Missed Opportunities For Earlier Diagnosis Of Hiv In British Columbia, Canada: A Retrospective Cohort Study

BackgroundLate HIV diagnosis is associated with increased AIDS-related morbidity and mortality as well as an increased risk of HIV transmission. In this study, we quantified and characterized missed opportunities for earlier HIV diagnosis in British Columbia (BC), Canada.DesignRetrospective cohort.MethodsA missed opportunity was defined as a healthcare encounter due to a clinical manifestation which may be caused by HIV infection, or is frequently present among those with HIV infection, but no HIV diagnosis followed within 30 days. We developed an algorithm to identify missed opportunities within one, three, and five years prior to diagnosis. The algorithm was applied to the BC STOP HIV/AIDS population-based cohort. Eligible individuals were. 18 years old, and diagnosed from 2001-2014. Multivariable logistic regression identified factors associated with missed opportunities.ResultsOf 2119 individuals, 7%, 12% and 14% had >= 1 missed opportunity during one, three and five years prior to HIV diagnosis, respectively. In all analyses, individuals aged. 40 years, heterosexuals or people who ever injected drugs, and those residing in Northern health authority had increased odds of experiencing. 1 missed opportunity. In the three and five-year analysis, individuals with a CD4 count <350 cells/mm(3) were at higher odds of experiencing >= 1 missed opportunity. Prominent missed opportunities were related to recurrent pneumonia, herpes zoster/shingles among younger individuals, and anemia related to nutritional deficiencies or unspecified cause.ConclusionsBased on our newly-developed algorithm, this study demonstrated that HIV-diagnosed individuals in BC have experienced several missed opportunities for earlier diagnosis. Specific clinical indicator conditions and population sub-groups at increased risk of experiencing these missed opportunities were identified. Further work is required in order to validate the utility of this proposed algorithm by establishing the sensitivity, specificity, positive and negative predictive values corresponding to the incidence of the clinical indicator conditions among both HIV-diagnosed and HIV-negative populations.