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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 detectio

Background and Objectives: Tumor necrosis factor-alpha (TNF-α) inhibitors are efficacious and considered generally safe in adults. However, pediatric-specific safety evidence is scarce. The aim of this study was to screen for signals of previously unknown adverse events of TNF-α inhibitors in pediatric patients. Methods: We conducted a data-mining study based on routinely collected, nationwide Danish healthcare data for 2004–2016. Using tree-based scan statistics to identify events with unexpectedly high incidence during TNF-α inhibitor use among patients with inflammatory bowel disease or juvenile idiopathic arthritis, two analyses were performed: comparison with episodes of no use and with other time periods from the same patient. Based on incident physician-assigned diagnosis codes from outpatient and inpatient visits in specialist care, we screened thousands of potential adverse events while adjusting for multiple testing. Results: We identified 1310 episodes of new TNF-α inhibitor use that met the eligibility criteria. Two signals of adverse events of TNF-α inhibitors, as compared with no use, were detected. First, there were excess events of dermatologic complications (ICD-10: L00-L99, 87 vs. 44 events, risk difference [RD] 3.3%), which have been described previously in adults and children. Second, there were excess events of psychiatric diagnosis adjustment disorders (ICD-10: F432, 33 vs. 7 events, RD 2.0%), which was likely associated with the underlying disease and its severity, rather than with the treatment. The self-controlled analysis generated no signal. Conclusions: No signals of previously unknown adverse events of TNF-α inhibitors in pediatric patients were detected. The study showed that real-world data and newly developed methods for adverse events data mining can play a particularly important role in pediatrics where pre-approval drug safety data are scarce.

BACKGROUND AND OBJECTIVES: Tumor necrosis factor-alpha (TNF-α) inhibitors are efficacious and considered generally safe in adults. However, pediatric-specific safety evidence is scarce. The aim of this study was to screen for signals of previously unknown adverse events of TNF-α inhibitors in pediatric patients. METHODS: We conducted a data-mining study based on routinely collected, nationwide Danish healthcare data for 2004-2016. Using tree-based scan statistics to identify events with unexpectedly high incidence during TNF-α inhibitor use among patients with inflammatory bowel disease or juvenile idiopathic arthritis, two analyses were performed: comparison with episodes of no use and with other time periods from the same patient. Based on incident physician-assigned diagnosis codes from outpatient and inpatient visits in specialist care, we screened thousands of potential adverse events while adjusting for multiple testing. RESULTS: We identified 1310 episodes of new TNF-α inhibitor use that met the eligibility criteria. Two signals of adverse events of TNF-α inhibitors, as compared with no use, were detected. First, there were excess events of dermatologic complications (ICD-10: L00-L99, 87 vs. 44 events, risk difference [RD] 3.3%), which have been described previously in adults and children. Second, there were excess events of psychiatric diagnosis adjustment disorders (ICD-10: F432, 33 vs. 7 events, RD 2.0%), which was likely associated with the underlying disease and its severity, rather than with the treatment. The self-controlled analysis generated no signal. CONCLUSIONS: No signals of previously unknown adverse events of TNF-α inhibitors in pediatric patients were detected. The study showed that real-world data and newly developed methods for adverse events data mining can play a particularly important role in pediatrics where pre-approval drug safety data are scarce., Funding Agencies:Frimurare Barnhuset FoundationStrategic Research Area Epidemiology program at Karolinska Institutet

ObjectiveTo assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.DesignMulticenter retrospective cohort study.SettingThe study included 43 hospitals using a common infection prevention surveillance system.MethodsA 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.ResultsWe 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.ConclusionsAutomated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

ObjectiveTo assess the utility of an automated, statistically-based outbreak detection system to identify clusters of hospital-acquired microorganisms.DesignMulticenter retrospective cohort study.SettingThe study included 43 hospitals using a common infection prevention surveillance system.MethodsA 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.ResultsWe 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.ConclusionsAutomated, statistically-based outbreak detection can increase the consistency, scope, and comprehensiveness of detecting hospital-associated transmission.

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.

Reported habits of red meat consumption, particularly red meat that has been cooked to the degree termed ‘well-done', is a positive risk factor for colorectal cancer. Under high, pyrolytic temperatures, heterocyclic amines (HCA) and benzo[a]pyrene (BP) molecules can form inside and on the surface of red meat, respectively. These compounds are precursors that are metabolically converted to compounds known to act as mutagens and carcinogens in animal models, yet their role in human colorectal carcinogenesis remains to be clarified. We investigated whether intake of these compounds is associated with risk of colorectal adenoma in the context of a polyp-screening study conducted in Southern California. Using a database of individual HCAs and BP in meats of various types and subjected to specified methods and degrees of cooking, we estimated nanogram consumption of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine, 2-amino-3,4,8-trimethylimidazo[4,5-f] quinoxaline, 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline and benzo[a]pyrene (BP). We observed a 6% increased risk of large (>1 cm) adenoma per 10 ng/day consumption of BP [OR = 1.06 (95% CI, 1.00-1.12), P (trend) = 0.04]. A major source of BP is red meat exposed to a naked flame, as occurs during the barbecuing process. Consistent with this finding an incremental increase of 10 g of barbecued red meat per day was associated with a 29% increased risk of large adenoma [OR = 1.29 (95% CI, 1.02-1.63), P (trend) = 0.04]. Individuals in the top quintile of barbecued red meat intake were at increased risk of large adenoma [OR = 1.90 (95% CI, 1.04-3.45)], compared with never consuming barbecued red meat. The consumption of oven-broiled red meat was inversely related to adenoma risk compared with non-consumers [OR = 0.49 (95% CI, 0.28-0.85)]. We did not identify any association with consumption of individual HCAs and colorectal adenoma risk. These results support the hypothesis that BP contributes to colorectal carcinogenesis

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.

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.

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.

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.

Methods for the assessment of spatial variations in temporal trends (SVTT) are important tools for disease surveillance, which can help governments to formulate programs to prevent diseases, and measure the progress, impact, and efficacy of preventive efforts already in operation. The linear SVTT method is designed to detect areas with unusual different disease linear trends. In some situations, however, its estimation trend procedure can lead to wrong conclusions. In this article, the quadratic SVTT method is proposed as alternative of the linear SVTT method. The quadratic method provides better estimates of the real trends, and increases the power of detection in situations where the linear SVTT method fails. A performance comparison between the linear and quadratic methods is provided to help illustrate their respective properties. The quadratic method is applied to detect unusual different cervical cancer trends in white women in the United States, over the period 1969 to 1995.

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.

Methods for the assessment of spatial variations in temporal trends (SVTT) are important tools for disease surveillance, which can help governments to formulate programs to prevent diseases, and measure the progress, impact, and efficacy of preventive efforts already in operation. The linear SVTT method is designed to detect areas with unusual different disease linear trends. In some situations, however, its estimation trend procedure can lead to wrong conclusions. In this article, the quadratic SVTT method is proposed as alternative of the linear SVTT method. The quadratic method provides better estimates of the real trends, and increases the power of detection in situations where the linear SVTT method fails. A performance comparison between the linear and quadratic methods is provided to help illustrate their respective properties. The quadratic method is applied to detect unusual different cervical cancer trends in white women in the United States, over the period 1969 to 1995.

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

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

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.

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

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

Reported habits of red meat consumption, particularly red meat that has been cooked to the degree termed ‘well-done', is a positive risk factor for colorectal cancer. Under high, pyrolytic temperatures, heterocyclic amines (HCA) and benzo[a]pyrene (BP) molecules can form inside and on the surface of red meat, respectively. These compounds are precursors that are metabolically converted to compounds known to act as mutagens and carcinogens in animal models, yet their role in human colorectal carcinogenesis remains to be clarified. We investigated whether intake of these compounds is associated with risk of colorectal adenoma in the context of a polyp-screening study conducted in Southern California. Using a database of individual HCAs and BP in meats of various types and subjected to specified methods and degrees of cooking, we estimated nanogram consumption of 2-amino-1-methyl-6-phenylimidazo[4,5-b]pyridine, 2-amino-3,4,8-trimethylimidazo[4,5-f] quinoxaline, 2-amino-3,8-dimethylimidazo[4,5-f]quinoxaline and benzo[a]pyrene (BP). We observed a 6% increased risk of large (>1 cm) adenoma per 10 ng/day consumption of BP [OR = 1.06 (95% CI, 1.00-1.12), P (trend) = 0.04]. A major source of BP is red meat exposed to a naked flame, as occurs during the barbecuing process. Consistent with this finding an incremental increase of 10 g of barbecued red meat per day was associated with a 29% increased risk of large adenoma [OR = 1.29 (95% CI, 1.02-1.63), P (trend) = 0.04]. Individuals in the top quintile of barbecued red meat intake were at increased risk of large adenoma [OR = 1.90 (95% CI, 1.04-3.45)], compared with never consuming barbecued red meat. The consumption of oven-broiled red meat was inversely related to adenoma risk compared with non-consumers [OR = 0.49 (95% CI, 0.28-0.85)]. We did not identify any association with consumption of individual HCAs and colorectal adenoma risk. These results support the hypothesis that BP contributes to colorectal carcinogenesis