Objective
The purpose of this study was to determine the rates of late preterm inductions without a medical indication from birth certificate data and to compare them with rates that were obtained from medical charts.
Study Design
The Ohio Perinatal Quality Collaborative, which comprises 20 hospitals in Ohio that came together in 2008 for the purpose of decreasing nonmedically indicated scheduled deliveries, abstracted data on all scheduled births between 36 weeks and 38 weeks 6 days of gestation. We compared labor inductions with “elective” documented or no indication documented in charts to birth certificate data for inductions with no maternal or fetal complications recorded.
Results
Birth certificates overestimate rates of induction without medical indication compared with chart abstraction (11% vs 1%; P < .0001). The monthly difference between chart abstraction and birth certificates averages 10.1%.
Conclusion
Birth certificates overestimate nonmedically indicated inductions by 11-fold. Until birth certificate data improve, nonmedically indicated induction rates that are calculated from birth certificates should be interpreted with caution.
Nonmedically indicated induction of labor, when labor is induced for the convenience of the patient or doctor, is rising. Estimates on the prevalence of nonmedically indicated induction vary greatly. A large series from a single center estimated that 6.3% of all inductions (or 2.7% of all deliveries) were not medically indicated. However, a population-based study from Washington State showed that 33% of all women had an induction of labor; of those women, 85.7% had a standard reason for induction of labor, which left 14.3% (4.7% of the overall population) with either no reason listed or a nonstandard reason for induction.
For Editors’ Commentary, see Table of Contents
See related editorial, page 190
Review of medical records is the gold standard for the evaluation of whether a patient had an nonmedically indicated or an indicated induction. However, chart reviews are work intensive and are limited generally to 1 medical center, which minimizes the generalizability of the findings. Birth certificate data offer the advantage of being population based and thus more generalizable, but the quality of the clinical data is suspect. In general, complications on birth certificate data are thought to be specific but not sensitive.
The Ohio Perinatal Quality Collaborative (OPQC) is a group of 20 hospitals in Ohio that came together in 2008 for the purpose of decreasing the rate of nonmedically indicated scheduled deliveries at <39 weeks of gestation. As part of this effort, we performed chart abstractions on all scheduled deliveries between 36 0/7 and 38 6/7 weeks of gestation. Birth certificate data from these same hospitals were also tracked during this time. We sought to determine the magnitude of the difference between chart abstracted data and birth certificate data in estimating nonmedically indicated induction rates. If the magnitude of the difference is known, birth certificate data, which is cheap and easily available, can be better interpreted. This work is a secondary analysis of OPQC data.
Materials and Methods
OPQC collected data on all scheduled deliveries from 36 0/7 to 38 6/7 from the 20 hospitals in OPQC from August 2008 through June 2009. Hospitals that participated in OPQC included all of the level-3 centers (tertiary) in Ohio and many of the level-2 centers. Level-1 centers are not included in these data. These 20 OPQC hospitals represent 47% of the deliveries in Ohio during those years. This project significantly lowered the rate of nonmedically indicated scheduled deliveries from 25% to <5% during the course of the project.
Scheduled deliveries were determined from labor and delivery schedules and review of charts. We limited our sample to those women who had a scheduled induction of labor. Because there is not a gold standard of legitimate medical reasons for the inducement of labor, to be conservative, we counted all indications as medically indicated, unless they had no reason that was documented for the induction or “elective” delivery was documented as a reason for the induction. Thus, some of the induction reasons that we considered indicated were not medically standard. An example of a nonmedically standard indication might be “lives far from hospital.” Although the definition of nonmedically indicated induction can be debated, by defining it in this way, our definition was as conservative as possible and gave the full benefit of the doubt to the provider’s reasoning and judgment.
Birth certificate data from OPQC hospitals was obtained from the state repository for women who underwent an induction of labor between 36 and 38 completed weeks of gestation. An induction for the purpose of documentation on the birth certificate is when contractions are started medically or mechanically before the onset of spontaneous labor. There is no specific birth certificate item for reason for induction. Thus, we categorized the birth certificates as a nonmedically indicated induction if the following were absent from the birth certificate: a baby with a birthweight <10th percentile for gestational age, diabetes mellitus, prepregnancy hypertension, gestational hypertension, hypertension eclampsia, history of poor pregnancy outcomes, premature rupture of membranes, augmentation of labor, chorioamnionitis, and fetal anomalies. Unfortunately, not all fields that we ideally would have liked to include as medical reasons for induction are listed on the birth certificate. These items were chosen from the available fields on the Ohio birth certificate.
We compared rates of nonmedically indicated induction that were calculated by the OPQC data and compared them to rates of nonmedically indicated induction that were calculated by the birth certificate using a Student t test. The total number of monthly deliveries (the denominator) was obtained from birth certificate data for both rates because birth certificate data are known to be complete for the population. We also describe the differences by data type in the rates month by month. Last, we looked at the magnitude of the difference between the OPQC data and the birth certificate data within each of the 20 centers to assess the range of differences between the data types and to assess whether a few large centers were driving the findings.
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