Model-based disease mapping using primary care registry data

• Published in: Spatial and spatio-temporal epidemiology Vol. 49; p. 100654
• Main Authors: Janssens, Arne, Vaes, Bert, Van Pottelbergh, Gijs, Libin, Pieter J.K., Neyens, Thomas
• Format: Journal Article
• Language: English
• Published: Netherlands Elsevier Ltd 01.06.2024
• Subjects: Adolescent; Adult; Aged; Bayesian spatial modeling; Child; Child, Preschool; Female; Humans; Logistic Models; Lower respiratory tract infections; Male; Middle Aged; Models, Statistical; Passive sentinel surveillance; Primary care registry data; Primary Health Care - statistics & numerical data; Registries; Respiratory Tract Infections - epidemiology; Simulation study; Spatial Analysis; Spatial epidemiology; Young Adult; Lower respiratory tract infections; Spatial epidemiology; Bayesian spatial modeling; Passive sentinel surveillance; Simulation study; Primary care registry data
• ISSN: 1877-5845; 1877-5853
• DOI: 10.1016/j.sste.2024.100654

Spatial modeling of disease risk using primary care registry data is promising for public health surveillance. However, it remains unclear to which extent challenges such as spatially disproportionate sampling and practice-specific reporting variation affect statistical inference. Using lower respiratory tract infection data from the INTEGO registry, modeled with a logistic model incorporating patient characteristics, a spatially structured random effect at municipality level, and an unstructured random effect at practice level, we conducted a case and simulation study to assess the impact of these challenges on spatial trend estimation. Even with spatial imbalance and practice-specific reporting variation, the model performed well. Performance improved with increasing spatial sample balance and decreasing practice-specific variation. Our findings indicate that, with correction for reporting efforts, primary care registries are valuable for spatial trend estimation. The diversity of patient locations within practice populations plays an important role. •It is unclear if primary care registry data are useful for disease mapping.•We performed a case and simulation study on lower respiratory tract infections.•Opportunistic samples estimated spatial trends reasonably well.•It is beneficial that general practice populations come from multiple regions.•Limiting reporting variability is important.