Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study
Introduction The COVID-19 pandemic has affected communities of colour the hardest. Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation,...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2022-06-01
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| Series: | BMJ Open |
| Online Access: | https://bmjopen.bmj.com/content/12/6/e062294.full |
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| author | Bankole Olatosi Jiajia Zhang Xiaoming Li Chen Liang Jihong Liu Peiyin Hung Shan Qiao Berry A Campbell Myriam E Torres Neset Hikmet |
| author_facet | Bankole Olatosi Jiajia Zhang Xiaoming Li Chen Liang Jihong Liu Peiyin Hung Shan Qiao Berry A Campbell Myriam E Torres Neset Hikmet |
| author_sort | Bankole Olatosi |
| collection | DOAJ |
| description | Introduction The COVID-19 pandemic has affected communities of colour the hardest. Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (>200 000 births). We will also conduct similar analyses using EHR data from the National COVID-19 Cohort Collaborative including >270 000 women who had a childbirth between 2018 and 2021 in the USA. We will use a convergent parallel design which includes a quantitative analysis of data from the 2018–2021 SC Pregnancy Risk Assessment and Monitoring System (unweighted n>2000) and in-depth interviews of 40 postpartum women and 10 maternal care providers to identify distinct mediating pathways.Ethics and dissemination The study was approved by institutional review boards at the University of SC (Pro00115169) and the SC Department of Health and Environmental Control (DHEC IRB.21-030). Informed consent will be provided by the participants in the in-depth interviews. Study findings will be disseminated with key stakeholders including patients, presented at academic conferences and published in peer-reviewed journals. |
| format | Article |
| id | doaj-art-642e5d6fa7f048de9be0015c6c331d3a |
| institution | Kabale University |
| issn | 2044-6055 |
| language | English |
| publishDate | 2022-06-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open |
| spelling | doaj-art-642e5d6fa7f048de9be0015c6c331d3a2025-01-28T04:40:10ZengBMJ Publishing GroupBMJ Open2044-60552022-06-0112610.1136/bmjopen-2022-062294Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods studyBankole Olatosi0Jiajia Zhang1Xiaoming Li2Chen Liang3Jihong Liu4Peiyin Hung5Shan Qiao6Berry A Campbell7Myriam E Torres8Neset Hikmet9South Carolina SmartState Center for Healthcare Quality, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USA1Bloomberg~Kimmel Institute for Cancer Immunotherapy, Baltimore, MD, USADepartment of Health Promotion, Education, & Behavior, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USA1 Fralin Biomedical Research Institute at VTC, Roanoke, Virginia, USA2Sun Yat-sen University, Guangzhou, ChinaDepartment of Health Services Policy and Management, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USADepartment of Health Promotion, Education and Behavior, Arnold School of Public Health, University of South Carolina, Columbia, South Carolina, USADepartment of Health Services Policy & Management, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USADepartment of Epidemiology & Biostatistics, University of South Carolina Arnold School of Public Health, Columbia, South Carolina, USADepartment of Integrated Information Technology, University of South Carolina College of Engineering and Computing, Columbia, South Carolina, USAIntroduction The COVID-19 pandemic has affected communities of colour the hardest. Non-Hispanic black and Hispanic pregnant women appear to have disproportionate SARS-CoV-2 infection and death rates.Methods and analysis We will use the socioecological framework and employ a concurrent triangulation, mixed-methods study design to achieve three specific aims: (1) examine the impacts of the COVID-19 pandemic on racial/ethnic disparities in severe maternal morbidity and mortality (SMMM); (2) explore how social contexts (eg, racial/ethnic residential segregation) have contributed to the widening of racial/ethnic disparities in SMMM during the pandemic and identify distinct mediating pathways through maternity care and mental health; and (3) determine the role of social contextual factors on racial/ethnic disparities in pregnancy-related morbidities using machine learning algorithms. We will leverage an existing South Carolina COVID-19 Cohort by creating a pregnancy cohort that links COVID-19 testing data, electronic health records (EHRs), vital records data, healthcare utilisation data and billing data for all births in South Carolina (SC) between 2018 and 2021 (>200 000 births). We will also conduct similar analyses using EHR data from the National COVID-19 Cohort Collaborative including >270 000 women who had a childbirth between 2018 and 2021 in the USA. We will use a convergent parallel design which includes a quantitative analysis of data from the 2018–2021 SC Pregnancy Risk Assessment and Monitoring System (unweighted n>2000) and in-depth interviews of 40 postpartum women and 10 maternal care providers to identify distinct mediating pathways.Ethics and dissemination The study was approved by institutional review boards at the University of SC (Pro00115169) and the SC Department of Health and Environmental Control (DHEC IRB.21-030). Informed consent will be provided by the participants in the in-depth interviews. Study findings will be disseminated with key stakeholders including patients, presented at academic conferences and published in peer-reviewed journals.https://bmjopen.bmj.com/content/12/6/e062294.full |
| spellingShingle | Bankole Olatosi Jiajia Zhang Xiaoming Li Chen Liang Jihong Liu Peiyin Hung Shan Qiao Berry A Campbell Myriam E Torres Neset Hikmet Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study BMJ Open |
| title | Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study |
| title_full | Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study |
| title_fullStr | Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study |
| title_full_unstemmed | Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study |
| title_short | Multilevel determinants of racial/ethnic disparities in severe maternal morbidity and mortality in the context of the COVID-19 pandemic in the USA: protocol for a concurrent triangulation, mixed-methods study |
| title_sort | multilevel determinants of racial ethnic disparities in severe maternal morbidity and mortality in the context of the covid 19 pandemic in the usa protocol for a concurrent triangulation mixed methods study |
| url | https://bmjopen.bmj.com/content/12/6/e062294.full |
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