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Am J Perinatol
DOI: 10.1055/a-2251-6238
Original Article

Validation of a Costing Algorithm and Cost Drivers for Neonates Admitted to the Neonatal Intensive Care Unit

Elias Jabbour
1   Division of Neonatology, Department of Pediatrics, McGill University Health Center Research Institute, Montreal, Canada
,
Sharina Patel
1   Division of Neonatology, Department of Pediatrics, McGill University Health Center Research Institute, Montreal, Canada
2   Division of Neonatology, Department of Pediatrics, McGill University Health Center, Montreal, Canada
,
Guy Lacroix
3   Department of Economics, University of Laval, Montreal, Canada
,
Petros Pechlivanoglou
4   Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
,
Prakesh S. Shah
4   Child Health Evaluative Sciences, The Hospital for Sick Children, Toronto, Canada
5   Maternal-Infant Care Research Centre and Department of Pediatrics, Mount Sinai Hospital, Toronto, Canada
,
Marc Beltempo
1   Division of Neonatology, Department of Pediatrics, McGill University Health Center Research Institute, Montreal, Canada
2   Division of Neonatology, Department of Pediatrics, McGill University Health Center, Montreal, Canada
,
On behalf of the Canadian Preterm Birth Network Investigators Canadian Neonatal Network Investigators › Author Affiliations Funding M.B. holds an Early Career Investigator Grant from the CIHR Institute of Human Development, Child and Youth Health (IHDCYH), a research grant funding from the FRSQ Clinical Research Scholar Career Award Junior 1, and an Early Career Investigator Grant from the Montreal Children's Hospital Foundation.
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Abstract

Objective Neonatal intensive care units (NICUs) account for over 35% of pediatric in-hospital costs. A better understanding of NICU expenditures may help identify areas of improvements. This study aimed to validate the Canadian Neonatal Network (CNN) costing algorithm for seven case-mix groups with actual costs incurred in a tertiary NICU and explore drivers of cost.

Study Design A retrospective cohort study of infants admitted within 24 hours of birth to a Level-3 NICU from 2016 to 2019. Patient data and predicted costs were obtained from the CNN database and were compared to actual obtained from the hospital accounting system (Coût par Parcours de Soins et de Services). Cost estimates (adjusted to 2017 Canadian Dollars) were compared using Spearman correlation coefficient (rho).

Results Among 1,795 infants included, 169 (9%) had major congenital anomalies, 164 (9%) with <29 weeks' gestational age (GA), 189 (11%) with 29 to 32 weeks' GA, and 452 (25%) with 33 to 36 weeks' GA. The rest were term infants: 86 (5%) with hypoxic–ischemic encephalopathy treated with therapeutic hypothermia, 194 (11%) requiring respiratory support, and 541 (30%) admitted for other reasons. Median total NICU costs varied from $6,267 (term infants admitted for other reasons) to $211,103 (infants born with <29 weeks' GA). Median daily costs ranged from $1,613 to $2,238. Predicted costs correlated with actual costs across all case-mix groups (rho range 0.78–0.98, p < 0.01) with physician and nursing representing the largest proportion of total costs (65–82%).

Conclusion The CNN algorithm accurately predicts NICU total costs for seven case-mix groups. Personnel costs account for three-fourths of in-hospital total costs of all infants in the NICU.

Key Points

  • Very preterm infants born below 33 weeks of gestation account for most of NICU resource use.

  • Human resources providing direct patient care represented the largest portion of costs.

  • The algorithm strongly predicted total costs for all case-mix groups.

Keywords

costing - preterm infants - neonatal intensive care unit - algorithm - term infants

Note

Retrospective cohort study validating a case costing algorithm and determining cost drivers of admission in the NICU. Preterm infants are the highest resource users within the NICUs. There are limited studies that compared costing estimates per patient using different algorithms. Use of case-mix groups, patient-level costs, and dividing length of stay into tertiles of stay provide insight on resource use within NICUs. The CNN algorithm strongly predicts total costs of admission for all populations admitted within tertiary-level NICUs. Personnel, namely physician and nurses, remained the highest contributors to cost in the NICU.


Authors' Contributions

E.J. led the study design progress, data acquisition, statistical analyses, and drafted the manuscript. S.P., G.L., and P.P. assisted in study design and data acquisition. P.S.S. oversaw data collection validity and original algorithm development. M.B. designed the study, supervised, and facilitated data collection, monitored analysis, and communicated insightful reviews of the manuscript. All authors have approved the final draft and agreed to be accountable for all aspects of their work.


Supplementary Material



Publication History

Received: 24 August 2023

Accepted: 19 January 2024

Accepted Manuscript online:
23 January 2024

Article published online:
16 February 2024

© 2024. Thieme. All rights reserved.

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