The purpose of this study was to develop a standardized methodology using readily available administrative data to identify indications for elective primary cesarean and to describe normative rates for these indications. Our findings suggest that elective primary cesarean delivery accounts for approximately 4 percent of all births to women without a previous cesarean. We were able to explain 93 percent of elective primary cesarean deliveries with 12 specific clinical indications. It is noteworthy that many of the indications were strongly associated with cesarean (i.e., strong RR); however, for all indications evaluated (except uterine scar unrelated to cesarean), a trial of labor was still more likely than elective primary cesarean delivery. This suggests that there are very few absolute indications for elective primary cesarean delivery. This has important implications when evaluating variation in cesarean rates, when determining which factors should be considered for case-mix adjustment to make comparisons meaningful, and when developing a consensus regarding what constitutes an appropriate rate (or an appropriate indication) for cesarean delivery.
The “unspecified” group ranks third in the proportion of women who undergo elective cesarean delivery (7.1 percent of all elective primary cesarean procedures). While it is possible that this category represents cases where there was inaccurate or suboptimal coding, it is also possible that this category represents cases associated with no medical condition, and therefore truly reflects elective or “patient choice” procedures. There is speculation that this may be a growing trend, and warrants further monitoring (Showalter and Griffin 1999
; Harer 2000
; Tranquilli and Garzetti 1997
; Shelton 1999
The utilization of such methods should increase the face validity of the cesarean rate for clinicians, and begin to illustrate the complexity of normative rates by clinical and nonclinical conditions. In particular, the 12 indications for elective primary cesarean assigned within the hierarchy are supported by both clinical evidence and standards of practice (Henry et al. 1995
). However, the indications found farthest down in the hierarchy may have less evidence to support them as absolute indications and/or have such a low prevalence that they carry little importance with respect to general obstetrical practice. For example, data regarding the optimum method of delivery for breech presentation, while still controversial, appears to favor cesarean delivery (Hannah et al. 2000
), and this is clearly the normative standard in the United States, where 90 percent of infants with breech presentation are born via cesarean (Ventura et al. 2000
). On the other hand, the data to support cesarean delivery to optimize outcome for preterm infants is minimal, and even with regard to selected fetal anomalies, the benefits have not been clearly demonstrated (How et al. 2000
; Lurie, Sherman, and Bukovsky 1999
Other factors contributing to the variation in elective primary cesarean among the clinical categories may relate to temporal issues with coding, and uncertain severity of disease. For example, suspected macrosomia is presumed to be a “prelabor” diagnosis, but could have been assigned by the coder based on actual infant birthweight. Additionally, ICD-9-CM codes often do not reflect the severity of disease, and the severity of the condition, and range of delivery options for antepartum hemorrhage is likely to vary based on the timing, severity, and cause of the bleeding. Furthermore, the proposed hierarchy is not sensitive to the clinical judgment likely to be exercised when patients have more than one clinical diagnosis. Delivery options for women with severe hypertension may vary depending on their previous obstetrical history, gestational age, and cervical exam at time of diagnosis, and on their physician's clinical experience and hospital resources. None of these factors is considered in the models presented.
Recursive partitioning algorithms have been in use since the mid-1970s. They were integral to the development of diagnosis related groups (DRGs) for the Medicare Prospective Payment System (Fetter et al. 1980
). The goals of DRG models include the following: The patient groups have to make good clinical sense, they have to be based on routinely collected data, and there has to be a manageable number of groups (Fetter et al. 1980
). Although DRGs were developed to provide a basis for uniform payment across hospitals, the attempt to use similar methodologies to measure the quality of care is becoming increasingly popular (Fetter et al. 1980
; Feinglass et al. 1998
We feel the technique has added value to traditional regression methods because of the opportunity to create clinically homogeneous groups for comparison of rates of cesarean delivery. Such cohorts can then be examined for variation in practice across regions, populations, and hospital organizational factors. Traditional risk-adjustment methodology has focused on interhospital comparisons, and has relied on the definition of a standard “low-risk” patient as a reference. Specifically, such methodology is intended to “level the playing field” among hospitals with varying proportions of patients at risk for cesarean delivery (Iezzoni 1994
). Some investigators have proposed reporting cesarean rates by parity, or have argued for “labor-adjusted” cesarean rates, which would exclude patients at “high risk” for cesarean (Elliott, Russell, and Dickason 1997
). Many of the “high-risk” conditions that would be excluded on the basis of clinical “reasonableness” proposed by other investigators are represented in the clinical hierarchy derived here. However, we found the cesarean rates for these “high-risk” conditions varied widely (4–64 percent), with none approaching 100 percent. In fact, apart from malpresentation, the relatively low prevalence of each of the remaining conditions illustrates the difficulty encountered by obstetricians as they try to interpret the appropriateness of cesareans represented by a single overall rate. Hence we propose that it is necessary to monitor cesarean rates for these “high-risk” conditions as well as for low-risk conditions if one hopes to identify normative rates for specific indications. Such methods should encourage consensus regarding those conditions that are both statistically and clinically meaningful. Subsequent efforts can then be directed toward understanding factors contributing to the variation in these rates.
Compared with traditional risk-adjustment techniques, recursive partitioning algorithms can more easily allow for multiple simultaneous comparisons within large datasets to identify relatively homogeneous subgroups of patients at various levels of risk. The overlap among clinical conditions shown in is substantial among obstetrical patients, yet the final classification of cases into the hierarchy appears clinically consistent and acceptable. The tradeoff for creating these clinically homogeneous groups of patients is notable. The parameters in the model do not need to remain fixed. Rather, they can vary as needed by the population. Because the groups are mathematically independent, they can be examined separately, the definition of an “average” rate by which to adjust all others is not necessary, and the nonclinical variation in the use of elective primary cesarean for that clinically identifiable group can be examined across hospitals. Reference groups can be identified by hospital organizational factors and structures, such as public versus corporate hospitals, and are not hampered by the concept of a “standard” patient (Gregory, Korst, and Platt 2001
In conclusion, we set out to establish a method whereby normative rates for specific indications for elective primary cesarean delivery could be reported from readily available administrative data. An increased understanding of the reasons for elective primary cesarean delivery should improve our knowledge regarding normative practices, and identify an “at risk” subgroup of patients for whom the use of cesarean delivery is considered to be appropriate. Although this study does not solidify the link between normative rates and quality of care, the ability to determine clinically applicable normative rates should provide a foundation for benchmarking best practices and identifying outliers. Admittedly, it is unclear how identification of hospital-based differences in rates of elective primary cesarean might impact decision making that occurs prior to hospitalization. Theoretically, examination across hospitals of the maternal and neonatal morbidities associated with each of the clinical cohorts could lead to refined assessments of the costs of care and impact contract negotiations among insurers and hospitals. Additionally, given recent interest in the use of elective primary cesarean delivery because of patient preference, this methodology may assist women and their physicians in the identification of hospitals that are more likely to be supportive of this option. The current national primary cesarean rate is 16 percent (Ventura et al. 2000
). Based on this study's findings, we can extrapolate that a quarter of these patients are “high risk,” do not experience labor, and undergo elective primary cesarean delivery. Hospitals that vary widely (range 0.53–11.33 percent) from the mean elective primary cesarean rate (roughly 4 percent) may represent hospitals with under- or overutilization, and an opportunity for improved patient outcomes (Brook et al. 1984
). As cesarean rates are examined, an effort must be made to focus the analysis so that it applies to clinical decision making. The development of clinically appropriate denominators in conjunction with a greater understanding of normative rates should have the potential to improve the clinical response desired from the surveillance of maternal health care quality indicators.