PhD Scientific Days 2026

Health Sciences 2.

Geospatial Distribution and Potential Economic Implications of Health Development Offices in Hungary

Name of the presenter

Domjan, Peter

Institute/workplace of the presenter

Semmelweis University, Doctoral College, Health Sciences Division Interdisciplinary Applied Health Sciences Program

Authors

MSc Domján Péter¹, Dr. Bertalan Ádám², Angyal Viola², Petrov Iván³, Dr. Vingender István⁴
1: Semmelweis University, Doctoral College, Health Sciences Division Interdisciplinary Applied Health Sciences Program
2: Semmelweis University, Doctoral College, Health Sciences Division Institute of Digital Health Sciences
3: Semmelweis University, Heart and Vascular Center, Department of Sports Medicine
4: Semmelweis University, Faculty of Health Sciences, Department of Social Sciences

Text of the abstract

Introduction
Health Development Offices (HDOs) are key elements of preventive healthcare in Hungary. However, regional differences in their spatial distribution raise questions regarding equitable access to preventive services.

Aims
This study aims to describe the geospatial distribution of HDOs and to explore their potential economic implications using a model-based approach.

Methods
A county-level geospatial analysis was conducted using population data and the proportion of elderly residents to assess service coverage. National-level epidemiological rates for type 2 diabetes, cardiovascular diseases, and mental health service utilization (based on available data from 2019, 2021, and 2023) were used to estimate baseline disease burden. These rates were applied to county-level population data. A simplified budget impact framework was used to estimate potential changes in disease burden under conservative assumptions. Relative risk reductions (1–3%) and adherence rates (30–70%) were defined based on literature-informed ranges. Uncertainty was explored using Monte Carlo simulation.

Results
The geospatial analysis identified regional differences in HDO distribution, with relatively higher coverage in rural areas. Model-based estimates suggest that even small reductions in disease burden could be associated with measurable changes in healthcare utilization. Results remained stable across a wide range of parameter values.

Conclusion
Geospatial and model-based approaches can provide useful insights into the potential role of preventive services. However, results should be interpreted cautiously due to data limitations and the use of aggregated national-level inputs.

Funding
No external funding.