Hospitalized patients with pulmonary tuberculosis in Shizuoka Prefecture in 2009-2020: risk factors for dead cases

Hospitalized patients with pulmonary tuberculosis in Shizuoka Prefecture in 2009-2020: risk factors for dead cases | 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 Hospitalized patients with pulmonary tuberculosis in Shizuoka Prefecture in 2009-2020: risk factors for dead cases Kei Kanata, Toshihiro Shirai, Yutaro Kishimoto, Taisuke Akamatsu, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5190034/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract Background : Japan had long been a medium-burden country for tuberculosis (TB) but became a low-burden country in 2021. Shizuoka Prefecture is geographically located in Central Japan, and the incidence rate of TB is also at the average level in Japan. This study aimed to understand TB patients' characteristics and risk factors by analyzing the data from 2009 to 2020 in Shizuoka Prefecture, a representative sample for Japan. Methods: We retrospectively collected 1,132 patients with smear-positive pulmonary TB who were admitted to Shimada General Medical Center or Shizuoka General Hospital in the central area of Shizuoka Prefecture from 2009 to 2020. Patients were divided into alive and dead (i.e., all-cause in-hospital death) groups, and clinical parameters were compared between the two groups. Results: The median age of all patients was 80. The number of patients decreased from 2009 to 2020. Moreover, the number of patients over 70 gradually reduced, which was similar for all patients. Immigrant TB patients were 5% of all patients, which was very small compared to the total number of patients. Almost all were from Southeast Asian countries. In multivariate analysis, older age, low body mass index, respiratory failure, low serum albumin, lymphocyte, high serum creatinine levels, and neutrophil were independent prognostic factors. Conclusions: The decline in TB among elderly patients, who comprise most of the population, might be one reason why Japan has become a low-burden country. Although the rate of immigrants is minimal, caution is needed about the increase, especially from Southeast Asian countries. There is a need for a TB medical system that can deal with complications and malnutrition, which are risk factors for death. tuberculosis low-burden country immigrants prognostic factors elderly patients Figures Figure 1 Figure 2 Figure 3 Figure 4 Background Tuberculosis (TB) is an airborne disease that infects 10.6 million people worldwide. Before the emergence of the novel coronavirus disease (COVID-19), TB was the leading cause of death from a single pathogen worldwide. Even now, 1.3 million people die in 2022. The incidence of TB is exceptionally high in developing countries and tends to be low in developed countries. In the 2021 analysis, the country with the highest incidence of TB is the Philippines (incidence rate 650), followed by the Kingdom of Lesotho (614), which is surrounded by the Republic of South Africa (513). The lowest incidence rate is in the United States (2.6) [ 2 ]. Japan had been a medium-burden country for TB for a long time but became a low-burden country in 2021 [ 3 ]. Hagiya et al. reported that TB contact investigations routinely performed by public health centers, as government support, could have contributed to the decrease in TB prevalence in Japan [ 4 ]. The Japan Anti-Tuberculosis Association suggested that a decrease in the incidence rate was detected after the TB emergency was declared in 1999, and this declaration may have had some effect [ 5 ]. But the direct cause remains unclear. Imported TB from immigrants is also a challenge in eliminating TB because Japan has an increasing number of immigrants, mainly from Asia. The proportion of immigrants among all TB cases in Japan has steadily increased from 2.4% in 2000 to 10.7% in 2019 [ 6 ]. Shizuoka Prefecture is geographically located in Central Japan (Fig. 1 ), and the incidence rate of TB is also at the average level in Japan [ 7 ]. Therefore, this study aimed to understand TB patients' characteristics and risk factors by analyzing the data from 2009 to 2020 in Shizuoka Prefecture, a representative sample of Japan. Methods Study design and data collection We applied for permission to use data from Shimada General Medical Center and Shizuoka General Hospital and received approval from the institutional review board (R 6 − 5). The board waived patient approval or informed consent because the study was a retrospective review of patient records. We retrospectively collected 1,132 patients with smear-positive pulmonary TB who were admitted to Shimada General Medical Center or Shizuoka General Hospital in the central area of Shizuoka Prefecture from 2009 to 2020. The inclusion criteria for this study were as follows: pulmonary TB as a definitive diagnosis. Acid-fast sputum smears were positive; subsequently, Mycobacterium tuberculosis was isolated by culture in all patients. We excluded cases with no definitive diagnosis of TB, no description of outcome, or smear-negative patients. Medical records were used to collect clinical and demographic information and peripheral blood tests. Sputum smears were obtained before treatment and classified according to the Gaffky scale. Patients were divided into alive and dead (i.e., all-cause in-hospital death) groups, and clinical parameters were compared between the two groups. Statistical analysis Categorical variables are summarized as frequencies and proportions, and continuous variables are expressed as medians and interquartile ranges. We used the Mann-Whitney U and Fisher’s exact test for nonparametric data to evaluate the difference between alive and dead groups. Variables contributing to death were subjected to multivariate analysis by logistic regression after univariate analysis. All analyses were performed with R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). A two-tailed p-value < 0.05 was considered significant. Results Characteristics of the subjects The clinical characteristics of the study population on admission are shown in Table 1. The median age of all patients was 80, and male patients were dominant. The median body mass index (BMI) was 18.6. Respiratory failure, corticosteroid use, and malignant disease were the main primary pre-existing diseases. Slightly low albumin and hemoglobin were observed in laboratory data. Dead patients were older, had lower BMI, more immigrants, more extended hospital stays, more miliary tuberculosis, respiratory failure, corticosteroid users, chronic renal failure, lower levels of serum albumin, lymphocytes, hemoglobin, and higher levels of serum creatinine, AST, neutrophils, Gaffky scale than discharged alive patients. Table 1 Characteristics of the patients Characteristics All patients (n = 1132) Discharged alive group (n = 906) Dead group (n = 226) P value Age, yr 80 (68–87) 78 (63–85) 87 (80–90) < 0.001 Sex, male 709 (63) 563 (62) 146 (65) 0.49 BMI, kg/m 2 18.6 (16.5–20.9) 19.0 (17.1–21.2) 16.8 (15.2–18.7) < 0.001 Immigrant patients 58 (5) 58 (5) 0 (0) < 0.001 Length of hospital stays, d 64 (46–99) 69 (51–104) 29 (12–64) < 0.001 Comorbidity Miliary tuberculosis 60 (5) 35 (4) 25 (11) < 0.001 Respiratory failure 352 (31) 189 (21) 163 (72) < 0.001 Corticosteroid user 176 (16) 111 (12) 65 (23) < 0.001 Malignant disease 109 (10) 86 (9) 23 (10) 0.76 Cardiac failure 59 (5) 40 (4) 19 (8) 0.02 Diabetes 49 (4) 41 (5) 8 (4) 0.52 COPD 22 (2) 14 (2) 8 (4) 0.08 Cerebral infarction 24 (2) 17 (2) 7 (3) 0.28 Liver cirrhosis/hepatitis 12 (1) 9 (1) 3 (1) 0.67 Bronchial asthma 11 (1) 7 (1) 4 (2) 0.21 Chronic renal failure 9 (1) 3 (0) 6 (3) 0.002 Interstitial pneumonia 8 (1) 5 (1) 3 (1) 0.25 Laboratory data Alb, g/dL 2.90 (2.35–3.20) 3.10 (2.50–3.60) 2.20 (1.80–2.60) < 0.001 Crt, mg/dL 0.74 (0.60–0.96) 0.70 (0.60–0.93) 0.87 (0.60-1.00) < 0.001 AST, IU/L 25 (20–34) 22 (17–31) 31 (21–48) < 0.001 ALT, IU/L 16 (11–23) 15 (11–23) 18 (11–31) 0.71 WBC, cells/µL 6650 (5275–8350) 6900 (5500–8600) 7100 (4900–9500) 0.12 Neutrophil, cells/µL 5290 (4020–7038) 5225 (4015–6777) 5929 (4111–8569) < 0.001 Lymphocyte, cells/µL 852 (527–1236) 953 (624–1320) 476 (298–735) < 0.001 Hemoglobin, g/dL 11.4 (10.3–13.0) 11.8 (10.5–13.2) 10.7 (9.2–12.1) < 0.001 Gaffky scale 5 (2–8) 5 (2–8) 6 (2–9) 0.004 Drug resistance (MDR/XDR) 14 (1) 12 (1) 2 (1) 0.68 Definition of abbreviations : Alb = albumin; ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; COPD = chronic obstructive pulmonary disease; Crt = creatinine; MDR = multi-drug-resistant; WBC = white blood cell; XDR = extensively drug-resistant. Data are shown as median (interquartile range) or frequency (percentage). Changes in the patient number by background over 12 years The number of all patients decreased from 2009 to 2020 (Fig. 2). The number of patients over 70 years old also gradually reduced. The ratio of immigrants gradually increased until 2018 and decreased after that. Characteristics of immigrant TB patients Immigrant TB patients were 5% of all patients, which was very small compared to the total number of patients. Almost all were from Southeast Asian countries, including the Philippines, Indonesia, and Myanmar (Fig. 3). There were few patients from South American countries. Immigrant patients were younger than Japanese patients (Fig. 4), and all were discharged alive (Table 1). Multiple logistic regression analysis for factors associated with dead patients As shown in univariate analysis (Table 2), older age, low BMI, miliary TB, respiratory failure, corticosteroid use, chronic renal failure, low levels of serum albumin, lymphocyte, hemoglobin, high levels of serum creatinine, AST, and neutrophils were independent prognostic factors. In multivariate analysis, older age, low BMI, respiratory failure, low serum albumin, lymphocyte, high serum creatinine levels, and neutrophil were independent prognostic factors. Table 2 Univariate and multivariate analysis of factors contributing to death from hospital discharge Univariate analysis Multivariate analysis Odds ratio 95% CI P value Odds ratio 95% CI P value Age, yr 7.72 4.79–12.4 < 0.001 1.11 1.08–1.14 < 0.001 Sex, male 1.11 0.82–1.51 0.494 BMI, kg/m 2 0.30 0.21–0.42 < 0.001 0.87 0.80–0.95 0.001 Comorbidity Miliary tuberculosis 3.05 1.81–5.16 < 0.001 0.91 0.41–2.03 0.818 Respiratory failure 9.75 7.00-13.6 < 0.001 6.41 4.05–10.2 < 0.001 Corticosteroid user 3.08 2.17–4.37 < 0.001 1.45 0.85–2.50 0.176 Malignant disease 1.08 0.67–1.75 0.755 Cardiac failure 1.60 0.91–2.83 0.025 Diabetes 0.77 0.36–1.68 0.515 COPD 2.34 0.97–5.64 0.094 Cerebral infarction 1.67 0.68–4.08 0.204 Liver cirrhosis/hepatitis 1.34 0.36–4.99 0.661 Bronchial asthma 2.31 0.67–7.97 0.172 Chronic renal failure 8.21 2.04–33.1 0.002 1.23 0.06–23.6 0.890 Interstitial pneumonia 2.42 0.58–10.2 0.213 Laboratory data Alb, g/dL 0.08 0.05–0.12 < 0.001 0.25 0.16–0.40 < 0.001 Crt, mg/dL 2.43 1.80–3.27 < 0.001 1.45 1.17–1.78 < 0.001 AST, IU/L 3.36 2.46–4.59 < 0.001 1.01 1.00-1.01 0.153 ALT, IU/L 1.01 0.96–1.06 0.710 WBC, cells/µL 1.01 0.99–1.01 0.110 Neutrophil, cells/µL 1.82 1.36–2.44 < 0.001 1.22 1.09–1.36 < 0.001 Lymphocyte, cells/µL 0.16 0.11–0.24 < 0.001 1.26 1.08–1.46 0.003 Hemoglobin, g/dL 0.43 0.32–0.59 < 0.001 1.04 0.91–1.18 0.57 Gaffky scale 1.07 1.02–1.11 0.004 1.05 0.97–1.12 0.234 Definition of abbreviations : Alb = albumin; ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; COPD = chronic obstructive pulmonary disease; Crt = creatinine; WBC = white blood cell. Discussion This study shows TB patients' characteristics and risk factors in Shizuoka Prefecture, a representative sample of Japan, helping us understand the subjects' characteristics and changes in the patient number by background and factors associated with dead patients. Shizuoka Prefecture is one of the prefectures with an aging population, which is the same trend in Japan. The results show that the median age of TB patients in Shizuoka Prefecture was very old. Moreover, they are gradually getting older compared with a few decades ago [ 3 ]. The poor outcomes in patients who had low BMI and complicated diseases were thought to be due to a decline in nutritional status with age. This study also indicated that TB is approximately 1.7 times more common in males than in females. Previous research examining the incidence of TB in the aspect of sex revealed that men are more likely to be diagnosed with TB than women, with a male-to-female ratio of 1.6:1 globally [ 8 ]. This sex gap has been proposed to be explained by biological differences in disease and disease presentation and different access to health care, specifically in developing countries [ 9 – 11 ]. Additionally, men are more likely to report risk factors associated with TB exposure [ 9 , 11 – 14 ]. Our analysis also found that the number of patients over 70, who comprise most of the population, is decreasing. In Japan, TB patients have decreased due to the widespread use of BCG vaccines and antibiotics to prevent TB, improved sanitary conditions, and more substantial guidance on medication administration by public health nurses. Experts mention that the declining TB cases could be partly explained by a drop in the number of people seeing a doctor and undergoing scaled-back health screenings by public health centers amid the coronavirus pandemic [ 15 ]. For these reasons, TB among elderly patients, who comprise most of the population, has declined, which may be one reason why Japan has become a low-burden country. In our analysis, almost all immigrants of central Shizuoka Prefecture were from Southeast Asia, and there were few patients from South American countries. Looking at the western Shizuoka Prefecture, the number of TB cases among immigrants from South America is most prevalent. This is because Hamamatsu City, the largest city in the west of Shizuoka prefecture, has the transportation equipment industry, such as automobiles and motorcycles, and immigrants from South America, including Brazil, tend to work in these companies [ 16 ]. This study found differences in immigrants' countries of origin in western and central Shizuoka Prefecture. Immigrant patients were younger than Japanese patients, which means they had good nutrition and few complicated diseases, which led to all of the immigrants being discharged alive. Though immigrants are very small now, the number of immigrants may increase in the future. Thus, it must be necessary to prepare for immigration outbreaks. Other novel findings of our study were that low BMI, respiratory failure, low serum albumin levels, lymphocyte, and high serum creatinine levels were independent prognostic factors for death. Osawa et al. reported a clinically applicable mortality risk prediction system for pulmonary TB. The disease severity score (named the AHL score) included daily living, hypoxemia, and lymphocytes (< 720/µL). This score showed good discrimination of mortality risk [ 17 ]. However, there were no reports about these comprehensive data about prognostic factors by clinical and demographic information and peripheral blood tests. In Japan, the number of TB patients is getting older, and complicated diseases will increase compared with the past. Therefore, we must create a medical system to deal with this problem. Conclusions The analysis of two hospitals in Shizuoka Prefecture suggests that the decline in TB among elderly patients, who comprise most of the population, might be one reason why Japan has become a low-burden country. Although immigrants are very small now, caution about the increase in immigrants from Southeast Asian countries is needed. In addition to patients’ age, it is expected that the number of patients with complications and malnutrition, which are factors for poor prognosis, will increase. There is a need for a TB medical system that can deal with these complex underlying diseases. Abbreviations TB Tuberculosis COVID-19 Coronavirus disease 2019 BMI body mass index AST aspartate aminotransferase BCG Bacille de Calmette et Guérin Declarations Ethics approval and consent to participate The present study was conducted with the approval of the research ethics committee at Shimada General Medical Center (Approval number: R 6-5). Consent for publication Not applicable. Availability of data and materials No, I do not have any research data outside the submitted manuscript file. Funding There was no Funding. Authors’ contributions All authors made substantial contributions to this article. KK conducted the data analysis and prepared all the tables, figures, and manuscripts. TS supervised them. YK, TA, YI, KI, and MU contributed to collecting data. Acknowledgements The authors would like to thank the many patients who contributed to the success of this analysis. We would like to thank Philip Hawke for English language editing. Consent for publication Not applicable. Competing interests No, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper. Clinical trial number Not applicable. References World Health Organization. Global Tuberculosis Report 2022. World Health Organization. https://www.who.int/teams/global-tuberculosis-programme/tbreports/global-tuberculosis-report-2022. National Public Health Service - Northern Region. IMMUNISATION. National Public Health Service website. https://www.arphs.health.nz/public-health-topics/immunisation/countries-with-a-high-incidence-of-tb/. Kawatsu L, Kasuya S, Imai A, Yoshie A, Uchimura K. Tuberculosis in Japan: Annual Report. 2022. Hagiya H, Koyama T, Zamami Y, Minato Y, Tatebe Y, Mikami N, et al. Trends in incidence and mortality of tuberculosis in Japan: a population-based study, 1997–2016. Epidemiology and Infection. 2018; 147, e38: 1–10. https://doi.org/10.1017/S095026881800290X. Research Institute of Tuberculosis/JATA. TB Control Program in Japan. Research Institute of Tuberculosis/JATA website. https://jata.or.jp/Department_of_International_Cooperation/TB_Control_in_Japan.html. Ota M, Nishimura T, Uchimura K, Hirao S. Epidemiology of tuberculosis in foreign students in Japan, 2015–2019: a comparison with the notification rates in their countries of origin. Epidemiology and Infection. 2021. 149, e202, 1–6. The Tuberculosis Surveillance Center. Number of newly registered tuberculosis cases (rate) by prefecture/city. https://jata-ekigaku.jp/archive. WHO. Global tuberculosis report 2016. Geneva: WHO; 2016. http://apps.who.int/. van den Hof S, Najlis CA, Bloss E, Straetemans M. A systematic review on the role of gender in tuberculosis control. KNCV Tuberculosis Foundation; 2010. https://www.kncvtbc.org/uploaded/2015/09/Role_of_Gender_in_TB_Control.pdf. Karim F, Ahmed F, Begum I, Johansson E, Diwan VK. Female-male differences at various clinical steps of tuberculosis management in rural Bangladesh. Int J Tuberc Lung Dis. 2008; 12: 1336-9. Jiménez-Corona ME, García-García L, DeRiemer K, Ferreyra-Reyes L, Bobadilla-del-Valle M, Cano-Arellano B, et al. Gender differentials of pulmonary tuberculosis transmission and reactivation in an endemic area. Thorax. 2006; 61: 348-53. Lim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012; 380: 2224-60. Feng JY, Huang SF, Ting WY, Chen YC, Lin YY, Huang RM, et al. Gender differences in treatment outcomes of tuberculosis patients in Taiwan: a prospective observational study. Clin Microbiol Infect. 2012; 18: 331-7. Smith GS, Van Den Eeden SK, Baxter R, Shan J, Van Rie A, Herring AH, et al. Cigarette smoking and pulmonary tuberculosis in northern California. J Epidemiol Community Health. 2015; 69: 568-73. Komiya K, Yamasue M, Takahashi O, Hiramatsu K, Kadota K, Kato S. The COVID-19 pandemic and the true incidence of Tuberculosis in Japan. Journal of Infection. 81 2020; e24–e25. Ikegami S. The increase in the number of immigrants to Japan mainly focused on the Brazilians living in Hamamatsu city in Shizuoka Prefecture.2005. Osawa T, Watanabe M, Morimoto K, Yoshiyama T, Matsuda S, Fujiwara K et al. Activities of Daily Living, Hypoxemia, and Lymphocytes Score for Predicting Mortality Risk in Patients With Pulmonary TB. CHEST 2024; 165(2): 267-277. Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 17 Oct, 2024 Submission checks completed at journal 16 Oct, 2024 First submitted to journal 16 Oct, 2024 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. 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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-5190034","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":367059077,"identity":"d4711c58-82f2-4155-a479-127cad3453eb","order_by":0,"name":"Kei Kanata","email":"data:image/png;base64,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","orcid":"","institution":"Shimada General Medical Center","correspondingAuthor":true,"prefix":"","firstName":"Kei","middleName":"","lastName":"Kanata","suffix":""},{"id":367059078,"identity":"72278e1f-5852-4889-884b-726516d427d4","order_by":1,"name":"Toshihiro Shirai","email":"","orcid":"","institution":"Shizuoka General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Toshihiro","middleName":"","lastName":"Shirai","suffix":""},{"id":367059079,"identity":"8f92b94c-7414-4a27-ad4e-8bb9ccb06f4c","order_by":2,"name":"Yutaro Kishimoto","email":"","orcid":"","institution":"Shizuoka General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Yutaro","middleName":"","lastName":"Kishimoto","suffix":""},{"id":367059080,"identity":"76c11352-617a-45a5-b617-2cef150479e5","order_by":3,"name":"Taisuke Akamatsu","email":"","orcid":"","institution":"Shizuoka General Hospital","correspondingAuthor":false,"prefix":"","firstName":"Taisuke","middleName":"","lastName":"Akamatsu","suffix":""},{"id":367059081,"identity":"8412f7ae-313b-4570-83f9-32ab2d318287","order_by":4,"name":"Yutaro Ito","email":"","orcid":"","institution":"Shimada General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Yutaro","middleName":"","lastName":"Ito","suffix":""},{"id":367059082,"identity":"6e689571-d7dd-4fb8-a0e3-586f4ec18d94","order_by":5,"name":"Koshiro Ichijo","email":"","orcid":"","institution":"Shimada General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Koshiro","middleName":"","lastName":"Ichijo","suffix":""},{"id":367059083,"identity":"b1196a2c-bed8-48fb-b7db-423662388c48","order_by":6,"name":"Masahiro Uehara","email":"","orcid":"","institution":"Shimada General Medical Center","correspondingAuthor":false,"prefix":"","firstName":"Masahiro","middleName":"","lastName":"Uehara","suffix":""}],"badges":[],"createdAt":"2024-10-02 00:53:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5190034/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5190034/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":80998260,"identity":"f2ae1709-f06f-4b92-878a-b2dc9ed93938","added_by":"auto","created_at":"2025-04-21 05:41:53","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":87911,"visible":true,"origin":"","legend":"\u003cp\u003eLocation of Shizuoka Prefecture. It is geographically located in the center of Japan.\u003c/p\u003e","description":"","filename":"figure1jpg2024.07.08.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5190034/v1/126b7c5c4d4659ac00462428.jpg"},{"id":80997010,"identity":"a0a0fa53-1803-4bb2-a9d4-7cb5d6196a6a","added_by":"auto","created_at":"2025-04-21 05:33:54","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":141842,"visible":true,"origin":"","legend":"\u003cp\u003eChanges in the patient number and the percentage of immigrants. The number of smear-positive pulmonary TB patients decreased from 2009 to 2020. The number of patients over 70 years old also gradually reduced. The number of immigrants gradually increased until 2018 and declined after that.\u003c/p\u003e","description":"","filename":"figure2jpg2024.07.08.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5190034/v1/6d3b41cf7b81c4fd10d33b01.jpg"},{"id":80997012,"identity":"d8b3aa25-91a4-4033-b5b5-c54139ca6427","added_by":"auto","created_at":"2025-04-21 05:33:54","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":133255,"visible":true,"origin":"","legend":"\u003cp\u003eThe nationality of immigrants. They corresponded to 5% of all patients, most from Southeast Asia, including the Philippines, Indonesia, and Myanmar. There were few patients from South American countries.\u003c/p\u003e","description":"","filename":"figure3jpg2024.07.08.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5190034/v1/50b19b6b6beacea203a4ad60.jpg"},{"id":80997013,"identity":"0b2a8282-0dac-4340-8537-e27db714dd1f","added_by":"auto","created_at":"2025-04-21 05:33:54","extension":"jpg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":137428,"visible":true,"origin":"","legend":"\u003cp\u003eThe age distribution of hospitalized smear-positive patients. Immigrants were younger than Japanese patients.\u003c/p\u003e","description":"","filename":"figure4jpg2024.08.30.jpg","url":"https://assets-eu.researchsquare.com/files/rs-5190034/v1/b71e88af70581022e8f6146e.jpg"},{"id":80999159,"identity":"cc03cf67-3989-4c36-aaf1-b1d494a5288b","added_by":"auto","created_at":"2025-04-21 06:00:17","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1341258,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5190034/v1/3f8c5492-979b-4551-9a71-8dbda14969cc.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Hospitalized patients with pulmonary tuberculosis in Shizuoka Prefecture in 2009-2020: risk factors for dead cases","fulltext":[{"header":"Background","content":"\u003cp\u003e \u003cem\u003eTuberculosis\u003c/em\u003e (TB) is an airborne disease that infects 10.6\u0026nbsp;million people worldwide. Before the emergence of the novel coronavirus disease (COVID-19), TB was the leading cause of death from a single pathogen worldwide. Even now, 1.3\u0026nbsp;million people die in 2022. The incidence of TB is exceptionally high in developing countries and tends to be low in developed countries. In the 2021 analysis, the country with the highest incidence of TB is the Philippines (incidence rate 650), followed by the Kingdom of Lesotho (614), which is surrounded by the Republic of South Africa (513). The lowest incidence rate is in the United States (2.6) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eJapan had been a medium-burden country for TB for a long time but became a low-burden country in 2021 [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. Hagiya et al. reported that TB contact investigations routinely performed by public health centers, as government support, could have contributed to the decrease in TB prevalence in Japan [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. The Japan Anti-Tuberculosis Association suggested that a decrease in the incidence rate was detected after the TB emergency was declared in 1999, and this declaration may have had some effect [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. But the direct cause remains unclear.\u003c/p\u003e \u003cp\u003eImported TB from immigrants is also a challenge in eliminating TB because Japan has an increasing number of immigrants, mainly from Asia. The proportion of immigrants among all TB cases in Japan has steadily increased from 2.4% in 2000 to 10.7% in 2019 [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eShizuoka Prefecture is geographically located in Central Japan (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), and the incidence rate of TB is also at the average level in Japan [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Therefore, this study aimed to understand TB patients' characteristics and risk factors by analyzing the data from 2009 to 2020 in Shizuoka Prefecture, a representative sample of Japan.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eStudy design and data collection\u003c/p\u003e \u003cp\u003e We applied for permission to use data from Shimada General Medical Center and Shizuoka General Hospital and received approval from the institutional review board (R 6\u0026thinsp;\u0026minus;\u0026thinsp;5). The board waived patient approval or informed consent because the study was a retrospective review of patient records. We retrospectively collected 1,132 patients with smear-positive pulmonary TB who were admitted to Shimada General Medical Center or Shizuoka General Hospital in the central area of Shizuoka Prefecture from 2009 to 2020. The inclusion criteria for this study were as follows: pulmonary TB as a definitive diagnosis. Acid-fast sputum smears were positive; subsequently, Mycobacterium tuberculosis was isolated by culture in all patients. We excluded cases with no definitive diagnosis of TB, no description of outcome, or smear-negative patients.\u003c/p\u003e \u003cp\u003eMedical records were used to collect clinical and demographic information and peripheral blood tests. Sputum smears were obtained before treatment and classified according to the Gaffky scale. Patients were divided into alive and dead (i.e., all-cause in-hospital death) groups, and clinical parameters were compared between the two groups.\u003c/p\u003e \u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eCategorical variables are summarized as frequencies and proportions, and continuous variables are expressed as medians and interquartile ranges. We used the Mann-Whitney U and Fisher\u0026rsquo;s exact test for nonparametric data to evaluate the difference between alive and dead groups. Variables contributing to death were subjected to multivariate analysis by logistic regression after univariate analysis. All analyses were performed with R version 3.6.3 (R Foundation for Statistical Computing, Vienna, Austria). A two-tailed p-value\u0026thinsp;\u0026lt;\u0026thinsp;0.05 was considered significant.\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec5\"\u003e\n \u003ch2\u003eCharacteristics of the subjects\u003c/h2\u003e\n \u003cp\u003eThe clinical characteristics of the study population on admission are shown in Table\u0026nbsp;1. The median age of all patients was 80, and male patients were dominant. The median body mass index (BMI) was 18.6. Respiratory failure, corticosteroid use, and malignant disease were the main primary pre-existing diseases. Slightly low albumin and hemoglobin were observed in laboratory data. Dead patients were older, had lower BMI, more immigrants, more extended hospital stays, more miliary tuberculosis, respiratory failure, corticosteroid users, chronic renal failure, lower levels of serum albumin, lymphocytes, hemoglobin, and higher levels of serum creatinine, AST, neutrophils, Gaffky scale than discharged alive patients.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab1\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 1\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eCharacteristics of the patients\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eCharacteristics\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eAll patients\u003c/p\u003e\n \u003cp\u003e(n = 1132)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDischarged alive group\u003c/p\u003e\n \u003cp\u003e(n = 906)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003eDead group\u003c/p\u003e\n \u003cp\u003e(n = 226)\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\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\u003eAge, yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e80 (68–87)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e78 (63–85)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e87 (80–90)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e709 (63)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e563 (62)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e146 (65)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.49\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18.6 (16.5–20.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19.0 (17.1–21.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16.8 (15.2–18.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eImmigrant patients\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e58 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLength of hospital stays, d\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e64 (46–99)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e69 (51–104)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e29 (12–64)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidity\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\u003eMiliary tuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e60 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e35 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (11)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e352 (31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e189 (21)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e163 (72)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorticosteroid user\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e176 (16)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e111 (12)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e65 (23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e109 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e86 (9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e23 (10)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.76\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e59 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e40 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e19 (8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.02\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e49 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e41 (5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.52\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebral infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e24 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e17 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.28\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver cirrhosis/hepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.67\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBronchial asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4 (2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.21\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic renal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterstitial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory data\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\u003eAlb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.90 (2.35–3.20)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.10 (2.50–3.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.20 (1.80–2.60)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrt, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.74 (0.60–0.96)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.70 (0.60–0.93)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87 (0.60-1.00)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e25 (20–34)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e22 (17–31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e31 (21–48)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e16 (11–23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e15 (11–23)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e18 (11–31)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.71\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC, cells/µL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6650 (5275–8350)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6900 (5500–8600)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7100 (4900–9500)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil, cells/µL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5290 (4020–7038)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5225 (4015–6777)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5929 (4111–8569)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte, cells/µL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e852 (527–1236)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e953 (624–1320)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e476 (298–735)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.4 (10.3–13.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e11.8 (10.5–13.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e10.7 (9.2–12.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGaffky scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2–8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e5 (2–8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6 (2–9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDrug resistance (MDR/XDR)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e14 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e12 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2 (1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"char\"\u003e\n \u003cp\u003e0.68\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\u003cem\u003eDefinition of abbreviations\u003c/em\u003e: Alb = albumin; ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; COPD = chronic obstructive pulmonary disease; Crt = creatinine; MDR = multi-drug-resistant; WBC = white blood cell; XDR = extensively drug-resistant.\u003c/p\u003e\n \u003cp\u003eData are shown as median (interquartile range) or frequency (percentage).\u003c/p\u003e\n\u003c/div\u003e\n\u003ch3\u003eChanges in the patient number by background over 12 years\u003c/h3\u003e\n\u003cp\u003eThe number of all patients decreased from 2009 to 2020 (Fig.\u0026nbsp;2). The number of patients over 70 years old also gradually reduced. The ratio of immigrants gradually increased until 2018 and decreased after that.\u003c/p\u003e\n\u003ch3\u003eCharacteristics of immigrant TB patients\u003c/h3\u003e\n\u003cp\u003eImmigrant TB patients were 5% of all patients, which was very small compared to the total number of patients. Almost all were from Southeast Asian countries, including the Philippines, Indonesia, and Myanmar (Fig.\u0026nbsp;3). There were few patients from South American countries. Immigrant patients were younger than Japanese patients (Fig.\u0026nbsp;4), and all were discharged alive (Table\u0026nbsp;1).\u003c/p\u003e\n\u003cdiv id=\"Sec8\"\u003e\n \u003ch2\u003eMultiple logistic regression analysis for factors associated with dead patients\u003c/h2\u003e\n \u003cp\u003eAs shown in univariate analysis (Table\u0026nbsp;2), older age, low BMI, miliary TB, respiratory failure, corticosteroid use, chronic renal failure, low levels of serum albumin, lymphocyte, hemoglobin, high levels of serum creatinine, AST, and neutrophils were independent prognostic factors. In multivariate analysis, older age, low BMI, respiratory failure, low serum albumin, lymphocyte, high serum creatinine levels, and neutrophil were independent prognostic factors.\u003c/p\u003e\n \u003cdiv\u003e\n \u003ctable id=\"Tab3\" border=\"1\"\u003e\n \u003ccaption language=\"En\"\u003e\n \u003cdiv\u003eTable 2\u003c/div\u003e\n \u003cdiv\u003e\n \u003cp\u003eUnivariate and multivariate analysis of factors contributing to death from hospital discharge\u003c/p\u003e\n \u003c/div\u003e\n \u003c/caption\u003e\n \u003cthead\u003e\n \u003ctr\u003e\n \u003cth align=\"left\"\u003e\u0026nbsp;\u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eUnivariate analysis\u003c/p\u003e\n \u003c/th\u003e\n \u003cth align=\"left\" colspan=\"3\"\u003e\n \u003cp\u003eMultivariate analysis\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\u0026nbsp;\u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eOdds ratio\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e95% CI\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u003cem\u003eP\u003c/em\u003e value\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAge, yr\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.72\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.79–12.4\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08–1.14\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eSex, male\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.82–1.51\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.494\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBMI, kg/m\u003csup\u003e2\u003c/sup\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.30\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.21–0.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.87\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.80–0.95\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eComorbidity\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 \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\u003eMiliary tuberculosis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.81–5.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.41–2.03\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.818\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eRespiratory failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e9.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e7.00-13.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e6.41\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e4.05–10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCorticosteroid user\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.17–4.37\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.85–2.50\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.176\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eMalignant disease\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67–1.75\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.755\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCardiac failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91–2.83\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.025\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eDiabetes\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.77\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36–1.68\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.515\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCOPD\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97–5.64\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.094\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCerebral infarction\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.67\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.68–4.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.204\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLiver cirrhosis/hepatitis\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.34\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.36–4.99\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.661\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eBronchial asthma\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.31\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.67–7.97\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.172\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eChronic renal failure\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e8.21\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.04–33.1\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.002\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.23\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.06–23.6\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.890\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eInterstitial pneumonia\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.42\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.58–10.2\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.213\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLaboratory data\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 \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\u003eAlb, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.08\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.05–0.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.25\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16–0.40\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eCrt, mg/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.80–3.27\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.45\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.17–1.78\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eAST, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e3.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e2.46–4.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.00-1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.153\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eALT, IU/L\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.96–1.06\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.710\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eWBC, cells/µL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.99–1.01\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.110\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 \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eNeutrophil, cells/µL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.82\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.36–2.44\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.22\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.09–1.36\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eLymphocyte, cells/µL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.16\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.11–0.24\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.26\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.08–1.46\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.003\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eHemoglobin, g/dL\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.43\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.32–0.59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e\u0026lt; 0.001\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.04\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.91–1.18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.57\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003eGaffky scale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.07\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.02–1.11\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.004\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e1.05\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.97–1.12\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd align=\"left\"\u003e\n \u003cp\u003e0.234\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\u003cem\u003eDefinition of abbreviations\u003c/em\u003e: Alb = albumin; ALT = alanine aminotransferase; AST = aspartate aminotransferase; BMI = body mass index; COPD = chronic obstructive pulmonary disease; Crt = creatinine; WBC = white blood cell.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eThis study shows TB patients' characteristics and risk factors in Shizuoka Prefecture, a representative sample of Japan, helping us understand the subjects' characteristics and changes in the patient number by background and factors associated with dead patients.\u003c/p\u003e \u003cp\u003eShizuoka Prefecture is one of the prefectures with an aging population, which is the same trend in Japan. The results show that the median age of TB patients in Shizuoka Prefecture was very old. Moreover, they are gradually getting older compared with a few decades ago [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The poor outcomes in patients who had low BMI and complicated diseases were thought to be due to a decline in nutritional status with age.\u003c/p\u003e \u003cp\u003eThis study also indicated that TB is approximately 1.7 times more common in males than in females. Previous research examining the incidence of TB in the aspect of sex revealed that men are more likely to be diagnosed with TB than women, with a male-to-female ratio of 1.6:1 globally [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. This sex gap has been proposed to be explained by biological differences in disease and disease presentation and different access to health care, specifically in developing countries [\u003cspan additionalcitationids=\"CR10\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Additionally, men are more likely to report risk factors associated with TB exposure [\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e, \u003cspan additionalcitationids=\"CR12 CR13\" citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eOur analysis also found that the number of patients over 70, who comprise most of the population, is decreasing. In Japan, TB patients have decreased due to the widespread use of BCG vaccines and antibiotics to prevent TB, improved sanitary conditions, and more substantial guidance on medication administration by public health nurses. Experts mention that the declining TB cases could be partly explained by a drop in the number of people seeing a doctor and undergoing scaled-back health screenings by public health centers amid the coronavirus pandemic [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. For these reasons, TB among elderly patients, who comprise most of the population, has declined, which may be one reason why Japan has become a low-burden country.\u003c/p\u003e \u003cp\u003eIn our analysis, almost all immigrants of central Shizuoka Prefecture were from Southeast Asia, and there were few patients from South American countries. Looking at the western Shizuoka Prefecture, the number of TB cases among immigrants from South America is most prevalent. This is because Hamamatsu City, the largest city in the west of Shizuoka prefecture, has the transportation equipment industry, such as automobiles and motorcycles, and immigrants from South America, including Brazil, tend to work in these companies [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. This study found differences in immigrants' countries of origin in western and central Shizuoka Prefecture.\u003c/p\u003e \u003cp\u003eImmigrant patients were younger than Japanese patients, which means they had good nutrition and few complicated diseases, which led to all of the immigrants being discharged alive. Though immigrants are very small now, the number of immigrants may increase in the future. Thus, it must be necessary to prepare for immigration outbreaks.\u003c/p\u003e \u003cp\u003eOther novel findings of our study were that low BMI, respiratory failure, low serum albumin levels, lymphocyte, and high serum creatinine levels were independent prognostic factors for death. Osawa et al. reported a clinically applicable mortality risk prediction system for pulmonary TB. The disease severity score (named the AHL score) included daily living, hypoxemia, and lymphocytes (\u0026lt;\u0026thinsp;720/\u0026micro;L). This score showed good discrimination of mortality risk [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e]. However, there were no reports about these comprehensive data about prognostic factors by clinical and demographic information and peripheral blood tests. In Japan, the number of TB patients is getting older, and complicated diseases will increase compared with the past. Therefore, we must create a medical system to deal with this problem.\u003c/p\u003e "},{"header":"Conclusions","content":"\u003cp\u003eThe analysis of two hospitals in Shizuoka Prefecture suggests that the decline in TB among elderly patients, who comprise most of the population, might be one reason why Japan has become a low-burden country. Although immigrants are very small now, caution about the increase in immigrants from Southeast Asian countries is needed. In addition to patients\u0026rsquo; age, it is expected that the number of patients with complications and malnutrition, which are factors for poor prognosis, will increase. There is a need for a TB medical system that can deal with these complex underlying diseases.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cp\u003eTB Tuberculosis\u003c/p\u003e\n\u003cp\u003eCOVID-19 Coronavirus disease 2019\u003c/p\u003e\n\u003cp\u003eBMI body mass index\u003c/p\u003e\n\u003cp\u003eAST aspartate aminotransferase\u003c/p\u003e\n\u003cp\u003eBCG Bacille de Calmette et Gu\u0026eacute;rin \u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe present study was conducted with the approval of the research ethics committee at Shimada General Medical Center (Approval number: R 6-5).\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo, I do not have any research data outside the submitted manuscript file.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThere was no Funding.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026rsquo; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors made substantial contributions to this article. KK conducted the data analysis and prepared all the tables, figures, and manuscripts. TS supervised them. YK, TA, YI, KI, and MU contributed to collecting data.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors would like to thank the many patients who contributed to the success of this analysis. We would like to thank Philip Hawke for English language editing.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNo, I declare that the authors have no competing interests as defined by BMC, or other interests that might be perceived to influence the results and/or discussion reported in this paper.\u003c/p\u003e\n\n\u003cp\u003e\u003cstrong\u003eClinical trial number\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eNot applicable.\u003c/p\u003e\n\n\n"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. Global Tuberculosis Report 2022. World Health Organization. https://www.who.int/teams/global-tuberculosis-programme/tbreports/global-tuberculosis-report-2022.\u003c/li\u003e\n\u003cli\u003eNational Public Health Service - Northern Region. IMMUNISATION. National Public Health Service website. https://www.arphs.health.nz/public-health-topics/immunisation/countries-with-a-high-incidence-of-tb/. \u003c/li\u003e\n\u003cli\u003eKawatsu L, Kasuya S, Imai A, Yoshie A, Uchimura K. Tuberculosis in Japan: Annual Report. 2022.\u003c/li\u003e\n\u003cli\u003eHagiya H, Koyama T, Zamami Y, Minato Y, Tatebe Y, Mikami N, et al. Trends in incidence and mortality of tuberculosis in Japan: a population-based study, 1997\u0026ndash;2016. Epidemiology and Infection. 2018; 147, e38: 1\u0026ndash;10. https://doi.org/10.1017/S095026881800290X.\u003c/li\u003e\n\u003cli\u003eResearch Institute of Tuberculosis/JATA. TB Control Program in Japan. Research Institute of Tuberculosis/JATA website. https://jata.or.jp/Department_of_International_Cooperation/TB_Control_in_Japan.html.\u003c/li\u003e\n\u003cli\u003eOta M, Nishimura T, Uchimura K, Hirao S. Epidemiology of tuberculosis in foreign students in Japan, 2015\u0026ndash;2019: a comparison with the notification rates in their countries of origin. Epidemiology and Infection. 2021. 149, e202, 1\u0026ndash;6.\u003c/li\u003e\n\u003cli\u003eThe Tuberculosis Surveillance Center. Number of newly registered tuberculosis cases (rate) by prefecture/city. https://jata-ekigaku.jp/archive.\u003c/li\u003e\n\u003cli\u003eWHO. Global tuberculosis report 2016. Geneva: WHO; 2016. http://apps.who.int/.\u003c/li\u003e\n\u003cli\u003evan den Hof S, Najlis CA, Bloss E, Straetemans M. A systematic review on the role of gender in tuberculosis control. KNCV Tuberculosis Foundation; 2010. https://www.kncvtbc.org/uploaded/2015/09/Role_of_Gender_in_TB_Control.pdf.\u003c/li\u003e\n\u003cli\u003eKarim F, Ahmed F, Begum I, Johansson E, Diwan VK. Female-male differences at various clinical steps of tuberculosis management in rural Bangladesh. Int J Tuberc Lung Dis. 2008; 12: 1336-9.\u003c/li\u003e\n\u003cli\u003eJim\u0026eacute;nez-Corona ME, Garc\u0026iacute;a-Garc\u0026iacute;a L, DeRiemer K, Ferreyra-Reyes L, Bobadilla-del-Valle M, Cano-Arellano B, et al. Gender differentials of pulmonary tuberculosis transmission and reactivation in an endemic area. Thorax. 2006; 61: 348-53.\u003c/li\u003e\n\u003cli\u003eLim SS, Vos T, Flaxman AD, Danaei G, Shibuya K, Adair-Rohani H, et al. A comparative risk assessment of burden of disease and injury attributable to 67 risk factors and risk factor clusters in 21 regions, 1990-2010: a systematic analysis for the Global Burden of Disease Study 2010. Lancet. 2012; 380: 2224-60.\u003c/li\u003e\n\u003cli\u003eFeng JY, Huang SF, Ting WY, Chen YC, Lin YY, Huang RM, et al. Gender differences in treatment outcomes of tuberculosis patients in Taiwan: a prospective observational study. Clin Microbiol Infect. 2012; 18: 331-7.\u003c/li\u003e\n\u003cli\u003eSmith GS, Van Den Eeden SK, Baxter R, Shan J, Van Rie A, Herring AH, et al. Cigarette smoking and pulmonary tuberculosis in northern California. J Epidemiol Community Health. 2015; 69: 568-73.\u003c/li\u003e\n\u003cli\u003eKomiya K, Yamasue M, Takahashi O, Hiramatsu K, Kadota K, Kato S. The COVID-19 pandemic and the true incidence of Tuberculosis in Japan. Journal of Infection. 81 2020; e24\u0026ndash;e25.\u003c/li\u003e\n\u003cli\u003eIkegami S. The increase in the number of immigrants to Japan mainly focused on the Brazilians living in Hamamatsu city in Shizuoka Prefecture.2005.\u003c/li\u003e\n\u003cli\u003eOsawa T, Watanabe M, Morimoto K, Yoshiyama T, Matsuda S, Fujiwara K et al. Activities of Daily Living, Hypoxemia, and Lymphocytes Score for Predicting Mortality Risk in Patients With Pulmonary TB. CHEST 2024; 165(2): 267-277.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"tuberculosis, low-burden country, immigrants, prognostic factors, elderly patients","lastPublishedDoi":"10.21203/rs.3.rs-5190034/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5190034/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eJapan had long been a medium-burden country for tuberculosis (TB) but became a low-burden country in 2021. Shizuoka Prefecture is geographically located in Central Japan, and the incidence rate of TB is also at the average level in Japan.\u003c/p\u003e\n\u003cp\u003eThis study aimed to understand TB patients' characteristics and risk factors by analyzing the data from 2009 to 2020 in Shizuoka Prefecture, a representative sample for Japan.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods:\u003c/strong\u003e We retrospectively collected 1,132 patients with smear-positive pulmonary TB who were admitted to Shimada General Medical Center or Shizuoka General Hospital in the central area of Shizuoka Prefecture from 2009 to 2020. Patients were divided into alive and dead (i.e., all-cause in-hospital death) groups, and clinical parameters were compared between the two groups.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults:\u003c/strong\u003e The median age of all patients was 80. The number of patients decreased from 2009 to 2020. Moreover, the number of patients over 70 gradually reduced, which was similar for all patients. Immigrant TB patients were 5% of all patients, which was very small compared to the total number of patients. Almost all were from Southeast Asian countries. In multivariate analysis, older age, low body mass index, respiratory failure, low serum albumin, lymphocyte, high serum creatinine levels, and neutrophil were independent prognostic factors.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusions:\u003c/strong\u003e The decline in TB among elderly patients, who comprise most of the population, might be one reason why Japan has become a low-burden country. Although the rate of immigrants is minimal, caution is needed about the increase, especially from Southeast Asian countries. There is a need for a TB medical system that can deal with complications and malnutrition, which are risk factors for death.\u003c/p\u003e","manuscriptTitle":"Hospitalized patients with pulmonary tuberculosis in Shizuoka Prefecture in 2009-2020: risk factors for dead cases","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-21 05:33:49","doi":"10.21203/rs.3.rs-5190034/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-17T05:49:18+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-16T13:05:50+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2024-10-16T13:04:33+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"fab648ce-bb68-47fa-b989-f53b63a64ba6","owner":[],"postedDate":"April 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-10-08T02:53:18+00:00","versionOfRecord":[],"versionCreatedAt":"2025-04-21 05:33:49","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5190034","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5190034","identity":"rs-5190034","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}