Short-Term Elevation of Fine Particulate Matter Air Pollution and Acute Lower Respiratory Infection.

RATIONALE: Nearly 60% of U.S. children live in counties with particulate matter less than or equal to 2.5 µm in aerodynamic diameter (PM2.5) concentrations above air quality standards. Understanding the relationship between ambient air pollution exposure and health outcomes informs actions to reduce exposure and disease risk. OBJECTIVES: To evaluate the association between ambient PM2.5 levels and healthcare encounters for acute lower respiratory infection (ALRI). METHODS: Using an observational case-crossover design, subjects (n = 146,397) were studied if they had an ALRI diagnosis and resided on Utah's Wasatch Front. PM2.5 air pollution concentrations were measured using community-based air quality monitors between 1999 and 2016. Odds ratios for ALRI healthcare encounters were calculated after stratification by ages 0-2, 3-17, and 18 or more years. MEASUREMENTS AND MAIN RESULTS: Approximately 77% (n = 112,467) of subjects were 0-2 years of age. The odds of ALRI encounter for these young children increased within 1 week of elevated PM2.5 and peaked after 3 weeks with a cumulative 28-day odds ratio of 1.15 per +10 µg/m3 (95% confidence interval, 1.12-1.19). ALRI encounters with diagnosed and laboratory-confirmed respiratory syncytial virus and influenza increased following elevated ambient PM2.5 levels. Similar elevated odds for ALRI were also observed for older children, although the number of events and precision of estimates were much lower. CONCLUSIONS: In this large sample of urban/suburban patients, short-term exposure to elevated PM2.5 air pollution was associated with greater healthcare use for ALRI in young children, older children, and adults. Further exploration is needed of causal interactions between PM2.5 and ALRI.

Cross-immunity between strains explains the dynamical pattern of paramyxoviruses.

Viral respiratory tract diseases pose serious public health problems. Our ability to predict and thus, be able to prepare for outbreaks is strained by the complex factors driving the prevalence and severity of these diseases. The abundance of diseases and transmission dynamics of strains are not only affected by external factors, such as weather, but also driven by interactions among viruses mediated by human behavior and immunity. To untangle the complex out-of-phase annual and biennial pattern of three common paramyxoviruses, Respiratory Syncytial Virus (RSV), Human Parainfluenza Virus (HPIV), and Human Metapneumovirus (hMPV), we adopt a theoretical approach that integrates ecological and immunological mechanisms of disease interactions. By estimating parameters from multiyear time series of laboratory-confirmed cases from the intermountain west region of the United States and using statistical inference, we show that models of immune-mediated interactions better explain the data than those based on ecological competition by convalescence. The strength of cross-protective immunity among viruses is correlated with their genetic distance in the phylogenetic tree of the paramyxovirus family.

A Bayesian system to detect and characterize overlapping outbreaks.

Outbreaks of infectious diseases such as influenza are a significant threat to human health. Because there are different strains of influenza which can cause independent outbreaks, and influenza can affect demographic groups at different rates and times, there is a need to recognize and characterize multiple outbreaks of influenza. This paper describes a Bayesian system that uses data from emergency department patient care reports to create epidemiological models of overlapping outbreaks of influenza. Clinical findings are extracted from patient care reports using natural language processing. These findings are analyzed by a case detection system to create disease likelihoods that are passed to a multiple outbreak detection system. We evaluated the system using real and simulated outbreaks. The results show that this approach can recognize and characterize overlapping outbreaks of influenza. We describe several extensions that appear promising.

Predicting asthma control deterioration in children.

BACKGROUND: Pediatric asthma affects 7.1 million American children incurring an annual total direct healthcare cost around 9.3 billion dollars. Asthma control in children is suboptimal, leading to frequent asthma exacerbations, excess costs, and decreased quality of life. Successful prediction of risk for asthma control deterioration at the individual patient level would enhance self-management and enable early interventions to reduce asthma exacerbations. We developed and tested the first set of models for predicting a child's asthma control deterioration one week prior to occurrence. METHODS: We previously reported validation of the Asthma Symptom Tracker, a weekly asthma self-monitoring tool. Over a period of two years, we used this tool to collect a total of 2912 weekly assessments of asthma control on 210 children. We combined the asthma control data set with patient attributes and environmental variables to develop machine learning models to predict a child's asthma control deterioration one week ahead. RESULTS: Our best model achieved an accuracy of 71.8 %, a sensitivity of 73.8 %, a specificity of 71.4 %, and an area under the receiver operating characteristic curve of 0.757. We also identified potential improvements to our models to stimulate future research on this topic. CONCLUSIONS: Our best model successfully predicted a child's asthma control level one week ahead. With adequate accuracy, the model could be integrated into electronic asthma self-monitoring systems to provide real-time decision support and personalized early warnings of potential asthma control deteriorations.

Association Between Procalcitonin and Antibiotics in Children With Community-Acquired Pneumonia.

OBJECTIVE: To determine whether empirical antibiotic initiation and selection for children with pneumonia was associated with procalcitonin (PCT) levels when results were blinded to clinicians. METHODS: We enrolled children <18 years with radiographically confirmed pneumonia at 2 children's hospitals from 2014 to 2019. Blood for PCT was collected at enrollment (blinded to clinicians). We modeled associations between PCT and (1) antibiotic initiation and (2) antibiotic selection (narrow versus broad-spectrum) using multivariable logistic regression models. To quantify potential stewardship opportunities, we calculated proportions of noncritically ill children receiving antibiotics who also had a low likelihood of bacterial etiology (PCT <0.25 ng/mL) and those receiving broad-spectrum therapy, regardless of PCT level. RESULTS: We enrolled 488 children (median PCT, 0.37 ng/mL; interquartile range [IQR], 0.11-2.38); 85 (17%) received no antibiotics (median PCT, 0.32; IQR, 0.09-1.33). Among the 403 children receiving antibiotics, 95 (24%) received narrow-spectrum therapy (median PCT, 0.24; IQR, 0.08-2.52) and 308 (76%) received broad-spectrum (median PCT, 0.46; IQR, 0.12-2.83). In adjusted analyses, PCT values were not associated with antibiotic initiation (odds ratio [OR], 1.02, 95% confidence interval [CI], 0.97%-1.06%) or empirical antibiotic selection (OR 1.07; 95% CI, 0.97%-1.17%). Of those with noncritical illness, 246 (69%) were identified as potential targets for antibiotic stewardship interventions. CONCLUSION: Neither antibiotic initiation nor empirical antibiotic selection were associated with PCT values. Whereas other factors may inform antibiotic treatment decisions, the observed discordance between objective likelihood of bacterial etiology and antibiotic use suggests important opportunities for stewardship.

Predicting the start week of respiratory syncytial virus outbreaks using real time weather variables.

BACKGROUND: Respiratory Syncytial Virus (RSV), a major cause of bronchiolitis, has a large impact on the census of pediatric hospitals during outbreak seasons. Reliable prediction of the week these outbreaks will start, based on readily available data, could help pediatric hospitals better prepare for large outbreaks. METHODS: Naïve Bayes (NB) classifier models were constructed using weather data from 1985-2008 considering only variables that are available in real time and that could be used to forecast the week in which an RSV outbreak will occur in Salt Lake County, Utah. Outbreak start dates were determined by a panel of experts using 32,509 records with ICD-9 coded RSV and bronchiolitis diagnoses from Intermountain Healthcare hospitals and clinics for the RSV seasons from 1985 to 2008. RESULTS: NB models predicted RSV outbreaks up to 3 weeks in advance with an estimated sensitivity of up to 67% and estimated specificities as high as 94% to 100%. Temperature and wind speed were the best overall predictors, but other weather variables also showed relevance depending on how far in advance the predictions were made. The weather conditions predictive of an RSV outbreak in our study were similar to those that lead to temperature inversions in the Salt Lake Valley. CONCLUSIONS: We demonstrate that Naïve Bayes (NB) classifier models based on weather data available in real time have the potential to be used as effective predictive models. These models may be able to predict the week that an RSV outbreak will occur with clinical relevance. Their clinical usefulness will be field tested during the next five years.

A Bayesian approach for detecting a disease that is not being modeled.

Over the past decade, outbreaks of new or reemergent viruses such as severe acute respiratory syndrome (SARS) virus, Middle East respiratory syndrome (MERS) virus, and Zika have claimed thousands of lives and cost governments and healthcare systems billions of dollars. Because the appearance of new or transformed diseases is likely to continue, the detection and characterization of emergent diseases is an important problem. We describe a Bayesian statistical model that can detect and characterize previously unknown and unmodeled diseases from patient-care reports and evaluate its performance on historical data.

Should the pertussis case definition for public health reporting be refined?

BACKGROUND: The surveillance case definition for confirmed pertussis requires that an individual with a positive polymerase chain reaction (PCR) result for Bordetella pertussis have 2 weeks or more of cough and at least one of the following: paroxysmal coughing, inspiratory "whoop," or posttussive vomiting. OBJECTIVES: Determine (1) proportion of individuals with a positive PCR result who met additional criteria for surveillance confirmed pertussis, (2) whether the likelihood of PCR-positive individuals meeting additional elements of surveillance case definition varied by age or vaccination status, and (3) whether elements of the current case definition influence the likelihood of pertussis confirmation in PCR-positive individuals. METHODS: Pertussis PCR results were compared with case investigation data. RESULTS: Eighty-eight percent (165/188) of PCR-positive individuals met requirements for confirmed pertussis. Sixty-one percent (14/23) of PCR-positive individuals who had less than 2 weeks but more than 1 week of cough had at least one other reported sign or symptom. Fourteen (100%) reported paroxysmal coughing, 7 (50%) "whoop," and 7 (50%) posttussive vomiting. Infants who met case definition were more likely to have reported apnea than were older individuals (15/17 vs 45/86, OR = 6.8, 95% CI = 1.4-64.2). CONCLUSIONS: Decreasing cough duration from 2 weeks or more to more than 1 week would result in 95 percent of those with positive PCR results meeting confirmation criteria for pertussis. Apnea should be considered an additional sign for pertussis confirmation in infants.

Urgent care providers' knowledge and attitude about public health reporting and pertussis control measures: implications for informatics.

OBJECTIVES: We assessed urgent care providers' knowledge about public health reporting, guidelines, and actions for the prevention and control of pertussis; attitudes about public health reporting and population-based data; and perception of reporting practices in their clinic. METHODS: We identified the 106 providers (95% are physicians) employed in 28 urgent care clinics owned by Intermountain Healthcare located throughout Utah and Southern Idaho. We performed a descriptive, cross-sectional survey and assessed providers' knowledge, attitudes, beliefs, and behaviors associated with population-based data and public health mandates and recommendations. The online survey was completed between November 1, 2007, and February 29, 2008. RESULTS: Among 63 practicing urgent care providers (60% response rate), 19 percent knew that clinically diagnosed pertussis was reportable, and only half (52%) the providers correctly responded about current pertussis vaccination recommendations. Most (35%-78%) providers did not know the prevention and control measures performed by public health practitioners after reporting occurs, including contact tracing, testing, treatment, and prophylaxis. Half (48%) the providers did not know that health department personnel can prescribe antibiotics for contacts of a reported case, and only 22 percent knew that health department personnel may perform diagnostic testing on contacts. Attitudes about reporting are variable, and reporting responsibility is diffused. CONCLUSION: To improve our ability to meet public health goals, systems need to be designed that engage urgent care providers in the public health process, improve their knowledge and attitude about reporting, and facilitate the flow of information between urgent care and public health settings.

The design and evaluation of a Bayesian system for detecting and characterizing outbreaks of influenza.

The prediction and characterization of outbreaks of infectious diseases such as influenza remains an open and important problem. This paper describes a framework for detecting and characterizing outbreaks of influenza and the results of testing it on data from ten outbreaks collected from two locations over five years. We model outbreaks with compartment models and explicitly model non-influenza influenza-like illnesses.

Automated Real-Time Collection of Pathogen-Specific Diagnostic Data: Syndromic Infectious Disease Epidemiology.

BACKGROUND: Health care and public health professionals rely on accurate, real-time monitoring of infectious diseases for outbreak preparedness and response. Early detection of outbreaks is improved by systems that are comprehensive and specific with respect to the pathogen but are rapid in reporting the data. It has proven difficult to implement these requirements on a large scale while maintaining patient privacy. OBJECTIVE: The aim of this study was to demonstrate the automated export, aggregation, and analysis of infectious disease diagnostic test results from clinical laboratories across the United States in a manner that protects patient confidentiality. We hypothesized that such a system could aid in monitoring the seasonal occurrence of respiratory pathogens and may have advantages with regard to scope and ease of reporting compared with existing surveillance systems. METHODS: We describe a system, BioFire Syndromic Trends, for rapid disease reporting that is syndrome-based but pathogen-specific. Deidentified patient test results from the BioFire FilmArray multiplex molecular diagnostic system are sent directly to a cloud database. Summaries of these data are displayed in near real time on the Syndromic Trends public website. We studied this dataset for the prevalence, seasonality, and coinfections of the 20 respiratory pathogens detected in over 362,000 patient samples acquired as a standard-of-care testing over the last 4 years from 20 clinical laboratories in the United States. RESULTS: The majority of pathogens show influenza-like seasonality, rhinovirus has fall and spring peaks, and adenovirus and the bacterial pathogens show constant detection over the year. The dataset can also be considered in an ecological framework; the viruses and bacteria detected by this test are parasites of a host (the human patient). Interestingly, the rate of pathogen codetections, on average 7.94% (28,741/362,101), matches predictions based on the relative abundance of organisms present. CONCLUSIONS: Syndromic Trends preserves patient privacy by removing or obfuscating patient identifiers while still collecting much useful information about the bacterial and viral pathogens that they harbor. Test results are uploaded to the database within a few hours of completion compared with delays of up to 10 days for other diagnostic-based reporting systems. This work shows that the barriers to establishing epidemiology systems are no longer scientific and technical but rather administrative, involving questions of patient privacy and data ownership. We have demonstrated here that these barriers can be overcome. This first look at the resulting data stream suggests that Syndromic Trends will be able to provide high-resolution analysis of circulating respiratory pathogens and may aid in the detection of new outbreaks.

The effects of natural language processing on cross-institutional portability of influenza case detection for disease surveillance.

OBJECTIVES: This study evaluates the accuracy and portability of a natural language processing (NLP) tool for extracting clinical findings of influenza from clinical notes across two large healthcare systems. Effectiveness is evaluated on how well NLP supports downstream influenza case-detection for disease surveillance. METHODS: We independently developed two NLP parsers, one at Intermountain Healthcare (IH) in Utah and the other at University of Pittsburgh Medical Center (UPMC) using local clinical notes from emergency department (ED) encounters of influenza. We measured NLP parser performance for the presence and absence of 70 clinical findings indicative of influenza. We then developed Bayesian network models from NLP processed reports and tested their ability to discriminate among cases of (1) influenza, (2) non-influenza influenza-like illness (NI-ILI), and (3) 'other' diagnosis. RESULTS: On Intermountain Healthcare reports, recall and precision of the IH NLP parser were 0.71 and 0.75, respectively, and UPMC NLP parser, 0.67 and 0.79. On University of Pittsburgh Medical Center reports, recall and precision of the UPMC NLP parser were 0.73 and 0.80, respectively, and IH NLP parser, 0.53 and 0.80. Bayesian case-detection performance measured by AUROC for influenza versus non-influenza on Intermountain Healthcare cases was 0.93 (using IH NLP parser) and 0.93 (using UPMC NLP parser). Case-detection on University of Pittsburgh Medical Center cases was 0.95 (using UPMC NLP parser) and 0.83 (using IH NLP parser). For influenza versus NI-ILI on Intermountain Healthcare cases performance was 0.70 (using IH NLP parser) and 0.76 (using UPMC NLP parser). On University of Pisstburgh Medical Center cases, 0.76 (using UPMC NLP parser) and 0.65 (using IH NLP parser). CONCLUSION: In all but one instance (influenza versus NI-ILI using IH cases), local parsers were more effective at supporting case-detection although performances of non-local parsers were reasonable.

A study of the transferability of influenza case detection systems between two large healthcare systems.

OBJECTIVES: This study evaluates the accuracy and transferability of Bayesian case detection systems (BCD) that use clinical notes from emergency department (ED) to detect influenza cases. METHODS: A BCD uses natural language processing (NLP) to infer the presence or absence of clinical findings from ED notes, which are fed into a Bayesain network classifier (BN) to infer patients' diagnoses. We developed BCDs at the University of Pittsburgh Medical Center (BCDUPMC) and Intermountain Healthcare in Utah (BCDIH). At each site, we manually built a rule-based NLP and trained a Bayesain network classifier from over 40,000 ED encounters between Jan. 2008 and May. 2010 using feature selection, machine learning, and expert debiasing approach. Transferability of a BCD in this study may be impacted by seven factors: development (source) institution, development parser, application (target) institution, application parser, NLP transfer, BN transfer, and classification task. We employed an ANOVA analysis to study their impacts on BCD performance. RESULTS: Both BCDs discriminated well between influenza and non-influenza on local test cases (AUCs > 0.92). When tested for transferability using the other institution's cases, BCDUPMC discriminations declined minimally (AUC decreased from 0.95 to 0.94, p<0.01), and BCDIH discriminations declined more (from 0.93 to 0.87, p<0.0001). We attributed the BCDIH decline to the lower recall of the IH parser on UPMC notes. The ANOVA analysis showed five significant factors: development parser, application institution, application parser, BN transfer, and classification task. CONCLUSION: We demonstrated high influenza case detection performance in two large healthcare systems in two geographically separated regions, providing evidentiary support for the use of automated case detection from routinely collected electronic clinical notes in national influenza surveillance. The transferability could be improved by training Bayesian network classifier locally and increasing the accuracy of the NLP parser.

Detection of pediatric respiratory and diarrheal outbreaks from sales of over-the-counter electrolyte products.

OBJECTIVE: To determine whether sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal disease in children and, if so, how much earlier a signal relative to hospital diagnoses. DESIGN: Retrospective analysis was conducted of sales of electrolyte products and hospital diagnoses for six urban regions in three states for the period 1998 through 2001. MEASUREMENTS: Presence of signal was ascertained by measuring correlation between electrolyte sales and hospital diagnoses and the temporal relationship that maximized correlation. Earliness was the difference between the date that the exponentially weighted moving average (EWMA) method first detected an outbreak from sales and the date it first detected the outbreak from diagnoses. The coefficient of determination (r2) measured how much variance in earliness resulted from differences in sales' and diagnoses' signal strengths. RESULTS: The correlation between electrolyte sales and hospital diagnoses was 0.90 (95% CI, 0.87-0.93) at a time offset of 1.7 weeks (95% CI, 0.50-2.9), meaning that sales preceded diagnoses by 1.7 weeks. EWMA with a nine-sigma threshold detected the 18 outbreaks on average 2.4 weeks (95% CI, 0.1-4.8 weeks) earlier from sales than from diagnoses. Twelve outbreaks were first detected from sales, four were first detected from diagnoses, and two were detected simultaneously. Only 26% of variance in earliness was explained by the relative strength of the sales and diagnoses signals (r2 = 0.26). CONCLUSION: Sales of electrolyte products contain a signal of outbreaks of respiratory and diarrheal diseases in children and usually are an earlier signal than hospital diagnoses.

Technical description of RODS: a real-time public health surveillance system.

This report describes the design and implementation of the Real-time Outbreak and Disease Surveillance (RODS) system, a computer-based public health surveillance system for early detection of disease outbreaks. Hospitals send RODS data from clinical encounters over virtual private networks and leased lines using the Health Level 7 (HL7) message protocol. The data are sent in real time. RODS automatically classifies the registration chief complaint from the visit into one of seven syndrome categories using Bayesian classifiers. It stores the data in a relational database, aggregates the data for analysis using data warehousing techniques, applies univariate and multivariate statistical detection algorithms to the data, and alerts users of when the algorithms identify anomalous patterns in the syndrome counts. RODS also has a Web-based user interface that supports temporal and spatial analyses. RODS processes sales of over-the-counter health care products in a similar manner but receives such data in batch mode on a daily basis. RODS was used during the 2002 Winter Olympics and currently operates in two states-Pennsylvania and Utah. It has been and continues to be a resource for implementing, evaluating, and applying new methods of public health surveillance.

Automated syndromic surveillance for the 2002 Winter Olympics.

The 2002 Olympic Winter Games were held in Utah from February 8 to March 16, 2002. Following the terrorist attacks on September 11, 2001, and the anthrax release in October 2001, the need for bioterrorism surveillance during the Games was paramount. A team of informaticists and public health specialists from Utah and Pittsburgh implemented the Real-time Outbreak and Disease Surveillance (RODS) system in Utah for the Games in just seven weeks. The strategies and challenges of implementing such a system in such a short time are discussed. The motivation and cooperation inspired by the 2002 Olympic Winter Games were a powerful driver in overcoming the organizational issues. Over 114,000 acute care encounters were monitored between February 8 and March 31, 2002. No outbreaks of public health significance were detected. The system was implemented successfully and operational for the 2002 Olympic Winter Games and remains operational today.

Implementing syndromic surveillance: a practical guide informed by the early experience.

Syndromic surveillance refers to methods relying on detection of individual and population health indicators that are discernible before confirmed diagnoses are made. In particular, prior to the laboratory confirmation of an infectious disease, ill persons may exhibit behavioral patterns, symptoms, signs, or laboratory findings that can be tracked through a variety of data sources. Syndromic surveillance systems are being developed locally, regionally, and nationally. The efforts have been largely directed at facilitating the early detection of a covert bioterrorist attack, but the technology may also be useful for general public health, clinical medicine, quality improvement, patient safety, and research. This report, authored by developers and methodologists involved in the design and deployment of the first wave of syndromic surveillance systems, is intended to serve as a guide for informaticians, public health managers, and practitioners who are currently planning deployment of such systems in their regions.

A systematic review of predictive modeling for bronchiolitis.

PURPOSE: Bronchiolitis is the most common cause of illness leading to hospitalization in young children. At present, many bronchiolitis management decisions are made subjectively, leading to significant practice variation among hospitals and physicians caring for children with bronchiolitis. To standardize care for bronchiolitis, researchers have proposed various models to predict the disease course to help determine a proper management plan. This paper reviews the existing state of the art of predictive modeling for bronchiolitis. Predictive modeling for respiratory syncytial virus (RSV) infection is covered whenever appropriate, as RSV accounts for about 70% of bronchiolitis cases. METHODS: A systematic review was conducted through a PubMed search up to April 25, 2014. The literature on predictive modeling for bronchiolitis was retrieved using a comprehensive search query, which was developed through an iterative process. Search results were limited to human subjects, the English language, and children (birth to 18 years). RESULTS: The literature search returned 2312 references in total. After manual review, 168 of these references were determined to be relevant and are discussed in this paper. We identify several limitations and open problems in predictive modeling for bronchiolitis, and provide some preliminary thoughts on how to address them, with the hope to stimulate future research in this domain. CONCLUSIONS: Many problems remain open in predictive modeling for bronchiolitis. Future studies will need to address them to achieve optimal predictive models.

Rotavirus gastroenteritis and seizures in young children.

In this retrospective cohort study, a clinical and administrative database of children hospitalized at Primary Children's Medical Center, Salt Lake City, Utah, between January 1, 2002, and December 31, 2006, was used to identify those with laboratory-confirmed rotavirus infections and at least one seizure. In all, 59 children were identified, 34 of whom (58%) had no other potential medical explanation for their seizures. Of these 34 children, 23 (68%) were afebrile at seizure onset and 11 were febrile. Electroencephalography was performed for 21 of the 34 children (62%); all findings were normal, except for a child with slowing related to cerebral edema. Twenty-six of the 34 children (76%) had neuroimaging studies; all findings were normal, except for the child with cerebral edema and a child with an incidental arachnoid cyst. Twenty of the 34 children (59%) had a lumbar puncture; again, all findings were normal. All 34 children recovered uneventfully, including the 6 children who spent at least 1 day in an intensive care unit. Follow-up data on 27 of these children identified 2 children (7%) who required chronic anticonvulsant therapy. The results indicate that seizures associated with rotavirus infection are a relatively benign neurologic condition in young children. With few exceptions, neurodiagnostic studies do not influence management or outcome.

Feasibility of elementary school children's use of hand gel and facemasks during influenza season.

BACKGROUND: The feasibility of non-pharmacologic interventions to prevent influenza's spread in schools is not well known. OBJECTIVES: To determine the acceptability of, adherence with, and barriers to the use of hand gel and facemasks in elementary schools. INTERVENTION: We provided hand gel and facemasks to 20 teachers and their students over 4 weeks. Gel use was promoted for the first 2 weeks; mask use was promoted for the second 2 weeks. OUTCOMES: Acceptability, adherence, and barriers were measured by teachers' responses on weekly surveys. Mask use was also measured by observation. RESULTS: The weekly survey response rate ranged from 70% to 100%. Averaged over 2 weeks, 89% of teachers thought gel use was not disruptive (week 1--17/20, week 2--16/17), 95% would use gel next winter (week 1--19/20, week 2--16/17), and 97% would use gel in a pandemic (week 1--20/20, week 2--16/17). Averaged over 2 weeks, 39% thought mask use was not disruptive (week 1--6/17, week 2--6/14), 35% would use masks next winter (week 1--5/17, week 2--6/14), and 97% would use masks in a pandemic (week 1--16/17, week 2--14/14). About 70% estimated that their students used hand gel ≥ 4 x/day for both weeks (week 1--14/20, week 2--13/17). Students' mask use declined over time with 59% of teachers (10/17) estimating regular mask use during week 1 and 29% (4/14) during week 2. By observation, 30% of students wore masks in week 1, while 15% wore masks in week 2. Few barriers to gel use were identified; barriers to mask use were difficulty reading facial expressions and physical discomfort. CONCLUSIONS: Hand gel use is a feasible strategy in elementary schools. Acceptability and adherence with facemasks was low, but some students and teachers did use facemasks for 2 weeks, and most teachers would use masks in their classroom in a pandemic.