Understanding racial disparities in severe maternal morbidity using Bayesian network analysis.
PLoS One
; 16(10): e0259258, 2021.
Article
em En
| MEDLINE
| ID: mdl-34705872
ABSTRACT
Previous studies have evaluated the marginal effect of various factors on the risk of severe maternal morbidity (SMM) using regression approaches. We add to this literature by utilizing a Bayesian network (BN) approach to understand the joint effects of clinical, demographic, and area-level factors. We conducted a retrospective observational study using linked birth certificate and insurance claims data from the Arkansas All-Payer Claims Database (APCD), for the years 2013 through 2017. We used various learning algorithms and measures of arc strength to choose the most robust network structure. We then performed various conditional probabilistic queries using Monte Carlo simulation to understand disparities in SMM. We found that anemia and hypertensive disorder of pregnancy may be important clinical comorbidities to target in order to reduce SMM overall as well as racial disparities in SMM.
Texto completo:
1
Temas:
ECOS
/
Aspectos_gerais
/
Equidade_desigualdade
Bases de dados:
MEDLINE
Assunto principal:
Complicações na Gravidez
/
Disparidades nos Níveis de Saúde
/
Saúde Materna
Tipo de estudo:
Observational_studies
/
Prognostic_studies
Aspecto:
Determinantes_sociais_saude
/
Equity_inequality
/
Patient_preference
Limite:
Adolescent
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Adult
/
Female
/
Humans
/
Middle aged
/
Pregnancy
País/Região como assunto:
America do norte
Idioma:
En
Revista:
PLoS One
Assunto da revista:
CIENCIA
/
MEDICINA
Ano de publicação:
2021
Tipo de documento:
Article
País de afiliação:
Estados Unidos