scholarly journals Errors in postcode to enumeration district mapping and their effect on small area analyses of health data

1998 ◽  
Vol 20 (3) ◽  
pp. 325-330 ◽  
Author(s):  
S. E. Collins ◽  
R. P. Haining ◽  
I. R. Bowns ◽  
D. J. Crofts ◽  
T. S. Williams ◽  
...  
Keyword(s):  
Small Area ◽  
Health Data ◽  
Enumeration District

The Estimation of Population Microdata by Using Data from Small Area Statistics and Samples of Anonymised Records

1998 ◽  
Vol 30 (5) ◽  
pp. 785-816 ◽  
Author(s):  
P Williamson ◽  
M Birkin ◽  
P H Rees
Keyword(s):  
Small Area ◽  
Census Data ◽  
Population Data ◽  
Combinatorial Optimisation ◽  
Population Characteristics ◽  
Test Statistics ◽  
Enumeration District ◽  
Small Area Statistics ◽  
Using Data ◽  
Combining Information

Census data can be represented both as lists and as tabulations of household/individual attributes. List representation of Census data offers greater flexibility, as the exploration of interrelationships between population characteristics is limited only by the quality and scope of the data collected. Unfortunately, the released lists of household/individual attributes (Samples of Anonymised Records, SARs) are spatially referenced only to areas (single or merged districts) with populations of 120 000 or more, whereas released tabulations are available for units as small as single enumeration districts (Small Area Statistics, SAS). Intuitively, it should be possible to derive list-based estimates of enumeration district populations by combining information contained in the SAR and the SAS. In this paper we explore the range of solutions that could be adapted to this problem which, ultimately, is presented as a complex combinatorial optimisation problem. Various techniques of combinatorial optimisation are tested, and preliminary results from the best performing algorithm are evaluated. Through this process, the lack of suitable test statistics for the comparison of observed and expected tabulations of population data is highlighted.

scholarly journals Evaluation of Bayesian spatiotemporal latent models in small area health data

2011 ◽  
Vol 22 (8) ◽  
pp. 1008-1022 ◽  
Author(s):  
Jungsoon Choi ◽  
Andrew B. Lawson ◽  
Bo Cai ◽  
Md. Monir Hossain
Keyword(s):  
Small Area ◽  
Health Data ◽  
Latent Models ◽  
Area Health

scholarly journals Multiscale measurement error models for aggregated small area health data

2016 ◽  
Vol 25 (4) ◽  
pp. 1201-1223 ◽  
Author(s):  
Mehreteab Aregay ◽  
Andrew B Lawson ◽  
Christel Faes ◽  
Russell S Kirby ◽  
Rachel Carroll ◽  
...  
Keyword(s):  
Measurement Error ◽  
Small Area ◽  
Health Data ◽  
Error Models ◽  
Measurement Error Models ◽  
Area Health

Developments in General and Syndromic Surveillance for Small Area Health Data

2004 ◽  
Vol 31 (8) ◽  
pp. 951-966 ◽  
Author(s):  
Andrew Lawson ◽  
Allan Clark ◽  
Carmen Vidal Rodeiro
Keyword(s):  
Syndromic Surveillance ◽  
Small Area ◽  
Health Data ◽  
Area Health

Comparing multilevel and multiscale convolution models for small area aggregated health data

2017 ◽  
Vol 22 ◽  
pp. 39-49 ◽  
Author(s):  
Mehreteab Aregay ◽  
Andrew B Lawson ◽  
Christel Faes ◽  
Russell S Kirby ◽  
Rachel Carroll ◽  
...  
Keyword(s):  
Small Area ◽  
Health Data

scholarly journals A Bayesian unified framework for risk estimation and cluster identification in small area health data analysis

PLoS ONE ◽  
2020 ◽  
Vol 15 (5) ◽  
pp. e0231935
Author(s):  
K. C. Flórez ◽  
A. Corberán-Vallet ◽  
A. Iftimi ◽  
J. D. Bermúdez
Keyword(s):  
Data Analysis ◽  
Small Area ◽  
Risk Estimation ◽  
Health Data ◽  
Unified Framework ◽  
Cluster Identification ◽  
Area Health
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