scholarly journals Modeling the Relationship between the Gross Domestic Product and Built-Up Area Using Remote Sensing and GIS Data: A Case Study of Seven Major Cities in Canada

2016 ◽  
Vol 5 (3) ◽  
pp. 23 ◽  
Author(s):  
Kamil Faisal ◽  
Ahmed Shaker ◽  
Suhaib Habbani
Keyword(s):  
Remote Sensing ◽  
Gross Domestic Product ◽  
Remote Sensing And Gis ◽  
Domestic Product ◽  
Gis Data ◽  
The Relationship

Abstract P360: Hypertension Treatment Rates And Health Worker Density: An Analysis Of Worldwide Data

Circulation ◽  
2014 ◽  
Vol 129 (suppl_1) ◽  
Author(s):  
Rajesh Vedanthan ◽  
Mondira Ray ◽  
Valentin Fuster ◽  
Ellen Magenheim
Keyword(s):  
Gross Domestic Product ◽  
Health Worker ◽  
World Bank ◽  
Domestic Product ◽  
Hypertension Treatment ◽  
Treatment Rate ◽  
Capita Gross Domestic Product ◽  
The World ◽  
Per Capita ◽  
The Relationship

Introduction: Hypertension is the leading global risk for mortality and its prevalence is increasing in many low- and middle-income countries. Hypertension treatment rates are low worldwide, potentially in part due to insufficient human resources. However, the relationship between health worker density and hypertension treatment rates is unknown. Objective: To conduct an econometric analysis of the relationship between health worker density and hypertension treatment rates worldwide. Methods: Hypertension treatment rates were collected from published reports between 1980 and 2010. Data on health worker (physician and nurse) density were obtained from the World Health Organization (WHO). Data for potential confounding variables--per capita gross domestic product, hospital bed density, burden of infectious diseases, land area and urban population--were obtained from WHO and World Bank databases. Potential interaction by per capita gross domestic product was evaluated. Multivariable logistic-logarithmic regression analysis was performed using Stata. Results: Full data were available from 146 countries spanning all World Bank income classification categories. Health worker density was significantly associated with hypertension treatment rate in the unadjusted model (beta = 0.23; p < 0.005). In the fully adjusted model, the association remained positive but was not statistically significant (beta = 0.30; p = 0.078) (Figure). Hypertension treatment rates were more strongly related to physician than nurse density (beta = 0.21 vs 0.08; p = 0.10 vs 0.49). Conclusion: Hypertension treatment rates across the world appear to be related to health worker density, although the relationship does not achieve strict statistical significance. Our results suggest that a 10% increase in health worker density is associated with a 2-3% increase in hypertension treatment rate. Given the global burden of hypertension and other chronic diseases, WHO guidelines for health workforce staffing may need to be reconsidered.

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