Statistical Methodology:

• Quick, H. and Song, G. (2023+). "Reliable event rates for disease mapping." Accepted for publication in the Journal of Official Statistics. [arXiv]

  • Establishes a definition of "reliability" for model-based estimates of event rates and establishes a rationale for restricting spatial models like those in Quick et al. (2021) and Song et al. (2021+).

• Song, G., Tabb, L.P., and Quick, H. (2021+).  "Geographic and racial disparities in the incidence of low birthweight in Pennsylvania." Under revision. [arXiv]

  • Extends the work of Quick et al. (2021) from Poisson to binomially distributed count data.

• Quick, H., Song, G., and Tabb, L.P. (2021). "Evaluating the informativeness of the Besag-York-Mollie CAR model." Spatial Spatio-temporal Epidemiol., 37, 100420. [sste]

  • Compares the commonly used BYM CAR model to the simple conjugate Poisson-gamma setting to quantify the amount of information contributed by the CAR model framework.  Not only does this paper demonstrate that the BYM CAR model framework often corresponds to an overly informative model – or more specifically, that the CAR model often contributes more information than the data – but it also provides a mechanism for restricting the model's informativeness to avoid oversmoothing.

• Quick, H., Waller, L.A., and Casper, M. (2018a). “A multivariate space-time model for analyzing county-level heart disease death rates by race and sex.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 67, 291-304. [jrss-c]

  • Develops a nonseparable, multivariate space-time CAR model for the purpose of analyzing 38 years of county-level heart disease death rates by race (white and black) and sex.  The "nonseparable" aspect of the model is due to the presence of 4 group-specific temporal correlation parameters (i.e., one for each race/sex combination) and the temporal evolution of the 4 x 4 covariance matrices in the spatiotemporal process.

• Quick, H., Waller, L.A., and Casper, M. (2017). “Multivariate spatiotemporal modeling of age-specific stroke mortality.” Ann. Appl. Stat, 11, 2170-2182. [aoas]

  • Embeds the MSTCAR model of Quick et al. (2018a) inside a Poisson regression to model the number of stroke deaths (by age) in US counties between 1973 and 2013.

• Quick, H., Carlin, B.P., and Banerjee, S. (2015b).  “Heteroscedastic conditional auto-regression models for areally referenced temporal processes for analysing California asthma hospitalization data.” J. Roy. Statist. Soc., Ser. C (Applied Statistics), 64, 799-813. [jrss-c]

  • Extends the work of Quick et al. (2013) to allow for region-specific variance parameters in the spatiotemporal process and proposes a diagnostic for identifying regions which may be spatial outliers (i.e., regions whose data is dissimilar from their neighbors).

• Quick, H., Banerjee, S., and Carlin, B.P. (2015a).  “Bayesian modeling and analysis for gradients in spatiotemporal processes.” Biometrics, 71, 575-584. [biom]

  • Develops the theoretical framework for spatiotemporal gradient modeling.  In particular, this framework allows for inference on spatial gradients (i.e., sudden changes in the residual surface over space), temporal gradients (changes over time), and spatiotemporal mixed gradients (changes in space/time) for an arbitrary spatial location at an arbitrary point in time.  The method is then illustrated using an analysis of air quality data from California, where the gradient process detects sharp changes in the residual surface coinciding with wildfires in Northern California.

• Quick, H., Groth, C., Banerjee, S., Carlin, B.P., Stenzel, M.R., Stewart, P.A., Sandler, D.P., Engel, L.S., and Kwok, R.K. (2014). “Exploration of the use of Bayesian modeling of gradients for censored spatiotemporal data from the Deepwater Horizon oil spill.” Spatial Statistics, 9, 166-179. [spasta]

  • Extends the work of Quick et al. (2015a) to estimate the spatiotemporal gradient process in the presence of censored data.

• Quick, H., Banerjee, S., and Carlin, B.P. (2013). “Modeling temporal gradients in regionally aggregated California asthma hospitalization data.” Ann. Appl. Stat, 7, 154-176. [aoas]

  • Develops the theoretical framework for spatially-referenced temporal gradient modeling.  More specifically, this framework permits assessing temporal gradients (i.e., changes over time) in the residuals for any given spatial region.

Applications of spatial statistics to data privacy:

• Quick, H. and Waller, L.A. (2018).  "Using spatiotemporal models to generate synthetic data for public use." Spatial Spatio-temporal Epidemiol., 27, 37-45. [sste]

• Quick, H., Holan, S.H., and Wikle, C.K. (2018b). “Generating partially synthetic geocoded public use data with decreased disclosure risk using differential smoothing.” J. Roy. Statist. Soc., Ser. A (Statistics in Society), 181, 649-661. [jrss-a]

• Quick, H., Holan, S.H., and Wikle, C.K. (2015d). “Zeros and ones: A case for suppressing zeros in sensitive count data with an application to stroke mortality.” Stat, 4, 227-234. [stat]

  • Illustrates the risk of not suppressing zeros in sensitive count data by modeling county-level stroke mortality data using a CAR model.

  • See the Disclosure Limitation page for a more thorough summary of this work.

• Quick, H., Holan, S.H., Wikle, C.K., and Reiter, J.P. (2015c). “Bayesian marked point process modeling for generating fully synthetic public use data with point-referenced geography.” Spatial Statistics, 14, 439-451. [spasta]

  • Uses marked point process models to generate fully synthetic data for public use which preserve the spatial structure in the original, sensitive data.

  • See the Disclosure Limitation page for a more thorough summary of this work.

Epidemiologic applications of existing methods:

• Quick, H., Terloyeva, D., Wu, Y., Moore, K., and Diez Roux, A.V. (2020). "Trends in tract-level prevalence of obesity in Philadelphia by race, space, and time." Epidemiology, 31, 15-21. [epi]

• Quick, H. (2019). "Estimating county-level mortality rates using highly censored data from CDC WONDER.''  Prev. Chronic Dis., 16:180441. [pcd] 

• Schinasi, L.H., Quick, H., Clougherty, J.E., and De Roos, A.J. (2019). "Green space and infant mortality in Philadelphia, PA." J. Urban Health, 96, 497-506. [urban]

• Quick, H., Tootoo, J., Li, R., Vaughan, A., Schieb, L., Casper, M., and Miranda, M.L. (2019). "The Rate Stabilizing Tool: Generating stable local-level measures of chronic disease."  Prev. Chronic Dis., 16:180442. [pcd]

• Vaughan, A., Quick, H., Schieb, L., Kramer, M., Taylor, H., and Casper, M. (2019). "Changing rate orders of race-gender heart disease death rates: An exploration of county-level race-gender disparities." SSM -- Population Health, 7, 100334. [ssmph]

• Vaughan, A., Schieb, L., Quick, H., Kramer, M.R., and Casper, M. (2018). "Before the here and now: What can we learn from variation in spatiotemporal patterns of changing heart disease mortality by age group, time period, and birth cohort?" Soc. Sci. Med., 217, 97-105. [socscimed]

• Tabb, L.P., McClure, L.A., Quick, H., Purtle, J., and Diez Roux, A.V. (2018). "Assessing the spatial heterogeneity in overall health across the United States using spatial regression methods: The contribution of health factors and county-level demographics." Health & Place, 51, 68-77. [healthplace]

• Casper, M., Kramer, M., Quick, H., Schieb, L., Vaughan, A.S., and Greer, S. (2016). “Changes in the geographic patterns of heart disease mortality in the United States 1973 to 2010.” Circulation, 133, 1171-1180. [circ]

  • Applies a univariate version of the method from Quick et al. (2018a) to analyze 38 years of age-adjusted county-level heart disease death rates from the total population.

• Vaughan, A.S., Quick, H., Pathak, E., Kramer, M., and Casper, M. (2015). “Disparities in temporal and geographic patterns of declining heart disease mortality by race and sex in the United States, 1973--2010.” Journal of the American Heart Association, 4, doi: 10.1161/JAHA.115.002567. [jaha]

• Erickson, D.J., Carlin, B.P., Lenk, K.M., Quick, H.S., Harwood, E.M., and Toomey, T.L. (2015). “Do neighborhood attributes moderate the relationship between alcohol establishment density and crime?" Prevention Science, 16, 254-264.

• Toomey, T.L., Erickson, D.J., Carlin, B.P., Lenk, K.M., Quick, H.S., Jones, A.M., and Harwood, E.M. (2012b). “The association between density of alcohol establishments and violent crime within urban neighborhoods."  Alcoholism: Clinical and Experimental Research, 36, 1468-1473.

• Toomey, T.L., Erickson, D.J., Carlin, B.P., Quick, H.S., Harwood, E.M., Lenk, K.M., and Ecklund, A.M. (2012a). “Is the density of alcohol establishments related to non-violent crime?" Journal of Studies on Alcohol and Drugs, 73, 21-25.