Maze MJ, Sharples KJ, Allan KJ, Biggs HM, Cash-Goldwasser S, Galloway RL, De Glanville WA, Halliday JEB, Kazwala RR, Kibona T, Mmbaga BT, Maro VP, Rubach MP, Cleaveland S, Crump JA. Estimating acute human leptospirosis incidence in northern Tanzania using sentinel site and community behavioural surveillance. Zoonoses and public health. 2020 May 6. PMID: 32374085 [PubMed]
Many infectious diseases lack robust estimates of incidence from endemic areas, and extrapolating incidence when there are few locations with data remains a major challenge in burden of disease estimation. We sought to combine sentinel surveillance with community behavioural surveillance to estimate leptospirosis incidence. We administered a questionnaire gathering responses on established locally relevant leptospirosis risk factors and recent fever to livestock-owning community members across six districts in northern Tanzania and applied a logistic regression model predicting leptospirosis risk on the basis of behavioural factors that had been previously developed among patients with fever in Moshi Municipal and Moshi Rural Districts. We aggregated probability of leptospirosis by district and estimated incidence in each district by standardizing probabilities to those previously estimated for Moshi Districts. We recruited 286 community participants: Hai District (n = 11), Longido District (59), Monduli District (56), Moshi Municipal District (103), Moshi Rural District (44) and Rombo District (13). The mean predicted probability of leptospirosis by district was Hai 0.029 (0.005, 0.095), Longido 0.071 (0.009, 0.235), Monduli 0.055 (0.009, 0.206), Moshi Rural 0.014 (0.002, 0.049), Moshi Municipal 0.015 (0.004, 0.048) and Rombo 0.031 (0.006, 0.121). We estimated the annual incidence (upper and lower bounds of estimate) per 100,000 people of human leptospirosis among livestock owners by district as Hai 35 (6, 114), Longido 85 (11, 282), Monduli 66 (11, 247), Moshi Rural 17 (2, 59), Moshi Municipal 18 (5, 58) and Rombo 47 (7, 145). Use of community behavioural surveillance may be a useful tool for extrapolating disease incidence beyond sentinel surveillance sites.