• 1.

    Summers J, 1989. Soho – A History of London’s Most Colourful Neighborhood. London: Bloomsbury.

  • 2.

    Carter R, Mendis KN, Roberts D, 2000. Spatial targeting of interventions against malaria. Bull World Health Organ 78: 14011411.

  • 3.

    Roll Back Malaria Partnership, 2008. The Global Malaria Action Plan. Technical Report. Geneva, Switzerland: World Health Organization.

  • 4.

    Feachem RGA, Phillips AA, Targett GA, editors, 2009. Shrinking the Malaria Map: A Prospectus on Malaria Elimination. San Francisco, CA: The Global Health Group, Global Health Sciences, University of California, San Francisco.

    • Search Google Scholar
    • Export Citation
  • 5.

    Hay SI, Guerra CA, Tatem AJ, Noor AM, Snow RW, 2004. The global distribution and population at risk of malaria: past, present, and future. Lancet Infect Dis 4: 327336.

    • Search Google Scholar
    • Export Citation
  • 6.

    John CC, Riedesel MA, Magak NG, Lindblade KA, Menge DM, Hodges JS, Vulule JM, Akhwale W, 2009. Possible interruption of malaria transmission, highland Kenya, 2007–2008. Emerging Infect Dis 15: 19171924.

    • Search Google Scholar
    • Export Citation
  • 7.

    Mendis K, Rietveld A, Warsame M, Bosman A, Greenwood B, Wernsdorfer WH, 2009. From malaria control to eradication: the WHO perspective. Trop Med Int Health 14: 802809.

    • Search Google Scholar
    • Export Citation
  • 8.

    Ernst KC, Adoka SO, Kowuor DO, Wilson ML, John CC, 2006. Malaria hotspot areas in a highland Kenya site are consistent in epidemic and non-epidemic years and are associated with ecological factors. Malar J 5: 78.

    • Search Google Scholar
    • Export Citation
  • 9.

    Ernst KC, Lindblade KA, Koech D, Sumba PO, Kuwuor DO, John CC, Wilson ML, 2009. Environmental, socio-demographic and behavioural determinants of malaria risk in the western Kenyan highlands: a case-control study. Trop Med Int Health 14: 12581265.

    • Search Google Scholar
    • Export Citation
  • 10.

    World Health Organization, 1997. House-spraying with residual insecticides. Rozendaal JA, ed. Vector Control: Methods for Use by Individuals and Communities. Geneva, Switzerland: WHO.

    • Search Google Scholar
    • Export Citation
  • 11.

    Kenya Ministry of Health, 2007. Implementation of IRS Campaign in Malaria Epidemic Prone Districts in Kenya. Nairobi, Kenya: National Malaria Control Programme.

    • Search Google Scholar
    • Export Citation
  • 12.

    Hamre KES, Ayodo G, Hodges JS, John CC, 2020. A mass insecticide-treated bed net distribution campaign reduced malaria risk on an individual but not population level in a highland epidemic-prone area of Kenya. Am J Trop Med Hyg 103: 21832188.

    • Search Google Scholar
    • Export Citation
  • 13.

    ESRI, 2011. ArcGIS Desktop: Release 10. Redlands, CA: Environmental Systems Research Institute.

  • 14.

    R Core Team, 2015. R: A Language and Environment for Statistical Computing. Vienna, Austria: The R Foundation for Statistical Computing.

    • Search Google Scholar
    • Export Citation
  • 15.

    Bivand RS, Pebesma E, Gomez-Rubio V, 2013. Spatial Point Pattern Analysis. New York, NJ: Use R! Springer, Ch. 7, 173211.

  • 16.

    Kalkhan MA, 2011. Spatial Statistics: GeoSpatial Information Modeling and Thematic Mapping. Boca Raton, FL: CRC Press.

  • 17.

    Waller LA, Gotway CA, 2004. Analysis of Spatial Point Patterns. Hoboken, NJ: John Wiley & Sons, Inc., Ch. 5, 118154.

  • 18.

    Gatrell AC, Bailey TC, Diggle PJ, Rowlingson BS, 1996. Spatial point pattern analysis and its application in geographic epidemiology. Trans Inst Br Geogr 21: 256274.

    • Search Google Scholar
    • Export Citation
  • 19.

    Bithell JF, 1990. An application of density estimation to geographic epidemiology. Stat Med 9: 691701.

  • 20.

    Kelsall JE, Diggle PJ, 1995. Non-parametric estimation of spatial variation in relative risk. Stat Med 14: 23352342.

  • 21.

    Davies TM, Hazelton ML, Marshall JC, 2011. Sparr: analyzing spatial relative risk using fixed and adaptive kernel density estimation in R. J Stat Softw 39: 114.

    • Search Google Scholar
    • Export Citation
  • 22.

    Lemke D, Mattauch V, Heidinger O, Pebesma E, Hense HW, 2015. Comparing adaptive and fixed bandwidth-based kernel density estimates in spatial cancer epidemiology. Int J Health Geogr 14: 15.

    • Search Google Scholar
    • Export Citation
  • 23.

    Davies TM, Hazelton ML, 2010. Adaptive kernel estimation of spatial relative risk. Stat Med 29: 24232437.

  • 24.

    Bautista CT, Chan AS, Ryan JR, Calampa C, Roper MH, Hightower AW, Magill AJ, 2006. Epidemiology and spatial analysis of malaria in the northern Peruvian Amazon. Am J Trop Med Hyg 75: 12161222.

    • Search Google Scholar
    • Export Citation
  • 25.

    Gaudart J 2006. Space-time clustering of childhood malaria at the household level: a dynamic cohort in a Mali village. BMC Public Health 6: 286.

    • Search Google Scholar
    • Export Citation
  • 26.

    Nourein AB, Abass MA, Nugud AH, El Hassan I, Snow RW, Noor AM, 2011. Identifying residual foci of Plasmodium falciparum infections for malaria elimination: the urban context of Khartoum, Sudan. PLoS One 6: e16948.

    • Search Google Scholar
    • Export Citation
  • 27.

    Coleman M, Coleman M, Mabuza AM, Kok G, Coetzee M, Durheim DN, 2009. Using the SaTScan method to detect local malaria clusters for guiding malaria control programmes. Malar J 8: 68.

    • Search Google Scholar
    • Export Citation
  • 28.

    Platt A, Obala AA, MacIntyre C, Otsyula B, O’Meara WP, 2018. Dynamic malaria hotspots in an open cohort in western Kenya. Sci Rep 8: 647.

  • 29.

    Ihantamalala FA, Feno MJR, Tanjona R, Rakotondramanga JM, Pennober G, Ranotomanana F, Cauchemez S, Metcalf CJE, Herbreteau V, Wesolowski A, 2018. Spatial and temporal dynamics of malaria in Madagascar. Malar J 17: 58.

    • Search Google Scholar
    • Export Citation
  • 30.

    Soto-Calle V 2017. Spatio-temporal analysis of malaria incidence in the Peruvian Amazon region between 2002 and 2013. Sci Rep 7: 40350.

  • 31.

    Bejon P 2010. Stable and unstable malaria hotspots in longitudinal cohort studies in Kenya. PLoS Med 7: e1000304.

  • 32.

    Clark TD, Greenhouse B, Njama-Meya D, Nzarubara B, Maiteki-Sebuguzi C, Staedke SG, Seto E, Kamya MR, Rosenthal P, Dorsey G, 2008. Factors determining the heterogeneity of malaria incidence in children in Kampala, Uganda. J Infect Dis 198: 393400.

    • Search Google Scholar
    • Export Citation
  • 33.

    Rosas-Aguirre A, Guzman-Guzman M, Gamboa D, Chuquiyauri R, Ramirez R, Manrique P, Carrasco-Escobar G, Puemape C, Llanos-Cuentas A, Vinetz JM, 2017. Micro-heterogeneity of malaria transmission in the Peruvian Amazon: a baseline assessment underlying a population-based cohort study. Malar J 16: 312.

    • Search Google Scholar
    • Export Citation
  • 34.

    Oesterholt MJAM, Bousema JT, Mwerinde OK, Harris C, Lushino P, Masokoto A, Mwerinde H, Mosha FW, Drakeley CJ, 2006. Spatial and temporal variation in malaria transmission in a low endemicity area in northern Tanzania. Malar J 5: 98.

    • Search Google Scholar
    • Export Citation
  • 35.

    Mosha JF 2014. Hot spot or not: a comparison of spatial statistical methods to predict prospective malaria infections. Malar J 13: 53.

  • 36.

    Sumba PO, Wong SL, Kanzaria HK, Johnson KA, John CC, 2008. Malaria treatment-seeking behaviour and recovery from malaria in a highland area of Kenya. Malar J 7: 245.

    • Search Google Scholar
    • Export Citation
  • 37.

    Crowell V, Hardy D, Briet O, Chitnis N, Maire N, Smith T, 2012. Can we depend on case management to prevent re-establisment of P. falciparum malaria, after local interruption of transmission? Epidemics 4: 18.

    • Search Google Scholar
    • Export Citation
  • 38.

    Moonen B 2010. Operational strategies to achieve and maintain malaria elimination. Lancet 376: 15921603.

  • 39.

    Sabot O 2010. Costs and financial feasibility of malaria elimination. Lancet 376: 16041615.

  • 40.

    Sturrock HJ, Hsiang MS, Cohen JM, Smith DL, Greenhouse B, Bousema T, Gosling RD, 2013. Targeting asymptomatic malaria infections: active surveillance in control and elimination. PLoS Med 10: e1001467.

    • Search Google Scholar
    • Export Citation

 

 

 

 

Lack of Consistent Malaria Incidence Hotspots in a Highland Kenyan Area During a 10-Year Period of Very Low and Unstable Transmission

View More View Less
  • 1 Division of Global Pediatrics, University of Minnesota, Minneapolis, Minnesota;
  • 2 Division of Epidemiology and Community Health, University of Minnesota, Minneapolis, Minnesota;
  • 3 CDC Foundation, Atlanta, Georgia;
  • 4 Division of Biostatistics, University of Minnesota, Minneapolis, Minnesota;
  • 5 Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya;
  • 6 Jaramogi Oginga Odinga University of Science and Technology, Bondo, Kenya;
  • 7 Department of Pediatrics, Indiana University, Indianapolis, Indiana

ABSTRACT

The use of spatial data in malaria elimination strategies is important to understand whether targeted interventions against malaria can be used, particularly in areas with limited resources. We previously documented consistent areas of increased malaria incidence in the epidemic-prone area of Kipsamoite in highland Kenya from 2001 to 2004. In this area and a neighboring subcounty (Kapsisiywa), malaria incidence decreased substantially in 2005, going from peak incidence of 31.7 per 1,000 persons in June 2004 to peak incidence of 7.4 per 1,000 persons in May 2005. Subsequently, the use of indoor residual spraying and artemisinin combination therapy malaria treatment led to a possible interruption of malaria transmission for a 13-month period from 2007 to 2008, after which the incidence returned to very low levels until an epidemic in April–July 2013. In the present study, we used novel kernel density estimation methods to determine whether areas of increased malaria incidence were consistent in six periods of peak incidence from 2003 to 2013, and to assess patterns of incidence in the period before versus. after the period of possible interruption. Areas of highest incidence differed during peak malaria transmission periods over the years 2003–2013, and differed before and after the potential malaria interruption. In this epidemic-prone region with very low malaria transmission, consistent malaria “hotspots” identified in a time of higher transmission are no longer present. Ongoing assessment of spatial malaria epidemiology to identify and target current areas of elevated malaria risk may be important in campaigns to control or eliminate malaria in epidemic-prone areas.

    • Supplementary Materials
    • Supplementary Materials
    • Supplementary Materials

Author Notes

Address correspondence to Chandy C. John, Department of Pediatrics, Indiana University, 717 Delaware St. SE, 3rd Floor, Indianapolis, IN 46202. E-mail: chjohn@iu.edu

Financial support: This project was supported by grants from NIH-NIAID (NCT00393757), NIH Fogarty International Center (D43 TW0080085), the University of Minnesota Amplatz Children’s Hospital, and an NIH research training grant (R25 TW009345) awarded to the Northern Pacific Global Health Fellows Program by Fogarty International Center in partnership with several NIH Institutes (NIMH, NIGMS, NHLBI, OAR, and OWH).

Disclosure: This study was published with the permission of the director of the Kenya Medical Research Institute.

Disclaimer: The funding agencies were not involved in any aspect of the study including design, analysis, or interpretation of results.

Authors’ addresses: Karen E. S. Hamre, CDC Foundation, Atlanta, GA, E-mail: hamr0091@umn.edu. James S. Hodges, Division of Biostatistics, University of Minnesota, Minneapolis, MN, E-mail: hodge003@umn.edu. George Ayodo, Centre for Global Health Research, Kenya Medical Research Institute, Kisumu, Kenya, E-mail: gayodo@gmail.com. Chandy C. John, Department of Pediatrics, Indiana University, Indianapolis, IN, E-mail: chjohn@iu.edu.

Save