Missing in space: an evaluation of imputation methods for missing data in spatial analysis of risk factors for type II diabetes
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- 作者：Jannah Baker (1) (2)
Nicole White (1) (2)
Kerrie Mengersen (1) (2)
1. Queensland University of Technology School of Mathematical Sciences ; Brisbane ; Australia
2. Cooperative Research Centres for Spatial Information ; Melbourne ; Australia - 关键词：Imputation ; Missing ; Spatial ; Prevalence ; Diabetes
- 刊名：International Journal of Health Geographics
- 出版年：2014
- 出版时间：December 2014
- 年：2014
- 卷：13
- 期：1
- 全文大小：1,122 KB
- 参考文献：1. Earnest, A, Morgan, G, Mengersen, KL, Ryan, L, Summerhayes, R, Beard, J (2007) Evaluating the effect of neighbourhood weight matrices on smoothing properties of Conditional Autoregressive (CAR) models. Int J Health Geogr 6: pp. 54 CrossRef
2. Besag, J, York, J, Mollie, A (1991) Bayesian image restoration with two application in spatial statistics. Annc Inst Statist Math 43: pp. 1-59 CrossRef
Diabetes in the UK 2012. Diabetes UK.
3. Holden, SH, Barnett, AH, Peters, JR, Jenkins-Jones, S, Poole, CD, Morgan, CL, Currie, CJ (2010) The incidence of type 2 diabetes in the United Kingdom from 1991 to 2010. Diabetes Obes Metab 15: pp. 844-852 CrossRef
4. Palmer, AJ, Tucker, DM (2012) Cost and clinical implications of diabetes prevention in an Australian setting: a long-term modeling analysis. Prim Care Diabetes 6: pp. 109-121 CrossRef
5. Harris, MI, Eastman, RC (2000) Early detection of undiagnosed diabetes mellitus: a US perspective. Diabetes Metab Res Rev 16: pp. 230-236 CrossRef
6. Liese, AD, Lawson, A, Song, HR, Hibbert, JD, Porter, DE, Nichols, M, Lamichhane, AP, Dabelea, D, Mayer-Davis, EJ, Standiford, D, Liu, L, Hamman, RF, D鈥橝gostino, RB (2010) Evaluating geographic variation in type 1 and type 2 diabetes mellitus incidence in youth in four US regions. Health Place 16: pp. 547-556 CrossRef
7. Noble, D, Mathur, R, Dent, T, Meads, C, Greenhalgh, T (2011) Risk models and scores for type 2 diabetes: systematic review. BMJ 343: pp. d7163 CrossRef
8. Weng, C, Coppini, DV, Sonksen, PH (2000) Geographic and social factors are related to increased morbidity and mortality rates in diabetic patients. Diabet Med 17: pp. 612-617 CrossRef
9. Egede, LE, Gebregziabher, M, Hunt, KJ, Axon, RN, Echols, C, Gilbert, GE, Mauldin, PD (2011) Regional, geographic, and racial/ethnic variation in glycemic control in a national sample of veterans with diabetes. Diabetes Care 34: pp. 938-943 CrossRef
10. Green, C, Hoppa, RD, Young, TK, Blanchard, JF (2003) Geographic analysis of diabetes prevalence in an urban area. Soc Sci Med 57: pp. 551-560 CrossRef
11. Bocquier, A, Cortaredona, S, Nauleau, S, Jardin, M, Verger, P (2011) Prevalence of treated diabetes: Geographical variations at the small-area level and their association with area-level characteristics. A multilevel analysis in Southeastern France. Diabetes Metab 37: pp. 39-46 CrossRef
12. Geraghty, EM, Balsbaugh, T, Nuovo, J, Tandon, S (2010) Using Geographic Information Systems (GIS) to assess outcome disparities in patients with type 2 diabetes and hyperlipidemia. J Am Board Fam Med 23: pp. 88-96 CrossRef
13. Chaix, B, Billaudeau, N, Thomas, F, Havard, S, Evans, D, Kestens, Y, Bean, K (2011) Neighborhood effects on health: correcting bias from neighborhood effects on participation. Epidemiology 22: pp. 18-26 CrossRef
14. Congdon, P (2006) Estimating diabetes prevalence by small area in England. J Public Health (Oxf) 28: pp. 71-81 CrossRef
15. Kravchenko, VI, Tronko, ND, Pankiv, VI, Venzilovich Yu, M, Prudius, FG (1996) Prevalence of diabetes mellitus and its complications in the Ukraine. Diabetes Res Clin Pract 34: pp. S73-S78 CrossRef
16. Lee, JM, Davis, MM, Menon, RK, Freed, GL (2008) Geographic distribution of childhood diabetes and obesity relative to the supply of pediatric endocrinologists in the United States. J Pediatr 152: pp. 331-336 CrossRef
17. Noble, D, Smith, D, Mathur, R, Robson, J, Greenhalgh, T (2012) Feasibility study of geospatial mapping of chronic disease risk to inform public health commissioning. BMJ Open 2: pp. e000711 CrossRef
18. Magalhaes, RJ, Clements, AC (2011) Mapping the risk of anaemia in preschool-age children: the contribution of malnutrition, malaria, and helminth infections in West Africa. PLoS Med 8: pp. e1000438 CrossRef
19. Stromberg, U, Magnusson, K, Holmen, A, Twetman, S (2011) Geo-mapping of caries risk in children and adolescents - a novel approach for allocation of preventive care. BMC Oral Health 11: pp. 26 CrossRef
20. Joshua, V, Gupte, MD, Bhagavandas, M (2008) A Bayesian approach to study the space time variation of leprosy in an endemic area of Tamil Nadu, South India. Int J Health Geogr 7: pp. 40 CrossRef
21. Cocco, E, Sardu, C, Massa, R, Mamusa, E, Musu, L, Ferrigno, P, Melis, M, Montomoli, C, Ferretti, V, Coghe, G, Fenu, G, Frau, J, Lorefice, L, Carboni, N, Contu, P, Marrosu, MG (2011) Epidemiology of multiple sclerosis in south-western Sardinia. Mult Scler 17: pp. 1282-1289 CrossRef
22. Goovaerts, P (2006) Geostatistical analysis of disease data: accounting for spatial support and population density in the isopleth mapping of cancer mortality risk using area-to-point Poisson kriging. Int J Health Geogr 5: pp. 52 CrossRef
23. Hegarty, AC, Carsin, AE, Comber, H (2010) Geographical analysis of cancer incidence in Ireland: a comparison of two Bayesian spatial models. Cancer Epidemiol 34: pp. 373-381 CrossRef
24. Cramb, SM, Mengersen, KL, Baade, PD (2011) Developing the atlas of cancer in Queensland: methodological issues. Int J Health Geogr 10: pp. 9 CrossRef
25. Haque, U, Magalhaes, RJ, Reid, HL, Clements, AC, Ahmed, SM, Islam, A, Yamamoto, T, Haque, R, Glass, GE (2010) Spatial prediction of malaria prevalence in an endemic area of Bangladesh. Malar J 9: pp. 120 CrossRef
26. Zayeri, F, Salehi, M, Pirhosseini, H (2011) Geographical mapping and Bayesian spatial modeling of malaria incidence in Sistan and Baluchistan province, Iran. Asian Pac J Trop Med 4: pp. 985-992 CrossRef
27. Stensgaard, AS, Vounatsou, P, Onapa, AW, Simonsen, PE, Pedersen, EM, Rahbek, C, Kristensen, TK (2011) Bayesian geostatistical modelling of malaria and lymphatic filariasis infections in Uganda: predictors of risk and geographical patterns of co-endemicity. Malar J 10: pp. 298 CrossRef
28. Kang, SY, McGree, J, Mengersen, K (2013) The impact of spatial scales and spatial smoothing on the outcome of bayesian spatial model. PLoS One 8: pp. e75957 CrossRef
Australian Diabetes Map.
3218.0 Population Estimates by Local Government Area, 2001 to 2011.
Queensland self-reported health status 2009鈥?010: Local Government Area summary report.
29. Besag, J (1974) Spatial interaction and the statistical analysis of lattice systems. J Royal Sta Soc Ser B (Methodological) 36: pp. 192-236
30. Pascutto, C, Wakefield, JC, Best, NG, Richardson, S, Bernardinelli, L, Staines, A, Elliott, P (2000) Statistical issues in the analysis of disease mapping data. Stat Med 19: pp. 2493-519 CrossRef
The R Project for Statistical Computing.
WinBUGS.
31. Spiegelhalter, D, Best, NG, Carlin, B, Van Der Linde, A (2002) Bayesian measures of model complexity and fit. J Royal Sta Soc 64: pp. 583-639 CrossRef - 刊物主题：Public Health; Geographical Information Systems/Cartography; Human Geography; Epidemiology;
- 出版者：BioMed Central
- ISSN：1476-072X
文摘
Background Spatial analysis is increasingly important for identifying modifiable geographic risk factors for disease. However, spatial health data from surveys are often incomplete, ranging from missing data for only a few variables, to missing data for many variables. For spatial analyses of health outcomes, selection of an appropriate imputation method is critical in order to produce the most accurate inferences. Methods We present a cross-validation approach to select between three imputation methods for health survey data with correlated lifestyle covariates, using as a case study, type II diabetes mellitus (DM II) risk across 71 Queensland Local Government Areas (LGAs). We compare the accuracy of mean imputation to imputation using multivariate normal and conditional autoregressive prior distributions. Results Choice of imputation method depends upon the application and is not necessarily the most complex method. Mean imputation was selected as the most accurate method in this application. Conclusions Selecting an appropriate imputation method for health survey data, after accounting for spatial correlation and correlation between covariates, allows more complete analysis of geographic risk factors for disease with more confidence in the results to inform public policy decision-making.