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AIACC AF91. HEALTH 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 - PowerPoint PPT Presentation
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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
