Urban Water Consumption Patterns in an Adult Population in Wuxi, China: A Regression Tree Analysis
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Area and Sample
2.2. Data Collection
2.3. Variables
2.4. Statistical Analyses
2.4.1. Descriptive Analysis
2.4.2. Univariate and Multivariate Analysis
2.4.3. CART Analysis
2.4.4. Comparisons of CART and Regression Models
3. Results
3.1. General Characteristics of the Participants
3.2. The Total Water, Direct Water and Indirect Water Consumptions of the Participants
3.3. Impacts of Demographic Variables on the Water Consumption According to Multivariate Analysis
3.4. Impacts of Age, BMI, and Location on Water Consumption According to CART Analysis
3.5. Comparisons of the Results of Linear Regression and CART Analysis
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Acknowledgments
Conflicts of Interest
References
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Variables | Summer | Winter | Total | |||
---|---|---|---|---|---|---|
n | % | n | % | n | % | |
Total | 596 | 100 | 592 | 100 | 1188 | 100 |
Gender | ||||||
Man | 288 | 48.3 | 285 | 48.1 | 573 | 48.2 |
Woman | 308 | 51.7 | 307 | 51.9 | 615 | 51.8 |
Age (years) | ||||||
18–34 | 198 | 33.2 | 198 | 33.4 | 396 | 33.3 |
35–54 | 193 | 32.4 | 192 | 32.4 | 385 | 32.4 |
≥55 | 205 | 34.4 | 202 | 34.1 | 407 | 34.3 |
Location (Community) | ||||||
A | 198 | 33.2 | 202 | 34.1 | 400 | 33.7 |
B | 200 | 33.6 | 197 | 33.3 | 397 | 33.4 |
C | 198 | 33.2 | 193 | 32.6 | 391 | 32.9 |
Ethnicity | ||||||
Han | 590 | 99.0 | 588 | 99.3 | 1178 | 99.2 |
Others | 6 | 1.0 | 4 | 0.7 | 10 | 0.8 |
labor worker | ||||||
Yes | 76 | 12.8 | 73 | 12.3 | 149 | 12.5 |
No | 520 | 87.2 | 519 | 87.7 | 1039 | 87.5 |
BMI (kg/m2) | ||||||
<18.5 | 35 | 5.9 | 20 | 3.4 | 55 | 4.6 |
18.5–23.9 | 347 | 58.2 | 377 | 63.7 | 724 | 60.9 |
24–27.9 | 172 | 28.9 | 166 | 28.0 | 338 | 28.5 |
≥28 | 42 | 7.0 | 29 | 4.9 | 71 | 6.0 |
Variables | n | Water Total (mL/day) | Direct Water (mL/day) | Indirect Water (mL/day) | |||
---|---|---|---|---|---|---|---|
Median | P25–P75 | Median | P25–P75 | Median | P25–P75 | ||
Summer | 596 | 1525 | 1101–1970 | 1000 | 700–1400 | 471 | 343–612 |
Gender | |||||||
Man | 288 | 1646 | 1203–2203 | 1100 | 750–1550 | 515 | 364–669 |
Woman | 308 | 1442 | 1038–1825 | 960 | 600–1300 | 442 | 337–586 |
p < 0.001 | p < 0.001 | p = 0.001 | |||||
Age (years) | |||||||
18–34 | 198 | 1323 | 978–1886 | 865 | 600–1300 | 424b | 319–563 |
35–54 | 193 | 1511a | 1136–2006 | 1075a | 700–1400 | 463b | 346–592 |
≥55 | 205 | 1638a | 1237–2108 | 1100a | 750–1500 | 542 | 394–689 |
p < 0.001 | p = 0.001 | p < 0.001 | |||||
Location (Community) | |||||||
A | 198 | 1531 | 1083–1902 | 950 | 673–1300 | 476 | 344–628 |
B | 200 | 1640 | 1124–2107 | 1100 | 700–1500 | 504 | 359–621 |
C | 198 | 1442 | 1097–1928 | 1000 | 650–1400 | 444 | 338–587 |
p = 0.078 | p = 0.048 | p = 0.179 | |||||
BMI (kg/m2) | |||||||
<18.5 | 35 | 1074 | 840–1548 | 730 | 400–1100 | 381 | 284–471 |
18.5–23.9 | 347 | 1489c | 1099–1915 | 1000c | 680–1350 | 471c | 344–620 |
24–27.9 | 172 | 1638c | 1144–2190 | 1100c | 700–1500 | 507c | 344–612 |
≥28 | 42 | 1843c | 1268–2371 | 1225c | 750–1862 | 493 | 389–716 |
p < 0.001 | p < 0.001 | p = 0.028 | |||||
Winter | 592 | 1217 | 917–1555 | 850 | 600–1200 | 321 | 215–464 |
Gender | |||||||
Man | 285 | 1321 | 1050–1644 | 1000 | 668–1300 | 364 | 220–474 |
Woman | 307 | 1119 | 808–1458 | 800 | 550–1050 | 313 | 209–432 |
p < 0.001 | p < 0.001 | p = 0.004 | |||||
Age (years) | |||||||
18–34 | 198 | 1116 | 773–1410 | 800 | 500–1100 | 275b | 211–413 |
35–54 | 192 | 1231a | 920–1617 | 915a | 650–1250 | 315b | 214–431 |
≥55 | 202 | 1315a | 1058–1613 | 900a | 650–1200 | 410 | 220–514 |
p < 0.001 | p = 0.003 | p < 0.001 | |||||
Location (Community) | |||||||
A | 202 | 1276 | 1014–1567 | 900 | 700–1100 | 342 | 216–468 |
B | 197 | 1163 | 887–1516 | 800 | 550–1200 | 304 | 211–472 |
C | 193 | 1172 | 861–1565 | 850 | 550–1200 | 325 | 217–455 |
p = 0.085 | p = 0.157 | p = 0.767 | |||||
BMI (kg/m2) | |||||||
<18.5 | 20 | 1009 | 821–1450 | 800 | 425–850 | 270 | 210–463 |
18.5–23.9 | 377 | 1208d | 906–1516 | 850 | 600–1200 | 316d | 209–455 |
24–27.9 | 166 | 1320 | 1017–1680 | 900 | 700–1250 | 365 | 224–511 |
≥28 | 29 | 1016d | 767–1414 | 800 | 425–975 | 274 | 214–418 |
p = 0.003 | p = 0.021 | p = 0.025 |
Variables | Coefficient | p-Value | 95% (CI) |
---|---|---|---|
Summer (n = 596) | |||
Gender | |||
Woman | Referent | ||
Man | 0.186 | <0.001 | 0.099, 0.273 |
BMI | 0.019 | 0.008 | 0.005, 0.034 |
Location (Community) | |||
A | Referent | ||
B | −0.113 | 0.034 | −0.217, −0.008 |
C | −0.152 | 0.004 | −0.255, −0.049 |
Constant | 6.678 | <0.001 | 6.343, 7.013 |
Winter (n = 592) | |||
Gender | |||
Woman | Referent | ||
Man | 0.128 | 0.001 | 0.051, 0.204 |
Age | 0.004 | <0.001 | 0.002, 0.006 |
Location (Community) | |||
A | Referent | ||
B | −0.024 | 0.621 | −0.118, 0.070 |
C | 0.010 | 0.825 | −0.083, 0.104 |
Constant | 6.679 | <0.001 | 6.541, 6.844 |
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Zheng, H.; Zhou, W.; Zhang, L.; Li, X.; Cheng, J.; Ding, Z.; Xu, Y.; Hu, W. Urban Water Consumption Patterns in an Adult Population in Wuxi, China: A Regression Tree Analysis. Int. J. Environ. Res. Public Health 2020, 17, 2983. https://doi.org/10.3390/ijerph17092983
Zheng H, Zhou W, Zhang L, Li X, Cheng J, Ding Z, Xu Y, Hu W. Urban Water Consumption Patterns in an Adult Population in Wuxi, China: A Regression Tree Analysis. International Journal of Environmental Research and Public Health. 2020; 17(9):2983. https://doi.org/10.3390/ijerph17092983
Chicago/Turabian StyleZheng, Hao, Weijie Zhou, Lan Zhang, Xiaobo Li, Jian Cheng, Zhen Ding, Yan Xu, and Wenbiao Hu. 2020. "Urban Water Consumption Patterns in an Adult Population in Wuxi, China: A Regression Tree Analysis" International Journal of Environmental Research and Public Health 17, no. 9: 2983. https://doi.org/10.3390/ijerph17092983