de Fine Olivarius et al. BMC Public Health 2011, 11:80 http://www.biomedcentral.com/1471-2458/11/80 the statistical analysis is necessarily based on logMAR values and expressed as means which are greatly affected by patients with very poor eye sight (Table 1). When the changes in visual acuity were modelled as piecewise linear models with a breakpoint 3 years after diagnosis, the annual change in logMAR before (0.0268, p < 0.0001) and after this point (0.0231, p = 0.0008) was similar (p = 0.99 for the significance test of the time- logMAR interaction). Accordingly, visual loss during the first years after diabetes diagnosis did not seem to be greater than later on, which supports the underlying assumption in Figure 2 that visual loss follows a linear course. Adding the identification number of the eye doctors as a random effect to the models in Table 1 did not change the associations with developments in visual acuity. Therefore, it is unlikely that our results are affected by the fact that some patients were examined by the same eye doctor at baseline and follow up, while others were not. Discussion In a population-based cohort of patients newly diag- nosed with clinical, often symptomatic type 2 diabetes, 6.3% were visually impaired. Among those patients with reduced sight, 76% had cataract and 58% retinopathy, usually AMD, although many of these eye pathologies are not closely related to diabetes and metabolic control. During the first 6 years after diabetes diagnosis, the inci- dence of blindness was relatively high, 40 per 10,000 person-years. The baseline predictors of both the level of and speed of progressive visual impairment after diag- nosis were AMD, cataract, age at diagnosis, and living alone. The level of visual acuity over 6 years was lower in patients who had a low self-rated health or a seden- tary life-style. The rate of the 6-year visual loss increased if the patient had DR or high fasting triglycer- ides at diabetes diagnosis. Strengths and limitations of the study Our patients are likely to be representative of Danish patients with newly diagnosed, clinical, often sympto- matic diabetes in this age group because of the well- defined background population in each general practice, the unchanged inclusion activity during the inclusion period, the small number of primary exclusions, and the acceptable participation in the eye examinations [24]. Furthermore, data from studies including patients with known diabetes may be misleading because of selective survival of patients with a more favourable risk factor profile [29]. Our results, however, cannot be generalised to all countries because of marked disparities in causes of reduced vision across the world [8]. Our patient sample was established in the early 90’s and since then both surgical treatment of eye Page 8 of 13 pathologies and pharmacological treatment of hypergly- caemia, hypertension and dyslipidaemia have been intensified, as has screening for diabetes. These initia- tives to improve diabetes care and to identify patients earlier in the natural history of diabetes have probably decreased the variability of measures of treatment qual- ity, so if a similar follow up study were to be made today, the associations between variables would perhaps be harder to identify. There is, however, no reason to suppose that the causal patterns underlying the associa- tions that we have identified would be substantially different. By presenting results from the best seeing eye only, we are underestimating the prevalence of eye pathologies in this patient group. This is because our main purpose is to estimate the change in visual acuity and its predic- tors, and eye pathologies are important in this study only insofar they are predictors of visual loss. In most studies the main pathology in the better eye is consid- ered the cause of visual impairment [6,7]. We analysed predictors of vision impairment prospectively. Further- more, to improve statistical strength we chose to analyse visual acuity as a continuous outcome. Our ability to detect a true clinical change in visual acuity is inversely related to the measurement error, i.e. the test-retest variability, of the test used [30,31]. Unlike logMAR charts, the Snellen chart, which we had to rely on in this nationwide primary care study, has a large-scale increment resulting in a relatively high mea- surement error. Another factor contributing to the test- retest variability was differing routines for taking account of visual field loss [32]. The lower precision of the outcome, visual acuity, will however only tend to lessen the strength of the association between the out- come and a predictor. On the other hand, the true incidence of vision impairment may be underestimated if those patients who missed an eye examination experienced a relatively rapidly declining vision. However there is no reason to suppose that the possible imprecision and error intro- duced were associated with any of the possible predic- tors of visual loss that were examined. Comparison of visual acuity between studies, even when categorised, is in any case not feasible [31], and our prevalence and incidence figures for visual impairment should be inter- preted in this light. Almost all Danish ophthalmologists contributed to the study increasing the inter-rater variability, and their screening by funduscopy may have overlooked 10-40% of sight-threatening eye disease [33]. Such measurement errors in the predictor variables will tend to reduce a true association between e.g. an eye pathology and the outcome, i.e. visual acuity, but it does not invalidate the associations that we actually find. It can be assumed