The Burden of Alcohol, Tobacco and Others Drugs Among Prisoners Diagnosed With Tuberculosis: Time Trends and Spatial Determinants in Southern Brazil

The Burden of Alcohol, Tobacco and Others Drugs Among Prisoners Diagnosed With Tuberculosis: Time Trends and Spatial Determinants in Southern Brazil | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article The Burden of Alcohol, Tobacco and Others Drugs Among Prisoners Diagnosed With Tuberculosis: Time Trends and Spatial Determinants in Southern Brazil Alessandro Rolim Scholze, Josilene Dália Alves, Thaís Zamboni Berra, and 11 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-828111/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Tuberculosis is an infectious disease caused by Mycobacterium tuberculosis and is a public health problem worldwide. It is estimated that 90% of the patients diagnosed with TB live in vulnerable environments with limited health resources, such as individuals living in correctional facilities. This study aimed to identify the consumption of alcohol, tobacco, and other drugs among prisoners diagnosed with tuberculosis and the spatial determinants and time trends of the phenomenon in southern Brazil. Methods An ecological study using data from the Brazilian Notifiable Diseases Information System. Tuberculosis cases confirmed from 2014 to 2018 in prisons located in Paraná, Brazil, were selected. The Prais-Winsten procedure was performed to identify time trends by calculating monthly rates and the percentage of monthly variation. The Seasonal-Trend by Loess decomposition method was used to verify the time series and trends. The spatial association was verified with the Getis-Ord Gi* technique, and risk areas were identified using spatial scan statistics. Results A total of 1,099 TB cases were found in the studied population. The consumption of tobacco (n = 460; 41.9%), illegal drugs (n = 451; 41.0%), and alcohol (n = 179; 16.3%) stand out. An ascending trend was found for the consumption of alcohol [+ 19.4%/mo. (95%CI: 12.20-23.03)], tobacco [+ 20.2% (95%CI: 12.20-28.82)], and illegal drugs [+ 62.2%/mo. (95%CI: 44.54–81.97)]. Spatial analysis revealed clusters for the use of alcohol, tobacco, and illegal drugs. Conclusions This study advances knowledge presenting the burden of drug use and its typology among individuals diagnosed with tuberculosis in the prison system. There is a growing trend among patients to use drugs, especially illegal drugs. The clusters show differences between the places where the prisons are located. Tuberculosis within the prison system is already a complex disease, and it is even more challenging when drugs are consumed. The consumption of drugs increased threefold when tobacco and alcohol were considered. Health Economics & Outcomes Research Infectious Diseases Health Policy Tuberculosis Persons Deprived of Liberty Drug Abuse. Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Tuberculosis (TB) is an infectious disease caused by Mycobacterium tuberculosis [ 1 ] and is a public health problem worldwide, though developing countries are the most severely affected. Data show that 30 countries account for 87% of the disease burden, and Brazil ranks 19th [ 2 ]. Even though the TB burden declined in recent years before the COVID-19 pandemic, it is a disease difficult to control and eliminate due to co-infection with the Human Immunodeficiency Virus (HIV), antimicrobial resistance, multidrug-resistant TB, and increased consumption of alcohol, tobacco, and/or other drugs. In addition, TB is associated with social determinants of health, such as social vulnerability, poverty, and social exclusion to which populations such as immigrants, refugees, the homeless, and Persons Deprived of Liberty (PDL) are exposed [ 3 ]. It is estimated that 90% of the patients diagnosed with TB live in vulnerable environments with limited health resources [ 4 ], such as individuals living in correctional facilities. The World Health Organization (WHO) estimates that the prevalence of TB among PDL is 100 times higher than in the population in general [ 5 , 6 , 7 ] the prison environment contributes to TB mortality rates, revealing that the control of the disease is a priority neglected throughout the world [ 8 ]. Due to the unhealthy condition to which this population is exposed, Brazilian prisons are considered an important reservoir of TB bacillus and, consequently, a source of transmission among prisoners, prison officers, and their families [ 9 ]. A literature review, conducted with the following descriptors “incarcerated,” “drug abuse and tuberculosis,” and “temporal trends and clustering,” revealed that few studies address the use of alcohol, tobacco, and other drugs among PDL diagnosed with TB, showing a need for further research. The development of studies addressing this population is crucial to promote equity and mitigate the effects of the TB burden, especially in a country like Brazil with a large contingent of prisoners. Therefore, this study’s objective is to identify the use of alcohol, tobacco, and other drugs among PDL diagnosed with tuberculosis and the spatial determinants and time trends of the phenomenon in Brazil. Methods Study design Ecological study [ 10 ]. Study setting This study was conducted in the state of Paraná, in southern Brazil, divided into four health macro-regions (east, west, north, and northwest) with 399 cities and an estimated population of 11.34 million inhabitants. The 63 prison facilities distributed in 44 cities were considered in this study [ 11 ]. Figure 1 shows the spatial distribution of prisons in the state of Paraná according to the four health macro-regions. Population and study period It is estimated that Brazil has 748,009 PDL [ 11 ], ranking 3rd among the countries with the largest number of prisoners, with an incidence of 352.6 prisoners per 100,000 inhabitants [ 12 ]. Paraná has the 7th largest proportion of PDL in the country, with a population of approximately 29,831 [ 11 ]. The TB cases confirmed in the state’s prisons from 2014 to 2018 and reported to the Notifiable Diseases Information System (SINAN), Brazilian Ministry of Health, were selected. SINAN collects, reports, and disseminates data concerning reportable diseases according to information provided by the cities’ epidemiological surveillance agencies. Data were collected from the state Health Department using an electronic spreadsheet. The that data are anonymous, and patients cannot identified by name or facility. Inclusion criteria were PDL diagnosed with TB from 2014 and 2018 using alcohol, tobacco, or illegal drugs. The period between 2014 and 2018 was chosen because the reporting form was updated in 2014 and started including information regarding PDL and drug use. Data analysis The database was checked for consistency, and the cases were grouped according to the following characteristics: 1) total PDL considering all TB confirmed cases; 2) PDL consuming alcohol or reporting to be alcoholic; 3) PDL using tobacco or reporting to be a smoker, and 4) PDL using illegal drugs. Note that the same patient may use more than one drug and be included in more than one group. Analysis Procedures Exploratory Descriptive statistics were performed to present the absolute and relative frequencies of the variables sex, age, race, education; TB/HIV co-infection, diabetes mellitus, mental disorder, information regarding TB clinical profile such as admission, type, and whether the following were performed: radiograph, sputum smear microscopy, histopathology, molecular test; and treatment outcome, using Statistical Package for the Social Sciences (SPSS) version 25. Time Series Times series is characterized as a sequential collection of observations indexed over time [ 13 ]. Hence, the TB cases reported by the prison system and grouped as mentioned earlier were organized according to the month they were reported to obtain monthly rates. The Prais-Winsten self-regression method [ 14 ] was performed using Software for Statistics and Data Science (STATA) version 14 to classify the event’s time trend into ascending, descending, or stationary in the period. The Monthly Percent Change – MPC was calculated whenever a time trend was classified as ascending or descending along with its respective 95% confidence interval (95%CI) [ 13 ]. Note that a time trend refers to the ascending, descending, or stationary tendency of a time series in a given period [ 13 , 15 ]. Then, the robust Seasonal-Trend by Loess (STL by Loess) [ 15 ] method was used. This decomposition method is based on a locally weighted regression (Loess) so that it is used to estimate non-linear relationships, separating the components of a time series into trend, seasonality, or noise [ 15 , 16 ]. Rstudio was used with the forecast package. Opposed to the Prais-Winsten method in which time trend is globally assessed, and a constant is generated to classify the entire period, the STL method assesses the time trend over the period, verifying its variations over time and whether the trend was always ascending/descending or stationary or there were variations with peaks and/or decreases. Spatial analysis The Getis-Ord Gi* statistic, a technique consisting of a local indicator of spatial association, was used to identify the social determinants and cluster formation in each prison in the state, based on a neighborhood matrix. A z-score is generated for the statistically significant cities; the larger the z-score, the more intense the clustering of high values (hot spot). The same logic is used for a negative z-score, i.e., the smaller the z-score, the more intense is the clustering of low values (cold spot). In addition to the z-score, the p-value and significance level (Gi-Bin) determine whether hot spots and cold spots are statistically significant. For example, values may range between +/- 3 and reflect statistical significance with a 99% confidence level, between +/- 2 with a 95% confidence level, and +/- 1 with a 90% confidence level; zero corresponds to non-statistically significant areas. Spatial scan statistics was used to identify the TB risk spatial areas for PDL in Paraná, Brazil. First, spatial clusters were identified by finding circles with a variable radius around each center of the cities with prison facilities. Then, the number of observed and expected cases within each circle is calculated, and this procedure is performed until all centers are tested. A cluster is identified when the value observed in the area within the circle is greater or smaller than the expected [ 17 ]. In this stage, the following characteristics were considered: Poisson’s discrete model, only high-risk clusters, no overlapping geographic clusters, circular clusters, 999 Monte Carlo simulation, while the size of the population exposed was determined by the Gini coefficient in which the number of expected cases in each state is proportional to the size of the population at risk [ 17 , 18 ]. The relative risk (RR) and confidence interval (95%CI) of each cluster were also calculated; statistical significance was established at p < 0.05. Ethical Aspects The study was approved by the Research Ethics Committee at the University of São Paulo at Ribeirão Preto College of Nursing under Certificate of Presentation for Ethical Consideration number 24963319.1.0000.5393 and report number 3.836.401, issued on February 13th 2020 in accordance with the Guidelines and Regulatory Standards for Research with Human Subjects, Resolution number 466/2012 of the National Health Council of Brazilian Ministry of Health. Results A total of 1,099 TB cases were reported among PDL in Paraná. The use of tobacco was the most frequently reported (n = 460;41.8%), followed by illegal drugs (n = 451;41.0%), and alcohol (n = 179;16.3%). Regarding sociodemographic characteristics, most were Caucasian male individuals, aged between 18 and 29 years old, with a low educational level (< 8 years of schooling) (Table 1 ). Table 1 Sociodemographic characteristics of PDL diagnosed with TB according to the type of drug used and total PDL, Paraná, Brazil. (N = 1,099) Variables PDL using alcohol PDL using tobacco PDL using illegal drugs Total PDL n(%) n(%) n(%) n(%) Gender Male 173(96.6) 447(97.2) 440(97.6) 1064(96.8) Female 6(3.4) 13(2.8) 11(2.4) 35(3.2) Race Caucasian 112(62.6) 280(60.9) 290(64.3) 722(65.7) Mixed race 48(26.8) 136(29.6) 122(27.1) 275(25.0) African descent 19(10.6) 37(8.0) 34(7.5) 83(7.6) Asian descent - 3(0.7) 1(0.2) 3(0.3) Indigenous - - - 2(0.2) Ignored - 4(0.8) 2(0.4) 14(1.3) Age group (years) 18 to 29 78(43.6) 253(55.0) 269(59.6) 614(55.9) 30 to 39 64(35.8) 142(30.9) 138(30.6) 326(29.7) > 40 37(20.7) 64(13.9) 40(9.5) 156(14.2) Education Illiterate 9(5.0) 14(3.0) 8(1.8) 22(2.0) 1st to 4th grade 35(19.6) 96(20.9) 78(17.3) 198(18.0) 5th to 8th grade 82(45.8) 222(48.9) 240(53.2) 564(51.3) > 8 years 32(17.9) 77(16.7) 62(13.7) 297(27.0) Sources: study’s data TB/HIV co-infection was more prevalent among smokers and those using illegal drugs, whereas TB/diabetes mellitus and mental disorders were more frequent among those consuming alcohol. The TB clinical profile was: new cases, pulmonary TB, no radiograph, positive sputum smear microscopy, histopathology, and molecular test revealing sensitivity to rifampicin among the groups using tobacco and illegal drugs. Regarding TB treatment outcome, cure and DR-TB were predominant among smokers and individuals using illegal drugs, while alcohol consumers tended to abandon the treatment more frequently (Table 2 ). Table 2 – Clinical characteristics of PDL diagnosed with TB using alcohol, tobacco, or illegal drugs. Paraná, Brazil (N = 1,099) Variables PDL using alcohol PDL using tobacco PDL using illegal drugs Total PDL n(%) n(%) n(%) n(%) AIDS Yes 21(11.7) 39(8.5) 39(8.6) 82(7.5) No 155(86.6) 409(88.9) 394(87.4) 971(88.4) Diabetes Mellitus Yes 7(3.9) 13(2.8) 8(1.8) 21(1.9) No 170(95.0) 430(93.5) 422(93.6) 1025(93.3) Mental disorder Yes 4(2.2) 9(2.0) 8(1.8) 17(1.5) No 172(96.1) 435(94.6) 424(94.0) 1029(93.6) Admission New case 136(76.0) 359(78.0) 344(76.3) 880(80.1) Relapse 11(6.1) 29(6.3) 30(6.7) 70(6.4) Retreatment after treatment abandonment 20(11.2) 38(8.3) 39(8.6) 66(6.0) Transference 12(6.7) 34(7.4) 38(8.4) 79(7.2) Type Pulmonary 156(87.2) 410(89.1) 402(89.1) 968(88.1) Extrapulmonary 21(11.7) 40(8.7) 39(8.6) 105(9.6) Pulmonary + extrapulmonary 2(1.1) 10(2.2) 10(2.2) 26(2.4) Radiograph Suspected TB 148(82.7) 374(81.3) 360(79.8) 877(79.8) Normal 3(1.7) 10(2.2) 9(2.0) 32(2.9) Other pathology 1(0.6) - - 3(0.3) Not taken 26(14.5) 75(16.3) 80(17.7) 182(16.6) Sputum smear microscopy Positive 114(63.7) 288(62.6) 302(67.0) 703(64.0) Negative 35(19.6) 80(17.4) 65(14.4) 179(16.3) Not taken 25(14.0) 86(18.7) 78(17.3) 202(18.4) Histopathology Baar Positive 15(8.4) 16(3.5) 22(4.9) 69(6.3) Suggestive of TB 5(2.8) 21(4.6) 19(4.2) 44(4.0) Not suggestive of TB 3(1.7) 4(0.9) 4(0.9) 8(o.7) Not performed 153(85.5) 405(88.0) 396(87.8) 951(86.5) Molecular test Rifampicin sensitivity detected 65(36.3) 227(49.3) 223(49.4) 432(39.3) Rifampicin resistance detected 3(1.7) 11(2.4) 9(2.0) 21(1.9) Not detectable 9(5.0) 35(7.6) 29(6.4) 57(5.2) Not performed 97(54.2) 179(38.9) 182(40.4) 543(49.4) Outcome Cure 116(64.8) 312(67.8) 301(66.7) 743(67.6) Abandoned 25(14.0) 42(9.1) 40(8.9) 89(8.1) Death due to TB 2(1.1) 4(0.9) 3(0.7) 18(1.6) Death due to another cause 5(2.8) 15(3.3) 9(2.0) 31(2.8) Transferred 18(10.1) 43(9.3) 51(11.3) 119(10.8) DR-TB 12(6.7) 41(8.9) 43(9.5) 75(6.8) Source: Study’s data. Table 3 Time trend of TB incidence among PDL according to the consumption of psychoactive substances. Paraná, Brazil. (2014–2018). (N = 1,099) Variable Coefficient (95%CI)* Time trend MPC† (95%CI) Total PDL -0.30 (-0.19 - -0.40) Descending -49.88 (-35.43–60.19) PDL consuming alcohol 0.07 (0.05–0.09) Ascending 19.40 (12.20–23.03) PDL using tobacco 0.08 (0.05–0.11) Ascending 20.23 (12.20–28.82) PPL using illegal drugs 0.21 (0.16–0.26) Ascending 62.18 (44.54–81.97) Source: Study’s data. *95%CI = 95% Confidence Interval; †MPC = Monthly percentage change The time trend of TB cases in the total PDL was classified as descending, with a decrease of -49.8% a month (95%CI: -35.43 to -60.19). When analyzing the psychotic substances, an ascending time trend was found for alcohol consumers +19.4%/mo. (95%CI: 12.20 to 23.03); smokers +20.2% (95%CI: 12.20 to 28.82), and drug users +62.2% (95%CI: 44.54 to 81.97), as shown in (Table 3). The time series decomposition technique (Figs. 2 A, 2 B, and 2 C) showed an increase in the TB time trend among PDL using alcohol, tobacco, and illegal drugs, whereas Fig. 2 D shows a decrease in the time trend of the total population between 2014 and 2018. These findings corroborate data presented in Table 3 concerning the results from the Prais-Winsten analysis. Figure 3 A concerns the total PDL. The Getis-Ord Gi* technique identifies three hot spots: east (metropolitan region of Curitiba), north and west macro-regions. Figure 3 B reveals three hot spots, two in the east macro-region (metropolitan region of Curitiba and in Ponta Grossa) and in the north macro-region; the pseudo-significance test presented a z-score of 4.27 confirming the clusters’ non-randomness (p < 0.00). Figure 3 C shows a hot spot in the east macro-region (z-score = 2.41 and p < 0.01), and Fig. 4 D presents two hot spots in the east and north macro-regions (z-score = 2.13 and p < 0.03). Figure 4 A shows a relationship between TB and the total PDL, indicating four spatial risk clustering, namely: 1) one in the city of Pinhais located in the east macro-region (metropolitan region of Curitiba) presenting RR:17.75 (95%CI 15.40-19.65) with 832 PDL and 296 cases; 2) in the city of Paranaguá located in the east macro-region (Paraná coast region) presenting RR:6.75 (95%CI 3.84–11.18) with 56 PDL and five cases; 3) in the north macro-region presenting RR:2.87 (95%CI 2.11–3.66) with 616 PDL and 50 cases; and 4) one in the northwest macro-region presenting RR:2.11 (95%CI 1.76–2.36) with 3,911 PDL and 212 cases. Figure 4 B presents three risk clusters among PDL diagnosed with TB using alcohol: 1) one in the city of Ourizona located in the northwest macro-region presenting RR:20.93 (95%CI 8.86–48.96) with 56 PDL and five cases observed.; 2) one located in the east macro-regional in the metropolitan region of Curitiba presenting RR:9.95 (95%CI 6.99–14.08), with 1,670 PDL and 49 cases; 3) one cluster located in the north macro-region presenting RR:2.05 (95%CI 1.43–2.87) with 7,219 PDL and 52 cases. Figure 4 C presents four spatial risk clusters concerning PDL using tobacco: 1) one in the city of Pinhais in the east macro-region (metropolitan region of Curitiba) RR:30.49 (95%CI 24.56–37.41) with 832 PDL and 142 cases reported; 2) one in Paranaguá, east macro-region (Paraná’s coast region) RR:8.57 (95%CI 3.15–6.58) with 56 PDL and five cases observed; 3) north macro-region presenting RR:4.60 (95%CI 2.14–6.37) with 667 PDL and 30 cases; 4) one in Paranavaí in the northwest macro-region presenting RR:3.73 (95%CI 3.64–19.73) with 264 PDL and 9 cases observed. Figure 4 D presents two risk clusters of PDL using drugs: 1) in the city of Pinhais in the east macro-region (metropolitan region of Curitiba) presenting RR:23.81 (95%CI 19.41–28.77) with 832 PDL and 149 cases; and 2) one located in the north macro-region presenting RR:2.06 (95%CI 1.60–2.58) with 3,911 PDL and 87 cases. Discussion The use of tobacco and illegal drugs was prevalent among PDL diagnosed with TB, and an ascending trend was verified for alcohol, tobacco, and illegal drugs in prison facilities. The highest RR was found in the prisons located in the east macro-region (metropolitan region of Curitiba), showing that prisons contribute to TB burden and are an environment that favors the disease, considering a large number of alcohol consumers and drug users [ 5 , 7 , 9 , 19 , 20 ]. Note that the medical center providing care to prisoners is located in the east macro-region, which explains the high number of TB cases in this area. Data suggest that the availability of medical services indicates PDL have access to healthcare services, and the high rates of outpatient care consultations and hospitalizations suggest the quality of these individuals’ health conditions [ 21 ]. It is worth noting that in 2003 the Ministry of Health, together with the Ministry of Justice, launched the National Health Plan for the Prison System and in 2014 established the National Policy for Integral Health Care for Persons Deprived of Liberty in the Prison System to promote health and prevent diseases in the prison system and ensure PDL access integral and quality health care provided by the Brazilian Unified Health System [ 22 ]. However, a high number of TB cases is observed among PDL, mainly pulmonary TB, which is of concern considering that the bacillus is airborne and the prisons’ conditions such as overcrowding and poor ventilation favor the dissemination of TB [ 23 ]. Therefore, efficient public policies intended to provide health care to PDL. PDL include socially vulnerable people affected by different diseases,[ 22 ] among which dependency on alcohol, tobacco, and illegal drugs stand out. Even though alcohol and tobacco consumption decreased in the population in general, there was a significant increase in the number of smokers and drug users among PDL [ 19 ]. Studies report that approximately 80% of the PDL in the United States have a history of illegal drug use [ 20 ]. One study was conducted in Norway to investigate drinking habits before imprisonment, reporting that 55% of the prisoners presented alcohol problems of some severity, and 18% were possibly alcohol dependent [ 24 ]. Not much data are available in Brazil and research in the field is still incipient considering the difficulty in discussing this phenomenon in public security institutions [ 25 ]. Hence, the consumption of legal or illegal Psychoactive Substances (PS) within prisons contributes to an increase in the number of diseases, and as this study shows, contributes to the development and maintenance of TB [ 26 , 27 ]. Furthermore, these substances not only contribute to the development of TB but lead to unfavorable treatment outcomes, considering that PS is associated with higher rates of mental disorders, suicide, mortality, relapse after release [ 28 ], and violence within prisons. The consumption of PS favors the development of diseases among PDL and is associated with higher rates of physical violence and suicide attempts within prisons; suicide attempts in this population are estimated to be three to eight times higher than in the general population. Risk factors include mental disorders, substance use disorders, suicidal ideation, suicide attempt, self-injury behaviors, accommodation in single-occupancy cells, and conviction due to violent crimes [ 29 ]. The prisons’ social, spatial factors directly contribute to the maintenance of TB and other diseases considering many prisons are overcrowded, present high turnover of PDL, are poorly ventilated, and access to health services is restricted [ 30 ]. PDL live in an unhealthy environment with poor hygiene conditions, which often fail to ensure basic human needs to protect one’s physical and mental health, directly contributing to disseminating transmissible diseases, violence-related injuries, and mental disorders [ 31 ]. These are characteristics observed in the east, north, and northwest macro-regions, which host risk areas for total TB and TB associated with alcohol, tobacco, and illegal drugs. Note that the west macro-region did not present any risk area for TB associated with PS. The identification of risk prison facilities for the development of TB associated with PS consumption can support the implementation of preventive measures and quality health care, especially in those facilities with a large number of prisoners, that is, facilities exposed to a higher risk. However, there is usually a delay in the TB diagnosis, with a high prevalence of resistant bacteria, inadequate treatment and treatment abandonment, low educational level, malnutrition, mental disorders, previous diseases, TB-HIV co-infection, alcohol, tobacco, and/or illegal drugs consumption/dependency [ 5 , 6 , 32 ]. Therefore, prisons are a reservoir of various diseases, especially infectious-contagious diseases such as TB. The community is also exposed to TB when contacting prison officers, released prisoners, or families visiting prisoners. Hence, the prison environment promotes the incidence and maintenance of TB, and health actions are needed to break the transmission cycle and decrease the number of new cases and deaths [ 30 ]. Time trend analysis showed TB increased among PDL consuming alcohol, tobacco, or illegal drugs, which is similar in the general population. The fact that the consumption of these substances has increased worldwide is of concern, considering it is associated with worsened TB treatment outcomes [ 21 , 23 ]. Therefore, a screening protocol should be implemented in the prison system to identify SP consumption and TB and invest and give priority to early diagnosis and interventions, providing appropriate treatment to avoid interruptions and relapses [ 30 ]. Achieving the goals established by the End TB Strategy and eradicating TB by 2050 will only be possible by investing in preventive measures and appropriate TB treatment. Therefore, one of the most difficult challenges is to control the progress of the disease among subpopulations presenting high incidence rates, such as PDL [ 33 ]. In this sense, PDL is a social stratum at a higher risk of TB, [ 7 ] and strategies are needed to decrease the transmission of the disease and achieve the global goals. In this sense, the WHO recommends measures be implemented in prisons to prevent new cases, including screening protocols applied in the admission and discharge of inmates, in addition to periodical assessments of PDL or isoniazid preventive therapy [ 2 ]. Routinely screening prisoners, isolating confirmed cases, decreasing the number of inmates in a single cell, and avoiding agglomerations are efficient ways to decrease transmissions among prisoners, prison officers, families, and the community [ 7 ]. Programs intended to obtain early diagnoses, the report of new cases, and the implementation of proper treatment are vital. However, the access of PDL to health services is restricted, resulting in unfavorable outcomes and high rates of TB. Conclusions This study advances with knowledge in the field, as it shows the consumption of alcohol, tobacco, and illegal drugs among PDL diagnosed with TB. Clustering was found in some areas in the state with an ascending trend, highlighting the need to implement policies to control the disease in vulnerable areas and areas hosting prisons. Even though there are studies addressing TB in the prison system, this is one of the first studies investigating spatial differences and determinants using geostatistics. These techniques are seldom adopted to address these populations, showing the originality and relevance of this investigation. Decreasing disparities and inequalities involves understanding the burden of the disease and its determinants in the prison system so that innovative approaches and studies are also needed. Abbreviations CI Confidence Interval DR-TB Drug-resistant Tuberculosis HIV Human Immunodeficiency Virus MPC Monthly Percent Change PDL Persons Deprived of Liberty PS Psychoactive Substances RR Relative Risk SINAN Notifiable Diseases Information System SPSS Statistical Package for the Social Sciences STATA Software for Statistics and Data Science STL Seasonal-Trend by Loess TB Tuberculosis WHO World Health Organization Declarations Ethics approval and consent to participate The study was approved by the Research Ethics Committee at the University of São Paulo at Ribeirão Preto College of Nursing under Certificate of Presentation for Ethical Consideration number 24963319.1.0000.5393 and report number 3.836.401, issued on February 13th 2020 in accordance with the Guidelines and Regulatory Standards for Research with Human Subjects, Resolution number 466/2012 of the National Health Council of Brazilian Ministry of Health. Consent for publication Not applicable. Availability of data and materials All the data supporting the study findings are within the manuscript. Additional detailed information and raw data will be shared upon request addressed to the corresponding author. Competing interests The authors declare that they have no competing interests. Funding Not applicable. Author’s contributions The conception of the work, design of the work, acquisition of data, analysis, and interpretation of data was done by ARS, JDA, TZB, FLS, MJQG and RAA. Data curation, drafting the article, revising it critically for intellectual content, validation and final approval of the version to be published was done by ARS, RAA, TZB, ACVR, GLF, FMP, MAMA, SCP, AAM, IF and DG. All authors read and approved the final version of the manuscript. Acknowledgements Not applicable. References Seki M, Choi H, Kim K, Whang J, Sung J, Mitarai S. Tuberculosis: A persistent unpleasant neighbour of humans. 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In: Rothman KJ, Greenland S, Lash TL, editors. Modern Epidemiology. 3rd ed. Philadelphia: Lippincott Williams & Wilkins; 2008. p.512 – 31. National Penitentiary Department. National Prison Information Survey. Period from July 2019 to December 2019. Available from: https://app.powerbi.com/view?r=eyJrIjoiMmU4ODAwNTAtY2IyMS 00OWJiLWE3ZTgtZGNjY2ZhNTYzZDliIiwidCI6ImViMDkwNDIwLTQ0NGMtNDNmNy05MWYyLTRiOGRhNmJmZThlMSJ9 Palmeira JLM, Vasconcelos KIR, Lima KA, Santana JS, Santos L, Vasconcelos JMB. Tuberculosis treatment from the perspective of incarcerated individuals in a maximum security unit R. pesq. cuid. fundam. online. 2021;13:907 – 11. https://doi.org/10.9789/2175-5361.rpcfo.v13.9614 Antunes JLF, Cardoso MRA. Using time series analysis in epidemiological studies. Epidemiol Serv Saude. 2015;24(3):565–76. https://doi.org/10.5123/S1679-49742015000300024 Prais SJ, Winsten CB. Trend estimators and serial correlation. Trend Estimates and Serial Correlation. Cowles Commission Discussion Paper, Stat. No. 383, University of Chicago, Chicago. Cleveland RB, Cleveland WS, McRae JE, Terpenning I. STL: A Seasonal-Trend Decomposition Procedure Based on Loess. J Off Stat 1990: 6(1):3–73. Brockwell PJ, Davis RA. Introduction to Time Series and Forecasting. 2nd ed. New York: Springer-Verlag; 2002. Kulldorff M. SaTScan User Guide V9.4. SaTScan TM User Guid version 94. 2015. Han J, Zhu L, Kulldorff M, Hostovich S, Stinchcomb DG, Tatalovich Z, et al. Using Gini coefficient to determining optimal cluster reporting sizes for spatial scan statistics. Int J Health Geogr. 2016;15(1):27. https://doi.org/10.1186/s12942-016-0056-6 Woodall J, Tattersfield A. Perspectives on implementing smoke-free prison policies in England and Wales. Health Promot Int. 2018;33(6):1066–73. https://doi.org/10.1093/heapro/dax031 Rowell-Cunsolo TL, Sampong SA, Befus M, Mukherjee DV, Larson EL. Predictors of Illicit Drug Use Among Prisoners. Subst Use Misuse. 2016;51(2):261–7. https://doi.org/10.3109/10826084.2015.1082594 Kouyoumdjian FG, Cheng SY, Fung K, Orkin AM, McIsaac KE, Kendall C, et al. The health care utilization of people in prison and after prison release: A population-based cohort study in Ontario, Canada. PLoS One. 2018;13(8):e0201592. https://doi.org/10.1371/journal.pone.0201592 Ministry of Health. Health Care Secretariat. Department of Strategic Programmatic Actions. Health Coordination in the Prison System. National Policy for Comprehensive Health Care for Persons Deprived of Liberty in the Prison System (PNAISP). Available from: http://www.as.saude.ms.gov.br/wp-content/uploads/2016/06/Cartilha-PNAISP.pdf Alves KKAF, Borralho LM, Araújo AJ, Bernardino IM, Figueiredo TMRM. Factors associated with recovery and the abandonment of tuberculosis treatment in the incarcerated population. Rev Bras Epidemiol. 2020;23:e200079. https://doi.org/10.1590/1980-549720200079 . Pape H, Rossow I, Bukten A. Alcohol Problems among Prisoners: Subgroup Variations, Concurrent Drug Problems, and Treatment Needs. Eur Addict Res. 2021;27(3):179–88. https://doi.org/10.1159/000511253 Dalmaso TF, Meyer DEE. Drug circulation and consumption in a female penitentiary: perceptions of a prison health team. Saúde debate. 2017;41(115):1156–67. https://doi.org/10.1590/0103-1104201711513 Young JT, Puljević C, Love AD, Janca EK, Segan CJ, Baird D, et al. Staying Quit After Release (SQuARe) trial protocol: a randomised controlled trial of a multicomponent intervention to maintain smoking abstinence after release from smoke-free prisons in Victoria, Australia. BMJ Open. 2019;9(6):e027307. https://doi.org/10.1136/bmjopen-2018-027307 Winkelman TNA, Vickery KD, Busch AM. Tobacco use among non-elderly adults with and without criminal justice involvement in the past year: United States, 2008–2016. Addict Sci Clin Pract. 2019;14(1):2. https://doi.org/10.1186/s13722-019-0131-y Baranyi G, Scholl C, Fazel S, Patel V, Priebe S, Mundt AP. Severe mental illness and substance use disorders in prisoners in low-income and middle-income countries: a systematic review and meta-analysis of prevalence studies. Lancet Glob Health. 2019;7(4):e461-e471. https://doi.org/10.1016/S2214-109X(18)30539-4 Larney S, Topp L, Indig D, O'Driscoll C, Greenberg D. A cross-sectional survey of prevalence and correlates of suicidal ideation and suicide attempts among prisoners in New South Wales, Australia. BMC Public Health. 2012;12:14. https://doi.org/10.1186/1471-2458-12-14 Sequera VG, Aguirre S, Estigarribia G, Cellamare M, Croda J, Andrews JR, et al. Increased incarceration rates drive growing tuberculosis burden in prisons and jeopardize overall tuberculosis control in Paraguay. Sci Rep. 2020;10(1):21247. https://doi.org/10.1038/s41598-020-77504-1 Job Neto F, Miranda RB, Coelho RA, Gonçalves CP, Zandonade E, Miranda AE. Health morbidity in Brazilian prisons: a time trends study from national databases. BMJ Open. 2019;9(5):e026853. https://doi.org/10.1136/bmjopen-2018-026853 Allgayer MF, Ely KZ, Freitas GH, Valim ARM, Gonzales RIC, Krug SBF, et al. Tuberculosis: health care and surveillance in prisons. Rev. Bras. Enferm. 2019;72(5):1304–10. http://dx.doi.org/10.1590/0034-7167-2018-0260 . Mabud TS, Lourdes Delgado Alves M, Ko AI, Basu S, Walter KS, Cohen T, et al. Evaluating strategies for control of tuberculosis in prisons and prevention of spillover into communities: An observational and modeling study from Brazil. PLoS Med. 2019;16(1):e1002737. https://doi.org/10.1371/journal.pmed.1002737 Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Major revision 23 Mar, 2022 Reviews received at journal 21 Mar, 2022 Reviews received at journal 05 Feb, 2022 Reviewers agreed at journal 02 Feb, 2022 Reviewers invited by journal 30 Jan, 2022 Editor assigned by journal 20 Jan, 2022 Editor invited by journal 27 Aug, 2021 Submission checks completed at journal 27 Aug, 2021 First submitted to journal 19 Aug, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-828111","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":48465910,"identity":"82523090-a891-4b12-81b7-aed2588f1331","order_by":0,"name":"Alessandro Rolim 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Paulo","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ricardo","middleName":"Alexandre","lastName":"Arcêncio","suffix":""}],"badges":[],"createdAt":"2021-08-19 19:44:04","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-828111/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-828111/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":12932515,"identity":"95354fa2-bfcd-4563-9d92-a31727756bc4","added_by":"auto","created_at":"2021-08-31 14:07:12","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":132830,"visible":true,"origin":"","legend":"Distribution of macro-regions and prisons in the cities located in Paraná, Brazil 2021. ","description":"","filename":"fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-828111/v1/23b8820ef313ff08c9c7ab30.jpg"},{"id":12932516,"identity":"6129f801-8dfc-42c1-aab0-8ffb62241497","added_by":"auto","created_at":"2021-08-31 14:07:12","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":370121,"visible":true,"origin":"","legend":"TB time series concerning PDL in prisons located in Paraná, Brazil (2014-2019) (N=1,099).","description":"","filename":"Figure2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-828111/v1/392c353269484b7fa3cde3a3.jpeg"},{"id":12932833,"identity":"b47d2c03-a7d7-43c5-8d0e-7c1915d88294","added_by":"auto","created_at":"2021-08-31 14:10:12","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":311267,"visible":true,"origin":"","legend":"TB High-clusters e Low-clusters for the prison facilities. Paraná, Brazil, 2014-2018. ","description":"","filename":"fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-828111/v1/4e9eb20c093d3f3827d98373.jpg"},{"id":12932518,"identity":"2e16118c-a4c7-40b2-8748-68576bb557ff","added_by":"auto","created_at":"2021-08-31 14:07:12","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":337631,"visible":true,"origin":"","legend":"Areas of spatial risk for tuberculosis among PPL users of alcohol, tobacco and other drugs. Paraná, Brazil 2014-2018.","description":"","filename":"fig4.jpg","url":"https://assets-eu.researchsquare.com/files/rs-828111/v1/548c9878d4a8815667f337a1.jpg"},{"id":13712152,"identity":"e721a729-2321-431b-8648-c3e5a5f71f4c","added_by":"auto","created_at":"2021-09-17 14:26:50","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1190262,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-828111/v1/25095b6d-81f0-469c-aa7a-d3fd7a5e8bda.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eThe Burden of Alcohol, Tobacco and Others Drugs Among Prisoners Diagnosed With Tuberculosis: Time Trends and Spatial Determinants in Southern Brazil\u003c/p\u003e","fulltext":[{"header":"Background","content":"\u003cp\u003eTuberculosis (TB) is an infectious disease caused by \u003cem\u003eMycobacterium tuberculosis\u003c/em\u003e [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e] and is a public health problem worldwide, though developing countries are the most severely affected. Data show that 30 countries account for 87% of the disease burden, and Brazil ranks 19th [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eEven though the TB burden declined in recent years before the COVID-19 pandemic, it is a disease difficult to control and eliminate due to co-infection with the Human Immunodeficiency Virus (HIV), antimicrobial resistance, multidrug-resistant TB, and increased consumption of alcohol, tobacco, and/or other drugs. In addition, TB is associated with social determinants of health, such as social vulnerability, poverty, and social exclusion to which populations such as immigrants, refugees, the homeless, and Persons Deprived of Liberty (PDL) are exposed [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. It is estimated that 90% of the patients diagnosed with TB live in vulnerable environments with limited health resources [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e], such as individuals living in correctional facilities.\u003c/p\u003e \u003cp\u003eThe World Health Organization (WHO) estimates that the prevalence of TB among PDL is 100 times higher than in the population in general [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] the prison environment contributes to TB mortality rates, revealing that the control of the disease is a priority neglected throughout the world [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Due to the unhealthy condition to which this population is exposed, Brazilian prisons are considered an important reservoir of TB bacillus and, consequently, a source of transmission among prisoners, prison officers, and their families [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eA literature review, conducted with the following descriptors \u0026ldquo;incarcerated,\u0026rdquo; \u0026ldquo;drug abuse and tuberculosis,\u0026rdquo; and \u0026ldquo;temporal trends and clustering,\u0026rdquo; revealed that few studies address the use of alcohol, tobacco, and other drugs among PDL diagnosed with TB, showing a need for further research. The development of studies addressing this population is crucial to promote equity and mitigate the effects of the TB burden, especially in a country like Brazil with a large contingent of prisoners. Therefore, this study\u0026rsquo;s objective is to identify the use of alcohol, tobacco, and other drugs among PDL diagnosed with tuberculosis and the spatial determinants and time trends of the phenomenon in Brazil.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStudy design\u003c/h2\u003e \u003cp\u003eEcological study [\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eStudy setting\u003c/h2\u003e \u003cp\u003eThis study was conducted in the state of Paran\u0026aacute;, in southern Brazil, divided into four health macro-regions (east, west, north, and northwest) with 399 cities and an estimated population of 11.34\u0026nbsp;million inhabitants. The 63 prison facilities distributed in 44 cities were considered in this study [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Figure\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e shows the spatial distribution of prisons in the state of Paran\u0026aacute; according to the four health macro-regions.\u003c/p\u003e\u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003ePopulation and study period\u003c/h2\u003e \u003cp\u003eIt is estimated that Brazil has 748,009 PDL [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e], ranking 3rd among the countries with the largest number of prisoners, with an incidence of 352.6 prisoners per 100,000 inhabitants [\u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. Paran\u0026aacute; has the 7th largest proportion of PDL in the country, with a population of approximately 29,831 [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe TB cases confirmed in the state\u0026rsquo;s prisons from 2014 to 2018 and reported to the Notifiable Diseases Information System (SINAN), Brazilian Ministry of Health, were selected. SINAN collects, reports, and disseminates data concerning reportable diseases according to information provided by the cities\u0026rsquo; epidemiological surveillance agencies.\u003c/p\u003e \u003cp\u003eData were collected from the state Health Department using an electronic spreadsheet. The that data are anonymous, and patients cannot identified by name or facility. Inclusion criteria were PDL diagnosed with TB from 2014 and 2018 using alcohol, tobacco, or illegal drugs. The period between 2014 and 2018 was chosen because the reporting form was updated in 2014 and started including information regarding PDL and drug use.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eData analysis\u003c/h2\u003e \u003cp\u003eThe database was checked for consistency, and the cases were grouped according to the following characteristics: 1) total PDL considering all TB confirmed cases; 2) PDL consuming alcohol or reporting to be alcoholic; 3) PDL using tobacco or reporting to be a smoker, and 4) PDL using illegal drugs. Note that the same patient may use more than one drug and be included in more than one group.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eAnalysis Procedures\u003c/h2\u003e \u003cdiv id=\"Sec8\" class=\"Section3\"\u003e \u003ch2\u003eExploratory\u003c/h2\u003e \u003cp\u003eDescriptive statistics were performed to present the absolute and relative frequencies of the variables sex, age, race, education; TB/HIV co-infection, diabetes mellitus, mental disorder, information regarding TB clinical profile such as admission, type, and whether the following were performed: radiograph, sputum smear microscopy, histopathology, molecular test; and treatment outcome, using Statistical Package for the Social Sciences (SPSS) version 25.\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003eTime Series\u003c/h2\u003e \u003cp\u003eTimes series is characterized as a sequential collection of observations indexed over time [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Hence, the TB cases reported by the prison system and grouped as mentioned earlier were organized according to the month they were reported to obtain monthly rates.\u003c/p\u003e \u003cp\u003eThe Prais-Winsten self-regression method [\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e] was performed using Software for Statistics and Data Science (STATA) version 14 to classify the event\u0026rsquo;s time trend into ascending, descending, or stationary in the period. The Monthly Percent Change \u0026ndash; MPC was calculated whenever a time trend was classified as ascending or descending along with its respective 95% confidence interval (95%CI) [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e]. Note that a time trend refers to the ascending, descending, or stationary tendency of a time series in a given period [\u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e, \u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThen, the robust Seasonal-Trend by Loess (STL by Loess) [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e] method was used. This decomposition method is based on a locally weighted regression (Loess) so that it is used to estimate non-linear relationships, separating the components of a time series into trend, seasonality, or noise [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Rstudio was used with the forecast package.\u003c/p\u003e \u003cp\u003eOpposed to the Prais-Winsten method in which time trend is globally assessed, and a constant is generated to classify the entire period, the STL method assesses the time trend over the period, verifying its variations over time and whether the trend was always ascending/descending or stationary or there were variations with peaks and/or decreases.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eSpatial analysis\u003c/h2\u003e \u003cp\u003eThe Getis-Ord Gi* statistic, a technique consisting of a local indicator of spatial association, was used to identify the social determinants and cluster formation in each prison in the state, based on a neighborhood matrix. A z-score is generated for the statistically significant cities; the larger the z-score, the more intense the clustering of high values (hot spot). The same logic is used for a negative z-score, i.e., the smaller the z-score, the more intense is the clustering of low values (cold spot).\u003c/p\u003e \u003cp\u003eIn addition to the z-score, the p-value and significance level (Gi-Bin) determine whether hot spots and cold spots are statistically significant. For example, values may range between +/- 3 and reflect statistical significance with a 99% confidence level, between +/- 2 with a 95% confidence level, and +/- 1 with a 90% confidence level; zero corresponds to non-statistically significant areas.\u003c/p\u003e \u003cp\u003eSpatial scan statistics was used to identify the TB risk spatial areas for PDL in Paran\u0026aacute;, Brazil. First, spatial clusters were identified by finding circles with a variable radius around each center of the cities with prison facilities. Then, the number of observed and expected cases within each circle is calculated, and this procedure is performed until all centers are tested. A cluster is identified when the value observed in the area within the circle is greater or smaller than the expected [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIn this stage, the following characteristics were considered: Poisson\u0026rsquo;s discrete model, only high-risk clusters, no overlapping geographic clusters, circular clusters, 999 Monte Carlo simulation, while the size of the population exposed was determined by the Gini coefficient in which the number of expected cases in each state is proportional to the size of the population at risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. The relative risk (RR) and confidence interval (95%CI) of each cluster were also calculated; statistical significance was established at p\u0026thinsp;\u0026lt;\u0026thinsp;0.05.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEthical Aspects\u003c/h2\u003e \u003cp\u003eThe study was approved by the Research Ethics Committee at the University of S\u0026atilde;o Paulo at Ribeir\u0026atilde;o Preto College of Nursing under Certificate of Presentation for Ethical Consideration number 24963319.1.0000.5393 and report number 3.836.401, issued on February 13th 2020 in accordance with the Guidelines and Regulatory Standards for Research with Human Subjects, Resolution number 466/2012 of the National Health Council of Brazilian Ministry of Health.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cp\u003eA total of 1,099 TB cases were reported among PDL in Paran\u0026aacute;. The use of tobacco was the most frequently reported (n\u0026thinsp;=\u0026thinsp;460;41.8%), followed by illegal drugs (n\u0026thinsp;=\u0026thinsp;451;41.0%), and alcohol (n\u0026thinsp;=\u0026thinsp;179;16.3%). Regarding sociodemographic characteristics, most were Caucasian male individuals, aged between 18 and 29 years old, with a low educational level (\u0026lt;\u0026thinsp;8 years of schooling) (Table \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eSociodemographic characteristics of PDL diagnosed with TB according to the type of drug used and total PDL, Paran\u0026aacute;, Brazil. (N\u0026thinsp;=\u0026thinsp;1,099)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDL using alcohol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDL using tobacco\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDL using illegal drugs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal PDL\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eGender\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e173(96.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e447(97.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e440(97.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1064(96.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6(3.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35(3.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCaucasian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e112(62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e280(60.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e290(64.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e722(65.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMixed race\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e48(26.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e136(29.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e122(27.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e275(25.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAfrican descent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(10.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(8.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e83(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAsian descent\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(0.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIgnored\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2(0.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e14(1.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group (years)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 to 29\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(43.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e253(55.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e269(59.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e614(55.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30 to 39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(35.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e142(30.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e138(30.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e326(29.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e37(20.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64(13.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156(14.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eIlliterate\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14(3.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e22(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1st to 4th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e96(20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e198(18.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5th to 8th grade\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(45.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e222(48.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e240(53.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e564(51.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026gt;\u0026thinsp;8 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(17.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e77(16.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62(13.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e297(27.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSources:\u003c/strong\u003e study\u0026rsquo;s data\u003c/p\u003e\n\u003cp\u003eTB/HIV co-infection was more prevalent among smokers and those using illegal drugs, whereas TB/diabetes mellitus and mental disorders were more frequent among those consuming alcohol. The TB clinical profile was: new cases, pulmonary TB, no radiograph, positive sputum smear microscopy, histopathology, and molecular test revealing sensitivity to rifampicin among the groups using tobacco and illegal drugs. Regarding TB treatment outcome, cure and DR-TB were predominant among smokers and individuals using illegal drugs, while alcohol consumers tended to abandon the treatment more frequently (Table \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003e).\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab2\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026ndash;\u003c/strong\u003e Clinical characteristics of PDL diagnosed with TB using alcohol, tobacco, or illegal drugs. Paran\u0026aacute;, Brazil (N\u0026thinsp;=\u0026thinsp;1,099)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\" rowspan=\"2\"\u003e\n \u003cp\u003eVariables\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDL using alcohol\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDL using tobacco\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003ePDL using illegal drugs\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTotal PDL\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003en(%)\u003c/strong\u003e\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAIDS\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21(11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(8.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e82(7.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e155(86.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e409(88.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e394(87.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e971(88.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes Mellitus\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e7(3.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e13(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e170(95.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e430(93.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e422(93.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1025(93.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMental disorder\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eYes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e4(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(1.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17(1.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNo\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e172(96.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e435(94.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e424(94.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1029(93.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eAdmission\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNew case\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e136(76.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e359(78.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e344(76.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e880(80.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRelapse\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e11(6.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e30(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e70(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRetreatment after treatment abandonment\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e20(11.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(8.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e66(6.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransference\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e34(7.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e38(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e79(7.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eType\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e156(87.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e410(89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e402(89.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e968(88.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eExtrapulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e21(11.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(8.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e39(8.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e105(9.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePulmonary\u0026thinsp;+\u0026thinsp;extrapulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e26(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eRadiograph\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuspected TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e148(82.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e374(81.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e360(79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e877(79.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNormal\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10(2.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e32(2.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOther pathology\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e1(0.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(0.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e26(14.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(17.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182(16.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eSputum smear microscopy\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePositive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e114(63.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e288(62.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e302(67.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e703(64.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNegative\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e35(19.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80(17.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65(14.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179(16.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot taken\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86(18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78(17.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e202(18.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eHistopathology\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBaar Positive\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e15(8.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16(3.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22(4.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69(6.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSuggestive of TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(4.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19(4.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e44(4.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot suggestive of TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8(o.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e153(85.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e405(88.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e396(87.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e951(86.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eMolecular test\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRifampicin sensitivity detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e65(36.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e227(49.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e223(49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e432(39.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRifampicin resistance detected\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e3(1.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11(2.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e21(1.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot detectable\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e9(5.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35(7.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29(6.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e57(5.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNot performed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e97(54.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e179(38.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e182(40.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e543(49.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cstrong\u003eOutcome\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e116(64.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e312(67.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e301(66.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e743(67.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAbandoned\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e25(14.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e42(9.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40(8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e89(8.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath due to TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e2(1.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4(0.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3(0.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18(1.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDeath due to another cause\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e5(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15(3.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9(2.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31(2.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTransferred\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e18(10.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(9.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e51(11.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e119(10.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDR-TB\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e12(6.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41(8.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e43(9.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e75(6.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u003c/strong\u003e Study\u0026rsquo;s data.\u003c/p\u003e\n\u003cdiv class=\"gridtable\"\u003e\u0026nbsp;\u003ctable border=\"1\" id=\"Tab3\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e\n \u003cdiv class=\"CaptionContent\"\u003e\n \u003cp\u003eTime trend of TB incidence among PDL according to the consumption of psychoactive substances. Paran\u0026aacute;, Brazil. (2014\u0026ndash;2018). (N\u0026thinsp;=\u0026thinsp;1,099)\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003ccolgroup cols=\"5\"\u003e\u003c/colgroup\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eVariable\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCoefficient\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e(95%CI)*\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eTime trend\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eMPC\u0026dagger; (95%CI)\u003c/p\u003e\n \u003c/th\u003e\n \u003c/tr\u003e\n \u003c/thead\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eTotal PDL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e-0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(-0.19 - -0.40)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDescending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e-49.88 (-35.43\u0026ndash;60.19)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDL consuming alcohol\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05\u0026ndash;0.09)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAscending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.40 (12.20\u0026ndash;23.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePDL using tobacco\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.05\u0026ndash;0.11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAscending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e20.23 (12.20\u0026ndash;28.82)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003ePPL using illegal drugs\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e(0.16\u0026ndash;0.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAscending\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e62.18 (44.54\u0026ndash;81.97)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n \u003c/table\u003e\n\u003c/div\u003e\n\u003cp\u003e\u003cstrong\u003eSource:\u0026nbsp;\u003c/strong\u003eStudy\u0026rsquo;s data.\u003c/p\u003e\n\u003cp\u003e*95%CI = 95% Confidence Interval; \u0026dagger;MPC = Monthly percentage change\u003c/p\u003e\n\u003cp\u003eThe time trend of TB cases in the total PDL was classified as descending, with a decrease of -49.8% a month (95%CI: -35.43 to -60.19). When analyzing the psychotic substances, an ascending time trend was found for alcohol consumers +19.4%/mo. (95%CI: 12.20 to 23.03); smokers +20.2% (95%CI: 12.20 to 28.82), and drug users +62.2% (95%CI: 44.54 to 81.97), as shown in (Table 3).\u003c/p\u003e\n\u003cp\u003eThe time series decomposition technique (Figs.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eA, \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eB, and \u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eC) showed an increase in the TB time trend among PDL using alcohol, tobacco, and illegal drugs, whereas Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e2\u003c/span\u003eD shows a decrease in the time trend of the total population between 2014 and 2018. These findings corroborate data presented in Table\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003e concerning the results from the Prais-Winsten analysis.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eA concerns the total PDL. The Getis-Ord Gi* technique identifies three hot spots: east (metropolitan region of Curitiba), north and west macro-regions. Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eB reveals three hot spots, two in the east macro-region (metropolitan region of Curitiba and in Ponta Grossa) and in the north macro-region; the pseudo-significance test presented a z-score of 4.27 confirming the clusters\u0026rsquo; non-randomness (p\u0026thinsp;\u0026lt;\u0026thinsp;0.00). Figure\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e3\u003c/span\u003eC shows a hot spot in the east macro-region (z-score\u0026thinsp;=\u0026thinsp;2.41 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), and Fig.\u0026nbsp;\u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD presents two hot spots in the east and north macro-regions (z-score\u0026thinsp;=\u0026thinsp;2.13 and p\u0026thinsp;\u0026lt;\u0026thinsp;0.03).\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eA shows a relationship between TB and the total PDL, indicating four spatial risk clustering, namely: 1) one in the city of Pinhais located in the east macro-region (metropolitan region of Curitiba) presenting RR:17.75 (95%CI 15.40-19.65) with 832 PDL and 296 cases; 2) in the city of Paranagu\u0026aacute; located in the east macro-region (Paran\u0026aacute; coast region) presenting RR:6.75 (95%CI 3.84\u0026ndash;11.18) with 56 PDL and five cases; 3) in the north macro-region presenting RR:2.87 (95%CI 2.11\u0026ndash;3.66) with 616 PDL and 50 cases; and 4) one in the northwest macro-region presenting RR:2.11 (95%CI 1.76\u0026ndash;2.36) with 3,911 PDL and 212 cases.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eB presents three risk clusters among PDL diagnosed with TB using alcohol: 1) one in the city of Ourizona located in the northwest macro-region presenting RR:20.93 (95%CI 8.86\u0026ndash;48.96) with 56 PDL and five cases observed.; 2) one located in the east macro-regional in the metropolitan region of Curitiba presenting RR:9.95 (95%CI 6.99\u0026ndash;14.08), with 1,670 PDL and 49 cases; 3) one cluster located in the north macro-region presenting RR:2.05 (95%CI 1.43\u0026ndash;2.87) with 7,219 PDL and 52 cases.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eC presents four spatial risk clusters concerning PDL using tobacco: 1) one in the city of Pinhais in the east macro-region (metropolitan region of Curitiba) RR:30.49 (95%CI 24.56\u0026ndash;37.41) with 832 PDL and 142 cases reported; 2) one in Paranagu\u0026aacute;, east macro-region (Paran\u0026aacute;\u0026rsquo;s coast region) RR:8.57 (95%CI 3.15\u0026ndash;6.58) with 56 PDL and five cases observed; 3) north macro-region presenting RR:4.60 (95%CI 2.14\u0026ndash;6.37) with 667 PDL and 30 cases; 4) one in Paranava\u0026iacute; in the northwest macro-region presenting RR:3.73 (95%CI 3.64\u0026ndash;19.73) with 264 PDL and 9 cases observed.\u003c/p\u003e\n\u003cp\u003eFigure \u003cspan class=\"InternalRef\"\u003e4\u003c/span\u003eD presents two risk clusters of PDL using drugs: 1) in the city of Pinhais in the east macro-region (metropolitan region of Curitiba) presenting RR:23.81 (95%CI 19.41\u0026ndash;28.77) with 832 PDL and 149 cases; and 2) one located in the north macro-region presenting RR:2.06 (95%CI 1.60\u0026ndash;2.58) with 3,911 PDL and 87 cases.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eThe use of tobacco and illegal drugs was prevalent among PDL diagnosed with TB, and an ascending trend was verified for alcohol, tobacco, and illegal drugs in prison facilities. The highest RR was found in the prisons located in the east macro-region (metropolitan region of Curitiba), showing that prisons contribute to TB burden and are an environment that favors the disease, considering a large number of alcohol consumers and drug users [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e, \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eNote that the medical center providing care to prisoners is located in the east macro-region, which explains the high number of TB cases in this area. Data suggest that the availability of medical services indicates PDL have access to healthcare services, and the high rates of outpatient care consultations and hospitalizations suggest the quality of these individuals\u0026rsquo; health conditions [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eIt is worth noting that in 2003 the Ministry of Health, together with the Ministry of Justice, launched the National Health Plan for the Prison System and in 2014 established the National Policy for Integral Health Care for Persons Deprived of Liberty in the Prison System to promote health and prevent diseases in the prison system and ensure PDL access integral and quality health care provided by the Brazilian Unified Health System [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHowever, a high number of TB cases is observed among PDL, mainly pulmonary TB, which is of concern considering that the bacillus is airborne and the prisons\u0026rsquo; conditions such as overcrowding and poor ventilation favor the dissemination of TB [\u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. Therefore, efficient public policies intended to provide health care to PDL.\u003c/p\u003e \u003cp\u003ePDL include socially vulnerable people affected by different diseases,[\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e] among which dependency on alcohol, tobacco, and illegal drugs stand out. Even though alcohol and tobacco consumption decreased in the population in general, there was a significant increase in the number of smokers and drug users among PDL [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Studies report that approximately 80% of the PDL in the United States have a history of illegal drug use [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. One study was conducted in Norway to investigate drinking habits before imprisonment, reporting that 55% of the prisoners presented alcohol problems of some severity, and 18% were possibly alcohol dependent [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Not much data are available in Brazil and research in the field is still incipient considering the difficulty in discussing this phenomenon in public security institutions [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eHence, the consumption of legal or illegal Psychoactive Substances (PS) within prisons contributes to an increase in the number of diseases, and as this study shows, contributes to the development and maintenance of TB [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Furthermore, these substances not only contribute to the development of TB but lead to unfavorable treatment outcomes, considering that PS is associated with higher rates of mental disorders, suicide, mortality, relapse after release [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e], and violence within prisons.\u003c/p\u003e \u003cp\u003eThe consumption of PS favors the development of diseases among PDL and is associated with higher rates of physical violence and suicide attempts within prisons; suicide attempts in this population are estimated to be three to eight times higher than in the general population. Risk factors include mental disorders, substance use disorders, suicidal ideation, suicide attempt, self-injury behaviors, accommodation in single-occupancy cells, and conviction due to violent crimes [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe prisons\u0026rsquo; social, spatial factors directly contribute to the maintenance of TB and other diseases considering many prisons are overcrowded, present high turnover of PDL, are poorly ventilated, and access to health services is restricted [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePDL live in an unhealthy environment with poor hygiene conditions, which often fail to ensure basic human needs to protect one\u0026rsquo;s physical and mental health, directly contributing to disseminating transmissible diseases, violence-related injuries, and mental disorders [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. These are characteristics observed in the east, north, and northwest macro-regions, which host risk areas for total TB and TB associated with alcohol, tobacco, and illegal drugs. Note that the west macro-region did not present any risk area for TB associated with PS.\u003c/p\u003e \u003cp\u003eThe identification of risk prison facilities for the development of TB associated with PS consumption can support the implementation of preventive measures and quality health care, especially in those facilities with a large number of prisoners, that is, facilities exposed to a higher risk. However, there is usually a delay in the TB diagnosis, with a high prevalence of resistant bacteria, inadequate treatment and treatment abandonment, low educational level, malnutrition, mental disorders, previous diseases, TB-HIV co-infection, alcohol, tobacco, and/or illegal drugs consumption/dependency [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e, \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, prisons are a reservoir of various diseases, especially infectious-contagious diseases such as TB. The community is also exposed to TB when contacting prison officers, released prisoners, or families visiting prisoners. Hence, the prison environment promotes the incidence and maintenance of TB, and health actions are needed to break the transmission cycle and decrease the number of new cases and deaths [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTime trend analysis showed TB increased among PDL consuming alcohol, tobacco, or illegal drugs, which is similar in the general population. The fact that the consumption of these substances has increased worldwide is of concern, considering it is associated with worsened TB treatment outcomes [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eTherefore, a screening protocol should be implemented in the prison system to identify SP consumption and TB and invest and give priority to early diagnosis and interventions, providing appropriate treatment to avoid interruptions and relapses [\u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAchieving the goals established by the End TB Strategy and eradicating TB by 2050 will only be possible by investing in preventive measures and appropriate TB treatment. Therefore, one of the most difficult challenges is to control the progress of the disease among subpopulations presenting high incidence rates, such as PDL [\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. In this sense, PDL is a social stratum at a higher risk of TB, [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e] and strategies are needed to decrease the transmission of the disease and achieve the global goals.\u003c/p\u003e \u003cp\u003eIn this sense, the WHO recommends measures be implemented in prisons to prevent new cases, including screening protocols applied in the admission and discharge of inmates, in addition to periodical assessments of PDL or isoniazid preventive therapy [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. Routinely screening prisoners, isolating confirmed cases, decreasing the number of inmates in a single cell, and avoiding agglomerations are efficient ways to decrease transmissions among prisoners, prison officers, families, and the community [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003ePrograms intended to obtain early diagnoses, the report of new cases, and the implementation of proper treatment are vital. However, the access of PDL to health services is restricted, resulting in unfavorable outcomes and high rates of TB.\u003c/p\u003e"},{"header":"Conclusions","content":"\u003cp\u003eThis study advances with knowledge in the field, as it shows the consumption of alcohol, tobacco, and illegal drugs among PDL diagnosed with TB. Clustering was found in some areas in the state with an ascending trend, highlighting the need to implement policies to control the disease in vulnerable areas and areas hosting prisons. Even though there are studies addressing TB in the prison system, this is one of the first studies investigating spatial differences and determinants using geostatistics.\u003c/p\u003e \u003cp\u003eThese techniques are seldom adopted to address these populations, showing the originality and relevance of this investigation. Decreasing disparities and inequalities involves understanding the burden of the disease and its determinants in the prison system so that innovative approaches and studies are also needed.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eCI\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eConfidence Interval\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eDR-TB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDrug-resistant Tuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eHIV\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eHuman Immunodeficiency Virus\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eMPC\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMonthly Percent Change\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePDL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePersons Deprived of Liberty\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003ePS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePsychoactive Substances\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eRR\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative Risk\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSINAN\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNotifiable Diseases Information System\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSPSS\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eStatistical Package for the Social Sciences\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTATA\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSoftware for Statistics and Data Science\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eSTL\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSeasonal-Trend by Loess\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eTB\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTuberculosis\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003e\u003cb\u003eWHO\u003c/b\u003e\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eWorld Health Organization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study was approved by the Research Ethics Committee at the University of S\u0026atilde;o Paulo at Ribeir\u0026atilde;o Preto College of Nursing under Certificate of Presentation for Ethical Consideration number 24963319.1.0000.5393 and report number 3.836.401, issued on February 13th 2020 in accordance with the Guidelines and Regulatory Standards for Research with Human Subjects, Resolution number 466/2012 of the National Health Council of Brazilian Ministry of Health.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll the data supporting the study findings are within the manuscript. Additional detailed information and raw data will be shared upon request addressed to the corresponding author.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor\u0026rsquo;s contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe conception of the work, design of the work, acquisition of data, analysis, and interpretation of data was done by ARS, JDA, TZB, FLS, MJQG and RAA. Data curation, drafting the article, revising it critically for intellectual content, validation and final approval of the version to be published was done by ARS, RAA, TZB, ACVR, GLF, FMP, MAMA, SCP, AAM, IF and DG. All authors read and approved the final version of the manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eSeki M, Choi H, Kim K, Whang J, Sung J, Mitarai S. Tuberculosis: A persistent unpleasant neighbour of humans. J Infect Public Health. 2021;14(4):508\u0026ndash;513. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1016/j.jiph.2021.01.005\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWord Health Organization. Global Tuberculosis Report 2020. Geneva (2020). pp\u0026nbsp;208. 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Enferm. 2019;72(5):1304\u0026ndash;10. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://dx.doi.org/10.1590/0034-7167-2018-0260\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eMabud TS, Lourdes Delgado Alves M, Ko AI, Basu S, Walter KS, Cohen T, et al. Evaluating strategies for control of tuberculosis in prisons and prevention of spillover into communities: An observational and modeling study from Brazil. PLoS Med. 2019;16(1):e1002737. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.1371/journal.pmed.1002737\u003c/span\u003e\u003c/span\u003e\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, Persons Deprived of Liberty, Drug Abuse.","lastPublishedDoi":"10.21203/rs.3.rs-828111/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-828111/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eTuberculosis is an infectious disease caused by Mycobacterium tuberculosis and is a public health problem worldwide. It is estimated that 90% of the patients diagnosed with TB live in vulnerable environments with limited health resources, such as individuals living in correctional facilities. This study aimed to identify the consumption of alcohol, tobacco, and other drugs among prisoners diagnosed with tuberculosis and the spatial determinants and time trends of the phenomenon in southern Brazil.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eAn ecological study using data from the Brazilian Notifiable Diseases Information System. Tuberculosis cases confirmed from 2014 to 2018 in prisons located in Paran\u0026aacute;, Brazil, were selected. The Prais-Winsten procedure was performed to identify time trends by calculating monthly rates and the percentage of monthly variation. The Seasonal-Trend by Loess decomposition method was used to verify the time series and trends. The spatial association was verified with the Getis-Ord Gi* technique, and risk areas were identified using spatial scan statistics.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eA total of 1,099 TB cases were found in the studied population. The consumption of tobacco (n\u0026thinsp;=\u0026thinsp;460; 41.9%), illegal drugs (n\u0026thinsp;=\u0026thinsp;451; 41.0%), and alcohol (n\u0026thinsp;=\u0026thinsp;179; 16.3%) stand out. An ascending trend was found for the consumption of alcohol [+\u0026thinsp;19.4%/mo. (95%CI: 12.20-23.03)], tobacco [+\u0026thinsp;20.2% (95%CI: 12.20-28.82)], and illegal drugs [+\u0026thinsp;62.2%/mo. (95%CI: 44.54\u0026ndash;81.97)]. Spatial analysis revealed clusters for the use of alcohol, tobacco, and illegal drugs.\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eThis study advances knowledge presenting the burden of drug use and its typology among individuals diagnosed with tuberculosis in the prison system. There is a growing trend among patients to use drugs, especially illegal drugs. The clusters show differences between the places where the prisons are located. Tuberculosis within the prison system is already a complex disease, and it is even more challenging when drugs are consumed. The consumption of drugs increased threefold when tobacco and alcohol were considered.\u003c/p\u003e","manuscriptTitle":"The Burden of Alcohol, Tobacco and Others Drugs Among Prisoners Diagnosed With Tuberculosis: Time Trends and Spatial Determinants in Southern Brazil","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-08-31 14:07:10","doi":"10.21203/rs.3.rs-828111/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2022-03-23T06:18:21+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-03-21T13:33:49+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2022-02-05T11:01:01+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"1cd581b5-e3f4-46f1-9ff5-626cd3752e61","date":"2022-02-02T10:33:11+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2022-01-30T19:05:20+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2022-01-20T20:15:09+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-08-27T06:23:27+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-08-27T06:19:48+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Public Health","date":"2021-08-19T19:32:57+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-public-health","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pubh","sideBox":"Learn more about [BMC Public Health](http://bmcpublichealth.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pubh/default.aspx","title":"BMC Public Health","twitterHandle":"@BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"7490477f-895c-47d8-b4d7-21ce9794a84f","owner":[],"postedDate":"August 31st, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[{"id":6839252,"name":"Health Economics \u0026 Outcomes Research"},{"id":6839253,"name":"Infectious Diseases"},{"id":6839254,"name":"Health Policy"}],"tags":[],"updatedAt":"2022-05-12T08:44:18+00:00","versionOfRecord":[],"versionCreatedAt":"2021-08-31 14:07:10","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-828111","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-828111","identity":"rs-828111","version":["v1"]},"buildId":"7rjqhiLT3MXkJMwkYKINL","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}