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(searched for: doi:10.1093/jamia/ocab004)
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Published: 6 October 2022
Journal: JMIR Cancer
JMIR Cancer, Volume 8; https://doi.org/10.2196/35310

Abstract:
Background: Prior studies, generally conducted at single centers with small sample sizes, found that individuals with cancer experience more severe outcomes due to COVID-19, caused by SARS-CoV-2 infection. Although early examinations revealed greater risk of severe outcomes for patients with cancer, the magnitude of the increased risk remains unclear. Furthermore, prior studies were not typically performed using population-level data, especially those in the United States. Given robust prevention measures (eg, vaccines) are available for populations, examining the increased risk of patients with cancer due to SARS-CoV-2 infection using robust population-level analyses of electronic medical records is warranted. Objective: The aim of this paper is to evaluate the association between SARS-CoV-2 infection and all-cause mortality among recently diagnosed adults with cancer. Methods: We conducted a retrospective cohort study of newly diagnosed adults with cancer between January 1, 2019, and December 31, 2020, using electronic health records linked to a statewide SARS-CoV-2 testing database. The primary outcome was all-cause mortality. We used the Kaplan-Meier estimator to estimate survival during the COVID-19 period (January 15, 2020, to December 31, 2020). We further modeled SARS-CoV-2 infection as a time-dependent exposure (immortal time bias) in a multivariable Cox proportional hazards model adjusting for clinical and demographic variables to estimate the hazard ratios (HRs) among newly diagnosed adults with cancer. Sensitivity analyses were conducted using the above methods among individuals with cancer-staging information. Results: During the study period, 41,924 adults were identified with newly diagnosed cancer, of which 2894 (6.9%) tested positive for SARS-CoV-2. The population consisted of White (n=32,867, 78.4%), Black (n=2671, 6.4%), Hispanic (n=832, 2.0%), and other (n=5554, 13.2%) racial backgrounds, with both male (n=21,354, 50.9%) and female (n=20,570, 49.1%) individuals. In the COVID-19 period analysis, after adjusting for age, sex, race or ethnicity, comorbidities, cancer type, and region, the risk of death increased by 91% (adjusted HR 1.91; 95% CI 1.76-2.09) compared to the pre–COVID-19 period (January 1, 2019, to January 14, 2020) after adjusting for other covariates. In the adjusted time-dependent analysis, SARS-CoV-2 infection was associated with an increase in all-cause mortality (adjusted HR 6.91; 95% CI 6.06-7.89). Mortality increased 2.5 times among adults aged 65 years and older (adjusted HR 2.74; 95% CI 2.26-3.31) compared to adults 18-44 years old, among male (adjusted HR 1.23; 95% CI 1.14-1.32) compared to female individuals, and those with ≥2 chronic conditions (adjusted HR 2.12; 95% CI 1.94-2.31) compared to those with no comorbidities. Risk of mortality was 9% higher in the rural population (adjusted HR 1.09; 95% CI 1.01-1.18) compared to adult urban residents. Conclusions: The findings highlight increased risk of death is associated with SARS-CoV-2 infection among patients with a recent diagnosis of cancer. Elevated risk underscores the importance of adhering to social distancing, mask adherence, vaccination, and regular testing among the adult cancer population.
, Mahima Vijayaraghavan, Marianne Chronister, Sindhu Srinivas, Adel Bassily-Marcus, Jeffrey Gumprecht, Avniel Shetreat-Klein, Bruce Darrow, Catherine K. Craven
Published: 29 July 2022
Journal: ACI Open
Abstract:
Objective We detail inpatient electronic health record (EHR) system tools created at Mount Sinai Health System for the clinical management of patients with coronavirus disease 2019 (COVID-19) during the early pandemic months in the U.S. epicenter, New York City. We discuss how we revised these tools to create a robust Care pathway, unlike other tools reported, that helped providers care for these patients as guidelines evolved. Methods Mount Sinai Health System launched a Command Center on March 8, 2020. The Chief Medical Information Officer launched a workgroup of clinical informaticists and Epic analysts tasked with rapidly creating COVID-19-related EHR tools for the inpatient setting. Results Initial EHR tools focused on inpatient order sets for care standardization and resource utilization. In preparation for a fall 2020-winter 2021 surge, we created a clinician-facing, integrated Care pathway incorporating additional Epic System-specific tools: a Care Path, a dedicated Navigator, Summary and Timeline Reports, and SmartTexts. Discussion Initial tools offered standard functionality but included complex decision-making support to account for the lack of COVID-19 clinical knowledge, operational challenges during a dramatic patient surge, and resource limitations. We revised content and built a more comprehensive Care pathway that provided real-time clinical data along with treatment recommendations as knowledge evolved, e.g., convalescent plasma. Conclusion We have provided a framework that can inform future informaticists in developing EHR tools during an evolving pandemic.
Hye Ryeon Jang, Jordan Quinones-Marrero,
Published: 29 July 2022
Frontiers in Public Health, Volume 10; https://doi.org/10.3389/fpubh.2022.925808

Abstract:
Public dashboards have been one of the most effective tools to provide critical information about COVID-19 cases during the pandemic. However, dashboards for COVID-19 that have not received a lot of scrutiny are those from the public school system. We conducted an environmental scan of published dashboards that report and track new COVID-19 infections in the Florida public school system. We found that thirty-four percent of counties do not provide any public dashboard, and there was significant heterogeneity in the data quality and framework of existing systems. There were poor interfaces without visual tools to trace the trend of COVID-19 cases in public schools and significant limitations for data extraction. Given these observations, it is impossible to conduct meaningful policy evaluations and proper surveillance. Additional work and oversight are needed to improve public data reported.
, Catarina Camarinha, Miguel De Araújo Nobre, Cecília Elias, , Andreia Silva Costa, Cristina Furtado, Paulo Jorge Nogueira
Published: 1 July 2022
International journal of medical informatics, Volume 163; https://doi.org/10.1016/j.ijmedinf.2022.104763

Uba Backonja, Seungeun Park, Amae Kurre, Hayley Yudelman, Sam Heindel, Melinda Schultz, Greg Whitman, Anne M. Turner, Natasza T. Marchak, Betty Bekemeier
Published: 1 May 2022
Journal of Biomedical Informatics, Volume 129; https://doi.org/10.1016/j.jbi.2022.104051

, Shaun J. Grannis
Published: 20 April 2022
The publisher has not yet granted permission to display this abstract.
, Damir Ivanković, Karapet Davtyan, , Zhamin Yelgezekova, Claire Willmington, Bernardo Meza-Torres, Véronique L.L.C. Bos, Óscar Brito Fernandes, , et al.
Published: 1 January 2022
Journal: Digital Health
Digital Health, Volume 8; https://doi.org/10.1177/20552076221121154

Abstract:
Background: Governments across the World Health Organization (WHO) European Region have prioritised dashboards for reporting COVID-19 data. The ubiquitous use of dashboards for public reporting is a novel phenomenon. Objective: This study explores the development of COVID-19 dashboards during the first year of the pandemic and identifies common barriers, enablers and lessons from the experiences of teams responsible for their development. Methods: We applied multiple methods to identify and recruit COVID-19 dashboard teams, using a purposive, quota sampling approach. Semi-structured group interviews were conducted from April to June 2021. Using elaborative coding and thematic analysis, we derived descriptive and explanatory themes from the interview data. A validation workshop was held with study participants in June 2021. Results: Eighty informants participated, representing 33 national COVID-19 dashboard teams across the WHO European Region. Most dashboards were launched swiftly during the first months of the pandemic, February to May 2020. The urgency, intense workload, limited human resources, data and privacy constraints and public scrutiny were common challenges in the initial development stage. Themes related to barriers or enablers were identified, pertaining to the pre-pandemic context, pandemic itself, people and processes and software, data and users. Lessons emerged around the themes of simplicity, trust, partnership, software and data and change. Conclusions: COVID-19 dashboards were developed in a learning-by-doing approach. The experiences of teams reveal that initial underpreparedness was offset by high-level political endorsement, the professionalism of teams, accelerated data improvements and immediate support with commercial software solutions. To leverage the full potential of dashboards for health data reporting, investments are needed at the team, national and pan-European levels.
Katie S. Allen, Nader Zidan, Vishal Dey, Eneida A. Mendonca, Shaun Grannis, Suranga Kasturi, Babar Khan, Sarah R. Zappone, David Haggstrom, Laura Ruppert, et al.
Published: 18 December 2021
Abstract:
The primary objective of the COVID-19 Research Data Commons (CoRDaCo) is to provide broad and efficient access to a large corpus of clinical data related to COVID-19 in Indiana, facilitating research and discovery. This curated collection of data elements provides information on a significant portion of COVID-19 positive patients in the State from the beginning of the pandemic, as well as two years of health information prior its onset. CoRDaCo combines data from multiple sources, including clinical data from a large, regional health information exchange, clinical data repositories of two health systems, and state laboratory reporting and vital records, as well as geographic-based social variables. Clinical data cover information such as healthcare encounters, vital measurements, laboratory orders and results, medications, diagnoses, the Charlson Comorbidity Index and Pediatric Early Warning Score, COVID-19 vaccinations, mechanical ventilation, restraint use, intensive care unit and ICU and hospital lengths of stay, and mortality. Interested researchers can visit ridata.org or email [email protected] to discuss access to CoRDaCo.Key Features: CoRDaCo includes patient-level data on diagnosis and treatment, healthcare utilization, outcomes, and demographics. The level of detail available for each patient varies depending on the source of the clinical data. CoRDaCo uses geographic identifiers to link patient-specific data to area-level social factors, such as census variables and social deprivation indices. As of 4/30/21, the CoRDaCo cohort consists of over 776,000 cases, including granular data on over 15,000 patients who were admitted to an intensive care unit, and over 1,362,000 COVID-19-negative controls. Data is currently refreshed two times per month. The most prevalent comorbidities in the data set include hypertension, diabetes, chronic pulmonary disease, renal disease, cancer, and congestive heart failure.
, Tian He, Courtney Rowan, , Eneida Mendonca
Published: 2 December 2021
Journal of Asthma, Volume 59, pp 2421-2430; https://doi.org/10.1080/02770903.2021.2010750

Abstract:
Introduction: Pediatric asthma is a common cause of emergency department visits, hospital admissions, and mortality. Population incidence studies have historically used large-scale survey data. We measured these epidemiologic trends using a health information exchange. Methods: In this retrospective cohort study, we used electronic health record data from a regional health information exchange to study clinical trends in pediatric patients presenting to the hospital for asthma in the State of Indiana. Data was obtained from 2010 − 2019 and included all patients ages 2-18 years. Study participants were identified using international classification of disease codes. The measured outcomes were number of hospital encounters per year, percentage of admissions per year, and mortality rates. Results: Data included 50 393 unique patients and 88 772 unique encounters, with 57% male patients. Over the ten-year period, hospital encounters ranged from 5000 to 8000 per year with no change in trajectory. Between 2010 and 2012, the percent of encounters admitted to the hospital was ∼30%. This decreased to ∼20-25% for 2015-2019. Patient mortality rates increased from 1 to 3 per 1000 patient encounters in 2010-2014 to between 5 and 7 per 1000 patient encounters from 2016-2019. White patients had a significantly higher admission percentage compared to other racial groups, but no difference in mortality rate. Conclusions: Asthma continues to be a common condition requiring hospital care for pediatric patients. Regional health information exchanges can enable public health researchers to follow asthma trends in near real time, and have potential for informing patient-level public health interventions.
, , , , Zhamin Yelgezekova, Claire Willmington, , Véronique L.L.C. Bos, , , et al.
Published: 24 November 2021
Abstract:
Background: Governments across the WHO European Region prioritized dashboards for reporting COVID-19 data. The ubiquitous use of dashboards for public reporting is novel. This study explores the development of COVID-19 dashboards during the pandemic’s first year and common barriers, enablers and lessons from the experiences of teams responsible for their development.Methods: Multiple methods were applied to identify and recruit COVID-19 dashboard teams using a purposive, quota sampling approach. Semi-structured group interviews were conducted between April– June 2021. Using elaborative coding and thematic analysis, descriptive and explanatory themes were derived from interview data. A validation workshop with study participants was held in June 2021.Results: Eighty informants, representing 33 national COVID-19 dashboard teams across the WHO European Region participated. Most dashboards were launched swiftly in the first months of the pandemic, between February–May 2020. The urgency, intense workload, limited human resources, data and privacy constraints, and public scrutiny were common to the initial development stage. Themes related to barriers or enablers were identified pertaining to the pre-pandemic context, pandemic itself, people and processes, software, data, and users. Lessons emerged around the themes of simplicity, trust, partnership, software and data, and change.Conclusions: COVID-19 dashboards were developed in a learning-by-doing approach. The experiences of teams signal initial under-preparedness was compensated by high-level political endorsement, the professionalism of teams, accelerated data improvements, and immediate support of commercial software solutions. To leverage the full potential of dashboards, investments are needed at team-, national- and pan-European-level.
Weilin Meng, Kelly M Mosesso, Kathleen A Lane, Anna R Roberts, Ashley Griffith, Wanmei Ou,
Published: 12 October 2021
JMIR Public Health and Surveillance, Volume 9; https://doi.org/10.2196/29017

Abstract:
Background Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. Objective The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. Methods We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non–small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. Results For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. Conclusions Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.
, Patrick Y Liu, Joseph R Friedman, Alex A T Bui
Published: 1 October 2021
Journal: JAMIA Open
Abstract:
COVID-19 mortality forecasting models provide critical information about the trajectory of the pandemic, which is used by policymakers and public health officials to guide decision-making. However, thousands of published COVID-19 mortality forecasts now exist, many with their own unique methods, assumptions, format, and visualization. As a result, it is difficult to compare models and understand under which circumstances a model performs best. Here, we describe the construction and usability of covidcompare.io, a web tool built to compare numerous forecasts and offer insight into how each has performed over the course of the pandemic. From its launch in December 2020 to June 2021, we have seen 4600 unique visitors from 85 countries. A study conducted with public health professionals showed high usability overall as formally assessed using a Post-Study System Usability Questionnaire. We find that covidcompare.io is an impactful tool for the comparison of international COVID-19 mortality forecasting models.
Jenny Rees Patterson, Donna Shaw, Sharita R Thomas, Julie A Hayes, Christopher R Daley, Stefania Knight, Jay Aikat, Joanna O Mieczkowska, ,
Published: 2 September 2021
JMIR Public Health and Surveillance, Volume 7; https://doi.org/10.2196/29310

Abstract:
Background As the world faced the pandemic caused by the novel coronavirus disease 2019 (COVID-19), medical professionals, technologists, community leaders, and policy makers sought to understand how best to leverage data for public health surveillance and community education. With this complex public health problem, North Carolinians relied on data from state, federal, and global health organizations to increase their understanding of the pandemic and guide decision-making. Objective We aimed to describe the role that stakeholders involved in COVID-19–related data played in managing the pandemic in North Carolina. The study investigated the processes used by organizations throughout the state in using, collecting, and reporting COVID-19 data. Methods We used an exploratory qualitative study design to investigate North Carolina’s COVID-19 data collection efforts. To better understand these processes, key informant interviews were conducted with employees from organizations that collected COVID-19 data across the state. We developed an interview guide, and open-ended semistructured interviews were conducted during the period from June through November 2020. Interviews lasted between 30 and 45 minutes and were conducted by data scientists by videoconference. Data were subsequently analyzed using qualitative data analysis software. Results Results indicated that electronic health records were primary sources of COVID-19 data. Often, data were also used to create dashboards to inform the public or other health professionals, to aid in decision-making, or for reporting purposes. Cross-sector collaboration was cited as a major success. Consistency among metrics and data definitions, data collection processes, and contact tracing were cited as challenges. Conclusions Findings suggest that, during future outbreaks, organizations across regions could benefit from data centralization and data governance. Data should be publicly accessible and in a user-friendly format. Additionally, established cross-sector collaboration networks are demonstrably beneficial for public health professionals across the state as these established relationships facilitate a rapid response to evolving public health challenges.
, William E. Trick
Published: 4 August 2021
Infectious disease clinics of North America, Volume 35, pp 755-769; https://doi.org/10.1016/j.idc.2021.04.010

Published: 26 July 2021
Journal of Medical Internet Research, Volume 23; https://doi.org/10.2196/28812

Abstract:
Background The COVID-19 pandemic has changed public health policies and human and community behaviors through lockdowns and mandates. Governments are rapidly evolving policies to increase hospital capacity and supply personal protective equipment and other equipment to mitigate disease spread in affected regions. Current models that predict COVID-19 case counts and spread are complex by nature and offer limited explainability and generalizability. This has highlighted the need for accurate and robust outbreak prediction models that balance model parsimony and performance. Objective We sought to leverage readily accessible data sets extracted from multiple states to train and evaluate a parsimonious predictive model capable of identifying county-level risk of COVID-19 outbreaks on a day-to-day basis. Methods Our modeling approach leveraged the following data inputs: COVID-19 case counts per county per day and county populations. We developed an outbreak gold standard across California, Indiana, and Iowa. The model utilized a per capita running 7-day sum of the case counts per county per day and the mean cumulative case count to develop baseline values. The model was trained with data recorded between March 1 and August 31, 2020, and tested on data recorded between September 1 and October 31, 2020. Results The model reported sensitivities of 81%, 92%, and 90% for California, Indiana, and Iowa, respectively. The precision in each state was above 85% while specificity and accuracy scores were generally >95%. Conclusions Our parsimonious model provides a generalizable and simple alternative approach to outbreak prediction. This methodology can be applied to diverse regions to help state officials and hospitals with resource allocation and to guide risk management, community education, and mitigation strategies.
, Shaun J. Grannis, Lauren R. Lembcke, Nimish Valvi, Anna R. Roberts,
Published: 23 July 2021
Journal: PLOS ONE
Abstract:
Background: Early studies on COVID-19 identified unequal patterns in hospitalization and mortality in urban environments for racial and ethnic minorities. These studies were primarily single center observational studies conducted within the first few weeks or months of the pandemic. We sought to examine trends in COVID-19 morbidity, hospitalization, and mortality over time for minority and rural populations, especially during the U.S. fall surge. Methods: Data were extracted from a statewide cohort of all adult residents in Indiana tested for SARS-CoV-2 infection between March 1 and December 31, 2020, linked to electronic health records. Primary measures were per capita rates of infection, hospitalization, and death. Age adjusted rates were calculated for multiple time periods corresponding to public health mitigation efforts. Comparisons across time within groups were compared using ANOVA. Results: Morbidity and mortality increased over time with notable differences among sub-populations. Initially, hospitalization rates among racial minorities were 3–4 times higher than whites, and mortality rates among urban residents were twice those of rural residents. By fall 2020, hospitalization and mortality rates in rural areas surpassed those of urban areas, and gaps between black/brown and white populations narrowed. Changes across time among demographic groups was significant for morbidity and hospitalization. Cumulative morbidity and mortality were highest among minority groups and in rural communities. Conclusions: The synchronicity of disparities in COVID-19 by race and geography suggests that health officials should explicitly measure disparities and adjust mitigation as well as vaccination strategies to protect those sub-populations with greater disease burden.
Weilin Meng, Kelly M Mosesso, Kathleen A Lane, Anna R Roberts, Ashley Griffith, Wanmei Ou,
Published: 22 March 2021
Abstract:
BACKGROUND Extraction of line-of-therapy (LOT) information from electronic health record and claims data is essential for determining longitudinal changes in systemic anticancer therapy in real-world clinical settings. OBJECTIVE The aim of this retrospective cohort analysis is to validate and refine our previously described open-source LOT algorithm by comparing the output of the algorithm with results obtained through blinded manual chart review. METHODS We used structured electronic health record data and clinical documents to identify 500 adult patients treated for metastatic non–small cell lung cancer with systemic anticancer therapy from 2011 to mid-2018; we assigned patients to training (n=350) and test (n=150) cohorts, randomly divided proportional to the overall ratio of simple:complex cases (n=254:246). Simple cases were patients who received one LOT and no maintenance therapy; complex cases were patients who received more than one LOT and/or maintenance therapy. Algorithmic changes were performed using the training cohort data, after which the refined algorithm was evaluated against the test cohort. RESULTS For simple cases, 16 instances of discordance between the LOT algorithm and chart review prerefinement were reduced to 8 instances postrefinement; in the test cohort, there was no discordance between algorithm and chart review. For complex cases, algorithm refinement reduced the discordance from 68 to 62 instances, with 37 instances in the test cohort. The percentage agreement between LOT algorithm output and chart review for patients who received one LOT was 89% prerefinement, 93% postrefinement, and 93% for the test cohort, whereas the likelihood of precise matching between algorithm output and chart review decreased with an increasing number of unique regimens. Several areas of discordance that arose from differing definitions of LOTs and maintenance therapy could not be objectively resolved because of a lack of precise definitions in the medical literature. CONCLUSIONS Our findings identify common sources of discordance between the LOT algorithm and clinician documentation, providing the possibility of targeted algorithm refinement.
, , Lauren Lembcke, Anna Roberts,
Published: 6 March 2021
Abstract:
Background Early studies on COVID-19 identified unequal patterns in hospitalization and mortality in urban environments for racial and ethnic minorities. These studies were primarily single center observational studies conducted within the first few weeks or months of the pandemic. We sought to examine trends in COVID-19 morbidity and mortality over time for minority and rural populations, especially during the U.S. fall surge. Methods Statewide cohort of all adult residents in Indiana tested for SARS-CoV-2 infection between March 1 and December 31, 2020, linked to electronic health records. Primary measures were per capita rates of infection, hospitalization, and death. Age adjusted rates were calculated for multiple time periods corresponding to public health mitigation efforts. Results Morbidity and mortality increased over time with notable differences among sub-populations. Initially, per capita hospitalizations among racial minorities were 3-4 times higher than whites, and per capita deaths among urban residents were twice those of rural residents. By fall 2020, per capita hospitalizations and deaths in rural areas surpassed those of urban areas, and gaps between black/brown and white populations narrowed. Cumulative morbidity and mortality were highest among minority groups and in rural communities. Conclusions Burden of COVID-19 morbidity and mortality shifted over time, creating a twindemic involving disparities in outcomes based on race and geography. Health officials should explicitly measure disparities and adjust mitigation and vaccination strategies to protect vulnerable sub-populations with greater disease burden.
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