title: Environmental Predictors of US County Mortality Patterns on a National Basis.
abstract: A growing body of evidence has found that mortality rates are positively correlated with social inequalities, air pollution, elevated ambient temperature, availability of medical care and other factors. This study develops a model to predict the mortality rates for different diseases by county across the US. The model is applied to predict changes in mortality caused by changing environmental factors. A total of 3,110 counties in the US, excluding Alaska and Hawaii, were studied. A subset of 519 counties from the 3,110 counties was chosen by using systematic random sampling and these samples were used to validate the model. Step-wise and linear regression analyses were used to estimate the ability of environmental pollutants, socio-economic factors and other factors to explain variations in county-specific mortality rates for cardiovascular diseases, cancers, chronic obstructive pulmonary disease (COPD), all causes combined and lifespan across five population density groups. The estimated models fit adequately for all mortality outcomes for all population density groups and, adequately predicted risks for the 519 validation counties. This study suggests that, at local county levels, average ozone (0.07 ppm) is the most important environmental predictor of mortality. The analysis also illustrates the complex inter-relationships of multiple factors that influence mortality and lifespan, and suggests the need for a better understanding of the pathways through which these factors, mortality, and lifespan are related at the community level.
authors.affiliation: Environmental Sciences Program, Southern Illinois University Edwardsville, Edwardsville, IL, 62026, 2, Independent Researcher and Journalist, Tucson, AZ, 85736, United States of America.
authors.forename: Melissa P L
authors.initials: MP
authors.lastname: Chan
authors.affiliation: Independent Researcher and Journalist, Colorado City, CO, 81019, United States of America.
authors.forename: Robert S
authors.initials: RS
authors.lastname: Weinhold
authors.affiliation: School of Public Health, University of California, Berkeley, CA, 85736, United States of America.
authors.forename: Reuben
authors.initials: R
authors.lastname: Thomas
authors.affiliation: School of Public Health, University of Alabama, Birmingham, AL, 35294, United States of America.
authors.forename: Julia M
authors.initials: JM
authors.lastname: Gohlke
authors.affiliation: National Center for Environmental Health and Agency for Toxic Substances and Disease Registry, U.S. Centers for Disease and Prevention, Atlanta, GA 30341, United States of America.
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: Portier
mesh_terms: D000293:Adolescent; D000328:Adult; D000393:Air Pollutants / Q000009:adverse effects*; D002318:Cardiovascular Diseases / Q000209:etiology / Q000401:mortality*; D004781:Environmental Exposure / Q000009:adverse effects*; D004784:Environmental Monitoring; D005260:Female; D006801:Humans; D008017:Life Expectancy / Q000639:trends*; D008297:Male; D008875:Middle Aged; D008962:Models, Theoretical; D009026:Mortality / Q000639:trends*; D012140:Respiratory Tract Diseases / Q000209:etiology / Q000401:mortality*; D018570:Risk Assessment; D012621:Seasons; D012959:Socioeconomic Factors; D013696:Temperature; D014481:United States; D055815:Young Adult
chemical_list: D000393:Air Pollutants