Published in:
Open Access 01-12-2014 | Research article
The frequency of tetracycline resistance genes co-detected with respiratory pathogens: a database mining study uncovering descriptive trends throughout the United States
Published in: BMC Infectious Diseases | Issue 1/2014
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Background
The Center for Disease Control and Prevention (CDC) indicates that one of the largest problems threatening healthcare includes antibiotic resistance. Tetracycline, an effective antibiotic that has been in use for many years, is becoming less successful in treating certain pathogens. To better understand the temporal patterns in the growth of antibiotic resistance, patient diagnostic test records can be analyzed.
Methods
Data mining methods including frequent item set mining and association rules via the Apriori algorithm were used to analyze results from 80,241 Target Enriched Multiplex-PCR (TEM-PCR) reference laboratory tests. From the data mining results, five common respiratory pathogens and their co-detection rates with tetracycline resistance genes (TRG) were further analyzed and organized according to year, patient age, and geography.
Results
From 2010, all five pathogens were associated with at least a 24% rise in co-detection rate for TRGs. Patients from 0–2 years old exhibited the lowest rate of TRG co-detection, while patients between 13–50 years old displayed the highest frequency of TRG co-detection. The Northeastern region of the United States recorded the highest rate of patients co-detected with a TRG and a respiratory pathogen. Along the East–west gradient, the relative frequency of co-detection between TRGs and respiratory pathogens decreased dramatically.
Conclusions
Significant trends were uncovered regarding the co-detection frequencies of TRGs and respiratory pathogens over time. It is valuable for the field of public health to monitor trends regarding the spread of resistant infectious disease, especially since tetracycline continues to be utilized a treatment for various microbial infections. Analyzing large datasets containing TEM-PCR results for co-detections provides valuable insights into trends of antibiotic resistance gene expression so that the effectiveness of first-line treatments can be continuously monitored.