Small area estimation and childhood obesity surveillance using electronic health records.
There is an urgent need for childhood surveillance systems to design, implement, and evaluate interventions at the local level. We estimated obesity prevalence for individuals aged 5-17 years using a southcentral Wisconsin EHR data repository, Public Health Information Exchange (PHINEX, 2007-2012)....
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| Format: | Article |
| Language: | English |
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Public Library of Science (PLoS)
2021-01-01
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| Series: | PLoS ONE |
| Online Access: | https://doi.org/10.1371/journal.pone.0247476 |
| _version_ | 1818741793012842496 |
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| author | Ying-Qi Zhao Derek Norton Larry Hanrahan |
| author_facet | Ying-Qi Zhao Derek Norton Larry Hanrahan |
| author_sort | Ying-Qi Zhao |
| collection | DOAJ |
| description | There is an urgent need for childhood surveillance systems to design, implement, and evaluate interventions at the local level. We estimated obesity prevalence for individuals aged 5-17 years using a southcentral Wisconsin EHR data repository, Public Health Information Exchange (PHINEX, 2007-2012). The prevalence estimates were calculated by aggregating the estimated probability of each individual being obese, which was obtained via a generalized linear mixed model. We incorporated the random effects at the area level into our model. A weighted procedure was employed to account for missingness in EHR data. A non-parametric kernel smoothing method was used to obtain the prevalence estimates for locations with no or little data (<20 individuals) from the EHR. These estimates were compared to results from newly available obesity atlas (2015-2016) developed from various EHRs with greater statewide representation. The mean of the zip code level obesity prevalence estimates for males and females aged 5-17 years is 16.2% (SD 2.72%); 17.9% (SD 2.14%) for males and 14.4% (SD 2.00%) for females. The results were comparable to the Wisconsin Health Atlas (WHA) estimates, a much larger dataset of local community EHRs in Wisconsin. On average, prevalence estimates were 2.12% lower in this process than the WHA estimates, with lower estimation occurring more frequently for zip codes without data in PHINEX. Using this approach, we can obtain estimates for local areas that lack EHRs data. Generally, lower prevalence estimates were produced for those locations not represented in the PHINEX database when compared to WHA estimates. This underscores the need to ensure that the reference EHRs database can be made sufficiently similar to the geographic areas where synthetic estimates are being created. |
| first_indexed | 2024-12-18T02:02:16Z |
| format | Article |
| id | doaj.art-b4b427e0b0484f1b8e9dbce03bf785bb |
| institution | Directory Open Access Journal |
| issn | 1932-6203 |
| language | English |
| last_indexed | 2024-12-18T02:02:16Z |
| publishDate | 2021-01-01 |
| publisher | Public Library of Science (PLoS) |
| record_format | Article |
| series | PLoS ONE |
| spelling | doaj.art-b4b427e0b0484f1b8e9dbce03bf785bb2022-12-21T21:24:45ZengPublic Library of Science (PLoS)PLoS ONE1932-62032021-01-01162e024747610.1371/journal.pone.0247476Small area estimation and childhood obesity surveillance using electronic health records.Ying-Qi ZhaoDerek NortonLarry HanrahanThere is an urgent need for childhood surveillance systems to design, implement, and evaluate interventions at the local level. We estimated obesity prevalence for individuals aged 5-17 years using a southcentral Wisconsin EHR data repository, Public Health Information Exchange (PHINEX, 2007-2012). The prevalence estimates were calculated by aggregating the estimated probability of each individual being obese, which was obtained via a generalized linear mixed model. We incorporated the random effects at the area level into our model. A weighted procedure was employed to account for missingness in EHR data. A non-parametric kernel smoothing method was used to obtain the prevalence estimates for locations with no or little data (<20 individuals) from the EHR. These estimates were compared to results from newly available obesity atlas (2015-2016) developed from various EHRs with greater statewide representation. The mean of the zip code level obesity prevalence estimates for males and females aged 5-17 years is 16.2% (SD 2.72%); 17.9% (SD 2.14%) for males and 14.4% (SD 2.00%) for females. The results were comparable to the Wisconsin Health Atlas (WHA) estimates, a much larger dataset of local community EHRs in Wisconsin. On average, prevalence estimates were 2.12% lower in this process than the WHA estimates, with lower estimation occurring more frequently for zip codes without data in PHINEX. Using this approach, we can obtain estimates for local areas that lack EHRs data. Generally, lower prevalence estimates were produced for those locations not represented in the PHINEX database when compared to WHA estimates. This underscores the need to ensure that the reference EHRs database can be made sufficiently similar to the geographic areas where synthetic estimates are being created.https://doi.org/10.1371/journal.pone.0247476 |
| spellingShingle | Ying-Qi Zhao Derek Norton Larry Hanrahan Small area estimation and childhood obesity surveillance using electronic health records. PLoS ONE |
| title | Small area estimation and childhood obesity surveillance using electronic health records. |
| title_full | Small area estimation and childhood obesity surveillance using electronic health records. |
| title_fullStr | Small area estimation and childhood obesity surveillance using electronic health records. |
| title_full_unstemmed | Small area estimation and childhood obesity surveillance using electronic health records. |
| title_short | Small area estimation and childhood obesity surveillance using electronic health records. |
| title_sort | small area estimation and childhood obesity surveillance using electronic health records |
| url | https://doi.org/10.1371/journal.pone.0247476 |
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