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Transcript of GIS
Methods: In 10 villages in malaria-endemic Kenya, household SES was calculated and mapped. Malaria incidence was monitored from local clinic and mapped.
Results: The GIS map shows that households with lower SES also had higher incidence of malaria.
Research question: Does this design demonstrate an accurate representation of the relationship between malaria and SES? Public Health Applications of GIS A Presentation by:
Denise Hartsock, Suzanne Lewis, & Erin Sears (Image Source: http://www.gis.rgs.org/images/front/main.jpg) GIS: Geographic Information Systems Africa Tanzania Computer-based systems for integrating and analyzing geographic & spatial) data GIS can be used to map & analyze geographic distributions of health risks & outcomes in order to assess possible associations Explore & address health problems to improve population health Human Health can have a geographical context (Image Source: http://www.co.goodhue.mn.us/departments/landuse/gis/files/place_to_place_gis.jpg) Where do people live?
What are the agents of disease we are looking at? Where can we intervene to eliminate risks/improve health services delivery? GIS for Public Health looks at: GIS is a very useful tool esp. for
identifying populations and places w/greatest need for public health interventions Both people and the agents that cause human disease are dispersed across different regions Population Distributions + Exposures +Risk Factors GIS can be used to make maps of these distributions & clusters of health events Disease mapping makes important contributions to ph & epi & has for centuries GIS also helps link data from surveillance systems to other info about environment Overview Background of GIS and their purpose
Describe an actual application and
critique its use of GIS
Malaria in Tanzania John Snow -mapping Msitu wa Tembo, Tanzania Karanga River REFERENCES Comley, E. K., McLafferty, S. L. (2012). GIS and public health (2nd ed.). New York, NY: The Guilford Press Oesterholt, M., Bousema, J., Mwerindel, O., Harris, C., Lushinol, P., Masokotol, A., Mwerinde, H., & Drakeley, C. (2006). Spatial and temporal variation in malaria transmission in a low endemicity area in northern Tanzania. Malaria Journal, 5(98), 1-7. Tanser, F., & le Sueur, D. (2002). The application of geographical information systems to important public health problems in Africa. International Journal of Health Geographics, 1(4), 1-9. General Indicators Total Population 44,841,000
Gross National Income per capita ($) 1,440
Total Expenditure on Health as % GDP (2010) 6.0
Adult Literacy Rate (%, 15 & above) 73
Adult Female Literacy Rate (%, 15 & above) 68 Health/Primary Care Indicators Life expectancy at birth m/f (years) 58/61
Under 5 mortality rate (per 1,000 live births) 68
Maternal mortality ratio (per 100,000 live births) 578
Low birthweight babies (% of births) 9.5
Nurses and Midwives (per 1,000,000 people) 242
Physicians (per 1,000,000 people) 8
Births attended by skilled health staff (% total) 49 Malaria Specific Indicators Notified cases of malaria 2008 (per 100,000) 24,088
Use of insecticide-treated bed net (% under 5 population)
2008 - 25.7 2010 - 63.6
Children w/ fever receiving anti-malarial drugs (% under 5)
2008 - 56.7 2010 - 59.1 Female Anopholes Mosquito Insecticide-treated bed net Malaria Cases per Season Figure 1
Rainfall, numbers of mosquito and patients during 2004. Bars indicate the total rainfall per week collected in 21 rain gauges at the nearby sugar plantation (left hand y-axis). The solid line indicates the total proportion of female Anopheles mosquitoes caught per week (left hand y-axis). The broken line the total number of malaria cases seen by the health centre per week (right hand Y-axis). http://thesocietypages.org/graphicsociology/2009/02/05/john-snows-cholera-map/ www.google.com www.google.com personal archives www.google.com www.google.com The WOrld Bank Group. (2013). Data: Tanzania. Retrieved from http://data.worldbank.org/country/tanzania World Health Organization. (2013). WHO Africa Region: Tanzania, United Republic of Tanzania. Retrieved from http://www.who.int/countries/tza/en/ Critical Thinking Question Do you think the use of GIS in this study was cost-effective and WHY? GIS Study in Tanzania Malaria incidence is affected by the entomological inoculation rate (EIR)
Particularly important in areas of low transmission intensity
GIS maps environmental and clinical data to determine areas that carry the heaviest burden of malaria Study Methods Msitu wa Tembo village in lower Moshi
863 households for 3,388 individuals
hypoendemic for malaria
Karanga River allows for small scale farming
Houses mapped with handheld GPS units
Mosquito traps placed weekly for one year in 10 representative houses
One local clinic collected malaria diagnosis data from suspected malaria patients Methods/Results Entomological testing
-510 traps, 12859 mosquitoes caught
-3 positive mosquitoes (1 in March, 2 in May), 2 in the same house
-130 malaria episodes in 122 individuals from 105 households
-17 in dry season, 85 in wet season, 28 in cool season
-Higher number of cases closer to river
-Most frequently occuring in children 5-14 years old
-Higher cases in houses with no window screens and larger windows Discussion Describes pattern of malaria transmission in an area of low transmission intensity
Strongest relation was distance to river
Significant effect of the rainy season and high mosquito numbers in 2-4 weeks after the heavy rainfall
Provide basis for transmission reducing interventions
Improve housing conditions
Target households close to breeding sites
Increase strategies for larval control Critique of GIS 1) Positive social and health consequences
-Greater access to health information
-Innovative way to visualize and analyze health data
-Spatial modeling directly applies to the variations in environment and disease factors
2) Potential negative social consequences
-Requires resource-intensive disease reporting
-Reinforces the power of state agencies
-Spatial perspective could be misleading
-Promotes technocratic view of social problems Obstacles to GIS development in Africa Capacity development controlled by outside funders and resources
Lack of suitable GIS databases
Difficulty determining data resolution requirements
Must prove cost-effectiveness to key role players Critique of GIS in the Tanzania study Positive: Accurately linked geographic and health data to visualize the spatial variations of disease
Provided transmission reducing interventions targeted to where it was needed most
Highlighted a breeding ground and when it would be most important to focus on larval control strategies
Negative: GIS studies associations, not causal factors
Researchers didn't factor in other risk factors that may have played role
Limited number of distance categories
Requires use of outside resources and is costly with limited transferability to the local people Research Hypothesis Hypothesis: Lower SES is associated with higher rates of malaria incidence.
Methods: In 10 villages in malaria-endemic Tanzania, household SES was calculated and mapped. Malaria incidence rates were monitored from a local clinic and mapped.
Results: The GIS map shows that households with lower sES also had higher incidence of malaria.
Research question: Does this design demontstrate an accurate representation of the relationship between malaria and SES?