AIMC Topic: Residence Characteristics

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Evaluating the performance of a predictive modeling approach to identifying members at high-risk of hospitalization.

Journal of medical economics
To evaluate the risk-of-hospitalization (ROH) models developed at Blue Cross Blue Shield of Louisiana (BCBSLA) and compare this approach to the DxCG risk-score algorithms utilized by many health plans. Time zero for this study was December 31, 2016....

Alcohol outlets and firearm violence: a place-based case-control study using satellite imagery and machine learning.

Injury prevention : journal of the International Society for Child and Adolescent Injury Prevention
INTRODUCTION: This article proposes a novel method for matching places based on visual similarity, using high-resolution satellite imagery and machine learning. This approach strengthens comparisons when the built environment is a potential confounde...

Using street view data and machine learning to assess how perception of neighborhood safety influences urban residents' mental health.

Health & place
Previous studies have shown that perceptions of neighborhood safety are associated with various mental health outcomes. However, scant attention has been paid to the mediating pathways by which perception of neighborhood safety affects mental health....

The linkage between the perception of neighbourhood and physical activity in Guangzhou, China: using street view imagery with deep learning techniques.

International journal of health geographics
BACKGROUND: Neighbourhood environment characteristics have been found to be associated with residents' willingness to conduct physical activity (PA). Traditional methods to assess perceived neighbourhood environment characteristics are often subjecti...

Value of Neighborhood Socioeconomic Status in Predicting Risk of Outcomes in Studies That Use Electronic Health Record Data.

JAMA network open
IMPORTANCE: Data from electronic health records (EHRs) are increasingly used for risk prediction. However, EHRs do not reliably collect sociodemographic and neighborhood information, which has been shown to be associated with health. The added contri...

Use of Deep Learning to Examine the Association of the Built Environment With Prevalence of Neighborhood Adult Obesity.

JAMA network open
IMPORTANCE: More than one-third of the adult population in the United States is obese. Obesity has been linked to factors such as genetics, diet, physical activity, and the environment. However, evidence indicating associations between the built envi...

The Association of Urban Greenness and Walking Behavior: Using Google Street View and Deep Learning Techniques to Estimate Residents' Exposure to Urban Greenness.

International journal of environmental research and public health
Many studies have established that urban greenness is associated with better health outcomes. Yet most studies assess urban greenness with overhead-view measures, such as park area or tree count, which often differs from the amount of greenness perce...

Residential scene classification for gridded population sampling in developing countries using deep convolutional neural networks on satellite imagery.

International journal of health geographics
BACKGROUND: Conducting surveys in low- and middle-income countries is often challenging because many areas lack a complete sampling frame, have outdated census information, or have limited data available for designing and selecting a representative s...

Estimating the Causal Impact of Proximity to Gold and Copper Mines on Respiratory Diseases in Chilean Children: An Application of Targeted Maximum Likelihood Estimation.

International journal of environmental research and public health
In a town located in a desert area of Northern Chile, gold and copper open-pit mining is carried out involving explosive processes. These processes are associated with increased dust exposure, which might affect children's respiratory health. Therefo...

Racial/Ethnic Disparities in Rates of Traumatic Injury in Arizona, 2011-2012.

Public health reports (Washington, D.C. : 1974)
OBJECTIVE: The purpose of this study was to compare the rates of traumatic injury among five racial/ethnic groups in Arizona and to identify which mechanisms and intents of traumatic injury were predominant in each group.