scholarly journals Effects of Built Environmental Factors on Obesity and Self-reported Health Status in Seoul Metropolitan Area Using Spatial Regression Model

2011 ◽  
Vol 68 (null) ◽  
pp. 85-98 ◽  
Author(s):  
김은정 ◽  
강민규
Keyword(s):  
Health Status ◽  
Environmental Factors ◽  
Regression Model ◽  
Metropolitan Area ◽  
Spatial Regression ◽  
Spatial Regression Model ◽  
Seoul Metropolitan Area ◽  
Reported Health ◽  
Self Reported Health Status

The Influence of the Perceptions of Environmental Factors on Self-Reported Health Status Among Thais

2012 ◽  
Vol 4 (3-4) ◽  
pp. 35-46 ◽  
Author(s):  
Manyat Ruchiwit ◽  
Kampol Ruchiwit ◽  
Lisa Pawloski ◽  
Kevin M. Curtin
Keyword(s):  
Health Status ◽  
Environmental Factors ◽  
Reported Health ◽  
Self Reported Health Status

Social inequalities, stressors and self reported health status among African American and white women in the Detroit metropolitan area

2000 ◽  
Vol 51 (11) ◽  
pp. 1639-1653 ◽  
Author(s):  
A Schulz ◽  
B Israel ◽  
D Williams ◽  
E Parker ◽  
A Becker ◽  
...  
Keyword(s):  
African American ◽  
Health Status ◽  
Metropolitan Area ◽  
White Women ◽  
Social Inequalities ◽  
Detroit Metropolitan Area ◽  
Reported Health ◽  
Self Reported Health Status

Insights From a Developing Country: Self-Reported Health Status of Elderly Men (60 Years and Over) in Jamaica

2013 ◽  
Vol 12 (2) ◽  
pp. 106-120 ◽  
Author(s):  
Chloe Morris ◽  
Kenneth James ◽  
Desmale Holder-Nevins ◽  
Denise Eldemire-Shearer
Keyword(s):  
Health Status ◽  
Developing Country ◽  
Elderly Men ◽  
Reported Health ◽  
Self Reported Health Status

scholarly journals Use of Complementary and Alternative Medical Services by HMO Members, 1995-1996

2001 ◽  
Vol 7 (2) ◽  
pp. 85-98 ◽  
Author(s):  
Evelyn P. Whitlock
Keyword(s):  
Multivariate Analysis ◽  
Patient Satisfaction ◽  
Health Status ◽  
Service Users ◽  
Improve Patient Satisfaction ◽  
Kaiser Permanente ◽  
Reported Health ◽  
Self Reported Health Status ◽  
Improve Patient ◽  
Complementary And Alternative

We investigated HMO members' use of complementary and alternative medicine (CAM) providers outside the HMO in 1995-1996. A random 2% survey of Kaiser Permanente Northwest members addressed HMO service satisfaction, self-reported health status and behaviors, and HMO utilization. Among respondents, 15.7% (n = 380) used CAM providers (chiropractors, naturopaths, acupuncturists, others) in the prior 12 months, while 35% were ever users. Multivariate analysis found that those more likely to consult CAM providers were females, more educated, and more dissatisfied with the HMO. These results suggest that HMOs may wish to focus efforts to improve patient satisfaction among CAM service users.

Field level digital soil mapping of cation exchange capacity using electromagnetic induction and a hierarchical spatial regression model

Soil Research ◽  
2009 ◽  
Vol 47 (7) ◽  
pp. 651 ◽  
Author(s):  
John Triantafilis ◽  
Scott Mitchell Lesch ◽  
Kevin La Lau ◽  
Sam Mostyn Buchanan
Keyword(s):  
Regression Model ◽  
Cation Exchange Capacity ◽  
Spatial Regression ◽  
Soil Management ◽  
Exchange Capacity ◽  
Ancillary Data ◽  
Trend Surface ◽  
Spatial Regression Model ◽  
Field Level ◽  
Mlr Model

At the field level the demand for spatial information of soil properties is rapidly increasing owing to its requirements in precision agriculture and soil management. One of the most important properties is the cation exchange capacity (CEC, cmol(+)/kg) because it is an index of the shrink–swell potential and hence is a measure of soil structural resilience to tillage. However, CEC is time-consuming and expensive to measure. Various ancillary datasets and statistical methods can be used to predict CEC, but there is little scientific literature which implements this approach to map CEC or addresses the issue of the amount of ancillary data required to maximise precision and minimise bias of spatial prediction at the field level. We compare a standard least-squares multiple linear regression (MLR) model which includes 2 proximally sensed (EM38 and EM31), 3 remotely sensed (Red, Green and Blue spectral brightness), and 2 trend surface (Easting and Northing) variables as ancillary data or independent variables, and a stepwise MLR model which only includes the statistically valid EM38 signal data and the Easting trend surface vector. The latter is used as the basis for developing a hierarchical spatial regression model to predict CEC. The reliability of the model is analysed by comparing prediction precision (root mean square error) and bias (mean error) using degraded EM38 transect spacing (i.e. 96, 144, 192, 240, and 288 m) and comparing these with predictions achieved with the 48-m spacing. We conclude that the EM38 data available on the 96- and 144-m spacing are suitable at a reconnaissance level (i.e. broad-scale farming) and 24- or 48-m spacing are suitable at smaller levels where detailed information is necessary for siting the location of water reservoirs. In terms of soil management, CEC predictions determine where suitable subsoil exists for the purpose of soil profile inversion to improve the structural resilience of a topsoil that is susceptible to dispersion and surface crusting.

scholarly journals PRO2 SELF-REPORTED HEALTH STATUS PREDICTORS OF MEDICATION ADHERENCE IN OLDER ADULTS WITH CHRONIC DISEASES

2006 ◽  
Vol 9 (3) ◽  
pp. A169
Author(s):  
R Balkrishnan ◽  
SS Jayawant ◽  
MJ Bhosle ◽  
RT Anderson
Keyword(s):  
Older Adults ◽  
Health Status ◽  
Medication Adherence ◽  
Chronic Diseases ◽  
Reported Health ◽  
Self Reported Health Status

SPATIAL AUTOREGRESSIVE (SAR) MODEL WITH ENSEMBLE LEARNING-MULTIPLICATIVE NOISE WITH LOGNORMAL DISTRIBUTION (CASE ON POVERTY DATA IN EAST JAVA)

2021 ◽  
Vol 14 (1) ◽  
pp. 89-97
Author(s):  
Dewi Retno Sari Saputro ◽  
Sulistyaningsih Sulistyaningsih ◽  
Purnami Widyaningsih
Keyword(s):  
Regression Model ◽  
Ensemble Learning ◽  
Spatial Data ◽  
Lognormal Distribution ◽  
Multiplicative Noise ◽  
Spatial Regression ◽  
Spatial Error ◽  
Spatial Regression Model ◽  
Sar Model ◽  
Spatial Autoregressive

The regression model that can be used to model spatial data is Spatial Autoregressive (SAR) model. The level of accuracy of the estimated parameters of the SAR model can be improved, especially to provide better results and can reduce the error rate by resampling method. Resampling is done by adding noise (noise) to the data using Ensemble Learning (EL) with multiplicative noise. The research objective is to estimate the parameters of the SAR model using EL with multiplicative noise. In this research was also applied a spatial regression model of the ensemble non-hybrid multiplicative noise which has a lognormal distribution of cases on poverty data in East Java in 2016. The results showed that the estimated value of the non-hybrid spatial ensemble spatial regression model with multiplicative noise with a lognormal distribution was obtained from the average parameter estimation of 10 Spatial Error Model (SEM) resulting from resampling. The multiplicative noise used is generated from lognormal distributions with an average of one and a standard deviation of 0.433. The Root Mean Squared Error (RMSE) value generated by the non-hybrid spatial ensemble regression model with multiplicative noise with a lognormal distribution is 22.99.

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