Comparison of Nurse-Midwife and Physician Birth Outcomes in the Military Health System.
Lynette HamlinLindsay GrunwaldRodney X SturdivantTracey P KoehlmoosPublished in: Policy, politics & nursing practice (2021)
The purpose of this study is to identify the socioeconomic and demographic characteristics of women cared for by Certified Nurse-Midwives (CNMs) versus physicians in the Military Health System (MHS) and compare birth outcomes between provider types. The MHS is one of America's largest and most complex health care systems. Using the Military Health System Data Repository, this retrospective study examined TRICARE beneficiaries who gave birth during 2012-2014. Analysis included frequency of patients by perinatal services, descriptive statistics, and logistic regression analysis by provider type. To account for differences in patient and pregnancy risk, odds ratios were calculated for both high-risk and general risk population. There were 136,848 births from 2012 to 2014, and 30.8% were delivered by CNMs. Low-risk women whose births were attended by CNMs had lower odds of a cesarean birth, induction/augmentation of labor, complications of birth, postpartum hemorrhage, endometritis, and preterm birth and higher odds of a vaginal birth, vaginal birth after cesarean, and breastfeeding than women whose births were attended by physicians. These results have implications for the composition of the women's health workforce. In the MHS, where CNMs work to the fullest scope of their authority, CNMs attended almost 4 times more births than our national average. An example to other U.S. systems and high-income countries, this study adds to the growing body of evidence demonstrating that when CNMs practice to the fullest extent of their education, they provide quality health outcomes to more women.
Keyphrases
- gestational age
- preterm birth
- pregnancy outcomes
- primary care
- healthcare
- polycystic ovary syndrome
- pregnant women
- low birth weight
- public health
- quality improvement
- emergency department
- end stage renal disease
- type diabetes
- risk factors
- posttraumatic stress disorder
- ejection fraction
- preterm infants
- big data
- metabolic syndrome
- risk assessment
- deep learning
- electronic health record
- health information
- data analysis
- patient reported outcomes