Student or educator? Sign up with your school email and get 6 months of Pro free.
Cited by 618

The DOI matches its Crossref record; 5 of the 5 references it cites were found in a registry or an index.

Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study

Open access
Rosengren, Annika, Smyth, Andrew, Rangarajan, Sumathy, Ramasundarahettige, Chinthanie, Bangdiwala, Shrikant I, AlHabib, Khalid F, Avezum, Alvaro, Bengtsson Boström, Kristina, Chifamba, Jephat, Gulec, Sadi, Gupta, Rajeev, Igumbor, Ehi U, Iqbal, Romaina, Ismail, Norhassim, Joseph, Philip, Kaur, Manmeet, Khatib, Rasha, Kruger, Iolanthé M, Lamelas, Pablo, Lanas, Fernando, Lear, Scott A, Li, Wei, Wang, Chuangshi, Quiang, Deren, Wang, Yang, Lopez-Jaramillo, Patricio, Mohammadifard, Noushin, Mohan, Viswanathan, Mony, Prem K, Poirier, Paul, Srilatha, Sarojiniamma, Szuba, Andrzej, Teo, Koon, Wielgosz, Andreas, Yeates, Karen E, Yusoff, Khalid, Yusuf, Rita, Yusufali, Afzalhusein H, Attaei, Marjan W, McKee, Martin, Yusuf, Salim·2019·The Lancet Global Health

Abstract

Background Socioeconomic status is associated with differences in risk factors for cardiovascular disease incidence and outcomes, including mortality. However, it is unclear whether the associations between cardiovascular disease and common measures of socioeconomic status—wealth and education—differ among high-income, middle-income, and low-income countries, and, if so, why these differences exist. We explored the association between education and household wealth and cardiovascular disease and mortality to assess which marker is the stronger predictor of outcomes, and examined whether any differences in cardiovascular disease by socioeconomic status parallel differences in risk factor levels or differences in management. Methods In this large-scale prospective cohort study, we recruited adults aged between 35 years and 70 years from 367 urban and 302 rural communities in 20 countries. We collected data on families and households in two questionnaires, and data on cardiovascular risk factors in a third questionnaire, which was supplemented with physical examination. We assessed socioeconomic status using education and a household wealth index. Education was categorised as no or primary school education only, secondary school education, or higher education, defined as completion of trade school, college, or university. Household wealth, calculated at the household level and with household data, was defined by an index on the basis of ownership of assets and housing characteristics. Primary outcomes were major cardiovascular disease (a composite of cardiovascular deaths, strokes, myocardial infarction, and heart failure), cardiovascular mortality, and all-cause mortality. Information on specific events was obtained from participants or their family. Findings Recruitment to the study began on Jan 12, 2001, with most participants enrolled between Jan 6, 2005, and Dec 4, 2014. 160 299 (87·9%) of 182 375 participants with baseline data had available follow-up event data and were eligible for inclusion. After exclusion of 6130 (3·8%) participants without complete baseline or follow-up data, 154 169 individuals remained for analysis, from five low-income, 11 middle-income, and four high-income countries. Participants were followed-up for a mean of 7·5 years. Major cardiovascular events were more common among those with low levels of education in all types of country studied, but much more so in low-income countries. After adjustment for wealth and other factors, the HR (low level of education vs high level of education) was 1·23 (95% CI 0·96–1·58) for high-income countries, 1·59 (1·42–1·78) in middle-income countries, and 2·23 (1·79–2·77) in low-income countries (p interaction <0·0001). We observed similar results for all-cause mortality, with HRs of 1·50 (1·14–1·98) for high-income countries, 1·80 (1·58–2·06) in middle-income countries, and 2·76 (2·29–3·31) in low-income countries (p interaction <0·0001). By contrast, we found no or weak associations between wealth and these two outcomes. Differences in outcomes between educational groups were not explained by differences in risk factors, which decreased as the level of education increased in high-income countries, but increased as the level of education increased in low-income countries (p interaction <0·0001). Medical care (eg, management of hypertension, diabetes, and secondary prevention) seemed to play an important part in adverse cardiovascular disease outcomes because such care is likely to be poorer in people with the lowest levels of education compared to those with higher levels of education in low-income countries; however, we observed less marked differences in care based on level of education in middle-income countries and no or minor differences in high-income countries. Interpretation Although people with a lower level of education in low-income and middle-income countries have higher incidence of and mortality from cardiovascular disease, they have better overall risk factor profiles. However, these individuals have markedly poorer health care. Policies to reduce health inequities globally must include strategies to overcome barriers to care, especially for those with lower levels of education. Funding Full funding sources are listed at the end of the paper (see Acknowledgments).

Overview

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Integrity signals

Registry

Crossref · 10.1016/s2214-109x(19)30045-2

Cites

5 of 5 references verified0 citation errors · 0 not checkable

Statistics

Verification

Claims the paper makes

The claims and statistics of this paper haven’t been checked yet.

Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.

A few free checks a day · papers already verified open instantly · results are public

Self-check

Percentages, sums and reported differences re-computed from the paper’s own numbers.

The self-check needs the paper’s full text.

Citations

Cites

Show all 5 references

Research works printed but not linked: 39

  1. Veronesi, 2017, Combined effect of educational status and cardiovascular risk factors on the incidence of coronary heart disease and stroke in European cohorts: Implications for prevention, Eur J Prev Cardiol, 24, 437, 10.1177/2047487316679521 · DOI
  2. Roth, 2015, Global and regional patterns in cardiovascular mortality from 1990 to 2013, Circulation, 132, 1667, 10.1161/CIRCULATIONAHA.114.008720 · DOI · Paper page
  3. Gupta, 2009, Coronary heart disease in low socioeconomic status subjects in India: “an evolving epidemic”, Indian Heart J, 61, 358
  4. Reddy, 2007, Educational status and cardiovascular risk profile in Indians, Proc Natl Acad Sci USA, 104, 16263, 10.1073/pnas.0700933104 · DOI · Paper page
  5. Subramanian, 2013, Jumping the gun: the problematic discourse on socioeconomic status and cardiovascular health in India, Int J Epidemiol, 42, 1410, 10.1093/ije/dyt017 · DOI
  6. Braveman, 2005, Socioeconomic status in health research: one size does not fit all, JAMA, 294, 2879, 10.1001/jama.294.22.2879 · DOI · Paper page
  7. Pednekar, 2011, Illiteracy, low educational status, and cardiovascular mortality in India, BMC Public Health, 11, 567, 10.1186/1471-2458-11-567 · DOI
  8. Vathesatogkit, 2014, Socioeconomic disadvantage and disease-specific mortality in Asia: systematic review with meta-analysis of population-based cohort studies, J Epidemiol Community Health, 68, 375, 10.1136/jech-2013-203053 · DOI
  9. Stringhini, 2014, Association of socioeconomic status with overall and cause specific mortality in the Republic of Seychelles: results from a cohort study in the African region, PLoS One, 9, e102858, 10.1371/journal.pone.0102858 · DOI
  10. Goyal, 2010, Attained educational level and incident atherothrombotic events in low- and middle-income compared with high-income countries, Circulation, 122, 1167, 10.1161/CIRCULATIONAHA.109.919274 · DOI
  11. Woodward, 2015, Socioeconomic status in relation to cardiovascular disease and cause-specific mortality: a comparison of Asian and Australasian populations in a pooled analysis, BMJ Open, 5, e006408, 10.1136/bmjopen-2014-006408 · DOI
  12. Kumar, 2015, Low socioeconomic status is an independent risk factor for ischemic stroke: a case-control study in North Indian population, Neuroepidemiology, 44, 138, 10.1159/000374118 · DOI
  13. Teo, 2009, The Prospective Urban Rural Epidemiology (PURE) study: examining the impact of societal influences on chronic noncommunicable diseases in low-, middle-, and high-income countries, Am Heart J, 158, 1, 10.1016/j.ahj.2009.04.019 · DOI · Paper page
  14. Corsi, 2013, Prospective Urban Epidemiology (PURE) study: baseline characteristics of the household sample and comparative analyses with national data in 17 countries, Am Heart J, 166, 636, 10.1016/j.ahj.2013.04.019 · DOI
  15. McGorrian, 2011, Estimating modifiable coronary heart disease risk in multiple regions of the world: the INTERHEART Modifiable Risk Score, Eur Heart J, 32, 581, 10.1093/eurheartj/ehq448 · DOI · Paper page
  16. Gupta, 2015, Socioeconomic factors and use of secondary preventive therapies for cardiovascular diseases in South Asia: the PURE study, Eur J Prev Cardiol, 22, 1261, 10.1177/2047487314540386 · DOI
  17. Erreygers, 2011, Measuring socioeconomic inequality in health, health care and health financing by means of rank-dependent indices: a recipe for good practice, J Health Econ, 30, 685, 10.1016/j.jhealeco.2011.04.004 · DOI · Paper page
  18. Wagstaff, 2005, The bounds of the concentration index when the variable of interest is binary, with an application to immunization inequality, Health Econ, 14, 429, 10.1002/hec.953 · DOI · Paper page
  19. Mackenbach, 2008, Socioeconomic inequalities in health in 22 European countries, N Engl J Med, 358, 2468, 10.1056/NEJMsa0707519 · DOI · Paper page
  20. Avendano, 2006, Socioeconomic status and ischaemic heart disease mortality in 10 western European populations during the 1990s, Heart, 92, 461, 10.1136/hrt.2005.065532 · DOI · Paper page
  21. Moran, 2014, Variations in ischemic heart disease burden by age, country, and income: the Global Burden of Diseases, Injuries, and Risk Factors 2010 study, Glob Heart, 9, 91, 10.1016/j.gheart.2013.12.007 · DOI
  22. Kreatsoulas, 2010, The impact of social determinants on cardiovascular disease, Can J Cardiol, 26, 8
  23. Rosengren, 2009, Education and risk for acute myocardial infarction in 52 high, middle and low-income countries: INTERHEART case-control study, Heart, 95, 2014, 10.1136/hrt.2009.182436 · DOI
  24. Mirowsky, 2005, Education, learned effectiveness and health, London Rev Educ, 3, 205, 10.1080/14748460500372366 · DOI · Paper page
  25. Camacho, 2016, Social disparities explain differences in hypertension prevalence, detection and control in Colombia, J Hypertens, 34, 2344, 10.1097/HJH.0000000000001115 · DOI
  26. Khatib, 2016, Availability and affordability of cardiovascular disease medicines and their effect on use in high-income, middle-income, and low-income countries: an analysis of the PURE study data, Lancet, 387, 61, 10.1016/S0140-6736(15)00469-9 · DOI · Paper page
  27. Xavier, 2008, Treatment and outcomes of acute coronary syndromes in India (CREATE): a prospective analysis of registry data, Lancet, 371, 1435, 10.1016/S0140-6736(08)60623-6 · DOI · Paper page
  28. Miller, 2016, Availability, affordability, and consumption of fruits and vegetables in 18 countries across income levels: findings from the Prospective Urban Rural Epidemiology (PURE) study, Lancet Glob Health, 4, e695, 10.1016/S2214-109X(16)30186-3 · DOI · Paper page
  29. Wallack, 2016, Developmental origins, epigenetics, and equity: moving upstream, Matern Child Health J, 20, 935, 10.1007/s10995-016-1970-8 · DOI
  30. Eriksson, 1999, Catch-up growth in childhood and death from coronary heart disease: longitudinal study, BMJ, 318, 427, 10.1136/bmj.318.7181.427 · DOI · Paper page
  31. Ohlsson, 2016, Association between excessive BMI increase during puberty and risk of cardiovascular mortality in adult men: a population-based cohort study, Lancet Diabetes Endocrinol, 4, 1017, 10.1016/S2213-8587(16)30273-X · DOI
  32. Rosengren, 1999, Weight gain from age 20 to middle age and long-term mortality from coronary, cardiovascular and all causes, Eur Heart J, 20, 269, 10.1053/euhj.1998.1235 · DOI · Paper page
  33. Meneton, 2018, Work environment mediates a large part of social inequalities in the incidence of several common cardiovascular risk factors: findings from the Gazel cohort, Soc Sci Med, 216, 59, 10.1016/j.socscimed.2018.09.042 · DOI
  34. Brunner, 2017, Social factors and cardiovascular morbidity, Neurosci Biobehav Rev, 74, 260, 10.1016/j.neubiorev.2016.05.004 · DOI
  35. Kakinami, 2013, Comparison of three lifecourse models of poverty in predicting cardiovascular disease risk in youth, Ann Epidemiol, 23, 485, 10.1016/j.annepidem.2013.05.012 · DOI
  36. Bambra, 2009, Welfare state regimes, unemployment and health: a comparative study of the relationship between unemployment and self-reported health in 23 European countries, J Epidemiol Community Health, 63, 92, 10.1136/jech.2008.077354 · DOI · Paper page
  37. Pollack, 2013, Do wealth disparities contribute to health disparities within racial/ethnic groups?, J Epidemiol Community Health, 67, 439, 10.1136/jech-2012-200999 · DOI
  38. Chae, 2015, Association between an internet-based measure of area racism and black mortality, PLoS One, 10, e0122963, 10.1371/journal.pone.0122963 · DOI · Paper page
  39. Kunitz, 2010, State political cultures and the mortality of African Americans and American Indians, Health Place, 16, 558, 10.1016/j.healthplace.2010.02.002 · DOI

Cited by

See all 618

Metadata

Authors
Rosengren, A., Smyth, A., Rangarajan, S., Ramasundarahettige, C., Bangdiwala, S. I., AlHabib, K. F., Avezum, A., Bengtsson Boström, K., Chifamba, J., Gulec, S., Gupta, R., Igumbor, E. U., Iqbal, R., Ismail, N., Joseph, P., Kaur, M., Khatib, R., Kruger, I. M., Lamelas, P., Lanas, F., Lear, S. A., Li, W., Wang, C., Quiang, D., Wang, Y., Lopez-Jaramillo, P., Mohammadifard, N., Mohan, V., Mony, P. K., Poirier, P., Srilatha, S., Szuba, A., Teo, K., Wielgosz, A., Yeates, K. E., Yusoff, K., Yusuf, R., Yusufali, A. H., Attaei, M. W., McKee, M., & Yusuf, S.
Venue
The Lancet Global Health
Publisher
Elsevier BV
Published
2019-06
Volume
7
Issue
6
Pages
e748-e760
ISSN
2214-109X (ISSN-L)
OpenAlex
W2942480445
Cited by
618
References
5
FWCI
43.52
Type
journal article
Language
en
Open access
gold
Licence
Funders
  • Deanship of Scientific Research, King Saud University (RG-1436-013)
  • Ministry of Science, Technology and Space (100-IRDC/BIOTEK 16/6/21 [13/2007, 07-05-IFN-BPH 010)
  • Servier
  • AstraZeneca
  • Swedish Insitute
  • Philippine Council for Health Research and Development
  • United Arab Emirates University
  • Universidad de La Frontera
  • Vetenskapsrådet
  • Canadian Institutes of Health Research
  • Ontario Ministry of Health and Long-Term Care
  • Indian Council of Medical Research
  • North-West University
  • Ministry of Higher Education, Malaysia (600-RMI/LRGS/5/3 [2/2011)
  • National Center for Research and Development (6517-777-58228, 6566-04-18062)
  • Universiti Teknologi MARA (UKM-Hejim-Komuniti-15-2010)
  • South African Medical Research Council
  • National Research Foundation
  • GlaxoSmithKline
  • Sanofi
  • Uniwersytet Medyczny im. Piastów Slaskich we Wroclawiu
  • Boehringer Ingelheim

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review

How to cite

Cite CoChat for the checks on this page, and the paper for its own findings.

This page
CoChat. (2026). Citation check: Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study. https://app.cochat.ai/papers/10.1016/s2214-109x(19)30045-2
The paper
Rosengren, A., Smyth, A., Rangarajan, S., Ramasundarahettige, C., Bangdiwala, S. I., AlHabib, K. F., Avezum, A., Bengtsson Boström, K., Chifamba, J., Gulec, S., Gupta, R., Igumbor, E. U., Iqbal, R., Ismail, N., Joseph, P., Kaur, M., Khatib, R., Kruger, I. M., Lamelas, P., . . . Yusuf, S. (2019). Socioeconomic status and risk of cardiovascular disease in 20 low-income, middle-income, and high-income countries: the Prospective Urban Rural Epidemiologic (PURE) study. The Lancet Global Health. https://doi.org/10.1016/s2214-109x(19)30045-2

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.