Dataset Information

Development and Evaluation of Statewide Prospective Spatiotemporal Legionellosis Cluster Surveillance, New Jersey, USA.

ABSTRACT: Incidence of Legionnaires' disease is increasing, particularly in the Mid-Atlantic states in the United States; since 2015, New Jersey has documented ≈250-350 legionellosis cases per year. We used SaTScan software to develop a semiautomated surveillance tool for prospectively detecting legionellosis clusters in New Jersey. We varied temporal window size and baseline period to evaluate optimal parameter selections. The surveillance system detected 3 community clusters of Legionnaires' disease that were subsequently investigated. Other, smaller clusters were detected, but standard epidemiologic data did not identify common sources or new cases. The semiautomated processing is straightforward and replicable in other jurisdictions, likely by persons with even basic programming skills.

SUBMITTER: Gleason JA

PROVIDER: S-EPMC8888220 | biostudies-literature | 2022 Mar

REPOSITORIES: biostudies-literature

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Development and Evaluation of Statewide Prospective Spatiotemporal Legionellosis Cluster Surveillance, New Jersey, USA.

Gleason Jessie A JA Ross Kathleen M KM

Emerging infectious diseases 20220301 3

Incidence of Legionnaires' disease is increasing, particularly in the Mid-Atlantic states in the United States; since 2015, New Jersey has documented ≈250-350 legionellosis cases per year. We used SaTScan software to develop a semiautomated surveillance tool for prospectively detecting legionellosis clusters in New Jersey. We varied temporal window size and baseline period to evaluate optimal parameter selections. The surveillance system detected 3 community clusters of Legionnaires' disease tha ...[more]

PMID: 35202521

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Project description:BackgroundCOVID-19 brought enormous challenges to public health surveillance and underscored the importance of developing and maintaining robust systems for accurate surveillance. As public health data collection efforts expand, there is a critical need for infectious disease modeling researchers to continue to develop prospective surveillance metrics and statistical models to accommodate the modeling of large disease counts and variability. This paper evaluated different likelihoods for the disease count model and various spatiotemporal mean models for prospective surveillance.MethodsWe evaluated Bayesian spatiotemporal models, which are the foundation for model-based infectious disease surveillance metrics. Bayesian spatiotemporal mean models based on the Poisson and the negative binomial likelihoods were evaluated with the different lengths of past data usage. We compared their goodness of fit and short-term prediction performance with both simulated epidemic data and real data from the COVID-19 pandemic.ResultsThe simulation results show that the negative binomial likelihood-based models show better goodness of fit results than Poisson likelihood-based models as deemed by smaller deviance information criteria (DIC) values. However, Poisson models yield smaller mean square error (MSE) and mean absolute one-step prediction error (MAOSPE) results when we use a shorter length of the past data such as 7 and 3 time periods. Real COVID-19 data analysis of New Jersey and South Carolina shows similar results for the goodness of fit and short-term prediction results. Negative binomial-based mean models showed better performance when we used the past data of 52 time periods. Poisson-based mean models showed comparable goodness of fit performance and smaller MSE and MAOSPE results when we used the past data of 7 and 3 time periods.ConclusionWe evaluate these models and provide future infectious disease outbreak modeling guidelines for Bayesian spatiotemporal analysis. Our choice of the likelihood and spatiotemporal mean models was influenced by both historical data length and variability. With a longer length of past data usage and more over-dispersed data, the negative binomial likelihood shows a better model fit than the Poisson likelihood. However, as we use a shorter length of the past data for our surveillance analysis, the difference between the Poisson and the negative binomial models becomes smaller. In this case, the Poisson likelihood shows robust posterior mean estimate and short-term prediction results.

Project description:State freedom of information laws are vital mechanisms for providing public access to government records and supporting civic engagement through the effectuation of a public policy of transparency at the state level within the United States, not unlike their federal counterpart, the Freedom of Information Act (FOIA). New Jersey state law facilitates public access to government records under the Open Public Records Act (OPRA). Codified at N.J.S.A. 47:1A-1 et seq., OPRA applies to state, county and local public authorities but exempts the judicial and legislative branches from its disclosure requirements. Since OPRA took effect in 2002, it has been difficult to track the full extent of law's impact across New Jersey's 21 counties, 565 municipalities, and numerous state agencies, school districts and independent authorities, all of which must individually respond to requests under the law. To the best of the author's knowledge, no official source has compiled detailed metadata tracking the content and disposition of OPRA requests at the state, regional and municipal levels within New Jersey using individual requests, and authorities rarely proactively disclose their responses to requests they receive, necessitating further data collection to support research into the impacts of this law. This article presents the OPRAmachine dataset: data containing detailed metadata on public records requests submitted to state & local public authorities in New Jersey since October 2017 collected through the implementation of information and communication technologies (ICT) to facilitate the freedom of information request process. The data was collected using an open-source web interface that allowed users to submit an OPRA request to public authorities, with responses stored in a database and made available via the internet. After their request received a response, users were asked to answer a single survey question describing the status of their request, with their answer used to classify the request. Descriptive statistics, tables and frequencies were produced for the dataset and are included in this article. These data will assist state policymakers and other interested parties with assessing trends in OPRA requests across multiple types of public authorities & geographic regions. These data can inform more efficient government records management procedures, foster civic engagement by increasing government transparency and can inform the development of possible reforms to the OPRA law by showing trends in requests & responses that can be used to evaluate the law's implementation throughout the state.

Dataset Information

Development and Evaluation of Statewide Prospective Spatiotemporal Legionellosis Cluster Surveillance, New Jersey, USA.

Publications

Development and Evaluation of Statewide Prospective Spatiotemporal Legionellosis Cluster Surveillance, New Jersey, USA.

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