Dataset Information

Population-attributable fraction of risk factors for severe maternal morbidity.

ABSTRACT:

Background

Severe maternal morbidity is an important proxy for maternal mortality. Population attributable fraction is the proportion of a disease that is attributable to a given risk factor and can be used to estimate the reduction in the disease that would be anticipated if a risk factor was reduced or eliminated.

Objective

We sought to determine the population-attributable fraction (PAF) of potentially modifiable risk factors for severe maternal morbidity.

Study design

We used a retrospective cohort of 86,260 delivery hospitalizations from Magee-Womens Hospital, Pittsburgh, PA for this analysis (2003-2012). Severe maternal morbidity was defined as any of the following: Centers for Disease Control and Prevention International Classification of Diseases 9^th Revision diagnosis and procedure codes for the identification of maternal morbidity; prolonged postpartum length of stay (defined as >3 standard deviations beyond the mean length of stay: >3 days for vaginal deliveries and >5 days for Cesarean deliveries); or maternal intensive care unit admission. We used multivariable logistic regression with generalized estimating equations to estimate the association of prepregnancy overweight or obesity, maternal age ?35 years, preexisting hypertension, preexisting diabetes, excessive gestational weight gain, smoking, education, and marital status with severe maternal morbidity. We then calculated the PAF for each risk factor. We also examined the impact of modest reductions and elimination of risk factors on the PAF of severe maternal morbidity.

Results

The overall rate of severe maternal morbidity was 2.0%. Overweight and obesity, maternal age ?35 years, preexisting hypertension, excessive gestational weight gain, and lack of a college degree had PAF ranging from 4.5% to 13%. If all risk factors were eliminated, 36% of cases could have been prevented. Modest reductions in the prevalence of excessive BMI and advanced maternal age had minimal impact on preventing severe maternal morbidity. Smoking during pregnancy and marital status were not associated with severe maternal morbidity.

Conclusions

Our data suggest maternal morbidity can be reduced by modifying common, individual-level risk factors. Nevertheless, the majority of cases were not attributable to the patient level risk factors we examined. These data support the need for large studies of patient-, provider-, system- and population-level factors to identify high-impact interventions to reduce maternal morbidity.

SUBMITTER: Freese KE

PROVIDER: S-EPMC7450808 | biostudies-literature | 2020 Feb

REPOSITORIES: biostudies-literature

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Publications

Population-attributable fraction of risk factors for severe maternal morbidity.

Freese Kyle E KE Bodnar Lisa M LM Brooks Maria M MM McTIGUE Kathleen K Himes Katherine P KP

American journal of obstetrics & gynecology MFM 20191122 1

<h4>Background</h4>Severe maternal morbidity is an important proxy for maternal mortality. Population attributable fraction is the proportion of a disease that is attributable to a given risk factor and can be used to estimate the reduction in the disease that would be anticipated if a risk factor was reduced or eliminated.<h4>Objective</h4>We sought to determine the population-attributable fraction (PAF) of potentially modifiable risk factors for severe maternal morbidity.<h4>Study design</h4>W ...[more]

PMID: 32864602

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Project description:BackgroundSevere maternal morbidity (SMM) is a key indicator of maternal health. Generally explored without distinction by the timing of the event, it mainly reflects postpartum SMM. Although antepartum (pre-labour) SMM presents specific challenges in its need to optimise the risk-benefit balance for both mother and foetus, its features remain inadequately explored.ObjectivesWe explored risk factors of antepartum SMM and described adverse delivery and neonatal outcomes associated with antepartum SMM.MethodsWe designed a population-based nested case-control study based on data from the EPIMOMS study (119 maternity hospitals of 6 French regions, 2012-2013, N = 182,309 deliveries in the source cohort). This study included all women with antepartum SMM (cases, n = 601) compared to a randomly selected sample of women who gave birth without SMM in the same hospitals (controls, n = 3651). Antepartum SMM risk factors were identified with multivariable logistic regression following imputations for missing data.ResultsAntepartum SMM complicated 0.33% (95% confidence interval [CI] 0.30, 0.36) of pregnancies. Antepartum SMM risk factors were maternal age ≥35 years (adjusted odds ratio [OR] 1.55, 95% CI 1.22, 1.97), increased body mass index (OR for 5 kg/m2 increase, 1.24, 95% CI 1.14, 1.36), maternal birth in sub-Saharan Africa (OR 1.80, 95% CI 1.29, 2.53), pre-existing medical condition (OR 2.56, 95% CI 1.99, 3.30), nulliparity (OR 2.26, 95% CI 1.83, 2.80), previous pregnancy-related hypertensive disorders (OR 4.94, 95% CI 3.36, 7.26), multiple pregnancy (OR 5.79, 95% CI 3.75, 7.26), irregular prenatal care (OR 1.86, 95% CI 1.27, 2.72). For women with antepartum SMM, preterm delivery, neonatal mortality and transfer to the neonatal intensive care unit were 10 times more frequent than for controls. Emergency caesarean and general anaesthesia were more frequent in women with antepartum SMM.ConclusionsAntepartum SMM is rare but associated with increased rates of adverse delivery and neonatal outcomes.

Project description:OBJECTIVE:To determine risk factors of severe acute maternal morbidity in women with twin pregnancies and identify subgroups at high risk. METHODS:In a prospective, population-based study of twin deliveries, the JUMODA cohort, all women with twin pregnancies at or after 22 weeks of gestation were recruited in 176 French hospitals. Severe acute maternal morbidity was a composite criterion. We determined its risk factors by multilevel multivariate Poisson regression modeling and identified high-risk subgroups by classification and regression tree (CART) analysis, in two steps: first considering only characteristics known at the beginning of pregnancy and then adding factors arising during its course. RESULTS:Among the 8,823 women with twin pregnancies, 542 (6.1%, 95% confidence interval (CI) 5.6-6.6) developed severe acute maternal morbidity. Risk factors for severe maternal morbidity identified at the beginning of pregnancy were maternal birth in sub-Saharan Africa (adjusted relative risk (aRR) 1.6, 95% CI 1.1-2.3), preexisting insulin-treated diabetes (aRR 2.2, 95% CI 1.1-4.4), nulliparity (aRR 1.6, 95% CI 1.3-2.0), IVF with autologous oocytes (aRR, 1.3, 95% CI, 1.0-1.6), and oocyte donation (aRR 2.0, 95% CI 1.4-2.8); CART analysis identified nulliparous women with oocyte donation as the subgroup at highest risk (SAMM rate: 14.7%, 95% CI, 10.3-19.1). At the end of pregnancy, additional risk factors identified were placenta praevia (aRR 3.5, 95% CI 2.3-5.3), non-severe preeclampsia (aRR 2.5, 95% CI 1.9-3.2), and macrosomia for either twin (aRR 1.7, 95% CI 1.3-2.1); CART analysis identified women with both oocyte donation and non-severe preeclampsia (SAMM rate: 28.9%, 95% CI, 19.9-37.9) and sub-Saharan nulliparous women with non-severe preeclampsia (SAMM rate: 26.9%, 95% CI, 9.9-43.9) as the two subgroups at highest risk. CONCLUSION:In woman with twin pregnancy, rates of severe acute maternal morbidity vary between subgroups from 4.6% to 14.7% and from 3.8% to 28.9% at the beginning and at the end of pregnancy respectively, depending on the combined presence of risk factors.

Project description:BackgroundCurrent interest in using severe maternal morbidity (SMM) as a quality indicator for maternal healthcare will require the development of a standardized method for estimating hospital or regional SMM rates that includes adjustment and/or stratification for risk factors.ObjectiveTo perform a scoping review to identify methodological considerations and potential covariates for risk adjustment for delivery-associated SMM.Search methodsFollowing the guidelines for Preferred Reporting Items for Systematic Reviews and Meta-analyses Extension for Scoping Reviews, systematic searches were conducted with the entire PubMed and EMBASE electronic databases to identify publications using the key term "severe maternal morbidity."Selection criteriaIncluded studies required population-based cohort data and testing or adjustment of risk factors for SMM occurring during the delivery admission. Descriptive studies and those using surveillance-based data collection methods were excluded.Data collection and analysisInformation was extracted into a pre-defined database. Study design and eligibility, overall quality and results, SMM definitions, and patient-, hospital-, and community-level risk factors and their definitions were assessed.Main resultsEligibility criteria were met by 81 studies. Methodological approaches were heterogeneous and study results could not be combined quantitatively because of wide variability in data sources, study designs, eligibility criteria, definitions of SMM, and risk-factor selection and definitions. Of the 180 potential risk factors identified, 41 were categorized as pre-existing conditions (e.g., chronic hypertension), 22 as obstetrical conditions (e.g., multiple gestation), 22 as intrapartum conditions (e.g., delivery route), 15 as non-clinical variables (e.g., insurance type), 58 as hospital-level variables (e.g., delivery volume), and 22 as community-level variables (e.g., neighborhood poverty).ConclusionsThe development of a risk adjustment strategy that will allow for SMM comparisons across hospitals or regions will require harmonization regarding: a) the standardization of the SMM definition; b) the data sources and population used; and c) the selection and definition of risk factors of interest.

Project description:Severe maternal morbidity conditions such as sepsis, embolism and cardiac arrest during the delivery hospitalization period can lead to extended length of hospital stays, life-long maternal health problems, and high medical costs. Most importantly, these conditions also contribute to the risk of maternal death. This population-based observational study proposed and evaluated the impact of expanding the Centers for Disease Control and Prevention (CDC) measure of severe maternal morbidity by including additional comorbidities and intensive care admissions during delivery hospitalizations and examined associated factors. A New York State linked hospitalization and birth record database was used. Study participants included all New York State female residents, ages 10 to 55 years, who delivered a live infant in a New York acute care hospital between 2008 and 2013, inclusive. Incidence trends for both severe maternal morbidity measures were evaluated longitudinally. Associations between covariates and the two severe maternal morbidity measures were examined with logistic regression models, solved using generalized estimating equations and stratified by method of delivery. The New York expanded severe maternal morbidity measure identified 34,478 cases among 1,352,600 hospital deliveries (estimated incidence 2.55%) representing a 3% increase in the number of cases compared to the CDC measure. Both estimates increased over the study period (p<0.001). Covariates with an odds ratio > 1.5 included most measured comorbidities (e.g., pregnancy-induced hypertension, placentation disorder), multiple births, preterm birth, no prenatal care, hospitalization prior to delivery, higher levels of perinatal care birthing facilities and race/ethnicity. Expanding the measure for severe maternal morbidity during delivery to capture intensive care admissions provides a more sensitive estimate of disease burden. Perinatal regionalization in New York appears effective in routing high risk pregnancies to higher levels of perinatal care birthing facilities.

Dataset Information

Population-attributable fraction of risk factors for severe maternal morbidity.

Background

Objective

Study design

Results

Conclusions

Publications

Population-attributable fraction of risk factors for severe maternal morbidity.

Similar Datasets

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