Estimates of TB incidence,

prevalence and mortality

Philippe Glaziou

Cairo, October 2009

Outline

• Main sources of information

• Incidence

• From incidence to prevalence

• From incidence to mortality

• TB/HIV

• MDR-TB

Main sources of information

• Measurements– Mortality (Vital Registration)

– Prevalence (Prevalence survey)

– Service coverage, (inventory, capture re-capture)

• Trends– Time series of notifications and programmatic

data

• Expert opinion– Size of non-notified TB population

Incidence

Estimating incidence (2007)• Reference year: 1997 global consultation process. 64

country estimates updated later)• Of proportion of cases being notified (expert opinion)

N = notifications / year, r = case detection ratio

• From surveys of infection

λ denotes the percent risk of TB infection, l is expressed per 100,000/year

This approximation is very uncertain, it assumes that 1 ss(+) remains infectious for 2 years and transmits infection to 10 susceptible individuals every year

Incidence: other methods

• From disease prevalence surveys

P = prevalence, d = weighted duration

• From mortality data (vital registration)

m = deaths, f = case fatality rate

• Capture re-capture, ≥3 lists required, log-linear modelling to estimate cases not in any list, adjustment for dependencies

Incidence: Main source of

information (reference year)

Trends in incidence may reflect

trends in:

• Notifications (when there is no significant change in case finding effort)

• annual risk of infection (repeat tuberculin surveys)

• mortality (Brazil, South Africa)

• else, a flat trend (zero slope) when data are too difficult to interpret. E.g. Iraq, Pakistan

Trends in incidence (2007)

18flat

161trends in notifications

18trends in ARI (tuberculin surveys)

3trends in mortality

12trends in prevalence

Number of countries

Trends in incidence assumed to mirror:

Limitations

• In most countries, trends in incidence mirror trends in notifications -> constant case finding effort assumed

• Difficult to interpret trends in infection measured through repeat PPD surveys

• Trends in prevalence (repeat prev. surveys) and trends in incidence not necessarily parallel

• Difficult to incorporate several sources of data as the estimation process is constrained to one year of reference and one model for trends

• Uncertainty not documented

Upcoming changes

• Three main sources of data

– From measurements of prevalence, using

simpler method

– From measurements of mortality

– Assessment of surveillance data using WHO

Task Force framework and quantification of expert opinion (onion model)

• Improve assessment of trends

• Documentation of uncertainty

•Time-changes in notifications of cases and

deaths

•Changes in case-finding, case definitions,

ICD codes, coverage of surveillance

systems, TB determinants

• Apply "onion" model to identify

where cases may be missing

• Inventory studies with existing or

newly developed study registries

• Capture re-capture studies

Are data reliable and complete?

Do notifications

reflect trends in incidence?

Does VR data reflect changes in TB

mortality

Do notifications

include all incident cases?

Does the VR system include all

TB deaths?

IMPROVE surveillance system

If appropriate, CERTIFY

TB surveillance data as a direct measure of TB incidence and mortality

UPDATE estimates of TB incidence and

mortality

TB notification data

•Complete, consistent

Vital registration (VR) data

• Accurate and with high coverage

Evaluate

trends and impact of TB control

notifications ≈ incidence

VR mortality data ≈ deaths

WHO Task Force Framework

Data reliability

1. completeness of notification data and other

quality checks

• are all reports complete and compiled?

2. internal consistency

• is there more sub-national variability in notification

rates than expected?

• is there more variability over time than expected?

• is laboratory diagnosis of documented quality?

3. external consistency

• are proportions and rates consistent with current knowledge on TB epidemiology?

Removing duplicates in Brazil

(2005)

+6.764.560.5-9.740.244.274,11381,33019,064

afterbeforeafterbeforeafterbefore

change(%)

Cured

(%)

change(%)

incidence

rate

new casesdups

Source: Bierrenbach A et al. Rev Saúde Pública 2007; 41(Supl. 1): 67-76

Misclassifications

• Are case definitions consistent with WHO definitions?

• Is laboratory performance satisfactory?

– Microscopy units with satisfactory EQA results

(no major error AND less than 3 minor errors)

> 90% of all units

– If culture used, positive growth in untreated

smear positives > 90%

The Onion Model

All TB cases

Undiagnosed cases

Diagnosed but not notified cases

Notified cases

Recorded in notification data

Diagnosed by NTP or collaborating

providers

Diagnosed by public or private providers, but

not notified

Access to health facilities, but don't go

No access to health care

Presenting to health facilities, but undiagnosed

Documented guess of the size of

the non-notified TB population

percent

Bangladesh

Bhutan

Indonesia

Maldives

Myanmar

Nepal

Sri Lanka

Thailand

Timor-Leste

Bangladesh

Bhutan

Indonesia

Maldives

Myanmar

Nepal

Sri Lanka

Thailand

Timor-Leste

do not go

not diagnosed

0 10 20 30 40 50 60 70

no access

ntp not notified

0 10 20 30 40 50 60 70

non ntp not notified

total

0 10 20 30 40 50 60 70

From Incidence to Prevalence

Assumptions 1990-2007

• P = I . d

• Duration d provided as point estimate for– 12 categories of patients:

• Shorter in HIV+

• DOTS < non-DOTS < untreatedmedian DOTS = 1yr, non-DOTS = 1.8 yrs, untreated = 2yrs

• Smear neg mostly similar to smear pos

– Durations in HIV- vary between countries

– Proportion smear positive vary between regions

All incidentcases

HIV+ve HIV-ve

smear-positive

(35%)

smear-negative

(65%)

smear-positive

(45%)

smear-negative

(55%)

DOTS

nonDOTS

untreated

DOTS

nonDOTS

untreated

DOTS

nonDOTS

untreated

DOTS

nonDOTS

untreated

notificatio

ns

(DO

TS

/ nonD

OT

S,

ss+

/oth

er)

From incidence to prevalence

Estimation of

%HIV+ presented

separately

Limitations

• Need estimates in 12 case categories for

– Incidence

– Duration

• Inconsistent definitions for DOTS and non DOTS patients between countries

• No analysis of propagation of errors

• Very large number of uncertain quantities and parameters

Upcoming simplifications

• N/I = N/P / (N/P + 1/d)d denotes the average duration of disease in untreated TB [1],

N: notifs, I: incidence, P: prevalence

• Duration: triangular distribution from 1 to 4 years, mode at 2 years (Hanoi)

• HIV+: ratio d+/d- ~N (0.31, 0.088) [2]

[1] Borgdorff M. New measurable indicator for tuberculosis case detection. EID

2004; 10(9): 1523-1528

[2] Williams et al. Anti-retroviral therapy for the control of HIV-associated

tuberculosis: modelling the potential effects in nine African countries.

Submitted.

Global prevalence (all forms),

old and new method, by WHO region

Ra

te p

er

10

0,0

00 100

200

300

400

500

600

0

20

40

60

80

100

120

140

AFR

EUR

1990 1995 2000 2005

50

100

150

200

100

200

300

400

500

600

700

AMR

SEA

1990 1995 2000 2005

0

100

200

300

400

100

200

300

400

EMR

WPR

1990 1995 2000 2005

rates

notifs

prev.best

prev.old

Mortality

• Ideally: directly measured – Vital Registration with high coverage and low rate of

ill-specified causes of deaths

– Interim systems: sample VR, verbal autopsy studies

• Indirectly estimated:

∑= ii fIM .

Where i is a case category (notification and

HIV status), f denotes case fatality

TBHIV

Measurements of TB/HIV incidence

• Empirical measurements from 64 countries (7 national surveys, 8 sentinel surveillance, 49 provider-initiated HIV testing data with > 50% of new TB cases tested for HIV)

t = I+ / I ; proportion HIV-positive among incident TB; h = N+/ N , HIV in general population (UNAIDS); ρ, Incidence rate ratio

Prediction of TB/HIV incidence

• Linear model of logit-transformed t using logit-transformed h, slope constrained to 1

t denotes HIV in TB h denotes HIV in general population

Three estimates of incidence rate ratio

Upcoming change

• Account for ART: multiply the IRR by a best estimate of TB risk ratio on/off ART

– Rifabutin projections:

RR ~ Triangular (0.15, 0.3, 0.55)

• Sources of uncertainty:

– IRR (HIV pos/neg)

– RR (on/off ART)

MDRTB

Multidrug Resistant TB

• Direct measurements in 113 countries (new cases), of which 102 countries also have measurements on retreatment cases

with π = Pr(MDR | new), c = incident cases (new or retreatment), r = reported retreatment episodes and n =

notified new cases

MDR-TB (cont)

• In countries with no direct measurement, ppredicted from logistic regression model with indirect predictors such as Gross National Income, retreatment ratio r/n; % HIV in TB

• Model predictions should be replaced with measurements from quality surveillance data

Very weak indirect estimates of

MDR-TB

• Predictive model very weak, the predictors are only indirectly related to the outcome

• Input data from DRS often outdated

• Limited data on MDR in categories of retreatment cases

• Double counting (new patient re-registered as retreatment during the same year)

• Misclassifications (retx -> new)

During this workshop, we would

like to

• review the quality of surveillance data

• update

– assessment of trends

– changes in case finding efforts

– changes in predictors of incidence (e.g. HIV,

GDP, MDR?)

• update estimates of incidence