Creating local estimates from a population health survey: practical application of small area estimation methods
Regular health surveys can produce reliable estimates at higher geographic levels but not for small areas. Alternatives are to aggregate data over several years or use model-based methods. We created and evaluated model-based estimates for four health-related outcomes by gender, for 153 Local Govern...
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doaj-46e7a81b278243dba3552e893f37fec82020-11-25T02:51:10ZengAIMS PressAIMS Public Health2327-89942020-06-017240342410.3934/publichealth.2020034Creating local estimates from a population health survey: practical application of small area estimation methodsDiane Hindmarsh0David Steel11 Bureau of Health Information, Level 2, 1 Reserve Road St Leonards, NSW, Australia 2 National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, Australia2 National Institute for Applied Statistics Research Australia, University of Wollongong, Wollongong, NSW, AustraliaRegular health surveys can produce reliable estimates at higher geographic levels but not for small areas. Alternatives are to aggregate data over several years or use model-based methods. We created and evaluated model-based estimates for four health-related outcomes by gender, for 153 Local Government Areas using data from the New South Wales Population Health Survey. The evaluation examined evidence on bias and determined the covariates available and appropriate for each outcome variable. The evaluation considered the likely precision of the resulting estimates. The bias and precision of results for single years (2006–2008) for each outcome variable using six covariate specifications were compared with direct survey estimates based on a single year’s data and those obtained by aggregating over seven years. A practical issue is how to choose covariates to include in the models as the best covariate specification varies between outcome variables. Model-based results had median root mean squared errors between 3.3% and 5.5% (max 5.2% and 11.3% respectively) and median relative root mean squared errors between 6.8% and 24.5% (max 11.7% and 41.5% respectively). The model-based estimates were unbiased compared with direct estimates based on one or seven years of data and when aggregated to a point where direct estimates were reliable. The bias and reliability assessment process provides a way for policymakers to have confidence in model-based estimates.https://www.aimspress.com/article/10.3934/publichealth.2020034/fulltext.htmlsaesmall area estimationsurveyhealthrisk factorspopulation healthsmoking ratesrisk alcohol drinking |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Diane Hindmarsh David Steel |
spellingShingle |
Diane Hindmarsh David Steel Creating local estimates from a population health survey: practical application of small area estimation methods AIMS Public Health sae small area estimation survey health risk factors population health smoking rates risk alcohol drinking |
author_facet |
Diane Hindmarsh David Steel |
author_sort |
Diane Hindmarsh |
title |
Creating local estimates from a population health survey: practical application of small area estimation methods |
title_short |
Creating local estimates from a population health survey: practical application of small area estimation methods |
title_full |
Creating local estimates from a population health survey: practical application of small area estimation methods |
title_fullStr |
Creating local estimates from a population health survey: practical application of small area estimation methods |
title_full_unstemmed |
Creating local estimates from a population health survey: practical application of small area estimation methods |
title_sort |
creating local estimates from a population health survey: practical application of small area estimation methods |
publisher |
AIMS Press |
series |
AIMS Public Health |
issn |
2327-8994 |
publishDate |
2020-06-01 |
description |
Regular health surveys can produce reliable estimates at higher geographic levels but not for small areas. Alternatives are to aggregate data over several years or use model-based methods. We created and evaluated model-based estimates for four health-related outcomes by gender, for 153 Local Government Areas using data from the New South Wales Population Health Survey. The evaluation examined evidence on bias and determined the covariates available and appropriate for each outcome variable. The evaluation considered the likely precision of the resulting estimates. The bias and precision of results for single years (2006–2008) for each outcome variable using six covariate specifications were compared with direct survey estimates based on a single year’s data and those obtained by aggregating over seven years. A practical issue is how to choose covariates to include in the models as the best covariate specification varies between outcome variables. Model-based results had median root mean squared errors between 3.3% and 5.5% (max 5.2% and 11.3% respectively) and median relative root mean squared errors between 6.8% and 24.5% (max 11.7% and 41.5% respectively). The model-based estimates were unbiased compared with direct estimates based on one or seven years of data and when aggregated to a point where direct estimates were reliable. The bias and reliability assessment process provides a way for policymakers to have confidence in model-based estimates. |
topic |
sae small area estimation survey health risk factors population health smoking rates risk alcohol drinking |
url |
https://www.aimspress.com/article/10.3934/publichealth.2020034/fulltext.html |
work_keys_str_mv |
AT dianehindmarsh creatinglocalestimatesfromapopulationhealthsurveypracticalapplicationofsmallareaestimationmethods AT davidsteel creatinglocalestimatesfromapopulationhealthsurveypracticalapplicationofsmallareaestimationmethods |
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