Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis

Sourya Shrestha; Gokul Mishra; Mukesh Hamal; Raghu Dhital; Suvesh Shrestha; Ashish Shrestha; Naveen Prakash Shah; Mukti Khanal; Suman Gurung; Maxine Caws

doi:10.1136/bmjopen-2022-062123

Quantifying the potential epidemiological impact of a 2-year active case finding for tuberculosis in rural Nepal: a model-based analysis

BMJ Open. 2023 Nov 1;13(11):e062123. doi: 10.1136/bmjopen-2022-062123.

Authors

Sourya Shrestha¹, Gokul Mishra^{2

3}, Mukesh Hamal⁴, Raghu Dhital³, Suvesh Shrestha⁵, Ashish Shrestha⁶, Naveen Prakash Shah⁶, Mukti Khanal⁶, Suman Gurung^{2

3}, Maxine Caws^{3

7}

Affiliations

¹ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA sshres14@jh.edu.
² Department of Clinical Sciences, Liverpool School of Tropical Medicine, Liverpool, Liverpool, UK.
³ Birat Nepal Medical Trust, Kathmandu, Nepal.
⁴ Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, Maryland, USA.
⁵ Save the Children Nepal, Kathmandu, Nepal.
⁶ National Tuberculosis Center, Bhaktapur, Nepal.
⁷ Liverpool School of Tropical Medicine, Liverpool, UK.

Abstract

Objectives: Active case finding (ACF) is an important tuberculosis (TB) intervention in high-burden settings. However, empirical evidence garnered from field data has been equivocal about the long-term community-level impact, and more data at a finer geographic scale and data-informed methods to quantify their impact are necessary.

Methods: Using village development committee (VDC)-level data on TB notification and demography between 2016 and 2017 in four southern districts of Nepal, where ACF activities were implemented as a part of the IMPACT-TB study between 2017 and 2019, we developed VDC-level transmission models of TB and ACF. Using these models and ACF yield data collected in the study, we estimated the potential epidemiological impact of IMPACT-TB ACF and compared its efficiency across VDCs in each district.

Results: Cases were found in the majority of VDCs during IMPACT-TB ACF, but the number of cases detected within VDCs correlated weakly with historic case notification rates. We projected that this ACF intervention would reduce the TB incidence rate by 14% (12-16) in Chitwan, 8.6% (7.3-9.7) in Dhanusha, 8.3% (7.3-9.2) in Mahottari and 3% (2.5-3.2) in Makwanpur. Over the next 10 years, we projected that this intervention would avert 987 (746-1282), 422 (304-571), 598 (450-782) and 197 (172-240) cases in Chitwan, Dhanusha, Mahottari and Makwanpur, respectively. There was substantial variation in the efficiency of ACF across VDCs: there was up to twofold difference in the number of cases averted in the 10 years per case detected.

Conclusion: ACF data confirm that TB is widely prevalent, including in VDCs with relatively low reporting rates. Although ACF is a highly efficient component of TB control, its impact can vary substantially at local levels and must be combined with other interventions to alter TB epidemiology significantly.

Keywords: EPIDEMIOLOGY; Public health; Tuberculosis.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Humans
Incidence
Mass Screening* / methods
Nepal / epidemiology
Tuberculosis* / epidemiology