Predicting Increased Incidence of Common Antibiotic-Resistant and Antibiotic-Associated Pathogens Using Ensemble Species Distribution Modeling.

The Centers for Disease Control estimates antibiotic-associated pathogens result in 2.8 million infections and 38,000 deaths annually in the United States. This study applies species distribution modeling to elucidate the impact of environmental determinants of human infectious disease in an era of rapid global change. We modeled methicillin-resistant Staphylococcus aureus and Clostridioides difficile using 31 publicly accessible bioclimatic, healthcare, and sociodemographic variables. Ensemble models were created from 8 unique statistical and machine learning algorithms. Using International Classification of Diseases, 10th Edition codes, we identified 305,528 diagnoses of methicillin-resistant S.aureus and 302,001 diagnoses of C.difficile presence. Three environmental factors - average maximum temperature, specific humidity, and agricultural land density - emerged as major predictors of increased methicillin-resistant S.aureus and C.difficile presence; variables representing healthcare availability were less important. Species distribution modeling may be a powerful tool for identifying areas at increased risk for disease presence and have important implications for disease surveillance systems.

Effect of climate on surgical site infections and anticipated increases in the United States.

Surgical site infections (SSI) are one of the most common and costly hospital-acquired infections in the United States. Meteorological variables such as temperature, humidity, and precipitation may represent a neglected group of risk factors for SSI. Using a national private insurance database, we collected admission and follow-up records for National Healthcare Safety Network-monitored surgical procedures and associated climate conditions from 2007 to 2014. We found that every 10 cm increase of maximum daily precipitation resulted in a 1.09 odds increase in SSI after discharge, while every g/kg unit increase in specific humidity resulted in a 1.03 odds increase in SSI risk after discharge. We identified the Southeast region of the United States at highest risk of climate change-related SSI, with an estimated 3% increase in SSI by 2060 under high emission assumptions. Our results describe the effect of climate on SSI and the potential burden of climate-change related SSI in the United States.