Seasonal patterns in monthly hemoglobin A1c values

Am J Epidemiol. 2005 Mar 15;161(6):565-74. doi: 10.1093/aje/kwi071.

Abstract

The purpose of this study was to investigate seasonal variations in population monthly hemoglobin A(1c) (A1c) values over 2 years (from October 1998 to September 2000) among US diabetic veterans. The study cohort included 285,705 veterans with 856,181 A1c tests. The authors calculated the monthly average A1c values for the overall population and for subpopulations defined by age, sex, race, insulin use, and climate regions. A1c values were higher in winter and lower in summer with a difference of 0.22. The proportion of A1c values greater than 9.0% followed a similar seasonal pattern that varied from 17.3% to 25.3%. Seasonal autoregressive models including trigonometric function terms were fit to the monthly average A1c values. There were significant seasonal effects; the seasonal variation was consistent across different subpopulations. Regions with colder winter temperatures had larger winter-summer contrasts than did those with warmer winter temperatures. The seasonal patterns followed trends similar to those of many physiologic markers, cardiovascular and other diabetes outcomes, and mortality. These findings have implications for health-care service research in quality-of-care assessment, epidemiologic studies investigating population trends and risk factors, and clinical trials or program evaluations of treatments or interventions.

Publication types

  • Multicenter Study
  • Research Support, U.S. Gov't, Non-P.H.S.

MeSH terms

  • Age Distribution
  • Aged
  • Cohort Studies
  • Confidence Intervals
  • Diabetes Mellitus / blood*
  • Diabetes Mellitus / classification
  • Diabetes Mellitus / drug therapy
  • Female
  • Glycated Hemoglobin / metabolism*
  • Hospitals, Veterans
  • Humans
  • Insulin / therapeutic use
  • Male
  • Middle Aged
  • Risk Factors
  • Seasons
  • Severity of Illness Index
  • United States
  • Veterans

Substances

  • Glycated Hemoglobin A
  • Insulin