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Abstract

Objective

This investigation examines burden of comorbidity measured by the Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) among inpatients based on age, sex, and race.

Methods

Cross-sectional analysis of 2012-2018 US NIS datasets. Participants were inpatients 55y+. ICD-9/10 codes for admitting diagnoses were used to calculate disease burden using the CCI and ECI. Unweighted mean CCI and ECI scores were compared across demographic variables.

Results

An increase in mean CCI and ECI scores across age, sex, and races (p<.001) was identified. Compared to the youngest age group (55-59y), all age groups had higher mean CCI and ECI adjusting for time (p<.001). Increases were greatest in older age groups until age 80-84 for CCI and 85-89 for ECI. The female group had lower CCI adjusting for time (p<.001) compared to males. There was no difference between sex groups in mean ECI (p=.409). Compared with the White group, all other race groups had higher mean CCI adjusting for time (p<.001). Black inpatients had the highest CCI followed by Native American inpatients. Findings were similar for ECI, but with no difference between Hispanic and White groups (p=.434).

Conclusions

Growing multimorbidity burden among adult inpatients across age, sex, and race supports the continued need for programs for preventing and reducing multimorbidity, especially among communities that experience health inequity including older, Black, and Native American patients.

Keywords

Multimorbidity older adult hospitalization charlson comorbidity index elixhauser comorbidity index

Introduction

Multimorbidity is the experience of having multiple chronic health conditions (or comorbidities) simultaneously.¹ Disease burden arising from multimorbidity has been a focus of researchers and healthcare providers alike due to its impact on patient outcomes and disease management. Studies show that multimorbidity results in increased risk of death,² functional decline and disability,³ diminished quality of life,¹ psychological distress,^4,5 polypharmacy⁶ and elevated healthcare use and cost.^7-9 Additionally, multimorbidity elevates risk of fragmented care and physiological complications affecting disease treatment efficacy.^10-12 Evidence supports that multimorbidity prevalence among community-dwelling adults is common and that it is increasing across the age continuum.^13,14 Key contributors to this growth are population aging, medical advances enhancing survival of individuals living with chronic health conditions, and changes in health policy.¹⁵ Increasing multimorbidity not only negatively affects patients but also puts strain on the healthcare system through increased costs and utilization.

While many adults with chronic disease suffer from multimorbidity, older hospitalized patients are particularly at risk. Hospitalized patients are commonly sicker than those in the general population, yet, to the best of our knowledge, it has not been investigated if disease burden has changed across time among inpatients. To address this gap, this study aimed to determine the burden of multimorbidity among a large nation-wide sample of adult inpatients (55y+) admitted to American hospitals over a 7-year interval based on age, race, and sex.

Methods

Data source

The study sample was collected from the United States (US) National Inpatient Sample (NIS) database via the Agency of Healthcare Research and Quality (AHRQ). Data from calendar years 2012-2018 were used in the analysis. The NIS is the largest national all-payer inpatient care database housing data from over 4,000 non-federal hospitals from across the US.¹⁶ The data collected each year and stored in the NIS datasets originated from hospitals across the US that voluntarily participated in providing data to the AHRQ for that particular year and represents more than 97 percent of inpatient discharges from community hospitals in the US.¹⁶ While each year’s data provides a national sample, it could be originating from different hospitals.

The NIS datasets include hospital admission information, demographic information, and primary and secondary diagnoses for patients admitted from short and long-term care and rehabilitation facilities as well as the community. The data on diagnoses are provided as International Classification of Disease (ICD) 9th and 10th revision codes.¹⁷ ICD code data for primary and secondary discharge diagnoses were used in analysis. ICD-9 codes were used to calculate comorbidity index scores for 2012-2014, and Q1-Q3 of 2015. ICD-10 codes were used to calculate scores for Q4 of 2015 and 2016-2018. The year 2015 is represented in 2 parts, 2015-Q1-3 and 2015-Q4, to ensure that all data was captured for this year despite the ICD-9 to -10 code switch in 2015-Q4. All study procedures for this retrospective cross-sectional analysis were exempted by the Institutional Review Board, thus informed consent was not needed to complete this study.

Study population

The population of interest for this analysis was US inpatients aged 55y+ due to the increased disease burden experienced by the older adult population.¹⁸ We extracted ICD codes, race, and sex data for all inpatients 55y+ for each year, 2012-2018. The total combined sample size for all inpatients 55y+ was N=24,947,829. The mean CCI and ECI scores were calculated as described above and stratified by age group, race group, sex group, and year. Age grouping used 5-year intervals (55-59; 60-64; 65-69; 70-74; 75-79; 80-84; 85-89; and 90y+), race grouping included White, Black, Hispanic, Asian/Pacific Islander, Native American, and sex grouping included male and female.

Measures of disease burden

Two common comorbidity measures were used to quantify disease burden among hospitalized patients using ICD codes for each year across age groups, sex, and race. The Charlson Comorbidity Index (CCI)¹⁹ identifies 17 conditions (acute myocardial infarction, congestive heart failure, peripheral vascular disease, cerebrovascular, dementia, chronic pulmonary disease, rheumatoid arthritis, peptic ulcer disease, mild liver disease, diabetes, diabetes with complication, hemiplegia or paraplegia, renal disease, cancer (any malignancy), moderate or severe liver disease, metastatic solid tumor, AIDS/HIV) and was used along with the Elixhauser Comorbidity Index (ECI)²⁰ which identifies 31 conditions (congestive heart failure, cardiac arrhythmias, valvular disease, pulmonary circulation disorders, vascular disorders, uncomplicated hypertension, complicated hypertension, paralysis, other neurological disorders, chronic pulmonary disease, uncomplicated diabetes, complicated diabetes, hypothyroidism, renal failure, liver disease, peptic ulcer disease, AIDS/HIV, lymphoma, metastatic cancer, solid tumour (without metastasis), rheumatoid arthritis / collaged vascular disease, coagulopathy, obesity, weight loss, fluid and electrolyte disorders, blood loss anemia, deficiency anemia, alcohol abuse, drug abuse, psychoses, depression). Both indexes combine the number of conditions yielding a summary score with higher scores indicating increased multimorbidity and risk for poorer health outcomes including increased length of stay, 30-day readmission, and mortality.^21-24 Conditions for each index were not weighted but provide a count of the number of comorbidities as an overall assessment of disease burden.

Statistical analysis

The characteristics of the study sample were described using mean (95% CI) for continuous variables (age) and frequency (95% CI) for categorical variables (age group, sex, race). Differences between categorical variables were assessed using Pearson’s chi-square test. CCI and ECI summary scores were calculated using primary and secondary admission diagnoses using the R survey package, version 3.5.1. The summary scores were presented as means and SDs. Linear regression was used to examine the relationship between mean CCI or ECI scores with demographic characteristics (age, sex, race) and time.

Results

Table 1 contains the sample characteristics. Across 2012-2018, the mean age of the inpatients was approximately 72y and the highest hospitalization rates occurred among the 60-64y, 65-69y, and 70-74y age groups. Majority of inpatients in the sample were female (54.2% in 2012 to 51.8% in 2018) and White (78.2% in 2012 to 75.9% in 2018). The mean CCI and mean ECI scores increased each year. The mean CCI score was 1.69 (SD=1.36) in 2012 and 1.97 (SD=1.51) in 2018. The mean ECI score was 3.66 (SD=2.08) in 2012 and 4.14 (SD=2.24) in 2018.

Table 1.

Characteristics of the sample population by year are presented. Mean age and mean Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores by year, and number of hospitalizations per year based on age group, race group, and sex group.

Year	2012	2013	2014	2015	2016	2017	2018
N	N=3530762	N=3476318	N=3454614	N=3550112	N=3572836	N=3671456	N=3691731
Age (years)	72.4 (10.5)	72.3 (10.5)	72.1 (10.5)	72.1 (10.5)	72.0 (10.4)	72.0 (10.4)	72.1 (10.3)
Age group:
55-59	486164 (13.8%)	481186 (13.8%)	485928 (14.1%)	497226 (14.0%)	500751 (14.0%)	498854 (13.6%)	492857 (13.4%)
60-64	503657 (14.3%)	497456 (14.3%)	503133 (14.6%)	523424 (14.7%)	532738 (14.9%)	546660 (14.9%)	551201 (14.9%)
65-69	531122 (15.0%)	529853 (15.2%)	534245 (15.5%)	554294 (15.6%)	575653 (16.1%)	580399 (15.8%)	578068 (15.7%)
70-74	490322 (13.9%)	494982 (14.2%)	495848 (14.4%)	512042 (14.4%)	521109 (14.6%)	558018 (15.2%)	568088 (15.4%)
75-79	470935 (13.3%)	461532 (13.3%)	454775 (13.2%)	464156 (13.1%)	466615 (13.1%)	487565 (13.3%)	505829 (13.7%)
80-84	452445 (12.8%)	429921 (12.4%)	413142 (12.0%)	415979 (11.7%)	407929 (11.4%)	417931 (11.4%)	420564 (11.4%)
85-89	360008 (10.2%)	346919 (9.98%)	334991 (9.70%)	339762 (9.57%)	329663 (9.23%)	333208 (9.08%)	326616 (8.85%)
90 and above	236109 (6.69%)	234469 (6.74%)	232552 (6.73%)	243229 (6.85%)	238378 (6.67%)	248821 (6.78%)	248508 (6.73%)
Sex:
Male	1617697 (45.8%)	1611523 (46.4%)	1613750 (46.7%)	1670720 (47.1%)	1696157 (47.5%)	1754055 (47.8%)	1777898 (48.2%)
Female	1912928 (54.2%)	1864452 (53.6%)	1840390 (53.3%)	1878575 (52.9%)	1874407 (52.5%)	1917289 (52.2%)	1913674 (51.8%)
Race:
White	2547224 (78.2%)	2500330 (77.8%)	2488971 (77.6%)	2538751 (76.9%)	2580304 (77.0%)	2650565 (76.5%)	2670402 (75.9%)
Black	393825 (12.1%)	389303 (12.1%)	392871 (12.3%)	421324 (12.8%)	423286 (12.6%)	441176 (12.7%)	448734 (12.8%)
Hispanic	233176 (7.15%)	238183 (7.41%)	238960 (7.45%)	249735 (7.56%)	257148 (7.67%)	277547 (8.01%)	296177 (8.42%)
Asian or Pacific Islander	66169 (2.03%)	69683 (2.17%)	69176 (2.16%)	77507 (2.35%)	75932 (2.26%)	80637 (2.33%)	83420 (2.37%)
Native American	18756 (0.58%)	15171 (0.47%)	15739 (0.49%)	15581 (0.47%)	15866 (0.47%)	16919 (0.49%)	18305 (0.52%)
CCI	1.69 (1.36)	1.71 (1.37)	1.75 (1.39)	Q1-Q3 1.78 (1.41)Q4 1.84 (1.45)	1.88 (1.47)	1.93 (1.50)	1.97 (1.51)
ECI	3.66 (2.08)	3.75 (2.10)	3.84 (2.15)	Q1-Q3 3.93 (2.19)Q4 3.89 (2.19)	3.96 (2.20)	4.05 (2.23)	4.14 (2.24)

Mean scores for CCI and ECI significantly increased across age groups, sex groups, and race groups (all p<.001; Table 2, 3, 4) during the study period. Using age group 55-59y (the youngest group) as a reference, all other age groups had significantly higher CCI and ECI scores when adjusting for time (p<.001, Table 2). The 85-89y and 90y+ age groups had the highest average CCI score increases peaking at 2.19, SD=1.47 and 2.09, SD=1.38. The 80-84y age group had the highest average ECI score increase peaking at 4.46, SD=2.20 followed by the 75-79y and 85-89y age groups that peaked at 4.35, SD=2.25 and 4.50, SD=2.14 (Supplemental Table 1-2).

Table 2.

Linear regression of mean Charlson Comorbidity (CCI) and Elixhauser Comorbidity Index (ECI) scores based on age group. The reference is the 55-59 age group.

	CCI			ECI
Predictors	Estimates	CI	p	Estimates	CI	p
Time (year)	0.05	0.05 – 0.06	<0.001	0.08	0.07 – 0.08	<0.001
Age group (ref = age 55-59)
Age 60-64	0.16	0.12 – 0.21	<0.001	0.20	0.18 – 0.23	<0.001
Age 65-69	0.28	0.24 – 0.32	<0.001	0.36	0.33 – 0.38	<0.001
Age 70-74	0.41	0.37 – 0.45	<0.001	0.54	0.51 – 0.56	<0.001
Age 75-79	0.50	0.45 – 0.54	<0.001	0.69	0.67 – 0.72	<0.001
Age 80-84	0.53	0.49 – 0.57	<0.001	0.79	0.77 – 0.82	<0.001
Age 85-89	0.51	0.47 – 0.55	<0.001	0.84	0.81 – 0.86	<0.001
Age 90+	0.39	0.35 – 0.44	<0.001	0.72	0.69 – 0.74	<0.001
Observations	64			64
R²/R² adjusted	0.963/0.957			0.995/0.994

Table 3.

Linear regression of mean Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores based on sex group.

	CCI			ECI
Predictors	Estimates	CI	p	Estimates	CI	p
Time (year)	0.05	0.04 – 0.05	<0.001	0.08	0.07 – 0.08	<0.001
Female vs. Male	-0.21	-0.23 – -0.19	<0.001	-0.01	-0.04 – 0.02	0.409
Observations	16			16
R²/R² adjusted	0.984/0.982			0.973/0.968

Table 4.

Linear regression of mean Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores based on race group. The reference is the White group.

	CCI			ECI
Predictors	Estimates	CI	p	Estimates	CI	p
Time (year)	0.06	0.05 – 0.06	<0.001	0.08	0.07 – 0.08	<0.001
Race (ref = White)
Black	0.39	0.36 – 0.42	<0.001	0.42	0.39 – 0.45	<0.001
Hispanic	0.17	0.14 – 0.19	<0.001	0.01	-0.02 – 0.04	0.434
Asian or Pacific Islander	0.18	0.16 – 0.21	<0.001	-0.07	-0.10 – -0.04	<0.001
Native American	0.25	0.23 – 0.28	<0.001	0.21	0.18 – 0.24	<0.001
Observations	40			40
R²/R² adjusted	0.979/0.976			0.984/0.982

Compared to the male group, the female group had significantly lower mean CCI scores when adjusting for time (p<.001). Male group mean CCI scores increased peaking at 2.08, SD=1.54, while the female group increased peaking at 1.86, SD=1.47 (Supplemental Table 3). While there was a significant increase in mean ECI scores for male and female groups, there was no significant difference between the groups (p=.409; Table 3). Male group mean ECI scores increased peaking at 4.15, SD=2.24 while the female group increased peaking at 4.12, SD=2.24 (Supplemental Table 4).

All race groups had a significant increase in mean CCI and ECI scores (p<.001, Table 4). Compared to the White group, the Black/African American, Hispanic, Asian/Pacific Islander, and Native American groups had significantly higher mean CCI score adjusting for time (p<.001, Table 4). Findings were similar for mean ECI scores, except there was no difference between Hispanic and White groups (p=.434). The Black/African American group had the highest mean comorbidity scores (CCI 2.32, SD=1.55; ECI 4.54, SD=2.19) followed by the Native American group (CCI 2.17, SD=1.51; ECI 4.27, SD=2.18). The White group had the lowest mean CCI score each year analyzed while Asian/Pacific Islander group had the lowest mean ECI score each year (Supplemental Tables 5-6).

Discussion

Measuring multimorbidity provides an understanding of a patient’s disease burden and risk for outcomes including death, disability, and reduced quality of life.^1-3 This study used unweighted mean Charlson Comorbidity Index (CCI) and Elixhauser Comorbidity Index (ECI) scores to quantify multimorbidity among a large adult (55y+) inpatient sample for years 2012-2018. While evidence of change in multimorbidity among community-dwelling adults, and inpatients of specific races, has been reported previously,^25,26 to our knowledge this is the first report of change in multimorbidity among a national sample of adult inpatients using two validated measures across age, sex, and race. The findings presented show that multimorbidity increased over time in this hospitalized cohort and there were unique trends across age, sex and race. It is possible that the results presented reflect changes to the hospital system policies restricting hospitalization to only the sickest patients each year analyzed. If only patients with higher multimorbidity were admitted, it might appear that hospitalized patients are sicker. Yet, there is strong community-based evidence supporting that multimorbidity is increasing among US adults, so the possibility that multimorbidity among inpatients is also increasing is likely considering most patients admitted came from the community.^25,27

Evidence that older age is associated with higher rates of multimorbidity has been described previously.^25,28,29 Our study generally agreed with prior work, we showed that most older groups had higher mean comorbidity scores than the younger groups except for the 90y+ group. However, we also identified that most hospitalizations in the sample were among the younger age groups and declined among the 80-84y and 85-89y age groups over time. This is consistent with initiatives for enhancing community-based healthcare services for older adults such as the Geriatric Resource Teams and the Community Aging in Place to aid in managing complex care needs outside of the hospital to reduce hospitalization.^30,31 Future research should examine this topic to confirm a causal effect. Our analysis also uncovered a distinct increase in mean CCI score among the 90y+ group during the year 2015 following the ICD version 9 to version 10 code transition. This was largely due to the increased rate of hospitalization for dementia in this group (9.9% in 2015Q-3 to 34.6% in 2015Q4; data not shown). It is possible that change in coding for dementia contributed to this increase because younger age groups also showed a sharp increase after the ICD code transition. Furthermore, another study showed change in prevalence for multiple neurological conditions with this code transition.³²

Assessment of sex and multimorbidity over time revealed that there was an increase across male and female inpatients. Male inpatients had higher mean CCI scores than female inpatients each year. Yet this was not observed using the mean ECI, likely, because the conditions listed for each tool are different.^19,20 Other research identified that women were more likely to report having multimorbidity than men.¹³ Importantly, this prior study included women 18y+ and determined that lower income and smoking contributed to this finding. Our study did not differentiate female inpatients based on socio-demographic or behavioral risk factors, which could account for this difference. Additionally, females composed a decreasing proportion of overall hospitalizations from 2012-2018. Women are known to visit ambulatory care more often than men,³³ thus these results could signify that females are improving management of chronic disease using out-of-hospital care programs.

Research aiming to better understand racial health disparities has included a focus on assessment of multimorbidity burden among underrepresented groups.³⁴ Prior studies among community-dwelling adults showed that Black individuals experience multimorbidity earlier in life and are more likely than White individuals to have multisystem multimorbidity.^34,35 Another study showed that Native American, Hispanic, and Black individuals are at increased risk of multimorbidity compared to White individuals.³⁶ The authors indicate that barriers to receiving healthcare and racial discrimination contribute to these findings and that better screening and care management are needed to reduce multimorbidity in minority populations.³⁶ Our findings further support the need for improved racial health equity by showing that the greatest increases in multimorbidity in the sample were among Black and Native American inpatients.

A key strength of this study is that, to the best of our knowledge, this is the first analysis of change in multimorbidity among US adult inpatients using two validated assessments to quantify disease burden. While the weighted CCI and ECI are often used to predict risk, our group used unweighted values for each comorbidity of the CCI and ECI to ensure the summary scores calculated provided an overall assessment of disease burden. Other work has examined change in multimorbidity by categorizing inpatients as multimorbid if they had 3+ conditions but without calculating a comorbidity summary score.²⁶ Furthermore, our analysis involved a very large and racially diverse sample that spanned hospitals across the US providing a strong assessment of the US adult inpatient population at large. Despite the strengths of this analysis, some limitations should be noted. This study used an inpatient sample dataset collected prior to the COVID-19 pandemic. The pandemic resulted in a shift to using telemedicine for outpatient services, which may have reinforced structural barriers to receiving community-based services in groups already experiencing health disparities thus influencing disease burden and hospitalization in these groups.³⁷ Additional research should examine changes to multimorbidity burden across demographic variables before and after the pandemic among inpatients to assess the impact. Furthermore, since the NIS datasets are de-identified, we are unable to determine if the same patient was admitted more than once to the same hospital during a single year of this study. Additionally, the hospitals that provided data for the NIS datasets from 2012-2018 might have been different, though each annual dataset provided a representative sample for the US inpatient population for that year. Finally, the NIS dataset categorizes sex as male or female, and thus does not provide information regarding transgender, non-binary, or other gender non-conforming individuals, precluding assessment of multimorbidity among these groups.

Conclusions

This study highlights that multimorbidity significantly increased overtime among a large sample of male and female adult inpatients of diverse races in the US. The findings highlight that multimorbidity increased most among older adult inpatients and minority underserved groups, especially the Black and Native American groups, supporting the need for continued growth of geriatric care and programs that aim to provide health equity to minority communities.

Supplemental Material

Supplemental Material - Changes in multimorbidity among hospitalized adults in the US

Supplemental Material for Changes in multimorbidity among hospitalized adults in the US by Christine Loyd, Lauren Picken, Richelle Sanders, Yue Zhang, Richard E. Kennedy, and Cynthia J. Brown in Journal of Multimorbidity and Comorbidity

Footnotes

Author contributions

CL – conceptualization, data curation, methodology, funding acquisition, investigation, supervision, resources, validation, project administration, visualization, writing – original draft, writing – review and editing, final draft approval. LP – investigation, data curation, writing- original draft, writing – review and editing, final draft approval. RS – investigation, writing- original draft, writing – review and editing, final draft approval. YZ – data curation, formal analysis, methodology, software, writing – original draft, final draft approval. REK – conceptualization, data curation, formal analysis, investigation, methodology, software, resources, validation, visualization, supervision, writing- original draft, writing- review and editing, final draft approval. CJB - conceptualization, methodology, validation, resources, writing- original draft, writing- review and editing, final draft approval.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

Ethical statement

ORCID iD

Christine Loyd

Data availability statement

The NIS databases are publically available and can be purchased from the US Agency for Healthcare Research and Quality.

Supplemental Material

Supplemental material for this article is available online.

References

Fortin

Lapointe

Hudon

Vanasse

Ntetu

Maltais

. Multimorbidity and quality of life in primary care: a systematic review. Health Qual Life Outcomes. 2004;2:51.

Nunes

Flores

Mielke

Thumé

Facchini

. Multimorbidity and mortality in older adults: A systematic review and meta-analysis. Archives of Gerontology and Geriatrics. 2016;67:130-138.

Ryan

Wallace

O'Hara

Smith

. Multimorbidity and functional decline in community-dwelling adults: a systematic review. Health Qual Life Outcomes. 2015;13:168.

Fortin

Bravo

Hudon

Lapointe

Dubois

Almirall

. Psychological distress and multimorbidity in primary care. Ann Fam Med. 2006;4(5):417-422.

Gunn

Ayton

Densley

Pallant

Chondros

Herrman

, et al. The association between chronic illness, multimorbidity and depressive symptoms in an Australian primary care cohort. Soc Psychiatry Psychiatr Epidemiol. 2012;47(2):175-184.

Nguyen

Ngangue

Haggerty

Bouhali

Fortin

. Multimorbidity, polypharmacy and primary prevention in community-dwelling adults in Quebec: a cross-sectional study. Fam Pract. 2019;36(6):706-712.

Bähler

Huber

Brüngger

Reich

. Multimorbidity, health care utilization and costs in an elderly community-dwelling population: a claims data based observational study. BMC Health Serv Res. 2015;15:23.

Soley-Bori

Ashworth

Bisquera

Dodhia

Lynch

Wang

, et al. Impact of multimorbidity on healthcare costs and utilisation: a systematic review of the UK literature. British Journal of General Practice. 2021;71(702):e39-e46.

Zhao

Zhang

Haregu

Wang

. Impacts of multimorbidity on medication treatment, primary healthcare and hospitalization among middle-aged and older adults in China: evidence from a nationwide longitudinal study. BMC Public Health. 2021;21(1):1380.

10.

Ploeg

Matthew-Maich

Fraser

Dufour

McAiney

Kaasalainen

, et al. Managing multiple chronic conditions in the community: a Canadian qualitative study of the experiences of older adults, family caregivers and healthcare providers. BMC Geriatrics. 2017;17(1):40.

11.

Barnett

Mercer

Norbury

Watt

Wyke

Guthrie

. Epidemiology of multimorbidity and implications for health care, research, and medical education: a cross-sectional study. Lancet. 2012;380(9836):37-43.

12.

Townsend

Hunt

Wyke

. Managing multiple morbidity in mid-life: a qualitative study of attitudes to drug use. BMJ. 2003;327(7419):837.

13.

Canizares

Hogg-Johnson

Gignac

MAM

Glazier

Badley

. Increasing Trajectories of Multimorbidity Over Time: Birth Cohort Differences and the Role of Changes in Obesity and Income. The Journals of Gerontology: Series B. 2017;73(7):1303-1314.

14.

Nguyen

Manolova

Daskalopoulou

Vitoratou

Prince

Prina

. Prevalence of multimorbidity in community settings: A systematic review and meta-analysis of observational studies. Journal of Comorbidity. 2019;9:2235042X19870934.

15.

Fortin

Bravo

Hudon

Lapointe

Almirall

Dubois

, et al. Relationship between multimorbidity and health-related quality of life of patients in primary care. Qual Life Res. 2006;15(1):83-91.

16.

US Department of Health and Human Services . Healthcare Cost and Utilization Project. Available from: https://www.hcup-us.ahrq.gov/nisoverview.jsp.

17.

World Health Organization . ICD-10: international statistical classification of diseases and related health problems: tenth revision, 2nd ed. 2004. Available from: https://apps.who.int/iris/handle/10665/42980.

18.

Prince

Guo

Gutierrez Robledo

O'Donnell

Sullivan

, et al. The burden of disease in older people and implications for health policy and practice. Lancet. 2015;385(9967):549-562.

19.

Charlson

Pompei

Ales

MacKenzie

. A new method of classifying prognostic comorbidity in longitudinal studies: development and validation. J Chronic Dis. 1987;40(5):373-383.

20.

Elixhauser

Steiner

Harris

Coffey

. Comorbidity Measures for Use with Administrative Data. Medical Care. 1998;36(1).

21.

Voskuijl

Hageman

Ring

. Higher Charlson Comorbidity Index Scores are associated with readmission after orthopaedic surgery. Clin Orthop Relat Res. 2014;472(5):1638-1644.

22.

Menendez

Neuhaus

van Dijk

Ring

. The Elixhauser comorbidity method outperforms the Charlson index in predicting inpatient death after orthopaedic surgery. Clin Orthop Relat Res. 2014;472(9):2878-2886.

23.

Lakomkin

Kothari

Dodd

VanHouten

Yarlagadda

Collinge

, et al. Higher Charlson Comorbidity Index Scores Are Associated With Increased Hospital Length of Stay After Lower Extremity Orthopaedic Trauma. J Orthop Trauma. 2017;31(1):21-26.

24.

Shu

Lin

Hsu

. Risk factors for 30-day readmission in general medical patients admitted from the emergency department: a single centre study. Intern Med J. 2012;42(6):677-682.

25.

Quiñones

Hwang

Heintzman

Huguet

Lucas

Schmidt

, et al. Trajectories of Chronic Disease and Multimorbidity Among Middle-aged and Older Patients at Community Health Centers. JAMA Network Open. 2023;6(4):e237497.

26.

Mohamud

Campbell

DJT

Wick

Leung

Fabreau

Tonelli

, et al. 20-year trends in multimorbidity by race/ethnicity among hospitalized patient populations in the United States. International Journal for Equity in Health. 2023;22(1):137.

27.

Paez

Zhao

Hwang

. Rising out-of-pocket spending for chronic conditions: a ten-year trend. Health Aff (Millwood). 2009;28(1):15-25.

28.

Boogaerts

Loyd

Zhang

Kennedy

Brown

. National Norms for the Elixhauser and Charlson Comorbidity Indexes Among Hospitalized Adults. J Gerontol A Biol Sci Med Sci. 2023;78(2):365-372.

29.

Freid

Bernstein

Bush

. Multiple chronic conditions among adults aged 45 and over: trends over the past 10 years. NCHS Data Brief. 2012(100):1-8.

30.

Buhr

Dixon

Dillard

Nickolopoulos

Bowlby

Canupp

, et al. Geriatric Resource Teams: Equipping Primary Care Practices to Meet the Complex Care Needs of Older Adults. Geriatrics (Basel). 2019;4(4).

31.

Spoelstra

Sikorskii

Gitlin

Schueller

Kline

Szanton

. Dissemination of the CAPABLE Model of Care in a Medicaid Waiver Program to Improve Physical Function. Journal of the American Geriatrics Society. 2019;67(2):363-370.

32.

Hamedani

Blank

Thibault

Willis

. Impact of ICD-9 to ICD-10 Coding Transition on Prevalence Trends in Neurology. Neurol Clin Pract. 2021;11(5):e612.

33.

Centers for Disease Control and Prevention . Women more likely than men to visit the doctor, more likely to have annual exams 2001. Available from: https://www.cdc.gov/nchs/pressroom/01news/newstudy.htm.

34.

Quiñones

Botoseneanu

Markwardt

Nagel

Newsom

Dorr

, et al. Racial/ethnic differences in multimorbidity development and chronic disease accumulation for middle-aged adults. PLoS One. 2019;14(6):e0218462.

35.

Quiñones

Newsom

Elman

Markwardt

Nagel

Dorr

, et al. Racial and Ethnic Differences in Multimorbidity Changes Over Time. Med Care. 2021;59(5):402-409.

36.

Jackson

Young

Onsando

. High Rates of Multimorbidity Reported Among People of Color Despite Healthy Weight. Health Equity. 2022;6(1):662-668.

37.

Satish

Raghunathan

Prigoff

Wright

Hillyer

Trivedi

, et al. Care Delivery Impact of the COVID-19 Pandemic on Breast Cancer Care. JCO Oncol Pract. 2021;17(8):e1215-e1224.

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