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Figure.
Distribution of Adjusted 30-Day Hospital Readmission Rates
Distribution of Adjusted 30-Day Hospital Readmission Rates

The box plot shows the median hospital readmission rate and surrounding interquartile range (IQR). Whiskers extend 1.5 times the IQR beyond the box. Rates outside of the whiskers are considered outliers (34 of 1228 hospitals).

Table.  
Variance of Postpartum Maternal Readmission Rates Attributed to the Hospital Among Random-Effects Linear Regression Models
Variance of Postpartum Maternal Readmission Rates Attributed to the Hospital Among Random-Effects Linear Regression Models
1.
Zuckerman  RB, Sheingold  SH, Orav  EJ, Ruhter  J, Epstein  AM.  Readmissions, observation, and the Hospital Readmissions Reduction Program.  N Engl J Med. 2016;374(16):1543-1551.PubMedGoogle ScholarCrossref
2.
Society for Maternal-Fetal Medicine Quality and Safety and Health Policy Committees, Bailit  JL, Gregory  KD, Srinivas  S, Westover  T, Grobman  WA, Saade  GR.  Society for Maternal-Fetal Medicine (SMFM) special report: current approaches to measuring quality of care in obstetrics.  Am J Obstet Gynecol. 2016;215(3):B8-B16.PubMedGoogle ScholarCrossref
3.
Agency for Healthcare Research and Quality.  Health care cost and utilization project: Nationwide Readmissions Database documentation. https://www.hcup-us.ahrq.gov/db/nation/nrd/nrddbdocumentation.jsp. Accessed November 22, 2016.
4.
Kuklina  EV, Whiteman  MK, Hillis  SD,  et al.  An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity.  Matern Child Health J. 2008;12(4):469-477.PubMedGoogle ScholarCrossref
5.
Haymart  MR, Banerjee  M, Stewart  AK, Koenig  RJ, Birkmeyer  JD, Griggs  JJ.  Use of radioactive iodine for thyroid cancer.  JAMA. 2011;306(7):721-728.PubMedGoogle ScholarCrossref
6.
Fingar  K, Washington  R.  Healthcare Cost and Utilization Project statistical briefs: trends in hospital readmissions for 4 high-volume conditions, 2009–2013: statistical brief #196. https://www.ncbi.nlm.nih.gov/books/NBK338299/. Accessed August 27, 2016. PubMed
Research Letter
May 23/30, 2017

Hospital-Level Variation in Postpartum Readmissions

Author Affiliations
  • 1Department of Obstetrics and Gynecology, Massachusetts General Hospital, Boston
  • 2Department of Obstetrics and Gynecology, Brigham and Women’s Hospital, Boston, Massachusetts
  • 3Department of Health Policy and Management, Harvard T.H. Chan School of Public Health, Boston, Massachusetts
JAMA. 2017;317(20):2128-2129. doi:10.1001/jama.2017.2830

Readmission rates are used as a quality indicator and linked to reimbursement for certain medical and surgical conditions.1 Obstetric maternal readmissions have not been rigorously studied as a quality measure, though their use has been proposed.2 The goal of this study was to determine the potential utility of this metric and its ability to accurately reflect quality by quantifying (1) the variance in hospital postpartum readmission rates and (2) the percentage of the variance that was attributed to the effect of the hospital after controlling for case mix.

Methods

The Partners HealthCare institutional review board exempted this study from review. Childbirth hospitalizations were identified using a previously validated method in the 2013 National Readmission Database, which contains discharge data from 14 million hospitalizations from 21 states.3,4 The following hospitalizations were excluded: those in December (per database use guidelines), patients with more than 1 childbirth hospitalization, and those in hospitals performing less than 100 deliveries per year. The primary outcome was any readmission within 30 days of the childbirth hospitalization discharge. Logistic regression models using patient-level data were constructed to calculate risk-adjusted readmission rates for each hospital, accounting for clustering at the level of the hospital. The rates were adjusted for patient age, payer, median income quartile of their zip code, and comorbidities (obesity, hypertensive disorder, pregnancy-related hypertensive disorder, diabetes, asthma, smoking, thyroid disease, psychiatric disease, and seizure disorder), which were identified by International Classification of Disease, Ninth Revision, codes. To understand the amount of variation explained by the hospital, a series of hierarchical random-effects linear regression models were created. Although a binary outcome, this linear approximation allows the within-hospital variability to be estimated from the within-hospital residuals; this method has been previously described.5 The percentage of overall variability attributed to the random effect of the hospital was calculated before and after case-mix adjustment. A sensitivity analysis was performed that accounted for intrapartum events, including mode of delivery and delivery complications (infection, hemorrhage, operative injury, uterine rupture), because these events may be on the causal pathway for readmission. Attributable primary indications for readmission were tabulated to provide clinical context. Analyses were performed using SAS, version 9.4, and JMP 13 software (both from SAS Institute).

Results

Of the 1 664 472 childbirth hospitalizations identified, 1 517 683 from 1228 hospitals met the inclusion criteria. There were 17 508 30-day readmissions. The median unadjusted hospital readmission rate was 1.01% (interquartile range [IQR], 0.67%-1.42%). There was little change after adjustment. The Figure shows the distribution of the adjusted readmission rates. The median was 1.06% (IQR, 0.73%-1.43%). There was little hospital-attributable variance prior to case mix adjustment; 0.11% of the variation was attributed to the hospital (Table). This percentage variance was unchanged after case-mix adjustment and in the sensitivity analysis. The most common primary indications for readmission were hypertension (21.6%), wound infection (13.0%), endometritis (10.2%), hemorrhage (5.8%), urinary tract infection (3.7%), sepsis (3.1%), thrombotic disease (3.1%), mastitis (2.8%), and psychiatric disease (2.5%).

Discussion

In this study, postpartum readmissions were rare events and attributable to a variety of causes. Fifty percent of hospitals had postpartum readmission rates of 1% or less; in contrast, well-studied medical and surgical readmission rates exceed 20% for some conditions.6 The low frequency of readmissions resulted in rate data that were unstable for analysis, especially for lower volume facilities. Furthermore, of the little variability that existed between the hospital readmission rates, less than 1% of this variation was attributed to the hospital, limiting their use as a quality metric.

This study was limited in that diagnoses were dependent on individual hospital coding practices, and the database lacks the clinical and geographic details to perform more granular analyses. Although a percentage of some indications for readmission may be potentially avoidable, the rarity of events would make studying preventability challenging.

In the search for appropriate metrics, these findings caution against the assumption that postpartum readmission rates accurately reflect obstetrical care quality. The adoption of an insufficient quality metric may negatively affect patient care and reimbursement.

Section Editor: Jody W. Zylke, MD, Deputy Editor.
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Article Information

Accepted for Publication: March 2, 2017.

Corresponding Author: Mark A. Clapp, MD, MPH, Department of Obstetrics and Gynecology, Massachusetts General Hospital, 55 Fruit St, Boston, MA 02114 (mark.clapp@mgh.harvard.edu).

Author Contributions: Drs Clapp and Zheng had full access to all of the data in the study, performed the statistical analyses, and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Clapp, Little, Robinson.

Acquisition, analysis, or interpretation of data: All authors.

Drafting of the manuscript: Clapp, Little, Robinson.

Critical revision of the manuscript for important intellectual content: All authors.

Statistical analysis: Clapp, Zheng.

Supervision: Little, Kaimal, Robinson.

Conflict of Interest Disclosures: All authors have completed and submitted the ICMJE Form for Disclosure of Potential Conflicts of Interest and none were reported.

Funding/Support: This study was supported in part by the Department of Obstetrics and Gynecology at Brigham and Women’s Hospital through an intradepartmental research grant, which was used for data acquisition, database processing, and statistical analysis support.

Role of the Funder/Sponsor: The funder was not involved in the design and conduct of the study; the collection, management, analysis, and interpretation of the data; the preparation, review, or approval of the manuscript; or the decision to submit the manuscript for publication.

References
1.
Zuckerman  RB, Sheingold  SH, Orav  EJ, Ruhter  J, Epstein  AM.  Readmissions, observation, and the Hospital Readmissions Reduction Program.  N Engl J Med. 2016;374(16):1543-1551.PubMedGoogle ScholarCrossref
2.
Society for Maternal-Fetal Medicine Quality and Safety and Health Policy Committees, Bailit  JL, Gregory  KD, Srinivas  S, Westover  T, Grobman  WA, Saade  GR.  Society for Maternal-Fetal Medicine (SMFM) special report: current approaches to measuring quality of care in obstetrics.  Am J Obstet Gynecol. 2016;215(3):B8-B16.PubMedGoogle ScholarCrossref
3.
Agency for Healthcare Research and Quality.  Health care cost and utilization project: Nationwide Readmissions Database documentation. https://www.hcup-us.ahrq.gov/db/nation/nrd/nrddbdocumentation.jsp. Accessed November 22, 2016.
4.
Kuklina  EV, Whiteman  MK, Hillis  SD,  et al.  An enhanced method for identifying obstetric deliveries: implications for estimating maternal morbidity.  Matern Child Health J. 2008;12(4):469-477.PubMedGoogle ScholarCrossref
5.
Haymart  MR, Banerjee  M, Stewart  AK, Koenig  RJ, Birkmeyer  JD, Griggs  JJ.  Use of radioactive iodine for thyroid cancer.  JAMA. 2011;306(7):721-728.PubMedGoogle ScholarCrossref
6.
Fingar  K, Washington  R.  Healthcare Cost and Utilization Project statistical briefs: trends in hospital readmissions for 4 high-volume conditions, 2009–2013: statistical brief #196. https://www.ncbi.nlm.nih.gov/books/NBK338299/. Accessed August 27, 2016. PubMed
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