Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors.
Community-Engaged Modeling of Geographic and Demographic Patterns of Multiple Public Health Risk Factors.
复制标题
DOI：
10.3390/ijerph14070730
复制
发表时间：
2017-07-06
影响因子：
--
通讯作者：
Levy JI
中科院分区：
文献类型：
--
作者：
Basra K;Fabian MP;Holberger RR;French R;Levy JI
许多健康风险因素是社区内的干预目标，但很少有与社区规模干预相关的地理分辨率的关于高危亚人群的信息。美国马萨诸塞州新贝德福德的研究人员和社区合作伙伴共同确定了行为风险因素监测系统（BRFSS）中可获得的高优先级行为和健康结果。我们从BRFSS中开发了多变量回归模型，解释了运动、水果和蔬菜消费、体重指数和糖尿病患病率作为人口统计学和行为特征的函数的可变性，并将这些模型与使用空间微观模拟开发的人口微观数据联系起来，以表征高风险人群和地点。收入和受教育程度较低的人有较低的多种促进健康的行为（例如，水果和蔬菜的消费和运动）和较高的自我报告糖尿病的比例。我们的模型结合模拟的人口微观数据，确定了高风险亚人群比例较高的人口普查区，社区合作伙伴可以利用这些信息来确定资金和干预计划的优先次序。利用公共数据库数据和微观模拟方法进行多压力源建模，以表征人口属性的高分辨率空间格局，再加上强大的社区合作伙伴参与，可以为干预提供重要的见解。我们的方法可以推广到其他社区。
Many health risk factors are intervention targets within communities, but information regarding high-risk subpopulations is rarely available at a geographic resolution that is relevant for community-scale interventions. Researchers and community partners in New Bedford, Massachusetts (USA) collaboratively identified high-priority behaviors and health outcomes of interest available in the Behavioral Risk Factor Surveillance System (BRFSS). We developed multivariable regression models from the BRFSS explaining variability in exercise, fruit and vegetable consumption, body mass index, and diabetes prevalence as a function of demographic and behavioral characteristics, and linked these models with population microdata developed using spatial microsimulation to characterize high-risk populations and locations. Individuals with lower income and educational attainment had lower rates of multiple health-promoting behaviors (e.g., fruit and vegetable consumption and exercise) and higher rates of self-reported diabetes. Our models in combination with the simulated population microdata identified census tracts with an elevated percentage of high-risk subpopulations, information community partners can use to prioritize funding and intervention programs. Multi-stressor modeling using data from public databases and microsimulation methods for characterizing high-resolution spatial patterns of population attributes, coupled with strong community partner engagement, can provide significant insight for intervention. Our methodology is transferrable to other communities.
登录
查看更多内容
影响因子：
4.4
作者：
Riva, Mylene;Smith, Dianna M.
通讯作者：
Smith, Dianna M.
影响因子：
4.9
作者：
Koh, Keumseok;Grady, Sue C.;Vojnovic, Igor
通讯作者：
Vojnovic, Igor
影响因子：
1.6
作者：
Colasanti, Kathryn J. A.;Conner, David S.;Smalley, Susan B.
通讯作者：
Smalley, Susan B.
影响因子：
5.5
作者：
Humpel, N;Owen, N;Leslie, E
通讯作者：
Leslie, E
DOI：
10.1007/s12134-014-0340-x
发表时间：
2015-05-01
影响因子：
1.3
作者：
Kennedy, Steven;Kidd, Michael P.;Biddle, Nicholas
通讯作者：
Biddle, Nicholas