The frequency of tetracycline resistance genes co-detected with respiratory pathogens: a database mining study uncovering descriptive trends throughout the United States

Abstract

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.

Figure 1

The frequency of TRGs Detected with Respiratory Pathogens Increased from 2009–2013. The scatterplot shows the rise in co-detection between TRGs and respiratory pathogens in recent years. The data is aggregated by week. Loess smoothing curves pass through the data points and are shaded by a 95% confidence interval.

Figure 2

The Frequency of TRGs Detected with Respiratory Pathogens Increased in Each Age Group over Time. Each graph displays the co-detection frequency for each age group and pathogen. The data is aggregated by month. A loess smoothing line passes through data points and are shaded by 95% confidence intervals.

Figure 3

Heatmap Displaying the Longitudinal Effect of TRG Co-detection across the United States. The heat map is ordered by state on the bottom where the eastern states are on the right and the western states are on the left side of the map. The circles which are most red are the states that have the highest levels of tetracycline resistance co-detection regarding each pathogen, while states that are the most blue have the lowest rates. Size of the circles correspond to sample size of the data.