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Impact of multimorbidity: acute morbidity, area of residency and use of health services across the life span in a region of south Europe

Impact of multimorbidity: acute morbidity, area of residency and use of health services across... Background: Concurrent diseases, multiple pathologies and multimorbidity patterns are topics of increased interest as the world’s population ages. To explore the impact of multimorbidity on affected patients and the consequences for health services, we designed a study to describe multimorbidity by sex and life-stage in a large population sample and to assess the association with acute morbidity, area of residency and use of health services. Methods: A cross-sectional study was conducted in Catalonia (Spain). Participants were 1,749,710 patients aged 19+ years (251 primary care teams). Primary outcome: Multimorbidity (≥2 chronic diseases). Secondary outcome: Number of new events of each acute disease. Other variables: number of acute diseases per patient, sex, age group (19–24, 25–44, 45–64, 65–79, and 80+ years), urban/rural residence, and number of visits during 2010. Results: Multimorbidity was present in 46.8% (95% CI, 46.7%-46.8%) of the sample, and increased as age increased, being higher in women and in rural areas. The most prevalent pair of chronic diseases was hypertension and lipid disorders in patients older than 45 years. Infections (mainly upper respiratory infection) were the most common acute diagnoses. In women, the highest significant RR of multimorbidity vs. non-multimorbidity was found for teeth/gum disease (aged 19–24) and acute upper respiratory infection. In men, this RR was only positive and significant for teeth/gum disease (aged 65–79). The adjusted analysis showed a strongly positive association with multimorbidity for the oldest women (80+ years) with acute diseases and women aged 65–79 with 3ormoreacute diseases, compared to patients with no acute diseases (OR ranged from 1.16 to 1.99, p < 0.001). Living in a rural area was significantly associated with lower probability of having multimorbidity. The odds of multimorbidity increased sharply as the number of visits increased, reaching the highest probability in those aged 65–79 years. Conclusions: Multimorbidity is related to greater use of health care services and higher incidence of acute diseases, increasing the burden on primary care services. The differences related to sex and life-stage observed for multimorbidity and acute diseases suggest that further research on multimorbidity should be stratified according to these factors. Keywords: Multimorbidity, Chronic disease, Acute disease, Life-stage * Correspondence: qfoguet@idiapjgol.org Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Gran Via Corts Catalanes, 587 àtic, 08007 Barcelona, Spain Universitat Autònoma de Barcelona, Plaza Cívica, Campus de la UAB, 08193 Bellaterra, Cerdanyola del Vallès, Spain Full list of author information is available at the end of the article © 2014 Foguet-Boreu et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 2 of 11 http://www.biomedcentral.com/1471-2296/15/55 Background clinical information from the EHR system. A subset of Concurrent diseases, multiple pathologies and multimor- SIDIAP records meeting the highest quality criteria for bidity patterns are topics of increased interest as the clinical data (SIDIAP-Q) includes 40% of the SIDIAP world’s population ages [1]. Multimorbidity is the coex- population (1,936,443 patients), attended by the 1,319 istence of two or more chronic health problems in the general practitioners (GP) whose data recording scored same person at one point in time [2], and multimorbidity highest in a validated comparison process. The sample patterns are any combination of chronic diseases [3]. Both is representative of the general Catalan population in considerations have important consequences for the indi- terms of geography, age and sex distributions, according vidual and for health services [4]. Multimorbidity is a chal- to the official 2010 census [13]. lenge for industrialized countries and can jeopardize the A sample of 1,749,710 patients aged 19 years or older, viability of national health systems. assigned to 251 PHCT during the period of study (1 Traditionally, the construct of multimorbidity has been January- 31 December 2010), was selected from the inherently associated with persistent or chronic disease. SIDIAP-Q database. Methods to measure multimorbidity include disease scores, case-mix systems, indexes and disease counts, Coding of diseases the latter being the common method [5]. Far too little International Classification of Diseases (ICD-10) codes were attention has been paid to the use of health services and mapped to the International Classification of Primary Care the role of urban or rural residency in patients with (ICPC-2e-v.4.2, available at: http://www.kith.no/templates/ multimorbidity [6,7]. Furthermore, the literature lacks kith_WebPage____1111.aspx). R codes (symptoms, signs comparisons by sex of acute morbidity in patients with and abnormal clinical and laboratory findings, not else- multimorbidity in a large population sample [8,9]. where classified) and Z codes (factors influencing health The classification of acute and chronic disease remains status and contact with health services) were excluded, controversial. Acute disease is characterized by a single or resulting in 686 included codes. Each diagnosis was then repeated episode of relatively rapid onset and short dur- classified using O’Halloran criteria for chronic disease [14]. ation with a recovery to previous stage of activity [10]. We included all 146 diagnoses considered as chronic Nevertheless, some diseases fall into a grey area. Know- diseases by O’Halloran criteria: (i) have a duration that ledge of specific acute diseases that may occur more fre- has lasted, or is expected to last, at least 6 months; (ii) quently than expected and of the underlying vulnerabilities have a pattern of recurrence or deterioration; (iii) have a [11] could help to focus attention on the patients with poor prognosis and (iv) produce consequences, or se- multimorbidity rather than emphasizing the diseases. quelae, that have an impact on the individual’squality of To explore the impact of multimorbidity on affected life [14,15]. Any disease not meeting the O’Halloran cri- patients and the consequences for health services, we teria was considered an acute disease. designed a study to describe multimorbidity by sex and All results were described with ICPC-2 codes. Diseases life-stage in a large population sample and to assess the were classified as acute if diagnosed during the study association with acute morbidity, area of residency and period and chronic if recorded as such in EHR as of 31 use of health services. December 2010. Methods Outcomes and variables Data source and study population The main outcome was multimorbidity, defined as the Cross-sectional study of adults resident in Catalonia, a coexistence of 2 or more chronic diseases. Secondary Mediterranean region of southern European with 7,434,632 outcome was the number of new events of each acute inhabitants (2010 census), 16% of the population of disease. Other variables recorded for each patient were Spain. In Catalonia, 358 primary health care teams the following: number of all acute diseases (0, 1, 2, > = 3), (PHCT) comprised of doctors, nurses, social workers sex (male, female), age (young adult, 19 to 24; adult, 25– and support staff are assigned by geographical area and 44; older adult, 45–64; elderly, 65–79; and oldest adult, responsible for the health care of the population in their 80+), number of visits during the study period (0, 1–2, areas. The Catalan Health Institute (CHI) manages 274 3–5, 6–10, ≥11), and area of residence (rural if <10,000 PHCT (76.5%), serving a population of 4,859,725 adults; inhabitants and/or population density <150 people/km , the remaining PHCT are managed by other providers. otherwise urban) [14]. Number of all acute diseases (or Doctors and nurses systematically use electronic health 0 diseases) and visits (or 0 visits) were categorized as records (EHR) to record diagnoses, prescriptions and quartiles of the study population. Number of visits was other clinical, patient management and administrative ac- used as a proxy of use of health services and included tivities. The CHI Information System for the Development visits recorded in EHR by GP, nurses or social workers, of Research in Primary Care (SIDIAP) [12] compiles coded either at the primary care centre or as home health care. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 3 of 11 http://www.biomedcentral.com/1471-2296/15/55 Statistical analysis aged 19–44); acne (men aged 19–24) and lipid disorder Analysis was stratified by sex and age group. Descriptive (men aged 25–44). After 45 both sex groups first chronic statistics were used to summarize overall information. disease was lipid disorder in 45–64 and uncomplicated Categorical variables were expressed as frequencies (per- hypertension in 65-80+ (Table 1). Upper respiratory infec- centage) and continuous as mean (Standard deviation, SD) tion acute is the most incident acute disease in all age or median (interquartile range, IQR). groups (except in 80+). Cumulative incidence of acute morbidity events was Multimorbidity prevalence increased as age increased, calculated as the number of new acute events during the being higher in female (ranged from 19.0% to 92.1%) than study period divided by the at-risk population in the male (ranged from 12.9% to 92.0%). In patients with multi- sample (e.g., if a patient had bronchitis twice in the one- morbidity, the number of acute diseases was higher in year study period, the total number of events accounted female than male and decreased as age increased, except for was 2). We took into account the five acute diseases in male older than 65. In addition, the number of visits with the highest cumulative incidence within each stratum. increased as age increased, and was higher for female Risk ratios (RR) of multimorbidity vs. non-multimorbidity than male in all age groups except 80+ (Table 2). were calculated for the number of events for each acute Patients with multimorbidity had a higher incidence of disease, using Poisson, negative binomial (if overdispersion acute diseases and number of visits in all age strata than was present) or zero inflated (when data had an excess of non-multimorbidity patients; in both cases, the incidence zero counts) equations, as appropriate. All models were was higher for female than male (Table 2). Overall, the adjusted for number of visits and area of residency. median (IQR) of number of visits was 8(4–14) in patients To determine the most prevalent multimorbidity pat- with multimorbidity vs. 1(0–4) in the non-multimorbidity terns, all possible combinations of any two chronic dis- group. eases and their frequencies were calculated. Observed (O) The two most prevalent combinations of two chronic and expected (E) prevalence of those two chronic diseases diseases were hypertension and lipid disorders in patients with each acute disease was then computed. Expected co- older than 45 years. The only acute disease that appeared occurrence of diseases was obtained as the product of in both sexes was “bursitis/tendinitis/synovitis NOS” in these prevalences, assuming statistical independence of the oldest age group (80+). In the other age groups, the the diseases concerned. The overlapping of those combi- acute health disease varied by sex (Figure 1). nations that presented the highest O/E ratio was reported. The five acute diseases with the highest cumulative in- Logistic regression was used to assess the association cidence were similar by sex in any age group. Infections between multimorbidity and the variables listed above. were the most common diagnosis. Cystitis/urinary infec- All statistical tests were two-sided at the 5% signifi- tion was present among the five most prevalent acute cance level. The analyses were performed using SPSS for conditions only in women and in the oldest men. In Windows, version 18 (SPSS Inc., Chicago, IL, USA), Stata/ women, the highest significant RR of multimorbidity vs. SE version 11 for Windows (Stata Corp. LP, College non-multimorbidity was found for teeth/gum disease Station, TX, USA) and R version 2.15.2 (R Foundation (aged 19–24) and upper respiratory infection, acute (80+). for Statistical Computing, Vienna, Austria). In men, this RR was only positive and significant for teeth/ gum disease (aged 65–79) (Table 3). Ethical considerations The adjusted analysis of factors associated with multi- The study protocol was approved by the Committee on morbidity showed that the oldest patients (80+ years) the Ethics of Clinical Research, Institut Universitari with acute diseases and women aged 65–79 with 3 or d’Investigació en Atenció Primària (IDIAP) Jordi Gol more acute diseases were more likely to have multimor- (Protocol No: P12/28). All data were anonymized and bidity than patients with no acute diseases. This positive the confidentiality of EHR was respected at all times in association was only significant in women. Living in a accordance with international law. rural area was significantly associated with lower prob- ability of having multimorbidity. Patients who visited a Results GP more often were more likely than those without We included 1,749,710 patients; mean age was 47.4 years visits to have multimorbidity, reaching the highest prob- (SD: 17.8), 50.7% were female and 16% lived in rural areas. ability in those aged 65–79 years (Table 4). Multimorbidity (≥ 2 diseases) was present in 46.8% (95% CI, 46.7%-46.8%) of the sample, being higher in female Discussion (52.3%) than in male (41.1%) and in rural areas (47.6%) Statement of principal findings than in urban areas (46.6%). Almost half of the study population had multimorbidity, The prevalence of the most common chronic diseases with infections (mainly acute upper respiratory infection) differed by sex below 45: anxiety disorder/anxiety (women the most common acute disease in both sexes and all age Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 4 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 1 Five highest cumulative incidence of acute and prevalence of chronic diseases by sex and age groups Female Male Age ICPC Chronic diseases Prevalence ICPC Acute diseases Cumulative ICPC Chronic diseases Prevalence ICPC Acute diseases Cumulative groups (%, CI) incidence (%, CI) (%, CI) incidence (%, CI) 19-24 P74 Anxiety disorder/ 8.4 (8.2-8.7) R74 Upper respiratory 9.1 (8.9-9.3) S96 Acne 7.7 (7.5-7.9) R74 Upper respiratory 6.9 (6.7-7.1) anxiety state infection acute infection acute S96 Acne 7.8 (7.6-8.0) R76 Tonsillitis acute 4.4 (4.3-4.6) R96 Asthma 6.0 (5.8-6.2) R76 Tonsillitis acute 3.4 (3.3-3.6) R96 Asthma 5.4 (5.2-5.6) U71 Cystitis/urinary 4.1 (4.0-4.3) P74 Anxiety disorder/ 3.9 (3.7-4.0) D73 Gastroenteritis 2.9 (2.8-3.0) infection other anxiety state presumed infection T82 Obesity 5.0 (4.8-5.1) D82 Teeth/gum disease 3.7 (3.5-3.8) T82 Obesity 3.4 (3.3-3.6) D82 Teeth/gum disease 2.5 (2.4-2.7) L85 Acquired deformity 4.6 (4.4-4.8) D73 Gastroenteritis 3.6 (3.5-3.8) L85 Acquired deformity 3.1 (3.0-3.2) S16 Bruise/contusion 2.3 (2.2-2.4) of spine presumed infection of spine 25-44 P74 Anxiety disorder/ 12.2 (12.1-12.3) R74 Upper respiratory 7.8 (7.7-7.9) T93 Lipid disorder 7.3 (7.2-7.4) R74 Upper respiratory 5.9 (5.8-5.9) anxiety state infection acute infection acute P76 Depressive disorder 8.8 (8.7-8.9) L03 Low back symptom/ 3.4 (3.3-3.5) P74 Anxiety disorder/ 6.5 (6.4-6.6) D73 Gastroenteritis 2.4 (2.4-2.5) complaint anxiety state presumed infection T82 Obesity 6.8 (6.7-6.9) U71 Cystitis/urinary 2.9 (2.8-2.9) T82 Obesity 4.4 (4.4-4.5) L03 Low back symptom/ 2.4 (2.3-2.4) infection other complaint T93 Lipid disorder 5.0 (4.9-5.0) D73 Gastroenteritis 2.8 (2.8-2.9) P76 Depressive disorder 3.7 (3.7-3.8) D82 Teeth/gum disease 2.1 (2.1-2.2) presumed infection N89 Migraine 4.9 (4.8-4.9) R76 Tonsillitis acute 2.6 (2.6-2.7) L86 Back syndrome with 3.5 (3.4-3.5) R76 Tonsillitis acute 1.8 (1.8-1.9) radiating pain 45-64 T93 Lipid disorder 28.4 (28.2-28.5) R74 Upper respiratory 7.0 (6.9-7.1) T93 Lipid disorder 29.9 (29.7-30.1) R74 Upper respiratory 4.8 (4.7-4.9) infection acute infection acute K86 Hypertension 21.2 (21.1-21.4) L03 Low back symptom/ 3.2 (3.1-3.3) K86 Hypertension 24.6 (24.4-24.7) L03 Low back symptom/ 2.6 (2.5-2.7) uncomplicated complaint uncomplicated complaint P76 Depressive disorder 18.9 (18.8-19.1) U71 Cystitis/urinary 2.9 (2.8-3.0) T82 Obesity 10.9 (10.8-11.0) R78 Acute bronchitis/ 2.0 (2.0-2.1) infection other bronchiolitis T82 Obesity 15.7 (15.6-15.9) R78 Acute bronchitis/ 2.8 (2.7-2.9) T90 Diabetes non-insulin 10.3 (10.2-10.5) D82 Teeth/gum disease 2.0 (2.0-2.1) bronchiolitis dependent P74 Anxiety disorder/ 13.5 (13.4-13.6) L20 Joint symptom/ 2.6 (2.6-2.7) L86 Back syndrome with 7.6 (7.5-7.7) H81 Excessive ear wax 1.9 (1.9-2.0) anxiety state complaint NOS radiating pain 65-79 K86 Hypertension 60.3 (60.0-60.6) R74 Upper respiratory 7.2 (7.1-7.3) K86 Hypertension 56.2 (55.9-56.5) R74 Upper respiratory 6.4 (6.2-6.5) uncomplicated infection acute uncomplicated infection acute T93 Lipid disorder 52.4 (52.1-52.7) U71 Cystitis/urinary 4.3 (4.2-4.4) T93 Lipid disorder 44.6 (44.3-44.9) H81 Excessive ear wax 3.9 (3.8-4.0) infection other T82 Obesity 24.9 (24.7-25.1) R78 Acute bronchitis/ 3.8 (3.7-3.9) Y85 Benign prostatic 28.4 (28.1-28.7) R78 Acute bronchitis/ 3.5 (3.4-3.6) bronchiolitis hypertrophy bronchiolitis Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 5 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 1 Five highest cumulative incidence of acute and prevalence of chronic diseases by sex and age groups (Continued) L95 Osteoporosis 22.8 (22.5-23.0) H81 Excessive ear wax 3.0 (2.9-3.1) T90 Diabetes non-insulin 25.6 (25.4-25.9) D82 Teeth/gum disease 2.4 (2.3-2.5) dependent P76 Depressive disorder 22.3 (22.1-22.5) L03 Low back symptom/ 2.8 (2.7-2.9) T82 Obesity 15.4 (15.2-15.6) L03 Low back symptom/ 2.3 (2.2-2.4) complaint complaint 80+ K86 Hypertension 73.1 (72.7-73.4) R74 Upper respiratory 5.1 (4.9-5.2) K86 Hypertension 63.4 (62.9-63.9) H81 Excessive ear wax 5.8 (5.6-6.1) uncomplicated infection acute uncomplicated T93 Lipid disorder 44.5 (44.1-44.9) U71 Cystitis/urinary 5.0 (4.9-5.2) Y85 Benign prostatic 37.3 (36.8-37.8) R74 Upper respiratory 5.4 (5.1-5.6) infection other hypertrophy infection acute L91 Osteoarthrosis other 25.7 (25.4-26.1) R78 Acute bronchitis/ 4.5 (4.4-4.7) T93 Lipid disorder 35.0 (34.5-35.5) R78 Acute bronchitis/ 4.9 (4.7-5.2) bronchiolitis bronchiolitis F92 Cataract 23.5 (23.2-23.8) H81 Excessive ear wax 4.2 (4.0-4.4) T90 Diabetes non-insulin 25.4 (24.9-25.9) S18 Laceration/cut 3.8 (3.6-4.0) dependent T90 Diabetes non- 22.8 (22.5-23.1) S18 Laceration/cut 3.4 (3.3-3.6) F92 Cataract 21.9 (21.4-22.3) U71 Cystitis/urinary 2.7 (2.6-2.9) insulin dependent infection other Abbreviations: ICPC 2 International Classification of Primary Care, CI Confidence interval. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 6 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 2 Multimorbidity prevalence and acute diseases, area of residency and visits according to multimorbidity status stratified by sex and age groups Female 19-24 25-44 45-64 65-79 ≥80 MM Non-MM MM Non-MM MM Non-MM MM Non-MM MM Non-MM n = 12,804 n = 54,700 n = 105,463 n = 253,771 n = 167,778 n = 97,089 n = 119,528 n = 13,021 n = 58,533 n = 5,021 (19.0%) (81.0%) (29.4%) (70.6%) (63.3%) (36.7%) (90.2%) (9.8%) (92.1%) (7.9%) Number of acute diseases Median (IQR) 1 (0–2) 0 (0–1) 1 (0–2) 0 (0–1) 1 (0-2) 0 (0–1) 1 (0–1) 0 (0–0) 0 (0–1) 0 (0–0) 0 36.4 55.3 40.2 60.3 44.8 68.2 47.7 78.0 51.5 82.3 1 26.8 23.7 27.1 22.3 27.6 19.4 27.4 14.9 26.2 12.3 2 16.9 11.6 16.1 10.2 14.7 7.8 13.7 4.9 12.7 3.7 ≥3 19.9 9.4 16.6 7.2 12.9 4.6 11.2 2.3 9.7 1.8 Living in a rural 14.4 14.9 15.3 15.1 15.4 16.7 15.4 17.6 18.4 18.7 area Number of visits Median (IQR) 6 (3–10) 2 (0–5) 6 (3–11) 2 (0–5) 8 (4–13) 2 (0–5) 11 (7–18) 2 (0–5) 14 (8–24) 1 (0–7) 0 8.8 28.4 9.8 34.2 5.8 38.3 2.6 38.9 3.0 40.4 1-2 15.4 24.4 14.7 22.9 10.4 21.6 4.4 18.6 4.2 16.2 3-5 25.0 23.6 23.1 21.5 20.5 20.1 11.9 18.0 9.1 14.0 6-10 27.1 15.8 26.7 14.5 28.7 13.5 26.5 15.5 20.4 13.4 ≥11 23.6 7.8 25.7 6.9 34.6 6.4 54.7 9.0 63.4 16.0 Male 19-24 25-44 45-64 65-79 ≥80 MM Non-MM MM Non-MM MM Non-MM MM Non-MM MM Non-MM n = 8,916 n = 60,373 n = 75,556 n = 311,124 n = 139,776 n = 119,008 n = 97,044 n = 14,584 n = 32,773 n = 2,948 (12.9%) (87.1%) (19.5%) (80.5%) (54.0%) (46.0%) (86.9%) (13.1%) (92.0%) (8.0%) Number of acute diseases Median (IQR) 1 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–0) 0 (0–1) 0 (0–0) 0 49.0 64.6 50.8 68.1 55.3 74.0 53.2 76.4 52.0 78.9 1 26.1 21.7 26.4 20.0 26.0 17.3 26.9 16.0 27.1 13.7 2 13.7 8.4 12.8 7.6 11.3 5.9 12.0 5.3 11.9 4.6 ≥3 11.1 5.2 10.0 4.4 7.4 2.8 8.0 2.3 8.9 2.8 Living in a rural 15.0 15.1 15.6 15.3 17.3 17.5 16.7 18.8 21.3 21.5 area Number of visits Median (IQR) 4 (1–8) 1 (0–4) 5 (2–9) 1 (0–3) 6 (3–12) 1 (0–3) 11 (6–17) 2 (0–6) 14 (8–24) 2 (0–8) 0 15.0 38.7 14.2 43.8 8.9 46.1 2.8 36.0 2.3 35.2 1-2 21.6 26.3 18.4 24.0 13.4 21.7 5.3 19.4 3.5 16.3 3-5 26.1 19.8 24.4 18.0 22.1 17.1 13.4 18.8 8.8 15.5 6-10 22.5 10.8 23.2 10.0 26.9 10.3 28.1 16.1 20.8 14.2 ≥11 14.8 4.4 19.8 4.2 28.7 4.7 50.4 9.7 64.5 18.8 Abbreviations: MM multimorbidity, IQR interquartile range. Data are expressed as percentage, unless otherwise stated. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 7 of 11 http://www.biomedcentral.com/1471-2296/15/55 19-24 25-44 45-64 65-79 80+ Female Male Chronic diseases: green and red lettering. Acute disease: blue lettering. Value of observed prevalence expressed as percentage. * Expected co-occurrence of diseases was obtained as the product of the separate prevalences, assuming statistical independence of the diseases concerned. Figure 1 Most prevalent multimorbidity patterns of two chronic diseases and the corresponding acute disease with the highest observed/expected ratio , by sex and age groups. groups. The most frequent multimorbidity pattern of Some possible biases could have influenced our results. chronic diseases was the combination of hypertension First, diseases could be underreported, especially for male and dyslipidemia in adults over 45 years of age. of normal workforce age who tend to see their doctors less We observed a decrease in the number of acute dis- often than other strata of patients. This effect would dimin- eases recorded as age increased. Nonetheless, in adjusted ish in the two oldest age groups because of the retirement models female older than 65 who had acute diseases age (65 years) in Spain. In patients with multimorbidity, the were more likely to have multimorbidity. true incidence of acute disease could be underreported be- Finally, the use of health services was positively associ- cause theGPwould placehigherpriorityonthe chronic ated with a diagnosis of multimorbidity. Living in a rural problems in these patients. On the other hand, there could area decreased the probability of multimorbidity. be an over-representation of chronic diagnoses (e.g., hyper- tension, diabetes, hyperlipidemia, etc.) that are included in the goals/incentives contracts of Catalan PHCT. The diseases that form part of the CatSalut treatment Strengths and weaknesses of the study objectives may be more carefully recorded than other con- A major strength of this study is the analysis of a large, ditions. However, these same diseases are the most preva- high-quality database of primary-care records, represen- lent (high blood pressure, diabetes, hypercholesterolemia, tative of a large population. In the context of a national smoking, dyslipidemia, ischemic heart disease, atrial fibril- health system with universal coverage, EHR data have lation) and therefore have the greatest impact on popula- been shown to yield more reliable and representative tion health. The quality-recorders database (SIDIAP-Q) conclusions than those derived from survey-based studies used in this study minimizes the under-reporting of dis- [16]. Another important strength was the inclusion of all eases not included in the CatSalut objectives. chronic and acute diagnoses registered in EHR, which Furthermore, the stratified analysis allows more accurate contributed to a more accurate analysis of the association estimation within each age-sex strata and universal access between acute and chronic diseases and of the disease to free health care and medications makes it more likely combinations present in multimorbidity in this popula- that patients seeking care will acquire a diagnosis, either tion. To synthesize the results, we present here only the acute or chronic [17,18]. Second, there is no universally most frequent combinations. Finally, few studies have in- accepted criterion for consensus classification of acute and corporated acute diseases in the study of multimorbidity chronic disease. This lack of accurate case definitions im- patterns [8] and none analyzed the relationship between pedes the establishment of the true incidence/prevalence multimorbidity and acute morbidity. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 8 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 3 Cumulative incidence and risk ratio (RR) of multimorbidity for the five acute diseases with the highest cumulative incidence by sex and age groups* Female Male Age ICPC 2 Acute diseases MM Non-MM Risk ratio 95% confidence ICPC 2 Acute diseases MM Non-MM Risk ratio 95% confidence groups code CI CI interval code CI* CI interval (%) (%) (%) (%) 19-24 R74 Upper respiratory infection, acute 16.2 8.8 1.10 (1.04-1.16) R74 Upper respiratory infection, acute 11.9 7.0 0.96 (0.89-1.03) R76 Tonsillitis, acute 6.7 4.3 0.94 (0.86-1.02) R76 Tonsillitis, acute 5.5 3.4 0.92 (0.83-1.02) U71 Cystitis/urinary infection, other 6.3 4.1 0.85 (0.78-0.93) D73 Gastroenteritis, presumed infection 4.8 2.8 0.93 (0.83-1.04) D82 Teeth/gum disease 6.2 3.6 1.14 (1.04-1.25) D82 Teeth/gum disease 4.3 2.7 0.63 (0.44-0.89) D73 Gastroenteritis, presumed infection 6.3 3.4 1.06 (0.97-1.16) S16 Bruise/contusion 3.2 2.4 0.75 (0.66-0.86) 25-44 R74 Upper respiratory infection, acute 12.8 6.9 1.01 (0.98-1.03) R74 Upper respiratory infection, acute 9.9 5.7 0.85 (0.82-0.87) L03 Low back symptom/complaint 4.4 3.1 0.73 (0.71-0.76) D73 Gastroenteritis, presumed infection 3.8 2.3 0.79 (0.76-0.83) U71 Cystitis/urinary infection other 4.4 2.6 0.86 (0.83-0.90) L03 Low back symptom/complaint 3.5 2.2 0.75 (0.71-0.78) D73 Gastroenteritis presumed infection 4.3 2.5 0.90 (0.87-0.94) D82 Teeth/gum disease 3.5 2.2 0.82 (0.72-0.93) R76 Tonsillitis, acute 3.6 2.5 1.08 (0.91-1.29) R76 Tonsillitis, acute 2.4 1.8 0.69 (0.65-0.73) 45-64 R74 Upper respiratory infection, acute 9.7 4.5 0.92 (0.88-0.95) R74 Upper respiratory infection, acute 6.7 3.4 0.79 (0.76-0.82) L03 Low back symptom/complaint 3.9 2.1 0.78 (0.74-0.82) L03 Low back symptom/complaint 3.2 2.1 0.58 (0.55-0.62) U71 Cystitis/urinary infection, other 4.0 1.7 0.86 (0.81-0.92) R78 Acute bronchitis/bronchiolitis 3.0 1.3 0.86 (0.80-0.92) R78 Acute bronchitis/bronchiolitis 3.9 1.6 0.95 (0.89-1.01) D82 Teeth/gum disease 2.9 1.6 0.83 (0.70-0.98) L20 Joint symptom/complaint NOS 3.3 1.5 0.97 (0.82-1.15) H81 Excessive ear wax 2.5 1.4 0.74 (0.69-0.78) 65-79 R74 Upper respiratory infection, acute 8.4 3.2 0.92 (0.70-1.20) R74 Upper respiratory infection, acute 7.6 3.3 0.93 (0.84-1.03) U71 Cystitis/urinary infection other 5.1 1.4 1.09 (0.91-1.30) H81 Excessive ear wax 4.4 2.3 0.74 (0.65-0.83) R78 Acute bronchitis/bronchiolitis 4.5 1.3 0.71 (0.36-1.39) R78 Acute bronchitis/bronchiolitis 4.2 1.5 0.99 (0.85-1.14) H81 Excessive ear wax 3.3 1.2 0.88 (0.74-1.04) D82 Teeth/gum disease 2.9 1.2 1.31 (1.10-1.57) L03 Low back symptom/complaint 3.0 1.1 0.76 (0.54-1.07) L03 Low back symptom/complaint 2.5 1.1 0.87 (0.73-1.03) 80+ R74 Upper respiratory infection, acute 5.8 1.5 1.47 (1.16-1.86) H81 Excessive ear wax 6.5 2.7 0.98 (0.77-1.24) U71 Cystitis/urinary infection, other 5.9 1.5 1.40 (1.10-1.78) R74 Upper respiratory infection, acute 6.2 2.0 1.29 (0.98-1.70) R78 Acute bronchitis/bronchiolitis 5.3 2.0 0.99 (0.80-1.23) R78 Acute bronchitis/bronchiolitis 5.7 2.0 1.06 (0.80-1.40) H81 Excessive ear wax 4.7 1.3 1.34 (1.04-1.73) S18 Laceration/cut 4.3 2 0.75 (0.57-0.99) S18 Laceration/cut 3.9 1.6 0.78 (0.61-0.99) U71 Cystitis/urinary infection, other 3.0 1.5 0.73 (0.53-1.01) Abbreviations: MM multimorbidity, RR risk ratio, ICPC 2 International Classification of Primary Care, CI Cumulative incidence, NOS not otherwise specified. *Cumulative incicidence was calculated as the sum of the number of new acute events during the study period divided by the 2010 population (e.g. if a patient passed two bronchitis in one year. the total number of events accounted for was two). The outcome is the number of acute diseases in each person for each acute disease considered. RR of multimorbidity versus non-multimorbidity was calculated by Poisson, negative binomial (if overdispersion is present) or zero inflated (when data showed excess of zero counts) equation as appropriate and adjusted for number of visits and area of residency. In bold P < 0.05. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 9 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 4 Factors associated with multimorbidity by sex and age groups Female 19-24 25-44 45-64 65-79 80+ OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value Number of acute diseases(ref.0) 0.030 <0.001 <0.001 <0.001 <0.001 1 0.94 0.89-0.99 0.88 0.86-0.90 0.82 0.80-0.84 0.98 0.92-1.03 1.16 1.05-1.28 2 0.95 0.89-1.01 0.86 0.83-0.88 0.76 0.73-0.78 1.05 0.96-1.15 1.54 1.31-1.80 ≥3 1.02 0.95-1.09 0.89 0.87-0.92 0.78 0.75-0.81 1.29 1.14-1.46 1.99 1.59-2.47 Rural area (ref. urban) 0.94 0.89-0.99 0.025 0.94 0.92-0.96 <0.001 0.80 0.78-0.82 <0.001 0.69 0.65-0.73 <0.001 0.76 0.70-0.83 <0.001 Number of visits (ref. 0) <0.001 <0.001 <0.001 <0.001 <0.001 1-2 2.09 1.93-2.26 2.34 2.28-2.14 3.40 3.30-3.51 3.62 3.38-3.87 3.41 3.08-3.78 3-5 3.52 3.25-3.80 4.04 3.93-4.15 7.54 7.31-7.77 10.18 9.54-10.86 8.36 7.55-9.27 6-10 5.69 5.25-6.17 7.11 6.91-7.32 16.28 15.76-16.81 25.98 24.28-27.80 18.86 16.99-20.94 ≥11 9.93 9.09-10.85 14.52 14.05-14.99 43.03 41.39-44.73 90.81 83.74-98.48 45.19 40.69-50.19 Male 19-24 25-44 45-64 65-79 80+ OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value Number of acute diseases(ref.0) <0.001 <0.001 <0.001 <0.001 0.590 1 0.78 0.73-0.83 0.71 0.70-0.73 0.67 0.65-0.69 0.84 0.80-0.89 1.07 0.95-1.21 2 0.79 0.73-0.86 0.64 0.62-0.66 0.58 0.56-0.60 0.78 0.72-0.85 1.08 0.89-1.31 ≥3 0.76 0.69-0.83 0.60 0.58-0.62 0.55 0.52-0.57 0.86 0.77-0.97 1.12 0.88-1.42 Rural area (ref. urban) 0.94 0.88-1.00 0.045 0.95 0.93-0.97 <0.001 0.87 0.85-0.89 <0.001 0.71 0.68-0.75 <0.001 0.79 0.71-0.88 <0.001 Number of visits (ref. 0) <0.001 <0.001 <0.001 <0.001 <0.001 1-2 2.32 2.15-2.50 2.67 2.60-2.75 3.57 3.47-3.67 3.58 3.35-3.82 3.33 2.89-3.85 3-5 3.93 3.64-4.25 5.18 5.04-5.33 8.20 7.97-8.43 9.66 9.06-10.30 8.62 7.50-9.92 6-10 6.51 5.97-7.09 9.62 9.33-9.92 17.78 17.24-18.34 24.51 22.93-26.19 22.07 19.13-25.47 ≥11 10.86 9.82-12.01 20.58 19.86-21.32 45.13 43.43-46.90 76.41 70.68-82.60 51.32 44.49-59.19 Logistic regression. Abbreviations: OR odds ratio, CI confidence interval, ref. reference. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 10 of 11 http://www.biomedcentral.com/1471-2296/15/55 of a disease [19]. Finally, a residual confounding cannot be the implementation of personalized disease prevention completely excluded, and could occur because of epi- and health promotion activities. demiological factors not considered in this study, such as At the level of health policy and health care administra- patients’ socioeconomic status [20]. tion, the organization of services should be reviewed to ensure that continuity and coordination of patient care are Strengths and weaknesses in relation to other studies guaranteed; current evidence suggests the potential for The estimated multimorbidity prevalence in our sample improvement in this regard [35]. is higher than in other European studies [21-24], perhaps because of the analysis of a greater number of diseases Unanswered questions and future research in our study than in most other published studies [25]. The classification of chronic and acute disease remains Nonetheless, the patterns of multimorbidity observed unresolved, and there is no consensus on the type and were similar to those observed in other studies [26]. number of chronic diseases that define multimorbidity. As in other studies, multimorbidity was more prevalent A personalized measure to determine the severity of among female [21,22,24]. This could be due to the longer diagnosed multimorbidity is also needed. female life expectancy and worse health status, compared If longitudinal studies confirm a higher incidence of to male, differences that are due to both biological and so- morbidity in patients with multimorbidity, evidence-based cial factors [26]. In addition, sex is a social determinant interventions will be needed to prevent the onset of acute that influences health status, health behaviours and the use disease. Further studies are needed to study possible gen- of health services [27-30]. Recent studies, however, suggest etic and pathophysiological explanations that corroborate a dismantling of this paradigm based on sex-stratified ana- the observed multimorbidity patterns. lysis of consultations for common symptoms [31]. In-depth analysis of other contextual factors related to Acute problems are time-consuming for health profes- multimorbidity is also required, along with studies of the sional [32], and therefore should be considered part of the relationship between area of residence and multimorbidity primary care workload. Although current health policy, and of the differences in health status that may exist be- health care services, and research are all heavily focused tween different territories. Finally, there is a need for the on chronic diseases, we must not forget that 41% of pri- implementation and evaluation of health literacy and self- mary care visits are motivated by an acute disease [33]. management interventions to improve patient competence The incidence of acute diseases observed in our study in resolving routine acute diseases, which in turn will concurs with other reports of acute upper respiratory decrease the care burden in primary care systems. infection and other health problems of infectious aeti- ology (acute tonsillitis, cystitis) as the primary reasons Conclusions for seeking primary care, along with non-infectious dis- Multimorbidity is related to more use of health services eases such as dorsalgia [33,34]. and higher incidence of acute diseases, which increases Our study observed a higher prevalence of multimorbid- the burden on primary care services. Residence in urban ity in rural settings. Other studies conducted in rural areas vs rural settings is a factor for future in-depth study. have reported only a greater prevalence of multimorbidity The association of acute morbidity, area of residency in elderly people [6,7]. Nonetheless, our adjusted analysis or use of health services with multimorbidity differs showed that living in a rural area is negatively associated according to life-stage and sex. Therefore, the study of with multimorbidity. This phenomenon could be due to multimorbidity should be stratified by life-stage and sex. the environmental and sociocultural context and access to Understanding these trends across life-stages will allow both public and private services [28]. health systems to adjust their clinical and management models to adapt and prioritize interventions. Implications for clinicians and policymakers Abbreviations Our study considered multimorbidity in patients who PHCT: Primary health care teams; CHI: Catalan Health Institute; received primary medical care, considering all visits and EHR: Electronic health records; SIDIAP: Information System for the Development of Research in Primary Care; GP: General practitioners; diagnoses (acute and chronic diseases). This approach ICD: International Classification of Diseases; ICPC: International Classification allowed the identification of vulnerable subgroups in of Primary Care; IDIAP: Institut Universitari d’Investigació en Atenció Primària; our population. A major advantage of our methodology SD: Standard deviation; IQR: Interquartile range; RR: Risk ratios. is the use of data obtained directly from standard clin- Competing interests ical practice. Knowing the distribution of acute and The authors declare that they have no competing interests. chronic diseases by life-stage and sex will help the clin- Authors’ contributions ician faced with a particular patient to anticipate disease All authors contributed to the design of the study, revised the article, and patterns based on the patient’s sex and stage of life, rec- approved the final version. CV, QFB, JMV, MMP, ARL drafted the study ognizing that these vary with age. This will encourage protocol and obtained the funding. TRB, CV, QFB, JMV, ARL contributed to Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 11 of 11 http://www.biomedcentral.com/1471-2296/15/55 the analysis and interpretation of data. CV, QFB, JMV, TRB, ARL, MPV, MMP, 16. Violán C, Foguet-Boreu Q, Hermosilla-Pérez E, Valderas JM, Bolíbar B, Fàbregas- and EPR wrote the first draft, and all authors contributed ideas, interpreted Escurriola M, Brugulat-Guiteras P, Muñoz-Pérez MÁ: Comparison of the the findings and reviewed rough drafts of the manuscript. All authors read information provided by electronic health records data and a popula- and approved the final manuscript. tion health survey to estimate prevalence of selected health conditions and multimorbidity. BMC Public Health 2013, 13:251. 17. Jordan K, Porcheret M, Croft P: Quality of morbidity coding in general Acknowledgments practice computerized medical records: a systematic review. Fam Pract We thank the Catalan Health Institute and especially the SIDIAP Unit, which 2004, 21:396–412. provided the database for the study. The authors also appreciate the English 18. Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M: Defining language review by Elaine Lilly, PhD. comorbidity: implications for understanding health and health services. Ann Fam Med 2009, 7:357–363. Funding 19. de Lusignan S, Tomson C, Harris K, van Vlymen J, Gallagher H: Creatinine This study was funded by the Ministry of Science and Innovation through fluctuation has a greater effect than the formula to estimate glomerular the Instituto Carlos III (ISCiii) as part of the Primary Care Prevention and filtration rate on the prevalence of chronic kidney disease. Nephron Clin Health Promotion Research Network (rediAPP), by ISCiii-RETICS (RD12/0005), Pract 2011, 117:c213–c224. by a grant for research projects ISCiii (PI12/00427), and by a 2011–2013 20. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B: Epidemiology scholarship that aims to promote research in Primary Health Care by health of multimorbidity and implications for health care, research, and professionals who have completed their specialty training, awarded by Institut medical education: a cros-sectional study. Lancet 2012, 380:37–43. Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol). The 21. Prados-Torres A, Poblador-Plou B, Calderón-Larrañaga A, Gimeno-Feliu LA, funders had no role in the study design, collection, analysis and interpretation González-Rubio F, Poncel-Falcó A, Sicras-Mainar A, Alcalá-Nalvaiz JT: of data, writing of the manuscript and decision to submit for publication. Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis. PLoS One 2012, 7:e32190. Author details 1 22. Glynn LG, Valderas JM, Healy P, Burke E, Newell J, Gillespie P, Murphy AW: Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi 2 The prevalence of multimorbidity in primary care and its effect on Gol), Gran Via Corts Catalanes, 587 àtic, 08007 Barcelona, Spain. Universitat health care utilization and cost. Fam Pract 2011, 28:516–523. Autònoma de Barcelona, Plaza Cívica, Campus de la UAB, 08193 Bellaterra, 3 23. Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA: Cerdanyola del Vallès, Spain. Hospital de Campdevànol, Ctra. de Gombrèn, 4 Epidemiology and impact of multimorbidity in primary care: a 20, 17530 Campdevànol, Spain. Health Services & Policy Research Group, retrospective cohort study. Br J Gen Pract 2011, 61:e12–e21. School of Medicine, University of Exeter, Exeter EX1 2LU, UK. 24. García-Olmos L, Salvador CH, Alberquilla Á, Lora D, Carmona M, García- Sagredo P, Pascual M, Muñoz A, Monteagudo JL, García-López F: Received: 31 December 2013 Accepted: 21 March 2014 Comorbidity patterns in patients with chronic diseases in general Published: 26 March 2014 practice. PLoS One 2012, 7:e32141. 25. Marengoni A, Angleman S, Melis R, Mangialasche F, Karp A, Garmen A, References Meinow B, Fratiglioni L: Aging with multimorbidity: a systematic review of 1. Bodenheimer T, Wagner EH, Grumbach K: Improving primary care for the literature. Ageing Res Rev 2011, 10:430–439. patients with chronic illness. JAMA 2002, 288:1775–1779. 26. Laux G, Kuehlein T, Rosemann T, Szecsenyi J: Co- and multimorbidity 2. Van den Akker M, Buntinx F, Knottnerus JA: Comorbidity or multimorbidity: patterns in primary care based on episodes of care: results from the what’s in a name? A review of literature. Eur J Gen Pract 1996, 2:65–70. German CONTENT project. BMC Health Serv Res 2008, 8:14. 3. 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O’Halloran J, Miller GC, Britt H: Defining chronic conditions for primary Cite this article as: Foguet-Boreu et al.: Impact of multimorbidity: acute care with ICPC-2. Fam Pract 2004, 21:381–386. morbidity, area of residency and use of health services across the life 15. Defining Chronic Conditions for Primary Care Using ICPC-2. Available in: span in a region of south Europe. BMC Family Practice 2014 15:55. http://www.fmrc.org.au/Download/DefiningChronicConditions.pdf. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png BMC Family Practice Springer Journals

Impact of multimorbidity: acute morbidity, area of residency and use of health services across the life span in a region of south Europe

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Springer Journals
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Copyright © 2014 by Foguet-Boreu et al.; licensee BioMed Central Ltd.
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Medicine & Public Health; General Practice / Family Medicine; Primary Care Medicine
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1471-2296
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Abstract

Background: Concurrent diseases, multiple pathologies and multimorbidity patterns are topics of increased interest as the world’s population ages. To explore the impact of multimorbidity on affected patients and the consequences for health services, we designed a study to describe multimorbidity by sex and life-stage in a large population sample and to assess the association with acute morbidity, area of residency and use of health services. Methods: A cross-sectional study was conducted in Catalonia (Spain). Participants were 1,749,710 patients aged 19+ years (251 primary care teams). Primary outcome: Multimorbidity (≥2 chronic diseases). Secondary outcome: Number of new events of each acute disease. Other variables: number of acute diseases per patient, sex, age group (19–24, 25–44, 45–64, 65–79, and 80+ years), urban/rural residence, and number of visits during 2010. Results: Multimorbidity was present in 46.8% (95% CI, 46.7%-46.8%) of the sample, and increased as age increased, being higher in women and in rural areas. The most prevalent pair of chronic diseases was hypertension and lipid disorders in patients older than 45 years. Infections (mainly upper respiratory infection) were the most common acute diagnoses. In women, the highest significant RR of multimorbidity vs. non-multimorbidity was found for teeth/gum disease (aged 19–24) and acute upper respiratory infection. In men, this RR was only positive and significant for teeth/gum disease (aged 65–79). The adjusted analysis showed a strongly positive association with multimorbidity for the oldest women (80+ years) with acute diseases and women aged 65–79 with 3ormoreacute diseases, compared to patients with no acute diseases (OR ranged from 1.16 to 1.99, p < 0.001). Living in a rural area was significantly associated with lower probability of having multimorbidity. The odds of multimorbidity increased sharply as the number of visits increased, reaching the highest probability in those aged 65–79 years. Conclusions: Multimorbidity is related to greater use of health care services and higher incidence of acute diseases, increasing the burden on primary care services. The differences related to sex and life-stage observed for multimorbidity and acute diseases suggest that further research on multimorbidity should be stratified according to these factors. Keywords: Multimorbidity, Chronic disease, Acute disease, Life-stage * Correspondence: qfoguet@idiapjgol.org Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol), Gran Via Corts Catalanes, 587 àtic, 08007 Barcelona, Spain Universitat Autònoma de Barcelona, Plaza Cívica, Campus de la UAB, 08193 Bellaterra, Cerdanyola del Vallès, Spain Full list of author information is available at the end of the article © 2014 Foguet-Boreu et al.; licensee BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 2 of 11 http://www.biomedcentral.com/1471-2296/15/55 Background clinical information from the EHR system. A subset of Concurrent diseases, multiple pathologies and multimor- SIDIAP records meeting the highest quality criteria for bidity patterns are topics of increased interest as the clinical data (SIDIAP-Q) includes 40% of the SIDIAP world’s population ages [1]. Multimorbidity is the coex- population (1,936,443 patients), attended by the 1,319 istence of two or more chronic health problems in the general practitioners (GP) whose data recording scored same person at one point in time [2], and multimorbidity highest in a validated comparison process. The sample patterns are any combination of chronic diseases [3]. Both is representative of the general Catalan population in considerations have important consequences for the indi- terms of geography, age and sex distributions, according vidual and for health services [4]. Multimorbidity is a chal- to the official 2010 census [13]. lenge for industrialized countries and can jeopardize the A sample of 1,749,710 patients aged 19 years or older, viability of national health systems. assigned to 251 PHCT during the period of study (1 Traditionally, the construct of multimorbidity has been January- 31 December 2010), was selected from the inherently associated with persistent or chronic disease. SIDIAP-Q database. Methods to measure multimorbidity include disease scores, case-mix systems, indexes and disease counts, Coding of diseases the latter being the common method [5]. Far too little International Classification of Diseases (ICD-10) codes were attention has been paid to the use of health services and mapped to the International Classification of Primary Care the role of urban or rural residency in patients with (ICPC-2e-v.4.2, available at: http://www.kith.no/templates/ multimorbidity [6,7]. Furthermore, the literature lacks kith_WebPage____1111.aspx). R codes (symptoms, signs comparisons by sex of acute morbidity in patients with and abnormal clinical and laboratory findings, not else- multimorbidity in a large population sample [8,9]. where classified) and Z codes (factors influencing health The classification of acute and chronic disease remains status and contact with health services) were excluded, controversial. Acute disease is characterized by a single or resulting in 686 included codes. Each diagnosis was then repeated episode of relatively rapid onset and short dur- classified using O’Halloran criteria for chronic disease [14]. ation with a recovery to previous stage of activity [10]. We included all 146 diagnoses considered as chronic Nevertheless, some diseases fall into a grey area. Know- diseases by O’Halloran criteria: (i) have a duration that ledge of specific acute diseases that may occur more fre- has lasted, or is expected to last, at least 6 months; (ii) quently than expected and of the underlying vulnerabilities have a pattern of recurrence or deterioration; (iii) have a [11] could help to focus attention on the patients with poor prognosis and (iv) produce consequences, or se- multimorbidity rather than emphasizing the diseases. quelae, that have an impact on the individual’squality of To explore the impact of multimorbidity on affected life [14,15]. Any disease not meeting the O’Halloran cri- patients and the consequences for health services, we teria was considered an acute disease. designed a study to describe multimorbidity by sex and All results were described with ICPC-2 codes. Diseases life-stage in a large population sample and to assess the were classified as acute if diagnosed during the study association with acute morbidity, area of residency and period and chronic if recorded as such in EHR as of 31 use of health services. December 2010. Methods Outcomes and variables Data source and study population The main outcome was multimorbidity, defined as the Cross-sectional study of adults resident in Catalonia, a coexistence of 2 or more chronic diseases. Secondary Mediterranean region of southern European with 7,434,632 outcome was the number of new events of each acute inhabitants (2010 census), 16% of the population of disease. Other variables recorded for each patient were Spain. In Catalonia, 358 primary health care teams the following: number of all acute diseases (0, 1, 2, > = 3), (PHCT) comprised of doctors, nurses, social workers sex (male, female), age (young adult, 19 to 24; adult, 25– and support staff are assigned by geographical area and 44; older adult, 45–64; elderly, 65–79; and oldest adult, responsible for the health care of the population in their 80+), number of visits during the study period (0, 1–2, areas. The Catalan Health Institute (CHI) manages 274 3–5, 6–10, ≥11), and area of residence (rural if <10,000 PHCT (76.5%), serving a population of 4,859,725 adults; inhabitants and/or population density <150 people/km , the remaining PHCT are managed by other providers. otherwise urban) [14]. Number of all acute diseases (or Doctors and nurses systematically use electronic health 0 diseases) and visits (or 0 visits) were categorized as records (EHR) to record diagnoses, prescriptions and quartiles of the study population. Number of visits was other clinical, patient management and administrative ac- used as a proxy of use of health services and included tivities. The CHI Information System for the Development visits recorded in EHR by GP, nurses or social workers, of Research in Primary Care (SIDIAP) [12] compiles coded either at the primary care centre or as home health care. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 3 of 11 http://www.biomedcentral.com/1471-2296/15/55 Statistical analysis aged 19–44); acne (men aged 19–24) and lipid disorder Analysis was stratified by sex and age group. Descriptive (men aged 25–44). After 45 both sex groups first chronic statistics were used to summarize overall information. disease was lipid disorder in 45–64 and uncomplicated Categorical variables were expressed as frequencies (per- hypertension in 65-80+ (Table 1). Upper respiratory infec- centage) and continuous as mean (Standard deviation, SD) tion acute is the most incident acute disease in all age or median (interquartile range, IQR). groups (except in 80+). Cumulative incidence of acute morbidity events was Multimorbidity prevalence increased as age increased, calculated as the number of new acute events during the being higher in female (ranged from 19.0% to 92.1%) than study period divided by the at-risk population in the male (ranged from 12.9% to 92.0%). In patients with multi- sample (e.g., if a patient had bronchitis twice in the one- morbidity, the number of acute diseases was higher in year study period, the total number of events accounted female than male and decreased as age increased, except for was 2). We took into account the five acute diseases in male older than 65. In addition, the number of visits with the highest cumulative incidence within each stratum. increased as age increased, and was higher for female Risk ratios (RR) of multimorbidity vs. non-multimorbidity than male in all age groups except 80+ (Table 2). were calculated for the number of events for each acute Patients with multimorbidity had a higher incidence of disease, using Poisson, negative binomial (if overdispersion acute diseases and number of visits in all age strata than was present) or zero inflated (when data had an excess of non-multimorbidity patients; in both cases, the incidence zero counts) equations, as appropriate. All models were was higher for female than male (Table 2). Overall, the adjusted for number of visits and area of residency. median (IQR) of number of visits was 8(4–14) in patients To determine the most prevalent multimorbidity pat- with multimorbidity vs. 1(0–4) in the non-multimorbidity terns, all possible combinations of any two chronic dis- group. eases and their frequencies were calculated. Observed (O) The two most prevalent combinations of two chronic and expected (E) prevalence of those two chronic diseases diseases were hypertension and lipid disorders in patients with each acute disease was then computed. Expected co- older than 45 years. The only acute disease that appeared occurrence of diseases was obtained as the product of in both sexes was “bursitis/tendinitis/synovitis NOS” in these prevalences, assuming statistical independence of the oldest age group (80+). In the other age groups, the the diseases concerned. The overlapping of those combi- acute health disease varied by sex (Figure 1). nations that presented the highest O/E ratio was reported. The five acute diseases with the highest cumulative in- Logistic regression was used to assess the association cidence were similar by sex in any age group. Infections between multimorbidity and the variables listed above. were the most common diagnosis. Cystitis/urinary infec- All statistical tests were two-sided at the 5% signifi- tion was present among the five most prevalent acute cance level. The analyses were performed using SPSS for conditions only in women and in the oldest men. In Windows, version 18 (SPSS Inc., Chicago, IL, USA), Stata/ women, the highest significant RR of multimorbidity vs. SE version 11 for Windows (Stata Corp. LP, College non-multimorbidity was found for teeth/gum disease Station, TX, USA) and R version 2.15.2 (R Foundation (aged 19–24) and upper respiratory infection, acute (80+). for Statistical Computing, Vienna, Austria). In men, this RR was only positive and significant for teeth/ gum disease (aged 65–79) (Table 3). Ethical considerations The adjusted analysis of factors associated with multi- The study protocol was approved by the Committee on morbidity showed that the oldest patients (80+ years) the Ethics of Clinical Research, Institut Universitari with acute diseases and women aged 65–79 with 3 or d’Investigació en Atenció Primària (IDIAP) Jordi Gol more acute diseases were more likely to have multimor- (Protocol No: P12/28). All data were anonymized and bidity than patients with no acute diseases. This positive the confidentiality of EHR was respected at all times in association was only significant in women. Living in a accordance with international law. rural area was significantly associated with lower prob- ability of having multimorbidity. Patients who visited a Results GP more often were more likely than those without We included 1,749,710 patients; mean age was 47.4 years visits to have multimorbidity, reaching the highest prob- (SD: 17.8), 50.7% were female and 16% lived in rural areas. ability in those aged 65–79 years (Table 4). Multimorbidity (≥ 2 diseases) was present in 46.8% (95% CI, 46.7%-46.8%) of the sample, being higher in female Discussion (52.3%) than in male (41.1%) and in rural areas (47.6%) Statement of principal findings than in urban areas (46.6%). Almost half of the study population had multimorbidity, The prevalence of the most common chronic diseases with infections (mainly acute upper respiratory infection) differed by sex below 45: anxiety disorder/anxiety (women the most common acute disease in both sexes and all age Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 4 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 1 Five highest cumulative incidence of acute and prevalence of chronic diseases by sex and age groups Female Male Age ICPC Chronic diseases Prevalence ICPC Acute diseases Cumulative ICPC Chronic diseases Prevalence ICPC Acute diseases Cumulative groups (%, CI) incidence (%, CI) (%, CI) incidence (%, CI) 19-24 P74 Anxiety disorder/ 8.4 (8.2-8.7) R74 Upper respiratory 9.1 (8.9-9.3) S96 Acne 7.7 (7.5-7.9) R74 Upper respiratory 6.9 (6.7-7.1) anxiety state infection acute infection acute S96 Acne 7.8 (7.6-8.0) R76 Tonsillitis acute 4.4 (4.3-4.6) R96 Asthma 6.0 (5.8-6.2) R76 Tonsillitis acute 3.4 (3.3-3.6) R96 Asthma 5.4 (5.2-5.6) U71 Cystitis/urinary 4.1 (4.0-4.3) P74 Anxiety disorder/ 3.9 (3.7-4.0) D73 Gastroenteritis 2.9 (2.8-3.0) infection other anxiety state presumed infection T82 Obesity 5.0 (4.8-5.1) D82 Teeth/gum disease 3.7 (3.5-3.8) T82 Obesity 3.4 (3.3-3.6) D82 Teeth/gum disease 2.5 (2.4-2.7) L85 Acquired deformity 4.6 (4.4-4.8) D73 Gastroenteritis 3.6 (3.5-3.8) L85 Acquired deformity 3.1 (3.0-3.2) S16 Bruise/contusion 2.3 (2.2-2.4) of spine presumed infection of spine 25-44 P74 Anxiety disorder/ 12.2 (12.1-12.3) R74 Upper respiratory 7.8 (7.7-7.9) T93 Lipid disorder 7.3 (7.2-7.4) R74 Upper respiratory 5.9 (5.8-5.9) anxiety state infection acute infection acute P76 Depressive disorder 8.8 (8.7-8.9) L03 Low back symptom/ 3.4 (3.3-3.5) P74 Anxiety disorder/ 6.5 (6.4-6.6) D73 Gastroenteritis 2.4 (2.4-2.5) complaint anxiety state presumed infection T82 Obesity 6.8 (6.7-6.9) U71 Cystitis/urinary 2.9 (2.8-2.9) T82 Obesity 4.4 (4.4-4.5) L03 Low back symptom/ 2.4 (2.3-2.4) infection other complaint T93 Lipid disorder 5.0 (4.9-5.0) D73 Gastroenteritis 2.8 (2.8-2.9) P76 Depressive disorder 3.7 (3.7-3.8) D82 Teeth/gum disease 2.1 (2.1-2.2) presumed infection N89 Migraine 4.9 (4.8-4.9) R76 Tonsillitis acute 2.6 (2.6-2.7) L86 Back syndrome with 3.5 (3.4-3.5) R76 Tonsillitis acute 1.8 (1.8-1.9) radiating pain 45-64 T93 Lipid disorder 28.4 (28.2-28.5) R74 Upper respiratory 7.0 (6.9-7.1) T93 Lipid disorder 29.9 (29.7-30.1) R74 Upper respiratory 4.8 (4.7-4.9) infection acute infection acute K86 Hypertension 21.2 (21.1-21.4) L03 Low back symptom/ 3.2 (3.1-3.3) K86 Hypertension 24.6 (24.4-24.7) L03 Low back symptom/ 2.6 (2.5-2.7) uncomplicated complaint uncomplicated complaint P76 Depressive disorder 18.9 (18.8-19.1) U71 Cystitis/urinary 2.9 (2.8-3.0) T82 Obesity 10.9 (10.8-11.0) R78 Acute bronchitis/ 2.0 (2.0-2.1) infection other bronchiolitis T82 Obesity 15.7 (15.6-15.9) R78 Acute bronchitis/ 2.8 (2.7-2.9) T90 Diabetes non-insulin 10.3 (10.2-10.5) D82 Teeth/gum disease 2.0 (2.0-2.1) bronchiolitis dependent P74 Anxiety disorder/ 13.5 (13.4-13.6) L20 Joint symptom/ 2.6 (2.6-2.7) L86 Back syndrome with 7.6 (7.5-7.7) H81 Excessive ear wax 1.9 (1.9-2.0) anxiety state complaint NOS radiating pain 65-79 K86 Hypertension 60.3 (60.0-60.6) R74 Upper respiratory 7.2 (7.1-7.3) K86 Hypertension 56.2 (55.9-56.5) R74 Upper respiratory 6.4 (6.2-6.5) uncomplicated infection acute uncomplicated infection acute T93 Lipid disorder 52.4 (52.1-52.7) U71 Cystitis/urinary 4.3 (4.2-4.4) T93 Lipid disorder 44.6 (44.3-44.9) H81 Excessive ear wax 3.9 (3.8-4.0) infection other T82 Obesity 24.9 (24.7-25.1) R78 Acute bronchitis/ 3.8 (3.7-3.9) Y85 Benign prostatic 28.4 (28.1-28.7) R78 Acute bronchitis/ 3.5 (3.4-3.6) bronchiolitis hypertrophy bronchiolitis Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 5 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 1 Five highest cumulative incidence of acute and prevalence of chronic diseases by sex and age groups (Continued) L95 Osteoporosis 22.8 (22.5-23.0) H81 Excessive ear wax 3.0 (2.9-3.1) T90 Diabetes non-insulin 25.6 (25.4-25.9) D82 Teeth/gum disease 2.4 (2.3-2.5) dependent P76 Depressive disorder 22.3 (22.1-22.5) L03 Low back symptom/ 2.8 (2.7-2.9) T82 Obesity 15.4 (15.2-15.6) L03 Low back symptom/ 2.3 (2.2-2.4) complaint complaint 80+ K86 Hypertension 73.1 (72.7-73.4) R74 Upper respiratory 5.1 (4.9-5.2) K86 Hypertension 63.4 (62.9-63.9) H81 Excessive ear wax 5.8 (5.6-6.1) uncomplicated infection acute uncomplicated T93 Lipid disorder 44.5 (44.1-44.9) U71 Cystitis/urinary 5.0 (4.9-5.2) Y85 Benign prostatic 37.3 (36.8-37.8) R74 Upper respiratory 5.4 (5.1-5.6) infection other hypertrophy infection acute L91 Osteoarthrosis other 25.7 (25.4-26.1) R78 Acute bronchitis/ 4.5 (4.4-4.7) T93 Lipid disorder 35.0 (34.5-35.5) R78 Acute bronchitis/ 4.9 (4.7-5.2) bronchiolitis bronchiolitis F92 Cataract 23.5 (23.2-23.8) H81 Excessive ear wax 4.2 (4.0-4.4) T90 Diabetes non-insulin 25.4 (24.9-25.9) S18 Laceration/cut 3.8 (3.6-4.0) dependent T90 Diabetes non- 22.8 (22.5-23.1) S18 Laceration/cut 3.4 (3.3-3.6) F92 Cataract 21.9 (21.4-22.3) U71 Cystitis/urinary 2.7 (2.6-2.9) insulin dependent infection other Abbreviations: ICPC 2 International Classification of Primary Care, CI Confidence interval. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 6 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 2 Multimorbidity prevalence and acute diseases, area of residency and visits according to multimorbidity status stratified by sex and age groups Female 19-24 25-44 45-64 65-79 ≥80 MM Non-MM MM Non-MM MM Non-MM MM Non-MM MM Non-MM n = 12,804 n = 54,700 n = 105,463 n = 253,771 n = 167,778 n = 97,089 n = 119,528 n = 13,021 n = 58,533 n = 5,021 (19.0%) (81.0%) (29.4%) (70.6%) (63.3%) (36.7%) (90.2%) (9.8%) (92.1%) (7.9%) Number of acute diseases Median (IQR) 1 (0–2) 0 (0–1) 1 (0–2) 0 (0–1) 1 (0-2) 0 (0–1) 1 (0–1) 0 (0–0) 0 (0–1) 0 (0–0) 0 36.4 55.3 40.2 60.3 44.8 68.2 47.7 78.0 51.5 82.3 1 26.8 23.7 27.1 22.3 27.6 19.4 27.4 14.9 26.2 12.3 2 16.9 11.6 16.1 10.2 14.7 7.8 13.7 4.9 12.7 3.7 ≥3 19.9 9.4 16.6 7.2 12.9 4.6 11.2 2.3 9.7 1.8 Living in a rural 14.4 14.9 15.3 15.1 15.4 16.7 15.4 17.6 18.4 18.7 area Number of visits Median (IQR) 6 (3–10) 2 (0–5) 6 (3–11) 2 (0–5) 8 (4–13) 2 (0–5) 11 (7–18) 2 (0–5) 14 (8–24) 1 (0–7) 0 8.8 28.4 9.8 34.2 5.8 38.3 2.6 38.9 3.0 40.4 1-2 15.4 24.4 14.7 22.9 10.4 21.6 4.4 18.6 4.2 16.2 3-5 25.0 23.6 23.1 21.5 20.5 20.1 11.9 18.0 9.1 14.0 6-10 27.1 15.8 26.7 14.5 28.7 13.5 26.5 15.5 20.4 13.4 ≥11 23.6 7.8 25.7 6.9 34.6 6.4 54.7 9.0 63.4 16.0 Male 19-24 25-44 45-64 65-79 ≥80 MM Non-MM MM Non-MM MM Non-MM MM Non-MM MM Non-MM n = 8,916 n = 60,373 n = 75,556 n = 311,124 n = 139,776 n = 119,008 n = 97,044 n = 14,584 n = 32,773 n = 2,948 (12.9%) (87.1%) (19.5%) (80.5%) (54.0%) (46.0%) (86.9%) (13.1%) (92.0%) (8.0%) Number of acute diseases Median (IQR) 1 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–1) 0 (0–0) 0 (0–1) 0 (0–0) 0 49.0 64.6 50.8 68.1 55.3 74.0 53.2 76.4 52.0 78.9 1 26.1 21.7 26.4 20.0 26.0 17.3 26.9 16.0 27.1 13.7 2 13.7 8.4 12.8 7.6 11.3 5.9 12.0 5.3 11.9 4.6 ≥3 11.1 5.2 10.0 4.4 7.4 2.8 8.0 2.3 8.9 2.8 Living in a rural 15.0 15.1 15.6 15.3 17.3 17.5 16.7 18.8 21.3 21.5 area Number of visits Median (IQR) 4 (1–8) 1 (0–4) 5 (2–9) 1 (0–3) 6 (3–12) 1 (0–3) 11 (6–17) 2 (0–6) 14 (8–24) 2 (0–8) 0 15.0 38.7 14.2 43.8 8.9 46.1 2.8 36.0 2.3 35.2 1-2 21.6 26.3 18.4 24.0 13.4 21.7 5.3 19.4 3.5 16.3 3-5 26.1 19.8 24.4 18.0 22.1 17.1 13.4 18.8 8.8 15.5 6-10 22.5 10.8 23.2 10.0 26.9 10.3 28.1 16.1 20.8 14.2 ≥11 14.8 4.4 19.8 4.2 28.7 4.7 50.4 9.7 64.5 18.8 Abbreviations: MM multimorbidity, IQR interquartile range. Data are expressed as percentage, unless otherwise stated. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 7 of 11 http://www.biomedcentral.com/1471-2296/15/55 19-24 25-44 45-64 65-79 80+ Female Male Chronic diseases: green and red lettering. Acute disease: blue lettering. Value of observed prevalence expressed as percentage. * Expected co-occurrence of diseases was obtained as the product of the separate prevalences, assuming statistical independence of the diseases concerned. Figure 1 Most prevalent multimorbidity patterns of two chronic diseases and the corresponding acute disease with the highest observed/expected ratio , by sex and age groups. groups. The most frequent multimorbidity pattern of Some possible biases could have influenced our results. chronic diseases was the combination of hypertension First, diseases could be underreported, especially for male and dyslipidemia in adults over 45 years of age. of normal workforce age who tend to see their doctors less We observed a decrease in the number of acute dis- often than other strata of patients. This effect would dimin- eases recorded as age increased. Nonetheless, in adjusted ish in the two oldest age groups because of the retirement models female older than 65 who had acute diseases age (65 years) in Spain. In patients with multimorbidity, the were more likely to have multimorbidity. true incidence of acute disease could be underreported be- Finally, the use of health services was positively associ- cause theGPwould placehigherpriorityonthe chronic ated with a diagnosis of multimorbidity. Living in a rural problems in these patients. On the other hand, there could area decreased the probability of multimorbidity. be an over-representation of chronic diagnoses (e.g., hyper- tension, diabetes, hyperlipidemia, etc.) that are included in the goals/incentives contracts of Catalan PHCT. The diseases that form part of the CatSalut treatment Strengths and weaknesses of the study objectives may be more carefully recorded than other con- A major strength of this study is the analysis of a large, ditions. However, these same diseases are the most preva- high-quality database of primary-care records, represen- lent (high blood pressure, diabetes, hypercholesterolemia, tative of a large population. In the context of a national smoking, dyslipidemia, ischemic heart disease, atrial fibril- health system with universal coverage, EHR data have lation) and therefore have the greatest impact on popula- been shown to yield more reliable and representative tion health. The quality-recorders database (SIDIAP-Q) conclusions than those derived from survey-based studies used in this study minimizes the under-reporting of dis- [16]. Another important strength was the inclusion of all eases not included in the CatSalut objectives. chronic and acute diagnoses registered in EHR, which Furthermore, the stratified analysis allows more accurate contributed to a more accurate analysis of the association estimation within each age-sex strata and universal access between acute and chronic diseases and of the disease to free health care and medications makes it more likely combinations present in multimorbidity in this popula- that patients seeking care will acquire a diagnosis, either tion. To synthesize the results, we present here only the acute or chronic [17,18]. Second, there is no universally most frequent combinations. Finally, few studies have in- accepted criterion for consensus classification of acute and corporated acute diseases in the study of multimorbidity chronic disease. This lack of accurate case definitions im- patterns [8] and none analyzed the relationship between pedes the establishment of the true incidence/prevalence multimorbidity and acute morbidity. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 8 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 3 Cumulative incidence and risk ratio (RR) of multimorbidity for the five acute diseases with the highest cumulative incidence by sex and age groups* Female Male Age ICPC 2 Acute diseases MM Non-MM Risk ratio 95% confidence ICPC 2 Acute diseases MM Non-MM Risk ratio 95% confidence groups code CI CI interval code CI* CI interval (%) (%) (%) (%) 19-24 R74 Upper respiratory infection, acute 16.2 8.8 1.10 (1.04-1.16) R74 Upper respiratory infection, acute 11.9 7.0 0.96 (0.89-1.03) R76 Tonsillitis, acute 6.7 4.3 0.94 (0.86-1.02) R76 Tonsillitis, acute 5.5 3.4 0.92 (0.83-1.02) U71 Cystitis/urinary infection, other 6.3 4.1 0.85 (0.78-0.93) D73 Gastroenteritis, presumed infection 4.8 2.8 0.93 (0.83-1.04) D82 Teeth/gum disease 6.2 3.6 1.14 (1.04-1.25) D82 Teeth/gum disease 4.3 2.7 0.63 (0.44-0.89) D73 Gastroenteritis, presumed infection 6.3 3.4 1.06 (0.97-1.16) S16 Bruise/contusion 3.2 2.4 0.75 (0.66-0.86) 25-44 R74 Upper respiratory infection, acute 12.8 6.9 1.01 (0.98-1.03) R74 Upper respiratory infection, acute 9.9 5.7 0.85 (0.82-0.87) L03 Low back symptom/complaint 4.4 3.1 0.73 (0.71-0.76) D73 Gastroenteritis, presumed infection 3.8 2.3 0.79 (0.76-0.83) U71 Cystitis/urinary infection other 4.4 2.6 0.86 (0.83-0.90) L03 Low back symptom/complaint 3.5 2.2 0.75 (0.71-0.78) D73 Gastroenteritis presumed infection 4.3 2.5 0.90 (0.87-0.94) D82 Teeth/gum disease 3.5 2.2 0.82 (0.72-0.93) R76 Tonsillitis, acute 3.6 2.5 1.08 (0.91-1.29) R76 Tonsillitis, acute 2.4 1.8 0.69 (0.65-0.73) 45-64 R74 Upper respiratory infection, acute 9.7 4.5 0.92 (0.88-0.95) R74 Upper respiratory infection, acute 6.7 3.4 0.79 (0.76-0.82) L03 Low back symptom/complaint 3.9 2.1 0.78 (0.74-0.82) L03 Low back symptom/complaint 3.2 2.1 0.58 (0.55-0.62) U71 Cystitis/urinary infection, other 4.0 1.7 0.86 (0.81-0.92) R78 Acute bronchitis/bronchiolitis 3.0 1.3 0.86 (0.80-0.92) R78 Acute bronchitis/bronchiolitis 3.9 1.6 0.95 (0.89-1.01) D82 Teeth/gum disease 2.9 1.6 0.83 (0.70-0.98) L20 Joint symptom/complaint NOS 3.3 1.5 0.97 (0.82-1.15) H81 Excessive ear wax 2.5 1.4 0.74 (0.69-0.78) 65-79 R74 Upper respiratory infection, acute 8.4 3.2 0.92 (0.70-1.20) R74 Upper respiratory infection, acute 7.6 3.3 0.93 (0.84-1.03) U71 Cystitis/urinary infection other 5.1 1.4 1.09 (0.91-1.30) H81 Excessive ear wax 4.4 2.3 0.74 (0.65-0.83) R78 Acute bronchitis/bronchiolitis 4.5 1.3 0.71 (0.36-1.39) R78 Acute bronchitis/bronchiolitis 4.2 1.5 0.99 (0.85-1.14) H81 Excessive ear wax 3.3 1.2 0.88 (0.74-1.04) D82 Teeth/gum disease 2.9 1.2 1.31 (1.10-1.57) L03 Low back symptom/complaint 3.0 1.1 0.76 (0.54-1.07) L03 Low back symptom/complaint 2.5 1.1 0.87 (0.73-1.03) 80+ R74 Upper respiratory infection, acute 5.8 1.5 1.47 (1.16-1.86) H81 Excessive ear wax 6.5 2.7 0.98 (0.77-1.24) U71 Cystitis/urinary infection, other 5.9 1.5 1.40 (1.10-1.78) R74 Upper respiratory infection, acute 6.2 2.0 1.29 (0.98-1.70) R78 Acute bronchitis/bronchiolitis 5.3 2.0 0.99 (0.80-1.23) R78 Acute bronchitis/bronchiolitis 5.7 2.0 1.06 (0.80-1.40) H81 Excessive ear wax 4.7 1.3 1.34 (1.04-1.73) S18 Laceration/cut 4.3 2 0.75 (0.57-0.99) S18 Laceration/cut 3.9 1.6 0.78 (0.61-0.99) U71 Cystitis/urinary infection, other 3.0 1.5 0.73 (0.53-1.01) Abbreviations: MM multimorbidity, RR risk ratio, ICPC 2 International Classification of Primary Care, CI Cumulative incidence, NOS not otherwise specified. *Cumulative incicidence was calculated as the sum of the number of new acute events during the study period divided by the 2010 population (e.g. if a patient passed two bronchitis in one year. the total number of events accounted for was two). The outcome is the number of acute diseases in each person for each acute disease considered. RR of multimorbidity versus non-multimorbidity was calculated by Poisson, negative binomial (if overdispersion is present) or zero inflated (when data showed excess of zero counts) equation as appropriate and adjusted for number of visits and area of residency. In bold P < 0.05. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 9 of 11 http://www.biomedcentral.com/1471-2296/15/55 Table 4 Factors associated with multimorbidity by sex and age groups Female 19-24 25-44 45-64 65-79 80+ OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value Number of acute diseases(ref.0) 0.030 <0.001 <0.001 <0.001 <0.001 1 0.94 0.89-0.99 0.88 0.86-0.90 0.82 0.80-0.84 0.98 0.92-1.03 1.16 1.05-1.28 2 0.95 0.89-1.01 0.86 0.83-0.88 0.76 0.73-0.78 1.05 0.96-1.15 1.54 1.31-1.80 ≥3 1.02 0.95-1.09 0.89 0.87-0.92 0.78 0.75-0.81 1.29 1.14-1.46 1.99 1.59-2.47 Rural area (ref. urban) 0.94 0.89-0.99 0.025 0.94 0.92-0.96 <0.001 0.80 0.78-0.82 <0.001 0.69 0.65-0.73 <0.001 0.76 0.70-0.83 <0.001 Number of visits (ref. 0) <0.001 <0.001 <0.001 <0.001 <0.001 1-2 2.09 1.93-2.26 2.34 2.28-2.14 3.40 3.30-3.51 3.62 3.38-3.87 3.41 3.08-3.78 3-5 3.52 3.25-3.80 4.04 3.93-4.15 7.54 7.31-7.77 10.18 9.54-10.86 8.36 7.55-9.27 6-10 5.69 5.25-6.17 7.11 6.91-7.32 16.28 15.76-16.81 25.98 24.28-27.80 18.86 16.99-20.94 ≥11 9.93 9.09-10.85 14.52 14.05-14.99 43.03 41.39-44.73 90.81 83.74-98.48 45.19 40.69-50.19 Male 19-24 25-44 45-64 65-79 80+ OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value OR 95% CI P-value Number of acute diseases(ref.0) <0.001 <0.001 <0.001 <0.001 0.590 1 0.78 0.73-0.83 0.71 0.70-0.73 0.67 0.65-0.69 0.84 0.80-0.89 1.07 0.95-1.21 2 0.79 0.73-0.86 0.64 0.62-0.66 0.58 0.56-0.60 0.78 0.72-0.85 1.08 0.89-1.31 ≥3 0.76 0.69-0.83 0.60 0.58-0.62 0.55 0.52-0.57 0.86 0.77-0.97 1.12 0.88-1.42 Rural area (ref. urban) 0.94 0.88-1.00 0.045 0.95 0.93-0.97 <0.001 0.87 0.85-0.89 <0.001 0.71 0.68-0.75 <0.001 0.79 0.71-0.88 <0.001 Number of visits (ref. 0) <0.001 <0.001 <0.001 <0.001 <0.001 1-2 2.32 2.15-2.50 2.67 2.60-2.75 3.57 3.47-3.67 3.58 3.35-3.82 3.33 2.89-3.85 3-5 3.93 3.64-4.25 5.18 5.04-5.33 8.20 7.97-8.43 9.66 9.06-10.30 8.62 7.50-9.92 6-10 6.51 5.97-7.09 9.62 9.33-9.92 17.78 17.24-18.34 24.51 22.93-26.19 22.07 19.13-25.47 ≥11 10.86 9.82-12.01 20.58 19.86-21.32 45.13 43.43-46.90 76.41 70.68-82.60 51.32 44.49-59.19 Logistic regression. Abbreviations: OR odds ratio, CI confidence interval, ref. reference. Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 10 of 11 http://www.biomedcentral.com/1471-2296/15/55 of a disease [19]. Finally, a residual confounding cannot be the implementation of personalized disease prevention completely excluded, and could occur because of epi- and health promotion activities. demiological factors not considered in this study, such as At the level of health policy and health care administra- patients’ socioeconomic status [20]. tion, the organization of services should be reviewed to ensure that continuity and coordination of patient care are Strengths and weaknesses in relation to other studies guaranteed; current evidence suggests the potential for The estimated multimorbidity prevalence in our sample improvement in this regard [35]. is higher than in other European studies [21-24], perhaps because of the analysis of a greater number of diseases Unanswered questions and future research in our study than in most other published studies [25]. The classification of chronic and acute disease remains Nonetheless, the patterns of multimorbidity observed unresolved, and there is no consensus on the type and were similar to those observed in other studies [26]. number of chronic diseases that define multimorbidity. As in other studies, multimorbidity was more prevalent A personalized measure to determine the severity of among female [21,22,24]. This could be due to the longer diagnosed multimorbidity is also needed. female life expectancy and worse health status, compared If longitudinal studies confirm a higher incidence of to male, differences that are due to both biological and so- morbidity in patients with multimorbidity, evidence-based cial factors [26]. In addition, sex is a social determinant interventions will be needed to prevent the onset of acute that influences health status, health behaviours and the use disease. Further studies are needed to study possible gen- of health services [27-30]. Recent studies, however, suggest etic and pathophysiological explanations that corroborate a dismantling of this paradigm based on sex-stratified ana- the observed multimorbidity patterns. lysis of consultations for common symptoms [31]. In-depth analysis of other contextual factors related to Acute problems are time-consuming for health profes- multimorbidity is also required, along with studies of the sional [32], and therefore should be considered part of the relationship between area of residence and multimorbidity primary care workload. Although current health policy, and of the differences in health status that may exist be- health care services, and research are all heavily focused tween different territories. Finally, there is a need for the on chronic diseases, we must not forget that 41% of pri- implementation and evaluation of health literacy and self- mary care visits are motivated by an acute disease [33]. management interventions to improve patient competence The incidence of acute diseases observed in our study in resolving routine acute diseases, which in turn will concurs with other reports of acute upper respiratory decrease the care burden in primary care systems. infection and other health problems of infectious aeti- ology (acute tonsillitis, cystitis) as the primary reasons Conclusions for seeking primary care, along with non-infectious dis- Multimorbidity is related to more use of health services eases such as dorsalgia [33,34]. and higher incidence of acute diseases, which increases Our study observed a higher prevalence of multimorbid- the burden on primary care services. Residence in urban ity in rural settings. Other studies conducted in rural areas vs rural settings is a factor for future in-depth study. have reported only a greater prevalence of multimorbidity The association of acute morbidity, area of residency in elderly people [6,7]. Nonetheless, our adjusted analysis or use of health services with multimorbidity differs showed that living in a rural area is negatively associated according to life-stage and sex. Therefore, the study of with multimorbidity. This phenomenon could be due to multimorbidity should be stratified by life-stage and sex. the environmental and sociocultural context and access to Understanding these trends across life-stages will allow both public and private services [28]. health systems to adjust their clinical and management models to adapt and prioritize interventions. Implications for clinicians and policymakers Abbreviations Our study considered multimorbidity in patients who PHCT: Primary health care teams; CHI: Catalan Health Institute; received primary medical care, considering all visits and EHR: Electronic health records; SIDIAP: Information System for the Development of Research in Primary Care; GP: General practitioners; diagnoses (acute and chronic diseases). This approach ICD: International Classification of Diseases; ICPC: International Classification allowed the identification of vulnerable subgroups in of Primary Care; IDIAP: Institut Universitari d’Investigació en Atenció Primària; our population. A major advantage of our methodology SD: Standard deviation; IQR: Interquartile range; RR: Risk ratios. is the use of data obtained directly from standard clin- Competing interests ical practice. Knowing the distribution of acute and The authors declare that they have no competing interests. chronic diseases by life-stage and sex will help the clin- Authors’ contributions ician faced with a particular patient to anticipate disease All authors contributed to the design of the study, revised the article, and patterns based on the patient’s sex and stage of life, rec- approved the final version. CV, QFB, JMV, MMP, ARL drafted the study ognizing that these vary with age. This will encourage protocol and obtained the funding. TRB, CV, QFB, JMV, ARL contributed to Foguet-Boreu et al. BMC Family Practice 2014, 15:55 Page 11 of 11 http://www.biomedcentral.com/1471-2296/15/55 the analysis and interpretation of data. CV, QFB, JMV, TRB, ARL, MPV, MMP, 16. Violán C, Foguet-Boreu Q, Hermosilla-Pérez E, Valderas JM, Bolíbar B, Fàbregas- and EPR wrote the first draft, and all authors contributed ideas, interpreted Escurriola M, Brugulat-Guiteras P, Muñoz-Pérez MÁ: Comparison of the the findings and reviewed rough drafts of the manuscript. All authors read information provided by electronic health records data and a popula- and approved the final manuscript. tion health survey to estimate prevalence of selected health conditions and multimorbidity. BMC Public Health 2013, 13:251. 17. Jordan K, Porcheret M, Croft P: Quality of morbidity coding in general Acknowledgments practice computerized medical records: a systematic review. Fam Pract We thank the Catalan Health Institute and especially the SIDIAP Unit, which 2004, 21:396–412. provided the database for the study. The authors also appreciate the English 18. Valderas JM, Starfield B, Sibbald B, Salisbury C, Roland M: Defining language review by Elaine Lilly, PhD. comorbidity: implications for understanding health and health services. Ann Fam Med 2009, 7:357–363. Funding 19. de Lusignan S, Tomson C, Harris K, van Vlymen J, Gallagher H: Creatinine This study was funded by the Ministry of Science and Innovation through fluctuation has a greater effect than the formula to estimate glomerular the Instituto Carlos III (ISCiii) as part of the Primary Care Prevention and filtration rate on the prevalence of chronic kidney disease. Nephron Clin Health Promotion Research Network (rediAPP), by ISCiii-RETICS (RD12/0005), Pract 2011, 117:c213–c224. by a grant for research projects ISCiii (PI12/00427), and by a 2011–2013 20. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B: Epidemiology scholarship that aims to promote research in Primary Health Care by health of multimorbidity and implications for health care, research, and professionals who have completed their specialty training, awarded by Institut medical education: a cros-sectional study. Lancet 2012, 380:37–43. Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi Gol). The 21. Prados-Torres A, Poblador-Plou B, Calderón-Larrañaga A, Gimeno-Feliu LA, funders had no role in the study design, collection, analysis and interpretation González-Rubio F, Poncel-Falcó A, Sicras-Mainar A, Alcalá-Nalvaiz JT: of data, writing of the manuscript and decision to submit for publication. Multimorbidity patterns in primary care: interactions among chronic diseases using factor analysis. PLoS One 2012, 7:e32190. Author details 1 22. Glynn LG, Valderas JM, Healy P, Burke E, Newell J, Gillespie P, Murphy AW: Institut Universitari d’Investigació en Atenció Primària Jordi Gol (IDIAP Jordi 2 The prevalence of multimorbidity in primary care and its effect on Gol), Gran Via Corts Catalanes, 587 àtic, 08007 Barcelona, Spain. Universitat health care utilization and cost. Fam Pract 2011, 28:516–523. Autònoma de Barcelona, Plaza Cívica, Campus de la UAB, 08193 Bellaterra, 3 23. Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA: Cerdanyola del Vallès, Spain. Hospital de Campdevànol, Ctra. de Gombrèn, 4 Epidemiology and impact of multimorbidity in primary care: a 20, 17530 Campdevànol, Spain. Health Services & Policy Research Group, retrospective cohort study. Br J Gen Pract 2011, 61:e12–e21. School of Medicine, University of Exeter, Exeter EX1 2LU, UK. 24. 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