Painter Ian

The effectiveness of emergency preparedness and response systems depends, in part, on the effectiveness of communication between agencies and individuals involved in emergency response, including health care providers who play a significant role in planning, event detection, response and communication with the public. Although much attention has been paid to the importance of communicating clinical data from health care providers to public health agencies for purposes of early event detection and situational awareness (e.g., BioSense) and to the need for alerting health care providers of public health events (e.g., Health Alert Networks), no studies to date have systematically identified the most effective methods of communication between public health agencies and community health care providers for purposes of public health emergency preparedness and response. The REACH (Rapid Emergency Alert Communication in Health) study is a 4-year randomized controlled trial to evaluate and compare the effectiveness of mobile (SMS) and traditional (email, FAX) communication strategies for sending public health messages to health care providers—physicians, pharmacists, nurse practitioners, physician’s assistants and veterinarians.

Objective:

To systematically compare mobile (SMS) and traditional (email, FAX) communication strategies to identify which modality is most effective for communication of health alerts and advisories between public health agencies and health care providers in order to improve emergency preparedness and response.

 

Data consisting of counts or indicators aggregated from multiple sources pose particular problems for data quality monitoring when the users of the aggregate data are blind to the individual sources. This arises when agencies wish to share data but for privacy or contractual reasons are only able to share data at an aggregate level. If the aggregators of the data are unable to guarantee the quality of either the sources of the data or the aggregation process then the quality of the aggregate data may be compromised. This situation arose in the Distribute surveillance system (1). Distribute was a national emergency department syndromic surveillance project developed by the International Society for Disease Surveillance for influenza-like-illness (ILI) that integrated data from existing state and local public health department surveillance systems, and operated from 2006 until mid 2012. Distribute was designed to work solely with aggregated data, with sites providing data aggregated from sources within their jurisdiction, and for which detailed information on the un-aggregated ‘raw’ data was unavailable. Previous work (2) on Distribute data quality identified several issues caused in part by the nature of the system: transient problems due to inconsistent uploads, problems associated with transient or long-term changes in the source make up of the reporting sites and lack of data timeliness due to individual site data accruing over time rather than in batch. Data timeliness was addressed using prediction intervals to assess the reliability of the partially accrued data (3). The types of data quality issues present in the Distribute data are likely to appear to some extent in any aggregate data surveillance system where direct control over the quality of the source data is not possible.

Objective

In this work we present methods for detecting both transient and long-term changes in the source data makeup.

Data quality monitoring is necessary for accurate disease surveillance. However it can be challenging, especially when “real-time” data are required. Data quality has been broadly defined as the degree to which data are suitable for use by data consumers. When compromised at any point in a health information system, data of low quality can impair the detection of data anomalies, delay the response to emerging health threats, and result in inefficient use of staff and financial resources. While the impacts of poor data quality on biosurveillance are largely unknown, and vary depending on field and business processes, the information management literature includes estimates for increased costs amounting to 8-12% of organizational revenue and, in general, poorer decisions that take longer to make.

Objective

To highlight how data quality has been discussed in the biosur- veillance literature in order to identify current gaps in knowledge and areas for future research. 

Effective use of data for disease surveillance depends critically on the ability to trust and quantify the quality of source data. The Scalable Data Integration for Disease Surveillance project is developing tools to integrate and present surveillance data from multiple sources, with an initial focus on malaria. Consideration of data quality is particularly important when integrating data from diverse clinical, population-based, and other sources. Several global initiatives to reduce the burden of malaria (Presidents Malaria Initiative, Roll Back Malaria Initiative and The Global Fund to Fight AIDS, Tuberculosis and Malaria) have published lists of recommended indicators. Values for these indicators can be obtained from different data sources, with each source having different data quality properties as a consequence of the type of data collected and the method used to collect the data. Our goal is to develop a framework for organizing the data quality (DQ) properties of indicators used for disease surveillance in this setting.

There is growing recognition that an inability to access timely health indicators can hamper both the design and the effective implementation of infectious diseases control interventions. In malaria control, the global use of standard interventions has driven down the burden of disease in many regions. Further gains in high transmission areas and elimination in lower transmission settings, however, will require an enhanced understanding of malaria epidemiology, population characteristics, and efficacy of clinical and public health programs at the local level. Currently, there is a dearth of information available to fine-tune malaria control interventions at the local level. A key obstacle is the fragmentation of data into silos, as existing data cannot be brought together to estimate accurate and timely health metrics.

Objective

Driven by the need to bring malaria surveillance data from different sources together to support evidence-based decision making, we are conducting the “Scalable Data Integration for Disease Surveillance” (SDIDS) project. This project aims to foster the integration of existing surveillance data to support evidence-based decision-making in malaria control and demonstrate a model applicable to other diseases. Central to this initiative is collaboration between academia, governmental and NGO sectors.

The International Society for Disease Surveillance (ISDS) will hold its thirteenth annual conference in Philadelphia on December 10th and 11th, 2014.  The society’s mission is to improve population health by advancing the science and practice of disease surveillance, and the annual conference advances this mission by bringing together practitioners and researchers from multiple fields involved in disease surveillance, including public health, epidemiology, health policy, biostatistics and mathematical modeling, informatics and computer science. This year the conference received a record number of abstract submissions (267), from 33 countries. We accepted 102 abstracts for oral presentations, along with 40 lightning talks and 100 posters.

Public health departments need enhanced surveillance tools for population monitoring, and external researchers have expertise and methods to provide these tools. However, collaboration with potential solution developers and students in academia, industry, and government has not been sufficiently close or well informed for rapid progress. Many peer-reviewed papers on biosurveillance methods have been published by researchers, but few methods have been adopted in systems used by health departments. In a 2013 BioSense User Group survey with responses from users in more than 40 U.S. states, access to improved analytic methods was a top priority. Among the tools most desired by respondents were the ESSENCE biosurveillance system with multiple analytic tools and statistical software packages such as SAS. Multiple obstacles have slowed the progress of practitioners and developers who seek the development and implementation of useful analytic tools. First, the epidemiological challenges and associated operational constraints are not sufficiently understood among academic developers. Many health departments do not have the resources to hire such developers beyond maintenance of information technology, and the health monitors are typically too busy to publish in peer-reviewed journals. Second, data cannot be shared because of privacy and proprietary limitations with varying local rules. Data-sharing has posed difficult administrative problems, both within and external to health departments, in the course of ISDS Technical Conventions committee efforts to promote interactions through use case problems. Third, aspects of situational awareness vary widely among health monitors at different jurisdictional levels, so analytical challenges and constraints vary widely among potential users. Practitioners have pointed out that “surveillance is local”, but local operational and data environments vary widely. A fourth main issue is cross-cultural: Understaffed health departments must respond to successive crises and often lack the time for requirements analysis and technical publication. Such client work situations complicate interaction with academic environments of semester schedules and limited grants and transient student support. This panel brings together academic statisticians who have had successful direct relationships with public health departments to discuss how they have dealt with these challenges.

Objective

The session will explore past collaborations between the scientist panelists and public health departments to highlight approaches that have and have not been effective and to recommend effective, sustainable relationship strategies for the mutual advancement of practical disease surveillance and relevant academic research.

This year’s conference theme is “Harnessing Data to Advance Health Equity” – and Washington State researchers and practitioners at the university, state, and local levels are leading the way in especially novel approaches to visualize health inequity and the effective translation of evidence into surveillance practice.

Objective

Washington is leading the way in especially novel approaches. Our goal is to share some of these innovative methods and discuss how these are used in State and Local monitoring of Health