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Article ; Online: Chlorthalidone in Advanced Chronic Kidney Disease - Have We Missed a Trick?

Wheeler, David C

2021 Volume 385, Issue 27, Page(s) 2574–2575

MeSH term(s)	Antihypertensive Agents/therapeutic use ; Chlorthalidone/therapeutic use ; Humans ; Renal Insufficiency, Chronic/drug therapy
Chemical Substances	Antihypertensive Agents ; Chlorthalidone (Q0MQD1073Q)
Language	English
Publishing date	2021-12-29
Publishing country	United States
Document type	Editorial ; Comment
ZDB-ID	207154-x
ISSN	1533-4406 ; 0028-4793
ISSN (online)	1533-4406
ISSN	0028-4793
DOI	10.1056/NEJMe2118149
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Article ; Online: Knot selection for low-rank kriging models of spatial risk in case-control studies.

Boyle, Joseph / Wheeler, David C

Spatial and spatio-temporal epidemiology

2022 Volume 41, Page(s) 100483

Abstract: Many spatial analysis methods have been used to identify potential geographic clusters of disease in case-control studies. Low-rank kriging (LRK) models reduce the computational burden in generalized additive models by using a set of knot locations ... ...

Abstract	Many spatial analysis methods have been used to identify potential geographic clusters of disease in case-control studies. Low-rank kriging (LRK) models reduce the computational burden in generalized additive models by using a set of knot locations instead of the observed subject locations for estimating spatial risk. However, there is little guidance regarding selection of the number and location of the knots in case-control studies. We perform an extensive simulation study that compares a commonly-used method of knot selection in LRK models with two proposed methods and varies the number of knots. We find the commonly-used method is vastly outperformed by those that consider the locations of cases. We find that the Teitz and Bart heuristic allows the highest spatial sensitivity and power to detect zones of elevated risk, and recommend its use with a number of knots as close to the number of case locations as computation time will allow.
MeSH term(s)	Case-Control Studies ; Computer Simulation ; Humans ; Spatial Analysis
Language	English
Publishing date	2022-01-21
Publishing country	Netherlands
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	2515896-X
ISSN	1877-5853 ; 1877-5845
ISSN (online)	1877-5853
ISSN	1877-5845
DOI	10.1016/j.sste.2022.100483
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Article ; Online: Modeling variation in mixture effects over space with a Bayesian spatially varying mixture model.

Boyle, Joseph / Ward, Mary H / Cerhan, James R / Rothman, Nat / Wheeler, David C

Statistics in medicine

2024 Volume 43, Issue 7, Page(s) 1441–1457

Abstract: Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and ... ...

Abstract	Mixture analysis is an emerging statistical tool in epidemiological research that seeks to estimate the health effects associated with mixtures of several exposures. This approach acknowledges that individuals experience many simultaneous exposures and it can estimate the relative importance of components in the mixture. Health effects due to mixtures may vary over space driven by to political, demographic, environmental, or other differences. In such cases, estimating a global mixture effect without accounting for spatial variation would induce bias in effect estimates and potentially lower statistical power. To date, no methods have been developed to estimate spatially varying chemical mixture effects. We developed a Bayesian spatially varying mixture model that estimates spatially varying mixture effects and the importance weights of components in the mixture, while adjusting for covariates. We demonstrate the efficacy of the model through a simulation study that varies the number of mixtures (one and two) and spatial pattern (global, one-dimensional, radial) and magnitude of mixture effects, showing that the model is able to accurately reproduce the spatial pattern of mixture effects across a diverse set of scenarios. Finally, we apply our model to a multi-center case-control study of non-Hodgkin lymphoma (NHL) in Detroit, Iowa, Los Angeles, and Seattle. We identify significant spatially varying positive and inverse associations with NHL for two mixtures of pesticides in Iowa and do not find strong spatial effects at the other three centers. In conclusion, the Bayesian spatially varying mixture model represents a novel method for modeling spatial variation in mixture effects.
MeSH term(s)	Humans ; Case-Control Studies ; Bayes Theorem ; Computer Simulation ; Epidemiologic Studies ; Iowa
Language	English
Publishing date	2024-02-02
Publishing country	England
Document type	Journal Article
ZDB-ID	843037-8
ISSN	1097-0258 ; 0277-6715
ISSN (online)	1097-0258
ISSN	0277-6715
DOI	10.1002/sim.10022
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Article: Hypertension and small vessel disease: do the drugs work?

Nash, Philip S / Simister, Rob J / Wheeler, David C / Werring, David J

British journal of hospital medicine (London, England : 2005)

2023 Volume 84, Issue 9, Page(s) 1–11

Abstract: Associations of hypertension with ischaemic stroke and intracerebral haemorrhage, particularly when attributed to cerebral small vessel disease, are well established. While it seems plausible that treating hypertension should prevent small vessel disease ...

Abstract	Associations of hypertension with ischaemic stroke and intracerebral haemorrhage, particularly when attributed to cerebral small vessel disease, are well established. While it seems plausible that treating hypertension should prevent small vessel disease from developing or progressing, there is limited evidence demonstrating this. This article critically appraises the evidence answering this clinical question. Hypertension is also closely associated with chronic kidney disease, with anatomical and functional similarities between the vasculature of the brain and kidneys leading to the hypothesis that shared multi-system pathophysiological processes may be involved. Therefore, the article also summarises data on prevention of progression of chronic kidney disease. Evidence supports a target blood pressure of <130/80 mmHg to optimally prevent progression of both small vessel disease and chronic kidney disease. However, future studies are needed to determine long-term effects of more intensive blood pressure treatment targets on small vessel disease progression and incident dementia.
Language	English
Publishing date	2023-09-06
Publishing country	England
Document type	Journal Article ; Review
ISSN	1750-8460
ISSN	1750-8460
DOI	10.12968/hmed.2023.0092
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Article: Modeling annual elevated blood lead levels among children in Maryland in relation to neighborhood deprivation

Wheeler, David C. / Boyle, Joseph / Nelson, Erik J.

Science of the total environment. 2022 Jan. 20, v. 805

2022

Abstract: Estimating environmental lead exposure using ecologic risk models is an inexpensive strategy to inform public health departments and to develop location-based intervention strategies such as targeted screening and mitigation. Importantly, studies in this ...

Abstract	Estimating environmental lead exposure using ecologic risk models is an inexpensive strategy to inform public health departments and to develop location-based intervention strategies such as targeted screening and mitigation. Importantly, studies in this area have not assessed temporal and spatio-temporal lead exposure risk trends. Due to lead abatement efforts and targeted screening efforts, it is anticipated that lead exposure risk has decreased over time. However, it is unknown if decreases have occurred, and if the decreases are evenly distributed across neighborhoods. Thus, the purpose of this study was to examine the association between neighborhood deprivation and risk of elevated blood lead levels (EBLLs) in both temporal and spatio-temporal contexts within the US state of Maryland in 2005–2015. To consider the temporal dimension of lead risk, we used a novel extension of Bayesian index models to estimate time-varying neighborhood deprivation indices along with time-varying index effects. The results showed that overall EBLL proportion decreased over time, from a high of 0.11 in 2006 to a low of 0.02 in 2015. The association between neighborhood deprivation and EBLL risk was positive and significant annually, but generally diminished over time. The most important variables in the neighborhood deprivation index were percent of houses built before 1940 and median household income. In summary, using Bayesian index models that can account for both temporal and spatio-temporal contexts is a promising approach to inform public health efforts to remediate lead and focus testing efforts and may be useful in studies in other geographic areas and times.
Keywords	Bayesian theory ; blood ; environment ; household income ; lead ; public health ; risk ; Maryland
Language	English
Dates of publication	2022-0120
Publishing place	Elsevier B.V.
Document type	Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2021.150333
Database	NAL-Catalogue (AGRICOLA)

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Z 74/341: Show issues

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Article ; Online: Accounting for the uncertainty due to chemicals below the detection limit in mixture analysis.

Hargarten, Paul M / Wheeler, David C

Environmental research

2020 Volume 186, Page(s) 109466

Abstract: Simultaneous exposure to a mixture of chemicals over a lifetime may increase an individual's risk of disease to a greater extent than individual exposures. Researchers have used weighted quantile sum (WQS) regression to estimate the effect of multiple ... ...

Abstract	Simultaneous exposure to a mixture of chemicals over a lifetime may increase an individual's risk of disease to a greater extent than individual exposures. Researchers have used weighted quantile sum (WQS) regression to estimate the effect of multiple exposures in a manner that identifies the important (etiologically relevant) components in the mixture. However, complications arise when an experimental apparatus detects concentrations for each chemical with a different detection limit. Current strategies to account for values below the detection limit (BDL) in WQS include single imputation or placing the BDL values into the first quantile of the weighted index (BDLQ1), which do not fully capture the uncertainty in the data when estimating mixture effects. In response, we integrated WQS regression into the multiple imputation framework (MI-WQS). In a simulation study, we compared the BDLQ1 approach to MI-WQS when using either a Bayesian imputation or bootstrapping imputation approach over a range of BDL values. We examined the ability of each method to estimate the mixture's overall effect and to identify important chemicals. The results showed that as the number of BDL values increased, the accuracy, precision, model fit, and power declined for all imputation approaches. When chemical values were missing at 10%, 33%, or 50%, the MI approaches generally performed better than single imputation and BDLQ1. In the extreme case of 80% of all the chemical values were missing, the BDLQ1 approach was superior in some examined metrics.
MeSH term(s)	Bayes Theorem ; Data Collection ; Limit of Detection ; Research Design ; Uncertainty
Language	English
Publishing date	2020-04-04
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2020.109466
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Zs.A 726: Show issues

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Article ; Online: Modeling historic neighborhood deprivation and non-Hodgkin lymphoma risk

Boyle, Joseph / Ward, Mary H. / Cerhan, James R. / Rothman, Nathaniel / Wheeler, David C.

Elsevier Inc. Environmental Research. 2023 Sept., v. 232 p.116361-

2023

Abstract: Many studies have identified associations between neighborhood deprivation and disease, emphasizing the importance of social determinants of health. However, when studying diseases with long latency periods such as cancers, considering the timing of ... ...

Abstract	Many studies have identified associations between neighborhood deprivation and disease, emphasizing the importance of social determinants of health. However, when studying diseases with long latency periods such as cancers, considering the timing of exposures for deprivation becomes more important. In this study, we estimated the associations between neighborhood deprivation indices at several time points and risk of non-Hodgkin lymphoma (NHL) in a population-based case-control study at four study centers – Detroit, Iowa, Los Angeles County, and Seattle (1998–2000). We used the Bayesian index regression model and residential histories to estimate neighborhood deprivation index effects in crude models and adjusted for four chemical mixtures measured in house dust and individual-level covariates. We found that neighborhood deprivation in 1980, approximately twenty years before study entry, provided better model fit than did neighborhood deprivation at 1990 and 2000. We identified several statistically significant associations between neighborhood deprivation in 1980 and NHL risk in Iowa and among long-term (20+ years) residents of Detroit. The most important variables in these indices were median gross rent as a percentage of household income in Iowa and percent of single-parent households with at least one child and median household income in Detroit. Associations remained statistically significant after adjustment for individual-level covariates and chemical mixtures, providing evidence for historic neighborhood deprivation as a risk factor for NHL and motivating future research to uncover the specific carcinogens driving these associations in deprived areas.
Keywords	Bayesian theory ; case-control studies ; children ; dust ; household income ; non-Hodgkin lymphoma ; regression analysis ; risk factors ; Iowa ; Exposome ; Neighborhood deprivation ; Historic exposures ; Mixture analysis ; Residential history
Language	English
Dates of publication	2023-09
Publishing place	Elsevier Inc.
Document type	Article ; Online
ZDB-ID	205699-9
ISSN	1096-0953 ; 0013-9351
ISSN (online)	1096-0953
ISSN	0013-9351
DOI	10.1016/j.envres.2023.116361
Database	NAL-Catalogue (AGRICOLA)

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Article ; Online: Imputation of Below Detection Limit Missing Data in Chemical Mixture Analysis with Bayesian Group Index Regression.

Carli, Matthew / Ward, Mary H / Metayer, Catherine / Wheeler, David C

International journal of environmental research and public health

2022 Volume 19, Issue 3

Abstract: There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian ...

Abstract	There is growing scientific interest in identifying the multitude of chemical exposures related to human diseases through mixture analysis. In this paper, we address the issue of below detection limit (BDL) missing data in mixture analysis using Bayesian group index regression by treating both regression effects and missing BDL observations as parameters in a model estimated through a Markov chain Monte Carlo algorithm that we refer to as pseudo-Gibbs imputation. We compare this with other Bayesian imputation methods found in the literature (Multiple Imputation by Chained Equations and Sequential Full Bayes imputation) as well as with a non-Bayesian single-imputation method. To evaluate our proposed method, we conduct simulation studies with varying percentages of BDL missingness and strengths of association. We apply our method to the California Childhood Leukemia Study (CCLS) to estimate concentrations of chemicals in house dust in a mixture analysis of potential environmental risk factors for childhood leukemia. Our results indicate that pseudo-Gibbs imputation has superior power for exposure effects and sensitivity for identifying individual chemicals at high percentages of BDL missing data. In the CCLS, we found a significant positive association between concentrations of polycyclic aromatic hydrocarbons (PAHs) in homes and childhood leukemia as well as significant positive associations for polychlorinated biphenyls (PCBs) and herbicides among children from the highest quartile of household income. In conclusion, pseudo-Gibbs imputation addresses a commonly encountered problem in environmental epidemiology, providing practitioners the ability to jointly estimate the effects of multiple chemical exposures with high levels of BDL missingness.
MeSH term(s)	Bayes Theorem ; Child ; Computer Simulation ; Humans ; Limit of Detection ; Monte Carlo Method ; Research Design
Language	English
Publishing date	2022-01-26
Publishing country	Switzerland
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, N.I.H., Intramural
ZDB-ID	2175195-X
ISSN	1660-4601 ; 1661-7827
ISSN (online)	1660-4601
ISSN	1661-7827
DOI	10.3390/ijerph19031369
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Article ; Online: Relationship Between Life-Space Mobility and Health Characteristics in Older Adults Using Global Positioning System Watches.

Chung, Jane / Boyle, Joseph / Wheeler, David C

Journal of applied gerontology : the official journal of the Southern Gerontological Society

2021 Volume 41, Issue 4, Page(s) 1186–1195

Abstract: This study aimed to examine the feasibility of using global positioning system (GPS) watches to examine relationships between GPS-based life-space mobility (LSM) metrics and self-report LSM and health measures (physical, psychological, and cognitive ... ...

Abstract	This study aimed to examine the feasibility of using global positioning system (GPS) watches to examine relationships between GPS-based life-space mobility (LSM) metrics and self-report LSM and health measures (physical, psychological, and cognitive function) among older adults. Thirty participants wore a Fitbit Surge for 3 days. Eight spatial and temporal LSM measures were derived from GPS data. About 90% of in-home movement speeds were zero, indicating the sedentary lifestyle, but they made some active out-of-home trips as the total distance traveled and size of movement area indicated. There was a significant difference in total distance traveled and 95th percentile of movement speed between mild cognitive and intact cognition groups. GPS-based higher proportion of out-of-home time was significantly associated with greater functional fitness. Greater GPS use hours were significantly associated with higher cognition. These findings suggest the potential of GPS watches to continuously monitor changes in functional health to inform prevention efforts.
MeSH term(s)	Aged ; Geographic Information Systems ; Humans ; Self Report
Language	English
Publishing date	2021-10-31
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ZDB-ID	155897-3
ISSN	1552-4523 ; 0733-4648
ISSN (online)	1552-4523
ISSN	0733-4648
DOI	10.1177/07334648211054834
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Article ; Online: Modeling annual elevated blood lead levels among children in Maryland in relation to neighborhood deprivation.

Wheeler, David C / Boyle, Joseph / Nelson, Erik J

The Science of the total environment

2021 Volume 805, Page(s) 150333

Abstract	Estimating environmental lead exposure using ecologic risk models is an inexpensive strategy to inform public health departments and to develop location-based intervention strategies such as targeted screening and mitigation. Importantly, studies in this area have not assessed temporal and spatio-temporal lead exposure risk trends. Due to lead abatement efforts and targeted screening efforts, it is anticipated that lead exposure risk has decreased over time. However, it is unknown if decreases have occurred, and if the decreases are evenly distributed across neighborhoods. Thus, the purpose of this study was to examine the association between neighborhood deprivation and risk of elevated blood lead levels (EBLLs) in both temporal and spatio-temporal contexts within the US state of Maryland in 2005-2015. To consider the temporal dimension of lead risk, we used a novel extension of Bayesian index models to estimate time-varying neighborhood deprivation indices along with time-varying index effects. The results showed that overall EBLL proportion decreased over time, from a high of 0.11 in 2006 to a low of 0.02 in 2015. The association between neighborhood deprivation and EBLL risk was positive and significant annually, but generally diminished over time. The most important variables in the neighborhood deprivation index were percent of houses built before 1940 and median household income. In summary, using Bayesian index models that can account for both temporal and spatio-temporal contexts is a promising approach to inform public health efforts to remediate lead and focus testing efforts and may be useful in studies in other geographic areas and times.
MeSH term(s)	Bayes Theorem ; Child ; Environmental Exposure/analysis ; Humans ; Lead ; Maryland ; Residence Characteristics ; Socioeconomic Factors
Chemical Substances	Lead (2P299V784P)
Language	English
Publishing date	2021-09-15
Publishing country	Netherlands
Document type	Journal Article
ZDB-ID	121506-1
ISSN	1879-1026 ; 0048-9697
ISSN (online)	1879-1026
ISSN	0048-9697
DOI	10.1016/j.scitotenv.2021.150333
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