ObjectiveTo better understand the importance of monitoring responders during public health emergencies and to learn how the Georgia Department of Public Health (DPH) developed and deployed an electronic responder monitoring tool.IntroductionDuring an emergency, the state of Georgia depends on public health staff and volunteers to respond. It is imperative that staff are safe before, during and after deployment. Emergency response workers must be protected from the hazardous conditions that disasters and other emergencies create1. In October 2016 and September 2017, Hurricanes Matthew and Irma caused widespread evacuation of Georgia residents, initiating a tremendous sheltering effort. Hundreds of public health responders were deployed to assist with sheltering and other aspects of the response. DPH rapidly developed a novel electronic Responder Safety, Tracking and Resilience module, which was used to track public health responders and monitor their health and safety while deployed.MethodsDPH rapidly developed a novel electronic Responder Safety, Tracking, and Resilience module (R-STaR), within the existing State Electronic Notifiable Disease Surveillance System to monitor the health and safety of responders. R-STaR was originally used during Hurricane Matthew, where it was launched the day of the storm, and was launched again four days before Hurricane Irma made landfall. Responders were emailed a web-based link to register, indicating demographic information, contact information, work location, subject area, vaccination status, and whether they considered themselves mentally and physically fit to deploy. Responders then received a daily email with a link to document their daily deployment location, duties, and whether they had any hazardous exposures, illness, or injuries while deployed. A post-deployment survey was sent to responders after Hurricane Matthew to solicit feedback about the responder safety module.ResultsDuring Hurricane Matthew, 128 responders representing 11 Georgia Public Health Districts registered in R-STaR. Seven responders reported illness or injury and were contacted to determine if medical services were needed; all remained healthy post-deployment. During Hurricane Irma, 1240 responders representing DPH and 16 Public Health Districts, and other employers, including law enforcement, fire, and education, registered in R-STaR. Of 472 responders completing daily health checks during their Irma deployment, 48 reported an injury, illness, or exposure, and were contacted to determine if services were needed. The daily health checks led to the identification of an outbreak of influenza in one of the shelters and resulted in vaccination or antiviral prophylaxis administration to 76 responders. Fifty responders to Hurricane Matthew completed the post-deployment survey; 95% found R-STaR easy to use, and 92% indicated that they liked being monitored. Supervisors indicated that the module could be used to: 1) roster and credential responders prior to an event; 2) track where responders are, monitor their health and safety during an event, and quantify the human resources deployed during a declared emergency; and, 3) to distribute post-response responder resources, monitor responder health, and gather information for after-action reports.ConclusionsR-STaR was widely used and well received despite being implemented with no prior training, with a dramatic increase in the number of responders registering from the first implementation in 2016 to the second implementation in September 2017. Monitoring responder health and safety is crucial to responding to and preventing outbreaks during a response, and ensuring responders get appropriate mental and physical support after a deployment. Lessons learned from both events will be used to create a just-in-time training curriculum, and develop a more robust R-STaR, which will enable responder rostering, credentialing, tracking and monitoring before, during, and after an event to ensure the health and safety of our responders as well as for future planning.References1. Centers for Disease Control and Prevention (2017). EMERGENCY RESPONDER HEALTH MONITORING AND SURVEILLANCE (ERHMS). Retrieved from Centers for Disease Control and Prevention: https://www.cdc.gov/niosh/erhms/default.html.

Background:In the United States, human immunodeficiency virus (HIV) remains a substantial public health issue. There is evidence that the use of antiretroviral medications such as pre-exposure prophylaxis (PrEP) can be a safe and effective primary prevention strategy to reduce new cases of HIV infection. Provider practice behavior as it relates to prescribing PrEP and the potential impact on specific vulnerable populations needs increased attention. Few studies have evaluated the attitudes of physicians towards ethical issues related to prescribing PrEP. Methods:The purpose of the present literature review was to evaluate provider attitudes toward the ethics of prescribing PrEP for individuals at risk of acquiring HIV infection. Searches of the PubMed and Cochrane databases were conducted. Three reviewers independently assessed the relevance of articles and discarded those not directly related to the attitudes of physicians toward ethics of the cost, safety, and resource allocation of PrEP. A total of twenty-one articles were included in the review.Results: Provider attitudes and perceptions focused on three areas: resource allocation, cost, and safety or effectiveness of PrEP. Providers who were hesitant in prescribing PrEP were concerned with the availability of resources, patient adherence, risk of drug resistance, and toxicity. In the studies reviewed, few providers had prescribed PrEP; however, prescribing practices trended upward with time and awareness. Conclusions:Realization of the benefits of PrEP will require a utilitarian ethical approach to identifying the populations that will benefit most, monitoring for adverse effects, addressing costs, and educating and training providers to prescribe PrEP responsibly. Ensuring that PrEP fulfills its potential as part of a combination regimen for HIV prevention requires identification of additional evidence, education, support services, and resources that are needed, as well as the regulatory framework and cost scenarios for access to PrEP.

ObjectiveTo explore the timeliness of emergency room surveillance data after the advent of federal Meaningful Use initiatives and determine potential areas for improvement.IntroductionTimeliness of emergency room (ER) data is arguably its strongest attribute in terms of its contribution to disease surveillance. Timely data analyses may improve the efficacy of prevention and control measures.There are a number of studies that have looked at timeliness prior to the advent of Meaningful Use, and these studies note that ER data were not fast enough for them to be useful in real time2,3. However, the change in messaging practices in the Meaningful Use era potentially changes this.Other studies have shown that changes in processes and protocol can dramatically improve timeliness1,4 and this motivates the current study of timeliness to identify processes that can be changed to improve timeliness.MethodsER data were collected from March 2017 through September 2017 from both the Georgia Department of Public Health’s (GDPH) State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance Module and the Centers for Disease Control and Prevention (CDC) National Syndromic Surveillance Program’s (NSSP) ESSENCE systems. Patients from hospitals missing 10 or more days of data, as well as patients with missing or invalid triage times, and all visits after August 1st were excluded in order to ensure data were representative of a “typical” time period and that a sufficient amount of time was given for visits to arrive from hospitals.The timeliness of individual records was determined in a number of different ways. All timeliness measurements were determined by subtracting the earlier time event from the later time of the event. The overall measure of timeliness is the time between the patient’s triage time and the data being present in the ESSENCE data system. In between, Georgia’s SendSS system receives and processes the data. This is illustrated in Figure 1. Due to the skewed nature of these measures, they were analyzed using medians and Gaussian kernel density plots.ResultsThe study in total included records from 118 Georgia hospitals, 14,203 data files and 1,897,501 patient records. Overall median timeliness of data from Triage Time to being available in SendSS for analyses was 33.62 hours (IQR=28.5), and in ESSENCE was 45.08 hours (IQR=37.05).The distributions of Triage Time of Day, Time Available in SendSS Staging, and Time Available in ESSENCE Analysis can be seen in Figure 2. Additionally, lines were added for when SendSS makes data available for its own analyses and when it sends data to ESSENCE. These latter lines represent places where the SendSS system itself could improve, and potential improved times were noted based on the kernel densities.Peak triage times for Georgia hospitals were between 10 am to 11 pm, shown in black. This represents the ideal timeliness if Hospitals sent their data immediately. However, data was all batched by Georgia hospitals and sent at different times of the day. The distribution of the time patient records arrived at SendSS staging was indicated in blue.During the period of this study, Georgia processed data into its SendSS system at 6:30am and 11:30am every day and sent data to the ESSENCE system at 1pm each day. These times are highlighted on the plot in green, and red respectively. New potential improved times, based on the kernel density of data being available in SendSS staging, are shown in the lighter shades of these colors at 8:30am and 12pm every day, while being sent to ESSENCE at 9am and 12:30pm to ensure time for data to be properly processed. These were determined to be optimal times for reducing lag in the data, however, may not be optimal for daily analysis.The purple line on the plot represents the times that data were available in ESSENCE’s system for analysis. This was notably delayed by a median 4.15 hours after the data was sent to ESSENCE on a typical day.ConclusionsA data driven approach to choosing processing times could improve timeliness of data analyses in the SendSS and ESSENCE systems. By conducting this type of analysis in an ongoing periodic basis, processing lag times can be kept at a minimum.1. Centers for Disease Control. Progress in improving state and local disease surveillance--United States, 2000-2005. MMWR Morbidity and mortality weekly report. 2005;54(33):822-825.2. Jajosky R, Groseclose S. Evaluation of reporting timeliness of public health surveillance systems for infectious diseases. BMC Public Health. 2004;4(1).3. Travers D, Barnett C, Ising A, Waller A. Timeliness of emergency department diagnoses for syndromic surveillance. AMIA Annual Symposium Proceedings. 2006;Vol. 2006:769.4. Ward M, Brandsema P, van Straten E, Bosman A. Electronic reporting improves timeliness and completeness of infectious disease notification, The Netherlands, 2003. Eurosurveillance. 2005;10(1):7-8.

Background: Notifiable disease reporting, although required by law, does not always occur. For this reason, it is crucial for local public health agencies to leverage new partnerships for reporting of notifiable diseases. Schools represent sites within communities that experience relatively high numbers of cases of notifiable disease and clusters of illness. By leveraging partnerships with schools, an increase in disease reporting can occur within communities.

Objective: To describe how the Georgia Department of Public Health (DPH) uses ICD-9 and ICD-10-based discharge diagnoses (DDx) codes assigned to Emergency Department (ED) patients to support the early detection and investigation of outbreaks, clusters, and individual cases of reportable diseases.Introduction: The Georgia DPH has used its State Electronic Notifiable Disease Surveillance System (SendSS) Syndromic Surveillance (SS) module to collect, analyze and display analyses of ED patient visits, including DDx data from hospitals throughout Georgia for early detection and investigation of cases of reportable diseases before laboratory test results are available. Evidence on the value of syndromic surveillance approaches for outbreak or event detection is limited (1, 2). Use of the DDx field within datasets, specifically as it might be used for investigation of outbreaks, clusters, and / or individual cases of reportable diseases, has not been widely discussed.Methods: The DDx field of the ED data set sent to DPH by 120 facilities was queried for diseases that are immediately-reportable, as well as those reportable within 7 days of diagnosis. The query was performed twice a day using a combination of SAS 9.4 and the internal query capabilities of SendSS. District Epidemiologists (DE) were notified by email, with an Excel file attached that includes the details of the patient’s visit. DEs contacted Infection Control Practitioners (ICPs) of the facilities where the patients had received a discharge diagnosis of a reportable disease. True or false positives were determined after the outcome of the follow-up with the ICP had been known and after the patient had been entered as a case of reportable disease in SendSS by the DE. Hence, if the patient was a confirmed or probable case of a reportable disease, it was recorded as a True Positive, and True Negative otherwise. This led to the calculation of Predictive Value Positive (PVP) by reportable disease.Results: Table 1 shows the number of notifications sent to DEs that were later demonstrated to be True Positives and False Positives. It also shows the PVP by diseases being reported, for the period spanning from 05/01/2016 to 08/31/2017. Use of these notifications has allowed early investigation and identification of 158 cases of notifiable diseases by DEs. This includes 25 epi-linked cases (varicella=12, pertussis=4, cryptosporidiosis=3, shigellosis=2, malaria=2, and viral meningitis=2), as well as two clusters of varicella, one cluster of pertussis, and one outbreak of varicella in an elementary school that had not been reported to the local health department. A notable limitation of this study is that no systematic and exhaustive tracking is done of all notifications, as DEs have latitude to decide whether to follow up on a specific notification, depending on other local data that could be related to the event. Therefore, the PPVs may be biased due to over- / under-estimation of unknown size and direction. One exception to this is varicella notifications, whose outcomes have been exhaustively tracked by the DPH surveillance coordinator of this disease.Conclusions: The use of ED discharge diagnoses to examine potential cases of reportable diseases can help improve detection and early response by local health departments to outbreaks, clusters, and individual cases of reportable diseases. Exhaustive tracking of all the notifications by specific diseases may improve the estimation of the PPVs of the notifications sent to DEs.