Abstract
Morbidities generally show patterns of concentration that vary by space and time. Disease mapping models are useful in estimating the spatiotemporal patterns of disease risks and are therefore pivotal for effective disease surveillance, resource allocation, and the development of prevention strategies. This study considers six spatiotemporal Bayesian hierarchical models based on two spatial conditional autoregressive priors. It could serve as a guideline on the development and application of Bayesian hierarchical models to assess the emerging risk trends, risk clustering, and spatial inequality trends, with estimation of covariables’ effects on the interested disease risk. The method is applied to the Florida Birth Record data between 2006 and 2015 to study two cardiovascular risk factors: preeclampsia and gestational diabetes. High-risk clusters were detected in North Central Florida for preeclampsia and in Central Florida for gestational diabetes. While the adjusted disease trend was stable, spatial inequality peaked in 2011–2012 for both diseases. Exposure to PM2.5 at first or/and second trimester increased the risk of preeclampsia and gestational diabetes, but the magnitude is less severe compared to previous studies. In conclusion, this study underscores the significance of selecting appropriate disease mapping models in estimating the intricate spatiotemporal patterns of disease risk and suggests the importance of localized interventions to reduce health disparities. The result also identified an opportunity to study potential risk factors of preeclampsia, as the spike of risk in North Central Florida cannot be explained by current covariables.
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Data availability
Data use approval is needed from the Florida Department of Health.
Abbreviations
- AR1:
-
first order autoregressive
- BHM:
-
Bayesian hierarchical model
- BMI:
-
body mass index
- CAR:
-
conditional autoregressive
- DIC:
-
deviance information criterion
- GDM:
-
gestational diabetes mellitus
- IQR:
-
interquartile range
- LISA:
-
local indicators of spatial association
- PE:
-
preeclampsia
- SD:
-
standard deviation
- SIR:
-
standardized incidence ratio
- WAIC:
-
widely applicable information criterion
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Acknowledgements
The authors would like to thank the FL-DOH for providing the data and acknowledge that the findings and conclusions are those of the authors and do not necessarily represent the official position of the Florida Department of Health.
Funding
This work is supported by The National Heart Lung and Blood Institute (Grant number: K01 HL146944).
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Conceptualization, B.I.; methodology, N.S., B.I., S.D.; formal analysis, N.S.; resources, B.I.; writing—original draft preparation, N.S.; writing—review and editing, B.I., R.L, Z.B., I.D.. All authors have read and agreed to the published version of the manuscript.
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Sun, N., Bursac, Z., Dryden, I. et al. Bayesian spatiotemporal modelling for disease mapping: an application to preeclampsia and gestational diabetes in Florida, United States. Environ Sci Pollut Res 30, 109283–109298 (2023). https://doi.org/10.1007/s11356-023-29953-0
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DOI: https://doi.org/10.1007/s11356-023-29953-0