Background: Modeling suggests that climate change mitigation actions can have substantial human health benefits that accrue quickly and locally. Documenting the benefits can help drive more ambitious and health-protective climate change mitigation actions; however, documenting the adverse health effects can help to avoid them. Estimating the health effects of mitigation (HEM) actions can help policy makers prioritize investments based not only on mitigation potential but also on expected health benefits. To date, however, the wide range of incompatible approaches taken to developing and reporting HEM estimates has limited their comparability and usefulness to policymakers. Objective: The objective of this effort was to generate guidance for modeling studies on scoping, estimating, and reporting population health effects from climate change mitigation actions. Methods: An expert panel of HEM researchers was recruited to participate in developing guidance for conducting HEM studies. The primary literature and a synthesis of HEM studies were provided to the panel. Panel members then participated in a modified Delphi exercise to identify areas of consensus regarding HEM estimation. Finally, the panel met to review and discuss consensus findings, resolve remaining differences, and generate guidance regarding conducting HEM studies. Results: The panel generated a checklist of recommendations regarding stakeholder engagement: HEM modeling, including model structure, scope and scale, demographics, time horizons, counterfactuals, health response functions, and metrics; parameterization and reporting; approaches to uncertainty and sensitivity analysis; accounting for policy uptake; and discounting. Discussion: This checklist provides guidance for conducting and reporting HEM estimates to make them more comparable and useful for policymakers. Harmonization of HEM estimates has the potential to lead to advances in and improved synthesis of policy-relevant research that can inform evidence-based decision making and practice. https://doi.org/10.1289/EHP6745
While COVID-19 continues to challenge the world, meteorological variables are thought to impact COVID-19 transmission. Previous studies showed evidence of negative associations between high temperature and absolute humidity on COVID-19 transmission. Our research aims to fill the knowledge gap on the modifying effect of vaccination rates and strains on the weather-COVID-19 association.
BACKGROUND AND AIM: Current evidence on Kawasaki Disease (KD) in the tropical countries are not well-established. We, thus, aimed to describe the incidence of KD in children, assess its seasonality, and determine its association with ambient air temperature in the National Capital Region (NCR), Philippines from January 2009 to December 2019. METHOD: Monthly number of KD cases from NCR were collected using the publicly available Philippine Pediatric Society (PPS) disease registry to determine the incidence of KD. We used a generalized linear model (GLM) with quasi-Poisson regression to assess the seasonality of KD and determine its association with ambient air temperature in dry (December to May) and wet (June to November) seasons. RESULTS: Majority of KD cases in NCR belonged to zero to four years old age group (68.52%) with a 1.43:1 male-to-female ratio. KD incidence in 2015 was 18.49 cases per 100,000 population of children less than 5 years old. Seasonal variation had a unimodal shape with rate ratio of 1.13 times from the average with increase of cases in March and decrease in September. Every one-degree Celsius increase in the monthly mean air temperature significantly increased the risk of developing KD by 8.28% (95% CI: 2.12%, 14.80%). Season-specific analysis revealed a positive association during the dry season (RR: 1.06, 95% CI: 1.01, 1.11), whereas no evidence of association was found during the wet season (RR: 1.10, 95% CI: 0.95, 1.27). CONCLUSIONS: This study was able to determine the incidence of KD in children in NCR, Philippines, and has shown a unimodal seasonality with peak in March and nadir in September. Year-round ambient air temperature was linearly associated with KD cases, especially during the dry season.
TPS 664: Climate change: temperature effects 2, Exhibition Hall, Ground floor, August 27, 2019, 3:00 PM - 4:30 PM Background: The seasonality of mortality can be a major contributor to a consistent pattern of health-care demands throughout the year. Temperature- one of the key season defining factors, has been consistently linked with increasing mortality. However, little is known about its impact on seasonality of mortality. Investigating seasonality and the effect of temperature on it will provide important information for health risk management in different seasons. Aims: To assess seasonality of mortality with temperature adjustment in Japan and its temporal changes Method: Daily mean temperature and daily death cases for all-cause, circulatory and respiratory mortality from 1972 to 2015 were collected for 47 prefectures in Japan. A generalized linear model with quasi-Poisson distribution was used, with a one-dimensional cyclic spline function with 4 degrees of freedom for the calendar day. The long term trend and day of week were controlled. A cross-basis function, with natural cubic B-spline functions and extended lag period to 21 days, was used for temperature. The ratio of estimated mortality counts of peak versus trough day (RR) was estimated with and without temperature adjustment, by treating trough day as a reference day. RR was estimated for whole country over 44 years first and then for each single year to investigate the temporal changes. All estimates were conducted for all-cause, circulatory and respiratory mortality, respectively. Results: U-shaped relationships were observed for both adjusted and unadjusted seasonality, with peaks in cold seasons and troughs in warm seasons. Seasonality became less obvious after adjusting for temperature. Both adjusted and unadjusted RR showed a decreasing trend for all-cause and respiratory mortality. No significant trend was observed for circulatory mortality. Conclusions: Seasonality risk was higher in cold seasons. Temperature may be an important driver. Additionally, seasonality of mortality decreased over 44 years. Its underlying reasons should be investigated in the future.
Background and aim: Short-term associations between air pollution and mortality have been well reported in Japan, but the historical changes of mortality risks remain unknown while Japan undergoes rapid aging. We examined temporal changes in the mortality risks associated with short-term exposure to four criteria air pollutants in selected Japanese cities. Methods: We collected daily mortality data for non-accidental causes (n=5,748,206), cardiovascular (n=1,938,743) and respiratory diseases (n=777,266), and air pollutants – SO₂, NO₂, suspended particulate matter (SPM) and oxidants (Ox) – of 10 cities from 1977 to 2015. We performed a two-stage analysis with 5-year stratification to estimate the relative risk (RR) of mortality per 10 unit increase in 2-day moving average of air pollutant concentrations. In the first stage, city-specific associations were assessed using a quasi-Poisson generalized linear regression model. In the second stage, the city-specific estimates were pooled using a random-effects meta-analysis. Ratio of relative risks (RRR) was computed to examine temporal changes. Results: Average concentrations in each stratified period decreased for SO₂, NO₂, and SPM (14.2–2.3 ppb, 29.4–17.5 ppb, 52.1–20.6 μg/m³, respectively) but increased for Ox (29.1–39.1 ppb), over the study period. When stratifying the analysis by every 5 years, the estimated risks of non-accidental mortality with these pollutants remained positive but did not show any clear trend. Meanwhile, the risk of respiratory mortality with SPM had increased (RRR of the latest period to that of the earliest period: 1.008, 95% CI: 1.002–1.015). The risks posed by these pollutants were slightly to moderately heterogeneous for the different cities. Conclusions: The respiratory mortality risk per 10 unit increase of SPM concentration was significantly higher in the latest period than in the earliest period. Other pollutant–mortality associations indicated either decrease or non-significant risk change in Japan between 1977 and 2015. Keywords: air pollution, daily mortality, time-varying effects, long-term trend
Tropical cyclones bring health risks and can trigger outbreaks of diarrheal diseases in affected populations. There are several reviews that mention the relationship between tropical cyclones and diarrheal diseases. However, there is no dedicated review of the current evidence and extent of research on the association between tropical cyclones and diarrheal diseases. We performed a scoping review to thoroughly examine the available literature. We also thematically analyzed the transmission pathways explained in the literature. A total of 96 studies were included. Twenty-three studies quantitatively measured the tropical cyclone–diarrhea associations, with half reporting positive associations. We found that the studies defined and measured tropical cyclone exposure differently and mostly analyzed a single event. The study designs employed were mostly pre-post comparisons that had several limitations affecting internal validity. These differences also prevent the quantitative pooling of evidence. A standardized approach to measuring the association between tropical cyclones and diarrheal diseases can be devised by suggesting the most appropriate exposure definition and modeling.
Abstract Background Ambient temperature may contribute to seasonality of mortality; in particular, a warming climate is likely to influence the seasonality of mortality. However, few studies have investigated seasonality of mortality under a warming climate. Methods Daily mean temperature, daily counts for all-cause, circulatory, and respiratory mortality, and annual data on prefecture-specific characteristics were collected for 47 prefectures in Japan between 1972 and 2015. A quasi-Poisson regression model was used to assess the seasonal variation of mortality with a focus on its amplitude, which was quantified as the ratio of mortality estimates between the peak and trough days (peak-to-trough ratio (PTR)). We quantified the contribution of temperature to seasonality by comparing PTR before and after temperature adjustment. Associations between annual mean temperature and annual estimates of the temperature-unadjusted PTR were examined using multilevel multivariate meta-regression models controlling for prefecture-specific characteristics. Results The temperature-unadjusted PTRs for all-cause, circulatory, and respiratory mortality were 1.28 (95% confidence interval (CI): 1.27–1.30), 1.53 (95% CI: 1.50–1.55), and 1.46 (95% CI: 1.44–1.48), respectively; adjusting for temperature reduced these PTRs to 1.08 (95% CI: 1.08–1.10), 1.10 (95% CI: 1.08–1.11), and 1.35 (95% CI: 1.32–1.39), respectively. During the period of rising temperature (1.3 °C on average), decreases in the temperature-unadjusted PTRs were observed for all mortality causes except circulatory mortality. For each 1 °C increase in annual mean temperature, the temperature-unadjusted PTR for all-cause, circulatory, and respiratory mortality decreased by 0.98% (95% CI: 0.54–1.42), 1.39% (95% CI: 0.82–1.97), and 0.13% (95% CI: − 1.24 to 1.48), respectively. Conclusion Seasonality of mortality is driven partly by temperature, and its amplitude may be decreasing under a warming climate.
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