Examinando por Autor "Raman Kutty, Vellappillil"

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    Modifiable risk factors, cardiovascular disease, and mortality in 155 722 individuals from 21 high-income, middle-income, and low-income countries (PURE)
    (Elsevier, 2019-09-03) Yusuf, Salim; Joseph, Philip; Rangarajan, Sumathy; Islam, Shofiqul; Mente, Andrew; Hystad, Perry; Brauer, Michael; Raman Kutty, Vellappillil; Gupta, Rajeev; Wielgosz, Andreas; AlHabib, Khalid F.; Dans, Antonio; Lopez-Jaramillo, Patricio; Avezum, Alvaro; Lanas, Fernando; Oguz, Aytekin; Kruger, Iolanthe M.; Diaz, Rafael; Yusoff, Khalid; Mony, Prem; Chifamba, Jephat; Yeates, Karen; Kelishadi, Roya; Yusufali, Afzalhussein; Khatib, Rasha; Rahman, Omar; Zatonska, Katarzyna; Iqbal, Romaina; Wei, Li; Bo, Hu; Rosengren, Annika; Kaur, Manmeet; Mohan, Viswanathan; Lear, Scott A.; Teo, Koon K.; Leong, Darryl; O'Donnell, Martin; McKee, Martin; Dagenais, Gilles; Everest
    Background Global estimates of the effect of common modifiable risk factors on cardiovascular disease and mortality are largely based on data from separate studies, using different methodologies. The Prospective Urban Rural Epidemiology (PURE) study overcomes these limitations by using similar methods to prospectively measure the effect of modifiable risk factors on cardiovascular disease and mortality across 21 countries (spanning five continents) grouped by different economic levels. Methods In this multinational, prospective cohort study, we examined associations for 14 potentially modifiable risk factors with mortality and cardiovascular disease in 155 722 participants without a prior history of cardiovascular disease from 21 high-income, middle-income, or low-income countries (HICs, MICs, or LICs). The primary outcomes for this paper were composites of cardiovascular disease events (defined as cardiovascular death, myocardial infarction, stroke, and heart failure) and mortality. We describe the prevalence, hazard ratios (HRs), and population-attributable fractions (PAFs) for cardiovascular disease and mortality associated with a cluster of behavioural factors (ie, tobacco use, alcohol, diet, physical activity, and sodium intake), metabolic factors (ie, lipids, blood pressure, diabetes, obesity), socioeconomic and psychosocial factors (ie, education, symptoms of depression), grip strength, and household and ambient pollution. Associations between risk factors and the outcomes were established using multivariable Cox frailty models and using PAFs for the entire cohort, and also by countries grouped by income level. Associations are presented as HRs and PAFs with 95% CIs. Findings Between Jan 6, 2005, and Dec 4, 2016, 155 722 participants were enrolled and followed up for measurement of risk factors. 17 249 (11·1%) participants were from HICs, 102 680 (65·9%) were from MICs, and 35 793 (23·0%) from LICs. Approximately 70% of cardiovascular disease cases and deaths in the overall study population were attributed to modifiable risk factors. Metabolic factors were the predominant risk factors for cardiovascular disease (41·2% of the PAF), with hypertension being the largest (22·3% of the PAF). As a cluster, behavioural risk factors contributed most to deaths (26·3% of the PAF), although the single largest risk factor was a low education level (12·5% of the PAF). Ambient air pollution was associated with 13·9% of the PAF for cardiovascular disease, although different statistical methods were used for this analysis. In MICs and LICs, household air pollution, poor diet, low education, and low grip strength had stronger effects on cardiovascular disease or mortality than in HICs. Interpretation Most cardiovascular disease cases and deaths can be attributed to a small number of common, modifiable risk factors. While some factors have extensive global effects (eg, hypertension and education), others (eg, household air pollution and poor diet) vary by a country's economic level. Health policies should focus on risk factors that have the greatest effects on averting cardiovascular disease and death globally, with additional emphasis on risk factors of greatest importance in specific groups of countries. Funding Full funding sources are listed at the end of the paper (see Acknowledgments).
  • Publicación
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    The household economic burden of non-communicable diseases in 18 countries
    (BMJ Global Health, 2020-02-11) Murphy, Adrianna; Palafox, Benjamin; Walli-Attaei, Marjan; Powell-Jackson, Timothy; Rangarajan, Sumathy; Alhabib, Khalid F.; Avezum, Alvaro; Tumerdem Calik, Kevser Burcu; Chifamba, Jephat; Choudhury, Tarzia; Dagenais, Gilles; Dans, Antonio; Gupta, Rajeev; Iqbal, Romaina; Kaur, Manmeet; Kelishadi, Roya; Khatib, Rasha; Kruger, Iolanthe Marike; Raman Kutty, Vellappillil; Lear, Scott A.; Li, Wei; Lopez-Jaramillo, Patricio; Mohan, Viswanathan; Mony, Prem K.; Orlandin, Andres; Rosengren, Annika; Rosnah, Ismail; Seron, Pamela; Teo, Koon; Tse, Lap Ah; Tsolekile, Lungiswa; Wang, Yang; Wielgosz, Andreas; Yan, Ruohua; Yeates, Karen; Yusoff, Khalid; Zatonska, Katarzyna; Hanson, Kara; Yusuf, Salim; McKee, Martin; Everest
    Abstract Background Non-communicable diseases (NCDs) are the leading cause of death globally. In 2014, the United Nations committed to reducing premature mortality from NCDs, including by reducing the burden of healthcare costs. Since 2014, the Prospective Urban and Rural Epidemiology (PURE) Study has been collecting health expenditure data from households with NCDs in 18 countries. Methods Using data from the PURE Study, we estimated risk of catastrophic health spending and impoverishment among households with at least one person with NCDs (cardiovascular disease, diabetes, kidney disease, cancer and respiratory diseases; n=17 435), with hypertension only (a leading risk factor for NCDs; n=11 831) or with neither (n=22 654) by country income group: high-income countries (Canada and Sweden), upper middle income countries (UMICs: Brazil, Chile, Malaysia, Poland, South Africa and Turkey), lower middle income countries (LMICs: the Philippines, Colombia, India, Iran and the Occupied Palestinian Territory) and low-income countries (LICs: Bangladesh, Pakistan, Zimbabwe and Tanzania) and China. Results The prevalence of catastrophic spending and impoverishment is highest among households with NCDs in LMICs and China. After adjusting for covariates that might drive health expenditure, the absolute risk of catastrophic spending is higher in households with NCDs compared with no NCDs in LMICs (risk difference=1.71%; 95% CI 0.75 to 2.67), UMICs (0.82%; 95% CI 0.37 to 1.27) and China (7.52%; 95% CI 5.88 to 9.16). A similar pattern is observed in UMICs and China for impoverishment. A high proportion of those with NCDs in LICs, especially women (38.7% compared with 12.6% in men), reported not taking medication due to costs. Conclusions Our findings show that financial protection from healthcare costs for people with NCDs is inadequate, particularly in LMICs and China. While the burden of NCD care may appear greatest in LMICs and China, the burden in LICs may be masked by care foregone due to costs. The high proportion of women reporting foregone care due to cost may in part explain gender inequality in treatment of NCDs.
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    Variations in risks from smoking between high-income, middle-income, and low-income countries. An analysis of data from 179 000 participants from 63 countries
    (The Lancet Global Health, 2022-02-24) Sathish, Thirunavukkarasu; Teo, Koon; Britz-McKibbin, Philip; Gill, Biban; Islam, Shofiqul; Pare, Guillaume; Rangarajan, Sumathy; Duong, MyLinh; Lanas, Fernando; Lopez-Jaramillo, Patricio; Mony, Prem; Pinnaka, Lakshmi; Raman Kutty, Vellappillil; Orlandini, Andres; Avezum, Alvaro; Wielgosz, Andreas; Poirier, Paul; Alhabib, Khalid F.; Temizhan, Ahmet; Chifamba, Jephat; Yeates, Karen; Kruger, Iolanthé M.; Khatib, Rasha; Yusuf, Rita; Rosengren, Annika; Zatonska, Katarzyna; Iqbal, Romaina; Lui, Weida; Lang, Xinyue; Li, Sidong; Hu, Bo; Dans, Antonio; Yusufali, Afzalhussein; Bahonar, Ahmad; O’Donnell, Martin J.; McKee, Martin; Yusuf, Salim; Masira
    Background Separate studies suggest that the risks from smoking might vary between high-income (HICs), middle-income (MICs), and low-income (LICs) countries, but this has not yet been systematically examined within a single study using standardised approaches. We examined the variations in risks from smoking across different country income groups and some of their potential reasons. Methods We analysed data from 134 909 participants from 21 countries followed up for a median of 11·3 years in the Prospective Urban Rural Epidemiology (PURE) cohort study; 9711 participants with myocardial infarction and 11 362 controls from 52 countries in the INTERHEART case-control study; and 11 580 participants with stroke and 11 331 controls from 32 countries in the INTERSTROKE case-control study. In PURE, all-cause mortality, major cardiovascular disease, cancers, respiratory diseases, and their composite were the primary outcomes for this analysis. Biochemical verification of urinary total nicotine equivalent was done in a substudy of 1000 participants in PURE. Findings In PURE, the adjusted hazard ratio (HR) for the composite outcome in current smokers (vs never smokers) was higher in HICs (HR 1·87, 95% CI 1·65–2·12) than in MICs (1·41, 1·34–1·49) and LICs (1·35, 1·25–1·46; interaction p<0·0001). Similar patterns were observed for each component of the composite outcome in PURE, myocardial infarction in INTERHEART, and stroke in INTERSTROKE. The median levels of tar, nicotine, and carbon monoxide displayed on the cigarette packs from PURE HICs were higher than those on the packs from MICs. In PURE, the proportion of never smokers reporting high second-hand smoke exposure (≥1 times/day) was 6·3% in HICs, 23·2% in MICs, and 14·0% in LICs. The adjusted geometric mean total nicotine equivalent was higher among current smokers in HICs (47·2 μM) than in MICs (31·1 μM) and LICs (25·2 μM; ANCOVA p<0·0001). By contrast, it was higher among never smokers in LICs (18·8 μM) and MICs (11·3 μM) than in HICs (5·0 μM; ANCOVA p=0·0001). Interpretation The variations in risks from smoking between country income groups are probably related to the higher exposure of tobacco-derived toxicants among smokers in HICs and higher rates of high second-hand smoke exposure among never smokers in MICs and LICs.