Short Communication, Gigs Vol: 9 Issue: 6

When And Why Public Health Needs Geoinformatics System

Mahyar Yousefi

Department of geostatic, Afghan University, Afghanistan

*Corresponding author: Mahyar Yousefi, Department of geostatic, Afghan University, Afghanistan. E-mail: yousefi.eng@gmail.com

Received: June 5, 2021 Accepted: June 19, 2021 Published: June 26 ,2021

Citation: Yousef M. (2021) When And Why Public Health Needs Geoinformatics System Geo infor Geo stat: An Overview 9(6).308

Abstract

Keywords: Geoinformatics System, Public Health

Introduction

Geographic issues to public health research and policy, particularly through the lens of geographic information systems (GIS). It covers six themes with a stress on methodological issues. Facilities (supply) and patients (demand) during a health-care market interact with one another across geopolitical borders, and measures of health-care accessibility got to capture that. Disease rates in areas of small population are unreliable, and one effective thanks to mitigate the matter is to construct a bigger, internally homogenous and comparable square measure. Defining a scientific geographic unit for health-care market is critical for researchers, practitioners, and policy makers to gauge health-care delivery, and GIS enables us to define the unit (e.g. medical care service areas, hospital service areas, and cancer service areas) automatically, efficiently and optimally. apart from various optimization objectives around ‘efficiency’, it's as important to plan the situation and allocation of health-care resources towards maximum equality in health-care access. Case studies are cited for instance each theme. Public health also features a long tradition of examining the effect of urbanicity (i.e., degree of urbanization) on health behaviour and outcome. Both can benefit an excellent deal from GIS. for instance, classify the postcode areas in Illinois into five categories of urbanicity like the densely populated City of Chicago, Chicago suburbs, other smaller metropolitan areas, large towns (with population 10–50k), and rural. an equivalent multilevel logistic model on late-stage cancer risk of every cancer type is implemented for cancer patients in postcode areas grouped in each of the five urbanicity classifications, and therefore the derived odd ratios for late stage are compared across these five categories. After controlling for individual attributes, zip-code-level socioeconomic characteristics and spatial access to health care, they find that late-stage cancer risks for four major sorts of cancer (breast, colorectal, lung, and prostate) are highest within the most highly urbanized area and reduce as urbanicity declines, with a little upturn in risk within the most isolated rural areas. Similarly, during a study on on the association of built environments with individual physical inactivity and obesity within the U.S., divide the info set into several subsets consistent with the urbanicity levels of counties where individuals reside, and apply a multilevel regression model in each subset. They use the Urban–Rural Classification Scheme for Counties prepared by the National Centre for Health Statistics and also divide the counties into five categories supported urban population. The latter is achieved by overlaying counties and concrete areas defined by the U.S. Bureau of Census, since urban areas (including urbanized areas and concrete clusters) are made from geographic units of fine scale like census tracts and census block, a more accurate measure of urbanicity is attained. In short, separate models are run in areas of distinctive urbanicity so as to capture possible varying behavior and relationship across these areas. By extension, public policies can't be one-size-fits-all and wish to be geographically adaptable. When geographic boundary for examining heterogeneity in relationship isn't clearly defined as within the aforementioned cases, one can use the geographically weighted regression (GWR) method to permit regression coefficients to vary across space and detect whether and the way the consequences are more significant in some areas than others. in additional recent versions of GWR, the semi-parametric geographically weighted regression (SGWR) model detects whether the effect of an experimental variable is global and thus a spatially homogenous determinant, or local and thus a spatially heterogeneous determinant. Using the SGWR model to elucidate the extent of energy poverty in Netherland, identify two global.

References

1. Cooper MM. (1996). The Dartmouth Atlas of Health Care: what is it telling us? Health Syst Rev. 29:44–45.
2. Boulos MN. (2004) Towards evidence-based, GIS-driven national spatial health information infrastructure and surveillance services in the United Kingdom. Int J Health Geogr.3:1.
3. Joyce K. (2009) "To me it's just another tool to help understand the evidence": public health decision-makers' perceptions of the value of geographical information systems (GIS) Health Place. 15:801–810.
4. Clarke KC, McLafferty SL, Tempalski BJ. (1996) On epidemiology and geographic information systems: a review and discussion of future directions. Emerg Infect Dis. 2:85–92.
5. Clark RA, Driscoll A. (2009) Access and quality of heart failure management programs in Australia. Aust Crit Care.22:111–116.