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.

Marching band (MB) artists are subject to exertional heat illnesses (EHIs) similar to other active groups like laborers and athletes. Yet, they are an understudied population with no evidence-based heat safety guidelines. Presented here is a case study of the 233rd annual Bristol, RI Independence Day Parade in 2018 that resulted in over 50 EHIs, including 25 from the Saint Anthony Village marching band (MB) from suburban Minneapolis, MN. This research aims to identify the contributing factors that led to the large number of EHIs, as well as guide ensuring the safety of MB artists in future events. A human heat balance model in conjunction with local weather data was used to simulate heat stress on MB artists. Three modeling scenarios were used to isolate the roles of clothing (band uniform vs. t-shirt and shorts), weather (July 4, 2018 vs. 30-year climatology), and metabolic rate (slow, moderate, and brisk marching pacing) on heat stress. The results identify several key factors that increased heat stress. The meteorological conditions were unusually hot, humid, and sunny for Bristol, resulting in reduced cooling from evaporation and convection, and increased radiant heating. Behavioral factors also affect heat stress. The full marching band uniforms reduced evaporative cooling by 50% and the activity levels of marching 4 km over several hours without breaks resulted in conditions that were uncompensable. Finally, it is speculated that a lack of acclimatization for participants from cooler regions may have exacerbated heat-related impacts. These findings highlight several recommendations for MB directors and race organizers, including the use of summer uniforms for anticipated hot conditions, and advance parade planning that includes providing shade/hydration before and after the parade for participants, considering cooler routes that reduce radiant heating and preparing for anticipated heat-related health impacts appropriate for anticipated hot conditions.

BACKGROUND: Climate change increasingly impacts health, particularly of rural populations in sub-Saharan Africa due to their limited resources for adaptation. Understanding these impacts remains a challenge, as continuous monitoring of vital signs in such populations is limited. Wearable devices (wearables) present a viable approach to studying these impacts on human health in real time.
OBJECTIVE: The aim of this study was to assess the feasibility and effectiveness of consumer-grade wearables in measuring the health impacts of weather exposure on physiological responses (including activity, heart rate, body shell temperature, and sleep) of rural populations in western Kenya and to identify the health impacts associated with the weather exposures.
METHODS: We conducted an observational case study in western Kenya by utilizing wearables over a 3-week period to continuously monitor various health metrics such as step count, sleep patterns, heart rate, and body shell temperature. Additionally, a local weather station provided detailed data on environmental conditions such as rainfall and heat, with measurements taken every 15 minutes.
RESULTS: Our cohort comprised 83 participants (42 women and 41 men), with an average age of 33 years. We observed a positive correlation between step count and maximum wet bulb globe temperature (estimate 0.06, SE 0.02; P=.008). Although there was a negative correlation between minimum nighttime temperatures and heat index with sleep duration, these were not statistically significant. No significant correlations were found in other applied models. A cautionary heat index level was recorded on 194 (95.1%) of 204 days. Heavy rainfall (>20 mm/day) occurred on 16 (7.8%) out of 204 days. Despite 10 (21%) out of 47 devices failing, data completeness was high for sleep and step count (mean 82.6%, SD 21.3% and mean 86.1%, SD 18.9%, respectively), but low for heart rate (mean 7%, SD 14%), with adult women showing significantly higher data completeness for heart rate than men (2-sided t test: P=.003; Mann-Whitney U test: P=.001). Body shell temperature data achieved 36.2% (SD 24.5%) completeness.
CONCLUSIONS: Our study provides a nuanced understanding of the health impacts of weather exposures in rural Kenya. Our study\'s application of wearables reveals a significant correlation between physical activity levels and high temperature stress, contrasting with other studies suggesting decreased activity in hotter conditions. This discrepancy invites further investigation into the unique socioenvironmental dynamics at play, particularly in sub-Saharan African contexts. Moreover, the nonsignificant trends observed in sleep disruption due to heat expose the need for localized climate change mitigation strategies, considering the vital role of sleep in health. These findings emphasize the need for context-specific research to inform policy and practice in regions susceptible to the adverse health effects of climate change.

Occurrence of cryptosporidiosis has been associated with weather conditions in many settings internationally. We explored statistical clusters of human cryptosporidiosis and their relationship with severe weather events in New Zealand (NZ). Notified cases of cryptosporidiosis from 1997 to 2015 were obtained from the national surveillance system. Retrospective space-time permutation was used to identify statistical clusters. Cluster data were compared to severe weather events in a national database. SaTScan analysis detected 38 statistically significant cryptosporidiosis clusters. Around a third (34.2%, 13/38) of these clusters showed temporal and spatial alignment with severe weather events. Of these, nearly half (46.2%, 6/13) occurred in the spring. Only five (38%, 5/13) of these clusters corresponded to a previously reported cryptosporidiosis outbreak. This study provides additional evidence that severe weather events may contribute to the development of some cryptosporidiosis clusters. Further research on this association is needed as rainfall intensity is projected to rise in NZ due to climate change. The findings also provide further arguments for upgrading the quality of drinking water sources to minimize contamination with pathogens from runoff from livestock agriculture.

OBJECTIVE: The weather is frequently blamed for changes in musculoskeletal health behaviour and adverse events. However, despite the frequency with which this phenomenon is endorsed, past research is largely conflicting. This meta-analysis has reviewed, appraised and summarised case-crossover studies assessing the transient risk of musculoskeletal health events associated with weather parameters (e.g. temperature, relative humidity, air pressure, and precipitation).
METHODS: A meta-analysis of case-crossover studies was conducted. Two reviewers independently searched MEDLINE, EMBASE, CINAHL, Web of Science, Scopus, and PsycINFO from inception to 10/09/2023. Published studies that employed a case-crossover design to evaluate the risk of musculoskeletal health events (e.g. symptoms, hospitalisation) associated with weather parameters were included. Primary outcome was pain (new episodes of pain or flares). Quality of included studies was assessed based on selection bias, exposure assessment, confounding, and outcome assessment. Pooling of results was conducted using random effects models and separately performed for each condition and weather factor. Heterogeneity among included studies was assessed using I2 measures.
RESULTS: Of the 1,107 studies identified in the search, 11 were included (15,315 participants), providing data on 28,010 events (102,536 control periods), for seven musculoskeletal conditions. Pooled analyses showed no association between relative humidity, air pressure, temperature, or precipitation and the risk of rheumatoid arthritis, knee pain or low back pain. High temperatures combined with low humidity were associated with increased pain, redness, and joint swelling in people with gout (Odds Ratio: 2.04; 95 % Confidence Interval: 1.26 to 3.30).
CONCLUSIONS: Despite anecdotal reports from patients, changes in weather factors do not seem to be risk factors for rheumatoid arthritis, knee, hip, or low back pain, but may have a significant influence in gout disease.

BACKGROUND: Atrial fibrillation (AF) is highly prevalent in the population, yet the factors contributing to AF events in susceptible individuals remain partially understood. The potential relationship between meteorological factors and AF, particularly with abnormal electrocardiograph (ECG) repolarization, has not been adequately studied. This case-crossover study aims to investigate the association between meteorological factors and daily hospital visits for AF with abnormal ECG repolarization in Shanghai, China.
METHODS: The study cohort comprised 10,325 patients with ECG-confirmed AF who sought treatment at Shanghai Sixth People\'s Hospital between 2015 and 2018. Meteorological and air pollutant concentration data were matched with the patient records. Using a case-crossover design, we analyzed the association between meteorological factors and the daily count of hospital visitors for AF with abnormal ECG repolarization at our AF center. Lag analysis models were applied to examine the temporal relationship between meteorological factors and AF events.
RESULTS: The analysis revealed statistically significant associations between AF occurrence and specific meteorological factors. AF events were significantly associated with average atmospheric pressure (lag 0 day, OR 0.9901, 95% CI 0.9825-0.9977, P < 0.05), average temperature (lag 1 day, OR 0.9890, 95% CI 0.9789-0.9992, P < 0.05), daily pressure range (lag 7 days, OR 1.0195, 95% CI 1.0079-1.0312, P < 0.01), and daily temperature range (lag 5 days, OR 1.0208, 95% CI 1.0087-1.0331, P < 0.01). Moreover, a significant correlation was observed between daily pressure range and daily temperature range with AF patients, particularly those with abnormal ECG repolarization, as evident in the case-crossover analysis.
CONCLUSIONS: This study highlights a significant correlation between meteorological factors and daily hospital visits for AF accompanied by abnormal ECG repolarization in Shanghai, China. In addition, AF patients with abnormal ECG repolarization were found to be more vulnerable to rapid daily changes in pressure and temperature compared to AF patients without such repolarization abnormalities.

Valley Fever is a respiratory disease caused by inhalation of arthroconidia, a type of spore produced by fungi within the genus Coccidioides spp. which are found in dry, hot ecosystems of the Western Hemisphere. A quantitative microbial risk assessment (QMRA) for the disease has not yet been performed due to a lack of dose-response models and a scarcity of quantitative occurrence data from environmental samples. A literature review was performed to gather data on experimental animal dosing studies, environmental occurrence, human disease outbreaks, and meteorological associations. As a result, a risk framework is presented with information for parameterizing QMRA models for Coccidioides spp., with eight new dose-response models proposed. A probabilistic QMRA was conducted for a Southwestern US agricultural case study, evaluating eight scenarios related to farming occupational exposures. Median daily workday risks for developing severe Valley Fever ranged from 2.53 × 10-7 (planting by hand while wearing an N95 facemask) to 1.33 × 10-3 (machine harvesting while not wearing a facemask). The literature review and QMRA synthesis confirmed that exposure to aerosolized arthroconidia has the potential to result in high attack rates but highlighted that the mechanistic relationships between environmental conditions and disease remain poorly understood. Recommendations for Valley Fever risk assessment research needs in order to reduce disease risks are discussed, including interventions for farmers.

Disentangling the impact of the weather on transmission of infectious diseases is crucial for health protection, preparedness and prevention. Because weather factors are co-incidental and partly correlated, we have used geography to separate out the impact of individual weather parameters on other seasonal variables using campylobacteriosis as a case study. Campylobacter infections are found worldwide and are the most common bacterial food-borne disease in developed countries, where they exhibit consistent but country specific seasonality. We developed a novel conditional incidence method, based on classical stratification, exploiting the long term, high-resolution, linkage of approximately one-million campylobacteriosis cases over 20 years in England and Wales with local meteorological datasets from diagnostic laboratory locations. The predicted incidence of campylobacteriosis increased by 1 case per million people for every 5° (Celsius) increase in temperature within the range of 8°-15°. Limited association was observed outside that range. There were strong associations with day-length. Cases tended to increase with relative humidity in the region of 75-80%, while the associations with rainfall and wind-speed were weaker. The approach is able to examine multiple factors and model how complex trends arise, e.g. the consistent steep increase in campylobacteriosis in England and Wales in May-June and its spatial variability. This transparent and straightforward approach leads to accurate predictions without relying on regression models and/or postulating specific parameterisations. A key output of the analysis is a thoroughly phenomenological description of the incidence of the disease conditional on specific local weather factors. The study can be crucially important to infer the elusive mechanism of transmission of campylobacteriosis; for instance, by simulating the conditional incidence for a postulated mechanism and compare it with the phenomenological patterns as benchmark. The findings challenge the assumption, commonly made in statistical models, that the transformed mean rate of infection for diseases like campylobacteriosis is a mere additive and combination of the environmental variables.

Although the relationship between the environmental factors, such as weather conditions and air pollution, and COVID-19 case fatality rate (CFR) has been found, the impacts of these factors to which infected cases are exposed at different infectious stages (e.g., virus exposure time, incubation period, and at or after symptom onset) are still unknown. Understanding this link can help reduce mortality rates. During the first wave of COVID-19 in the United Kingdom (UK), the CFR varied widely between and among the four countries of the UK, allowing such differential impacts to be assessed. We developed a generalized linear mixed-effect model combined with distributed lag nonlinear models to estimate the odds ratio of the weather factors (i.e., temperature, sunlight, relative humidity, and rainfall) and air pollution (i.e., ozone, [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] and [Formula: see text]) using data between March 26, 2020 and September 15, 2020 in the UK. After retrospectively time adjusted CFR was estimated using back-projection technique, the stepwise model selection method was used to choose the best model based on Akaike information criteria and the closeness between the predicted and observed values of CFR. The risk of death reached its maximum level when the low temperature (6 °C) occurred 1 day before (OR 1.59; 95% CI 1.52-1.63), prolonged sunlight duration (11-14 h) 3 days after (OR 1.24; 95% CI 1.18-1.30) and increased [Formula: see text] (19 μg/m3) 1 day after the onset of symptom (OR 1.12; 95% CI 1.09-1.16). After reopening, many COVID-19 cases will be identified after their symptoms appear. The findings highlight the importance of designing different preventive measures against severe illness or death considering the time before and after symptom onset.

Inflammatory bowel disease (IBD) is an idiopathic inflammatory disorder characterized by chronic and relapsing manifestations. Several environmental factors are known as triggers for exacerbation of IBD. However, an association between exacerbation of IBD and ambient temperature is uncertain. This study aimed to estimate the risk of acute exacerbation of IBD due to ambient temperature. We performed a bidirectional case-crossover study using a nationwide claim data from South Korea. The external validation was conducted with a large prospective cohort in the United Kingdom. We confirmed significant associations between acute exacerbation of IBD and the short-term ambient temperature changes toward severe temperatures, in the cold weather (-19.4°C-4.3°C) (odd ratio [OR] = 1.13, 95% confidence interval [CI]: 1.13-1.14) and in the hot weather (21.3°C-33.5°C) (OR = 1.16, 95% CI: 1.15-1.17). However, the association was not significant in the moderate weather (4.3°C-21.3°C). The external validation suggested consistent results with additional elevation of acute exacerbation risk in the colder weather (-13.4°C to 2.6°C) (OR = 1.90, 95% CI: 1.62-2.22) and in the hotter weather (15.7°C-28.4°C) (OR = 1.41, 95% CI: 1.32-1.51). We observed and validated that the short-term ambient temperature changes were associated with acute exacerbation of IBD in the cold and hot weathers. Our findings provide evidence that temperature changes are associated with the acute exacerbation of IBD.