Emilia Clementi1, Sofia Bartlett2, Michael Otterstatter1, Jane A Buxton1, Stanley Wong3, Amanda Yu3, Zahid A Butt4, James Wilton3, Margo Pearce1, Dahn Jeong1, Mawuena Binka3, Prince Adu1, Maria Alvarez3, Hasina Samji5, Younathan Abdia1, Jason Wong1, Mel Krajden6, Naveed Z Janjua7. 1. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; BC Centre for Disease Control, Vancouver, BC, Canada. 2. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; BC Centre for Disease Control, Vancouver, BC, Canada; University of New South Wales, Sydney, New South Wales, Australia. 3. BC Centre for Disease Control, Vancouver, BC, Canada. 4. BC Centre for Disease Control, Vancouver, BC, Canada; University of Waterloo Faculty of Applied Health Sciences, Waterloo, ON, Canada. 5. BC Centre for Disease Control, Vancouver, BC, Canada; Faculty of Health Sciences, Simon Fraser University, Burnaby, BC, Canada. 6. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; Department of Pathology and Laboratory Medicine, University of British Columbia, Vancouver, BC, Canada. 7. School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada; BC Centre for Disease Control, Vancouver, BC, Canada; Centre for Health Evaluation and Outcome Sciences, Vancouver, BC, Canada. Electronic address: naveed.janjua@bccdc.ca.
Abstract
BACKGROUND: Hepatitis C (HCV) affects diverse populations such as people who inject drugs (PWID), 'baby boomers,' gay/bisexual men who have sex with men (gbMSM), and people from HCV endemic regions. Assessing HCV syndemics (i.e.relationships with mental health/chronic diseases) among subpopulations using Latent Class Analysis (LCA) may facilitate targeted program planning. METHODS: The BC Hepatitis Testers Cohort(BC-HTC) includes all HCV cases identified in BC between 1990 and 2015, integrated with medical administrative data. LCA grouped all BC-HTC HCV diagnosed people(n = 73,665) by socio-demographic/clinical indicators previously determined to be relevant for HCV outcomes. The final model was chosen based on fit statistics, epidemiological meaningfulness, and posterior probability. Classes were named by most defining characteristics. RESULTS: The six-class model was the best fit and had the following names and characteristics: 'Younger PWID'(n =11,563): recent IDU (67%), people born >1974 (48%), mental illness (62%), material deprivation (59%). 'Older PWID'(n =15,266): past IDU (78%), HIV (17%), HBV (17%) coinfections, alcohol misuse(68%). 'Other Middle-Aged People'(n = 9019): gbMSM (26%), material privilege (31%), people born between 1965-1974 (47%). 'People of Asian backgrounds' (n = 4718): East/South Asians (92%), no alcohol misuse (97%) or mental illness (93%), people born <1945 (26%), social privilege (66%). 'Rural baby boomers' (n = 20,401): rural dwellers (32%), baby boomers (79%), heterosexuals (99%), no HIV (100%). 'Urban socially deprived baby boomers' (n = 12,698): urban dwellers (99%), no IDU (100%), liver disease (22%), social deprivation (94%). CONCLUSIONS: Differences between classes suggest variability in patients' service needs. Further analysis of health service utilization patterns may inform optimal service layout.
BACKGROUND: Hepatitis C (HCV) affects diverse populations such as people who inject drugs (PWID), 'baby boomers,' gay/bisexual men who have sex with men (gbMSM), and people from HCV endemic regions. Assessing HCV syndemics (i.e.relationships with mental health/chronic diseases) among subpopulations using Latent Class Analysis (LCA) may facilitate targeted program planning. METHODS: The BC Hepatitis Testers Cohort(BC-HTC) includes all HCV cases identified in BC between 1990 and 2015, integrated with medical administrative data. LCA grouped all BC-HTC HCV diagnosed people(n = 73,665) by socio-demographic/clinical indicators previously determined to be relevant for HCV outcomes. The final model was chosen based on fit statistics, epidemiological meaningfulness, and posterior probability. Classes were named by most defining characteristics. RESULTS: The six-class model was the best fit and had the following names and characteristics: 'Younger PWID'(n =11,563): recent IDU (67%), people born >1974 (48%), mental illness (62%), material deprivation (59%). 'Older PWID'(n =15,266): past IDU (78%), HIV (17%), HBV (17%) coinfections, alcohol misuse(68%). 'Other Middle-Aged People'(n = 9019): gbMSM (26%), material privilege (31%), people born between 1965-1974 (47%). 'People of Asian backgrounds' (n = 4718): East/South Asians (92%), no alcohol misuse (97%) or mental illness (93%), people born <1945 (26%), social privilege (66%). 'Rural baby boomers' (n = 20,401): rural dwellers (32%), baby boomers (79%), heterosexuals (99%), no HIV (100%). 'Urban socially deprived baby boomers' (n = 12,698): urban dwellers (99%), no IDU (100%), liver disease (22%), social deprivation (94%). CONCLUSIONS: Differences between classes suggest variability in patients' service needs. Further analysis of health service utilization patterns may inform optimal service layout.
Authors: Kiana Yazdani; Katerina Dolguikh; Wendy Zhang; Sara Shayegi-Nik; Jessica Ly; Shaughna Cooper; Jason Trigg; Sophia Bartlett; Rolando Barrios; Julio S G Montaner; Kate Salters Journal: PLoS One Date: 2022-03-23 Impact factor: 3.240
Authors: Jennifer L Smith; Davis Mumbengegwi; Erastus Haindongo; Carmen Cueto; Kathryn W Roberts; Roly Gosling; Petrina Uusiku; Immo Kleinschmidt; Adam Bennett; Hugh J Sturrock Journal: PLoS One Date: 2021-06-25 Impact factor: 3.240