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Abstract Infant mortality remains high and uneven in much of sub-Saharan Africa. Even low-cost, highly effective therapies can only save lives in proportion to how successfully they can be targeted to those children who, absent the treatment, would have died. This places great value on maximizing the accuracy of any targeting or means-testing algorithm. Yet, the interventions that countries deploy in hopes of reducing mortality are often targeted based on simple models of wealth or income or a few additional variables. Examining 22 countries in sub-Saharan Africa, we illustrate the use of flexible (machine learning) risk models employing up to 25 generally available pre-birth variables from the Demographic and Health Surveys. Using these models, we construct risk scores such that the 10 percent of the population at highest risk account for 15-30 percent of infant mortality, depending on the country. Successful targeting in these models turned on several variables other than wealth, while models that employ only wealth data perform little or no better than chance. Consequently, employing such data and models to predict high-risk births in the countries studied flexibly could substantially improve the targeting and thus the life-saving potential of existing interventions.

Summary: Coronavirus disease 2019 (COVID-19) has exposed health care disparities in minority groups including Hispanics/Latinxs (HL). Studies of COVID-19 risk factors for HL have relied on county-level data. We investigated COVID-19 risk factors in HL using individual-level, electronic health records in a Los Angeles health system between March 9, 2020, and August 31, 2020. Of 9,287 HL tested for SARS-CoV-2, 562 were positive. HL constituted an increasing percentage of all COVID-19 positive individuals as disease severity escalated. Multiple risk factors identified in Non-Hispanic/Latinx whites (NHL-W), like renal disease, also conveyed risk in HL. Pre-existing nonrheumatic mitral valve disorder was a risk factor for HL hospitalization but not for NHL-W COVID-19 or HL influenza hospitalization, suggesting it may be a specific HL COVID-19 risk. Admission laboratory values also suggested that HL presented with a greater inflammatory response. COVID-19 risk factors for HL can help guide equitable government policies and identify at-risk populations.

Event-related potentials (ERP) waveforms are the summation of many overlapping signals. Changes in the peak or mean amplitude of a waveform over a given time period, therefore, cannot reliably be attributed to a particular ERP component of ex ante interest, as is the standard approach to ERP analysis. Though this problem is widely recognized, it is not well addressed in practice. Our approach begins by presuming that any observed ERP waveform — at any electrode, for any trial type, and for any participant — is approximately a weighted combination of signals from an underlying set of what we refer to as principle ERPs, or pERPs. We propose an accessible approach to analyzing complete ERP waveforms in terms of their underlying pERPs. First, we propose the principle ERP reduction (pERP-RED) algorithm for investigators to estimate a suitable set of pERPs from their data, which may span multiple tasks. Next, we provide tools and illustrations of pERP-space analysis, whereby observed ERPs are decomposed into the amplitudes of the contributing pERPs, which can be contrasted across conditions or groups to reveal which pERPs differ (substantively and/or significantly) between conditions/groups. Differences on all pERPs can be reported together rather than selectively, providing complete information on all components in the waveform, thereby avoiding selective reporting or user discretion regarding the choice of which components or windows to use. The scalp distribution of each pERP can also be plotted for any group/condition. We demonstrate this suite of tools through simulations and on real data collected from multiple experiments on participants diagnosed with Autism Spectrum Disorder and Attention Deficit Hyperactivity Disorder. Software for conducting these analyses is provided in the pERPred package for R.