1. Impact of Climate Change on Emergency
Hospital Admission: A case study for
Greater London
Muhammad Saiful Islam
Health and Social Care Modelling Group (HSCMG)
University of Westminster, London
www.healthcareanalytics.co.uk
2. Outline:
• Background
• Objectives
• Data
• Missing values and AIRGENE algorithm
• Methodology
• Results
• Conclusion
• Limitations and Future Works
2Facing the Future 2013
3. Background
• Climate is changing at an
unprecedented alarming
rate
• Crucial to understand
disease~ meteorological.
• Asthma ~ exposure of
climate change.
3Facing the Future 2013
4. Objectives:
• Explore and measure the relationships of some selected
climate factors and emergency Asthma admissions.
• Measure the impact of meteorological variables on inpatient
hospital admissions.
• Develop a statistical model for measuring the impact of
climate on Asthma .
4Facing the Future 2013
5. Data:
• Inpatient Admissions of Hospital Episode Statistics (HES), for
COPD (ICD-10, J40-J44)
• Met office observational data from weather stations at
Heathrow and Saint James Park, London.
• Daily Mean Temperature (Ti), daily total rainfall (Ri), daily wind
speed (Wi), daily sun hours (Si), daily radiation (RDi), daily
relative humidity, daily pressure (Pi).
• Emergency asthma admissions for 10 years (2000-2009) for
the Greater London. They were linked based on matching the
region and time (e.g. Postcode, admission date and daily
temperature).
5Facing the Future 2013
6. Missing Values @ Weather Station:
• Mainly used Heathrow weather station (NGR = 5077E 1767N, Altitude =
25 metres, Latitude = 51:48 N, Longitude = 00:45 W)
• . Missing values:
− Wind Speed and Radiation
− Very few missing for Rainfall, Humidity and Pressure
− No missing Temperature
• Used AIRGENE algorithm, incorporating the observations of
London, ST James’s Park Weather station (NGR = 5298E 1801N,
Altitude = 5 metres, Latitude = 51:50 N Longitude = 00:13 W)
6Facing the Future 2013
8. Methodology:
• Exploratory data analysis.
• Generalized Linear Model (GLM) dealing the over dispersion of
the data.
• The response variable will be the daily number of emergency
asthma hospital admissions which is expected to vary as a
function of the selected meteorological variables.
8Facing the Future 2013
9. Rationale for using GLM:
• One of the building block of statistical modelling.
• Nature of the response variable (hospital admission count) and
explanatory variables (climate variables).
• Flexibility of GLM and computational benefits.
9Facing the Future 2013
11. Poisson Generalized Linear Model:
• Overdispersion:
– Can be a problem when working with Poisson or binomial errors
– Violation of the Poisson GLM: mean = variance
– For our data mean (27.57) ≠ Variance (105.39)
– Overdispersion can also be confirmed from model fitting results (if the residual
deviance is larger than the residual degrees of freedom)
• Dealing with Overdispersion:
– Refitting the model using quasi-Poisson rather than Poisson errors
– Another option is to use a negative binomial model
• We used negative binomial model.
– (a) nice characteristics of likelihood (AICs, likelihood ratio tests, use of step or
stepAIC),
– (b) For the ease of interpretations (simplicity) and model comparisons
11Facing the Future 2013
12. Exploratory Data Analysis:
12
Showing the
relationships of the
asthma count and
other climate
variables
Autumn (29.3%)
and winter (25.9%)
showing more
emergency
admissions
Sign of correlations
among the climate
variables (possibility
of multicollinearity Figure 1: Scatter Plot Matrix of emergency asthma Patients and climate Variables
Facing the Future 2013
13. Exploratory Data Analysis (Cont.….):
13
Skewed distribution
of the count
(Poisson)
Possible
multicollinearity
among
temperature, Sun
hours, Radiation
and Humidity
Figure 2: Scatter plot and correlation Matrix of emergency asthma Patients and climate
Facing the Future 2013
14. Exploratory Data Analysis (Cont.….):
14
Seasonality in
Asthma Emergency
admissions
High peak at the
start of Autumn
(August-
September)
Figure 3: Seasonality of daily emergency asthma admissions (2000-2009)
0
5
10
15
20
25
30
35
40
45
50
Admission Count
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15. Exploratory Data Analysis (Cont.….):
15
Figures 4 and 5: Seasonality of daily emergency asthma admissions with daily temperature and daily rainfall (2000-2009)
0
5
10
15
20
25
30
35
40
45
50
Admission Count
Daily Temp
0
1
2
3
4
5
6
7
0
5
10
15
20
25
30
35
40
45
50
Admission Count
Rainfall
Visible change of trend as
temperature starts to fall at the start
of autumn (fig4)
No traceable trends in asthma
counts with rain (fig5)
Facing the Future 2013
16. Exploratory Data Analysis (Cont.….):
16
0
2
4
6
8
10
12
0
5
10
15
20
25
30
35
40
45
50
Admission Count
Wind Speed
0
2
4
6
8
10
12
0
5
10
15
20
25
30
35
40
45
50
Admission Count
Daily Sun Hours
Figures 6 and 7: Seasonality of daily emergency asthma admissions with daily wind Speed and daily sun hours (2000-2009)
No visible change of trends between
daily wind speed and asthma counts
(fig6)
Sunhours: Same trends like
temperature (fig7)
Facing the Future 2013
17. Exploratory Data Analysis (Cont.….):
17
0
5000
10000
15000
20000
25000
0
5
10
15
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25
30
35
40
45
50
Admission Count
Daily Radiation
0
10
20
30
40
50
60
70
80
90
100
0
5
10
15
20
25
30
35
40
45
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Admission Count
Humidity
Figures 8 and 9: Seasonality of daily emergency asthma admissions with daily radiation and daily humidity (2000-2009)
Higher Radiation during Summer
(fig8)
No visible change of trends
between Humidity and asthma
counts (fig9)
Facing the Future 2013
18. Exploratory Data Analysis (Cont.….):
18
990
995
1000
1005
1010
1015
1020
1025
1030
0
5
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25
30
35
40
45
50
Admission Count
Pressure
Figures 10: Seasonality of daily emergency asthma admissions with daily
pressure (2000-2009)
No visible change of trends
between pressure and asthma
counts (fig10)
Facing the Future 2013
19. Checking Multicollinearity:
19
Variance Inflation Factor
− A diagnostic approach to check
multicollinearity
− Measure the variance of an estimated
coefficient increases if the explanatory
variables are correlated
− The higher the value ofVIF the greater is the
degree of collinearity
− VIFs exceeding 4 warrant further investigation,
− VIFs exceeding 10 are signs of serious
multicollinearity requiring correction
Variable
Name
VIF
Multicollinearity
(Yes/No)
Temperature 2.035 No
Rain 1.301 No
Wind Speed 1.312 No
Sun Hours 3.697 No
Radiation 6.263 Yes
Humidity 3.162 No
Pressure 1.411 No
Table 1: Variance Inflation Factor of the full model
Facing the Future 2013
20. GLM Model formation and checking:
20
− Likelihood ratio
test was
performed at each
stage
− Radiation was
neglected in the
final model
because of
multicollinearity
− Model8 appeared
to be as the final
model.
Model Model Form AIC Likelihood
Ratio (LR)
test: Pr(Chi)
Improved
/
Significant
(YES / No)
Model1 Count ~ Temp 26856
Model2 Count ~ Temp + Rain 26856 0.183 No
Model3 Count ~ Temp + Rain + Wind Speed 26855 0.062 No
Model4 Count ~ Temp + Rain + Wind Speed +
Sun Hours
26800 5.262e-14 Yes
Model5 Count ~ Temp + Rain + Wind Speed +
Sun Hours + Radiation
26762 2.043e-10 Yes
Model6 Count ~ Temp + Rain + Wind Speed +
Sun Hours + Radiation + R.Humidity
26748 9.592e-05 Yes
Model7 Count ~ Temp + Rain + Wind Speed +
Sun Hours + Radiation + R.Humidity
+ Pressure
26743 0.0074 Yes
Model8 Count ~ Temp + Rain + Wind Speed +
Sun Hours + R.Humidity + Pressure
26757 Final
Model
Model9 Count ~ Temp + Wind Speed +
R.Humidity + Pressure
26758 0.084 No
Table 2: Model checking and Comparing
Facing the Future 2013
21. Model fitting results:
Final model: Generalized Negative binomial model
Asthma Count ~ Temp + Rain + Wind Speed + Sun Hours + R.Humidity + Pressure
with poisson errors and log link.
21
Coefficients: Estimate Std. Error z value Pr(>|z|)
Intercept 0.8829958 0.6868876 1.286 0.19862
Temperature -0.0030107 0.0011955 -2.518 0.01179 *
Rain -0.0028764 0.0018176 -1.583 0.11352
Wind Speed 0.0060899 0.0019573 3.111 0.00186 **
Sun Hours -0.0035182 0.0022457 -1.567 0.11720
R. Humidity 0.0060248 0.0009263 6.504 7.8e-11 ***
Pressure 0.0019537 0.0006558 2.979 0.00289 **
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Table 3: Model fitting results
− Temperature, Wind
Speed, Relative
Humidity and Pressure
are found to be
significant
− The LR test doesn’t
show any improvement
without rain and sun
hours.
Facing the Future 2013
22. Model fitting results (Cont.….):
22
Coefficients: Estimate Std. Error z value Pr(>|z|)
Intercept 0.8829958 0.6868876 1.286 0.19862
Temperature -0.0030107 0.0011955 -2.518 0.01179 *
Rain -0.0028764 0.0018176 -1.583 0.11352
Wind Speed 0.0060899 0.0019573 3.111 0.00186 **
Sun Hours -0.0035182 0.0022457 -1.567 0.11720
R. Humidity 0.0060248 0.0009263 6.504 7.8e-11 ***
Pressure 0.0019537 0.0006558 2.979 0.00289 **
Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
Table 4: Model fitting resultsInterpretations
− The expected logAsthma
count for 1 unit increase
inTemperature is -
0.0030107
− The expected logAsthma
count for 1 unit increase
inWind Speed is
0.0060899
− Similarly for R.Humidity
and Pressure it is
0.0060248 and 0.0019537
respectively
Facing the Future 2013
23. Model Diagnostics:
23
− The model is
reasonably OK
− No clear trend in
the residual plots
and any influential
data point in the
leverage plot
Figures 11: Model diagnostic plots for Generalized Negative
Binomial Model
Facing the Future 2013
25. Limitations and Future Works:
• Limitations of data. More variables like:
− Pollutions factors (e.g. Ozone, Particulate matters)
− Socio-economic factors (House heating system, food habit, income)
− Others (pollen in the air for asthma)
• Non-linearity among the relationships of the variables. Non-linear model
like Generalized additive model and various smoothing techniques need to
be checked.
• Lag structure of the delayed effects of the factors.
• Calculation of a Threshold for climate variables for specific diseases and
regions.
25Facing the Future 2013
Climate is changing at an unprecedented alarming rate (UK Met Office and World Meteorological Organization).The last decade was the warmest since the instrumental records began.Crucial to understand disease~ meteorological relationships. Asthma are one of the leading causes of morbidity and mortality through out the world. It is the 6th most common cause of death in England and Wales
Inpatient Admissions of Hospital Episode Statistics (HES), NHS England for COPD (ICD-10, J40-J44) Met office observational data from weather stations at Heathrow and Saint James Park, London. Data were collected on temperature (dailymean), daily total rainfall, mean wind speed, daily sun hours, radiation, relative humidity, daily mean pressure.The period of the data 2000-2009.They were linked based on matching the region and time (e.g. admission date and daily temperature).
Mainly used Heathrow weather station (NGR = 5077E 1767N, Altitude = 25 metres, Latitude = 51:48 N, Longitude = 00:45 W) . Missing values:Wind Speed and RadiationVery few missing for Rainfall, Humidity and PressureNo missing TemperatureUsed AIRGENE algorithm for these missing values incorporating the observations of London, ST James’s Park Weather station (NGR = 5298E 1801N, Altitude = 5 metres, Latitude = 51:50 N Longitude = 00:13 W)
AIRGENE: (air pollution and inflammatory response in myocardial infarction survivors: gene-environment interaction in a high riskgroup)To replace missing values on the aggregate level.A missing value on day i from monitor j is replaced by the period average of monitor j plus a standardized value of day i over all monitors multiplied by the period standard deviation of monitor j. 𝑋𝑖𝑗= 𝑋.𝑗+ 𝑍𝑖.𝑆.𝑗 ; 𝑊h𝑒𝑟𝑒 𝑍𝑖.= 𝑗=1𝑛𝑥𝑖𝑗− 𝑥.𝑗𝑆.𝑗𝑛Consider not only differences in mean values, but also differences in variability between monitors.If all monitors are missing for one day, the averages from the day before and after will be taken.References: Bhaskaran, Hajat et al. (2010); Ruckerl et al. (2007) AIRGENE: (air pollution and inflammatory response in myocardial infarction survivors: gene-environment interaction in a high riskgroup)To replace missing values on the aggregate level.A missing value on day i from monitor j is replaced by the period average of monitor j plus a standardized value of day i over all monitors multiplied by the period standard deviation of monitor j. 𝑋 ̂_𝑖𝑗= 𝑋 ̅_(.𝑗)+ 𝑍 ̅_(𝑖.) 𝑆 ̅_(.𝑗) ; 𝑊ℎ𝑒𝑟𝑒 𝑍 ̅_(𝑖.)= (∑1_(𝑗=1)^𝑛▒((𝑥_𝑖𝑗− 𝑥 ̅_(.𝑗))/𝑆_(.𝑗) ) )/𝑛Consider not only differences in mean values, but also differences in variability between monitors.If all monitors are missing for one day, the averages from the day before and after will be taken.References: Bhaskaran, Hajat et al. (2010); Ruckerl et al. (2007)
Rationale for using GLM: One of the building block of statistical modelling.Even though climate change health relationship does not necessarily follow a straight forward linear pattern, GLM should be the first statistical model to check the relationships. Nature of the response variable (hospital admission count) and explanatory variables (climate variables).Flexibility of GLM and computational benefits.
Showing the relationships of the response (asthma count) and other climate variablesAutumn (29.3%) and winter (25.9%) showing more emergency admissionsSign of correlations among the climate variables (possibility of multicollinearity
From the scatter plot, we can see that there is no visible pattern of relationships between the COPD count with any of the meteorological variables indicating the non-linear relationships of health outcomes.Among the meteorological variables we can see that only sunshine hours and radiation showing strong positive relationships. It is also visible that there are more cases of COPD admission during summer and winter seasons.
Variance Inflation FactorA diagnostic approach to check multicollinearityMeasure the variance of an estimated coefficient increases if the explanatory variables are correlatedThe higher the value of VIF the greater is the degree of collinearityVIFs exceeding 4 warrant further investigation,VIFs exceeding 10 are signs of serious multicollinearity requiring correction
Likelihood ratio test was performed at each stageRadiation was neglected in the final model because of multicollinearityModel9 appeared to be as the final model.
Temperature, Wind Speed, Relative Humidity and Pressure are found to be significant Final model. The LR test doesn’t show any improvement without rain and sun hours
The expected log Asthma count for 1 one unit increase in Temperature is -0.0030107The expected log Asthma count for 1 one unit increase in Wind Speed is 0.0060899Similarly for R.Humidity and Pressure it is 0.0060248 and 0.0019537 respectively
The model diagnostic plots show that the model is reasonably OKNo clear trend in the residual plots and any influential data point in the leverage plot
Conclusion:Temperature, Wind Speed, Relative Humidity and Pressure are found to be significantly effecting the emergency asthma admission count.However the Generalized R2 (NagelkerkeR2) for the final model is 𝑅2=1−𝐿0𝐿𝜃2𝑛=7.4%indicates a very poor fit of the data.Need to improve the model.Conclusion:Temperature, Wind Speed, Relative Humidity and Pressure are found to be significantly effecting the emergency asthma admission count.However the Generalized R2 (NagelkerkeR2) for the final model is 𝑅^2=1−(𝐿(0)/𝐿(𝜃 ̂ ) )^(2/𝑛)=7.4%indicates a very poor fit of the data.Need to improve the model.
