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Can PAs help address the pressing public health problem of rising maternal mortality?

2018· article· en· W2800137779 on OpenAlexaboutno aff
Tamara S. Ritsema, Amy Mercantini Klingler

Bibliographic record

VenueJAAPA · 2018
Typearticle
Languageen
FieldEconomics, Econometrics and Finance
TopicHealthcare Policy and Management
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineDeep veinPulmonary embolismChildbirthDistressHematomaPregnancyGeneral surgeryThrombosisSurgeryMedical emergency

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.796
Threshold uncertainty score0.979

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.182
GPT teacher head0.326
Teacher spread0.144 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designTheoretical or conceptual
Domainnot available
GenreEmpirical

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".

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Citations1
Published2018
Admission routes1
Has abstractyes

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