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Accounting for multimorbidity can affect the estimation of the Burden of Disease: a comparison of approaches

Overview of attention for article published in Archives of Public Health, August 2016
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  • Good Attention Score compared to outputs of the same age and source (71st percentile)

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1 policy source
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3 X users

Citations

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40 Dimensions

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73 Mendeley
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Title
Accounting for multimorbidity can affect the estimation of the Burden of Disease: a comparison of approaches
Published in
Archives of Public Health, August 2016
DOI 10.1186/s13690-016-0147-7
Pubmed ID
Authors

Henk B. M. Hilderink, Marjanne H. D. Plasmans, Bianca E. P. Snijders, Hendriek C. Boshuizen, M. J. J. C. (René) Poos, Coen H. van Gool

Abstract

Various 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. We 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). This 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 %). We 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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The data shown below were collected from the profiles of 3 X users who shared this research output. Click here to find out more about how the information was compiled.
Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 73 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 73 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 15 21%
Student > Master 10 14%
Student > Ph. D. Student 9 12%
Student > Bachelor 6 8%
Other 4 5%
Other 9 12%
Unknown 20 27%
Readers by discipline Count As %
Medicine and Dentistry 19 26%
Nursing and Health Professions 8 11%
Business, Management and Accounting 4 5%
Economics, Econometrics and Finance 4 5%
Social Sciences 3 4%
Other 8 11%
Unknown 27 37%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 4. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 14 February 2019.
All research outputs
#7,356,550
of 25,374,917 outputs
Outputs from Archives of Public Health
#432
of 1,144 outputs
Outputs of similar age
#109,216
of 355,244 outputs
Outputs of similar age from Archives of Public Health
#4
of 14 outputs
Altmetric has tracked 25,374,917 research outputs across all sources so far. This one has received more attention than most of these and is in the 69th percentile.
So far Altmetric has tracked 1,144 research outputs from this source. They typically receive a little more attention than average, with a mean Attention Score of 7.1. This one has gotten more attention than average, scoring higher than 60% of its peers.
Older research outputs will score higher simply because they've had more time to accumulate mentions. To account for age we can compare this Altmetric Attention Score to the 355,244 tracked outputs that were published within six weeks on either side of this one in any source. This one has gotten more attention than average, scoring higher than 68% of its contemporaries.
We're also able to compare this research output to 14 others from the same source and published within six weeks on either side of this one. This one has gotten more attention than average, scoring higher than 71% of its contemporaries.