Fluctuating temperature modifies heat-mortality association around the globe

Studies have investigated the effects of heat and temperature variability (TV) on mortality. However, few assessed whether TV modifies the heat-mortality association. Data on daily temperature and mortality in the warm season were collected from 717 locations across 36 countries. TV was calculated as the standard deviation of the average of the same and previous days’ minimum and maximum temperatures. We used location-specific quasi-Poisson regression models with an interaction term between the cross-basis term for mean temperature and quartiles of TV to obtain heat-mortality associations under each quartile of TV, and then pooled estimates at the country, regional, and global levels. Results show the increased risk in heat-related mortality with increments in TV, accounting for 0.70% (95% confidence interval [CI]: −0.33 to 1.69), 1.34% (95% CI: −0.14 to 2.73), 1.99% (95% CI: 0.29–3.57), and 2.73% (95% CI: 0.76–4.50) of total deaths for Q1–Q4 (first quartile–fourth quartile) of TV. The modification effects of TV varied geographically. Central Europe had the highest attributable fractions (AFs), corresponding to 7.68% (95% CI: 5.25–9.89) of total deaths for Q4 of TV, while the lowest AFs were observed in North America, with the values for Q4 of 1.74% (95% CI: −0.09 to 3.39). TV had a significant modification effect on the heat-mortality association, causing a higher heat-related mortality burden with increments of TV. Implementing targeted strategies against heat exposure and fluctuant temperatures simultaneously would benefit public health

Comparison of weather station and climate reanalysis data for modelling temperature-related mortality

Multi-Country Multi-City (MCC) Collaborative Research Network: Barrak Alahmad, Rosana Abrutzky, Paulo Hilario Nascimento Saldiva, Patricia Matus Correa, Nicolás Valdés Orteg, Haidong Kan, Samuel Osorio, Ene Indermitte, Jouni J K Jaakkola, Niilo Ryti, Alexandra Schneider, Veronika Huber, Klea Katsouyanni, Antonis Analitis, Alireza Entezari, Fatemeh Mayvaneh, Paola Michelozzi, Francesca de'Donato, Masahiro Hashizume, Yoonhee Kim, Magali Hurtado Diaz, César De la Cruz Valencia, Ala Overcenco, Danny Houthuijs, Caroline Ameling, Shilpa Rao, Xerxes Seposo, Baltazar Nunes, Iulian-Horia Holobaca, Ho Kim, Whanhee Lee, Carmen Íñiguez, Bertil Forsberg, Christofer Åström, Martina S Ragettli, Yue-Liang Leon Guo, Bing-Yu Chen, Valentina Colistro, Antonella Zanobetti, Joel Schwartz, Tran Ngoc Dang, Do Van DungErratum in: Author Correction: Sci Rep. 2022 May 13;12(1):7960. doi: 10.1038/s41598-022-11769-6. https://www.nature.com/articles/s41598-022-11769-6Epidemiological analyses of health risks associated with non-optimal temperature are traditionally based on ground observations from weather stations that offer limited spatial and temporal coverage. Climate reanalysis represents an alternative option that provide complete spatio-temporal exposure coverage, and yet are to be systematically explored for their suitability in assessing temperature-related health risks at a global scale. Here we provide the first comprehensive analysis over multiple regions to assess the suitability of the most recent generation of reanalysis datasets for health impact assessments and evaluate their comparative performance against traditional station-based data. Our findings show that reanalysis temperature from the last ERA5 products generally compare well to station observations, with similar non-optimal temperature-related risk estimates. However, the analysis offers some indication of lower performance in tropical regions, with a likely underestimation of heat-related excess mortality. Reanalysis data represent a valid alternative source of exposure variables in epidemiological analyses of temperature-related risk.The study was primarily supported by Grants from the European Commission’s Joint Research Centre Seville (Research Contract ID: JRC/SVQ/2020/MVP/1654), Medical Research Council-UK (Grant ID: MR/R013349/1), Natural Environment Research Council UK (Grant ID: NE/R009384/1), European Union’s Horizon 2020 Project Exhaustion (Grant ID: 820655). The following individual Grants also supported this work: J.K and A.U were supported by the Czech Science Foundation, project 20-28560S. A.T was supported by MCIN/AEI/10.13039/501100011033, Grant CEX2018-000794-S. V.H was supported by the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie Grant agreement No 101032087.info:eu-repo/semantics/publishedVersio

Geographical Variations of the Minimum Mortality Temperature at a Global Scale

Background: Minimum mortality temperature (MMT) is an important indicator to assess the temperature-mortality association, indicating long-term adaptation to local climate. Limited evidence about the geographical variability of the MMT is available at a global scale.Methods: We collected data from 658 communities in 43 countries under different climates. We estimated temperature-mortality associations to derive the MMT for each community using Poisson regression with distributed lag nonlinear models. We investigated the variation in MMT by climatic zone using a mixed-effects meta-analysis and explored the association with climatic and socioeconomic indicators.Results: The geographical distribution of MMTs varied considerably by country between 14.2 and 31.1 °C decreasing by latitude. For climatic zones, the MMTs increased from alpine (13.0 °C) to continental (19.3 °C), temperate (21.7 °C), arid (24.5 °C), and tropical (26.5 °C). The MMT percentiles (MMTPs) corresponding to the MMTs decreased from temperate (79.5th) to continental (75.4th), arid (68.0th), tropical (58.5th), and alpine (41.4th). The MMTs indreased by 0.8 °C for a 1 °C rise in a community’s annual mean temperature, and by 1 °C for a 1 °C rise in its SD. While the MMTP decreased by 0.3 centile points for a 1 °C rise in a community’s annual mean temperature and by 1.3 for a 1 °C rise in its SD.Conclusions: The geographical distribution of the MMTs and MMTPs is driven mainly by the mean annual temperature, which seems to be a valuable indicator of overall adaptation across populations. Our results suggest that populations have adapted to the average temperature, although there is still more room for adaptation

Global, regional, and national burden of mortality associated with non-optimal ambient temperatures from 2000 to 2019: a three-stage modelling study.

BACKGROUND: Exposure to cold or hot temperatures is associated with premature deaths. We aimed to evaluate the global, regional, and national mortality burden associated with non-optimal ambient temperatures. METHODS: In this modelling study, we collected time-series data on mortality and ambient temperatures from 750 locations in 43 countries and five meta-predictors at a grid size of 0·5° × 0·5° across the globe. A three-stage analysis strategy was used. First, the temperature-mortality association was fitted for each location by use of a time-series regression. Second, a multivariate meta-regression model was built between location-specific estimates and meta-predictors. Finally, the grid-specific temperature-mortality association between 2000 and 2019 was predicted by use of the fitted meta-regression and the grid-specific meta-predictors. Excess deaths due to non-optimal temperatures, the ratio between annual excess deaths and all deaths of a year (the excess death ratio), and the death rate per 100 000 residents were then calculated for each grid across the world. Grids were divided according to regional groupings of the UN Statistics Division. FINDINGS: Globally, 5 083 173 deaths (95% empirical CI [eCI] 4 087 967-5 965 520) were associated with non-optimal temperatures per year, accounting for 9·43% (95% eCI 7·58-11·07) of all deaths (8·52% [6·19-10·47] were cold-related and 0·91% [0·56-1·36] were heat-related). There were 74 temperature-related excess deaths per 100 000 residents (95% eCI 60-87). The mortality burden varied geographically. Of all excess deaths, 2 617 322 (51·49%) occurred in Asia. Eastern Europe had the highest heat-related excess death rate and Sub-Saharan Africa had the highest cold-related excess death rate. From 2000-03 to 2016-19, the global cold-related excess death ratio changed by -0·51 percentage points (95% eCI -0·61 to -0·42) and the global heat-related excess death ratio increased by 0·21 percentage points (0·13-0·31), leading to a net reduction in the overall ratio. The largest decline in overall excess death ratio occurred in South-eastern Asia, whereas excess death ratio fluctuated in Southern Asia and Europe. INTERPRETATION: Non-optimal temperatures are associated with a substantial mortality burden, which varies spatiotemporally. Our findings will benefit international, national, and local communities in developing preparedness and prevention strategies to reduce weather-related impacts immediately and under climate change scenarios. FUNDING: Australian Research Council and the Australian National Health and Medical Research Council

Global, regional, and national burden of mortality associated with short-term temperature variability from 2000-19: a three-stage modelling study.

BACKGROUND: Increased mortality risk is associated with short-term temperature variability. However, to our knowledge, there has been no comprehensive assessment of the temperature variability-related mortality burden worldwide. In this study, using data from the MCC Collaborative Research Network, we first explored the association between temperature variability and mortality across 43 countries or regions. Then, to provide a more comprehensive picture of the global burden of mortality associated with temperature variability, global gridded temperature data with a resolution of 0·5° × 0·5° were used to assess the temperature variability-related mortality burden at the global, regional, and national levels. Furthermore, temporal trends in temperature variability-related mortality burden were also explored from 2000-19. METHODS: In this modelling study, we applied a three-stage meta-analytical approach to assess the global temperature variability-related mortality burden at a spatial resolution of 0·5° × 0·5° from 2000-19. Temperature variability was calculated as the SD of the average of the same and previous days' minimum and maximum temperatures. We first obtained location-specific temperature variability related-mortality associations based on a daily time series of 750 locations from the Multi-country Multi-city Collaborative Research Network. We subsequently constructed a multivariable meta-regression model with five predictors to estimate grid-specific temperature variability related-mortality associations across the globe. Finally, percentage excess in mortality and excess mortality rate were calculated to quantify the temperature variability-related mortality burden and to further explore its temporal trend over two decades. FINDINGS: An increasing trend in temperature variability was identified at the global level from 2000 to 2019. Globally, 1 753 392 deaths (95% CI 1 159 901-2 357 718) were associated with temperature variability per year, accounting for 3·4% (2·2-4·6) of all deaths. Most of Asia, Australia, and New Zealand were observed to have a higher percentage excess in mortality than the global mean. Globally, the percentage excess in mortality increased by about 4·6% (3·7-5·3) per decade. The largest increase occurred in Australia and New Zealand (7·3%, 95% CI 4·3-10·4), followed by Europe (4·4%, 2·2-5·6) and Africa (3·3, 1·9-4·6). INTERPRETATION: Globally, a substantial mortality burden was associated with temperature variability, showing geographical heterogeneity and a slightly increasing temporal trend. Our findings could assist in raising public awareness and improving the understanding of the health impacts of temperature variability. FUNDING: Australian Research Council, Australian National Health & Medical Research Council

Constructing synthetic populations in the age of big data

Abstract Background To develop public health intervention models using micro-simulations, extensive personal information about inhabitants is needed, such as socio-demographic, economic and health figures. Confidentiality is an essential characteristic of such data, while the data should reflect realistic scenarios. Collection of such data is possible only in secured environments and not directly available for open-source micro-simulation models. The aim of this paper is to illustrate a method of construction of synthetic data by predicting individual features through models based on confidential data on health and socio-economic determinants of the entire Dutch population. Methods Administrative records and health registry data were linked to socio-economic characteristics and self-reported lifestyle factors. For the entire Dutch population (n = 16,778,708), all socio-demographic information except lifestyle factors was available. Lifestyle factors were available from the 2012 Dutch Health Monitor (n = 370,835). Regression model was used to sequentially predict individual features. Results The synthetic population resembles the original confidential population. Features predicted in the first stages of the sequential procedure are virtually similar to those in the original population, while those predicted in later stages of the sequential procedure carry the accumulation of limitations furthered by data quality and previously modelled features. Conclusions By combining socio-demographic, economic, health and lifestyle related data at individual level on a large scale, our method provides us with a powerful tool to construct a synthetic population of good quality and with no confidentiality issues

Assessment of spatial and physical neighborhood characteristics that influence sound quality and herewith well-being and health.

Environmental noise and health studies seldom address the positive effect of environments with high acoustic quality. Sound quality, in turn, is influenced by a large number of factors, including the spatial-physical characteristics of a neighborhood. In general, these characteristics cannot be retrieved from existing databases. In this article, we describe the design of an audit instrument and demonstrate its value for gathering information about these characteristics of neighborhoods. The audit instrument used was derived from research in other fields than environmental health. The instrument was tested in 33 neighborhoods in the Dutch cities of Amsterdam, Rotterdam, and Arnhem. In these neighborhoods, more or less homogeneous subareas were identified that were subject of the audit. The results show that the audit approach is suitable to gather neighborhood data that are relevant for the sound quality of neighborhoods. Together with survey data, they provide information that could further the field of soundscape and health. Several suggestions for improvement of the audit instrument were made

Estimating the prevalence of 26 health-related indicators at neighbourhood level in the Netherlands using structured additive regression.

Local policy makers increasingly need information on health-related indicators at smaller geographic levels like districts or neighbourhoods. Although more large data sources have become available, direct estimates of the prevalence of a health-related indicator cannot be produced for neighbourhoods for which only small samples or no samples are available. Small area estimation provides a solution, but unit-level models for binary-valued outcomes that can handle both non-linear effects of the predictors and spatially correlated random effects in a unified framework are rarely encountered