|C.I.S.F.A.M.: Consolidated Information System for Famine Management in Africa - Phase One Report (Centre for Research on the Epidemiology of Disasters - World Health Organisation, 1987, 33 p.)|
|CHAPTER 3: Information Systems, Databases and the CISFAM Project: Overview of General Findings|
CISFAM addresses issues in famine information systems within the health service sector, in the Sudano-Sahelian countries. Properly formulated, such a limited system can report, at international and national levels, on epidemiological, infrastructural, socio-economic profiles, providing corrective feedback and objective basis for effective resource allocation.
During the last decade, there has been considerable progress in developing statistical information systems in Africa, but serious deficiencies remain in terms of coverage and the availability of reliable and timely indicators of the human condition. Economic indicators have generally been available and have dominated both international and national development strategies despite their inadequacy to address distributional problems that are frequently the critical issues in development. Even where priority has been placed on monitoring and evaluation systems, it has been largely unrelated to long-term capability building.
The statistical services in Africa, due to serious resource constraints, are limited and on occasion non-existent. In the last two decades, famine interventions implemented by different agencies were dependent on data they collected themselves on the field or simply put in operation activities guided by the most publicised pieces of information. The governments often finding themselves in the position of having a multitude of agencies demanding (or as the case was, not demanding) information, lost control and relief assistance was inequitably distributed over the country.
CISFAM is founded on the concept of Management Information Systems (MIS) for health. Although, this concept is no longer controversial, a certain fuzziness exists about the elements and purposes of such systems. Data of many kinds are fed into a MIS, but unless selectively compiled and processed, they convey little other than archival data. The value of data and statistics in a management information system is judged by their usefulness in programme evaluation and policy formulation. In Africa, however, the value is questionable because data are subject on omissions and misreporting.
For comparative analyses in a multi-sectoral database like CISFAM, additional problems are introduced by the use of non-comparable definitions in data collection procedures and by differences in tabulation. Given the incomplete, fragmentary and defective nature of the data available, their interpretation must remain somewhat tentative and uncritical acceptance of a single set of data may be very misleading.
The constraint on monitoring development and health is not the inability to provide reliable and relevant indicators. This can be done with a degree of confidence but the key element is the appreciation of the part of the international donors and national policy makers of the importance of collecting and using relevant data to monitor conditions and plan programmes on conditions affecting human development and the will to use tools, techniques and materials that are already available.
Like most computer based information systems, CISFAM has a series of basic features: data capture, storage and retrieval, analyses, output and display. Data capture involves putting information into the computer and organising it in memory. The key element is how a soft ware handles each piece of information. Retrieval is the reversal of storage, recovering ordered data from the computer memory, mass storage disks or magnetic tapes for use. It also involves searching for information with certain characteristics, for example all hospitals less than 20 kilometeres from a paved road or with maternity wards. A special feature of CISFAM is its capacity to analyse and display information. This involves the retrieval of data files or parts of them in any combination and their analyses to generate tables, graphs, maps or charts. These display possibilities help planners manipulate and prioritise needs for resource allocation.
The project depends on two basic techniques of data use: overlays and statistics. In overlaying, the GRID software is particularly remarkable. For any defined area, if the information exists, data sets can be overlaid in an electronic version of stacking maps with different information for the same area. The greater the number of data sets, the greater the number of possible comparisons. For example, in a study of malaria control, the software will be able to compare water distribution, health centres, domestic animal distribution, and endemic malaria incidence by overlaying these sets one by one. In emergency health planning, this has special implications in pinpointing vector-breeding sites. Presently, both FAO and GRID use Landsat and the Advanced Very High Resolution Radiometer Data to pin-point locust and other vector breeding areas.