Integration of environmental, social and health data using GIS: Lessons learned from three disease outbreaks investigations in rural areas

Christovam Barcellos Walter RamalhoWaneska Alves

Brazilian Health Ministry

Objectives

• 3 Outbreak investigations

• Structure of Brazilian health

surveillance system

• Low cost alternatives for data

acquisition and analysis

Leishmaniosis outbreak

• Rural settlement since 1998• 706 inhabitants• Family farm• 70 suspected cases during 2002

Outbreak dimensions

• Total population: 706

• Number of cases: 20

• Households with cases: 16

• Attack rate: 3%

Settlement characteristics

Subtropical climate

Recent provisonary settlementLandless Workers' Movement (MST)

Close contact with animals(dogs, chicken, pig)

Proximity to forest (rain forest and riparian vegetation)(use of wood and hunting)

Methodology

Mapping and questionnaire

Case location and habits characterization

Interactions people and environment

Pavlovski, 1939, theory of natural nidality of transmissible diseases

GIS was employed to

• Characterize local landscape

(RS)

• Measure distance between

houses and suspected risk

sources

• Identify clusters of disease

(spatial statistics)

Clusters of disease

Dual kernel rate smoothing

Primary layer: households with cases

Secondary layer: All households

Red – households with casesYellow – households without cases

Investigation participants and partners

Brazilian Health Ministry

Paraná State Health Secretary (SES)

Mariluz Municipal Health Secretary (SMS)

Research Institutes (Fiocruz, Brasilia University, Maringá University)

Landless Workers' Movement (MST)

Waterborne Toxoplasmosis, Brazil

• Unusual acute toxoplasmosis cases in an urban area• Mapping the city water supply system • Residence location used as a proxy of exposure

Waterborne Toxoplasmosis, BrazilMoura et al. (2006) Emerging Infectious Diseases, CDC

Santa Isabel do Ivai

138 (88%) of cases lived in the area served by reservoir A and 17 individuals lived in area served

by reservoir B

Water reservoir contamination by cat faeces

Henkes, 2004

Hantavirosis transmission foci identification Rio Grande do Sul

• Hantavirus Pulmonary Syndrome (HPS) is a disease of increasing incidence in Rio Grande do Sul state• The spatial distribution of cases is apparently scattered in the state • The aim of spatial analysis was to investigate the role of agriculture activities and changing ecosystem in the virus transmission Case location > Transmission pattern identification > Preventive

measures

Henkes, 2004

Hantavirosis transmission foci identification Rio Grande do Sul

The majority of cases occurred during spring, in highland areas dominated by secondary vegetation and agricultural activity

An example of mapping and deciding in a regional level

Ministry of Health (National)

Ministry of Health (National)

State Health Secretary (State)

State Health Secretary (State)

Local Health Secretary (Municipality)

Local Health Secretary (Municipality)

Other institutionsOther institutionsBasic Health Care

Service (local)

Basic Health Care Service (local)

Information flux and Health Surveillance Network

Case diagnostic and notification

Primary epidemiological investigation

Data consolidation and analysisLaboratory confirmation

Epidemiological investigation and technical support

• Highly hierarchical (different roles in each level)• Decentralized (present in all municipalities)• Unequal (different capabilities and resources)

Health information systemsLive newborn Information system – SINASC

National Disease Notification System – SINAN

Mortality Information system – SIM

Hospital Information system – SIH

Plenty of dataBut... Poor quality, Incomplete coverageLow capacity to analysis

National disease surveillance

Imediate notification of:

Suspected or confirmed case of: Botulism, Carbuncle or Anthrax, Cholera, Yellow Fever, West Nile Fever, Hantavirus, Human Influenza by a new sub-type, Plague, Poliomyelitis, Human Rabies, Measles, Acute Icterohemorrhagic Fever, SARS, Smallpox and Tularemia

Outbreak or clustering of cases or deaths by: Unusual aggravations (unknown disease or epidemiologic changes in known diseases), Diphtheria, Acute Chagas Disease, Meningococcal Disease

Epizootic and/or death of animals that could precede the occurrence of diseases in humans: Epizootic in non-human primates, other epizootics of epidemiologic importance

GIS and RS demands for Public Health

Peopleware (Courses)

Software (Free and open)

Dataware (Health, population and cartographical data)

Technological and methodological development

RIPSA, 2003

TerraReads images and shapefiles, several spatial analysis tools.

TabwinCalculates health indicators and produces simple thematic maps

Brazilian free (and user-friendly) programs

Other available programs

Coordenadas do centro do aglomerado

Raio de 1 km

Available satellite images

Embrapa: Composed Landsat-TM bands 3, 4 and 5INPE: Raw and classified CBERS images

A long and winding road…

Investigation

Field workGeocoding

Gathering data

Spatial analysis

Analysis

Interpretation

• What kind of data we need? Where are them?• Which objects must be mapped and how to georeference data?• What kind of statistics do we use? Which software?

The agricultural activities provide intensive contact with the virus. The degradation of naturally forested areas and the invasion of intensive agriculture practices alter the habitat of rodents, increasing food availability due to grain storage.

Alarm Knowledge

Necessities

Free data and softwareAll data used are free and available

Decentralized and coordinated

actionsOutbreaks are detected, investigated and

followed by local health authorities, supported by

a national task force and accompanied by NGO

Theory-driven investigationsInstead of technology-driven