Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana

International Journal of Health Geographics

Jürgen May⁴  Udo Schickhoff³  Wibke Loag⁶  Christof Vinnemeier⁵  Andreas Hahn⁴  Robin Kobbe²  Matilda Ayim-Akonor⁷  Ohene Adjei¹  Ralf Krumkamp⁴  Benno Kreuels⁴  Anne Caroline Krefis⁶  Lutz Ehlkes⁶ 

[1] Komfo Anokye Teaching Hospital, Kumasi, Ghana;Department of Paediatrics, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany;Department of Geography, University of Hamburg, Hamburg, Germany;German Center for Infection Research (DZIF), Hamburg-Borstel-Lübeck, Germany;First Department for Internal Medicine, Division for Tropical Medicine, University Medical Centre Hamburg-Eppendorf, Hamburg, Germany;Bernhard-Nocht-Institute for Tropical Medicine, Research Group Infectious Disease Epidemiology, Hamburg, Germany;Animal Health and Food Safety Division, Animal Research Institute, Achimota-Accra, Ghana

关键词: Geographically weighted regression;    Malaria risk;    Land cover;    Land use;    Spatial epidemiology;   

Others  :  1141625
DOI  :  10.1186/1476-072X-13-35

 received in 2014-05-23, accepted in 2014-09-03,  发布年份 2014

【 摘 要 】

Background

Malaria is a mosquito-borne parasitic disease that causes severe mortality and morbidity, particularly in Sub-Saharan Africa. As the vectors predominantly bite between dusk and dawn, risk of infection is determined by the abundance of P. falciparum infected mosquitoes in the surroundings of the households. Remote sensing is commonly employed to detect associations between land use/land cover (LULC) and mosquito-borne diseases. Due to challenges in LULC identification and the fact that LULC merely functions as a proxy for mosquito abundance, assuming spatially homogenous relationships may lead to overgeneralized conclusions.

Methods

Data on incidence of P. falciparum parasitaemia were recorded by active and passive follow-up over two years. Nine LULC types were identified through remote sensing and ground-truthing. Spatial associations of LULC and P. falciparum parasitaemia rate were described in a semi-parametric geographically weighted Poisson regression model.

Results

Complete data were available for 878 individuals, with an annual P. falciparum rate of 3.2 infections per person-year at risk. The influences of built-up areas (median incidence rate ratio (IRR): 0.94, IQR: 0.46), forest (median IRR: 0.9, IQR: 0.51), swampy areas (median IRR: 1.15, IQR: 0.88), as well as banana (median IRR: 1.02, IQR: 0.25), cacao (median IRR: 1.33, IQR: 0.97) and orange plantations (median IRR: 1.11, IQR: 0.68) on P. falciparum rate show strong spatial variations within the study area. Incorporating spatial variability of LULC variables increased model performance compared to the spatially homogenous model.

Conclusions

The observed spatial variability of LULC influence in parasitaemia would have been masked by traditional Poisson regression analysis assuming a spatially constant influence of all variables. We conclude that the spatially varying effects of LULC on P. falciparum parasitaemia may in fact be associated with co-factors not captured by remote sensing, and suggest that future studies assess small-scale spatial variation of vegetation to circumvent generalised assumptions on ecological associations that may in fact be artificial.

【 授权许可】

   
2014 Ehlkes et al.; licensee BioMed Central Ltd.

【 图 表 】

Figure 1.

Figure 1.

【 参考文献 】

• [1]WHO: World Malaria Report: 2013. 2013, 284.
• [2]Krefis AC, Schwarz NG, Krüger A, Fobil J, Nkrumah B, Acquah S, Loag W, Sarpong N, Adu-Sarkodie Y, Ranft U, May J: Modeling the relationship between precipitation and malaria incidence in children from a holoendemic area in Ghana. Am J Trop Med Hyg 2011, 84:285-291.
• [3]Ijumba JN, Mosha FW, Lindsay SW: Malaria transmission risk variations derived from different agricultural practices in an irrigated area of northern Tanzania. Med Vet Entomol 2002, 16:28-38.
• [4]Tatem AJ, Gething PW, Smith DL, Hay SI: Urbanization and the global malaria recession Urbanization and the global malaria recession. Malar J 2013, 12:133. BioMed Central Full Text
• [5]Mushinzimana E, Munga S, Minakawa N, Li L, Feng C-C, Bian L, Kitron U, Schmidt C, Beck L, Zhou G, Githeko AK, Yan G: Landscape determinants and remote sensing of anopheline mosquito larval habitats in the western Kenya highlands. Malar J 2006, 5:13. BioMed Central Full Text
• [6]Oesterholt MJ, Bousema JT, Mwerinde OK, Harris C, Lushino P, Masokoto A, Mwerinde H, Mosha FW, Drakeley CJ: Spatial and temporal variation in malaria transmission in a low endemicity area in northern Tanzania. Malar J 2006, 5:98. BioMed Central Full Text
• [7]Bousema T, Griffin JT, Sauerwein RW, Smith DL, Churcher TS, Takken W, Ghani A, Drakeley C, Gosling R: Hitting hotspots: spatial targeting of malaria for control and elimination. PLoS Med 2012, 9:e1001165.
• [8]Brooker S, Clarke S, Njagi JK, Polack S, Mugo B, Estambale B, Muchiri E, Magnussen P, Cox J: Spatial clustering of malaria and associated risk factors during an epidemic in a highland area of western Kenya. Trop Med Int Health 2004, 9:757-766.
• [9]Kreuels B, Kobbe R, Adjei S, Kreuzberg C, von Reden C, Bäter K, Klug S, Busch W, Adjei O, May J: Spatial variation of malaria incidence in young children from a geographically homogeneous area with high endemicity. J Infect Dis 2008, 197:85-93.
• [10]Barros FSM, Arruda ME, Gurgel HC, Honório NA: Spatial clustering and longitudinal variation of Anopheles darlingi (Diptera: Culicidae) larvae in a river of the Amazon: the importance of the forest fringe and of obstructions to flow in frontier malaria. Bull Entomol Res 2011, 101:643-658.
• [11]Clark TD, Greenhouse B, Njama-meya D, Nzarubara B, Maiteki-sebuguzi C, Staedke SG, Seto E, Kamya MR, Rosenthal PJ, Dorsey G: Factors determining the heterogeneity of malaria incidence in children in Kampala, Uganda. J Infect Dis 2008, 198:393-400.
• [12]Krefis AC, Schwarz NG, Nkrumah B, Acquah S, Loag W: Spatial analysis of land cover determinants of malaria incidence in the Ashanti Region, Ghana. PLoS ONE 2011, 6:e17905.
• [13]Wagner T, Benbow ME, Brenden TO, Qi J, Johnson C: Buruli ulcer disease prevalence in Benin, West Africa : associations with land use/cover and the identification of disease clusters. Int J Health Geogr 2008, 13:1-13.
• [14]Ernst KC, Lindblade KA, Koech D, Sumba PO, Kuwuor DO, John CC, Wilson ML: Environmental, socio-demographic and behavioural determinants of malaria risk in the western Kenyan highlands: a case–control study. Trop Med Int Health 2009, 14:1258-1265.
• [15]Abonuusum A, Owusu-Daako K, Tannich E, May J, Garms R, Kruppa T: Malaria transmission in two rural communities in the forest zone of Ghana. Parasitol Res 2011, 108:1465-1471.
• [16]Gimnig JE, Ombok M, Kamau L, Hawley WA: Characteristics of larval anopheline (Diptera: Culicidae) habitats in Western Kenya. J Med Entomol 2001, 38:282-288.
• [17]Oduola AO, Olojede JB, Oyewole IO, Otubanjo OA, Awolola TS: Abundance and diversity of Anopheles species (Diptera: Culicidae) associated with malaria transmission in human dwellings in rural and urban communities in Oyo State, Southwestern Nigeria. Parasitol Res 2013, 112:3433-3439.
• [18]Dambach P, Machault V, Lacaux J, Vignolles C, Sié A, Sauerborn R: Utilization of combined remote sensing techniques to detect environmental variables influencing malaria vector densities in rural West Africa. Int J Health Geogr 2012, 11:8. BioMed Central Full Text
• [19]Stefani A, Roux E, Fotsing J-M, Carme B: Studying relationships between environment and malaria incidence in Camopi (French Guiana) through the objective selection of buffer-based landscape characterisations. Int J Health Geogr 2011, 10:65. BioMed Central Full Text
• [20]Achee NL, Grieco JP, Masuoka P, Richard G, Roberts DR, Thomas J, Briceno I, King R, Andre RG: Use of Remote Sensing and Geographic Information Systems to predict locations of Anopheles darlingi - positive breeding sites within the Sibun river in Belize, Central America. J Med Entomol 2006, 43(2):382-392.
• [21]Kobbe R, Kreuzberg C, Adjei S, Thompson B, Langefeld I, Thompson PA, Abruquah HH, Kreuels B, Ayim M, Busch W, Marks F, Amoah K, Opoku E, Meyer CG, Adjei O, May J: A randomized controlled trial of extended intermittent preventive antimalarial treatment in infants. Clin Infect Dis 2007, 45:16-25.
• [22]Trape JF: Rapid evaluation of malaria parasite density and standardization of thick smear examination for epidemiological investigations. Trans R Soc Trop Med Hyg 1985, 79:181-184.
• [23]Kobbe R, Neuhoff R, Marks F, Adjei S, Langefeld I, von Reden C, Adjei O, Meyer CG, May J: Seasonal variation and high multiplicity of first Plasmodium falciparum infections in children from a holoendemic area in Ghana, West Africa. Trop Med Int Health 2006, 11:613-619.
• [24]Kobbe R, Hogan B, Adjei S, Klein P, Kreuels B, Loag W, Adjei O, May J: Follow-up survey of children who received sulfadoxine-pyrimethamine for intermittent preventive antimalarial treatment in infants. J Infect Dis 2011, 203:556-560.
• [25]Kreuels B, Kreuzberg C, Kobbe R, Ayim-Akonor M, Apiah-Thompson P, Thompson B, Ehmen C, Adjei S, Langefeld I, Adjei O, May J: Differing effects of HbS and HbC traits on uncomplicated falciparum malaria, anemia, and child growth. Blood 2010, 115:4551-4558.
• [26]ITT Visual Information Solutions: Envi 4.4. 2009.
• [27]Lawrence R, Hurst R, Weaver T, Aspinall R: Mapping prairie pothole communities with multitemporal IKONOS satellite imagery. Photogramm Eng Remote Sens 2006, 72:169-174.
• [28]Wei W, Zhang X, Chen X, Tang J, Jiang M: Wetland mapping using subpixel analysis and decision tree classification in the Yellow River delta area. ISPRS Archives 2008, 38(B7):667-670.
• [29]Huho B, Briët O, Seyoum A, Sikaala C, Bayoh N, Gimnig J, Okumu F, Diallo D, Abdulla S, Smith T, Killeen G: Consistently high estimates for the proportion of human exposure to malaria vector populations occurring indoors in rural Africa. Int J Epidemiol 2013, 42:235-247.
• [30]Midega JT, Mbogo CM, Mwnambi H, Wilson MD, Ojwang G, Mwangangi JM, Nzovu JG, Githure JI, Yan G, Beier JC: Estimating dispersal and survival of Anopheles gambiae and Anopheles funestus along the Kenyan coast by using mark-release-recapture methods. J Med Entomol 2007, 44:923-929.
• [31]Krefis AC, Schwarz NG, Nkrumah B, Acquah S, Loag W, Sarpong N, Adu-Sarkodie Y, Ranft U, May J: Principal component analysis of socioeconomic factors and their association with malaria in children from the Ashanti Region. Ghana Malar J 2010, 9:201. BioMed Central Full Text
• [32]StataCorp LP: Stata Statistical Software: Release 12. 2011.
• [33]Nakaya T: GWR 4GWR4.0. Kyoto, Japan: Ritsumeikan University; 2009.
• [34]Nakaya T, Fotheringham AS, Brunsdon C, Charlton M: Geographically weighted Poisson regression for disease association mapping. Statist Med 2005, 24:2695-2717.
• [35]Wheeler D, Tiefelsdorf M: Multicollinearity and correlation among local regression coefficients in geographically weighted regression. J Geogr Syst 2005, 7:161-187.
• [36]O’Brien RM: A caution regarding rules of thumb for variance inflation factors. Qual Quant 2007, 41:673-690.
• [37]ESRI: ArcGis Desktop: Release 10. 2011.
• [38]Barbieri AF, Sawyer IO, Soares-Filho BS: Population and land use effects on malaria prevalence in the Southern Brazilian Amazon. Hum Ecol 2005, 33:847-874.
• [39]Afrane YA, Little TJ, Lawson BW, Githeko AK, Yan G: Deforestation and vectorial capacity of Anopheles gambiae Giles mosquitoes in malaria transmission, Kenya. Emerg Infect Dis 2008, 14:1533-1538.
• [40]Vittor AY, Gilman RH, Tielsch J, Glass G, Shields T, Lozano WS, Pinedo-Cancino V, Patz JA: The effect of deforestation on the human-biting rate of Anopheles darlingi, the primary vector of Falciparum malaria in the Peruvian Amazon. Am J Trop Med Hyg 2006, 74:3-11.
• [41]Stefani A, Dusfour I, Corrêa APS, Cruz MCB, Dessay N, Galardo AKR, Galardo CD, Girod R, Gomes MSM, Gurgel H, Lima ACF, Moreno ES, Musset L, Nacher M, Soares ACS, Carme B, Roux E: Land cover, land use and malaria in the Amazon: a systematic literature review of studies using remotely sensed data. Malar J 2013, 12:192. BioMed Central Full Text
• [42]Vlahov D, Galea S: Urbanization, urbanicity, and health. J Urban Health 2002, 79:S1-S12.
• [43]Overgaard HJ, Ekbom B, Suwonkerd W, Takagi M: Effect of landscape structure on anopheline mosquito density and diversity in northern Thailand : Implications for malaria transmission and control. Landsc Ecol 2003, 18:605-619.
• [44]Kovendan K, Mahesh Kumar P, Amerasan D, Murugan K, Subrmaniam J, Hwang J-S: Larvicidal, pupicidal, repellent and adulticidal activity of Citrus sinensis orange peel extract against Anopheles stephensi, Aedes aegypti and Culex quinquefasciatus (Diptera: Culicidae). Parasitol Res 2012, 111(4):1757-1769.
• [45]Giatropoulos A, Papachristos DP, Kimbaris A, Koliopoulos G, Polissiou MG, Emmanouel N, Michaelakis A: Evaluation of bioefficacy of three Citrus essential oils against the dengue vector Aedes albopictus (Diptera: Culicidae) in correlation to their components enantiomeric distribution. Parasitol Res 2012, 111(6):2253-2263.
• [46]Müller GC, Beier JC, Traore SF, Toure MB, Traore MM, Bah S, Doumbia S, Schlein Y: Field experiments of Anopheles gambiae attraction to local fruits/seedpods and flowering plants in Mali to optimize strategies for malaria vector control in Africa using attractive toxic sugar bait methods. Malar J 2010, 9:262. BioMed Central Full Text
• [47]Appawu M, Owusu-Agyei S, Dadzie S, Asoala V, Anto F, Koram K, Rogers W, Nkrumah F, Hoffman SL, Fryauff DJ: Malaria transmission dynamics at a site in northern Ghana proposed for testing malaria vaccines. Trop Med Int Health 2004, 9:164-170.
• [48]Grieco JP, Johnson S, Achee NL, Masuoka P, Pope K, Rejmánková EKA, Andre R, Roberts D, Rejma KA: Distribution of Anopheles albimanus, Anopheles vestitipennis, and Anopheles crucians associated with land use in Northern Belize. J Med Entomol 2006, 43(3):614-622.
• [49]Matthys B, Koudou BG, N’Goran EK, Vounatsou P, Gosoniu L, Koné M, Gissé G, Utzinger J: Spatial dispersion and characterisation of mosquito breeding habitats in urban vegetable-production areas of Abidjan, Côte d’Ivoire. Ann Trop Med Parasitol 2010, 104:649-666.
• [50]Bejon P, Williams TN, Liljander A, Noor AM, Wambua J, Ogada E, Olotu A, Osier FH A, Hay SI, Färnert A, Marsh K: Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. PLoS Med 2010, 7:e1000304.
• [51]Tran TM, Ongoiba A, Coursen J, Crosnier C, Diouf A, Huang C-Y, Li S, Doumbo S, Doumtabe D, Kone Y, Bathily A, Dia S, Niangaly M, Dara C, Sangala J, Miller LH, Doumbo OK, Kayentao K, Long CA, Miura K, Wright GJ, Traore B, Crompton PD: Naturally acquired antibodies specific for plasmodium falciparum RH5 inhibit parasite growth and predict protection from malaria. J Infect Dis 2013, 1-10.
• [52]Roussilhon C, Oeuvray C, Müller-Graf C, Tall A, Rogier C, Trape J-F, Theisen M, Balde A, Pérignon J-L, Druilhe P: Long-term clinical protection from falciparum malaria is strongly associated with IgG3 antibodies to merozoite surface protein 3. PLoS Med 2007, 4:e320.
• [53]Google: Google Earth. 2005.