Biomedical subjects

Shaun J Grannis

Publications and source records attributed to Shaun J Grannis.

8 recordsLinked to original sources

PubMed2006

Privacy protection versus cluster detection in spatial epidemiology.

OBJECTIVES: Patient data that includes precise locations can reveal patients' identities, whereas data aggregated into administrative regions may preserve privacy and confidentiality. We investigated the effect of varying degrees of address precision (exact latitude and longitude vs the center points of zip code or census tracts) on detection of spatial clusters of cases. METHODS: We simulated disease outbreaks by adding supplementary spatially clustered emergency department visits to authentic hospital emergency department syndromic surveillance data. We identified clusters with a spatial scan statistic and evaluated detection rate and accuracy. RESULTS: More clusters were identified, and clusters were more accurately detected, when exact locations were used. That is, these clusters contained at least half of the simulated points and involved few additional emergency department visits. These results were especially apparent when the synthetic clustered points crossed administrative boundaries and fell into multiple zip code or census tracts. CONCLUSIONS: The spatial cluster detection algorithm performed better when addresses were analyzed as exact locations than when they were analyzed as center points of zip code or census tracts, particularly when the clustered points crossed administrative boundaries. Use of precise addresses offers improved performance, but this practice must be weighed against privacy concerns in the establishment of public health data exchange policies.

Algorithms↗

PubMed2005

A context-sensitive approach to anonymizing spatial surveillance data: impact on outbreak detection.

OBJECTIVE: The use of spatially based methods and algorithms in epidemiology and surveillance presents privacy challenges for researchers and public health agencies. We describe a novel method for anonymizing individuals in public health data sets by transposing their spatial locations through a process informed by the underlying population density. Further, we measure the impact of the skew on detection of spatial clustering as measured by a spatial scanning statistic. DESIGN: Cases were emergency department (ED) visits for respiratory illness. Baseline ED visit data were injected with artificially created clusters ranging in magnitude, shape, and location. The geocoded locations were then transformed using a de-identification algorithm that accounts for the local underlying population density. MEASUREMENTS: A total of 12,600 separate weeks of case data with artificially created clusters were combined with control data and the impact on detection of spatial clustering identified by a spatial scan statistic was measured. RESULTS: The anonymization algorithm produced an expected skew of cases that resulted in high values of data set k-anonymity. De-identification that moves points an average distance of 0.25 km lowers the spatial cluster detection sensitivity by less than 4% and lowers the detection specificity less than 1%. CONCLUSION: A population-density-based Gaussian spatial blurring markedly decreases the ability to identify individuals in a data set while only slightly decreasing the performance of a standardly used outbreak detection tool. These findings suggest new approaches to anonymizing data for spatial epidemiology and surveillance.

Algorithms↗

PubMed2005

How disease surveillance systems can serve as practical building blocks for a health information infrastructure: the Indiana experience.

Although many organizations are beginning to develop strategies to implement and study regional and national health information exchanges, there are few operational examples to date. The Indiana Network for Patient Care (INPC) is an example of a currently operational Regional Health Information Organization (RHIO) built upon a foundation of open, robust healthcare information standards. Having demonstrated the scalability of this design, the Indiana State Department of Health (ISDH) contracted with the Regenstrief Institute to implement a statewide disease surveillance system incorporating encounter data from all 114 Indiana hospitals with emergency departments. We describe the 4-year implementation plan, including our design rationale and how we plan to address the specific implementation challenges of data collection, connectivity in diverse environments and current hospital buy-in. To date, 36 hospitals are in various stages of engagement, with 19 hospitals actively providing real-time surveillance data. We will discuss how this project creates the foundation for a potential statewide health information exchange.

Computer Communication Networks↗

PubMed2005

Reviewing and managing syndromic surveillance SaTScan datasets using an open source data visualization tool.

SaTScan is a popular, free software tool used to identify disease clusters early in the course of an outbreak. Using geographic and time-based surveillance data, SaTScan can generate large datasets that are difficult for humans to interpret. Tracing disease clusters through space and time using text tables is a challenging cognitive task. To simplify this process, we developed a Java-based open-source tool to transform SaTScan analytic datasets into easily navigable data visualizations.

Cluster Analysis↗

PubMed2004

Real world performance of approximate string comparators for use in patient matching.

Medical record linkage is becoming increasingly important as clinical data is distributed across independent sources. To improve linkage accuracy we studied different name comparison methods that establish agreement or disagreement between corresponding names. In addition to exact raw name matching and exact phonetic name matching, we tested three approximate string comparators. The approximate comparators included the modified Jaro-Winkler method, the longest common substring, and the Levenshtein edit distance. We also calculated the combined root-mean square of all three. We tested each name comparison method using a deterministic record linkage algorithm. Results were consistent across both hospitals. At a threshold comparator score of 0.8, the Jaro-Winkler comparator achieved the highest linkage sensitivities of 97.4% and 97.7%. The combined root-mean square method achieved sensitivities higher than the Levenshtein edit distance or long-est common substring while sustaining high linkage specificity. Approximate string comparators increase deterministic linkage sensitivity by up to 10% compared to exact match comparisons and represent an accurate method of linking to vital statistics data.

Algorithms↗

PubMed2004

The Indiana network for patient care: an integrated clinical information system informed by over thirty years of experience.

Presented in this article is the Indiana Network for Patient Care, an integrated citywide medical record system that promotes health quality by enabling efficient access to clinical information. It begins with a description of the system's infrastructure, which includes an explanation of how the system accomplishes data integration. This is followed by a series of descriptions and rationales behind the many clinical applications that interface these data. In doing so, some of the factors that we feel contribute to the success of the system are illustrated.

Databases, Factual↗

PubMed2003

Analysis of a probabilistic record linkage technique without human review.

We previously developed a deterministic record linkage algorithm demonstrating sensitivities approaching 90% while maintaining 100% specificity. Substantially better performance has been reported using probabilistic linkage techniques; however, such methods often incorporate human review into the process. To avoid human review, we employed an estimator function using the Expectation Maximization (EM) algorithm to establish a single true-link threshold. We compared the unsupervised probabilistic results against the manually reviewed gold-standard for two hospital registries, as well against our previous deterministic results. At an estimated specificity of 99.95%, actual specificities were 99.43% and 99.42% for registries A and B, respectively. At an estimated sensitivity of 99.95%, actual sensitivities were 99.19% and 98.99% for registries A and B, respectively. The EM algorithm estimated linkage parameters with acceptable accuracy, and was an improvement over the deterministic algorithm. Such a methodology may be used where record linkage is required, but human intervention is not possible or practical.

Algorithms↗

PubMed2002

Analysis of identifier performance using a deterministic linkage algorithm.

As part of developing a record linkage algorithm using de-identified patient data, we analyzed the performance of several demographic variables for making linkages between patient registry records from two hospital registries and the Social Security Death Master File. We analyzed samples from each registry totaling 6,000 record-pairs to establish a linkage gold-standard. Using Social Security Number as the exclusive linkage variable resulted in substantial linkage error rates of 4.7% and 9.2%. The best single variable combination for finding links was Social Security Number, phonetically compressed first name, birth month, and gender. This found 87% and 88% of the links without any false links. We achieved sensitivities of 90% to 92% while maintaining 100% specificity using combinations of social security number, gender, name, and birth date fields. This represents an accurate method for linking patient records to death data and is the basis for a more generalized de-identified linkage algorithm.

Algorithms↗