Clinical and Socioeconomic Dataset of Thalassemia Patients in Bangladesh

Description

This dataset comprises comprehensive clinical, laboratory, socioeconomic, and standardized mental health data for 617 thalassemia patients treated at a major diagnostic center, LABAID LTD., Pabna, Bangladesh. The data were collected through structured face-to-face interviews and medical record reviews between March and June 2025. It includes 24 variables covering demographic characteristics (age, gender, residence, education, income), treatment history (transfusion frequency, iron chelation therapy, splenectomy status), laboratory findings (hemoglobin levels, serum ferritin, and red blood cell indices), and mental health assessment. Mental health status was quantified using the PHQ-9, a validated clinical instrument for assessing depressive symptoms. Each patient’s PHQ-9 score ranges from 0 to 27 and was used to derive a binary mental health classification based on established clinical thresholds, where scores below 10 indicate low risk (Good) and scores of 10 or above indicate clinically significant risk (Bad). Key Features Patient age range: 2 to 60 years, covering pediatric to adult populations. Thalassemia types: Major, Intermedia, and Minor, enabling subtype-specific analysis. Mental health distribution: Approximately 64.83% of patients fall into the “at-risk” category based on PHQ-9 clinical thresholds. Standardized mental health measure: PHQ-9 score (0–27) provides a clinically validated and reproducible outcome variable. Data format: De-identified CSV file (UTF-8 encoded), ensuring privacy and reproducibility. Variable types: A combination of numerical, ordinal, and categorical variables suitable for advanced statistical and machine learning analyses. Ethical Considerations Ethical approval was obtained from LABAID LTD., with informed consent from all participants and guardians for pediatric cases. All data were fully anonymized to ensure confidentiality. Significance This dataset supports analysis of treatment, socioeconomic factors, and mental health in thalassemia patients. By incorporating standardized PHQ-9 data, it enables reliable machine learning research and contributes to personalized care and public health decision-making.

Institutions

• American International University-Bangladesh

  Dhaka Division, Dhaka
• Southeast University

  Dhaka Division, Dhaka
• Eastern Institute of Technology

  Hawke's Bay Region, Napier City

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