Nutritional Surveillance - Malawi(Continued)

Nutritional Surveillance - Malawi

The INFSS system faced several challenges

  • Delays in transmission of data: there is an average of two to three months delay between data collection at health clinics and delivery of the actual forms at the government level, since data is recorded on paper and sent via mail or alternative means of transport to a centralized location. At the national level, lack of human resources lead to long delays before data is analyzed.
  • Poor data quality: paper data collection forms are sometimes illegible due to poor handwriting, or are incomplete or contain significant outliers (eg. height of a child listedas seven meters). In 2007, 14.2 percent of all forms were discarded by ACF as unusable.
  • One way flow of information: field based Health Surveillance Assistants (HSAs) rarely received any feedback or are given access to the analysis done at the national level, leading to low levels of ownership.
  • High operational costs: the paper based system was particularly labor intensive, with full time staff needed at a national level just for data entry.

Since chronic and widespread child malnutrition remains a serious problem in Malawi, the shortcomings of the system are a serious threat to the country’s ability to anticipate and plan for current and future food security crises. As a result of these limitations, there are too few complete datasets to analyze on a monthly basis, and effectiveness of the early warning system is subsequently compromised. Policy makers and development practitioners are unable to receive timely information regarding trends in child malnutrition throughout the country and are subsequently unable to react appropriately with increased support to areas facing high levels of malnutrition.

 

RapidSMS Implementation and Results

The Malawi RapidSMS platform was designed to keep the data input format as close to the paper format as possible. In order to make RapidSMS compatible with all mobile phones, the platform uses a string of variables entered in a predefined order within a single SMS, with the same health indicators inputted in the same order as the original system.

The RapidSMS platform was also designed to provide immediate feedback to the HSAs. Feedback loops were incorporated to guide HSAs in their work and alert them of any data entry errors. For example, after submitting a child’s measurements via SMS, HSAs automatically received an SMS confirming the data submission. In the event that the data submitted indicated malnutrition, the HSA would also receive an SMS providing them with specific instructions for treating the child. In the event of a significant data entry error (for example, a height measurement outside the range of physical possibility), the HSA would automatically receive an SMS instructing them to re-send the corrected data.

In January 2009, the pilot study was launched at three GMC sites in central Malawi. Approximately 30 health surveillance assistants, typically holding a secondary school diploma and paid by the government for their services, were given a two-hour training in RapidSMS reporting. They registered two hundred and ten children and tracked them for a period of four months using RapidSMS. During this period, total of 535 unique data sets were generated.

  • Delays in transmission of data: RapidSMS eliminated both the data transfer and data entry time delays by implementing automated data-entry into a central database. Once data was collected by the HSA and sent by SMS, it was also immediately stored, analyzed, and accessible to stakeholders at all levels. Transmission times that previously took from two to three months were reduced to an average of two minutes. This is essentially 64,800 times faster than the paper-based system.
  • Poor data quality: there were 15 data entry errors, representing an error rate of 2.8 percent. These errors consisted of wrong measurements entered, wrong association of child ID number with measurements, or entry of data strings with one or more missing values. This was a significant improvement over the 14.2 percent error rate under the previous system in 2007. Moreover, all the errors occurred in the first reporting period. During the final three months, there was not a single unusable data set.
  • One way flow of information: Approximately 30 feedback loops were programmed to assist health care workers in accurately reporting patient data and offer specific information on patient current health status. One of the most significant automated functions is the weight for height calculations. Despite being the most reliable method of identifying malnutrition in children, most HSAs stated that they were not trained in these calculations and did not feel competent carrying them out. RapidSMS automatically performed this calculation for every patient and instantly alerted HSAs of their patient’s nutritional status. At one GMC, HSAs proudly noted that they had identified and treated ten mildly malnourished children who would have otherwise been missed.
  • High operational costs: The need for centrally-based full-time staff dedicated to manually inputting the data was eliminated, along with the high cost of transporting the forms. After the two-hour initial training, the only additional operational cost was for the texts themselves, which was dramatically reduced through an agreement with the mobile providers.

The Government of Malawi, pleased by the results of the pilot, plans on scaling RapidSMS up nationally later in the year. They are also interested in expanding this to a country wide campaign to register child births, as well as deploying RapidSMS in other sectors, including education and HIV/AIDS.