Characterization of SARS-CoV-2 antibodies in human milk from 21 women with confirmed COVID-19 infection.

BACKGROUND: One potential mechanism for protection from SARS-CoV-2 in children is through passive immunity via breast milk from a mother infected with the novel coronavirus. The primary objectives of this study were to establish the presence of SARS-CoV-2-specific IgA and IgG and to characterize the antigenic regions of SARS-CoV-2 proteins that were reactive with antibodies in breast milk. METHODS: Between March 2020 and September 2020, 21 women with confirmed SARS-CoV-2 infection were enrolled in Mommy's Milk. Participants donated serial breast milk samples around their time of illness. Breast milk samples were used to probe a multi-coronavirus protein microarray containing full-length and variable-length overlapping fragments of SARS-CoV-2 proteins. Samples were also tested against S and N proteins by electrochemiluminescence assay. RESULTS: The breast milk samples contained IgA reactive with a variety of SARS-CoV-2 antigens. The most IgA-reactive SARS-CoV-2 proteins were N (42.9% of women responded to ≥1 N fragment) and S proteins (23.9% responded to ≥1 fragment of S1 or S2). IgG responses were similar. A striking observation was the dissimilarity between mothers in antibody recognition, giving distinct antibody reactivity and kinetic profiles. CONCLUSIONS: Individual COVID-19 cases had diverse and unique milk IgA profiles following the onset of symptoms. IMPACT: In this observational longitudinal case series of 21 women with confirmed SARS-CoV-2 infection, IgA binding to SARS-CoV-2 proteins detected by orthologous proteome microarray and electrochemiluminescence assays was observed in >75% of women, but there was heterogeneity in which antigens and how many were reactive between women. Immunological profiles of protein regions recognized by each woman were distinct. Diverse repertoires of mucosal breast milk antibody to SARS-CoV-2 reflect heterogeneous passive transfer of maternal antibody to exposed breastfeeding infants.

Changing drug overdose deaths patterns where US rates are highest: global implications

BackgroundWest Virginia(WV) drug poisoning rates were low before 2000 but now are the nation’s highest. We explore the role of excessive opioid prescribing in imitating this epidemic.MethodsWV age-adjusted overdose fatality trends 1979 to 2020 were examined along with toxicology data from our medical examiner(ME) containing drug levels on all drug-involved deaths in WV. Percapita opioid prescribing rates was available 1997–2017.ResultsFatal drug overdose rates increased from 3.3/100,000 in 2000 to 83.6 in 2000. WV percapita morphine milligram equivalent (MME) opioid prescription rates increased from 137 in 1997 to 1,171 in 2011 (850% increase) but decreased to 554 in 2017. Corresponding with 2012 decline in prescribed opioids, prescription drug involvement deaths declined steadily while 2011–2013 overall overdose rates flattened. However, in 2012 heroin deaths began rising. In 2014 introduction of fentanyl began an unprecedented increase in overall fatality rates peaking in 2017. In 2018–19 rates declined but began to rise again corresponding with COVID-19 pandemic. More detailed time series analyses of temporal correlations of opioid prescription and overdose rates, including multivariate trends will be presented.DiscussionUnlike urban drug problems, the WV epidemic origins are largely iatrogenic, starting with extremely high opioid prescription rates. As predicted by the Iron Law of Prohibition, sudden decreases in the availability of prescription opioids were associated with an increased use of illicit, more potent opioids. This presentation will outline the progression of the rural drug problem in WV including harm-reduction efforts.Learning OutcomesDiscuss global implications of findings and lessons learned.

If You Build It, Will They Come? Is Test Site Availability a Root Cause of Geographic Disparities in COVID-19 Testing?

Objectives The purpose of this study was to examine the relationship between test site availability and testing rate within the context of social determinants of health. Study Design A retrospective ecological investigation was conducted using statewide COVID-19 testing data between March 2020 and December 2021. Methods Ordinary least squares and geographically weighted regression were used to estimate state and zip code level associations between testing rate and testing sites per capita, adjusting for neighbourhood level confounders. Results Findings indicate that site availability is positively associated with the zip code level testing rate and that this association is amplified in communities of greater economic deprivation. Additionally, economic deprivation is a key factor for consideration when examining ethnic differences in testing in medically underserved states. Conclusion The study findings could be used to guide delivery of testing facilities in resource-constrained states.

Brief report: Cause of death among people discharged from infective endocarditis related hospitalization-West Virginia, 2016-2019.

BACKGROUND AND OBJECTIVES: Compare proportion of all-cause and cause-specific mortality among West Virginia Medicaid enrollees who were discharged from infective endocarditis (IE) hospitalization with and without opioid use disorder (OUD) diagnosis. METHODS: The proportions of cause-specific deaths among those who were discharged from IE-related hospitalizations were compared by OUD diagnosis. RESULTS: The top three underlying causes of death discharged from IE hospitalization were accidental drug poisoning, mental and behavioral disorders due to polysubstance use, and cardiovascular diseases. Of the total deaths occurring among patients discharged after IE-related hospitalization, the proportion has increased seven times from 2016 to 2019 among the OUD deaths while it doubled among the non-OUD deaths. DISCUSSION AND CONCLUSIONS: Of the total deaths occurring among patients discharged after IE-related hospitalization, the increase is higher in those with OUD diagnosis. OUD is becoming a significantly negative impactor on the survival outcome among IE patients. It is of growing importance to deliver medication for OUD treatment and harm reduction efforts to IE patients in a timely manner, especially as the COVID-19 pandemic persists.

Predicting increases in COVID-19 incidence to identify locations for targeted testing in West Virginia: A machine learning enhanced approach.

During the COVID-19 pandemic, West Virginia developed an aggressive SARS-CoV-2 testing strategy which included utilizing pop-up mobile testing in locations anticipated to have near-term increases in SARS-CoV-2 infections. This study describes and compares two methods for predicting near-term SARS-CoV-2 incidence in West Virginia counties. The first method, Rt Only, is solely based on producing forecasts for each county using the daily instantaneous reproductive numbers, Rt. The second method, ML+Rt, is a machine learning approach that uses a Long Short-Term Memory network to predict the near-term number of cases for each county using epidemiological statistics such as Rt, county population information, and time series trends including information on major holidays, as well as leveraging statewide COVID-19 trends across counties and county population size. Both approaches used daily county-level SARS-CoV-2 incidence data provided by the West Virginia Department Health and Human Resources beginning April 2020. The methods are compared on the accuracy of near-term SARS-CoV-2 increases predictions by county over 17 weeks from January 1, 2021- April 30, 2021. Both methods performed well (correlation between forecasted number of cases and the actual number of cases week over week is 0.872 for the ML+Rt method and 0.867 for the Rt Only method) but differ in performance at various time points. Over the 17-week assessment period, the ML+Rt method outperforms the Rt Only method in identifying larger spikes. Results show that both methods perform adequately in both rural and non-rural predictions. Finally, a detailed discussion on practical issues regarding implementing forecasting models for public health action based on Rt is provided, and the potential for further development of machine learning methods that are enhanced by Rt.

Maternal and Child Outcomes Reported by Breastfeeding Women Following Messenger RNA COVID-19 Vaccination.

Background: In December 2020, two novel messenger RNA (mRNA) vaccines for severe acute respiratory syndrome coronavirus-2 received emergency use authorization from the U.S. Food and Drug Administration; however, the early trials excluded lactating women. Methods: Breastfeeding women residing in the United States who received either of the two mRNA vaccines were enrolled into the Mommy's Milk Human Milk Research Biorepository at the University of California, San Diego. From December 14, 2020 to February 1, 2021, 180 women who received two doses of either mRNA vaccine were recruited into the study. Results: Similar proportions of women reported any one or more symptoms following vaccination with either mRNA vaccine. In addition, the frequency by specific type of symptom did not differ by brand. However, following the second dose of vaccine, women who received the Moderna brand were significantly more likely to report symptoms. A small proportion of women following the first dose of either vaccine brand reported a reduction in milk supply, and significantly, more women reported a reduction in milk supply following the second dose of Moderna. Few infant events were reported for either vaccine brand following either dose, and no serious adverse events were reported. Conclusions: These data are reassuring regarding the safety of vaccination in breastfeeding women and their breastfed children with either of the mRNA COVID-19 vaccines.