Strategic Planning for Tuberculosis Control in the Republic of Fiji

A social network analysis model approach to understand tuberculosis transmission in remote rural Madagascar

BACKGROUND

Quality surveillance data used to build tuberculosis (TB) transmission models are frequently unavailable and may overlook community intrinsic dynamics that impact TB transmission. Social network analysis (SNA) generates data on hyperlocal social-demographic structures that contribute to disease transmission.

METHODS

We collected social contact data in five villages and built SNA-informed village-specific stochastic TB transmission models in remote Madagascar. A name-generator approach was used to elicit individual contact networks. Recruitment included confirmed TB patients, followed by snowball sampling of named contacts. Egocentric network data were aggregated into village-level networks. Network- and individual-level characteristics determining contact formation and structure were identified by fitting an exponential random graph model (ERGM), which formed the basis of the contact structure and model dynamics. Models were calibrated and used to evaluate WHO-recommended interventions and community resiliency to foreign TB introduction.

RESULTS

Inter- and intra-village SNA showed variable degrees of interconnectivity, with transitivity (individual clustering) values of 0.16, 0.29, and 0.43. Active case finding and treatment yielded 67%-79% reduction in active TB disease prevalence and a 75% reduction in TB mortality in all village networks. Following hypothetical TB elimination and without specific interventions, networks A and B showed resilience to both active and latent TB reintroduction, while Network C, the village network with the highest transitivity, lacked resiliency to reintroduction and generated a TB prevalence of 2% and a TB mortality rate of 7.3% after introduction of one new contagious infection post hypothetical elimination.

CONCLUSION

In remote Madagascar, SNA-informed models suggest that WHO-recommended interventions reduce TB disease (active TB) prevalence and mortality while TB infection (latent TB) burden remains high. Communities' resiliency to TB introduction decreases as their interconnectivity increases. "Top down" population level TB models would most likely miss this difference between small communities. SNA bridges large-scale population-based and hyper focused community-level TB modeling.

Estimating the long-term effects of mass screening for latent and active tuberculosis in the Marshall Islands

BACKGROUND

Ambitious population-based screening programmes for latent and active tuberculosis (TB) were implemented in the Republic of the Marshall Islands in 2017 and 2018.

METHODS

We used a transmission dynamic model of TB informed by local data to capture the Marshall Islands epidemic's historical dynamics. We then used the model to project the future epidemic trajectory following the active screening interventions, as well as considering a counterfactual scenario with no intervention. We also simulated future scenarios including periodic interventions similar to those previously implemented, to assess their ability to reach the End TB Strategy targets and TB pre-elimination in the Marshall Islands.

RESULTS

The screening activities conducted in 2017 and 2018 were estimated to have reduced TB incidence and mortality by around one-third in 2020, and are predicted to achieve the End TB Strategy milestone of 50% incidence reduction by 2025 compared with 2015. Screening interventions had a considerably greater impact when latent TB screening and treatment were included, compared with active case finding alone. Such combined programmes implemented at the national level could achieve TB pre-elimination around 2040 if repeated every 2 years.

CONCLUSIONS

Our model suggests that it would be possible to achieve TB pre-elimination by 2040 in the Marshall Islands through frequent repetition of the same interventions as those already implemented in the country. It also highlights the importance of including latent infection testing in active screening activities.

Antimicrobial resistance management in Pacific Island countries: Current status, challenges, and strategic solutions

Antimicrobial resistance (AMR) is currently recognized as a major emerging threat to human and animal health. The burden of antimicrobial-resistant infections affects the economy in developed and developing countries. There is a rapid rise in AMR in human and veterinary medicine globally. AMR profiles are poorly documented in Fiji, and limited data are accessible. Fiji currently has no national veterinary antibiotic resistance surveillance network or regulations and guidelines on veterinary drug use. However, available literature shows that although human drugs are better managed than veterinary drugs, the knowledge is still constrained and dispersed. Furthermore, Fiji was chosen as a case study to develop a prototype AMR surveillance and control in the Pacific region. Pacific Island countries share similar geographic and climatic conditions. Currently, the Australian Centre for International Agricultural Research has funded an AMR project that addresses some gaps in managing AMR in the region. The project is the first to adopt the One Health approach to research the AMR in humans, animals, and the Pacific region's environment. Combating AMR needs human health and veterinary personnel to work with all other stakeholders. Continuous surveillance for resistant clinical isolates in humans and animals and the development of appropriate policy intervention measures in human and veterinary drug use are necessary to alleviate AMR burden. Therefore, there is a need to educate farmers, human patients, and the public on the fight against AMR. In addition, AMR data are necessary to develop effective AMR control strategies. This review gives a comprehensive information assessment on AMR in Fiji and the other South Pacific Islands in relation to global trends. Suggestions on the most appropriate ways of effectively managing AMR in Fiji have been made.

Revisiting the Natural History of Pulmonary Tuberculosis: A Bayesian Estimation of Natural Recovery and Mortality Rates

BACKGROUND

Tuberculosis (TB) natural history remains poorly characterized, and new investigations are impossible as it would be unethical to follow up TB patients without treatment.

METHODS

We considered the reports identified in a previous systematic review of studies from the prechemotherapy era, and extracted detailed data on mortality over time. We used a Bayesian framework to estimate the rates of TB-induced mortality and self-cure. A hierarchical model was employed to allow estimates to vary by cohort. Inference was performed separately for smear-positive TB (SP-TB) and smear-negative TB (SN-TB).

RESULTS

We included 41 cohorts of SP-TB patients and 19 cohorts of pulmonary SN-TB patients in the analysis. The median estimates of the TB-specific mortality rates were 0.389 year-1 (95% credible interval [CrI], .335-.449) and 0.025 year-1 (95% CrI, .017-.035) for SP-TB and SN-TB patients, respectively. The estimates for self-recovery rates were 0.231 year-1 (95% CrI, .177-.288) and 0.130 year-1 (95% CrI, .073-.209) for SP-TB and SN-TB patients, respectively. These rates correspond to average durations of untreated TB of 1.57 years (95% CrI, 1.37-1.81) and 5.35 years (95% CrI, 3.42-8.23) for SP-TB and SN-TB, respectively, when assuming a non-TB-related mortality rate of 0.014 year-1 (ie, a 70-year life expectancy).

CONCLUSIONS

TB-specific mortality rates are around 15 times higher for SP-TB than for SN-TB patients. This difference was underestimated dramatically in previous TB modeling studies, raising concerns about the accuracy of the associated predictions. Despite being less infectious, SN-TB may be responsible for equivalent numbers of secondary infections as SP-TB due to its much longer duration.

Tuberculosis in the Western Pacific Region: Estimating the burden of disease and return on investment 2020-2030 in four countries

Background

We aimed to estimate the disease burden of Tuberculosis (TB) and return on investment of TB care in selected high-burden countries of the Western Pacific Region (WPR) until 2030.

Methods

We projected the TB epidemic in Viet Nam and Lao People's Democratic Republic (PDR) 2020–2030 using a mathematical model under various scenarios: counterfactual (no TB care); baseline (TB care continues at current levels); and 12 different diagnosis and treatment interventions. We retrieved previous modeling results for China and the Philippines. We pooled the new and existing information on incidence and deaths in the four countries, covering >80% of the TB burden in WPR. We estimated the return on investment of TB care and interventions in Viet Nam and Lao PDR using a Solow model.

Findings

In the baseline scenario, TB incidence in the four countries decreased from 97•0/100,000/year (2019) to 90•1/100,000/year (2030), and TB deaths from 83,300/year (2019) to 71,100/year (2030). Active case finding (ACF) strategies (screening people not seeking care for respiratory symptoms) were the most effective single interventions. Return on investment (2020–2030) for TB care in Viet Nam and Lao PDR ranged US$4-US$49/dollar spent; additional interventions brought up to US$2•7/dollar spent.

Interpretation

In the modeled countries, TB incidence will only modestly decrease without additional interventions. Interventions that include ACF can reduce TB burden but achieving the End TB incidence and mortality targets will be difficult without new transformational tools (e.g. vaccine, new diagnostic tools, shorter treatment). However, TB care, even at its current level, can bring a multiple-fold return on investment.

Funding

World Health Organization Western Pacific Regional Office; Swiss National Science Foundation Grant 163878.