Table 3.
Study characteristics of the 34 included papers [97].
| Ref | Country (year) | Neurodege-Generative Disease | Environmental Factor | Geographic Factor | Study Design | Study Limitations | Statistical Methods | Outcome |
|---|---|---|---|---|---|---|---|---|
| [97] | Canada (2012) | Multiple sclerosis | Sun exposure | GIS, Remote Sensing | Methodological | Exposure assessment | None | None |
| [98] | Israel (1971) | Multiple sclerosis | Sun exposure, Temperature, Precipitation, Humidity | Residence | Review | None given by the authors | None | None |
| [99] | Bulgaria (1987) | Multiple sclerosis | Sun exposure, Temperature, Precipitation | Administrative division, Latitude | Cross-sectional | Unassessed patients | Correlation, Chi-squared, Linear regression | Correlation, Coefficients |
| [100] | Australia (2001) | Multiple sclerosis | Sun exposure, Temperature, Precipitation | Administrative division, Latitude, Remote Sensing | Ecological | Confounding, Exposure assessment | Correlation, Poisson regression | Prevalence, Correlation |
| [101] | Canada (2011) | Multiple sclerosis | Sun exposure | Latitude, Longitude, Remote Sensing | Cross-sectional | None given by the authors | Correlation, Linear regression | Correlation |
| [102] | England (2011) | Multiple sclerosis | Sun exposure | GIS, Remote Sensing | Cross-sectional | Confounding, Sampling, Statistics | Correlation, Linear regression | Correlation, Coefficients |
| [103] | USA (2017) | Multiple sclerosis | Sun exposure, Temperature | Administrative division, GIS, Remote Sensing | Cross-sectional | Confounding, Statistics | Correlation, Linear regression | Correlation, Coefficients |
| [104] | USA (2018) | Multiple sclerosis | Sun exposure | Residence, Remote Sensing | Cohort | Confounding, Exposure assessment, Interpolation, Recall bias, Migration, Survival bias, Time related | Cox regression | Relative risk, Hazard ratio |
| [105] | USA (1983) | Multiple sclerosis | Sun exposure, Temperature, Precipitation, Humidity | Latitude | Case-control | None given by the authors | Logistic regression | Relative risk |
| [106] | Italy (2016) | Multiple sclerosis | Sun exposure | Administrative division, GIS | Cross-sectional | Confounding, Ecological bias, Time related | Correlation, Linear regression | Correlation, Odds ratio |
| [107] | Canada (2018) | Multiple sclerosis | Sun exposure | Residence, Remote Sensing | Cohort | Confounding, Exposure assessment, Time related | Linear regression | Coefficients |
| [108] | Norway (2010) | Multiple sclerosis | Sun exposure, Temperature, Precipitation | Administrative division | Cross-sectional | Migration, Statistics | ANOVA, Poisson regression | Prevalence |
| [109] | Italy (2018) | Multiple sclerosis | PM2.5 | Residence, Remote Sensing | Cross-sectional | Conflict of interests, Confounding, Study design | Correlation, Chi-squared | Correlation, Coefficients |
| [110] | USA (2008) | Multiple sclerosis | PM10, PM2.5, NOX, SO2, CO | Administrative division | Cross-sectional | None given by the authors | Correlation, T-test, Linear regression | Correlation, Coefficients |
| [111] | Italy (2005) | Multiple sclerosis | SO2 | Administrative division, Latitude | Cross-sectional | Exposure assessment, Interpolation | Correlation, Linear regression | Correlation, Coefficients |
| [112] | Iran (2014) | Multiple sclerosis | PM10, NOX, SO2 | Clustering, GIS | Cross-sectional | Confounding, Statistics, Study design | Correlation, Clustering | Correlation, Coefficients |
| [113] | Iran (2018) | Multiple sclerosis | Index | Administrative division, GIS, Residence | Cross-sectional | Exposure assessment, Statistics | Correlation, Logistic regression | Odds ratio, Coefficients |
| [114] | Norway (1997) | Multiple sclerosis | Mg | Administrative division | Methodological | Confounding | Correlation | None |
| [115] | England (2016) | Multiple sclerosis | Rn | Residence | Ecological | Sampling, Statistics, Unassessed patients | Correlation, Chi-squared, Linear regression | Correlation, Coefficients |
| [116] | USA (2017) | Paediatric Multiple sclerosis | Index | GIS, Residence | Case-control | Exposure assessment, Statistics, Time related, Unassessed patients | Logistic regression | Odds ratio, Coefficients |
| [117] | USA (2018) | Paediatric Multiple sclerosis | PM10, PM2.5, NOX, SO2, CO, O3, Pb | Administrative division, GIS, Residence | Case-control | Exposure assessment, Referral bias, Time related | T-test, Logistic regression | Odds ratio |
| [118] | USA (2010) | Parkinson’s disease | Cu, Pb, Mg | Administrative division | Ecological | Confounding, Exposure assessment, Statistics | Logistic regression, Sensitivity analysis | Relative risk, Odds ratio |
| [119] | Spain (2016) | Parkinson’s disease | Pb | Administrative division, GIS | Ecological | Exposure assessment, Sampling, Unassessed patients | Correlation, T-test | Correlation, Coefficients |
| [120] | Canada (2007) | Parkinson’s disease | NOX, Mn | Residence, Remote Sensing, Spatial interpolation | Case-control | Confounding, Exposure assessment, Interpolation, Study design, Time related | Correlation, Linear regression, Logistic regression, Cox regression, Sensitivity analysis | Prevalence, Correlation, Odds ratio |
| [121] | USA (2016) | Parkinson’s disease | PM10, PM2.5, NOX | GIS, Residence | Case-control | Exposure assessment, Recall bias, Statistics, Time related | Correlation, Logistic regression, Sensitivity analysis | Correlation, Odds ratio |
| [122] | Australia (2020) | Parkinson’s disease | PM2.5, NOX | Residence, Remote Sensing | Cross-sectional | Recall bias, Referral bias, Sampling | Logistic regression, Sensitivity analysis | Odds ratio |
| [123] | Taiwan (2016) | Parkinson’s disease | NOX, CO | GIS, Residence | Case-control | Confounding, Sampling, Statistics | Correlation, Chi-squared, Logistic regression, Sensitivity analysis | Correlation, Odds ratio |
| [124] | France (2017) | Parkinson’s disease | Sun exposure, PM2.5 | Administrative division, Remote Sensing | Ecological | Ecological bias, Exposure assessment, Migration | Correlation, Poisson regression, Sensitivity analysis | Correlation, Relative risk |
| [125] | USA (2019) | Dementia | Temperature | Administrative division, Residence, Remote Sensing | Cohort | Confounding, Exposure assessment, Statistics | Correlation, Cox regression, Sensitivity analysis | Correlation, Hazard ratio |
| [126] | Taiwan (2019) | Dementia | PM10, NOX, SO2, CO, O3 | Clustering | Case-control | Confounding, Exposure assessment, Statistics, Unassessed patients | Correlation, Logistic regression, Sensitivity analysis | Odds ratio |
| [127] | Canada (2017) | Dementia | PM2.5, NOX, O3 | GIS, Residence, Remote Sensing | Cohort | Confounding, Exposure assessment, Time related, Unassessed patients | Cox regression, Sensitivity analysis | Hazard ratio |
| [128] | Spain (2018) | Amyotrophic lateral sclerosis | PM10, PM25, NOX, SO2, CO, O3, Cu, Pb, As, Ni, Cd, C6H6, H2S, C6OH12 | GIS | Case-control | Ecological bias, Exposure assessment, Sampling, Statistics | T-test, Chi-squared, Linear regression, Sensitivity analysis | Prevalence, Odds ratio |
| [129] | Taiwan (2013) | Amyotrophic lateral sclerosis | Sun exposure, Temperature, Precipitation, Humidity, Pressure | Administrative division | Case-control | Exposure assessment, Migration, Sampling, Time related | Correlation, Spatial autoregressive model, Clustering | Correlation, Coefficients |
| [130] | Spain (2016) | Motor neuron disease | Pb | Administrative division, GIS | Ecological | None given by the authors | Correlation, T-test, ANOVA | Correlation, Coefficients |