Climate Change Vulnerability

Assessment in Ghana (2006 -2008)

Edith Clarke

Occupational & Environmental Health Unit

Ghana Health Service

Ministry of Health, Ghana

San Jose, Costa Rica (20-23 July 2010)

Ghana• Land Area: 240,000km2

• Bordered in south by 560km long coastline & north by Latitude 11.15o

• Population:22 million

• Workforce: >50% in agricul-ture contributing 30% GDP

• High Illiteracy rate: 40%

• Life Expectancy: 60.5yrs

Vulnerability Assessment & Health

Adaptation : Mechanisms

I. Integrated Assessment of Sectoral Vulnerabilities & Adaptation : 2006 – 2008

II. Adaptation Project : Development of Proposal : To be co-sponsored by GEF Facility

III. Consideration of Climate Change within the SANA process of the Libreville Declaration

Integrated Assessment

• 2006- 2008 : Integrated assessment of sectoral

Vulnerabilities & Adaptation led by the EPA /MEST

• Purpose: Was to provide inputs towards the

formulation of an Integrated National Climate

Change Adaptation Policy Framework.

• Health Vulnerability & Adaptation assessment was

conducted as part of integrated national

assessment

Health Assessment within Integrated

Assessment Objective:

• Examine the association between climate change and human health vulnerability.

• Identify impact of climate change on 5 endemic tropical diseases: malaria, diarrhoeal diseases, guinea worm, measles and cerebrospinal meningitis

• Investigate consequences of climate change on the livelihood systems of poor communities.

2009 Draft WHO Guidelines for V&A assessment for the assessment not yet available

The Process

• Process led by Environmental Protection Agency :

focal point for Climate Change

• Study conducted by researchers from the

University of science & Technology

• Stakeholder involvement : at inception and

dissemination of results

: though involved MOH, excluded managers of

environmental health programme and disease

programme areas under consideration

Methodology i. Observation of time series data of;

• Monthly incidence of outpatient morbidity from 2 regions :Ashanti and Upper West Regions : 1999 -2004

• Climate variables (mean, maximum & minimum air temperature, rainfall and relative humidity data for period 1961 - 1990 for of Ghana.

ii. studied above against their seasonal changes & modelling scenarios for 2020, 2050 and 2080

iii. Estimation of the socio-economic burden :direct & indirect cost of treating malaria.

Major Findings:

Results:

• Analysis predicts general increases in temperature

and decreases in rainfall

• Predicts: upsurge of diarrhoeal diseases, meningitis

and guinea worm and

:decreases in incidence of malaria

• Total Disease Cost/Direct & Indirect to patient &

carers : Approximately $5 million Dollars per annum

(2003)

Results. Fig. 1 Climate scenario for baseline and the years 2020, 2050 and 2080

26.5

27.1

28.5

29.6

27.9 27.9

27.6

27

25.8

24.924.8

25.3

26

26.6

26.1

28.628.6

28.2

27.2

26.3

25.4 25.3

25.8

26.5

27.2

26.7

30.130 29.9

29.2

27.7

26.6 26.6

26.9

27.6

28.6

28.1

30.1

30.5

29.4

28.528.2

28.4

31.4

32.132.132.1

30.6

24

25

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32

33

Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec

Period (Months)

Mean air temperature

Mean air temperature baseline

Mean air temperature 2020

Mean air temperature2050

Mean air temperature2080

M

Distribution of number of malaria cases and mean air temperature : Inverse relation (1999 –

2004).

Dec-01

40463

Jan-00

17941

Sep-04

71416

Aug-02

80323

May-01

60280Jun-00

55983

Jun-99

48013

Feb-03

39886

Jul-04

38343

Oct-03

68043

Aug-04

24.8Aug-03

24.5

Aug-02

24.22Aug-01

24.5

Jul-00

24.6

Sep-99

25.1

Mar-04

27.9

Mar-03

29.3

Feb-02

29.6

Feb-01

28.8

Mar-00

29.3

Mar-99

28.1

10000

20000

30000

40000

50000

60000

70000

80000

90000

Jan-

99

Apr-

99

Jul-

99

Oct-

99

Jan-

00

Apr-

00

Jul-

00

Oct-

00

Jan-

01

Apr-

01

Jul-

01

Oct-

01

Jan-

02

Apr-

02

Jul-

02

Oct-

02

Jan-

03

Apr-

03

Jul-

03

Oct-

03

Jan-

04

Apr-

04

Jul-

04

Oct-

04

Time (1999 - 2004)

Number of malaria cases

24

25

26

27

28

29

30

Mean air temperature

Number of malaria cases Mean air temperature

Generally high mean air temperature values corresponds to periods of

low number of malaria cases and vice versa.

Baseline values of Rainfall and Average Number of Outpatient Malaria Cases.

17.1

54.8

114.9

135.0

169.4

121.9

153.8

67.080.9

31.9

142.0

215.2

20000

25000

30000

35000

40000

45000

50000

55000

60000

65000

January

February

March

April

May

June

July

August

Septem

ber

October

Novem

ber

Decem

ber

Period (1999 - 2004)

Number of outpatient malaria cases (Averaged)

0.0

50.0

100.0

150.0

200.0

250.0

Baseline rainfall amount(mm)

Average number of malaria cases

Rainfall baseline

The maximum number of malaria cases occurs in June and the mean air

temperature is 25.8°C. Low number of malaria cases occur in February,

March and Aril where mean air temperature ranges from 27.6°C in April,

27.9°C in both February and March respectively.

Meningitis and Climate Change

Distribution of meningitis cases and mean air temperature.

Mar-00

18

Mar-99

28.1

Feb-01

28.8

Feb-02

29.6Mar-03

29.3

Feb-04

27.9

0

10

20

30

40

50

60

70

80

Jan-99Apr-99Jul-99

Oct-99Jan-00Apr-00Jul-00

Oct-00Jan-01Apr-01Jul-01

Oct-01Jan-02Apr-02Jul-02

Oct-02Jan-03Apr-03Jul-03

Oct-03Jan-04Apr-04Jul-04

Oct-04

Period (1999 - 2004)

Number of meningitis cases

24

25

26

27

28

29

30

Mean air temperature

Number of meningitis cases

Mean air temperature

High meningitis cases corresponds to periods of high

mean air temperature and vice versa.

Process of the GEF Adaptation Project

• Process initiated by EPA / UNDP

• An Adaptation project (for 3 diseases) NOT Vulnerability Assessment Project

• Did not take cognizance of gaps in Health assessment within the Integrated assessment

• Health sector’s /WHO input received only after agreement to project , Hence limiting extent of inclusion of comprehensive sector Vulnerability Assessment & Health System Strengthening approach it proposed.

• Final proposal submitted

GEF Adaptation Project• Objective

Identify, implement & evaluate adaptations to

reduce current & likely future burdens of Malaria,

Diarrhoeal diseases & meningococcal meningits

Project Components

i. Strengthening technical capacities to manage

climate change related health risks

ii. Mainstreaming CC into decision making

iii.Strengthen CC health risk knowledge

SANA: 19 Country Task Team MembersLed by 2 Focal Persons : Hlth & Env

• MOH:1

• GHS : 3 – PPME, Environmental Health, Disease Control

• Min of Env: 1

• EPA : 3

• Ministry of Local Government :Env Health Dept

• Min of Agriculture

• Min of Finance

• National Development Planning Commission

• Academia

• Community Water & Sanitation

• Standards Board

• WHO

• (UNDP)

Analysis by Country Task Team of SANA

Driving Forces for Health

Effects of Climate Change

Potential Impacts on Health

Drought

Flooding

Heat Waves

Dry winds

Pre-existing Poor Sanitation

• Increase in Vectors of disease

transmission eg mosquitoes

•Increase or decrease in vector –borne

diseases : Malaria, yellow fever, filariasis

•Increase in water-related diseases eg

diarrhoeal diseases, Guinea worm,

•Increasing malnutrition

•Increase in Cardiovascular & respiratory

ailments

•Risks to vaccine potency due to

increasing unreliability of energy supply

•Increasing pressure on National Health

Insurance Scheme

Major Challenges to Integrated

Assessment As assessed also by SANA process-

Limitations of study include:

i. Inadequate Data

• daily morbidity data not available at regional

level to compare with meteorological data;

• short period of disease incidence data used for

establishing time series trends

ii. Influence of natural increases in population on

incidence of the diseases studied not explicitly

factored into projections of disease incidences

Limitations of study (II)

iii. Limited scope of analysis: eg

• Absence of research into CC impacts on vectors of disease transmission eg mosquito vector of malaria

• Does not consider information on perceptions of the population regarding knowledge and understanding of the disease conditions & local prevention & treatment practices

• excludes potential impacts of health systems and impact of on-going disease control interventions /programmes including vulnerability of infrastructure & support services like energy supplies for cold chain maintenance on evolution of selected diseases

19

Views of a ravaged Health Center in Northern Ghana

(roof blown off by storm) due to bad weather (2008)

Limitations of study (III)

iv. Involvement of stakeholders in health not broad

enough eg program managers, field service staff

excluded

v. Health implications of potential impact of climate

variability on other sectors not determined e.g.

�agricultural productivity increasing malnutrition as

a result of decreased food security

Utility of Assessment of Integrated Study

• Study succeeded in demonstrating that there is a linkage between trends of some common endemic communicable diseases and climate variability.

• It demonstrated fact of increase in disease burden over time but does not analyse the extent to which CC effects contribute to additional burden of disease

• Demonstrated cost of managing malaria but does not relate it to earning capacity / ability to pay

• Since modifying effects of disease control programs, human treatment seeking behaviours etc omitted, may weaken evidence base for some of the adaptation decisions

Lessons Learnti.Processes employed in integrated assessment have

led to incomplete health V & A assessment

ii. Need for thorough stakeholder analysis at onset of

V & A projects cannot be overemphasized

iii. Need for health sector to be more pro-active in CC

issues

iv. Urgent Need for a more comprehensive

assessment incorporating all the elements in

WHO’s V&A guidelines still exists via

? modification of GEF Adaptation project or

?New project within Libreville Declaration framework