It has been well established that perinatal outcomes vary by the race and ethnicity of the mother. Prematurity, cesarean delivery, infant death, and maternal death are higher in the black population than in the white population. 1,2 The causes of these differences have been of great interest to the obstetric community. 3,4 There are many possible causes of racial disparities in obstetrics, including economics, biology, and discrimination. 5 There is likely overlap in these categories and it is unclear whether a lucid answer will ever be delineated.
Despite this uncertainty regarding the mechanism by which race and ethnicity influence perinatal outcomes, perinatal outcomes are used as a measure of the quality of obstetric care. Outcomes are often measured at the hospital level. Case mix adjustment is a technique used to account for differences in baseline patient characteristics that influence outcomes. This is done so that hospitals caring for sicker patients who are more likely to have poor outcomes are not penalized in the evaluation of quality. 6
Risk-adjusted primary cesarean rates are a promising new measure of obstetrical quality that can be used to effectively identify hospitals with poorer outcomes.7,8,9 A risk-adjustment model is first developed to predict the probability of a cesarean delivery for each patient associated with the institution, using a series of well-accepted risk factors for cesarean delivery that were developed by practicing obstetricians 10, 11. The estimated probabilities of cesarean delivery for each patient are then summed across the institution in order to create an institutional predicted primary cesarean rate. These rates are then directly compared to actual observed rates of primary cesarean delivery. Risk-adjusted primary cesarean rates are particularly appealing because they are associated with both maternal and neonatal outcomes. 12 Hospitals that have risk-adjusted primary cesarean rates that are below expected have higher rates of poor maternal and neonatal outcomes. 13,12,14,15 Risk-adjusted cesarean rates do not provide a “target” cesarean rate. They do not pass judgment as to whether any particular cesarean was appropriate, and they do not attempt to assess the quality of surgical technique. The model simply predicts each patient’s chance of a cesarean delivery given their personal risk factors in the hands of a typical provider. An institution’s predicted rate is based solely on its case mix.
In assessing the quality of a risk-adjustment model, it is important to address predictive accuracy in terms of both discrimination and calibration. Discrimination may be thought of as the ability of the model to accurately separate patients into those who will undergo a primary cesarean delivery and those who will not. The C statistic (also called the area under the receiver operating characteristic curve) is the standard approach to quantifying discrimination, The C statistic falls between 0.5 (worst case) and 1.0 (ideal scenario), with larger values indicating better discrimination 16.
In addition to discrimination, any risk-adjustment model should be assessed in terms of its calibration. This may be thought of as the model’s ability to predict accurately throughout the range of possible probabilities (i.e., for patients at all levels of risk). Calibration of a risk-adjustment model is most commonly assessed using a statistical test of goodness of fit due to Hosmer and Lemeshow, where small p-values indicate poor model calibration.
Because race impacts perinatal outcomes and because the race/ethnicities of patients are not equally distributed among hospitals, race is a potential variable for risk-adjustment models. The inclusion of race in risk-adjustment models is controversial. If racial differences are based in economics or biology, this is a legitimate addition to a risk adjustment model. On the other hand, if racial differences are due to discrimination, race/ethnicity should not be included in risk-adjustment models because such an approach would mask an important social issue. To better understand the role that race and ethnicity play in risk-adjustment models for primary cesarean delivery, our study sought to compare models with and without race and ethnicity to assess their impact on the discrimination and calibration of risk-adjustment models.