PMID- 38222066
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20240215
IS  - 0035-9254 (Print)
IS  - 1467-9876 (Electronic)
IS  - 0035-9254 (Linking)
VI  - 73
IP  - 1
DP  - 2024 Jan
TI  - Bayesian kernel machine regression for count data: modelling the association 
      between social vulnerability and COVID-19 deaths in South Carolina.
PG  - 257-274
LID - 10.1093/jrsssc/qlad094 [doi]
AB  - The COVID-19 pandemic created an unprecedented global health crisis. Recent 
      studies suggest that socially vulnerable communities were disproportionately 
      impacted, although findings are mixed. To quantify social vulnerability in the 
      US, many studies rely on the Social Vulnerability Index (SVI), a county-level 
      measure comprising 15 census variables. Typically, the SVI is modelled in an 
      additive manner, which may obscure non-linear or interactive associations, 
      further contributing to inconsistent findings. As a more robust alternative, we 
      propose a negative binomial Bayesian kernel machine regression (BKMR) model to 
      investigate dynamic associations between social vulnerability and COVID-19 death 
      rates, thus extending BKMR to the count data setting. The model produces a 
      'vulnerability effect' that quantifies the impact of vulnerability on COVID-19 
      death rates in each county. The method can also identify the relative importance 
      of various SVI variables and make future predictions as county vulnerability 
      profiles evolve. To capture spatio-temporal heterogeneity, the model incorporates 
      spatial effects, county-level covariates, and smooth temporal functions. For 
      Bayesian computation, we propose a tractable data-augmented Gibbs sampler. We 
      conduct a simulation study to highlight the approach and apply the method to a 
      study of COVID-19 deaths in the US state of South Carolina during the 2021 
      calendar year.
CI  - (c) The Royal Statistical Society 2023. All rights reserved. For permissions, 
      please e-mail: journals.permissions@oup.com.
FAU - Mutiso, Fedelis
AU  - Mutiso F
AD  - Division of Biostatistics, Department of Public Health Sciences, Medical 
      University of South Carolina, Charleston, SC, USA.
FAU - Li, Hong
AU  - Li H
AD  - Division of Biostatistics, Department of Public Health Sciences, University of 
      California, Davis, CA, USA.
FAU - Pearce, John L
AU  - Pearce JL
AD  - Division of Environmental Health, Department of Public Health Sciences, Medical 
      University of South Carolina, Charleston, SC, USA.
FAU - Benjamin-Neelon, Sara E
AU  - Benjamin-Neelon SE
AD  - Department of Health, Behavior and Society, Johns Hopkins Bloomberg School of 
      Public Health, Baltimore, MD, USA.
FAU - Mueller, Noel T
AU  - Mueller NT
AD  - Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, 
      Baltimore, MD, USA.
FAU - Neelon, Brian
AU  - Neelon B
AD  - Division of Biostatistics, Department of Public Health Sciences, Medical 
      University of South Carolina, Charleston, SC, USA.
LA  - eng
GR  - R21 LM012866/LM/NLM NIH HHS/United States
GR  - P30 CA138313/CA/NCI NIH HHS/United States
GR  - P30 AR072582/AR/NIAMS NIH HHS/United States
GR  - R21 MD016947/MD/NIMHD NIH HHS/United States
GR  - UL1 TR001450/TR/NCATS NIH HHS/United States
PT  - Journal Article
DEP - 20231103
PL  - England
TA  - J R Stat Soc Ser C Appl Stat
JT  - Journal of the Royal Statistical Society. Series C, Applied statistics
JID - 101086541
PMC - PMC10782459
OTO - NOTNLM
OT  - B-splines
OT  - Gaussian process
OT  - Polya-Gamma distribution
OT  - conditionally autoregressive prior
OT  - negative binomial
OT  - spatial confounding
COIS- Conflict of interest None declared.
EDAT- 2024/01/15 06:42
MHDA- 2024/01/15 06:43
PMCR- 2024/11/03
CRDT- 2024/01/15 04:28
PHST- 2022/07/21 00:00 [received]
PHST- 2023/04/24 00:00 [revised]
PHST- 2023/09/19 00:00 [accepted]
PHST- 2024/11/03 00:00 [pmc-release]
PHST- 2024/01/15 06:43 [medline]
PHST- 2024/01/15 06:42 [pubmed]
PHST- 2024/01/15 04:28 [entrez]
AID - qlad094 [pii]
AID - 10.1093/jrsssc/qlad094 [doi]
PST - epublish
SO  - J R Stat Soc Ser C Appl Stat. 2023 Nov 3;73(1):257-274. doi: 
      10.1093/jrsssc/qlad094. eCollection 2024 Jan.