Assessing the Impact of Visual Acuity on Quality of Life in Individuals With Type 2 Diabetes Using the Short Form-36

Clarke PM, Simon J, Cull CA, Holman RR. Diabetes Care. 2006;29:1506-1511.  

Quality-of-life measurements are a key strategy for determining the overall burden of disease in a population. Utility values also assess quality of life, with the inclusion of societal preferences for individual health states. This study aims to evaluate the relationship between quality-of- life measures and visual acuity in patients with type 2 diabetes. Quality-of-life analysis followed by a utility assessment is generally regarded as the preferred method of determining outcomes in measures such as quality of life-years.

A total of 4051 individuals with type 2 diabetes were recruited from the Lipids in Diabetes Study. Participants were characterized by the absence of clinically significant cardiovascular disease, LDL cholesterol between ≥1.5 and <4.1 mmol/L, and a triglyceride level <4.5 mmol/L. Best corrected visual acuity (BCVA) was assessed in both eyes for each patient using the Early Treatment of Diabetic Retinopathy Study (ETDRS) chart, expressed as a LogMAR score ranging from -0.3 to 1.0; a score of 0.0 was considered normal vision and a score of 1.0 was deemed legally blind. Additionally, health-related quality of life (HRQOL) was calculated for each patient in the study using the Medical Outcome Study Short Form with 36 items (SF-36), a self-administered questionnaire comprised of 8 health categories: physical functioning, role physical, role emotional, bodily pain, general health, social functioning, mental health, and energy and vitality. Individual scores for each item were subsequently transformed into a scale ranging from 0 (worst possible health) to 100 (best possible health). However, as this scale does not represent a single overall quality-of-life index score, utility values were calculated by merging the SF-36 into a Short Form with 6 dimensions (SF-6D). These values corresponded to health states in which 1.0 represents full health and 0.296 as the lowest possible health.

The study population was characterized by a higher percentage of men (65%) and had a median duration of diagnosed diabetes of 6 years. Additionally, the mean age for patients was 61.6 ± 8.6, and the average A1C and systolic blood pressure were 8.2 ± 1.5% and 143 ± 19 mm Hg, respectively. LogMAR scores indicate that 58% of patients were considered to have normal or better-than-normal vision in their best corrected eye (LogMAR ≤0). A total of 41% had reduced vision in the best corrected eye (LogMAR >0.0 and ≤0.5), and only 1% showed signs of blindness (LogMAR >0.5). In a similar pattern, quality-of-life assessment, as measured by SF-36 scores, showed mean values ranging from a high of 86.2 ± 21.9 for role emotional to a low of 59.7 ± 19.6 for energy and vitality. The overall utility value, derived from the 8 individual SF-36 domain scores, was 0.76 ± 0.11. Results demonstrate a notable negative association for all 8 SF-36 domain scores with LogMAR category that persisted after adjusting for potential confounders such as age, systolic blood pressure, A1C, duration of diabetes, ethnicity, sex, BMI, smoking status, and complication status.

To determine the nature of the relationship between visual acuity and utility, a regression model was used to assess the association between utility and LogMAR score in the best and worst eye. This model assumes a linear decline represented by lower levels of utility associated with higher LogMAR scores. Patients whose LogMAR scores equated to being legally blind had on average 0.054 lower utility compared with patients with normal or better than normal visual acuity. This indicates a 7% lower utility score associated with vision loss.

HRQOL can be used as an outcome measurement for future treatment of prevention methods. The burden of retinopathy is distinct for each person and differs according to individual health state. However, the findings in this study demonstrate the need for further evaluation in how changes in visual acuity affect a person’s response to the SF-36 and how adjustments made after vision loss can affect one’s HRQOL. Only then will the true impact be understood and corrected.