Epidemiological Bulletin
      Vol. 17, No. 1
March 1996  



Epidemiology in Health Programs

One of the main applications of epidemiology is to facilitate the identification of geographical areas and population groups that present a greater disease or premature death risk and therefore require more preventive or curative care and health promotion. Epidemiology also allows to recognize that the distribution and significance of the factors that lead to the increase of a given risk are not necessarily the same in all population groups. Nevertheless, groups that share similar health risk determinants may also be identified.

Recognition of these groups allows the selection of appropriate social and health interventions in order to reduce or eliminate specific risk factors. These choices often mean that the health services must be reorganized in order to address those unmet needs. Once interventions have been carried out, their impact on health status must be evaluated. The purpose of this evaluation is to determine if it is necessary to make adjusments in the interventions because goals were met or not, or if it is best to continue them until the proposed goals are attained. This dynamic process of diagnosis-action-evaluation-adjustment is part of the methodology known as epidemiological stratification (1).

The current resource constraints and the decentralization process of health services under way in most of the countries demand health programs that are more effective and efficient in their decision-making. For this purpose, health programs require expeditions information systems that allow them to identify the areas or populations with the greatest unmet health needs and to target interventions for those priority groups. With the advent of personal computers and the information and communications highways, efficient health service information systems are now more feasible.

Maps, particularly computerized ones, are valuable in increasing the effectiveness of decision-making. It has been estimated that approximately 80% of the information needs of local government decision-makers and policymakers relate to geographical location (2). In this context, geographical information systems should be considered as one of the existing technologies that facilitate information and decision-making processes in the health services.

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What is a Geographic Information System?

A geographic information system (GIS) is a constellation of computer hardware and software that integrates maps and graphics with a database related to a defined geographical space (3). Thegeographical data may be spatial or descriptive in nature. A GIS can be defined as an integrated set of tools within an automated system capable of collecting, storing, handling, analyzing, and displaying geographically referenced information.

A GIS has several components, each with different functions, including the capability to :

  1. Digitize maps, which means that the system captures spatial data for map preparation;

  2. Store, handle, and integrate geographically referenced data from different sources, that is, it performs some functions of a database manager system;

  3. Retrieve or locate geographic data, which means that by locating points on a spatial surface, a number of attributes can be determined for that unit in the system;

  4. Produce various types of data analyses, including a capability for defining such conditions as adjacency, inclusion, and proximity;

  5. Produce output in several formats such as maps, graphs, and tables; and

  6. Produce high-quality thematic maps, since different output formats can be employed simultaneously and versatile editing tools are available (Figure 1).

Some software packages-EpiMap (4) and SiMap (5) are two-have some of these capabilities, such as retrieving, storing, and displaying data through maps and may be used for basic GIS and epidemiological surveillance. If simultaneous management of different variables or databases is required, then more powerful computer programs are available.

A GIS makes it possible to produce different types of analytical maps. One of them is the reference map, which shows the boundaries of certain areas and the location of different objects within them that are usually labeled. An example is a highway map with several types of roads, municipal boundaries, distances, towns, and other features. Due to their conceptual simplicity, superimposing layers of information, reference maps are attractive because they can be easily handled as a set of images. They do not, however, lend themselves to other GIS operations, such as those mentioned below.

Thematic or choropleth maps have areas that are colored or marked in accordance with a key so that the color or marking reflects the intensity of a mapped variable. Examples of these types of maps include, among others: the area map, which shows a phenomenon according to a particular territory; the symbol map, which shows scattered objects in relation to points on the map; the isarithmic o isoline map, which shows phenomena with uniform changes in a pattern of uninterrupted diffusion; the dot-density map, which shows the occurrence of a phenomenon with non-uniform distribution; the cartodiagram in which the size of territorial units represents the magnitude of a phenomenon.

A GIS makes it possible to perform other operations that are valuable in decision analysis and decision-making: redistricting of boundaries, defining of buffer areas or buffering, and determining the distance between objects. In redistricting, the boundaries of one territory can be modified or joined to those of another in order to form a new territory including in these operations the sum of the values of their attributes into a single unit. Buffering allows contiguous or non-contiguous territories or objects of different shapes and dimensions to be selected in order to form a virtual region or area without having to modify boundaries. Both redistricting and buffering capture the information on attributes of an area or region so that they can be managed or analyzed. Distance determination makes it possible to calculate the real distance between two or more points on a map or the area of a territory.

Finally, some GIS packages have the ability to process images such as aerial or satellite photographs, thereby offering continuous and systematic coverage of different types of information within large geographical tracts. Examples include precipitation and frontal systems, vegetation, soil types, and erosion.

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Data and Files Required for a GIS and their Sources

There are essentially two types of data used in a geographical information system: cartographic data and descriptive or attribute data. Cartographic data provide a spatial or geographical reference for an object, whereas attribute information indicates the characteristics of the object.

In general, a GIS contains four types of information and computer files: geographic, map, attribute, and data-point files. Geographic files are the heart of a GIS; they contain the data, including the coordinates defining each unit, that are going to be mapped. The map files contain information on the names of geographical files and other related files forming the GIS; e.g., names or labels, coverage, colors, map scale, and lines. Attribute files are rectangular data files whose columns list variables and whose rows correspond to individual cases or geographical points. Finally, data-point files are those produced by linking interfacing attribute files and geographic files through a process called geocoding, using an identifier.

It is commonly believed that cartographic data are available with software packages, and that all that needs to be done is to add the databases with the attributes that are to be analyzed. This is certainly the case in some circumstances, such as those in countries where information development is well advanced and were digitized maps are commercially available at an additional cost. Their cost depends on the level of aggregation required; whether it be, regional, state, municipal, or some other definition like zip codes in the United States or basic geostatistical areas in Mexico.

If such a map is not available, the clear alternative is to prepare a map independently. This can be done through one of three methods: electronic digitizing, data conversion, or the use of global positioning systems (GPS).

Digitizing consists of manually transferring maps in hardcopy to an electronic format. This method requires a digitizing tablet to place points on a map, which are subsequently joined by lines. In contrast to points situated with a mouse, which only supply their relative position on a plane, the absolute position is provided for the point entered with a digitizing tablet, and the defined point will always have the same position of coordinates available, so distances can also be determined. Alternatively, scanners may be employed to reproduce hardcopy maps, although expensive precision equipment is required to transform images to vector formats that can be handled by the GIS. As might be expected, preparation of maps with these procedures requires a considerable time investment, and their quality depends on the accuracy of source maps and the skills of the technician.

Data conversion is done through the selection of control points on maps by their known coordinates for latitude and longitude; the values are then entered for a number of points whose coordinates have been defined through formulas. In this case as well, precision in making calculations and quality of the source maps for the control points are essential.

Finally, the global positioning system (GPS) is used to find a location in the field through radio transmission to satellites, which provide the coordinates for the point of transmission. Each option has a different level of precision and cost, which means the decision on what to use must take account of needs and available resources.

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Use of GIS in Epidemiology (Epi-GIS)

GIS use in the field of public health is quite new. The development of these systems gained momentum in other areas such as marketing, transportation, law enforcement, and monitoring geological and climatic phenomena around the world. GIS can be applied in Epidemiology (Epi-GIS), for different aspects most of which are interconnected. Some of the most common uses are the determination of the health situation in an area, generation and analysis of research hypotheses, identification of high-risk health groups, planning and programming of activities, and monitoring and evaluation of interventions.

Epi-GIS can be used to determine patterns or differences in health situations through different levels of aggregation ranging from the hemispheric level, to the regional, national,departmental, district, or local level. For example, basic health indicators are being mapped (6) at the hemispheric level, which has helped to determine which countries have the highest infant mortality rates (Figure 2). Althought there may be many reasons for the infant mortality situation, --lack of drinking water in the home,--on of its causal factors, seems to coincide with higher infant mortality rates (Figure 3). If available resources are limited and a decision must be made about where to use them, it may be suggested at this level that priority be given to water supply systems in places with high infant mortality and low levels of drinking water coverage.

Epi-GIS have also been used to map the risk of malaria in Brazil (Figure 4), where it was observed that almost half of the cases occur in a single state containing a small fraction of the population. It was also employed to monitor malaria in Central American and Caribbean countries between 1990 and 1993 (Figure 5), where downward trends were generally observed.

As an analytical tool, Epi-GIS facilitate data uses in ways other than simple projection on a map. For example, the system can calculate the surface of a territorial polygon while the database manager can utilize demographic data sources. In this way, population density estimates were obtained at the municipal level in Guatemala (Figure 6). Due to their considerable variation, the results were grouped and displayed for interpretation to determine the presence of patterns in municipal jurisdictions.

Epi-GIS were used for epidemiological surveillance in Cuba during an epidemic of meningococcal disease to determine the municipal areas of the country at greatest risk, to define the absolute magnitude of the problem and the type of disease that was diagnosed most frequently at the provincial level (R. Martinez, R. González, and the PAHO/Epi-GIS Collaborating Center, unpublished). To do this, three different databases were used: one contained individual case data, another consolidated population data, and the third consolidated data for types of care. A complex thematic map was prepared showing mortality at the municipal level with overlays of cartograms indicating the number of cases and the disease profile by province (Figure 7).

As a support for planning, Epi-GIS have been used to evaluate the adequacy of health services to the health care needs for the treatment of severe malaria in the Department of Petén, in northern Guatemala (N. Ceron, H. Altan, Malaria Research Group in Petén and GIS-Epi/PAHO Collaborating Center, unpublished). First, the localities with the greatest risk of severe malaria, caused mainly by Plasmodium falciparum, were determined. The next step was to determine if the health facilities were geographically accessible and whether they were distributed according to the needs of the locality. This was done by displaying the roads and buffer areas around the health facilities, which delineate the areas of influence or coverage of the localities within a 12-km radius (Figure 8). As can be inferred from the map, it is necessary to adapt health services and provide them to certain widely scattered high-risk localities that are difficult to reach by road, perhaps by using mobile units. Moreover, since it is possible to determine the number of people living in the localities covered by a given health facility, then the size of the population that remains to be served can be calculated accordingly, and the number and type of resources required can also be planned.

According to a bibliographic search on Epi-GIS using the MEDLARS databases for the years 1993-1995, the systems have been utilized in public health in the following ways:

  1. identification and characterization of populations that live near high-voltage transmission lines (7);

  2. mapping environmental release of toxic chemicals (8);

  3. estimates of the risk of fascioliasis (9);

  4. monitoring trypanosomiasis in space and time (10);

  5. analysis of infant mortality (11);

  6. identification of mistakes in road accident records (12);

  7. accessibility of hospitals to a population (13);

  8. entomological surveillance of vector-borne diseases, such as malaria, dengue, and borreliosis (14-17);

  9. factors that affect the non-response to cervical cytology screening (18); and

  10. factors associated with child pedestrian injuries (19).

This search indicates that the field has only been explored in a limited fashion, mainly for the diagnosis and the epidemiological surveillance of health problems or risk factors. Most of the work has been performed by professional researchers. However, it is clear that many different possibilities for work in this field remain.

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Conclusions and Recommendations

Epi-GIS represent a powerful tool that supports health situation analysis, operations research, and surveillance for the prevention and control health problems. Moreover, these systems provide analytical support for the planning, programming, and evaluation of activities and interventions in the health sector. Thus, GIS can be considered part of the decision-support systems for people who formulate and follow health policy. GIS represents a new technology in the field of public health, which offers many applications and can strengthen the managerial capacity of health services.

Unfortunately, as with statistical software, GIS are also becoming part of the xclusive domain of specialists in organization rather than being used as a generic tool. Thus, the Health Situation Analysis Program of PAHO's Division of Health and Human Development (HDA/HDP) is encouraging an initiative to develop GIS in the countries of the Region aiming to facilitate access to this tool for information management and analysis. This effort is intended to strengthen another initiative, which is the development and strengthening of epidemiology in the health services. As a part of this development and in order to provide coverage for needs in the Region, institutions have been identified to form a network of reference centers for technical support and training in GIS. The first centers are operating in Chile, Cuba, Guatemala, and Mexico in institutions that represent different academic, research, and health service sectors. It is expected that they will support the training of health professionals in this area, the digitizing of priority geographical areas, the design and implementation of GIS for different needs and resources, and the development of more friendly applications for end-users.

For more information please contact:

Dr. Carlos Castillo-Salgado
Coordinator Health Situation Analysis Program
Division of Health and Human Development
Tel: (202) 974-3327

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Bibliographic References

  1. Castillo-Salgado C. Estratificación epidemiológica de la malaria en la región de las Américas. Mem Inst Oswaldo Cruz, 1993.

  2. Williams RE. Selling a geographical information system to government policy makers. URISA, 1987; 3:150-156.

  3. Garson GD, Biggs RS. Analytic mapping and geographic databases. Series: Quantitative applications in the Social Sciences. Sage University Papers. Sage Publications, Newbury Park. 1992. 89p.

  4. Dean JA, Burton AH, Dean AG, Brendel KA. Epi Map: A mapping program for IBM-compatible microcomputers. Centers for Disease Control and Prevention, Atlanta, Georgia, USA, 1993. 104p.

  5. Instituto de Nutrición de Centroamérica y Panamá (INCAP). Sistema de Información por Mapas - SIMAP, versión 2.0. INCAP, Organización Panamericana de la Salud. Publ INCAP L-61, 1992.56p.

  6. Organización Panamericana de la Salud, Programa de Análisis de Situación de Salud (SHA). Situación de salud en las Américas. Indicadores Básicos 1995. PAHO/HDP/sha/95.03.

  7. Wartenber D, Greenberg M, Lathrop R. Identification and characterization of populations living near high-voltage transmission lines: a pilot study. Environ Health Perspect, 1993.101: 626-632.

  8. Stockwell JR, Sorensen JW, Eckert JW, Carreras EM. The U.S. EPA Geographic Information System for mapping environmental releases of Toxic Chemical Release Inventory (TRI) chemicals. Risk Anal, 1993. 13: 155-164.

  9. Zukowski SH, Wilkerson GW, Malone JB. Fasciolosis in cattle in Louisiana. II. Development of a system to use soil maps in a geographic information system to estimate disease risk on Louisiana coastal marsh rangeland. Vet Parasitol, 1993. 47: 51-65.

  10. Rogers DJ, Williams BG. Monitoring trypanosomiasis in space and time. Parasitology, 1993. 106: S77-92.

  11. Andes N, Davis, JE. Linking public health data using geographic information system techniques: Alaskan community characteristics and infant mortality. Stat Med, 1995. 14: 481-490.

  12. Austin K. The identification of mistakes in road accident records: Part 1, Locational variables. Accid Anal Prev, 1995. 27: 261-276.

  13. Love D, Lindquist P. The geographical accessibility of hospitals to the aged: a geographic information systems analysis within Illinois. Health Serv Res, 1995. 29: 629-651.

  14. Su MD, Chang NT. Framework for application of geographic information system to the monitoring of dengue vectors. Kao Hsiung I Hsueh Ko Hseuh Tsa Chih, 1994. 10: S94-101.

  15. Beck LR, Rodriguez MH, Dister SW, Rodriguez AD, Rejmankova E, Ulloa A, Meza RA, Roberts DR, Paris JF, Spanner MA et al. Remote sensing as a landscape epidemiologic tool to identify villages at high risk for malaria transmission. Am J Trop Med Hyg, 1994. 51: 271-280.

  16. Kitron U, Pener H, Costin C, Orshan L, Greenberg Z, Shalom U. Geographic information system in malaria surveillance: mosquito breeding and imported cases in Israel, 1992. Am J Trop Med Hyg, 1994. 50: 550-556.

  17. Glass GE, Amerasinghe FP, Morgan JM, Scott TW. Predicting Ixodes scapularis abundance on white-tailed deer using geographic information systems. Am J Trop Med Hyg, 1994. 51: 538-544.

  18. Bentham G, Hinton J, Haynes R, Lovett A, Bestwick C. Factors affecting non-response to cervical citology screening in Norfolk, England. Soc Sci Med, 1995. 40: 131-135.

  19. Braddock M, Lapidus G, Cromley E, Cromley R, Burke G, Banco L. Using a geographic information system to understand child pedestrian injury. Am J Public Health, 1994. 84: 1158-1161.

Source: Division of Health and Human Development, Health Situation Analysis Program, SHA, PAHO.

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