The joint and interaction effect of high temperature and humidity on mortality in China

The trends of non-accidental mortality burden attributed to compound hot-dry events in China and its provinces in a global warming world

BACKGROUND

Global warming has provoked more co-occurrence of hot extreme and dry extreme, namely compound hot-dry events (CHDEs). However, their health impacts have seldom been investigated. This study aimed to characterize CHDEs and assess its mortality burden in China from 1990 to 2100.

METHODS

CHDEs were defined as a day when daily maximum temperature > its 90th percentile and Standardized Precipitation Index < its 50th percentile. A two-stage approach, including a distributed lag nonlinear model (DLNM) and a multivariate meta-analysis, was used to estimate exposure-response associations of CHDEs with mortality in 358 counties/districts during 2006-2017 in China, which was then applied to assess the national mortality burden attributable to CHDEs from 1990 to 2100.

FINDINGS

We observed a significant increasing trend of CHDEs in China until mid-21st century, and then flatted, while the duration and intensity of CHDEs continuously increased across the 21st century. CHDEs were much riskier (ER=17.82 %, 95 %CI: 14.17 %-21.60 %) than independent hot events (ER=5.86 %,95 %CI: -0.04 %,12.45 %) or dry events (ER=0.07 %,95 %CI: -1.22 %, 1.38 %), and there was significantly additive interaction between hot events and dry events (AP=0.10,95 %CI: 0.04, 0.16). Females (ER=24.28 %, 95 %CI: 19.21 %-29.56 %), the elderly (ER=23.28 %, 95 %CI: 18.23 %-28.55 %), and people living in humid area (ER=18.98 %, 95 %CI: 15.08 %-23.02 %) had higher mortality risks than their counterparts. Mortality burden attributed to CHDEs significantly increased during historical observation and became stable since mid-21st century in China.

INTERPRETATION

CHDEs would significantly increase mortality with higher risk for females, the elderly and people living in humid areas. Mortality burden has significantly increased during historical observation and will keep relatively steady since mid-21st century.

The synergistic effect of high temperature and relative humidity on non-accidental deaths at different urbanization levels

Numerous studies have examined the impact of temperature on mortality, yet research on the combined effect of temperature and humidity on non-accidental deaths remains limited. This study investigates the synergistic impact of high temperature and humidity on non-accidental deaths in China, assessing the influence of urban development and urbanization level. Utilizing the distributed lag nonlinear model (DLNM) of quasi-Poisson regression, we analyzed the relationship between Wet Bulb Globe Temperature (WBGT) and non-accidental deaths in 30 Chinese cities from 2010 to 2016, including Guangzhou during 2012-2016. We stratified temperature and humidity across these cities to evaluate the influence of varying humidity levels on deaths under high temperatures. Then, we graded the duration of heat and humidity in these cities to assess the impact of deaths with different durations. Additionally, the cities were categorized based on gross domestic product (GDP), and a vulnerability index was calculated to examine the impact of urban development and urbanization level on non-accidental deaths. Our findings reveal a pronounced synergistic effect of high temperature and humidity on non-accidental deaths, particularly at elevated humidity levels. The synergies of high temperature and humidity are extremely complex. Moreover, the longer the duration of high temperature and humidity, the higher the risk of non-accidental death. Furthermore, areas with higher urbanization exhibited lower relative risks (RR) associated with the synergistic effects of heat and humidity. Consequently, it is imperative to focus on damp-heat related mortality among vulnerable populations in less developed regions.

Impact of Ambient Temperature on Mortality Burden and Spatial Heterogeneity in 16 Prefecture-Level Cities of a Low-Latitude Plateau Area in Yunnan Province: Time-Series Study

Background

The relation between climate change and human health has become one of the major worldwide public health issues. However, the evidence for low-latitude plateau regions is limited, where the climate is unique and diverse with a complex geography and topography.

objectives

This study aimed to evaluate the effect of ambient temperature on the mortality burden of nonaccidental deaths in Yunnan Province and to further explore its spatial heterogeneity among different regions.

Methods

We collected mortality and meteorological data from all 129 counties in Yunnan Province from 2014 to 2020, and 16 prefecture-level cities were analyzed as units. A distributed lagged nonlinear model was used to estimate the effect of temperature exposure on years of life lost (YLL) for nonaccidental deaths in each prefecture-level city. The attributable fraction of YLL due to ambient temperature was calculated. A multivariate meta-analysis was used to obtain an overall aggregated estimate of effects, and spatial heterogeneity among 16 prefecture-level cities was evaluated by adjusting the city-specific geographical characteristics, demographic characteristics, economic factors, and health resources factors.

Results

The temperature-YLL association was nonlinear and followed slide-shaped curves in all regions. The cumulative cold and heat effect estimates along lag 0-21 days on YLL for nonaccidental deaths were 403.16 (95% empirical confidence interval [eCI] 148.14-615.18) and 247.83 (95% eCI 45.73-418.85), respectively. The attributable fraction for nonaccidental mortality due to daily mean temperature was 7.45% (95% eCI 3.73%-10.38%). Cold temperature was responsible for most of the mortality burden (4.61%, 95% eCI 1.70-7.04), whereas the burden due to heat was 2.84% (95% eCI 0.58-4.83). The vulnerable subpopulations include male individuals, people aged <75 years, people with education below junior college level, farmers, nonmarried individuals, and ethnic minorities. In the cause-specific subgroup analysis, the total attributable fraction (%) for mean temperature was 13.97% (95% eCI 6.70-14.02) for heart disease, 11.12% (95% eCI 2.52-16.82) for respiratory disease, 10.85% (95% eCI 6.70-14.02) for cardiovascular disease, and 10.13% (95% eCI 6.03-13.18) for stroke. The attributable risk of cold effect for cardiovascular disease was higher than that for respiratory disease cause of death (9.71% vs 4.54%). Furthermore, we found 48.2% heterogeneity in the effect of mean temperature on YLL after considering the inherent characteristics of the 16 prefecture-level cities, with urbanization rate accounting for the highest proportion of heterogeneity (15.7%) among urban characteristics.

Conclusions

This study suggests that the cold effect dominated the total effect of temperature on mortality burden in Yunnan Province, and its effect was heterogeneous among different regions, which provides a basis for spatial planning and health policy formulation for disease prevention.

The temporal variation in the effects of extreme temperature on respiratory mortality: Evidence from 136 cities in China, 2006-2019

BACKGROUND

In the context of climate change and urbanization, the temporal variation of the adverse health effect of extreme temperature has attracted increasing attention.

METHODS

The meteorological data and the daily death records of mortality from respiratory diseases of 136 Chinese cities were from 2006 to 2019. Heat wave and cold spell were selected as the indicator events of extreme high temperature and extreme low temperature, respectively. The generalized linear model and time-varying distributed lag model were used to perform a two-stage time-series analysis to evaluate the temporal variation of the mortality risk associated with extreme temperature in the total population, sub-populations (sex- and age- specific) and different regions (climatic zone and relative humidity level).

RESULTS

During the study period, relative risk (RR) of respiratory mortality associated with heat wave decreased from 1.22 (95 %CI: 1.07-1.39) to 1.13 (95 %CI: 1.01-1.26) in the total population, and RR of respiratory mortality associated with cold spell decreased from 1.30 (95 %CI: 1.14-1.49) to 1.17 (95 %CI: 1.08-1.26). The impact of heat wave reduced in the males (P = 0.044) and in the females as with cold spell (P < 0.001). The respiratory mortality risk of people over 65 associated with cold spell decreased (P = 0.040 for people aged 65-74 and P < 0.001 for people over 75). The effect of cold spell reduced in cities from tropical or arid zone (P = 0.035). The effects of both heat wave and cold spell decreased in cities with the relative humidity in the first quartile (P = 0.046 and 0.010, respectively).

CONCLUSION

The impact of heat wave on mortality of respiratory diseases decreased mainly in males and cities with the lowest relative humidity, while the impact of cold spell reduced in females, people over 65 and tropical and arid zone, suggesting adaptation to extreme temperature of Chinese residents to some extent.

Optimal decision-making in relieving global high temperature-related disease burden by data-driven simulation

The rapid acceleration of global warming has led to an increased burden of high temperature-related diseases (HTDs), highlighting the need for advanced evidence-based management strategies. We have developed a conceptual framework aimed at alleviating the global burden of HTDs, grounded in the One Health concept. This framework refines the impact pathway and establishes systematic data-driven models to inform the adoption of evidence-based decision-making, tailored to distinct contexts. We collected extensive national-level data from authoritative public databases for the years 2010-2019. The burdens of five categories of disease causes - cardiovascular diseases, infectious respiratory diseases, injuries, metabolic diseases, and non-infectious respiratory diseases - were designated as intermediate outcome variables. The cumulative burden of these five categories, referred to as the total HTD burden, was the final outcome variable. We evaluated the predictive performance of eight models and subsequently introduced twelve intervention measures, allowing us to explore optimal decision-making strategies and assess their corresponding contributions. Our model selection results demonstrated the superior performance of the Graph Neural Network (GNN) model across various metrics. Utilizing simulations driven by the GNN model, we identified a set of optimal intervention strategies for reducing disease burden, specifically tailored to the seven major regions: East Asia and Pacific, Europe and Central Asia, Latin America and the Caribbean, Middle East and North Africa, North America, South Asia, and Sub-Saharan Africa. Sectoral mitigation and adaptation measures, acting upon our categories of Infrastructure & Community, Ecosystem Resilience, and Health System Capacity, exhibited particularly strong performance for various regions and diseases. Seven out of twelve interventions were included in the optimal intervention package for each region, including raising low-carbon energy use, increasing energy intensity, improving livestock feed, expanding basic health care delivery coverage, enhancing health financing, addressing air pollution, and improving road infrastructure. The outcome of this study is a global decision-making tool, offering a systematic methodology for policymakers to develop targeted intervention strategies to address the increasingly severe challenge of HTDs in the context of global warming.

Effects of ambient temperature and humidity on COPD mortality in Ganzhou city, China

This study used the time series data of Ganzhou city to explore the individual and interaction effects of temperature and humidity on COPD death, and identify vulnerable subgroups of the population. We collected daily COPD mortality and meteorological data in Ganzhou from 2016 to 2019. The nonlinear distribution lag model was used to examine the associations and interaction between daily mean temperature and humidity and COPD mortality. For the total population, male and 65 years old or above, the relative risk (RR) for COPD mortality could be significant at extremely low temperature (3.3 ℃), reaching 1.799 (95% confidence interval [CI]: 1.216, 2.662), 1.894 (95% CI: 1.164, 3.084) and 1.779 (95% CI:1.185, 2.670). Also, at extremely low humidity (47.8%), the risk reached 1.888 (95% CI: 1.217, 2.930), 1.837 (95% CI: 1.066, 3.165) and 2.166 (95% CI: 1.375, 3.414). The cumulative COPD death risk for females was 3.524 (95% CI: 1.340, 9.267) at high temperature (30.7 ℃), 1.953(95% CI: 1.036, 3.683) at low humidity (47.8%) and 1.726 (95% CI: 1.048, 2.845) at high humidity (96.7%). For the total COPD deaths and subgroups, the interaction effects between daily temperature and humidity were not significant (p > 0.05). Both extremely low temperature and low humidity increased the risk of COPD death in Ganzhou city, especially for males and people over 65 years old. Females were more sensitive to extremely high temperature and humidity. Patients with COPD should pay attention to self-protection under extreme temperature and humidity weather conditions.

Comparison for the effects of different components of temperature variability on mortality: A multi-country time-series study

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.

Heat and Cardiovascular Mortality: An Epidemiological Perspective

As global temperatures rise, extreme heat events are projected to become more frequent and intense. Extreme heat causes a wide range of health effects, including an overall increase in morbidity and mortality. It is important to note that while there is sufficient epidemiological evidence for heat-related increases in all-cause mortality, evidence on the association between heat and cause-specific deaths such as cardiovascular disease (CVD) mortality (and its more specific causes) is limited, with inconsistent findings. Existing systematic reviews and meta-analyses of epidemiological studies on heat and CVD mortality have summarized the available evidence. However, the target audience of such reviews is mainly limited to the specific field of environmental epidemiology. This overarching perspective aims to provide health professionals with a comprehensive overview of recent epidemiological evidence of how extreme heat is associated with CVD mortality. The rationale behind this broad perspective is that a better understanding of the effect of extreme heat on CVD mortality will help CVD health professionals optimize their plans to adapt to the changes brought about by climate change and heat events. To policymakers, this perspective would help formulate targeted mitigation, strengthen early warning systems, and develop better adaptation strategies. Despite the heterogeneity in evidence worldwide, due in part to different climatic conditions and population dynamics, there is a clear link between heat and CVD mortality. The risk has often been found to be higher in vulnerable subgroups, including older people, people with preexisting conditions, and the socioeconomically deprived. This perspective also highlights the lack of evidence from low- and middle-income countries and focuses on cause-specific CVD deaths. In addition, the perspective highlights the temporal changes in heat-related CVD deaths as well as the interactive effect of heat with other environmental factors and the potential biological pathways. Importantly, these various aspects of epidemiological studies have never been fully investigated and, therefore, the true extent of the impact of heat on CVD deaths remains largely unknown. Furthermore, this perspective also highlights the research gaps in epidemiological studies and the potential solutions to generate more robust evidence on the future consequences of heat on CVD deaths.

Joint effects of heat-humidity compound events on drowning mortality in Southern China

BACKGROUND

Several previous studies have examined the association of ambient temperature with drowning. However, no study has investigated the effects of heat-humidity compound events on drowning mortality.

METHODS

The drowning mortality data and meteorological data during the five hottest months (May to September) were collected from 46 cities in Southern China (2013-2018 in Guangdong, Hunan and Zhejiang provinces). Distributed lag non-linear model was first conducted to examine the association between heat-humidity compound events and drowning mortality at city level. Then, meta-analysis was employed to pool the city-specific exposure-response associations. Finally, we analysed the additive interaction of heat and humidity on drowning mortality.

RESULTS

Compared with wet-non-hot days, dry-hot days had greater effects (excess rate (ER)=32.34%, 95% CI: 24.64 to 40.50) on drowning mortality than wet-hot days (ER=14.38%, 95%CI: 6.80 to 22.50). During dry-hot days, males (ER=42.40%, 95% CI: 31.92 to 53.72), adolescents aged 0-14 years (ER=45.00%, 95% CI: 21.98 to 72.35) and urban city (ER=36.91%, 95% CI: 23.87 to 51.32) showed higher drowning mortality risk than their counterparts. For wet-hot days, males, adolescents and urban city had higher ERs than their counterparts. Attributable fraction (AF) of drowning attributed to dry-hot days was 23.83% (95% CI: 21.67 to 26.99) which was significantly higher than that for wet-hot days (11.32%, 95% CI: 9.64 to 13.48%). We also observed that high temperature and low humidity had an additive interaction on drowning mortality.

CONCLUSION

We found that dry-hot days had greater drowning mortality risk and burden than wet-hot days, and high temperature and low humidity might have synergy on drowning mortality.

The impact of long-term PM1 exposure on all-cause mortality and its interaction with BMI: A nationwide prospective cohort study in China

BACKGROUND

China has a serious air pollution problem and a high prevalence of obesity. The interaction between the two and its impact on all-cause mortality is a public health issue of great concern.

OBJECTIVES

This study aimed to investigate the association between long-term exposure to particulate matter with aerodynamic diameter ≤ 1 μm (PM1) and all-cause mortality, as well as the interaction effect of body mass index (BMI) in the association.

METHODS

A total of 33,087 participants from 162 counties in 25 provinces in China were included, with annual average PM1 exposure being estimated based on the county address. The PM1-mortality relation was evaluated using the time-varying Cox proportional hazards models, with the dose-response relationship being fitted using the penalized splines. Besides, the potential interaction effect of BMI in the PM1-mortality relation was evaluated.

RESULTS

The incidence of all-cause deaths was 76.99 per 10,000 person-years over a median of 8.2 years of follow-up. After controlling for potential confounders, the PM1-mortality relation was approximately J-shaped. The full-adjustment analysis observed the hazard ratio (HR) of all-cause mortality was 1.114 [95 % confidence interval (CI): 1.017-1.220] corresponding to a 10 μg/m3 rise in PM1 concentration. Further stratified analyses suggested the adverse effects of PM1 might be more pronounced among the underweight.

DISCUSSION

Higher PM1 concentrations were associated with an increase in all-cause mortality. The BMI might further alter the relation, and the underweight population was the sensitive subgroup of the population that needed to be protected.

Heatwave characteristics complicate the association between PM2.5 components and schizophrenia hospitalizations in a changing climate: Leveraging of the individual residential environment

BACKGROUND

In the era characterized by global environmental and climatic changes, understanding the effects of PM2.5 components and heatwaves on schizophrenia (SCZ) is essential for implementing environmental interventions at the population level. However, research in this area remains limited, which highlights the need for further research and effort. We aim to assess the association between exposure to PM2.5 components and hospitalizations for SCZ under different heatwave characteristics.

METHODS

We conducted a 16 municipalities-wide, individual exposure-based, time-stratified, case-crossover study from January 1, 2017, to December 31, 2020, encompassing 160736 hospitalizations in Anhui Province, China. Daily concentrations of PM2.5 components were obtained from the Tracking Air Pollution in China dataset. Conditional logistic regression models were used to investigate the association between PM2.5 components and hospitalizations. Additionally, restricted cubic spline models were used to identify protective thresholds of residential environment in response to environmental and climate change.

RESULTS

Our findings indicate a positive correlation between PM2.5 and its components and hospitalizations. Significantly, a 1 μg/m3 increase in black carbon (BC) was associated with the highest risk, at 1.58% (95%CI: 0.95-2.25). Exposure to heatwaves synergistically enhanced the impact of PM2.5 components on hospitalization risks, and the interaction varied with the intensity and duration of heatwaves. Under the 99th percentile heatwave events, the impact of PM2.5 and its components on hospitalizations was most pronounced, which were PM2.5 (2-4d: 4.59%, 5.09%, and 5.09%), sulfate (2-4d: 21.73%, 23.23%, and 25.25%), nitrate (2-4d: 17.51%, 16.93%, and 20.31%), ammonium (2-4d: 27.49%, 31.03%, and 32.41%), organic matter (2-4d: 32.07%, 25.42%, and 24.48%), and BC (2-4d: 259.36%, 288.21%, and 152.52%), respectively. Encouragingly, a protective effect was observed when green and blue spaces comprised more than 17.6% of the residential environment.

DISCUSSION

PM2.5 components and heatwave exposure were positively associated with an increased risk of hospitalizations, although green and blue spaces provided a mitigating effect.

Gut microbiota associated with appetite suppression in high-temperature and high-humidity environments

BACKGROUND

Food is crucial for maintaining vital human and animal activities. Disorders in appetite control can lead to various metabolic disturbances. Alterations in the gut microbial composition can affect appetite and energy metabolism. While alterations in the gut microbiota have been observed in high-temperature and high-humidity (HTH) environments, the relationship between the gut microbiota during HTH and appetite remains unclear.

METHODS

We utilised an artificial climate box to mimic HTH environments, and established a faecal bacteria transplantation (FMT) mouse model. Mendelian randomisation (MR) analysis was used to further confirm the causal relationship between gut microbiota and appetite or appetite-related hormones.

FINDINGS

We found that, in the eighth week of exposure to HTH environments, mice showed a decrease in food intake and body weight, and there were significant changes in the intestinal microbiota compared to the control group. After FMT, we observed similar changes in food intake, body weight, and gut bacteria. Appetite-related hormones, including ghrelin, glucagon-like peptide-1, and insulin, were reduced in DH (mice exposed to HTH conditions) and DHF (FMT from mice exposed to HTH environments for 8 weeks), while the level of peptide YY initially increased and then decreased in DH and increased after FMT. Moreover, MR analysis further confirmed that these changes in the intestinal microbiota could affect appetite or appetite-related hormones.

INTERPRETATION

Together, our data suggest that the gut microbiota is closely associated with appetite suppression in HTH. These findings provide novel insights into the effects of HTH on appetite.

FUNDING

This work was supported by the National Natural Science Foundation of China and Guangzhou University of Chinese Medicine.

Impacts of exposure to humidex on cardiovascular mortality: a multi-city study in Southwest China

BACKGROUND

Many studies have reported the association between ambient temperature and mortality from cardiovascular disease (CVD). However, the health effects of humidity are still unclear, much less the combined effects of temperature and humidity. In this study, we used humidex to quantify the effect of temperature and humidity combined on CVD mortality.

METHODS

Daily meteorological, air pollution, and CVD mortality data were collected in four cities in southwest China. We used a distributed lag non-linear model (DLNM) in the first stage to assess the exposure-response association between humidex and city-specific CVD mortality. A multivariate meta-analysis was conducted in the second stage to pool these effects at the overall level. To evaluate the mortality burden of high and low humidex, we determined the attributable fraction (AF). According to the abovementioned processes, stratified analyses were conducted based on various demographic factors.

RESULTS

Humidex and the CVD exposure-response curve showed an inverted "J" shape, the minimum mortality humidex (MMH) was 31.7 (77th percentile), and the cumulative relative risk (CRR) was 2.27 (95% confidence interval [CI], 1.76-2.91). At extremely high and low humidex, CRRs were 1.19 (95% CI, 0.98-1.44) and 2.52 (95% CI, 1.88-3.38), respectively. The burden of CVD mortality attributed to non-optimal humidex was 21.59% (95% empirical CI [eCI], 18.12-24.59%), most of which was due to low humidex, with an AF of 20.16% (95% eCI, 16.72-23.23%).

CONCLUSIONS

Low humidex could significantly increase the risk of CVD mortality, and vulnerability to humidex differed across populations with different demographic characteristics. The elderly (> 64 years old), unmarried people, and those with a limited level of education (1-9 years) were especially susceptible to low humidex. Therefore, humidex is appropriate as a predictor in a CVD early-warning system.

Improving interventional causal predictions in regulatory risk assessment

In 2022, the US EPA published an important risk assessment concluding that "Compared to the current annual standard, meeting a revised annual standard with a lower level is estimated to reduce PM2.5-associated health risks in the 30 annually-controlled study areas by about 7-9% for a level of 11.0 µg/m3… and 30-37% for a level of 8.0 µg/m3." These are interventional causal predictions: they predict percentage reductions in mortality risks caused by different counterfactual reductions in fine particulate (PM2.5) levels. Valid causal predictions are possible if: (1) Study designs are used that can support valid causal inferences about the effects of interventions (e.g., quasi-experiments with appropriate control groups); (2) Appropriate causal models and methods are used to analyze the data; (3) Model assumptions are satisfied (at least approximately); and (4) Non-causal sources of exposure-response associations such as confounding, measurement error, and model misspecification are appropriately modeled and adjusted for. This paper examines two long-term mortality studies selected by the EPA to predict reductions in PM2.5-associated risk. Both papers use Cox proportional hazards (PH) models. For these models, none of these four conditions is satisfied, making it difficult to interpret or validate their causal predictions. Scientists, reviewers, regulators, and members of the public can benefit from more trustworthy and credible risk assessments and causal predictions by insisting that risk assessments supporting interventional causal conclusions be based on study designs, methods, and models that are appropriate for predicting effects caused by interventions.