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Page 1© Crown copyright 2004 Health Forecasting Home Energy Conference May 11 2005 Dr William Bird Clinical Director, Health Forecasting.

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Presentation on theme: "Page 1© Crown copyright 2004 Health Forecasting Home Energy Conference May 11 2005 Dr William Bird Clinical Director, Health Forecasting."— Presentation transcript:

1 Page 1© Crown copyright 2004 Health Forecasting Home Energy Conference May 11 2005 Dr William Bird Clinical Director, Health Forecasting

2 Page 2© Crown copyright 2004 THE EFFECT OF COLD ON HEALTH

3 Page 3© Crown copyright 2004 The Effect of Cold on Hospital Admissions

4 Page 4© Crown copyright 2004 0 0.2 0.4 0.6 0.8 1.0 1.2 1.4 North Finland South Finland Baden-Wurttemberg Netherlands London North Italy Public Extra winter mortality % increase in mortality for each 1ºC fall from 18ºC Keatinge et al, 1997

5 Page 5© Crown copyright 2004 Public Prevention Regression coefficients for cold-related mortality from respiratory disease standardised at 7 0 C. *p<0.05 **p<0.01 Encourage AnorakAnorak Hat Warm Housing Warm Housing -3 -5* -7** Shivering+24** Stationary (>2 mins) +13* Shivering+24** Stationary (>2 mins) +13* Avoid

6 Page 6© Crown copyright 2004 Respiratory Effects of Cold  Cold causes bronchoconstriction  Cold inhaled air on the lower airways  Facial cooling  In COPD patients cold bedroom temperatures are related to the development of a ‘cold’ and an exacerbation. This may be related to cooling of nasal passages.  Increase in exacerbations related to cold outdoor temperatures.  Following a fall in temperature there is a lag for respiratory deaths peaking at 12 days.

7 Page 7© Crown copyright 2004 Keeping the house warm  There is good evidence that cold houses cause increased mortality across all social classes. Indoor temperatures are related to respiratory deaths.  A study in London demonstrated that cold bedroom temperatures are related to increased “common colds” in patients with COPD.  There is no evidence in the misconceptions that cold houses or that sleeping with the bedroom window open is “healthy” despite 40% of elderly doing so.

8 Page 8© Crown copyright 2004 HEALTH FORECASTING FOR COPD

9 Page 9© Crown copyright 2004 The Effect of Cold on Different Groups Elderly, Old Houses, Post Code COPD, CHD, Chronic Disease, Health Centre, Out of Hours, Social Services Managers, A&E, Clinicians COPD PATIENT PATHWAY IDENTIFY PATIENTS STRATIFY PATIENTS BASELINE TREATMENT FORECAST INTERVENTION

10 Page 10© Crown copyright 2004 Met Office Winter 04/05 Trial  COPD forecast for PCTs and hospitals to allow anticipatory care.  COPD advisory Group chaired by David Halpin (recent chair of NICE guideline committee).  Workload Forecast for Hospitals based on  Historic data  Real time admission data  Environmental factors  Evaluation by London School Hygiene Tropical Medicine funded by DH.

11 Page 11© Crown copyright 2004 SHA pilot project agreed. 8 Met Office service Developers Admissions & COPD Prevention. DoH funded evaluation COPD project

12 Page 12© Crown copyright 2004 SERVICE DEVELOPERS Facilitate Actions Feedback of current situation Feedback of service

13 Page 13© Crown copyright 2004 COPD Burden  A PCT serving a population of 250,000 will have about 14,200 GP consultations every year for people with COPD.  680 patients will be admitted to hospital, accounting for 9800 bed days.  Admission costs about £1700  GP Consultation costs £56

14 Page 14© Crown copyright 2004 Results so far  One PCT has noted an 85% reduction in COPD admissions.  This could “save” the PCT £1.36 million a year  The forecasts are acting as a catalyst for integrated care between the patient social care, primary care, secondary care and the local authority.  The forecasts are 75% accurate.

15 Page 15© Crown copyright 2004 COPD Admissions Plymouth hospitals

16 Page 16© Crown copyright 2004 Positive correlation: Cold snaps lead to increased COPD admissions, peaking 1-2 weeks later Weekly “Coldness” measure vs COPD admissions N.B. “Coldness” is the weekly sum of a threshold temperature minus daily max temperature Cross-correlations / lags of COPD with weather

17 Page 17© Crown copyright 2004 Temperature and EWM

18 Page 18© Crown copyright 2004 Cold-only model

19 Page 19© Crown copyright 2004 Creating a COPD forecast for each PCT Rule-based COPD predictive model Other weather data e.g. pressure, RH Local information/ Feedback/ Evaluation Health forecaster web interface COPD forecast for each Primary Care Trust (PCT) Average, Above Average, High, Very High Health data e.g. latest admission data, virus load

20 Page 20© Crown copyright 2004 Herald period conditions and calendar correction are also taken into account in this model, along with the cold.

21 Page 21© Crown copyright 2004 Treating an Exacerbation

22 Page 22© Crown copyright 2004 Anticipatory Care

23 Page 23© Crown copyright 2004 Anticipatory Care

24 Page 24© Crown copyright 2004 COPD Actions  Phone call to check:  Heating, insulation  Diet  Medication  Social Support  Early symptoms  Activity levels  Depression/anxiety Patient report early symptoms that could herald an exacerbation.

25 Page 25© Crown copyright 2004 STRATIFICATION OF PATIENTS

26 Page 26© Crown copyright 2004 Above Average Workload / Risk of Admission Forecast

27 Page 27© Crown copyright 2004 High Workload / Risk of Admission Forecast

28 Page 28© Crown copyright 2004 VERY HIGH Workload / Risk of Admission Forecast

29 Page 29© Crown copyright 2004 PCT (s)Action 1 Action 2 Action 3 Action 4 Very High Workload  High Workload  Above average Workload   Action 1 (Individual) medication, social support, heating, early symptoms etc. Action 2 (PCT) Increased resource required to deal with larger numbers of high risk. Action 3 (PCT) Increased resource required to deal with moderate admissions. Action 4 (PCT) Baseline resource to attend to small numbers of very high risk group Converting risk into action

30 Page 30© Crown copyright 2004 SUMMARY  By understanding the relationship between health and Cold many clinical conditions may be helped by:  Targeting the vulnerable by place and time  Forecasting periods of increased risk  Delivering interventions that can effectively prevent ill health.  Integrating many partners to deliver


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