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. Author manuscript; available in PMC: 2022 Dec 4.
Published in final edited form as: Int J Prison Health. 2021 Aug 16;18(3):233–244. doi: 10.1108/IJPH-01-2021-0012

Pulmonary tuberculosis among prisoners in Southern Thailand: prevalence and its association with imprisonment status

Bintinee Bilmumad 1, Tippawan Liabsuetrakul 2, Nipa Ngamtrairai 3, Virasakdi Chongsuvivatwong 4
PMCID: PMC9719586  NIHMSID: NIHMS1853081  PMID: 34464526

Abstract

Purpose –

This study aims to measure the prevalence of pulmonary tuberculosis (PTB), its association with imprisonment status and to document the treatment success rate (TSR) among prisoners in Songkhla province, Southern Thailand.

Design/methodology/approach –

A retrospective cross-sectional study was conducted in five prisons in Songkhla province, including all prisoners in the fiscal of year 2019, who had an annual chest radiography (CXR) screening result. Information of prisoners who had been imprisoned from 1 October 2018 to 30 September 2019, were reviewed for PTB diagnosis. Imprisonment status and other associated factors with PTB were analyzed using multiple logistic regression.

Findings –

The prevalence of PTB was 2.72%. Prisoners having new or transfer-in status were more likely to have PTB. Those aged 40–80 years, who had smoked for ten years or more, or who were underweight, had higher odds of having PTB. TSR among prisoners with PTB in this study was 94.9%.

Originality/value –

The prevalence of PTB among prisoners having annual CXR screening was high. Detection of PTB was higher among new or transfer-in prisoners; therefore, the CXR for PTB screening before admission to prison should be performed to prevent transmission to other prisoners.

Keywords: Pulmonary tuberculosis, Prisoner, Imprisonment status, Chest radiography screening, Treatment success rate, Thailand

Background

Globally, tuberculosis (TB) is one of the top ten causes of mortality (World Health Organization, 2019). A systematic review reported higher incidence of TB in prisoners than in the general population (Baussano et al., 2010), and global mapping showed the prevalence in Asia Pacific as 1,173 per 100,000 prisoners (Dolan et al., 2016). The prevalence of PTB among prisoners and their treatment outcomes vary, which may be because of different associated factors in individual prisoners, different prison environments and PTB screening methods (Al-Darraji et al., 2016; Jittimanee et al., 2007; Morasert et al., 2018; Morishita et al., 2017). In South-East Asia, studies assessing the prevalence of PTB among prisoners in Malaysia and the Philippines, using a cross-sectional design, have reported rates of 6,082 and 6,163 cases per 100,000 prisoners, respectively (Al-Darraji et al., 2016; Morishita et al., 2017). Screening using CXR among prisoners has been found more reliable in the detection of PTB than the use of a signs and symptoms assessment questionnaire alone (Al-Darraji et al., 2016; Jittimanee et al., 2007; Morasert et al., 2018; Morishita et al., 2017; Sanchez et al., 2005).

TB infection and transmission depends on the virulence of the pathogen, host susceptibility and environment (The Stop TB Partnership, 2019; World Health Organization, 2019). Prisoners are vulnerable to the risk of infection and transmission of PTB, owing to having a baseline status of malnutrition or chronic illness and/or undertaking risky behaviors such as smoking, history of illicit drug use or alcohol consumption, as well as being exposed to crowded prisons with insufficient ventilation (O’Grady et al., 2011; Santos et al., 2012). Duration of imprisonment and previous imprisonment in another prison were more likely to be found in CXR-confirmed TB in the studies in Brazil, Côte d’Ivoire and Thailand (Morasert et al., 2018; Pelissari et al., 2018; Séri et al., 2017). However, these findings were inconsistent. In addition, no study considered other imprisonment status and policies of inter-prison transfers, which may increase the risk of transmission, if infective prisoners are transferred.

The treatment guidelines for PTB in prisoners are the same as for the general population. Treatment outcomes of PTB are divided into cure or treatment completion, failure, relapse or survival/death. Treatment success is classified as both cure and treatment completion. A patient with bacteriologically confirmed TB at the beginning of treatment that is then smear- or culture-negative in the last month of treatment completion is defined as cured, whereas a patient who has completed treatment without evidence of failure is defined as treatment completed (Bureau of Tuberculosis, 2018a, 2018b; World Health Organization, 2019). Variation of treatment success rates were found in previous studies (Berihun et al., 2018; Singano et al., 2020), ranging from 63.6% (Berihun et al., 2018) in Ethiopia to 93.0% in Malawi (Singano et al., 2020).

The detection rates of PTB have been found to be higher in prisoners than in the general population (Bureau of Tuberculosis, 2018a, 2018b). From the global TB report, the incidence of TB accounted for 153 per 100,000 of the population in Thailand (World Health Organization, 2019). Four published studies reported the prevalence of PTB among prisoners in Thailand ranging from 354.8 per 100,000 prisoners in 2007, to 2,096 cases per 100,000 prisoners in 2018 (Jittimanee et al., 2007; Morasert et al., 2018; Imduang et al., 2018; Wiriyaprasobchok et al., 2017). Songkhla is the largest province in lower, Southern Thailand and consequently has the highest number of prisons. Annual CXR has been used for TB screening in prisoners as the national guideline (Bureau of Tuberculosis, 2018a, 2018b); however, there has been no study on the prevalence and treatment outcomes of PTB. Hence, this study aimed to measure the prevalence of PTB, its association with imprisonment status and document the treatment success rate (TSR) among prisoners in Songkhla province, Southern Thailand.

Methods

Study design and setting

A retrospective cross-sectional study was conducted from November 2019 to April 2020, in five prisons in Songkhla province, Southern Thailand. All prisoners who had been imprisoned between 1 October 2018 and 30 September 2019 (fiscal year 2019) and who had information on their individual characteristics and a result of PTB screening and diagnosis in their prison profiles or hospital records were included. Prisoners diagnosed with PTB, who continued their TB treatment, were registered with treatment after failure (TAF) or treatment after loss to follow up (TALF) before the fiscal year, of 2019, those who did not have sputum results, CXR or treatment, were excluded.

Since 2017, as per national strategic planning, the National Tuberculosis Control Program Thailand has recommended that all new prisoners should undergo CXR for TB screening when they enter prison and subsequently on an annual basis. For those who have CXR suggestive of TB, the Xpert Mycobacterium tuberculosis (MTB)/rifampicin (RIF), Xpert MTB/RIF, is required. A six-month regimen of treatment (2HRZE/4HR) is recommended for new drug-susceptible TB prisoners, including isoniazid (H), rifampicin (R), pyrazinamide (Z) and ethambutol (E) for the intensive phase (the first two months), followed by isoniazid (H) and rifampicin (R) for the continuation phase (the next four months). The recommended six-month regimen of treatment for TB, as mentioned above, is the identical treatment used for the general population of Thailand (Bureau of Tuberculosis, 2018a, 2018b).

Sample size and sampling

For prevalence of PTB and treatment outcomes, the infinite population proportion formula was used. Considering 2% for prevalence of PTB in prisoners from a previous study (Morasert et al., 2018), a 95% confidence interval (CI), a 0.4% acceptable error and a design effect of 2; at least 9,412 prisoners were required. Previous records of the number of prisoners in the five study prisons ranged from 12,000 to 13,000; therefore, all prisoners screened during the study period were used. For treatment outcomes, based on a 75% treatment success rate from a previous study (Wiriyaprasobchok et al., 2017), a 95% CI, a 10% acceptable error, a design effect of 2 and 15% of incomplete records; at least 172 prisoners diagnosed with PTB were required. To explore the association of imprisonment status and other factors with PTB, the two-proportion formula of estimated exposure rates of 70% in PTB, and 60% in non-PTB, with a ratio of 4, a type I error of 5% and a type II error of 20% was used. At least 231 prisoners with PTB and 924 prisoners with non-PTB were required.

To obtain sufficient samples, 250 prisoners with PTB and 1,000 prisoners without PTB were recruited. Detainees from each prison were randomly selected, considering probability proportional to size on the number of prisoners in each prison, using simple random sampling.

Data collection

After the study was approved by the Human Research Ethics Committee, Faculty of Medicine, Prince of Songkla University, the Songkhla Public Health Office and the Department of Corrections, the number of study prisoners and their histories of PTB screening were retrieved from the existing electronic-based records of the Songkhla Public Health Office, so as to assess the prevalence of PTB. This was based on the diagnosis of a physician, using CXR and/or AFB (acid-fast bacilli) smear sputum. Treatment success was defined as cure and completion of treatment, following the World Health Organization (WHO) guidelines (World Health Organization, 2019). Any personal identity was encrypted before we obtained the data from the Songkhla Public Health Office.

The information of the selected 250 prisoners with PTB and the 1,000 prisoners with non-PTB was collected from the prison records using an anonymized structured questionnaire. Independent variables included: socio-demographics, malnutrition, substance use and imprisonment status. Baseline information included: prison, gender, age, religion, education and occupation prior to imprisonment. Information relating to malnutrition and substance use included: body mass index (BMI), history of smoking, history of alcohol consumption and history of illicit drug use.

The imprisonment status in our study was defined as: the offense (drug-related, violent, property or concurrent/consecutive crime), number of previous imprisonments (no, yes or unknown), inter-prison transfer (no or yes), original prison (no, another or southern region), status of prisoner (current or new), duration of sentence (<15 or ≥15 years) and duration of imprisonment (<3 or ≥3 years). This three-year cut-off duration of imprisonment was chosen because in a previous study conducted in Thailand (Morasert et al., 2018) the prevalence of PTB trebled in prisoners who had prolonged imprisonment ≥3 years. If a prisoner was transferred, the origin of transfer, either from local southern transfer or transfer from another region, was recorded. Prisoner status was divided into new (new prisoners who underwent a screening CXR in the fiscal year of 2019) and current (prisoners who entered prison before the fiscal year of 2019).

Data management and analysis

Double data entry and analyses were performed using Epidata version 3.1 (Lauritsen et al., 2004), and R statistical software version 3.5.3 (R Core Team, 2019). Prevalence and treatment success rates were descriptively calculated as frequencies and percentages. Association of imprisonment status and other factors with PTB were analyzed using univariate analysis and multiple logistic regression, with backward stepwise selection. The factors with a p-value less than 0.2 in univariate analysis were selected for use in the first model of multiple regression and the associated factors with a p-value less than 0.05 were kept in the final model. The multicollinearity of factors in the final model was tested by using the variance inflation factor (VIF).

Ethical considerations

The study protocol was submitted to the Human Research Ethics Committee, Faculty of Medicine, Prince of Songkla University, the Songkhla Public Health Office and the Department of Corrections for approval. The first author was mainly responsible for contacting the chief nurse in each study prison and for retrieving the data using the study’s unique code without disclosure of personal identity. All data obtained were stored with privacy and confidentiality in mind.

Results

During the study period of the fiscal year of 2019, there were a total of 14,879 prisoners recorded in the selected prisons. Of these, 1,057 were excluded (7.1%) from the study. This included those who had been diagnosed with PTB before the fiscal year of 2019 but continued treatment in the fiscal year of 2019 (n = 314). In addition, those registered with TAF or TALF (n = 4), and those who did not have information of sputum results or CXR and treatment (n = 739) were also excluded. This resulted in 13,818 prisoners being included in this study. The flow diagram of screening and diagnosis of PTB, among the 13,818 screened prisoners, is shown in Figure 1. The prevalence of PTB was 2.72% (376 of 13,818), of which 369 cases were detected by annual CXR screening (2.67%) and seven were diagnosed by AFB smear sputum, owing to having signs and symptoms of PTB (0.05%). Only four prisoners with abnormal CXR were tested for the Xpert MTB/RIF. The duration from the time of entering prison to the annual CXR ranged from 1 to 438 days. All prisoners with PTB were treated with a six-month treatment regimen.

Figure 1.

Figure 1

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Diagram of PTB screening results among the study prisoners

The socio-demographic characteristics and the characteristics of malnutrition, substance use and imprisonment status of the 1,250 prisoners, 250 prisoners with PTB and 1,000 prisoners without PTB, are shown in Tables 1–3.

Table 1.

Baseline information of the study prisoners

Information Total (N = 1,250) n (%) Non-PTB (N = 1,000) n (%) PTB (N = 250) n (%) p-value
Prison <0.001
A    343 (27.4) 280 (28.0)   63 (25.2)
B    338 (27.1) 240 (24.0)   98 (39.2)
C    241 (19.3) 200 (20.0)   41 (16.4)
D    135 (10.8) 120 (12.0)   15 (6.0)
E    193 (15.4) 160 (16.0)   33 (13.2)
Gender <0.001
Female    163 (13.0) 147 (14.7)   16 (6.4)
Male 1,087 (87.0) 853 (85.3) 234 (93.6)
Age group (years) <0.001
18-39 years    902 (72.2) 745 (74.5) 157 (62.8)
40-80 years    348 (27.8) 255 (25.5)   93 (37.2)
Religion   0.071
Buddhist    581 (46.5) 481 (48.1) 100 (40)
Islam    340 (27.2) 264 (26.4)   76 (30.4)
Unknown    329 (26.3) 255 (25.5)   74 (29.6)
Education   0.147
Secondary or lower    598 (47.9) 489 (48.9) 109 (43.6)
Higher than Secondary    184 (14.7) 150 (15.0)   34 (13.6)
Unknown    468 (37.4) 361 (36.1) 107 (42.8)
Occupation <0.001
Unemployed/housewife   83 (6.6)   73 (7.3)   10 (4.0)
Laborer    613 (49.0) 495 (49.5) 118 (47.2)
Business/officer    181 (14.5) 153 (15.3)   28 (11.2)
Farmer/fisherman    257 (20.6) 207 (20.7)   50 (20.0)
Unknown    116 (9.3)   72 (7.2)   44 (17.6)
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Table 3.

Imprisonment history of study prisoners

Information Total (N = 1,250) n (%) Non-PTB (N = 1,000) n (%) PTB (N = 250) n (%) p-value
Offense 0.577
Drug-related crime    900 (72.0) 722 (72.2) 178 (71.2)
Violent crime    195 (15.6) 150 (15.0)   45 (18.0)
Property crime    114 (9.1)   95 (9.5)   19 (7.6)
Concurrent/consecutive crime   41 (3.3)   33 (3.3)  8 (3.2)
Number of previous imprisonments 0.124
No    706 (56.5) 573 (57.3) 133 (53.2)
Yes    401 (32.1) 305 (30.5)   96 (38.4)
Unknown    143 (11.4) 122 (12.2)   21 (8.4)
Inter-prison transfer 0.013
No    488 (39.0) 408 (40.8)   80 (32.0)
Yes    762 (61.0) 592 (59.2) 170 (68.0)
Original prison 0.008
No    488 (39.1) 408 (40.8)   80 (32.0)
Another region   18 (1.4)   11 (1.1)  7 (2.8)
Southern region    744 (59.5) 581 (58.1) 163 (65.2)
Status of prisoners 0.137
Current 1,009 (80.7) 816 (81.6) 193 (77.2)
New   241 (19.3) 184 (18.4)   57 (22.8)
Duration of sentence 0.372
Less than 15 years   748 (59.9) 608 (60.8) 140 (56.0)
15 years or more   468 (37.4) 366 (36.6) 102 (40.0)
Unknown   34 (2.7)   26 (2.6)  8 (3.2)
Duration of imprisonment 0.501
Less than 3 years    719 (57.5) 570 (57.0) 149 (59.0)
3 years or more    531 (42.5) 430 (43.0) 101 (40.0)
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Factors associated with PTB in prisoners, analyzed by multiple logistic regression, are presented in Table 4. All factors in the model showed VIF of 1, indicating no multicollinearity. Only two factors in the imprisonment status, inter-prison transfers and new, were significantly associated with PTB. Prisoners who were transferred into (1.58, 95% CI: 1.14-2.19), were newly entering (1.84, 95% CI: 1.26-2.69), aged 40-80 years (1.64, 95% CI: 1.19-2.26), had a history of smoking for ten or more years (3.98, 95% CI: 2.39-6.64) or were underweight (2.16, 95% CI: 1.4, 3.34) were significantly more likely to have PTB. There were no interactions among these associated factors.

Table 4.

Factors associated with pulmonary tuberculosis (PTB) among prisoners using multivariate analysis

Factors Crude OR (95% CI) Adj. OR (95% CI) P (Wald’s test) P (LR-test)
Type of inter-prison transfer (ref = No)   0.006
Yes    1.46 (1.09-1.97)   1.58 (1.14-2.19)   0.006
Status of prisoner (ref = Current)   0.002
New    1.31 (0.94-1.83)   1.84 (1.60-2.69)   0.002
Age group: (ref = 18-39 years)   0.003
40-80 years    1.73 (1.29-2.32)   1.64 (1.19-2.26)   0.002
Smoking duration (ref = Never smoked) <0.001
Less than 10 years    0.92 (0.53-1.6)   0.96 (0.55-1.69)   0.891
10 years or more    3.82 (2.32-6.27)   3.98 (2.39-6.64) <0.001
Body mass index (ref = Normal weight) <0.001
Underweight    1.69 (1.13-2.53)   2.16 (1.4-3.34) <0.001
Overweight    0.26 (0.15-0.45)   0.26 (0.15-0.45) <0.001
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Notes: OR: odds ratio; CI: confidence interval; LR: likelihood ratio

From 376 prisoners with PTB, 357 had successful treatment (94.9%), including cured in 32 prisoners (8.5%), and treatment completion in 325 prisoners (86.4%). Unsuccessful treatment outcomes were found in 19 prisoners (5.1%), which included failure (n = 5, 1.3%), transferred out (n = 5, 1.3%) and default (n = 9, 2.4%).

Discussion

The prevalence of PTB in this sample of prisoners in Songkhla province was 2.72%. Prisoners aged 40–80 years, having a history of smoking for ten or more years, were transferred in newly entering prison or were underweight were more likely to have PTB. High TSR among prisoners with PTB was found in our study.

Wide variations of PTB prevalence among prisoners have been reported within and across Thailand, Malaysia, Ethiopia and all lower- or middle-income countries, ranging from 349 to 7,692 per 100,000 of the prison populations (Fuge and Ayanto, 2016; Al-Darraji et al., 2016; Gizachew Beza et al., 2017; Imduang et al., 2018; Jittimanee et al., 2007; Morasert et al., 2018). These variations may be because of differences in sample characteristics, study settings, screening methods and/or policies for active case findings. The positive cases in our study showed higher TB prevalence than in two previous studies from upper Southern Thailand, which used the same CXR screening method (Imduang et al., 2018; Morasert et al., 2018) and a survey in Thailand using signs and symptoms screening (Jittimanee et al., 2007). Two studies from Ethiopia used signs and symptoms screening and further testing with sputum AFB smears or Xpert MTB/RIF in cases with positive signs and symptoms. These yielded a higher prevalence of PTB from the Xpert MTB/RIF than AFB smears (Fuge and Ayanto, 2016; Gizachew Beza et al., 2017).

Universal screening using CXR is recommended by the WHO, for use among high-risk populations including prisoners. One study reported that the sensitivity (87-98%) and specificity (46-89%) of CXR to detect PTB were better than screening using signs and symptoms; however, the accuracy of diagnosis depends on the interpretation of radiologists (World Health Organization, 2016). A literature review found that the average sensitivities and specificities of mobile CXR compared with the culture-confirmed gold standard were 81.8% (95% CI 64.5-93.0) and 99.2% (95% CI 99.1-99.3) for the detection of PTB in a mixed high-risk population, respectively (Canadian Agency for Drugs and Technologies in Health, 2016). A study in Myanmar, which screened for household contacts aged 15 years or more, found false positive CXRs in 18% compared with positive Xpert MTB/RIF as a reference test (Htet et al., 2018).

In our study, prisoners with increasing age were more likely to have PTB. This was also the case in previous studies, although the cut-off points of age used in our study was 40 years, whereas previous studies used 30 years of age (Al-Darraji et al., 2016; Séri et al., 2017). This finding is not surprising, as increasing age is thought to be associated with increasingly impaired function of T helper type1 (TH1) cells that play a role in the protective immunity response to Mycobacterium tuberculosis (Mtb) antigens (Winslow et al., 2008). One study suggested that prisoners may become infected by Mtb when they are young, but the Mtb becomes exacerbated when they age, and their immune function becomes less effective, resulting in detection of active PTB in older people (Wang, 2012). Age was not shown to be a significantly associated factor with Brazilian prisoners with PTB screened by CXR (Sanchez et al., 2005).

A history of smoking was one of the associated factors for PTB in prisoners, similar to the findings of previous studies (Gebrecherkos et al., 2016; Nyasulu et al., 2015). This can be explained by smoking-caused impaired phagocytic activity of alveolar macrophages providing immunity against Mtb (Bothamley, 2005). Although we could not find any studies examining an association between duration of smoking and PTB among prisoners, we did find one study in the general population, which concluded that a smoking duration of at least ten years was associated with higher odds of contracting PTB (Alavi-Naini et al., 2012).

Our study found higher odds of TB in underweight prisoners than other studies, but these studies used different definitions. We used a BMI of less than 18.5 kg/m2 to define underweight, the same as two previous studies conducted in Ethiopia and the Congo (Gebrecherkos et al., 2016; Kalonji et al., 2016). However, another study from Georgia used a BMI of less than 20 kg/m2 (Aerts et al., 2000). Being underweight indicates malnutrition, which decreases the circulating levels of pro-inflammatory cytokines (Anuradha et al., 2016b). In contrast, being overweight (BMI ≥ 25 kg/m2) was negatively associated with PTB in our study; however, earlier studies did not assess the effect of being overweight on the risk of developing PTB (Gebrecherkos et al., 2016; Kalonji et al., 2016). Three previous studies, conducted among general populations, found that household contacts and elderly people who were overweight had lower odds of PTB compared with people with normal weight (Aibana et al., 2016; Kim et al., 2018). Overweight individuals have increased circulating levels of pro-inflammatory cytokines – chemicals that play a role in the immune response against Mtb (Anuradha et al., 2016a).

The prisoners who were categorized as “new” to each prison, who were actually new prisoners or inter-prison transfers, were associated with higher odds of detecting PTB in our study. However, there are to our knowledge no previous studies exploring this factor, and we could not identify the factors related to new prisoners or inter-prison transfers. This may be explained through the epidemiological triad of tuberculosis on the vulnerable host and unhealthy lifestyles for new prisoners as well as exposure to the poor environments of the prison for inter-prison transfers. Screening on entry is helpful in detecting undiagnosed TB (Dara et al., 2009). Our findings confirmed that a delay in TB screening among prisoners upon entry is risky. Only duration of imprisonment and previous imprisonment in another prison were previously reported (Morasert et al., 2018; Pelissari et al., 2018; Séri et al., 2017).

The overall TSR in our study were high compared to the findings of previous studies from Brazil, Ethiopia and Thailand, which varied from 63.62% to 94% (Berhe et al., 2012; Berihun et al., 2018; Hasan et al., 2008; Macedo et al., 2013; Wiriyaprasobchok et al., 2017). The variations in TSR may be because of differences in prisoner characteristics, drug administration policies and the prisoner transfer system. High TSR for TB in prisoners can be supported by the success of Directly Observed Treatment for TB (Berhe et al., 2012).

Our study has some limitations. First, we conducted this retrospective study using existing electronic records; therefore, the diagnosis of PTB from prisoner’s screening in our study relied on the responsible physicians’ decision using abnormal CXR and/or positive bacteriology. Second, the factors collected in our study depended on existing variables in the databases, in which some important, potentially confounding factors were not adequately recorded such as, HIV status, information on contacts in confined areas within the same prison or other risk factors. Third, the self-reported variables of each individual prisoner recorded may introduce information bias and attrition bias, due to them being transferred out. Fourth, we could not analyze the factors associated with treatment success rates using multiple regression, owing to the small number of unsuccessful outcomes. Fifth, we did not consider a clustering effect for analysis, as we found that the individual characteristics of the prisoners were not correlated with prison-related factors. Finally, the wide range in time to CXR found in our study was because of the fact that some prisoners entered the prisons after the annual schedule of CXR screening. Thailand recently updated its policy for screening TB for prisoners in the fiscal year of 2021 and now has more mobile CXRs for TB screening on a monthly basis.

The findings of our study reflect the results of the annual CXR screening policy of the country to combat PTB among prisoners, a group which is classified as one of the vulnerable global target populations at high risk for TB. High detection of PTB in prisoners is still a challenge for TB prevention and control in Thailand. Therefore, strengthening the policy of CXR at prison entry, by performing a CXR and ensuring negative PTB results before allocation to cells with other people for all prisoners, is required. Additionally, further studies are required to monitor the PTB status among prisoners. Continuously monitoring the effect of the recent policy for monthly CXR on the magnitude of TB in prisoners should also be studied. More collaboration between prison health personnel and TB coordinators of hospitals and communities is required.

Conclusion

A high prevalence of PTB, using routine CXR screening, was found among prisoners in Southern Thailand. New or inter-prison transfer prisoners and prisoners who were older, heavy smokers or underweight had increased risk of PTB detection. The treatment success rate among the PTB prisoners in our study met the national target goals, as suggested in the global recommendations.

Table 2.

Information of malnutrition and substance use among prisoners

Information Total (N = 1,250) n (%) Non-PTB (N = 1,000) n (%) PTB (N = 250) n (%) p-value
Body mass index <0.001
Normal weight   898 (71.9) 704 (70.4) 194 (77.6)
Underweight   129 (10.3)   88 (8.8)   41 (16.4)
Overweight   223 (17.8) 208 (20.8)   15 (6.0)
Smoking status <0.001
Never smoked   185 (14.8) 165 (16.5)   20 (8.0)
Less than 10 years   235 (18.8) 446 (44.6)   50 (20.0)
10 years or more   569 (45.5) 389 (38.9) 180 (72.0)
History of alcohol consumption   0.848
No   447 (35.8) 365 (36.5)   89 (35.6)
Yes   742 (59.3) 635 (63.5) 161 (64.4)
History of illicit drug use   0.462
Never used   212 (17.0) 174 (17.4)   38 (15.2)
Used 1038 (83.0) 826 (82.6) 212 (84.8)
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Acknowledgements

This study was part of the thesis of the first author; to fulfil the requirements of a Master’s degree in Epidemiology at Prince of Songkla University. Scholarship and research funding were supplied by the Fogarty International Center – National Institutes of Health, for the project: “TB/MDR-TB Research Capacity Building in low-and middle-income countries in Southeast Asia” (Grant No. D43TW009522 to V.C.). The authors sincerely thank the Director of the Department of Corrections, Ministry of Justice, Thailand, for permission to collect and use the study data.

Competing interests:

T.L. and V.C. declare no competing interests. Although B.B. and N.N. are health personnel under the Ministry of Justice, they declare no benefits arising from the study and freedom from pressure concerning any presentation of the study findings.

Contributor Information

Bintinee Bilmumad, Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand, and Songkhla Correctional Institution for Drug Addicts, Department of Corrections Thailand, Mueang, Thailand.

Tippawan Liabsuetrakul, Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.

Nipa Ngamtrairai, Songkhla Central Prison, Department of Corrections Thailand, Mueang, Thailand.

Virasakdi Chongsuvivatwong, Epidemiology Unit, Faculty of Medicine, Prince of Songkla University, Songkhla, Thailand.

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