Influence of geospatial resolution on sociodemographic predictors of COVID-19 in Massachusetts.
COVID-19
Mixed-effect modeling
Regression analysis
Spatial resolution
Journal
Annals of epidemiology
ISSN: 1873-2585
Titre abrégé: Ann Epidemiol
Pays: United States
ID NLM: 9100013
Informations de publication
Date de publication:
04 2023
04 2023
Historique:
received:
10
10
2022
revised:
27
01
2023
accepted:
16
02
2023
medline:
28
3
2023
pubmed:
24
2
2023
entrez:
23
2
2023
Statut:
ppublish
Résumé
When studying health risks across a large geographic region such as a state or province, researchers often assume that finer-resolution data on health outcomes and risk factors will improve inferences by avoiding ecological bias and other issues associated with geographic aggregation. However, coarser-resolution data (e.g., at the town or county-level) are more commonly publicly available and packaged for easier access, allowing for rapid analyses. The advantages and limitations of using finer-resolution data, which may improve precision at the cost of time spent gaining access and processing data, have not been considered in detail to date. We systematically examine the implications of conducting town-level mixed-effect regression analyses versus census-tract-level analyses to study sociodemographic predictors of COVID-19 in Massachusetts. In a series of negative binomial regressions, we vary the spatial resolution of the outcome, the resolution of variable selection, and the resolution of the random effect to allow for more direct comparison across models. We find stability in some estimates across scenarios, changes in magnitude, direction, and significance in others, and tighter confidence intervals on the census-tract level. Conclusions regarding sociodemographic predictors are robust when regions of high concentration remain consistent across town and census-tract resolutions. Inferences about high-risk populations may be misleading if derived from town- or county-resolution data, especially for covariates that capture small subgroups (e.g., small racial minority populations) or are geographically concentrated or skewed (e.g., % college students). Our analysis can help inform more rapid and efficient use of public health data by identifying when finer-resolution data are truly most informative, or when coarser-resolution data may be misleading.
Identifiants
pubmed: 36822278
pii: S1047-2797(23)00038-8
doi: 10.1016/j.annepidem.2023.02.007
pmc: PMC9942453
pii:
doi:
Types de publication
Journal Article
Research Support, N.I.H., Extramural
Research Support, Non-U.S. Gov't
Langues
eng
Sous-ensembles de citation
IM
Pagination
62-68.e3Subventions
Organisme : NIMHD NIH HHS
ID : P50 MD010428
Pays : United States
Organisme : NIEHS NIH HHS
ID : T32 ES014562
Pays : United States
Informations de copyright
Copyright © 2023 The Authors. Published by Elsevier Inc. All rights reserved.
Références
BMC Infect Dis. 2021 Jul 16;21(1):686
pubmed: 34271870
Lancet Infect Dis. 2022 Jan;22(1):43-55
pubmed: 34480857
N Engl J Med. 2017 Jun 29;376(26):2513-2522
pubmed: 28657878
Environ Res. 2018 Feb;161:76-86
pubmed: 29101831
Am J Epidemiol. 2006 Sep 15;164(6):586-90
pubmed: 16893922
J Public Health Manag Pract. 2021 Jan/Feb;27 Suppl 1, COVID-19 and Public Health: Looking Back, Moving For:S43-S56
pubmed: 32956299
Am Stat. 2022;76(2):142-151
pubmed: 35531350
Influenza Other Respir Viruses. 2022 Mar;16(2):213-221
pubmed: 34761531
Health Serv Res. 2019 Aug;54(4):860-869
pubmed: 30937888
Ann Intern Med. 2021 May;174(5):649-654
pubmed: 33513035
Nature. 2020 Aug;584(7821):430-436
pubmed: 32640463
NAM Perspect. 2021 Apr 07;2021:
pubmed: 34532688