Temporal Patterns Of The Top Ten Diseases At Darussalam, Community Health Center In 2025 : An Epidemiological Analysis Using Routine Health Information System Data

Abstract

This study aimed to analyze the temporal patterns of the top ten diseases reported at Darussalam Community Health Center in 2025 using routine health information system (RHIS) data. Understanding disease patterns over time is essential for strengthening primary health care services, improving disease prevention strategies, and supporting evidence-based decision-making at the local level. By examining temporal variations in morbidity, this study seeks to identify priority health problems and periods of increased disease burden that require targeted interventions. This study employed a quantitative descriptive epidemiological approach with a retrospective design. Secondary data were obtained from RHIS records of outpatient visits at Darussalam Community Health Center for the period January–December 2025. The study population comprised all recorded morbidity cases during the study period. A total population approach was used, and the top ten diseases were identified based on the highest cumulative number of reported cases. Data were analyzed using descriptive epidemiological methods, including monthly and quarterly trend analysis, and were presented in the form of tables and temporal distributions to illustrate disease patterns over time.
The findings showed that a limited number of disease categories accounted for the majority of outpatient visits in 2025. Acute respiratory infections consistently ranked as the leading cause of morbidity and exhibited clear temporal variation, with higher incidence during specific months. Non-communicable diseases, particularly hypertension and diabetes mellitus, demonstrated stable patterns throughout the year, indicating a persistent demand for chronic disease management. Gastrointestinal and skin-related diseases showed seasonal fluctuations, with increased cases during certain periods. Overall, the highest disease burden was observed in the later months of the year. This study highlights the value of RHIS data in identifying temporal disease patterns at the primary health care level. Regular analysis of routine morbidity data can enhance service responsiveness, support targeted preventive and promotive interventions, and improve resource allocation. Integrating temporal analysis into routine monitoring activities is essential for strengthening evidence-based primary health care planning.