Transcript Methods and Tools for the Human Health Sector

Methods and Tools for the
Human Health Sector
Kristie L. Ebi, Ph.D., MPH
Washington, DC USA
[email protected]
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
Distal Causes
Temperature
Humidity
Precipitation
Living conditions
(water supply and
sanitation)
Food sources and
hygiene practices
Proximal Causes
Infection Hazards
Survival/ replication
of pathogens in the
environment
Consumption of
contaminated water
Contamination of
water sources
Consumption of
contaminated food
Contamination of
food sources
Contact with
infected persons
Rate of person
to person contact
WHO
Health Outcome
Incidence of
mortality and
morbidity
attributable
to diarrhea
Vulnerability
(e.g. age and
nutrition)
Pathways from Driving Forces to Potential
Health Impacts
Corvalan et al. 2003
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
entail
changes 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
Venezuela
Landslide
25,000
N/A
1999
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
•Immediate deaths and injuries
•Non specific increases in
mortality
•Infectious diseases –
leptospirosis, hepatitis,
diarrhoeal, respiratory, & vectorborne diseases
•Exposure to toxic substances
•Mental health effects
•Increased demands on health
systems
Philip Wijmans, LWF/ACT Mozambique, March 2000
Proportion of malaria case s and
anomalie s in maximum te mpe rture : Ke nya
70
4
60
3
50
2
40
1
0
30
-1
20
Jan 97May SepJan 98May SepJan 99May Sep
Temperature anomalies
Percent of malaria cases in hospital
5
-2
Time
Malaria cases
Dr. Githeko, personal communication
Maximum temp Minimum Temp
Climate Change and Malaria
Under Different Scenarios (2080)
• Increase: East Africa, Central Asia, Russian Federation
• Decrease: Central America, Amazon
[within current vector limits]
Change of consecutive months
A1
> +2
+2
A2
-2
< -2
B1
B2
Van Lieshout et al. 2004
China Haze 10 January 2003
NASA
Effect of Temperature Variation on
Diarrheal Incidence in Lima, Peru
Daily Diarrhea
Admissions
Daily
Temperature
Diarrhea increases by 8% for each 1 ºC increase in temperature
Checkley et al. 2000
Number of Cholera cases in Uganda 1997-2002
Number of cases
50000
40000
El Nino stops
El Nino starts
30000
20000
10000
0
1996
1997
1998
1999
2000
Time in years
Dr. Githeko, personal communication
2001
2002
2003
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/cchhsumma
ry/
• 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 ClimateSensitive 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/databa
se/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)
Population
360,965,000
Total deaths
6,007,000
HIV/AIDS
1,616,000
Diarrheal diseases
356,000
Malaria
579,000
Protein-energy
malnutrition
World Health Report 2004
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 VectorBorne 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 Change
Campbell-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
Greenhouse gas
emissions scenarios
Time
2020s
2050s
Global climate modelling:
2080s
Generates series of maps
of predicted future climate
Health impact model:
Estimates the change in relative
risk of specific diseases
2020s
Campbell-Lendrum et al. 2003
2050s
2080s
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
8
Relative Risk of Deaths and Injuries in Inland
Floods in 2030, by Region
7
s550
s750
UE
5
4
3
2
1
Wpr B
Wpr A
Sear D
Sear B
Eur C
Eur B
Eur A
Emr D
Emr B
Amr D
Amr B
Amr A
Afr E
0
Afr D
Relative Risk
6
Relative Risk of Diarrheoa in 2030, by Region
1.1
Climate
scenarios,
s550
as function
ofs750
GHG
emissions
1.08
UE
1.04
1.02
1
0.98
0.96
Wpr B
Wpr A
Sear D
Sear B
Eur C
Eur B
Eur A
Emr D
Emr B
Amr D
Amr B
Amr A
Afr E
0.94
Afr D
Relative Risk
1.06
Estimated Death and DALYs
Attributable to Climate Change
2000
Floods
2020
Malaria
Diarrhea
Malnutrition
120 100 80
60
40
20
Deaths (thousands)
Campbell-Lendrum et al. 2003
0
2
4
6
8
DALYs (millions)
10
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
Mean Temperature (°C)
40
38
36
34
32
30
28
26
24
22
20
18
.1
1.00
0.90
0.80
0.70
0.60
0.50
0.40
0.30
0.20
0.10
0.00
16
Proportion of M osquitoes
Surviving One Day
Relationship Between Temperature and
Daily Survivorship of Anopheles
Mean Temperature (°C)
39
37
35
33
31
29
27
25
23
21
19
0.40
0.35
0.30
0.25
0.20
0.15
0.10
0.05
0.00
17
Proportion Surviving
Proportion of Vectors Surviving Time
Required for Parasite Development
Relationship Between Temperature and Time
Required for Parasite Development
120
100
Days
80
60
40
20
0
17
19
21
23
25
27
29
31
Mean Temperature (°C )
33
35
37
39
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 Scenarios
Ebi 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
Cases by Month
Source:
South African Malaria Research Programme
Ebi et al. Climatic Change
• 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
Methods
• Baseline climatology determined
• COSMIC was used to generate Zimbabwespecific 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
1.2
Fuzzy Value
1
0.8
0.6
0.4
0.2
39.5
37.5
35.5
33.5
31.5
29.5
27.5
25.5
23.5
21.5
19.5
17.5
0
Mean Temperature (°C)
Fuzzy Logic Value for Minimum Temperature
1.2
1
1
Precipitation (mm)
Minimum Temperature (°C)
6.5
6.3
6.1
5.9
5.7
5.5
5.3
5.1
4.9
4.7
4.5
84
80
76
72
68
64
60
56
52
48
44
40
36
32
28
24
20
16
0
8
0
12
0.2
4
0.2
4.3
0.4
4.1
0.4
0.6
3.9
0.6
0.8
3.7
0.8
3.5
Fuzzy Value
1.2
0
Fuzzy Value
Fuzzy Logic Value for Precipitation
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 value
Ebi 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/inde
x.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 19611990 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
Emergency
relief
Yes
Burton and Ebi, in preparation
Closed
Yes
Yes
Yes
Open
Practical
Range of
Choice
Size of
Events/
Exposure
Intensity
Technically
viable?
Economically
possible
(includes
needed
infrastructure
available)?
Institutional
support and
human capital
available?
Land use
planning to
reduce risk
exposure
Yes
Yes
Over 400 local Variable
planning
authorities; little
central
coordination
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
Compatible
with current
policies?
Policy
Transchange
boundary
needed? issue?
Thank You