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Explaining Racial/Ethnic Disparities in Children’s Dental Health: A Decomposition Analysis
No full textAbstract available at publisher's website
Attributes of researchers and their strategies to recruit minority populations: Results of a national survey
Get PDFDespite NIH mandates for inclusion, recruiting minorities is challenging for biomedical and
public health researchers. Little is known about how attributes of researchers affect their
choice of recruitment strategies. The purpose of this study was to address this gap by
examining how use of recruitment strategies relates to other researcher characteristics. To do
this, we conducted an online survey from May to August 2010 with researchers (principal
investigators, research staff, and IRB members) in which we measured the number and types
of recruitment strategies utilized, along with other characteristics of the researchers and their
research. We identified two clusters of researchers: comprehensive researchers who utilized a
greater number and more diverse and active recruitment strategies, and traditional
researchers, who utilized fewer and more passive strategies. Additional characteristics that
distinguished the two groups were that comprehensive researchers were more likely than
traditional researchers to 1) report racial and ethnic differences as one of their specific aims or
hypotheses, 2) receive federal (CDC and NIH) funding, 3) conduct behavioral or epidemiological
research, and 4) have received training in conducting research with and recruiting
minorities. Traditional researchers, on the other hand, were more likely to conduct clinical
research and a greater (though non-significant) percentage received funding from pharmaceutical
sources. This study provides a novel description of how researcher attributes are
related to their recruitment strategies and raises a number of future research questions to
further examine the implications of this relationship
Health Impacts of Yoga and Pranayama: A State‑of‑the‑Art Review
No full textThousands of years ago yoga originated in India, and in present day and age, an alarming awareness was observed in health and natural remedies among people by yoga and pranayama which has been proven an effective method for improving health in addition to prevention and management of diseases. With increasing scientific research in yoga, its therapeutic aspects are also being explored. Yoga is reported to reduce stress and anxiety, improves autonomic functions by triggering neurohormonal mechanisms by the suppression of sympathetic activity, and even, now‑a‑days, several reports suggested yoga is beneficial for physical health of cancer patients. Such global recognition of yoga also testifies to India’s growing cultural influence
Physical Activity and Physical Fitness: Standardizing Assessment with the PhenX Toolkit
No full textThe focus of the PhenX (Phenotypes and eXposures) Toolkit is to provide researchers whose expertise lies outside a particular area with key measures identified by experts for uniform use in large-scale genetic studies and other extensive epidemiologic efforts going forward. The current paper specifically addresses the PhenX Toolkit research domain of physical activity and physical fitness (PA/PF), which are often associated with health outcomes. A Working Group (WG) of content experts completed a 6-month consensus process in which they identified a set of 14 high-priority, low-burden, and scientifically supported measures. During this process, the WG considered self-reported and objective measures that included the latest technology (e.g., accelerometers, pedometers, and heart-rate monitors). They also sought the input of measurement experts and other members of the research community during their deliberations. A majority of the measures include protocols for children (or adolescents), adults, and older adults or are applicable to all ages.
Measures from the PA/PF domain and 20 other domains are publicly available and found at the PhenX Toolkit website, www.phenxtoolkit.org. The use of common measures and protocols across large studies enhances the capacity to combine or compare data across studies, benefiting both PA/PF experts and non-experts. Use of these common measures by the research community should increase statistical power and enhance the ability to answer scientific questions that previously might have gone unanswered
Effect of Regression from Prediabetes to Normal Glucose Regulation on Long-Term Reduction in Diabetes Risk: Results from the Diabetes Prevention Program Outcomes Study
No full textSummary
Background
Our objective was to quantify and predict diabetesriskreduction during the DiabetesPreventionProgramOutcomes Study (DPPOS) in participants who returned to normalglucoseregulation at least once during the DiabetesPreventionProgram (DPP) compared with those who consistently met criteria for prediabetes.
Methods
DPPOS is an ongoing observational study of participants from the DPP randomised trial. For this analysis, diabetes cumulative incidence in DPPOS was calculated for participants with normalglucoseregulation or prediabetes status during DPP with and without stratification by previous randomised treatment group. Cox proportional hazards modelling and generalised linear mixed models were used to quantify the effect of previous (DPP) glycaemic status on risk of later (DPPOS) diabetes and normalglucoseregulation status, respectively, per SD in change. Included in this analysis were 1990 participants of DPPOS who had been randomly assigned to treatment groups during DPP (736 intensive lifestyle intervention, 647 metformin, 607 placebo). These studies are registered at ClinicalTrials.gov, NCT00004992 (DPP) and NCT00038727 (DPPOS).
Findings
Diabetesrisk during DPPOS was 56% lower for participants who had returned to normalglucoseregulation versus those who consistently had prediabetes (hazard ratio [HR] 0·44, 95% CI 0·37–0·55, p<0·0001) and was unaffected by previous group assignment (interaction test for normalglucoseregulation and lifestyle intervention, p=0·1722; normalglucoseregulation and metformin, p=0·3304). Many, but not all, of the variables that increased diabetesrisk were inversely associated with the chance of a participant reaching normalglucoseregulation status in DPPOS. Specifically, previous achievement of normalglucoseregulation (odds ratio [OR] 3·18, 95% CI 2·71–3·72, p<0·0001), increased β-cell function (OR 1·28; 95% CI 1·18–1·39, p<0·0001), and insulin sensitivity (OR 1·16, 95% CI 1·08–1·25, p<0·0001) were associated with normalglucoseregulation in DPPOS, whereas the opposite was true for prediction of diabetes, with increased β-cell function (HR 0·80, 95% CI 0·71–0·89, p<0·0001) and insulin sensitivity (HR 0·83, 95% CI 0·74–0·94, p=0·0001) having a protective effect. Among participants who did not return to normalglucoseregulation in DPP, those assigned to the intensive lifestyle intervention had a higher diabetesrisk (HR 1·31, 95% CI 1·03–1·68, p=0·0304) and lower chance of normalglucoseregulation (OR 0·59, 95% CI 0·42–0·82, p=0·0014) than did the placebo group in DPPOS.
Interpretation
We conclude that prediabetes is a high-risk state for diabetes, especially in patients who remain with prediabetes despite intensive lifestyle intervention. Reversion to normalglucoseregulation, even if transient, is associated with a significantly reduced risk of future diabetes independent of previous treatment group
Racial and Ethnic Disparities in Depression Care in Community-Dwelling Elderly in the United States
No full textAbstract available at publisher's website
The Missing "One-Offs": The Hidden Supply of High Achieving, Low Income Students
Get PDFWe show that the vast majority of very high-achieving students who are low-income do not apply to any selective college or university. This is despite the fact that selective institutions would often cost them less, owing to generous financial aid, than the resource-poor two-year and non-selective four-year institutions to which they actually apply. Moreover, high-achieving, low-income students who do apply to selective institutions are admitted and graduate at high rates. We demonstrate that these low-income students' application behavior differs greatly from that of their high-income counterparts who have similar achievement. The latter group generally follows the advice to apply to a few "par" colleges, a few "reach" colleges, and a couple of "safety" schools. We separate the low-income, high-achieving students into those whose application behavior is similar to that of their high-income counterparts ("achievement-typical" behavior) and those whose apply to no selective institutions ("income-typical" behavior). We show that income-typical students do not come from families or neighborhoods that are more disadvantaged than those of achievement-typical students. However, in contrast to the achievement-typical students, the income-typical students come from districts too small to support selective public high schools, are not in a critical mass of fellow high achievers, and are unlikely to encounter a teacher or schoolmate from an older cohort who attended a selective college. We demonstrate that widely-used policies–college admissions staff recruiting, college campus visits, college access programs–are likely to be ineffective with income-typical students, and we suggest policies that will be effective must depend less on geographic concentration of high achievers
