Abstract
Objective
To assess i) the epidemiology of cardiodiabesity, ii) its association with healthcare resource utilization and cost of care, as well as iii) provide recommendations for its management.
Methods
A cohort study of insured adults with early-stage and/or active cardiodiabesity from January 2019 to December 2021 identified through a longitudinal, and de-identified medical and pharmacy claims database was conducted. All patients were followed for one year through December 2022. Conditions include cardiovascular disease, prediabetes, Type 2 diabetes (T2D), chronic kidney disease (CKD), overweight and/or obesity. Rates of progression from early-stage cardiodiabesity to active cardiodiabesity and/or advanced cardiodiabesity with complications; frequency of emergency department, inpatient and outpatient visits; as well as total cost of care over one year were analyzed.
Results
A total of 3,273,813 and 1,628,407 patients had at least one of the comorbid conditions for early-stage and active cardiodiabesity, respectively. Among those with all early-stage cardiodiabesity conditions, 27.4 % progressed to active cardiodiabesity, while 88.4 % of those with all active cardiodiabesity conditions progressed to complications within one year. Predictors of progression from early-stage to active cardiodiabesity were hypertension (OR: 2.31, 95 % CI: 2.29–2.33, p < 0.001), hyperlipidemia (OR: 1.77, 95 % CI: 1.76–1.79, p < 0.001), CKD stages 1 and 2 (OR: 1.74, 95 % CI: 1.69–1.79, p < 0.001), prediabetes (OR: 1.64, 95 % CI: 1.63–1.66, p < 0.001) and living in areas with very high social needs (OR: 1.25, 95 % CI: 1.23–1.26, p < 0.001). Significant predictors of progression from active cardiodiabesity to complications were T2D (OR: 1.88, 95 % CI: 1.81–1.96, p < 0.001), CVD (OR: 1.47, 95 % CI: 1.44–1.51, p < 0.001), CKD stages 3 and 4 (OR: 1.37, 95 % CI: 1.34–1.41, p < 0.001) and obesity (OR: 1.29, 95 % CI: 1.26–1.32, p < 0.001). Average total cost of care increased significantly among those who progressed from one disease phase to the next (p < 0.05).
Conclusions
Cardiodiabesity is deadly and rapidly progressive with substantial economic burden on the healthcare system. However, it is preventable. Innovative approaches to better understand the holistic impact of cardiodiabesity on total cost of care, early intervention or management to halt disease progression and promote equity, as well as decrease resource utilization are needed.
Keywords: Cardiodiabesity, Diabetes, Obesity, Cardiovascular disease, Progression
Graphical abstract
1. Introduction
Cardiodiabesity is a term used to define and describe the interrelationship between cardiovascular disease (CVD), Type II Diabetes (T2D) and obesity [1]. Though knowledge of this interrelationship has been building since the 1970s [[2], [3], [4]], the term is yet to attain mainstream visibility. It is an important concept in that it highlights the synergistic effect of all three conditions on health outcomes. These conditions are well documented global health problems, with CVD-related complications being the leading cause of morbidity and mortality. A study by O'Hearn et al. reported that <7 % of the U.S. adult population has optimal cardiometabolic health [5]. Individually, these conditions present a significant economic burden to the U.S. healthcare system with estimates putting the combined cost at a minimum $50.4 billion annually, of which 84.3 % are attributed to acute care [6]. Unfortunately, these conditions are projected to rise exponentially by 2030 [[7], [8], [9]], putting additional constraints on healthcare resources. In the U.S., 50 % of adults are projected to be obese by 2030 [10] and the number of people with diabetes will increase by 39.3 % by the year 2026 [11]. Furthermore, medications geared towards treating these conditions and their risk factors are estimated to experience significant growth between 2022 and 2029 [12]. Therefore, the collective cost of these conditions will likely worsen.
Though many of these cases are preventable, the increasing prevalence of diabetes, obesity and CVD will increase the occurrence of severe complications (e.g. stroke, amputation), death, disability, and healthcare costs. Most of the morbidity and cost come from CVD complications rather than from diabetes or obesity alone. As such, studying the trends, outcomes, and composite impact on healthcare resource utilization is important. Cardiodiabesity is a progressive disease, so we study it through three lenses: prevention (i.e., at-risk state or early-stage cardiodiabesity), diagnosis (i.e., active cardiodiabesity) and post (i.e., advanced state or cardiodiabesity with complications). Doing so allows us the opportunity to better understand, develop and tailor cost-saving intervention strategies for each phase of the condition. The aims of our study were to assess: i) the epidemiology of cardiodiabesity, ii) its association with healthcare resource utilization and cost of care, as well as iii) provide recommendations for its management.
2. Design and methods
Data Source and Study Population: Using a longitudinal, and de-identified pharmacy and medical claims database, we identified insured adult patients (≥18 years) across the U.S. from 2019 to 2021 in two distinct cohorts: ‘early-stage’ and ‘active disease’ phases. Both groups were mutually exclusive based on diagnoses (eFigure 1 in Supplement 1). As a progressive disease, there are ‘at-risk’ markers for early-stage cardiodiabesity that caught early enough, can slow the progression (Fig. 1). The database is representative across various age groups and geographic locations of the insured U.S. population derived directly from payer sources, including 100 % fully integrated fee-for-service Medicare, Medicare Advantage, Commercial, and Medicaid claims. The study was exempt from institution review board (IRB) review.
Fig. 1.
Disease progression: phases of cardiodiabesity.
Inclusion and Exclusion Criteria: For the early-stage cohort, we included patients with any diagnoses of overweight (i.e., body mass index [BMI] 25–30 lbs/in2), prediabetes or early kidney disease (i.e., chronic kidney disease [CKD] stages 1 and 2), or CVD risk factors such as early hypertension and high cholesterol from 2019 to 2021. For the active cardiodiabesity cohort, patients were included if they had at least one of the comorbid diagnoses of obesity (i.e., BMI>30 lbs/in2), controlled or uncontrolled T2D or CKD stages 3 and 4, and CVD without severe complications over the same period (eTable 1 in Supplement 1). Diagnoses were identified using the International Classification of Diseases Tenth Edition (ICD-10). In each cohort, patients were categorized as having one, two, or all three condition categories during the study period. The index date was defined as the first date in the study period when patients had any one of the conditions, as applicable. In order to ascertain a complete clinical and treatment profile of the study population, patients were required to have continuous medical benefits (allowing gaps of at most 45 days) for at least 12 months prior to (baseline) and after (follow-up) the index date. Patients with missing data on sex, region of residence; those without any commercial, Medicare, Medicaid, or dual Medicare-Medicaid health plan on the index date; those <18 years of age on the index date; and those living outside of the 50 states and D.C. were excluded. All patients were followed for one year post index date with the final year being 2022.
Outcome Variables: These were defined for each disease phase but centered on adverse outcomes linked to the next phase along the disease progression spectrum (eTable 2 in Supplement 1) during the follow-up period. Healthcare resource utilization was assessed by the frequency of inpatient, outpatient, and emergency department (ED) visits in the baseline and follow-up periods. Total healthcare costs were estimated as the sum of medical allowed amounts and pharmacy costs in the baseline and follow-up periods. We imputed medical allowed amounts by assigning missing amounts with the average amount from procedures that had complete cost data, stratifying by procedure type, place of service, patient's region at the time of service (Midwest, Northeast, South, and West), payer type (commercial, Medicare, and Medicaid), and the month and year of service. Similarly, we imputed missing pharmacy cost separately for plan pay and patient pay based on the national drug code, days-supply, place of service, patient's region at the time of service, payer type, and the month and year of service. After imputation, the missing rates for medical allowed amounts and pharmacy costs were <9 % and 4 %, respectively. Medical and pharmacy costs were top-coded at the 99th percentile to avoid the influence of outliers onto the analysis. All costs were adjusted for inflation and reflected the dollar amounts in 2022.
Covariates: Sociodemographic (gender, age, and social demographic index), geographic region (Midwest, Northeast, South, and West) and insurance type data were analyzed. Social determinants of health was assessed using the Evernorth Social Demographic index (ESDI) version 1.0. The ESDI is a composite community-level score of several known domains such as education, health insurance coverage, infrastructure, economic factors, culture and language, and food access that may impact the health of people living in that community. Patients were assigned an ESDI score based on their current zip3. A higher score indicated a higher level of social needs. The score was then categorized into low, medium, high, and very high based on its quantile distribution (eMethods in Supplement 2).
2.1. Statistical analysis
Descriptive statistics were used to estimate means, medians, standard deviations and range for continuous variables, as well as frequencies and percentages for categorical variables. Chi-square test was used to compare categorical variables across patient groups with one, two, or three condition categories, and a Kruskal-Wallis ranked test was used for continuous variables. Multivariate logistic regression was employed to identify significant predictors of progression from one disease phase to the next, and increase in total cost of care for each disease phase. Odds ratios (OR) and incidence risk ratios (IRR) with corresponding 95 % confidence intervals (CI) were estimated. Statistical significance was set at p < 0.05. Analyses were conducted using RStudio version 1.4.1564 [13].
3. Results
A total of 3273,813 and 1,628,407 patients were identified with at least one of the comorbid conditions for early-stage and active cardiodiabesity, respectively.
Early-stage Cardiodiabesity: Among patients with all three early-stage cardiodiabesity conditions at baseline, the average age was 56.8 (±13.1) years, with the majority being ≥50 years of age (73.5 %). Approximately 57.6 % and 33.8 % were female and lived in areas with very high social needs, respectively (Table 1). Those living in the south accounted for 31.4 % of the cohort, though the distribution of burden across the U.S. varied by condition (eFigures 2a-d in Supplement 1). The average total cost of care for baseline and follow-up collectively for patients with all three early-stage conditions was $28,807 (±45,917) versus $22,451 (±40,502) for one condition only with medical costs accounting for a larger portion ($22,696±40,695 versus $17,480±35,990) of total cost of care (p < 0.001) (Fig. 2). Progression to active cardiodiabesity within a year was 22.3 % among those with one condition and 27.4 % among those with all three conditions present (all p < 0.001) (Table 1). They also had more healthcare resource utilization.
Table 1.
Summary characteristics of patients by early-stage cardiodiabesity condition categories.
| Variable | All 3 Conditions N = 36,599 |
2 Conditions N = 483,798 |
1 Condition N = 2,753,416 |
p-value |
|---|---|---|---|---|
| Age in years | 56.8 (±13.1) | 54.5 (±14.1) | 51.5 (±15.3) | <0.001* |
| 18–34 | 2,043 (5.6 %) | 44,882 (9.3 %) | 425,274 (15.4 %) | |
| 35–49 | 7,629 (20.8 %) | 114,622 (23.7 %) | 727,708 (26.4 %) | |
| 50–64 | 17,393 (47.5 %) | 219,961(45.5 %) | 1,124,021 (40.8 %) | |
| 65+ | 9,534 (26.0 %) | 104,333 (21.6 %) | 476,413 (17.3 %) | |
| Sex | <0.001* | |||
| Male | 15,511 (42.4 %) | 216,690 (44.8 %) | 1,291,033 (46.9 %) | |
| Female | 21,088 (57.6 %) | 267,108 (55.2 %) | 1,462,383 (53.1 %) | |
| Region | <0.001* | |||
| Midwest | 5,739 (15.7 %) | 102,602 (21.2 %) | 705,051 (25.6 %) | |
| Northeast | 9,794 (26.8 %) | 116,340 (24.0 %) | 655,373 (23.8 %) | |
| South | 11,502 (31.4 %) | 149,199 (30.8 %) | 851,579 (30.9 %) | |
| West | 9,564 (26.1 %) | 115,657 (23.9 %) | 541,413 (19.7 %) | |
| Social Determinants of Health Index | <0.001* | |||
| Low | 9,846 (26.9 %) | 133,783 (27.7 %) | 788,431 (28.6 %) | |
| Medium | 8,124 (22.2 %) | 119,328 (24.7 %) | 712,986 (25.9 %) | |
| High | 6,107 (16.7 %) | 82,266 (17.0 %) | 493,095 (17.9 %) | |
| Very High | 12,381 (33.8 %) | 146,655 (30.3 %) | 749,539 (27.2 %) | |
| Health Insurance Type | <0.001* | |||
| Commercial | 19,999 (54.6 %) | 302,348 (62.5 %) | 1,888,842 (68.6 %) | |
| Medicaid | 9,786 (26.7 %) | 114,463 (23.7 %) | 584,809 (21.2 %) | |
| Medicare | 5,887 (16.1 %) | 60,117 (12.4 %) | 259,019 (9.4 %) | |
| Dual | 927 (2.5 %) | 6,870 (1.4 %) | 20,746 (0.8 %) | |
| Baseline Screening / Testing for | ||||
| Hemoglobin A1c Levels | 23,119 (63.2 %) | 204,683 (42.3 %) | 448,739 (16.3 %) | <0.001* |
| Lipids Levels | 25,975 (71.0 %) | 276,591 (57.2 %) | 969,167 (35.2 %) | <0.001* |
| Annual Wellness Visits Completed | ||||
| Baseline | 20,935 (57.2 %) | 230,379 (47.6 %) | 982,782 (35.7 %) | <0.001* |
| Follow-up | 22,732 (62.1 %) | 282,144 (58.3 %) | 1,486,126 (54.0 %) | <0.001* |
| §Annual Screening / Testing for | ||||
| Hemoglobin A1c Levels | 24,965 (68.2 %) | 274,614 (56.8 %) | 869,193 (31.6 %) | <0.001* |
| High Lipid Levels | 26,616 (72.7 %) | 332,937 (68.8 %) | 1,717,695 (62.4 %) | <0.001* |
| §Progressed to Cardiodiabesity | 10,013 (27.4 %) | 131,258 (27.1 %) | 613,852 (22.3 %) | <0.001* |
| Resource Utilization (per 1,000 patients) | ||||
| Number of Outpatient Visits | 8,060 (±6,060) | 6,940 (±5,520) | 5,780 (±4,960) | <0.001* |
| Number of Emergency Department Visits | 430 (±1,120) | 430 (±1,180) | 500 (±1,260) | <0.001* |
| Number of Inpatient Visits | 190 (±970) | 200 (±1,030) | 220 (±1,080) | <0.001* |
| Cost of Care Per Patient Per Year (U.S.$) | ||||
| Total Cost at Baseline | 12,279 (±21,464) | 10,714 (±20,331) | 8,549 (±18,109) | <0.001* |
| Medical Cost | 9,452 (±18,908) | 8148 (±17,883) | 6,275 (±15,701) | <0.001* |
| Pharmacy Cost | 2,827 (±8,000) | 2,566 (±7,609) | 2,275 (±7,234) | <0.001* |
| Total Cost at Follow-up | 16,528 (±31,990) | 15,269 (±30,741) | 13,902 (±29,325) | <0.001* |
| Medical Cost | 13,244 (±29,446) | 12,233 (±28,434) | 11,206 (±27,192) | <0.001* |
| Pharmacy Cost | 3,284 (±8,866) | 3,036 (±8,456) | 2,696 (±8,028) | <0.001* |
| Total Cost at Baseline + Follow-up | 28,807 (±45,917) | 25,983 (±43,616) | 22,451 (±40,502) | <0.001* |
| Medical Cost | 22,696 (±40,695) | 20,381 (±38,783) | 17,480 (±35,990) | <0.001* |
| Pharmacy Cost | 6,111 (±15,570) | 5,602 (±14,756) | 4,971 (±14,010) | <0.001* |
Values are presented as means with standard deviations or counts with percentages.
Statistically significant at p < 0.001
Within 1 year post index diagnosis
Fig. 2.
Average total cost of care per patient per year by early-stage cardiodiabesity condition categories (Baseline versus Follow-Up).
Active Cardiodiabesity: Among patients with all three active cardiodiabesity conditions at baseline, the average age of patients was 62.0 (±12.0) years with almost all patients being in the ≥50 years age group (86.8 %) (Table 2). Approximately, 54.7 % and 29.9 % were male and lived in areas with very high social needs, respectively. Similar to early-stage, those in the south accounted for 30.9 % of the cohort (eFigures 3a-d in Supplement 1). Progression to complications within a year was very high (45.9 %) among those with one condition and extremely high (88.4 %) among those with all three conditions present (all p < 0.001) (Table 2). They also had more healthcare resource utilization. Specifically, patients with all three conditions had 1.5 times the number of outpatient visits compared to patients with one condition. The increase was 1.8 times and 2.9 times higher for ED and inpatient visits, respectively (all p < 0.001) (Table 2). When analyzed for baseline and follow-up collectively, the average total cost of care for those with all active cardiodiabesity conditions was $58,185 (±72,536) versus $26,686 (±44,988) for one condition only with medical costs accounting for a larger portion ($46,555 versus $21,139) of all costs (p < 0.001) (Fig. 3). The two-year cost of taking care of a patient with all three cardiodiabesity conditions was 2.2 times higher than the cost of a patient with only one condition (all p < 0.001).
Table 2.
Summary characteristics of patients by active cardiodiabesity condition categories.
| Variable | All 3 Conditions N = 11,181 |
2 Conditions N = 181,314 |
1 Condition N = 1,435,912 |
p-value |
|---|---|---|---|---|
| Age in years | 62.0 (±12.0) | 55.3 (±14.7) | 47.5 (±16.10) | <0.001* |
| 18–34 | 227 (2.0 %) | 17,456 (9.6 %) | 354,736 (24.7 %) | |
| 35–49 | 1,246 (11.1 %) | 40,349 (22.3 %) | 407,731 (28.4 %) | |
| 50–64 | 5,302 (47.4 %) | 78,354 (43.2 %) | 476,161 (33.2 %) | |
| 65+ | 4,406 (39.4 %) | 45,155 (24.9 %) | 197,284 (13.7 %) | |
| Sex | <0.001* | |||
| Male | 6,114 (54.7 %) | 83,803 (46.2 %) | 572,877 (39.9 %) | |
| Female | 5,067 (45.3 %) | 97,511 (53.8 %) | 863,035 (60.1 %) | |
| Region | <0.001* | |||
| Midwest | 2,763 (24.7 %) | 44,524 (24.6 %) | 380,523 (26.5 %) | |
| Northeast | 3,091 (27.6 %) | 45,848 (25.3 %) | 327,221 (22.8 %) | |
| South | 3,456 (30.9 %) | 56,634 (31.2 %) | 450,123 (31.3 %) | |
| West | 1,871 (16.7 %) | 34,308 (18.9 %) | 278,045 (19.4 %) | |
| Social Determinants of Health Index | <0.001* | |||
| Low | 2,732 (24.4 %) | 45,825 (25.3 %) | 373,763 (26.0 %) | |
| Medium | 2,925 (26.2 %) | 45,841 (25.3 %) | 365,979 (25.5 %) | |
| High | 2,150 (19.2 %) | 34,401 (19.0 %) | 267,209 (18.6 %) | |
| Very High | 3,343 (29.9 %) | 54,659 (30.1 %) | 424,043 (29.5 %) | |
| Health Insurance Type | <0.001* | |||
| Commercial | 5,335 (47.7 %) | 99,400 (54.8 %) | 893,329 (62.2 %) | |
| Medicaid | 3,004 (26.9 %) | 52,944 (29.2 %) | 418,685 (29.2 %) | |
| Medicare | 2,493 (22.3 %) | 25,899 (14.3 %) | 111,525 (7.8 %) | |
| Dual | 349 (3.1 %) | 3,071 (1.7 %) | 12,373 (0.9 %) | |
| Baseline Screening / Testing | ||||
| Calcium Score Test | 63 (0.6 %) | 488 (0.3 %) | 1,391 (0.1 %) | <0.001* |
| Stress Test | 1,340 (12.0 %) | 11,254 (6.2 %) | 33,729 (2.3 %) | <0.001* |
| Electrophysiology Studies | 14 (0.1 %) | 110 (0.1 %) | 346 (0.0 %) | <0.001* |
| Annual Wellness Visits Completed | ||||
| Baseline | 4,401 (39.4 %) | 73,960 (40.8 %) | 534,020 (37.2 %) | <0.001* |
| Follow-up | 5,199 (46.5 %) | 87,841 (48.4 %) | 747,274 (52.0 %) | <0.001* |
| §Progressed to Complications | 9,883 (88.4 %) | 146,004 (80.5 %) | 659,479 (45.9 %) | <0.001* |
| Resource Utilization (per 1,000 patients) | ||||
| Number Outpatient Visits | 9,490 (±6,880) | 8,130 (±6,240) | 6,470 (±5,370) | <0.001* |
| Number Emergency Department Visits | 1,020 (±2,040) | 760 (±1,630) | 580 (±1,350) | <0.001* |
| Number Inpatient Visits | 800 (±2,220) | 470 (±1,640) | 280 (±1,110) | <0.001* |
| Cost of Care Per Patient Per Year (U.S.$) | ||||
| Total Cost at Baseline | 20,576 (±31,058) | 15,013 (±25,906) | 10,256 (±20,475) | <0.001* |
| Medical Cost | 15,472 (±27,675) | 11,264 (±22,994) | 7,813 (±18,121) | <0.001* |
| Pharmacy Cost | 5,104 (±11,070) | 3,749 (±9,491) | 2,443 (±7,734) | <0.001* |
| Total Cost at Follow-up | 37,609 (±55,824) | 24,988 (±42,516) | 16,430 (±32,513) | <0.001* |
| Medical Cost | 31,083 (±52,789) | 19,895 (±39,681) | 13,325 (±30,257) | <0.001* |
| Pharmacy Cost | 6,526 (±12,840) | 5,094 (±11,424) | 3,105 (±9,087) | <0.001* |
| Total Cost at Baseline + Follow-up | 58,185 (±72,536) | 40,001 (±57,847) | 26,686 (±44,988) | <0.001* |
| Medical Cost | 46,555 (±66,186) | 31,159 (±52,156) | 21,139 (±40,377) | <0.001* |
| Pharmacy Cost | 11,630 (±22,014) | 8,842 (±19,094) | 5,547 (±15,405) | <0.001* |
Values are presented as means with standard deviations or counts with percentages;.
Statistically significant at p < 0.001.
Within 1 year post index diagnosis.
Fig. 3.
Average total cost of care per patient per year by active cardiodiabesity condition categories (Baseline versus Follow-Up).
3.1. Regression analysis
Similar trends were observed in the multivariate regression analysis. The most significant predictors of progression from early-stage to active cardiodiabesity were living in areas with very high social needs (OR: 1.25, 95 % CI: 1.23–1.26, p < 0.001), hypertension (OR: 2.31, 95 % CI: 2.29–2.33, p < 0.001), hyperlipidemia (OR: 1.77, 95 % CI: 1.76–1.79, p < 0.001), CKD stages 1 and 2 (OR: 1.74, 95 % CI: 1.69–1.79, p < 0.001) and prediabetes (OR: 1.64, 95 % CI: 1.63–1.66, p < 0.001). Significant predictors of progression from active cardiodiabesity to complications were T2D (OR: 1.88, 95 % CI: 1.81–1.96, p < 0.001), CVD (OR: 1.47, 95 % CI: 1.44–1.51, p < 0.001), CKD stages 3 and 4 (OR: 1.37, 95 % CI: 1.34–1.41, p < 0.001) and obesity (OR: 1.29, 95 % CI: 1.26–1.32, p < 0.001) (Table 3). Factors associated with increased cost of care among progressors over time are in Table 4.
Table 3.
Predictors of progression from one disease phase to the next.
|
Early-stage to Active Cardiodiabesity |
Active Cardiodiabesity to Complications |
||||||
|---|---|---|---|---|---|---|---|
| Variable | Odds Ratio | 95 % Confidence Interval | p-value | Variable | Odds Ratio | 95 % Confidence Interval | p-value |
| Age in years | Age in years | ||||||
| 18–34 | — | — | 18–34 | — | — | ||
| 35–49 | 1.08 | 1.06, 1.09 | <0.001 | 35–49 | 1.01 | 0.99, 1.02 | 0.36 |
| 50–64 | 0.98 | 0.97, 1.00 | 0.009 | 50–64 | 0.94 | 0.92, 0.95 | <0.001 |
| 65+ | 0.98 | 0.96, 0.99 | 0.004 | 65+ | 1.06 | 1.03, 1.08 | <0.001 |
| Sex | Sex | ||||||
| Female | — | — | Female | — | — | ||
| Male | 0.97 | 0.96, 0.98 | <0.001 | Male | 0.84 | 0.83, 0.85 | <0.001 |
| Region | Region | ||||||
| Midwest | — | — | Midwest | — | — | ||
| Northeast | 1.00 | 0.99, 1.01 | 0.86 | Northeast | 0.92 | 0.90, 0.93 | <0.001 |
| South | 0.88 | 0.88, 0.89 | <0.001 | South | 1.03 | 1.01, 1.04 | <0.001 |
| West | 0.82 | 0.81, 0.83 | <0.001 | West | 0.85 | 0.83, 0.86 | <0.001 |
| Social Determinants of Health Index | Social Determinants of Health Index | ||||||
| Low | — | — | Low | — | — | ||
| Medium | 1.07 | 1.06, 1.08 | <0.001 | Medium | 1.06 | 1.04, 1.07 | <0.001 |
| High | 1.16 | 1.15, 1.17 | <0.001 | High | 1.08 | 1.06, 1.10 | <0.001 |
| Very High | 1.25 | 1.23, 1.26 | <0.001 | Very High | 1.07 | 1.05, 1.09 | <0.001 |
| Prediabetes | 1.64 | 1.63, 1.66 | <0.001 | Diabetes | 1.88 | 1.81, 1.96 | <0.001 |
| CKD Stages 1 and 2 | 1.74 | 1.69, 1.79 | <0.001 | CKD Stages 3 and 4 | 1.37 | 1.34, 1.41 | <0.001 |
| Overweight | 0.79 | 0.78, 0.80 | <0.001 | Obesity | 1.29 | 1.26, 1.32 | <0.001 |
| Hyperlipidemia | 1.77 | 1.76, 1.79 | <0.001 | CVD | 1.47 | 1.44, 1.51 | <0.001 |
| Hypertension | 2.31 | 2.29, 2.33 | <0.001 | Had wellness visits completed | 0.82 | 0.81, 0.83 | <0.001 |
| Had wellness visits completed | 0.77 | 0.76, 0.77 | <0.001 | Had A1c test completed | 1.11 | 1.10, 1.13 | <0.001 |
| Had A1c test completed | 1.39 | 1.38, 1.41 | <0.001 | Had lipid screening completed | 0.91 | 0.90, 0.92 | <0.001 |
| Had lipid screening completed | 0.66 | 0.66, 0.67 | <0.001 | ||||
CKD = chronic kidney disease; CVD = cardiovascular disease.
Table 4.
Factors Associated with increased total cost of care among progressors.
|
Early-stage to Active Cardiodiabesity |
Active Cardiodiabesity to Complications |
||||||
|---|---|---|---|---|---|---|---|
| Variable | Incidence Risk Ratio | 95 % Confidence Interval | p-value | Variable | Incidence Risk Ratio | 95 % Confidence Interval | p-value |
| Age in years | Age in years | ||||||
| 18–34 | — | — | 18–34 | — | — | ||
| 35–49 | 1.10 | 1.10, 1.10 | <0.001 | 35–49 | 1.08 | 1.08, 1.08 | <0.001 |
| 50–64 | 1.43 | 1.43, 1.43 | <0.001 | 50–64 | 1.18 | 1.18, 1.18 | <0.001 |
| 65+ | 1.70 | 1.70, 1.70 | <0.001 | 65+ | 1.14 | 1.14, 1.14 | <0.001 |
| Sex | Sex | ||||||
| Female | — | — | Female | — | — | ||
| Male | 1.13 | 1.13, 1.13 | <0.001 | Male | 1.01 | 1.01, 1.01 | <0.001 |
| Region | Region | ||||||
| Midwest | — | — | Midwest | — | — | ||
| Northeast | 0.97 | 0.97, 0.97 | <0.001 | Northeast | 0.95 | 0.95, 0.95 | <0.001 |
| South | 0.97 | 0.97, 0.97 | <0.001 | South | 0.93 | 0.93, 0.93 | <0.001 |
| West | 1.04 | 1.04, 1.04 | <0.001 | West | 1.05 | 1.05, 1.05 | <0.001 |
| Social Determinants of Health Index | Social Determinants of Health Index | ||||||
| Low | — | — | Low | — | — | ||
| Medium | 0.93 | 0.93, 0.93 | <0.001 | Medium | 1.00 | 1.00, 1.00 | <0.001 |
| High | 0.99 | 0.99, 0.99 | <0.001 | High | 1.01 | 1.01, 1.01 | <0.001 |
| Very High | 0.99 | 0.99, 0.99 | <0.001 | Very High | 1.01 | 1.01, 1.01 | <0.001 |
| Prediabetes | 0.86 | 0.86, 0.86 | <0.001 | Type 2 Diabetes | 0.82 | 0.82, 0.82 | <0.001 |
| CKD Stages 1 and 2 | 1.13 | 1.13, 1.13 | <0.001 | CKD Stages 3 and 4 | 1.48 | 1.48, 1.48 | <0.001 |
| Overweight | 1.05 | 1.05, 1.05 | <0.001 | Obesity | 1.05 | 1.05, 1.05 | <0.001 |
| Hyperlipidemia | 1.10 | 1.10, 1.10 | <0.001 | CVD | 2.00 | 2.00, 2.00 | <0.001 |
| Hypertension | 1.64 | 1.64, 1.64 | <0.001 | Had wellness visits completed | 0.92 | 0.92, 0.92 | <0.001 |
| Had wellness visits completed | 0.90 | 0.90, 0.90 | <0.001 | Had A1c test completed | 0.99 | 0.99, 0.99 | <0.001 |
| Had A1c test completed | 0.92 | 0.92, 0.92 | <0.001 | Had lipid screening completed | 0.91 | 0.91, 0.91 | <0.001 |
| Had lipid screening completed | 1.01 | 1.01, 1.01 | <0.001 | Total cost of care at baseline | 1.29 | 1.29, 1.29 | <0.001 |
| Total cost of care at baseline | 1.23 | 1.23, 1.23 | <0.001 | ||||
CKD = chronic kidney disease; CVD = cardiovascular disease.
4. Discussion
Exorbitant resource utilization brought about by serious health conditions that are preventable remain a major concern and threat to the U.S. healthcare system. In our study, we found that cardiodiabesity has substantial rates of progression, increases healthcare resource utilization, and is a significant cost driver. Several U.S.-based studies have shown the impact of diabetes on healthcare resource utilization [[14], [15], [16]]. King et al. in a study of adult patients with a baseline diagnosis of T2D reported that without proper treatment, 0.9 % developed stroke in the year following diagnosis [14]. Furthermore, those with comorbid diagnosis of T2D and stroke reported higher healthcare utilization and cost compared to adults with T2D alone [14]. Weng et al. in another study which evaluated the impact of atherosclerotic cardiovascular disease (ASCVD) on healthcare resource utilization and costs in patients with T2D, reported that those with comorbid diagnoses had significantly higher outpatient, inpatient and emergency resource utilization as well as total healthcare cost than those without ASCVD ($22,977 versus $9,735) [16]. Similar observations have also been reported for obesity [17]. These studies and our findings reinforce the need for interventions that lower the risk of cardiodiabesity progression. Below are key insights and recommendations from our analysis.
4.1. Cardiodiabesity is a burden and cost driver to the healthcare system
While each component condition can cause health problems on its own, a combination of all synergistically increases more severe health risks. However, simply having 1 or 2 of the conditions poses significant risks. In our analysis, many patients that had 1 to 2 conditions in each stage had significant rates of progression. In the one-year follow-up period, 27.4 % and 88.4 % of patients in the early-stage and active cardiodiabesity phases, progressed to more adverse phases. Even those with only one risk factor or condition have a substantial 22.3 % progression from early-stage to active cardiodiabesity in one year. Furthermore, with resource utilization and total cost of care being a central focus, our analyses show that cardiodiabesity is a problem that poses a significant economic risk to the U.S. healthcare system with average medical cost over a one-year period being as high as $46,555 for a patient with all cardiodiabesity conditions compared to $21,139 for a patient with one condition. The average pharmacy cost was $11,630 versus $5,547, respectively indicating a similar compounding of cost above what is expected for individual conditions. Differences in resource utilization such as inpatient, outpatient and ED visits were also observed, with higher rates for those with active cardiodiabesity.
4.2. Disease burden and progression varies by sociodemographic group(s)
Health disparities remain a major problem in disease distribution and healthcare. These disparities are evident in the manifestation and distribution of cardiodiabesity among certain sociodemographic groups. In our analysis, the highest prevalence was among those 50 years and above, suggesting that the burden attributable to cardiodiabesity risks increases with age. While no race/ethnicity or educational level data were included in our analysis, previous studies have shown rates of these conditions to be significantly higher among minorities (i.e., non-Hispanic whites and blacks) and those with low education attainment [5,[18], [19]]. Similar to previous research findings, the burden of this condition was highest among those living in areas with very high social needs and those predominantly in the Southern, Eastern and Midwestern regions of the U.S [20]. Interestingly, early-stage cardiodiabesity is more prevalent in women while active cardiodiabesity and the risk of progression to complications is more prevalent in men. Although this might exemplify biological differences, future work need to delve further into possible diagnostic delays or inaccuracies in women as some studies have started to do already [[21], [22]]. These findings inform the need for clinical and/or population health interventions to address health equity in the management of cardiodiabesity and tailor these interventions according to the needs of those most affected [5].
4.3. Prevention is key to slow or halt disease progression
The biggest priority regarding cardiodiabesity should be prevention. The underlying principle of prevention is to limit the development of risk factors such as hypertension, pre-diabetes, and high cholesterol or to control at-risk conditions before the patient develops active cardiodiabesity. Measures would include identifying at-risk patients early enough, understanding the role of micro and macro level ecosystem factors (e.g. emotional, social, geographic and economic factors), targeting patient barriers to follow-up and disease control, and developing performance quality metrics that improve health outcomes and equity. Strategies should also aim to prevent disease progression in a susceptible individual. Examples include screening, early identification and helping patients understand how to reduce their risk. These practices must be carried out under a healthcare ecosystem that supports incentivizing preventative behaviors that lowers risk, providing convenient access to best possible evidence-based care, encouraging care providers to practice in a population-health orientation, delivering best possible tailored care to each patient, by decreasing the administrative burden associated with payer prior authorization, and maximizing reimbursement for effective prevention strategies. Of note also is the rate of disease progression despite record of annual wellness visits among patients. In our analysis, almost half of patients with two or more early-stage conditions had an annual wellness visit; yet 27.4 % of them worsened and developed active cardiodiabesity. Similar trends were observed for those with active cardiodiabesity patients where 88.4 % subsequently worsened to more advanced disease (i.e., they developed complications). This finding suggests the need to look into which provider models are more effective at utilizing evidence-based guidelines for risk reduction and control of at-risk condition since access alone is not sufficient.
4.4. Limitations
While this study has several strengths such as the use of a large and nationally representative sample of U.S. adults, there are a few limitations to consider. The analysis is limited to the insured adult population; and there is reason to be concerned about the growing cardiodiabesity trend among the uninsured as well as in pediatric populations. Second, we might have underestimated the actual number of adults with these conditions and any associated outcomes because it relied on medical claims data. Third, causal inference could not be drawn due to the cross-sectional nature of the analysis. Fourth, no information on race/ethnicity or education level were included; thus, we were unable to assess prevalence and disparities by these factors. Fifth, some conditions or variables were identified using ‘E’ ICD-10 codes which may have resulted in an underestimation of counts in instances where additional ‘Z’ or historical ICD-10 codes could be used. Nonetheless, the insights derived from this analysis are important and could drive future research in better understanding cardiodiabesity.
5. Conclusion and relevant implications for clinical practice
In summary, it is important to consider the synergies and components of cardiodiabesity. These are important set of conditions, whose prevalence and cost are already staggering and increasing. Cardiodiabesity, its associated risk factors and complications, are significant drivers of resource utilization posing substantial economic burden on the healthcare system. Reaffirming our commitment to identify and serve those individuals as well as tailor medical care to their health and social needs are necessary. Innovative approaches to better understand the holistic impact of cardiodiabesity on total cost of care, early intervention or management to halt disease progression and promote equity, as well as decrease resource utilization are needed.
Supplemental Files
Supplement 1. eFigures and eTables
Supplement 2. eMETHODS: Detailed Methodology
CRediT authorship contribution statement
Duy Do: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Data curation. Tiffany Lee: Writing – review & editing, Writing – original draft, Methodology, Formal analysis. Calie Santana: Writing – review & editing, Writing – original draft, Methodology, Conceptualization. Angela Inneh: Writing – review & editing, Writing – original draft, Validation, Supervision, Project administration, Methodology, Conceptualization. Urvashi Patel: Writing – review & editing, Writing – original draft, Supervision, Project administration, Methodology, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgements
The authors express special thanks to the clinicians and data scientists at Evernorth Health Services and Cigna Healthcare for the insights and expertise in the execution as well as review of this study.
Footnotes
No conflicts of interest or financial disclosures were reported by the authors of this paper. All authors have each met all criteria for authorship of the paper.
Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.ajpc.2024.100887.
Appendix. Supplementary materials
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