Decomposing multidimensional child poverty and its drivers in the Mouhoun region of Burkina Faso, West Africa.

BACKGROUND: The global poverty profile shows that Africa and Asia bear the highest burden of multidimensional child poverty. Child survival and development therefore depend on socioeconomic and environmental factors that surround a child.The aim of this paper is to measure multidimensional child poverty and underpin what drives it among children aged 5 to 18 years in a resource poor region of Burkina Faso. METHODS: Using primary data collected from a cross sectional study of 722 households in the Mouhoun region of Burkina Faso, the Alkire-Foster methodology was applied to estimate and decompose child poverty among children aged 5-18 years. Seven broad dimensions guided by the child poverty literature, data availability and the country's SDGs were used. A binary logistic regression model was applied to identify drivers of multidimensional child poverty in the region. RESULTS: The highest prevalence of deprivations were recorded in water and sanitation (91%), information and leisure (89%) followed by education (83%). Interestingly, at k = 3 (the sum of weighted indicators that a child must be deprived to be considered multidimensionally poor), about 97% of children are deprived in at least three of the seven dimensions. At k = 4 to k = 6, between 88.7 and 30.9% of children were equally classified as suffering from multidimensional poverty. The odds of multidimensional poverty were reduced in children who belonged to households with a formally educated mother (OR = 0.49) or stable sources of income (OR = 0.31, OR = 0.33). The results equally revealed that being an adolescent (OR = 0.67), residing in the urban area of Boromo (OR = 0.13) and rural area of Safané (OR = 0.61) reduced the odds of child poverty. On the other hand, child poverty was highest among children from the rural area of Yé (OR = 2.74), polygamous households (OR = 1.47, OR = 5.57 and OR = 1.96), households with an adult head suffering from a longstanding illness (OR = 1.61), households with debts (OR = 1.01) and households with above five number of children/woman (OR = 1.49). CONCLUSION: Child poverty is best determined by using a multidimensional approach that involves an interplay of indicators and dimensions, bearing in mind its causation.

A Spatio-temporal Model of African Animal Trypanosomosis Risk.

BACKGROUND: African animal trypanosomosis (AAT) is a major constraint to sustainable development of cattle farming in sub-Saharan Africa. The habitat of the tsetse fly vector is increasingly fragmented owing to demographic pressure and shifts in climate, which leads to heterogeneous risk of cyclical transmission both in space and time. In Burkina Faso and Ghana, the most important vectors are riverine species, namely Glossina palpalis gambiensis and G. tachinoides, which are more resilient to human-induced changes than the savannah and forest species. Although many authors studied the distribution of AAT risk both in space and time, spatio-temporal models allowing predictions of it are lacking. METHODOLOGY/PRINCIPAL FINDINGS: We used datasets generated by various projects, including two baseline surveys conducted in Burkina Faso and Ghana within PATTEC (Pan African Tsetse and Trypanosomosis Eradication Campaign) national initiatives. We computed the entomological inoculation rate (EIR) or tsetse challenge using a range of environmental data. The tsetse apparent density and their infection rate were separately estimated and subsequently combined to derive the EIR using a "one layer-one model" approach. The estimated EIR was then projected into suitable habitat. This risk index was finally validated against data on bovine trypanosomosis. It allowed a good prediction of the parasitological status (r2 = 67%), showed a positive correlation but less predictive power with serological status (r2 = 22%) aggregated at the village level but was not related to the illness status (r2 = 2%). CONCLUSIONS/SIGNIFICANCE: The presented spatio-temporal model provides a fine-scale picture of the dynamics of AAT risk in sub-humid areas of West Africa. The estimated EIR was high in the proximity of rivers during the dry season and more widespread during the rainy season. The present analysis is a first step in a broader framework for an efficient risk management of climate-sensitive vector-borne diseases.

Using species distribution models to optimize vector control in the framework of the tsetse eradication campaign in Senegal.

Tsetse flies are vectors of human and animal trypanosomoses in sub-Saharan Africa and are the target of the Pan African Tsetse and Trypanosomiasis Eradication Campaign (PATTEC). Glossina palpalis gambiensis (Diptera: Glossinidae) is a riverine species that is still present as an isolated metapopulation in the Niayes area of Senegal. It is targeted by a national eradication campaign combining a population reduction phase based on insecticide-treated targets (ITTs) and cattle and an eradication phase based on the sterile insect technique. In this study, we used species distribution models to optimize control operations. We compared the probability of the presence of G. p. gambiensis and habitat suitability using a regularized logistic regression and Maxent, respectively. Both models performed well, with an area under the curve of 0.89 and 0.92, respectively. Only the Maxent model predicted an expert-based classification of landscapes correctly. Maxent predictions were therefore used throughout the eradication campaign in the Niayes to make control operations more efficient in terms of deployment of ITTs, release density of sterile males, and location of monitoring traps used to assess program progress. We discuss how the models' results informed about the particular ecology of tsetse in the target area. Maxent predictions allowed optimizing efficiency and cost within our project, and might be useful for other tsetse control campaigns in the framework of the PATTEC and, more generally, other vector or insect pest control programs.

The effect of protest zeros on estimates of willingness to pay in healthcare contingent valuation analysis.

'Protest zeros' occur when respondents reject some aspect of the contingent valuation (CV) market scenario by reporting a zero value even though they place a positive value on the amenity being valued. This is inevitable even in the best-designed CV study, and, when excluded on an ad hoc basis, may cause a selection bias problem. This could affect the reliability of the willingness to pay (WTP) estimates obtained for preference assessment. Treatment of 'protest zeros' in general, and particularly in the context of developing countries, has been rather unsatisfactory. Most case studies employ the Heckman 2-step approach, which is much less robust to co-linearity problems than the Full Information Maximum Likelihood (FIML) estimator. The main objective of this article is to illustrate a sequential procedure to simultaneously deal with co-linearity and selectivity bias resulting from excluding 'protest zeros' in CV analysis. The sequential procedure involves different levels of estimation and diagnostics with the 2-step and FIML estimators; the duration of the procedure depends on the diagnostic test results at each stage of the estimations. The data used for the analysis were elicited using the conventional dichotomous choice buttressed with an open-ended follow-up question. The survey was designed to elicit households' WTP for a proposed community-based malaria control scheme in rural Cameroon. In the application context, we found that the different levels of estimation and diagnostics resulted in reliable WTP estimates from the FIML approach, which would obviously have been overlooked in the absence of such diagnostics.