Data Analytics

When a reportable condition is identified, clinicians and laboratories are required to report the case to public health authorities. These case reports help public health officials to make informed decisions and implement appropriate control measures to prevent the spread of disease. Incomplete or delayed case reports can result in new occurrences of disease that could have been prevented. To improve the disease reporting and surveillance processes, the Utah Department of Health is collaborating with Intermountain Healthcare and the University of Utah to electronically transmit case reports from healthcare facilities to public health entities using Health Level Seven v2.5, SNOMED CT, and LOINC. As part of the Utah Center of Excellence in Public Health Informatics, we conducted an observation study in 2009 to identify metrics to evaluate the impact of electronic systems. We collected baseline data in 2009 and in this paper we describe preliminary results from a follow-up study conducted in 2010.

Objective

This paper describes a comparison study conducted to identify quality of reportable disease case reports received at Salt Lake Valley health department in 2009 and 2010.

Most, if not all, disease surveillance systems are federated in the sense that hospitals, doctors’ offices, pharmacies are the source of most surveillance data. Although a health department may request or mandate that these organizations report data, we are not aware of any requirements about the method of data collection or audits or other measures of quality control.

Because of the heterogeneity and lack of control over the processes by which the data are generated, data sources in a federated disease surveillance system are black boxes the reliability, completeness, and accuracy of which are not fully understood by the recipient.

In this paper, we use the variance-to-mean ratio of daily counts of surveillance events as a metric of data quality. We use thermometer sales data as an example of data from a federated disease surveillance system. We test a hypothesis that removing stores with higher baseline variability from pooled surveillance data will improve the signal-to-noise ratio of thermometer sales for an influenza outbreak.

Objective

We developed a novel method for monitoring the quality of data in a federated disease surveillance system, which we define as ‘a surveillance system in which a set of organizations that are not owned or controlled by public health provide data.’

Given the clear relationship between spatial contexts and health, the Indiana Center of Excellence in Public Health Informatics (ICEPHI) aims to serve both the needs of public health researchers and practitioners by contextualizing the health information of large populations. Specifically, ICEPHI will integrate one of the nation’s largest health information exchanges, the Indiana Network for Patient Care, with well-established community information systems that collect, geocode, organize, and present integrated data on communities in Indiana and surrounding states, including data on public safety, welfare, education, economics, and demographics.

Objective

This presentation describes a collaborative approach for realizing the public health potential of a geospatially enabled statewide health information exchange.

Recently published studies evaluate statistical alerting methods for disease surveillance based on detection of modeled signals in a data background of either authentic historical data or randomized samples. Differences in regional and jurisdictional data, collection and filtering methods, investigation resources, monitoring objectives, and systemrequirements have hindered acceptance of standard monitoring methodology. The signature of a disease outbreak and the baseline data behavior depend on various factors, including population coverage, quality and timeliness of data, symptomatology, and the careseeking behavior of the monitored population. For this reason, statistical process control methods based on standard data distributions or stylized signals may not alert as desired. Practical algorithm evaluation and adjustment may be possible by judging algorithmperformance according to the preferences of experienced human monitors.

Objective

This presentation gives a method of monitoring surveillance time series on the basis of the human expert preference. The method does not require detailed history for the current series, modeling expertize, or a well-defined data signal. It is designed for application to many data types and without need for a sophisticated environment or historical data analysis. 

Scan statistics are highly successful for the evaluation of space-time clusters. Recently, concepts from the graph theory were applied to evaluate the set of potential clusters. Wieland et al. introduced a graph theoretical method for detecting arbitrarily shaped clusters on the basis of the Euclidean minimum spanning tree of cartogram transformed case locations, which is quite effective, but the cartogram construction step of this algorithm is computationally expensive and complicated.

Objective

We describe a method for prospective space-time cluster detection of point event data based on the scan statistic. Our aim is to detect as early as possible the appearance of an emerging cluster of syndromic individuals because of a real outbreak of disease amidst the heterogeneous population at risk.

The goal of disease and syndromic surveillance is to monitor and detect aberrations in disease prevalence across space and time. Disease surveillance typically refers to the monitoring of confirmed cases of disease, whereas syndromic surveillance uses syndromes associated with disease to detect aberrations. In either situation, any proper surveillance system should be able to (i) detect, as early as possible, potentially harmful deviations from baseline levels of disease while maintaining low false positive detection rates, (ii) incorporate the spatial and temporal dynamics of a disease system, (iii) be widely applicable to multiple diseases or syndromes, (iv) incorporate covariate information and (v) produce results that are readily interpretable by policy decision makers.

Early approaches to surveillance were primarily computational algorithms. For example, the CUSUM technique and its variants (see, for example, Fricker et al.) monitor the cumulative deviation (over time) of disease counts from some baseline rate. A second line of work uses spatial scan statistics, originally proposed by Kulldorff with later extensions given in Walther and Neill et al.

Objective

Syndromic surveillance for new disease outbreaks is an important problem in public health. Many statistical techniques have been devised to address the problem, but none are able to simultaneously achieve important practical goals (good sensitivity and specificity, proper use of domain information, and transparent support to decision-makers). The objective, here, is to improve model-based surveillance methods by (i) detailing the structure of a hierarchical hidden Markov model for the surveillance of disease across space and time and (ii) proposing a new, non-separable spatio-temporal autoregressive model.

Predictionmarkets have been successfully used to forecast future events in other fields. We adapted this method to provide estimates of the likelihood of H5N1 influenza related events.

Objective

The purpose of this study is to compare the results of an H5N1 influenza prediction market model with a standard statistical model.

Reporting notifiable conditions to public health authorities by health-care providers and laboratories is fundamental to the prevention, control, and monitoring of population-based disease. To successfully develop community centered health, public health strives to understand and to manage the diseases in its community. Public health surveillance systems provide the mechanisms for public health professionals to ascertain the true disease burden of the population in their community. The information

necessary to determine the disease burden is primarily found in the data generated during clinical care processes.

Objective

This poster will present a predictive model to describe the actual number of confirmed cases for an outbreak (H1N1) based on the current number of confirmed cases reported to public health. The model describes the methods used to calculate the number of cases expected in a community based on the lag time in the diagnosis and reporting of these cases to public health departments.

In disease surveillance, an outbreak is often present in more than one data type. If each data type is analyzed separately rather than combined, the statistical power to detect an outbreak may suffer because no single data source captures all the individuals in the outbreak. Researchers, thus, started to take multivariate approaches to syndromic surveillance. The data sources often analyzed include emergency department data, categorized by chief complaint; over-thecounter pharmaceutical sales data collected by the National Retail Data Monitor (NRDM), and some other syndromic data.

Objective

This study proposes a simulation model to generate the daily counts of over-the-counter medication sales, such as thermometer sales from all ZIP code areas in a study region that include the areas without retail stores based on the daily sales collected from the ZIP codes with retail stores through the NRDM. This simulation allows us to apply NRDM data in addition to other data sources in a multivariate analysis in order to rapidly detect outbreaks.

One objective of public health surveillance is detecting disease outbreaks by looking for changes in the disease occurrence, so that control measures can be implemented and the spread of disease minimized. For this purpose, the Florida Department of Health (FDOH) employs the Electronic Surveillance System for Early Notification of Community-based Epidemics (ESSENCE). The current problem was spawned by a laborintensive process at the FDOH: authentic outbreaks were detected by epidemiologists inspecting ESSENCE time series and derived event lists. The corresponding records indicated that patients arrived at an ED within a short interval, often less than 30minutes. The time-of-arrival (TOA) task was to develop and automate a capability to detect events with clustered patient arrival times at the hospital level for a list of subsyndrome categories of concern to the monitoring counties.

Objective

This presentation discusses the approach and results of collaboration to enable a solution of a hospital TOA monitoring problemin syndromic surveillance applied to public health data at the hospital level for county monitoring.