Download Comparison of indoor contact time data in Zambia and

Comparison of indoor contact
time data in Zambia and
Western Cape, South Africa
Nicky McCreesh1, Clare Looker1, Peter J. Dodd1,2, Ian D Plumb1,
Kwame Shanaube3, Monde Muyoyeta3,4, Peter Godfrey-Faussett1,
Elizabeth L Corbett1,5, Helen Ayles1,3, Richard G. White1
1. LSHTM, 2. University of Sheffield. 3. University of Zambia. 4. Centre for Infectious Disease
Research in Zambia. 5. Malawi Liverpool Wellcome Trust Clinical Research Programme
Improving health worldwide
www.lshtm.ac.uk
Background
• We have very little idea where the majority of M.tb. transmission
occurs in high incidence settings
• Effective infection control measures exist, but are not widely
employed outside clinics
• A better understanding of important locations of transmission could
inform the targeting of infection control measures, reducing M.tb.
incidence
• Contact data provides a starting point for identifying key locations
• Aim: to compare patterns of indoor contact in urban Zambia and
Western Cape, South Africa
Methods
• CODA study (Dodd, 2015) - cluster randomised sample of adults
enrolled in ZAMSTAR final prevalence survey
• Data from 3206 respondents. 40% from Western Cape, 60% from
Zambia
• Respondents asked about buildings entered on day before interview:
•
•
•
•
Building type (shop, church, bar, etc)
Duration of visit
Number of adults and youth (>12 years) present
Number of children (5-12 years) present
• Results weighted to account for sampling design (age, gender and
neighbourhood), and day of week
Dodd PJ, Looker C, Plumb I, Bond G, Schaap A, Shanaube K et al. Age and gender specific social contact
patterns and incidence of Mycobacterium tuberculosis infection. Am J Epidemiol. 2015;kwv160.
Proportion of people who visited
30%
Mean visit duration (hours)
8
25%
6
20%
15%
4
10%
2
5%
0%
0
Mean adults/youths per visit
Mean children per visit
30
30
25
25
20
Adult contact hours
20
15
20
15
10
15
10
5
10
5
0
5
0
0
Western Cape
Zambia
p<0.05
*Estimates for bars could not be
*calculated for Western Cape
Proportion of people who visited
30%
Mean visit duration (hours)
8
25%
6
20%
15%
4
10%
2
5%
0%
0
Mean adults/youths per visit
Mean children per visit
30
30
25
25
20
Adult contact hours
20
15
20
15
10
15
10
5
10
5
0
5
0
0
Western Cape
Zambia
p<0.05
*Estimates for bars could not be
*calculated for Western Cape
Proportion of people who visited
30%
Mean visit duration (hours)
8
25%
6
20%
15%
4
10%
2
5%
0%
0
Mean adults/youths per visit
Mean children per visit
30
30
25
25
20
Adult contact hours
20
15
20
15
10
15
10
5
10
5
0
5
0
0
Western Cape
Zambia
p<0.05
*Estimates for bars could not be
*calculated for Western Cape
Overall adults/youths met per day
Overall children met per day
4
4
3
3
2
2
1
1
0
0
Adult contact hours
Child contact hours
20
20
15
10
5
Adult contact hours
15
10
20
15
5
10
0
0
5
0
Western Cape
Zambia
p<0.05
*Estimates for bars could not be
*calculated for Western Cape
Overall adults/youths met per day
Overall children met per day
4
4
3
3
2
2
1
1
0
0
Adult contact hours
Child contact hours
20
20
15
Adult contact hours
15
10
20
10
5
15
5
0
10
0
5
0
Western Cape
Zambia
p<0.05
*Estimates for bars could not be
*calculated for Western Cape
Discussion - limitations
• Social desirability bias
• No comparable data collected on public transport
• Retrospective data collection, however comparable results to
prospective contact diary data from Cape Town (JohnstoneRobertson, 2011; Wood 2012)
Johnstone-Robertson SP, Mark D, Morrow C, Middelkoop K, Chiswell M, Aquino LD et al. Social mixing patterns within a South
African township community: implications for respiratory disease transmission and control. Am J Epidemiol. 2011:kwr251
Wood R, Racow K, Bekker L-G, Morrow C, Middelkoop K, Mark D et al. Indoor social networks in a South African township:
potential contribution of location to tuberculosis transmission. PLoS One. 2012;7(6):e39246.
Discussion - moving from
contacts to transmission risk
• Contact data tell us where people spend their time and meet
people
• Gives some indication of locations where a substantial
proportion of M.tb. is likely to occur
• Doesn’t tell the whole story:
• Buildings will vary in their suitability for transmission (Murray 2009,
Wood 2014)
• Some types of building will be visited more frequently by people who
are infectious and/or highly susceptible
• (Highly) infectious TB disease very rare, and large amounts of variation
in susceptibility – leads to more transmission in high mixing locations
Murray EJ, Marais BJ, Mans G, Beyers N, Ayles H, Godfrey-Faussett P et al. A multidisciplinary method to map potential
tuberculosis transmission 'hot spots' in high-burden communities. Int J Tuberc Lung Dis. 2009;13(6):767-74
Wood R, Morrow C, Ginsberg S, Piccoli E, Kalil D, Sassi A, Walensky RP, Andrews JR (2014) Quantification of Shared Air: A Social
and Environmental Determinant of Airborne Disease Transmission. PloS One 9(9): e106622.
Conclusions
• Patterns of indoor contact vary by setting
• Targeting of infection control measures needs to be informed by local
data
• In Zambia, churches may be important locations for transmission. In
Western Cape, indoor workplaces may be important
• The potential for targeting infection control measures at these
locations should be considered
• More widely, a better understanding of transmission locations in
different settings could improve TB control
Acknowledgments
• Bill and Melinda Gates Foundation
• ZAMSTAR study team
• LSHTM
•
•
•
•
Ken Eames
John Edmunds
Amelia Crampin
Immo Kleinschmidt
• Stellenbosch University
• Rory Dunbar
• Study communities and participants
Results
Number of buildings visited
60%
50%
p<0.001
40%
30%
20%
10%
0%
0
1
Western Cape
2
Zambia
3+