Babette A Brumback

University of Florida, Gainesville, Florida, United States

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Publications (70)200.54 Total impact

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    ABSTRACT: Objective: This prospective investigation examined: 1) processing speed and working memory relative to other cognitive domains in non-demented medically managed idiopathic Parkinson's disease, and 2) the predictive role of cortical/subcortical gray thickness/volume and white matter fractional anisotropy on processing speed and working memory. Methods: Participants completed a neuropsychological protocol, Unified Parkinson's Disease Rating Scale, brain MRI, and fasting blood draw to rule out vascular contributors. Within group a priori anatomical contributors included bilateral frontal thickness, caudate nuclei volume, and prefrontal white matter fractional anisotropy. Results: Idiopathic Parkinson's disease (n = 40; Hoehn & Yahr stages 1-3) and non-Parkinson's disease 'control' peers (n = 40) matched on demographics, general cognition, comorbidity, and imaging/blood vascular metrics. Cognitively, individuals with Parkinson's disease were significantly more impaired than controls on tests of processing speed, secondary deficits on working memory, with subtle impairments in memory, abstract reasoning, and visuoperceptual/spatial abilities. Anatomically, Parkinson's disease individuals were not statistically different in cortical gray thickness or subcortical gray volumes with the exception of the putamen. Tract Based Spatial Statistics showed reduced prefrontal fractional anisotropy for Parkinson's disease relative to controls. Within Parkinson's disease, prefrontal fractional anisotropy and caudate nucleus volume partially explained processing speed. For controls, only prefrontal white matter was a significant contributor to processing speed. There were no significant anatomical predictors of working memory for either group. Conclusions: Caudate nuclei volume and prefrontal fractional anisotropy, not frontal gray matter thickness, showed unique and combined significance for processing speed in Parkinson's disease. Findings underscore the relevance for examining gray-white matter interactions and also highlight clinical processing speed metrics as potential indicators of early cognitive impairment in PD.
    Full-text · Article · Jan 2016 · PLoS ONE
  • Babette A. Brumback · Li Li · Zhuangyu Cai
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    ABSTRACT: Between-within models are generalized linear mixed models (GLMMs) for clustered data that incorporate a random intercept together with fixed effects for within-cluster and between-cluster covariates; the between-cluster covariates represent the cluster means of the within-cluster covariates. One popular use of these models is to adjust for confounding of the effect of within-cluster covariates due to unmeasured between-cluster covariates. Previous research has shown via simulations that using this approach can yield inconsistent estimators. We present theory and simulations as evidence that a primary cause of the inconsistency is heteroscedasticity of the linearized version of the GLMM used for estimation.
    No preview · Article · Dec 2015 · Communication in Statistics- Simulation and Computation
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    ABSTRACT: Background: There is remarkable heterogeneity in clinical Alzheimer's disease (AD) or vascular dementia (VaD). Objectives: 1) To statistically exam neuropsychological data to determine dementia subgroups for individuals clinically diagnosed with AD or VaD and then 2) examine group differences in specific gray/white matter regions of interest. Methods: A k-means cluster analysis requested a 3-group solution from neuropsychological data acquired from individuals diagnosed clinically with AD/VaD. MRI measures of hippocampal, caudate, ventricular, subcortical lacunar infarction, whole brain volume, and leukoaraiosis (LA) were analyzed. Three regions of LA volumes were quantified and these included the periventricular (5 mm around the ventricles), infracortical (5 mm beneath the gray matter), and deep (between periventricular and infracortical) regions. Results: Cluster analysis sorted AD/VaD patients into single domain amnestic (n = 41), single-domain dysexecutive (n = 26), and multi-domain (n = 26) phenotypes. Multi-domain patients exhibited worst performance on language tests; however, multi-domain patients were equally impaired on memory tests when compared to amnestic patients. Statistically-determined groups dissociated using neuroradiological parameters: amnestic and multi-domain groups presented with smaller hippocampal volume while the dysexecutive group presented with greater deep, periventricular, and whole brain LA. Neither caudate nor lacunae volume differed by group. Caudate nucleus volume negatively correlated with total LA in the dysexecutive and multi-domain groups. Conclusions: There are at least three distinct subtypes embedded within patients diagnosed clinically with AD/VaD spectrum dementia. We encourage future research to assess a) the neuroradiological substrates underlying statistically-determined AD/VaD spectrum dementia and b) how statistical modeling can be integrated into existing diagnostic criteria.
    No preview · Article · Sep 2015 · Journal of Alzheimer's disease: JAD
  • Zhuangyu Cai · Babette A Brumback
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    ABSTRACT: Model-based standardization uses a statistical model to estimate a standardized, or unconfounded, population-averaged effect. With it, one can compare groups had the distribution of confounders been identical in both groups to that of the standard population. We develop two methods for model-based standardization with complex survey data that accommodate a categorical confounder that clusters the individual observations into a very large number of subgroups. The first method combines a random-intercept generalized linear mixed model with a conditional pseudo-likelihood estimator of the fixed effects. The second method combines a between-within generalized linear mixed model with census data on the cluster-level means of the individual-level covariates. We conduct simulation studies to compare the two approaches. We apply the two methods to the 2008 Florida Behavioral Risk Factor Surveillance System survey data to estimate standardized proportions of people who drink alcohol, within age groups, adjusting for measured individual-level and unmeasured cluster-level confounders. Copyright © 2015 John Wiley & Sons, Ltd. Copyright © 2015 John Wiley & Sons, Ltd.
    No preview · Article · Apr 2015 · Statistics in Medicine
  • Amy Dailey · Babette Brumback
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    ABSTRACT: Racial/ethnic disparities in access to oral health care have been documented for decades in the United States, yet little progress has been made in reducing these inequalities. Using 2008 Florida Behavioral Risk Factor Surveillance System data, dramatic changes in the estimates for receiving recent dental cleanings by race/ethnicity were observed after appropriately accounting for neighborhood confounding using conditional pseudolikelihood methods with an ordinal dental cleaning outcome (the methodological aspects of this work have been published). This analysis revealed that if zip code differences were accounted for, minority populations had equal or better outcomes than Whites. For example, with income and neighborhood included in the model, in addition to age, gender, education, and health insurance, Hispanics had significantly higher odds of receiving recent dental cleanings than Whites (OR 3.67, 95% CI: 1.79, 7.52). These findings highlight the immense impact that area-level factors can have on racial/ethnic differences in dental care utilization. One way to facilitate progress in identifying and acting upon underlying disparities mechanisms is to make area-level data more accessible for communities to use and easily link to surveillance data. Regional discussions addressing social disparities across areas are also needed, along with increased awareness among practitioners and communities that determinants of racial/ethnic inequalities in oral health extend beyond behavioral inadequacies. Significant disparities are likely to persist if we continue to fall short of addressing the underlying structural and social determinants.
    No preview · Conference Paper · Nov 2014
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    ABSTRACT: doi: 10.1164/rccm.201405-0993LE
    Full-text · Article · Oct 2014 · American Journal of Respiratory and Critical Care Medicine
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    Full-text · Dataset · Oct 2014
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    ABSTRACT: The purpose of this study was to quantify how school sanitation conditions are associated with pupils' use of sanitation facilities. We conducted a longitudinal assessment in 60 primary schools in Nyanza Province, Kenya, using structured observations to measure facility conditions and pupils' use at specific facilities. We used multivariable mixed regression models to characterize how pupil to toilet ratio was associated with toilet use at the school-level and also how facility conditions were associated with pupils' use at specific facilities. We found a piecewise linear relationship between decreasing pupil to toilet ratio and increasing pupil toilet use (p < 0.01). Our data also revealed significant associations between toilet use and newer facility age (p < 0.01), facility type (p < 0.01), and the number of toilets in a facility (p < 0.01). We found some evidence suggesting facility dirtiness may deter girls from use (p = 0.06), but not boys (p = 0.98). Our study is the first to rigorously quantify many of these relationships, and provides insight into the complexity of factors affecting pupil toilet use patterns, potentially leading to a better allocation of resources for school sanitation, and to improved health and educational outcomes for children.
    Full-text · Article · Sep 2014 · International Journal of Environmental Research and Public Health
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    ABSTRACT: Much attention has been paid to estimating the causal effect of adherence to a randomized protocol using instrumental variables to adjust for unmeasured confounding. Researchers tend to use the instrumental variable within one of the three main frameworks: regression with an endogenous variable, principal stratification, or structural-nested modeling. We found in our literature review that even in simple settings, causal interpretations of analyses with endogenous regressors can be ambiguous or rely on a strong assumption that can be difficult to interpret. Principal stratification and structural-nested modeling are alternative frameworks that render unambiguous causal interpretations based on assumptions that are, arguably, easier to interpret. Our interest stems from a wish to estimate the effect of cluster-level adherence on individual-level binary outcomes with a three-armed cluster-randomized trial and polytomous adherence. Principal stratification approaches to this problem are quite challenging because of the sheer number of principal strata involved. Therefore, we developed a structural-nested modeling approach and, in the process, extended the methodology to accommodate cluster-randomized trials with unequal probability of selecting individuals. Furthermore, we developed a method to implement the approach with relatively simple programming. The approach works quite well, but when the structural-nested model does not fit the data, there is no solution to the estimating equation. We investigate the performance of the approach using simulated data, and we also use the approach to estimate the effect on pupil absence of school-level adherence to a randomized water, sanitation, and hygiene intervention in western Kenya. Copyright © 2013 John Wiley & Sons, Ltd.
    No preview · Article · Apr 2014 · Statistics in Medicine
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    Babette A Brumback · Zhuangyu Cai · Amy B Dailey
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    ABSTRACT: Reasons for health disparities may include neighborhood-level factors, such as availability of health services, social norms, and environmental determinants, as well as individual-level factors. Investigating health inequalities using nationally or locally representative data often requires an approach that can accommodate a complex sampling design, in which individuals have unequal probabilities of selection into the study. The goal of the present article is to review and compare methods of estimating or accounting for neighborhood influences with complex survey data. We considered 3 types of methods, each generalized for use with complex survey data: ordinary regression, conditional likelihood regression, and generalized linear mixed-model regression. The relative strengths and weaknesses of each method differ from one study to another; we provide an overview of the advantages and disadvantages of each method theoretically, in terms of the nature of the estimable associations and the plausibility of the assumptions required for validity, and also practically, via a simulation study and 2 epidemiologic data analyses. The first analysis addresses determinants of repeat mammography screening use using data from the 2005 National Health Interview Survey. The second analysis addresses disparities in preventive oral health care using data from the 2008 Florida Behavioral Risk Factor Surveillance System Survey.
    Preview · Article · Apr 2014 · American journal of epidemiology
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    ABSTRACT: An epidemic of cholera infections was documented in Haiti for the first time in more than 100 years during October 2010. Cases have continued to occur, raising the question of whether the microorganism has established environmental reservoirs in Haiti. We monitored 14 environmental sites near the towns of Gressier and Leogane during April 2012-March 2013. Toxigenic Vibrio cholerae O1 El Tor biotype strains were isolated from 3 (1.7%) of 179 water samples; nontoxigenic O1 V. cholerae was isolated from an additional 3 samples. All samples containing V. cholerae O1 also contained non-O1 V. cholerae. V. cholerae O1 was isolated only when water temperatures were ≥31°C. Our data substantiate the presence of toxigenic V. cholerae O1 in the aquatic environment in Haiti. These isolations may reflect establishment of long-term environmental reservoirs in Haiti, which may complicate eradication of cholera from this coastal country.
    Full-text · Article · Mar 2014 · Emerging Infectious Diseases
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    ABSTRACT: Age-related medical conditions such as Parkinson's disease (PD) compromise driver fitness. Results from studies are unclear on the specific driving errors that underlie passing or failing an on-road assessment. In this study, we determined the between-group differences and quantified the on-road driving errors that predicted pass or fail on-road outcomes in 101 drivers with PD (mean age = 69.38 ± 7.43) and 138 healthy control (HC) drivers (mean age = 71.76 ± 5.08). Participants with PD had minor differences in demographics and driving habits and history but made more and different driving errors than HC participants. Drivers with PD failed the on-road test to a greater extent than HC drivers (41% vs. 9%), χ²(1) = 35.54, HC N = 138, PD N = 99, p < .001. The driving errors predicting on-road pass or fail outcomes (95% confidence interval, Nagelkerke R² =.771) were made in visual scanning, signaling, vehicle positioning, speeding (mainly underspeeding, t(61) = 7.004, p < .001, and total errors. Although it is difficult to predict on-road outcomes, this study provides a foundation for doing so.
    No preview · Article · Dec 2013
  • Zhulin He · Babette A. Brumback
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    ABSTRACT: Motivated by an application with complex survey data, we show that for logistic regression with a simple matched-pairs design, infinitely replicating observations and maximizing the conditional likelihood results in an estimator exactly identical to the unconditional maximum likelihood estimator based on the original sample, which is inconsistent. Therefore, applying conditional likelihood methods to a pseudosample with observations replicated a large number of times can lead to an inconsistent estimator; this casts doubt on one possible approach to conditional logistic regression with complex survey data. We speculate that for more general designs, an asymptotic equivalence holds.
    No preview · Article · Sep 2013 · Communication in Statistics- Theory and Methods
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    ABSTRACT: SUMMARY The impact of improved water, sanitation, and hygiene (WASH) access on mitigating illness is well documented, although impact of school-based WASH on school-aged children has not been rigorously explored. We conducted a cluster-randomized trial in Nyanza Province, Kenya to assess the impact of a school-based WASH intervention on diarrhoeal disease in primary-school pupils. Two study populations were used: schools with a nearby dry season water source and those without. Pupils attending 'water-available' schools that received hygiene promotion and water treatment (HP&WT) and sanitation improvements showed no difference in period prevalence or duration of illness compared to pupils attending control schools. Those pupils in schools that received only the HP&WT showed similar results. Pupils in 'water-scarce' schools that received a water-supply improvement, HP&WT and sanitation showed a reduction in diarrhoea incidence and days of illness. Our study revealed mixed results on the impact of improvements to school WASH improvements on pupil diarrhoea.
    Full-text · Article · May 2013 · Epidemiology and Infection
  • Babette A Brumback · Hao W Zheng · Amy B Dailey
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    ABSTRACT: When investigating health disparities, it can be of interest to explore whether adjustment for socioeconomic factors at the neighborhood level can account for, or even reverse, an unadjusted difference. Recently, we proposed new methods to adjust the effect of an individual-level covariate for confounding by unmeasured neighborhood-level covariates using complex survey data and a generalization of conditional likelihood methods. Generalized linear mixed models (GLMMs) are a popular alternative to conditional likelihood methods in many circumstances. Therefore, in the present article, we propose and investigate a new adaptation of GLMMs for complex survey data that achieves the same goal of adjusting for confounding by unmeasured neighborhood-level covariates. With the new GLMM approach, one must correctly model the expectation of the unmeasured neighborhood-level effect as a function of the individual-level covariates. We demonstrate using simulations that even if that model is correct, census data on the individual-level covariates are sometimes required for consistent estimation of the effect of the individual-level covariate. We apply the new methods to investigate disparities in recency of dental cleaning, treated as an ordinal outcome, using data from the 2008 Florida Behavioral Risk Factor Surveillance System (BRFSS) survey. We operationalize neighborhood as zip code and merge the BRFSS data with census data on ZIP Code Tabulated Areas to incorporate census data on the individual-level covariates. We compare the new results to our previous analysis, which used conditional likelihood methods. We find that the results are qualitatively similar. Copyright © 2012 John Wiley & Sons, Ltd.
    No preview · Article · Apr 2013 · Statistics in Medicine
  • Babette A Brumback · Zhuangyu Cai · Zhulin He · Hao W Zheng · Amy B Dailey
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    ABSTRACT: In order to adjust individual-level covariate effects for confounding due to unmeasured neighborhood characteristics, we have recently developed conditional pseudolikelihood methods to estimate the parameters of a proportional odds model for clustered ordinal outcomes with complex survey data. The methods require sampling design joint probabilities for each within-neighborhood pair. In the present article, we develop a similar methodology for a baseline category logit model for clustered multinomial outcomes and for a loglinear model for clustered count outcomes. All of the estimators and asymptotic sampling distributions we present can be conveniently computed using standard logistic regression software for complex survey data, such as sas proc surveylogistic. We demonstrate validity of the methods theoretically and also empirically by using simulations. We apply the new method for clustered multinomial outcomes to data from the 2008 Florida Behavioral Risk Factor Surveillance System survey in order to investigate disparities in frequency of dental cleaning both unadjusted and adjusted for confounding by neighborhood. Copyright © 2012 John Wiley & Sons, Ltd.
    No preview · Article · Apr 2013 · Statistics in Medicine
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    ABSTRACT: Model-based standardization enables adjustment for confounding of a population-averaged exposure effect on an outcome. It requires either a model for the probability of the exposure conditional on the confounders (an exposure model) or a model for the expectation of the outcome conditional on the exposure and the confounders (an outcome model). The methodology can also be applied to estimate averaged exposure effects within categories of an effect modifier and to test whether these effects differ or not. Recently, we extended that methodology for use with complex survey data, to estimate the effects of disability status on cost barriers to health care within three age categories and to test for differences. We applied the methodology to data from the 2007 Florida Behavioral Risk Factor Surveillance System Survey (BRFSS). The exposure modeling and outcome modeling approaches yielded two contrasting sets of results. In the present paper, we develop and apply to the BRFSS example two doubly robust approaches to testing and estimating effect modification with complex survey data; these approaches require that only one of these two models be correctly specified. Furthermore, assuming that at least one of the models is correctly specified, we can use the doubly robust approaches to develop and apply goodness-of-fit tests for the exposure and outcome models. We compare the exposure modeling, outcome modeling, and doubly robust approaches in terms of a simulation study and the BRFSS example. Copyright © 2012 John Wiley & Sons, Ltd.
    No preview · Article · Feb 2013 · Statistics in Medicine
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    ABSTRACT: The CHIRP Study is a two-arm, pilot randomized controlled trial assessing the effectiveness of a behavioral family weight management intervention in an important and at-risk population, overweight young children, 3 to 6years of age, and their parents from underserved rural counties. Participants will include 96 parent-child dyads living in rural counties in north central Florida. Families will be randomized to one of two conditions: (a) Behavioral Family Based Intervention or (b) a Waitlist Control. Child and parent participants will be assessed at baseline (month 0), post-treatment (month 4), and follow-up (month 10). Assessments and intervention sessions will be held at the Cooperative Extension office in each participating rural county. The primary outcome measure is change in child body mass index (BMI) z-score. Additional key outcome measures include child dietary intake, physical activity, and parent BMI. This study is unique because (1) it is one of the few randomized controlled trails examining a behavioral family intervention to address healthy habits and improved weight status in young overweight and obese children, (2) addresses health promotion in rural settings, (3) examines intervention delivery in real world community settings through the Cooperative Extension Service offices. If successful, this research has potential implications for medically underserved rural communities and preventative health services for young children and their families.
    No preview · Article · Nov 2012 · Contemporary clinical trials
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    ABSTRACT: Due to time-dependent confounding by blood pressure and differential loss to follow-up, it is difficult to estimate the effectiveness of aggressive versus conventional antihypertensive combination therapies in non-randomized comparisons. We utilized data from 22,576 hypertensive coronary artery disease patients, prospectively enrolled in the INternational VErapamil-Trandolapril STudy (INVEST). Our post-hoc analyses did not consider the randomized treatment strategies, but instead defined exposure time-dependently as aggressive treatment (≥3 concomitantly used antihypertensive medications) versus conventional treatment (≤2 concomitantly used antihypertensive medications). Study outcome was defined as time to first serious cardiovascular event (non-fatal myocardial infarction, non-fatal stroke, or all-cause death). We compared hazard ratio (HR) estimates for aggressive vs. conventional treatment from a Marginal Structural Cox Model (MSCM) to estimates from a standard Cox model. Both models included exposure to antihypertensive treatment at each follow-up visit, demographics, and baseline cardiovascular risk factors, including blood pressure. The MSCM further adjusted for systolic blood pressure at each follow-up visit, through inverse probability of treatment weights. 2,269 (10.1%) patients experienced a cardiovascular event over a total follow-up of 60,939 person-years. The HR for aggressive treatment estimated by the standard Cox model was 0.96 (95% confidence interval 0.87-1.07). The equivalent MSCM, which was able to account for changes in systolic blood pressure during follow-up, estimated a HR of 0.81 (95% CI 0.71-0.92). Using a MSCM, aggressive treatment was associated with a lower risk for serious cardiovascular outcomes compared to conventional treatment. In contrast, a standard Cox model estimated similar risks for aggressive and conventional treatments. Trial registration Clinicaltrials.gov Identifier: NCT00133692
    Full-text · Article · Aug 2012 · BMC Medical Research Methodology
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    ABSTRACT: To examine the concept of leukoaraiosis thresholds on working memory, visuoconstruction, memory, and language in dementia. A consecutive series of 83 individuals with insidious onset/progressive dementia clinically diagnosed with Alzheimer disease (AD) or small vessel vascular dementia (VaD) completed neuropsychological measures assessing working memory, visuoconstruction, episodic memory, and language. A clinical MRI scan was used to quantify leukoaraiosis, total white matter, hippocampus, lacune, and intracranial volume. We performed analyses to detect the lowest level of leukoaraiosis associated with impairment on the neuropsychological measures. Leukoaraiosis ranged from 0.63% to 23.74% of participants' white matter. Leukoaraiosis explained a significant amount of variance in working memory performance when it involved 3% or more of the white matter with curve estimations showing the relationship to be nonlinear in nature. Greater leukoaraiosis (13%) was implicated for impairment in visuoconstruction. Relationships between leukoaraiosis, episodic memory, and language measures were linear or flat. Leukoaraiosis involves specific threshold points for working memory and visuoconstructional tests in AD/VaD spectrum dementia. These data underscore the need to better understand the threshold at which leukoaraiosis affects and alters the phenotypic expression in insidious onset dementia syndromes.
    No preview · Article · Jul 2012 · Neurology

Publication Stats

4k Citations
200.54 Total Impact Points

Institutions

  • 2008-2015
    • University of Florida
      Gainesville, Florida, United States
  • 2003-2005
    • University of California, Los Angeles
      • Division of Adult Psychiatry
      Los Angeles, California, United States
  • 2000-2003
    • University of Washington Seattle
      • Department of Biostatistics
      Seattle, Washington, United States
  • 1998-2000
    • Harvard Medical School
      • Department of Medicine
      Boston, Massachusetts, United States