Research partnerships between blood services and public health authorities: An international, cross-sectional survey.

BACKGROUND AND OBJECTIVES: The COVID-19 pandemic has brought to the fore how blood services can partner with public health (PH) authorities to inform decisions. Yet the scope of partnerships between blood services and PH authorities is inadequately documented. We explored how blood services partner with PH authorities outside the scope of COVID-19. MATERIALS AND METHODS: On 19 January 2022, survey was sent to employees of blood services located throughout the world. Survey questions mainly pertained to partnerships with PH authorities, including how blood specimens are used and collected. RESULTS: Twenty-seven recipients-4 (14.8%) in Africa, 3 (11.1%) in Asia, 9 (33.3%) in Europe, 6 (22.2%) in North America, 2 (7.4%) in Oceania and 3 (11.1%) in South America-completed the survey. Fifteen recipients (55.6%) indicated their blood service was directly or indirectly supervised by PH authorities. Twenty-four recipients (88.9%) indicated currently using or planning to use blood donor data or samples for PH research or pathogen surveillance. A substantial proportion of respondents reported using samples or results from non-routine tests for the surveillance of non-transfusion-transmitted infectious disease pathogens (n = 13 [48.1%]); samples or results of non-routine tests for PH research unrelated to pathogens (n = 10 [37.0%]); donor data for PH research unrelated to pathogens (n = 12 [44.4%]) and donor data for PH research unrelated to transfusion safety (n = 11 [40.7%]). Fourteen (51.9%) had established (or planned to establish) longitudinal cohorts and 19 (70.4%) biobanks. CONCLUSION: The majority of responding blood services were already involved in or planned to be involved in PH research or pathogen surveillance.

How do we decide how representative our donors are for public health surveillance?

BACKGROUND: Surveillance of blood donors is fundamental to safety of the blood supply. Such data can also be useful for public health policy but tend to be underutilized. When the COVID-19 pandemic arrived, blood centers around the world measured blood donor SARS-CoV-2 seroprevalence to inform public health policy. There is now a movement toward blood centers becoming more involved in public health research and surveillance post-pandemic. However, blood donors are a healthy population and not representative of all segments of the general population. In this article, we explain how blood centers can evaluate their donor base to understand which part of the general population they are representative of. STUDY DESIGN AND METHODS: Methodologic approaches for evaluating samples relative to the target population were reviewed. Blood donor data that are available to most blood centers were identified and application to assess representativeness of blood donors was evaluated. RESULTS: Key aspects of blood donor data to compare with general population data include donor selection criteria, health indicators, geography, and demographics. In some cases, statistical adjustment can improve representativeness. DISCUSSION: Comparing key blood donor data with corresponding general population data can define the subset of the general population for which a particular blood center's donors may be representative of. We suggest that donors are an ideal convenience population for surveillance of infectious agents which are frequently asymptomatic and main routes of transmission are not deferrable, for studying the natural history of disease in an initially well population, and for vaccination serology surveillance.

Monitoring Approaches for a Pediatric Chronic Kidney Disease Machine Learning Model.

OBJECTIVE: The purpose of this study is to evaluate the ability of three metrics to monitor for a reduction in performance of a chronic kidney disease (CKD) model deployed at a pediatric hospital. METHODS: The CKD risk model estimates a patient's risk of developing CKD 3 to 12 months following an inpatient admission. The model was developed on a retrospective dataset of 4,879 admissions from 2014 to 2018, then run silently on 1,270 admissions from April to October, 2019. Three metrics were used to monitor its performance during the silent phase: (1) standardized mean differences (SMDs); (2) performance of a "membership model"; and (3) response distribution analysis. Observed patient outcomes for the 1,270 admissions were used to calculate prospective model performance and the ability of the three metrics to detect performance changes. RESULTS: The deployed model had an area under the receiver-operator curve (AUROC) of 0.63 in the prospective evaluation, which was a significant decrease from an AUROC of 0.76 on retrospective data (p = 0.033). Among the three metrics, SMDs were significantly different for 66/75 (88%) of the model's input variables (p <0.05) between retrospective and deployment data. The membership model was able to discriminate between the two settings (AUROC = 0.71, p <0.0001) and the response distributions were significantly different (p <0.0001) for the two settings. CONCLUSION: This study suggests that the three metrics examined could provide early indication of performance deterioration in deployed models' performance.