Analisis Spasial dan Perhitungan Epidemiologi Kejadian Hipertensi di Kota Palembang Tahun 2024 - Nurul Rahma; Siti Halimatul Munawarah; Najmah Najmah; Iche Andriyani Liberty; Rahmatillah Razak; Najmah Maulaya; Nafisah Shakira Putri Yarna; Tarisha Kahla Sabitha; Yudhi Setiawan; Dedi Sandra; Ratna Dewi (Master of Epidemiology, Faculty of Public Health, Diponegoro University, Indonesia, 2026)
Background: Hypertension is one of the most prevalent and impactful non-communicable diseases in Indonesia, including in Palembang City, contributing to a substantial health and economic burden. Spatial analysis is needed to understand the distribution of cases and support more effective interventions.Methods: This study employed a quantitative descriptive observational design using secondary data from the Non-Communicable Disease surveillance system and Minimum Service Standards reports of the Palembang City Health Office in 2024, with sub-districts as the unit of analysis. Two indicators were applied, estimated prevalence based on SPM assumptions and case detection rate, calculated as the proportion of detected cases among the at-risk population. Descriptive analysis was conducted using Google Sheets, and descriptive spatial mapping was performed using QGIS 3.40 with a quantile classification approach.Result: Among 1,313,535 residents aged over 15 years, an estimated 409,823 people (31.2%) were living with hypertension based on the national SPM reference. Sukarami sub-district had the highest number of cases (46,253), while Bukit Kecil had the lowest (9,221). Surveillance data reported 89,548 detected cases, with Gandus highest burden area (9,663 cases). Hypertension-related deaths totaled 57 (2.2% of total mortality), with uneven distribution across sub-districts.Conclusion : A difference between estimated and detected hypertension cases was observed, with spatial variation across sub-districts. Estimated prevalence was based on SPM references, while detected cases reflect surveillance findings (case detection rate). This gap indicates differences between expected burden and identified cases. Strengthening early detection and area-based interventions is needed.