Last data update: May 06, 2024. (Total: 46732 publications since 2009)
Records 1-2 (of 2 Records) |
Query Trace: Sanderson JM[original query] |
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Increasing the efficiency and yield of a tuberculosis contact investigation through electronic data systems matching
Sanderson JM , Proops DC , Trieu L , Santos E , Polsky B , Ahuja SD . J Am Med Inform Assoc 2015 22 (5) 1089-93 BACKGROUND: Electronic health data may improve the timeliness and accuracy of resource-intense contact investigations (CIs) in healthcare settings. METHODS: In September 2013, we initiated a CI around a healthcare worker (HCW) with infectious tuberculosis (TB) who worked in a maternity ward. Two sources of electronic health data were employed: hospital-based electronic medical records (EMRs), to identify patients exposed to the HCW, and an electronic immunization registry, to obtain contact information for exposed infants and their providers at two points during follow-up. RESULTS: Among 954 patients cared for in the maternity ward during the HCW's infectious period, the review of EMRs identified 285 patients (30%) who interacted with the HCW and were, thus, exposed to TB. Matching infants to the immunization registry offered new provider information for 52% and 30% of the infants in the first and second matches. Providers reported evaluation results for the majority of patients (66%). CONCLUSION: Data matching improved the efficiency and yield of this CI, thereby demonstrating the usefulness of enhancing CIs with electronic health data. |
Re: "Estimated rate of reactivation of latent tuberculosis infection in the United States, overall and by population subgroup"
Sanderson JM , Meissner JS , Ahuja SD . Am J Epidemiol 2014 180 (5) 556-7 In an investigation using national data sources, Shea et al. (1) estimated the rate of reactivation tuberculosis (TB) to be 0.084 cases per 100 person-years among persons with latent TB infection (LTBI) in the United States. The authors present these findings as the overall rate of reactivation TB in the United States, and they state that the groups identified as having higher rates of reactivation TB “have increased rates of progression and will receive even greater benefit from testing and treatment” (1, p. 223) for LTBI. While this study represents an important attempt to quantify the contribution of reactivation TB to the overall TB burden in the United States, this extrapolation has significant implications for TB control programs, and we urge caution in the interpretation and application of these results. | Shea et al. (1) differentiated reactivation TB cases from primary TB cases on the basis of cluster status, with cases that clustered being considered cases of primary TB. A cluster was defined as “at least 2 cases with indistinguishable TB genotypes reported within statistically significant geospatial zones” (1, p. 217). While using genotyping to distinguish primary TB from reactivation TB is a common molecular epidemiologic technique, limitations with this method, such as sampling bias (2), unknown strain variation (3), and genotyping methods with limited discriminatory power (4), have been documented. |
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