Causes and predictors of mortality in Asian Indians with and without diabetes–10 year follow-up of the Chennai Urban Rural Epidemiology Study (CURES - 150)

Anjana, Ranjit Mohan; Unnikrishnan, Ranjit; Poongkunran, Mugilan; Jagdish, Padoor Sethuraman; Parthasarathy, Balasubramanian; Deepa, Mohan; Geetha, L.; Kumar, Ritesh; Rahulashankiruthiyayan, Thangarajan; Sankari, Ganesan Uma; Venkatesan, Ulagamathesan; Mohan, Viswanathan; Rani, Coimbatore Subramanian Shanthi

doi:10.1371/journal.pone.0197376

Cited by 17 publications

(15 citation statements)

References 42 publications

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“…Over the working lifespan of the diabetes cohort, higher all-cause mortality risk in those with diabetes resulted in a 5.5% reduction in years of life lived and was similar in men and women. This is consistent with the previous study conducted in India 21 but is contrary to the findings of a study conducted in China, which showed a higher mortality risk among working age Chinese men than women. 29 The relative impact of diabetes on years of life lost was higher among Bangladeshi younger people due to their longer working lifespan.…”

Section: Discussionsupporting

confidence: 88%

“…20 Since there were no mortality rates in Bangladesh, age and sex-specific mortality data for 2017 were extrapolated from the Chennai Urban Rural Epidemiology Study-150. 21 These were attributed to those with and without diabetes based on age and sex-specific diabetes prevalence and the relative risk of all-cause mortality associated with diabetes in Bangladeshi populations. Based on an assumption that the mortality rate for a 5 years age group applied to people in the midpoint of that age band, mortality rates for age in single years were derived using exponential functions (online supplimentary EMS table 2).…”

Section: Population and Mortality Ratesmentioning

confidence: 99%

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The impact of diabetes on the productivity and economy of Bangladesh

et al. 2020

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AimsTo estimate the impact of type 2 diabetes in terms of mortality, years of life lost (YLL) and productivity-adjusted life years (PALY) lost in Bangladesh.MethodsA life table model was constructed to estimate the productivity of the Bangladeshi population of current working age (20–59 years) with diabetes. Follow-up to 60 years (retirement age) was simulated. The life table analysis was then repeated assuming that the cohort did not have diabetes, with subsequent improvement in productivity. Differences in the results of the two analyses reflected the impact of diabetes on health and productivity. Demographic and the prevalence of diabetes data were sourced from the International Diabetes Foundation estimates for 2017 and mortality data were based on the 2017 Global Burden of Disease study. Relative risk and productivity indices were based on an Indian and Bangladeshi study, respectively. The cost of each PALY was assumed to be equivalent to gross domestic product (GDP) per equivalent full-time worker (US$8763). Future costs and years of life, and PALYs lived were discounted at an annual rate of 3%.ResultsAssuming a follow-up of this population (aged 20–59 years) until age 60 years or death, an estimated 813 807 excess deaths, loss of 4.0 million life years (5.5%) and 9.2 million PALYs (20.4%) were attributable to having diabetes. This was equivalent to 0.7 YLL, and 1.6 PALYs lost per person. The loss in PALYs equated to a total of US$97.4 billion lost (US$16 987 per person) in GDP. The results of the scenario analysis showed that the estimation was robust.ConclusionIn Bangladesh, the impact of diabetes on productivity loss and the broader economy looms large, and poses a substantial risk to the country’s future prosperity. This highlights the critical importance of health strategies aimed at the control of diabetes.

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Section: Discussionsupporting

confidence: 88%

Section: Population and Mortality Ratesmentioning

confidence: 99%

The impact of diabetes on the productivity and economy of Bangladesh

et al. 2020

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“…9 However, only a few studies have been conducted to describe the patterns and predictors of mortality in South Asian populations and even fewer are based on population-based cohorts. 10 Well organised and functioning vital registration systems provide high quality and timely mortality data that are of great value for epidemiological purposes. At present, the rate of death registration in Sri Lanka exceeds 99% and on registration, a death certificate is issued.…”

Section: Open Accessmentioning

confidence: 99%

Patterns and predictors of mortality in a semi-urban population-based cohort in Sri Lanka: findings from the Ragama Health Study

et al. 2020

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ObjectiveTo describe patterns and predictors of mortality in a semi-urban population in Sri Lanka.DesignA prospective population-based cohort study.SettingRagama Medical Officer of Health area in the Gampaha district, Sri Lanka.ParticipantsAdults between 35 and 64 years of age were recruited using an age stratified random sampling technique in 2007.MeasuresAt baseline, we recorded socio-demographic, lifestyle, anthropometric, biochemical and clinical data of the participants. Over 10 years, we obtained the cause and date of death from the death registration documents of deceased participants. We determined the survival probability of the cohort over 10 years and estimated Hazard ratios (HRs) for all-cause mortality (ACM), cardiovascular mortality (CVM) and cancer-related mortality (CRM) using Cox’s proportional hazards model. We also estimated the survival probabilities for men and women in each 10-year age group and standardised mortality ratio relative to the source population.ResultsThere were 169 deaths over 10 years with standardised mortality rates of 5.3 and 2.4 per 1000 years of follow-up for men and women, respectively. Independent predictors of: ACM were older age, lower income, smoking and diabetes mellitus while gender, education, occupation, harmful alcohol use, waist circumference and hypertension were not; CVM were older age, lower income, smoking, diabetes and hypertension while gender and harmful alcohol use were not; CRM was older age while gender, smoking and diabetes were not. Those engaged in clerical and technical occupations or unemployed had a lower risk of CRM as compared with those engaged in elementary occupations.ConclusionsOlder age, lower income, smoking, diabetes and hypertension strongly predict mortality in this cohort. Addressing the identified modifiable predictors through behavioural modification will improve longevity in similar populations.

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“…2 The high prevalence of ASCVD in persons with diabetes is well documented in many populations of the world. 3 Individuals with diabetes have higher atheroma volume, more atherosclerotic plaque and much narrower coronary lumen than do nondiabetics. 4 This makes them at great risk for a much worse myocardial infarction and occurrence of silent myocardial infarction; however, the concept of diabetes as a risk equivalent for established coronary artery disease is still not universally accepted.…”

Section: Clinical Summarymentioning

confidence: 99%