Search

Objective: To quantify the degree to which health care service area (HCSA) definitions captured hospitalizations and heterogeneity in social determinants of health (SDOH). Data Sources and Study Setting: Geospatial data from the Centers for Medicare and Medicaid Services, the Census Bureau, and the Dartmouth Institute. Drive-time isochrones from MapBox. Area Deprivation Index (ADI) data. 2017 inpatient discharge data from Arizona, Florida, Iowa, Maryland, Nebraska, New Jersey, New York, and Wisconsin, State Emergency Department Databases and State Inpatient Databases, Healthcare Cost and Utilization Project, Agency for Healthcare Research and Quality; and Fee-For-Service Medicare data in 48 states. Study Design: Cross-sectional, descriptive analysis. Data Collection/Extraction Methods: The capture rate was the percentage of inpatient discharges occurring in the same HCSA as the hospital. We compared capture rates for each HCSA definition for different populations and by hospital type. We measured SDOH heterogeneity using the coefficient of variation of the ADI among ZIP codes within each HCSA. Principal Findings: HCSA definitions captured a wide range of inpatient discharges, ranging from 20% to 50% for Public Use Microdata Areas (PUMAs) to 93%-97% for Metropolitan Statistical Areas (MSAs). Three-quarters of inpatient discharges were from facilities within the same county as the patient's residential ZIP code, while nearly two-thirds were within the same Hospital Service Area. From the hospital perspective, 74.7% of inpatient discharges originated from within a 30-min drive and 90.1% within a 60-min drive. Capture rates were the lowest for teaching hospitals. PUMAs and drive-time-based HCSAs encompassed more homogenous populations while MSAs, Commuting Zones, and Hospital Referral Regions captured the most variation. Conclusions: The proportion of hospital discharges captured by each HCSA varied, with MSAs capturing the highest proportion of discharges and PUMAs capturing the lowest. Additionally, researchers face a trade-off between capture rate and population homogeneity when deciding which HCSA to use. KEYWORDS access/demand/utilization of services, geographic/spatial factors/small-area variations, hospitals, social determinants of health, 1 | INTRODUCTION The geography of health care utilization is important to the substance, methods, and impact of health policy and health services research. For example, geographic service area boundaries [...]

Hospitalizations for children with medical complexity (CMC) have increased substantially over the past 2 decades and constitute a disproportionate percentage of hospitalization rates and costs among children. We sought to describe the etiology and utilization for hospitalizations of CMC using the Pediatric Clinical Classification System (PECCS).Using the 2019 Kids’ Inpatient Database, we classified hospitalizations for CMC using the PECCS, which groups diagnoses into mutually exclusive, pediatric-specific categories. For the medical, surgical, and medical/surgical PECCS clinical groups, we reported diagnosis groups accounting for ≥1% of hospital encounters for that group. We described admission frequency, cost, payer, length of stay, and mortality rates within each diagnosis grouping using survey-weighted statistics.We identified 2 315 743 nonlivebirth hospitalizations, of which 712 139 (30.8%) were for CMC. Most (94.4%) hospitalizations occurred at a teaching hospital. Medical diagnosis comprised most hospitalizations (69.2%), whereas hospitalizations for surgical and medical/surgical conditions had a higher median cost. The most common diagnosis groups overall were encounters for chemotherapy, diabetic ketoacidosis, and respiratory failure, whereas the costliest were for necrotizing enterocolitis, transposition of the great vessels, and hypoplastic left heart syndrome.We evaluated the most common diagnoses and their associated resource use for hospitalized CMC using the PECCS, providing a more granular view on the etiology, utilization, cost, and outcomes of hospitalizations for CMC. These topics represent high-impact areas for further research and quality efforts for CMC.

Objective: To create definitions for complications for 16 serious pediatric conditions using the International Classification of Diseases, 10th Revision, Clinical Modification or Procedure Coding System (ICD-10-CM/PCS), and to assess whether complication rates are similar to those measured with ICD-9-CM/PCS. Data Sources: The Healthcare Cost and Utilization Project State Emergency Department and Inpatient Databases from five states between 2014 and 2017 were used to identify cases and assess complication rates. Incidences were calculated using population counts from the 5-year American Community Survey. Data Collection/Extraction Methods: Patients were identified by the presence of a diagnosis code for one of the 16 serious conditions. Only the first encounter for a given condition by a patient was included. Encounters resulting in transfer were excluded as the presence of complications was unknown. Study Design: We defined complications using data elements routinely available in administrative databases including ICD-10-CM/PCS codes. The definitions were adapted from ICD-9-CM/PCS using general equivalence mappings and refined using consensus opinion. We included 16 serious conditions: appendicitis, bacterial meningitis, compartment syndrome, new-onset diabetic ketoacidosis (DKA), ectopic pregnancy, empyema, encephalitis, intussusception, mastoiditis, myocarditis, orbital cellulitis, ovarian torsion, sepsis, septic arthritis, stroke, and testicular torsion. Using data from children under 18 years, we compared incidences and complication rates across the ICD-10-CM/PCS transition for each condition using interrupted time series. Principal Findings: There were 61314 ED visits for a serious condition; the most common was appendicitis (n = 37 493). Incidence rates for each condition were not significantly different across the ICD-10-CM/PCS transition for 13/16 conditions. Three differed: empyema (increased 42%), orbital cellulitis (increased 60%), and sepsis (increased 26%). Complication rates were not significantly different for each condition across the ICD-10-CM/PCS transition, except appendicitis (odds ratio 0.62, 95% CI 0.57-0.68), DKA (OR 3.79, 95% CI 1.92-7.50), and orbital cellulitis (OR 0.53, 95% CI 0.30-0.95). Conclusions: For most conditions, incidences and complication rates were similar before and after the transition to ICD-10-CM/PCS codes, suggesting our system identifies complications of conditions in administrative data similarly using ICD-9-CM/PCS and ICD-10-CM/PCS codes. This system may be applied to screen for cases with complications and in health services research. KEYWORDS administrative data, claims data, complications, outcomes, pediatrics, 1 | INTRODUCTION Efficient diagnosis and management of emergency conditions is a core function of emergency departments (EDs). Understanding and comparing how health systems perform in this role requires accurate [...]