2_Introdução à epidemiologia espacial

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Introdução à Epidemiologia Espacial
PROGRAMA DE PÓS-GRADUAÇÃO EM 
DOENÇAS TROPICAIS
Josafá Barreto, Prof. Dr.
josabarreto@gmail.com
mailto:josabarreto@gmail.com
Saúde Pública 
de Precisão
“Uso de dados para guiar intervenções de 
saúde que beneficiem a população de 
forma mais eficiente”!
https://www.nature.com/news/four-steps-to-precision-public-health-1.21089
Saúde pública 
de precisão!
O que?
Quem?
Como?Quando?
Onde?
“Geographic precision 
also means that public-
health resources are used 
more efficiently…”
https://www.nature.com/news/four-steps-to-precision-public-health-1.21089
Spatial epidemiology
▪Geographic variations in health/disease.
▪Risk factors:
▪ Demographic, environmental, behavioral. socioeconomic, genetic, and 
infectious.
Elliott P, Wartenberg D. Environ Health Perspect. 2004 
Spatial epidemiology
Parauapebas, PA, Brazil. Fort Collins, CO, USA.
Elliott P, Wartenberg D. Environ Health Perspect. 2004 
Place and health
Source: LDI
Spatial epidemiology
Altamira, PA, Brazil. Atlanta, GA, USA.
Elliott P, Wartenberg D. Environ Health Perspect. 2004 
Place and health
Source: LDI Source: LDI
Spatial epidemiology
Snow, John. On the Mode of Communication of Cholera, 1855.
McLeod KS, Social Science & Medicine. 2000
Source: McLeod KS, 2000
John Snow 
Cholera in London, 1854
Source: The Guardian, 2013
Spatial epidemiology
▪ Recent increase in popularity.
▪ Global Positioning System (GPS) – 1970-1990 – 24 to 31 satellites.
▪ Geographic information systems (GIS).
▪ Better personal computers.
▪ Spatial data availability.
Auchincloss AH, et al. Annu Rev Public Health. 2012
Scientific Journals
1. International Journal of Health Geographics
2. Spatial and Spatio-temporal Epidemiology.
3. Health & Place.
4. Geospatial Health. 
5. Social Science & Medicine. Part D: Medical Geography. 
Spatial epidemiology
▪Spatial distribution pattern.
▪ Identify pockets of high endemicity.
▪ Target extra resources.
Source: http://www.who.int/lep/monitor/gis/en/index.html
Objectives
1. Identify the spatial distribution pattern of leprosy in Castanhal.
2. Describe the spatio-temporal distribution.
3. Correlate with new cases and subclinical infection among 
HHC and SC.
Methods
▪Castanhal.
Source: https://maps.google.com.br/
Methods
▪Data from SINAN (leprosy).
▪Data from IBGE (base map, demographic).
▪ Field work data on HHC and SC.
Methods
▪ 173,149 inhabitants (IBGE, 2010).
▪ 88.5% in the urban area.
▪ 633 new cases (2004 to February 2010).
▪Hyperendemic.
Methods
▪ Mapping.
▪ Cases (2004-2010).
▪ GPS (50%).
▪ Remote geocoding.
▪ Examined students.
Methods
▪Maps draw:
▪ Spatial reference SIRGAS 2000 UTM Zone 23S.
▪ ArcGIS 10 (ESRI, Redlands, CA, USA).
Spatial analysis
▪Spatial statistics:
• Global and local Moran’s I.
• Kulldorff spatial scan statistics.
• Ripley’s K-function.
• Knox space-time test.
Results
Results
▪ 570/633 (90%) lived in the urban area.
▪ 46/633 (7.3%) in rural areas.
▪ 17/633 (2.7%) unavailable.
▪ 499/570 (87.5%) were mapped.
▪ 71/570 (12.5%) inconsistent information.
Results
499 (87.5%) mapped cases.
Results
499 (87.5%) mapped cases
Spatio-temporal 
clusters
p < 0.05
▪ 134 Students (6-14 years).
▪ 11 (8%) new cases (p<0,05).
▪ 4 PB and 7 as MB (4 BT and 3 
BB).
▪ No physical disability. 
▪ 104 (77%) seropositive.
▪ 42 HCSDL.
▪ 7 (16.6%) new cases.
▪ 23 (54.7%) seropositive.
▪ 83 (62%) within 100m of a 
case.
▪ 121 (90.3%) within 200m.
▪ All new cases < 200m. 
| https://doi.org/10.1371/journal.pntd.0006532
Leprosy in Diamantina, MG
Leprosy in Diamantina, MG
PEP++ project
https://zeroleprosy.org/subtheme/detect/
2019
Necessidade de 
mapear os casos no 
mundo
https://www.jstor.org/stable/resrep38997
Global Leprosy Mapping Initiative
• Develop tools, processes, and best practices to support 
MoH to map leprosy.
• Mapping last 5-10 years and develop a systematic 
approach for mapping future cases. 
Global map of the predicted 
distribution of Ae. aegypti.
The map depicts the probability of occurrence (from 0 blue to 1 red) at a spatial resolution of 5 km × 5 km.
Kraemer, M. U. G. et al. eLife 4, e08347 (2015).
“Geographic precision 
also means that public-
health resources are used 
more efficiently…”
https://www.nature.com/news/four-steps-to-precision-public-health-1.21089#/b4
Spatial variation for five independent covariates associated with an increased probability of leptospirosis infection in Fiji
Graphs show (A) cattle density per km2, (B) maximum rainfall in the wettest month (mm), (C) distance to river (m), (D) 
poverty rate, and (E) residential setting.
Cluster analysis showing areas predicted by geographically weighted logistic regression (GWLR) model as having significantly 
higher (hot spot) or lower (cold spot) chance of infection than average probability of leptospirosis infection.
Agressões por 
morcegos em Curuçá.