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Influenza infects an estimated 9–35 million individuals each year in the United States and is a contributing cause for between 12,000 and 56,000 deaths annually. Seasonal outbreaks of influenza are common in temperate regions of the world, with highest incidence typically occurring in colder and drier months of the year. Real-time forecasts of influenza transmission can inform public health response to outbreaks. We present the results of a multiinstitution collaborative effort to standardize the collection and evaluation of forecasting models for influenza in the United States for the 2010/2011 through 2016/2017 influenza seasons. For these seven seasons, we assembled weekly real-time forecasts of seven targets of public health interest from 22 different models. We compared forecast accuracy of each model relative to a historical baseline seasonal average. Across all regions of the United States, over half of the models showed consistently better performance than the historical baseline when forecasting incidence of influenza-like illness 1 wk, 2 wk, and 3 wk ahead of available data and when forecasting the timing and magnitude of the seasonal peak. In some regions, delays in data reporting were strongly and negatively associated with forecast accuracy. More timely reporting and an improved overall accessibility to novel and traditional data sources are needed to improve forecasting accuracy and its integration with real-time public health decision making.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is determined by maximizing overall ensemble accuracy over past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

The large quantities of chemical oil dispersants used in the oil spill response and cleanup (OSRC) work following the disaster provide an opportunity to study associations between dispersant exposure (Corexit™ EC9500A or EC9527A) and human health. Our objectives were to examine associations between potential exposure to the dispersants and adverse respiratory, dermal, and eye irritation symptoms. Using data from detailed Gulf Long-term Follow-up ( GuLF) Study enrollment interviews, we determined potential exposure to either dispersant from participant-reported tasks during the OSRC work. Between 27,659 and 29,468 participants provided information on respiratory, dermal, and eye irritation health. We estimated prevalence ratios (PRs) to measure associations with symptoms reported during the OSRC work and at study enrollment, adjusting for potential confounders including airborne total hydrocarbons exposure, use of cleaning chemicals, and participant demographics. Potential exposure to either of the dispersants was significantly associated with all health outcomes at the time of the OSRC, with the strongest association for burning in the nose, throat, or lungs [adjusted PR (aPR)=1.61 (95% CI: 1.42, 1.82)], tightness in chest [aPR=1.58 (95% CI: 1.37, 1.81)], and burning eyes [aPR=1.48 (95% CI: 1.35, 1.64). Weaker, but still significant, associations were found between dispersant exposure and symptoms present at enrollment. Potential exposure to Corexit™ EC9527A or EC9500A was associated with a range of health symptoms at the time of the OSRC, as well as at the time of study enrollment, 1-3 y after the spill. https://doi.org/10.1289/EHP1677.

Growth in early life is associated with various individual health outcomes in adulthood, but limited research has been done on associations with a more comprehensive measure of health. Combining information from multiple biological systems, allostatic load (AL) provides such a quantitative measure of overall physiological health. We used longitudinal data from the Birth to Twenty Plus cohort in South Africa to calculate an AL score at age 22 years and examined associations with birth weight and linear growth and weight gain from age 0 to 2 years and 2 to 5 years, as attenuated by trajectories of body mass index and pubertal development in later childhood and adolescence. Differences in total AL score between males and females were small, though levels of individual biological factors contributing to AL differed by sex. Increased weight gain from age 2 to 5 years among males was associated with an increased risk of high AL, but no other early-life measures were associated with AL. Increased adiposity through childhood and adolescence in females was associated with higher AL in early adulthood. These results illustrate that patterns of early-life growth are not consistently associated with higher AL. While more research is needed to link AL in young adulthood to later health outcomes, these results also suggest increased adiposity during childhood and adolescence represents a potential early sign of later physiological risk.

BACKGROUND: The large quantities of chemical oil dispersants used in the oil spill response and cleanup (OSRC) work following the Deepwater Horizon disaster provide an opportunity to study associations between dispersant exposure (Corexit[TM] EC9500A or EC9527A) and human health. OBJECTIVES: Our objectives were to examine associations between potential exposure to the dispersants and adverse respiratory, dermal, and eye irritation symptoms. METHODS: Using data from detailed Gulf Long-term Follow-up (GuLF) Study enrollment interviews, we determined potential exposure to either dispersant from participant-reported tasks during the OSRC work. Between 27,659 and 29,468 participants provided information on respiratory, dermal, and eye irritation health. We estimated prevalence ratios (PRs) to measure associations with symptoms reported during the OSRC work and at study enrollment, adjusting for potential confounders including airborne total hydrocarbons exposure, use of cleaning chemicals, and participant demographics. RESULTS: Potential exposure to either of the dispersants was significantly associated with all health outcomes at the time of the OSRC, with the strongest association for burning in the nose, throat, or lungs [adjusted PR (aPR) = 1.61 (95% CI: 1.42, 1.82)], tightness in chest [aPR= 1.58 (95% CI: 1.37, 1.81)], and burning eyes [aPR = 1.48 (95% CI: 1.35, 1.64). Weaker, but still significant, associations were found between dispersant exposure and symptoms present at enrollment. CONCLUSIONS: Potential exposure to Corexit[TM] EC9527A or EC9500A was associated with a range of health symptoms at the time of the OSRC, as well as at the time of study enrollment, 1-3 y after the spill.

Since 2013, the Centers for Disease Control and Prevention (CDC) has hosted an annual influenza season forecasting challenge. The 2015–2016 challenge consisted of weekly probabilistic forecasts of multiple targets, including fourteen models submitted by eleven teams. Forecast skill was evaluated using a modified logarithmic score. We averaged submitted forecasts into a mean ensemble model and compared them against predictions based on historical trends. Forecast skill was highest for seasonal peak intensity and short-term forecasts, while forecast skill for timing of season onset and peak week was generally low. Higher forecast skill was associated with team participation in previous influenza forecasting challenges and utilization of ensemble forecasting techniques. The mean ensemble consistently performed well and outperformed historical trend predictions. CDC and contributing teams will continue to advance influenza forecasting and work to improve the accuracy and reliability of forecasts to facilitate increased incorporation into public health response efforts.

Seasonal influenza results in substantial annual morbidity and mortality in the United States and worldwide. Accurate forecasts of key features of influenza epidemics, such as the timing and severity of the peak incidence in a given season, can inform public health response to outbreaks. As part of ongoing efforts to incorporate data and advanced analytical methods into public health decision-making, the United States Centers for Disease Control and Prevention (CDC) has organized seasonal influenza forecasting challenges since the 2013/2014 season. In the 2017/2018 season, 22 teams participated. A subset of four teams created a research consortium called the FluSight Network in early 2017. During the 2017/2018 season they worked together to produce a collaborative multi-model ensemble that combined 21 separate component models into a single model using a machine learning technique called stacking. This approach creates a weighted average of predictive densities where the weight for each component is based on that component’s forecast accuracy in past seasons. In the 2017/2018 influenza season, one of the largest seasonal outbreaks in the last 15 years, this multi-model ensemble performed better on average than all individual component models and placed second overall in the CDC challenge. It also outperformed the baseline multi-model ensemble created by the CDC that took a simple average of all models submitted to the forecasting challenge. This project shows that collaborative efforts between research teams to develop ensemble forecasting approaches can bring measurable improvements in forecast accuracy and important reductions in the variability of performance from year to year. Efforts such as this, that emphasize real-time testing and evaluation of forecasting models and facilitate the close collaboration between public health officials and modeling researchers, are essential to improving our understanding of how best to use forecasts to improve public health response to seasonal and emerging epidemic threats.

Whether a conservative strategy of medical therapy alone or a strategy of medical therapy plus invasive treatment is more beneficial in older adults with non-ST-segment elevation myocardial infarction (NSTEMI) remains unclear. We conducted a prospective, multicenter, randomized trial involving patients 75 years of age or older with NSTEMI at 48 sites in the United Kingdom. The patients were assigned in a 1:1 ratio to a conservative strategy of the best available medical therapy or an invasive strategy of coronary angiography and revascularization plus the best available medical therapy. Patients who were frail or had a high burden of coexisting conditions were eligible. The primary outcome was a composite of death from cardiovascular causes (cardiovascular death) or nonfatal myocardial infarction assessed in a time-to-event analysis. A total of 1518 patients underwent randomization; 753 patients were assigned to the invasive-strategy group and 765 to the conservative-strategy group. The mean age of the patients was 82 years, 45% were women, and 32% were frail. A primary-outcome event occurred in 193 patients (25.6%) in the invasive-strategy group and 201 patients (26.3%) in the conservative-strategy group (hazard ratio, 0.94; 95% confidence interval [CI], 0.77 to 1.14; P = 0.53) over a median follow-up of 4.1 years. Cardiovascular death occurred in 15.8% of the patients in the invasive-strategy group and 14.2% of the patients in the conservative-strategy group (hazard ratio, 1.11; 95% CI, 0.86 to 1.44). Nonfatal myocardial infarction occurred in 11.7% in the invasive-strategy group and 15.0% in the conservative-strategy group (hazard ratio, 0.75; 95% CI, 0.57 to 0.99). Procedural complications occurred in less than 1% of the patients. In older adults with NSTEMI, an invasive strategy did not result in a significantly lower risk of cardiovascular death or nonfatal myocardial infarction (the composite primary outcome) than a conservative strategy over a median follow-up of 4.1 years. (Funded by the British Heart Foundation; BHF SENIOR-RITA ISRCTN Registry number, ISRCTN11343602.).

In 2010, Parties to the Convention on Biological Diversity (CBD) adopted the Strategic Plan for Biodiversity 2011–2020 to address the loss and degradation of nature. Subsequently, most biodiversity indicators continued to decline. Nevertheless, conservation actions can make a positive difference for biodiversity. The emerging Post‐2020 Global Biodiversity Framework has potential to catalyze efforts to “bend the curve” of biodiversity loss. Thus, the inclusion of a goal on species, articulated as Goal B in the Zero Draft of the Post‐2020 Framework, is essential. However, as currently formulated, this goal is inadequate for preventing extinctions, and reversing population declines; both of which are required to achieve the CBD's 2030 Mission. We contend it is unacceptable that Goal B could be met while most threatened species deteriorated in status and many avoidable species extinctions occurred. We examine the limitations of the current wording and propose an articulation with robust scientific basis. A goal for species that strives to end extinctions and recover populations of all species that have experienced population declines, and especially those at risk of extinction, would help to align actors toward the transformative actions and interventions needed for humans to live in harmony with nature.

Hopping kangaroos exhibit remarkably little change in their rate of metabolic energy expenditure with locomotor speed compared to other running animals. This phenomenon may be related to greater elastic energy savings due to increasing tendon stress; however, the mechanisms which enable the rise in stress without additional muscle work remain poorly understood. In this study, we created a three-dimensional (3D) kangaroo musculoskeletal model, integrating 3D motion capture and force plate data, to analyse the kinematics and kinetics of hopping red and grey kangaroos. Using our model, we evaluated how body mass and speed influence (i) hindlimb posture, (ii) effective mechanical advantage (EMA), (iii) the associated tendon stress in the ankle extensors, and (iv) ankle work during hopping. We found that increasing ankle dorsiflexion and metatarsophalangeal plantarflexion likely played an important role in decreasing ankle EMA by altering both the muscle and external moment arms, which subsequently increased energy absorption and peak tendon stress at the ankle. Surprisingly, kangaroo hindlimb posture changes appeared to contribute to increased tendon stress, allowing more elastic energy storage at faster speeds. These posture-mediated increases in elastic energy storage and return could be a key factor enabling kangaroos to achieve energetic benefits at faster hopping speeds, but may limit the performance of large kangaroos due to the risk of tendon rupture.