It is reported that sudden sensorineural hearing loss (SSNHL) is closely related to diabetes, hypertension, and hyperlipidemia. While the metabolic syndrome (MetS) is a multifactorial disease that includes diabetes, hypertension, dyslipidemia, and obesity, which are known to be associated with SSNHL. Weather conditions have long been known to affect the SSNHL. This study aimed to make a clear connection between MetS, or weather conditions, and the severity and prognosis of SSNHL. 127 SSNHL patients have been divided into the MetS group and the non-MetS group, and the demographic and clinical characteristics of the 2 groups have been analyzed retrospectively. There were 52 (40.9%) patients in the MetS group, while there were 75 (59.1%) patients in the non-MetS group. The rate of vertigo, hypertension, diabetes, lower high-density lipoprotein cholesterol (HDL-C) levels, high triglyceride (TG), and body mass index (BMI) ≥25 (kg/m2 ) were significantly higher in the MetS group than those in non-MetS group. Vertigo, hypertension, and Mets were linked to the severity of hearing loss. The rate of complete recovery and partial recovery in the MetS group was clearly lower than that in non-MetS group. According to the multivariate analysis, MetS was significantly associated with a poorer prognosis of SSNHL; a high ambient temperature difference at onset and hypertension were correlated with a poor prognosis. These results demonstrate that the severity and prognosis of SSNHL can be influenced by the MetS. High ambient temperature differences at onset and hypertension were indicators of a poor prognosis for SSNHL.

Expanding road networks to accommodate various activities has significantly increased urban noise pollution, adversely affecting human health and quality of life. Numerous factors influence the noise level in urban areas, including road characteristics, traffic characteristics, surrounding buildings, and weather conditions. While previous studies have considered many of these factors individually, this study aims to integrate all relevant variables to comprehensively monitor and analyze their combined effects on noise levels. The objective is to determine the most influential factors that could be incorporated into effective noise reduction strategies. This research focuses on Cairo, one of the most densely populated cities in the world, where high noise levels are a persistent issue. A detailed case study of Tahrir Street in Dokki, Cairo, provides the basis for this investigation. One of the most crowded areas is El-Tahrir Street in Al-Dokki, which was selected as a case study in this research. This area experiences high traffic volume, with up to 1700 vehicles passing through within a 15-min interval. This significant traffic volume is the primary driver of the elevated noise levels in the area. Traffic and noise level monitoring was conducted using a field survey using the sound level meter. Consequently, a statistical analysis was conducted to investigate the correlation between different factors and the noise level and determine the most influential factors. The study revealed that traffic volume and congestion are the most significant factors influencing noise levels on Tahrir Street, exhibiting strong positive correlations (R = 0.38). Additionally, the study found an inverse relationship between vehicle speed and noise level due to high traffic volumes and identified that building characteristics and wind direction also play roles, albeit to a lesser extent.

The cotton or solenopsis mealybug, Phenacoccus solenopsis (Tinsley, 1898) (Hemiptera: Pseudococcidae), infests various host plants in Egypt. A study was conducted to observe the incidence of mealybugs and the possible influences of meteorological variables and plant age on the insect population of maize (single-hybrid 168 yellow maize cultivar) plants in Esna district, Luxor governorate, Egypt, in two consecutive seasons (2021 and 2022). P. solenopsis infested maize plants from the 3rd week of June to harvest, and had three peaks of seasonal incidence/season namely; in the 1st week of June in the 3rd/4th week of July, and the 2nd week of August. Similarly, there were three peaks in the percent of infestations per season. In the first season, the average population density of P. solenopsis per sample was 174.04 ± 16.93 individuals, and in the second season, 156.72 ± 14.28 individuals. The most favorable climate for P. solenopsis population increase and infestation occurred in August in the first season and in September in the second season, while June was less suitable in both growing seasons (as estimated by weekly surveys). The combined effects of weather conditions and plant age are significantly related to the estimates of P. solenopsis populations, with an explained variance (E.V.) of 93.18 and 93.86%, respectively, in the two seasons. In addition, their influences explained differences in infestation percentages of 93.30 and 95.54%, respectively, in the two seasons. Maize plant age was the most effective factor in determining changes in P. solenopsis population densities in each season. The mean daily minimum temperature in the first season and mean daily dew point in the second season were the most important factors affecting the percent changes in infestation. However, in both seasons, the mean daily maximum temperature was the least effective variable in population and infestation variation. This study paves the way for monitoring and early detection of mealybugs in maize; as well as the optimal climatic conditions for its development.

BACKGROUND: The government of Lao PDR has increased efforts to control malaria transmission in order to reach its national elimination goal by 2030. Weather can influence malaria transmission dynamics and should be considered when assessing the impact of elimination interventions but this relationship has not been well characterized in Lao PDR. This study examined the space-time association between climate variables and Plasmodium falciparum and Plasmodium vivax malaria incidence from 2010 to 2022.
METHODS: Spatiotemporal Bayesian modelling was used to investigate the monthly relationship, and model selection criteria were used to evaluate the performance of the models and weather variable specifications. As the malaria control and elimination situation was spatially and temporally dynamic during the study period, the association was examined annually at the provincial level.
RESULTS: Malaria incidence decreased from 2010 to 2022 and was concentrated in the southern regions for both P. falciparum and P. vivax. Rainfall and maximum humidity were identified as most strongly associated with malaria during the study period. Rainfall was associated with P. falciparum incidence in the north and central regions during 2010-2011, and with P. vivax incidence in the north and central regions during 2012-2015. Maximum humidity was persistently associated with P. falciparum and P. vivax incidence in the south.
CONCLUSIONS: Malaria remains prevalent in Lao PDR, particularly in the south, and the relationship with weather varies between regions but was strongest for rainfall and maximum humidity for both species. During peak periods with suitable weather conditions, vector control activities and raising public health awareness on the proper usage of intervention measures, such as indoor residual spraying and personal protection, should be prioritized.

BACKGROUND: The role of the climate regarding atopic dermatitis (AD) in infants is still unclear. This study aimed to determine the relationship between meteorological conditions and the incidence of early AD.
METHODS: The study was conducted using a retrospective design. We analyzed children aged 0-24 months with clinically diagnosed AD (n = 603), including infantile eczema (IE, n = 292), in relation to the mean monthly meteorological data in Minsk. The Mantel-Haenszel method was used to study the association between an AD outcome and meteorological variables, stratifying by potential confounders. Seasons of birth were analyzed in children diagnosed with AD before 6 months of age (n = 567) and at 12 months of age (n = 350) from 2005 to 2019.
RESULTS: The incidence rate of IE was negatively associated with air temperature (adjusted incidence rate ratio = 0.75; 95% confidence interval (CI) 0.59-0.94), precipitation (0.74; 95% CI 0.58-0.93), and positively associated with atmospheric pressure (1.31; 95% CI 1.04-1.66). The highest incidence rate of IE was during spring, and the lowest was during summer. Incidences of AD were less frequent among infants born in the spring (18.1% vs. 29.4%, P < 0.001) than among older children. The principal component analysis identified three meteorological combinations where the first one (warm, low humidity) was negatively associated with the incidence rate of AD among children aged 0-24 months (0.77; 95% CI 0.65-0.92), and the third one (rainy, low atmospheric pressure) with IE (0.70; 95% CI 0.54-0.90).
CONCLUSIONS: Continental seasonal cold-humid weather may influence early AD incidence. Moreover, short-term meteorological factors may play an important role in the onset of IE.

Objective weather classification methods have been extensively applied to identify dominant ozone-favorable synoptic weather patterns (SWPs), however, the consistency of different classification methods is rarely examined. In this study, we apply two widely-used objective methods, the self-organizing map (SOM) and K-means clustering analysis, to derive ozone-favorable SWPs at four Chinese megacities in 2015-2022. We find that the two algorithms are largely consistent in recognizing dominant ozone-favorable SWPs for four Chinese megacities. In the case of classifying six SWPs, the derived circulation fields are highly similar with a spatial correlation of 0.99 between the two methods, and the difference in the mean frequency of each SWP is less than 7%. The six dominant ozone-favorable SWPs in Guangzhou are all characterized by anomaly higher radiation and temperature, lower cloud cover, relative humidity, and wind speed, and stronger subsidence compared to climatology mean. We find that during 2015-2022, the occurrence of ozone-favorable SWPs days increases significantly at a rate of 3.2 day/year, faster than the increases in the ozone exceedance days (3.0 day/year). The interannual variability between the occurrence of ozone-favorable SWPs and ozone exceedance days are generally consistent with a temporal correlation coefficient of 0.6. In particular, the significant increase in ozone-favorable SWPs in 2022, especially the Subtropical High type which typically occurs in September, is consistent with a long-lasting ozone pollution episode in Guangzhou during September 2022. Our results thus reveal that enhanced frequency of ozone-favorable SWPs plays an important role in the observed 2015-2022 ozone increase in Guangzhou.

UNASSIGNED: Many respiratory viruses and their associated diseases are sensitive to meteorological factors. For SARS-CoV-2 and COVID-19, evidence on this sensitivity is inconsistent. Understanding the influence of meteorological factors on SARS-CoV-2 transmission and COVID-19 epidemiology can help to improve pandemic preparedness.
UNASSIGNED: This review aimed to examine the recent evidence about the relation between meteorological factors and SARS-CoV-2/COVID-19.
UNASSIGNED: We conducted a global scoping review of peer-reviewed studies published from January 2020 up to January 2023 about the associations between temperature, solar radiation, precipitation, humidity, wind speed, and atmospheric pressure and SARS-CoV-2/COVID-19.
UNASSIGNED: From 9,156 initial records, we included 474 relevant studies. Experimental studies on SARS-CoV-2 provided consistent evidence that higher temperatures and solar radiation negatively affect virus viability. Studies on COVID-19 (epidemiology) were mostly observational and provided less consistent evidence. Several studies considered interactions between meteorological factors or other variables such as demographics or air pollution. None of the publications included all determinants holistically.
UNASSIGNED: The association between short-term meteorological factors and SARS-CoV-2/COVID-19 dynamics is complex. Interactions between environmental and social components need further consideration. A more integrated research approach can provide valuable insights to predict the dynamics of respiratory viruses with pandemic potential.

Previous correlative and modeling approaches indicate influences of environmental factors on COVID-19 spread through atmospheric conditions\' impact on virus survival and transmission or host susceptibility. However, causal connections from environmental factors to the pandemic, mediated by human mobility, received less attention. We use the technique of Convergent Cross Mapping to identify the causal connections, beyond correlation at the country level, between pairs of variables associated with weather conditions, human mobility, and the number of COVID-19 cases for 32 European states. Here, we present data-based evidence that the relatively reduced number of cases registered in Northern Europe is related to the causal impact of precipitation on people\'s decision to spend more time at home and that the relatively large number of cases observed in Southern Europe is linked to people\'s choice to spend time outdoors during warm days. We also emphasize the channels of the significant impact of the pandemic on human mobility. The weather-human mobility connections inferred here are relevant not only for COVID-19 spread but also for any other virus transmitted through human interactions. These results may help authorities and public health experts contain possible future waves of the COVID-19 pandemic or limit the threats of similar human-to-human transmitted viruses.

UNASSIGNED: Although the regular exercise population is a key metric for gaging the success of China\'s fitness-for-all activities, effective policy approaches to increase mass sports participation remain unclear. Previous research suggests that GDP, educational attainment, sports resources, and meteorological conditions could influence regular exercise participation. Therefore, this study first analyzed the macro-level correlates influencing China\'s regular exercise population.
UNASSIGNED: We utilize ordinary least squares (OLS) regression and geographical weighted regression (GWR) to theorize the relationship. The analysis encompasses data from the 31 administrative regions of Mainland China, as reported at the end of the 13th Five-Year Plan period. The log-log model enables us to quantify the marginal effect (elasticity) of the explanatory variables.
UNASSIGNED: The OLS regression showed that regional GDP and the proportion of the population with a university education were significant predictors. In the global model, the marginal effects of regional GDP and university education were 0.048 and 0.173, respectively. Furthermore, the GWR revealed a distinct geographic pattern that corresponds to the classic Hu Line.
UNASSIGNED: While regional GDP was also a significant correlate in our model, the elasticity demonstrates that university education had an asymmetric effect on China\'s regular exercise population. Therefore, this paper sheds light on a policy priority for the upcoming 15th Five-Year Plan, emphasizing the strategic importance of expanding university education to enhance mass sports participation. In turn, a better-educated populace may yield significant secondary effects on public health and contribute to the high-quality development of the Chinese path to modernization.

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