Video Abstract

Video Abstract

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BACKGROUND AND OBJECTIVES:

Hospitals treating patients with greater diagnosis diversity may have higher fixed and overhead costs. We assessed the relationship between hospitals’ diagnosis diversity and cost per hospitalization for children.

METHODS:

Retrospective analysis of 1 654 869 all-condition hospitalizations for children ages 0 to 21 years from 2816 hospitals in the Kids’ Inpatient Database 2016. Mean hospital cost per hospitalization, Winsorized and log-transformed, was assessed for freestanding children’s hospitals (FCHs), nonfreestanding children’s hospitals (NFCHs), and nonchildren’s hospitals (NCHs). Hospital diagnosis diversity index (HDDI) was calculated by using the D-measure of diversity in Shannon–Wiener entropy index from 1254 diagnosis and severity-of-illness groups distinguished with 3M Health’s All Patient Refined Diagnosis Related Groups. Log-normal multivariable models were derived to regress hospital type on cost per hospitalization, adjusting for hospital-level HDDI in addition to patient-level demographic (eg, age, race and ethnicity) and clinical (eg, chronic conditions) characteristics and hospital teaching status.

RESULTS:

Admission counts were 383 789 (23.2%) in FCHs, 588 463 (35.6%) in NFCHs, and 682 617 (41.2%) in NCHs. Unadjusted mean cost per hospitalization was $10 757 (95% confidence interval [CI]: $9451 to $12 243) in FCHs, $6264 (95% CI: $5830 to $6729) in NFCHs, and $4192 (95% CI: $4121 to $4265) in NCHs. HDDI was significantly (P < .001) higher in FCHs and NFCHs (median 9.2 and 6.4 times higher, respectively) than NCHs. Across all hospitals, greater HDDI was associated (P = .002) with increased cost. Adjusting for HDDI resulted in a nonsignificant (P = .1) difference in cost across hospital types.

CONCLUSIONS:

Greater diagnosis diversity was associated with increased cost per hospitalization and should be considered when assessing associated costs of inpatient care for pediatric patients.

What’s Known on This Subject:

Hospitals vary substantially in the diversity of diagnoses treated for pediatric patients. Hospitals treating patients with greater diagnosis diversity may have higher fixed and overhead costs, resulting in higher average costs per hospitalization.

What This Study Adds:

Greater diagnosis diversity was associated with increased cost per hospitalization. Hospitals’ diagnosis diversity is important to consider when assessing associated costs of inpatient care for pediatric patients.

Hospital care accounts for one-third of total US health care spending and over 5% of US gross domestic product.1  Studies in hospitalized adult patients report dramatic variation in cost-per-case across hospitals, ranging from 150% to 300%, which persists after adjustment for differences in patient severity of illness (SOI).2,3  Similar variation across hospitals, including children’s and nonchildren’s hospitals (NCHs), has been reported for pediatric patients.46  As a result, hospital care has been historically targeted for cost containment under the assumption that some hospital care might be excessive, overpriced, or associated with substandard quality of care.7,8 

Largely absent from assessments of hospital expenditures, the diversity of diagnoses treated by a hospital may also influence cost of hospitalization. Increased outpatient health care costs correlate with the range of specialized health service conducted by a clinical practice.9,10  The specialization of personnel and treatments required to handle the diagnosis diversity may lead to higher direct costs, including salaries and training for clinical staff as well as purchasing, maintenance, and administration of expensive medical supplies and medications. Additionally, circumstances involving the limited supply of, and minimal competition among, manufacturers and vendors of highly specialized treatments might contribute to increase cost of care.11  Specialization may require adjustments to building infrastructure and maintenance to support specific populations and equipment. An example of this would be the need for highly purified air for immune-compromised patients, such as bone marrow transplant recipients.

Most reasons for hospitalization in children occur because of a limited set of common health problems (eg, bronchiolitis, gastroenteritis) that are associated with low severity and complexity of illness, mainstay treatments, fast recovery, and modest health care costs. Most pediatric clinicians, and most hospitals that admit children, are equipped to provide high quality of care for these conditions. Fewer pediatric clinicians are equipped to provide high quality of care for children with rare, high severity, complex health problems (eg, solid-organ transplant, cystic fibrosis exacerbation) that require specialized care and treatment at tertiary referral centers such as children’s hospitals (CHs). Consequently, CHs likely treat more diverse diagnoses, including rare conditions, with high severity and complexity, as well as common diagnoses that are typically managed at NCHs.

To evaluate this possibility that hospital-level differences in diagnostic diversity may be associated with differences in costs, the current study sought to (1) assess the association between hospitals’ diversity of diagnoses and cost per hospitalization of their pediatric patients and (2) measure how much this diversity explains the variation in cost per hospitalization across different hospital types, including CHs and NCHs.

This is a retrospective analysis of hospitalizations for children age 0 to 21 years in the Agency for Healthcare Research and Quality, Healthcare Cost and Utilization Project Kids’ Inpatient Database (KID) 2016. The 2016 KID database contains ∼3.1 million hospitalizations in 2016, representing an estimated 6.3 million hospital discharges nationally; the KID draws on data from 4200 hospitals in 46 states and Washington DC.12  The study cohort was derived with the following discharge exclusions: (1) missing diagnosis or cost data (n = 40 034, 0.6%), (2) healthy newborn care (n = 1 044 084, 16.7%), and (3) labor and delivery care (n = 243 505, 3.9%). In addition, hospitals, and all of their accompanying discharges, were excluded if they (1) had a low volume (<10 annually) of pediatric discharges (n = 922 hospitals [2995 discharges]) or (2) were specialty hospitals that predominately admitted 1 type of health problem (n = 412 hospitals [121 122 discharges]) (Supplemental Fig 3). This analysis of publicly available deidentified data did not constitute human subjects research.

Hospital cost per hospitalization was the main outcome measure. Hospital costs were estimated from charges by using hospital-specific cost-to-charge ratios provided by Healthcare Cost and Utilization Project as a supplement to the 2016 KID. Cost estimates associated with a specific hospitalization were estimated by multiplying the total charges reported in the data by the appropriate hospital-specific cost-to-charge ratio and then adjusting for price factors beyond a hospital’s control by using their area wage index. Costs per discharge were Winsorized at the fifth and 95th percentiles (ie, costs lower than the fifth percentile were set to the fifth percentile, and costs over the 95th percentile were set to the 95th percentile) and then log-transformed to mitigate the effect of outliers and nonnormality, respectively. The geometric mean (ie, the back-transformed mean from the log state) of each hospital’s cost per hospitalization (across all hospitalized children in each hospital) was then calculated.

Cost per hospitalization was compared across 3 types of acute-care hospital: freestanding children’s hospital (FCHs), nonfreestanding children’s hospital (NFCHs), and NCHs. FCHs were (1) labeled FCH by the KID data element, KID_STRATUM and (2) had ≥2550 annual volume of pediatric admissions. All acute-care, general CHs had ≥2550 admissions in 2016 (correspondence Children’s Hospital Association). There were 16 KID_STRATUM-identified FCHs that did not reach this admission threshold. Those 16 hospitals were small, specialty CHs that admitted children with a select group of patients (eg, orthopedic rehabilitation patients). The discharges (n = 10 804) from those hospitals were therefore removed from analysis. NFCHs were hospitals with ≥2550 annual pediatric admissions and no freestanding designation by the KID_STRATUM data element. The remaining hospitals were designated NCHs.

The main independent variable was hospitals’ diagnosis diversity experienced by their hospitalized patients. To assess this diversity, we used 3M Health System’s All Patient Refined Diagnosis Related Groups (APR-DRG). APR-DRGs assess the array the International Classification of Diseases, 10th Revision, Clinical Modification codes (up to 25 per admission) assigned to each hospitalization at discharge and categorize the hospitalization into 1 of 316 mutually exclusive diagnosis groups (128 surgical and 188 medical). APR-DRGs encompass the spectrum of health problems necessitating hospitalization, including ones that are common, rare, simple, and complex to treat. For reach diagnosis group, the APR-DRGs also assign 1 of 4 subclasses (minor, moderate, major, and extreme) that indicate SOI.

Using the APR-DRGs, we calculated a hospital diagnosis diversity index (HDDI) for each hospital by using the Shannon–Wiener entropy index, a concept adapted from ecology that measures the complexity of an ecosystem.13,14  We used the D-measure of diversity based on Shannon–Wiener entropy index H.14  First, we used the possible APR-DRG diagnosis groups (n = 316) and SOI (n = 4) to calculate the effective number (maximum = 1264) of total diagnosis variety treated by each hospital. HDDI increased with increasing variety. Second, we assessed the proportion of total bed days for all inpatient admissions attributable to each diagnosis. HDDI increased with decreasing proportions of bed days attributable to each diagnosis. The HDDI formula is:

formula

where = proportion of bed days attributed to ith diagnosis, categorized by APR-DRG SOI, and = the number of APR-DRG SOI combinations (maximum = 1254).

An example of a low-diversity hospital is one that treated 4 diagnoses that each accounted for 25% of that hospital’s bed days (HDDI = 4). In contrast, a high-diversity hospital is one that treated 400 diagnoses that each accounted for 0.25% of the hospital’s bed days (HDDI = 400).

We assessed patient demographic characteristics, including age (neonatal [0–28 days], infant [29–364 days], preschool [1–4 years], primary school [5–12 years], adolescence [13–18 years], and young adulthood [19–21 years]), sex (male, female), race and ethnicity (non-Hispanic white, non-Hispanic Black, Hispanic, Asian American or Pacific Islander, other), payer (public, private, other), median family income by zip code, elective versus nonelective admission, and transfer status (ie, transferred versus not from another hospital at admission). We assessed patient’s clinical characteristics, including case-mix index (using APR-DRGs15 ) and the presence of complex chronic conditions (CCCs).16,17  Discharges of children with a CCC were classified by using Feudtner’s International Classification of Diseases, 10th Revision, Clinical Modification diagnosis classification scheme.16  CCCs represent defined diagnosis groupings expected to last longer than 12 months and involve either a single organ system severely enough to require specialty pediatric care and hospitalization or multiple organ systems.17  We also assessed hospitals’ teaching status, assigned from membership in the Council of Teaching Hospitals or having residency training approved by the Accreditation Council for Graduate Medical Education.

In bivariable analysis, we used χ2 tests to assess differences in characteristics by hospital type (FCH, NFCH, NCH). We used Wilcoxon rank tests to assess differences in median HDDI by hospital type. To assess the bivariable association of HDDI and cost per hospitalization, we regressed HDDI on the geometric, unadjusted, log-transformed mean cost per hospitalization across all hospitals. We used a cubic model for this analysis because model fit statistics and visual inspection (Fig 1) revealed that the HDDI-cost relationship was nonlinear.

FIGURE 1

Bivariable association of cost per hospitalization and hospital-level diagnosis diversity for all-condition pediatric admissions in US hospitals. The y-axis displays Winsorized, log-transformed, unadjusted, mean hospital cost per hospitalization for all-condition admissions at FCHs (dark dots), NFCHs (dark gray dots), and NCHs (light gray dots). The x-axis displays HDDI, calculated as number of distinct APR-DRG diagnosis groups and SOI levels (1254 possible) with the proportion of total bed days within each APR-DRG SOI as proportional abundances. The solid black line is the cubic regression line for the association between HDDI and cost per hospitalization.

FIGURE 1

Bivariable association of cost per hospitalization and hospital-level diagnosis diversity for all-condition pediatric admissions in US hospitals. The y-axis displays Winsorized, log-transformed, unadjusted, mean hospital cost per hospitalization for all-condition admissions at FCHs (dark dots), NFCHs (dark gray dots), and NCHs (light gray dots). The x-axis displays HDDI, calculated as number of distinct APR-DRG diagnosis groups and SOI levels (1254 possible) with the proportion of total bed days within each APR-DRG SOI as proportional abundances. The solid black line is the cubic regression line for the association between HDDI and cost per hospitalization.

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To assess variation in cost per hospitalization by hospital type, we derived a series of nested log-normal regression models. In the first model, we regressed hospital type (FCH, NFCH, and NCH) on the geometric mean cost per hospitalization without case-mix adjustments. In the second model, we adjusted for patient-level demographic (eg, age, sex, race and ethnicity, insurance, income), clinical characteristics (eg, presence of CCCs), and hospital teaching status. We then retransformed adjusted cost estimates by hospital type using Duan’s smearing estimator.18  This resource intensity adjustment was applied to all patients, irrespective of the hospital at which they were treated. In the third model, we estimated the mean of the log-transformed patient-level, resource intensity–adjusted costs for each hospital, with additional hospital-level adjustment for HDDI, assuming a cubic relationship between HDDI and log-costs. None of the hospital-level models were fit with both hospital-level HDDI and hospital-level resource intensity covariates. Covariance estimates from the log-normal models were used to assess how much variation in cost per hospitalization was explained independently by all of the covariates included in each model. The statistical significance threshold was P < .05. All analyses were performed by using SAS, Version 9.4 (SAS Institute, Inc, Cary, NC).

The analytic sample included 2816 hospitals: 1.6% (n = 46) were in FCHs, 5.3% (n = 150) were in NFCHs, and 93.0% (n = 2620) were in NCHs (Table 1). There were 1 654 869 discharges across all hospitals, including 383 789 (23.2%) in FCHs, 588 463 (35.6%) in NFCHs, and 682 617 (41.2%) in NCHs.

TABLE 1

Demographic Characteristics of Hospitalizations by Type of Hospital

CharacteristicAll HospitalsCHsNCHs
FreestandingNonfreestanding
No. discharges, n (%) 1 654 869 383 789 (23.2) 588 463 (35.6) 682 617 (41.2) 
Age at admission, n (%)     
 0–29 d 345 819 (20.9) 34 311 (8.9) 98 978 (16.8) 212 530 (31.1) 
 30–364 d 193 458 (11.7) 61 433 (16) 74 364 (12.6) 57 661 (8.4) 
 1–4 y 268 153 (16.2) 88 091 (23) 106 811 (18.2) 73 251 (10.7) 
 5–12 y 309 282 (18.7) 108 361 (28.2) 126 328 (21.5) 74 593 (10.9) 
 13–18 y 360 589 (21.8) 83 538 (21.8) 137 357 (23.3) 139 694 (20.5) 
 19–21 y 177 568 (10.7) 8055 (2.1) 44 625 (7.6) 124 888 (18.3) 
Sex, n (%)     
 Male 864 875 (52.3) 205 065 (53.4) 311 045 (52.9) 348 765 (51.1) 
 Female 789 436 (47.7) 178 558 (46.5) 277 288 (47.1) 333 590 (48.9) 
 Missing 558 (0.0) 166 (0.0) 130 (0.0) 262 (0.0) 
Race and ethnicity, n (%)     
 White 758 537 (45.8) 153 155 (39.9) 279 295 (47.5) 326 087 (47.8) 
 Black 273 689 (16.5) 51 957 (13.5) 111 890 (19) 109 842 (16.1) 
 Hispanic 325 004 (19.6) 87 158 (22.7) 110 241 (18.7) 127 605 (18.7) 
 Asian American or PI 51 407 (3.1) 11 926 (3.1) 19 071 (3.2) 20 410 (3) 
 American Indian 14 794 (0.9) 1479 (0.4) 4805 (0.8) 8510 (1.2) 
 Other 83 514 (5) 13 977 (3.6) 36 792 (6.3) 32 745 (4.8) 
 Missing 147 924 (8.9) 64 137 (16.7) 26 369 (4.5) 57 418 (8.4) 
Payer, n (%)     
 Government 875 272 (52.9) 200 309 (52.2) 316 835 (53.8) 358 128 (52.5) 
 Private 653 235 (39.5) 155 372 (40.5) 231 237 (39.3) 266 626 (39.1) 
 Other 124 315 (7.5) 27 689 (7.2) 40 009 (6.8) 56 617 (8.3) 
 Missing 2047 (0.1) 419 (0.1) 382 (0.1) 1246 (0.2) 
CharacteristicAll HospitalsCHsNCHs
FreestandingNonfreestanding
No. discharges, n (%) 1 654 869 383 789 (23.2) 588 463 (35.6) 682 617 (41.2) 
Age at admission, n (%)     
 0–29 d 345 819 (20.9) 34 311 (8.9) 98 978 (16.8) 212 530 (31.1) 
 30–364 d 193 458 (11.7) 61 433 (16) 74 364 (12.6) 57 661 (8.4) 
 1–4 y 268 153 (16.2) 88 091 (23) 106 811 (18.2) 73 251 (10.7) 
 5–12 y 309 282 (18.7) 108 361 (28.2) 126 328 (21.5) 74 593 (10.9) 
 13–18 y 360 589 (21.8) 83 538 (21.8) 137 357 (23.3) 139 694 (20.5) 
 19–21 y 177 568 (10.7) 8055 (2.1) 44 625 (7.6) 124 888 (18.3) 
Sex, n (%)     
 Male 864 875 (52.3) 205 065 (53.4) 311 045 (52.9) 348 765 (51.1) 
 Female 789 436 (47.7) 178 558 (46.5) 277 288 (47.1) 333 590 (48.9) 
 Missing 558 (0.0) 166 (0.0) 130 (0.0) 262 (0.0) 
Race and ethnicity, n (%)     
 White 758 537 (45.8) 153 155 (39.9) 279 295 (47.5) 326 087 (47.8) 
 Black 273 689 (16.5) 51 957 (13.5) 111 890 (19) 109 842 (16.1) 
 Hispanic 325 004 (19.6) 87 158 (22.7) 110 241 (18.7) 127 605 (18.7) 
 Asian American or PI 51 407 (3.1) 11 926 (3.1) 19 071 (3.2) 20 410 (3) 
 American Indian 14 794 (0.9) 1479 (0.4) 4805 (0.8) 8510 (1.2) 
 Other 83 514 (5) 13 977 (3.6) 36 792 (6.3) 32 745 (4.8) 
 Missing 147 924 (8.9) 64 137 (16.7) 26 369 (4.5) 57 418 (8.4) 
Payer, n (%)     
 Government 875 272 (52.9) 200 309 (52.2) 316 835 (53.8) 358 128 (52.5) 
 Private 653 235 (39.5) 155 372 (40.5) 231 237 (39.3) 266 626 (39.1) 
 Other 124 315 (7.5) 27 689 (7.2) 40 009 (6.8) 56 617 (8.3) 
 Missing 2047 (0.1) 419 (0.1) 382 (0.1) 1246 (0.2) 

PI, Pacific Islander.

Median age at admission was 5 years (interquartile range [IQR]: 0–15). Age at admission varied across hospital type. For example, the percentage of neonatal discharges (0–28 days old) was 31.1%, 16.8%, and 8.9% and in FCHs, NCHs, and NFCHs, respectively (P < .001) (Table 1). Of all discharges, 47.7% were female, 45.8% were non-Hispanic white, and 52.9% were for children with public insurance. Although demographic characteristics (eg, sex and payer) varied significantly (P < .001 for all) by hospital type, the absolute differences in percentages were small across subcategories for these demographic attributes. FCHs had a larger percentage of discharges for individuals with CCCs (49.0%) than NFCHs (38.7%) and NCHs (23.7%), P < .001 (Table 2)

TABLE 2

Clinical Characteristics of Hospitalizations by Type of Hospital

CharacteristicAll HospitalsCHsNCHs
FreestandingNonfreestanding
Hospital births, n (%) 260 802 (15.8) 3187 (0.8) 70 917 (12.1) 186 698 (27.4) 
Transfers, n (%)     
 Admitted without transfer 1 429 676 (86.4) 318 233 (82.9) 486 260 (82.6) 625 183 (91.6) 
 Transferred from an acute-care facility 188 478 (11.4) 57 916 (15.1) 86 296 (14.7) 44 266 (6.5) 
 Transferred from a nonacute-care facility 29 931 (1.8) 7250 (1.9) 13 096 (2.2) 9585 (1.4) 
 Missing 6784 (0.4) 390 (0.1) 2811 (0.5) 3583 (0.5) 
CCC, n (%) 582 188 (35.2) 188 279 (49.1) 230 419 (39.2) 163 490 (24.0) 
Mortality, n (%) 14 614 (0.9) 3162 (0.8) 6046 (1.0) 5406 (0.8) 
CharacteristicAll HospitalsCHsNCHs
FreestandingNonfreestanding
Hospital births, n (%) 260 802 (15.8) 3187 (0.8) 70 917 (12.1) 186 698 (27.4) 
Transfers, n (%)     
 Admitted without transfer 1 429 676 (86.4) 318 233 (82.9) 486 260 (82.6) 625 183 (91.6) 
 Transferred from an acute-care facility 188 478 (11.4) 57 916 (15.1) 86 296 (14.7) 44 266 (6.5) 
 Transferred from a nonacute-care facility 29 931 (1.8) 7250 (1.9) 13 096 (2.2) 9585 (1.4) 
 Missing 6784 (0.4) 390 (0.1) 2811 (0.5) 3583 (0.5) 
CCC, n (%) 582 188 (35.2) 188 279 (49.1) 230 419 (39.2) 163 490 (24.0) 
Mortality, n (%) 14 614 (0.9) 3162 (0.8) 6046 (1.0) 5406 (0.8) 
TABLE 3

Diagnosis Diversity by Type of Hospital

CharacteristicAll HospitalsCHsNCHs
FreestandingNonfreestanding
No. diagnosis groups,a median (IQR)     
 All 41 (21–84) 252 (241–262) 224 (197–246) 38 (19–72) 
 Medical 33 (17–64) 150 (144–155) 141 (132–151) 30 (16–56) 
 Surgical 8 (3–19) 97 (86–102) 79 (63–93) 7 (3–15) 
Hospital diagnosis diversity index,b median (IQR) 29 (16–52) 249 (213–276) 173 (139–210) 27 (15–46) 
CharacteristicAll HospitalsCHsNCHs
FreestandingNonfreestanding
No. diagnosis groups,a median (IQR)     
 All 41 (21–84) 252 (241–262) 224 (197–246) 38 (19–72) 
 Medical 33 (17–64) 150 (144–155) 141 (132–151) 30 (16–56) 
 Surgical 8 (3–19) 97 (86–102) 79 (63–93) 7 (3–15) 
Hospital diagnosis diversity index,b median (IQR) 29 (16–52) 249 (213–276) 173 (139–210) 27 (15–46) 
a

Diagnosis groups were distinguished from 3M Health System’s APR-DRG. There were 316 possible diagnosis groups.

b

The HDDI was calculated number of distinct APR-DRG diagnosis groups and SOI levels (1254 possible), and we used the proportion of total bed days within each APR-DRG SOI as proportional abundances. HDDI is multiplicative; that is, if Hospital A has an HDDI value of 50 and Hospital B has an HDDI value of 100, Hospital B has twice as many equi-probable APR-DRG SOIs (ie, twice as much diagnosis diversity responsibility for the patients’ admissions) as Hospital A.

The median number of diagnosis groups (ie, APR-DRG SOI groups) was higher in FCHs (252; IQR: 241–262) and NFCHs (224; IQR: 197–246) than in NCHs (38; IQR: 19–72), P < .001 (Table 3). The most dramatic differences were observed for the number of surgical diagnosis groups: 97 (IQR 86–102) for FCHs, 79 (IQR 63–93) for NFCHs, and 7 (IQR 3–15) for NCHs. The median HDDI varied significantly (P < .001) by hospital type: 249 (IQR: 213–276) for FCHs, 173 (IQR: 139–210) for NFCHs, and 27 (IQR: 15–46) for NCHs. From these findings, the diversity of diagnosis groups was a median 9.2 times greater in FCHs versus NCHs (Table 3).

Across all hospitals, greater HDDI was associated (P = .002) with increased cost per hospitalization (Fig 1). Although the HDDI-cost association was nonlinear, in general greater diagnosis diversity was associated with higher cost per hospitalization. For example, HDDI of 25 and 250 was associated with cost per hospitalization of $3266 and $8977, respectively (Fig 1).

Unadjusted Analysis

Without adjusting for patient-level or hospital-level characteristics, the geometric mean cost per hospitalization varied significantly (P < .001) by hospital type: $10 757 (95% confidence interval [CI]: $9451 to $12 243) for FCHs, $6264 (95% CI: $5830 to $6729) for NFCHs, and $4192 (95% CI: $4121 to $4265) for NCHs. Hospital type explained 11.6% of the total variance in cost per hospitalization (Fig 2).

FIGURE 2

Multivariable analysis of cost per hospitalization by type of hospital. Displayed are the Winsorized, log-transformed, mean hospital cost per hospitalization for all-condition admissions at FCHs, NFCHs, and NCHs. Three log-normal linear models were derived to regress hospital type on cost per hospitalization with (1) no case-mix adjustment (ie, unadjusted; P < .001), (2) adjustment for patient-level demographic (age, sex, race and ethnicity, insurance, income) and clinical (severity, type, and number of chronic conditions) characteristics (P < .001), and (3) adjustment for those patient-level characteristics as well as the hospital-level diagnosis diversity index (P = .1).

FIGURE 2

Multivariable analysis of cost per hospitalization by type of hospital. Displayed are the Winsorized, log-transformed, mean hospital cost per hospitalization for all-condition admissions at FCHs, NFCHs, and NCHs. Three log-normal linear models were derived to regress hospital type on cost per hospitalization with (1) no case-mix adjustment (ie, unadjusted; P < .001), (2) adjustment for patient-level demographic (age, sex, race and ethnicity, insurance, income) and clinical (severity, type, and number of chronic conditions) characteristics (P < .001), and (3) adjustment for those patient-level characteristics as well as the hospital-level diagnosis diversity index (P = .1).

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Adjustments for Patient-Level Characteristics and Hospital Teaching Status

After those adjustments, the mean cost per hospitalization was attenuated by 9.0% in FCHs ($−967; 95% CI: −$691 to −$1302). In contrast, the adjustment increased (33.2% and 25.4%, respectively) the estimated cost per hospitalization in NFCHs (+$2803; 95% CI: $2018 to $2148) and in NCHs ($+1067; 95% CI: $1061 to $1072). Together, patient-level clinical and demographic characteristics and hospital teaching status explained 33.3% of the total variance in cost per hospitalization (Fig 2).

Multilevel Adjustments for Patient-Level Characteristics, Hospital Teaching Status, and Hospital-Level Diagnosis Diversity

After adding HDDI to the adjustment of patient-level characteristics and hospital teaching status, the adjusted cost per hospitalization decreased by 34.8% in FCHs (−$3406; 95% CI: −$3313 to −$3406) This adjustment significantly decreased the cost per hospitalization by an additional 32.7% in NFCHs (−$2727; 95% CI: −$2702 to −$2745) and had a modest effect (3.1% increase) on the cost per hospitalization in NCHs (−$161; 95% CI: −$158 to −$164). This adjustment resulted in a nonsignificant (P = .1) difference in cost by hospital type. Together, hospital type, the patient-level clinical and demographic characteristics, hospital teaching status, and HDDI explained 44.0% of the total variance in cost per hospitalization (Fig 2).

The current study advances understanding of the relationship between hospitals’ diagnosis diversity and associated costs for pediatric patients. This diversity contributed significantly to hospitals’ costs, adding explanatory power about the variance of cost beyond that provided by patient-level characteristics alone. Hospital-level diagnosis diversity had the largest influence on cost per hospitalization in CHs. Diversity adjustment at the hospital level lowered the patient-level adjusted costs in CHs by one-third. With hundreds of diagnosis groups treated, CHs had substantially higher diagnosis diversity than NCHs.

Further investigation is needed to understand the reasons why hospitals’ diagnosis diversity and cost per hospitalization are related. In a post hoc analysis, we excluded admissions from the top 10% of APR-DRG SOI combinations by mean cost (eg, solid-organ transplant, spinal fusion) and the results did not change: increased diversity remained associated with higher costs (P < .001). Therefore, the diversity itself appears to be an independent driver of cost, even when diagnoses that are associated with the most expensive hospitalizations are excluded. Hospitals striving to be full-service for the treatment of diverse pediatric diagnoses may require a broad and complex network of expensive, customized expenses related to highly specialized infrastructure, supplies, treatments, and staff. Clinical staff, in particular, are believed to be one of the largest contributors of hospitals’ operating costs.9  A variety of clinical personnel (eg, specialized physicians, nurses, therapists, technicians) on hospital staff is required to effectively care for patients with a diverse array of diagnoses. In addition, the continuous demand for hospitals to be equipped to treat a vast array of diagnoses may have necessitated increased supply and overhead costs, especially with the purchasing and maintenance of specialized medications, high-technology equipment (surgical and diagnostic), and therapies. This demand may have been particularly pertinent when no other local or regional hospital in proximity to the high-diversity hospital offered such specialized items.

The low complexity of patients and low diversity of diagnosis groups in NCHs may help explain why their costs were minimally influenced by adjustments for patient complexity and diversity. In another post hoc analysis, we remeasured diversity in NCHs when excluding admissions for infants age 0 to 29 days of age, who accounted for nearly one-third of admissions in NCHs and could have had low diagnosis diversity. This exclusion actually lowered the diagnosis diversity of NCHs. Complexity and diversity had a larger impact on costs in CHs, where the complexity and diversity of patients was much higher. Treatment of hundreds of rare conditions, both acute and chronic, was likely required to achieve the magnitude of diagnosis diversity measured across CHs in the current study. Further evaluation of the contribution of rare diagnoses to HDDI and to increased cost is warranted. It is possible that hospitals specializing in treating rare conditions may inevitably incur higher cost of care.

The framework used by hospitals to direct their clinical operations may also have influenced the relationship between diagnosis diversity and cost per hospitalization.19  Hospitals treating a limited set of diagnoses may be better positioned to operate as a “focused factory,” characterized by a uniform, engineered, evidence-based, and highly efficient approach to each diagnosis encountered.10  Hospitals treating a diverse array of diagnoses may operate more like a manufacturing “solution shop” in which creativity and calculated deviation from traditional approaches to treatment are essential to solve the rare and unusual health problems experienced by their patients.20  In-depth case studies that compare care processes, operation approaches, and cost of care in hospitals with low and high diagnosis diversity are warranted.

Hospitals’ diagnosis diversity may be an important attribute to consider when assessing other outcomes beyond cost per hospitalization, including length of stay and hospital readmission. Most traditional case-mix methods used to assess hospital performance focus predominately, and appropriately, on patient-level factors, including type and number of chronic conditions.21  Although useful, these methods are perceived to not fully capture the full spectrum of complexity and SOI experienced across a hospital’s population of patients.22  Hospital-level diagnosis diversity may help account for these attributes. In the current study, hospital-level diagnosis diversity did not behave as a “double adjuster” of hospital costs. Rather, it made a significant, independent contribution to the explanation of costs beyond that of patient-level factors. Additional study is necessary to understand the meaning and value of including hospital-level factors, such as diagnosis diversity, in case-mix adjustment, especially when comparing performance across different types of hospitals that care for children.

This study has several limitations. The nationally representative but limited administrative data in this study cannot be used to precisely determine if one type of hospital provides more or less expensive care than another. Because KID does not contain information on itemized cost, we could not assess the cost of specific aspects of clinical care (eg, pharmacy, medical equipment, personnel). Detailed clinical data (eg, electronic health data) may be preferable to administrative billing data to measure the type and number of patients’ chronic conditions as well as their reason for hospitalization. KID data cannot be used to fully account for SOI, functional status, social and family factors, and other attributes that may influence hospital cost. Specific information on hospital location (beyond US region) was not available in KID, and therefore, we could not assess how each hospital’s local market position may have been associated with hospital cost. Although we used a common definition of CHs in KID, there is no universally accepted standard or definition for a CH, and a different definition may yield different results. We excluded hospitals that appeared to be specialty hospitals, focused on a particular type or small set of admission diagnoses. Future investigations should explore diversity and cost in children’s specialty hospitals.

Despite these limitations, the findings from the current study may be useful in several ways. First, hospitals and payers may leverage the findings on diagnosis diversity to generate discussions about expected cost of inpatient care for children. Second, the methods and findings may prompt use of diagnosis diversity by quality improvement specialists and health services researchers for case-mix adjustment with additional, important aspects of inpatient care, beyond hospital cost, including efficiency, timeliness, and safety. Third, the findings may prompt hospitals with high diagnosis diversity to explore cross-cutting opportunities for cost containment through their approach to clinical operations. Fourth, pediatric health systems may benefit from exploring regional variation in the complexity and diversity of pediatric inpatient care across hospitals to understand the supply and demand for expensive, highly complex and diverse care.

Dr Berry made substantial contributions to conception and design, analysis, and interpretation of data and is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved; Drs Hall and Richardson made substantial contributions to conception and design, acquisition, analysis, and interpretation of data; Drs Cohen, Feudtner, Chiang, Chung, Gay, Shah, and Casto made substantial contributions to conception and design and interpretation of data; and all authors drafting the article and revising it critically for important intellectual content, and final approval of the version to be published.

FUNDING: No external funding.

COMPANION PAPER: A companion to this article can be found online at www.pediatrics.org/cgi/doi/10.1542/2020-043521.

     
  • APR-DRG

    All Patient Refined Diagnosis Related Groups

  •  
  • CCC

    complex chronic condition

  •  
  • CH

    children’s hospital

  •  
  • FCH

    freestanding children’s hospital

  •  
  • HDDI

    hospital diagnosis diversity index

  •  
  • IQR

    interquartile range

  •  
  • KID

    Kids’ Inpatient Database

  •  
  • NCH

    nonchildren’s hospital

  •  
  • NFCH

    nonfreestanding children’s hospital

  •  
  • SOI

    severity of illness

1
Centers for Medicare & Medicaid Services
.
National Health Expenditures 2017 Highlights
.
Baltimore, MD
:
Centers for Medicare & Medicaid Services
;
2017
2
Billig
JI
,
Lu
Y
,
Momoh
AO
,
Chung
KC
.
A nationwide analysis of cost variation for autologous free flap breast reconstruction
.
JAMA Surg
.
2017
;
152
(
11
):
1039
1047
3
Reinhardt
UE
.
The pricing of U.S. hospital services: chaos behind a veil of secrecy
.
Health Aff (Millwood)
.
2006
;
25
(
1
):
57
69
4
Gupta
RS
,
Bewtra
M
,
Prosser
LA
,
Finkelstein
JA
.
Predictors of hospital charges for children admitted with asthma
.
Ambul Pediatr
.
2006
;
6
(
1
):
15
20
5
Merenstein
D
,
Egleston
B
,
Diener-West
M
.
Lengths of stay and costs associated with children’s hospitals
.
Pediatrics
.
2005
;
115
(
4
):
839
844
6
Jen
HC
,
Shew
SB
.
The impact of hospital type and experience on the operative utilization in pediatric intussusception: a nationwide study
.
J Pediatr Surg
.
2009
;
44
(
1
):
241
246
7
Schwartz
WB
,
Mendelson
DN
.
Hospital cost containment in the 1980s. Hard lessons learned and prospects for the 1990s
.
N Engl J Med
.
1991
;
324
(
15
):
1037
1042
8
Wakeam
E
,
Molina
G
,
Shah
N
, et al
.
Variation in the cost of 5 common operations in the United States
.
Surgery
.
2017
;
162
(
3
):
592
604
9
Wu
VY
,
Shen
YC
,
Yun
MS
,
Melnick
G
.
Decomposition of the drivers of the U.S. hospital spending growth, 2001-2009
.
BMC Health Serv Res
.
2014
;
14
:
230
10
Skinner
W
.
The focused factory
.
Harv Bus Rev
.
1974
;
52
(
3
):
113
121
11
Luzzatto
L
,
Hyry
HI
,
Schieppati
A
, et al.;
Second Workshop on Orphan Drugs participants
.
Outrageous prices of orphan drugs: a call for collaboration
.
Lancet
.
2018
;
392
(
10149
):
791
794
12
Agency for Healthcare Research and Quality
. Overview of the Kids' Inpatient Database (KID).
2019
. Available at: www.hcup-us.ahrq.gov/kidoverview.jsp. Accessed January 16, 2020
13
Pourmohammadi
K
,
Shojaei
P
,
Rahimi
H
,
Bastani
P
.
Evaluating the health system financing of the Eastern Mediterranean Region (EMR) countries using Grey Relation Analysis and Shannon Entropy
.
Cost Eff Resour Alloc
.
2018
;
16
:
31
14
Rajaram
R
,
Castellani
B
.
An entropy based measure for comparing distributions of complexity
.
JPhysica A
.
2016
;
453
:
35
43
15
Richardson
T
,
Rodean
J
,
Harris
M
,
Berry
J
,
Gay
JC
,
Hall
M
.
Development of Hospitalization resource Intensity Scores for Kids (H-RISK) and comparison across pediatric populations
.
J Hosp Med
.
2018
;
13
(
9
):
602
608
16
Feudtner
C
,
Feinstein
JA
,
Zhong
W
,
Hall
M
,
Dai
D
.
Pediatric complex chronic conditions classification system version 2: updated for ICD-10 and complex medical technology dependence and transplantation
.
BMC Pediatr
.
2014
;
14
:
199
17
Feudtner
C
,
Christakis
DA
,
Connell
FA
.
Pediatric deaths attributable to complex chronic conditions: a population-based study of Washington State, 1980-1997
.
Pediatrics
.
2000
;
106
(
1 pt 2
):
205
209
18
Duan
N
.
Smearing estimate: a nonparametric retransformation method
.
Journal of the American Statistical Association
.
1983
;
78
(
383
):
605
610
19
Huckman
RS
.
Are you having trouble keeping your operations focused?
Harv Bus Rev
.
2009
;
87
(
9
):
90
95
20
Christensen
CMGJ
,
Hwang
J
.
The Innovator’s Prescription: A Disruptive Solution for Health Care
.
New York, NY
:
McGraw-Hill
;
2009
21
Söderlund
N
,
Milne
R
,
Gray
A
,
Raftery
J
.
Differences in hospital casemix, and the relationship between casemix and hospital costs
.
J Public Health Med
.
1995
;
17
(
1
):
25
32
22
Kansagara
D
,
Englander
H
,
Salanitro
A
, et al
.
Risk prediction models for hospital readmission: a systematic review
.
JAMA
.
2011
;
306
(
15
):
1688
1698

Competing Interests

POTENTIAL CONFLICT OF INTEREST: The authors have indicated they have no potential conflicts of interest to disclose.

FINANCIAL DISCLOSURE: The authors have indicated they have no financial relationships relevant to this article to disclose.

Supplementary data