Extending Income Measurement and Modeling Options for Injury Epidemiology

Janene Rae Brown

doi:10.17918/00000906

Fatal intentional and unintentional injuries significantly impact victims in years of life lost and can traumatize victims' families and communities. Non-fatal injuries, such as falls, can result in expensive healthcare costs associated with post-fall rehabilitation and treating associated injuries,1 and there remains a significant racial disparity in rates of fatal motor vehicle accidents, and a lingering racial disparity in homicides, despite decreases in nationwide rates. Income and elements of the built environment in neighborhoods also influence health, including fatal injuries, and further existing health associated inequities. The proposed research study will explore the association between income with injury outcomes across a set of refinements. Chapter 2 will distinguish fatal injury risks' association with gross and disposable household income, adjusting for neighborhood confounders beyond area-level income. Chapter 3 will explore source specific income inequality as ranked from most exacerbating to most reducing of inequality and their association with risk for fatal injuries. Chapter 4 analyses will allow for the slope between household income and non-fatal injury risk to change at an empirically determined joinpoint. In this thesis, the 2008 American Community Survey (ACS) and the REasons for Geographic and Racial Differences in Stroke (REGARDS) cohort studies will provide income and demographic data. ACS data have been individually linked to National Death Index (NDI) data through the Mortality Disparities in American Communities (MDAC) effort to identify decedents and causes of death from fatal injuries. REGARDS will provide data related to non-fatal falls injuries. The Retail Environment and Cardiovascular Disease (RECVD) database will provide neighborhood business data at the census tract spatial unit linked to both data sources. In Chapter 2, we found that individual level demographics and neighborhood characteristics partially attenuated the association between household income and risk of injuries however, disposable household income predicted cause-specific mortality more precisely compared to gross household income. In Chapter 3 we found that wage-based income sources were associated with inequality exacerbation, and there was some evidence of effect modification by income source in that inequality exacerbating sources were generally associated with injury risk reduction and inequality reducing sources were generally associated with injury risk exacerbation. Chapter 4 showed that there was evidence of a two-segment non-linear association between past-year household income and recurrent non-fatal falls risk and a significant threshold effect above which risk of falls was attenuated. Results from this thesis provide additional relevance to the consideration of income associated with health and injuries. It points to considering income source as it is associated with income inequality and health including risk for injuries. It also sheds light on the potential non-linear shape of the association between income and falls, to help reduce the costly burden of non-fatal falls among older adults.

Extending Income Measurement and Modeling Options for Injury Epidemiology

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Extending Income Measurement and Modeling Options for Injury Epidemiology

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