Prevalence of self-reported multimorbidity in the general population and in primary care practices: a cross-sectional study

Study design and setting

The present cross-sectional study is a secondary analysis conducted among parallel
samples of the general population and primary care patients recruited for a larger
cohort study (the Program of Research on the Evolution of a Cohort Investigating Health
System Effects, PRECISE) 5] within the geographic boundaries of four local healthcare networks in Quebec, Canada.
These networks are located in metropolitan, urban, rural and remote areas. The study
was approved by the scientific and ethics committees of the Centre de santé et de
services sociaux de Chicoutimi, as well as the Research Ethics Committee of Hôpital
Charles Lemoyne, Quebec.

Participants

All participants had to be aged between 25 and 75 years, able to respond to written
and oral questions in English or French and reside in one of the four networks identified.
The general population sample was selected by random digit dialing from March to April
2010 within the administrative boundaries of the four networks. Once contact was made,
staff selected the eligible adult in the household with the most recent birthday to
ensure random selection.

The primary care patient sample was also recruited from March to April 2010 by research
assistants recruiting patients in the waiting room of 12 primary care clinics located
within the networks identified. In each of the four networks, we purposefully selected
three sentinel clinics typical of the dominant forms of primary healthcare organizations:
private medical clinics, community health clinics, and Family Medicine Groups. To
be included in the study, participants had to be a regular patient of the clinic and
be consulting for themselves in addition to be aged between 25 and 75 years, able
to respond to written and oral questions in English or French and reside in one of
the four networks identified.

Data collection

At recruitment, participants reported on socio-demographic information (age, gender,
education level, perceived financial situation, house ownership, presence or absence
of medical insurance, the possession of a retirement plan). Based on these data, we
produced a data-driven classification of socio-economic status and classified patients
into four socio-economic clusters: elite group, middle-high, middle-low, and low.
Definitional criteria for the four socio-economic clusters have been previously described
6]. After recruitment, a self-administered questionnaire was mailed to the subjects
included in the general population sample. The same questionnaire was given to the
patients recruited in primary care. Among other instruments, the questionnaire included
the disease burden morbidity assessment (DBMA) 7], 8]. The instrument elicits whether the patient has been diagnosed by a health professional
with or is taking any medications from a list of 21 conditions 7], 8]. For each condition present, the patient assesses the degree to which the condition
limits his/her daily activities on a five-point descriptive scale from 1 = “not at
all” to 5 = “a lot”; absence of the conditions is scored zero. The total DBMA score
is the sum of the limitation from all conditions. We also made a simple count of chronic
conditions present in each subject from the list of 21 conditions.

We used three operational definitions for multimorbidity: (1) the presence of two
or more chronic conditions (MM 2+); (2) the presence of three or more chronic conditions
(MM 3+); (3) DBMA score of 10 or higher (DBMA 10+). The definition of multimorbidity
based on DBMA 10+ arises from the clinical experience that it is a threshold of the
DBMA score that may correspond to patients with several chronic diseases that individually
have a minimal impact on the daily living of a patient, or the presence of at least
two chronic diseases with an important impact on patient’s daily living. This definition
of multimorbidity was proposed in the protocol of the project PRECISE 5], and used in that study.

Data analysis

The sociodemographic characteristics as well as number of diseases and DBMA scores
of the samples were analyzed with descriptive statistics. To compare the extent to
which the two samples differed, the Student’s t test was used to measure possible
differences of continuous variables, and the Chi squared test was used to test possible
differences between categorical variables. Subsequently, we calculated age-standardized
prevalence by direct standardization using the general population of Quebec as a Ref.
9]. To determine the extent to which the estimates where statistically significantly
in the samples, we calculated Fisher’s 95 % confidence intervals (95 % CI) looking
for overlap. No overlap of 95 % CI was considered a statistical significant difference.
All the analyses were done using SPSS version 20. The alpha significance level was
set at 0.05.