The economic impact of schistosomiasis

Abstract

Background: The economic impact of schistosomiasis and the underlying tradeoffs between water resources development and public health concerns have yet to be quantified. Schistosomiasis exerts large health, social and financial burdens on infected individuals and households. While irrigation schemes are one of the most important policy responses designed to reduce poverty, particularly in sub-Saharan Africa, they facilitate the propagation of schistosomiasis and other diseases.

Methods: We estimate the economic impact of schistosomiasis in Burkina Faso via its effect on agricultural production. We create an original dataset that combines detailed household and agricultural surveys with high-resolution geo-statistical disease maps. We develop new methods that use the densities of the intermediate host snails of schistosomiasis as instrumental variables together with panel, spatial and machine learning techniques.

Results: We estimate that the elimination of schistosomiasis in Burkina Faso would increase average crop yields by around 7%, rising to 32% for high infection clusters. Keeping schistosomiasis unchecked, in turn, would correspond to a loss of gross domestic product of approximately 0.8%. We identify the disease burden as a shock to the agricultural productivity of farmers. The poorest households engaged in subsistence agriculture bear a far heavier disease burden than their wealthier counterparts, experiencing an average yield loss due to schistosomiasis of between 32 and 45%. We show that the returns to water resources development are substantially reduced once its health effects are taken into account: villages in proximity of large-scale dams suffer an average yield loss of around 20%, and this burden decreases as distance between dams and villages increases.

Conclusions: This study provides a rigorous estimation of how schistosomiasis affects agricultural production and how it is both a driver and a consequence of poverty. It further quantifies the tradeoff between the economics of water infrastructures and their impact on public health. Although we focus on Burkina Faso, our approach can be applied to any country in which schistosomiasis is endemic.

Keywords: Agriculture; Neglected Tropical Diseases; Poverty; Schistosomiasis; Sub-Saharan Africa; Water Resources Development.

Fig. 1

(Upper panel) Mechanisms studied in the paper. Our main interest lies in estimating (1), the causal effect of schistosomiasis on agriculture. To achieve identification we use a set of variables (instruments) that influence disease intensity without directly affecting agriculture: we use the densities of the different snail species that act as intermediate hosts for the Schistosoma. (2). Poverty is shown to have a reinforcing effect on the burden the disease exerts on agricultural production, as well as being its consequence (3). Water resources development (4) is shown to boost agriculture and development, but also to increase the adverse effects of schistosomiasis via both the increase of snail habitat and human-water contact. (Lower panel) Data overview. a Villages included in the agricultural surveys, the capital Ouagadougou (white point) and level 1 (regions, black lines) and level 2 (provinces, white lines) administrative subdivisions. b River network (blue lines, width proportional to upstream area) and water resources infrastructure in the country. c Estimated schistosomiasis prevalence up to 2010. d Estimated schistosomiasis prevalence for 2011–2017

Fig. 2

(Upper panel) Estimates of the yield loss (in %) due to schistosomiasis. Each label reports the average loss and in parentheses the loss at the top 5% infection intensity clusters. 95% error bands are cluster-bootstrapped at the village level. (Lower panel) Nonlinear effect of the disease intensity. Dotted lines represent the 95% confidence interval in function fitting

Fig. 3

(Upper panel) Added schistosomiasis-induced yield loss due to poverty. Each point on the middle surface represents the extra loss due to the disease for plots below the respective crop weight and surface quantiles. The upper and lower transparent surfaces are cluster-bootstrapped 95% confidence intervals. (Lower panel) Losses to yield suffered by households above and below threshold levels of poverty, defined by the left tail of the joint harvest weight and plot surface distribution

Fig. 4

(Top left) Added effect of schistosomiasis on yields caused by the presence of a large dam. (Bottom left) Joint effect of schistosomiasis, distance from water resources networks and dam size. Estimations account for spatial correlation. (Right) Joint effect of schistosomiasis and distance (in km) from dams and water networks. Each point in the fitted surface represents the effect of schistosomiasis on yield for a village at the corresponding distance from a dam or a water reservoir: the darker the color, the more negative is the effect