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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| Veröffentlicht in: | PloS one 2021-02, Vol.16 (2), p.e0247476-e0247476 |
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| 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 ( |
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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.</description><identifier>ISSN: 1932-6203</identifier><identifier>EISSN: 1932-6203</identifier><identifier>DOI: 10.1371/journal.pone.0247476</identifier><identifier>PMID: 33606784</identifier><language>eng</language><publisher>United States: Public Library of Science</publisher><subject>Age ; Biology and Life Sciences ; Children ; Community ; Computer and Information Sciences ; Editing ; Electronic health records ; Electronic medical records ; Electronic records ; Engineering and Technology ; Estimates ; Ethnicity ; Health care ; Health surveillance ; Hispanic people ; Informatics ; Medical records ; Medicine and Health Sciences ; Methods ; Obesity ; Obesity in children ; Pediatric research ; People and places ; Population ; Postal codes ; Public health ; Research and Analysis Methods ; Research facilities ; Reviews ; Risk factors ; Rural areas ; Sentinel health events ; Socioeconomic factors ; Statistics ; Surveillance systems ; Trends</subject><ispartof>PloS one, 2021-02, Vol.16 (2), p.e0247476-e0247476</ispartof><rights>COPYRIGHT 2021 Public Library of Science</rights><rights>2021 Zhao et al. This is an open access article distributed under the terms of the Creative Commons Attribution License: http://creativecommons.org/licenses/by/4.0/ (the “License”), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. 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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.</description><subject>Age</subject><subject>Biology and Life Sciences</subject><subject>Children</subject><subject>Community</subject><subject>Computer and Information Sciences</subject><subject>Editing</subject><subject>Electronic health records</subject><subject>Electronic medical records</subject><subject>Electronic records</subject><subject>Engineering and Technology</subject><subject>Estimates</subject><subject>Ethnicity</subject><subject>Health care</subject><subject>Health surveillance</subject><subject>Hispanic people</subject><subject>Informatics</subject><subject>Medical records</subject><subject>Medicine and Health Sciences</subject><subject>Methods</subject><subject>Obesity</subject><subject>Obesity in children</subject><subject>Pediatric research</subject><subject>People and 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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.</abstract><cop>United States</cop><pub>Public Library of Science</pub><pmid>33606784</pmid><doi>10.1371/journal.pone.0247476</doi><tpages>e0247476</tpages><orcidid>https://orcid.org/0000-0002-2851-2257</orcidid><oa>free_for_read</oa></addata></record> |
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| subjects | Age Biology and Life Sciences Children Community Computer and Information Sciences Editing Electronic health records Electronic medical records Electronic records Engineering and Technology Estimates Ethnicity Health care Health surveillance Hispanic people Informatics Medical records Medicine and Health Sciences Methods Obesity Obesity in children Pediatric research People and places Population Postal codes Public health Research and Analysis Methods Research facilities Reviews Risk factors Rural areas Sentinel health events Socioeconomic factors Statistics Surveillance systems Trends |
| title | Small area estimation and childhood obesity surveillance using electronic health records |
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