Applied Statistical Methods for Analyzing Disease Prevalence in Urban Areas
Abstract
The proposed paper seeks to examine and discuss disease prevalence trend in cities through the application of statistical tools, in a practical case study of Baghdad City
The analysis was based on officially reported neighborhood-level data for the year 2023. The study not only deals with the uneven distribution of the incidence of diseases among urban neighborhoods, but also investigates the influence of demographic, socioeconomic, and service-related factors that affect such patterns. The data on the neighborhood level were obtained based on the official health, demographic and municipal data and analyzed in terms of the descriptive statistics and the Negative Binomial Regression model which is appropriate in terms of count data characterized by overdispersion.
The analysis that was applied showed that there was great difference in incidence of diseases in the neighborhoods that were studied. The results indicated that population density and poverty rate were statistically and positively correlated with higher incidence of the disease whereas better levels of the public services were correlated with a significant decrease in the reported cases. The spatial and graphical analyses also established the neighborhoods of a particular area to be hotspots of disease according to which the clear clustering of cases is observed in the densely crowded and under-serviced neighborhoods.
The data were analyzed in statistical software (SPSS) and descriptive statistics and the Negative Binomial Regression were used to investigate the prevalence of the disease across selected neighborhoods.
The results justify the usefulness of practical statistical approaches in converting the ordinary health data to useful analytical information that can be used to make evidence-based decisions in the area of healthcare. The paper has also given prominence to statistical analysis integration in the health planning of urban areas in order to enhance the efficiency of monitoring diseases, interventions targeting and the effectiveness of eliminating health resources in an urban setting.
Downloads
References
Cacciatore, S., et al. (2025). Urban Health Inequities & Healthy Longevity: Risks & Policy.
Reiner, R. C., Jr., et al. (2020). Mapping geographical inequalities in childhood diarrhoeal morbidity and mortality across low-income and middle-income countries.
Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.). Chapman & Hall/CRC.
Hilbe, J. M. (2014). Modeling Count Data. Cambridge University Press. (Poisson & Negative Binomial and overdispersion).
Lee, J. H., et al. (2011). Analysis of overdispersed count data: application to the health sciences.
World Health Organization (WHO). (2025). Urban health (Fact sheet).
World Health Organization (WHO). (2025). World report on social determinants of health equity
Bain, R., Cronk, R., Wright, J., Yang, H., Slaymaker, T., & Bartram, J. (2014). Fecal contamination of drinking-water in low- and middle-income countries: A systematic review and meta-analysis. PLoS Medicine, 11(5), e1001644
Pope, C. A., III, & Dockery, D. W. (2006). Health effects of fine particulate air pollution: Lines that connect. Journal of the Air & Waste Management Association, 56(6), 709–742.
Brunekreef, B., & Holgate, S. T. (2002). Air pollution and health. The Lancet, 360(9341), 1233–1242.
Berkman, N. D., Sheridan, S. L., Donahue, K. E., Halpern, D. J., & Crotty, K. (2011). Low health literacy and health outcomes: An updated systematic review. Annals of Internal Medicine, 155(2), 97–10
Prüss-Ustün, A., Bartram, J., Clasen, T., Colford, J. M., Cumming, O., Curtis, V., Bonjour, S., Dangour, A. D., De France, J., Fewtrell, L., Freeman, M. C., Gordon, B., Hunter, P. R., Johnston, R. B., Mathers, C., Mäusezahl, D., Medlicott, K., Neira, M., Stocks, M., … Cairncross, S. (2014). Burden of disease from inadequate water, sanitation and hygiene in low- and middle-income settings: A retrospective analysis of data from 145 countries. Tropical Medicine & International Health, 19(8), 894–905.
Cutler, D. M., & Lleras-Muney, A. (2006). Education and health: Evaluating theories and evidence. National Bureau of Economic Research Working Paper No. 12352
Cardoso, M. R. A., Cousens, S. N., de Góes Siqueira, L. F., Alves, F. M., & D’Angelo, L. A. V. (2004). Crowding: Risk factor or protective factor for lower respiratory disease in young children? BMC Public Health, 4, 19.
Li, Y., Leung, G. M., Tang, J. W., Yang, X., Chao, C. Y. H., Lin, J. Z., Lu, J. W., Nielsen, P. V., Niu, J., Qian, H., Sleigh, A. C., Su, H.-J. J., Sundell, J., Wong, T. W., & Yuen, P. L. (2007). Role of ventilation in airborne transmission of infectious agents in the built environment: A multidisciplinary systematic review. Indoor Air, 17(1), 2–18
Gascon, M., Triguero-Mas, M., Martínez, D., Dadvand, P., Forns, J., Plasència, A., & Nieuwenhuijsen, M. J. (2015). Mental health benefits of long-term exposure to residential green and blue spaces: A systematic review. International Journal of Environmental Research and Public Health, 12(4), 4354–4379.
Centers for Disease Control and Prevention (CDC). (2016). Pulmonary Health Effects of Air Pollution (review/summary).
Lawson, A. B. (2018). Bayesian Disease Mapping: Hierarchical Modeling in Spatial Epidemiology (3rd ed.).
UN-Habitat. (2020). World Cities Report: The Value of Sustainable Urbanization.
Central Statistical Organization (CSO), Iraq. (Latest available year). Statistical Abstract of Iraq.
