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Review
. 2017 Mar 30:9:1178222617700626.
doi: 10.1177/1178222617700626. eCollection 2017.

Using Spatial Analysis to Inform Community Immunization Strategies

Moises E Maravi  1 , Lauren E Snyder  1 , L Dean McEwen  1 , Kathryn DeYoung  1 , Arthur J Davidson  1   2   3
Affiliations
Review

Using Spatial Analysis to Inform Community Immunization Strategies

Moises E Maravi et al. Biomed Inform Insights. .

Abstract

Introduction: Recent pertussis outbreaks in the United States suggest our response to local disease outbreaks (eg, vaccine-preventable Bordetella pertussis) may benefit from understanding and applying spatial analytical methods that use data from immunization information systems at a subcounty level.

Methods: A 2012 study on Denver, CO, residents less than 19 years of age confirmed pertussis cases and immunization information system records were geocoded and aggregated to the census tract (CT) level. An algorithm assessed whether individuals were up-to-date (UTD) for pertussis vaccines. Pearson, Spearman, and Kendall correlations assessed relations between disease incidence and pertussis vaccine coverage. Using spatial analysis software, disease incidence and UTD rates were spatially weighted, and smoothed. Global and local autocorrelations based on univariate Moran's I spatial autocorrelation statistics evaluated whether a CT's rate belong to a cluster based on incidence or UTD measures.

Results: Overall disease incidence rate was 116.8/100 000. Assessment of pertussis vaccination coverage was available for 90% of the population. Among 134 672 Denver residents less than 19 years old, 103 496 (77%) were UTD for pertussis vaccines. Raw correlation coefficients showed weak relationships between incidence and immunization rates due to the presence of outliers. With geospatial and clustering analysis, estimates and correlation coefficients were improved with statistically significant Moran's I values for global and local autocorrelations rejecting the null hypothesis that incidence or UTD rates were randomly distributed. With evidence indicating the presence of clusters, smoothed and weighted disease incidence and UTD rates in 144 CTs identified 21 CTs (15%) for potential public health intervention.

Conclusions: Correlation of raw disease incidence and vaccine UTD rates in subcounty regions showed limited association, providing limited information for decision making. By assessing for clusters using spatial analysis methods, we identified CTs with higher incidence and lower immunization coverage for targeted public health interventions.

Keywords: Autocorrelation; Immunization; Pertussis; Spatial Analysis.

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Conflict of interest statement

DECLARATION OF CONFLICTING INTERESTS: The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article. DISCLOSURES AND ETHICS As a requirement of publication, author(s) have provided to the publisher signed confirmation of compliance with legal and ethical obligations including, but not limited to, the following: authorship and contributorship, conflicts of interest, privacy and confidentiality, and (where applicable) protection of human and animal research subjects. The authors have read and confirmed their agreement with the ICMJE authorship and conflict of interest criteria. The authors have also confirmed that this article is unique and not under consideration or published in any other publication, and that they have permission from rights holders to reproduce any copyrighted material. Any disclosures are made in this section. The external blind peer reviewers report no conflicts of interest.

Figures

Figure 1.
Figure 1.
Bordetella pertussis incidence and pertussis immunization up-to-date (UTD) unweighted rates, Denver, CO, 2012: (A) raw incidence rate per 100 000 and (B) raw UTD rate (%).
Figure 2.
Figure 2.
Standardized correlation plot for unweighted Bordetella pertussis disease incidence rate and immunization up-to-date rate (hinge: 1.5), Denver, CO, 2012. Pearson correlation coefficient = 0.126; P = .133.
Figure 3.
Figure 3.
Exploratory box plot geospatial analysis of unweighted Bordetella pertussis incidence and pertussis immunization up-to-date (UTD) rates, Denver, CO, 2012: (A) incidence rate per 100 000 and (B) UTD rates (%).
Figure 4.
Figure 4.
Exploratory (unweighted) box map geospatial analysis of Bordetella pertussis disease incidence and pertussis immunization up-to-date (UTD), Denver, CO, 2012: (A) incidence rate hinge = 1.5 and (B) UTD rate hinge = 1.5.
Figure 5.
Figure 5.
Spatial rate smoothed Bordetella pertussis incidence rate with queen contiguity weights Moran’s I scatterplot (hinge: 1.5), Denver, CO, 2012. Global autocorrelation spatial rate smoothed with QC weighting.
Figure 6.
Figure 6.
Local autocorrelation spatial rate smoothed Bordetella pertussis incidence weighting, rates with queen contiguity map, Denver, CO, 2012: (A) local indicators of spatial association cluster map and (B) Gi and Gi* cluster map.
Figure 7.
Figure 7.
Map of combined spatial weight scoringa for pertussis vaccine up-to-date (UTD) rates by census tract, Denver, CO, 2012. aScore determination: the score is determined by adding the number of times a census tract (CT) appeared in any Gi and Gi* high-high cluster. Maximum possible CT score was 12. Gi and Gi* inclusions were counted only when they corresponded to high-high UTD LISA clusters generated using the spatial rate smoothing method combined with queen contiguity, rook contiguity, and k-nearest neighbor spatial weighting types.
Figure 8.
Figure 8.
Intersection of census tracts with high Bordetella pertussis incidence and low pertussis vaccine up-to-date immunization rates using spatial rate smoothing and queen contiguity weighting, Denver, CO, 2012.
Figure 9.
Figure 9.
Pearson correlation plots between Bordetella pertussis incidence and pertussis vaccine up-to-date immunization smoothed rates using spatial rate smoothing method, Denver, CO, 2012: (A) QC smoothed incidence rate and QC smoothed UTD rate, (B) QC smoothed incidence rate and RC smoothed UTD rate, and (C) QC smoothed incidence rate and KN smoothed UTD rate.
Figure 10.
Figure 10.
Census tracts (CTs) with high Bordetella pertussis incidence and low pertussis vaccine up-to-date (UTD) immunization (unweighted and unsmoothed) rates, Denver, CO, 2012.
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References

    1. Centers for Disease Control and Prevention. Pertussis cases by year (1922–2014). http://www.cdc.gov/pertussis/surv-reporting/cases-by-year.html. Accessed July 20, 2016.
    1. Centers for Disease Control and Prevention. 2012. final pertussis surveillance report. https://www.cdc.gov/pertussis/downloads/pertuss-surv-report-2012.pdf. Accessed July 17, 2016.
    1. Clark TA. Status of pertussis control in the United States. http://www.hhs.gov/sites/default/files/nvpo/nvac/meetings/pastmeetings/2.... Published 2013. Accessed July 17, 2016.
    1. Klein NP, Bartlett J, Fireman B, Rowhani-Rahbar A, Baxter R. Comparative effectiveness of acellular versus whole-cell pertussis vaccines in teenagers. Pediatrics. 2013;131:e1716–e1722. - PubMed
    1. Centers for Disease Control and Prevention. Epidemiology and prevention of vaccine-preventable diseases: Pertussis. http://www.cdc.gov/vaccines/pubs/pinkbook/downloads/pert.pdf. Accessed July 20, 2016.

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