Correlation of neonatal intensive care unit performance across multiple measures of quality of care

Jochen Profit, John A F Zupancic, Jeffrey B Gould, Kenneth Pietz, Marc A Kowalkowski, David Draper, Sylvia J Hysong, Laura A Petersen, Jochen Profit, John A F Zupancic, Jeffrey B Gould, Kenneth Pietz, Marc A Kowalkowski, David Draper, Sylvia J Hysong, Laura A Petersen

Abstract

Objectives: To examine whether high performance on one measure of quality is associated with high performance on others and to develop a data-driven explanatory model of neonatal intensive care unit (NICU) performance.

Design: We conducted a cross-sectional data analysis of a statewide perinatal care database. Risk-adjusted NICU ranks were computed for each of 8 measures of quality selected based on expert input. Correlations across measures were tested using the Pearson correlation coefficient. Exploratory factor analysis was used to determine whether underlying factors were driving the correlations.

Setting: Twenty-two regional NICUs in California.

Patients: In total, 5445 very low-birth-weight infants cared for between January 1, 2004, and December 31, 2007.

Main outcomes measures: Pneumothorax, growth velocity, health care-associated infection, antenatal corticosteroid use, hypothermia during the first hour of life, chronic lung disease, mortality in the NICU, and discharge on any human breast milk.

Results: The NICUs varied substantially in their clinical performance across measures of quality. Of 28 unit-level correlations, 6 were significant (ρ < .05). Correlations between pairs of measures of quality of care were strong (ρ ≥ .5) for 1 pair, moderate (range, ρ ≥ .3 to ρ < .5) for 8 pairs, weak (range, ρ ≥ .1 to ρ < .3) for 5 pairs, and negligible (ρ < .1) for 14 pairs. Exploratory factor analysis revealed 4 underlying factors of quality in this sample. Pneumothorax, mortality in the NICU, and antenatal corticosteroid use loaded on factor 1; growth velocity and health care-associated infection loaded on factor 2; chronic lung disease loaded on factor 3; and discharge on any human breast milk loaded on factor 4.

Conclusion: In this sample, the ability of individual measures of quality to explain overall quality of neonatal intensive care was modest.

Figures

Figure 1
Figure 1
Observed and expected probability of a neonatal intensive care unit (NICU) being in the top 4 ranks for each measure was not different from random variaBon (random binomial distribuBon). This indicates that NICUs are not consistent with regard to performance across 8 measures of quality. If NICUs that performed in the top 4 on one measure of quality also were more likely to perform well on other measures, we would see a U-shaped distribution.
Figure 2
Figure 2
Correlation between survival rank and the sum of neonatal intensive care unit (NICU) ranks across 7 measures of quality of care shows a trend indicating that NICUs performing well on survival tend to perform well on the other measures. This trend was not apparent in correlations between pairs of measures of quality of care and suggests that composite measurement may provide a better global picture of quality of care delivery.

Source: PubMed

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