COVID-19 Outbreak - New York City, February 29-June 1, 2020.

New York City (NYC) was an epicenter of the coronavirus disease 2019 (COVID-19) outbreak in the United States during spring 2020 (1). During March-May 2020, approximately 203,000 laboratory-confirmed COVID-19 cases were reported to the NYC Department of Health and Mental Hygiene (DOHMH). To obtain more complete data, DOHMH used supplementary information sources and relied on direct data importation and matching of patient identifiers for data on hospitalization status, the occurrence of death, race/ethnicity, and presence of underlying medical conditions. The highest rates of cases, hospitalizations, and deaths were concentrated in communities of color, high-poverty areas, and among persons aged ≥75 years or with underlying conditions. The crude fatality rate was 9.2% overall and 32.1% among hospitalized patients. Using these data to prevent additional infections among NYC residents during subsequent waves of the pandemic, particularly among those at highest risk for hospitalization and death, is critical. Mitigating COVID-19 transmission among vulnerable groups at high risk for hospitalization and death is an urgent priority. Similar to NYC, other jurisdictions might find the use of supplementary information sources valuable in their efforts to prevent COVID-19 infections.

Characteristics of Persons Who Died with COVID-19 - United States, February 12-May 18, 2020.

During January 1, 2020-May 18, 2020, approximately 1.3 million cases of coronavirus disease 2019 (COVID-19) and 83,000 COVID-19-associated deaths were reported in the United States (1). Understanding the demographic and clinical characteristics of decedents could inform medical and public health interventions focused on preventing COVID-19-associated mortality. This report describes decedents with laboratory-confirmed infection with SARS-CoV-2, the virus that causes COVID-19, using data from 1) the standardized CDC case-report form (case-based surveillance) (https://www.cdc.gov/coronavirus/2019-ncov/php/reporting-pui.html) and 2) supplementary data (supplemental surveillance), such as underlying medical conditions and location of death, obtained through collaboration between CDC and 16 public health jurisdictions (15 states and New York City).

Description of a school nurse visit syndromic surveillance system and comparison to emergency department visits, New York City.

OBJECTIVES: We compared school nurse visit syndromic surveillance system data to emergency department (ED) visit data for monitoring illness in New York City schoolchildren. METHODS: School nurse visit data recorded in an electronic health record system are used to conduct daily surveillance of influenza-like illness, fever-flu, allergy, asthma, diarrhea, and vomiting syndromes. We calculated correlation coefficients to compare the percentage of syndrome visits to the school nurse and ED for children aged 5 to 14 years, from September 2006 to June 2011. RESULTS: Trends in influenza-like illness correlated significantly (correlation coefficient = 0.89; P < .001) and 72% of school signals occurred on days that ED signaled. Trends in allergy (correlation coefficient = 0.73; P < .001) and asthma (correlation coefficient = 0.56; P < .001) also correlated and school signals overlapped with ED signals on 95% and 51% of days, respectively. Substantial daily variation in diarrhea and vomiting visits limited our ability to make comparisons. CONCLUSIONS: Compared with ED syndromic surveillance, the school nurse system identified similar trends in influenza-like illness, allergy, and asthma syndromes. Public health practitioners without school-based surveillance may be able to use age-specific analyses of ED syndromic surveillance data to monitor illness in schoolchildren.

Reassessing Google Flu Trends data for detection of seasonal and pandemic influenza: a comparative epidemiological study at three geographic scales.

The goal of influenza-like illness (ILI) surveillance is to determine the timing, location and magnitude of outbreaks by monitoring the frequency and progression of clinical case incidence. Advances in computational and information technology have allowed for automated collection of higher volumes of electronic data and more timely analyses than previously possible. Novel surveillance systems, including those based on internet search query data like Google Flu Trends (GFT), are being used as surrogates for clinically-based reporting of influenza-like-illness (ILI). We investigated the reliability of GFT during the last decade (2003 to 2013), and compared weekly public health surveillance with search query data to characterize the timing and intensity of seasonal and pandemic influenza at the national (United States), regional (Mid-Atlantic) and local (New York City) levels. We identified substantial flaws in the original and updated GFT models at all three geographic scales, including completely missing the first wave of the 2009 influenza A/H1N1 pandemic, and greatly overestimating the intensity of the A/H3N2 epidemic during the 2012/2013 season. These results were obtained for both the original (2008) and the updated (2009) GFT algorithms. The performance of both models was problematic, perhaps because of changes in internet search behavior and differences in the seasonality, geographical heterogeneity and age-distribution of the epidemics between the periods of GFT model-fitting and prospective use. We conclude that GFT data may not provide reliable surveillance for seasonal or pandemic influenza and should be interpreted with caution until the algorithm can be improved and evaluated. Current internet search query data are no substitute for timely local clinical and laboratory surveillance, or national surveillance based on local data collection. New generation surveillance systems such as GFT should incorporate the use of near-real time electronic health data and computational methods for continued model-fitting and ongoing evaluation and improvement.

Syndromic surveillance during pandemic (H1N1) 2009 outbreak, New York, New York, USA.

We compared emergency department and ambulatory care syndromic surveillance systems during the pandemic (H1N1) 2009 outbreak in New York City. Emergency departments likely experienced increases in influenza-like-illness significantly earlier than ambulatory care facilities because more patients sought care at emergency departments, differences in case definitions existed, or a combination thereof.

First 12 Months of Life for Infants in New York City, New York, With Possible Congenital Zika Virus Exposure.

BACKGROUND: Our goal was to characterize the epidemiology and clinical significance of congenital Zika virus (ZIKV) exposure by prospectively following a cohort of infants with possible congenital exposure through their first year of life. METHODS: We included infants born in New York City between 2016 and 2017 who had or were born to a woman who had laboratory evidence of ZIKV infection during pregnancy. We conducted provider/patient interviews and reviewed medical records to collect information about the pregnant women and, for infants, clinical and neurodevelopmental status at birth and 2, 6, and 12 months of age. RESULTS: Of the 404 infants who met inclusion criteria, most (385 [95.3%]) appeared well, whereas 19 (4.7%) had a possible ZIKV-associated birth defect. Seven had congenital ZIKV syndrome, and 12 were microcephalic without other abnormalities. Although infants with congenital ZIKV syndrome manifested clinical and neurodevelopmental sequelae during their first year of life, all 12 infants with isolated microcephaly were normocephalic and appeared well by 2 months of age. Laboratory evidence of ZIKV was detected for 22 of the infants, including 7 (31.8%) with a birth defect. Among 148 infants without a birth defect and negative/no laboratory results on ZIKV testing, and for whom information was available at 1 year, 4 presented with a developmental delay. CONCLUSIONS: Among infants with possible congenital ZIKV exposure, a small proportion had possible ZIKV-associated findings at birth or at follow-up, or laboratory evidence of ZIKV. Identifying and monitoring infants with possible ZIKV exposure requires extensive efforts by providers and public health departments. Longitudinal studies using standardized clinical and developmental assessments are needed for infants after possible congenital ZIKV exposure.

Maternal Zika Virus Infection: Association With Small-for-Gestational-Age Neonates and Preterm Birth.

OBJECTIVE: To evaluate whether antenatal Zika virus infection is associated with risk of having a small-for-gestational-age (SGA) neonate, risk of preterm birth, and lower mean birth weight of term neonates. METHODS: For this retrospective observational study, we linked birth record data for women who delivered liveborn singleton neonates in New York City in 2016 to data for pregnant women with Zika virus infection reported to the New York City Health Department. We restricted the analysis to nonsmoking, nonwhite women and adjusted for maternal characteristics. Among women with antenatal Zika virus infection, we used modified Poisson regression to assess risks of having an SGA neonate and of delivering preterm, and linear regression to assess the association of infection with mean birth weight of term neonates. RESULTS: Of 116,034 deliveries of singleton neonates in New York City in 2016, 251 (0.2%) were to women with antenatal Zika virus infection. A higher percentage of women with Zika virus infection delivered an SGA neonate compared with those without (11.2% vs 5.8%; adjusted relative risk [RR] 1.8; 95% CI 1.3-2.6). There was no difference in preterm birth prevalence for women with and without Zika virus infection (adjusted RR 1.0; 95% CI 0.69-1.6). Mean birth weight of term neonates born to women with Zika virus infection was 47 g less (95% CI -105 to 11 g); this difference was not statistically significant in crude or adjusted analyses. CONCLUSION: For a cohort of New York City women, antenatal Zika virus infection was associated with an increased risk of having an SGA neonate, but not preterm birth or lower mean birth weight of term neonates. This supports a putative association between Zika virus infection during pregnancy and SGA.

Effectiveness of mosquito traps in measuring species abundance and composition.

Mosquito species abundance and composition estimates provided by trapping devices are commonly used to guide control efforts, but knowledge of trap biases is necessary for accurately interpreting results. We tested the hypothesis that commercially available traps (Mosquito Magnet-Pro, the Mosquito Magnet-X) would be significant improvements over the CDC Miniature Light Trap with respect to abundance, species diversity, and measures of recruitment in a wooded area of the Bronx Zoo in New York City, NY. The Mosquito Magnet-Pro collected significantly more mosquitoes (n = 1,117; mean per night, 124 +/- 28.3) than the CDC Miniature Light Trap (n = 167; mean per night, 19 +/- 5.5). The Simpson's diversity index was greatest for the Mosquito Magnet-Pro. A CDC light trap from a simultaneous surveillance project was located 15 m away and used as a control trap to test for significant differences in mosquito counts on nights with or without the experimental traps. There were no significant differences between nights, indicating the test traps did not recruit beyond 15 m. The traps differed significantly in abundance, but they had similarly limited sampling areas. Measured differences in abundance were independent of differences in diversity. This study highlights how differences between traps might affect species abundance and composition estimates.

Monitoring the impact of influenza by age: emergency department fever and respiratory complaint surveillance in New York City.

BACKGROUND: The importance of understanding age when estimating the impact of influenza on hospitalizations and deaths has been well described, yet existing surveillance systems have not made adequate use of age-specific data. Monitoring influenza-related morbidity using electronic health data may provide timely and detailed insight into the age-specific course, impact and epidemiology of seasonal drift and reassortment epidemic viruses. The purpose of this study was to evaluate the use of emergency department (ED) chief complaint data for measuring influenza-attributable morbidity by age and by predominant circulating virus. METHODS AND FINDINGS: We analyzed electronically reported ED fever and respiratory chief complaint and viral surveillance data in New York City (NYC) during the 2001-2002 through 2005-2006 influenza seasons, and inferred dominant circulating viruses from national surveillance reports. We estimated influenza-attributable impact as observed visits in excess of a model-predicted baseline during influenza periods, and epidemic timing by threshold and cross correlation. We found excess fever and respiratory ED visits occurred predominantly among school-aged children (8.5 excess ED visits per 1,000 children aged 5-17 y) with little or no impact on adults during the early-2002 B/Victoria-lineage epidemic; increased fever and respiratory ED visits among children younger than 5 y during respiratory syncytial virus-predominant periods preceding epidemic influenza; and excess ED visits across all ages during the 2003-2004 (9.2 excess visits per 1,000 population) and 2004-2005 (5.2 excess visits per 1,000 population) A/H3N2 Fujian-lineage epidemics, with the relative impact shifted within and between seasons from younger to older ages. During each influenza epidemic period in the study, ED visits were increased among school-aged children, and each epidemic peaked among school-aged children before other impacted age groups. CONCLUSIONS: Influenza-related morbidity in NYC was highly age- and strain-specific. The impact of reemerging B/Victoria-lineage influenza was focused primarily on school-aged children born since the virus was last widespread in the US, while epidemic A/Fujian-lineage influenza affected all age groups, consistent with a novel antigenic variant. The correspondence between predominant circulating viruses and excess ED visits, hospitalizations, and deaths shows that excess fever and respiratory ED visits provide a reliable surrogate measure of incident influenza-attributable morbidity. The highly age-specific impact of influenza by subtype and strain suggests that greater age detail be incorporated into ongoing surveillance. Influenza morbidity surveillance using electronic data currently available in many jurisdictions can provide timely and representative information about the age-specific epidemiology of circulating influenza viruses.

Child care-associated outbreak of Escherichia coli O157:H7 and hemolytic uremic syndrome.

We present an outbreak of E. coli O157:H7 diarrhea in an urban child care center. Eleven of 45 attendees with diarrhea had positive tests (stool culture or shiga-like toxin assay) for E. coli O157:H7. Two of these 11 (18%) progressed to hemolytic uremic syndrome. Diarrheal illness in child care centers should be considered a public health risk. Staff education, hand washing, and cohorting or exclusion of attendees with diarrhea must be performed to help control infectious outbreaks.

Evaluating and implementing temporal, spatial, and spatio-temporal methods for outbreak detection in a local syndromic surveillance system.

The New York City Department of Health and Mental Hygiene has operated an emergency department syndromic surveillance system since 2001, using temporal and spatial scan statistics run on a daily basis for cluster detection. Since the system was originally implemented, a number of new methods have been proposed for use in cluster detection. We evaluated six temporal and four spatial/spatio-temporal detection methods using syndromic surveillance data spiked with simulated injections. The algorithms were compared on several metrics, including sensitivity, specificity, positive predictive value, coherence, and timeliness. We also evaluated each method's implementation, programming time, run time, and the ease of use. Among the temporal methods, at a set specificity of 95%, a Holt-Winters exponential smoother performed the best, detecting 19% of the simulated injects across all shapes and sizes, followed by an autoregressive moving average model (16%), a generalized linear model (15%), a modified version of the Early Aberration Reporting System's C2 algorithm (13%), a temporal scan statistic (11%), and a cumulative sum control chart (<2%). Of the spatial/spatio-temporal methods we tested, a spatial scan statistic detected 3% of all injects, a Bayes regression found 2%, and a generalized linear mixed model and a space-time permutation scan statistic detected none at a specificity of 95%. Positive predictive value was low (<7%) for all methods. Overall, the detection methods we tested did not perform well in identifying the temporal and spatial clusters of cases in the inject dataset. The spatial scan statistic, our current method for spatial cluster detection, performed slightly better than the other tested methods across different inject magnitudes and types. Furthermore, we found the scan statistics, as applied in the SaTScan software package, to be the easiest to program and implement for daily data analysis.

Evaluating the New York City Emergency Department Syndromic Surveillance for Monitoring Influenza Activity during the 2009-10 Influenza Season.

OBJECTIVE: To use laboratory data to assess the specificity of syndromes used by the New York City emergency department (ED) syndromic surveillance system to monitor influenza activity. DESIGN: For the period from October 1, 2009 through March 31, 2010, we examined the correlation between citywide ED syndrome assignment and laboratory-confirmed influenza and respiratory syncytial virus (RSV). In addition, ED syndromic data from five select NYC hospitals were matched at the patient and visit level to corresponding laboratory reports of influenza and RSV. The matched dataset was used to evaluate syndrome assignment by disease and to calculate the sensitivity and specificity of the influenza-like illness (ILI) syndrome. RESULTS: Citywide ED visits for ILI correlated well with influenza laboratory diagnoses (R=0.92). From October 1, 2009, through March 31, 2010, there were 264,532 ED visits at the five select hospitals, from which the NYC Department of Health and Mental Hygiene (DOHMH) received confirmatory laboratory reports of 655 unique cases of influenza and 1348 cases of RSV. The ED visit of most (56%) influenza cases had been categorized in the fever/flu syndrome; only 15% were labeled ILI. Compared to other influenza-related syndromes, ILI had the lowest sensitivity (15%) but the highest specificity (90%) for laboratory-confirmed influenza. Sensitivity and specificity varied by age group and influenza activity level. CONCLUSIONS: The ILI syndrome in the NYC ED syndromic surveillance system served as a specific but not sensitive indicator for influenza during the 2009-2010 influenza season. Despite its limited sensitivity, the ILI syndrome can be more informative for tracking influenza trends than the fever/flu or respiratory syndromes because it is less likely to capture cases of other respiratory viruses. However, ED ILI among specific age groups should be interpreted alongside laboratory surveillance data. ILI remains a valuable tool for monitoring influenza activity and trends as it facilitates comparisons nationally and across jurisdictions and is easily communicated to the public.

Applying a New Model for Sharing Population Health Data to National Syndromic Influenza Surveillance: DiSTRIBuTE Project Proof of Concept, 2006 to 2009.

The Distributed Surveillance Taskforce for Real-time Influenza Burden Tracking and Evaluation (DiSTRIBuTE) project began as a pilot effort initiated by the International Society for Disease Surveillance (ISDS) in autumn 2006 to create a collaborative electronic emergency department (ED) syndromic influenza-like illness (ILI) surveillance network based on existing state and local systems and expertise. DiSTRIBuTE brought together health departments that were interested in: 1) sharing aggregate level data; 2) maintaining jurisdictional control; 3) minimizing barriers to participation; and 4) leveraging the flexibility of local systems to create a dynamic and collaborative surveillance network. This approach was in contrast to the prevailing paradigm for surveillance where record level information was collected, stored and analyzed centrally. The DiSTRIBuTE project was created with a distributed design, where individual level data remained local and only summarized, stratified counts were reported centrally, thus minimizing privacy risks. The project was responsive to federal mandates to improve integration of federal, state, and local biosurveillance capabilities. During the proof of concept phase, 2006 to 2009, ten jurisdictions from across North America sent ISDS on a daily to weekly basis year-round, aggregated data by day, stratified by local ILI syndrome, age-group and region. During this period, data from participating U.S. state or local health departments captured over 13% of all ED visits nationwide. The initiative focused on state and local health department trust, expertise, and control. Morbidity trends observed in DiSTRIBuTE were highly correlated with other influenza surveillance measures. With the emergence of novel A/H1N1 influenza in the spring of 2009, the project was used to support information sharing and ad hoc querying at the state and local level. In the fall of 2009, through a broadly collaborative effort, the project was expanded to enhance electronic ED surveillance nationwide.

Developing syndrome definitions based on consensus and current use.

OBJECTIVE: Standardized surveillance syndromes do not exist but would facilitate sharing data among surveillance systems and comparing the accuracy of existing systems. The objective of this study was to create reference syndrome definitions from a consensus of investigators who currently have or are building syndromic surveillance systems. DESIGN: Clinical condition-syndrome pairs were catalogued for 10 surveillance systems across the United States and the representatives of these systems were brought together for a workshop to discuss consensus syndrome definitions. RESULTS: Consensus syndrome definitions were generated for the four syndromes monitored by the majority of the 10 participating surveillance systems: Respiratory, gastrointestinal, constitutional, and influenza-like illness (ILI). An important element in coming to consensus quickly was the development of a sensitive and specific definition for respiratory and gastrointestinal syndromes. After the workshop, the definitions were refined and supplemented with keywords and regular expressions, the keywords were mapped to standard vocabularies, and a web ontology language (OWL) ontology was created. LIMITATIONS: The consensus definitions have not yet been validated through implementation. CONCLUSION: The consensus definitions provide an explicit description of the current state-of-the-art syndromes used in automated surveillance, which can subsequently be systematically evaluated against real data to improve the definitions. The method for creating consensus definitions could be applied to other domains that have diverse existing definitions.

Daily Emergency Department Surveillance System --- Bergen County, New Jersey.

The purpose of the Daily Emergency Department Surveillance System (DEDSS) is to provide consistent, timely, and robust data that can be used to guide public health activities in Bergen County, New Jersey. DEDSS collects data on all emergency department visits in four hospitals in Bergen County and analyzes them for aberrant patterns of disease or single instances of certain diseases or syndromes. The system monitors for clusters of patients with syndromes consistent with the prodrome of a terrorism-related illness (e.g., anthrax or smallpox) or naturally occurring disease (e.g., pandemic influenza or food and waterborne outbreaks). The health department can use these data to track and characterize the temporal and geographic spread of a known outbreak or demonstrate the absence of cases during the same period (e.g., severe acute respiratory syndrome [SARS] or anthrax). DEDSS was designed to be flexible and readily adaptable as local, state, or federal surveillance needs evolve.