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Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses

Spatial Demography volume 4, pages 155–173 (2016)Cite this article

Abstract

We evaluate the impacts of random spatial displacements on analyses that involve distance measures from displaced Demographic and Health Survey clusters to nearest ancillary point or line features, such as health resources or roads. We use simulation and case studies to address the effects of this introduced error, and propose use of regression calibration (RC) to reduce its impact. Results suggest that RC outperforms analyses involving naive distance-based covariate assignments by reducing the bias and MSE of the main estimator in most settings. Proposed guidelines also address the effect of the spatial density of destination features on observed bias.

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References

  • Armstrong, M. P., Rushton, G., & Zimmerman, D. L. (1999). Geographically masking health data to preserve confidentiality. Statistics in medicine, 18(5), 497–525.

    Article  Google Scholar 

  • Bonner, M. R., Han, D., Nie, J., Rogerson, P., Vena, J. E., & Freudenheim, J. L. (2003). Positional accuracy of geocoded addresses in epidemiologic research. Epidemiology, 14(4), 408–412.

    Google Scholar 

  • Burgert, C.R., Colston, J., Roy, T., & Zachary, B. (2013). Geographic displacement procedure and georeferenced data release policy for the Demographic and Health Surveys. DHS Spatial Analysis Report No. 7. Calverton, Maryland, USA: ICF International.

  • Burra, T., Jerrett, M., Burnett, R. T., & Anderson, M. (2002). Conceptual and practical issues in the detection of local disease clusters: A study of mortality in hamilton, ontario. The Canadian Geographer/Le Géographe Canadien, 46(2), 160–171.

    Article  Google Scholar 

  • Byass, P., Worku, A., Emmelin, A., & Berhane, Y. (2007). Dss and dhs: Longitudinal and cross-sectional viewpoints on child and adolescent mortality in ethiopia. Population Health Metrics, 5(1), 12.

    Article  Google Scholar 

  • Carroll, R. J., Ruppert, D., Stefanski, L. A., & Crainiceanu, C. M. (2010). Measurement error in nonlinear models: A modern perspective. Boca Raton, FL: CRC Press.

    Google Scholar 

  • Carroll, R. J., & Stefanski, L. A. (1990). Approximate quasi-likelihood estimation in models with surrogate predictors. Journal of the American Statistical Association, 85, 652–663.

    Article  Google Scholar 

  • Cayo, M. R., & Talbot, T. O. (2003). Positional error in automated geocoding of residential addresses. International Journal of Health Geographics, 2(1), 10.

    Article  Google Scholar 

  • DeLuca, P., & Kanaroglou, P. (2008). Effects of alternative point pattern geocoding procedures on first and second order statistical measures. Journal of Spatial Science, 53(1), 131–141.

    Article  Google Scholar 

  • Feldacker, C., Emch, M., & Ennett, S. (2010). The who and where of HIV in rural Malawi: Exploring the effects of person and place on individual HIV status. Health & Place, 16, 996–1006.

    Article  Google Scholar 

  • Goldberg, D. W., Wilson, J. P., & Knoblock, C. A. (2007). From text to geographic coordinates: The current state of geocoding. Urisa Journal, 19(1), 33–46.

    Google Scholar 

  • Hardin, J. W., Schmiediche, H., & Carroll, R. J. (2003). The regression-calibration method for fitting generalized linear models with additive measurement error. The Stata Journal, 3, 361–372.

    Google Scholar 

  • Jacquez, G. M., & Rommel, R. (2009). Local indicators of geocoding accuracy (LIGA): Theory and application. International Journal of Health Geographics, 8, 60.

    Article  Google Scholar 

  • Kalemli-Ozcan, S., & Turan, B. (2011). Hiv and fertility revisited. Journal of Development Economics, 96(1), 61–65.

    Article  Google Scholar 

  • Kamel Boulos, M. N., Cai, Q., Padget, J. A., & Rushton, G. (2006). Using software agents to preserve individual health data confidentiality in micro-scale geographical analyses. Journal of Biomedical Informatics, 39(2), 160–170.

    Article  Google Scholar 

  • Kravets, N., & Hadden, W. C. (2007). The accuracy of address coding and the effects of coding errors. Health & Place, 13(1), 293–298.

    Article  Google Scholar 

  • Leitner, M., & Curtis, A. (2004). Cartographic guidelines for geographically masking the locations of confidential point data. Cartographic Perspectives, 49, 22–39.

    Article  Google Scholar 

  • Leitner, M., & Curtis, A. (2006). A first step towards a framework for presenting the location of confidential point data on mapsresults of an empirical perceptual study. International Journal of Geographical Information Science, 20(7), 813–822.

    Article  Google Scholar 

  • Lohela, T. J., Campbell, O. M., & Gabrysch, S. (2012). Distance to care, facility delivery and early neonatal mortality in malawi and zambia. PloS ONE, 7(12), e52110.

    Article  Google Scholar 

  • Mazumdar, S., Rushton, G., Smith, B. J., Zimmerman, D. L., & Donham, K. J. (2008). Geocoding accuracy and the recovery of relationships between environmental exposures and health. International Journal of Health Geographics, 7, 13.

    Article  Google Scholar 

  • Mekonnen, W., & Worku, A. (2011). Determinants of fertility in rural ethiopia: The case of butajira demographic surveillance system (dss). BMC Public Health, 11(1), 782.

    Article  Google Scholar 

  • Nhacolo, A. Q., Nhalungo, D. A., Sacoor, C. N., Aponte, J. J., Thompson, R., & Alonso, P. (2006). Levels and trends of demographic indices in southern rural mozambique: Evidence from demographic surveillance in manhica district. BMC Public Health, 6(1), 291.

    Article  Google Scholar 

  • Olson, K. L., Grannis, S. J., & Mandl, K. D. (2006). Privacy protection versus cluster detection in spatial epidemiology. Journal Information, 96(11), 2002–2008.

    Google Scholar 

  • Core Team, R. (2012). R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing.

    Google Scholar 

  • Rushton, G., Armstrong, M. P., Gittler, J., Greene, B. R., Pavlik, C. E., West, M. M., et al. (2006). Geocoding in cancer research: A review. American Journal of Preventive Medicine, 30(2), S16–S24.

    Article  Google Scholar 

  • Uganda Bureau of Statistics (UBOS) and ICF International (2012). Uganda Demographic and Health Survey 2011 [Datasets]. UGIR60FL and UGGE61FL Kampala, Uganda: UBOS and Calverton, Maryland: ICF International [Producers]. ICF International [Distributor].

  • Ward, M. H., Nuckols, J. R., Giglierano, J., Bonner, M. R., Wolter, C., Airola, M., et al. (2005). Positional accuracy of two methods of geocoding. Epidemiology, 16(4), 542–547.

    Article  Google Scholar 

  • Whitsel, E. A., Quibrera, P. M., Smith, R. L., Catellier, D. J., Liao, D., Henley, A. C., et al. (2006). Accuracy of commercial geocoding: Assessment and implications. Epidemiologic Perspectives & Innovations, 3(1), 8.

    Article  Google Scholar 

  • Wieland, S. C., Cassa, C. A., Mandl, K. D., & Berger, B. (2008). Revealing the spatial distribution of a disease while preserving privacy. Proceedings of the National Academy of Sciences, 105(46), 17608–17613.

    Article  Google Scholar 

  • World Health Organization (WHO) (2009). Uganda health facilities.

  • Zandbergen, P. A. (2007). Influence of geocoding quality on environmental exposure assessment of children living near high traffic roads. BMC Public Health, 7(1), 37.

    Article  Google Scholar 

  • Zandbergen, P. A. (2008). A comparison of address point, parcel and street geocoding techniques. Computers, Environment and Urban Systems, 32(3), 214–232.

    Article  Google Scholar 

  • Zandbergen, P. A. (2011). Influence of street reference data on geocoding quality. Geocarto International, 26(1), 35–47.

    Article  Google Scholar 

  • Zandbergen, P. A., & Hart, T. C. (2009). Geocoding accuracy considerations in determining residency restrictions for sex offenders. Criminal Justice Policy Review, 20(1), 62–90.

    Article  Google Scholar 

  • Zandbergen, P. A., Ignizio, D. A., & Lenzer, K. E. (2011). Positional accuracy of tiger 2000 and 2009 road networks. Transactions in GIS, 15(4), 495–519.

    Article  Google Scholar 

  • Zhan, F. B., Brender, J. D., De Lima, I., Suarez, L., & Langlois, P. H. (2006). Match rate and positional accuracy of two geocoding methods for epidemiologic research. Annals of Epidemiology, 16(11), 842–849.

    Article  Google Scholar 

  • Zimmerman, D. L., Fang, X., Mazumdar, S., & Rushton, G. (2007). Modeling the probability distribution of positional errors incurred by residential address geocoding. International Journal of Health Geographics, 6, 1.

    Article  Google Scholar 

  • Zimmerman, D. L., Li, J., & Fang, X. (2010). Spatial autocorrelation among automated geocoding errors and its effects on testing for disease clustering. Statistics in Medicine, 29(9), 1025–1036.

    Google Scholar 

  • Zinszer, K., Jauvin, C., Verma, A., Bedard, L., Allard, R., Schwartzman, K., et al. (2010). Residential address errors in public health surveillance data: A description and analysis of the impact on geocoding. Spatial and Spatio-temporal Epidemiology, 1(2), 163–168.

    Article  Google Scholar 

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Acknowledgments

This research was supported in part by grants from the National Institute of Environmental Health Sciences (T32ES007018, P30ES010126) and the United States Agency for International Development (USAID) through the MEASURE DHS project (Contract No. GPO-C-00-08-00008-00).

Confilct of interests

The authors declare that they have no competing interests.

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Authors and Affiliations

  1. Department of Biostatistics, Yale School of Public Health, Yale University, New Haven, CT, USA

    Joshua L. Warren

  2. Department of Biological Sciences, Meredith College, Raleigh, NC, USA

    Carolina Perez-Heydrich

  3. ICF International, Rockville, MD, USA

    Clara R. Burgert

  4. Department of Geography, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA

    Michael E. Emch

Authors
  1. Joshua L. Warren
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  2. Carolina Perez-Heydrich
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  3. Clara R. Burgert
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  4. Michael E. Emch
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Correspondence to Joshua L. Warren.

Electronic supplementary material

Below is the link to the electronic supplementary material.

40980_2015_14_MOESM1_ESM.pdf

Appendix A Appendix A is a .pdf file which shows that the random displacement of DHS cluster locations leads to a classical measurement error problem when a distance-based covariate is used.(PDF 84KB)

40980_2015_14_MOESM2_ESM.txt

Appendix B Appendix B is a .txt file which provides R code to randomly displace DHS survey cluster locations based on the DHS Program geographic displacement procedure.(TXT 8KB)

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Warren, J.L., Perez-Heydrich, C., Burgert, C.R. et al. Influence of Demographic and Health Survey Point Displacements on Distance-Based Analyses. Spat Demogr 4, 155–173 (2016). https://doi.org/10.1007/s40980-015-0014-0

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  • DOI: https://doi.org/10.1007/s40980-015-0014-0

Keywords

  • Global Position System
  • Mean Square Error
  • Global Position System Data
  • True Distance
  • Random Displacement