Disease Surveillance

The OKC-Co Health Department deployed a phased vectorborne response plan to address multiple diseases, including Zika Virus and West Nile Virus. This plan is scalable and flexible, but must necessarily prepare for the worst case scenario. Although not currently a local threat in OKC-Co, Zika virus response planning requires early coordination between state, local and federal agencies in order to mitigate risk to the population. The backbone of the Vectorborne response planning has been proven successful through West Nile Virus response in which Oklahoma has experienced three outbreak seasons: 2003, 2007 and 2012. (OSDH) In 2015, the OKC area experienced a greater than 112% increase in the number of vectors and 18 WNV positive test pools were observed. The heightened number of vectors and positive test pools did not translate to the same escalation in human cases, which demonstrates the strength that Public Health collaboration between surrounding municipalities and community members has on reducing the potential impact of this seasonal epidemic. During the most recent 2017 mosquito season, local code enforcement, city officials and consumer protection deployed a total of 18 CDC Gravid and BG Sentinel traps. The final day of sorting took place during the last week of October, as consistent with the decrease in mosquito numbers. There were 23 trapping and testing weeks with a total of 43,079 mosquitos trapped and 33, 846 mosquitos tested. An average of 66% of all trapped mosquitos were tested each week. The Maximum Likelihood Estimation (MLE) Infection Rate is calculated each week.

Objective: Demonstrate the impact of surveillance and media engagement on Public Health protection during a Vectorborne disease response.Identify surveillance and reporting methods for timely response to positive cases steps.Explore and apply best practices for collaboration with partners and surrounding municipalities in order to reduce disease impact

The West Africa Ebola outbreak of 2014-2016 demonstrated the importance of strong disease surveillance systems and the severe consequences of weak capacity to detect and respond to cases quickly. Challenges in the transmission and management of surveillance data were one factor that contributed to the delay in detecting and confirming the Ebola outbreak. To help address this challenge, we have collaborated with the U.S. Centers for Disease Control and Prevention (CDC), the Ministry of Health (MOH) in Guinea, the World Health Organization and various partners to strengthen the disease surveillance system through the implementation of an electronic reporting system using an open source software tool, the District Health Information Software Version 2 (DHIS 2). These efforts are part of the Global Health Security Agenda objective to strengthen real-time surveillance. This online system enables prefecture health offices to enter aggregate weekly disease reports from health facilities and for that information to be immediately accessible to designated staff at prefecture, regional and national levels. Incorporating DHIS 2 includes several advantages for the surveillance system. For one, the data is available in real time and can be analyzed quickly using built-in data analysis tools within DHIS 2 or exported to other analysis tools. In contrast, the existing system of reporting using Excel spreadsheets requires the MOH to manually compile spreadsheets from all the 38 prefectures to have case counts for the national level. For the individual case notification system, DHIS 2 enables a similar accessibility of information that does not exist with the current paper-based reporting system. Once a case notification form is completed in DHIS 2, the case-patient information is immediately accessible to the laboratories receiving specimens and conducting testing for case confirmation. The system is designed so that laboratories enter the date and time that a specimen is received, and any test results. The results are then immediately accessible to the reporting district and to the stakeholders involved including the National Health Security Agency and the Expanded Program on Vaccination. In addition, DHIS 2 can generate email and short message service (SMS) messages to notify concerned parties at critical junctures in the process, for example, when a laboratory result is available for a given case.

Objective: The objective is to share the progress and challenges in the implementation of the District Health Information Software Version 2 (DHIS 2) as an electronic disease surveillance system platform in Guinea, West Africa, to inform Global Health Security Agenda efforts to strengthen real-time surveillance in low-resource settings.

Effective and valid surveillance of syndromes can be extremely useful in the early detection of outbreaks and disease trends. However, medical chart checks without patient identifiers and lack of diagnoses in A08 data has made validation difficult. With the rising availability of electronic health records (EHRs) to local health departments, the ability to evaluate syndromic surveillance systems (SSS) has improved. In LAC, ED data are collected from hospitals and classified into categories based on chief complaints. The most reported syndrome in LAC is the respiratory classification, which is intended to broadly capture respiratory pathogen activity trends. To test the validity of the LAC Department of Public Health (DPH) respiratory syndrome classification, ED syndromic surveillance data were analyzed using corresponding EHRs from one hospital in LAC.

Objective

To compare and validate syndromic surveillance categorization against electronic health records at one hospital emergency department (ED) in Los Angeles County (LAC).

The Electronic Integrated Disease Surveillance System (EIDSS) is a computer-based disease reporting application funded under the Cooperative Biological Engagement Program of the U.S. Defense Threat Reduction Agency. EIDSS deployment includes the Republics of Georgia (GG) and Azerbaijan (AJ) where personnel in the Ministry of Health and the Ministry of Agriculture in each country enter case-based disease reports. The potential benefits obtained through surveillance of infectious diseases across species have been widely discussed. A limitation of such practice has been the paucity of single applications that collect information about disease in both human and other animal populations (Scotch 2009). A unique feature of EIDSS is the use of a single platform to enter reports of disease in humans and other animals. Records are stored in a common database enabling ready access to information on multiple diseases and provide a quantitative linkage between human and animal data. An integrated analysis and reporting (AVR) module further supports timely investigation of disease events across the epizootic barrier.

Objective

We describe an electronic disease reporting system that integrates case-based disease information from humans and other animals in a single database and examine the utility for supporting disease surveillance functions through access to longitudinal case reports of multiple diseases across multiple species provided by the system.

OpenMRS has global presence as an open source web based medical record system (MRS). It is built on an extensible, modular framework that allows the user to create a MRS that is as simple or complex as needed. OE is a multi-user network accessible analysis and visualization tool that enables users to monitor the populationÕs health from any computer connected to that network. OE was created as an open source solution using features and lessons learned from Enterprise ESSENCE, (Electronic Surveillance System for Early Notification of Community-Based Epidemics.) OE provides analyses, maps, graphs, charts, crosstabs, as well as detail tables and export functionality.

Objective

By pairing an open source medical record system, OpenMRS, with the Suite for Automated Global Electronic bioSurveillance (SAGES) disease surveillance tool, OpenESSENCE (OE), we have prototyped an open source solution for passive disease and program surveillance using an active medical record system.

The time series of syphilis cases has been studied at the country and state level at the yearly basis, and it has been found that syphilis has a periodicity of approximately 10 years. However, to inform prevention efforts, it is important to understand the short term dynamics of disease activity.

Objective

(i) To forecast syphilis cases per state in the US to support early containment of outbreaks. (ii) For each state, to determine which states are most correlated, to find "bellwether" states to inform surveillance efforts. (iii) To determine a small collection of states whose syphilis incidence patterns are most closely correlated with all the states.

Public health surveillance (also called field epidemiology) as defined by Centres for Disease Control and Prevention (CDC) is the ongoing systematic, collection, analysis, and interpretation of outcome specific data essential to the planning , implementation and evaluation of public health practises closely integrated with the timely dissemination of this data to those who need to know(1). This modern concept of surveillance includes 3 main features; the systematic collection of all relevant data, orderly consolidation and evaluation of this data and the prompt dissemination of the results to those who need to know (2). The IDSR is a strategy of the WHO Afro region adopted by the member states in 1998 as a regional strategy for strengthening weak national surveillance systems in the African region (3, 4). The DSNOs under the supervision of the Medical Officers of Health (MOHs) are responsible for surveillance activities within their Local Government catchment area. Therefore their role is very crucial to the success of the IDSR strategy. 

Objective

To evaluate the immediate impact of training on Integrated Disease Surveillance and Response (IDSR) on the knowledge of Disease Surveillance and Notification Officers (DSNOs) and the demographic characteristics associated with the change in knowledge.

Data obtained through public health surveillance systems are used to detect and locate clusters of cases of diseases in space-time, which may indicate the occurrence of an outbreak or an epidemic. We present a methodology based on adaptive likelihood ratios to compare the null hypothesis (no outbreaks) against the alternative hypothesis (presence of an emerging disease cluster).

Objective

Disease surveillance is based on methodologies to detect outbreaks as soon as possible, given an acceptable false alarm rate. We present an adaptive likelihood ratio method based on the properties of the martingale structure which allows the determination of an upper limit for the false alarm rate.