Data Analysis

The NNDSS is a nationwide collaboration that enables all levels of public health (local, state, territorial, federal and international) to monitor, control, and prevent the occurrence and spread of state-reportable and nationally notifiable diseases and conditions. The NNDSS data are a critical source of information for monitoring disease trends, effectiveness of prevention and control programs, and policy development. To provide timely NNDSS data, state and territorial health departments voluntarily report notifiable disease incidence data to CDC when they become aware of these cases and as per recommended national notification timeframes. These provisional data are published each week in Morbidity and Mortality Weekly Report (MMWR). Great strides have been made exploring and exploiting new and different sources of disease surveillance data and developing robust statistical methods for analyzing the collected data. However, there have been fewer efforts in the area of online dissemination of surveillance data. Appropriate dissemination of surveillance data is important to maximize the utility of collected data.

Objective

The purpose of this study was to identify ideas for an enhanced dissemination of the US National Notifiable Diseases Surveillance System (NNDSS) provisional data.

Syndromic surveillance can supplement diagnosis-based surveillance in resource-limited settings with limited laboratory infrastructure. Syndromic surveillance allows for early outbreak detection relative to traditional systems and enables community health monitoring during outbreaks. Monitoring and disease diagnosis can be strengthened using pre-diagnostic data and statistical algorithms to detect morbidity trends.

Alerta (2002-11) and Vigila (2011-present) are sequentially implemented electronic disease surveillance systems created by the Peruvian Navy to improve the detection, prevention, and control of disease outbreaks. The phone-, internet-, and radio-based reporting system now covers over 97.5% of the Navy population, encompassing 169 reporting establishments that treat active and retired service members, dependents, and civilian employees. Acute diarrheal disease, respiratory infections, and pneumonias are reported weekly, whereas specific notifiable diseases such as malaria, dengue, and tuberculosis are reported immediately after case detection.

Objective

To use data from the Peruvian Navy’s electronic syndromic surveillance systems to estimate the baseline incidence of acute diarrheal disease (ADD) and detect outbreaks among individuals accessing military medical facilities from 2009-13.

Absenteeism has been considered as a potential indicator for the early detection of infectious disease outbreaks in population, especially in primary schools. However, in practice this data are often characterized by an excess of zeros and spatial heterogeneity. In a project on integrated syndromic surveillance system (ISSC) in rural China, Random effect zero-inflated Poisson (RE-ZIP) model was applied to simultaneously quantify the spatial heterogeneity for “occurrence” and “intensity” on school absenteeism data.

Objective

To describe and explore the spatial heterogeneity via Random effects zero-inflated Poisson model (RE-ZIP) for absenteeism surveillance in primary school for early detection of infectious disease outbreak in rural China.

The mission of the ISDS TCC is to bridge the gap between the analytic needs of public health practitioners and the expertise of researchers from other fields for the enhancement of disease surveillance, including situational awareness of chronic as well as infectious threats and follow-up activities such as case linkage and contact tracing. Committee activities to achieve this mission are identifying practical use cases, refining technical specifications in open forums, obtaining benchmark datasets for controlled dissemination, validating candidate methods, and sharing method documentation. In its first 2 years, the TCC has worked on three use cases and assisted with development of data use agreements to permit posting of benchmark datasets, http://www.syndromic.org/ communities/technical-conventions. Recent polling of the Biosense User Group indicated widespread interest in developing additional use cases. The proposed panel is intended to focus on practical applications of common interest, refine the use case development and dissemination process, and foster global interest in this process.

Objective

The main objective is to broaden the collection of use cases developed by the ISDS Technical Conventions Committee (TCC) to enhance effective collaboration between public health practice and analyst researchers in various disciplines and institutions. Panellists will present and motivate use case concepts including requirements for practical solution methods. Component objectives are to refine the presented use cases and to stimulate formation of new ones at local, state, and national levels.

The Affordable Care Act (ACA) was promoted with two goals: expanding health insurance coverage and reducing healthcare costs. Expanded coverage is expected to partially reduce costs. Emergency department (ED) visits are costlier than comparable primary care physician visits. If uninsured patients use the local ED more often than insured patients with comparable conditions, insuring them may change usage and lower costs. Some reports in the literature do not fit this model of ED usage. In one study, nonurgent ED visits were mainly the result of patient uncertainty about the severity of their condition. While trained medical personnel distinguished urgent and nonurgent cases after the fact, initial presentations were similar. In Oregon, an expansion of Medicaid increased health insurance coverage; ED usage increased rather than decreased. Thus, the motivating narrative about insurance coverage and ED usage informing the ACA may not be the complete story. Reduction of hospital readmissions is also expected to cut costs under the ACA. Hospital process improvements are expected to realize this reduction. Recently it was reported that up to 60% of hospital readmissions are predicted by patient demographics, raising questions about how much control a hospital has over its readmission rate. This research will examine whether data collected via syndromic surveillance can corroborate these findings.

Objective

To determine if data collected for syndromic surveillance can inform policy questions related to emergency department utilization and inpatient readmission.

 

The Research Electronic Data Capture (REDCap) application has been used to build and manage online surveys and databases in academic research settings. Public health agencies have begun to use REDCap to manage disease outbreak data. In addition to survey and database development, and data management and analysis, REDCap allows users to track data manipulation and user activity, automate export procedures for data downloads, and use ad hoc reporting tools and advanced features, such as branching logic, file uploading, and calculated fields. REDCap supports HIPAA compliance through userbased permissions and audit trails. These additional capabilities may provide an advantage over commonly used outbreak management tools such as Epi Info and Microsoft Access. The Illinois Department of Public Health (IDPH) has not used REDCap to date. Prior to adopting this web-based application, an evaluation was conducted to assess how REDCap may facilitate outbreak data management.

Objective

To evaluate the use of the Research Electronic Data Capture (REDCap) application to manage outbreak data at the local, state, and multi-jurisdictional level.

 

The use of R is increasing in the public health disease surveillance community. The ISDS pre-conference workshops and newly formed R Group for Surveillance have been well attended and continue to grow in popularity. The use of R in the National Syndromic Surveillance Program (NSSP) has also been of value to many users who wish to analyze and visualize public health data using custom R scripts. This interest in R, combined with a desire from many ESSENCE users to create custom analytics and visualizations, led to a summer internship project to look into the feasibility and ways R could be integrated into ESSENCE.

Objective

The objective of this project is to give users the ability to run custom R scripts from within the ESSENCE system. This capability would allow for custom analytics and visualizations to be baked into the system for daily use. It would also provide a sandbox area for new ideas and features to be tested before being developed more fully into the ESSENCE codebase for a more seamless use in the future. The project must do this while maintaining a secure environment for public health data to reside.