Most at-risk populations: contextualising HIV prevention programmes targeting marginalised groups in Zanzibar, Tanzania

2014 ◽  
Vol 13 (3) ◽  
pp. 205-213 ◽  
Author(s):  
Naheed Ahmed
Keyword(s):  
At Risk ◽  
Hiv Prevention ◽  
Marginalised Groups ◽  
Prevention Programmes ◽  
At Risk Populations ◽  
Most At Risk Populations

scholarly journals Expanding the Space: Inclusion of Most-at-Risk Populations in HIV Prevention, Treatment, and Care Services

2011 ◽  
Vol 57 ◽  
pp. S96-S99 ◽  
Author(s):  
Chris Beyrer ◽  
Stefan Baral ◽  
Deanna Kerrigan ◽  
Nabila El-Bassel ◽  
Linda-Gail Bekker ◽  
...  
Keyword(s):  
At Risk ◽  
Hiv Prevention ◽  
Care Services ◽  
At Risk Populations ◽  
Most At Risk Populations

scholarly journals Using social networks to understand and overcome implementation barriers in the global HIV response

2019 ◽  
Author(s):  
Guy Harling ◽  
Alexander Tsai
Keyword(s):  
At Risk ◽  
Social Network ◽  
Hiv Prevention ◽  
Implementation Barriers ◽  
Treatment Interventions ◽  
Social Network Data ◽  
Prevention And Treatment ◽  
At Risk Populations ◽  
Most At Risk Populations ◽  
Roll Out

Despite the development of several efficacious HIV prevention and treatment methods in the past decades, HIV continues to spread globally. Uptake of interventions is non-randomly distributed across populations, and such inequality is socially patterned both statically (due to homophily) and dynamically (due to social selection and influence). Social network analysis (SNA) methods, including egocentric, sociocentric, and respondent-driven sampling, provide tools to measure most-at-risk populations, to understand how epidemics spread, and to evaluate intervention take-up. SNA informed designs can improve intervention effectiveness by reaching otherwise inaccessible populations and improve efficiency by maximizing spillovers to at-risk but susceptible individuals through social ties; they thus have the potential to be both more effective and less unequal in their effects than SNA-naïve approaches. While SNA-informed designs are often resource-intensive, they are uniquely able to help reach those most in need of HIV prevention and treatment interventions. Increased collection of social network data during both research and implementation work would provide important information to improve the roll-out of existing studies in the present and to inform the design of more data-efficient, SNA-informed interventions in the future.

scholarly journals Factors Associated with Willingness to Accept Oral Fluid HIV Rapid Testing among Most-at-Risk Populations in China

PLoS ONE ◽  
2013 ◽  
Vol 8 (11) ◽  
pp. e80594 ◽  
Author(s):  
Huanmiao Xun ◽  
Dianmin Kang ◽  
Tao Huang ◽  
Yuesheng Qian ◽  
Xiufang Li ◽  
...  
Keyword(s):  
At Risk ◽  
Oral Fluid ◽  
Willingness To Accept ◽  
Rapid Testing ◽  
Factors Associated ◽  
Hiv Rapid Testing ◽  
At Risk Populations ◽  
Most At Risk Populations

scholarly journals Key factors associated with malaria infection among patients seeking care through the public sector in endemic townships of Ayeyarwady Region, Myanmar

2021 ◽  
Author(s):  
Jillian Dunning ◽  
Nang Khaing Zar Aung ◽  
Abigail Ward ◽  
Moe Moe Aye ◽  
Christopher Lourenço ◽  
...  
Keyword(s):  
Risk Factors ◽  
At Risk ◽  
Malaria Elimination ◽  
Risk Groups ◽  
Behavioral Risk ◽  
Rubber Plantation ◽  
Public Facilities ◽  
Causal Pathway ◽  
At Risk Populations ◽  
Most At Risk Populations

Abstract BackgroundAyeyarwady Region in Myanmar has made significant progress towards malaria elimination, with confirmed cases decreasing from 13,522 in 2013 to 122 in 2019. As transmission declines, malaria becomes increasingly focalized both in geographic hotspots and among population groups sharing certain risk factors. Developing a thorough profile of high-risk activities associated with malaria infections is critical to ensure intervention approaches are evidence-based. MethodsA test-negative study was conducted from September 2017 to May 2018 in Ngaputaw, Pathein and Thabaung townships in Ayeyarwady Region. Patients that presented to selected public facilities or community health volunteers with fever answered survey questions on demographic and behavioral risk factors, including exposure to malaria interventions, and were assigned to case and control groups based on the result of a malaria rapid diagnostic test. A random-effects logistic regression model adjusted for clustering at the facility level, as well as any variables along the causal pathway described by a directed acyclic graph, was used to determine odds ratios and association with malaria infections. ResultsA total of 119 cases and 1,744 controls were recruited from 41 public facilities, with a mean age of 31.3 and 63.7% percent male. Higher risk groups were identified as males (aOR 1.8, 95% CI: 1.2–2.9) and those with a worksite located within the forest (aOR 2.8, 95% CI: 1.4–5.3), specifically working in the logging (aOR 2.7, 95% CI: 1.5–4.6) and rubber plantation (aOR 3.0, 95% CI: 1.4–6.8) industries. Additionally, links between forest travel and malaria were observed, with risk factors identified to be sleeping in the forest within the past month (aOR 2.6, 95% CI: 1.1–6.3), and extended forest travel with durations from 3 to 14 days (aOR 8.6, 95% CI: 3.5–21.4) or longer periods (aOR 8.4, 95% CI: 3.2–21.6). ConclusionMalaria transmission is highly focalized in Ayeyarwady, and results illustrate the need to target interventions to the most at-risk populations of working males and forest goers. It will become increasingly necessary to ensure full intervention coverage of at-risk populations active in forested areas as Myanmar moves closer to malaria elimination goals.

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