We explored multimorbidity patterns and their 6-year evolution in people aged 65 years and older with multimorbidity attended in PHC. The most prevalent chronic diseases, Hypertension, uncomplicated and Lipid disorder, were represented in all clusters in all four groups (i.e., men and women aged 65–79 and ≥80 years). We found 6 clusters per group, 5 of them with a specific pattern related to an organic system: Musculoskeletal, Endocrine-metabolic, Digestive/Digestive-respiratory, Neuropsychiatric and Cardiovascular patterns. We analysed multimorbidity patterns over 6 years and found that they remained quite similar from the beginning to the end of the study period.
We observed a high prevalence of multimorbidity in our population sample, with a higher proportion for women, as in other published studies [5, 8] and described 6 patterns in each study group. In addition, the prevalence of chronic diseases and multimorbidity patterns was similar to previous studies in Catalonia [22] and in other developed countries [23,24,25]. In a separate study in the same sample, we analysed mortality rates and observed higher mortality among men with Digestive-respiratory patterns and among women with Cardiovascular pattern [26].
In both age groups, both men and women had the same 5 multimorbidity pattern names plus one additional cluster: a Digestive disease pattern in women and a Digestive-respiratory pattern in men. This difference is probably related to the smoking and alcohol habits that were more common among men than among women in the age groups studied [27]. The differences observed between age groups were related to disease prevalence and O/E ratio; no significant differences between men and women were found in the systems that were most commonly affected by the prevalent diseases. As a result, future clinical guidelines could focus on improving common management of multimorbidity in all older patients.
It is particularly noteworthy that more than 50% of those showing the Nonspecific pattern remained in that same pattern across the period analysed, without moving on to a specific pattern; a few degenerative diseases were added in the older groups. In addition, this first (Nonspecific) cluster was defined by highly prevalent diseases, with no over-represented chronic diseases, so that the association between diseases could exist by chance. Consequently, this first cluster showed that a considerable portion of the sample had no system-specific pattern.
In contrast, across the specific patterns we also observed a large proportion (range from 42.5 to 64.7%) of people remaining stable (in terms of chronic disease prevalence) in the same pattern. Maximum stability was observed for the Nonspecific pattern in both groups aged 65 to 79 years and in older women; for men aged 80 and older, the Cardiovascular pattern showed the greatest stability. Moreover, some people changed from one pattern to another but the multimorbidity pattern kept mostly stable during the 6 years studied, confirming the long-term stability of the multimorbidity pattern composition. In view of these results, an association could be hypothesized between multimorbidity and specific genetic conditions, as well as previously suggested associations with lifestyle and environmental conditions [28].
Estimates of multimorbidity pattern prevalences differ deeply in the literature because of variations in methods, data sources and structures, populations and diseases studied. Although this makes it challenging to compare study results [5, 29, 30], there are some similarities between the present and previous studies. For instance, the most common organic systems affected in previous studies of multimorbidity patterns were cardiovascular/metabolic, neuropsychiatric (mental health), and musculoskeletal [30]. Our study found patterns affecting these same organic systems; however, it offers another point of view for defining multimorbidity patterns. Cluster analysis shows the complexity of multimorbidity in persons aged 65 years and older and is likely to be helpful in shaping future strategies to continue studying this important health issue.
Previous studies have analysed no more than four years of data [29], compared to six years of information about the evolution of a multimorbidity pattern in our study. As a result, we identified long-term stability in multimorbidity patterns, observing some differences between age groups, related to prevalence and O/E ratio in chronic diseases. Useful information can be extracted from our study for the monitoring and treatment of each multimorbidity pattern.
Strengths and limitations
A major strength of this study is the analysis of a large, high-quality EHR database, representative of a large population. In the context of a national health system with universal coverage, EHR data have been shown to yield more reliable and representative conclusions than those derived from survey-based studies [25]. The inclusion of all chronic diagnoses registered in EHR contributed to a more accurate analysis of the multimorbidity patterns in this population. Moreover, the use of data collected by the primary health care system increased the external validation of the information extracted because primary care centres in Barcelona attended more than 70% of the population at least once a year during the study period. As the nonspecific pattern contained well-known chronic diseases with established clinical guidance, the information extracted is relevant but less useful in clinical practice than the specific patterns defined. The long time period observed provided information on the stability of the patterns during six years, enabling us to focus on creating better strategies to address all five specific patterns in terms of prevention, diagnosis, and treatment of these systemic clusters of prevalent diseases.
A number of limitations must be taken into account as well. First, EHR accuracy depends on the data entered by each general physician or nurse, and EHR systems are not designed as general-purpose research tools [31]. Another weakness could be the attention only to chronic diseases, which precludes awareness of acute diseases or bio-psychosocial factors [2]. Nonetheless, the inclusion of a wide range of diseases makes it possible to find multimorbidity patterns not previously obtained and increases complexity in terms of assembling patterns. Finally, we did not have data on cause of death.
In addition, using MCA can produce low percentages of variation on principal axes, complicating the choice of the number of dimensions to retain. We assumed a five-dimension solution, using the elbow rule in the scree plot to have the most accurate solution possible without including an unwieldy number of dimensions in the analysis [19]. Although we did not retain the total variance of the dataset, clustering techniques can be applied to the reduced dataset while preserving its complexity.
The strength of using k-means cluster analysis is that the results are less susceptible to outliers in the data, the influence of the chosen distance measure, or the inclusion of inappropriate or irrelevant variables. The method can also analyse extremely large data sets (as in this study), as no distance matrix is required. On the other hand, some disadvantages of the method are that different solutions can occur for each set of seed points and there is no guarantee of optimal clustering [11]. To minimize this shortcoming, we tested the internal validity of our solution using bootstrap methods [32], and the results were highly stable (Jaccard > 0.85). However, the method is not efficient when a large number of potential cluster solutions are to be considered [11]; to address this limitation, we computed the optimal number using analytical indexes like Calinski Harabasz [33].
Future research
With this confirmation of the stability of multimorbidity patterns across age groups, sex, and time, some actions could be considered to improve multimorbidity management. For instance, clinical guidance could encompass a specific pattern to deal with its complexity rather than creating multiple guidelines for each of the chronic diseases. Relevant information could be extracted from our study for the monitoring and treatment of each multimorbidity pattern. Finally, genetic factors, as well as socioeconomic status, should be taken into account in future studies.