rural South Africa population between

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1 Spatial Analysis of HIV/AIDS mortality events in a rural South Africa population between Elias Namosha 1, 3, Frank Tanser ², Benn Sartorius 1 1 Witwatersrand University, School of Public Health, Faculty of Health Sciences,7 York krd, Parktown 2193, South hafrica 2 Africa Centre for Health and Population Studies, P.O. Box 198, Mtubatuba, 3935, South Africa 3 PNG Institute of Medical Research, P.O. Box 400, Maprik 533, ESP, PNG 23 nd September, 2008

2 Outline Introduction Aim & Objectives Methodology Results Discussion & Conclusions Acknowledgements

3 Introduction (1) Global estimation of HIV/AIDS: Global estimation of persons living with HIV/AIDS 2005 (millions); total 40.3 ( ) million. (Source: UNAID/WHO, 2005) HIV/AIDS is the major burden globally, affecting an estimated 65 million people worldwide and more than 25 million have died d of AIDS

4 Introduction (2) In Africa (African continent) it is a leading cause of mortality and morbidity South Africa has more people living with HIV/AIDS then any other country in the world. In rural South Africa HIV/AIDS is a major health problem It is now becoming necessary to look at new public health approaches to help manage HIV/AIDS at the local, provincial or national level and GIS can be an appropriate approach Spatial clustering (GIS) of AIDS mortality has never been done before at the community level l so this study is the first to address this using the Africa Centre HDSS data

5 Study Aim & Objectives Aim: To apply GIS tools to investigate and demonstrate how GIS and spatial scan statistic technology can be optimized for public health management and intervention Primary Objectives 1. To describe the mortality profile of the Africa Centre demographic surveillance site (ACDSS) population with a particular focus on AIDS mortality between To map the distribution of adult AIDS and all-cause mortality across the DSS ( 15 years) using spatial scan statistic and GIS technology

6 Methodology (1) Study area and population size Africa Centre HDSS established in 2000 Area = 438 km 2 with pop. approx 85,000 & (11,000 household) Population = both resident and non-residents Pop. density ranging from people/km 2 Source of income is waged employment and state pensions > agriculture In 2006, approx 77% of households have access to piped water and toilet facilities

7 Methodology (2) Study design and database A secondary data analysis study using data from the population-based longitudinal cohort study Exposure data and mortality ty events e from the ACDIS (Africa Centre e demographic information systems) database during were used Database stores data on registered subjects or individuals, physical structures (e.g. homesteads, clinics and schools) and household Adult residents ( 15yrs) both males and females forms the study sample

8 Methodology (3) Cluster detection method Mortality events and person-exposure were aggregated by homestead using MapInfo Program version 7 Analyses were performed for adults ( 15 years) across the two time periods ( and ). All results were adjusted for age and sex A spatial scan statistic analysis implemented in SaTScan 7.0.3, was used to identify statistically ti ti significant ifi mortality clusters (p 0.05) 05) The circle with the maximum likelihood is the most likely cluster (p 0.05) - least likely to have occurred by chance

9 Results (1) Causes of deaths by year and sex for adult population ( 15yrs)

10 Results (2) AIDS mortality clusters for time period and ( 15yrs)

11 Results (3) Standardized maps across the two time periods &

12 Results (4) All-cause mortality cluster for time periods and

13 Discussion & Conclusion Mapping is a powerful tool for helping people visualize the geographic distribution of health problems GIS and spatial scan statistic was used as a quick research tool to investigate potential mortality clusters in the Africa Centre DSS Cluster detection analysis can be an appropriate approach to identify critical HIV/AIDS mortality locations for investigation and interventions to be carried out Further work (socio-economic, health seeking behaviour) is needed to understand the underlying mechanisms responsible for the spatial clustering tendencies (p 0.05) This study may be regarded as a first step in prioritizing areas that needs further investigation and public health effort in the Africa Centre DSS With this sort of analytical study health officers can come up with better strategic With this sort of analytical study health officers can come up with better strategic health plans for intervention now and in the future

14 Acknowledgements Tropical Disease Research (TDR/WHO) Grant, Geneva INDEPTH Network Wits University, School of Public Health Staff & Management of Africa Centre HDSS Dr. Ivo Mueller (PNGIMR) Prof. Peter Siba ( PNGIMR) Wosera HDSS,PNG (PNGIMR)

15 Thank you