Computational protocol: Performance of Kala-Azar Surveillance in Gaffargaon Subdistrict of Mymensingh, Bangladesh

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Protocol publication

[…] We expected 1.4 kala-azar cases per 100 households (based on an unpublished 2009 survey data). Also, using Epi Info StatCalc, we calculated that we would need a sample of 68 cases (i.e. 4857 households) to detect 50% under-reporting through the national surveillance system with 10% precision and 90% confidence []. First, we excluded two of the 15 unions that were part of central administration. Care seeking experience of kala-azar patients from these two unions would be different than that of the patients from the other unions because of the proximity to the Gaffargaon upazila health complex (UHC). Then we randomly sampled three unions from the sampling frame of 13 unions. In each of these sampled unions we randomly selected three villages. We then sampled households in each village, aiming for about 600 households per village. If a village had more than 600 households we divided it into different paras or localities and randomly sampled paras until we got 600 households. If a village had less than 600 households, we sampled all the households of that village. This resulted in sampling of less number of households than expected. The resulting sample included a total of 4703 households from nine sampled villages from three sampled unions of Gaffargaon.Our case definition required diagnosis by a qualified health care provider based on clinical presentation and a positive confirmatory diagnostic test. Most of the study respondents could not mention the name of the confirmatory diagnostic test. But they mentioned that the providers who confirmed kala-azar did so based on positive diagnostic test results for kala-azar. Going from sampled house to house in the period December 2011 to May 2012, we sought cases that had occurred between January 2010 and December 2011. In each household the informant was a case or a senior family member. [...] Quantitative data were digitized using Epi Info (version 3.5.3). Information from the patients and families was recorded in MS Excel in a file which also noted whether the patient was recorded in the UHC registers. Qualitative data included extensive notes on general observations. The recordings were transcribed in the Bengali language for the three UHC staff.Quantitative data were analysed using STATA version 8 []. We compared frequencies of patient and health system factors for those found and not found in hospital records. The factors were age, sex, place of diagnosis, treatment location, drug and year. Any difference with p<0.05 was considered statistically significant.Interview transcripts were contrasted for recurring themes and informative quotations related to the research questions. Quotations used in this publication were translated into English by author KMR. All analyses were supported by the general observations. […]

Pipeline specifications

Software tools Epi Info, Stata
Application Population genetic analysis
Organisms Homo sapiens
Diseases Leishmaniasis, Visceral