Muhammad Saiful Islam @FTF2013

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  • 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.
  • Muhammad Saiful Islam @FTF2013

    1. 1. Impact of Climate Change on EmergencyHospital Admission: A case study forGreater LondonMuhammad Saiful IslamHealth and Social Care Modelling Group (HSCMG)University of Westminster, Londonwww.healthcareanalytics.co.uk
    2. 2. Outline:• Background• Objectives• Data• Missing values and AIRGENE algorithm• Methodology• Results• Conclusion• Limitations and Future Works2Facing the Future 2013
    3. 3. Background• Climate is changing at anunprecedented alarmingrate• Crucial to understanddisease~ meteorological.• Asthma ~ exposure ofclimate change.3Facing the Future 2013
    4. 4. Objectives:• Explore and measure the relationships of some selectedclimate factors and emergency Asthma admissions.• Measure the impact of meteorological variables on inpatienthospital admissions.• Develop a statistical model for measuring the impact ofclimate on Asthma .4Facing the Future 2013
    5. 5. Data:• Inpatient Admissions of Hospital Episode Statistics (HES), forCOPD (ICD-10, J40-J44)• Met office observational data from weather stations atHeathrow and Saint James Park, London.• Daily Mean Temperature (Ti), daily total rainfall (Ri), daily windspeed (Wi), daily sun hours (Si), daily radiation (RDi), dailyrelative humidity, daily pressure (Pi).• Emergency asthma admissions for 10 years (2000-2009) forthe Greater London. They were linked based on matching theregion and time (e.g. Postcode, admission date and dailytemperature).5Facing the Future 2013
    6. 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 ofLondon, ST James’s Park Weather station (NGR = 5298E 1801N,Altitude = 5 metres, Latitude = 51:50 N Longitude = 00:13 W)6Facing the Future 2013
    7. 7. 7Facing the Future 2013
    8. 8. Methodology:• Exploratory data analysis.• Generalized Linear Model (GLM) dealing the over dispersion ofthe data.• The response variable will be the daily number of emergencyasthma hospital admissions which is expected to vary as afunction of the selected meteorological variables.8Facing the Future 2013
    9. 9. Rationale for using GLM:• One of the building block of statistical modelling.• Nature of the response variable (hospital admission count) andexplanatory variables (climate variables).• Flexibility of GLM and computational benefits.9Facing the Future 2013
    10. 10. 10Facing the Future 2013
    11. 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 residualdeviance 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 orstepAIC),– (b) For the ease of interpretations (simplicity) and model comparisons11Facing the Future 2013
    12. 12. Exploratory Data Analysis:12 Showing therelationships of theasthma count andother climatevariables Autumn (29.3%)and winter (25.9%)showing moreemergencyadmissions Sign of correlationsamong the climatevariables (possibilityof multicollinearity Figure 1: Scatter Plot Matrix of emergency asthma Patients and climate VariablesFacing the Future 2013
    13. 13. Exploratory Data Analysis (Cont.….):13 Skewed distributionof the count(Poisson) Possiblemulticollinearityamongtemperature, Sunhours, Radiationand HumidityFigure 2: Scatter plot and correlation Matrix of emergency asthma Patients and climateFacing the Future 2013
    14. 14. Exploratory Data Analysis (Cont.….):14 Seasonality inAsthma Emergencyadmissions High peak at thestart of Autumn(August-September)Figure 3: Seasonality of daily emergency asthma admissions (2000-2009)05101520253035404550Admission CountFacing the Future 2013
    15. 15. Exploratory Data Analysis (Cont.….):15Figures 4 and 5: Seasonality of daily emergency asthma admissions with daily temperature and daily rainfall (2000-2009)05101520253035404550Admission CountDaily Temp0123456705101520253035404550Admission CountRainfall Visible change of trend astemperature starts to fall at the startof autumn (fig4) No traceable trends in asthmacounts with rain (fig5)Facing the Future 2013
    16. 16. Exploratory Data Analysis (Cont.….):1602468101205101520253035404550Admission CountWind Speed02468101205101520253035404550Admission CountDaily Sun HoursFigures 6 and 7: Seasonality of daily emergency asthma admissions with daily wind Speed and daily sun hours (2000-2009) No visible change of trends betweendaily wind speed and asthma counts(fig6) Sunhours: Same trends liketemperature (fig7)Facing the Future 2013
    17. 17. Exploratory Data Analysis (Cont.….):17050001000015000200002500005101520253035404550Admission CountDaily Radiation010203040506070809010005101520253035404550Admission CountHumidityFigures 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 trendsbetween Humidity and asthmacounts (fig9)Facing the Future 2013
    18. 18. Exploratory Data Analysis (Cont.….):18990995100010051010101510201025103005101520253035404550Admission CountPressureFigures 10: Seasonality of daily emergency asthma admissions with dailypressure (2000-2009) No visible change of trendsbetween pressure and asthmacounts (fig10)Facing the Future 2013
    19. 19. Checking Multicollinearity:19Variance Inflation Factor− A diagnostic approach to checkmulticollinearity− Measure the variance of an estimatedcoefficient increases if the explanatoryvariables are correlated− The higher the value ofVIF the greater is thedegree of collinearity− VIFs exceeding 4 warrant further investigation,− VIFs exceeding 10 are signs of seriousmulticollinearity requiring correctionVariableNameVIFMulticollinearity(Yes/No)Temperature 2.035 NoRain 1.301 NoWind Speed 1.312 NoSun Hours 3.697 NoRadiation 6.263 YesHumidity 3.162 NoPressure 1.411 NoTable 1: Variance Inflation Factor of the full modelFacing the Future 2013
    20. 20. GLM Model formation and checking:20− Likelihood ratiotest wasperformed at eachstage− Radiation wasneglected in thefinal modelbecause ofmulticollinearity− Model8 appearedto be as the finalmodel.Model Model Form AIC LikelihoodRatio (LR)test: Pr(Chi)Improved/Significant(YES / No)Model1 Count ~ Temp 26856Model2 Count ~ Temp + Rain 26856 0.183 NoModel3 Count ~ Temp + Rain + Wind Speed 26855 0.062 NoModel4 Count ~ Temp + Rain + Wind Speed +Sun Hours26800 5.262e-14 YesModel5 Count ~ Temp + Rain + Wind Speed +Sun Hours + Radiation26762 2.043e-10 YesModel6 Count ~ Temp + Rain + Wind Speed +Sun Hours + Radiation + R.Humidity26748 9.592e-05 YesModel7 Count ~ Temp + Rain + Wind Speed +Sun Hours + Radiation + R.Humidity+ Pressure26743 0.0074 YesModel8 Count ~ Temp + Rain + Wind Speed +Sun Hours + R.Humidity + Pressure26757 FinalModelModel9 Count ~ Temp + Wind Speed +R.Humidity + Pressure26758 0.084 NoTable 2: Model checking and ComparingFacing the Future 2013
    21. 21. Model fitting results:Final model: Generalized Negative binomial modelAsthma Count ~ Temp + Rain + Wind Speed + Sun Hours + R.Humidity + Pressurewith poisson errors and log link.21Coefficients: Estimate Std. Error z value Pr(>|z|)Intercept 0.8829958 0.6868876 1.286 0.19862Temperature -0.0030107 0.0011955 -2.518 0.01179 *Rain -0.0028764 0.0018176 -1.583 0.11352Wind Speed 0.0060899 0.0019573 3.111 0.00186 **Sun Hours -0.0035182 0.0022457 -1.567 0.11720R. 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 ‘ ’ 1Table 3: Model fitting results− Temperature, WindSpeed, RelativeHumidity and Pressureare found to besignificant− The LR test doesn’tshow any improvementwithout rain and sunhours.Facing the Future 2013
    22. 22. Model fitting results (Cont.….):22Coefficients: Estimate Std. Error z value Pr(>|z|)Intercept 0.8829958 0.6868876 1.286 0.19862Temperature -0.0030107 0.0011955 -2.518 0.01179 *Rain -0.0028764 0.0018176 -1.583 0.11352Wind Speed 0.0060899 0.0019573 3.111 0.00186 **Sun Hours -0.0035182 0.0022457 -1.567 0.11720R. 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 ‘ ’ 1Table 4: Model fitting resultsInterpretations− The expected logAsthmacount for 1 unit increaseinTemperature is -0.0030107− The expected logAsthmacount for 1 unit increaseinWind Speed is0.0060899− Similarly for R.Humidityand Pressure it is0.0060248 and 0.0019537respectivelyFacing the Future 2013
    23. 23. Model Diagnostics:23− The model isreasonably OK− No clear trend inthe residual plotsand any influentialdata point in theleverage plotFigures 11: Model diagnostic plots for Generalized NegativeBinomial ModelFacing the Future 2013
    24. 24. 24Facing the Future 2013
    25. 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 modellike Generalized additive model and various smoothing techniques need tobe checked.• Lag structure of the delayed effects of the factors.• Calculation of a Threshold for climate variables for specific diseases andregions.25Facing the Future 2013
    26. 26. Thanks26Facing the Future 2013

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