International Journal of Environmental Research and Public Health Article Combined Multimorbidity and Polypharmacy Patterns in the Elderly: A Cross-Sectional Study in Primary Health Care Grant Stafford 1,2 , Noemí Villén 3,4 , Albert Roso-Llorach 2,4,5 , Amelia Troncoso-Mariño 3,6 , Mònica Monteagudo 2,5 and Concepción Violán 5,7, * 1 Programa de Máster en Salud Pública, Universitat Pompeu Fabra, 08003 Barcelona, Spain; grant.stafford95@gmail.com 2 Unitat Transversal de Recerca (UTR), Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08007 Barcelona, Spain; aroso@idiapjgol.org (A.R.-L.); mmonteagudo@idiapjgol.org (M.M.) 3 Àrea del Medicament i Servei de Farmàcia, Atenció Primària Barcelona Ciutat, Institut Català de la Salut (ICS), 08015 Barcelona, Spain; nvillenr.bcn.ics@gencat.cat (N.V.); atroncoso@gencat.cat (A.T.-M.) 4 Programa de Doctorat en Metodologia de la Recerca Biomèdica i Salut Pública, Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), 08193 Barcelona, Spain 5 Universitat Autònoma de Barcelona, Bellaterra (Cerdanyola del Vallès), 08193 Barcelona, Spain 6 Department of Clinical Sciences, University of Barcelona and IDIBELL, L’Hospitalet de Llobregat, 08907 Barcelona, Spain 7 Unitat de Suport a la Recerca Metropolitana Nord, Fundació Institut Universitaria per a la Recerca a l’Atenció  Primària de Salut Jordi Gol i Gurina (IDIAPJGol), 08303 Mataró, Spain  * Correspondence: cviolanf.mn.ics@gencat.cat Citation: Stafford, G.; Villén, N.; Roso-Llorach, A.; Troncoso-Mariño, Abstract: (1) Background: The acquisition of multiple chronic diseases, known as multimorbidity, A.; Monteagudo, M.; Violán, C. is common in the elderly population, and it is often treated with the simultaneous consumption Combined Multimorbidity and of several prescription drugs, known as polypharmacy. These two concepts are inherently related Polypharmacy Patterns in the Elderly: and cause an undue burden on the individual. The aim of this study was to identify combined A Cross-Sectional Study in Primary multimorbidity and polypharmacy patterns for the elderly population in Catalonia. (2) Methods: Health Care. Int. J. Environ. Res. A cross-sectional study using electronic health records from 2012 was conducted. A mapping Public Health 2021, 18, 9216. https:// process was performed linking chronic disease categories to the drug categories indicated for their doi.org/10.3390/ijerph18179216 treatment. A soft clustering technique was then carried out on the final mapped categories. (3) Results: Academic Editors: Belchin Kostov, 916,619 individuals were included, with 93.1% meeting the authors’ criteria for multimorbidity and Luis González-de Paz and 49.9% for polypharmacy. A seven-cluster solution was identified: one non-specific (Cluster 1) and six Antoni Sisó-Almirall specific, corresponding to diabetes (Cluster 2), neurological and musculoskeletal, female dominant (Clusters 3 and 4) and cardiovascular, cerebrovascular and renal diseases (Clusters 5 and 6), and multi- Received: 22 June 2021 system diseases (Cluster 7). (4) Conclusions: This study utilized a mapping process combined with a Accepted: 26 August 2021 soft clustering technique to determine combined patterns of multimorbidity and polypharmacy in Published: 1 September 2021 the elderly population, identifying overrepresentation in six of the seven clusters with chronic disease and chronic disease-drug categories. These results could be applied to clinical practice guidelines in Publisher’s Note: MDPI stays neutral order to better attend to patient needs. This study can serve as the foundation for future longitudinal with regard to jurisdictional claims in regarding relationships between multimorbidity and polypharmacy. published maps and institutional affil- iations. Keywords: multimorbidity; polypharmacy; elderly; primary healthcare; chronic disease; clustering; combined patterns; machine learning Copyright: © 2021 by the authors. Licensee MDPI, Basel, Switzerland. 1. Introduction This article is an open access article The global life expectancy at birth has increased from 52.6 years in 1960 to 72.6 years distributed under the terms and conditions of the Creative Commons in 2018 [1]. While it is certain that people are living longer on average, this does not Attribution (CC BY) license (https:// necessarily mean they are living healthier lives, as an increase in life expectancy anticipates creativecommons.org/licenses/by/ an increase in morbidity [2,3]. As individuals age, the body changes and experiences a 4.0/). state of physical decline, resulting in weaker defenses and easier acquisition of chronic Int. J. Environ. Res. Public Health 2021, 18, 9216. https://doi.org/10.3390/ijerph18179216 https://www.mdpi.com/journal/ijerph Int. J. Environ. Res. Public Health 2021, 18, 9216 2 of 22 illnesses in the later years of life [2,4]. The diagnosis of two or more chronic diseases in the same individual is referred to as multimorbidity [5]. Multimorbid individuals tend to be prescribed a high number of medications in order to combat their diagnosed chronic illnesses. Consumption of prescribed drugs holds a higher prevalence and relevance in older adults, and complications could include po- tentially inappropriate prescribing [6]. While a homogeneous operational definitional is lacking throughout the field, literature supports the definition of polypharmacy as the consumption of five or more drugs daily in the same individual [7]. Polypharmacy is considered a critical public health problem that is related to drug-drug and drug-disease interactions, adverse drug events [8–12], falls, hospital admissions and mortality [13,14]. Polypharmacy has been on the rise over the past several decades [11] and is highly associ- ated to multimorbidity [8]. In a world with an ageing population, the burdens of multimorbidity and polyphar- macy have undue individual and system-wide impacts on health. While there exists a growing amount of literature regarding multimorbidity and polypharmacy, the vast major- ity of studies analyze polypharmacy as descriptive drugs in multimorbidity patterns, focus almost exclusively on one topic or the other without meaningfully connecting the two, or examine the disease rather than the individual as the unit of analysis [15–17]. Furthermore, medication is considered a proxy variable to disease [15,18,19], and, for this reason, jointly analyzing multimorbidity and polypharmacy can produce an overestimation error due to the fact that people with prevalent diseases such as diabetes or cardiovascular diseases are treated with many medications for both clinical conditions and risk factors and, for this reason, are overestimated. To avoid this, drug groups can be analyzed according to their associated disease, thereby preventing prevalent diseases from being overestimated. This type of approach would permit a better understanding of the patient groups and, at the same time, facilitate strategies aimed at prevention, diagnosis, and treatment because it includes diseases with or without drug treatment. As far as we understand, little research has been completed regarding methods that simultaneously analyze the combined patterns of multimorbidity and polypharmacy at an individual level. Machine-learning soft clustering models are a robust tool capable of performing such an analysis. Cluster analysis involves assigning individuals to a certain cluster so that the items (i.e., units of analysis—diseases and drugs) are as similar as possible, while individuals in different clusters are as least similar as possible. Cluster identification is based on similarity measures, and their choice is reliant upon the data and/or the reason for analysis [20]. Hard clustering forces each individual to belong to only one cluster, while soft clustering (also called fuzzy clustering) grants varying degrees of membership, thus allowing for the individual to pertain to multiple clusters [20]. The aim of this study was to determine combined patterns of multimorbidity and polypharmacy in the Catalan population 65–99 years of age through a machine-learning soft clustering technique that incorporates the research team’s mapping of chronic disease and drug associations. 2. Materials and Methods 2.1. Setting, Design, and Population Catalonia, an autonomous community of Spain, is a Mediterranean region with 7,515,398 reported inhabitants for the year 2012 [21]. Universal health coverage is es- tablished for residents in Spain by the National Health Service and is implemented in a decentralized fashion through each of the seventeen autonomous communities [22]. In Cat- alonia, the Catalan Health Institute (CHI) manages over 283 primary care centers, offering health services to over six million residents [23]. A cross-sectional study was performed on the baseline year (2012) of a longitudinal study (2012–2016) using electronic health records (EHRs) in Catalonia. Inclusion criteria for the cross-sectional study population allowed for individuals 65–99 years of age on 31 December 2011, who survived until 31 December 2012, and had at least one visit to Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 3 of 22 Int. J. Environ. Res. Public Health 2021, 18, 9216 3 of 22 for the cross-sectional study population allowed for individuals 65–99 years of age on 31 December 2011, who survived until 31 December 2012, and had at least one visit to a CHI- managed primaryprimary a CHI-managed care center careduring center the longitudinal during study period the longitudinal study(2012–2016). No new period (2012–2016). entries No new were permitted entries in the study, were permitted andstudy, in the dropouts and were due to dropouts either were duedeath or transfer to either death to or another transfer primary careprimary to another center outside of CHI care center governance. outside A total of 916,619 of CHI governance. eligible A total indi- of 916,619 viduals eligible were included individuals at the were baseline included yearbaseline at the (Figureyear 1). (Figure 1). Figure Figure 1. 1. Estimated Estimatedpopulation population study study according according to to selection selection criteria. criteria. 2.2. Data 2.2. DataSource Source The Information The Information System System for for Research Research in in Primary Primary Care Care (SIDIAP) (SIDIAP) contains contains EHRs EHRs from from the primary care centers managed by the CHI [24]. The SIDIAP database, in addition the primary care centers managed by the CHI [24]. The SIDIAP database, in addition to to clinical information, contains demographic, laboratory, and invoiced drug information, clinical information, contains demographic, laboratory, and invoiced drug information, with every datapoint linked to the individual via an anonymous and unique personal with every datapoint linked to the individual via an anonymous and unique personal identifier. identifier. 2.3. Variables 2.3. Variables The SIDIAP database was the single source of information for all variables. All The SIDIAP variables database were analyzed was the single exclusively source within of information the study for all variables. All vari- period (2012). ables were analyzed exclusively within the study period (2012). 2.3.1. Chronic Diseases and Multimorbidity 2.3.1. All Chronic Diseases diseases in the and Multimorbidity SIDIAP database were coded according to the International Classi- All of fication diseases in the Diseases, SIDIAP Version database An 10 (ICD-10). were coded according operational to for definition themultimorbidity International Clas- was sification of Diseases, Version 10 (ICD-10). An operational definition based on the 60 chronic disease categories determined by Calderón-Larrañaga for multimorbidity et al. in the was basedNational Swedish on the 60 chronic study disease of Aging andcategories determined by Care in Kungsholmen Calderón-Larrañaga (SNAC-K) et al. [25]. Each chronic in the Swedish disease categoryNational study of was included as Aging and Care an individual in Kungsholmen binary variable, and(SNAC-K) [25]. Each multimorbidity was chronic defineddisease category was via a dichotomous included variable as as theanpresence individual binary of two variable, or more and multimorbid- diagnoses from the 60 ity was defined chronic disease via a dichotomous categories. However,variable as the presence only chronic disease categories with ≥ of two or more diagnoses from 2% prevalence the 60 study in the chronic disease categories. population were includedHowever, for finalonly chronic analysis, disease thus leavingcategories 47 SNAC-K with ≥2% chronic disease categories prevalence in the in totalpopulation study (Appendix A Table were A1). included for final analysis, thus leaving 47 SNAC-K chronic disease categories in total (Appendix A Table A1). 2.3.2. Drugs and Classification 2.3.2. Invoiced Drugs and Classification drugs recorded in the SIDIAP database included drugs dispensed in phar- macies. Drugs received in hospital Invoiced drugs recorded in theand/or SIDIAPdispensed databaseby a hospital included pharmacy drugs and in dispensed allphar- other drugs not subsidized through the national health system were excluded from macies. Drugs received in hospital and/or dispensed by a hospital pharmacy and all other this study. Drugsnot drugs were coded according subsidized throughtothe thenational Anatomical Therapeutic health system wereChemical excluded(ATC) fromClassification this study. System, which categorizes drugs through various levels of specificity into groups Drugs were coded according to the Anatomical Therapeutic Chemical (ATC) Classifica- (hereafter tion System, which categorizes drugs through various levels of specificity intochemical, referred to as “drug categories”) according to the targeted organ/system and their groups pharmacological, and therapeutic properties [26]. Drug categories with ≥1% prevalence in the study population were included for final analysis. Chronic use for invoiced drugs Int. J. Environ. Res. Public Health 2021, 18, x FOR PEER REVIEW 4 of 22 (hereafter Int. J. Environ. Res. Public Health 2021, 18, 9216referred to as “drug categories”) according to the targeted organ/system and 4 of 22 their chemical, pharmacological, and therapeutic properties [26]. Drug categories with ≥1% prevalence in the study population were included for final analysis. Chronic use for invoiced drugs was determined for individuals who were invoiced three or more pack- ageswas determined of the same drugforcategories individuals who the during were invoiced study three period. or more While packages not meeting theofpreva- the same lence or chronic use criteria, the drug category Other Drugs Affecting Bone Structure and use drug categories during the study period. While not meeting the prevalence or chronic criteria, the(ATC Mineralization drug 4th category Other“M05BX”) level code Drugs Affecting Bone Structure was added and Mineralization to the study (ATC 4th due to its twice-a- year treatment regimen for chronic diseases. Each drug category was included as an regimen level code “M05BX”) was added to the study due to its twice-a-year treatment indi- for chronic diseases. Each drug category was included as an individual vidual binary variable, and polypharmacy was defined via a dichotomous variable as binary variable, and polypharmacy was defined via a dichotomous variable as chronic use in the same chronic use in the same individual for five or more different drug categories (ATC 4th individual for five or more different drug categories (ATC 4th level) from the eighty-nine level) from the eighty-nine drug categories outlined in the following section. drug categories outlined in the following section. 2.3.3. Grouping 2.3.3. Grouping of Drugs andand of Drugs Mapping Mappingto Chronic to Chronic Diseases Diseases TheThe research team identified 89 different drug research team identified 89 different drug categories categories (ATC (ATC 4th level) 4th level)(Appendix (Appendix A A Table A2) associated to the 60 chronic disease categories Table A2) associated to the 60 chronic disease categories mentioned prior mentioned prior via via a thorough a thorough revision revision of several databases that are well-known international clinical guidelines each of several databases that are well-known international clinical guidelines for for each disease [27–29]. disease [27–29].A mapping A mapping was wasdone done ofofallall6060SNAC-K SNAC-Kchronic chronic disease categories[25] disease categories [25]and and89 89drug drugcategories. categories. Drug categories were mapped to the SNAC-K Drug categories were mapped to the SNAC-K chronic disease categories chronic disease categories for which for they whicharethey are prescribed prescribed to treat. to treat. disease-drug Chronic Chronic disease-drugcategoriescategories were thenwere created thenincreated in the form of dichotomous variables for individuals the form of dichotomous variables for individuals who were diagnosed with who were diagnosed a SNAC-K withchronic a SNAC-K chronic disease disease category and,category and, depending depending on disease on disease management management criteria,0, 1, criteria, invoiced invoiced or 1+0,of1,the or 1+ of the mapped mapped drug categories. drug categories. (For example, (For example, the Allergythe Allergy diseasedisease category cat-was egory was mapped with seven drug categories. An individual mapped with seven drug categories. An individual would qualify in this category if would qualify in this cate- gorydiagnosed if diagnosed withwith a disease a disease pertaining pertaining to theto Allergy the Allergy disease disease category category and and invoiced invoiced at least at least one of the seven drug categories). A total of seven of the one of the seven drug categories). A total of seven of the final 47 SNAC-K chronic final 47 SNAC-K chronic diseases diseases categories categories requirerequire non-pharmacological non-pharmacological treatmentstreatments or treatment or treatment withexcluded with drugs drugs ex- from cluded this from studythis and,study and, therefore, therefore, could not be could mapped.not beThe mapped. remaining The40 remaining SNAC-K 40 SNAC-K chronic disease chronic disease categories categories were mappedwere to drugmapped to drug categories, categories, resulting in 29 resulting in 29 chronic chronic disease-drug dis- categories ease-drug containingcategories containingin≥the ≥ 2% prevalence 2%study prevalence population in the(see study population Appendix (see A Table A3Appendix for mapping A Table A3 for example; seemapping example;Materials Supplementary see Supplementary Table S1 forMaterials completeTable S1 forprocess). mapping completeThe mapping seven process). The seven chronic disease chronicrequiring categories disease categories requiring non-pharmacological non-pharmacological treatment or treatment with drugs treatment excludedwith or treatment fromdrugsthis study and from excluded the 29thischronic studydisease-drug categories, and the 29 chronic all with disease- drug categories,≥all prevalence 2%, were with included≥to2%, prevalence determine were included combined patterns of to determine multimorbidity combined patternsand polypharmacy and of multimorbidity (Figure 2). polypharmacy (Figure 2). Figure Figure 2. Number 2. Number of significant of significant chronic chronic disease disease or chronic or chronic disease-drug disease-drug categories categories afterafter mapping. mapping. 2.3.4. Other Variables Pertinent demographic data analyzed in the study includes age (measured in years), sex (female or male), and socioeconomic status (measured by the MEDEA [Mortality Int. J. Environ. Res. Public Health 2021, 18, 9216 5 of 22 in Spanish Small Areas and Socioeconomic and Environmental Inequalities] Index via quintiles from least deprived to most deprived for urban areas, while rural areas were sorted into an independent category [30]). 2.4. Statistical Analysis Descriptive statistics were applied to summarize preliminary findings. Due to the high dimensionality of the SIDIAP database, dimension-reduction tech- niques were exercised through PCA Mix, an application of principal component analysis (PCA) for numeric original variables and multiple correspondence analysis (MCA) for binary variables. This method reduced the size of the database while maintaining the complexity of the original data. The Karlis–Saporta–Spinaki rule was applied in order to select the appropriate number of dimensions to preserve [31]. Using the reduced database, the combined multimorbidity and polypharmacy patterns were determined through a fuzzy c-means (FCM) clustering algorithm [32], incorporating the twenty-nine chronic disease-drug categories and the seven chronic disease categories with non-pharmacological treatment or treatment with drugs excluded from this study, all of which satisfied the prevalence threshold of ≥2% in the study population. To obtain a range with the ideal number of clusters, validation indices [33] were calculated (Supplemen- tary Materials Figure S1). The outcome of the FCM clustering algorithm was a determined number of models with different numbers of clusters in each model, as calculated by the validation indices. Each model contained varying degrees of disease-medication associ- ation, and the final model of clusters was determined by the research team according to clinical relevance. The clusters were described in two parts: (1) observed/expected ratios (O/E ratios) were calculated by dividing the prevalence of a chronic disease or chronic disease-drug category in a specific cluster by the prevalence of the same chronic disease or chronic disease-drug category in the entire study population; (2) exclusivity was determined by dividing the number of individuals with a chronic disease or chronic disease-drug category in a specific cluster by the amount of all individuals with the same chronic disease or chronic disease-drug category in the entire study population. A threshold of two for the O/E ratio was set in order for a disease/medication to be considered a relevant part of a cluster [34,35]. An exclusivity threshold of 30% was a secondary, but not determining, factor when evaluating the chronic disease or chronic disease-drug categories association with a cluster. All analyses were performed in R version 4.0.3 and Stata version 15. Specifically, R was used to run the PCA mix and FCM clustering algorithm; Stata was used for data management. 3. Results Of the 916,619 eligible individuals 65 years and over (women: 57.8%; mean age: 75.4; standard deviation; 7.4), 853,085 (93.1%) satisfied the criteria for multimorbidity, and 457,576 (49.9%) for polypharmacy (Figure 3). The most frequent chronic disease categories in the population were hypertension (71%), dyslipidemia (50.9%), osteoarthritis and other degenerative joint diseases (32.8%), obesity (28.7%), and diabetes (25.1%) [Appendix A Table A1], with a median of six chronic diseases (interquartile range [IQR] 4.0–8.0) per person. The most prevalent drug categories included proton pump inhibitors (44.3%), HMG CoA reductase inhibitors (38.2%), anilides (28.4%), platelet aggregation inhibitors, excluding heparin (35.6%), and benzodiazepine derivatives (20.9%) [Appendix A Table A2], with a median of 5 drugs (IQR 2.0–8.0) per person. Int. J. Environ. Int. Res. J. Environ. Public Res. Health Public 2021, Health 18,18, 2021, x FOR 9216 PEER REVIEW 6 of 2222 6 of Figure Figure Multimorbid 3. 3.Multimorbid and and polymedicated polymedicated individuals individuals in in the the study study aged aged 65–99 65–99 years years (n(n == 916,619, 916,619, Catalonia, 2012). Catalonia, 2012). TheTheauthors authorsidentified identifieda aseven-cluster seven-cluster solution solution forfor combined combined patterns patterns ofof multimor- multimor- bidity and polypharmacy. Cluster 2 to Cluster 7 contained over 99% multimorbidity inin bidity and polypharmacy. Cluster 2 to Cluster 7 contained over 99% multimorbidity each each cluster cluster andand reported reported higher higher overrepresentation overrepresentation values values (O/E(O/E ratioratio > 2) > 2) for atfor at one least least ofone the of the chronic chronic diseasedisease or chronic or chronic disease-drug disease-drug categories. categories. Characteristics Characteristics of the par- of the study study participants in each cluster are detailed in Appendix A Table A4. Principal ticipants in each cluster are detailed in Appendix A Table A4. Principal results and theresults and the most frequent chronic disease and chronic disease-drug categories by cluster most frequent chronic disease and chronic disease-drug categories by cluster (Table 1) are (Table 1) are highlighted below: highlighted below: Cluster 1 (non-specific) included a substantial number of individuals that do not pre- Table 1. Most frequent 15 chronic disease or chronic disease-drug categories in individuals aged 65–99 years by cluster sent any overrepresented chronic disease or chronic disease-drug category (O/E ratios are (n = 916,619, Catalonia, 2012). below two and exclusivity values are below 30%). Cluster 1 was also the cluster with the Pattern lowest average age Disease or (74.20 years, SD 7.47) Disease-Medication and the lowest percentage Category O ofO/E individuals Ratio with EX multimorbidity (81.74%) and polypharmacy (15.38%). Chronic disease group for solid neoplasms 12.86 0.86 32.37 Diabetes (Cluster 2): The only category that surpassed the O/E ratio threshold was chronicChronic disease-drug disease-drug groupgroup for prostate(O/E for “diabetes” diseases ratio 2.15), with 7.82 0.77 exclusivity 29.11 of 41.93%. Chronic disease-drug group for osteoporosis 7.74 0.74 27.74 Table 1. Most frequent 15 chronicChronic disease disease or chronic disease-drug categories in individuals group for deafness and hearing loss aged 65–99 5.91 years by cluster 0.60 (n22.58 = 916,619, Catalonia, 2012). Chronic disease-drug group for COPD, emphysema, and chronic 4.26 0.53 20.03 Pattern bronchitis Disease or Disease-Medication Category O O/E Ratio EX 1 Chronic disease Chronic disease-drug group group for for solid esophagus, neoplasms stomach, and duodenum 12.86 0.86 32.37 3.77 0.52 19.54 Non-Specific Chronic disease-drugdiseases group for prostate diseases 7.82 0.77 29.11 (n = 344,958: 37.63%) Chronic disease-drug Chronic disease-drug group groupforfor thyroid diseases osteoporosis 2.84 7.74 0.51 0.74 19.34 27.74 Chronic Chronic diseasegroup disease group for for cataract deafness and lens and diseasesloss hearing 8.47 5.91 0.50 0.60 18.65 22.58 Chronic disease-drug group for COPD, Chronic disease-drug emphysema, group for dementia and chronic 1.56 0.47 17.61 4.26 0.53 20.03 bronchitis Chronic disease-drug group for hypertension 26.41 0.46 17.48 Chronic disease-drug Chronic group disease group forfor esophagus, bradycardias andstomach, and conduction duodenum 1.12 diseases 0.40 15.13 1 3.77 0.52 19.54 diseases Non-Specific Chronic disease-drug group for sleep disorders 2.22 0.39 14.64 Chronic disease-drug group for thyroid diseases 2.84 0.51 19.34 (n = 344,958: 37.63%) Chronic disease group for obesity 11.29 0.39 14.82 Chronic disease group for cataract and lens diseases 8.47 0.50 18.65 Chronic disease-drug group for dyslipidemia 12.32 0.38 14.40 Chronic disease-drug group for dementia 1.56 0.47 17.61 Chronic disease group for chronic pancreas, biliary tract, and Chronic disease-drug group for hypertension 26.41 1.09 0.46 0.37 17.48 13.78 gallbladder diseases Chronic disease group for bradycardias and conduction diseases 1.12 0.40 15.13 Chronic disease-drug group for sleep disorders 2.22 0.39 14.64 Chronic disease group for obesity 11.29 0.39 14.82 Chronic disease-drug group for dyslipidemia 12.32 0.38 14.40 Int. J. Environ. Res. Public Health 2021, 18, 9216 7 of 22 Table 1. Cont. Pattern Disease or Disease-Medication Category O O/E Ratio EX Chronic disease-drug group for diabetes 39.52 2.15 41.93 Chronic disease-drug group for glaucoma 10.75 1.78 34.65 Chronic disease group for obesity 49.61 1.73 33.68 Chronic disease-drug group for dyslipidemia 55.12 1.71 33.33 Chronic disease-drug group for hypertension 84.37 1.48 28.89 Chronic disease-drug group for thyroid diseases 7.44 1.34 26.17 2 Chronic disease-drug group for chronic kidney diseases 13.92 1.32 25.69 Diabetes Chronic disease-drug group for ischemic heart disease 8.52 1.11 21.61 (n = 178,457: 19.47%) Chronic disease-drug group for cerebrovascular diseases 6.54 1.01 19.69 Chronic disease group for cataract and lens diseases 17.15 1.00 19.54 Chronic disease-drug group for peripheral vascular disease 2.55 1.00 19.47 Chronic disease-drug group for prostate diseases 9.58 0.95 18.46 Chronic disease group for solid neoplasms 14.06 0.94 18.31 Chronic disease group for deafness and hearing loss 8.42 0.85 16.64 Chronic disease group for chronic pancreas, biliary tract, and 2.40 0.80 15.67 gallbladder diseases Chronic disease-drug group for peripheral neuropathy 9.73 3.08 34.56 Chronic disease-drug group for dorsopathies 24.05 2.88 32.24 Chronic disease-drug group for other musculoskeletal and joint 22.42 2.73 30.64 diseases Chronic disease-drug group for other genitourinary diseases 8.68 2.44 27.32 Chronic disease-drug group for glaucoma 14.68 2.43 27.25 Chronic disease-drug group for osteoarthritis and other 41.87 2.16 24.19 degenerative joint diseases 3 Neurological and Chronic disease group for deafness and hearing loss 19.68 2.00 22.40 Musculoskeletal, Chronic disease-drug group for neurotic, stress-related, and 20.38 1.99 22.36 Female Dominant somatoform diseases (n = 102,750: 11.21%) Chronic disease group for cataract and lens diseases 33.75 1.98 22.14 Chronic disease-drug group for depression and mood diseases 23.16 1.86 20.90 Chronic disease-drug group for osteoporosis 18.91 1.80 20.17 Chronic disease-drug group for colitis and related diseases 18.22 1.79 20.09 Chronic disease-drug group for sleep disorders 9.93 1.74 19.51 Chronic disease-drug group for other psychiatric and behavioral 3.18 1.58 17.71 diseases Chronic disease-drug group for esophagus, stomach, and duodenum 11.29 1.55 17.41 diseases Int. J. Environ. Res. Public Health 2021, 18, 9216 8 of 22 Table 1. Cont. Pattern Disease or Disease-Medication Category O O/E Ratio EX Chronic disease-drug group for other psychiatric and behavioral 7.42 3.69 36.34 diseases Chronic disease-drug group for neurotic, stress-related, and 37.33 3.65 36.00 somatoform diseases Chronic disease-drug group for peripheral neuropathy 10.93 3.46 34.09 Chronic disease-drug group for depression and mood diseases 41.75 3.36 33.10 Chronic disease-drug group for dorsopathies 27.22 3.25 32.06 4 Chronic disease-drug group for other musculoskeletal and joint Behavioral, 26.65 3.25 32.00 diseases Neurological, and Musculoskeletal, Chronic disease-drug group for other genitourinary diseases 9.81 2.76 27.14 Female Dominant Chronic disease-drug group for sleep disorders 15.07 2.64 26.02 (n = 90,287: 9.85%) Chronic disease-drug group for osteoarthritis and other 47.21 2.43 23.97 degenerative joint diseases Chronic disease-drug group for colitis and related diseases 22.46 2.21 21.76 Chronic disease-drug group for osteoporosis 20.89 1.99 19.58 Chronic disease-drug group for esophagus, stomach, and duodenum 13.80 1.90 18.70 diseases Chronic disease-drug group for thyroid diseases 8.76 1.58 15.60 Chronic disease-drug group for autoimmune diseases 3.12 1.43 14.13 Chronic disease group for deafness and hearing loss 13.77 1.40 13.77 Chronic disease-drug group for peripheral vascular disease 12.02 4.71 41.57 Chronic disease-drug group for ischemic heart disease 29.85 3.89 34.32 Chronic disease-drug group for cerebrovascular diseases 19.34 2.99 26.37 Chronic disease-drug group for heart failure 21.10 2.83 24.94 Chronic disease group for bradycardias and conduction diseases 7.04 2.53 22.33 Chronic disease-drug group for atrial fibrillation 15.98 2.39 21.06 5 Chronic disease-drug group for other psychiatric and behavioral 4.63 2.30 20.33 Cardio-cerebrovascular diseases and Renal Chronic disease-drug group for chronic kidney diseases 21.85 2.07 18.27 (n = 80,855: 8.82%) Chronic disease-drug group for COPD, emphysema, chronic bronchitis 15.85 1.98 17.45 Chronic disease-drug group for colitis and related diseases 19.64 1.93 17.04 Chronic disease-drug group for anemia 11.32 1.93 17.00 Chronic disease-drug group for neurotic, stress-related, and 18.28 1.79 15.79 somatoform diseases Chronic disease-drug group for prostate diseases 17.96 1.78 15.67 Chronic disease-drug group for depression and mood diseases 21.75 1.75 15.45 Chronic disease-drug group for sleep disorders 9.50 1.67 14.69 Int. J. Environ. Res. Public Health 2021, 18, 9216 9 of 22 Table 1. Cont. Pattern Disease or Disease-Medication Category O O/E Ratio EX Chronic disease-drug group for atrial fibrillation 40.07 5.99 45.54 Chronic disease-drug group for heart failure 42.57 5.70 43.38 Chronic disease group for bradycardias and conduction diseases 11.50 4.14 31.48 Chronic disease-drug group for inflammatory arthropathies 11.16 3.34 25.41 Chronic disease-drug group for autoimmune diseases 7.07 3.25 24.69 Chronic disease-drug group for anemia 17.61 3.00 22.81 6 Chronic disease-drug group for chronic kidney diseases 31.24 2.96 22.52 Cardiovascular, Renal, Chronic disease-drug group for COPD, emphysema, chronic Inflammatory, and 20.53 2.56 19.49 bronchitis Respiratory (n = 69,720: 7.61%) Chronic disease-drug group for ischemic heart disease 17.01 2.22 16.86 Chronic disease-drug group for peripheral vascular disease 5.36 2.10 15.97 Chronic disease group for chronic pancreas, biliary tract, and 4.88 1.64 12.45 gallbladder diseases Chronic disease-drug group for cerebrovascular diseases 10.29 1.59 12.10 Chronic disease-drug group for colitis and related diseases 15.91 1.56 11.90 Chronic disease-drug group for prostate diseases 14.98 1.48 11.27 Chronic disease-drug group for hypertension 83.43 1.47 11.16 Chronic disease group for other digestive diseases 23.29 9.70 52.46 Chronic disease-drug group for dementia 21.63 6.48 35.07 Chronic disease group for chronic pancreas, biliary tract, and 16.95 5.69 30.77 gallbladder diseases Chronic disease-drug group for autoimmune diseases 10.95 5.03 27.19 Chronic disease-drug group for inflammatory arthropathies 14.80 4.43 23.97 Chronic disease-drug group for anemia 19.82 3.37 18.25 Chronic disease-drug group for atrial fibrillation 14.80 2.21 11.96 7 Chronic disease-drug group for heart failure 16.37 2.19 11.87 Multisystem (n = 49,592: 5.41%) Chronic disease-drug group for colitis and related diseases 19.34 1.90 10.29 Chronic disease-drug group for chronic kidney diseases 19.73 1.87 10.12 Chronic disease group for bradycardias and conduction diseases 4.49 1.61 8.73 Chronic disease-drug group for COPD, emphysema, chronic bronchitis 12.50 1.56 8.44 Chronic disease-drug group for cerebrovascular diseases 10.06 1.55 8.41 Chronic disease-drug group for esophagus, stomach, and duodenum 9.95 1.37 7.41 diseases Chronic disease-drug group for osteoarthritis and other degenerative 25.47 1.31 7.10 joint diseases Categories highlighted in gray are chronic disease categories; those in white are chronic disease-drug categories. Categories in bold reach the O/E ratio threshold of two. Abbreviations: O: disease prevalence in the cluster; O/E ratio: observed/expected ratio; Ex: exclusivity; COPD: chronic obstructive pulmonary disease. Cluster 1 (non-specific) included a substantial number of individuals that do not present any overrepresented chronic disease or chronic disease-drug category (O/E ratios are below two and exclusivity values are below 30%). Cluster 1 was also the cluster with the lowest average age (74.20 years, SD 7.47) and the lowest percentage of individuals with multimorbidity (81.74%) and polypharmacy (15.38%). Int. J. Environ. Res. Public Health 2021, 18, 9216 10 of 22 Diabetes (Cluster 2): The only category that surpassed the O/E ratio threshold was chronic disease-drug group for “diabetes” (O/E ratio 2.15), with exclusivity of 41.93%. Neurological and musculoskeletal, female dominant (Cluster 3): 69.05% of the indi- viduals in this cluster were female, with exclusivity for the chronic disease-drug groups for “peripheral neuropathy”, “dorsopathies”, and “other musculoskeletal and joint diseases” all surpassing 30%. Similar to Cluster 3 is behavioral, neurological, and musculoskeletal, female dominant (Cluster 4), which also has a high proportion of females (75.08%) and contains many of the chronic disease-drug groups from the neurological and musculoskele- tal categories. However, the differentiating factor is the emphasis on behavioral chronic disease-drug groups, with several groups pertaining to this category as well. Cardio-cerebrovascular and renal (Cluster 5): The vast majority (six out of eight) of the significant groups in this cluster are cardiovascular related, with exclusivity of chronic disease-drug groups for “peripheral vascular disease” and “ischemic heart disease” both exceeding 30%. This cluster contained the highest proportion of polypharmacy (83.65%). Cardiovascular, renal, inflammatory, and respiratory (Cluster 6): Cardiovascular categories occupy the top three spots, with chronic disease-drug categories for “atrial fibrillation” (O/E ratio 5.99) and “heart failure” (O/E ratio 5.70), and chronic disease category “bradycardias and conduction disorders” (O/E ratio 4.14), all exceeding an exclusivity of 30%. This cluster contained the highest proportions for individuals diagnosed with 10 or more chronic diseases (30.82%), individuals prescribed 10 or more chronic drugs (27.71%), and individuals with 10 or more primary care visits (75.38%). Polypharmacy was nearly that of Cluster 5 (83.54%). The multisystem pattern identified in Cluster 7 represents the smallest group of indi- viduals from the study population (5.41%) and contains several overrepresented diseases corresponding to multiple systems, in which individuals with digestive disease dominate the cluster. Socioeconomic status (measured by MEDEA index) between categories appears to remain relatively stable in Cluster 2 to Cluster 7, with most deprived fluctuating between 13.63–15.79% and least deprived between 14.90–16.57%. Those in rural health settings fluctuate between 19.34–23.83% in Cluster 2 to Cluster 7. 4. Discussion 4.1. Key Results In this article, patterns for multimorbidity and polypharmacy were analyzed in a joint manner by means of creating a variable relating each drug to one or more diseases for which they are indicated. Such patterns were obtained via specified criteria that allowed for the identification of multimorbid individuals that also qualified as polymedicated (those who were invoiced more than five distinct drugs in one year). This study ultimately identified distinct patterns with singular characteristics, with some more profound in women than in men. This is a key point in order to carry out a stricter follow up of the individuals who have a higher risk of presenting secondary effects caused by drugs or drug interaction. Cluster 1 (non-specific) did not overrepresent any disease and Cluster 7 (multisys- temic) overrepresented diseases from many systems are less specific than Clusters 2 to 6. These cluster types have been identified in other studies [35,36]. In our study, we were able to identify overrepresented diseases that do not have any prescribed drug in primary healthcare, such as neoplastic disease, deafness and hearing loss, and bradycardias and conduction diseases. These data suggest that diseases and drugs should be considered in combination to more efficiently identify multimorbidity patterns [37]. It was observed that the chronic disease-drug categories “hypertension” and “dys- lipidemia” are distributed throughout the seven obtained clusters. Individuals with these common pathologies are therefore not grouped together in any one specific cluster; rather, they are spread throughout all seven. The fuzzy c-means method is a technique that classifies individuals based on cluster probability membership, and, given that there were Int. J. Environ. Res. Public Health 2021, 18, 9216 11 of 22 many individuals with these chronic diseases and associated drugs in the study, the results are homogenously distributed throughout the study population [37]. The patterns from Cluster 2 to Cluster 6 group individuals who share similar chronic disease-drug categories. Cluster 2 is classified as diabetes because 41.93% of individuals included in the cluster are diabetic. Although not reaching the O/E threshold of two, associations are still observed between diabetes and obesity, cataracts, and neoplasms, all diseases that are frequently presented in diabetic individuals [38–40]. Cluster 3 (neurological and musculoskeletal) and Cluster 4 (behavioral, neurological, and musculoskeletal) include patterns predominantly in women. The clustering model identified these two groups of individuals that present similar health problems with singular characteristics. The patterns within behavioral, neurological, and musculoskeletal show an association with autoimmune diseases that are also more frequent in women [41]. Cluster 5 (cardio-cerebrovascular and renal) and Cluster 6 (cardiovascular, renal, in- flammatory, and respiratory) include chronic disease-drug patterns that primarily treat cardiac, cardiovascular, cerebrovascular, and renal pathologies. This association, which has been observed in a previous study that indicated a pattern of increased mortality [16], concerns closely related pathologies that also share risk factors and even treatments. This study allows for the grouping of individuals with similar chronic disease and chronic disease-drug categories into two differentiated and specific clusters, given that Cluster 6 (cardiovascular, renal, inflammatory, and respiratory) includes a chronic disease-drug cate- gory for inflammatory pathologies as well as a chronic disease-drug group for respiratory diseases. Chronic inflammation as a mechanism in atherosclerosis carries a higher risk for cardiovascular and cerebrovascular events [42]. Autoimmune diseases and arthrosis have also been linked to an increase in cardiovascular disease, although this could be due to adverse effects of the treatments for these diseases, such as corticoids [43,44]. A small group of individuals who presented polypharmacy did not satisfy the con- dition for multimorbidity. This could be due to the individuals having received multiple treatments during the study period without them coinciding. On the other hand, there is also the possibility that some pathologies could be treated with five or more drugs simultaneously for certain patients, with neuropathy being a prime example. The inclusion of the same study variable for chronic diseases and drugs indicated for their respective diseases’ treatment is an advantage that allows for a better identification of the individuals and, thereby, allows to better orient the clinical management of these groups of individuals. The methodology used for mapping drugs and chronic diseases permitted the iden- tification of a new chronic diseases-drug category. This new variable applied to fuzzy clustering methods is less susceptible to outliers in the data, choice of distance measure, and the inclusion of inappropriate or irrelevant variables [35,45,46]. This mapping process between chronic diseases and drugs, together with the applica- tion of the fuzzy method, will be very useful for multimorbidity studies where drugs are used as a proxy variable. Soft clustering methods offer a new methodological approach towards understanding the relationships between specific diseases or drugs in individuals. This is an essential step in improving the care of patients and the quality of health systems. Analyzing multimorbidity patterns based on drugs permits the identification of patient subgroups with different clinical approaches and attention. Our analysis focuses on groups of patients with specific diseases and drugs as opposed to other studies centered in diseases. This methodology can be applied in a large variety of studies using electronic health records to study polypharmacy and multimorbidity. 4.2. Comparison to the Literature Studies performed with clustering methods concerning multimorbidity and polyphar- macy are novel but becoming more frequent [47]; however, these studies generally approach multimorbidity with a polypharmacy perspective or vice versa. To the authors’ knowledge, Int. J. Environ. Res. Public Health 2021, 18, 9216 12 of 22 this study is the first in conveying a combined approach to multimorbidity and polyphar- macy by combining both topics into one inclusive variable to determine joint outcomes. Although, as previously mentioned, some of the patterns obtained (neurological and musculoskeletal, female dominant; behavioral, neurological, and musculoskeletal, female dominant; and cardio-cerebrovascular and renal) coincide with the literature [17,37,48]. 4.3. Strengths and Limitations The sample size, with nearly one million individuals, is a clear advantage of the study. Considering individuals as the primary variable for analysis rather than diseases [34], the study employs a large, high-quality database composed of primary health care records representative of the Catalan population aged ≥ 65 years [24], an age group that is more susceptible to present health problems related to drugs. The inclusion of all drugs that are indicated for different diseases with a minimum number of packages facilitates the identification of those individuals who should be more thoroughly monitored. Regarding the method, soft clustering offers a methodologic focus in understanding the relations be- tween specific diseases and individuals. The analysis of multimorbidity and polypharmacy patterns can identify subgroups of patients with different associated diseases and drugs. The extensive mapping process of drug categories to their respective SNAC-K chronic disease categories provides a further level of detail than most other clustering techniques within the literature. This can ultimately serve as a framework for future studies that wish to map diseases and drugs under certain conditions to determine combined patterns. Some limitations of this study should be considered. Individuals who met initial selection criteria but sought care outside of CHI governance were ineligible, possibly introducing selection bias for individuals who chose to seek care in a private healthcare facility. However, this is a small group of the population, and the results of this study can be applied to the general population. Drugs were recorded via invoices, and hospital drugs were not included, which could have influenced the calculation for polypharmacy. Concerning the clusters, the results obtained in this study are similar to those obtained in other studies without prior mapping; nevertheless, we consider that this methodology more precisely determines which pathologies and drugs are overrepresented in each cluster, thus more adequately defining patient profiles. Finally, while the fuzzy c-means clustering technique used in this study is an unsupervised, exploratory method, the authors believe that, combined with a vigorous internal validation system, this technique produces robust results and minimizes potential pitfalls. 5. Conclusions This study utilized a mapping process combined with a soft clustering technique to determine combined patterns of multimorbidity and polypharmacy in the elderly Catalan population in 2012, identifying overrepresentation in 6 of the 7 clusters with chronic disease and chronic disease-drug categories. Cluster 2 to Cluster 6 provided recognizable patterns, predominantly in diabetes; neurological and musculoskeletal, female dominant and behav- ioral, neurological, and musculoskeletal, female dominant; and cardio-cerebrovascular and renal and cardiovascular, renal, inflammatory, and respiratory. These patterns further high- light the differences between sexes, specifically within neurological and musculoskeletal, female dominant and behavioral, neurological, and musculoskeletal, female dominant. The combined patterns of multimorbidity and polypharmacy identified in this study will contribute key information to the evaluation of multimorbid and polymedicated indi- viduals, facilitating the identification of these subgroups of individuals that require specific attention. The obtained results could be applied to clinical practice guidelines differen- tiating distinct population groups, such as multimorbid individuals with and without associated diseases and/or polypharmacy. Due to the relationship between multimorbidity and polypharmacy over long periods of time, this analysis could serve as the base to deepen this relationship in further longitudinal studies. The patterns obtained in this research will Int. J. Environ. Res. Public Health 2021, 18, 9216 13 of 22 allow for an in-depth study of the prescription of multiple medications in elderly people in relation to medication-related problems. Supplementary Materials: The following are available online at https://www.mdpi.com/article/ 10.3390/ijerph18179216/s1, Figure S1: validation indices, Table S1: mapping of SNAC-K chronic disease categories and their associated ATC drugs categories. Author Contributions: Conceptualization, A.R.-L. and C.V.; methodology, G.S., N.V., A.R.-L., A.T.-M., and C.V.; software, G.S. and A.R.-L.; validation, N.V. and A.T.-M.; formal analysis, G.S. and A.R.-L.; investigation, G.S., N.V., A.R.-L., A.T.-M., and C.V.; resources, A.R.-L. and C.V.; data curation, G.S. and A.R.-L.; writing—original draft preparation, G.S., N.V., and C.V.; writing—review and editing, G.S., N.V., A.R.-L., A.T.-M., M.M., and C.V.; visualization, G.S. and A.R.-L.; supervision, A.R.-L. and C.V.; project administration, C.V.; funding acquisition, C.V. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by a research grant from the Instituto de Salud Carlos III, under the auspices of the Ministry of Science, Innovation and Universities, awarded in the 2016 call under the Health Strategy Action 2013–2016, within the National Research Program oriented to Societal Challenges, within the Technical, Scientific and Innovation Research National Plan 2013–2016 (Grant Number PI16/00639), co-funded with the EU European Regional Development Fund and the Department of Health of the Catalan Government, in the 2017 call that awarded subsidies for the Strategic Plan for Research in Health (Pla Estratègic de Recerca i Innovació en Salut, PERIS) 2016–2020, modality research oriented to primary care (Grant Number SLT002/16/00058) and by the Catalan Government (Grant Number AGAUR 2017 SGR 578). This project has been partially funded by the Carlos III Health Institute (Ministry of Economy and Competitiveness, Spain) through the Network for Prevention and Health Promotion in Primary Care (redIAPP, RD16/0007/0001) and by European Union European Regional Development Fund (ERDF) funds (European Regional Development Fund). Institutional Review Board Statement: The study was conducted according to the guidelines of the Declaration of Helsinki, and approved by the Clinical Research Ethics Committee, Fundació Institut Universitari per a la Recerca a l’Atenció Primària de Salut Jordi Gol i Gurina (IDIAPJGol) (Protocol No: P17/080). All data were anonymized in agreement with national and international law. Informed Consent Statement: Not applicable. Data Availability Statement: The datasets are not available, since researchers signed an agreement with the Information System for the Development of Research in Primary Care (SIDIAP) concerning confidentiality and security of the dataset, which forbids providing data to third parties. The SIDIAP is subject to periodic audits. Acknowledgments: We thank the Catalan Institute of Health and the SIDIAP, which provided the database for the study. Conflicts of Interest: The authors declare no conflict of interest. Appendix A Table A1. Prevalence of the 60 chronic diseases included in the study in individuals aged 65–99 years (n = 916,619, Catalonia, 2012). In the last column, list of diseases included by prevalence cut off of 2%. Rank Chronic Conditions Frequency Percentage (%) Cut Off of 2% 1 Hypertension 650,899 71.0 2 Dyslipidemia 466,585 50.9 3 Osteoarthritis and other degenerative joint diseases 300,803 32.8 4 Obesity 262,888 28.7 5 Diabetes 230,460 25.1 6 Anemia 167,577 18.3 7 Cataract and other lens diseases 156,622 17.1 Int. J. Environ. Res. Public Health 2021, 18, 9216 14 of 22 Table A1. Cont. Rank Chronic Conditions Frequency Percentage (%) Cut Off of 2% 8 Chronic kidney diseases 153,756 16.8 9 Prostate diseases 153,635 16.8 10 Osteoporosis 151,847 16.6 11 Depression and mood diseases 148,751 16.2 12 Solid neoplasms 137,045 15.0 13 Colitis and related diseases 131,512 14.4 14 Venous and lymphatic diseases 126,997 13.9 15 Other musculoskeletal and joint diseases 124,765 13.6 16 Dorsopathies 124,603 13.6 17 Neurotic, stress-related and somatoform diseases 123,395 13.5 18 COPD, emphysema, chronic bronchitis 109,603 12.0 19 Ischemic heart disease 95,434 10.4 20 Deafness, hearing impairment 90,261 9.9 21 Sleep disorders 88,739 9.7 22 Thyroid diseases 88,445 9.7 23 Other genitourinary diseases 85,468 9.3 24 Cerebrovascular disease 80,264 8.8 25 Atrial fibrillation 80,247 8.8 26 Esophagus, stomach and duodenum diseases 80,043 8.7 27 Heart failure 74,077 8.1 28 Other eye diseases 68,939 7.5 29 Glaucoma 66,162 7.2 30 Inflammatory arthropathies 62,450 6.8 31 Dementia 59,213 6.5 32 Cardiac valve diseases 52,100 5.7 33 Peripheral neuropathy 49,127 5.4 34 Other psychiatric and behavioral diseases 46,841 5.1 35 Asthma 43,663 4.8 36 Allergy 40,394 4.4 37 Autoimmune diseases 39,350 4.3 38 Ear, nose, throat diseases 38,752 4.2 39 Peripheral vascular disease 30,674 3.4 40 Other neurological diseases 28,541 3.1 41 Chronic pancreas, biliary tract and gallbladder diseases 27,321 3.0 42 Migraine and facial pain syndromes 25,999 2.8 43 Bradycardias and conduction diseases 25,476 2.8 44 Chronic liver diseases 22,633 2.5 45 Other digestive diseases 22,022 2.4 46 Parkinson and parkinsonism 20,833 2.3 47 Other metabolic diseases 18,997 2.1 Int. J. Environ. Res. Public Health 2021, 18, 9216 15 of 22 Table A1. Cont. Rank Chronic Conditions Frequency Percentage (%) Cut Off of 2% 48 Other cardiovascular diseases 16,833 1.8 49 Other skin diseases 15,363 1.7 50 Chronic ulcer of the skin 13,869 1.5 51 Blood and blood forming organ diseases 13,575 1.5 52 Other respiratory diseases 9974 1.1 53 Epilepsy 8981 1.0 54 Hematological neoplasms 8174 0.9 55 Chronic infectious diseases 6647 0.7 56 Inflammatory bowel diseases 5549 0.6 57 Schizophrenia and delusional diseases 4792 0.5 58 Blindness, visual impairment 4772 0.5 59 Multiple sclerosis 576 0.1 60 Chromosomal abnormalities 77 0.0 Abbreviations: COPD: chronic obstructive pulmonary disease. The black shading in the lower part of the table IS necessary, as it indicates a cut off of 2%, as defined in the first line. Table A2. Prevalence of the 89 medication categories diseases included in the study in individuals aged 65–99 years (n = 916,619, Catalonia, 2012). Rank ATC-5 Code Medication Categories Frequency Percentage (%) 1 A02BC Proton pump inhibitors 405,942 44.29 2 C10AA HMG CoA reductase inhibitors 349,676 38.15 3 N02BE Anilides 260,018 28.37 4 B01AC Platelet aggregation inhibitors excl. heparin 234,306 25.56 5 N05BA Benzodiazepine derivatives 191,870 20.93 6 C09AA ACE inhibitors, plain 182,906 19.95 7 A10BA Biguanides 119,955 13.09 8 C08CA Dihydropyridine derivatives 116,713 12.73 9 N06AB Selective serotonin reuptake inhibitors 116,497 12.71 10 C07AB Beta blocking agents, selective 102,442 11.18 11 C03CA Sulfonamides, plain 101,933 11.12 12 C09CA Angiotensin II antagonists, plain 99,742 10.88 13 A12AX Calcium, combinations with vitamin D and/or other drugs 93,581 10.21 14 C03AA Thiazides, plain 86,414 9.43 15 C09DA Angiotensin II antagonists and diuretics 80,115 8.74 16 M01AE Propionic acid derivatives 79,393 8.66 17 C09BA ACE inhibitors and diuretics 74,494 8.13 18 G04CA Alpha-adrenoreceptor antagonists 71,028 7.75 19 B01AA Vitamin K antagonists 62,331 6.80 20 M05BA Bisphosphonates 62,313 6.80 21 R03BB Anticholinergics 53,159 5.80 22 N05CD Benzodiazepine derivatives 52,859 5.77 23 A10BB Sulfonylureas 50,812 5.54 24 H03AA Thyroid hormones 50,699 5.53 Adrenergics in combination with corticosteroids or other 25 R03AK 50,644 5.53 drugs, excl. anticholinergics Int. J. Environ. Res. Public Health 2021, 18, 9216 16 of 22 Table A2. Cont. Rank ATC-5 Code Medication Categories Frequency Percentage (%) 26 N02AX Other opioids 48,388 5.28 27 M04AA Preparations inhibiting uric acid production 47,629 5.20 28 N06AX Other antidepressants 45,357 4.95 29 R03AC Selective beta-2-adrenoreceptor agonists 43,972 4.80 30 S01ED Beta blocking agents 42,513 4.64 31 C01DA Organic nitrates 41,674 4.55 32 S01EE Prostaglandin analogues 40,643 4.43 33 N03AX Other antiepileptics 37,873 4.13 34 B03AA Iron bivalent, oral preparations 35,13 3.83 Other anti-inflammatory and antirheumatic agents, 35 M01AX 34,490 3.76 non-steroids 36 M01AB Acetic acid derivatives and related substances 30,848 3.37 37 N02BB Pyrazolones 30,600 3.34 38 C02CA Alpha-adrenoreceptor antagonists 26,333 2.87 39 N07CA Antivertigo preparations 25,290 2.76 40 C01EB Other cardiac preparations 24,272 2.65 41 G04BD Drugs for urinary frequency and incontinence 23,646 2.58 42 D01AC Imidazole and triazole derivatives 23,459 2.56 43 A03FA Propulsives 22,461 2.45 44 A10AE Insulins and analogues for injection, long-acting 21,678 2.36 45 H02AB Glucocorticoids 21,493 2.34 46 C01AA Digitalis glycosides 21,190 2.31 47 D07AC Corticosteroids, potent (Group III) 20,599 2.25 48 G04CB Testosterone-5-alpha reductase inhibitors 19,379 2.11 49 N06DA Anticholinesterases 19,366 2.11 50 C08DB Benzothiazepine derivatives 19,211 2.10 51 R03BA Glucocorticoids 19,143 2.09 52 R06AX Other antihistamines for systemic use 18,609 2.03 53 C07AG Alpha and beta blocking agents 18,198 1.99 54 A02BA H2-receptor antagonists 18,158 1.98 55 C03BA Sulfonamides, plain 17,998 1.96 56 C10AB Fibrates 17,874 1.95 57 N05CF Benzodiazepine related drugs 17,914 1.95 58 G03CA Natural and semisynthetic estrogens, plain 16,513 1.80 59 C03DA Aldosterone antagonists 16,367 1.79 60 N06DX Other anti-dementia drugs 16,292 1.78 61 A10BD Combinations of oral blood glucose lowering drugs 15,397 1.68 62 N06AA Non-selective monoamine reuptake inhibitors 15,364 1.68 63 N05AH Diazepines, oxazepines, thiazepines and oxepines 14,249 1.55 64 R01AD Corticosteroids 13,880 1.51 65 G04CX Other drugs used in benign prostatic hypertrophy 13,684 1.49 Int. J. Environ. Res. Public Health 2021, 18, 9216 17 of 22 Table A2. Cont. Rank ATC-5 Code Medication Categories Frequency Percentage (%) 66 J01MA Fluoroquinolones 13,610 1.48 67 C03EA Low-ceiling diuretics and potassium-sparing agents 13,179 1.44 68 A10BX Other blood glucose lowering drugs, excl. insulins 12,933 1.41 69 A12AA Calcium 12,809 1.40 70 C09DB Angiotensin II antagonists and calcium channel blockers 12,698 1.39 71 A12BA Potassium 12,625 1.38 72 J01CR Combinations of penicillins, incl. beta-lactamase inhibitors 12,658 1.38 73 C04AD Purine derivatives 12,589 1.37 74 C10AX Other lipid modifying agents 12,579 1.37 75 M01AH Coxibs 12,537 1.37 76 N04BA Dopa and dopa derivatives 12,280 1.34 77 D01AE Other antifungals for topical use 11,514 1.26 78 A11CC Vitamin D and analogues 11,262 1.23 Insulins and analogues for injection, intermediate- or 79 A10AD 11,056 1.21 long-acting combined with fast-acting 80 A10AC Insulins and analogues for injection, intermediate-acting 10,925 1.19 81 N02AB Phenylpiperidine derivatives 10,802 1.18 82 B03BA Vitamin B12 (cyanocobalamin and analogues) 10,613 1.16 83 C01BD Antiarrhythmics, class III 10,511 1.15 84 N05AX Other antipsychotics 10,404 1.14 85 C07AA Beta blocking agents, non-selective 10,231 1.12 86 S01EC Carbonic anhydrase inhibitors 9737 1.06 87 B03AB Iron trivalent, oral preparations 9504 1.04 88 N03AE Benzodiazepine derivatives 9211 1.00 89 M05BX Other drugs affecting bone structure and mineralization 7652 0.83 Abbreviations: HMGCoA-reductase: 3-Hidroxi-3-metil-glutaril-CoA reductase; ACE inhibitors: angiotensin converting enzyme inhibitors. Table A3. Mapping of SNAC-K chronic disease categories and their associated ATC drugs categories (first two categories). ALLERGY ATC-5 codes and labels for drugs associated to chronic disease ICD-10 codes and labels included in chronic disease category category. Includes none or any of the following: J301 Allergic rhinitis due to pollen R01AD Corticosteroids (nasal use) J302 Other seasonal allergic rhinitis R03AC Selective beta-2-adrenoreceptor agonists Adrenergics in combination with corticosteroids J303 Other allergic rhinitis R03AK or other drugs, excl. anticholinergics J304 Allergic rhinitis, unspecified R03BA Glucocorticoids (inhalation) J450 Predominantly allergic asthma R03BB Anticholinergics K522 Allergic and dietetic gastroenteritis and colitis R06AX Other antihistamines for systemic use L20 Atopic dermatitis D07AC Corticosteroids, potent (Group III) (topical use) L23 Allergic contact dermatitis L500 Allergic urticaria Z516 Desensitization to allergens Int. J. Environ. Res. Public Health 2021, 18, 9216 18 of 22 Table A3. Cont. ANEMIA ATC-5 codes and labels for drugs associated to chronic disease ICD-10 codes and labels included in chronic disease category category. Includes none or any of the following: D50 Iron deficiency anaemia B03AA Iron bivalent, oral preparations D51 Vitamin B12 deficiency anaemia B03AB Iron trivalent, oral preparations D52 Folate deficiency anaemia B03BA Vitamin B12 (cyanocobalamin and analogues) D53 Other nutritional anaemias H02AB Glucocorticoids (systemic use, plain) D55 Anaemia due to enzyme disorders D56 Thalassaemia D57 Sickle-cell disorders D58 Other hereditary haemolytic anaemias D59 Acquired haemolytic anaemia Acquired pure red cell aplasia D60 [erythroblastopenia] D61 Other aplastic anaemias D63 Anaemia in chronic diseases classified elsewhere D64 Other anaemias Only chronic diseases and their associated ATC drug categories resulting in chronic disease or chronic disease-drug categories ≥ 2% prevalence are included in this table. For the sake of brevity, only the first two mapped categories are shown. The complete mapping table can be found in Supplementary Materials Table S1. Remaining categories with <2% prevalence can be sent to readers upon request. Int. J. Environ. Res. Public Health 2021, 18, 9216 19 of 22 Table A4. Variables characterizing each cluster for chronic disease or chronic disease-drug category threshold of 2% (n = 916,619). 4. Behavioral, 6. Cardiovascular, 3. Neurological and 5. Cardio- Neurological, and Renal, Study Population 1. Non-Specific 2. Diabetes Musculoskeletal, Cerebrovascular 7. Multisystem Musculoskeletal, Inflammatory, and (All) Female Dominant and Renal Female Dominant Respiratory Number of people, n 344,958 178,457 102,750 90,287 80,855 69,720 49,592 916,619 Multimorbidity, n (%) 281,952 (81.74) 178,412 (99.97) 102,629 (99.88) 90,245 (99.95) 80,819 (99.96) 69,680 (99.94) 49,347 (99.51) 853,084 (93.07) Polypharmacy, n (%) 53,070 (15.38) 105,323 (59.02) 69,503 (67.64) 68,276 (75.62) 67,635 (83.65) 58,245 (83.54) 35,523 (71.63) 457,575 (49.92) Women, n (%) 187,691 (54.41) 98,251 (55.06) 70,946 (69.05) 67,785 (75.08) 39,649 (49.04) 35,130 (50.39) 29,679 (59.85) 529,131 (57.73) Men, n (%) 157,267 (45.59) 80,206 (44.94) 31,803 (30.95) 22,502 (24.92) 41,207 (50.96) 34,590 (49.61) 19,913 (40.15) 387,488 (42.27) Age, mean (SD) 74.28 (7.47) 74.61 (6.86) 75.82 (7.07) 75.43 (7.06) 77.06 (7.34) 78.47 (7.30) 78.27 (7.52) 75.41 (7.39) Age (categories), n (%) [65,70) 119,921 (34.76) 51,741 (28.99) 24,050 (23.41) 23,121 (25.61) 15,649 (19.35) 9796 (14.05) 7901 (15.93) 252,178 (27.51) [70,80) 140,724 (40.79) 83,142 (46.59) 47,314 (46.05) 41,082 (45.5) 34,708 (42.93) 28,197 (40.44) 19,419 (39.16) 394,586 (43.05) [80,90) 71,317 (20.67) 39,345 (22.05) 27,970 (27.22) 23,398 (25.92) 26,574 (32.87) 27,204 (39.02) 18,936 (38.18) 234,744 (25.61) [90,99] 12,997 (3.77) 4229 (2.37) 3416 (3.32) 2686 (2.97) 3924 (4.85) 4524 (6.49) 3336 (6.73) 35,111 (3.83) MEDEA * index, n (%) R 71,007 (21.98) 35,761 (21.22) 18,739 (19.54) 16,239 (19.34) 15,426 (21.01) 14,514 (23.28) 10,563 (23.83) 182,249 (19.88) U1 63,526 (19.67) 25,114 (14.90) 14,580 (15.20) 13,131 (15.64) 11,684 (15.91) 9413 (15.10) 7344 (16.57) 144,792 (15.80) U2 53,337 (16.51) 26,606 (15.79) 15,239 (15.89) 13,307 (15.85) 11,546 (15.72) 9563 (15.34) 6832 (15.41) 136,430 (14.88) U3 51,420 (15.92) 28,004 (16.62) 15,988 (16.67) 13,976 (16.64) 11,928 (16.24) 9839 (15.78) 7068 (15.94) 138,223 (15.08) U4 46,598 (14.43) 28,059 (16.65) 16,264 (16.96) 14,066 (16.75) 11,795 (16.06) 9726 (15.60) 6480 (14.62) 132,988 (14.51) U5 37,128 (11.49) 24,998 (14.83) 15,109 (15.75) 13,264 (15.79) 11,050 (15.05) 9290 (14.90) 6043 (13.63) 116,882 (12.75) Number of chronic 3 [2–5] 6 [4–7] 7 [5–9] 8 [6–10] 8 [6–10] 8 [6–10] 7 [6–10] 6 [4–8] diseases, median [IQR] Number of chronic diseases (categories), n (%) 0 25,380 (7.36) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 0 (0) 25,380 (2.77) 1 37,626 (10.91) 45 (0.03) 121 (0.12) 42 (0.05) 36 (0.04) 40 (0.06) 244 (0.49) 38,154 (4.16) [2,5) 176,529 (51.17) 50,912 (28.53) 12,934 (12.59) 7896 (8.75) 7990 (9.88) 6133 (8.8) 6441 (12.99) 268,836 (29.33) [5,10) 99,930 (28.97) 114,840 (64.35) 67,586 (65.78) 58,154 (64.41) 51,082 (63.18) 42,058 (60.32) 30,059 (60.61) 463,709 (50.59) ≥10 5493 (1.59) 12,660 (7.09) 22,109 (21.52) 24,195 (26.8) 21,747 (26.9) 21,489 (30.82) 12,847 (25.91) 120,540 (13.15) Int. J. Environ. Res. Public Health 2021, 18, 9216 20 of 22 Table A4. Cont. 4. Behavioral, 6. Cardiovascular, 3. Neurological and 5. Cardio- Neurological, and Renal, Study Population 1. Non-Specific 2. Diabetes Musculoskeletal, Cerebrovascular 7. Multisystem Musculoskeletal, Inflammatory, and (All) Female Dominant and Renal Female Dominant Respiratory Number of medications, 2 [0–3] 5 [3–7] 6 [4–8] 6 [5–9] 7 [5–9] 7 [5–10] 6 [4–9] 4 [2–7] median [IQR] Number of medications, (categories), n (%) 0 113,702 (32.96) 536 (0.3) 1414 (1.38) 132 (0.15) 24 (0.03) 180 (0.26) 1801 (3.63) 117,789 (12.85) 1 50,694 (14.7) 5232 (2.93) 3057 (2.97) 1282 (1.42) 581 (0.72) 602 (0.86) 1364 (2.75) 62,812 (6.85) [2,5) 127,492 (36.96) 67,366 (37.75) 28,777 (28.01) 20,597 (22.81) 12,615 (15.6) 10,692 (15.34) 10,904 (21.99) 278,442 (30.38) [5,10) 50,510 (14.64) 90,914 (50.94) 55,102 (53.63) 52,401 (58.04) 47,710 (59.01) 38,927 (55.83) 25,494 (51.41) 361,058 (39.39) ≥10 2560 (0.74) 14,410 (8.07) 14,401 (14.02) 15,875 (17.58) 19,924 (24.64) 19,318 (27.71) 10,029 (20.22) 96,518 (10.53) Number of visits 2012, 6 [2–10] 10 [6–16] 12 [7–18] 12 [7–19] 14 [8–23] 18 [10–29] 13 [7–23] 9 [5–16] median [IQR] Number of visits 2012 (categories), n (%) 0 44,954 (13.03) 825 (0.46) 637 (0.62) 317 (0.35) 242 (0.3) 204 (0.29) 766 (1.54) 47,945 (5.23) 1 25,389 (7.36) 3155 (1.77) 1463 (1.42) 1213 (1.34) 1026 (1.27) 683 (0.98) 954 (1.92) 33,884 (3.7) [2,5) 75,895 (22) 20,685 (11.59) 9889 (9.62) 8168 (9.05) 6708 (8.3) 4336 (6.22) 4759 (9.6) 130,439 (14.23) [5,10) 101,438 (29.41) 56,518 (31.67) 27,564 (26.83) 22,805 (25.26) 18,077 (22.36) 11,938 (17.12) 11,008 (22.2) 249,349 (27.2) ≥10 97,283 (28.2) 97,274 (54.51) 63,197 (61.51) 57,783 (64) 54,802 (67.78) 52,558 (75.38) 32,105 (64.74) 455,002 (49.64) For the sake of simplicity, all numbers in the table were rounded to its closest natural number. * MEDEA index starts with U1 (urban setting, least deprived) and ends with U5 (urban setting, most deprived). 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