Vincent Ricciardi

Buruli Ulcer Disease and Its Association with Land Cover in Southwestern Ghana

Background Buruli ulcer (BU), one of 17 neglected tropical diseases, is a debilitating skin and soft tissue infection caused by Mycobacterium ulcerans. In tropical Africa, changes in land use and proximity to water have been associated with the disease. This study presents the first analysis of BU at the village level in southwestern Ghana, where prevalence rates are among the highest globally, and explores fine and medium-scale associations with land cover by comparing patterns both within BU clusters and surrounding landscapes. Methodology/Principal Findings We obtained 339 hospital-confirmed BU cases in southwestern Ghana between 2007 and 2010. The clusters of BU were identified using spatial scan statistics and the percentages of six land cover classes were calculated based on Landsat and Rapid Eye imagery for each of 154 villages/towns. The association between BU prevalence and each land cover class was calculated using negative binomial regression models. We found that older people had a significantly higher risk for BU after considering population age structure. BU cases were positively associated with the higher percentage of water and grassland surrounding each village, but negatively associated with the percent of urban. The results also showed that BU was clustered in areas with high percentage of mining activity, suggesting that water and mining play an important and potentially interactive role in BU occurrence. Conclusions/Significance Our study highlights the importance of multiple land use changes along the Offin River, particularly mining and agriculture, which might be associated with BU disease in southwestern Ghana. Our study is the first to use both medium- and high-resolution imagery to assess these changes. We also show that older populations (≥ 60 y) appear to be at higher risk of BU disease than children, once BU data were weighted by population age structures.

Situated knowledge of pathogenic landscapes in Ghana: Understanding the emergence of Buruli ulcer through qualitative analysis.

Successfully addressing neglected tropical diseases requires nuanced understandings of pathogenic landscapes that incorporate situated, contexualized community knowledge. In the case of Buruli ulcer (BU), the role of social science is vital to investigate complex human-environment interactions and navigate different ways of knowing. We analyze a set of qualitative data from our interdisciplinary project on BU in Ghana, drawing from participatory mapping, focus group discussions, semi-structured interviews, and open-ended survey questions to explore how people in endemic and non-endemic areas see themselves embedded in changing environmental and social landscapes. We pay particular attention to landscape disturbance through logging and small-scale alluvial gold mining. The results from our participatory research underscore the holistic nature of BU emergence in landscapes, encapsulated in partial and incomplete local descriptions, the relevance of collective learning to distill complexity, and the potential of rich qualitative data to inform quantitative landscape-disease models.

An open-access dataset of crop production by farm size from agricultural censuses and surveys

This dataset is a cross-country convenience sample of primary data measuring crop production and/or area by farm size for 55 countries that underlies the article entitled “How much of the world׳s food do smallholders produce?” (DOI: https://doi.org/10.1016/j.gfs.2018.05.002). The harmonized dataset is nationally representative with subnational resolution, sourced from agricultural censuses and household surveys. The dataset covers 154 crop species and 11 farm size classes, and is ontologically interoperable with other global agricultural datasets, such as the Food and Agricultural Organization׳s statistical database (FAOSTAT), and the World Census of Agriculture (WCA). The dataset includes estimates of the quantity of food, feed, processed agricultural commodities, seed, waste (post-harvest loss), or other uses; and potential human nutrition (i.e., kilocalories, fats, and proteins) generated by each farm size class. We explain the details of the dataset, the inclusion criteria used to assess each data source, the data harmonization procedures, and the spatial coverage. We detail assumptions underlying the construction of this dataset, including the use of aggregate field size as a proxy for farm size in some cases, and crop species omission biases resulting from converting local species names to harmonized names. We also provide bias estimates for commonly used methods for estimating food production by farm size: use of constant yields across farm size classes when crop production is not available, and relying on nationally representative household sample surveys that omitted non-family farms. Together this dataset represents the most complete empirically grounded estimate of how much food and nutrition smallholder farmers produce from crops.