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TYPE Original Research
PUBLISHED 13 January 2023
DOI 10.3389/fpubh.2022.1082183
OPEN ACCESS
EDITED BY
Jiayuan Wu,
Aliated Hospital of Guangdong Medical
University, China
REVIEWED BY
Suresh Munuswamy,
Public Health Foundation of India, India
Juan Su,
Xiangya Hospital, Central South
University, China
*CORRESPONDENCE
John P. Ansah
jxp992@case.edu
Chi-Tsun Chiu
ctchiu@gate.sinica.edu.tw
SPECIALTY SECTION
This article was submitted to
Aging and Public Health,
a section of the journal
Frontiers in Public Health
RECEIVED 27 October 2022
ACCEPTED 28 December 2022
PUBLISHED 13 January 2023
CITATION
Ansah JP and Chiu C-T (2023) Projecting the
chronic disease burden among the adult
population in the United States using a
multi-state population model.
Front. Public Health 10:1082183.
doi: 10.3389/fpubh.2022.1082183
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comply with these terms.
Projecting the chronic disease
burden among the adult population
in the United States using a
multi-state population model
John P. Ansah1*and Chi-Tsun Chiu2*
1Center for Community Health Integration, Case Western Reserve University, Cleveland, OH, United States,
2Institute of European and American Studies, Academia Sinica, New Taipei, Taiwan
Introduction: As the United States population ages, the adult population with chronic
diseases is expected to increase. Exploring credible, evidence-based projections of
the future burden of chronic diseases is fundamental to understanding the likely
impact of established and emerging interventions on the incidence and prevalence
of chronic disease. Projections of chronic disease often involve cross-sectional data
that fails to account for the transition of individuals across dierent health states. Thus,
this research aims to address this gap by projecting the number of adult Americans
with chronic disease based on empirically estimated age, gender, and race-specific
transition rates across predetermined health states.
Methods: We developed a multi-state population model that disaggregates the adult
population in the United States into three health states, i.e., (a) healthy, (b) one chronic
condition, and (c) multimorbidity. Data from the 1998 to 2018 Health and Retirement
Study was used to estimate age, gender, and race-specific transition rates across the
three health states, as input to the multi-state population model to project future
chronic disease burden.
Results: The number of people in the United States aged 50 years and older will
increase by 61.11% from 137.25 million in 2020 to 221.13 million in 2050. Of the
population 50 years and older, the number with at least one chronic disease is
estimated to increase by 99.5% from 71.522 million in 2020 to 142.66 million by
2050. At the same time, those with multimorbidity are projected to increase 91.16%
from 7.8304 million in 2020 to 14.968 million in 2050. By race by 2050, 64.6%
of non-Hispanic whites will likely have one or more chronic conditions, while for
non-Hispanic black, 61.47%, and Hispanic and other races 64.5%.
Conclusion: The evidence-based projections provide the foundation for
policymakers to explore the impact of interventions on targeted population
groups and plan for the health workforce required to provide adequate care for
current and future individuals with chronic diseases.
KEYWORDS
chronic disease, adult population, multi-state population projection, United States of
America, projections
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Ansah and Chiu 10.3389/fpubh.2022.1082183
What is already known about this topic?
- Adults population in the United States with chronic diseases is
expected to increase.
What is added by this research?
- An evidence-based age, gender, and race-specific projections of
the burden of chronic diseases show that the majority of the
adult population 50 years and older, across all races, will have
at least one chronic disease by 2050, with the majority between
the ages of 60 to 79 years.
What are the implications for public
health?
- The importance of prioritizing the promotion of access to
high-quality primary care to provide whole-person care that
ensures prevention and management of chronic disease care
and addresses evidence-based social determinants of health that
increase the risk of developing chronic diseases.
Introduction
According to the US Centers for Disease Control and Prevention,
in 2019, 54.1 million US adults were 65 years or older, representing
16% of the population. By 2040, it is estimated that the number of
older adults 65 years and older is expected to reach 80.8 million
and 94.7 million by 2060, representing 25% of the US population.
An aging population is characterized by the co-occurrence of
more than one chronic condition, which is referred to as
multimorbidity (1–4).
A meta-analysis of the prevalence of multimorbidity in high, low,
and middle-income countries found an overall pooled prevalence
of 33.1% (30.0–36.3). There was a considerable difference in
the pooled estimates between high-income countries and low
and middle-income countries, with prevalence ranging between
37.9 (32.5–43.5) and 29.7% (26.4–33.0), respectively (5). In the
United States, data from the 2018 National Health Interview
Survey (NHIS) indicates that 27.2% of US adults had multiple
chronic conditions. While multimorbidity is not new, there is
greater recognition of its impact and the importance of improving
outcomes for individuals affected. Multimorbidity is associated with
increased mortality (6), reduced quality of life, and functional status
(2,7,8), increased health services use (3,9), and higher cost
of care.
To better understand the future chronic disease burden, as
well as explore the effectiveness of various interventions on the
incidence and prevalence of chronic disease, including quality of
life outcomes for people with chronic disease, requires an evidence-
based and credible forecast of the current and a future number
of American adults with chronic disease. Projections of chronic
disease often involve cross-sectional data that fails to account for
the transition of individuals across different health states. Thus,
this research aims to address this gap by projecting the number of
adult Americans with chronic disease based on empirically estimated
age, gender, and race-specific transition rates across predetermined
health states. The evidence-based projections from this research
could help healthcare providers to implement interventions for
targeted population groups to prevent and or manage their chronic
disease and plan for the health workforce required to provide
adequate care for current and future individuals with chronic
diseases to achieve the quadruple aim of healthcare, i.e., improve
population health, reduce cost, and increase patients’ and providers
satisfaction (10,11).
Methods
Model design
To project the number of Americans 50 years and older with one
or more chronic conditions, we developed and validated a dynamic
multi-state population model (12–15) to simulate the population
of the United States and track their transition to and from three
health states. The health states are (a) healthy (adults with no chronic
condition), (b) one chronic condition (adult with any one of the nine
chronic conditions indicated in the Health and Retirement Survey),
and (c) multimorbidity (adults with at least two chronic conditions
indicated in the Health and Retirement Survey). For each health
state, adult individuals were further divided into a three-dimensional
vector: age (from age 50–100 and older), gender (male and female),
and race (non-Hispanic white, non-Hispanic black, Hispanic, and
other races). To ensure consistency and validation of the model
output, an additional state that accounts for the population below
50 years was included to ensure that individuals aged 50 transitions
to the adult population’s health states. To ensure a consistent aging
process, the population aged 50 years and younger was subdivided by
age (age 0–age 49). The number of people below age 50 increases by
births and net migration (estimated by calibration) and decreases by
deaths and becoming age 50. Births were estimated using race-specific
fertility rates from the National Vital Statistics report and the fecund
female population age 15–49, while life tables informed deaths (16).
At the end of each year, the surviving population in each age cohort
flows to the subsequent cohort, except the final age cohort, age 100
and older. Transition across health states was determined by 1-year
age-gender-race specific transition rates.
Health states
The chronic conditions in the Health and Retirement Survey
record self-reported lifetime histories of a modest number of illnesses
and conditions that are very important to older persons and account
for much of the morbidity and mortality among older persons in
western societies. The conditions consist of: (a) hypertension, (b)
diabetes mellitus, (c) cancer (various types at all bodily sites, except
minor skin cancers), (d) chronic lung diseases (often including
emphysema, but not asthma), (e) coronary health disease, (f)
congestive health failure, (g) stroke (cerebrovascular disease), (h)
arthritis (a collection of heterogeneous diseases and Musculoskeletal
pain syndromes), and (i) psychiatric problem (in general, not
further defined or categorized, except major depressive, depressive
symptoms, and dementia). Adult individuals who reported no
presence of any chronic conditions were classified as healthy; those
who reported only one of the chronic conditions were classified
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Ansah and Chiu 10.3389/fpubh.2022.1082183
as adults with a single chronic condition, whereas those with at
least two or more chronic conditions were classified as adults
with multimorbidity.
Model assumption
Constant age-gender-race-specific mortality rates were used for
the population 50 years and younger. For the adult population, the
1000 bootstrap estimates for all the transitions across the health states
accounted for future improvement or deterioration. A race-specific
fertility rate was used, while we assumed a constant fertility rate
from 2018 over the simulation time. This assumption was deemed
appropriate because a fertility rate change will not impact the adult
population by 2060. The net migration rate, estimated via calibration,
was assumed to be constant over the simulation time.
Estimation of transition rates
The 1998 to 2018 Health and Retirement Study data (17)
was used to estimate the transition rates across health states. The
Health and Retirement Study is a longitudinal panel study that
surveys a representative sample of more than 26,000 Americans
over the age of 50 every two years. The study explores changes in
labor force participation and the health transitions that individuals
undergo toward the end of their work lives. Since its inception, the
study has collected information about income, work, assets, pension
plans, health insurance, disability, physical health and functioning,
cognitive impairment, and healthcare expenditure.
The input data to the transition rate estimate is in an extended
format, and each observation occupies one line of data. Each line
of observation includes the individual’s age and values of covariates
in the model. Everyone has multiple lines of observation. Since
the Health and Retirement Study is not an annual survey, we fill
in gaps with pseudo-data representing successive years to obtain
annual transition probabilities (18). If starting and ending states of
an interval are the same, the filled-in data assume that states. If an
interval’s starting and ending states differ, the filled-in data assume
one transition at a random time. Multinomial logistic regressions
are then fitted to estimate the probability of transitioning from a
starting health state to one of ending health states (including death).
Multinomial logistic regression models estimate age, gender, and
race-specific transition rates.
Model validation
The model structure has been validated and used for several
publications on similar chronic conditions in other countries (13–
15). Thus, the structure of the model has been presented to
researchers familiar with chronic disease care in several countries to
verify the conceptual framework of the model and its assumptions
regarding causal relationships as indicated in the literature cited
(19–21). The model structure is grounded in evidence of how
individuals transition from a healthy state to a single and multiple
chronic conditions over their lifetime. To ensure that the model
output is consistent with available data, selected simulated outcomes
were compared with available data. The results suggest that the
simulated model outputs compare favorably with the available data,
demonstrating that the model performs credibly.
Results
Transition rates by age, gender, and race
Figure 1 shows the age, gender, and race-specific transition
rates across the three health states and death. For both gender
and race, the progression to worse health status (healthy to one
chronic condition, healthy to multimorbidity, and from one chronic
condition to multimorbidity) increases with age, except for the
progression from healthy to one chronic condition where the
transition rates begin to decrease significantly from age 90. Also,
mortality across all the health states increases with age. On the
contrary, for both gender and race, the regression to a better health
status from multimorbidity to one chronic condition decreases
with age.
For gender differences, males have a higher rate of progression
to a worse health state compared to females, while regression to a
better health state was better for females than males. Also, females
are more likely to maintain their health status than males. In the
case of mortality, males have higher death rates than females. For
race differences, non-Hispanic White had a higher transition rate
while non-Hispanic Black had the lowest transition rate from a
healthy to one chronic condition. For individuals transitioning from
healthy to multimorbidity, Hispanics had a higher transition rate for
all races, while non-Hispanic Whites had the lowest transition rate.
Likewise, Hispanics had the highest transition rates for individuals
transitioning from one chronic condition to multimorbidity, while
other races had the lowest transition rates. Hispanics had the highest
transition rates for regression from multimorbidity to one chronic
condition, whereas non-Hispanic Blacks had the lowest transition
rates. The transition rates from healthy to death and one chronic
condition to death show that non-Hispanic Blacks have the highest
transition rates among all the races, whereas Hispanics have the
lowest transition rates. For the transition from multimorbidity to
death, non-Hispanic Blacks have the highest transition rates, while
non-Hispanic Whites have the lowest transition rates.
The results in Table 1 suggest that the number of people in
the United States aged 50 years and older will increase by 61.11%
(100% confidence interval 57.2%−66.2%) from 137.25 million
(135.64–139.18) in 2020 to 221.13 million (213.24–231.34) in 2050.
Remarkably, the number of people aged 80 years and older will
increase by 137.26% (116.0%−164.6%), from 16.935 million (16.148–
17.863) in 2020 to 40.181 million (34.881–47.272) in 2050. Of the
population 50 years and older, the number with at least one chronic
disease is estimated to increase by 99.5% (95.1%−107.9%) from
71.522 million (69.065–73.781) in 2020 to 142.66 million (134.74–
153.39) by 2050. At the same time, those with multimorbidity are
projected to increase 91.16% (79.09%−103.24%) from 7.8304 million
(6.5965–9.4853) in 2020 to 14.968 million (11.813–19.277) in 2050.
The analysis suggests that by 2035, 35.66% (33.36–36.04) of the adult
population 50 years and older will have at least one chronic condition,
which is expected to increase to 47.81% (46.09–49.71) by 2050. At the
same time, 3.659% (2.905–4.696) of the adult population is expected
to have multimorbidity, increasing to 5.017% (3.948–6.481) by 2050.
Most individuals with at least one chronic condition (62.75% in
2020 and 58.54% in 2050) or multimorbidity (62.9% in 2020 and
58.9% in 2050) are between the ages of 60 to 79 years. However,
individuals aged 80 years and older with one chronic condition and
multimorbidity are projected to have the highest increase (244.3%
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Ansah and Chiu 10.3389/fpubh.2022.1082183
FIGURE 1
Transition rates across health states. Mwhite: is male non-Hispanic white; Mblack: is male non-Hispanic black; Mhispanic: is male Hispanic; and Mother: is
male other races; while Fwhite: is female non-Hispanic white; Fblack: is female non-Hispanic black; Fhispanic: is female Hispanic; and Fothers: is female
other races.
for one chronic condition and 202.7% for multimorbidity) from 2020
to 2050.
For gender differences, by 2050, 53.33% (52.07–54.88) of the
individuals with one chronic condition are projected to be females,
while males form the majority of those with multimorbidity with an
estimated 50.71% (44.43–55.79) by 2050.
Tables 2–5show the race-specific projections. The number of
non-Hispanic Whites adults 50 years and older with at least one
chronic condition is projected to increase from 46.6159 million
(44.786–48.339) in 2020 to 93.026 million (86.516–99.839) by
2050, representing an increase of 99.55% (93.2%−106.5%). Most
non-Hispanic Whites with one chronic condition are females
between the ages of 60 to 79 years. In addition, the age
group with the highest increase in one chronic condition is
individuals 80 years and older. Similarly, the number of non-
Hispanic Whites with multimorbidity is estimated to increase
from 4.8927 million (4.0826–5.9523) in 2020 to 9.12 million
(6.9983–12.6401) by 2050. For multimorbidity among the non-
Hispanic Whites, the majority are males between the ages of 60 to
79 years.
The projected number of non-Hispanic Blacks with at least one
chronic condition is 8.1994 million (7.6355–8.6193) in 2020 and
is expected to increase to 15.2213 million (13.33–16.98) by 2050
[that is a relative change between 2020 and 2050 of 85.64% (74.64–
97.01)]. Most non-Hispanic Blacks with one chronic condition and
multimorbidity are females between 60 to 79 years old. Similarly, to
all the races, the age group with the highest increase is individuals 80
years and older for both one chronic condition and multimorbidity.
The number of non-Hispanic Blacks with multimorbidity is projected
to increase from 0.9625 million (0.7294–1.2165) in 2020 to 1.7505
million (1.1798–2.489) by 2050, representing a relative increase of
82.87% (61.75%−104.6%).
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Ansah and Chiu 10.3389/fpubh.2022.1082183
TABLE 1 Projected number of adults 50 years and older with chronic disease in the United States.
Age cohort 2020 2035 2050 Relative change
between 2020 and
2050 (%)
Population (million)
Total adult population 137.25
[135.64–139.18]
180.40
[175.17–186.85]
221.13
[213.24–231.34]
61.11%
[57.21% to 66.22%]
50–59 years 48.318
[48.212–48.419]
55.755
[55.634–55.870]
68.555
[68.401–68.700]
41.88%
[41.88% to 41.89%]
60–79 years 72.001
[71.212–72.910]
94.610
[92.326–97.129]
112.40
[109.69–115.39]
56.11%
[54.04% to 58.27%]
80 and older 16.935
[16.148–17.863]
30.042
[26.998–33.875]
40.181
[34.881–47.272]
137.267%
[116.0% to 164.6%]
≥1 chronic condition (million)
Adult population 71.522
[69.065–73.781]
114.48
[108.87–121.36]
142.66
[134.74–153.39]
99.5%
[95.1% to 107.9%]
50–59 years 15.929
[15.181–16.460]
17.688
[16.820–18.305]
22.277
[21.192–23.047]
39.85%
[39.59% to 40.01%]
60–79 years 44.881
[43.462–46.090]
70.752
[67.520–73.550]
83.511
[79.670–86.834]
86.07%
[83.31% to 88.40%]
80 and older 10.711
[10.012–11.662]
26.040
[23.190–29.936]
36.875
[31.757–44.038]
244.3%
[217.2% to 277.6%]
Multimorbidity (million)
Adult population 7.8304
[6.5965–9.4853]
12.085
[9.6091–15.490]
14.968
[11.813–19.277]
91.16%
[79.09% to 103.24%]
50–59 years 1.6234
[1.3501–1.9254]
1.7968
[1.4913–2.1314]
2.2662
[1.8802–2.6889]
39.6%
[39.26% to 39.66%]
60–79 years 4.9258
[4.1510–6.0167]
7.4836
[5.8964–9.7171]
8.8249
[6.9446–11.454]
79.16%
[67.30% to 90.39%]
80 and older 1.2812
[0.9838–1.5817]
2.8050
[1.9896–3.8781]
3.8777
[2.6510–5.5552]
202.7%
[169.5% to 251.2%]
Prevalence of ≥1 chronic condition (%) 21.77
[21.05–22.37]
34.66
[33.36–36.04]
47.81
[46.09–49.71]
119.7%
[118.9% to 122.2%]
Fraction female 52.57
[51.77–53.20]
53.37
[52.43–54.62]
53.22
[52.07–54.88]
1.25%
[0.57% to 3.16%]
Fraction male 47.43
[46.80–48.23]
46.63
[45.38–47.57]
46.78
[45.12–47.93]
−1.39%
[−3.59 to −0.61%]
Prevalence of multimorbidity (%) 2.383
[2.007–2.887]
3.659%
[2.905–4.696]
5.017
[3.948–6.481]
110.5%
[96.7% to 124.5%]
Fraction female 48.33
[44.71–52.14]
49.40
[44.54–55.21]
49.29
[44.21–55.57]
1.98%
[−1.10% to 6.59%]
Fraction male 51.67
[47.86–55.29]
50.60
[44.79–55.46]
50.71
[44.43–55.79]
−1.86%
[−7.18% to 0.89%]
Hispanic adults 50 years and older with at least one chronic
condition are estimated to increase from 11.7996 million (11.125–
12.546) in 2020 to 24.732 million (22.214–28.613) by 2050. This
change represents an increase of 109.61% (99.67–128.1). Like all
races, most Hispanics with one chronic condition are females within
the age group of 60 to 79 years, and the age group with the
highest increase in the number of people with at least one chronic
condition and multimorbidity is individuals aged 80 years and older.
Also, the number of Hispanics with multimorbidity is projected to
increase from 1.4632 million (1.0713–1.902) in 2020 to 2.9136 million
(1.8776–4.2586) by 2050. Most Hispanics with multimorbidity are
males between 60 to 79 years old.
Lastly, the number of other races who are not non-Hispanic
Whites, Blacks, or Hispanics in the United States with at least
one chronic condition is projected to increase from 4.9072 million
(4.3938–5.3519) in 2020 to 9.684 million (7.8591–11.6347) by 2050,
representing a relative increase of 97.34% (78.87–117.4) from 2020
to 2050. Most of the other races with one chronic condition are
females between the ages of 60–79 years. Among the other race, the
age group with the highest increase in the number of people with at
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Ansah and Chiu 10.3389/fpubh.2022.1082183
TABLE 2 Projected number of non-Hispanic White adults 50 years and older with chronic disease (in millions by sex and age) in the United States.
Age cohort 2020 2035 2050 Relative change
between 2020 and
2050 (%)
≥1 chronic condition (million)
50–59 years 10.347
[9.8407–10.739]
11.483
[10.893–11.942]
14.464
[13.725–15.038]
39.79%
[39.48%−40.03%]
60–79 years 29.260
[28.395–30.063]
46.139
[44.027–47.866]
54.453
[51.942–56.510]
86.10%
[82.93%−87.97%]
80 and older 7.0089
[6.5512–7.5374]
17.040
[15.209–19.300]
24.109
[20.849–28.291]
244.0%
[218.3%−275.4%]
Total 46.6159
[44.786–48.339]
74.662
[70.129–79.108]
93.026
[86.516–99.839]
99.55%
[93.2%−106.5%]
Female
50–59 years 5.1688
[4.8547–5.4175]
5.7010
[5.3377–5.9886]
7.0841
[6.6341–7.4400]
37.05%
[36.65%−37.33%]
60–79 years 15.1119
[14.500–15.652]
24.070
[22.700–25.213]
28.092
[26.472–29.445]
85.89%
[82.56%−88.12%]
80 and older 4.2173
[3.8823–4.6583]
10.054
[8.7272–11.916]
14.308
[11.917–17.784]
239.3%
[207.0%−281.8%]
Male
50–59 years 5.1783
[4.9860–5.3623]
5.7828
[5.5556–6.0003]
7.3799
[7.0912–7.6559]
42.52%
[42.22%−42.77%]
60–79 years 14.148
[13.862–14.451]
22.068
[21.327–22.789]
26.360
[25.470–27.223]
86.32%
[83.74%−88.38%]
80 and older 2.7916
[2.6611–2.9202]
6.9860
[6.4725–7.5923]
9.8011
[8.9301–10.897]
251.1%
[235.6%−273.2%]
Multimorbidity (million)
50–59 years 0.9972
[0.8259–1.1720]
1.1019
[0.9096–1.2939]
1.3900
[1.1474–1.6326]
39.39%
[38.93%−39.31%]
60–79 years 3.0740
[2.6021–3.7479]
4.6747
[3.6213–6.1113]
5.5100
[4.2812–7.1998]
79.24%
[64.53%−92.10%]
80 and older 0.8215
[0.6546–1.0324]
1.8122
[1.2028–2.5807]
2.5120
[1.5697–3.8077]
205.8%
[139.8%−268.8%]
Total 4.8927
[4.0826–5.9523]
7.5888
[5.7337–9.9859]
9.412
[6.9983–12.6401]
92.368%
[71.4%−112.4%]
Female
50–59 years 0.4511
[0.3521–0.5682]
0.4962
[0.3860–0.6250]
0.6167
[0.4795–0.7779]
36.70%
[36.17%−36.91%]
60–79 years 1.4434
[1.1306–1.9062]
2.2337
[1.5682–3.2361]
2.6012
[1.8290–3.7738]
80.21%
[61.77%−97.98%]
80 and older 0.4669
[0.3514–0.6284]
1.0161
[0.5919–1.6156]
1.4210
[0.7573–2.3860]
204.3%
[115.5%−279.7%]
Male
50–59 years 0.5461
[0.4727–0.6140]
0.6058
[0.5224–0.6831]
0.7734
[0.6667–0.8722]
41.61%
[41.04%−42.05%]
60–79 years 1.6306
[1.4566–1.8417]
2.4410
[2.0531–2.8751]
2.9088
[2.4522–3.4260]
78.39%
[68.35%−86.02%]
80 and older 0.3546
[0.3032–0.4072]
0.7961
[0.6109–1.0118]
1.0910
[0.8124–1.4387]
207.75
[167.9%−253.3%]
least one chronic condition and multimorbidity is individuals aged
80 years and older. The projected number of adults 50 years and
older categorized as other races with multimorbidity is estimated
to increase from 0.5119 million (0.2233–0.8173) in 2020 to 0.8927
million (0.2893–1.9205) by 2050. Most of the other races with
multimorbidity are females.
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TABLE 3 Projected number of non-Hispanic Black adults 50 years and older with chronic disease (in millions by sex and age) in the United States.
Age cohort 2020 2035 2050 Relative change
between 2020 and
2050 (%)
≥1 chronic disease (million)
50–59 years 1.8920
[1.7732–1.9764]
2.0731
[1.9372–2.1709]
2.5934
[2.4242–2.7151]
37.07%
[36.71%−37.37%]
60–79 years 5.1999
[4.9250–5.4070]
7.9304
[7.2875–8.4762]
9.2501
[8.5027–9.8901]
77.89%
[72.64%−82.91%]
80 and older 1.1075
[0.9373–1.2359]
2.4979
[1.8941–3.0597]
3.3778
[2.4076–4.3753]
205.0%
[156.9%−254.0%]
Total 8.1994
[7.6355–8.6193]
12.5014
[11.118–13.706]
15.2213
[13.33–16.98]
85.64%
[74.64%−97.01%]
Female
50–59 years 0.9467
[0.8744–0.9975]
1.0329
[0.9505–1.0909]
1.2791
[1.1773–1.3507]
35.11%
[34.65%−35.41%]
60–79 years 2.7028
[2.5303–2.8349]
4.1817
[3.8298–4.4699]
4.8336
[4.4260–5.1707]
78.84%
[74.92%−82.39%]
80 and older 0.6737
[0.5631–0.07625]
1.5029
[1,1131–1.8587]
2.0498
[1.4143–2.6938]
204.3%
[151.2%−253.3%]
Male
50–59 years 0.9453
[0.8974–0.9870]
1.0402
[0.9848–1.0890]
1.3143
[1.2445–1.3755]
39.03%
[38.68%−39.37%]
60–79 years 2.4972
[2.3780–2.5996]
3.7487
[3.4577–4.0099]
4.4165
[4.0767–4.7237]
76.86%
[71.44%−81.71%]
80 and older 0.4337
[0.3742–0.4853]
0.9949
[0.7809–1.2010]
1.3280
[0.9934–1.6815]
206.2%
[165.5%−246.5%]
Multimorbidity (million)
50–59 years 0.2130
[0.1681–0.2580]
0.2338
[0.1843–0.2848]
0.2929
[0.2307–0.3565]
37.48%
[37.27%−38.16%]
60–79 years 0.6167
[0.4735–0.7695]
0.9357
[0.6438–1.2800]
1.0918
[0.7531–1.4922]
77.05%
[59.05%−93.91%]
80 and older 0.1328
[0.0878–0.1890]
0.2765
[0.1597–0.4568]
0.3658
[0.1960–0.6403]
175.5%
[123.1%−238.9%]
Total 0.9625
[0.7294–1.2165]
1.446
[0.9878–2.0216]
1.7505
[1.1798–2.489]
81.87%
[61.75%−104.6%]
Female
50–59 years 0.0972
[0.0737–0.1241]
0.1064
[0.0805–0.1365]
0.1318
[0.0997–0.1691]
35.53%
[35.18%−36.26%]
60–79 years 0.2963
[0.2135–0.3879]
0.4604
[0.2880–0.6765]
0.5319
[0.3329–0.7824]
79.53%
[55.93%−101.6%]
80 and older 0.0778
[0.0506–0.1117]
0.1622
[0.0862–0.2635]
0.2171
[0.1065–0.3743]
178.9%
[110.5%−234.9%]
Male
50–59 years 0.1158
[0.0944–0.1375]
0.1274
[0.1038–0.1517]
0.1611
[0.1311–0.1919]
39.13%
[38.905–39.57%]
60–79 years 0.3204
[0.2523–0.3943]
0.4753
[0.3457–0.6236]
0.5599
[0.4082–0.7334]
74.75%
[61.83%−86.00%]
80 and older 0.0550
[0.0372–0.0778]
0.1144
[0.0692–0.1933]
0.1487
[0.0851–0.2667]
170.6%
[128.7%−243.1%]
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TABLE 4 Projected number of Hispanic adults 50 years and older with chronic disease (in millions by sex and age) in the United States.
Age cohort 2020 2035 2050 Relative change
between 2020 and
2050 (%)
≥1 chronic disease (million)
50–59 years 2.5907
[2.4400–2.7267]
2.9035
[2.7270–3.0625]
3.6682
[3.4463–3.8678]
41.59%
[41.24%−41.85%]
60–79 years 7.3341
[7.0190–7.6569]
11.835
[11.153–12.659]
14.056
[13.239–15.044]
91.66%
[88.62%−96.49%]
80 and older 1.8748
[1.6667–2.1631]
4.7988
[3.9698–6.1682]
7.0086
[5.5290–9.7020]
273.8%
[231.7%−348.5%]
Total 11.7996
[11.125–12.546]
19.537
[17.849–21.889]
24.732
[22.214–28.613]
109.61%
[99.67%−128.1%]
Female
50–59 years 1.2915
[1.2059–1.3694]
1.4372
[1.3377–1.5272]
1.7908
[1.6673–1.9025]
38.66%
[38.26%−38.93%]
60–79 years 3.7812
[3.6062–3.9682]
6.1549
[5.7926–6.5917]
7.2247
[6.7941–7.7456]
91.07%
[88.40%−95.19%]
80 and older 1.1254
[0.9961–1.3028]
2.8186
[2.3239–3.6155]
4.1383
[3.2314–5.7171]
267.7%
[224.4%−338.8%]
Male
50–59 years 1.2992
[1.2295–1.3581]
1.4663
[1.3837–1.5361]
1.8774
[1.7722–1.9664]
44.51%
[44.14%−44.79%]
60–79 years 3.5529
[3.4094–3.7141]
5.6809
[5.3608–6.0676]
6.8316
[6.4450–7.2994]
92.28%
[89.03%−96.53%]
80 and older 0.7494
[0.6629–0.8603]
1.9802
[1.6114–2.5527]
2.8703
[2.2253–3.9849]
283.0%
[235.7%−363.2%]
Multimorbidity (million)
50–59 years 0.3028
[0.2302–0.3902]
0.3381
[0.2555–0.4365]
0.4278
[0.3235–0.5528]
41.27%
[40.53%−41.68%]
60–79 years 0.9176
[0.6792–1.1838]
1.4275
[0.9545–1.9999]
1.6943
[1.1353–2.3721]
84.65%
[67.15%−100.3%]
80 and older 0.2428
[0.1619–0.3280]
0.5578
[0.3114–0.8892]
0.7915
[0.4188–1.3337]
225.9%
[158.6%−306.7%]
Total 1.4632
[1.0713–1.902]
2.3234
[1.5214–3.3156]
2.9136
[1.8776–4.2586]
99.13%
[75.26%−123.9%]
Female
50–59 years 0.1371
[0.0973–0.1837]
0.1522
[0.1074–0.2045]
0.1897
[0.1337–0.2551]
38.31%
[37.40–38.87%]
60–79 years 0.4331
[0.2953–0.5854]
0.6847
[0.4105–0.9842]
0.8024
[0.4811–1.1539]
85.24%
[62.94%−97.13%]
80 and older 0.1389
[0.0864–0.1937]
0.3143
[0.1603–0.5314]
0.4495
[0.2165–0.8224]
223.7%
[150.6%−324.5%]
Male
50–59 years 0.1657
[0.1329–0.2096]
0.1859
[0.1482–0.2356]
0.2381
[0.1898–0.3021]
43.73%
[42.83%−44.15%]
60–79 years 0.4844
[0.3747–0.6135]
0.7428
[0.5270–1.0157]
0.8919
[0.6336–1.2181]
84.12%
[69.12%−98.55%]
80 and older 0.1040
[0.0753–0.1359]
0.2436
[0.1511–0.3677]
0.3420
[0.2014–0.5492]
228.9%
[167.4%−304.2%]
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TABLE 5 Projected number of other adults 50 years and older with chronic disease (in millions by sex and age) in the United States.
Age cohort 2020 2035 2050 Relative change
between 2020 and
2050 (%)
≥1 chronic disease (million)
50–59 years 1.0999
[0.9828–1.1846]
1.2283
[1.0907–1.3279]
1.5519
[1.3788–1.6769]
41.10%
[40.29%−41.56%]
60–79 years 3.0872
[2.8448–3.3042]
4.8468
[4.2676–5.3310]
5.7521
[5.0644–6.3334]
86.32%
[78.02%−91.68%]
80 and older 0.7201
[0.5662–0.8631]
1.7029
[1.1205–2.3582]
2.3800
[1.4159–3.6244]
230.5%
[150.1%−319.9%]
Total 4.9072
[4.3938–5.3519]
7.778
[6.4788–9.0171]
9.684
[7.8591–11.6347]
97.34%
[78.87%−117.4%]
Female
50–59 years 0.5487
[0.4836–0.5946]
0.6086
[0.5328–0.6619]
0.7583
[0.6642–0.8245]
38.19%
[37.33%−38.66%]
60–79 years 1.5952
[1.4542–1.7136]
2.5305
[2.2494–2.7866]
2.9676
[2.6363–3.2725]
86.03%
[81.28%−90.97%]
80 and older 0.4340
[0.3396–0.5224]
1.0081
[0.6614–1.3750]
1.4154
[0.8355–2.1206]
226.1%
[146.1%−305.9%]
Male
50–59 years 0.5511
[0.4992–0.5923]
0.6198
[0.5579–0.6688]
0.7936
[0.7146–0.8560]
43.99%
[43.16%−44.51%]
60–79 years 1.4920
[1.3693–1.5906]
2.3163
[2.0182–2.5507]
2.7844
[2.4282–3.0656]
86.63%
[77.33%−92.73%]
80 and older 0.2861
[0.2266–0.3423]
0.6948
[0.4591–0.9844]
0.9646
[0.5804–1.5037]
237.2%
[156.2%−339.3%]
Multimorbidity (million)
50–59 years 0.1103
[0.0614–0.1523]
0.1231
[0.0693–0.1695]
0.1555
[0.0871–0.2146]
40.98%
[41.78%−40.89%]
60–79 years 0.3176
[0.1315–0.4941]
0.4458
[0.1377–0.8072]
0.5289
[0.1653–0.9555]
66.51%
[2.74%−93.39%]
80 and older 0.0840
[0.0304–0.1709]
0.1584
[0.0335–0.4794]
0.2083
[0.0369–0.7504]
148.0%
[21.5%−339.1%]
Total 0.5119
[0.2233–0.8173]
0.7273
[0.2405–1.4561]
0.8927
[0.2893–1.9205]
74.39%
[29.56%−134.9%]
Female
50–59 years 0.0498
[0.0270–0.0723]
0.0552
[0.0301–0.0800]
0.0688
[0.0373–0.0999]
38.01%
[38.28%−38.12%]
60–79 years 0.1493
[0.0611–0.2508]
0.2125
[0.0635–0.4316]
0.2487
[0.0749–0.5054]
66.63%
[22.70%−101.5%]
80 and older 0.0478
[0.0172–0.0957]
0.0890
[0.0185–0.2656]
0.1175
[0.0201–0.4220]
145.9%
[16.7%−341.1%]
Male
50–59 years 0.0605
[0.0345–0.0807]
0.0678
[0.0392–0.0910]
0.0867
[0.0498–0.1165]
43.42%
[44.52%−44.43%]
60–79 years 0.1683
[0.0704–0.2506]
0.2332
[0.0742–0.4081]
0.2801
[0.0904–0.4886]
66.41%
[28.38%−94.96%]
80 and older 0.0362
[0.0132–0.0752]
0.0695
[0.0150–0.2137]
0.0908
[0.0168–0.3283]
150.8%
[27.8%−336.5%]
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Discussion
The results show that the number of people in the United States
aged 50 years and older is projected to increase significantly.
Consequently, by 2050, most individuals 50 years and older will have
one or more chronic conditions. Most of the population 50 years
and older with one or more chronic conditions are projected to be
between the ages of 60 to 79 years, and the number of individuals
80 years and older with one or more chronic conditions is expected
to more than double from 2020 to 2050. Most individuals 50 years
and older with one chronic condition are females, while that with
multimorbidity are males.
The insight that the majority of the adult population 50 years and
older, across all races, will have at least one chronic condition has
health and economic implications. Within the health domain, these
insights emphasize the importance of prioritizing the promotion of
access to high-quality primary care services that can provide whole-
person care that ensures prevention and management of chronic
disease care and address evidence-based social determinants of health
that increase the risk of developing chronic diseases. Moreover,
individual, family and community-oriented health education that
highlights the importance of a healthy lifestyle and addresses
structural issues that perpetuate health disparities should be a
vital part of the health system to change the trajectory of chronic
disease. The health education provided to individuals, families
and the community and care models offered to the population
should emphasize the continuous care models for addressing chronic
conditions that help individuals to lead better lives. This health
education and care models should focus on self-care (i.e., tasks
performed by healthy people to stay healthy) and self-management
(i.e., day-to-day tasks undertaken to reduce the impact of chronic
disease on physical health status) approaches. These approaches
should focus on encouraging the individual to stay healthy and for
those with chronic conditions, the ability to manage the symptoms,
treatment, physical and psychosocial consequences, and lifestyle
changes inherent in living with a chronic condition.
Chronic disease and especially multimorbidity, is associated with
increased mortality (6), reduced quality of life, and functional status
(2,7,8), increased health services use (3,9), and higher cost of care.
As a result, health care systems and policymakers should prioritize
cost-effective interventions that have the potential to reduce the cost
of chronic disease management to the health care system. Chronic
disease is associated with substantial work productivity losses. Thus,
policymakers and employers should focus on programs and resource
allocation to reduce the incidence and prevalence of chronic disease
and absenteeism resulting from chronic diseases to maintain and
increase productivity.
The main strength of this paper is the use of 20 years’
worth of data to estimate the incidence and prevalence of chronic
diseases among the adult population in the United States. The
main limitation of this research is, first, the list of chronic
diseases included in the Health and Retirement Study is not a
comprehensive list of chronic diseases, and the chronic diseases
reported in the survey are self-reported. A broader definition
of chronic diseases would include more conditions that are not
captured in this study. These can potentially underestimate the
incidence and prevalence of chronic diseases projected in this
study. Hence, the numbers provided in the research should be
interpreted within the context of the chronic diseases captured
in the survey used herein. Another important limitation is that
individuals transitioning to the adult population are assumed
to have similar chronic disease transition patterns observed in
the Health and Retirement Survey. Lastly, a limitation of the
statistic model is that since the data used for this study (Health
and Retirement Study) is not an annual survey, we fill in gaps
with pseudo-data representing successive years to obtain annual
transition probabilities.
Data availability statement
Publicly available datasets were analyzed in this study. This data
can be found at: The Health and Retirement Study.
Author contributions
JA conceived and designed the study, developed the multi-state
population model to simulate the chronic disease burden among
the adult population in the USA, and conducted the analysis and
manuscript writing. C-TC conducted the statistical analysis for the
transition probabilities using the Health and Retirement Study and
developed the R algorithm used for data analytics. All authors
contributed to the article and approved the submitted version.
Funding
This research was supported by the Center for Community
Health Integration at Case Western Reserve University.
Conflict of interest
The authors declare that the research was conducted in the
absence of any commercial or financial relationships that could be
construed as a potential conflict of interest.
Publisher’s note
All claims expressed in this article are solely those of the
authors and do not necessarily represent those of their affiliated
organizations, or those of the publisher, the editors and the reviewers.
Any product that may be evaluated in this article, or claim that may
be made by its manufacturer, is not guaranteed or endorsed by the
publisher.
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References
1. Barnett K, Mercer SW, Norbury M, Watt G, Wyke S, Guthrie B. Epidemiology
of multimorbidity and implications for health care, research, and medical education: a
cross-sectional study. Lancet. (2012) 380:37–43. doi: 10.1016/S0140-6736(12)60240-2
2. van den Akker M, Buntinx F, Metsemakers JF, Roos S, Knottnerus JA.
Multimorbidity in general practice: prevalence, incidence, and determinants of co-
occurring chronic and recurrent diseases. J Clin Epidemiol. (1998) 51:367–75.
doi: 10.1016/S0895-4356(97)00306-5
3. Walker AE. Multiple chronic diseases and quality of life: patterns
emerging from a large national sample, Australia. Chronic Illn. (2007) 3:202–18.
doi: 10.1177/1742395307081504
4. Wolff JL, Starfield B, Anderson G. Prevalence, expenditures, and complications
of multiple chronic conditions in the elderly. Arch Intern Med. (2002) 162:2269–76.
doi: 10.1001/archinte.162.20.2269
5. Salisbury C, Johnson L, Purdy S, Valderas JM, Montgomery AA. Epidemiology and
impact of multimorbidity in primary care: a retrospective cohort study. Br J Gen Pract.
(2011) 61:e12–21. doi: 10.3399/bjgp11X548929
6. Fortin M, Lapointe L, Hudon C, Vanasse A, Ntetu AL, Maltais D. Multimorbidity
and quality of life in primary care: a systematic review. Health Qual Life Outcomes. (2004)
2:51. doi: 10.1186/1477-7525-2-51
7. Kadam UT, Croft PR, North Staffordshire GP, Consortium Group. Clinical
multimorbidity and physical function in older adults: a record and health status linkage
study in general practice. Fam Pract. (2007) 24:412–9. doi: 10.1093/fampra/cmm049
8. Gijsen R, Hoeymans N, Schellevis FG, Ruwaard D, Satariano WA, van den Bos GA.
Causes and consequences of comorbidity: a review. J Clin Epidemiol. (2001) 54:661–74.
doi: 10.1016/S0895-4356(00)00363-2
9. Nguyen H, Manolova G, Daskalopoulou C, Vitoratou S, Prince M, Prina
AM. Prevalence of multimorbidity in community settings: a systematic review
and meta-analysis of observational studies. J Comorb. (2019) 9:2235042X19870934.
doi: 10.1177/2235042X19870934
10. Bodenheimer T, Sinsky C. From triple to quadruple aim: care of the patient requires
care of the provider. Ann Fam Med. (2014) 12:573–6. doi: 10.1370/afm.1713
11. Arnetz BB, Goetz CM, Arnetz JE, Sudan S, vanSchagen J, Piersma K, et al. Enhancing
healthcare efficiency to achieve the Quadruple Aim: an exploratory study. BMC Res Notes.
(2020) 13:362. doi: 10.1186/s13104-020-05199-8
12. Schoen R. Practical uses of multistate population models. Annu Rev Sociol. (1988)
14:341–61. doi: 10.1146/annurev.so.14.080188.002013
13. Ansah JP, Koh V, Chiu C-T, Chei C-L, Zeng Y, Yin Z-X, et al. Projecting the number
of elderly with cognitive impairment in china using a multi-state dynamic population
model. Syst Dyn Rev. (2017) 33:89–111. doi: 10.1002/sdr.1581
14. Ansah JP, Inn RLH, Ahmad S. An evaluation of the impact of aggressive
hypertension, diabetes, and smoking cessation management on CVD outcomes at the
population level: a dynamic simulation analysis. BMC Public Health. (2019) 19:1105.
doi: 10.1186/s12889-019-7429-2
15. Ahmad SC. Evaluating the impact of upstream and downstream interventions on
chronic kidney disease and dialysis care: a simulation analysis. Syst Dyn Rev. (2021)
37:32–58. doi: 10.1002/sdr.1676
16. Arias E. United States life tables, 2010. Natl Vital Stat Rep. (2014) 63:1–63. Available
online at: https://www.cdc.gov/nchs/data/nvsr/nvsr63/nvsr63_07.pdf
17. Fisher GG, Ryan LH. Overview of the health and retirement study and introduction
to the special issue. Work Aging Retire. (2018) 4:1–9. doi: 10.1093/workar/wax032
18. Cai L, Hayward MD, Saito Y, Lubitz J, Hagedorn A, Crimmins E. Estimation of
multi-state life table functions and their variability from complex survey data using the
SPACE Program. Demogr Res. (2010) 22:129–58. doi: 10.4054/DemRes.2010.22.6
19. Matchar DB, Ansah JP, Koh V, Whitson HE. Trajectories of functional ability over
the life course: a conceptual model of the interaction of stressor-induced functional loss
and resilience. Syst Dyn Rev. (2018) 34:481–502. doi: 10.1002/sdr.1611
20. Urwannachotima N, Hanvoravongchai P, Ansah JP, Prasertsom P, Koh VRY. Impact
of sugar-sweetened beverage tax on dental caries: a simulation analysis. BMC Oral Health.
(2020) 20:76. doi: 10.1186/s12903-020-1061-5
21. Sterman JD. System dynamics modeling: tools for learning in a complex world. Calif
Manage Rev. (2001) 43:8–25. doi: 10.2307/41166098
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