Conceived and designed the experiments: APT BPP ACL JTAN. Performed the experiments: BPP JTAN. Analyzed the data: BPP JTAN. Contributed reagents/materials/analysis tools: LAGF FGR APF ASM. Wrote the paper: APT ACL LAGF FGR.
The authors have declared that no competing interests exist.
The primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The secondary objective of this study was to generate evidence regarding the pathophysiological processes underlying multimorbidity and to understand the interactions and synergies among the various diseases.
This observational, retrospective, multicentre study utilised information from the electronic medical records of 19 primary care centres from 2008. To identify multimorbidity patterns, an exploratory factor analysis was carried out based on the tetra-choric correlations between the diagnostic information of 275,682 patients who were over 14 years of age. The analysis was stratified by age group and sex.
Multimorbidity was found in all age groups, and its prevalence ranged from 13% in the 15 to 44 year age group to 67% in those 65 years of age or older. Goodness-of-fit indicators revealed sample values between 0.50 and 0.71. We identified five patterns of multimorbidity: cardio-metabolic, psychiatric-substance abuse, mechanical-obesity-thyroidal, psychogeriatric and depressive. Some of these patterns were found to evolve with age, and there were differences between men and women.
Non-random associations between chronic diseases result in clinically consistent multimorbidity patterns affecting a significant proportion of the population. Underlying pathophysiological phenomena were observed upon which action can be taken both from a clinical, individual-level perspective and from a public health or population-level perspective.
The simultaneous occurrence of various health problems in a given individual, which is defined as multimorbidity, has become the norm rather than the exception
Understanding the types of multimorbidity patterns that appear in the elderly, especially the chronic diseases that comprise these patterns, demands an analysis of what happens in the earlier stages of life, how those patterns are shaped from a young age, in what way these diseases are aggregated, which diseases are associated with each other and which are not, as well as the pathophysiological mechanisms that may explain these associations. An improved global understanding of the disease process and the interactions present in an affected individual, rather than the study of individual diseases, is essential so that health systems can design appropriate and timely strategies for raising awareness and improving preventative and clinical approaches to multimorbidity, which currently is a health problem in and of itself
Multimorbidity has extraordinary importance not only for the general population but also for healthcare systems. It has been shown to be associated with increased mortality
Despite these results, there are very few studies that have analysed multimorbidity in depth; for example, it is unknown to what degree multimorbidity is caused by chance, how multimorbidity patterns differ between age and sex groups, what the possible existence of index diseases is that would in turn give rise to new symptoms and diseases or what the roles of iatrogenic issues are in this phenomenon. Some of these aspects need to be addressed promptly.
There are three main methodological reasons for this lack of evidence:
First, there is uncertainty among the scientific community regarding the application of statistical methods for the exploration of simultaneously occurring health problems
Moreover, progress towards understanding the causes of multimorbidity requires a standardised conceptualisation of the various clinical entities that compose it, as the number and type of diseases together with the various ways of grouping them can significantly affect estimates of the prevalence of multimorbidity and the strength of the identified associations
Finally, the performance of large-scale population studies focused on this and other public health problems requires sources of information of proven quality that are accessible to the entire scientific community. In this regard, it is important to highlight the enormous potential of clinical-administrative databases, which are available as a result of the computerisation of medical records in both primary and specialised care. Compared with studies that have been based on survey results, these databases provide access to diagnostic information at the individual and population levels.
With respect to these three aspects, this work was based on a statistical methodology that allowed for the identification of statistically relevant patterns of multimorbidity. It applied a previously validated categorisation system for chronic diseases and used diagnostic information from primary care electronic medical records.
The primary objective of this study was to identify the existence of chronic disease multimorbidity patterns in the primary care population, describing their clinical components and analysing how these patterns change and evolve over time both in women and men. The secondary objective of this study was to generate evidence regarding the pathophysiological processes underlying multimorbidity and to understand the interactions and synergies among the various diseases.
The hypothesis was that there would be patterns of multimorbidity partially expected, predictable and explainable that evolve along patients' life. Such patterns would be composed of risk factors in the early stages of life, of organ disorders in the middle ages, and of disease-related complications in the later years of life. Healthcare systems should consider the resulting evidence when organising and planning their service offerings. Moreover, this study pretends to generate new hypotheses related to unknown causal mechanisms underlying multimorbidity such as iatrogenic effects.
This multicentre, retrospective, observational study was performed in primary care centres of the Spanish National Health System. From 1986 on, Spain has a tax-based health system with universal coverage for the entire population. The primary care model is based on multidisciplinary teams consisting of family physicians and nurses. The former coordinate prevention, health promotion, treatment, and community care and they also act as gatekeepers to the healthcare system and therefore to more specialized care. Each team has a geographically delimited population assigned to it and all types of patients and health problems, except for medical emergencies, are initially attended in primary care centres.
The selection of centres to participate in this study was conducted based on the following quality inclusion criteria: (a) centres with computerised records for all appointments and with more than two years of experience using this system by all physicians and nurses, (b) those with a percentage of uncoded episodes less than 20%, (c) those with a percentage of notes (e.g., prescription, observation, etc.) listed in uncoded episodes less than 15%, (d) those with a percentage of prescriptions linked to uncoded episodes less than 10%, (e) those with an average number of diagnoses higher than 3.5, and (f) those with a percentage of patients with no diagnostic information less than 10%.
After selecting the centres, all patients over the age of 14 who were seen at least once during 2008 by their family doctor were included in the study. The study population was composed of 275,682 individuals belonging to 19 urban health centres (7 in Aragón and 12 in Catalonia). For each of the patients included in the study, demographic and diagnostic variables were extracted from electronic medical records, including the age of the patient as of December 31, 2008, the sex of the patient, and the chronic diagnoses coded according to the International Classification of Primary Care (ICPC)
To facilitate the management of the diagnostic information, the episodes were grouped according to the Expanded Diagnostic Clusters (EDC) of the ACG® system. To use this system, a conversion from the ICPC to the International Classification of Diseases (ICD-9-CM)
The selection of which chronic diseases to study was based on the recent study by Salisbury et al.
This study was favourably evaluated by the Clinical Research Ethics Committee of Aragon (CEICA). Written consent by patients was not needed since the study does not involve interventions on individuals, the use of human biological samples, or the analysis of personally identifiable data. Instead, the present work is based on the statistical analysis of anonymous data contained in previously existing databases which were obtained with prior permission from the corresponding entity.
A descriptive analysis of the population was performed by calculating the frequencies of demographic variables. To facilitate the analysis of age, three age ranges were created: 15–44, 45–64 and 65 and over. The remaining analyses were performed separately for each age and sex group. To provide an initial description of the prevalence of multimorbidity, the frequency of EDCs per patient for each group of age and sex was calculated.
To determine the associations between diseases that exhibited multimorbidity patterns, an exploratory factor analysis was applied. The aim of this method was to identify sets of variables with a common underlying causal factor. This technique was chosen because, in addition to identifying associations among groups of variables, it allowed for variables to be included in the various patterns.
To increase the epidemiological interest of the study, only those EDCs with a prevalence equal to or greater than 1% in each group were included (
The extraction of the disease factors was performed using the principal factor method, and it was assumed that the extracted factors did not explain the total variance of the analysed EDCs. To determine the number of factors to extract, a scree plot was utilised in which the eigenvalues of the correlation matrix were represented in descending order. The number of factors extracted corresponds to the sequence number of the eigenvalue that produces the inflection point of the curve
The adequacy of the sample used to perform the factor analysis was analysed by measuring the Kaiser-Meyer-Olkin (KMO) for each age and sex group. This parameter takes values between 0 and 1, which are closer to 1 with a greater goodness of fit. In addition, as a measure of model goodness-of-fit, the proportion of cumulative variance was obtained, which describes the variability of the diagnostic data explained by the patterns.
For determining which EDCs formed each multimorbidity pattern, those with scores equal to or greater than 0.25 for each factor were selected.
Given that multimorbidity is defined as the simultaneous presence of two or more chronic diseases
STATA 11.0 software was used to conduct the statistical analysis, and Excel 2007 was used to prepare the graphs.
Clinical relevance was studied in three phases. First, two qualified primary care physicians with research and clinical experience independently reviewed the clinical plausibility of disease interactions. Next, a joint analysis of both independent reports was performed by a third physician specialized in public health and experienced in health services research, identifying agreements and disagreements between both family physicians. Last, results were resubmitted to the primary care professionals and discussed in a final consensus meeting. The main arguments and conclusions were also contrasted with a literature review by other members of the research team.
The study population consisted of 275,682 patients over the age of 14, and 56.6% of this population was female.
15–44 years | 45–64 years | ≥65 years | ||||
n | % | n | % | n | % | |
Men | 54,705 | 19.8 | 35,587 | 12.9 | 29,361 | 10.7 |
Women | 66,540 | 24.1 | 46,035 | 16.7 | 43,454 | 15.8 |
Multimorbidity existed in all of the studied groups (
15–44 years | 45–64 years | ≥65 years | ||||||||||
Men | Women | Men | Women | Men | Women | |||||||
No_ chronic EDCs | N | % | n | % | n | % | n | % | n | % | n | % |
0 | 32,422 | 59.27 | 34,824 | 52.34 | 9,940 | 27.93 | 10,413 | 22.62 | 3,486 | 11.87 | 4,642 | 10.68 |
1 | 16,293 | 29.78 | 20,911 | 31.43 | 11,683 | 32.83 | 14,021 | 30.46 | 6,855 | 23.35 | 8,687 | 19.99 |
2 | 4,442 | 8.12 | 7,403 | 11.13 | 7,291 | 20.49 | 10,065 | 21.86 | 6,948 | 23.66 | 9,703 | 22.33 |
3 | 1,132 | 2.07 | 2,370 | 3.56 | 3,871 | 10.88 | 6,140 | 13.34 | 5,345 | 18.2 | 8,306 | 19.11 |
4 | 299 | 0.55 | 737 | 1.11 | 1,703 | 4.79 | 3,075 | 6.68 | 3,425 | 11.67 | 5,775 | 13.29 |
5 | 85 | 0.16 | 205 | 0.31 | 687 | 1.93 | 1,406 | 3.05 | 1,785 | 6.08 | 3,374 | 7.76 |
6 | 23 | 0.04 | 76 | 0.11 | 250 | 0.7 | 575 | 1.25 | 829 | 2.82 | 1,649 | 3.79 |
7 | 5 | 0.01 | 10 | 0.02 | 102 | 0.29 | 214 | 0.46 | 414 | 1.41 | 759 | 1.75 |
8 | 3 | 0.01 | 4 | 0.01 | 46 | 0.13 | 80 | 0.17 | 169 | 0.58 | 311 | 0.72 |
9 | 1 | 0 | – | – | 10 | 0.03 | 36 | 0.08 | 74 | 0.25 | 153 | 0.35 |
10 | – | – | – | – | 3 | 0.01 | 7 | 0.02 | 17 | 0.06 | 64 | 0.15 |
11 | – | – | – | – | – | – | 2 | 0 | 11 | 0.04 | 21 | 0.05 |
12 | – | – | – | – | 1 | 0 | 1 | 0 | 1 | 0 | 6 | 0.01 |
13 | – | – | – | – | – | – | – | – | – | – | 4 | 0.01 |
14 | – | – | – | – | – | – | – | – | 2 | 0.01 | – | – |
Total | 54,705 | 100 | 66,540 | 100 | 35,587 | 100 | 46,035 | 100 | 29,361 | 100 | 43,454 | 100 |
This age and sex group had a KMO sampling adequacy index of 0.66 and a cumulative variance percentage of 26.87% (
15–44 years | 45–64 years | ≥65 years | ||||
Men | Women | Men | Women | Men | Women | |
KMO | 0.66 | 0.71 | 0.50 | 0.68 | 0.57 | 0.68 |
% Accumulated Variance | 26.87 | 24.72 | 14.80 | 23.70 | 22.81 | 24.68 |
Chronic EDCs | Factor1 | Factor2 |
|
|
|
|
|
0.13 |
|
|
0.05 |
|
|
|
|
0.03 |
|
|
−0.08 |
|
|
0.01 |
|
Asthma, w/o status asthmaticus | −0.10 | 0.04 |
Thyroid disease | 0.14 | 0.15 |
Arthropathy | 0.12 | −0.03 |
Cervical pain syndromes | 0.14 | 0.02 |
Low back pain | 0.04 | −0.02 |
Dermatitis and eczema | 0.09 | 0.03 |
Note: factor scores ≥0.25 have been highlighted in bold.
For this age and sex group, a KMO value of 0.50 was obtained with a cumulative variance percentage of 14.80% (
Factor1 | Factor2 | |
|
|
−0.08 |
|
|
−0.08 |
|
|
−0.01 |
|
|
−0.03 |
|
|
0.14 |
|
|
−0.07 |
|
|
−0.06 |
|
|
|
|
|
−0.14 |
|
|
0.09 |
|
−0.08 |
|
|
−0.05 |
|
|
0.01 |
|
|
0.08 |
|
|
0.02 |
|
|
−0.02 |
|
Disorders of lipoid metabolism | 0.24 | 0.17 |
Haematologic disorders, other | 0.21 | 0.02 |
Asthma, w/o status asthmaticus | −0.08 | 0.23 |
Cervical pain syndromes | −0.01 | 0.22 |
Dermatitis and eczema | 0.05 | 0.23 |
Cardiovascular disorders, other | 0.19 | 0.02 |
Thyroid disease | 0.19 | 0.06 |
Glaucoma | 0.10 | 0.10 |
Renal calculi | 0.06 | 0.15 |
Peripheral neuropathy, neuritis | 0.09 | 0.12 |
Anxiety, neuroses | 0.17 | 0.10 |
Schizophrenia and affective psychosis | 0.02 | −0.08 |
Gout | 0.16 | 0.17 |
Psoriasis | 0.04 | 0.06 |
Note: factor scores ≥0.25 have been highlighted in bold.
For this age and sex group, a KMO value of 0.57 was obtained with a cumulative variance percentage of 22.81% (
Factor1 | Factor2 | Factor3 | |
|
|
|
0.03 |
|
|
0.19 | 0.09 |
|
|
0.17 | 0.08 |
|
|
−0.10 | 0.09 |
|
|
0.03 | −0.09 |
|
|
|
0.15 |
|
|
−0.07 | 0.01 |
|
|
−0.22 | 0.04 |
|
|
−0.08 | 0.03 |
|
|
0.05 | 0.00 |
|
−0.21 |
|
|
|
0.02 |
|
0.00 |
|
−0.08 |
|
0.10 |
|
0.04 |
|
0.04 |
|
0.23 |
|
−0.15 |
|
0.20 |
|
0.00 |
|
−0.06 | 0.09 |
|
|
−0.07 | −0.04 |
|
|
0.03 | −0.02 |
|
|
−0.03 | −0.06 |
|
|
−0.01 | −0.02 |
|
|
0.03 | 0.20 |
|
|
0.00 | 0.04 |
|
Acute myocardial infarction | 0.23 | −0.02 | −0.06 |
Ischemic heart disease (excluding infarction) | 0.20 | 0.04 | 0.18 |
Thyroid disease | 0.06 | 0.16 | 0.20 |
Emphysema, chronic bronchitis, COPD | 0.15 | 0.12 | 0.24 |
Malignant neoplasms, colorectal | 0.07 | 0.07 | 0.00 |
Disorders of lipoid metabolism | 0.09 | −0.17 | 0.17 |
Malignant neoplasms, prostate | −0.03 | 0.19 | −0.02 |
Peripheral neuropathy, neuritis | 0.19 | 0.01 | 0.15 |
Cataract, aphakia | 0.09 | −0.05 | 0.19 |
Glaucoma | 0.04 | 0.00 | 0.11 |
Psoriasis | 0.04 | −0.08 | 0.11 |
Deafness, hearing loss | −0.04 | −0.11 | 0.14 |
Varicose veins of lower extremities | 0.05 | 0.05 | 0.19 |
Low impact malignant neoplasms | 0.00 | 0.05 | 0.14 |
Asthma, w/o status asthmaticus | −0.12 | −0.08 | 0.18 |
Note: factor scores ≥0.25 have been highlighted in bold.
The sampling adequacy of this group had a KMO value of 0.71 with a cumulative variance percentage of 24.72% (
Factor1 | Factor2 | |
|
|
0.16 |
|
|
0.04 |
|
|
0.09 |
|
−0.02 |
|
|
0.03 |
|
|
0.09 |
|
|
−0.04 |
|
|
0.04 |
|
|
0.03 |
|
|
−0.09 |
|
|
0.14 |
|
Low back pain | 0.05 | 0.22 |
Anxiety, neuroses | 0.10 | 0.22 |
Asthma, w/o status asthmaticus | 0.18 | −0.02 |
Note: factor scores ≥0.25 have been highlighted in bold.
The sampling adequacy of this group had a KMO value of 0.68 with a cumulative variance percentage of 23.70% (
Factor1 | Factor2 | Factor3 | |
|
|
−0.11 | 0.02 |
|
|
−0.10 | 0.02 |
|
|
0.16 | 0.03 |
|
0.07 |
|
−0.16 |
|
0.05 |
|
0.04 |
|
0.01 |
|
0.07 |
|
−0.07 |
|
0.10 |
|
0.02 |
|
0.16 |
|
−0.01 |
|
−0.16 |
|
−0.03 |
|
−0.14 |
|
0.02 |
|
0.05 |
|
−0.02 | 0.20 |
|
|
−0.01 | 0.06 |
|
Asthma | 0.03 | 0.18 | −0.01 |
Disorders of lipoid metabolism | 0.21 | 0.19 | −0.08 |
Cardiovascular disorders, other | 0.07 | 0.17 | 0.02 |
Other endocrine disorders | 0.05 | 0.03 | 0.21 |
Glaucoma | 0.16 | 0.12 | 0.11 |
Iron deficiency, other deficiency anaemia | −0.02 | 0.17 | −0.02 |
Haematologic disorders, other | 0.08 | 0.13 | −0.06 |
Low impact malignant neoplasms | 0.00 | 0.12 | −0.33 |
Arthropathy | 0.09 | 0.21 | 0.19 |
Peripheral neuropathy, neuritis | 0.03 | 0.23 | 0.06 |
Note: factor scores ≥0.25 have been highlighted in bold.
The sampling adequacy of this group had a KMO value of 0.68 with a cumulative variance percentage of 24.68% (
Factor1 | Factor2 | Factor3 | Factor4 | |
|
|
−0.16 | 0.04 | −0.06 |
|
|
−0.07 | −0.17 | −0.07 |
|
|
0.01 | 0.02 | −0.13 |
|
|
0.13 | −0.14 | 0.09 |
|
|
0.14 | −0.08 | 0.01 |
|
|
0.03 | 0.05 | −0.02 |
|
|
−0.04 | 0.09 | 0.00 |
|
|
−0.09 | −0.02 | 0.01 |
|
|
−0.06 | 0.05 | 0.07 |
|
|
0.18 | −0.07 | −0.13 |
|
|
0.02 | 0.03 | 0.04 |
|
−0.04 |
|
|
0.03 |
|
−0.02 |
|
|
−0.14 |
|
0.10 |
|
0.14 | 0.00 |
|
−0.03 |
|
0.08 | 0.08 |
|
−0.01 |
|
−0.13 | 0.00 |
|
−0.16 |
|
0.10 | 0.02 |
|
0.09 | 0.18 |
|
−0.06 |
|
0.11 | −0.08 |
|
0.03 |
|
−0.13 | 0.04 |
|
−0.02 |
|
0.01 | 0.12 |
|
0.05 |
|
0.13 | −0.25 |
|
0.13 |
|
0.17 | 0.19 | −0.06 |
|
|
0.06 | −0.06 | 0.05 |
|
Asthma, w/o status asthmaticus | 0.23 | −0.02 | 0.12 | −0.01 |
Cataract, aphakia | 0.22 | −0.01 | 0.06 | 0.09 |
Glaucoma | 0.20 | −0.01 | 0.07 | 0.02 |
Generalised atherosclerosis | 0.07 | 0.23 | 0.09 | −0.14 |
Cardiovascular disorders, other | 0.07 | 0.10 | 0.21 | 0.09 |
Deafness, hearing loss | 0.17 | 0.03 | 0.02 | 0.13 |
Peripheral neuropathy, neuritis | 0.15 | −0.05 | 0.10 | −0.17 |
Emphysema, chronic bronchitis, COPD | 0.15 | 0.18 | 0.05 | 0.00 |
Low impact malignant neoplasms | 0.11 | 0.10 | −0.05 | −0.33 |
Other endocrine disorders | 0.10 | 0.00 | 0.14 | 0.18 |
Note: factor scores ≥0.25 have been highlighted in bold.
Most of the studied diseases were present in one single pattern, and only 6 of the 37 diseases that comprised the 5 patterns were associated with more than one pattern; these included lipid metabolism disorders (in men), obesity (in men), congestive heart failure (in women and men), osteoporosis (in men), cardiac arrhythmia (in women) and iron deficiency (in men).
This study revealed five specific clinically consistent patterns of multimorbidity in the adult population: cardio-metabolic, psychiatric-substance abuse, mechanical-obesity-thyroidal, psychogeriatric and depressive. Two of them were found to evolve throughout life and differed in their presentation for men and women (i.e., cardio-metabolic and mechanical-obesity-thyroidal), three of them affected both sexes (i.e., cardio-metabolic, mechanical-obesity-thyroidal and psychogeriatric), and two of them were present exclusively in men or women (i.e., psychiatric-substance abuse and depressive, respectively) (
Men | Women | |||
Multimorbidity pattern | Prevalence (%) | Multimorbidity pattern | Prevalence (%) | |
15–45 years | Cardio-metabolic | 0.93 | cardio-metabolic | 0.38 |
psychiatric-substance abuse | 1.53 | mechanical-obesity-thyroidal | 2.69 | |
45–64 years | Cardio-metabolic | 9.20 | cardio-metabolic | 4.05 |
mechanical-obesity-thyroidal | 4.86 | mechanical-obesity-thyroidal | 11.69 | |
depressive | 0.12 | |||
≥65 years | Cardio-metabolic | 20.25 | cardio-metabolic | 33.30 |
mechanical-obesity-thyroidal | 1.67 | mechanical-obesity-thyroidal | 3.48 | |
psychogeriatric | 13.56 | psychogeriatric | 17.30 | |
depressive | 0.16 |
Those two patterns suggesting an evolution throughout life were composed mainly of risk factors in the youngest age group, of organ disorders in the middle-aged group, and of various disease-related complications in the eldest group.
Although there are many examples in the literature of the association between the specific diseases that comprise these patterns, there have been very few population-wide published studies on multimorbidity including the non-elderly population.
This multimorbidity pattern was present in both sexes and for all of the age ranges analysed. Although it was especially prevalent among individuals 65 years of age or older (i.e., one in three women and one out of every five men in this age group), the high prevalence of this pattern in the middle-aged groups should be noted (i.e., 22% in women and about 10% in men).
Its development in men and women was consistent with the disease pathophysiology. In young patients, it was manifested as diabetes, hypertension, obesity and dyslipidaemia
In men, this pattern presented at a young age and with a possible common pathophysiological basis of insulin resistance, obesity and their associated inflammatory processes, and negative lifestyles (e.g., physical inactivity, poor diet, etc.). During middle age, ischemic heart disease, atherosclerosis, myocardial infarction, arrhythmias, dyslipidaemia (with a factor score very close to the established cut-off point), substance abuse, COPD and chronic liver disease were also observed within this pattern. Although this study did not have access to specific information regarding tobacco smoking, it is likely that its prevalence was high in those cases where the abuse of other substances was also high (this information was available). The alcohol-tobacco association could have been the probable cause of the diseases observed, such as chronic liver disease and other related associations. In addition, there are possible underlying iatrogenic causes due to the use of drugs such as statins and fibrates, which can cause the elevation of transaminases at medium and high dosages
The presence and the evolution of this cardio-metabolic pattern revealed differences between women and men. In women, although hypertension, obesity and dyslipidaemia were present at a young age, diabetes did not appear until the age range 45–64. Moreover, in this second age group, the expected cardiac complications were not observed, contrary to what happened in men of the same age. Oestrogenic protection and reduced smoking have been previously reported as the primary reasons for reduced cardiac complications in women
This multimorbidity pattern appeared only in young men and was found to affect 2% of the individuals studied (n = 837). This pattern consisted of psychopathological processes, such as psychosis and neurosis, which are both likely related to the toxic substance abuse that is also present within this pattern and that commonly affects men at this stage of life
In men, this was a complex pattern that could also be denominated mechanical-obesity-thyroidal. It affected 5% of middle-aged individuals and was found to decrease to 2% among individuals aged 65 and over. For men aged 45–64, it is likely that obesity acted as an index disease that favoured the emergence of mechanical disorders due to excess body weight, such as arthropathy, cervical and low back pain, varicose veins of lower extremities, and gastro-oesophageal reflux
In women, the mechanical pattern was manifest somewhat differently than in men. It appeared early in life and it was never associated with obesity but rather with thyroid problems, likely in the form of hypothyroidism
This multimorbidity pattern appeared in individuals 65 years of age and older and was the second-most prevalent pattern after cardio-metabolic. It was more common in women (17%) than men (14%) and was manifest in the form of dementia, behavioural problems, Parkinson's disease, osteoporosis, chronic skin ulcers and iron deficiency. Heart failure and stroke showed factor scores very close to the established cut-off-point and are among the leading causes of dementia
The presence of dementia and Parkinson's disease together with age-related osteoporosis can lead to falls, fractures, the immobility of the patients and the appearance of skin ulcers caused by bed rest among these patients
In women, this pattern was slightly different than in men: dementia, cerebrovascular disease and chronic skin ulcers were present. In addition, heart failure, iron deficiencies and cardiac arrhythmia (mostly in the form of atrial fibrillation) were present in this pattern, and these were clearly associated with and were the cause of cerebrovascular disease
This multimorbidity pattern consisted of only two conditions: depression and behavioural disorders (mainly insomnia). These were strongly associated with one another and were present only in women aged 45–64 and 65 years and over. This was the least prevalent of the five patterns described, as the frequency was less than 0.2% in both age groups. The association between these two clinical conditions has been widely described
The two main aspects that influence result-stability are the nature and number of individuals and diseases that are included in the analysis
Regarding the statistical methodology used, the exploratory factor analysis applied in this study is the preferred method when the objective is to explore statistically significant stable disease clusters
It is noteworthy that this study met most of these standards. The decision to reduce the minimum factor score to 0.25 (or even 0.20) was due to the expectation that there would be a significant number of associations among diseases due to chance (i.e., concurrent multimorbidity). Therefore, a more permissive threshold was established. On the other hand, despite the fact that the depressive pattern comprised only two diseases (depression and behavioural disorders, such as insomnia), this pattern has been previously identified in the literature
Although several hypothesis have been put forward on the pathophysiological processes underlying the five multimorbidity patterns brought to light in this study, the former must be interpreted with the necessary caution since the study design (i.e. transversal) does not allow to establish the sequence in which diseases cluster within a pattern. Longitudinal studies would be required to corroborate the suggested causal associations as well as to help elucidate those disease associations that could not be explained in the present study.
As stated earlier, this study was based on information that was recorded in the primary care electronic medical record system during medical visits. Although there are many benefits of this methodology, it can also limit the data. The workload of healthcare professionals and the structure of the applied diagnostic coding system (i.e. ICPC) often lead certain information regarding disease history not to be recorded in the individual patient medical histories. Therefore, the frequencies of many diseases are often underestimated. We suspect that this may have occurred for smoking, which is systematically underreported in a population with a very high prevalence of smokers
A potential overrepresentation of certain diagnoses may exist when these are associated with other diseases for which treatment protocols recommend periodic health reviews. In other words, the higher frequency of visits by patients with these diseases would affect the likelihood that associated diseases were diagnosed (i.e., observer bias). To avoid this bias, future studies should be based on the entire population rather than solely on those users of the primary care services.
It is important to note that this work focused on chronic diseases. Although this may ensure comparability with other large studies across different international contexts, future research should consider including the whole range of diseases seen in patients. This would be especially true where the boundaries between “chronic” and “acute” diseases are not always clear, as was demonstrated by Starfield
Although the different age groups were defined according to the expected biological homogeneity of individuals among groups, the selected thresholds could have influenced the nature of the obtained multimorbidity patterns. The use of different and/or narrower age groups may be advisable in future studies.
To conclude with the potential limitations of this study, it should be noted that this study focused on the occurrence and the simultaneous association of diseases that were defined and registered from the perspective of the professionals caring for the patient and not by the patient him/herself. It is possible that many of the disease manifestations affecting the quality of life of the patient are not met by the defined criteria. Understanding the diseases from this perspective would require complementary methodological approaches that are beyond the scope of this work.
It is difficult to compare these results with those of other studies because of differences in the disease inclusion criteria, the study populations and the data sources used. This study was also the first to extend the analysis to age groups under 65. The study by Schäfer et al.
Despite the significance of multimorbidity, especially in older individuals, the scientific evidence provided by the clinical practice guidelines is not appropriate for patients with multiple diseases
From the standpoint of healthcare management, strong and developed primary care is the best solution to the complex needs of patients with multimorbidity
Moreover, the importance of other approaches beyond those based on health services, which are needed to ensure the health of the population, should be stressed. For example, the promotion of a healthy lifestyle is closely linked to a decline in the incidence of obesity
Finally, a change in the focus of etiologic research is required for the study of diseases and their relationship to joint presentations, and an analysis of underlying factors, including pathophysiological, genetic, iatrogenic and/or socioeconomic factors should be promoted.
The results of this work have demonstrated that the existence of non-random associations between chronic diseases is a reality for the entire population, not only the elderly.
These disease associations give rise to clinically consistent multimorbidity patterns that affect a significant proportion of the population. Most importantly, when studying the lifetime disease process, there are underlying pathophysiological phenomena upon which action can be taken both from a clinical, individual-level perspective and from a public health or population-level perspective.
Knowledge regarding the main disease patterns and their evolution with age should facilitate the design of tools, such as clinical practice guidelines, treatment protocols for patients with multimorbidity and healthcare information systems with alarm signals to monitor for the duplication of visits and diagnostic tests, the incidence of polypharmacy and potential adverse drug reactions, inappropriate hospitalisations and even mortality.
All information available in the health systems should be properly managed to maximise the individual attention given to these patients and to ensure the efficient use of resources. This will positively impact the overall health of the population.
The integration of information is a basic strategy that should be accompanied by structural and functional measures that promote coordination between different actors and levels of the care and that ensure safe and adequate communication among the professionals who care for such patients.
Multimorbidity is a public health problem that must be considered by the planning and organisational frameworks of the health system. This work aimed to provide clinically relevant information to improve this process.
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We would have liked to share this work with Barbara Starfield, as she inspired and supported it until a few days before her death.