AIACC AF91

AIACC AF91HEALTH

Presented by

Dr. A. K Githeko PhD

Head: Climate and Human Health Research Unit,

Kenya Medical Research Institute

Scope of primary data collection Indicators of wealth Knowledge, attitude and practice Impacts

This presentation will focus on malaria in Western Kenya Highlands

A typical malaria epidemic in Western KenyaKISII DISTRICT MALARIA THRESHOLD

0

5000

10000

15000

20000

25000

J an Feb Mar Apr May J un J ul Aug Sep Oct Nov Dec

MONTHS

MA

LA

RIA

CA

SE

S

MEDIAN QUARTILE 3 CURRENT YEAR

NORMAL

ALERT

OUTBREAK

Factors affecting Malaria impacts Geographic location Climate variability Quality of housing Vector control Immunity Accuracy of

diagnoses Availability of

medical facilities

Accessibility of health facilities

Efficacy of drugs Affordability of drugs Frequency of

infections Size of susceptible

population

Parameterization of factors The contributing factors to vulnerability will be

converted into numerical parameters reflecting their linearity or non-linearity

Summation of the factors will provide a composite index of estimating vulnerability

This index is a measure of departure from the ideal health conditions required for adaptation

Risk parameterization matrix

House

location

Population freq

Risk level

Max Temp

Anomaly

Level of risk

Hill 32.5% 0.26 1 O.03

Mid-hill 48.3% 0.40 2 0.13

Valley 19.3% 0.70 3 0.30

4 0.54

Conceptual framework Exposure + vulnerability = Disease (Impacts)

Adaptation is the ability to reduce exposure and vulnerability to diseases

Exposure to infection is reduced by vector control

Disease is controlled with effective drugs

Location of households

Location of Households

0102030405060

Val

ley

Hill

side

Hill

top

Sta

gnan

tw

ater

Location

Pro

po

rtio

n o

f h

ou

ses

Level of education

Level of education

0

10

20

30

40

50

60

None Primary Secondary Tertiary

Level

Pro

port

ion o

f re

spondents

Marital status

Marital stutus

01020304050607080

Single Married Divorced Widowed

Status

Pro

po

rtio

n

Number of people in house hold

Number of people in household

0

5

10

15

20

0 5 10 15 20

Number of people

Pro

po

rtio

n

Source of income

Source of income

020

4060

80100

120

Formal Self Farming

Source

Pro

po

rtio

n

Total cash per month

Range KSh US$Minimum 300.00 4.00Medium 3000.00 39.00Maximum 30000.00 390.00

Proportion of families without enough food in some days

Food Security

25%

75%

Insufficient

Sufficient

Land ownership

Land ownership: Acreage

0

10

20

30

40

1-2 2-4 5-10 >10

Acres

Pro

po

rtio

n

Access to information

Access to information

0

50

100

Radio Newspaper

Source

Pro

po

rtio

n

House quality

House quality

0

10

20

30

40

50

60

Low Medium Good

House type

Pro

po

rtio

n

Accessibility of health facilities

Type of facility

Proportion

of people using facility

Accessibility

by foot

Owner of

facility

Dispensary 64.9 98 GK

Health Centre

33.4 98 GK

Proportion of people visiting or admitted in hospitals in the last three months

Proportion of people visiting or admitted in hospital

05

1015202530

Number of people per house

Pro

po

rtio

n

Cost of last treatment in K.Sh

Cost of last treatment

0

200

400

600

800

1000

1200

1400

Minimum Medium Maximum

Statistics

Am

ount

How do you consider the cost of treatment ?

Fair 24.5%

High 72.7%

How do you cope with more malaria ?

Sell some animals 30%

Sell some food 56.1%

Drugs bought for self treatment

Drugs bought for self treatment

0

20

40

60

80

100

QC SP QN Others

Drug

Pro

po

rtio

n

Proportion

Resistance level

Awareness about malaria treatment and prevention

Category Proportion aware %

Linking health and weather

94.5

Correct treatment of malaria

71

Prevention with bed nets 23.1

Malaria is a serious diseases

96

Number of bed nets per house

Number of bed nets per house

020406080

100

None One Two

Number of nets

Pro

po

rtio

n

Other malaria control methods

Method Proportion using method

Indoor spraying IRS 0

Mosquito coils 3.3

Bush clearing 62.3

Drainage 11.3

Screening 0.7

Future directions in data collection

Anomalies in Max temperatures in Western Kenya

Frequency of = >3C events: observed and expected: Possible scenarios

0

5

10

15

20

25

Freq

uen

cy

of a

no

ma

lies >

3 d

eg

rees C

70/80 81/90 91/2000 01/10 11/20 21/30Time in decades

ObservedProjected

Frequency of mean maximum temperature

Observed and projected

Increase in significant events per decade follows an exponential model

Frequency of siginificant anomalies per decade

y = 0.3816e0.8959x

R2 = 0.9832

0153045607590

0 2 4 6 8 10 12

Decade

Fre

qu

en

cy

Malaria outbreaks occur after positive maximum temperature anomalies

20

30

40

50

60

70

Pro

po

rtio

n o

f m

ala

ria

Cas

es

-2

-1

0

1

2

3

4

5

Tem

peratu

re an

om

alies

JAN97MAY

SEPJAN98

MAYSEP

JAN99MAY

SEP

Month

CasesTmax Tmin

Malaria cases and maximumtemperature anomalies: western Kenya

Acknowledgement Faith Githui MSc PhD trainee Richard Oriedo MSc PhD Trainee James Kathuri MSc Eugene Apindi BSc Lydia Olaka MSc Dr. Maggie Opondo PhD Dr. Dan Olago PhD Prof. Shem Wandiga