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

Clare MacRae*, Megan McMinn, Stewart W Mercer, David Henderson, David McAllister, Iris Ho, Emily Jefferson, Daniel R Morales, Jane Lyons, Ronan Lyons, Chris Dibben, Bruce Guthrie

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

Abstract / Description of output

Background AU Multimorbidity: Pleasec onfirmthatall headinglevelsarerepresentedcorrectly 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 1, 168, 260 participants who were all people alive and permanently registered with 149 included general practices. Outcome measures of the study were prevalence estimates of multimorbidity (defined as≥2 conditions) when varying the number and selection of conditions considered for 80 conditions. Included conditions featured in ≥1 of the 9 published lists of conditions examined in the study and/or phenotyping algorithms in the Health Data Research UK (HDR-UK) Phenotype Library. First, multimorbidity prevalence was calculated when considering the individually most common 2 conditions, 3 conditions, etc., up to 80 conditions. Second, prevalence was calculated using 9 condition-lists from published studies. Analyses were stratified by dependent variables age, socioeconomic position, and sex. Prevalence when only the 2 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- to 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 multimor piledforthoseusedinthetext bidity, and to facilitate :Pleaseverifythatallentriesarecorrect this, researchers can use : existing condition-lists associated with highest multimorbidity prevalence.

Original languageEnglish
Pages (from-to)e1004208
JournalPLoS Medicine
Issue number4
Publication statusPublished - 4 Apr 2023

Keywords / Materials (for Non-textual outputs)

  • Chronic Disease
  • Comorbidity
  • Cross-Sectional Studies
  • Humans
  • Multimorbidity
  • Prevalence
  • Primary Health Care


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