Lab Matters Fall 2023 | Page 64

APHL 2023 POSTER ABSTRACTS

Due to the high degree of variability in food matrices , a robust method for selecting reference materials for food chemistry testing quality assurance is critical . This is especially important when results may be used for compliance or regulatory actions . Purpose and Scope : This poster describes the methods by which the NH PHL selects reference materials for food chemistry testing , with considerations for matrix matching , analyte matching , processing for high quality results , and addressing processes that can affect the reference material performance over time . In particular , this poster presents a case study on transpiration and its effects on liquid reference materials and certified standards . Transpiration is the process by which liquid evaporates through a closed container . It is influenced by light intensity , time , humidity , and temperature . In a laboratory , the environment can either increase or decrease the transpiration rate . The transpiration rate can also be affected by physical movement or agitation of the container . Transpiration affects the concentration by reducing the volume of the solution , thereby increasing the concentration . This can affect the measured results relative to the Certificate of Analysis , and therefore must be evaluated to determine if this difference has implications for quality control and quality assurance . Methods : To evaluate the effect of transpiration on a liquid reference material , this case study used a NIST-traceable multi-element standard solution containing Arsenic , Chromium , Lead , Molybdenum , Selenium , Zinc , Cadmium , Copper , Manganese , Nickel , and Thallium . Over the course of eight months , the solution was weighed before and after being placed into storage to measure mass loss due to transpiration . The mass loss was converted to a percent change in concentration of the target analytes relative to the certified values on the Certificate of Analysis . Results and Conclusions : We calculated an overall transpiration loss of 0.2692g , or 0.22 % of the starting mass of the solution over a period of eight months . The average daily transpiration rate for the period of eight months is 0.039 mg / day . In contrast , the percent uncertainties on the certified concentrations of target analytes from the certificate of analysis range from 0.4 % to 0.8 %. We determined that the effect of transpiration on target analyte concentrations in a multi-element standard solution was not significant over a period of eight months . Transpiration is one if the many important to considerations in the context of maintaining reference materials for robust chemical analysis .

Presenter : Boone Harris , boone . k . harris @ affiliate . dhhs . nh . gov

INFORMATICS

Asked on Order Entry Questions : Quality and Value of Data for Oregon COVID-19 Surveillance and Response

M . Moore 1 , L . Boyd 1 , K . Cogswell 2 ; 1 Oregon State Public Health Laboratory , 2 Acute and Communicable Disease Prevention , Oregon Public Health Division

Background : The COVID-19 pandemic response identified challenges about how best to gather important epidemiological information to guide pandemic control and response . One tactic employed by the Department of Health and Human Services ( HHS ) under the Coronavirus Aid , Relief , and Economic Security ( CARES ) Act was to mandate that providers report epidemiological risk questions ( e . g , is this the patient ’ s first COVID-19 test ?) to testing laboratories who were then required to report COVID-19

“ Ask on Order Entry ” ( AOE ) info on results sent to HHS . The Oregon State Public Health Laboratory ( OSPHL ) entered AOE info into the laboratory information management system ( LIMS ) primarily from paper requisition forms submitted by providers . At the time of the mandate , there was no available reporting solution from the LIMS so the OSPHL built an in-house coding solution to enable reporting . Due to the high costs of collecting and reporting this information , the OSPHL set out to determine the quality of AOE data and utility for local epidemiological response . Method : This investigation sought to determine the accuracy and completeness of AOE info , including key attributes of interest for state COVID epidemiology : “ hospitalized ” ( either hospitalized or admitted to intensive care unit ) and “ pregnant ” along with an epidemiologic review of how these data were actually used . AOE info from specimens tested for SARS- Cov2 at the OSPHL in May 2021 ( pre-Delta surge ) and September 2022 ( post-Delta surge ) were assessed for completeness and epidemiologic utility . Results : The OSPHL conducted 5,764 SARS-Cov2 tests in May 2021 . Of these tests , only 16 reported a “ hospitalized ” status , a completion rate of 0.3 %, and only 7 reported a “ pregnant ” status , a 0.1 % completion rate . The most consistently reported AOEs were “ first COVID-19 test ” ( 87.1 % completion rate ) and “ symptomatic ” ( 80.1 % completion rate ). In September 2022 , the OSPHL conducted 1,179 SARS-Cov2 tests . “ Hospitalized ” status was not provided for any specimen and “ pregnant ” status was only reported for 1 patient , a < 0.0 % completion rate . “ First COVID-19 test ” ( 100 % completion rate ) and “ symptomatic ” ( 92.8 % completion rate ) were the most consistently reported . The comparison of these data to other data sources indicated issues with data quality that complicated their practical use . Conclusion : The OSPHL found interesting trends in AOE completeness in two different timeframes . It is not clear if attributes such as pregnancy and hospitalization status were under-reported or if these were not as relevant for the populations being tested . This reporting mandate required a significant level of effort from the OSPHL and well-populated attributes did not guide local response . This reporting paradigm may benefit from replacement with FHIR ( Fast Health Interoperability Resources ) as that becomes more widely available .

Presenter : Laure Boyd , laurel . boyd @ oha . state . or . us

Building a Sustainable Model for Electronic Test Orders and Results and Surveillance Reporting

L . Boyd 1 , M . O ’ Malley Moore 1 , M . Yungclas 1 , S . Hearn 1 , J . Kondamuri 2 , M . Kourbage 2 , A . Prada 2 ; 1 Oregon Health Authority 2 J . Michael Consulting

Background : The ability to share data is central to the public health laboratory ’ s mission . Yet maintaining multiple pointto-point connections , each with their own standards and surveillance implications , can be time-consuming and resourceintensive for the public health laboratory to maintain . CDC ’ s Data Modernization Initiative challenges public health partners at every level to create solutions that “ move from siloed and brittle public health data systems to connected , resilient , adaptable , and sustainable ‘ response-ready ’ systems that can help us solve problems before they happen and reduce the harm caused by the problems that do happen .” Method : With this DMI goal in mind , the Oregon State Public Health Laboratory ( OSPHL ) has partnered with J Michael Consulting to design an in-house technical solution that can manage the laboratory ’ s data exchange needs , including surveillance reporting and electronic test orders and results ( ETOR )