Search

O-5 Uncovering the driving forces behind the trend in coronary heart disease mortality in Barbados from 2009– 2018

Author(s): F Carter , NP Sobers , W Jones
Type Of Study:
  • Evidence Synthesis
Country(ies) Of Focus:
  • Barbados
Year of Presentation: 2022

Abstract

Objective: To investigate the risk factors contributing to trend in coronary heart disease (CHD) mortality over the ten-year period 2009–2018

Methods: Secondary data analysis was conducted on existing databases: the Barbados National Registry for chronic non-communicable diseases and the Non-Communicable Disease Risk Factor Collaboration. We calculated agestandardized incidence rate from 2009-2018. Using cases of myocardial infarction from the Barbados National Registry, we examined the impact of BMI, hypertension, diabetes and raised cholesterol on death before discharge, using multivariable logistic regression analysis.

Results: In 2009, CHD mortality rates were higher in men 77.8 per 100,000 [95% UI 70.7–84.1] compared to women 63.0 per 100,000 [95% UI 55.5–68.9]. After declining to the lowest rates in 2015, they rose to 66.0 per 100,000 [95% UI 55.5–78.0] in men and 40.0 per 100,000 [95% UI 32.99– 48.30] in women by 2018. Trends in risk factor prevalence revealed increases in diabetes and obesity and stable raised blood pressure and mean cholesterol rates. Patients with diabetes are 2.97 times [95% CI 1.93–4.56] more likely to die from CHD than non-diabetics. Hypertensives are 2.37 times [95% CI 1.42–3.96] more likely to die from CHD than non-hypertensives. Previous aspirin use significantly reduced the odds of dying from CHD by 0.71 times [95% CI 0.64–0.79].

Conclusion: Gains made to decrease CHD mortality in Barbados appear to be reversing. Diabetes, hypertension and obesity appear to be the main drivers of this reversal.

Previous Article O-5 The double burden of COVID-19 and a natural disaster on food production and security in a Small Island Developing State
Next Article O-50 Predicting virological failure in pediatric and adolescent human immunodeficiency virus (HIV) patients in Haiti: a crisis-adjusted machine learning approach
Print
11 Rate this article:
No rating

Comments

Please login or register to post comments.