Long-term risk of death after Tuberculosis diagnosis and treatment in Brazil: a nationwide longitudinal study using linked routine data between 2004 and 2018.

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Thiago Cerqueira-Silva, Viviane Boaventura, Enny Paixao, Mauro Sanchez, and 4 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7133626/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Nature Medicine → Version 1 posted You are reading this latest preprint version Abstract Tuberculosis (TB) remains a major societal burden, yet data on long-term mortality following diagnosis and treatment are limited. We conducted a nationwide Brazilian cohort study using linked data (2004-2018) to quantify long-term mortality (up to 14 years) following TB. We matched: (i)individuals diagnosed with TB or (ii)individuals who had completed TB treatment to TB-free individuals. We used competing risk methods to analyse natural causes (i.e., defined as deaths excluding TB, HIV, and external causes) and cause-specific mortality. In the diagnosed cohort (185,921 pairs), the risk of 14-year natural cause mortality was significantly higher (Risk Ratio [RR]=2.16, 95%CI=1.96-2.37); RRs were significantly elevated for deaths due to cancer, cardiovascular, endocrine, respiratory, and external causes. The treated cohort (111,871 pairs) showed elevated natural cause mortality risk (RR=1.77,1.55-2.03), with similarly increased RRs across specific causes. We showed that TB survivors, even after treatment, faced a significantly elevated, prolonged risk of death from various causes up to 14 years later. This emphasises the need for long-term monitoring to reduce the burden of TB. Health sciences/Medical research/Epidemiology Health sciences/Health care/Public health/Epidemiology Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Tuberculosis (TB) continues to be one of the deadliest infectious diseases globally. In 2023, approximately 10.8 million individuals developed TB, and 1.25 million died from this disease. 1 These alarming figures highlight the critical importance of the World Health Organisation’s (WHO) End TB Strategy, which aims to reduce TB incidence by 50% and TB mortality by 75% by 2025, relative to 2015 levels. However, progress has been insufficient: by 2023, the global TB incidence had declined by only about 8% and TB mortality by approximately 23%. 1 Addressing this gap requires a comprehensive approach that extends beyond treating active TB, considering its long-term impact on health systems through increased demand for chronic disease services and its role in perpetuating health inequities. While TB treatment is effective in curing the disease and significantly decreasing mortality during the active phase, 1 emerging evidence suggests that individuals who survive TB remain at an elevated risk of death long after completing treatment. 2–4 This ongoing risk may stem from factors such as lasting lung damage, chronic inflammation, coexisting health conditions, and poor social circumstances. 2,5,6 Despite the magnitude of this issue, the long-term burden of mortality following TB remains mostly overlooked in public health strategies; notably, the WHO guidelines contain no recommendations for addressing post-TB conditions. 4 Studies assessing post-TB mortality have relied exclusively on relative risk measures, without estimating absolute risks, thereby limiting the ability to quantify the excess mortality attributable to TB. 7–10 In addition, most studies on mortality post-TB to date compared the all-cause mortality to the general population, adjusting only by age and sex, but without properly controlling for socioeconomic variables associated with the risk of tuberculosis and death. 7,8 To overcome these limitations and provide a clearer understanding of TB’s lasting impact, a comprehensive evaluation of adverse outcomes post-TB with finer socioeconomic characteristics control is needed. Closing this knowledge gap is critical for improving the understanding of post-TB health trajectories and informing preventive and long-term care strategies for affected individuals. Leveraging nationwide administrative data from the 100 Million Brazilian Cohort, which represents the poorest half of the population of Brazil, this study investigates the risk of death following TB diagnosis and TB treatment completion. Specifically, we (i) compared the risk of death between TB cases after diagnosis and after treatment completion and control participants over time, by natural deaths (defined as deaths excluding TB, HIV, and external causes), all-cause mortality and cause-specific of death (cancer, cardiovascular, etc), and (ii) assessed whether there is a difference in the risk of death by subgroups of sex, age, race, presence of comorbidities and tuberculosis classification. Methods We used data from the 100 Million Brazilian Cohortlinked with nationwide death and tuberculosis registries. The 100 Million Brazilian Cohort is a dynamic cohort comprising over 130 million individuals from the Unified Registry for Social Programs (CadÚnico). CadÚnico serves as Brazil's primary tool for identifying and registering low-income families for social welfare programs, thus predominantly capturing individuals from the lower socioeconomic strata of the country. We linked the CadÚnico database to Tuberculosis disease records from Jan 1, 2004, to Dec 31, 2018, registered in the National Notifiable Disease Information System (SINAN) and the Mortality Information System (SIM). In Brazil, TB is a mandatory notification disease, i.e., any healthcare professional is legally required to report all suspected and confirmed cases of tuberculosis to the Ministry of Health. The linkage between tuberculosis registries and the CadÚnico had a sensitivity (94·6%) and specificity (93·6%) calculated based on false or true links between the two databases. 11 The linkage between mortality registries and the CadÚnico calculated by year had a sensitivity that ranged between 97·8% and 100·0% and a specificity between 96·6% and 99·9%. 11 From the linked dataset, we built two cohorts: one matching persons with a TB diagnosis to TB-free controls, and another matching participants who had completed TB treatment to TB-free controls. Exposure and outcomes The exposure was an individual's first SINAN record of tuberculosis between Jan 1, 2004, and Dec 31, 2018 for the diagnosed cohort and an individual's first SINAN record of treatment completion for tuberculosis between Jan 1, 2004, and Dec 31, 2018 for the treated cohort. In SINAN, patients with treatment completion are those who have two negative smear tests according to national guidelines or who do not have evidence of treatment failure based on clinical or radiological criteria. 12 Unexposed individuals were those tuberculosis-free and alive during the study period. The primary outcome was deaths by natural causes, defined as any cause of death excluding external causes (International Classification of Diseases, 10 th edition (ICD-10), Chapter XX) and causes related to Tuberculosis (ICD-10 A15-A19) and HIV (ICD-10 B20-B24). Given the well-established link between HIV and TB, 13 and to prevent overestimation of mortality directly due to active TB itself, excluding deaths directly attributed to TB and HIV allows for a clearer assessment of TB’s broader adverse effects, potentially indirect or long-term, on health. Secondary outcomes included all-cause mortality and cause-specific mortality defined by ICD-10 codes from causes with higher proportion of deaths in previous studies, 3,5 specifically (i) Cardiovascular causes (Chapter IX), and blocks: Ischaemic heart diseases (I20-I25), and Cerebrovascular diseases (I60-I69); (ii) Metabolic causes (Chapter IV); (iii) Respiratory System (Chapter X); (iv) Cancer (Chapter II), and blocks: Malignant neoplasms of respiratory and intrathoracic organs (C30-C39), and Malignant neoplasms of digestive organs (C15-C26); and (v) External Deaths (Chapter XX), and blocks: Accidents (V01-X59), and Assaults (X85-Y09). Study population and statistical analysis Exposed individuals were exactly matched to an unexposed individual (control-participants not linked to a TB record) on the day of diagnosis of TB (diagnosis cohort) or date of treatment completion (treated cohort). Exact matching was performed without replacement on year of birth (in 5-year bins), sex, race or ethnicity, city of residence, household location, household water supply type, material of the household, year of registration in the CadÚnico (in 3-year bins), and household crowding (Details on appendix methods). When multiple controls were available for a single case, one was chosen at random. Matching by these factors provided demonstrable control of bias in a previous study. 14 Controls matched on a given day who acquired TB on a subsequent date became a case and could be matched to a new control. In this case, they could contribute first as controls and later as cases. We excluded individuals (i) aged 100 years or older at CadÚnico registration, (ii) diagnosed with tuberculosis before the CadÚnico registration, (iii) with missing data in one of the variables used in the matching (Supplementary Table 1), (iv) data inconsistencies in date of death before diagnosis date or date of death before date of CadÚnico registration, (v) people experiencing homelessness due to the impossibility of assessing variables related to the household. For the treated cohort, to remove cases with inconsistent treatment length, we also excluded individuals with treatment completion date less than 138 days after the notification date or more than 2 years after the notification date. We used this cutoff considering the treatment length of 6 to 12 months, as recommended by the Ministry of Health in Brazil, depending on the type of Tuberculosis, the maximum of two years was chosen to allow for delays in the start of treatment after diagnosis. 12 For the diagnosed cohort, each matched pair was followed up from the matching date (i.e., date of TB diagnosis for the exposed individual) until the earliest of the following events: death, or Dec 31, 2018 (final data collection date), diagnosis of TB in the unexposed control individual (in this case, both members of the matched pair were censored). For the treated cohort, pairs’ follow-up started on the day of TB treatment completion and ended on the earliest of the following events: death or Dec 31, 2018 (final data collection date). We estimated the cumulative incidence function for each outcome using the Aalen-Johansen estimator, considering the competing risk of death from other causes, for example, in the model for natural deaths, all other deaths were considered as competing causes. This estimates the total effect of TB on the cause of interest, capturing both the direct pathway by which TB affects the cause of interest and the indirect effect of TB on the competing causes. 15 We estimated marginal period-specific risks, risk differences (RD), risk ratios (RR), and incidence rate ratios (IRR) comparing the exposed group to the unexposed group for each outcome. The period-specific intervals were demarcated on days 30, 90, 180, 365 and yearly intervals up to 14 years. Subgroup analyses were conducted by sex, age at diagnosis of TB (<18, 18-59 and ≥60 years), race (white/mixed/black), type of TB (pulmonary, extrapulmonary and extrapulmonary + pulmonary), diagnosis of HIV, and diagnosis of diabetes mellitus (DM). We used non-parametric bootstrapping (resampling only matched pairs) with 500 iterations to calculate percentile-based 95% confidence intervals for all measures. This procedure has been proved valid when conducting matching without replacement. 16 All analyses were conducted using R, with the package survival. Sensitivity analysis - Household contacts To further understand how much of the excess burden could be due to shared social factors and estimate the residual confounding by unmeasured variables, we conducted an additional cohort matching TB contacts to individuals free of TB (i.e., without TB diagnosis and not a TB contact). TB household contacts were eligible if they resided in the same household as a TB case and were alive at the moment of the first TB case diagnosis, i.e., we did not include persons born after the first TB case was diagnosed in the household. The same matching variables used in the main analysis was employed. A TB contact who becomes a TB case afterwards was censored on the day of TB diagnosis (pair censoring). The time zero in this cohort was the date of the first TB case in the household. Each matched pair were followed up from the matching date until the earliest of the following events: death, December 31, 2018 or diagnosis of TB (in this case, both members of the matched pair were censored). We also evaluated the comparison directly between the first TB case and the household contacts, matching only on year of birth (in 5-year bins) and sex. The time zero in this analysis was the date of the first diagnosis of a TB case in the household. Each matched pair were followed up from the matching date until the earliest of the following events: death, December 31, 2018, or diagnosis of TB in the unexposed control individual (in this case, both members of the matched pair were censored) We calculated RD, IRR, and RR, as well as 95% confidence intervals, similarly to the main analysis. Protocol The study design and statistical analysis plan were specified in advance of analysing the data and are described in a publicly available protocol (https://github.com/csthiago/tuberculosis_death). Results Among the 95,789,738 eligible people who entered the 100 Million Brazilian Cohort between 2004 and 2018, 209,598 had TB. A total of 185,921 (88.4%) diagnosed TB cases were matched with an unexposed individual. Among the 125,989 treated TB cases, 111,871 (88.8%) were matched with an unexposed individual. (Figure 1 and Table 1) Table 1: Baseline characteristics of diagnosed and treated TB cases. Characteristic Control N = 185,921 Diagnosed N = 185,921 Control N = 111,871 Treated N = 111,871 Age (years), at CadUnico registration – median (IQR) 25 (15 - 39) 25 (15 - 39) 25 (15 - 39) 25 (15 - 39) Age (years), at index date – median (IQR) 33 (24 – 47) 33 (24 – 47) 33 (25 – 47) 33 (25 – 47) Age group, at index date <18 12,520 (6.7%) 12,520 (6.7%) 6,963 (6.2%) 6,963 (6.2%) 18-59 154,081 (82.9%) 154,081 (82.9%) 92,998 (83.1%) 92,998 (83.1%) ≥60 19,320 (10.4%) 19,320 (10.4%) 11,910 (10.6%) 11,910 (10.6%) Sex Female 68,470 (36.8%) 68,470 (36.8%) 42,829 (38.3%) 42,829 (38.3%) Male 117,451 (63.2%) 117,451 (63.2%) 69,042 (61.7%) 69,042 (61.7%) Race/Ethnicity White 49,599 (26.7%) 49,599 (26.7%) 28,695 (25.7%) 28,695 (25.7%) Black 21,183 (11.4%) 21,183 (11.4%) 12,116 (10.8%) 12,116 (10.8%) Mixed 113,311 (60.9%) 113,311 (60.9%) 69,769 (62.4%) 69,769 (62.4%) Asian 264 (0.1%) 264 (0.1%) 156 (0.1%) 156 (0.1%) Indigenous 1,564 (0.8%) 1,564 (0.8%) 1,135 (1.0%) 1,135 (1.0%) Education Level No school 24,436 (13.1%) 24,436 (13.1%) 14,806 (13.2%) 14,806 (13.2%) Nursery 1,503 (0.8%) 1,503 (0.8%) 951 (0.9%) 951 (0.9%) Infant School 1,720 (0.9%) 1,720 (0.9%) 1,033 (0.9%) 1,033 (0.9%) Elementary School 70,454 (37.9%) 70,454 (37.9%) 41,865 (37.4%) 41,865 (37.4%) Middle school 65,170 (35.1%) 65,170 (35.1%) 38,951 (34.8%) 38,951 (34.8%) High School 21,962 (11.8%) 21,962 (11.8%) 13,833 (12.4%) 13,833 (12.4%) Higher education 676 (0.4%) 676 (0.4%) 432 (0.4%) 432 (0.4%) Overcrowded* 27,968 (15.0%) 27,968 (15.0%) 16,240 (14.5%) 16,240 (14.5%) Water system Public system 147,196 (79.2%) 147,196 (79.2%) 87,296 (78.0%) 87,296 (78.0%) Water well 26,747 (14.4%) 26,747 (14.4%) 17,381 (15.5%) 17,381 (15.5%) Other 11,978 (6.4%) 11,978 (6.4%) 7,194 (6.4%) 7,194 (6.4%) Location of household Urban 164,096 (88.3%) 164,096 (88.3%) 97,496 (87.2%) 97,496 (87.2%) Rural 21,825 (11.7%) 21,825 (11.7%) 14,375 (12.8%) 14,375 (12.8%) Material of household Masonry/brick 147,173 (79.2%) 147,173 (79.2%) 87,086 (77.8%) 87,086 (77.8%) Coated Taipa 2,929 (1.6%) 2,929 (1.6%) 1,926 (1.7%) 1,926 (1.7%) Uncoated Taipa 2,930 (1.6%) 2,930 (1.6%) 2,003 (1.8%) 2,003 (1.8%) Wood 24,712 (13.3%) 24,712 (13.3%) 15,674 (14.0%) 15,674 (14.0%) Other 8,177 (4.4%) 8,177 (4.4%) 5,182 (4.6%) 5,182 (4.6%) Year of CadUnico registration 2004-2006 105,041 (56.5%) 105,041 (56.5%) 63,757 (57.0%) 63,757 (57.0%) 2007-2009 41,269 (22.2%) 41,269 (22.2%) 25,078 (22.4%) 25,078 (22.4%) 2010-2012 23,137 (12.4%) 23,137 (12.4%) 13,764 (12.3%) 13,764 (12.3%) 2013-2015 11,417 (6.1%) 11,417 (6.1%) 7,138 (6.4%) 7,138 (6.4%) 2016-2018 5,057 (2.7%) 5,057 (2.7%) 2,134 (1.9%) 2,134 (1.9%) Diabetes - 9,768 (5.3%) - 5,908 (5.3%) HIV - 15,286 (8.2%) - 6,031 (5.4%) Geographic region North 21,560 (11.6%) 21,560 (11.6%) 14,525 (13.0%) 14,525 (13.0%) Northeast 61,861 (33.3%) 61,861 (33.3%) 39,603 (35.4%) 39,603 (35.4%) Southeast 75,008 (40.3%) 75,008 (40.3%) 40,272 (36.0%) 40,272 (36.0%) South 19,828 (10.7%) 19,828 (10.7%) 12,619 (11.3%) 12,619 (11.3%) Central west 7,664 (4.1%) 7,664 (4.1%) 4,852 (4.3%) 4,852 (4.3%) Tuberculosis classification Pulmonary 157,540 (84.7%) 96,328 (86.1%) Extrapulmonary 23,134 (12.4%) 13,110 (11.7%) Both 5,178 (2.8%) 2,433 (2.2%) Missing 69 (<0.1%) 0 (0%) * Overcrowded was defined as a household density (residents per room) greater than 2. IQR = Interquartile range The unmatched TB exposed (23,667 [11.6%] of cases diagnosed with TB, and 14,118 [11.4%] of those treated for TB) were older, had a higher proportion of minorities (Asian/Indigenous), resided more in rural areas, and had worse socioeconomic conditions (higher proportion of overcrowded houses, with worse access to water). (Supplementary Table 2) The median follow-up period in the diagnosed cohort was 4.5 years (interquartile range – IQR 1.9 to 7.7) for the control group and 3.9 years (IQR 1.5 to 7.2) for the diagnosed group. For the treated cohort, the median follow-up was 4.0 years (IQR 1.8 to 7.2) for the control group and 3.8 years (IQR 1.7 to 7.0) for the treated group. The median time to treatment completion was 6.6 months (IQR, 6.2 to 7.7). In the diagnosed cohort, 29,226 deaths occurred during follow-up, 23,900 (12.6%) in the TB-diagnosed group and 5326 (2.2%) in the control group. (Supplementary Figure 1) In the treated cohort, 10,631 deaths occurred, with 7,677 (6.6%) in the TB-treated group and 2,954 (2.3%) in the control group. (Supplementary Figure 2) Natural deaths (excluding external, HIV and TB causes) Table 2: Estimated risk of death from natural causes (excluding external causes, HIV and TB deaths) of diagnosed and treated tuberculosis cases compared to non-exposed controls. Cumulative Number of events Risk per 100 000 people (95% CI) Risk Difference per 100 000 people (95% CI) Risk Ratio (95% CI) Control group Tuberculosis group Control group Tuberculosis group Diagnosed cohort 30 days 54 1524 29.2 (22.2 to 37.3) 822.4 (782.8 to 860.9) 793.2 (753.7 to 832.9) 28.18 (21.68 to 37.04) 1 year 673 4636 389.2 (360.5 to 418.5) 2600.9 (2526.9 to 2667.0) 2211.7 (2139.2 to 2286.0) 6.68 (6.20 to 7.26) 5 years 2746 9518 2116.3 (2033.0 to 2197.4) 6562.7 (6433.3 to 6699.4) 4446.4 (4277.5 to 4602.6) 3.10 (2.97 to 3.24) 10 years 3892 11847 4327.3 (4166.6 to 4484.2) 10945.4 (10719.2 to 11176.8) 6618.1 (6344.0 to 6888.7) 2.53 (2.43 to 2.64) 14 years 4060 12151 6451.7 (5886.5 to 6975.2) 13947.2 (13413.3 to 14553.7) 7495.5 (6652.7 to 8257.8) 2.16 (1.96 to 2.37) Treated cohort 30 days 39 106 35.1 (24.3 to 46.8) 95.3 (78.2 to 112.3) 60.2 (39.1 to 80.0) 2.72 (1.94 to 3.92) 1 year 380 1206 364.7 (327.1 to 400.4) 1158.2 (1090.1 to 1225.0) 793.5 (719.7 to 870.4) 3.18 (2.83 to 3.55) 5 years 1578 3950 2111.8 (2005.8 to 2213.4) 5093.1 (4943.8 to 5257.1) 2981.3 (2800.1 to 3177.8) 2.41 (2.29 to 2.55) 10 years 2181 5215 4283.7 (4080.3 to 4510.5) 9607.3 (9290.8 to 9930.4) 5323.6 (4961.2 to 5662.3) 2.24 (2.12 to 2.37) 14 years 2258 5336 6655.3 (5826.3 to 7501.2) 11756.4 (11220.3 to 12389.8) 5101.0 (4105.4 to 6128.4) 1.77 (1.55 to 2.03) In the diagnosed cohort, the risk of death within 30 days of diagnosis was markedly higher among people diagnosed with TB compared to controls (RR: 28.18 - 95% CI 21.68 to 37.04), decreasing to 6.68 (6.20 to 7.26) at the end of the first year and 2.16 (1.96 to 2.37) at the end of 14 years. (Table 2 and Supplementary Table 3) The yearly estimates (IRR) showed values close to 2.4 (ranging from 1.99 to 3.22) between 2 and 10 years. (Figure 2 and Supplementary Table 3) In the treated cohort, the risk of death among people treated for TB compared to controls was more stable over time, with an RR of 2.72 (1.94 to 3.92) within 30 days of treatment completion, 3.33 at 90 days (2.69 to 4.27), and of 1.77 (1.55 to 2.03) at the end of the 14 years of follow-up. (Table 2) Similarly to the diagnosed cohort, the yearly IRRs were close to 2.2 (ranging from 2.03 to 2.62) between 2 and 10 years after treatment completion. (Figure 2 and Supplementary Table 4) All-cause mortality In the diagnosed cohort, the all-cause mortality RR comparing the TB group with the control group was markedly higher in the first month after diagnosis (RR: 58.10; 47.05 to 73.04). The RR decreased to 12.66 at one year and 2.89 (2.69 to 3.11) at 14 years. At 14 years, the risk difference per 100,000 persons was 15,167.8 (14343.2 to 16015.5) (Supplementary Table 3 and Supplementary Figure 1) For the treated cohort, the RR in the first month after treatment completion was 3.22 (2.41 to 4.31), decreasing to 2.01 (1.81 to 2.28) at 14 years, with a risk difference of 8,206.6 (7,131.3 to 9,453.2). (Supplementary Table 4 and Supplementary Figure 2) Cause-specific mortality The cause-specific analysis showed a similar pattern to the analysis of natural death. In the diagnosed cohort, the highest RRs were seen within 30 days of the diagnosis, which decreased over time. (Supplementary Tables 3 and 6) In the treated cohort, the highest RR for cancer and respiratory deaths were seen within 90 days of treatment completion. (Supplementary Tables 4 and 7) The treated cohort showed lower RRs for all cause-specific deaths, except for external causes, which presented similar values across both cohorts (ranging from 1.72 to 2.47 in the diagnosed cohort and 1.74 to 2.10 in the treated cohort). (Figure 3 and Supplementary Tables 3 and 4) Notably, within the specific cancer types, we found an increased risk for cancer of the digestive organs in both cohorts; the RR at 14 years were 1.97 (1.55 to 2.57) in the diagnosed cohort and 1.98 (1.40 to 2.89) in the treated cohort. (Supplementary Tables 6 and 7) Subgroup analyses The subgroup analysis for the risk of death due to natural causes exhibited the same pattern as the main analysis, with the RR decreasing over time. In the diagnosed cohort, females had slightly higher RRs than males throughout the entire follow-up period. Participants aged 18-59 years had a higher RR than those aged 60 years or older (RR at 14 years: 2.71 (2.35 to 3.14) and 1.43 (1.28 to 1.60), respectively). However, on the RD scale, participants aged 60 years or older had nearly three times the risk of death compared to those aged 18-59 years (RD at 14 years for 18-59 years: 7,047.4 (6,165.6 to 7,858.2); RD at 14 years for ≥ 60 years: 14,900.6 (10,525.5 to 19,344.6)). Participants with pulmonary tuberculosis exhibited lower RR and RD than those with extrapulmonary or both extrapulmonary/pulmonary tuberculosis. (Extended Data Figure 1 and Supplementary Table 9) In the treated cohort, males and females had similar RR. The age group pattern was maintained, with the 18-59 age group showing a higher RR and lower RD than those ≥60 years. (Figure 4 and Supplementary Tables 10) In both cohorts, we did not observe any substantial differences in the temporal patterns or baseline risk magnitude by race/ethnicity or TB classification. Participants with diabetes mellitus had a similar RR to those with HIV in the diagnosed cohort and a higher RR in the treated cohort. Notably, the absolute excess mortality (risk difference) in TB patients with DM was substantially larger, exceeding double the RD observed in TB patients with HIV. (Figure 4, Extended Figure 1, Supplementary Tables 9 and 10) We also evaluated the cause-specific mortality by sex. Both cohorts (diagnosed and treated) showed similar RR for cardiovascular, respiratory and endocrine causes for males and females, while the RR for cancer deaths was slightly higher in males than in females. (Supplementary Tables 11 - 14) Household contacts A total of 448,825 household contacts were matched to unexposed control persons. The risk of death among the TB household contacts was elevated when compared to their unexposed counterparts. Within 5 years, the RR for all-cause mortality was 1.19 (RR 1.14 to 1.23), for natural deaths was 1.11 (1.06 to 1.16), and for external deaths it was 1.29 (1.20 to 1.39). At 14 years, the RR for all-cause mortality was 1.09 (1.00 to 1.19), for natural deaths 1.04 (0.93 to 1.15), and for external causes of death 1.16 (1.00 to 1.34). (Supplementary Tables 5 and 8). A total of 12,948 (6.2%) TB cases were matched to household contacts. The median age of the pairs was 14 years (IQR 11 to 18), and 8,980 pairs (69.4%) were male. The direct comparison between TB cases and household contacts for natural deaths showed similar results to the main analysis, with an RR at 1, 5 and 10 years of 11.15 (6.49 to 23.10), 3.97 (2.94 to 5.70) and 2.52 (1.83 to 3.66), respectively. For external causes of death, the RR at 1, 5 and 10 years were 2.29 (1.48 to 4.09), 1.66 (1.29 to 2.12) and 1.40 (1.06 to 1.84), respectively. Discussion In this large, population-based cohort study conducted in Brazil, we found a significantly elevated risk of mortality among individuals diagnosed and treated for TB compared to TB-free participants with similar socioeconomic characteristics. Over 14 years of follow-up, diagnosed TB patients had 15,168 more deaths per 100,000 persons compared to TB-free participants. After treatment, we observed a decrease in the risk difference, but it remained substantial at 8,206 more deaths per 100,000 persons. Our findings reveal that individuals successfully treated for TB still experience excess mortality across multiple organ systems and causes, highlighting TB’s lasting impact on overall health. Previous studies have also evaluated mortality following TB. 5,7–10,17,18 However, our study provides finer control over socioeconomic characteristics, particularly for having detailed information for the TB-free participants. Controlling for socioeconomic variables is extremely important when evaluating the residual burden of TB, as the heightened vulnerability of individuals who develop TB also contributes to the increased risk of mortality compared to the general population, partly due to poverty. 19 We have also conducted a comprehensive evaluation of mortality risks using a robust methodology, which includes exact matching of multiple characteristics within a competing risks framework for individuals with a TB diagnosis and those who have completed TB treatment. Second, we have presented both absolute and relative measures for risks and the rate ratios by year of follow-up in all comparisons. These measures complement one another, offering a thorough view of the TB burden. Third, we have assessed the risks after confirmed treatment completion, with clinical or microbiological confirmation of treatment success, rather than defining the post-TB period as 12 months following diagnosis, as in previous studies. 7,8 Relying solely on time after diagnosis can group individuals who abandoned treatment, experienced treatment failure, who are classified as drug-resistant TB, or were lost to follow-up with those who completed treatment, likely biasing the results. 7 In our study, half of the patients completed their treatment at 199 days post-diagnosis, with most finishing it before 8 months. Finally, we used as the main outcome mortality for natural causes, but also excluding TB and HIV related deaths. The curated outcome definition and use of cases with proof of treatment completion allow us to quantify the residual burden of TB with greater accuracy. Our findings showed that diagnosed and treated TB cases were associated with a significant increase in mortality across a broad range of causes, including respiratory, cardiovascular, endocrine and cancer. Our results complement previous evidence showing that TB is associated with increased risk of respiratory mortality, mainly due to direct lung damage caused by TB, increasing the risk of recurrent pneumonia and chronic obstructive pulmonary disease, bronchiectasis and other specific infections, such as aspergillomas. 2,6 The sustained increased risk of post-TB cases dying from cardiovascular diseases for more than a decade complements previous epidemiological studies and may be driven by persistent systemic inflammation and chronic immune activation, 20,21 potentially accelerating atherogenesis through mechanisms such as inflammaging or sustained endothelial dysfunction. 20,21 The prolonged immune dysregulation, characterised by elevated levels of pro-inflammatory cytokines (such as TNF-alpha and IL-6), can accelerate atherosclerosis, thereby increasing cardiovascular risk. 20–22 Additionally, endocrine-related deaths were notably increased, potentially reflecting a bidirectional relationship between diabetes mellitus and TB. 23,24 Diabetes can predispose individuals to TB through impaired immune responses, while chronic inflammation from TB can exacerbate insulin resistance and metabolic dysregulation, thereby increasing the risk of diabetes-related mortality. 24,25 Shared inflammatory mechanisms and dysregulated immune responses likely underpin the mutual exacerbation observed between these two conditions. 24,26 Our findings showed that TB is associated with an increased risk of deaths from cancer, including cancer in the digestive organs. This finding is consistent with a growing body of evidence; notably, a prior meta-analysis of 11 studies showed an elevated risk of cancer in TB patients for more than five years after diagnosis. 27 Although the mechanisms underlying this association are not fully understood, several hypotheses have been proposed. The chronic systemic inflammation caused by TB can promote carcinogenesis through the promotion of reactive oxygen species and DNA damage, 28–30 27–2928–30 while lasting immune dysregulation, leading to the exhaustion of T-cells and NK cells, may compromise anti-tumour mechanisms. 30–32 Furthermore, residual lung fibrosis can establish a pro-tumorigenic microenvironment. 28,30 Last, the association may be partially explained by a higher prevalence of shared lifestyle risk factors for cancer and TB, such as smoking and alcohol use. 9,33–35 Our work extends this body of evidence by providing detailed, year-by-year risk estimates, highlighting the significant long-term outcomes of TB and supporting the need for continued patient care for tuberculosis-related sequelae after successful treatment. We also observed an increased risk of deaths due to external causes, with similar magnitudes in the diagnosed and treated TB cases. While we cannot dismiss the possibility that this finding is due to residual confounding, as there is no biological plausibility for this relationship, it may also reflect the social stigma experienced by tuberculosis patients. This stigma can lead to social isolation, limited economic opportunities, and potentially worsen underlying or new-onset mental health issues such as depression or anxiety, which, in turn, could promote riskier behaviours behaviour. 36–38 However, there is also the possibility that poor mental health or living in contexts of violence could increase the chances of TB reactivation 39 and further lead to higher external-causes related mortality after TB diagnosis or treatment. However, similarities in the RR between the treated and diagnosed cohorts reinforce the plausibility of these increased risks being due to a societal rather than a biological phenomenon. Our direct comparison of TB cases to household contacts with the same sex and similar age also showed elevated risk of deaths from external causes in the TB group. This indicates that, even under similar socioeconomic conditions, TB patients experienced an increased risk of death from external causes, while some of this association can be related to residual confounding, it is unlikely to account for all of it. In this scenario, increased awareness about TB stigma and interventions to deal with it, such as TB support groups, training of healthcare workers to provide non-judgmental care, and community-wide educational campaigns to dispel myths and misinformation about TB can improve the lives of TB survivors. 40 In our study, we observed a slight increase in the risk of death among household tuberculosis contacts compared to unexposed individuals, suggesting some degree of residual confounding likely due to heightened social vulnerability in households with a tuberculosis case. However, the excess risk of death among patients diagnosed with, and even those treated for, tuberculosis far exceeds the risk found among household contacts. This suggests that while there are indeed connections between poverty and tuberculosis, 19 the increased mortality after tuberculosis treatment cannot be attributed solely to poverty, as indicated in previous studies. 3 Our study has several limitations. First, like any observational study, it is susceptible to residual confounding. We also could not account for potential time-varying confounders after the index date, such as loss or decrease of family income. Our attempt to quantify this issue involved conducting an additional analysis to evaluate mortality among household contacts, using the same approach as the main analysis, since we do not expect a strong causal link between being a TB contact and an increased risk of death. This analysis revealed a slightly elevated risk of death in this group. However, the excess risk of death among patients diagnosed with and even those treated for tuberculosis far exceeds the risk found among household contacts. Additionally, our analysis is restricted to the poorer half of Brazil. While this may limit generalisability to wealthier populations, it likely enhances control for confounding due to socioeconomic characteristics by studying a more homogenous group and focuses on a highly vulnerable population where the impact of TB is often most severe. Nevertheless, even in this large cohort, we were unable to match all participants, with the unmatched group exhibiting worse socioeconomic conditions than the matched sample. Our decision to match to increase internal generalisability, providing adjustment by design without making any assumptions on the exposure or outcome model. However, even with a higher percentage of matched cases (>88%), the matching may lead to an underestimation of TB effects, considering that the unmatched group had poorer socioeconomic conditions, and likely a higher baseline mortality risk. The presence of unmatched cases also changes the estimand of the average exposure effect in the exposed to average exposure effect in the matched sample. 41 Second, our results may be subject to reverse causality, particularly for cancer and endocrine-related mortality. Given that active malignancies and diabetes are known risk factors for TB and that we were unable to adjust for pre-exposure comorbidity, it is possible that an undiagnosed underlying condition precipitated the onset of active TB in some individuals. 6,25 In such cases, TB would be a marker of underlying vulnerability or an exacerbating factor, rather than the primary cause of developing and dying from cancer or endocrine diseases. While our long-term follow-up and analysis of the treated cohort mitigate this concern for deaths occurring many years post-diagnosis, the possibility cannot be fully excluded, especially for mortality observed in the early follow-up period. Third, comorbidity data on HIV and DM were available only for the TB group. Consequently, our subgroup analyses estimate the joint effect of TB plus the comorbidity, rather than the effect of TB in isolation. Fourth, the low number of identified drug-resistant TB cases (n = 945; 0.5%) precluded subgroup analysis. Lastly, after 11 years of follow-up, we had a relatively small number of participants at risk, resulting in a limited number of events and imprecise estimates by risk period. This issue also occurred in the subgroup analysis, requiring caution when interpreting the point estimates from those analyses. A fundamental limitation of the global tuberculosis response has long been its focus solely on diagnosing and curing active disease. For decades, WHO guidelines have appropriately emphasised the diagnosis and bacteriological cure of active disease, considering this a complete return to health, while overlooking the substantial burden of post-TB complications. The issue of neglecting the long-term health of survivors will become more evident due to ongoing global reductions in TB funding, 42 which threaten to reverse progress towards TB control and elimination, primarily impacting low- and middle-income countries. Our findings strongly support the integration of long-term clinical follow-up into routine TB care. Integrating post-TB assessments, such as lung function testing, cardiovascular risk screening, and cancer surveillance, into national guidelines for post-TB management is essential. Such measures will enhance clinician awareness of post-TB complications, ensure timely management, and direct resources towards truly comprehensive, patient-centred care. Declarations Contributions T.C-S, E.P. and J.P. conceived the idea for the study. All authors contributed to the study design, with J.P. and T.C.-S. drafting the statistical analysis plan. T.C-S. conducted the statistical analysis. J.P. and O.R. oversaw the analysis. M.L.B and M.S acquired the data. T.C.S. drafted the manuscript, with assistance from J.P. All authors critically revised the manuscript and approved the final version for submission. References Global Tuberculosis Report 2024 . (World Health Organization, Geneva, 2024). Allwood, B. W. et al. Post-Tuberculosis Lung Disease: Clinical Review of an Under-Recognised Global Challenge. Respiration 100 , 751–763 (2021). Romanowski, K. et al. Long-term all-cause mortality in people treated for tuberculosis: a systematic review and meta-analysis. The Lancet Infectious Diseases 19 , 1129–1137 (2019). Yarbrough, C. et al. Post-tuberculosis lung disease: Addressing the policy gap. PLOS Global Public Health 4 , e0003560 (2024). Gorvetzian, S., Pacheco, A. G., Anderson, E., Ray, S. M. & Schechter, M. C. Mortality Rates after Tuberculosis Treatment, Georgia, USA, 2008–2019 - Volume 30, Number 11—November 2024 - Emerging Infectious Diseases journal - CDC. doi:10.3201/eid3011.240329. Trajman, A. et al. Tuberculosis. The Lancet 405 , 850–866 (2025). Kim, S. et al. Long-term Mortality Trends Among Individuals With Tuberculosis: A Retrospective Cohort Study of Individuals Diagnosed With Tuberculosis in Brazil. Clinical Infectious Diseases ciaf206 (2025) doi:10.1093/cid/ciaf206. Ranzani, O. T. et al. Long-term survival and cause-specific mortality of patients newly diagnosed with tuberculosis in São Paulo state, Brazil, 2010–15: a population-based, longitudinal study. The Lancet Infectious Diseases 20 , 123–132 (2020). Nordholm, A. C. et al. Mortality, risk factors, and causes of death among people with tuberculosis in Denmark, 1990-2018. International Journal of Infectious Diseases 130 , 76–82 (2023). Liu, Y. et al. Tuberculosis-associated mortality and its risk factors in a district of Shanghai, China: a retrospective cohort study. Int J Tuberc Lung Dis 22 , 655–660 (2018). Pinto, P. F. P. S. et al. Incidence and risk factors of tuberculosis among 420 854 household contacts of patients with tuberculosis in the 100 Million Brazilian Cohort (2004–18): a cohort study. The Lancet Infectious Diseases 24 , 46–56 (2024). Saúde, M. da. Manual de recomendações para o controle da tuberculose no Brasil . (Ms, 2018). Meintjes, G. & Maartens, G. HIV-Associated Tuberculosis. New England Journal of Medicine 391 , 343–355 (2024). Cerqueira-Silva, T. et al. Risk of death following chikungunya virus disease in the 100 Million Brazilian Cohort, 2015–18: a matched cohort study and self-controlled case series. The Lancet Infectious Diseases 24 , 504–513 (2024). Rojas-Saunero, L. P., Young, J. G., Didelez, V., Ikram, M. A. & Swanson, S. A. Considering Questions Before Methods in Dementia Research With Competing Events and Causal Goals. American Journal of Epidemiology 192 , 1415–1423 (2023). Abadie, A. & Spiess, J. Robust Post-Matching Inference. Journal of the American Statistical Association 117 , 983–995 (2022). Baluku, J. B. et al. Death after cure: Mortality among pulmonary tuberculosis survivors in rural Uganda. International Journal of Infectious Diseases 144 , 107069 (2024). Fox, G. J. et al. Post-treatment Mortality Among Patients With Tuberculosis: A Prospective Cohort Study of 10 964 Patients in Vietnam. Clinical Infectious Diseases 68 , 1359–1366 (2019). Lönnroth, K., Jaramillo, E., Williams, B. G., Dye, C. & Raviglione, M. Drivers of tuberculosis epidemics: the role of risk factors and social determinants. Soc Sci Med 68 , 2240–2246 (2009). Huaman, M. A., Henson, D., Ticona, E., Sterling, T. R. & Garvy, B. A. Tuberculosis and cardiovascular disease: linking the epidemics. Trop Dis Travel Med Vaccines 1 , 10 (2015). Marcu, D. T. M. et al. Cardiovascular Involvement in Tuberculosis: From Pathophysiology to Diagnosis and Complications—A Narrative Review. Diagnostics (Basel) 13 , 432 (2023). Feria, M. G. et al. Pro-Inflammatory Alterations of Circulating Monocytes in Latent Tuberculosis Infection. Open Forum Infectious Diseases 9 , ofac629 (2022). Boadu, A. A., Yeboah-Manu, M., Osei-Wusu, S. & Yeboah-Manu, D. Tuberculosis and diabetes mellitus: The complexity of the comorbid interactions. International Journal of Infectious Diseases 146 , 107140 (2024). Bisht, M. K., Dahiya, P., Ghosh, S. & Mukhopadhyay, S. The cause–effect relation of tuberculosis on incidence of diabetes mellitus. Front. Cell. Infect. Microbiol. 13 , (2023). Pitua, I., Raizudheen, R., Muyanja, M., Nyero, J. & Obol, M. O. Diabetes and tuberculosis: a systematic review and meta-analyis of mendelian randomization evidence. Diabetology & Metabolic Syndrome 17 , 46 (2025). Huangfu, P., Ugarte-Gil, C., Golub, J., Pearson, F. & Critchley, J. The effects of diabetes on tuberculosis treatment outcomes: an updated systematic review and meta-analysis. Int J Tuberc Lung Dis 23 , 783–796 (2019). Luczynski, P., Poulin, P., Romanowski, K. & Johnston, J. C. Tuberculosis and risk of cancer: A systematic review and meta-analysis. PLOS ONE 17 , e0278661 (2022). Malik, A. A., Sheikh, J. A., Ehtesham, N. Z., Hira, S. & Hasnain, S. E. Can Mycobacterium tuberculosis infection lead to cancer? Call for a paradigm shift in understanding TB and cancer. International Journal of Medical Microbiology 312 , 151558 (2022). Qin, Y. et al. The relationship between previous pulmonary tuberculosis and risk of lung cancer in the future. Infect Agent Cancer 17 , 20 (2022). Zhou, W. et al. Coexisting Lung Cancer and Pulmonary Tuberculosis: A Comprehensive Review From Incidence to Management. Cancer Rep (Hoboken) 8 , e70213 (2025). Wang, Y. et al. Systemic immune dysregulation in severe tuberculosis patients revealed by a single-cell transcriptome atlas. Journal of Infection 86 , 421–438 (2023). Kang, K. et al. T cell exhaustion in human cancers. Biochimica et Biophysica Acta (BBA) - Reviews on Cancer 1879 , 189162 (2024). Everatt, R., Kuzmickiene, I., Davidaviciene, E. & Cicenas, S. Non-pulmonary cancer risk following tuberculosis: a nationwide retrospective cohort study in Lithuania. Infectious Agents and Cancer 12 , 33 (2017). Sjödahl, K. et al. Smoking and alcohol drinking in relation to risk of gastric cancer: A population-based, prospective cohort study. International Journal of Cancer 120 , 128–132 (2007). Rehm, J. et al. The association between alcohol use, alcohol use disorders and tuberculosis (TB). A systematic review. BMC Public Health 9 , 450 (2009). Courtwright, A. & Turner, A. N. Tuberculosis and Stigmatization: Pathways and Interventions. Public Health Rep 125 , 34–42 (2010). Kılıç, A. et al. A systematic review exploring the role of tuberculosis stigma on test and treatment uptake for tuberculosis infection. BMC Public Health 25 , 628 (2025). Meghji, J. et al. The long term effect of pulmonary tuberculosis on income and employment in a low income, urban setting. Thorax 76 , 387–395 (2021). Sweetland, A. C. et al. Addressing the tuberculosis–depression syndemic to end the tuberculosis epidemic. The International Journal of Tuberculosis and Lung Disease 21 , 852–861 (2017). Foster, I. et al. Analysing interventions designed to reduce tuberculosis-related stigma: A scoping review. PLOS Glob Public Health 2 , e0000989 (2022). Greifer, N. & Stuart, E. A. Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist’s Toolbox. Epidemiologic Reviews 43 , 118–129 (2022). Funding cuts impact access to TB services endangering millions of lives. https://www.who.int/news/item/05-03-2025-funding-cuts-to-tuberculosis-programmes-endanger-millions-of-lives. Additional Declarations There is NO Competing Interest. Supplementary Files suplementarynonterminated.docx Supplementary Information image5.png Extended Figure 1: Estimated risk of death from natural causes (excluding external causes, HIV and TB deaths) of diagnosed TB cases compared to non-exposed controls by subgroup. In Tuberculosis classification, “both” indicates pulmonary plus extrapulmonary. Error bars represent 95% confidence intervals Cite Share Download PDF Status: Published Journal Publication published 19 Mar, 2026 Read the published version in Nature Medicine → Version 1 posted 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. 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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-7133626","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":490851831,"identity":"76babb25-420f-4ec4-851d-0c470f484a83","order_by":0,"name":"Thiago Cerqueira-Silva","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAABBUlEQVRIie2RsWrDMBCGf3PgLKJjiVEgr6AQKC0x6FUkMmQJpdDFk3EwKIsfINCXSRFkygMkdOwLJLSDwUul0pQuSjoWqm+QTpw+7o4DIpG/SD+pPu+0547DA9QpIUIG+1YISFYCil1WTpFTiP1GkbxeHN8LyCtio7dclPfyep0cWthxsMrgueaDLbQhNuZzYR8ZV5Q1sDfhxnTFMwOVUqqcstYNV+CAzc8odecU6ZRpdytKr1B3QTHZ0SAxRBsOQV5JfZVwYzttJtg60Sl3jbC6edHGBbPg+L3V7HXfFrkcLhf1ri1KvXyaWhdMRlXIgV8H+j/ffrnBrXx9ac/nI5FI5L/zAbtPSRxXq5SLAAAAAElFTkSuQmCC","orcid":"https://orcid.org/0000-0003-4534-2509","institution":"London School of Hygiene and Tropical Medicine","correspondingAuthor":true,"prefix":"","firstName":"Thiago","middleName":"","lastName":"Cerqueira-Silva","suffix":""},{"id":490851832,"identity":"0723903b-6402-4948-8ca9-972badd8283c","order_by":1,"name":"Viviane Boaventura","email":"","orcid":"https://orcid.org/0000-0002-7241-6844","institution":"Federal University of Bahia","correspondingAuthor":false,"prefix":"","firstName":"Viviane","middleName":"","lastName":"Boaventura","suffix":""},{"id":490851833,"identity":"37657426-eb35-4ba3-92c2-a8bd666dbcd8","order_by":2,"name":"Enny Paixao","email":"","orcid":"https://orcid.org/0000-0002-4797-908X","institution":"London School of Hygiene and Tropical Medicine","correspondingAuthor":false,"prefix":"","firstName":"Enny","middleName":"","lastName":"Paixao","suffix":""},{"id":490851834,"identity":"4778fbab-9a2b-4a9e-9110-f1a454b8266a","order_by":3,"name":"Mauro Sanchez","email":"","orcid":"","institution":"University of Brasília","correspondingAuthor":false,"prefix":"","firstName":"Mauro","middleName":"","lastName":"Sanchez","suffix":""},{"id":490851835,"identity":"c615708e-e0bb-43b8-896a-f4bc8d7270fe","order_by":4,"name":"Clemence Leyrat","email":"","orcid":"","institution":"LSHTM","correspondingAuthor":false,"prefix":"","firstName":"Clemence","middleName":"","lastName":"Leyrat","suffix":""},{"id":490851836,"identity":"9dc7c1f3-6bd3-4e94-a9a5-24e63c5acc4d","order_by":5,"name":"Otavio Ranzani","email":"","orcid":"https://orcid.org/0000-0002-4677-6862","institution":"ISGlobal","correspondingAuthor":false,"prefix":"","firstName":"Otavio","middleName":"","lastName":"Ranzani","suffix":""},{"id":490851837,"identity":"c0b16c06-99ca-46b7-a9f1-d379728d681a","order_by":6,"name":"Mauricio Barreto","email":"","orcid":"","institution":"Universidade Federal de Bahia","correspondingAuthor":false,"prefix":"","firstName":"Mauricio","middleName":"","lastName":"Barreto","suffix":""},{"id":490851838,"identity":"f98995a8-abdf-4882-b386-a4cb0da5a905","order_by":7,"name":"Julia Pescarini","email":"","orcid":"","institution":"LSHTM","correspondingAuthor":false,"prefix":"","firstName":"Julia","middleName":"","lastName":"Pescarini","suffix":""}],"badges":[],"createdAt":"2025-07-15 19:35:13","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7133626/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7133626/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41591-026-04294-w","type":"published","date":"2026-03-19T04:00:00+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":87700026,"identity":"3ff67fb2-51f8-4c28-b104-f2721a8de2bc","added_by":"auto","created_at":"2025-07-28 07:09:47","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":253940,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eSelection process for the (A) matched cohort for diagnosed TB cases and (B) matched cohort for treated TB cases.\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image1.png","url":"https://assets-eu.researchsquare.com/files/rs-7133626/v1/8d55c24e3fc5e31562757ad7.png"},{"id":87700023,"identity":"a75f46e2-50b8-4063-9b4c-e67192ccda08","added_by":"auto","created_at":"2025-07-28 07:09:46","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":72453,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eYearly Incidence rate ratios from natural causes (excluding external causes, HIV and TB deaths) of diagnosed and treated TB cases compared to non-exposed controls. Error bars represent 95% confidence intervals\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image2.png","url":"https://assets-eu.researchsquare.com/files/rs-7133626/v1/b15726e91344f92890690014.png"},{"id":87700022,"identity":"f31af96c-6370-4b57-8157-901995fa7c97","added_by":"auto","created_at":"2025-07-28 07:09:46","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":96173,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEstimated cause-specific risk of death of diagnosed and treated tuberculosis cases compared to non-exposed controls in the first five years. Causes of death defined by ICD-10 codes:\u003c/em\u003e \u003cem\u003eCardiovascular causes (Chapter IX), (ii) Endocrine causes (Chapter IV); (iii) Respiratory System (Chapter X); (iv) Cancer (Chapter II); (v) External Deaths. Error bars represent 95% confidence intervals\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image3.png","url":"https://assets-eu.researchsquare.com/files/rs-7133626/v1/a069d11e400208b94d06c904.png"},{"id":87700025,"identity":"ae459c12-ecbe-4069-bcf6-9fb448a0848c","added_by":"auto","created_at":"2025-07-28 07:09:47","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":108171,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eEstimated risk of death from natural causes (excluding external causes, HIV and TB deaths) of treated TB cases compared to non-exposed controls by subgroup. In Tuberculosis classification, “both” indicates pulmonary plus extrapulmonary. Error bars represent 95% confidence intervals\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image4.png","url":"https://assets-eu.researchsquare.com/files/rs-7133626/v1/b120db3200985d531e93e14e.png"},{"id":105038638,"identity":"60c47887-0c9c-4530-8712-c4ee50c77ed2","added_by":"auto","created_at":"2026-03-20 07:44:07","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1503481,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7133626/v1/23f7b62a-92a2-4a91-9acb-d139b8c99047.pdf"},{"id":87700030,"identity":"df60ea13-8e1e-4736-afe2-c4e03d250510","added_by":"auto","created_at":"2025-07-28 07:09:51","extension":"docx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":764769,"visible":true,"origin":"","legend":"Supplementary Information","description":"","filename":"suplementarynonterminated.docx","url":"https://assets-eu.researchsquare.com/files/rs-7133626/v1/8bd3bdea8a9503f1aec4cfeb.docx"},{"id":87700027,"identity":"977f730b-2e91-4f5d-9874-a3c6d3fd4be7","added_by":"auto","created_at":"2025-07-28 07:09:47","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":109060,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eExtended Figure 1: Estimated risk of death from natural causes (excluding external causes, HIV and TB deaths) of diagnosed TB cases compared to non-exposed controls by subgroup. In Tuberculosis classification, “both” indicates pulmonary plus extrapulmonary. Error bars represent 95% confidence intervals\u003c/em\u003e\u003c/p\u003e","description":"","filename":"image5.png","url":"https://assets-eu.researchsquare.com/files/rs-7133626/v1/9d657a5abe6abf8bf4c2b122.png"}],"financialInterests":"There is \u003cb\u003eNO\u003c/b\u003e Competing Interest.","formattedTitle":"Long-term risk of death after Tuberculosis diagnosis and treatment in Brazil: a nationwide longitudinal study using linked routine data between 2004 and 2018.","fulltext":[{"header":"Introduction","content":"\u003cp\u003eTuberculosis (TB) continues to be one of the deadliest infectious diseases globally. In 2023, approximately 10.8 million individuals developed TB, and 1.25 million died from this disease.\u003csup\u003e1\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThese alarming figures highlight the critical importance of the World Health Organisation\u0026rsquo;s (WHO) End TB Strategy, which aims to reduce TB incidence by 50% and TB mortality by 75% by 2025, relative to 2015 levels. However, progress has been insufficient: by 2023, the global TB incidence had declined by only about 8% and TB mortality by approximately 23%.\u003csup\u003e1\u003c/sup\u003e Addressing this gap requires a comprehensive approach that extends beyond treating active TB, considering its long-term impact on health systems through increased demand for chronic disease services and its role in perpetuating health inequities.\u003c/p\u003e\n\u003cp\u003eWhile TB treatment is effective in curing the disease and significantly decreasing mortality during the active phase,\u003csup\u003e1\u003c/sup\u003e emerging evidence suggests that individuals who survive TB remain at an elevated risk of death long after completing treatment.\u003csup\u003e2\u0026ndash;4\u003c/sup\u003e This ongoing risk may stem from factors such as lasting lung damage, chronic inflammation, coexisting health conditions, and poor social circumstances.\u003csup\u003e2,5,6\u003c/sup\u003e Despite the magnitude of this issue, the long-term burden of mortality following TB remains mostly overlooked in public health strategies; notably, the WHO guidelines contain no recommendations for addressing post-TB conditions.\u003csup\u003e4\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eStudies assessing post-TB mortality have relied exclusively on relative risk measures, without estimating absolute risks, thereby limiting the ability to quantify the excess mortality attributable to TB.\u003csup\u003e7\u0026ndash;10\u003c/sup\u003e In addition, most studies on mortality post-TB to date compared the all-cause mortality to the general population, adjusting only by age and sex, but without properly controlling for socioeconomic variables associated with the risk of tuberculosis and death. \u003csup\u003e7,8\u003c/sup\u003e\u0026nbsp; To overcome these limitations and provide a clearer understanding of TB\u0026rsquo;s lasting impact, a comprehensive evaluation of adverse outcomes post-TB with finer socioeconomic characteristics control is needed. Closing this knowledge gap is critical for improving the understanding of post-TB health trajectories and informing preventive and long-term care strategies for affected individuals.\u003c/p\u003e\n\u003cp\u003eLeveraging nationwide administrative data from the 100 Million Brazilian Cohort, which represents the poorest half of the population of Brazil, this study investigates the risk of death following TB diagnosis and TB treatment completion. Specifically, we (i) compared the risk of death between TB cases after diagnosis and after treatment completion and control participants over time, by natural deaths (defined as deaths excluding TB, HIV, and external causes), all-cause mortality and cause-specific of death (cancer, cardiovascular, etc), and (ii) assessed whether there is a difference in the risk of death by subgroups of sex, age, race, presence of comorbidities and tuberculosis classification.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eWe used data from the 100 Million Brazilian Cohortlinked with nationwide death and tuberculosis registries. The 100 Million Brazilian Cohort is a dynamic cohort comprising over 130 million individuals from the Unified Registry for Social Programs (Cad\u0026Uacute;nico). Cad\u0026Uacute;nico serves as Brazil\u0026apos;s primary tool for identifying and registering low-income families for social welfare programs, thus predominantly capturing individuals from the lower socioeconomic strata of the country. We linked the Cad\u0026Uacute;nico database to Tuberculosis disease records from Jan 1, 2004, to Dec 31, 2018, registered in the National Notifiable Disease Information System (SINAN) and the Mortality Information System (SIM). In Brazil, TB is a mandatory notification disease, i.e., any healthcare professional is legally required to report all suspected and confirmed cases of tuberculosis to the Ministry of Health.\u003c/p\u003e\n\u003cp\u003eThe linkage between tuberculosis registries and the Cad\u0026Uacute;nico had a sensitivity (94\u0026middot;6%) and specificity (93\u0026middot;6%) calculated based on false or true links between the two databases.\u003csup\u003e11\u003c/sup\u003eThe linkage between mortality registries and the Cad\u0026Uacute;nico calculated by year had a sensitivity that ranged between 97\u0026middot;8% and 100\u0026middot;0% and a specificity between 96\u0026middot;6% and 99\u0026middot;9%.\u003csup\u003e11\u003c/sup\u003e From the linked dataset, we built two cohorts: one matching persons with a TB diagnosis to TB-free controls, and another matching participants who had completed TB treatment to TB-free controls.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eExposure and outcomes\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe exposure was an individual\u0026apos;s first SINAN record of tuberculosis between Jan 1, 2004, and Dec 31, 2018 for the diagnosed cohort and an individual\u0026apos;s first SINAN record of treatment completion for tuberculosis between Jan 1, 2004, and Dec 31, 2018 for the treated cohort. \u0026nbsp;In SINAN, patients with treatment completion are those who have two negative smear tests according to national guidelines or who do not have evidence of treatment failure based on clinical or radiological criteria.\u003csup\u003e12\u003c/sup\u003e Unexposed individuals were those tuberculosis-free and alive during the study period.\u003c/p\u003e\n\u003cp\u003eThe primary outcome was deaths by natural causes, defined as any cause of death excluding external causes (International Classification of Diseases, 10\u003csup\u003eth\u003c/sup\u003e edition (ICD-10), Chapter XX) and causes related to Tuberculosis (ICD-10 A15-A19) and HIV (ICD-10 B20-B24). Given the well-established link between HIV and TB,\u003csup\u003e13\u003c/sup\u003e and to prevent overestimation of mortality directly due to active TB itself, excluding deaths directly attributed to TB and HIV allows for a clearer assessment of TB\u0026rsquo;s broader adverse effects, potentially indirect or long-term, on health.\u003c/p\u003e\n\u003cp\u003eSecondary outcomes included all-cause mortality and cause-specific mortality defined by ICD-10 codes from causes with higher proportion of deaths in previous studies,\u003csup\u003e3,5\u003c/sup\u003e specifically (i) Cardiovascular causes (Chapter IX), and blocks: Ischaemic heart diseases (I20-I25), and Cerebrovascular diseases (I60-I69); (ii) Metabolic causes (Chapter IV); (iii) Respiratory System (Chapter X); (iv) Cancer (Chapter II), and blocks: Malignant neoplasms of respiratory and intrathoracic organs (C30-C39), and Malignant neoplasms of digestive organs (C15-C26); and (v) External Deaths (Chapter XX), and blocks: Accidents (V01-X59), and Assaults (X85-Y09).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy population and statistical analysis\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eExposed individuals were exactly matched to an unexposed individual (control-participants not linked to a TB record) on the day of diagnosis of TB (diagnosis cohort) or date of treatment completion (treated cohort). Exact matching was performed without replacement on year of birth (in 5-year bins), sex, race or ethnicity, city of residence, household location, household water supply type, material of the household, year of registration in the Cad\u0026Uacute;nico (in 3-year bins), and household crowding (Details on appendix methods). When multiple controls were available for a single case, one was chosen at random. Matching by these factors provided demonstrable control of bias in a previous study.\u003csup\u003e14\u003c/sup\u003e Controls matched on a given day who acquired TB on a subsequent date became a case and could be matched to a new control. In this case, they could contribute first as controls and later as cases.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe excluded individuals (i) aged 100 years or older at Cad\u0026Uacute;nico registration, (ii) diagnosed with tuberculosis before the Cad\u0026Uacute;nico registration, (iii) with missing data in one of the variables used in the matching (Supplementary Table 1), (iv) data inconsistencies in date of death before diagnosis date or date of death before date of Cad\u0026Uacute;nico registration, (v) people experiencing homelessness due to the impossibility of assessing variables related to the household. For the treated cohort, to remove cases with inconsistent treatment length, we also excluded individuals with treatment completion date less than 138 days after the notification date or more than 2 years after the notification date. We used this cutoff considering the treatment length of 6 to 12 months, as recommended by the Ministry of Health in Brazil, depending on the type of Tuberculosis, the maximum of two years was chosen to allow for delays in the start of treatment after diagnosis.\u003csup\u003e12\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eFor the diagnosed cohort, each matched pair was followed up from the matching date (i.e., date of TB diagnosis for the exposed individual) until the earliest of the following events: death, or Dec 31, 2018 (final data collection date), diagnosis of TB in the unexposed control individual (in this case, both members of the matched pair were censored). For the treated cohort, pairs\u0026rsquo; follow-up started on the day of TB treatment completion and ended on the earliest of the following events: death or Dec 31, 2018 (final data collection date).\u003c/p\u003e\n\u003cp\u003eWe estimated the cumulative incidence function for each outcome using the Aalen-Johansen estimator, considering the competing risk of death from other causes, for example, in the model for natural deaths, all other deaths were considered as competing causes. This estimates the total effect of TB on the cause of interest, capturing both the direct pathway by which TB affects the cause of interest and the indirect effect of TB on the competing causes.\u003csup\u003e15\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe estimated marginal period-specific risks, risk differences (RD), risk ratios (RR), and incidence rate ratios (IRR) comparing the exposed group to the unexposed group for each outcome. The period-specific intervals were demarcated on days 30, 90, 180, 365 and yearly intervals up to 14 years.\u003c/p\u003e\n\u003cp\u003eSubgroup analyses were conducted by sex, age at diagnosis of TB (\u0026lt;18, 18-59 and \u0026ge;60 years), race (white/mixed/black), type of TB (pulmonary, extrapulmonary and extrapulmonary + pulmonary), diagnosis of HIV, and diagnosis of diabetes mellitus (DM).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe used non-parametric bootstrapping (resampling only matched pairs) with 500 iterations to calculate percentile-based 95% confidence intervals for all measures. This procedure has been proved valid when conducting matching without replacement.\u003csup\u003e16\u003c/sup\u003e All analyses were conducted using R, with the package survival.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSensitivity analysis - Household contacts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo further understand how much of the excess burden could be due to shared social factors and estimate the residual confounding by unmeasured variables, we conducted an additional cohort matching TB contacts to individuals free of TB (i.e., without TB diagnosis and not a TB contact). TB household contacts were eligible if they resided in the same household as a TB case and were alive at the moment of the first TB case diagnosis, i.e., we did not include persons born after the first TB case was diagnosed in the household. The same matching variables used in the main analysis was employed. A TB contact who becomes a TB case afterwards was censored on the day of TB diagnosis (pair censoring). The time zero in this cohort was the date of the first TB case in the household. Each matched pair were followed up from the matching date until the earliest of the following events: death, December 31, 2018 or diagnosis of TB (in this case, both members of the matched pair were censored).\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eWe also evaluated the comparison directly between the first TB case and the household contacts, matching only on year of birth (in 5-year bins) and sex. The time zero in this analysis was the date of the first diagnosis of a TB case in the household. Each matched pair were followed up from the matching date until the earliest of the following events: death, December 31, 2018, or diagnosis of TB in the unexposed control individual (in this case, both members of the matched pair were censored)\u003c/p\u003e\n\u003cp\u003eWe calculated RD, IRR, and RR, as well as 95% confidence intervals, similarly to the main analysis.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eProtocol\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe study design and statistical analysis plan were specified in advance of analysing the data and are described in a publicly available protocol (https://github.com/csthiago/tuberculosis_death). \u0026nbsp;\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003eAmong the 95,789,738 eligible people who entered the 100 Million Brazilian Cohort between 2004 and 2018, 209,598 had TB. A total of 185,921 (88.4%) diagnosed TB cases were matched with an unexposed individual. Among the 125,989 treated TB cases, 111,871 (88.8%) were matched with an unexposed individual. (Figure 1 and Table 1)\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 1: Baseline characteristics of diagnosed and treated TB cases.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"602\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCharacteristic\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 185,921\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosed\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003eN = 185,921\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl\u003cbr\u003e\u0026nbsp;N = 111,871\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreated\u0026nbsp;\u003cbr\u003e\u0026nbsp;N = 111,871\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), at CadUnico registration \u0026ndash; median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e25 (15 - 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (15 - 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (15 - 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25 (15 - 39)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge (years), at index date \u0026ndash; median (IQR)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e33 (24 \u0026ndash; 47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e33 (24 \u0026ndash; 47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e33 (25 \u0026ndash; 47)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e33 (25 \u0026ndash; 47)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eAge group, at index date\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026lt;18\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e12,520 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12,520 (6.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e6,963 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e6,963 (6.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e18-59\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e154,081 (82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e154,081 (82.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e92,998 (83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e92,998 (83.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u0026ge;60\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19,320 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e19,320 (10.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e11,910 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e11,910 (10.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eSex\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eFemale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e68,470 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e68,470 (36.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e42,829 (38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e42,829 (38.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMale\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e117,451 (63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e117,451 (63.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e69,042 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e69,042 (61.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRace/Ethnicity\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eWhite\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e49,599 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e49,599 (26.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e28,695 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e28,695 (25.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eBlack\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21,183 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e21,183 (11.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12,116 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12,116 (10.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMixed\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e113,311 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e113,311 (60.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e69,769 (62.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e69,769 (62.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eAsian\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e264 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e264 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e156 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e156 (0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eIndigenous\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,564 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,564 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,135 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,135 (1.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eEducation Level\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNo school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24,436 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e24,436 (13.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e14,806 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e14,806 (13.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNursery\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,503 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,503 (0.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e951 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e951 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eInfant School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e1,720 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,720 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,033 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,033 (0.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eElementary School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e70,454 (37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e70,454 (37.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e41,865 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e41,865 (37.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMiddle school\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e65,170 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e65,170 (35.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e38,951 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e38,951 (34.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHigh School\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21,962 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e21,962 (11.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e13,833 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e13,833 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eHigher education\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e676 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e676 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e432 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e432 (0.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eOvercrowded*\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e27,968 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e27,968 (15.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e16,240 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e16,240 (14.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eWater system\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePublic system\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e147,196 (79.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e147,196 (79.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e87,296 (78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e87,296 (78.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eWater well\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e26,747 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e26,747 (14.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e17,381 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e17,381 (15.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11,978 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e11,978 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7,194 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7,194 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eLocation of household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eUrban\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e164,096 (88.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e164,096 (88.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e97,496 (87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e97,496 (87.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eRural\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21,825 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e21,825 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e14,375 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e14,375 (12.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eMaterial of household\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMasonry/brick\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e147,173 (79.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e147,173 (79.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e87,086 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e87,086 (77.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCoated Taipa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,929 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,929 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,926 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e1,926 (1.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eUncoated Taipa\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e2,930 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,930 (1.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,003 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,003 (1.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eWood\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e24,712 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e24,712 (13.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e15,674 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e15,674 (14.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eOther\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e8,177 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e8,177 (4.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5,182 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5,182 (4.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eYear of CadUnico registration\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2004-2006\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e105,041 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e105,041 (56.5%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e63,757 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e63,757 (57.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2007-2009\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e41,269 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e41,269 (22.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25,078 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e25,078 (22.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2010-2012\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e23,137 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e23,137 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e13,764 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e13,764 (12.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2013-2015\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e11,417 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e11,417 (6.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7,138 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7,138 (6.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e2016-2018\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e5,057 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5,057 (2.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,134 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,134 (1.9%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiabetes\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e9,768 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5,908 (5.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eHIV\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e15,286 (8.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e-\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e6,031 (5.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eGeographic region\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNorth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e21,560 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e21,560 (11.6%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e14,525 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e14,525 (13.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eNortheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e61,861 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e61,861 (33.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e39,603 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e39,603 (35.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSoutheast\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e75,008 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e75,008 (40.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e40,272 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e40,272 (36.0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eSouth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e19,828 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e19,828 (10.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12,619 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e12,619 (11.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eCentral west\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e7,664 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e7,664 (4.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e4,852 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e4,852 (4.3%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculosis classification\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003ePulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e157,540 (84.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e96,328 (86.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eExtrapulmonary\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e23,134 (12.4%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e13,110 (11.7%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eBoth\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e5,178 (2.8%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e2,433 (2.2%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 142px;\"\u003e\n \u003cp\u003eMissing\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 108px;\"\u003e\u003cbr\u003e\u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e69 (\u0026lt;0.1%)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e\u0026nbsp;\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 117px;\"\u003e\n \u003cp\u003e0 (0%)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cem\u003e* Overcrowded was defined as a household density (residents per room) greater than 2. IQR = Interquartile range\u003c/em\u003e\u003c/p\u003e\n\u003cp\u003eThe unmatched TB exposed (23,667 [11.6%] of cases diagnosed with TB, and 14,118 [11.4%] of those treated for TB) were older, had a higher proportion of minorities (Asian/Indigenous), resided more in rural areas, and had worse socioeconomic conditions (higher proportion of overcrowded houses, with worse access to water). (Supplementary Table 2)\u003c/p\u003e\n\u003cp\u003eThe median follow-up period in the diagnosed cohort was 4.5 years (interquartile range \u0026ndash; IQR 1.9 to 7.7) for the control group and 3.9 years (IQR 1.5 to 7.2) for the diagnosed group. For the treated cohort, the median follow-up was 4.0 years (IQR 1.8 to 7.2) for the control group and 3.8 years (IQR 1.7 to 7.0) for the treated group. The median time to treatment completion was 6.6 months (IQR, 6.2 to 7.7).\u003c/p\u003e\n\u003cp\u003eIn the diagnosed cohort, 29,226 deaths occurred during follow-up, 23,900 (12.6%) in the TB-diagnosed group and 5326 (2.2%) in the control group. (Supplementary Figure 1) In the treated cohort, 10,631 deaths occurred, with 7,677 (6.6%) in the TB-treated group and 2,954 (2.3%) in the control group. (Supplementary Figure 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eNatural deaths (excluding external, HIV and TB causes)\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cem\u003eTable 2: Estimated risk of death from natural causes (excluding external causes, HIV and TB deaths) of diagnosed and treated tuberculosis cases compared to non-exposed controls.\u003c/em\u003e\u003c/p\u003e\n\u003ctable border=\"0\" cellspacing=\"0\" cellpadding=\"0\" width=\"100%\"\u003e\n \u003ctbody\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 23px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eCumulative Number of events\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd colspan=\"2\" valign=\"bottom\" style=\"width: 33px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk per 100 000 people (95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Difference per 100 000 people\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd rowspan=\"2\" valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eRisk Ratio\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003cp\u003e\u003cstrong\u003e(95% CI)\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e\u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculosis group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eControl group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTuberculosis group\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eDiagnosed cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e54\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1524\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e29.2 (22.2 to 37.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e822.4 (782.8 to 860.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e793.2 (753.7 to 832.9)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e28.18 (21.68 to 37.04)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e673\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e4636\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e389.2 (360.5 to 418.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e2600.9 (2526.9 to 2667.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2211.7 (2139.2 to 2286.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e6.68 (6.20 to 7.26)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2746\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e9518\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2116.3 (2033.0 to 2197.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e6562.7 (6433.3 to 6699.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e4446.4 (4277.5 to 4602.6)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.10 (2.97 to 3.24)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e3892\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e11847\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4327.3 (4166.6 to 4484.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e10945.4 (10719.2 to 11176.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e6618.1 (6344.0 to 6888.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.53 (2.43 to 2.64)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e14 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e4060\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e12151\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6451.7 (5886.5 to 6975.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e13947.2 (13413.3 to 14553.7)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e7495.5 (6652.7 to 8257.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.16 (1.96 to 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd colspan=\"7\" valign=\"bottom\" style=\"width: 100px;\"\u003e\n \u003cp\u003e\u003cstrong\u003eTreated cohort\u003c/strong\u003e\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e30 days\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e39\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e106\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e35.1 (24.3 to 46.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e95.3 (78.2 to 112.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e60.2 (39.1 to 80.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.72 (1.94 to 3.92)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e1 year\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e380\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1206\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e364.7 (327.1 to 400.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e1158.2 (1090.1 to 1225.0)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e793.5 (719.7 to 870.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3.18 (2.83 to 3.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e5 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e1578\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e3950\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e2111.8 (2005.8 to 2213.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e5093.1 (4943.8 to 5257.1)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e2981.3 (2800.1 to 3177.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.41 (2.29 to 2.55)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e10 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2181\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5215\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e4283.7 (4080.3 to 4510.5)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e9607.3 (9290.8 to 9930.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e5323.6 (4961.2 to 5662.3)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e2.24 (2.12 to 2.37)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003ctr\u003e\n \u003ctd valign=\"bottom\" style=\"width: 6px;\"\u003e\n \u003cp\u003e14 years\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 10px;\"\u003e\n \u003cp\u003e2258\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e5336\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 15px;\"\u003e\n \u003cp\u003e6655.3 (5826.3 to 7501.2)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 17px;\"\u003e\n \u003cp\u003e11756.4 (11220.3 to 12389.8)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 21px;\"\u003e\n \u003cp\u003e5101.0 (4105.4 to 6128.4)\u003c/p\u003e\n \u003c/td\u003e\n \u003ctd valign=\"bottom\" style=\"width: 13px;\"\u003e\n \u003cp\u003e1.77 (1.55 to 2.03)\u003c/p\u003e\n \u003c/td\u003e\n \u003c/tr\u003e\n \u003c/tbody\u003e\n\u003c/table\u003e\n\u003cp\u003e\u003cbr\u003e\u003c/p\u003e\n\u003cp\u003eIn the diagnosed cohort, the risk of death within 30 days of diagnosis was markedly higher among people diagnosed with TB compared to controls (RR: 28.18 - 95% CI 21.68 to 37.04), decreasing to 6.68 (6.20 to 7.26) at the end of the first year and 2.16 (1.96 to 2.37) at the end of 14 years. (Table 2 and Supplementary Table 3) The yearly estimates (IRR) showed values close to 2.4 (ranging from 1.99 to 3.22) between 2 and 10 years. (Figure 2 and Supplementary Table 3)\u003c/p\u003e\n\u003cp\u003eIn the treated cohort, the risk of death among people treated for TB compared to controls was more stable over time, with an RR of 2.72 (1.94 to 3.92) within 30 days of treatment completion, 3.33 at 90 days (2.69 to 4.27), and of 1.77 (1.55 to 2.03) at the end of the 14 years of follow-up. (Table 2) Similarly to the diagnosed cohort, the yearly IRRs were close to 2.2 (ranging from 2.03 to 2.62) between 2 and 10 years after treatment completion. (Figure 2 and Supplementary Table 4)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eAll-cause mortality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn the diagnosed cohort, the all-cause mortality RR comparing the TB group with the control group was markedly higher in the first month after diagnosis (RR: 58.10; 47.05 to 73.04). The RR decreased to 12.66 at one year and 2.89 (2.69 to 3.11) at 14 years. At 14 years, the risk difference per 100,000 persons was 15,167.8 (14343.2 to 16015.5) (Supplementary Table 3 and Supplementary Figure 1)\u003c/p\u003e\n\u003cp\u003eFor the treated cohort, the RR in the first month after treatment completion was 3.22 (2.41 to 4.31), decreasing to 2.01 (1.81 to 2.28) at 14 years, with a risk difference of 8,206.6 (7,131.3 to 9,453.2). (Supplementary Table 4 and Supplementary Figure 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e\u003cem\u003eCause-specific mortality\u003c/em\u003e\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe cause-specific analysis showed a similar pattern to the analysis of natural death. In the diagnosed cohort, the highest RRs were seen within 30 days of the diagnosis, which decreased over time. \u0026nbsp;(Supplementary Tables 3 and 6)\u003c/p\u003e\n\u003cp\u003eIn the treated cohort, the highest RR for cancer and respiratory deaths were seen within 90 days of treatment completion. (Supplementary Tables 4 and 7)\u003c/p\u003e\n\u003cp\u003eThe treated cohort showed lower RRs for all cause-specific deaths, except for external causes, which presented similar values across both cohorts (ranging from 1.72 to 2.47 in the diagnosed cohort and 1.74 to 2.10 in the treated cohort). (Figure 3 and Supplementary Tables 3 and 4)\u003c/p\u003e\n\u003cp\u003eNotably, within the specific cancer types, we found an increased risk for cancer of the digestive organs in both cohorts; the RR at 14 years were 1.97 (1.55 to 2.57) in the diagnosed cohort and 1.98 (1.40 to 2.89) in the treated cohort. (Supplementary Tables 6 and 7)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSubgroup analyses\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe subgroup analysis for the risk of death due to natural causes exhibited the same pattern as the main analysis, with the RR decreasing over time. In the diagnosed cohort, females had slightly higher RRs than males throughout the entire follow-up period. Participants aged 18-59 years had a higher RR than those aged 60 years or older (RR at 14 years: 2.71 (2.35 to 3.14) and 1.43 (1.28 to 1.60), respectively). However, on the RD scale, participants aged 60 years or older had nearly three times the risk of death compared to those aged 18-59 years (RD at 14 years for 18-59 years: 7,047.4 (6,165.6 to 7,858.2); RD at 14 years for \u0026ge; 60 years: 14,900.6 (10,525.5 to 19,344.6)). Participants with pulmonary tuberculosis exhibited lower RR and RD than those with extrapulmonary or both extrapulmonary/pulmonary tuberculosis. (Extended Data Figure 1 and Supplementary Table 9)\u003c/p\u003e\n\u003cp\u003eIn the treated cohort, males and females had similar RR. The age group pattern was maintained, with the 18-59 age group showing a higher RR and lower RD than those \u0026ge;60 years. (Figure 4 and Supplementary Tables 10)\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eIn both cohorts, we did not observe any substantial differences in the temporal patterns or baseline risk magnitude by race/ethnicity or TB classification. Participants with diabetes mellitus had a similar RR to those with HIV in the diagnosed cohort and a higher RR in the treated cohort. Notably, the absolute excess mortality (risk difference) in TB patients with DM was substantially larger, exceeding double the RD observed in TB patients with HIV. (Figure 4, Extended Figure 1, Supplementary Tables 9 and 10)\u003c/p\u003e\n\u003cp\u003eWe also evaluated the cause-specific mortality by sex. Both cohorts (diagnosed and treated) showed similar RR for cardiovascular, respiratory and endocrine causes for males and females, while the RR for cancer deaths was slightly higher in males than in females. (Supplementary Tables 11 - 14)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHousehold contacts\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eA total of 448,825 household contacts were matched to unexposed control persons. The risk of death among the TB household contacts was elevated when compared to their unexposed counterparts. Within 5 years, the RR for all-cause mortality was 1.19 (RR 1.14 to 1.23), for natural deaths was 1.11 (1.06 to 1.16), and for external deaths it was 1.29 (1.20 to 1.39). At 14 years, the RR for all-cause mortality was 1.09 (1.00 to 1.19), for natural deaths 1.04 (0.93 to 1.15), and for external causes of death 1.16 (1.00 to 1.34). (Supplementary Tables 5 and 8).\u003c/p\u003e\n\u003cp\u003eA total of 12,948 (6.2%) TB cases were matched to household contacts. The median age of the pairs was 14 years (IQR 11 to 18), and 8,980 pairs (69.4%) were male. The direct comparison between TB cases and household contacts for natural deaths showed similar results to the main analysis, with an RR at 1, 5 and 10 years of 11.15 (6.49 to 23.10), 3.97 (2.94 to 5.70) and 2.52 (1.83 to 3.66), respectively. For external causes of death, the RR at 1, 5 and 10 years were 2.29 (1.48 to 4.09), 1.66 (1.29 to 2.12) and 1.40 (1.06 to 1.84), respectively.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this large, population-based cohort study conducted in Brazil, we found a significantly elevated risk of mortality among individuals diagnosed and treated for TB compared to TB-free participants with similar socioeconomic characteristics. Over 14 years of follow-up, diagnosed TB patients had 15,168 more deaths per 100,000 persons compared to TB-free participants. After treatment, we observed a decrease in the risk difference, but it remained substantial at 8,206 more deaths per 100,000 persons. Our findings reveal that individuals successfully treated for TB still experience excess mortality across multiple organ systems and causes, highlighting TB\u0026rsquo;s lasting impact on overall health.\u003c/p\u003e\n\u003cp\u003ePrevious studies have also evaluated mortality following TB. \u003csup\u003e5,7\u0026ndash;10,17,18\u003c/sup\u003e\u0026nbsp; \u0026nbsp;However, our study provides finer control over socioeconomic characteristics, particularly for having detailed information for the TB-free participants. Controlling for socioeconomic variables is extremely important when evaluating the residual burden of TB, as the heightened vulnerability of individuals who develop TB also contributes to the increased risk of mortality compared to the general population, partly due to poverty.\u003csup\u003e19\u003c/sup\u003e We have also conducted a comprehensive evaluation of mortality risks using a robust methodology, which includes exact matching of multiple characteristics within a competing risks framework for individuals with a TB diagnosis and those who have completed TB treatment. Second, we have presented both absolute and relative measures for risks and the rate ratios by year of follow-up in all comparisons. These measures complement one another, offering a thorough view of the TB burden. Third, we have assessed the risks after confirmed treatment completion, with clinical or microbiological confirmation of treatment success, rather than defining the post-TB period as 12 months following diagnosis, as in previous studies.\u003csup\u003e7,8\u003c/sup\u003e Relying solely on time after diagnosis can group individuals who abandoned treatment, experienced treatment failure, who are classified as drug-resistant TB, or were lost to follow-up with those who completed treatment, likely biasing the results.\u003csup\u003e7\u003c/sup\u003e In our study, half of the patients completed their treatment at 199 days post-diagnosis, with most finishing it before 8 months. Finally, we used as the main outcome mortality for natural causes, but also excluding TB and HIV related deaths. The curated outcome definition and use of cases with proof of treatment completion allow us to quantify the residual burden of TB with greater accuracy.\u003c/p\u003e\n\u003cp\u003eOur findings showed that diagnosed and treated TB cases were associated with a significant increase in mortality across a broad range of causes, including respiratory, cardiovascular, endocrine and cancer. Our results complement previous evidence showing that TB is associated with increased risk of respiratory mortality, mainly due to direct lung damage caused by TB, increasing the risk of recurrent pneumonia and chronic obstructive pulmonary disease, bronchiectasis and other specific infections, such as aspergillomas.\u003csup\u003e2,6\u003c/sup\u003e The sustained increased risk of post-TB cases dying from cardiovascular diseases for more than a decade complements previous epidemiological studies and may be driven by\u0026nbsp;persistent systemic inflammation and chronic immune activation,\u003csup\u003e20,21\u003c/sup\u003e potentially accelerating atherogenesis through mechanisms such as inflammaging or sustained endothelial dysfunction.\u003csup\u003e20,21\u003c/sup\u003e The prolonged immune dysregulation, characterised by elevated levels of pro-inflammatory cytokines (such as TNF-alpha and IL-6), can accelerate atherosclerosis, thereby increasing cardiovascular risk.\u003csup\u003e20\u0026ndash;22\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eAdditionally, endocrine-related deaths were notably increased, potentially reflecting a bidirectional relationship between diabetes mellitus and TB.\u003csup\u003e23,24\u003c/sup\u003e Diabetes can predispose individuals to TB through impaired immune responses, while chronic inflammation from TB can exacerbate insulin resistance and metabolic dysregulation, thereby increasing the risk of diabetes-related mortality.\u003csup\u003e24,25\u003c/sup\u003e Shared inflammatory mechanisms and dysregulated immune responses likely underpin the mutual exacerbation observed between these two conditions.\u003csup\u003e24,26\u003c/sup\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eOur findings showed that TB is associated with an increased risk of deaths from cancer, including cancer in the digestive organs. This finding is consistent with a growing body of evidence; notably, a prior meta-analysis of 11 studies showed an elevated risk of cancer in TB patients for more than five years after diagnosis.\u003csup\u003e27\u003c/sup\u003e Although the mechanisms underlying this association are not fully understood, several hypotheses have been proposed. The chronic systemic inflammation caused by TB can promote carcinogenesis through the promotion of reactive oxygen species and DNA damage,\u003csup\u003e28\u0026ndash;30\u003c/sup\u003e \u003csup\u003e27\u0026ndash;2928\u0026ndash;30\u003c/sup\u003ewhile lasting immune dysregulation, leading to the exhaustion of T-cells and NK cells, may compromise anti-tumour mechanisms.\u0026nbsp;\u003csup\u003e30\u0026ndash;32\u003c/sup\u003e Furthermore, residual lung fibrosis can establish a pro-tumorigenic microenvironment.\u003csup\u003e28,30\u003c/sup\u003e Last, the association may be partially explained by a higher prevalence of shared lifestyle risk factors for cancer and TB, such as smoking and alcohol use.\u003csup\u003e9,33\u0026ndash;35\u003c/sup\u003e Our work extends this body of evidence by providing detailed, year-by-year risk estimates, highlighting the significant long-term outcomes of TB and supporting the need for continued patient care for tuberculosis-related sequelae after successful treatment.\u003c/p\u003e\n\u003cp\u003eWe also observed an increased risk of deaths due to external causes, with similar magnitudes in the diagnosed and treated TB cases. While we cannot dismiss the possibility that this finding is due to residual confounding, as there is no biological plausibility for this relationship, it may also reflect the social stigma experienced by tuberculosis patients. This stigma can lead to social isolation, limited economic opportunities, and potentially worsen underlying or new-onset mental health issues such as depression or anxiety, which, in turn, could promote riskier behaviours behaviour.\u003csup\u003e36\u0026ndash;38\u003c/sup\u003e However, there is also the possibility that poor mental health or living in contexts of violence could increase the chances of TB reactivation\u003csup\u003e39\u003c/sup\u003e and further lead to higher external-causes related mortality after TB diagnosis or treatment. However, similarities in the RR between the treated and diagnosed cohorts reinforce the plausibility of these increased risks being due to a societal rather than a biological phenomenon. Our direct comparison of TB cases to household contacts with the same sex and similar age also showed elevated risk of deaths from external causes in the TB group. This indicates that, even under similar socioeconomic conditions, TB patients experienced an increased risk of death from external causes, while some of this association can be related to residual confounding, it is unlikely to account for all of it. In this scenario, increased awareness about TB stigma and interventions to deal with it, such as TB support groups, training of healthcare workers to provide non-judgmental care, and community-wide educational campaigns to dispel myths and misinformation about TB can improve the lives of TB survivors.\u003csup\u003e40\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eIn our study, we observed a slight increase in the risk of death among household tuberculosis contacts compared to unexposed individuals, suggesting some degree of residual confounding likely due to heightened social vulnerability in households with a tuberculosis case. However, the excess risk of death among patients diagnosed with, and even those treated for, tuberculosis far exceeds the risk found among household contacts. This suggests that while there are indeed connections between poverty and tuberculosis,\u003csup\u003e19\u003c/sup\u003e the increased mortality after tuberculosis treatment cannot be attributed solely to poverty, as indicated in previous studies.\u003csup\u003e3\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eOur study has several limitations. First, like any observational study, it is susceptible to residual confounding. We also could not account for potential time-varying confounders after the index date, such as loss or decrease of family income. Our attempt to quantify this issue involved conducting an additional analysis to evaluate mortality among household contacts, using the same approach as the main analysis, since we do not expect a strong causal link between being a TB contact and an increased risk of death. This analysis revealed a slightly elevated risk of death in this group. However, the excess risk of death among patients diagnosed with and even those treated for tuberculosis far exceeds the risk found among household contacts. Additionally, our analysis is restricted to the poorer half of Brazil. While this may limit generalisability to wealthier populations, it likely enhances control for confounding due to socioeconomic characteristics by studying a more homogenous group and focuses on a highly vulnerable population where the impact of TB is often most severe. Nevertheless, even in this large cohort, we were unable to match all participants, with the unmatched group exhibiting worse socioeconomic conditions than the matched sample. Our decision to match to increase internal generalisability, providing adjustment by design without making any assumptions on the exposure or outcome model. However, even with a higher percentage of matched cases (\u0026gt;88%), the matching may lead to an underestimation of TB effects, considering that the unmatched group had poorer socioeconomic conditions, and likely a higher baseline mortality risk. The presence of unmatched cases also changes the estimand of the average exposure effect in the exposed to average exposure effect in the matched sample.\u003csup\u003e41\u003c/sup\u003e\u003c/p\u003e\n\u003cp\u003eSecond, our results may be subject to reverse causality, particularly for cancer and endocrine-related mortality. Given that active malignancies and diabetes are known risk factors for TB and that we were unable to adjust for pre-exposure comorbidity, it is possible that an undiagnosed underlying condition precipitated the onset of active TB in some individuals.\u003csup\u003e6,25\u003c/sup\u003e In such cases, TB would be a marker of underlying vulnerability or an exacerbating factor, rather than the primary cause of developing and dying from cancer or endocrine diseases. While our long-term follow-up and analysis of the treated cohort mitigate this concern for deaths occurring many years post-diagnosis, the possibility cannot be fully excluded, especially for mortality observed in the early follow-up period. Third, comorbidity data on HIV and DM were available only for the TB group. Consequently, our subgroup analyses estimate the joint effect of TB plus the comorbidity, rather than the effect of TB in isolation. Fourth, the low number of identified drug-resistant TB cases (n = 945; 0.5%) precluded subgroup analysis. Lastly, after 11 years of follow-up, we had a relatively small number of participants at risk, resulting in a limited number of events and imprecise estimates by risk period. This issue also occurred in the subgroup analysis, requiring caution when interpreting the point estimates from those analyses.\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eA fundamental limitation of the global tuberculosis response has long been its focus solely on diagnosing and curing active disease. For decades, WHO guidelines have appropriately emphasised the diagnosis and bacteriological cure of active disease, considering this a complete return to health, while overlooking the substantial burden of post-TB complications. The issue of neglecting the long-term health of survivors will become more evident due to ongoing global reductions in TB funding,\u003csup\u003e42\u003c/sup\u003e which threaten to reverse progress towards TB control and elimination, primarily impacting low- and middle-income countries. Our findings strongly support the integration of long-term clinical follow-up into routine TB care. Integrating post-TB assessments, such as lung function testing, cardiovascular risk screening, and cancer surveillance, into national guidelines for post-TB management is essential. Such measures will enhance clinician awareness of post-TB complications, ensure timely management, and direct resources towards truly comprehensive, patient-centred care.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eContributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eT.C-S, E.P. and J.P. conceived the idea for the study. All authors contributed to the study design, with J.P. and T.C.-S. drafting the statistical analysis plan. T.C-S. conducted the statistical analysis. J.P. and O.R. oversaw the analysis. M.L.B and M.S acquired the data. T.C.S. drafted the manuscript, with assistance from J.P. All authors critically revised the manuscript and approved the final version for submission.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003e\u003cem\u003eGlobal Tuberculosis Report 2024\u003c/em\u003e. (World Health Organization, Geneva, 2024).\u003c/li\u003e\n\u003cli\u003eAllwood, B. 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Matching Methods for Confounder Adjustment: An Addition to the Epidemiologist\u0026rsquo;s Toolbox. \u003cem\u003eEpidemiologic Reviews\u003c/em\u003e \u003cstrong\u003e43\u003c/strong\u003e, 118\u0026ndash;129 (2022).\u003c/li\u003e\n\u003cli\u003eFunding cuts impact access to TB services endangering millions of lives. https://www.who.int/news/item/05-03-2025-funding-cuts-to-tuberculosis-programmes-endanger-millions-of-lives.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"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":"nature-portfolio","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"","title":"Nature Portfolio","twitterHandle":"","acdcEnabled":false,"dfaEnabled":false,"editorialSystem":"ejp","reportingPortfolio":"","inReviewEnabled":true,"inReviewRevisionsEnabled":false},"keywords":"","lastPublishedDoi":"10.21203/rs.3.rs-7133626/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7133626/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Tuberculosis (TB) remains a major societal burden, yet data on long-term mortality following diagnosis and treatment are limited. We conducted a nationwide Brazilian cohort study using linked data (2004-2018) to quantify long-term mortality (up to 14 years) following TB. We matched: (i)individuals diagnosed with TB or (ii)individuals who had completed TB treatment to TB-free individuals. We used competing risk methods to analyse natural causes (i.e., defined as deaths excluding TB, HIV, and external causes) and cause-specific mortality. In the diagnosed cohort (185,921 pairs), the risk of 14-year natural cause mortality was significantly higher (Risk Ratio [RR]=2.16, 95%CI=1.96-2.37); RRs were significantly elevated for deaths due to cancer, cardiovascular, endocrine, respiratory, and external causes. The treated cohort (111,871 pairs) showed elevated natural cause mortality risk (RR=1.77,1.55-2.03), with similarly increased RRs across specific causes. We showed that TB survivors, even after treatment, faced a significantly elevated, prolonged risk of death from various causes up to 14 years later. This emphasises the need for long-term monitoring to reduce the burden of TB.","manuscriptTitle":"Long-term risk of death after Tuberculosis diagnosis and treatment in Brazil: a nationwide longitudinal study using linked routine data between 2004 and 2018.","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-28 07:09:10","doi":"10.21203/rs.3.rs-7133626/v1","editorialEvents":[],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"nature-medicine","isNatureJournal":true,"hasQc":false,"allowDirectSubmit":false,"externalIdentity":"nm","sideBox":"Learn more about [Nature Medicine](http://www.nature.com/nm/)","snPcode":"","submissionUrl":"","title":"Nature Medicine","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"ejp","reportingPortfolio":"Nature Research","inReviewEnabled":true,"inReviewRevisionsEnabled":false}}],"origin":"","ownerIdentity":"b6f04205-8af3-42b6-a502-a159a1f8fdf0","owner":[],"postedDate":"July 28th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":52121852,"name":"Health sciences/Medical research/Epidemiology"},{"id":52121853,"name":"Health sciences/Health care/Public health/Epidemiology"}],"tags":[],"updatedAt":"2026-03-20T07:37:21+00:00","versionOfRecord":{"articleIdentity":"rs-7133626","link":"https://doi.org/10.1038/s41591-026-04294-w","journal":{"identity":"nature-medicine","isVorOnly":false,"title":"Nature Medicine"},"publishedOn":"2026-03-19 04:00:00","publishedOnDateReadable":"March 19th, 2026"},"versionCreatedAt":"2025-07-28 07:09:10","video":"","vorDoi":"10.1038/s41591-026-04294-w","vorDoiUrl":"https://doi.org/10.1038/s41591-026-04294-w","workflowStages":[]},"version":"v1","identity":"rs-7133626","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7133626","identity":"rs-7133626","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}