From the clinic to the community: Can health system data accurately estimate population obesity prevalence?
From the clinic to the community: Can health system data accurately estimate population obesity prevalence?
复制标题
DOI：
10.1002/oby.23273
复制
发表时间：
2021-11
期刊：
影响因子：
--
通讯作者：
Arterburn DE
中科院分区：
文献类型：
--
作者：
Mooney SJ;Song L;Drewnowski A;Buskiewicz J;Mooney SD;Saelens BE;Arterburn DE
我们评估了卫生系统数据在多大程度上可以估计人口普查区的肥胖患病率。临床访视数据来自华盛顿金县的两个大型卫生系统（Kaiser Permanente华盛顿和华盛顿医学大学），行为风险因素监测系统（BRFSS）的人口普查区水平肥胖患病率估计值也是如此。我们对卫生系统数据进行了地理编码，以确定每位患者的居住区域，并评估了2005 - 2006年两个卫生系统计算的人口普查区域级肥胖患病率估计值之间的横截面一致性，以及2012 - 2016年华盛顿大学医学院和BRFSS之间的一致性。肥胖的空间分布在卫生系统之间相似（斯皮尔曼r = 0.63）。华盛顿大学医学院估计的等级顺序与BRFSS估计值相关良好（Spearman r = 0.85），尽管BRFSS的患病率估计值较低（华盛顿大学医学院的平均肥胖患病率= 26%，BRFSS为20%，Wilcoxon秩和检验p值<0.001）。在所有数据来源中，肥胖在教育程度较低的地区更为普遍。卫生系统的临床体重数据可以可靠地复制人口普查区一级的空间模式，在肥胖患病率的排名。卫生系统数据可能是地理肥胖监测的有效资源。
We assessed how well health system data can estimate obesity prevalence in census tracts. Clinical visit data were available from two large health systems (Kaiser Permanente Washington and University of Washington Medicine) in King County, Washington, as were census tract-level obesity prevalence estimates from Behavioral Risk Factor Surveillance System (BRFSS). We geocoded the health system data to identify each patient’s tract of residence and assessed the cross-sectional concordance between census tract-level obesity prevalence estimates computed from the two health systems in 2005–2006 and concordance between University of Washington Medicine and BRFSS from 2012–2016. The spatial distribution of obesity was similar between the health systems (Spearman r = 0.63). University of Washington Medicine estimates rank-order correlated well with BRFSS estimates (Spearman r = 0.85), though prevalence estimates from BRFSS were lower (mean obesity prevalence = 26% for University of Washington Medicine vs. 20% for BRFSS, Wilcoxon rank-sum test p-value < 0.001). Across all data sources, obesity was more prevalent in tracts with less educational attainment. Health system clinical weight data can reliably replicate census tract-level spatial patterns in the ranking of obesity prevalence. Health system data may be an efficient resource for geographic obesity surveillance.
登录
查看更多内容
影响因子：
10.4
作者：
Duncan DT;Sharifi M;Melly SJ;Marshall R;Sequist TD;Rifas-Shiman SL;Taveras EM
通讯作者：
Taveras EM
影响因子：
1.4
作者：
Arterburn, David;Alexander, Gwen;Sherwood, Nancy
通讯作者：
Sherwood, Nancy
影响因子：
2.3
作者：
Boone-Heinonen, J.;Tillotson, C. J.;DeVoe, J. E.
通讯作者：
DeVoe, J. E.
影响因子：
2.2
作者：
Drewnowski A;Buszkiewicz J;Aggarwal A;Cook A;Moudon AV
通讯作者：
Moudon AV
影响因子：
4.9
作者：
Lee, J. M.;Pilli, S.;Gebremariam, A.;Keirns, C. C.;Davis, M. M.;Vijan, S.;Freed, G. L.;Herman, W. H.;Gurney, J. G.
通讯作者：
Gurney, J. G.