Cardiovascular Mortality in a Western Asian Country: Results From the Iran Cohort Consortium Publisher Pubmed



Fahimfar N¹ ; Khalili D^{2, 3} ; Sepanlou SG⁴ ; Malekzadeh R^{4, 5} ; Azizi F² ; Mansournia MA¹ ; Roohafza H⁶ ; Emamian MH⁷ ; Hadaegh F² ; Poustchi H⁴ ; Mansourian M^{6, 8} ; Hashemi H⁹ ; Sharafkhah M^{1, 4} ; Pourshams A⁴ Show All Authors Show Affiliations
Source: BMJ Open Published:2018

Abstract

Objectives Cardiovascular mortality in Western Asia is high and still rising. However, most data documented on risk prediction has been derived from Western countries and few population-based cohort studies have been conducted in this region. The current study aimed to present the process of pooling data and cardiovascular disease (CVD) mortality incidences for four Iranian cohorts. Methods From the Iran Cohort Consortium, the Golestan Cohort Study (GCS), Tehran Lipid and Glucose Study, Isfahan Cohort Study (ICS) and the Shahroud Eye Cohort Study (ShECS) were eligible for the current study since they had appropriate data and follow-up visits. Age-standardised CVD mortality rates were estimated for ages 40-80 and 40-65 years. Cox regression was used to compare mortalities among cohorts. Adjusted marginal rates were calculated using Poisson regression. Results Overall, 61 291 participants (34 880 women) aged 40-80 years, free of CVD at baseline, were included. During 504 606 person-years of follow-up, 1981 CVD deaths (885 women) occurred. Age-standardised/sex-standardised premature CVD mortality rates were estimated from 133 per 100 000 person-years (95% CI 81 to 184) in ShECS to 366 (95% CI 342 to 389) in the GCS. Compared with urban women, rural women had higher CVD mortality in the GCS but not in the ICS. The GCS population had a higher risk of CVD mortality, compared with the others, adjusted for conventional CVD risk factors. Conclusions The incidence of CVD mortality is high with some differences between urban and rural cohorts in Iran as a Western Asian country. Pooling data facilitates the opportunity to globally evaluate risk prediction models. © 2018 author(s).