The potential for glycemic control monitoring and screening for diabetes at dental visits using oral blood.

OBJECTIVES: We examined the potential for glycemic control monitoring and screening for diabetes in a dental setting among adults (n = 408) with or at risk for diabetes. METHODS: In 2013 and 2014, we performed hemoglobin A1c (HbA1c) tests on dried blood samples of gingival crevicular blood and compared these with paired "gold-standard" HbA1c tests with dried finger-stick blood samples in New York City dental clinic patients. We examined differences in sociodemographics and diabetes-related risk and health care characteristics for 3 groups of at-risk patients. RESULTS: About half of the study sample had elevated HbA1c values in the combined prediabetes and diabetes ranges, with approximately one fourth of those in the diabetes range. With a correlation of 0.991 between gingival crevicular and finger-stick blood HbA1c, measures of concurrence between the tests were extremely high for both elevated HbA1c and diabetes-range HbA1c levels. Persons already diagnosed with diabetes and undiagnosed persons aged 45 years or older could especially benefit from HbA1c testing at dental visits. CONCLUSIONS: Gingival crevicular blood collected at the dental visit can be used to screen for diabetes and monitor glycemic control for many at-risk patients.

Bayesian estimates of autocorrelations in single-case designs.

Researchers in the single-case design tradition have debated the size and importance of the observed autocorrelations in those designs. All of the past estimates of the autocorrelation in that literature have taken the observed autocorrelation estimates as the data to be used in the debate. However, estimates of the autocorrelation are subject to great sampling error when the design has a small number of time points, as is typically the situation in single-case designs. Thus, a given observed autocorrelation may greatly over- or underestimate the corresponding population parameter. This article presents Bayesian estimates of the autocorrelation that greatly reduce the role of sampling error, as compared to past estimators. Simpler empirical Bayes estimates are presented first, in order to illustrate the fundamental notions of autocorrelation sampling error and shrinkage, followed by fully Bayesian estimates, and the difference between the two is explained. Scripts to do the analyses are available as supplemental materials. The analyses are illustrated using two examples from the single-case design literature. Bayesian estimation warrants wider use, not only in debates about the size of autocorrelations, but also in statistical methods that require an independent estimate of the autocorrelation to analyze the data.

Enhancing Drug Treatment Program Staff's Self-Efficacy to Support Patients' HCV Needs.

To increase HCV-related support for patients in substance abuse treatment programs, we implemented an on-site staff training in 16 programs throughout the United States. It aimed to increase participants' self-efficacy in assisting patients with their HCV-related needs. Findings indicate that participants' self-efficacy increased both 1- and 3-months post-training, resulting in providers' perceptions that they were better able to support patients regarding HCV. Implementing an engaging and interactive HCV training for social workers and other substance abuse treatment program staff has the potential to increase their HCV knowledge, self-efficacy, and the HCV-related assistance provided to patients both in the short- and longer-term.

Screening patients in busy hospital-based HIV care centers for hazardous and harmful drinking patterns: the identification of an optimal screening tool.

Because many HIV care providers fail to detect patients' hazardous drinking, we examined the potential use of the AUDIT-C, the first 3 of the 10 items comprising the Alcohol Use Disorders Identification Test (AUDIT), to efficiently screen patients for alcohol abuse. To perform this examination, we used Item Response Theory (IRT) involving individual AUDIT items and AUDIT instruments completed by patients (N = 400) at a Designated AIDS Center (DAC) in New York City. At various AUDIT-C cutoff scores, specificities and sensitivities were then examined using the AUDIT as a ''gold standard.'' For cutoff scores on the AUDIT from 4 to 8, cutoff scores on the AUDIT-C of 3 and 4, respectively, resulted in sensitivities between .94-.98 and .81-.89, respectively, and specificities between .82-.91 and .91-1.0, respectively. In busy HIV care centers, the AUDIT-C with cutoff scores of 3 or 4 is a reasonable alternative to the full AUDIT as an alcohol screening instrument.

Using Bayesian Correspondence Criteria to Compare Results From a Randomized Experiment and a Quasi-Experiment Allowing Self-Selection.

BACKGROUND: Randomized experiments yield unbiased estimates of treatment effect, but such experiments are not always feasible. So researchers have searched for conditions under which randomized and nonrandomized experiments can yield the same answer. This search requires well-justified and informative correspondence criteria, that is, criteria by which we can judge if the results from an appropriately adjusted nonrandomized experiment well-approximate results from randomized experiments. Past criteria have relied exclusively on frequentist statistics, using criteria such as whether results agree in sign or statistical significance or whether results differ significantly from each other. OBJECTIVES: In this article, we show how Bayesian correspondence criteria offer more varied, nuanced, and informative answers than those from frequentist approaches. RESEARCH DESIGN: We describe the conceptual bases of Bayesian correspondence criteria and then illustrate many possibilities using an example that compares results from a randomized experiment to results from a parallel nonequivalent comparison group experiment in which participants could choose their condition. RESULTS: Results suggest that, in this case, the quasi-experiment reasonably approximated the randomized experiment. CONCLUSIONS: We conclude with a discussion of the advantages (computation of relevant quantities, interpretation, and estimation of quantities of interest for policy), disadvantages, and limitations of Bayesian correspondence criteria. We believe that in most circumstances, the advantages of Bayesian approaches far outweigh the disadvantages.

Using latent class analysis to identify patterns of hepatitis C service provision in drug-free treatment programs in the U.S.

Hepatitis C virus (HCV) infection is a global health problem, and in many countries (including the U.S.), illicit drug users constitute the group at greatest risk for contracting and transmitting HCV. Drug treatment programs are therefore unique sites of opportunity for providing medical care and support for many HCV infected individuals. This paper determines subtypes of a large sample of U.S. drug-free treatment programs (N=333) according to services they provide to patients with HCV infection, and examines the organizational and aggregate patient characteristics of programs in these subtypes. A latent class analysis identified four subtypes of HCV service provision: a "Most Comprehensive Services" class (13% of the sample), a "Comprehensive Off-Site Medical Services" class (54%), a "Medical Monitoring Services" class (8%) and a "Minimal Services" class (25%). "Comprehensive" services class programs were less likely to be outpatient and private for profit than those in the other two classes. It is of concern that so many programs belong to the "Minimal Services" class, especially because some of these programs serve many injection drug users. "Minimal Services" class programs in the U.S. need to innovate services so that their HCV infected patients can get the medical and support care they need. Similar analyses in other countries can inform their policy makers about the capacity of their drug treatment programs to provide support to their HCV infected patients.

Single-case experimental design yielded an effect estimate corresponding to a randomized controlled trial.

OBJECTIVES: We reanalyzed data from a previous randomized crossover design that administered high or low doses of intravenous immunoglobulin (IgG) to 12 patients with hypogammaglobulinaemia over 12 time points, with crossover after time 6. The objective was to see if results corresponded when analyzed as a set of single-case experimental designs vs. as a usual randomized controlled trial (RCT). STUDY DESIGN AND SETTINGS: Two blinded statisticians independently analyzed results. One analyzed the RCT comparing mean outcomes of group A (high dose IgG) to group B (low dose IgG) at the usual trial end point (time 6 in this case). The other analyzed all 12 time points for the group B patients as six single-case experimental designs analyzed together in a Bayesian nonlinear framework. RESULTS: In the randomized trial, group A [M = 794.93; standard deviation (SD) = 90.48] had significantly higher serum IgG levels at time six than group B (M = 283.89; SD = 71.10) (t = 10.88; df = 10; P < 0.001), yielding a mean difference of MD = 511.05 [standard error (SE) = 46.98]. For the single-case experimental designs, the effect from an intrinsically nonlinear regression was also significant and comparable in size with overlapping confidence intervals: MD = 495.00, SE = 54.41, and t = 495.00/54.41 = 9.10. Subsequent exploratory analyses indicated that how trend was modeled made a difference to these conclusions. CONCLUSIONS: The results of single-case experimental designs accurately approximated results from an RCT, although more work is needed to understand the conditions under which this holds.

Predictors of Depression Among Adult Women With Diabetes in the United States: An Analysis Using National Health and Nutrition Examination Survey Data From 2007 to 2012.

PURPOSE: The purpose of the study was to identify the sex-specific characteristics that predict depression among adult women with diabetes. METHODS: Data from the 2007-2012 National Health and Nutrition Examination Survey in the United States were used to identify the predictors of depression in a large sample of women ages 20 years and older with diabetes (n = 946). RESULTS: When extrapolated to almost 9 million women in the United States ≥ 20 years of age with diabetes, 19.0% had depression. Female-specific significant predictors of depression included younger age (< 65 years old), less than high school graduation, self-rated fair or poor health, inactivity due to poor health, and pain that interferes with usual activities. Marital status and diabetes-related factors (years living with diabetes, use of insulin, parent or sibling with diabetes) were not significant predictors of depression in adult women with diabetes. CONCLUSION: When educating and counseling women with diabetes, diabetes educators should be aware that some of the predictors of depression in women with diabetes differ from those of populations that include both sexes. Depression screening, although important for all women with diabetes, should especially be performed among women with female-specific depression predictors.

Latinas with elevated fasting plasma glucose: an analysis using NHANES 2009-2010 data.

For Latinas with fasting plasma glucose (FPG) levels in the prediabetes and diabetes ranges, early detection can support steps to optimize their health. Data collected in 2009-2010 indicate that 36.7% of Latinas in the United States had elevated FPG levels. Latinas with elevated FPG who were unaware of their diabetes status were significantly less likely than non-Hispanic White and non-Hispanic Black women to have seen a health care provider in the past year (75.8%, 92.9%, and 90.2%, respectively; p = .018). With almost 1 million Latinas in the United States with elevated FPG unaware of their diabetes risk, and less likely than other at-risk women to see health care providers, there is an urgent need to establish alternate sites of opportunity for their diabetes screening.

Analyzing data from single-case designs using multilevel models: new applications and some agenda items for future research.

Several authors have proposed the use of multilevel models to analyze data from single-case designs. This article extends that work in 2 ways. First, examples are given of how to estimate these models when the single-case designs have features that have not been considered by past authors. These include the use of polynomial coefficients to model nonlinear change, the modeling of counts (Poisson distributed) or proportions (binomially distributed) as outcomes, the use of 2 different ways of modeling treatment effects in ABAB designs, and applications of these models to alternating treatment and changing criterion designs. Second, issues that arise when multilevel models are used for the analysis of single-case designs are discussed; such issues can form part of an agenda for future research on this topic. These include statistical power and assumptions, applications to more complex single-case designs, the role of exploratory data analyses, extensions to other kinds of outcome variables and sampling distributions, and other statistical programs that can be used to do such analyses.

Alcohol education provided to opioid treatment program patients: results of a nationwide survey.

Alcohol-related problems are especially common among opioid treatment program (OTP) patients, suggesting that educating OTP patients about alcohol and its harmful effects needs to be a priority in OTPs. Using data collected in interviews with a nationwide U.S. sample of OTP directors (N = 200) in 25 states, we identified factors that differentiate OTPs that provided this education to all OTP patients from those that did not. Findings indicate that these factors include (1) providing this education in a greater variety of ways, (2) having a larger percent of staff knowledgeable about alcohol-related issues, (3) having a director who views alcohol issues as a high priority, and (4) having a written OTP policy.

Using UnGraph to extract data from image files: verification of reliability and validity.

Certain research tasks require extracting data points from graphs and charts. Using 91 graphs that presented results from single-case designs, we investigated whether pairs of coders extract similar data from the same graphs (reliability), and whether the extracted data match numerical descriptions of the graph that the original author may have presented in tables or text (validity). Coders extracted data using the UnGraph computer program. Extraction proved highly reliable over several different kinds of analyses. Coders nearly always extracted identical numbers of data points, and the values they assigned to those data points were nearly identical. Extraction also proved highly valid, with the means of extracted data correlating nearly perfectly with means reported in tables or text and with very few discrepancies in any single case. These results suggest that researchers can use extracted data with a high degree of confidence that they are nearly identical to the original data.

Assessing the Consequences of Using Self-report Data to Determine the Correlates of HIV Status: Conditional and Marginal Approaches.

This article examines whether relationships between individual characteristics and HIV status can be identified when self-report data are used as a proxy for HIV serotest results. The analyses use data obtained from HIV serotests and face-to-face interviews with 7,256 out-of-treatment drug users in ten sites from 1992 to 1998. Relationships between 17 individual characteristics and HIV status (measured both by self-report and serotest results) were determined by (a) comparing and evaluating the fit of both standard and "nonstandard" loglinear models, and (b) evaluating the fit of marginal homogeneity models. The loglinear analyses showed that HIV serostatus was related to individual characteristics in 38 percent of the relationships. In most cases, the strength of the relationships between HIV status and individual characteristics did not differ when HIV status was measured with self-report data.