Predictors of Evacuation Rates during Hurricane Laura: Weather Forecasts, Twitter, and COVID-19

Machine learning was applied to predict evacuation rates for all census tracts affected by Hurricane Laura. The evacuation ground truth was derived from cellular telephone–based mobility data. Twitter data, census data, geographical data, COVID-19 case rates, the social vulnerability index from the Centers for Disease Control and Prevention (CDC)/Agency for Toxic Substances and Disease Registry (ATSDR), and relevant weather and physical data were used to do the prediction. Random forests were found to perform well, with a mean absolute percent error of 4.9% on testing data. Feature importance for prediction was analyzed using Shapley additive explanations and it was found that previous evacuation, rainfall forecasts, COVID-19 case rates, and Twitter data rank highly in terms of importance. Social vulnerability indices were also found to show a very consistent relationship with evacuation rates, such that higher vulnerability consistently implies lower evacuation rates. These findings can help with hurricane evacuation preparedness and planning as well as real-time assessment. © 2023 American Meteorological Society.

Senegal Farmer Networks Respond to COVID-19

This study leveraged existing data infrastructure and relationships from the Feed the Future Senegal Naatal Mbay ("flourishing agriculture") project, funded by the US Agency for International Development (USAID) and implemented by RTI International from 2015 to 2019. The research informed and empowered farmer organizations to track and respond to rural households in 2020 as they faced the COVID-19 pandemic. Farmer organizations, with support from RTI and local ICT firm STATINFO, administered a survey to a sample of 800 agricultural households that are members of four former Naatal Mbay-supported farmer organizations in two rounds in August and October 2020. Focus group discussions were conducted with network leadership pre- and post-data collection to contextualize the experience of the COVID-19 shock and to validate findings. The results showed that farmers were already reacting to the effects of low rainfall during the 2019 growing season and that COVID-19 compounded the shock through disrupted communications and interregional travel bans, creating food shortages and pressure to divert seed stocks for food. Food insecurity effects, measured through the Household Food Insecurity Access Scale and cereals stocks, were found to be greater for households in the Casamance region than in the Kaolack and Kaffrine regions. The findings also indicate that farmer networks deployed a coordinated response comprising food aid and access to personal protective equipment, distribution of short-cycle legumes and grains (e.g., cowpea, maize) and vegetable seeds, protection measures for cereals seeds, and financial innovations with banks. However, food stocks were expected to recover as harvesting began in October 2020, and the networks were planning to accelerate seed multiplication, diversify crops beyond cereals, improve communication across the network. and mainstream access to financial instruments in the 2021 growing season. The research indicated that the previous USAID-funded project had likely contributed to the networks' COVID-19 resilience capacities by building social capital and fostering the new use of tools and technologies over the years it operated.

Health disparities stemming from race and ethnicity differences: best practices for reporting demographics in newborn screening research

he recent and persistent COVID-19 pandemic highlights the mounting published data on health disparities in the United States, including higher mortality in minority communities due to systemic racism embedded in our society. Throughout history, “race” has been supposition as a biological variable instead of a social and political construct that has changed throughout history. Using race and ethnicity as variables in human genomic research has had negative consequences for how the research is translated into clinical practice, incorporated into public health programs, and implemented in public policy. Newborn screening (NBS) is one of few public health programs that does not target a particular population and is available to every infant born in the United States regardless of race or socioeconomic status. Each year during the process of screening 4 million newborns for over 80 disorders, state-based public health programs collect a variety of demographic and birth-related data. The potential to leverage the data collected could improve our understanding of diseases and interventions, and in time, could transform healthcare by reducing the health disparity gap. However, inaccuracies or misuse of non-biological variables such as race or ethnicity can lead to social harms and unvalidated conclusions. NBS disorders are screened using a combination of biological and physiological assessments and are conducted either in the birthing hospital or in a state public health laboratory. The laboratory measurements are performed using a blood sample collected on filter paper card. These dried blood spot (DBS) cards also list demographic and birth data that is vital to interpreting test results. Although the list of data collected varies across state programs, most programs collect sex, birth weight, gestational age, the use of antibiotics, feeding type, and/or transfusion status. Residual DBS are a valuable resource and state programs store them for use in program improvement activities and research. Over two-thirds of state programs store residual DBS for longer than one year, and at least 18 include consent for research as one of the collected data points. While NBS research studies often rely on data collected on the DBS card for reliable variables, some of the data represents demographic information provided by the parents and collected at the birthing center. It is not uncommon for healthcare professionals who collect the DBS specimen to infer the newborn’s ascriptive race and/or ethnicity. This leads to potentially inaccurate data that has been used in NBS research studies to characterize study populations and provide conclusions about rare mendelian disorders in specific racial and ethnic populations. The accurate representation of race and ethnicity is always important, especially when a condition is added to nationwide screening. In 2010, NBS for severe combined immunodeficiency (SCID), a life-threating disorder caused by the lack of T-cells, was recommended for nationwide screening. Prior to screening, diagnosed patients that were followed long-term were predominately white (81%). However, a recent publication of screening results from 3.25 million California infants reported that SCID did not occur more frequently in any ethnic group, and found no predominant founder mutation. SCID frequently occurred because of homozygous autosomal recessive inheritance, and 80% of cases have no family history. Accurate representation of race and ethnicity could be used to assess health outcomes and disparities across all racial groups and other biological variables such as genetic ancestry should be considered to help advance the understanding of etiology of SCID.This presentation will exam how race and ethnicity is collected from NBS programs in the United States and how race is used in published NBS literature. Additionally, we will explore the lack of standardized language used to collect information on race and ethnicity in NBS and the incorrect assumption that race and ethnic information is based on parent report. We wil discuss the impact of these practices on NBS research, propose best practices for reporting race and/or ethnicity to ensure accurate evaluation of health outcomes and disparities, and recommend that NBS researchers use other biological variables such as genetic ancestry in research to assess true disease risk