Skip to main content
Download PDF

Ethnic differences between South Asians and White Caucasians in cardiovascular disease-related mortality in developed countries: a systematic literature review

Systematic Reviews volume 11, Article number: 207 (2022) Cite this article

Abstract

Background

Cardiovascular disease is the leading cause of death worldwide, with significantly worse mortality-related outcomes in ethnic minorities in developed countries. A systematic literature review and meta-analysis of observational studies was conducted to investigate cardiovascular disease-related mortality inequalities between South Asian and White Caucasian ethnic groups.

Methods

Published studies on mortality between South Asians and Whites in developed countries were retrieved from MEDLINE, PubMed, Embase, Web of Science, and grey literature sources (inception—April 2021) and critically appraised using the Quality in Prognosis Studies tool. Bayesian random-effects meta-analyses were performed for both primary and secondary outcomes. Heterogeneity was determined using the I2 statistic.

Results

Of the 9879 studies screened originally, 41 were deemed eligible. A further 3 studies were included via the later search. Of these, 15 reported cardiovascular disease-related mortality, 23 reported all-cause mortality, and 6 reported both.

The meta-analysis results showed that South Asians had a significantly increased risk of cardiovascular disease mortality compared to Whites (risk ratio = 1.32; 95% credible interval = 1.14 to 1.54) and a decreased risk of all-cause mortality (risk ratio = 0.95; 95% credible interval = 0.83 to 1.12).

Discussion

South Asians had statistically significantly higher odds of cardiovascular disease-related mortality compared to Whites, but not for all-cause mortality. Risk of bias was a serious concern mainly due to a lack of confounders being reported.

Systematic review registration

PROSPERO: CRD42021240865

Peer Review reports

Introduction

Rationale

Cardiovascular diseases (CVD) are a group of disorders affecting the heart and blood vessels and are the leading cause of death globally, taking an estimated 17.9 million in 2019 [1]. Over 75% of these deaths take place in low- and middle-income countries, but CVD still poses a significant risk in developed countries, amounting to one-quarter of deaths in countries like the UK [2], the United States of America (USA) [3], Canada [4], and Australia [5].

The South Asian (SA) diaspora, consisting of people from countries such as India, Pakistan, and Bangladesh, make up a major migrant group in the western world. The 2011 census found the South Asian population was the largest minority ethnic group in the United Kingdom (UK) [6]. Of the overall population, 2.5% were Indian, 2.0% were Pakistani, and 0.8% were Bangladeshi. In the USA, South Asians make up 1.9% of the American population [7]; in Canada, South Asians make up 5.6% of the Canadian population [8]; and in Australia, South Asians make up approximately 4.0% of the population [9].

Current knowledge of CVD and its relationship with ethnicity is largely derived from studies of Caucasians of European ethnicity [10]. Ethnic minority groups are underrepresented in studies; however, one study found that there are no differences in the willingness of minorities to participate in health research compared to non-Hispanic Whites in the USA [11].

In the UK, CVD is more common in people of South Asian, African, or Caribbean background [12], as people of these ethnicities are more likely to have other risk factors for CVD, such as hypertension or type 2 diabetes mellitus [13,14,15]. In most cases, the risk of first heart attack is thought to be related to modifiable risk factors, for example smoking, high cholesterol, inactivity, and excess alcohol consumption [16].

A 2017 study [17] investigating the ethnic differences in the initial lifetime presentation of clinical CVD in over one million people from the CALIBER platform found that age of CVD onset was the lowest in South Asians and significantly lower in South Asian women compared to South Asian men. However, an older study [18] found CVD deaths rates were significantly lower in all Asian ethnic groups compared to the other groups from the REACH registry.

Objectives

This systematic literature review (SLR) was undertaken to systematically identify and review all original studies relating to the South Asian ethnic group and CVD-related mortality, to critically examine the quality of studies, and to elucidate the relationship between South Asian and White ethnicities with respect to CVD-related and all-cause mortality in four developed countries which have a sizeable South Asian migrant population.

Methods

This SLR was prepared according to the 2020 versions of the Preferred Reporting Items for Systematic Reviews and Meta-Analysis Protocols (PRISMA-P) and PRISMA for abstracts checklist (Additional file 1a and b).

Eligibility criteria

Studies were included if they fulfilled the following predefined population, indicator, comparator, and outcomes (PICO) criteria:

  • Population: patients with any form of CVD. The location was restricted to the UK and other western, more economically developed countries (MEDC) where the prevailing ethnicity is White and who have a significant number of South Asian migrants: North America and Australia.

  • Indicator: the prevailing-White population of the aforementioned countries

  • Comparator: South Asian population, either by individual ethnicities (Indian, Pakistani, Bangladeshi) or as a combined group

  • Outcome of CVD-related mortality or all-cause mortality (included as a secondary outcome)

  • Patients aged 18 years or older

  • There were no restriction on study design, though mainly observational studies meet the PICO criteria

  • Publications that are available in the English language

Information sources

Literature searches were conducted from inception to 22nd April 2021 using the following electronic databases: Ovid MEDLINE, PubMed, Embase, Web of Science, and the Cochrane Library. In addition to this, the PROSPERO database was searched to find similar reviews, OpenGrey and EThOS was searched for grey literature, and Google Scholar was searched to find any potentially missed papers.

The search strategies were conducted once more prior to publication to find any new articles that were published between April 2021 and April 2022.

Search strategy

The search terms used to identify relevant publications were based on the PICO criteria. Population-based terms included “United Kingdom” OR “Europe” OR “United States”. The comparator ethnicity was searched using terms such as “Ethnic groups” OR “South Asian” OR “India*” OR “Pakistan*”. CVD-related terms included “CVD” OR “cardio*” OR “heart*”. Outcome-based terms included “Death” OR “mortality” OR “risk factors”.

The full search strategy for Ovid MEDLINE is presented in Additional file 2.

Selection process

All publications found from the databases searched were exported from corresponding databases into EndNote X9, and duplicates were removed. Two authors, MP and SA, independently screened the titles and abstracts of the remaining publications to assess their eligibility. The publications which passed this first round of screenings were then read in completion to further assess their eligibility against the prespecified eligibility criteria, as specified in the protocol [19]. Any disagreements, in any phase of screening, were resolved via consensus between the two authors.

No automation tools were used in any stage of the selection and screening process.

Data collection process

The relevant data from the publications that passed the full-text review part of the screening process were independently abstracted by two reviewers, MP and SA. MP designed and created the data extraction form which was then pilot tested by both MP and SA prior to data extraction. Where data were unclear or missing, attempts were made to contact the author of the publication. Any disagreements were resolved via consensus between the two authors.

Data items

The primary outcome sought for extraction was cardiovascular disease-related mortality. All-cause mortality was extracted as a secondary outcome measure.

Other variables that were extracted were author’s name, publication year, country (or countries) of study, number of sites, study start and end date, funding, conflicts of interest, study design, inclusion and exclusion criteria, participant disposition, age, gender, ethnicity, type of CVD, other baseline characteristics reported, number of deaths, statistical methods used to assess mortality, and results of the statistical analyses.

Study risk-of-bias assessment

For each publication, the study quality and risk of bias were assessed, independently by MP and SA, using the Quality in Prognosis Studies (QUIPS) tool [20]. This tool assessed study participation, study attrition, prognostic factor measurement, outcome measurement, study confounding, and statistical analysis and reporting. Each domain was rated as having either “low”, “moderate”, or “high” risk of bias. A study with “low” risk in all six domains was rated as having a low risk of bias. A study that has a “high” risk of bias for any domain was rated as having a high risk of bias. All other studies were rated as having a moderate risk of bias. Disagreements were resolved by consensus.

Effect measures

The results for the primary outcome, CVD-related mortality, and the secondary outcome, all-cause mortality, between South Asians and the local-White ethnicity were presented as a hazard ratio, relative risk ratio, rate ratio, or odds ratio. Only three papers presented results as standardised mortality ratios. Due to the low numbers, these were not included in analyses.

Synthesis methods

The PICO criteria of individual studies were tabulated and compared against the prespecified PICO criteria in the SLR protocol to test study eligibility.

The baseline characteristics were extracted and tabulated alongside the outcomes that were reported by individual studies. If key confounders or outcome variables were missing, authors were contacted to attempt to fill in the missing gaps in the tables.

A Bayesian random-effects meta-analysis was conducted to synthesise the results of the individual studies, for both CVD-related and all-cause mortality separately using weakly informative priors for the true pooled effect size, μ, and the between-study heterogeneity, τ2. The model used 75,000 burn-in samples and then 75,000 iterations to draw the posterior samples. Trace and density plots were used to assess model convergence. If the model converged, then the estimates and 95% credible intervals (CrI) were obtained. To test the assumptions of the meta-analysis, the prediction interval was calculated, which presented the expected range of true effects between the studies. Corresponding forest plots were constructed for both outcomes.

To assess the robustness of the pooled results in the meta-analysis, the following sensitivity analyses were conducted:

  • Studies with a “high” risk of bias according to the QUIPS tool were removed.

  • By study design

  • By effect measure

  • After the primary analysis, the studies were assessed for influence or being outliers. Outlying studies or highly influential studies were removed from the meta-analysis.

To examine outlying studies, studentised residuals were calculated. Studies were deemed to be outliers if the absolute value of the studentised residual is > 3 [21]. To examine studies with high influence, Cook’s distance was calculated, and a study with a high Cook’s distance was deemed as highly influential [22].

In the systematic literature review protocol, there were two additional sensitivity analyses planned but, ultimately, were not undertaken. The reasons are as follows:

  • “Removal of non-peer-reviewed articles”: Where such articles were available, such as conference abstracts, the corresponding peer-reviewed paper was also available and was used in the selection process due to having more data.

  • “Method of imputation”: No studies used imputation methods for missing data.

Multiple testing

As the statistical analysis for this review was Bayesian in nature, there were no p-values presented. Typical methods employed to correct for multiple testing, such as the Bonferroni method [23], was not used as they are frequentist methods. One approach to multiplicity in the Bayesian setting is accommodated through prior probabilities associated with the multiplicities. Typically, the more possible hypotheses there are, the lower prior probabilities they each receive [24,25,26,27,28]. To control for multiplicity in this review, lower prior probabilities were assigned. Results remained the same after controlling for multiple testing.

Reporting bias assessment

Reporting bias was assessed using a funnel plot, where publication bias is present if the funnel plot is asymmetrical, and using Egger’s test. Publication bias is considered to exist if p < 0.05.

The meta-analyses were conducted using RStudio [29] using the R2WinBUGS package. All descriptive analyses were conducted using RStudio.

Certainty assessment

The certainty of evidence for both outcomes, CVD-related and all-cause mortality, were assessed using the Grading of Recommendations Assessment, Development and Evaluation (GRADE) criteria. The domains assessed risk of bias, inconsistency of effect, indirectness, imprecision, and publication bias. These were summarised alongside the main results in a key findings table which details any domains of concern and explanations why.

Results

Study selection and characteristics

The search identified 9879 records through the literature search and 10 records via other methods. Among them, 41 studies fulfilled the predetermined inclusion and exclusion criteria [17, 30,31,32,33,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52,53,54,55,56,57,58,59,60,61,62,63,64,65,66,67,68,69]. A further 3 publications were included via the search done prior to publication [70,71,72]. Of these, 15 studies reported CVD-related mortality only, 23 studies reported all-cause mortality only, and 6 studies reported both CVD-related and all-cause mortality included as outcomes (Additional file 3). Studies were identified by the search strategy and appear to meet the inclusion criteria but were excluded from review and are presented in Supplementary Table 1. The main reasons for exclusion were composite outcomes which included mortality with a non-mortality outcome, such as event, or combining the South Asian population with other Asian groups to analyse an overall Asian population.

The eligible articles were conducted in 4 countries (Australia, Canada, the UK, and the USA). Study designs were either retrospective (48%), prospective (32%), observational (9%), or cross-sectional (9%). One study [64] combined data from different sources which were either observational or cross-sectional in nature. Across all eligible studies, South Asians amounted to an average of 17% of the study size. One study reported person-years; 1% of person-years were from South Asian people.

Key study characteristics of the eligible studies included in this SLR are presented in Table 1. Baseline characteristics are shown in Table 2. Most of the studies reported gender, age, smoking status, participants with hypertension, and diabetes. However, the majority of studies did not report on BMI, alcohol consumption, or cholesterol levels.

Table 1 Characteristics of eligible studies
Full size table
Table 2 Reported baseline characteristics in the eligible studies
Full size table

Results of individual studies and risk-of-bias assessment

Details of individual study baseline characteristics and risk of bias (RoB) are presented in Table 2. A summary of endpoints and results are presented in Table 3. The majority of eligible studies were deemed as having a low risk of bias (59%). Five studies had a high risk of bias, mainly due to the lack of reporting confounding variables. For the studies that reported CVD-related mortality, 38% were deemed a low risk of bias, and 43% were moderately biased.

Table 3 Results from included studies
Full size table

Results of syntheses

Compared to Whites, people of South Asian ethnicity had a statistically meaningful increased risk of CVD-related mortality (RR = 1.32; 95% CrI = 1.14 to 1.54; I2 = 53%) (Fig. 1: forest plot showing relative effect for CVD-related mortality between South Asians and Whites (RE < 1.00 favours South Asians)). A nonmeaningful decrease in all-cause mortality risk was found for South Asian participant versus Whites (RR = 0.95; 95% CrI = 0.83 to 1.12; I2 = 41%) (Fig. 2: forest plot showing relative effect for all-cause mortality between South Asians and Whites (RE < 1.00 favours South Asians)).

Fig. 1
figure 1

Forest plot showing relative effect for CVD-related mortality between South Asians and Whites (RE < 1.00 favours South Asians)

Full size image
Fig. 2
figure 2

Forest plot showing relative effect for all-cause mortality between South Asians and Whites (RE < 1.00 favours South Asians)

Full size image

Subgroup analyses

In subgroup analysis stratifying based on study location, South Asians in North America had a lower risk of CVD-related mortality compared to Whites, and this result was meaningfully different to the pooled CVD-related mortality result, as were studies deemed a high risk of bias where South Asians have more than double the risk of CVD-related mortality compared to Whites. The remaining subgroup analyses revealed a nonmeaningful difference between age group and type of CVD (Fig. 3: forest plot showing relative effect of subgroup analyses for CVD-related mortality between South Asians and Whites (RE < 1.00 favours South Asians)).

Fig. 3
figure 3

Forest plot showing relative effect of subgroup analyses for CVD-related mortality between South Asians and Whites (RE < 1.00 favours South Asians)

Full size image

Testing for heterogeneity

The I2 value of the primary outcome, CVD-related mortality, was 53%. For all-cause mortality, I2 = 41%. As I2 < 60% for both outcomes, per the protocol, further analyses to explore heterogeneity were not undertaken.

Sensitivity analyses

Upon sensitivity analysis, there were no meaningful differences between the results of CVD-related mortality of each sensitivity analysis subgroup and the overall result (Fig. 4: forest plot showing relative effect of sensitivity analyses for CVD-related mortality between South Asians and Whites (RE < 1.00 favours South Asians)).

Fig. 4
figure 4

Forest plot showing relative effect of sensitivity analyses for CVD-related mortality between South Asians and Whites (RE < 1.00 favours South Asians)

Full size image

Publication bias

Egger’s test and visual inspection of funnel plots (Fig. 5: funnel plots for publication bias: (L) CVD-related mortality; (R) all-cause mortality) did not suggest any small study effect for both CVD-related (p = 0.06) and all-cause mortality (p = 0.59), at the 5% significance level.

Fig. 5
figure 5

Funnel plots for publication bias: (L) CVD-related mortality; (R) all-cause mortality

Full size image

Certainty of evidence

CVD-related mortality was given a moderate certainty of evidence grade; all-cause mortality was given a low grade. Certainty assessment and the reasons for downgrading the certainty of evidence for both outcomes are presented in the footnotes of Table 4.

Table 4 Certainty of the evidence using GRADE and summary of results for CVD-related mortality and all-cause mortality
Full size table

Synthesis of other ethnicities

Results from ethnicities other than South Asians that were presented in the eligible studies in this review were synthesised, the results of which are presented in Supplementary Fig. 1 (forest plot showing relative effect for CVD-related mortality between other ethnicities and Whites (RE < 1.00 favours the other ethnicities)). The ethnicities whose credible intervals did not overlap with the South Asian vs White result were East Asian and other, where the risk of East Asian participants dying due to a CVD-related issue was almost half that for Whites.

Discussion

General interpretation

This systematic literature review summarised the available scientific evidence pertaining to CVD-related mortality between South Asians and Whites in four developed countries. To the best of the authors’ knowledge, it is the first to do so. Overall, a total of 41 studies were reviewed. South Asian participants had an increased odds of CVD-related mortality compared to Whites, but not in all-cause mortality.

Most studies suggest higher mortality in SA compared to Whites which are reflected in the overall results. Subgroup analyses found that CVD type did not have a meaningful effect on CVD-related mortality between SA and Whites but that may be due to the small number of studies that reported the exact type of CVD being measured.

One review conduced in Canada [73] found similar results to the present review, where South Asian Canadians had higher rates of hypertension and other determinants of CVD, resulting in higher rates of CVD and worse outcomes. This is backed up by results in other studies which looked at CVD burden in South Asians [74,75,76,77,78] and found South Asians at higher risk of CVD determinants, often leading to poorer outcomes.

Methodological differences in capturing mortality rates existed which made it more difficult to compare estimates, for example standardised mortality rates that are calculated based on the number of deaths in a population per person-year. Methods like hazard ratios (HR) or rate ratios (RR), whose calculations are based on a deaths in a sample and can be controlled for variables, which are then easier to synthesise in a meta-analysis.

Strengths

This is the first SLR that quantifies how South Asians, a major migrant group, differ to the White population with respect to CVD-related and all-cause mortality in four major countries, comprehensively reviewing data from a relatively geographically diverse range of studies, across three continents, and the large overall sample size increased the robustness and reliability of the results presented.

Each step of the SLR is described in detail, reducing the possibility of bias in the method of identifying and selecting studies for review. This includes the inclusion of the search strategy used and specifying inclusion criteria a priori.

A comprehensive set of analyses were performed to test assumptions, including the use of a random-effects model, subgroup analyses, testing for publication bias, and adjusting for multiple testing in the Bayesian setting.

Limitations

A limitation of the evidence was that all-cause mortality was more often reported compared to CVD-related mortality.

We found that many studies did not report either some or all of the baseline characteristics based on key confounding variables. This introduces a high level of bias since there may exist extraneous factors, other than ethnicity, which influenced the results, and these were not accounted or adjusted for in these studies.

The majority of articles reported estimates on the South Asian ethnic group as a whole, but did not analyse the individual South Asian countries, such as analysing only Indian or only Pakistani participants. Oftentimes, this was due to the small number of participants in individual groups. However, more detailed analysis could be used to ascertain how different countries of origin within South Asia compare to each other and where health resources should be directed, if required.

Although we presented results for other ethnicities, our search strategy focused on the South Asian and White comparison. Therefore, the exploratory analysis of CVD-related mortality for the other ethnicities is not robust and should not be used for interpretation. For example, several publications fit the PICO criteria for this review except that they focused on the African-American population instead of South Asians These studies were removed during the screening process but would be needed to properly access the impact of different ethnicities on CVD-related mortality.

Where publications did not provide adequate data, usually in terms of baseline characteristics of participants, we attempted to contact authors. However, contacting authors of some of the older papers was unsuccessful; thus, confounding bias is present in the results.

Implications and future research

The foremost implication of this review is that it provides an estimate on mortality between a major ethnic group versus the local ethnicity; South Asians have a higher risk of CVD-related mortality compared to their White counterparts. However, the heterogeneity of observational studies in this area makes it difficult to draw precise conclusions.

There is a need to collect more evidence regarding current mortality rates and long-term outcomes beyond the most common 1-year mortality outcome, more consistent statistical analyses, and clearer information on confounders. Furthermore, investigating how confounders mediate the relationship between ethnicity and mortality, which then highlights the need for additional research in this area, both by presenting all relevant confounders at baseline, reduces bias, and adjusting for them in analyses, results in more robust conclusions.

Conclusions

In conclusion, this SLR presents the available evidence concerning mortality rates for South Asians vs Whites in four developed countries and demonstrated that people of South Asian ethnicity living in western, developed countries, were at a higher risk of CVD-related mortality, but not of all-cause mortality, compared to their White counterparts.

Availability of data and materials

Raw data used in the meta-analysis can be found in Table 3.

Abbreviations

CVD:

Cardiovascular disease

ACS:

Acute coronary syndrome

AF:

Atrial fibrillation

AIH:

Acute ischemic haemorrhage

AMI:

Acute myocardial infarction

BMI:

Body mass index

CABG:

Coronary artery bypass grafting

CAD:

Coronary arterial disease

CD:

Circulatory disease

CHD:

Coronary heart disease

CrI:

Credible interval

GRADE:

Grading of Recommendations Assessment, Development and Evaluation

HF:

Heart failure

HR:

Hazard ratio

IHD:

Ischaemic heart disease

MEDC:

More economically developed country

MI:

Myocardial infarction

NR:

Not reported

OR:

Odds ratios

PCI:

Percutaneous coronary intervention

PICO:

Patient, Intervention, Comparison, Outcome

PRISMA:

Preferred Reporting Items for Systematic Reviews and Meta-Analysis

PRISMA-P:

Preferred Reporting Items for Systematic Reviews and Meta-Analysis-Protocols

PROSPERO:

International prospective register of systematic reviews

QUIPS:

Quality in Prognosis Studies

REACH:

REduction of Atherothrombosis for Continued Health

RiR:

Risk ratio

RoB:

Risk of bias

RR:

Rate ratios

SLR:

Systematic literature review

TIA:

Transient ischemic attack

UK:

United Kingdom

USA:

United States of America

References

  1. (WHO) WHO. Cardiovascular diseases (CVDs). 2021. Available from: https://www.who.int/en/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds).

    Google Scholar 

  2. Foundation BH. Facts and figures. 2021. Available from: https://www.bhf.org.uk/what-we-do/news-from-the-bhf/contact-the-press-office/facts-and-figures.

    Google Scholar 

  3. Virani SS, Alonso A, Aparicio HJ, Benjamin EJ, Bittencourt MS, Callaway CW, et al. Heart disease and stroke statistics-2021 update. Circulation. 2021;143(8):e254–743.

    Article  PubMed  Google Scholar 

  4. Department SR. Death rate for major cardiovascular diseases in Canada from 2000 to 2019. Available from: https://www.statista.com/statistics/434439/death-rate-for-major-cardiovascular-diseases-in-canada/. Accessed 12 Mar 2022.

  5. Australia NHFo. Key statistics: cardiovascular disease. Available from: https://www.heartfoundation.org.au/activities-finding-or-opinion/key-stats-cardiovascular-disease.

  6. White E. Ethnicity and national identity in England and Wales: 2011. In: Statistics OfN, editor. 2012.

  7. Bureau USC. US census Bereau July 1 2019 estimates. 2019. Available from: https://www.census.gov/quickfacts/fact/table/US/PST045219.

    Google Scholar 

  8. Canada S. Data tables, 2016 census. 2016. Available from: https://www12.statcan.gc.ca/census-recensement/2016/dp-pd/dt-td/Rp-eng.cfm?LANG=E&APATH=3&DETAIL=0&DIM=0&FL=A&FREE=0&GC=0&GID=0&GK=0&GRP=1&PID=110528&PRID=10&PTYPE=109445&S=0&SHOWALL=0&SUB=0&Temporal=2017&THEME=120&VID=0&VNAMEE=&VNAMEF=.

    Google Scholar 

  9. Statistics ABo. 2016 census QuickStats. 2016. Available from: https://quickstats.censusdata.abs.gov.au/census_services/getproduct/census/2016/quickstat/0.

    Google Scholar 

  10. Forouhi NG, Sattar N. CVD risk factors and ethnicity—a homogeneous relationship? Atheroscler Suppl. 2006;7(1):11–9.

    Article  PubMed  Google Scholar 

  11. Wendler D, Kington R, Madans J, Wye GV, Christ-Schmidt H, Pratt LA, et al. Are racial and ethnic minorities less willing to participate in health research? PLoS Med. 2005;3(2):e19.

    Article  PubMed  PubMed Central  Google Scholar 

  12. Tillin T, Hughes AD, Whincup P, Mayet J, Sattar N, McKeigue PM, et al. Ethnicity and prediction of cardiovascular disease: performance of QRISK2 and Framingham scores in a UK tri-ethnic prospective cohort study (SABRE—Southall And Brent REvisited). Heart. 2014;100(1):60.

    Article  PubMed  Google Scholar 

  13. Shah A, Kanaya AM. Diabetes and associated complications in the South Asian population. Curr Cardiol Rep. 2014;16(5):476.

    Article  PubMed  PubMed Central  Google Scholar 

  14. Anand SS, Yusuf S, Vuksan V, Devanesen S, Teo KK, Montague PA, et al. Differences in risk factors, atherosclerosis, and cardiovascular disease between ethnic groups in Canada: the Study of Health Assessment and Risk in Ethnic groups (SHARE). Lancet. 2000;356(9226):279–84.

    Article  CAS  PubMed  Google Scholar 

  15. Quan H, Chen G, Walker RL, Wielgosz A, Dai S, Tu K, et al. Incidence, cardiovascular complications and mortality of hypertension by sex and ethnicity. Heart. 2013;99(10):715–21.

    Article  CAS  PubMed  Google Scholar 

  16. Barnes AS. Emerging modifiable risk factors for cardiovascular disease in women: obesity, physical activity, and sedentary behavior. Tex Heart Inst J. 2013;40(3):293–5.

    PubMed  PubMed Central  Google Scholar 

  17. George J, Mathur R, Shah AD, Pujades-Rodriguez M, Denaxas S, Smeeth L, et al. Ethnicity and the first diagnosis of a wide range of cardiovascular diseases: associations in a linked electronic health record cohort of 1 million patients. PLoS One. 2017;12(6):e0178945.

    Article  PubMed  PubMed Central  Google Scholar 

  18. Meadows TA, Bhatt DL, Cannon CP, Gersh BJ, Röther J, Goto S, et al. Ethnic differences in cardiovascular risks and mortality in atherothrombotic disease: insights from the Reduction of Atherothrombosis for Continued Health (REACH) registry. Mayo Clin Proc. 2011;86(10):960–7.

    Article  PubMed  PubMed Central  Google Scholar 

  19. Patel M, Abatcha S, Uthman OA. Ethnic differences between South Asians and White Caucasians in cardiovascular disease-related mortality in developed countries: a systematic literature review protocol. BMJ Open. 2022;12(7):e052487.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Hayden JA, van der Windt DA, Cartwright JL, Côté P, Bombardier C. Assessing bias in studies of prognostic factors. Ann Intern Med. 2013;158(4):280–6.

    Article  PubMed  Google Scholar 

  21. Stevens JP. Outliers and influential data points in regression analysis. Psychol Bull. 1984;95(2):334–44.

    Article  Google Scholar 

  22. Cook RD. Detection of influential observation in linear regression. Technometrics. 1977;19(1):15–8.

    Google Scholar 

  23. Armstrong RA. When to use the Bonferroni correction. Ophthalmic Physiol Opt. 2014;34(5):502–8.

    Article  PubMed  Google Scholar 

  24. Scott G, Berger JO. An exploration of Bayesian multiple testing. To appear in volume in honour of Shanti Gupta; 2003.

    Google Scholar 

  25. Balarajan R. Ethnic differences in mortality from ischaemic heart disease and cerebrovascular disease in England and Wales. BMJ (Clinical research ed). 1991;302(6776):560–4.

    Article  CAS  Google Scholar 

  26. Genovese C, Wasserman L. Bayesian and frequentist multiple testing: Department of Statistics, Carnegie-Mellon University; 2002. Tech. Rep

    Google Scholar 

  27. DuMouchel WH. A Bayesian model and graphical elicitation model for multiple comparison. In: Bernardo JM, DeGroot MH, Lindey DV, Smith AFM, editors. Bayesian statistics 3: Oxford University Press; 1998. p. 127–46.

  28. Duncan DB. A Bayesian approach to multiple comparisons. Technometrics. 1965;7:171–222.

    Article  Google Scholar 

  29. Team RDC. R: a language and environment for statistical computing. Vienna: R Foundation for Statistical Computing; 2010. Retrieved from http://www.R-project.org

    Google Scholar 

  30. Adil M, Khan S, Khan M, Rahim B, Qureshi A. Paradoxical increase in stroke mortality among Asian Indians in United States. Neurology. 2013;80:1.

    Google Scholar 

  31. Albarak J, Nijjar AP, Aymong E, Wang H, Quan H, Khan NA. Outcomes in young South Asian Canadians after acute myocardial infarction. Can J Cardiol. 2012;28(2):178–83.

    Article  PubMed  Google Scholar 

  32. Bansal N, Fischbacher CM, Bhopal RS, Brown H, Steiner MFC, Capewell S, et al. Myocardial infarction incidence and survival by ethnic group: Scottish Health and Ethnicity Linkage retrospective cohort study. BMJ Open. 2013;3(9):9.

    Article  Google Scholar 

  33. Bellary S, O'Hare JP, Raymond NT, Mughal S, Hanif WM, Jones A, et al. Premature cardiovascular events and mortality in South Asians with type 2 diabetes in the United Kingdom Asian Diabetes Study - effect of ethnicity on risk. Curr Med Res Opin. 2010;26(8):1873–9.

    Article  PubMed  Google Scholar 

  34. Blackledge HM, Newton J, Squire IB. Prognosis for South Asian and white patients newly admitted to hospital with heart failure in the United Kingdom: historical cohort study. Br Med J. 2003;327(7414):526–30.

    Article  Google Scholar 

  35. Chaturvedi N, Fuller JH. Ethnic differences in mortality from cardiovascular disease in the UK: do they persist in people with diabetes? J Epidemiol Community Health. 1996;50(2):137–9.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  36. Deb S, Tu JV, Austin PC, Ko DT, Rocha R, Mazer CD, et al. Impact of South Asian ethnicity on long-term outcomes after coronary artery bypass grafting surgery: a large population-based propensity matched study. J Am Heart Assoc. 2016;5(7):12.

    Article  Google Scholar 

  37. Elahi M, Chetty G, Matata B. Ethnic differences in the management of coronary heart disease patients: lessons to be learned in indo-asians. Med Princ Pract. 2005;15(1):69–73.

    Article  Google Scholar 

  38. Feder G, Crook AM, Magee P, Banerjee S, Timmis AD, Hemingway H. Ethnic differences in invasive management of coronary disease: prospective cohort study of patients undergoing angiography. Br Med J. 2002;324(7336):511–6.

    Article  Google Scholar 

  39. Fischbacher CM, Bhopal R, Povey C, Steiner M, Chalmers J, Mueller G, et al. Record linked retrospective cohort study of 4.6 million people exploring ethnic variations in disease: myocardial infarction in South Asians. BMC Public Health. 2007;7:142.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  40. Forouhi NG, Sattar N, Tillin T, McKeigue PM, Chaturvedi N. Do known risk factors explain the higher coronary heart disease mortality in South Asian compared with European men? Prospective follow-up of the Southall and Brent studies, UK. Diabetologia. 2006;49(11):2580–8.

    Article  CAS  PubMed  Google Scholar 

  41. Gahungu N, Tewari S, Liu YW, Inacio JR, Chow BJ, Dwivedi G. Quantified coronary plaque characteristics between Caucasian and Morise score-matched South Asian populations. Int J Cardiovasc Imaging. 2020;36(12):2347–55.

    Article  PubMed  Google Scholar 

  42. Gasevic D, Khan NA, Qian H, Karim S, Simkus G, Quan HD, et al. Outcomes following percutaneous coronary intervention and coronary artery bypass grafting surgery in Chinese, South Asian and white patients with acute myocardial infarction: administrative data analysis. BMC Cardiovasc Disord. 2013;13:7.

    Article  Google Scholar 

  43. Gholap NN, Khunti K, Davies MJ, Bodicoat DH, Squire IB. Survival in South Asian and White European patients after acute myocardial infarction. Heart. 2015;101(8):630–+.

    Article  PubMed  Google Scholar 

  44. Gray L, Harding S, Reid A. Evidence of divergence with duration of residence in circulatory disease mortality in migrants to Australia. Eur J Public Health. 2007;17(6):550–4.

    Article  PubMed  Google Scholar 

  45. Gunarathne A, Patel J, Potluri R, Gill PS, Hughes EA, Lip GYH. Secular trends in the cardiovascular risk profile and mortality of stroke admissions in an inner city, multiethnic population in the United Kingdom (1997-2005). J Hum Hypertens. 2008;22(1):18–23.

    Article  CAS  PubMed  Google Scholar 

  46. Gupta M, Doobay AV, Singh N, Anand SS, Raja F, Mawji F, et al. Risk factors, hospital management and outcomes after acute myocardial infarction in South Asian Canadians and matched control subjects. Can Med Assoc J. 2002;166(6):717–22.

    Google Scholar 

  47. Hadjinikolaou L, Klimatsidas M, Maria Iacona G, Spyt T, Samani NJ. Short- and medium-term survival following coronary artery bypass surgery in British Indo-Asian and White Caucasian individuals: impact of diabetes mellitus. Interact Cardiovasc Thorac Surg. 2009;10(3):389–93.

    Article  PubMed  Google Scholar 

  48. Harding S, Rosato M, Teyhan A. Trends for coronary heart disease and stroke mortality among migrants in England and Wales, 1979-2003: slow declines notable for some groups. Heart. 2008;94(4):463–70.

    Article  CAS  PubMed  Google Scholar 

  49. Hsu RT, Ardron ME, Brooks W, Cherry D, Taub NA, Botha JL. The 1996 Leicestershire Community Stroke & Ethnicity Study: differences and similarities between South Asian and white strokes. Int J Epidemiol. 1999;28(5):853–8.

    Article  CAS  PubMed  Google Scholar 

  50. Jones DA, Rathod KS, Sekhri N, Junghans C, Gallagher S, Rothman MT, et al. Case death rates for South Asian and Caucasian patients show no difference 2.5 years after percutaneous coronary intervention. Heart. 2012;98:414-9.

  51. Jones DA, Gallagher S, Rathod KS, Redwood S, de Belder MA, Mathur A, et al. Mortality in South Asians and Caucasians after percutaneous coronary intervention in the United Kingdom. JACC Cardiovasc Interv. 2014;7(4):362–71.

    Article  PubMed  Google Scholar 

  52. Kaila KS, Norris CM, Graham MM, Ali I, Bainey KR. Long-term survival with revascularization in South Asians admitted with an acute coronary syndrome (from the Alberta Provincial Project for Outcomes Assessment in Coronary Heart Disease Registry). Am J Cardiol. 2014;114(3):395–400.

    Article  PubMed  Google Scholar 

  53. Khan NA, Grubisic M, Hemmelgarn B, Humphries K, King KM, Quan HD. Outcomes after acute myocardial infarction in South Asian, Chinese, and White patients. Circulation. 2010;122(16):1570–7.

    Article  PubMed  Google Scholar 

  54. Khan NA, Quan HD, Hill MD, Pilote L, McAlister FA, Palepu A, et al. Risk factors, quality of care and prognosis in South Asian, East Asian and White patients with stroke. BMC Neurol. 2013;13:8.

    Article  Google Scholar 

  55. Krishnamurthy A, Keeble C, Burton-Wood N, Somers K, Anderson M, Harland C, et al. Clinical outcomes following primary percutaneous coronary intervention for ST-elevation myocardial infarction according to sex and race. Eur Heart J Acute Cardiovasc Care. 2019;8(3):264–72.

    Article  PubMed  Google Scholar 

  56. Lane DA, Lip GYH, Beevers DG. Ethnic differences in cardiovascular and all-cause mortality in Birmingham, England: the Birmingham Factory Screening Project. J Hypertens. 2005;23(7):1347–53.

    Article  CAS  PubMed  Google Scholar 

  57. Mackay MH, Singh R, Boone RH, Park JE, Humphries KH. Outcomes following percutaneous coronary revascularization among South Asian and Chinese Canadians. BMC Cardiovasc Disord. 2017;17:7.

    Article  Google Scholar 

  58. Muilwijk M, Ho F, Waddell H, Sillars A, Welsh P, Iliodromiti S, et al. Contribution of type 2 diabetes to all-cause mortality, cardiovascular disease incidence and cancer incidence in white Europeans and South Asians: findings from the UK Biobank population-based cohort study. BMJ Open Diabetes Res Care. 2019;7(1):9.

    Article  Google Scholar 

  59. Mukhtar HT, Littler WA. Survival after acute myocardial infarction in Asian and White patients in Birmingham. Br Heart J. 1995;73(2):122–4.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  60. Nijjar APK, Wang H, Dasgupta K, Rabi DM, Quan H, Khan NA. Outcomes in a diabetic population of South Asians and whites following hospitalization for acute myocardial infarction: a retrospective cohort study. Cardiovasc Diabetol. 2010;9:7.

    Article  Google Scholar 

  61. O’Neill J, Jegodzinski L, Tayebjee MH. Incidence of subclinical atrial fibrillation in a South Asian population. Pacing Clin Electrophysiol. 2018;41(12):1600–5.

    Article  PubMed  Google Scholar 

  62. Pursnani S, Merchant M. South Asian ethnicity as a risk factor for coronary heart disease. Atherosclerosis. 2020;315:126–30.

    Article  CAS  PubMed  Google Scholar 

  63. Quan H, Khan N, Li B, Humphries KH, Faris P, Galbraith PD, et al. Invasive cardiac procedure use and mortality among South Asian and Chinese Canadians with coronary artery disease. Can J Cardiol. 2010;26(7):e236–e42.

    Article  PubMed  PubMed Central  Google Scholar 

  64. Rafnsson SB, Bhopal RS, Agyemang C, Fagot-Campagna A, Harding S, Hammar N, et al. Sizable variations in circulatory disease mortality by region and country of birth in six European countries. Eur J Public Health. 2013;23(4):594–605.

    Article  PubMed  Google Scholar 

  65. Raghavan R, Rahme E, Nedjar H, Huynh T. Long-term prognosis of South Asians following acute coronary syndromes. Can J Cardiol. 2008;24(7):585–7.

    Article  PubMed  PubMed Central  Google Scholar 

  66. Sheth T, Nair C, Nargundkar M, Anand S, Yusuf S. Cardiovascular and cancer mortality among Canadians of European, South Asian and Chinese origin from 1979 to 1993: an analysis of 1.2 million deaths. Can Med Assoc J. 1999;161(2):132–8.

    CAS  Google Scholar 

  67. Sun LY, Kimmoun A, Takagi K, Liu PP, Eddeen AB, Mebazaa A. Ethnic differences in acute heart failure outcomes in Ontario. Int J Cardiol. 2019;291:177–82.

    Article  PubMed  Google Scholar 

  68. Toor IS, Jaumdally R, Lip GYH, Pagano D, Dimitri W, Millane T, et al. Differences between South Asians and White Europeans in five year outcome following percutaneous coronary intervention. Int J Clin Pract. 2011;65(12):1259–66.

    Article  CAS  PubMed  Google Scholar 

  69. Wilkinson P, Sayer J, Laji K, Grundy C, Marchant B, Kopelman P, et al. Comparison of case fatality in South Asian and white patients after acute myocardial infarction: observational study. BMJ. 1996;312(7042):1330–3.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  70. Vyas MV, Austin PC, Fang JM, Laupacis A, Silver FL, Kapral MK. Immigration Status, ethnicity, and long-term outcomes following ischemic stroke. Neurology. 2021;96(8):E1145–E55.

    Article  CAS  Google Scholar 

  71. Vyas MV, Chaturvedi N, Hughes AD, Marmot M, Tillin T. Cardiovascular disease recurrence and long-term mortality in a tri-ethnic British cohort. Heart. 2021;107(12):996–1002.

    Article  CAS  Google Scholar 

  72. Patel AP, Wang MX, Kartoun U, Ng K, Khera AV. Quantifying and understanding the higher risk of atherosclerotic cardiovascular disease among South Asian individuals results from the UK Biobank Prospective Cohort Study. Circulation. 2021;144(6):410–22.

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  73. Rana A, de Souza RJ, Kandasamy S, Lear SA, Anand SS. Cardiovascular risk among South Asians living in Canada: a systematic review and meta-analysis. CMAJ Open. 2014;2(3):E183.

    Article  PubMed  PubMed Central  Google Scholar 

  74. Fernando E, Razak F, Lear SA, Anand SS. Cardiovascular disease in South Asian migrants. Can J Cardiol. 2015;31(9):1139–50.

    Article  PubMed  Google Scholar 

  75. Gunarathne A, Patel JV, Gammon B, Gill PS, Hughes EA, Lip GYH. Ischemic stroke in South Asians. Stroke. 2009;40(6):e415–e23.

    Article  PubMed  Google Scholar 

  76. Hussain SM, Oldenburg B, Wang Y, Zoungas S, Tonkin AM. Assessment of cardiovascular disease risk in South Asian populations. Int J Vasc Med. 2013;2013:786801.

    PubMed  PubMed Central  Google Scholar 

  77. Singh V, Prabhakaran S, Chaturvedi S, Singhal A, Pandian J. An examination of stroke risk and burden in South Asians. J Stroke Cerebrovasc Dis. 2017;26(10):2145–53.

    Article  PubMed  Google Scholar 

  78. Volgman AS, Palaniappan LS, Aggarwal NT, Gupta M, Khandelwal A, Krishnan AV, et al. Atherosclerotic cardiovascular disease in South Asians in the United States: epidemiology, risk factors, and treatments: a scientific statement from the American Heart Association. Circulation. 2018;138(1):e1–e34.

    Article  PubMed  Google Scholar 

Download references

Funding

Open access fees were covered by financial support from the University of Warwick Research Development Fund. This review received no specific funding.

Author information

Authors and Affiliations

  1. Warwick Evidence, Warwick Medical School (WMS), University of Warwick, Coventry, CV47AL, UK

    Mubarak Patel

  2. Warwick Medical School (WMS), University of Warwick, Coventry, CV47AL, UK

    Salim Abatcha & Olalekan Uthman

Authors
  1. Mubarak Patel
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Salim Abatcha
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Olalekan Uthman
    View author publications

    You can also search for this author in PubMed Google Scholar

Contributions

MP and SA screened publications throughout all stages of the review, from searching to data extraction. OA was approached to settle any disagreements in the screening process. MP undertook all statistical analyses, which was independently checked by SA. The authors read and approved the final manuscript.

Corresponding author

Correspondence to Mubarak Patel.

Ethics declarations

Ethics approval and consent to participate

In the conduct of the present systematic review and meta-analysis, approval from an ethics review board was not sought as it focused on summarising findings of studies made public. However, the procedures followed clear steps from the literature search to the synthesis of findings.

Competing interests

The authors declare that they have no competing interests.

Additional information

Publisher’s Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Supplementary Information

Additional file 1.

a. PRISMA 2020 Checklist. b. PRISMA 2020 abstract checklist.

Additional file 2.

OVID MEDLINE search strategy.

Additional file 3.

PRISMA flowchart.

Additional file 4: Supplementary Table 1.

Eligible studies that were excluded from the review with reasons.

Additional file 5: Supplementary Figure 1.

Forest plot showing the relative effect of exploratory analyses for CVD-related mortality between other ethnicities and Whites.

Rights and permissions

Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated in a credit line to the data.

Reprints and Permissions

About this article

Check for updates. Verify currency and authenticity via CrossMark

Cite this article

Patel, M., Abatcha, S. & Uthman, O. Ethnic differences between South Asians and White Caucasians in cardiovascular disease-related mortality in developed countries: a systematic literature review. Syst Rev 11, 207 (2022). https://doi.org/10.1186/s13643-022-02079-z

Download citation

  • Received:

  • Accepted:

  • Published:

  • DOI: https://doi.org/10.1186/s13643-022-02079-z

Keywords

  • Cardiovascular diseases
  • Ethnic groups
  • Mortality

Systematic Reviews

ISSN: 2046-4053