Step 2: Assessment of Content Validity
2A. Expert Panel Review
To assess the content validity of the survey items, a panel of 32 local, national, and international asthma experts was identified. This panel included clinicians who were members of the National Asthma Education and Prevention Program’s expert panel or participated in the Global Initiative on Asthma. Read the rest of this entry »
The clinical input, together with the Health Belief Model, produced a set of nine domains: (1) symptoms; (2) stigma/acceptability, (3) seriousness/severity; (4) perceptions of susceptibility; (5) consequences; (6) barriers to care; (7) perceptions of quality of life; (8) treatment/ utilization of health care; and (9) triggers/environmental risk. This set of domains formed the conceptual structure used for the subsequent developmental steps. comments
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The result of this three-step process was a 32-item survey of asthma knowledge, attitudes, and perceptions, the Chicago Community Asthma Survey (CCAS-32). Finally, the survey introduction was examined to see if modification would alter the responses to survey items (Step 4).
This project was approved by the Institutional Review Board of Rush-Presbyterian-St. Luke’s Medical Center.
Step 2: Initial Construction Step 1A: Assessment of Relevant Issues
As seen in Figure 1, the survey development process began by identifying relevant content areas. This was accomplished by reviewing published literature in the area of asthma education and by gathering the advice of local practitioners who participate in asthma care. Read the rest of this entry »
The Chicago Community Asthma Survey
Recent trends in the social burden of asthma have become part of the debate about the health of the general public. However, there is very little information about the general public’s perception of the diagnosis of asthma and its impact on individuals, their families, and their communities. If the public’s knowledge about asthma is very good, national campaigns targeting asthma awareness, such as the National Asthma Education and Prevention Program, are likely to have little impact. If, on the other hand, the general public has some misconceptions about asthma and its impact, these misconceptions may need to be addressed if asthma outcomes are to be improved. Read the rest of this entry »
Various oximetric indices have been studied for case finding of OSA, with sensitivities ranging from 40 to 100% and specificities ranging from 39 to 100%. In patients with OSA, we found that minSaO2 and mSa02 were lower, and CT80 higher, than in patients without OSA. Indeed, AHI was negatively correlated with minSa02 and mSa02, and positively correlated with CT80. Finally, mSa02 was a predictor of OSA according to logistic regression analysis. After determination of optimal thresholds by ROC curves, the oximetric criteria were the variables that had the best diagnostic values, as expressed by the area under the ROC curve (Table 3); however, this diagnostic value was not good enough to be useful as a screening technique (Fig 2). Read the rest of this entry »
We observed a lower diurnal Pa02 in patients with OSA than in patients without OSA. Because only a small proportion of OSA patients had an associated bronchial obstruction (6/40, 15%), this resting hypoxemia may be in part explained by high BMI. Indeed, several studies conducted in predominantly obese populations found values of Pa02 similar to those of our patients. Among them, Gold et al also found a higher PaC02 in sleep apnea patients than in control subjects, which was not the case in our study; this discrepancy is likely related to a higher proportion of overlap syndromes in their population, because their patients with OSA had lower FEV1 and FVC than patients without OSA, which we did not find. there
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Our data show that in an overweight population, CS, PFTs, and nocturnal oximetry taken alone may not accurately predict the presence or absence of OSA. As shown in Figures 1, 2, there is a considerable overlap between patients with and without OSA, even for variables that independently predict OSA according to logistic regression, and even when ROC curve analysis is used to determine the best thresholds for each of these variables. In 72.5% of the population, a complex logistic regression model would predict the presence or absence of OSA with a positive predictive value of 94% and a negative predictive value of 90%. Read the rest of this entry »