Measuring multimorbidity in family practice—a comparison of two methods

Measuring multimorbidity in family practice—a comparison of two methods Abstract Background The presence of multimorbidity in the family practice setting is as evident as it is hard to measure. Objective The objective of this study was to describe the differences in the prevalence of multimorbidity in a single primary care population, through the use of the only two available lists of chronic conditions based on the International Classification for Primary Care coding system. Methods This is a cross-sectional, analytical study. Secondary analysis of existing chronic conditions data involved 1279 women and 714 men attending primary care centres in mainland Portugal. Multimorbidity was measured by the presence in each patient of both ≥2 and ≥3 chronic conditions, from both a list of 147 conditions and another list of 75 conditions. Logistic regression analyses were conducted to study the association of multimorbidity with sex, age, education and income by the type of list of chronic conditions. Results Multimorbidity prevalence estimates are modified by (i) the number of chronic conditions included in the lists used and (ii) the number of conditions necessary to define the cut-off for multimorbidity. The use of different lists of conditions modifies not only the multimorbidity prevalence estimates but also the evaluation of the determinants of multimorbidity. Conclusions The use of different lists of chronic conditions produces different research results. Even the use of lists designed to be more general practice-oriented may underestimate the frequency of multimorbidity by limiting the number of conditions considered. Further research is still needed to grasp the full implications of using different lists of chronic conditions in multimorbidity research. Chronic disease, family practice, Portugal, prevalence, primary health care Introduction The presence of multimorbidity—coexistence of more than one chronic condition in a person (1)—in the family practice/primary care setting is as evident (2) as it is hard to measure (3). The negative impact of multimorbidity on patients is well known (4) in terms of high mortality (5), poor physical function (6), low quality of life (7) and high health care costs (8). In a recent literature review (9), which included primary care data from 12 countries, multimorbidity prevalence was found to be between 12.9% and 95.1% (9). The wide difference of the published results is due to the characteristics of the studied populations and the definition of multimorbidity in such studies (10,11). Although there has been a growing theoretical reasoning on the definition of multimorbidity during the past years, there is still no consensual definition (12). The number of chronic conditions collected and the number of conditions for which the person is considered to have multimorbidity vary between studies (9). These factors make comparisons between studies difficult. Furthermore, most of the published measures of multimorbidity use disease classification systems that do not reflect the health problems found in primary care. The International Classification of Primary Care (ICPC) is recognized by the World Health Organization as an ideal classification system for primary care and is now widely used internationally (13). Currently, it is in its second version: ICPC-2 (13). A recent study that compared prevalence estimates of multimorbidity (3) noticed that the list of 147 chronic conditions developed by O’Halloran et al. (14) (based on ICPC-2 coding system) is a good reference point for estimating the prevalence of multimorbidity (3). This list originated from a literature review process, specifically for the Australian general practice setting, and although it has the purpose of measuring the prevalence of chronic conditions (14) it was not developed within the context of multimorbidity. Nonetheless, it was used in several multimorbidity studies. For example, in Portugal, the prevalence of multimorbidity (2+ chronic conditions) is estimated to be 72.7% among adult patients attending primary care consultations (15), by using O’Halloran et al.’s list (14). In 2016, more than 10 years after the publication of the aforementioned list, and because the standards for calculating and reporting multimorbidity are still lacking, N’Goran et al. (16) published, in the same journal, the second list of chronic conditions based on the ICPC-2 coding system. This list has gathered 75 chronic conditions thought to be the most relevant in the context of multimorbidity, after obtained consensus from a large panel of Swiss family physicians (16). As it has fewer conditions than O’Halloran et al.’s list, it is considered to be less time-consuming for GPs. Similar to O’Halloran et al.’s list, N’Goran et al.’s list was also used in multimorbidity prevalence studies (17). These are the only two available lists of chronic conditions based on the ICPC-2 coding system. Most importantly, N’Goran et al.’s list was the first to be developed within the context of multimorbidity (16). This study used cross-sectional data from an existing primary care sample patient population in Portugal, published in 2015 (15). In this previous study, multimorbidity was measured at 2+ and 3+ conditions per patient using O’Halloran et al.’s list. The current study is the next step to compare the results from that study with measurements using the new list of N’Goran et al. This study tests the extent to which the number of conditions in the lists affects the outcome of the multimorbidity measure. Using the same target population allows a better, less biased comparison than relating different lists in different population samples. We hypothesize that the prevalence estimates for multimorbidity in family practice are vulnerable to the chronic conditions comprised in the lists. Methods Family practice data were extracted from the first phase of the MM-PT project (multimorbidity in primary care in Portugal), a cross-sectional study conducted by the authors, from October 2013 to December 2014, in the five mainland Portuguese Healthcare Administrative Regions (15). Full methods of the MM-PT project were published elsewhere (18). Over the study period, enrolled GPs recorded demographic and clinical variables (chronic conditions) from 1993 adult patients (1279 women, 714 men) that attended primary care consultations and that gave formal consent (mean = 28.9 patients recruited per GP) (15). The original study was performed in accordance with the ethical standards of the Declaration of Helsinki (19) and received institutional ethics committee approval. For the current study, demographic variables including sex, age, educational level and income were extracted for each patient from the MM-PT project database. Data on chronic conditions, coded according to the ICPC-2 (13), were also obtained. Multimorbidity was defined as the co-occurrence of (i) ≥2 chronic conditions and (ii) ≥3 chronic conditions in the same individual. For comparison purposes, two lists of chronic conditions based on ICPC-2 coding system (13) were used (i) 147 conditions list developed by O’Halloran et al. (14) and (ii) 75 conditions list developed by N’Goran et al. (16). Data analyses were done using the IBM SPSS Statistics for Windows, Version 21.0 (IBM Corporation, Armonk, NY). Descriptive statistics were used to summarize variables: mean and standard deviation (SD) for numerical variables and absolute and relative frequencies for categorical variables. Chi-square tests for group comparisons were performed between the prevalence of multimorbidity for both lists across sample’s characteristics. Multiple binary logistic regression analysis for the presence of multimorbidity (for both lists) was conducted using the variables sex, age, education and income and a stepwise selection method. P values of <0.05 were deemed statistically significant. Results Table 1 shows the demographic characteristics of the study population and the global prevalence of multimorbidity. The sample was aged between 18 and 95 years (mean ± SD = 56.3 ± 17.5), 64.2% were women. The two lists considered in the current study, which have a different number of chronic conditions, affected the estimated prevalence of multimorbidity—or more exactly—if one considers N’Goran et al.’s list of 75 chronic conditions, the average number of conditions present in the sample is reduced by half, compared with the average number from O’Halloran et al.’s list of 147 conditions (1.7 ± 1.5 conditions versus 3.4 ± 2.6 conditions per patient). This is even more apparent when multimorbidity is defined by considering a high number of conditions. For example, the global prevalence of 6+ diseases using N’Goran et al.’s list is almost one tenth the prevalence using O’Halloran et al.’s list (Table 1). Table 1. Demographic and health characteristics of the sample (Data are from Portugal 2015 study (15).) n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 View Large Table 1. Demographic and health characteristics of the sample (Data are from Portugal 2015 study (15).) n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 View Large Table 2 shows the most common chronic conditions in the sample by using O’Halloran et al.’s and N’Goran et al.’s lists. The largest difference between the two lists was the absence in N’Goran et al.’s list of some frequent conditions present in O’Halloran et al.’s list (e.g. lipid disorder, back syndrome with radiating pain, overweight, varicose veins of leg, anxiety disorder/anxiety state, and osteoarthrosis/other). Other differences when using N’Goran et al.’s list were as follows: (i) the rise in rank of osteoporosis, atrial fibrillation/flutter, osteoarthrosis of hip, asthma and chronic obstructive pulmonary disease to the top 12 most common chronic conditions and also (ii) the inclusion of tobacco abuse as one of the most common chronic conditions. Table 2. Twelve most common chronic conditions using O’Halloran et al.’s and N’Goran et al.’s lists (Data are from Portugal 2015 study (15).) Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 View Large Table 2. Twelve most common chronic conditions using O’Halloran et al.’s and N’Goran et al.’s lists (Data are from Portugal 2015 study (15).) Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 View Large Figure 1 depicts the prevalence of multimorbidity across sex, age, educational level and income for both lists. Compared with O’Halloran et al.’s list, the prevalence of multimorbidity by N’Goran et al.’s list was statistically significantly lower in view of all sociodemographic variables. Even so, the prevalence estimates of multimorbidity, defined as 2+ chronic conditions using N’Goran et al.’s list, were close to the prevalence estimates of multimorbidity defined as 3+ chronic conditions using O’Halloran et al.’s list. No statistical differences were found between the prevalence estimates of multimorbidity (2+ chronic conditions using N’Goran et al.’s list versus 3+ chronic conditions using O’Halloran et al.’s list) in the 18–34 years (P = 0.57), 35–49 years (P = 0.10) and 50–64 years (P = 0.66) age groups. Figure 1. View largeDownload slide Prevalence of multimorbidity across (I) sex, (II) age, (III) educational level and (IV) income for O’Halloran et al.’s list and N’Goran et al.’s list. *Significant difference from 2+ O’Halloran et al.’s list (chi-square test, P < 0.05). #Significant difference from 3+ O’Halloran et al.’s list (chi-square test, P < 0.05). &No significant difference from 3+ O’Halloran et al.’s list (chi-square test, P > 0.05). Data are from Portugal 2015 study (15). Figure 1. View largeDownload slide Prevalence of multimorbidity across (I) sex, (II) age, (III) educational level and (IV) income for O’Halloran et al.’s list and N’Goran et al.’s list. *Significant difference from 2+ O’Halloran et al.’s list (chi-square test, P < 0.05). #Significant difference from 3+ O’Halloran et al.’s list (chi-square test, P < 0.05). &No significant difference from 3+ O’Halloran et al.’s list (chi-square test, P > 0.05). Data are from Portugal 2015 study (15). Table 3 shows the association of multimorbidity with sex, age, educational level and income by type of list of chronic conditions. In all cases, old age and low education increased the likelihood of multimorbidity (P < 0.05). Male gender and low income was associated with higher multimorbidity only when using N’Goran et al.’s list (P <0.05). Table 3. Association of multimorbidity with sex, age, educational level and income by type of list of chronic conditions (ORs and 95% CIs) (Data are from Portugal 2015 study (15).) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) View Large Table 3. Association of multimorbidity with sex, age, educational level and income by type of list of chronic conditions (ORs and 95% CIs) (Data are from Portugal 2015 study (15).) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) View Large Discussion Strengths of the study To the extent of our knowledge, the present study is the first to measure multimorbidity by comparing the only two prevailing lists of chronic conditions based on the ICPC-2 (13) coding system—O’Halloran et al.’s list and N’Goran et al.’s list—on the same target population sample. The major strength of this study is that by using only one sample, the compared prevalence estimates of multimorbidity were not affected by different data collection methods. The study’s results will be useful for further epidemiology research in primary care and family medicine settings. Overall findings and relationship with other studies The current study seems to be consistent with previous research (11) which found that multimorbidity prevalence estimates are modified by (i) the number of chronic conditions included in the lists used and (ii) the number of conditions necessary to define the cut-off for multimorbidity (11). The higher the number of chronic conditions in the list, the higher the prevalence of multimorbidity found (20). In relation to the cut-off in number of chronic conditions, a count of 3+ conditions results in a lower prevalence of multimorbidity (20), for both lists of chronic conditions. The similarity found in the prevalence estimates of multimorbidity between the 2+ N’Goran et al.’s list (50.2%) and the 3+ O’Halloran et al.’s list (57.2%), which is maintained throughout the different sociodemographic characteristics, may suggest that when studying similar populations, multimorbidity measured by 2+ chronic conditions from a list of a lower number of conditions assessed may be comparable to multimorbidity measured by 3+ conditions from a list of a higher number of conditions, especially in younger age groups. These results are of importance as the number of chronic conditions used between published studies varies and so no direct comparisons can be made so far (9,10). The use of different lists of conditions modifies not only the multimorbidity prevalence estimates but also the evaluation of the determinants of multimorbidity. Older age, gender and lower socioeconomic status are known determinants of multimorbidity (9). In the present study, the use of N’Goran et al.’s list—a list developed to characterize the variety and complexity of patients found in the clinical settings of family doctors (17)—revealed determinants of multimorbidity that were not present when using O’Halloran et al.’s list. This may suggest that some conditions included in these lists may contribute to the likelihood of multimorbidity at a higher extent than others. Nonetheless, whether this is a real difference between the two lists, or the consequence of the sample’s characteristics, will remain the subject of future studies. A note of caution is due: as stated in previous research, N’Goran et al.’s list does not take into consideration some chronic condition commonly found in the context of multimorbidity (17); the current study showed that this affects the identification of the most common conditions in the sample (see Table 2). Thus, the use of N’Goran et al.’s list in future prevalence studies will have to take this limitation into consideration and their findings will have to be carefully interpreted. For some authors (3), the exclusion of conditions that require medical treatment and the use of lists with limited number of conditions that may not reflect the full heterogeneity of primary care can create biased results. Limitations of the study The present study has some limitations inherent to secondary analysis of existing data (21). The available data were not collected specifically for the current study and variables could not be modified. Nevertheless, as the authors were familiar with the existing data and no extra variables were needed, this was not considered to be a problem. Its low cost was a significant advantage for an unfunded research such as this. Conclusion The current study contributes to increase the understanding of the epidemiology of multimorbidity. 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World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects . JAMA 2013 ; 310 : 2191 – 4 . Crossref Search ADS PubMed 20. Fortin M , Stewart M , Poitras ME , Almirall J , Maddocks H . A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology . Ann Fam Med 2012 ; 10 : 142 – 51 . Google Scholar Crossref Search ADS PubMed 21. Cheng HG , Phillips MR . Secondary analysis of existing data: opportunities and implementation . Shanghai Arch Psychiatry 2014 ; 26 : 371 – 5 . Google Scholar PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model) http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Family Practice Oxford University Press

Measuring multimorbidity in family practice—a comparison of two methods

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10.1093/fampra/cmy014
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Abstract

Abstract Background The presence of multimorbidity in the family practice setting is as evident as it is hard to measure. Objective The objective of this study was to describe the differences in the prevalence of multimorbidity in a single primary care population, through the use of the only two available lists of chronic conditions based on the International Classification for Primary Care coding system. Methods This is a cross-sectional, analytical study. Secondary analysis of existing chronic conditions data involved 1279 women and 714 men attending primary care centres in mainland Portugal. Multimorbidity was measured by the presence in each patient of both ≥2 and ≥3 chronic conditions, from both a list of 147 conditions and another list of 75 conditions. Logistic regression analyses were conducted to study the association of multimorbidity with sex, age, education and income by the type of list of chronic conditions. Results Multimorbidity prevalence estimates are modified by (i) the number of chronic conditions included in the lists used and (ii) the number of conditions necessary to define the cut-off for multimorbidity. The use of different lists of conditions modifies not only the multimorbidity prevalence estimates but also the evaluation of the determinants of multimorbidity. Conclusions The use of different lists of chronic conditions produces different research results. Even the use of lists designed to be more general practice-oriented may underestimate the frequency of multimorbidity by limiting the number of conditions considered. Further research is still needed to grasp the full implications of using different lists of chronic conditions in multimorbidity research. Chronic disease, family practice, Portugal, prevalence, primary health care Introduction The presence of multimorbidity—coexistence of more than one chronic condition in a person (1)—in the family practice/primary care setting is as evident (2) as it is hard to measure (3). The negative impact of multimorbidity on patients is well known (4) in terms of high mortality (5), poor physical function (6), low quality of life (7) and high health care costs (8). In a recent literature review (9), which included primary care data from 12 countries, multimorbidity prevalence was found to be between 12.9% and 95.1% (9). The wide difference of the published results is due to the characteristics of the studied populations and the definition of multimorbidity in such studies (10,11). Although there has been a growing theoretical reasoning on the definition of multimorbidity during the past years, there is still no consensual definition (12). The number of chronic conditions collected and the number of conditions for which the person is considered to have multimorbidity vary between studies (9). These factors make comparisons between studies difficult. Furthermore, most of the published measures of multimorbidity use disease classification systems that do not reflect the health problems found in primary care. The International Classification of Primary Care (ICPC) is recognized by the World Health Organization as an ideal classification system for primary care and is now widely used internationally (13). Currently, it is in its second version: ICPC-2 (13). A recent study that compared prevalence estimates of multimorbidity (3) noticed that the list of 147 chronic conditions developed by O’Halloran et al. (14) (based on ICPC-2 coding system) is a good reference point for estimating the prevalence of multimorbidity (3). This list originated from a literature review process, specifically for the Australian general practice setting, and although it has the purpose of measuring the prevalence of chronic conditions (14) it was not developed within the context of multimorbidity. Nonetheless, it was used in several multimorbidity studies. For example, in Portugal, the prevalence of multimorbidity (2+ chronic conditions) is estimated to be 72.7% among adult patients attending primary care consultations (15), by using O’Halloran et al.’s list (14). In 2016, more than 10 years after the publication of the aforementioned list, and because the standards for calculating and reporting multimorbidity are still lacking, N’Goran et al. (16) published, in the same journal, the second list of chronic conditions based on the ICPC-2 coding system. This list has gathered 75 chronic conditions thought to be the most relevant in the context of multimorbidity, after obtained consensus from a large panel of Swiss family physicians (16). As it has fewer conditions than O’Halloran et al.’s list, it is considered to be less time-consuming for GPs. Similar to O’Halloran et al.’s list, N’Goran et al.’s list was also used in multimorbidity prevalence studies (17). These are the only two available lists of chronic conditions based on the ICPC-2 coding system. Most importantly, N’Goran et al.’s list was the first to be developed within the context of multimorbidity (16). This study used cross-sectional data from an existing primary care sample patient population in Portugal, published in 2015 (15). In this previous study, multimorbidity was measured at 2+ and 3+ conditions per patient using O’Halloran et al.’s list. The current study is the next step to compare the results from that study with measurements using the new list of N’Goran et al. This study tests the extent to which the number of conditions in the lists affects the outcome of the multimorbidity measure. Using the same target population allows a better, less biased comparison than relating different lists in different population samples. We hypothesize that the prevalence estimates for multimorbidity in family practice are vulnerable to the chronic conditions comprised in the lists. Methods Family practice data were extracted from the first phase of the MM-PT project (multimorbidity in primary care in Portugal), a cross-sectional study conducted by the authors, from October 2013 to December 2014, in the five mainland Portuguese Healthcare Administrative Regions (15). Full methods of the MM-PT project were published elsewhere (18). Over the study period, enrolled GPs recorded demographic and clinical variables (chronic conditions) from 1993 adult patients (1279 women, 714 men) that attended primary care consultations and that gave formal consent (mean = 28.9 patients recruited per GP) (15). The original study was performed in accordance with the ethical standards of the Declaration of Helsinki (19) and received institutional ethics committee approval. For the current study, demographic variables including sex, age, educational level and income were extracted for each patient from the MM-PT project database. Data on chronic conditions, coded according to the ICPC-2 (13), were also obtained. Multimorbidity was defined as the co-occurrence of (i) ≥2 chronic conditions and (ii) ≥3 chronic conditions in the same individual. For comparison purposes, two lists of chronic conditions based on ICPC-2 coding system (13) were used (i) 147 conditions list developed by O’Halloran et al. (14) and (ii) 75 conditions list developed by N’Goran et al. (16). Data analyses were done using the IBM SPSS Statistics for Windows, Version 21.0 (IBM Corporation, Armonk, NY). Descriptive statistics were used to summarize variables: mean and standard deviation (SD) for numerical variables and absolute and relative frequencies for categorical variables. Chi-square tests for group comparisons were performed between the prevalence of multimorbidity for both lists across sample’s characteristics. Multiple binary logistic regression analysis for the presence of multimorbidity (for both lists) was conducted using the variables sex, age, education and income and a stepwise selection method. P values of <0.05 were deemed statistically significant. Results Table 1 shows the demographic characteristics of the study population and the global prevalence of multimorbidity. The sample was aged between 18 and 95 years (mean ± SD = 56.3 ± 17.5), 64.2% were women. The two lists considered in the current study, which have a different number of chronic conditions, affected the estimated prevalence of multimorbidity—or more exactly—if one considers N’Goran et al.’s list of 75 chronic conditions, the average number of conditions present in the sample is reduced by half, compared with the average number from O’Halloran et al.’s list of 147 conditions (1.7 ± 1.5 conditions versus 3.4 ± 2.6 conditions per patient). This is even more apparent when multimorbidity is defined by considering a high number of conditions. For example, the global prevalence of 6+ diseases using N’Goran et al.’s list is almost one tenth the prevalence using O’Halloran et al.’s list (Table 1). Table 1. Demographic and health characteristics of the sample (Data are from Portugal 2015 study (15).) n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 View Large Table 1. Demographic and health characteristics of the sample (Data are from Portugal 2015 study (15).) n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 n 1993 Women, % 64.2 Age  Mean (SD) 56.3 (17.5)  18–34 years, % 14.5  35–49 years, % 19.5  50–64 years, % 30.7  65+ years, % 35.4 Education  Low education (6 or less years), % 58.3  High education (more than 6 years), % 41.7 Income  Low income,% 27.5  High income, % 72.5 n chronic conditions (147 conditions list)  Mean (SD) 3.4 (2.6)  0 diseases, % 13.0  1 disease, % 14.4  ≥2 diseases, % 72.7  ≥3 diseases, % 57.2  ≥4 diseases, % 43.5  ≥5 diseases, % 30.0  ≥6 diseases, % 20.6 n chronic conditions (75 conditions list)  Mean (SD) 1.7 (1.5)  0 diseases, % 24.0  1 disease, % 25.8  ≥2 diseases, % 50.2  ≥3 diseases, % 28.2  ≥4 diseases, % 12.2  ≥5 diseases, % 5.0  ≥6 diseases, % 1.8 View Large Table 2 shows the most common chronic conditions in the sample by using O’Halloran et al.’s and N’Goran et al.’s lists. The largest difference between the two lists was the absence in N’Goran et al.’s list of some frequent conditions present in O’Halloran et al.’s list (e.g. lipid disorder, back syndrome with radiating pain, overweight, varicose veins of leg, anxiety disorder/anxiety state, and osteoarthrosis/other). Other differences when using N’Goran et al.’s list were as follows: (i) the rise in rank of osteoporosis, atrial fibrillation/flutter, osteoarthrosis of hip, asthma and chronic obstructive pulmonary disease to the top 12 most common chronic conditions and also (ii) the inclusion of tobacco abuse as one of the most common chronic conditions. Table 2. Twelve most common chronic conditions using O’Halloran et al.’s and N’Goran et al.’s lists (Data are from Portugal 2015 study (15).) Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 View Large Table 2. Twelve most common chronic conditions using O’Halloran et al.’s and N’Goran et al.’s lists (Data are from Portugal 2015 study (15).) Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 Rank Chronic conditions (O’Halloran et al.’s list) n Rank Chronic conditions (N’Goran et al.’s list) n 1 Lipid disorder 776 1 Hypertension, uncomplicated 740 2 Hypertension, uncomplicated 740 2 Depressive disorder 340 3 Depressive disorder 340 3 Diabetes, noninsulin dependent 333 4 Diabetes, noninsulin dependent 333 4 Obesity 323 5 Obesity 323 5 Hypertension, complicated 206 6 Back syndrome with radiating pain 250 6 Osteoarthritis of knee 191 7 Overweight 213 7 Tobacco abuse 170 8 Hypertension, complicated 206 8 Osteoporosis 105 9 Varicose veins of leg 195 9 Atrial fibrillation/flutter 84 10 Osteoarthritis of knee 191 10 Osteoarthrosis of hip 78 11 Anxiety disorder/anxiety state 176 11 Asthma 74 12 Osteoarthrosis, other 130 12 Chronic obstructive pulmonary disease 59 View Large Figure 1 depicts the prevalence of multimorbidity across sex, age, educational level and income for both lists. Compared with O’Halloran et al.’s list, the prevalence of multimorbidity by N’Goran et al.’s list was statistically significantly lower in view of all sociodemographic variables. Even so, the prevalence estimates of multimorbidity, defined as 2+ chronic conditions using N’Goran et al.’s list, were close to the prevalence estimates of multimorbidity defined as 3+ chronic conditions using O’Halloran et al.’s list. No statistical differences were found between the prevalence estimates of multimorbidity (2+ chronic conditions using N’Goran et al.’s list versus 3+ chronic conditions using O’Halloran et al.’s list) in the 18–34 years (P = 0.57), 35–49 years (P = 0.10) and 50–64 years (P = 0.66) age groups. Figure 1. View largeDownload slide Prevalence of multimorbidity across (I) sex, (II) age, (III) educational level and (IV) income for O’Halloran et al.’s list and N’Goran et al.’s list. *Significant difference from 2+ O’Halloran et al.’s list (chi-square test, P < 0.05). #Significant difference from 3+ O’Halloran et al.’s list (chi-square test, P < 0.05). &No significant difference from 3+ O’Halloran et al.’s list (chi-square test, P > 0.05). Data are from Portugal 2015 study (15). Figure 1. View largeDownload slide Prevalence of multimorbidity across (I) sex, (II) age, (III) educational level and (IV) income for O’Halloran et al.’s list and N’Goran et al.’s list. *Significant difference from 2+ O’Halloran et al.’s list (chi-square test, P < 0.05). #Significant difference from 3+ O’Halloran et al.’s list (chi-square test, P < 0.05). &No significant difference from 3+ O’Halloran et al.’s list (chi-square test, P > 0.05). Data are from Portugal 2015 study (15). Table 3 shows the association of multimorbidity with sex, age, educational level and income by type of list of chronic conditions. In all cases, old age and low education increased the likelihood of multimorbidity (P < 0.05). Male gender and low income was associated with higher multimorbidity only when using N’Goran et al.’s list (P <0.05). Table 3. Association of multimorbidity with sex, age, educational level and income by type of list of chronic conditions (ORs and 95% CIs) (Data are from Portugal 2015 study (15).) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) View Large Table 3. Association of multimorbidity with sex, age, educational level and income by type of list of chronic conditions (ORs and 95% CIs) (Data are from Portugal 2015 study (15).) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) List of chronic conditions 2+ O’Halloran et al.’s list 3+ O’Halloran et al.’s list 2+ N’Goran et al.’s list 3+ N’Goran et al.’s list Sex  Women 1 1 1 1  Men nonsignificant nonsignificant 1.3 (1.1–1.6) 1.3 (1.0–1.6) Age (years)  18–34 1 1 1 1  35–49 3.8 (2.7–5.3) 2.8 (1.9–4.3) 2.7 (1.8–4.1) 3.2 (1.6–6.3)  50–64 9.5 (6.6–13.6) 9.3 (6.2–13.9) 6.7 (4.5–10.1) 8.5 (4.4–16.2)  ≥65 23.3 (15.1–36.1) 21.6 (14.0–33.4) 14.3 (9.3–22.1) 15.2 (7.9–29.3) Education  High education 1 1 1 1  Low education 1.9 (1.5–2.5) 1.7 (1.3–2.1) 1.5 (1.2–1.9) 1.4 (1.1–1.9) Income  High income 1 1 1 1  Low income nonsignificant nonsignificant 1.4 (1.1–1.7) 1.5 (1.2–1.9) View Large Discussion Strengths of the study To the extent of our knowledge, the present study is the first to measure multimorbidity by comparing the only two prevailing lists of chronic conditions based on the ICPC-2 (13) coding system—O’Halloran et al.’s list and N’Goran et al.’s list—on the same target population sample. The major strength of this study is that by using only one sample, the compared prevalence estimates of multimorbidity were not affected by different data collection methods. The study’s results will be useful for further epidemiology research in primary care and family medicine settings. Overall findings and relationship with other studies The current study seems to be consistent with previous research (11) which found that multimorbidity prevalence estimates are modified by (i) the number of chronic conditions included in the lists used and (ii) the number of conditions necessary to define the cut-off for multimorbidity (11). The higher the number of chronic conditions in the list, the higher the prevalence of multimorbidity found (20). In relation to the cut-off in number of chronic conditions, a count of 3+ conditions results in a lower prevalence of multimorbidity (20), for both lists of chronic conditions. The similarity found in the prevalence estimates of multimorbidity between the 2+ N’Goran et al.’s list (50.2%) and the 3+ O’Halloran et al.’s list (57.2%), which is maintained throughout the different sociodemographic characteristics, may suggest that when studying similar populations, multimorbidity measured by 2+ chronic conditions from a list of a lower number of conditions assessed may be comparable to multimorbidity measured by 3+ conditions from a list of a higher number of conditions, especially in younger age groups. These results are of importance as the number of chronic conditions used between published studies varies and so no direct comparisons can be made so far (9,10). The use of different lists of conditions modifies not only the multimorbidity prevalence estimates but also the evaluation of the determinants of multimorbidity. Older age, gender and lower socioeconomic status are known determinants of multimorbidity (9). In the present study, the use of N’Goran et al.’s list—a list developed to characterize the variety and complexity of patients found in the clinical settings of family doctors (17)—revealed determinants of multimorbidity that were not present when using O’Halloran et al.’s list. This may suggest that some conditions included in these lists may contribute to the likelihood of multimorbidity at a higher extent than others. Nonetheless, whether this is a real difference between the two lists, or the consequence of the sample’s characteristics, will remain the subject of future studies. A note of caution is due: as stated in previous research, N’Goran et al.’s list does not take into consideration some chronic condition commonly found in the context of multimorbidity (17); the current study showed that this affects the identification of the most common conditions in the sample (see Table 2). Thus, the use of N’Goran et al.’s list in future prevalence studies will have to take this limitation into consideration and their findings will have to be carefully interpreted. For some authors (3), the exclusion of conditions that require medical treatment and the use of lists with limited number of conditions that may not reflect the full heterogeneity of primary care can create biased results. Limitations of the study The present study has some limitations inherent to secondary analysis of existing data (21). The available data were not collected specifically for the current study and variables could not be modified. Nevertheless, as the authors were familiar with the existing data and no extra variables were needed, this was not considered to be a problem. Its low cost was a significant advantage for an unfunded research such as this. Conclusion The current study contributes to increase the understanding of the epidemiology of multimorbidity. The use of different lists of chronic conditions produces different research results. Even the use of lists designed to be more general practice-oriented (i.e. N’Goran et al.’s list) may underestimate the frequency of multimorbidity by limiting the number of conditions considered. Further research is still needed to grasp the full implications of using different lists of chronic conditions in multimorbidity research. Declaration Funding: This research did not receive any specific grant from funding agencies in the public, commercial or not-for-profit sectors. Ethical approval: N/A. Conflict of interest: The authors declare no conflict of interest related to this article. References 1. Mercer SW , Smith SM , Wyke S , O’Dowd T , Watt GC . Multimorbidity in primary care: developing the research agenda . Fam Pract 2009 ; 26 : 79 – 80 . Google Scholar Crossref Search ADS PubMed 2. Fortin M , Lapointe L , Hudon C , Vanasse A . Multimorbidity is common to family practice: is it commonly researched ? Can Fam Physician 2005 ; 51 : 244 – 5 . Google Scholar PubMed 3. Fortin M , Hudon C , Haggerty J , Akker Mv , Almirall J . Prevalence estimates of multimorbidity: a comparative study of two sources . BMC Health Serv Res 2010 ; 10 : 111 . Google Scholar Crossref Search ADS PubMed 4. Fortin M , Soubhi H , Hudon C , Bayliss EA , van den Akker M . Multimorbidity’s many challenges . BMJ 2007 ; 334 : 1016 – 7 . Google Scholar Crossref Search ADS PubMed 5. Steiner CA , Friedman B . Hospital utilization, costs, and mortality for adults with multiple chronic conditions, Nationwide Inpatient Sample, 2009 . Prev Chronic Dis 2013 ; 10 : E62 . Google Scholar Crossref Search ADS PubMed 6. Kadam UT , Croft PR ; North Staffordshire GP Consortium Group . Clinical multimorbidity and physical function in older adults: a record and health status linkage study in general practice . Fam Pract 2007 ; 24 : 412 – 9 . Google Scholar Crossref Search ADS PubMed 7. Prazeres F , Santiago L . Relationship between health-related quality of life, perceived family support and unmet health needs in adult patients with multimorbidity attending primary care in Portugal: a multicentre cross-sectional study . Health Qual Life Outcomes 2016 ; 14 : 156 . Google Scholar Crossref Search ADS PubMed 8. Glynn LG , Valderas JM , Healy P , et al. The prevalence of multimorbidity in primary care and its effect on health care utilization and cost . Fam Pract 2011 ; 28 : 516 – 23 . Google Scholar Crossref Search ADS PubMed 9. Violan C , Foguet-Boreu Q , Flores-Mateo G , et al. Prevalence, determinants and patterns of multimorbidity in primary care: a systematic review of observational studies . PLoS One 2014 ; 9 : e102149 . Google Scholar Crossref Search ADS PubMed 10. Stewart M , Fortin M , Britt HC , Harrison CM , Maddocks HL . Comparisons of multi-morbidity in family practice—issues and biases . Fam Pract 2013 ; 30 : 473 – 80 . Google Scholar Crossref Search ADS PubMed 11. Harrison C , Britt H , Miller G , Henderson J . Examining different measures of multimorbidity, using a large prospective cross-sectional study in Australian general practice . BMJ Open 2014 ; 4 : e004694 . Google Scholar Crossref Search ADS PubMed 12. Willadsen TG , Bebe A , Køster-Rasmussen R , et al. The role of diseases, risk factors and symptoms in the definition of multimorbidity—a systematic review . Scand J Prim Health Care 2016 ; 34 : 112 – 21 . Google Scholar Crossref Search ADS PubMed 13. WONCA . International Classification of Primary Care 2016 . http://www.globalfamilydoctor.com/site/DefaultSite/filesystem/documents/Groups/WICC/International%20Classification%20of%20Primary%20Care%20Dec16.pdf (accessed on 3 March 2018). 14. O’Halloran J , Miller GC , Britt H . Defining chronic conditions for primary care with ICPC-2 . Fam Pract 2004 ; 21 : 381 – 6 . Google Scholar Crossref Search ADS PubMed 15. Prazeres F , Santiago L . Prevalence of multimorbidity in the adult population attending primary care in Portugal: a cross-sectional study . BMJ Open 2015 ; 5 : e009287 . Google Scholar Crossref Search ADS PubMed 16. N’Goran AA , Blaser J , Deruaz-Luyet A , et al. From chronic conditions to relevance in multimorbidity: a four-step study in family medicine . Fam Pract 2016 ; 33 : 439 – 44 . Google Scholar Crossref Search ADS PubMed 17. Déruaz-Luyet A , N’Goran AA , Senn N , et al. Multimorbidity and patterns of chronic conditions in a primary care population in Switzerland: a cross-sectional study . BMJ Open 2017 ; 7 : e013664 . Google Scholar Crossref Search ADS PubMed 18. Prazeres F , Santiago L . Multimorbidity in primary care in Portugal (MM-PT): a cross-sectional three-phase observational study protocol . BMJ Open 2014 ; 4 : e004113 . Google Scholar Crossref Search ADS PubMed 19. World Medical Association . World Medical Association Declaration of Helsinki: ethical principles for medical research involving human subjects . JAMA 2013 ; 310 : 2191 – 4 . Crossref Search ADS PubMed 20. Fortin M , Stewart M , Poitras ME , Almirall J , Maddocks H . A systematic review of prevalence studies on multimorbidity: toward a more uniform methodology . Ann Fam Med 2012 ; 10 : 142 – 51 . Google Scholar Crossref Search ADS PubMed 21. Cheng HG , Phillips MR . Secondary analysis of existing data: opportunities and implementation . Shanghai Arch Psychiatry 2014 ; 26 : 371 – 5 . Google Scholar PubMed © The Author(s) 2018. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com. This article is published and distributed under the terms of the Oxford University Press, Standard Journals Publication Model (https://academic.oup.com/journals/pages/open_access/funder_policies/chorus/standard_publication_model)

Journal

Family PracticeOxford University Press

Published: Sep 18, 2018

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