Cover Image
close this bookC.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.)
View the document(introduction...)
View the documentEXECUTIVE SUMMARY
Open this folder and view contentsCHAPTER 1: Famine, Health and Relief: Issues and Observations
Open this folder and view contentsCHAPTER 2: CISFAM: An Experimental Information System
Open this folder and view contentsCHAPTER 3: Information Systems, Databases and the CISFAM Project: Overview of General Findings
Open this folder and view contentsCHAPTER 4: Plan of Activities: Phase I and II
View the documentFindings and Conclusions

(introduction...)

CISFAM - CONSOLIDATED INFORMATION SYSTEM FOR FAMINE MANAGEMENT IN AFRICA


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CRED - CENTRE FOR RESEARCH ON THE EPIDEMIOLOGY OF DISASTERS

In co-operation with: WORLD HEALTH ORGANIZATION (ORGANISATION MONDIALE DE LA SANTE)

Debarati GUHA-SAPIR
Jean-Pierre REVEL
Michel F. LECHAT

UNIVERSITY OF LOUVAIN - BRUSSELS - BELGIUM
APRIL 1987

The authors gratefully acknowledge the invaluable assistance rendered by:

C. BOROWIAK
I. DARGENT
Y. de KETTENIS
D. TOLLET

This study has been financed by WHO Emergency
Preparedness and Response Unit, Geneva,
Contract N° E17/181/2 1986

EXECUTIVE SUMMARY

Funded by the World Health Organisation, the CISFAM project is an initiative to address the observed deficiency of appropriate multi-sectoral information for rapid decision making, planning and allocation of relief during the African crisis in the recent past. CISFAM is a tool for programme planning, covering defined geographic region for relief, rehabilitation and health development and is a measure to improve data use in disaster preparedness programmes. The project expects to address ultimately famine prevention rather than exclusively famine response programmes.

Phase I of this project is conceived as an experimental effort and explores the viability of developing a standardized database on selected sectors drawing exclusively on existing sources at specialised U.N. agencies and other official organizations. The countries covered in the pilot phase are Senegal, Mauritania, Mali, Niger, for national and district-levels data gathering and Burkina-Faso, Chad, Sudan, Ethiopia and Somalia for national level only. In addition to health and nutrition, five sectors or subject areas were selected as being of direct relevance to famine control or preventive action. These are demographic, agriculture, logistics and infrastructure, socio-economic, environment and meteorology.

The database should:

(i) be an information source to all implementing agencies in countries;

(ii) service the needs of governments, which frequently are unable to use existing information due to financial, manpower and technical constraints;

(iii) serve the interests of research institutes and professional associations for operational and other food crises management research.

The report concludes that:

(i) despite the general consensus on the discouraging state of statistics on sahelian Africa, adequate data and information do exist for minimal planning needs;

(ii) existing data is scattered amongst agencies in formats specialised to the discipline;

(iii) non-governmental agencies collect large quantities of reliable data which remain unprocessed.

(iv) given the financial, technical and political realities of the CISFAM countries, a basic and cheap information system needs to be developed for application at national levels.

Depending on available funding, CISFAM plans to address in Phase II:

(i) Development of a low-cost, data management kit for national use at relief and rehabilitation units. This will consist of a model data base skeleton; initial data files; software; short training program.

(ii) Establishment of a CISFAM focal centre at WHO with support from WHO Divisions of Health Statistics (HST) and Information Systems (ISS); and WHO Collaborating Centre for Research on Epidemiology of Disasters (CRED)

(iii) Development of map-linked databases and image processing capabilities with the collaboration of United Nations Environmental Programme I Global Resource Information Database (UNEP/GRID) Project for rapid and planned response in future crises situations in Africa.

1.1. The African Food Crisis

Famines and acute food shortages are usually caused by a combination of natural disasters such as droughts, floods, cyclones; socio-economic problems such as income inequities, unemployment, migration, marketing constraints; and man-made crises such as wars, civil strife or fires. It is not enough only to recognise the existance of the food scarcity situations but is more important to prevent the situation or, in case of occurrence to minimise the costs of relief activities, and develop community resistance.

Africa’s economic and social conditions began to deteriorate in the 1970’s and continue to do so. Gross domestic product grew at an average of 3.6 percent a year between 1970 and 1980, but has fallen every year since then. With the population rising at over 3 percent a year, income per capita has declined since 1980, and food imports have increased. The effects of drought have understandably claimed international attention and immediate priority has been given to saving human lives through emergency relief operations. The effects of the drought are, however, only the most extreme and distressing aspects of the more pervasive developmental crises in Africa. Pressing as these current problems are, it is important to emphasize that emergency relief as applied provide traditionally short term solutions.

Prolonged food and water shortages, inadequate shelter, contaminated water sources, deteriorating sanitary conditions and breakdown in already inadequate access to basic health services had initiated an explosion in communicable and parasitic diseases to which these weakened communities are susceptible. Dramatic increases in the incidence of malaria, respiratory infections, trachoma, meningitis has been reported in Niger within the last three years. Increasing infant mortality rates in the famine struck countries exceeding 250 in some provinces of Mozambique and 225 per 1000 in some regions of Mali, testify to the synergism between malnutrition and disease. In most of these countries, infant mortality rates that had been showing some signs of decline have made a volte face and have, in certain cases surpassed levels of the past 15 years. (The Human Face of Famine; UNICEF, 1986).

Over the recent years however, India, Bangladesh and a few African countries have demonstrated that with careful planning and management, very poor developing countries can block the chain of events that leads from crop failure to widespread death (J. Mellor and S. Gavian Science, vol. 235,1987, pp 540-545).

1.2. Famine Relief, Health and Development: Policy Issues

National and international policy set the stage for all famines. Poor policies, both in assistance and in public sector along with armed conflicts heighten a nation’s vulnerability to famine. If proper policies are in place, natural disasters should not evolve into famine.

In Africa, international relief assistance played a major role in response to the 1984 famine. Despite the urgency of the situation, the lesson to be learnt is clearly that of providing relief in ways that could strengthen continuing development activities. Frequently however, emergency mandates of international and multi-lateral bodies lack the flexibility required to integrate relief with development. These restricted mandates preclude integrated planning for relief and development, making it impossible for public sector relief units to plan jointly with development units. Unfortunately, famine relief policies, define a limited period as emergency and thereby encourage thoughtless spending, inappropriate and ineffective action. Emergency funds for famine often can not be used to strengthen primary structures to prevent and mitigate future crises. As a result, the international community repeatedly pays for “curative” response of limited effect, as illustrated by the two massive Sahelian famines within the span of just one decade.

The health sector in acute food crisis, is necessarily pulled into centre stage due to death, disease and acute malnutrition that accompanies famine. Despite the fundamentally socio-economic nature of famines, the potential for the health infrastructure especially within a primary health care context to play a major role in famine management and prevention is significant.

The inadequacies in current emergency and relief policies, both amongst the donors as well as the executing bodies, are perhaps best illustrated by the proliferation of early warning systems in various famine struck countries. Systems financed at great cost, manned by qualified expatriates and requiring stable and functioning logistical and administrative structures have been installed in many countries under the wave of donor empathy with the media coverage of the appalling African famine. As is frequently the case in disaster programmes elsewhere, these efforts are beginning to suffer the effects of lack of interest from both host governments and donor agencies, whose priorities have changed with the easing of the crisis. (International Disaster Institute Seminar, Draft Report, January 1987).

This example only serves to illustrate that disaster or relief programmes in the Third World can only succeed and be maintained on an on-going basis, if it can be approached like any other social or human welfare programme and be completely integrated within the appropriate plan at different administrative levels.

Recently a great deal of effort, good-will and resources have been generated to combat this disaster. Nevertheless, there remains considerable room for improvement in the efficiency and effectiveness of the employment of available resources. Famine is clearly a multisectoral problem that requires long-term solutions. The complexity of the problem explains to a certain extent, the relatively disappointing outcome of action so far taken in Africa.

The principle hurdles to effective interventions were, frequently, inappropriate action, delayed action, insufficient local information, logistical back ups and duplication of efforts. Fortunately, most of these can generally be avoided by timely information and planning, especially if long-term goals are to be achieved via such programmes. Although a great deal of information exists scattered in different organizations. Ignorance of their existance as well as their lack of standardization have resulted in a gross underuse of existing sources.


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(introduction...)

Information systems, in the context of famines are, principally, of two kinds: early warning systems and management systems. The former has had, as mentioned earlier, great success in the last two years. The latter has been, on the contrary, visibly neglected. The Organization of Emergency Operations in Africa (OEOA) has noted in its final report, that it lacked specific data to support agency activities on the field. Although the field agencies presented their data at monthly meetings, little effort was made subsequently to co-ordinate, compile or otherwise record the data routinely collected by NGOs.

While many programmes and activities are governed by political pressures, and improved information may be of little value, it is nevertheless a vital and worthwhile investment for programmes that have the will, authority and resources to manage by objectives. The availability of selected, easy-to-understand information can encourage national, international and voluntary agencies to improve programme implementations in famine relief.

The purpose of CISFAM is to provide a centralised information service for decision makers to make educated and appropriate decisions for famine interventions and policy. It would respond to requests from national, regional and international offices, governmental and non-governmental agencies, for planning, targeting and policy-making in famine management programmes. It would serve as a centralized source where limited information on sectors such as agriculture, meteorology or economy, in addition to health, would be quickly and easily available. This would eliminate the need for planners and managers to go to different specialized agencies for information on various sectors.

2.1. Background and Rationale

CISFAM was launched in March, 1986, as a joint initiative of Emergency Preparedness and Response Unit (WHO) and Centre for Research on Epidemiology of Disasters (CRED) as a initial effort to upgrade the preparedness and management concept in famine relief and recovery in Africa.


COUNTRIES COVERED BY CISFAM PHASE I

The actual project was a result of four main observations.

(i) Resource constraints were getting increasingly serious with grave implications for continued international assistance;

(ii) While the health sector is a focal point in famine relief, the crisis is essentially a multisectoral problem and requires multi-disciplinary data for effective programme planning and resource allocation;

(iii) Large quantities of data existed in specialized agencies of the larger U.N. family, and in national archives. While the international data-collections are frequently in sophisticated and technical form that are inaccessible to the uninitiated, the national ones on the other hand are non-standardized, non-computerized and dusty;

(iv) The NGOs were observed to frequently have regular data reporting systems which were not adequately processed or used either by themselves or the governments with whom they work. These agencies formed a potentially important repository of sub-national data.

Beside the principal sector of health, five additional sectors were selected as being relevant to any famine programme planning and these were: demography, agriculture, logistic and infrastructure, socio-economic, environment and meteorology.

A quick inventory of the large databases revealed that, even at the international reporting level, most of these countries had poor data, particularly in health.

The project is currently housed at the WHO Collaborating Centre for Research on Epidemiology of Disaster, (CRED), Catholic University of Louvain, Brussels, Belgium. The CISFAM project has a team of six members at CRED, with varied responsibilities in their respective areas of competence.

Its main functions are to acquire collaboration from the source agencies, collate, digest and classify the data to ensure ease of access, comprehensibility, and useability of different types of data by field planners and managers of famine and food programmes at short notice. Furthermore, it is expected once the process becomes operational, to ensure system-wide compatibility of information flows.

CISFAM OBJECTIVES

To provide quick and easy access to country data on multiple sectors to:

. National governments
. International and bi-lateral agencies
. Non-governmental organizations.

To conceptualize and design a ready to use information system, (including staff training), for transfer to the national health authorities or the relief and recovery management unit.

To identify and develop image, graphic and map-linked databases for quick interpretation and operational decision-making.

2.2. Typology and Framework

CISFAM is restricted to nine countries as a function of their high susceptibility to acute food shortages and famine conditions. Of these, four were chosen for sub-national data collection, while the rest remained at the aggregated national level. Other countries also threatened by famine are not included in the interest of manageability of this pilot phase. Moreover, the CISFAM project does not make any effort to collect any primary data from the field in this project.

The database is not conceived to be an exhaustive source of information on each of the represented sectors. It compiles some of the most important items in each sector which have significance in famine relief and prevention planning

The structure is defined by country blocks. Information by category, as available, is collected for each country. Any additional information generated by special survey and studies generated by other bodies is appended in the informational annexes to the country data block. These annexes are restricted to only those surveys dealing with food, nutrition and health. A graphic representation of the CISFAM structure is shown below.


Figure 1: Schematic Representation of the CISFAM Database

(introduction...)

Several data bases were examined including both electronically maintained and data on cards, reports and other forms of hard copy. Certain CISFAM countries had better data reporting than others and the variability was significant

Statistical services of certain countries had been out of operation for several years and therefore the only viable data sources were non-governmental organizations and sample surveys and studies. Data quality also, varied widely from sector to sector.

Meteorological, ecological and climate data bases provided the best quality information in terms of reliability, coverage, accuracy and time series. The Climate System Monitoring database of the World Meteorological Organisation initiated as a response to the occurrence of significant climate anomalies over the last decades with associated adverse socio-economic effects provided potentially useful information. It carried synthesized information on climate anomalies, rainfall variability and vegetation data by small geographic areas. It has available time series data for the past 110 years on African rainfall. The data, however, is fairly technical and is not divided into the political and administrative boundaries of the countries. Similarly, the soils and temperature databases of Africa held by United Nations Environmental Programme are also very robust and reliable.

Several databases from the six different sectors were explored. A summary of the electronically registered data-sets are presented below:

Table 1:
Summary of International Computerized Database Relevant to CISFAM

SYSTEM NAME

AVAILABILITY

SUBJECT COVERAGE

GEOGRAPHICAL COVERAGE

GLOBAL INFORMATION AND EARLY WARNING SYSTEM (GIEWS)

Agency staff; other U.N. agency staff; External users

Food supply agricultural production, food aid; pest control; meteorology; commodity markets; and prices.

Worldwide

INTERLINKED COMPUTERIZED STORAGE AND PROCESSING SYSTEM OF FOOD AND AGRICULTURAL COMMODITY DATA

Agency staff; Other U.N. agency staff; External users

Food statistics; agricultural statistics; crops; agricultural production; imports/exports; land use; agricultural workers and machinery; fertilizers; forestry and fishery statistics.

Worldwide; Regional; National; Sub-national

DATA PROCESSING AND COMMUNICATIONS UNIT

Agency staff; Other U.N. agency staff; External users

Consultants; disasters; devastation; regional analyses damages compensation emergency relief; supply management; disaster prevention.

Worldwide

DISASTER HISTORY PROFILE DATA EASE (HISTORY)

Agency Staff, Other U.N. agency staff; External users

Disasters; natural disasters; disaster prevention

COMMODITY MARKET PRICES SERVICE

Agency staff; Other U.N. agency staff; External users

Prices, commodity prices and markets.

Worldwide

COMPARATIVE ANALYSIS AND PROJECTIONS DATA BASE

Agency staff; Other U.N. Agency staff; External users

Economic indicators; social indicators.

Worldwide; Regional; National

EPIDEMIOLOGICAL INFORMATION INDEX

Agency staff: Other U.N. agency staff; External users

Infectious diseases; transmission epidemiology

GLOBAL EPIDEMIOLOGICAL SURVEILLANCE AND HEALTH SITUATION ASSESSMENT

Agency staff; Other U.N. agency staff; External users

Mortality; causes of death; infectious diseases; health personnel; hospitals demographic and health statistics Morbidity

Worldwide; Regional; National

HEALTH LEGISLATION

Agency staff; Other U.N. agency staff; External users

Health; legislation; environmental legislation; aging; human nutrition; food standards; pharmaceuticals; poisons occupational hygiene; health statistics

Worldwide; Regional; National; Sub-national

WORLD CLIMATE DATA INFORMATION REFERRAL SERVICE (INFOCLIMA)

Agency staff; Other U.N. agency staff; External users with restrictions

Climate; meteorology; geophysics; climatology

Worldwide

The health sector information on the other hand, was disappointing in both coverage, quality and continuity. The physical resources, such as hospitals, dispensaries, health centres and skilled personnel enumerated in the source agencies, were the only items with regular reporting. However, a major caveat in these items are that frequently hospitals and health centres are, in fact, inoperative and therefore, the value of these numbers are questionable for certain objectives. For crisis management and long-term planning, however, the knowledge of their existence can be useful. Several sample surveys on nutritional status and incidence of nutritional deficiency diseases are available but no officially reported figures on on-going basis. Continuous data collection in this field is undertaken only by large non-governmental organisations, who, generally execute this function as an incidental by-product of their principal activities. Country statistical annuaires and card files were used for data on four of the CISFAM countries at provincial levels. Table 2 presents a summary of the CISFAM database contents listing gross categories of variables by sectors.

Table 2:
CISFAM Database Contents: Sectors files, Items Categories and Sources

CLASS

CONTENTS

FORM

SOURCE

COMMENTS

Health

Basic Health Indicators
WHO HFA Indicators
Primary causes of morbidity
Health service coverage
Health establishments and personnel
Cholera, yellow fever: incidence and deaths

Numeric

Weekly Epidemiological records/WHO HFA Indicator Data Set U.N. Population Division Global Epidemiological Surveillance/WHO Country Annual Reports

Available on sub-national levels for Senegal, Mali, Niger, Mauritania

Food Agriculture

Food supply, cereal availability; cash/food crop production; livestock products prices; food intake, irrigation; means of production; land use; food aid; agricultural population

Numeric

Agricultural production database; GIEWS/FAO Development Centre OECD

National; occasional sub-national; large survey data available

Ecology

Soil status; animal pressure; population pressure; desertification hazard index; vegetation cover

Geographic

Global Environmental Monitoring System

Sub-national; by small geographic areas with no administrative boundaries

Demography

Population estimates projections; mortality, fertility and other vital statistics

Numeric

U.N. population division; World bank; World Fertility Survey International Statistical Institute

National level large survey data

Climate and meteorology

Precipitation; geographical co-ordinates; temperatures; windspeed, growing season

Numeric; raster

Climate monitoring system/WMO

Georeferenced data

Social and economic

Macro-economic indicators; education and social indicators food import/exports

Numeric

OECD; World Bank Country annual reports

Maps

Desertification index; population and animal pressure

1:5,000,000

UNEP/GRID

All countries

Administrative and communications

1:500,000 digitized

Institut Graphique National, Paris.

Only Senegal, Mali, Niger

Administrative and geographic

1:1,000,000

All countries

Limitations and Caveats in the Data

The confidentiality of the data returns is usually provided by law in particular, for manufacturing and production data. However, these legal provisions are applied somewhat indiscriminately in many African countries. The problem in this project was more unuseability rather than unaccessability of the data.

The limited data collected from archival records are uneven in coverage and quality in certain sectors, in particular health. The cholera and yellow fever data transferred from cards filed on the Weekly Epidemiological Reports were about the only regular incidence data to be reported to WHO by province. The data are under-reported for want of coverage and the magnitude of this under-reporting is unknown. The problem is aggravated when the data are compiled by different agencies. Estimates are different for the same variable according to the source, and on occasion delayed reporting caused inexplicable increases in incidence of diseases.

Most of the statistics from these records are either reported late or not reported at all. Obviously, the former situation is preferable to the latter, although none is desirable. An argument often advanced in favour of the late publication is that it improves the quality of statistics. But the timeliness of statistical information can be increased only at the expense of accuracy, while improvements in quality require more time and increase the cost of the information. Comparability of the statistics in Africa, both temporal and spatial, is limited by the use of non-comparable definitions, difference in coverage and the timing of the data collection. Comparability is further reduced by the details of tabulations.

Different types of conceptual problems arise in interpreting and collecting data. In agriculture, for example, under traditional African conditions, it is not always clear what is to be considered the main occupation of the holder, as persons can be occupied in different occupations at different times of the year.

In certain countries of the region, farmers frequently work away from their holdings during a large part of the year, for example, on plantations, in mines or nearby towns. The data of economically active women in the country are extremely unreliable due to the confusion arising from the definition of economic activity. The number of employees may be regarded as reliable, but a lot of confusion arise in classifying people, particularly women, into own-account or family workers. Furthermore, the fact that a man can have more than one wife who independently cultivate separate pieces of land though the land may customarily belong to the head of household create problems for both demographic and agricultural information.

The data on health, like data from other sectors, are subject to several limitations. They vary from incomplete coverage to details in the tabulations provided by various countries. There is also lack of uniformity in data collection procedures and the publication of such information. As the extent of undernotification is high and often unknown, the statistics collected are essentially useful only for the operational purposes of controlling diseases by taking immediate steps for prevention and disease surveillance, and for rough assessment of trends in the incidence of the disease.

The data also suffers from other limitations, the most important besides coverage being selectivity. Hospital statistics are, no doubt, an accurate source of diagnostic information, but they suffer from bias arising from selectivity in relation to factors such as location, type of disease, provision of health facilities, age and sex of the patient, and social and economic factors. The population served is also unknown. It is not, therefore, possible to generalize the hospital experience in respect of many diseases to community level, and these statistics cannot give a true picture of prevalence or distribution of morbid conditions in a community as a whole. However, they serve the purpose of measuring the relative distribution of diseases in the areas covered, and can be treated as valuable adjuncts to mortality statistics, suggesting priorities for provision of more medical facilities and efficient medical care.

Nevertheless, the importance of data in national disaster policies requires a reappraisal of the data needs to formulate, monitor and evaluate these policies. These demands a critical examination of the relevance, adequacy and reliability of current statistical data and of the tools used to collect and analyse these data.

2.4 Data Source Agencies and Negotiations

Since the system is not designed to create any new sources of information, efforts have been made to avoid duplication with existing systems. The project has reviewed the existing data-banks and information systems of the UN agencies and allied bodies. Efforts are made to use CISFAM as means to enhance the utilization of these databases, especially by users in developing countries. These sources of information and databases are little known and even less utilized by most of the targeted audience. A very small proportion of the potential users showed any knowledge of the UN systems databases and fewer acknowledged ever requesting or receiving any information from them.

The project has established working relations with the following agencies:

- Food and Agricultural Organisation,
- United Nations Disaster Co-ordination Office,
- World Meteorological Organisation,
- United Nations Environmental Program,
- Global Environmental Monitoring System (GEMS),
- Global Resources Information Database (GRID),
- World Food Program,
- Organisation for Economic Co-operation and Development,
- European Commission,
- World Bank,
- World Health Organisation and its internal departmental data collections,
- Centers for Disease Control, Atlanta,
- Office de la Recherche Scientifique et Technique d’Outre-Mer (ORSTOM), France, as well as: Mcins Sans Frontis, (Belgique,) Laboratoire International de Calcul et d’Intelligence Artificielle (LICIA), Paris, International Statistical Institute, The Hague.

Discussions with these agencies centred around the access of data for the demonstration module of CISFAM and the possibility of establishing co-operative linkages between CISFAM and their respective data-banks and information systems as well as for the feeding, procurement and consultancy services for CISFAM. Some agencies, moreover, expressed the view that CISFAM could actually enhance the utilization of their existing information system by becoming their “First link” with the users of their information services.


Plate 1: UNEP/GRID AFRICAN DATABASE - Desertification Hazard

(introduction...)

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.

3.1 GRID Technology Applied to CISFAM Data: Image Processing Potential

One of the most important developments in computer technology has been emergence of Geographic Information Systems (GIS). Many types of GIS are currently in use. For CISFAM the most useful GIS system will integrate tabular (i.e., vital statistics) and thematic (i.e., ecological) maps with digital satellite data (i.e., vegetation) in a geographical context which will allow the analysis of physical, biological, economic, and social information by appropriate specialists and associations in era of epidemiological concern.


Plate 2: UNEP/GRID model for population density estimation

The above maps have been developed based on available health resource and population data reported to the various international organizations. They were generated as an experiment to test the model, pending acquisition of additional data. The iterations were made successively, as a function of constraints to produce topographical distributions of the variables of interest. The top photograph shows the population and major road network, based on the model explained below. The second is the possible health distribution as a function of the facilities available and the estimated population density.


Plate 3: Possible Health Distribution in Senegal

PRELIMINARY MODEL FOR ESTIMATING POPULATION DENSITY.

In the absence of detailed data, the development of a population density dataplane for Senegal has been accomplished at UNEP/GRID using a very rough estimation technique. The latitude and longitude of major transportation route and 118 towns in Senegal were digitized from 1:500 000 scale maps obtained by CRED personnel from the Institut Graphique National, Paris. Each of the towns was assigned a rank from one (village) to five (metropolitan area) based upon the relative size of each as depicted on the maps.

The Earth Resources Laboratory Application Software (ELAS) was utilized on the computer systems at UNEP/GRID to develop six raster or gridded cell (pixels) dataplanes generated at 30 seconds of latitude I longitude resolution (approximatively one square kilometre), one for the transportation network and one for each of the five ranked town sizes. Dataplanes for distances away from each town and road was then developed using ELAS overlay DIST. The final estimated population density dataplane was then generated by the summation of each of these distances indexed through tables with different weights for the roads and relative size of towns as contained in the following chart using ELAS overlay DBAS. The results are shown in the attached print where relative population densities have been color coded.


WEIGHTS FOR RELATIVE POPULATION DENSITY


WEIGHT FOR RELATIVE POPULATION DENSITY

Understanding and controlling famines, mass migrations, abnormally high mortality, collapse of social and economical structures and epidemic diseases may be effectively accomplished when vital statistics in tabular form are linked to thematic forms of environmental and socio-economic parameters, using qualified population density distribution, allowing policy makers to take informed and productive decisions.

(introduction...)

Two factors have a bearing on judgement of what statistics to collect: first, technology can almost certainly be counted on to cut down costs of producing and collecting figures (e.g. processing); and second, data compilation is only the start of a whole chain of processes, which include interpretation of the data collected, statistical analysis and research, which are very dependent on man, even if they make use of machines. Not withstanding these problems, every African country is trying to develop, within its limitations, the collection of better data. A further problem, however, is how to reconcile the needs for international comparability, and hence conformity with the standards laid down by the international agencies, with the specific domestic needs of an individual country.

The ELAS system software was developed mainly for processing of raster based satellite imagery, although it has routines for digitizing polygons and processing vector based data. Conversion between two data formats are also possible. The main routines of ELAS are as follows:

(i) Image analyses, histograms, scattergrams, other image area statistics;

(ii) Supervised and unsupervised image classification

(iii) Map transformations in 24 projections;

(iv) Geo-referencing of images and registration of maps and images using control points and non-linear distortion techniques;

(v) Logical and mathematical function of up to 20 different maps or images.

All of ELAS database is converted and stored in the latitude-longitude projection with a grid resolution of 30 seconds. This corresponds to pixel areas of 927 m × 927 m at the equator. At this resolution only one map in the African database contains 256 × 512 × 512 = approximately 67 million pixels.


Plate 4: Desertification Hazard in Senegal

Using data from CISFAM, some maps have been digitized, along with the communication networks. Health data are partially digitized and work is on-going in developing models and images with the data.

3.2.1. Computer Software Use and Development: An Overview of Low-cost Options

With the introduction of the personal computer, data processing equipment has become more affordable, easier to install and maintain, and no longer demands specialized skills for full utilization. These characteristics tend to facilitate the use of computer systems in developing countries, where information technology continues to be an essential tool for development. Increasingly, the personal computer is being applied to the planning and managing of development. The result is high-quality information for use in decision-making to help determine what projects are needed, to aid in implementing and monitoring them, and to evaluate their results. On a much wider scale than ever professional and technical personnel in developing countries have available the computing power needed to organize and analyze large quantities of data.

Appropriate software is the key, in fact, to the effectiveness of personal computers in the development process. Such software has been prepared by United Nations specialized agencies, multilateral and bilateral donor agencies, universities and foundations. In most instances, it is available free of charge from the organizations that produced it, on request of developing-country governmental agencies. In addition to appropriate software, decision-makers and users must have the personal computer hardware. With these two elements in place, expanding the use of information technology is a matter of connecting the information need, the software package and the personal computer. Because of the way software is designed, no package works on a personal computer without an operative system. One or more supporting packages may be required, depending on the application programme.

Besides the ELAS system, operational in CISFAM, there are two other possibilities that require lighter support mechanisms for the international CISFAM focal point. These are as follows:

1) The ARC/INFO is a system running on the following hardware:

. PRIME 750 CPU with 6 Mb memory
. 2 × 675 Mb fixed disks
. 1 CALCOMP 1044 Pen Plotter and 9100 Digitizer
. 1 Textronix 4109 colour display

The ARC/INFO digitizes and processes vector based cartographic information and has many routines for operations research applications (optimizing networks etc.). It supports raster-based data and can do overlays. Whereas ELAS can overlay twenty raster maps, ARC can deal with two at a time. The INFO routine can carry out database inquiries on attribute data, which has special implications for the type of information available in health and other population-based data.

2) A PC option for similar analyses and processing is the ERDAS system. This designed to run on IBM and IBM compatible personal computers. The support hardware required are:

. IBM PC AT with 85 Mb fixed disk and 1 floppy disk units
. Cartridge Tape Unit
. Colour Image Display, 512 × 512, 19”
. CALCOMP digitizer
. Colour inkjet plotter

The ERDAS system supports both vector and raster-based data and is very user-friendly. It has a well developed digitizing and editing routines and a large number of image-processing routines.

Table 3
Software Available for Low-cost Information Systems: Sources and Types

PACKAGE NAME

APPLICATION

USER SKILL

HARD/SOFTWARE

FAO FARM ANALYSIS PACKAGE (FARMAP)

Processes farm survey data for farmers, agricultural experts and government officials

Familiarity with word processing, spreadsheet and/or databases helpful. Users must receive one month of initial training at FAO.

IBM PC (256 KB) with 2 × 360 KB diskette drives, IBM portable PC or PC XT; IBM monochrome display and Graphics Printer or equivalent DOS 2.10; PC-SORT

MULTIPURPOSE AGRICULTURAL DATA SYSTEMS (MADS III)

Produces computer models of agricultural development projects

Familiarity with word processing spreadsheet and/or databases essential. Ability to programme helpful

IBM PC (256 KB) with 2 × 360 KB diskette drives or PC XT; IBM monochrome display, IBM Graphics Printer or equivalent DOS 2.10

DEMOGRAPHIC PROJECTION MODEL FOR MICROCOMPUTERS

Projects population growth and change

No computer expertise required. Comes with an user’s manual giving step-by-step instructions on using the programme and a sample database on diskette suitable for training.

IBM PC (256 KB) with 2 × 360 KB diskette drives or PC XT; IBM monochrome display; IBM Graphics Printer; or equivalent. DOS 2.10

CENSUS TABULATION SYSTEM (CENTS 4)

Processes data from census questionnaires

Familiarity with word processing, spreadsheet and/or databases essential. Ability to programme helpful.

IBM PC XT with expansion unit; IBM Monochrome display; IBM Graphics Printer or equivalent DOS 2.10; COBOL Compiler, ASCII text editor

BACHUE-INTERNATIONAL LONG-TERM SIMULATION DEMO-ECONOMIC MODEL

Analyzes economic and demographic data to estimate economic growth.

Familiarity with word processing spreadsheet and/or databases essential. Ability to programme helpful

IBM PC (256 KB) with 2 × 360 KB diskette drives or PC XT; IBM Monochrome display; IBM Graphics Printer, or equivalent. DOS 2.10

3.2.2. Hard and Software Capabilities and Needs: CRED

The current useable hardware at the Centre for Research of Disaster Epidemiology is as follows:

1) PRIME 2250 (300 Mb capability with 150 Mb dedicated to the CISFAM Project);
2) TEXTRONIX 4691 Pen Plotter;
3) TEXTRONIX 4109 Smart Terminal

The short term hardware requirements for CISFAM operations are:

1) Image Processing Display System (IPDS) 512 × 512

. One Image Plane
. Four Graphic Plane

2) Hardcopy Report Quality Printout

. Matrix camera
. Dunn camera

Both cameras use signals of blue, red, green on IPDS to produce:

. A4 size Polaroid print and transparencies
. 35 mm colour slides and prints.

The long-term requirements are:

1) Ancillary Data Digitization
2) Remotely Sensed Data Processing Capability

The short-term hardware acquisition options are the following:

1) Upgrade PRIME 2250

. Gould/DeANZA equipment with CONTROLLER, REFRESH, CRT
. ERDAS IBM-PC complete system with add-on digitizer.

Alternatively:

2) DIAD Systems Ltd. Complete system for PC Image Processor.

The procurement and installation of the hard and software required to make CISFAM operational at CRED, Brussels, in terms of using different types of data and image-processing, were explored. In view of the time and budget constraints vis-a-vis the CRED staff training, administrative and technical processes involved, this was undertaken via the co-operation of UNEP/GRID Project.


Plate 5: UNEP/GRID/CISFAM AFRICAN DATABASE

Estimation of Population Density, the UNEP/GRID model applied to Senegal integrating major cities and transportation routes.

4.1. Work in Progress: Phase One

CISFAM is conceived to function as a network of different units holding data on the African countries under the threat of famine.

The data base is currently being reorganized and additional data received is being introduced into the structure. Completion of the sub-national or district level data for the Senegal case is envisaged towards the end of 1987 and complete annexes prepared. The project continues to work closely with GRID in developing appropriate models and applications of the image-processing software to the available health data. A minimum information system kit for use at the CISFAM country level will be prepared. This will consist of the data base framework for time series and transversal data, the country data as collected, the softwares and support programmes; users manual and a three day training session programme.

A closed workshop to discuss the possibility of an Africa Health and Disaster Data Cooperative is planned. All CISFAM participating countries, non-governmental agencies and other operational organizations working in this area, will be invited to participate. The use and applicability of a CISFAM type database for famine management and procedures for its transfer to country or regional levels will be discussed. The workshop would also include a discussion on the ways and means of inter-acting with the users and the development of a data network and co-operative. Time would be devoted to discussing country papers on each participating country’s information needs, capabilities and sectoral issues. Background and country papers will be prepared for this purpose.

In addition to the Workshop, CISFAM plans on a programme to create among the user community awareness of CISFAM and especially promote information use in participating countries. Presentations in appropriate WHO workshops will be organized to inform the potential users as to how they can participate in CISFAM and benefits to be derived from this information exchange. Participation in these presentations will be encouraged amongst NGO’s involved in disaster relief operations.

4.2. Future Plans: Phase Two

The second phase of CISFAM depends on funding resources and collaboration of the participating agencies. It will focus on the operational aspects of adapting the information system at country level and possibly, developing its focal point at the appropriate unit in WHO.


Figure 2: Possible Structural Framework: CISFAM II

The second phase will focus on district level data as being the greatest lacunae today for operations in Africa. After completion of the first phase of CISFAM, the operating system could be based in the statistical division of WHO. Besides greater technical support in terms of computer capabilities and programming services, it is a more logical centre in terms of access to data from co-operating governments and other U.N. agencies. The data from NGO’s and special surveys may be handled through CRED in this case, due to easier procedural mechanisms and contact Moreover each NGO reporting system will be unique, requiring substantial and particular attention in developing the database. Existing Primary Health Care Surveillance systems can be made to function jointly with systems such as CISFAM for mutual benefit. It can, furthermore, foster collaboration in the snaring and communication of information between specialized disciplines.

CISFAM expects to actively pursue endorsements from various technical panels of the United Nations or non U.N. agencies. It will seek support for the view that systems such as CISFAM can provide important networking facilities and communication links for more efficient disaster management. Such joint ventures seem particularly appropriate at a time when single sector approaches have become less and less effective.

Phase II of CISFAM is planned as follows:

- Development and implementation of data management training to national personnel, NGO staff involved in famine prevention and food programmes.

- Confirmation of co-operating countries and identification of key health statistical persons in charge of general statistical services.

- Collection of relevant data from national archives on district level.

- Digitization of maps and data as appropriate.

- Transfer and installation of limited data systems in cooperating countries.

The plan requires active collaboration between the Emergency Preparedness and Response office and the statistical services office, both at the nodal point and in the country. Together with Emergency Preparedness and Response, and Health Statistics & Methodology, HST/WHO, CRED would continue to function in the conceptualization, design and organization of systems. Collaboration with United Nations Environmental Programme, Global Resource Information Database UNEP/GRID will need to be defined and a formal agreement established before data are extracted from country level.


Photo

Findings and Conclusions

CISFAM is conceived to function as:

(i) a network of different units, international and non-governmental organizations holding data on the African countries under the threat of famine.

(ii) an organisational framework/administrative framework for core data files;

(iii) a simulator of small-scale data base programmes at national level;

(iv) a data resource for organisations operating in food shortage countries;

At the current stage of the study, it was found that large databases on different sectors exist, however most are on national levels and sub-national information is almost non-existant.

It was also found that improved exploitation of existing databases is not only possible but indicated, given the amount of unused data regularly collected. Evidently, for a multisectorial database, centred on health this requires identification of data subsets in the non-health sectors. Furthermore, substantial quantities of time series and baseline information are recorded on hard copy such as archives and card-file. Finally, reporting format for the same country is frequently not standardized and definitions vary between the provincial reports and over time.

The principle conclusions of this study are as follows:

(i) despite the general consensus on the discouraging state of statistics on sahelian Africa, adequate data and information exist for minimal planning needs.

(ii) the existing data is scattered amongst agencies and is frequently in technically difficult formats, principally in climate and agriculture.

(iii) non-governmental agencies collect large quantities of reliable data which remain unprocessed and there is a critical lack of standardization within a single sector.

(iv) given the financial, technical and political realities of the CISFAM countries, a basic and cheap information system should be envisaged for application at national levels.