Data Completeness

Spatial cluster analysis is considered an important technique for the elucidation of disease causes and epidemiological surveillance. Kulldorff's spatial scan statistic, defined as a likelihood ratio, is the usual measure of the strength of geographic clusters. The circular scan, a particular case of the spatial scan statistic, is currently the most used tool for the detection and inference of spatial clusters of disease.

Kulldorff's spatial scan statistic for aggregated area maps searches for clusters of cases without specifying their size (number of areas) or geographic location in advance. Their statistical significance is tested while adjusting for the multiple testing inherent in such a procedure. However, as is shown in this work, this adjustment is not done in an even manner for all possible cluster sizes. We propose a modification to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found.

Objective

We propose a modification to the usual inference test of the spatial scan statistic, incorporating additional information about the size of the most likely cluster found.

A common problem in syndromic surveillance using ED department data is temporary gaps in the data received from individual ED departments caused by delays in receiving the data.

Currently most syndromic surveillance systems provide information about the status of the data sources feeding into the system, for example on the home page of the system, but do not show the effects of any missing data sources on individual derived data elements (except in that graphs may show obvious drops in counts on days when data sources are missing).

Disease surveillance is a core public health (PH) function. To manage and adjudicate cases of suspected notifiable disease, PH workers gather data elements about persons, clinical care, and providers from various clinical sources, including providers, laboratories, among others. Current processes often yield incomplete and untimely reporting across different diseases requiring time-consuming follow-up by PH to get needed information [1,2]. To improve the completeness and timeliness of case reporting, health departments have explored accessing EHR systems, which are increasingly available. We examine whether providing PH with EHR access to gather notifiable disease case information affects data completeness.

Objective

To assess the effect of electronic health record (EHR) system access on notifiable disease case data completeness.

Clinician reporting of notifiable diseases has historically been slow, labor intensive, and incomplete. Manual and electronic laboratory reporting (ELR) systems have increased the timeliness, efficiency, and completeness of notifiable disease reporting but cannot provide full demographic information about patients, integrate an array of pertinent lab tests to yield a diagnosis, describe patient signs and symptoms, pregnancy status, treatment rendered, or differentiate a new diagnosis or from follow-up of a known old diagnosis. Electronic medical record (EMR) systems are a promising resource to combine the timeliness and completeness of ELR systems with the clinical perspective of clinician initiated reporting. We describe an operational system that detects and reports patients with notifiable diseases to the state health department using EMR data.

Objective

To leverage EMR systems to improve the timeliness, completeness, and clinical detail of notifiable disease reporting.