Literature DB >> 29549756

Assessing the spatial heterogeneity in overall health across the United States using spatial regression methods: The contribution of health factors and county-level demographics.

Loni Philip Tabb1, Leslie A McClure2, Harrison Quick2, Jonathan Purtle3, Ana V Diez Roux2.   

Abstract

Mesh：

Year:  2018        PMID： 29549756     DOI： 10.1016/j.healthplace.2018.02.012

Source DB:  PubMed          Journal:  Health Place        ISSN： 1353-8292            Impact factor:   4.078


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  7 in total

1.  Spatiotemporal Analysis of Overall Health in the United States Between 2010 and 2018.

Authors:  Binod Acharya
Journal:  Cureus       Date:  2021-09-26

2.  Spatio-Temporal Distribution, Spillover Effects and Influences of China's Two Levels of Public Healthcare Resources.

Authors:  Xueqian Song; Yongping Wei; Wei Deng; Shaoyao Zhang; Peng Zhou; Ying Liu; Jiangjun Wan
Journal:  Int J Environ Res Public Health       Date:  2019-02-17       Impact factor: 3.390

3.  Modeling the Importance of Within- and Between-County Effects in an Ecological Study of the Association Between Social Capital and Mental Distress.

Authors:  Tse-Chuan Yang; Stephen A Matthews; Feinuo Sun; Marina Armendariz
Journal:  Prev Chronic Dis       Date:  2019-06-13       Impact factor: 2.830

4.  Spatial variability of COVID-19 and its risk factors in Nigeria: A spatial regression method.

Authors:  Taye Bayode; Ayobami Popoola; Olawale Akogun; Alexander Siegmund; Hangwelani Magidimisha-Chipungu; Olusiyi Ipingbemi
Journal:  Appl Geogr       Date:  2021-12-03

5.  Association between county-level sociodemographic characteristics and county-level differences in opioid dispensing.

Authors:  Laura J Cremer; Natasha Underwood; Amber Robinson; Gery P Guy; Cherie R Rooks-Peck
Journal:  Prev Med Rep       Date:  2021-10-25

6.  Multilevel and spatial analyses of childhood malnutrition in Uganda: examining individual and contextual factors.

Authors:  Prince M Amegbor; Zhaoxi Zhang; Rikke Dalgaard; Clive E Sabel
Journal:  Sci Rep       Date:  2020-11-18       Impact factor: 4.379

7.  Demographics, politics, and health factors predict mask wearing during the COVID-19 pandemic: a cross-sectional study.

Authors:  George B Cunningham; Calvin Nite
Journal:  BMC Public Health       Date:  2021-07-15       Impact factor: 3.295
  7 in total

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