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Ulrich SE, Sugg MM, Desjardins MR, Runkle JD. Disparities in spatiotemporal clustering of maternal mental health conditions before and during the COVID-19 pandemic. Health Place 2024; 89:103307. [PMID: 38954963 DOI: 10.1016/j.healthplace.2024.103307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/21/2023] [Revised: 04/15/2024] [Accepted: 06/23/2024] [Indexed: 07/04/2024]
Abstract
Mounting evidence indicates the worsening of maternal mental health conditions during the COVID-19 pandemic. Mental health conditions are the leading cause of preventable death during the perinatal and postpartum periods. Our study sought to detect space-time patterns in the distribution of maternal mental health conditions in pregnant women before (2016-2019) and during (2020-2021) the COVID-19 pandemic in North Carolina, USA. Using the space-time Poisson model in SaTScan, we performed univariate and multivariate cluster analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), maternal mental disorders of pregnancy (MDP), suicidal thoughts, and suicide attempts during the pre-pandemic and pandemic periods. Clusters were adjusted for age, race, and insurance type. Significant multivariate and univariate PMAD, SMI, and MDP clustering persisted across both periods in North Carolina, while univariate clustering for both suicide outcomes decreased during the pandemic. Local relative risk (RR) for all conditions increased drastically in select locations. The number of zip code tabulation areas (ZCTAs) included in clusters decreased, while the proportion of urban locations included in clusters increased for non-suicide outcomes. Average yearly case counts for all maternal mental health outcomes increased during the pandemic. Results provide contextual and spatial information concerning at-risk maternal populations with a high burden of perinatal mental health disorders before and during the pandemic and emphasize the necessity of urgent and targeted expansion of mental health resources in select communities.
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Affiliation(s)
- Sarah E Ulrich
- Department of Geography and Planning, P.O. Box 32066, Appalachian State University, Boone, NC, 28608, USA.
| | - Margaret M Sugg
- Department of Geography and Planning, P.O. Box 32066, Appalachian State University, Boone, NC, 28608, USA.
| | - Michael R Desjardins
- Department of Epidemiology & Spatial Science for Public Health Center, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, 21205, USA
| | - Jennifer D Runkle
- North Carolina Institute for Climate Studies, North Carolina State University, 151 Patton Avenue, Asheville, NC, 28801, USA
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Ulrich SE, Sugg MM, Ryan SC, Runkle JD. Mapping high-risk clusters and identifying place-based risk factors of mental health burden in pregnancy. SSM - MENTAL HEALTH 2023; 4:100270. [PMID: 38230394 PMCID: PMC10790331 DOI: 10.1016/j.ssmmh.2023.100270] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/18/2024] Open
Abstract
Purpose Despite affecting up to 20% of women and being the leading cause of preventable deaths during the perinatal and postpartum period, maternal mental health conditions are chronically understudied. This study is the first to identify spatial patterns in perinatal mental health conditions, and relate these patterns to place-based social and environmental factors that drive cluster development. Methods We performed spatial clustering analysis of emergency department (ED) visits for perinatal mood and anxiety disorders (PMAD), severe mental illness (SMI), and maternal mental disorders of pregnancy (MDP) using the Poisson model in SatScan from 2016 to 2019 in North Carolina. Logistic regression was used to examine the association between patient and community-level factors and high-risk clusters. Results The most significant spatial clustering for all three outcomes was concentrated in smaller urban areas in the western, central piedmont, and coastal plains regions of the state, with odds ratios greater than 3 for some cluster locations. Individual factors (e.g., age, race, ethnicity) and contextual factors (e.g., racial and socioeconomic segregation, urbanity) were associated with high risk clusters. Conclusions Results provide important contextual and spatial information concerning at-risk populations with a high burden of maternal mental health disorders and can better inform targeted locations for the expansion of maternal mental health services.
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Affiliation(s)
- Sarah E. Ulrich
- Department of Geography and Planning, P.O. Box 32066, Appalachian State University, Boone, NC, 28608, USA
| | - Margaret M. Sugg
- Department of Geography and Planning, P.O. Box 32066, Appalachian State University, Boone, NC, 28608, USA
| | - Sophia C. Ryan
- Department of Geography and Planning, P.O. Box 32066, Appalachian State University, Boone, NC, 28608, USA
| | - Jennifer D. Runkle
- North Carolina Institute for Climate Studies, North Carolina State University, 151 Patton Avenue, Asheville, NC, 28801, USA
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Liu J, Hung P, Zhang J, Olatosi B, Shih Y, Liang C, Campbell BA, Hikmet N, Li X. Severe maternal morbidity by race and ethnicity before vs. during the COVID-19 pandemic. Ann Epidemiol 2023; 88:51-61. [PMID: 37952778 PMCID: PMC10843780 DOI: 10.1016/j.annepidem.2023.11.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/10/2023] [Revised: 11/06/2023] [Accepted: 11/08/2023] [Indexed: 11/14/2023]
Abstract
PURPOSE To examine the change in racial disparity in severe maternal morbidity (SMM) during the COVID-19 pandemic and the associations between SARS-CoV-2 infection and SMM. METHODS This retrospective cohort study used linked databases of all livebirths delivered between 2018 and 2021 in South Carolina (n = 162,576). Exposures were 1) pre-pandemic and pandemic periods (before vs. March 2020 onwards); 2) SARS-CoV-2 infection, severity, and timing of first infection. Log-binomial regression models were used. RESULTS SMM rate was higher among pandemic childbirths than pre-pandemic period (p = 0.06). The risk of SMM among Hispanics was doubled from pre-pandemic to pandemic periods (adjusted relative risk (aRR)= 2.50, 95% CI: 1.27, 4.94). During pre-pandemic, compared to White women, Black women (aRR=1.37, 95% CI: 1.14-1.64), while Hispanics had lower risk of SMM (aRR=0.42, 95% CI: 0.24-0.73). During the pandemic, the Black-White difference in the risk of SMM persisted (aRR=1.24, 95% CI: 1.00-1.54) and Hispanic-White difference in SMM risk became insignificant (aRR=0.85, 95% CI: 0.54-1.34). SARS-CoV-2 infection, its severity, and the late diagnosis were associated with 1.78-5.06 times higher risk of SMM. CONCLUSIONS During pandemic, Black-White racial disparity in SMM persisted but the relative pre-pandemic advantage in SMM among Hispanic women over White women disappeared during the pandemic.
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Affiliation(s)
- Jihong Liu
- Department of Epidemiology & Biostatistics, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA.
| | - Peiyin Hung
- Department of Health Services Policy & Management, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA; Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA
| | - Jiajia Zhang
- Department of Epidemiology & Biostatistics, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA; Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA
| | - Bankole Olatosi
- Department of Health Services Policy & Management, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA; Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA
| | - Yiwen Shih
- Department of Health Services Policy & Management, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA
| | - Chen Liang
- Department of Health Services Policy & Management, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA; Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA
| | - Berry A Campbell
- Department of Obstetrics and Gynecology, School of Medicine, University of South Carolina, Two Medical Park, Columbia, SC 29203, USA
| | - Neset Hikmet
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA; Department of Integrated Information Technology, College of Engineering and Computing, University of South Carolina, 550 Assembly Street, Columbia, SC 29208, USA
| | - Xiaoming Li
- Big Data Health Science Center, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA; Department of Health Promotion, Education, & Behavior, Arnold School of Public Health, University of South Carolina, 915 Greene Street, Columbia, SC 29208, USA
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Sugg MM, Runkle JD, Ryan S, Wertis L. A Difference-In Difference Analysis of the South Carolina 2015 Extreme Floods and the Association with Maternal Health. INTERNATIONAL JOURNAL OF DISASTER RISK REDUCTION : IJDRR 2023; 97:104037. [PMID: 38525445 PMCID: PMC10956501 DOI: 10.1016/j.ijdrr.2023.104037] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
Research documenting the public health impacts of natural disasters often focuses on adults and children. Little research has examined the influence of extreme events, like floods, on maternal health, and less has examined the effect of disasters on maternal indicators like severe maternal morbidity (SMM) or unexpected outcomes of labor and delivery that result in significant short-or long-term consequences to a woman's health. The aim of this study is to identify the impacts of the 2015 flood events on maternal health outcomes in South Carolina, USA. We employ a quasi-experimental design using a difference-in-difference analysis with log-binomial regressions to evaluate maternal outcomes for impacted and control locations during the disaster event. Unlike previous studies, we extended our difference-in-difference analysis to include a trimester of exposure to assess the timing of flood exposure. We did not find evidence of statistically significant main effects on maternal health from the 2015 flood events related to preterm birth, gestational diabetes, mental disorders of pregnancy, depression, and generalized anxiety. However, we did find a statistically significant increase in SMM and low birth weight during the flood event for women in select trimester periods who were directly exposed. Our work provides new evidence on the effects of extreme flood events, like the 2015 floods, which can impact maternal health during specific exposure periods of pregnancy. Additional research is needed across other extreme weather events, as the unique context of the 2015 floods limits the generalizability of our findings.
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Affiliation(s)
- Maggie M Sugg
- Department of Geography and Planning, Appalachian State University, Boone, North Carolina
| | - Jennifer D Runkle
- North Carolina Institute for Climate Studies, North Carolina State University, Asheville, North Carolina
| | - Sophie Ryan
- Department of Geography and Planning, Appalachian State University, Boone, North Carolina
| | - Luke Wertis
- Department of Geography and Planning, Appalachian State University, Boone, North Carolina
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Jiao A, Sun Y, Avila C, Chiu V, Slezak J, Sacks DA, Abatzoglou JT, Molitor J, Chen JC, Benmarhnia T, Getahun D, Wu J. Analysis of Heat Exposure During Pregnancy and Severe Maternal Morbidity. JAMA Netw Open 2023; 6:e2332780. [PMID: 37676659 PMCID: PMC10485728 DOI: 10.1001/jamanetworkopen.2023.32780] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/24/2023] [Accepted: 07/31/2023] [Indexed: 09/08/2023] Open
Abstract
Importance The rate of severe maternal morbidity (SMM) is continuously increasing in the US. Evidence regarding the associations of climate-related exposure, such as environmental heat, with SMM is lacking. Objective To examine associations between long- and short-term maternal heat exposure and SMM. Design, Setting, and Participants This retrospective population-based epidemiological cohort study took place at a large integrated health care organization, Kaiser Permanente Southern California, between January 1, 2008, and December 31, 2018. Data were analyzed from February to April 2023. Singleton pregnancies with data on SMM diagnosis status were included. Exposures Moderate, high, and extreme heat days, defined as daily maximum temperatures exceeding the 75th, 90th, and 95th percentiles of the time series data from May through September 2007 to 2018 in Southern California, respectively. Long-term exposures were measured by the proportions of different heat days during pregnancy and by trimester. Short-term exposures were represented by binary variables of heatwaves with 9 different definitions (combining percentile thresholds with 3 durations; ie, ≥2, ≥3, and ≥4 consecutive days) during the last gestational week. Main Outcomes and Measures The primary outcome was SMM during delivery hospitalization, measured by 20 subconditions excluding blood transfusion. Discrete-time logistic regression was used to estimate associations with long- and short-term heat exposure. Effect modification by maternal characteristics and green space exposure was examined using interaction terms. Results There were 3446 SMM cases (0.9%) among 403 602 pregnancies (mean [SD] age, 30.3 [5.7] years). Significant associations were observed with long-term heat exposure during pregnancy and during the third trimester. High exposure (≥80th percentile of the proportions) to extreme heat days during pregnancy and during the third trimester were associated with a 27% (95% CI, 17%-37%; P < .001) and 28% (95% CI, 17%-41%; P < .001) increase in risk of SMM, respectively. Elevated SMM risks were significantly associated with short-term heatwave exposure under all heatwave definitions. The magnitude of associations generally increased from the least severe (HWD1: daily maximum temperature >75th percentile lasting for ≥2 days; odds ratio [OR], 1.32; 95% CI, 1.17-1.48; P < .001) to the most severe heatwave exposure (HWD9: daily maximum temperature >95th percentile lasting for ≥4 days; OR, 2.39; 95% CI, 1.62-3.54; P < .001). Greater associations were observed among mothers with lower educational attainment (OR for high exposure to extreme heat days during pregnancy, 1.43; 95% CI, 1.26-1.63; P < .001) or whose pregnancies started in the cold season (November through April; OR, 1.37; 95% CI, 1.24-1.53; P < .001). Conclusions and Relevance In this retrospective cohort study, long- and short-term heat exposure during pregnancy was associated with higher risk of SMM. These results might have important implications for SMM prevention, particularly in a changing climate.
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Affiliation(s)
- Anqi Jiao
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine
| | - Yi Sun
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine
- Institute of Medical Information, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, China
| | - Chantal Avila
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena
| | - Vicki Chiu
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena
| | - Jeff Slezak
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena
| | - David A. Sacks
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena
- Department of Obstetrics and Gynecology, University of Southern California, Keck School of Medicine, Los Angeles
| | | | - John Molitor
- College of Public Health and Human Sciences, Oregon State University, Corvallis
| | - Jiu-Chiuan Chen
- Department of Population and Public Health Sciences, University of Southern California, Los Angeles
| | - Tarik Benmarhnia
- Scripps Institution of Oceanography, University of California, San Diego
| | - Darios Getahun
- Department of Research & Evaluation, Kaiser Permanente Southern California, Pasadena
- Department of Health Systems Science, Kaiser Permanente Bernard J. Tyson School of Medicine, Pasadena, California
| | - Jun Wu
- Department of Environmental and Occupational Health, Program in Public Health, University of California, Irvine
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Jeffers NK, Berger BO, Marea CX, Gemmill A. Investigating the impact of structural racism on black birthing people - associations between racialized economic segregation, incarceration inequality, and severe maternal morbidity. Soc Sci Med 2023; 317:115622. [PMID: 36542927 PMCID: PMC9910389 DOI: 10.1016/j.socscimed.2022.115622] [Citation(s) in RCA: 12] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/24/2022] [Revised: 12/12/2022] [Accepted: 12/14/2022] [Indexed: 12/23/2022]
Abstract
Black birthing people are twice as likely to experience severe maternal morbidity (SMM) as their white counterparts. Structural racism provides a framework for understanding root causes of perinatal health disparities. Our objective was to investigate associations between measures of structural racism and severe maternal morbidity (SMM) among Black birthing people in the US. We linked delivery hospitalizations for Black birthing people in the National Inpatient Sample (2008-2011) with data from the American Community Survey 5-year estimates and the Vera Institute of Justice Incarceration Trends datasets (2008-2011). Structural racism measures included the Index of Concentration at the Extremes for race and income (i.e., racialized economic segregation) and Black-white incarceration inequality, assessed as quintiles by hospital county. Multilevel logistic regression assessed the relationship between these county-level indicators of structural racism and SMM. Black birthing people delivering in quintiles 5 (concentrated deprivation; OR = 1.45, 95% CI = 1.16-1.81) and 3 (OR = 1.27, 95% CI = 1.04-1.56) experienced increased odds of SMM compared to those in quintile 1 (concentrated privilege). After adjusting for individual characteristics, obstetric comorbidities, and hospital characteristics the odds of SMM remained elevated for Black birthing people delivering in quintiles 5 (aOR = 1.32, 95% CI = 1.02-1.71) and 3 (aOR = 1.24, 95% CI = 1.02-1.51). Delivering in the quintile with the highest incarceration inequality (Q5) was not significantly associated with SMM (aOR = 0.95, 95% CI = 0.72-1.25) compared to those delivering in counties with the lowest incarceration inequality (Q1). In this national-level study, racialized economic segregation was associated with SMM among Black birthing people. Our findings highlight the need to promote maternal and perinatal health equity through actionable policies that prioritize investment in communities experiencing deprivation.
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Affiliation(s)
- Noelene K Jeffers
- Johns Hopkins Bloomberg School of Public Health, Department of Population Family, And Reproductive Health, 615 N. Wolfe Street, Baltimore, MD, 21205, United States.
| | - Blair O Berger
- Johns Hopkins Bloomberg School of Public Health, Department of Population Family, And Reproductive Health, 615 N. Wolfe Street, Baltimore, MD, 21205, United States.
| | - Christina X Marea
- Georgetown University School of Nursing & Health Studies, Department of Advanced Nursing Practice, St. Mary's Hall 3700 Reservoir Road, N.W., Washington D.C, 20057-1107, United States.
| | - Alison Gemmill
- Johns Hopkins Bloomberg School of Public Health, Department of Population Family, And Reproductive Health, 615 N. Wolfe Street, Baltimore, MD, 21205, United States.
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Spatial analysis of mental health and suicide clustering among older adults in North Carolina: An exploratory analysis. SSM - MENTAL HEALTH 2022. [DOI: 10.1016/j.ssmmh.2022.100162] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022] Open
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