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close this bookMalaria Epidemics, Detection and Control Forecasting and Prevention (WHO - RBM - WHO - OMS, 1998, 90 p.)
close this folderIII. EPIDEMIOLOGICAL SURVEILLANCE, FORECASTING AND PREVENTION OF EPIDEMICS
close this folder9. Epidemiological Information Systems
View the document(introduction...)
View the document9.1. Identification of indicators of epidemic risk
View the document9.2. Field investigations
View the document9.3. Geographical information systems

(introduction...)

There can be no doubt that an information system is vital for the proper functioning of any programme. Information systems have been designed to provide the managerial and epidemiological data necessary to monitor the impact of interventions on the malaria problem and the implementation of programmed activities. Such information systems have led to the belief that the main causes of changes in the epidemiological situation are the control interventions themselves, and that the only response to an unexpected deterioration in the situation is to intensify those interventions. A broader look at concomitant processes might have suggested a more critical determinant factor and a more effective control policy.

While information is essential for the management of any control activity, it must be kept in mind that the objective is control and not the acquisition of information for its own sake, no matter how interesting it may be. During an epidemic, the first priority should be the appropriate care of the cases, which in most instances will require an improvement in the accessibility and efficiency of diagnostic and treatment facilities. This must take precedence in the distribution of resources and an important aspect to consider is the optimization of the deployment of microscopical diagnostic facilities.

At present, most of the established antimalarial programmes are reorganizing their information systems in response to the emphasis on disease management required by the Global Malaria Control Strategy, in contrast with the almost exclusive attention previously given to parasite infections during the eradication era. There is therefore a need for certain redefinitions of reportable variables and a search for relationships between the new and the old variables. This is particularly important for trend analyses, which will require the establishment of relationships between old and new time series of not fully comparable variables. It will also be necessary to collect information from general health services that had not previously been received by malaria programmes.

The new emphasis on disease management requires the separate monitoring of ‘clinical malaria’ and ‘laboratory confirmed cases’ reported by formal health services and by lay community health workers. It can be assumed that reported ‘clinical malaria’ will be more or less equivalent to the ‘slides collected’ previously by formal health services and by community workers. Both time series can be compared to test their hypothesis and to see how previous definitions of normality may be translated to the varaibles used in the new systems. New time series will have to be started for the new data collected on the main forms of severe malaria, treatment failures and deaths.

An information system should not be limited to the routine reporting and subsequent analysis of data. It is essential that all echelons of the health services should be aware of the importance of particular indicators of risk defined for each area, and that they should be required to communicate abnormalities in those indicators to the level capable of generating an appropriate response as soon as possible. Routine information and trend analyses should then be supplemented by properly planned epidemiological surveys or specific studies conducted by the specialized services to confirm, evaluate or study particular problems or situations, especially when an emerging epidemic is suspected.

All echelons of the services reporting epidemiological information should be considered full participants in the overall process of malaria control. Peripheral services should receive not only consolidated reports but also be able to exchange information with neighbouring areas and receive technical assistance when necessary, so that they appreciate the usefulness of reporting. This should never become a burden and a cause of non-compliance or inaccurate reports. It is also essential to stress, reinforce or stimulate an interest in understanding time and spatial variability and to avoid the tendency to rely only on averages of scattered observations, to extrapolate to large areas or to disregard past information. It is unfortunately still very common for epidemiological analyses to be limited only to the comparison of current data with past year indicators. It is also common to find services that neglect or destroy past reports.

The renewed interest in malaria epidemics has made malariologists aware of the fact that most epidemics are owing to, or greatly influenced by, meteorological or social determinants (Kouznetzov, 1989; Nájera and Beales, 1989), yet most antimalarial services still fail to monitor these variables since the indicators of risk for their epidemic-prone areas have not been determined. Thus in most of the recent descriptions of malaria epidemics, and even in those where exceptionally heavy rains or population displacements are recognized as the main cause, those concerned fail to analyse the details of that relationship, or to show any interest in monitoring such determinants. As a result, they continue to build so-called warning systems based on the classic malariometric variables.

The recognition that the risk of malaria epidemics is in most cases determined by ecological and social variables should lead to their monitoring. Most of the variables concerned are carefully studied by other public services, such as those responsible for agriculture, public works or economic development. Their monitoring therefore requires the involvement of a variety of collaborating partners through the establishment of truly functional intersectoral links. As examples of these situations, graphs 13-15 show how known epidemic years in Khartoum and Wad Medani in Sudan are linked to early deviations from normal Nile river levels and rainfall. Epidemics seem to have been caused by me floods resulting from the joint effects of river overflow and excessive local rainfall, the latter being of particular importance in 1988 in Khartoum and in 1975 in Wad Medani.