Seasonal Trends and Antibiotic Resistance Profiles of Bacterial Pathogens in Indian Clinical Isolates

Introduction Bacterial diseases exhibit seasonal trends, necessitating their monitoring for outbreak prediction, treatment optimization, and infection control. This study explores seasonal trends, temperature correlations, and antimicrobial resistance profiles of key pathogens in an Indian tertiary care setting. Methodology This cross-sectional study analyzed bacterial isolates from 1,562 patient samples, including <i>Staphylococcus aureus</i>, <i>Klebsiella pneumoniae</i>, <i>Pseudomonas aeruginosa</i>, <i>Acinetobacter baumannii</i>, <i>Escherichia coli</i>, and <i>Enterococcus faecalis</i>. Monthly infection rates and seasonal patterns were visualized using heatmaps and time-series graphs. Pearson's correlation assessed the relationship between these infection rates and temperature. Antibiotic susceptibility was evaluated using VITEK2, with resistance patterns visualized in R. Results Infections peaked in April (<i>n</i> = 163, 10.43%) and March (<i>n</i> = 161, 10.30%), with <i>S</i> <i>. aureus</i> as the most common pathogen (<i>n</i> = 271, 25.64%), followed by <i>K</i> <i>. pneumoniae</i> (<i>n</i> = 201, 19.02%) and <i>P</i> <i>. aeruginosa</i> (<i>n</i> = 178, 16.84%). Seasonal trends showed <i>S. aureus</i> infections peaked in summer (<i>n</i> = 45, 16.6%), while <i>P. aeruginosa </i>(<i>n</i> = 27, 15.2%) and <i>E. faecalis</i> (<i>n</i> = 24, 25.5%) peaked in winter. Temperature correlated positively with <i>S. aureus</i> infection (<i>r</i> = 0.814, <i>P</i> = 0.001) and negatively with <i>P. aeruginosa</i> (<i>r</i> = -0.845, <i>P</i> = 0.001), and <i>E</i> <i>. faecalis</i> (<i>r</i> = -0.618, <i>P</i> = 0.032), with no correlation observed for <i>K. pneumoniae</i>, <i>A. baumannii</i>, and <i>E. coli</i>. Multi-drug resistance (MDR), extensively drug resistance (XDR), and pandrug resistance were more prevalent in Gram-negative than in Gram-positive bacteria. Conclusions This study reveals temperature-driven seasonal patterns in bacterial infections, aiding outbreak prediction and prevention. The findings emphasize the threat of multidrug resistance, particularly in Gram-negative bacteria, reinforcing the need for enhanced infection control and targeted antibiotic stewardship.

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Journal
Cureus
Published
2025-01-10
DOI
https://doi.org/10.7759/cureus.77255
Citations
4
Primary Topic
Antibiotic Resistance in Bacteria
Type
article
Field-Weighted Citation Impact
2.36

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article

Seasonal Trends and Antibiotic Resistance Profiles of Bacterial Pathogens in Indian Clinical Isolates

Vanya Singh, Balram Ji Omar, B. Nirmala
4 citations
Cureus
Antibiotic Resistance in Bacteria
2.36
article

Seasonal Trends and Antibiotic Resistance Profiles of Bacterial Pathogens in Indian Clinical Isolates

Vanya Singh, Balram Ji Omar, B. Nirmala
article en
4 citations

Abstract

Introduction Bacterial diseases exhibit seasonal trends, necessitating their monitoring for outbreak prediction, treatment optimization, and infection control. This study explores seasonal trends, temperature correlations, and antimicrobial resistance profiles of key pathogens in an Indian tertiary care setting. Methodology This cross-sectional study analyzed bacterial isolates from 1,562 patient samples, including <i>Staphylococcus aureus</i>, <i>Klebsiella pneumoniae</i>, <i>Pseudomonas aeruginosa</i>, <i>Acinetobacter baumannii</i>, <i>Escherichia coli</i>, and <i>Enterococcus faecalis</i>. Monthly infection rates and seasonal patterns were visualized using heatmaps and time-series graphs. Pearson's correlation assessed the relationship between these infection rates and temperature. Antibiotic susceptibility was evaluated using VITEK2, with resistance patterns visualized in R. Results Infections peaked in April (<i>n</i> = 163, 10.43%) and March (<i>n</i> = 161, 10.30%), with <i>S</i> <i>. aureus</i> as the most common pathogen (<i>n</i> = 271, 25.64%), followed by <i>K</i> <i>. pneumoniae</i> (<i>n</i> = 201, 19.02%) and <i>P</i> <i>. aeruginosa</i> (<i>n</i> = 178, 16.84%). Seasonal trends showed <i>S. aureus</i> infections peaked in summer (<i>n</i> = 45, 16.6%), while <i>P. aeruginosa </i>(<i>n</i> = 27, 15.2%) and <i>E. faecalis</i> (<i>n</i> = 24, 25.5%) peaked in winter. Temperature correlated positively with <i>S. aureus</i> infection (<i>r</i> = 0.814, <i>P</i> = 0.001) and negatively with <i>P. aeruginosa</i> (<i>r</i> = -0.845, <i>P</i> = 0.001), and <i>E</i> <i>. faecalis</i> (<i>r</i> = -0.618, <i>P</i> = 0.032), with no correlation observed for <i>K. pneumoniae</i>, <i>A. baumannii</i>, and <i>E. coli</i>. Multi-drug resistance (MDR), extensively drug resistance (XDR), and pandrug resistance were more prevalent in Gram-negative than in Gram-positive bacteria. Conclusions This study reveals temperature-driven seasonal patterns in bacterial infections, aiding outbreak prediction and prevention. The findings emphasize the threat of multidrug resistance, particularly in Gram-negative bacteria, reinforcing the need for enhanced infection control and targeted antibiotic stewardship.

CureusVol. 17(1)
All India Institute of Medical Sciences Rishikesh (IN)
Department of Biotechnology, Ministry of Science and Technology, India, All-India Institute of Medical Sciences
Zero hunger
Openalex Percentile: Top 13%
Antibiotic Resistance in Bacteria
2.36
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