A systematic review of factors associated with side-effect expectations from medical interventions

Background: Fear of side-effects can result in non-adherence to medical interventions, such as medication and chemotherapy. Side-effect expectations have been identified as strong predictors of later perception of side-effects. However, research investigating predictors of side-effect expectations is disparate.

Objective: To identify factors associated with side-effect expectations.

Search strategy: We systematically searched Embase, Ovid MEDLINE, Global Health, PsycARTICLES, PsycINFO, Web of Science and Scopus.

Inclusion criteria: Studies were included if they investigated associations between any predictive factor and expectations of side-effects from any medical intervention.

Data extraction and synthesis: We extracted information about participant characteristics, medication, rates of side-effects expected, and predictors of side-effect expectations. Data were narratively synthesised.

Main results: We identified sixty-four citations, reporting on seventy-two studies. Predictors fell into five categories: personal characteristics; clinical characteristics; psychological traits and state; presentation format of information; and information sources used. Using verbal risk descriptors (e.g. ‘common’) compared to numerical descriptors (e.g. percentages), having lower quality of life or wellbeing, and currently experiencing symptoms were associated with increased side-effect expectations.

Discussion and conclusions: Decreasing unrealistic side-effect expectations may lead to decreased experience of side-effects and increased adherence to medical interventions. Widespread communications about medical interventions should describe the incidence of side-effects numerically. Evidence suggests that clinicians should take particular care with patients with lower quality of life, who are currently experiencing symptoms and who have previously experienced symptoms from treatment. Further research should investigate different clinical populations and aim to quantify the impact of the media and social media on side-effect expectations.