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Spatial variation of pneumonia hospitalization risk in Twin Cities metro area, Minnesota

  • P. Y. IROH TAM (a1) (a2), B. KRZYZANOWSKI (a3), J. M. OAKES (a4), L. KNE (a3) (a5) and S. MANSON (a3)...


Fine resolution spatial variability in pneumonia hospitalization may identify correlates with socioeconomic, demographic and environmental factors. We performed a retrospective study within the Fairview Health System network of Minnesota. Patients 2 months of age and older hospitalized with pneumonia between 2011 and 2015 were geocoded to their census block group, and pneumonia hospitalization risk was analyzed in relation to socioeconomic, demographic and environmental factors. Spatial analyses were performed using Esri's ArcGIS software, and multivariate Poisson regression was used. Hospital encounters of 17 840 patients were included in the analysis. Multivariate Poisson regression identified several significant associations, including a 40% increased risk of pneumonia hospitalization among census block groups with large, compared with small, populations of ⩾65 years, a 56% increased risk among census block groups in the bottom (first) quartile of median household income compared to the top (fourth) quartile, a 44% higher risk in the fourth quartile of average nitrogen dioxide emissions compared with the first quartile, and a 47% higher risk in the fourth quartile of average annual solar insolation compared to the first quartile. After adjusting for income, moving from the first to the second quartile of the race/ethnic diversity index resulted in a 21% significantly increased risk of pneumonia hospitalization. In conclusion, the risk of pneumonia hospitalization at the census-block level is associated with age, income, race/ethnic diversity index, air quality, and solar insolation, and varies by region-specific factors. Identifying correlates using fine spatial analysis provides opportunities for targeted prevention and control.

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Corresponding author

*Author for correspondence: P. Y. Iroh Tam, Malawi-Liverpool Wellcome Trust Clinical Research Programme, P.O. Box 30096, Chichiri, Blantyre 3, Malawi. (Email:


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Both authors contributed equally to this work.



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1. File, TM Jr., Marrie, TJ. Burden of community-acquired pneumonia in North American adults. Postgraduate Medicine 2010; 122(2): 130141.
2. Keren, R, et al. Prioritization of comparative effectiveness research topics in hospital pediatrics. Archives of Pediatrics & Adolescent Medicine 2012; 166(12): 11551164.
3. National Center for Health Statistics. National Vital Statistics Report. Deaths: Final Data for 2006, vol. 57. 2009.
4. Griffin, MR, et al. U.S. hospitalizations for pneumonia after a decade of pneumococcal vaccination. New England Journal of Medicine 2013; 369(2): 155163.
5. Grijalva, CG, et al. Decline in pneumonia admissions after routine childhood immunisation with pneumococcal conjugate vaccine in the USA: a time-series analysis. Lancet 2007; 369(9568): 11791186.
6. Iroh Tam, PY, Koopmeiners, JS. Trends in pneumonia hospitalizations in Hennepin County, Minnesota, 1999–2010. Mayo Clinic Proceedings 2013; 88(10): 11811182.
7. Thorn, LK, et al. Pneumonia and poverty: a prospective population-based study among children in Brazil. BMC Infectious Diseases 2011; 11: 180.
8. Beck, AF, et al. Geographic variation in hospitalization for lower respiratory tract infections across one county. JAMA Pediatrics 2015; 169(9): 846854.
9. Crighton, EJ, et al. A spatial analysis of the determinants of pneumonia and influenza hospitalizations in Ontario (1992–2001). Social Science & Medicine 2007; 64(8): 16361650.
10. Blain, AP, et al. Spatial variation in the risk of hospitalization with childhood pneumonia and empyema in the North of England. Epidemiology and Infection 2014; 142(2): 388398.
11. Anon. Esri's Business Analyst ( Accessed 23 September 2016.
12. Anon. American Fact Finder ( Accessed 23 September 2016.
13. Brink, C, et al. Solar insolation, Minnesota (2006–2012) ( Accessed 6 January 2017.
14. Anon. Marshall Research Group ( Accessed 23 September 2016.
15. Reese-Cassal, K. 2014/2019 Esri Diversity Index, 2014.
16. National Centers for Environmental Information. Metereological versus astronomical seasons. In: National Oceanic and Atmospheric Administration ( Accessed 22 April 2017.
17. Cliff, A, Ord, K. Testing for spatial autocorrelation among regression residuals. Geographical Analysis 1972; 4(3): 267284.
18. Ord, JK, Getis, A. Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis 1995; 27(4): 286306.
19. Epstein, D, et al. The effect of neighborhood and individual characteristics on pediatric critical illness. Journal of Community Health 2014; 39(4): 753759.
20. Feemster, KA, et al. Risk of invasive pneumococcal disease varies by neighbourhood characteristics: implications for prevention policies. Epidemiology and Infection 2013; 141(8): 16791689.
21. Wang, C, et al. Neighborhood income and health outcomes in infants: how do those with complex chronic conditions fare? Archives of Pediatric and Adolescent Medicine 2009; 163(7): 608615.
22. Yoo, JP, Slack, KS, Holl, JL. Material hardship and the physical health of school-aged children in low-income households. American Journal of Public Health 2009; 99(5): 829836.
23. Beck, AF, et al. Inequalities in neighborhood child asthma admission rates and underlying community characteristics in one US county. Journal of Pediatrics 2013; 163(2): 574580.
24. Gorton, CP, Jones, JL. Wide geographic variation between Pennsylvania counties in the population rates of hospital admissions for pneumonia among children with and without comorbid chronic conditions. Pediatrics 2006; 117(2): 176180.
25. Gupta, RS, et al. Geographic variability in childhood asthma prevalence in Chicago. Journal of Allergy and Clinical Immunology 2008; 121(3): 639645.e1.
26. Yousey-Hindes, KM, Hadler, JL. Neighborhood socioeconomic status and influenza hospitalizations among children: New Haven County, Connecticut, 2003–2010. American Journal of Public Health 2011; 101(9): 17851789.
27. Ghio, AJ. Particle exposures and infections. Infection 2014; 42(3): 459467.
28. Larson, K, Halfon, N. Family income gradients in the health and health care access of US children. Maternal and Child Health Journal 2010; 14(3): 332342.
29. Hardt, NS, et al. Neighborhood-level hot spot maps to inform delivery of primary care and allocation of social resources. The Permanente Journal 2013; 17(1): 49.
30. Ram, PK, et al. Household air quality risk factors associated with childhood pneumonia in urban Dhaka, Bangladesh. American Journal of Tropical Medicine and Hygiene 2014; 90(5): 968975.
31. Kravchenko, J, et al. Long-term dynamics of death rates of emphysema, asthma, and pneumonia and improving air quality. International Journal of Chronic Obstructive Pulmonary Disease 2014; 9: 613627.
32. Ware, DN, et al. Household reporting of childhood respiratory health and air pollution in rural Alaska Native communities. International Journal of Circumpolar Health 2014; 73: 110.
33. Qiu, H, et al. Coarse particulate matter associated with increased risk of emergency hospital admissions for pneumonia in Hong Kong. Thorax 2014; 69(11): 10271033.
34. Schwartz, J. PM10, ozone, and hospital admissions for the elderly in Minneapolis-St. Paul, Minnesota. Archives of Environmental Health 1994; 49(5): 366374.
35. Grant, WB. The role of geographical ecological studies in identifying diseases linked to UVB exposure and/or vitamin D. Dermatoendocrinology 2016; 8(1): e1137400.
36. Juzeniene, A, et al. The seasonality of pandemic and non-pandemic influenzas: the roles of solar radiation and vitamin D. International Journal of Infectious Diseases 2010; 14(12): e1099e1105.
37. Grant, WB, Giovannucci, E. The possible roles of solar ultraviolet-B radiation and vitamin D in reducing case-fatality rates from the 1918–1919 influenza pandemic in the United States. Dermatoendocrinology 2009; 1(4): 215219.
38. Muhe, L, et al. Case-control study of the role of nutritional rickets in the risk of developing pneumonia in Ethiopian children. Lancet 1997; 349(9068): 18011804.
39. Wayse, V, et al. Association of subclinical vitamin D deficiency with severe acute lower respiratory infection in Indian children under 5 y. European Journal of Clinical Nutrition 2004; 58(4): 563567.
40. Gunier, RB, et al. Traffic density in California: socioeconomic and ethnic differences among potentially exposed children. Journal of Exposure Analysis and Environmental Epidemiology 2003; 13(3): 240246.
41. Jenkin, ME, Clemitshaw, KC. Ozone and other secondary photochemical pollutants: chemical processes governing their formation in the planetary boundary layer. Atmospheric Environment 2000; 34(16): 24992527.


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