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Seasonal Variation in Diagnoses and Visits to Family Physicians

Department of Family Medicine, University of Colorado Health Sciences, Center, Aurora, Colo

CORRESPONDING AUTHOR: Wilson D. Pace, MD, Department of Family Medicine, UCHSC at Fitzsimons, PO Box 6508 Mail Stop F496, Aurora, CO 80045-0508, wilson.pace{at}uchsc.edu

BACKGROUND Practice-based research networks (PBRNs) replicating the National Ambulatory Medical Care Survey (NAMCS) must sample more than 1 year to account for presumed seasonal variation in illnesses. This study evaluated the effects of seasonality on diagnoses within NAMCS family physician data.

METHODS Using combined data from the 1995–1998 NAMCS, diagnostic clusters that accounted for more than 1% of total visits were analyzed for seasonality. Seasons were coded categorically as dummy variables with summer as the reference category. A logistic regression was performed with each diagnosis as an outcome on the full data. To examine the ability of alternative sampling strategies to replicate the full year of data, a simulation study was carried out drawing 50 samples of 1,000 visits each for winter-summer and spring-fall sampling periods.

RESULTS We found 23 diagnostic clusters that had a frequency more than 1%, of which 10 had seasonal variations (P .001), primarily between winter and summer. If sampling were restricted to spring, the diagnostic clusters of pregnancy and coronary artery disease would account for less than 1% of visits. All other diagnostic clusters, though changing rank order, would account for more than 1% if sampled in a single quarter. In the simulated sampling strategy, visit prevalence dropped below 1% for at least 1 diagnosis in 24 of 50 samples in spring-fall compared with 20 of 50 samples for winter-summer (P >.20).

CONCLUSIONS There is little seasonal variation in the 23 diagnoses that occur in more than 1% of visits to family physicians. There is, however, important seasonal variation in the rank order of these diagnoses. A sampling strategy that uses any quarter of the year but spring (March, April, May) could be used to understand what diagnoses are frequently seen within a PBRN.

Key Words: Practice-based research network • seasonal variation • family practice/statistics & numerical data • statistics • logistic models

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