scholarly journals Breaking the paradigm: Optimized Case Finding multiplies tuberculosis detection among key populations in Ukraine

2021 ◽  
Vol 15 (09.1) ◽  
pp. 75S-81S
Author(s):  
Liliia Masiuk ◽  
Olga Denisiuk ◽  
Evgenia Geliukh ◽  
Zahedul Islam ◽  
Garry Aslanyan ◽  
...  
Keyword(s):  
Social Network ◽  
General Population ◽  
People Who Inject Drugs ◽  
Scale Up ◽  
Case Finding ◽  
Key Populations ◽  
Case Detection Rate ◽  
Tb Treatment ◽  
The Social ◽  
Close Contacts

Introduction: In 2018, there were 3 million “missed” tuberculosis (TB) cases globally, much of which was disproportionally concentrated among key populations. To enhance TB case-finding, an Optimized Case Finding (OCF) strategy involving all contacts within the social network of an index TB case was introduced in five regions of Ukraine. We assessed TB detection and linkage to TB treatment using OCF in key populations. Methodology: A cohort study using routine programme data (July 2018 – March 2020). OCF empowers the index TB case to identify and refer up to eight close contacts within his/her social network for TB investigations. Results: Of 726 index TB cases in key populations, 6998 close contacts were referred for TB investigations and 275 were diagnosed with TB (183 drug-sensitive and 92 drug-resistant TB). The TB case detection rate was 3930/100,000 and the Numbers Needed to Investigate to detect one TB case was 25. TB was most frequent among people who inject drugs and homeless groups. Compared to TB detection using routine household case finding within the general population (1090/100,000), OCF was 3.6-fold more effective and when compared to passive case finding in the general population (60/100,000), OCF was 66 times more effective. 99% (273) of TB patients were linked to care and initiated TB treatment. Conclusions: The OCF strategy among key populations is highly effective in detecting TB cases and linking them to care. We advocate to scale-up this case finding strategy in Ukraine and beyond.

scholarly journals Examination of ‘Interests’ and ‘Activities’ of Social Network users

2019 ◽  
Vol 9 (2) ◽  
pp. 2688-2693
Keyword(s):  
Machine Learning ◽  
Social Media ◽  
Social Network ◽  
General Population ◽  
Social Media Marketing ◽  
Family Status ◽  
Learning Methods ◽  
Attribute Data ◽  
Machine Learning Methods ◽  
The Social

The present study relates to the analysis of attribute data related to users of the social network VK. The general population N = 52,614 users is the intersection of audiences from two communities for social media marketing. Based on the collected statistics on the “interests” attribute, one can compile a generalized portrait of an IT specialist and online marketer: this is a man aged about 30 years old, not married, or who defines his family status as “everything is complicated”. He speaks an average of two languages, works for an organization, or studies at a university. He has about 370 followers on VK. The result based on the data from the field 'activities' is very close to the data from the field 'interests', and gives a similar picture of the generalized portrait of a specialist. As part of the study, the authors have learned how to segment users into the users that identify themselves as „IT specialists or online marketers‟, and „other‟ users, using machine learning methods

network scale-up method

2004 ◽  
Vol 1 (2) ◽  
pp. 395-405
Author(s):  
Silvia Snidero ◽  
Roberto Corradetti ◽  
Dario Gregori
Keyword(s):  
Social Network ◽  
Scale Up ◽  
Monte Carlo Simulation Study ◽  
The Social ◽  
Network Scale ◽  
Ideal Number ◽  
Unbiased Estimates ◽  
The Mean ◽  
Ml Estimator ◽  
Biased Estimates

The network scale-up method is a social network estimator for the size of hidden or hard-to-count subpopulations. These estimators are based on a simple model which have however strong assumptions. The basic idea is that the proportion of the mean number of people known by respondent in a subpopulation E of T of size e is the same of the proportion that the e subpopulation E forms in general population T of size t: mc = t , where c is the number of persons known by each respondent and m is the mean number of persons known by each respondent in the subpopulation E. The persons known by every subject is called the "social network", and its size is c, estimated by several estimators proposed in the recent literature. In this paper we present a Monte Carlo simulation study aimed at understanding the behavior of the scale-up method type estimators under several conditions. The first goal was to understand what would be the ideal number of subpopulations of known size to be used in planning the research. The second goal was to analyze what happens when we use overlapped subpopulations. Our results showed that with the scale-up estimator we always obtain biased estimates for any number of subpopulations employed in estimates. With the Killworth's ML estimator, the improvement of scale-up method, we have substantially unbiased estimates under any condition. Also in case of overlapping, and increasing the degree of it among subpopulations, bias raises with scale-up method, instead it remains close to zero with ML estimator.

scholarly journals People Who Inject Drugs and have tuberculosis: Opioid Substitution Therapy improves treatment outcomes in Ukraine

2021 ◽  
Vol 15 (09.1) ◽  
pp. 51S-57S
Author(s):  
Tetiana Fomenko ◽  
Anna Meteliuk ◽  
Larysa Korinchuk ◽  
Olga Denisiuk ◽  
Garry Aslanyan ◽  
...  
Keyword(s):  
Treatment Outcomes ◽  
People Who Inject Drugs ◽  
Treatment Success ◽  
Scale Up ◽  
Drug Resistant ◽  
Health Coverage ◽  
Substitution Therapy ◽  
Opioid Substitution Therapy ◽  
Tb Treatment ◽  
Opioid Substitution

Introduction: Opioid substitution therapy (OST) is one of the pillars of harm reduction strategies for People Who Inject Drugs (PWID). It should be an integral part of tuberculosis (TB) care to increase the uptake, compliance and effectiveness of treatment and also curtail risk behaviors. We aimed to compare TB treatment outcomes in relation to OST among PWID in six regions of Ukraine. Methodology: A retrospective cohort study using routine programmatic data from centers offering integrated TB and OST (December 2016 – May 2020). OST involved use of methadone or buprenorphine. TB treatment outcomes were standardized. Results: Of 228 PWID (85% male) diagnosed with TB, 104 (46%) had drug-sensitive and 124 (64%) drug-resistant TB. The majority had pulmonary TB (95%), 64 (28%) were HCV-positive and 179 (78%) were HIV-positive, 91% of the latter were also on antiretroviral therapy. There were 114 (50%) PWID with TB on OST. For drug-sensitive TB (n=104), treatment success was significantly higher (61%) in those on adjunctive OST than those not on OST (42%, P<0.001). Similarly, for drug-resistant TB (n=124) treatment success was also significantly higher when individuals were on OST (43%) compared to when not on OST (26%, P<0.001). Conclusions: This operational research study shows that OST is associated with significantly improved treatment success in PWID and can contribute to achieving Universal Health Coverage and the WHO Flagship Initiative “Find.Treat.All. #End TB”. We advocate for the scale-up of this intervention in Ukraine.

Reconstructing the social network of HIV key populations from locally observed information

AIDS Care ◽  
2021 ◽  
pp. 1-8
Author(s):  
Fengshi Jing ◽  
Qingpeng Zhang ◽  
Weiming Tang ◽  
Johnson Zixin Wang ◽  
Joseph Tak-fai Lau ◽  
...  
Keyword(s):  
Social Network ◽  
Key Populations ◽  
Observed Information ◽  
The Social

scholarly journals The TB REACH Initiative: Supporting TB Elimination Efforts in the Asia-Pacific

2020 ◽  
Vol 5 (4) ◽  
pp. 164
Author(s):  
Jacob Creswell ◽  
Amera Khan ◽  
Mirjam I Bakker ◽  
Miranda Brouwer ◽  
Vishnu Vardhan Kamineni ◽  
...  
Keyword(s):  
Scale Up ◽  
Case Finding ◽  
Asia Pacific ◽  
Active Case Finding ◽  
Asia Pacific Region ◽  
Tb Control ◽  
Tb Treatment ◽  
Collective Response ◽  
Development Evaluation ◽  
Using Data

After many years of TB ‘control’ and incremental progress, the TB community is talking about ending the disease, yet this will only be possible with a shift in the way we approach the TB response. While the Asia-Pacific region has the highest TB burden worldwide, it also has the opportunity to lead the quest to end TB by embracing the four areas laid out in this series: using data to target hotspots, initiating active case finding, provisioning preventive TB treatment, and employing a biosocial approach. The Stop TB Partnership’s TB REACH initiative provides a platform to support partners in the development, evaluation and scale-up of new and innovative technologies and approaches to advance TB programs. We present several approaches TB REACH is taking to support its partners in the Asia-Pacific and globally to advance our collective response to end TB.

scholarly journals Social network methods for HIV case-finding among people who inject drugs in Tajikistan

2018 ◽  
Vol 21 ◽  
pp. e25139 ◽  
Author(s):  
Maxim Kan ◽  
Danielle B Garfinkel ◽  
Olga Samoylova ◽  
Robert P Gray ◽  
Kristen M Little
Keyword(s):  
Social Network ◽  
People Who Inject Drugs ◽  
Case Finding ◽  
Network Methods

scholarly journals Strategies for active detection of tuberculosis in Ukraine: Comparative effectiveness amongst key populations (2014-2018)

2019 ◽  
Vol 13 (07.1) ◽  
pp. 89S-94S
Author(s):  
Natalia Kamenska ◽  
Dilyara Nabirova ◽  
Karapet Davtyan ◽  
Hayk Davtyan ◽  
Rony Zachariah ◽  
...  
Keyword(s):  
Sex Workers ◽  
Case Finding ◽  
Key Populations ◽  
Active Case Finding ◽  
Active Case ◽  
Tb Treatment ◽  
Number Needed To Screen ◽  
Symptom Screening ◽  
Specimen Collection ◽  
Sex With Men

Introduction: Ukraine has gaps in Tuberculosis (TB) service coverage, especially in key populations (KPs). We compared effectiveness of three different strategies for active TB detection among KPs and their linkage to TB treatment during three time periods. Methodology: The KPs included people who inject drugs (PWID), sex workers (SW), men who have sex with men (MSM) and groups at-risk of TB (ex-prisoners, Roma and homeless). The active case finding included decentralized symptom screening and specimen collection (2014, strategy-1), decentralized screening with patient referred for specimen collection (2015-2017, strategy-2) and strategy-2 plus GeneXpert (2018, strategy-3). Results: In total 680,760 KPs were screened, of whom 68% were PWID. TB case detection per 100,000 populations was 1,191 in strategy-1, 302 in strategy-2, and 235 in strategy-3. The number needed to screen (NNS) to identify one case was respectively 84, 332, and 425. TB detection was highest among homeless (range: 1,839-2,297 per 100,000 population). The lowest detection was among the MSM and SW. Between 2014 and 2018, 82-94% of all diagnosed TB patients in KPs started TB treatment. Conclusions: The active case finding in KPs increased detection of TB cases in Ukraine, and the majority of diagnosed KPs initiated TB treatment. Centralization of diagnosis reduced the effectiveness of TB screening. Each region in Ukraine should assess the composition and the needs of KPs which will allow for adoption of specific strategies to detect TB among KPs with high TB prevalence.

The social network

2013 ◽  
Vol 44 (2) ◽  
pp. 22
Author(s):  
ALAN ROCKOFF
Keyword(s):  
Social Network ◽  
The Social
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