Network Surveillance System Research Articles

Vacation in Egypt associated with Shiga toxin-producing Escherichia coli infection in children and adolescents, northern Italy, 2023.

BackgroundHaemolytic uremic syndrome (HUS) is a severe complication of infection with Shiga toxin-producing Escherichia coli (STEC). Although the reservoirs of STEC are known, the source of the infection of sporadic cases is often unknown. In 2023, we observed several cases of bloody diarrhoea with STEC infection in children and adolescents returning from vacations.AimWe aimed to explore the association between travel and bloody diarrhoea with STEC infection in children and adolescents.MethodsWe included all children and adolescents with bloody diarrhoea with STEC infection identified in 2023 by the ItalKid-HUS Network surveillance system in northern Italy. We interviewed children's families and sent a questionnaire on recent travels abroad. The exposure time was between 3 days after arrival abroad and 5 days after return home. A self-controlled case series (SCCS) design was used in the analysis.ResultsOf the 43 cases, 11 developed HUS. Twenty-three cases did not travel abroad, while 20 had travelled to several destinations. The incidence rate ratio (IRR) associated with travel to Egypt was 88.6 (95% confidence interval (CI): 17.0-462). Serotype analysis excluded the possibility of a single strain causing the infections. We did not find the source of the infections.ConclusionThere is an elevated risk of acquiring STEC infection with bloody diarrhoea and HUS associated with travel to Egypt. Specific investigations to identify the source are needed to implement effective preventive measures.

Antimicrobial resistance of clinical bacterial isolates in China: current status and trends.

Antimicrobial resistance surveillance systems have been established in China. Two representative national surveillance networks are the China Antimicrobial Surveillance Network (CHINET) and China Antimicrobial Resistance Surveillance System (CARSS), both of which were established in 2005. For all clinical isolates collected in both of these surveillance networks, the ratio of Gram-negative bacilli to Gram-positive cocci was approximately 7:3 during the past 18 years. Generally, Gram-negative bacilli have a higher antimicrobial resistance profile in China. The prevalence of ESBLs in Escherichia coli is as high as approximately 50%. Acinetobacter baumannii-calcoaceticus complex (ABC) has a high antimicrobial resistance profile, with a carbapenem resistance rate of approximately 66%. However, the prevalence of carbapenem-resistant ABC has also shown a decreasing trend from 2018 to 2022. The prevalence of vancomycin-resistant Enterococcus was low, and the prevalence of MRSA and carbapenem-resistant Pseudomonas aeruginosa showed decreasing trends from 2005 to 2022. CHINET surveillance data demonstrated that the prevalence of carbapenem-resistant Klebsiella pneumoniae showed a remarkable increasing trend from 2.9% (imipenem resistance) in 2005 to 25.0% in 2018, and then slightly decreased to 22.6% in 2022. The decreasing trends may reflect the antimicrobial stewardship efforts in China: a professional consensus on the rational clinical use of carbapenems was issued by the National Health Commission of China and was well implemented nationally; after that, the clinical use of carbapenems decreased slightly in China.

Analysis of time-series monitoring data for early detection of CO2 leakage to shallow subsurface environments

Time-series data of environmental parameters have been gathered in numerous artificial CO2 release experiments performed globally; however, their usability must be re-evaluated because they are generally utilized as auxiliary data, such as model construction or system operation checks. In this study, we propose a process for determining the environmental changepoint (EnCP) using continuous data for the reliable fast detection of unexpected CO2 intrusion into a shallow subsurface. From a controlled CO2 gas release test, continuous data on electrical conductivity, pH, and partial pressure of CO2 (pCO2) in the saturated zone, as well as CO2 concentration in the vadose zone were obtained and analyzed using four methods (simple excess, simple moving average, offline, and online change-point detection) to determine the environmental change-point caused by CO2 appearance. We found that changes caused by CO2 gas injection occurred rapidly, allowing the EnCP detected in the time-series analysis to identify changes more efficiently than the occasional sampling, which was performed at least once a day. It was confirmed that pCO2 had the highest sensitivity among the saturated zone indicators, and the online change-point method was more successful than other methods in determining EnCP effectively. Furthermore, the determined EnCPs at each monitoring point, showing the front boundary of the injected CO2, allowed for the comprehensive understanding of CO2 behavior in both the saturated and vadose zones. This may be used to aid in the design and operation of a real-time CO2 leakage surveillance network system for the early detection of CO2 intrusion into shallow subsurface environments.

Blood and Body Fluid Exposure Among Healthcare Workers and Personal Protective Equipment Usage in the United States.

The International Safety Center disseminates the Exposure Prevention Information Network (EPINet) surveillance system to standardize a system for healthcare facilities to track mucocutaneous blood and body fluid exposures. Occupational exposure incidents to blood and body fluids were recorded within the participant health systems and hospitals (N = 41), using the EPINet Blood and Body Fluid Exposure Report Form. Forms include detailed questions about the circumstances surrounding the exposure, including the type of exposure, body part(s) involved, and if the employee reporting the incident was wearing personal protective equipment (PPE). There were statistically significant differences between participants who wore PPE at time of exposure versus those who did not. Differences were noted by job category (χ2 =32.91, p-value = <.001); where the exposure occurred (χ2 = 32.31, p-value = <.001); what the exposure was a result of (χ2 = 50.19, p-value = <.001); and day versus night shift (χ2 = 11.47, p-value = .001). The study found that occupational exposure to blood and body fluids in 2021 remain high risk given the frequency with which they happen, the exposure site (face) and lack of PPE use. The pandemic seemed to matter little in changing frequencies despite high awareness and growing PPE availability and supply. The findings provide robust information about how exposures occur, why they remain high risk, and how important it is to improve reporting and surveillance to prevent occupational exposures and disease in healthcare in future.

Recent Advances in Video Analytics for Rail Network Surveillance for Security, Trespass and Suicide Prevention-A Survey.

Railway networks systems are by design open and accessible to people, but this presents challenges in the prevention of events such as terrorism, trespass, and suicide fatalities. With the rapid advancement of machine learning, numerous computer vision methods have been developed in closed-circuit television (CCTV) surveillance systems for the purposes of managing public spaces. These methods are built based on multiple types of sensors and are designed to automatically detect static objects and unexpected events, monitor people, and prevent potential dangers. This survey focuses on recently developed CCTV surveillance methods for rail networks, discusses the challenges they face, their advantages and disadvantages and a vision for future railway surveillance systems. State-of-the-art methods for object detection and behaviour recognition applied to rail network surveillance systems are introduced, and the ethics of handling personal data and the use of automated systems are also considered.

The severity and clinical characteristics of COVID-19 among patients with type 2 diabetes mellitus in Jazan, Saudi Arabia.

Background:The objectives of the current study were to assess the severity and clinical characteristics of coronavirus disease 2019 (COVID-19) among Saudi adults with type 2 diabetes mellitus (T2DM) in Jazan region, Saudi Arabia.Methods:This retrospective cohort study included 412 patients with COVID-19 selected randomly from the Health Electronic Surveillance Network system, which contains the primary data on COVID-19 infections in Jazan.Results:COVID-19 disease duration was significantly longer in patients with T2DM (mean = 10.7 days) compared with those without T2DM (mean = 8.3 days) (P = .01). Six (7%) patients experienced an increase in blood glucose concentrations and had to escalate their total daily insulin dose accordingly. Median fasting and random blood glucose levels increased after infection with COVID-19 (pre-COVID median = 119 and 172 mg/dL, respectively; post-COVID median = 148 and 216 mg/dL, respectively) (P = .02). The total insulin dose pre-COVID (median = 42 units/d) increased after infection with COVID-19 (median = 58 units/d) (P = .01). Most patients with T2DM had clinical COVID-19 symptoms (91%) and the remainder (9%) were asymptomatic. A large proportion (80%) of T2DM patients with mild COVID-19 symptoms self-isolated at home. COVID-19 patients with T2DM (11%) who had an oxygen saturation of ≤ 90% and admitted to the intensive care unit were higher than those without T2DM (5%) (P = < .001). COVID-19 patients with T2DM (9%) had higher mortality rate than COVID-19 patients without T2DM (1%) (P = < .001).Conclusion:COVID-19 patients with T2DM were associated with a higher risk of admission to the intensive care unit and mortality than COVID-19 patients without T2DM.

Analysis of national surveillance of respiratory pathogens for community-acquired pneumonia in children and adolescents

BackgroundRespiratory infections among children, particularly community-acquired pneumonia (CAP), is a major disease with a high frequency among outpatient and inpatient visits. The causes of CAP vary depending on individual susceptibility, the epidemiological characteristics of the community, and the season. We performed this study to establish a nationwide surveillance network system and identify the causative agents for CAP and antibiotic resistance in Korean children with CAP.MethodsThe monitoring network was composed of 28 secondary and tertiary medical institutions. Upper and lower respiratory samples were assayed using a culture or polymerase chain reaction (PCR) from August 2018 to May 2020.ResultsA total of 1023 cases were registered in patients with CAP, and PCR of atypical pneumonia pathogens revealed 422 cases of M. pneumoniae (41.3%). Respiratory viruses showed a positivity rate of 65.7% by multiplex PCR test, and human rhinovirus was the most common virus, with 312 cases (30.5%). Two hundred sixty four cases (25.8%) were isolated by culture, including 131 cases of S. aureus (12.8%), 92 cases of S. pneumoniae (9%), and 20 cases of H. influenzae (2%). The cultured, isolated bacteria may be colonized pathogen. The proportion of co-detection was 49.2%. The rate of antibiotic resistance showed similar results as previous reports.ConclusionsThis study will identify the pathogens that cause respiratory infections and analyze the current status of antibiotic resistance to provide scientific evidence for management policies of domestic respiratory infections. Additionally, in preparation for new epidemics, including COVID-19, monitoring respiratory infections in children and adolescents has become more important, and research on this topic should be continuously conducted in the future.

Clinical characteristics and ABO blood groups in COVID-19 patients, Saudi Arabia.

This study assessed the proportion of ABO blood groups and clinical characteristics among Saudi patients with coronavirus disease 2019 (COVID-19) in Jazan, Saudi Arabia.This retrospective cohort study included 404 Saudi adults with COVID-19, confirmed by the real-time reverse transcription-polymerase chain reaction. The participants were selected randomly between July 1, 2020, and July 31, 2020, from the Health Electronic Surveillance Network system, which contains the primary data on COVID-19 infections in Jazan.Blood type O (62.4%) represented the highest proportion in COVID-19 Saudi patients followed by the other blood groups which distributed as follows: blood type A (25.5%), blood type B (10.1%), and blood type AB (2%). Men, and people aged 18–44 years, represented the higher percentage than women and those of a younger age. The majority of the patients with COVID-19 had clinical symptoms (88.4%), and the remainder (11.6%) were asymptomatic. Ninety four percent of the patients had mild COVID-19 symptoms and self-isolated at home. Only 6.4% of the cases were severe and admitted to hospital. There was no significant association between a specific ABO blood group and COVID-19 clinical symptoms (P = .950), incubation period (P = .780), disease duration (P = .430), and disease severity (P = .340). Old age and diabetes were the significant predictors of COVID-19 severity and hospital admission (P = .010).Blood group O represented the highest proportion of COVID-19 Saudi patients as it is the most common blood group in Saudi individuals in Jazan. However, no specific blood group was associated with COVID-19 severity and hospital admission. Old age and diabetes mellitus were shown to be significant predictors of severe COVID-19 and hospital admission.

Candidemia: An Infection Prevention Issue?

(See the Major Article by Suzuki et al on pages 689–96.) Candidemia has consistently ranked in the top 5 causes of nosocomial bloodstream infections in the past 2 decades [1, 2]. In this issue of Clinical Infectious Diseases, Suzuki and colleagues present the temporal trends of candidemia incidence over a period of 18 years within the US veterans healthcare system, concluding that infection prevention interventions implemented over the time span of their study have had an effect in decreasing the incidence of this infection. The National Health and Safety Network surveillance (NHSN) system of the Centers for Disease Control and Prevention (CDC) has indeed shown a gradual decrease central line-associated blood stream infections (CLABSI) over the past 2 decades or so in the United States [2], this being a result of focus on this infection as a national healthcare safety priority since...

Co-morbidity and blood group type risk in coronavirus disease 2019 patients: A case–control study

ObjectiveThe objective of the study was to assess mortality risk associated with co-morbidities and blood group type in coronavirus disease 2019 (COVID-19) patients in Jazan, Saudi Arabia. MethodsThis case–control study enrolled 323 Saudi adults with COVID-19, confirmed by real-time reverse transcription–polymerase chain reaction. The participants were selected randomly between August 31, 2020, and July 1, 2020, from the Health Electronic Surveillance Network system, which contains the primary data on COVID-19 infections in Jazan, Saudi Arabia. The sample included 108 patients who died due to COVID-19 disease and 215 controls who recovered from it (1:2 ratio). The chi-square test, independent samples t-test, and logistic regression were used to perform the statistical analysis. ResultsMortality was higher in older age patients with COVID-19 (mean = 65.4 years, standard deviation [SD] = 15.6) compared to recovered patients (mean = 39.5 years, SD = 14.8) (p<0.001) with a moderate effect size (eta squared = 0.06). Diabetes mellitus (odds ratio [OR] = 9.4), hypertension (OR = 8.6), cardiovascular disease (OR = 7.4), chronic kidney disease (OR = 3.3), and obesity (OR = 2.0) were significantly associated with death due to COVID-19. Using logistic regression analysis, older age and diabetes mellitus were the primary independent predictors of COVID-19 mortality. However, there was no significant association between a specific ABO blood group and mortality risk (P = 0.07). ConclusionOlder age and the presence of co-morbidities, especially diabetes mellitus, increased the risk of death in patients with COVID-19. Establishing the causality of death in patients with COVID-19 should be a key aim of future studies.

Detecting Unauthorized Network Intrusion based on Network Traffic using Behavior Analysis Techniques

Nowadays, network intrusion detection is an essential problem because cyber-attacks are increasing in both the number and extent of the danger. Network intrusion techniques often use various methods to bypass the oversight of anomaly detection and surveillance systems. This paper proposes to use behavior analysis techniques, machine learning, and deep learning algorithms for the task of detecting network intrusions. The practical and scientific significance of our paper includes two issues: (1) Regarding the process of selecting and extracting features: instead of using typical abnormal behaviors of attacks, this study will use statistical behaviors that are easy to calculate and extract while still ensuring the effectiveness of the method; (2) Regarding the detection process, this study proposes to use the Random Forest (RF) classification algorithm, the Multilayer Perceptron (MLP) and the Convolutional Neural Network (CNN) deep learning model. The experimental results in Section IV have proven that our proposal in this paper is completely correct and reasonable. Based on the results shown in Section IV, this study has provided network surveillance systems with a number of abnormal behaviors as the basis for detecting network intrusions.

Risk Factors for Coronavirus Disease 2019 (COVID-19)-Associated Hospitalization: COVID-19-Associated Hospitalization Surveillance Network and Behavioral Risk Factor Surveillance System.

BackgroundData on risk factors for COVID-19-associated hospitalization are needed to guide prevention efforts and clinical care. We sought to identify factors independently associated with COVID-19-associated hospitalizationsMethodsU.S. community-dwelling adults (≥18 years) hospitalized with laboratory-confirmed COVID-19 during March 1–June 23, 2020 were identified from the COVID-19-Associated Hospitalization Surveillance Network (COVID-NET), a multi-state surveillance system. To calculate hospitalization rates by age, sex, and race/ethnicity strata, COVID-NET data served as the numerator and Behavioral Risk Factor Surveillance System estimates served as the population denominator for characteristics of interest. Underlying medical conditions examined included hypertension, coronary artery disease, history of stroke, diabetes, obesity [BMI ≥30 kg/m 2], severe obesity [BMI≥40 kg/m 2], chronic kidney disease, asthma, and chronic obstructive pulmonary disease. Generalized Poisson regression models were used to calculate adjusted rate ratios (aRR) for hospitalizationResultsAmong 5,416 adults, hospitalization rates were higher among those with ≥3 underlying conditions (versus without)(aRR: 5.0; 95%CI: 3.9, 6.3), severe obesity (aRR:4.4; 95%CI: 3.4, 5.7), chronic kidney disease (aRR:4.0; 95%CI: 3.0, 5.2), diabetes (aRR:3.2; 95%CI: 2.5, 4.1), obesity (aRR:2.9; 95%CI: 2.3, 3.5), hypertension (aRR:2.8; 95%CI: 2.3, 3.4), and asthma (aRR:1.4; 95%CI: 1.1, 1.7), after adjusting for age, sex, and race/ethnicity. Adjusting for the presence of an individual underlying medical condition, higher hospitalization rates were observed for adults aged ≥65, 45-64 (versus 18-44 years), males (versus females), and non-Hispanic black and other race/ethnicities (versus non-Hispanic whites)ConclusionOur findings elucidate groups with higher hospitalization risk that may benefit from targeted preventive and therapeutic interventions

Suspicious activity detection using deep learning in secure assisted living IoT environments

Children who are left alone in environments such as daycares and creches require constant monitoring and care to protect them from abuse. In this paper, we propose a novel deep learning-based method for predicting the occurrence of abnormal events using footage gathered from networked surveillance systems and notifying users of those events in an Internet of Things (IoT) environment. Sequences of images are converted to still frames and de-blurred using adaptive motion detection techniques. Then, abnormal activities are predicted using random forest differential evolution with kernel density (RFKD), and any abnormal activities that are detected cause signals to be sent to IoT devices via the MQTT protocol. The proposed work consists of a multi-classifier, deep neural network and kernel density functions. The multi-classifier is used for input classifications from the sequence of frames of videos. The deep neural network is used to learn and train the data and kernel density is used clustering and prediction of data. The novelty of the proposed work is in the dynamic nature of activity prediction. Most of the previous work in this research area concentrated on static activity prediction. The proposed work is able to support both static and dynamic activities of daycare environments. In our experimental trials, our novel method’s performance is shown to be superior to that of the ReHAR method.

Foreground Extraction and Motion Recognition Technology for Intelligent Video Surveillance

With the rapid development of computer technology and network technology, it has become possible to build a large-scale networked video surveillance system. The video surveillance system has become a new type of infrastructure necessary for modern cities. In this paper, the problem of foreground extraction and motion recognition in intelligent video surveillance is studied. The three key sub-problems, namely the extraction of motion foreground in video, the deblurring of motion foreground and the recognition of human motion, are studied and corresponding solutions are proposed. A background modeling technique based on video block is proposed. The background is modeled at the block level, which greatly reduces the spatial complexity of the algorithm. It solves the problem that the traditional Gaussian model (GMM) moving target enters the static state and is integrated into the background process. The target starts to move for a long time and there are ghosts and other problems, which reduce the processing efficiency of the lifting algorithm. The test results on the Weizmann dataset show that the proposed algorithm can achieve high human motion recognition accuracy and recognition with low computational complexity. The rate can reach 100%; the local constrained group sparse representation classification (LGSRC) model is used to classify it. The experimental results on Weizmann, KTH, UCF sports and other test datasets confirm the validity of the algorithm in this chapter. KNN, SRC voting classification accuracy.

Ed-PUF: Event-Driven Physical Unclonable Function for Camera Authentication in Reactive Monitoring System

As surveillance footage plays an increasingly significant role in law enforcement, it is imperative to ensure the integrity of recorded video data and the authenticity of its originator, and instill situation awareness into these monitoring systems with a fidelity record of the incidents. Unfortunately, existing frame-based networked surveillance systems could only partially fulfill these requirements. The emerging Dynamic Vision Sensor (DVS) sheds new light on solving this problem with its completely different sensor design, i.e., DVS responds only to temporal intensity change and records only sparse asynchronous address-events with precise timing information. Motivated by the reduced data size of activities and the prevention of privacy intrusion of subjects under surveillance as well as other appealing attributes, this work introduces the first event-driven physical unclonable function (Ed-PUF) system to fill the forensic gap of simultaneously authenticating the event data integrity and source camera identity for reactive monitoring by DVS camera. New DVS sensor architecture is proposed with negligible modifications made to the original DVS pixel. The Ed-PUF response bit can only be triggered by and uniquely dependent on the asynchronous addressed event without being interfered by the simultaneous firing of other address events. Address event streams are securely transmitted with an event package tag created by a keyed hash-based message authentication code with the key being the Ed-PUF response. A secure protocol to authenticate the identity of DVS camera and the integrity of address events transmitted through cellular network is also proposed. A camera lock is embedded to protect against severing and splicing the inter-chip connectivity within the camera for raw PUF responses. The proposed system is evaluated using raw PUF data obtained by post-layout Monte Carlo simulation in UMC 180nm technology and real event stream captured by a DVS camera. The proposed Ed-PUF has been demonstrated to have excellent uniqueness, randomness and reliability. Collision test is also conducted to show that the quality of DVS imaging is not compromised. Besides keeping the hardware/power/timing overheads low, the proposed scheme is also analyzed to be resilient against multiple attack scenarios.

Evaluation of the application of moving epidemic method on making influenza epidemic thresholds in the 7 climate zones in China

Objective: We planned to evaluate the effectiveness of moving epidemic method (MEM) in calculating influenza epidemic threshold of 7 climatic zones in China mainland. Methods: The positive rate of influenza virus was obtained from the National Influenza Surveillance Network System from 2010/2011 to 2017/2018. We divided the 31 provinces into 7 climatic zones according to previous literatures and applied MEM to calculate the influenza epidemic threshold of 2018/2019 influenza season for these climatic zones. Sensitivity, specificity, positive predictive value and negative predictive value were calculated to evaluate the effectiveness of MEM. Results: Pre-epidemic threshold (the positive rate of influenza virus) varied from 9.66% (temperate zone) to 16.36% (subtropical zone) for 2018/2019 influenza season. The gap between pre-epidemic and post-epidemic thresholds was less than 5% except for plateau zone. The sensitivity was 86.16% (95CI:66.81%-98.23%), the specificity was 94.92% (95CI: 91.13%-98.41%), the positive predictive value was 89.87% (95%CI: 84.39%-94.38%), the negative predictive value was 92.96% (95%CI: 84.46%-99.17%). Conclusion: Overall, moving epidemic Method performs well in calculating influenza epidemic threshold in China, much better than the previous study.

522. Impact of a Multicomponent Intervention Bundle on Healthcare Facility-Onset Clostridium difficile Rates

Background Clostridium difficile is the most common cause of healthcare-associated infections in US hospitals. The National Healthcare Safety Network (NHSN) surveillance system tracks C. difficile infections (CDI), one of the measures used by Centers for Medicare and Medicaid Services (CMS) to determine a hospital’s total Hospital-Acquired Conditions (HAC) score and ranking among other hospitals. This is then used to calculate Value-Based Purchasing pay-for-performance incentive payments, which may lead to significant reductions in a facility’s reimbursement if rates are too high. The objective of this study was to assess the effectiveness of a multicomponent intervention bundle in reducing our healthcare facility-onset (HO) CDI rates.MethodsThis was a pre-post quasi-experimental retrospective study comparing CDI rate per 1,000 patient days and Standardized Infection Ratio (SIR) in the preintervention period from January 1, 2017 to December 31, 2017 to the intervention period from January 1, 2018 to March 31, 2018 in a 319-bed teaching hospital in northwest Ohio. We implemented a testing algorithm to guide physicians and nurses, focusing on increasing early detection and decreasing inappropriate testing. We enforced re-testing criteria, which did not allow re-testing within 7 days and in those who were positive during the admission. Infection Preventionists provided staff education. A dedicated C. diff isolation cart was created. Contact isolation, hand hygiene, enhanced environmental cleaning and disinfection were reinforced. Treatment guidelines were established and antimicrobial stewardship reviews were performed on all cases to discourage unnecessary medications, encourage judicious use of antimicrobials, and ensure appropriate treatment.ResultsOur C. difficile rate per 1,000 patient days decreased from 0.826 in the preintervention period to 0.495 in the postintervention period, which resulted in 60% reduction in HO-CDI rate. The SIR also decreased from 1.207 to 0.677, yielding a 55% reduction.ConclusionImplementing a C. difficile multicomponent intervention bundle that emphasizes early and appropriate testing may reduce HO-CDI rates.Disclosures All authors: No reported disclosures.

Human Detection in Intelligent Video Surveillance: A Review

With the rapid development of networked video surveillance systems, human detection is essential. These tasks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, such as security and surveillance. This review paper extensively surveys the current progress made toward human detection in intelligent video surveillance. The algorithms presented in this paper are classified as either human detection without classifier training or human detection with classifier training. In the core techniques of human detection without classifier training, three critical processing stages are discussed including background subtraction, Gaussian mixture model (GMM) and skin color model. In the core techniques of human detection with classifier training, two main types are mentioned including holistic human detector, and part-based human detector. Our survey aims to address existing problems, challenges and future research directions based on the analyses of the current progress made toward human detection techniques in computer vision.

Utilization of Syndromic Surveillance to Identify Naled-Related Illness in Florida

Objective: To describe the use of Florida Poison Information Center Network (FPICN) and Electronic Surveillance System for the Early Notification of Community-based Epidemics (ESSENCE-FL) emergency department (ED) chief complaints data to identify acute naled-related illness following aerial spraying in Miami-Dade county, Florida in response to the Zika outbreak.Introduction: Pesticide-related illness and injury is a reportable condition in Florida. In August and September 2016, aerial spraying for mosquito control was conducted in an effort to reduce the population of Aedes aegyptimosquitoes in Miami-Dade County.1 Two areas Wynwood (in August) and Miami Beach (in September) were sprayed with naled. Naled is an organophosphate insecticide registered with the U.S. Environmental Protection Agency (EPA) which is applied via aerial ultra-low volume (ULV) spraying. In addition to routine surveillance using FPICN and reportable disease surveillance data to identify acute naled-related illness, the Florida Department of Health (DOH) also monitored ED chief complaints data to identify any associated increase in ED visits.Methods: In 2016, DOH used three datasets to monitor illness related to naled exposure: FPICN call data, reportable disease (Merlin) data, and ED chief complaints. Product code 2327991 was used to search FPICN data for naled-related exposure calls. FPICN calls with the following medical outcomes were excluded: no health effect, not followed - judged as nontoxic exposure, unrelated effect, and confirmed non-exposure. Individuals who met the reportable disease surveillance case definition for pesticide-related illness and injury2 were entered into Merlin. ESSENCE-FL was monitored to evaluate ED visits in Miami-Dade County for the syndrome categories and free text chief complaints involving eyes (free text queries for eye irritation, eye burning, eye redness, and conjunctivitis), skin (syndrome category for rash), and respiratory (sub-syndrome categories for shortness of breath, wheezing, acute bronchitis, sore throat, and asthma) illnesses.Results: Twenty-two exposure calls were identified through FPICN data in 2016. Seven calls were excluded after DOH review determined that these individuals were not exposed to naled. Fifteen exposure calls were investigated and eight individuals met the surveillance case definition for pesticide-related illness and injury. Among the eight DOH cases, one individual was exposed in August (12.5%), and seven in September (87.5%). Everyone had low severity illness, five (62.5%) were female, and the mean age was 39.6 years (range: 27 to 46 years). Two cases (25%) were work-related. Review of ESSENCE ED chief complaints data for eye, skin, and respiratory illnesses showed a few statistically significant increases in daily patient visits. However, these increases were not attributed to aerial spraying.Conclusions: FPICN data are useful in identifying cases of naled-related illness. Near real-time access to ED chief complaints data along with FPICN and Merlin data has enhanced surveillance capability for DOH and helped address public health concerns related to naled-related illness following aerial spraying in Miami-Dade County.

Risk factors of mortality in nosocomial infected traumatic patients in a trauma referral center in south of Iran

PurposeTrauma-related injuries are the leading cause of death worldwide. Some risk factors make traumatic patients susceptible to infection. Furthermore, some mortality risk factors, including length of hospitalization and increasing age, were detected in non-traumatic infected patients. This study aimed to assess mortality risk factors among nosocomial infected traumatic patients in Rajaee trauma center, Shiraz, Iran. MethodsThis prospective cohort study was conducted during a period of 2 years since April 2015 to March 2017 in Rajaee hospital, which is the center of emergency medical services for traumatic injuries in Shiraz, Iran. Centers for Disease Control and Prevention/National Healthcare Safety Network surveillance system criteria were applied to define 5 types of nosocomial infections. The variables analyzed as the risk factors of infection and mortality included sex, age, mechanism of injury, site of injury, injury severity score (ISS), surgical intervention, length of hospitalization, intensive care unit (ICU) admission, and type of pathogen. Then, the incidence of nosocomial infection and also risk factors of mortality in traumatic patients were evaluated. All data analyses were performed using the statistical package for social sciences, version 15 (SPSS Inc., Chicago) and p ≤ 0.05 is considered to be statistically significant. ResultsThe incidence of nosocomial infection was 7.2% (p < 0.001). Pneumonia was the most common type of infection detected in our study. Infection led to a 7.8-fold increase in mortality of the traumatic patients (p < 0.001). Admission in intensive care units and old age were the main risk factors of mortality in infected traumatic patients. Old age, gunshot and motor vehicle accidents, trauma to extremities and abdomen, higher injury severity score, and prolonged hospitalization, made the traumatic patients more susceptible to infection. ConclusionThe really high incidence of nosocomial infection in traumatic patients in Iran depends on some risk factors that should be considered. Also infection increases the mortality rate in the traumatic patients, which could be reduced by eliminating its risk factors.