Heart failure readmissions in urban and rural hospital settings: An analysis of 30‐day readmissions using the 2021 Nationwide Readmissions Database

Abstract

Purpose

Heart failure is a leading cause of hospital readmissions in the United States. Rural populations experience higher mortality and fragmented care, yet limited research has explored how demographic, clinical, structural, and socioeconomic factors influence 30‐day readmission risk across geographic locations. This study examined predictors of 30‐day heart failure readmissions and assessed how associations differ between rural and urban hospitals.

Methods

This secondary analysis used the 2021 Nationwide Readmissions Database and included adults hospitalized with a primary diagnosis of heart failure. The primary outcome was 30‐day hospital readmission, defined as the first non‐elective inpatient admission for heart failure occurring within 30 days of discharge from an index hospitalization. Covariates included age, sex, diabetes, chronic obstructive pulmonary disease, length of hospital stay, discharge disposition, insurance payer type, median household income, hospital bed size, teaching status, geographic location, and total charges. Hospital rural–urban classification was based on the Urban–Rural Classification Scheme (HOSP_URCAT4) provided by the Healthcare Cost and Utilization Project. Weighted multivariable logistic regression with interaction terms was used to assess geographic variation in readmission risk.

Findings

Rural hospitals had lower overall odds of readmission; however, this pattern did not extend to patients with self‐pay or no‐charge status. Among rural patients, those with no‐charge hospitalizations had the highest risk (aOR = 1.830, 95% CI [1.059, 3.161]), followed by self‐pay (aOR = 1.301, 95% CI [1.143, 1.480]). Diabetes (aOR = 1.181), COPD (aOR = 1.258), and longer hospital stays (aOR = 1.246) were strong clinical predictors. Private insurance (aOR = 0.684) and higher income (aOR = 0.917) were protective. Model performance was modest (AUC = 0.589; Brier score = 0.167).

Conclusions

Findings indicate that structural inequities persist in rural heart failure readmissions, warranting targeted policy and care interventions.

INTRODUCTION

Heart failure (HF) is a leading cause of 30‐day hospital readmissions in the United States and contributes to adverse patient outcomes, increased health care expenditures, and strain on the health care system. ¹ , ² , ³ Despite national quality initiatives aimed at reducing preventable readmissions, progress has been uneven across geographic locations. Rural populations experience disproportionately higher HF‐related mortality, limited access to specialty care, and fewer transitional services, which collectively increase the risk of readmission. ⁴ , ⁵ , ⁶ , ⁷

Rural hospitals frequently serve older, medically complex, and socioeconomically disadvantaged populations while operating with fewer clinical resources, limited workforce capacity, and reduced access to timely outpatient care. ⁴ , ⁶ , ⁸ These challenges are compounded by structural barriers such as long travel distances, inconsistent broadband coverage for telehealth, and fragmented care coordination. ⁴ , ⁵ , ⁶ Although these issues are well documented in the literature, limited research has examined how predictors of HF readmission vary between rural and urban hospital settings.

Most predictive models of HF readmission emphasize clinical factors such as age, sex, and comorbid conditions. However, structural and socioeconomic characteristics, including insurance coverage, median household income, hospital bed size, hospital teaching status, and geographic location, also shape care delivery and patient outcomes. ⁵ , ⁹ , ¹⁰ These variables may play a more prominent role in rural environments where care infrastructure is less robust. ⁶

This study was guided by Donabedian's structure–process–outcome framework, emphasizing the role of structural characteristics in shaping readmission outcomes and examined the following research question: How do demographic factors (age and sex), clinical factors (diabetes, COPD, length of hospital stay, discharge disposition), and structural and socioeconomic variables (insurance payer type, median household income, hospital bed size, hospital teaching status, hospital geographic location, and total charges) influence the likelihood of 30‐day hospital readmissions among patients with HF, and how do these associations differ between urban and rural hospitals?

While prior studies have identified clinical predictors of HF readmission, fewer have examined how structural and socioeconomic factors interact with hospital geographic context to shape readmission risk. This study extends existing work by explicitly testing effect modification between insurance payer type and rural versus urban hospital settings using a nationally representative inpatient dataset.

METHODS

Study design and population

This secondary analysis used the 2021 Nationwide Readmissions Database (NRD), developed by the Healthcare Cost and Utilization Project (HCUP) and sponsored by the Agency for Healthcare Research and Quality (AHRQ). The NRD enables analysis of national hospital readmission rates by linking inpatient discharge records across the same calendar year. The 2021 database includes more than 16 million unweighted discharges from 28 states, representing approximately 60% of the US population. Discharge‐level weights allow extrapolation to a nationally representative estimate of more than 33 million hospitalizations. Hospitals are selected using a multistage stratified design based on geographic location, ownership, hospital bed size, and hospital teaching status. ¹¹

The primary outcome variable was 30‐day hospital readmission, defined as the first non‐elective inpatient admission for HF occurring within 30 days of discharge from an index hospitalization for HF. Index hospitalizations were defined as inpatient stays with a primary diagnosis of HF, classified using the Clinical Classifications Software Refined (CCSR) category CIR019. Index admissions occurring between January and November 2021 were eligible to allow complete 30‐day follow‐up within the calendar year.

To avoid misclassification of inter‐hospital transfers as readmissions, hospitalizations resulting in transfer to another acute‐care facility were treated as a single episode of care in accordance with HCUP guidance. Same‐day admissions following discharge were not considered readmissions and were treated as continuation of the index hospitalization. Readmissions were identified using the NRD VisitLink variable, which enables longitudinal tracking of individuals across hospitals within a state during a single calendar year.

When patients experienced multiple eligible HF hospitalizations during the study period, each qualifying hospitalization could serve as an index admission, provided that it was preceded by at least 30 days without an inpatient admission. Only the first readmission within 30 days of each index hospitalization was counted. Patients who died during the index hospitalization were excluded. This approach is consistent with HCUP‐recommended methods for estimating condition‐specific 30‐day readmissions using the NRD.

Independent variables

Variable selection was guided by the Donabedian framework, which emphasizes the role of structural, process, and outcome factors in shaping health care quality. These variables were categorized into three corresponding domains and are summarized in Table 1.

TABLE 1.

Independent variables.

Domain	Variable	Description
Demographic	Age	Age in years at the time of the index hospitalization.
	Sex	Biological sex categorized as male or female.
Clinical/process	Diabetes	Presence of diabetes identified using the Clinical Classifications Software Refined (CCSR) categories for diabetes during the index hospitalization.
	COPD	Presence of chronic obstructive pulmonary disease identified using the Clinical Classifications Software Refined (CCSR) categories during the index hospitalization.
	Length of hospital stay	Total number of inpatient days for the index hospitalization.
	Discharge disposition	Discharge status categorized using the HCUP DISPUNIFORM variable (e.g., routine discharge, home health care, skilled nursing facility, hospice, discharge against medical advice).
	Hospital bed size	Hospital bed size categorized as small, medium, or large based on HCUP definitions that account for hospital region, location, and teaching status.
	Hospital teaching status	Hospital teaching status categorized as metropolitan teaching, metropolitan non‐teaching, or non‐metropolitan, as defined by HCUP.
	Geographic location	Hospital geographic location classified using the HCUP Urban–Rural Classification Scheme (HOSP_URCAT4), including large metropolitan, small metropolitan, micropolitan, and rural/non‐core areas.
	Costs	Total charges for the index hospitalization, reported in US dollars.

Note: All variables and coding categories were defined according to HCUP guidelines for the 2021 NRD. Hospital geographic location was based on the HCUP‐provided Urban–Rural Classification Scheme (HOSP_URCAT4), which is derived from Urban Influence Codes developed by the US Department of Agriculture (USDA).

Geographic location

Table 2 shows the hospital geographic location classifications. These were defined using the NRD's HOSP_URCAT4 variable, which categorizes hospitals into four levels based on the Urban Influence Codes developed by the US Department of Agriculture (USDA): large metropolitan (code 1), small metropolitan (code 2), micropolitan (code 3), and rural/non‐core (code 4). Geographic location was used both as an independent variable and within interaction terms to assess variation in 30‐day readmission risk.

TABLE 2.

Descriptions of geographic location.

HOSP_URCAT4 Code	Classification	Description
1	Large metropolitan	Hospitals located in large metropolitan areas.
2	Small metropolitan	Hospitals located in small metropolitan areas.
3	Micropolitan	Hospitals located in micropolitan (non‐metropolitan) areas.
4	Rural (non‐core)	Hospitals located in rural, non‐core areas.

Note: Hospital geographic location categories are based on the HCUP‐provided Urban–Rural Classification Scheme (HOSP_URCAT4), which is derived from Urban Influence Codes developed by the USDA.

Missing data

Missingness was evaluated for all study variables. Approximately 1% of observations were missing income data, 0.3% were missing hospital charges, and 0.2% were missing insurance payer type information. Little's MCAR test indicated that the data were not missing completely at random (p < 0.001). Multiple imputation was conducted using fully conditional specification (FCS) with ten imputations. FCS was selected because it's flexibly in accommodating different variable types and complex relationships among predictors, which is essential in large administrative datasets with mixed categorical and continuous variables. ¹² All study variables were included in the imputation model, and pooled estimates were used in all regression analyses.

Weighting and national estimates

To generate nationally representative estimates, the study conducted all analyses using weighted data, as recommended by HCUP for the NRD. The discharge weight variable (DISCWT) was applied using the SPSS weight by command, which adjusted for the complex sampling design. ¹¹ This allowed for the extrapolation of the sample data to reflect national hospital discharges. All descriptive statistics, regression models, and imputations involving inferential procedures were conducted using the weighted dataset. Additionally, the stratum variable (NRD_STRATUM) was applied to account for stratification in variance estimation and standard error calculations. The stratum variable reflected strata on a combination of hospital characteristics, such as hospital bed size, teaching status, and geographic location (urban/rural). This preserved the survey's complex sampling structure.

Statistical analysis

All analyses incorporated HCUP‐provided discharge weights and accounted for complex survey design using sampling strata. Descriptive statistics summarized patient and hospital characteristics. Chi‐square tests and one‐way analysis of variance (ANOVA) were used to assess bivariate associations between independent variables and 30‐day readmission status. Chi‐square tests were selected for categorical variables to evaluate distributional differences in readmission rates across groups, while ANOVA was used for continuous variables to compare mean values between readmitted and non‐readmitted patients. These methods allowed for a comprehensive assessment of variation across all levels of the independent variables in the context of a large, nationally representative sample. ¹³

To address the primary research question, a weighted binary logistic regression model was constructed. All predictors were entered simultaneously. To assess whether the association between insurance payer type and readmission varied by geographic location, interaction terms were created between insurance category and hospital location (urban vs. rural) and included in the final multivariable model. Adjusted odds ratios (aOR) and 95% confidence intervals (CI) were reported. Model fit was assessed using the Brier score and the area under the receiver operating characteristic curve (AUC). Statistical significance was set at p < 0.05. Analyses were conducted using IBM SPSS Statistics, version 29.0. The analytic approach was explanatory rather than predictive, with model performance metrics reported to characterize overall discrimination rather than to support individual‐level risk prediction. In addition, total hospital charges were included as a proxy for resource intensity during the index hospitalization and were interpreted as a contextual marker rather than a causal predictor of readmission risk.

Ethical considerations

This was a secondary analysis using de‐identified administrative data and thus qualified for exemption from institutional review board (IRB) review under federal guidelines. All analyses complied with the HCUP Data Use Agreement and the Health Insurance Portability and Accountability Act (HIPAA).

Consent for publication

No written consent has been obtained from the patients as there is no patient identifiable data included in this case report/series.

RESULTS

The final weighted sample included 4,693,859 adult discharges. Table 3 summarizes the demographic, clinical, and structural characteristics of the study sample. The mean age was 70.8 years (SD = 13.7) with the majority being male. In the sample, 48.3% of patients had diabetes, 32.6% had COPD, and 15.7% had both conditions. The median length of hospital stay was 4 days. Most patients were discharged home without home health care (HHC) or other outpatient services such as a skilled nursing facility or hospice. The majority insurer was Medicare. Approximately one‐third resided in ZIP Codes within the lowest national income quartile. Over half of the sample received care in large hospitals, and the majority were treated in metropolitan teaching hospitals. Hospitalization was most common in large metropolitan areas and least common in rural facilities.

TABLE 3.

Weighted demographic and clinical characteristics (n = 4,693,859).

	n	%
Sex
Male	2,458,910	52.4
Female	2,234,949	47.6
Diabetes
No	2,426,359	51.7
Yes	2,267,500	48.3
COPD
No	3,163,926	67.4
Yes	1,529,933	32.6
Diabetes and COPD
No	3,956,547	84.3
Yes	737,312	15.7
Length of stay (days)—Skew [IQR]	4 [3–8]
Discharge disposition
Routine discharge to home or self‐care	2,155,111	45.9
Transfer to a short‐term hospital	48,792	1.0
Transfer to other facilities	1,087,672	23.2
Discharge to home health care	1,310,532	28.0
Left against medical advice (AMA)	89,713	1.9
Insurance payer type
Medicare	3,469,612	73.9
Medicaid	506,474	10.8
Private insurance	512,486	10.9
Self‐pay	82,001	1.7
No charge	7,648	0.2
Other	115,639	2.5
Median household income national quartile for patient ZIP Code
$1–$51,999	1,520,250	32.4
$52,000–$65,999	1,284,653	27.4
$66,000–$87,999	1,071,951	22.8
$88,000 or more	817,005	17.4
Hospital bed size
Small	906,099	19.3
Medium	1,225,862	26.1
Large	2,561,898	54.6
Hospital teaching status
Metropolitan non‐teaching	816,265	17.4
Metropolitan teaching	3,432,012	73.1
Non‐metropolitan	445,582	9.5
Hospital geographic location (urban/rural)
Large metropolitan areas with at least 1 million residents	2,560,395	54.5
Small metropolitan areas with less than 1 million residents	1,687,882	36.0
Micropolitan areas	337,915	7.2
Not metropolitan or micropolitan	107,667	2.3
Total charges ($)—Skew [IQR]	48,802 [26,322–97,235]

Note: 1. Frequencies and percentages reflect weighted estimates based on discharge weights (DISCWT). 2. Unweighted sample size = 3,406,226.

M = mean; SD = standard deviation; Skew = median; IQR = interquartile range.

Table 4 presents continuous variables, showing raw values for age and both raw and log‐transformed values for length of stay and total hospital charges. Length of stay and total hospital charges exhibited substantial positive skewness and were log‐transformed for regression analyses, whereas age was approximately normally distributed and therefore remained untransformed.

TABLE 4.

Descriptive statistics for continuous study variables.

	n	Min	Max	M	SD	Skew	Kurt
Age at admission	4,693,859	18	90	70.800	13.742	0.13	−0.67
Length of stay	4,693,859	0	365	6.770	8.395	4	142.347
Log Transform length of stay	4,693,859	0	5.9	1.764	0.709	1.609	0.578
Total charges	4,693,859	−510,269	10,000,000	88,424.550	148,088.217	48,801	409.989
Log Transform total charges	4,689,047	1.76	16.12	10.859	0.978	10.797	0.287

Note: Descriptive statistics are shown for continuous study variables using weighted estimates from the 2021 Nationwide Readmissions Database. Age was approximately normally distributed and is presented in its raw form. Length of stay and total hospital charges exhibited substantial positive skewness and were log‐transformed for regression analyses; both raw and log transformed values are shown for descriptive purposes. M = mean; SD = standard deviation; Skew = skewness; Kurt = kurtosis.

Abbreviation: M = mean; SD = standard deviation; Skew = skewness; Kurt = kurtosis.

To assess multicollinearity among the demographic, clinical, and structural characteristics, Tolerance and Variance Inflation Factor (VIF) values were examined. As shown in Table 5, all VIF values were well below the common threshold of 2.0, indicating no evidence of collinearity among variables included in the regression model.

TABLE 5.

Collinearity diagnostics for independent variables.

	Tolerance	VIF
Age	0.804	1.243
Sex	0.972	1.028
Diabetes	0.990	1.010
COPD	0.987	1.013
Length of stay	0.583	1.717
Discharge disposition	0.929	1.076
Insurance payer type	0.859	1.164
Median household income	0.867	1.153
Hospital bed size	0.981	1.019
Hospital teaching status	0.780	1.281
Hospital geographic region (urban/rural)	0.713	1.403
Total charges	0.581	1.720

Note: Tolerance and VIF values were calculated to assess multicollinearity among independent variables.

Bivariate analyses

To examine unadjusted relationships between demographic, clinical, and selected hospital characteristics and 30‐day hospital readmission among patients with HF, bivariate analyses were conducted. Pearson's chi‐square tests were used for categorical variables, and one‐way analysis of variance (ANOVA) was applied to continuous variables. As shown in Table 6, statistically significant associations were observed across multiple demographic, clinical, and hospital characteristics.

TABLE 6.

Bivariate associations between patient and hospital characteristics and 30‐day readmission.

	30‐day readmission
	Total	No	Yes	p
Sex				<0.001
Male	2,458,910 (52.4)	1,920,306 (52.3)	538,604 (52.8)
Female	2,234,949 (47.6)	1,753,273 (47.7)	481,676 (47.2)
Diabetes
No	2,426,359 (51.7)	1,938,860 (52.8)	487,499 (47.8)	<0.001
Yes	2,267,500 (48.3)	1,734,719 (47.2)	532,781 (52.2)
COPD
No	3,163,926 (67.4)	2,519,798 (68.6)	644,128 (63.1)	<0.001
Yes	1,529,933 (32.6)	1,153,781 (31.4)	376,152 (36.9)
Discharge disposition
Routine discharge to home or self	2,155,111 (45.9)	1,736,803 (47.3)	418,308 (41.0)
Transfer to a short‐term hospital	48,792 (1.0)	34,072 (0.9)	14,720 (1.4)
Transfer to other facilities	1,087,672 (23.2)	840,205 (22.9)	247,467 (24.3)	<0.001
Discharge to home health care	1,310,533 (27.9)	1,005,288 (27.4)	305,245 (29.9)
Left against medical advice	89,713 (1.9)	55,173 (1.5)	34,540 (3.4)
Discharged alive, unknown	2039 (0.0)	2039 (0.1)	0 (0.0)
Insurance payer type				<0.001
Medicare	3,469,612 (73.9)	2,711,726 (73.8)	757,886 (74.3)
Medicaid	506,474 (10.8)	373,728 (10.2)	132,746 (13.0)
Private insurance	512,486 (10.9)	422,268 (11.5)	90,218 (8.8)
Self‐pay	82,001 (1.7)	67,592 (1.8)	14,409 (1.4)
No charge	7647 (0.2)	6204 (0.2)	1443 (0.1)
Other	115,638 (2.5)	92,061 (2.5)	23,577 (2.3)
Median household income				<0.001
$1–$51,999	1,520,250 (32.4)	1,169,621 (31.8)	350,629 (34.4)
$52,000–$65,999	1,284,653 (27.4)	1,007,682 (27.4)	276,971 (27.1)
$66,000–$87,999	1,071,951 (22.8)	847,425 (23.1)	224,526 (22.0)
$88,000 or more	817,006 (17.4)	648,852 (17.7)	168,154 (16.5)
Hospital bed size				<0.001
Small	906,098 (19.3)	713,796 (19.4)	192,302 (18.8)
Medium	1,225,862 (26.1)	958,463 (26.1)	267,399 (26.2)
Large	2,561,899 (54.6)	2,001,320 (54.5)	560,579 (54.9)
Hospital geographic location
Large metropolitan areas with at least 1 million residents	2,560,395 (54.5)	1,985,441 (54.0)	574,954 (56.4)	<0.001
Small metropolitan areas with less than 1 million residents	1,687,883 (36.0)	1,331,334 (36.2)	356,549 (34.9)
Micropolitan areas	337,916 (7.2)	269,300 (7.3)	68,616 (6.7)
Not metropolitan or micropolitan	107,667 (2.3)	87,506 (2.4)	20,161 (2.0)

Note: Values represent weighted frequencies and column percentages based on the 2021 Nationwide Readmissions Database. Bivariate associations were assessed using Pearson's chi‐square tests for categorical variables. Table 6 presents selected demographic, clinical, and hospital characteristics associated with 30‐day readmission; hospital teaching status is described in Table 3 and retained in the multivariable regression model (Table 8). Percentages may not sum to 100 due to rounding.

Patient sex was significantly associated with 30‐day readmission. Males experienced slightly higher readmission rates (52.4%) compared to females (47.6%), χ²(1, 4,693,859) = 85.364, p < 0.001. Insurance payer type was also significantly associated with readmission, with patients insured through Medicare accounting for a greater proportion of readmissions (74.3%) compared to those with Medicaid (13.0%) and private insurance (8.8%), χ²(5, 4,693,859) = 12,114.463, p < 0.001. Socioeconomic status, measured using median household income quartiles, was similarly associated with readmission, with patients from the lowest income quartile comprising a larger proportion of readmissions than those from the highest income quartile.

Discharge disposition was significantly associated with 30‐day readmission outcomes. Patients who left against medical advice had the highest proportion of readmissions (3.4%), despite representing a small percentage of overall discharges (1.9%). Patients discharged to home health care also experienced higher readmission rates (29.9%) compared to those not readmitted (27.4%). In contrast, patients discharged routinely to home or self‐care had a lower proportion of 30‐day readmissions (41.0%) relative to their overall discharge distribution (45.9%).

Comorbid conditions were significantly associated with readmission status. Patients with diabetes were more likely to experience a 30‐day readmission (52.2%) compared to those without diabetes (47.8%). Similarly, patients with COPD demonstrated higher readmission rates (36.9%) compared to patients without COPD (31.4%), consistent with increased clinical complexity. Additionally, 15.7% of patients had diagnoses of both diabetes and COPD.

Hospital characteristics were also associated with readmission in bivariate analyses. Patients treated in large hospitals accounted for a greater proportion of 30‐day readmissions compared to those treated in small or medium‐sized hospitals. With respect to geographic location, a higher proportion of readmitted patients were hospitalized in large metropolitan areas (56.4%) compared to other hospital geographic categories.

To further examine differences in continuous variables by readmission status, one‐way ANOVA tests were conducted for age, length of stay, and total hospital charges. As shown in Table 7, all three variables differed significantly between patients who were and were not readmitted (p < 0.001). However, effect sizes were small (η² ≤ .002), indicating that these differences, while statistically significant due to the large sample size, explained only a small proportion of variance in readmission outcomes.

TABLE 7.

Differences in age, length of stay, and total charges by 30‐day readmission status.

	30‐day readmission
	No		Yes
	M	SD	M	SD	df1	df2	F	p	η 2
Age at admission	71.13	13.701	69.57	13.819	1	4693857	10343.060	<0.001	0.002
Length of stay	6.60	8.241	7.39	8.897	1	4693857	7111.475	<0.001	0.002
Total charges	87235.49	144545.57	92736.70	160066.538	1	4693857	1102.381	<0.001	0.000

Note: One‐way analysis of variance (ANOVA) was used to compare mean values between patients who were and were not readmitted.

Abbreviation: M = mean; SD = standard deviation; df = degrees of freedom; F = F‐statistic; p = significance level; η ² = effect size.

Multivariate analyses

To assess adjusted associations between demographic, clinical, structural, and socioeconomic variables and 30‐day readmission, a multivariable logistic regression model was conducted. Table 8 presents adjusted odds ratios (aOR), confidence intervals (CI), and significant values for all variables. Overall model discrimination was modest (AUC = 0.589; Brier score = 0.167), consistent with an explanatory analysis using administrative data rather than an individual‐level predictive model.

TABLE 8.

Association of demographic, clinical, COPD, and hospital‐level factors with 30‐day readmissions among HF patients in urban versus rural populations.

	95% CI
	aOR	Lower	Upper	SE	p
Age	0.989	0.989	0.990	0.000	<0.001
Sex
Male	Reference
Female	0.976	0.971	0.980	0.002	<0.001
Diabetes
No	Reference
Yes	1.181	1.175	1.186	0.002	<0.001
COPD
No	Reference
Yes	1.258	1.252	1.264	0.002	<0.001
Both diabetes and COPD
No	Reference
Yes	0.960 0.951 0.969 0.005 <0.001
Length of stay	1.246	1.240	1.251	0.002	<0.001
Discharge disposition
Routine discharge to home or self‐care	Reference
Transfer to a short‐term hospital	1.836	1.800	1.873	0.010	<0.001
Transfer to other facilities	1.159	1.152	1.166	0.003	<0.001
Discharge to home health care	1.224	1.217	1.231	0.003	<0.001
Left against medical advice (AMA)	2.419	2.385	2.453	0.007	<0.001
Discharged alive, destination unknown†	0.000	0.000	.	891.17	0.982
Insurance payer type
Medicare	Reference
Medicaid	1.005	0.996	1.015	0.005	0.265
Private insurance	0.684	0.677	0.691	0.005	<0.001
Self‐pay	0.653	0.636	0.670	0.013	<0.001
No charge	0.762	0.707	0.821	0.038	<0.001
Other	0.791	0.772	0.811	0.012	<0.001
Median household income
$1–$51,999	Reference
$52,000–$65,999	0.952	0.946	0.957	0.003	<0.001
$66,000–$87,999	0.921	0.916	0.927	0.003	<0.001
$88,000 or more	0.917	0.911	0.924	0.004	<0.001
Hospital bed size
Small	Reference
Medium	1.034	1.027	1.041	0.003	<0.001
Large	1.034	1.028	1.041	0.003	<0.001
Hospital teaching status
Metropolitan non‐teaching	Reference
Metropolitan teaching	0.994	0.988	1.000	0.003	0.043
Hospital geographic location
Large metropolitan	Reference
Small metropolitan	0.910	0.905	0.915	0.003	<0.001
Micropolitan	0.824	0.814	0.833	0.006	<0.001
Not metropolitan or micropolitan	0.745	0.731	0.759	0.010	<0.001
Insurance payer by geographic location
Large metropolitan with Medicare	Reference
Small metropolitan with Medicaid	0.983	0.968	0.998	0.008	0.024
Small metropolitan with private insurance	1.037	1.019	1.054	0.008	<0.001
Small metropolitan with self‐pay	0.970	0.932	1.008	0.020	0.124
Small metropolitan with no charge	0.915	0.808	1.035	0.063	0.159
Small metropolitan with other	1.137	1.101	1.174	0.016	<0.001
Micropolitan with Medicaid	0.984	0.954	1.015	0.016	0.313
Micropolitan with private insurance	1.109	1.074	1.146	0.017	<0.001
Micropolitan with self‐pay	1.077	1.006	1.152	0.035	0.032
Micropolitan with no charge	0.511	0.374	0.698	0.159	<0.001
Micropolitan with other	1.143	1.088	1.200	0.025	<0.001
Rural with Medicaid	0.954	0.898	1.013	0.031	0.122
Rural with private insurance	1.082	1.018	1.150	0.031	0.012
Rural with self‐pay	1.301	1.143	1.480	0.066	<0.001
Rural with no charge	1.830	1.059	3.161	0.279	0.030
Rural with other	1.284	1.164	1.417	0.050	<0.001
Total charges	0.933	0.931	0.936	0.002	<0.001
Constant	0.728	0.703	0.754	0.018	<0.001

Note: CI = confidence interval; aOR = adjusted odds ratio; SE = standard error; p = significance level (2‐sided). Estimates are based on pooled, weighted analyses using imputed data from the 2021 NRD. Interaction terms between insurance payer type and hospital geographic location (urban vs. rural) were included to test for effect modification.

†Category had low frequency (n = 2039; 0.04%), resulting in unstable estimates.

Younger age (aOR = 0.989, 95% CI [0.989, 0.990], p < 0.001) and female sex (aOR = 0.976, 95% CI [0.971, 0.980], p < 0.001) were associated with lower odds of readmission. Diabetes (aOR = 1.181, 95% CI [1.175, 1.186], p < 0.001) and COPD (aOR = 1.258, 95% CI [1.252, 1.264], p < 0.001) were both significantly associated with increased odds of readmission. Patients with both diabetes and COPD had slightly lower odds of readmission (aOR = 0.960, 95% CI [0.951, 0.969], p < 0.001). Longer hospital length of stay was also associated with increased odds (aOR = 1.246, 95% CI [1.240, 1.251], p < 0.001).

Discharge disposition was a significant predictor of readmission. Compared to patients discharged home without services, those transferred to a short‐term hospital (aOR = 1.836, 95% CI [1.800, 1.873], p < 0.001), to other facilities (aOR = 1.159, 95% CI [1.152, 1.166], p < 0.001), or with home health care (aOR = 1.224, 95% CI [1.217, 1.231], p < 0.001) had higher odds of readmission. Patients discharged against medical advice had the highest odds (aOR = 2.419, 95% CI [2.385, 2.453], p < 0.001). The category “discharged alive, destination unknown” could not be estimated due to low frequency and model convergence issues.

Among insurance payer types, patients with private insurance (aOR = 0.684, 95% CI [0.675, 0.693], p < 0.001), self‐pay (aOR = 0.653, 95% CI [0.636, 0.670], p < 0.001), no charge (aOR = 0.762, 95% CI [0.707, 0.821], p < 0.001), and other payer types (aOR = 0.791, 95% CI [0.772, 0.811], p < 0.001) had lower odds of readmission compared to those with Medicare. Medicaid was not significantly different (aOR = 1.005, 95% CI [0.996, 1.015], p = 0.265).

Median household income was inversely associated with readmission risk. Compared to patients in the lowest income quartile, those in the second (aOR = 0.952, 95% CI [0.946, 0.957], p < 0.001), third (aOR = 0.921, 95% CI [0.916, 0.927], p < 0.001), and highest quartile (aOR = 0.917, 95% CI [0.911, 0.924], p < 0.001) had significantly lower odds of readmission.

Compared to small hospitals, medium (aOR = 1.034, 95% CI [1.027, 1.041], p < 0.001) and large hospitals (aOR = 1.034, 95% CI [1.028, 1.041], p < 0.001) were associated with higher odds of readmission. Hospital teaching status demonstrated a statistically significant but clinically negligible association with 30‐day readmission (aOR = 0.994, 95% CI [0.988, 1.000], p = 0.043).

Geographic location was associated with readmission risk. Compared to patients in large metropolitan hospitals, those in small metropolitan (aOR = 0.910, 95% CI [0.905, 0.915], p < 0.001), micropolitan (aOR = 0.824, 95% CI [0.814, 0.833], p < 0.001), and rural (aOR = 0.745, 95% CI [0.731, 0.759], p < 0.001) hospitals had lower odds of readmission.

Interaction terms revealed variation by insurance payer and geographic location. The interaction between hospital geographic location and insurance payer type was statistically significant (p < 0.001), indicating that the association between insurance status and 30‐day readmission differed across rural and non‐rural hospital settings. Among patients in rural hospitals, those with self‐pay (aOR = 1.301, 95% CI [1.143, 1.480], p < 0.001) and no‐charge status (aOR = 1.830, 95% CI [1.059, 3.161], p = 0.030) had significantly higher odds of readmission compared to the large metropolitan Medicare reference group. Other notable interactions included higher odds for micropolitan patients with private insurance (aOR = 1.109, 95% CI [1.074, 1.146], p < 0.001) and lower odds for micropolitan patients with no‐charge status (aOR = 0.511, 95% CI [0.374, 0.698], p < 0.001), though not all combinations were statistically significant. Total hospital charges were inversely associated with readmission (aOR = 0.933, 95% CI [0.931, 0.936], p < 0.001).

DISCUSSION

This study examined predictors of 30‐day HF readmissions and assessed how associations differ between rural and urban hospitals. The findings reveal that while rural hospitals were associated with lower average odds of readmission overall, this protective effect did not extend to patients without adequate insurance coverage. In fact, the highest observed readmission risks occurred among rural patients with self‐pay or no‐charge status, underscoring a sharp vulnerability at the intersection of geographic and financial disadvantage. ⁶ , ⁷

The “no charge” designation in administrative datasets reflects patients for whom hospitals did not expect payment often due to charity care, clinical trials, or extraordinary socioeconomic circumstances. The finding that those in rural hospitals face the highest risk of readmission suggests that structural barriers to continuity of care may be most pronounced in settings with limited financial safety nets. This is consistent with prior research demonstrating that rural hospitals operate with fewer care coordination resources, constrained staffing capacity, and reduced access to outpatient specialty care. ⁴ , ⁶ While previous readmission models have focused primarily on clinical predictors such as age, comorbidity burden, and discharge disposition, the findings in this study suggest that insurance payer status and hospital geographic location play an equally important role, especially when considered together. ¹⁰ , ¹⁴ Among clinical variables, both diabetes and COPD were independently associated with increased readmission risk. Of note, 15.7% of patients had both conditions, representing a substantial burden of multimorbidity. Interestingly, despite the elevated risk linked to each condition individually, patients with both diabetes and COPD had slightly lower odds of 30‐day readmission compared to those without both diagnoses (aOR = 0.960, 95% CI [0.951, 0.969], p < 0.001). This may reflect closer monitoring and/or higher baseline acuity triggering additional post‐discharge services. This finding aligns with previous research in which patients with both diabetes and COPD had a 4.55‐fold increased risk of developing chronic HF and a 3.72‐fold increased risk of all‐cause mortality compared to individuals without either condition. ¹⁵

Prior research examining discharge disposition reported that patients discharged to home with home health care (HHC) had significantly higher risk of 90‐day readmission or mortality compared to those discharged home without these services (44.2% vs. 36.9%, HR = 1.27, 95% CI [1.24, 1.31]). ¹⁶ These results align with the present study's findings that discharge with HHC was associated with increased odds of 30‐day readmission relative to routine discharge, even after adjusting for clinical and structural factors. Although HHC is intended to support recovery, it may be an indicator for ongoing disease severity or insufficient transitional planning. These findings underscore the clinical complexity and post‐discharge vulnerabilities of patients with overlapping cardiopulmonary and metabolic conditions, particularly in settings with limited outpatient care infrastructure or rural health access barriers.

Hospital teaching status was not associated with a clinically meaningful reduction in readmission risk. This contrasts with assumptions in prior studies suggesting that non‐teaching hospitals may lack the specialized services that support HF management. ¹⁷ Similarly, although rural hospitals demonstrated lower odds of readmission overall, this may reflect differences in admission thresholds, greater reliance on observation status, or care transfers that obscure true readmission rates. Prior literature has raised concerns about the validity of readmission metrics in rural contexts, where transportation barriers and post‐acute care shortages may affect care‐seeking behavior and hospital utilization. ⁸ , ¹⁸

The inverse association observed between total hospital charges and 30‐day readmission should be interpreted cautiously. In this analysis, hospital charges function as a proxy for resource intensity during the index hospitalization rather than a direct measure of disease severity or quality of care. Higher charges may reflect more extensive diagnostic evaluation, longer inpatient monitoring, or greater use of multidisciplinary services, which could plausibly reduce short‐term readmission risk. However, because hospital charges are influenced by payer mix, hospital billing practices, and regional pricing structures, they should be viewed as contextual indicators rather than causal drivers of readmission outcomes.

These findings support Donabedian's structure–process–outcome framework by illustrating how the structural context of care, including geographic location, hospital characteristics, and financial access, shapes both discharge processes and patient outcomes. ⁹ , ¹⁹ In rural hospitals, where flexibility in care transitions is often limited and community‐based services are sparse, financial vulnerability becomes a critical driver of risk. These patterns are unlikely to be addressed through readmission penalties alone and may, in fact, be worsened if structural disadvantage is not included in risk adjustment methodologies. ²⁰

The elevated readmission risk among patients who are self‐pay and no‐charge in rural hospitals is a clear signal that targeted interventions are needed. Strategies may include expanding eligibility for transitional care programs regardless of insurance payer type, investing in rural care coordination infrastructure, and increasing access to telehealth‐supported HF follow‐up services. ² , ⁵ However, these approaches stand in stark contrast to recent federal efforts aimed at reducing Medicare and Medicaid spending, which risk undermining the very programs needed to support vulnerable rural populations. ²¹ Without attention to these systemic gaps, patients at the intersection of geographic and economic disadvantage will continue to face avoidable harm.

Limitations

This study used a large, nationally representative dataset and applied rigorous analytic methods, including survey weighting and multiple imputation, to enhance validity and generalizability. However, several limitations should be acknowledged. The dataset is based on hospital discharge data from 28 states rather than all 50 states and excludes federal facilities, which may limit national representativeness. In addition, the analysis relied on administrative data that lack granular clinical detail, such as HF phenotype, disease severity measures, medication adherence, and social supports, all of which may influence readmission risk. The dataset also does not capture out‐of‐hospital mortality, which may lead to underestimation of readmissions among patients who died before follow‐up.

Although insurance payer type and median household income were included as proxies for socioeconomic status, more granular measures, such as education, housing instability, employment status, and neighborhood‐level deprivation, were unavailable. The NRD also does not capture behavioral risk factors, including smoking and substance use, nor does it include measures of patient travel distance or transportation barriers, which may differentially influence readmission risk in rural settings. State‐level variation in Medicare policy and care delivery could not be examined, as the NRD is not designed for state‐specific policy analyses.

Although prior inpatient admissions were considered in defining eligible index hospitalizations, the dataset does not allow distinction between first‐ever and recurrent HF admissions. Moreover, because a single‐year NRD dataset does not permit tracking of hospitalizations that occurred in prior calendar years, prior HF history may be incompletely captured, potentially influencing baseline readmission risk. Additionally, although hospitalizations during the COVID‐19 pandemic were included in the 2021 dataset, COVID‐19 infection status was not available as a covariate and may have influenced discharge planning or readmission patterns.

Because total hospital charges reflect downstream care processes and institutional billing practices, their inclusion as a covariate may introduce residual confounding despite adjustment for other clinical and structural variables. The modest model discrimination (AUC = 0.589) reflects the exploratory and explanatory purpose of the analysis and the absence of granular clinical and social variables in administrative data; the model was not intended for individual‐level risk prediction. Finally, no formal adjustment for multiple comparisons was applied, and findings should be interpreted as exploratory rather than confirmatory.

CONCLUSIONS

This study examined how demographic, clinical, structural, and socioeconomic factors influenced the likelihood of 30‐day hospital readmissions among patients with HF across urban and rural settings. Although rural hospitals were associated with lower overall readmission rates, this pattern masked important subgroup disparities. Patients with self‐pay or no‐charge status treated in rural hospitals experienced the highest odds of readmission, highlighting the compounded effects of financial vulnerability and geographic context. Unlike prior readmission models that treat insurance status and geography as independent predictors, this analysis demonstrates that these factors interact to amplify readmission risk, identifying a subgroup of patients for whom standard readmission metrics and interventions may be least effective.

These findings underscore the need for tailored interventions in rural hospitals, particularly for patients with limited or no insurance coverage. Expanding transitional care services regardless of payer type, implementing inpatient risk screening, and strengthening partnerships with community‐based organizations may improve continuity of care after discharge. From a policy perspective, readmission penalties and reimbursement models that do not account for insurance payer type or rural hospital resource constraints may inadvertently exacerbate disparities; risk adjustment strategies should better reflect the intersecting challenges of rural care delivery and socioeconomic disadvantage.

Future research should examine community‐level infrastructure, including transportation access, telehealth capacity, and public health resources, as potential modifiers of rural readmission risk. Mixed‐methods approaches may further clarify how structural gaps in care transitions disproportionately affect uninsured and under‐resourced populations. Reducing HF readmissions in rural settings will ultimately require care models and policies that explicitly account for both geographic and financial vulnerability.

FUNDING

The authors received no financial support for the research, authorship, and/or publication of this article.

CONFLICT OF INTEREST STATEMENT

The authors declare that they have no conflict of interest with respect to the research, authorship, and/or publication of this article.

DATA AVAILABILITY STATEMENT

The data that support the findings of this study are available from the corresponding author upon reasonable request.