This document discusses work package 2.3.1 which aims to assess climate risk for vulnerable patient groups in Berlin and Potsdam from heat stress and air pollution and investigate if climate-adapted hospitals can reduce impacts. The work will create models for each district to estimate future healthcare burdens and evaluate adaptation strategies. Next steps include defining the modeling strategy, creating datasets for each district, and mapping hospital admissions. Analysis shows radiant cooling systems in patient rooms provide clinical benefits. Stakeholders were identified and services proposed including a risk database and operational forecasting system.

1. Work package 2.3.1
Christina Hoffmann
07.05.2019
CHARITÉ - UNIVERSITÄTSMEDIZIN BERLIN
CC12 Department of Outpatient Pneumology
ARIA Technologies
Climate Services for the Health Sector
Samya de Lara Pinheiro

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Introduction and Objectives
2.3.1 Climate services for the health sector
• Assessment of climate risk for vulnerable patient groups and therefore the health insurance sector for two
European cities, metropolitan Berlin and the mid-sized town Potsdam
• The focus lies on (urban) heat stress and air pollution affecting people with respiratory diseases
• In addition, we will investigate whether climate adapted hospitals are able to reduce the climate impacts for
vulnerable groups
• The resulting model lets us estimate future burdens to the healthcare system and enables an evaluation of
adaptation strategies

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Timetable 2019
2.3.1 Climate services for the health sector
• Definition of modelling strategy:
 R package dlnm
 Distributed lag non-linear model for each
district
 Combine results in a multivariate meta
regression
Next steps:
 Create dataset for each district
(12 datasets for Berlin, 1 for Potsdam)
 Adjust hospital admissions by population
density
 Map hospital admissions by district for
each season

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Hospital admissions by district and season
2.3.1 Climate services for the health sector
Mitte

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Dlnm model of Berlin Mitte
Relevant variables for the model
date
admissions
temperature
sunshine
humidity
windspeed
airpressure
water vapor
pressure
PM2.5
NOx
ozone

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Dlnm model of Berlin Mitte
Adjustment for seasonality

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Importance of model adjustment
RR CI low CI high
Unadjusted 0.983 0.969 0.996
Plus season/trend 1.014 0.993 1.035
Dlnm model of Berlin Mitte

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Dlnm model of Berlin Mitte
Lag day analysis for NOx

9. 9
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Dlnm model of Berlin Mitte
Verification of the model

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Analysis of adaptation measures
Hospital patient rooms with radiant cooling
Parameter
Median, (range)
No climatized room Climatized room p value
Length of hospital stay 8 days (3 – 18) 6 days (2 – 14) 0.006**
CAT admission 27 (6 – 36) 27 (8 – 40) 0.989
CAT discharge 23 (1 – 36) 23 (6 – 36) 0.472
mMRC admission 3 (0 – 4) 3 (0 – 4) 0.865
mMRC discharge 3 (0 – 4) 3 (0 – 4) 0.746
Body weight admission 68 kg (44 – 152) 74 kg (48 – 138) 0.084
Body weight discharge 66 kg (44 – 103) 74 kg (46 – 127) 0.334
Body temperature 36.0 °C (34.8 – 37.7) 35.8 °C (34.5 – 38.0) 0.034*
Blood pressuresystolic 130 mmHg (90 – 180)
125 mmHg (100 –
170)
0.389
Blood pressurediastolic 80 mmHg (60 – 100) 70 mmHg (60 – 100) 0.014*
Heart rate 74 bpm (48 – 108) 82 bpm (56 – 116) 0.002**
Oxygen saturation 0.95 (0.80 – 1.00) 0.94 (0.81 – 0.99) 0.227
Daily fluid intake 2.1 (1.0 – 4.0) 1.7 (0.8 – 2.8) <0.001***
Numberofsteps
Day of hospital stay
Standard patient room
Patient room with radiant cooling system
Radiant cooling systems in hospital patient rooms
provide clinical benefits and ensure an early mobilization

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Climate Services for the health sector
Overview
Tasks 2.3.1.1, 2.3.1.2, 2.3.1.3 Tasks 2.3.1.4, 2.3.1.5 Task 2.3.1.6
Cordex,
Impact2C, etc

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• No premium change by exposure profile (ethics)
• Charité interviews:
• 3 private and 3 statutory health insurers
• No specific demand; No internal evaluation of environmental
burden
• Public sector interest: infrastructure planning
• ARIA experience:
• EUROPE: VYV Group, Climate KIC, Swiss Re, KYOMED, TERA,
GROUPAMA, EIT Health, VEOLIA Health, AirLiquide, Birmingham
Community Healthcare
• CHILE: Santa Maria Clinic, MINSAL-UdeC, Engie Factory, BUPA,
FUNDAMAS-ZEKE (pollens), AccuHealth, Hospital JJ Aguirre,
CLC (pediatry)
• Reasonable cost + high resolution (scalability = any city of the
world)
• Time scales: past (for insurers), present (information to citizens),
and near future (for health professionals)
• Prior to be involved, they would like to see DEMO results
(H2020_Insurance, Atmodata4Health, CAMS, etc)
• Health data access issue
Definition of health climate services
T2.3.1.6 – Identifying demands (Health Insurance sector – private and public)
Stakeholders
• Swiss Re Ltd. has been contacted with the assistance of
our partners from Genillard & Co. GmbH
• The Charité, ARIA, and PIK started a cooperation with
Swiss Re to develop an OASIS prototype
model together with OASIS LMF

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• ADAPTATION
• Avoid the COPD exacerbation  PREVENTIVE
• Improve the treatment options  REDUCE PATIENT STAY
Definition of health climate services
T2.3.1.6 – Cost-benefit analysis of adaptation measures (burden of environmental risks)
Radiant cooling system in hospital patient rooms at the Charité
Telemedicine

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Definition of health climate services
T2.3.1.6 – Services design
2. OPERATIONAL FORECAST1. RISK DATABASE
DESCRIPTION
Georeferenced information on chronic exposure:
risk index per neighborhood
VALUE PROPOSITION
Assess risks on customer’s operations
and minimize it accordingly
CUSTOMERS AND CHANNELS
Public health authorities, health insurance;
customized web access and yearly improvement
DESCRIPTION
Alert system to manage peaks of consultations for
respiratory diseases
VALUE PROPOSITION
Optimize medical resources distribution through a
better anticipation of peak frequentation
CUSTOMERS AND CHANNELS
Health operators (authorities, hospitals, clinics, doctors);
customized web access and yearly improvement

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Definition of health climate services
T2.3.1.6 – Services design
2. OPERATIONAL FORECAST1. RISK DATABASE
DESCRIPTION
Georeferenced information on chronic exposure:
risk index per neighborhood
VALUE PROPOSITION
Assess risks on customer’s operations and
minimize it accordingly
CUSTOMERS AND CHANNELS
Public health authorities, health insurance;
customized web access and yearly improvement
DESCRIPTION
Alert system to manage peaks of consultations for
respiratory diseases
VALUE PROPOSITION
Optimize medical resources distribution through a
better anticipation of peak frequentation
CUSTOMERS AND CHANNELS
Health operators (authorities, hospitals, clinics,
doctors); customized web access and yearly
improvement
NEXT STEPS
- Format service PRESENTATION
- Retrieve the CURRENT COST associated
with the burden of environmental risk factors
on COPD morbidity
- OASIS prototype model
- Go back to stakeholders

16. Dr. Christina Hoffmann
Charité - Universitätsmedizin Berlin
christina.hoffmann2@charite.de
https://pneumologie.charite.de/
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