Surveillance and epidemiology of Clostridioides difficile infection using the national health surveillance network criteria: A 7-year study from Mumbai, India

Abstract

IntroductionClostridioides difficile (CD) is a significant cause of morbidity and mortality. While considerable data is available in the developed world regarding Clostridioides difficile infection (CDI), Indian data is sparse especially using the standardized surveillance systems. Aim and objectives of the studyTo identify the incidence, risk factors, and mortality rate associated with CDI in a tertiary care hospital based on the Laboratory-Identified (LabID) event criteria of the Centers for Disease Control and Prevention (CDC) National Health Surveillance Network (NHSN). Materials and methodsDuring a 7- year prospective observational study, CDI was diagnosed using CD polymerase chain reaction (PCR). CDI Laboratory-Identified (LabID) events were classified using the CDC NHSN surveillance definition, and CDI incidence was calculated per 10,000 Patient Days (PDs). Clinical details were collected as part of healthcare-associated infection (HCAI) surveillance. Healthcare Facility-Onset (HO) and Community-Onset Healthcare Facility-Associated (CO-HCFA) incident CDI events were analyzed further. ResultsAmong 898 tested stool samples, 77 CDI LabID events were observed, with 68 being Incident events. Of 68 events, 76.5% (52/68), 19.1% (13/68), and 4.4% (3/68) were HO, Community-Onset (CO), and CO-HCFA CDI events respectively. The overall incidence of CDI events was 1.66 per 10,000 PDs. Risk factors observed were antibiotics exposure (100%), comorbidities (87.3%), antacids exposure (87.3%), age over 60 years (80%), and hospitalization within the past 6 months (67.3%). The crude mortality rate was 25.5% (14/55). ConclusionThese findings highlight the predominance of HO-CDI and the need for further investigation into CO-CDI in the Indian context to identify at-risk populations. Utilizing standardized surveillance methods such as NHSN definitions can facilitate accurate disease burden estimation, trend monitoring, and meaningful comparisons with global data.