754: Population attributable fraction of modifiable risk factors of severe maternal morbidity

2018 ◽  
Vol 218 (1) ◽  
pp. S452
Author(s):  
Kyle Freese ◽  
Lisa M. Bodnar ◽  
Maria M. Brooks ◽  
Katherine P. Himes
2020 ◽  
Vol 2 (1) ◽  
pp. 100066 ◽  
Author(s):  
Kyle E. Freese ◽  
Lisa M. Bodnar ◽  
Maria M. Brooks ◽  
Kathleen McTigue ◽  
Katherine P. Himes

2012 ◽  
Vol 26 (6) ◽  
pp. 506-514 ◽  
Author(s):  
Kristen E. Gray ◽  
Erin R. Wallace ◽  
Kailey R. Nelson ◽  
Susan D. Reed ◽  
Melissa A. Schiff

Author(s):  
Lisa M. Korst ◽  
Kimberly D. Gregory ◽  
Lisa A. Nicholas ◽  
Samia Saeb ◽  
David J. Reynen ◽  
...  

Abstract Background Current 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. Objective To perform a scoping review to identify methodological considerations and potential covariates for risk adjustment for delivery-associated SMM. Search methods Following 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 criteria Included 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 analysis Information 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 results Eligibility 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). Conclusions The 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.


2018 ◽  
Vol 56 (2) ◽  
pp. 151-158 ◽  
Author(s):  
Janhavi R. Raut ◽  
Regina M. Simeone ◽  
Sarah C. Tinker ◽  
Mark A. Canfield ◽  
R. Sue Day ◽  
...  

Objective: Estimate the population attributable fraction (PAF) for a set of recognized risk factors for orofacial clefts. Design: We used data from the National Birth Defects Prevention Study. For recognized risk factors for which data were available, we estimated crude population attributable fractions (cPAFs) to account for potential confounding, average-adjusted population attributable fractions (aaPAFs). We assessed 11 modifiable and 3 nonmodifiable parental/maternal risk factors. The aaPAF for individual risk factors and the total aaPAF for the set of risk factors were calculated using a method described by Eide and Geffler. Setting: Population-based case–control study in 10 US states. Participants: Two thousand seven hundred seventy-nine cases with isolated cleft lip with or without cleft palate (CL±P), 1310 cases with isolated cleft palate (CP), and 11 692 controls with estimated dates of delivery between October 1, 1997, and December 31, 2011. Main Outcome Measures: Crude population attributable fraction and aaPAF. Results: The proportion of CL±P and CP cases attributable to the full set of examined risk factors was 50% and 43%, respectively. The modifiable factor with the largest aaPAF was smoking during the month before pregnancy or the first month of pregnancy (4.0% for CL±P and 3.4% for CP). Among nonmodifiable factors, the factor with the largest aaPAF for CL±P was male sex (27%) and for CP it was female sex (16%). Conclusions: Our results may inform research and prevention efforts. A large proportion of orofacial cleft risk is attributable to nonmodifiable factors; it is important to better understand the mechanisms involved for these factors.


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