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Methods and Tools for the Human Health Sector
Kristie L. Ebi, Ph.D., MPH
Washington, DC USA
V&A Assessment Hands-On Training Workshop
April 2005
Outline
1. Overview of the potential health impacts of climate variability & change
2. Health data to determine the current burden of climate-sensitive diseases
3. Methods and tools for V&A assessment in the health sector
4. Methods for determining a health adaptation baseline
Overview of the Potential Health Impacts of Climate
Variability & Change
Topics• Pathways for weather to affect health• Potential health impacts of climate change
– Extreme weather events• Temperature• Floods
– Vector-borne diseases– Diseases related to air pollution– Diarrheal diseases
Pathways for Weather to Affect Health: Example = Diarrheal Disease
TemperatureHumidityPrecipitation
Distal Causes Proximal Causes Infection Hazards Health Outcome
Living conditions(water supply andsanitation)
Food sources andhygiene practices
Survival/ replicationof pathogens in theenvironment
Contamination ofwater sources
Rate of personto person contact
Consumption ofcontaminated water
Consumption ofcontaminated food
Contact withinfected persons
Incidence of mortality andmorbidityattributableto diarrhea
Vulnerability(e.g. age andnutrition)
Contamination of food sources
WHO
Corvalan et al. 2003
Pathways from Driving Forces to Potential Health Impacts
Drivers of Health Issues• Population density• Urbanization• Public health infrastructure• Economic and technologic development• Environmental conditions• Populations at risk
– Poor– Children– Increasing population of elderly residents– Immunocompromised
Climate change may entailchanges in variance, as well as changes in mean
Temperature Extremes in
the Caribbean,1955-2000
Climate Variability & Change Impacts in the Caribbean
DATE COUNTRY EVENT DEATH ESTIMATED COSTS(US$ million, 1998)
1974 Honduras Hurricane Fifi 7,000 1,331
1982/3 Bolivia, Ecuador, Peru El Niño 0 5,661
1997/98
Bolivia, Colombia, Ecuador, Peru
El Niño 600 7,694
1998 Central America Hurricane Mitch 9,214 6,008
1998 Dominican Republic Hurricane Georges 235 2,193
Cuba Hurricane Georges 6 N/A
1999 Venezuela Landslide 25,000 N/A
Fuente: ECLAC, América Latina y El Caribe: El Impacto de los Desastres Naturales en el Desarrollo, 1972-1999, LC/MEX/L.402; OFDA, Venezuela- Floods, Fact Sheet #10, 1/12/ 2000.
2000 Flood in Mozambique• Heavy rains from Cyclones Connie and Eline in
February 2000 caused large scale flooding of the Limpopo, Incomati, Save, and Umbeluzi rivers– Environmental degradation and poor river system
management and protection contributed to the crisis
• 700 people died, 250,000 people were displaced and 950,000 required humanitarian assistance (of which 190,000 were children under the age of 5)– 14,800 people were rescued by helicopter
Health Impacts of Floods
Philip Wijmans, LWF/ACT Mozambique, March 2000
•Immediate deaths and injuries
•Non specific increases in mortality
•Infectious diseases – leptospirosis, hepatitis, diarrhoeal, respiratory, & vector-borne diseases
•Exposure to toxic substances
•Mental health effects
•Increased demands on health systems
20
30
40
50
60
70P
erc
en
t o
f m
ala
ria c
ases in
ho
sp
ital
-2
-1
0
1
2
3
4
5
Tem
pera
ture
an
om
alies
Jan 97May SepJan 98May SepJan 99May SepTime
Malaria cases Maximum temp Minimum Temp
Proportion of malaria cases andanomalies in maximum temperture: Kenya
Dr. Githeko, personal communication
Climate Change and Malaria Under Different Scenarios (2080)• Increase: East Africa, Central Asia, Russian Federation• Decrease: Central America, Amazon
[within current vector limits]
C hange o f consecutive m onths
> +2
+2
-2
< -2
A1
B2
A2
B1 Van Lieshout et al. 2004
China Haze 10 January 2003
NASA
Effect of Temperature Variation on Diarrheal Incidence in Lima, Peru
Daily Temperature
Daily Diarrhea Admissions
Diarrhea increases by 8% for each 1 ºC increase in temperature
Checkley et al. 2000
Number of Cholera cases in Uganda 1997-2002
0
10000
20000
30000
40000
50000
1996 1997 1998 1999 2000 2001 2002 2003
Time in years
Num
ber o
f cas
es
El Nino starts El Nino stops
Dr. Githeko, personal communication
Resources• McMichael AJ, Campbell-Lendrum DH, Corvalan
CF, Ebi KL, Githeko A, Scheraga JD, Woodward A (eds.). Climate Change and Human Health: Risks and Responses. WHO, Geneva, 2003.– Summary pdf available at
http://www.who.int/globalchange/publications/cchhsummary/
• Kovats RD, Ebi KL, Menne B. Methods of Assessing Human Health Vulnerability and Public Health Adaptation to Climate Change. WHO/Health Canada/UNEP, 2003.– Pdf available at http://www.who.dk/document/E81923.pdf
Health Data to Determine the Current Burden of Climate-
Sensitive Diseases
Questions to be Addressed
• What climate-sensitive diseases are important in your country or region?– What is the current burden of these diseases?
• What factors other than climate should be considered?– Water, sanitation, etc.
• Where are data available?• Are health services able to satisfy current
demands?
Health Data Sources
• World Health Report provides regional level data for all major diseases– http://www.who.int/whr/en– Annual data in Statistical Annex
• WHO databases– Malnutrition http://www.who.int/nutgrowth/db– Water and sanitation
http://www.who.int/entity/water_sanitation_health/database/en
• Ministry of Health– Disease surveillance/reporting branch
Health Data Sources - Other
• UNICEF at http://www.unicef.org
• CRED-EMDAT provides data on disasters– http://www.em-dat.net
• Mission hospitals
• Government district hospitals
Mozambique
• Total population = 18,863,000• Annual population growth rate = 2.4%• Life expectancy at birth = 45 years• Under age 5 mortality rate = 158/1000
– 72% of 1-year-olds immunized with 3 doses of DTP
• 5.8% of gross domestic product spent on health
World Health Report 2005
WHO Region Afr-E (Countries with High Child & Very High Adult Mortality)
World Health Report 2004
Population 360,965,000
Total deaths 6,007,000
HIV/AIDS 1,616,000
Diarrheal diseases 356,000
Malaria 579,000
Protein-energy malnutrition
54,000
Seychelles National Communication
Methods and Tools for V&A Assessment in the Health
Sector
Methods and Tools
• Qualitative assessments• Methods of assessing human health
vulnerability to climate change• MARA/ARMA -- climate suitability for stable
malaria transmission• WHO Global Burden of Disease Comparative
Risk Assessment– Environmental Burden of Disease
• Other models
Qualitative Assessments
• Available data allows for qualitative assessment of vulnerability
• For example, given current burden of diarrheal diseases and projected changes in precipitation, will vulnerability likely remain the same, increase, or decrease?
Methods of Assessing Human Health Vulnerability
and Public Health Adaptation to Climate
Change
Kovats et al. 2003
Methods for:
• Estimating the current distribution and burden of climate-sensitive diseases
• Estimating future health impacts attributable to climate change
• Identifying current and future adaptation options to reduce the burden of disease
Kovats et al. 2003
Estimate Potential Future Health Impacts
• Requires using climate scenarios• Can use top-down or bottom-up approaches
– Models can be complex spatial models or be based on a simple exposure-response relationship
• Should include projections of how other relevant factors may change
• Uncertainty must be addressed explicitly
Kovats et al. 2003
Case Study: Risk of Vector-Borne Diseases in Portugal
• 4 qualitative scenarios developed of changes in climate and in vector populations– Vector not present– Focal distribution of vector– Widespread distribution of vector– Change from focal to potentially regional
distribution
• Expert judgment determined likely risk under each scenario for 5 vector-borne diseases
Kovats et al. 2003
Sources of Uncertainty
• Data– Missing data or errors in data
• Models– Uncertainty regarding predictability of the system– Uncertainty introduced by simplifying relationships
• Other– Inappropriate spatial or temporal data– Inappropriate assumptions– Uncertainty about predictive ability of scenarios
Kovats et al. 2003
Estimating the Global Health Impacts of Climate ChangeCampbell-Lendrum et al. 2003 (pdf available)
• What will be the total potential health impact caused by climate change (2000 to 2030)?
• How much of this could be avoided by reducing the risk factor (i.e. stabilizing greenhouse gas (GHG) emissions)?
Comparative Risk Assessment
2020s
2050s
2080s
Greenhouse gas emissions scenarios
Global climate modelling:
Generates series of maps of predicted future climate
Health impact model: Estimates the change in relative risk of specific diseases
Campbell-Lendrum et al. 2003
Time
2080s2050s2020s
Criteria for Selection of Health Outcomes
• Sensitive to climate variation
• Important global health burden
• Quantitative model available at the global scale– Malnutrition (prevalence)– Diarrhoeal disease (incidence)– VBD – dengue and Falciparum malaria – Inland and coastal floods (mortality)– Heat and cold related CVD mortality
Campbell-Lendrum et al. 2003
Exposure: Alternative Future Projections of GHG Emissions
• Unmitigated current GHG emissions trends
• Stabilization at 750 ppm CO2-equivalent
• Stabilization at 550 ppm CO2-equivalent
• 1961-1990 levels of GHGs with associated climate
Source: UK Hadley Centre models
Campbell-Lendrum et al. 2003
Relative Risk of Deaths and Injuries in Inland Floods in 2030, by Region
0
1
2
3
4
5
6
7
8
Afr
D
Afr
E
Am
r A
Am
r B
Am
r D
Em
r B
Em
r D
Eur
A
Eur
B
Eur
C
Sea
r B
Sea
r D
Wpr
A
Wpr
B
Rel
ativ
e R
isk
s550
s750
UE
Climate scenarios, as function of GHG emissions
Relative Risk of Diarrheoa in 2030, by Region
0.94
0.96
0.98
1
1.02
1.04
1.06
1.08
1.1
Afr
D
Afr
E
Am
r A
Am
r B
Am
r D
Em
r B
Em
r D
Eur
A
Eur
B
Eur
C
Sea
r B
Sea
r D
Wpr
A
Wpr
B
Rel
ativ
e R
isk
s550
s750
UE
Floods
Malaria
Diarrhea
Malnutrition
020406080100120 2 4 6 8 10
DALYs (millions)Deaths (thousands)
2000
2020
Estimated Death and DALYs Attributable to Climate Change
Campbell-Lendrum et al. 2003
Conclusions• Climate change may already be causing a
significant burden in developing countries
• Unmitigated climate change is likely to cause significant public health impacts out to 2030– Largest impacts from diarrhea, malnutrition,
and vector-borne diseases
• Uncertainties include:– Uncertainties in projections– Effectiveness of interventions– Changes in non-climatic factors
Campbell-Lendrum et al. 2003
Environmental Burden of Disease
• Introduction and Methods: Assessing the Environmental Burden of Disease at National and Local Levels by A Pruss-Ustun, C Mathers, C Corvalan, and A Woodward [pdf available at http://www.who.int/peh/burden/burdenindex.html]
• Climate change document will be published soon
The website [http://www.mara.org.za] contains prevalence and population data, and regional and county-level maps
Climate and Stable Malaria Transmission
• Climate suitability is a primary determinant of whether the conditions in a particular location are suitable for stable malaria transmission
• A change in temperature may lengthen or shorten the season in which mosquitoes or parasites can survive
• Changes in precipitation or temperature may result in conditions during the season of transmission that are conducive to increased or decreased parasite and vector populations
Climate and Stable Malaria Transmission (continued)
• Changes in precipitation or temperature may cause previously inhospitable altitudes or ecosystems to become conducive to transmission. Higher altitudes that were formerly too cold or desert fringes that were previously too dry for mosquito populations to develop may be rendered hospitable by small changes in temperature or precipitation.
MARA/ARMA Model
• Biological model that defines a set of decision rules based on minimum and mean temperature constraints on the development of the Plasmodium falciparum parasite and the Anopheles vector, and on precipitation constraints on the survival and breeding capacity of the mosquito
• CD-ROM $5 or can download components from website
Relationship Between Temperature and Daily Survivorship of Anopheles
0.000.100.200.300.400.500.600.700.800.901.00
Mean Temperature (°C)
Prop
ortio
n of
Mos
quito
es
Surv
ivin
g O
ne D
ay
Proportion of Vectors Surviving Time Required for Parasite Development
0.00
0.05
0.10
0.15
0.20
0.25
0.30
0.35
0.40
Mean Temperature (°C)
Pro
port
ion
Sur
vivi
ng
Relationship Between Temperature and Time Required for Parasite Development
0
20
40
60
80
100
120
Mean Temperature (°C )
Days
Mozambique – Endemic Malaria Season Length
Mozambique – Endemic Malaria Prevalence
Mozambique – Endemic Malaria Prevalence by Age
Climate Suitability for Stable Malaria Transmission in Zimbabwe Under
Different Climate Change ScenariosEbi et al. Climatic Change
Objective: to look at the range of responses in the climatic suitability for stable falciparum malaria transmission under different climate change scenarios in Zimbabwe
Malaria in Zimbabwe
• Patterns of stable transmission follow pattern of precipitation and elevation (which in turn influences temperature)
• >9,500 deaths and 6.4 million cases between 1989-1996
• Recent high-altitude outbreaks
Cases by Month
Source:
South African Malaria Research Programme
Ebi et al. Climatic Change
Methods
• Baseline climatology determined
• COSMIC was used to generate Zimbabwe-specific scenarios of climate change; changes were added to baseline climatology
• Outputs from COSMIC were used as inputs for the MARA/ARMA (Mapping Malaria Risk in Africa) model of climate suitability for stable Plasmodium falciparum malaria transmission
Ebi et al. Climatic Change
Data Inputs
• Climate data– Mean 60 year climatology of Zimbabwe on
a 0.05° lat/long grid (1920-1980)– Monthly minimum and maximum
temperature and total precipitation
• COSMIC output– Projected mean monthly temperature and
precipitation (1990-2100)
Ebi et al. Climatic Change
Climate in Zimbabwe• Rainy warm austral summer October – April• Dry and cold May-September• Heterogeneous elevation-dictated temperature
range• Strong interannual and decadal variability in
precipitation • Decrease in precipitation in the last 100 years
(about 1% per decade) • Temperature changes 1933-1993
– Increase in maximum temperatures +0.6°C– Decrease in minimum temperatures –0.2 °C
Ebi et al. Climatic Change
GCMs
• Canadian Centre for Climate Research (CCC)• United Kingdom Meteorological Office
(UKMO)• Goddard Institute for Space Studies (GISS)• Henderson-Sellers model using the CCM1 at
NCAR (HEND)
Ebi et al. Climatic Change
Scenarios
• Climate sensitivity– High = 4.5ºC– Low = 1.4ºC
• Equivalent carbon dioxide (ECD) analogues to the 350 ppmv and 750 ppmv greenhouse gas emission stabilization scenarios of the IPCC SAR
Ebi et al. Climatic Change
Assumptions
• No change in the monthly range in minimum and maximum temperatures
• Permanent water bodies do not meet the precipitation requirements
• Climate did not change between the baseline (1920-1980) and 1990
Ebi et al. Climatic Change
Fuzzy Logic Value
• Fuzzy logic boundaries established for minimum, mean temperature and precipitation
• 0 = unsuitable
• 1 = suitable for seasonal endemic malaria
Ebi et al. Climatic Change
Assignment of Fuzzy Logic Values to Climate Variables
Fuzzy Logic Value for Mean Temperature
0
0.2
0.4
0.6
0.8
1
1.2
17.5
19.5
21.5
23.5
25.5
27.5
29.5
31.5
33.5
35.5
37.5
39.5
Mean Temperature (°C)
Fu
zzy
Val
ue
Fuzzy Logic Value for Precipitation
0
0.2
0.4
0.6
0.8
1
1.2
0 4 8 12 16 20 24 28 32 36 40 44 48 52 56 60 64 68 72 76 80 84
Precipitation (mm)
Fu
zzy
Val
ue
Fuzzy Logic Value for Minimum Temperature
0
0.2
0.4
0.6
0.8
1
1.2
3.5
3.7
3.9
4.1
4.3
4.5
4.7
4.9
5.1
5.3
5.5
5.7
5.9
6.1
6.3
6.5
Minimum Temperature (°C)
Fu
zzy
Val
ue
Climate Suitability Criteria
• Fuzzy values assigned to each grid• For each month, determined the lowest fuzzy
value for precipitation and mean temperature
• Determined moving 5-month minimum fuzzy values
• Compared these with the fuzzy value for the lowest monthly average of daily minimum temperature
• Assigned the lowest fuzzy valueEbi et al. Climatic Change
UKMO• S750 ECD stabilization scenario with 4.5°C
climate sensitivity• Model output
– Precipitation• Rainy season (ONDJFMA) increase in precipitation
of 8.5% from 1990 to 2100
– Temperature• Annual mean temperature increase by 3.5°C from
1990 to 2100, with October temperatures increasing more than July temperatures.
Ebi et al. Climatic Change
Baseline
Ebi et al. Climatic Change
2025
Ebi et al. Climatic Change
2050
Ebi et al. Climatic Change
2075
Ebi et al. Climatic Change
2100
Ebi et al. Climatic Change
Conclusions
• Assuming no future human-imposed constraints on malaria transmission, changes in temperature and precipitation could alter the geographic distribution of stable malaria transmission in Zimbabwe
• Among all scenarios, the highlands become more suitable for transmission
• The lowveld and areas currently limited by precipitation show varying degrees of change
• The results illustrate the importance of using several climate scenarios
Ebi et al. Climatic Change
Other Models
• MIASMA– Global malaria model
• CiMSiM and DENSim for dengue– Weather and habitat-driven entomological
simulation model that links with a simulation model of human population dynamics to project disease outbreaks
– http://daac.gsfc.nasa.gov/IDP/models/index.html
Sudan National Communication
• Using an Excel spreadsheet, modeled malaria based on relationships described in MIASMA
• Calculated monthly changes in transmission potential for the Kordofan Region for the years 2030-2060, relative to the period 1961-1990 using the IPCC IS92A scenario, simulation results of HADCM2, GFDL, and BMRC, and MAGICC/SCENGEN
Sudan – Projected Increase in Transmission Potential of Malaria in 2030
Sudan – Projected Increase in Transmission Potential of Malaria in 2060
Sudan – Malaria Projections
• Malaria in Kordofan Region could increase significantly during the winter months in the absence of effective adaptation measures– The transmission potential during these months is
75% higher than without climate change
• Under HADCM2, the transmission potential in 2060 is more than double baseline
• Transmission potential is projected to decrease during May-August due to increased temperature
Methods for Determining a Health Adaptation Baseline
Questions for Designing Adaptation Policies & Measures• Adaptation to what?• Is additional intervention needed?• What are the future projections for the outcome?
Who is vulnerable?– On scale relevant for adaptation
• Who adapts? How does adaptation occur?• When should interventions be implemented?• How good or likely is the adaptation?
Current and Future Adaptation Options
• What is being done now to reduce the burden of disease? How effective are these policies and measures?
• What measures should begin to be implemented to increase the range of possible future interventions?
• When and where should new policies be implemented?– Identify strengths and weaknesses, as well as threats
and opportunities to implementation
Kovats et al. 2003
Public Health Adaptation to Climate Change
• Existing risks– Modifying existing prevention strategies
– Reinstitute effective prevention programs that have been neglected or abandoned
– Apply win/win or no-regrets strategies
• New risks
Policy Analysis of Flooding Adaptation Strategies, Policies and Measures in
the UK
Theoretical Range of Choice
Technically feasibility demonstrated?
Economically feasible?
Socially and Legally Acceptable?
Effective to address health outcome?
Closed/Open (Practical Range of Choice)
Land use planning to reduce risk exposure
Yes at County and District levels only
Yes Yes Yes Open
Engineering works to reduce risk exposure
Yes Yes Yes Yes Open
Insurance Generally not available
Closed
Emergency relief
Yes Yes Yes Yes Open
Burton and Ebi, in preparation
Practical Range of Choice
Size of Events/ Exposure Intensity
Technically viable?
Economically possible (includes needed infrastructure available)?
Institutional support and human capital available?
Compatible with current policies?
Policy change needed?
Trans-boundary issue?
Land use planning to reduce risk exposure
Yes Yes Over 400 local planning authorities; little central coordination
Variable Variable No
Engineering works to reduce risk exposure
Yes Grant aid to supplement local resources for flood defense is provided only for capital schemes
Through Environment Agency and County Councils
Variable Variable No
Emergency relief
Yes Yes County and District Councils; emergency services; local and regional health authorities
Yes No No
Burton and Ebi, in preparation
Thank You