Aim

To investigate the relationship between breastmilk feeding in very-low-birth-weight infants in the neonatal intensive care unit and breastmilk feeding rates for all newborns by hospital.

Methods

This was a cross-sectional study of 111 California hospitals in 2007 and 2008. Correlation coefficients were calculated between overall hospital breastfeeding rates and breastmilk feeding rates of very-low-birth-weight infants. Hospitals were categorized in quartiles by crude and adjusted very-low-birth-weight infant rates to compare rankings between measures.

Results

Correlation between breastmilk feeding rates of very-low-birth-weight infants and overall breastfeeding rates varied by neonatal intensive care unit level of care from 0.13 for intermediate hospitals to 0.48 for regional hospitals. For hospitals categorized in the top quartile according to overall breastfeeding rate, only 46% were in the top quartile for both crude and adjusted very-low-birth-weight infant rates. On the other hand, when considering the lowest quartile for overall breastfeeding hospitals, three of 27 (11%) actually were performing in the top quartile of performance for very-low-birth-weight infant rates.

Conclusions

Reporting hospital overall breastfeeding rates and neonatal intensive care unit breastmilk provision rates separately may give an incomplete picture of quality of care.

OBJECTIVE: To assess variation among hospitals on pediatric readmission and revisit rates and to determine the number of high- and low-performing hospitals. METHODS: In a retrospective analysis using the State Inpatient and Emergency Department Databases from the Healthcare Cost and Utilization Project with revisit linkages available, we identified pediatric (ages 1-20 years) visits with 1 of 7 common inpatient pediatric conditions (asthma, dehydration, pneumonia, appendicitis, skin infections, mood disorders, and epilepsy). For each condition, we calculated rates of all-cause readmissions and rates of revisits (readmission or presentation to the emergency department) within 30 and 60 days of discharge. We used mixed logistic models to estimate hospital-level risk-standardized 30-day revisit rates and to identify hospitals that had performance statistically different from the group mean. RESULTS: Thirty-day readmission rates were low (<10.0%) for all conditions. Thirty-day rates of revisit to the inpatient or emergency department setting ranged from 6.2% (appendicitis) to 11.0% (mood disorders). Study hospitals (n = 958) had low condition-specific visit volumes (37.0%-82.8% of hospitals had <25 visits). The only condition with >1% of hospitals labeled as different from the mean on 30-day risk-standardized revisit rates was mood disorders (4.2% of hospitals [n = 15], range of hospital performance 6.3%-15.9%). CONCLUSIONS: We found that when comparing hospitals performances to the average, few hospitals that care for children are identified as high- or low-performers for revisits, even for common pediatric diagnoses, likely due to low hospital volumes. This limits the usefulness of condition-specific readmission or revisit measures in pediatric quality measurement.

BACKGROUND: Adjustment for differing risks among patients is usually incorporated into newer payment approaches, and current risk models rely on age, sex, and diagnosis codes. It is unknown the extent to which controlling additionally for disease severity improves cost prediction. Failure to adjust for within-disease variation may create incentives to avoid sicker patients. We address this issue among patients with chronic obstructive pulmonary disease (COPD). METHODS: Cost and clinical data were collected prospectively from 1202 COPD patients at Kaiser Permanente. Baseline analysis included age, sex, and diagnosis codes (using the Diagnostic Cost Group Relative Risk Score) in a general linear model predicting total medical costs in the following year. We determined whether adding COPD severity measures-forced expiratory volume in 1 second, 6-Minute Walk Test, dyspnea score, body mass index, and BODE Index (composite of the other 4 measures)-improved predictions. Separately, we examined household income as a cost predictor. RESULTS: Mean costs were $12,334/y. Controlling for Relative Risk Score, each ½ SD worsening in COPD severity factor was associated with $629 to $1135 in increased annual costs (all P<0.01). The lowest stratum of forced expiratory volume in 1 second (<30% normal) predicted $4098 (95% confidence interval, $576-$8773) additional costs. Household income predicted excess costs when added to the baseline model (P=0.038), but this became nonsignificant when also incorporating the BODE Index. CONCLUSIONS: Disease severity measures explain significant cost variations beyond current risk models, and adding them to such models appears important to fairly compensate organizations that accept responsibility for sicker COPD patients. Appropriately controlling for disease severity also accounts for costs otherwise associated with lower socioeconomic status.