scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Vol 28 (1) ◽  
pp. e100439
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
Data Framework ◽  
The Impact ◽  
Machine Learning Models

IntroductionThe SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the USA, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities.MethodsWe combined county-level COVID-19 testing data, COVID-19 vaccination rates and SDOH information in Tennessee. Between February and May 2021, we trained machine learning models on a semimonthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance.ResultsOur results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased.ConclusionIncorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policy-makers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals Influence of social determinants of health and county vaccination rates on machine learning models to predict COVID-19 case growth in Tennessee

2021 ◽  
Author(s):  
Lukasz S Wylezinski ◽  
Coleman R Harris ◽  
Cody N Heiser ◽  
Jamieson D Gray ◽  
Charles F Spurlock
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
The United States ◽  
Determinants Of Health ◽  
Learning Models ◽  
Vaccination Rates ◽  
The Impact ◽  
Machine Learning Models

The SARS-CoV-2 (COVID-19) pandemic has exposed health disparities throughout the United States, particularly among racial and ethnic minorities. As a result, there is a need for data-driven approaches to pinpoint the unique constellation of clinical and social determinants of health (SDOH) risk factors that give rise to poor patient outcomes following infection in US communities. We combined county-level COVID-19 testing data, COVID-19 vaccination rates, and SDOH information in Tennessee. Between February-May 2021, we trained machine learning models on a semi-monthly basis using these datasets to predict COVID-19 incidence in Tennessee counties. We then analyzed SDOH data features at each time point to rank the impact of each feature on model performance. Our results indicate that COVID-19 vaccination rates play a crucial role in determining future COVID-19 disease risk. Beginning in mid-March 2021, higher vaccination rates significantly correlated with lower COVID-19 case growth predictions. Further, as the relative importance of COVID-19 vaccination data features grew, demographic SDOH features such as age, race, and ethnicity decreased while the impact of socioeconomic and environmental factors, including access to healthcare and transportation, increased. Incorporating a data framework to track the evolving patterns of community-level SDOH risk factors could provide policymakers with additional data resources to improve health equity and resilience to future public health emergencies.

scholarly journals Comparing Decision Tree-Based Ensemble Machine Learning Models for COVID-19 Death Probability Profiling

2020 ◽  
Author(s):  
Carlos Pedro Gonçalves ◽  
José Rouco
Keyword(s):  
Machine Learning ◽  
Risk Factors ◽  
Decision Tree ◽  
Age Group ◽  
Learning Models ◽  
Ensemble Machine Learning ◽  
Death Probability ◽  
Group Sex ◽  
The Impact ◽  
Machine Learning Models

AbstractWe compare the performance of major decision tree-based ensemble machine learning models on the task of COVID-19 death probability prediction, conditional on three risk factors: age group, sex and underlying comorbidity or disease, using the US Centers for Disease Control and Prevention (CDC)’s COVID-19 case surveillance dataset. To evaluate the impact of the three risk factors on COVID-19 death probability, we extract and analyze the conditional probability profile produced by the best performer. The results show the presence of an exponential rise in death probability from COVID-19 with the age group, with males exhibiting a higher exponential growth rate than females, an effect that is stronger when an underlying comorbidity or disease is present, which also acts as an accelerator of COVID-19 death probability rise for both male and female subjects. The results are discussed in connection to healthcare and epidemiological concerns and in the degree to which they reinforce findings coming from other studies on COVID-19.

Assessing baccalaureate nursing students’ knowledge and attitudes of social determinants of health after a health equity simulation

2020 ◽  
Vol 17 (1) ◽  
Author(s):  
Quyen Phan ◽  
Naomi Johnson ◽  
JoAnna Hillman ◽  
Daniel Geller ◽  
Laura P. Kimble ◽  
...  
Keyword(s):  
Nursing Students ◽  
Social Determinants Of Health ◽  
Health Equity ◽  
Social Determinants ◽  
Baccalaureate Nursing ◽  
Baccalaureate Nursing Students ◽  
Determinants Of Health ◽  
Marginalized Populations ◽  
Knowledge And Attitudes ◽  
The Impact

AbstractObjectiveFor nursing students, competency in population health management involves acquiring knowledge and forming attitudes about the impact of the social determinants of health (SDoH) on health equity. The purpose of this pilot study was to assess nursing students’ knowledge and attitudes about the SDoH and health equity following a focused simulation activity.MethodBaccalaureate nursing students (N=182) participated in a ninety-minute health equity simulation and a post-simulation debrief. Forty-four students (23%) completed a 19-item post-simulation survey.ResultsSixty-four percent of participants reported positive attitude change in working with marginalized populations caused by the SDoH, and 89% reported being knowledgeable about the role of the registered nurse in addressing health equity. Seventy-five percent reported enhanced knowledge of the SDoH through the health equity simulation.ConclusionUsing health equity simulation may be effective in enhancing students’ knowledge, as well as their attitudes in caring for the health of marginalized populations by addressing the SDoH.

Implementing clinical decision support for oncology advanced care planning: A systems engineering framework to optimize the usability and utility of a machine learning predictive model in clinical practice.

2021 ◽  
Vol 39 (28_suppl) ◽  
pp. 330-330
Author(s):  
Teja Ganta ◽  
Stephanie Lehrman ◽  
Rachel Pappalardo ◽  
Madalene Crow ◽  
Meagan Will ◽  
...  
Keyword(s):  
Machine Learning ◽  
High Risk ◽  
Predictive Model ◽  
Systems Engineering ◽  
Care Planning ◽  
Learning Models ◽  
Predictive Tool ◽  
Risk Of Death ◽  
The Impact ◽  
Machine Learning Models

330 Background: Machine learning models are well-positioned to transform cancer care delivery by providing oncologists with more accurate or accessible information to augment clinical decisions. Many machine learning projects, however, focus on model accuracy without considering the impact of using the model in real-world settings and rarely carry forward to clinical implementation. We present a human-centered systems engineering approach to address clinical problems with workflow interventions utilizing machine learning algorithms. Methods: We aimed to develop a mortality predictive tool, using a Random Forest algorithm, to identify oncology patients at high risk of death within 30 days to move advance care planning (ACP) discussions earlier in the illness trajectory. First, a project sponsor defined the clinical need and requirements of an intervention. The data scientists developed the predictive algorithm using data available in the electronic health record (EHR). A multidisciplinary workgroup was assembled including oncology physicians, advanced practice providers, nurses, social workers, chaplain, clinical informaticists, and data scientists. Meeting bi-monthly, the group utilized human-centered design (HCD) methods to understand clinical workflows and identify points of intervention. The workgroup completed a workflow redesign workshop, a 90-minute facilitated group discussion, to integrate the model in a future state workflow. An EHR (Epic) analyst built the user interface to support the intervention per the group’s requirements. The workflow was piloted in thoracic oncology and bone marrow transplant with plans to scale to other cancer clinics. Results: Our predictive model performance on test data was acceptable (sensitivity 75%, specificity 75%, F-1 score 0.71, AUC 0.82). The workgroup identified a “quality of life coordinator” who: reviews an EHR report of patients scheduled in the upcoming 7 days who have a high risk of 30-day mortality; works with the oncology team to determine ACP clinical appropriateness; documents the need for ACP; identifies potential referrals to supportive oncology, social work, or chaplain; and coordinates the oncology appointment. The oncologist receives a reminder on the day of the patient’s scheduled visit. Conclusions: This workgroup is a viable approach that can be replicated at institutions to address clinical needs and realize the full potential of machine learning models in healthcare. The next steps for this project are to address end-user feedback from the pilot, expand the intervention to other cancer disease groups, and track clinical metrics.

Limitations on Participation and Their Relation to Social Determinants of Health Among Occupational Therapists and Occupational Therapy Students During Coronavirus Lockdown

2021 ◽  
Vol 75 (6) ◽  
Author(s):  
Nuria Menéndez Álvarez ◽  
Emiliano Diez Villoria ◽  
Estíbaliz Jimenez Arberas ◽  
Ana María Castaño Pérez ◽  
Antonio León García Izquierdo
Keyword(s):  
Occupational Therapy ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Occupational Therapists ◽  
Determinants Of Health ◽  
World Health ◽  
International Classification Of Functioning ◽  
Occupational Therapy Students ◽  
The Impact ◽  
The Relationship

Importance: For the first time in recent history, people worldwide have faced severe restrictions in occupations because of the measures adopted by governments to contain the coronavirus disease 2019 (COVID-19) crisis. Objective: To determine the limitations on participation of occupational therapists and occupational therapy students during “lockdown” and their impact on social determinants of health. Design: A cross-sectional, descriptive study conducted via an online survey. Participants: A total of 488 occupational therapists and occupational therapy students in North America, South America, and Europe. Outcomes and Measures: A questionnaire consisting of the World Health Organization Disability Assessment Schedule 2.0 of the International Classification of Functioning, Disability and Health and items developed to assess the impact of lockdown on daily life was emailed to occupational therapy professional associations, organizations, and universities between April and June 2020. It was available in English, Spanish, and Portuguese and met all the parameters listed in the Declaration of Helsinki. Results: The roles and routines of people across the developed world have been affected by lockdown measures. The study shows marked differences between participants in the domains of getting along and life activities, as well as influence on the environment. Moreover, South American participants experienced these difficulties to a greater extent than European participants. Conclusions and Relevance: This study quantifies the limitations in the participation of occupational therapists and occupational therapy students and the relationship of occupation to social determinants of health. What This Article Adds: The results of this research corroborate the relationship between health and occupation and highlight elements, such as the environment and context, that are important in occupational therapy. Therapists’ ability to analyze occupation in relation to contextual and cultural factors will benefit clients.

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