Pattern Analysis

The nature of Emergency Room services makes the patients' visits hard to predict and control and the services incur high costs. Chronic patients should not require urgent care to treat their chronic illness, if they were properly managed in primary care. We track frequency of emergency room visits by chronically ill when the primary complaint of record is their chronic condition. We use a record of institutional insurance claims collected in over 400 hospitals in California between 2006 and 2010. We identify dimensions of data that provide statistically significant differences of utilization between strata. We found particularly significant differences in resource utilization subject to type of insurance coverage carried by the patient, and subject to patient's age. We studied Diabetes, Asthma, and Arthritis patients from 8 age groups spanning ages 5 to 85, and 13 insurance payer types.

Objective

To study patterns of utilization of emergency care resources by chronically ill in order to identify efficiency and quality of care improvement opportunities.

In some influenza seasons, morbidity and mortality closely follow the expected seasonal variation. In these years, approaches such as Serflingís model and seasonal-based syndromic outbreak detectors, in use in EARS, work well. In other years, though, short but intense variations occur in addition to the longer term seasonal variation. These intense outbreaks, which are often multimodal, have important implications for both syndromic surveillance and influenza epidemiology. Unfortunately, they are both difficult to characterize and poorly understood. In this paper, we apply techniques from time-frequency distribution theory to identify the temporal location, duration, and amplitude of intense outbreaks occurring in the presence of longer time scale variations.

Computational and statistical methods for detecting disease clusters, such as the spatial scan statistic, have become frequently used tools in epidemiology. However, they simply tell the user where a cluster is, and leave the analysis task to the user. Multivariate visualization tools provide one way for this analysis. The approach developed in this research is computational in nature, using computer vision techniques to analyze the shape of the cluster. Shapes are used here because different spatial processes that cause clusters, such as pollution along a river, create clusters with different shapes. Thus, it may be possible to categorize clusters by their respective spatial processes by analyzing the cluster shapes.

OBJECTIVE

There are plenty of computational and statistical methods for detecting spatial clusters, although the interpretation of these clusters is a task left to the user. This research develops computational methods to not just detect, but also analyze the cluster to hypothesize one or more potential causes.

Definitions of “re-emerging infectious diseases” typically encompass any disease occurrence that was a historic public health threat, declined dramatically, and has since presented itself again as a significant health problem. Examples include antimicrobial resistance leading to resurgence of tuberculosis, or measles re-appearing in previously protected communities. While the language of this verbal definition of “re-emergence” is sensitive enough to capture most epidemiologically relevant resurgences, its qualitative nature obfuscates the ability to quantitatively classify disease re-emergence events as such.

Objective:

Although relying on verbal definitions of "re-emergence", descriptions that classify a “re-emergence” event as any significant recurrence of a disease that had previously been under public health control, and subjective interpretations of these events is currently the conventional practice, this has the potential to hinder effective public health responses. Defining re-emergence in this manner offers limited ability for ad hoc analysis of prevention and control measures and facilitates non-reproducible assessments of public health events of potentially high consequence. Re-emerging infectious disease alert (RED Alert) is a decision-support tool designed to address this issue by enhancing situational awareness by providing spatiotemporal context through disease incidence pattern analysis following an event that may represent a local (country-level) re-emergence. The tool’s analytics also provide users with the associated causes (socioeconomic indicators) related to the event, and guide hypothesis-generation regarding the global scenario.