MODELING TO INFORM ACTIVE CASE FINDING OF TB IN RURAL NEPAL

Sourya Shrestha sourya@jhu.edu

Johns Hopkins School of Public Health, USA

ACKNOWLEDGEMENTS

Gabriella Gomes

Maxine Caws

Gokul Mishra

Raghu Dhital

FUNDING:

CONTRIBUTION:

BACKGROUND

Fox et al, 2013

U

Prevalence of TB disease among

contacts of TB patients.

• Active Case Finding (ACF) is considered to be an important tool for curbing TB, particularly in settings with high TB transmission, like Nepal.

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• However, it has been challenging to develop clear (quantitative) understanding of the effectiveness and epidemiological impact of ACF.
• Its effectiveness — in terms of number TB cases found, or yield per person screened — can be heterogenous.
• Its impact — population-level, epidemiological impact in terms of reduction in TB incidence — is difficult to demonstrate in empirical studies.

BACKGROUND

• Active Case Finding (ACF) is considered to be an important tool for curbing TB, particularly in settings with high TB transmission, like Nepal.

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• However, it has been challenging to develop clear (quantitative) understanding of the effectiveness and epidemiological impact of ACF.
• Its effectiveness — in terms of number TB cases found, or yield per person screened — can be heterogenous.
• Its impact — population-level, epidemiological impact in terms of reduction in TB incidence — is difficult to demonstrate in empirical studies.

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• Ultimately, if ACF is to be a reliable tool for TB intervention across many settings, we need to start developing a better quantitative understanding of ACF: How can data and model be used to design and plan effective ACF?

AIMS

• To construct mathematical models of ACF that can aid in strategic design and implementation of ACF in Nepal. (e.g., geographic targeting, prioritizing between different ACF implementations)

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• To use field data from ongoing ACF to validate model, which can estimate the impact (and assist in estimate of cost effectiveness) of ACF in rural Nepal.
• Embedded into IMPACT TB ACF in four rural districts in Nepal conducted by BNMT (2017-2019).

APPROACH

DATA: TB NOTIFICATION

TB case notification rates

(per 100,000/year)

Chitwan

( ) no of cases

X

DMC

Population: ~650,000

Total notified

TB cases per year ~1,000

DATA: TB NOTIFICATION

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TB case notification rates

(per 100,000/year)

Mahottari

( ) no of cases

X

DMC

Population: ~750,000

Total notified

TB cases per year ~850

DATA: TB NOTIFICATION

TB case notification rates

(per 100,000/year)

Makwanpur

( ) no of cases

X

DMC

Population: ~450,000

Total notified

TB cases per year ~700

DATA: TB NOTIFICATION

TB case notification rates

(per 100,000/year)

Dhanusha

( ) no of cases

X

DMC

Population: ~800,000

Total notified

TB cases per year ~700

DATA: TB NOTIFICATION

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TB case notification rates

(per 100,000/year)

( ) no of cases

X

DMC

Substantial geographical heterogeneity in TB case notification in all of the four districts: notification rates varied by ten-folds from 20-40 to up to 400 per 100,000/year.

MODELING

We developed VDC-level simple transmission models, capturing local epidemiology, and heterogeneity in each of the four IMPACT TB districts.

Calibrated to TB incidence rates in 2016 and 2017 estimated at the VDC level (shown in maps)

Modeled ACF and preventive therapy (PT), and estimated projected epidemiological impact of these interventions (% reduction in TB incidence after 10 years of intervention).

Does not account of migration/movement of individuals, and

assumes no ongoing decline in TB incidence rates

PRELIMINARY RESULTS

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ACF

ACF modeled to capture about 10% of TB cases, was projected to result in 15.2% (11.4 — 19.6) reduction in TB incidence after 10 years of intervention, or about 197 cases averted each year in Chitwan.

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ACF

The impact (reduction in TB incidence rates) varied substantially between VFCs: impact in highest incidence VDC was up to 10 times larger.

PRELIMINARY RESULTS

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ACF

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ACF and PT

Combining ACF with PT was projected to result in 26.2% (21.1 — 31.2) reduction in TB incidence after 10 years of intervention, or about 339 cases averted each year in Chitwan.

PRELIMINARY RESULTS

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ACF

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Similar epidemiological impact across the districts, but larger heterogeneity in Mahottari and Makwanpur

15.2% (11.4 — 19.6) reduction in Chitwan

14.5% (10.7 — 19.1) in Dhanusha

16% (12.3 — 20.4) reduction in Mahottari; and

17.1% (13.4 — 21.5) reduction in Makwanpur

PRELIMINARY RESULTS

DRIVERS OF HETEROGENEITY?

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Some of the heterogeneity could be driven by access to TB care

VDCs with microscopy centers have in

general higher (~50%) TB notification rates, compared to those that do not.

SUMMARY

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Household and social contacts are generally at high risk of TB, and ACF targeting these contacts are an efficient way of finding TB cases (although the reported yields tend vary quite substantially between studies)

VDC-level TB notification data in the IMPACT TB districts in Nepal shows:

(i) high incidence of TB (indicating that transmission is driving local TB epidemics), and

(ii) substantial geographic heterogeneity (indicating potential heterogeneity in transmission)

Active case finding (IMPACT TB-like) is projected to have moderate, but important epidemiological impact (14 to 17% reductions in TB incidence). Prioritizing higher incidence VDCs could be more impactful, if the observed heterogeneity are driven by differences in transmission rates.

Combining ACF with preventive therapy could substantially increase the impact (almost double).

Better data and understanding of the underlying drivers of heterogeneity (such as under notification rates), ongoing transmission rates, and migration and mixing patterns, can improve model projections.

NEXT STEPS

Considering alternative drivers of heterogeneity; e.g., heterogeneity driven by differences in notification rates (as opposed to transmission).

Model Validation: compare projected yields of the model to the observed yields from IMPACT TB study.

Model sensitivity; e.g., considering data from prevalence survey, fine tuning specifics of the interventions.

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THANK YOU!