Central Lymph Node Dissection In Patients With Papillary Thyroid Cancer: A Population Level Analysis Of 14257 Cases

CENTRAL LYMPH NODE DISSECTION IN PATIENTS WITH PAPILLARY THYROID CANCER: A POPULATION LEVEL ANALYSIS OF 14,257 CASES. Chineme Enyioha, Sanziana Roman and Julie Ann Sosa. Department of Surgery, Yale School of Medicine, New Haven, CT. The role of prophylactic central lymph node dissection (CLND) in patients with differentiated thyroid cancer has been controversial. This study analyzes the impact of patient demographic factors and tumor size on surgery with CLND in patients with papillary thyroid cancer (PTC) in the U.S. All patients \u3e/= 18 with PTC and follicular variant-PTC, who underwent thyroidectomy with or without CLND in SEER, 2004-08, were included. Bivariate and multivariate analyses were performed to determine effects of patient demographic and clinical characteristics on the likelihood of undergoing CLND. Of 14,257 patients in the study, 80.3% were women, 84.3% white, and the average age was 50.1 years. 79.6% had a total thyroidectomy, and 37.1% had a CLND. Bivariate analysis revealed that patients who were older, black, and from the South were less likely to undergo CLND (all p\u3c. 001). Patients with T1 tumors were least likely to undergo CLND (36.6% compared to 57.2% of T4 tumors, p\u3c. 01). 32.1% of patients with 2 cm had CLND, of which 3.6% (microPTC) and 8.8% (tumors 1-2cm) had positive nodes compared to 34.2% of patients with T4 tumors. From 2004 to 2008, there was an 18.3% increase in overall use of CLND. On multivariate analysis, younger age, female gender, white race, and Northeast region were independently associated with an increased likelihood of undergoing CLND. While the use of CLND has increased over time even in patients with T1 tumors, several demographic factors remain associated with lower likelihood of receiving CLND. This variation in practice suggests potential disparity in access and quality of surgical care for PTC in the U.S

Willingness-To-Try Various Tobacco Cessation Methods Among US Adult Cigarette Smokers

Introduction: Long-term smoking cessation success rates without substantive intervention remain abysmal. Some studies suggest an association between sociodemographic factors andtobacco cessation success. We sought to explore US adult tobacco users’ willingness-to-try diverse tobacco cessation methods by sociodemographics and tobacco use habits.Methods: We electronically surveyed a convenience sample of 562 US adults to explore willingness-to-try various cessation methods among those who reported current tobacco cigarette use. Participants rated their willingness-to-try different cessation methods. Logistic regression models examined associations between willingness-to-try tobacco cessation methods based onsociodemographic and tobacco use characteristics.Results: Non-whites were more likely to report willingness-to-try counseling (RR 1.32, 95% CI 1.14, 1.52) and those with high school education or less were less likely to report willingness-to-try counseling (RR 0.78, 95% CI 0.64, 0.95). Those with lower income were less likely to report willingness-to-try any medication (RR 0.84, 95% CI 0.73, 0.98). High nicotine dependence wasassociated with a high likelihood of reporting willingness-to-try any evidence-based method (RR 1.07, 95% CI 1.04, 1.10) and a history of quit attempts was associated with likelihood to reportwillingness-to-try any evidence-based method (RR 1.31, 95% CI 1.10, 1.56).Conclusion: Sociodemographics and nicotine dependence may affect preferences for tobacco cessation methods and should be considered when counseling patients on tobacco cessationMaster of Public Healt

Tobacco Use and Treatment among Cancer Survivors

Tobacco use is causally associated with the risk of developing multiple health conditions, including over a dozen types of cancer, and is responsible for 30% of cancer deaths in the U [...

Black Smokers’ Preferences for Features of a Smoking Cessation App: Qualitative Study

BackgroundMobile health (mHealth) interventions for smoking cessation have grown extensively over the last few years. Although these interventions improve cessation rates, studies of these interventions consistently lack sufficient Black smokers; hence knowledge of features that make mHealth interventions attractive to Black smokers is limited. Identifying features of mHealth interventions for smoking cessation preferred by Black smokers is critical to developing an intervention that they are likely to use. This may in turn address smoking cessation challenges and barriers to care, which may reduce smoking-related disparities that currently exist. ObjectiveThis study aims to identify features of mHealth interventions that appeal to Black smokers using an evidence-based app developed by the National Cancer Institute, QuitGuide, as a reference. MethodsWe recruited Black adult smokers from national web-based research panels with a focus on the Southeastern United States. Participants were asked to download and use QuitGuide for at least a week before participation in remote individual interviews. Participants gave their opinions about features of the QuitGuide app and other mHealth apps they may have used in the past and suggestions for future apps. ResultsOf the 18 participants, 78% (n=14) were women, with age ranging from 32 to 65 years. Themes within five major areas relevant for developing a future mHealth smoking cessation app emerged from the individual interviews: (1) content needs including health and financial benefits of quitting, testimonials from individuals who were successful in quitting, and strategies for quitting; (2) format needs such as images, ability to interact with and respond to elements within the app, and links to other helpful resources; (3) functionality including tracking of smoking behavior and symptoms, provision of tailored feedback and reminders to users, and an app that allows for personalization of functions; (4) social network, such as connecting with friends and family through the app, connecting with other users on social media, and connecting with a smoking cessation coach or therapist; and (5) the need for inclusivity for Black individuals, which may be accomplished through the inclusion of smoking-related information and health statistics specific for Black individuals, the inclusion of testimonials from Black celebrities who successfully quit, and the inclusion of cultural relevance in messages contained in the app. ConclusionsCertain features of mHealth interventions for smoking cessation were highly preferred by Black smokers based on their use of a preexisting mHealth app, QuitGuide. Some of these preferences are similar to those already identified by the general population, whereas preferences for increasing the inclusivity of the app are more specific to Black smokers. These findings can serve as the groundwork for a large-scale experiment to evaluate preferences with a larger sample size and can be applied in developing mHealth apps that Black smokers may be more likely to use

Data from: Genetic diversity and population structure of Trypanosoma brucei in Uganda: implications for the epidemiology of sleeping sickness and Nagana

Background: While Human African Trypanosomiasis (HAT) is in decline on the continent of Africa, the disease still remains a major health problem in Uganda. There are recurrent sporadic outbreaks in the traditionally endemic areas in south-east Uganda, and continued spread to new unaffected areas in central Uganda. We evaluated the evolutionary dynamics underpinning the origin of new foci and the impact of host species on parasite genetic diversity in Uganda. We genotyped 269 Trypanosoma brucei isolates collected from different regions in Uganda and southwestern Kenya at 17 microsatellite loci, and checked for the presence of the SRA gene that confers human infectivity to T. b. rhodesiense. Results: Both Bayesian clustering methods and Discriminant Analysis of Principal Components partition Trypanosoma brucei isolates obtained from Uganda and southwestern Kenya into three distinct genetic clusters. Clusters 1 and 3 include isolates from central and southern Uganda, while cluster 2 contains mostly isolates from southwestern Kenya. These three clusters are not sorted by subspecies designation (T. b. brucei vs T. b. rhodesiense), host or date of collection. The analyses also show evidence of genetic admixture among the three genetic clusters and long-range dispersal, suggesting recent and possibly on-going gene flow between them. Conclusions: Our results show that the expansion of the disease to the new foci in central Uganda occurred from the northward spread of T. b. rhodesiense (Tbr). They also confirm the emergence of the human infective strains (Tbr) from non-infective T. b. brucei (Tbb) strains of different genetic backgrounds, and the importance of cattle as Tbr reservoir, as confounders that shape the epidemiology of sleeping sickness in the region

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microsatellite dat

The 19 Ugandan and Kenyan districts from which <i>T</i>. <i>brucei</i> samples were collected.

<p>The dotted lines indicate the <i>G</i>. <i>f</i>. <i>fuscipes</i> distribution in the study region, and thus the distribution of <i>T</i>. <i>brucei</i>; there is a disjunct area of <i>G</i>. <i>f</i>. <i>fuscipes</i> around Lake George. Lakes (grey shading) are indicated by name. Districts are identified by two/three letter abbreviations (expanded in <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003353#pntd.0003353.t001" target="_blank">Table 1</a> and <a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003353#pntd.0003353.s001" target="_blank">S1 Table</a>). Districts are color-coded as follows: green—new foci of <i>T</i>. <i>b</i>. <i>rhodesiense</i> (<i>Tbr</i>) in central Uganda; blue—old foci of <i>Tbr</i> in southeastern Uganda; orange—foci of <i>Tbr</i> in western Kenya. The blue and green shaded areas separated by Lake Kyoga also demarcate the genetically distinct northern and southern <i>G</i>. <i>f</i>. <i>fuscipes</i> populations[<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003353#pntd.0003353.ref016" target="_blank">16</a>–<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003353#pntd.0003353.ref007" target="_blank">7</a>].</p

AMOVA results.

<p>Results of AMOVA analyses on seventeen microsatellite loci of <i>T</i>. <i>brucei</i> isolates partitioned into four groups: host (human, cattle, sheep, pig, dog, wild animals and tsetse flies), year of isolation (decade), subspecies, and structure/DAPC inferred genetic clusters. Asterisks denote comparisons with significant p values (<0.05).</p><p>AMOVA results.</p

Data from: Genetic diversity and population structure of Trypanosoma brucei in Uganda: implications for the epidemiology of sleeping sickness and Nagana

Background: While Human African Trypanosomiasis (HAT) is in decline on the continent of Africa, the disease still remains a major health problem in Uganda. There are recurrent sporadic outbreaks in the traditionally endemic areas in south-east Uganda, and continued spread to new unaffected areas in central Uganda. We evaluated the evolutionary dynamics underpinning the origin of new foci and the impact of host species on parasite genetic diversity in Uganda. We genotyped 269 Trypanosoma brucei isolates collected from different regions in Uganda and southwestern Kenya at 17 microsatellite loci, and checked for the presence of the SRA gene that confers human infectivity to T. b. rhodesiense. Results: Both Bayesian clustering methods and Discriminant Analysis of Principal Components partition Trypanosoma brucei isolates obtained from Uganda and southwestern Kenya into three distinct genetic clusters. Clusters 1 and 3 include isolates from central and southern Uganda, while cluster 2 contains mostly isolates from southwestern Kenya. These three clusters are not sorted by subspecies designation (T. b. brucei vs T. b. rhodesiense), host or date of collection. The analyses also show evidence of genetic admixture among the three genetic clusters and long-range dispersal, suggesting recent and possibly on-going gene flow between them. Conclusions: Our results show that the expansion of the disease to the new foci in central Uganda occurred from the northward spread of T. b. rhodesiense (Tbr). They also confirm the emergence of the human infective strains (Tbr) from non-infective T. b. brucei (Tbb) strains of different genetic backgrounds, and the importance of cattle as Tbr reservoir, as confounders that shape the epidemiology of sleeping sickness in the region.,alltrypsmicrosatellite data

Sampling locality details.

<p>Sample sizes and genetic diversity statistics for seventeen microsatellite loci across <i>Trypanosoma brucei</i> isolates from 19 districts (<a href="http://www.plosntds.org/article/info:doi/10.1371/journal.pntd.0003353#pntd.0003353.g001" target="_blank">Fig. 1</a>). N = number of samples analyzed, A_R = allele richness, H_E = expected heterozygosity, H_O = observed heterozygosity and F_IS = Fisher’s inbreeding coefficient. N/A = data not available because only a single sample was collected.</p><p>Sampling locality details.</p