Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis
Abstract
:1. Introduction
2. Methods and Materials
2.1. Search Strategy and Study Criteria
2.2. Study Selection
2.3. Quality Assessment
2.4. Data Extraction and Publication Bias
2.5. Statistical Analysis
2.5.1. The First Stage
2.5.2. The Second Stage
3. Results
Authors and year of publication | Outcomes Investigated | Published journal | Location and Period of Data Obtained | Study Design | model | No. Events | Variables Controlled | Lags |
---|---|---|---|---|---|---|---|---|
Kyobutungi et al. [30], 2005 | stroke morbidity | Eur. J. Epidemiol. | Germany, 1998–2000 | case-crossover | conditional logistic regression model | 303 | not mention | both |
Wang et al. [31], 2014 | emergency room visits | PLoS ONE | China, 2000–2009 | time-series | DLNM | 6,962 | air pollution, pneumonia and influenza, holidays, DOW, trends | both |
Dawson et al. [32],2008 | stroke admissions | Acta Neurol. Scand. | Scotland, 1990–2006 | time-series | negative binomial regression | 6,389 | trends, season and DOW | null |
Chen et al. [33], 2013 | stroke mortality | Neurology | China, 1996–2002 | time-series | GAM and DLNM | 127,750 | risk factors, socio-demographic characteristics, air pollution and relative humidity | both |
Matsumoto et al. [34], 2010 | stroke morbidity | J. Epidemiol. | Japan, 1995–2005 | case-crossover | Multilevel logistic regression | 450 | age, obesity, smoking, total cholesterol, systolicblood pressure, diabetes, all the meteorologicalparameters | null |
Hori et al. [35], 2012 | emergency admission | Int. J. Environ. Health Res. | Japan, 2006–2010 | time-series | generalized linear Poisson regression model | 778 | DOW, holidays, influenza, air pollution, other meteorological factors | average |
Morabito et al. [36], 2011 | stroke admissions | Stroke | Italy, 1997–2007 | time-series | GLM | 112,870 | temporal variables, categorical factors | single day |
Hong et al.[37], 2003 | stroke admissions | Epidemiology | Korea, 1998–2000 | case-crossover | conditional logistic regression model | 545 | humidity and air pressure | single day |
Wang et al.[38], 2013 | hospital admissions for ischemic stroke | PLoS ONE | China, 1990–2009 | time-series | DLNM | 1,908 | season, trend, DOW and public holidays, season | both |
Atsumi et al. [39], 2013 | cardiovascular mortality | Circ. J. | Japan, 1993–2008 | time stratified case-crossover | conditional logistic regression model | 1,709 | relative humidity and air pollution. | both |
Mostofsky et al. [40], 2014 | stroke morbidity | Cerebrovasc. Dis. Extra | USA, 1999–2008 | Time stratified case-crossover | conditional logistic regression model | 1,763 | PM 2.5, ozone and relative humidity | both |
Breitner et al. [41], 2014 | cardiovascular mortality | Heart | Germany, 1990–2006 | time-series | DLNM | 187,943 | trend, season, DOW, influenza, relative humidity and barometric pressure | both |
Wang et al. [42], 2009 | stroke admissions | Int. J. Biometeorol. | Australia, 1996–2005 | time-series | GEE | 12,387 | humidity, PM10,NO2, O3 and SO2 | null |
Cevik et al. [43], 2014 | Emergency stroke admissions | Int. J. Biometeorol. | Turkey, 2009–2010 | time-series | GAM(generalized additive models) and DLNM | 373 | wind speed and air pressure | single day |
Basu et al.[44], 2012 | emergency room visits | Epidemiology | USA, 2005–2008 | time-series | conditional logistic regression model | 1,215,023 | air pollution | both |
Green et al. [45], 2010 | hospital admissions | Int. J. Public Health | USA, 1995–2005 | case-crossover | conditional logistic regression model | 91,806 | season,DOW,air pollution | both |
Zhang et al. [46], 2014 | cerebrovascular mortality | Environ. Health | China, 2004–2008 | time-series | DLNM(distributed lag nonlinear model) | 20,308 | season, trends,DOW, relative humidity, air pollution | average |
Shaposhnikov et al. [47], 2014 | hospitalizations | Int. J. Biometeorol. | Russia, 1992–2005 | time-series | generalized linear Poisson regression model | 1,096 | DOW, and geomagnetic storms | single day |
Lim et al. [48], 2013 | stroke mortality | Int. J. Biometeorol. | Korea, 1992–2007 | time-series | GAM GLM | 149,598 | humidity, air pressure, air pollution, DOW, season, and year | both |
Goggins et al. [49], 2012 | stroke admissions | Int. J. Biometeorol. | China, 1999–2006 | time-series | GAM | 130,962 | DOW and holiday, air pollution, other meteorological factors, influenza rates, season and trends | both |
3.1. Overall Analyses
Egger’s test, P | Total | Morbidity | Mortality | HS | IS |
---|---|---|---|---|---|
Hot effect | 0.196 | 1.000 | 0.536 | 0.221 | 0.803 |
Cold effect | 0.071 | 0.711 | 0.148 | 0.734 | 0.178 |
3.2. Sensitivity Analyses
Subtypes | <65 | ≥65 | Male | Female | Lag0 | Lag1 |
---|---|---|---|---|---|---|
Number of estimated areas | 7 | 7 | 6 | 6 | 6 | 3 |
Number of estimated articles | 4 | 4 | 3 | 3 | 4 | 3 |
Effect Size (95% CI) | 1.000 (1.000–1.001) | 1.008 (1.002–1.015) | 1.017 (1.003–1.030) | 1.019 (0.993–1.046) | 1.045 (1.007–1.082) | 1.010 (1.000–1.019) |
Heterogeneity, I2,% | 0 | 78.4 | 0 | 62.3 | 93.2 | 9.9 |
Publication Bias (Egger’s test, p) | 0.368 | 0.260 | 0.260 | 0.707 | 0.216 | 0.540 |
Model | Fixed | Random | Fixed | Random | Random | Fixed |
Age and Gender | <65 | ≥65 | Male | Female |
---|---|---|---|---|
Number of estimated areas | 6 | 6 | 5 | 5 |
Number of estimated articles | 3 | 3 | 3 | 3 |
Effect Size (95% CI) | 1.001 (1.000–1.002) | 1.005 (1.001–1.009) | 1.007 (1.002–1.011) | 1.009 (1.004–1.014) |
Heterogeneity, I2,% | 0 | 60.9 | 40.4 | 51.8 |
Publication Bias (Egger’s test, P) | 0.133 | 0.133 | 0.452 | 0.566 |
Model | Fixed | Random | Random | Random |
Subtypes | Lag0 | Lag1 | Lag2 | Lag3 | Lag4 | Lag02 |
---|---|---|---|---|---|---|
Number of estimated areas | 9 | 4 | 11 | 4 | 11 | 7 |
Number of estimated articles | 6 | 4 | 4 | 4 | 4 | 3 |
Effect Size (95% CI) | 0.999 (0.997–1.002) | 1.006 (0.996–1.016) | 1.003 (1.001–1.004) | 1.007 (1.002–1.012) | 1.002 (1.001–1.003) | 1.010 (1.000–1.021) |
Heterogeneity, I2,% | 41.4% | 27.6 | 50.9 | 0 | 40.8 | 38.3 |
Publication Bias (Egger’s test, P) | 0.002 | 0.308 | 0.349 | 0.734 | 0.814 | 0.764 |
Model | Random | Random | Random | Fixed | Random | Random |
4. Discussion
5. Conclusions
Acknowledgments
Author Contributions
Conflicts of Interest
Abbreviations
CNKI | China National Knowledge Infrastructure |
MACBE | major adverse cerebrovascular events |
CI | confidence intervals |
HS | hemorrhage stroke |
IS | ischemic stroke |
NE | note express |
GBD | global burden of diseases |
DOW | day of the week |
RR | relative risk |
OR | odds ratio |
EN | expected number |
AF | attribute fraction |
GLM | generalized linear model |
GAM | generalized additive models |
GEE | generalized estimating equation |
DLNM | distributed lag nonlinear model |
AT | apparent temperature |
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Lian, H.; Ruan, Y.; Liang, R.; Liu, X.; Fan, Z. Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis. Int. J. Environ. Res. Public Health 2015, 12, 9068-9088. https://doi.org/10.3390/ijerph120809068
Lian H, Ruan Y, Liang R, Liu X, Fan Z. Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis. International Journal of Environmental Research and Public Health. 2015; 12(8):9068-9088. https://doi.org/10.3390/ijerph120809068
Chicago/Turabian StyleLian, Hui, Yanping Ruan, Ruijuan Liang, Xiaole Liu, and Zhongjie Fan. 2015. "Short-Term Effect of Ambient Temperature and the Risk of Stroke: A Systematic Review and Meta-Analysis" International Journal of Environmental Research and Public Health 12, no. 8: 9068-9088. https://doi.org/10.3390/ijerph120809068