Effect of timeframes to define long term conditions and sociodemographic factors on prevalence of multimorbidity using disease code frequency in primary care electronic health records: retrospective study

Abstract

ObjectiveTo determine the extent to which the choice of timeframe used to define a long term condition affects the prevalence of multimorbidity and whether this varies with sociodemographic factors.DesignRetrospective study of disease code frequency in primary care electronic health records.Data sourcesRoutinely collected, general practice, electronic health record data from the Clinical Practice Research Datalink Aurum were used.Main outcome measuresAdults (≥18 years) in England who were registered in the database on 1 January 2020 were included. Multimorbidity was defined as the presence of two or more conditions from a set of 212 long term conditions. Multimorbidity prevalence was compared using five definitions. Any disease code recorded in the electronic health records for 212 conditions was used as the reference definition. Additionally, alternative definitions for 41 conditions requiring multiple codes (where a single disease code could indicate an acute condition) or a single code for the remaining 171 conditions were as follows: two codes at least three months apart; two codes at least 12 months apart; three codes within any 12 month period; and any code in the past 12 months. Mixed effects regression was used to calculate the expected change in multimorbidity status and number of long term conditions according to each definition and associations with patient age, gender, ethnic group, and socioeconomic deprivation.Results9 718 573 people were included in the study, of whom 7 183 662 (73.9%) met the definition of multimorbidity where a single code was sufficient to define a long term condition. Variation was substantial in the prevalence according to timeframe used, ranging from 41.4% (n=4 023 023) for three codes in any 12 month period, to 55.2% (n=5 366 285) for two codes at least three months apart. Younger people (eg, 50-75% probability for 18-29 yearsv1-10% for ≥80 years), people of some minority ethnic groups (eg, people in the Other ethnic group had higher probability than the South Asian ethnic group), and people living in areas of lower socioeconomic deprivation were more likely to be re-classified as not multimorbid when using definitions requiring multiple codes.ConclusionsChoice of timeframe to define long term conditions has a substantial effect on the prevalence of multimorbidity in this nationally representative sample. Different timeframes affect prevalence for some people more than others, highlighting the need to consider the impact of bias in the choice of method when defining multimorbidity.

Multimorbidity research: where one size does not fit all

“…In their paper, Beaney and colleagues deal with this technical question within multimorbidity research, with implications from the viewpoint of inequality (doi:10.1136/bmjmed-2022-000474). 6 Using a sample of primary care electronic health records from the Clinical Practice Research Datalink Aurum database of adults registered at general practices in England on 1 January 2020, the authors determined the impact of timeframes used in defining long term conditions on the prevalence of multimorbidity, and whether prevalence differed by sociodemographic factors. The authors defined multimorbidity as two or more diseases from a list of 212 chronic conditions, and they calculated the prevalence of multimorbidity when a single code ever recorded denoted the existence of all the conditions.…”