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Ir J Med Sci ; 2021 Dec 02.
Article in English | MEDLINE | ID: covidwho-1544557


BACKGROUND: The COVID-19 pandemic has changed how maternity care services are provided worldwide. To contain the virus, many providers reduced the number of face-to-face visits for women. In addition, partner attendance was prohibited in many circumstances to protect staff, and other service users, from potential infection. AIMS: To explore women's experience of pregnancy and birth in the Republic of Ireland during the COVID-19 pandemic. METHODS: A qualitative study with 14 women was conducted using a grounded theory approach. Data were collected between April and July 2020, and in-depth interviews were conducted either in pregnancy or in the first 12 weeks after the birth. RESULTS: Six categories emerged: loss of normality, navigating "new" maternity care systems, partners as bystanders, balancing information, uncertainty, and unexpected benefits of pregnancy during the pandemic. While benefits were reported (working from home and additional time spent with partners during the "fourth trimester"), in general, the themes were of increased anxiety and uncertainty. CONCLUSION: The pandemic caused additional anxiety for pregnant women. This was exacerbated by uncertainty about the effects of COVID-19 on pregnancy and unclear messaging about restrictions. More interactive and personalized communication is required to support women to cope with uncertainty during a pandemic. The birth partner plays an important role as an advocate for women and excluding them from pregnancy care caused additional anxiety for pregnant women. Containment strategies for a pandemic should be developed with this in mind, to view the family as a unit rather than the woman in isolation.

Mil Med ; 2021 Oct 06.
Article in English | MEDLINE | ID: covidwho-1455331


INTRODUCTION: Early identification of patients with coronavirus disease 2019 (COVID-19) who are at risk for hospitalization may help to mitigate disease burden by allowing healthcare systems to conduct sufficient resource and logistical planning in the event of case surges. We sought to develop and validate a clinical risk score that uses readily accessible information at testing to predict individualized 30-day hospitalization risk following COVID-19 diagnosis. METHODS: We assembled a retrospective cohort of U.S. Veterans Health Administration patients (age ≥ 18 years) diagnosed with COVID-19 between March 1, 2020, and December 31, 2020. We screened patient characteristics using Least Absolute Shrinkage and Selection Operator logistic regression and constructed the risk score using characteristics identified as most predictive for hospitalization. Patients diagnosed before November 1, 2020, comprised the development cohort, while those diagnosed on or after November 1, 2020, comprised the validation cohort. We assessed risk score discrimination by calculating the area under the receiver operating characteristic (AUROC) curve and calibration using the Hosmer-Lemeshow (HL) goodness-of-fit test. This study was approved by the Veteran's Institutional Review Board of Northern New England at the White River Junction Veterans Affairs Medical Center (Reference no.:1473972-1). RESULTS: The development and validation cohorts comprised 11,473 and 12,970 patients, of whom 4,465 (38.9%) and 3,669 (28.3%) were hospitalized, respectively. The independent predictors for hospitalization included in the risk score were increasing age, male sex, non-white race, Hispanic ethnicity, homelessness, nursing home/long-term care residence, unemployed or retired status, fever, fatigue, diarrhea, nausea, cough, diabetes, chronic kidney disease, hypertension, and chronic obstructive pulmonary disease. Model discrimination and calibration was good for the development (AUROC = 0.80; HL P-value = .05) and validation (AUROC = 0.80; HL P-value = .31) cohorts. CONCLUSIONS: The prediction tool developed in this study demonstrated that it could identify patients with COVID-19 who are at risk for hospitalization. This could potentially inform clinicians and policymakers of patients who may benefit most from early treatment interventions and help healthcare systems anticipate capacity surges.