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Chenzhi Wang
Nonlinear relationship
between extreme
temperature and
mortality across
mainland in China
----- A comprehensive study
based on 127 communities
Beijing Normal University
CONTENTS
Background
Data & Method
Result
Conclusion & Innovation
1
2
3
4
PART I
Background
PART ONE Background
 With the intensification of extreme weather
events, the impact of global climate change
has been ubiquitous
Climate change
1stthreat
 Climate change was considered to be the
most serve global health threat in the 21st
century
 Specialists concern on investigating public
exposure-response relation under global
warming
Research summary
PART ONE Background
First research Second research
2types
Extreme
temperature
influence
Ambient
temperature
relationship
4problems
Few study
focus on
China
Communities
not Enough
Lack thought on
impact of
temperature
zones
Seldom
compare
the rural
and urban
04OBJECT03OBJECT02OBJECT01
Characterize the
temperature-mortality
association。
Explore the relationship
Identify the hotspots of
extreme temperature
risk
Hotspot analysis
Find the potential
drivers of this pattern
Compare the difference Find the factor
PART ONE Background
OBJECT
Research goals
Compare the risk
difference between
rural and urban areas
PART II
Data & Method
PART TWO Data
• Mortality data: The daily non-accidental
mortality data of each community we
obtained from DSP (Death Surveillance
Points System) is continuous and ranges from
1 January 2007 to 31 December 2012
Dataset
• Meteorological data: We selected the
daily meteorological variables from the
dataset including daily mean temperature
(Tm), maximum temperature (Tmax),
minimum temperature (Tmin) and relative
humidity (Rh)
• Social-economic data: Community level
data is also indispensable and we obtained
these data from the statistical yearbook,
including population size, per capita GDP and
medical data
Model and describe the temperature-
mortality relationship at community
level
01
Investigate the spatial autocorrelation
of temperature-mortality association
02
Analyze the different temperature-
mortality relationship at various
temperature zones
04
Analyze temperature-mortality relationship
through a meta-analysis method at national
or regional level03
Compare the difference between
rural and urban with the geostatistics
05
Utilize the statistics to analyze the
driven factors
06
PART TWO Methods
PART TWO Method
DLNM Model
• A quasi-Poisson regression
with Distributed Lag Non-
linear Model (DLNM) was
applied to estimate the
relationship between daily
mean temperature and
mortality in each
community .
• During this analysis, we
used the ‘cross-basis’
function to describe the
daily mean temperature-
mortality relationship and
lag days.
( ) ( , ) ( , 3) ( , 9)mean t t t ttLogE Y cb T lag ns Rh df ns Time df Dow Hoilday         
Core Model
 Yt represents the number of deaths on day t; α is intercept; cb means “cross-
basis” function defined by a B-spline with 5 degree of freedom (df) for the
space of temperature and 4 df for lag spaces.
 To ensure that the model can cover the tails of the distribution, we placed 3
knots at 10th percentile, 50th percentile and 90th percentile of mean
temperature distribution of each community.
Model and describe the temperature-
mortality relationship at community
level
01 Investigate the spatial autocorrelation
of temperature-mortality association
02
Analyze the different temperature-
mortality relationship at various
temperature zones
04
Analyze temperature-mortality relationship
through a meta-analysis method at national
or regional level03
Compare the difference between
rural and urban with the geostatistics
05
Utilize the statistics to analyze the
driven factors
06
PART TWO Methods
PART III
Result
PART Three Result
Modeling temperature-mortality
curves at the community level
Community level
Meta-analysis on
temperature-mortality
curves in different
temperature zones
Regional level
A comparison of extreme
temperature effected risk
between rural and urban
areas
Rural vs. Urban area
Find the potential factor and
analyze the coefficient
between those factors and
risk
Influence factor
PART THREE Result
• The curves which reflect the temperature-morality
relationship, are typically U-, J-, V-, and W-shaped as
many previous studies have pointed out.
• Different shapes of the temperature-mortality
curves represent different risk characteristics of the
temperature.
• It is obvious that both low and high temperatures
are likely to increase the mortality risk.
Temperature-mortality relationship
Community level
PART THREE Result
• Temperature–mortality curves at the community level
showed huge differences, the pooled curves were
generally U- or J- shaped in these five zones.
• The temperature–mortality curves in three zones were
all U-shaped, indicating both low and high temperatures
could increase significantly mortality risk.
• The curves appeared J-shaped in other two zones (the
middle subtropical region and south subtropical region),
implying more mortality risk from cold temperature than
those from hot stress
Pooled result across
Regional level
PART THREE Result
• Temperature–mortality curves at the community level
showed huge differences, the pooled curves were
generally U- or J- shaped in these five zones.
• The temperature–mortality curves in three zones were
all U-shaped, indicating both low and high temperatures
could increase significantly mortality risk.
• The curves appeared J-shaped in other two zones (the
middle subtropical region and south subtropical region),
implying more mortality risk from cold temperature than
those from hot stress
Extreme temperature risk
Regional level
Region Relative Risk
Cold Effect Heat Effect
Sub-temperate (29communities) 1.61(0.82,3.36) 1.24(0.70,2.47)
Warm temperate(34 communities) 1.78(0.97,3.53) 1.37(0.72,2.83)
North subtropical(28 communities) 1.27(0.94,1.72) 1.16(0.89,1.53)
Middle subtropical(21 communities) 1.93(1.08,3.60) 1.04(0.65,1.83)
South subtropical(14 communities) 1.56(1.09,2.24) 1.05(0.74,1.51)
PART THREE Result
• For cold effect in rural reflected in central area from
West to East the risk is higher than two edges ;the
trend of latitude-effected risk rise first and fall later
• For hot effect in rural area from West to East the risk
is especially higher in central than two edges ;the
trend from the North to South appeared a declining
pattern
Compare urban to rural
Rural area
Cold
Hot
PART THREE Result
• For cold effect in urban appeared a U-shaped curve
from West to East; the trend of latitude-effected risk
fall quickly
• For hot effect in urban area, the longitude-effect is
not so significant while the latitude-effect seems a
obvious inverse U-shaped curve
Compare urban to rural
Urban area
Cold
Hot
PART THREE Result
• Many study thought the social-economic factor
influence the temperature-mortality relationship,
we pick the per GDP as well as health workers per
thousand people own as two main factors
• For economical factor, we found that the highest
per capita GDP was in the north subtropical zone,
while the related lower per capita GDP was in the
middle subtropical zone
• For the warm zone, it is seem that the social-
economic factors have important impact on the
cold risk
Influence factor
Social & medical factor
PART THREE Result
• Temperature–mortality curves at the community level
showed huge differences, the pooled curves were
generally U- or J- shaped in these five zones.
• The temperature–mortality curves in three zones were
all U-shaped, indicating both low and high temperatures
could increase significantly mortality risk.
• The curves appeared J-shaped in other two zones (the
middle subtropical region and south subtropical region),
implying more mortality risk from cold temperature than
those from hot stress
Extreme temperature risk
Regional level
Region Relative Risk
Cold Effect Hot Effect
Sub-temperate (29communities) 1.61(0.82,3.36) 1.24(0.70,2.47)
Warm temperate(34 communities) 1.78(0.97,3.53) 1.37(0.72,2.83)
North subtropical(28 communities) 1.27(0.94,1.72) 1.16(0.89,1.53)
Middle subtropical(21 communities) 1.93(1.08,3.60) 1.04(0.65,1.83)
South subtropical(14 communities) 1.56(1.09,2.24) 1.05(0.74,1.51)
PART THREE Result
• Many study thought the social-economic factor
influence the temperature-mortality relationship,
we pick the per GDP as well as health workers per
thousand people own as two main factors
• For economical factor, we found that the highest
per capita GDP was in the north subtropical zone,
while the related lower per capita GDP was in the
middle subtropical zone
• For the warm zone, it is seem that the social-
economic factors have important impact on the
cold risk
Influence factor
Social & medical factor
PART THREE Result
• Many study thought the social-economic
factor influence the temperature-mortality
relationship, we pick the per GDP as well as
health workers per thousand people own as
two main factors
• For medical factor ,we found a obvious
correlation between the cold risk and the
urban medical
• However, when we consider the rural cold risk
we found the relationship with the medical
factor is not so significant
Influence factor
Social & medical factor
Urban cold
risk
Urban medical
factor
Rural cold
risk
Rural medical
factor
PART IV
Conclusion &
Innovation
PART FOUR Conclusion
The different characteristics of mortality responding to cold and
hot stress highlighted that not only circumstance temperature but
also social-economic condition can be a main factor controlling
health risk
Multi-factors effect
Nonlinear relationship
Difference between urban and rural
The temperature-mortality relationship showed a distinct spatial
heterogeneity along temperature zones across Chinese mainland.
A systematic research
A comprehensive research on the temperature-mortality relationship
and extreme temperature effect about China.
A obvious difference for both cold and hot risk between urban and
rural areas in China
PART Four Innovation
Study based on the most
communities of China
Multi-scales research
A new method to combine
the tiny scale result
New relationship between
temperature
and cold risk
First research compare
the rural and urban
THANK YOU
FOR
ATTENTION

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A Comparative Study of the Mortality Risk of Extreme Temperature in Urban and Rural Areas of China An Analysis Based on 122 Communities, Chenzhi WANG

  • 1. Chenzhi Wang Nonlinear relationship between extreme temperature and mortality across mainland in China ----- A comprehensive study based on 127 communities Beijing Normal University
  • 4. PART ONE Background  With the intensification of extreme weather events, the impact of global climate change has been ubiquitous Climate change 1stthreat  Climate change was considered to be the most serve global health threat in the 21st century  Specialists concern on investigating public exposure-response relation under global warming
  • 5. Research summary PART ONE Background First research Second research 2types Extreme temperature influence Ambient temperature relationship 4problems Few study focus on China Communities not Enough Lack thought on impact of temperature zones Seldom compare the rural and urban
  • 6. 04OBJECT03OBJECT02OBJECT01 Characterize the temperature-mortality association。 Explore the relationship Identify the hotspots of extreme temperature risk Hotspot analysis Find the potential drivers of this pattern Compare the difference Find the factor PART ONE Background OBJECT Research goals Compare the risk difference between rural and urban areas
  • 7. PART II Data & Method
  • 8. PART TWO Data • Mortality data: The daily non-accidental mortality data of each community we obtained from DSP (Death Surveillance Points System) is continuous and ranges from 1 January 2007 to 31 December 2012 Dataset • Meteorological data: We selected the daily meteorological variables from the dataset including daily mean temperature (Tm), maximum temperature (Tmax), minimum temperature (Tmin) and relative humidity (Rh) • Social-economic data: Community level data is also indispensable and we obtained these data from the statistical yearbook, including population size, per capita GDP and medical data
  • 9. Model and describe the temperature- mortality relationship at community level 01 Investigate the spatial autocorrelation of temperature-mortality association 02 Analyze the different temperature- mortality relationship at various temperature zones 04 Analyze temperature-mortality relationship through a meta-analysis method at national or regional level03 Compare the difference between rural and urban with the geostatistics 05 Utilize the statistics to analyze the driven factors 06 PART TWO Methods
  • 10. PART TWO Method DLNM Model • A quasi-Poisson regression with Distributed Lag Non- linear Model (DLNM) was applied to estimate the relationship between daily mean temperature and mortality in each community . • During this analysis, we used the ‘cross-basis’ function to describe the daily mean temperature- mortality relationship and lag days. ( ) ( , ) ( , 3) ( , 9)mean t t t ttLogE Y cb T lag ns Rh df ns Time df Dow Hoilday          Core Model  Yt represents the number of deaths on day t; α is intercept; cb means “cross- basis” function defined by a B-spline with 5 degree of freedom (df) for the space of temperature and 4 df for lag spaces.  To ensure that the model can cover the tails of the distribution, we placed 3 knots at 10th percentile, 50th percentile and 90th percentile of mean temperature distribution of each community.
  • 11. Model and describe the temperature- mortality relationship at community level 01 Investigate the spatial autocorrelation of temperature-mortality association 02 Analyze the different temperature- mortality relationship at various temperature zones 04 Analyze temperature-mortality relationship through a meta-analysis method at national or regional level03 Compare the difference between rural and urban with the geostatistics 05 Utilize the statistics to analyze the driven factors 06 PART TWO Methods
  • 13. PART Three Result Modeling temperature-mortality curves at the community level Community level Meta-analysis on temperature-mortality curves in different temperature zones Regional level A comparison of extreme temperature effected risk between rural and urban areas Rural vs. Urban area Find the potential factor and analyze the coefficient between those factors and risk Influence factor
  • 14. PART THREE Result • The curves which reflect the temperature-morality relationship, are typically U-, J-, V-, and W-shaped as many previous studies have pointed out. • Different shapes of the temperature-mortality curves represent different risk characteristics of the temperature. • It is obvious that both low and high temperatures are likely to increase the mortality risk. Temperature-mortality relationship Community level
  • 15. PART THREE Result • Temperature–mortality curves at the community level showed huge differences, the pooled curves were generally U- or J- shaped in these five zones. • The temperature–mortality curves in three zones were all U-shaped, indicating both low and high temperatures could increase significantly mortality risk. • The curves appeared J-shaped in other two zones (the middle subtropical region and south subtropical region), implying more mortality risk from cold temperature than those from hot stress Pooled result across Regional level
  • 16. PART THREE Result • Temperature–mortality curves at the community level showed huge differences, the pooled curves were generally U- or J- shaped in these five zones. • The temperature–mortality curves in three zones were all U-shaped, indicating both low and high temperatures could increase significantly mortality risk. • The curves appeared J-shaped in other two zones (the middle subtropical region and south subtropical region), implying more mortality risk from cold temperature than those from hot stress Extreme temperature risk Regional level Region Relative Risk Cold Effect Heat Effect Sub-temperate (29communities) 1.61(0.82,3.36) 1.24(0.70,2.47) Warm temperate(34 communities) 1.78(0.97,3.53) 1.37(0.72,2.83) North subtropical(28 communities) 1.27(0.94,1.72) 1.16(0.89,1.53) Middle subtropical(21 communities) 1.93(1.08,3.60) 1.04(0.65,1.83) South subtropical(14 communities) 1.56(1.09,2.24) 1.05(0.74,1.51)
  • 17. PART THREE Result • For cold effect in rural reflected in central area from West to East the risk is higher than two edges ;the trend of latitude-effected risk rise first and fall later • For hot effect in rural area from West to East the risk is especially higher in central than two edges ;the trend from the North to South appeared a declining pattern Compare urban to rural Rural area Cold Hot
  • 18. PART THREE Result • For cold effect in urban appeared a U-shaped curve from West to East; the trend of latitude-effected risk fall quickly • For hot effect in urban area, the longitude-effect is not so significant while the latitude-effect seems a obvious inverse U-shaped curve Compare urban to rural Urban area Cold Hot
  • 19. PART THREE Result • Many study thought the social-economic factor influence the temperature-mortality relationship, we pick the per GDP as well as health workers per thousand people own as two main factors • For economical factor, we found that the highest per capita GDP was in the north subtropical zone, while the related lower per capita GDP was in the middle subtropical zone • For the warm zone, it is seem that the social- economic factors have important impact on the cold risk Influence factor Social & medical factor
  • 20. PART THREE Result • Temperature–mortality curves at the community level showed huge differences, the pooled curves were generally U- or J- shaped in these five zones. • The temperature–mortality curves in three zones were all U-shaped, indicating both low and high temperatures could increase significantly mortality risk. • The curves appeared J-shaped in other two zones (the middle subtropical region and south subtropical region), implying more mortality risk from cold temperature than those from hot stress Extreme temperature risk Regional level Region Relative Risk Cold Effect Hot Effect Sub-temperate (29communities) 1.61(0.82,3.36) 1.24(0.70,2.47) Warm temperate(34 communities) 1.78(0.97,3.53) 1.37(0.72,2.83) North subtropical(28 communities) 1.27(0.94,1.72) 1.16(0.89,1.53) Middle subtropical(21 communities) 1.93(1.08,3.60) 1.04(0.65,1.83) South subtropical(14 communities) 1.56(1.09,2.24) 1.05(0.74,1.51)
  • 21. PART THREE Result • Many study thought the social-economic factor influence the temperature-mortality relationship, we pick the per GDP as well as health workers per thousand people own as two main factors • For economical factor, we found that the highest per capita GDP was in the north subtropical zone, while the related lower per capita GDP was in the middle subtropical zone • For the warm zone, it is seem that the social- economic factors have important impact on the cold risk Influence factor Social & medical factor
  • 22. PART THREE Result • Many study thought the social-economic factor influence the temperature-mortality relationship, we pick the per GDP as well as health workers per thousand people own as two main factors • For medical factor ,we found a obvious correlation between the cold risk and the urban medical • However, when we consider the rural cold risk we found the relationship with the medical factor is not so significant Influence factor Social & medical factor Urban cold risk Urban medical factor Rural cold risk Rural medical factor
  • 24. PART FOUR Conclusion The different characteristics of mortality responding to cold and hot stress highlighted that not only circumstance temperature but also social-economic condition can be a main factor controlling health risk Multi-factors effect Nonlinear relationship Difference between urban and rural The temperature-mortality relationship showed a distinct spatial heterogeneity along temperature zones across Chinese mainland. A systematic research A comprehensive research on the temperature-mortality relationship and extreme temperature effect about China. A obvious difference for both cold and hot risk between urban and rural areas in China
  • 25. PART Four Innovation Study based on the most communities of China Multi-scales research A new method to combine the tiny scale result New relationship between temperature and cold risk First research compare the rural and urban

Editor's Notes

  1. Title of my research is nonlinear relationship between extreme temperature and mortality across mainland in China a comprehensive study based on 127 communities
  2. I will introduce my research from the following five parts.
  3. The previous studies can be classified into two types: First study focus on exploring the relationship between ambient temperature and mortality; The other category investigate the adverse impact of extreme temperature on public health. However, four problems can be summarized from these studies: At first, fewer studies focus on China. Secondly, even though there are some studies focusing on China in recent years, the number of communities they used were not large enough to have an effective representative of China. Furthermore, few studies pay attention to the effect of temperature zones. The last but not least point is, lots of studies pointed out for those developing countries, like China, rapid urbanization have important impact on public health, however, few studies compare the difference between urban and rural areas.
  4. Considering all of above problems, our research included the following four goals: first, we tried to explore the temperature-mortality relationship of plentiful communities and analyze the mortality risk of extreme temperature at community level; based on this, we pooled these result at community level to get the regional result and identify those hotspot suffering from extreme temperature. Thirdly, we compared the temperature-mortality relationship between urban and rural areas and find their difference. Lastly, we tried to make out how other factors such as social factors can influence this difference.
  5. Here is the second part: data and methods
  6. Our research data contains three components: mortality data, meteorological data and social economy data. We collected non-accidental mortality data of each community from Death Surveillance Points System of China. The continuous data ranges from 1 January 2007 to 31 December 2012.Here is spatial distribution of communities, different colors represents different temperature zones. Meteorological data is a daily interpolation dataset of the same period. Here we selected daily mean temperature (Tm) and relative humidity (Rh) to build our model and max/ min temperature to describe the characteristics of the community. Previous studies reported that social economy factors have impact on the temperature-mortality relationship. In our study we used the per capita GDP and the number of medical staff per thousand people possess as indicators
  7. Here is our technical route. And the key part of our method is how to model and describe the temperature-mortality relationship at community level. The core model is the distributed lag non-linear model. This model becomes popular in the environment health assessment field recently.
  8. Here is our technical route. And the key part of our method is how to model and describe the temperature-mortality relationship at community level. The core model is the distributed lag non-linear model. This model becomes popular in the environment health assessment field recently.
  9. So, based on the model at community level, we calculated the mortality risk of extreme temperature. Then we analyze whether there is a spatial autocorrelation of the cold-related risk or heat-related risk. The result shows no spatial autocorrelation. So we can investigate the regional temperature-mortality relationship with the meta-analysis method. Then we compare the difference of this relationship at different temperature zones. The next step is comparing the difference of the mortality risk between urban and rural areas. Here we used some geo-statistical method. At the last period, we tried to analyze what factors can lead to the difference of the characteristics among different areas.
  10. 第三部分我们将分享我们的研究结果
  11. Research results included following four aspects: temperature-mortality relationship at community level; temperature-mortality relationship at regional level; a comparison about mortality risk of extreme temperature between rural and urban areas and the analysis on how social economy factors modify the relationship.
  12. Temperature-mortality curves of community level were listed in this table. Roman numerals I to V represent the mid-temperature zones, the warm temperature zones, north subtropics, middle subtropics and south subtropics. However, for lack of space, only some results were listed. Actually, all of the 127 communities had the curves. For each curve, the x-coordinate represents local temperature, the y-coordinate represents the risk. From the table, we can find even though there were difference among this curves, they are typically U-, J-, V-, and W-shaped. This means both low temperature and high temperature can increase the mortality risk.
  13. The result at regional levels seems a lot different. Here we dived the regions from the temperature zones and do the meta-analysis. We can find the temperature-mortality relationship among those five temperature zones are usually U- shaped or J-shaped, which shows a more obvious characteristic: for the mid-temperature zone, warm-temperature zone and north subtropics, their curves are U-shaped which means in this areas both high and low temperature can increase risk; while in the mid-subtropics and south subtropics the curves are J-shaped. This means only low temperature can increase mortality risk. This reflects the acclimatization of human to local environment.  
  14. To calculate the risk of extreme temperature, we defined the risk of 1st percentile of the local mean temperature distribution as the cold effect risk while defined the risk of 99th percentile as the heat effect risk. This table shows the result of cold and heat effect of the five temperature zones. From the box plot, we can find with the temperature increasing from the north to south, the heat effect appears a declining trend while the cold effect looks like an M-shaped curve. This is very interesting, and I will give my explanation in the following parts.
  15. This part is about the comparison of the temperature-mortality relationship between rural and urban areas. We used the spatial trend method to analyze the spatial difference. In the picture x-coordinate represents the longitude and y-coordinate represents the latitude, the z-coordinate represent the cold or heat effect risk. We can see in rural areas, for the cold effect, with the longitude increases, there seems no obvious change while with the latitude decrease, it increases at first then decline in the later. For the heat effect, with the longitude increase, risk in north and south areas is low while in central area is high. With latitude decreases, the risk is obviously declining.
  16. Compared with rural areas, the cold and heat effect in urban areas shows a different spatial characteristics. For the cold effect, with the longitude increases, the risk drop at first then rise in the later but it appears a declining trend with the latitude decreases. For the heat effect, longitude have no significant impact but with the latitude decreases and temperature increase, the change is obvious. It rise at first then decline.
  17. The last part is the influence factors. When we look back to the characteristics of temperature-mortality relationship at regional level, we found with the regional temperature raised, the heat related risk shows a declining trend while the cold related risk presents an M-shaped curve. We think environment factors affect the heat-related risk while the social-economy factors can influence the cold-related risk. Look at the picture, bar graph is the per capita GDP of each temperature zone while the plot represent the cold effect. There seems a negative correlation between the cold effect and the economy. We gave this explanation to this phenomenon: In those cold or hot regions, such as the mid-temperature zones and south subtropics, temperature occupy the leading position influence human health; while in those warm areas, such as the warm-temperature zones and north subtropics, social and economy factors have a significant impact on human health. The lowest cold-related risk area, north subtopics, contains the most developed regions in China: the Yangtze River Delta and the Yangtze River Basin.
  18. 1.区域差异的规律是,随着温度升高,高温致死风险下降,而低温影响的死亡风险呈现M型
  19. The last part is the influence factors. When we look back to the characteristics of temperature-mortality relationship at regional level, we found with the regional temperature raised, the heat related risk shows a declining trend while the cold related risk presents an M-shaped curve. We think environment factors affect the heat-related risk while the social-economy factors can influence the cold-related risk. Look at the picture, bar graph is the per capita GDP of each temperature zone while the plot represent the cold effect. There seems a negative correlation between the cold effect and the economy. We gave this explanation to this phenomenon: In those cold or hot regions, such as the mid-temperature zones and south subtropics, temperature occupy the leading position influence human health; while in those warm areas, such as the warm-temperature zones and north subtropics, social and economy factors have a significant impact on human health. The lowest cold-related risk area, north subtopics, contains the most developed regions in China: the Yangtze River Delta and the Yangtze River Basin.
  20. To explain the difference of cold related risk between rural and urban areas, we used the medical indicator: medical staff per thousand people possess to get a continuous trend surface. We can find for urban areas, where the medical indicator is higher, the cold risk is lower. However, for rural areas, this spatial pattern only appears in the south areas.
  21. 我们的结论包含以下四个方面
  22. Our conclusion include the following four aspects: our research is a systematic research about the temperature-mortality relationship of China. And this relationship shows a distinct spatial heterogeneity along temperature zones across Chinese mainland. Meanwhile, there is an obvious difference of this relationship and mortality risk between urban and rural areas. Moreover, all of this difference is a multi-factors result, economy and medical factors have important impact on these difference.
  23. There are five innovations in our research: First, Compared to previous studies on temperature-mortality relationships in China, the number of communities involved in our study was the largest. Secondly, we studied the influence of temperature on human health from multi-scales. Moreover, compared with previous study, our study combine the result of community scale to get the regional result from the perspective of temperature zones. Based on this, we found an interesting M-shaped curve of the cold-related risk across different temperature zones. The last but least point is we firstly compare the urban and rural areas of the temperature-mortality relationship.
  24. 谢谢