Modelling the Impact of Different Tuberculosis Control Interventions on the Prevalence of Tuberculosis in an Overcrowded Prison

Assessment of tuberculosis transmission probability in three Thai prisons based on five dynamic models

This study aimed to assess and compare the probability of tuberculosis (TB) transmission based on five dynamic models: the Wells-Riley equation, two Rudnick & Milton-proposed models based on air changes per hour (ACH) and liters per second per person (L/s/p), the model proposed by Issarow et al, and the Applied Susceptible-Exposed-Infected-Recovered (SEIR) TB transmission model. This study also aimed to determine the impact of model parameters on such probabilities in three Thai prisons. A cross-sectional study was conducted using data from 985 prison cells. The TB transmission probability for each cell was calculated using parameters relevant to the specific model formula, and the magnitude of the model agreement was examined by Spearman's rank correlation and Bland-Altman plot. Subsequently, a multiple linear regression analysis was conducted to investigate the influence of each model parameter on the estimated probability. Results revealed that the median (Quartiles 1 and 3) of TB transmission probability among these cells was 0.052 (0.017, 0.180). Compared with the pioneered Wells-Riley's model, the remaining models projected discrepant TB transmission probability from less to more commensurate to the degree of model modification from the pioneered model as follows: Rudnick & Milton (ACH), Issarow et al., and Rudnick & Milton (L/s/p), and the applied SEIR models. The ventilation rate and number of infectious TB patients in each cell or zone had the greatest impact on the estimated TB transmission probability in most models. Additionally, the number of inmates in each cell, the area per person in square meters, and the inmate turnover rate were identified as high-impact parameters in the applied SEIR model. All stakeholders must urgently address these influential parameters to reduce TB transmission in prisons. Moreover, further studies are required to determine their relative validity in accurately predicting TB incidence in prison settings.

Spread of Mycobacterium tuberculosis in Southern Brazilian persons deprived of liberty: a molecular epidemiology study

To evaluate the genetic diversity and clustering rates of M. tuberculosis strains to better understand transmission among persons deprived of liberty (PDL) in Rio Grande do Sul (RS), southern Brazil. This is a cross-sectional study, including strains of M. tuberculosis isolated from PDL, stored at the Central Laboratory of RS, in the period from 2013 to 2018. The molecular characterization was performed using the MIRU-VNTR 15 loci method. A total of 598 M. tuberculosis strains were genotyped, and 37.5% were grouped into 53 clusters. Cluster sizes ranged from 2 to 34 strains. The largest cluster of the study had strains from 34 PDL, and 58.8% of the PDL of this cluster were in P01. Among the clusters formed, in 60.3%, there was at least one strain from P01. The most common strains in RS were LAM (53.2%) and Haarlem (31.1%). The LAM strain was the most likely to form clusters, and Haarlem was associated with anti-TB drug resistance. This was translational research, and the results can collaborate with the TB control programs, leading to improved strategies that allow the reduction of the TB burden in prisons.

Environmental factors associated with the time to tuberculosis diagnosis in prisoners in São Paulo, Brazil

BACKGROUND

Environmental conditions play an important role in the high incidence of tuberculosis in prisons. We estimated the effect of environmental factors, including measurements based on cell dimensions, on the time to tuberculosis diagnosis in prison population of Brazil.

METHODS

This is a retrospective cohort of 2,257 prisoners diagnosed with tuberculosis in 2014 and 2015. We collected environmental data from 105 prisons and linked with routine tuberculosis surveillance and prison data. We estimated tuberculosis-free survival time with Cox risk models, guided by a validated directed acyclic graph.

RESULTS

The median disease-free time was 1.71 years (95% confidence interval [95% CI] 1.64-1.78). Each 50% increase in occupancy-rate, increased the tuberculosis speed incidence by 16% (95% CI 8%-25%) in the first 2 years, and 9% (95% CI 3%-16%) up to 5 years. An increase in the cell area per person (ln[m2/person]) reduced the hazard of tuberculosis by 13% (95% CI 3%-23%) for up to 2, and 12% (95% CI 3%-21%) for up to 5 years.

DISCUSSION

Most tuberculosis cases were diagnosed within 2 years of incarceration. Prison overcrowding and physical space per person in the cell were associated with the tuberculosis-free disease time.

CONCLUSIONS

Interventions to reduce overcrowding or increase physical space are crucial to prevent tuberculosis in prisons.

Impact of incarceration on tuberculosis incidence and its interaction with income distribution inequality in Brazil

BACKGROUND

Deteriorated conditions in the non-prison population can lead to an approximation of its tuberculosis (TB) risk to that in the prison population. We evaluated the association between incarceration and TB incidence rate and its interaction with population income distribution inequality in Brazilian municipalities (2013-2015).

METHODS

We included 954 municipalities with at least one prison. Interaction between the Gini coefficient and prison exposure was analysed in a multiple regression model. We estimated the fraction of TB in the population attributable fraction (PAF) to exposure to prisons according the Gini coefficient.

RESULTS

Compared with the non-prison population, the prisoners had 22.07 times (95% confidence interval [CI] 20.38 to 23.89) the risk of TB in municipalities where the Gini coefficient was <0.60 and 14.96 times (95% CI 11.00 to 18.92) the risk where the Gini coefficient was ≥0.60. A negative interaction in the multiplicative scale was explained by a higher TB incidence in the non-prison population in municipalities with a Gini coefficient ≥0.60. The PAF ranged from 50.06% to 5.19% in municipalities with Gini coefficients <0.40 and ≥0.60, respectively.

CONCLUSIONS

Interventions to reduce prison exposure would have an ostensible impact in population TB incidence rates mainly in settings with lower Gini coefficients. In those with extreme inequality in income distribution, strategies focused on mitigating the effects of socio-economic factors should also be prioritized.