Prospective Spatiotemporal Cluster Detection Using SaTScan: Tutorial for Designing and Fine-Tuning a System to Detect Reportable Communicable Disease Outbreaks.

Staff at public health departments have few training materials to learn how to design and fine-tune systems to quickly detect acute, localized, community-acquired outbreaks of infectious diseases. Since 2014, the Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene has analyzed reportable communicable diseases daily using SaTScan. SaTScan is a free software that analyzes data using scan statistics, which can detect increasing disease activity without a priori specification of temporal period, geographic location, or size. The Bureau of Communicable Disease's systems have quickly detected outbreaks of salmonellosis, legionellosis, shigellosis, and COVID-19. This tutorial details system design considerations, including geographic and temporal data aggregation, study period length, inclusion criteria, whether to account for population size, network location file setup to account for natural boundaries, probability model (eg, space-time permutation), day-of-week effects, minimum and maximum spatial and temporal cluster sizes, secondary cluster reporting criteria, signaling criteria, and distinguishing new clusters versus ongoing clusters with additional events. We illustrate how to support health equity by minimizing analytic exclusions of patients with reportable diseases (eg, persons experiencing homelessness who are unsheltered) and accounting for purely spatial patterns, such as adjusting nonparametrically for areas with lower access to care and testing for reportable diseases. We describe how to fine-tune the system when the detected clusters are too large to be of interest or when signals of clusters are delayed, missed, too numerous, or false. We demonstrate low-code techniques for automating analyses and interpreting results through built-in features on the user interface (eg, patient line lists, temporal graphs, and dynamic maps), which became newly available with the July 2022 release of SaTScan version 10.1. This tutorial is the first comprehensive resource for health department staff to design and maintain a reportable communicable disease outbreak detection system using SaTScan to catalyze field investigations as well as develop intuition for interpreting results and fine-tuning the system. While our practical experience is limited to monitoring certain reportable diseases in a dense, urban area, we believe that most recommendations are generalizable to other jurisdictions in the United States and internationally. Additional analytic technical support for detecting outbreaks would benefit state, tribal, local, and territorial public health departments and the populations they serve.

Epidemiological characteristics of the B.1.526 SARS-CoV-2 variant.

To characterize the epidemiological properties of the B.1.526 SARS-CoV-2 (severe acute respiratory syndrome coronavirus 2) variant of interest, here we used nine epidemiological and population datasets and model-inference methods to reconstruct SARS-CoV-2 transmission dynamics in New York City, where B.1.526 emerged. We estimated that B.1.526 had a moderate increase (15 to 25%) in transmissibility, could escape immunity in 0 to 10% of previously infected individuals, and substantially increased the infection fatality risk (IFR) among adults 65 or older by >60% during November 2020 to April 2021, compared to estimates for preexisting variants. Overall, findings suggest that new variants like B.1.526 likely spread in the population weeks before detection and that partial immune escape (e.g., resistance to therapeutic antibodies) could offset prior medical advances and increase IFR. Early preparedness for and close monitoring of SARS-CoV-2 variants, their epidemiological characteristics, and disease severity are thus crucial to COVID-19 (coronavirus disease 2019) response.

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.

Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis - New York City, May-June 2019.

In May 2019, the New York City Department of Health and Mental Hygiene (NYCDOHMH) detected an unusual cluster of five salmonellosis patients via automated spatiotemporal analysis of notifiable diseases using free SaTScan software (1). Within 1 day of cluster detection, graduate student interviewers determined that three of the patients had eaten prepared food from the same grocery store (establishment A) located inside the cluster area. NYCDOHMH initiated an investigation to identify additional cases, establish the cause, and provide control recommendations. Overall, 15 New York City (NYC) residents with laboratory-diagnosed salmonellosis who reported eating food from establishment A were identified. The most commonly consumed food item was chicken, reported by 10 patients. All 11 clinical isolates available were serotyped as Salmonella Blockley, sequenced, and analyzed by core genome multilocus sequence typing; isolates had a median difference of zero alleles. Environmental assessments revealed food not held at the proper temperature, food not cooled properly, and potential cross-contamination during chicken preparation. Elevated fecal coliform counts were found in two of four ready-to-eat food samples collected from establishment A, and Bacillus cereus was detected in three. The outbreak strain of Salmonella was isolated from one patient's leftover chicken. Establishing automated spatiotemporal cluster detection analyses for salmonellosis and other reportable diseases could aid in the detection of geographically focused, community-acquired outbreaks even before laboratory subtyping results become available.

Detecting Drop-offs in Electronic Laboratory Reporting for Communicable Diseases in New York City.

CONTEXT: The Bureau of Communicable Disease at the New York City Department of Health and Mental Hygiene receives an average of more than 1000 reports daily via electronic laboratory reporting. Rapid recognition of any laboratory reporting drop-off of test results for 1 or more diseases is necessary to avoid delays in case investigation and outbreak detection. PROGRAM: We modified our outbreak detection approach using the prospective space-time permutation scan statistic in SaTScan. Instead of searching for spatiotemporal clusters of high case counts, we reconceptualized "space" as "laboratory" and instead searched for clusters of recent low reporting, overall and for each of 52 diseases and 10 hepatitis test types, within individual laboratories. Each analysis controlled for purely temporal trends affecting all laboratories and accounted for multiple testing. IMPLEMENTATION: A SAS program automatically created input files, invoked SaTScan, and further processed SaTScan analysis results and output summaries to a secure folder. Analysts reviewed output weekly and reported concerning drop-offs to coordinators, who liaised with reporting laboratory staff to investigate and resolve issues. EVALUATION: During a 42-week evaluation period, October 2017 to July 2018, we detected 62 unique signals of reporting drop-offs. Of these, 39 (63%) were verified as true drop-offs, including failures to generate or transmit files and programming errors. For example, a hospital laboratory stopped reporting influenza after changing a multiplex panel result from "positive" to "detected." Six drop-offs were detected despite low numbers of expected reports missing (<10 per drop-off). DISCUSSION: Our novel application of SaTScan identified a manageable number of possible electronic laboratory reporting drop-offs for investigation. Ongoing maintenance requirements are minimal but include accounting for laboratory mergers and referrals. Automated analyses facilitated rapid identification and correction of electronic laboratory reporting errors, even with small numbers of expected reports missing, suggesting that our approach might be generalizable to smaller jurisdictions.

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.

Survival of adults with systemic sclerosis following lung transplantation: a nationwide cohort study.

OBJECTIVE: Many transplant programs are hesitant to offer lung transplantation to patients with systemic sclerosis (SSc) due to concerns about extrapulmonary involvement that might affect survival. The aim of this study was to determine whether adults with SSc have higher 1-year mortality rates after lung transplantation compared to those with interstitial lung disease (ILD) or pulmonary arterial hypertension (PAH) not due to SSc. METHODS: Using data provided by the United Network for Organ Sharing, we performed a retrospective cohort study of 229 adults with SSc, 201 with PAH, and 3,333 with ILD who underwent lung transplantation in the US between May 4, 2005 and September 14, 2012. We examined associations between diagnosis and 1-year survival after lung transplantation using stratified Cox models adjusted for recipient, donor, and procedure factors. RESULTS: Adults with SSc undergoing lung transplantation in the US had a multivariable-adjusted 48% relative increase in the 1-year mortality rate compared to those with non-SSc-related ILD (hazard ratio 1.48 [95% confidence interval 1.01-2.17]). However, we did not detect a difference in the risk of death at 1 year between those with SSc and those with non-SSc-related PAH (hazard ratio 0.85 [95% confidence interval 0.50-1.44]). CONCLUSION: A diagnosis of SSc may confer an increased risk of death 1 year following lung transplantation compared to a diagnosis of ILD, but this risk is similar to that of PAH, a widely accepted indication for lung transplantation. Future work should identify modifiable risk factors that can improve transplant outcomes in this population.

Body composition and mortality after adult lung transplantation in the United States.

RATIONALE: Obesity and underweight are contraindications to lung transplantation based on their associations with mortality in studies performed before implementation of the lung allocation score (LAS)-based organ allocation system in the United States Objectives: To determine the associations of body mass index (BMI) and plasma leptin levels with survival after lung transplantation. METHODS: We used multivariable-adjusted regression models to examine associations between BMI and 1-year mortality in 9,073 adults who underwent lung transplantation in the United States between May 2005 and June 2011, and plasma leptin and mortality in 599 Lung Transplant Outcomes Group study participants. We measured body fat and skeletal muscle mass using whole-body dual X-ray absorptiometry in 142 adult lung transplant candidates. MEASUREMENTS AND MAIN RESULTS: Adjusted mortality rates were similar among normal weight (BMI 18.5-24.9 kg/m(2)), overweight (BMI 25.0-29.9), and class I obese (BMI 30-34.9) transplant recipients. Underweight (BMI < 18.5) was associated with a 35% increased rate of death (95% confidence interval, 10-66%). Class II-III obesity (BMI ≥ 35 kg/m(2)) was associated with a nearly twofold increase in mortality (hazard ratio, 1.9; 95% confidence interval, 1.3-2.8). Higher leptin levels were associated with increased mortality after transplant surgery performed without cardiopulmonary bypass (P for interaction = 0.03). A BMI greater than or equal to 30 kg/m(2) was 26% sensitive and 97% specific for total body fat-defined obesity. CONCLUSIONS: A BMI of 30.0-34.9 kg/m(2) is not associated with 1-year mortality after lung transplantation in the LAS era, perhaps because of its low sensitivity for obesity. The association between leptin and mortality suggests the need to validate alternative methods to measure obesity in candidates for lung transplantation. A BMI greater than or equal to 30 kg/m(2) may no longer contraindicate lung transplantation.

Merck Ad5/HIV induces broad innate immune activation that predicts CD8⁺ T-cell responses but is attenuated by preexisting Ad5 immunity.

To better understand how innate immune responses to vaccination can lead to lasting protective immunity, we used a systems approach to define immune signatures in humans over 1 wk following MRKAd5/HIV vaccination that predicted subsequent HIV-specific T-cell responses. Within 24 h, striking increases in peripheral blood mononuclear cell gene expression associated with inflammation, IFN response, and myeloid cell trafficking occurred, and lymphocyte-specific transcripts decreased. These alterations were corroborated by marked serum inflammatory cytokine elevations and egress of circulating lymphocytes. Responses of vaccinees with preexisting adenovirus serotype 5 (Ad5) neutralizing antibodies were strongly attenuated, suggesting that enhanced HIV acquisition in Ad5-seropositive subgroups in the Step Study may relate to the lack of appropriate innate activation rather than to increased systemic immune activation. Importantly, patterns of chemoattractant cytokine responses at 24 h and alterations in 209 peripheral blood mononuclear cell transcripts at 72 h were predictive of subsequent induction and magnitude of HIV-specific CD8(+) T-cell responses. This systems approach provides a framework to compare innate responses induced by vectors, as shown here by contrasting the more rapid, robust response to MRKAd5/HIV with that to yellow fever vaccine. When applied iteratively, the findings may permit selection of HIV vaccine candidates eliciting innate immune response profiles more likely to drive HIV protective immunity.