Principal findings

Different count measures of multimorbidity demonstrated similar poor discrimination for the outcome of emergency admission in this study with the Rx-Risk-V performing best (c-statistic: 0.63). Predictive accuracy for ACS admission was marginally better and three measures (RxRisk-V, number of medications and Charlson index) demonstrated equivalent predictive accuracy (c-statistic 0.67). All measures demonstrated poor discrimination for functional decline over 2-year follow-up (c-statistic range: 0.55–0.61), indicating that measuring multimorbidity in isolation is insufficient if aiming to predict these outcomes.

Regarding multimorbidity prevalence, a total of 626 participants (73%) met the definition for multimorbidity using the total disease counts measure and 484 (56%) according to the selected condition count measure. Three hundred and fourteen (37%) had ≥2 of the Charlson comorbidity index conditions. For medication count measures, a total of 789 participants (91%) had ≥2 chronic RxRisk-V conditions and 73% (n=632) met the definition for polypharmacy (≥4 medications) with 13% (n=119) prescribed ≥10 medications (high-risk polypharmacy). These findings indicate that multimorbidity prevalence in this older community-based cohort varies considerably depending on the measure selected to define it. Considering more than one drug will often be needed to treat a single condition, it seems clinically intuitive that a higher medication count is required to define multimorbidity.

Comparison with existing literature

Few primary care studies have compared the performance of different multimorbidity measures for the outcome of emergency admission. One US study (n=14, 192 adult veterans) reported similar performance of the RxRisk, Charlson index and ACG system in predicting hospital admission (c-statistic range: 0.61–0.64).24 Additional US population-based studies found that the RxRisk-V predicted future healthcare use better than diagnosis-based measures.16 ,25 A Canadian study, which used administrative data (n=137, 700, aged ≥65 years) and examined different measures of multimorbidity in predicting hospital admission, reported only modest discrimination for the best-performing measure of simple disease counts (c-statistic 0.67).26 In the UK, a large-scale cross-sectional study (n=95,372 adults) reported that medication count was the more powerful predictor of future primary care consultations when compared to diagnosis-based measures.27

The current study adds to the literature in this area and suggests that while all measures demonstrate poor discrimination, medication-based measures, such as the RxRisk-V, performed marginally better than diagnosis-based measures in predicting emergency and ACS admissions. For research purposes, these measures can be applied to pharmacy claims databases rather than requiring medical record review and simple medication counts are easy to conduct in clinical practice. However, it is important to highlight that emergency admission is an inherently difficult outcome to predict accurately.28 Existing admission risk prediction models, which include a variety of clinical, socioeconomic and prior healthcare usage variables, rarely achieve model discrimination of ≥0.8, and the performance of the various multimorbidity measures should be judged in this context.29

There has been very limited research comparing the performance of different multimorbidity measures in predicting functional decline. One Italian study (n=633 aged ≥65 years) measured incident basic activities of daily living (BADL) disability over 5-year follow-up.8 The functional status was assessed using a modified version of the Guralnik's lower physical performance battery and 9.6% developed incident disability. Disease counts had the highest predictive value (c-statistic 0.85).8 However, all study participants had an index condition of congestive heart failure which limits the generalisability of this study to typical primary care populations.

Interestingly, a recent analysis argues that increasing levels of multimorbidity in older people may be related to increased diagnosis through screening and more vigilant recording of diagnoses rather than worsening health.30 This is supported by the fact that older people's self-rated health has not declined in tandem with increasing morbidity levels.30 If this is the case, then perhaps it is not surprising that count-based multimorbidity measures in isolation are not enough to predict emergency admission and functional decline. What is needed is a way of incorporating the burden and severity of conditions into multimorbidity measures to allow better capture of disease combinations that predict poorer health outcomes.31 Research efforts to conceptualise and capture the burden of multiple diseases for patients are ongoing.32 ,33

Clinical and research implications

The current study indicates that the choice of measure for multimorbidity will have implications in terms of prevalence estimates. Using the cut-point of ≥2 to define multimorbidity for the RxRisk-V measure would result in almost the entire study population categorised as multimorbid (91%), while using the selected condition list would identify a smaller proportion (56%). Existing multimorbidity intervention studies have largely used diagnosis-based inclusion criteria, with a smaller number of studies using this approach in combination with medication count.34 An example is the OPTIMAL primary care randomised controlled trial that recruited patients with two or more chronic conditions prescribed four or more medications for a self-management intervention that succeeded in improving activity participation.35 Using this combined approach seems pragmatic and should ensure patients with more complex multimorbidity are included. Future research studies need to carefully consider participant selection, choice of intervention and relevant outcomes in community-based multimorbidity trials.36

A systematic review examining multimorbidity measures included in risk prediction models designed to predict future hospital admission and readmission reported that of 21 studies which included a multimorbidity measure, a total of 15 (71%) used the Charlson comorbidity index (or adapted version).37 Only two studies used a medication-based measure of multimorbidity.37 The current study indicates that medication-based measures may be considered for use in risk prediction models with this outcome of interest. However, as mentioned, higher cut-points are needed to achieve optimal discrimination.

Strengths and limitations

This prospective primary care study adds to the limited literature in comparing different multimorbidity measures to predict health outcomes for older community-dwelling people. The study population was not selected based on the presence of any one index condition, which improves the generalisability of the findings. The robustness of the multimorbidity measures calculation is enhanced by diagnosis measurement using GP electronic medical record data for and use of linked pharmacy claims data for the medication-based measures. The outcomes of emergency admission and ACS admission were recorded from review of the GP electronic medical record. ACS emergency admissions are increasingly of interest due to their perceived preventability through primary care interventions. However, only a limited number of risk prediction models have been developed specifically to identify ACS admissions.29 Understanding which multimorbidity measure to include preferentially in predicting ACS admission is important and topical.

Regarding study limitations, the study involves 15 general practices from Ireland and as such may not be generalisable to other settings. As one of the outcome measurements depended on participants' filling in a postal questionnaire those with cognitive impairment at the level that would impact their ability to complete the outcome measure (defined as Mini Mental State Examination ≤20) were excluded from this study which may also affect on generalisability of the findings. Accuracy of diagnosis information was dependent on accurate recording of information taken from the GP electronic medical record. All GP medical record data was recorded by the same researcher and a random sample of 10% of all data was double checked by an independent reviewer with extensive data cleaning undertaken to maximise accuracy and reducing the likelihood of misclassification bias. A small number (n=21, 3%) of study participants had some missing data for the outcome measure of functional decline and were excluded.8

Conclusions

This study indicates that though all multimorbidity measures demonstrate poor discrimination, medication-based measures are equivalent to diagnosis-based measures in predicting emergency admission. The choice of multimorbidity measure used may have a significant impact on prevalence. Consideration of multimorbidity in isolation is insufficient in predicting which primary care patients are most likely to experience emergency admission or functional decline.