Journal
PLOS ONE
Volume 9, Issue 8, Pages -Publisher
PUBLIC LIBRARY SCIENCE
DOI: 10.1371/journal.pone.0103678
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Funding
- Special Programme for Research and Training in Tropical Diseases (TDR/WHO/World bank) [A60443, A60482]
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Background: The visceral leishmaniasis (VL) elimination program in Bangladesh is in its attack phase. The primary goal of this phase is to decrease the burden of VL as much as possible. Active case detection (ACD) by the fever camp method and an approach using past VL cases in the last 6-12 months have been found useful for detection of VL patients in the community. We aimed to explore the yield of Accelerated Active Case Detection (AACD) of non-self reporting VL as well as the factors that are associated with non-self reporting to hospitals in endemic communities of Bangladesh. Methods: Our study was conducted in the Trishal sub-district of Mymensingh, a highly VL endemic region of Bangladesh. We used a two-stage sampling strategy from 12 VL endemic unions of Trishal. Two villages from each union were selected at random. We looked for VL patients who had self-reported to the hospital and were under treatment from these villages. Then we conducted AACD for VL cases in those villages using house-to-house visit. Suspected VL cases were referred to the Trishal hospital where diagnosis and treatment of VL was done following National Guidelines for VL case management. We collected socio-demographic information from patients or a patient guardian using a structured questionnaire. Results: The total number of VL cases was 51. Nineteen of 51 (37.3%) were identified by AACD. Poverty, female gender and poor knowledge about VL were independent factors associated with non self-reporting to the hospital. Conclusion: Our primary finding is that AACD is a useful method for early detection of VL cases that would otherwise go unreported to the hospital in later stage due to poverty, poor knowledge about VL and gender inequity. We recommend that the National VL Program should consider AACD to strengthen its early VL case detection strategy.
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