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
Authors: MacRae Clare , McMinn Megan , Mercer Stewart W , Henderson David , McAllister David A , Ho Iris , Jefferson Emily , Morales Daniel R , Lyons Jane , Lyons Ronan A , Dibben Chris , Guthrie Bruce 
Published: February 2023

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.

Author summary

Why was this study done?

There is wide variety in the conditions considered by researchers when measuring multimorbidity prevalence.

A systematic review of 566 studies, published in 2021, found lack of consensus in the selection of conditions considered.

In half of studies only eight conditions (diabetes, stroke, cancer, chronic obstructive pulmonary disease, hypertension, coronary heart disease, chronic kidney disease, and heart failure) were consistently considered; and the number of conditions considered varied from 2 to 285 (median 17).

A more consistent approach to measuring multimorbidity is needed to facilitate comparability and generalisability across studies.

What did the researchers do and find?

This study investigated the relationship between the number and selection of conditions considered and the impact on multimorbidity prevalence.

There are large differences in prevalence, a range of 4.6% to 40.5%, when different numbers and selections of conditions are considered.

Nine published condition-lists were examined; including those recommended for measuring multimorbidity, previously used to measure multimorbidity prevalence, or measures of ‘comorbidity’.

Highest multimorbidity prevalence was found when using Ho always + usually (a list derived from a recent Delphi consensus study), Barnett (widely used to measure multimorbidity prevalence), and Fortin (a list recommended for use in measuring multimorbidity).

People who are the oldest, living in the most deprived areas, and men require fewer conditions to be considered to reach close to multimorbidity prevalence when considering all 80 conditions (the ceiling effect, where the prevalence approaches the upper limit of prevalence possible in the study).

What do these findings mean?

All conditions were counted in the same way (the presence of the condition ever recorded) to improve comparability, however in previous studies conditions were counted according to varying rules, highlighting that further variability in prevalence estimates across studies will happen because of variation in how each condition is measured.

There is a need for standardisation when measuring multimorbidity prevalence so that results across studies are comparable and population subgroups are accurately represented.

To address this, researchers can consider using the Ho always + usually, Barnett, or Fortin condition lists.