Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits
- PMID: 31304329
- PMCID: PMC6550159
- DOI: 10.1038/s41746-018-0056-y
Sociomarkers and biomarkers: predictive modeling in identifying pediatric asthma patients at risk of hospital revisits
Abstract
The importance of social components of health has been emphasized both in epidemiology and public health. This paper highlights the significant impact of social components on health outcomes in a novel way. Introducing the concept of sociomarkers, which are measurable indicators of social conditions in which a patient is embedded, we employed a machine learning approach that uses both biomarkers and sociomarkers to identify asthma patients at risk of a hospital revisit after an initial visit with an accuracy of 66%. The analysis has been performed over an integrated dataset consisting of individual-level patient information such as gender, race, insurance type, and age, along with ZIP code-level sociomarkers such as poverty level, blight prevalence, and housing quality. Using this uniquely integrated database, we then compare the traditional biomarker-based risk model and the sociomarker-based risk model. A biomarker-based predictive model yields an accuracy of 65% and the sociomarker-based model predicts with an accuracy of 61%. Without knowing specific symptom-related features, the sociomarker-based model can correctly predict two out of three patients at risk. We systematically show that sociomarkers play an important role in predicting health outcomes at the individual level in pediatric asthma cases. Additionally, by merging multiple data sources with detailed neighborhood-level data, we directly measure the importance of residential conditions for predicting individual health outcomes.
Keywords: Population screening; Risk factors.
Conflict of interest statement
Competing interestsThe authors declare no competing interests.
Figures
Similar articles
-
Health care access, concentrated poverty, and pediatric asthma hospital care use in California's San Joaquin Valley: A multilevel approach.J Asthma. 2018 Nov;55(11):1253-1261. doi: 10.1080/02770903.2017.1409234. Epub 2017 Dec 20. J Asthma. 2018. PMID: 29261336
-
A Machine Learning Approach to Predicting Need for Hospitalization for Pediatric Asthma Exacerbation at the Time of Emergency Department Triage.Acad Emerg Med. 2018 Dec;25(12):1463-1470. doi: 10.1111/acem.13655. Epub 2018 Nov 29. Acad Emerg Med. 2018. PMID: 30382605
-
Emergency department revisits for pediatric acute asthma exacerbations: association of factors identified in an emergency department asthma tracking system.Pediatr Emerg Care. 2008 Aug;24(8):505-10. doi: 10.1097/PEC.0b013e318180fdcb. Pediatr Emerg Care. 2008. PMID: 18645538
-
Adding Social Determinant Data Changes Children's Hospitals' Readmissions Performance.J Pediatr. 2017 Jul;186:150-157.e1. doi: 10.1016/j.jpeds.2017.03.056. Epub 2017 May 2. J Pediatr. 2017. PMID: 28476461
-
A machine learning approach to predict early outcomes after pituitary adenoma surgery.Neurosurg Focus. 2018 Nov 1;45(5):E8. doi: 10.3171/2018.8.FOCUS18268. Neurosurg Focus. 2018. PMID: 30453460
Cited by
-
Environmental risk factors and their footprints in vivo - A proposal for the classification of oxidative stress biomarkers.Redox Biol. 2020 Jul;34:101442. doi: 10.1016/j.redox.2020.101442. Epub 2020 Jan 31. Redox Biol. 2020. PMID: 32035921 Free PMC article. Review.
-
A scoping review on the use of machine learning in research on social determinants of health: Trends and research prospects.SSM Popul Health. 2021 Jun 5;15:100836. doi: 10.1016/j.ssmph.2021.100836. eCollection 2021 Sep. SSM Popul Health. 2021. PMID: 34169138 Free PMC article.
-
The Impact of Socioeconomic Status on Pediatric Facial Trauma.Craniomaxillofac Trauma Reconstr. 2024 Dec;17(4):NP242-NP248. doi: 10.1177/19433875241280214. Epub 2024 Sep 3. Craniomaxillofac Trauma Reconstr. 2024. PMID: 39553817 Free PMC article.
-
Indoor Bacterial and Fungal Burden in "Moldy" versus "Non-Moldy" Homes: A Case Study Employing Advanced Sequencing Techniques in a US Metropolitan Area.Pathogens. 2023 Aug 1;12(8):1006. doi: 10.3390/pathogens12081006. Pathogens. 2023. PMID: 37623966 Free PMC article.
-
Geo-clustered chronic affinity: pathways from socio-economic disadvantages to health disparities.JAMIA Open. 2019 Aug 1;2(3):317-322. doi: 10.1093/jamiaopen/ooz029. eCollection 2019 Oct. JAMIA Open. 2019. PMID: 31984364 Free PMC article.
References
-
- World Health Organization. A conceptual framework for action on the social determinants of health. (2010).
-
- Booske, B. C., Athens, J. K., Kindig, D. A., Park, H. & Remington, P. L. Different Perspectives For Assigning Weights to Determinants Of Health (University of Wisconsin: Population Health Institute, 2010).
LinkOut - more resources
Full Text Sources