Four indirect measures of TB incidence Incidence, prevalence, deaths derived by rearranging 4 equations

From infection prevalence From disease prevalence ESTIMATING TB INCIDENCE 300 300 250 incidence 250 falling 200 200 b > 50 incidence incidence ss+/100,000 150 incidence ss+/100,000 steady 150 100 b = 50 100 weighted duration illness = 2y 50 50 ss+/- HIV+/- DOTS+/- 1 2 3 4 5 6 200 400 600 ARI (%/year) prevalence ss+/100,000 From case notifications From HIV prevalence 300 300 250 250 200 200 incidence ss+/100,000 ss+/100,000 150 incidence 150 100 100 no HIV 50 proportion detected = 0.8 50 50 100 150 200 250 0.1 0.2 0.3 0.4 notification ss+/100,000 prevalence HIV/100,000

“Proportion detected”: guess from quality of surveillance system e. g “Proportion detected”: guess from quality of surveillance system e.g. USA probably detects about 95% of cases More objective e.g. % health units reporting in any year Beware circular arguments Method 1 weak with poor surveillance, but strong M&S is the ultimate goal

Disease prevalence from population surveys e. g Disease prevalence from population surveys e.g. Philippines, China, Cambodia Duration: time span of the condition measured in prevalence survey e.g. ss+ disease Duration from e.g. patients and physicians asked about reporting and treatment delays (often underestimated)

Estimating mean duration: Egypt Proportion ss+ cases treated DOTS 0.4 non-DOTS 0.5 untreated 0.1 Estimated ss+ durations (years): DOTS 1.0 non-DOTS 1.5 untreated 2.0

Prevalence vs incidence: Korean civil servants Tubercle and Lung Disease 76, 534 (1995) Prevalence PTB 1990 241/100K Incidence PTB 1989-90 84/100K/yr Estimated duration = 241/84 = 2.9 years (bigger ratio for older age groups)

Estimating incidence from prevalence: Cambodia prevalence ss+ 270/100K in 2002 incidence DOTS (case notification rate) 141/100K in 2002 duration DOTS (questionnaire survey) 1 year duration nonDOTS (no treatment) 2 years Therefore: incidence ss+ nonDOTS 64/100K total incidence (DOTS + nonDOTS) 205/100K in 2002 NB: usually wide range on estimates

Styblo ratio: 1% ARI to 50 ss+/100,000 population (range 40-60) Accuracy of ARI from tuberculin surveys? 1:50 breaks down when TB incidence not stable (gets bigger in decline), and in presence of HIV

Origin of Styblo’s rule Bull IUAT vol 60, 1985

Accuracy of deaths from vital registration? Case fatality more accurately measured from observed cohorts (but fate of defaulters, transfers?) CFR less accurately from unseen patients, whether treated or untreated

Estimating case fatality rate: Egypt Proportion ss+ cases treated DOTS 0.4 non-DOTS 0.5 untreated 0.1 Estimated case fatality ss+ (years): DOTS 0.1 non-DOTS 0.3 untreated 0.7

Estimating proportion of TB patients infected with HIV T = AR/[1+A(R-1)] T = proportion TB patients infected with HIV A = proportion adults infected with HIV R = incidence rate ratio (TB incidence in HIV+/TB incidence in HIV-)

TB incidence closely correlated with HIV prevalence in Africa 1000 800 600 Estimated TB incidence (per 100,000 population) 400 200 10 20 30 40 HIV prevalence, adults 15-49y

TB incidence weakly related to social and economic variables: infant mortality 6.5 6.0 5.5 AF AS 5.0 CA Ln (estimated TB incidence) 4.5 EE SA 4.0 US WP 3.5 ME 3.0 WE Series10 2.5 2.0 1.5 1.5 2.0 2.5 3.0 3.5 4.0 4.5 5.0 5.5 Ln (Infant mortality rate 1991)

Association between the rate of TB among country-of-birth-specific groups in Australia and the rate of TB in the country of birth. From Watkins & Plant 2003.

What’s wrong with the estimation process? Cannot survey the whole world (infection or disease) Many estimates are based on guesses about case detection Estimates often too inaccurate or too biased to judge progress to case detection targets National estimates do not apply sub-nationally  value of exploiting surveillance data