BackgroundVarious Burden of Disease (BoD) studies do not account for multimorbidity in their BoD estimates. Ignoring multimorbidity can lead to inaccuracies in BoD estimations, particularly in ageing populations that include large proportions of persons with two or more health conditions. The objective of this study is to improve BoD estimates for the Netherlands by accounting for multimorbidity. For this purpose, we analyzed different methods for 1) estimating the prevalence of multimorbidity and 2) deriving Disability Weights (DWs) for multimorbidity by using existing data on single health conditions.MethodsWe included 25 health conditions from the Dutch Burden of Disease study that have a high rate of prevalence and that make a large contribution to the total number of Years Lived with a Disability (YLD). First, we analyzed four methods for estimating the prevalence of multimorbid conditions (i.e. independent, independent age- and sex-specific, dependent, and dependent sex- and age-specific). Secondly, we analyzed three methods for calculating the Combined Disability Weights (CDWs) associated with multimorbid conditions (i.e. additive, multiplicative and maximum limit). A combination of these two approaches was used to recalculate the number of YLDs, which is a component of the Disability-Adjusted Life Years (DALY).ResultsThis study shows that the YLD estimates for 25 health conditions calculated using the multiplicative method for Combined Disability Weights are 5 % lower, and 14 % lower when using the maximum limit method, than when calculated using the additive method. Adjusting for sex- and age-specific dependent co-occurrence of health conditions reduces the number of YLDs by 10 % for the multiplicative method and by 26 % for the maximum limit method. The adjustment is higher for health conditions with a higher prevalence in old age, like heart failure (up to 43 %) and coronary heart diseases (up to 33 %). Health conditions with a high prevalence in middle age, such as anxiety disorders, have a moderate adjustment (up to 13 %).ConclusionsWe conclude that BoD calculations that do not account for multimorbidity can result in an overestimation of the actual BoD. This may affect public health policy strategies that focus on single health conditions if the underlying cost-effectiveness analysis overestimates the intended effects. The methodology used in this study could be further refined to provide greater insight into co-occurrence and the possible consequences of multimorbid conditions in terms of disability for particular combinations of health conditions.

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Background The Disability Adjusted Life Year (DALY) is a measure to prioritize in the public health field. In the Netherlands, the DALY estimates are calculated since 1997 and are included in the Public Health Status and Foresight studies which is an input for public health priority setting and policy making. Over these 20 years, methodological advancements have been made, including accounting for multimorbidity and performing projections for DALYs into the future. Most important methodological choices and improvements are described and results are presented. Methods The DALY is composed of the two components years of life lost (YLL) due to premature mortality and years lost due to disability (YLD). Both the YLL and the YLD are distinguished by sex, age and health condition, allowing aggregation to the ICD-10 chapters. The YLD is corrected for multimorbidity, assuming independent occurrence of health conditions and a multiplicative method for the calculation of combined disability weights. Future DALYs are calculated based on projections for causes of death, and prevalence and incidence. Results The results for 2015 show that cancer is the ICD-10 chapter with the highest disease burden, followed by cardiovascular diseases and mental disorders. For the individual health conditions, coronary heart disease had the highest disease burden in 2015. In 2040, we see a strong increase in disease burden of dementia and arthrosis. For dementia this is due to a threefold increase in dementia as a cause of death, while for arthrosis this is mainly due to the increase in prevalence. Conclusions To calculate the DALY requires a substantial amount of data, methodological choices, interpretation and presentation of results, and the personnel capacity to carry out all these tasks. However, doing a National Burden of Disease study, and especially doing that for more than 20 years, proved to have an enormous additional value in population health information and thus supports better public health policies.

BackgroundUtilities and disability weights (DWs) are metrics used for calculating Quality-Adjusted Life Years and Disability-Adjusted Life Years (DALYs), respectively. Utilities can be obtained with multi-attribute instruments such as the EuroQol 5 dimensions questionnaire (EQ-5D). In 2010 and 2013, Salomon et al. proposed a set of DWs for 220 and 183 health states, respectively. The objective of this study is to develop an approach for mapping EQ-5D utilities to existing GBD 2010 and GBD 2013 DWs, allowing to predict new GBD 2010/2013 DWs based on EQ-5D utilities.MethodsWe conducted two pilot studies including respectively four and twenty-seven health states selected from the 220 DWs of the GBD 2010 study. In the first study, each participant evaluated four health conditions using the standard written EQ-5D-5 L questionnaire. In the second study, each participant evaluated four health conditions randomly selected among the twenty-seven health states using a previously developed web-based EQ-5D-5 L questionnaire. The EQ-5D responses were translated into utilities using the model developed by Cleemput et al. A loess regression allowed to map EQ-5D utilities to logit transformed DWs.ResultsOverall, 81 and 393 respondents completed the first and the second survey, respectively. In the first study, a monotonic relationship between derived utilities and predicted GBD 2010/2013 DWs was observed, but not in the second study. There were some important differences in ranking of health states based on utilities versus GBD 2010/2013 DWs. The participants of the current study attributed a relatively higher severity level to musculoskeletal disorders such as ‘Amputation of both legs’ and a relatively lower severity level to non-functional disorders such as ‘Headache migraine’ compared to the participants of the GBD 2010/2013 studies.ConclusionThis study suggests the possibility to translate any utility derived from EQ-5D scores into a DW, but also highlights important caveats. We observed a satisfactory result of this methodology when utilities were derived from a population of public health students, a written questionnaire and a small number of health states in the presence of a study leader. However the results were unsatisfactory when utilities were derived from a sample of the general population, using a web-based questionnaire. We recommend to repeat the study in a larger and more diverse sample to obtain a more representative distribution of educational level and age.Electronic supplementary materialThe online version of this article (doi:10.1186/s13690-017-0174-z) contains supplementary material, which is available to authorized users.

BackgroundDisability Adjusted Life Years (DALYs) quantify the loss of healthy years of life due to dying prematurely and due to living with diseases and injuries. Current methods of attributing DALYs to underlying risk factors fall short on two main points. First, risk factor attribution methods often unjustly apply incidence-based population attributable fractions (PAFs) to prevalence-based data. Second, it mixes two conceptually distinct approaches targeting different goals, namely an attribution method aiming to attribute uniquely to a single cause, and an elimination method aiming to describe a counterfactual situation without exposure. In this paper we describe dynamic modeling as an alternative, completely counterfactual approach and compare this to the approach used in the Global Burden of Disease 2010 study (GBD2010).MethodsUsing data on smoking in the Netherlands in 2011, we demonstrate how an alternative method of risk factor attribution using a pure counterfactual approach results in different estimates for DALYs. This alternative method is carried out using the dynamic multistate disease table model DYNAMO-HIA. We investigate the differences between our alternative method and the method used by the GBD2010 by doing additional analyses using data from a synthetic population in steady state.ResultsWe observed important differences between the outcomes of the two methods: in an artificial situation where dynamics play a limited role, DALYs are a third lower as compared to those calculated with the GBD2010 method (398,000 versus 607,000 DALYs). The most important factor is newly occurring morbidity in life years gained that is ignored in the GBD2010 approach. Age-dependent relative risks and exposures lead to additional differences between methods as they distort the results of prevalence-based DALY calculations, but the direction and magnitude of the distortions depend on the particular situation.ConclusionsWe argue that the GBD2010 approach is a hybrid of an attributional and counterfactual approach, making the end result hard to understand, while dynamic modelling uses a purely counterfactual approach and thus yields better interpretable results.