Impact of variable look-back periods on the incidence rates of chronic diseases using real world data

Pharmacoepidemiol Drug Saf. 2020 Sep;29(9):1086-1092. doi: 10.1002/pds.5066. Epub 2020 Jul 9.

Abstract

Purpose: Estimating disease incidence based on secondary data requires a look-back period to exclude patients with pre-existing disease from the incidence risk set. However, the optimal length of the look-back period and its impact on incidence rates are often unknown. We studied the impact of the length of the look-back period on incidence rates of 24 different chronic diseases.

Methods: Everyone residing in Sweden between January 1, 2005 and December 31, 2013 were identified from national registries and followed up to 2 years (through December 31, 2015). Outcome events were identified from inpatient and outpatient hospital contacts and incidence rates were calculated per 100 000 person-years. The length of the look-back period was varied with 6-month increments, starting at 6 months. The maximum look-back period of 9 years was used as reference period.

Results: There were 7 943 807 individuals with a look-back period of at least 9 years (mean age 46.5 years) and a mean follow-up time of 1.97 years. Incidence rates were higher across all diseases when restricting the look-back to 1 year compared to 9 years, with a magnitude of overestimation of the incidence rates between 13% (temporal arteritis) and 174% (type 1 diabetes). However, for most diseases the effect of extending the look-back period beyond 3-5 years appeared comparably small.

Conclusions: This study illustrates how short look-back periods cause overestimation of the incidence rates of chronic diseases, suggesting that sensitivity analyses with respect to look-back period are considered, particularly using data sources with limited information on past medical history.

Keywords: chronic disease; epidemiology; incidence; methodology; pharmacoepidemiology.

Publication types

  • Research Support, Non-U.S. Gov't

MeSH terms

  • Adult
  • Chronic Disease / epidemiology*
  • Cohort Studies
  • Datasets as Topic
  • Humans
  • Incidence
  • Middle Aged
  • Pharmacoepidemiology / methods*
  • Registries / statistics & numerical data
  • Sweden / epidemiology
  • Time Factors
  • Young Adult