The influence of non-response in a population-based cohort study on type 2 diabetes evaluated by the Swedish Prescribed Drug Register
The influence of non-response in a population-based cohort study on type 2 diabetes evaluated by the Swedish Prescribed Drug Register
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DOI：
10.1007/s10654-011-9630-1
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发表时间：
2012-03-01
影响因子：
13.6
通讯作者：
Ostenson, Claes-Goran
中科院分区：
文献类型：
--
作者：
Eriksson, Anna-Karin;Ekbom, Anders;Ostenson, Claes-Goran
Bias due to non-response in observational studies may lead to false risk estimates. We evaluated potential selective non-response in Stockholm Diabetes Prevention Program (SDPP) using a population-based drug register. A cohort of 12,952 men and 19,416 women, aged 35-56 years, was screened for diabetes and family history of diabetes (FHD). Response rate: 79% in men and 85% in women. Of the 4,209 men and 6,916 women that were invited, 70 and 68% participated in a baseline health examination including oral glucose tolerance test. A follow-up was performed in 79% of male and 70% of female baseline participants. We used data from the prescription register to estimate absolute risks and ORs for drug-treated diabetes in the non-response/non-participation groups. At both screening and baseline steps absolute risks of drug-treated diabetes were equal in non-participants and participants. Adjusted ORs were 0.9-1.0. At follow-up, absolute risks were higher among non-participants than participants, men 6.2/4.4% and women 2.6/1.6%, adjusted ORs 1.4 (0.9-2.3) and 1.5 (0.9-2.4), suggesting selective non-participation. Further analyses of FHD, smoking, physical activity, socioeconomic position and psychological distress demonstrated no previous false risk estimates for type 2 diabetes. However, for body mass index, there were indications of overestimation in women. We conclude that bias from non-response was not present at screening or baseline steps, suggesting that diabetes prevalence and risks may be estimated from a population-based cohort study with high attendance rate such as the SDPP. However, follow-up data should be treated with some caution, since the sample may have become biased.
