دانلود مقاله ISI انگلیسی شماره 97916
ترجمه فارسی عنوان مقاله

اهداف استنتاج مبتنی بر مدل و طراحی مبتنی بر طراحی چگونگی حسابداری برای خوشه بندی محله در مطالعات بهداشتی در انواع همپوشانی متنی

عنوان انگلیسی
Model-based and design-based inference goals frame how to account for neighborhood clustering in studies of health in overlapping context types
کد مقاله سال انتشار تعداد صفحات مقاله انگلیسی
97916 2017 38 صفحه PDF
منبع

Publisher : Elsevier - Science Direct (الزویر - ساینس دایرکت)

Journal : SSM - Population Health, Volume 3, December 2017, Pages 600-608

ترجمه کلمات کلیدی
تجزیه و تحلیل چندسطحی، اندازه گیری اپیدمیولوژیک، نظرسنجی های بهداشتی،
کلمات کلیدی انگلیسی
Multilevel analysis; Epidemiologic measurement; Health surveys;
پیش نمایش مقاله
پیش نمایش مقاله  اهداف استنتاج مبتنی بر مدل و طراحی مبتنی بر طراحی چگونگی حسابداری برای خوشه بندی محله در مطالعات بهداشتی در انواع همپوشانی متنی

چکیده انگلیسی

Accounting for non-independence in health research often warrants attention. Particularly, the availability of geographic information systems data has increased the ease with which studies can add measures of the local “neighborhood” even if participant recruitment was through other contexts, such as schools or clinics. We highlight a tension between two perspectives that is often present, but particularly salient when more than one type of potentially health-relevant context is indexed (e.g., both neighborhood and school). On the one hand, a model-based perspective emphasizes the processes producing outcome variation, and observed data are used to make inference about that process. On the other hand, a design-based perspective emphasizes inference to a well-defined finite population, and is commonly invoked by those using complex survey samples or those with responsibility for the health of local residents. These two perspectives have divergent implications when deciding whether clustering must be accounted for analytically and how to select among candidate cluster definitions, though the perspectives are by no means monolithic. There are tensions within each perspective as well as between perspectives. We aim to provide insight into these perspectives and their implications for population health researchers. We focus on the crucial step of deciding which cluster definition or definitions to use at the analysis stage, as this has consequences for all subsequent analytic and interpretational challenges with potentially clustered data.