American Journal of Theoretical and Applied Statistics

| Peer-Reviewed |

Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches

Received: 14 September 2017    Accepted: 25 September 2017    Published: 03 November 2017
Views:       Downloads:

Share This Article

Abstract

The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information.

DOI 10.11648/j.ajtas.20170606.11
Published in American Journal of Theoretical and Applied Statistics (Volume 6, Issue 6, November 2017)
Page(s) 254-269
Creative Commons

This is an Open Access article, distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution and reproduction in any medium or format, provided the original work is properly cited.

Copyright

Copyright © The Author(s), 2024. Published by Science Publishing Group

Keywords

Malaria, Spatial Autocorrelation, Malaria Prevalence, Bayesian Spatial Analysis

References
[1] Ahmad, R., Ali, W., Nor, Z., Ismail, Z., Hadi, A., Ibrahim, M., and Lim, L. (2011): Mapping of mosquito breeding sites in malaria endemic areas in Pos Lenjang, Kuala Lipis, Pahang, Malaysia. Malaria Journal, 10(1), 361.
[2] Anselin, L. (1995): Local indicators of spatial associations-LISA. Geographical Analysis, 27:93-115.
[3] Anselin, L. (1998): Exploratory Spatial Data Analysis in a Geocomputational Environment, PP. 77-94 in Geocomputation, A Primer, edited by P. A. Longley, S. Brooks, B. Macmillan and R. McDonnell. New York: John Wile.
[4] Anselin, L. (2005): Exploring Spatial Data with GeoDa: A Workbook. Spatial Analysis Laboratory, Department of Geography University of Illinois, Urbana-Champaign, Urban IL 61801.
[5] Asnakew, K., Sucharita, G., Afework, T., Dereje, O., and Hrishikesh, P. (2009): Spatial analysis of malaria incidence at the village level in areas with unstable transmission in Ethiopia. International Journal of Health Geographics, 8:5 doi:10.1186/1476-072X-8-5.
[6] Central Statistical Agency(CSA). 2007: Survey report, Ethiopia.
[7] Charles, D. (2012): Bayesian Hierarchical Approaches to Spatial Analysis of Injury and Disaster Data. Columbia University.
[8] Cressie, N. (1993): Statistics for Spatial Data. In revised ed. Wiley, New York.
[9] Congdon, P. (2003): Applied Bayesian Modelling. Chichester: Wiley.
[10] Chikodzi, D. (2013): Spatial Modelling of Malaria Risk Zones Using Environmental, Anthropogenic Variables and Geogra-Phical Information Systems Techniques. Journal of Geosciences and Geomatics 1, no. 1: 8-14. doi: 10.12691/jgg-1-1-2.
[11] Darper, N. and Smith, H. (1998): Applied regression Analysis, Third edition, John Wiley and Sons, New York.
[12] Donnelly, M. J., PJ, M., Lengeler, C., Bates, I., D’Alessandro, U., Barnish, G., Mutero, C. (2005): Malaria and urbanization in sub-Saharan Africa. Malaria Journal, 4:12.
[13] Duncan, L. (2017): An R Package for Spatial Areal Unit Modelling with Conditional Autoregressive Priors. University of Glasgow.
[14] Drissa, C., Stanislas, R., Mark, T., Youssouf, T., Matthew, L., Abdoulaye, K., Karim, T., Ando, G., Issa, D., Amadou, N., Modibo, D., Ahmadou, D., Mody, S., Bourema, K., Nadine, D., Jean, G., Renaud, P., Mahamadou, T., Christopher, P. and Ogobara, D. (2013): Spatio-temporal analysis of malaria within a transmission season in Bandiagara, Mali. Malaria Journal, 12:82.
[15] English, D. (1992): Geographical epidemiology and ecological studies. In Elliott, P., Cuzick, J., English, D., and Stern, R., editors, Geographical and Environmental Epidemiology. Methods for Small-Area Studies. Oxford University Press, Oxford, pp 3-13.
[16] Federal Ministry of Health (FMOH). (2014): National Strategic Plan for Malaria Prevention, Control and Elimination in Ethiopia:2014-2020. FMOH;
[17] Faraway, J. J. (2004): Linear Models with R. Chapman Hall, Boca Raton.
[18] Field, A. (2009): Discovering Statistics Using SPSS, Third Edition. London Thousand Oaks-New Delhi: SAGE Publications.
[19] Gemperli, A., Vounatsou, P., Sogoba, N., Smith, T. (2006): Malaria mapping using transmission models: Applications to survey data from Mali. Am J Epidemiology, 163:289-297.
[20] Getis, A. and Ord J. (1992): The Analysis of Spatial Association by Use of Distance Statistics, Geographical Analysis, 24(3):189-206.
[21] Ghebreyesus, T., Haile, M., Witten, H., Getachew, A., Yohannes, M., Yohannes, M., Teklehaimanot, D., Lindsay, W., Byass, P. (1999): Incidence of malaria among children living near dams in northern Ethiopia: community based incidence survey. BMJ, 319:663-666.
[22] Ghebreyesus, T., Haile, M., Witten, H., Getachew, A., Yohannes, M., Lindsay, W., Byass, P. (2000): Household risk factors for malaria among children in the Ethiopian highlands. Trans R Soc Trop Med Hyg, 94(1):17-21.
[23] Gosoniu, L., Andre, M., and Penelope, V. (2008): Bayesian Geostistical modeling of malaria Indicator survey data in Angola, Journal of Epidemology, Vol. 9:8-12.
[24] Githeko, K., John, A., Peter, O., Francis, A., Bryson, N., John, G. and Guiyun, Y., 2006: Topography and malaria transmission heterogeneity in western Kenya highlands: prospects for focal vector control Malaria Journal, 5:107
[25] Guofa, Z., Noboru, M. and Githeko, K. (2003): Association between climate variability and malaria epidemics in the East African highlands, Journal of Malaria, vol. 6:1-2.
[26] Haining, R. (2010): Spatial Data Analysis in the Social and Environmental Sciences, Cambridge University Press: Cambridge.
[27] Ingrid Peterson, Luisa N. Borrell, Wafaa El-Sadr and Awash Teklehaimanot. (2009): Individual and Household Level Factors Associated with Malaria Incidence in a Highland Region of Ethiopia: The American Society of Tropical Medicine and Hygiene: pp. 103-111.
[28] Tuyishimire, J. (2013): Spatial Modelling of Malaria Risk Factors in Ruhuha sector, Rwanda.
[29] Kiszewski, E., Tekelehaimanot, A. (2002): A review of the clinical and epidemiologic burdens of epidemic malaria.
[30] Kleinschmidt I, Bagayoko M, Clarke GPY, Craig M, Le Sueur D. (2000): A spatial statistical approach to malaria mapping. Int J Epidemiol, 29:355-61.
[31] LeSage, J., and Pace, K. (2009): Introduction to Spatial Econometrics, Boca Raton, Florida: CRC Press.
[32] Lubetzky-Vilnai, A., Ciol, M., McCoy, S. W. (2013): Statistical Analysis of Clinical Prediction Rules for Rehabilitation Interventions: Current State of the Literature. Archives of Physical Medicine and Rehabilitation.
[33] Matheron, G. (1971): The theory of regionalized variables and its applications. Les Cahiers du Centre de Morphologie Mathematique de Fontainebleau.
[34] Merkle, E., Shev, C. and Trisha, G. (2005): Simulation Based Bayesian Inference Using Winbugs. Winbugs Tutorial Outline.
[35] Mitiku M. and Bute G. (2011): Statistical Analysis of Spatial Distribution of Malaria in West Shoa Zone, Ethiopia. Journal of Ethiopian Statistical Association, AA.
[36] Molla, E., and Ayele, B. (2015): Prevalence of Malaria and Associated Factors in Dilla Town and the Surrounding Rural Areas, Gedeo Zone, Southern Ethiopia. J Bacteriol Parasitol, 6:242.
[37] Omumbo, J., Hay, I., Snow, W., Tatem, J., Rogers, J. (2005): Modeling malaria risk in East Africa at high-spatial resolution. Trop Med Int Health, 10:557-566.
[38] Raso, G., Schur, N., Utzinger, J., Koudou, B., Tchicaya, E., Rohner, F., N’ Goran, E., Silué, D., Matthys, B., Assi, S., Tanner, M. (2012): Mapping malaria risk among children in Côte d’Ivoire using Bayesian geo-statistical models. Malaria Journal, 11:160.
[39] Rosas, A, Oscar, J., Gabriel, C., Niko, S., Juan, C., Dionicia, G., Edwar, P., Socrates, H. and Alejandro, L. (2015): Spatial clustering and risk factors in a low endemicity urban area of the northwestern Peruvian coast, Malaria Journal, 14:176.
[40] Robert, W., Marsh, K. (2002): The consequences of reducing transmission of Plasmodium falciparum in Africa.
[41] Stratton, L., O’Neill, M. S., Kruk, M. E., & Bell, M. L. (2008): The persistent problem of malaria: Addressing the fundamental causes of a global killer. Social Science & Medicine, 67(5), 854-862.
[42] Thomas, J., Lindsay, W. (2000): Local-scale variation in malaria infection amongst rural Gambian children estimated by satellite remote sensing. Trans R Soc Trop Med Hyg, 94(2):159-63.
[43] Wakefield, J., N. Best, and L. Waller. (2000): Bayesian Approaches to Disease Mapping. In Spatial Epidemiology Methods and Applications, edited by P. Elliott, J. Wakefield, N. Best, and D. J. Briggs. Oxford: Oxford University Press.
[44] Yamamoto, S., Louis, R, A., Sauerborn, R. (2010): Household risk factors for clinical malaria in a semi-urban area of Burkina Faso: a case–control study. Transactions of the Royal Society of Tropical Medicine and Hygiene, 104(1), 61-65.
Author Information
  • Department of Statistics, Madda Walabu University, Bale Robe, Ethiopia

Cite This Article
  • APA Style

    Dereje Bekele Dessie. (2017). Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches. American Journal of Theoretical and Applied Statistics, 6(6), 254-269. https://doi.org/10.11648/j.ajtas.20170606.11

    Copy | Download

    ACS Style

    Dereje Bekele Dessie. Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches. Am. J. Theor. Appl. Stat. 2017, 6(6), 254-269. doi: 10.11648/j.ajtas.20170606.11

    Copy | Download

    AMA Style

    Dereje Bekele Dessie. Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches. Am J Theor Appl Stat. 2017;6(6):254-269. doi: 10.11648/j.ajtas.20170606.11

    Copy | Download

  • @article{10.11648/j.ajtas.20170606.11,
      author = {Dereje Bekele Dessie},
      title = {Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches},
      journal = {American Journal of Theoretical and Applied Statistics},
      volume = {6},
      number = {6},
      pages = {254-269},
      doi = {10.11648/j.ajtas.20170606.11},
      url = {https://doi.org/10.11648/j.ajtas.20170606.11},
      eprint = {https://download.sciencepg.com/pdf/10.11648.j.ajtas.20170606.11},
      abstract = {The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information.},
     year = {2017}
    }
    

    Copy | Download

  • TY  - JOUR
    T1  - Spatial Modelling of Malaria Prevalence and Its Risk Factors in Rural SNNPR, Ethiopia: Classical and Bayesian Approaches
    AU  - Dereje Bekele Dessie
    Y1  - 2017/11/03
    PY  - 2017
    N1  - https://doi.org/10.11648/j.ajtas.20170606.11
    DO  - 10.11648/j.ajtas.20170606.11
    T2  - American Journal of Theoretical and Applied Statistics
    JF  - American Journal of Theoretical and Applied Statistics
    JO  - American Journal of Theoretical and Applied Statistics
    SP  - 254
    EP  - 269
    PB  - Science Publishing Group
    SN  - 2326-9006
    UR  - https://doi.org/10.11648/j.ajtas.20170606.11
    AB  - The purpose of this study was to assess the spatial distribution of malaria prevalence rates among selected rural part of woredas in SNNPR, Ethiopia. This work is based on data available from the 2011 malaria indicator survey (MIS 2011) of Ethiopian Public Health Institution. ESDA, Spatial regression model and Bayesian Spatial analysis were employed for data analysis. From ESDA, we found positive spatial autocorrelation in malaria prevalence rate. Relying on specification diagnostics and measures of fit; Spatial lag model was found to be the best model for modeling malaria prevalence rate data. The relationship between malaria prevalence and its risk factors was assessed using spatial models. The spatial models also showed an increase of malaria prevalence with a number of factors. From results, increase in the proportion of households sprayed in 12 months and the average altitude in the woreda estimated to decrease the average malaria prevalence. The result also demonstrated that increase in the House hold size of the district, proportion of households having access to piped water, proportion of households having access to radio, proportion of households having access to radio and Main construction material of the room’s wall are estimated to raise the average malaria prevalence rate. Finally, the study concluded that malaria is spatially clustered in space and the risk factors exhibit effect on the malaria prevalence in the study area. Based on the results of the study, We recommend for policy makers on the way to reduce malaria prevalence in the rural part of woreda of SNNPR using spatial information.
    VL  - 6
    IS  - 6
    ER  - 

    Copy | Download

  • Sections