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OP19 Quantifying multi-morbidity in an ethnically-diverse inner city population: exploring the health burden of households using a retrospective e-cohort
  1. G Harper1,
  2. J Lyons2,
  3. A Akbary2,
  4. R Fry2,
  5. Z Ahmed1,
  6. R Lyons2,
  7. C Dexateux1,
  8. J Robson1
  1. 1Clinical Effectiveness Group, Institute of Population Health Science, QMUL, London, UK
  2. 2Biomedical Sciences/Medicine, Swansea University, UK


Background Multi-morbidity is a growing challenge globally. New insights and approaches into the patterns of, and contributing factors to, multi-morbidity, using large routinely-collected patient data resources, are current research priorities. There is evidence that individuals who live with people with a long-term condition are at increased risk of a long-term condition themselves, however to date there has been no assessment of multi-morbidity at a household level.

General practitioner (GP) Electronic Health Records (EHRs) contain rich demographic and clinical data for research to quantify and explore household multi-morbidity. We investigated this by creating and linking GP-EHRs to a unique household identifier based on the patient address.

Methods GP-EHRs for 1,164,736 patients registered with GP practices in four London boroughs at mid-2018 were extracted to create a retrospective e-cohort. Patient addresses were matched to Unique Property Reference Numbers (UPRNs) using a validated deterministic address-matching algorithm, and pseudonymised into Residential Anonymised Linking Fields (RALFs). GP-EHRs were linked to the RALF. Exclusion criteria were selected using sensitivity analyses as per STROBE guidelines, based on GP registration status and date, property type, and data quality.

The main outcome was multi-morbidity in patients aged ≥18 years in mid-2018 with two or more chronic long-term conditions identified from their GP-EHRs based on diagnostic criteria and their associated READ codesets developed in the Quality and Outcomes Framework. We assigned individuals to their households on the basis of shared RALFs. We calculated age-specific multi-morbidity prevalences and their ratios by individual-level factors, and estimated the number of adults with multi-morbidity in each household. We investigated the characteristics of households with ≥2 adults with multi-morbidity.

Results The e-cohort comprised 923,995 patients (48.6% female, 44.6% Black and Minority Ethnic [BAME] backgrounds, 68% aged 20–64 years) living in 332,661 households (median [IQR] occupancy: 2 [1–3]). Multi-morbidity was identified in 104,082 patients (14%) and was more prevalent in women (53%), those from BAME backgrounds (51%), or those of working age (58% 20–64 years). Overall, 87,889 (26%) households included at least one, and 14,563 (4%) two or more, adults with multi-morbidity. Age-specific prevalence and prevalence ratios will be presented.

Conclusion This is the first time multi-morbidity has been quantified at the household level. We have demonstrated a high burden of multi-morbidity in women, working-age adults and those from BAME backgrounds in a geographically-defined, ethnically diverse, urban population. Factors contributing to multi-morbidity at a household level will be explored and compared to findings from a harmonised dataset for Wales.

  • electronic health records
  • multi-morbidity
  • household

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