Using Linear Regression Correlational Analysis to Understand the Relationship Between Cyanobacterial Harmful Algal Blooms and Parkinson's Disease Prevalence in Various Regions of the US

Esha Agarwal

Cyanobacteria/Cyanobacterial Harmful Algal Blooms, Linear Regression Correlation Analysis, Parkinson’s/Parkinsonian Disease, Remote Sensing & Satellite Imaging.

Parkinson’s Disease (PD) is a movement/behavioral disorder that affects millions worldwide. Due to its neurodegenerative nature, PD’s induction and prognosis remain unknown; thus, it has an uncharacteristically high misdiagnosis rate. Scientists are investigating genetic predispositions and environmental factors that may result in PD. BMAA, a neurotoxic byproduct of Cyanobacterial Harmful Algal Blooms (CyanoHABs), has been found to cause neurodegeneration [1]. To better understand this relation, this study aims to understand whether there is a correlation between CyanoHABs and PD prevalence in the US, especially in a period of climate change. After finding CyanoHAB prevalence data of four states that fit given criteria from the past 4-5 years, PD prevalence was obtained by determining a PD state proportion and estimating the PD prevalence in each state. A Python algorithm was designed to conduct a linear regression correlational analysis on the data, and the correlation and determination coefficients were determined. Based on the coefficients of determination, each state was found to have a moderate-high correlation between the independent variable of CyanoHABs and dependent variable of PD and a positive correlation between CyanoHABs and PD was supported. Limitations for this study included PD prevalence estimation and lack of CyanoHAB data, both of which limited the duration and accuracy of this study. In the future, using satellite imagery and contacting medical centers directly can overcome these issues. Given that the elderly population is rising in many developed nations and increasing climate change and CyanoHABs in the future, it is becoming extremely important to better understand the disease [2],[3].

https://doi.org/10.31871/IJNTR.7.7.16