[show abstract][hide abstract] ABSTRACT: The degree of health disparities present in rural communities is of growing concern and is considered "urgent" since rural residents lag behind their urban counterparts in health status. Understanding the prevalence and type of chronic diseases in rural communities is often difficult since Americans living in rural areas are reportedly less likely to have access to quality health care, although there are some exceptions. Data suggest that rural residents are more likely to engage in higher levels of behavioral and health risk-taking than urban residents, and newer evidence suggests that there are differences in health risk behavior within rural subgroups. The objective of this report is to characterize the prevalence of four major and costly chronic diseases (diabetes, cardiovascular disease, cancer, and arthritis) and putative risk factors including depressive symptoms within an understudied rural region of the United States. These four chronic conditions remain among the most common and preventable of health problems across the United States.
Using survey data (N = 2526), logistic regression models were used to assess the association of the outcome and risk factors adjusting for age, gender, and race.
Key findings are (1) Lower financial security was associated with higher prevalence of cardiovascular disease, arthritis, and diabetes, but not cancer. (2) Higher levels of depressive symptoms were associated with higher prevalence of cardiovascular disease, arthritis, and diabetes. (3) Former or current smoking was associated with higher prevalence of cardiovascular disease and cancer. (4) Blacks reported higher prevalence of diabetes than Whites; Black women were more likely to report diabetes than all other groups; prevalence of diabetes was greater among women with lower education than among women with higher education. (5) Overall, the prevalence of diabetes and arthritis was higher than that reported by Florida and national data.
The findings presented in this paper are derived from one of only a few studies examining patterns of chronic disease among residents of both a rural and lower income geographic region. Overall, the prevalence of these conditions compared to the state and nation as a whole is elevated and calls for increased attention and tailored public health interventions.
BMC Public Health 10/2013; 13(1):906. · 2.08 Impact Factor
[show abstract][hide abstract] ABSTRACT: Suboptimal vitamin B-6 status, as reflected by low plasma pyridoxal 5'-phosphate (PLP) concentration, is associated with increased risk of vascular disease. PLP plays many roles, including in one-carbon metabolism for the acquisition and transfer of carbon units and in the transsulfuration pathway. PLP also serves as a coenzyme in the catabolism of tryptophan. We hypothesize that the pattern of these metabolites can provide information reflecting the functional impact of marginal vitamin B-6 deficiency. We report here the concentration of major constituents of one-carbon metabolic processes and the tryptophan catabolic pathway in plasma from 23 healthy men and women before and after a 28-d controlled dietary vitamin B-6 restriction (<0.35 mg/d). liquid chromatography-tandem mass spectrometry analysis of the compounds relevant to one-carbon metabolism showed that vitamin B-6 restriction yielded increased cystathionine (53% pre- and 76% postprandial; P < 0.0001) and serine (12% preprandial; P < 0.05), and lower creatine (40% pre- and postprandial; P < 0.0001), creatinine (9% postprandial; P < 0.05), and dimethylglycine (16% postprandial; P < 0.05) relative to the vitamin B-6-adequate state. In the tryptophan pathway, vitamin B-6 restriction yielded lower kynurenic acid (22% pre- and 20% postprandial; P < 0.01) and higher 3-hydroxykynurenine (39% pre- and 34% postprandial; P < 0.01). Multivariate ANOVA analysis showed a significant global effect of vitamin B-6 restriction and multilevel partial least squares-discriminant analysis supported this conclusion. Thus, plasma concentrations of creatine, cystathionine, kynurenic acid, and 3-hydroxykynurenine jointly reveal effects of vitamin B-6 restriction on the profiles of one-carbon and tryptophan metabolites and serve as biomarkers of functional effects of marginal vitamin B-6 deficiency.
Journal of Nutrition 08/2013; · 4.20 Impact Factor
[show abstract][hide abstract] ABSTRACT: Many researchers favor repeated measures designs because they allow the detection of within-person change over time and typically have higher statistical power than cross-sectional designs. However, the plethora of inputs needed for repeated measures designs can make sample size selection, a critical step in designing a successful study, difficult. Using a dental pain study as a driving example, we provide guidance for selecting an appropriate sample size for testing a time by treatment interaction for studies with repeated measures. We describe how to (1) gather the required inputs for the sample size calculation, (2) choose appropriate software to perform the calculation, and (3) address practical considerations such as missing data, multiple aims, and continuous covariates.
BMC Medical Research Methodology 07/2013; 13(1):100. · 2.21 Impact Factor
[show abstract][hide abstract] ABSTRACT: The aim of this study was to examine risk factors for reduced mouth or throat cancer (MTC) knowledge using a sample of rural North Floridian adults.
Telephone interviews were conducted across rural census tracts throughout North Florida in 2009-2010, using a survey adapted for cultural appropriateness. The sample consisted of 2,393 individuals (1,059 males and 1,334 females; 1,681 whites and 712 blacks).
Only 9% of the study respondents indicated they had not heard of MTC; however, only 12% endorsed knowing "a lot." Higher education levels and health literacy indicated they had more MTC knowledge. Among female participants, whites had more knowledge than blacks (OR = 1.9). Among black participants, males had more knowledge than females (OR = 1.7). Conversely, greater concern with MTC was associated with lower education levels, health literacy, and financial status, but higher depression scores. Awareness that excessive sun exposure is a risk factor for MTC was lower than for earlier studies using more urban samples.
This study adds to the literature on MTC knowledge and concern because this sample was drawn exclusively from rural populations in North Florida, a group with the highest MTC morbidity and mortality. An unanticipated finding was that blacks were more concerned than their white rural counterparts. This study was also the first to report that depression was associated with increased concern about MTC. The goal is to persuade at-risk groups to obtain MTC screenings with the goal of reducing disparities in MTC whenever they occur.
The Journal of Rural Health 06/2013; 29(3):294-303. · 1.44 Impact Factor
[show abstract][hide abstract] ABSTRACT: Scientists often use a paired comparison of the areas under the receiver
operating characteristic curves to decide which continuous cancer screening
test has the best diagnostic accuracy. In the paired design, all participants
are screened with both tests. Participants with unremarkable screening results
enter a follow-up period. Participants with suspicious screening results and
those who show evidence of disease during follow-up receive the gold standard
test. The remaining participants are classified as non-cases, even though some
may have occult disease. The standard analysis includes all study participants
in the analysis, which can create bias in the estimates of diagnostic accuracy.
If the bias affects the area under the curve for one screening test more than
the other screening test, scientists may make the wrong decision as to which
screening test has better diagnostic accuracy. We describe a weighted maximum
likelihood bias correction method to reduce decision errors. We assessed the
ability of the bias correction method to reduce decision errors via simulation
studies. The simulations compared the Type I error rate and power of the
standard analysis with that of the bias-corrected analysis. The performance of
the bias correction method depends on characteristics of the screening tests
and the disease, and on the percentage of study participants who receive the
gold standard test. In studies with a large amount of bias in the difference in
the full area under the curve, the bias correction method reduces the Type I
error rate and improves power for the correct decision. In order to determine
if bias correction is needed for a specific screening trial, we recommend the
investigator conduct a simulation study using our free software.
[show abstract][hide abstract] ABSTRACT: Background Using covariance or mean estimates from previous dataintroduces randomness into each power value in a power curve. Creatingconfidence intervals about the power estimates improves study planning byallowing scientists to account for the uncertainty in the power estimates.Driving examples arise in many imaging applications.Methods We use both analytical and Monte Carlo simulation methods. Our analytical derivationsapply to power for tests with the univariate approach to repeated measures(UNIREP). Approximate confidence intervals and regions for power based onan estimated covariance matrix and fixed means are described. Extensivesimulations are used to examine the properties of the approximations.Results Closed-form expressions are given for approximate power andconfidence intervals and regions. Monte Carlo simulations support theaccuracy of the approximations for practical ranges of sample size, rank ofthe design matrix, error degrees of freedom, and the amount of deviation fromsphericity. The new methods provide accurate coverage probabilities for allfour UNIREP tests, even for small sample sizes. Accuracy is higher forhigher power values than for lower power values, making the methodsespecially useful in practical research conditions. The new techniquesallow the plotting of power confidence regions around an estimated powercurve, an approach that has been well received by researchers. Freesoftware makes the new methods readily available.Conclusions The new techniques allow a convenient way to account for the uncertainty of using anestimated covariance matrix in choosing a sample size for a repeated measuresANOVA design. Medical imaging and many other types of healthcare researchoften use repeated measures ANOVA.
BMC Medical Research Methodology 04/2013; 13(1):57. · 2.21 Impact Factor
[show abstract][hide abstract] ABSTRACT: BACKGROUND: Oral and pharyngeal cancer is a serious health threat that goes unnoticed by most people. Increasing screenings for oral and pharyngeal cancer is essential to achieving early detection when the disease is most treatable. PURPOSE: We tested the effectiveness of a media campaign designed to increase intentions to seek an oral and pharyngeal cancer screening. We further examined whether concern and knowledge of oral and pharyngeal cancer mediated screening intentions. METHODS: Participants in the intervention condition received messages on posters, handheld fans, pamphlets, and magnets displayed on the sides of cars or trucks. Participants in the intervention and comparison conditions (N = 1,790) were surveyed prior to and after the intervention. RESULTS: Intervention participants reported greater intentions to seek free oral and pharyngeal cancer screenings. Concern about oral and pharyngeal cancer partially mediated the effect whereas knowledge did not. CONCLUSIONS: Our media campaign successfully increased screening intentions by heightening concerns.
Annals of Behavioral Medicine 03/2013; · 4.20 Impact Factor
[show abstract][hide abstract] ABSTRACT: Marginal deficiency of vitamin B-6 is common among segments of the population worldwide. Because pyridoxal 5'-phosphate (PLP) serves as a coenzyme in the metabolism of amino acids, carbohydrates, organic acids, and neurotransmitters, as well as in aspects of one-carbon metabolism, vitamin B-6 deficiency could have many effects. Healthy men and women (age: 20-40 y; n = 23) were fed a 2-day controlled, nutritionally adequate diet followed by a 28-day low-vitamin B-6 diet (<0.5 mg/d) to induce marginal deficiency, as reflected by a decline of plasma PLP from 52.6±14.1 (mean ± SD) to 21.5±4.6 nmol/L (P<0.0001) and increased cystathionine from 131±65 to 199±56 nmol/L (P<0.001). Fasting plasma samples obtained before and after vitamin B6 restriction were analyzed by (1)H-NMR with and without filtration and by targeted quantitative analysis by mass spectrometry (MS). Multilevel partial least squares-discriminant analysis and S-plots of NMR spectra showed that NMR is effective in classifying samples according to vitamin B-6 status and identified discriminating features. NMR spectral features of selected metabolites indicated that vitamin B-6 restriction significantly increased the ratios of glutamine/glutamate and 2-oxoglutarate/glutamate (P<0.001) and tended to increase concentrations of acetate, pyruvate, and trimethylamine-N-oxide (adjusted P<0.05). Tandem MS showed significantly greater plasma proline after vitamin B-6 restriction (adjusted P<0.05), but there were no effects on the profile of 14 other amino acids and 45 acylcarnitines. These findings demonstrate that marginal vitamin B-6 deficiency has widespread metabolic perturbations and illustrate the utility of metabolomics in evaluating complex effects of altered vitamin B-6 intake.
PLoS ONE 01/2013; 8(6):e63544. · 3.73 Impact Factor
[show abstract][hide abstract] ABSTRACT: Previous animal studies showed that severe vitamin B-6 deficiency altered fatty acid profiles of tissue lipids, often with an increase of linoleic acid and a decrease of arachidonic acid. However, little is known about the extent to which vitamin B-6 deficiency affects human fatty acid profiles. The aim of this study was to determine the effects of marginal vitamin B-6 deficiency on fatty acid profiles in plasma, erythrocytes, and peripheral blood mononuclear cells (PBMC) of healthy adults fed a 28-d, low-vitamin B-6 diet. Healthy participants (n = 23) received a 2-d, controlled, vitamin B-6-adequate diet followed by a 28-d, vitamin B-6-restricted diet to induce a marginal deficiency. Plasma HDL and LDL cholesterol concentrations, FFA concentrations, and erythrocyte and PBMC membrane fatty acid compositions did not significantly change from baseline after the 28-d restriction. Plasma total arachidonic acid, EPA, and DHA concentrations decreased from (mean ± SD) 548 ± 96 to 490 ± 94 μmol/L, 37 ± 13 to 32 ± 13 μmol/L, and 121 ± 28 to 109 ± 28 μmol/L [positive false discovery rate (pFDR) adjusted P < 0.05], respectively. The total (n-6):(n-3) PUFA ratio in plasma exhibited a minor increase from 15.4 ± 2.8 to 16.6 ± 3.1 (pFDR adjusted P < 0.05). These data indicate that short-term vitamin B-6 restriction decreases plasma (n-3) and (n-6) PUFA concentrations and tends to increase the plasma (n-6):(n-3) PUFA ratio. Such changes in blood lipids may be associated with the elevated risk of cardiovascular disease in vitamin B-6 insufficiency.
Journal of Nutrition 09/2012; 142(10):1791-7. · 4.20 Impact Factor
[show abstract][hide abstract] ABSTRACT: Adaptive designs allow planned modifications based on data accumulating within a study. The promise of greater flexibility and efficiency stimulates increasing interest in adaptive designs from clinical, academic, and regulatory parties. When adaptive designs are used properly, efficiencies can include a smaller sample size, a more efficient treatment development process, and an increased chance of correctly answering the clinical question of interest. However, improper adaptations can lead to biased studies. A broad definition of adaptive designs allows for countless variations, which creates confusion as to the statistical validity and practical feasibility of many designs. Determining properties of a particular adaptive design requires careful consideration of the scientific context and statistical assumptions. We first review several adaptive designs that garner the most current interest. We focus on the design principles and research issues that lead to particular designs being appealing or unappealing in particular applications. We separately discuss exploratory and confirmatory stage designs in order to account for the differences in regulatory concerns. We include adaptive seamless designs, which combine stages in a unified approach. We also highlight a number of applied areas, such as comparative effectiveness research, that would benefit from the use of adaptive designs. Finally, we describe a number of current barriers and provide initial suggestions for overcoming them in order to promote wider use of appropriate adaptive designs. Given the breadth of the coverage all mathematical and most implementation details are omitted for the sake of brevity. However, the interested reader will find that we provide current references to focused reviews and original theoretical sources which lead to details of the current state of the art in theory and practice.
[show abstract][hide abstract] ABSTRACT: We examined the knowledge and prevalence of mouth and throat cancer examinations in a sample drawn from rural populations in north Florida.
Telephone interviews were conducted across rural census tracts throughout north Florida in 2009 and 2010, in a survey that had been adapted for cultural appropriateness using cognitive interviews. The sample consisted of 2526 respondents (1132 men and 1394 women; 1797 Whites and 729 African Americans).
Awareness of mouth and throat cancer examination (46%) and lifetime receipt (46%) were higher than reported in statewide studies performed over the past 15 years. Only 19% of the respondents were aware of their examination, whereas an additional 27% reported having the examination when a description was provided, suggesting a lack of communication between many caregivers and rural patients. Surprisingly, anticipated racial/ethnic differences were diminished when adjustments were made for health literacy and several measures of socioeconomic status.
These findings support the notion that health disparities are multifactorial and include characteristics such as low health literacy, lack of access to care, and poor communication between patient and provider.
American Journal of Public Health 02/2012; 102(2):e7-14. · 3.93 Impact Factor
[show abstract][hide abstract] ABSTRACT: High-throughput technology in metabolomics, genomics, and proteomics gives rise to high dimension, low sample size data when the number of metabolites, genes, or proteins exceeds the sample size. For a limited class of designs, the classic 'univariate approach' for Gaussian repeated measures can provide a reasonable global hypothesis test. We derive new tests that not only accurately allow more variables than subjects, but also give valid analyses for data with complex between-subject and within-subject designs. Our derivations capitalize on the dual of the error covariance matrix, which is nonsingular when the number of variables exceeds the sample size, to ensure correct statistical inference and enhance computational efficiency. Simulation studies demonstrate that the new tests accurately control Type I error rate and have reasonable power even with a handful of subjects and a thousand outcome variables. We apply the new methods to the study of metabolic consequences of vitamin B6 deficiency. Free software implementing the new methods applies to a wide range of designs, including one group pre-intervention and post-intervention comparisons, multiple parallel group comparisons with one-way or factorial designs, and the adjustment and evaluation of covariate effects.
Statistics in Medicine 12/2011; 31(8):724-42. · 2.04 Impact Factor
[show abstract][hide abstract] ABSTRACT: Analysis of a large longitudinal study of children motivated our work. The results illustrate how accurate inference for fixed effects in a general linear mixed model depends on the covariance model selected for the data. Simulation studies have revealed biased inference for the fixed effects with an underspecified covariance structure, at least in small samples. One underspecification common for longitudinal data assumes a simple random intercept and conditional independence of the within-subject errors (i.e., compound symmetry). We prove that the underspecification creates bias in both small and large samples, indicating that recruiting more participants will not alleviate inflation of the Type I error rate associated with fixed effect inference. Enumerations and simulations help quantify the bias and evaluate strategies for avoiding it. When practical, backwards selection of the covariance model, starting with an unstructured pattern, provides the best protection. Tutorial papers can guide the reader in minimizing the chances of falling into the often spurious software trap of nonconvergence. In some cases, the logic of the study design and the scientific context may support a structured pattern, such as an autoregressive structure. The sandwich estimator provides a valid alternative in sufficiently large samples. Authors reporting mixed-model analyses should note possible biases in fixed effects inference because of the following: (i) the covariance model selection process; (ii) the specific covariance model chosen; or (iii) the test approximation.
Statistics in Medicine 07/2011; 30(22):2696-707. · 2.04 Impact Factor
[show abstract][hide abstract] ABSTRACT: An internal pilot with interim analysis (IPIA) design combines interim power analysis (an internal pilot) with interim data analysis (two stage group sequential). We provide IPIA methods for single df hypotheses within the Gaussian general linear model, including one and two group t tests. The design allows early stopping for efficacy and futility while also re-estimating sample size based on an interim variance estimate. Study planning in small samples requires the exact and computable forms reported here. The formulation gives fast and accurate calculations of power, type I error rate, and expected sample size.
Communication in Statistics- Theory and Methods 12/2010; 39(20):3717-3738. · 0.30 Impact Factor
[show abstract][hide abstract] ABSTRACT: Longitudinal imaging studies have moved to the forefront of medical research
due to their ability to characterize spatio-temporal features of biological
structures across the lifespan. Credible models of the correlations in
longitudinal imaging require two or more pattern components. Valid inference
requires enough flexibility of the correlation model to allow reasonable
fidelity to the true pattern. On the other hand, the existence of computable
estimates demands a parsimonious parameterization of the correlation structure.
For many one-dimensional spatial or temporal arrays, the linear exponent
autoregressive (LEAR) correlation structure meets these two opposing goals in
one model. The LEAR structure is a flexible two-parameter correlation model
that applies in situations in which the within-subject correlation decreases
exponentially in time or space. It allows for an attenuation or acceleration of
the exponential decay rate imposed by the commonly used continuous-time AR(1)
structure. Here we propose the Kronecker product LEAR correlation structure for
multivariate repeated measures data in which the correlation between
measurements for a given subject is induced by two factors. We also provide a
scientifically informed approach to assessing the adequacy of a Kronecker
product LEAR model and a general unstructured Kronecker product model. The
approach provides useful guidance for high dimension, low sample size data that
preclude using standard likelihood based tests. Longitudinal medical imaging
data of caudate morphology in schizophrenia illustrates the appeal of the
Kronecker product LEAR correlation structure.
[show abstract][hide abstract] ABSTRACT: In repeated measures settings, modeling the correlation pattern of the data can be immensely important for proper analyses. Accurate inference requires proper choice of the correlation model. Optimal efficiency of the estimation procedure demands a parsimonious parameterization of the correlation structure, with sufficient sensitivity to detect the range of correlation patterns that may occur. Many repeated measures settings have within-subject correlation decreasing exponentially in time or space. Among the variety of correlation patterns available for this context, the continuous-time first-order autoregressive correlation structure, denoted AR(1), sees the most utilization. Despite its wide use, the AR(1) structure often poorly gauges within-subject correlations that decay at a slower or faster rate than required by the AR(1) model. To address this deficiency we propose a two-parameter generalization of the continuous-time AR(1) model, termed the linear exponent autoregressive (LEAR) correlation structure, which accommodates much slower and much faster decay patterns. Special cases of the LEAR family include the AR(1), compound symmetry, and first-order moving average correlation structures. Excellent analytic, numerical, and statistical properties help make the LEAR structure a valuable addition to the suite of parsimonious correlation models for repeated measures data. Both medical imaging data concerning neonate neurological development and longitudinal data concerning diet and hypertension [DASH (Dietary Approaches to Stop Hypertension) study] exemplify the utility of the LEAR correlation structure.
Statistics in Medicine 07/2010; 29(17):1825-38. · 2.04 Impact Factor
[show abstract][hide abstract] ABSTRACT: Automatic computer segmentation in three dimensions creates opportunity to reduce the cost of three-dimensional treatment planning of radiotherapy for cancer treatment. Comparisons between human and computer accuracy in seg-menting kidneys in CT scans generate distance values far larger in number than the number of CT scans. Such high dimension, low sample size (HDLSS) data present a grand challenge to statisticians: how do we find good estimates and make credible inference? We recommend discovering and using scientifically and statistically sufficient statistics as an additional strategy for overcoming the curse of di-mensionality. First, we reduced the three-dimensional array of distances for each image comparison to a histogram to be modeled individually. Second, we used non-parametric kernel density estimation to explore distributional patterns and assess multi-modality. Third, a systematic exploratory search for parametric distributions and truncated variations led to choosing a Gaussian form as approximating the dis-tribution of a cube root transformation of distance. Fourth, representing each histogram by an individually estimated distribution eliminated the HDLSS problem by reducing on average 26,000 distances per histogram to just 2 parame-ter estimates. In the fifth and final step we used classical statistical methods to demonstrate that the two human ob-servers disagreed significantly less with each other than with the computer segmentation. Nevertheless, the size of all dis-agreements was clinically unimportant relative to the size of a kidney. The hierarchal modeling approach to object-oriented data created response variables deemed sufficient by both the scientists and statisticians. We believe the same strategy provides a useful addition to the imaging toolkit and will succeed with many other high throughput tech-nologies in genetics, metabolomics and chemical analysis.
Statistics and Its Interface Volume. 01/2010; 3:91-101.
[show abstract][hide abstract] ABSTRACT: Mixed effects models have become very popular, especially for the analysis of longitudinal data. One challenge is how to build a good enough mixed effects model. In this paper, we suggest a systematic strategy for addressing this challenge and introduce easily implemented practical advice to build mixed effects models. A general discussion of the scientific strategies motivates the recommended five-step procedure for model fitting. The need to model both the mean structure (the fixed effects) and the covariance structure (the random effects and residual error) creates the fundamental flexibility and complexity. Some very practical recommendations help to conquer the complexity. Centering, scaling, and full-rank coding of all the predictor variables radically improve the chances of convergence, computing speed, and numerical accuracy. Applying computational and assumption diagnostics from univariate linear models to mixed model data greatly helps to detect and solve the related computational problems. Applying computational and assumption diagnostics from the univariate linear models to the mixed model data can radically improve the chances of convergence, computing speed, and numerical accuracy. The approach helps to fit more general covariance models, a crucial step in selecting a credible covariance model needed for defensible inference. A detailed demonstration of the recommended strategy is based on data from a published study of a randomized trial of a multicomponent intervention to prevent young adolescents' alcohol use. The discussion highlights a need for additional covariance and inference tools for mixed models. The discussion also highlights the need for improving how scientists and statisticians teach and review the process of finding a good enough mixed model.
Statistics in Medicine 12/2009; 29(4):504-20. · 2.04 Impact Factor