44 research outputs found

    Complex differences in infection rates between ethnic groups in Scotland: a retrospective, national census-linked cohort study of 1.65 million cases

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    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

    Where next for understanding race/ethnic inequalities in severe mental illness? Structural, interpersonal and institutional racism.

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    In this article we use the example of race/ethnic inequalities in severe mental illness to demonstrate the utility of a novel integrative approach to theorising the role of racism in generating inequality. Ethnic minority people in the UK are at much greater risk than White British people of being diagnosed with a severe - psychosis related - mental illness, and this is particularly the case for those with Black Caribbean or Black African origins. There is entrenched dispute about how we might understand the drivers of this inequality. To address this dispute we build on, and to a certain extent refine, established approaches to theorising structural and institutional racism, and integrate this within a theoretical framework that also incorporates racist/discriminatory interactions (interpersonal racism). We argue that this provides a conceptually robust and thorough analysis of the role of inter-related dimensions of racism in shaping risks of severe mental illness, access to care, and policy and practice responses. This analysis carries implications for a broader, but integrated, understanding of the fundamental drives of race/ethnic inequalities in health and for an anti-racism public health agenda

    Users' perspectives on epidemiological, GIS and point pattern approaches to analysing environment and health data

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    Despite examples showing the usefulness of geographical information systems (GIS) and spatial point pattern analysis in health research, there remain barriers to their widespread use within health service settings. This paper explores potential users’ views on the relative usefulness of such approaches for analysing spatially referenced environmental health data. Our findings indicate that researchers and practitioners do not always prefer the approach with which they are most familiar. In addition, there is a need for higher levels of understanding of, and confidence in, GIS and point pattern analysis techniques amongst health service professionals. The greatest need is for multi-disciplinary research which uses the most appropriate approach for each investigation, rather than that with which researchers are most familiar

    Meteorological conditions and incidence of Legionnaires' disease in Glasgow, Scotland: application of statistical modelling

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    This study investigated the relationships between Legionnaires' disease (LD) incidence and weather in Glasgow, UK, by using advanced statistical methods. Using daily meteorological data and 78 LD cases with known exact date of onset, we fitted a series of Poisson log-linear regression models with explanatory variables for air temperature, relative humidity, wind speed and year, and sine-cosine terms for within-year seasonal variation. Our initial model showed an association between LD incidence and 2-day lagged humidity (positive, P = 0·0236) and wind speed (negative, P = 0·033). However, after adjusting for year-by-year and seasonal variation in cases there were no significant associations with weather. We also used normal linear models to assess the importance of short-term, unseasonable weather values. The most significant association was between LD incidence and air temperature residual lagged by 1 day prior to onset (P = 0·0014). The contextual role of unseasonably high air temperatures is worthy of further investigation. Our methods and results have further advanced understanding of the role which weather plays in risk of LD infection
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