Note:
In this demo version of the web map, the visualization of Health Indicators has been intentionally disabled to ensure data privacy and confidentiality.

This section presents health vulnerability indicators. These indicators are based on estimates of prevalent cases in 2024, distributed over a grid of 100⨉100m cells (10,000 m²), for the following groups of conditions:

• Mental Disorders, defined according to the International Classification of Diseases, 9th Revision, Clinical Modification (ICD-9-CM). This includes conditions such as psychosis, bipolar disorder, schizophrenia, major depression, and dementia. Individuals with mental disorders who also have other chronic conditions are included in this category.
• Multi-morbidity: individuals affected by two or more major chronic conditions (from a set of thirty conditions considered). For the full list of conditions, see the methodological notes.
• Health Vulnerability Indicator (overall): includes individuals who fall into at least one of the two categories above. This indicator is not the simple sum of the previous two, as each person is counted only once, even if they meet both criteria.

Prevalent cases were identified through record linkage across 13 regional healthcare databases, using anonymized individual identifiers. Condition groups were defined based on ICD-9-CM diagnosis and procedure codes recorded over the past 4 years (including the reference year), ATC drug codes from the past 2 years, and disease-specific exemption status active in the reference year. For more details, see the methodological notes. Individuals who are not self-sufficient, living in residential care facilities for the elderly, or receiving integrated home care, were excluded.

Each prevalent case was initially assigned to its census section of residence using georeferencing techniques. Within each section, cases were then spatially redistributed according to population distribution, using data from the Global Human Settlement Layer Project of the European Commission, on a 100⨉100m grid. This downscaling approach produces more uniform and finer spatial detail, as the median area of an inhabited census section is A = 26,379 m² (approximately equivalent to a 162⨉162m cell), and allows easier comparison across different areas. In this way, the spatial distribution of heat-sensitive health conditions reflects the higher likelihood that individuals are located in more densely populated areas within each census section.