Complex differences in infection rates between ethnic groups in Scotland: a retrospective, national census-linked cohort study of 1.65 million cases. (22nd January 2021)

• Record Type: Journal Article
• Authors: Gruer, L D; Cézard, G I; Wallace, L A; Hutchinson, S J; Douglas, A F; Buchanan, D; Katikireddi, S V; Millard, A D; Goldberg, D J; Sheikh, A; Bhopal, R S
• Is Part Of: Journal of public health. Volume 44:Number 1(2022)
• Page Start: 60
• Page End: 69
• Publication Date: 2021-01-22
• Subjects: epidemiology -- ethnicity -- infectious disease; Public health -- Periodicals; Public health -- Great Britain -- Periodicals; Medical policy -- Periodicals; Medical policy -- Great Britain -- Periodicals; 362.1
• DOI: 10.1093/pubmed/fdaa267
• Languages: English
• ISSNs: 1741-3842

Abstract

Abstract: Background: Ethnicity can influence susceptibility to infection, as COVID-19 has shown. Few countries have systematically investigated ethnic variations in infection. Methods: We linked the Scotland 2001 Census, including ethnic group, to national databases of hospitalizations/deaths and serological diagnoses of bloodborne viruses for 2001–2013. We calculated age-adjusted rate ratios (RRs) in 12 ethnic groups for all infections combined, 15 infection categories, and human immunodeficiency virus (HIV), hepatitis B (HBV) and hepatitis C (HCV) viruses. Results: We analysed over 1.65 million infection-related hospitalisations/deaths. Compared with White Scottish, RRs for all infections combined were 0.8 or lower for Other White British, Other White and Chinese males and females, and 1.2–1.4 for Pakistani and African males and females. Adjustment for socioeconomic status or birthplace had little effect. RRs for specific infection categories followed similar patterns with striking exceptions. For HIV, RRs were 136 in African females and 14 in males; for HBV, 125 in Chinese females and 59 in males, 55 in African females and 24 in males; and for HCV, 2.3–3.1 in Pakistanis and Africans. Conclusions: Ethnic differences were found in overall rates and many infection categories, suggesting multiple causative pathways. We recommend census linkage as a powerful method for studying the disproportionate impact of COVID-19.