Identification of Early Onset Dementia in Population-Based Health Administrative Data: A Validation Study Using Primary Care Electronic Medical Records

Identification of homelessness using health administrative data in Ontario, Canada following a national coding mandate: a validation study

OBJECTIVES

Conducting longitudinal health research about people experiencing homelessness poses unique challenges. Identification through administrative data permits large, cost-effective studies; however, case validity in Ontario is unknown after a 2018 Canada-wide policy change mandating homelessness coding in hospital databases. We validated case definitions for identifying homelessness using Ontario health administrative databases after introduction of this coding mandate.

STUDY DESIGN AND SETTING

We assessed 42 case definitions in a representative sample of people experiencing homelessness in Toronto (n = 640) from whom longitudinal housing history (ranging from 2018 to 2022) was obtained, and a randomly selected sample of presumably housed people (n = 128,000) in Toronto. We evaluated sensitivity, specificity, positive and negative predictive values, and positive likelihood ratios to select an optimal definition, and compared the resulting true positives against false positives and false negatives to identify potential causes of misclassification.

RESULTS

The optimal case definition included any homelessness indicator during a hospital-based encounter within 180 days of a period of homelessness (sensitivity = 52.9%; specificity = 99.5%). For periods of homelessness with ≥1 hospital-based healthcare encounter, the optimal case definition had greatly improved sensitivity (75.1%) while retaining excellent specificity (98.5%). Review of false positives suggested that homeless status is sometimes erroneously carried forward in healthcare databases after an individual transitioned out of homelessness.

CONCLUSION

Case definitions to identify homelessness using Ontario health administrative data exhibit moderate to good sensitivity and excellent specificity. Sensitivity has more than doubled since the implementation of a national coding mandate. Mandatory collection and reporting of homelessness information within administrative data present invaluable opportunities for advancing research on the health and healthcare needs of people experiencing homelessness.

Algorithmic identification of persons with dementia for research recruitment: ethical considerations

Underdiagnosis, misdiagnosis, and patterns of social inequality that translate into unequal access to health systems all pose barriers to identifying and recruiting diverse and representative populations into research on Alzheimer's disease and Alzheimer's disease related dementias. In response, some have turned to algorithms to identify patients living with dementia using information that is associated with this condition but that is not as specific as a diagnosis. This paper explains six ethical issues associated with the use of such algorithms including the generation of new, sensitive, identifiable medical information for research purposes without participant consent, issues of justice and equity, risk, and ethical communication. It concludes with a discussion of strategies for addressing these issues and prompting valuable research.