<?xml version="1.0" encoding="UTF-8"?><xml><records><record><source-app name="Biblio" version="7.x">Drupal-Biblio</source-app><ref-type>17</ref-type><contributors><authors><author><style face="normal" font="default" size="100%">McDonald, Scott A</style></author><author><style face="normal" font="default" size="100%">Brecht Devleesschauwer</style></author><author><style face="normal" font="default" size="100%">Wallinga, Jacco</style></author></authors></contributors><titles><title><style face="normal" font="default" size="100%">The impact of individual-level heterogeneity on estimated infectious disease burden: a simulation study.</style></title><secondary-title><style face="normal" font="default" size="100%">Popul Health Metr</style></secondary-title><alt-title><style face="normal" font="default" size="100%">Popul Health Metr</style></alt-title></titles><keywords><keyword><style  face="normal" font="default" size="100%">Age factors</style></keyword><keyword><style  face="normal" font="default" size="100%">Bias (Epidemiology)</style></keyword><keyword><style  face="normal" font="default" size="100%">Communicable Diseases</style></keyword><keyword><style  face="normal" font="default" size="100%">Computer Simulation</style></keyword><keyword><style  face="normal" font="default" size="100%">Cost of Illness</style></keyword><keyword><style  face="normal" font="default" size="100%">Disabled Persons</style></keyword><keyword><style  face="normal" font="default" size="100%">Disease Progression</style></keyword><keyword><style  face="normal" font="default" size="100%">Humans</style></keyword><keyword><style  face="normal" font="default" size="100%">Models, Biological</style></keyword><keyword><style  face="normal" font="default" size="100%">probability</style></keyword><keyword><style  face="normal" font="default" size="100%">Quality-Adjusted Life Years</style></keyword><keyword><style  face="normal" font="default" size="100%">risk</style></keyword></keywords><dates><year><style  face="normal" font="default" size="100%">2016</style></year><pub-dates><date><style  face="normal" font="default" size="100%">2016 12 08</style></date></pub-dates></dates><volume><style face="normal" font="default" size="100%">14</style></volume><pages><style face="normal" font="default" size="100%">47</style></pages><language><style face="normal" font="default" size="100%">eng</style></language><abstract><style face="normal" font="default" size="100%">&lt;p&gt;&lt;b&gt;BACKGROUND: &lt;/b&gt;Disease burden is not evenly distributed within a population; this uneven distribution can be due to individual heterogeneity in progression rates between disease stages. Composite measures of disease burden that are based on disease progression models, such as the disability-adjusted life year (DALY), are widely used to quantify the current and future burden of infectious diseases. Our goal was to investigate to what extent ignoring the presence of heterogeneity could bias DALY computation.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;METHODS: &lt;/b&gt;Simulations using individual-based models for hypothetical infectious diseases with short and long natural histories were run assuming either &amp;quot;population-averaged&amp;quot; progression probabilities between disease stages, or progression probabilities that were influenced by an a priori defined individual-level frailty (i.e., heterogeneity in disease risk) distribution, and DALYs were calculated.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;RESULTS: &lt;/b&gt;Under the assumption of heterogeneity in transition rates and increasing frailty with age, the short natural history disease model predicted 14% fewer DALYs compared with the homogenous population assumption. Simulations of a long natural history disease indicated that assuming homogeneity in transition rates when heterogeneity was present could overestimate total DALYs, in the present case by 4% (95% quantile interval: 1-8%).&lt;/p&gt;

&lt;p&gt;&lt;b&gt;CONCLUSIONS: &lt;/b&gt;The consequences of ignoring population heterogeneity should be considered when defining transition parameters for natural history models and when interpreting the resulting disease burden estimates.&lt;/p&gt;
</style></abstract><issue><style face="normal" font="default" size="100%">1</style></issue><custom1><style face="normal" font="default" size="100%">http://www.ncbi.nlm.nih.gov/pubmed/27931225?dopt=Abstract</style></custom1></record></records></xml>