Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S.
This paper assesses concordance and inconsistency among three small area estimation methods that are currently providing county-level health indicators in the United States. The three methods are multi-level logistic regression, spatial logistic regression, and spatial Poison regression, all propose...
| Main Authors: | , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Ubiquity Press
2018-04-01
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| Series: | Data Science Journal |
| Subjects: | |
| Online Access: | https://datascience.codata.org/articles/763 |
| _version_ | 1811309192412135424 |
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| author | Lung-Chang Chien Ge Lin Xiao Li Xingyou Zhang |
| author_facet | Lung-Chang Chien Ge Lin Xiao Li Xingyou Zhang |
| author_sort | Lung-Chang Chien |
| collection | DOAJ |
| description | This paper assesses concordance and inconsistency among three small area estimation methods that are currently providing county-level health indicators in the United States. The three methods are multi-level logistic regression, spatial logistic regression, and spatial Poison regression, all proposed since 2010. Diabetes prevalence is estimated for each county in the continental United States from the 2012 sample of Behavioral Risk Factor Surveillance System. The mapping results show that all three methods displayed elevated diabetes prevalence in the South. While the Pearson correlation coefficients among three model-based estimates were all above 0.60, the highest one was 0.80 between the multilevel and spatial logistic methods. While point estimates are apparently different among the three small area estimate methods, their top and bottom of quintile distributions are fairly consistent based on Bangdiwala’s B-statistic, suggesting that outputs from each method would support consistent policy making in terms of identifying top and bottom percent counties. |
| first_indexed | 2024-04-13T09:36:40Z |
| format | Article |
| id | doaj.art-857e072cacdf4c0abd77c303419cc569 |
| institution | Directory Open Access Journal |
| issn | 1683-1470 |
| language | English |
| last_indexed | 2024-04-13T09:36:40Z |
| publishDate | 2018-04-01 |
| publisher | Ubiquity Press |
| record_format | Article |
| series | Data Science Journal |
| spelling | doaj.art-857e072cacdf4c0abd77c303419cc5692022-12-22T02:52:03ZengUbiquity PressData Science Journal1683-14702018-04-011710.5334/dsj-2018-008666Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S.Lung-Chang Chien0Ge Lin1Xiao Li2Xingyou Zhang3University of Nevada, Las VegasUniversity of Nevada, Las VegasUniversity of Texas Health Science Center at Houston (UTHealth) School of Public HealthU.S. Census BureauThis paper assesses concordance and inconsistency among three small area estimation methods that are currently providing county-level health indicators in the United States. The three methods are multi-level logistic regression, spatial logistic regression, and spatial Poison regression, all proposed since 2010. Diabetes prevalence is estimated for each county in the continental United States from the 2012 sample of Behavioral Risk Factor Surveillance System. The mapping results show that all three methods displayed elevated diabetes prevalence in the South. While the Pearson correlation coefficients among three model-based estimates were all above 0.60, the highest one was 0.80 between the multilevel and spatial logistic methods. While point estimates are apparently different among the three small area estimate methods, their top and bottom of quintile distributions are fairly consistent based on Bangdiwala’s B-statistic, suggesting that outputs from each method would support consistent policy making in terms of identifying top and bottom percent counties.https://datascience.codata.org/articles/763Small area estimatediabetes prevalencemulti-level logistic regressionspatial logistic regressionspatial Poisson regression |
| spellingShingle | Lung-Chang Chien Ge Lin Xiao Li Xingyou Zhang Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S. Data Science Journal Small area estimate diabetes prevalence multi-level logistic regression spatial logistic regression spatial Poisson regression |
| title | Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S. |
| title_full | Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S. |
| title_fullStr | Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S. |
| title_full_unstemmed | Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S. |
| title_short | Disparity of Imputed Data from Small Area Estimate Approaches – A Case Study on Diabetes Prevalence at the County Level in the U.S. |
| title_sort | disparity of imputed data from small area estimate approaches a case study on diabetes prevalence at the county level in the u s |
| topic | Small area estimate diabetes prevalence multi-level logistic regression spatial logistic regression spatial Poisson regression |
| url | https://datascience.codata.org/articles/763 |
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