scholarly journals A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects

2021 ◽  
pp. 100836
Author(s):  
Shiho Kino ◽  
Yu-Tien Hsu ◽  
Koichiro Shiba ◽  
Yung-Shin Chien ◽  
Carol Mita ◽  
...  
Keyword(s):  
Machine Learning ◽  
Social Determinants Of Health ◽  
Social Determinants ◽  
Scoping Review ◽  
Determinants Of Health ◽  
Health Trends

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 Meaningful Information in the Age of Big Data: A Scoping Review on Social Determinants of Health Data Collection for Electronic Health Records

2019 ◽  
Author(s):  
Kelsey Berg ◽  
Chelsea Doktorchik ◽  
Hude Quan ◽  
Vineet Saini
Keyword(s):  
Big Data ◽  
Data Collection ◽  
Electronic Health Records ◽  
Social Determinants Of Health ◽  
Health Systems ◽  
Social Determinants ◽  
Scoping Review ◽  
Determinants Of Health ◽  
Health Records ◽  
Automated Technology

Abstract Background: Electronic Health Records (EHRs) are key tools for integrating patient data into health information systems (IS). Advances in automated data collection methodology, particularly the collection of social determinants of health (SDOH), provide opportunities to advance health promotion and illness prevention through advanced analytics (i.e. “Big Data” techniques). We ask how current data collection processes in EHRs permit SDOH data to flow throughout health systems. Methods: Using a scoping review framework, we searched through medical literature to identify current practices in SDOH data collection within EHR systems. We extracted relevant information on data collection methodology, specifically focusing on uses of automated technology. We discuss our findings in the context of research methodology and potential for health equity. Results: Practitioners collect a variety of SDOH data at point of care through EHR, predominantly via embedded screening tools and clinical notes, and primarily capturing data on financial security, housing status, and social support. Health systems are increasingly using digital technology in data collection, including natural language processing algorithms. However overall use of automated technology is limited to date. End uses of data pertain to improving system efficiency, patient care-coordination, and addressing health disparities. Discussion & Conclusion: EHRs can realistically promote collection and meaningful use of SDOH data, although EHRs have not extensively been used to collect and manage this type of information. Future applied research on systems-level application of SDOH data is necessary, and should incorporate a range of stakeholders and interdisciplinary teams of researchers and practitioners in fields of health, computing, and social sciences.

scholarly journals Teaching the Social Determinants of Health in Undergraduate Medical Education: a Scoping Review

2019 ◽  
Vol 34 (5) ◽  
pp. 720-730 ◽  
Author(s):  
Ashti Doobay-Persaud ◽  
Mark D. Adler ◽  
Tami R. Bartell ◽  
Natalie E. Sheneman ◽  
Mayra D. Martinez ◽  
...  
Keyword(s):  
Medical Education ◽  
Social Determinants Of Health ◽  
Undergraduate Medical Education ◽  
Social Determinants ◽  
Scoping Review ◽  
Determinants Of Health ◽  
The Social

scholarly journals Social determinants of health associated with the development of sepsis in adults: a scoping review protocol

BMJ Open ◽  
2020 ◽  
Vol 10 (10) ◽  
pp. e039146
Author(s):  
Christina Machon ◽  
Fatima Sheikh ◽  
Alison Fox-Robichaud
Keyword(s):  
Social Determinants Of Health ◽  
Social Determinants ◽  
Scoping Review ◽  
Smoking Status ◽  
Social Determinant ◽  
Determinants Of Health ◽  
Drug Use Disorders ◽  
Potential Link ◽  
Life Threatening ◽  
The Impact

IntroductionSepsis, the life-threatening immune response to infection, affects millions of people annually. Understanding of the factors associated with the development of sepsis is crucial for improving population health and public health efforts; in particular, literature exploring the relationship between sepsis and social determinants of health is lacking. This review seeks to establish and amalgamate existing evidence of the relationships between sepsis and the following social determinants: frailty, registration with a family physician, mental illness, alcohol abuse, social support levels, smoking status, illicit drug use disorders, socioeconomic status, gender and race/ethnicity.Methods and analysisThis study will analyse qualitative and quantitative studies using standard processes. The selected social determinants of health and their potential link to adult sepsis will be analysed separately under distinct headings. Findings will be consolidated in a final discussion. PubMed and Medline will be searched for articles published between 1970 and 2020 using search strings combining ‘sepsis’ and other variations, such as ‘septicaemia’ with each social determinant of interest. ‘Sepsis’ and at least one social determinant of interest must be present in a study’s title for inclusion in the review; the results of the initial search will be filtered based on predetermined inclusion and exclusion criteria. Evidence from this scoping review will provide information on the impact of social determinants of health on the risk of developing adult sepsis, which can inform clinicians of the various risk factors to consider when admitting patients.Ethics and disseminationApproval from a research ethics board is not needed for this amalgamation of information from studies for which the primary investigators have obtained their own, respective ethics board approval. Once completed, the review will be submitted for publication in a peer-reviewed journal, and findings will be presented in local and national forums.

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