Regional Variation in Standardized Costs of Care at Children’s Hospitals
BACKGROUND: Though regional variation in healthcare spending has received national attention, it has not been widely studied in pediatrics.
OBJECTIVES: (1) To evaluate regional variation in costs of care for 3 inpatient pediatric conditions, (2) assess potential drivers of variation, and (3) estimate cost savings from reducing variation.
DESIGN/SETTING/PATIENTS: Retrospective cohort study of hospitalizations for asthma, diabetic ketoacidosis (DKA), and acute gastroenteritis (AGE) at 46 children’s hospitals from October 2014 to September 2015.
INTERVENTION/MEASUREMENTS: Variation in trimmed standardized costs were assessed within and across regions. Linear mixed effects models were adjusted for patient- and encounter-level variables to assess drivers of variation.
RESULTS: After adjusting for patient-level factors, variation remained. Using census division clusters, mean trimmed and adjusted total standardized costs were 120% higher for asthma ($1920 vs $4227), 46% higher for DKA ($7429 vs $10,881), and 150% higher for AGE ($3316 vs $8292) in the highest-cost compared with the lowest-cost region. Comparing hospitals in the same region, standardized costs were significantly different (P < 0.001) for each condition in each region. Drivers of variation were encounter-level variables including length of stay and intensive care unit utilization. For this cohort, annual savings from reducing variation would equal $69.1 million at the interregional level and $25.2 million at the intraregional level.
CONCLUSIONS: Pediatric hospital costs vary between and within regions. Future studies should examine how much of this variation is avoidable. To the extent that less spending does not compromise outcomes, care models may be adjusted to eliminate unwarranted variation and reduce costs.
© 2017 Society of Hospital Medicine
For the hospital-level analysis, differences in “other” remained the largest driver of cost variation. For asthma, “other” explained 61% of variation, while pharmacy, laboratory, and imaging each accounted for <8%. For DKA, differences in imaging accounted for 18% of the variation and laboratory charges accounted for 12%. For AGE, imaging accounted for 15% of the variation. Adding the 4 cost components to the other patient- and encounter-level covariates, the model explained 81%, 72%, and 67% of the variation for asthma, DKA, and AGE.
Cost Savings
If all hospitals in this cohort with adjusted standardized costs above the national PHIS average achieved costs equal to the national PHIS average, estimated annual savings in adjusted standardized costs for these 3 conditions would be $69.1 million. If each hospital with adjusted costs above the average within its census region achieved costs equal to its regional average, estimated annual savings in adjusted standardized costs for these conditions would be $25.2 million.
DISCUSSION
This study reported on the regional variation in costs of care for 3 conditions treated at 46 children’s hospitals across 7 geographic regions, and it demonstrated that variations in costs of care exist in pediatrics. This study used standardized costs to compare utilization patterns across hospitals and adjusted for several patient-level demographic and illness-severity factors, and it found that differences in costs of care for children hospitalized with asthma, DKA, and AGE remained both between and within regions.
These variations are noteworthy, as hospitals strive to improve the value of healthcare. If the higher-cost hospitals in this cohort could achieve costs equal to the national PHIS averages, estimated annual savings in adjusted standardized costs for these conditions alone would equal $69.1 million. If higher-cost hospitals relative to the average in their own region reduced costs to their regional averages, annual standardized cost savings could equal $25.2 million for these conditions.
The differences observed are also significant in that they provide a foundation for exploring whether lower-cost regions or lower-cost hospitals achieve comparable quality outcomes.28 If so, studying what those hospitals do to achieve outcomes more efficiently can serve as the basis for the establishment of best practices.29 Standardizing best practices through protocols, pathways, and care-model redesign can reduce potentially unnecessary spending.30
Our findings showed that patient-level demographic and illness-severity covariates, including community-level HHI and SOI, did not consistently explain cost differences. Instead, LOS and ICU utilization were associated with higher costs.17,19 When considering the effect of the 4 cost components on the variation in total standardized costs between regions and between hospitals, the fact that the “other” category accounted for the largest percent of the variation is not surprising, because the cost of room occupancy and nursing services increases with longer LOS and more time in the ICU. Other individual cost components that were major drivers of variation were laboratory utilization for asthma and imaging for DKA and AGE31 (though they accounted for a much smaller proportion of total adjusted costs).19
To determine if these factors are modifiable, more information is needed to explain why practices differ. Many factors may contribute to varying utilization patterns, including differences in capabilities and resources (in the hospital and in the community) and patient volumes. For example, some hospitals provide continuous albuterol for status asthmaticus only in ICUs, while others provide it on regular units.32 But if certain hospitals do not have adequate resources or volumes to effectively care for certain populations outside of the ICU, their higher-value approach (considering quality and cost) may be to utilize ICU beds, even if some other hospitals care for those patients on non-ICU floors. Another possibility is that family preferences about care delivery (such as how long children stay in the hospital) may vary across regions.33
Other evidence suggests that physician practice and spending patterns are strongly influenced by the practices of the region where they trained.34 Because physicians often practice close to where they trained,35,36 this may partially explain how regional patterns are reinforced.
Even considering all mentioned covariates, our model did not fully explain variation in standardized costs. After adding the cost components as covariates, between one-third and one-fifth of the variation remained unexplained. It is possible that this unexplained variation stemmed from unmeasured patient-level factors.
In addition, while proxies for SES, including community-level HHI, did not significantly predict differences in costs across regions, it is possible that SES affected LOS differently in different regions. Previous studies have suggested that lower SES is associated with longer LOS.37 If this effect is more pronounced in certain regions (potentially because of differences in social service infrastructures), SES may be contributing to variations in cost through LOS.
Our findings were subject to limitations. First, this study only examined 3 diagnoses and did not include surgical or less common conditions. Second, while PHIS includes tertiary care, academic, and freestanding children’s hospitals, it does not include general hospitals, which is where most pediatric patients receive care.38 Third, we used ZIP code-based median annual HHI to account for SES, and we used ZIP codes to determine the distance to the hospital and rural-urban location of patients’ homes. These approximations lack precision because SES and distances vary within ZIP codes.39 Fourth, while adjusted standardized costs allow for comparisons between hospitals, they do not represent actual costs to patients or individual hospitals. Additionally, when determining whether variation remained after controlling for patient-level variables, we included SOI as a reflection of illness-severity at presentation. However, in practice, SOI scores may be assigned partially based on factors determined during the hospitalization.18 Finally, the use of other regional boundaries or the selection of different hospitals may yield different results.
