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Article

Changes in Multimorbidity and Polypharmacy Patterns in Young and Adult Population over a 4-Year Period: A 2011–2015 Comparison Using Real-World Data

by
Sara Mucherino
1,2,†,
Antonio Gimeno-Miguel
3,4,†,
Jonas Carmona-Pirez
3,4,
Francisca Gonzalez-Rubio
3,4,5,
Ignatios Ioakeim-Skoufa
3,5,6,
Aida Moreno-Juste
3,4,
Valentina Orlando
1,2,
Mercedes Aza-Pascual-Salcedo
3,4,
Beatriz Poblador-Plou
3,4,
Enrica Menditto
1,2,*,‡ and
Alexandra Prados-Torres
3,4,‡
1
CIRFF, Center of Drug Utilization and Pharmacoeconomics, University of Naples Federico II, 80131 Naples, Italy
2
Department of Pharmacy, University of Naples Federico II, 80131 Naples, Italy
3
EpiChron Research Group, Aragon Health Sciences Institute (IACS), IIS Aragón, Miguel Servet University Hospital, 50009 Zaragoza, Spain
4
Health Services Research on Chronic Patients Network (REDISSEC), ISCIII, 28029 Madrid, Spain
5
Drug Utilization Work Group, Spanish Society of Family and Community Medicine (SemFYC), 28004 Madrid, Spain
6
Vaksinasjonssenter BSN, Bydel Søndre Nordstrand, Oslo Kommune, 1252 Oslo, Norway
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work and served as co-first authors.
These authors also contributed equally to this work and served as co-lead authors.
Int. J. Environ. Res. Public Health 2021, 18(9), 4422; https://doi.org/10.3390/ijerph18094422
Submission received: 22 March 2021 / Revised: 14 April 2021 / Accepted: 15 April 2021 / Published: 21 April 2021

Abstract

:
The pressing problem of multimorbidity and polypharmacy is aggravated by the lack of specific care models for this population. We aimed to investigate the evolution of multimorbidity and polypharmacy patterns in a given population over a 4-year period (2011–2015). A cross-sectional, observational study among the EpiChron Cohort, including anonymized demographic, clinical and drug dispensation information of all users of the public health system ≥65 years in Aragon (Spain), was performed. An exploratory factor analysis, stratified by age and sex, using an open cohort was carried out based on the tetra-choric correlations among chronic diseases and dispensed drugs during 2011 and compared with 2015. Seven baseline patterns were identified during 2011 named as: mental health, respiratory, allergic, mechanical pain, cardiometabolic, osteometabolic, and allergic/derma. Of the epidemiological patterns identified in 2015, six were already present in 2011 but a new allergic/derma one appeared. Patterns identified in 2011 were more complex in terms of both disease and drugs. Results confirmed the existing association between age and clinical complexity. The systematic associations between diseases and drugs remain similar regarding their clinical nature over time, helping in early identification of potential interactions in multimorbid patients with a high risk of negative health outcomes due to polypharmacy.

1. Introduction

Polypharmacy is referred to as the concurrent use of multiple drugs, and it can be the natural consequence of multimorbidity, more often intended as the coexistence of two or more chronic diseases [1]. However, inappropriate polypharmacy increases the risk of unnecessary drug use, potential drug–drug and drug–disease interactions, and adverse drug reactions (ADRs) [2,3], representing an economic and public health issue related to the quality and efficiency of health care [4,5]. The lack of development of specific care models for this population aggravates multimorbidity and polypharmacy [6].
Large-scale population studies based on real-world data represent an excellent opportunity to analyze the complexity of drug prescribing and clinical conditions and allow us to investigate the existence of systematic associations among drugs and diseases [7,8,9,10]. Factor analysis can improve the understanding of multimorbidity and polypharmacy in a real-world context. In 2015, we conducted a study that revealed the existence of systematic associations among chronic diseases and dispensed drugs, identifying up to six patterns of multimorbidity and polypharmacy [11]. Hence, this study aims to compare the baseline epidemiological patterns of multimorbidity and polypharmacy of the EpiChron Cohort in 2011 with those published in 2015 and to describe the clinical evolution of the clinical clusters identified.

2. Materials and Methods

2.1. Design, Study Population, and Variables

We performed an observational, cross-sectional study in the EpiChron Cohort [12]. This cohort includes the anonymized demographic, drug dispensation and clinical information of 98% of users of the public health system in Aragon, Spain (about 1.3 million inhabitants). We collected data from 2011 and compared them with previously published data from 2015 [11] in order to make a 4-year comparison.
The study population included all the subjects living in the Aragon region up to 65 years of age who were users of the public health system. Patients aged 65 and older were excluded from the study to allow for focus on young and adult populations for reasons already explained [11]. We stratified the population by sex and into three age groups: 0–14, 15–44 and 45–65 years, as for the previous analysis to compare the same age groups. For each subject, we analyzed all the diagnoses of chronic diseases from primary care and hospital electronic health records and all dispensed drugs from pharmacy billing records during 2011.
Diagnoses were coded initially based, first on the International Classification of Primary Care (ICPC) and then converted to codes of the International Classification of Diseases 9th Revision (ICD-9). Finally, they were grouped in the Expanded Diagnostic Clusters (EDC) of the ACG System (version 11.0, The Johns Hopkins University, Baltimore, MD, USA). We included in the analysis all 114 diseases classified as chronic by Salisbury et al. [13] and coded in binary format (i.e., presence/absence of the disease). As in the 2015 study, we also included rhinitis, following the World Health Organization (WHO) indications [14], and acute lower respiratory tract infection, as it can generate chronic sequelae. We classified dispensed drugs according to their Anatomical Therapeutic Chemical (ATC) code at the third level and included chronic and acute drug dispensation with a prevalence of at least 3% in 2015. The Clinical Research Ethics Committee of Aragón (CEICA) approved the study (ethical approval code: PI18/041) and waived the requirement for patient consent, since data of the EpiChron Cohort are anonymized, and no interventions on individuals were performed.

2.2. Statistical Analyses

As we used an open cohort, we performed a descriptive analysis of both 2011 and 2015 populations by describing demographic and clinical information expressed as frequencies, means, standard deviations (SD), and medians. We compared differences between patient characteristics using the chi-squared test for categorical variables or the unpaired t-test for numerical variables, as appropriate, considering statistically significant a p value < 0.05. Patients’ characteristics compared were age, area of living, immigrant status, deprivation index and number of chronic diseases, multimorbidity, and number of drugs related to the reference year. The deprivation index is strictly related to the census section of subjects, which represents the degree of deprivation from the lowest (Q1) to the highest (Q4) of the administrative health area to which it belongs.
An exploratory factor analysis was performed to identify multimorbidity and polypharmacy patterns according to a correlation matrix to decide which diagnoses and dispensed drugs comprised each pattern. Tetra-choric correlation matrices were used due to the dichotomous nature of both chronic diagnoses and administrated medicines. We performed factor extraction based on the principal factor method. We also applied an oblique rotation (Oblimin) to facilitate factor interpretation.
Scree plots were used to decide the number of factors to extract in each group. To determine which codes formed each pattern, we included those with scores >0.30 for each factor. This is the threshold factor loading traditionally used when deciding whether to accept a variable as belonging to a factor [11]. Nonetheless, as done in the previous study, EDCs and ATC codes with scores between 0.25 and 0.30 were included in a factor if considered relevant in the clinical explanation of the pattern.
As done in the previous work [11], we included EDCs with a prevalence >1–2% and ATC codes with a prevalence >3–5% in each age and sex group. Some ATCs with lower prevalence were also covered based on their potential relevance for interactions or side effects. The inclusion and exclusion criteria of EDCs and ATC codes used for each sex and age group were the same, explicitly explained in the 2015 study [11]. We used this prevalence threshold to increase the epidemiological interest of the study, and for statistical reasons regarding collinearity amongst some of the studied variables. The order of factors depends on the prevalence of its components. ATCs and EDCs with higher prevalence values will be identified in the first factors.
We evaluated sample adequacy using the Kaiser–Meyer–Olkin (KMO) test. We only considered values >0.60 as acceptable. Moreover, we calculated the proportion of cumulative variance as a measure of the model’s goodness-of-fit. This measurement describes the data variability explained by the patterns. We conducted all statistical analyses in STATA (version 12.0, StataCorp LLC, College Station, TX, USA).

2.3. Differences in the Clinical Patterns Evaluation Process

Once we obtained the data, the clinical nature of the patterns identified, and the comparability of the patterns over the 4-year period analyzed, we identified the presence of potential interactions between diseases and drugs within the patterns and the substantial differences observed. The associations found in each pattern were independently reviewed by three pharmacists (E.M., V.O., and S.M.) and seven physicians (F.G.R., M.A.S., A.M.J., A.J.M., I.I.S., J.C.P. and A.P.T.) from the research team. Subsequently, a consensus meeting was held to discuss and analyze the differences that existed at the turn of four years. We retained the names of the clusters given in the previous published study with 2015 data, wherever possible, to ensure a better reading of the difference over the years. Finally, the differences observed between 2011 and 2015 were compared with existing literature.

3. Results

Subjects identified up to 65 years old in the Aragon region were 1,000,390 during 2011 and 887,572 during 2015. Comparison and description of demographic and clinical characteristics of the two study populations are shown in Table 1 for women and Table 2 for men. Firstly, for both the years 2011 and 2015, we detected a statistically significant increase in the number of drugs and chronic conditions for both sexes as age increases.

3.1. Comparison of Multimorbidity and Polypharmacy Patterns

All the six epidemiological patterns identified in 2015 were also maintained during 2011, named as respiratory, mental health, cardiometabolic, endocrinological, osteometabolic, and mechanical pain. In addition, a new one appeared in 2011 mainly in younger age groups, recognized as an allergic/derma factor. Comparison of multimorbidity and polypharmacy patterns are detailed in Table 3.

3.1.1. Girls Aged 0–14 Years

Scree plot identified four factors during 2011 versus three during 2015 (Table 4). Factors identified in 2015 in girls in this age group were generally already present in 2011, but with the addition of an allergic/derma component recognized in 2011 and not maintained in 2015. In contrast, the first factor of 2015 was identified as respiratory/acute infection due to the presence of acute lower respiratory tract infection conditions and anti-infectives, corticosteroids, antifungals, and antibiotics. Second factors were similar in both years, having respiratory/asthmatic character due to the equal presence of asthma but differed for drugs-related such as adrenergics and corticosteroids for 2011 and antihistamines and decongestants for 2015. The third factor, the allergic one, with allergic rhinitis and antihistamines and decongestants, appeared only in 2011 in the pediatric population. The last factor identified as mental health remained unchanged over the years due to the presence of developmental disorders and psychosocial disorders of childhood as frequent childhood mental conditions. The KMO sampling adequacy index was 0.72 in 2011 and 0.73 in 2015, while a cumulative variance percentage was of 34.0% in 2011 and 33.2% in 2015.

3.1.2. Women Aged 15–44 Years

In women in this age group, the epidemiological factors identified in 2015 were already similar in 2011 but appearing less complex (Table 4). Mechanical pain factor was identified in 2011, factor not maintained during 2015, characterized by low back pain as the only condition and drugs such as opioids, muscle relaxants, NSAID. Other factors identified are comparable, such as the respiratory one, which includes asthma, allergic rhinitis, acute lower respiratory tract infection, but more pertaining drugs were recorded during 2015. The mental health factor was also comparable but appeared as a third factor in 2011 and as the first factor in 2015. Depression and anxiety were recorded during the mental health of 2011 with antidepressants, anxiolytics, and antiepileptics, while, during 2015, sleep, neurologic, and peripheral disorders were also recorded. The last factor identified was the endocrinological with iron deficiency in both years and hypothyroidism only in 2015. The KMO sampling adequacy index was 0.77 in 2011 and 0.74 in 2015 and a cumulative variance percentage was 47.0% in 2011 and 35.6% in 2015.

3.1.3. Women Aged 45–65 Years

Scree plot identified the same four factors of 2015, in the same order but, generally, factors identified in 2011 were more complex in terms of clinical conditions number (Table 4). Anxiety, depression, sleep, neurologic, and peripheral disorders were recorded during 2011, while only anxiety, depression, and sleep disorder remained in the 2015 factor. Related drugs were comparable, as opioids remained presents for both years. The second factor identified was respiratory due to the presence of asthma and allergic rhinitis, with the addition of acute lower respiratory tract infection during 2011. This factor was mostly made up of related drugs such as antibiotics, adrenergics, decongestants, and corticosteroids. The third cardiometabolic factor was composed of diabetes, hypertension, obesity, disorders of lipid metabolism equally for both years, but more conditions appeared in 2011, such as glaucoma. The last factor identified for both was the osteometabolic, which was similarly made up of osteoporosis and calcium. The KMO index was 0.86 in 2011 and 0.80 in 2015, while the cumulative variance percentage was 55.0% in 2011 and 31.3% in 2015.

3.1.4. Boys Aged 0–14 Years

This profile was similar to that observed for girls aged 0–14 years, both for 2011 and 2015 factors (Table 5). In fact, likewise, factors identified in 2015 in boys in this age group were generally already detected in 2011, but with the presence of an allergic/derma component. The same differences observed for girls in terms of conditions and factor were observed for boys. The KMO sampling adequacy index was 0.72 in 2011 and 0.74 in 2015, while the cumulative variance percentage was 34.0% in 2011 and 35.6% in 2015.

3.1.5. Men Aged 15–44 Years

Among men of this age group, the order and the composition of epidemiological patterns identified in 2015 were not maintained in 2011 (Table 5). In fact, during 2011, four factors were identified. The first one, recognized as mental health/mechanical pain, was comparable with the first two identified during 2015. Moreover, this factor appeared without condition but was only made up of drugs such as opioids, antiepileptics, anxiolytics, and NSAID. During 2015, mental health and mechanical pain were split into two factors containing, in the first case, depression, substance use, anxiety, and sleep disorders with related drugs, and, in the second case, low back pain. Respiratory factor observed during 2015 was already present in 2011 both for disease and drugs. The last two 2011 factors identified as cardiometabolic and derma were not present in 2015. The KMO sampling adequacy index was 0.85 in 2011 and 0.75 in 2015, while the cumulative variance percentage of 26.0% in 2011 and 37.0% in 2015.

3.1.6. Men Aged 45–65 Years

All three factors identified in 2015 were already present in 2011 but in a different order (Table 5). In this age group, respiratory factor was enriched with emphysema, chronic bronchitis, COPD for rather than other age groups, equally during 2015 and 2011. This factor appeared first during 2011 and last in 2015. The cardiometabolic factor seemed more complex in this age group for both years, due to the number of conditions that emerged such as hypertension, obesity, diabetes, ischemic heart disease, and gout. Mental health factor appeared firstly during 2015 and has become more complex than in 2011. The KMO sampling adequacy index was 0.82 in 2011 and 0.63 in 2015, while the cumulative variance percentage was 40.0% in 2011 and 30.4% in 2015.

4. Discussion

This study found that baseline epidemiologic patterns of multimorbidity and polypharmacy identified in the young and adult Spanish population during 2015 were already present in 2011 but with the addition of an allergic/derma pattern, which is not maintained in 2015. Globally, our findings also revealed that patterns identified in 2011 were more complex in terms of both disease and drugs; this could be a sign of an improvement and greater accuracy over the years in the computerized medical records systems. Other reason for the decreasing in the number of drugs taken by all age groups between 2011 and 2015 can be explained by the fact that after 2011, some medication was no longer reimbursed by the Spanish NHS, so this cannot be translated into a decrease in their use. We found that the complexity of patterns in terms of diseases and drugs, identified in both sexes, increases with age, and this trend remains unchanged in 2015.
The first difference identified can be represented by the presence of dermatitis and eczema as a condition more often diagnosed during 2011. In young subjects, the respiratory pattern was the most prevalent, even after four years. During 2015, the respiratory allergic component was predominant in children. This aspect was recorded during 2011, but it seems that respiratory conditions were better registered during 2015, as shown from the more accurate patterns resulted. Corroborating with our results, the high frequency of allergic and asthmatic components in childhood was widely discussed in the literature [15,16,17,18]. Similar was the case of childhood mental disorders and illnesses, conditions also found in 2011, with the addition in 2015 of the drugs for peptic ulcers and GERD, highlighting an increase in their use over the years attributable to prescriptive inappropriateness [19,20]. Additionally, a register of developmental and psychosocial disorders in children associated with antiepileptic treatments and attention deficit hyperactivity disorder (ADHD) treatments were established in both 2011 and 2015. The same pattern of drugs appeared in both sexes, but the diagnosis in girls seemed less accurate than in boys [21]. This could be explained as, in general, the clinic is more evident in boys, and among girls the symptoms are less intense, and therefore, a more general descriptor is used. For these reasons, since 2011, pediatricians started to collaborate with psychiatrists in the follow-up and treatment of affected children [22].
Various changes have been highlighted over the years among the age group 15–44 years in both sexes. Drugs such as cough suppressants and propulsives were dispensed to both men and women in 2011, also in younger and older age groups, but not in 2015, but this can be explained by the fact that after 2011, they were no longer reimbursed by the Spanish NHS. Another considerable difference is related to the mental factor that has become more complex in 2015, differently for men and women. Hence, during 2015, the mental factor was more prevalent among women. The prevalence of depression increased from 4.5% in 2011 to 6.7% in 2015, and more neurological disorders were diagnosed. This could be partly explained by an increase in psychophysical stress caused by more accelerated life rhythms over the years [23]. Similarly, in 2015, men were diagnosed with more disorders not present in 2011, and there was also evidence of substance use disorder, not present in women [24]. Substance use in men, in this age group, could be the cause of the worsening of the diagnosis picture in 2015; in fact, it appears to be a mechanical pain factor that was not present at all in 2011.
It is likely that as polypharmacy increases, drug dependence also increases, which leads to the development of a phenomenon of drug tolerance that complicates the overall clinical framework [25]. In women, it is noteworthy that the mental factor appeared in some psychosocial disorders, such as psychosocial disorders of childhood, combined with a drug cluster in which opioids appeared only in 2015. Perhaps this could be related to the higher prescription of tramadol in 2011, as this molecule was associated with the mechanical pattern. To date, several observational studies are alerting health authorities due to the adverse effects of opioid drugs associated with gabapentin. In fact, in Canada and France, there has been a warning about the risk of combining gabapentin and opioids, both in clinical practice and for recreational use [26,27]. In Ireland, the Medical Council has urged doctors to reduce the prescription of sedative drugs, including gabapentin [28]. Additionally, a recently published study linked the use of these drugs, especially pregabalin, to an increased risk of suicidal behavior, involuntary overdoses, injuries, traffic accidents, and crime [29]. Furthermore, among women, mechanical pain was detected in 2011 but not in 2015; in this year, the neurologic disorders that produce pain as neurologic disorders and peripheral neuropathy are included in the mental health patterns. A significant difference is, in fact, evident with men in the same age group, for whom, as in 2011, the mechanical pain factor remained in 2015.
Our results showed that in 2011 a cardiometabolic factor appeared in men in the 15–44 age group, while during 2015, in the older age group. It could be that until 2011, the occurrence of an episode of hypertension was sufficient to be diagnosed; however, with the subsequent establishment of new guidelines, the diagnosis has to be more accurate and well confirmed [30].
Furthermore, our findings also revealed that in 2011, as for 2015, the association between age and epidemiological pattern complexity is confirmed, as already discussed in literature [31,32]. Therefore, both for 2011 and 2015, among adults until 65 years, all the patterns appeared more complex than other age groups. In fact, the most predominant factors maintained over time were respiratory, cardiometabolic, and mental factors. Respiratory factor generally appeared more complex in 2011 than 2015, because it has been widely studied and identified the systematic association between asthma and allergic rhinitis; this has allowed for making a more accurate diagnosis [33,34,35]. Cardiometabolic factor appears similar for men and women with the addition of gout in men. This is in line with other studies, reporting that a prevalence rate of 1–2% for adults, underlining that it represents the most common inflammatory arthritis in men [36,37]. Another difference between sex was that this pattern in men included consequences of metabolic syndrome such as cardiovascular disease, ischemic heart disease, and cardiac arrhythmia, which is possibly due to increased cardiovascular risk in men, together with an increased incidence of ischemic heart and cerebrovascular diseases [38].
The mechanical pain in men aged 15–44 group in 2011 is included in the mental health pattern, while is separated in 2015. Contrarily, for women of the same age group, mechanical pain appeared only in 2011. The association of anxiety, depression, and somatic symptoms displayed in this pattern is well described, and somatic symptoms are mainly associated with emotional and brain functions, and they may reflect potential emotional conflicts that patients cannot face [39].
Finally, for the 45–56 age group, another gender difference can be highlighted, such as the presence of osteometabolic factor among women. This factor made up of osteoporosis and calcium, during 2011 also contained drugs affecting bone structure and mineralization that disappeared during 2015. The absence of these drugs in 2015 could be partly explained by the restrictions in use of bisphosphonates, recommended by the Spanish Agency of Medicines and Medical Devices in 2011, due to their association with a higher risk of atypical fractures [40].
In various patterns, we revealed potential DDIs, which could increase the risk of adverse health outcomes. Among them, we could highlight the use of inhaled beta-adrenergic agonists and corticosteroids, which decreased potassium levels, thus increasing the risk of arrhythmia [41]; the use of macrolides with inhaled beta-adrenergic and antihistamines, producing a QT prolongation and thus increasing the risk of arrhythmia [42]; the combined use of benzodiazepines and opioids, which increases sedation and respiratory depression [41].

4.1. Comparison with Other Studies

Multiple studies have been published in the recent years describing the different multimorbidity patterns, such as a study conducted in patients over 14 years old that described the existence of mechanical obesity, metabolic, neurovascular, liver disease, psychiatric substance abuse, anxiety, and depression-related patterns [8]. In addition, others studies only described the polypharmacy patterns [35]. However, in 2019, a study on multimorbidity and polypharmacy patterns showed the existence of some unexpected systematic associations among chronic diseases and drugs, as well as potential DDIs and prescribing cascades described in multimorbid patients [11]. Other authors had identified patterns between drugs and chronic disease in populations with a specific disease. For example, Hanlon et al. in 2018 describe the pattern and extent of multimorbidity and polypharmacy in patients with chronic obstructive pulmonary disease [43]. Nevertheless, our study described the patterns that influence to all the population. Aoki et al. in 2018 developed a study similar to ours identifying the multimorbidity patterns in a Japanese population, determining the effects on polypharmacy and dosage frequency [44].
The present study could be considered more exhaustive, because it compared the evolution of multimorbidity and polypharmacy patterns between 4 years in the same population, although this time span is not enough to detect long-term changes.

4.2. Strengths and Limitations

To our knowledge, this is the first large-scale population study comparing the differences observed in 4 years in the systematic associations among chronic diseases and dispensed drugs. The large population size of the EpiChron Cohort, together with the quality of data, resulting in reliable and representative results compared to those based only on medical records or drug use surveys [11]. In order to compare the same population at two different times, in this study, we have considered the population as an open cohort and, thus, not a cohort composed of a fixed number of members, but a dynamic cohort in which over time some subjects became lost and others are involved in the study. A population residing in a geographical area is, by definition, an open (or dynamic) cohort made up of individuals who contribute their personal time to the cohort, as long as they meet the membership criteria, i.e., place of residence, age, and health status. Therefore, having analyzed the variations in terms of multimorbidity and polypharmacy patterns in the population of Aragon, the cohort observed in 2011 and 2015 was considered as dynamic.
During the last five years, valuable information has been published regarding the security profile of numerous drugs, as was the case of benzodiazepines and opioids, allowing us to discuss our findings from both 2011 and 2015 in a more comprehensive manner. One of the essential methodological limitations of this study concerns the impossibility of including some drugs in the analyses due to multicollinearity with specific diseases, thus leading to the absence of specific drugs that would be, a priori, expected in some patterns. The issue of multicollinearity was also responsible for excluding the population aged >65 years from the analysis, which limited the comprehensiveness of the study. Nevertheless, in the present study, we used the same methodological criteria as the reference study to compare two populations that are as homogeneous as possible [11]. Furthermore, we conducted this study in order to assess the variations in most common clinical profiles among real-world population over the years. The 4 years evaluated were from 2011 to 2015 due to the availability of such data; in the future, a further survey may be carried out over more recent years. Providing information based on real-world data [45,46,47,48,49,50,51] may be a useful way to explore the dynamics in real clinical practice and to improve single-patient care model.

5. Conclusions

This study investigated the nature and complexity of a population, investigating the presence of systematic associations between diseases and drugs at two different times. We found that most clinical profiles were maintained over time as in the case of mental, cardiometabolic, mechanical, endocrinological, and osteometabolic patterns. Our findings revealed that baseline multimorbidity and polypharmacy patterns are maintained over time, as the nature of patterns identified in 2011 was also confirmed in 2015. Furthermore, our results also confirmed the existing association between age and clinical complexity, confirming a correlation between multimorbidity and ageing. The present study, therefore, confirmed systematic associations between diseases and drugs in the patterns over time. This could help in the early identification of potential interactions in multimorbid patients with a high risk of adverse health outcomes due to polypharmacy.

Author Contributions

Conceptualization, E.M. and A.P.-T.; methodology, S.M., A.G.-M. and B.P.-P.; formal analysis, S.M. and A.G.-M.; data curation, B.P.-P.; writing—original draft preparation, S.M. and A.G.-M.; writing—review and editing, J.C.-P., F.G.-R., I.I.-S., A.M.-J., V.O., M.A.-P.-S., E.M. and A.P.-T.; supervision, E.M. and A.P.-T. All authors have read and agreed to the published version of the manuscript.

Funding

This work was supported by Gobierno de Aragón, grant number B01_20R, and from the Progetto Regionale AIFA “Analisi delle Prescrizioni Farmaceutiche in Regione Campania” (Pharmacovigilance grant).

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data used in this study cannot be publicly shared, because of restrictions imposed by the Aragon Health Sciences Institute (IACS) and asserted by the Clinical Research Ethics Committee of Aragon (CEICA, [email protected]). The authors who accessed the data belong to the EpiChron Research Group of IACS, and received permission from IACS to utilize the data for this specific study, thus implying its exclusive use by the researchers appearing in the project protocol approved by CEICA.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Sirois, C.; Domingues, N.S.; Laroche, M.-L.; Zongo, A.; Lunghi, C.; Guénette, L.; Kröger, E.; Émond, V. Polypharmacy definitions for multimorbid older adults need stronger foundations to guide research, clinical practice and public health. Pharmacy 2019, 7, 126. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  2. Bradley, M.C.; Motterlini, N.; Padmanabhan, S.; Cahir, C.; Williams, T.; Fahey, T.; Hughes, C.M. Potentially inappropriate prescribing among older people in the United Kingdom. BMC Geriatr. 2014, 14, 72. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  3. Wallace, E.; Stuart, E.; Vaughan, N.; Bennett, K.; Fahey, T.; Smith, S.M. Risk prediction models to predict emergency hospital admission in community-dwelling adults. Med. Care 2014, 52, 751–765. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  4. Monegat, M.; Sermet, C.; Perronnin, M.; Rococo, E. Polypharmacy: Definitions, Measurement and Stakes Involved. Review of the Literature and Measurement Tests. IRDES 2014. Available online: www.irdes.fr/english/issues-in-health-economics/204-polypharmacy-definitions-measurement-and-stakesinvolved.pdf (accessed on 5 April 2017).
  5. Chang, T.I.; Park, H.; Kim, D.W.; Jeon, E.K.; Rhee, C.M.; Kalantar-Zadeh, K.; Kang, E.W.; Kang, S.-W.; Han, S.H. Polypharmacy, hospitalization, and mortality risk: A nationwide cohort study. Sci. Rep. 2020, 10, 1–9. [Google Scholar] [CrossRef] [PubMed]
  6. Prados-Torres, D.; Del Cura-González, I.; Prados-Torres, A. Hacia un modelo de atención a la multimorbilidad en atención primaria. Atención Primaria 2017, 49, 261–262. [Google Scholar] [CrossRef]
  7. Poblador-Plou, B.; Akker, M.V.D.; Vos, R.; Calderón-Larrañaga, A.; Metsemakers, J.; Prados-Torres, A. Similar multimorbidity patterns in primary care patients from two european regions: Results of a factor analysis. PLoS ONE 2014, 9, e100375. [Google Scholar] [CrossRef] [Green Version]
  8. Prados-Torres, A.; Calderón-Larrañaga, A.; Hancco-Saavedra, J.; Poblador-Plou, B.; Akker, M.V.D. Multimorbidity patterns: A systematic review. J. Clin. Epidemiol. 2014, 67, 254–266. [Google Scholar] [CrossRef]
  9. Staff, T.P.O.; Staff, P.O. Correction: Multimorbidity patterns in a national representative sample of the spanish adult population. PLoS ONE 2015, 10, e0123037. [Google Scholar] [CrossRef] [Green Version]
  10. Islam, M.M.; Valderas, J.M.; Yen, L.; Dawda, P.; Jowsey, T.; McRae, I.S. Multimorbidity and comorbidity of chronic diseases among the senior Australians: Prevalence and patterns. PLoS ONE 2014, 9, e83783. [Google Scholar] [CrossRef] [PubMed]
  11. Menditto, E.; Miguel, A.G.; Juste, A.M.; Plou, B.P.; Pascual-Salcedo, M.A.; Orlando, V.; Rubio, F.G.; Torres, A.P. Patterns of multimorbidity and polypharmacy in young and adult population: Systematic associations among chronic diseases and drugs using factor analysis. PLoS ONE 2019, 14, e0210701. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  12. Wang, I.-J.; Tung, T.-H.; Tang, C.-S.; Zhao, Z.-H. Allergens, air pollutants, and childhood allergic diseases. Int. J. Hyg. Environ. Health 2016, 219, 66–71. [Google Scholar] [CrossRef]
  13. Pinart, M.; Keller, T.; Reich, A.; Fröhlich, M.; Cabieses, B.; Hohmann, C.; Postma, D.S.; Bousquet, J.; Antó, J.M.; Keil, T. Sex-related allergic rhinitis prevalence switch from childhood to adulthood: A systematic review and meta-analysis. Int. Arch. Allergy Immunol. 2017, 172, 224–235. [Google Scholar] [CrossRef]
  14. World Health Organization. Prevalence of Asthma and Allergies in Children. European Environment and Health Information System. FACT SHEET NO. 3.1 MAY 2007 CODE: RPG3_Air_E1. 2007. Available online: https://www.euro.who.int/__data/assets/pdf_file/0012/96996/3.1.pdf (accessed on 1 February 2020).
  15. Menditto, E.; Costa, E.; Midão, L.; Bosnic-Anticevich, S.; Novellino, E.; Bialek, S.; Briedis, V.; Mair, A.; Rajabian-Soderlund, R.; Arnavielhe, S.; et al. Adherence to treatment in allergic rhinitis using mobile technology. The MASK Study. Clin. Exp. Allergy 2018, 49, 442–460. [Google Scholar] [CrossRef] [Green Version]
  16. Chia, C.T.W.; Lim, W.P.; Vu, C.K.F. Inappropriate use of proton pump inhibitors in a local setting. Singap. Med. J. 2014, 55, 363–366. [Google Scholar] [CrossRef]
  17. Molloy, D.; Molloy, A.; O’Loughlin, C.; Falconer, M.; Hennessy, M. Inappropriate use of proton pump inhibitors. Ir. J. Med. Sci. 2009, 179, 73–75. [Google Scholar] [CrossRef] [PubMed]
  18. Tung, I.; Li, J.J.; Meza, J.I.; Jezior, K.L.; Kianmahd, J.S.; Hentschel, P.G.; O’Neil, P.M.; Lee, S.S. Patterns of comorbidity among girls with ADHD: A meta-analysis. Pediatrics 2016, 138, e20160430. [Google Scholar] [CrossRef] [Green Version]
  19. Gobierno de Aragon. Plan Integral de Infancia y Adolescencia de Aragón 2010–2014. Available online: https://www.aragon.es/documents/20127/674325/Plan_Integral_de_Infancia_y_Adolescencia._nov_2010.pdf/89b9de94-592c-c68c-fab3-66d0b15a1b16 (accessed on 27 July 2020).
  20. Fowles, E.R.; Stang, J.; Bryant, M.; Kim, S. Stress, Depression, social support, and eating habits reduce diet quality in the first trimester in low-income women: A Pilot Study. J. Acad. Nutr. Diet. 2012, 112, 1619–1625. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  21. Teixidó-Compañó, E.; Espelt, A.; Sordo, L.; Bravo, M.J.; Sarasa-Renedo, A.; Indave, B.I.; Bosque-Prous, M.; Brugal, M.T. Differences between men and women in substance use: The role of educational level and employment status. Gac. Sanit. 2018, 32, 41–47. [Google Scholar] [CrossRef]
  22. Montamat, S.C.; Cusack, B. Overcoming problems with polypharmacy and drug misuse in the elderly. Clin. Geriatr. Med. 1992, 8, 143–158. [Google Scholar] [CrossRef]
  23. Governament of Canada. Health Canada advises Canadians to Exercise Caution When Taking Gabapentin or Pregabalin with Opioids. 2019. Available online: https://www.canada.ca/en/health-canada.html (accessed on 28 July 2020).
  24. Le Quotidien du Farmacien. Prégabaline: Toujours plus de Mésusage. 2019. Available online: https://www.lequotidiendupharmacien.fr/actualite-pharmaceutique/article/2019/09/16/pregabaline-toujours-plus-de-mesusage_279763 (accessed on 28 July 2020).
  25. The Irish Times. Doctors Warned to Reduce Prescribing of Sedatives. Available online: news/doctors-warned-to-reduce-prescribing-of-sedatives-1.4022751 (accessed on 28 July 2020).
  26. Molero, Y.; Larsson, H.; D’Onofrio, B.M.; Sharp, D.J.; Fazel, S. Associations between gabapentinoids and suicidal behaviour, unintentional overdoses, injuries, road traffic incidents, and violent crime: Population based cohort study in Sweden. BMJ 2019, 365, l2147. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  27. Williams, B.; Mancia, G.; Spiering, W.; Rosei, E.A.; Azizi, M.; Burnier, M.; Clement, D.L.; Coca, A.; de Simone, G.; Dominiczak, A.F.; et al. 2018 ESC/ESH Guidelines for the management of arterial hypertension. Eur. Heart J. 2018, 39, 3021–3104. [Google Scholar] [CrossRef] [PubMed]
  28. Illario, M.; Vollenbroek-Hutten, M.; Molloy, D.W.; Menditto, E.; Iaccarino, G.; Eklund, P. Active and healthy ageing and independent living. J. Aging Res. 2015, 2015, 1–3. [Google Scholar] [CrossRef] [PubMed]
  29. Illario, M.; Vollenbroek-Hutten, M.M.R.; Molloy, D.W.; Menditto, E.; Iaccarino, G.; Eklund, P. Active and healthy ageing and independent living 2016. J. Aging Res. 2016, 2016, 1–3. [Google Scholar] [CrossRef] [PubMed]
  30. Khan, D.A. Allergic rhinitis and asthma: Epidemiology and common pathophysiology. Allergy Asthma Proc. 2014, 35, 357–361. [Google Scholar] [CrossRef] [PubMed]
  31. Morjaria, J.B.; Caruso, M.; Emma, R.; Russo, C.; Polosa, R. Treatment of allergic rhinitis as a strategy for preventing asthma. Curr. Allergy Asthma Rep. 2018, 18, 23. [Google Scholar] [CrossRef]
  32. March, M.E.; Sleiman, P.M.A.; Hakonarson, H. The genetics of asthma and allergic disorders. Discov. Med. 2011, 11, 35–45. [Google Scholar]
  33. Robinson, P.C. Gout—An update of aetiology, genetics, co-morbidities and management. Maturitas 2018, 118, 67–73. [Google Scholar] [CrossRef] [Green Version]
  34. Richette, P.; Bardin, T. Gout. Lancet 2010, 375, 318–328. [Google Scholar] [CrossRef]
  35. Calderón-Larrañaga, A.; Gimeno-Feliu, L.A.; González-Rubio, F.; Poblador-Plou, B.; José, M.L.-S.; Abad-Díez, J.M.; Poncel-Falcó, A.; Prados-Torres, A. Polypharmacy patterns: Unravelling systematic associations between prescribed medications. PLoS ONE 2013, 8, e84967. [Google Scholar] [CrossRef] [Green Version]
  36. Zheng, F.; Duan, Y.; Li, J.; Lai, L.; Zhong, Z.; Hu, M.; Ding, S. Somatic symptoms and their association with anxiety and depression in Chinese patients with cardiac neurosis. J. Int. Med. Res. 2019, 47, 4920–4928. [Google Scholar] [CrossRef]
  37. Agencia Española de Medicamentos y Productos Sanitarios, AEMPS. Bisfosfonatos y Riesgo de Fracturas Atípicas de Fémur. 15 de Abril de 2011. Available online: https://www.aemps.gob.es/informa/notasInformativas/medicamentosUsoHumano/seguridad/2011/docs/NI-MUH_04-2011.pdf?x57200 (accessed on 1 June 2020).
  38. Madero, A.D.; Hernández, C.R.; Pollo, D.R.; González, M.M.; Martínez, N.G.; Arruz, A.B. Interacciones farmacológicas en una unidad de cuidados paliativos. Med. Paliativa 2012, 19, 17–23. [Google Scholar] [CrossRef]
  39. Committee for Medicinal Products for Human Use (CHMP). European Medicines Agency—Find Medicine—European Public Assessment Reports. Available online: http://www.ema.europa.eu/ema/index.jsp?curl=pages/medicines/landing/epar_search.jsp&mid=WC0b01ac058001d124 (accessed on 20 March 2018).
  40. Prados-Torres, A.; Poblador-Plou, B.; Calderón-Larrañaga, A.; Gimeno-Feliu, L.A.; González-Rubio, F.; Poncel-Falcó, A.; Sicras-Mainar, A.; Alcalá-Nalvaiz, J.T. Multimorbidity patterns in primary care: Interactions among chronic diseases using factor analysis. PLoS ONE 2012, 7, e32190. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  41. Russo, V.; Monetti, V.M.; Guerriero, F.; Trama, U.; Guida, A.; Menditto, E.; Orlando, V. Prevalence of antibiotic prescription in southern Italian outpatients: Real-world data analysis of socioeconomic and sociodemographic variables at a municipality level. Clin. Outcomes Res. 2018, ume 10, 251–258. [Google Scholar] [CrossRef] [Green Version]
  42. Guerriero, F.; Orlando, V.; Monetti, V.M.; Russo, V.; Menditto, E. Biological therapy utilization, switching, and cost among patients with psoriasis: Retrospective analysis of administrative databases in Southern Italy. Clin. Outcomes Res. 2017, 9, 741–748. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  43. Hanlon, P.; Nicholl, B.I.; Jani, B.D.; McQueenie, R.; Lee, D.; I Gallacher, K.; Mair, F.S. Examining patterns of multimorbidity, polypharmacy and risk of adverse drug reactions in chronic obstructive pulmonary disease: A cross-sectional UK Biobank study. BMJ Open 2018, 8, e018404. [Google Scholar] [CrossRef] [PubMed] [Green Version]
  44. Aoki, T.; Yamamoto, Y.; Ikenoue, T.; Onishi, Y.; Fukuhara, S. Multimorbidity patterns in relation to polypharmacy and dosage frequency: A nationwide, cross-sectional study in a Japanese population. Sci. Rep. 2018, 8, 1–8. [Google Scholar] [CrossRef]
  45. Orlando, V.V.; Menditto, E.; Coretti, S.; Putignano, D.; Fiorentino, D.; Ruggeri, M. Doctors commitment and long-term effectiveness for cost containment policies: Lesson learned from biosimilar drugs. Clin. Outcomes Res. 2015, 7, 575–581. [Google Scholar] [CrossRef] [Green Version]
  46. Iolascon, G.; Gimigliano, F.; Orlando, V.; Capaldo, A.; Di Somma, C.; Menditto, E. Osteoporosis drugs in real-world clinical practice: An analysis of persistence. Aging Clin. Exp. Res. 2013, 25, 137–141. [Google Scholar] [CrossRef]
  47. Malo, S.; Aguilar-Palacio, I.; Feja, C.; Lallana, M.J.; Rabanaque, M.J.; Armesto, J.; Menditto, E. Different approaches to the assessment of adherence and persistence with cardiovascular-disease preventive medications. Curr. Med. Res. Opin. 2017, 33, 1329–1336. [Google Scholar] [CrossRef]
  48. Cammarota, S.; Bruzzese, D.; Catapano, A.L.; Citarella, A.; De Luca, L.; Manzoli, L.; Masulli, M.; Menditto, E.; Mezzetti, A.; Riegler, S.; et al. Lower incidence of macrovascular complications inpatients on insulin glargine versus those on basal human insulins: A population-based cohort study in Italy. Nutr. Metab. Cardiovasc. Dis. 2014, 24, 10–17. [Google Scholar] [CrossRef]
  49. Casula, M.; Catapano, A.L.; Piccinelli, R.; Menditto, E.; Manzoli, L.; De Fendi, L.; Orlando, V.; Flacco, M.E.; Gambera, M.; Filippi, A.; et al. Assessment and potential determinants of compliance and persistence to antiosteoporosis therapy in Italy. Am. J. Manag. Care 2014, 20, 138–145. [Google Scholar]
  50. Prados-Torres, A.; Poblador-Plou, B.; Gimeno-Miguel, A.; Calderón-Larrañaga, A.; Poncel-Falcó, A.; Gimeno-Feliú, L.A.; González-Rubio, F.; Laguna-Berna, C.; Marta-Moreno, J.; Clerencia-Sierra, M.; et al. Cohort profile: The epidemiology of chronic diseases and multimorbidity. The EpiChron Cohort Study. Int. J. Epidemiol. 2018, 47, 382–384. [Google Scholar] [CrossRef] [PubMed]
  51. Salisbury, C.; Johnson, L.; Purdy, S.; Valderas, J.M.; Montgomery, A.A. Epidemiology and impact of multimorbidity in primary care: A retrospective cohort study. Br. J. Gen. Pr. 2011, 61, e12–e21. [Google Scholar] [CrossRef] [PubMed]
Table 1. Demographic and clinical characteristics of women in 2011 and 2015.
Table 1. Demographic and clinical characteristics of women in 2011 and 2015.
Subjects’ Characteristics0–14 Years15–44 Years45–65 Years
Women20112015p Value20112015p Value20112015p Value
DEMOGRAPHIC
Population (N)72,94078,534 245,171205,122 170,584168,587
Age (mean (SD))7.79
(3.71)
7.03
(4.21)
<0.00131.57
(8.21)
31.71 (8.45)<0.00154.23
(6.03)
54.43
(5.96)
<0.001
Area of living (n (%)) 0.001 a <0.001 a <0.001 a
Urban43,911 (60.20%)46,649 (59.40%) 155,773 (63.54%)127,450 (62.13%) 109,249 (64.04%)106,244 (63.02%)
Rural29,008 (39.77%)31,885 (40.60%) 89,252 (36.40%)77,672 (37.87%) 61,279 (35.92%)62,343 (36.98%)
Unknown21
(0.03%)
- 146
(0.06%)
- 56
(0.03%)
Immigrant status (n (%)) 0.032 a <0.001 a <0.001 a
Native61,997 (85.00%)67,740 (86.26%) 199,026 (81.18%)168,839 (82.31%) 159,239 (93.35%)156,311 (92.72%)
Immigrant10,168 (13.94%)10,761 (13.70%) 46,100 (18.80%)36,277 (17.69%) 11,331
(6.64%)
12,275 (7.28%)
Unknown775
(1.06%)
33
(0.04%)
45
(0.02%)
6
(0.00%)
14
(0.01%)
1
(0.00%)
Deprivation index (n (%)) b <0.001 a <0.001 a 0.007 a
Q120,305 (27.84%)22,448 (28.58%) 69,079 (28.18%)55,733 (27.17%) 44,754 (26.24%)43,546 (25.83%)
Q218,719 (25.66%)19,019 (24.22%) 60,847 (24.82%)50,671 (24.70%) 43,587 (25.55%)43,732 (25.94%)
Q314,137 (19.38%)15,556 (19.81%) 48,256 (19.68%)41,415 (20.19%) 35,696 (20.93%)35,040 (20.78%)
Q419,743 (27.07%)21,511 (27.39%) 66,913 (27.29%)57,303 (27.94%) 46,512 (27.27%)46,269 (27.45%)
Unknown36
(0.05%)
- 76
(0.03%)
- 35
(0.02%)
-
CLINICAL
Number of chronic diseases e <0.001 <0.001 <0.001
 mean (SD)0.67
(0.92)
1.00
(1.05)
0.89
(1.24)
1.47
(1.47)
2.28
(2.18)
3.06
(2.34)
 median (P25; P75)0
(0; 1)
1
(0; 2)
0
(0; 1)
1
(0; 2)
2
(1; 3)
3
(1; 4)
Multimorbidity
(n (%)) c
11,525 (15.80%)20,022 (25.49%)<0.00156,798 (23.17%)80,521 (39.26%)<0.00195,722 (56.11%)120,101 (71.24%)<0.001
Number of drugsd,e <0.001 <0.001 <0.001
 mean (SD)2.40
(2.42)
2.16
(2.09)
2.80
(3.12)
2.67
(2.71)
5.13
(4.66)
4.34
(3.75)
 median (P25; P75)2
(0; 4)
2
(0; 3)
2
(0; 4)
2
(0; 4)
4
(1; 8)
4
(1; 6)
a Missing values were not considered when performing test and p value. b Deprivation index: degree of deprivation from the lowest (Q1) to the highest (Q4) of the administrative health area to which it belongs. c Defined as the coexistence of 2 or more chronic diseases. d Refers to different drugs dispensed at the third level of the anatomical, therapeutic, chemical (ATC) classification system. e Non-parametric test.
Table 2. Demographic and clinical characteristics of men in 2011 and 2015.
Table 2. Demographic and clinical characteristics of men in 2011 and 2015.
Subjects’ Characteristics0–14 Years15–44 Years45–65 Years
Men 20112015p Value20112015p Value20112015p Value
DEMOGRAPHIC
Population (N)77,39182,893 260,915190,658 173,389161,778
Age (mean (SD))7.82
(3.72)
7.04
(4.21)
<0.00131.68
(8.18)
31.54
(8.67)
0,76854.00
(6.01)
54.36
(5.93)
<0.001
Area of living (n (%)) <0.001 a <0.001 a <0.001 a
Urban46,346 (59.89%)48,943 (59.04%) 160,106 (61.36%)113,262 (59.41%) 102,994 (59.40%)94,223 (58.24%)
Rural31,022 (40.08%)33,950 (40.96%) 100,728 (38.61%)77,396 (40.59%) 70,349 (40.57%)67,555 (41.76%)
Unknown23
(0.03%)
- 81
(0.03%)
- 46
(0.03%)
Immigrant status (n (%)) <0.001 a <0.001 a <0.001 a
Native65,525 (84.67%)71,506 (86.26%) 206,631 (79.19%)160,073 (83.96%) 159,095 (91.76%)149,258 (92.26%)
Immigrant11,040 (14.27%)11,357 (13.70%) 54,219 (20.78%)30,577 (16.04%) 14,292 (8.24%)12,519 (7.74%)
Unknown826
(1.07%)
30
(0.04%)
65
(0.02%)
8
(0.00%)
2
(0.00%)
1
(0.00%)
Deprivation index (n (%)) b <0.001 a <0.001 a 0.011 a
Q121,455 (27.72%)23,695 (28.59%) 69,997 (26.83%)49,759 (26.10%) 43,513 (25.10%)40,042 (24.75%)
Q219,695 (25.45%)19,725 (23.80%) 64,037 (24.54%)46,709 (24.50%) 44,237 (25.51%)41,522 (25.67%)
Q315,168 (19.60%)16,465 (19.86%) 52,872 (20.26%)39,962 (20.96%) 37,330 (21.53%)34,511 (21.33%)
Q421,052 (27.20%)23,008 (27.76%) 73,953 (28.34%)54,228 (28.44%) 48,285 (27.85%)45,703 (28.25%)
Unknown21
(0.03%)
- 56
(0.02%)
- 24
(0.01%)
-
CLINICAL
Number of chronic diseases e <0.001 <0.001 <0.001
 mean (SD)0.76
(0.99)
1.12
(1.11)
0.62
(1.01)
1.14
(1.24)
1.70
(1.94)
2.48
(2.12)
 median (P25; P75)0
(0; 1)
1
(0; 2)
0
(0; 1)
1
(0; 2)
1
(0; 3)
2
(1; 3)
Multimorbidity (n (%)) c14,748 (19.06%)24,386 (29.42%)<0.00138,788 (14.87%)55,704 (29.22%)<0.00175,251 (43.40%)99,176 (61.30%)<0.001
Number of drugsd,e <0.001 <0.001 <0.001
 mean (SD)2.50
(2.50)
2.27
(2.20)
1.71
(2.34)
1.78
(2.10)
3.53
(3.88)
3.42
(3.32)
 median (P25; P75)2
(0; 4)
2
(0; 3)
1
(0; 3)
1
(0; 3)
2
(0; 5)
3
(1; 5)
a Missing values were not considered when performing test and p value. b Deprivation index: degree of deprivation from the lowest (Q1) to the highest (Q4) of the administrative health area to which it belongs. c Defined as the coexistence of 2 or more chronic diseases. d Refers to different drugs dispensed at the third level of the anatomical, therapeutic, chemical (ATC) classification system. e Non-parametric test.
Table 3. Comparison of multimorbidity and polypharmacy patterns identified in each age and sex group in 2011 and 2015.
Table 3. Comparison of multimorbidity and polypharmacy patterns identified in each age and sex group in 2011 and 2015.
Gender0–14 Years15–44 Years45–65 Years
201120152011201520112015
WomenAllergic–DermaRespiratory–Acute InfectionMechanical
Pain
Mental HealthMental HealthMental Health
Respiratory-Asthma–Acute InfectionRespiratory–Asthma–AllergicRespiratoryRespiratoryRespiratoryRespiratory
AllergicMental HealthMental
Health
EndocrinologicalCardiometabolicCardiometabolic
Mental Health Endocrinological OsteometabolicOsteometabolic
MenAllergic–DermaRespiratory–Acute InfectionMental Health–PainMental HealthRespiratoryMental Health
Respiratory–Asthma–Acute InfectionRespiratory–Asthma–AllergicRespiratory–AllergicMechanical PainCardiometabolicCardiometabolic
AllergicMental HealthCardiometabolicRespiratoryMental HealthRespiratory
Mental Health Derma
Table 4. Patterns of chronic diseases (EDC codes) and drugs (ATC codes) and factor loading scores in women. Diseases are highlighted in bold.
Table 4. Patterns of chronic diseases (EDC codes) and drugs (ATC codes) and factor loading scores in women. Diseases are highlighted in bold.
Year 2011PrevalencesValuesYear 2015PrevalencesValues
0–14 years
FACTOR 1: ALLERGIC/DERMAPrev (%)ValuesFACTOR 1: RESPIRATORY/ACUTE INFECTIONPrev (%)Values
M01AAnti-inflammatory and antirheumatic products, non-steroids38.650.6462H02ACorticosteroids for systemic use, pain 9.400.6427
J01CBeta-lactam antibacterials, penicillins34.110.6454RES02Acute lower respiratory tract infection 11.060.6355
N02BOther analgesics and antipyretics17.850.5855R03AAdrenergics, inhalants 10.680.6224
R05CExpectorants, excl. combinations with cough suppressants22.570.5845J01CBeta-lactam antibacterials, penicillins 33.570.5882
R05DCough suppressants, excl, combinations with expectorants22.140.5616N02BOther analgesics and antipyretics 22.570.5116
J01DOther beta-lactam antibacterials5.300.4862J01FMacrolides, lincosamides, and streptogramins8.760.4816
S01AAnti-infectives6.860.4411N05BAnxiolytics3.640.4570
J01FMacrolides, lincosamides and streptogramins8.240.4225S01AAnti-infectives 9.630.4271
D07ACorticosteroids, plain7.870.4198M01AAnti-inflammatory and antirheumatic products, non-steroids 34.450.4174
A03FPropulsives2.040.3905D07ACorticosteroids, plain 8.050.4097
D01AAntifungals for topical use3.440.3817D01AAntifungals for topical use 3.970.3684
N05BAnxiolytics3.710.3750A07CElectrolytes with carbohydrates 4.150.3648
D06AAntibiotics for topical use4.340.3681D06AAntibiotics for topical use 5.070.3583
SKN02Dermatitis and eczema18.130.2929
FACTOR 2: RESPIRATORY/ASTHMA/
ACUTE INFECTION
Prev (%)ValuesFACTOR 2: RESPIRATORY/ASTHMA/
ALLERGIC
Prev (%)Values
H02ACorticosteroids for systemic use, plain6.930.4682R06AAntihistamines for systemic use 13.630.6105
R03AAdrenergics, inhalants7.500.8946ALL03Allergic rhinitis 4.230.7546
RES02Acute lower respiratory tract infection8.210.7506S01GDecongestants and antiallergics2.680.7419
ASMAAsthma6.250.6038R01ADecongestants and other nasal preparations for topical use3.900.6744
ASMAAsthma 7.180.3489
FACTOR 3: ALLERGICPrev (%)Values
R06AAntihistamines for systemic use10.500.5823
ALL03Allergic rhinitis2.900.8316
S01GDecongestants and antiallergics1.920.7065
R01ADecongestants and other nasal preparations for topical use3.030.6528
FACTOR 4: MENTAL HEALTHPrev (%)ValuesFACTOR 3: MENTAL HEALTHPrev (%)Values
N06BPsychostimulants, agents used for ADHD and nootropics0.890.7123N03AAntiepileptics0.360.6693
N03AAntiepileptics0.360.6379N06BPsychostimulants, agents used for ADHD and nootropics0.740.5403
NUR19Developmental disorder1.190.6150NUR19Developmental disorder2.150.3793
PSY14Psychosocial disorders of childhood3.400.3113A02BDrugs for peptic ulcers and GERD0.690.3761
PSY14Psychosocial disorders of childhood5.360.3287
15–44 years
FACTOR 1: MECHANICAL PAINPrev (%)Values
M01AAnti-inflammatory and antirheumatic products, non-steroids30.970.7664
M03BMuscle relaxants, centrally acting agents4.080.5416
A02BDrugs for peptic ulcer and GERD10.670.5046
N02BOther analgesics and antipyretics19.650.5007
M02ATopical products for joint and muscular pain3.800.4578
N02AOpioids2.680.4304
J01CBeta-lactam antibacterials, penicillins19.960.3998
MUS14Low back pain4.200.3607
R05DCough suppressants, excl. combinations with expectorants9.300.3497
A03FPropulsives4.270.3157
FACTOR 2: RESPIRATORYPrev (%)ValuesFACTOR 1: RESPIRATORYPrev (%)Values
R05CExpectorants, excl. combinations with cough suppressants14.620.4734M01AAnti-inflammatory and antirheumatic products, non-steroids30.850.3224
J01FMacrolides. lincosamides and streptogramins7.940.3563R06AAntihistamines for systemic use 14.830.8167
S01CAnti-inflammatory agents and anti-infectives in combination2.150.9123R03AAdrenergics, inhalants 5.240.7087
R03AAdrenergics, inhalants3.950.8991R01ADecongestants and other nasal reparations for topical use 8.500.6800
ASMAAsthma4.150.6915S01GDecongestants and antiallergics 3.100.6329
R06AAntihistamines for systemic use11.120.6647ASMAAsthma 6.670.4935
R01ADecongestants and other nasal preparations for topical use6.370.5650RES02Acute lower respiratory tract infection 2.380.4617
RES02Acute lower respiratory tract infection2.250.5564ALL03Allergic rhinitis 12.640.4243
ALL03Allergic rhinitis7.270.3956H02ACorticosteroids for systemic use. plain 3.300.4065
H02ACorticosteroids for systemic use, plain2.350.3574J01FMacrolides, lincosamides and streptogramins 9.550.3837
J01CBeta-lactam antibacterials, penicillins 21.020.3651
J01MQuinolone antibacterials 3.640.3413
J01DOther beta-lactam antibacterials 3.420.3320
N02BOther analgesics and antipyretics 20.890.3169
D07ACorticosteroids, plain 5.540.3086
FACTOR 3: MENTAL HEALTHPrev (%)ValuesFACTOR 2: MENTAL HEALTHPrev (%)Values
N06AAntidepressants6.100.9314N06AAntidepressants 6.950.8600
N05BAnxiolytics8.860.7156N03AAntiepileptics2.750.7610
N03AAntiepileptics2.220.6426N05BAnxiolytics11.110.7584
PSY09Depression4.550.6301N05AAntipsychotics2.030.5738
N05AAntipsychotics1.830.5151PSY09Depression6.760.5535
PSY01Anxiety, neuroses2.650.4704A02BDrugs for peptic ulcers and GERD 10.420.4688
N02CAntimigraine preparations1.480.2683N02AOpioids3.830.4575
PSY01Anxiety, neuroses 4.890.4333
PSY19Sleep disorders of nonorganic origin 3.650.3776
N02CAntimigraine preparations 1.740.3742
NUR21Neurologic disorders, other 2.330.3556
NUR03Peripheral neuropathy, neuritis2.600.3093
FACTOR 4: ENDOCRINOLOGICALPrev (%)ValuesFACTOR 3: ENDOCRINOLOGICALPrev (%)Values
B03AIron preparations7.730.9181B03AIron preparations 8.970.7959
H03CIodine therapy4.240.7731H03CIodine therapy 5.610.6469
HEM02Iron deficiency, other deficiency anemias4.100.5908HEM02Iron deficiency, other deficiency anemias6.180.5369
B03BVitamin B12 and folic acid3.530.5032B03BVitamin B12 and folic acid 3.990.4798
G03AHormonal contraceptives for systemic use3.030.3399H03AThyroid preparations 4.300.4306
C05CCapillary stabilizing agents3.020.2817END04Hypothyroidism6.290.3658
45–65 years
FACTOR 1: MENTAL HEALTHPrev (%)ValuesFACTOR 1: MENTAL HEALTHPrev (%)Values
N06AAntidepressants16.630.8254N06AAntidepressants18.210.8980
N05BAnxiolytics22.350.7021N05BAnxiolytics24.670.6682
N03AAntiepileptics5.700.5976PSY09Depression16.810.6131
N05CHypnotics and sedatives6.120.5944N05CHypnotics and sedatives 6.080.5592
PSY09Depression12.930.5871N03AAntiepileptics7.010.5406
N02AOpioids8.240.4676PSY01Anxiety, neuroses 6.700.4116
A02BDrugs for peptic ulcer and GERD 30.900.4483N02AOpioids 10.240.3805
PSY01Anxiety, neuroses4.130.4187PSY19Sleep disorders of nonorganic origin10.290.3618
PSY19Sleep disorders of nonorganic origin6.540.4111A02BDrugs for peptic ulcers and GERD29.060.3379
M01AAnti-inflammatory and antirheumatic products, non-steroids46.560.4095
A03FPropulsives6.280.3674
M03BMuscle relaxants, centrally acting agents7.110.3614
MUS13Cervical pain syndromes2.380.3128
MUS14Low back pain7.520.2733
NUR21Neurologic disorders, other3.570.2617
NUR03Peripheral neuropathy, neuritis4.590.2524
FACTOR 2: RESPIRATORYPrev (%)ValuesFACTOR 2: RESPIRATORYPrev (%)Values
N02BOther analgesics and antipyretics30.150.3050R03A Adrenergics, inhalants7.930.7548
R03AAdrenergics, inhalants6.210.8711R06A Antihistamines for systemic use16.740.7487
R05CExpectorants, excl. combinations with cough suppressants20.410.7092R01A Decongestants and other nasal preparations for topical use 8.220.6301
RES02Acute lower respiratory tract infection4.460.7032ASMA Asthma6.380.5872
R06AAntihistamines for systemic use13.510.6205H02A Corticosteroids for systemic use, pain 6.770.4867
ASMAAsthma4.450.5862J01F Macrolides, lincosamides, and streptogramins 10.820.4468
R01ADecongestants and other nasal preparations for topical use7.030.5761J01M Quinolone antibacterials 6.610.4313
J01FMacrolides, lincosamides, and streptogramins9.290.5400ALL03 Allergic rhinitis10.500.4032
J01MQuinolone antibacterials6.420.5128J01C Beta-lactam antibacterials, penicillins 17.970.3853
H02ACorticosteroids for systemic use, plain5.250.5007N02BOther analgesics and antipyretics29.090.3269
J01CBeta-lactam antibacterials, penicillins17.980.4622
R05DCough suppressants, excl. combinations with expectorants11.740.4230
ALL03Allergic rhinitis6.330.2741
FACTOR 3: CARDIOMETABOLICPrev (%)ValuesFACTOR 3: CARDIOMETABOLICPrev (%)Values
DIABDiabetes4.990.7288HTAHypertension20.490.9601
HTAHypertension19.060.6791C09AACE inhibitors, plain 5.060.7041
NUT03Obesity9.000.6258DIABDiabetes5.580.5854
B01AAntithrombotic agents6.490.4258NUT03Obesity11.620.5014
CAR11Disorders of lipid metabolism23.700.3817B01AAntithrombotic agents6.510.3699
ARTRITISDegenerative joint disease11.660.3318CAR11Disorders of lipid metabolism32.890.2951
C05CCapillary stabilizing agents9.780.2882
EYE08Glaucoma2.930.2823
GSU08Varicose veins of lower extremities15.780.2771
FACTOR 4: OSTEOMETABOLICPrev (%)ValuesFACTOR 4: OSTEOMETABOLICPrev (%)Values
M05BDrugs affecting bone structure and mineralization6.290.9690A12ACalcium6.100.8032
A12ACalcium8.960.8944END02Osteoporosis8.980.7869
END02Osteoporosis9.450.8609
Abbreviations: ATC, anatomical therapeutic chemical classification; COPD, chronic obstructive pulmonary disease; EDC, expanded diagnostic clusters; GERD, gastro-esophageal reflux disease; Prev, prevalenc.
Table 5. Patterns of chronic diseases (EDC codes) and drugs (ATC codes) and factor loading scores in men. Diseases are highlighted in bold.
Table 5. Patterns of chronic diseases (EDC codes) and drugs (ATC codes) and factor loading scores in men. Diseases are highlighted in bold.
Year 2011Year 2015
0–14 years
FACTOR 1: ALLERGIC/DERMAPrev
(%)
ValuesFACTOR 1: RESPIRATORY/ACUTE INFECTIONPrev
(%)
Values
J01CBeta-lactam antibacterials, penicillins34.080.6579H02A Corticosteroids for systemic use, pain11.770.6877
M01AAnti-inflammatory and antirheumatic products, non-steroids38.230.6372RES02 Acute lower respiratory tract infection 13.690.6748
N02BOther analgesics and antipyretics17.980.6097R03A Adrenergics, inhalants 13.790.6683
R05CExpectorants, excl. combinations with cough suppressants22.800.5800J01C Beta-lactam antibacterials, penicillins33.480.5854
R05DCough suppressants, excl. combinations with expectorants22.400.5611R03B Other drugs for obstructive airway diseases, inhalants 4.050.5520
J01DOther beta-lactam antibacterials4.750.4832N02B Other analgesics and antipyretics 22.760.5332
S01AAnti-infectives6.970.4410J01F Macrolides, lincosamides, and streptogramins8.830.5120
A07CElectrolytes with carbohydrates3.120.4302N05B Anxiolytics 3.490.4556
D07ACorticosteroids, plain9.500.4195S01A Anti-infective 9.790.4545
J01FMacrolides, lincosamides, and streptogramins8.420.4027D07A Corticosteroids, plain 9.670.4018
N05BAnxiolytics3.560.4009M01A Anti-inflammatory and antirheumatic products, non-steroids 34.580.3990
A03FPropulsives1.910.3946A07C Electrolytes with carbohydrates4.480.3666
D06AAntibiotics for topical use5.000.3710D01A Antifungals for topical use3.360.3452
H02ACorticosteroids for systemic use, plain8.800.3262D06A Antibiotics for topical use 5.640.3344
SKN02Dermatitis and eczema16.840.2812
FACTOR 2: RESPIRATORY/ASTHMA/ACUTE INFECTIONPrev
(%)
ValuesFACTOR 2: RESPIRATORY/ASTHMA/
ALLERGIC
Prev
(%)
Values
R03AAdrenergics, inhalants10.090.9215R06A Antihistamines for systemic use14.540.6159
R03BOther drugs for obstructive airway diseases, inhalants3.590.8434ALL03 Allergic rhinitis 5.340.7213
RES02Acute lower respiratory tract infection10.080.7459S01G Decongestants and antiallergics 3.860.6773
ASMAAsthma9.280.6818R01A Decongestants and other nasal preparations for topical use 4.330.6734
ASMA Asthma 10.960.4222
FACTOR 3: ALLERGICPrev
(%)
Values
R06AAntihistamines for systemic use11.260.6047
ALL03Allergic rhinitis3.770.7499
R01ADecongestants and other nasal preparations for topical use3.400.7494
S01GDecongestants and antiallergics2.940.6728
FACTOR 4: MENTAL HEALTHPrev
(%)
ValuesFACTOR 3: MENTAL HEALTHPrev
(%)
Values
N06BPsychostimulants, agents used for ADHD and nootropics2.460.9564N06B Psychostimulants, agents used for ADHD and nootropics 2.180.7213
PSY05Attention deficit disorder1.970.8148N03A Antiepileptics 0.390.6562
NUR19Developmental disorder2.100.3823PSY05Attention deficit disorder1.920.5889
PSY14Psychosocial disorders of childhood5.700.3139PSY14Psychosocial disorders of childhood8.590.3968
NUR19 Developmental disorder 3.890.3857
A02B Drugs for peptic ulcers and GERD0.600.3324
15–44 years
FACTOR 1: MENTAL HEALTH/MECHANICAL PAINPrev
(%)
ValuesFACTOR 1: MENTAL HEALTHPrev
(%)
Values
N03AAntiepileptics1.670.7159N06A Antidepressants 3.740.8979
N05CHypnotics and sedatives0.970.6100N05C Hypnotics and sedatives 1.100.7614
N05BAnxiolytics4.600.6036N05A Antipsychotics 2.000.7482
N05AAntipsychotics1.530.5564N05B Anxiolytics 6.920.6522
A02BDrugs for peptic ulcer and GERD 7.940.5129N03A Antiepileptics 2.450.6442
N02AOpioids1.900.5024PSY09 Depression 3.470.6005
M01AAnti-inflammatory and antirheumatic products, non-steroids23.050.4126PSY02 Substance use 2.790.4973
N02BOther analgesics and antipyretics14.240.3849PSY01 Anxiety neuroses 2.550.4801
PSY19 Sleep disorders of nonorganic origin 3.120.4604
FACTOR 2: MECHANICAL PAINPrev
(%)
Values
M01A Anti-inflammatory and antirheumatic products, non-steroids25.680.7741
N02B Other analgesics and antipyretics17.050.6115
A02B Drugs for peptic ulcers and GERD 8.490.5996
J01C Beta-lactam antibacterials, penicillins 18.010.5105
N02A Opioids 2.920.4920
MUS14 Low back pain 4.180.4663
H02A Corticosteroids for systemic use, pain 2.580.4642
J01F Macrolides, lincosamides, and streptogramins 7.100.4037
B01A Antithrombotic agents 2.010.3980
FACTOR 2: RESPIRATORYPrev
(%)
ValuesFACTOR 3: RESPIRATORYPrev
(%)
Values
H02ACorticosteroids for systemic use, plain1.650.3883RES02 Acute lower respiratory tract infection2.050.3838
R01ADecongestants and other nasal preparations for topical use4.730.7461R03A Adrenergics, inhalants 4.540.7900
R06AAntihistamines for systemic use7.830.6764R06A Antihistamines for systemic use 11.970.7005
R05CExpectorants, excl. combinations with cough suppressants10.130.5909ASMA Asthma 6.890.6227
ALL03Allergic rhinitis6.060.5124R01A Decongestants and other nasal preparations for topical use6.990.5562
J01FMacrolides, lincosamides, and streptogramins5.220.4957ALL03 Allergic rhinitis12.120.4093
ASMAAsthma3.530.4573
R05DCough suppressants, excl. combinations with expectorants5.990.4383
J01CBeta-lactam antibacterials, penicillins15.470.3796
FACTOR 3: CARDIOMETABOLICPrev
(%)
Values
HTAHypertension2.050.7494
NUT03Obesity2.620.5822
CAR11Disorders of lipid metabolism6.100.5101
B01AAntithrombotic agents1.610.5063
FACTOR 4: DERMAPrev
(%)
Values
SKN02Dermatitis and eczema4.940.8586
D07ACorticosteroids, plain3.620.6772
D01AAntifungals for topical use3.150.4985
45–65 years
FACTOR 1: RESPIRATORYPrev
(%)
ValuesFACTOR 3: RESPIRATORYPrev
(%)
Values
R05CExpectorants, excl. combinations with cough suppressants14.430.7394N02B Other analgesics and antipyretics22.350.3056
R06AAntihistamines for systemic use8.060.6970RES04 Emphysema, chronic bronchitis, COPD3.640.3491
R01ADecongestants and other nasal preparations for topical use5.050.6485R03A Adrenergics, inhalants 5.880.8130
RES02Acute lower respiratory tract infection3.150.5805R06A Antihistamines for systemic use 11.100.7063
J01FMacrolides, lincosamides, and streptogramins5.570.5787RES02 Acute lower respiratory tract infection 3.450.5897
R05DCough suppressants, excl. combinations with expectorants7.000.5409R01A Decongestants and other nasal preparations for topical use 6.260.5803
J01CBeta-lactam antibacterials, penicillins14.330.5140ASMA Asthma 3.430.5666
N02BOther analgesics and antipyretics21.110.5065J01M Quinolone antibacterials 5.530.4548
M01AAnti-inflammatory and antirheumatic products, non-steroids32.370.4865J01FMacrolides, lincosamides, and streptogramins 6.910.4383
J01MQuinolone antibacterials 0.4676J01C Beta-lactam antibacterials, penicillins 15.460.3981
ALL03Allergic rhinitis4.030.4222ALL03 Allergic rhinitis7.530.3589
ASMAAsthma2.050.4190
D07ACorticosteroids, plain32.370.3434
M02ATopical products for joint and muscular pain6.250.3159
RES04Emphysema, chronic bronchitis. COPD2.690.2818
FACTOR 2: CARDIOMETABOLICPrev
(%)
ValuesFACTOR 2: CARDIOMETABOLICPrev
(%)
Values
A02BDrugs for peptic ulcer and GERD24.460.3434A02BDrugs for peptic ulcer and gastro-esophageal reflux disease (gord)25.260.3952
HTAHypertension22.120.8007B01AAntithrombotic agents11.010.7832
B01AAntithrombotic agents9.930.6619HTAHypertension27.960.6610
DIABDiabetes8.730.6547IHDIschemic heart disease4.070.6085
C09CAngiotensin II receptor blockers (ARBs), plain6.490.6080DIABDiabetes11.000.5750
IHDIschemic heart disease3.230.5763C09CAngiotensin II receptor blockers (ARBs), plain6.850.5396
NUT03Obesity6.730.5377CAR16Cardiovascular disorders, other2.140.4854
CAR11Disorders of lipid metabolism26.320.4817CAR09 Cardiac arrhythmia2.500.4723
RHU02Gout2.880.3703NUT03Obesity10.240.4283
CAR11 Disorders of lipid metabolism 39.370.3296
RHU02 Gout 4.170.3014
FACTOR 3: MENTAL HEALTHPrev
(%)
ValuesFACTOR 1: MENTAL HEALTHPrev
(%)
Values
N06AAntidepressants6.040.9434N06AAntidepressants7.220.7887
PSY09Depression4.420.7844N05BAnxiolytics12.790.7326
N05BAnxiolytics10.250.7607N03A Antiepileptics 5.100.6613
PSY19Sleep disorders of nonorganic origin3.600.3751PSY09 Depression 6.860.5530
PSY02Substance use2.610.3104N02A Opioids 6.600.4891
PSY01 Anxiety, neuroses 3.040.4447
M01A Anti-inflammatory and antirheumatic products, non-steroids 31.420.4166
PSY19 Sleep disorders of nonorganic origin 6.660.3594
MUS14 Low back pain 6.130.3367
MUS13 Cervical pain syndromes 2.480.3161
NUR21 Neurologic disorders, other 3.690.2959
Abbreviations: ATC, anatomical therapeutic chemical classification; COPD, chronic obstructive pulmonary disease; EDC, expanded diagnostic clusters; GERD, gastro-esophageal reflux disease; Prev, Prevalence.
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Mucherino, S.; Gimeno-Miguel, A.; Carmona-Pirez, J.; Gonzalez-Rubio, F.; Ioakeim-Skoufa, I.; Moreno-Juste, A.; Orlando, V.; Aza-Pascual-Salcedo, M.; Poblador-Plou, B.; Menditto, E.; et al. Changes in Multimorbidity and Polypharmacy Patterns in Young and Adult Population over a 4-Year Period: A 2011–2015 Comparison Using Real-World Data. Int. J. Environ. Res. Public Health 2021, 18, 4422. https://doi.org/10.3390/ijerph18094422

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Mucherino S, Gimeno-Miguel A, Carmona-Pirez J, Gonzalez-Rubio F, Ioakeim-Skoufa I, Moreno-Juste A, Orlando V, Aza-Pascual-Salcedo M, Poblador-Plou B, Menditto E, et al. Changes in Multimorbidity and Polypharmacy Patterns in Young and Adult Population over a 4-Year Period: A 2011–2015 Comparison Using Real-World Data. International Journal of Environmental Research and Public Health. 2021; 18(9):4422. https://doi.org/10.3390/ijerph18094422

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Mucherino, Sara, Antonio Gimeno-Miguel, Jonas Carmona-Pirez, Francisca Gonzalez-Rubio, Ignatios Ioakeim-Skoufa, Aida Moreno-Juste, Valentina Orlando, Mercedes Aza-Pascual-Salcedo, Beatriz Poblador-Plou, Enrica Menditto, and et al. 2021. "Changes in Multimorbidity and Polypharmacy Patterns in Young and Adult Population over a 4-Year Period: A 2011–2015 Comparison Using Real-World Data" International Journal of Environmental Research and Public Health 18, no. 9: 4422. https://doi.org/10.3390/ijerph18094422

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