2-PG TB estimates
Transcript of 2-PG TB estimates
Estimates of TB burden
Philippe Glaziou Harare, 2010
Can we measure TB incidence?
Sources of data on incidence
• Incidence surveys
• PPD surveys
• Notifications
• Capture-recapture
• Indirect estimation
http://www.flickr.com/photos/giacomofrigerio
duration
prevalenceincidence =
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ratefatalitycase
mortalityincidence =
ratedetectioncase
onsnotificatiincidence =
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Onion model: size of the non-
notified TB population
Inventory study
Survey
Survey
DHS
Source
Capture-
recapture
Diagnosed but not
reported
Seek care but not
diagnosed
Have access but do not
seek care
Do not have access to
health care
Sources of data
19Tuberculin surveys
3Inventory / capture re-capture
6Prevalence surveys of disease
89Mortality (vital registration)
212Case notifications
Number of
countries
Mortality
• Measurements from VR in 88 countries– Coverage > 80%
– Ill-defined causes < 20%
• Prior distributions of Case Fatality Rates (CFR)– Commissioned literature review (Masja
Straetemans)
– WHO database
• Posterior distributions of CFRs from Bayesian models
Interpreting mortality time trends
in South Africa
0%
4%
8%
12%
1988 1992 1996 2000 2004
TB
& H
IV
0%
5%
10%
15%
20%
25%
Ill-
de
fin
ed
VR: HIV/AIDS
VR: ill-defined
VR: TB
Vital registration:
More reported deaths (VR) per
notified TB case in East Europe
Trends in TB incidence and
mortality in Latvia
From Vital
Registrations
Trends in TB incidence and
mortality in Brazil
CFR = probability of death at 4 years of follow-up among linked records
probability of linkage of TB death records
From Vital
Registrations
How about prevalence?
• Measurable through cross-sectional
surveys, but …
• Indirect estimates very uncertain
P = I * d
Kenya profile
TB incidence all forms
(including HIV+)
TB incidence all forms
(including HIV+)
TB notifications (black)
TB prevalence all
forms (including HIV+)
Tb prevalence per 100,000 population
Disaggregation of estimates
• Smear status: estimates discontinued
• Age group and sex
– Based on distribution of TB deaths by age
and sex in countries with VR
• HIV-status
• MDR-TB
HIV in TB indirectly estimated in 13
out of 46 countries in AFR
logit
(HIV
in T
B)
logit (HIV in the general population)
Insufficient coverage of drug
resistance surveillance in Africa
Percent MDR in
previously untreated
Insufficient capacity
to diagnose MDR-TB
Insufficient lab capacity
to diagnose TB
In conclusion
1. Incomplete data leading to uncertainty
2. Use of vital registration records
3. Incidence trajectories based on notifications and time changes in case finding effort
4. Need to strengthen surveillance, including VR, drug resistance
5. Targets and planning should be based on measured indicators (as opposed to indicators estimated with uncertainty)
Acknowledgements
• Katherine Floyd
• Babis Sismanidis
• Ana Bierrenbach
• the TB estimates sub-group of the Task Force on TB Impact Measurement