During a 22-month period, 78,000 blood donors were screened for human T-lymphotropic virus types I and II (HTLV-I/II) at Belle Bonfils Memorial Blood Center (Denver, Colorado). Positive donors and the living recipients of their previously donated blood components were evaluated for risk factors and symptoms related to HTLV-I infection, were screened by enzyme immunoassay, confirmed by Western blot for HTLV-I/II, and subsequently tested by polymerase chain reaction and peptide enzyme immunoassay to distinguish between HTLV-I and -II infection. Six seropositive blood donors (0.008%) were identified; four were typed as having HTLV-I infection and two as having HTLV-II. Of 18 living recipients of components from seropositive donors, none had risk factors for HTLV-I infection prior to transfusion and none had signs or symptoms of HTLV-I infection at follow-up. The mean time from transfusion to testing was 6.4 years. Seven recipients of HTLV-I-infected components were HTLV seropositive; all were typed as having HTLV-I. A possible case of posttransfusion HTLV-I-associated myelopathy was identified in one patient who died before complete evaluation. One possible case of transfusion-associated HTLV-II was identified. These data further support the continued screening of blood donors for HTLV-I/II.
A method for estimating the socioeconomic impact of Earth observations is proposed and deployed. The core of the method is the analysis of outcomes of hypothetical fire suppression scenarios generated using a coupled atmosphere–fire behaviour model, based on decisions made by an experienced wildfire incident management team with and without the benefits of MODIS (Moderate Resolution Imaging Spectroradiometer) satellite observations and the WRF-SFIRE wildfire behaviour simulation system. The scenarios were based on New Mexico’s 2011 Las Conchas fire. For each scenario, fire break line location decisions served as inputs to the model, generating fire progression outcomes. Fire model output was integrated with a property database containing thousands of coordinates and property values and other asset values to estimate the total losses associated with each scenario. An attempt to estimate the socioeconomic impact of satellite and modelling data used during the decision-making process was made. We analysed the impact of Earth observations and include considerations for estimating other socioeconomic impacts.
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