Zobrazeno 1 - 10
of 149
pro vyhledávání: '"Xingyou Zhang"'
Publikováno v:
International Journal of Health Geographics, Vol 17, Iss 1, Pp 1-13 (2018)
Abstract Objective To assess spatial accessibility measures to on-premise alcohol outlets at census block, census tract, county, and state levels for the United States. Methods Using network analysis in a geographic information system, we computed di
Externí odkaz:
https://doaj.org/article/447e4f0e3d0e4e85a6d7a956994365f4
Autor:
Meredith Deutscher, Chris Van Beneden, Deron Burton, Alvin Shultz, Oliver W. Morgan, Shadi Chamany, Hannah T. Jordan, Xingyou Zhang, Brendan Flannery, Daniel R. Feikin, Beatrice Olack, Kim A. Lindblade, Robert F. Breiman, Sonja J. Olsen
Publikováno v:
Journal of Epidemiology and Global Health, Vol 2, Iss 2 (2019)
Background: Surveillance is essential to estimating the global burden of pneumonia, yet differences in surveillance methodology and health care-seeking behaviors limit inter-country comparisons. Methods: Results were compared from community surveys
Externí odkaz:
https://doaj.org/article/b6c5535b0c194e778ec85183868738eb
Publikováno v:
Data Science Journal, Vol 17 (2018)
This paper assesses concordance and inconsistency among three small area estimation methods that are currently providing county-level health indicators in the United States. The three methods are multi-level logistic regression, spatial logistic regr
Externí odkaz:
https://doaj.org/article/857e072cacdf4c0abd77c303419cc569
Autor:
Zahava, Berkowitz, Xingyou, Zhang, Thomas B, Richards, Susan A, Sabatino, Lucy A, Peipins, Judith Lee, Smith
Publikováno v:
Journal of Women's Health. 32:216-223
Autor:
Hua Lu, Yan Wang, Yong Liu, James B. Holt, Catherine A. Okoro, Xingyou Zhang, Qing C. Zhang, Kurt J. Greenlund
Publikováno v:
Preventing Chronic Disease. 20
Autor:
James Holt, S. Jane Henley, Lucy Peipins, Thomas B. Richards, Xingyou Zhang, Zahava Berkowitz
Background: Smoking is the leading preventable cause of death; however, small-area estimates for detailed smoking status are limited. We developed multilevel small-area estimate mixed models to generate county-level estimates for six smoking status c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::75b0a07a1c7fb9787e460a295adaa470
https://doi.org/10.1158/1055-9965.c.6515736
https://doi.org/10.1158/1055-9965.c.6515736
Autor:
James Holt, S. Jane Henley, Lucy Peipins, Thomas B. Richards, Xingyou Zhang, Zahava Berkowitz
Supplementary Methods and Materials
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::4fb11c96b703c87734cfc19a98c04734
https://doi.org/10.1158/1055-9965.22437117
https://doi.org/10.1158/1055-9965.22437117
Publikováno v:
Open Journal of Statistics. 12:70-81
Generalized Linear Mixed Model (GLMM) has been widely used in small area estimation for health indicators. Bayesian estimation is usually used to construct statistical intervals, however, its computational intensity is a big challenge for large compl
Autor:
Zahava Berkowitz, Susan A. Sabatino, Xingyou Zhang, Lucy A. Peipins, Judith Lee Smith, Thomas B. Richards
Publikováno v:
The Journal of the American Board of Family Medicine. 34:634-647
Background: In 2018, the US Preventive Services Task Force (USPSTF) recommended prostate cancer screening for men aged 55 to 69 years who express a preference for being screened after being informed about and understanding prostate-specific antigen (
Publikováno v:
The Science of Health Disparities Research