About this free course
Due to the consistent reporting and wide coverage of routine data collected through the HEP — therapeutic feeding programme TFP , trends have been established for several years. Initial data collectors are volunteers and frontline health practitioners. Where officials have been provided with a computer, data appears to have been regularly updated. In Ethiopia, in line with governmental decentralisation, woreda and kebele level administrations have been given increased power to analyse, assess and act on their own changing situation. This will be most effective if a consensus is reached on key indicators, in particular for timely warning. Added value of NIS:
Inclusion of routine nutrition data in the NIS and the early warning system
You will also learn about the service generated data sources and the key nutritional indicators that can be calculated at local level and at national level. Much of this data will be generated by the routine community-based nutrition interventions that you are participating in as a Health Extension Practitioner.
The materials below are provided for offline use for your convenience and are not tracked. If you wish to save your progress, please go through the online version. For further information, take a look at our frequently asked questions which may give you the support you need.
Skip to main content. Course content Expand Contents Nutrition Module: Nutrients and their Sources Nutrition Module: Nutritional Requirements Throughout the Lifecycle Nutrition: Common Nutritional Problems in Ethiopia Nutrition: Household Food Security Nutrition: These information are collected monthly and reported quarterly.
Ethiopia has introduced a child survival score card in an effort to reduce child mortality. The scorecard consists of three components: Nutrition indicators such as stunting, breastfeeding practices, vitamin A and de-worming capsule coverage are included on the score card.
Furthermore, the National Nutrition Coordination Body NNCB led by MoH is currently working to develop a multisectoral nutrition scorecard that would facilitate high level decision making.
Hence decentralization of the system has facilitated local interpretation and use of information. Frontline workers in Ethiopia have been gathering nutrition information particularly data for CMAM since Over the years, the skills of these workers have been developed and data have become very reliable. For instance, in when the Horn of Africa was hit by food shortages, the situation was picked up by frontline workers in Ethiopia very early and corrective measures were put in place — hence the number of affected children was minimised and the death rate remained very low.
There are three constituent parts to this role. This comprehensive vision for the NIS is to inform understanding of the nutritional situation with respect to chronic and newly occurring problems, as well as the causes of these problems, and how these change over time in order to help in decision-making at all levels. These conditions ultimately determine the basic parameters upon which the initial choice of information for the NIS is made.
Ethiopia is in quite a unique position because, over the last thirty years, large amounts of data have been collected by the Early Warning System EWS , including health and nutrition information.
In recent years, targeting of surveys has been improved through increased use of routine data sources, at least to indicate where an assessment is most urgently needed. Nutrition data are now available and accessible on a monthly and quarterly basis at the lowest levels due in large measure to three programmes: These routine systems are the monitoring backbone of the NNP, which — at least theoretically — can be combined to inform timely warning and be shared with other sectors.
Thus, there is a very real potential for the EWS to systematically tap into specific data from existing health information sources and vice-versa. This will be most effective if a consensus is reached on key indicators, in particular for timely warning.
The key question, ultimately, is whether decision-makers from all sectors are willing to exchange and use available routine data to inform their decisions and response. Initial data collectors are volunteers and frontline health practitioners. Many report that data collection is an additional burden to their already crowded agenda. After the initial collection, data flows up through various levels via supervisors and health officials.
However, little feedback is given through the system so that people directly involved have a limited sense of what is actually done with the information provided. Currently, asking for nutrition information from a woreda official leads to a paper-chase given the amount of report forms collated. Where officials have been provided with a computer, data appears to have been regularly updated. Given the increased requirements for information management, it seems inevitable that woreda Health Offices will move from a paper-based system to a computerised one, allowing them to perform data quality checks that otherwise are time consuming and prone to mistakes if done manually.
The implication here is that woreda level officials are mostly young, often computer-literate, professionals with degrees. This implies that collected data are not interpreted in isolation but are brought together from different sources.
Frontline practitioners in health-posts have access to nutrition and health information through regular contact with patients. An example where this could be used is in chronically food insecure areas supported by the Productive Safety Net Programme PSNP where risk financing mechanisms exist to address new chronic or temporary food insecurity.
By monitoring increases in underweight as an early indicator and OTP admissions as a late indicator , frontline health practitioners, who are members of the Food Security Task Forces FSTF , can play a crucial role in providing information for appeal processes. However, the credibility of their information will depend on their full understanding that risk financing mechanisms are only accessible when malnutrition is linked to food insecurity.