The Environment and Headache: a Narrative Review

PURPOSE OF REVIEW

In this narrative review, we summarize the peer-reviewed literature published between 2017 and 2022 that evaluated ambient environmental risk factors for primary headache disorders, which affect more than half of the population globally. Primary headache disorders include migraine, tension-type headache (TTH), and trigeminal and autonomic cephalalgias (TAC).

RECENT FINDINGS

We identified 17 articles that met the inclusion criteria via PubMed or Google Scholar. Seven studies (41%) relied on data from US populations. The remaining studies were conducted in China, Taiwan, Germany, Ghana, Japan, the Netherlands, South Korea, and Turkey. Air pollution was the most frequently assessed environmental risk factor. Most studies were cross-sectional and focused on all-cause or migraine headaches; one study included TTH, and none included TAC. Short-term exposure to fine particulate matter (PM2.5) was not consistently associated with headache endpoints, but long-term exposure to PM2.5 was associated with migraine headache prevalence and severity across multiple studies. Elevated ambient temperature, changes in weather, oil and gas well exposure, and less natural greenspace, but not noise pollution, were also associated with headache. No studies considered water pollution, metal exposure, ultrafine particulate matter, or wildfire smoke exposure. There is a need for ongoing research focused on headache and the environment. Study designs with the greatest explanatory power may include longitudinal studies that capture the episodic nature of headache and case-crossover analysis, which control for time-invariant individual-level confounders by design. There is also a clear need for research that considers comorbid psychiatric illness and socioeconomic position as powerful modifiers of the effect of the environment on headache.

Short-term effects of air pollution and weather on physical activity in patients with chronic obstructive pulmonary disease (COPD)

INTRODUCTION

Patients with chronic obstructive pulmonary disease (COPD) accumulate low levels of physical activity. How environmental factors affect their physical activity in the short-term is uncertain.

AIM

to assess the short-term effects of air pollution and weather on physical activity levels in COPD patients.

METHODS

This multi-center panel study assessed 408 COPD patients from Catalonia (Spain). Daily physical activity (i.e., steps, time in moderate-to-vigorous physical activity (MVPA), locomotion intensity, and sedentary time) was recorded in two 7-day periods, one year apart, using the Dynaport MoveMonitor. Air pollution (nitrogen dioxide (NO2), particulate matter below 10 μm (PM10) and a marker of black carbon (absorbance of PM2.5: PM2.5ABS), and weather (average and maximum temperature, and rainfall) were estimated the same day (lag zero) and up to 5 days prior to each assessment (lags 1-5). Mixed-effect distributed lag linear regression models were adjusted for age, sex, weekday, public holidays, greenness, season, and social class, with patient and city as random effects.

RESULTS

Patients (85% male) were on average (mean ± SD) 68 ± 9 years old with a post-bronchodilator forced expiratory volume in 1 s (FEV1) of 57 ± 18% predicted. Higher NO2, PM10 and PM2.5ABS levels at lag four were associated with fewer steps, less time in MVPA, reduced locomotion intensity, and longer sedentary time (e.g., coefficient (95% CI) of -60 (-105, -15) steps per 10 μg/m3 increase in NO2). Higher average and maximum temperatures at lag zero were related to more steps and time in MVPA, and less sedentary time (e.g., +85 (15, 154) steps per degree Celsius). Higher rainfall at lag zero was related to fewer steps and more sedentary time.

CONCLUSION

Air pollution affects the amount and intensity of physical activity performed on the following days in COPD patients, whereas weather affects the amount of physical activity performed on the same day.

Vulnerability to environmental and climatic health provocations among women and men hospitalized with chronic heart disease: insights from the RESILIENCE TRIAL cohort

AIMS

We aimed to recruit a representative cohort of women and men with multi-morbid chronic heart disease as part of a trial testing an innovative, nurse-co-ordinated, multi-faceted intervention to lower rehospitalization and death by addressing areas of vulnerability to external challenges to their health.

METHODS AND RESULTS

The prospective, randomized open, blinded end-point RESILIENCE Trial recruited 203 hospital inpatients (mean age 75.7 ± 10.2 years) of whom 51% were women and 94% had combined coronary artery disease, heart failure, and/or atrial fibrillation. Levels of concurrent multi-morbidity were high (mean Charlson Index of Comorbidity Score 6.5 ± 2.7), and 8.9% had at least mild frailty according to the Rockwood Clinical Frailty Scale. Including the index admission, 19-20% of women and men had a pre-existing pattern of seasonally linked hospitalization (seasonality). Detailed phenotyping revealed that 48% of women and 40% of men had ≥3 physiological factors, and 15% of women and 16% of men had ≥3 behavioural factors likely to increase their vulnerability to external provocations to their health. Overall, 61-62% of women and men had ≥4 combined factors indicative of such vulnerability. Additional factors such as reliance on the public health system (63 vs. 49%), lower education (30 vs. 14%), and living alone (48 vs. 29%) were more prevalent in women.

CONCLUSION

We successfully recruited women and men with multi-morbid chronic heart disease and bio-behavioural indicators of vulnerability to external provocations to their health. Once completed, the RESILIENCE TRIAL will provide important insights on the impact of addressing such vulnerability (promoting resilience) on subsequent health outcomes.

REGISTRATION

ClinicalTrials.org: NCT04614428.

Emerging effects of temperature on human cognition, affect, and behaviour

Human body core temperature is tightly regulated within approximately 37 °C. Global near surface temperature has increased by over 1.2 °C between 1850 and 2020. In light of the challenge this poses to human thermoregulation, the present perspective article sought to provide an overview on the effects of varying ambient and body temperature on cognitive, affective, and behavioural domains of functioning. To this end, an overview of observational and experimental studies in healthy individuals and individuals with mental disorders was provided. Within body core temperature at approximately 37 °C, relatively lower ambient and skin temperatures appear to evoke a need for social connection, whereas comparably higher temperatures appear to facilitate notions of other as closer and more sociable. Above-average ambient temperatures are associated with increased conflicts as well as incident psychotic and depressive symptoms, mental disorders, and suicide. With mild hypo- and hyperthermia, paradoxical effects are observed: whereas the acute states are generally characterised by impairments in cognitive performance, anxiety, and irritability, individuals with depression experience longer-term symptom improvements with treatments deliberately inducing these states for brief amounts of time. When taken together, it has thus become clear that temperature is inexorably associated with human cognition, affect, and (potentially) behaviour. Given the projected increase in global warming, further research into the affective and behavioural sequelae of heat and the mechanisms translating it into mental health outcomes is urgently warranted.

The Slip and Fall Index: Assessing the risk of slipping and falling on ice

OBJECTIVES

Canadians are at an increased risk of outdoor slip and fall accidents during periods of ice and snow. The aim of this study was to create an index to alert the public of slippery outdoor conditions and promote pedestrian safety.

METHODS

Emergency department (ED) presentations from the four adult hospitals in Calgary, Alberta, Canada, over an 11-year period (January 2008‒December 2018) were extracted and filtered using the ICD-10 code W00 (fall due to ice and snow). Multivariable dispersion-corrected Poisson regression models were used to determine the variables most predictive of these presentations. Month of year, the presence of ice, snow on ground (per 10 cm), and interactions between ice and snow, all up to 3 days prior, were used to create the Slip and Fall Index (SFI).

RESULTS

The dataset included 14,977 slip and fall on ice/snow ED presentations. Females (57.36%, n = 8591) accounted for more presentations than males (42.64%, n = 6386). All months had a significant effect, either being predictive or protective of slip and falls on ice/snow. Current-day ice, snow on ground, and ice up to 3 days prior were predictive of increased presentations. Month and measurements of ice and snow can be input into the SFI, which generates the level of daily risk.

CONCLUSION

The SFI is the first Canadian index with the purpose of measuring the risk of having a slip and fall accident on ice/snow.

Come rain or shine: Is weather a risk factor for musculoskeletal pain? A systematic review with meta-analysis of case-crossover studies

OBJECTIVES

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.

FINDINGS

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).

INTERPRETATION

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.

Whether Weather Matters with Migraine

PURPOSE OF REVIEW

Many patients with migraine report their attacks are triggered by various weather anomalies. Studies have shown mixed results regarding the association of migraine to weather changes. The purpose of the current review is to compile the most up-to-date research studies on how weather may affect migraine. In addition, we explore the association between weather and other inflammatory disease states as well as neurotransmitters.

RECENT FINDINGS

Migraine attacks can be related to weather variables such as barometric pressure, humidity, and wind. However, the results of recent studies are inconsistent; weathers' effect on migraine attacks is around 20%. However, very strong weather factors have a more significant effect on migraine attack variables. Many individuals identify weather as a migraine attack trigger, yet we see no causative relationship between weather and migraine patterns. The outcomes of studies indicate mixed results and reflect individual variation in how weather can impact migraine patterns. Similar relationships can be seen with other rheumatologic and pain conditions in general. Overall, the combination of weather plus other factors appears to be a more significant migraine trigger.

Trend and Seasonality of Hip Fractures in Catalonia, Spain: Exploring the Influence of Climate

To describe the secular trend and seasonality of the incidence of hip fracture (HF) and its relationship with climatic variables during the period 2010-2019 in Catalonia in people aged ≥ 65 years. The results were analyzed by sex, age groups (65-74, 75-84, and ≥ 85), and types of fracture (extracapsular and intracapsular). Data on sex, age, type of fracture, year, and month of hospitalization of patients admitted with a diagnosis of HF between January 1, 2010 and December 31, 2019 were collected. Crude and standardized HF incidence (HFi) rates were obtained. Data on the monthly mean of climatological variables (temperature, insolation, icy days, rain, relative humidity, atmospheric pressure, and wind force) were obtained from the network of meteorological stations in Catalonia. Time series analytical statistics were used to identify trends and seasonality. Linear regression and a seasonal autoregressive integrated moving average (ARIMA) were used to analyze the relationship of each climatic parameter with fracture rates. In addition, generalized additive models were used to ascertain the best predictive model. The total number of HF episodes was 90,149 (74.1% in women and 25.9% in men). The total number of HFs increased by 6.4% between 2010 and 2019. The median age (SD) was 84.5 (7.14) and 54% of patients were ≥ 85 years of age. Extracapsular fractures were the most common (55%). The standardized incidence rates decreased from 728.1/100,000 (95% CI 738.6-769.3) to 624.5/100,000 (95% CI 648.7-677.0), which represents a decrease of 14.2% (p < 0.05). The decline was greater at older ages. There were seasonal variations, with higher incidences in autumn (27.2%) and winter (25.7%) and lower rates in summer (23.5%) and spring (23.6%). Seasonality was more pronounced in elderly people and men. In the bivariate regression analysis, high temperatures and greater insolation were negatively associated with the HF rate, while the number of icy days, rainy days, and high relative humidity were associated with a higher incidence of fractures in all age groups and sexes. In the regression analysis using the seasonal ARIMA model, only insolation had a consistently significant association with overall HFi, after adjusting by trend and other climatic parameters. While the global number of HFs grew in Catalonia due to increases in the elderly population, the standardized HF rate decreased during the years 2010-2019. There was a seasonal trend, with predominance in the cold months and correlations with climatic parameters, especially with insolation.

A microbial risk assessor's guide to Valley Fever (Coccidioides spp.): Case study and review of risk factors

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.

Investigating the Effect of Weather Events on Primary Care Clinicians Across the United States

BACKGROUND

When we consider weather impacts, we mainly consider how the event affects the person, not the clinicians treating them. There is a paucity of studies discussing the effect of weather on the clinicians and the care of their patients.

METHODS

A survey covering weather effects was distributed to American Academy of Family Physicians National Research Network (AAFP NRN) members in August 2020. Descriptive statistics and Fisher's exact tests were completed on the survey responses. Postsurvey interviews were conducted with selected respondents about specific weather events.

RESULTS

Survey respondents were US physicians (88.7%) and 84.9% indicated more than 1 type of event has affected their practice. Respondents were most affected by snow/snowstorm (81.1%) and indicated they had to close for the day or longer and staff were unable to make it into clinic (79.2%). Respondents indicated respiratory (94.5%), mental health (81.8%), and musculoskeletal conditions (50.9%) were most affected by weather. Interviews with selected respondents covered weather topics including winter, summer, and flooding.

DISCUSSION

Survey respondents/interviewees indicated weather affects them in a variety of ways including issues with patients' conditions and practice effects. Clinicians have noticed a change to their areas' weather over the years, but, generally, warming is occurring.

Adolescent mental distress in the wake of climate disasters

Objective

Retrospective exposure to a higher number and prolonged duration of climate-related disasters could be positively associated with adolescent mental distress.

Methods

Person-level data came from 38,616 high-school students residing in 22 urban public-school districts in 14 states (U.S. Youth Risk Behavior Survey, 2019). Each district's federally declared climate-related catastrophes (severe storms, floods, wildfire, etc.) came from the Federal Emergency Management Agency. Logistic regression models estimated the adjusted odds ratios (aOR) of adolescent mental distress (MD, using survey responses feeling prolonged sadness/ hopelessness and short sleep duration) according to disaster events and days during three exposure periods (past 2-, 5-, 10-years); adjusted for age, gender, race/ethnicity, socio-economic disadvantage, feeling unsafe at school, district area size, district poverty, and region.

Results

Over 10 years, the median number of disaster events was 3 and total disaster days was 64. Adolescents experiencing the highest number of disaster days (top quartile vs. less) had 25% higher odds of MD when exposed within the past 2-years (aOR 1.25 [95% CI 1.14, 1.38]), and 20% higher odds of MD when exposed within the past 5-years (aOR 1.20 95% CI 1.07, 1.35). The odds of MD were not statistically associated with exposure periods that extended to 10 years, nor disaster events (instead of disaster days, all p-values > 0.1).

Conclusions

Severe weather will become more frequent and last longer with human-induced climate warming. More studies like this are needed to understand the broad range of adverse effects and enhance planning and preparedness including preparing for worsening mental health among adolescents.

Effects of weather and air pollution on outpatient visits for insect-and-mite-caused dermatitis: an empirical and predictive analysis

BACKGROUND

Dermatitis caused by insects and mites, diagnosed as papular urticaria or scabies, is a common skin disease. However, there is still a lack of studies about the effects of weather and air pollution on outpatient visits for this disease. This study aims to explore the impacts of meteorological and environmental factors on daily visits of dermatitis outpatients.

METHODS

Analyses are conducted on a total of 43,101 outpatient visiting records during the years 2015-2020 from the largest dermatology specialist hospital in Guangzhou, China. Hierarchical cluster models based on Pearson correlation between risk factors are utilized to select regression variables. Linear regression models are fitted to identify the statistically significant associations between the risk factors and daily visits, taking into account the short-term effects of temperatures. Permutation importance is adopted to evaluate the predictive ability of these factors.

RESULTS

Short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. In terms of total outpatients, the one-day lagged temperature not only has a significant impact on daily visits, but also has the highest median value of permutation importance. This conclusion is robust across most subgroups except for subgroups of summer and scabies, wherein the three-day lagged temperature has a negative effect. By contrast, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. Moreover, weekdays, holidays and trends have significant impacts on daily visits, but with weak predictive abilities.

CONCLUSIONS

Our study suggests that short-term temperatures have positive associations with daily visits and exhibit strong predictive abilities. Nevertheless, air pollution has insignificant associations with daily visits and exhibits weak predictive abilities. The results of this study provide a reference for local authorities to formulate intervention measures and establish an environment-based disease early warning system.

Variability in weather and site properties affect fuel and fire behavior following fuel treatments in semiarid sagebrush-steppe

Fuel-treatments targeting shrubs and fire-prone exotic annual grasses (EAGs) are increasingly used to mitigate increased wildfire risks in arid and semiarid environments, and understanding their response to natural factors is needed for effective landscape management. Using field-data collected over four years from fuel-break treatments in semiarid sagebrush-steppe, we asked 1) how the outcomes of EAG and sagebrush fuel treatments varied with site biophysical properties, climate, and weather, and 2) how predictions of fire behavior using the Fuel Characteristic Classification System fire model related to land-management objectives of maintaining fire behavior expected of low-load, dry-climate grasslands. Generalized linear mixed effect modeling with build-up model selection was used to determine best-fit models, and marginal effects plots to assess responses for each fuel type. EAG cover decreased as antecedent-fall precipitation increased and increased as antecedent-spring temperatures and surface soil clay contents increased. Herbicides targeting EAGs were less effective where pre-treatment EAG cover was >40 % and antecedent spring temperatures were >9.5 °C. Sagebrush cover was inversely related to soil clay content, especially where clay contents were >17 %. Predicted fire behavior exceeded management objectives under 1) average fire weather conditions when EAG or sagebrush cover was >50 % or >26 %, respectively, or 2) extreme fire weather conditions when EAG or sagebrush cover was >10 % or >8 %, respectively. Consideration of the strong effects of natural variability in site properties and antecedent weather can help in justifying, planning and implementing fuel-treatments.

Causal effect of air pollution and meteorology on the COVID-19 pandemic: A convergent cross mapping approach

Environmental factors have been suspected to influence the propagation and lethality of COVID-19 in the global population. However, most of the studies have been limited to correlation analyses and did not use specific methods to address the dynamic of the causal relationship between the virus and its external drivers. This work focuses on inferring and understanding the causal effect of critical air pollutants and meteorological parameters on COVID-19 by using an Empirical Dynamic Modeling approach called Convergent Cross Mapping. This technique allowed us to identify the time-delayed causation and the sign of interactions. Considering its remarkable urban environment and mortality rate during the pandemic, Quito, Ecuador, was chosen as a case study. Our results show that both urban air pollution and meteorology have a causal impact on COVID-19. Even if the strength and the sign of the causality vary over time, a general trend can be drawn. NO2, SO2, CO and PM2.5 have a positive causation for COVID-19 infections (ρ > 0.35 and ∂ > 9.1). Contrary to current knowledge, this study shows a rapid effect of pollution on COVID-19 cases (1 < lag days <24) and a negative impact of O3 on COVID-19-related deaths (ρ = 0.53 and ∂ = -0.3). Regarding the meteorology, temperature (ρ = 0.24 and ∂ = -0.4) and wind speed (ρ = 0.34 and ∂ = -3.9) tend to mitigate the epidemiological consequences of SARS-CoV-2, whereas relative humidity seems to increase the excess deaths (ρ = 0.4 and ∂ = 0.05). A causal network is proposed to synthesize the interactions between the studied variables and to provide a simple model to support the management of coronavirus outbreaks.

Significance of weather condition, human mobility, and vaccination on global COVID-19 transmission

The transmission growth rate of infectious diseases, particularly COVID-19, has forced governments to take immediate control decisions. Previous studies have shown that human mobility, weather condition, and vaccination are potential factors influencing virus transmission. This study investigates the contribution of weather conditions, namely temperature and precipitation, human mobility, and vaccination to coronavirus transmission. Three machine learning models: random forest (RF), XGBoost, and neural networks, are applied to predict the confirmed cases based on three aforementioned variables. All models' prediction are evaluated via spatial and temporal analysis. The spatial analysis observes the model performance over countries on certain times. The temporal analysis looks at the model prediction of each country during the specified period. The models' prediction results effectively indicate the transmission trend. The RF model performs best with a coefficient of determination of up to 89%. Meanwhile, all models confirm that vaccination is most significantly associated with COVID-19 cases.

Revisiting the importance of temperature, weather and air pollution variables in heat-mortality relationships with machine learning

Extreme heat events have significant health impacts that need to be adequately quantified in the context of climate change. Traditionally, heat-health association methods have relied on statistical models using a single air temperature index, without considering other heat-related variables that may influence the relationship and their potentially complex interactions. This study aims to introduce and compare different machine learning (ML) models, which naturally consider interactions between predictors and non-linearities, to re-examine the importance of temperature, weather and air pollution predictors in modeling the heat-mortality relationship. ML approaches based on tree ensembles and neural networks, as well as non-linear statistical models, were used to model the heat-mortality relationship in the two most populated metropolitan areas of the province of Quebec, Canada. The models were calibrated using a comprehensive database of heat-related predictors including various lagged temperature indices, temperature variations, meteorological and air pollution variables. Performance was evaluated based on out-of-sample summer mortality predictions. For the two studied regions, models relying only on lagged temperature indices performed better, or equally well, than models considering more heat-related predictors such as temperature variations, weather and air pollution variables. The temperature index with the best performance differed by region, but both mean temperature and humidex were among the best indices. In terms of modeling approaches, non-linear statistical models were as competent as more advanced ML models for predicting out-of-sample summer mortality. This research validated the current use of non-linear statistical models with the appropriate lagged temperature index to model the heat-mortality relationship. Although ML models have not improved the performance of all-cause mortality modeling, these approaches should continue to be explored, particularly for other health effects that may be more directly linked to heat exposure and, in the future, when more data become available.

Effect of warm and humid days on the positivity rate of anti-Trichosporon asahii antibody test for the diagnosis of summer-type hypersensitivity pneumonitis

BACKGROUND

Summer-type hypersensitivity pneumonitis (SHP) has been reported to occur during warm and humid summer seasons in Japan; however, the effect of weather conditions on SHP remains unknown. Anti-Trichosporon asahii antibody (TaAb) test is highly specific and useful for the diagnosing SHP. Therefore, we aimed to investigate the impact of weather conditions on SHP by examining the relationship between the positivity rate of TaAb and warm and humid days.

METHODS

TaAb test data from June 2013 to June 2020 were obtained from major commercial laboratories to determine the number of samples and positivity rate of TaAb by prefecture. Using the Japan Meteorological Agency database, we counted the warm and humid days (maximum temperature ≥25 °C and average humidity ≥80 %) for each prefecture. Negative binomial regression was employed to examine the relationship between the positivity rate of TaAb and the number of warm and humid days per month.

RESULTS

A total of 79,211 samples and 7626 positive samples (9.6 %) were identified. We found that the number of warm and humid days, 1 or 2 months prior to testing for TaAb, was associated with the positivity rate of the test. An increase in the positivity rate by 1.6 % and 2.9 % was observed with every 1-day increase in warm and humid days 1 month and 2 months before the test, respectively.

CONCLUSIONS

Our TaAb analysis revealed a significant increase in TaAb positivity 1 or 2 months after periods of warm and humid days.

Ambient temperature and kidney function in primary care patients

INTRODUCTION

Exposure to high ambient temperatures is associated with a risk of acute kidney injury. However, evidence comes from emergency departments or extreme weather exposures. It is unclear whether temperature-related adverse kidney outcomes can also be detected at a community level in a temperate climate zone.

METHODS

In a 9.5-year retrospective cohort study we correlated estimated glomerular filtration rate (eGFR) values of Swiss adult primary care patients from the FIRE cohort (Family medicine Research using Electronic medical records) with same-day maximum local ambient temperature data. We investigated 5 temperature groups (< 15 °C, 15-19 °C, 20-24 °C, 25-29 °C and  ≥ 30 °C) as well as possible interactions for patients with increased kidney vulnerability (chronic heart failure, diabetes, chronic kidney disease, therapy with renin-angiotensin-aldosterone-system (RAAS) inhibitors, diuretics or non-steroidal anti-inflammatory drugs).

RESULTS

We included 18,000 primary care patients who altogether provided 132,176 creatinine measurements. In the unadjusted analysis, higher ambient temperatures were associated with lower eGFR across all age and vulnerability groups. In the adjusted models, we did not find a consistent association.The highest ambient temperature differences (> 25 or > 30 versus < 15 °C) were associated with marginally reduced kidney function only in patients with ≥ 3 risk factors for kidney vulnerability, with a maximum estimated glomerular filtration rate reduction of -2.9 ml/min/1.73m2 (SE 1.0), P 0.003.

DISCUSSION

In a large primary care cohort from a temperate climate zone, we did not find an association between ambient temperatures and kidney function. A marginal inverse association in highly vulnerable patients is of unclear clinical relevance.

Radiology weather forecast: A retrospective analysis of predictability of median daily polytrauma-CT occurrence based on weather data

OBJECTIVES

Resource planning is a crucial component in hospitals, particularly in radiology departments. Since weather conditions are often described to correlate with emergency room visits, we aimed to forecast the amount of polytrauma-CTs using weather information.

DESIGN

All polytrauma-CTs between 01/01/2011 and 12/31/2022 (n = 6638) were retrieved from the radiology information system. Local weather data was downloaded from meteoblue.com. The data was normalized and smoothened. Daily polytrauma-CT occurrence was stratified into below median and above median number of daily polytrauma-CTs. Logistic regression and machine learning algorithms (neural network, random forest classifier, support vector machine, gradient boosting classifier) were employed as prediction models. Data from 2012 to 2020 was used for training, data from 2021 to 2022 for validation.

RESULTS

More polytrauma-CTs were acquired in summer compared with winter months, demonstrating a seasonal change (median: 2.35; IQR 1.60-3.22 vs. 2.08; IQR 1.36-3.03; p <.001). Temperature (rs = 0.45), sunshine duration (rs = 0.38) and ultraviolet light amount (rs = 0.37) correlated positively, wind velocity (rs = -0.57) and cloudiness (rs = -0.28) correlated negatively with polytrauma-CT occurrence (all p <.001). The logistic regression model for identification of days with above median number of polytrauma-CTs achieved an accuracy of 87 % on training data from 2011 to 2020. When forecasting the years 2021-2022 an accuracy of 65 % was achieved. A neural network and a support vector machine both achieved a validation accuracy of 72 %, whereas all classifiers regarded wind velocity and ultraviolet light amount as the most important parameters.

CONCLUSION

It is possible to forecast above or below median daily number of polytrauma-CTs using weather data.

CLINCICAL RELEVANCE STATEMENT

Prediction of polytrauma-CT examination volumes may be used to improve resource planning.

The effects of warm weather on children's outdoor heat stress and physical activity in a preschool yard in Gothenburg, Sweden

Hot weather conditions can have negative impacts on the thermal comfort and physical activity of vulnerable groups such as children. The aim of this study is to analyze the effects of warm weather on 5-year-old children's thermal comfort and physical activity in a preschool yard in Gothenburg, Sweden. In situ measurements were conducted for 1-1.5 h in the early afternoon on 8 days in May, June, and August of 2022. The thermal comfort and physical activity was estimated with GPS-tracks, heart rate monitors, and step counts and compared to observed weather conditions. Results show that physical activity decreases under warmer weather conditions, depicted by a decrease in distance moved, step counts, and highest registered pulse. Moreover, on warm days, the children avoid sunlit areas. For 50% or more of the time spent in sunlit areas, the children are exposed to cautious levels of heat. In shaded areas, on the other hand, the children are less exposed, with five out of 8 days having 50% or more of the time at neutral levels. The study demonstrates the importance of access to shaded areas in preschool yards where children can continue their active play while simultaneously maintaining a safe thermal status.

FROST 2.0: Factors Predicting Orthopaedic Trauma Volumes - A Validation Study

The anecdotal relationship between weather, season, and orthopaedic trauma volumes is understood by patients and healthcare providers: when the winter ground is icy, people fall. Previous research established that winter and summer months as well as ice and snow were significant predictors of orthopaedic trauma and surges in volumes. The current study validates the previously established predictive model in one Canadian city; the original methods were repeated using a new, yet geographically similar dataset. The effect of month and ice were consistent, which suggests the proposed model has sufficient external validity to guide resource allocation and primary prevention strategies.

Climatic variations and pollution on benign paroxysmal positional vertigo in Kars, Türkiye

Benign paroxysmal positional vertigo (BPPV) is the most common diagnosis for peripheral vertigo. Although pathophysiological mechanisms remain unclear, BPPV is mostly idiopathic and factors related to BPPV are still being investigated. Knowing these factors can contribute to the prevention and management of BPPV. In this study, we investigated the correlations between climatic variations, pollution, and BPPV retrospectively. 262 patients diagnosed with BPPV between 2019 and 2021 in Kars, Türkiye, were included in our study. Meteorological parameters were obtained from Turkish State Meteorological Service. Horizontal BPPV increased significantly with the humidity (p < 0.05). In addition, carbon monoxide levels significantly increased the potantial of BPPV (p < 0.05). Surprisingly, BPPV increased in the summertime and showed a significant relationship with humidity. We believe this change is related with the city-specific features as it is the coldest place in the country, emigrant province and crowded in the summer times.

Effects of weather conditions on endovascular treatment case volume for patients with ischemic stroke

BACKGROUND

Weather conditions have been shown to influence the occurrence of cardiovascular events. We tested the hypothesis that weather parameters may be associated with variations of case volume of endovascular treatment (EVT) for acute ischemic stroke.

METHODS

Individual data from the ETIS (Endovascular Treatment in Ischemic Stroke) French national registry were matched to local weather stations. Meteorological parameters (rainfall, humidity, atmospheric pressure, air temperature) were gathered from national online resources. Weather readings and EVT case volumes were annually standardized per weather station and EVT center, and their associations tested with non-parametric univariable and generalized linear statistical models.

RESULTS

Between 2015 and 2021, 9913 EVT procedures addressed by 135 primary stroke units were matched to weather conditions. The mean daily case volume per center was 0.41 [StDev 0.33], and there was a median of 0.84 procedures daily linked to a weather station [StDev 0.47]. We found lower atmospheric pressure (β estimate -0.04; 95%CI[-0.07;-0.03], p<0.001), higher humidity (β estimate 0.07; 95%CI [0.05;0.09], p<0.001) and lower temperatures (β estimate -0.08; 95%CI[-0.10;-0.06], p<0.001) to be associated with higher standardized EVT daily case volumes. These associations were stable when testing them across strata of binned EVT standardized case volumes.

CONCLUSIONS

Our study suggests that lower ambient temperature, lower atmospheric pressure, and higher air humidity are associated with significantly more daily EVT cases in a European temperate country. These results may provide insight into both system of care optimization at times of climate change and intracranial LVO pathophysiology. REGISTRATION-URL: https://clinicaltrials.gov/ct2/show/NCT03776877.

Prevalence and Subtype Characterization of Campylobacter in Ceca of Commercial Broiler Chickens at Processing - A 452 Flock, Seven-year Survey

Human Campylobacter infections have been associated with chicken and other poultry meat products. Environmental conditions such as temperature and season can affect Campylobacter recoverability from chicken meat products. In the presented study, we sought to investigate the relationship between ambient weather conditions and the isolation of Campylobacter from chicken flocks, as well as the subtype of these isolates. Campylobacter was isolated from the ceca of broilers collected in a commercial processing facility over 7 years, representing 452 flocks. Isolates were subjected to whole-genome sequencing and subtyping by multilocus sequence typing (MLST). Approximately 60% (269/452) of flocks sampled were positive for Campylobacter. There was no significant effect on the presence of detectable Campylobacter by month, season, temperature, or rainfall during grow-out or transportation. Sixty-eight different STs were detected; 45 C. jejuni and 23 C. coli. Diversity as measured by Shannon's diversity index was higher in the spring and fall than in mid-winter and summer. We concluded that in the warm temperate climate of the Southeastern U.S., seasonality does not affect the rate of Campylobacter isolation from broilers, but the diversity of isolates was higher in the milder spring and fall seasons.

Short-term effect of meteorological factors and extreme weather events on daily outpatient visits for dry eye disease between 2013 and 2020: a time-series study in Urumqi, China

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.

Mask Adherence and the Relationship Between Masking and Weather-Related Metrics

Little is known about adherence to COVID-19 masking mandates on college campuses or the relationship between weather-related variables and masking. This study aimed to (1) observe students' adherence to on-campus mask mandates and (2) estimate the effect of weather on mask-wearing. Temple University partnered in the Centers for Disease Control and Prevention's observational Mask Adherence Surveillance at Colleges and Universities Project. February-April 2021, weekly observations were completed at 12 on-campus locations to capture whether individuals wore masks, wore them correctly, and the type of mask worn. Fashion and university masks also were recorded. Weekly average temperature, humidity, and precipitation were calculated. Descriptive statistics were calculated for masking adherence overall, over time, and by location. Statistical significance was assessed between correct mask use and mask type and the linear relationships between weekly weather metrics and mask use. Overall, 3508 individuals were observed with 89.6% wearing masks. Of those, 89.4% correctly wore masks. Cloth (58.7%) and surgical masks (35.3%) were most commonly observed and 21.3% wore fashion masks. N95/KN95 masks were correctly worn in 98.3% of observations and surgical and cloth masks were correctly worn ~ 90% of the time. Weekly adherence varied over time and by campus location. Significant inverse linear relationships existed between weekly temperature (r = - 0.72; p < 0.05) and humidity (r = - 0.63; p ≤ 0.05) and masking. Mask adherence and correct use was high. Temperature and humidity inversely affected adherence. Adherence varied by on-campus location, which suggests the locations (e.g., academic buildings, recreational center) and possibly the characteristics of individuals who frequent certain areas impacted adherence.

Impacts of weather and air pollution on Legionnaires' disease in Switzerland: A national case-crossover study

BACKGROUND

The number of reported cases of Legionnaires' disease (LD) has risen markedly in Switzerland (6.5/100,000 inhabitants in 2021) and abroad over the last decade. Legionella, the causative agent of LD, are ubiquitous in the environment. Therefore, environmental changes can affect the incidence of LD, for example by increasing bacterial concentrations in the environment or by facilitating transmission.

OBJECTIVES

The aim of this study is to understand the environmental determinants, in particular weather conditions, for the regional and seasonal distribution of LD in Switzerland.

METHODS

We conducted a series of analyses based on the Swiss LD notification data from 2017 to 2021. First, we used a descriptive and hotspot analysis to map LD cases and identify regional clusters. Second, we applied an ecological model to identify environmental determinants on case frequency at the district level. Third, we applied a case-crossover design using distributed lag non-linear models to identify short-term associations between seven weather variables and LD occurrence. Lastly, we performed a sensitivity analysis for the case-crossover design including NO2 levels available for the year 2019.

RESULTS

Canton Ticino in southern Switzerland was identified as a hotspot in the cluster analysis, with a standardised notification rate of 14.3 cases/100,000 inhabitants (CI: 12.6, 16.0). The strongest association with LD frequency in the ecological model was found for large-scale factors such as weather and air pollution. The case-crossover study confirmed the strong association of elevated daily mean temperature (OR 2.83; CI: 1.70, 4.70) and mean daily vapour pressure (OR: 1.52, CI: 1.15, 2.01) 6-14 days before LD occurrence.

DISCUSSION

Our analyses showed an influence of weather with a specific temporal pattern before the onset of LD, which may provide insights into the effect mechanism. The relationship between air pollution and LD and the interplay with weather should be further investigated.

Assessment of growing condition variables on alfalfa productivity

This study was conducted to assess the impact of growing condition variables on alfalfa (Medicago sativa L.) productivity. A total of 197 alfalfa yield results were acquired from the alfalfa field trials conducted by the South Korean National Agricultural Cooperative Federation or Rural Development Administration between 1983 and 2008. The corresponding climate and soil data were collected from the database of the Korean Meteorological Administration. Twenty-three growing condition variables were developed as explaining variables for alfalfa forage biomass production. Among them, twelve variables were chosen based on the significance of the partial-correlation coefficients or potential agricultural values. The selected partial correlation coefficients between the variables and alfalfa forage biomass ranged from -0.021 to 0.696. The influence of the selected twelve variables on yearly alfalfa production was summarized into three dominant factors through factor analysis. Along with the accumulated temperature variables, the loading scores of the daily mean temperature higher than 25°C were over 0.88 in factor 1. The sunshine duration at temperature between 0°C-25°C was 0.939 in factor 2. Precipitation days were 0.82, which was the greatest in factor 3. Stepwise regression applied with the three dominant factors resulted in the coefficients of factors 1, 2, and 3 for 0.633, 0.485, and 0.115, respectively, and the R-square of the model was 0.602. The environmental conditions limiting alfalfa growth, such as daily temperature higher than 25°C or daily mean temperature affected annual alfalfa production most substantially among the growing condition variables. Therefore, future cultivar selection should consider the capability of alfalfa to be tolerant to extreme summer weather along with biomass production potential.

Quantification of rainfall, temperature, and reference evapotranspiration trend and their interrelationship in sub-climatic zones of Bangladesh

Rainfall, temperature, and reference evapotranspiration (ET0) have a significant influence on irrigation, aridity, flooding, and crop water requirements. The primary aims of this study were to analyze the trends in rainfall, temperature, and ET0 in seven sub-climatic zones of Bangladesh from 1989 to 2020, as well as examine their interrelationships. The Modified Mann-Kendall method was employed to assess trends, while linear regression was used for trend validation. ET0 was calculated using the FAO-56 Penman-Monteith method, and Sen's slope was utilized to quantify the magnitude. Spatial analysis was conducted using Inverse Distance Weighting techniques. The findings revealed that annual rainfall increased only in the south-eastern zone, while the other zones experienced a decline. No significant changes were observed in annual maximum temperature, except in the south-eastern, north-eastern, and south-central zones, which showed variations ranging from 0.02 to 0.05 (°C/year). However, the yearly minimum temperature increased in all zones. Additionally, negative changes were observed in the annual magnitude of ET0 for all zones and seasons, except for the south-eastern and north-eastern zones, with a range of 0.01-0.02 mm/year. It was also noted that rainfall and ET0 displayed a strong decreasing relationship, except during the pre-monsoon season. Regarding regional variation, the northern regions exhibited a significant decreasing trend in both rainfall and ET0. The study identified key challenges, including water scarcity and irrigation difficulties due to declining rainfall and evapotranspiration, increased aridity, changing flood patterns, temperature-related impacts on crop growth, regional disparities in climate trends, and the need for effective climate change adaptation measures. Therefore, the study's findings can contribute to knowledge in areas such as irrigation scheduling, promoting climate-smart agricultural practices, encouraging crop diversification to reduce dependence on water-intensive crops cultivation, and planning resilient water resource management to minimize the effects of environmental shifts, regulate human operations, and implement disaster remedial actions in Bangladesh.

The complex interplay between weather, social activity, and COVID-19 in the US

Empirical studies on the impact of weather and policy interventions on Covid-19 infections have dedicated little attention to the mediation role of social activity. In this study, we combine mobile locations, weather, and COVID-19 data in a two-way fixed effects mediation model to estimate the impact of weather and policy interventions on the COVID-19 infection rate in the US before the availability of vaccines, disentangling their direct impact from the part of the effect that is mediated by the endogenous response of social activity. We show that, while temperature reduces viral infectiousness, it also increases the amount of time individuals spend out of home, which instead favours the spread of the virus. This second channel substantially attenuates the beneficial effect of temperature in curbing the spread of the virus, offsetting one-third of the potential seasonal fluctuations in the reproduction rate. The mediation role of social activity is particularly pronounced when viral incidence is low, and completely offsets the beneficial effect of temperature. Despite being significant predictors of social activity, wind speed and precipitation do not induce sufficient variation to affect infections. Our estimates also suggest that school closures and lockdowns are effective in reducing infections. We employ our estimates to quantify the seasonal variation in the reproduction rate stemming from weather seasonality in the US.

Meteorological extremes and their impact on tinnitus-related emergency room visits: a time-series analysis

PURPOSE

Extreme weather events are rising due to the accelerating pace of climate change. These events impact human health and increase emergency room visits (EV) for many morbidities. Tinnitus is a common cause of EVs within otolaryngology in Germany and Austria. The effect of extreme weather conditions on tinnitus-related EVs is unknown.

METHODS

A total of 526 tinnitus-related EVs at a tertiary care hospital in Vienna were identified. A distributed lag non-linear model with a maximum lag period of 14 days was fitted to investigate the immediate and delayed effect of single-day and prolonged (three-day) extreme atmospheric pressure, relative humidity, mean temperature, precipitation and mean wind speed on EV rates. Extreme conditions were defined as the 1st, 5th, 95th, and 99th percentile of the meteorological variables. Relative risk (RR) is defined as risk for tinnitus-related EVs at an extreme condition compared to the risk at the median weather condition. Cumulative RR (cRR) is the total cumulated EV risk for a given time period.

RESULTS

High relative humidity increased same-day RR for tinnitus-related EVs to 1.75. Both low and high atmospheric pressure raised cRR as early as three days after an event to a maximum of 3.24. Low temperatures mitigated cRR within 4 days, while high temperatures tended to increase risk. Prolonged precipitation reduced cRR within one day.

CONCLUSION

Extreme meteorological conditions are associated with tinnitus-related EV rates. Further investigation into potential causative links and underlying pathophysiological mechanisms is warranted.

Predicting the dengue cluster outbreak dynamics in Yogyakarta, Indonesia: a modelling study

Background

Human mobility and climate conditions are recognised key drivers of dengue transmission, but their combined and individual role in the local spatiotemporal clustering of dengue cases is not well understood. This study investigated the effects of human mobility and weather conditions on dengue risk in an urban area in Yogyakarta, Indonesia.

Methods

We established a Bayesian spatiotemporal model for neighbourhood outbreak prediction and evaluated the performances of two different approaches for constructing an adjacency matrix: one based on geographical proximity and the other based on human mobility patterns. We used population, weather conditions, and past dengue cases as predictors using a flexible distributed lag approach. The human mobility data were estimated based on proxies from social media. Unseen data from February 2017 to January 2020 were used to estimate the one-month ahead prediction accuracy of the model.

Findings

When human mobility proxies were included in the spatial covariance structure, the model fit improved in terms of the log score (from 1.748 to 1.561) and the mean absolute error (from 0.676 to 0.522) based on the validation data. Additionally, showed only few observations outside the credible interval of predictions (1.48%) and weather conditions were not found to contribute additionally to the clustering of cases at this scale.

Interpretation

The study shows that it is possible to make highly accurate predictions of the within-city cluster dynamics of dengue using mobility proxies from social media combined with disease surveillance data. These insights are important for proactive and timely outbreak management of dengue.

Funding

Swedish Research Council Formas, Umeå Centre for Global Health Research, Swedish Council for Working Life and Social Research, Swedish research council VINNOVA and Alexander von Humboldt Foundation (Germany).

The impact of weather on time allocation to physical activity and sleep of child-parent dyads

PURPOSE

Previous studies showed that unfavourable weather conditions discourage physical activity. However, it remains unclear whether unfavourable weather conditions have a differential impact on physical activity in children compared with adults. We aim to explore the differential impact of weather on time allocation to physical activity and sleep by children and their parents.

METHOD

We use nationally representative data with time use indicators objectively measured on multiple occasions for >1100 Australian pairs of 12-13-year-old children and their middle-aged parents, coupled with daily meteorological data. We employ an individual fixed effects regression model to estimate the causal impact of weather.

RESULTS

We find that unfavourable weather conditions, as measured by cold or hot temperatures or rain, cause children to reduce moderate- and vigorous-intensity physical activity time and increase sedentary time. However, such weather conditions have little impact on children's sleep time or the time allocation of their parents. We also find substantial differential weather impact, especially on children's time allocation, by weekdays/weekends and parental employment status, suggesting that these factors may contribute to explaining the differential weather impact that we observed. Our results additionally provide evidence of adaptation, as temperature appears to have a more pronounced impact on time allocation in colder months and colder regions.

CONCLUSION

Our finding of a negative impact of unfavourable weather conditions on the time allocated to physical activity by children indicates a need to design policies to encourage them to be more physically active on days with unfavourable weather conditions and hence improve child health and wellbeing. Evidence of a more pronounced and negative impact on the time allocated to physical activity by children than their parents suggests that extreme weather conditions, including those associated with climate change, could make children vulnerable to reduced physical activity.

Climate and human mortality in Virginia, 2005-2020

Using an extensive database of every resident death in Virginia from 2005 to 2020, climate-mortality relationships are examined for 12 climatically homogeneous regions within the Commonwealth. Each region is represented by a first-order weather station from which archived temperature and humidity data are used to generate a variety of biometeorologically relevant indices. Using these indices and other variables (such as air quality and heat and cold waves), daily mortality and climate relationships are modeled for each region over a 21-day lag period utilizing generalized additive models and distributed lag non-linear models. Optimal models are identified for each region, and a consensus model was also run based on maximum temperature to facilitate inter-regional comparisons. The relative risk of mortality varies markedly as a function of climate between regions, with U-shaped, J-shaped, and inverse linear relationships evident. Cold mortality exceeds heat mortality across most of Virginia (typical relative risks are 1.10 for cold and 1.03 for heat), with cold risks strongest at lags 3 to 10. Low temperatures (or low humidity) are protective at lags 0-2 days except in the colder, western parts of state. Heat mortality occurs at short lags (0-2 days) for three-fourths of the stations, but the spatial pattern is random. Mortality displacement is evident for most regions for several days following the heat-related spike. Although the use of region-specific models is justified, the simple consensus model based on a consistent set of predictors provides similar results.

Association between ambient air pollutants and upper respiratory tract infection and pneumonia disease burden in Thailand from 2000 to 2022: a high frequency ecological analysis

BACKGROUND

A pertinent risk factor of upper respiratory tract infections (URTIs) and pneumonia is the exposure to major ambient air pollutants, with short term exposures to different air pollutants being shown to exacerbate several respiratory conditions.

METHODS

Here, using disease surveillance data comprising of reported disease case counts at the province level, high frequency ambient air pollutant and climate data in Thailand, we delineated the association between ambient air pollution and URTI/Pneumonia burden in Thailand from 2000 - 2022. We developed mixed-data sampling methods and estimation strategies to account for the high frequency nature of ambient air pollutant concentration data. This was used to evaluate the effects past concentrations of fine particulate matter (PM_2.5), sulphur dioxide (SO₂), and carbon monoxide (CO) and the number of disease case count, after controlling for the confounding meteorological and disease factors.

RESULTS

Across provinces, we found that past increases in CO, SO_2, and PM_2.5 concentration were associated to changes in URTI and pneumonia case counts, but the direction of their association mixed. The contributive burden of past ambient air pollutants on contemporaneous disease burden was also found to be larger than meteorological factors, and comparable to that of disease related factors.

CONCLUSIONS

By developing a novel statistical methodology, we prevented subjective variable selection and discretization bias to detect associations, and provided a robust estimate on the effect of ambient air pollutants on URTI and pneumonia burden over a large spatial scale.

Atmospheric pressure predicts probability of departure for migratory songbirds

BACKGROUND

Weather can have both delayed and immediate impacts on animal populations, and species have evolved behavioral adaptions to respond to weather conditions. Weather has long been hypothesized to affect the timing and intensity of avian migration, and radar studies have demonstrated strong correlations between weather and broad-scale migration patterns. How weather affects individual decisions about the initiation of migratory flights, particularly at the beginning of migration, remains uncertain.

METHODS

Here, we combine automated radio telemetry data from four species of songbirds collected at five breeding and wintering sites in North America with hourly weather data from a global weather model. We use these data to determine how wind profit, atmospheric pressure, precipitation, and cloud cover affect probability of departure from breeding and wintering sites.

RESULTS

We found that the probability of departure was related to changes in atmospheric pressure, almost completely regardless of species, season, or location. Individuals were more likely to depart on nights when atmospheric pressure had been rising over the past 24 h, which is predictive of fair weather over the next several days. By contrast, wind profit, precipitation, and cloud cover were each only informative predictors of departure probability in a single species.

CONCLUSIONS

Our results suggest that individual birds actively use weather information to inform decision-making regarding the initiation of departure from the breeding and wintering grounds. We propose that birds likely choose which date to depart on migration in a hierarchical fashion with weather not influencing decision-making until after the departure window has already been narrowed down by other ultimate and proximate factors.

'I don't really associate climate change with actual people's health': a qualitative study in England of perceptions of climate change and its impacts on health

OBJECTIVES

The health impacts of climate change are increasing, but qualitative evidence on people's perceptions is limited. This qualitative study investigated people's perceptions of climate change and its impacts on health.

STUDY DESIGN

This was an online study using semistructured interviews.

METHODS

A total of 41 semistructured interviews were conducted in 2021 with members of the public aged ≥15 years living in England, recruited via community-based groups. Data were analysed using reflexive thematic analysis.

RESULTS

Participants were concerned about climate change, which was often perceived as extreme weather events happening elsewhere. Changes in the UK's seasons and weather patterns were noted, but participants were uncertain whether these changes resulted from climate change. Participants often struggled to identify health impacts of climate change; where health impacts were described, they tended to be linked to extreme weather events outside the United Kingdom and their associated threats to life. The mental health impacts of such events were also noted.

CONCLUSIONS

The study found that most participants did not perceive climate change to be affecting people's health in England. This raises questions about whether framing climate change as a health issue, an approach advocated for countries less exposed to the direct effects of climate change, will increase its salience for the British public.

Relationship of emergency department visits for suicide attempts with meteorological and air pollution conditions

BACKGROUND

Environmental factors such as meteorological and air pollution conditions have been identified as risk factors for suicide. This study aimed to clarify the relationship of the number of visits to the emergency department for suicide attempts with meteorological and air pollution conditions.

METHODS

This cross-sectional study included patients who attempted suicide and were transported to Yokohama City University Medical Center from April 2005 to March 2022. The meteorological conditions recorded at the time of transport included mean atmospheric pressure, mean temperature, maximum temperature, minimum temperature, mean humidity, wind speed, and sunshine hours, and the air pollution conditions included SO₂ (ppm), NO (ppm), NO₂ (ppm), NOX (ppm), OX (ppm), CH₄ (ppmC), NMHC (ppmC), THC (ppmC), SPM (μg/m³), and PM2.5 (μg/m³). Poisson regression analysis was used to examine the association between the number of suicide attempts and the meteorological and air pollution conditions. Subgroup analyses were conducted by classifying the subjects according to the means of suicide attempt; comparisons were performed using t-tests.

RESULTS

The study included 1737 patients. Multivariate Poisson regression analyses revealed a significant positive relationship between the number of suicide attempts and SO₂ levels and a significant negative relationship between the number of suicide attempts and NO levels. When subjects were divided by means of suicide attempt, different relationships with meteorological and air pollution conditions were observed.

CONCLUSION

Meteorological and air pollution conditions are environmental factors that can enable a more detailed understanding of suicide behavior according to the means of suicide attempts.

Forecasting daily emergency ambulance service demand using biometeorological indexes

This study aims to study the effectiveness of using biometeorological indexes in the development of a daily emergency ambulance service demand forecast system for Taipei City, Taiwan, compared to typical weather factors. Around 370,000 emergency ambulance service patient records were aggregated into a daily emergency ambulance service demand time series as the study's dependent variable. To assess the effectiveness of biometeorological indexes in making a 1 to 7-day forecast of daily emergency ambulance service demand, five forecast models were developed to make the comparison. The model with average temperature as the only predictor performed the best consistently from 1 to 7-day forecasts. The models with net effective temperature and apparent temperature as their only predictors ranked second and third, respectively. It is surprising that the model with both average temperature and relative humidity as predictors only ranked fourth. The unexpected outperformance of average temperature over net effective temperature and apparent temperature in forecasting daily emergency ambulance service demand suggested the need to develop updated locational-specific biometeorological indexes so that the benefit of the indexes can be fully utilized. Although adopting popular biometeorological indexes that are already available would be cheap and convenient, the benefit from these general indexes may not be guaranteed.

Impact of non-pharmaceutical interventions, weather, vaccination, and variants on COVID-19 transmission across departments in France

BACKGROUND

Multiple factors shape the temporal dynamics of the COVID-19 pandemic. Quantifying their relative contributions is key to guide future control strategies. Our objective was to disentangle the individual effects of non-pharmaceutical interventions (NPIs), weather, vaccination, and variants of concern (VOC) on local SARS-CoV-2 transmission.

METHODS

We developed a log-linear model for the weekly reproduction number (R) of hospital admissions in 92 French metropolitan departments. We leveraged (i) the homogeneity in data collection and NPI definitions across departments, (ii) the spatial heterogeneity in the timing of NPIs, and (iii) an extensive observation period (14 months) covering different weather conditions, VOC proportions, and vaccine coverage levels.

FINDINGS

Three lockdowns reduced R by 72.7% (95% CI 71.3-74.1), 70.4% (69.2-71.6) and 60.7% (56.4-64.5), respectively. Curfews implemented at 6/7 pm and 8/9 pm reduced R by 34.3% (27.9-40.2) and 18.9% (12.04-25.3), respectively. School closures reduced R by only 4.9% (2.0-7.8). We estimated that vaccination of the entire population would have reduced R by 71.7% (56.4-81.6), whereas the emergence of VOC (mainly Alpha during the study period) increased transmission by 44.6% (36.1-53.6) compared with the historical variant. Winter weather conditions (lower temperature and absolute humidity) increased R by 42.2% (37.3-47.3) compared to summer weather conditions. Additionally, we explored counterfactual scenarios (absence of VOC or vaccination) to assess their impact on hospital admissions.

INTERPRETATION

Our study demonstrates the strong effectiveness of NPIs and vaccination and quantifies the role of weather while adjusting for other confounders. It highlights the importance of retrospective evaluation of interventions to inform future decision-making.

Determinants of farms' antibiotic consumption - A longitudinal study of pig fattening farms in Germany

As high consumption of antibiotics in livestock production poses risks to public health, Germany has implemented a monitoring system to decrease their administration to farm animals. Data from 1,984 German pig farms are used to describe prescription trends for different antibiotic subclasses between Autumn 2017 and Autumn 2019. A panel Tobit model with control function approach is implemented to identify determinants of antibiotic consumption, where variables studied include farm, farmer, and county characteristics as well as weather variables. The overall quantity of prescribed antibiotics has been stable but with seasonal fluctuations and a shift away from critically important antibiotics used. Biosecurity factors such as livestock farm density in a county and pigs per farm are shown to be important drivers of antibiotic consumption. In addition, the number of cold days within a season increases antibiotic consumption but precipitation and the number of hot days have no significant effect.

Cold temperatures induce priming of the glucose stress response in tree swallows

Capricious environments often present wild animals with challenges that coincide or occur in sequence. Conceptual models of the stress response predict that one threat may prime or dampen the response to another. Although evidence has supported this for glucocorticoid responses, much less is known about the effects of previous challenges on energy mobilization. Food limitation may have a particularly important effect, by altering the ability to mobilize energy when faced with a subsequent challenge. We tested the prediction that challenging weather conditions, which reduce food availability, alter the energetic response to a subsequent acute challenge (capture and restraint). Using a three-year dataset from female tree swallows measured during three substages of breeding, we used a model comparison approach to test if weather (temperature, wind speed, and precipitation) over 3- or 72-hour timescales predicted baseline and post-restraint glucose levels, and if so which environmental factors were the strongest predictors. Contrary to our predictions, weather conditions did not affect baseline glucose; however, birds that had experienced lower temperatures over the preceding 72 h tended to have higher stress-induced glucose when faced with an acute stressor. We also saw some support for an effect of rainfall on stress-induced glucose: around the time that eggs hatched, birds that had experienced more rainfall over the preceding 72 h mounted lower responses. Overall, we find support in a wild animal for the idea that the glucose stress response may be primed by exposure to prior challenges.

Diverse environmental cues drive the size of reproductive aggregation in a rheophilic fish

BACKGROUND

Animal migrations are periodic and relatively predictable events, and their precise timing is essential to the reproductive success. Despite large scientific effort in monitoring animal reproductive phenology, identification of complex environmental cues that determine the timing of reproductive migrations and temporal changes in the size of reproductive aggregations in relation to environmental variables is relatively rare in the current scientific literature.

METHODS

We tagged and tracked 1702 individuals of asp (Leuciscus aspius), a large minnow species, and monitored with a resolution of one hour the size of their reproductive aggregations (counts of sexes present at the breeding grounds standardized by the sum of individuals in the season) over seven breeding seasons using passive integrated transponder tag systems. We examined the size of reproductive aggregations in relation to environmental cues of day number within a reproductive season (intra-year seasonality), water temperature, discharge, hour in a day (intra-day pattern), temperature difference between water and air, precipitation, atmospheric pressure, wind speed and lunar phase. A generalized additive model integrating evidence from seven breeding seasons and providing typical dynamics of reproductive aggregations was constructed.

RESULTS

We demonstrated that all environmental cues considered contributed to the changes in the size of reproductive aggregations during breeding season, and that some effects varied during breeding season. Our model explained approximately 50% of the variability in the data and the effects were sex-dependent (models of the same structure were fitted to each sex separately, so that we effectively stratified on sex). The size of reproductive aggregations increased unimodally in response to day in season, correlated positively with water temperature and wind speed, was highest before and after the full moon, and highest at night (interacting with day in a season). Males responded negatively and females positively to increase in atmospheric pressure.

CONCLUSION

The data demonstrate complex utilization of available environmental cues to time reproductive aggregations in freshwater fish and their interactions during the reproductive season. The study highlights the need to acquire diverse data sets consisting of many environmental cues to achieve high accuracy of interpretation of reproductive timing.

Weather associations with physical activity, sedentary behaviour and sleep patterns of Australian adults: a longitudinal study with implications for climate change

BACKGROUND

Weather is a potentially important influence on how time is allocated to sleep, sedentary behaviour and physical activity across the 24-h day. Extremes of weather (very hot, cold, windy or wet) can create undesirable, unsafe outdoor environments for exercise or active transport, impact the comfort of sleeping environments, and increase time indoors. This 13-month prospective cohort study explored associations between weather and 24-h movement behaviour patterns.

METHODS

Three hundred sixty-eight adults (mean age 40.2 years, SD 5.9, 56.8% female) from Adelaide, Australia, wore Fitbit Charge 3 activity trackers 24 h a day for 13 months with minute-by-minute data on sleep, sedentary behaviour, light physical activity (LPA), and moderate-to-vigorous physical activity (MVPA) collected remotely. Daily weather data included temperature, rainfall, wind, cloud and sunshine. Multi-level mixed-effects linear regression analyses (one model per outcome) were used.

RESULTS

Ninety thousand eight hundred one days of data were analysed. Sleep was negatively associated with minimum temperature (-12 min/day change across minimum temperature range of 31.2 °C, p = 0.001). Sedentary behaviour was positively associated with minimum temperature (+ 12 min/day, range = 31.2 oC, p = 0.006) and wind speed (+ 10 min/day, range = 36.7 km/h, p< 0.001), and negatively associated with sunshine (-17 min/day, range = 13.9 h, p < 0.001). LPA was positively associated with minimum temperature (+ 11 min/day, range = 31.2 °C, p = 0.002), cloud cover (+ 4 min/day, range = 8 eighths, p = 0.008) and sunshine (+ 17 min/day, range = 13.9 h, p < 0.001), and negatively associated with wind speed (-8 min/day, range = 36.7 km/h, p < 0.001). MVPA was positively associated with sunshine (+ 3 min/day, range = 13.9 h, p < 0.001) and negatively associated with minimum temperature (-13 min/day, range = 31.2 oC, p < 0.001), rainfall (-3 min/day, range = 33.2 mm, p = 0.006) and wind speed (-4 min/day, range = 36.7 km/h, p < 0.001). For maximum temperature, a significant (p < 0.05) curvilinear association was observed with sleep (half-U) and physical activity (inverted-U), where the decrease in sleep duration appeared to slow around 23 °C, LPA peaked at 31 oC and MVPA at 27 °C.

CONCLUSIONS

Generally, adults tended to be less active and more sedentary during extremes of weather and sleep less as temperatures rise. These findings have the potential to inform the timing and content of positive movement behaviour messaging and interventions.

TRIAL REGISTRATION

The study was prospectively registered on the Australian New Zealand Clinical Trial Registry (Trial ID: ACTRN12619001430123).

Risk factors for fracture by same-level falls among workers across sectors: a cross-sectional study of national open database of the occupational injuries in Japan

OBJECTIVES

The hospitalisation rate for work-related injuries among older workers is double that of younger workers; however, the risk factors for same-level fall fractures sustained during industrial accidents remain unclear. This study aimed to estimate the influence of worker age, time of day and weather conditions on the risk of same-level fall fractures in all industrial sectors in Japan.

STUDY DESIGN

This was a cross-sectional study.

METHODS

This study used the population-based national open database of worker death and injury reports in Japan. In total, 34,580 reports of occupational same-level falls between 2012 and 2016 were used in this study. Multiple logistic regression analysis was performed.

RESULTS

In primary industries, workers aged ≥55 years had a 1.684 times greater risk of fracture (95% confidence interval [CI]: 1.167-2.430) compared with workers aged ≤54 years. In tertiary industries, relative to the odds ratio (OR) of injuries recorded at 0:00-2:59 a.m., the ORs recorded at 6:00-8:59 p.m., 6:00-8:59 a.m., 9:00-11:59 p.m. and 0:00-2:59 p.m. were 1.516 (95% CI: 1.202, 1.912), 1.502 (95% CI: 1.203-1.876), 1.348 (95% CI: 1.043-1.741) and 1.295 (95% CI: 1.039-1.614), respectively. The risk of fracture increased with a 1-day increase in the number of snowfall days were per month in secondary (OR = 1.056, 95% CI: 1.011-1.103) and tertiary (OR = 1.034, 95% CI: 1.009-1.061) industries. The risk of fracture decreased with every 1-degree increase in the lowest temperature in primary (OR = 0.967, 95% CI: 0.935-0.999) and tertiary (OR = 0.993, 95% CI: 0.988-0.999) industries.

CONCLUSIONS

With the increasing number of older workers and changing environmental conditions, the risk of falls in the tertiary sector industries is increasing, particularly just before and just after shift change hours. These risks may be associated with environmental obstacles during work migration. It is also important to consider the weather-associated risks of fracture.

Using weather factors and google data to predict COVID-19 transmission in Melbourne, Australia: A time-series predictive model

Background

Forecast models have been essential in understanding COVID-19 transmission and guiding public health responses throughout the pandemic. This study aims to assess the effect of weather variability and Google data on COVID-19 transmission and develop multivariable time series AutoRegressive Integrated Moving Average (ARIMA) models for improving traditional predictive modelling for informing public health policy.

Methods

COVID-19 case notifications, meteorological factors and Google data were collected over the B.1.617.2 (Delta) outbreak in Melbourne, Australia from August to November 2021. Timeseries cross-correlation (TSCC) was used to evaluate the temporal correlation between weather factors, Google search trends, Google Mobility data and COVID-19 transmission. Multivariable time series ARIMA models were fitted to forecast COVID-19 incidence and Effective Reproductive Number (R _eff ) in the Greater Melbourne region. Five models were fitted to compare and validate predictive models using moving three-day ahead forecasts to test the predictive accuracy for both COVID-19 incidence and R _eff over the Melbourne Delta outbreak.

Results

Case-only ARIMA model resulted in an R squared (R²) value of 0.942, Root Mean Square Error (RMSE) of 141.59, and Mean Absolute Percentage Error (MAPE) of 23.19. The model including transit station mobility (TSM) and maximum temperature (Tmax) had greater predictive accuracy with R² 0.948, RMSE 137.57, and MAPE 21.26.

Conclusion

Multivariable ARIMA modelling for COVID-19 cases and R _eff was useful for predicting epidemic growth, with higher predictive accuracy for models including TSM and Tmax. These results suggest that TSM and Tmax would be useful for further exploration for developing weather-informed early warning models for future COVID-19 outbreaks with potential application for the inclusion of weather and Google data with disease surveillance in developing effective early warning systems for informing public health policy and epidemic response.

Do environmental temperatures and altitudes affect physical outputs of elite football athletes in match conditions? A systematic review of the 'real world' studies

Players involved in the various football codes compete throughout the calendar year around the world. Therefore, environmental stressors such as temperature and altitude should be considered in preparation for, and during, matches. We aimed to systematically review the observational and quasi-experimental studies that have been specifically designed to quantify the effects of temperature (hot or cold) high altitude on in-match physical performance indicators. A search of electronic databases (Web of Science, Scopus, SPORTDiscus, PubMed/MEDLINE) was conducted, with 19,424 papers identified as relevant. Following sifting in relation to the eligibility criteria, 12 papers were deemed directly relevant. The reviewed studies scored 6-9 (on a 0-9 scale) for quality assessment using a previously used scale. The major outcome variables relevant to the current review were total distance (m), high-speed running (m) and high-speed runs (count) measured during matches. Standardized effect sizes (ES) were heterogeneous across studies for total distance (ES: -0.96 to -0.14) and high-speed running (ES: -0.69 to 0.12) for >1000 m vs sea-level, time spent at the given altitude being a putative factor for this heterogeneity. Heat had mainly detrimental effects on performance, but ES were, again, heterogeneous across studies (ES: -1.25 to 0.26), dependent on temperature. Given the small number of studies that involved mostly male athletes, and large heterogeneity across studies, more research needs be conducted on physical performance in these environmental conditions, with attention paid to standardizing outcomes and broadening the approaches of studies to guide future decision-making in professional sporting environments.

Impact of meteorological factors and population density on COVID-19 pandemic in Saudi Arabia

Transmission and increase in cases and fatalities of coronavirus disease-2019 (COVID-19) are significantly influenced by the parameters of weather, human activities and population factors. However, study gap on the seasonality of COVID-19 and impact of environmental factors on the pandemic in Saudi Arabia is present. The main aim of the study is to evaluate the impact of environment on the COVID-19 pandemic. Data were analyzed from January 2020 to July 2021. The generalized estimating equation (GEE) was used to determine the effect of environmental variables on longitudinal outcomes. Spearman's rank correlation coefficient (r_s ) was used to analyze the impact of different parameters on the outcome of the pandemic. Multiple sequence alignment was performed by using ClustalW. Vaccination and fatalities (r _s = -0.85) had the highest association followed by vaccination with cases (r _s = -0.81) and population density with the fatalities (r_s  = 0.71). The growth rate had the highest correlation with sun hours (r _s = -0.63). Isolates from variant of concern alpha and beta were detected. Most of the reference sequences in Saudi Arabia were closely related with B.1.427/429 variant. Clade GH (54%) was the most prevalent followed by O (27%), GR (9%), G (6%), and S (4%), respectively. Male to female patient ratio was 1.4:1. About 95% fatality and hospitalization were reported in patients aged >60 years. This study will create a comprehensive insight of the interaction of environmental factors and the pandemic and add knowledge on seasonality of COVID-19 in Saudi Arabia.

The effect of weather and temporal factors on emergency hand trauma consultations

INTRODUCTION

Fluctuations in the numbers of patient consultations in hand trauma emergency units are challenging in terms of both scheduling and the provision of sufficient resources. Trauma consultations in general are affected by both temporal and meteorological variables. As the genesis and epidemiology of hand trauma have their own characteristics, this study aimed to identify the influence of temporal and meteorological factors on hand trauma consultations.

MATERIALS AND METHODS

All patients treated for hand trauma in our level one trauma center in 2019 were included in the study population and the data were analyzed in retrospect. The daily weather data, including temperature, sunshine duration, precipitation, humidity and wind speed, as well as temporal factors such as time of day, weekday and public holidays were considered and correlated with patient consultations. Gender differences were studied as well.

RESULTS

We included 4787 hand trauma patients (66.4% male, mean age 38.4 ± 19.3 years, 31.7% occupational injuries). Significantly more consultations occurred on Saturdays as compared to weekdays (14.8 ± 0.6, n = 52 vs. 13.0 ± 0.2, n = 261; p = 0.028), and fewer occurred on official holidays (11.8 ± 0.5, n = 63 vs. 13.4 ± 0.2, n = 302; p = 0.0047). We found a significant positive correlation between daily consultations, sunshine duration (r = 0.14, p = 0.0056) and the mean temperature (r = 0.20, p < 0.0001); in contrast, a significant negative correlation between daily consultations and humidity (r = - 0.17, p = 0.001) was observed. Furthermore, fewer consultations were seen on days with precipitation (12.7 ± 0.3, n = 219 vs. 13.8 ± 0.3, n = 146; p = 0.009). The variation was considerably stronger in men.

CONCLUSIONS

Hand trauma consultations increased with increasing temperatures, duration of sunshine, and decreasing humidity. Peak admissions were seen on Fridays and Saturdays. These findings can assist in predicting days with peak admissions to allocate resources appropriately.