Pattern and Predicting Risk Factors of Multi-Morbidity in the XXX Cohort
Population Using Structural Equation Model
Abstract
Aims: The aims of this study were to determine pattern and predicting
risk factors of multi-morbidity in the Azar Cohort population using
Structural Equation Model (SEM).
Methods: In this study, the prevalence of MM in 15006 XXX cohort
population was evaluated. MM was defined as the co-existence of two or
more CDs. The information regarding socio-economic, demographic,
sleeping habits, and physical activity were collected by questionnaires.
A multi-group SEM was employed to model complex relationships between
directly- and indirectly-observed variables.
Results: The overall MM was seen in 28.8% of the population. The most
prevalent chronic diseases were obesity, hypertension, depression, and
diabetes, respectively. Obesity, depression, and diabetes were the most
co-occurring CDs in our population. The SEM diagram indicated the
overall effect of socio-demographic (predictors) and sleep and physical
activity (mediators) on the number of CDs. The number of CDs in the
active participants and those who sleep 6.6-7.3 hours/day was lower than
the inactive participants and those who sleep ≤6.5 hours/day.
Conclusions: According to our results, it seems that the reduction of MM
is possible through promoting public health from an early age and for a
wide range of socio-economic conditions, provided that the necessary
support for general health is offered for the aging population.
Keywords: Chronic disease, Mmultimorbidity, Structural Equation Model,
cohort study
What’s known?
-Many of the risk factors (including age, obesity, smoking, and
hypertension) are linked with multi morbidity (MM).
- Cohort studies show a direct association between high age and low
socioeconomic populations, with a growing trend in women.
- A large number of cohort MM studies have focused on old ages,
especially those over 65.
- Studies that evaluated the predictor risk factors of MM using the
Structural Equation Model (SEM) are rare.
What’s new?
-In our study, using SEM method, physical activity and sleeping habits
are mediators, while wealth score index, the number of years of
schooling, marital status, and residence regions (rural) influence the
number of chronic diseases through physical activity and sleeping
habits.
- Another finding in this study indicated depression as the pivotal
factor in multidisciplinary patterns, which is closely associated with
an increased risk of chronic diseases.
Introduction:
Multimorbidity (MM) is often described as the co-existence of two or
more chronic diseases, including physical non-communicable long-term
conditions and mental health
problems1. The
prevalence of MM is reported to vary from 12.9% in the general
population to 95.1% in older people. Many of the risk factors
(including age, obesity, smoking, and hypertension) are linked with
multimorbidity 2. In
other words, each factor can result in co-morbidities, yield vulnerable
conditions for patients, and establish a generally unfavorable quality
of life3. Over the past
decades, with improving health-care systems and increased life
expectancy, the average age has gradually increased. However, as people
move toward adulthood, MM and related complications are more likely to
affect individuals. Consequently, due to enduring diseases for long
periods of time and increasing age, people experience mental, emotional,
and physical weakness. This simultaneous endurance of physical and
mental hardships causes excessive fatigue that prevents people from
having physical activity, which in turn, influences social participation
and cognitive health4.
Therefore, this chain of risk factors has increased the prevalence rate
of non-communicable diseases in recent decades, despite the advancement
of diagnostic and screening capabilities.
The most common classification method to study the prevalence of MM is
based on demographic characteristics such as age, sex, level of income,
and socioeconomic
status5. Cohort studies
show a direct association between high age and low socioeconomic
populations, with a growing trend in
women3. Because of
continuous monitoring requirements, MM patients are imposed to
overwhelming treatments, frequent hospitalizations, and
polypharmacy6. In
specific, polypharmacy is a significant risk factor that results in a
vicious cycle and more risk factors by increasing hospitalizations and
presentation of new multimorbidities, such as liver disease due to the
disruption of liver
enzymes6,7.
Several studies investigated the relationship between physical activity
and the emergence of MM in different populations. A study showed that
the requirement of polypharmacy in MM patients with low levels of
physical activity is doubled in comparison with active patients.
Moreover, decreased physical exercise negatively impacts the lipid
profile and insulin sensitivity and could develop anxiety and depression
in COPDs8. In one of
these studies, the relationship between watching television and physical
activity was examined. As demonstrated in this study, increasing the
hours of watching television is directly associated with a rising
prevalence of multimorbidity. In other words, decreasing physical
activity enhances the influence of sedentary behaviors9. Another study on the
Chinese adult population by Wang et al. demonstrated that inadequate
sleeping alters the natural metabolism of the body, which plays a key
role in the emergence of MMs such as cerebrovascular disease,
cardiovascular disease, and diabetes. However, this study found no
association between sleep duration, anemia, and chronic kidney disease10.
Understanding the general framework of MM in accordance with well-known
risk factors can assist in finding direct and indirect relationships
between them. This will enable predicting their occurrence sequence in
people with different features. Moreover, using this information, we
will be able to identify and prevent the defective cycles of the
diseases at an appropriate and reversible
point11.
However, these complex demands increase the workload of health-care
centers and create unnecessary expenses for the system and patients.
Thus, managing the prevalence of MM in patients is among the most
crucial challenges in modern medicine and is a global research
priority12.
Nevertheless, despite its critical significance, there are many profound
gaps in the exact relationships between each demographic factor and
multimorbidity. Usually, age and sex are considered as demographic
characteristics to express the prevalence of
multimorbidity11.
A large number of cohort MM studies have focused on old ages, especially
those over 65. Therefore, it is impossible to determine the behavioral
pattern of MMs in primary ages. In addition, these studies have
investigated a few specific diseases, and no investigation had been
carried out to clarify their relationship with other
diseases13.
Therefore, the purpose of this study is to determine the prevalence of
MM by considering all demographic factors to express a comprehensive
relationship pattern between various diseases (such as cardiovascular
diseases, cancers, asthma, and other frequent diseases) and their risk
factors, using a structural equation modeling approach in the XXX cohort
population.
Method and Material:
In the following cross-sectional study, the prevalence of MM in 15006
subjects who participated in the XXX cohort study was evaluated. XXX
cohort study is part of a large prospective epidemiological research
studies in XXX (XXX cohort)14 and has been
approved by the Ethical Committee of XXX University of medical sciences
(XXXmed.Rec.1393.205). The pilot and the enrollment phases for this
study was launched in 2014, and it was concluded in 2017. Comprehensive
details about the XXXcohort study are provided in other published
article 15.
MM is defined as the co-existence of two or more Chronic Diseases (CD),
including hypertension, diabetes, cardiovascular diseases (CVD),
cerebrovascular diseases, asthma, cancers (gastrointestinal, breast,
prostate, skin, bladder, lung, head and neck, and hematopoietic),
depression, fatty livers, rheumatoid disease, and obesity.
In the questionnaires, participants were considered to have these
diseases when they answered yes to the following question: “Has any
doctor ever told you that you have -?” Moreover, obesity was defined as
a body mass index of 30kg/m2 or higher.
Anthropometric Measurements:
The weight and height of all subjects were measured, and the body mass
index was determined using the standard formula: weight (kg)/height
(m2). The anthropometric measurements are described in
detail elsewhere14.
Information regarding age, gender, education level, marital status,
smoking status, and sleep habits were collected using the
questionnaires.
Socioeconomic status was evaluated using Wealth Score Index (WSI), which
is calculated by Multiple Correspondence Analysis (MCA). Ownership of a
variety of durable assets (e.g., dishwasher, car, and television),
household condition (e.g., the number of rooms, type of ownership), and
education level were used in the calculation of WSI for each
participant. Participants of the study were categorized into five SES
quintiles, from the lowest (1st quintile) to the highest (5th quintile).
In this study, the daily activity of the participants was evaluated
using a questionnaire recorded by the participants. For this purpose, a
criterion called MET has been employed. Each MET is equal to the amount
of energy that each person consumes relative to their weight. For
instance, one MET is equal to the amount of oxygen used by each person
while resting per kilogram of their body weight per minute, which is 3.5
ml of oxygen, and 4 MET equals 16 milliliters of oxygen used per
kilogram of their body weight per minute. Through this criterion, we
obtained the level of activity based on its respective MET for each
person.
Statistical Analysis:
In this study, the STATA software (version 16, Stata Corp, College
Station, Texas) was employed for data analysis. The normality of data
was assessed using the Kolmogorov-Smirnov test and descriptive
statistics. The mean (standard deviation) was reported for the
quantitative data, and the frequency (percentage) was reported for
qualitative data. In the current analysis, quantitative variables were
age, BMI, while qualitative variables were gender, marital status,
education level, residential region, smoking status, WSI, and sleep
duration. One-Way ANOVA, chi-square, and Kruskal-Wallis H were used to
compare quantitative, qualitative, and categorical qualitative
variables, respectively, between multimorbidity classifications. The MMs
were classified into four groups: 0 (no chronic diseases), 1 (one
chronic disease), 2 (two chronic diseases), 3 (3 chronic diseases), and
4 (≥ four chronic diseases). Moreover, WSI, METS, and sleep duration
were categorized into tertile, quartile, and quintiles, respectively.
A multi-group Structural Equation Model (SEM) was employed to model
complex relationships between directly- and indirectly-observed
variables. In this model, MM was considered as multiple chronic
diseases. Age, gender, marital status, WSI, the number of years in
school, residence regions, and current smoking status were predictors,
sleeping habits and physical activity were the moderators, and the
number of chronic diseases was the outcome. The theoretical model was
illustrated based on a review of the literature.
To compensate for the missing predictors, the maximum likelihood method
(with the missing values) was employed. Stata’s sem command is capable
of estimating models with missing data using the Full Information
Maximum Likelihood estimation (FIML) method16Prior to SEM
analysis, multivariate normality was evaluated by examining the
normality variables (i.e., estimating Mardia’s coefficient of
multivariate skewness and kurtosis). To assess the significance of the
relationship (the significance level of 5%), appropriate fitness,
Comparative Fit Index (CFI), Tucker-Lewis Index (TLI), Root Mean Square
Error of Approximation (RMSEA), and Standardized Root Mean Square
Residual (SRMR) were employed. In large sample sizes, χ2/df is not a
suitable choice for model fitness. Hence, for this study, the criteria
employed were RMSEA ≤ .06, SRMR ≤ .08, and CFI ≥ .95. Since latent
variables possess several observed variables, it is necessary to assign
an arbitrary value, usually 1.0, to the path that links the latent
variable to one of its indicator variables to provide a unit of
measurement for each latent variable17.
Results
The Total Model: This model appeared to fit the data well: CFI (0.969),
TLI (0.965), RMSEA (0.051), and SRMR (0.052).
Our findings indicated that in the cohort population (n=15006), 32.8%
of participants had at least one chronic disease (table1). The overall
MM (i.e., CD ≥ 2) was seen in 28.8% of the XXX cohort population. The
most prevalent chronic diseases were obesity (37.6%), hypertension
(20.2%), depression (17.1%), and diabetes (11.6/%), respectively. In
addition, obesity, depression, and diabetes were the most co-occurring
CDs in our population (table2).
The baseline characteristics of the participants stratified by the
number of chronic diseases are demonstrated in table 3. The frequency of
chronic diseases was significantly higher in female, urban residents,
and unmarried participants. Compared with the subjects in the
3rd tertile METS and the 5th quintiles of WSI, the
prevalence of multimorbidity was significantly higher in subjects with
low physical activity (1st
tertile METS) and low WSI (poorest; 1st quintiles of
WSI).
The SEM diagram with the standardized estimates indicated the total
effect of socio-demographic (as predictors) and sleeping habits and
physical activity (as the mediator) on the outcome of the study (number
of CDs) (Figure 1). In this model, age was inversely associated with
sleeping habits (P<0.001). Moreover, marital status had a
positive effect on physical activity (B=2.07; P<0.001), while
the significant negative effect of WSI, age, and gender (female) was
observed on physical activity (P<0.001).
As presented in table 4, age and gender had significant effects on the
number of CDs, both directly and through physical activity and sleeping
habits (P<0.001). A significant and direct effect of the
number of years of schooling on WSI and the number of CDs were found in
our SEM model (P<0.001). Moreover, it seems that the number of
years of schooling indirectly mediated its effect on the number of CDs
via physical activity (B=-0.11, P<0.001). In specific, people
who had higher WSI were more inactive (B=-1.09; P <0.001).
Discussion
The findings of this cross-sectional study indicated that 28.8% of the
total population had MM (i.e., ≥2 CDs). A wide range of the prevalence
of MM was reported in different studies. The prevalence of MM in the
Golestan cohort study, conducted by Malekzadeh et al. in the population
aged 40-70 years, was
19.4%18. In another
study, according to the dates provided by the health insurance
organization, MM has been reported in 21.1% of the included
population19. In the
aged Kurdish population (i.e., >50 years), MM was seen in
36.1% of participants20. Moreover, Aoki et
al. studied the pattern of MM in the Japanese population, noting the
prevalence of MM to be 29.9% in all participants, which was higher
(62.8%) in the older population (i.e., ≥ 65
years)21. Additionally,
in a cohort study in Germany, the prevalence of MM was 67.3% in the
50-70-year-old
population22. The
difference observed in the prevalence of MM in studies may be due to
differences in sample size, the types of chronic diseases considered as
an MM component, methodology, and ethnicity.
In this study, the most common CDs were obesity, hypertension,
depression, and diabetes. Moreover, the most co-occurring CDs were
obesity, diabetes, and depression. It should be noted that our findings
are in line with previous studies that reported hypertension, diabetes,
dyslipidemia, and obesity as the most common CDs in population23. Also, the most
prevalent CDs reported in the population studied in Blümela et al. were
hypertension, arthrosis, diabetes, and
depression12. In this
regard, Read et al. reported that the risk of depression was doubled in
patients with MM, compared with subjects without MM24.
As indicated in previous studies, there is a mutual association between
depression and CDs 25.
However, the mechanism of this bi-directional relationship is not
adequately recognized. Some studies proposed that mechanisms such as the
complication of CDs, including disability, decreasing quality of life,26 pain,27and belief about the
disease and adapting manners are involved in increasing the risk of
depression 28. On the
other hand, people with depression are less likely to follow treatment
protocols for their CD, which will increase their risk of developing MM
and poor control over their illness29. Nevertheless,
disorders in both metabolic and immune-inflammatory pathways, which
occur in many CDs, are associated with depression30. Therefore, in
patients with CDs, depression should be considered as an important
factor.
Studies that evaluated the predictor risk factors of MM using the SEM
model are rare. In our SEM model, age and gender influence the number of
CDs both directly and indirectly through physical activity and sleeping
habits. In studies similar to ours, it has been documented that age and
gender were positively associated with MMs18,19In this regard, Boutayeb et al. reported that females and older people
are the predictor factors of MM in WHO Eastern Mediterranean countries31. However, in some
studies, there are no differences in the prevalence of MM among genders23,32For instance, the EpiChron Cohort Study indicated that the number of CDs
increased with age in both genders23.
Our analysis revealed that the number of years of schooling is inversely
associated with the number of CDs, which is in line with Blümela et al.
that observed an increase of 40% in the risk of MM among low-educated
women who had an unqualified
job33. Moreover,
Johnson-Lawrence et al. showed that the risk of MM in the population of
60-64 years with bachelor’s degree (or higher) was lower than less
educated people34.
Since the level of education is a key factor in social/life elements
such as employment, health insurance, and housing, lower education
levels may lead to lower income, poor living conditions, and
psychological stress. These conditions can prevent people from
exercising proper health practices such as a healthy diet, physical
exercise, and access to preventive health care, resulting in a higher
risk of chronic diseases35. In this regard, our
findings indicated that the number of years of schooling is positively
related to WSI. However, it demonstrated a negative effect on physical
activity level, confirming the mentioned association between education
level, poor social condition, and physical inactivity.
In previous studies, it was reported that socioeconomic status is
correlated with MM. In specific, the prevalence of MM was higher in low
socioeconomic groups19,36As
confirmed in this study, the frequency of MM decreased with increasing
WSI, which confirms the findings of the aforementioned studies.
Moreover, living in rural areas had a significantly negative effect on
WSI, and the number of years of schooling was positively associated with
WSI. It has been suggested that the higher number of CDs in the
population with low WSI can be attributed to less knowledge about
symptoms of CDs, fewer checkups, and the consumption of unhealthy food.
The other finding of the present study indicated that smoking was
inversely associated with the number of CDs. We asked the participants
about their current smoking status. Therefore, stopping smoking after
developing CDs may be one of the reasons for this result37. On the other hand,
in previous studies, a history of smoking has been documented as a risk
factor for MM18,38.
We found that the number of CDs in the active participants
(3rd tertile METS) and those who sleep 6.6-7.3
hours/day were lower than the inactive population and those who sleep
≤6.5 hours/day the SEM pathway. In other words, physical activity and
sleeping habits had a significant effect on the number of CDs. Similar
to the present study, Ruiz-Castel et al. noted that the chance of having
two, or three or more chronic conditions increased by 7.30 and 6.79
times, respectively, in participants who sleep <6 hours/day39. Finally, Nicholson
et al. reported that the risk of MM was higher in participants who sleep
either <6 hours/day or >8 hours/day33.
It appears that the association between sleeping habits and the number
of CDs is reciprocal. To elaborate, the sleeping habits in subjects with
CDs are subject to change by the pain caused by some CDs,
medications/treatments used, and mood disorders40. On the other hand,
it is well-known that sustained sleep deprivation demonstrates negative
effects on cardio-metabolic, endocrine, immune system, and inflammatory
pathways41,42.
Moreover, shorter sleep duration could modify the circadian rhythm and
alter the hormonal system (e.g., insulin resistance and decreased
leptin)43.
The association between physical activity and the number of CDs in the
present study is in agreement with Christofoletti et al. In that study,
it was reported that the frequency of co-existing CDs was greater among
those who had more leisure time and watched TV ≥2 hours/day22. Moreover, Ryan et
al. noted a significant association between physical activity and
MM44.
Physical activity and sleeping habits are mediators, while WSI, the
number of years of schooling, marital status, and residence regions
(rural) influence the number of CDs through physical activity and
sleeping habits.
The main strength of this cohort study is the use of data obtained from
a large population. In addition, there are other strengths to this
study, including the large number of participants and the data obtained
with details of the lifestyle, social, and demographic status of the
participants.
Similar to all studies, this study had its limitations, including the
type of study (i.e., cross-sectional) and the illnesses expressed by
each individual, which is likely to bias the data due to the difference
in the level of literacy and information retrieval from the
participants.
Conclusion
Our study is valuable since it examined the multimorbidity patterns from
multiple economic, social, and epidemiologic aspects in parts of XXX
where the general population is at an increased risk of depression and
components of the metabolic syndrome (obesity, hypertension, and
diabetes).
This increased risk is specifically more prevalent among the least
disadvantaged population with risk factors such as old age, gender
(female), low socioeconomic status, low education levels, improper
sleeping habits, inadequate hygiene, and inactivity.
Therefore, adequate sleeping habits and physical activity should be
considered effective factors in the health of the population. Since
multimorbidity are very common in our population, a comprehensive
program should be developed to promote sleep health and reduce
multimorbidity.
Another finding in this study indicated depression as the pivotal factor
in multidisciplinary patterns, which is closely associated with an
increased risk of chronic diseases. Depressed people are at risk of
chronic disease. Hence, they should be screened and treated. Finally, it
should be noted that the reduction in multimorbidity is possible by
promoting public health from an early age and among a wide range of
socio-economic conditions, provided that the necessary support for
health is offered to the aging population of XXX.