A socio-ecological model of injury mortality in Texas using Bayesian modelsCorey Sparks and Susanne SchmidtDepartment of DemographyThe University of Texas at San Antonio

Introduction•Injury mortality has been recognized as a pressing public health concern, due its high number of years of potential life lost.•Investigating injuries has been noted to be particularly beneficial because prevention measures may have a more immediate effect both on health and economic cost reduction than compared to efforts to reduce chronic conditions.•In this analysis, we consider a socio-ecological model of injury mortality using county level data from the state of Texas between 2000 and 2009•Bayesian hierarchical models are used to examine spatial and spatio-temporal dynamics of injury risk•This research can provide insights into what features constitute areas high in injury risk and can help policy makers identify areas for injury prevention measures.

Data•The data for this paper consist of all deaths in the state of Texas between 2000 and 2009, geocoded to the county of residence. These are provided by the Texas Department of Health Services.

•Environmental variables include land use mix, violent crime rates, and alcohol availability.

•Social/Human factors include county level socioeconomic deprivation and metropolitan residence

•Healthcare access is measured as whether a county is classified as a medically underserved area and number of ER doctors per capita.

Methodology•Bayesian hierarchical models are used•Models are specified as a generalized linear mixed model, assuming a binomial distribution to the mortality rate for each county and a structured space-time interaction:

•With vague Normal priors on regression effects, and vague Gamma priors on all random effect precisions•The Integrated Nested Laplace Approximation (INLA) approach is used to calculate the posterior density•Posterior 95% credible intervals are used to summarize regression effects•Exceedence probabilities are used to visualize spatio-temporal risk

Conceptual Socio-Ecological Model

•Injuries as a cause of death can be divided into human and environmental causes.

•Generally, this means that injuries neither occur in a social nor in a physical vacuum but instead in a complex context.

•For injury risk as the health outcome of interest, local, or neighborhood factors are often influential in injury risk.

• Socioeconomic factors

• Behavioral factorsHuman Causes

• Crime• Alcohol availability• Land Use MixEnvironmental

Causes

• Medically underserved area• ER doctors per capitaHealthcare

Access

Injury Mortality

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Results

Parameter Model 1 Model 2 Model 3Socioeconomic Deprivation

Metro County

1.00 (0.95 – 1.05)

0.94 (0.91 – 0.97)

-

-

1.00(0.95 – 1.06)

0.97(0.94 – 1.01)

Violent Crime Rate

Alcohol Availability

Land Use Mix

-

-

-

0.99(0.96 – 1.03)

1.10 (1.04 – 1.16)

0.94(0.90 – 0.97)

0.99(0.96 – 1.03)

1.09(1.03 – 1.15)

0.95(0.91 – 0.99)

Medically Underserved Area

ER Doctors per capita

-

-

-

-

1.02(0.98 – 1.05)

0.98(0.95 – 1.02)

Hyperparametersτvτuτγ

0.0950.0230.001

0.0990.0190.001

0.1110.0150.001

DIC 13,101.2 13,098.7 13,101.2

Bayesian Risk Estimates, Exceedence Probabilities

Discussion•Results show that medical access medicates a nonmetro disadvantage in risk

•Counties with higher alcohol availability and a lower land use mixture have consistently higher injury mortality risk

•This points to environmental effects as being more important influences on injury mortality in this context

•When spatio-temporal exceedence probabilities are examined, consistent clusters of high risk are found in areas of east Texas, northeast of Houston