Trend estimation and short-term forecasting of COVID-19 cases and deaths worldwide.

Since the beginning of the COVID-19 pandemic, many dashboards have emerged as useful tools to monitor its evolution, inform the public, and assist governments in decision-making. Here, we present a globally applicable method, integrated in a daily updated dashboard that provides an estimate of the trend in the evolution of the number of cases and deaths from reported data of more than 200 countries and territories, as well as 7-d forecasts. One of the significant difficulties in managing a quickly propagating epidemic is that the details of the dynamic needed to forecast its evolution are obscured by the delays in the identification of cases and deaths and by irregular reporting. Our forecasting methodology substantially relies on estimating the underlying trend in the observed time series using robust seasonal trend decomposition techniques. This allows us to obtain forecasts with simple yet effective extrapolation methods in linear or log scale. We present the results of an assessment of our forecasting methodology and discuss its application to the production of global and regional risk maps.

[Epidemic forecasting and COVID-19]. / Prévisions épidémiques et Covid-19.

Since December 2019, the COVID-19 pandemic has had a major impact on global health and the economy. Epidemiological forecasts are crucial for governmental decisions, healthcare officials, and the general public. A collaboration between the Institute of Global Health at the University of Geneva and the Swiss Data Science Center created an interactive dashboard providing forecasts for over 200 countries and territories. This dashboard has been a valuable tool for the public and authorities alike. The pandemic has highlighted the importance of international collaborations and a robust national surveillance system. Data collection systems, pathogen-agnostic models, and communication tools need to be consolidated and maintained in operation.

Depuis décembre 2019, la pandémie de Covid-19 a eu un impact majeur sur la santé et l'économie mondiales. Les prévisions épidémiques sont essentielles pour les décisions gouvernementales, les responsables de la santé et le public. Un projet entre l'Institut de santé globale de l'Université de Genève et le Swiss Data Science Center a créé un tableau de bord interactif fournissant des prévisions pour plus de 200 pays et territoires, qui fut un outil précieux pour le public et les autorités. La pandémie a souligné l'importance des collaborations internationales et d'un système de surveillance national solide. Les systèmes de collecte de données, les modèles agnostiques aux pathogènes et les outils de communication doivent être consolidés et maintenus en fonctionnement.

Disconnectomics of the Rich Club Impacts Motor Recovery After Stroke.

BACKGROUND AND PURPOSE: Structural brain networks possess a few hubs, which are not only highly connected to the rest of the brain but are also highly connected to each other. These hubs, which form a rich-club, play a central role in global brain organization. To investigate whether the concept of rich-club sheds new light on poststroke recovery, we applied a novel network-theoretical quantification of lesions to patients with stroke and compared the outcomes with what lesion size alone would indicate. METHODS: Whole-brain structural networks of 73 patients with ischemic stroke were reconstructed using diffusion-weighted imaging data. Disconnectomes, a new type of network analyses, were constructed using only those fibers that pass through the lesion. Fugl-Meyer upper extremity scores and their changes were used to determine whether the patients show natural recovery or not. RESULTS: Cluster analysis revealed 3 patient clusters: small-lesion-good-recovery, midsized-lesion-poor-recovery (MLPR), and large-lesion-poor-recovery (LLPR). The small-lesion-good-recovery consisted of subjects whose lesions were small, and whose prospects for recovery were relatively good. To explain the nondifference in recovery between the MLPR and LLPR clusters despite the difference (LLPR>MLPR) in lesion volume, we defined the [Formula: see text] metric to be the sum of the entries in the disconnectome and, more importantly, the [Formula: see text] to be the sum of all entries in the disconnectome corresponding to edges with at least one node in the rich-club. Unlike lesion volume and corticospinal tract damage (MLPRLLPR) or showed no difference for [Formula: see text]. CONCLUSIONS: Smaller lesions that focus on the rich-club can be just as devastating as much larger lesions that do not focus on the rich-club, pointing to the role of the rich-club as a backbone for functional communication within brain networks and for recovery from stroke.

[Swiss Precision and Global Predictions]. / Covid-19 : précision suisse et prévisions globales.

A consortium of Swiss universities has set up a dashboard providing daily 7-day epidemic forecasting for 209 countries and territories around the world. Relayed on social networks, international media, and the sites of major public health agencies, these forecasts can help guiding public policy. However, the time horizon of these forecasts is limited and their accuracy is sometimes questionable, even at 7 days. Interdisciplinary research aimed at increasing the complexity of mathematical models can improve the accuracy of the forecasts provided.

Un consortium émanant de hautes écoles suisses a mis en place un tableau de bord fournissant quotidiennement des prévisions épidémiologiques à 7 jours pour 209 pays et territoires dans le monde. Relayées sur les réseaux sociaux, les médias internationaux et les sites des grandes agences de sécurité sanitaire, ces prévisions peuvent aider au guidage des politiques publiques. Cependant l'horizon de temps de ces prévisions est limité et leur précision parfois questionnable, même à 7 jours. Des pistes sont proposées à travers une recherche interdisciplinaire visant à complexifier les modèles mathématiques pour améliorer la précision des prévisions fournies.

Air Pollution and Mortality in the Medicare Population.

BACKGROUND: Studies have shown that long-term exposure to air pollution increases mortality. However, evidence is limited for air-pollution levels below the most recent National Ambient Air Quality Standards. Previous studies involved predominantly urban populations and did not have the statistical power to estimate the health effects in underrepresented groups. METHODS: We constructed an open cohort of all Medicare beneficiaries (60,925,443 persons) in the continental United States from the years 2000 through 2012, with 460,310,521 person-years of follow-up. Annual averages of fine particulate matter (particles with a mass median aerodynamic diameter of less than 2.5 µm [PM2.5]) and ozone were estimated according to the ZIP Code of residence for each enrollee with the use of previously validated prediction models. We estimated the risk of death associated with exposure to increases of 10 µg per cubic meter for PM2.5 and 10 parts per billion (ppb) for ozone using a two-pollutant Cox proportional-hazards model that controlled for demographic characteristics, Medicaid eligibility, and area-level covariates. RESULTS: Increases of 10 µg per cubic meter in PM2.5 and of 10 ppb in ozone were associated with increases in all-cause mortality of 7.3% (95% confidence interval [CI], 7.1 to 7.5) and 1.1% (95% CI, 1.0 to 1.2), respectively. When the analysis was restricted to person-years with exposure to PM2.5 of less than 12 µg per cubic meter and ozone of less than 50 ppb, the same increases in PM2.5 and ozone were associated with increases in the risk of death of 13.6% (95% CI, 13.1 to 14.1) and 1.0% (95% CI, 0.9 to 1.1), respectively. For PM2.5, the risk of death among men, blacks, and people with Medicaid eligibility was higher than that in the rest of the population. CONCLUSIONS: In the entire Medicare population, there was significant evidence of adverse effects related to exposure to PM2.5 and ozone at concentrations below current national standards. This effect was most pronounced among self-identified racial minorities and people with low income. (Supported by the Health Effects Institute and others.).

Adjusting for unmeasured spatial confounding with distance adjusted propensity score matching.

Propensity score matching is a common tool for adjusting for observed confounding in observational studies, but is known to have limitations in the presence of unmeasured confounding. In many settings, researchers are confronted with spatially-indexed data where the relative locations of the observational units may serve as a useful proxy for unmeasured confounding that varies according to a spatial pattern. We develop a new method, termed distance adjusted propensity score matching (DAPSm) that incorporates information on units' spatial proximity into a propensity score matching procedure. We show that DAPSm can adjust for both observed and some forms of unobserved confounding and evaluate its performance relative to several other reasonable alternatives for incorporating spatial information into propensity score adjustment. The method is motivated by and applied to a comparative effectiveness investigation of power plant emission reduction technologies designed to reduce population exposure to ambient ozone pollution. Ultimately, DAPSm provides a framework for augmenting a "standard" propensity score analysis with information on spatial proximity and provides a transparent and principled way to assess the relative trade-offs of prioritizing observed confounding adjustment versus spatial proximity adjustment.

Assessing NO2 Concentration and Model Uncertainty with High Spatiotemporal Resolution across the Contiguous United States Using Ensemble Model Averaging.

NO2 is a combustion byproduct that has been associated with multiple adverse health outcomes. To assess NO2 levels with high accuracy, we propose the use of an ensemble model to integrate multiple machine learning algorithms, including neural network, random forest, and gradient boosting, with a variety of predictor variables, including chemical transport models. This NO2 model covers the entire contiguous U.S. with daily predictions on 1-km-level grid cells from 2000 to 2016. The ensemble produced a cross-validated R2 of 0.788 overall, a spatial R2 of 0.844, and a temporal R2 of 0.729. The relationship between daily monitored and predicted NO2 is almost linear. We also estimated the associated monthly uncertainty level for the predictions and address-specific NO2 levels. This NO2 estimation has a very high spatiotemporal resolution and allows the examination of the health effects of NO2 in unmonitored areas. We found the highest NO2 levels along highways and in cities. We also observed that nationwide NO2 levels declined in early years and stagnated after 2007, in contrast to the trend at monitoring sites in urban areas, where the decline continued. Our research indicates that the integration of different predictor variables and fitting algorithms can achieve an improved air pollution modeling framework.

Accountability Assessment of Health Improvements in the United States Associated with Reduced Coal Emissions Between 2005 and 2012.

BACKGROUND: National, state, and local policies contributed to a 65% reduction in sulfur dioxide emissions from coal-fired power plants between 2005 and 2012 in the United States, providing an opportunity to directly quantify public health benefits attributable to these reductions under an air pollution accountability framework. METHODS: We estimate ZIP code-level changes in two different-but related-exposure metrics: total PM2.5 concentrations and exposure to coal-fired power plant emissions. We associate changes in 10 health outcome rates among approximately 30 million US Medicare beneficiaries with exposure changes between 2005 and 2012 using two difference-in-difference regression approaches designed to mitigate observed and unobserved confounding. RESULTS: Rates per 10,000 person-years of six cardiac and respiratory health outcomes-all cardiovascular disease, chronic obstructive pulmonary disorder, cardiovascular stroke, heart failure, ischemic heart disease, and respiratory tract infections-decreased by between 7.89 and 1.95 per (Equation is included in full-text article.)decrease in PM2.5, with comparable decreases in coal exposure leading to slightly larger rate decreases. Results for acute myocardial infarction, heart rhythm disorders, and peripheral vascular disease were near zero and/or mixed between the various exposure metrics and analyses. A secondary analysis found that nonlinearities in relationships between changing health outcome rates and coal exposure may explain differences in their associations. CONCLUSIONS: The direct analyses of emissions reductions estimate substantial health benefits via coal power plant emission and PM2.5 concentration reductions. Differing responses associated with changes in the two exposure metrics underscore the importance of isolating source-specific impacts from those due to total PM2.5 exposure.

Relative Effects of the Hospital Readmissions Reduction Program on Hospitals That Serve Poorer Patients.

IMPORTANCE: Hospitals that serve poorer populations have higher readmission rates. It is unknown whether these hospitals effectively lowered readmission rates in response to the Hospital Readmissions Reduction Program (HRRP). OBJECTIVE: To compare pre-post differences in readmission rates among hospitals with different proportion of dual-eligible patients both generally and among the most highly penalized (ie, low performing) hospitals. DESIGN: Retrospective cohort study using piecewise linear model with estimated hospital-level risk-standardized readmission rates (RSRRs) as the dependent variable and a change point at HRRP passage (2010). Economic burden was assessed by proportion of dual-eligibles served. SETTING: Acute care hospitals within the United States. PARTICIPANTS: Medicare fee-for-service beneficiaries aged 65 years or older discharged alive from January 1, 2003 to November 30, 2014 with a principal discharge diagnosis of acute myocardial infarction (AMI), congestive heart failure (CHF), and pneumonia. MAIN OUTCOME AND MEASURE: Decrease in hospital-level RSRRs in the post-law period, after controlling for the pre-law trend. RESULTS: For AMI, the pre-post difference between hospitals that service high and low proportion of dual-eligibles was not significant (-65 vs. -64 risk-standardized readmissions per 10000 discharges per year, P=0.0678). For CHF, RSRRs declined more at high than low dual-eligible hospitals (-79 vs. -75 risk-standardized readmissions per 10000 discharges per year, P=0.0006). For pneumonia, RSRRs declined less at high than low dual-eligible hospitals (-44 vs. -47 risk-standardized readmissions per 10000 discharges per year, P=0.0003). Among the 742 highest penalized hospitals and all conditions, the pre-post decline in rate of change of RSRRs was less for high dual-eligible hospitals than low dual-eligible hospitals (-68 vs. -74 risk-standardized readmissions per 10000 discharges per year for AMI, -88 vs. -97 for CHF, and -47 vs. -56 for pneumonia, P<0.0001 for all). CONCLUSIONS AND RELEVANCE: For all hospitals, differences in pre-post trends in RSRRs varied with disease conditions. However, for the highest-penalized hospitals, the pre-post decline in RSRRs was greater for low than high dual-eligible hospitals for all penalized conditions. These results suggest that high penalty, high dual-eligible hospitals may be less able to improve performance on readmission metrics.

Characterizing population exposure to coal emissions sources in the United States using the HyADS model.

In anticipation of the expanding appreciation for air quality models in health outcomes studies, we develop and evaluate a reduced-complexity model for pollution transport that intentionally sacrifices some of the sophistication of full-scale chemical transport models in order to support applicability to a wider range of health studies. Specifically, we introduce the HYSPLIT average dispersion model, HyADS, which combines the HYSPLIT trajectory dispersion model with modern advances in parallel computing to estimate ZIP code level exposure to emissions from individual coal-powered electricity generating units in the United States. Importantly, the method is not designed to reproduce ambient concentrations of any particular air pollutant; rather, the primary goal is to characterize each ZIP code's exposure to these coal power plants specifically. We show adequate performance towards this goal against observed annual average air pollutant concentrations (nationwide Pearson correlations of 0.88 and 0.73 with SO 4 2 - and PM2.5, respectively) and coal-combustion impacts simulated with a full-scale chemical transport model and adjusted to observations using a hybrid direct sensitivities approach (correlation of 0.90). We proceed to provide multiple examples of HyADS's single-source applicability, including to show that 22% of the population-weighted coal exposure comes from 30 coal-powered electricity generating units.

Impact of National Ambient Air Quality Standards Nonattainment Designations on Particulate Pollution and Health.

BACKGROUND: Despite dramatic air quality improvement in the United States over the past decades, recent years have brought renewed scrutiny and uncertainty surrounding the effectiveness of specific regulatory programs for continuing to improve air quality and public health outcomes. METHODS: We employ causal inference methods and a spatial hierarchical regression model to characterize the extent to which a designation of "nonattainment" with the 1997 National Ambient Air Quality Standard for ambient fine particulate matter (PM2.5) in 2005 causally affected ambient PM2.5 and health outcomes among over 10 million Medicare beneficiaries in the Eastern United States in 2009-2012. RESULTS: We found that, on average across all retained study locations, reductions in ambient PM2.5 and Medicare health outcomes could not be conclusively attributed to the nonattainment designations against the backdrop of other regional strategies that impacted the entire Eastern United States. A more targeted principal stratification analysis indicates substantial health impacts of the nonattainment designations among the subset of areas where the designations are estimated to have actually reduced ambient PM2.5 beyond levels achieved by regional measures, with noteworthy reductions in all-cause mortality, chronic obstructive pulmonary disorder, heart failure, ischemic heart disease, and respiratory tract infections. DISCUSSION: These findings provide targeted evidence of the effectiveness of local control measures after nonattainment designations for the 1997 PM2.5 air quality standard.

Readmission Rates After Passage of the Hospital Readmissions Reduction Program: A Pre-Post Analysis.

BACKGROUND: Whether hospitals with the highest risk-standardized readmission rates (RSRRs) subsequently experienced the greatest improvement after passage of the Medicare Hospital Readmissions Reduction Program (HRRP) is unknown. OBJECTIVE: To evaluate whether passage of the HRRP was followed by acceleration in improvement in 30-day RSRRs after hospitalizations for acute myocardial infarction (AMI), congestive heart failure (CHF), or pneumonia and whether the lowest-performing hospitals had faster acceleration in improvement after passage of the law than hospitals that were already performing well. DESIGN: Pre-post analysis stratified by hospital performance groups. SETTING: U.S. acute care hospitals. PATIENTS: 15 170 008 Medicare patients discharged alive from 2000 to 2013. INTERVENTION: Passage of the HRRP. MEASUREMENTS: 30-day readmission rates after hospitalization for AMI, CHF, or pneumonia for hospitals in the highest-performance (0% penalty), average-performance (>0% and <0.50% penalty), low-performance (≥0.50% and <0.99% penalty), and lowest-performance (≥0.99% penalty) groups. RESULTS: Of 2868 hospitals serving 1 109 530 Medicare discharges annually, 30.1% were highest performers, 44.0% were average performers, 16.8% were low performers, and 9.0% were lowest performers. After controlling for prelaw trends, an additional 67.6 (95% CI, 66.6 to 68.4), 74.8 (CI, 74.0 to 75.4), 85.4 (CI, 84.0 to 86.8), and 95.1 (CI, 92.6 to 97.5) readmissions per 10 000 discharges were found to have been averted per year in the highest-, average-, low-, and lowest-performance groups, respectively, after passage of the law. LIMITATION: Inability to distinguish between improvement caused by the magnitude of the penalty or by different levels of health improvement in different patient populations. CONCLUSION: After passage of the HRRP, 30-day RSRRs for myocardial infarction, heart failure, and pneumonia decreased more rapidly than before the law's passage. Improvement was most marked for hospitals with the lowest prelaw performance. PRIMARY FUNDING SOURCE: National Institutes of Health.

Association of Short-term Exposure to Air Pollution With Mortality in Older Adults.

Importance: The US Environmental Protection Agency is required to reexamine its National Ambient Air Quality Standards (NAAQS) every 5 years, but evidence of mortality risk is lacking at air pollution levels below the current daily NAAQS in unmonitored areas and for sensitive subgroups. Objective: To estimate the association between short-term exposures to ambient fine particulate matter (PM2.5) and ozone, and at levels below the current daily NAAQS, and mortality in the continental United States. Design, Setting, and Participants: Case-crossover design and conditional logistic regression to estimate the association between short-term exposures to PM2.5 and ozone (mean of daily exposure on the same day of death and 1 day prior) and mortality in 2-pollutant models. The study included the entire Medicare population from January 1, 2000, to December 31, 2012, residing in 39 182 zip codes. Exposures: Daily PM2.5 and ozone levels in a 1-km × 1-km grid were estimated using published and validated air pollution prediction models based on land use, chemical transport modeling, and satellite remote sensing data. From these gridded exposures, daily exposures were calculated for every zip code in the United States. Warm-season ozone was defined as ozone levels for the months April to September of each year. Main Outcomes and Measures: All-cause mortality in the entire Medicare population from 2000 to 2012. Results: During the study period, there were 22 433 862 million case days and 76 143 209 control days. Of all case and control days, 93.6% had PM2.5 levels below 25 µg/m3, during which 95.2% of deaths occurred (21 353 817 of 22 433 862), and 91.1% of days had ozone levels below 60 parts per billion, during which 93.4% of deaths occurred (20 955 387 of 22 433 862). The baseline daily mortality rates were 137.33 and 129.44 (per 1 million persons at risk per day) for the entire year and for the warm season, respectively. Each short-term increase of 10 µg/m3 in PM2.5 (adjusted by ozone) and 10 parts per billion (10-9) in warm-season ozone (adjusted by PM2.5) were statistically significantly associated with a relative increase of 1.05% (95% CI, 0.95%-1.15%) and 0.51% (95% CI, 0.41%-0.61%) in daily mortality rate, respectively. Absolute risk differences in daily mortality rate were 1.42 (95% CI, 1.29-1.56) and 0.66 (95% CI, 0.53-0.78) per 1 million persons at risk per day. There was no evidence of a threshold in the exposure-response relationship. Conclusions and Relevance: In the US Medicare population from 2000 to 2012, short-term exposures to PM2.5 and warm-season ozone were significantly associated with increased risk of mortality. This risk occurred at levels below current national air quality standards, suggesting that these standards may need to be reevaluated.

Causal Inference Methods for Estimating Long-Term Health Effects of Air Quality Regulations.

INTRODUCTION: The regulatory and policy environment surrounding air quality management warrants new types of epidemiological evidence. Whereas air pollution epidemiology has typically informed previous policies with estimates of exposure-response relationships between pollution and health outcomes, new types of evidence can inform current debates about the actual health impacts of air quality regulations. Directly evaluating specific regulatory strategies is distinct from and complements estimating exposure-response relationships; increased emphasis on assessing the effectiveness of well-defined regulatory interventions will enhance the evidence supporting policy decisions. The goal of this report is to provide new analytic perspectives and statistical methods for what we refer to as "direct"-accountability assessment of the effectiveness of specific air quality regulatory interventions. Toward this end, we sharpened many of the distinctions surrounding accountability assessment initially raised by the HEI Accountability Working Group (2003) through discussion, development, and deployment of statistical methods for drawing causal inferences from observational data. The methods and analyses presented here are unified in their focus on anchoring accountability assessment to the estimation of the causal consequences of well-defined actions or interventions. These analytic perspectives are discussed in the context of two direct-accountability case studies pertaining to four different links in the so-called chain of accountability, the related series of events leading from the intervention to the expected outcomes (see Preface; HEI Accountability Working Group 2003). METHODS: The statistical methods described in this report consist of both established methods for drawing causal inferences from observational data and newly developed methods for assessing causal accountability. We have sharpened the analytic distinctions between studies that directly evaluated the effectiveness of specific policies and those that estimated exposure-response relationships between pollution and health. We emphasized how a potential-outcomes paradigm for causal inference can elevate policy debates by means of more direct evidence of the extent to which complex regulatory interventions affect pollution and health outcomes. We also outlined the potential-outcomes perspective and promoted its use as a means to frame observational studies as approximate randomized experiments. Our newly developed methods for assessing causal accountability draw on propensity scores, principal stratification, causal mediation analysis, spatial hierarchical models, and Bayesian estimation. The first case study made use of health outcomes among approximately four million Medicare beneficiaries living in the Western United States to estimate the causal health impacts of areas designated as being in nonattainment for particulate matter ≤10 µm in aerodynamic diameter (PM10*) according to the 1987 National Ambient Air Quality Standards (NAAQS). The second case study focused on developing and testing our new, advanced methodology for multipollutant accountability assessment by examining the extent to which sulfur dioxide (SO2) scrubbers on coal-fired power plants causally affect emissions of SO2, nitrogen oxides (NO(x)), and carbon dioxide (CO2) as well as the extent to which emissions reductions mediate the causal effect of a scrubber on ambient concentrations of PM2.5. Both case studies were anchored in our compilation of national, linked data on ambient air quality monitoring, weather, population demographics, Medicare hospitalization and mortality outcomes, continuous-emissions monitoring for electricity-generating units (EGUs) in power plants, and a variety of regulatory control interventions. The resulting database has unprecedented accuracy and granularity for conducting the types of accountability assessments presented in this report. A key component of our work was the creation of tools to help distribute our linked database and to facilitate reproducible research. RESULTS: In the first case study, we focused on illustrating the most fundamental features of a causal-inference perspective on direct-accountability assessment. The results indicated that all-cause Medicare mortality and respiratory-related hospitalization rates were causally reduced in areas designated as nonattainment for PM10 during 1990 to 1995 compared with the rates that would have occurred without the designation. In the second case study, which examined power-plant emissions and illustrated our newly developed statistical methods, the results indicated that the presence of an SO2 scrubber causally reduced ambient PM2.5 and that this reduction was mediated almost entirely through causal reductions in SO2 emissions. The results were interpreted in light of the well-documented relationships between scrubbers, power-plant emissions, and PM2.5. CONCLUSION: By grounding accountability research in a potential-outcomes framework and applying our new methods to our collection of national data sets, we were able to provide additional sound evidence of the health effects of long-term, large-scale air quality regulations. This additional, rigorous evidence of the causal effects of well-defined actions augments the existing body of research and ensures that the highest-level epidemiological evidence will continue to support regulatory policies. Ultimately, our research contributed to the evidence available to support to the U.S. Environmental Protection Agency (U.S. EPA) and other stakeholders for incorporating health outcomes research into policy development.

Redefining pandemic preparedness: Multidisciplinary insights from the CERP modelling workshop in infectious diseases, workshop report.

In July 2023, the Center of Excellence in Respiratory Pathogens organized a two-day workshop on infectious diseases modelling and the lessons learnt from the Covid-19 pandemic. This report summarizes the rich discussions that occurred during the workshop. The workshop participants discussed multisource data integration and highlighted the benefits of combining traditional surveillance with more novel data sources like mobility data, social media, and wastewater monitoring. Significant advancements were noted in the development of predictive models, with examples from various countries showcasing the use of machine learning and artificial intelligence in detecting and monitoring disease trends. The role of open collaboration between various stakeholders in modelling was stressed, advocating for the continuation of such partnerships beyond the pandemic. A major gap identified was the absence of a common international framework for data sharing, which is crucial for global pandemic preparedness. Overall, the workshop underscored the need for robust, adaptable modelling frameworks and the integration of different data sources and collaboration across sectors, as key elements in enhancing future pandemic response and preparedness.

Mortality risk from United States coal electricity generation.

Policy-makers seeking to limit the impact of coal electricity-generating units (EGUs, also known as power plants) on air quality and climate justify regulations by quantifying the health burden attributable to exposure from these sources. We defined "coal PM2.5" as fine particulate matter associated with coal EGU sulfur dioxide emissions and estimated annual exposure to coal PM2.5 from 480 EGUs in the US. We estimated the number of deaths attributable to coal PM2.5 from 1999 to 2020 using individual-level Medicare death records representing 650 million person-years. Exposure to coal PM2.5 was associated with 2.1 times greater mortality risk than exposure to PM2.5 from all sources. A total of 460,000 deaths were attributable to coal PM2.5, representing 25% of all PM2.5-related Medicare deaths before 2009 and 7% after 2012. Here, we quantify and visualize the contribution of individual EGUs to mortality.

Inequitable Exposures to U.S. Coal Power Plant-Related PM2.5: 22 Years and Counting.

BACKGROUND: Emissions from coal power plants have decreased over recent decades due to regulations and economics affecting costs of providing electricity generated by coal vis-à-vis its alternatives. These changes have improved regional air quality, but questions remain about whether benefits have accrued equitably across population groups. OBJECTIVES: We aimed to quantify nationwide long-term changes in exposure to particulate matter (PM) with an aerodynamic diameter ≤2.5µm (PM2.5) associated with coal power plant SO2 emissions. We linked exposure reductions with three specific actions taken at individual power plants: scrubber installations, reduced operations, and retirements. We assessed how emissions changes in different locations have influenced exposure inequities, extending previous source-specific environmental justice analyses by accounting for location-specific differences in racial/ethnic population distributions. METHODS: We developed a data set of annual PM2.5 source impacts ("coal PM2.5") associated with SO2 emissions at each of 1,237 U.S. coal-fired power plants across 1999-2020. We linked population-weighted exposure with information about each coal unit's operational and emissions-control status. We calculate changes in both relative and absolute exposure differences across demographic groups. RESULTS: Nationwide population-weighted coal PM2.5 declined from 1.96µg/m3 in 1999 to 0.06 µg/m3 in 2020. Between 2007 and 2010, most of the exposure reduction is attributable to SO2 scrubber installations, and after 2010 most of the decrease is attributable to retirements. Black populations in the South and North Central United States and Native American populations in the western United States were inequitably exposed early in the study period. Although inequities decreased with falling emissions, facilities in states across the North Central United States continue to inequitably expose Black populations, and Native populations are inequitably exposed to emissions from facilities in the West. DISCUSSION: We show that air quality controls, operational adjustments, and retirements since 1999 led to reduced exposure to coal power plant related PM2.5. Reduced exposure improved equity overall, but some populations continue to be inequitably exposed to PM2.5 associated with facilities in the North Central and western United States. https://doi.org/10.1289/EHP11605.

Multi-syndrome, multi-gene risk modeling for individuals with a family history of cancer with the novel R package PanelPRO.

Identifying individuals who are at high risk of cancer due to inherited germline mutations is critical for effective implementation of personalized prevention strategies. Most existing models focus on a few specific syndromes; however, recent evidence from multi-gene panel testing shows that many syndromes are overlapping, motivating the development of models that incorporate family history on several cancers and predict mutations for a comprehensive panel of genes.We present PanelPRO, a new, open-source R package providing a fast, flexible back-end for multi-gene, multi-cancer risk modeling with pedigree data. It includes a customizable database with default parameter values estimated from published studies and allows users to select any combinations of genes and cancers for their models, including well-established single syndrome BayesMendel models (BRCAPRO and MMRPRO). This leads to more accurate risk predictions and ultimately has a high impact on prevention strategies for cancer and clinical decision making. The package is available for download for research purposes at https://projects.iq.harvard.edu/bayesmendel/panelpro.

Genetic mutations that increase cancer risk can be passed down from parents to their children, which can affect families across many generations. In these families, multiple members may be affected by different types of cancer, and these cancers often develop at an early age. Unaffected family members are often referred to genetic counselling, where they can explore their own risk of cancer. Clinicians and genetic counselors can provide recommendations to minimize cancer risk and inform personal choices on how to manage that risk, such as opting for preventative surgeries or participating in regular screening. In genetic counselling sessions, highly trained clinicians and specialists use software that takes an individual's family history of cancer and uses it to estimate their individual risk of carrying certain genetic mutations. These estimates can in turn help to predict their future risk of cancer. Many existing software packages are limited to estimating risks based on mutations in well-known cancer-related genes, such as BRCA1 and BRCA2 in breast and ovarian cancer. However, emerging evidence suggests that many of the genes associated with cancer risk work as part of a complex and overlapping network. Since current risk-profiling software packages are only designed to consider such genes in isolation, they cannot generate the most robust, accurate or comprehensive cancer risk profiles. To address this challenge, Lee, Liang et al. have developed a new risk-profiling software that can integrate a large number of gene mutations and a wide range of potential cancer types to provide more accurate estimates of individual cancer risk. This software, called PanelPRO, uses evidence identified from extensive literature reviews to model the complex interplay between genes and cancer risk. The software not only calculates risks based on known genes, but also allows other developers to integrate new cancer-related genes that may be identified in the future. Importantly, the software is compatible with genetic counselling applications, since it returns answers within seconds when reasonable family and gene database sizes are used. PanelPRO is a new, modern, flexible and efficient software package that provides an important advance towards modelling the vast genetic and biological complexity that contributes to inherited cancer risk. This software is designed to provide a more accurate and comprehensive estimate of cancer risk for individuals with family histories of cancer. As an open-source software, it is freely available for research purposes, and can be licensed by software companies and healthcare organizations to integrate electronic patient records and rapidly identify at-risk individuals across larger patient groups. Ultimately, this software has the potential to improve cancer prevention strategies and optimize the personalized decision-making processes around cancer risk.