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Objective  To estimate the magnitude in which Parkinson’s disease (PD) symptoms and health- related quality of life (HRQoL) determined PD costs over a 4-year period.    Materials and Methods  Data collected during 3-month, each year, for 4 years, from the ELEP study, included sociodemographic, clinical and use of resources information. Costs were calculated yearly, as mean 3-month costs/patient and updated to Spanish €, 2012. Mixed linear models were performed to analyze total, direct and indirect costs based on symptoms and HRQoL.    Results  One-hundred and seventy four patients were included. Mean (SD) age: 63 (11) years, mean (SD) disease duration: 8 (6) years. Ninety-three percent were HY I, II or III (mild or moderate disease). Forty-nine percent remained in the same stage during the study period. Clinical evaluation and HRQoL scales showed relatively slight changes over time, demonstrating a stable group overall. Mean (SD) PD total costs augmented 92.5%, from €2,082.17 (€2,889.86) in year 1 to €4,008.6 (€7,757.35) in year 4. Total, direct and indirect cost incremented 45.96%, 35.63%, and 69.69% for mild disease, respectively, whereas increased 166.52% for total, 55.68% for direct and 347.85% for indirect cost in patients with moderate PD. For severe patients, cost remained almost the same throughout the study. For each additional point in the SCOPA-Motor scale total costs increased €75.72 (p = 0.0174); for each additional point on SCOPA-Motor and the SCOPA-COG, direct costs incremented €49.21 (p = 0.0094) and €44.81 (p = 0.0404), respectively; and for each extra point on the pain scale, indirect costs increased €16.31 (p = 0.0228).    Conclusions  PD is an expensive disease in Spain. Disease progression and severity as well as motor and cognitive dysfunctions are major drivers of costs increments. Therapeutic measures aimed at controlling progression and symptoms could help contain disease expenses.

Parkinson Symptoms and Health Related Quality of Life as Predictors of Costs: A Longitudinal Observational Study with Linear Mixed Model Analysis