Quality of ethnicity data within Scottish health records and implications of misclassification for ethnic inequalities in severe COVID-19: a national linked data study.

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.

Ethnic inequalities in positive SARS-CoV-2 tests, infection prognosis, COVID-19 hospitalisations and deaths: analysis of 2 years of a record linked national cohort study in Scotland.

BACKGROUND: This study aims to estimate ethnic inequalities in risk for positive SARS-CoV-2 tests, COVID-19 hospitalisations and deaths over time in Scotland. METHODS: We conducted a population-based cohort study where the 2011 Scottish Census was linked to health records. We included all individuals ≥ 16 years living in Scotland on 1 March 2020. The study period was from 1 March 2020 to 17 April 2022. Self-reported ethnic group was taken from the census and Cox proportional hazard models estimated HRs for positive SARS-CoV-2 tests, hospitalisations and deaths, adjusted for age, sex and health board. We also conducted separate analyses for each of the four waves of COVID-19 to assess changes in risk over time. FINDINGS: Of the 4 358 339 individuals analysed, 1 093 234 positive SARS-CoV-2 tests, 37 437 hospitalisations and 14 158 deaths occurred. The risk of COVID-19 hospitalisation or death among ethnic minority groups was often higher for White Gypsy/Traveller (HR 2.21, 95% CI (1.61 to 3.06)) and Pakistani 2.09 (1.90 to 2.29) groups compared with the white Scottish group. The risk of COVID-19 hospitalisation or death following confirmed positive SARS-CoV-2 test was particularly higher for White Gypsy/Traveller 2.55 (1.81-3.58), Pakistani 1.75 (1.59-1.73) and African 1.61 (1.28-2.03) individuals relative to white Scottish individuals. However, the risk of COVID-19-related death following hospitalisation did not differ. The risk of COVID-19 outcomes for ethnic minority groups was higher in the first three waves compared with the fourth wave. INTERPRETATION: Most ethnic minority groups were at increased risk of adverse COVID-19 outcomes in Scotland, especially White Gypsy/Traveller and Pakistani groups. Ethnic inequalities persisted following community infection but not following hospitalisation, suggesting differences in hospital treatment did not substantially contribute to ethnic inequalities.

Ethnic and social inequalities in COVID-19 outcomes in Scotland: protocol for early pandemic evaluation and enhanced surveillance of COVID-19 (EAVE II).

INTRODUCTION: Evidence from previous pandemics, and the current COVID-19 pandemic, has found that risk of infection/severity of disease is disproportionately higher for ethnic minority groups, and those in lower socioeconomic positions. It is imperative that interventions to prevent the spread of COVID-19 are targeted towards high-risk populations. We will investigate the associations between social characteristics (such as ethnicity, occupation and socioeconomic position) and COVID-19 outcomes and the extent to which characteristics/risk factors might explain observed relationships in Scotland.The primary objective of this study is to describe the epidemiology of COVID-19 by social factors. Secondary objectives are to (1) examine receipt of treatment and prevention of COVID-19 by social factors; (2) quantify ethnic/social differences in adverse COVID-19 outcomes; (3) explore potential mediators of relationships between social factors and SARS-CoV-2 infection/COVID-19 prognosis; (4) examine whether occupational COVID-19 differences differ by other social factors and (5) assess quality of ethnicity coding within National Health Service datasets. METHODS AND ANALYSIS: We will use a national cohort comprising the adult population of Scotland who completed the 2011 Census and were living in Scotland on 31 March 2020 (~4.3 million people). Census data will be linked to the Early Assessment of Vaccine and Anti-Viral Effectiveness II cohort consisting of primary/secondary care, laboratory data and death records. Sensitivity/specificity and positive/negative predictive values will be used to assess coding quality of ethnicity. Descriptive statistics will be used to examine differences in treatment and prevention of COVID-19. Poisson/Cox regression analyses and mediation techniques will examine ethnic and social differences, and drivers of inequalities in COVID-19. Effect modification (on additive and multiplicative scales) between key variables (such as ethnicity and occupation) will be assessed. ETHICS AND DISSEMINATION: Ethical approval was obtained from the National Research Ethics Committee, South East Scotland 02. We will present findings of this study at international conferences, in peer-reviewed journals and to policy-makers.

Are there ethnic and religious variations in uptake of bowel cancer screening? A retrospective cohort study among 1.7 million people in Scotland.

OBJECTIVE: Cancer screening should be equitably accessed by all populations. Uptake of colorectal cancer screening was examined using the Scottish Health and Ethnicity Linkage Study that links the Scottish Census 2001 to health data by individual-level self-reported ethnicity and religion. SETTING: Data on 1.7 million individuals in two rounds of the Scottish Bowel Cancer Screening Programme (2007-2013) were linked to the 2001 Census using the Scottish Community Health Index number. MAIN OUTCOME MEASURE: Uptake of colorectal cancer screening, reported as age-adjusted risk ratios (RRs) by ethnic group and religion were calculated for men and women with 95% CI. RESULTS: In the first, incidence screening round, compared with white Scottish men, Other White British (RR 109.6, 95% CI 108.8 to 110.3) and Chinese (107.2, 95% CI 102.8 to 111.8) men had higher uptake. In contrast, men of all South Asian groups had lower uptake (Indian RR 80.5, 95% CI 76.1 to 85.1; Pakistani RR 65.9, 95% CI 62.7 to 69.3; Bangladeshi RR 76.6, 95% CI 63.9 to 91.9; Other South Asian RR 88.6, 95% CI 81.8 to 96.1). Comparable patterns were seen among women in all ethnic groups, for example, Pakistani (RR 55.5, 95% CI 52.5 to 58.8). Variation in uptake was also observed by religion, with lower rates among Hindu (RR (95%CI): 78.4 (71.8 to 85.6)), Muslim (69.5 (66.7 to 72.3)) and Sikh (73.4 (67.1 to 80.3)) men compared with the reference population (Church of Scotland), with similar variation among women: lower rates were also seen among those who reported being Jewish, Roman Catholic or with no religion. CONCLUSIONS: There are important variations in uptake of bowel cancer screening by ethnic group and religion in Scotland, for both sexes, that require further research and targeted interventions.

Theory driven analysis of social class and health outcomes using UK nationally representative longitudinal data.

BACKGROUND: Social class is frequently used as a means of ranking the population to expose inequalities in health, but less often as a means of understanding the social processes of causation. We explored how effectively different social class mechanisms could be measured by longitudinal cohort data and whether those measures were able to explain health outcomes. METHODS: Using a theoretically informed approach, we sought to map variables within the National Child Development Study (NCDS) to five different social class mechanisms: social background and early life circumstances; habitus and distinction; exploitation and domination; location within market relations; and power relations. Associations between the SF-36 physical, emotional and general health outcomes at age 50 years and the social class measures within NCDS were then assessed through separate multiple linear regression models. R2 values were used to quantify the proportion of variance in outcomes explained by the independent variables. RESULTS: We were able to map the NCDS variables to the each of the social class mechanisms except 'Power relations'. However, the success of the mapping varied across mechanisms. Furthermore, although relevant associations between exposures and outcomes were observed, the mapped NCDS variables explained little of the variation in health outcomes: for example, for physical functioning, the R2 values ranged from 0.04 to 0.10 across the four mechanisms we could map. CONCLUSIONS: This study has demonstrated both the potential and the limitations of available cohort studies in measuring aspects of social class theory. The relatively small amount of variation explained in the outcome variables in this study suggests that these are imperfect measures of the different social class mechanisms. However, the study lays an important foundation for further research to understand the complex interactions, at various life stages, between different aspects of social class and subsequent health outcomes.

Does ethnic diversity explain intra-UK variation in mortality? A longitudinal cohort study.

OBJECTIVES: It has been proposed that part of the explanation for higher mortality in Scotland compared with England and Wales, and Glasgow compared with other UK cities, relates to greater ethnic diversity in England and Wales. We sought to assess the extent to which this excess was attenuated by adjusting for ethnicity. We additionally explored the role of country of birth in any observed differences. SETTING: Scotland and England and Wales; Glasgow and Manchester. PARTICIPANTS: We used the Scottish Longitudinal Study and the Office for National Statistics Longitudinal Study of England and Wales (2001-2010). Participants (362 491 in total) were aged 35-74 years at baseline. PRIMARY OUTCOME MEASURES: Risk of all-cause mortality between 35 and 74 years old in Scotland and England and Wales, and in Glasgow and Manchester, adjusting for age, gender, socioeconomic position (SEP), ethnicity and country of birth. RESULTS: 18% of the Manchester sample was non-White compared with 3% in Glasgow (England and Wales: 10.4%; Scotland: 1.2%). The mortality incidence rate ratio was 1.33 (95% CI 1.13 to 1.56) in Glasgow compared with Manchester. This reduced to 1.25 (1.07 to 1.47) adjusting for SEP, and to 1.20 (1.02 to 1.42) adjusting for ethnicity and country of birth. For Scotland versus England and Wales, the corresponding figures were 18% higher mortality, reducing to 10%, and then 7%. Non-Whites born outside the UK had lower mortality. In the Scottish samples only, non-Whites born in the UK had significantly higher mortality than Whites born in the UK. CONCLUSIONS: The research supports the hypothesis that ethnic diversity and migration from outside UK play a role in explaining Scottish excess mortality. In Glasgow especially, however, a large excess remains: thus, previously articulated policy implications (addressing poverty, vulnerability and inequality) still apply.

Assessment of health care, hospital admissions, and mortality by ethnicity: population-based cohort study of health-system performance in Scotland.

BACKGROUND: Ethnic minorities often experience barriers to health care. We studied six established quality indicators of health-system performance across ethnic groups in Scotland. METHODS: In this population-based cohort study, we linked ethnicity from Scotland's Census 2001 (April 29, 2001) to hospital admissions and mortality records, with follow-up until April 30, 2013. Indicators of health-system performance included amenable deaths (ie, deaths avertable by effective treatment), preventable deaths (ie, deaths avertable by public health policy), avoidable deaths (combined amenable and preventable deaths), avoidable hospital admissions, unplanned readmissions, and length of stay. We calculated rate ratios and odds ratios (with 95% CIs) using Poisson and logistic regression, which we multiplied by 100, adjusting first for age-related covariates and then for socioeconomic-related and birthplace-related covariates. The white Scottish population was the reference (rate ratio [RR] 100). FINDINGS: The results are based on 4·61 million people. During the 50·5 million person-years of study, 1·17 million avoidable hospital admissions, 587 740 unplanned readmissions, and 166 245 avoidable deaths occurred. South Asian groups had higher avoidable hospital admissions than the white Scottish group, with the highest reported RRs in Pakistani groups (RR 140·6 [95% CI 131·9-150·0] in men; RR 141·0 [129·0-154·1] in women). There was little variation between ethnic groups in length of stay or unplanned readmission. Preventable and amenable mortality were higher in the white Scottish group than several ethnic minorities including other white British, other white, Indian, and Chinese groups. Such differences were partly diminished by adjustment for socioeconomic status, whereas adjustment for country of birth had little additional effect. INTERPRETATION: These data suggest concerns about the access to and quality of primary care to prevent avoidable hospital admissions, especially for south Asians. Relatively high preventable and amenable deaths in white Scottish people, compared with several ethnic minority populations, were unexpected. Future studies should both corroborate and examine explanations for these patterns. Studies using several indicators simultaneously are also required internationally. FUNDING: Chief Scientist's Office, Medical Research Council, NHS Research Scotland, Farr Institute.

Mortality, ethnicity, and country of birth on a national scale, 2001-2013: A retrospective cohort (Scottish Health and Ethnicity Linkage Study).

BACKGROUND: Migrant and ethnic minority groups are often assumed to have poor health relative to the majority population. Few countries have the capacity to study a key indicator, mortality, by ethnicity and country of birth. We hypothesized at least 10% differences in mortality by ethnic group in Scotland that would not be wholly attenuated by adjustment for socio-economic factors or country of birth. METHODS AND FINDINGS: We linked the Scottish 2001 Census to mortality data (2001-2013) in 4.62 million people (91% of estimated population), calculating age-adjusted mortality rate ratios (RRs; multiplied by 100 as percentages) with 95% confidence intervals (CIs) for 13 ethnic groups, with the White Scottish group as reference (ethnic group classification follows the Scottish 2001 Census). The Scottish Index of Multiple Deprivation, education status, and household tenure were socio-economic status (SES) confounding variables and born in the UK or Republic of Ireland (UK/RoI) an interacting and confounding variable. Smoking and diabetes data were from a primary care sub-sample (about 53,000 people). Males and females in most minority groups had lower age-adjusted mortality RRs than the White Scottish group. The 95% CIs provided good evidence that the RR was more than 10% lower in the following ethnic groups: Other White British (72.3 [95% CI 64.2, 81.3] in males and 75.2 [68.0, 83.2] in females); Other White (80.8 [72.8, 89.8] in males and 76.2 [68.6, 84.7] in females); Indian (62.6 [51.6, 76.0] in males and 60.7 [50.4, 73.1] in females); Pakistani (66.1 [57.4, 76.2] in males and 73.8 [63.7, 85.5] in females); Bangladeshi males (50.7 [32.5, 79.1]); Caribbean females (57.5 [38.5, 85.9]); and Chinese (52.2 [43.7, 62.5] in males and 65.8 [55.3, 78.2] in females). The differences were diminished but not eliminated after adjusting for UK/RoI birth and SES variables. A mortality advantage was evident in all 12 minority groups for those born abroad, but in only 6/12 male groups and 5/12 female groups of those born in the UK/RoI. In the primary care sub-sample, after adjustment for age, UK/RoI born, SES, smoking, and diabetes, the RR was not lower in Indian males (114.7 [95% CI 78.3, 167.9]) and Pakistani females (103.9 [73.9, 145.9]) than in White Scottish males and females, respectively. The main limitations were the inability to include deaths abroad and the small number of deaths in some ethnic minority groups, especially for people born in the UK/RoI. CONCLUSIONS: There was relatively low mortality for many ethnic minority groups compared to the White Scottish majority. The mortality advantage was less clear in UK/RoI-born minority group offspring than in immigrants. These differences need explaining, and health-related behaviours seem important. Similar analyses are required internationally to fulfil agreed goals for monitoring, understanding, and improving health in ethnically diverse societies and to apply to health policy, especially on health inequalities and inequities.

Life expectancy of different ethnic groups using death records linked to population census data for 4.62 million people in Scotland.

BACKGROUND: Few countries record the data needed to estimate life expectancy by ethnic group. Such information is helpful in assessing the extent of health inequality. METHOD: Life tables were created using 3 years of deaths (May 2001-April 2004) linked to Scottish 2001 Census data for 4.62 million individuals with self-reported ethnicity. We created 8 ethnic groups based on the census definitions, each with at least 5000 individuals and 40 deaths. Life expectancy at birth was calculated using the revised Chiang method. RESULTS: The life expectancy of White Scottish males at birth was 74.7 years (95% CI 74.6 to 74.8), similar to Mixed Background (73.0; 70.2 to 75.8) and White Irish (75.0; 74.0 to 75.9), but shorter than Indian (80.9; 78.4 to 83.4), Pakistani (79.3; 76.9 to 81.6), Chinese (79.0; 76.5 to 81.5), Other White British (78.9; 78.6 to 79.2) and Other White (77.2; 76.4 to 78.1). The life expectancy of White Scottish females was 79.4 years (79.3 to 79.5), similar to mixed background (79.3; 76.6 to 82.0), but shorter than Pakistani (84.6; 82.0 to 87.3), Chinese (83.4; 81.1 to 85.7), Indian (83.3; 80.7 to 85.9), Other White British (82.6; 82.3 to 82.9), other White (82.0; 81.3 to 82.8) and White Irish (81; 80.2 to 81.8). CONCLUSIONS: Males and females in most of the larger ethnic minority groups in Scotland have longer life expectancies than the majority White Scottish population.

Dying younger in Scotland: Trends in mortality and deprivation relative to England and Wales, 1981-2011.

Given previous evidence that not all Scotland's higher mortality compared to England & Wales (E&W) can be explained by deprivation, the aim was to enhance understanding of this excess by analysing changes in deprivation and mortality in Scotland and E&W between 1981 and 2011. Mortality was compared by means of direct standardisation and log-linear Poisson regression models, adjusting for age, sex and deprivation. Different measures of deprivation were employed, calculated at different spatial scales. Results show that Scotland became less deprived compared to E&W between 1981 and 2011. However, the Scottish excess (the difference in mortality rates relative to E&W after adjustment for deprivation) increased from 4% higher (c.1981) to 10% higher in 2010-12. The latter figure equates to c. 5000 extra deaths per year. The increase was driven by higher mortality from cancer, suicide, alcohol related causes and drugs-related poisonings. The size and increase in Scottish excess mortality are major concerns. Investigations into its underlying causes continue, the findings of which will be relevant to other populations, given that similar excesses have been observed elsewhere in Britain.

Always looking on the bright side of life? Exploring optimism and health in three UK post-industrial urban settings.

BACKGROUND: Many theories have been proposed to explain the high levels of 'excess' mortality (i.e. higher mortality over and above that explained by differences in socio-economic circumstances) shown in Scotland-and, especially, in its largest city, Glasgow-compared with elsewhere in the UK. One such proposal relates to differences in optimism, given previously reported evidence of the health benefits of an optimistic outlook. METHODS: A representative survey of Glasgow, Liverpool and Manchester was undertaken in 2011. Optimism was measured by the Life Orientation Test (Revised) (LOT-R), and compared between the cities by means of multiple linear regression models, adjusting for any differences in sample characteristics. RESULTS: Unadjusted analyses showed LOT-R scores to be similar in Glasgow and Liverpool (mean score (SD): 14.7 (4.0) for both), but lower in Manchester (13.9 (3.8)). This was consistent in analyses by age, gender and social class. Multiple regression confirmed the city results: compared with Glasgow, optimism was either similar (Liverpool: adjusted difference in mean score: -0.16 (95% CI -0.45 to 0.13)) or lower (Manchester: -0.85 (-1.14 to -0.56)). CONCLUSIONS: The reasons for high levels of Scottish 'excess' mortality remain unclear. However, differences in psychological outlook such as optimism appear to be an unlikely explanation.

Comparing Antonovsky's sense of coherence scale across three UK post-industrial cities.

OBJECTIVES: High levels of 'excess' mortality (ie, that seemingly not explained by deprivation) have been shown for Scotland compared to England and Wales and, especially, for its largest city, Glasgow, compared to the similarly deprived English cities of Liverpool and Manchester. It has been suggested that this excess may be related to differences in 'Sense of Coherence' (SoC) between the populations. The aim of this study was to ascertain whether levels of SoC differed between these cities and whether, therefore, this could be a plausible explanation for the 'excess'. SETTING: Three post-industrial UK cities: Glasgow, Liverpool and Manchester. PARTICIPANTS: A representative sample of more than 3700 adults (over 1200 in each city). PRIMARY AND SECONDARY OUTCOME MEASURES: SoC was measured using Antonovsky's 13-item scale (SOC-13). Multivariate linear regression was used to compare SoC between the cities while controlling for characteristics (age, gender, SES etc) of the samples. Additional modelling explored whether differences in SoC moderated city differences in levels of self-assessed health (SAH). RESULTS: SoC was higher, not lower, among the Glasgow sample. Fully adjusted mean SoC scores for residents of Liverpool and Manchester were, respectively, 5.1 (-5.1 (95% CI -6.0 to -4.1)) and 8.1 (-8.1 (-9.1 to -7.2)) lower than those in Glasgow. The additional modelling confirmed the relationship between SoC and SAH: a 1 unit increase in SoC predicted approximately 3% lower likelihood of reporting bad/very bad health (OR=0.97 (95% CI 0.96 to 0.98)): given the slightly worse SAH in Glasgow, this resulted in slightly lower odds of reporting bad/very bad health for the Liverpool and Manchester samples compared to Glasgow. CONCLUSIONS: The reasons for the high levels of 'excess' mortality seen in Scotland and particularly Glasgow remain unclear. However, on the basis of these analyses, it appears unlikely that a low SoC provides any explanation.

The influence of primary care prescribing rates for new drugs on spontaneous reporting of adverse drug reactions.

INTRODUCTION: Adverse drug reaction (ADR) reporting makes a vital contribution to pharmacovigilance, although the factors that influence the reporting rate remain unclear. The aim of this study was to investigate whether the variation in the rate of reporting of suspected ADRs in different regions of Scotland was explained by differences in local prescribing practice and to quantify the extent of this influence. METHODS: Population and primary care prescribing data were obtained for ten geographical areas based on the 15 administrative regions of the National Health Service in Scotland. All reports of suspected ADRs received from within Scotland for 2000 and 2001 were available from the regional monitoring centre (Committee on Safety of Medicines, Scotland). The primary analysis was based on 14 medications that appeared in the 'top ten' list for the frequency of reported ADRs for either year. Reporting rates for each area were expressed both in terms of population (reports per million people) and in terms of estimated exposure to those medications in primary care (reports per 1000 prescriptions). For each analysis, the Pearson correlation coefficient between reporting and prescribing data was calculated using SPSS software. RESULTS: The 'top ten' medications accounted for 1715 of 2817 (60.9%, 95% CI 59.1, 62.7) ADR reports but only 2.2 million out of a total of 128 million primary care prescriptions (1.7%). Although there was a 3-fold geographical variation in the per-population ADR reporting rate, there was a close correlation between local reporting of ADRs and prescribing of the index medications (p = 0.66, p = 0.04, respectively). This implies that 44% of the observed variation in reporting rate can be attributed to variation in prescribing within the same population. DISCUSSION: Spontaneous ADR reporting in Scotland over the 2 years studied was highly concentrated on a small number of medications that were under intensive surveillance. Although there was a 3-fold variation in reporting rates from individual geographic areas when corrected for the size of the population, primary care prescribing data showed nearly half of this local variation in reporting rates could be explained by differences in prescribing. This study highlights the importance of considering prescribing practice when interpreting spontaneous ADR reporting data.