Profile Of Patient Diagnosed With Depression In Bandar Lampung In 2023 (BPJS Kesehatan Sample Data From 2024): A Descriptive Study - Syaripah Ulandari; Yulianasari Pulungan; Dwi Ismayati; Made Laksmi Meiliana (International Journal of Health Systems and Policy, 2026)
Depression is a major global public health concern and a leading cause of disability worldwide, affecting individuals across various demographic groups. Despite its high burden, local descriptive data on depression remain limited, particularly in Bandar Lampung. This study aims to describe the sociodemographic profile of patients diagnosed with depression in Bandar Lampung in 2023 using BPJS Kesehatan sample data. This study employed a descriptive, observational, cross-sectional design using secondary data from the 2023 BPJS Kesehatan dataset. The sample included patients diagnosed with depression based on ICD-10 codes F32 and F33 who accessed referral healthcare facilities (FKRTL). A total of 41 patients from Bandar Lampung were included after data cleaning. Descriptive statistical analysis was conducted to summarize patient characteristics, including diagnosis type, generation, gender, participant segmentation, marital status, and healthcare costs. The results showed that the most common diagnosis was unspecified depressive episode (F329) (31.71%). The majority of patients were from Generation Z (41.46%), female (85.37%), and formally employed participants (31.71%). Most patients were married (53.66%). The total healthcare cost for depression cases reached IDR 10,929,600. These findings highlight the predominance of depression among younger individuals, women, and working populations. In conclusion, depression in Bandar Lampung is influenced by demographic and socioeconomic factors, with higher prevalence observed among vulnerable groups such as Generation Z and women. These findings underscore the need for improved mental health screening, targeted interventions, and strengthened healthcare system support to address depression effectively. Further studies with larger, more comprehensive datasets are recommended.