1Thavorn K, et al. BMJ Open 2017;7:e017264. doi:10.1136/bmjopen-2017-017264
Open Access
Effect of socio-demographic factors on
the association between multimorbidity
and healthcare costs: a population-
based, retrospective cohort study
Kednapa Thavorn,1,2,3 Colleen J Maxwell,3,4 Andrea Gruneir,3,5,6,7
Susan E Bronskill,3,5 YuQing Bai,3 Anna J Koné Pefoyo,8,9 Yelena Petrosyan,1,5
Walter P Wodchis3,5,10
To cite: Thavorn K,
Maxwell CJ, Gruneir A, et al.
Effect of socio-demographic
factors on the association
between multimorbidity
and healthcare costs: a
population-based, retrospective
cohort study. BMJ Open
2017;7:e017264. doi:10.1136/
bmjopen-2017-017264
► Prepublication history and
additional material for this paper
are available online. To view,
please visit the journal (http://
dx. doi. org/ 10. 1136/ bmjopen-
2017- 017264).
Received 11 April 2017
Revised 3 August 2017
Accepted 11 August 2017
For numbered affiliations see
end of article.
Correspondence to
Dr Walter P Wodchis;
walter. wodchis@ utoronto. ca
Research
AbstrACt
Objectives To estimate the attributable costs of
multimorbidity and assess whether the association
between the level of multimorbidity and health system
costs varies by socio-demographic factors in young (<65
years) and older (≥65 years) adults living in Ontario,
Canada.
Design A population-based, retrospective cohort study
setting The province of Ontario, Canada
Participants 6 639 089 Ontarians who were diagnosed
with at least one of 16 selected medical conditions on 1
April 2009.
Main outcome measures From the perspective of
the publicly funded healthcare system, total annual
healthcare costs were derived from linked provincial health
administrative databases using a person-level costing
method. We used generalised linear models to examine
the association between the level of multimorbidity
and healthcare costs and the extent to which socio-
demographic variables modified this association.
results Attributable total costs of multimorbidity ranged
from C$377 to C$2073 for young individuals and C$1026
to C$3831 for older adults. The association between
the degree of multimorbidity and healthcare costs was
significantly modified by age (p<0.001), sex (p<0.001)
and neighbourhood income (p<0.001) in both age groups,
and the positive association between healthcare costs
and levels of multimorbidity was statistically stronger
for older than younger adults. For individuals aged 65
years or younger, the increase in healthcare costs was
more gradual in women than in their male counterparts,
however, for those aged 65 years or older, the increase in
healthcare costs was significantly greater among women
than men. Lastly, we also observed that the positive
association between the level of multimorbidity and
healthcare costs was significantly greater at higher levels
of marginalisation.
Conclusion Socio-demographic factors are important
effect modifiers of the relationship between multimorbidity
and healthcare costs and should therefore be considered
in any discussion of the implementation of healthcare
policies and the organisation of healthcare services
aimed at controlling healthcare costs associated with
multimorbidity.
bACkgrOunD
Multimorbidity, the presence of two or more
coexisting conditions within a single person,
is increasingly prevalent due to advances
in life-extending medical treatments and
increases in life expectancy.1 2 Internationally,
the prevalence of multimorbidity has been
shown to range from 17% in young adults3 to
82% in older adults living in nursing homes.4
In the province of Ontario, Canada, the prev-
alence of multimorbidity based on 16 selected
conditions rose from 17.4% in 2003 to 24.3%
in 2009, and this increase was evident across
all age groups.5
Higher levels of multimorbidity are asso-
ciated with impaired physical functioning,6
strengths and limitations of this study
► This population-based study was based on a large
sample size and used robust costing and generalised
linear model regression techniques.
► The availability of linked and patient-level health
administrative databases allows the estimation
of the total health system costs associated with
multimorbidity from all healthcare sectors.
► The use of health administrative databases can also
minimise potential recall and non-response biases
that are commonly found in survey data.
► The total healthcare costs reported in this study
may be underestimated because they were
derived based on 16 selected medical conditions.
Moreover, it was not possible to measure certain
costs (eg, deductibles and copayments borne by
supplemental health insurance, out-of-pocket
beneficiary payments and indirect costs associated
with caregiving) with our data.
► The study did not take into account particular
clusters of medical conditions. It is possible that the
relationship between multimorbidity and healthcare
costs may vary according to the types and patterns
of comorbid medical conditions.
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2 Thavorn K, et al. BMJ Open 2017;7:e017264. doi:10.1136/bmjopen-2017-017264
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poorer quality of life,7 more frequent use of health
services and higher risk of death.8 In addition, individ-
uals with multimorbidity may experience faster disease
progression and require more complex medical care.9
Consequently, these individuals may be at a higher risk
of receiving suboptimal care,10 inappropriate prescrip-
tions11 and experiencing potentially preventable hospital-
isations.12 These adverse health outcomes can impose a
substantial burden on patients, family caregivers and the
healthcare system.
The relationship between multimorbidity and health-
care costs is well-documented and has been shown to
be curvilinear or exponential across jurisdictions. The
average Medicare payments in the USA ranged from
US$1154 among part A and part B beneficiaries with one
chronic condition to US$13 973 among beneficiaries with
at least four chronic conditions (a 12-fold difference).12
Similarly, the mean total health system costs among older
adults with multimorbidity in Switzerland were nearly six
times higher than among those without multimorbidity.13
Despite an abundance of research describing the rela-
tionship between multimorbidity and healthcare costs,
existing studies have some important methodological
and conceptual limitations. Some previous studies14 15
used ordinary least squares (OLS) regression despite the
fact that the positively skewed distribution of cost data
often violates the normality assumption of OLS.16 Others
attempted to overcome this problem by transforming
cost data to the logarithmic scale13 17; however, this trans-
formation may still result in interpretation problems,
as regression on transformed costs provides the predic-
tion of a median instead of the arithmetic mean costs.18
Importantly, the role of socio-demographic characteris-
tics as effect modifiers of the relationship between multi-
morbidity and healthcare costs remains poorly described,
although previous research has shown that the specific
types of disease clusters vary by age and sex2 19 and that
multimorbidity is more prominent in selected visible
minority and low-socioeconomic status populations.20
The objectives of this study were, therefore, to estimate
the health system costs attributable to multimorbidity
using a more rigorous and appropriate approach, and to
assess the extent to which the relationship between the
level of multimorbidity and health system costs varies
according to socio-demographic characteristics.
MethODs
study design and sample
This population-based, retrospective cohort study
included all residents of the province of Ontario between
1 April 2001 and 31 March 2010, who were enrolled in
the Ontario Health Insurance Plan (OHIP), and were
diagnosed with at least one of the following selected 16
medical conditions between 1 April 2001 and 31 March
2009 (study index date): acute myocardial infarction
(AMI), arthritis, asthma, cancer, cardiac arrhythmia,
chronic coronary syndrome, chronic obstructive
pulmonary disorder (COPD), congestive heart failure,
dementia, depression, diabetes, hypertension, osteopo-
rosis, renal failure, rheumatoid arthritis and stroke. These
conditions were selected because previous research and
clinical experts agreed that they were highly prevalent
and represented a substantial care and economic burden
for Canada’s healthcare system.5 21 We excluded indi-
viduals if they met the following criteria: had an invalid
health card number, were older than 105 years, died or
moved out of the province prior to the index date. Indi-
viduals with no contact with the healthcare system within
the past 5 years prior to the index date were also excluded
(excepting infants), as they may have left the province or
experienced an unreported death.
Data sources
We linked multiple provincial health administrative data-
bases anonymously using unique encrypted identifiers.
The Discharge Abstract Database provides data for all
hospital discharges in Ontario, and the OHIP claims
database includes billing claims for all physician encoun-
ters. We used the Registered Persons Database to iden-
tify Ontarians who were eligible for health insurance
coverage and derive their age. The linked database was
housed and secured at the Institute for Clinical Evaluative
Sciences (ICES) under data security and privacy policies
and procedures approved by the Information and Privacy
Commissioner of Ontario. This study was approved by the
Research Ethics Board at Sunnybrook Health Sciences
Centre, Toronto, Ontario, Canada.
Each medical condition was defined using diagnostic
algorithms and consultation codes that have been vali-
dated or used in previous studies. We defined six condi-
tions (AMI, asthma, chronic heart failure, COPD, diabetes
and hypertension) based on validated population-derived
registries held at ICES.22–28 These conditions were all
defined based on one diagnosis recorded in acute care or
two diagnoses recorded in ambulatory care (physician)
records within a 2-year period (ie, between 2007/2008
and 2008/2009), except for AMI, which was defined using
acute care records in 2008/2009. A similar approach was
adopted to define the remaining medical conditions
including arthritis, cancer, cardiac arrhythmia, chronic
coronary syndrome, dementia, depression, osteoporosis,
renal failure, rheumatoid arthritis and stroke. A list of
diagnostic codes used to define these medical conditions
are shown in online supplementary appendix 1.
Measures
Healthcare costs
Healthcare costs were estimated from the perspective of
the publicly funded healthcare system; accordingly, only
direct costs borne to the Ontario Ministry of Health and
Long-Term Care were considered. In Ontario, medically
necessary hospital and physician services are paid for by
the publicly financed health insurance plan; however,
public coverage for prescription drugs is primarily limited
to residents aged 65 years and over, social assistance
https://dx.doi.org/10.1136/bmjopen-2017-017264
3Thavorn K, et al. BMJ Open 2017;7:e017264. doi:10.1136/bmjopen-2017-017264
Open Access
recipients as well as those with high prescription drug
costs compared with their net household income.
We identified, measured and valued direct healthcare
costs by applying a person-level costing technique that
was developed and validated based on the Ontario health
administrative data.29 We calculated the costs of inpatient
hospitalisations, emergency department visits, same day
surgeries and inpatient rehabilitation by multiplying the
weighted volume of services by the average provincial costs
per weighted case. We obtained the costs of fee-for-ser-
vice physician and outpatient diagnostic or laboratory
services through OHIP fee approved as outlined in the
Ontario Health Insurance Schedule of Benefits and
Fees.30 Non-fee-for-service physician payments were calcu-
lated by applying applicable capitation payments or the
median amount reimbursed for the same service code
for the specific fiscal year.29 Costs for high-cost medical
device equipment were estimated from the amount
reimbursed to patients recorded in the Assistive Devices
Program database. Complex continuing care and inpa-
tient psychiatric costs were based on case mix, number
of days in care and resource utilisation groups.31 Patient
costs for long-term care were estimated based on a fixed
per diem according to prevailing government payment
rates, and costs for home care were estimated using
the average cost per hour. We used pharmacy payments
recorded in the Ontario Drug Benefit database to capture
prescription medication costs for individuals eligible for
public coverage. Annual total direct healthcare costs were
the sum of costs across healthcare sectors for each patient
for a 1-year period after the study index date, that is, from
April 2009 to March 2010.
We categorised healthcare costs into five components:
physician, hospital, drug, continuing care and other
healthcare delivery costs. Physician costs included profes-
sional fees paid by the provincial insurance plan directly
to physicians in private practice. Hospital costs included
amounts paid to healthcare institutions, including those
providing acute care, extended and chronic care, rehabil-
itation and convalescent care, psychiatric care as well as
drugs dispensed in hospitals. Drug costs consisted of the
costs of prescriptions dispensed at outpatient pharma-
cies to individuals eligible for provincial coverage while
continuing care costs included expenditure on home
care and residential long-term (nursing-home) care.
The other healthcare delivery costs category represented
expenditures on an assistive device programme that subsi-
dises high-cost equipment, such as wheel chairs, walkers,
continuous positive airway pressure devices and insulin
pumps, for patients with physical disabilities. All costs
were expressed in 2009 Canadian Dollars (C$).
Independent variables
Multimorbidity was defined as the occurrence of two or
more chronic diseases among the 16 selected conditions
within a single individual and was categorised into five
groups. A categorical variable was created to capture
those with no multimorbidity (single disease only), two,
three, four and five or more multimorbid conditions.
Socio-demographic variables included age, sex, income
and level of marginalisation. As prescription drug costs
among Ontarians aged <65 years were primarily covered
by private drug plans, we ran separate regressions for
younger (<65 years) and older (65+ years) cohorts, and
also included a continuous variable for age in the models.
Income level was categorised into five quintiles, with the
lowest quintile reflecting the lowest income level. We used
the Ontario Marginalisation Index, a validated census-
based and geographically based index, as a proxy for
individual-level socio-demographic factors.32 The index
consisted of four dimensions of marginalisation: mate-
rial deprivation; residential instability; ethnic concentra-
tion and dependency. Lower scores on each dimension
represent areas that are the least marginalised and higher
scores represent areas that are the most marginalised.
This index has been shown to be associated with several
health outcomes.33
We also controlled for other factors that may confound
the impact of multimorbidity on healthcare costs, such as
the type of primary care model and geographic location.
Selection of such factors was guided by previous health-
care cost studies12 34 35 and was subject to the availability
of data on these factors in Ontario administrative data-
bases. The payment scheme of primary care services was
categorised into three groups: group-based teams with
capitation/salary and team-based payment (family health
teams/other group models); capitation or blended
payment models (family health networks/family health
organisations) or primarily fee for service (family health
groups and non-rostered patients). Lastly, we assigned a
geographic location to each individual using the Rurality
Index for Ontario,36 whereby a value >40 was considered
to be a designated rural area.
Analysis
Annual healthcare costs per capita were described by
health service sector, age group (<65 vs ≥65 years), the
degree of multimorbidity and each of the independent
factors, such as sex, age group and level of marginalisa-
tion. Multivariate regression analyses were used to assess
the incremental costs of interest in this study. To iden-
tify the regression model that best fits the cost data, we
followed the steps suggested by Manning and Mullahy.37
We first ran OLS of the logarithmic transformation of
cost data on the number of medical conditions and other
confounding factors; however, the OLS regression was
deemed inappropriate because the residuals were not
normally distributed. Therefore, the generalised linear
model (GLM) with a log-link function and a gamma distri-
bution was chosen because a modified Park test suggested
that the variance was proportional to the conditional
mean. The GLM allows us to estimate mean healthcare
costs without the need for retransformation.
Attributable costs due to multimorbidity were esti-
mated by subtracting the mean predicted cost of one
medical condition from the mean predicted cost of two
4 Thavorn K, et al. BMJ Open 2017;7:e017264. doi:10.1136/bmjopen-2017-017264
Open Access
conditions, two from three conditions, three from four
conditions and four from at least five conditions, respec-
tively. To investigate whether the relationship between
the level of multimorbidity and healthcare costs was
moderated by socio-demographic factors, we added
two-way interaction terms between the level of multimor-
bidity and each socio-demographic factor, including sex,
age, income level, deprivation quintile, instability quin-
tile, dependency quintile and ethnic concentration quin-
tile. The significance of interaction terms was assessed
by comparing the likelihood ratio of the full model with
all interaction terms with the model without interaction
terms using the likelihood ratio test.
The model performance, including goodness of fit
and specifications, was examined by checking the scaled
deviance, Pearson’s χ2 statistics and residual plots, respec-
tively. All analyses were performed using SAS statistical
software for UNIX (V.9.3; SAS Institute, Cary, North
Carolina, USA).
results
We identified a cohort of 6 639 089 individuals living
with at least one of the selected 16 medical conditions in
Ontario in 2009 (see online supplementary appendix 2
for baseline characteristics). Our cohort represents about
50% of the total population in the province of Ontario
in 2009. Close to half of the study cohort (48%) had at
least two selected medical conditions, and this prevalence
was found to increase with age. The majority of the study
cohort was younger than 65 years of age (75%) and just
over half was female (53%). Nearly all individuals (91%)
resided in non-rural areas, and about one-third (33%)
lived in neighbourhoods with a high proportion of diverse
ethnic groups.
The total annual healthcare cost estimated for the
study cohort was C$26.5 billion. As shown in figure 1,
individuals living with at least two selected medical
conditions represented 24.4% of the total population of
Ontario (~13 million) but accounted for approximately
two-thirds (67.9%) of total allocatable healthcare costs
in 2009/2010. By contrast, individuals without multimor-
bidity who accounted for 76% of the total population were
responsible for only 32.1% of total allocatable healthcare
costs. On average, annual total costs per capita amounted
to C$2217 in individuals <65 years and C$9398 in those
aged 65 years or older.
Table 1 shows the annual total costs per capita by base-
line characteristics for young and older adults. For both
age groups, per capita total healthcare costs were higher
in women than in men. The average healthcare costs
increased with older age, and greater levels of marginali-
sation were associated with higher healthcare costs in both
age groups. Mean total healthcare costs were the highest
among individuals living in the most deprived and most
unstable areas as well as those who were highly depen-
dent; however, mean total costs decreased as income level
increased.
Figure 2 illustrates the distribution of total cost per
capita by type of services. Among individuals <65 years,
hospitalisation was the primary cost driver and respon-
sible for 47% of total healthcare costs, followed by physi-
cian costs (32%), drug costs (10%) and continuing care
costs (6%). For older adults, hospital costs remained the
largest cost component (41%), followed by continuing
care costs (23%), drug costs (19%) and physician costs
(15%). Figure 2 also reveals that unadjusted mean total
costs increased with additional numbers of medical
conditions, ranging from C$1352 in individuals <65 years
without multimorbidity to C$13 105 in those living with
Figure 1 Distribution of total number of population and total health system costs in Ontario from 1 April 2009 to
31 March 2010.
https://dx.doi.org/10.1136/bmjopen-2017-017264
5Thavorn K, et al. BMJ Open 2017;7:e017264. doi:10.1136/bmjopen-2017-017264
Open Access
Table 1 Annual per capita healthcare costs by baseline characteristics and age group, 1 April 2009 to 31 March 2010
<65 years
(n=5 004 699)
≥65 years
(n=1 634 390)
N
Per capita healthcare cost (C$)
N
Per capita healthcare cost (C$)
Mean (SD) Median (IQR) Mean (SD) Median (IQR)
All cohort 5 004 699 2217
(9630)
502
(193–1317)
1 634 390 9398
(19 796)
2982
(1448–7178)
Sex
Female 2 618 591 2311
(9044)
624
(248–1546)
923 053 9526.96
(19 245)
2991.97
(1461–7344)
Male 2 386 108 2113
(10 233)
378.67
(132–1058)
711 337 9230.31
(20 488)
2968.13
(1431–6982)
Age group (years)
<20 809 782 997
(6420)
257
(103–600)
20–44 1 784 314 1835
(7997)
440
(155–1171)
45–64 3 247 243 2910
(11 414)
684
(291–1725)
65–74 1 219 877 6424
(16 464)
2363
(1173–4757)
75+ 797 750 12 517
(22 351)
3964
(1884–12 277)
Income quintile
Lowest 935 048 2822
(11 333)
580
(206–1699)
314 616 10 646
(21 501)
3325
(1596–8667)
Middle-low 970 797 2360
(10 276)
521
(199–1380)
336 928 9529
(20 218)
3053
(1501–7296)
Middle 999 087 2107
(9146)
498
(195–1268)
318 557 9319
(19 552)
2992
(1470–7114)
Middle-high 1 042 284 2008
(8 899)
487
(195–1226)
322 798 9120
(19 279)
2916
(1426–6873)
Highest 1 009 890 1903
(8391)
475
(192–1180)
331 022 8549
(18 309)
2747
(1351–6352)
Rurality index
Non-rural 4 579 691 2206
(9 605)
509
(197–1320)
1 459 014 9448
(19 998)
3005
(1470–7161)
Rural 356 361 2522
(10 112)
501
(197–1441)
157 864 9333
(18 303)
2918
(1400–7798)
Deprivation quintile
Least deprived 1 282 898 1894.17
(8 596 59)
476
(193–1170)
371 547 9167
(19 628)
2823
(1380–6709)
Less deprived 1 136 731 2015
(8 810)
489
(196–1231)
368 124 8935
(18 928)
2898
(1423–6759)
Somewhat
deprived
982 133 2193
(9 240)
504
(196–1311)
346 326 9165
(19 300)
2978
(1463–7030)
Very deprived 808 152 2438
(10 281)
511
(200–1443)
293 434 9541
(19 951)
3100
(1520–7467)
Most deprived 705 593 2941
(11 861)
600
(210–179)
228 501 10 517
(21 250)
3326
(1599–8570)
Instability quintile
Least dependent 1 211 734 2007
(8 674)
489
(188–1250)
188 787 8149
(19 413)
2713
(1307–5882)
Less dependent 1 179 936 2078
(9 134)
500
(195–1275)
276 819 8359
(18 652)
2777
(1353–6167)
Somewhat
dependent
976 538 2230
(9 793)
506
(198–1320)
303 853 8717
(19 018)
2849
(1401–6548)
Very dependent 808 196 2349
(9 954)
515
(201–1375)
326 662 9068
(19 195)
2944
(1458–6958)
Continued
6 Thavorn K, et al. BMJ Open 2017;7:e017264. doi:10.1136/bmjopen-2017-017264
Open Access
five or more medical conditions, corresponding to a
10-fold increase. On the other hand, while C$4185 was
spent on older adults without multimorbidity, spending
increased by about fivefold to C$19 196 in those living
with five or more medical conditions.
Table 2 shows adjusted attributable costs of multimor-
bidity after controlling for other factors. Among individ-
uals <65 years, the attributable total cost was C$377 in
those living with two medical conditions and C$2073 in
those living with at least five medical conditions, corre-
sponding to a sixfold increase in attributable cost. Simi-
larly, attributable total costs in older adults also rose with
increasing number of medical conditions, ranging from
C$1026 in those with two medical conditions to C$3831
in those with five or more. The magnitude of an incre-
mental cost, however, depended on the reference cate-
gory. Specifically, one additional medical condition in
young adults without multimorbidity led to an attribut-
able cost of C$377, while for young adults who already
had three medical conditions, one additional health
condition resulted in a total cost of C$798. These incre-
mental costs were even greater in older adults, among
whom the incremental cost rose from C$1026 (one vs two
conditions) to C$1652 (three vs four conditions). Similar
patterns were observed for subdivided healthcare costs,
which varied across age groups (table 2). An additional
<65 years
(n=5 004 699)
≥65 years
(n=1 634 390)
N
Per capita healthcare cost (C$)
N
Per capita healthcare cost (C$)
Mean (SD) Median (IQR) Mean (SD) Median (IQR)
Most dependent 739 103 2650
(10 947)
550
(213–1507)
511 811 10 961
(20 953)
3381
(1636–9336)
Ethnic concentration quintile
Lowest 564 476 2398
(9 766)
500
(200–1370)
283 980 9309
(18 529)
2983
(1463–7533)
Middle-low 756 120 2288
(9 552)
491
(196–1317)
304 526 9170
(18 773)
2969
(1458–7283)
Middle 854 573 2280
(9 780)
497
(196–1317)
305 524 9540
(19 678)
3011
(1478–7419)
Middle-high 1 028 876 2190
(9 565)
502
(195–1309)
294 164 9600
(20 240)
3012
(1473–7266)
Highest 1 711 462 2124
(9 468)
528
(199–1331)
419 738 9288
(20 751)
2981
(1441–6694)
Table 1 Continued
Figure 2 Unadjusted mean total healthcare cost per capita for Ontario adults, by service type, number of conditions and age
group from 1 April 2009 to 31 March 2010.
7Thavorn K, et al. BMJ Open 2017;7:e017264. doi:10.1136/bmjopen-2017-017264
Open Access
medical condition caused a onefold to threefold increase
in the costs of each health sector except for hospital care,
for which incremental costs increased steadily from C$185
to C$802 in the younger cohort and C$232 to C$1060 in
the older adult cohort.
We also found that the association between the number
of medical conditions (ie, the degree of multimorbidity)
and healthcare costs was significantly modified by age and
sex for both young and older adults (table 3), and the
positive association between healthcare costs and levels of
multimorbidity was significantly stronger for older than
younger adults. For individuals aged 65 years or younger,
the increase in healthcare costs was more gradual in
women than in their male counterparts, however, among
those aged 65 years or older, the increase in healthcare
costs in women was significantly greater than in men.
For both age groups, we observed small interaction
effects between the number of medical conditions and
other socio-demographic factors. The rise in healthcare
costs as the level of multimorbidity increased was less
pronounced among high-income individuals than low-in-
come individuals, and the association between the level
of multimorbidity and healthcare costs was significantly
modified by the level of deprivation, instability, depen-
dency and ethnic concentration. The positive …
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