Detecting COVID-19 Clusters at High Spatiotemporal Resolution, New York City, New York, USA, June-July 2020.

A surveillance system that uses census tract resolution and the SaTScan prospective space-time scan statistic detected clusters of increasing severe acute respiratory syndrome coronavirus 2 test percent positivity in New York City, NY, USA. Clusters included one in which patients attended the same social gathering and another that led to targeted testing and outreach.

Daily Reportable Disease Spatiotemporal Cluster Detection, New York City, New York, USA, 2014-2015.

Each day, the New York City Department of Health and Mental Hygiene uses the free SaTScan software to apply prospective space-time permutation scan statistics to strengthen early outbreak detection for 35 reportable diseases. This method prompted early detection of outbreaks of community-acquired legionellosis and shigellosis.

Refining historical limits method to improve disease cluster detection, New York City, New York, USA.

Since the early 2000s, the Bureau of Communicable Disease of the New York City Department of Health and Mental Hygiene has analyzed reportable infectious disease data weekly by using the historical limits method to detect unusual clusters that could represent outbreaks. This method typically produced too many signals for each to be investigated with available resources while possibly failing to signal during true disease outbreaks. We made method refinements that improved the consistency of case inclusion criteria and accounted for data lags and trends and aberrations in historical data. During a 12-week period in 2013, we prospectively assessed these refinements using actual surveillance data. The refined method yielded 74 signals, a 45% decrease from what the original method would have produced. Fewer and less biased signals included a true citywide increase in legionellosis and a localized campylobacteriosis cluster subsequently linked to live-poultry markets. Future evaluations using simulated data could complement this descriptive assessment.

Disparities in Reportable Communicable Disease Incidence by Census Tract-Level Poverty, New York City, 2006-2013.

OBJECTIVES: We described disparities in selected communicable disease incidence across area-based poverty levels in New York City, an area with more than 8 million residents and pronounced household income inequality. METHODS: We geocoded and categorized cases of 53 communicable diseases diagnosed during 2006 to 2013 by census tract-based poverty level. Age-standardized incidence rate ratios (IRRs) were calculated for areas with 30% or more versus fewer than 10% of residents below the federal poverty threshold. RESULTS: Diseases associated with high poverty included rickettsialpox (IRR = 3.69; 95% confidence interval [CI] = 2.29, 5.95), chronic hepatitis C (IRR for new reports = 3.58; 95% CI = 3.50, 3.66), and malaria (IRR = 3.48; 95% CI = 2.97, 4.08). Diseases associated with low poverty included domestic tick-borne diseases acquired through travel to areas where infected vectors are prevalent, such as human granulocytic anaplasmosis (IRR = 0.08; 95% CI = 0.03, 0.19) and Lyme disease (IRR = 0.34; 95% CI = 0.32, 0.36). CONCLUSIONS: Residents of high poverty areas were disproportionately affected by certain communicable diseases that are amenable to public health interventions. Future work should clarify subgroups at highest risk, identify reasons for the observed associations, and use findings to support programs to minimize disparities.

Built by epidemiologists for epidemiologists: an internal COVID-19 dashboard for real-time situational awareness in New York City.

Objective: New York City (NYC) experienced a large first wave of coronavirus disease 2019 (COVID-19) in the spring of 2020, but the Health Department lacked tools to easily visualize and analyze incoming surveillance data to inform response activities. To streamline ongoing surveillance, a group of infectious disease epidemiologists built an interactive dashboard using open-source software to monitor demographic, spatial, and temporal trends in COVID-19 epidemiology in NYC in near real-time for internal use by other surveillance and epidemiology experts. Materials and methods: Existing surveillance databases and systems were leveraged to create daily analytic datasets of COVID-19 case and testing information, aggregated by week and key demographics. The dashboard was developed iteratively using R, and includes interactive graphs, tables, and maps summarizing recent COVID-19 epidemiologic trends. Additional data and interactive features were incorporated to provide further information on the spread of COVID-19 in NYC. Results: The dashboard allows key staff to quickly review situational data, identify concerning trends, and easily maintain granular situational awareness of COVID-19 epidemiology in NYC. Discussion: The dashboard is used to inform weekly surveillance summaries and alleviated the burden of manual report production on infectious disease epidemiologists. The system was built by and for epidemiologists, which is critical to its utility and functionality. Interactivity allows users to understand broad and granular data, and flexibility in dashboard development means new metrics and visualizations can be developed as needed. Conclusions: Additional investment and development of public health informatics tools, along with standardized frameworks for local health jurisdictions to analyze and visualize data in emergencies, are warranted.

Fatalities associated with the 2009 H1N1 influenza A virus in New York city.

BACKGROUND. When the 2009 H1N1 influenza A virus emerged in the United States, epidemiologic and clinical information about severe and fatal cases was limited. We report the first 47 fatal cases of 2009 H1N1 influenza in New York City. METHODS. The New York City Department of Health and Mental Hygiene conducted enhanced surveillance for hospitalizations and deaths associated with 2009 H1N1 influenza A virus. We collected basic demographic and clinical information for all patients who died and compared abstracted data from medical records for a sample of hospitalized patients who died and hospitalized patients who survived. RESULTS. From 24 April through 1 July 2009, 47 confirmed fatal cases of 2009 H1N1 influenza were reported to the New York City Department of Health and Mental Hygiene. Most decedents (60%) were ages 18-49 years, and only 4% were aged 65 years. Many (79%) had underlying risk conditions for severe seasonal influenza, and 58% were obese according to their body mass index. Thirteen (28%) had evidence of invasive bacterial coinfection. Approximately 50% of the decedents had developed acute respiratory distress syndrome. Among all hospitalized patients, decedents had presented for hospitalization later (median, 3 vs 2 days after illness onset; P < .05) and received oseltamivir later (median, 6.5 vs 3 days; P < .01) than surviving patients. Hospitalized patients who died were less likely to have received oseltamivir within 2 days of hospitalization than hospitalized patients who survived (61% vs 96%; P < .01). CONCLUSIONS. With community-wide transmission of 2009 H1N1 influenza A virus, timely medical care and antiviral therapy should be considered for patients with severe influenza-like illness or with underlying risk conditions for complications from influenza.

Infectious Disease Hospitalizations, New York City, 2001-2014.

OBJECTIVE: Hospital discharge data are a means of monitoring infectious diseases in a population. We investigated rates of infectious disease hospitalizations in New York City. METHODS: We analyzed data for residents discharged from New York State hospitals with a principal diagnosis of an infectious disease during 2001-2014 by using the Statewide Planning and Research Cooperative System. We calculated annual age-adjusted hospitalization rates and the percentage of hospitalizations in which in-hospital death occurred. We examined diagnoses by site of infection or sepsis and by pathogen type. RESULTS: During 2001-2014, the mean annual age-adjusted rate of infectious disease hospitalizations in New York City was 1661.6 (95% CI, 1659.2-1663.9) per 100 000 population; the mean annual age-adjusted hospitalization rate decreased from 2001-2003 to 2012-2014 (rate ratio = 0.9; 95% CI, 0.9-0.9). The percentage of in-hospital death during 2001-2014 was 5.9%. The diagnoses with the highest mean annual age-adjusted hospitalization rates among all sites of infection and sepsis diagnoses were the lower respiratory tract, followed by sepsis. From 2001-2003 to 2012-2014, the mean annual age-adjusted hospitalization rate per 100 000 population for HIV decreased from 123.1 (95% CI, 121.7-124.5) to 40.0 (95% CI, 39.2-40.7) and for tuberculosis decreased from 10.2 (95% CI, 9.8-10.6) to 4.6 (95% CI, 4.4-4.9). CONCLUSIONS: Although hospital discharge data are subject to limitations, particularly for tracking sepsis, lower respiratory tract infections and sepsis are important causes of infectious disease hospitalizations in New York City. Hospitalizations for HIV infection and tuberculosis appear to be declining.

A Large Community Outbreak of Legionnaires' Disease Associated With a Cooling Tower in New York City, 2015.

OBJECTIVES: Infections caused by Legionella are the leading cause of waterborne disease outbreaks in the United States. We investigated a large outbreak of Legionnaires' disease in New York City in summer 2015 to characterize patients, risk factors for mortality, and environmental exposures. METHODS: We defined cases as patients with pneumonia and laboratory evidence of Legionella infection from July 2 through August 3, 2015, and with a history of residing in or visiting 1 of several South Bronx neighborhoods of New York City. We describe the epidemiologic, environmental, and laboratory investigation that identified the source of the outbreak. RESULTS: We identified 138 patients with outbreak-related Legionnaires' disease, 16 of whom died. The median age of patients was 55. A total of 107 patients had a chronic health condition, including 43 with diabetes, 40 with alcoholism, and 24 with HIV infection. We tested 55 cooling towers for Legionella, and 2 had a strain indistinguishable by pulsed-field gel electrophoresis from 26 patient isolates. Whole-genome sequencing and epidemiologic evidence implicated 1 cooling tower as the source of the outbreak. CONCLUSIONS: A large outbreak of Legionnaires' disease caused by a cooling tower occurred in a medically vulnerable community. The outbreak prompted enactment of a new city law on the operation and maintenance of cooling towers. Ongoing surveillance and evaluation of cooling tower process controls will determine if the new law reduces the incidence of Legionnaires' disease in New York City.

Building-level analyses to prospectively detect influenza outbreaks in long-term care facilities: New York City, 2013-2014.

BACKGROUND: Timely outbreak detection is necessary to successfully control influenza in long-term care facilities (LTCFs) and other institutions. To supplement nosocomial outbreak reports, calls from infection control staff, and active laboratory surveillance, the New York City (NYC) Department of Health and Mental Hygiene implemented an automated building-level analysis to proactively identify LTCFs with laboratory-confirmed influenza activity. METHODS: Geocoded addresses of LTCFs in NYC were compared with geocoded residential addresses for all case-patients with laboratory-confirmed influenza reported through passive surveillance. An automated daily analysis used the geocoded building identification number, approximate text matching, and key-word searches to identify influenza in residents of LTCFs for review and follow-up by surveillance coordinators. Our aim was to determine whether the building analysis improved prospective outbreak detection during the 2013-2014 influenza season. RESULTS: Of 119 outbreaks identified in LTCFs, 109 (92%) were ever detected by the building analysis, and 55 (46%) were first detected by the building analysis. Of the 5,953 LTCF staff and residents who received antiviral prophylaxis during the 2013-2014 season, 929 (16%) were at LTCFs where outbreaks were initially detected by the building analysis. CONCLUSIONS: A novel building-level analysis improved influenza outbreak identification in LTCFs in NYC, prompting timely infection control measures.

Assessment of reportable disease incidence after Hurricane Sandy, New York City, 2012.

OBJECTIVE: Hurricane Sandy's October 29, 2012 arrival in New York City caused flooding, power disruption, and population displacement. Infectious disease risk may have been affected by floodwater exposure, residence in emergency shelters, overcrowding, and lack of refrigeration or heating. For 42 reportable diseases that could have been affected by hurricane-related exposures, we developed methods to assess whether hurricane-affected areas had higher disease incidence than other areas of NYC. METHODS: We identified post-hurricane cases as confirmed, probable, or suspected cases with onset or diagnosis between October 30 and November 26 that were reported via routine passive surveillance. Pre-hurricane cases for the same 4-week period were identified in 5 prior years, 2007-2011. Cases were geocoded to the census tract of residence. Using data compiled by the NYC Office of Emergency Management, we determined (1) the proportion of the population in each census tract living in a flooded block and (2) the subset of flooded tracts severely "impacted", e.g., by prolonged service outages or physical damage. A separate multivariable regression model was constructed for each disease, modeling the outcome of case counts using a negative binomial distribution. Independent variables were: neighborhood poverty; whether cases were pre- or post-hurricane (time); the proportion of the population flooded in impacted and not impacted tracts; and interaction terms between the flood/impact variables and time. Models used repeated measures to adjust for correlated observations from the same tract and an offset term of the log of the population size. Sensitivity analyses assessed the effects of case count fluctuations and accounted for variations in reporting volume by using an offset term of the log of total cases. RESULTS: Only legionellosis was statistically significantly associated with increased occurrence in flooded/impacted areas post-hurricane, adjusting for baseline differences (P = .04). However, there was only 1 legionellosis case post-hurricane in a flooded/impacted area. CONCLUSIONS: Hurricane Sandy did not appear to elevate reportable disease incidence in NYC. Defining and acquiring reliable data and meta-data regarding hurricane-affected areas was a challenge in the weeks post-storm. Relevant metrics could be developed during disaster preparedness planning. These methods to detect excess disease can be adapted for future emergencies.