Multimorbidity patterns and premature mortality in a prospective cohort: effect modifications by socioeconomic status and healthy lifestyles

Abstract

Background
Few studies have explored the impact of multimorbidity patterns on premature mortality. This study aimed to assess the associations between multimorbidity patterns and long-term mortality and whether the associations were modified by socioeconomic status (SES) and healthy lifestyles.

Methods
Data were from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 in the US. The latent class analysis was used to establish multimorbidity patterns based on 11 chronic conditions. Mortality outcomes were ascertained by linking with the public-use mortality data from the National Death Index through December 31, 2019. Accelerated failure time models were used to estimate time ratios (TRs) and corresponding 95% confidence intervals (CIs) for the associations between multimorbidity patterns and all-cause and CVD mortality and to exmine the extent to which SES and healthy lifestyles modified those associations.

Results
In our study, six multimorbidity patterns were identified, including “relatively healthy”, “hypercholesterolemia”, “metabolic”, “arthritis-respiratory”, “CKD-vascular-cancer”, and “severely impaired” classes. Compared with the “relatively healthy” class, TRs for all-cause and CVD mortality progressively decreased across the multimorbidity classes, with the “severely impaired” class showing the shortest survival time (TR, 0.53; 95% CI: 0.48, 0.58 for all-cause mortality; 0.42; 0.35, 0.50 for CVD mortality). A significant interaction was noted between SES and multimorbidity patterns for survival time, with a stronger positive association in individuals with low SES. Adherence to healthy lifestyles was related to longer survival time across all multimorbidity patterns, especially in those with relatively less severe multimorbidity.

Conclusions
Multiple multimorbidity patterns were identified and associated with mortality. Lower SES was associated with higherexcess multimorbidity-associated mortality, while adopting healthy lifestyles contributed to longer survival regardless of multimorbidity patterns. Efforts should be mobilized to reduce SES gaps and promote healthy lifestyles to alleviate the health burden of multimorbidity.

Full text

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Xue et al. BMC Public Health (2025) 25:1262
https://doi.org/10.1186/s12889-025-22216-2 BMC Public Health
†Yunhaonan Yang and Xiong-Fei Pan contributed equally to this
paper.
*Correspondence:
Yunhaonan Yang
yunhaonanyang@scu.edu.cn
Xiong-Fei Pan
pxiongfei@scu.edu.cn
Full list of author information is available at the end of the article
Abstract
Background Few studies have explored the impact of multimorbidity patterns on premature mortality. This
study aimed to assess the associations between multimorbidity patterns and long-term mortality and whether the
associations were modied by socioeconomic status (SES) and healthy lifestyles.
Methods Data were from the National Health and Nutrition Examination Survey (NHANES) 1999–2018 in the US.
The latent class analysis was used to establish multimorbidity patterns based on 11 chronic conditions. Mortality
outcomes were ascertained by linking with the public-use mortality data from the National Death Index through
December 31, 2019. Accelerated failure time models were used to estimate time ratios (TRs) and corresponding 95%
condence intervals (CIs) for the associations between multimorbidity patterns and all-cause and CVD mortality and to
exmine the extent to which SES and healthy lifestyles modied those associations.
Results In our study, six multimorbidity patterns were identied, including “relatively healthy”, “hypercholesterolemia”,
“metabolic”, “arthritis-respiratory”, “CKD-vascular-cancer”, and “severely impaired” classes. Compared with the “relatively
healthy” class, TRs for all-cause and CVD mortality progressively decreased across the multimorbidity classes, with the
“severely impaired” class showing the shortest survival time (TR, 0.53; 95% CI: 0.48, 0.58 for all-cause mortality; 0.42;
0.35, 0.50 for CVD mortality). A signicant interaction was noted between SES and multimorbidity patterns for survival
time, with a stronger positive association in individuals with low SES. Adherence to healthy lifestyles was related to
longer survival time across all multimorbidity patterns, especially in those with relatively less severe multimorbidity.
Conclusions Multiple multimorbidity patterns were identied and associated with mortality. Lower SES was
associated with higherexcess multimorbidity-associated mortality, while adopting healthy lifestyles contributed to
Multimorbidity patterns and premature
mortality in a prospective cohort: eect
modications by socioeconomic status
and healthy lifestyles
QingpingXue1,2, ShanshanZhang2,3, XueYang4, Yan-BoZhang5, YidanDong2, FanLi2, ShuoLi2, NianweiWu2,
TongYan6, YingWen7, Chun-XiaYang3, Jason HYWu8, AnPan9, YunhaonanYang2*† and Xiong-FeiPan2,10,11*†
Content courtesy of Springer Nature, terms of use apply. Rights reserved.

Page 2 of 13
Xue et al. BMC Public Health (2025) 25:1262
Introduction
Multimorbidity, usually dened as the co-occurrence of
two or more chronic conditions, has increased world-
wide due to population aging and improved medical care
[1]. Among US adults, the prevalence of multimorbidity
steadily increased from 45.7% in 1988–1994 to 59.6% in
2013–2014 [2]. Multimorbidity has been reported to lead
to higher functional limitations [3], increased medical
service utilization, and healthcare expenditure [4]. us,
multimorbidity poses substantial challenges to healthcare
systems.
Evidence accumulates that multimorbidity is associ-
ated with elevated mortality, with more morbidities being
related to a higher risk of death [5]. However, previous
studies mainly measured multimorbidity by counting
the number of conditions rather than proling condition
patterns. Certain morbidities often cluster as they share
similar pathological pathways [6], potentially resulting in
synergistic eects on mortality. Prior studies used latent
class analysis (LCA) to identify multimorbidity patterns,
and reported associations between certain multimorbid-
ity patterns (i.e., cardiometabolic group, and depression/
cardiovascular disease/cancer group) and higher mortal-
ity risk [7, 8]. However, evidence from the US is limited. A
study among 166,126 US older adults identied ve multi-
morbidity groups, with the complex cardiometabolic class
showing the strongest eects on mortality [9]. Of note, this
study only included older US adults. Although the preva-
lence of multimorbidity is higher in US middle-aged (50%
for 45–60 years) and older adults (81% for 65 years and
older), 18% of 18–44 years US adults had multimorbidity
in 2016 [10]. Evidence on the associations between multi-
morbidity patterns and mortality is still needed in a repre-
sentative US population.
In addition, recent studies suggested that SES and
healthy lifestyles might modify the process of progres-
sion from a single disease or multimorbidity into mortal-
ity [11–15]. Previous studies showed that SES disparities
in survival increased [16] and adherence to healthy life-
styles decreased in the US [17]. us, whether mortality
associated with multimorbidity patterns diered by SES,
and whether adherence to healthy lifestyles could dif-
ferentially reduce mortality by multimorbidity patterns
are urgently needed to be examined. ese questions
have important public health implications for minimizing
health inequality caused by SES and improving long-term
survival in individuals with multimorbidity.
To address the knowledge gap, we used data from the
National Health and Nutrition Examination Survey
(NHANES) to assess the associations between multimor-
bidity patterns and premature mortality and whether the
associations were modied by SES. In addition, we exam-
ined whether the associations of combined lifestyle fac-
tors and mortality diered by multimorbidity patterns.
Methods
Study design and participants
e NHANES is an ongoing series of cross-sectional,
nationally representative surveys conducted by the
National Center for Health Statistics (NCHS) of the Cen-
ters for Disease Control and Prevention to evaluate the
health and nutritional status of the US general popula-
tion. e study design, sampling procedures, and sur-
vey methods were described in detail elsewhere [18].
Briey, the NHANES used a stratied, multistage prob-
ability cluster design to enroll a nationally representa-
tive sample. Data were collected from study participants
at enrollment using questionnaires, physical examina-
tions, and laboratory tests. All NHANES protocols were
approved by the NCHS Research Review Board, and writ-
ten informed consent was provided by study participants.
For our analyses, we used data from 52,398 partici-
pants aged more than 20 years old recruited in ten cycles
(NHANES 1999–2000, 2001–2002, 2003–2004, 2005–
2006, 2007–2008, 2009–2010, 2011–2012, 2013–2014,
2015–2016, and 2017–2018). We excluded (1) par-
ticipants without any of the eleven chronic conditions
(n = 6,349: see the “identication of chronic conditions
and multimorbidity” section); (2) participants without
information for SES, including family poverty-to-income
ratio, educational level, and insurance status (n = 4,084);
(3) participants without information for healthy lifestyles
(n = 4,084); and (4) participants without information for
healthcare utilizations (n = 33; Supplementary Fig. 1).
38,011 participants were included for nal analyses.
Identication of chronic conditions and Multimorbidity
Eleven chronic conditions (i.e., stroke, arthritis, cancer,
chronic obstructive pulmonary disease [COPD], asthma,
cardiovascular disease [CVD], diabetes, hypertension,
hypercholesterolemia, obesity, chronic kidney disease
[CKD]) were determined based on self-reported doctor
diagnosis, physical examinations, and laboratory tests
according to previous work [2]. Stroke, arthritis, and can-
cer were dened as self-reported doctor diagnoses. All
study participants were asked three following questions:
(1) “has a doctor or other health professional ever told you
that you had a stroke?” (2) “has a doctor or other health
longer survival regardless of multimorbidity patterns. Eorts should be mobilized to reduce SES gaps and promote
healthy lifestyles to alleviate the health burden of multimorbidity.
Keywords Multimorbidity, Socioeconomic status, Healthy lifestyles, Mortality, Cohort study
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Xue et al. BMC Public Health (2025) 25:1262
professional ever told you that you had an arthritis?” (3)
“has a doctor or other health professional ever told you that
you had a cancer?”. COPD was dened as a composite of
self-reported doctor diagnoses of emphysema or chronic
bronchitis [19]. All study participants were asked three
following questions: (1) “has a doctor or other health pro-
fessional ever told you that you had emphysema?” (2) “has
a doctor or other health professional ever told you that
you had chronic bronchitis?” (3) “has a doctor or other
health professional ever told you that you had COPD?” for
NHANES 2013–2018. Asthma was dened if participants
reported ever being told that they had asthma and still had
it [20] using the following two questions: (1) “Has a doctor
ever told you that you had asthma?” (2) “Do you still have
asthma?”. CVD was dened as a composite of self-reported
doctor diagnoses of coronary heart disease, myocardial
infarction, angina pectoris, and congestive heart failure.
All participants were asked the following four questions:
(1) “has a doctor or other health professional ever told you
that you had a coronary heart disease?” (2) “has a doctor or
other health professional ever told you that you had a heart
attack (also called myocardial infarction)?” (3) “has a doc-
tor or other health professional ever told you that you had
an angina, also called angina pectoris?” (4) “has a doctor or
other health professional ever told you that you had a con-
gestive heart failure?”. Diabetes was determined by fasting
glucose measures (HbA1c ≥ 6.5%) or self-reported antidia-
betic medication taken or self-reported doctor diagnosis of
diabetes [21]. Hypertension was determined by blood pres-
sure (systolic blood pressure (SBP) ≥ 140mm Hg or dia-
stolic blood pressure (DBP) ≥ 90mm Hg) or self-reported
antihypertension medication taken or self-reported doctor
diagnosis of hypertension via questionnaires. Hypercho-
lesterolemia [2] was dened as total cholesterol ≥ 200mg/
dL or self-reported anti-hypercholesterolemia medica-
tion taken [22]. Obesity was dened as body mass index
(BMI) ≥ 30 kg/m2 at enrollment. CKD was determined by
eGFR < 60 ml/min/1.73m2 or a one-time urine albumin-
to-creatinine ratio (ACR) ≥ 30mg/g. eGFR was calculated
using the Chronic Kidney Disease Epidemiology Collabo-
ration (CKD-EPI) formula [23]: eGFR CKD-EPI = 141 ×
min (SCr/κ,1)α × max (SCr/κ,1)−1.209 × 0.993Age × 1.018 (if
female), where SCr is serum creatinine, κ is 0.7 for females
and 0.9 for males, α is − 0.329 for females and − 0.411 for
males, min indicates the minimum of SCr/κ or 1 and max
indicates the maximum of SCr/κ or 1.
LCA is a statistical procedure used to identify qualita-
tively dierent subgroups (also known as latent classes)
within populations who share similar characteristics [24].
It was used to dene multimorbidity patterns based on
these 11 chronic conditions (Supplementary methods).
Assessment of SES
Self-reported family poverty-to-income ratio, educa-
tional level, and insurance status were used to measure
SES according to previous work [14, 25]. e family
poverty-to-income ratio was calculated as the total fam-
ily income divided by the poverty threshold, and a higher
poverty-to-income ratio indicated a higher family income
status. e family poverty-to-income ratio was grouped
into three groups: ≤1, 1–4, and ≥ 4 [14]. Educational level
was classied into three groups: less than high school, high
school, and college or above. Health insurance status was
categorized into no insurance, public health insurance
only (including Medicare, Medicaid, military healthcare,
Indian Health Service, State Sponsored Health Plan, or
other government program), and private health insur-
ance (including any private health insurance, Medi-Gap,
or single-service plan). LCA was used to dene the overall
SES based on the three components according to the item-
response probabilities (Supplementary methods).
Assessments of lifestyle factors
Based on previous work [14] and evidence on modi-
able lifestyle factors for the prevention of major noncom-
municable diseases recommended by the World Health
Organization [26], a combined healthy lifestyle score was
assessed using four major modiable lifestyle factors,
including smoking, alcohol drinking, diet, and physi-
cal activity. A healthy level for smoking was dened as
never smoking (participants who smoked fewer than 100
cigarettes in a life-long time). A healthy level of alcohol
drinking was dened as daily consumption of one drink
or fewer for women and two drinks or fewer for men.
Diet quality was assessed in terms of the healthy eating
index-2015 (HEI-2015) using 24-hour dietary recalls [27].
A total score of diet quality ranged from 0 to 100, with a
higher total diet quality score indicating better diet qual-
ity. A healthy diet was dened as a HEI-2015 score in the
top 40% of distributions. A healthy physical activity was
dened as more than 150min of moderate-to-vigorous lei-
sure-time physical activity per week. We assigned 1 point
for the healthy level and 0 point otherwise for each lifestyle
factor. e total healthy lifestyle score was calculated as a
sum of four factors and ranged from 0 to 4, with a higher
score indicating a healthier lifestyle. e healthy lifestyle
score was categorized into three groups: 0–1 (low), 2
(medium), and 3–4 (high).
Assessment of healthcare utilizations
Healthcare utilization was determined by self-reported
use of outpatient and inpatient services in line with pre-
vious studies [28, 29]. e outpatient utilization was
dened according to whether the participants ever had
at least one visit to a doctor or other health professional
in the past year. e inpatient utilization was dened
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Xue et al. BMC Public Health (2025) 25:1262
according to whether participants had at least one over-
night hospital stay in the past year, excluding an overnight
stay in the emergency room.
Measurement of covariates
Several potential covariates were included in our analy-
ses, including NHANES cycles, age, sex, and race/eth-
nicity. e NHANES cycles were treated as an ordinal
variable (0–9 from 1999–2000 to 2017–2018). Age was
treated as a continuous variable in the analyses. Sex
was categorized as male and female. Race/ethnicity was
grouped as Hispanic (including Mexican and non-Mexi-
can Hispanic), non-Hispanic white, non-Hispanic black,
and others.
Outcome ascertainment
Mortality outcomes were ascertained using the pub-
lic-use mortality data from the National Death Index
through December 31, 2019, to which the NHANES
1999–2018 eligible participants were matched using a
probabilistic matching algorithm. e International Clas-
sication of Diseases Tenth Revision (ICD-10) codes
were used to identify causes of death, and codes I00-I09,
I11, I13, and I20-51 were used to ascertain deaths from
CVD. Participants not matched with a death record were
regarded as alive throughout the entire follow-up period.
Statistical analysis
Complex survey design and sampling weights were con-
sidered in our analyses according to the NHANES ana-
lytic guidelines. Baseline characteristics were compared
across six multimorbidity patterns using Rao-Scott Chi-
square (for categorical variables) and Wald F-tests (for
continuous variables). Logistic regression models were
conducted to generate odds ratios (ORs) and corre-
sponding 95% condence intervals (CIs) for the associa-
tions between multimorbidity patterns and inpatient and
outpatient healthcare utilization, respectively. We added
product terms of multimorbidity patterns and healthcare
utilization in the model to examine potential modica-
tion using the Wald test. e Poisson regression model
was conducted to assess incident rate ratios (IRRs) and
95% CIs for the association between SES and the number
of chronic conditions.
As the proportion hazards assumption was not met,
we did not use the Cox proportional hazard regression
models to assess the associations between multimor-
bidity patterns and all-cause and CVD mortality. We
tted the accelerated failure time with Weibull distribu-
tion for the baseline hazard function to assess the time
ratios (TRs) and 95% CIs for our analyses [30]. Covariates
were adjusted in a stepwise manner: Model 1 included
NHANES cycles, age, sex, and race/ethnicity, and Model
2 additionally included SES and healthy lifestyle score.
We further assessed the associations between mul-
timorbidity patterns and all-cause and CVD mortality
in subgroups stratied by SES. We included a product
term of multimorbidity patterns and SES to examine the
potential eect modication using the Wald test. We also
cross-classied participants according to categories of
multimorbidity patterns and SES, and explored the com-
bined associations of multimorbidity patterns and SES
with all-cause and CVD mortality.
In addition, we conducted analyses stratied by mul-
timorbidity patterns to assess the associations between
healthy lifestyles and all-cause and CVD mortality in dif-
ferent multimorbidity pattern subgroups.We evaluated
the eect modication of multimorbidity patterns for
the association between healthy lifestyles and mortality
using the Wald test. We also cross-classied participants
according to categories of multimorbidity patterns and
healthy lifestyles and explored the combined associations
of multimorbidity patterns and healthy lifestyle with all-
cause and CVD mortality.
We conducted subgroup analyses by stratifying par-
ticipants by age (< 40, 40–60, and ≥ 60 years), sex (male
and female), and years of enrollment (1999–2008 and
2009–2018). We added a product term of the stratify-
ing variables and multimorbidity patterns to the nal
models to examine potential eect modications by the
stratifying variables using the Wald test. We conducted
two sensitivity analyses. First, we adjusted for three indi-
vidual SES components and individual lifestyle factors to
assess the associations between multimorbidity patterns
and mortality. Second, instead of using LCA, we classi-
ed the SES based on the levels of three SES components.
We assigned 0, 1, and 2 to represent the following catego-
ries respectively: PIR ≤ 1, PIR 1–4, and PIR ≥ 4; less than
high school, high school, and college or above; no insur-
ance, public insurance only, and private insurance. SES
score was calculated as a sum of three SES indicators and
ranged from 0 to 6, with a higher score indicating a higher
SES. e SES score was categorized into three groups: low
(0–2), medium (3–4), and high SES (5–6).
e LCA for the complex survey was conducted with
Mplus 8 following the Mplus User Guide Version 8, and
diet quality was calculated in SAS 9.4. All other statisti-
cal analyses were conducted with R 4.2.0 and STATA/
SE 17.0, and all signicance tests were two-sided with
P < 0.05 indicating statistical signicance.
Results
Of 38,011 participants, the mean age was 46.90 (SE,
0.20) and 49.01% were males. Six multimorbidity pat-
terns were identied through the LCA according to the
item-response probabilities, including “relatively healthy”,
“hypercholesterolemia”, “metabolic”, “arthritis-respira-
tory”, “CKD-vascular-cancer”, and “severely impaired”
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Xue et al. BMC Public Health (2025) 25:1262
Characteristics Total Relatively healthy Hypercholesterolemia Metabolic Arthritis-respiratory CKD-
vascular-cancer
Severely impaired P*
(n = 38,011) (n = 19,995) (n = 5,465) (n = 5,609) (n = 2,154) (n = 2,793) (n = 1,995)
Age, mean (SE), years 46.90 (0.20) 38.76 (0.18) 58.02 (0.26) 52.81 (0.28) 53.31 (0.40) 68.50 (0.35) 65.43 (0.35) < 0.001
Sex, n (%) < 0.001
Male 18,654 (49.01) 9,973 (50.85) 2,583 (46.40) 2,732 (50.98) 789 (33.55) 1,608 (52.06) 969 (45.71)
Female 19,357 (50.99) 10,022 (49.15) 2,882 (53.60) 2,877 (49.02) 1,365 (66.45) 1,185 (47.94) 1,026 (54.29)
Race/ethnicity, n (%) < 0.001
Hispanic 9,525 (12.86) 5,687 (15.73) 1,062 (6.74) 1,592 (13.98) 322 (6.59) 535 (8.47) 327 (6.38)
Non-Hispanic white 17,949 (71.08) 8,698 (67.69) 3,169 (82.16) 2,087 (65.13) 1,368 (80.70) 1,535 (75.56) 1,092 (76.15)
Non-Hispanic black 7,446 (9.96) 3,616 (9.66) 886 (6.63) 1,620 (15.93) 329 (7.24) 509 (9.71) 486 (12.43)
Others 3,091 (6.11) 1,994 (6.92) 348 (4.48) 310 (4.95) 135 (5.46) 214 (6.26) 90 (5.05)
SES, n (%) < 0.001
High SES 10,589 (38.30) 5,983 (39.68) 1,817 (45.15) 1,416 (36.16) 499 (31.16) 547 (28.02) 327 (23.19)
Median SES 17,531 (45.31) 8,991 (44.20) 2,514 (43.64) 2,644 (46.84) 997 (46.96) 1,422 (52.19) 963 (51.32)
Low SES 9,891 (16.39) 5,021 (16.11) 1,134 (11.21) 1,549 (17.00) 658 (21.89) 824 (19.80) 705 (25.48)
Insurance status, n (%) < 0.001
Private insurance 21,657 (67.02) 11,587 (67.70) 3,413 (72.46) 3,162 (67.34) 1,106 (59.89) 1,469 (61.54) 920 (53.45)
Public insurance only 8,631 (16.19) 2,955 (10.66) 1,428 (18.86) 1,490 (19.49) 719 (26.58) 1,088 (31.24) 951 (40.23)
No insurance 7,723 (16.79) 5,453 (21.64) 624 (8.69) 957 (13.17) 329 (13.53) 236 (7.22) 124 (6.33)
Education level, n (%) < 0.001
College or higher 19,784 (60.50) 11,274 (63.79) 2,878 (62.27) 2,610 (55.50) 1,062 (54.68) 1,173 (48.92) 787 (45.94)
High school 8,833 (23.96) 4,393 (22.43) 1,295 (24.01) 1,397 (26.97) 548 (26.70) 673 (26.55) 527 (28.86)
Less than high school 9,394 (15.54) 4,328 (13.79) 1,292 (13.72) 1,602 (17.53) 544 (18.62) 947 (24.53) 681 (25.20)
PIR, n (%) < 0.001
≥ 4 10,227 (37.41) 5,669 (38.33) 1,810 (45.13) 1,401 (35.88) 474 (29.76) 561 (28.75) 312 (22.32)
1–4 20,401 (49.45) 10,312 (47.99) 2,888 (46.64) 3,149 (51.60) 1,160 (52.24) 1,713 (58.71) 1,179 (58.31)
≤ 1 7,383 (13.14) 4,014 (13.67) 767 (8.23) 1,059 (12.51) 520 (18.01) 519 (12.54) 504 (19.37)
Lifestyle score, n (%) < 0.001
Low 6,306 (16.18) 2,832 (13.87) 946 (17.43) 895 (16.35) 556 (25.04) 564 (20.36) 513 (25.46)
Moderate 13,249 (34.09) 6,644 (32.52) 1,832 (32.92) 2,084 (37.60) 841 (39.20) 1,043 (35.87) 805 (40.05)
High 18,456 (49.72) 10,519 (53.61) 2,687 (49.65) 2,630 (46.05) 757 (35.76) 1,186 (43.77) 677 (34.48)
Diet quality, n (%) < 0.001
Unhealthy 22,345 (60.00) 12,355 (62.17) 2,791 (52.62) 3,306 (61.40) 1,352 (64.98) 1,432 (51.19) 1,109 (56.28)
Healthy 15,666 (40.00) 7,640 (37.83) 2,674 (47.38) 2,303 (38.60) 802 (35.02) 1,361 (48.81) 886 (43.72)
Smoking, n (%) < 0.001
Unhealthy 17,566 (46.49) 8,202 (41.90) 2,793 (51.75) 2,421 (44.20) 1,398 (64.16) 1,502 (53.93) 1,250 (63.50)
Healthy 20,445 (53.51) 11,793 (58.10) 2,672 (48.25) 3,188 (55.80) 756 (35.84) 1,291 (46.07) 745 (36.50)
Alcohol consumption, n (%) < 0.001
Unhealthy 3,299 (10.30) 1,787 (10.41) 614 (13.52) 364 (7.58) 198 (9.38) 227 (10.42) 109 (6.34)
Table 1 Basic characteristics across dierent multimorbidity patterns in the NHANES from 1999 to 2018
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Xue et al. BMC Public Health (2025) 25:1262
classes(Supplementary Methods, Supplementary Tables
1–2, and Supplementary Fig.2). ree SES classes were
also established, including the low, medium, and high
SES classes (Supplementary Tables 3 and Supplementary
Fig.3). 38.30% had high SES and 16.39% had low SES;
49.72% had a high lifestyle score and 16.18% had a low
lifestyle score.
Participants from the “CKD-vascular-cancer” class had
the oldest age (P < 0.001), while those from the “severely
impaired” class were more likely to have low SES
(P < 0.001), and to have a low lifestyle score (P < 0.001;
Table1). e aged- and sex-standardized prevalence of
“relatively healthy” class and “CKD-vascular-cancer”
class decreased from 56.98% and 5.53% in 1999–2000
to 54.81% and 4.21% in 2017–2018 (P = 0.01 and < 0.001;
Supplementary Fig.4), respectively. In contrast, the age-
and sex-standardized prevalence of “metabolic” and
“severely impaired” increased from 9.79% and 3.48% in
1999–2000 to 15.48% and 4.73% in 2017–2018 (P = 0.01
and < 0.001), respectively. Meanwhile, the prevalence of
“metabolic” class and “severely impaired” class increased
from 1999 to 2018 across all SES classes (P ≤ 0.02 for all),
while the prevalence of “CKD-vascular-cancer” decreased
for participants in the medium and low SES classes
(P ≤ 0.003 for all). Low SES was associated with a higher
number of chronic conditions (IRR, 1.16; 95% CI: 1.13,
1.19; Supplementary Table 4) after adjusting for covariates.
Compared with study participants in the “relatively
healthy” class, the odds of having at least one outpatient
visit in the past year and at least one inpatient overnight
hospital stay were progressively elevated in “metabolic”,
“hypercholesterolemia”, “arthritis-respiratory”, “CKD-
vascular-cancer”, and “severely impaired” classes (ranging
from 2.49 to 17.43 for the outpatient visit; 1.60 to 6.48 for
inpatient admission; Supplementary Table 5). ere was
no signicant interaction between SES and multimorbidity
patterns for outpatient visits or inpatient overnight hospi-
tal stays (Supplementary Table 6).
During a median follow-up of 9.58 years, 5,425 deaths
(1,688 from CVD) were documented. Compared with the
“relatively healthy” class, other multimorbidity classes
showed shorter survival time after adjustment for the
NHANES cycle, age, sex, race/ethnicity, SES, and healthy
lifestyles. e “severely impaired” class had the shortest
survival time with TR (95% CI) of 0.53 (0.48, 0.58), fol-
lowed by the “CKD-vascular-cancer” class (0.61; 0.56,
0.67), the “arthritis-respiratory” class (0.70; 0.64, 0.77),
the “metabolic” class (0.76; 0.70, 0.83), and the “hyper-
cholesterolemia” class (0.89; 0.82, 0.97). e “severely
impaired” class had the shortest survival time due to
CVD mortality with TR (95% CI) of 0.42 (0.35, 0.50),
followed by the “CKD-vascular-cancer” class (0.52;
0.44, 0.62), the “metabolic” class (0.59; 0.50, 0.70), the
“arthritis-respiratory” class (0.65; 0.54, 0.79), and the
Characteristics Total Relatively healthy Hypercholesterolemia Metabolic Arthritis-respiratory CKD-
vascular-cancer
Severely impaired P*
(n = 38,011) (n = 19,995) (n = 5,465) (n = 5,609) (n = 2,154) (n = 2,793) (n = 1,995)
Healthy 34,712 (89.70) 18,208 (89.59) 4,851 (86.48) 5,245 (92.42) 1,956 (90.62) 2,566 (89.58) 1,886 (93.66)
Physical activity, n (%) < 0.001
Unhealthy 15,759 (36.25) 6,960 (30.17) 2,293 (37.31) 2,734 (46.01) 1,021 (44.22) 1,516 (49.89) 1,235 (59.08)
Healthy 22,252 (63.75) 13,035 (69.83) 3,172 (62.69) 2,875 (53.99) 1,133 (55.78) 1,277 (50.11) 760 (40.92)
Abbreviations: CKD, chronic kid ney disease; NHANES, Nati onal Health and Nutriti on Examination Sur vey; PIR, Family povert y-to-income rati o; SE, standard error; SE S, socioeconomic s tatus
*P values were ca lculated using the Wald F-test for continuous variables and Rao-Scott χ2 test for categorical variables after adjusting for sampling weights
Table 1 (continued)
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Xue et al. BMC Public Health (2025) 25:1262
“hypercholesterolemia” class (0.84; 0.71, 0.99; Table2).
e eect estimates did not change appreciably after fur-
ther adjustment for SES components and lifestyle factors
(Supplementary Table 7) or using an alternative SES mea-
surement (Supplementary Table 8). We found some het-
erogeneities in the associations between multimorbidity
patterns and survival time by age, sex, and years of enroll-
ment (all P for interaction ≤ 0.04; Supplementary Table 9).
As for SES, study participants with low SES had a
shorter survival time, compared to those with high
SES (Supplementary Table 10). Signicant interactions
between SES and multimorbidity were noted for survival
time due to all-cause (P for interaction = 0.01) and CVD
mortality (P for interaction < 0.001), with stronger nega-
tive associations between multimorbidity and survival
time in study participants with low SES than those with
high SES for all multimorbidity patterns (Fig.1). ere
was no noticeable change after further adjustment for
SES components and lifestyle factors (Supplementary
Fig.5) or using an alternative SES measurement (Supple-
mentary Fig.6). In a joint analysis to investigate the com-
bined association of SES and multimorbidity patterns with
survival time, TR (95% CI) for individuals of low SES and
the “severely impaired” class compared with those with
high SES and the “relatively healthy” class were 0.32 (95%
CI: 0.27, 0.38) for all-cause mortality and 0.23 (95% CI:
0.17, 0.32; Fig.2) for CVD mortality.
Individuals with higher lifestyle scores (3–4) had a lon-
ger survival time than those with lower lifestyle scores
(0–1; Supplementary Table 11). A signicant interaction
between healthy lifestyles and multimorbidity patterns
was noted for survival time (all P for interaction < 0.001).
e positive association between healthy lifestyles and
survival time due to all-cause mortality was stronger in
the less severe multimorbidity class: TRs were from 1.64
to 1.25 for the “relatively healthy”, “hypercholesterolemia”,
“metabolic”, “severely impaired”, and “CKD-vascular-can-
cer” classes, but TR was 1.68 for the “arthritis-respiratory”
classes (Fig.3). Similar associations were found after fur-
ther adjustment for SES components and individual life-
style factors (Supplementary Fig.7).
Discussion
In our large prospective study, six statistically distinct
and clinically meaningful classes of multimorbidity were
identied in the US population. Compared with study
participants in the “relatively healthy” class, those in the
“severely impaired” class showed the lowest survival time
as well as the highest healthcare utilization (i.e., outpa-
tient visits and inpatient overnight stay). In addition,
SES modied the associations between multimorbidity
patterns and survival time, with stronger associations
in those with lower SES. We also noted signicant inter-
actions between multimorbidity patterns and healthy
lifestyles for survival time: while adherence to healthy life-
styles seemed protective across multimorbidity patterns,
healthy lifestyles showed stronger positive associations
with survival time among participants with less severe
multimorbidity patterns.
Given that specic clusters of multimorbidity often
share common pathophysiological pathways, identify-
ing these clusters and their associations with long-term
mortality can provide valuable insights to enhance
eective management of multimorbidity and optimize
Table 2 Associations of multimorbidity with survival years
Multimorbidity patterns Cases/person years Model 1aModel 2b
TR (95% CI) TR (95% CI)
All-cause mortality
Relatively healthy 945/2,562,678 1.00 (Ref.) 1.00 (Ref.)
Hypercholesterolemia 1,024/660,292 0.88 (0.81, 0.95) 0.89 (0.82, 0.97)
Metabolic 909/621,133 0.73 (0.68, 0.79) 0.76 (0.70, 0.83)
Arthritis-respiratory 420/229,533 0.64 (0.58, 0.71) 0.70 (0.64, 0.77)
CKD-vascular-cancer 1,328/259,823 0.57 (0.52, 0.62) 0.61 (0.56, 0.67)
Severely impaired 799/169,477 0.47 (0.43, 0.52) 0.53 (0.48, 0.58)
CVD mortality
Relatively healthy 205/256,2678 1.00 (Ref.) 1.00 (Ref.)
Hypercholesterolemia 289/660,292 0.84 (0.71, 0.98) 0.84 (0.71, 0.99)
Metabolic 314/621,133 0.58 (0.49, 0.68) 0.59 (0.50, 0.70)
Arthritis-respiratory 109/229,533 0.61 (0.51, 0.74) 0.65 (0.54, 0.79)
CKD-vascular-cancer 471/259,823 0.50 (0.42, 0.59) 0.52 (0.44, 0.62)
Severely impaired 300/169,477 0.38 (0.32, 0.46) 0.42 (0.35, 0.50)
Abbreviations: CI, condence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; NHANES, National Health and Nutrition Examination Survey; TR,
time ratio
aModel 1: adjusted for NHANES cycles, age (continuous, years), sex (male and female), and race/ethnicity (Hispanic, non-Hispanic white, non-Hispanic black, and
others)
bModel 2: a djusted for SES (low, medium , and high SES), and healthy life style score (0–1, 2, and 3–4) p lus variables in Model 2
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Xue et al. BMC Public Health (2025) 25:1262
Fig. 1 Associations of multimorbidity patterns with survival years by SES
Abbreviations: CI, condence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; NHANES, National Health and Nutrition Examination Sur-
vey; SES, socioeconomic status; TR, time ratio. All models were adjusted for NHANES cycles, age (continuous, years), sex (male and female), race/ethnicity
(Hispanic, non-Hispanic white, non-Hispanic black, and others), and healthy lifestyle score (0–1, 2, and 3–4).
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Page 9 of 13
Xue et al. BMC Public Health (2025) 25:1262
Fig. 2 Joint associations of multimorbidity patterns and SES with survival years
Abbreviations: CI, condence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; NHANES, National Health and Nutrition Examination Sur-
vey; SES, socioeconomic status; TR, time ratio. All models were adjusted for NHANES cycles, age (continuous, years), sex (male and female), race/ethnicity
(Hispanic, non-Hispanic white, non-Hispanic black, and others), and healthy lifestyle score (0–1, 2, and 3–4)
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Page 10 of 13
Xue et al. BMC Public Health (2025) 25:1262
healthcare utilization. Instead of dening multimorbid-
ity by the number of chronic conditions [5, 31], we used
LCA to identify six distinctive multimorbidity patterns
and showed that these dierent patterns had dierential
eects on premature mortality. Study participants in the
“severely impaired” class (i.e., high prevalence of most 11
diseases), and the “CKD-vascular-cancer” class character-
ized by high prevalence of cancer, CKD, stroke, CVD, and
hypertension, had the shortest survival time due to all-
cause and CVD mortality, which was consistent with the
reported dose-response relationship between the number
of chronic conditions and mortality [5, 31]. Consistently, a
study among 166,126 US adults aged more than 50 years
from the National Health Interview Survey 2002–2014
identied ve multimorbidity classes, and the “complex
cardiometabolic” class showed higher mortality than the
“healthy” class [9]. Another study that used data from
512,723 Chinese participants aged 30 to 79 years from
the China Kadoorie Biobank identied four multimorbid-
ity patterns based on 15 chronic conditions and showed
that the cardiometabolic multimorbidity class had higher
mortality, compared with participants without multimor-
bidity [32]. Individuals in the “metabolic” and “arthritis-
respiratory” classes showed similarly higher mortality in
our work, compared to the “relatively healthy” class. In
addition, the prevalence of the “metabolic” class (from
11.55 to 15.48%) persistently increased in the US, espe-
cially in adults with high SES. ese ndings highlight the
importance of monitoring the health of the “metabolic”
class to reduce premature mortality. Of note, we found
Fig. 3 Associations of healthy lifestyles with survival years by multimorbidity patterns
Abbreviations: CI, condence interval; CKD, chronic kidney disease; CVD, cardiovascular disease; HL, healthy lifestyle; NHANES, National Health and Nutri-
tion Examination Survey; SES, socioeconomic status; TR, time ratio. All models were adjusted for NHANES cycles, age (continuous, years), sex (male and
female), race/ethnicity (Hispanic, non-Hispanic white, non-Hispanic black, and others), and SES (low, medium, and high)
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Xue et al. BMC Public Health (2025) 25:1262
that participants in relatively severe multimorbidity classes
were more likely to use healthcare services in both out-
patient and inpatient settings, which was consistent with
ndings from previous studies [33, 34]. Current models
of healthcare services mainly focus on the treatments of
single diseases, and multimorbidity receives inadequate
attention [35]. Since the rising prevalence of multimorbid-
ity poses a huge challenge to healthcare systems world-
wide, integrated patient-centered healthcare services are
needed for the management of multimorbidity [36].
SES inequalities in morbidity and mortality are widely
explored. Consistent with previous studies [37], our study
observed that lower SES was associated with a greater
burden of multimorbidity. However, very few studies
explored whether SES inequalities existed for mortal-
ity associated with multimorbidity, especially multimor-
bidity patterns. Our study showed that SES modied the
associations of multimorbidity patterns with all-cause
and CVD mortality; in particular, the negative associa-
tions of most multimorbidity patterns with survival time
were stronger in those with low SES. Consistently, a study
among 50,807 Norwegian participants aged 35–75 years
showed an interaction between socioeconomic position
(measured by occupation) and multimorbidity in relation
to mortality in men, with a stronger association between
multimorbidity and mortality in the lower occupational
group [11]. However, a study among 500,769 adults from
the UK Biobank revealed no signicant interactions
between the number of chronic conditions and socioeco-
nomic status (based on Townsend quintiles) in risk predic-
tion for all-cause mortality [38]. Data for 6,425 participants
from the Whitehall II study showed that the SES based on
education, occupation, and literacy did not modify the
transition to mortality from multimorbidity [12], and a
similar nding was observed in 1,768 participants from the
Health, Well-being, and Ageing Cohort Study [13]. ese
inconsistent ndings may be due to dierent population
characteristics, and dierent denitions of SES and multi-
morbidity. As extant evidence was mainly from European
adults, our work added data from a nationally representa-
tive US general population. In addition, most studies used
SES based on a single measure that could not capture the
full domains of SES. Our study addressed this limitation by
generating SES using LCA based on PIR, education, and
insurance status in US adults. Our ndings suggest that
SES inequalities may inuence not only the development
of adverse health conditions but also their clinical trajec-
tories, which could partially account for the documented
widening mortality gap and diverging life expectancy
trends across SES groups in US adults [39]. As the rising
prevalence of multimorbidity might further widen the SES
inequalities in health, health policies for the management
of multimorbidity should consider sociodemographic
factors.
Our previous work showed that adherence to healthy
lifestyles could be an eective way to reduce long-term
mortality in both the general population [40] and those
with chronic diseases [41]. However, these studies did not
address the topic with considerations of multimorbidity,
although the presence of multimorbidity could compli-
cate treatment and management. Recently, some stud-
ies suggested that adherence to a healthy lifestyle could
benet those with multimorbidity. For example, a study
among 480,940 middle-aged adults from the UK Bio-
bank showed that adherence to a healthy lifestyle could
be associated with lower mortality in individuals with
or without multimorbidity [42]. Another study among
290,795 participants with cardiometabolic multimorbidity
from the UK Biobank revealed that regular physical activ-
ity and non-current smoking can increase life expectancy
in patients with specic cardiometabolic multimorbidity
patterns [43]. But no studies ever explored the interac-
tion between healthy lifestyles and multimorbidity status
for mortality risk. Our study showed that healthy lifestyles
were associated with elevated survival time in participants
with any of the six identied multimorbidity patterns, and
the benet was particularly stronger in those with less
severe multimorbidity (e.g., “relatively healthy”, “hyper-
cholesterolemia”, and “metabolic” classes). us, adopt-
ing healthy lifestyles should be considered as an eective
intervention in the management of multimorbidity, espe-
cially in individuals with relatively less severe multimor-
bidity. It also suggests that public health recommendations
involving engagement in healthy lifestyles should also be
emphasized in individuals who already have multimorbid-
ity. Although healthy lifestyles were reported to partially
explain the SES inequalities in health [44], our previous
work suggested that adopting healthy lifestyles could not
substantially reduce the SES inequalities in health for gen-
eral populations [14, 45]. As SES was negatively related
to multimorbidity and modied the association between
multimorbidity and mortality in our current work, the
policies for reducing SES inequalities in mortality for gen-
eral populations or those with multimorbidity should go
beyond adherence to healthy lifestyles.
Our study has major strengths, including prospective
study design, a large sample size, and a well-characterized
study population. However, several limitations should be
acknowledged. First, the denitions of certain diseases
(i.e. COPD, asthma, arthritis, and cancer) were based on
self-reported doctor diagnoses and mental health condi-
tions were not considered due to lack of accurate infor-
mation. SES and healthy lifestyle factors were also mainly
self-reported, which could lead to measurement errors.
Second, certain healthy lifestyle factors, such as sleep
duration, were not included as information was not fully
available. ird, chronic conditions were assessed at a
single time point, and thus could not capture the impact
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Page 12 of 13
Xue et al. BMC Public Health (2025) 25:1262
of dynamic changes of multimorbidity patterns on long-
term mortality. Future studies with measurements at
multiple time points are needed to address the dynamic
changes of multimorbidity patterns. Fourth, although our
study adjusted for multiple covariates, residual confound-
ing could still be possible as disease-specic treatments
and other unascertained factors were not collected. Fifth,
the median follow-up time was 9.58 years, which was
moderate to examine the associations between multimor-
bidity patterns and mortality. Large prospective studies
with longer follow-ups are needed to conrm our nd-
ings. Sixth, the multimorbidity patterns identied by data-
driven methods showed high variations due to dierent
populations, methods, and eligible chronic conditions [46,
47], and a consensus is still needed for clustering chronic
conditions in future studies.
Conclusion
In conclusion, we identied six multimorbidity patterns
in US adults, and their associations with all-cause and
CVD mortality were noted across multimorbidity pat-
terns and were strongest in the study participants with
low SES. In addition, healthy lifestyles were associated
with longer survival time in all multimorbidity patterns,
particularly in those with relatively less severe multimor-
bidity patterns. Our ndings highlight that eorts should
be made to address SES inequalities in multimorbidity-
related deaths and to promote healthy lifestyles for mortal-
ity reductions among individuals with multimorbidity.
Supplementary Information
The online version contains supplementary material available at h t t p s : / / d o i . o r
g / 1 0 . 1 1 8 6 / s 1 2 8 8 9 - 0 2 5 - 2 2 2 1 6 - 2.
Supplementary Material 1
Author contributions
Qingping Xue: Data curation, Writing- Original draft preparation,
Conceptualization, Methodology, Software, Visualization, Investigation.
Shanshan Zhang: Validation. Xue Yang: Writing- Reviewing and Editing. Yan-Bo
Zhang: Conceptualization, Methodology, Writing- Reviewing and Editing.
Yidan Dong: Writing- Reviewing and Editing. Fan Li: Writing- Reviewing and
Editing. Nianwei Wu: Writing- Reviewing and Editing. Tong Yan: Writing-
Reviewing and Editing. Ying Wen: Writing- Reviewing and Editing. Chun-Xia
Yang: Writing- Reviewing and Editing. Jason Wu: Writing- Reviewing and
Editing. An Pan: Writing- Reviewing and Editing. Yunhaonan Yang: Supervision,
Conceptualization, Methodology, Writing- Reviewing and Editing. Xiong-Fei
Pan: Supervision, Conceptualization, Methodology, Writing- Reviewing and
Editing.
Funding
The work was supported by the Fundamental Research Funds for the Central
Universities (YJ202346) (Xiong-Fei Pan) and the National Natural Science
Foundation of China (72204031) (Qingping Xue) and the CMC Excellent-talent
Program (No. 2024qnGzn17) (Qingping Xue). The funders were not involved in
the study design, collection, analysis, and interpretation of data.
Data availability
This research has been conducted using the NHANES1999-2019. The NHANES
data are available online (https:/ /www.cd c.gov/n chs/ nhanes/index.htm).
Declarations
Ethics approval and consent to participate
The U.S. National Centre for Health Statistics Ethics Review Board approved
the NHANES survey protocols (Protocol #98 − 12, #2005-06 and #2011–2017),
and the study complied with the Declaration of Helsinki. All participants gave
written informed consent.
Consent for publication
Not applicable.
Competing interests
The authors declare no competing interests.
Clinical trial number
Not applicable.
Author details
1Department of Epidemiology and Health Statistics, School of Public
Health, Chengdu Medical College, Chengdu, Sichuan, China
2Section of Epidemiology and Population Health, Department of
Gynecology and Obstetrics, Ministry of Education Key Laboratory of
Birth Defects and Related Diseases of Women and Children & Children’s
Medicine Key Laboratory of Sichuan Province, West China Second
University Hospital, Sichuan University, 3-17 Renmin Nanlu,
Chengdu 610041, Sichuan, China
3Department of Epidemiology and Biostatistics, West China School
of Public Health and West China Fourth Hospital, Sichuan University,
Chengdu, Sichuan, China
4Center for Immunological and Metabolic Diseases, MED-X Institute, the
First Aliated Hospital of Xi’an Jiaotong University, Xi’an, Shaanxi, China
5Department of Epidemiology and Population Health, Albert Einstein
College of Medicine, Bronx, NY, USA
6Center for Obesity and Metabolic Health & Center of Gastrointestinal
and Minimally Invasive Surgery, Department of General Surgery, The
Third People’s Hospital of Chengdu & The Aliated Hospital of Southwest
Jiaotong University, Chengdu, Sichuan, China
7Shenzhen Center for Disease Control and Prevention, Shenzhen,
Guangdong, China
8The George Institute for Global Health, University of New South Wales,
Sydney, NSW, Australia
9Department of Epidemiology and Biostatistics, Ministry of Education
Key Laboratory of Environment and Health, School of Public Health,
Tongji Medical College, Huazhong University of Science and Technology,
Wuhan, Hubei, China
10Shuangliu Institute of Women’s and Children’s Health, Shuangliu
Maternal and Child Health Hospital, Chengdu, Sichuan, China
11West China Hospital, West China Biomedical Big Data Center, Sichuan
University, Chengdu, Sichuan, China
Received: 14 December 2024 / Accepted: 6 March 2025
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