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Geostatistical analysis of Malawi’s changing malaria transmission from 2010 to 2017

Chipeta, Michael Give, Giorgi, Emanuele, Mategula, Donnie, Macharia, Peter M., Ligomba, Chimwemwe, Munyenyembe, Alinane, Chirombo, James, Gumbo, Austin, Terlouw, Dianne J., Snow, Robert W., Kayange, Michael·2019·Wellcome Open Research
DOI 10.12688/wellcomeopenres.15193.2PMC PMC6662685
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Abstract

Background: The prevalence of malaria infection in time and space provides important information on the likely sub-national epidemiology of malaria burdens and how this has changed following intervention. Model-based geostatitics (MBG) allow national malaria control programmes to leverage multiple data sources to provide predictions of malaria prevalance by district over time. These methods are used to explore the possible changes in malaria prevalance in Malawi from 2010 to 2017. Methods: Plasmodium falciparum parasite prevalence ( Pf PR) surveys undertaken in Malawi between 2000 and 2017 were assembled. A spatio-temporal geostatistical model was fitted to predict annual malaria risk for children aged 2–10 years ( Pf PR 2–10 ) at 1×1 km spatial resolutions. Parameter estimation was carried out using the Monte Carlo maximum likelihood methods. Population-adjusted prevalence and populations at risk by district were calculated for 2010 and 2017 to inform malaria control program priority setting. Results: 2,237 surveys at 1,834 communities undertaken between 2000 and 2017 were identified, geo-coded and used within the MBG framework to predict district malaria prevalence properties for 2010 and 2017. Nationally, there was a 47.2% reduction in the mean modelled Pf PR 2-10 from 29.4% (95% confidence interval (CI) 26.6 to 32.3%) in 2010 to 15.2% (95% CI 13.3 to 18.0%) in 2017. Declining prevalence was not equal across the country, 25 of 27 districts showed a substantial decline ranging from a 3.3% reduction to 79% reduction. By 2017, 16% of Malawi’s population still lived in areas that support Pf PR 2-10 ≥ 25%. Conclusions: Malawi has made substantial progress in reducing the prevalence of malaria over the last seven years. However, Malawi remains in meso -endemic malaria transmission risk. To sustain the gains made and continue reducing the transmission further, universal control interventions need to be maintained at a national level.

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Research works printed but not linked: 37

  1. A Bennett, 2013, Mapping malaria transmission intensity in Malawi, 2000-2010., Am J Trop Med Hyg., 89, 840-849, 10.4269/ajtmh.13-0028 · DOI
  2. A Buchwald, 2016, Bed net use among school-aged children after a universal bed net campaign in Malawi., Malar J., 15, 127, 10.1186/s12936-016-1178-9 · DOI
  3. E Chanda, 2015, Preventing malaria transmission by indoor residual spraying in Malawi: grappling with the challenge of uncertain sustainability., Malar J., 14, 254, 10.1186/s12936-015-0759-3 · DOI
  4. E Chanda, 2016, Scale-up of integrated malaria vector control: lessons from Malawi., Bull World Health Organ., 94, 475-480, 10.2471/BLT.15.154245 · DOI
  5. M Chipeta, 2019, Geostatistical analysis of Malawi’s changing malaria transmission from 2010 to 2017., figshare.
  6. C Chunga, 2014, Community satisfaction with indoor residual spraying for malaria control in Karonga, Northern Malawi., Malawi Med J., 26, 71-77
  7. J Cohen, 2010, How absolute is zero? An evaluation of historical and current definitions of malaria elimination., Malar J., 9, 213, 10.1186/1475-2875-9-213 · DOI
  8. P Diggle, 2019, Model-based Geostatistics for Global Public Health: Methods and Applications, 10.1201/9781315188492 · DOI
  9. P Diggle, 1998, Model-based geostatistics., J Roy Stat Soc C App Stat., 47, 299-350, 10.1111/1467-9876.00113 · DOI
  10. V Escamilla, 2017, Effects of community-level bed net coverage on malaria morbidity in Lilongwe, Malawi., Malar J., 16, 142, 10.1186/s12936-017-1767-2 · DOI
  11. P Gething, 2011, Modelling the global constraints of temperature on transmission of Plasmodium falciparum and P. vivax., Parasit Vectors., 4, 92, 10.1186/1756-3305-4-92 · DOI
  12. E Giorgi, 2017, PrevMap: an R package for prevalence mapping., J Stat Softw., 78, 29, 10.18637/jss.v078.i08 · DOI
  13. E Giorgi, 2018, Geostatistical methods for disease mapping and visualisation using data from spatio-temporally referenced prevalence surveys., Int Stat Rev., 86, 571-597, 10.1111/insr.12268 · DOI
  14. A Kabaghe, 2018a, Fine-scale spatial and temporal variation of clinical malaria incidence and associated factors in children in rural Malawi: a longitudinal study., Parasit Vectors., 11, 129, 10.1186/s13071-018-2730-y · DOI
  15. A Kabaghe, 2017, Adaptive geostatistical sampling enables efficient identification of malaria hotspots in repeated cross-sectional surveys in rural Malawi., PLoS One., 12, e0172266, 10.1371/journal.pone.0172266 · DOI
  16. A Kabaghe, 2018b, Access and adequate utilization of malaria control interventions in rural Malawi: a descriptive quantitative study., Malar J., 17, 104, 10.1186/s12936-018-2253-1 · DOI
  17. L Kazembe, 2006, Spatial analysis and mapping of malaria risk in Malawi using point-referenced prevalence of infection data., Int J Health Geogr., 5, 41, 10.1186/1476-072X-5-41 · DOI
  18. C Linard, 2012, Population distribution, settlement patterns and accessibility across Africa in 2010., PLoS One., 7, e31743, 10.1371/journal.pone.0031743 · DOI
  19. P Macharia, 2018, Spatio-temporal analysis of Plasmodium falciparum prevalence to understand the past and chart the future of malaria control in Kenya., Malar J., 17, 340, 10.1186/s12936-018-2489-9 · DOI · Paper page
  20. G Malenga, 2009, Developing national treatment policy for falciparum malaria in Africa: Malawi experience., Trans R Soc Trop Med Hyg., 103 Suppl 1, S15-S18, 10.1016/j.trstmh.2009.02.003 · DOI
  21. D Mathanga, 2010, Comparison of anaemia and parasitaemia as indicators of malaria control in household and EPI-health facility surveys in Malawi., Malar J., 9, 107, 10.1186/1475-2875-9-107 · DOI
  22. D Mathanga, 2012, Malaria control in Malawi: current status and directions for the future., Acta Trop., 121, 212-217, 10.1016/j.actatropica.2011.06.017 · DOI
  23. R McCann, 2017, Assessment of the effect of larval source management and house improvement on malaria transmission when added to standard malaria control strategies in southern Malawi: study protocol for a cluster-randomised controlled trial., BMC Infect Dis., 17, 639, 10.1186/s12879-017-2749-2 · DOI
  24. D Metselaar, 1959, Classification of malaria., Trop Geogr Med., 11, 157-161
  25. T Mzilahowa, 2016, Increasing insecticide resistance in Anopheles funestus and Anopheles arabiensis in Malawi, 2011-2015., Malar J., 15, 563, 10.1186/s12936-016-1610-1 · DOI · Paper page
  26. 2010, Malawi Malaria Program Performance Review
  27. E Okiro, 2013, Childhood malaria admission rates to four hospitals in Malawi between 2000 and 2010., PLoS One., 8, e62214, 10.1371/journal.pone.0062214 · DOI
  28. J Omumbo, 2013, How well are malaria maps used to design and finance malaria control in Africa?, PLoS One., 8, e53198, 10.1371/journal.pone.0053198 · DOI · Paper page
  29. T Phiri, 2016, Feasibility, acceptability and impact of integrating malaria rapid diagnostic tests and pre-referral rectal artesunate into the integrated community case management programme. A pilot study in Mchinji district, Malawi., Malar J., 15, 177, 10.1186/s12936-016-1237-2 · DOI
  30. A Roca-Feltrer, 2012a, Lack of decline in childhood malaria, Malawi, 2001-2010., Emerg Infect Dis., 18, 272-278, 10.3201/eid1802.111008 · DOI
  31. A Roca-Feltrer, 2012b, Rolling Malaria Indicator Surveys (rMIS): a potential district-level malaria monitoring and evaluation (M&E) tool for program managers., Am J Trop Med Hyg., 86, 96-98, 10.4269/ajtmh.2012.11-0397 · DOI
  32. R Snow, 2017, The prevalence of Plasmodium falciparum in sub Saharan Africa since 1900, 10.1038/nature24059 · DOI · Paper page
  33. R Snow, 1996, The need for maps of transmission intensity to guide malaria control in Africa., Parasitol Today., 12, 455-457, 10.1016/S0169-4758(96)30032-X · DOI
  34. R Snow, 2017, The prevalence of Plasmodium falciparum in sub-Saharan Africa since 1900., Nature., 550, 515-518, 10.1038/nature24059 · DOI · Paper page
  35. F Stevens, 2015, Disaggregating census data for population mapping using random forests with remotely-sensed and ancillary data., PLoS One., 10, e0107042, 10.1371/journal.pone.0107042 · DOI
  36. J Walldorf, 2015, School-Age Children Are a Reservoir of Malaria Infection in Malawi., PLoS One., 10, e0134061, 10.1371/journal.pone.0134061 · DOI
  37. C Wondji, 2012, Impact of pyrethroid resistance on operational malaria control in Malawi., Proc Natl Acad Sci U S A., 109, 19063-19070, 10.1073/pnas.1217229109 · DOI

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Metadata

Authors
Chipeta, M. G., Giorgi, E., Mategula, D., Macharia, P. M., Ligomba, C., Munyenyembe, A., Chirombo, J., Gumbo, A., Terlouw, D. J., Snow, R. W., & Kayange, M.
Venue
Wellcome Open Research
Publisher
F1000 Research Ltd
Published
2019-07-04
Volume
4
Pages
57
ISSN
2398-502X
DOI
10.12688/wellcomeopenres.15193.2
PMC
PMC6662685
Cited by
30
References
1
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (unspecified)
Funders
  • Department for International Development, UK Government (203155)
  • Wellcome Trust (103602, 203077, 101113, 206545, 107769)

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