Understanding the demographic and socioeconomic determinants of morbidity in Eastern Uganda: a retrospective analysis of the Iganga-Mayuge health and demographic surveillance data.
Steve Bicko Cygu, Betty Nabukeera, Lindsey English, Shakira Babirye, Collins Gyezaho, Maureen Ng'etich, Michael Ochola, David Amadi, Henry Owoko Odero, Grace Banturaki, Damazo Twebaze Kadengye, Agnes Kiragga, Dan Kajungu
Author Information
Steve Bicko Cygu: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
Betty Nabukeera: Centre for Health and Population Research (MUCHAP), Iganga, Makerere University, Kampala, Uganda. ORCID
Lindsey English: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
Shakira Babirye: Statistics, Infectious Diseases Research Collaboration, Kampala, Uganda. ORCID
Collins Gyezaho: Centre for Health and Population Research (MUCHAP), Iganga, Makerere University, Kampala, Uganda. ORCID
Maureen Ng'etich: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
Michael Ochola: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
David Amadi: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
Henry Owoko Odero: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
Grace Banturaki: Infectious Diseases Institute, Makerere University College of Health Sciences, Kampala, Uganda. ORCID
Damazo Twebaze Kadengye: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
Agnes Kiragga: Data Science Program, African Population and Health Research Center, Nairobi, Kenya. ORCID
Dan Kajungu: Centre for Health and Population Research (MUCHAP), Makerere University, Kampala, Uganda. ORCID
Introduction: Understanding the determinants of disease burden is imperative in enhancing population health outcomes. This study uses data from the Iganga-Mayuge Health and Demographic Surveillance Site, to understand demographic and socioeconomic factors influencing morbidity. Methods: We analysed secondary data from 2018 to 2023. We employed graphs and tables to present morbidity patterns across different sociodemographic factors and applied mixed-effects multinomial multivariate logistic regression model to understand the correlates of morbidity. Results: The findings reveal a predominant prevalence of malaria, lower respiratory tract infections, coryza, gastric acid-related and urinary tract infections, collectively constituting 83% of diagnosed diseases. Noteworthy demographic variations, particularly gender and age, significantly impact disease distribution, revealing higher diagnosis rates among females. Additionally, socioeconomic factors, including education and wealth status, contribute to discernible differences in disease burden. Conclusion: This research provides crucial insights into the implications of demographic and socioeconomic factors on disease burden in Uganda. The results contribute to evidence-based policy-making, highlighting the necessity for targeted interventions addressing specific health challenges encountered by diverse populations. The study advocates for continuous assessment of the epidemiological landscape to inform more tailored and effective health strategies, ultimately enhancing resilience in disease control efforts.
