Alina Schnake-Mahl, Giancarlo Anfuso, Stephanie M Hernandez, Usama Bilal
{"title":"新地理区域的地理空间数据聚合方法:验证国会选区的预期寿命估计值。","authors":"Alina Schnake-Mahl, Giancarlo Anfuso, Stephanie M Hernandez, Usama Bilal","doi":"10.1097/EDE.0000000000001797","DOIUrl":null,"url":null,"abstract":"<p><strong>Background: </strong>Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy to the congressional district level to derive local estimates, but such an approach has not been validated.</p><p><strong>Methods: </strong>Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010-2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project (LEEP) and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland-Altman plots to visualize the agreement between the two measures.</p><p><strong>Results: </strong>We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric LEEP model-based approach and Vital Statistics direct estimates approach, though life expectancy at older ages (75 and older) showed weak correlations.</p><p><strong>Conclusion: </strong>This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policy making aimed at improving population health outcomes.</p>","PeriodicalId":11779,"journal":{"name":"Epidemiology","volume":" ","pages":""},"PeriodicalIF":4.7000,"publicationDate":"2024-09-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Geospatial Data Aggregation Methods for Novel Geographies: Validating Congressional District Life Expectancy Estimates.\",\"authors\":\"Alina Schnake-Mahl, Giancarlo Anfuso, Stephanie M Hernandez, Usama Bilal\",\"doi\":\"10.1097/EDE.0000000000001797\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Background: </strong>Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy to the congressional district level to derive local estimates, but such an approach has not been validated.</p><p><strong>Methods: </strong>Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010-2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project (LEEP) and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland-Altman plots to visualize the agreement between the two measures.</p><p><strong>Results: </strong>We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric LEEP model-based approach and Vital Statistics direct estimates approach, though life expectancy at older ages (75 and older) showed weak correlations.</p><p><strong>Conclusion: </strong>This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policy making aimed at improving population health outcomes.</p>\",\"PeriodicalId\":11779,\"journal\":{\"name\":\"Epidemiology\",\"volume\":\" \",\"pages\":\"\"},\"PeriodicalIF\":4.7000,\"publicationDate\":\"2024-09-27\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Epidemiology\",\"FirstCategoryId\":\"3\",\"ListUrlMain\":\"https://doi.org/10.1097/EDE.0000000000001797\",\"RegionNum\":2,\"RegionCategory\":\"医学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Epidemiology","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1097/EDE.0000000000001797","RegionNum":2,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"PUBLIC, ENVIRONMENTAL & OCCUPATIONAL HEALTH","Score":null,"Total":0}
Geospatial Data Aggregation Methods for Novel Geographies: Validating Congressional District Life Expectancy Estimates.
Background: Place is a critical determinant of health. Recent novel analyses have explored health outcome estimation for small geographies, such as census tracts, as well as health outcome aggregation to geopolitical geographies with accountable political representatives, such as congressional districts. In one such application, combining these approaches, researchers aggregated census tract estimates of life expectancy to the congressional district level to derive local estimates, but such an approach has not been validated.
Methods: Here, we compared two sources and approaches to calculating life expectancy data for Pennsylvania congressional districts. We used 2010-2015 census tract life expectancy estimates from the US Small-area Life Expectancy Estimates Project (LEEP) and dasymetric methods to compute population-weighted life expectancy aggregated to the congressional district level. Using georeferenced Vital Statistics data, we aggregated age-specific census tract death and population counts to congressional districts and used abridged life tables to estimate life expectancy. To validate the dasymetric aggregated estimates we compared absolute differences, assessed the correlation, and created Bland-Altman plots to visualize the agreement between the two measures.
Results: We found strong agreement between congressional district estimates of life expectancy at birth derived using the dasymetric LEEP model-based approach and Vital Statistics direct estimates approach, though life expectancy at older ages (75 and older) showed weak correlations.
Conclusion: This validation contributes to our understanding of geospatial aggregation methods for novel geographies including congressional districts. Health outcome data aggregated to the congressional district geography can support congressional policy making aimed at improving population health outcomes.
期刊介绍:
Epidemiology publishes original research from all fields of epidemiology. The journal also welcomes review articles and meta-analyses, novel hypotheses, descriptions and applications of new methods, and discussions of research theory or public health policy. We give special consideration to papers from developing countries.