Dataset Information

Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach.

ABSTRACT: While there is a literature on the distribution of food stores across geographic and social space, much of this research uses cross-sectional data. Analyses attempting to understand whether the availability of stores across neighborhoods is associated with diet and/or health outcomes are limited by a lack of understanding of factors that shape the emergence of new stores and the closure of others. We used quarterly data on supermarket and convenience store locations spanning seven years (2006-2012) and tract-level census data in four US cities: Birmingham, Alabama; Chicago, Illinois; Minneapolis, Minnesota; San Francisco, California. A spatial discrete-time survival model was used to identify factors associated with an earlier and/or later closure time of a store. Sales volume was typically the strongest indicator of store survival. We identified heterogeneity in the association between tract-level poverty and racial composition with respect to store survival. Stores in high poverty, non-White tracts were often at a disadvantage in terms of survival length. The observed patterns of store survival varied by some of the same neighborhood sociodemographic factors associated with lifestyle and health outcomes, which could lead to confusion in interpretation in studies of the estimated effects of introduction of food stores into neighborhoods on health.

SUBMITTER: Warren JL

PROVIDER: S-EPMC5990034 | biostudies-literature | 2018 Jun

REPOSITORIES: biostudies-literature

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Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach.

Warren Joshua L JL Gordon-Larsen Penny P

Journal of the Royal Statistical Society. Series A, (Statistics in Society) 20171022 3

While there is a literature on the distribution of food stores across geographic and social space, much of this research uses cross-sectional data. Analyses attempting to understand whether the availability of stores across neighborhoods is associated with diet and/or health outcomes are limited by a lack of understanding of factors that shape the emergence of new stores and the closure of others. We used quarterly data on supermarket and convenience store locations spanning seven years (2006-20 ...[more]

PMID: 29887675

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Project description:IntroductionTo inform tobacco policy and prevention efforts, we examined youth and young adult behaviors at gas-station-convenience stores and whether these behaviors varied by demographics or tobacco use.Aims and methodsBetween June and July 2017, we conducted 990 intercept surveys at convenience stores in North Carolina among 16- to 25-year olds who were susceptible to or used tobacco. We used logistic regression to examine whether demographics or tobacco use predicted pumping gas, entering the store, or purchasing tobacco, gas, food, lottery tickets, or other items inside the store.ResultsMost participants (85.3%) reported ever using tobacco. Most visited that store at least once per week (40.3%) or once within the past month (41.2%). Just over half (55.0%) reported pumping gas at the store. Of those who went or planned to go inside (68.8%), 43.2% purchased food, 39.2% purchased gas, 33.1% purchased tobacco, and 6.8% purchased lottery tickets. Those who were aged 21-25, visited the store two to three times a week, and went inside already were more likely to purchase tobacco (all p < .05). Past 30-day cigarette, cigar, smokeless, and other tobacco uses were positively associated with purchasing tobacco (all p < .05).ConclusionsOur sample of young people who were susceptible to or ever used tobacco regularly visited gas-station-convenience stores, and one-third purchased or planned to purchase tobacco during their visit. Convenience stores appear to be an important access point for young people. Practitioners and policy makers should consider a comprehensive set of strategies to reduce access among youth and young adults.ImplicationsWe conducted 990 intercept surveys among youth and young adults at gas-station-convenience stores. Among our sample of those who had ever used tobacco or were susceptible to use, most visited the store frequently and one-third purchased tobacco, particularly those who used cigarettes and cigars. Tobacco companies heavily market in convenience stores, and our results show tobacco is a product often purchased by susceptible young adults. Practitioners and policy makers should consider prioritizing efforts at convenience stores to reduce product purchase and use.

Project description:BackgroundPrediction models for time-to-event outcomes are commonly used in biomedical research to obtain subject-specific probabilities that aid in making important clinical care decisions. There are several regression and machine learning methods for building these models that have been designed or modified to account for the censoring that occurs in time-to-event data. Discrete-time survival models, which have often been overlooked in the literature, provide an alternative approach for predictive modeling in the presence of censoring with limited loss in predictive accuracy. These models can take advantage of the range of nonparametric machine learning classification algorithms and their available software to predict survival outcomes.MethodsDiscrete-time survival models are applied to a person-period data set to predict the hazard of experiencing the failure event in pre-specified time intervals. This framework allows for any binary classification method to be applied to predict these conditional survival probabilities. Using time-dependent performance metrics that account for censoring, we compare the predictions from parametric and machine learning classification approaches applied within the discrete time-to-event framework to those from continuous-time survival prediction models. We outline the process for training and validating discrete-time prediction models, and demonstrate its application using the open-source R statistical programming environment.ResultsUsing publicly available data sets, we show that some discrete-time prediction models achieve better prediction performance than the continuous-time Cox proportional hazards model. Random survival forests, a machine learning algorithm adapted to survival data, also had improved performance compared to the Cox model, but was sometimes outperformed by the discrete-time approaches. In comparing the binary classification methods in the discrete time-to-event framework, the relative performance of the different methods varied depending on the data set.ConclusionsWe present a guide for developing survival prediction models using discrete-time methods and assessing their predictive performance with the aim of encouraging their use in medical research settings. These methods can be applied to data sets that have continuous time-to-event outcomes and multiple clinical predictors. They can also be extended to accommodate new binary classification algorithms as they become available. We provide R code for fitting discrete-time survival prediction models in a github repository.

Project description:Time series analysis of fMRI data is an important area of medical statistics for neuroimaging data. Spatial models and Bayesian approaches for inference in such models have advantages over more traditional mass univariate approaches; however, a major challenge for such analyses is the required computation. As a result, the neuroimaging community has embraced approximate Bayesian inference based on mean-field variational Bayes (VB) approximations. These approximations are implemented in standard software packages such as the popular statistical parametric mapping software. While computationally efficient, the quality of VB approximations remains unclear even though they are commonly used in the analysis of neuroimaging data. For reliable statistical inference, it is important that these approximations be accurate and that users understand the scenarios under which they may not be accurate. We consider this issue for a particular model that includes spatially varying coefficients. To examine the accuracy of the VB approximation, we derive Hamiltonian Monte Carlo (HMC) for this model and conduct simulation studies to compare its performance with VB in terms of estimation accuracy, posterior variability, the spatial smoothness of estimated images, and computation time. As expected, we find that the computation time required for VB is considerably less than that for HMC. In settings involving a high or moderate signal-to-noise ratio (SNR), we find that the 2 approaches produce very similar results suggesting that the VB approximation is useful in this setting. On the other hand, when one considers a low SNR, substantial differences are found, suggesting that the approximation may not be accurate in such cases and we demonstrate that VB produces Bayes estimators with larger mean squared error. A comparison of the 2 computational approaches in an application examining the hemodynamic response to face perception in addition to a comparison with the traditional mass univariate approach in this application is also considered. Overall, our work clarifies the usefulness of VB for the spatiotemporal analysis of fMRI data, while also pointing out the limitation of VB when the SNR is low and the utility of HMC in this case.

Dataset Information

Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach.

Publications

Factors associated with supermarket and convenience store closure: a discrete time spatial survival modelling approach.

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