Kumaraswamy Distribution in Analyzing the Health-related Quality of Life and Effective Factors in Elderly Patients with Epilepsy
Message:
Abstract:
Background

Epilepsy, which develops in the elderly, is recognized as a major health burden. Although health-related quality of life (HRQoL) is an essential element in the medical treatment of elderly patients with epilepsy, it is a question whether epilepsy and its treatment effectively influence the quality of life (QoL) in the elderly.

Objectives

The current study aimed at evaluating the relationship between demographic and clinical aspects of epilepsy in HRQoL of elderly patients. Since HRQoL scores are bounded, the Kumaraswamy (Kum) regression model was used to analyze the data.

Methods

The current study was conducted on 766 elderly patients diagnosed with epilepsy taking at least one antiepileptic drug (AED) selected from six neurologic clinics in Iran. In addition to demographic information, the Liverpool seizure severity scale (LSSS), medication adherence report scale (MARS-5), and quality of life in epilepsy (QoLIE-31) questionnaire were completed for patients. Data were analyzed using multiple linear regression (MLR) and the Kum regression models.

Results

Most of the patients included in the study had focal (70.2%) epilepsy. Mean duration of disease was 17.71 ± 4.56 years and the average number of seizures was 3.4 ± 3.2 episodes per month. The Kum regression model indicated that seizure frequency (β = -0.157, P < 0.0001) and LSSS score (β = -0.003, P = 0.009) were significant and negative predictors of overall QoLEI-31 score; MARS-5 score was a positive predictor of overall QoLEI-31 score (β = 0.014, P = 0.002). However, disease duration and serum AED level had no significant effects on overall QoLEI-31 score.

Conclusions

The findings suggested that increased seizure frequency and severity were associated with lower QoL and medication adherence was directly associated with HRQoL. The Kum regression could be a suitable alternative to the methods currently used in the analysis of HRQoL data

Article Type:
Research/Original Article
Language:
English
Published:
Archives of Neuroscience, Volume:6 Issue:4, 2019
Page:
4
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