Surveillance Systems

Integrated Disease Surveillance and Response (IDSR) is a World Health Organization (WHO) Regional Office for Africa (AFRO) strategy for strengthening national public health surveillance and response systems in African countries. The strategy incorporates the International Health Regulations (2005) core capacities for public health surveillance and response systems. Since 2010, more than 30 countries have conducted at least one IDSR training workshop. Limited resources preclude conducting workshops in each of the 4,500 districts in all WHO-AFRO region. One solution is to implement an electronic version for IDSR training.

In collaboration with WHO-AFRO, we conducted a literature search to identify e-Learning best practices, and transformed the IDSR workshop training materials into electronic modules using measurable performance objectives, realistic examples, meaningful practices, and real time feedback to the learner. We also utilized an online learning management platform that lets course managers track learner progress and share supporting materials. The IDSR e-Learning course, available in English, French and Portuguese, aims to increase access to skills that support the prevent-detect-respond goal areas of the Global Health Security Agenda.

Objective

This presentation addresses the challenges of expanding district level surveillance training in Africa. We developed an e-Learning course and field tested the modules using an innovative approach to assess the feasibility of delivering electronic surveillance training.

Clinical data captured in electronic health records (EHR) for patient health care could be used for chronic disease surveillance, helping to inform and prioritize interventions at a state or community level. While there has been significant progress in the collection of clinical information such as immunizations for public health purposes, greater attention could be paid to the collection of data on chronic illness. Obesity is a chronic disease that affects over a third of the US adult population1 , making it an important public health concern. Both HL7 v.2.5.12 and Clinical Document Architecture (CDA) messages3 can be used to facilitate the collection of HW EHR data. These standards include anthropometric and demographic information along with the option to transmit behavioral, continuity of care, community resource identification and care plan information. We worked with vendors participating in the Integrating the Healthcare Enterprise initiative (IHE) in developing, testing and showcasing scenarios to facilitate system development, increase the visibility of HW standards and demonstrate potential usages of obesity-related information.

Objective

To demonstrate the feasibility of using healthy weight (HW) IT standards in public health surveillance through the collection and visualization of patient height, weight and behavioral data.

Per a frequently asked questions document on the ISDS website, approximately two thirds of HL7 records received in BioSense do not provide a Visit ID. As a result, BioSense data processing rules use the patient ID, facility ID and earliest date in the record to identify a unique visit. If the earliest dates in records with the same patient ID and facility ID occur within the same 24-hour time frame, those two visits are combined into one visit and the earliest date will be stored. The ED data sent by hospitals to NC DETECT include unique visit IDs and these are used to identify unique visits in NC DETECT. These data are also sent twice daily to BioSense. In order to assess the potential differences between the NC DETECT ED data in NC DETECT and the NC DETECT ED data in BioSense, an initial analysis of the 24-hour rule was performed.

Objective

NC DETECT emergency department (ED) data were analyzed to assess the impact of applying the BioSense “24-hour rule” that combines ED visits into a single visit if the patient ID and facility ID are the same and the earliest recorded dates occur within the same 24-hour time frame.

In 2011, injury by firearms accounted for 32,351 deaths (10.4 deaths per 100,000 population) in the United States. This rate was higher than any infectious or parasitic disease (the highest being 2.5 for both viral hepatitis and HIV disease). Furthermore, death by gunshots accounted for over half of all suicides and over two-thirds of all homicides in the US. Despite the disproportionate media coverage of mass shootings and assault weapon violence, the vast majority of these deaths are attributable to non-mass shootings and to handguns. Though a contentious issue in the United States, understanding this cause of death is vital to confronting the issue locally and nationally. Traditionally, death certificates, crime data, cross-sectional studies, and retrospective studies have most commonly been utilized in this endeavor; however, the collection of real-time emergency department (ED) visit information presents a unique opportunity to track gunrelated injuries to supplement our current understanding of this issue. The Houston Department of Health and Human Services (HDHHS) has been receiving this information for over a decade from EDs in the greater-Houston area, and the department is currently connected to 32 of the largest EDs in the area. The current study aims to enhance the understanding of gunshot-related injuries in the Houston area and present a model for utilizing RODS information for this purpose.

Objective

To introduce a model to track gunshot-related injuries, describe gun-related injuries in Houston, and investigate the association between gun-related injuries and social determinants of health using syndromic surveillance data.

The Connecticut Department of Public Health (DPH), in collaboration with Yale Emerging Infections Program (EIP), receives funding to particpate in the Foodborne Diseases Active Surveillance Network (FoodNet) and Foodborne Disease Centers for Outbreak Response Enhancement (FoodCORE). FoodNet is an active population-based surveillance network that monitors trends for ten enteric diseases and conducts special studies to better understand the causes of foodborne illness. FoodCORE develops best practices related to the detection, investigation, and control or disease outbreaks, particularly those due to to Salmonella, Shiga toxin-producing E. coli, and Listeria (SSL). Foodborne disease surveillance and response is a collaborative effort requiring real-time data sharing between key stakeholders including: DPH Epidemiology, DPH Laboratory, DPH Food Protection Program, Yale EIP, and local health department (LHD) staff.

Objective

To develop an integrated system for routine enteric disease surveillance, cluster detection and monitioring, information sharing among key stakeholders, and documentation.

Seasonal rises in respiratory illnesses are a major burden on primary care services. Public Health England (PHE), in collaboration with NHS 111, coordinate a national surveillance system based upon the daily calls received at the NHS 111 telehealth service. Daily calls are categorized according to the clinical ‘pathway’ used by the call handler to assess the presenting complaints of the caller e.g. cold/flu, diarrhoea, rash.

Objective

We compared weekly laboratory reports for a number of seasonal respiratory pathogens with telehealth calls (NHS 111) to assess the burden of seasonal pathogens on this syndromic surveillance system and investigate any potential for providing additional early warning of seasonal outbreaks.

Several countries prospectively monitor influenza-attributable mortality using a variation of the Serfling seasonal time series model that uses sinusoidal terms for seasonality. Typically, a seasonal model from previous years is used to forecast current expected mortality. Using laboratory surveillance time series data in the model may enhance interpretation of the surveillance information.

Objective

To demonstrate use of routine laboratory-confirmed influenza surveillance data to forecast predicted influenza-attributable deaths during the current influenza season. We also assessed whether including information on influenza type produced better surveillance forecasts.

The general health-seeking behavior has been well described in different populations. However, how different symptoms have driven health-seeking behavior was less explored. From the patient’s perspective, health-seeking behavior tends to be responsive to discomfort or symptoms rather than the type of diseases which is unknown before medical consultation, hence symptom-specific behavior may more realistically reflect responses from the public which is subsequently captured by syndromic surveillance. In Hong Kong, sentinel surveillance of common diseases, such as ILI and acute diarrhoeal diseases, consists of general practitioners (GP), general outpatient clinics (GOPC) and Chinese medicine practitioners (CMP). These existing sources of syndromic surveillance data are affected by the choice of health services and health seeking behavior and hence may over- or under-represent actual disease burden. By understanding health-seeking behavior at different times of the year, we could estimate the disease burden in the population, and population subgroup from multiple surveillance data.

Objective

This study described health-seeking behavior of the general population specific to different symptoms, at different times of the year. This information allows the estimation of population disease burden over the year using sentinel surveillance data. We will use influenza-like illness (ILI) as an example.

Existing EVD surveillance strategies in Sierra Leone use a centralized live alert system to refer suspect cases from the community to Ebola treatment centers. As EVD case burden declined in Port Loko District, so did the number of reported alerts. As EVD presents similarly to malaria, the number of alerts reported are expected to remain consistent with malaria prevalence in malaria-endemic areas, irrespective of a reduction in true EVD cases. Declines in reported suspect cases from the community alluded to the possibility that individuals were returning to healthcare centers to seek treatment for malaria, and that PHUs were not adequately reporting suspect EVD cases. District surveillance officers (DSOs) were used to investigate the usage of PHUs by community members, as well as the mechanisms that health center staff used in recording patient visits. Surveillance methods specific to PHUs were introduced to increase the number of reported EVD alerts, as well as establish the foundation for future integrated disease surveillance response strategies.

Objective

A community-based EVD surveillance system with improved symptom recording and follow-up of malaria positive patients at PHUs was implemented during low EVD transmission. The rationale and methodology in implementing a PHU-focused approach to strengthen surveillance system sensitivity is described.