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BackgroundDetection and containment of hospital outbreaks currently depend on variable and personnel-intensive surveillance methods. Whether automated statistical surveillance for outbreaks of health care-associated pathogens allows earlier containment efforts that would reduce the size of outbreaks is unknown.MethodsWe conducted a cluster-randomized trial in 82 community hospitals within a larger health care system. All hospitals followed an outbreak response protocol when outbreaks were detected by their infection prevention programs. Half of the hospitals additionally used statistical surveillance of microbiology data, which alerted infection prevention programs to outbreaks. Statistical surveillance was also applied to microbiology data from control hospitals without alerting their infection prevention programs. The primary outcome was the number of additional cases occurring after outbreak detection. Analyses assessed differences between the intervention period (July 2019 to January 2022) versus baseline period (February 2017 to January 2019) between randomized groups. A post hoc analysis separately assessed pre-coronavirus disease 2019 (Covid-19) and Covid-19 pandemic intervention periods.ResultsReal-time alerts did not significantly reduce the number of additional outbreak cases (intervention period versus baseline: statistical surveillance relative rate [RR]=1.41, control RR=1.81; difference-in-differences, 0.78; 95% confidence interval [CI], 0.40 to 1.52; P=0.46). Comparing only the prepandemic intervention with baseline periods, the statistical outbreak surveillance group was associated with a 64.1% reduction in additional cases (statistical surveillance RR=0.78, control RR=2.19; difference-in-differences, 0.36; 95% CI, 0.13 to 0.99). There was no similarly observed association between the pandemic versus baseline periods (statistical surveillance RR=1.56, control RR=1.66; difference-in-differences, 0.94; 95% CI, 0.46 to 1.92).ConclusionsAutomated detection of hospital outbreaks using statistical surveillance did not reduce overall outbreak size in the context of an ongoing pandemic. (Funded by the Centers for Disease Control and Prevention; ClinicalTrials.gov number, NCT04053075. Support for HCA Healthcare's participation in the study was provided in kind by HCA.).

OBJECTIVE: To assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms. DESIGN: Multicenter retrospective cohort study. SETTING: The study included 43 hospitals using a common infection prevention surveillance system. METHODS: A space-time permutation scan statistic was applied to hospital microbiology, admission, discharge, and transfer data to identify clustering of microorganisms within hospital locations and services. Infection preventionists were asked to rate the importance of each cluster. A convenience sample of 10 hospitals also provided information about clusters previously identified through their usual surveillance methods. RESULTS: We identified 230 clusters in 43 hospitals involving Gram-positive and -negative bacteria and fungi. Half of the clusters progressed after initial detection, suggesting that early detection could trigger interventions to curtail further spread. Infection preventionists reported that they would have wanted to be alerted about 81% of these clusters. Factors associated with clusters judged to be moderately or highly concerning included high statistical significance, large size, and clusters involving Clostridioides difficile or multidrug-resistant organisms. Based on comparison data provided by the convenience sample of hospitals, only 9 (18%) of 51 clusters detected by usual surveillance met statistical significance, and of the 70 clusters not previously detected, 58 (83%) involved organisms not routinely targeted by the hospitals surveillance programs. All infection prevention programs felt that an automated outbreak detection tool would improve their ability to detect outbreaks and streamline their work. CONCLUSIONS: Automated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

Spatiotemporal analysis of high-resolution coronavirus disease (COVID-19) data can help health officials monitor disease spread and target interventions (1,2). Publicly available data have been used to detect COVID-19 spatiotemporal clusters [...]

BackgroundWhile antimicrobial resistance threatens the prevention, treatment, and control of infectious diseases, systematic analysis of routine microbiology laboratory test results worldwide can alert new threats and promote timely response. This study explores statistical algorithms for recognizing geographic clustering of multi-resistant microbes within a healthcare network and monitoring the dissemination of new strains over time.MethodsEscherichia coli antimicrobial susceptibility data from a three-year period stored in WHONET were analyzed across ten facilities in a healthcare network utilizing SaTScan's spatial multinomial model with two models for defining geographic proximity. We explored geographic clustering of multi-resistance phenotypes within the network and changes in clustering over time.ResultsGeographic clustering identified from both latitude/longitude and non-parametric facility groupings geographic models were similar, while the latter was offers greater flexibility and generalizability. Iterative application of the clustering algorithms suggested the possible recognition of the initial appearance of invasive E. coli ST131 in the clinical database of a single hospital and subsequent dissemination to others.ConclusionSystematic analysis of routine antimicrobial resistance susceptibility test results supports the recognition of geographic clustering of microbial phenotypic subpopulations with WHONET and SaTScan, and iterative application of these algorithms can detect the initial appearance in and dissemination across a region prompting early investigation, response, and containment measures.

BackgroundThe Massachusetts Childhood Obesity Research Demonstration (MA-CORD) project is a 2-year, multilevel, multisector community intervention to prevent and control obesity among children 2-12 years of age from two predominantly low-income communities in Massachusetts. MA-CORD includes evidence-based interventions in multiple sectors, including community health centers, early care and education centers, schools, afterschool programs, the Special Supplemental Nutrition Program for Women, Infants and Children, and the broader community. Currently, implementation of MA-CORD is complete and the final year of data collection is in progress. Here, the MA-CORD evaluation plan is described and baseline data are presented.Methods/designThe impact of MA-CORD on children's BMI, lifestyle behaviors, obesity-related care, and quality of life will be assessed using sector-specific, pre/post, time-series, and quasi-experimental designs. Change in the primary outcomes will be compared for intervention and comparison communities. Additionally, change in mean BMI and obesity prevalence in intervention school districts will be compared to similar districts throughout the state.ResultsAt baseline in 2012, approximately 16% of preschool-aged and 25% of school-aged children were obese. Moreover, 15-40% of children consumed no vegetables on the previous day, 25-75% drank a sugar-sweetened beverage on the previous day, up to 87% had insufficient physical activity, 50-75% had a television in the room where they slept, and 50-80% obtained insufficient sleep.ConclusionsThere is ample room for improvement in BMI and health behaviors in children in MA-CORD communities. If successful, MA-CORD may serve as a model for multilevel, multisector approaches to childhood obesity prevention and control.

BackgroundPassive reporting and laboratory testing delays may limit gastrointestinal (GI) disease outbreak detection. Healthcare systems routinely collect clinical data in electronic medical records (EMRs) that could be used for surveillance. This study's primary objective was to identify data streams from EMRs that may perform well for GI outbreak detection.MethodsZip code-specific daily episode counts in 2009 were generated for 22 syndromic and laboratory-based data streams from Kaiser Permanente Northern California EMRs, covering 3.3 million members. Data streams included outpatient and inpatient diagnosis codes, antidiarrheal medication dispensings, stool culture orders, and positive microbiology tests for six GI pathogens. Prospective daily surveillance was mimicked using the space-time permutation scan statistic in single and multi-stream analyses, and space-time clusters were identified. Serotype relatedness was assessed for isolates in two Salmonella clusters.ResultsPotential outbreaks included a cluster of 18 stool cultures ordered over 5 days in one zip code and a Salmonella cluster in three zip codes over 9 days, in which at least five of six cases had the same rare serotype. In all, 28 potential outbreaks were identified using single stream analyses, with signals in outpatient diagnosis codes most common. Multi-stream analyses identified additional potential outbreaks and in one example, improved the timeliness of detection.ConclusionsGI disease-related data streams can be used to identify potential outbreaks when generated from EMRs with extensive regional coverage. This process can supplement traditional GI outbreak reports to health departments, which frequently consist of outbreaks in well-defined settings (e.g., day care centers and restaurants) with no laboratory-confirmed pathogen. Data streams most promising for surveillance included microbiology test results, stool culture orders, and outpatient diagnoses. In particular, clusters of microbiology tests positive for specific pathogens could be identified in EMRs and used to prioritize further testing at state health departments, potentially improving outbreak detection.

National infectious disease incidence rates are often estimated by standardizing locally derived rates using national-level age and race distributions. Data on other factors potentially associated with incidence are often not available in the form of patient-level covariates. Including characteristics of patients' area of residence may improve the accuracy of national estimates. The authors used data from the Centers for Disease Control and Prevention's Active Bacterial Core Surveillance program (2004-2005), adjusted for census-based variables, to estimate the national incidence of invasive pneumococcal disease (IPD). The authors tested Poisson and negative binomial models in a cross-validation procedure to select variables best predicting the incidence of IPD in each county. Including census-level information on race and educational attainment improved the fit of both Poisson and negative binomial models beyond that achieved by adjusting for other census variables or by adjusting for an individual's race and age alone. The Poisson model with census-based predictors led to a national estimate of IPD of 16.0 cases per 100,000 persons as compared with 13.5 per 100,000 persons using an individual's age and race alone. Accuracy of, and confidence intervals for, these estimates can only be determined by obtaining data from other randomly selected US counties. However, incorporating census-derived characteristics should be considered when estimating national incidence of IPD and other diseases.

Background: Detection of outbreaks of hospital-acquired infections is often based on simple rules, such as the occurrence of three new cases of a single pathogen in two weeks on the same ward. These rules typically focus on only a few pathogens, and they do not account for the pathogens' underlying prevalence, the normal random variation in rates, and clusters that may occur beyond a single ward, such as those associated with specialty services. Ideally, outbreak detection programs should evaluate many pathogens, using a wide array of data sources. Methods and Findings: We applied a space-time permutation scan statistic to microbiology data from patients admitted to a 750-bed academic medical center in 2002-2006, using WHONET-SaTScan laboratory information software from the World Health Organization (WHO) Collaborating Centre for Surveillance of Antimicrobial Resistance. We evaluated patients' first isolates for each potential pathogenic species. In order to evaluate hospital-associated infections, only pathogens first isolated >2d after admission were included. Clusters were sought daily across the entire hospital, as well as in hospital wards, specialty services, and using similar antimicrobial susceptibility profiles. We assessed clusters that had a likelihood of occurring by chance less than once per year. For methicillin-resistant Staphylococcus aureus (MRSA) or vancomycin-resistant enterococci (VRE), WHONET-SaTScan-generated clusters were compared to those previously identified by the Infection Control program, which were based on a rule-based criterion of three occurrences in two weeks in the same ward. Two hospital epidemiologists independently classified each cluster's importance. From 2002 to 2006, WHONET-SaTScan found 59 clusters involving 2-27 patients (median 4). Clusters were identified by antimicrobial resistance profile (41%), wards (29%), service (13%), and hospital-wide assessments (17%). WHONET-SaTScan rapidly detected the two previously known gram-negative pathogen clusters. Compared to rule-based thresholds, WHONET-SaTScan considered only one of 73 previously designated MRSA clusters and 0 of 87 VRE clusters as episodes statistically unlikely to have occurred by chance. WHONET-SaTScan identified six MRSA and four VRE clusters that were previously unknown. Epidemiologists considered more than 95% of the 59 detected clusters to merit consideration, with 27% warranting active investigation or intervention. Conclusions: Automated statistical software identified hospital clusters that had escaped routine detection. It also classified many previously identified clusters as events likely to occur because of normal random fluctuations. This automated method has the potential to provide valuable real-time guidance both by identifying otherwise unrecognized outbreaks and by preventing the unnecessary implementation of resource-intensive infection control measures that interfere with regular patient care.

Temporal, spatial and space-time scan statistics are commonly used to detect and evaluate the statistical significance of temporal and/or geographical disease clusters, without any prior assumptions on the location, time period or size of those clusters. Scan statistics are mostly used for count data, such as disease incidence or mortality. Sometimes there is an interest in looking for clusters with respect to a continuous variable, such as lead levels in children or low birth weight. For such continuous data, we present a scan statistic where the likelihood is calculated using the the normal probability model. It may also be used for other distributions, while still maintaining the correct alpha level. In an application of the new method, we look for geographical clusters of low birth weight in New York City.

The Bureau of Communicable Disease (BCD) at the New York City Department of Health and Mental Hygiene (DOHMH) monitors and investigates >70 reportable diseases among the city's 8.49 million residents. [...]

Background Kawasaki disease is an acute vasculitis that primarily affects children younger than 5 years of age. Its etiology is unknown. The United States Vaccine Safety Datalink conducted postlicensure safety surveillance for 13-valent pneumococcal conjugate vaccine (PCV13), comparing the risk of Kawasaki disease within 28 days of PCV13 vaccination with the historical risk after 7-valent PCV (PCV7) vaccination and using chart-validation. A relative risk (RR) of 2.38 (95% CI 0.92-6.38) was found. Concurrently, the Food and Drug Administration (FDA) conducted a postlicensure safety review that identified cases of Kawasaki disease through adverse event reporting. The FDA decided to initiate a larger study of Kawasaki disease risk following PCV13 vaccination in the claims-based Sentinel/Postlicensure Rapid Immunization Safety Monitoring (PRISM) surveillance system. The objective of this study was to determine the existence and magnitude of any increased risk of Kawasaki disease in the 28 days following PCV13 vaccination. Methods and findings The study population included mostly commercially insured children from birth to Conclusions With more than 6 million doses of PCV13 administered, no evidence was found of an association between PCV13 vaccination and Kawasaki disease onset in the 4 weeks after vaccination nor of an elevated risk extending or concentrated somewhat beyond 4 weeks. These null results were consistent across alternative designs, age-adjustment methods, control intervals, and categories of Kawasaki disease case included., Author(s): Meghan A. Baker 1,2, Bethany Baer 3, Martin Kulldorff 4, Lauren Zichittella 1, Rebecca Reindel 3, Sandra DeLuccia 1, Hana Lipowicz 1, Katherine Freitas 1, Robert Jin 1, W. [...]

Tree-based scan statistics have been successfully used to study the safety of several vaccines without prespecifying health outcomes of concern. In this study, the binomial tree-based scan statistic was applied sequentially to detect adverse events in days 1–28 compared with days 29–56 after recombinant herpes zoster (RZV) vaccination, with 5 looks at the data and formal adjustment for the repeated analyses over time. IBM MarketScan data on commercially insured persons ≥50 years of age receiving RZV during January 1, 2018, to May 5, 2020, were used. With 999,876 doses of RZV included, statistically significant signals were detected only for unspecified adverse effects/complications following immunization, with attributable risks as low as 2 excess cases per 100,000 vaccinations. Ninety percent of cases in the signals occurred in the week after vaccination and, based on previous studies, likely represent nonserious events like fever, fatigue, and headache. Strengths of our study include its untargeted nature, self-controlled design, and formal adjustment for repeated testing. Although the method requires prespecification of the risk window of interest and may miss some true signals detectable using the tree-temporal variant of the method, it allows for early detection of potential safety problems through early initiation of ongoing monitoring. [ABSTRACT FROM AUTHOR]