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Assessment of seasonal and climatic effects
on the incidence and species composition of
malaria by using GIS methods
Ali-Akbar Haghdoost
Neal Alexander (supervisor)
Main objectives
1. Assessment of the feasibility of an early
warning system based on ground climate and
remote sensing data
2. Assessment of the interaction between
Plasmodium spp from different points of view:
meta-analysis, modelling, and extended
analysis of a large epidemiological dataset
Feasibility of the early warning (1)
ppv
P. vivax
fitted value
ppf
P. falciparum
0
0
100
100
200
200
300
300
fitted value
Jun 94
Jun 96
Jun 98
date
all species
0
500
fitted value
Jun 00
Jun 94
Jun 96
Jun 98
date
Jun 00
Jun 94
Jun 96
Jun 98
date
Jun 00
The fitted values of models
based on seasonality, time
trend and meteorological
variables classified by
species, observed numbers
(dashes) and model
estimated number (solid line)
Feasibility of the early warning (2)
• Main findings
– Ground climate data explained around 80% of P. vivax
and 85% of P. falciparum variations one month ahead
– Comparing to the extrapolation of data from previous
month, ground climate data improve the accuracies
around 10%; but remote sensing data does not improve
– The ground climate data are freely available in the filed;
therefore, it was concluded that the models based on
ground climate data are feasible.
What is the interaction?
The difference between the observed number
of mixed infections in blood slides and the
expected number if infection with one species
is independent of infection with other species
Why the interaction is important?
• To know more about the pathogenesis of
Plasmodium spp
• To know more about the immunity mechanisms
against Plasmodium spp
• To estimate the impact of vaccine against one
species on the other species
Positive interaction
1. Similarity in transmission routes
2. Higher susceptibility of a subgroup of people
Negative interaction
1.Suppression
2.Cross immunity
3.Differences in the biology of Plasmodium spp
4.Environmental factors
5.Missed mixed infections in blood slides
Background
Howard (2001) showed that the logarithm of odds
ratio between P. falciparum and P. vivax changed in a
wide rage from –5.08 (in Bangladesh) to 2.56 (in
Sierra Leone).
He found that in Asian countries, the associations
were largely negative; however, positive associations
were seen in Tanzania, Papua New Guinea and
USA.
Questions
• What is the overall association between species?
• How we can explain the differences between
study findings?
Sections
1. Meta-analysis
• To quantify the interaction between P. falciparum and
P. vivax
• To assess the source of the heterogeneities
2. Modelling the heterogeneity effect
3. To measure the association between Plasmodium
spp in the Garki region of Sudan Savanna of west
Africa
Meta-analysis (1)
Database
number of citations
Medline: 1966-2001
395
Embase: 1980-2001
77
CAB-Health: 1973-2001
455
Merged database (excluding repeated citation)
829
Meta-analysis (2)
• Reviewing abstracts (829)
– Non eligible papers
– Eligible papers
– Uncertain
657 (72.2%)
104 (12.5%)
68 ( 8.3%)
• Reviewing full texts of papers (172)
– Eligible for meta-analysis
– Non eligible for meta-analysis
– Was not available (from China)
62 (36.1%)
108 (63.3%)
1 ( 0.6%)
Meta-analysis (3)
Number of
studies
Percentage
52
4
6
83.9
6.4
9.7
36
16
10
58.1
25.8
16.1
26
12
5
19
41.9
19.3
8.1
30.7
5
57
8.1
91.9
26
36
41.9
58.1
Continent
Asia
Africa
America
Spatial span
Villages
District
Province or larger
Temporal span
Month
Season
Year
Greater than one year
Age group
Children
All age groups or adults
Samples
Febrile
Normal
Meta-analysis (4)
Minimum OR=0.02
Maximum OR= 10.9
Summary OR=0.6 (0.49-0.79)
Number of studies with OR<1=41
Number of studies with OR>1=32
•
•
Overall (95% CI)
.00167
•
•
Odds ratio
100
Meta-analysis (5)
Subgroup (number of
studies)
Continent
Asia (52)
South America (6)
Africa(4)
Age group
Children(5)
Mixed(57)
Subjects
Normal(36)
Febrile(26)
Spatial span
A few villages(36)
District(16)
Larger than a district(10)
Odds ratio
(95%CI)
Subgroup (number of studies)
Odds ratio
(95%CI)
Temporal Span
Month(26)
Season(12)
Year or longer(24)
0.81(0.56-1.17)
0.97(0.52-1.79)
0.39(0.26-0.6)
1.38(0.31-6.08)
0.56(0.43-0.75)
P. falciparum risk (%)
<10(23)
10-14.99(10)
≥15(29)
1.06(0.54-2.1)
0.75(0.42-1.35)
0.4(0.28-0.57)
0.9(0.65-1.24)
0.35(0.21-0.58)
P. vivax risk (%)
<5(27)
5-9.99(18)
≥10(17)
1.43(0.98-2.1)
0.49(0.32-0.75)
0.25(0.13-0.5)
Both species risk (%)
<15(18)
15-29.99(22)
≥30(22)
2.51(1.66-3.8)
0.5(0.36-0.7)
0.32(0.22-0.47)
0.62(0.46-0.83)
0.21(0.16-0.26)
1.76(0.47-6.6)
0.5(0.33-0.75)
0.99(0.591-1.63)
0.49(0.3-0.82)
Meta-analysis (6)
The results of meta-reg analysis
Subgroup
Tau
square*
Model 1: no explanatory variable
0.91
Model2: explanatory variables were age group, subjects
(febrile or normal), spatial and temporal span of studies
and continent
1.18
Model3: the only explanatory variable was the frequencies
of all species (all Plasmodium species considered
together) and temporal span of studies
0.72
*a measure of between studies heterogeneity
Meta-analysis (7)
• Main findings:
– The overall OR (between P. vivax and P. falciparum)
was less than 1
– There were negative associations (weaker) between
the prevalence of species and the overall OR
– There was a negative association between the temporal
span of studies and the overall OR
Modelling (1)
Positive associations between species mean that a
subgroup of people, in terms of time or space, has
higher infection risks for all species, i.e.,
heterogeneity in infection risks within the population.
Therefore, infection risk could be considered as a
confounder.
Modelling (2)
Main question:
Can the confounding effect of the
heterogeneity in infection risks explain
OR as large as 11 by its own?
Modelling (3)
Model specification:
– Population has been divided into low and high risk strata
– The OR between species in each stratum was 1
– The risk ratio of infection with species i in high risk
versus low risk stratum (k1) was varied from 1 up to its
maximum possible values
– The ratio of the populations in low and high risk strata
(m) was varied in a wide range (0.2-5)
– The prevalence of species were varied in a wide range
from 0.05 to 0.8
Modelling (4)
• The impact of ki on the overall OR in the whole population
105 OR
16.6
15
13
k2
11
9
5.8
7
5
3
1
90-105
90
75-90
75
60-75
60
45-60
45
30-45
30
15-30
15
0-15
0
3
1 k1
Modelling (5)
The impact of m on the overall OR in the whole population
7
6
5
4
3
2
1
0
0.2
0.6
1
1.4
1.8
2.2
2.6
3
3.4
3.8
4.2
The ratio of populations in high and low risk groups
4.6
5
Modelling (6)
• Greatest ORs were observed when the prevalence
of species were equal
• By increasing the prevalence of species in low risk
stratum, the overall OR was decreased
Modelling (7)
Conclusion
Just heterogeneity in infection risk
can explain an OR as large as 11
Garki (1)
The Garki project was one of the largest
epidemiological studies on malaria, with data
comprised from more than 12,000 people in 23
surveys.
It was conducted in a highly endemic area in
northern Nigeria from 1969 to 1976 by co-operation
between the World Health Organisation (WHO) and
the Nigerian government.
Garki (2)
The published results of the Garki data had not
thoroughly explored the interactions between
Plasmodium species, and that too had only
approached this issue cross-sectionally using very
simple methods.
Garki (3)
Objectives
To measure the associations between Plasmodium
spp cross-sectionally and longitudinally; and assess
the effects of:
repeated infections (i.e., within subject clustering)
Age
spatial and temporal distribution of individual species
Garki (4)
• Cross-sectional analysis: the presence of
P. falciparum in each survey was considered as a
risk factor for the presence of the other species in
the same survey
• Longitudinal analysis: the presence of one species
in each survey was considered as a risk factor for
the presence of the other species in the following
survey
Garki (5)
Frequencies of single and mixed Plasmodium spp in 118,346 blood slides
P. falciparum
43,713
9,588
435
P. malariae
12,761
Negative for all species
(49,742)
703
1,37
2 P. ovale
32
Garki (6)
Annual variation of Plasmodium spp prevalence, based on 6 years data
(%)
70
Dry-cool
Dry-hot
Wet
Dry-cool
60
P. falciparum
50
40
30
20
P. malariae
10
P. ovale
0
•
Jan
1•
2 • Mar
3•
Month
4•
5•
May
6•
7•
July
8 •Sep9•
10•
11
Nov
Garki (7)
Multi-level models showed that the risk
of P.falciparum had the largest within
person-variation, and also within and
between village variations
Garki (8)
The risk of infection with Plasmodium spp classified by age
Age group
P. falciparum
OR
(95% CI)
<4 months
Number (%)
4-7 months
Number (%)
Number (%)
0.75
(0.64-0.9)
2.52
(2.2-2.9)
3.9
(3.41-4.56)
8-12 months
1-9.9 year
Number (%)
≥10 year
Number (%)
11.68
(11.13-12.27)
1
-
5.9
(5.63-6.2)
1
-
4.2
(3.72-4.75)
1
-
OR for the whole first year: 2.1 (1.8-2.4)
P. malariae
OR
(95% CI)
0.56
(0.39-0.8)
1.31
(1.04-1.65)
1.95
(1.6-2.37)
OR for the whole first year: 1.3 (1.1-1.5)
P. ovale
OR
(95% CI)
1
0.47-2.12
2.59
(1.68-4)
2.2
(1.38-3.49)
OR for the whole first year: 4.2 (3.6-5.0)
Garki (9)
The associations of P. falciparum (as risk factor) with other species
adjusted for intra-person clustering effect in cross-sectional analysis
P. malariae
OR (95% CI)
P. ovale
OR (95% CI)
Rho=0.34
3.64(3.4-3.9)
Rho=0.25
5.1 (4.33-6.0)
Age (year)
<1
1-9
>=10
6.25(2.63-14.82)
2.32(1.70-3.16)
3.97(3.24-4.85)
6.26(2.64-14.83)
2.19(1.59-3.03)
3.95(3.23-4.84)
Season
Dry and cool
Dry and hot
Wet
4.02(3.7-4.35)
6.32(5.48-7.29)
3.58(3.3-3.9)
5.53(4.6-6.68)
3.94(2.18-7.12)
3.76(2.78-5.07)
All subjects
Garki (10)
The associations between P. falciparum in a former survey with species in
the latter survey, adjusted for intra-person clustering effect
P. falciparum
OR (95% CI)
P. malariae
OR (95% CI)
P. ovale
OR (95% CI)
Rho=0.73
1.9(1.9-2)
Rho=0.44
2.7(2.5-2.9)
Rho=0.34
3.6(3-4.4)
Age (year)
<1
1-9
>=10
9.3(7.6-11.5)
3.1(2.7-3.6)
1.5(1.4-1.6)
11.6(6.8-20)
2(1.7-2.3)
1.8(1.7-2)
6.9(2.7-17.7)
2.0(1.4-2.7)
2.7(2.2-3.4)
Season
Dry and cool
Dry and hot
Wet
4.3(3.9-4.6)
9.8(9-10.6)
4.3(3.9-4.6)
4.1(3.7-4.5)
5.5(4.8-6.2)
3.6(3.2-4.1)
2.6(2-3.5)
4(2.8-5.7)
4.7(3.5-6.4)
All subjects
Garki (11)
Why the ORs were greater in infants?
– Heterogeneity in infection risk (as the source of positive
associations depends on:
• The heterogeneity in exposure to mosquitoes
• The heterogeneity in acquired protective immunity
– It is reasonable to assume a positive association
between the strength of acquired immunity and
exposure to mosquitoes in adults. Therefore, these two
factors somehow decreased their impacts on the
heterogeneity in infection risk in adults.
Garki (12)
The relationship between P. falciparum density and the
risk of other species based on cross-sectional data
Density* 0
1-50
>50
P. malariae
1
4.05
8.66
P. Ovale
1
4.05
8.73
* number of positive filed in 200 examined fields
Garki (13)
The association between Plasmodium spp adjusted for intra-person
clustering effect in cross-sectional analysis
Latter survey
P. falciparum
P. malariae
P. ovale
P. falciparum
OR(95% CI)
Rho
1.9(1.9-2)
0.73
2.63(2.5-2.9)
0.44
3.6(3-4.4)
0.34
P. malariae
OR(95% CI)
Rho
1.7(1.5-2)
0.22
2.7(2.5-2.9)
0.33
2.6(2.2-3.0)
0.03
P. ovale
OR(95% CI)
Rho
1.9(1.3-2.8)
0.22
2.8(2.3-3.4)
0.29
5.3(3.9-7.2)
0.17
Former Survey
Garki (14)
Estimated daily clearance and acquisition rates of P. malariae and P. ovale
classified by the presence of P. falciparum in the former survey
Daily conversion rates in
logarithmic scale
1
0.1
0.01
0.001
0.0001
>1
1-9
>=10
Plasmodium malariae
pf negative acquisition rate
pf positive acquisition rate
age group (year)
>1
1-9
>=10
Plasmodium ovale
pf negative clearance rate
pf positive clearance rate
Garki (15): conclusion
• Cross-sectional analysis:
– Suppression decreases the association between
species
• Longitudinal analysis:
– Cross immunity, suppression and changing one’s
behaviour (such as the exposure risk to mosquitoes)
after contracting the first infection decrease the
association between species
Garki (16): conclusion
• P. falciparum suppress other species particularly
P. malaria
• The suppression is not just due to the competition
for host cells or nutrients. It is most probably due
to heterologous immunity
• Low level of acquired immunity suppresses the
other species; stronger immunity increases the
clearance rate, and very strong immunity
decreases the acquisition rate as well.
Summary (1)
• A very wide range of associations between
Plasmodium spp was observed in meta-analysis
which was partly explained by the prevalence of
species and the temporal span of studies
• The heterogeneity in infection risk (due to
heterogeneity in exposure risk or immunity) can
explain the observed high ORs in meta-analysis
Summary (2)
• The ORs in longitudinal analysis of the Garki data
was smaller than those in cross-sectional analysis
• The ORs in infants were less than others which
can be explained by the heterogeneity in infection
risk theory
• P. falciparum suppresses other species, probably
via immunological pathways
• People obtained protective immunity after many
infections; therefore, the frequency of species had
direct association with the variation of infection
risk within and between subjects and villages
Time for your comments
Thanks for you kind
attention