Mapping the geographical distribution of schistosomiasis in Nigeria from compiled survey data AUTHORS 1Uwem F. Ekpo 2,3Eveline Huerlimann 2,3Nadine Schur 1Akinola S. Oluwole 1Eniola M. Abe 4Magaret A. Mafe 5Obiagele J. Nebe 6Sunday Isiaku 7Fransicca Olamiju 1Mukiala Kadiri 8Temi.O.S. Poopola 9Ekanem I. Braide 10Yisa Saka 11Chiedu F. Mafiana 12Thomas K. Kristensen 2,3Juerg Utzinger 2,3Penelope Vounatsou ADDRESSES 1Department of Biological Sciences, Federal University of Agriculture, Abeokuta Nigeria 2 Department of Public Health and Epidemiology, Swiss Tropical and Public Health Institute, Basel, Switzerland 3 University of Basel, Petersplatz 1, CH-4003 Basel, Switzerland 4 Department of Public Health, National Institute for Medical Research, Yaba, Nigeria 5 Schistosomiasis/STH control Programme, Department of Public Health, Federal Ministry of Health, Phase 3, Abuja, Nigeria 6 SightSavers Nigeria Country Office, 1 Golf Course Road, Kaduna, Nigeria 7 MITOSATH, 605, Hospital Place, P.O. Box 205, Jos 930001 Plateau State, Nigeria 8 Department of Microbiology, Federal University of Agriculture, Abeokuta Nigeria 9 Department of Animal and Environmental Biology, University of Calabar, Nigeria 10 National Onchocerciasis control Programme, Department of Public Health, Federal Ministry of Health, Phase 3, Abuja, Nigeria 11 Office of the Executive Secretary, National Universities Commission, Abuja Nigeria 12 Department of Veterinary Disease Biology, DBL-Centre for Health Research and Development, University of Copenhagen, Frederiksberg, Denmark

Introduction Methodology Results What next? Acknowledgements

Introduction Schistosomiasis is a water-based disease caused by trematode parasites belonging to the genus Schistosoma. These worm-like invertebrates dwell in the blood stream of vertebrate hosts including human (WHO, 1985; Steinmann et al. 2006). Current estimates suggest that schistosomiasis affects 207 million people worldwide with 201·5 million of them living in Africa (Steinmann et al. 2006; Utzinger et al. 2009). Three species are known to be endemic in Nigeria: Schistosoma haemtobium, Schistosoma mansoni and Schistosoma intercalatum (WHO 1985)

Introduction contd Even though schistosomiasis has been documented in Nigeria prior to independence (Ramsay, 1934). Government commitment to control, however did not commence until 1988, with establishment of a national control programme. The control programme since inception have suffered from the following absence of reliable baseline data on the geographical distribution of the disease to help guide control. Inadequate and limited global support and Poor governmental response to control effort.

Why we must control schistosomiasis in nigeria 29 million people infected (Steinmann et al., 2006) 113 million people are at risk of infection Nigeria abhors 14% of global burden of schistosomiasis The most endemic country in the world Infection are also widespread among infant and preschool-aged children (Ekpo et al., 2011; Ekpo et al., 2012) Reduction in morbidity estimates for Nigeria will significantly result in the reduction in morbidity estimate for Sub-Saharan Africa and the world. For Nigeria to benefit from global support, disease distribution maps are needed. Attempts to provide such maps have been undertaken previously for Ogun State (Ekpo et al., 2008).

This project was essentially to provide model risk maps for Nigerian control programme using compiled survey data. OBJECTIVES Develop a nationwide GIS database of infection prevalence distribution using historical and current information in the country. Predict and estimate prevalence at locations without survey data Provide reliable estimate of population infected and at risk of infection. Create advocacy for international support of the control programme in Nigeria.

Methodology A literature search on schistosomiasis prevalence in Nigeria from peer-reviewed local and international journals was conducted from 2009 until 2010 conjunction with Swiss TPH within the EU-CONTRAST project. Relevant schistosomiasis and snail data were extracted and geo-referenced to create a nationwide geographical information system (GIS) database. Geo-reference information were gotten from GEOnet Name Servers, Google map, Google earth and using a GPS. The data is online, open-access after registration at Global Neglected Tropical Disease (GNTD) database (http://www.gntd.org)(See Hürlimann et al., 2011) Additional information obtained from reports of surveys by the Federal Ministry of Health, State ministries of health and Non-Governmental Developmental Organization (NGDOs).

Results Schistosomiasis is endemic in 35 States plus FCT Abuja, with the exception of Akwa Ibom State in South-South Nigeria. 833 locations were identified from complied survey data 463 locations are endemic with schistosomiasis S. haematobium is endemic in 31 States and in 368 (79.8%) locations S. mansoni is endemic in 22 States and in 78 (16.7%) locations S. intercalatum is endemic in 2 States and in 17 (3.7%) locations There are several areas of co-distribution of S. haematobium and S. mansoni in 22 States. Co-distribution of all the three Schistosoma species was only reported in Rivers State in the Niger delta area.

SH= S. HEAMTOBUIM, SM= S. MANSONI, SI= S. INTERCALATUM Table 1: Number of locations of schistosomiasis reports in Nigeria by species Geo political zone State Number of location with infection Species reported by location SH SM SI North East Adamawa 8 6 2   Bauchi 18 9 Bornu 14 Gombe 1 Taraba Yobe North Central Abuja Benue 5 3 Kwara 16 11 Niger 31 26 Plateau 20 North West Kaduna 7 Katsina Kebbi Kano Sokoto 10 4 Zamfara 57 47 South East Abia 19 Anambra 17 Ebonyi Enugu Imo South West Ekiti Lagos Ogun 64 63 Ondo Osun 21 Oyo South South Akwa Ibom Bayelsa Edo Cross River Delta Rivers 32 Total 463 368 78 SH= S. HEAMTOBUIM, SM= S. MANSONI, SI= S. INTERCALATUM

Geographical distribution of reported schistosomiasis cases in Nigeria stratified by the three different Schistosoma species

Observed prevalence of S. mansoni Observed prevalence of S. mansoni. The data include 78 georeferenced survey locations. Prevalence equal to low infection rates (0.1–9.9%), moderate infection rates (10.0–49.9%) and high infection rates (≥50%) Cut-offs follow WHO classifications of endemicity

Observed prevalence of S. haematobium Observed prevalence of S. haematobium. The data include 368 georeferenced survey locations. Prevalence equal to low infection rates (0.1–9.9%), moderate infection rates (10.0–49.9%) and high infection rates (≥50%) Cut-offs follow WHO classifications of endemicity.

Prediction map of S. haematobium in Nigeria prediction error map

What next? Create high-resolution disease risk maps using Bayesian geostatistical methods to predict prevalence for unobserved locations using environmental features that support transmission e.g. rainfall, temperature, land cover, vegetation and socio-economy. Estimate number of persons infected and cost of treatment. Provide risk maps at implementation level for planning control programme. Incorporate other NTDs such as Soil transmitted helminths to predict areas of co-distribution for integrated control

GNOSISGIS Training Course in Database Development, LSU, USA, 2008 IEA Training Course in Epidemiological Methods, Riyadh, Saudi Arabia, 2010 1st Scientific visit to STI, Basel, March, 2009 2nd Scientific visit to STI and training in Bayesian Disease Mapping, Basel, July 2010

AcknowledgEments THANK YOU European Foundations Initiative for African Research into Neglected Tropical Diseases (EFINTD) Post Doctoral Fellowship grant project no: AZ:I/84003 Schistosomiasis/Soil Transmitted Helminths Control Unit, Federal Ministry of Health, Abuja Federal University of Agriculture, Abeokuta National Institute for Medical Research, NIMR, Yaba Swiss Tropical and Public Health Institute, Basel, Switzerland. Prof Thomas K. Kristensen and British Society for Parasitology for inviting me to this meeting. THANK YOU