Mutational spectrum of adult T-ALL.

BACKGROUND

Certain industries and occupations (I&O) are linked to higher SARS-CoV-2 risk. Research on this association among pregnant women has been limited to clinical settings or small samples.

OBJECTIVES

To estimate occupational risk of SARS-CoV-2 infection during pregnancy by applying machine-learning-based I&O coding to population-based data.

METHODS

We performed population-based analyses of 116,424 Massachusetts residents aged 18-54 with in-state live-birth deliveries during 4/1/2020-12/31/2021. SARS-CoV-2 infections were obtained from statewide case surveillance and matched to birth certificates. We used the NIOSH Industry and Occupation Computerized Coding System (NIOCCS) to code I&O text from birth certificates. We used generalized estimating equations with log link, Poisson distribution and exchangeable correlation to estimate adjusted risk ratios (aRR) and 95% confidence intervals (CI) for SARS-CoV-2 infection comparing each I&O group to low-risk I&O reference groups, adjusting for race/ethnicity, age, education, insurance, language, and nativity.

RESULTS

Among the study population, 66.7% (n = 77,596) had complete employment data; of these, 4744 (6.1%) had a laboratory-confirmed SARS-CoV-2 infection during pregnancy. By industry, women working in healthcare/social assistance (aRR = 1.63; 95%CI 1.45-1.84) and public administration (aRR = 1.59; 95%CI 1.31-1.93) had highest risk of infection. By occupation, women in protective services (aRR = 1.82; 95%CI 1.32-2.51) and health care practitioners and technical occupations (aRR = 1.49; 95%CI 1.32-1.68) had highest infection risk.

CONCLUSIONS

During the first two years of the COVID-19 pandemic, women in healthcare and social assistance industries and protective service occupations had highest risk of SARS-CoV-2 infection in pregnancy. NIOCCS facilitates use of birth certificate I&O text data to assess risk of occupational exposures on maternal/infant health outcomes.