Seasonality of mortality under a changing climate: a time-series analysis of mortality in Japan between 1972 and 2015

Beyond the sine wave: unveiling climate-driven baseline mortality cycles

The relationship between temperature and mortality is well-established, with higher mortality rates occurring in moderate climates during winter. Studies on COVID-19 and influenza-related excess deaths often assume a sine-like wave pattern for baseline mortality. This study aims to assess the accuracy of this approximation in capturing the observed mortality pattern and explore its linkage with climate. Weekly mortality data from European regions (2000-2019) were modeled using the seasonal-trend decomposition procedure based on Loess. Cycles were grouped into clusters, and underlying trends were extracted using principal component analysis. Generalized linear models assuming a sine-like pattern were used to test predictive value. Cluster analysis divided the regional cycles approximately into continental and temperate climate regions, further subdivided into oceanic and Mediterranean. While the continental region exhibited a sine-like mortality pattern, it displayed modest deviations that compounded further south. The period of elevated winter mortality became shorter but more intense, while decreased summer mortality became more pronounced yet delayed. This study improves weekly estimations of excess mortality models by providing enhanced baselines. The deviation from the sine-like approximation mirrors the idealized outbreak pattern from epidemiological models with sharper surges and more gradual declines. The results point to winter infections, impacted by acquired immunity and weather conditions, as the primary drivers of fluctuations in mortality. In warmer regions, there is an apparent shift toward a lower number of overall infections within a compressed time span.

Seasonal variation of Covid-19 incidence and role of land surface and air temperatures: a case study in the west of Iran

.In this study, we assessed the impact of satellite-based Land Surface Temperature (LST) and Air Temperature (AT) on covid-19. First, we spatio-temporally kriged the LST and applied bias correction. The epidemic shape, timing, and size were compared after and before adjusting for the predictors. Given the non-linear behavior of a pandemic, a semi-parametric regression model was used. In addition, the interaction effect between the predictors and season was assessed. Before adjusting for the predictors, the peak happened at the end of hot season. After adjusting, it was attenuated and slightly moved forward. Moreover, the Attributable Fraction (AF) and Peak to Trough Relative (PTR) were % 23 (95% CI; 15, 32) and 1.62 (95%CI; 1.34, 1.97), respectively. We found that temperature might have changed the seasonal variation of covid-19. However, given the large uncertainty after adjusting for the variables, it was hard to provide conclusive evidence in the region we studied.

Exploring the paradoxical nature of cold temperature mortality in Europe

While low winter temperatures are associated with increased mortality, this phenomenon has been suggested to be most severe in regions with seemingly mild winters. The study aimed to establish a temperature-based formula that could elucidate the previously ambiguous regional differences in vulnerability to low temperature. European weekly mortality data (2000-2019) were matched with meteorological data to determine for each region vulnerability to temperature decrease and the optimal temperature with lowest mortality. Regression models were developed to generalize and explain these findings considering regional temperature characteristics. Optimal temperature could be predicted based on local average summer temperature (R2 = 85.6%). Regional vulnerability to temperature decrease could be explained by combination of winter and summer temperatures (R2 = 86.1%). Regions with warm winters and cold summers showed the highest vulnerability to decrease of temperature during winter. Contrary to theories about economic disparities Eastern Europe exhibited resistance comparable to Scandinavia. The southern edges of Europe demonstrated serious low temperature vulnerability to decreased temperatures, even if temperature was relatively high around 20 °C. This suggests that the observed connection primarily reflects the modulation of the length of respiratory virus infection seasons by climate conditions, counterbalanced by varying levels of acquired immunity and the presence of heatwaves eliminating the most frail individuals. Thus, relatively low vulnerability and a flat mortality cycle in countries with harsh climates paradoxically imply the presence of threats throughout the whole year.

Seasonality of mortality under climate change: a multicountry projection study

BACKGROUND

Climate change can directly impact temperature-related excess deaths and might subsequently change the seasonal variation in mortality. In this study, we aimed to provide a systematic and comprehensive assessment of potential future changes in the seasonal variation, or seasonality, of mortality across different climate zones.

METHODS

In this modelling study, we collected daily time series of mean temperature and mortality (all causes or non-external causes only) via the Multi-Country Multi-City Collaborative (MCC) Research Network. These data were collected during overlapping periods, spanning from Jan 1, 1969 to Dec 31, 2020. We projected daily mortality from Jan 1, 2000 to Dec 31, 2099, under four climate change scenarios corresponding to increasing emissions (Shared Socioeconomic Pathways [SSP] scenarios SSP1-2.6, SSP2-4.5, SSP3-7.0, and SSP5-8.5). We compared the seasonality in projected mortality between decades by its shape, timings (the day-of-year) of minimum (trough) and maximum (peak) mortality, and sizes (peak-to-trough ratio and attributable fraction). Attributable fraction was used to measure the burden of seasonality of mortality. The results were summarised by climate zones.

FINDINGS

The MCC dataset included 126 809 537 deaths from 707 locations within 43 countries or areas. After excluding the only two polar locations (both high-altitude locations in Peru) from climatic zone assessments, we analysed 126 766 164 deaths in 705 locations aggregated in four climate zones (tropical, arid, temperate, and continental). From the 2000s to the 2090s, our projections showed an increase in mortality during the warm seasons and a decrease in mortality during the cold seasons, albeit with mortality remaining high during the cold seasons, under all four SSP scenarios in the arid, temperate, and continental zones. The magnitude of this changing pattern was more pronounced under the high-emission scenarios (SSP3-7.0 and SSP5-8.5), substantially altering the shape of seasonality of mortality and, under the highest emission scenario (SSP5-8.5), shifting the mortality peak from cold seasons to warm seasons in arid, temperate, and continental zones, and increasing the size of seasonality in all zones except the arid zone by the end of the century. In the 2090s compared with the 2000s, the change in peak-to-trough ratio (relative scale) ranged from 0·96 to 1·11, and the change in attributable fraction ranged from 0·002% to 0·06% under the SSP5-8.5 (highest emission) scenario.

INTERPRETATION

A warming climate can substantially change the seasonality of mortality in the future. Our projections suggest that health-care systems should consider preparing for a potentially increased demand during warm seasons and sustained high demand during cold seasons, particularly in regions characterised by arid, temperate, and continental climates.

FUNDING

The Environment Research and Technology Development Fund of the Environmental Restoration and Conservation Agency, provided by the Ministry of the Environment of Japan.

Peculiar weather patterns effects on air pollution and COVID-19 spread in Tokyo metropolis

As a pandemic hotspot in Japan, between March 1, 2020-October 1, 2022, Tokyo metropolis experienced seven COVID-19 waves. Motivated by the high rate of COVID-19 incidence and mortality during the seventh wave, and environmental/health challenges we conducted a time-series analysis to investigate the long-term interaction of air quality and climate variability with viral pandemic in Tokyo. Through daily time series geospatial and observational air pollution/climate data, and COVID-19 incidence and death cases, this study compared the environmental conditions during COVID-19 multiwaves. In spite of five State of Emergency (SOEs) restrictions associated with COVID-19 pandemic, during (2020-2022) period air quality recorded low improvements relative to (2015-2019) average annual values, namely: Aerosol Optical Depth increased by 9.13% in 2020 year, and declined by 6.64% in 2021, and 12.03% in 2022; particulate matter PM2.5 and PM10 decreased during 2020, 2021, and 2022 years by 10.22%, 62.26%, 0.39%, and respectively by 4.42%, 3.95%, 5.76%. For (2021-2022) period the average ratio of PM2.5/PM10 was (0.319 ± 0.1640), showing a higher contribution to aerosol loading of traffic-related coarse particles in comparison with fine particles. The highest rates of the daily recorded COVID-19 incidence and death cases in Tokyo during the seventh COVID-19 wave (1 July 2022-1 October 2022) may be attributed to accumulation near the ground of high levels of air pollutants and viral pathogens due to: 1) peculiar persistent atmospheric anticyclonic circulation with strong positive anomalies of geopotential height at 500 hPa; 2) lower levels of Planetary Boundary Layer (PBL) heights; 3) high daily maximum air temperature and land surface temperature due to the prolonged heat waves (HWs) in summer 2022; 4) no imposed restrictions. Such findings can guide public decision-makers to design proper strategies to curb pandemics under persistent stable anticyclonic weather conditions and summer HWs in large metropolitan areas.

Development and validation of ischemic heart disease and stroke prognostic models using large-scale real-world data from Japan

BACKGROUND

Previous cardiovascular risk prediction models in Japan have utilized prospective cohort studies with concise data. As the health information including health check-up records and administrative claims becomes digitalized and publicly available, application of large datasets based on such real-world data can achieve prediction accuracy and support social implementation of cardiovascular disease risk prediction models in preventive and clinical practice. In this study, classical regression and machine learning methods were explored to develop ischemic heart disease (IHD) and stroke prognostic models using real-world data.

METHODS

IQVIA Japan Claims Database was searched to include 691,160 individuals (predominantly corporate employees and their families working in secondary and tertiary industries) with at least one annual health check-up record during the identification period (April 2013-December 2018). The primary outcome of the study was the first recorded IHD or stroke event. Predictors were annual health check-up records at the index year-month, comprising demographic characteristics, laboratory tests, and questionnaire features. Four prediction models (Cox, Elnet-Cox, XGBoost, and Ensemble) were assessed in the present study to develop a cardiovascular disease risk prediction model for Japan.

RESULTS

The analysis cohort consisted of 572,971 invididuals. All prediction models showed similarly good performance. The Harrell's C-index was close to 0.9 for all IHD models, and above 0.7 for stroke models. In IHD models, age, sex, high-density lipoprotein, low-density lipoprotein, cholesterol, and systolic blood pressure had higher importance, while in stroke models systolic blood pressure and age had higher importance.

CONCLUSION

Our study analyzed classical regression and machine learning algorithms to develop cardiovascular disease risk prediction models for IHD and stroke in Japan that can be applied to practical use in a large population with predictive accuracy.

Temperature, cardiovascular mortality, and the role of hypertension and renin-angiotensin-aldosterone axis in seasonal adversity: a narrative review

Environmental temperature is now well known to have a U-shaped relationship with cardiovascular (CV) and all-cause mortality. Both heat and cold above and below an optimum temperature, respectively, are associated with adverse outcomes. However, cold in general and moderate cold specifically is predominantly responsible for much of temperature-attributable adversity. Importantly, hypertension-the most important CV risk factor-has seasonal variation such that BP is significantly higher in winter. Besides worsening BP control in established hypertensives, cold-induced BP increase also contributes to long-term BP variability among normotensive and pre-hypertensive patients, also a known CV risk factor. Disappointingly, despite the now well-stablished impact of temperature on BP and on CV mortality separately, direct linkage between seasonal BP change and CV outcomes remains preliminary. Proving or disproving this link is of immense clinical and public health importance because if seasonal BP variation contributes to seasonal adversity, this should be a modifiable risk. Mechanistically, existing evidence strongly suggests a central role of the sympathetic nervous system (SNS), and secondarily, the renin-angiotensin-aldosterone axis (RAAS) in mediating cold-induced BP increase. Though numerous other inflammatory, metabolic, and vascular perturbations likely also contribute, these may also well be secondary to cold-induced SNS/RAAS activation. This review aims to summarize the current evidence linking temperature, BP and CV outcomes. We also examine underlying mechanisms especially in regard to the SNS/RAAS axis, and highlight possible mitigation measures for clinicians.

Global Population Exposure to Extreme Temperatures and Disease Burden

The frequency and duration of extreme temperature events continues to increase worldwide. However, the scale of population exposure and its quantitative relationship with health risks remains unknown on a global scale, limiting our ability to identify policy priorities in response to climate change. Based on data from 171 countries between 2010 and 2019, this study estimated the exposure of vulnerable populations to extreme temperatures, and their contemporary and lag associations with disease burden attributed to non-optimal temperatures. Fixed-effects models and dynamic panel models were applied. Increased vulnerable population exposure to extreme temperatures had adverse contemporary effects on the burden of disease attributed to non-optimal temperature. Health risks stemming from extreme cold could accumulate to a greater extent, exhibiting a larger lag effect. Population exposure to extreme cold was mainly distributed in high-income countries, while extreme heat occurred more in low-income and middle-income countries. However, the association between population exposure to extreme cold and burden of disease was much stronger in low-income and middle-income countries than in high-income countries, whereas the effect size of population exposure to extreme heat was similar. Our study highlighted that differential strategies should be determined and implemented according to the characteristics in different countries.

Association between ambient temperature and ambulance dispatch: a systematic review and meta-analysis

Previous studies have quantified the associations between ambient temperature and dispatch of ambulances, but the conclusions are still controversial. Therefore, a systematic review and meta-analysis were conducted to summarize all the current evidence. A systematic review of published literature was undertaken to characterize the effect of temperature on ambulance dispatch. We completed the literature search by the end of January 5, 2022. The pooled estimates for different temperature exposures were calculated using a random effects model. Differences among temperature pooled estimates were determined using subgroup analysis. This study was registered with PROSPERO under the number CRD42021284434. This is the first meta-analysis investigating the association between temperature and ambulance dispatch. A total of 25 studies were eligible for this study. The overall increased risks of high temperature, expressed as relative risks, were 1.734 (95% CI: 1.481-2.031). Subgroup analysis found that for the study using daily mean temperature, the high temperature increased the risk of ambulance dispatch by 15.2% (RR = 1.152, 95%CI: 1.081-1.228). In the ambulance dispatch of all-cause subgroups, the RR was 1.179 (95% CI: 1.085-1.282). The results also reported a significant association between low temperature and ambulance dispatch (RR = 1.130, 95% CI: 1.052-1.213). In the subgroup, the RR for cardiovascular disease was 1.209 (95% CI: 1.033-1.414), and respiratory disease was 1.126 (95% CI: 1.012-1.253). Sensitivity analysis indicated that the results were robust, and no obvious publication bias was observed. High temperature and low temperature are important factors influencing the dispatch of ambulances. These findings help improve the understanding of temperature effect on ambulance dispatch, demonstrating the need to consider wider surveillance of acute health outcomes in different environments.

Electrocardiogram abnormalities in residents in cold homes: a cross-sectional analysis of the nationwide Smart Wellness Housing survey in Japan

Background

Excess winter mortality caused by cardiovascular disease is particularly profound in cold houses. Consistent with this, accumulating evidence indicates that low indoor temperatures at home increase blood pressure. However, it remains unclear whether low indoor temperatures affect other cardiovascular biomarkers. In its latest list of priority medical devices for management of cardiovascular diseases, the World Health Organization (WHO) included electrocardiography systems as capital medical devices. We therefore examined the association between indoor temperature and electrocardiogram findings.

Methods

We collected electrocardiogram data from 1480 participants during health checkups. We also measured the indoor temperature in the living room and bedroom for 2 weeks in winter, and divided participants into those living in warm houses (average exposure temperature ≥ 18 °C), slightly cold houses (12–18 °C), and cold houses (< 12 °C) in accordance with guidelines issued by the WHO and United Kingdom. The association between indoor temperature (warm vs. slightly cold vs. cold houses) and electrocardiogram findings was analyzed using multivariate logistic regression models, with adjustment for confounders such as demographics (e.g., age, sex, body mass index, household income), lifestyle (e.g., eating habit, exercise, smoking, alcohol drinking), and region.

Results

The average temperature at home was 14.7 °C, and 238, 924, and 318 participants lived in warm, slightly cold, and cold houses, respectively. Electrocardiogram abnormalities were observed in 17.6%, 25.4%, and 30.2% of participants living in warm, slightly cold, and cold houses, respectively (p = 0.003, chi-squared test). Compared to participants living in warm houses, the odds ratio of having electrocardiogram abnormalities was 1.79 (95% confidence interval: 1.14–2.81, p = 0.011) for those living in slightly cold houses and 2.18 (95% confidence interval: 1.27–3.75, p = 0.005) for those living in cold houses.

Conclusions

In addition to blood pressure, living in cold houses may have adverse effects on electrocardiogram. Conversely, keeping the indoor thermal environment within an appropriate range through a combination of living in highly thermal insulated houses and appropriate use of heating devices may contribute to good cardiovascular health.

Trial registration

The trial was retrospectively registered on 27 Dec 2017 to the University hospital Medical Information Network Clinical Trials Registry (UMIN-CTR, https://www.umin.ac.jp/ctr/, registration identifier number UMIN000030601).

Supplementary Information

The online version contains supplementary material available at 10.1186/s12199-021-01024-1.