Related Experiment Video
Updated: Aug 14, 2025

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
Published on: January 8, 2020
Industry and occupation in California birth certificates (1998-2019): Reporting disparities and classification
Linda L Remy1, Louise Kaseff1, Rita Shiau1
1Family Health Outcomes Project (FHOP), Family and Community Medicine, School of Medicine, University of California San Francisco, San Francisco, California, USA.
Background:
Missing and noncodable parental industry and occupation (I/O) information on birth certificates (BCs) can bias analyses informing parental worksite exposures and family economic stability.
Methods:
We used the National Institute for Occupational Safety and Health (NIOSH) software to code parental I/O in 1989-2019 California BC data (N = 21,739,406). We assessed I/O missingness and codability by reporting period, parental sex, race/ethnicity, age, and education.
Results:
During 1989-2019, records missing I/O increased from 4.4% to 9.4%. I/O was missing more frequently from parents who were male (7.8% vs. 4.4%), Black or American Indian/Alaska Native (AIAN) (9.3% and 8.9% vs. 3.2%-4.7% in others), and had high school or less education (4.0%-5.9% vs. 1.4%-2.6% in others). Of records with I/O, less than 2% were noncodable by NIOSH software. Noncodable entries were more common for parents who were male (industry (1.9% vs. 1.0%); occupation (1.5% vs. 0.7%)), Asian/Pacific Islander (industry (2.4% vs. 1.2%-1.6% in other groups); occupation (1.7% vs. 0.7%-1.5% in other groups)), age 40 and older (industry (2.1% vs. 0.4%-1.7% in younger groups); occupation (1.7% vs. 0.3%-1.3% in younger groups)), and 4-year college graduates (industry (2.0% vs. 1.0%-1.9% in other groups); occupation (1.7% vs. 0.5%-1.4%)).
Conclusions:
In California BC, I/O missingness was systematically higher among parents who are male, Black, AIAN, less than 20 years old, and report no college education. I/O codability is high when information is reported, with small percentage disparities. Improving data collection is vital to equitably describe economic contexts that determine important family outcomes.
More Related Videos
07:50A Metadata Extraction Approach for Clinical Case Reports to Enable Advanced Understanding of Biomedical Concepts
Published on: September 20, 2018
09:55Bridging the Technology Divide in the COVID-19 Era: Using Virtual Outreach to Expose Middle and High School Students to Imaging Technology
Published on: September 28, 2022
Related Concept Videos
Data Reporting and Recording
How Data are Classified: Categorical Data
Data are classified based on whether they are measurable or not. Categorical data cannot be measured; instead, it can be divided into categories. For example, if Y denotes a person's party affiliation, some examples of Y include...
Applications of Life Tables
Classification of Illness
An illness is a response to a disease in which the person's level of functioning is changed compared with a previous level. The general classification of illness includes acute and chronic.
Acute illness is severe...
Statistical Methods for Analyzing Epidemiological Data
How Data are Classified: Numerical Data
Quantitative data may be either discrete or continuous. All quantitative data that take on only specific numerical...