Barriers and Delays in Tuberculosis Diagnosis and Treatment Services: Does Gender Matter?

Background:. Tuberculosis (TB) remains a global public health problem with known gender-related disparities. We reviewed the quantitative evidence for gender-related differences in accessing TB services from symptom onset to treatment initiation. Methods:. Following a systematic review process, we: searched 12 electronic databases; included quantitative studies assessing gender differences in accessing TB diagnostic and treatment services; abstracted data; and assessed study validity. We defined barriers and delays at the individual and provider/system levels using a conceptual framework of the TB care continuum and examined gender-related differences. Results:. Among 13,448 articles, 137 were included: many assessed individual-level barriers (52%) and delays (42%), 76% surveyed persons presenting for care with diagnosed or suspected TB, 24% surveyed community members, and two-thirds were from African and Asian regions. Many studies reported no gender differences. Among studies reporting disparities, women faced greater barriers (financial: 64% versus 36%; physical: 100% versus 0%; stigma: 85% versus 15%; health literacy: 67% versus 33%; and provider-/system-level: 100% versus 0%) and longer delays (presentation to diagnosis: 45% versus 0%) than men. Conclusions:. Many studies found no quantitative gender-related differences in barriers and delays limiting access to TB services. When differences were identified, women experienced greater barriers and longer delays than men

Early Mortality in Adults Initiating Antiretroviral Therapy (ART) in Low- and Middle-Income Countries (LMIC): A Systematic Review and Meta-Analysis

BackgroundWe systematically reviewed observational studies of early mortality post-antiretroviral therapy (ART) initiation in low- and middle-income countries (LMIC) in Asia, Africa, and Central and South America, as defined by the World Bank, to summarize what is known.Methods and FindingsStudies published in English between January 1996 and December 2010 were searched in Medline and EMBASE. Three independent reviewers examined studies of mortality within one year post-ART. An article was included if the study was conducted in a LMIC, participants were initiating ART in a non-clinical trial setting and were ≥15 years. Fifty studies were included; 38 (76%) from sub-Saharan Africa (SSA), 5 (10%) from Asia, 2 (4%) from the Americas, and 5 (10%) were multi-regional. Median follow-up time and pre-ART CD4 cell count ranged from 3–55 months and 11–192 cells/mm3, respectively. Loss-to-follow-up, reported in 40 (80%) studies, ranged from 0.3%–27%. Overall, SSA had the highest pooled 12-month mortality probability of 0.17 (95% CI 0.11–0.24) versus 0.11 (95% CI 0.10–0.13) for Asia, and 0.07 (95% CI 0.007–0.20) for the Americas. Of 14 (28%) studies reporting cause-specific mortality, tuberculosis (TB) (5%–44%), wasting (5%–53%), advanced HIV (20%–37%), and chronic diarrhea (10%–25%) were most common. Independent factors associated with early mortality in 30 (60%) studies included: low baseline CD4 cell count, male sex, advanced World Health Organization clinical stage, low body mass index, anemia, age greater than 40 years, and pre-ART quantitative HIV RNA.ConclusionsSignificant heterogeneity in outcomes and in methods of reporting outcomes exist among published studies evaluating mortality in the first year after ART initiation in LMIC. Early mortality rates are highest in SSA, and opportunistic illnesses such as TB and wasting syndrome are the most common reported causes of death. Strategies addressing modifiable risk factors associated with early death are urgently needed

Coronary artery dissection following chest trauma

Chest trauma has a high rate of mortality. Coronary dissection causing myocardial infarction (MI) following blunt chest trauma is rare. We describe the case of an anterior MI following blunt chest trauma. A 39-year-old male was received in our hospital following a motorcycle accident. The patient was asymptomatic before the accident. The patient underwent craniotomy for evacuation of hematoma. He developed severe chest pain and an electrocardiogram (ECG) revealed anterior ST segment elevation following surgery. Acute coronary event was medically managed; subsequently, coronary angiogram was performed that showed dissection in the left anterior coronary artery, which was stented

Bilateral spontaneous internal carotid artery dissection managed with endovascular stenting – A case report

AbstractCarotid artery dissection (CAD) is a frequent cause of stroke, accounting for up to 25% of all ischemic strokes in young and middle-aged patients.1,2 It may be traumatic or spontaneous, with multi-factorial etiology. A tear in the arterial wall causes intrusion of blood within its layers, producing intra-luminal stenosis, or aneurysmal dilatation.3 Thrombo-embolism arising from this anatomic disruption has been postulated as the essential stroke mechanism in CAD.4 Bilateral internal carotid artery dissection (ICAD) has been rarely reported.1,4Antiplatelets and anticoagulation remain standard therapy for CAD.5 However, in patients with either expanding pseudoaneurysms, severe flow compromise, worsening symptoms despite anticoagulation or contraindication to anticoagulation, endovascular stenting is beneficial.6We describe a patient with ischemic stroke from spontaneous bilateral ICAD with completely occluded left ICA. Having failed medical therapy with antiplatelets and anticoagulants due to extensive loss of carotid vascular supply, he was managed successfully with endovascular stenting with good neurological recovery

Cognos: crowdsourcing search for topic experts in microblogs

Finding topic experts on microblogging sites with millions of users, such as Twitter, is a hard and challenging problem. In this paper, we propose and investigate a new methodology for discovering topic experts in the popular Twitter social network. Our methodology relies on the wisdom of the Twitter crowds – it leverages Twitter Lists, which are often carefully curated by individual users to include experts on topics that interest them and whose meta-data (List names and descriptions) provides valuable semantic cues to the experts’ domain of expertise. We mined List information to build Cognos, a system for finding topic experts in Twitter. Detailed experimental evaluation based on a real-world deployment shows that: (a) Cognos infers a user’s expertise more accurately and comprehensively than state-of-the-art systems that rely on the user’s bio or tweet content, (b) Cognos scales well due to built-in mechanisms to efficiently update its experts ’ database with new users, and (c) Despite relying only on a single feature, namely crowdsourced Lists, Cognos yields results comparable to, if not better than, those given by the official Twitter experts search engine for a wide range of queries in user tests. Our study highlights Lists as a potentially valuable source of information for future content or expert search systems in Twitter

On Sampling the Wisdom of Crowds: Random vs. Expert Sampling of the Twitter Stream

Several applications today rely upon content streams crowdsourced from online social networks. Since real-time processing of large amounts of data generated on these sites is difficult, analytics companies and researchers are increasingly resorting to sampling. In this paper, we investigate the crucial question of how to sample the data generated by users in social networks. The traditional method is to randomly sample all the data. We analyze a different sampling methodology, where content is gathered only from a relatively small subset (< 1%) of the user population namely, the expert users. Over the duration of a month, we gathered tweets from over 500,000 Twitter users who are identified as experts on a diverse set of topics, and compared the resulting expert-sampled tweets with the 1 % randomly sampled tweets provided publicly by Twitter. We compared the sampled datasets along several dimensions, including the diversity, timeliness, and trustworthiness of the information contained within them, and find important differences between the datasets. Our observations have major implications for applications such as topical search, trustworthy content recommendations, and breaking news detection

Understanding and Combating Link Farming in the Twitter Social Network

Recently, Twitter has emerged as a popular platform for discovering real-time information on the Web, such as news stories and people’s reaction tothem. Like theWeb, Twitter has become a target for link farming, where users, especially spammers, try to acquire large numbers of follower links in the social network. Acquiring followers not only increases the size of a user’s direct audience, but also contributes to the perceived influence of the user, which in turn impacts the ranking of the user’s tweets by search engines. In this paper, we first investigate link farming in the Twitter network and then explore mechanisms to discourage the activity. To this end, we conducted a detailed analysis of links acquired by over 40,000 spammer accounts suspended by Twitter. We find that link farming is wide spread and that a majority of spammers ’ links are farmed from a small fraction of Twitter users, the social capitalists, who are themselves seeking to amass social capital and links by following back anyone who follows them. Our findings shed light on the social dynamics that are at the root of the link farming problem in Twitter network and they have important implications for future designs of link spam defenses. In particular, we show that a simple user ranking scheme that penalizes users for connecting to spammers can effectively address the problem by disincentivizing users from linking with other users simply to gain influence. Categories andSubject Descriptors H.3.5 [Online Information Services]: Web-based services

Inferring who-is-who in the twitter social network

In this paper, we design and evaluate a novel who-is-who service for inferring attributes that characterize individual Twitter users. Our methodology exploits the Lists feature, which allows a user to group other users who tend to tweet on atopic thatis ofinterest toher, andfollow theircollective tweets. Our key insight is that the List meta-data (names anddescriptions)providesvaluablesemantic cuesaboutwho the users included in the Lists are, including their topics of expertise and how they are perceived by the public. Thus, we can infer a user’s expertise by analyzing the meta-data of crowdsourced Lists that contain the user. We show that our methodology can accurately and comprehensively infer attributes of millions of Twitter users, including a vast majority ofTwitter’s influentialusers(basedonrankingmetrics like number of followers). Our work provides a foundation for building better search and recommendation services on Twitter. to the success of Twitter, but it also poses a big challenge: how can microbloggers tell who is who in Twitter? Knowing the credentials of a Twitter user can crucially help others determine how much trust or importance they should place in the content posted by the user. In this paper, we present the design and evaluation of a novel who-is-who inference system for users on the popular Twitter microblogging site. Figure 1 shows an illustrative tag cloud of attributes inferred by our service for Lada Adamic, who is an active Twitter user and a wellknown researcher in the area of social networks [9]. Note that these attributes not only contain her biographical information (she is a professor at umsi, umich – University of Michigan’s School of Information), but also capture her expertise (she is an expert on social media, network-analysis, social networks, csresearch, hci, statphysics) as well as popular perceptions about her (she is a bigname, a thinker, and a goodblogger(s).

Early Mortality in Adults Initiating Antiretroviral Therapy (ART) in Low- and Middle-Income Countries (LMIC): A Systematic Review and Meta-Analysis

BACKGROUND: We systematically reviewed observational studies of early mortality post-antiretroviral therapy (ART) initiation in low- and middle-income countries (LMIC) in Asia, Africa, and Central and South America, as defined by the World Bank, to summarize what is known. METHODS AND FINDINGS: Studies published in English between January 1996 and December 2010 were searched in Medline and EMBASE. Three independent reviewers examined studies of mortality within one year post-ART. An article was included if the study was conducted in a LMIC, participants were initiating ART in a non-clinical trial setting and were ≥15 years. Fifty studies were included; 38 (76%) from sub-Saharan Africa (SSA), 5 (10%) from Asia, 2 (4%) from the Americas, and 5 (10%) were multi-regional. Median follow-up time and pre-ART CD4 cell count ranged from 3–55 months and 11–192 cells/mm(3), respectively. Loss-to-follow-up, reported in 40 (80%) studies, ranged from 0.3%–27%. Overall, SSA had the highest pooled 12-month mortality probability of 0.17 (95% CI 0.11–0.24) versus 0.11 (95% CI 0.10–0.13) for Asia, and 0.07 (95% CI 0.007–0.20) for the Americas. Of 14 (28%) studies reporting cause-specific mortality, tuberculosis (TB) (5%–44%), wasting (5%–53%), advanced HIV (20%–37%), and chronic diarrhea (10%–25%) were most common. Independent factors associated with early mortality in 30 (60%) studies included: low baseline CD4 cell count, male sex, advanced World Health Organization clinical stage, low body mass index, anemia, age greater than 40 years, and pre-ART quantitative HIV RNA. CONCLUSIONS: Significant heterogeneity in outcomes and in methods of reporting outcomes exist among published studies evaluating mortality in the first year after ART initiation in LMIC. Early mortality rates are highest in SSA, and opportunistic illnesses such as TB and wasting syndrome are the most common reported causes of death. Strategies addressing modifiable risk factors associated with early death are urgently needed