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.

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.

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.

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.

Modeling and Surveillance of Reporting Delays of Mosquitoes and Humans Infected With West Nile Virus and Associations With Accuracy of West Nile Virus Forecasts.

Importance: West Nile virus (WNV) is the leading cause of domestically acquired arboviral disease. Objective: To develop real-time WNV forecasts of infected mosquitoes and human cases. Design, Setting, and Participants: Real-time forecasts of WNV in 4 geographically dispersed locations in the United States were generated using a WNV model-inference forecasting system previously validated with retrospective data. Analysis was performed to evaluate how observational reporting delays of mosquito WNV assay results and human medical records were associated with real-time forecast accuracy. Exposures: Mosquitoes positive for WNV and human cases. Main Outcomes and Measures: Delays in reporting mosquito WNV assay results and human medical records and the association of these delays with real-time WNV forecast accuracy. Results: Substantial delays in data reporting exist for both infected mosquitoes and human WNV cases. For human cases, confirmed data (n = 37) lagged behind the onset of illness by a mean (SD) of 5.5 (2.3) weeks (range, 2-14 weeks). These human case reporting lags reduced mean forecast accuracy for the total number of human cases over the season in 110 simulated outbreaks for 2 forecasting systems by 26% and 14%, from 2 weeks before to 3 weeks after the predicted peak of infected mosquitoes. This period is the time span during which 47% of human cases are reported. Of 7064 mosquito pools, 500 (7%) tested positive; the reporting lag for these data associated with viral testing at a state laboratory was a mean (SD) of 6.6 (2.6) days (range, 4-11 days). This reporting lag was associated with decreased mean forecast accuracy for the 3 mosquito infection indicators, timing, magnitude, and season, by approximately 5% for both forecasting systems. Conclusions and Relevance: Delays in reporting human WNV disease and infected mosquito information are associated with difficulties in outbreak surveillance and decreased real-time forecast accuracy. Infected mosquito lags were short enough that skillful forecasts could still be generated for mosquito infection indicators, but the human WNV case lags were too great to support accurate forecasting in real time. Forecasting WNV is potentially an important evidence-based decision support tool for public health officials and mosquito abatement districts; however, to operationalize real-time forecasting, more resources are needed to reduce human case reporting lags between illness onset and case confirmation.

Use of temperature to improve West Nile virus forecasts.

Ecological and laboratory studies have demonstrated that temperature modulates West Nile virus (WNV) transmission dynamics and spillover infection to humans. Here we explore whether inclusion of temperature forcing in a model depicting WNV transmission improves WNV forecast accuracy relative to a baseline model depicting WNV transmission without temperature forcing. Both models are optimized using a data assimilation method and two observed data streams: mosquito infection rates and reported human WNV cases. Each coupled model-inference framework is then used to generate retrospective ensemble forecasts of WNV for 110 outbreak years from among 12 geographically diverse United States counties. The temperature-forced model improves forecast accuracy for much of the outbreak season. From the end of July until the beginning of October, a timespan during which 70% of human cases are reported, the temperature-forced model generated forecasts of the total number of human cases over the next 3 weeks, total number of human cases over the season, the week with the highest percentage of infectious mosquitoes, and the peak percentage of infectious mosquitoes that on average increased absolute forecast accuracy 5%, 10%, 12%, and 6%, respectively, over the non-temperature forced baseline model. These results indicate that use of temperature forcing improves WNV forecast accuracy and provide further evidence that temperature influences rates of WNV transmission. The findings provide a foundation for implementation of a statistically rigorous system for real-time forecast of seasonal WNV outbreaks and their use as a quantitative decision support tool for public health officials and mosquito control programs.

Ensemble forecast of human West Nile virus cases and mosquito infection rates.

West Nile virus (WNV) is now endemic in the continental United States; however, our ability to predict spillover transmission risk and human WNV cases remains limited. Here we develop a model depicting WNV transmission dynamics, which we optimize using a data assimilation method and two observed data streams, mosquito infection rates and reported human WNV cases. The coupled model-inference framework is then used to generate retrospective ensemble forecasts of historical WNV outbreaks in Long Island, New York for 2001-2014. Accurate forecasts of mosquito infection rates are generated before peak infection, and >65% of forecasts accurately predict seasonal total human WNV cases up to 9 weeks before the past reported case. This work provides the foundation for implementation of a statistically rigorous system for real-time forecast of seasonal outbreaks of WNV.

Reducing Emergency Department Visits for Acute Gastrointestinal Illnesses in North Carolina (USA) by Extending Community Water Service.

BACKGROUND: Previous analyses have suggested that unregulated private drinking water wells carry a higher risk of exposure to microbial contamination than regulated community water systems. In North Carolina, ~35% of the state's population relies on private wells, but the health impact associated with widespread reliance on such unregulated drinking water sources is unknown. OBJECTIVES: We estimated the total number of emergency department visits for acute gastrointestinal illness (AGI) attributable to microbial contamination in private wells in North Carolina per year, the costs of those visits, and the potential health benefits of extending regulated water service to households currently relying on private wells for their drinking water. METHODS: We developed a population intervention model using 2007-2013 data from all 122 North Carolina emergency departments along with microbial contamination data for all 2,120 community water systems and for 16,138 private well water samples collected since 2008. RESULTS: An estimated 29,400 (95% CI: 26,600, 32,200) emergency department visits per year for acute gastrointestinal illness were attributable to microbial contamination in drinking water, constituting approximately 7.3% (95% CI: 6.6, 7.9%) of all AGI-related visits. Of these attributable cases, 99% (29,200; 95% CI: 26,500, 31,900) were associated with private well contamination. The estimated statewide annual cost of emergency department visits attributable to microbiological contamination of drinking water is 40.2 million USD (95% CI: 2.58 million USD, 193 million USD), of which 39.9 million USD (95% CI: 2.56 million USD, 192 million USD) is estimated to arise from private well contamination. An estimated 2,920 (95% CI: 2,650, 3,190) annual emergency department visits could be prevented by extending community water service to 10% of the population currently relying on private wells. CONCLUSIONS: This research provides new evidence that extending regulated community water service to populations currently relying on private wells may decrease the population burden of acute gastrointestinal illness. CITATION: DeFelice NB, Johnston JE, Gibson JM. 2016. Reducing emergency department visits for acute gastrointestinal illnesses in North Carolina (USA) by extending community water service. Environ Health Perspect 124:1583-1591; http://dx.doi.org/10.1289/EHP160.

Acute Gastrointestinal Illness Risks in North Carolina Community Water Systems: A Methodological Comparison.

The magnitude and spatial variability of acute gastrointestinal illness (AGI) cases attributable to microbial contamination of U.S. community drinking water systems are not well characterized. We compared three approaches (drinking water attributable risk, quantitative microbial risk assessment, and population intervention model) to estimate the annual number of emergency department visits for AGI attributable to microorganisms in North Carolina community water systems. All three methods used 2007-2013 water monitoring and emergency department data obtained from state agencies. The drinking water attributable risk method, which was the basis for previous U.S. Environmental Protection Agency national risk assessments, estimated that 7.9% of annual emergency department visits for AGI are attributable to microbial contamination of community water systems. However, the other methods' estimates were more than 2 orders of magnitude lower, each attributing 0.047% of annual emergency department visits for AGI to community water system contamination. The differences in results between the drinking water attributable risk method, which has been the main basis for previous national risk estimates, and the other two approaches highlight the need to improve methods for estimating endemic waterborne disease risks, in order to prioritize investments to improve community drinking water systems.