Predictive Malaria Risk and Uncertainty Mapping in Nchelenge District, Zambia: Evidence of Widespread, Persistent Risk and Implications for Targeted Interventions

Jessie Pinchoff Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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Mike Chaponda Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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Timothy Shields Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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James Lupiya Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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Tamaki Kobayashi Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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Modest Mulenga Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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William J. Moss Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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Frank C. Curriero Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, Maryland; Tropical Disease Research Centre, Ndola, Zambia

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Malaria risk maps may be used to guide policy decisions on whether vector control interventions should be targeted and, if so, where. Active surveillance for malaria was conducted through household surveys in Nchelenge District, Zambia from April 2012 through December 2014. Households were enumerated based on satellite imagery and randomly selected for study enrollment. At each visit, participants were administered a questionnaire and a malaria rapid diagnostic test (RDT). Logistic regression models were used to construct spatial prediction risk maps and maps of risk uncertainty. A total of 461 households were visited, comprising 1,725 participants, of whom 48% were RDT positive. Several environmental features were associated with increased household malaria risk in a multivariable logistic regression model adjusting for seasonal variation. The model was validated using both internal and external evaluation measures to generate and assess root mean square error, as well as sensitivity and specificity for predicted risk. The final, validated model was used to predict and map malaria risk including a measure of risk uncertainty. Malaria risk in a high, perennial transmission setting is widespread but heterogeneous at a local scale, with seasonal variation. Targeting malaria control interventions may not be appropriate in this epidemiological setting.

Author Notes

* Address correspondence to Jessie Pinchoff, Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, 615 N Wolfe Street, Baltimore, MD 21205. E-mail: jpincho1@jhu.edu

Financial support: This work was supported by the Johns Hopkins Malaria Research Institute, the Bloomberg Family Foundation, and the Division of Microbiology and Infectious Diseases, National Institutes of Allergy and Infectious Diseases, National Institutes of Health as part of the International Centers of Excellence for Malaria Research (U19 AI089680).

Authors' addresses: Jessie Pinchoff, Timothy Shields, Tamaki Kobayashi, William J. Moss, and Frank C. Curriero, Department of Epidemiology, Johns Hopkins University Bloomberg School of Public Health, Baltimore, MD, E-mails: jpinchof@jhu.edu, tshield2@jhu.edu, tkobaya2@jhu.edu, wmoss1@jhu.edu, and fcurriero@jhu.edu. Mike Chaponda, James Lupiya, and Modest Mulenga, Tropical Disease Research Centre, Ndola, Zambia, E-mails: chapondam@tdrc.org.zm, jamluipiya@gmail.com, and mulengam@tdrc.org.zm.

  • 1.

    Kamuliwo M, Chanda E, Haque U, Mwanza-Ingwe M, Sikaala C, Katebe-Sakala C, Mukonka V, Norris D, Smith D, Glass G, Moss W, 2013. The changing burden of malaria and association with vector control interventions in Zambia using district-level surveillance data, 2006–2011. Malar J 12: 437.

    • Search Google Scholar
    • Export Citation
  • 2.

    Coleman C, Coleman M, Mabuza A, Kok G, Coetzee M, Durrheim D, 2009. Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes. Malar J 8: 68.

    • Search Google Scholar
    • Export Citation
  • 3.

    Smith D, McKenzie F, Snow R, Hay S, 2007. Revisiting the basic reproductive number for malaria and its implications for malaria control. PLoS Biol 5: 531542.

    • Search Google Scholar
    • Export Citation
  • 4.

    Clennon JAKA, Musapa M, Shiff C, Glass GE, 2010. Identifying malaria vector breeding habitats with remote sensing data and terrain-based landscape indices in Zambia. Int J Health Geogr 9: 58.

    • Search Google Scholar
    • Export Citation
  • 5.

    Moss W, Hamapumbu H, Kobayashi T, Shields T, Kamanga A, Clennon J, Mharakurwa S, Thuma P, Glass G, 2011. Use of remote sensing to identify spatial risk factors for malaria in a region of declining transmission: a cross-sectional and longitudinal community. Malar J 10: 163.

    • Search Google Scholar
    • Export Citation
  • 6.

    Ghebreyesus T, Haile M, Witten K, Getachew A, Yohannes AM, Yohannes M, Teklehaimanot HD, Lindsay SW, Byass P, 1999. Incidence of malaria among children living near dams in northern Ethiopia: community based incidence survey. BMJ 319: 663666.

    • Search Google Scholar
    • Export Citation
  • 7.

    Nmor J, Sunahara T, Goto K, Futami K, Sonye G, Akewywa P, Dida G, Minakawa N, 2013. Topographic models for predicting malaria vector breeding habitats: potential tools for vector control managers. Parasit Vectors 6: 14.

    • Search Google Scholar
    • Export Citation
  • 8.

    Bejon P, Williams T, Liljander A, Noor A, Wambua J, Ogada E, Olotu A, Osier F, Hay S, Farnert A, Marsh K, 2010. Stable and unstable malaria hostpots in longitudinal cohort studies in Kenya. PLoS Med 7: e1000304.

    • Search Google Scholar
    • Export Citation
  • 9.

    Eisele T, Keating J, Swalm C, Mbogo C, Githeko A, Regens J, Githure J, Andrews L, Beier J, 2003. Linking field-based ecological data with remotely sensed data using a geographic information system in two malaria endemic urban areas of Kenya. Malar J 2: 44.

    • Search Google Scholar
    • Export Citation
  • 10.

    Pinault L, Hunter F, 2012. Larval habitat associations with human land uses, roads, rivers, and land cover for Anopheles albimanus, A. pseudopunctipennis, and A. punctimacula (Diptera:Culicidae) in coastal and highland Ecuador. Front Physiol 3: 59.

    • Search Google Scholar
    • Export Citation
  • 11.

    Omukunda E, Githeko A, Ndong'a M, Mushinzimana E, Yan G, 2012. Effect of swamp cultivation on distribution of anopheline larval habitats in western Kenya. J Vector Borne Dis 49: 6171.

    • Search Google Scholar
    • Export Citation
  • 12.

    Raso G, Schur N, Utzinger J, Koudou B, Tchicaya E, Rohner F, N'Goran E, Silue K, Matthys B, Assi S, Tanner M, Vounatsou P, 2012. Mapping malaria risk among children in Cote d'Ivoire using Bayesian geo-statistical models. Malar J 11: 160.

    • Search Google Scholar
    • Export Citation
  • 13.

    Craig M, Sharp B, Mabaso M, Kleinschmidt I, 2007. Developing a spatial-statistical model and map of historical malaria prevalence in Botswana using a staged variable selection procedure. Int J Health Geogr 6: 44.

    • Search Google Scholar
    • Export Citation
  • 14.

    Yeshiwondim A, Gopal S, Hailemariam A, Dengela D, Patel H, 2009. Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia. Int J Health Geogr 8: 5.

    • Search Google Scholar
    • Export Citation
  • 15.

    Riedel N, Vounatsou P, Miller J, Gosniu L, Chizema-Kawesha E, Mukonka V, Steketee R, 2010. Geographical patterns and predictors of malaria risk in Zambia: Bayesian geostatistical modelling of the 2006 Zambia national malaria indicator survey (ZMIS). Malar J 9: 37.

    • Search Google Scholar
    • Export Citation
  • 16.

    Gemperli A, Sogoba N, Fondjo E, Mabaso M, Bagayoko M, Briet O, Anderegg D, Liebe J, Smith T, Vounatsou P, 2006. Mapping malaria transmission in west and central Africa. Trop Med Int Health 11: 10321046.

    • Search Google Scholar
    • Export Citation
  • 17.

    Hay S, Guerra C, Gething P, Patil A, Tatem A, Noor A, Kabaria C, Manh B, Elyazar I, Brooker S, Smith D, Moyeed R, Snow R, 2009. A world malaria map: Plasmodium falciparum endemicity in 2007. PLoS Med 6: e100048.

    • Search Google Scholar
    • Export Citation
  • 18.

    Cohen J, Dlamini S, Novotny J, Kandula D, Kunene S, Tatem A, 2013. Rapid case-based mapping of seasonal malaria transmission risk for strategic elimination planning in Swaziland. Malar J 12: 61.

    • Search Google Scholar
    • Export Citation
  • 19.

    Noor A, Moloney G, Borle M, Fegan G, Shewchuk T, Snow R, 2008. The use of mosquito nets and the prevalence of Plasmodium falciparum infection in rural south central Somalia. PLoS One 3: e2081.

    • Search Google Scholar
    • Export Citation
  • 20.

    Rulisa S, Kateera F, Bizimana J, Agaba S, Dukuzumuremyi J, Baas L, Harelimana J, Mens P, Boer K, de Vries PJ, 2012. Malaria prevalence, spatial clustering and risk factors in a low endemic area of eastern Rwanda: a cross sectional study. PLoS One 8: e69443.

    • Search Google Scholar
    • Export Citation
  • 21.

    Magalhaes R, Langa A, Sousa-Figueiredo J, Clements A, Nery SV, 2012. Finding malaria hot-spots in northern Angola: the role of individual, household and environmental factors within a meso-endemic area. Malar J 11: 385.

    • Search Google Scholar
    • Export Citation
  • 22.

    Noor A, El Mardi K, Abdelgader T, Patil A, Amine A, Bakhier S, Mukhtar M, Snow R, 2012. Malaria risk mapping for control in the Republic of Sudan. Am J Trop Med Hyg 87: 10121021.

    • Search Google Scholar
    • Export Citation
  • 23.

    MOH, 2012. Zambia National Malaria Indicator Survey 2012. Lusaka, Zambia: Ministry of Health Government of the Republic of Zambia.

  • 24.

    Mukonka V, Chanda E, Haque U, Kamuliwo M, Mushinge G, Chileshe J, Chibwe K, Norris D, Mulenga M, Chaponda M, Muleba M, Glass G, Moss W, 2014. High burden of malaria following scale-up of control interventions in Nchelenge district, Luapula province, Zambia. Malar J 13: 153.

    • Search Google Scholar
    • Export Citation
  • 25.

    Lowther S, Curriero F, Shields T, Ahmed S, Monze M, Moss W, 2009. Feasibility of satellite image-based sampling for a health survey among urban townships of Lusaka, Zambia. Trop Med Int Health 14: 778.

    • Search Google Scholar
    • Export Citation
  • 26.

    Maidment D, 2002. Arc Hydro: Gis for Water Resources. Press E, ed. Redlands, CA: ESRI.

  • 27.

    Tarboton D, Bras R, Rodriguez-Iturbe I, 1991. On the extraction of channel networks from digital elevation data. Hydrol Processes 5: 81100.

    • Search Google Scholar
    • Export Citation
  • 28.

    Waller LA, Gotway CA, 2004. Applied Spatial Statistics for Public Health Data. Hoboken, NJ: Wiley.

  • 29.

    R Core Team, 2013. R: A Language and Environment for Statistical Computing. Vienna, Austria: R Foundation for Statistical computing.

  • 30.

    Hahn D, Wanjala P, Marx M, 2013. Where is information quality lost at clinical level? A mixed-method study on information systems and data quality in three urban Kenyan ANC clinics? Glob Health Action 6: 21424.

    • Search Google Scholar
    • Export Citation
  • 31.

    Chandler C, Jones C, Boniface G, Juma K, Reyburn H, Whitty C, 2008. Guidlines and mindlines: why do clinical staff over-diagnose malaria in Tanzania? A qualitative study. Malar J 7: 5366.

    • Search Google Scholar
    • Export Citation
  • 32.

    Githinji S, Kigen S, Memusi D, Nyandigisi A, Wamari A, Muturi A, Jagoe G, Ziegler R, Snow R, Zurovac D, 2014. Using mobile phone text messaging for malaria surveillance in rural Kenya. Malar J 13: 107116.

    • Search Google Scholar
    • Export Citation
  • 33.

    Valle D, Lima JT, 2014. Large-scale drivers of malaria and priority areas for prevention and control in the Brazilian amazon region using a novel multi-pathogen geospatial model. Malar J 13: 443457.

    • Search Google Scholar
    • Export Citation
  • 34.

    Vittor A, Gilman R, Tielsch J, Glass G, Shields T, Lozano W, Pinedo-Cancino V, Patz J, 2006. The effects 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 74: 311.

    • Search Google Scholar
    • Export Citation
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