Buckeridge David

In 2010, as rules for the Centers for Medicaid and Medicare Electronic Heatlh Record (EHR) Incentive Programs (Meaningful Use)(1), were finalized, ISDS became aware of a trend towards new EHR systems capturing or sending emergency department (ED) chief complaint (CC) data as structured variables without including the free-text. This perceived shift in technology was occurring in the absence of consensus-based technical requirements for syndromic surveillance and survey data on the value of free-text CC to public health practice. On 1/31/11, ISDS, in collaboration with CDC BioSense, recommended a core set of data for public health syndromic surveillance (PHSS) to support public health's participation in Meaningful Use.

Objective

This study was conducted to better support a requirement for ED CC as free-text, by investigating the relationship between the unstructured, free-text form of CC data and its usefulness in public health practice. To better inform health IT standardization practices, specifically related to Meaningful Use, by describing how US public health agencies use unstructured, free-text EHR data to monitor, assess, investigate and manage issues of public health interest.

Work on vaccination timing and promotion largely precedes the 2009 pandemic. Post-pandemic studies examining the wide range of local vaccination efforts mostly have been limited to surveys assessing the role of administrative strategies, logistical challenges, and perceived deterrents of vaccination [1].

Objective

To assess the effectiveness of a Public Health automated phone campaign to increase vaccination uptake in targeted neighborhoods. To identify alternative predictors of variation in vaccination uptake, specifically to assess the association between vaccination uptake, and weather conditions and day-of-week.

There is a clear need for improved surveillance of chronic diseases to guide public health practice and policy. Chronic disease surveillance has tended to use administrative data, due to the need to link encounters for an individual over time and to have complete capture of all encounters. Case-detection algorithms generally combine variables found in the data using Boolean operators (i.e., AND, OR, NOT). For example, a commonly used algorithm for DM surveillance requires a patient to have one hospitalization or two physician visits within two years with a diagnostic code for DM. While this approach to defining case-detection algorithms is straightforward, it has limitations. For example, if more than simple combinations of one or two variables are used, then it becomes unwieldy to represent the algorithm and it can be difficult to identity how different variables in the definition contribute to detection accuracy. A multivariable probabilistic case-detection algorithm can address these problems and facilitate exploration of how the multiple variables available from different data sources might improve case-detection accuracy1. In this research, we develop an approach for probabilistic multivariable case-detection and apply the method to a cohort of older adults with known DM status to demonstrate and evaluate the method.

Objective

To develop and validate a multivariable probabilistic algorithm for detecting cases of diabetes mellitus (DM) using clinical and demographic data.

While there has been some work to evaluate different data sources for syndromic surveillance of influenza, no one has yet assessed the utility of simultaneously restricting data to specific visit settings and patient age-groups using data drawn from a single source population. Furthermore, most studies have been limited to the emergency departments (ED), with few evaluating the timeliness of data from community-based primary care.

Objective

Using physician billing data from a single source population, we aimed to compare age-group and visit setting specific patterns in the timing of patients presenting to community-based healthcare settings and hospital ED for influenza-like-illnesses (ILI). We thus evaluate the utility of focusing on particular age-groups and care settings for syndromic surveillance of ILI in ambulatory care.

Tuberculosis (TB) has reemerged as a global health epidemic in recent years. Although several researchers have examined the use of space-time surveillance to detect TB clusters, they have not used genetic information to verify that detected clusters are due to person-to-person transmission. Using genetic fingerprinting data for TB cases, we sought to determine whether detected clusters were due to recent transmission.

Objective

This paper describes the utility of prospective spacetime surveillance to detect genetic clusters of TB due to person-to-person spread.

Though spatio-temporal patterns of influenza spread have often suggested that environmental factors, such as temperature, solar radiation and humidity play a key role, few studies have directly assessed their effect on the timing of annual epidemics. Finkelman et al observed a significant positive relationship between the latitudinal position of temperate countries and epidemic timing. It is hypothesized that during winter months, in temperate regions, decreased skin exposure to sunlight affects immune function by altering the production of certain immunomodulators (e.g. melatonin and Vitamin D3). Other studies have linked temperature and humidity conditions to the rate of transmission of the influenza virus.

Objective 

To assess the strength of the association between peak influenza activity and dew point, average daily temperature, solar radiation, latitude and longitude so that we may better understand the factors that affect virus transmission and/or innate immunity and to determine whether these climate variables should be used as covariates in the surveillance of influenza.

Most research in syndromic surveillance has emphasized early detection, but clinical diagnosis of the index case will tend to occur before detection by syndromic surveillance for certain types of outbreaks [1]. Syndromic surveillance may, however, still play an important role in rapidly characterizing the outbreak size because there will be additional non-diagnosed symptomatic cases in the medical system when the index case is identified. Other authors have shown that the temporal pattern of symptomatic cases could be used to project the total outbreak size, but their approach requires a priori knowledge of the incubation curve for the specific anthrax strain and exposure level [2]. In this paper, we focus on estimating the number of non-diagnosed symptomatic cases at the time of detection without making assumptions about the exposure level or disease course.

Objective 

Upon detection of an inhalational anthrax attack, a critical priority for the public health response would be to characterize the size and extent of the outbreak. Our objective is to assess the potential role of syn-dromic surveillance in estimating the outbreak size.

To assess the accuracy of community-based physician claims for identifying 5 syndromes: fever, gastrointestinal, neurological, rash, and respiratory.

Objective: To enable improved health surveillance and clinical decision support within ambulatory Electronic Health Record (EHR) systems.