Spatial distribution and sociodemographic risk factors of malaria in Nigerian children less than 5 years old

Submitted: 28 September 2019
Accepted: 28 January 2020
Published: 29 December 2020
Abstract Views: 2845
PDF: 1337
HTML: 444
Publisher's note
All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.

Authors

Malaria remains a leading cause of morbidity and mortality among children in Nigeria less than 5 years old (under-5). This study utilized nationally representative secondary data extracted from the 2015 Nigeria Malaria Indicator Survey (NMIS) to investigate the spatial variability in malaria distribution in those under- 5 and to explore the influence of socioeconomic and demographic factors on malaria prevalence in this population group. To account for spatial correlation, a Spatially Generalized Linear Mixed Model (SGMM) was employed and predictive risk maps was developed using Kriging. Highly significant spatial variability in under-5 malaria distribution was observed (P<0.0001) with a higher likelihood of malaria prevalence in this group in the Northwest and North-east of the country. The number of malaria infections increased with age, children aged between 49-59 months were found to be at a higher risk (Odds Ratio=4.680, 95% CI=3.674 to 5.961 at P<0.0001). After accounting for spatial correlation, we observed a strong significant association between the non-availability or non-use of mosquito bed-nets, low household socioeconomic status, low level of mother's educational attainment, family size, anaemia prevalence, rural type of residence and under-5 malaria prevalence. Faced with a high rate of under-5 mortality due to malaria in Nigeria, targeted interventions (which requires the identification of the child's location) may reduce malaria prevalence, and we conclude that socioeconomic impediments need to be confronted to reduce the burden of childhood malaria infection.

Dimensions

Altmetric

PlumX Metrics

Downloads

Download data is not yet available.

Citations

Adebayo SB, Gayawan E, Heumann C, Seiler C, 2016. Joint modeling of Anaemia and Malaria in children under five in Nigeria. Spat Spatiotemporal Epidemiol 17:105-15. doi: https://doi.org/10.1016/j.sste.2016.04.011 DOI: https://doi.org/10.1016/j.sste.2016.04.011
Adigun AB, Gajere EN, Oresanya O, Vounatsou P, 2015. Malaria risk in Nigeria: Bayesian geostatistical modelling of 2010 malaria indicator survey data. Malaria J 14:156. doi: https://doi.org/10.1186/s12936-015-0683-6 DOI: https://doi.org/10.1186/s12936-015-0683-6
Akpan GE, Adepoju KA, Oladosu OR, 2019. Potential distribution of dominant malaria vector species in tropical region under climate change scenarios. PloS One, 2019. 14:e0218523. doi: https://doi.org/10.1371/journal.pone.0218523 DOI: https://doi.org/10.1371/journal.pone.0218523
Andrew O, 2014. An assesment of the spatial pattern of malaria infection in Nigeria. Int J Med Med Sci 6: 80-6. doi: 10.5897/IJMMS2013.1006 DOI: https://doi.org/10.5897/IJMMS2013.1006
Awolola TS, Oduola AO, Obansa JB, Chukwurar NJ, Unyimadu JP, 2007. Anopheles gambiae ss breeding in polluted water bodies in urban Lagos, southwestern Nigeria. J Vector Borne Dis 44:241.
Ayele DG, Zewotir TT, Mwambi HG, 2013. Spatial distribution of malaria problem in three regions of Ethiopia. Malaria J 12:207. doi: https://doi.org/10.1186/1475-2875-
Cressie N, 1992. Statistics for spatial data. Terra Nova 4:613-17. doi: https://doi.org/10.1111/j.1365-3121.1992.tb00605.x DOI: https://doi.org/10.1111/j.1365-3121.1992.tb00605.x
Diggle P, Moyeed R, Rowlingson B, Thomson M, 2002. Childhood malaria in the Gambia: a case-study in model-based geostatistics. J R Stat Soc C-Appl 51:493-506. doi: https://doi.org/10.1111/1467-9876.00283 DOI: https://doi.org/10.1111/1467-9876.00283
Ebenezer A, Noutcha AEM, Agi PI, Okiwelu SN, Commander T, 2014. Spatial distribution of the sibling species of A nopheles gambiae sensu lato (Diptera: Culicidae) and malaria prevalence in Bayelsa State, Nigeria. Parasit Vectors 7:32. doi: https://doi.org/10.1186/1756-3305-7-32 DOI: https://doi.org/10.1186/1756-3305-7-32
Efe SI, Ojoh CO, 2013. Spatial distribution of malaria in Warri metropolis. Open J Epidemiol 3:118. doi: http://dx.doi.org/10.4236/ojepi.2013.33018 DOI: https://doi.org/10.4236/ojepi.2013.33018
Ferrao J, Niquisse S, Mendes J, Painho M, 2018. Mapping and modelling malaria risk areas using climate, socio-demographic and clinical variables in Chimoio, Mozambique. Int J Environ Res Public Health 15:795. doi: https://doi.org/10.3390/ijerph15040795 DOI: https://doi.org/10.3390/ijerph15040795
Gayawan E, Arogundade ED, Adebayo SB, 2014. A Bayesian multinomial modeling of spatial pattern of co-morbidity of malaria and non-malarial febrile illness among young children in Nigeria. Trans R Soc Trop Med Hyg 108:415-24. DOI: https://doi.org/10.1093/trstmh/tru068
Gotway CA, Stroup WW, 1997. A generalized linear model approach to spatial data analysis and prediction. J Agr Biol Envir Stat 157-78. doi: https://www.jstor.org/stable/1400401 DOI: https://doi.org/10.2307/1400401
Idowu AP, Okoronkwo N, Adagunodo RE, 2009. Spatial predictive model for malaria in Nigeria. J Health Informatics dev ctries 3.
Kalu MK, Obasi NA, Nduka FO, Otuchristian G, 2012. A comparative study of the prevalence of malaria in Aba and Umuahia urban areas of Abia State, Nigeria. Research Journal of Parasitology 7:17-24. doi: https://10.3923/jp.2012.17.24 DOI: https://doi.org/10.3923/jp.2012.17.24
Kazembe LN, Mathanga DP, 2016. Estimating risk factors of urban malaria in Blantyre, Malawi: A spatial regression analysis. Asian Pac J Trop Biomed 2016. 6. doi: 376--381 DOI: https://doi.org/10.1016/j.apjtb.2016.03.011
Keranen K, Kolvoord R, 2017. Making Spatial Decisions Using ArcGIS Pro, A Workbook. Esri Press Redlands, California, USA
Kincaid C, 2005. Guidelines for selecting the covariance structure in mixed model analysis. Paper presented at the Proceedings of the thirtieth annual SAS users group international conference. Available from: https://support.sas.com › papers › proceedings › proceedings › sugi30 (accessed Jan. 2019)
Kreuels B, Kobbe R, Adjei S, Kreuzberg C, von Reden C, B{"a}ter K, May J, 2008. Spatial variation of malaria incidence in young children from a geographically homogeneous area with high endemicity. J. Infect Dis 197:85-93. doi: https://doi.org/10.1086/524066 DOI: https://doi.org/10.1086/524066
Laird NM, Ware JH, 1982. Random-effects models for longitudinal data. Biometrics 38:963-74. DOI: https://doi.org/10.2307/2529876
Machault V, Vignolles C, Pages F, Gadiaga L, Gaye A, Sokhna C, Trape J, Lacaux J, Rogier C, 2010. Spatial heterogeneity and temporal evolution of malaria transmission risk in Dakar, Senegal, according to remotely sensed environmental data. Malaria J 9:252. doi: https://doi.org/10.1186/1475-2875-9-252 DOI: https://doi.org/10.1186/1475-2875-9-252
McCulloch CE, Searle SR, 2001. Generalized Linearand Mixed Models Wiley Series in Probability and Statistics, Wiley & Sons, New York, United States. DOI: https://doi.org/10.1002/0471722073
NMC P, 2015. Nigeria Malaria Indicator Survey Final Report Abuja, Nigeria: Federal Republic of Nigeria. Available from https://dhsprogram.com/pubs/pdf/MIS20/MIS20.pdf:(accessed Jan. 2019)
NDHS N, 2016. Nigeria Demographic and health survey: key indicators report. The DHS Program ICF. Available from https://microdata.worldbank.org › Home › Central Data Catalog › DHS (accessed Jan 2019)
Nelder JA, Wedderburn RWM, 1972. Generalized linear models. J R Stat Soc Ser A 135:370-84. doi: https://doi.org/10.2307/2344614 DOI: https://doi.org/10.2307/2344614
Njau JD, Stephenson R, Menon MP, Kachur SP, McFarland DA, 2014. Investigating the important correlates of maternal education and childhood malaria infections. Am J Trop Med 91:509-19. doi: https://doi.org/10.4269/ajtmh.13-0713 DOI: https://doi.org/10.4269/ajtmh.13-0713
Okeke TA, Okeibunor JC, 2010. Rural-urban differences in health-seeking for the treatment of childhood malaria in south-east Nigeria. Health policy 95:62-8. doi: https://doi.org/10.1016/j.healthpol.2009.11.005Get
Okonko IO, Soleye FA, Amusan TA, Ogun AA, Udeze AO, Nkang AO, Faleye TOC, 2009. Prevalence of malaria plasmodium in Abeokuta, Nigeria. Malaysian J Microb 5:113-8. DOI: https://doi.org/10.21161/mjm.16509
Onwujekwe O, Uzochukwu B, Dike N, Okoli C, Eze S, Chukwuogo O, 2009. Are there geographic and socio-economic differences in incidence, burden and prevention of malaria? A study in southeast Nigeria. Int J Equity Health 8:45. doi: https://doi.org/10.1186/1475-9276-8-45 DOI: https://doi.org/10.1186/1475-9276-8-45
Onyiri N, 2015. Estimating malaria burden in Nigeria: a geostatistical modelling approach. Geospatial Health 10: 163-69. doi: https://doi.org/10.4081/gh.2015.306 DOI: https://doi.org/10.4081/gh.2015.306
Samadoulougou S, Maheu-Giroux M, Kirakoya-Samadoulougou F, De Keukeleire M, Castro MC, Robert A, 2014. Multilevel and geo-statistical modeling of malaria risk in children of Burkina Faso. Parasites vectors 7:350. doi: https://doi.org/10.1186/1756-3305-7-350 DOI: https://doi.org/10.1186/1756-3305-7-350
Stroup WW, 2012. Generalized linear mixed models: modern concepts, methods and applications: CRC press.
Stroup WW, Baenziger PS, Mulitze DK, 1994. Removing spatial variation from wheat yield trials: a comparison of methods. Crop Science 34:62-6. doi: 10.2135/cropsci1994.0011183X003400010011x DOI: https://doi.org/10.2135/cropsci1994.0011183X003400010011x
Umer M, Zofeen S, Majeed A, Hu W, Qi X, Zhuang G, 2018. Spatiotemporal Clustering Analysis of Malaria Infection in Pakistan. Int J Environ Res Public Health 15:1202. doi: https://doi.org/10.3390/ijerph15061202 DOI: https://doi.org/10.3390/ijerph15061202
UN, 2015. Malaria and the UN Sustainable Development Goals (SDGs) 2030. Available from https://www.swissmalariagroup.ch › assets › uploads › files › New factsheet (accessed 01.10.2020)
Verly G, David M, Journel AG, Marechal A, 2013. Geostatistics for natural resources characterization. Springer Science & Business Media, New York, United States.
Weli VE, Efe SI, 2015. Climate and epidemiology of malaria in Port Harcourt Region, Nigeria. AJCC, 4:40. DOI: https://doi.org/10.4236/ajcc.2015.41004
WHO, 2018. World Health Organization, World malaria report, Geneva, Switzerland, Available from https://www.who.int/malaria/publications/world-malaria-report-2018/en/ Accessed: 10 June 2019.
WHO, 2019. World Health Organization, Global technical strategy for malaria 2016-203. Available from https://www.who.int › malaria › publications › atoz (accessed 01.10.2020)
WHO, 2015. World Health Organization, Guidelines for the treatment of malaria. Available from https://www.who.int › malaria › publications › atoz(accessed Jan. 2019)
Wong G, Manson W, 1985. Generalized Linear Models: A Pseudo Likelihood Approach. J Stat Comput Sim 80:513-24.
Yaya S, Uthman O, Amouzou A, Bishwajit G, 2018. Use of intermittent preventive treatment among pregnant women in sub-Saharan Africa: Evidence from malaria indicator surveys. Trop Med Infect Dis 3:18. doi: https://doi.org/10.3390/tropicalmed3010018 DOI: https://doi.org/10.3390/tropicalmed3010018
Yusuf OB, Adeoye BW, Oladepo OO, Peters DH, Bishai D, 2010. Poverty and fever vulnerability in Nigeria: a multilevel analysis. Malaria J 9:235. doi: https://doi.org/10.1186/1475-2875-9-235 DOI: https://doi.org/10.1186/1475-2875-9-235
Zimmerman DL, Harville DA, 1991. A random field approach to the analysis of field-plot experiments and other spatial experiments. Biometrics 47:223-39. doi: https://www.jstor.org/stable/2532508 DOI: https://doi.org/10.2307/2532508

How to Cite

Ugwu, C. L. J., & Zewotir, T. (2020). Spatial distribution and sociodemographic risk factors of malaria in Nigerian children less than 5 years old. Geospatial Health, 15(2). https://doi.org/10.4081/gh.2020.819