The authors declare that they have no competing interests.
‡ These authors also contributed equally to this work.
This study aimed to analyze the factors associated with likely TB deaths, likely TB-related deaths and deaths from other causes. Understanding the factors associated with mortality could help the strategy to End TB, especially the goal of reducing TB deaths by 95% between 2015 and 2035.
A retrospective, population-based cohort study of the causes of death was performed using a competing risk model in patients receiving treatment for TB. Patients had started TB treatment in Brazil 2008–2013 with any death certificates dated in the same period. We used three categories of deaths, according to ICD-10 codes: i) probable TB deaths; ii) TB-related deaths; iii) deaths from other causes.
In this cohort, 39,997 individuals (14.1%) died, out of a total of 283,508 individuals. Of these, 8,936 were probable TB deaths (22.4%) and 3,365 TB-related deaths (8.4%), illustrating high mortality rates. 27,696 deaths (69.2%) were from other causes. From our analysis, factors strongly associated with probable TB deaths were male gender (sHR = 1.33, 95% CI: 1.26–1.40), age over 60 years (sHR = 9.29, 95% CI: 8.15–10.60), illiterate schooling (sHR = 2.33, 95% CI: 2.09–2.59), black (sHR = 1.33, 95% CI: 1.26–1.40) and brown (sHR = 13, 95% CI: 1.07–1.19) color/race, from the Southern region (sHR = 1.19, 95% CI: 1.10–1.28), clinical mixed forms (sHR = 1.91, 95% CI: 1.73–2.11) and alcoholism (sHR = 1.90, 95% CI: 1.81–2.00). Also, HIV positive serology was strongly associated with probable TB deaths (sHR = 62.78; 95% CI: 55.01–71.63).
In conclusion, specific strategies for active surveillance and early case detection can reduce mortality among patients with tuberculosis, leading to more timely detection and treatment.
Although there is highly effective treatment, tuberculosis (TB) remains the ninth leading cause of death in the world and the leading cause of death among infectious diseases. In 2018, 1.2 million deaths from TB among seronegative individuals and 251,000 deaths among HIV-positive people were estimated [
Identifying patients at risk of death during TB treatment should be a priority for health surveillance: it is essential for assessing programmatic needs and has the potential to contribute to the targeting of interventions and improvement of treatment monitoring, thus contributing to the End TB Strategy and to reduce TB mortality by 95% [
Several studies have suggested that factors associated with survival in cases of TB are related to the presence of specific comorbidities, including HIV/AIDS and diabetes mellitus [
Therefore, this study aimed to analyze the factors associated with probable TB deaths, TB-related deaths and deaths from other causes, using competing risk model in a cohort of patients receiving treatment for TB from 2008 to 2013, across Brazil.
This is a retrospective cohort study including all patients who initiated treatment for TB in Brazil from January 1, 2008, to December 31, 2013.
Two national data sources were used: The Notifiable Diseases Information System (SINAN, acronym in Portuguese) and the Mortality Information System (SIM). The SINAN database contained cases reported between January 1, 2008, and December 31, 2011, and was extracted on 09/20/2013. The SIM database contained deaths reported between January 1, 2008, and December 31, 2013, and was extracted on 04/01/2016. Both databases had nominal information.
The study population consisted of all new cases of tuberculosis reported in SINAN that began treatment in the period from January 1, 2008 (date of first entry) until December 31, 2011 (date of last entry). Through record linkage of data with the SIM database, the cases were followed up until the occurrence of deaths or until December 31, 2013, when administrative censoring was considered (end of the follow-up period).
In order to guarantee the quality of information on TB treatment episodes, an automatic monitoring method adopted by Bierrenbach et al. [
This study was approved by the Research Ethics Committee of the National School of Public Health/FIOCRUZ, under the protocol: CAAE: 14643713.0.0000.5240. The nominal identifiers were removed from the database after the data linkage, ensuring the privacy of the subjects involved in the study.
No informed consent was obtained since only the secondary notification data were analyzed.
SINAN, linked to SIM has been used to evaluate tuberculosis outcomes [
In Brazil, the National Tuberculosis Control Program (NTCP) uses the following definition to case of tuberculosis: any individual with a diagnosis confirmed by smear microscopy or culture, and one in which the physician, based on clinical-epidemiological data and the result of complementary tests, makes the diagnosis of tuberculosis [
The outcome categories of the study were created for the causes of death according to ICD-10 codes: i) probable TB deaths, those that had underlying cause with codes A15 to A19 of ICD-10, ‘probable’ is used as definition relies on death certificate and not autopsy; ii) TB-related deaths, those in which there was mention of any of the ICD-10 codes (A15-A19), referring to TB in any line of part 1 of the death certificate; iii) death with no mention of TB, those deaths in which there was no mention of TB (codes A15-A19 of ICD-10) in any part of the death certificate, considered here as other causes of deaths.
Based on the literature review, we tested the following covariables as associated factors with probable TB deaths: sex (female/male); schooling (illiterate, under 8 years old, over 8 years old and ignored); age group (0 to 19 years, 20 to 39 years, 40 to 59 years and 60 or more); color or race (white, black, brown, yellow, indigenous and ignored); macro-region of residence (North, Northeast, Southeast, South, and Central-West); clinical forms (pulmonary, extrapulmonary and mixed); number of treatments (1; 2 to 3; 4 or more); HIV serology (positive, negative, in progress and not performed); alcoholism (yes and no); diabetes (yes and no). The variable number of treatments refers to the number of episodes that a patient underwent treatment during the follow-up period.
The variable color or race, according to the categories adopted by the Brazilian Geography and Statistics Institute (IBGE) was introduced in information systems managed by the Ministry of Health from 2000. In practice, the color/race variable in SINAN notification form is reported from the patient’s self-declaration, based on the color of their skin, according to the five terms used by the IBGE: white, black, brown, yellow, and indigenous [
The main tool of the SINAN database is the notification/investigation form that includes both individual data of the patients as well as epidemiological and follow-up data until the end of treatment, on average 6 to 9 months. This information must be sent regularly from the local health units, since the municipality level, to the Ministry of Health, in order to produce the TB monitoring bulletin [
SIM is the oldest health information system in the country, created in 1975 by the Ministry of Health to address civil registry failures. It is a system with high population coverage, which aims to record data on mortality in Brazil, comprehensively, and reliably. Currently, SIM’s coverage is estimated at more than 95% in Brazil [
Death certificates are the fundamental sources for SIM. Adequate completion of the death certificate, which must necessarily be performed by physicians [
According to the Brazilian legislation regulating access to secondary data [
The database linkage between SINAN and SIM was performed in a three-step process. The first one was conducted in SINAN’s database using a deterministic algorithm for semi-automatic linking records, similar to those validated by Pacheco et al. [
The four study groups [probable TB deaths, TB-related deaths, death with no mention of TB, and no death (censoring) reported until December 31, 2013] were compared in a descriptive analysis regarding the variables of interest.
Survival analysis was used to elucidate factors associated with probable TB deaths (TB as the underlying cause) considering the presence of competing risks. For this analysis, the “censoring” outcome category was used as a reference for the outcome response variable, being compared with censoring vs. probable TB deaths; censoring vs. TB-related death and censoring vs. death with no mention TB. The Fine & Gray sub-distribution model based on the cumulative incidence function (CIF) was used as a reference [
Survival time was measured in days between the date of the beginning of first treatment and the date of the end of treatment or of the event of interest [probable TB death, TB-related death, death with no mention of TB or censoring (end of follow-up on 12/31/2013)]. On the other hand, from the deterministic linkage of the data, the fatal outcomes were divided into three groups (above mentioned) of analysis for competing events according to the ICD-10 codes listed in part I of the death certificates and made available in the SIM.
The cumulative incidence function was used to describe the probability of TB mortality in the presence of competing events and the Gray test was used to compare the differences between the groups. The Fine-Gray subdistribution hazard model was used to identify factors associated with mortality among TB cases. Based on a list of selected variables that showed previous association with death due to TB (presented above), we tested each of them individually, verifying its statistical significance, considering a p-value <0.20. The variables that had a p-value lower than 0.20 in the univariate analysis were selected as a candidate for the Fine & Gray multiple model. In the final model, we considered the level of significance of 5% (p<0.05). The risk measure was the subdistribution hazard ratio (sHR) with its respective 95% confidence intervals. The proportionality assumption of the Fine-Gray model was initially checked for CIF and Schoenfeld residuals tests.
Microsoft Excel spreadsheets 2016 were used to structure the data (Microsoft Corp., Redmond, WA, USA). We conducted the statistical analysis in STATA software, College Station, TX, USA [
During the study period, a total of 283,508 subjects met inclusion criteria, including 39,997 (14.1%) who died. A number of 8,936 deaths (22.4%) were attributed to tuberculosis as the probable cause, 3,365 deaths (8.4%) had tuberculosis as an associated cause, and 27,696 deaths (69.2%) had no mention of tuberculosis in the death certificate. Together, probable TB deaths and TB-related deaths made up 30.8% of the total deaths, demonstrating a high mortality rate in patients receiving TB treatment in Brazil, between 2008–2013. The median follow-up of the entire cohort was 1,348 days (IQR: 949–1,761). The median elapsed time since treatment start for probable TB deaths was 27 days (IQR: 5–126), for TB-related deaths was 52 days (IQR: 9–196) and among deaths for other causes/with no mention of TB was 383.5 days (IQR: 73–875).
The majority of deaths (29,322, 15.7%) in our analysis occurred in males, regardless of the cause. The majority of the cases occurred among patients aged 20 to 39 years old. Higher death proportions were observed among the oldest age group for probable TB deaths and deaths with no mention of TB (8.9% and 23.0%, respectively). There were higher proportions of probable TB deaths (5.2%) and deaths with no mention of TB (12.9%) among the patients who had no schooling (
Variables | Censoring | Likely TB-related deaths | Death associated with TB | Death with no mention of TB |
Total | |||||
---|---|---|---|---|---|---|---|---|---|---|
1,450 |
27 (5–126) | 52 (9–196) | 383,5 (73–876 | 1,348 |
||||||
Male | 157,455 | 84.3 | 6,729 | 3.6 | 2,306 | 1.2 | 20,287 | 10.9 | 186,777 | 65.9 |
Female | 86,056 | 89.0 | 2,207 | 2.3 | 1,059 | 1.1 | 7,409 | 7.7 | 96,731 | 34.1 |
0 a 19 | 25,930 | 96.2 | 243 | 0.9 | 71 | 0.3 | 699 | 2.6 | 26,943 | 9.5 |
20 a 39 | 115,640 | 91.6 | 1,693 | 1.3 | 1,631 | 1.3 | 7,341 | 5.8 | 126,305 | 44.5 |
40 a 59 | 76,544 | 82.8 | 3,620 | 3.9 | 1,342 | 1.5 | 10,941 | 11.8 | 92,447 | 32.6 |
60+ | 25,397 | 67.2 | 3,380 | 8.9 | 321 | 0.8 | 8,715 | 23.0 | 37,813 | 13.4 |
Illiterate | 16,112 | 81.0 | 1,029 | 5.2 | 174 | 0.9 | 2,566 | 12.9 | 19,881 | 7.0 |
Less 8 years | 91,104 | 85.6 | 3,192 | 3.0 | 1,346 | 1.3 | 10,787 | 10.1 | 106,429 | 37.5 |
Greater 8 years | 46,369 | 92.5 | 560 | 1.1 | 343 | 0.7 | 2,837 | 5.7 | 50,109 | 17.7 |
Ignored | 89,926 | 84.0 | 4,155 | 3.9 | 1,502 | 1.4 | 11,506 | 10.7 | 107,089 | 37.8 |
White | 83,321 | 85.3 | 2,885 | 3,0 | 1,157 | 1.2 | 10,268 | 10.5 | 97,631 | 34.4 |
Black | 30,569 | 85.2 | 1,187 | 3,3 | 541 | 1.5 | 3,580 | 10.0 | 35,877 | 12.6 |
Brown | 98,499 | 86.8 | 3,578 | 3,2 | 1,227 | 1.1 | 10,168 | 9.0 | 113,472 | 40.0 |
Yellow | 2,336 | 87.2 | 85 | 3,2 | 21 | 0.8 | 238 | 8.9 | 2,680 | 0.9 |
Indigenous | 2,941 | 91.6 | 89 | 2,8 | 11 | 0.3 | 171 | 5.3 | 3,212 | 1.1 |
Ignored | 25,845 | 84.4 | 1,112 | 3,6 | 408 | 1.3 | 3,271 | 10.7 | 30,636 | 10.8 |
North | 25,123 | 88.2 | 727 | 2.6 | 232 | 0.8 | 2,409 | 8.5 | 28,491 | 10.1 |
Northeast | 67,368 | 86.9 | 2,560 | 3.3 | 611 | 0.8 | 6,964 | 9.0 | 77,503 | 27.3 |
Southeast | 111,411 | 85.8 | 4,153 | 3.2 | 1,563 | 1.2 | 12,747 | 9.8 | 129,874 | 45.8 |
South | 29,231 | 83.0 | 1,048 | 3.0 | 775 | 2.2 | 4,143 | 11.8 | 35,197 | 12.4 |
Central-West | 10,378 | 83.4 | 448 | 3.6 | 184 | 1.5 | 1,433 | 11.5 | 12,443 | 4.4 |
1 | 234,851 | 86.1 | 8,514 | 3.1 | 3,065 | 1.1 | 26,473 | 9.7 | 272,903 | 96.3 |
2 a 3 | 8,473 | 81.8 | 404 | 3.9 | 283 | 2.7 | 1,198 | 11.6 | 10,358 | 3.7 |
4 or more | 187 | 75.7 | 18 | 7.3 | 17 | 6.9 | 25 | 10.1 | 247 | 0.1 |
Pulmonary | 202,510 | 86.6 | 7,640 | 3.3 | 2,227 | 1.0 | 21,603 | 9.2 | 233,980 | 82.5 |
Extrapulmonary | 34,191 | 84.8 | 876 | 2.2 | 654 | 1.6 | 4,581 | 11.4 | 40,302 | 14.2 |
Mixed forms | 6,810 | 73.8 | 420 | 4.6 | 484 | 5.2 | 1,512 | 16.4 | 9,226 | 3.3 |
Positive | 17,442 | 64.9 | 319 | 1.2 | 2,703 | 10.1 | 6391 | 23.8 | 26,855 | 9.5 |
Negative | 127,027 | 90.1 | 3,453 | 2.4 | 251 | 0.2 | 10211 | 7.2 | 140,942 | 49.7 |
In progress | 20,663 | 88.6 | 655 | 2.8 | 92 | 0.4 | 1901 | 8.2 | 23,311 | 8.2 |
Not performed | 78,379 | 84.8 | 4,509 | 4.9 | 319 | 0.3 | 9193 | 9.9 | 92,400 | 32.6 |
Yes | 30,302 | 79.0 | 2,241 | 5.8 | 669 | 1.7 | 5,126 | 13.4 | 38,338 | 13.5 |
No | 213,209 | 87.0 | 6,695 | 2.7 | 2,696 | 1.1 | 22,570 | 9.2 | 245,170 | 86.5 |
Yes | 13,174 | 78.2 | 826 | 4.9 | 122 | 0.7 | 2,718 | 16.1 | 16,840 | 5.9 |
No | 230,337 | 86.4 | 8,110 | 3.0 | 3,243 | 1.2 | 24,978 | 9.4 | 266,668 | 94.1 |
a median (IQR)
Death proportions were lower among self-reported indigenous cases (2.8% for probable TB deaths, 0.3% for TB-related deaths, and 5.3% for deaths with no mention of TB). Most cases were from the Southeast region 129,874 (45.8%); however, higher proportions of probable TB deaths were from the Central-West region (3.6%). The Southern region had higher death rates with TB as an associated cause and deaths with no mention of TB (2.2% and 11.8%, respectively) (
Almost all cases (96.3%) had been subject to a single treatment. However, higher proportions of probable TB deaths and TB-related deaths were observed among individuals with multiple treatments (4 or more) when compared to those with a single or with 2–3 treatments (7.3% and 6.9%, respectively). While the pulmonary form was the most frequent one (82.5%) of the total cohort, cases with “mixed forms” proved most fatal (4.6% for probable TB deaths, 5.2% for TB-related deaths, and 16.4% for deaths with no mention of TB). HIV serology was more frequent among “TB-related deaths” and “deaths with no mention of TB” (10.1% and 23.8%), being less frequent among probable TB deaths (4.9%) (
For 13.5% of patients, alcohol abuse was identified, with the highest proportion of alcoholism among those that died with no mention of TB (13.4%) and among deaths that had TB as the underlying cause (5.2%). The presence of diabetes mellitus was reported in 5.9% of patients and was more frequent among deaths with no mention of TB (16.1%) and probable TB deaths (4.9%) (
The cumulative incidence functions for the risk sub-distribution ratio (sHR) of causes of probable TB death, TB-related death and death with no mention of TB in a competing risk structure are presented in
Cause of death | Likely TB-related deaths | Death associated with TB | Death with no mention of TB |
|||
---|---|---|---|---|---|---|
Variables | sHR |
IC 95% | sHR | IC 95% | sHR | IC 95% |
Male | 1.33 | 1.26–1.40 |
0.94 | 0.87–1.01 | 1.29 | 1.25–1.32 |
Female | Ref e | Ref | Ref | |||
0 a 19 | Ref | Ref | Ref | |||
20 a 39 | 1.53 | 1.34–1.76 |
2.09 | 1.64–2.66 |
1.89 | 1.74–2.03 |
40 a 59 | 3.95 | 3.46–4.50 |
2.53 | 1.98–3.22 |
3.87 | 3.58–4.18 |
60+ | 9.29 | 8.15–10.60 |
4.26 | 3.29–5.52 |
10.36 | 9.59–11.20 |
Illiterate | 2.33 | 2.09–2.59 |
1.72 | 1.43–2.08 |
1.64 | 1.55–1.73 |
Less 8 years | 1.74 | 1.59–1.91 |
1.56 | 1.38–1.76 |
1.41 | 1.35–1.47 |
Greater 8 years | Ref | Ref | Ref | |||
Ignored | 2.57 | 2.35–2.81 |
1.73 | 1.53–1.96 |
1.58 | 1.51–1.65 |
White | Ref | Ref | Ref | |||
Black | 1.10 | 1.02–1.18 |
1.29 | 1.16–1.43 |
0.98 | 0.94–1.01 |
Brown | 1.13 | 1.07–1.19 |
1.24 | 1.13–1.35 |
0.96 | 0.93–0.99 |
Yellow | 1.01 | 0.82–1.26 | 1.22 | 0.79–1.89 | 0.92 | 0.80–1.04 |
Indigenous | 0.96 | 0.77–1.19 | 0.70 | 0.39–1.28 | 0.59 | 0,51–0,69 |
Ignored | 1.07 | 0.99–1.16 | 1.18 | 1.05–1.33 |
0.99 | 0.95–1.03 |
North | 0.81 | 0.75–0.89 |
0.85 | 0.73–0.98 |
0,95 | 0.91–1.00 |
Northeast | 0.84 | 0.80–0.89 |
0.85 | 0.77–0.94 |
0,89 | 0.86–0.92 |
Southeast | Ref | Ref | Ref | |||
South | 1.19 | 1.10–1.28 |
1.21 | 1.10–1.34 |
1,08 | 1.04–1.12 |
Central-West | 1.07 | 0.97–1.64 | 1.40 | 1.20–1.64 |
1,16 | 1.10–1.23 |
Pulmonary | Ref | Ref | Ref | |||
Extrapulmonary | 0.82 | 0.76–0.88 |
0.92 | 0.85–1.01 | 1.15 | 1.11–1.19 |
Mixed forms | 1.91 | 1.73–2.11 |
1.62 | 1.46–1.79 |
1.41 | 1.34–1.49 |
Positive | 0.59 | 0.53–0.67 |
62.78 | 55.01–71.63 |
4.44 | 4.29–4.58 |
Negative | Ref | Ref | Ref | |||
In progress | 1.17 | 1.08–1.28 |
2.27 | 1.78–2.88 |
1.11 | 1.05–1.16 |
Not performed | 2.00 | 1.91–2.10 |
2.01 | 1.70–2.38 |
1.31 | 1.27–1.35 |
Yes | 1.90 | 1.81–2.00 |
1.38 | 1.27–1.51 |
1.35 | 1.31–1.38 |
No | Ref | Ref | Ref |
Ref: Reference category
***
**
*
a as mentioned in the methods.
The Fine-Gray models indicated that male subjects had a higher risk of probable TB death TB (sHR: 1.33 95% CI: 1.26–1.40) and deaths with no mention of TB (sHR: 1.29 (95% CI: 1.25–1.32). Patients over 60 years of age were more likely to die, irrespective of cause. For probable TB deaths and for those with no mention of TB, the sHR, as compared to the age group of 0–19 years was 9.29 (95% CI: 8.15–10.60) and 10.36 (95% CI: 9.59–11.20) respectively. The Ignored and illiterate schooling categories were strongly associated with death irrespective of cause, presenting a risk gradient, as schooling declined. Regarding the risk of death among color or race categories, browns had a higher risk of probable TB death (sHR = 1.13 95% CI: 1.07–1.19). Among the TB-related deaths, the highest risk was among blacks (sHR = 1.29, 95% CI: 1.16–1.43), and a lower risk of deaths with no mention of TB was observed among indigenous and brown persons (sHR = 0.59, 95% CI: 0.51–0.69 and sHR = 0.96, 95% CI: 0.93–0.99, respectively) (
Patients from the South and Central-West regions of the country had higher risks of probable TB death (sHR = 1.19 95% CI: 1.19–1.28), while TB-related death and deaths with no mention of TB (sHR = 0.59, 95% CI: 0.51–0.69 and sHR = 0.96, 95% CI: 0.93–0.99, respectively) were less frequent in that regions. The mixed clinical form presented higher risk of death from all causes (sHR = 1.91, 95% CI: 1.73–2.11, sHR = 1.62, 95% CI: 1.46–1.79; sHR = 1.41; 95% CI: 1.34–1.49, for probable TB death, TB-related death and death with no mention of TB, respectively) (
HIV positive serology showed a higher risk of death for TB-related death (sHR = 62.78 CI95%: 55.01–71.63) as compared to HIV negative individuals. On the other hand, positive serology was a protective factor for probable TB death (sHR = 0.59 95% CI: 0.53–0.67). Individuals with alcoholism comorbidity presented a higher risk among probable TB deaths (sHR = 1.90 CI95%: 1.81–2.00) as compared to those without this comorbidity.
This study is the first to draw on Brazil’s national databases to investigate factors associated with death in patients receiving treatment for TB, taking into account the presence of competing events in survival analysis. While studies usually look at survival analysis with only two categories, probable TB deaths and deaths from other causes, this study includes TB-related deaths as a third category of analysis. Our findings indicate that the main factors associated with deaths, regardless of the cause, include age group, schooling, mixed clinical form, HIV serology and alcoholism.
There was a dose-response effect in the risk of mortality for differing age groups from 20 years old, with a significant risk for patients over 60, particularly in deaths from other causes and probable TB deaths. Looking at schooling, illiterate patients presented the highest risk of mortality, the effect being more evident in probable TB deaths. The association of mixed clinical form with higher rates of death with any cause likely reflects the clinical severity of the cases, particularly among those where treatment began shortly before the patient died as a result of probable TB. Within HIV serology however, the data are contradictory. While positive HIV serology was strongly associated with TB-related deaths and deaths from other causes, probable TB deaths often presented with HIV serology in progress or not performed. It could be that these inconclusive HIV serology tests act as an indicator of the poor performance of the relevant health service. Finally, alcoholism was associated with all forms of mortality, most strongly associated with probable TB death.
The Fine-Gray model, considering the presence of competing risks, revealed significant changes in hazard risks when considering three different groups of causes of death (probable TB deaths, TB-related deaths and deaths from other causes) among patients undergoing treatment for TB in a cohort of approximately 300,000 people throughout the national territory.
We understand that classical survival analysis is not appropriate when analyzing time elapsed from an initial event to the occurrence of complex events, or in this case competing events, where the individual is at risk of more than one cause of death. A possibility for analyzing data in the presence of competing risks is the Fine-Gray sub-distribution hazard model of risk that has proved useful in the analysis of the factors associated with death for cases receiving treatment for TB in Brazil.
Death due to TB is considered a preventable sentinel event. The disease has a straightforward diagnosis, there are free medications available in the public health network throughout Brazil, a complete treatment with first-line drugs is relatively inexpensive and the disease is curable in almost 100% of cases. Therefore, the high number of deaths analyzed here points to weaknesses in the Brazilian model of care offered to these patients. These failures range from difficulties in access to diagnosis and treatment in the primary care services to access to emergency services and hospitalization for patients with advanced stages of illness [
As reported elsewhere [
In this analysis, individuals aged 60 years or older had a higher chance of death in the different causes analyzed. Similar results have been described by other authors [
Interestingly, although the incidence of TB in indigenous populations is consistently higher than that observed among the general population, as reported by several authors [
Although the concept of race is widely recognized as a social construct, it represents an important indicator of health and is mostly based on a social definition of race, rather than a biological or genetic characterization. It is worth noting the higher chances of probable TB death and TB-related death among individuals classified as "black" and "brown" as compared to “white”. The identification of racial groups at higher risk is crucial for National Tuberculosis Control Programs which should lead to the development of differential strategies of follow-up, in an attempt to close the historical, sociological, socioeconomic, and access to health services gaps [
TB is the most common opportunistic infection, and several studies have shown that it is the leading cause of death among people living with HIV/AIDS [
People infected with the human immunodeficiency virus (HIV) have 20–30 times greater risk of developing tuberculosis than those without HIV. Thus, the need for treatment of latent TB infection has increased due to its high frequency in people with HIV, if treated prophylactically they can prevent outcomes such as death [
The prevalence of alcohol abuse disorders in Brazil is around 13.7% in the adult population [
Record linkage between SINAN and SIM databases is a widely used strategy to identify the underreporting of TB cases and TB deaths in Brazil [
Despite the illustrative findings and the national scope of this study, it is important to take some limitations into account. As with any study based on reporting secondary data, there may have been underreporting of cases and/or deaths, the incompleteness of some variables, errors in classification, and/or coding of cases. Misclassification errors in particular may have occurred for causes of death in the death certificates and with variables color or race. Another important limitation is that, since it is a study of secondary databases, there is the possibility of residual confounding factors (smoking, COPD, etc.) that are not identified or not measured. Thus, causal inferences cannot be made.
In our analyses, we included 266,668 cases. On the one hand, the survival estimates presented here, in the presence of competing events, identified significant differences in death risks with notable statistical significance and narrow confidence intervals. On the other hand, due to the large size of the sample, the standard errors became extremely small; however, the associations found in this study show biological plausibility and are widely recognized in the literature, so that we do not consider our results to be unfeasible.
Finally, our findings indicate that probable TB deaths or TB-related deaths occurred in the first weeks of treatment, with median times of 27 and 52 days, respectively, while deaths from other causes occurred at a median of more than a year after treatment started when most patients had finished their treatment. This result indicates that patients were probably diagnosed with TB late and at an advanced stage of the disease. This situation is perhaps related to the high rate of mortality reported here. Early diagnosis is key to a favourable outcome as established in the literature [
Probable TB deaths and TB-related deaths were strongly associated with males, age over 60, illiterate people, individuals of black/brown color, those who presented with mixed clinical forms and were patients with HIV and suffering from alcoholism. It is therefore possible to assume that if there was active surveillance of cases being treated, together with strategies for early detection and adequate follow-up of the patients, mortality due to tuberculosis could be reduced.
In conclusion, understanding the factors associated with mortality in patients receiving TB treatment may be a useful contribution to the End TB strategy, particularly in working towards the target to reduce TB deaths by 95%, between 2015 and 2035. It could also help to organize local health services to offer optimal support to patients and to prevent avoidable deaths.
(DOCX)
(RAR)
The Brazilian Ministry of Health and the National Tuberculosis Control Program (PNCT) for the availability of data, especially Maria de Fatima Marino de Souza, Denise Arakaki-Sanchez, Patricia Barthlomay and Danielle Pelissari. Noteworthy is the important support of Antony Stevens in one of the stages of database linkage. We also thank MD Joseph Kempton of the University Hospitals Plymouth NHS Trust for their critical review of the manuscript. We are grateful for support from the Coordination for the Improvement of Higher Education Personnel (CAPES). The support and assistance offered by the Graduate Program in Epidemiology in Public Health of the National School of Public Health (ENSP) under the coordination of Leticia Oliveira Cardoso and the administrative support provided by Marcella Fagundes and Joyce Torres. Thank you all very much!
PONE-D-19-34302
Factors associated with death in patients with tuberculosis in Brazil: survival analysis with competitive risks
PLOS ONE
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Reviewer #1: Yes
Reviewer #2: Partly
Reviewer #3: Partly
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2. Has the statistical analysis been performed appropriately and rigorously?
Reviewer #1: Yes
Reviewer #2: No
Reviewer #3: No
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3. Have the authors made all data underlying the findings in their manuscript fully available?
The
Reviewer #1: No
Reviewer #2: Yes
Reviewer #3: No
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Reviewer #1: Yes
Reviewer #2: No
Reviewer #3: Yes
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5. Review Comments to the Author
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Reviewer #1: This article presents an analysis of TB-related mortality in a TB-patient cohort in Brazil from 2008-2013. Reducing TB related mortality is a global goal, as TB is the greatest cause of mortality from infectious disease, so this article is of interest. The analysis links a TB database and a mortality database, which both have restrictions on public access, which is why the answer to the question above about providing underlying data with the submission was "no."
The article is generally well written, but the methods section has redundancies, is not concisely written, and is too detailed. The paper could be improved by moving some of this material to an online appendix.
I have the following additional comments that need to be addressed:
* deaths caused by TB can only be accurately assessed using autopsies. Death certificates are notorious for their errors and cannot be solely relied upon to determine cause of death. However, this analysis still has merits in the absence of autopsies. Instead of the terminology "deaths caused by TB", you should change it to be deaths probably due to TB, or something similar.
* TB diagnosed at death in patients not started on TB treatment are excluded, but this is not mentioned. This can be a substantial proportion of TB in some settings and for populations having disparities in access to care.
* For persons with HIV, the underlying cause of death on the death certificate is nearly always chosen to be HIV and not TB. This is not an error in coding, but the perception of physicians completing the forms. This is also shown in your data, as there were 209 HIV+ "deaths due to TB" and 1762 HIV+ associated TB deaths. That doesn't mean that TB did not cause their deaths (see above). How does this affect your analysis?
* Did you not have data on ART use by HIV+ during or before TB treatment? This should be a major part of the Discussion section, including mentioning of ART for HIV, and LTBI treatment among persons with HIV as a means to decrease HIV-related TB deaths, and overall TB mortality.
* in several places you write "anti-HIV serology" and in others "HIV serology". The latter is correct. And on p. 9, you have omitted the word "test" for "in progress" and "not performed".
* in the Methods, please explicitly describe the outcomes analyzed and their comparisons for each model. For typical survival analyses, the comparison is those who did not die (or a subset of those who did not die). Also include the comparison on the titles of Table 2.
* p. 11, no informed consent was "obtained" not "used"
* also p. 11 and elsewhere, interquartile range is typically abbreviated as IQR. You have it as IQ and IR in the document.
* p. 12 please present the percentages of deaths, not of cases
* Were all those who died tested for HIV? if not, how might this have biased your analysis?
* are "number of treatments" referring to separate episodes of TB disease? or of number of medications? What does this mean for deaths with no mention of TB? please describe this variable better.
* put the N at the top of Table 1, and on the columns of Table 2
* what does the category "ignored" mean for the variable "schooling"? Is this "refused to answer" or "unknown"?
* Discussion summary: please include the referent for each summary population mentioned. Also, the HR of mortality was significantly higher for all age groups beginning from 20+ compared with <20. Males were not significantly associated with TB associated deaths, so that statement is inaccurate.
* you mention "relative risks" but you analyzed hazard ratios, not relative risks.
* you discuss diabetes, but don't state that you did not find a statistically significant association with mortality. The concluding sentence of that paragraph was not shown in the study findings: "findings suggest the need for better screening of DM". What evidence do you have that DM diagnoses were missed?
* deaths early in TB treatment are common among persons with HIV. I suggest looking at the time to death, stratified by HIV, to see and present how this differs. TB prevention through LTBI treatment, and early access to HIV treatment soon after HIV infection, may be the only ways to reduce these early TB deaths.
Reviewer #2: Summary: The authors have performed a retrospective cohort study to quantify mortality among patients with TB and causes of death for patients with TB in Brazil. While this is an important study in the context of the End TB Strategy’s goal of 95% reduction in TB deaths, I think the manuscript could be significantly strengthened by addressing the major and minor comments listed below.
Major Comments
1-The abstract could be strengthened as this may be the only part of the manuscript that some will read. In the objective section, would drive home the significance with some text about TB in Brazil, End TB strategy goals, and what would be done with the results. Would include some definitions in the methods-specifically with respect to the outcome-deaths due to TB, deaths associated with TB, deaths from other causes. The conclusions are not supported by the results as active surveillance and early case finding were not assessed. Would revise the conclusions section to summarize the main findings and state what will be done with the results.
2-Would add some text about the End TB strategy in the Introduction and throughout to further highlight the importance of TB mortality studies.
3-The Methods section would benefit from some reorganization for flow. The first section could be “Study Population” in which you state the study design, setting source of study population, eligibility criteria, follow-up definition (start and stop), and ethics statement. The second section could be “Study Definitions” in which you define the TB case definition, covariates of interest, and mortality data source and mortality outcomes (death due to TB, death associated with TB, and deaths from other causes). The fourth section could describe the linkage procedures and the statistical analyses. The Strobe Checklist can be helpful with manuscript organization for observational studies (
4-Would specifically state the TB case definition used in SINAN. Are only laboratory confirmed TB cases included or are clinical cases for which a TB treatment course is prescribed also used?
5-Why wasn’t the WHO definition of TB death considered for this study. A TB patient who dies for any reason before starting or during the course of treatment (
6-In the variables of interest section it was noted that these variables have all been shown to be associated with death due to TB in previous literature. Why later was it noted that the adjusted analysis only included those with p<0.20 using the Wald test in a simple model and those with p<0.05 in a “multiple model”? Why not include all variables identified as important in the previous literature?
7-In describing Table 1 data in the text, be cautious using the term rate as these are actually frequencies and proportions. Additionally, would be careful in comparing groups as it does not appear that any statistical testing was done using the data in Table 1.
8-Figure 2 appears to be the results of the unadjusted model. It would be more illustrative to show the results of the adjusted model either in addition to or in place of the unadjusted model.
9-Was year of cohort entry considered as a variable of interest? TB screening and treatment or other important practices may have changed over the study period.
10-Were patients with documented drug resistance or patients not treated with a drug-susceptible TB regimen (HRZE) excluded? Similarly, can you somehow account adherence by looking at the time it takes the patient to complete treatment? These are both important factors when studying mortality among patients with TB.
11-The first paragraph of the Discussion jumps right to risk factors for death. The Discussion would benefit from description of the main findings of the study followed by discussion of these findings in the context of other studies conducted both within Brazil and outside Brazil.
12-The Discussion includes a paragraph about the importance of screening for DM; however, DM is not included in the adjusted model. This gets to the comment about how variables were chosen for inclusion in the multivariable model.
13-The methods section should describe how missing data were handled. Were these people excluded, was multiple imputation performed, other? What does HIV serology in progress mean?
14-The Conclusions section could be strengthened by again summarizing the main findings as well as how the results can be used to improve outcomes in Brazil (such as future studies, etc).
15-Additional limitations to consider are the presence of unmeasured confounders (smoking, chronic lung disease, etc) and generalizability of the study findings.
Minor Comments
1-Would update Reference #1 to the 2019 Global TB Report.
2-The flow chart could be made more clear by using arrows with boxes to show those excluded and those who enter due to re-classification. Would need to explicitly define the exclusions. For example, you note exclusion of non-TB cases from SINAN.
Reviewer #3: In general, this study used a national-level cohort to examine the mortality risk among people with tuberculosis. The risk factors were examined by category of deaths due to tuberculosis, associated with tuberculosis, and from other causes after the tuberculosis treatment. Several risk factors were found, with some differences by cause of death category. However, the article needs extensive editing for language and several critical points need to be clarified before considering for publication.
1. In page 5, I think it is important to provide a more complete SINAN context. For example, how does this system collect data? Who actually does the monitoring, doctor? What criteria are used to diagnoses cases? Another important thing is how do authors identify tuberculosis cases form SINAN? By using ICD code or extracting manually from medication notes? If manually, how to evaluate the reliability?
2. In page 5 and 6, the paragraph “The variable that identifies the color or race of individuals according to the categories adopted by the Brazilian Geography and Statistics Institute (IBGE)…” presents twice.
3. In page 8, what did it mean by “ii) associated TB deaths, those deaths in which there was no mention of any of the ICD-10 codes (A15-A19), referring to TB in any line of part 1 of the death certificate”? Does that mean that tuberculosis was not coded as the underlying cases (part 1), but the terminal or intermediate cause?
4. The figure 1 is confusing and cannot be matched with the “Inclusion and exclusion criteria” part totally. This part should be combined with “ Record linkage procedures and study groups definition” part and restructured.
5. In addition to reporting the mortality rate, it is more valuable to report the incidence per 100,000 patients in different follow-up time periods.
6. How did authors deal with the missing value?
7. In page 10, what is the rationale of p-value>0.20 were used to select covariates? How about adding some sensitivity analysis by adopting different cut-off p values to validate the robustness of the results?
8. The cases used in this study were patients with tuberculosis treatment. What kind of treatments did they received? The treatment information should be controlled in the model as they also ave effects on the survival time.
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Reviewer #1: Yes: Suzanne Marks
Reviewer #2: No
Reviewer #3: No
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Author's reply to comments:
Dear Dr. David J Horne, PLOS ONE,
First, I would like to thank you for considering our manuscript for publication in PLOS ONE. We are pleased to send the revised version of the manuscript entitled “Factors associated with death in patients with tuberculosis in Brazil: survival analysis with competitive risks” for your consideration for publication in PLOS ONE.
We also thank the reviewers for their meticulous review and appreciate the constructive criticism of our work. We received from the reviewers and hope to point out all comments appropriately. We have made a wide revision and changes to the manuscript, which are highlighted and we also addressed the comments and changes, point to point, below. It was an immense privilege to get such valuable feedback that allowed us to address several gaps in our manuscript.
In this letter, you will find our responses to each question and concern.
Kind regards,
The authors
RESPONSES TO THE REVIEWER #1
1) This article presents an analysis of TB-related mortality in a TB-patient cohort in Brazil from 2008-2013. Reducing TB related mortality is a global goal, as TB is the greatest cause of mortality from infectious disease, so this article is of interest. The analysis links a TB database and a mortality database, which both have restrictions on public access, which is why the answer to the question above about providing underlying data with the submission was "no."
Authors comments: Thank you for your kind words; they were very well received. Your comments were invaluable in helping us improve the manuscript. In Brazil, there is a Law, known as the Access to Information Law, which protects the confidentiality of patients' information reported in their information systems. Thus, for the linkage of the databases, we researchers signed a document making us responsible for the integrity of the information made available. The individual data sets of tuberculosis cases and deaths in Brazil analyzed during the present study are available upon request in the repository “Electronic System of the Citizen Information Service”, [
2) The article is generally well written, but the methods section has redundancies, is not concisely written, and is too detailed. The paper could be improved by moving some of this material to an online appendix.
Authors comments: We are grateful for your comments about the importance of the methods of study. We clarified the linkage process section in the supplementary file.
3) Deaths caused by TB can only be accurately assessed using autopsies. Death certificates are notorious for their errors and cannot be solely relied upon to determine cause of death. However, this analysis still has merits in the absence of autopsies. Instead of the terminology "deaths caused by TB", you should change it to be deaths probably due to TB, or something similar.
Authors comments: We agree with the reviewer that the gold standard for death certifications is autopsies. Throughout the manuscript, we replaced the term "death due TB" with " probable TB deaths ".
4) TB diagnosed at death in patients not started on TB treatment are excluded, but this is not mentioned. This can be a substantial proportion of TB in some settings and for populations having disparities in access to care.
Authors comments: We agree with the reviewer that we were unable to capture the underreporting of TB cases from death certificates (SIM). This limitation was included in the discussion section, such weakness. Due to some restrictions that we had faced to the national death database in Brazil, the Brazilian government only provided us with the deaths that have already linked with the notification cases database, i.e. which were under treatment (Sinan).
5) For persons with HIV, the underlying cause of death on the death certificate is nearly always chosen to be HIV and not TB. This is not an error in coding, but the perception of physicians completing the forms. This is also shown in your data, as there were 209 HIV+ "deaths due to TB" and 1762 HIV+ associated TB deaths. That doesn't mean that TB did not cause their deaths (see above). How does this affect your analysis?
Authors comments: We appreciate your comments on the relevance of this placement. Such concern was discussed as limitations in the discussion section of the article, regarding the aspect of coding underlying causes in HIV positive patients.
6) Did you not have data on ART use by HIV+ during or before TB treatment? This should be a major part of the Discussion section, including mentioning of ART for HIV, and LTBI treatment among persons with HIV as a means to decrease HIV-related TB deaths, and overall TB mortality.
Authors comments: Unfortunately, in the Brazilian compulsory notification system (Sinan), we do not have information on ART. We discussed the limitations of this information among HIV-positive patients in our manuscript. In addition, we have included a discussion on the identification and treatment of latent TB infection in people living with HIV.
7) In several places you write "anti-HIV serology" and in others "HIV serology". The latter is correct. And on p. 9, you have omitted the word "test" for "in progress" and "not performed".
Authors comments: We appreciate your important observation in terminology. We conducted a comprehensive review of the terms and all have been replaced for “HIV serology”.
8) In the Methods, please explicitly describe the outcomes analyzed and their comparisons for each model. For typical survival analyses, the comparison is those who did not die (or a subset of those who did not die). Also, include the comparison on the titles of Table 2.
Authors comments: We appreciate your observation. In the methods section, we inserted the comparison categories for each outcome compared to the censoring, as well as introducing this information in table 2.
9) p. 11, no informed consent was "obtained" not "used"
Author comments: Thanks for your observation, we changed the word to obtained
10) Also p. 11 and elsewhere, interquartile range is typically abbreviated as IQR. You have it as IQ and IR in the document.
Author comments: Thanks for your observation, we changed the abbreviation to IQR
11) p. 12 please present the percentages of deaths, not of cases
Author comments: We corrected and presented the percentages for deaths in the results section
12) Were all those who died tested for HIV? if not, how might this have biased your analysis?
Author comments: Although the main international recommendations are to test all cases for HIV serology, unfortunately in Brazil due to their continental geographical characteristics, many regions are unable to test their patients satisfactorily for various reasons, that include the poor organization of the health care network, the lack of HIV tests and the not awareness of the health workers. This point was also addressed in the discussion of the study.
13) Are "number of treatments" referring to separate episodes of TB disease? or of number of medications? What does this mean for deaths with no mention of TB? please describe this variable better.
Author comments: This variable reports the number of times that a patient started a new treatment for tuberculosis during the follow-up, for example, the same patient who was a new case in 2008, re-entered in the system at other times to start a new treatment in later years, whether due to recurrence or by re-entry after abandoning treatment. As for deaths with no mention of tuberculosis, this points to an important weakness of health services that are unaware that the patient was undergoing treatment for tuberculosis and end up not reporting in the death certificate.
14) Put the N at the top of Table 1, and on the columns of Table 2
Author comments: Thanks for the comment, information placed in the tables
15) What does the category "ignored" mean for the variable "schooling"? Is this "refused to answer" or "unknown"?
Author comments: They were included in the category ignored cases with absence of information and children under 5 years
16) Discussion summary: please include the referent for each summary population mentioned. Also, the HR of mortality was significantly higher for all age groups beginning from 20+ compared with <20. Males were not significantly associated with TB associated deaths, so that statement is inaccurate
Authors comments: We appreciate the concern in structuring the summary of the discussion and restructured the first paragraph better.
17) * you mention "relative risks" but you analyzed hazard ratios, not relative risks.
Authors comments: Thank you very much for the observation we corrected the mistakes and replaced the term “relative risk” for “hazard ratio”.
18) You discuss diabetes, but don't state that you did not find a statistically significant association with mortality. The concluding sentence of that paragraph was not shown in the study findings: "findings suggest the need for better screening of DM". What evidence do you have that DM diagnoses were missed?
Authors comments: In our study, we propose the hypothesis that diabetes could contribute to the mortality in patients undergoing treatment for tuberculosis. But, in fact, this variable did not show statistical significance in our analyzes. Therefore, we are not be able to evaluate if there was underdiagnoses of diabetes in the TB patients, in Brazil, and due to this, following your recommendations we removed that sentence from the discussion.
19) Deaths early in TB treatment are common among persons with HIV. I suggest looking at the time to death, stratified by HIV, to see and present how this differs.
Authors comments: We included a figure in the Supporting Information showing the survival curves stratified by HIV serology.
20) TB prevention through LTBI treatment, and early access to HIV treatment soon after HIV infection, may be the only ways to reduce these early TB deaths.
Authors comments: We thank the reviewer for this important statement. In fact, the early initiation of antiretroviral therapy and the early identification of latent TB infection is an important way to reduce mortality in this group. We’ve included an excerpt addressing this in the discussion section.
RESPONSES TO THE REVIEWER #2
Major Comments
1) The abstract could be strengthened as this may be the only part of the manuscript that some will read. In the objective section, would drive home the significance with some text about TB in Brazil, End TB strategy goals, and what would be done with the results. Would include some definitions in the methods-specifically with respect to the outcome-deaths due to TB, deaths associated with TB, deaths from other causes. The conclusions are not supported by the results as active surveillance and early case finding were not assessed. Would revise the conclusions section to summarize the main findings and state what will be done with the results.
Authors comments: We appreciate your important contributions in the structure of the abstract of the manuscript. We improved the summary according to your comments.
2) Would add some text about the End TB strategy in the Introduction and throughout to further highlight the importance of TB mortality studies.
Authors comments: We included a sentence in the introduction to the manuscript describing the End TB strategy
3)The Methods section would benefit from some reorganization for flow.
The first section could be “Study Population” in which you state the study design, setting source of study population, eligibility criteria, follow-up definition (start and stop), and ethics statement.
The second section could be “Study Definitions” in which you define the TB case definition, covariates of interest, and mortality data source and mortality outcomes (death due to TB, death associated with TB, and deaths from other causes).
The fourth section could describe the linkage procedures and the statistical analyses. The Strobe Checklist can be helpful with manuscript organization for observational studies (
Authors comments: We agree with your valuable comments and we replaced the structure of the methods section, according your suggestions.
4) Would specifically state the TB case definition used in SINAN. Are only laboratory confirmed TB cases included or are clinical cases for which a TB treatment course is prescribed also used?
Authors comments: We have included in the methods section a brief description of the case definition of tuberculosis, used in SINAN database in Brazil. By this criterion, we use not only bacteriologically confirmed TB case and clinically diagnosed TB case, as proposed by WHO definition, but also cases with epidemiological link to other confirmed TB cases.
5) Why wasn’t the WHO definition of TB death considered for this study. A TB patient who dies for any reason before starting or during the course of treatment (
Authors comments: According to cited document by the reviewer, for country-specific purposes, deaths due to TB and deaths due to other causes could be separated in the treatment outcomes section. However, the two need to be added together for treatment outcome monitoring.
In spite of we have studied different kinds of mortality among patients under TB treatment, for the SINAN database all case have had death as an outcome of treatment were counted for treatment outcome monitoring.
6) In the variables of interest section it was noted that these variables have all been shown to be associated with death due to TB in previous literature. Why later was it noted that the adjusted analysis only included those with p<0.20 using the Wald test in a simple model and those with p<0.05 in a “multiple model”? Why not include all variables identified as important in the previous literature?
Authors comments: We appreciate the concern of the reviewer, in our study, although we selected an already known group of variables associated with death, this set of variables was still relatively large. Therefore, with the concern of being as flexible as possible and based on other studies in the literature, we also decided to adopt a cut-off point of p-value of 0.20 in order to seek a more parsimonious model as possible to describe the data, which also results better numerical stability and generalization of results. Thus, to better clarify the process of selecting variables for the Fine & Gray regression model, we rewrite the following sentence in the methods section.
“In our study, based on a group of variables associated with TB death in the literature, we based their choice individually, verifying the significance of each variable and the elimination of non-significant variables. Any variable with a p-value of 0.20 in the univariate test was selected as a candidate for the Fine & Gray multiple model, and the level of significance for choosing the final model was 5%”.
7) In describing Table 1 data in the text, be cautious using the term rate as these are actually frequencies and proportions. Additionally, would be careful in comparing groups as it does not appear that any statistical testing was done using the data in Table 1.
Authors comments: We are grateful for the reviewer's comment, in fact analyzes of proportions were performed in table 1 and not rates. We corrected this mistake throughout the results section.
8) Figure 2 appears to be the results of the unadjusted model. It would be more illustrative to show the results of the adjusted model either in addition to or in place of the unadjusted model.
Authors comments: In fact, in figure 2, the survival curves for the unadjusted data are presented in an exploratory way to better guide our analyzes in the Fine & Gray regression model.
9) Was year of cohort entry considered as a variable of interest? TB screening and treatment or other important practices may have changed over the study period?
Authors comments: In our study, the year of entry into the cohort was not considered an important variable for analysis. In Brazil, during our analysis period, there was no change in the type of treatment of tuberculosis cases.
10) Were patients with documented drug resistance or patients not treated with a drug-susceptible TB regimen (HRZE) excluded? Similarly, can you somehow account adherence by looking at the time it takes the patient to complete treatment? These are both important factors when studying mortality among patients with TB.
Authors comments: In our study, we excluded resistant cases from the study, as they are followed up in another information system focused exclusively on drug-resistant tuberculosis cases.
11) The first paragraph of the Discussion jumps right to risk factors for death. The Discussion would benefit from description of the main findings of the study followed by discussion of these findings in the context of other studies conducted both within Brazil and outside Brazil.
Authors comments: We thank you for your important contribution. We inform that we included a brief description with the main results of the study, in the first paragraph of the discussion.
12) The Discussion includes a paragraph about the importance of screening for DM; however, DM is not included in the adjusted model. This gets to the comment about how variables were chosen for inclusion in the multivariable model.
Authors comments: In our study, we propose the hypothesis that diabetes could contribute to the mortality in patients undergoing treatment for tuberculosis. But, in fact, this variable did not show statistical significance in our analyzes. Therefore, we are not be able to evaluate if there was underdiagnoses of diabetes in the TB patients, in Brazil, and due to this, following your recommendations we removed that sentence from the discussion.
13) The methods section should describe how missing data were handled. Were these people excluded, was multiple imputation performed, other? What does HIV serology in progress mean?
Authors comments: In our study, the missing data in the categories of variables were not excluded, but classified as ignored to avoid loss of information in the study. Thus, we do not adopt multiple imputation. When a specific exam is not collected, health professionals tend not to fill in the variable, leaving it with missing data. That is why, we believe that the missing data are completely random, the incompleteness is not related to exposure, covariates or the outcome.
In Brazil, serology in progress means that the patient has been tested but is still waiting for a positive or negative result. Unfortunately, due to logistic or operational issues at the local health units, often some patients have never get access these results.
14) The Conclusions section could be strengthened by again summarizing the main findings as well as how the results can be used to improve outcomes in Brazil (such as future studies, etc).
Authors comments: We appreciate your important contribution and adjust the conclusion according to your suggestions.
It is the first time that databases of TB cases and of mortality, from a nationwide cohort, in Brazil, were used to investigate associated factors to death in patients under treatment for TB, considering the presence of competitive events in a survival analysis. Usually, authors performing survival analysis using only two categories, deaths due to TB and deaths for other causes. In this study, we consider other deaths associated with TB and its associated factors as a third category of analysis.
Our findings indicate that the main factors associated with deaths, regardless of the cause, include age group, schooling, mixed clinical form, HIV serology and alcoholism.
Considering age-groups, there was a dose-response effect in the risk of mortality from 20 years old, reaching the remarkable risk in patients over than 60, especially in the deaths for other causes and probable TB deaths, respectively. Taking into account the schooling, patients that were illiterate presented the highest risk of mortality, being the effect more evident to probable TB deaths. The association of mixed clinical form with all kind of deaths, likely, is result of the clinical severity of the cases, especially among those where the beginning of the treatment and the probable TB deaths occurred in short time lapse. When we focus on HIV serology, the data are contradictory. While, HIV serology positive was strongly related to TB-related deaths and to other causes of deaths, for probable TB deaths the HIV serology in progress and not performed have presented strong association. Perhaps, these inconclusive HIV serology tests acted as an indicator of the bad performance of the health services. Finally, alcoholism was associated with all forms of mortality, being stronger among probable TB deaths.
Considering the factors associated with probable TB deaths, in addition to the factors above mentioned, the following stand out: male gender, "black" and "brown" color or race, and the situation in the South region of the country. Among the TB-related deaths, in addition to the factors above mentioned, we highlighted color or race "black" and "brown" and the situation in the South and Central-West regions of the country. In turn, among the deaths from other causes, we underlined male sex, the situation in the South and Central-West regions, as well extrapulmonary clinical forms.
15) Additional limitations to consider are the presence of unmeasured confounders (smoking, chronic lung disease, etc) and generalizability of the study findings.
Authors comments: We appreciate your observation; we have included these questions in the discussion section of the manuscript.
Minor Comments
1) Would update Reference #1 to the 2019 Global TB Report.
Authors comments: We updated this bibliographic reference of the Global Report
2) The flow chart could be made clearer by using arrows with boxes to show those excluded and those who enter due to re-classification. Would need to explicitly define the exclusions. For example, you note exclusion of non-TB cases from SINAN.
Authors comments: We improved figure 1 flowchart as suggested.
RESPONSES TO THE REVIEWER #3
1) In page 5, I think it is important to provide a more complete SINAN context. For example, how does this system collect data? Who actually does the monitoring, doctor? What criteria are used to diagnoses cases? Another important thing is how do authors identify tuberculosis cases form SINAN? By using ICD code or extracting manually from medication notes? If manually, how to evaluate the reliability?
Authors comments: In the methods section of the manuscript we include a description of how Sinan information is operated, as well as its filling and analysis routine.
2) In page 5 and 6, the paragraph “The variable that identifies the color or race of individuals according to the categories adopted by the Brazilian Geography and Statistics Institute (IBGE)…” presents twice.
Authors comments: Thank you for your observation and perform the correction in the manuscript.
3) In page 8, what did it mean by “ii) associated TB deaths, those deaths in which there was no mention of any of the ICD-10 codes (A15-A19), referring to TB in any line of part 1 of the death certificate”? Does that mean that tuberculosis was not coded as the underlying cases (part 1), but the terminal or intermediate cause?
Authors comments: Yes, tuberculosis was not a cause of death, but an intermediate or even terminal cause
4) The figure 1 is confusing and cannot be matched with the “Inclusion and exclusion criteria” part totally. This part should be combined with “Record linkage procedures and study groups definition” part and restructured.
Authors comments: We appreciate your important placement. This information was included in the supplementary section of the manuscript describing the database linkage process.
5) How did authors deal with the missing value?
Authors comments: In our study, the missing data in the categories of variables were not excluded, but classified as ignored to avoid loss of information in the study. Thus, we do not adopt multiple imputation. When a specific exam is not collected, health professionals tend not to fill in the variable, leaving it with missing data. That is why, we believe that the missing data are completely random, the incompleteness is not related to exposure, covariates or the outcome.
6) In page 10, what is the rationale of p-value>0.20 were used to select covariates? How about adding some sensitivity analysis by adopting different cut-off p values to validate the robustness of the results?
Authors comments: We appreciate the concern of the reviewer, in our study, although we selected an already known group of variables associated with death, this set of variables was still relatively large. Therefore, with the concern of being as flexible as possible and based on other studies in the literature, we also decided to adopt a cut-off point of p-value of 0.20 in order to seek a more parsimonious model as possible to describe the data, which also results better numerical stability and generalization of results. Thus, to better clarify the process of selecting variables for the Fine & Gray regression model, we rewrite the following sentence in the methods section.
“In our study, based on a group of variables associated with TB death in the literature, we based their choice individually, verifying the significance of each variable and the elimination of non-significant variables. Any variable with a p-value of 0.20 in the univariate test was selected as a candidate for the Fine & Gray multiple model, and the level of significance for choosing the final model was 5%”.
7) The cases used in this study were patients with tuberculosis treatment. What kind of treatments did they received? The treatment information should be controlled in the model as they also have effects on the survival time.
Authors comments: Unfortunately, in our database of Sinan there was no information on the type of drugs used in the treatment for tuberculosis, this can be explained that TB cases in Brazil receive a single standardized treatment.
Submitted filename:
PONE-D-19-34302R1
Factors associated with death in patients with tuberculosis in Brazil: survival analysis with competitive risks
PLOS ONE
Dear Dr. Viana,
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Additional Editor Comments (if provided):
In addition to the comments below, Reviewer 2 had previously requested that reference 1 be updated to the 2019 WHO Global Tuberculosis Report. Although the citation was updated, the language in the introduction was not. You still refer to 2017 deaths from TB -- the 2019 report is on deaths in 2018. Please update the year and the accurate number of deaths for 2018.
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Reviewers' comments:
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Reviewer #3: (No Response)
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Reviewer #3: Yes
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Reviewer #3: Yes
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Reviewer #3: No
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Reviewer #3: Although the authors have addressed the majority of my previous comments, some issues remain.
Some terms adopted for presenting the statistical methods need to be revised.
I think “survival analysis with competitive risk” is a rarely used term. “survival regression model” is also strange. It should be Cox regression/Cox proportional hazard model, or in your context, Fine and Gray competing risk regression model. Please go through all statistical terms again and make sure that they are standard terms commonly used in literature.
Besides, expressions such as “we believe”, “databases are fed continuously” should not be in academic writing.
Regarding my previous comments about a more detailed description of the data source, the sample representativeness is still not entirely clear to me. I don’t think “good coverage throughout the country” is precise enough. As this is critical for evaluating the external validity of the results, please try to add more accurate information.
Regarding the response about how missing values were handled. I think using complete cases is not a big issue. The important thing is to report the procedure - how the final sample size was arrived at (see the STROBE checklist). Even if you did not impute the data (which sometimes, indeed, may create another set of problems), the number of participants involved in each exclusion step needs to be reported (a flow chart is helpful, particularly for cohort study using electronic health records).
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Reviewer #3: No
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Author's reply to comments:
Dear Dr. David J Horne, PLOS ONE,
First, I would like to thank you for considering our manuscript for publication in PLOS ONE. We are pleased to send the revised version of the manuscript entitled “Factors associated with death in patients with tuberculosis in Brazil: survival analysis with competitive risks” for your consideration for publication in PLOS ONE.
We also thank the reviewers for their meticulous review and appreciate the constructive criticism of our work. We received from the reviewers and hope to point out all comments appropriately. We have made a wide revision and changes to the manuscript, which are highlighted and we also addressed the comments and changes, point to point, below. It was an immense privilege to get such valuable feedback that allowed us to address several gaps in our manuscript.
In this letter, you will find our responses to each question and concern.
Kind regards,
The authors
Additional Editor Comments (if provided):
In addition to the comments below, Reviewer 2 had previously requested that reference 1 be updated to the 2019 WHO Global Tuberculosis Report. Although the citation was updated, the language in the introduction was not. You still refer to 2017 deaths from TB -- the 2019 report is on deaths in 2018. Please update the year and the accurate number of deaths for 2018.
Authors comments: We appreciate the observation, we have corrected the epidemiological data of the global tuberculosis report for the year 2018.
RESPONSES TO THE REVIEWER #3
1) Some terms adopted for presenting the statistical methods need to be revised.
I think “survival analysis with competitive risk” is a rarely used term. “survival regression model” is also strange. It should be Cox regression/Cox proportional hazard model, or in your context, Fine and Gray competing risk regression model. Please go through all statistical terms again and make sure that they are standard terms commonly used in literature.
Authors comments: We appreciate your important placement. We carried out an extensive review and correction using the same nomenclatures used by one of the creators of the method (the researcher JP Fine), in a recent manuscript "Practical recommendations for reporting Fine-Gray model analyzes for competing risk data", for more details: https: / /pubmed.ncbi.nlm.
2) Besides, expressions such as “we believe”, “databases are fed continuously” should not be in academic writing.
Authors comments: Thank you for your observation and we have corrected these expressions in the manuscript.
3) Regarding my previous comments about a more detailed description of the data source, the sample representativeness is still not entirely clear to me. I don’t think “good coverage throughout the country” is precise enough. As this is critical for evaluating the external validity of the results, please try to add more accurate information.
Authors comments: We appreciate your important placement. More information was included in the aforementioned manuscript that describes the data source used to link the records, reporting their potential and also limitations of use in Brazil.
4) Regarding the response about how missing values were handled. I think using complete cases is not a big issue. The important thing is to report the procedure - how the final sample size was arrived at (see the STROBE checklist). Even if you did not impute the data (which sometimes, indeed, may create another set of problems), the number of participants involved in each exclusion step needs to be reported (a flow chart is helpful, particularly for cohort study using electronic health records).
Authors comments: We are grateful for the reviewer's concern and when re-evaluating the figure of the linkage process presented, it was confusing, as we mixed reclassification information of "treatment status" and exclusion criteria for observations (duplicate observations, inconsistencies in dates, among others). We redid the flowchart only with the information on the exclusions of the observations according to the exclusion criteria of the study cohort.
Submitted filename:
Factors associated with death in patients with tuberculosis in Brazil: competing risks analysis
PONE-D-19-34302R2
Dear Dr. Viana,
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David J Horne, MD, MPH
Academic Editor
PLOS ONE
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Reviewers' comments:
PONE-D-19-34302R2
Factors associated with death in patients with tuberculosis in Brazil: competing risks analysis
Dear Dr. Viana:
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