Risk factors for community-associated Clostridioides difficile infection in young children.
M K WengS H AdkinsW BambergM M FarleyC C EspinosaL WilsonR PerlmutterS HolzbauerT WhittenE C PhippsE B HancockG DumyatiD S NelsonZ G BeldavsV OcampoC M DavisB RueL KorhonenL C McDonaldAlice Y GuhPublished in: Epidemiology and infection (2020)
The majority of paediatric Clostridioides difficile infections (CDI) are community-associated (CA), but few data exist regarding associated risk factors. We conducted a case-control study to evaluate CA-CDI risk factors in young children. Participants were enrolled from eight US sites during October 2014-February 2016. Case-patients were defined as children aged 1-5 years with a positive C. difficile specimen collected as an outpatient or ⩽3 days of hospital admission, who had no healthcare facility admission in the prior 12 weeks and no history of CDI. Each case-patient was matched to one control. Caregivers were interviewed regarding relevant exposures. Multivariable conditional logistic regression was performed. Of 68 pairs, 44.1% were female. More case-patients than controls had a comorbidity (33.3% vs. 12.1%; P = 0.01); recent higher-risk outpatient exposures (34.9% vs. 17.7%; P = 0.03); recent antibiotic use (54.4% vs. 19.4%; P < 0.0001); or recent exposure to a household member with diarrhoea (41.3% vs. 21.5%; P = 0.04). In multivariable analysis, antibiotic exposure in the preceding 12 weeks was significantly associated with CA-CDI (adjusted matched odds ratio, 6.25; 95% CI 2.18-17.96). Improved antibiotic prescribing might reduce CA-CDI in this population. Further evaluation of the potential role of outpatient healthcare and household exposures in C. difficile transmission is needed.
Keyphrases
- healthcare
- clostridium difficile
- end stage renal disease
- emergency department
- risk factors
- ejection fraction
- air pollution
- chronic kidney disease
- prognostic factors
- primary care
- peritoneal dialysis
- mental health
- intensive care unit
- young adults
- machine learning
- risk assessment
- preterm birth
- artificial intelligence
- health insurance
- big data