Search

Abstract Exposure to high and low ambient temperatures increases the risk of neonatal mortality, but the contribution of climate change to temperature-related neonatal deaths is unknown. We use Demographic and Health Survey (DHS) data (n = 40,073) from 29 low- and middle-income countries to estimate the temperature-related burden of neonatal deaths between 2001 and 2019 that is attributable to climate change. We find that across all countries, 4.3% of neonatal deaths were associated with non-optimal temperatures. Climate change was responsible for 32% (range: 19-79%) of heat-related neonatal deaths, while reducing the respective cold-related burden by 30% (range: 10-63%). Climate change has impacted temperature-related neonatal deaths in all study countries, with most pronounced climate-induced losses from increased heat and gains from decreased cold observed in countries in sub-Saharan Africa. Future increases in global mean temperatures are expected to exacerbate the heat-related burden, which calls for ambitious mitigation and adaptation measures to safeguard the health of newborns.

Introduction: A causal link between air pollution exposure and cardiovascular events has been suggested. However fewer studies have investigated the shape of the associations at low levels of air pollution and identified the most important temporal window of exposure. Here we assessed long-term associations between particulate matter 40) from the UK Biobank cohort, we investigated the associations between 1-year, 3-year and 5-year time-varying averages of PM2.5 and incidence of major adverse cardiovascular events (MACE), myocardial infarction (MI), heart failure, atrial fibrillation and flutter and cardiac arrest. We also investigated outcome subtypes for MI and stroke. Events were defined as hospital inpatient admissions. We fitted Cox proportional hazard regression models applying extensive control for confounding at both individual and area level. Finally, we assessed the shape of the exposure–response functions to assess effects at low levels of exposure. Results: We analysed data from 377,736 study participants after exclusion of prevalent subjects. The average follow-up (2006–2021) was 12.9 years. We detected 19,353 cases of MACE, 6,562 of acute MI, 6,278 of heart failure, 1,258 for atrial fibrillation and flutter, and 16,327 for cardiac arrest. Using a 5-year exposure window, we detected positive associations (for 5 μg/m3 increase in PM2.5) for 5-point MACE of [1.12 (95 %CI: 1.00–1.26)], heart failure [1.22 (1.00–1.50)] and cardiac arrest [1.16 (1.03–1.31)]. We did not find any association with acute MI, while non-ST-elevation MI was associated with the 1-year exposure window [1.52 (1.12–2.07)]. The assessment of the shape of the exposure–response relationships suggested that risk is approximately linear for most of the outcomes. Conclusions: We found positive associations between long-term exposure to PM2.5 and multiple cardiovascular outcomes for different exposure windows. The cardiovascular risk tends to rise even at exposure concentrations below 12–15 μg/m3, indicating high risk below UK national and international thresholds.

Background Throughout the Syrian conflict, the Syrian government has intentionally attacked health facilities, violating International Humanitarian Law. Previous studies have qualitatively described health system disruptions following attacks on healthcare or established associations between armed conflict and health service utilisation, but there are no quantitative studies exploring the effects of health facility attacks. Our unprecedented study addresses this gap by quantifying the effects of health facility attacks on health service use during the Syrian conflict.Methods This retrospective observational study uses 18 537 reports capturing 2 826 627 consultations from 18 health facilities in northwest Syria and 69 attacks on these facilities. The novel study applies case time series design with a generalised non-linear model and stratification by facility type, attack mechanism and corroboration status.Results The study found significant, negative associations between health facility attacks and outpatient, trauma and facility births. On average, a health facility attack was associated with 51% and 38% reductions in outpatient, RR 0.49 (95% CI 0.43 to 0.57) and trauma consultations, RR 0.62 (95% CI 0.53 to 0.72), the day after an attack, with significant reductions continuing for 37 and 20 days, respectively. Health facility attacks were associated with an average 23% reduction in facility births, the second day after an attack, RR 0.77 (95% CI 0.66 to 0.89), with significant reductions continuing for 42 days.Conclusions Attacks on health facilities in northwest Syria are strongly associated with significant reductions in outpatient, trauma and facility births. These attacks exacerbate the adverse effects of armed conflict and impede the fundamental right to health. The findings provide evidence that attacks on health facilities, violations of international humanitarian law by themselves, also negatively affect human rights by limiting access to health services, underscoring the need to strengthen health system resilience in conflict settings, expand systematic reporting of attacks on healthcare and hold perpetrators accountable.

Background: While vegetation type, population density and proximity to greenspaces have been linked to human health, what type and location of greenspace matter most have remained unclear. In this context, there are question marks over investment-style metrics. Objectives: This paper aims at establishing what vegetation type may matter most in modifying heat-mortality associations, and what the optimal buffer distances of total and specific types of greenspace exposure associated with reduced heat-related mortality risks are. Methods: We conducted small-area analyses using daily mortality data for 286 Territory Planning Units (TPUs) across Hong Kong and 1 × 1 km gridded air temperature data for the summer months (2005–2018). Using a case time series design, we examined effect modifications of total and specific types of greenspaces, as well as population-weighted exposure at varying buffer distances (200–4000 m). We tested the significance of effect modifications by comparing relative risks (RRs) between the lowest and highest quartiles of each greenspace exposure metric; and explored the strength of effect modifications by calculating the ratio of RRs. Results: Forests, unlike grasslands, showed significant effect modifications on heat-mortality associations, with RRs rising from 0.98 (95 %CI: 0.92,1.05) to 1.06 (1.03, 1.10) for the highest to lowest quartiles (p-value = 0.037) The optimal distances associated with the most apparent effects were around 1 km for population-weighted exposure, with the ratio of RRs being 1.424 (1.038,1.954) for NDVI, 1.191 (1.004,1.413) for total greenspace, and 1.227 (1.024,1.470) for forests. A marked difference was observed in terms of the paired area-level and optimal distance-based exposure to total greenspace and forests under extreme heat (p-values

Abstract Extreme weather and coronavirus-type pandemics are both leading global health concerns. Until now, no study has quantified the compound health consequences of the co-occurrence of them. We estimate the mortality attributable to extreme heat and cold events, which dominate the UK health burden from weather hazards, in England and Wales in the period 2020-2022, during which the COVID-19 pandemic peaked in terms of mortality. We show that temperature-related mortality exceeded COVID-19 mortality by 8% in South West England. Combined, extreme temperatures and COVID-19 led to 19 (95% confidence interval: 16–22 in North West England) to 24 (95% confidence interval: 20–29 in Wales) excess deaths per 100,000 population during heatwaves, and 80 (95% confidence interval: 75–86 in Yorkshire and the Humber) to 127 (95% confidence interval: 123–132 in East of England) excess deaths per 100,000 population during cold snaps. These numbers are at least ~2 times higher than the previous decade. Society must increase preparedness for compound health crises such as extreme weather coinciding with pandemics.

Background: Mental-health-related stigma prevents youth from seeking help for mental health problems. Limited studies in low- and middle-income countries assess the effect of arts-based education in reducing such stigma among youth, thereby leaving a gap in evidence-based, age- and culturally-appropriate interventions. Objective: To evaluate the impact, feasibility, and acceptability of CREATORS, an arts-based educational program on reducing mental-health-related stigma among youth in India. Methods: We conducted a mixed-methods, pre-post control group study among college-going adolescents in Hyderabad, India. At baseline and post-intervention (after six weeks), we examined differences in intended behavior towards people with mental health problems. Results: Our study involved 432 participants across three study groups: participants creating art on the theme of mental-health-related stigma over six weeks (n = 123), a student audience viewing 2-h arts show by participants (n = 244), and a control group (n = 65). Between baseline and post-test, participants creating art showed significantly lower stigma towards people with mental health problems compared to members of the control group (coefficient = 1.55, 95%CI 0.06–3.04, p = 0.041). Participants found the intervention useful and enjoyable (>95%; n = 773 across six weeks). Participants identified that collaborative creation of art made the subject of mental health interesting and relatable. Conclusions: Participating in an arts-based educational program was associated with significantly lower mental-health-related stigma among youth compared to a control group in the short term. High acceptability of the program demonstrates the utility of arts-based education to address mental-health-related stigma. With community partners and artists as facilitators, our program may support mental health specialists in mental health promotion.

Abstract Older adults are generally amongst the most vulnerable to heat and cold. While temperature-related health impacts are projected to increase with global warming, the influence of population aging on these trends remains unclear. Here we show that at 1.5 °C, 2 °C, and 3 °C of global warming, heat-related mortality in 800 locations across 50 countries/areas will increase by 0.5%, 1.0%, and 2.5%, respectively; among which 1 in 5 to 1 in 4 heat-related deaths can be attributed to population aging. Despite a projected decrease in cold-related mortality due to progressive warming alone, population aging will mostly counteract this trend, leading to a net increase in cold-related mortality by 0.1%–0.4% at 1.5–3 °C global warming. Our findings indicate that population aging constitutes a crucial driver for future heat- and cold-related deaths, with increasing mortality burden for both heat and cold due to the aging population.

BackgroundThe regional disparity of heatwave-related mortality over a long period has not been sufficiently assessed across the globe, impeding the localisation of adaptation planning and risk management towards climate change. We quantified the global mortality burden associated with heatwaves at a spatial resolution of 0.5°×0.5° and the temporal change from 1990 to 2019.Methods and findingsWe collected data on daily deaths and temperature from 750 locations of 43 countries or regions, and 5 meta-predictors in 0.5°×0.5° resolution across the world. Heatwaves were defined as location-specific daily mean temperature ≥95th percentiles of year-round temperature range with duration ≥2 days. We first estimated the location-specific heatwave-mortality association. Secondly, a multivariate meta-regression was fitted between location-specific associations and 5 meta-predictors, which was in the third stage used with grid cell-specific meta-predictors to predict grid cell-specific association. Heatwave-related excess deaths were calculated for each grid and aggregated. During 1990 to 2019, 0.94% (95% CI: 0.68-1.19) of deaths [i.e., 153,078 cases (95% eCI: 109,950-194,227)] per warm season were estimated to be from heatwaves, accounting for 236 (95% eCI: 170-300) deaths per 10 million residents. The ratio between heatwave-related excess deaths and all premature deaths per warm season remained relatively unchanged over the 30 years, while the number of heatwave-related excess deaths per 10 million residents per warm season declined by 7.2% per decade in comparison to the 30-year average. Locations with the highest heatwave-related death ratio and rate were in Southern and Eastern Europe or areas had polar and alpine climates, and/or their residents had high incomes. The temporal change of heatwave-related mortality burden showed geographic disparities, such that locations with tropical climate or low incomes were observed with the greatest decline. The main limitation of this study was the lack of data from certain regions, e.g., Arabian Peninsula and South Asia.ConclusionsHeatwaves were associated with substantial mortality burden that varied spatiotemporally over the globe in the past 30 years. The findings indicate the potential benefit of governmental actions to enhance health sector adaptation and resilience, accounting for inequalities across communities.

Background: Temperature variability (TV) is associated with increased mortality risk. However, it is still unknown whether intra-day or inter-day TV has different effects. Objectives: We aimed to assess the association of intra-day TV and inter-day TV with all-cause, cardiovascular, and respiratory mortality. Methods: We collected data on total, cardiovascular, and respiratory mortality and meteorology from 758 locations in 47 countries or regions from 1972 to 2020. We defined inter-day TV as the standard deviation (SD) of daily mean temperatures across the lag interval, and intra-day TV as the average SD of minimum and maximum temperatures on each day. In the first stage, inter-day and intra-day TVs were modelled simultaneously in the quasi-Poisson time-series model for each location. In the second stage, a multi-level analysis was used to pool the location-specific estimates. Results: Overall, the mortality risk due to each interquartile range [IQR] increase was higher for intra-day TV than for inter-day TV. The risk increased by 0.59% (95% confidence interval [CI]: 0.53, 0.65) for all-cause mortality, 0.64% (95% CI: 0.56, 0.73) for cardiovascular mortality, and 0.65% (95% CI: 0.49, 0.80) for respiratory mortality per IQR increase in intra-day TV0–7 (0.9 °C). An IQR increase in inter-day TV0–7 (1.6 °C) was associated with 0.22% (95% CI: 0.18, 0.26) increase in all-cause mortality, 0.44% (95% CI: 0.37, 0.50) increase in cardiovascular mortality, and 0.31% (95% CI: 0.21, 0.41) increase in respiratory mortality. The proportion of all-cause deaths attributable to intra-day TV0–7 and inter-day TV0–7 was 1.45% and 0.35%, respectively. The mortality risks varied by lag interval, climate area, season, and climate type. Conclusions: Our results indicated that intra-day TV may explain the main part of the mortality risk related to TV and suggested that comprehensive evaluations should be proposed in more countries to help protect human health.

Assessing the association between temperature frequency and mortality can provide insights into human adaptation to local ambient temperatures. We collected daily time-series data on mortality and temperature from 757 locations in 47 countries/regions during 1979–2020. We used a two-stage time series design to assess the association between temperature frequency and all-cause mortality. The results were pooled at the national, regional, and global levels. We observed a consistent decrease in the risk of mortality as the normalized frequency of temperature increases across the globe. The average increase in mortality risk comparing the 10th to 100th percentile of normalized frequency was 13.03% (95% CI: 12.17–13.91), with substantial regional differences (from 4.56% in Australia and New Zealand to 33.06% in South Europe). The highest increase in mortality was observed for high-income countries (13.58%, 95% CI: 12.56–14.61), followed by lower-middle-income countries (12.34%, 95% CI: 9.27–15.51). This study observed a declining risk of mortality associated with higher temperature frequency. Our findings suggest that populations can adapt to their local climate with frequent exposure, with the adapting ability varying geographically due to differences in climatic and socioeconomic characteristics.

Introduction In Kenya, non-communicable diseases (NCDs) are estimated to account for almost one-third of all deaths and this is likely to rise by over 50% in the next 10 years. The Primary Health Integrated Care for Chronic Conditions (PIC4C) project aims to strengthen primary care by integrating comprehensive NCD care into existing HIV primary care platform. This paper evaluates the association of PIC4C implementation on clinical outcomes.Methods Outcomes included proportion of new patients, systolic blood pressure (SBP), fasting plasma glucose (FPG), diastolic blood pressure, hypertension control, random plasma glucose, diabetes control, viral load and HIV viral suppression. We used interrupted time series and binomial regression with random effects for facility-level data and generalised mixed-effects regression for visit-level data to examine the association between PIC4C and outcomes between January 2017 and December 2021. We conducted sensitivity analysis with restrictions on sites and the number of visits.Results Data from 66 641 visits of 13 046 patients with hypertension, 24 005 visits of 7267 patients with diabetes and 84 855 visits of 21 186 people with HIV were analysed. We found evidence of association between PIC4C and increase in proportion of new patients per month with hypertension (adjusted OR (aOR) 1.57, 95% CI 1.39 to 1.78) and diabetes (aOR 1.31, 95% CI 1.19 to 1.45), small increase in SBP (adjusted beta (aB) 1.7, 95% CI 0.8 to 2.7) and FPG (aB 0.6, 95% CI 0.0 to 1.1). There was no strong evidence of association between PIC4C and viral suppression (aOR 1.20, 95% CI 0.98 to 1.47). In sensitivity analysis, there was no strong evidence of association between PIC4C and SBP (aB 1.74, 95% CI −0.70 to 4.17) or FPG (aB 0.52, 95% CI −0.64 to 1.67)Conclusions PIC4C implementation was associated with increase in proportion of new patients attending clinics and a slight increase in SBP and FPG. The immediate post-PIC4C implementation period coincided with the COVID-19 pandemic, which is likely to explain some of our findings.

Abstract As the climate warms, increasing heat-related health risks are expected, and can be exacerbated by the urban heat island (UHI) effect. UHIs can also offer protection against cold weather, but a clear quantification of their impacts on human health across diverse cities and seasons is still being explored. Here we provide a 500 m resolution assessment of mortality risks associated with UHIs for 85 European cities in 2015-2017. Acute impacts are found during heat extremes, with a 45% median increase in mortality risk associated with UHI, compared to a 7% decrease during cold extremes. However, protracted cold seasons result in greater integrated protective effects. On average, UHI-induced heat-/cold-related mortality is associated with economic impacts of €192/€ − 314 per adult urban inhabitant per year in Europe, comparable to air pollution and transit costs. These findings urge strategies aimed at designing healthier cities to consider the seasonality of UHI impacts, and to account for social costs, their controlling factors, and intra-urban variability.

Abstract Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5 °C and 2 °C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives.

BackgroundMore intense tropical cyclones (TCs) are expected in the future under a warming climate scenario, but little is known about their mortality effect pattern across countries and over decades. We aim to evaluate the TC-specific mortality risks, periods of concern (POC) and characterize the spatiotemporal pattern and exposure-response (ER) relationships on a multicountry scale.Methods and findingsDaily all-cause, cardiovascular, and respiratory mortality among the general population were collected from 494 locations in 18 countries or territories during 1980 to 2019. Daily TC exposures were defined when the maximum sustained windspeed associated with a TC was ≥34 knots using a parametric wind field model at a 0.5° × 0.5° resolution. We first estimated the TC-specific mortality risks and POC using an advanced flexible statistical framework of mixed Poisson model, accounting for the population changes, natural variation, seasonal and day of the week effects. Then, a mixed meta-regression model was used to pool the TC-specific mortality risks to estimate the overall and country-specific ER relationships of TC characteristics (windspeed, rainfall, and year) with mortality. Overall, 47.7 million all-cause, 15.5 million cardiovascular, and 4.9 million respiratory deaths and 382 TCs were included in our analyses. An overall average POC of around 20 days was observed for TC-related all-cause and cardiopulmonary mortality, with relatively longer POC for the United States of America, Brazil, and Taiwan (>30 days). The TC-specific relative risks (RR) varied substantially, ranging from 1.04 to 1.42, 1.07 to 1.77, and 1.12 to 1.92 among the top 100 TCs with highest RRs for all-cause, cardiovascular, and respiratory mortality, respectively. At country level, relatively higher TC-related mortality risks were observed in Guatemala, Brazil, and New Zealand for all-cause, cardiovascular, and respiratory mortality, respectively. We found an overall monotonically increasing and approximately linear ER curve of TC-related maximum sustained windspeed and cumulative rainfall with mortality, with heterogeneous patterns across countries and regions. The TC-related mortality risks were generally decreasing from 1980 to 2019, especially for the Philippines, Taiwan, and the USA, whereas potentially increasing trends in TC-related all-cause and cardiovascular mortality risks were observed for Japan.ConclusionsThe TC mortality risks and POC varied greatly across TC events, locations, and countries. To minimize the TC-related health burdens, targeted strategies are particularly needed for different countries and regions, integrating epidemiological evidence on region-specific POC and ER curves that consider across-TC variability.

Background: Recent studies have reported that air pollution is related to kidney diseases. However, the global evidence on the risk of death from acute kidney injury (AKI) owing to air pollution is limited. Therefore, we investigated the association between short-term exposure to air pollution—particulate matter ≤ 2.5 μm (PM2.5), ozone (O3), and nitrogen dioxide (NO2)—and AKI-related mortality using a multi-country dataset. Methods: This study included 41,379 AKI-related deaths in 136 locations in six countries during 1987–2018. A novel case time-series design was applied to each air pollutant during 0–28 lag days to estimate the association between air pollution and AKI-related deaths. Moreover, we calculated AKI deaths attributable to non-compliance with the World Health Organization (WHO) air quality guidelines. Results: The relative risks (95% confidence interval) of AKI-related deaths are 1.052 (1.003, 1.103), 1.022 (0.994, 1.050), and 1.022 (0.982, 1.063) for 5, 10, and 10 µg/m3 increase in lag 0–28 days of PM2.5, warm-season O3, and NO2, respectively. The lag-distributed association showed that the risk appeared immediately on the day of exposure to air pollution, gradually decreased, and then increased again reaching the peak approximately 20 days after exposure to PM2.5 and O3. We also found that 1.9%, 6.3%, and 5.2% of AKI deaths were attributed to PM2.5, warm-season O3, and NO2 concentrations above the WHO guidelines. Conclusions: This study provides evidence that public health policies to reduce air pollution may alleviate the burden of death from AKI and suggests the need to investigate the several pathways between air pollution and AKI death.

Abstract Heatwaves are one of the leading causes of climate-induced mortality. Using the examples of recent heatwaves in Europe, the United States and Asia, we illustrate how the communication of dangerous conditions based on temperature maps alone can lead to insufficient societal perception of health risks. Comparison of maximum daily values of temperature with physiological heat stress indices accounting for impacts of both temperature and humidity, illustrates substantial differences in geographical extent and timing of their respective peak values during these recent events. This signals the need to revisit how meteorological heatwaves and their expected impacts are communicated. Close collaboration between climate and medical communities is needed to select the best heat stress indicators, establish them operationally, and introduce them to the public.

Background: The epidemiological evidence on the interaction between heat and ambient air pollution on mortality is still inconsistent. Objectives: To investigate the interaction between heat and ambient air pollution on daily mortality in a large dataset of 620 cities from 36 countries. Methods: We used daily data on all-cause mortality, air temperature, particulate matter ≤ 10 μm (PM10), PM ≤ 2.5 μm (PM2.5), nitrogen dioxide (NO2), and ozone (O3) from 620 cities in 36 countries in the period 1995–2020. We restricted the analysis to the six consecutive warmest months in each city. City-specific data were analysed with over-dispersed Poisson regression models, followed by a multilevel random-effects meta-analysis. The joint association between air temperature and air pollutants was modelled with product terms between non-linear functions for air temperature and linear functions for air pollutants. Results: We analyzed 22,630,598 deaths. An increase in mean temperature from the 75th to the 99th percentile of city-specific distributions was associated with an average 8.9 % (95 % confidence interval: 7.1 %, 10.7 %) mortality increment, ranging between 5.3 % (3.8 %, 6.9 %) and 12.8 % (8.7 %, 17.0 %), when daily PM10 was equal to 10 or 90 μg/m3, respectively. Corresponding estimates when daily O3 concentrations were 40 or 160 μg/m3 were 2.9 % (1.1 %, 4.7 %) and 12.5 % (6.9 %, 18.5 %), respectively. Similarly, a 10 μg/m3 increment in PM10 was associated with a 0.54 % (0.10 %, 0.98 %) and 1.21 % (0.69 %, 1.72 %) increase in mortality when daily air temperature was set to the 1st and 99th city-specific percentiles, respectively. Corresponding mortality estimate for O3 across these temperature percentiles were 0.00 % (-0.44 %, 0.44 %) and 0.53 % (0.38 %, 0.68 %). Similar effect modification results, although slightly weaker, were found for PM2.5 and NO2. Conclusions: Suggestive evidence of effect modification between air temperature and air pollutants on mortality during the warm period was found in a global dataset of 620 cities.

Background: Short-term associations between heat and cardiovascular disease (CVD) mortality have been examined mostly in large cities. However, different vulnerability and exposure levels may contribute to spatial heterogeneity. This study assessed heat effects on CVD mortality and potential vulnerability factors using data from three European countries, including urban and rural settings. Methods: We collected daily counts of CVD deaths aggregated at the small-area level in Norway (small-area level: municipality), England and Wales (lower super output areas), and Germany (district) during the warm season (May-September) from 1996 to 2018. Daily mean air temperatures estimated by spatial–temporal models were assigned to each small area. Within each country, we applied area-specific Quasi-Poisson regression using distributed lag nonlinear models to examine the heat effects at lag 0–1 days. The area-specific estimates were pooled by random-effects meta-analysis to derive country-specific and overall heat effects. We examined individual- and area-level heat vulnerability factors by subgroup analyses and meta-regression, respectively. Results: We included 2.84 million CVD deaths in analyses. For an increase in temperature from the 75th to the 99th percentile, the pooled relative risk (RR) for CVD mortality was 1.14 (95% CI: 1.03, 1.26), with the country-specific RRs ranging from 1.04 (1.00, 1.09) in Norway to 1.24 (1.23, 1.26) in Germany. Heat effects were stronger among women [RRs (95% CIs) for women and men: 1.18 (1.08, 1.28) vs. 1.12 (1.00, 1.24)]. Greater heat vulnerability was observed in areas with high population density, high degree of urbanization, low green coverage, and high levels of fine particulate matter. Conclusion: This study provides evidence for the heat effects on CVD mortality in European countries using high-resolution data from both urban and rural areas. Besides, we identified individual- and area-level heat vulnerability factors. Our findings may facilitate the development of heat-health action plans to increase resilience to climate change.

Abstract Background Cardiovascular diseases (CVDs), the leading cause of death worldwide, are sensitive to temperature. In light of the reported climate change trends, it is important to understand the burden of CVDs attributable to temperature, both hot and cold. The association between CVDs and temperature is region-specific, with relatively few studies focusing on low-and middle-income countries. This study investigates this association in Puducherry, a district in southern India lying on the Bay of Bengal, for the first time. Methods Using in-hospital CVD mortality data and climate data from the Indian Meteorological Department, we analyzed the association between apparent temperature (Tapp) and in-hospital CVD mortalities in Puducherry between 2011 and 2020. We used a case-crossover model with a binomial likelihood distribution combined with a distributed lag non-linear model to capture the delayed and non-linear trends over a 21-day lag period to identify the optimal temperature range for Puducherry. The results are expressed as the fraction of CVD mortalities attributable to heat and cold, defined relative to the optimal temperature. We also performed stratified analyses to explore the associations between Tapp and age-and-sex, grouped and considered together, and different types of CVDs. Sensitivity analyses were performed, including using a quasi-Poisson time-series approach. Results We found that the optimal temperature range for Puducherry is between 30°C and 36°C with respect to CVDs. Both cold and hot non-optimal Tapp were associated with an increased risk of overall in-hospital CVD mortalities, resulting in a U-shaped association curve. Cumulatively, up to 17% of the CVD deaths could be attributable to non-optimal temperatures, with a slightly higher burden attributable to heat (9.1%) than cold (8.3%). We also found that males were more vulnerable to colder temperature; females above 60 years were more vulnerable to heat while females below 60 years were affected by both heat and cold. Mortality with cerebrovascular accidents was associated more with heat compared to cold, while ischemic heart diseases did not seem to be affected by temperature. Conclusion Both heat and cold contribute to the burden of CVDs attributable to non-optimal temperatures in the tropical Puducherry. Our study also identified the age-and-sex and CVD type differences in temperature attributable CVD mortalities. Further studies from India could identify regional associations, inform our understanding of the health implications of climate change in India and enhance the development of regional and contextual climate-health action-plans.

The development of innovative tools for real-time monitoring and forecasting of environmental health impacts is central to effective public health interventions and resource allocation strategies. Though a need for such generic tools has been previously echoed by public health planners and regional authorities responsible for issuing anticipatory alerts, a comprehensive, robust and scalable real-time system for predicting temperature-related excess deaths at a local scale has not been developed yet. Filling this gap, we propose a flexible operational framework for coupling publicly available weather forecasts with temperature-mortality risk functions specific to small census-based zones, the latter derived using state-of-the-art environmental epidemiological models. Utilising high-resolution temperature data forecast by a leading European meteorological centre, we demonstrate a real-time application to forecast the excess mortality during the July 2022 heatwave over England and Wales. The output, consisting of expected temperature-related excess deaths at small geographic areas on different lead times, can be automated to generate maps at various spatio-temporal scales, thus facilitating preventive action and allocation of public health resources in advance. While the real-case example discussed here demonstrates an application for predicting (expected) heat-related excess deaths, the framework can also be adapted to other weather-related health risks and to different geographical areas, provided data on both meteorological exposure and the underlying health outcomes are available to calibrate the associated risk functions. The proposed framework addresses an urgent need for predicting the short-term environmental health burden on public health systems globally, especially in low- and middle-income regions, where rapid response to mitigate adverse exposures and impacts to extreme temperatures are often constrained by available resources.

Climate change could lead to high economic burden for individuals (i.e. low income and high prices). While economic conditions are important determinants of climate change vulnerability, environmental epidemiological studies focus primarily on the direct impact of temperature on morbidity and mortality without accounting for climate-induced impacts on the economy. More integrated approaches are needed to provide comprehensive assessments of climate-induced direct and indirect impacts on health. This paper provides some perspectives on how epidemiological and economic impact assessments could be better integrated. We argue that accounting for the economic repercussions of climate change on people’s health and, vice versa, the consequences of health effects on the economy could provide more realistic scenario projections and could be more useful for adaptation policy.

Background: Evidence on the potential interactive effects of heat and ambient air pollution on cause-specific mortality is inconclusive and limited to selected locations. Objectives: We investigated the effects of heat on cardiovascular and respiratory mortality and its modification by air pollution during summer months (six consecutive hottest months) in 482 locations across 24 countries. Methods: Location-specific daily death counts and exposure data (e.g., particulate matter with diameters ≤ 2.5 µm [PM2.5]) were obtained from 2000 to 2018. We used location-specific confounder-adjusted Quasi-Poisson regression with a tensor product between air temperature and the air pollutant. We extracted heat effects at low, medium, and high levels of pollutants, defined as the 5th, 50th, and 95th percentile of the location-specific pollutant concentrations. Country-specific and overall estimates were derived using a random-effects multilevel meta-analytical model. Results: Heat was associated with increased cardiorespiratory mortality. Moreover, the heat effects were modified by elevated levels of all air pollutants in most locations, with stronger effects for respiratory than cardiovascular mortality. For example, the percent increase in respiratory mortality per increase in the 2-day average summer temperature from the 75th to the 99th percentile was 7.7% (95% Confidence Interval [CI] 7.6–7.7), 11.3% (95%CI 11.2–11.3), and 14.3% (95% CI 14.1–14.5) at low, medium, and high levels of PM2.5, respectively. Similarly, cardiovascular mortality increased by 1.6 (95%CI 1.5–1.6), 5.1 (95%CI 5.1–5.2), and 8.7 (95%CI 8.7–8.8) at low, medium, and high levels of O3, respectively. Discussion: We observed considerable modification of the heat effects on cardiovascular and respiratory mortality by elevated levels of air pollutants. Therefore, mitigation measures following the new WHO Air Quality Guidelines are crucial to enhance better health and promote sustainable development.

Abstract Background The two-stage design has become a standard tool in environmental epidemiology to model multi-location data. However, its standard form is rather inflexible and poses important limitations for modelling complex risks associated with environmental factors. In this contribution, we illustrate multiple design extensions of the classical two-stage method, all implemented within a unified analytic framework. Methods We extended standard two-stage meta-analytic models along the lines of linear mixed-effects models, by allowing location-specific estimates to be pooled through flexible fixed and random-effects structures. This permits the analysis of associations characterised by combinations of multivariate outcomes, hierarchical geographical structures, repeated measures, and/or longitudinal settings. The analytic framework and inferential procedures are implemented in the R package mixmeta. Results The design extensions are illustrated in examples using multi-city time series data collected as part of the National Morbidity, Mortality and Air Pollution Study (NMMAPS). Specifically, four case studies demonstrate applications for modelling complex associations with air pollution and temperature, including non-linear exposure–response relationships, effects clustered at multiple geographical levels, differential risks by age, and effect modification by air conditioning in a longitudinal analysis. Conclusions The definition of several design extensions of the classical two-stage design within a unified framework, along with its implementation in freely-available software, will provide researchers with a flexible tool to address novel research questions in two-stage analyses of environmental health risks.

Abstract Background The increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels. Methods This tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level. Results The application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions. Conclusions The extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.

Abstract Epidemiological 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.

Abstract Higher availability of administrative data and better infrastructure for electronic surveys allow for large sample sizes in evaluations of national and other large scale policies. Although larger datasets have many advantages, the use of big disaggregate data (e.g., on individuals, households, stores, municipalities) can be challenging in terms of statistical inference. Measurements made at the same point in time may be jointly influenced by contemporaneous factors and produce more variation across time than suggested by the model. This excess variation, or co-movement over time, produce observations that are not truly independent (i.e., cross-sectional dependence). If this dependency is not accounted for, statistical uncertainty will be underestimated, and studies may indicate reform effects where there is none. In the context of interrupted time series (segmented regression), we illustrate the potential for bias in inference when using large disaggregate data, and we describe two simple solutions that are available in standard statistical software.

Abstract Previous studies have reported a decrease in air pollution levels following the enforcement of lockdown measures during the first wave of the COVID-19 pandemic. However, these investigations were mostly based on simple pre-post comparisons using past years as a reference and did not assess the role of different policy interventions. This study contributes to knowledge by quantifying the association between specific lockdown measures and the decrease in NO2, O3, PM2.5, and PM10 levels across 47 European cities. It also estimated the number of avoided deaths during the period. This paper used new modelled data from the Copernicus Atmosphere Monitoring Service (CAMS) to define business-as-usual and lockdown scenarios of daily air pollution trends. This study applies a spatio-temporal Bayesian non-linear mixed effect model to quantify the changes in pollutant concentrations associated with the stringency indices of individual policy measures. The results indicated non-linear associations with a stronger decrease in NO2 compared to PM2.5 and PM10 concentrations at very strict policy levels. Differences across interventions were also identified, specifically the strong effects of actions linked to school/workplace closure, limitations on gatherings, and stay-at-home requirements. Finally, the observed decrease in pollution potentially resulted in hundreds of avoided deaths across Europe.

Background: The causal association between mesothelioma and asbestos exposure is conclusive, and many studies have proved that the trend in asbestos use is a strong predictor of the pattern in mesothelioma cases with an adequate latency time (generally around 30–40 years or more). Recently, a novel approach for predicting malignant pleural mesothelioma, based on asbestos consumption trend and using distributed non-linear models, has been applied. Objectives: The purpose of this study is to analyse trends in asbestos consumption and malignant mesothelioma mortality in the major asbestos-user countries. Furthermore, we applied distributed non-linear models to estimate and compare epidemiological relationships between asbestos consumption and mesothelioma mortality across these countries. Methods: The study involves major asbestos-user countries in which historical asbestos consumption and mesothelioma mortality data are available. Data on asbestos consumption were derived from worldwide asbestos supply and mesothelioma mortality data from World Health Organization (WHO) mortality archives. A quasi-Poisson generalized linear model was used to model past asbestos exposure and male mesothelioma mortality rates in each country. Exposure-response associations have been modelled using distributed lag non-linear models. Findings and conclusions: According to the criteria defined above, we selected 18 countries with raw asbestos cumulative consumptions higher than two million tons in the period 1933–2012. Overall, a clear linear relationship can be observed between total consumption and total deaths for mesothelioma. Country-specific exposure, lag and age-response relationships were identified and common functions extracted by a meta-analysis procedure. Non-linear models appear suitable and flexible tools for investigating the association between mesothelioma mortality and asbestos consumption. There is a need to improve the global epidemiological surveillance of asbestos-related diseases, particularly mesothelioma mortality, and the absence of reliable data for some major asbestos-user countries is a real concern. A reliable assessment of mesothelioma mortality is a fundamental step towards increasing the awareness of related risks and the need of an international ban on asbestos.

Possible effects of weather conditions on COVID-19 transmission are debated. Here, the authors analyse data from early in the pandemic and show that although temperature and humidity had small effects on transmission, they were far out-weighed by the effects of non-pharmaceutical interventions.

Summary: Background: Acute aortic dissection (AAD) is a life-threatening cardiovascular emergency with high mortality, so identifying modifiable risk factors of AAD is of great public health significance. The associations of non-optimal temperature and temperature variability with AAD onset and the disease burden have not been fully understood. Methods: We conducted a time-stratified case-crossover study using a nationwide registry dataset from 1,868 hospitals in 313 Chinese cities. Conditional logistic regression and distributed lag models were used to investigate associations of temperature and temperature changes between neighboring days (TCN) with the hourly AAD onset and calculate the attributable fractions. We also evaluated the heterogeneity of the associations. Findings: A total of 40,270 eligible AAD cases were included. The exposure-response curves for temperature and TCN with AAD onset risk were both inverse and approximately linear. The risks were present on the concurrent hour (for temperature) or day (for TCN) and lasted for almost 1 day. The cumulative relative risks of AAD were 1.027 and 1.026 per 1°C lower temperature and temperature decline between neighboring days, respectively. The associations were significant during the non-heating period, but were not present during the heating period in cities with central heating. 23.13% of AAD cases nationwide were attributable to low temperature and 1.58% were attributable to temperature decline from the previous day. Interpretation: This is the largest nationwide study demonstrating robust associations of low temperature and temperature decline with AAD onset. We, for the first time, calculated the corresponding disease burden and further showed that central heating may be a modifier for temperature-related AAD risk and burden. Funding: This work was supported by the National Natural Science Foundation of China (92043301 and 92143301), Shanghai International Science and Technology Partnership Project (No. 21230780200), the Medical Research Council-UK (MR/R013349/1), and the Natural Environment Research Council UK (NE/R009384/1).

Summary: Background: Identifying how greenspace impacts the temperature-mortality relationship in urban environments is crucial, especially given climate change and rapid urbanization. However, the effect modification of greenspace on heat-related mortality has been typically focused on a localized area or single country. This study examined the heat-mortality relationship among different greenspace levels in a global setting. Methods: We collected daily ambient temperature and mortality data for 452 locations in 24 countries and used Enhanced Vegetation Index (EVI) as the greenspace measurement. We used distributed lag non-linear model to estimate the heat-mortality relationship in each city and the estimates were pooled adjusting for city-specific average temperature, city-specific temperature range, city-specific population density, and gross domestic product (GDP). The effect modification of greenspace was evaluated by comparing the heat-related mortality risk for different greenspace groups (low, medium, and high), which were divided into terciles among 452 locations. Findings: Cities with high greenspace value had the lowest heat-mortality relative risk of 1·19 (95% CI: 1·13, 1·25), while the heat-related relative risk was 1·46 (95% CI: 1·31, 1·62) for cities with low greenspace when comparing the 99th temperature and the minimum mortality temperature. A 20% increase of greenspace is associated with a 9·02% (95% CI: 8·88, 9·16) decrease in the heat-related attributable fraction, and if this association is causal (which is not within the scope of this study to assess), such a reduction could save approximately 933 excess deaths per year in 24 countries. Interpretation: Our findings can inform communities on the potential health benefits of greenspaces in the urban environment and mitigation measures regarding the impacts of climate change. Funding: This publication was developed under Assistance Agreement No. RD83587101 awarded by the U.S. Environmental Protection Agency to Yale University. It has not been formally reviewed by EPA. The views expressed in this document are solely those of the authors and do not necessarily reflect those of the Agency. EPA does not endorse any products or commercial services mentioned in this publication. Research reported in this publication was also supported by the National Institute on Minority Health and Health Disparities of the National Institutes of Health under Award Number R01MD012769. The content is solely the responsibility of the authors and does not necessarily represent the official views of the National Institutes of Health. Also, this work has been supported by the National Research Foundation of Korea (2021R1A6A3A03038675), Medical Research Council-UK (MR/V034162/1 and MR/R013349/1), Natural Environment Research Council UK (Grant ID: NE/R009384/1), Academy of Finland (Grant ID: 310372), European Union's Horizon 2020 Project Exhaustion (Grant ID: 820655 and 874990), Czech Science Foundation (22-24920S), Emory University's NIEHS-funded HERCULES Center (Grant ID: P30ES019776), and Grant CEX2018-000794-S funded by MCIN/AEI/ 10.13039/501100011033 The funders had no role in the design, data collection, analysis, interpretation of results, manuscript writing, or decision to publication.

Summary: 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.

Introduction Amid the rising number of people with non-communicable diseases (NCDs), Kenya has invested in strengthening primary care and in efforts to expand existing service delivery platforms to integrate NCD care. One such approach is the AMPATH (Academic Model Providing Access to Healthcare) model in western Kenya, which provides the platform for the Primary Health Integrated Care Project for Chronic Conditions (PIC4C), launched in 2018 to further strengthen primary care services for the prevention and control of hypertension, diabetes, breast and cervical cancer. This study seeks to understand how well PIC4C delivers on its intended aims and to inform and support scale up of the PIC4C model for integrated care for people with NCDs in Kenya.Methods and analysis The study is guided by a conceptual framework on implementing, sustaining and spreading innovation in health service delivery. We use a multimethod design combining qualitative and quantitative approaches, involving: (1) in-depth interviews with health workers and decision-makers to explore experiences of delivering PIC4C; (2) a cross-sectional survey of patients with diabetes or hypertension and in-depth interviews to understand how well PIC4C meets patients’ needs; (3) a cohort study with an interrupted time series analysis to evaluate the degree to which PIC4C leads to health benefits such as improved management of hypertension or diabetes; and (4) a cohort study of households to examine the extent to which the national hospital insurance chronic care package provides financial risk protection to people with hypertension or diabetes within PIC4C.Ethics and dissemination The study has received approvals from Moi University Institutional Research and Ethics Committee (FAN:0003586) and the London School of Hygiene & Tropical Medicine (17940). Workshops with key stakeholders at local, county, national and international levels will ensure early and wide dissemination of our findings to inform scale up of this model of care. We will also publish findings in peer-reviewed journals.

Abstract Background Regression analyses of time series of disease counts on environmental determinants are a prominent component of environmental epidemiology. For planning such studies, it can be useful to predict the precision of estimated coefficients and power to detect associations of given magnitude. Existing generic approaches for this have been found somewhat complex to apply and do not easily extend to multiple series studies analysed in two stages. We have sought a simpler approximate approach which can easily extend to multiple series and give insight into factors determining precision. Methods We derive approximate expressions for precision and hence power in single and multiple time series studies of counts from basic statistical theory, compare the precision predicted by these with that estimated by analysis in real data from 51 cities of varying size, and illustrate the use of these estimators in a realistic planning scenario. Results In single series studies with Poisson outcome distribution, precision and power depend only on the usable variation of exposure (i.e. that conditional on covariates) and the total number of disease events, regardless of how many days those are spread over. In multiple time series (eg multi-city) studies focusing on the meta-analytic mean coefficient, the usable exposure variation and the total number of events (in all series) are again the sole determinants if there is no between-series heterogeneity or within-series overdispersion. With heterogeneity, its extent and the number of series becomes important. For all but the crudest approximation the estimates of standard errors were on average within + 20% of those estimated in full analysis of actual data. Conclusions Predicting precision in coefficients from a planned time series study is possible simply and given limited information. The total number of disease events and usable exposure variation are the dominant factors when overdispersion and between-series heterogeneity are low.

Heat- and cold-related mortality risks are highly variable across different geographies, suggesting a differential distribution of vulnerability factors between and within countries, which could partly be driven by urban-to-rural disparities. Identifying these drivers of risk is crucial to characterize local vulnerability and design tailored public health interventions to improve adaptation of populations to climate change. We aimed to assess how heat- and cold-mortality risks change across urban, peri-urban and rural areas in Switzerland and to identify and compare the factors associated with increased vulnerability within and between different area typologies. We estimated the heat- and cold-related mortality association using the case time-series design and distributed lag non-linear models over daily mean temperature and all-cause mortality series between 1990–2017 in each municipality in Switzerland. Then, through multivariate meta-regression, we derived pooled heat and cold-mortality associations by typology (i.e. urban/rural/peri-urban) and assessed potential vulnerability factors among a wealth of demographic, socioeconomic, topographic, climatic, land use and other environmental data. Urban clusters reported larger pooled heat-related mortality risk (at 99th percentile, vs. temperature of minimum mortality (MMT)) (relative risk = 1.17 (95%CI: 1.10; 1.24), vs peri-urban 1.03 (1.00; 1.06), and rural 1.03 (0.99; 1.08)), but similar cold-mortality risk (at 1st percentile, vs. MMT) (1.35 (1.28; 1.43), vs rural 1.28 (1.14; 1.44) and peri-urban 1.39 (1.27–1.53)) clusters. We found different sets of vulnerability factors explaining the differential risk patterns across typologies. In urban clusters, mainly environmental factors (i.e. PM _2.5 ) drove differences in heat-mortality association, while for peri-urban/rural clusters socio-economic variables were also important. For cold, socio-economic variables drove changes in vulnerability across all typologies, while environmental factors and ageing were other important drivers of larger vulnerability in peri-urban/rural clusters, with heterogeneity in the direction of the association. Our findings suggest that urban populations in Switzerland may be more vulnerable to heat, compared to rural locations, and different sets of vulnerability factors may drive these associations in each typology. Thus, future public health adaptation strategies should consider local and more tailored interventions rather than a one-size fits all approach.

Climate change can substantially affect temperature-related mortality and morbidity, especially under high greenhouse gas emission pathways. Achieving the Paris Agreement goals require not only drastic reductions in fossil fuel-based emissions but also land-use and land-cover changes (LULCC), such as reforestation and afforestation. LULCC has been mainly analysed in the context of land-based mitigation and food security. However, growing scientific evidence shows that LULCC can also substantially alter climate through biogeophysical effects. Little is known about the consequential impacts on human health. LULCC-related impact research should broaden its scope by including the human health impacts. LULCC are relevant to several global agendas (i.e. Sustainable Development Goals). Thus, collaboration across research communities and stronger stakeholder engagement are required to address this knowledge gap.

Summary: 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.

Abstract Background Numerous studies have examined the association between air pollution and preterm birth (

Abstract New gridded climate datasets (GCDs) on spatially resolved modeled weather data have recently been released to explore the impacts of climate change. GCDs have been suggested as potential alternatives to weather station data in epidemiological assessments on health impacts of temperature and climate change. These can be particularly useful for assessment in regions that have remained understudied due to limited or low quality weather station data. However to date, no study has critically evaluated the application of GCDs of variable spatial resolution in temperature‐mortality assessments across regions of different orography, climate, and size. Here we explored the performance of population‐weighted daily mean temperature data from the global ERA5 reanalysis dataset in the 10 regions in the United Kingdom and the 26 cantons in Switzerland, combined with two local high‐resolution GCDs (HadUK‐grid UKPOC‐9 and MeteoSwiss‐grid‐product, respectively) and compared these to weather station data and unweighted homologous series. We applied quasi‐Poisson time series regression with distributed lag nonlinear models to obtain the GCD‐ and region‐specific temperature‐mortality associations and calculated the corresponding cold‐ and heat‐related excess mortality. Although the five exposure datasets yielded different average area‐level temperature estimates, these deviations did not result in substantial variations in the temperature‐mortality association or impacts. Moreover, local population‐weighted GCDs showed better overall performance, suggesting that they could be excellent alternatives to help advance knowledge on climate change impacts in remote regions with large climate and population distribution variability, which has remained largely unexplored in present literature due to the lack of reliable exposure data.

Summary: Background: Epidemiological evidence on short-term association between ambient carbon monoxide (CO) and mortality is inconclusive and limited to single cities, regions, or countries. Generalisation of results from previous studies is hindered by potential publication bias and different modelling approaches. We therefore assessed the association between short-term exposure to ambient CO and daily mortality in a multicity, multicountry setting. Methods: We collected daily data on air pollution, meteorology, and total mortality from 337 cities in 18 countries or regions, covering various periods from 1979 to 2016. All included cities had at least 2 years of both CO and mortality data. We estimated city-specific associations using confounder-adjusted generalised additive models with a quasi-Poisson distribution, and then pooled the estimates, accounting for their statistical uncertainty, using a random-effects multilevel meta-analytical model. We also assessed the overall shape of the exposure–response curve and evaluated the possibility of a threshold below which health is not affected. Findings: Overall, a 1 mg/m3 increase in the average CO concentration of the previous day was associated with a 0·91% (95% CI 0·32–1·50) increase in daily total mortality. The pooled exposure–response curve showed a continuously elevated mortality risk with increasing CO concentrations, suggesting no threshold. The exposure–response curve was steeper at daily CO levels lower than 1 mg/m3, indicating greater risk of mortality per increment in CO exposure, and persisted at daily concentrations as low as 0·6 mg/m3 or less. The association remained similar after adjustment for ozone but was attenuated after adjustment for particulate matter or sulphur dioxide, or even reduced to null after adjustment for nitrogen dioxide. Interpretation: This international study is by far the largest epidemiological investigation on short-term CO-related mortality. We found significant associations between ambient CO and daily mortality, even at levels well below current air quality guidelines. Further studies are warranted to disentangle its independent effect from other traffic-related pollutants. Funding: EU Horizon 2020, UK Medical Research Council, and Natural Environment Research Council.

Summary: Background: Temperature and rainfall patterns are known to influence seasonal patterns of dengue transmission. However, the effect of severe drought and extremely wet conditions on the timing and intensity of dengue epidemics is poorly understood. In this study, we aimed to quantify the non-linear and delayed effects of extreme hydrometeorological hazards on dengue risk by level of urbanisation in Brazil using a spatiotemporal model. Methods: We combined distributed lag non-linear models with a spatiotemporal Bayesian hierarchical model framework to determine the exposure-lag-response association between the relative risk (RR) of dengue and a drought severity index. We fit the model to monthly dengue case data for the 558 microregions of Brazil between January, 2001, and January, 2019, accounting for unobserved confounding factors, spatial autocorrelation, seasonality, and interannual variability. We assessed the variation in RR by level of urbanisation through an interaction between the drought severity index and urbanisation. We also assessed the effect of hydrometeorological hazards on dengue risk in areas with a high frequency of water supply shortages. Findings: The dataset included 12 895 293 dengue cases reported between 2001 and 2019 in Brazil. Overall, the risk of dengue increased between 0–3 months after extremely wet conditions (maximum RR at 1 month lag 1·56 [95% CI 1·41–1·73]) and 3–5 months after drought conditions (maximum RR at 4 months lag 1·43 [1·22–1·67]). Including a linear interaction between the drought severity index and level of urbanisation improved the model fit and showed the risk of dengue was higher in more rural areas than highly urbanised areas during extremely wet conditions (maximum RR 1·77 [1·32–2·37] at 0 months lag vs maximum RR 1·58 [1·39–1·81] at 2 months lag), but higher in highly urbanised areas than rural areas after extreme drought (maximum RR 1·60 [1·33–1·92] vs 1·15 [1·08–1·22], both at 4 months lag). We also found the dengue risk following extreme drought was higher in areas that had a higher frequency of water supply shortages. Interpretation: Wet conditions and extreme drought can increase the risk of dengue with different delays. The risk associated with extremely wet conditions was higher in more rural areas and the risk associated with extreme drought was exacerbated in highly urbanised areas, which have water shortages and intermittent water supply during droughts. These findings have implications for targeting mosquito control activities in poorly serviced urban areas, not only during the wet and warm season, but also during drought periods. Funding: Royal Society, Medical Research Council, Wellcome Trust, National Institutes of Health, Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro, and Conselho Nacional de Desenvolvimento Científico e Tecnológico. Translation: For the Portuguese translation of the abstract see Supplementary Materials section.

Summary: Background: Various retrospective studies have reported on the increase of mortality risk due to higher diurnal temperature range (DTR). This study projects the effect of DTR on future mortality across 445 communities in 20 countries and regions. Methods: DTR-related mortality risk was estimated on the basis of the historical daily time-series of mortality and weather factors from Jan 1, 1985, to Dec 31, 2015, with data for 445 communities across 20 countries and regions, from the Multi-Country Multi-City Collaborative Research Network. We obtained daily projected temperature series associated with four climate change scenarios, using the four representative concentration pathways (RCPs) described by the Intergovernmental Panel on Climate Change, from the lowest to the highest emission scenarios (RCP 2.6, RCP 4.5, RCP 6.0, and RCP 8.5). Excess deaths attributable to the DTR during the current (1985–2015) and future (2020–99) periods were projected using daily DTR series under the four scenarios. Future excess deaths were calculated on the basis of assumptions that warmer long-term average temperatures affect or do not affect the DTR-related mortality risk. Findings: The time-series analyses results showed that DTR was associated with excess mortality. Under the unmitigated climate change scenario (RCP 8.5), the future average DTR is projected to increase in most countries and regions (by −0·4 to 1·6°C), particularly in the USA, south-central Europe, Mexico, and South Africa. The excess deaths currently attributable to DTR were estimated to be 0·2–7·4%. Furthermore, the DTR-related mortality risk increased as the long-term average temperature increased; in the linear mixed model with the assumption of an interactive effect with long-term average temperature, we estimated 0·05% additional DTR mortality risk per 1°C increase in average temperature. Based on the interaction with long-term average temperature, the DTR-related excess deaths are projected to increase in all countries or regions by 1·4–10·3% in 2090–99. Interpretation: This study suggests that globally, DTR-related excess mortality might increase under climate change, and this increasing pattern is likely to vary between countries and regions. Considering climatic changes, our findings could contribute to public health interventions aimed at reducing the impact of DTR on human health. Funding: Korea Ministry of Environment.

It has been reported that suicide is associated with ambient temperature; however, the heterogeneity in this association and its underlying factors have not been extensively investigated. Therefore, we investigated the spatial and temporal variation in the temperature–suicide association and examined climatic, demographic, and socioeconomic factors that may underlie such heterogeneity. We analyzed the daily time-series data for the suicide counts and ambient temperature, which were collected for the 47 prefectures of Japan from 1972 to 2015, using a two-stage analysis. In the first stage, the prefecture-specific temperature–suicide association was estimated by using a generalized linear model. In the second stage, the prefecture-specific associations were pooled, and key factors explaining the spatial and temporal variation were identified by using mixed effects meta-regression. Results showed that there is an inverted J-shape nonlinear association between temperature and suicide; the suicide risk increased with temperature but leveled off above 24.4 °C. The nationwide relative risk (RR) for the maximum suicide temperature versus 5th temperature percentile (2.9 °C) was estimated as 1.26 (95% CI: 1.22, 1.29). The RRs were larger for females than for males (1.32 vs. 1.22) and larger for elderly people (≥65 y) than for the non-elderly (15–64 y) (1.51 vs. 1.18). The RRs were larger for rural prefectures, which are characterized by smaller population, higher proportions of females and elderly people, and lower levels of financial capability and the proportion of highly educated people. The RRs were also larger in colder and less humid prefectures. These findings may help in understanding the potential mechanism of the temperature–suicide association and projecting the future risk of suicide under climate change.

BackgroundUntil 2011, stockouts of family planning commodities were common in Senegalese public health facilities. Recognizing the importance of addressing this problem, the Government of Senegal implemented the Informed Push Model (IPM) supply system, which involves logisticians to collect facility-level stock turnover data once a month and provide contraceptive supplies accordingly. The aims of this paper were to evaluate the impact of IPM on contraceptive availability and on stockout duration.Methods and findingsTo estimate the impact of the IPM on contraceptive availability, stock card data were obtained from health facilities selected through multistage sampling. A total number of 103 health facilities pertaining to 27 districts and nine regions across the country participated in this project. We compared the odds of contraceptive stockouts within the health facilities on the 23 months after the intervention with the 18 months before. The analysis was performed with a logistic model of the monthly time-series. The odds of stockout for any of the five contraceptive products decreased during the 23 months post-intervention compared to the 18 months pre-intervention (odds ratio, 95%CI: 0.34, 0.22-0.51). To evaluate the impact of the IPM on duration of stockouts, a mixed negative binomial zero-truncated regression analysis was performed. The IPM was not effective in reducing the duration of contraceptive stockouts (incidence rate ratio, 95%CI: 0.81, 0.24-2.7), except for the two long-acting contraceptives (intrauterine devices and implants). Our model predicted a decrease in stockout median duration from 23 pre- to 4 days post-intervention for intrauterine devices; and from 19 to 14 days for implants.ConclusionsWe conclude that the IPM has resulted in greater efficiency in contraceptive stock management, increasing the availability of contraceptive methods in health facilities in Senegal. The IPM also resulted in decreased duration of stockouts for intrauterine devices and implants, but not for any of the short-acting contraception (pills and injectables).