Geospatial and Path Analysis for Enhancing Malaria Control and Primary Healthcare Delivery in Low-Income Nations: A Case Study of Uganda

Maria Assumpta Komugabe^{1, 2,} , Richard Caballero¹, Itamar Shabtai³, Zhaoxia Yi⁴ and Zachary Dodds⁵

¹Center of Information Systems and Technology, Claremont Graduate University, Claremont, USA

²Information Technology Department, University of Kisubi, Kampala, Uganda

³Center of Information Systems and Technology, Claremont Graduate University, Claremont

⁴Drucker School of Management, Claremont Graduate University, Claremont, USA

⁵Computer Science, Harvey Mudd College, Claremont, USA

Cite this paper:
Maria Assumpta Komugabe, Richard Caballero, Itamar Shabtai, Zhaoxia Yi and Zachary Dodds. Geospatial and Path Analysis for Enhancing Malaria Control and Primary Healthcare Delivery in Low-Income Nations: A Case Study of Uganda. American Journal of Epidemiology and Infectious Disease. 2024; 12(3):44-54. doi: 10.12691/ajeid-12-3-3

Abstract

This research investigated the use of Geospatial and Path Analysis for Enhancing Malaria Control and Primary Healthcare Delivery in Low-Income Nations. Utilizing methods such as generalized linear regression (GLR), ordinary least squares (OLS) regression, and spatial autocorrelation (Moran's I), the study identified key factors influencing malaria incidence rates: mean temperature, antimalarial treatment, mosquito net access, total population, and total health centers. The GLR and OLS analyses showed a moderate model fit (Adjusted R² = 0.443), highlighting the importance of these predictors. Path analysis was used to determine both the direct and indirect effects of these variables on malaria incidence rates, leading to the creation of a new model. In this model, mean temperature showed a significant direct effect (β = 0.658) and a small indirect effect (β = 0.002779), resulting in a total effect of 0.660779. Antimalarial treatment had a strong negative direct effect (β = -0.189) with a negligible indirect effect, yielding a total effect of -0.18947. Mosquito net access demonstrated a notable direct effect (β = 0.074) and a substantial indirect effect (β = 2.5214437), culminating in a total effect of 2.59544. Total population exhibited a small direct effect (β = -0.180) and a minimal indirect effect (β = -0.0001927), leading to a total effect of -0.18019. Finally, the number of health centers showed no direct effect but a significant indirect effect (β = 1.0956237), resulting in a total effect of 1.0956237. Spatial autocorrelation revealed significant clustering of malaria rates, highlighting the need for targeted interventions. Bivariate color maps underscored the critical role of health centers in improving healthcare access and controlling malaria, suggesting that expanding health center networks in underserved regions could enhance healthcare outcomes

Keywords:
Geospatial analysis path analysis primary healthcare malaria Low-income nations Universal Health Coverage (UHC) Uganda

This work is licensed under a Creative Commons Attribution 4.0 International License. To view a copy of this license, visit http://creativecommons.org/licenses/by/4.0/

References:

[1]	Wolfle, L. M. (1980). Strategies of Path Analysis. American Educational Research Journal, 17(2), 183–209.
[2]	Health equity: Challenges in low income countries. (2009). African Health Sciences, 9(Suppl 2), S49–S51.
[3]	Shaltynov, A., Rocha, J., Jamedinova, U., & Myssayev, A. (2022). Assessment of primary healthcare accessibility and inequality in north-eastern Kazakhstan. Geospatial Health, 17(1).
[4]	Dowhaniuk, N. (2021). Exploring country-wide equitable government health care facility access in Uganda. International Journal for Equity in Health, 20(1), 38.
[5]	Khashoggi, B. F., & Murad, A. (2020). Issues of Healthcare Planning and GIS: A Review. ISPRS International Journal of Geo-Information, 9(6), Article 6.
[6]	Kripa, P. K., Thanzeen, P. S., Jaganathasamy, N., Ravishankaran, S., Anvikar, A. R., & Eapen, A. (2024). Impact of climate change on temperature variations and extrinsic incubation period of malaria parasites in Chennai, India: Implications for its disease transmission potential. Parasites & Vectors, 17(1), 134.
[7]	Obeagu, E. I., & Obeagu, G. U. (2024). Adapting to the shifting landscape: Implications of climate change for malaria control: A review. Medicine, 103(29), e39010.
[8]	Suh, E., Stopard, I. J., Lambert, B., Waite, J. L., Dennington, N. L., Churcher, T. S., & Thomas, M. B. (2024). Estimating the effects of temperature on transmission of the human malaria parasite, Plasmodium falciparum. Nature Communications, 15(1), 3230.
[9]	Katushabe, J., Nnyanzi, J. B., & Muwanga, G. S. (2024). Exploring the role of spending on malaria incidence in Uganda using the auto-regressive distributed lag approach. Malaria Journal, 23(1), 129.
[10]	Komugabe, M. A., Caballero, R., Shabtai, I., & Musinguzi, S. P. (2024). Advancing Malaria Prediction in Uganda through AI and Geospatial Analysis Models. Journal of Geographic Information System, 16(2), Article 2.
[11]	Zhang, J., Shahbaz, M., Ijaz, M., & Zhang, H. (2024). Bibliometric analysis of antimalarial drug resistance. Frontiers in Cellular and Infection Microbiology, 14.
[12]	Chen, L., Chen, T., Lan, T., Chen, C., & Pan, J. (2023). The Contributions of Population Distribution, Healthcare Resourcing, and Transportation Infrastructure to Spatial Accessibility of Health Care. INQUIRY: The Journal of Health Care Organization, Provision, and Financing, 60, 00469580221146041.
[13]	Manda, S., Haushona, N., & Bergquist, R. (2020). A Scoping Review of Spatial Analysis Approaches Using Health Survey Data in Sub-Saharan Africa. International Journal of Environmental Research and Public Health, 17(9), 3070.
[14]	Akpan, G. U., Mohammed, H. F., Touray, K., Kipterer, J., Bello, I. M., Ngofa, R., Stein, A., Seaman, V., Mkanda, P., & Cabore, J. (2022). Conclusions of the African Regional GIS Summit (2019): Using geographic information systems for public health decision-making. BMC Proceedings, 16(1), 3.
[15]	Tessema, Z. T., Worku, M. G., Tesema, G. A., Alamneh, T. S., Teshale, A. B., Yeshaw, Y., Alem, A. Z., Ayalew, H. G., & Liyew, A. M. (2022). Determinants of accessing healthcare in Sub-Saharan Africa: A mixed-effect analysis of recent Demographic and Health Surveys from 36 countries. BMJ Open, 12(1), e054397.
[16]	GIS and Public Health at CDC | cdc.gov. (2020, March 16). https://www.cdc.gov/gis/index.htm.
[17]	Esri Global Public Health Grant Program for Health & Human Services. (n.d.). Retrieved October 20, 2023, from https://www.esri.com/en-us/industries/health/segments/public-health/modernization-overview.
[18]	Roth, S., Landry, M., Ebener, S., Marcelo, A., Kijsanayotin, B., & Parry, J. (n.d.). The Geography of Universal Health Coverage. 55.
[19]	Gregor, S. (2006). The nature of theory in information systems. MIS Quarterly, 611-642. (n.d.).
[20]	Malaria Atlas Project | Data. (n.d.). Retrieved June 15, 2024, from https:// data.malariaatlas.org/ trends? year= 2020 & metricGroup=Malaria&geographicLevel=admin0&metricSubcategory=Pf&metricType=rate&metricName=incidence.
[21]	World Bank Climate Change Knowledge Portal. (n.d.). Retrieved June 15, 2024, from https:// climateknowledgeportal. worldbank.org/.
[22]	Explore Statistics. (n.d.). Uganda Bureau of Statistics. Retrieved June 15, 2024, from https://www.ubos.org/explore-statistics/.
[23]	COMPLETE LIST OF ALL HEALTH FACILITIES IN UGANDA. (n.d.). Ministry of Health | Government of Uganda. Retrieved June 15, 2024, from https:// www.health.go.ug/ cause/ nkwanzi-rakai-lwengo-kalangala-mukono-buikwe-mpigi-butambala-butam-butamba-wakiso-mubende-lyantonde-n-n-n-sembabule-buvuma-kampala-m-m-a-complete-list-of-all-health-facilities-in-uganda/.