General Hospital Psychiatry xxx (2014) xxx–xxx
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Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study☆,☆☆ Mythily Subramaniam, M.H.S.M. ⁎, Edimansyah Abdin, Ph.D., Louisa Picco, M.P.H., Janhavi Ajit Vaingankar, M.Sc., Siow Ann Chong, M.Med. Research Division Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747
a r t i c l e
i n f o
Article history: Received 7 July 2013 Revised 5 February 2014 Accepted 3 March 2014 Available online xxxx Keywords: Multiple chronic medical conditions Composite international diagnostic interview Mood disorder Alcohol use disorder
a b s t r a c t Objective: The objective was to establish the prevalence and risk factors for multiple chronic medical conditions (MCMC) in the Singapore population. Methods: Data for the study were extracted from the Singapore Mental Health Study, a population-based, cross-sectional, epidemiological study of Singapore residents aged 18 years and above. Face-to-face interviews were completed with 6616 respondents from December 2009 to December 2010, and the survey response rate was 75.9%. The assessment of psychiatric conditions was established using version 3.0 of the WHO World Mental Health Composite International Diagnostic Interview (WMH-CIDI). A modified version of the CIDI checklist of chronic medical conditions was used to capture data on 15 chronic medical conditions which were reclassified into eight types of physical conditions. Results: A total of 25.4% of the population reported having one chronic condition, and 16.3% had MCMC. Those who were older (aged 35 years and above vs. those aged 18–34 years), economically inactive, unemployed, overweight and obese had higher odds of having MCMC. Adjusting for covariates in multinomial regression analyses, mood and alcohol use disorder (AUD) were significantly associated with higher risk of MCMC. Conclusions: The study identified two important yet potentially modifiable risk factors for MCMC — psychiatric conditions and obesity — in the general population. Screening for mood and alcohol use disorder, as well as lifestyle interventions targeted at obesity, must be a part of disease management for MCMC. © 2014 Elsevier Inc. All rights reserved.
1. Introduction The US Department of Health and Human Services defines chronic conditions as “conditions that last a year or more and require ongoing medical attention and/or limit activities of daily living” [1]. These include both physical conditions such as arthritis, asthma, cancer and human immunodeficiency virus infection, as well as mental disorders such as depression, developmental disabilities and dementia. “Comorbidity” and “multimorbidity” have been used to define the presence of two or more chronic conditions that affect a person at the same time. The term comorbidity was introduced by Feinstein in 1970 [2] and refers to the “additional co-existing ailment” beyond an index disorder. The index condition is the main focus, while other disorders are considered in the context of their possible effects on the prognosis of this disease. Multimorbidity on the other hand refers to the co-occurrence of diseases in the same person, indicating a shift of interest from a given index condition to individuals who suffer from multiple diseases [3]. ☆ Conflicts of interest and source of funding: no conflicts of interest. ☆☆ This study was supported by funding from the Singapore Millennium Foundation and the Ministry of Health, Singapore. ⁎ Corresponding author. Tel.: +65 6389 3633; fax: +65 6343 7962.
Prevalence estimates of multimorbidity vary considerably among studies, with wide variations mainly being due to dissimilar study populations or data sources, with most studies limited to primary care setting [4,5] or large administrative databases that examine specific but varied lists of chronic conditions [6]. Multiple chronic medical conditions (MCMC) are associated with adverse health outcomes, including higher mortality, reduced functioning, unnecessary and/or prolonged hospitalizations, higher cost per discharge and adverse drug events [5,7–9]. The reasons for poor outcomes in patients with multimorbidity are yet to be elucidated; however, plausible causes include biological interactions between disorders [10], poor self-management, limited financial resources, high levels of morbidity and coexisting depressive symptoms [11], as well as the fact that treatments for one disorder may have the potential to worsen another. There are few studies that have evaluated the prevalence of multimorbidity across age groups within the general population, including younger adults, or in multiethnic populations [12,13]. Ethnicity may influence the prevalence, health service utilization and outcomes for a variety of disorders [14,15]. Studying the relationships between ethnicity and MCMC may therefore improve our understanding of the underlying factors influencing MCMC. Additionally, such studies may identify ethnic groups in a given
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Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
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M. Subramaniam et al. / General Hospital Psychiatry xxx (2014) xxx–xxx
population with higher risk for MCMC related to cultural, genetic or environmental factors who might benefit from targeted interventions. Singapore is a city-state country in South-East Asia with a population of approximately 5 million of which 3.7 million are Singapore residents (Singapore citizens or permanent residents) [16]. It has a multiethnic urban population comprising mainly of Chinese, Malays and Indians, each a major ethnic group in Asia, who represent more than two thirds of world's population. Thus, a study in this setting provides a unique opportunity to study MCMC among different Asian ethnicities, the results of which can be extrapolated to other countries with similar ethnic composition. Noncommunicable chronic diseases are a significant cause of illness and death in Singapore. Four common chronic diseases — diabetes mellitus, hypertension, lipid disorders and stroke — affect about 1 million Singaporeans [17]. Ethnic differences have been suggested in the prevalence of these chronic diseases [18,19]; however, few studies have examined MCMC or their association with ethnicity in this population. The aim of the current study was to establish the prevalence and risk factors for MCMC in the Singapore population. We hypothesized that the prevalence of MCMC in Singapore would be similar to that reported in population studies elsewhere and that ethnicity would be associated with multimorbidity. 2. Methodology 2.1. Study design and participants The data for the current study were extracted from the Singapore Mental Health Study (SMHS), a survey of Singapore residents aged 18 years and above conducted from December 2009 to December 2010. This was a population-based, cross-sectional, epidemiological study which has been described previously in greater details [20]. The SMHS surveyed Singapore residents (including Singapore citizens and permanent residents) aged 18 years and above. The study involved a single-stage design without geographic clustering. The sample size numbers were calculated by running statistical power calculations for single and two proportions to determine what sample sizes were necessary overall, as well as for subgroups (i.e., age and ethnicity), to produce a precise estimate with a margin of error equal to 0.05 for different disorders. We assumed certain realistic sample sizes (e.g., n= 5000, 6500, 7000) and computed the margin of error for key disorders of interest. We found that the margin of error for the overall prevalence estimate was between 1.5% and 3.0%, while the margin of error for the strata defined by age and ethnic groups was 1.0%–3.5% when those above 65 years of age and Malay and Indian ethnicity were oversampled to ensure that a sufficient sample size would be achieved to improve the reliability of estimates for the subgroups analysis. Our design effect after oversampling by age and ethnicity was in the range of 1.1–2.17. We found that the relative standard error was appropriate for more prevalent disorders but high and above 30% for low-prevalence disorders. Using these two approaches, we determined that a target sample size of 6500 samples would provide sufficient precision to measure the prevalence of these conditions. Thus, assuming an eligibility rate of 65% and a response rate of 65%, a sample of approximately 15,000 persons was drawn for this study. The sampling frame was based on an administrative database of all citizens, permanent residents and foreigners in Singapore, which is updated based on administrative data. Residents who were younger than 18 years, without any address or with overseas addresses as of July 2009 and all foreigners were excluded from the frame. A probability sample was randomly selected using a disproportionate stratified sampling design with 16 strata defined according to ethnicity (Chinese, Malay, Indian, Others) and age groups (18–34, 35–49, 50–64, 65 and above). In order to make inferences of prevalence rates of mental disorders to the entire population of
Singapore residents, the final data were weighted back to the population. The data were adjusted accordingly to represent the Singapore population based on the 2007 census. The study was approved by the ethics committee (National Healthcare Group, Domain Specific Review Board), and all participants and parents/ guardians of those aged below 21 years gave written informed consent for participating in the study.
2.2. Data collection and instruments The assessment of mental disorders was established using version 3.0 of the WMH-CIDI [21]. Diagnostic modules for lifetime and 12month prevalence of mood disorders including major depressive disorder (MDD) and bipolar disorder; anxiety disorders, including generalized anxiety disorder (GAD) and obsessive compulsive disorder (OCD); and alcohol use disorder (AUD), i.e., alcohol abuse and alcohol dependence, were included in the survey. CIDI organic exclusion rules as well as diagnostic hierarchy rules were applied to generate the final diagnoses. We used aggregate variables for any mood disorder (including MDD, dysthymia and bipolar disorder), any anxiety disorder (including GAD and OCD) and any AUD (including alcohol abuse and alcohol dependence) in the lifetime for the analysis. The interview also gathered information on a range of chronic medical conditions. We used a modified version of the CIDI checklist of chronic medical conditions for this purpose, and respondents were asked to report any of the conditions listed in the checklist. The list comprised 15 chronic medical conditions which were reclassified into 8 types of physical disorders: (a) respiratory disorders (asthma, chronic lung disease such as chronic bronchitis or emphysema), (b) diabetes, (c) hypertension and high blood pressure, (d) chronic pain (arthritis or rheumatism, back problems including disk or spine, migraine headaches), (e) cancer, (f) neurological disorders (epilepsy, convulsion, Parkinson's disease), (g) cardiovascular disorders (stroke or major paralysis, heart attack, coronary heart disease, angina, congestive heart failure or other heart disease) and (h) ulcer and chronic inflamed bowel (stomach ulcer, chronic inflamed bowel, enteritis, or colitis) [22]. Cabassa et al. [12] reported on MCMC using a community sample from the National Epidemiologic Survey on Alcohol and Related Conditions. Using a checklist of seven common chronic medical conditions, they classified respondents into three mutually exclusive groups: no chronic medical condition, one chronic medical condition or at least two chronic medical conditions. We adopted their study methodology and used it to examine the data from the SMHS, and this categorical variable served as our main dependent variable. Health-related quality of life was measured using the EuroQOL five dimensions questionnaire (EQ-5D). The EQ-5D provides a simple, generic measure of health for clinical and economic appraisal [23]. It comprises a descriptive system and a visual analogue scale. The descriptive system assesses five domains (i.e., mobility, self-care, usual activities, pain/discomfort, anxiety/depression), and respondents were asked to rate their health on a 3-point severity scale (no problem/moderate problem/extreme problem). The EQ-5D defines a total of 243 health states. We used the UK time tradeoff values [24] to convert the states to health utility scores. The scores range from − 0.59 to 1.00, with negative values representing health states worse than being dead, 0 representing being dead and 1.00 representing the state of full health [25]. All other sociodemographic data such as age, gender, ethnicity, marital status, education, employment status, income and smoking status were collected using structured questionnaires. We used selfreported height and weight to compute participants' body mass index (BMI). Participants were classified into four groups: underweight (BMI b 18.5), normal weight (BMI 18.5–24.9), overweight (BMI 25– 29.9) and obese (BMI N30) [26].
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
M. Subramaniam et al. / General Hospital Psychiatry xxx (2014) xxx–xxx
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2.3. Field supervision and quality control
3. Results
Field supervisors were assigned to monitor interviewer's work including direct observations of actual interviews. Rigorous efforts were made to contact each respondent to complete the entire interview. Regular meetings were held with the interviewers and field supervisors to ensure early identification of any emergent problems in the field. A systematic quality assurance process was implemented. The number of interviews completed by each interviewer was tracked, and 20% of the cases (5% through face-to-face validation and 15% via telephone calls to the respondents) were subjected to detailed verification to detect any falsification of data.
Overall, 13,500 cases were released for the study. Face-to-face interviews were completed with 6616 respondents, and the overall response rate for the study, after excluding the 396 ineligible cases, was 75.9% [20] (Fig. 1). Characteristics of the study sample are presented in Table 1, which shows that 25.4% of the population reported having one chronic condition and 16.3% reported having two or more chronic medical conditions (MCMC). The most frequently reported chronic medical condition was hypertension or high blood pressure (19.7%), followed by high blood sugar or diabetes (9%), asthma (8.9%), back problems (7%) and arthritis or rheumatism (6%). Among respondents with any two chronic conditions (n= 648), the most common combinations were “hypertension/high blood pressure and high blood sugar/diabetes” (23.9%), followed by “hypertension/ high blood pressure and arthritis/rheumatism” (9%) and “hypertension/high blood pressure and asthma” (7.4%). Among respondents with any three chronic conditions (n= 218), the most common combinations were “asthma, high blood sugar/diabetes and hypertension/high blood pressure” (7%), followed by “asthma, back problems and migraine headaches” (3.9%) and “asthma, hypertension/high blood pressure and migraine headaches” (3%). Table 2 shows the sociodemographic correlates of these chronic medical conditions. Those who were older (aged 35 years and above vs. 18–34 years), economically inactive, unemployed, overweight or obese had higher odds of having MCMC. Malays had significantly lower odds of having MCMC compared to Chinese. Table 3 shows rates and ORs of psychiatric disorders among those with chronic medical conditions. The rate of one chronic medical condition and MCMC was significantly higher in those with mood disorder and AUD than those without these disorders. Adjusting for covariates in multinomial regression analyses, we found that mood disorder and AUD were significantly associated with higher risk of MCMC but not with one chronic medical condition. The mean EQ-5D index in those with one chronic medical condition (0.94 vs. 0.97, P
2.4. Statistical analysis Listwise deletion method was used for handling missing data. The weighting of the data was taken into account in data analyses using the Statistical Analysis Software (SAS) software version 9.2. Weighted mean and standard deviations were calculated for continuous variables, and weighted frequencies and percentages for categorical variables. Multiple linear regression was used to determine the mean difference in the EQ-5D index across groups. Multinomial regression models were used to generate odd ratios (ORs) and 95% confidence intervals (CIs) using morbidity as the main outcome variable and lifetime mental disorders as well as sociodemographic characteristics (i.e., age group, gender, ethnicity, marital status, employment status, income, smoking status, BMI) as predictor variables. Standard errors (S.E.) and significance tests were estimated using the Taylor series linearization method. Multivariate significance was evaluated using Wald χ 2 tests based on design corrected coefficient variance– covariance matrices. Statistical significance was evaluated at the b0.05 level using two-sided tests. All statistical analyses were carried out using SAS system version 9.2 (Cary, NC, USA) and STATA version 10 for Windows.
Fig. 1. Survey fieldwork statistics.
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
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Table 1 Sociodemographic distribution of the sample
Age group 18–34 35–49 50–64 65+ Ethnicity Chinese Malay Indian Others Sex Male Female Marital status Single Married Divorced/Separated Widowed Education Primary and below Secondary Pre-U/Junior College/Diploma Vocational University Employment Employed Economically inactive⁎ Unemployed Income Below $SD 20,000 $SD 20,000–49,999 $SD 50,000 and above BMI Underweight Normal Overweight Obese Smoking No Yes Chronic medical condition No chronic medical condition One chronic medical condition Two or more chronic medical conditions Lifetime psychiatric disorders No Yes Mood disorder No Yes Anxiety disorder No Yes Alcohol use disorder No Yes
n
Unweighted %
Weighted %
S.E.
2293 2369 1542 412
34.7 35.8 23.3 6.2
31.7 34.1 23.1 11.1
b0.01 b0.01 b0.01 b0.01
2006 2373 1969 268
30.2 35.9 29.8 4.1
76.9 12.3 8.3 2.4
b0.01 b0.01 b0.01 b0.01
3299 3317
49.9 50.1
48.5 51.5
0.9 0.9
1825 4290 262 237
27.6 64.8 4.0 3.6
28.9 62.4 4.2 4.4
0.6 0.8 0.4 0.4
1236 1975 1342 721 1342
18.7 29.8 20.3 10.9 20.3
20.2 27.6 22.4 7.9 21.9
0.7 0.8 0.7 0.4 0.7
4594 1522 313
71.4 23.7 4.9
71.0 24.5 4.5
0.8 0.7 0.4
3392 1924 962
54.0 30.7 15.3
51.3 31.2 17.5
0.8 0.8 0.7
446 3330 1717 788
7.1 53.0 27.3 12.6
8.4 60.2 23.6 7.8
0.5 0.9 0.8 0.4
5290 1326
80.0 20.0
84.0 16.0
0.6 0.6
3917 1706 993
59.2 25.8 15.0
58.3 25.4 16.3
0.8 0.8 0.6
5742 874
86.8 13.2
88.0 12.0
0.6 0.6
6109 507
92.3 7.7
93.0 7.0
0.4 0.4
6331 285
95.7 4.3
96.4 3.6
0.3 0.3
6360 256
96.1 3.9
96.4 3.6
0.3 0.3
⁎ Includes homemakers, students and retirees/pensioners.
value b.001) and MCMC (0.88 vs. 0.97, P value b.001) were lower than those with no chronic conditions (EQ-5D of 0.97). After adjusting for age and gender, these differences remained statistically significant.
4. Discussion We found that 16.3% of the Singapore population reported having two or more chronic conditions. It is somewhat difficult to compare prevalence estimates across studies as the estimates of multimorbidity vary considerably due to dissimilar study populations or data sources.
Examining administrative claims data for Medicare beneficiaries and 15 chronic medical conditions, Lochner et al. [6] reported that 68.4% of Medicare beneficiaries had two or more chronic conditions and 36.4% had four or more chronic conditions [6]. Data from the US National Ambulatory Medical Care Survey, examining 13 chronic conditions, revealed that 29.2% of physician office visits were made by adult patients with two or three chronic conditions and 8.4% of visits were made by patients with four or more chronic conditions [4]. A Scottish study using a national dataset held by the Primary Care Clinical Informatics Unit, which examined 40 conditions, reported that 23.2% of patients had multimorbidity [27]. The prevalence estimates of our study are comparable to those from epidemiological studies conducted in Canada and Australia [28,29]. Agborsangaya et al. [28] examined 16 chronic conditions in the Health Quality Council of Alberta 2010 Patient Experience Survey and established the prevalence of overall age- and sex-standardized prevalence of multimorbidity as 19.0%. Fortin et al. [13] looked at seven chronic conditions among the general population of Quebec aged 25 years and above and established that 10.1% of men and 13.3% of women had two or more diseases. The same study found the prevalence of multimorbidity in the practice based survey to be much higher: 51.9% of men and 46.1% of women had two or more diseases. We also found that those with MCMC had a lower quality of life than those with one medical illness (0.88 vs. 0.94, P value b.001). A recent systematic review on multimorbidity among the elderly found that most studies reviewed found a significant effect of multimorbidity on disability and poor quality of life [29]. Our study identified older age, ethnicity, employment status and overweight as well as obesity as risk factors for MCMC. Older age is a well-documented risk factor for MCMC [27,30], with studies reporting that the number of morbidities and the proportion of people with multimorbidity increase substantially with age [31]. While we did not find an association of MCMC with economic status, as reported in a number of other studies [27,32], we did find an association with employment status, whereby those who were unemployed and economically inactive, including students, homemakers and retirees, had increased risk of MCMC. Similar findings were reported by Andrade and colleagues [33] who studied medical and psychiatric multimorbidity whilst also examining the sociodemographic determinants of these multimorbidities. Their findings, like ours, showed that being retired or a homemaker increased the risk of somatic multimorbidity. We found that ethnicity was associated with multimorbidity — those of Malay ethnicity had a lower risk of multimorbidity as compared to Chinese. A recent national health survey [34] has established ethnic differences in the prevalence of chronic conditions. While diabetes was more prevalent among Indians followed by Malays and Chinese, hypertension and cholesterol were more prevalent among those of Malay ethnicity followed by Chinese and Indians. However, a number of other medical conditions were included in our study whose prevalence and risk factors by ethnicity have not been previously established. Studies have also shown that psychiatric morbidity [15] and comorbid mental–physical disorder [22] differ by ethnicity in this multiethnic population. Indians (as compared to Chinese) have a higher risk of MDD and mental– physical comorbidity. Our findings suggest that ethnicity and the presence of psychiatric disorders may interact in complex ways, increasing the risk of chronic medical conditions, which are yet to be elucidated. Cabassa et al. [12] examined differences in the 12-month prevalence of MCMC by race/ethnicity, and the interactions between race/ethnicity and psychiatric diagnosis. They found that, compared to non-Hispanic whites and adjusting for psychiatric disorders and all other covariates, the likelihood of MCMC was significantly higher among African Americans and significantly lower among Hispanics. This finding is consistent with the elevated rates of chronic medical conditions (e.g., hypertension, coronary heart disease) among African Americans. They postulated that African Americans also experience substantial morbidities associated with chronic diseases and lower
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
M. Subramaniam et al. / General Hospital Psychiatry xxx (2014) xxx–xxx
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Table 2 Sociodemographic correlates of chronic medical conditions No chronic condition
Age group 18–34 35–49 50–64 65+ Ethnicity Chinese Malay Indian Others Sex Male Female Marital status Single Married Divorced/Separated Widowed Education Primary and below Secondary Pre-U/Junior College/Diploma Vocational University Employment Employed Economically inactive Unemployed Income below $SD 20,000 $SD 20,000–49,999 $SD 50,000 and above BMI Underweight Normal Overweight Obese Smoking No Yes
One chronic condition
Two or more chronic conditions
Multinomial regression analyses One chronic vs. no chronic condition
Two or more chronic vs. no chronic condition
%
S.E.
%
S.E.
%
S.E.
OR
95% CI
P value
OR
95% CI
P value
72.4 65.0 47.9 19.0
1.3 1.4 1.8 2.8
21.6 25.0 29.1 29.6
1.2 1.3 1.7 3.3
5.9 10.0 23.0 51.3
0.6 0.9 1.5 3.6
1 1.1 1.7 3
(0.8–1.4) (1.3–2.2) (1.6–5.6)
.62 .001 .0004
1 1.5 4.1 18.4
(1.1–2.2) (2.8–6) (9.7–34.8)
.029 .0001 .0001
58.3 59.8 56.4 57.4
1.0 1.0 1.1 3.1
25.2 26.3 25.4 27.3
1.0 0.9 1.0 2.8
16.5 13.9 18.2 15.2
0.8 0.7 0.8 2.3
1 0.9 1 1.2
(0.8–1.1) (0.9–1.2) (0.8–1.7)
.41 .69 .29
1 0.7 1.2 1.3
(0.6–0.9) (1–1.5) (0.8–2.2)
.006 .13 .27
55.9 60.5
1.2 1.2
26.8 24.1
1.1 1.1
17.3 15.4
1.0 0.9
1 0.9
(0.7–1.1)
.16
1 0.8
(0.6–1.1)
.23
70.3 55.3 54.1 25.5
1.5 1.1 4.4 4.2
21.5 26.6 23.2 36.2
1.3 1.0 3.8 5.0
8.2 18.1 22.7 38.3
0.9 0.9 3.9 5.1
1 1.2 1.2 3.2
(1–1.6) (0.7–1.9) (1.6–6.5)
.08 .49 .002
1 1.3 1.7 1.7
(0.9–1.9) (1–3.1) (0.7–4)
.15 .07 .24
43.9 52.8 64.8 66.3 69.0
2.1 1.7 1.8 2.7 1.7
27.1 28.1 24.8 24.3 21.5
1.9 1.5 1.6 2.4 1.5
29.0 19.1 10.4 9.4 9.5
2.0 1.4 1.2 1.7 1.1
1.1 1.4 1.2 1.2 1
(0.7–1.6) (1.1–1.9) (0.9–1.6) (0.8–1.7)
.69 .024 .13 .40
(0.6–1.7) (0.9–2) (0.6–1.4) (0.6–1.8)
.93 .23 .61 .98
62.4 46.3 53.6
1.0 1.8 4.2
26.0 25.4 20.4
0.9 1.7 3.4
11.6 28.3 26.1
0.7 1.8 3.8
1 1.1 0.9
(0.8–1.5) (0.5–1.4)
.54 .61
1 1.8 2
(1.3–2.5) (1.1–3.5)
.001 .017
53.8 65.7 61.4
1.2 1.5 2.1
25.2 24.9 26.8
1.1 1.4 1.9
21.1 9.4 11.8
1.1 0.9 1.5
1 0.9 1.2
(0.7–1.2) (0.9–1.6)
.64 .33
1 0.8 1
(0.6–1.2) (0.6–1.6)
.35 .95
65.2 62.4 51.1 46.2
3.1 1.1 1.8 2.8
23.6 23.6 29.2 30.4
2.7 1.0 1.7 2.6
11.2 14.0 19.8 23.4
2.2 0.9 1.6 2.5
1.2 1 1.4 1.8
(0.8–1.6)
.39
(0.5–1.5)
.62
(1.1–1.7) (1.3–2.4)
.002 .0002
0.9 1 1.5 2.7
(1.1–2) (1.9–3.9)
.008 .0001
58.5 57.2
0.9 2.1
25.1 27.2
0.9 1.9
16.4 15.6
0.7 1.7
1 1.2
(0.9–1.5)
.25
1 1.3
(0.9–1.9)
.19
1 1.3 0.9 1 1
Bold font indicates significant p values.
quality of medical care, which could increase their risk of MCMC. The lower odds of MCMC among Hispanics were surprising given that this population reports higher prevalence rates than non-Hispanic whites for several chronic medical conditions. Lochner et al. [6] also
reported that the prevalence of MCMC varied by race/ethnicity. NonHispanic black and Hispanic women often had the highest prevalence of MCMC, although the authors did not elaborate further on the findings. Although ethnicity is associated with MCMC, it is necessary
Table 3 Prevalence rates and ORa of psychiatric disorders in chronic medical conditions
No psychiatric disorder Any psychiatric disorder Any mood disorder No Yes Any anxiety disorder No Yes Any alcohol use disorder No Yes
χ2
One chronic vs. no chronic condition
Two or more chronic vs. no chronic condition
S.E.
OR
P value
OR
95% CI
P value
0.7 2.1
10.6
.005
Reference 1.4
1.1–1.9
.006
Reference 2.7
1.9–3.7
b.001
15.8 22.8
0.7 2.9
8.3
.016
Reference 1.3
0.9–1.9
.110
Reference 2.3
1.5–3.6
b.001
0.8 3.8
16.2 18.4
0.7 3.2
0.9
.65
Reference 1.2
0.7–1.9
.471
Reference 1.4
0.8–2.5
.2
0.8 4.0
16.0 24.2
0.7 4.1
7.9
.019
Reference 1.5
1–2.4
.069
Reference 3
1.7–5.3
b.001
No chronic condition
One chronic condition
Two or more chronic conditions
%
S.E.
%
S.E.
%
59.2 51.4
0.9 2.5
25.1 27.6
0.8 2.2
15.6 21.1
58.8 51.3
0.9 3.2
25.4 25.9
0.8 2.8
58.4 54.6
0.8 4.3
25.3 26.9
58.7 47.2
0.8 4.4
25.3 28.6
P value
95% CI
Bold font indicates significant p values. a ORs were derived using multinomial regression analyses after adjusting for sociodemographic variables and BMI.
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
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to identify the underlying factors of which ethnicity may be a marker that might account for these observed differences. Such factors, if modifiable, may be targeted to prevent or manage MCMC in specific ethnic groups. Obesity has been described by the World Health Organization as an “escalating epidemic” and “one of the greatest public health problems of our times” [35]. It is not surprising that we found obesity and overweight were associated with a significantly higher risk of MCMC as persons with obesity are at a greater than average risk for a variety of comorbid conditions like type 2 diabetes, stroke, coronary heart disease and osteoarthritis. Other studies have similarly reported an association between excess weight and MCMC [36,37]. Compared to people without psychiatric disorders and adjusting for other covariates, the odds of MCMC in our study were significantly higher among people with psychiatric disorders. When examining specific disorders, we found that the risk of MCMC was highest among those with mood disorder or AUD. This finding of elevated risk of MCMC among those with psychiatric disorders is consistent with a number of other studies [12,27,38]. People with psychiatric disorders have been identified to be at increased risk for chronic medical conditions especially cardiovascular disease and diabetes in a number of studies [39,40]. While mentally ill people may have greater difficulties than other patients in interpreting and understanding physical signs and symptoms, as well as solving their health problems, the problem of MCMC in this group of individuals is often the consequence of the failed systems of care [41]. On the other hand, mental disorders in individuals with multimorbidity are also often neglected, and this might be due to competing demands [42]. This has important clinical implications as studies have shown that treatment of the psychiatric illness results in better management of the physical illness [43] and thus screening and treatment of psychiatric illnesses must be actively pursued. The strengths of our study are that it is the first study that examined MCMC in a multiethnic Asian population. It is one of the few that has examined the risk of BMI, sociodemographic correlates and psychiatric disorders on multimorbidity. We used a structured instrument to establish the diagnosis of psychiatric illnesses, and our response rate of 75.9% further ensures the generalizability of our results to the population. A limitation of our study is the reliance on self- report of the physical disorders rather than by clinical assessment or verification through medical records, but studies have indicated that self-report of chronic physical diseases showed moderate to strong agreement with information obtained from medical records [44]. Only specific physical and psychiatric disorders were included as part of the SMHS; thus, the true prevalence of MCMC may be underreported in our study. We used aggregate variables for any mood disorder, any anxiety disorder and any AUD; therefore, the association with individual disorders was not analyzed, and specific associations may have been missed. Ideally, studies about the prevalence of multimorbidity should be based on a standard list of chronic conditions that includes at a minimum the most frequent diagnoses; otherwise, prevalence and risk factors are contingent upon the number and types of conditions assessed and how multimorbidity is defined. Bayliss and colleagues [45] have compiled a list of 24 health conditions most frequently assessed for the measurement of comorbidity to develop an instrument for the assessment of disease burden. Such lists should be used for estimating the prevalence of multimorbidity in different populations in future studies. The crosssectional nature of the study limits our ability to make causal inferences, and thus, risk factors like psychiatric illnesses and high BMI may be either a cause or effect of MCMC. Multimorbidity presents an important clinical and organizational challenge to the current single system approach to chronic disease management. Much of the current practice of medicine and the search for new treatments takes a single-disorder approach ranging
from disease-specific treatment guidelines, disease management protocols that were not developed for patients with multiple chronic conditions to clinical trials. This has created a “silo mentality” that is reinforced by multiple stakeholders [8]. Transformation of health care systems that focus on the patient-centered care with a holistic approach to MCMC is urgently needed. The US Department of Health and Human Services has outlined strategies for maximizing care coordination and for improving health and quality of life for individuals with MCMC [46]. The vision articulated by the workgroup is that of “optimum health and quality of life for individuals with multiple chronic conditions.” In conclusion, our study identified two important yet potentially modifiable risk factors for MCMC: psychiatric disorders and obesity. The implications of this finding in Singapore's context are manifold. Given that depression is highly prevalent in individuals with multimorbidity, it is important to screen for depression and to ensure that it is adequately treated among individuals with multimorbidity. Patients with chronic physical conditions are often followed up in busy primary care settings; therefore, easy-to-administer, wellvalidated screening instruments should be used to routinely screen for patients who have depression. AUD on the other hand is often neglected, and screening for AUD must also be a part of disease management for MCMC. Overweight and obesity are difficult yet modifiable risk factors for multimorbidity, and lifestyle interventions must be included while providing care to these patients. Screening, early identification and treatment of these factors could potentially result in lowering the risk of development of MCMC. Our findings reemphasize the need to integrate health care systems and community-based health programs and for engaging care providers across systems and disciplines to ensure high quality and continuity of care for individuals with MCMC. There is also a need to develop local clinical practice guidelines based on evidence-based practices to treat those with MCMC, thereby standardizing care. Lastly, health care policies need to prioritize MCMC, given the complex needs and demands for care of this group of patients.
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Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study☆,☆☆ Mythily Subramaniam, M.H.S.M. ⁎, Edimansyah Abdin, Ph.D., Louisa Picco, M.P.H., Janhavi Ajit Vaingankar, M.Sc., Siow Ann Chong, M.Med. Research Division Institute of Mental Health, Buangkok Green Medical Park, 10 Buangkok View, Singapore 539747
a r t i c l e
i n f o
Article history: Received 7 July 2013 Revised 5 February 2014 Accepted 3 March 2014 Available online xxxx Keywords: Multiple chronic medical conditions Composite international diagnostic interview Mood disorder Alcohol use disorder
a b s t r a c t Objective: The objective was to establish the prevalence and risk factors for multiple chronic medical conditions (MCMC) in the Singapore population. Methods: Data for the study were extracted from the Singapore Mental Health Study, a population-based, cross-sectional, epidemiological study of Singapore residents aged 18 years and above. Face-to-face interviews were completed with 6616 respondents from December 2009 to December 2010, and the survey response rate was 75.9%. The assessment of psychiatric conditions was established using version 3.0 of the WHO World Mental Health Composite International Diagnostic Interview (WMH-CIDI). A modified version of the CIDI checklist of chronic medical conditions was used to capture data on 15 chronic medical conditions which were reclassified into eight types of physical conditions. Results: A total of 25.4% of the population reported having one chronic condition, and 16.3% had MCMC. Those who were older (aged 35 years and above vs. those aged 18–34 years), economically inactive, unemployed, overweight and obese had higher odds of having MCMC. Adjusting for covariates in multinomial regression analyses, mood and alcohol use disorder (AUD) were significantly associated with higher risk of MCMC. Conclusions: The study identified two important yet potentially modifiable risk factors for MCMC — psychiatric conditions and obesity — in the general population. Screening for mood and alcohol use disorder, as well as lifestyle interventions targeted at obesity, must be a part of disease management for MCMC. © 2014 Elsevier Inc. All rights reserved.
1. Introduction The US Department of Health and Human Services defines chronic conditions as “conditions that last a year or more and require ongoing medical attention and/or limit activities of daily living” [1]. These include both physical conditions such as arthritis, asthma, cancer and human immunodeficiency virus infection, as well as mental disorders such as depression, developmental disabilities and dementia. “Comorbidity” and “multimorbidity” have been used to define the presence of two or more chronic conditions that affect a person at the same time. The term comorbidity was introduced by Feinstein in 1970 [2] and refers to the “additional co-existing ailment” beyond an index disorder. The index condition is the main focus, while other disorders are considered in the context of their possible effects on the prognosis of this disease. Multimorbidity on the other hand refers to the co-occurrence of diseases in the same person, indicating a shift of interest from a given index condition to individuals who suffer from multiple diseases [3]. ☆ Conflicts of interest and source of funding: no conflicts of interest. ☆☆ This study was supported by funding from the Singapore Millennium Foundation and the Ministry of Health, Singapore. ⁎ Corresponding author. Tel.: +65 6389 3633; fax: +65 6343 7962.
Prevalence estimates of multimorbidity vary considerably among studies, with wide variations mainly being due to dissimilar study populations or data sources, with most studies limited to primary care setting [4,5] or large administrative databases that examine specific but varied lists of chronic conditions [6]. Multiple chronic medical conditions (MCMC) are associated with adverse health outcomes, including higher mortality, reduced functioning, unnecessary and/or prolonged hospitalizations, higher cost per discharge and adverse drug events [5,7–9]. The reasons for poor outcomes in patients with multimorbidity are yet to be elucidated; however, plausible causes include biological interactions between disorders [10], poor self-management, limited financial resources, high levels of morbidity and coexisting depressive symptoms [11], as well as the fact that treatments for one disorder may have the potential to worsen another. There are few studies that have evaluated the prevalence of multimorbidity across age groups within the general population, including younger adults, or in multiethnic populations [12,13]. Ethnicity may influence the prevalence, health service utilization and outcomes for a variety of disorders [14,15]. Studying the relationships between ethnicity and MCMC may therefore improve our understanding of the underlying factors influencing MCMC. Additionally, such studies may identify ethnic groups in a given
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Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
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population with higher risk for MCMC related to cultural, genetic or environmental factors who might benefit from targeted interventions. Singapore is a city-state country in South-East Asia with a population of approximately 5 million of which 3.7 million are Singapore residents (Singapore citizens or permanent residents) [16]. It has a multiethnic urban population comprising mainly of Chinese, Malays and Indians, each a major ethnic group in Asia, who represent more than two thirds of world's population. Thus, a study in this setting provides a unique opportunity to study MCMC among different Asian ethnicities, the results of which can be extrapolated to other countries with similar ethnic composition. Noncommunicable chronic diseases are a significant cause of illness and death in Singapore. Four common chronic diseases — diabetes mellitus, hypertension, lipid disorders and stroke — affect about 1 million Singaporeans [17]. Ethnic differences have been suggested in the prevalence of these chronic diseases [18,19]; however, few studies have examined MCMC or their association with ethnicity in this population. The aim of the current study was to establish the prevalence and risk factors for MCMC in the Singapore population. We hypothesized that the prevalence of MCMC in Singapore would be similar to that reported in population studies elsewhere and that ethnicity would be associated with multimorbidity. 2. Methodology 2.1. Study design and participants The data for the current study were extracted from the Singapore Mental Health Study (SMHS), a survey of Singapore residents aged 18 years and above conducted from December 2009 to December 2010. This was a population-based, cross-sectional, epidemiological study which has been described previously in greater details [20]. The SMHS surveyed Singapore residents (including Singapore citizens and permanent residents) aged 18 years and above. The study involved a single-stage design without geographic clustering. The sample size numbers were calculated by running statistical power calculations for single and two proportions to determine what sample sizes were necessary overall, as well as for subgroups (i.e., age and ethnicity), to produce a precise estimate with a margin of error equal to 0.05 for different disorders. We assumed certain realistic sample sizes (e.g., n= 5000, 6500, 7000) and computed the margin of error for key disorders of interest. We found that the margin of error for the overall prevalence estimate was between 1.5% and 3.0%, while the margin of error for the strata defined by age and ethnic groups was 1.0%–3.5% when those above 65 years of age and Malay and Indian ethnicity were oversampled to ensure that a sufficient sample size would be achieved to improve the reliability of estimates for the subgroups analysis. Our design effect after oversampling by age and ethnicity was in the range of 1.1–2.17. We found that the relative standard error was appropriate for more prevalent disorders but high and above 30% for low-prevalence disorders. Using these two approaches, we determined that a target sample size of 6500 samples would provide sufficient precision to measure the prevalence of these conditions. Thus, assuming an eligibility rate of 65% and a response rate of 65%, a sample of approximately 15,000 persons was drawn for this study. The sampling frame was based on an administrative database of all citizens, permanent residents and foreigners in Singapore, which is updated based on administrative data. Residents who were younger than 18 years, without any address or with overseas addresses as of July 2009 and all foreigners were excluded from the frame. A probability sample was randomly selected using a disproportionate stratified sampling design with 16 strata defined according to ethnicity (Chinese, Malay, Indian, Others) and age groups (18–34, 35–49, 50–64, 65 and above). In order to make inferences of prevalence rates of mental disorders to the entire population of
Singapore residents, the final data were weighted back to the population. The data were adjusted accordingly to represent the Singapore population based on the 2007 census. The study was approved by the ethics committee (National Healthcare Group, Domain Specific Review Board), and all participants and parents/ guardians of those aged below 21 years gave written informed consent for participating in the study.
2.2. Data collection and instruments The assessment of mental disorders was established using version 3.0 of the WMH-CIDI [21]. Diagnostic modules for lifetime and 12month prevalence of mood disorders including major depressive disorder (MDD) and bipolar disorder; anxiety disorders, including generalized anxiety disorder (GAD) and obsessive compulsive disorder (OCD); and alcohol use disorder (AUD), i.e., alcohol abuse and alcohol dependence, were included in the survey. CIDI organic exclusion rules as well as diagnostic hierarchy rules were applied to generate the final diagnoses. We used aggregate variables for any mood disorder (including MDD, dysthymia and bipolar disorder), any anxiety disorder (including GAD and OCD) and any AUD (including alcohol abuse and alcohol dependence) in the lifetime for the analysis. The interview also gathered information on a range of chronic medical conditions. We used a modified version of the CIDI checklist of chronic medical conditions for this purpose, and respondents were asked to report any of the conditions listed in the checklist. The list comprised 15 chronic medical conditions which were reclassified into 8 types of physical disorders: (a) respiratory disorders (asthma, chronic lung disease such as chronic bronchitis or emphysema), (b) diabetes, (c) hypertension and high blood pressure, (d) chronic pain (arthritis or rheumatism, back problems including disk or spine, migraine headaches), (e) cancer, (f) neurological disorders (epilepsy, convulsion, Parkinson's disease), (g) cardiovascular disorders (stroke or major paralysis, heart attack, coronary heart disease, angina, congestive heart failure or other heart disease) and (h) ulcer and chronic inflamed bowel (stomach ulcer, chronic inflamed bowel, enteritis, or colitis) [22]. Cabassa et al. [12] reported on MCMC using a community sample from the National Epidemiologic Survey on Alcohol and Related Conditions. Using a checklist of seven common chronic medical conditions, they classified respondents into three mutually exclusive groups: no chronic medical condition, one chronic medical condition or at least two chronic medical conditions. We adopted their study methodology and used it to examine the data from the SMHS, and this categorical variable served as our main dependent variable. Health-related quality of life was measured using the EuroQOL five dimensions questionnaire (EQ-5D). The EQ-5D provides a simple, generic measure of health for clinical and economic appraisal [23]. It comprises a descriptive system and a visual analogue scale. The descriptive system assesses five domains (i.e., mobility, self-care, usual activities, pain/discomfort, anxiety/depression), and respondents were asked to rate their health on a 3-point severity scale (no problem/moderate problem/extreme problem). The EQ-5D defines a total of 243 health states. We used the UK time tradeoff values [24] to convert the states to health utility scores. The scores range from − 0.59 to 1.00, with negative values representing health states worse than being dead, 0 representing being dead and 1.00 representing the state of full health [25]. All other sociodemographic data such as age, gender, ethnicity, marital status, education, employment status, income and smoking status were collected using structured questionnaires. We used selfreported height and weight to compute participants' body mass index (BMI). Participants were classified into four groups: underweight (BMI b 18.5), normal weight (BMI 18.5–24.9), overweight (BMI 25– 29.9) and obese (BMI N30) [26].
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
M. Subramaniam et al. / General Hospital Psychiatry xxx (2014) xxx–xxx
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2.3. Field supervision and quality control
3. Results
Field supervisors were assigned to monitor interviewer's work including direct observations of actual interviews. Rigorous efforts were made to contact each respondent to complete the entire interview. Regular meetings were held with the interviewers and field supervisors to ensure early identification of any emergent problems in the field. A systematic quality assurance process was implemented. The number of interviews completed by each interviewer was tracked, and 20% of the cases (5% through face-to-face validation and 15% via telephone calls to the respondents) were subjected to detailed verification to detect any falsification of data.
Overall, 13,500 cases were released for the study. Face-to-face interviews were completed with 6616 respondents, and the overall response rate for the study, after excluding the 396 ineligible cases, was 75.9% [20] (Fig. 1). Characteristics of the study sample are presented in Table 1, which shows that 25.4% of the population reported having one chronic condition and 16.3% reported having two or more chronic medical conditions (MCMC). The most frequently reported chronic medical condition was hypertension or high blood pressure (19.7%), followed by high blood sugar or diabetes (9%), asthma (8.9%), back problems (7%) and arthritis or rheumatism (6%). Among respondents with any two chronic conditions (n= 648), the most common combinations were “hypertension/high blood pressure and high blood sugar/diabetes” (23.9%), followed by “hypertension/ high blood pressure and arthritis/rheumatism” (9%) and “hypertension/high blood pressure and asthma” (7.4%). Among respondents with any three chronic conditions (n= 218), the most common combinations were “asthma, high blood sugar/diabetes and hypertension/high blood pressure” (7%), followed by “asthma, back problems and migraine headaches” (3.9%) and “asthma, hypertension/high blood pressure and migraine headaches” (3%). Table 2 shows the sociodemographic correlates of these chronic medical conditions. Those who were older (aged 35 years and above vs. 18–34 years), economically inactive, unemployed, overweight or obese had higher odds of having MCMC. Malays had significantly lower odds of having MCMC compared to Chinese. Table 3 shows rates and ORs of psychiatric disorders among those with chronic medical conditions. The rate of one chronic medical condition and MCMC was significantly higher in those with mood disorder and AUD than those without these disorders. Adjusting for covariates in multinomial regression analyses, we found that mood disorder and AUD were significantly associated with higher risk of MCMC but not with one chronic medical condition. The mean EQ-5D index in those with one chronic medical condition (0.94 vs. 0.97, P
2.4. Statistical analysis Listwise deletion method was used for handling missing data. The weighting of the data was taken into account in data analyses using the Statistical Analysis Software (SAS) software version 9.2. Weighted mean and standard deviations were calculated for continuous variables, and weighted frequencies and percentages for categorical variables. Multiple linear regression was used to determine the mean difference in the EQ-5D index across groups. Multinomial regression models were used to generate odd ratios (ORs) and 95% confidence intervals (CIs) using morbidity as the main outcome variable and lifetime mental disorders as well as sociodemographic characteristics (i.e., age group, gender, ethnicity, marital status, employment status, income, smoking status, BMI) as predictor variables. Standard errors (S.E.) and significance tests were estimated using the Taylor series linearization method. Multivariate significance was evaluated using Wald χ 2 tests based on design corrected coefficient variance– covariance matrices. Statistical significance was evaluated at the b0.05 level using two-sided tests. All statistical analyses were carried out using SAS system version 9.2 (Cary, NC, USA) and STATA version 10 for Windows.
Fig. 1. Survey fieldwork statistics.
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
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Table 1 Sociodemographic distribution of the sample
Age group 18–34 35–49 50–64 65+ Ethnicity Chinese Malay Indian Others Sex Male Female Marital status Single Married Divorced/Separated Widowed Education Primary and below Secondary Pre-U/Junior College/Diploma Vocational University Employment Employed Economically inactive⁎ Unemployed Income Below $SD 20,000 $SD 20,000–49,999 $SD 50,000 and above BMI Underweight Normal Overweight Obese Smoking No Yes Chronic medical condition No chronic medical condition One chronic medical condition Two or more chronic medical conditions Lifetime psychiatric disorders No Yes Mood disorder No Yes Anxiety disorder No Yes Alcohol use disorder No Yes
n
Unweighted %
Weighted %
S.E.
2293 2369 1542 412
34.7 35.8 23.3 6.2
31.7 34.1 23.1 11.1
b0.01 b0.01 b0.01 b0.01
2006 2373 1969 268
30.2 35.9 29.8 4.1
76.9 12.3 8.3 2.4
b0.01 b0.01 b0.01 b0.01
3299 3317
49.9 50.1
48.5 51.5
0.9 0.9
1825 4290 262 237
27.6 64.8 4.0 3.6
28.9 62.4 4.2 4.4
0.6 0.8 0.4 0.4
1236 1975 1342 721 1342
18.7 29.8 20.3 10.9 20.3
20.2 27.6 22.4 7.9 21.9
0.7 0.8 0.7 0.4 0.7
4594 1522 313
71.4 23.7 4.9
71.0 24.5 4.5
0.8 0.7 0.4
3392 1924 962
54.0 30.7 15.3
51.3 31.2 17.5
0.8 0.8 0.7
446 3330 1717 788
7.1 53.0 27.3 12.6
8.4 60.2 23.6 7.8
0.5 0.9 0.8 0.4
5290 1326
80.0 20.0
84.0 16.0
0.6 0.6
3917 1706 993
59.2 25.8 15.0
58.3 25.4 16.3
0.8 0.8 0.6
5742 874
86.8 13.2
88.0 12.0
0.6 0.6
6109 507
92.3 7.7
93.0 7.0
0.4 0.4
6331 285
95.7 4.3
96.4 3.6
0.3 0.3
6360 256
96.1 3.9
96.4 3.6
0.3 0.3
⁎ Includes homemakers, students and retirees/pensioners.
value b.001) and MCMC (0.88 vs. 0.97, P value b.001) were lower than those with no chronic conditions (EQ-5D of 0.97). After adjusting for age and gender, these differences remained statistically significant.
4. Discussion We found that 16.3% of the Singapore population reported having two or more chronic conditions. It is somewhat difficult to compare prevalence estimates across studies as the estimates of multimorbidity vary considerably due to dissimilar study populations or data sources.
Examining administrative claims data for Medicare beneficiaries and 15 chronic medical conditions, Lochner et al. [6] reported that 68.4% of Medicare beneficiaries had two or more chronic conditions and 36.4% had four or more chronic conditions [6]. Data from the US National Ambulatory Medical Care Survey, examining 13 chronic conditions, revealed that 29.2% of physician office visits were made by adult patients with two or three chronic conditions and 8.4% of visits were made by patients with four or more chronic conditions [4]. A Scottish study using a national dataset held by the Primary Care Clinical Informatics Unit, which examined 40 conditions, reported that 23.2% of patients had multimorbidity [27]. The prevalence estimates of our study are comparable to those from epidemiological studies conducted in Canada and Australia [28,29]. Agborsangaya et al. [28] examined 16 chronic conditions in the Health Quality Council of Alberta 2010 Patient Experience Survey and established the prevalence of overall age- and sex-standardized prevalence of multimorbidity as 19.0%. Fortin et al. [13] looked at seven chronic conditions among the general population of Quebec aged 25 years and above and established that 10.1% of men and 13.3% of women had two or more diseases. The same study found the prevalence of multimorbidity in the practice based survey to be much higher: 51.9% of men and 46.1% of women had two or more diseases. We also found that those with MCMC had a lower quality of life than those with one medical illness (0.88 vs. 0.94, P value b.001). A recent systematic review on multimorbidity among the elderly found that most studies reviewed found a significant effect of multimorbidity on disability and poor quality of life [29]. Our study identified older age, ethnicity, employment status and overweight as well as obesity as risk factors for MCMC. Older age is a well-documented risk factor for MCMC [27,30], with studies reporting that the number of morbidities and the proportion of people with multimorbidity increase substantially with age [31]. While we did not find an association of MCMC with economic status, as reported in a number of other studies [27,32], we did find an association with employment status, whereby those who were unemployed and economically inactive, including students, homemakers and retirees, had increased risk of MCMC. Similar findings were reported by Andrade and colleagues [33] who studied medical and psychiatric multimorbidity whilst also examining the sociodemographic determinants of these multimorbidities. Their findings, like ours, showed that being retired or a homemaker increased the risk of somatic multimorbidity. We found that ethnicity was associated with multimorbidity — those of Malay ethnicity had a lower risk of multimorbidity as compared to Chinese. A recent national health survey [34] has established ethnic differences in the prevalence of chronic conditions. While diabetes was more prevalent among Indians followed by Malays and Chinese, hypertension and cholesterol were more prevalent among those of Malay ethnicity followed by Chinese and Indians. However, a number of other medical conditions were included in our study whose prevalence and risk factors by ethnicity have not been previously established. Studies have also shown that psychiatric morbidity [15] and comorbid mental–physical disorder [22] differ by ethnicity in this multiethnic population. Indians (as compared to Chinese) have a higher risk of MDD and mental– physical comorbidity. Our findings suggest that ethnicity and the presence of psychiatric disorders may interact in complex ways, increasing the risk of chronic medical conditions, which are yet to be elucidated. Cabassa et al. [12] examined differences in the 12-month prevalence of MCMC by race/ethnicity, and the interactions between race/ethnicity and psychiatric diagnosis. They found that, compared to non-Hispanic whites and adjusting for psychiatric disorders and all other covariates, the likelihood of MCMC was significantly higher among African Americans and significantly lower among Hispanics. This finding is consistent with the elevated rates of chronic medical conditions (e.g., hypertension, coronary heart disease) among African Americans. They postulated that African Americans also experience substantial morbidities associated with chronic diseases and lower
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
M. Subramaniam et al. / General Hospital Psychiatry xxx (2014) xxx–xxx
5
Table 2 Sociodemographic correlates of chronic medical conditions No chronic condition
Age group 18–34 35–49 50–64 65+ Ethnicity Chinese Malay Indian Others Sex Male Female Marital status Single Married Divorced/Separated Widowed Education Primary and below Secondary Pre-U/Junior College/Diploma Vocational University Employment Employed Economically inactive Unemployed Income below $SD 20,000 $SD 20,000–49,999 $SD 50,000 and above BMI Underweight Normal Overweight Obese Smoking No Yes
One chronic condition
Two or more chronic conditions
Multinomial regression analyses One chronic vs. no chronic condition
Two or more chronic vs. no chronic condition
%
S.E.
%
S.E.
%
S.E.
OR
95% CI
P value
OR
95% CI
P value
72.4 65.0 47.9 19.0
1.3 1.4 1.8 2.8
21.6 25.0 29.1 29.6
1.2 1.3 1.7 3.3
5.9 10.0 23.0 51.3
0.6 0.9 1.5 3.6
1 1.1 1.7 3
(0.8–1.4) (1.3–2.2) (1.6–5.6)
.62 .001 .0004
1 1.5 4.1 18.4
(1.1–2.2) (2.8–6) (9.7–34.8)
.029 .0001 .0001
58.3 59.8 56.4 57.4
1.0 1.0 1.1 3.1
25.2 26.3 25.4 27.3
1.0 0.9 1.0 2.8
16.5 13.9 18.2 15.2
0.8 0.7 0.8 2.3
1 0.9 1 1.2
(0.8–1.1) (0.9–1.2) (0.8–1.7)
.41 .69 .29
1 0.7 1.2 1.3
(0.6–0.9) (1–1.5) (0.8–2.2)
.006 .13 .27
55.9 60.5
1.2 1.2
26.8 24.1
1.1 1.1
17.3 15.4
1.0 0.9
1 0.9
(0.7–1.1)
.16
1 0.8
(0.6–1.1)
.23
70.3 55.3 54.1 25.5
1.5 1.1 4.4 4.2
21.5 26.6 23.2 36.2
1.3 1.0 3.8 5.0
8.2 18.1 22.7 38.3
0.9 0.9 3.9 5.1
1 1.2 1.2 3.2
(1–1.6) (0.7–1.9) (1.6–6.5)
.08 .49 .002
1 1.3 1.7 1.7
(0.9–1.9) (1–3.1) (0.7–4)
.15 .07 .24
43.9 52.8 64.8 66.3 69.0
2.1 1.7 1.8 2.7 1.7
27.1 28.1 24.8 24.3 21.5
1.9 1.5 1.6 2.4 1.5
29.0 19.1 10.4 9.4 9.5
2.0 1.4 1.2 1.7 1.1
1.1 1.4 1.2 1.2 1
(0.7–1.6) (1.1–1.9) (0.9–1.6) (0.8–1.7)
.69 .024 .13 .40
(0.6–1.7) (0.9–2) (0.6–1.4) (0.6–1.8)
.93 .23 .61 .98
62.4 46.3 53.6
1.0 1.8 4.2
26.0 25.4 20.4
0.9 1.7 3.4
11.6 28.3 26.1
0.7 1.8 3.8
1 1.1 0.9
(0.8–1.5) (0.5–1.4)
.54 .61
1 1.8 2
(1.3–2.5) (1.1–3.5)
.001 .017
53.8 65.7 61.4
1.2 1.5 2.1
25.2 24.9 26.8
1.1 1.4 1.9
21.1 9.4 11.8
1.1 0.9 1.5
1 0.9 1.2
(0.7–1.2) (0.9–1.6)
.64 .33
1 0.8 1
(0.6–1.2) (0.6–1.6)
.35 .95
65.2 62.4 51.1 46.2
3.1 1.1 1.8 2.8
23.6 23.6 29.2 30.4
2.7 1.0 1.7 2.6
11.2 14.0 19.8 23.4
2.2 0.9 1.6 2.5
1.2 1 1.4 1.8
(0.8–1.6)
.39
(0.5–1.5)
.62
(1.1–1.7) (1.3–2.4)
.002 .0002
0.9 1 1.5 2.7
(1.1–2) (1.9–3.9)
.008 .0001
58.5 57.2
0.9 2.1
25.1 27.2
0.9 1.9
16.4 15.6
0.7 1.7
1 1.2
(0.9–1.5)
.25
1 1.3
(0.9–1.9)
.19
1 1.3 0.9 1 1
Bold font indicates significant p values.
quality of medical care, which could increase their risk of MCMC. The lower odds of MCMC among Hispanics were surprising given that this population reports higher prevalence rates than non-Hispanic whites for several chronic medical conditions. Lochner et al. [6] also
reported that the prevalence of MCMC varied by race/ethnicity. NonHispanic black and Hispanic women often had the highest prevalence of MCMC, although the authors did not elaborate further on the findings. Although ethnicity is associated with MCMC, it is necessary
Table 3 Prevalence rates and ORa of psychiatric disorders in chronic medical conditions
No psychiatric disorder Any psychiatric disorder Any mood disorder No Yes Any anxiety disorder No Yes Any alcohol use disorder No Yes
χ2
One chronic vs. no chronic condition
Two or more chronic vs. no chronic condition
S.E.
OR
P value
OR
95% CI
P value
0.7 2.1
10.6
.005
Reference 1.4
1.1–1.9
.006
Reference 2.7
1.9–3.7
b.001
15.8 22.8
0.7 2.9
8.3
.016
Reference 1.3
0.9–1.9
.110
Reference 2.3
1.5–3.6
b.001
0.8 3.8
16.2 18.4
0.7 3.2
0.9
.65
Reference 1.2
0.7–1.9
.471
Reference 1.4
0.8–2.5
.2
0.8 4.0
16.0 24.2
0.7 4.1
7.9
.019
Reference 1.5
1–2.4
.069
Reference 3
1.7–5.3
b.001
No chronic condition
One chronic condition
Two or more chronic conditions
%
S.E.
%
S.E.
%
59.2 51.4
0.9 2.5
25.1 27.6
0.8 2.2
15.6 21.1
58.8 51.3
0.9 3.2
25.4 25.9
0.8 2.8
58.4 54.6
0.8 4.3
25.3 26.9
58.7 47.2
0.8 4.4
25.3 28.6
P value
95% CI
Bold font indicates significant p values. a ORs were derived using multinomial regression analyses after adjusting for sociodemographic variables and BMI.
Please cite this article as: Subramaniam M., et al, Multiple chronic medical conditions: prevalence and risk factors — results from the Singapore Mental Health Study, Gen Hosp Psychiatry (2014), http://dx.doi.org/10.1016/j.genhosppsych.2014.03.002
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M. Subramaniam et al. / General Hospital Psychiatry xxx (2014) xxx–xxx
to identify the underlying factors of which ethnicity may be a marker that might account for these observed differences. Such factors, if modifiable, may be targeted to prevent or manage MCMC in specific ethnic groups. Obesity has been described by the World Health Organization as an “escalating epidemic” and “one of the greatest public health problems of our times” [35]. It is not surprising that we found obesity and overweight were associated with a significantly higher risk of MCMC as persons with obesity are at a greater than average risk for a variety of comorbid conditions like type 2 diabetes, stroke, coronary heart disease and osteoarthritis. Other studies have similarly reported an association between excess weight and MCMC [36,37]. Compared to people without psychiatric disorders and adjusting for other covariates, the odds of MCMC in our study were significantly higher among people with psychiatric disorders. When examining specific disorders, we found that the risk of MCMC was highest among those with mood disorder or AUD. This finding of elevated risk of MCMC among those with psychiatric disorders is consistent with a number of other studies [12,27,38]. People with psychiatric disorders have been identified to be at increased risk for chronic medical conditions especially cardiovascular disease and diabetes in a number of studies [39,40]. While mentally ill people may have greater difficulties than other patients in interpreting and understanding physical signs and symptoms, as well as solving their health problems, the problem of MCMC in this group of individuals is often the consequence of the failed systems of care [41]. On the other hand, mental disorders in individuals with multimorbidity are also often neglected, and this might be due to competing demands [42]. This has important clinical implications as studies have shown that treatment of the psychiatric illness results in better management of the physical illness [43] and thus screening and treatment of psychiatric illnesses must be actively pursued. The strengths of our study are that it is the first study that examined MCMC in a multiethnic Asian population. It is one of the few that has examined the risk of BMI, sociodemographic correlates and psychiatric disorders on multimorbidity. We used a structured instrument to establish the diagnosis of psychiatric illnesses, and our response rate of 75.9% further ensures the generalizability of our results to the population. A limitation of our study is the reliance on self- report of the physical disorders rather than by clinical assessment or verification through medical records, but studies have indicated that self-report of chronic physical diseases showed moderate to strong agreement with information obtained from medical records [44]. Only specific physical and psychiatric disorders were included as part of the SMHS; thus, the true prevalence of MCMC may be underreported in our study. We used aggregate variables for any mood disorder, any anxiety disorder and any AUD; therefore, the association with individual disorders was not analyzed, and specific associations may have been missed. Ideally, studies about the prevalence of multimorbidity should be based on a standard list of chronic conditions that includes at a minimum the most frequent diagnoses; otherwise, prevalence and risk factors are contingent upon the number and types of conditions assessed and how multimorbidity is defined. Bayliss and colleagues [45] have compiled a list of 24 health conditions most frequently assessed for the measurement of comorbidity to develop an instrument for the assessment of disease burden. Such lists should be used for estimating the prevalence of multimorbidity in different populations in future studies. The crosssectional nature of the study limits our ability to make causal inferences, and thus, risk factors like psychiatric illnesses and high BMI may be either a cause or effect of MCMC. Multimorbidity presents an important clinical and organizational challenge to the current single system approach to chronic disease management. Much of the current practice of medicine and the search for new treatments takes a single-disorder approach ranging
from disease-specific treatment guidelines, disease management protocols that were not developed for patients with multiple chronic conditions to clinical trials. This has created a “silo mentality” that is reinforced by multiple stakeholders [8]. Transformation of health care systems that focus on the patient-centered care with a holistic approach to MCMC is urgently needed. The US Department of Health and Human Services has outlined strategies for maximizing care coordination and for improving health and quality of life for individuals with MCMC [46]. The vision articulated by the workgroup is that of “optimum health and quality of life for individuals with multiple chronic conditions.” In conclusion, our study identified two important yet potentially modifiable risk factors for MCMC: psychiatric disorders and obesity. The implications of this finding in Singapore's context are manifold. Given that depression is highly prevalent in individuals with multimorbidity, it is important to screen for depression and to ensure that it is adequately treated among individuals with multimorbidity. Patients with chronic physical conditions are often followed up in busy primary care settings; therefore, easy-to-administer, wellvalidated screening instruments should be used to routinely screen for patients who have depression. AUD on the other hand is often neglected, and screening for AUD must also be a part of disease management for MCMC. Overweight and obesity are difficult yet modifiable risk factors for multimorbidity, and lifestyle interventions must be included while providing care to these patients. Screening, early identification and treatment of these factors could potentially result in lowering the risk of development of MCMC. Our findings reemphasize the need to integrate health care systems and community-based health programs and for engaging care providers across systems and disciplines to ensure high quality and continuity of care for individuals with MCMC. There is also a need to develop local clinical practice guidelines based on evidence-based practices to treat those with MCMC, thereby standardizing care. Lastly, health care policies need to prioritize MCMC, given the complex needs and demands for care of this group of patients.
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