Pediatric Inpatient-Status Volume and Cost at Children's and Nonchildren's Hospitals in the United States: 2000-2009

Do Unpaid Children's Hospital Account Balances Correlate with Family Income or Insurance Type?

Background

Current understanding of medical debt among various income ranges and insurance carriers is limited. We analyzed median household incomes, insurance carriers, and medical debt of plastic surgery patients at a major metropolitan children's hospital.

Methods

A retrospective chart review for zip codes, insurance carriers, and account balances was conducted for 2018-2021. All patients were seen by members of the Division of Pediatric Plastic Surgery at Ann and Robert H. Lurie Children's Hospital of Chicago. Blue Cross was reported separately among other commercial insurance carriers by the hospital's business analytics department. Median household income by zip code was obtained. IBM SPSS Statistics was used to perform chi-squared tests to study the distribution of unpaid account balances by income ranges and insurance carriers.

Results

Of the 6877 patients, 630 had unpaid account balances. Significant differences in unpaid account balances existed among twelve insurance classes (P < 0.001). There were significant differences among unpaid account balances when further examined by median household income ranges for Blue Cross (P < 0.001) and other commercial insurance carriers (P < 0.001).

Conclusions

Although patients with insurance policies requiring higher out-of-pocket costs (ie, Blue Cross and other commercial insurance carriers) are generally characterized by higher household incomes, these patients were found to have higher unpaid account balances than patients with public insurance policies. This suggests that income alone is not predictive of unpaid medical debt and provides greater appreciation of lower income families who may make a more consistent effort in repaying their medical debt.

Identification and assessment of a comprehensive set of structural factors associated with hospital costs in Switzerland

Structural factors can influence hospital costs beyond case-mix differences. However, accepted measures on how to distinguish hospitals with regard to cost-related organizational and regional differences are lacking in Switzerland. Therefore, the objective of this study was to identify and assess a comprehensive set of hospital attributes in relation to average case-mix adjusted costs of hospitals. Using detailed hospital and patient-level data enriched with regional information, we derived a list of 23 cost predictors, examined how they are associated with costs, each other, and with different hospital types, and identified principal components within them. Our results showed that attributes describing size, complexity, and teaching-intensity of hospitals (number of beds, discharges, departments, and rate of residents) were positively related to costs and showed the largest values in university (i.e., academic teaching) and central general hospitals. Attributes related to rarity and financial risk of patient mix (ratio of rare DRGs, ratio of children, and expected loss potential based on DRG mix) were positively associated with costs and showed the largest values in children's and university hospitals. Attributes characterizing the provision of essential healthcare functions in the service area (ratio of emergency/ ambulance admissions, admissions during weekends/ nights, and admissions from nursing homes) were positively related to costs and showed the largest values in central and regional general hospitals. Regional attributes describing the location of hospitals in large agglomerations (in contrast to smaller agglomerations and rural areas) were positively associated with costs and showed the largest values in university hospitals. Furthermore, the four principal components identified within the hospital attributes fully explained the observed cost variations across different hospital types. These uncovered relationships may serve as a foundation for objectifying discussions about cost-related heterogeneity in Swiss hospitals and support policymakers to include structural characteristics into cost benchmarking and hospital reimbursement.

Technology-Dependent Pediatric Inpatients at Children's Versus Nonchildren's Hospitals

BACKGROUND AND OBJECTIVE

Technology-dependent children (TDC) are admitted to both children's hospitals (CHs) and nonchildren's hospitals (NCHs), where there may be fewer pediatric-specific specialists or resources. Our objective was to compare the characteristics of TDC admitted to CHs versus NCHs.

METHODS

This was a multicenter, retrospective study using the 2012 Kids' Inpatient Database. We included patients aged 0 to 18 years with a tracheostomy, gastrostomy, and/or ventricular shunt. We excluded those who died, were transferred into or out of the hospital, had a length of stay (LOS) that was an extreme outlier, or had missing data for key variables. We compared patient and hospital characteristics across CH versus NCH using χ² tests and LOS and cost using generalized linear models.

RESULTS

In the final sample of 64 521 discharges, 55% of discharges of TDC were from NCHs. A larger proportion of those from CHs had higher disease severity (55% vs 49%; P < .001) and a major surgical procedure during hospitalization (28% vs 24%; P < .001). In an adjusted generalized linear model, the mean LOS was 4 days at both hospital types, but discharge from a CH was associated with a higher adjusted mean cost ($16 754 vs $12 023; P < .001).

CONCLUSIONS

Because the majority of TDC are hospitalized at NCHs, future research on TDC should incorporate NCH settings. Further studies should investigate if some may benefit from regionalization of care or earlier transfer to a CH.

Assessment of social factors influencing hospitalization cost of US preterm newborns, 2016

Background and objective: Preterm newborn (PTN) care is costly in the US, and the preterm birth (PTB) rate has not seen substantial improvement. PTB remains a significant public health issue because of risks of adverse health outcomes and the associated economic burden. Assessing factors leading to high hospital cost is imperative in order to ease economic burden associated with PTB. Social factors influencing hospitalization cost for PTN have not been studied extensively. This study aims to examine the contributions of hospital type, race/ethnicity, and median household income quartile to the cost of the PTN's initial hospitalization after birth.Materials and methods: Data used in this study originated from the 2016 Healthcare Cost and Utilization Project Kids' Inpatient Database (KID) which provided the most recent national records of pediatric hospitalization in the US. International Classification of Diseases, Tenth Revision, Clinical Modification (ICD-10-CM) codes P07.2x and P07.3x were used to identify birth gestational age (GA) in complete weeks. Q00-Q99 codes were used to identify congenital anomalies. Data records reflecting hospitalization of newborns without congenital anomalies were extracted to estimate total cost in 2016. Analysis of factors influencing total cost was performed on the subset of newborns that were in-born and with birth GA from 24 to 36 weeks. Total hospital cost was calculated by multiplying total charges and cost-to-charge ratios. Per-diem cost was calculated by dividing total hospital cost by length of stay. Linear regression modeling was performed using weighted data, and the best model was selected using the Akaike information criteria. Results: Hospitalization cost for PTNs, who constituted 7.6% of all studied newborns, comprised 45% of total newborn hospitalization cost. PTNs in freestanding children's hospitals had significantly higher total cost compared to non-children's teaching and non-teaching hospitals. The cost difference was GA-dependent. The latter two hospital types reported similar cost for PTNs. Although race/ethnicity and median household income quartile were related, the two covariates had independent effects on total hospitalization cost. A model built to assess the roles of covariates in the association between GA and total hospitalization cost found that all three covariates independently affected total cost when controlling for one another, with hospital type also showing an interaction effect with GA.Conclusions: Hospitalization cost is highly influenced by hospital type, race/ethnicity, and median household income quartile. The modifiable aspects of these factors may be further dissected and targeted in order to ease the burden of high hospitalization cost associated with PTB.