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Impact of an integrated mother-preterm infant intervention on birth hospitalization charges.


ABSTRACT: OBJECTIVE:To examine whether the H-HOPE (Hospital to Home: Optimizing the Preterm Infant's Environment) intervention reduced birth hospitalization charges yielding net savings after adjusting for intervention costs. STUDY DESIGN:One hundred and twenty-one mother-preterm infant dyads randomized to H-HOPE or a control group had birth hospitalization data. Neonatal intensive care unit costs were based on billing charges. Linear regression, propensity scoring and regression analyses were used to describe charge differences. RESULTS:Mean H-HOPE charges were $10,185 lower than controls (p?=?0.012). Propensity score matching showed the largest savings of $14,656 (p?=?0.003) for H-HOPE infants, and quantile regression showed a savings of $13,222 at the 75th percentile (p?=?0.015) for H-HOPE infants. Cost savings increased as hospital charges increased. The mean intervention cost was $680 per infant. CONCLUSIONS:Lower birth hospitalization charges and the net cost savings of H-HOPE infants support implementation of H-HOPE as the standard of care for preterm infants.

SUBMITTER: Vonderheid SC 

PROVIDER: S-EPMC7253350 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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Impact of an integrated mother-preterm infant intervention on birth hospitalization charges.

Vonderheid Susan C SC   Park Chang G CG   Rankin Kristin K   Norr Kathleen F KF   White-Traut Rosemary R  

Journal of perinatology : official journal of the California Perinatal Association 20200108 6


<h4>Objective</h4>To examine whether the H-HOPE (Hospital to Home: Optimizing the Preterm Infant's Environment) intervention reduced birth hospitalization charges yielding net savings after adjusting for intervention costs.<h4>Study design</h4>One hundred and twenty-one mother-preterm infant dyads randomized to H-HOPE or a control group had birth hospitalization data. Neonatal intensive care unit costs were based on billing charges. Linear regression, propensity scoring and regression analyses w  ...[more]

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