Background.An increasing proportion of people are living with multiple health conditions, or ‘multimorbidity’. Measures of multimorbidity are useful in studies of interventions in primary care to take account of confounding due to differences in case-mix.Objectives.Assess the predictive validity of commonly used measures of multimorbidity in relation to a health outcome (mortality) and a measure of health service utilization (consultation rate).Methods.We included 95372 patients registered on 1 April 2005 at 174 English general practices included in the General Practice Research Database. Using regression models we compared the explanatory power of six measures of multimorbidity: count of chronic diseases from the Quality and Outcomes Framework (QOF); Charlson index; count of prescribed drugs; three measures from the John Hopkins ACG software [Expanded Diagnosis Clusters count (EDCs), Adjusted Clinical Groups (ACGs), Resource Utilisation Bands (RUBs)].Results.A model containing demographics and GP practice alone explained 22% of the uncertainty in consultation rates. The number of prescribed drugs, ACG category, EDC count, RUB category, QOF disease count, or Charlson index increased this to 42%, 37%, 36%, 35%, 30%, and 26%, respectively. Measures of multimorbidity made little difference to the fit of a model predicting 3-year mortality. Nonetheless, Charlson index score was the best performing measure, followed by the number of prescribed drugs.Conclusion.The number of prescribed drugs is the most powerful measure for predicting future consultations and the second most powerful measure for predicting mortality. It may have potential as a simple proxy measure of multimorbidity in primary care.

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Supplementary Materials:We have developed user-friendly software for fitting the model described in the paper. The software is publically available as part of the rstanarm package, downloadable from the Comprehensive R Archive Network (https://cran.r-project.org/). The supplementary materials include an example of the code required to fit the model and additional details about the model estimation. However, the Iressa Pan-Asia Study (IPASS) dataset used in our application is not publicly available. AbstractJoint modelling of longitudinal and time-to-event data has received much attention recently.Increasingly, extensions to standard joint modelling approaches are being proposed to handle complex data structures commonly encountered in applied research. In this paper we propose a joint model for hierarchical longitudinal and time-to-event data. Our motivating application explores the association between tumor burden and progressionfree survival in non-small cell lung cancer patients. We define tumor burden as a function of the sizes of target lesions clustered within a patient. Since a patient may have more than one lesion, and each lesion is tracked over time, the data have a three-level hierarchical structure: repeated measurements taken at time points (level 1) clustered within lesions (level 2) within patients (level 3). We jointly model the lesion-specific longitudinal trajectories and patient-specific risk of death or disease progression by specifying novel association structures that combine information across lower level clusters (e.g. lesions) into patient-level summaries (e.g. tumor burden). We provide user-friendly software for fitting the model under a Bayesian framework. Lastly, we discuss alternative situations in which additional clustering factor(s) occur at a level higher in the hierarchy than the patient-level, since this has implications for the model formulation.

BackgroundThere are well-established risk factors, such as lower education, for attrition of study participants. Consequently, the representativeness of the cohort in a longitudinal study may deteriorate over time. Death is a common form of attrition in cohort studies of older people. The aim of this paper is to examine the effects of death and other forms of attrition on risk factor prevalence in the study cohort and the target population over time.MethodsDifferential associations between a risk factor and death and non-death attrition are considered under various hypothetical conditions. Empirical data from the Australian Longitudinal Study on Women's Health (ALSWH) for participants born in 1921-26 are used to identify associations which occur in practice, and national cross-sectional data from Australian Censuses and National Health Surveys are used to illustrate the evolution of bias over approximately ten years.ResultsThe hypothetical situations illustrate how death and other attrition can theoretically affect changes in bias over time. Between 1996 and 2008, 28.4% of ALSWH participants died, 16.5% withdrew and 10.4% were lost to follow up. There were differential associations with various risk factors, for example, non-English speaking country of birth was associated with non-death attrition but not death whereas being underweight (body mass index < 18.5) was associated with death but not other forms of attrition. Compared to national data, underrepresentation of women with non-English speaking country of birth increased from 3.9% to 7.2% and over-representation of current and ex-smoking increased from 2.6% to 5.8%.ConclusionsDeaths occur in both the target population and study cohort, while other forms of attrition occur only in the study cohort. Therefore non-death attrition may cause greater bias than death in longitudinal studies. However although more than a quarter of the oldest participants in the ALSWH died in the 12 years following recruitment, differences from the national population changed only slightly.