Kaohsiung Journal of Medical Sciences
Volume 24, Issue 11 , Pages 568-576, November 2008

A New Application of Spatiotemporal Analysis for Detecting Demographic Variations in AIDS Mortality: An Example from Florida

  • Yu-Wen Chiu

      Affiliations

    • Department of Public and Community Health, University of Maryland, Maryland, USA
    • Department of Community Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
  • ,
  • Min-Qi Wang

      Affiliations

    • Department of Public and Community Health, University of Maryland, Maryland, USA
  • ,
  • Hung-Yi Chuang

      Affiliations

    • Department of Community Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
    • Corresponding Author InformationAddress correspondence and reprint requests to: Dr Hung-Yi Chuang, Department of Community Medicine, Kaohsiung Medical University Hospital, 100 Shih-Chuan 1st Road, Kaohsiung 807, Taiwan
  • ,
  • Chiehwen Ed Hsu

      Affiliations

    • Department of Community Medicine, Kaohsiung Medical University Hospital, Kaohsiung, Taiwan
    • University of Texas School of Health Information Sciences and School of Public Health, UTHSC-Houston, Texas, USA
  • ,
  • Ella T. Nkhoma

      Affiliations

    • Department of Epidemiology, University of North Carolina at Chapel Hill, North Carolina, USA

Received 16 July 2008; accepted 18 November 2008.

Article Outline

The purpose of the present study was to characterize, geographically and temporally, the patterns of acquired immune deficiency syndrome (AIDS) death disparity in 67 Florida jurisdictions, and to determine if the detected trends varied according to age, race, and sex. The space-time scan statistic proposed by Kulldorff et al was used to examine the excess AIDS deaths that occurred between 1987 and 2004. Results were geographically referenced in maps using EpiInfo and EpiMap made available by the Centers for Disease Control. Miami-Dade and the nearby counties including Broward, Martin, and Palm Beach are the most likely clusters (observed/expected: 1505.16) with temporal dimension (also called cluster's age) persisting from 1996 to the present. Union county had the longest cluster for the cluster period 1987–1998, but not for 1999–2004. African-Americans contributed to more clusters compared with whites. Time trends indicated that AIDS mortality peaked in 1995 and then sharply dropped until 1998, when the decrease stopped. By accounting for the temporal dimension of disease clustering, the present study revealed the persistence of geographic clusters, which is not often provided by other geographic detection methods. These findings may be informative for medical resource allocation and better focus public health intervention strategies for AIDS care.

Key Words:  acquired immunodeficiency syndrome , cluster , mortality , spatiotemporal analysis

No full text is available. To read the body of this article, please view the PDF online.

 

Back to Article Outline

References 

  1. Centers for Disease Control  . Commentary. Cases of HIV infection and AIDS in the United States, 1981–2006 . MMWR Morb Mortal Wkly Rep . 2006;55:585–589
  2. Centers for Disease Control  . HIV/AIDS diagnoses among Blacks—Florida, 1999–2004 . MMWR Morb Mortal Wkly Rep . 2007;56:69–73
  3. Centers for Disease Control  . The global HIV/AIDS pandemic, 2006 . MMWR Morb Mortal Wkly Rep . 2006;55:841–844
  4. Centers for Disease Control. HIV/AIDS among women (available at http://www.cdc.gov/hiv/topics/women/resources/factsheets/pdf/women.pdf). CDC HIV/AIDS Fact Sheet 2007.
  5. Dean HD , Steele CB , Satcher AJ , et al.   HIV/AIDS among minority races and ethnicities in the United States, 1999–2003 . Natl Med Assoc . 2005;97:5S–12S
  6. 2006 Report on the Global AIDS epidemic . Geneva: UNAIDS; 2006; Available at http://www.unaids.org/en/ [Date accessed: May 18, 2006]
  7. Merson MH . The HIV-AIDS pandemic at 25—the global response . N Engl J Med . 2006;354:2414–2417
  8. Kulldorff M , Song C , Gregorio D , et al.   Cancer map patterns: are they random or not? . Am J Prev Med . 2006;30:S37–S49
  9. Ozdenerol E , Williams BL , Kang SY , et al.   Comparison of spatial scan statistic and spatial filtering in estimating low birth weight clusters . Int J Health Geogr . 2005;4:19
  10. Costa MA , Assunção RM . A fair comparison between the spatial scan and Besag-Newell disease clustering tests . Environ Ecol Stat . 2005;12:301–319
  11. Aamodt G , Samuelsen SO , Skrondal A . A simulation study of three methods for detecting disease clusters . Int J Health Geogr . 2006;5:15
  12. Pearl DL , Louie M , Chui L , et al.   The use of outbreak information in the interpretation of clustering of reported cases of Escherichia coli O157 in space and time in Alberta, Canada, 2000–2002 . Epidemiol Infect . 2006;134:699–711
  13. Jones RC , Liberatore M , Fernandez JR , et al.   Use of a prospective space-time scan statistic to prioritize shigellosis case investigations in an urban jurisdiction . Public Health Rep . 2006;121:133–139
  14. Kulldorff M , Athas WF , Feurer EJ , et al.   Evaluating cluster alarms: a space-time scan statistic and brain cancer in Los Alamos, New Mexico . Am J Public Health . 1998;88:1377–1380
  15. Kulldorff M , Heffernan R , Hartman J , et al.   A spacetime permutation scan statistic for disease outbreak detection . PLoS Med . 2005;2:e59
  16. Gething PW , Noor AM , Gikandi PW , et al.   Improving imperfect data from health management information systems in Africa using space-time geostatistics . PLoS Med . 2006;3:e271
  17. Kulldorff M , Nagarwalla N . Spatial disease clusters: detection and inference . Stat Med . 1995;14:799–810
  18. Song C , Kulldorff M . Power evaluation of disease clustering tests . Int J Health Geogr . 2003;2:9
  19. Kulldorff M , Zhang Z , Hartman J , et al.   Benchmark data and power calculations for evaluating disease outbreak detection methods . MMWR Morb Mortal Wkly Rep . 2004;53:144–151
  20. Kulldorff M. Information Management Services, Inc. SaTScan™ User Guide. SaTScan v. 7.0: Software for the Spatial and Space-time Scan Statistics, 2006.
  21. Akin M , Fernandez MI , Bowen GS , et al.   HIV risk behaviors of Latin American and Caribbean men who have sex with men in Miami, Florida, USA . Rev Panam Salud Publica . 2008;23:341–348
  22. Marcelin LH , McCoy HV , Diclemente RJ . HIV/AIDS knowledge and beliefs among Haitian adolescents in Miami-Dade County, Florida . J HIV AIDS Prev Child Youth . 2006;7:121–138
  23. Lieb S , Rosenberg R , Arons P , et al.   Age shift in patterns of injection drug use among the HIV/AIDS population in Miami-Dade County, Florida . Subst Use Misuse . 2006;41:1623–1635
  24. Yeni P . Update on HAART in HIV . J Hepatol . 2006;44:S100–S103
  25. Sukasem C , Churdboonchart V , Sukeepaisarncharoen W , et al.   Genotypic resistance profiles in antiretroviralnaive HIV-1 infections before and after initiation of firstline HAART: impact of polymorphism on resistance to therapy . Int J Antimicrob Agents . 2008;31:277–281
  26. Maggiolo F , Airoldi M , Kleinloog HD , et al.   Effect of adherence to HAART on virologic outcome and on the selection of resistance-conferring mutations in NNRTIor PI-treated patients . HIV Clin Trials . 2007;8:282–292
  27. Moore RD , Stanton D , Gopalan R , et al.   Racial differences in the use of drug therapy for HIV disease in an urban community . N Engl J Med . 1994;330:763–768
  28. Stone VE . Optimizing the care of minority patients with HIV/AIDS . Clin Infect Dis . 2004;38:400–404
  29. Turner BJ , Cunningham WE , Duan N , et al.   Delayed medical care after diagnosis in a US national probability sample of persons infected with human immunodeficiency virus . Arch Intern Med . 2000;160:2614–2622
  30. Sackoff JE , Hanna DB , Pfeiffer MR , et al.   Causes of death among persons with AIDS in the era of highly active antiretroviral therapy: New York City . Ann Intern Med . 2006;145:397–406

PII: S1607-551X(09)70018-X

doi:10.1016/S1607-551X(09)70018-X

Kaohsiung Journal of Medical Sciences
Volume 24, Issue 11 , Pages 568-576, November 2008