Disparities in Healthcare Utilization: Superfund Site vs. Neighboring Comparison Site.
Crystal StephensYoung-Il KimRekha RamachandranMonica L BaskinVeena AntonySejong BaePublished in: International journal of environmental research and public health (2022)
Inequities in pollution-attributable health disparities are similar in most urban areas throughout the United States, and appear to encompass racial and socio-demographic differences, thereby suggesting increased health risks for those living in these areas. Individuals residing in close proximity to Superfund sites, predominantly people of color, are increasingly stricken with lung diseases. The prevalence of chronic lung diseases, such as chronic obstructive pulmonary disease (COPD), asthma in children, and lower respiratory tract infections (LRTI), is significantly higher in the affected area compared to the neighboring control area, irrespective of smoking, socio-economic status, or demographics. We conducted a retrospective analysis using data collected from patients who obtained healthcare from the University of Alabama at Birmingham (UAB) Health System. The data were procured from the Enterprise Data Warehouse (UAB Informatics for Integrating Biology and the Bedside (i2b2)). We evaluated healthcare utilization and classification of disease (defined by ICD-10 codes) of patients residing in zip codes: affected (35207, 35217) and neighboring comparison (35214). The results of the analysis may provide evidence that can be used for risk mitigation strategies or outreach education campaign(s) for those who live in the affected area.
Keyphrases
- healthcare
- chronic obstructive pulmonary disease
- electronic health record
- respiratory tract
- lung function
- big data
- end stage renal disease
- chronic kidney disease
- newly diagnosed
- affordable care act
- machine learning
- ejection fraction
- risk factors
- mental health
- deep learning
- climate change
- risk assessment
- data analysis
- peritoneal dialysis
- prognostic factors
- smoking cessation
- cystic fibrosis
- particulate matter
- artificial intelligence
- african american
- patient reported
- clinical evaluation