Int J Health Geogr - Small-scale health-related indicator acquisition using secondary data spatial interpolation.

Tópicos

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Resumo

CKGROUND: Due to the lack of small-scale neighbourhood-level health related indicators, the analysis of social and spatial determinants of health often encounter difficulties in assessing the interrelations of neighbourhood and health. Although secondary data sources are now becoming increasingly available, they usually cannot be directly utilized for analysis in other than the designed study due to sampling issues. This paper aims to develop data handling and spatial interpolation procedures to obtain small area level variables using the Canadian Community Health Surveys (CCHS) data so that meaningful small-scale neighbourhood level health-related indicators can be obtained for community health research and health geographical analysis.RESULTS: Through the analysis of spatial autocorrelation, cross validation comparison, and modeled effect comparison with census data, kriging is identified as the most appropriate spatial interpolation method for obtaining predicted values of CCHS variables at unknown locations. Based on the spatial structures of CCHS data, kriging parameters are suggested and potential small-area-level health-related indicators are derived. An empirical study is conducted to demonstrate the effective use of derived neighbourhood variables in spatial statistical modeling. Suggestions are also given on the accuracy, reliability and usage of the obtained small area level indicators, as well as further improvements of the interpolation procedures.CONCLUSIONS: CCHS variables are moderately spatially autocorrelated, making kriging a valid method for predicting values at unsampled locations. The derived variables are reliable but somewhat smoother, with smaller variations than the real values. As potential neighbourhood exposures in spatial statistical modeling, these variables are more suitable to be used for exploring potential associations than for testing the significance of these associations, especially for associations that are barely significant. Given the spatial dependency of current health-related risks, the developed procedures are expected to be useful for other similar health surveys to obtain small area level indicators.

Resumo Limpo

ckground due lack smallscal neighbourhoodlevel health relat indic analysi social spatial determin health often encount difficulti assess interrel neighbourhood health although secondari data sourc now becom increas avail usual direct util analysi design studi due sampl issu paper aim develop data handl spatial interpol procedur obtain small area level variabl use canadian communiti health survey cchs data meaning smallscal neighbourhood level healthrel indic can obtain communiti health research health geograph analysisresult analysi spatial autocorrel cross valid comparison model effect comparison census data krige identifi appropri spatial interpol method obtain predict valu cchs variabl unknown locat base spatial structur cchs data krige paramet suggest potenti smallarealevel healthrel indic deriv empir studi conduct demonstr effect use deriv neighbourhood variabl spatial statist model suggest also given accuraci reliabl usag obtain small area level indic well improv interpol proceduresconclus cchs variabl moder spatial autocorrel make krige valid method predict valu unsampl locat deriv variabl reliabl somewhat smoother smaller variat real valu potenti neighbourhood exposur spatial statist model variabl suitabl use explor potenti associ test signific associ especi associ bare signific given spatial depend current healthrel risk develop procedur expect use similar health survey obtain small area level indic

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