JP Journal of Biostatistics

The JP Journal of Biostatistics is a highly regarded open-access international journal indexed in the Emerging Sources Citation Index (ESCI). It focuses on the application of statistical theory and methods in resolving problems in biological, biomedical, and agricultural sciences. The journal encourages the submission of experimental papers that employ relevant algorithms and also welcomes survey articles in the fields of biostatistics and epidemiology.

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EXAMINING DIALYSIS PATIENTS FOR SIGNS OF DEPRESSION AND IDENTIFYING POTENTIAL RISK FACTORS USING LOGISTIC REGRESSION MODEL

Authors

  • Ehab Ahmed Frah

Keywords:

depression, hemodialysis, logistic models, marital status, patient health questionnaire, prevalence, renal dialysis

DOI:

https://doi.org/10.17654/0973514325032

Abstract

Chronic kidney disease is a distressing condition characterized by diminished function and severe damage to both kidneys. Poor renal function results in the accumulation of excessive amounts of fluid and waste in the bloodstream. Hemodialysis is the ubiquitous treatment for end-stage renal disease patients who are unable to have a kidney transplant, but this life-preserving treatment can lead to depression in many patients. This study aims to determine the incidence of depression among patients experiencing kidney failure and to examine the correlation between its prevalence and specific sociodemographic factors. This study sought to evaluate the levels of depressive symptoms and identify the factors influencing them. This study enrolled a convenience sample of 114 hemodialysis patients at the King Salman Armed Forces Hospital (KSAFH) in Tabuk, using a cross-sectional study design, using convenience-sampling strategy.

We assessed depression using the Patient Health Questionnaire 9 (PHQ-9). We used descriptive frequency tables and statistical tests, such as independent t-tests and one-way ANOVA, to explore  the relationship between patients’ depression levels and their sociodemographic information. We then utilized multinomial logistic regression to forecast the primary risk factors contributing to depression in kidney failure patients. The study revealed no statistically significant variation in depression levels among patients with renal failure based on factors such as age, gender, occupational position, duration of the disease, and frequency of dialysis per week. However, a significant difference in depression levels was observed based on marital status. Moreover, the logistic regression model included all questionnaire items, except for three non-significant items.

Received: August 11, 2025
Accepted: September 25, 2025

References

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Published

2025-10-25

Issue

Vol. 25 No. 3 (2025)

Section

Articles

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How to Cite

EXAMINING DIALYSIS PATIENTS FOR SIGNS OF DEPRESSION AND IDENTIFYING POTENTIAL RISK FACTORS USING LOGISTIC REGRESSION MODEL. (2025). JP Journal of Biostatistics, 25(3), 581-597. https://doi.org/10.17654/0973514325032

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