Social disparities in the prevalence of multimorbidity – A register-based population study
© The Author(s). 2017
Received: 5 October 2016
Accepted: 26 April 2017
Published: 10 May 2017
Prevalences of multimorbidity vary between European studies and several methods and definitions are used. In this study we examine the prevalence of multimorbidity in relation to age, gender and educational attainment and the association between physical and mental health conditions and educational attainment in a Danish population.
A cross-sectional design was used to study the prevalence of multimorbidity, defined as two or more chronic conditions, and of comorbid physical and mental health conditions across age groups and educational attainment levels among 1,397,173 individuals aged 16 years and older who lived in the Capital Region of Denmark on January 1st, 2012. After calculating prevalence, odds ratios for multimorbidity and mental health conditions were derived from logistic regression on gender, age, age squared, education and number of physical conditions (only for odds ratios for mental health conditions). Odds ratios for having multimorbidity and mental health conditions for each variable were adjusted for all other variables.
Multimorbidity prevalence was 21.6%. Half of the population aged 65 and above had multimorbidity, and prevalence was inversely related to educational attainment: 26.9% (95% CI, 26.8–26.9) among those with lower secondary education versus 13.5% (95% CI, 13.5–13.6) among people with postgraduate education. Adjusted odds ratios for multimorbidity were 0.50 (95% CI, 0.49–0.51) for people with postgraduate education, compared to people with lower secondary education. Among all population members, 4.9% (95% CI, 4.9–4.9) had both a physical and a mental health condition, a proportion that increased to 22.6% of people with multimorbidity. Physical and mental health comorbidity was more prevalent in women (6.33%; 95% CI, 6.3–6.4) than men (3.34%; 95% CI, 3.3–3.4) and approximately 50 times more prevalent among older persons than younger ones. Physical and mental health comorbidity was also twice as prevalent among people with lower secondary education than among those with postgraduate education. The presence of a mental health condition was strongly associated with the number of physical conditions; those with five or more physical conditions had an adjusted odds ratio for a mental health condition of 3.93 (95% CI, 3.8–4.1), compared to those with no physical conditions.
Multimorbidity prevalence and patterns in the Danish population are comparable to those of other European populations. The high prevalence of mental and physical health conditions highlights the need to ensure that healthcare systems deliver care that takes physical and mental comorbidity into account. Further, the higher prevalence of multimorbidity among persons with low educational attainment emphasizes the importance of having a health care system providing care that is beneficial to all regardless of socioeconomic status.
KeywordsMultimorbidity Epidemiology Prevalence Socioeconomic factors
The increasing number of people living with multiple chronic conditions, also known as multimorbidity, is a growing public health problem and a challenge for current and future healthcare systems . In addition, multimorbidity is associated with increased risk of mortality, reduced functional status and increased use of healthcare services . Recent studies show that the prevalence of multimorbidity is increasing, rises with age  and is higher in socioeconomically deprived groups . However, there are marked variations among studies of the prevalence of multimorbidity with respect to both methodology and findings . Numerous definitions of multimorbidity have been used . According to Almirall et al., the most frequent definition of multimorbidity was “more than one or multiple chronic or long-term diseases/conditions” (including physical or mental diseases or both), which was closely followed by “more than one disease or condition” without specifying chronic or long-term duration . The former is consistent with the definition used by the World Health Organization, which defines multimorbidity as “being affected by two or more chronic health conditions” . Furthermore, methods used to identify persons with multimorbidity vary across prevalence studies. A key issue is how many conditions should be studied . Recent reviews conclude that a minimum of 11 or 12  conditions should be included when measuring multimorbidity prevalence.
The prevalence of multimorbidity, defined as two or more chronic conditions, identified in European studies varies substantially from 72.7% of a Portuguese population with two or more of 147 listed chronic conditions  to 13% of a Dutch population with two or more of 29 listed conditions . These large differences in the prevalence of multimorbidity may to some extent be explained by the use of different methodological approaches both with regard to numbers of conditions included as well as whether self-reported questionnaires, data from patient records or registers are used. Also, the differences in prevalence of multimorbidity may reflect real differences among different European populations. In Denmark, central registries including information from the health care system as well as information about socioeconomic status make it possible to obtain data from an entire population. Information about the prevalence of chronic conditions based on validated algorithms can be obtained from the registries . Thus, it is possible to study prevalence and patterns of multimorbidity related to socioeconomic status in a full population. In a recent study focusing on the prevalence of multimorbidity in relation to age and socioeconomic deprivation in a Scottish population, Barnett et al. found that 23.2% of all patients had two or more of 40 selected conditions and that the onset of multimorbidity occurred 10–15 years earlier in people living in the most socioeconomically deprived areas; deprivation was particularly associated with multimorbidity that included mental health conditions . Additionally, it was found that some conditions were more prevalent as comorbidities among people living in deprived areas . Likewise, other studies find that mental health conditions often coexist with chronic physical health conditions [4, 12–14], leading to multimorbidity that includes mental health conditions. Multimorbidity that includes mental health conditions has been associated with greater symptom burden and functional impairment, poorer quality of life, higher costs and excess mortality [15, 16]. However, only a few studies have examined the prevalence and patterns of co-occurrence of mental health conditions across the entire life span using medical record data [4, 17, 18].
Thus, in order to examine how prevalence and patterns of multimorbidity in the Danish population compare to prevalence and patterns of multimorbidity in other European populations, the aim of the study was to examine: 1) the prevalence of multimorbidity in relation to age, gender and socioeconomic status; 2) the association between physical and mental health conditions and educational attainment; and 3) the prevalence of selected pairs of chronic conditions by socioeconomic status.
Algorithms used to define the 16 conditions
Defined as a physical or mental health condition in the study
ICD-10 from the Danish National Patient Register
All persons included in the Danish Diabetes Register where the inclusion date is before the population date (1/1 2012) 
C00-C43 or C45-C97
M80-M82and/or for persons aged 45 years and older contacts with the ICD-10 codes S22.0, S22.1, S32.0, S32.7, S32.8, S42.2, S42.3, S42.4, S42.7, S42.8, S42.9, S52.5, S52.6, S52.7, S52.8, S52.9, S62.0, S62.1, S72
(DIAG) a and/or (MEDICINE) b all medicine prescriptions with either ATC: M05B, G03XC01, H05AA02, H05AA03
M05, M06.0, M06.8, M07.0, M07.1, M07.3, M10.0, M10.9
J30 except J30.0
(DIAG) a and/or (MEDICINE) b all medicine prescriptions with either ATC: V01AA02, V01AA03; V01AA05, V01AA11; R01AC, R01AD, R06A, S01G, R01BA52.
Chronic obstructive pulmonary disease (COPD)
J40, J41, J42, J43, J44, J47, J96
(DIAG) a All patients of minimum 35 years of age at contact. And/or (MEDICINE1) b all medicine prescriptions with either ATC: R03AC, R03AK, R03BA, R03BB, R03CC, R03DA, R03DC, V03AN01. And/or (SERVICE10) c if patients have had a minimum of two lab services within the last 12 months. Lab services (80) 7113 (lung spirometer test), (80) 7121 (lung function test). MEDICINE and SERVICES are ignored if there are contacts with J45 or J46 diagnosis.
F00, G30, F01, F02.0, F03.9, G31.8B, G31.8E, G31.9, G31.0B
(DIAG) a All patients of minimum 60 years of age at contact and/or (MEDICINE) b all medicine with the prescriptions with the ATC N06D also for patients of minimum 60 years of age.
NB: Only one prescription (in contrast to two in the other algorithms)
F20, F21, F22, F25, F28, F29, F31
(DIAG) a and/or (MEDICINE) b all medicine prescriptions with either ATC: N05AX13, N05AX12, N05AH03, N05AX13, N05Ax08
NB: Only A-diagnosis not B-diagnosis
Long term use of antidepressants
(MEDICINE) b At least three medicine prescriptions with the ATC N06A.
NB: At least three prescriptions (instead of two) on deferent dates within the latest five year with at least 730 days (2 years) between the first and the last one.
Patients with a schizophrenia diagnosis or with a dementia diagnosis are excluded from this algorithm.
(DIAG) a and (MEDICINE) b At least three medicine prescriptions with the ATC N06A.
NB: At least three prescriptions (instead of two) on deferent dates within the latest five year with at least 730 days (2 years) between the first and the last one.
Patients with a depression diagnosis are excluded from this algorithm
E78.0, E78.2, E78.4, E78.5
(DIAG) a and/or (MEDICINE) b all medicine prescriptions with the ATC C10
I10, I11, I12, I13, I15
(DIAG) a and (MEDICINE) b all medicine prescriptions with either ATC: C07B, C03A, C03B, C03E, C03X and/or (MEDICINE) b all medicine prescriptions with either ATC: C03C, C03D, C07A, C09 IF the person DOES NOT have hospital or outpatient contact with the ICD-10 codes I20.0, I21, I25.1, I50 and/or two medicine prescriptions with ATC: C08 IF the person DOES NOT have a hospital or outpatient contact with the ICD-10 codes I20-I25
G45, G46, I60, I61, I62, I63, I64, I65, I66, I67, I68, I69
I20, I21, I23, I24, I25, I50, I11, I13
(DIAG) a and/or (MEDICINE) b all medicine prescriptions with either ATC: C01A, C01B, C01D, C01E
The analyses were based on Barnett et al.’s approach to presenting the prevalence of multimorbidity . Prevalence was calculated and depicted graphically. In addition, odds ratios for multimorbidity were derived from logistic regression, where presence or absence of multimorbidity was regressed on gender, age, age squared and educational level. Odds ratios for multimorbidity for each variable were then adjusted for all other variables. Similarly, odds ratios for mental health conditions were derived from logistic regression, where presence or absence of mental conditions was regressed on gender, age, age squared, education and number of physical conditions. Odds ratios for mental health conditions for each variable were then adjusted for all other variables. Age squared was added as an independent variable after observing a nonlinear age effect. Standard deviations (SD) for demographic variables were derived using the binomial formula, and 95% confidence intervals were calculated. Standard deviations for odds ratios were extracted from logistic regression models and used to construct 95% confidence intervals. All analyses were carried out using R software version 3.2.2 .
Demographics and prevalence of multimorbidity and physical and mental health comorbidity
Mean number of morbidities (SD)
Percent (95% CI) with multimorbidity
Percent (95% CI)
with physical and mental health comorbidity
Number of chronic conditions
Odds ratios and 95% CIs for any mental health condition by age, gender, socioeconomic status and number of physical conditions
Odds Ratio (95% CI)
Adjusted Odds Ratio (95% CI)a
Male (vs. female)
Lower secondary school
Number of physical conditions
Selected comorbidities in people with four common disorders by educational attainment
Persons with this condition
Persons who also have this condition (%)
Use of antidepressant medications
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, 25–27], and the multimorbidity prevalence we report is comparable to that reported by Orueta et al.  and Barnett et al. . Orueta et al. found that 23.6% of the total Basque population had two or more chronic conditions , and Barnet et al. found that 23.2% of all patients in Scotland had two or more chronic conditions , 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  and Barnet et al. used data from primary care to identify chronic conditions and included 40 conditions ; 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 . 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 . 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  and Ireland , 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 . 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  and the 7.9% reported by Bobo et al. in a US population . 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 . In the same population, McLean et al.  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 [31–33] 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 . 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 . In keeping with Barnett et al. , we found that comorbidities for heart disease, diabetes, chronic obstructive pulmonary disease or cancer were more common in people with low educational attainment . 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 .
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 ; 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].
Our results emphasize the magnitude of the prevalence of multimorbidity and reveal that prevalence patterns of multimorbidity found in this study are comparable to those of other European populations, which may indicate that the patterns are also likely to be found in other Western European countries. Furthermore, the results support evidence showing that variations in the number of chronic conditions used to calculate the prevalence of multimorbidity are immaterial.
Persons living with multimorbidity in the Danish Healthcare System often receive care for their different conditions from different health care providers . Often, mental health conditions are not taken into consideration when providing care for physical health conditions . The high proportion of the population that has physical and mental health comorbidity highlights the need to ensure that healthcare systems deliver care that takes both physical and mental health into account. Furthermore, the higher prevalence of multimorbidity among persons with low educational attainment emphasizes the importance of a healthcare system providing care that is beneficial to all, regardless of socioeconomic status and educational attainment. Finally, the findings highlight the need for additional research into life-course socioeconomic pathways associated with the absence of morbidity and the occurrence of multimorbidity later in life.
We thank Jennifer Green for skillful editing.
The 50 million Crown Foundation from The Capital Region of Denmark, which had no role in the design of the study, in collection, analysis and interpretation of data or writing the manuscript.
Availability of data and material
The data that support the findings of this study are available from Statistic Denmark but restrictions apply to the availability of these data, which were used under license for the current study, and so are not publicly available. Data are however available from the authors upon reasonable request and with permission of Statistic Denmark, The Danish National Board of Health Data and The Research Centre for Prevention and Health, The Capital Region.
MLS, DH and AF conceived the study, AS analyzed the data. MLS wrote the first and successive drafts of the manuscript, DH, AS and AF contributed to the writing of the manuscript. All authors critically revised the manuscript for important intellectual content and contributed to the interpretation of results. All authors have given final approval of the version to be published.
The authors declare that they have no competing interests.
Consent for publication
Ethics approval and consent to participate
Approval to conduct the study was obtained from the Danish Data Protection Agency. No informed consent was required.
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