The impact of varying the number and selection of conditions on estimated multimorbidity prevalence: a cross-sectional study using a large, primary care population dataset

Background Multimorbidity prevalence rates vary considerably depending on the conditions considered in the morbidity count, but there is no standardised approach to the number or selection of conditions to include. Methods and Findings We conducted a cross-sectional study using English primary care data for 1168260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity when varying the number and selection of conditions considered (≥two conditions) for 80 conditions. Included conditions featured in ≥one of the nine published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common two conditions, three conditions, etc, up to 80 conditions. Second, prevalence was calculated using nine condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the two commonest conditions were considered was 4.6% (95%CI [4.6,4.6] p <0.001), rising to 29.5% (95%CI [29.5,29.6] p <0.001) considering the 10 commonest, 35.2% (95%CI [35.1,35.3] p <0.001) considering the 20 commonest, and 40.5% (95%CI [40.4,40.6] p <0.001) when considering all 80 conditions. The threshold number of conditions at which multimorbidity prevalence was >99% of that measured when considering all 80 conditions was 52 for the whole population but was lower in older people (29 in >80 years) and higher in younger people (71 in 0–9-year-olds). Nine published condition-lists were examined; these were either recommended for measuring multimorbidity, used in previous highly cited studies of multimorbidity prevalence, or widely applied measures of ‘comorbidity’. Multimorbidity prevalence using these lists varied from 11.1% to 36.4%. A limitation of the study is that conditions were not always replicated using the same ascertainment rules as previous studies to improve comparability across condition lists, but this highlights further variability in prevalence estimates across studies. Conclusions In this study we observed that varying the number and selection of conditions results in very large differences in multimorbidity prevalence, and different numbers of conditions are needed to reach ceiling rates of multimorbidity prevalence in certain groups of people. These findings imply that there is a need for a standardised approach to defining multimorbidity, and to facilitate this, researchers can use existing condition-lists associated with highest multimorbidity prevalence. Why was this study done? What did the researchers do and find? What do these findings mean? ### Competing Interest Statement The authors have declared no competing interest. ### Funding Statement This work was supported by the Chief Scientist Office (HIPS/18/30) to BG, SWM, DA, EJ, DM, NHS Education for Scotland Academic Fellowship for CM, and Medical Research Council MR/W000253/1 fellowship for CM. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript. ### Author Declarations I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained. Yes The details of the IRB/oversight body that provided approval or exemption for the research described are given below: All data management, statistical analyses, and plotting was done in R version 3.6.2, available within in the ISO27001 and Scottish Government approved Health Informatics Centre Safe Haven. The analysis was approved by CPRD Independent Scientific Advisory Committee (reference 20_018) under the terms of CPRD NHS Research Ethics dataset approval. I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals. Yes I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance). Yes I have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable. Yes All relevant data are within the manuscript or available via GitHub repository at . Raw data cannot be made publicly available to protect data security.