Maarten J Bijlsma1, Fanny Janssen2, René Lub3, Jens H J Bos3, Folgerdiena M De Vries3, Stijn Vansteelandt4, Eelko Hak3. 1. Unit PharmacoEpidemiology & PharmacoEconomics (PE2), Department of Pharmacy, University of Groningen, A. Deusinglaan 1, Groningen 9713 AV, The Netherlands. Electronic address: maarten.bijlsma@rug.nl. 2. Unit PharmacoEpidemiology & PharmacoEconomics (PE2), Department of Pharmacy, University of Groningen, A. Deusinglaan 1, Groningen 9713 AV, The Netherlands; Population Research Centre (PRC), Faculty of Spatial Sciences, University of Groningen, P.O. Box 800, 9700 AV Groningen, The Netherlands. 3. Unit PharmacoEpidemiology & PharmacoEconomics (PE2), Department of Pharmacy, University of Groningen, A. Deusinglaan 1, Groningen 9713 AV, The Netherlands. 4. Department of Applied Mathematics, Computer Science and Statistics, Ghent University, Krijgslaan 281, S9, 9000 Gent, Belgium.
Abstract
OBJECTIVES: To investigate how birth cohorts can confound population-based intervention effect estimates. STUDY DESIGN AND SETTING: Interrupted time series design was applied to study the prevalence of statin use in Dutch diabetes patients over the period 1998-2011. Effects of guideline changes on the outcome were estimated using a Poisson regression model with and without the birth cohort dimension modeled through random intercepts. RESULTS: Both models estimated a stronger increase in prevalence of statin use after influential studies were published in 2003 for patients aged below 50 and above 70 years. The model that controlled for birth cohort also estimated an effect for patients aged 50-70 years from 2003 onward. The magnitude of the intervention effect for patients aged above 70 years when we controlled for birth cohort was reduced from 0.078 [95% confidence interval (CI): 0.065, 0.091] to 0.027 (95% CI: 0.013, 0.041). Similarly, for patients aged below 50 years, the estimated guideline effect was reduced from 0.070 (95% CI: 0.048, 0.092) to 0.055 (95% CI: 0.035, 0.075). CONCLUSION: In this case study, the birth cohort dimension appeared to confound population-level effect estimates of guideline changes on prevalence of statin use in patients with diabetes.
OBJECTIVES: To investigate how birth cohorts can confound population-based intervention effect estimates. STUDY DESIGN AND SETTING: Interrupted time series design was applied to study the prevalence of statin use in Dutch diabetespatients over the period 1998-2011. Effects of guideline changes on the outcome were estimated using a Poisson regression model with and without the birth cohort dimension modeled through random intercepts. RESULTS: Both models estimated a stronger increase in prevalence of statin use after influential studies were published in 2003 for patients aged below 50 and above 70 years. The model that controlled for birth cohort also estimated an effect for patients aged 50-70 years from 2003 onward. The magnitude of the intervention effect for patients aged above 70 years when we controlled for birth cohort was reduced from 0.078 [95% confidence interval (CI): 0.065, 0.091] to 0.027 (95% CI: 0.013, 0.041). Similarly, for patients aged below 50 years, the estimated guideline effect was reduced from 0.070 (95% CI: 0.048, 0.092) to 0.055 (95% CI: 0.035, 0.075). CONCLUSION: In this case study, the birth cohort dimension appeared to confound population-level effect estimates of guideline changes on prevalence of statin use in patients with diabetes.