Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China
Abstract
:1. Introduction
2. Materials and Methods
2.1. Study Selection
2.2. Inclusion and Exclusion Criteria
2.3. Data Extraction and Quality Assessment
3. Results
3.1. Overview of Studies Included
3.2. The Effects of Urbanization-Related Influencing Factors on HFRS Epidemic
3.2.1. Population Growth and Socioeconomic Development
3.2.2. Land Use/Land Cover Change
- Artificial facilities construction
- Land use change
- Vegetation
- Livestock husbandry
3.2.3. Vaccination Program and Rodent Control
3.3. The Mechanism of Urbanization’s Influence on HFRS Epidemic
4. Discussion
5. Conclusions
Supplementary Materials
Author Contributions
Funding
Institutional Review Board Statement
Informed Consent Statement
Data Availability Statement
Conflicts of Interest
References
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Influencing Factors | Indicators | Area (Scale); Time Span | Model; Influence Effect | Other Factors Considered |
---|---|---|---|---|
Socioeconomic factors | Socioeconomic and population (+, −) | Shaanxi Province (county) [41]; 2005–2018 | Geographical temporal weighted regression model; R = 0.07–0.47 (2005), −0.005–0.15 (2011), −0.18–0.19 (2018) | Climate and meteorological factors |
Comprehensive indicator of urbanization level (+, −) | Xi’an City (the total area and county) [26]; 2005–2018 | Scatter plots; two characteristic phases, a trend of continuously increasing and then decreasing, the sixth year as the peak | Water body; rainfall; vegetation | |
Component representing GDP and the urbanization rate (−) | Chenzhou City (the total area) [42]; 2006–2010 | Cross-correlation analysis; R = −0.376, p < 0.01 | Rodents; NDVI; climate | |
Urbanization rate (+, −) | Hunan Province (total province and city level) [30]; 1963–2010 | Scatter plot and plotted fitting lines; biphasic inverted U-shaped relationship between HFRS and urbanization | Elevation | |
Economic | GDP (+) | Hunan Province (1 km grid) [43]; 2010–2019 | Binary logistic regression model; B = 0.810, p < 0.01 (2nd) | Soil; land use; population (3rd); altitude; NDVI (1st); precipitation |
GDP (−) | Chongqing City (total city) [33]; 1997–2008 | Poisson regression models; R = −0.0044, p < 0.001 | Temperature and rainfall; rodents | |
Shaanxi Province (county) [25]; 2005–2016 | Multivariate boosted regression; relative contribution 5.99%; negative relationship, with the highest correlation corresponding to low GDPs of 0–2500 Yuan | Meteorological factors; elevation; land use (1st); population density (3rd); pig density (2nd); cattle density; goat density; selenium | ||
Huyi District (total district) [32]; 1984–2016 | Generalized additive models; negative nonlinear correlation between GDP and HFRS incidence (F1.70,9.02 =2.917, p < 0.05) (1st) | Temperature | ||
Agricultural mechanization (−) | Qingdao City (total city) [44]; 1985–2015 | Pearson correlation; R = −0.383 in Jiaozhou City, p < 0.05; R = −0.386 in Jimo City, p < 0.05 | Rodents | |
Population | Population density (+) | Hubei Province (county) [45]; 2005–2015 | Correlation analysis; R = 0.372, p < 0.01 | Meteorological factors; rodent density; water area; height |
Hubei Province (county) [46]; 2005–2014 | Spearman’s rank correlation analysis; R = 0.397, p < 0.001 | Meteorological factors | ||
Hubei Province (county) [31]; 2011–2015 | Ordinary least square; R = 0.317, p < 0.01 | Farmland area; rainfall; water area; average humidity | ||
Hunan Province (1 km grid) [43]; 2010–2019 | Logistic regression model; B = 0.437, p < 0.01 (3rd) | Soil; land use; altitude; NDVI (1st); precipitation; GDP (2nd) | ||
Population density (+, −) | Shandong Province (county) [47]; 2010–2018 | Boosted regression tree model; contribution rate = 15.9% (1st), a trend of firstly increasing and then decreasing after the peak | Elevation (2nd); land use (grassland, 3rd); meteorological factors; GDP; NDVI | |
Shaanxi Province (county) [25]; 2005–2016√ | Multivariate boosted regression; relative contribution = 8.69% (3rd); a trend of firstly increasing and then decreasing after the peak | Meteorological factors; elevation; land use (1st); GDP; pig density (2nd); cattle density; goat density; selenium | ||
Immigrants (+) | Hunan Province (total province and city level) [30]; 1963–2010 | Scatter plot and plotted fitting lines; a positive correlation between HFRS incidence and number of immigrants | Elevation | |
Land use change—Construction | Bridge construction (+) | Guizhou Province (total area) [48]; 1983–1998 | Rat surveillance; HFRS virus antigen infection rate changed from being never detected (0%) to a high level (13.85%) | None |
Dam construction (−) | Three Gorges Reservoir Region (total area) [49]; 1997–2012 | Surveillance; annual HFRS incidence rate decreased by 85.74% after the dam impoundment; the indoor rodent density (4.38% to 2.29%, χ2 = 193.4, p < 0.05) and outdoor rodent density (4.41% to 2.66%, χ2 = 188.4, p < 0.05) decreased | Rodents | |
Economic zone construction (−) | Huangdao and Jiaonan of Qingdao City [50]; 1979–2014 | Comparative study (chi-square test); rodent capture rate in Jiaonan (4.00%) vs. Huangdao (0.51%); HFRS virus detection rate in Jiaonan (2.81%) vs. Huangdao (0.00%) (χ2 = 172.38, p < 0.05) | None | |
Land use type | Artificial area (building land, urban and rural construction land, highways, railways) (+/−/risk area) | Beijing City (total city) [51]; 1997–2006 | Poisson regression; built-up land, negative correlation (−0.82%/1%, as built-up land increased by 1%, the HFRS incidence rate decreased by 0.82%) | Elevation |
Shandong Province (county) [47]; 2010–2018 | Boosted regression model; rural settlement contribution = 9.25%, negative correlation | Population density (1st); elevation (2nd); meteorological factors; GDP; NDVI | ||
Shaanxi Province (county) [25]; 2005–2016√ | Multivariate boosted regression; relative contribution = 23.02% (1st); initially increased significantly and then plateaued in response to increase in percentage coverage of artificial area | Meteorological factors; elevation; GDP; land use (1st); population density (3rd); pig density (2nd); cattle density; goat density; selenium | ||
Wei River Basin of Shaanxi Province (5 km grid) [24]; 2005–2015√ | Boosted regression model; building land contribution = 49.10% (1st), positive correlation | Elevation (2nd); farmland | ||
Dongting Lake District (nearly 1 km) [52]; 2005–2010 | Ecological niche models; cumulative contribution rate of land use = 7.9%, construction land was the main risk land type associated with HFRS transmission between June and September | Elevation; meteorological factors; NDVI (1st); land use (2nd); eco-geographical data; human footprint index (3rd); compound topographic index; distance to water source; slope | ||
Changsha City (nearly 1 km) [23]; 2005–2009 | Ecological niche models; risk area in urban land; the risk level is correlated with an increase in area of urban land | Elevation; temperature; precipitation; NDVI; rodent density | ||
Hunan Province (1 km grid) [43]; 2010–2019 | Logistic regression model; B = 0.435, p < 0.01, urban and rural construction land as high-risk area for HFRS | GDP (2nd); soil; population (3rd); altitude; NDVI (1st); precipitation | ||
Jiangsu Province (county) [53]; 2001–2011 | Single factor analysis; significant nonlinear correlation between HFRS incidence and distance to railways and highways | Meteorological factors; elevation; NDVI | ||
Cultivated land (+/risk area) | Wei River Basin of Shaanxi Province (5 km grid) [24]; 2005–2015 | Boosted regression model; farmland contribution = 8.70%, positive correlation | Building land (1st); elevation (2nd) | |
Beijing City (total city) [51]; 1997–2006 | Poisson regression; rice paddies, positive correlation (+27.8%/1%, as rice paddies increased by 1%, the HFRS incidence rate increased by 27.8%) | Elevation | ||
Shandong Province (county) [47]; 2010–2018 | Boosted regression tree model; cultivated land contribution = 9.98%, positive correlation | Population density (1st); elevation (2nd); meteorological factors; GDP; NDVI | ||
Hu County (total county) [22]; 1984–2014 | Bayesian state space approach; decreasing carrying capacity is associated with loss of farmland | Rainfall; resource availability; rodents | ||
Shandong Province (grid) [54]; 2005–2009 | Ecological niche model; contribution rate of land cover = 31.2%, with highest risk in rain-fed croplands and mosaics of vegetation/croplands | Meteorological factors; NDVI; land surface temperature during nighttime | ||
Shaanxi Province (county) [25]; 2005–2016 | Multivariate boosted regression; relative contribution = 13.21%; initially increased significantly and then plateaued | Meteorological factors; elevation; GDP; land use (1st); population density (3rd); pig density (2nd); cattle density; goat density; selenium | ||
Hunan Province [43]; 2010–2019 | Binary logistic regression model; B = 0.435, p < 0.01, cultivated land as high-risk area for HFRS | GDP (2nd); population (3rd); soil; altitude; NDVI (1st); precipitation | ||
Changsha City (nearly 1 km) [23]; 2005–2009 | Ecological niche models; risk area in cultivated land; the risk level is correlated with an increase in area of cultivated land | Elevation; temperature; precipitation; NDVI; rodent density | ||
Loudi City and Shaoyang City (total area) [20]; 2006–2013 | Matrix; cultivated land had the largest proportion of cases | Rodents | ||
Hubei Province (county) [31]; 2011–2015 | Ordinary least square; farmland area (R = 0.421, p < 0.01) | Population; rainfall; water area; average humidity | ||
Chenzhou City (total city) [55]; 2006–2015 | Matrix; the highest risk of HFRS occurred on cultivated land | Meteorological factors; NDVI; TVDI; rodents | ||
Forest land (+/risk area) | China (1 km grid) [21]; 1994–1998 | Multivariate logistic regression; OR = 2.04, p < 0.01 | Elevation; NDVI; soil; meteorological factors | |
China (province) [56]; 2005–2009 | Geographically weighted regression model; positive correlation, p < 0.01 in 2006, 2008, and 2009 | Meteorological factors; elevation; NDVI | ||
Shandong Province (county) [47]; 2010–2018 | Boosted regression tree model; woodland contribution = 8.71%, initially increased and then decreased | Population density (1st); elevation (2nd); meteorological factors; GDP; NDVI | ||
Grassland (+/risk area) | Big Three Gorges area (county) [57]; 1997–2007 | Spearman correlation; R = −0.676, p = 0.011 | Rodents | |
Shandong Province (county) [47]; 2010–2018 | Boosted regression tree model; grassland contribution = 11.06%, initially increased and then decreased | Population density; elevation; woodland; rural settlement; water body | ||
Orchard land (+/risk area) | China (1 km grid) [21]; 1994–1998 | Multivariate logistic regression; OR = 1.97, p < 0.01 | Elevation; NDVI; soil; meteorological factors | |
Beijing City (total city) [51]; 1997–2006 | Poisson regression; orchard, positive correlation (+4.33%/1%, as orchard land increased by 1%, the HFRS incidence rate increased by 4.33%) | Elevation | ||
Water body (+/risk area) | Shandong Province (county) [47]; 2010–2018 | Boosted regression tree model; water body contribution rate = 8.63%, initially increased and then decreased | Population density (1st); elevation (2nd); meteorological factors; GDP; NDVI | |
Hubei Province (county) [31]; 2011–2015 | Ordinary least square; R = 0.087, p < 0.01 | Population; farmland; meteorological factors | ||
Jiangsu Province (county) [53]; 2001–2011 | Single factor analysis; significant nonlinear correlation between HFRS incidence and distance to rivers and lakes | Meteorological factors; elevation; NDVI | ||
Xi’an City (the total area) [26]; 2005–2018 | Kernel density estimate; higher HFRS incidence rate within the radii of 696.15 m and 1575.39 m | Urbanization; rainfall; vegetation | ||
Hubei Province (county) [45]; 2005–2014√ | Correlation analysis; R = 0.352, p < 0.01 | Meteorological factors; rodent density; population density; height | ||
Land use—Vegetation | Specific normalized difference vegetation index (NDVI) value as risk area | China (1 km grid) [21]; 1994–1998 | Univariate analysis; ORNDVI = 0.1–0.2 = 3.69, ORNDVI = 0.2–0.3 = 3.99, p < 0.001 | Elevation; meteorological factors; land surface temperature |
109 counties in China (county and the total area) [28]; 2002.01–2013.12 | Quasi-Poisson regression with a distributed lag nonlinear model and multivariate meta-analysis; highest risk in 80th percentile of NDVI | Meteorological factors | ||
Dongting Lake District (nearly 1 km) [52]; 2005–2010 | Ecological niche models; cumulative contribution rate of the monthly average NDVI = 62.7% (1st influencing factor), the highest HFRS incidence was in spring (NDVI between 0.5 and 0.7) and in winter (NDVI between 0.4 and 0.5) | Elevation; meteorological factors; land use (2nd); eco-geographical data; human footprint index (3rd); compound topographic index; distance to water source; slope | ||
NDVI for specific months or with time lags, or for specific land use types (+) | China (province) [56]; 2005–2012 | Geographically weighted regression model; positive correlation, p < 0.01 in 2005, 2006, 2007, and 2008 | Meteorological factors; elevation; land use | |
Chenzhou City (the total area) [42]; 2006–2010 | Polynomial distributed lag model; R (NDVI with 5 months lag) = 0.49, p < 0.001 | Rodent; GDP and the urbanization rate; climate | ||
Raohe and Mishan County in Heilongjiang, Chang’an District, and Hu County in Shaanxi (county) [58]; 2002–2012 | Seasonal autoregressive integrated moving average model with exogenous variables; NDVI with 1-month lag is significantly associated with HFRS cases in Raohe, NDVI with 2-month lag is significantly associated with HFRS cases in Chang’an | Meteorological factors | ||
Changsha (total area) [29]; 2004.01–2011.12 | Autoregressive integrated moving average model; positive correlation between NDVI and HFRS host density with 3 months lag | Meteorological factors; rodents; TVDI | ||
Changsha (total area) [59]; 2005–2010 | Cross-correlation analysis; NDVI values for rice paddies, orchards, forest land, and residential areas were significantly correlated with the monthly notified number of HFRS cases with a lag time of 1–6 months | Meteorological factors; climate factor | ||
Dayangshu District (the total area) [60]; 2001–2005 | Linear regression analysis; R = 0.67 between HFRS cases in farmland and 3 months backward NDVI, p < 0.001 | None | ||
NDVI (−) | Changsha (nearly 1 km grid) [23]; 2005–2009 | Ecological niche models; NDVI value in areas predicted present is lower than in areas predicted absent | Elevation; temperature; precipitation; land use; rodent density | |
Hunan Province (1 km grid) [43]; 2010–2019 | Binary logistic regression model; B = 0.563, p < 0.01, 1st influencing factor, negatively correlated | Soil; land use; altitude; population (3rd); precipitation; GDP (2nd) | ||
Hebei Province (city) [61]; 1999–2011 | Correlation analysis; R = −0.463, p < 0.05 | Rodents; meteorological factors | ||
Land use—Husbandry | Deer husbandry (+) | Changchun City (total area) [62]; 1998–2012 | Poisson regression analysis; Shuangyang County, HFRS incidence increased by 70.7% as the deer density increased by 10 head per km2 (p < 0.001); combined other nine counties, HFRS incidence increased by 90.4% as the deer density increased by 10 head per km2 (p < 0.001) | Climate factors |
Pig density (+) | Shaanxi Province (county) [25]; 2005–2016 | Multivariate boosted regression; relative contribution = 9.86%; a trend of firstly decreasing and then increasing | Meteorological factors; elevation; GDP; land use (1st), population density (3rd); cattle density; goat density; selenium | |
Vaccination | Expanded program of immunization (−) | China (total area) [34]; 2005–2010 | The proportion of HFRS cases among EPI-targeted age group decreased from 86.9% in 2005 to 81.9% in 2010; unvaccinated group increased from 13.1% to 18.1%; the differences were significant | None |
Expanded program of immunization (−) | Yichun City (total city) [35]; 2005–2013 | HFRS incidence of EPI-targeted population remained stable while incidence of population in non-EPI-targeted regions and non-EPI-targeted population in targeted region presented increasing tendency | Rodents | |
Vaccination (−) | Hu County (total county) [22]; 1984–2014 | Significant decrease in the number of susceptible individuals observed after the mass vaccination | Rainfall; resource availability; rodents | |
Vaccination (−) | Hu County (total county) [63]; 1971–2011 | Cross-correlation analysis; negative correlation between HFRS incidence and vaccination compliance with lags of 1 and 2 years (R = −0.51 and −0.55) | None |
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Li, S.; Zhu, L.; Zhang, L.; Zhang, G.; Ren, H.; Lu, L. Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China. Int. J. Environ. Res. Public Health 2023, 20, 3328. https://doi.org/10.3390/ijerph20043328
Li S, Zhu L, Zhang L, Zhang G, Ren H, Lu L. Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China. International Journal of Environmental Research and Public Health. 2023; 20(4):3328. https://doi.org/10.3390/ijerph20043328
Chicago/Turabian StyleLi, Shujuan, Lingli Zhu, Lidan Zhang, Guoyan Zhang, Hongyan Ren, and Liang Lu. 2023. "Urbanization-Related Environmental Factors and Hemorrhagic Fever with Renal Syndrome: A Review Based on Studies Taken in China" International Journal of Environmental Research and Public Health 20, no. 4: 3328. https://doi.org/10.3390/ijerph20043328