Measuring multimorbidity in older adults: comparing different data sources

Multimorbidity (MM), the co-occurrence of multiple chronic conditions in an individual [1], is a prominent public health issue that is attracting increasing interest among researchers, practitioners and decision-makers from a variety of disciplines and fields. The primary care setting undertakes a substantial part of the management of multimorbid patients [2‐4], and although MM is not observed exclusively in older adults, this group is particularly affected [5, 6]. Since MM is undoubtedly a central element of the framework describing the needs of older adults, an adequate description of the epidemiology of concurrent physical and mental health conditions [3, 4, 6‐8] is essential in informing the responsiveness of health systems [4, 9].

The designs of epidemiological MM studies vary greatly, as do their results in terms of prevalence (ranging from 3.5 to 100%) [10] and associated outcomes [11‐14]. This can be partly explained by the lack of consensus concerning the conceptual or operational definitions (i.e., the length of the list of conditions, cut points, etc.) [10, 15, 16], choice of study populations [17, 18] and data sources [19‐21]. It is not always clear, however, which data source is most appropriate to use in epidemiological studies [22].

The objective of comparing different data sources has been well explored in studies considering individual diagnoses (e.g., diabetes, hypertension, cancer) and in those including a relatively small number of diseases [23‐26]. These studies have predominantly compared self-reports to chart reviews, medical examinations or practitioner reports [22, 27, 28]. Recently, survey/self-reported (SR) and administrative (Adm) data have been increasingly used in health research [23]. As suggested by Bayliss and colleagues [29], these data sources represent the subjective (patient’s perspective) and objective (health systems’ perspective) measurements that must be taken into account to make a more comprehensive assessment of morbidity.

We identified only two studies that simultaneously compared the prevalence of chronic conditions and MM using SR and Adm data [30, 31] with one study [30] reporting prevalence estimates measured with the combined (either) data sources. The use of combined data sources in epidemiological studies has been suggested as a promising approach since it allows the perspectives of both the patient and the health system to be taken into account [32‐34]. To date, no previous study of this nature (i.e., comparing and combining SR and health-Adm data) has been conducted among older populations of primary health care users, a well-known priority population in terms of MM. Using SR and health-Adm data, individually or combined, the current study aimed to (1) compare the prevalence of selected chronic conditions and MM; (2) examine the agreement between these data sources on selected chronic diseases; (3) examine the factors associated with MM as a function of data sources and (4) draw out implications for policy and future research.

Comparisons between individuals with (n = 1625) and without (n = 140) available Adm data did not show statistically significant differences with respect to gender (p = 0.08), age group (p = 0.13), income (p = 0.06), marital status (p = 0.65), smoking habit (p = 0.71), perceived mental (p = 0.99) or physical (p = 0.40) health and MMSE score (p = 0.17). However, those for whom matched health-Adm data were not possible had a significantly lower level of education (p = 0.04), fewer self-reported chronic conditions (p < 0.01) and less social support (p < 0.01). The overall sample characteristics are shown in Table 1.

The prevalence of each of the 17 chronic conditions ranged from 3 to 57.4%, 1.2 to 52.3% and 3.8 to 68.7% when using SR data only, Adm data only and either data sources, respectively (Fig. 1). Cochran’s Q test indicated statistically significant differences among the three proportions (SR vs Adm vs either data sources) for all conditions. Our analysis showed relative percent differences ranging from 7.1 to 87.7%, and when combining data sources, estimations of proportions increased from 4.4 to 50.8% for all conditions.

As Table 2 shows, concordance was highly variable, and the κ coefficients ranged from 0.05 to 0.73 (poor to substantial agreement), whereas the PABAK varied from 0.16 to 0.93. With the exception of hypertension, all chronic conditions had higher negative agreement than positive agreement.

The calculated prevalence of MM ranged from 61.9 to 95.9%, and regardless of the operational definition used (MM2 or MM3), combined sources presented a higher prevalence, followed by SR and Adm data. Cochran’s Q test indicated statistically significant differences among the three proportions (p < .001). The presence of MM (any definition) was associated with participant age and perceived health. In addition, health service utilization was consistently higher among participants with MM compared to those with no MM. The prevalence of self-reported MM (both cut-offs) was higher among females and participants without partners. These characteristics were also associated with the MM3_either definition, which is MM defined as ≥3 chronic conditions from either data source. Higher income and education levels were associated with a lower prevalence of self-reported MM2 and MM3 (any definition). Smoking was associated with MM only when it was defined under the threshold of 3 conditions (SR only and Adm data only). Social support was significantly lower among certain multimorbid participants (i.e., MM3_SR and MM3_either) (Table 3).

Regardless of the definition used, the prevalence of MM was high in this study. This finding is similar to results observed elsewhere [3, 5, 53, 54] that have included older adults in primary care settings and in which the assessment of MM included a list of at least 12 conditions [55]. Our findings add to the growing body of research highlighting MM not only as a major issue in primary care but also as a global health system issue for the care of older populations [43, 56].

To describe the health care needs and to propose appropriate management of care, it is necessary to take into account the heterogeneity of chronic diseases implicated in MM [4, 7]. This is particularly important in older people presenting with coexisting chronic physical and mental health conditions, who are considered high-level complexity groups [7] with poorer health outcomes that require an individualized care approach [56, 57]. Our results suggest that integrating information from different data sources increases the identification of the presence of chronic conditions. Interestingly, this study showed that the proportions of common mental health conditions (SR vs Adm) had a relative percentage difference of 22%. Moreover, when both data sources were combined, the estimation increased by 50.8%. These findings align with earlier studies, based on older adults in the US and general population studies in Canada, that propose combining different data sources for improved case identification [58], particularly in neuropsychiatry [48, 59]. Moreover, our study has been one of the first attempts to thoroughly examine the benefits of combining different data sources among a large sample of older adults consulting in primary care settings. This further highlights the important contribution of different data sources in the investigation of the prevalence of MM.

Furthermore, the estimated proportion of the sample with headaches, eye diseases, musculoskeletal and dermatologic conditions increased more than 40% when the data were combined. Based on a single source, 4 out of these 5 conditions had a higher prevalence according to the Adm database. The results also suggested the potential underestimation of chronic conditions when using one data source. We observed high relative percent differences (60 to 87.7%) for obesity, cancer, hyperlipidemia, gastrointestinal and liver diseases, with 4 out of 5 conditions showing higher SR prevalence. These findings are in line with previous studies reporting inconsistencies in prevalence estimations due to the use of different data sources [23‐25, 30, 31, 60]. Based on these results, if one were to use Adm data only, there would be an underestimate of the prevalence of obesity and hyperlipidemia. If one were to choose SR data only, there would be an underestimation of up to 3 times the prevalence of cancer as reported in Adm databases.

Moreover, even when the prevalence estimated with single datasets (not combined sources) was apparently similar (e.g., respiratory tract and eye diseases), the calculated percentage increase obtained when combining both data sources suggests that our data sources identify different cases [48, 58].

As reported in previous studies including older populations, the agreement between data sources for the 17 chronic conditions was highly variable [24, 27, 30, 50]. For some diagnoses, such as diabetes, agreement is good. This may be explained by the health policies aimed at chronic condition management programmes in primary care for diabetes including both health professional and patient participation in treatment management. For other diagnoses, such as cancer and mental health conditions, agreement is poorer. Although associated with a high burden on the health system, the episodic nature of common mental disorders and treatable cancers may have been underreported by patients if interviewed while in remission. Such variability in potential underreporting is an important aspect to be explored in future studies since it may play a central role in patient involvement in health care decision-making. In the particular case of obesity, the low agreement between data sources might have occurred because the cut-off for self-reported obesity (BMI ≥ 30) may not correspond to the one applied in clinical practice where clinicians would be more likely to code a diagnosis of obesity only when it aggravates another condition. Others have suggested that the low agreement may also be a case of less reliable Adm database coding where only the most severe cases of obesity would be recorded [61]. While this latter Canadian study was performed among hospitalized patients, our results support a similar pattern among older adults in the context of primary care. In terms of κ values, our results are very similar to those presented by Fortin and colleagues (2017) [30]. However, according to the literature [22, 49, 50, 60, 62], this coefficient should not be reported or interpreted alone but rather jointly with other indicators. The k statistic takes into account not only the agreement occurring by chance but also the complex influence of the difference in the disagreement cells (BI) and the agreement cells (PI) in a 2 × 2 table [49]. Regardless of the chronic conditions assessed, our results showed that most k coefficients were low (fair to poor) and that BI and PI varied widely and independently of each other. If we contrast pairs of conditions that showed the same k values (e.g., dermatologic conditions and obesity; headaches and arthropathy; cancer and chronic urinary problems), we notice that the other indicators showed different values (Fig. 1; Table 2). Evaluating the set of reported indicators, not only the kappa, (Fig. 1; Table 2), leads to a better understanding of which conditions may be better identified when the different data sources are combined.

The prevalence of self-reported MM was higher than that of the cases identified through Adm data. Combining both databases allowed an increase of 10.6 and 22.4% in the estimation of MM2 and MM3 cases, leading to 95.9% of participants being identified as having two or more chronic conditions. Previous studies focusing on older adults and using patient chart or electronic health records (EHR) in a primary care setting also reported a MM prevalence higher than 90% [17, 63]. Many public health systems in Canada are investing in EHR [64]. Future studies should focus on comparing EHR to datasets derived from combined data sources such as SR and Adm [23, 65]. Rendering this possible requires policies that facilitate data access.

This study also showed an association between a number of sociodemographic and behavioural factors and the presence of MM defined according to different data sources and cut-offs. For example, gender was not associated with MM measured with Adm data but was associated with self-reported MM (any cut-off). This finding is in line with the idea presented in previous studies [15, 30, 31] that different population subgroups are captured when different data sources and operational criteria are used to identify MM cases. This may highlight the influence of certain biopsychosocial factors reflected in SR data but less so in Adm data based on physician diagnoses.

This is the first study to address, in primary care older adults, the concordance between SR and Adm data in the context of a public health system where residents are covered for all hospitalizations and outpatient consultations with physicians. This study also goes beyond the present literature by simultaneously investigating MM (according to two operational definitions) and chronic condition prevalence. Our findings suggest that no single data source is more valid than another, and we highlight the usefulness of combining two data sources that may, in fact, reflect different patient (SR) and health system (Adm) perspectives. Furthermore, the populations identified by each data source may present different clinical and need profiles. Given the increasing prevalence of MM due to the ageing population, policy and decision makers need to consider both Adm and SR data when estimating population health needs to be able to adequately allocate health and human resources to ensure quality of care and to improve the efficiency of the healthcare system.