Assessment of independent comorbidities and comorbidity measures in predicting healthcare facility-onset Clostridioides difficile infection in Kenya

Winnie C Mutai; Marianne Mureithi; Omu Anzala; Brian Kullin; Robert Ofwete; Cecilia Kyany' A; Erick Odoyo; Lillian Musila; Gunturu Revathi

doi:10.1371/journal.pgph.0000090

Assessment of independent comorbidities and comorbidity measures in predicting healthcare facility-onset Clostridioides difficile infection in Kenya

PLOS Glob Public Health. 2022 Jan 31;2(1):e0000090. doi: 10.1371/journal.pgph.0000090. eCollection 2022.

Authors

Winnie C Mutai¹, Marianne Mureithi¹, Omu Anzala¹, Brian Kullin², Robert Ofwete¹, Cecilia Kyany' A³, Erick Odoyo³, Lillian Musila³, Gunturu Revathi⁴

Affiliations

¹ Department of Medical Microbiology, School of Medicine, University of Nairobi, Nairobi, Kenya.
² Division of Medical Virology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.
³ US Army Medical Research Directorate-Africa, Kenya, Nairobi, Kenya.
⁴ Department of Pathology, Division of Medical Microbiology, Aga Khan University Hospital, Nairobi, Kenya.

Abstract

Introduction: Clostridioides difficile is primarily associated with hospital-acquired diarrhoea. The disease burden is aggravated in patients with comorbidities due to increased likelihood of polypharmacy, extended hospital stays and compromised immunity. The study aimed to investigate comorbidity predictors of healthcare facility-onset C. difficile infection (HO-CDI) in hospitalized patients.

Methodology: We performed a cross sectional study of 333 patients who developed diarrhoea during hospitalization. The patients were tested for CDI. Data on demographics, admission information, medication exposure and comorbidities were collected. The comorbidities were also categorised according to Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI). Comorbidity predictors of HO-CDI were identified using multiple logistic regression analysis.

Results: Overall, 230/333 (69%) patients had comorbidities, with the highest proportion being in patients aged over 60 years. Among the patients diagnosed with HO-CDI, 63/71(88.7%) reported comorbidities. Pairwise comparison between HO-CDI patients and comparison group revealed significant differences in hypertension, anemia, tuberculosis, diabetes, chronic kidney disease and chronic obstructive pulmonary disease. In the multiple logistic regression model significant predictors were chronic obstructive pulmonary disease (odds ratio [OR], 9.51; 95% confidence interval [CI], 1.8-50.1), diabetes (OR, 3.56; 95% CI, 1.11-11.38), chronic kidney disease (OR, 3.88; 95% CI, 1.57-9.62), anemia (OR, 3.67; 95% CI, 1.61-8.34) and hypertension (OR, 2.47; 95% CI, 1.-6.07). Among the comorbidity scores, CCI score of 2 (OR 6.67; 95% CI, 2.07-21.48), and ECI scores of 1 (OR, 4.07; 95% CI, 1.72-9.65), 2 (OR 2.86; 95% CI, 1.03-7.89), and ≥ 3 (OR, 4.87; 95% CI, 1.40-16.92) were significantly associated with higher odds of developing HO-CDI.

Conclusion: Chronic obstructive pulmonary disease, chronic kidney disease, anemia, diabetes, and hypertension were associated with an increased risk of developing HO-CDI. Besides, ECI proved to be a better predictor for HO-CDI. Therefore, it is imperative that hospitals should capitalize on targeted preventive approaches in patients with these underlying conditions to reduce the risk of developing HO-CDI and limit potential exposure to other patients.

Copyright: © 2022 Mutai et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Grants and funding

WM was supported by the Consortium for Advanced Research Training in Africa (CARTA). CARTA is jointly led by the African Population and Health Research Center and the University of the Witwatersrand and funded by the Carnegie Corporation of New York (Grant No--B 8606.R02), Sida (Grant No:54100029), the DELTAS Africa Initiative (Grant No: 107768/Z/15/Z). The DELTAS Africa Initiative is an independent funding scheme of the African Academy of Sciences (AAS) 's Alliance for Accelerating Excellence in Science in Africa (AESA) and supported by the New Partnership for Africa's Development Planning and Coordinating Agency (NEPAD Agency) with funding from the Wellcome Trust (UK) (Grant No: 107768/Z/15/Z) and the UK government. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.