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

The study received ethical approval from the Ethical Review Board of Chulalongkorn University

Faculty of Medicine, with reference number 610/63.

Contact the academic department of the Department of Corrections to gained permission from

the Department of Corrections at the Ministry of Justice before being conducted.

Present all the details of the research study to the relevant stakeholders of all three sample prisons, ensuring they are informed and given the opportunity to inquire about any concerns. Additionally, the superintendent of each correctional facility reviewed the “Information Sheet for Participants” and signed the “Submission Agreement for Volunteers” before initiating the study. No personal information was collected during the study that could identify inmates; thus, individual inmate consent was not required.

Our research team receives training on regulations, rules, and protocols for conducting research in each of the sample prisons and adheres strictly to them.

The inmate teams were trained by the principal investigator (NM) and a coinvestigator (PS, who is also an industrial hygienist) for collecting the CO₂concentrations in the morning after at least 13 hours of lockup time using the Kimo HQ210 with SCOH 112 probe (Sauermann Industries, ZA BernardMoulinet, Montpon, France) within and outside each cell (985 cells in the winter; October 2020 to January 2021). These data use for calculating Germ-free ventilationrate, Q in liters/second/person (See S1 Table of PONE-D-24-02877R1 data Folder for more detail).

The wind speed (meters/second) in each cell was measured at the location of the opening facing prevailing winds using a hot wire thermo-anemometer and datalogger (Model SDL350, Extech Instruments,Waltham, MA). The credibility of the data was cooperatively ensured by the principal investigator (NM) and a coinvestigator (PS)(985 cells in the winter). These data use for calculating Germ-free ventilationrate, Q in air changes per hour (See S1 Table of PONE-D-24-02877R1 data Folder for more detail).

Working with HCW and Prison Health Volunteer for collecting the number of tuberculosis-infected patients in each cell and zone, which served as an indication of the prevalence at both the cell and zonal levels. These data were considered secondary data, comprising documents and computer databases sourced from the respective zonal and central administrative and medical facilities within the prisons. We cross-checked the data obtained from central administrative and medical facilities with the data from each zone to ensure consistency for all PTB cases. The data was recorded as a percentage for a period of 6 months, from July to December 2020, for Prisons A and B, and from October 2020 to March 2021 for Prison C.

Five inmates from each zone were recruited and trained by the principal investigator (NM) and

a coinvestigator (PS) to survey the architectural and environmental characteristics of each cell

within the zone. The researchers verified the collected data against the prison blueprints and randomly measured the dimensions of the cells themselves.

However, since March 2021, there has been a COVID-19 outbreak in Thai prisons, leading to measures that prohibit outsiders from entering without valid reasons. This has prevented researchers from collecting data continuously according to the planned schedule. Once the COVID-19 situation in the sample prisons improved, the prisons relaxed their measures, allowing researchers to enter under strict adherence to the prison's protocols. Consequently, the researchers resumed collecting the data including (1) the architectural and environmental characteristics of each cell such as room volume, opening facing prevailing winds, opening into the building, and number of cells per courtyard; and (2) the demographic

and health status composition of inmates, including the number of total and susceptible inmates per cell, residence time per day, and inmate turnover and (3) treatment effectiveness parameters, including period of infectiousness, number of recovered patients, and TB-related, natural, and other mortality rates (secondary data). These variables and parameters were collected or obtained as shown in S1 & S2 Table of the manuscript [Assessment of tuberculosis transmission probability in three Thai prisons based on five dynamic models PONE-D-24-02877R1]

Review literature related to the model parameters additionally

To calculated the estimated TB transmission probability within cells using five dynamic models (See excel data Folder)

Agreement among the TB transmission probabilityestimated using the different dynamic models was then assessed via Spearman’s rank correlation (r). The detailed pattern of agreement or difference was

further investigated using Bland–Altman plots representing absolute and percent differences.

To investigate theinfluence of model parameters on TB transmission probability,

two procedures were used separately for each TB transmission prediction model.

First, cells were categorized into four subgroups based on the quartile of predicted TB transmission probability. Furthermore, subgroups of model parameters were then compared using the Wilcoxon rank–sum (Mann–Whitney) test since the cell-specific probabilities of TB transmission were non-normally distributed. Second, the magnitude and pattern of change in the predicted TB transmission probability along each model’s parameter categories were investigated using a multiple linear regression incorporating all model parameters simultaneously for a specific prediction model. The adjusted

beta (i.e., the magnitude of change in the predicted TB transmission probability) and its 95% confidence

intervals were estimated. All statistical analyses were performed using Stata software, Version

13.0 (StataCorp. 2013, Stata Statistical Software: Release 13, College Station, TX: StataCorp LP).

This study showed that the variant models projected different values of TB transmission

probability. Althoughthe probability values differed, three models, i.e., the Wells–Riley model and the two Rudnick & Milton-proposed models, estimated similar patterns of TB transmission probability. Using the pioneered Wells–Riley’s model as the reference, 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. In terms of risk factors, our study identified two parameters that significantly contribute to ongoing TB transmission risk in all models: low ventilation rates and a high number of existing TB inmates in the cell or zone. All stakeholders must urgently address these issues to reduce TB transmission in prisons. Furthermore, since these five models produced varying estimates of TB transmission probabilities, further studies are required to determine their relative validity in accurately

predicting TB incidence in prison settings.