Skip to main content
Top
Published in: Population Health Metrics 1/2013

Open Access 01-12-2013 | Research

An evaluation of the accuracy of small-area demographic estimates of population at risk and its effect on prevalence statistics

Authors: Jack D Baker, Adelamar Alcantara, Xiaomin Ruan, Srini Vasan, Crouse Nathan

Published in: Population Health Metrics | Issue 1/2013

Login to get access

Abstract

Demographic estimates of population at risk often underpin epidemiologic research and public health surveillance efforts. In spite of their central importance to epidemiology and public-health practice, little previous attention has been paid to evaluating the magnitude of errors associated with such estimates or the sensitivity of epidemiologic statistics to these effects. In spite of the well-known observation that accuracy in demographic estimates declines as the size of the population to be estimated decreases, demographers continue to face pressure to produce estimates for increasingly fine-grained population characteristics at ever-smaller geographic scales. Unfortunately, little guidance on the magnitude of errors that can be expected in such estimates is currently available in the literature and available for consideration in small-area epidemiology. This paper attempts to fill this current gap by producing a Vintage 2010 set of single-year-of-age estimates for census tracts, then evaluating their accuracy and precision in light of the results of the 2010 Census. These estimates are produced and evaluated for 499 census tracts in New Mexico for single-years of age from 0 to 21 and for each sex individually. The error distributions associated with these estimates are characterized statistically using non-parametric statistics including the median and 2.5th and 97.5th percentiles. The impact of these errors are considered through simulations in which observed and estimated 2010 population counts are used as alternative denominators and simulated event counts are used to compute a realistic range fo prevalence values. The implications of the results of this study for small-area epidemiologic research in cancer and environmental health are considered.
Appendix
Available only for authorised users
Literature
1.
go back to reference Centers for Disease Control: National Program of Cancer Registries Cancer Surveillance System Rationale and Approach. Atlanta: CDC; 1999. Centers for Disease Control: National Program of Cancer Registries Cancer Surveillance System Rationale and Approach. Atlanta: CDC; 1999.
2.
go back to reference Elliot P, Wartenberg D: Spatial epidemiology: Current approaches and future challenges. Inf Syst 2004, 112: 998-1006. Elliot P, Wartenberg D: Spatial epidemiology: Current approaches and future challenges. Inf Syst 2004, 112: 998-1006.
3.
go back to reference Krieger N, Chen J, Waterman P, Soobader M, Subramanian SV, Carson R: Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: Does the choice of area-based measure and geographic level matter? Am J Epidemiol 2002, 156: 471-482. 10.1093/aje/kwf068CrossRefPubMed Krieger N, Chen J, Waterman P, Soobader M, Subramanian SV, Carson R: Geocoding and monitoring of US socioeconomic inequalities in mortality and cancer incidence: Does the choice of area-based measure and geographic level matter? Am J Epidemiol 2002, 156: 471-482. 10.1093/aje/kwf068CrossRefPubMed
4.
go back to reference Schottenfield C, Fraumeni J: Cancer Epidemiology and Prevention. New York: Oxford UP; 2006.CrossRef Schottenfield C, Fraumeni J: Cancer Epidemiology and Prevention. New York: Oxford UP; 2006.CrossRef
5.
go back to reference Goodman SN, Samet JM: Cause and Cancer Epidemiology. In Cancer Epidemiology and Prevention. 3rd edition. Edited by: Schottenfield Fraumeni J. New York: Oxford UP; 2006:1341-1353. Goodman SN, Samet JM: Cause and Cancer Epidemiology. In Cancer Epidemiology and Prevention. 3rd edition. Edited by: Schottenfield Fraumeni J. New York: Oxford UP; 2006:1341-1353.
6.
go back to reference Smith S, Tayman J, Swanson D: State and Local Population Projections: Methodology and Analysis. New York: Plenum; 2001. Smith S, Tayman J, Swanson D: State and Local Population Projections: Methodology and Analysis. New York: Plenum; 2001.
7.
go back to reference Baker J, Alcantara A, Ruan XM, Watkins K: The impact of incomplete geocoding on small area population estimates. J Popul Res 2012, 29: 91-112. 10.1007/s12546-011-9077-yCrossRef Baker J, Alcantara A, Ruan XM, Watkins K: The impact of incomplete geocoding on small area population estimates. J Popul Res 2012, 29: 91-112. 10.1007/s12546-011-9077-yCrossRef
8.
go back to reference Swanson D, Tayman J: Sub-National Population Estimates. New York: Springer; 2012.CrossRef Swanson D, Tayman J: Sub-National Population Estimates. New York: Springer; 2012.CrossRef
9.
go back to reference Smith S, Shahidullah M: An evaluation of projection errors for census tracts. J Am Stat Assoc 1995,90(429):64-71. 10.1080/01621459.1995.10476489CrossRefPubMed Smith S, Shahidullah M: An evaluation of projection errors for census tracts. J Am Stat Assoc 1995,90(429):64-71. 10.1080/01621459.1995.10476489CrossRefPubMed
10.
go back to reference Bryan T: Population Estimates. In The Methods and Materials of Demography. 2nd edition. Edited by: Siegel J, Swanson D. New York: Elsevier; 2004. Bryan T: Population Estimates. In The Methods and Materials of Demography. 2nd edition. Edited by: Siegel J, Swanson D. New York: Elsevier; 2004.
11.
go back to reference Shyrock H, Siegel J: The Methods and Materials of Demography. Washington DC: US Department of Commerce; 1980. Shyrock H, Siegel J: The Methods and Materials of Demography. Washington DC: US Department of Commerce; 1980.
12.
go back to reference Shlyakhter A, Wilson R: Monte Carlo Simulation of Uncertainties in Epidemiological Studies: An Example of False-Positive Findings due to Misclassification, Proceedings of the ISUMA- NAFIPS ’95. College Park: Maryland: IEEE Computer Society Press; 1995:685-689. Shlyakhter A, Wilson R: Monte Carlo Simulation of Uncertainties in Epidemiological Studies: An Example of False-Positive Findings due to Misclassification, Proceedings of the ISUMA- NAFIPS ’95. College Park: Maryland: IEEE Computer Society Press; 1995:685-689.
13.
go back to reference Bain C, Feskanich D, Speizer F, Thun M, Hertzmark E, Rosner B, Colditz GA: Lung cancer rates in mean and women with comparable histories of smoking. J Natl Cancer Inst 2004,96(11):826-834. 10.1093/jnci/djh143CrossRefPubMed Bain C, Feskanich D, Speizer F, Thun M, Hertzmark E, Rosner B, Colditz GA: Lung cancer rates in mean and women with comparable histories of smoking. J Natl Cancer Inst 2004,96(11):826-834. 10.1093/jnci/djh143CrossRefPubMed
14.
go back to reference Hankey BF, Feuer E, Clegg L, Hayes R, Legler J, Prorok P, Ries L, Merrill M, Kaplan R: Cancer surveillance series: interpreting trends in prostate cancer–part I: Evidence of the effects of screening in recent prostate cancer incidence, mortality, and survival rates. J Natl Cancer Inst 1999, 91: 1017-1024. 10.1093/jnci/91.12.1017CrossRefPubMed Hankey BF, Feuer E, Clegg L, Hayes R, Legler J, Prorok P, Ries L, Merrill M, Kaplan R: Cancer surveillance series: interpreting trends in prostate cancer–part I: Evidence of the effects of screening in recent prostate cancer incidence, mortality, and survival rates. J Natl Cancer Inst 1999, 91: 1017-1024. 10.1093/jnci/91.12.1017CrossRefPubMed
15.
go back to reference Price B: Analysis of current trends in united states mesothelioma incidence. Am J Epidemiol 1997, 145: 211-218. 10.1093/oxfordjournals.aje.a009093CrossRefPubMed Price B: Analysis of current trends in united states mesothelioma incidence. Am J Epidemiol 1997, 145: 211-218. 10.1093/oxfordjournals.aje.a009093CrossRefPubMed
16.
go back to reference Gaylor D, Chen J, Sheehan D: Uncertainty in cancer risk estimates. Risk Anal 1993,13(2):149-154. 10.1111/j.1539-6924.1993.tb01064.xCrossRefPubMed Gaylor D, Chen J, Sheehan D: Uncertainty in cancer risk estimates. Risk Anal 1993,13(2):149-154. 10.1111/j.1539-6924.1993.tb01064.xCrossRefPubMed
17.
go back to reference Thompson K, Burmaster D, Crouch E: Monte Carlo techniques for quantiative uncertainty analysis in public health risk assessments. Risk Anal 1992,12(1):53-63. 10.1111/j.1539-6924.1992.tb01307.xCrossRefPubMed Thompson K, Burmaster D, Crouch E: Monte Carlo techniques for quantiative uncertainty analysis in public health risk assessments. Risk Anal 1992,12(1):53-63. 10.1111/j.1539-6924.1992.tb01307.xCrossRefPubMed
19.
go back to reference Lunn D, Simpson S, Diamond I, Middleton L: The accuracy of age-specific population estimates for small AReas in Britain. Popul Stud 1998, 52: 327-344. 10.1080/0032472031000150506CrossRef Lunn D, Simpson S, Diamond I, Middleton L: The accuracy of age-specific population estimates for small AReas in Britain. Popul Stud 1998, 52: 327-344. 10.1080/0032472031000150506CrossRef
20.
go back to reference Popoff C, Judson D: Selected General Methods. In The Methods and Materials of Demography. 2nd edition. Edited by: Siegel JS, Swanson D. New York: Springer; 2004:644-675. Popoff C, Judson D: Selected General Methods. In The Methods and Materials of Demography. 2nd edition. Edited by: Siegel JS, Swanson D. New York: Springer; 2004:644-675.
21.
go back to reference McNeil DR, Trussell JT, Turner JC: Spline interpolation of demographic data. Demography 1977,14(2):245-252. 10.2307/2060581CrossRefPubMed McNeil DR, Trussell JT, Turner JC: Spline interpolation of demographic data. Demography 1977,14(2):245-252. 10.2307/2060581CrossRefPubMed
22.
go back to reference Smith L, Hyndman R, Wood S: Spline interpolation for demographic variables: The monotonicity problem. J Popul Res 2004,21(1):95-98. 10.1007/BF03032212CrossRef Smith L, Hyndman R, Wood S: Spline interpolation for demographic variables: The monotonicity problem. J Popul Res 2004,21(1):95-98. 10.1007/BF03032212CrossRef
23.
go back to reference Brass W: The graduation of fertility distributions by polynomial functions. Popul Stud 1960,14(2):148-162.CrossRef Brass W: The graduation of fertility distributions by polynomial functions. Popul Stud 1960,14(2):148-162.CrossRef
24.
go back to reference Keyfitz N: Interpolation and Graduation. Introduction to the Mathematics of Population. Ch.10. Reading. Boston: Addison-Wesley; 1968. Keyfitz N: Interpolation and Graduation. Introduction to the Mathematics of Population. Ch.10. Reading. Boston: Addison-Wesley; 1968.
25.
go back to reference Aitken AC: On interpolation by iteration of proportional parts, without the use of differences. Proc Edinb Math Soc 1932,2(3):56-76.CrossRef Aitken AC: On interpolation by iteration of proportional parts, without the use of differences. Proc Edinb Math Soc 1932,2(3):56-76.CrossRef
26.
go back to reference Beers HS: Six-term formulas for routine actuarial interpolation. Re Am Inst Actuaries 1944,33(68):245-260. Beers HS: Six-term formulas for routine actuarial interpolation. Re Am Inst Actuaries 1944,33(68):245-260.
27.
go back to reference Greville T: The general theory of osculatory interpolation. Trans Acoust Soc Am 1944,45(112):202-265. Greville T: The general theory of osculatory interpolation. Trans Acoust Soc Am 1944,45(112):202-265.
28.
go back to reference Greville T: Recent developments in graduation and interpolation. J Am Stat Assoc 1948,43(243):428-441. 10.1080/01621459.1948.10483272CrossRefPubMed Greville T: Recent developments in graduation and interpolation. J Am Stat Assoc 1948,43(243):428-441. 10.1080/01621459.1948.10483272CrossRefPubMed
29.
go back to reference Sprague TB: Explanation of a new formula for interpolation. J Inst Actuaries 1881,22(270):1880-1881. Sprague TB: Explanation of a new formula for interpolation. J Inst Actuaries 1881,22(270):1880-1881.
30.
go back to reference Wolfenden H: Population Statistics and Their Compilation. Chicago: University of Chicago Press; 1954. Wolfenden H: Population Statistics and Their Compilation. Chicago: University of Chicago Press; 1954.
31.
go back to reference Neter J, Kutner M, Wasserman M, Nacthschem C: Applied Linear Statistical Models. 4th edition. New York: McGraw-Hill; 1999. Neter J, Kutner M, Wasserman M, Nacthschem C: Applied Linear Statistical Models. 4th edition. New York: McGraw-Hill; 1999.
33.
go back to reference Esparza A, Donnelson A: Colonias in Arizona and New Mexico: Border Poverty and Community Development Solutions. Tucson: University of Arizona Press; 2008. Esparza A, Donnelson A: Colonias in Arizona and New Mexico: Border Poverty and Community Development Solutions. Tucson: University of Arizona Press; 2008.
34.
go back to reference Werner W: Polynomial Interpolation: Lagrange vs Newton. Math Comput 1984,43(167):205-217. 10.1090/S0025-5718-1984-0744931-0CrossRef Werner W: Polynomial Interpolation: Lagrange vs Newton. Math Comput 1984,43(167):205-217. 10.1090/S0025-5718-1984-0744931-0CrossRef
35.
go back to reference Derbyshire J: Unknown Quantity: A Real and Imaginary History of Algebra. New York: Plume; 2008. Derbyshire J: Unknown Quantity: A Real and Imaginary History of Algebra. New York: Plume; 2008.
36.
go back to reference Smith S, Sincich T: Evaluating the forecast accuracy and bias of alternative population projections for states. Int J Forecasting 1992, 8: 495-508. 10.1016/0169-2070(92)90060-MCrossRef Smith S, Sincich T: Evaluating the forecast accuracy and bias of alternative population projections for states. Int J Forecasting 1992, 8: 495-508. 10.1016/0169-2070(92)90060-MCrossRef
37.
go back to reference Efron W: Nonparametric estimates of standard error: The jack-knife, the bootstrap, and other methods. Biometrika 1983,68(3):589-599.CrossRef Efron W: Nonparametric estimates of standard error: The jack-knife, the bootstrap, and other methods. Biometrika 1983,68(3):589-599.CrossRef
38.
go back to reference Wachter K: Essential Demographic Methods. Oxford: Oxford University Press; 2012. Wachter K: Essential Demographic Methods. Oxford: Oxford University Press; 2012.
39.
go back to reference Harper G, Coleman C, Devine J: Evaluation of 2000 Subcounty Population Estimates. Working Paper Series No. 70. Washington DC: Population Division, US Census Bureau; 2003. Harper G, Coleman C, Devine J: Evaluation of 2000 Subcounty Population Estimates. Working Paper Series No. 70. Washington DC: Population Division, US Census Bureau; 2003.
40.
go back to reference Swanson D, Tayman J: Between a rock and a hard place: The evaluation of demographic forecasts. Popul Res Policy Rev 1995,14(2):233-249. 10.1007/BF01074460CrossRef Swanson D, Tayman J: Between a rock and a hard place: The evaluation of demographic forecasts. Popul Res Policy Rev 1995,14(2):233-249. 10.1007/BF01074460CrossRef
41.
go back to reference Baker J, Alcantara A, Ruan XM, Vasan S, Watkins K: A Comparative evaluation of error and bias in census tract level age/sex-specific popuation estimates: Component I (net-migration) vs Component III (Hamilton-Perry). Popul Res Policy Rev 2013, 32: 919-942. 10.1007/s11113-013-9295-4CrossRef Baker J, Alcantara A, Ruan XM, Vasan S, Watkins K: A Comparative evaluation of error and bias in census tract level age/sex-specific popuation estimates: Component I (net-migration) vs Component III (Hamilton-Perry). Popul Res Policy Rev 2013, 32: 919-942. 10.1007/s11113-013-9295-4CrossRef
42.
go back to reference De Bruin S, Bregt A: Assessing fitness for Use: The expected value of spatial datasets. Int J Geogr Inf Sci 2001,15(5):457-471. 10.1080/13658810110053116CrossRef De Bruin S, Bregt A: Assessing fitness for Use: The expected value of spatial datasets. Int J Geogr Inf Sci 2001,15(5):457-471. 10.1080/13658810110053116CrossRef
43.
go back to reference Gilboa SM: Comparison fo residential geocoding methods in a population-based study of Air quality and birth defects. Environ Res 2006, 101: 256-262. 10.1016/j.envres.2006.01.004CrossRefPubMed Gilboa SM: Comparison fo residential geocoding methods in a population-based study of Air quality and birth defects. Environ Res 2006, 101: 256-262. 10.1016/j.envres.2006.01.004CrossRefPubMed
44.
go back to reference Oliver MN: Geographic bias related to geocoding in epidemiologic studies. Int J Health Geogr 2005.,4(29): Online Oliver MN: Geographic bias related to geocoding in epidemiologic studies. Int J Health Geogr 2005.,4(29): Online
45.
go back to reference Haining R: Spatial data analysis: Theory and practice. Cambridge: New York; 2003.CrossRef Haining R: Spatial data analysis: Theory and practice. Cambridge: New York; 2003.CrossRef
46.
go back to reference Little R, Schenker N: Missing Data. In Handook for Statistical Modeling in the Social and Behavioral Sciences. Edited by: Arminger G, Clogg CC, Soebel ME. New York: Plenum; 1994:39-75. Little R, Schenker N: Missing Data. In Handook for Statistical Modeling in the Social and Behavioral Sciences. Edited by: Arminger G, Clogg CC, Soebel ME. New York: Plenum; 1994:39-75.
47.
go back to reference Le Sage J, Pace KR: Models for spatially-dependent missing data. J Real Estate Finance Econ 2004,29(2):233-254.CrossRef Le Sage J, Pace KR: Models for spatially-dependent missing data. J Real Estate Finance Econ 2004,29(2):233-254.CrossRef
48.
go back to reference Polissar L: The effect of migration on comparison of disease rates in geographic studies in the United States. Am J Epidemiol 1980,111(2):175-182.PubMed Polissar L: The effect of migration on comparison of disease rates in geographic studies in the United States. Am J Epidemiol 1980,111(2):175-182.PubMed
49.
go back to reference Gordis L: Epidemiology. Princeton: Princeton; 2000. Gordis L: Epidemiology. Princeton: Princeton; 2000.
50.
go back to reference Aschengrau A, Seage GA: Essentials of Epidemiology in Public Health. Jones and Bartlett: Sudbury; 2003. Aschengrau A, Seage GA: Essentials of Epidemiology in Public Health. Jones and Bartlett: Sudbury; 2003.
51.
go back to reference Lemieux C: Monte Carlo and Quasi-Monte Carlo Sampling. New York: Springer; 2009. Lemieux C: Monte Carlo and Quasi-Monte Carlo Sampling. New York: Springer; 2009.
52.
go back to reference Kulkarni VG: Introduction to Modeling and Analysis of Stochastic Systems. 2nd edition. New York: Springer; 2011.CrossRef Kulkarni VG: Introduction to Modeling and Analysis of Stochastic Systems. 2nd edition. New York: Springer; 2011.CrossRef
53.
go back to reference Drummond WJ: Address matching: GIS technology for mapping human activity patterns. J Am Plann Assoc 1995,61(2):240-251. 10.1080/01944369508975636CrossRef Drummond WJ: Address matching: GIS technology for mapping human activity patterns. J Am Plann Assoc 1995,61(2):240-251. 10.1080/01944369508975636CrossRef
54.
go back to reference Ratcliffe JH: On the accuracy of tiger-type geocoded address data in relation to cadastral and census area units. Int J Geogr Inf Sci 2001,15(5):473-485. 10.1080/13658810110047221CrossRef Ratcliffe JH: On the accuracy of tiger-type geocoded address data in relation to cadastral and census area units. Int J Geogr Inf Sci 2001,15(5):473-485. 10.1080/13658810110047221CrossRef
55.
go back to reference Karimi HA, Durcik M: Evaluation of uncertainties associated with geocoding techniques. Comput Aided Civil Infrastruct Eng 2004, 19: 170-185. 10.1111/j.1467-8667.2004.00346.xCrossRef Karimi HA, Durcik M: Evaluation of uncertainties associated with geocoding techniques. Comput Aided Civil Infrastruct Eng 2004, 19: 170-185. 10.1111/j.1467-8667.2004.00346.xCrossRef
56.
go back to reference Goldberg DW, Wilson JP, Knoblock CA: From text to geographic coordinates: The current state of geocoding. URISA J 2007,19(1):33-46. Goldberg DW, Wilson JP, Knoblock CA: From text to geographic coordinates: The current state of geocoding. URISA J 2007,19(1):33-46.
58.
go back to reference Zandbergen P: Geocoding quality and implications for spatial analysis. Geogr Compass 2009., 3: Online Zandbergen P: Geocoding quality and implications for spatial analysis. Geogr Compass 2009., 3: Online
59.
go back to reference Jarosz B: Using Assessor Parcel Data to Maintain Housing Unit Counts for Small-Area Population Estimates. In Applied Demography in the 21st Century. Edited by: Murdock S, Swanson D. New York: Springer; 2008:89-101.CrossRef Jarosz B: Using Assessor Parcel Data to Maintain Housing Unit Counts for Small-Area Population Estimates. In Applied Demography in the 21st Century. Edited by: Murdock S, Swanson D. New York: Springer; 2008:89-101.CrossRef
Metadata
Title
An evaluation of the accuracy of small-area demographic estimates of population at risk and its effect on prevalence statistics
Authors
Jack D Baker
Adelamar Alcantara
Xiaomin Ruan
Srini Vasan
Crouse Nathan
Publication date
01-12-2013
Publisher
BioMed Central
Published in
Population Health Metrics / Issue 1/2013
Electronic ISSN: 1478-7954
DOI
https://doi.org/10.1186/1478-7954-11-24

Other articles of this Issue 1/2013

Population Health Metrics 1/2013 Go to the issue