Prospective Spatio-Temporal and Temporal Cluster Detection by Salmonella Serotype

Peterson, Eric; Reddy, Vasudha; Waechter, HaeNa; Li, Lan; Forney, Kristen; Greene, Sharon K.

doi:10.5210/ojphi.v8i1.6443

Cited by 6 publications

(4 citation statements)

References 3 publications

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“…It is uncommon for NYCDOHMH to detect a salmonellosis outbreak in the absence of any laboratory subtyping data (8) or any single report of multiple ill patients. As of July 15, 2019, CDC PulseNet transitioned its primary molecular subtyping tool from PFGE to WGS, which will improve foodborne outbreak detection through detailed pathogen characterization (9,10).…”

Section: Discussionmentioning

confidence: 99%

Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis — New York City, May–June 2019

Latash¹,

Greene²,

Stavinsky³

et al. 2020

MMWR Morb. Mortal. Wkly. Rep.

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Section: Discussionmentioning

confidence: 99%

Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis — New York City, May–June 2019

Latash¹,

Greene²,

Stavinsky³

et al. 2020

MMWR Morb. Mortal. Wkly. Rep.

Self Cite

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“…Alternatively, space need not be conceptualized as geographical coordinates. To identify temporal clusters of infections by particular Salmonella serotypes, we replace space with arbitrary coordinates for serotype and detect citywide clusters of particular serotypes that could not be explained by the overall seasonality of Salmonella infections [ 25 ].…”

Section: Analysis Designmentioning

confidence: 99%

“…We use the prospective space-time permutation scan statistic for almost all routine SaTScan analyses conducted at the BCD, including the base analysis [ 2 , 17 , 25 ]. Prospective analyses detect currently active unusual clusters, evaluating only the subset of possible clusters that encompass the last day of the study period together with a flexible number of previous days.…”

Section: Analysis Designmentioning

confidence: 99%

“…Providing context and examples to build intuition, we discuss system setup requirements, data quality assurance, input file preparation, parameter setting recommendations derived from public health and statistical principles, and new features in the 2022 SaTScan release that simplify routine analyses. Multimedia Appendix 1 [ 2 , 13 , 14 , 25 - 37 ] provides further details about parameter settings and advanced options, demonstrates cluster interpretation using artificial but realistic data, and offers strategies for fine-tuning analyses. In Multimedia Appendix 2 , a video demonstrates how to set up a cluster detection system, and sample input and output files are provided.…”

Section: Introductionmentioning

confidence: 99%

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

Levin-Rector,

Kulldorff,

Peterson

et al. 2024

JMIR Public Health Surveill

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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.

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

Greene

Andrews

Lloyd

et al. 2019

Journal of Public Health Management and Practice

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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.

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Prospective Spatio-Temporal and Temporal Cluster Detection by Salmonella Serotype

Cited by 6 publications

References 3 publications

Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis — New York City, May–June 2019

Salmonellosis Outbreak Detected by Automated Spatiotemporal Analysis — New York City, May–June 2019

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

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

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