@article {601, title = {Underdiagnosis and undertreatment of asthma in the elderly. Cardiovascular Health Study Research Group.}, journal = {Chest}, volume = {116}, year = {1999}, month = {1999 Sep}, pages = {603-13}, abstract = {

OBJECTIVE: To describe the clinical correlates of asthma in a community-based sample of elderly persons.

PARTICIPANTS: A community sample of 4,581 persons > or = 65 years old from the Cardiovascular Health Study.

MEASUREMENTS: Standardized respiratory, sleep, and quality-of-life (QOL) questions, a medication inventory, spirometry, and ambulatory peak flow.

RESULTS: Four percent of the participants reported a current diagnosis of asthma (definite asthma), while another 4\% reported at least one attack of wheezing accompanied by chest tightness or dyspnea during the previous 12 months (probable asthma). Smokers and those with congestive heart failure were excluded from the subsequent analyses, leaving 2,527 participants. Of those who had definite asthma, 40\% were taking a sympathomimetic bronchodilator, 30\% inhaled corticosteroids, 21\% theophylline, and 18\% oral corticosteroids; 39\% were taking no asthma medications. The participants with definite or probable asthma were much more likely than the others to have a family history of asthma, childhood respiratory problems, a history of workplace exposures, dyspnea on exertion, hay fever, chronic bronchitis, nocturnal symptoms, and daytime sleepiness. They were also more likely to report poor general health, symptoms of depression, and limitation of activities of daily living. There was little difference in the morbidity and QOL of participants with recent asthma-like symptoms who had received the diagnosis of asthma versus those who had not.

CONCLUSIONS: Asthma in elderly persons is associated with a lower QOL and considerable morbidity when compared with those who do not have asthma symptoms. Asthma is underdiagnosed in this group and is often associated with allergic triggers; inhaled corticosteroids are underutilized.

}, keywords = {Age Factors, Aged, Aged, 80 and over, Asthma, Female, Humans, Male, Peak Expiratory Flow Rate, Quality of Life, Risk Factors, Spirometry, Vital Capacity}, issn = {0012-3692}, doi = {10.1378/chest.116.3.603}, author = {Enright, P L and McClelland, R L and Newman, A B and Gottlieb, D J and Lebowitz, M D} } @article {671, title = {Correlates of peak expiratory flow lability in elderly persons.}, journal = {Chest}, volume = {120}, year = {2001}, month = {2001 Dec}, pages = {1861-8}, abstract = {

OBJECTIVE: To determine the correlates of the lability of peak expiratory flow (PEF) in the elderly.

METHODS: A community sample of 4,581 persons > or = 65 years old from the Cardiovascular Health Study completed an asthma questionnaire and underwent spirometry. During a follow-up examination of the cohort, 1,836 persons agreed to measure PEF at home twice daily for 2 weeks, and 90\% successfully obtained at least 4 days of valid measurements. PEF lability was calculated as the highest daily (PEF maximum - PEF minimum)/mean PEF.

RESULTS: Mean PEF measured at home was accurate when compared to PEF determined by spirometry in the clinic. Mean PEF lability was 18\% in those with current asthma (n = 165) vs 12\% in healthy nonsmokers (upper limit of normal, 29\%). Approximately 26\% of those with asthma and 14\% of the other participants had abnormally high PEF lability (> 29\%). After excluding participants with asthma, other independent predictors of high PEF lability included black race, current and former smoking, airway obstruction on spirometry, daytime sleepiness, recent wheezing, chronic cough, emphysema, and wheezing from lying in a supine position. Despite having a lower mean PEF, those reporting congestive heart failure (n = 82) did not have significantly higher PEF lability.

CONCLUSIONS: Measurement of PEF lability at home is highly successful in elderly persons. PEF lability > or = 30\% is abnormal in the elderly and is associated with asthma.

}, keywords = {Aged, Aged, 80 and over, Aging, Asthma, Female, Heart Failure, Humans, Male, Monitoring, Ambulatory, Peak Expiratory Flow Rate, Predictive Value of Tests, Pulmonary Disease, Chronic Obstructive, Reference Values, Rhinitis, Allergic, Seasonal, Risk Factors, Signal Processing, Computer-Assisted, Spirometry}, issn = {0012-3692}, doi = {10.1378/chest.120.6.1861}, author = {Enright, P L and McClelland, R L and Buist, A S and Lebowitz, M D} } @article {681, title = {Regression-based variable clustering for data reduction.}, journal = {Stat Med}, volume = {21}, year = {2002}, month = {2002 Mar 30}, pages = {921-41}, abstract = {

In many studies it is of interest to cluster states, counties or other small regions in order to obtain improved estimates of disease rates or other summary measures, and a more parsimonious representation of the country as a whole. This may be the case if there are too many to summarize concisely, and/or many regions with a small number of cases. By merging the regions into larger geographic areas, we obtain more cases within each area (and hence lower standard errors for parameter estimates), as well as fewer areas to summarize in terms of disease rates. The resulting clusters should be such that regions within the same cluster are similar in terms of their disease rates. In this paper we present a clustering algorithm which uses data at the subject-specific level in order to cluster the original regions into a reduced set of larger areas. The proposed clustering algorithm expresses the clustering goals in terms of a regression framework. This formulation of the problem allows the regions to be clustered in terms of their association with the response, and confounding variables measured at the subject-specific level may be easily incorporated during the clustering process. Additionally, this framework allows estimation and testing of the association between the areas and the response. The statistical properties and performance of the algorithm were evaluated via simulation studies, and the results are promising. Additional simulations illustrate the importance of controlling for confounding variables during the clustering process, rather than after the clusters are determined. The algorithm is illustrated with data from the Cardiovascular Health Study. Although developed with a specific application in mind, the method is applicable to a wide range of problems.

}, keywords = {Aged, Algorithms, Cardiovascular Diseases, Cluster Analysis, Cognition, Computer Simulation, Humans, Infarction, Magnetic Resonance Imaging, Models, Statistical, Regression Analysis}, issn = {0277-6715}, doi = {10.1002/sim.1063}, author = {McClelland, R L and Kronmal, R A} }