This project has two purposes. The first is to develop an online algorithm for Nebraskans to estimate their life expectancy based on their demographics, where they live, body mass index, physical exercises, dietary behavior, and smoking status, which might help with their life planning and making informed decisions to extend life. The second purpose is to increase public awareness of regional and racial disparities in life expectancy in Nebraska and to help direct resources and interventions to the regions and racial groups whereby health care and health promotion resources are most needed for the state to accomplish better health equity.
This project was made possible through a close partnership between the Center for Reducing Health Disparities at the College of Public Health, University of Nebraska Medical Center and The Division of Public Health, Nebraska Department of Health and Human Services.
Dr. Ali S. Khan, Dean of the College of Public Health at the University of Nebraska Medical Center initiated the project idea.
We want to thank Dr. Ge Lin, Professor of Environmental and Occupational Health at the University of Nevada, Las Vegas, and Dr. Qiuming Zhu, Professor of Computer Science from the University of Nebraska-Omaha for their support to the project. We also want to thank Dr. Ali Khan, Dr. Jane Meza, Dr. Mohammad Siahpush, Dr. Christine Arcari, Dr. Brandon Grimm, Dr. Abbie Raikes, Dr. Todd Wyatt, Dr. Shannon Maloney, and Maria Teel from the College of Public Health at UNMC for offering their comments and suggestions for us to improve the app.
Estimates on life expectancy by age groups, gender, race, zip codes, and smoking status were based on pooled mortality data for the period between 2006 and 2015 collected by the Nebraska Department of Health and Human Services Office of Vital Records. The 2010 Census population at the zip code level was used as the population base for life expectancy estimates. The modest population sizes of some zip codes in Nebraska might have impacted the reliability of life expectancy estimates. For zip codes with less than 5,000 residents in 2010, they were combined with nearby zip codes for more reliable estimates. Geographic data on zip codes come from Free Zip Code Map as of 2013.Life expectancy estimates related to body mass index, physical exercise, and dietary behavior were based on reasonable assumptions derived from the following studies:
Whitlock G, Lewington S, Sherliker P, et al; Prospective Studies Collaboration. Body-mass index and cause-specific mortality in 900 000 adults: collaborative analyses of 57 prospective studies. Lancet. 2009;373(9669):1083-1096
Moore SC, Patel AV, Matthews CE, et al. Leisure time physical activity of moderate to vigorous intensity and mortality: a large pooled cohort analysis. PLoS Med. 2012;9(11):e1001335. doi:10.1371/journal.pmed.1001335.
Rogers RG, Powell-Griner E. Life expectancies of cigarette smokers and nonsmokers in the United States. Soc Sci Med, 1991, vol. 32 (pg. 1151-9).
The estimates on life expectancy are based on aggregated analysis of mortality data. Many factors related to longevity such as disease history, economic status, alcohol use, stress, use of illicit drugs, exposure to pollutants, social relationships and so forth have not been considered in these estimates. By no means can these estimates predict exactly how many additional years an individual will live. The suggested life style changes were based on assumptions derived from relevant analyses of aggregated data as reported in previous studies, which might not be uniquely fitting with each and every individual.
Dr. Dejun Su, PhD
Center for Reducing Health Disparities
Department of Health Promotion
College of Public Health
University of Nebraska Medical Center
984340 Nebraska Medical Center
Email : email@example.com