, the corresponding values for the high, moderate, and low risk of PE were ≤15, 15–23, and >23 ng/mL, respectively. The models were well-calibrated, based on the Hosmer–Lemeshow test. Results of the adjusted generalized linear model showed a significant increasing trend in the risk of pregnancy outcomes by decreasing 25(OH)D levels. Conclusion In the preconception period, a 25(OH)D cut-off level of >15 ng/mL is recommended for the prevention of adverse pregnancy outcomes.

Recently, particulate matter pollution has been worsening, which has been affecting the asthma visits in children. In this study, we assessed the short-term effects of PM10 and PM2.5 on asthma visits in children in Shanghai, China from January 1, 2009 and December 31, 2010, using a generalized additive model. We controlled the confounding factors, such as long-term trends, week day effect, and weather elements. The lag effects of different age subgroups (≤ 2 yr, 3–5 yr, and 6–18 yr subgroups) were performed. The results showed significant effects of PM10 and PM2.5 on asthma visits in children, though the seasonal lags varied for the three age subgroups. In general, the effect of PM2.5 on asthma visits in children was stronger and more acute than that of PM10. PM2.5 showed the highest relative risk of 1.192 at lag 0 day in summer; and PM10 showed the highest relative risk of 1.073 at lag 3 day in autumn. Overall, particulate matter pollution showed a greater effect on relatively younger children. In particular, the ≤ 2 yr subgroup showed the highest seasonal relative risk of PM10. Especially, seasonal relative risk of PM10 in autumn for the ≤ 2 yr subgroup was much higher than that for the other two subgroups. The 3–5 yr and 6–18 yr subgroups showed the highest seasonal relative risk of PM2.5 in summer and winter, respectively. But the pediatric visits data we obtained cannot reflect the true prevalence of asthma and multiple visits. Thus, selection bias may exist in our analysis.

The contradiction between the regional imbalance and an one-size-fits-all policy is one of the biggest challenges in current air pollution control in China. With the recent implementation of first-level public health emergency response (FLPHER) in response to the COVID-19 pandemic in China (a total of 77 041 confirmed cases by February 22, 2020), human activities were extremely decreased nationwide and almost all economic activities were suspended. Here, we show that this scenario represents an unprecedented "base period" to probe the short-term emission control effect of air pollution at a city level. We quantify the FLPHER-induced changes of NO2, SO2, PM2.5, and PM10 levels in 174 cities in China. A machine learning prediction model for air pollution is established by coupling a generalized additive model, random effects meta-analysis, and weather research and forecasting model with chemistry analysis. The short-term control effect under the current energy structure in each city is estimated by comparing the predicted and observed results during the FLPHER period. We found that the short-term emission control effect ranges within 53.0%-98.3% for all cities, and southern cities show a significantly stronger effect than northern cities (P < 0.01). Compared with megacities, small-medium cities show a similar control effect on NO2 and SO2 but a larger effect on PM2.5 and PM10.

Dengue transmission is climate-sensitive and permissive conditions regularly cause large outbreaks in Asia-Pacific area. As climate change progresses, extreme weather events such as heatwaves and unusually high rainfall are predicted more intense and frequent, but their impacts on dengue outbreaks remain unclear so far. This paper aimed to investigate the relationship between extreme weather events (i.e., heatwaves, extremely high rainfall and extremely high humidity) and dengue outbreaks in China. We obtained daily number of locally acquired dengue cases and weather factors for Guangzhou, China, for the period 2006-2015. The definition of dengue outbreaks was based on daily number of locally acquired cases above the threshold (i.e., mean + 2SD of daily distribution of dengue cases during peaking period). Heatwave was defined as ≥2 days with temperature ≥ 95th percentile, and extreme rainfall and humidity defined as daily values ≥95th percentile during 2006-2015. A generalized additive model was used to examine the associations between extreme weather events and dengue outbreaks. Results showed that all three extreme weather events were associated with increased risk of dengue outbreaks, with a risk increase of 115-251% around 6 weeks after heatwaves, 173-258% around 6-13 weeks after extremely high rainfall, and 572-587% around 6-13 weeks after extremely high humidity. Each extreme weather event also had good capacity in predicting dengue outbreaks, with the model's sensitivity, specificity, accuracy, and area under the receiver operating characteristics curve all exceeding 86%. This study found that heatwaves, extremely high rainfall, and extremely high humidity could act as potential drivers of dengue outbreaks.

SARS-CoV-2 (Coronavirus) disease represents the causative agent with a potentially fatal risk which is having great global human health concern. Earlier studies suggested that air pollutants and meteorological factors were considered as the risk factors for acute respiratory infection, which carries harmful pathogens and affects the immunity. The study intended to explore the correlation between air pollutants, meteorological factors and the daily reported infection cases caused by novel coronavirus in India. The daily positive infected cases, air pollution and meteorological factors in 288 districts were collected from January 30, 2020 to April 23, 2020 in India. Speraman’s correlation and generalised additive model were applied to investigate the correlations of four air pollutants (PM2.5, PM10, NO2 and SO2) and eight meteorological factors (Temp, DTR, RH, AH, AP, RF, WS and WD) with COVID-19 infected cases. The study indicated that a 10 µg/m3 increase during (Lag0-14) in PM2.5, PM10 and NO2 was resulted in 2.21% (95%CI: 1.13 to 3.29), 2.67% (95% CI: 0.33 to 5.01) and 4.56 (95% CI: 2.22 to 6.90) increase in daily counts of COVID 19 infected cases respectively. However, only 1 unit increase in meteorological factor levels in case of daily mean temperature and DTR during (Lag0-14) associated with 3.78% (95%CI: 1.81 to 5.75) and 1.82% (95% CI: -1.74 to 5.38) rise of COVID-19 infected cases respectively. In addition, SO2 and relative humidity were negatively associated with COVID-19 infected cases at Lag0-14 with decrease of 7.23% (95% CI: -10.99 to -3.47) and 1.11% (95% CI: -3.45 to 1.23) for SO2 and for relative humidity respectively. The study recommended that there is significant relationship between air pollutants and meteorological factors with COVID-19 infected cases, which substantially explain the effect of national lockdown and suggested positive implications for control and prevention of the spread of SARS-CoV-2 disease.

Background The Acute Physiology and Chronic Health Evaluation (APACHE) III-j model is widely used to predict mortality in Japanese intensive care units (ICUs). Although the model’s discrimination is excellent, its calibration is poor. APACHE III-j overestimates the risk of death, making its evaluation of healthcare quality inaccurate. This study aimed to improve the calibration of the model and develop a Japan Risk of Death (JROD) model for benchmarking purposes. Methods A retrospective analysis was conducted using a national clinical registry of ICU patients in Japan. Adult patients admitted to an ICU between April 1, 2018, and March 31, 2019, were included. The APACHE III-j model was recalibrated with the following models: Model 1, predicting mortality with an offset variable for the linear predictor of the APACHE III-j model using a generalized linear model; model 2, predicting mortality with the linear predictor of the APACHE III-j model using a generalized linear model; and model 3, predicting mortality with the linear predictor of the APACHE III-j model using a hierarchical generalized additive model. Model performance was assessed with the area under the receiver operating characteristic curve (AUROC), the Brier score, and the modified Hosmer–Lemeshow test. To confirm model applicability to evaluating quality of care, funnel plots of the standardized mortality ratio and exponentially weighted moving average (EWMA) charts for mortality were drawn. Results In total, 33,557 patients from 44 ICUs were included in the study population. ICU mortality was 3.8%, and hospital mortality was 8.1%. The AUROC, Brier score, and modified Hosmer–Lemeshow p value of the original model and models 1, 2, and 3 were 0.915, 0.062, and < .001; 0.915, 0.047, and < .001; 0.915, 0.047, and .002; and 0.917, 0.047, and .84, respectively. Except for model 3, the funnel plots showed overdispersion. The validity of the EWMA charts for the recalibrated models was determined by visual inspection. Conclusions Model 3 showed good performance and can be adopted as the JROD model for monitoring quality of care in an ICU, although further investigation of the clinical validity of outlier detection is required. This update method may also be useful in other settings.

Objectives As the largest organ of the human body, the skin is the major exposure route of NO2. However, the evidence for a relationship between NO2 exposure and dermatologic diseases (DMs) is limited. This time-series study was conducted to assess the short-term effect of nitrogen dioxide (NO2) exposure on DMs outpatient visits in Xinxiang, China. Methods Daily recordings of NO2 concentrations, meteorological data, and the outpatient visits data for DMs were collected in Xinxiang from January 1st, 2015, to December 31st, 2018. The analysis method used was based on the generalized additive model (GAM) with quasi-Poisson regression to investigate the relationship between NO2 exposure and DMs outpatient visits. Several covariates, such as long-term trends, seasonality, and weather conditions were controlled. Results A total of 164,270 DMs outpatients were recorded. A 10 μg/m3 increase in NO2 concentrations during the period was associated with a 1.86% increase in DMs outpatient visits (95% confidence intervals [Cl]: 1.06–2.66%). The effect was stronger (around 6 times) in the cool seasons than in warmer seasons and younger patients (

In order to assess the influence of atmospheric conditions and particulate matter (PM) on the seasonally varying incidence of influenza-like illnesses (ILI) in the capital of Poland—Warsaw, we analysed time series of ILI reported for the about 1.75 million residents in total and for different age groups in 288 approximately weekly periods, covering 6 years 2013–2018. Using Poisson regression, we predicted ILI by the Universal Thermal Climate Index (UTCI) as biometeorological indicator, and by PM2.5 and PM10, respectively, as air quality measures accounting for lagged effects spanning up to 3 weeks. Excess ILI incidence after adjusting for seasonal and annual trends was calculated by fitting generalized additive models. ILI morbidity increased with rising PM concentrations, for both PM2.5 and PM10, and with cooler atmospheric conditions as indicated by decreasingUTCI. While the PM effect focused on the actual reporting period, the atmospheric influence exhibited a more evenly distributed lagged effect pattern over the considered 3-week period. Though ILI incidence adjusted for population size significantly declined with age, age did not significantly modify the effect sizes of both PM andUTCI. These findings contribute to better understanding environmental conditionings of influenza seasonality in a temperate climate. This will be beneficial to forecasting future dynamics of ILI and to planning clinical and public health resources under climate change scenarios.

As the national reference laboratory for febrile illness in Madagascar, we processed samples from the first epidemic wave of COVID-19, between March and September 2020. We fit generalized additive models to cycle threshold (Ct) value data from our RT-qPCR platform, demonstrating a peak in high viral load, low-Ct value infections temporally coincident with peak epidemic growth rates estimated in real time from publicly-reported incidence data and retrospectively from our own laboratory testing data across three administrative regions. We additionally demonstrate a statistically significant effect of duration of time since infection onset on Ct value, suggesting that Ct value can be used as a biomarker of the stage at which an individual is sampled in the course of an infection trajectory. As an extension, the population-level Ct distribution at a given timepoint can be used to estimate population-level epidemiological dynamics. We illustrate this concept by adopting a recently-developed, nested modeling approach, embedding a within-host viral kinetics model within a population-level Susceptible-Exposed-Infectious-Recovered (SEIR) framework, to mechanistically estimate epidemic growth rates from cross-sectional Ct distributions across three regions in Madagascar. We find that Ct-derived epidemic growth estimates slightly precede those derived from incidence data across the first epidemic wave, suggesting delays in surveillance and case reporting. Our findings indicate that public reporting of Ct values could offer an important resource for epidemiological inference in low surveillance settings, enabling forecasts of impending incidence peaks in regions with limited case reporting.

There is some evidence about the short-term effects of air pollutants on adverse pregnancy outcomes. The aim of this study was to determine the association between air pollutants and spontaneous abortion, stillbirth, gestational hypertension, preeclampsia, gestational diabetes and macrosomia in Ahvaz, which is one of the most polluted cities in the Middle East. Data on adverse pregnancy outcomes and air pollutants including ozone (O3), nitric oxide (NO), nitrogen dioxide (NO2), sulfur dioxide (SO2), carbon monoxide (CO), particles with a diameter of less than 10 µm (PM10) and particles with a diameter less than 2.5 µm (PM2.5) were inquired from the Health Department of Ahvaz Jundishapur University of Medical Sciences and the Environmental Protection Agency of Khuzestan Province for the years 2008–2018. A time series analysis using the generalized additive model (GAM) with up to 6-day lags was used. The results showed that the SO2 pollutant on 0, 1, 3, 4, and 6-day lags and PM10 on lag 0 had direct and significant associations with spontaneous abortion. NO, NO2 and CO on 0–6-day lags, and O3 on 6-day lags showed direct and significant associations with preeclampsia. NO and NO2 pollutants showed significant and direct associations with gestational diabetes, during 0- and 6-day lags. NO on 0-, 3- and 4-day lags, CO in all 0–6-day lags and PM2.5 on 1-, 3-, 5-, and 6-day lags showed direct and significant associations with macrosomia. None of the pollutants showed significant associations with stillbirth or gestational hypertension. The results of this study suggest that some air pollutants are associated with spontaneous abortion, preeclampsia, gestational diabetes and macrosomia. This study further emphasizes the need to control ambient air pollution.

Cataract is the first cause of blindness and the major cause of visual impairment worldwide. Under conditions of global warming, researchers have begun to give attention to the influence of increasing temperature on cataract patients. Our paper aimed to investigate the association between extreme heat and hospital admissions for cataract in Hefei, China. Based on data from the New Rural Cooperative Medical System and National Meteorological Information Center, we used a generalized additive model and a distributed lag nonlinear model to examine the relationship between extreme heat and hospitalizations for cataract, with consideration of cumulative and lagged effects. When current mean temperature was above 28 °C, each 1 °C rise was associated with a 4% decrease in the number of cataract admissions (RR = 0.96, 95% CI = 0.94–0.98). The cumulative relative risk over 11 days of lag was the lowest, which indicated that every 1 °C increase in mean temperature above 28 °C was associated with a 19% decrease in the number of hospital admissions for cataract (RR = 0.81, 95% CI = 0.75–0.88). In subgroup analyses, the negative association between extreme heat and hospital admissions for cataract was stronger among patients who were not admitted to provincial-level hospitals. In conclusion, this paper found that extreme heat was negatively associated with cataract hospitalizations in Hefei, providing useful information for hospitals and policymakers. Electronic supplementary material The online version of this article (10.1007/s11356-020-10402-1) contains supplementary material, which is available to authorized users.

The outbreak of novel pneumonia coronavirus disease has become a public health concern worldwide. Here, for the first time, the association between Korean meteorological factors and air pollutants and the COVID‐19 infection was investigated. Data of air pollutants, meteorological factors, and daily COVID‐19 confirmed cases of seven metropolitan cities and nine provinces were obtained from 3 February 2020 to 5 May 2020 during the first wave of pandemic across Korea. We applied the generalized additive model to investigate the temporal relationship. There was a significantly nonlinear association between daily temperature and COVID‐19 confirmed cases. Each 1°C increase in temperature was associated with 9% (lag 0‐14; OR = 1.09; 95% CI = 1.03‐1.15) increase of COVID‐19 confirmed cases when the temperature was below 8°C. A 0.01 ppm increase in NO2 (lag 0‐7, lag 0.14, and lag 0‐21) was significantly associated with increases of COVID‐19 confirmed cases, with ORs (95% CIs) of 1.13 (1.02‐1.25), 1.19 (1.09‐1.30), and 1.30 (1.19‐1.41), respectively. A 0.1 ppm increase in CO (lag 0‐21) was associated with the increase in COVID‐19 confirmed cases (OR = 1.10, 95% CI = 1.04‐1.16). There was a positive association between per 0.001 ppm of SO2 concentration (lag 0, lag 0‐7, and lag 0‐14) and COVID‐19 confirmed cases, with ORs (95% CIs) of 1.13 (1.04‐1.22), 1.20 (1.11‐1.31), and 1.15 (1.07‐1.25), respectively. There were significantly temporal associations between temperature, NO2, CO, and SO2 concentrations and daily COVID‐19 confirmed cases in Korea., Highlights ‐ Data on air pollutants and meteorological factors from 16 cities and provinces in Korea were obtained. ‐ Temperature was associated with an increase of COVID‐19 confirmed cases when below 8oC. ‐ Positive associations between NO2, CO, and SO2 levels and COVID‐19 confirmed cases were observed.