Oncotarget · 2015
Mutational spectrum of adult T-ALL.
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Neumann Martin, Vosberg Sebastian, Schlee Cornelia, Heesch Sandra, Schwartz Stefan, Gökbuget Nicola, Hoelzer Dieter, Graf Alexander, Krebs Stefan, Bartram Isabelle, Blum Helmut, Brüggemann Monika, Hecht Jochen, Bohlander Stefan K, Greif Philipp A, Baldus Claudia D
Аннотация
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.
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.
Предметные рубрики MeSH
AdolescentAdultAgedBiomarkers, TumorDNA Mutational AnalysisDrug Resistance, NeoplasmFemaleGene FrequencyGenetic Predisposition to DiseaseHumansMaleMiddle AgedMutationPhenotypePolymorphism, Single NucleotidePrecision MedicinePrecursor T-Cell Lymphoblastic Leukemia-LymphomaPredictive Value of TestsRecurrenceReelin ProteinTreatment OutcomeYoung Adult