Spatial Heterogeneity Association of HIV Incidence with Socio-economic Factors in Zimbabwe

Tawanda Manyangadze (Geography Department, Faculty of Science and Engineering, Bindura University of Science Education, Bag 1020,Bindura, Zimbabwe;School of Nursing and Public Health, Department of Public Health Medicine University of KwaZulu-Natal, Durban,South Africa)
Moses J Chimbari (School of Nursing and Public Health, Department of Public Health Medicine University of KwaZulu-Natal, Durban, South Africa)
Emmanuel Mavhura (Geography Department, Faculty of Science and Engineering, Bindura University of Science Education, Bag 1020,Bindura, Zimbabwe)

Article ID: 3466



This study examined the spatial heterogeneity association of HIV incidence and socio-economic factors including poverty severity index,permanently employed females and males, unemployed females, percentage of poor households i.e., poverty prevalence, night lights index, literacy rate,household food security, and Gini index at district level in Zimbabwe.A mix of spatial analysis methods including Poisson model based on original log likelihood ratios (LLR), global Moran’s I, local indicator of spatial association - LISA were employed to determine the HIV hotspots.Geographically Weighted Poisson Regression (GWPR) and semi-parametric GWPR (s-GWPR) were used to determine the spatial association between HIV incidence and socio-economic factors. HIV incidence (number of cases per 1000) ranged from 0.6 (Buhera district) to 13.30 (Mangwe district). Spatial clustering of HIV incidence was observed (Global Moran’s I = - 0.150; Z score 3.038; p-value 0.002). Significant clusters of HIV were observed at district level. HIV incidence and its association with socioeconomic factors varied across the districts except percentage of females unemployed. Intervention programmes to reduce HIV incidence should address the identified socio-economic factors at district level.


HIV and AIDS;Spatial modelling;Geographical weighted Poisson regression model;Socio-economic factors;Zimbabwe

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[1] United Nations. (2017). UNAIDS Data 2017. United Nations,Geneva.

[2] UNAIDS. (2019).UNAIDS DATA 2019. Switzerland.Retrieved from

[3] Dwyer-lindgren, al. (2019) ‘Mapping HIV prevalence in sub-Saharan Africa between 2000 and 2017’, Nature. Springer US, 570(189), p. 195.DOI: 10.1038/s41586-019-1200-9.

[4] Pascoe, S. J. S. et al. (2015) ‘Poverty, Food Insufficiency and HIV Infection and Sexual Behaviour among Young Rural Zimbabwean Women’, PLoS ONE, 10(1), p. e0115290.DOI:10.1371/journal.pone.0115290.

[5] Nyandoro, M. and Hatti, N. (2018) ‘Poverty and the Politics of Poverty in Independent Zimbabwe,1980-2017’, Social Science Spectrum, 4(2), pp. 56-74.

[6] Alves, A. T. J., Nobre, F. F. and Waller, L. A. (2016) ‘Exploring spatial patterns in the associations between local AIDS incidence and socioeconomic and demographic variables in the state of Rio de Janeiro,Brazil’, Spatial and Spatio-temporal Epidemiology,17, pp. 85-93.DOI:10.1016/j.sste.2016.04.008.

[7] Schur, N. et al. (2015) ‘The effects of household wealth on HIV prevalence in Manicaland, Zimbabwe Á a prospective household census and population-based open cohort study’, Journal of the International AIDS Society, 18(20063).

[8] Gillespie, S., Kadiyala, S. and Greener, R. (2007) ‘Is poverty or wealth driving HIV transmission?’,AIDS,21(Suppl 7), pp. S5-S16.

[9] Shelton, J. D., Cassell, M. M. and Adetunji, J. (2005) ‘Is poverty or wealth at the root of HIV?’,Lancet,366(9491), pp. 1057-1058.DOI: 10.1016/S0140-6736(05)67401-6.

[10] Kongnyuy, E. J. et al. (2006) ‘Wealth and sexual behaviour among men in Cameroon’, BMC International Health and Human Rights, 6(11), pp. 1-8.DOI: 10.1186/1472-698X-6-11.

[11] United Nations Population Fund (UNFPA). (2007).The State of the World Population 2007: Unleashing the potential of urban growth. New York. Retrieved from

[12] Parkhurst, J. O. (2010) ‘Understanding the correlations between wealth, poverty and human immunodeficiency virus infection in African countries’,Bulletin of the World Health Organization, 88(7), pp.519-526.DOI: 10.2471/BLT.09.070185.

[13] Nyoni, T. (2018). A Critical Diagnosis of the Success/Failure of Economic Policies in Zimbabwe During the Banana (1980-1987) and the Mugabe (1988-2017) Administrations: Learning the Hard Way.Journal of Business and Management, 1(2), 27-33.

[14] The Zimbabwe Population-Based HIV Impact Assessment (ZIMPHIA). (2020). Zimbabwe Population-Based HIV Impact Assessment. Harare.

[15] Cuadros, D. F. et al. (2017) ‘Mapping the spatial variability of HIV infection in Sub-Saharan Africa:Effective information for localized HIV prevention and control’, Scientific Reports, 7(9093).DOI: 10.1038/s41598-017-09464-y.

[16] Cuadros, D. F. et al. (2018) ‘Capturing the spatial variability of HIV epidemics in South Africa and Tanzania using routine healthcare facility data’, International Journal of Health Geographics. BioMed Central, 17(27).DOI: 10.1186/s12942-018-0146-8.

[17] Boyda, D. C. et al. (2019) ‘Geographic Information Systems, spatial analysis, and HIV in Africa:A scoping review’, PLoS ONE, 14(5), p. e0216388.DOI: 10.1371/journal.pone.0216388.

[18] United Nations: Economic Commission for Africs.(2018). Country profile 2017: Zimbabwe.Addis Ababa. Retrieved from

[19] ZimStat (2015) Zimbabwe Poverty Atlas. Zimbabwe National Statistical Agency, Harare.

[20] Muzari, W., Nyamushamba, G. B. and Soropa, G.(2016) ‘Climate Change Adaptation in Zimbabwe’s Agricultural Sector’, International Journal of Science and Research, 5(1), pp.1762-1768.

[21] UNDP (2018) Human Development Indices and Indicators: 2018 Statistical pdate for Zimbabwe. Harare.

[22] Ministry of Health and Child Care Zimbabwe (MoHCC) (2018) Zimbabwe National and Sub-National: HIV Estimate Report 2017. Harare, Zimbabwe. Available at:

[23] Ministry of Health and Child Care Zimbabwe (MoHCC) (2020) Zimbabwe Population-Based Hiv Impact Assessment (ZIMPHIA) 2020

[24] Henderson, J. V., Storeygard, A. & Weil, D. N. (2012).Measuring Economic Growth from Outer Space. Am Econ Rev 102, 994-1028.

[25] Martinez, A. N. et al. (2014) ‘Spatial analysis of HIV positive injection drug users in San Francisco, 1987 to 2005’, Int J Environ Res Public Health, 11, pp. 3937-55.

[26] Takahashi, K., Yokoyama, T. and Tango, T. (2010) FleXScan User Guide. Available at:

[27] Tango, T. and Takahashi, K. (2005) ‘A flexibly shaped spatial scan statistic for detecting clusters’,International Journal of Health Geographics, 4(11).DOI:10.1186/1476-072X-Received.

[28] Takahashi, K. and Shimadzu, H. (2018) ‘Multiple-cluster detection test for purely temporal disease clustering: Integration of scan statistics and generalized linear models’, PLoS ONE, 13(11).

[29] Nakaya, T. (2016) ‘“Geographically Weighted Regression (GWR) Software. GWR 4.09.’ ASU GeoDa Center. Available at: website.

[30] Nakaya, T. et al. (2005) ‘Geographically weighted Poisson regression for disease association mapping’,Statistics in Medicine, 24(17), pp. 2695-2717.DOI: 10.1002/sim.2129.

[31] Manyangadze, T. et al. (2017) ‘Micro - spatial distribution of malaria cases and control strategies at ward level in Gwanda district, Matabeleland South, Zimbabwe’, Malaria Journal. BioMed Central, pp. 1-11.DOI: 10.1186/s12936-017-2116-1.

[32] Ehlkes, L. et al. (2014) ‘Geographically weighted regression of land cover determinants of Plasmodium falciparum transmission in the Ashanti Region of Ghana’, Int J Health Geogr, 13, p.35.DOI: 10.1186/1476-072X-13-35.

[33] Rogerson, P. A. (2001). Statistical methods for geography. London: Sage.

[34] Manyangadze, T. et al. (2016) ‘Risk factors and micro-geographical heterogeneity of Schistosoma haematobium in Ndumo area, uMkhanyakude district,KwaZulu-Natal, South Africa’,Acta Tropica, 159,pp. 176-184.DOI: 10.1016/j.actatropica.2016.03.028.

[35] Gwitira, I., Murwira, A., Mberikunashe, J. & Masocha, M. (2018). Spatial overlaps in the distribution of HIV/AIDS and malaria in Zimbabwe. BMC Infect.Dis. 18, 1-10.

[36] Gillespie, S., Kadiyala, S., & Greener, R. (2007). Is poverty or wealth driving HIV transmission ?AIDS,21(Suppl 7), S5-S16.

[37] Li, X., Ge, L. and Chen, X. (2013) ‘Detecting Zimbabwe’s Decadal Economic Decline Using Nighttime Light Imagery’, Remote Sensing, 5, pp. 4551-4570.DOI: 10.3390/rs5094551.

[38] Galimberti, J. K. (2020). Forecasting GDP Growth from Outer Space. Oxford Bulletin of Economics and Statistics, 82(4), 697-722.

[39] Hadley, C., Maxfield, A. and Hruschka, D. (2019) ‘Different forms of household wealth are associated with opposing risks for HIV infection in East Africa’,World Development journal.Elsevier Ltd, 113, pp.344-351.DOI: 10.1016/j.worlddev.2018.09.015.

[40] Masvawure, T. (2020) ‘“I just need to be flashy on campus”: Female students and transactional sex at a university in Zimbabwe’, Culture, Health & Sexuality, 12(8), pp. 857-870.

[41] Dunkle, K. L. et al. (2004) ‘Transactional sex among women in Soweto, South Africa: Prevalence, risk factors and association with HIV infection, 59 (8)’,Social Science and Medicine,59(8), pp. 1581-1592.

[42] Xiong, K. (2012). Review of the Evidence : Linkages between Livelihood , Food Security , Economic Strengthening , and HIV-Related Outcomes. North Carolina.

[43] Andrews, G.J., 2018. Health geographies I: The presence of hope. Progress in Human Geography,42(5),pp.789-798.

[44] Simandan, D., 2011. The wise stance in human geography. Transactions of the Institute of British Geographers, 36(2), pp.188-192.

[45] Simandan, D., 2019. Revisiting positionality and the thesis of situated knowledge. Dialogues in human geography, 9(2), pp.129-149.

[46] Andrews, G.J., 2020. Health geographies III:More-than-representational pushes and expressions.Progress in Human Geography, 44(5), pp.991-1003.

[47] Simandan, D., 2020. Being surprised and surprising ourselves: a geography of personal and social change. Progress in Human Geography, 44(1), pp.99-118.

[48] Andrews, G.J., 2018. Non-representational theory & health: The health in life in space-time revealing.Routledge.


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