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

Diagnostic codes and data cleaning

Multimorbidity was defined as the presence of two or more chronic conditions from a set of 212 conditions.⁴ We included 211 conditions selected by Head and colleagues, based on a set of 308 disease phenotypes developed by Kuan and colleagues.^{3 14} We reviewed these code lists and made changes for diabetes disease lists (online supplemental appendix) and also included "chronic primary pain" as a new condition. In the study by Head and colleagues, most conditions were defined by a diagnostic code ever being recorded, but for some conditions, the authors required three occurrences of a code within a 12 month period. Two clinicians (TB and JC) reviewed the codes and reassigned 41 diseases to require multiple codes where a single code might otherwise indicate an acute condition (see online supplemental appendix for further details and rationale). For diabetes, we assigned the most likely diabetes type (type 1, type 2, or other/unspecified) based on the frequency of codes. Further information on the long term condition categories and cleaning of ethnic group codes are given in the online supplemental appendix. Our code lists are available from: https://tbeaney.github.io/MMclustering/

Disease definitions

The number of chronic conditions for each person was calculated as of the 1 January 2020 under each of the following five definitions (figure 2). These definitions were selected to reflect the variety used in previous multimorbidity research and definitions of chronic conditions:

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Figure 2

Schematic diagram of assignment of a long term condition for a hypothetical patient under each alternative definition. Each cross represents a code for a condition in a single patient s electronic health record timeline from January 2018 to April 2019, with the colour representing the disease. The single code means a single code was sufficient for diagnosis results in all four long term conditions being counted. Two codes over three months and two codes over 12 months required at least two codes separated by at least three months and 12 months, respectively. Three within 12 months’ requires three codes occurring within a 12 month time window. Any in past 12 months requires at least one code occurring in the past 12 months (measured in the 12 months from 1 January 2019 to 1 January 2020. Diseases defined by problems not included here

1. Any disease code in the record for all 212 conditions, which was used as the reference definition (single code);¹⁴
2. two codes at least three months apart for the 41 conditions requiring multiple codes,⁷ or a single code for the remaining 171 conditions;
3. two codes at least 12 months apart for the 41 conditions requiring multiple codes,^{7 8} or a single code for the remaining 171 conditions;
4. three codes within any 12 month period for the 41 conditions requiring multiple codes,³ or a single code for the remaining 171 conditions; and
5. any code appearing within the past 12 months for the 41 conditions requiring multiple codes,⁹ or a single code for the remaining 171 conditions.

We also compared a definition of multimorbidity only including conditions defined as active problems (referred to here as problems). Diagnostic codes entered into the record can be marked by clinicians as problems, which then appear on a patient’s summary during a consultation.¹¹ These may be more visible to the clinician than historical diagnostic codes, which would require manual searching through the electronic notes and are also pulled across onto referral letters.¹⁰ Problems are recorded as either active or past problems and can be marked as ended if no longer active, with the end date recorded.¹¹ We included only those problems that were active, that is, those that did not have an associated end date before 1 January 2020 and were marked as active. The problem definition was applied to all 212 long term conditions and not only the 41 requiring multiple codes. Further details are given in the online supplemental appendix page 10.

Multimorbidity prevalence

The prevalence of people with one or more, two or more, or three or more long term conditions varied substantially according to definition used (table 2). Under the single code definition, the prevalence of multimorbidity (two or more long term conditions) was 73.9%, reducing to 41.4% using the three codes within 12 months’ definition and 35.2% using only conditions recorded as active in the problem list.

Of the 41 conditions included that were required to have multiple codes, elevated concentrations of total cholesterol, triglycerides, and low density lipoprotein; dermatitis; and enthesopathy and synovial disorders were the most common conditions, with a prevalence of at least 20% using the single code definition (online supplemental appendix figure A1). The prevalence of most of these conditions was substantially lower with each of the alternative definitions from two codes over three months to two codes over 12 months to three codes within 12 months, and any code in the past 12 months. However, reductions in the prevalence were not consistent between conditions, as evidenced by the prevalence of obesity, which was higher under the any in past 12 months definition compared with the three within 12 months definition.

For problem codes, the pattern was mixed according to condition. Hypertension, type 2 diabetes, thyroid disease, chronic kidney disease, atrial fibrillation, and chronic obstructive pulmonary disease had a similar prevalence applying the problems definition compared with using only the single code definition (online supplemental figure A2). However, those diagnoses based only on blood test results (raised concentrations of cholesterol or triglycerides) were represented rarely in the problem table (prevalence <0.1%; Online supplemental figure A2).

The prevalence of multimorbidity under each definition stratified by demographic group is shown in figure 3 (percentages are given in online supplemental table A2). Within all sociodemographic strata, multimorbidity prevalence was lowest applying the problems definition, from 12.1% in the 18-29 year age group to 87.8% in the 80 years and older age group, compared with 48.5% and 98.1%, respectively, applying a single code definition. Comparing the definitions using alternative timeframes, the absolute difference in prevalence between definitions was large for younger age groups but became smaller with increasing age. Within categories of gender, ethnic group, and Index of Multiple Deprivation group, absolute differences in multimorbidity prevalence between definitions remained similar in size (figure 3).

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Figure 3

Prevalence of multimorbidity using different definitions of long term condition, by demographic factors. Diamonds represent estimates and bars represent 95% confidence intervals from mixed effects logistic regression models. IMD=Index of Multiple Deprivation

Demographic differences in multimorbidity status according to disease timeframe

In people with multimorbidity based on the single code definition, we compared the expected probability of a change in multimorbidity status under each different timeframe, holding age, gender, ethnic group and Index of Multiple Deprivation at their mean values. The expected probabilities by definition of a person being reclassified as not multimorbid (compared with the single code definition) were 18.7% for two codes over three months, 21.9% for two codes over 12 months, 40.4% for three codes within 12 months, 37.5% for any code in the past 12 months, and 51.6% for problems.

Older people were substantially less likely to be reclassified as not multimorbid compared with younger people after controlling for gender, ethnicity and deprivation, under each of the four alternative definitions compared with a single code definition (figure 4 and online supplemental table A3). People aged 80 years or older had only a 1-10% probability of being reclassified under the different definitions, compared with those aged 18-29 years, who had a 50-75% range of being reclassified. Within each definition, the expected probabilities of a change in multimorbidity status were similar for men and women. People of South Asian ethnicity had the lowest expected probability of a change in multimorbidity status, under all alternative definitions, except for the three codes within 12 months and problems definition, where the probability was lowest for people of white ethnicity. People of other, or unknown ethnic groups had the highest expected probabilities of no longer being multimorbid under all alternative definitions. The data showed a strong gradient towards a lower chance of being reclassified as not multimorbid with increasing deprivation across all alternative definitions.

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Figure 4

Expected probabilities for being reclassified as not multimorbid with each definition compared with a single code definition. Diamonds represent estimates and bars represent 95% confidence intervals from mixed effects logistic regression models

Strengths and limitations

A strength of our study is the use of large sample of adults registered to primary care in England, with previous studies of the Clinical Practice Research Datalink Aurum data finding it to be representative of the national population.¹¹ We adopted a broad set of disease codes, many of which have particular relevance to primary care, and applied different definitions of long term condition timeframe using the same set of conditions to the same patient cohort. To an extent, these definitions are arbitrary, and our analysis presented a range of approaches used in the literature before, rather than attempting to analyse all possible definitions. For example, a condition lasting more than six months is sometimes used to define a long term condition,⁶ but we did not include this definition because estimates would lie between our range of three and 12 months.

A limitation of using routinely collected healthcare data is that some conditions may not be recorded, either because a person does not present with a condition or because when presenting, the condition is not coded by a clinician. The likelihood of missing codes is unlikely to be random with respect to either patients or diseases. Although a previous systematic review found good agreement in diseases recorded in Clinical Practice Research Datalink with other sources,²³ comparison against cancer diagnoses in the Clinical Practice Research Datalink with cancer registry data found a range of 9–26% for different cancers were missing in the Clinical Practice Research Datalink.²⁴ The financial incentives offered by quality and outcomes framework have improved data collection and coding in primary care²² and may therefore inflate the prevalence of long term conditions requiring multiple codes for conditions included in the framework relative to other conditions.

The code lists used for raised total cholesterol, raised low density lipoprotein cholesterol, low high density lipoprotein cholesterol and raised triglycerides available from the CALIBER study include test results (rather than diagnostic codes), which were rarely coded as problems, partially explaining the substantially lower prevalence of multimorbidity overall. Studies using problem codes alone would therefore need to include diagnostic codes, but was not feasible for comparison in our study as the granularity required in low density lipoprotein cholesterol and high density lipoprotein cholesterol measurements cannot be accounted for using diagnostic codes alone. A further issue of using problem codes is in defining active problems. In Clinical Practice Research Datalink Aurum no date is recorded at which a problem is changed from active to inactive. Our data extraction occurred in May 2022, and so some codes marked as inactive were likely active at the study start date, and so our findings may represent an underestimate. However, given our focus on chronic conditions, the number of conditions to have resolved is unlikely to be large.

We used a large set of chronic conditions, which will have unequal burden on clinical outcomes, healthcare use, and quality of life. Many may not be included in other multimorbidity measures, with a recent Delphi study identifying a core set of 24 conditions to always include and 35 to usually include and with no conditions identified for exclusion.²⁵ A smaller and standardised set of conditions can aid comparability and reproducibility but has drawbacks. Firstly, a smaller set limits the scope to detect novel associations between less common conditions. Secondly, choices over conditions are subjective and those that are less frequent can still have a large burden on individuals.

The overlap between some of the long term conditions included here may lead to double counting and over-estimation of multimorbidity, for example, combining myocardial infarction and angina as ischaemic heart disease. However, when investigating the cause of disease, more granular categories may be a benefit because a person with a myocardial infarction who subsequently develops angina may have a different trajectory and opportunities for intervention and prevention to a person with angina who subsequently develops a myocardial infarction, despite a common pathophysiology.

Combining diagnostic codes with relevant medications or treatments may help to distinguish active versus inactive conditions; for example, use of a proton pump inhibitor in gastritis. Similarly, the quality and outcomes framework excludes patients from the asthma register if no asthma related drugs were prescribed in the past 12 months.²⁶ However, a difficulty with this approach for some diseases is that drugs can have multiple indications; for example, proton pump inhibitors being co-prescribed with non-steroidal anti-inflammatory medications, which may be more common in those with a history of gastro-intestinal problems irrespective of active symptoms.²⁷

Data management was provided by the Big Data and Analytical Unit at the Institute of Global Health Innovation. We thank Mark Cunningham for assistance with data extraction and management.