A systematic evaluation of the lagged effects of spatiotemporally relative surface weather types on wintertime cardiovascular-related mortality across 19 US cities

The influence of air masses on human mortality in the contiguous United States

Temperature-related mortality is the leading cause of weather-related deaths in the United States. Herein, we explore the effect of air masses (AMs) - a relatively novel and holistic measure of environmental conditions - on human mortality across 61 cities in the United States. Geographic and seasonal differences in the effects of each AM on deseasonalized and detrended anomalous lagged mortality are examined using simple descriptive statistics, one-way analyses of variance, relative risks of excess mortality, and regression-based artificial neural network (ANN) models. Results show that AMs are significantly related to anomalous mortality in most US cities, and in most seasons. Of note, two of the three cool AMs (Cool and Dry-Cool) each show a strong, but delayed mortality response in all seasons, with peak mortality 2 to 4 days after they occur, with the Dry-Cool AM having nearly a 15% increased risk of excess mortality. Humid-Warm (HW) air masses are associated with increases in deaths in all seasons 0 to 1 days after they occur. In most seasons, these near-term mortality increases are offset by reduced mortality for 1-2 weeks afterwards; however, in summer, no such reduction is noted. The Warm and Dry-Warm AMs show slightly longer periods of increased mortality, albeit slightly less intensely as compared with HW, but with a similar lag structure by season. Meanwhile, the most seasonally consistent results are with transitional weather, whereby passing cold fronts are associated with a significant decrease in mortality 1 day after they occur, while warm fronts are associated with significant increases in mortality at that same lag time. Finally, ANN modeling reveals that AM-mortality relationships gleaned from a combined meta-analysis can actually lead to more skillful modeling of these relationships than models trained on some individual cities, especially in the cities where such relationships might be masked due to low average daily mortality.

Spatiotemporal variation in urban overheating magnitude and its association with synoptic air-masses in a coastal city

Urban overheating (UO) may interact with synoptic-scale weather conditions. The association between meteorological parameters and UO has already been a subject of considerable research, however, the impact of synoptic-scale weather conditions on UO magnitude, particularly in a coastal city that is also near the desert landmass (Sydney) has never been investigated before. The present research examines the influence of synoptic-scale weather conditions on UO magnitude in Sydney by utilizing the newly developed gridded weather typing classification (GWTC). The diurnal, and seasonal variations in suburban-urban temperature contrast (ΔT) in association with synoptic-scale weather conditions, and ΔT response to synoptic air-masses during extreme heat events are investigated in three zones of Sydney. Generally, an exacerbation in UO magnitude was reported at daytime over the years, whereas the nocturnal UO magnitude was alleviated over time. The humid warm (HW), and warm (W) air-masses were found primarily responsible for exacerbated daytime UO during extreme heat events and in all other seasons, raising the mean daily maximum ΔT to 8-10.5 °C in Western Sydney, and 5-6.5 °C in inner Sydney. The dry warm (DW), and W conditions were mainly responsible for urban cooling (UC) at nighttime, bringing down the mean daily minimum ΔT to - 7.5 to - 10 °C in Western Sydney, and - 6 to - 7.5 °C in inner Sydney. The appropriate mitigation technologies can be planned based on this study to alleviate the higher daytime temperatures in the Sydney suburbs.

Hot and cold weather based on the spatial synoptic classification and cause-specific mortality in Sweden: a time-stratified case-crossover study

The spatial synoptic classification (SSC) is a holistic categorical assessment of the daily weather conditions at specific locations; it is a useful tool for assessing weather effects on health. In this study, we assessed (a) the effect of hot weather types and the duration of heat events on cardiovascular and respiratory mortality in summer and (b) the effect of cold weather types and the duration of cold events on cardiovascular and respiratory mortality in winter. A time-stratified case-crossover design combined with a distributed lag nonlinear model was carried out to investigate the association of weather types with cause-specific mortality in two southern (Skåne and Stockholm) and two northern (Jämtland and Västerbotten) locations in Sweden. During summer, in the southern locations, the Moist Tropical (MT) and Dry Tropical (DT) weather types increased cardiovascular and respiratory mortality at shorter lags; both hot weather types substantially increased respiratory mortality mainly in Skåne. The impact of heat events on mortality by cardiovascular and respiratory diseases was more important in the southern than in the northern locations at lag 0. The cumulative effect of MT, DT and heat events lagged over 14 days was particularly high for respiratory mortality in all locations except in Jämtland, though these did not show a clear effect on cardiovascular mortality. During winter, the dry polar and moist polar weather types and cold events showed a negligible effect on cardiovascular and respiratory mortality. This study provides valuable information about the relationship between hot oppressive weather types with cause-specific mortality; however, the cold weather types may not capture sufficiently effects on cause-specific mortality in this sub-Arctic region.

Estimating personal ambient temperature in moderately cold environments for occupationally exposed populations

Despite high mortality and morbidity rates in the winter season, few studies have investigated the health effects from working in moderately cold environments, especially among vulnerable outdoor worker populations in the southeastern US. Yet recent research has shown that the mortality risk from cold events is greatest in southern cities compared to other US locations. We performed repeated personal cold exposure measurements in outdoor grounds management workers in the southeastern US using consumer-based sensors. We recruited outdoor workers from two locations (Raleigh, NC and Boone, NC) each characterized by climatological differences in cold temperature to participate in a 3-week data collection period at the peak of the winter (Jan/Feb 2018). Lower personal ambient temperatures were observed among participants who worked in a warmer climate (Raleigh, NC). The relative risk for cold symptomatology was higher in moderately cold personal ambient temperatures (0 °C to 20 °C) than extremely cold personal ambient temperatures (less than 0 °C). A weak significant relationship was observed between personal ambient temperatures and weather station measurements highlighting that epidemiological researchers should be cautious when investigating the health effects of ambient temperatures based on fixed site measurements. As mobile technology progresses, real-time temperature health monitoring and analysis of environmental conditions at the individual level across multiple occupational-settings will become more feasible and ultimately, we believe, a digitally enhanced workforce will become standard practice in the field.

Warm Front Passage on the Previous Day Increased Ischemic Stroke Events

BACKGROUND AND PURPOSE

The influence of a weather front passage is rarely evaluated on stroke events. We hypothesized that a weather front passage on the stroke onset day or during the previous days may play an important role in the incidence of stroke.

METHODS

A multicenter retrospective study was conducted to evaluate the frequency of stroke events and their interaction with weather front passages. Consecutive acute stroke patients (n = 3935, 73.5 ± 12.4 years, 1610 females) who were admitted to 7 stroke hospitals in 3 cities from January 2012 to December 2013 were enrolled in this study. Multivariate Poisson regression models involving time lag variables were used to compare the daily rates of stroke events with the day of a weather front passage and the previous 6 days, adjusting for considerable influences of ambient temperature and atmospheric pressure.

RESULTS

There were a total of 33 cold fronts and 13 warm fronts that passed over the 3 cities during the study period. The frequency of ischemic stroke significantly increased when a warm front passed on the previous day (risk ratio 1.34, 95% confidence interval 1.07-1.69, P= .016).

CONCLUSIONS

This study indicated that a weather front passage on the previous days may be associated with the occurrence of stroke.

The Mortality Response to Absolute and Relative Temperature Extremes

While the impact of absolute extreme temperatures on human health has been amply studied, far less attention has been given to relative temperature extremes, that is, events that are highly unusual for the time of year but not necessarily extreme relative to a location's overall climate. In this research, we use a recently defined extreme temperature event metric to define absolute extreme heat events (EHE) and extreme cold events (ECE) using absolute thresholds, and relative extreme heat events (REHE) and relative extreme cold events (RECE) using relative thresholds. All-cause mortality outcomes using a distributed lag nonlinear model are evaluated for the largest 51 metropolitan areas in the US for the period 1975-2010. Both the immediate impacts and the cumulative 20-day impacts are assessed for each of the extreme temperature event types. The 51 metropolitan areas were then grouped into 8 regions for meta-analysis. For heat events, the greatest mortality increases occur with a 0-day lag, with the subsequent days showing below-expected mortality (harvesting) that decreases the overall cumulative impact. For EHE, increases in mortality are still statistically significant when examined over 20 days. For REHE, it appears as though the day-0 increase in mortality is short-term displacement. For cold events, both relative and absolute, there is little mortality increase on day 0, but the impacts increase on subsequent days. Cumulative impacts are statistically significant at more than half of the stations for both ECE and RECE. The response to absolute ECE is strongest, but is also significant when using RECE across several southern locations, suggesting that there may be a lack of acclimatization, increasing mortality in relative cold events both early and late in winter.

The influence of extreme cold events on mortality in the United States

Many studies have analyzed the effects of extreme heat on human mortality, however fewer studies have focused on the effects of cold related mortality due to the complicated nature of the lagged response. This study utilized a Distributed Lag Non-Linear Model with a 30-day lag to determine the cumulative effects of extreme cold events (ECEs) on mortality across 32 cities in the United States for the period of 1975-2010. ECEs were divided into specific categories based on duration, magnitude, and timing of occurrence. Mortality was divided into all-age mortality as well as mortality of individuals >64 years old. The findings suggest a strong relationship between a city's latitude as well as the timing of an ECE with mortality. Early season ECEs result in a much higher relative risk of increased mortality, particularly in cities with higher mean winter temperatures, while the RR of mortality of individuals >64 was consistently higher for each city. This study suggests early season ECEs should receive enhanced preparedness efforts as individuals may be particularly vulnerable when not acclimatized to extreme cold.

A new approach to modeling temperature-related mortality: Non-linear autoregressive models with exogenous input

Temperature-mortality relationships are nonlinear, time-lagged, and can vary depending on the time of year and geographic location, all of which limits the applicability of simple regression models in describing these associations. This research demonstrates the utility of an alternative method for modeling such complex relationships that has gained recent traction in other environmental fields: nonlinear autoregressive models with exogenous input (NARX models). All-cause mortality data and multiple temperature-based data sets were gathered from 41 different US cities, for the period 1975-2010, and subjected to ensemble NARX modeling. Models generally performed better in larger cities and during the winter season. Across the US, median absolute percentage errors were 10% (ranging from 4% to 15% in various cities), the average improvement in the r-squared over that of a simple persistence model was 17% (6-24%), and the hit rate for modeling spike days in mortality (>80th percentile) was 54% (34-71%). Mortality responded acutely to hot summer days, peaking at 0-2 days of lag before dropping precipitously, and there was an extended mortality response to cold winter days, peaking at 2-4 days of lag and dropping slowly and continuing for multiple weeks. Spring and autumn showed both of the aforementioned temperature-mortality relationships, but generally to a lesser magnitude than what was seen in summer or winter. When compared to distributed lag nonlinear models, NARX model output was nearly identical. These results highlight the applicability of NARX models for use in modeling complex and time-dependent relationships for various applications in epidemiology and environmental sciences.

Application of spatial synoptic classification in evaluating links between heat stress and cardiovascular mortality and morbidity in Prague, Czech Republic

Spatial synoptic classification (SSC) is here first employed in assessing heat-related mortality and morbidity in Central Europe. It is applied for examining links between weather patterns and cardiovascular (CVD) mortality and morbidity in an extended summer season (16 May-15 September) during 1994-2009. As in previous studies, two SSC air masses (AMs)-dry tropical (DT) and moist tropical (MT)-are associated with significant excess CVD mortality in Prague, while effects on CVD hospital admissions are small and insignificant. Excess mortality for ischaemic heart diseases is more strongly associated with DT, while MT has adverse effect especially on cerebrovascular mortality. Links between the oppressive AMs and excess mortality relate also to conditions on previous days, as DT and MT occur in typical sequences. The highest CVD mortality deviations are found 1 day after a hot spell's onset, when temperature as well as frequency of the oppressive AMs are highest. Following this peak is typically DT- to MT-like weather transition, characterized by decrease in temperature and increase in humidity. The transition between upward (DT) and downward (MT) phases is associated with the largest excess CVD mortality, and the change contributes to the increased and more lagged effects on cerebrovascular mortality. The study highlights the importance of critically evaluating SSC's applicability and benefits within warning systems relative to other synoptic and epidemiological approaches. Only a subset of days with the oppressive AMs is associated with excess mortality, and regression models accounting for possible meteorological and other factors explain little of the mortality variance.