Browsing by Author "Hattendorf, J."
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Item Molecular epidemiology of Mycobacterium africanum in Ghana(BioMed Central Ltd., 2016) Asante-Poku, A.; Otchere, I.D.; Osei-Wusu, S.; Sarpong, E.; Baddoo, A.; Forson, A.; Laryea, C.; Borrell, S.; Bonsu, F.; Hattendorf, J.; Ahorlu, C.; Koram, K.A.; Gagneux, S.; Yeboah-Manu, D.Background: Mycobacterium africanum comprises two phylogenetic lineages within the M. tuberculosis complex (MTBC) and is an important cause of human tuberculosis (TB) in West Africa. The reasons for this geographic restriction of M. africanum remain unclear. Here, we performed a prospective study to explore associations between the characteristics of TB patients and the MTBC lineages circulating in Ghana. Method: We genotyped 1,211 MTBC isolates recovered from pulmonary TB patients recruited between 2012 and 2014 using single nucleotide polymorphism typing and spoligotyping. Associations between patient and pathogen variables were assessed using univariate and multivariate logistic regression. Results: Of the 1,211 MTBC isolates analysed, 71.9 % (871) belonged to Lineage 4; 12.6 % (152) to Lineage 5 (also known as M. africanum West-Africa 1), 9.2 % (112) to Lineage 6 (also known as M. africanum West-Africa 2) and 0. 6 % (7) to Mycobacterium bovis. Univariate analysis revealed that Lineage 6 strains were less likely to be isoniazid resistant compared to other strains (odds ratio = 0.25, 95 % confidence interval (CI): 0.05-0.77, P < 0.01). Multivariate analysis showed that Lineage 5 was significantly more common in patients from the Ewe ethnic group (adjusted odds ratio (adjOR): 2.79; 95 % CI: 1.47-5.29, P < 0.001) and Lineage 6 more likely to be found among HIV-co-infected TB patients (adjOR = 2.2; 95 % confidence interval (CI: 1.32-3.7, P < 0.001). Conclusion: Our findings confirm the importance of M. africanum in Ghana and highlight the need to differentiate between Lineage 5 and Lineage 6, as these lineages differ in associated patient variables.Item Mycobacterium africanum Is Associated with Patient Ethnicity in Ghana(Public Library of Science, 2015) Asante-Poku, A.; Yeboah-Manu, D.; Otchere, I.D.; Aboagye, S.Y.; Stucki, D.; Hattendorf, J.; Borrell, S.; Feldmann, J.; Danso, E.; Gagneux, S.Mycobacterium africanum is a member of the Mycobacterium tuberculosis complex (MTBC) and an important cause of human tuberculosis in West Africa that is rarely observed elsewhere. Here we genotyped 613 MTBC clinical isolates from Ghana, and searched for associations between the different phylogenetic lineages of MTBC and patient variables. We found that 17.1% (105/613) of the MTBC isolates belonged to M. africanum, with the remaining belonging to M. tuberculosis sensu stricto. No M. bovis was identified in this sample. M. africanum was significantly more common in tuberculosis patients belonging to the Ewe ethnic group (adjusted odds ratio: 3.02; 95% confidence interval: 1.67–5.47, p<0.001). Stratifying our analysis by the two phylogenetic lineages of M. africanum (i.e. MTBC Lineages 5 and 6) revealed that this association was mainly driven by Lineage 5 (also known as M. africanum West Africa 1). Our findings suggest interactions between the genetic diversity of MTBC and human diversity, and offer a possible explanation for the geographical restriction of M. africanum to parts of West Africa. © 2015 Asante-Poku et al.Item Raising the Political Profile of the Neglected Zoonotic Diseases: Three Complementary European Commission-Funded Projects to Streamline Research, Build Capacity and Advocate for Control(Public Library of Science, 2015) Okello, A.L.; Beange, I.; Shaw, A.; Moriyón, I.; Gabriël, S.; Bardosh, K.; Johansen, M.V.; Saarnak, C.; Mukaratirwa, S.; Berkvens, D.; Welburn, S.C.; Gibbs, P.; de Balogh, K.; Amajoh, C.; Meslin, F.X.; Kock, R.; Kazibwe, S.W.; Ducrotoy, M.; McManus, P.; Scudamore, J.; Hattendorf, J.; Schelling, E.; Zinsstag, J.; Tanner, M.; Dorny, P.; Victor, B.; Ocholi, R.; Kazwala, R.; Ngowi, H.; Moser, I.; Tanner, M.; Correia-Neves, M.; Källenius, G.; Boué, F.; Pétavy, A.F.; Fihri, O.F.; Traoré, A.; Afonso, S.; Nicolau, Q.; Phiri, I.; Sikasunge, C.; Mwape, K.E.; Makungu, C.; Waiswa, C.; Kabasa, J.D.; Hargrove, J.; Hendrickx, G.; Bryssinckx, W.; Marcotty, T.; Bottieau, E.; Thys, S.; Madder, M.; Schelling, E.; Zinsstag, J.; Hatz, C.; Hattendorf, J.; Thrusfield, M.; Källenius, G.; Kulane, A.; Marrone, G.; Hendrickx, G.; Ducheyne, E.; de Groot, B.; Michel, A.; Venter, L.; Perridge, R.; Jenkins, A.; Rhalem, A.; Sahibi, H.; Bouslikhane, M.; Bakou, S.; Kone, P.; Waladjo, A.K.; Gbati, O.B.; Binka, F.; Clerk, C.; Sackey, S.; Afari, E.; Yebuah, N.; Gibbs, P.; de Balogh, K.; Abela-Ridder, B.; Thiermann, A.; Scudamore, J.; Simoonga, C.; Hendrickx, G.; Ducheyne, E.; de Groot, B.; Bryssinckx, W.; Marcotty, T.; Thys, S.; Schelling, E.; Zinsstag, J.Item Relationship between animal health and livestock farmers’ wellbeing in Ghana: beyond zoonoses(BMC Public Health, 2023) Nuvey, F.S.; Haydon, D.T; Hattendorf, J.; Addo, K.K.; Mensah, G.I.; Fink, G.; Zinsstag, J.; Bonfoh, B.Introduction Livestock production is a key livelihood source for many people in developing countries. Poor control of livestock diseases hamper livestock productivity, threatening farmers’ wellbeing and food security. This study estimates the effect of livestock mortalities attributable to disease on the wellbeing of livestock farmers. Methods Overall, 350 ruminant livestock farmers were randomly selected from three districts located in the north, middle and southern belts of Ghana. Mixed-effect linear regression models were used to estimate the relationship between animal health and farmer wellbeing. Farmer wellbeing was assessed using the WHOQOL-BREF tool, as the mean quality-of-life in four domains (physical, psychological, social, and environmental). Animal health was assessed as annual livestock mortalities to diseases adjusted for herd size, and standardized in tropical livestock units to account for different ruminant livestock species. We adjusted for the potential confounding effect of farmers’ age, sex, educational attainment, farmland size, socio-economic status, perception of disease risk to herd, satisfaction with health, previous experience of disease outbreaks in herds, and social support availability by including these as fixed effects, and community as random effects, in a pre-specified model. Results Our results showed that farmers had a median score of 65.5 out of 100 (IQR: 56.6 to 73.2) on the wellbeing scale. The farmers’ reported on average (median) 10% (IQR: 0 to 23) annual herd mortalities to diseases. There was a significantly negative relationship between increasing level of animal disease-induced mortality in herds and farmers’ wellbeing. Specifically, our model predicted an expected difference in farmers’ wellbeing score of 7.9 (95%CI 1.50 to 14.39) between a farmer without any herd mortalities to diseases compared to a (hypothetical) farmer with 100% of herd mortalities caused by diseases in a farming year. Thus, there is a reduction of approximately 0.8 wellbeing points of farmers, for the average of 10% disease-induced herd mortalities experienced. Conclusions Disease-induced livestock mortalities have a significant negative effect on farmers’ wellbeing, particularly in the physical and psychological domains. This suggests that veterinary service policies addressing disease risks in livestock, could contribute to improving the wellbeing of livestock dependent populations, and public food securit