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The association between short-term exposure to ambient air pollution and blood pressure has been inconsistent, as reported in the literature.
This study aimed to investigate the relationship between short-term ambient air pollution exposure and patient-level home blood pressure (HBP).
Patients with chronic cardiovascular diseases from a telehealth care program at a university-affiliated hospital were enrolled as the study population. HBP was measured by patients or their caregivers. Hourly meteorological data (including temperature, relative humidity, wind speed, and rainfall) and ambient air pollution monitoring data (including CO, NO2, particulate matter with a diameter of <10 µm, particulate matter with a diameter of <2.5 µm, and SO2) during the same time period were obtained from the Central Weather Bureau and the Environmental Protection Administration in Taiwan, respectively. A stepwise multivariate repeated generalized estimating equation model was used to assess the significant factors for predicting systolic and diastolic blood pressure (SBP and DBP).
A total of 253 patients and 110,715 HBP measurements were evaluated in this study. On multivariate analysis, demographic, clinical, meteorological factors, and air pollutants significantly affected the HBP (both SBP and DBP). All 5 air pollutants evaluated in this study showed a significant, nonlinear association with both home SBP and DBP. Compared with demographic and clinical factors, environmental factors (meteorological factors and air pollutants) played a minor yet significant role in the regulation of HBP.
Short-term exposure to ambient air pollution significantly affects HBP in patients with chronic cardiovascular disease.
Air pollution is a great hazard to public health [
We retrospectively enrolled patients with chronic cardiovascular diseases (CVDs) who participated in telehealth care at National Taiwan University Hospital (NTUH), Taipei, Taiwan, between January 2009 and December 2013 as the study population. We excluded patients who did not reside in Taipei City during the study period. Informed consent was obtained from all participants. The study was approved by the institutional review board of NTUH. Chronic CVDs included coronary artery disease, prior myocardial infarction, heart failure, peripheral artery disease, prior stroke, and hypertension.
In this study, we used a fourth-generation telehealth program developed by Anker et al [
Demographic, clinical (diagnosis of specific diseases), and medication data were obtained from the electronic database of NTUH. All biometric data were measured at home and automatically transmitted to the NTUH server. Biometric data were meant to be measured at least twice daily, ideally after waking up and before sleeping, but each patient or caregiver could have his/her own habit to select a time point and interval within a day to measure biometric parameters. We used the AViTA BPM65ZB sphygmomanometer (AViTA Corp), which is an electronic digital upper arm BP monitor. Hourly meteorological data (including temperature, relative humidity, and wind speed) of Taipei City during the study period were obtained from the Central Weather Bureau, Taiwan. Hourly ambient air pollution monitoring data (including CO, NO2, PM10, PM2.5, and SO2) during the same study period were obtained from the Environmental Protection Administration, Taiwan.
Statistical analysis was performed using the R software (version 3.4.2, The R Foundation for Statistical Computing). In statistical testing, a 2-sided
Since the use of antihypertensive agents, values of meteorological factors, and concentrations of air pollutants varied over time, we defined and included the following three groups of time-dependent covariates in our linear regression analyses:
Antihypertensive agents: among the 6 most common classes of antihypertensive drugs, we considered the classes of antihypertensive medications and the number of classes of antihypertensive medications used on the day of BP measurement.
Meteorological factors: these included hourly averaged outdoor temperature, relative humidity, and wind speed within the hour of BP measurement.
Air pollutants: we determined hourly inverse-distance weighted mean concentrations of 5 air pollutants (CO, NO2, PM10, PM2.5, and SO2) and the amount of rainfall within the hour of BP measurement, where the distances were calculated from each patient’s home location to the 6 air quality monitoring stations in Taipei City, based on the corresponding latitudes and longitudes. Instant and cumulated air pollutant concentrations at hours 0, 3, 6, 12, and 18 and days 1, 2, 3, 4, 5, 6, and 7 were included in the multivariate analysis to evaluate the possible lag effect of each pollutant.
Simple and multiple generalized additive models (GAMs) were fitted to assess the nonlinear effects of continuous covariates and identify appropriate cut-off points for discretizing continuous covariates, if necessary, during stepwise variable selection. Further details on statistical analysis are provided in
A total of 253 patients with CVD who participated in the NTUH Telehealth Care Program from January 2009 to December 2013 were enrolled in this study. A total of 110,715 HBP measurements were carried out accordingly for these patients. The details of this patient population, including the per-patient and per-measurement demographics and clinical characteristics, have already been reported previously [
Data distribution of air pollutants from among 110,715 observations obtained by 253 patients included in this study.
Air pollutant | Mean (SD) | Minimum | Maximum | Median |
NO2 (ppb) | 23.67 (6.80) | 4.43 | 63.01 | 23.19 |
PM10a (μg/m3) | 46.83 (21.03) | 11.64 | 842.87 | 42.60 |
PM2.5b (μg/m3) | 27.96 (10.77) | 8.16 | 140.39 | 26.07 |
CO (ppm) | 0.74 (0.25) | 0.14 | 2.74 | 0.70 |
SO2 (ppb) | 3.05 (1.19) | 0.58 | 14.85 | 2.79 |
aPM10: particulate matter with a diameter of <10 µm.
bPM2.5: particulate matter with a diameter of <2.5 µm.
Multivariate analysis was conducted by fitting multiple linear regression models to estimate the adjusted effects of demographic, clinical, and meteorological factors and air pollutants on home SBP and DBP measurements. The use of antihypertensive agents, values of meteorological factors, and concentrations of air pollutants were all defined and computed as time-dependent covariates. Multivariate analysis of the predictors for SBP and DBP on fitting 1 multiple linear regression model with the stepwise variable selection procedure is shown in
The multiple linear regression model for SBP (n=110,715;
Multivariate analysis of the predictors for systolic blood pressure by fitting 1 multiple linear regression model with stepwise variable selection: demographic and clinical characteristics.
Covariate | Parameter estimate | SE | Pr>| |
|
Intercept | 125.0560 | 0.8216 | 152.2027 | <.001 |
Male | –0.5184 | 0.1133 | –4.5771 | <.001 |
AFa | 0.5663 | 0.1370 | 4.1323 | <.001 |
Coronary artery disease without myocardial infarction | 3.0135 | 0.1086 | 27.7503 | <.001 |
Coronary artery disease with myocardial infarction | –0.5264 | 0.1708 | –3.0820 | .002 |
Cancer | 3.0896 | 0.1402 | 22.0379 | <.001 |
Chronic heart failure | –4.0580 | 0.1228 | –33.0523 | <.001 |
CVAb | 1.1025 | 0.1336 | 8.2507 | <.001 |
PAODc | –1.7202 | 0.1855 | –9.2744 | <.001 |
ARBd × ABe | 4.1642 | 0.4037 | 10.3161 | <.001 |
ARB × BBf | 1.8479 | 0.2601 | 7.1044 | <.001 |
ARB × CCBg | –3.2486 | 0.2218 | –14.6451 | <.001 |
BB | –0.7201 | 0.1743 | –4.1310 | <.001 |
CCB × AB | –8.0115 | 0.3885 | –20.6223 | <.001 |
CCB × ACEIh | –8.3039 | 1.2350 | –6.7237 | <.001 |
CCB × BB | –3.9889 | 0.2950 | –13.5204 | <.001 |
CCB × Diuretics | 1.0011 | 0.2187 | 4.5766 | <.001 |
Diuretics × AB | 7.1806 | 0.2805 | 25.6013 | <.001 |
Diuretics × ACEI | 1.5441 | 0.4555 | 3.3902 | .001 |
Diuretics × BB | 2.0257 | 0.2549 | 7.9458 | <.001 |
aAF: atrial fibrillation.
bCVA: cardiovascular accident.
cPAOD: peripheral arterial occlusion disease.
dARB: angiotensin receptor blocker.
eAB: alpha blocker.
fBB: beta blocker.
gCCB: calcium channel blocker.
hACEI: angiotensin-converting enzyme inhibitor.
Multivariate analysis of the predictors for systolic blood pressure by fitting 1 multiple linear regression model with stepwise variable selection: meteorological factors.
Covariate | Parameter estimate | SE | Pr > | |
|
Temperature | –0.6352 | 0.0127 | –49.9892 | <.001 |
DMa × temperature | 0.1882 | 0.0045 | 41.4358 | <.001 |
HTNb × temperature | 0.2101 | 0.0047 | 44.5223 | <.001 |
ARBc × temperature | 0.1528 | 0.0057 | 26.6504 | <.001 |
CCBd × temperature | 0.0520 | 0.0078 | 6.6818 | <.001 |
Diuretics × temperature | –0.0217 | 0.0059 | –3.6496 | <.001 |
0.557 < Wind speed 12 hours ago ≤ 3.73 | 0.3100 | 0.1008 | 3.0752 | .002 |
1.976 < Wind speed on day 0 ≤ 4.43 | 0.5216 | 0.1099 | 4.7471 | <.001 |
1.983 < Wind speed 1 day ago ≤ 3.895 | 0.2267 | 0.1044 | 2.1707 | .03 |
1.793 < Wind speed 2 days ago ≤ 3.634 | 0.3138 | 0.0990 | 3.1702 | .002 |
1.587 < Wind speed 4 days ago ≤ 3.923 | 0.2128 | 0.0995 | 2.1380 | .03 |
1.855 < Wind speed 6 days ago ≤ 3.575 | 0.3155 | 0.0979 | 3.2221 | .001 |
Relative humidity ≤ 65.774 or > 84.596 | 0.5365 | 0.1000 | 5.3641 | <.001 |
Relative humidity 6 hours ago ≤ 72.967 or > 92.905 | 0.4753 | 0.1096 | 4.3364 | <.001 |
Relative humidity 12 hours ago ≤ 56.324 or > 78.989 | 0.3813 | 0.1178 | 3.2365 | .001 |
Relative humidity 24 hours ago > 76.11 | 0.4278 | 0.1209 | 3.5391 | <.001 |
Relative humidity 2 days ago ≤ 67.752 or > 82.532 | 0.3446 | 0.1003 | 3.4362 | .001 |
Relative humidity 4 days ago ≤ 65.366 or > 82.318 | 0.4172 | 0.1044 | 3.9960 | <.001 |
Relative humidity 6 days ago ≤ 58.849 or > 81.108 | 0.5261 | 0.1331 | 3.9527 | <.001 |
Log rainfall | –0.2074 | 0.0593 | –3.4984 | .001 |
Log cumulated rainfall in the past 4 days | –0.2981 | 0.0684 | –4.3564 | <.001 |
Log rainfall 6 days ago < –0.106 | 0.6989 | 0.1652 | 4.2296 | <.001 |
aDM: diabetes mellitus.
bHTN: hypertension.
cARB: angiotensin receptor blocker.
dCCB: calcium channel blocker.
Multivariate analysis of the predictors for systolic blood pressure by fitting 1 multiple linear regression model with stepwise variable selection: air pollutants.
Covariate | Parameter estimate | SE | Pr>| |
|
Log CO concentration 3 hours ago | –0.6620 | 0.1186 | –5.5800 | <.001 |
Log cumulated CO concentration in the past 5 days | 1.1985 | 0.2965 | 4.0422 | <.001 |
Log NO2 concentration | 0.4520 | 0.1493 | 3.0276 | .003 |
2.571 < Log NO2 concentration 3 hours ago ≤ 3.654 | 0.3311 | 0.1113 | 2.9752 | .003 |
Log NO2 concentration 6 hours ago | 0.7517 | 0.1154 | 6.5136 | <.001 |
2.188 < Log NO2 concentration 18 hours ago ≤ 3.185 | 0.4333 | 0.0986 | 4.3971 | <.001 |
3.062 < Log NO2 concentration 3 days ago ≤ 3.497 | 0.4392 | 0.1026 | 4.2810 | <.001 |
3.063 < Log NO2 concentration 4 days ago ≤ 3.483 | 0.3689 | 0.1027 | 3.5929 | <.001 |
2.567 < Log NO2 concentration 5 days ago ≤ 3.507 | 0.4131 | 0.1435 | 2.8781 | .004 |
3.059 < Log NO2 concentration 6 days ago ≤ 3.494 | 0.4837 | 0.1037 | 4.6650 | <.001 |
3.043 < Log NO2 concentration 7 days ago ≤ 3.468 | 0.3438 | 0.1004 | 3.4256 | .001 |
Log PM2.5a concentration 3 hours ago < 2.284 | 1.1068 | 0.3245 | 3.4105 | .001 |
Log PM2.5 concentration 4 days ago ≤ 3.243 or > 4.378 | 0.5000 | 0.1031 | 4.8481 | <.001 |
Log PM2.5 concentration 6 days ago ≤ 3.034 or > 3.826 | 0.3055 | 0.1076 | 2.8383 | .005 |
2.848 < Log PM10b concentration 18 hours ago ≤ 3.828 | 0.4819 | 0.1032 | 4.6690 | <.001 |
Log SO2 concentration 3 hours ago < –0.271 | 3.5314 | 1.0028 | 3.5216 | <.001 |
Log SO2 concentration 24 hours ago > 1.561 | 0.4552 | 0.1372 | 3.3164 | .001 |
Log SO2 concentration 5 days ago > 1.316 | 0.2970 | 0.1187 | 2.5025 | .01 |
aPM2.5: particulate matter with a diameter of <2.5 µm.
bPM10: particulate matter with a diameter of <10 µm.
Multivariate analysis of the predictors for diastolic blood pressure by fitting 1 multiple linear regression model with stepwise variable selection: demographic and clinical characteristics.
Covariate | Parameter estimate | SE | Pr>| |
|
Intercept | 65.7236 | 0.7686 | 85.5088 | <.001 |
Male | –1.2416 | 0.0793 | –15.6608 | <.001 |
Age < 72.424 years | 8.5878 | 0.0783 | 109.6523 | <.001 |
AFa | –0.8389 | 0.0916 | –9.1609 | <.001 |
Coronary artery disease with myocardial infarction | –0.3166 | 0.1088 | –2.9105 | .004 |
Cancer | 0.4197 | 0.0942 | 4.4572 | <.001 |
CHFb | –0.8912 | 0.0806 | –11.0540 | <.001 |
CVAc | 3.2332 | 0.0896 | 36.0651 | <.001 |
PAODd | 1.8971 | 0.1279 | 14.8373 | <.001 |
ARBe × ABf | 6.8305 | 0.2722 | 25.0906 | <.001 |
ARB × BBg | 5.6929 | 0.1636 | 34.7953 | <.001 |
ARB × CCBh | –3.3888 | 0.1369 | –24.7458 | <.001 |
ARB × diuretics | –3.4067 | 0.1401 | –24.3191 | <.001 |
CCB × ACEIi | –10.7720 | 0.8297 | –12.9827 | <.001 |
CCB × BB | –0.4592 | 0.1868 | –2.4583 | .01 |
CCB × diuretics | 2.2884 | 0.1298 | 17.6274 | <.001 |
Diuretics × AB | –3.4526 | 0.2494 | –13.8457 | <.001 |
Diuretics × ACEI | 2.5617 | 0.3943 | 6.4973 | <.001 |
Diuretics × BB | –1.7401 | 0.1616 | –10.7675 | <.001 |
aAF: atrial fibrillation.
bCHF: congestive heart failure.
cCVA: cerebrovascular accident.
dPAOD: peripheral occlusive arterial disease.
eARB: angiotensin receptor blocker.
fAB: alpha blocker.
gBB: beta blocker.
hCCB: calcium channel blocker.
iACEI: angiotensin converting enzyme inhibitor.
Multivariate analysis of the predictors for diastolic blood pressure by fitting 1 multiple linear regression model with stepwise variable selection: meteorological factors.
Covariate | Parameter estimate | SE | Pr>| |
|
Temperature | –0.1608 | 0.0130 | –12.3600 | <.001 |
Temperature 12 hours ago | –0.1058 | 0.0135 | –7.8265 | <.001 |
Temperature 18 hours ago ≤ 16.992 or > 28.649 | 0.5042 | 0.0714 | 7.0590 | <.001 |
Temperature 7 days ago | 0.0753 | 0.0124 | 6.0722 | <.001 |
DMa × temperature | 0.0694 | 0.0030 | 22.9718 | <.001 |
HTNb × temperature | 0.0857 | 0.0032 | 26.9498 | <.001 |
ABc × temperature | –0.1161 | 0.0083 | –13.9588 | <.001 |
ACEId × temperature | –0.0323 | 0.0096 | –3.3598 | .001 |
ARBe × temperature | 0.0623 | 0.0046 | 13.5546 | <.001 |
Diuretics × temperature | –0.0177 | 0.0045 | –3.9649 | <.001 |
Wind speed | 0.1122 | 0.0250 | 4.4919 | <.001 |
Wind speed 18 hours ago | 0.0612 | 0.0230 | 2.6618 | .008 |
Wind speed 3 days ago | 0.1358 | 0.0318 | 4.2695 | <.001 |
Wind speed 5 days ago > 3.734 | 0.3835 | 0.0892 | 4.3014 | <.001 |
Wind speed 7 days ago > 4.56 | 0.7900 | 0.1750 | 4.5145 | <.001 |
Relative humidity 6 hours ago ≤ 67.108 or > 91.436 | 0.3259 | 0.0719 | 4.5359 | <.001 |
Relative humidity 12 hours ago ≤ 57.1 or > 78.822 | 0.2567 | 0.0836 | 3.0700 | .002 |
Relative humidity 18 hours ≤ 34.851 or >77.249 | 0.2478 | 0.0983 | 2.5216 | .01 |
Relative humidity 24 hours ago ≤ 47.631 or >77.286 | 0.2813 | 0.0893 | 3.1501 | .002 |
Relative humidity 2 days ago ≤ 64.191 or > 81.286 | 0.2489 | 0.0728 | 3.4188 | .001 |
Relative humidity 4 days ago ≤ 64.291 or > 81.392 | 0.3521 | 0.0729 | 4.8281 | <.001 |
Relative humidity 5 days ago ≤ 54.611 or > 76.3 | 0.3089 | 0.0852 | 3.6268 | <.001 |
Relative humidity 6 days ago ≤ 54.383 or > 79.265 | 0.5044 | 0.0985 | 5.1202 | <.001 |
Relative humidity 7 days ago ≤ 58.455 or >76.46 | 0.5243 | 0.0856 | 6.1239 | <.001 |
Log rainfall > –2.252 | 0.3004 | 0.0824 | 3.6452 | <.001 |
Log rainfall 3 hours ago ≤ –0.372 or > 0.817 | 0.4580 | 0.1538 | 2.9783 | .003 |
–2.251 < Log rainfall 6 hours ago ≤ 0.379 | 0.3796 | 0.0856 | 4.4340 | <.001 |
–2.246 < Log rainfall 12 hours ago ≤ 0.177 | 0.3282 | 0.0874 | 3.7542 | <.001 |
–2.228 < Log rainfall 18 hours ago ≤ 3.202 | 0.1674 | 0.0846 | 1.9797 | .05 |
–2.165 < Log rainfall 2 days ago ≤ 0.145 | 0.4425 | 0.0681 | 6.4961 | <.001 |
–2.168 < Log rainfall 4 days ago ≤ 0.068 | 0.3491 | 0.0682 | 5.1162 | <.001 |
–2.153 < Log rainfall 5 days ago ≤ 0.026 | 0.3042 | 0.0691 | 4.4003 | <.001 |
–2.193 < Log rainfall 7 days ago ≤ –0.266 | 0.5065 | 0.0666 | 7.6091 | <.001 |
–2.044 < Log cumulated rainfall in the past 6 days ≤ –0.735 | 0.5517 | 0.0691 | 7.9805 | <.001 |
Log cumulated rainfall in the past 7 days | –0.5756 | 0.0538 | 10.7067 | <.001 |
aDM: diabetes mellitus.
bHTN: hypertension.
cAB: alpha blocker.
dACEI: angiotensin converting enzyme inhibitor.
Multivariate analysis of the predictors for diastolic blood pressure by fitting 1 multiple linear regression model with stepwise variable selection: air pollutants.
Covariate | Parameter estimate | SE | Pr>| |
|
Log CO concentration | 0.3966 | 0.0970 | 4.0907 | <.001 |
Log CO concentration 12 hours ago | –0.9318 | 0.0759 | –12.2815 | <.001 |
–0.41 < Log CO concentration 24 hours ago ≤ 0.527 | 0.4100 | 0.0713 | 5.7517 | <.001 |
Log CO concentration 2 days ago ≤ –0.426 or > 0.366 | 0.2789 | 0.0743 | 3.7546 | <.001 |
Log cumulated CO concentration in the past 5 days > 0.034 | 1.0558 | 0.2096 | 5.0366 | <.001 |
Log cumulated CO concentration in the past 7 days > 0.006 | 0.7773 | 0.2050 | 3.7921 | <.001 |
Log NO2 concentration 6 hours ago | 0.1645 | 0.0763 | 2.1566 | .03 |
2.226 < Log NO2 concentration 18 hours ago ≤ 3.209 | 0.3400 | 0.0689 | 4.9383 | <.001 |
Log cumulated NO2 cconcentration in the past 1 day > 3.163 | 0.1880 | 0.0887 | 2.1203 | .03 |
Log NO2 concentration 2 days ago < 2.695 | 0.6081 | 0.1352 | 4.4984 | <.001 |
3.1 < Log NO2 concentration 3 days ago ≤ 3.6 | 0.2480 | 0.0727 | 3.4093 | .007 |
Log NO2 concentration 7 days ago | –0.9739 | 0.1572 | –6.1965 | <.001 |
Log PM2.5a concentration 1 day ago ≥ 2.734 | 0.6872 | 0.1397 | 4.9188 | <.001 |
Log PM2.5 concentration 2 days ago > 3.547 | 0.2638 | 0.0871 | 3.0294 | .003 |
Log PM2.5 concentration 3 days ago ≥ 2.598 | 0.4660 | 0.1869 | 2.4936 | .01 |
Log PM2.5 concentration 6 days ago > 3.559 | 0.2502 | 0.0827 | 3.0256 | .003 |
Log PM10b concentration 1 day ago | –0.5895 | 0.1264 | –4.6622 | <.001 |
0.926 < Log SO2 concentration ≤ 2.325 | 0.2053 | 0.0768 | 2.6736 | .008 |
0.77 < Log SO2 concentration 3 hours ago ≤ 1.902 | 0.1994 | 0.0718 | 2.7766 | .006 |
0.73 < Log SO2 concentration 18 hours ago ≤ 1.819 | 0.2264 | 0.0683 | 3.3144 | .001 |
1.062 < Log SO2 concentration 24 hours ago ≤ 2.926 | 0.2025 | 0.0726 | 2.7908 | .005 |
0.772 < Log SO2 concentration 5 days ago ≤ 1.558 | 0.2316 | 0.0696 | 3.3270 | .001 |
0.946 < Log SO2 concentration 6 days ago ≤ 1.604 | 0.2500 | 0.0682 | 3.6660 | <.001 |
Log SO2 concentration 7 days ago | 0.5296 | 0.1162 | 4.5578 | <.001 |
aPM2.5: particulate matter with a diameter of <2.5 µm.
bPM10: particulate matter with a diameter of <10 µm.
Generalized additive model (GAM) plot showing the relationship between systolic blood pressure and CO exposure.
Generalized additive model (GAM) plot showing the relationship between systolic blood pressure and SO2 exposure.
Generalized additive model (GAM) plot showing the relationship between systolic blood pressure and NO2 exposure.
Generalized additive model (GAM) plot showing the relationship between systolic blood pressure and PM10 exposure. PM10: particulate matter with a diameter of <10 μm.
Generalized additive model (GAM) plot showing the relationship between systolic blood pressure and PM2.5 exposure. PM2.5: particulate matter with a diameter of <2.5 μm.
Human health is significantly correlated with genomic and environmental factors. Wild [
There are some notable findings in this study. First, at the patient level, ambient air pollution was significantly associated with HBP, both SBP and DBP, and the culprit pollutants included all of the 5 pollutants (PM10, PM2.5, SO2, NO2, and CO) that were included in this study. The association between HBP and ambient air pollution was surprisingly nonlinear, given that most previous studies used a linear model to evaluate the effect of ambient air pollution on BP and hypertension. Yang et al [
This study has a few limitations. First, this was a retrospective registry with a relatively small number of patients. Second, the study population comprised patients with chronic CVD with great adherence and health insights as they opted to participate in a telehealth care program. Thus, whether the study results can be extrapolated to other patient populations should be carefully considered. Third, individual ambient air pollutant concentrations were calculated using a spatial model that might not reflect true personal exposure. Jiang et al [
Short-term exposure to ambient air pollution significantly affects both home SBP and DBP in patients with chronic CVD, and the relationship between ambient air pollution and HBP is nonlinear.
More Statistical Details.
Additional figure and figure legends.
blood pressure
cardiovascular disease
diastolic blood pressure
generalized additive models
home blood pressure
National Taiwan University Hospital
particulate matter with a diameter of <10 μm
particulate matter with a diameter of <2.5 μm
systolic blood pressure
We thank Dr Fu-Chang Hu (Institute of Clinical Medicine, National Taiwan University) for statistical support. This study was funded by the National Taiwan University Hospital (106-A136).
None declared.