Risk factors for infections - an overview of the evidence base and challenges in data synthesis.

BACKGROUND

Recognition of a broad spectrum of disease and development of infection (CDI) and recurrent CDI (rCDI) in populations previously considered to be at low risk has renewed attention on differences in the risk profile of patients. In the absence of primary prevention for CDI and limited treatment options, it is important to achieve a deep understanding of the multiple factors that influence the risk of developing CDI and rCDI.

METHODS

We conducted a review of systematic reviews and meta-analyses on risk factors for CDI and rCDI published between 1990 and October 2016.

RESULTS

22 systematic reviews assessing risk factors for CDI (n = 19) and rCDI (n = 6) were included. Meta-analyses were conducted in 17 of the systematic reviews. Over 40 risk factors have been associated with CDI and rCDI and can be classified into three categories: pharmacological risk factors, host-related risk factors, and clinical characteristics or interventions. Most systematic reviews and meta-analyses have focused on antibiotic use (n = 8 for CDI, 3 for rCDI), proton pump inhibitors (n = 8 for CDI, 4 for rCDI), and histamine 2 receptor antagonists (n = 4 for CDI) and chronic kidney disease (n = 4 for rCDI). However, other risk factors have been assessed. We discuss the state of the evidence, methods, and challenges for data synthesis.

CONCLUSION

Several studies, synthesized in different systematic review, provide valuable insights into the role of different risk factors for CDI. Meta-analytic evidence of association has been reported for factors such as antibiotics, gastric acid suppressants, non-selective NSAID, and some co-morbidities. However, despite statistical significance, issues of high heterogeneity, bias and confounding remain to be addressed effectively to improve overall risk estimates. Large, prospective primary studies on risk factors for CDI with standardised case definitions and stratified analyses are required to develop more accurate and robust estimates of risk effects that can inform targeted-CDI clinical management procedures, prevention, and research.