Predictors of Hospital-Based Care in Asthma
To identify clinical predictors of episodes of hospital-based care in people with asthma.
|Study Design:||Observational Model: Natural History|
|Study Start Date:||April 1992|
|Estimated Study Completion Date:||March 1997|
This study was of great practical importance because asthma is a common condition (3-5 percent of the population) which has had a recent, unexplained rise in morbidity and mortality. Many previous epidemiological studies focused on predictors of hospitalization for people with asthma seen in the emergency room. These studies were of limited value for the practicing physician who sees the vast majority of patients with asthma.
Results from this study should, for the first time, enable a profile to be constructed of the high risk patient with asthma which includes identification of modifiable risk factors. These results can be used for physician and patient education programs as well as to target medical intervention.
The study developed a key piece of information needed for outpatient care of patients with asthma; a profile of patients at risk for severe, potentially life-threatening exacerbations of asthma. The experimental design was a prospective study of 800 patients with asthma drawn from a large, pre-paid health care plan. The key advantage of this population was that all the care occurred within the health plan. Characteristics identified included demographic factors, socioeconomic status; patient characteristics such as tobacco use, atopy, pattern of medication use, self-reporting of compliance/adherence, attitudes about asthma; historical assessment of indoor air quality; characteristics of asthma such as duration of asthma, lung function, and variation in asthma symptoms; and speciality of physician. The outcome measures were obtained over a three year period and included all episodes of hospital-based care including hospitalizations, emergency room visits, and urgency care clinic visits. Based on these data, models were developed which assigned relative risk to each of the independent variables. These models were validated in a subset of the population.
|Investigator:||Molly Osborne||Oregon Health and Science University|