Babette A Brumback

University of Florida, Gainesville, Florida, United States

Are you Babette A Brumback?

Claim your profile

Publications (66)183.12 Total impact

  • [Show abstract] [Hide abstract]
    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.
    Journal of Alzheimer's disease: JAD 09/2015; DOI:10.3233/JAD-150407 · 4.15 Impact Factor
  • Zhuangyu Cai · Babette A Brumback
    [Show abstract] [Hide abstract]
    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.
    Statistics in Medicine 04/2015; 34(15). DOI:10.1002/sim.6504 · 1.83 Impact Factor
  • Amy Dailey · Babette Brumback
    [Show abstract] [Hide abstract]
    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.
    142nd APHA Annual Meeting and Exposition 2014; 11/2014
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: doi: 10.1164/rccm.201405-0993LE
    American Journal of Respiratory and Critical Care Medicine 10/2014; 190(7-7):837-839. DOI:10.1164/rccm.201405-0993LE · 13.00 Impact Factor
  • Source
  • Source
    [Show abstract] [Hide abstract]
    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.
    International Journal of Environmental Research and Public Health 09/2014; 11(9):9694-9711. DOI:10.3390/ijerph110909694 · 2.06 Impact Factor
  • [Show abstract] [Hide abstract]
    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.
    Statistics in Medicine 04/2014; 33(9). DOI:10.1002/sim.6049 · 1.83 Impact Factor
  • Babette A Brumback · Zhuangyu Cai · Amy B Dailey
    [Show abstract] [Hide abstract]
    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.
    American journal of epidemiology 04/2014; 179(10). DOI:10.1093/aje/kwu040 · 5.23 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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.
    12/2013; 68(1):77-85. DOI:10.5014/ajot.2014.008698
  • Zhulin He · Babette A. Brumback
    [Show abstract] [Hide abstract]
    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.
    Communication in Statistics- Theory and Methods 09/2013; 42(18). DOI:10.1080/03610926.2011.626547 · 0.27 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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.
    Epidemiology and Infection 05/2013; 24(2):1-12. DOI:10.1017/S0950268813001118 · 2.54 Impact Factor
  • Babette A Brumback · Hao W Zheng · Amy B Dailey
    [Show abstract] [Hide abstract]
    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.
    Statistics in Medicine 04/2013; 32(8). DOI:10.1002/sim.5624 · 1.83 Impact Factor
  • Babette A Brumback · Zhuangyu Cai · Zhulin He · Hao W Zheng · Amy B Dailey
    [Show abstract] [Hide abstract]
    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.
    Statistics in Medicine 04/2013; 32(8). DOI:10.1002/sim.5625 · 1.83 Impact Factor
  • [Show abstract] [Hide abstract]
    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.
    Statistics in Medicine 02/2013; 32(4). DOI:10.1002/sim.5532 · 1.83 Impact Factor
  • [Show abstract] [Hide abstract]
    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.
    Contemporary clinical trials 11/2012; 34(2). DOI:10.1016/j.cct.2012.11.004 · 1.94 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    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 Identifier: NCT00133692
    BMC Medical Research Methodology 08/2012; 12(1):119. DOI:10.1186/1471-2288-12-119 · 2.27 Impact Factor
  • [Show abstract] [Hide abstract]
    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.
    Neurology 07/2012; 79(8):734-40. DOI:10.1212/WNL.0b013e3182661ef6 · 8.29 Impact Factor
  • Babette A Brumback · Amy B Dailey · Hao W Zheng
    [Show abstract] [Hide abstract]
    ABSTRACT: In social epidemiology, an individual's neighborhood is considered to be an important determinant of health behaviors, mediators, and outcomes. Consequently, when investigating health disparities, researchers may wish to adjust for confounding by unmeasured neighborhood factors, such as local availability of health facilities or cultural predispositions. With a simple random sample and a binary outcome, a conditional logistic regression analysis that treats individuals within a neighborhood as a matched set is a natural method to use. The authors present a generalization of this method for ordinal outcomes and complex sampling designs. The method is based on a proportional odds model and is very simple to program using standard software such as SAS PROC SURVEYLOGISTIC (SAS Institute Inc., Cary, North Carolina). The authors applied the method to analyze racial/ethnic differences in dental preventative care, using 2008 Florida Behavioral Risk Factor Surveillance System survey data. The ordinal outcome represented time since last dental cleaning, and the authors adjusted for individual-level confounding by gender, age, education, and health insurance coverage. The authors compared results with and without additional adjustment for confounding by neighborhood, operationalized as zip code. The authors found that adjustment for confounding by neighborhood greatly affected the results in this example.
    American journal of epidemiology 04/2012; 175(11):1133-41. DOI:10.1093/aje/kwr452 · 5.23 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We investigated the psychometric properties of the 68-item Safe Driving Behavior Measure (SDBM) with 80 older drivers, 80 caregivers, and 2 evaluators from two sites. Using Rasch analysis, we examined unidimensionality and local dependence; rating scale; item- and person-level psychometrics; and item hierarchy of older drivers, caregivers, and driving evaluators who had completed the SDBM. The evidence suggested the SDBM is unidimensional, but pairs of items showed local dependency. Across the three rater groups, the data showed good person (≥3.4) and item (≥3.6) separation as well as good person (≥.93) and item reliability (≥.92). Cronbach's α was ≥.96, and few items were misfitting. Some of the items did not follow the hypothesized order of item difficulty. The SDBM classified the older drivers into six ability levels, but to fully calibrate the instrument it must be refined in terms of its items (e.g., item exclusion) and then tested among participants of lesser ability.
    The American journal of occupational therapy.: official publication of the American Occupational Therapy Association 02/2012; 66(2):233-41. DOI:10.5014/ajot.2012.001834 · 1.70 Impact Factor
  • Source
    [Show abstract] [Hide abstract]
    ABSTRACT: We used Safe Driving Behavior Measure (SDBM) to determine rater reliability and rater effects (erratic responses, severity, leniency) in three rater groups: 80 older drivers (mean age = 73.26, standard deviation = 5.30), 80 family members or caregivers (age range = 20-85 yr), and two driving evaluators. Rater agreement was significant only between the evaluators and the family members or caregivers. Participants rated driving ability without erratic effects. We observed an overall rater effect only between the evaluator and family members or caregivers, with the evaluators being the more severe rater group. Training family members or caregivers to rate driving behaviors more consistently with the evaluator's ratings may enhance the SDBM's usability and provide a role for occupational therapists to interpret proxy reports as an entry point for logical and efficient driving safety interventions.
    The American journal of occupational therapy.: official publication of the American Occupational Therapy Association 01/2012; 66(1):69-77. DOI:10.5014/ajot.2012.002261 · 1.70 Impact Factor

Publication Stats

4k Citations
183.12 Total Impact Points


  • 2008–2014
    • University of Florida
      • Department of Occupational Therapy
      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