The potential impact of the COVID-19 pandemic on the tuberculosis epidemic a modelling analysis

Abstract

Background: Routine services for tuberculosis (TB) are being disrupted by stringent lockdowns against the novel SARS-CoV-2 virus. We sought to estimate the potential long-term epidemiological impact of such disruptions on TB burden in high-burden countries, and how this negative impact could be mitigated.

Methods: We adapted mathematical models of TB transmission in three high-burden countries (India, Kenya and Ukraine) to incorporate lockdown-associated disruptions in the TB care cascade. The anticipated level of disruption reflected consensus from a rapid expert consultation. We modelled the impact of these disruptions on TB incidence and mortality over the next five years, and also considered potential interventions to curtail this impact.

Findings: Even temporary disruptions can cause long-term increases in TB incidence and mortality. If lockdown-related disruptions cause a temporary 50% reduction in TB transmission, we estimated that a 3-month suspension of TB services, followed by 10 months to restore to normal, would cause, over the next 5 years, an additional 1⋅19 million TB cases (Crl 1⋅06-1⋅33) and 361,000 TB deaths (CrI 333-394 thousand) in India, 24,700 (16,100-44,700) TB cases and 12,500 deaths (8.8-17.8 thousand) in Kenya, and 4,350 (826-6,540) cases and 1,340 deaths (815-1,980) in Ukraine. The principal driver of these adverse impacts is the accumulation of undetected TB during a lockdown. We demonstrate how long term increases in TB burden could be averted in the short term through supplementary "catch-up" TB case detection and treatment, once restrictions are eased.

Interpretation: Lockdown-related disruptions can cause long-lasting increases in TB burden, but these negative effects can be mitigated with rapid restoration of TB services, and targeted interventions that are implemented as soon as restrictions are lifted.

Funding: USAID and Stop TB Partnership.

Keywords: Covid-19; Epidemiology; Mathematical modellingabstract; Tuberculosis.

Fig. 1

The potential impact of a lockdown on TB incidence in India, Kenya and Ukraine. Shown is monthly TB incidence in each country, in 2020 and 2021, for two disruption scenarios: (i) a ‘mild’ scenario with a 2-month lockdown and a 2-month restoration (orange), and (ii) a ‘severe’ scenario with a 3-month lockdown and a 10-month restoration (red). Bars labelled with 'S' and 'R' denote, respectively, the suspension and restoration periods, with numbers giving the duration in months in each period. As described in the main text, we assume that the disruptions in Table 1 are in full effect during the suspension period, and that they are reduced to zero in a linear way over the restoration period. Shaded intervals show 95% Bayesian credible intervals, reflecting uncertainty in pre-lockdown model parameters. Cumulative excess TB incidence over the period 2020–2025 is given in Table 2.

Fig. 2

The potential impact of a lockdown on TB deaths in India, Kenya and Ukraine. As for Fig. 1, but showing monthly TB deaths in each country. As in Fig. 1, bars labelled with 'S' and 'R' denote, respectively, the lockdown and restoration periods, with numbers giving the duration in months of each period. Shaded intervals show 95% Bayesian credible intervals, reflecting uncertainty in pre-lockdown model parameters. Excess TB deaths over the period 2020–2025 are listed in Table 2.

Fig. 3

Sensitivity analysis: influence of specific components of a lockdown on excess TB cases and deaths. Shown here is a ‘leave-one-out’ analysis, where we simulate a scenario with all disruptions in Table 1 in effect, with the exception of one (given by the label to the left). Bars in the figures show the excess TB burden between 2020 and 2025 arising from this scenario, relative to the scenario where all disruptions are in effect. Vertical lines mark median excess TB cases and deaths in the ‘full-impact’ scenario. The largest bars therefore indicate those types of disruption that are most influential, for excess TB burden. Left-hand panels show results in terms of excess TB incidence, and right-hand panels show excess TB deaths. Error bars show 95% credible intervals, calculated by iterating this process over 250 posterior samples for each country. Abbreviations: DST: drug susceptibility test, FL: first-line, HIV: human immunodeficiency virus, IPT: isoniazid preventive therapy, SL: second-line, Tx: treatment.

Fig. 4

The role of undetected prevalent TB and the impact of short-term supplementary measures to reduce this burden. The left-hand panel shows, in the example of India, the growth in the prevalence of undetected and untreated TB during the lockdown period, taking the example of a 2-month lockdown followed by a 2-month restoration. As described in the text, this expanded pool of prevalent TB is a source of short-term increase in TB mortality, as well as seeding new infections of latent TB that manifest as incident TB disease over the subsequent months and years. The right-hand panel shows the effect of ‘supplementary measures’ that are instigated immediately upon lifting the lockdown, and that operate over a two-month period to reach these missed cases and initiate them on treatment as rapidly as possible. In practical terms, such efforts could be guided by notification targets. Shown in the figure is the example of a mild lockdown scenario, followed by supplementary measures that aim to reach a peak target of 14 (95%CrI 13–16) monthly notifications per 100,000 population.

Fig. 5

Sensitivity analysis to the extent of TB transmission reduction during the suspension period. In other figures we assume that transmission is reduced by 10% (see Table 1), and here we examine the potential implications of more substantial reductions. Lines show the percent increase in cumulative incidence (upper row) and cumulative TB mortality (lower row) between 2020 and 2025, compared to a baseline of TB services continuing indefinitely at pre-lockdown levels. The horizontal dashed line in each figure indicates zero overall change; the region above this line corresponds to a net increase in TB burden over the next 5 years, and vice versa. Overall, and in agreement with recent analysis, the figure illustrates that TB transmission reductions are likely to lead to overall reductions in TB burden only when strong transmission reductions are combined with mild disruptions (orange lines, at >50% transmission reductions for Kenya, and >75% transmission reductions for India and Ukraine).