Category: Surveillance

Biomasse

West Africa Rain Forecasts and Biomass Vulnerability: April-July

ACMAD/MESA’s 2017 forecasts for the April-July period are available. As far as West Africa is concerned these are initial forecasts and cover mostly the coastal countries/ Gulf of Guinea. Significant weather incidents for the Sahelian belt are not yet detected, but more data will be available soon. This map will be updated with new data with the release of more forecasts from ACMAD. This dataset shows ACMAD/MESA’s currently available forecasts, with ACF’s Biomass Vulnerability Index. The Vulnerability Index is a recursive indicator of biomass anomalies and identifies areas with multiple years of negative biomass production. These zones, marked as “very vulnerable” or “vulnerable”, pose particular dangers for pastoral communities, whose herds can be structurally damaged by consecutive years of poor vegetation/ forage production. To note:

  • An unfavourable precipitation during the April-July period is predicted for most Burkina Faso, North-Western Nigeria, Northern Ghana and the southern region of Niger. This will likely have negative impacts on agricultural production. A late start of rains for Burkina Faso could indicate a prolonged transhumance of pastoral herds.
  • Unfavorable rains during this period predicted for much of Senegal, Guinea, Liberia and Sierra Leone
  • If this trend continues during the rest of the rainy season, highly probable that these areas will have poor agricultural yields
    • Pastoral impacts could include: less availability of agricultural residue and pasture for 2018 dry season, prolonged transhumance.

Full resolution map available here

Publications

Nutrition surveillance using a small open cohort: experience from Burkina Faso

Authors: Mathias Altmann  , Christophe Fermanian  , Boshen Jiao  , Chiara Altare  , Martin Loada  and Mark Myatt

Background: Nutritional surveillance remains generally weak and early warning systems are needed in areas with high burden of acute under-nutrition. In order to enhance insight into nutritional surveillance, a community-based sentinel sites approach, known as the Listening Posts (LP) Project, was piloted in Burkina Faso by Action Contre la Faim (ACF). This paper presents ACF’s experience with the LP approach and investigates potential selection and observational biases.

Methods: Six primary sampling units (PSUs) were selected in each livelihood zone using the centric systematic area sampling methodology. In each PSU, 22 children aged between 6 and 24 months were selected by proximity sampling. The prevalence of GAM for each month from January 2011 to December 2013 was estimated using a Bayesian normal–normal conjugate analysis followed by PROBIT estimation. To validate the LP approach in detecting changes over time, the time trends of MUAC from LP and from five cross-sectional surveys were modelled using polynomial regression and compared by using a Wald test. The differences between prevalence estimates from the two data sources were used to assess selection and observational biases.

Results: The 95 % credible interval around GAM prevalence estimates using LP approach ranged between +6.5 %/−6.0 % on a prevalence of 36.1 % and +3.5 %/−2.9 % on a prevalence of 10.8 %. LP and cross-sectional surveys time trend models were well correlated (p = 0.6337). Although LP showed a slight but significant trend for GAM to decrease over time at a rate of −0.26 %/visit, the prevalence estimates from the two data sources showed good agreement over a 3-year period.

Conclusions: The LP methodology has proved to be valid in following trends of GAM prevalence for a period of 3 years without selection bias. However, a slight observational bias was observed, requiring a periodical reselection of the sentinel sites. This kind of surveillance project is suited to use in areas with high burden of acute under-nutrition where early warning systems are strongly needed. Advocacy is necessary to develop sustainable nutrition surveillance system and to support the use of surveillance data in guiding nutritional programs.

 

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