Our results reveal that multimorbidity is common in the Danish population and is substantial among the elderly; more than half of the population aged 65 years and up has at least two chronic conditions and one-fourth of the population between the ages of 45 and 64 years is living with multimorbidity. Furthermore, multimorbidity is more prevalent among women than among men and twice as prevalent in the population with the lowest educational attainment, compared to those with postgraduate education. Accordingly, the results show that multimorbidity is negatively associated with educational attainment. The risk of having a mental health condition was higher for women and for people with lower secondary education or vocational training and increased with age and the number of physical conditions. Further, the results showed that comorbidities of heart disease, diabetes, chronic obstructive pulmonary disease or cancer were common for people with low educational attainment.

Our results are comparable to those of other studies finding that age, lower socioeconomic status and gender are associated with multimorbidity [4, 18, 25,26,27], and the multimorbidity prevalence we report is comparable to that reported by Orueta et al. [25] and Barnett et al. [4]. Orueta et al. found that 23.6% of the total Basque population had two or more chronic conditions [25], and Barnet et al. found that 23.2% of all patients in Scotland had two or more chronic conditions [4], compared to 21.6% in our study. Both studies found the same prevalence patterns across socioeconomic groups [4, 25]. Orueta et al. used health administrative databases from both primary care and hospitals and a list of 52 chronic conditions [25] and Barnet et al. used data from primary care to identify chronic conditions and included 40 conditions [4]; in contrast, we used data from both the primary and secondary sectors and included only 16 diagnoses. The study populations in Orueta et al.’s and Barnett et al.’s studies included children aged 0–15 years. This group was not included in our study population because very few children under the age of 16 years have multimorbidity [4]. Further, both studies defined socioeconomic status by the area in which a person lived [4, 25], whereas individual educational attainment was used as an indicator of socioeconomic status in this study. We believe that similar patterns identified in several European populations reflect the fact that the number of included conditions does not affect the overall results because the most prevalent conditions are included in all studies. Similarly, including diagnoses from both primary and secondary sectors, compared to including only diagnoses from the primary care sector, does not substantially affect the results as long as the primary care sector diagnoses conditions. This is consistent with Harrison et al., who concluded that multimorbidity defined as two or more diseases can be accurately measured using varying definitions that include as few as 12 prevalent chronic conditions [7]. Thus, we believe that the comparable patterns of multimorbidity prevalence suggest that similar patterns are likely to be found in other Western European countries.

In keeping with this, our findings related to the relationship of multimorbidity to age are also consistent with recent studies from the Netherlands [10] and Ireland [28], both based on data from general practice, measuring multimorbidity as two or more co-occurring chronic conditions and using a list of 29 and 147 chronic conditions, respectively. Our results are also comparable with those of an English study with regard to prevalence and age and socioeconomic status [29]. The study is based on data from primary care and uses two different approaches to define multimorbidity. The prevalence of physical and mental health comorbidity of 4.9% in our study was lower than the 8.3% reported by Barnett et al. in a Scottish population [4] and the 7.9% reported by Bobo et al. in a US population [17]. Chronic conditions in our study were identified by algorithms based on ICD-10 codes from the hospital system, medication prescriptions and some services provided by primary care; consequently, mental health conditions that were not treated with medications were not identified. This may explain the differences in reported prevalence of physical and mental health comorbidity because both studies noted above [4, 17] included data from primary care, where we assume that diagnoses of mental health conditions are not contingent on medication use.

The results also revealed that the risk of having a mental health condition increases with age, number of physical health conditions and educational attainment which is consistent with Barnett et al.’s results [4]. In the same population, McLean et al. [30] also found a strong association between prevalence of multimorbidity and socioeconomic deprivation. Several studies have investigated the association between childhood conditions and the development of chronic conditions [31,32,33] and conclude that early life conditions have a lasting influence on adult health. Tomasdottir et al. state that allostatic overload can be the underlying mechanism behind this association, providing a route by which childhood adversities become biologically embodied [33]. Thus, in our study, low educational attainment can be an indicator of poor childhood conditions. In addition, factors such as different working environments and differences in health behaviors such as smoking and diet can contribute to the educational differences in the prevalence of multimorbidity [34]. In keeping with Barnett et al. [4], we found that comorbidities for heart disease, diabetes, chronic obstructive pulmonary disease or cancer were more common in people with low educational attainment [4]. In contrast, Barnett et al. could not demonstrate socioeconomic effect for stroke and dementia. A likely explanation for the differences between these results is that our study used individual educational attainment as an indicator of socioeconomic status, whereas Barnett et al. used socioeconomic deprivation of the area in which a patient lived to define socioeconomic status [4].

Strengths of our study include a relatively large population consisting of all adults from the Danish Capital Region and including data from both the primary and secondary sectors. Study limitations include the use of algorithms to identify chronic conditions. Previous studies indicate that the algorithms do not capture all persons with rheumatoid arthritis, osteoarthritis, back conditions, lung diseases, mental health disorders and allergies [11]; hence, the prevalence of multimorbidity may be underestimated here. In addition, the use of algorithms based on register data from the healthcare system means that the prevalence of the included chronic conditions might be underestimated because the register includes only people who are in contact with the healthcare system. Not surprisingly, the prevalence of multimorbidity found in this study is lower than studies from European countries using information from self-reported questionnaires to identify chronic conditions [9, 35, 36].