Is individual smoking behaviour influenced by area-level ethnic density? A cross-sectional electronic health database study of inner south-east London

Mathur, Rohini; Schofield, Peter; Smith, Dianna; Gilkes, Alexander; White, Patrick; Hull, Sally

doi:10.1183/23120541.00130-2016

Cited by 9 publications

(15 citation statements)

References 44 publications

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“…16,18 To date, studies of COVID-19 have reported findings according to higher-level ethnic groupings, such as, white, south Asian, and black, which may conceal significant heterogeneity. 19,20 For example, while Bangladeshi and African populations are more likely to live in deprived areas than the general population, Indian and Chinese groups are more likely to live in more affluent areas and experience less material deprivation. 20,21 Therefore, it is vital to disaggregate broad ethnic groupings to better model the overlapping contributions of health and social factors on COVID-19 infection, severity, and mortality.…”

Section: Introductionmentioning

confidence: 99%

“…19,20 For example, while Bangladeshi and African populations are more likely to live in deprived areas than the general population, Indian and Chinese groups are more likely to live in more affluent areas and experience less material deprivation. 20,21 Therefore, it is vital to disaggregate broad ethnic groupings to better model the overlapping contributions of health and social factors on COVID-19 infection, severity, and mortality. is the author/funder, who has granted medRxiv a license to display the preprint in (which was not certified by peer review) preprint…”

Section: Introductionmentioning

confidence: 99%

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Ethnic differences in COVID-19 infection, hospitalisation, and mortality: an OpenSAFELY analysis of 17 million adults in England

Mathur

Rentsch

Morton

et al. 2020

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Background: COVID-19 has had a disproportionate impact on ethnic minority populations, both in the UK and internationally. To date, much of the evidence has been derived from studies within single healthcare settings, mainly those hospitalised with COVID-19. Working on behalf of NHS England, the aim of this study was to identify ethnic differences in the risk of COVID-19 infection, hospitalisation and mortality using a large general population cohort in England. Methods: We conducted an observational cohort study using linked primary care records of 17.5 million adults between 1 February 2020 and 3 August 2020. Exposure was self-reported ethnicity collapsed into the 5 and 16 ethnicity categories of the English Census. Multivariable Cox proportional hazards regression was used to identify ethnic differences in the risk of being tested and testing positive for SARS-CoV-2 infection, COVID-19 related intensive care unit (ICU) admission, and COVID-19 mortality, adjusted for socio-demographic factors, clinical co-morbidities, geographic region, care home residency, and household size. Results: A total of 17,510,002 adults were included in the study; 63% white (n=11,030,673), 6% south Asian (n=1,034,337), 2% black (n=344,889), 2% other (n=324,730), 1% mixed (n=172,551), and 26% unknown (n=4,602,822). After adjusting for measured explanatory factors, south Asian, black, and mixed groups were marginally more likely to be tested (south Asian HR 1.08, 95%CI 1.07-1.09; black HR 1.08; 95%CI 1.06-1.09, mixed HR 1.03, 95%CI 1.01-1.05), and substantially more likely to test positive for SARS-CoV-2 compared with white adults (south Asian HR 2.02. 95% CI 1.97-2.07; black HR 1.68, 95%CI 1.61-1.76; mixed HR 1.46, 95%CI 1.36-1.56). The risk of being admitted to ICU for COVID-19 was substantially increased in all ethnic minority groups compared with white adults (south Asian HR 2.22, 95%CI 1.96-2.52; black HR 3.07, 95%CI 2.61-3.61; mixed HR 2.86, 95%CI 2.19-3.75, other HR 2.86, 95%CI 2.31-3.63). Risk of COVID-19 mortality was increased by 25-56% in ethnic minority groups compared with white adults (south Asian HR 1.27, 95%CI 1.17-1.38; black HR 1.55, 95%CI 1.38-1.75; mixed HR 1.40, 95%CI 1.12-1.76; other HR 1.25, 95%CI 1.05-1.49). We observed heterogeneity of associations after disaggregation into detailed ethnic groupings; Indian and African groups were at higher risk of all outcomes; Pakistani, Bangladeshi and Caribbean groups were less or equally likely to be tested for SARS-CoV-2, but at higher risk of all other outcomes, Chinese groups were less likely to be tested for and test positive for SARS-CoV-2, more likely to be admitted to ICU, and equally likely to die from COVID-19. Conclusions: We found evidence of substantial ethnic inequalities in the risk of testing positive for SARS-CoV-2, ICU admission, and mortality, which persisted after accounting for explanatory factors, including household size. It is likely that some of this excess risk is related to factors not captured in clinical records such as occupation, experiences of structural discrimination, or inequitable access to health and social services. Prioritizing linkage between health, social care, and employment data and engaging with ethnic minority communities to better understand their lived experiences is essential for generating evidence to prevent further widening of inequalities in a timely and actionable manner.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Ethnic differences in COVID-19 infection, hospitalisation, and mortality: an OpenSAFELY analysis of 17 million adults in England

Mathur

Rentsch

Morton

et al. 2020

Preprint

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“…Most research on ethnic density has been conducted in the area of mental health where increasing own ethnic density has a protective effect on a range of mental health outcomes 9 15–20. Beyond mental health, increasing ethnic density has been found protective against smoking, where increasing own-group ethnic density was associated with reduced odds of smoking 17. The causal pathways through which the ethnic density effect operates have yet to be fully elucidated; the most researched hypothesis suggests that the buffering effect of increased own-group ethnic density protects individuals against experiences of racism and discrimination alongside increased opportunity to build social capital and reduce stress 12 15.…”

Section: Introductionmentioning

confidence: 99%

Does the ethnic density effect extend to obesity? A cross-sectional study of 415 166 adults in east London

Sutaria¹,

Mathur²,

Hull³

2019

BMJ Open

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ObjectivesTo examine the prevalence of obesity by ethnic group and to examine the association between ethnic density and obesity prevalence.Design and settingCross-sectional study utilising electronic primary care records of 128 practices in a multiethnic population of east London.ParticipantsElectronic primary care records of 415 166 adults with a body mass index recorded in the previous 3 years.Outcome measures(1) Odds of obesity for different ethnic groups compared with white British. (2) Prevalence of obesity associated with each 10% increase in own-group ethnic density, by ethnic group.ResultsUsing multilevel logistic regression models, we find that compared with white British/Irish males, the odds of obesity were significantly higher among black ethnic groups and significantly lower among Asian and white other groups. Among females, all ethnic groups except Chinese and white other were at increased odds of obesity compared with white British/Irish. There was no association between increasing ethnic density and obesity prevalence, except among black Africans and Indian females. A 10% increase in black ethnic density was associated with a 15% increase in odds of obesity among black African males (95% CI 1.07 to 1.24) and 18% among black African females (95% CI 1.08 to 1.30). Among Indian females, a 10% increase in Indian ethnic density was associated with a 7% decrease in odds of obesity (95% CI 0.88 to 0.99).ConclusionWider environmental factors play a greater role in determining obesity than the ethnic composition of the area for most ethnic groups. Further research is needed to understand the mechanism through which increasing ethnic density is associated with increased odds of obesity among black Africans and decreased odds of obesity among Indian females.

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“…No prior study has examined the association between ethnic density and physical activity in the UK (Bécares et al, 2012), but some studies on smoking have reported comparable results. In particular, a large study conducted using electronic health records of adults from the boroughs of Hackney, Lambeth, Newham and Tower Hamlets showed that the negative association between smoking and ethnic density was greater in ethnic minority groups where smoking was less socially accepted (Mathur et al, 2017). Another study conducted in a deprived population indicated that a higher South Asian density was associated with a lower probability of smoking during pregnancy in the Pakistani women, a group in which smoking is uncommon, whereas no protective effect was found amongst the White British women (Uphoff et al, 2016).…”

Section: Discussionmentioning

confidence: 99%

“…Ethnic density has been hypothesised to influence other health behaviours by increasing civic engagement, increasing social capital and social support, and reducing exposure to racism and discrimination (Bécares and Nazroo, 2013;Shaw et al, 2012). A handful of studies have investigated associations between ethnic density and health behaviours in the UK, finding some protective effect for alcohol consumption in ethnic minorities (Bécares et al, 2011), and differential effects for smoking, which appear to vary depending on the prevalence of smoking in the ethnic group in question (Mathur et al, 2017).…”

Section: Introductionmentioning

confidence: 99%

Associations between school and neighbourhood ethnic density and physical activity in adolescents: Evidence from the Olympic Regeneration in East London (ORiEL) study

Berger

Lewis

Quartagno

et al. 2019

Social Science & Medicine

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While most adolescents do not achieve the recommended level of physical activity in the UK, the risk of physical inactivity varies across ethnic groups. We investigated whether own-group school and neighbourhood ethnic density can explain ethnic differences in adolescent physical activity. We used longitudinal data from the Olympic Regeneration in East London (ORiEL) study. In 2012, 3,106 adolescents aged 11-12 were recruited from 25 schools in East London, UK. Adolescents were followed-up in 2013 and 2014. Own-group ethnic density was measured in 2012-2014 at school-level and in 2011 at neighbourhood-level, and calculated as the percentage of pupils/residents who were of the same ethnic group. Analyses were restricted to White British (n=382), White Mixed (n=190), Bangladeshi (n=337), and Black African groups (n=251). We estimated adjusted logistic regression models with generalised estimating equations for self-reported walking to school, walking for leisure, and outdoor physical activity. At school-level, there was consistent evidence that own-group ethnic density amplifies ethnic differences in walking to school. For each 10 percentage point increase in own-group ethnic density, there was evidence of increased probability of walking to school in Bangladeshi adolescents (OR=1.20; 95% CI 1.09-1.31) and decreased probability of walking to school in Black African (OR=0.58; 95% CI 0.45-0.75) and White Mixed adolescents (OR=0.51; 95%CI 0.35-0.76). Associations with walking for leisure and outdoor physical activity were in expected directions but not consistently observed in all ethnic groups. At neighbourhood-level, evidence was more restricted. Amplification of ethnic differences was found for walking to school in Bangladeshi adolescents (OR=1.31; 95% CI 1.14-1.51) and for outdoor physical activity in White British adolescents (OR=0.85; 95% CI 0.76-0.94). Our results suggest that own-group ethnic density contributes to explaining differences in physical activity by amplifying ethnic differences in some forms of physical activity.

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Is individual smoking behaviour influenced by area-level ethnic density? A cross-sectional electronic health database study of inner south-east London

Cited by 9 publications

References 44 publications

Ethnic differences in COVID-19 infection, hospitalisation, and mortality: an OpenSAFELY analysis of 17 million adults in England

Ethnic differences in COVID-19 infection, hospitalisation, and mortality: an OpenSAFELY analysis of 17 million adults in England

Does the ethnic density effect extend to obesity? A cross-sectional study of 415 166 adults in east London

Associations between school and neighbourhood ethnic density and physical activity in adolescents: Evidence from the Olympic Regeneration in East London (ORiEL) study

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