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Leading Causes of Mortality in People with Diabetes

Aug 29, 2020
Editor: Steve Freed, R.PH., CDE

Author: Joan Prifti, PharmD. Candidate, LECOM College of Pharmacy

How is mortality in diabetes patients related to ethnicity, age, sex, and other factors? 

Diabetes has become an essential key health priority, as the number of patients has doubled globally. More people are starting to be diagnosed with diabetes at younger ages, and approximately one in three people are expected to have diabetes. The most common risk factors associated with diabetes include obesity, family history of diabetes, African American or Hispanic ethnicity, age, not being physically active, etc. People with diabetes have a higher chance of mortality and shorter life expectancy compared to people without diabetes. Previous studies have concluded that mortality in the US has shown a promising decrease in people with diabetes. But it was not clear if the trend in mortality was seen in different populations with diabetes. Therefore, the purpose of this study was to examine common trends in mortality related to ethnicity, age, sex, and differences between people without and with diabetes.  

In this study, they used a systematic review to gather data from different studies for over 29 years, extracting standardized or crude, cumulative, and annual mortality rate among people with and without diabetes. For the age-specific mortality rate, they only used the data from the age group that was the most representative of the broader group. The studies were classified into two ethnic categories, Europid and non-Europid population. To establish a distinction in mortality rates for people with and without diabetes, they used a z-score point estimate and differences in variance in the APCs mortality rates. They conducted a sensitivity analysis to examine the different trends in mortality among the total population in each study. They used approximately 36 paper studies from 17 different countries.

The comprehensive data that they gathered was that the Europid population had a higher decrease in mortality rate from 1990-1999 (75% [9/12] vs. 14% [1/7], p = 0.01), but not statistically different from 2000-2016 (78% [28/36] vs. 57% [4/7], p = 0.25), compared to the non-Europid population. A few US studies among different ethnic groups had shown a decrease in the mortality rate for patients with diabetes in Europids but not in African Americans or Hispanics. Another promising dataset showed a declining mortality rate in men (75%) compared to women (69%) from 2000-2016. Since 2000, 80% of patients with diabetes with a European background showed a decrease in mortality, but only a 57% decrease in the non-Europid population. Another critical dataset showed a higher decrease in mortality for patients with diabetes compared to patients without diabetes.

Looking at this data is very promising for the new generation of diabetes patients. Some factors that might have contributed to this decrease in mortality for patients with diabetes could be aggressive management with anti-hypertensive drugs or statins, or even following a better lifestyle. The researchers noted the lack of decline in the mortality rate for the non-Europid population, and that could be because of limited access to healthcare facilities and lower socioeconomic status compared to the Europid population, which has more resources available. This study was eye-opening as one of its strengths was the data gathered over 29 years. One of the weaknesses was that they didn’t gather much data on the younger population and focused more on older ages.  

Practice Pearls: 

  • Knowing the risk factors that contribute to mortality in patients with diabetes can be crucial in preventing it. 
  • The Europid population had shown a higher decrease in mortality, about 80%, compared to the non-Europid population, only 57%. 
  • Another dataset showed a higher decline in mortality rate in men (75%) compared to women (69%). 


Chen, L., Islam, R.M., Wang, J. et al. “A systematic review of trends in all-cause mortality among people with diabetes.” Diabetologia 63, July 6. 2020. Web. August 19, 2020.  https://doi.org/10.1007/s00125-020-05199-0 

Abilash R., Charulatha B.S. et al. “Early Detection of Diabetes from Daily Routine Activities: Predictive Modeling Based on Machine Learning Techniques.” Intelligence in Big Data Technologies—Beyond the Hype. Advances in Intelligent Systems and Computing, vol 1167. Springer, Singapore. July 26. 2020. Web. August 19, 2020.  https://doi.org/10.1007/978-981-15-5285-4_10       


Joan Prifti, PharmD. Candidate, LECOM College of Pharmacy