Poster

The massive flow of people to mass gathering events, such as festivals or sports events like EURO 2016, may increase public health risks. In the particular context of several terrorist attacks that took place in France in 2015, the French national Public Health agency has decided to strengthen the population health surveillance systems using the mandatory notification disease system and the French national syndromic surveillance SurSaUD®. The objectives in terms of health surveillance of mass gathering are: 1/ the timely detection of a health event (infectious cluster, environmental exposure, collective foodborne disease…) 2/ the health impact assessment of an unexpected event such as a terrorist attack. In collaboration with the Regional Emergency Observatory (ORU), a procedure for the labeling of emergencies has been tested to identify the ED records that could be considered as linked to the event.

Objective:

To access the potential health impact on the population during mass gathering over time using labelling procedure in emergency department (ED).

Where we live' affects 'How we live'. Information about 'how one lives' collected from the public health surveillance data such as the Behavioral Risk Factor Surveillance System (BRFSS). Neighborhood environment surrounding individuals affects their health behavior or health status are influenced as well as their own traits. Meanwhile, geographical information of subjects recruited in the health behavior surveillance data is usually aggregated at administrative levels such as a county. Even if we do not know accurate addresses of individuals, we can allocate them to the random locations where is analogous to their real home within a locality using a geo-imputation method. In this study, we assess the association between obesity and built environment by applying random property allocation.

Objective:

This study aimed to assess the effects of urban physical environment on individual obesity using geographically aggregated health behavior surveillance data applying a geo-imputation method.

Surveillance in nursing homes (Enserink et al., 2011) and day care facilities (Enserink et al., 2012) has been conducted in the Netherlands, but is not commonly practiced in the United States (Buehler et al., 2008). Outbreaks of illnesses within these facilities are required to be reported to the Epidemiology Program, however a small fraction of outbreaks reported come from LTCFs. Without regular communication between LTCFs and the Epidemiology Program, it is likely that many outbreaks are going unreported due to lack of awareness of the reporting requirements by facility staff. To better understand the prevalence of illness in LTCFs and improve communication between LTCFs and DOH-Hillsborough a weekly surveillance survey was created using Epi Info web survey.

Objective:

The Florida Department of Health in Hillsborough County (DOH-Hillsborough) routinely reviews the ESSENCE-FL system to assess syndromic trends in emergency department (ED) and urgent care data (UCC). Collection of this type of symptom data from long term care facilities (LTCFs) and child care centers is of interest in order to better understand how these illness patterns present in vulnerable populations outside of the EDs.

The Karachi Health and Demographic Surveillance System was set up in year 2003 by the Department of Pediatrics and Child Health of the Aga Khan University, Karachi, Pakistan, in four peri-urban low socioeconomic communities of Karachi and covers an area of 17.6 square kilometers.(Figure 1).

Objective:

The mandate of establishing this DSS is to provide a research platform for both observational and interventional studies, with focus on maternal and child health, which could influence decision-making and planning for health strategies at local, national and international levels.

Varicella (chickenpox) is a highly transmissible childhood disease. Between 2010 and 2015,it displayed two epidemic waves annually among school populations in Shenzhen, China. However, their transmission dynamics remain unclear and there is no school-based vaccination programme in Shenzhen to-date. In this study, we developed a mathematical model to compare a school-based vaccination intervention scenario with a baseline (i.e. no intervention)scenario.

Objective:

To modell the transmission dynamics of varicella among school children in Shenzhen,to determine the effect of the school-based vaccination intervention.

Global Mass gatherings (MGs) such as Olympic Games, FIFA World Cup, and Hajj (Muslim pilgrimage to Makkah), attract millions of people from different countries. The gathering of a large population in a proximity facilitates transmission of infectious diseases. Attendees arrive from different geographical areas with diverse disease history and immune responses. The associated travel patterns with global events can contribute to a further disease spread affecting a large number of people within a short period and lead to a potential pandemic. Global MGs pose serious health threats and challenges to the hosting countries and home countries of the participants. Advanced planning and disease surveillance systems are required to control health risks in these events. The success of computational models in different areas of public health and epidemiology motivates using these models in MGs to study transmission of infectious diseases and assess the risk of epidemics. Computational models enable simulation and analysis of different disease transmission scenarios in global MGs. Epidemic models can be used to evaluate the impact of various measures of prevention and control of infectious diseases.

Objective:

To develop a computational model to assess the risk of epidemics in global mass gatherings and evaluate the impact of various measures of prevention and control of infectious diseases.

National initiatives, such as Meaningful Use, are automating the detection and reporting of reportable disease events to public health, which has led to more complete, timely, and accurate public health surveillance data. However, electronic reporting has also lead to significant increases in the number of cases reported to public health. In order for this data to be useful to public health, it must be processed and made available to epidemiologists and investigators in a timely fashion for intervention and monitoring. To meet this challenge, the Utah Department of Health (UDOH)’s Disease Control and Prevention Informatics Program (DCPIP) has developed the Electronic Message Staging Area (EMSA). EMSA is a system capable of automatically filtering, processing, and evaluating incoming electronic laboratory reporting (ELR) messages for relevance to public health, and entering those laboratory results into Utah’s integrated disease surveillance system (UT-NEDSS) without impacting the overall efficiency of UT-NEDSS or increasing the workload of epidemiologists.

Objective:

The objective of this abstract is to illustrate how the Utah Department of Health processes a high volume of electronic data in an automated way. We do this by a series of rules engines that does not require human intervention.

The Great American Solar Eclipse of 2017 provided a rare opportunity to view a complete solar eclipse on the American mainland. Much of Oregon was in the path of totality and forecasted to have clear skies. Ahead of the event, OPHD aggregated a list of 107 known gatherings in mostly rural areas across the state, some with estimated attendance of up to 30,000 attendees. Temporary food vendors and a range of sanitation solutions (including open latrines) were planned. International travelers were expected, along with large numbers of visitors traveling by car on the day of the eclipse. The potential for multiple simultaneous mass gatherings across the state prompted OPHD to activate an incident management team (IMT) and to create a Health Intelligence Section to design a mass gathering surveillance strategy. Statewide syndromic surveillance (Oregon ESSENCE) has been used to monitor previous mass gatherings (1) and captures statewide emergency department (ED), urgent care, Oregon Poison Center, and reportable disease data.

Objective:

Develop a public health surveillance plan for the Oregon Public Health Division (OPHD) in anticipation of the expected influx of visitors for the 2017 Great American Solar Eclipse.

EpiCenter, NJ’s statewide syndromic surveillance system, collects ED registration data. The system uses chief complaint data to classify ED visits into syndrome categories and provides alerts to state and local health departments for surveillance anomalies. After the 2014 Ebola outbreak in West Africa, the New Jersey Department of Health (NJDOH) started collecting medical notes including triage notes, which contain more specific ED visit information than chief complaint, from 10 EDs to strengthen HAI syndromic surveillance efforts. In 2017, the NJDOH was aware of one NJ resident whose surgical site was infected following a cosmetic procedure outside of the US. This event triggered an intensive data mining using medical notes collected in EpiCenter. The NJDOH staff searched one week of medical notes data in EpiCenter with a specific keyword to identify additional potential cases of surgical-site infections (SSI) that could be associated with medical tourism.

Objective:

Medical notes provide a rich source of information that can be used as additional supporting information for healthcare-associated infection (HAI) investigations. The medical notes from 10 New Jersey (NJ) emergency departments (ED) were searched to identify cases of surgical-site infections (SSI).

Influenza is one of the significant causes of morbidity and mortality globally. Previous studies have demonstrated the benefit of laboratory surveillance and its capability to accurately detect influenza outbreaks earlier than syndromic surveillance.1-3 Current laboratory surveillance has an approximately 4-week lag due to laboratory test turn-around time, data collection and data analysis. As part of strengthening influenza virus surveillance in response to the 2009 influenza A (H1N1) pandemic, the real-time laboratory-based influenza surveillance system, the Bangkok Dusit Medical Services Surveillance System (BDMS-SS), was developed in 2010 by the Bangkok Health Research Center (BHRC). The primary objective of the BDMS-SS is to alert relevant stakeholders on the incidence trends of the influenza virus. Type-specific results along with patient demographic and geographic information were available to physicians and uploaded for public health awareness within 24 hours after patient nasopharyngeal swab was collected. This system advances early warning and supports better decision making during infectious disease events.2 The BDMS-SS operates all year round collecting results of all routinely tested respiratory clinical samples from participating hospitals from the largest group of private hospitals in Thailand.

Objective:

We describe the Bangkok Dusit Medical Services Surveillance System (BDMS-SS) and use of surveillance efforts for influenza as an example of surveillance capability in near real-time among a network of 20 hospitals in the Bangkok Dusit Medical Services group (BDMS).