Impact of obesity and type 2 diabetes on health-related quality of life in the general population in England

Abstract

Background: Weight gain can contribute towards the development of type 2 diabetes (T2D), and some treatments for T2D can lead to weight gain. The aim of this study was to determine whether having T2D and also being obese had a greater or lesser impact on health-related quality of life (HRQoL) than having either of the two conditions alone. Methods: The 2003 dataset of the Health Survey for England (HSE) was analyzed using multiple regression analyses to examine the influence of obesity and T2D on HRQoL, and to determine whether there was any interaction between these two disutilities. Results: T2D reduced HRQoL by 0.029 points, and obesity reduced HRQoL by 0.027 points. There was no significant interaction effect between T2D and obesity, suggesting that the effect of having both T2D and being obese is simply additive and results in a reduction in HRQoL of 0.056. Conclusions: Based on analysis of HSE 2003 data, people with either T2D or obesity experience significant reduction in HRQoL and people with both conditions have a reduction in HRQoL equal to the sum of the two independent effects. The effect of obesity on HRQoL in people with T2D should be considered when selecting a therapy.

Estimation of the relationship between body mass index and EQ-5D health utilities in individuals with type 2 diabetes: Evidence from the population-based KORA studies

“…Existing studies on the relationship between BMI and health utilities in populations with diabetes mainly divided BMI into categories or assumed that the impact of BMI on utility scores was a linear function (Hakim, Wolf, & Garrison, 2002;Redekop et al, 2002;Lee et al, 2005;Dennett, Boye, & Yurgin, 2008;Gough et al, 2009;Solli, Stavem, & Kristiansen, 2010). However, both approaches suffer from drawbacks: In the first approach, grouping BMI values into categories may obscure meaningful effects of weight changes observed within categories.…”

“…In addition, 90 % of the self-reported diabetes diagnoses were validated by hospital records review and by contacting the patient's treating physician. We classified subjects as having type 2 diabetes if this diagnosis was clinically confirmed or, in the case that no clinical validation data were available, if they reported not being treated with insulin at the time of interview or having been diagnosed with diabetes after the age of 30 (McNeely & Boyko, 2005;Gough et al, 2009;Schunk et al, 2012). Subjects with confirmed or suspected type 1 diabetes, confirmed other types of diabetes and individuals without diabetes were excluded from analyses.…”

“…Several studies assessed the association between BMI and health utilities in patients with diabetes (Hakim, Wolf, & Garrison, 2002;Redekop et al, 2002;Lee et al, 2005;Gough et al, 2009;Solli, Stavem, & Kristiansen, 2010). In a systematic review, however, it was found that reported effects of BMI on utilities varied substantially between the studies.…”

Economic evaluation of a community-based diagnostic pathway to stratify adults for non-alcoholic fatty liver disease: a Markov model informed by a feasibility study

“…For CC+, the utility decrement of 0.1 was assumed to capture psychological effects of diagnosis; this was not added to NMD+ and SLD+ utilities since identification of these fibrosis stages indicates risk of developing cirrhosis and advanced liver disease in the future and is not considered as disease diagnosis. Data on QoL obtained using the standard EuroQol five-dimension three-level questionnaire with societal weights, 30 reported in the Health Survey for England, 31 and results of the feasibility study 23 were used to calculate age-dependent utility for NMD, SLD and CC health states. Specifically, the regression coefficients for age, sex, BMI and hypertension estimated for the general population, 31 together with data on demographics and prevalence of obesity and hypertension in the feasibility study cohort (47.4% patients with BMI >30, 63.5% with hypertension), 23 were applied to calculate utilities reflecting the model health states for the target population.…”

Associations between complications and health‐related quality of life in individuals with diabetes

“…In addition, studies have shown that diabetes is associated with worse HRQoL . However, these studies did not assess for the presence of diabetes‐related complications, so it is difficult to separate the role of diabetes‐associated complications from that of diabetes per se .…”