Mapping residual malaria transmission for targeted
response in urban Lusaka, Zambia
Daniel Bridges
ASTMH
Philadelphia
28th October 2015
Lusaka
•
•
•
•
•
Urban environment
Major transport hub
25 primary care public clinics
Population ~2 million
MIS surveys (U5)
– 0% since 2010
• Sub-national elimination agenda
Strategy
• Background of
– Universal LLIN coverage
– IRS
– High population mobility
• Since 2011
– Emphasis on CONFIRMATION
– Identify residual transmission foci
• Passive => Reactive
• Local cases only (no travel)
10 houses
1. Confirmed malaria constant
(increased testing)
2. Clinical malaria almost disappeared
Aims
1. Identify risk factors for local transmission in Lusaka from
historical data
2. Characterize and map malaria parasites by genotype
RCD Summary
Year
Total
Confirmed
RCD
Responses
RCD number
tested
RCD Positives
RCD test positivity
2011
855
60
2,011
33
2012
1,867
127
4,236
68
2013
899
51
1,617
29
2014
3,688
144
3,955
66
2015
1,411
51
1,196
10
1.64%
(1.1 – 2.2)
1.61%
(1.2 – 1.9)
1.9%
(1.2 – 2.6)
1.94%
(1.5 – 2.4)
0.95%
(0.4 – 1.6)
>85% index cases with recent travel history were excluded
Very few RCD positives
Age of incident malaria cases
80
60
40
20
0
2011 2012 2013 2014 2015
Year
0.5
0.92
R estimated from Churcher et al. 2014
Proportion of incident malaria cases reporting travel
Measures of transmission
0.88
0.84
0.80
0.4
0.3
0.2
0.1
0.0
0.76
2011 2012 2013 2014 2015
Year
Decreasing transmission
2011 2012 2013 2014 2015
Year
Index case summary
Factor
Categorization
Unadjusted IRR (95% CI)
Adjusted IRR (95% CI)
Age
> 15
5-14
<5
2011
2012
2013
2014
2015
Female
Male
Low transmission
High transmission
Reference
1.13 (0.81 – 1.59)
1.29 (0.94 – 1.79)
Reference
0.86 (0.59 – 1.27)
0.89 (0.54 – 1.45)
0.75 (0.51 – 1.09)
0.33 (0.16 – 0.67)*
Reference
1.14 (0.87 – 1.51)
Reference
1.28 (0.96 – 1.71)
Reference
1.12 (0.79 – 1.58)
1.16 (0.83 – 1.62)
Reference
0.78 (0.57 – 1.16)
0.76 (0.45 – 1.27)
0.71 (0.48 – 1.04)
0.26 (0.12 – 0.55)***
Reference
1.16 (0.88 – 1.53)
Reference
1.52 (1.12 – 2.05)**
Year
Sex
Season
No association found apart from seasonality
Other measures of local transmission?
Associations
• No spatial or temporal
correlation with:
– Topography
– Rainfall
– Ecology
• Suggestion by eye of clustering
around vacant land
Distance to uninhabited area
Distance
Case
investigations
People tested
RDT positives
Test positivity
< 50 m
46
1281
23
1.80%
50 – 100 m
46
1431
23
1.61%
100 – 150 m
46
1501
26
1.73%
150 – 200 m
48
1451
36
2.48%
200 – 250 m
28
872
11
1.26%
250 – 300 m
35
967
16
1.65%
300 – 350 m
11
304
4
1.32%
350 – 400 m
16
468
5
1.07%
400 – 450 m
9
269
3
1.11%
450+ m
3
42
1
2.38%
Periphery Clustering
Unadjusted IRR (95%
CI)
Adjusted IRR (95%
CI)
Low transmission, > 250 meters from periphery
Reference
Reference
Low transmission, < 250 meters from periphery
1.95
(0.84 – 4.53)
2.21
(0.95 – 5.14)
High transmission, > 250 meters from
periphery
1.77
(0.79 – 3.94)
1.82
(0.81 – 4.09)
High transmission, < 250 meters from
periphery
2.06
(0.97 – 4.39)
2.61*
(1.19 – 5.73)
Incident case >15 years old
Incident case 5-15 years old
Reference
1.23
(0.71 – 2.12)
Reference
1.12
(0.64 – 1.96)
Incident case < 5 years old
1.50
(0.89 – 2.50)
1.40
(0.83 – 2.37)
Incident case female
Incident case male
Reference
1.11
(0.72 – 1.73)
Reference
1.04
(0.69 – 1.63)
Models also accounted for year
Why genotype?
Process
Dried Blood
Spots (stored
at -20oC)
Chelex for initial
RDT –ve screen
Promega kit for
genotyping
Daniels, R et al., Mal J. 2008, Vol 7, p223
23 barcodes
(1 failed to
amplify
consistently)
Barcoding
• 1/3 of loci showed limited allelic variation
– 2 appear to be fixed
• No attempt made to quantify allele ratio if
mixed
Complexity of Infection reflects transmission
COIL
Infections
Number
Percent
Single infection
35
67.3%
2 infections
14
26.9%
3 infections
3
5.8%
Undetermined (< 80% confidence of single infection)
19
n/a
Travel history
Single infection
2-3 infections
No
11
0
Yes
17
10
Fischer’s exact test, p = 0.037
Galinsky, K et al. Malar J, 2015, 14, 4
Conclusions
• Transmission decreasing in Lusaka
• Clustering at periphery in high transmission season
– Association with breeding sites?
• Travel associated with increased COI
• Higher levels of genetic diversity than expected (Senegal)
• Ability to identify areas of ongoing transmission
mSpray
Ensuring that data is translated into action
IRS aim
Delivery of an efficacious insecticide with high spray coverage to
populations at risk
Real-time progress to inform operations
Performance tracked for all personnel
Akros
Sandra Chishimba
Roy Mwenechanya
Mulenga Mwenda
Mwetwa Sombe
Chama Chisha
Kiko Kamanga
Derek Pollard
For more information
please contact
dbridges@akros.com
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