Low ambient temperature as the only meteorological risk factor of seizure occurrence: A multivariate study

The Influence of Climatic Factors on the Provocation of Epileptic Seizures

Background/Objectives: Recent studies provide the first indications of the impact of climate factors on human health, especially with individuals already grappling with internal and neurological conditions being particularly vulnerable. In the face of escalating climate change, our research delves into the specific influence of a spectrum of climatic factors and seasonal variations on the hospital admissions of patients receiving treatment for epileptic seizures at our clinic in Kaiserslautern. Methods: Our study encompassed data from 9366 epilepsy patients who were admitted to hospital due to epileptic seizures. We considered seven climate parameters that Germany's National Meteorological Service made available. We employed the Kruskal-Wallis test to examine the correlation between the frequency of admittance to our hospital in the mentioned patient group and seasons. Furthermore, we used conditional Poisson regression and distributed lag linear models (DLMs) to scrutinize the coherence of the frequency of patient admittance and the investigated climate parameters. The mentioned parameters were also analyzed in a subgroup analysis regarding the gender and age of patients and the classification of seizures according to ILAE 2017. Results: Our results demonstrate that climatic factors, such as precipitation and air pressure, can increase the frequency of hospital admissions for seizures in patients with general-onset epilepsy. In contrast, patients with focal seizures are less prone to climatic changes. Consequently, admittance to the hospital for seizures is less affected by climatic factors in the latter patient group. Conclusions: The present study demonstrated that climatic factors are possible trigger factors for the provocation of seizures, particularly in patients with generalized seizures. This was determined indirectly by analyzing the frequency of seizure-related emergency admissions and their relation to prevailing climate factors. Our study is consistent with other studies showing that climate factors, such as cerebral infarcts or cerebral hemorrhages, influence patients' health.

Association of outdoor air and noise pollution with unprovoked seizures and new onset epilepsy: A systematic review and meta-analysis

Research has indicated that certain environmental exposures may increase the risk of unprovoked seizures and new onset epilepsy. This study aimed to synthesize the literature that has estimated the associations between short- and long-term exposure to outdoor air and noise pollution and the risk of unprovoked seizures and new onset epilepsy. We searched Embase, MEDLINE, Scopus, Web of Science, BIOSIS Previews, Latin American and Caribbean Health Sciences Literature, Proquest Dissertations and Theses, conference abstracts, and the gray literature and conducted citation tracing in June 2023. Observational and ecological studies assessing the associations of air and noise pollution with unprovoked seizures or new onset epilepsy were eligible. One reviewer extracted summary data. Using fixed and random effects models, we calculated the pooled risk ratios (RRs) for the studies assessing the associations between short-term exposure to air pollution and unprovoked seizures. Seventeen studies were included, 16 assessing the association of air pollution with seizures and one with epilepsy. Eight studies were pooled quantitatively. Ozone (O3; RR = .99, 95% confidence interval [CI] = .99-.99) and nitrogen dioxide (NO2) exposure adjusted for particulate matter (RR = 1.02, 95% CI = 1.01-1.02) on the same day, and carbon monoxide (CO) exposure 2 days prior (RR = 1.12, 95% CI = 1.02-1.22), were associated with seizure risk. A single study of air pollution and epilepsy did not report a significant association. The risk of bias and heterogeneity across studies was moderate or high. Short-term exposure to O3, NO2, and CO may affect the risk of seizures; however, the effect estimates for O3 and NO2 were minimal. Additional research should continue to explore these and the associations between outdoor air pollution and epilepsy and between noise pollution and seizures and epilepsy.

Association between short-term exposure to extreme humidity and painful diabetic neuropathy: a case-crossover analysis

Painful diabetic neuropathy (PDN) is a common complication of diabetes mellitus, which reduces the quality of life. However, the association between PDN and environmental factors, especially ambient humidity, remains unclear. Therefore, this study investigated the impact of extreme humidity events on PDN. Data on PDN-related hospital admissions to two tertiary hospitals in Hefei, China (2014-2019) were obtained. A distributed lag non-linear model with a case-crossover design was used to quantitatively estimate the effects of ambient humidity on PDN, and the results were stratified by sex and age. The 1st, 10th, 90th, and 99th percentiles of relative humidity (RHU) were defined as extreme humidity, and the average relative humidity (74.94%) was set as the reference value. Non-linear exposure-response curves between the RHU and PDN cases were obtained. Extreme humidity (92%) had a significant effect on PDN with a relative risk (RR) of 1.13 (95% confidence interval (CI): 1.01-1.26) on a particular day, which increased with the RHU (RR: 1.21, 95% CI: 1.02-1.45 at 98% extreme humidity). Stratification analysis showed that women (RR: 1.38, 95% CI: 1.07-1.77) and patients aged < 65 years (RR: 1.26, 95% CI: 1.01-1.57) were highly susceptible to this effect on the same day. The results suggest that extreme humidity is a crucial trigger for PDN onset in diabetes patients. Furthermore, the effects vary with sex and age. This study provides detailed evidence of the adverse effects of extreme weather on diabetes patients.

Air Pollution and Hospital Admission for Epilepsy in Kerman, Iran

Background: This study aimed to investigate the relation between air pollution and epilepsy admissions in Kerman, Iran. Methods: In this ecological study, the concentrations of ambient air pollutants and meteorological data were obtained from Kerman Environmental Protection Agency and Kerman Meteorology Organization, respectively. Additionally, epilepsy admission data were obtained from Kerman’s Shafa hospital epilepsy registry. Generalized additive models with lags up to 7 days were used to estimate rate ratios (RRs). Results: Within 2008 to 2020, 894 epilepsy admissions occurred in Kerman, 498 cases (55.7%) of whom were male. The strongest relations of epilepsy admission were observed in the over 59-year group for carbon monoxide (CO) in lag 0 (RR = 2.1455, 95% CI: 1.5823 - 2.9091), nitrogen dioxide (NO2) in lag 0 (RR = 1.0409, 95% CI: 1.0282 - 1.0537), and particulate matter under 2.5 microns (PM2.5) in lag 5 (RR = 1.0157, 95% CI: 1.0062 - 1.0252). There were also significant associations for particulate matter under 10 microns (PM10) in the under 18-year group in lag 2 (RR = 1.0064, 95% CI: 1.0029 - 1.0098), ozone in lag 0 (RR = 0.9671, 95% CI: 0.9581 - 0.9761), and sulfur dioxide in lag 5 (RR = 0.9937, 95% CI: 0.9891 - 0.9983). Conclusions: Exposure to CO, NO2, PM2.5, or PM10 air pollutants might be a risk factor for epilepsy admissions in Kerman. Epilepsy patients should better stay away from exposure to polluted air. Staying at home on polluted days or residing in areas with less air pollution might be an option.

Weather patterns and occurrence of epileptic seizures

Background

The results of various studies that have so far evaluated the influence of meteorological conditions on seizures are often divergent. No studies have been performed so far that evaluate the possible correlation between the occurrence of seizure-related events, surface and upper level atmospheric conditions and biometeorological forecasts. The aim of this study is to investigate those interactions.

Methods

Records of “Sveti Duh” University hospital, Zagreb, Croatia between January 2016 and May 2020 were analysed in order to determine number of patients per day who were assessed through Emergency room because of a recent epileptic seizure. The dates were assessed for surface and upper level atmospheric conditions and biometeorological forecasts. Analyses of these factors were performed for two consecutive days preceding that day, the day of seizure, as well as for the following day. Data was analysed using chi-squared test, Mann-Whitney U-test or Kruskal-Wallis test (with Conover’s test for post-hoc analyses), depending on variable type. Additionaly, logistic regression was used to study effects of variables on seizure occurrence. Statistical significance was set to p < 0.05.

Results

Results showed significant difference between the numbers of patients with seizure depending on upper level type on the following day. We also observed fewer daily patients with seizures when the synoptic situation on the following day was high pressure field then when it was low pressure or non-gradient pressure field (observed just during the colder part of the year), which was supported in our regression analyses. A greater frequency of seizures was observed if the biometerological prognosis was deemed unfavourable on the following day. Furthermore, our results showed significantly larger proportion of days with seizure patients in warmer, than in colder days of the year.

Conclusions

All of the analyzed weather-related parameters seem to be associated with daily numbers of seizures on the previous day. The reason behind this phenomenon is uncertain; however, the results indicate that weather patterns have a certain influence on patients with epilepsy, but are not yet fully understood or adequately appreciated.

Multivariate analysis of the impact of weather and air pollution on emergency department visits for unprovoked seizure among children: A retrospective clinical observational study

BACKGROUND

An unprovoked seizure is a seizure or a cluster of seizures occurring within 24 h in a patient older than 1 month of age without precipitating factors. Recent studies have reported that extrinsic factors, such as meteorological conditions and air pollutants, may be important in seizure occurrence. Thus, this study aimed to examine the association between the number of visits to the emergency department (ED) by children for nighttime unprovoked seizures and exposure to multi-faceted factors, such as meteorological conditions and air pollution.

METHODS

We conducted a clinical observational analysis and reviewed consecutive patients younger than 16 years of age who visited the primary ED center in Kobe City, Japan, during nighttime (7:30 p.m.-7:00 a.m.) between January 1, 2011 and December 31, 2015. We investigated the effects of meteorological factors and air pollutants on the number of patients with unprovoked seizures using multivariate analysis of Poisson regression estimates.

RESULTS

In total, 151,119 children visited the ED, out of which 97 patients presented with unprovoked seizures. The mean age of the patients was 4.7 years (range, 1 month to 15.3 years), and 54.6% of them were boys. The total number of patients with unprovoked seizures showed no significant changes with the seasons; however, there were dominant peaks during the fall and fewer visits during the summer. The multivariate analysis of Poisson regression estimates revealed a significant positive relationship between the number of patients presenting with unprovoked seizures and precipitation (+1 patient/87 mm; p = 0.03) and methane (+1 patient/0.14 ppm; p = 0.03) levels and a negative relationship between the number of patients presenting with unprovoked seizures and nitrogen dioxide level (-1 patient/0.02 ppm; p = 0.04).

CONCLUSIONS

The present study is the first to evaluate the association between the number of children who presented to the ED with nighttime unprovoked seizures and environmental factors after controlling for confounding factors.

Effect of meteorological factors and lunar phases on occurrence of psychogenic nonepileptic seizures

BACKGROUND

Psychogenicnonepilepticseizures (PNES) lack the abnormal electrographical discharges in electroencephalogram seen in epileptic seizures. The notion of the effects of meteorological factors and lunar phases onoccurrence of seizures in patients with PNES has been the subject of very few research studies and the available evidence in the literature is equivocal. In this study, we aimed to study the influence oflunarphases and meteorological factors on the frequency of PNES and its semiological categories.

METHODS

We retrospectively reviewed the medical records of patients discharged with a diagnosis of PNES from our epilepsy monitoring unit in a 3-year period. The 119 patients who met the inclusion criteria were categorized into hypermotor, hypomotor, or intact sensorium based on semiology of their seizures. The occurrence of PNES, in total and in each semiological category, was correlated with the daily average temperature, atmospheric pressure, and the 4 lunar phases.

RESULTS

There were 31.8% and 11.1% more-than-expected captured seizures when the average daily atmospheric pressure was lower or higher, respectively, of what is generally considered a comfortable atmospheric pressure. No consistent relation was found between the full moon phases and occurrence of PNES.

CONCLUSION

A significant association between atmospheric pressure and the occurrence of nonepileptic seizures was found, whereas no consistent increase in PNES was observed during the full moon phases. There is still an open debate about the effect of the lunar phases on human behavior and neurological disorders such as PNES.

Forecasting Seizure Likelihood With Wearable Technology

The unpredictability of epileptic seizures exposes people with epilepsy to potential physical harm, restricts day-to-day activities, and impacts mental well-being. Accurate seizure forecasters would reduce the uncertainty associated with seizures but need to be feasible and accessible in the long-term. Wearable devices are perfect candidates to develop non-invasive, accessible forecasts but are yet to be investigated in long-term studies. We hypothesized that machine learning models could utilize heart rate as a biomarker for well-established cycles of seizures and epileptic activity, in addition to other wearable signals, to forecast high and low risk seizure periods. This feasibility study tracked participants' (n = 11) heart rates, sleep, and step counts using wearable smartwatches and seizure occurrence using smartphone seizure diaries for at least 6 months (mean = 14.6 months, SD = 3.8 months). Eligible participants had a diagnosis of refractory epilepsy and reported at least 20 seizures (mean = 135, SD = 123) during the recording period. An ensembled machine learning and neural network model estimated seizure risk either daily or hourly, with retraining occurring on a weekly basis as additional data was collected. Performance was evaluated retrospectively against a rate-matched random forecast using the area under the receiver operating curve. A pseudo-prospective evaluation was also conducted on a held-out dataset. Of the 11 participants, seizures were predicted above chance in all (100%) participants using an hourly forecast and in ten (91%) participants using a daily forecast. The average time spent in high risk (prediction time) before a seizure occurred was 37 min in the hourly forecast and 3 days in the daily forecast. Cyclic features added the most predictive value to the forecasts, particularly circadian and multiday heart rate cycles. Wearable devices can be used to produce patient-specific seizure forecasts, particularly when biomarkers of seizure and epileptic activity cycles are utilized.

From Precision Metapharmacology to Patient Empowerment: Delivery of Self-Care Practices for Epilepsy, Pain, Depression and Cancer Using Digital Health Technologies

To improve long-term outcomes of therapies for chronic diseases, health promotion and lifestyle modifications are the most promising and sustainable strategies. In addition, advances in digital technologies provide new opportunities to address limitations of drug-based treatments, such as medication non-adherence, adverse effects, toxicity, drug resistance, drug shortages, affordability, and accessibility. Pharmaceutical drugs and biologics can be combined with digital health technologies, including mobile medical apps (digital therapeutics), which offer additional clinical benefits and cost-effectiveness. Promises of drug+digital combination therapies are recognized by pharmaceutical and digital health companies, opening opportunities for integrating pharmacotherapies with non-pharmacological interventions (metapharmacology). Herein we present unique features of digital health technologies which can deliver personalized self-care modalities such as breathing exercises, mindfulness meditation, yoga, physical activity, adequate sleep, listening to preferred music, forgiveness and gratitude. Clinical studies reveal how aforementioned complimentary practices may support treatments of epilepsy, chronic pain, depression, cancer, and other chronic diseases. This article also describes how digital therapies delivering “medicinal” self-care and other non-pharmacological interventions can also be personalized by accounting for: 1) genetic risks for comorbidities, 2) adverse childhood experiences, 3) increased risks for viral infections such as seasonal influenza, or COVID-19, and 4) just-in-time stressful and traumatic circumstances. Development and implementation of personalized pharmacological-behavioral combination therapies (precision metapharmacology) require aligning priorities of key stakeholders including patients, research communities, healthcare industry, regulatory and funding agencies. In conclusion, digital technologies enable integration of pharmacotherapies with self-care, lifestyle interventions and patient empowerment, while concurrently advancing patient-centered care, integrative medicine and digital health ecosystems.

The effect of weather, air pollution and seasonality on the number of patient visits for epileptic seizures: A population-based time-series study

OBJECTIVE

The objective of the study was to explore the influences of seasonality, meteorological conditions, and air pollution exposure on the number of patients who visit the hospital due to seizures.

METHODS

Outpatient and inpatient data from the National Health Insurance Database of Taiwan from 2009 to 2013, meteorological data from the Meteorological Bureau, and air pollution exposure data from the Taiwan Air Quality Monitoring Stations were collected and integrated into daily time series data. The following data processing and analysis results are based on the mean of the 7 days' lag data of the 18 meteorological condition/air pollution exploratory factors to identify the critical meteorological conditions and air pollution exposure factors by executing univariate analysis. The average hospital visits for seizure per day by month were used as an index of observation. The effect of seasonality has also been examined.

RESULTS

The average visits per day by month had a significant association with 10 variables. Overall, the number of visits due to these factors has been estimated to be 71.529 (13.7%). The most obvious factors affecting the estimated number of visits include ambient temperature, CH₄, and NO. Six air pollutants, namely CH₄, NO, CO, NO₂, PM2.5, and NMHC had a significantly positive correlation with hospital visits due to seizures. Moreover, the average daily number of hospital visits was significantly high in January and February (winter season in Taiwan) than in other months (R² = 0.422).

CONCLUSION

The prediction model obtained in this study indicates the necessity of rigorous monitoring and early warning of these air pollutants and climate changes by governments. Additionally, the study provided a firm basis for establishing prediction models to be used by other countries or for other diseases.