Skip to content
naulon
Not checkedNot checked for notices yet.

Evaluation of a tuberculosis active case finding project in peri-urban areas, Myanmar: 2014-2016

Sandar Aye, Suman S. Majumdar, Myo Minn Oo and 9 more

International Journal of Infectious Diseases · 2018

doi:10.1016/j.ijid.2018.02.012 (opens in a new tab)

We email you when a new notice or a question about this paper appears.

Abstract

Objectives: We assessed the effect of an active case finding (ACF) project on tuberculosis (TB) case notification and the yields from a household and neigbourhood intervention (screening contacts of historical index TB patients diagnosed >24 months ago) and a community intervention (screening attendants of health education sessions/mobile clinics). Design: Cross-sectional analysis of project records, township TB registers and annual TB reports. Results: In the household and neigbourhood intervention, of 56,709 people screened, 1,076 were presumptive TB and 74 patients were treated for active TB with a screening yield of 0.1% and a yield from presumptive cases of 6.9%. In the community intervention, of 162,881 people screened, 4,497 were presumptive TB and 984 were treated for active TB with a screening yield of 0.6% and yield from presumptive cases of 21.9%. Of active TB cases, 94% were new, 89% were pulmonary, 44% were bacteriologically-confirmed and 5% had HIV. Case notification rates per 100,000 in project townships increased from 142 during baseline (2011-2013) to 148 during intervention (2014-2016) periods. Conclusions: The yield from household and neigbourhood intervention was lower than community intervention. This finding highlights reconsidering the strategy of screening of contacts from historical index cases. Strategies to reach high-risk groups should be explored for future ACF interventions to increase yield of TB. Keywords: Tuberculosis, Community-based active case finding, TB screening, Myanmar

Abstract licence: CC BY-NC-ND

Cited by papers we hold1

Reads

Read by people on this page

None yet.

Checked or read by AI through naulon

None yet.

A person counts once a day per paper. We keep no cookie and no address: a reader is a hash that changes every day. Any month under five reads shows no number.

Are you an author?

Is this your paper?

Claim it to add your note and be paid when agents cite it. Free.