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Record W2022449751 · doi:10.1016/s0195-668x(02)00611-5

Singapore and coronary heart disease: a population laboratory to explore ethnic variations in the epidemiologic transition

2003· letter· en· W2022449751 on OpenAlexaff
Stephanie Ôunpuu

Bibliographic record

VenueEuropean Heart Journal · 2003
Typeletter
Languageen
FieldSocial Sciences
TopicSocioeconomic Development in Asia
Canadian institutionsMcMaster UniversityPopulation Health Research Institute
Fundersnot available
KeywordsMedicineEpidemiological transitionPopulationDeveloping countryEthnic groupContext (archaeology)Per capitaDiseaseStandard of livingDeveloped countryDemographyLife expectancyGerontologyDevelopment economicsEconomic growthEnvironmental healthGeographyPathology

Abstract

fetched live from OpenAlex

See doi:10.1053/S1095-668X(02)00423-2for the article to which this editorial refers. The majority of cardiovascular disease (CVD) now occurs in developing countries. This trend will continue. Globally, CVD mortality is projected to double between 1990 and 2020, with the developing countries experiencing approximately 80% of the increase. In this context, recent experiencesin Singapore provide an interesting case study ofa developing country that has experienced rapid economic and social development.1 Since independence in 1965, the economy has grownapproximately 8% per year. Per capita GNP is now among the highest in the region, and the population enjoys a standard of living comparable to that experienced in many developed countries.2 Changes in disease patterns, that are consistent with those described by ‘the epidemiologic transition’,3 have been observed in Singapore over this time period. With economic development, the major causes of death and disability in more advanced societies, have shifted from a predominance of nutritional deficiencies and infectious diseases, to those classified as degenerative [e.g. chronic diseases such as CVD, cancer and diabetes). Few other countries have experienced the level of rapid economic development that Singapore has experienced in recent decades. At the midpoint of the century (1950–1960), Singapore was characterized by a very young population, with those below 20 years of age making up over 50% of the total population.2 By the end of the century, the proportion of the population over 65 years had more than doubled, from 2.2% in 1970 to 5.7% in 1990 and is expected toreach 10% by 2012 and 20% by 2033.2 Infant mortality dropped by a remarkable 95% from 82/1000 live births in 1950 to 4.3/1000 live births (a rate that compares favourably with many developed countries) in 1994, and average life expectancy at birth had increased from 62 years of age in 1957 to 76.4 in 1994.4 Disease patterns in Singapore have also changed dramatically. At the midpoint of the century, the top two causes of death were tuberculosis and pneumonia. All-cause mortality has declined steadily from 769 to 352/100000 in 1957 and 1987 respectively. However, during this period, chronic disease has become more prominent. For example, the age adjusted mortality rate for CHD was 27/100000 in 1957, and peaked at 71/100000 in 1984.5 By 1990, heart disease was the second leading cause of death, after cancer.2 These changes in disease patterns should be viewed in the light of demographic changes (aging population), as well as the more general societal changes. For several decades following independence in 1965, Singapore enjoyed a successful economy, which enabled the development of public health programs such as housing, water, sanitation, mass-inoculation, improved nutrition, etc. Health services developed and expanded considerably, and now provide a well-developed and modern healthcare infrastructure with equitable access to the general population. In 1991, a report of the National Health Policies Committee provided the stimulus for national level programs in Singapore on healthy lifestyles (exercise, diet, smoking, obesity and AIDS awareness) which were initiated during the 1990s.4 In this context, the data presented for Singapore by Mak et al.1 in this issue are timely, as they describe CHD mortality trends during 1991 to 1999, which follows implementation of the abovenational policies. These data demonstrate annual declines in incidence of acute myocardial infarction of 2.3% (95% CI, 0.80, 3.71) during the period 1991 to 1999.1 Age-adjusted 28-day and 1-year case-fatality rates also fell during the study period. The declines reported are comparable to declines observed in several of the WHO MONICA populations, for example in Australia, Belgium, Denmark, France, Sweden, United Kingdom and United States.6 Mak et al. also document the differential CV disease incidence and survival rates for each of the three major ethnic groups in Singapore.1 The incidence of myocardial infarction (MI) for Malays and Indians were much higher than the Chinese, with overall ratios of 2.1 (95% CI 2.0 to 2.2) and 3.1 (95% CI, 2.9 to 3.2) respectively. This pattern of higher incidence of CHD among Indians hasnow been reported in multiple countries and regions.7–11 Several factors likely contribute to the inter-ethnic differences in CHD occurring in the same geographic area. First, the prevalence of environmental risk factors (e.g. diet, activity, smoking) may vary across these three ethnic groups, which is probably related to cultural factors. Second, there may be differences in the prevalence of traditional and novel risk factors. Several cross sectional studies have been conducted inSingapore to examine ethnic differences in the prevalence of risk factors. The greater susceptibility of Indians to CHD does not appear to be explained by higher levels of the traditional risk factors (cigarette smoking, hypertension or hypercholesterolemia),12 nor does hyperhomocysteinemia appear to account for the differences.13 However Indians have more abdominal obesity and insulin resistance, and some components of Syndrome X (lower high density lipoprotein cholesterol, more glucose intolerance), as well as higher levels of thrombogenic factors such as elevated plasminogen activator inhibitor type 1 and lipoprotein(a) (Lp(a)), when compared with theMalays and the Chinese,14 which may partly explain their increased susceptibility. Indians also have lower levels of the antioxidants vitamin C and selenium,15 but the importance of thesefactors to CHD is unclear. Most intriguing is the possibility that Indians may experience more clinical events for the same degree of atherosclerosis, suggesting greater propensity to plaque rupture and thrombosis.8 Genetic factors may also contribute to interpopulation differences, due to variable gene frequencies among ethnic groups. Lp(a), for example, is a genetically determined plasminogen-like apolipoprotein that may be related to both atherogenesis and thrombogenesis, and has been demonstrated to be a powerful independent risk factor for premature atherosclerosis.16 When compared to Malays and Chinese living in Singapore, Indians have higher levels of Lp(a).14 Finally, Mak et al. have shown a higher case fatality rate for the Malays, with the lowest case fatality rate among the Indians.1 The reasons for this are unclear, especially within Singapore where healthcare is readily accessible to the whole population. This difference may be related to the larger infarct size observed among the Malays. However, it may also be associated with their lower socioeconomic status, which may present barriers to fully utilizing the available healthcare. Exploring the role of thrombogenic risk factors, genetic factors, the composition of atherosclerotic plaque and their propensity to rupture may provide insights into why different ethnic groups experience markedly differing rates of CHD. Additionally, understanding the links between socioeconomic status and optimal use of healthcare and prevention resources could throw light on behavioralstrategies, leading to optimal use of healthcare resources. In conclusion, studies such as the one by Mak et al.1 are important because they document trends in CVD incidence and mortality overall and their variations by ethnicity. The variations observed in the patterns of disease suggest the need to explore the role of emerging risk factors, which may partly explain the differences.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Commentary · Consensus signal: none
Teacher disagreement score0.018
Threshold uncertainty score0.060

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.003
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0000.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0180.005

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.163
GPT teacher head0.372
Teacher spread0.209 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreCommentary

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations25
Published2003
Admission routes1
Has abstractyes

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