A Spatially Resolved and Environmentally Informed Forecast Model of West Nile Virus in Coachella Valley, California.

West Nile virus (WNV) is the most significant arbovirus in the United States in terms of both morbidity and mortality. West Nile exists in a complex transmission cycle between avian hosts and the arthropod vector, Culex spp. mosquitoes. Human spillover events occur when humans are bitten by an infected mosquito and predicting these rates of infection and therefore the risk to humans may be associated with fluctuations in environmental conditions. In this study, we evaluate the hydrological and meteorological drivers associated with mosquito biology and viral development to determine if these associations can be used to forecast seasonal mosquito infection rates with WNV in the Coachella Valley of California. We developed and tested a spatially resolved ensemble forecast model of the WNV mosquito infection rate in the Coachella Valley using 17 years of mosquito surveillance data and North American Land Data Assimilation System-2 environmental data. Our multi-model inference system indicated that the combination of a cooler and dryer winter, followed by a wetter and warmer spring, and a cooler than normal summer was most predictive of the prevalence of West Nile positive mosquitoes in the Coachella Valley. The ability to make accurate early season predictions of West Nile risk has the potential to allow local abatement districts and public health entities to implement early season interventions such as targeted adulticiding and public health messaging before human transmission occurs. Such early and targeted interventions could better mitigate the risk of WNV to humans.

Generalized maculopapular rash and fever.

Was this a case of the "great masquerader"? Or was it something else?

Impact of air pollution on running performance.

Air pollution exposures during training may impact race preformances. We aggregated data on 334 collegiate male track & field athletes from 46 universities across the United States over 2010-2014. Using distributed lag non-linear models, we analyzed the relationship between race time and PM2.5, ozone, and two versions of the Air Quality Index (AQI) exposures up to 21 days prior to the race. We observed a 12.8 (95% CI: 1.3, 24.2) second and 11.5 (95% CI: 0.8, 22.1) second increase in race times from 21 days of PM2.5 exposure (10.0 versus 5.0 µg/m3) and ozone exposure (54.9 versus 36.9 ppm), respectively. Exposure measured by the two-pollutant threshold (PM2.5 and ozone) AQI was not significantly associated with race time; however, the association for summed two-pollutant AQI (PM2.5 plus ozone) was similar to associations observed for the individual pollutants (12.4, 95% CI: 1.8, 23.0 s). Training and competing at elevated air pollution levels, even at exposures within AQI's good-to-moderate classifications, was associated with slower race times. This work provides an initial characterization of the effect of air pollution on running performance and a justification for why coaches should consider approaches to reduce air pollution exposures while training.

Evaluation of an open forecasting challenge to assess skill of West Nile virus neuroinvasive disease prediction.

BACKGROUND: West Nile virus (WNV) is the leading cause of mosquito-borne illness in the continental USA. WNV occurrence has high spatiotemporal variation, and current approaches to targeted control of the virus are limited, making forecasting a public health priority. However, little research has been done to compare strengths and weaknesses of WNV disease forecasting approaches on the national scale. We used forecasts submitted to the 2020 WNV Forecasting Challenge, an open challenge organized by the Centers for Disease Control and Prevention, to assess the status of WNV neuroinvasive disease (WNND) prediction and identify avenues for improvement. METHODS: We performed a multi-model comparative assessment of probabilistic forecasts submitted by 15 teams for annual WNND cases in US counties for 2020 and assessed forecast accuracy, calibration, and discriminatory power. In the evaluation, we included forecasts produced by comparison models of varying complexity as benchmarks of forecast performance. We also used regression analysis to identify modeling approaches and contextual factors that were associated with forecast skill. RESULTS: Simple models based on historical WNND cases generally scored better than more complex models and combined higher discriminatory power with better calibration of uncertainty. Forecast skill improved across updated forecast submissions submitted during the 2020 season. Among models using additional data, inclusion of climate or human demographic data was associated with higher skill, while inclusion of mosquito or land use data was associated with lower skill. We also identified population size, extreme minimum winter temperature, and interannual variation in WNND cases as county-level characteristics associated with variation in forecast skill. CONCLUSIONS: Historical WNND cases were strong predictors of future cases with minimal increase in skill achieved by models that included other factors. Although opportunities might exist to specifically improve predictions for areas with large populations and low or high winter temperatures, areas with high case-count variability are intrinsically more difficult to predict. Also, the prediction of outbreaks, which are outliers relative to typical case numbers, remains difficult. Further improvements to prediction could be obtained with improved calibration of forecast uncertainty and access to real-time data streams (e.g. current weather and preliminary human cases).

Impact of COVID-19 on pediatric asthma-related healthcare utilization in New York City: a community-based study.

BACKGROUND: COVID-19 disproportionately affects families of low socioeconomic status and may worsen health disparities that existed prior to the pandemic. Asthma is a common chronic disease in children exacerbated by environmental exposures. METHODS: A cross-sectional survey was conducted to understand the impact of the initial stage of the pandemic on environmental and social conditions, along with access to care for children with asthma in New York City (NYC). Participants were recruited from a community-based organization in East Harlem and a nearby academic Pediatric Pulmonary clinic and categorized as having either public or private insurance (n = 51). RESULTS: Factors significantly associated with public compared to private insurance respectively were: increased reports of indoor asthma triggers (cockroach 76% vs 23%; mold 40% vs 12%), reduced income (72% vs 27%), and housing insecurity (32% vs 0%). Participants with public insurance were more likely to experience conditions less conducive to social distancing compared to respondents with private insurance, such as remaining in NYC (92% vs 38%) and using public transportation (44% vs 4%); families with private insurance also had greater access to remote work (81% vs 8%). Families with public insurance were significantly more likely to test positive for SARS-CoV-2 (48% vs 15%) but less likely to have gotten tested (76% vs 100%). Families with public insurance also reported greater challenges accessing office medical care and less access to telehealth, although not statistically significant (44% vs 19%; 68% vs 85%, respectively). CONCLUSIONS: Findings highlight disproportionate burdens of the pandemic, and how these disparities affect children with asthma in urban environments.

COVID-19 subphenotypes at hospital admission are associated with mortality: a cross-sectional study.

BACKGROUND: We have an incomplete understanding of COVID-19 characteristics at hospital presentation and whether underlying subphenotypes are associated with clinical outcomes and therapeutic responses. METHODS: For this cross-sectional study, we extracted electronic health data from adults hospitalized between 1 March and 30 August 2020 with a PCR-confirmed diagnosis of COVID-19 at five New York City Hospitals. We obtained clinical and laboratory data from the first 24 h of the patient's hospitalization. Treatment with tocilizumab and convalescent plasma was assessed over hospitalization. The primary outcome was mortality; secondary outcomes included intubation, intensive care unit (ICU) admission and length of stay (LOS). First, we employed latent class analysis (LCA) to identify COVID-19 subphenotypes on admission without consideration of outcomes and assigned each patient to a subphenotype. We then performed robust Poisson regression to examine associations between COVID-19 subphenotype assignment and outcome. We explored whether the COVID-19 subphenotypes had a differential response to tocilizumab and convalescent plasma therapies. RESULTS: A total of 4620 patients were included. LCA identified six subphenotypes, which were distinct by level of inflammation, clinical and laboratory derangements and ranged from a hypoinflammatory subphenotype with the fewest derangements to a hyperinflammatory with multiorgan dysfunction subphenotypes. Multivariable regression analyses found differences in risk for mortality, intubation, ICU admission and LOS, as compared to the hypoinflammatory subphenotype. For example, in multivariable analyses the moderate inflammation with fever subphenotype had 3.29 times the risk of mortality (95% CI 2.05, 5.28), while the hyperinflammatory with multiorgan failure subphenotype had 17.87 times the risk of mortality (95% CI 11.56, 27.63), as compared to the hypoinflammatory subphenotype. Exploratory analyses suggested that subphenotypes may differential respond to convalescent plasma or tocilizumab therapy. CONCLUSION: COVID-19 subphenotype at hospital admission may predict risk for mortality, ICU admission and intubation and differential response to treatment.KEY MESSAGEThis cross-sectional study of COVID patients admitted to the Mount Sinai Health System, identified six distinct COVID subphenotypes on admission. Subphenotypes correlated with ICU admission, intubation, mortality and differential response to treatment.

Statistical Tools for West Nile Virus Disease Analysis.

West Nile virus (WNV) is the most widespread arbovirus in the world and endemic to much of the United States. Its range continues to expand as land use patterns change, creating more habitable environments for the mosquito vector. Though WNV is endemic, the year-to-year risk is highly variable, thus making it difficult to understand the risk for human spillover events. Abatement districts monitor for infected mosquitoes to help understand these potential risks and to help guide our understanding of the risk posed by these observed infected mosquitoes. Creating optimal monitoring networks will provide more informed decision-making tools for abatement districts and policy makers. Investment in these monitoring networks that capture robust observations on mosquito infection rates will allow for environmentally informed inference systems to help guide decision-making and WNV risk. In turn, enhanced decision-making tools allow for faster response times of more targeted and economical surveillance and mosquito population reduction efforts and the overall reduction of WNV transmission. Here we discuss the data streams, their processing, and specifically three ways to calculate WNV infection rates in mosquitoes.

Changes in depressive and anxiety symptoms during COVID-19 in children from the PROGRESS cohort.

BACKGROUND: We assessed associations between maternal stress, social support, and child resiliency during the COVID-19 pandemic in relation to changes in anxiety and depression symptoms in children in Mexico City. METHODS: Participants included 464 mother-child pairs from a longitudinal birth cohort in Mexico City. At ages 8-11 (pre-COVID, 2018-2019) and 9-12 (during COVID, May-Nov 2020) years, depressive symptoms were assessed using the child and parent-reported Children's Depressive Inventory. Anxiety symptoms were assessed using the child-reported Revised Manifest Anxiety Scale. Linear regression models were used to estimate associations between maternal stress, social support, and resiliency in relation to changes in depressive and anxiety symptoms. We additionally assessed outcomes using clinically relevant cut-points. Models were adjusted for child age and sex and maternal socioeconomic status and age. RESULTS: Higher continuous maternal stress levels during the COVID-19 pandemic were associated with increases in depressive symptoms (ß: 0.72; 95% CI: 0.12, 1.31), and higher odds of clinically relevant depressive and anxiety symptoms in the children. CONCLUSIONS: Maternal stress during the pandemic may increase mental health symptoms in pre-adolescent children. Additional studies are needed that examine the long-term pandemic-related impacts on mental health throughout the adolescent years. IMPACT: In this longitudinal cohort study of children in Mexico City, we observed that depressive symptoms were higher from before to during the pandemic. Maternal stress surrounding the pandemic may increase mental health symptoms in pre-adolescent children. Child resiliency may help to protect against pandemic-related stressors.

SARS-COV-2 infections in inborn errors of immunity: A single center study.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a single-stranded RNA virus that causes coronavirus disease 2019 (COVID-19). One of the main topics of conversation in these past months in the world of immunology has been the issue of how patients with immune defects will fare if they contract this infection. To date there has been limited data on larger cohorts of patients with Inborn Errors of Immunity (IEI) diagnosed with COVID-19. Here, we review the data of COVID-19 infections in a single center cohort of 113 patients from the Mount Sinai Immunodeficiency program, who had 132 infections between January 2020 and June 2022. This included 56 males and 57 females, age range 2 - 84 (median 42). The mortality rate was 3%. Comparison between admitted patients revealed a significantly increased risk of hospitalization amongst the unvaccinated patients, 4% vaccinated vs 40% unvaccinated; odds ratio 15.0 (95% CI 4.2 - 53.4; p <0.00001). Additionally, COVID anti-spike antibody levels, determined in 36 of these patients post vaccination and before infection, were highly variable.

Long-Term Air Pollution Exposure and COVID-19 Mortality: A Patient-Level Analysis from New York City.

Rationale: Risk factors for coronavirus disease (COVID-19) mortality may include environmental exposures such as air pollution. Objectives: To determine whether, among adults hospitalized with PCR-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), long-term air pollution exposure is associated with the risk of mortality, ICU admission, or intubation. Methods: We performed a retrospective analysis of SARS-CoV-2 PCR-positive patients admitted to seven New York City hospitals from March 8, 2020, to August 30, 2020. The primary outcome was mortality; secondary outcomes were ICU admission and intubation. We estimated the annual average fine particulate matter (particulate matter ⩽2.5 µm in aerodynamic diameter [PM2.5]), nitrogen dioxide (NO2), and black carbon (BC) concentrations at patients' residential address. We employed double robust Poisson regression to analyze associations between the annual average PM2.5, NO2, and BC exposure level and COVID-19 outcomes, adjusting for age, sex, race or ethnicity, hospital, insurance, and the time from the onset of the pandemic. Results: Among the 6,542 patients, 41% were female and the median age was 65 (interquartile range, 53-77) years. Over 50% self-identified as a person of color (n = 1,687 [26%] Hispanic patients; n = 1,659 [25%] Black patients). Air pollution exposure levels were generally low. Overall, 31% (n = 2,044) of the cohort died, 19% (n = 1,237) were admitted to the ICU, and 16% (n = 1,051) were intubated. In multivariable models, a higher level of long-term exposure to PM2.5 was associated with an increased risk of mortality (risk ratio, 1.11 [95% confidence interval, 1.02-1.21] per 1-µg/m3 increase in PM2.5) and ICU admission (risk ratio, 1.13 [95% confidence interval, 1.00-1.28] per 1-µg/m3 increase in PM2.5). In multivariable models, neither NO2 nor BC exposure was associated with COVID-19 mortality, ICU admission, or intubation. Conclusions: Among patients hospitalized with COVID-19, a higher long-term PM2.5 exposure level was associated with an increased risk of mortality and ICU admission.

A proposed framework for the development and qualitative evaluation of West Nile virus models and their application to local public health decision-making.

West Nile virus (WNV) is a globally distributed mosquito-borne virus of great public health concern. The number of WNV human cases and mosquito infection patterns vary in space and time. Many statistical models have been developed to understand and predict WNV geographic and temporal dynamics. However, these modeling efforts have been disjointed with little model comparison and inconsistent validation. In this paper, we describe a framework to unify and standardize WNV modeling efforts nationwide. WNV risk, detection, or warning models for this review were solicited from active research groups working in different regions of the United States. A total of 13 models were selected and described. The spatial and temporal scales of each model were compared to guide the timing and the locations for mosquito and virus surveillance, to support mosquito vector control decisions, and to assist in conducting public health outreach campaigns at multiple scales of decision-making. Our overarching goal is to bridge the existing gap between model development, which is usually conducted as an academic exercise, and practical model applications, which occur at state, tribal, local, or territorial public health and mosquito control agency levels. The proposed model assessment and comparison framework helps clarify the value of individual models for decision-making and identifies the appropriate temporal and spatial scope of each model. This qualitative evaluation clearly identifies gaps in linking models to applied decisions and sets the stage for a quantitative comparison of models. Specifically, whereas many coarse-grained models (county resolution or greater) have been developed, the greatest need is for fine-grained, short-term planning models (m-km, days-weeks) that remain scarce. We further recommend quantifying the value of information for each decision to identify decisions that would benefit most from model input.

Short-term PM2.5 and cardiovascular admissions in NY State: assessing sensitivity to exposure model choice.

BACKGROUND: Air pollution health studies have been increasingly using prediction models for exposure assessment even in areas without monitoring stations. To date, most studies have assumed that a single exposure model is correct, but estimated effects may be sensitive to the choice of exposure model. METHODS: We obtained county-level daily cardiovascular (CVD) admissions from the New York (NY) Statewide Planning and Resources Cooperative System (SPARCS) and four sets of fine particulate matter (PM2.5) spatio-temporal predictions (2002-2012). We employed overdispersed Poisson models to investigate the relationship between daily PM2.5 and CVD, adjusting for potential confounders, separately for each state-wide PM2.5 dataset. RESULTS: For all PM2.5 datasets, we observed positive associations between PM2.5 and CVD. Across the modeled exposure estimates, effect estimates ranged from 0.23% (95%CI: -0.06, 0.53%) to 0.88% (95%CI: 0.68, 1.08%) per 10 µg/m3 increase in daily PM2.5. We observed the highest estimates using monitored concentrations 0.96% (95%CI: 0.62, 1.30%) for the subset of counties where these data were available. CONCLUSIONS: Effect estimates varied by a factor of almost four across methods to model exposures, likely due to varying degrees of exposure measurement error. Nonetheless, we observed a consistently harmful association between PM2.5 and CVD admissions, regardless of model choice.

Neighborhood-level disparities and subway utilization during the COVID-19 pandemic in New York City.

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. We investigate the association between neighborhood social disadvantage and the ability to socially distance, infections, and mortality in Spring 2020. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with weighted social factors associated with infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood disadvantage is also associated with a proxy of the capacity to socially isolate, NYC subway usage data. Finally, our index is associated with COVID-19-related mortality.

Containment of COVID-19: Simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation.

Efficient contact tracing and testing are fundamental tools to contain the transmission of SARS-CoV-2. We used multi-agent simulations to estimate the daily testing capacity required to find and isolate a number of infected agents sufficient to break the chain of transmission of SARS-CoV-2, so decreasing the risk of new waves of infections. Depending on the non-pharmaceutical mitigation policies in place, the size of secondary infection clusters allowed or the percentage of asymptomatic and paucisymptomatic (i.e., subclinical) infections, we estimated that the daily testing capacity required to contain the disease varies between 0.7 and 9.1 tests per thousand agents in the population. However, we also found that if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of new daily infections did not always decrease and could even increase exponentially, irrespective of the testing capacity. Under these conditions, we show that population-level information about geographical distribution and travel behaviour could inform sampling policies to aid a successful containment, while avoiding concerns about government-controlled mass surveillance.

Assessing capacity to social distance and neighborhood-level health disparities during the COVID-19 pandemic.

The COVID-19 pandemic has yielded disproportionate impacts on communities of color in New York City (NYC). Researchers have noted that social disadvantage may result in limited capacity to socially distance, and consequent disparities. Here, we investigate the role of neighborhood social disadvantage on the ability to socially distance, infections, and mortality. We combine Census Bureau and NYC open data with SARS-CoV-2 testing data using supervised dimensionality-reduction with Bayesian Weighted Quantile Sums regression. The result is a ZIP code-level index with relative weights for social factors facilitating infection risk. We find a positive association between neighborhood social disadvantage and infections, adjusting for the number of tests administered. Neighborhood infection risk is also associated with capacity to socially isolate, as measured by NYC subway data. Finally, infection risk is associated with COVID-19-related mortality. These analyses support that differences in capacity to socially isolate is a credible pathway between disadvantage and COVID-19 disparities.

Containment of future waves of COVID-19: simulating the impact of different policies and testing capacities for contact tracing, testing, and isolation.

We used multi-agent simulations to estimate the testing capacity required to find and isolate a number of infections sufficient to break the chain of transmission of SARS-CoV-2. Depending on the mitigation policies in place, a daily capacity between 0.7 to 3.6 tests per thousand was required to contain the disease. However, if contact tracing and testing efficacy dropped below 60% (e.g. due to false negatives or reduced tracing capability), the number of infections kept growing exponentially, irrespective of any testing capacity. Under these conditions, the population's geographical distribution and travel behaviour could inform sampling policies to aid a successful containment.

Nucleus accumbens volume as a predictor of anxiety symptom improvement following CBT and SSRI treatment in two independent samples.

Structural variations of neural regions implicated in fear responses have been well documented in the pathophysiology of anxiety and may play an important role in treatment response. We examined whether gray matter volume of three neural regions supporting fear and avoidance responses [bilateral amygdala, nucleus accumbens (NAcc), and ventromedial prefrontal cortex (PFC)] predicted cognitive-behavioral therapy (CBT) and selective serotonin reuptake inhibitor (SSRI) treatment outcome in two independent samples of patients with anxiety disorders. Study 1 consisted of 81 adults with anxiety disorders and Study 2 included 55 children and adolescents with anxiety disorders. In both studies, patients completed baseline structural MRI scans and received either CBT or SSRI treatment. Clinician-rated interviews of anxiety symptoms were assessed at baseline and posttreatment. Among the adult sample, greater pre-treatment bilateral NAcc volume was associated with a greater reduction in clinician-rated anxiety symptoms pre-to-post CBT and SSRI treatment. Greater left NAcc volume also predicted greater decreases in clinician-rated anxiety symptoms pre-to-post CBT and SSRI treatment among youth with current anxiety. Across studies, results were similar across treatments, and findings were maintained when adjusting for patient's age, sex, and total intracranial brain volume. We found no evidence for baseline amygdala or ventromedial PFC volume serving as treatment predictors across the two samples. Together, these findings provide promising support for the role of NAcc volume as an objective marker of anxiety treatment improvement that spans across development. Future studies should clarify the specific mechanisms through which NAcc volume exerts its therapeutic effects.