Leveraging address data from clinical administrative systems to build community disease profiles
1
School of Public Health, University of Cape Town, Cape Town, South Africa
2
Provincial Health Data Centre, Western Cape Department of Health and Wellness, Cape Town, South Africa
3
CIDRI-Africa, Institute of Infectious Diseases and Molecular Medicine, University of Cape Town, and Western Cape Department of Health and Wellness, Cape Town, South Africa
Popul. Med. 2026;8(Supplement Supplement 1):A881
ABSTRACT
BACKGROUND:
The Provincial Health Data Centre (PHDC) collates all available person-level electronic health data in the Western Cape public sector using a unique patient identifier. Information on patient co-morbidities is inferred from laboratory and pharmacy data, disease registers and diagnostic coding. The PHDC also holds residential address data from patient administrative systems, allowing for the creation of localised disease descriptions. Cape Town Systematic Healthcare Action Research Project (C-SHARP) health and demographic surveillance system is in two demographically diverse communities, Bishop Lavis and Nomzamo.
METHODS:
Using fuzzy matching and machine learning geocoding approaches, residential suburb was inferred from the free text address data. Combined with manual refinement, this approach identified all individuals in the PHDC whose residential suburb falls within Bishop Lavis and Nomzamo. Age, sex and PHDC-inferred episodes of HIV, tuberculosis, diabetes, hypertension, and chronic kidney disease were assessed, in this cross-sectional analysis.
RESULTS:
37,721 individuals were included (55% female). The median age was 27 years at both communities, but 12% in Bishop Lavis were ≥60 compared to 3% in Nomzamo. HIV prevalence peaked at 52% in women aged 40-49 and at 35% in men aged 50-59 in Nomzamo, vs 12% for women and 9% for men aged 40-49 in Bishop Lavis. Non-communicable diseases (NCDs) were 2-3 times higher in Bishop Lavis than Nomzamo. In Nomzamo a high proportion of HIV positive individuals also had an NCD. All comorbidities, except tuberculosis, were more prevalent in women.
CONCLUSIONS:
Routine electronic health data can be used to describe localised disease patterns in diverse demographic settings, although not all individuals could be geocoded and address data is not always correct or current. Disease burdens were mostly higher in women, partly because of care-seeking behaviour. Bishop Lavis has a high NCD burden. Nomzamo has a high HIV burden, which is contributing to NCDs there.
The Provincial Health Data Centre (PHDC) collates all available person-level electronic health data in the Western Cape public sector using a unique patient identifier. Information on patient co-morbidities is inferred from laboratory and pharmacy data, disease registers and diagnostic coding. The PHDC also holds residential address data from patient administrative systems, allowing for the creation of localised disease descriptions. Cape Town Systematic Healthcare Action Research Project (C-SHARP) health and demographic surveillance system is in two demographically diverse communities, Bishop Lavis and Nomzamo.
METHODS:
Using fuzzy matching and machine learning geocoding approaches, residential suburb was inferred from the free text address data. Combined with manual refinement, this approach identified all individuals in the PHDC whose residential suburb falls within Bishop Lavis and Nomzamo. Age, sex and PHDC-inferred episodes of HIV, tuberculosis, diabetes, hypertension, and chronic kidney disease were assessed, in this cross-sectional analysis.
RESULTS:
37,721 individuals were included (55% female). The median age was 27 years at both communities, but 12% in Bishop Lavis were ≥60 compared to 3% in Nomzamo. HIV prevalence peaked at 52% in women aged 40-49 and at 35% in men aged 50-59 in Nomzamo, vs 12% for women and 9% for men aged 40-49 in Bishop Lavis. Non-communicable diseases (NCDs) were 2-3 times higher in Bishop Lavis than Nomzamo. In Nomzamo a high proportion of HIV positive individuals also had an NCD. All comorbidities, except tuberculosis, were more prevalent in women.
CONCLUSIONS:
Routine electronic health data can be used to describe localised disease patterns in diverse demographic settings, although not all individuals could be geocoded and address data is not always correct or current. Disease burdens were mostly higher in women, partly because of care-seeking behaviour. Bishop Lavis has a high NCD burden. Nomzamo has a high HIV burden, which is contributing to NCDs there.
| eISSN: | 2654-1459 |
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