Can PAs help address the pressing public health problem of rising maternal mortality?
Bibliographic record
Abstract
FigureFigureEditor's note: This is the first in a series of articles on public health. A 36-year-old woman, otherwise healthy except for a history of deep vein thrombosis (DVT) and pulmonary embolism (PE) after lower leg surgery, presents in labor with a full-term infant. She undergoes an emergency cesarean section due to fetal distress but delivers a healthy baby. One day after delivery, the patient becomes dyspneic and alerts a nurse that she may be having a recurrent PE. She asks for a CT and heparin. The nurse attributes the patient's symptoms to confusion from pain medication. The patient insists on an evaluation, so the medical team orders ultrasound scans of her legs. No DVTs are found. The patient continues to insist on a chest CT. When her request finally is granted, she is found to have multiple PEs. Heparin is initiated, but this causes a large internal hematoma at the surgical site. She goes to the OR for hematoma evacuation and placement of an inferior vena cava filter. Thankfully, she survives both the PEs and the surgery. Had she died, her death would have been broadcast around the world. Far from being another anonymous mother, this patient is Serena Williams, one of the greatest tennis players ever.1 Unlike many other disease processes for which death rates continue to fall, childbirth is more likely to kill women in the United States today than 25 years ago.2 Maternal mortality in the United States more than doubled from 1990 to 2013 and now stands at 28 maternal deaths per 100,000 live births, the highest of any developed nation. In comparison, Canada's maternal mortality is 11 per 100,000; the rates in Australia and Japan both are 6 per 100,000.2 Although maternal mortality in the United States is increasing for all women, black women are nearly three times as likely to die from pregnancy-related causes than non-Hispanic white women (56 per 100,000 compared with 20 per 100,000 for non-Hispanic white women).3 Many factors have been proposed for this increase in maternal mortality and the racial, ethnic, and socioeconomic disparities associated with it. These factors, like all healthcare disparities, occur at the patient, provider, and system levels.4 Patient factors for maternal death include advancing maternal age; increases in comorbidities such as obesity, diabetes, and hypertension; maternal drug addiction; multiple gestation pregnancies; lack of prenatal and preconception care; and closely spaced pregnancies.3,5 Provider factors include implicit racial bias, lack of cultural competency, high rates of labor induction and cesarean section deliveries, and lack of postpartum care.6 System factors include obstetric provider shortages; variations in the quality of hospital-based intrapartum care; inconsistent management of obstetric emergencies; lack of healthcare insurance for women before, during, and after pregnancy; lack of transportation for medical appointments; and inconsistent use of maternal mortality review committees to review and prevent maternal deaths.3,4,7 Interventions to reduce disparities in maternal mortality must occur at each of these same levels. Fortunately, as physician assistants (PAs), we are positioned to be a part of the solution to this public health crisis. We can help our patients understand the risks of advanced maternal age and adopt healthful lifestyle habits related to diet and exercise. We can provide treatment for their chronic medical conditions before pregnancy and make prenatal care a priority. We should offer long-acting reversible contraceptives immediately after delivery, educate ourselves about contraceptive options for our patients, and schedule postpartum follow-up visits earlier than 6 weeks.8 PAs can lobby against cuts to Medicaid, which provides medical coverage for 50% of pregnant women in the United States.5 We can advocate for local transportation systems to help patients get to and from their appointments. We can help our institutions adopt best practices and minimum standards of care for antenatal and intrapartum care as well as intrapartum emergencies, and reduce the rates of elective labor inductions and cesarean sections.7 Although none of these actions alone will solve the maternal health crisis, done together, they may help reverse this growing problem.6 Finally, Serena Williams' story also reminds us to go back to the fundamentals of our training: listen to the patient. Ms. Williams has good knowledge of her own health history, is a wealthy and famous person, and was able to advocate for herself. Despite these advantages, she had difficulty getting her medical team to take her potentially life-threatening symptoms seriously. PAs often are lauded for our ability to listen to our patients. We need to deploy this skill with excellence for every patient so that even patients who are anonymous, poor, or inarticulate can receive the highest quality care.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".