Evidence map›Paper›PMID 36962261›Full record

ArticlePLOS global public health2022

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

Open access · goldFull text read
In one paragraph

Article in PLOS global public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
0.4field-weighted citation impact, top 42% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed, 4 citations in OpenAlex.

  1. The Global Burden ofInfectious disease reports · 2025
    Review
  2. Article
  3. High Prevalence ofPathogens (Basel, Switzerland) · 2023
    Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 4 institutions in 2 countries.

Winnie C MutaiDepartment of Medical Microbiology, School of Medicine, University of Nairobi, Nairobi, Kenya.ORCID https://orcid.org/0000-0003-0612-9430
Marianne MureithiDepartment of Medical Microbiology, School of Medicine, University of Nairobi, Nairobi, Kenya.ORCID https://orcid.org/0000-0001-9119-3167
Omu AnzalaDepartment of Medical Microbiology, School of Medicine, University of Nairobi, Nairobi, Kenya.ORCID https://orcid.org/0000-0003-2892-7847
Brian KullinDivision of Medical Virology, Department of Pathology, Faculty of Health Sciences, University of Cape Town, Cape Town, South Africa.ORCID https://orcid.org/0000-0001-5460-1977
Robert OfweteDepartment of Medical Microbiology, School of Medicine, University of Nairobi, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-3758-624X
Cecilia Kyany' AUS Army Medical Research Directorate-Africa, Kenya, Nairobi, Kenya.ORCID https://orcid.org/0000-0001-6293-5264
Erick OdoyoUS Army Medical Research Directorate-Africa, Kenya, Nairobi, Kenya.
Lillian MusilaUS Army Medical Research Directorate-Africa, Kenya, Nairobi, Kenya.ORCID https://orcid.org/0000-0003-1418-6523
Gunturu RevathiDepartment of Pathology, Division of Medical Microbiology, Aga Khan University Hospital, Nairobi, Kenya.ORCID https://orcid.org/0000-0002-6938-8387
University of Nairobi · KEUnited States Army Medical Research Directorate - Africa · KEAga Khan University Hospital Nairobi · KEUniversity of Cape Town · ZA

Funding

Wellcome Trust
6 · The paper itself

Abstract

introductionClostridioides 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.

resultsOverall, 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.

conclusionChronic 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.

Identifiers

PMID36962261
PMCPMC10022263
OpenAlexW4210496442

What OpenQuestion holds

Textfull text, public
LicenceCC BY
measurements read56
identifiers read4
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.