Spatial analysis of influenza incidence in EMRO using flexible scan statistics

authors:

avatar Maryam Rezaei , avatar Manoochehr Karami , avatar Javad Faradmal , *

Koomesh: Vol.22, issue 1; 28-32
published online: March 03, 2020
article type: Research Article
received: January 03, 2019
accepted: July 03, 2019

how to cite: Rezaei M, Karami M, Faradmal J . Spatial analysis of influenza incidence in EMRO using flexible scan statistics. koomesh. 2020;22(1):e153145. 

Abstract

Introduction: Influenza is an infectious and severe respiratory disease. It is one of the major problems of public health. In order to determine the spatial distribution and areas with over-expected of a disease including influenza, it can be effective in identifying environmental hazards and fair distribution of health services. In this study, the geographical distribution of the influenza and the identification of the high risk clusters of this disease were investigated. Materials and Methods: In this study, the incidence information of influenza in a 21-month period (until October 2018) from eastern Mediterranean countries were used. The data were extracted from the world health organization;#39s reports and used to determine the areas with over-expected incidence of influenza using flexible scan statistics. Results: In total, 28055 cases of influenza have been reported in the countries of the Eastern Mediterranean region during the study period. Results detected four high-risk cluster in this region. According to the results, incidence of influenza in Bahrain, Kuwait and the Qatar was significantly higher than expected. In the second place, Oman and Tunisia were also considered as a high-risk region, separately. Jordan was the next cluster, however there was no statistically significant difference between expected and observed cases of influenza in this cluster. Conclusion: The results of flexible spatial exploration statistics indicated the high incidence of influenza in the countries of west of Asia and north of Africa in the Eastern Mediterranean region. Therefore, the influenza control system at the regional and national levels in high-risk countries is strongly recommended

References

  • 1.

    Influenza (Seasonal). In: World Health Organization [website]. Geneva: World Health Organization; 2018 [cited 2018 Nov 6]. Available from: URL: https://www.who.int/en/news-room/fact-sheets/detail/influenza-(seasonal).

  • 2.

    Abbasi P, Kiavarz M, Abbasi M. Spatial analysis and identification of high risk areas of human brucellosis by spatial autocorrelation measurement methods. J Health Biomed Inform Med Inform Res Center 2018; 5: 348-360. (Persian).

  • 3.

    Moore DA, Carpenter TE. Spatial analytical methods and geographic information systems: use in health research and epidemiology. Epidemiol Rev 1999; 21: 143-161.

  • 4.

    Stark JH, Sharma R, Ostroff S, Cummings DA, Ermentrout B, Stebbins S, Burke DS, Wisniewski SR. Local spatial and temporal processes of influenza in Pennsylvania, USA: 2003-2009. PloS One 2012; 7: e34245.

  • 5.

    Demicheli V, Jefferson T, Ferroni E, Rivetti A, Di Pietrantonj C. Vaccines for preventing influenza in healthy adults. Cochrane Database Syst Rev 2014; 3: CD001269.

  • 6.

    Grohskopf LA, Sokolow LZ, Broder KR, Walter EB, Fry AM, Jernigan DB. Prevention and control of seasonal influenza with vaccines: recommendations of the Advisory Committee on Immunization Practices-United States, 2018-19 influenza season. MMWR Recomm Rep 2018; 67: 1-20.

  • 7.

    Yavarian J, Shafiei Jandaghi NZ, Naseri M, Hemmati P, Dadras M, Gouya MM, Mokhtari Azad T. Influenza virus but not MERS coronavirus circulation in Iran, 2013-2016: Comparison between pilgrims and general population. Travel Med Infect Dis 2018; 21: 51-55.

  • 8.

    Mehdipour S, Zolala F, Hoseinnejad M, Zahedi R, Najafi E, Farrokhnia N, Fathi M. Factors Associated with Hospitalization in Patients with H1N1 Influenza in Afzalipour Hospital, Kerman, Iran, 2015: A case-control Study. Iran J Epidemiol 2018; 14: 136-143.

  • 9.

    Roshanaei G, Safari M, Faradmal J, Karami M, Kouselo Z. Ranking and clustering Iranian provinces based on important health indicators of vital horoscope in rural areas by using multivariate methods. Koomesh 2016; 17: 277-287. (Persian).

  • 10.

    Borumandnia N, Alavi Majd H, Zayeri F, Baghestani A, Faeghi F, Tabatabaei S. Bayesian spatiotemporal model for detecting of active areas in brain for analyzing of fMRI data. Koomesh 2017; 19: 845-851. (Persian).

  • 11.

    Vahedi M, Alavi Majd H, Mehrabi Y, Naghavi B. Gene expression data clustering and it's application in differential analysis of leukemia. 2008; 9: 163-169. (Persian).

  • 12.

    Geary RC. The contiguity ratio and statistical mapping. Incorporat Statist 1954; 5: 115-146.

  • 13.

    Grimson RC, Wang KC, Johnson PW. Searching for hierarchical clusters of disease: spatial patterns of sudden infant death syndrome. Soc Sci Med Part D Med Geography 1981; 15: 287-293.

  • 14.

    Assuncao R, Costa M, Tavares A, Ferreira S. Fast detection of arbitrarily shaped disease clusters. Stat Me 2006; 25: 723-742.

  • 15.

    Moran PA. Notes on continuous stochastic phenomena. Biometrika 1950; 37: 17-23.##https://doi.org/10.1093/biomet/37.1-2.17.

  • 16.

    Tango T. The detection of disease clustering in time. Biometrics 1984; 15-26.

  • 17.

    Whittemore A, Keller JB. On Tango's index for disease clustering in time. Biometrics 1986; 42: 218.

  • 18.

    Whittemore AS, Friend N, Brown BW, Holly EA. A test to detect clusters of disease. Biometrika 1987; 74: 631-635.

  • 19.

    Tango T. Asymptotic distribution of an index for disease clustering. Biometrics 1990; 351-357.

  • 20.

    Tango T, Takahashi K. A new space-time scan statistic for timely outbreak detection taking overdispersion into account. Adv Dis Surveil 2008; 5: 68.

  • 21.

    Turnbull BW, Iwano EJ, Burnett WS, Howe HL, Clark LC. Monitoring for clusters of disease; Application to leukemia incidence in upstate New York: Cornell University Operations Research and Industrial Engineering 1989; 132: 136-143.

  • 22.

    Openshaw S, Charlton M, Craft AW, Birch J. Investigation of leukaemia clusters by use of a geographical analysis machine. The Lancet 1988; 331: 272-273.

  • 23.

    Kulldorff M. A spatial scan statistic. Commun Stat Theor Methods 1997; 26: 1481-1496.

  • 24.

    Mousavi T, Nadi A, Moosazadeh M, Haghshenas M. Estimating co-morbidity of H1N1 pandemic and other diseases: a meta-analysis of countries in eastern mediterranean region. J Mazandaran Univ Med Sci 2017; 27: 189-211.

  • 25.

    World Health Organization. Influenza laboratory surveillance data from the latest week. Geneva: World Health Organization; 2018 [cited 2018 Nov 6]. Available from: http://apps.who.int/flumart/Default?ReportNo=2.

  • 26.

    Solgi M, Karami M, Poorolajal J. Timely detection of influenza outbreaks in Iran: Evaluating the performance of the exponentially weighted moving average. J Infect Public Health 2018; 11: 389-392.

  • 27.

    United Nations Population Division. World Population Prospects: 2017 Revision [cited 2018 Nov 6]. Available from: https://data.worldbank.org/indicator/SP.POP.TOTL.

  • 28.

    Buliva E, Elhakim M, Tran Minh NN, Elkholy A, Mala P, Abubakar A, Malik S. Emerging and reemerging diseases in the world health organization (WHO) eastern mediterranean region-progress, challenges, and WHO initiatives. Front Public Health 2017; 5: 276.

  • 29.

    Caini S, El-Guerche Seblain C, Ciblak MA, Paget J. Epidemiology of seasonal influenza in the Middle East and North Africa regions, 2010-2016: Circulating influenza A and B viruses and spatial timing of epidemics. Influenza Other Respir Viruses 2018; 12: 344-352.

  • 30.

    Mohebbi A, Fotouhi F, Jamali A, Yaghobi R, Farahmand B, Mohebbi R. Molecular epidemiology of the hemagglutinin gene of prevalent influenza virus A/H1N1/pdm09 among patient in Iran. Virus Res 2019; 259: 38-45.

  • 31.

    Khadadah M, Essa S, Higazi Z, Behbehani N, Al-Nakib W. Respiratory syncytial virus and human rhinoviruses are the major causes of severe lower respiratory tract infections in Kuwait. J Med Virol 2010; 82: 1462-1467.

  • 32.

    Manabe T, Yamaoka K, Tango T, Binh NG, Tuan ND, Izumi S, et al. Chronological, geographical, and seasonal trends of human cases of avian influenza A (H5N1) in Vietnam, 2003-2014: a spatial analysis. BMC Infect Dis 2015; 16: 64.

  • 33.

    Leveau CM, Uez O, Vacchino MN. Spatiotemporal trends of cases of pandemic influenza A (H1N1) pdm09 in Argentina, 2009-2012. Rev Inst Med Trop Sao Paulo 2015; 57: 133-138.##.