NCHS US Natality Data (2020)

Published: 19 December 2024| Version 2 | DOI: 10.17632/h2pnwgt5f2.2
Contributor:
Maria Ahmed

Description

In comparison to vaginal deliveries, caesarean (C-section) deliveries have been linked to poorer health outcomes for both mother and infant; nevertheless, the rate of C-section deliveries continues to rise worldwide (Keag et al., 2018; Negrini et al., 2021). Using the Natality Data (2020) from the US National Center for Health Statistics (NCHS) (N=3,619,826), this study analyzes structural inequalities and systemic racism that exist within ubiquitous social institutions, such as hospitals (Bonilla-Silva, 1997; Meyer & Rowan, 1977). We estimate multinomial logistical regression models to predict differences in risk of C-section deliveries across racialized and non-racialized mothers, as well as for immigrant women. We also investigate the effect of source of payment for the birth, both as a signal of socioeconomic status of the mother and cost to the hospital. This data set uses the following exclusion criteria: breech/transverse births, non-hospital births, "other" methods of payment. "Other" method of payment refers to any payment method used for the delivery other than private insurance, Medicaid, or self-pay. Moreover, any records missing data on race of mother were also excluded.

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Steps to reproduce

Dataset: National Center for Health Statistics (NCHS) . 2020. Natality Data [US], accessed January 20, 2022. Direct link to data: https://www.nber.org/research/data/vital-statistics-natality-birth-data Computational Requirements: Processor: Intel(R) Core (TM) i5-1035G1 CPU @ 1.00GHz 1.19 GHz Installed RAM: 8.00 GB (7.60 GB usable) System type: 64-bit operating system, x64-based processor Edition: Windows 10 Home Software: Stata/BE 18.0 for Windows (64-bit x86-64) Runtime: >1 minute per file Reproducibility Steps: Data cleaning (drop all missing data): Dependent Variable: 1. Method of Delivery: recode dmeth_rec (1=1 "Vaginal") (2=2 "C-Section") (9=.), gen (deliv) Independent Variables 1. Mother's Nativity: recode mbstate_rec (1=1 "Born in US") (2=0 "Immigrant") (3=.), gen (USborn) 2. Mother's Race: recode mracehisp (1=1 "White") (2=2 "Black") (7=3 "Hispanic") (4=4 "Asian") (3=5 "Other") (5/6=5 "Other") (8=.), gen (race5) 3. Method of Payment: recode pay_rec (1=1 "Medicaid") (2=2 "Private Insurance") (3=3 "Self-pay") (4=.) (9=.), gen (methpay) Covariates 1. Marital Status: recode dmar (1=1 "Married") (2=2 "Unmarried"), gen (marstat) 2. Mother's Education: recode meduc (1/3=1 "HS or less")(4/5=2 "Some College/Assoc") (6/8=3 "BA or more") (9=.), gen (edu) 3. Birth Location: recode bfacil (1=1 "Hospital") (2/3=2 "Birth Center/Clinic/Home") (6=2 "Birth Center/Clinic/Home") (4/5=.) (7=.) (9=.), gen (birthchoice) Controls 1. BMI: recode bmi_r (3/6=1 "Overweight") (1/2=2 "Not Overweight") (9=.), gen (obesity) 2. Smoking: recode smoke (1=1 "Smokes") (0=0 "Does Not Smoke") (9=.), gen (pregsmoke) 3. Prenatal care start term:recode precare5 (1=1 "1st term") (2/3=2 "2nd/3rd term") (4=3 "No care") (5=.), gen (prenatal) 4. Fetal Presentation: recode fet (10=1 "Cephalic") (11=2 "Breech") (12=3 "Other") (13=.), gen (fetalpres) 5. Pre-pregnancy diabetes: recode pdiab (1=1 "Pre-Diabetic") (0=0 "Not Diabetic") (9=.), gen (prediab) 6. Gestational diabetes: recode Gdiab (1=1 "Gest-Diabetes") (0=0 "No Gest-Diabetes") (9=.), gen (gestdiab) 7. Pre-pregnancy hypertension: recode phy (1=1 "Pre-Hypertension") (0=0 "No Hypertension") (9=.), gen (prehype) 8. Gestational hypertension: recode Ghyp (1=1 "Gest-Hypertension") (0=0 "No Gest-Hypertension") (9=.), gen (gesthype) 9. Previous C-section: recode pCS (1=1 "Previous CSection") (0=0 "No CSection") (9=.), gen (preCS) 10. Infertility treatment: recode infertreat (1=1 "Infertility Treatment") (0=0 "No Treatment") (9=.), gen (treat) 11. Mother's Age: recode mager9 (1/2=1 "<19yrs") (3/5=2 "20-34yrs") (6/9=3 "35+years"), gen (mage3) 12. Induction: recode ind (1=1 "Induced") (0=0 "Not Induced") (9=.), gen (induc) 13. Augmentation : recode aug (1=1 "Augmented") (0=0 "Not Augmented") (9=.), gen (augmen) Exclusion Criteria 1. Filter dataset based on hospital births and cephalic births only a. keep if (birthchoice==1) b. keep if (fetalpres==1)

Institutions

Western University

Categories

Social Inequality, Maternal Health, Socioeconomic Factor in Health, Asian Health, Black American Health, Hispanic Health, Marginalised Population

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