Benefits of small area measurements: A spatial clustering analysis on medicare beneficiaries in the USA

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Issue: Volume 7, Issue 1, 2013

Abstract


Small area estimates on where services for potential Medicare beneficiaries may be needed, could provide unique research opportunities for improving the healthcare quality of the ageing U.S. population. The project described in this paper validates this argument by contrasting the spatial clustering results from an analysis that uses large geographical units with proxy measures to the results from an analysis using small area geographic units with direct measures. Large-area proxy measures come from county-level U.S. Census Bureau 2010 cross sectional data on the number of people aged 65 and over. Medicare beneficiary estimates in 2007 with Primary Care Service Areas (PCSAs) make up the small-area direct-measure analysis. Findings show that the latter offers a more geographically defined appraisal of where healthcare quality efforts should focus to aid potential Medicare beneficiary populations. Because the healthcare quality of an aging population will only increase in importance as their numbers grow in the US, further research is needed.

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Authors Affiliations


Epidemiology Department, Graduate School of Public Health, University of Pittsburgh, USA
Email: cas271@pitt.edu

References


Anselin, L 1995, ̒Local indicators of spatial association—LISA̕, Geographical Analysis, vol. 27, pp 97-115.

Anselin, L 2006, ̒How (not) to lie with spatial statistics̕, American Journal if Preventive Medicine, vol. 30, S3 – S6. [CrossRef][PMid:16458788]

Arcaya, M, Brewster, M, Zigler, CM et al. 2012, ̒Area variations in health: a spatial multilevel approach̕, Health and Place, vol. 18, pp 824-831. [CrossRef] [PMid:22522099]

Arbia, G & Petrarca, F 2011, ̒Effects of MAUP on spatial econometric models̕, Letters in Spatial Resource Science, vol. 4, pp 173-185. [CrossRef]

Bazemore, A, Phillips, RL & Miyoshi, T 2011, ̒Harnessing geographic information systems (GIS) to enable community-oriented primary care̕, Journal of Map and Geography Libraries, vol. 7, pp 71-86. [CrossRef]

Box, GEP 1979, ̒Robustness in the Strategy of Scientific Model Building̕ in R Launer & G Wilderson (eds.), Robustness in Statistics, Academic Press, New York, pp 201-236.

Carpenter, TE 2011, ̒The spatial epidemiologic (r)evolution: A look back in time and forward to the future̕, Spatial and Spatio-temporal Epidemiology, vol. 2, pp 119–124. [CrossRef] [PMid:22748171]

Carstairs, V 1981, ̒Small are analysis and health service research̕, Journal of Public Health, vol. 3, pp 131-139.

Clark, W & Avery, KL 1976, ̒The effects of data aggregation in statistical analysis̕, Geographical Analysis, vol. 8, pp 428–438. [CrossRef]

ESRI 2011, ArcGIS Desktop: Release 10, CA: Environmental Systems Research Institute, Redlands.

Fotheringham, AS & Wong, DW 1991, ̒The modifiable areal unit problem in multivariate statistical analysis̕, Environment and Planning A, vol. 23, pp 1025–1044. [CrossRef]

Galvis, L, Guertin, PJ & Meyer, WD 2009, Actionable Cultural Understanding for Support to Tactical Operations: The effect of data quality on spatial analysis results, ERDC/CERL TR-09-15 Report, http://www.dtic.mil/cgi-bin/GetTRDoc?AD=ADA522284

Gehlke, CE & Biehl, K 1934, ̒Certain effects of grouping upon the size of the correlation coefficient in census tract material̕, Journal of American Statistical Association Supplemental, vol. 29, pp 169–170.

Goodchild, MF & Haining, RP 2004, ̒GIS and spatial data analysis: converging perspectives̕, Papers in Regional Science, vol. 83, pp 363–385. [CrossRef]

Goodman, DC, Mick, SS, Bott, D et al. 2003, ̒Primary care service areas: a new tool for the evaluation of primary care services̕, Health Service Research, vol. 3, pp 287-309. [CrossRef] [PMCid:1360885]

He, W & Muenchrath, MN 2011, 90+ in the United States: 2006-2008, U.S. Government Printing Office, Washington, DC.

Jerrett, M, Gale, S, & Kontgis, C 2010, ̒Spatial modeling in environmental and public health research̕, International Journal of Environmental Research and Public Health, vol. 7, pp 1302-1329. [CrossRef] [PMid:20617032][PMCid:2872363]

Lefebvre, H 1991, The Production of Space, translation by Donald Nicholson-Smith, Blackwell, UK.

Lu, M & Wang, F 2008, ̒A scale-space clustering method: mitigating the effect of scale in the analysis of zone-based data̕ Annals of the Association of American Geographers, vol. 98, pp 85-101. [CrossRef]

Luft, HA 2012, ̒From small area variations to accountable care organizations: how health services research can inform policy̕, Annual Review of Public Health, vol. 33, pp 377-392. [CrossRef] [PMid:22224884]

Mayer, JD 1983, ̒The role of spatial-analysis and geographic data in the detection of disease causation̕, Social Science and Medicine, vol. 17, pp. 1213-1221. [CrossRef]

Murray, AT, O’Kelly, ME & Church, RL 2008, ̒Regional service coverage modeling̕, Computers and Operations Research, vol. 35, pp 339–355. [CrossRef]

Openshaw, S 1984, The modifiable areal unit problem, Norwich, Geo Books.

Ripley, BD 1976, ̒The second-order analysis of stationary point processes̕, Journal of Applied Probability, vol. 13, pp 255-266. [CrossRef]

Riva, M, Guavin, L & Barnett, TA 2007, ̒Toward the next generation of research into small area effects on health: a synthesis of multilevel investigation published since July 1998̕, Journal of Epidemiology and Community Health, vol. 61, pp 853-861. [CrossRef] [PMid:17873220][PMCid:2652961]

Siordia, C & Fox, A 2013, ̒Public Use Microdata Area fragmentation: Research and policy implications of polygon discontiguity̕, Spatial Demography, vol. 1, pp 42-56.

Siordia, C & Saenz, J 2012, ̒What is a “neighborhood”? Definition in studies about depressive symptoms in older persons̕, The Journal of Frailty and Aging, forthcoming.

Siordia, C, Saenz, J & Tom, S 2012, ̒An introduction to macro-level spatial nonstationarity: a geographically weighted regression analysis of diabetes and poverty̕, Human Geographies, 6.2, pp 5-13.

Scott, LM & Janikas, MV 2010, ̒Spatial Statistics in ArcGIS̕ in Handbook of Applied Spatial Analysis: Software Tools, Methods and Applications, MM Fischer & A Getis (eds.), Springer-Verlag, Berlin.

Tong, D 2012, ̒Regional coverage maximization: a new model to account implicitly for complementary coverage̕ Geographical Analysis, vol. 44, pp 1-14. [CrossRef]

Tong, D & Chruch, RL 2013, ̒Aggregation in continuous space coverage modeling̕, International Journal of Geographical Information Science, vol. 26, pp 795-816. [CrossRef]

Tong, D & Murray, AT 2009, ̒Maximizing coverage of spatial demand for service̕, Papers in Regional Science, vol. 88, pp 85–97. [CrossRef]

Vincent, GK & Velkoff, VA 2010, The next four decades: the older population in the United States: 2010 to 2050. Current Population Reports, U.S. Census Bureau, Washington, DC, pp 25-1138. [PMid:19820001][PMCid:2842103]

U.S. Census Bureau 2012a, 2010 Census Summary File 1, Washington, DC.

U.S. Census Bureau 2012b, 2012 TIGER/Line® Shapefiles, Washington, DC.

Weden, MM, Bird, CE, Escarce, JJ et al. 2011, ̒Neighborhood archetypes for population health research: is there no place like home?̕, Health and Place, vol. 17, pp 289-299. [CrossRef] [PMid:21168356][PMCid:3085046]

Witkin, AP 1983, ̒Scale-space filtering̕ in 8th International Joint Conference of Artificial Intelligence, A Bundy (ed.), William Kaufmann, Karlsruhe, Germany, pp 1019–22.

Yule, U & Kendall, MS 1950, An Introduction to the Theory of Statistics, Griffin, London. [PMid:14781262]

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