  Importance: Following a century of increase, life expectancy in the United
States has stagnated and begun to decline in recent decades. Using satellite
images and street view images prior work has demonstrated associations of the
built environment with income, education, access to care and health factors
such as obesity. However, assessment of learned image feature relationships
with variation in crude mortality rate across the United States has been
lacking.
  Objective: Investigate prediction of county-level mortality rates in the U.S.
using satellite images.
  Design: Satellite images were extracted with the Google Static Maps
application programming interface for 430 counties representing approximately
68.9% of the US population. A convolutional neural network was trained using
crude mortality rates for each county in 2015 to predict mortality. Learned
image features were interpreted using Shapley Additive Feature Explanations,
clustered, and compared to mortality and its associated covariate predictors.
  Main Outcomes and Measures: County mortality was predicted using satellite
images.
  Results: Predicted mortality from satellite images in a held-out test set of
counties was strongly correlated to the true crude mortality rate (Pearson
r=0.72). Learned image features were clustered, and we identified 10 clusters
that were associated with education, income, geographical region, race and age.
  Conclusion and Relevance: The application of deep learning techniques to
remotely-sensed features of the built environment can serve as a useful
predictor of mortality in the United States. Tools that are able to identify
image features associated with health-related outcomes can inform targeted
public health interventions.
