BACKGROUND: The 2019 novel coronavirus disease (COVID-19) pandemic led many jurisdictions to close in-person school instruction. METHODS: We collected data about COVID-19 cases associated with New York City (NYC) public schools from polymerase chain reaction testing performed in each school on a sample of asymptomatic students and staff and from routine reporting. We compared prevalence from testing done in schools to community prevalence estimates from statistical models. We compared cumulative incidence for school-associated cases to all cases reported to the city. School-based contacts were monitored to estimate the secondary attack rate and possible direction of transmission. RESULTS: To assess prevalence, we analyzed data from 234 132 persons tested for severe acute respiratory syndrome coronavirus 2 infection in 1594 NYC public schools during October 9 to December 18, 2020; 986 (0.4%) tested positive. COVID-19 prevalence in schools was similar to or less than estimates of prevalence in the community for all weeks. To assess cumulative incidence, we analyzed data for 2231 COVID-19 cases that occurred in students and staff compared with the 86 576 persons in NYC diagnosed with COVID-19 during the same period; the overall incidence was lower for persons in public schools compared with the general community. Of 36 423 school-based close contacts, 191 (0.5%) subsequently tested positive for COVID-19; the likely index case was an adult for 78.0% of secondary cases. CONCLUSIONS: We found that in-person learning in NYC public schools was not associated with increased prevalence or incidence overall of COVID-19 infection compared with the general community.

Background New York City (NYC) reported a higher pneumonia and influenza death rate than the rest of New York State during 2010–2014. Most NYC pneumonia and influenza deaths are attributed to pneumonia caused by infection acquired in the community, and these deaths typically occur in hospitals. Methods We identified hospitalizations of New York State residents aged ≥20 years discharged from New York State hospitals during 2010–2014 with a principal diagnosis of community-setting pneumonia or a secondary diagnosis of community-setting pneumonia if the principal diagnosis was respiratory failure or sepsis. We examined mean annual age-adjusted community-setting pneumonia-associated hospitalization (CSPAH) rates and proportion of CSPAH with in-hospital death, overall and by sociodemographic group, and produced a multivariable negative binomial model to assess hospitalization rate ratios. Results Compared with non-NYC urban, suburban, and rural areas of New York State, NYC had the highest mean annual age-adjusted CSPAH rate at 475.3 per 100,000 population and the highest percentage of CSPAH with in-hospital death at 13.7%. NYC also had the highest proportion of CSPAH patients residing in higher-poverty-level areas. Adjusting for age, sex, and area-based poverty, NYC residents experienced 1.3 (95% confidence interval [CI], 1.2–1.4), non-NYC urban residents 1.4 (95% CI, 1.3–1.6), and suburban residents 1.2 (95% CI, 1.1–1.3) times the rate of CSPAH than rural residents. Conclusions In New York State, NYC as well as other urban areas and suburban areas had higher rates of CSPAH than rural areas. Further research is needed into drivers of CSPAH deaths, which may be associated with poverty.

Objective: To describe hospitalizations involving an intensive care unit (ICU) admission among patients aged 65 years and older within New York City (NYC) hospitals during 2000–2014. Design: Observational study using an all-payer hospital discharge dataset. Setting: The setting was in NYC hospitals. Patients: Patients aged 65 years and older admitted to an ICU within a NYC hospital during 2000–2014. Interventions: No interventions were carried out. Measurements and Main Results: We calculated the mean annual number of hospitalizations involving an ICU admission. We also examined characteristics of hospitalizations, including the occurrence of in-hospital death and principal diagnosis. There were 5,338,577 hospitalizations of patients aged ≥65 years within NYC hospitals during 2000–2014, of which 765,084 (14.3%) involved an ICU admission. The mean annual number of hospitalizations involving an ICU admission for this age group decreased from 57,938 during 2000–2002 to 45,785 during 2012–2014. The proportion of hospitalizations involving an ICU admission in which in-hospital death occurred decreased from 15.9% during 2000–2002 to 14.5% during 2012–2014. During 2000–2002, 11.6% of hospitalizations involving an ICU admission listed an “infectious” principal diagnosis, increasing to 20.7% during 2012–2014. Listing of a “cardiovascular” principal diagnosis decreased from 46.4% to 33.4% between these time periods. “Infectious” principal diagnoses accounted for 31.0% of all hospitalizations involving an ICU admission in which in-hospital death occurred during the entire study period, while “cardiovascular” principal diagnoses accounted for 21.3%. Conclusions: This investigation provides a clearer understanding of ICU utilization among patients aged 65 years and older in NYC. Ongoing monitoring is warranted given projections that the proportion of New Yorkers aged 65 years and older will increase in coming years. In particular, in light of the observed increase of infectious principal diagnoses during the study period, further investigation is needed into the role of infectious disease in causing critical illness in NYC.

ObjectiveThe objectives of this project were to rapidly build and deploy a web-based reporting platform in response to a canine influenza H3N2 outbreak in New York City (NYC) and provide aggregate data back to the veterinary community as an interactive dashboard.IntroductionData-driven decision-making is a cornerstone of public health emergency response; therefore, a highly-configurable and rapidly deployable data capture system with built-in quality assurance (QA; e.g., completeness, standardization) is critical.1 Additionally, to keep key stakeholders informed of developments during an emergency, data need to be shared in a timely and effective manner. Dynamic data visualization is a particularly useful means of sharing data with healthcare providers and the public.2During Spring 2018, detection of canine influenza H3N2 among dogs in NYC caused concern in the veterinary community. Canine influenza is a highly contagious respiratory infection caused by an influenza A virus.3 However, no central database existed in NYC to monitor the outbreak and no single agency was responsible for data capture. Our team at the NYC Department of Health and Mental Hygiene (DOHMH) partnered with the NYC Veterinary Medical Association (VMA) to monitor the canine influenza H3N2 outbreak by building a web-based reporting platform and interactive dashboard.MethodsThe NYC DOHMH built and deployed a web-based reporting platform to aid veterinarians in reporting cases of canine influenza. We leveraged REDCap Cloud, a cloud-based graphical user interface data capture and management software. REDCap Cloud collected information regarding the provider, owner, dog, residence of dog, illness history, and influenza testing. We leveraged REDCap QA functionality in the form of mandatory questions to ensure data completeness. Several different field types — including dropdown menus, mutually exclusive radio buttons, and multi-select check boxes — were used to ensure data standardization. Skip logic was incorporated to guide users through unique sequences of questions based on the answers they entered. Reporting was voluntary.ResultsAfter requirements were gathered, the REDCap web-based reporting platform was rapidly deployed in approximately two business days. Over the course of one week, multiple versions of the dashboard were produced and the final iteration was completed. The entire system was built on server-side software that is available as free or open-source for individual licenses. The dashboard can be found at the following link: http://www.vmanyc.org/canine_influenza_dashboard.html.A total of 28 cases were reported by 6 providers during June–August 2018. All of the 28 cases were reported from 2 of the 5 NYC counties (boroughs); 17/28 (60.7%) were reported from Brooklyn and 11/28 (39.3%) were reported from Manhattan. We were able to collect mostly complete data by leveraging REDCap QA functionality. The reporting facility was listed in all cases, and an owner was listed in all but two cases. All reported cases used a PCR test for the detection of canine influenza H3N2. One reported case indicated polymerase chain reaction (PCR) test results as “not detected” which suggests that one negative case was reported through the system.ConclusionsUsing REDCap Cloud and R, we were able to rapidly build and deploy a web-based reporting platform and dynamic data visualization during an emergency response to an outbreak of canine influenza H3N2. Our system was used by veterinarians to report 28 cases of canine influenza. Future emergency responses for human disease outbreaks will likely benefit from the experience our team gained during our partnership with the NYC VMA.References1. Centers for Disease Control and Prevention. Public Health Emergency Response Guide for State, Local, and Tribal Public Health Directors. https://emergency.cdc.gov/planning/pdf/cdcresponseguide.pdf.2. Meyer M. The Rise of Healthcare Data Visualization. http://journal.ahima.org/2017/12/21/the-rise-of-healthcare-data-visualization/.3. American Veterinary Medical Association. Canine Influenza FAQ. https://www.avma.org/KB/Resources/FAQs/Pages/Control-of-Canine-Influenza-in-Dogs.aspx.4. Wickham H. R packages. http://r-pkgs.had.co.nz/.

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