WEAB033 – Spatial Relationship Between Tb Infection And

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).

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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 Amref Health Africa in Kenya

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

Amref Health Africa in Kenya

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

Amref Health Africa in Kenya

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. Amref Health Africa in Kenya

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