Spatial Relationship between TB Infection
and Population, Poverty and HIV/AIDs in
Kenya
Presenter
Titus Kiptai
AMREF Kenya
Authors: - Titus Kiptai , Tabitha Abong’o, Samson Musau, Duke Mobegi, Benson Ulo and Margaret Mungai
Amref Health Africa in Kenya
Presentation Outline
• Background
• Objectives
• Methodology.
• Results
• Conclusion
• Recommendation
Amref Health Africa in Kenya
Background
• Tuberculosis is one of humanities greatest killer disease in the world.
• In 2012, an estimated 8.6 million people developed TB and 1.3 million
died from the disease (including 320 000 deaths among HIV-positive
people) (WHO 2013).
• The number of TB deaths is unacceptably large given that most are
preventable.
• In Kenya, TB cases increased tremendously from 11,625 in 1990 to
116,723 cases in 2007 and has declined to 88,204 in 2013 (WHO
2013).
• There is a need for innovative ways of understanding the scope of TB
in order to come up with strong control strategies to fully combat TB in
Kenya.
Amref Health Africa in Kenya
Background Cont..
• Geographic information system (GIS) is suitable for analyzing
epidemiological data, revealing spatial visualization, trends and
interrelationships that would be more difficult to discover in tabular
format (Beyers N,& Zietsman HL et al.,1996).
Amref Health Africa in Kenya
Objectives
1. To investigate the spatial relationship between Poverty, population
distribution, HIV/AIDs and TB infections in Kenya
2. To identify Counties with high tendency of Tuberculosis infections in
Kenya
Amref Health Africa in Kenya
Methodology
• Retrospective data of TB incidence per district for the year 2012,
HIV/AIDs infections for the year 2012 from the DHIS and 2009 census
data on Population and Poverty and per district were obtained.
• The data were then aggregated per county and shape files for the 47
counties were obtained where the data was added
• ArcGIS 10.1 software was used in analysis by coming up with
choropleth maps
• Graduated colours were used to display the quantitative values and all
the fields are grouped into ordered classes. Within a class, all features
are drawn with the same colour. Each class is assigned a graduated
colour from smallest to largest.
• Querying of the data using ArcGIS 10.1 was also done
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Results and Discussions
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Spatial Relationship between TB and HIV/AIDS per county
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Spatial Relationship between TB and Population per county
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Spatial Relationship between TB and Poverty per county
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Counties with high infections of TB
Through Querying of the data
layers using ArcGIS 10.1
(query Builder), the stated
counties were identified to
have high tendency of TB
infections
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Results and Discussions cont…
• TB is directly related to HIV/AIDs, Population and Poverty in the country.
• In densely populated and HIV/AIDs stricken areas, TB is high.
• There is relationship between TB infection and poverty, this is seen
especially in densely populated counties with high number of the poor
• HIV/AIDs is also directly related to TB in that area with high HIV/AIDs, TB
is also high.
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Conclusions and Recommendations
Conclusion
• HIV/AIDs, Population density is directly related to Tuberculosis and
Tuberculosis infection has largely depended on these factors.
• There is relationship between TB infection and poverty
• Therefore, all partners combating TB are urged to consider Population,
HIV/AIDs burden during distribution of resources per counties
Recommendation
• This study recommends for more studies to be carried out specifically
focusing on contribution of socioeconomic/cultural activities in increasing
tuberculosis infection.
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References
•
Beyers N, Gie RP, Zietsman HL et al. (1996) The use of a Geographical
Information System (GIS) to evaluate the distribution of tuberculosis in a highincidence community. South African Medical Journal 86
•
Kistemann T, Munzinger A & Dangendorf F. (2002). Spatial patterns of tuberculosis
incidence in Cologne (Germany). Social Science and Medicine.
•
WHO (2013). Global Tuberculosis Control:
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Acknowledgement
I acknowledge the National Tuberculosis Leprosy and Lung disease Unit,
National AIDS & STI Control Programme (Courtesy of DHIS), Kenya National
Bureau of Statistics (KBS) for generously sharing data and my colleagues
who contributed to make this work successful.
Amref Health Africa in Kenya
THANK YOU
Amref Health Africa in Kenya