A monte carlo simulation study examining illness representations as a factor in asthma health disparities

Objective: The factors leading to asthma health disparities among children are complex, yet little research has been conducted that integrates, in one explanatory model, the multiple factors that can lead to these disparities. The Common Sense Model of Illness Representation (IR) is an integrated model that takes into account environmental, social, and cultural factors as well as patients' beliefs about health and illness. The objective of these analyses will be to test this model as an alternative explanation for observed asthma health disparities.

Methods: Monte Carlo simulation will be used to further investigate promising results obtained from a secondary data analysis of 225 parents and their children with asthma enrolled in a study examining parental IRs and asthma management. While the hypothesized direction of effects was observed for many outcomes in the structural equation model, low study power (30%) may have contributed to the lack of statistical significance for several of these findings.

The Asthma Illness Representation Scale© (AIRS) assesses parents' beliefs surrounding asthma and its management. Higher scores indicate alignment with the professional model of asthma management which views asthma as a chronic disease, present even when under control and no symptoms are evident. The lay model views asthma as episodic or acute with uncontrollable symptoms, and parents take a negative view towards daily controller medication use. Higher IRs are hypothesized to lead to increased controller medication use and decreased complementary and alternative medicine (CAM) use.

Results: The original path model demonstrated acceptable fit (χ2=48.19, df=43, p=.25, CFI=.94, RMSEA=.04). Two statistically significant paths resulted: poverty and education through AIRS to controller medication use. Although not statistically significant, the indirect paths from ethnicity to controller medication use and ethnicity, education, and poverty on CAM use were in the hypothesized direction. All indirect paths from ethnicity, education, and poverty on symptom severity were in the hypothesized direction but not statistically significant. Monte Carlo simulation will clarify the null results and we will determine key effect sizes.

Conclusions: Monte Carlo simulation analyses will move the research from descriptive studies of individual constructs and contexts to testing an integrated, multi-factorial model. Before developing interventions targeting asthma health disparities, it is necessary to gain a more thorough understanding of the interaction of individual characteristics, cultural factors, social-environmental context, and healthcare system factors and how the interaction of these factors impacts children's asthma health outcomes through parents' IRs, use of CAM and controller medications.