Regional variation in late preterm births in North Carolina

Identification and classification of principal features for analyzing unwarranted clinical variation

RATIONALE, AIMS, AND OBJECTIVE

Unwarranted clinical variation (UCV) is an undesirable aspect of a healthcare system, but analyzing for UCV can be difficult and time-consuming. No analytic feature guidelines currently exist to aid researchers. We performed a systematic review of UCV literature to identify and classify the features researchers have identified as necessary for the analysis of UCV.

METHODS

The literature search followed the Preferred Reporting Items for Systematic Reviews and Meta-Analyses. We looked for articles with the terms 'medical practice variation' and 'unwarranted clinical variation' from four databases: Medline, Web of Science, EMBASE and CINAHL. The search was performed on 24 March 2023. The articles selected were original research articles in the English language reporting on UCV analysis in adult populations. Most of the studies were retrospective cohort analyses. We excluded studies reporting geographic variation based on the Atlas of Variation or small-area analysis methods. We used ASReview Lab software to assist in identifying articles for abstract review. We also conducted subsequent reference searches of the primary articles to retrieve additional articles.

RESULTS

The search yielded 499 articles, and we reviewed 46. We identified 28 principal analytic features utilized to analyze for unwarranted variation, categorised under patient-related or local healthcare context factors. Within the patient-related factors, we identified three subcategories: patient sociodemographics, clinical characteristics, and preferences, and classified 17 features into seven subcategories. In the local context category, 11 features are classified under two subcategories. Examples are provided on the usage of each feature for analysis.

CONCLUSION

Twenty-eight analytic features have been identified, and a categorisation has been established showing the relationships between features. Identifying and classifying features provides guidelines for known confounders during analysis and reduces the steps required when performing UCV analysis; there is no longer a need for a UCV researcher to engage in time-consuming feature engineering activities.

Regional Variation in Antenatal Late Preterm Steroid Use Following the ALPS Trial

Importance

The publication of the Antenatal Late Preterm Steroids (ALPS) trial in February 2016 demonstrated that antenatal administration of betamethasone in the late preterm period (between 34 to 36 weeks of gestation) for individuals with a high risk of delivery decreased neonatal respiratory morbidity. National estimates have suggested the trial did change obstetric practice, but little is known if the evidence was adopted uniformly or equitably.

Objective

To assess regional variation in the use of late preterm steroids after the publication of the Antenatal Late Preterm Steroids (ALPS) Trial and to understand factors associated with a region's pace of adoption.

Design, Setting, and Participants

This cross-sectional study used US natality data from February 2015 to October 2017 from hospital referral regions (HRRs) within the US. Inclusion criteria included live-born, nonanomalous, singleton, late preterm (34 to 36 completed weeks of gestation) neonates born to individuals without pregestational diabetes. This study was conducted from November 15, 2022, to January 13, 2023.

Main Outcome and Measures

HRRs were categorized as either a slower adopter or faster adopter of antenatal late preterm steroids based on the observed vs expected pace of antenatal steroid adoption in a 1-year period after the trial's dissemination. Patient and regional factors hypothesized a priori to be associated with the uptake of late preterm steroids were compared between faster and slower adopters. Comparisons were made using Student t test or Wilcoxon rank-sum test, as appropriate. A multivariable logistic regression was constructed to identify factors associated with faster adopter status in the postperiod.

Results

There were 666 097 late preterm births in 282 HRRs. The mean (SD) maternal age in HRRs was 27.9 (1.2) years. The median (IQR) percentage of births by race categories in HRRs for patients identifying as American Indian or Alaskan Native was 0.5% (0.2%-1.3%); Asian or Pacific Islander, 3.0% (1.7%-5.3%); Black, 12.9% (5.1%-29.1%); and White, 78.6% (66.6%-87.0%). The median percentage of births in HRRs to patients of Hispanic ethnicity was 11.2% (6.3%-27.4%). In this study, 136 HRRs (48.2%) were classified as faster adopters and 146 (51.8%) were classified as slower adopters. Faster adopters increased their steroid use by 12.1 percentage points (from 5.9% to 18.0%) compared with a 5.5 percentage point increase (from 3.7% to 9.2%) among slower adopters (P < .001). Most examined patient and regional factors were not associated with a region's pace of adoption, with the exception of the regional prevalence of prior preterm birth (adjusted odds ratio [aOR], 2.04 [95% CI, 1.48-2.82]) and the percentage of deliveries at 34 to 35 weeks of gestation (aOR, 0.68 [95% CI, 0.47-0.99]) compared with 36 weeks.

Conclusions and Relevance

In this cross-sectional study, there was widespread geographic variation in the adoption of antenatal steroid administration for late preterm births that largely remained unexplained by population factors. These findings should prompt further investigations to barriers to timely or equitable access to new evidence-based practices and guide future dissemination strategies with the goal of more uniform adoption.

Late preterms: are they all the same?

Very preterm infants (VPT) and, especially extreme low gestational age (ELGA) preterms, often on the threshold of viability, make the headlines of both, the scientific as well as the popular press. However, all together they represent between 1 and 2% of all livebirths. Late preterms (LPT) those born between 34/07 and 36/06 weeks, on the other hand, may account for up to 80% of all preterms and for some 5-8% of all births. Although mortality is low they are prone to increasing neonatal morbidities posing a considerable medical, financial and psychosocial burden. In the last years, for many reasons, LPT appear to have increased considerably throughout the western world. But are LPT neonates all the same? In spite of overlapping gestational ages (GA) LPT may behave quite differently depending on circumstances surrounding their pre- and postnatal events. We can identify three different classes of LPT neonates: spontaneous late preterms (SpLPT) born in the absence of previous maternal illnesses and/or pregnancy related disorders; Induced LPT (IdLPT) due to maternal/fetal complications and those babies being born after 34-week gestation from postponed delivery at an earlier GA (PtLPT) - and they are quite different babies, with different behavior, despite a common and same gestational age.

Center Variation in the Delivery of Indicated Late Preterm Births

Objective Evidence for optimal timing of delivery for some pregnancy complications at late preterm gestation is limited. The purpose of this study was to identify center variation of indicated late preterm births. Study design We performed an analysis of singleton late preterm and term births from a large U.S. retrospective obstetrical cohort. Births associated with spontaneous preterm labor, major congenital anomalies, chorioamnionitis, and emergency cesarean were excluded. We used modified Poisson fixed effects logistic regression with interaction terms to assess center variation of indicated late preterm births associated with four medical/obstetric comorbidities after adjusting for socio-demographics, comorbidities, and hospital/provider characteristics. Results We identified 150,055 births from 16 hospitals; 9,218 were indicated late preterm births. We found wide variation of indicated late preterm births across hospitals. The extent of center variation was greater for births associated with preterm premature rupture of membranes (risk ratio [RR] across sites: 0.45-3.05), hypertensive disorders of pregnancy (RR across sites: 0.36-1.27), and placenta previa/abruption (RR across sites: 0.48-1.82). We found less center variation for births associated with diabetes (RR across sites: 0.65-1.39). Conclusion Practice variation in the management of indicated late preterm deliveries might be a source of preventable late preterm birth.

Variation in hospital caesarean section rates for preterm births

BACKGROUND

Evidence about optimal mode of delivery for preterm birth is lacking, and there is thought to be considerable variation in practice.

OBJECTIVE

To assess whether variation in hospital preterm caesarean section rates (Robson Classification Group 10) and outcomes are explained by casemix, labour or hospital characteristics.

MATERIALS AND METHODS

Population-based cohort study in NSW, 2007-2011. Births were categorised according to degree of prematurity and hospital service capability: 26-31, 32-33 and 34-36 weeks' gestation. Hospital preterm caesarean rates were investigated using multilevel logistic regression models, progressively adjusting for casemix, labour and hospital factors. The association between hospital caesarean rates, and severe maternal and neonatal morbidity rates was assessed.

RESULTS

At 26-31 weeks' gestation, the caesarean rate was 55.2% (seven hospitals, range 43.4-58.4%); 50.9% at 32-33 weeks (12 hospitals, 43.4-58.1%); and 36.4% at 34-36 weeks (51 hospitals, 17.4-48.3%). At 26-31 weeks and 32-33 weeks' gestation, 81% and 59% of the variation between hospitals was explained with no hospital significantly different from the state average after adjustment. At 34-36 weeks' gestation, although 59% of the variation was explained, substantial unexplained variation persisted. Hospital caesarean rates were not associated with severe maternal morbidity rates at any gestational age. At 26-31 weeks' gestation, medium and high caesarean rates were associated with higher severe neonatal morbidity rates, but there was no evidence of this association ≥32 weeks.

CONCLUSION

Both casemix and practice differences contributed to the variation in hospital caesarean rates. Low preterm caesarean rates were not associated with worse outcomes.