James Nazroo’s research while affiliated with University of Manchester and other places

This paper examines the relationship between ethnicity and immigrant generation in relationship to early educational outcomes and their potential determinants. Using Born in Bradford, a large longitudinal birth cohort, and its linked education and health records, we investigated the associations between ethnicity, immigration generations and education measures (Early Years Foundation Stage Profile and National Curriculum Key Stage One). We looked at the children of both first- and second-generation immigrants and compared them with White British non-immigrant children. Logistic regressions were used to examine the explanatory factors of the differences. On the Early Years Foundation Stage Profile, children of first-generation Pakistani immigrants did less well than White British non-immigrant children. This was largely related to language barriers. There were no significant differences between the children of second-generation Pakistani families and children of White British non-immigrant families. In Key Stage One results, there were no differences in reading and maths between children of first-generation Pakistani immigrants and the White British children, however, children of first-generation Pakistani immigrants had better scores in Key Stage One writing. Children of second-generation Pakistani immigrants had better odds of achieving expected standards in most models for reading and writing (but not maths) than the White British children. This might be attributed to better socioeconomic circumstances. Immigrant generation is an independent predictor for early educational outcomes. There are different patterns associated with different immigrant generations. Early life policy interventions to help children of first-generation immigrant with their English language before school could improve these children’s school readiness.

Background Frailty is characterised by a decline in physical, cognitive, energy, and health reserves and is linked to greater functional dependency and higher social care utilisation. However, the relationship between receiving care, or receiving insufficient care among older people with different frailty status and the risk of unplanned admission to hospital for any cause, or the risk of falls and fractures remains unclear. Methods and findings This study used information from 7,656 adults aged 60 and older participating in the English Longitudinal Study of Ageing (ELSA) waves 6–8. Care status was assessed through received care and self-reported unmet care needs, while frailty was measured using a frailty index. Competing-risk regression analysis was used (with death as a potential competing risk), adjusted for demographic and socioeconomic confounders. Around a quarter of the participants received care, of which approximately 60% received low levels of care, while the rest had high levels of care. Older people who received low and high levels of care had a higher risk of unplanned admission independent of frailty status. Unmet need for care was not significantly associated with an increased risk of unplanned admission compared to those receiving no care. Older people in receipt of care had an increased risk of hospitalisation due to falls but not fractures, compared to those who received no care after adjustment for covariates, including frailty status. Conclusions Care receipt increases the risk of hospitalisation substantially, suggesting this is a group worthy of prevention intervention focus.

Was it their disproportionate presence in flexible employment or in shut-down occupations that made some ethnic minority groups vulnerable to adverse labour market outcomes during the coronavirus disease 2019 (COVID-19) recession? Using the COVID-19 recession in the UK as a case study, we employ weighted linear probability models with 2021 data from the Evidence for Equality National Survey to look at changes in economic indicators across ethnic groups and gender. We report heterogeneity in flexible employment rates within the non-White group and between the non-White and the White British group. By using a conditional decomposition method, we conclude that those ethnic minority groups who were disproportionately on flexible contracts experienced worse economic effects than the White British group.

This commentary will focus on health inequalities in relation to race/ethnicity, and in doing so, it will connect with the health inequalities article by Case and Kraftman and the race and ethnic inequalities article by Mirza and Warwick, both in this collection. The central argument of this commentary is that to make sense of the (complex and often confusing) patterning of race/ethnic inequalities in health in the UK and to move towards developing policy to address these inequalities, we must adopt a theoretically informed approach that centres on the fundamental causes of race/ethnic inequalities—processes that flow from (structural, interpersonal and institutional) racism. To make this argument, first I critically review the data on race/ethnic inequalities in health and interpretations of these data. Second, I discuss the central role of social and economic inequalities in driving these inequalities. Third, I provide a more detailed discussion of how racism operates to shape social and economic inequalities and thereby to shape health outcomes. Fourth, I discuss the implications of this analysis for policy, particularly how this points to the need to address institutional racism and how this might be done. Finally, I briefly revisit the question of fundamental causes and the implications of this for considerations of other dimensions of inequality, such as those related to class and gender.1

Background We compared the quality of ethnicity coding within the Public Health Scotland Ethnicity Look-up (PHS-EL) dataset, and other National Health Service datasets, with the 2011 Scottish Census. Methods Measures of quality included the level of missingness and misclassification. We examined the impact of misclassification using Cox proportional hazards to compare the risk of severe coronavirus disease (COVID-19) (hospitalization & death) by ethnic group. Results Misclassification within PHS-EL was higher for all minority ethnic groups [12.5 to 69.1%] compared with the White Scottish majority [5.1%] and highest in the White Gypsy/Traveller group [69.1%]. Missingness in PHS-EL was highest among the White Other British group [39%] and lowest among the Pakistani group [17%]. PHS-EL data often underestimated severe COVID-19 risk compared with Census data. e.g. in the White Gypsy/Traveller group the Hazard Ratio (HR) was 1.68 [95% Confidence Intervals (CI): 1.03, 2.74] compared with the White Scottish majority using Census ethnicity data and 0.73 [95% CI: 0.10, 5.15] using PHS-EL data; and HR was 2.03 [95% CI: 1.20, 3.44] in the Census for the Bangladeshi group versus 1.45 [95% CI: 0.75, 2.78] in PHS-EL. Conclusions Poor quality ethnicity coding in health records can bias estimates, thereby threatening monitoring and understanding ethnic inequalities in health.