Introduction: Tuberculosis incidence is disproportionately high among people in poverty. Cash transfer programs have become an important strategy in Brazil fight inequalities as part of comprehensive poverty alleviation policies. This study was aimed at assessing the effect of being a beneficiary of a governmental cash transfer program on tuberculosis (TB) treatment cure rates. Methods: We conducted a longitudinal database study including people ≥18 years old with confirmed incident TB in Brazil in 2015. We treated missing data with multiple imputation. Poisson regression models with robust variance were carried out to assess the effect of TB determinants on cure rates. The average effect of being beneficiary of cash transfer was estimated by propensity-score matching. Results: In 2015, 25,084 women and men diagnosed as new tuberculosis case, of whom 1,714 (6.8%) were beneficiaries of a national cash transfer. Among the total population with pulmonary tuberculosis several determinants were associated with cure rates. However, among the cash transfer group, this association was vanished in males, blacks, region of residence, and people not deprived of their freedom and who smoke tobacco. The average treatment effect of cash transfers on TB cure rates, based on propensity score matching, found that being beneficiary of cash transfer improved TB cure rates by 8% [Coefficient 0.08 (95% confidence interval 0.06–0.11) in subjects with pulmonary TB]. Conclusion: Our study suggests that, in Brazil, the effect of cash transfer on the outcome of TB treatment may be achieved by the indirect effect of other determinants. Also, these results suggest the direct effect of being beneficiary of cash transfer on improving TB cure rates.
Keywords: Research Article; Medicine and health sciences; Infectious diseases; Bacterial diseases; Tuberculosis; Tropical diseases; People and places; Geographical locations; South America; Brazil; Pulmonology; Smoking related disorders; Mental health and psychiatry; Substance-related disorders; Public and occupational health; Health care; Socioeconomic aspects of health; Clinical medicine; Clinical immunology; Immune deficiency; Biology and life sciences; Immunology; Psychology; Behavior; Habits; Smoking habits; Social sciences; Behavioral and social aspects of health; Diagnostic medicine; Tuberculosis diagnosis and management
Tuberculosis (TB) remains a major health concern and is the leading cause of infectious disease mortality worldwide [[
The effect of poverty on TB incidence is unquestionable. The World Health Organization's End TB Strategy emphasizes this relationship and supports the integration of social support policies in a comprehensive approach to TB elimination [[
Launched in 2003, the Bolsa Família Program (BFP) a national conditional cash transfer program that requires education and health conditionalities to be met by the household for the receipt of benefits, including school attendance, children's immunizations, and pre- and post-natal care [[
A systematic review showed that, in low and middle-income countries, effective social support, through income transfer mechanisms and comprehensive interventions, have beneficial influences on TB outcomes [[
In order to understand the role of these governmental benefits on people with TB, since 2014, the Brazilian National Tuberculosis Program requires indication of the BFP beneficiary status on the TB notification forms. This study was aimed at assessing the effect of being beneficiary of a Brazilian governmental cash transfer program (BFP) on TB treatment cure rates.
We investigated the association between TB determinants and the outcomes of TB treatment in people from Brazilian Notifiable Disease Information System (SINAN) through a longitudinal database study. We included people ≥18 years old with confirmed incident TB who started and finished their treatment in 2015. People with a previous TB treatment were excluded, as well as those from the state of São Paulo because, in 2015, this state did not incorporate the new set of SINAN variables including beneficiary status into their surveillance systems. SINAN is a public database for all notifiable diseases in the Brazilian Unified Healthcare System (SUS–Sistema Único de Saúde). The World Health Organization (WHO) uses SINAN data in estimating Brazilian TB incidence. These data also include socio-demographic and clinical characteristics, features of treatment and outcome for individuals diagnosed with TB [[
Since 2014, in the beginning of treatment, people diagnosed with TB were asked about their governmental social programs benefits. A cash transfer beneficiary is considered any household member in a beneficiary family. These beneficiaries were divided into two groups based on classification within the SINAN database: a cash transfer group (people who answered as being beneficiaries of cash transfer program), and a non-cash transfer group (people who answered as not being beneficiaries of cash transfer program).
Concerning the outcomes of TB treatment, studied participants were prospectively followed through the treatment, from diagnosis to outcome, with TB outcome categorized as cure, dropout, death (from TB or from another cause), change of drug regimen, development of drug-resistant TB, and treatment failure. Cure is defined as treatment success which includes bacteriologically confirmed cure and completed treatment. The variable 'treatment outcome' was dichotomized in 'cured' and 'not cured' (including all other outcomes).
In order to explore SINAN covariates involved in the causal pathway to determining TB treatment outcomes, we also assessed the determinants proposed by the theoretical model of Maciel and Reis-Santos (2015) according to their vulnerability axes [[
Missing data are a serious concern in epidemiologic studies, particularly those using secondary data. SINAN has reasonable completeness as previously reported, but missing data is a problem for some covariates [[
Poisson regression models with robust variance were carried out to assess the effect of TB determinants on cure rates in the whole population and among the cash transfer group [[
People covered by governmental social programs of cash transfer must meet eligibility criteria, including low income and compliance with conditions related to education and health [[
Propensity score indicates the probability of allocation to exposure group given the measured covariates [[
We used Poisson regression models with robust variance to evaluate the direct effect of government cash transfer program on TB treatment outcomes ("not cured" was the category of reference). The first adjusted model included the set of confounding variables to the direct effect of cash transfer program on the outcomes: schooling, skin color, area of residence, region of residence, being healthcare worker, and comorbidities. The second model was adjusted by the propensity score (as continuous variable) and, additionally, by skin color and region of residence, which were not contained in the propensity score. Again, the Poisson regression models were replicated in a subsample including only people with pulmonary TB.
The average effect of being beneficiary of cash transfer program was estimated by propensity-score matching in a 1:1 ratio (we consider a pair of observations a match if the absolute difference in the propensity score was less than 0.05) [[
In addition, all analyses mentioned above were replicated using a dataset which included only individuals without any missing SINAN data (so, not subject to imputation). The results are shown in the supplementary material.
Data were analyzed using Stata 14 (Stata Corp., College Station, USA).
The study was approved by the ethics committee of Centro de Ciências da Saúde from Universidade Federal do Espírito Santo under the number 242,826.
There was no involvement of the funding source in study design, collection, analysis, and interpretation of data, writing, or in the decision to submit the paper for publication. All the authors had full access to the data in the study and had final responsibility for this publication.
In 2015, there were 76,109 people with TB notified in SINAN, of which 46,568 finished their treatment in the same year. We excluded 14,504 people from São Paulo (without information about governmental benefits and 6,980 < 18 years old and/or with previous TB treatment (Fig 1). Thus, in the present analysis, we included 25,084 women and men aged ≥18 years old diagnosed as new TB case, of whom 1,714 (6.8%) were beneficiaries of governmental social program of cash transfer (cash transfer group).
Table 1 shows that 12,911 (51%) and 896 (52%) of whole population and cash transfer group were <40 years old, respectively. With respect to schooling, 2,405 (10%) of whole population and 328 (19%) of cash transfer group had no schooling and 7,360 (29%) of whole population and 598 (35%) of cash transfer group had one to four years of education. 7,405 (30%) of whole population, excluding all people from São Paulo state, live in the Southeast region while 763 (45%) of cash transfer group live in the Northeast region.
Table 1: Distribution of sociodemographic determinants of tuberculosis among people from the Brazilian Notifiable Disease Information System database (SINAN), 2015.
Determinants Total population Cash transfer group n % n % Sex Women 8,330 33 942 55 Men 16,754 67 772 45 Total 25,084 100 1,714 100 Age <40 years 12,911 51 896 52 40–59 years 8,066 32 493 29 >60 years 4,107 16 325 19 Total 25,084 100 1,714 100 Schooling No schooling 2,405 10 328 19 1–4 years 7,360 29 598 35 5–8 years 7,467 30 495 29 >8 years 7,852 31 293 17 Total 25,084 100 1,714 100 Skin color White 7,832 31 309 18 Black 3,544 14 281 16 Brown 13,164 53 973 57 Yellow/Indigenous 544 2 151 9 Total 25,084 100 1,714 100 Place of residence North 3,739 15 280 16 Northeast 8,226 33 763 45 Southeast 7,405 30 283 17 South 4,113 16 214 12 Middle-West 1,601 6 174 10 Total 25,084 100 1,714 100 Area of residence Urban 22,511 90 1,297 76 Rural/peri-urban 2,573 10 417 24 Total 25,084 100 1,714 100 People deprived of their freedom No 23,185 92 1,624 95 Yes 1,899 8 90 5 Total 25,084 100 1,714 100 Homeless No 24,555 98 1,697 99 Yes 529 2 17 1 Total 25,084 100 1,714 100 Healthcare worker No 24,739 99 1,705 99 Yes 345 1 9 1 Total 25,084 100 1,714 100 Immigrant No 25,003 100 1,710 100 Yes 81 0 4 0 Total 25,084 100 1,714 100
Concerning behaviors and comorbidities (Table 2), among whole population and cash transfer group, respectively, 4,314 (17%) and 281 (16%) smoked tobacco, 4,314 (17%) and 214 (12%) lived with alcohol use disorder, 2,350 (9%) and 96 (6%) substance abuse disorder, 2,109 (8%) and 165 (10%) lived with diabetes, 609 (2%) and 53 (3%) lived with mental health disorder, and 2,815 (11%) and 128 (7%) lived with HIV.
Table 2: Distribution of clinical determinants of tuberculosis among people from the Brazilian Notifiable Disease Information System database (SINAN), 2015.
Determinants Total population Cash transfer group n % n % Tobacco smoking No 20,770 83 1,433 84 Yes 4,314 17 281 16 Total 25,084 100 1,714 100 Alcohol use disorder No 20,770 83 1,500 88 Yes 4,314 17 214 12 Total 25,084 100 1,714 100 Drug abuse disorder No 22,734 91 1,618 94 Yes 2,350 9 96 6 Total 25,084 100 1,714 100 Diabetes No 22,975 92 1,549 90 Yes 2,109 8 165 10 Total 25,084 100 1,714 100 Mental health disorder No 24,475 98 1,661 97 Yes 609 2 53 3 Total 25,084 100 1,714 100 HIV/AIDS No 22,269 89 1,586 93 Yes 2,815 11 128 7 Total 25,084 100 1,714 100 Clinical form Pulmonary 21,163 84 1,504 88 EPTB or pulmonary + EPTB 3,921 16 210 12 Total 25,084 100 1,714 100 DOT No 12,643 50 578 34 Yes 12,441 50 1,136 66 Total 25,084 100 1,714 100 Cash transfer program No 23,370 93 0 0 Yes 1,714 7 1,714 100 Total 25,084 100 1,714 100 Treatment outcome Not cure 7,859 31 383 22 Cured 17,225 69 1,331 78 Total 25,084 100 1,714 100
1 DOT: directly observed therapy; EPTB: extrapulmonary tuberculosis; HIV/AIDS: human immunodeficiency virus/ acquired immunodeficiency syndrome; n: number of observations; TB: tuberculosis.
The association of TB determinants and treatment outcome is showed in Table 3. Men demonstrated a lower probability of cure when compared with women (RR 0.90; 95%CI 0.88 to 0.91 in participants with all TB forms and RR 0.90; 95%CI 0.88 to 0.92 in participants with pulmonary TB). However, this difference vanished among men and women in the cash transfer group (RR 0.97; 95%CI 0.92 to 1.02 for participants with all TB forms and RR 0.98; 95%CI 0.93 to 1.03 for participants with pulmonary TB). When compared with people with no schooling, the risk ratio for cure was higher for those who studied > 8 years among total population (RR 1.19; 95%CI 1.15 to 1.23 to people with pulmonary TB), but was not seen among cash transfer group (RR 0.93; 95%CI 0.86 to 1.02 to people with pulmonary TB). Among the regions of Brazil, only people from the Northeast, when compared with those from Southeast, had higher cure rates (RR 1.12; 95%CI 1.02 to 1.21 in people with pulmonary TB).
Table 3: Adjusted* risk ratios of hierarchical Poisson regression models of the association of determinants of TB and treatment cure rates to 25,084 people from the Brazilian Notifiable Disease Information System database (SINAN), 2015.
Determinants Risk ratios (95% confidence intervals) * Total population Cash transfer group All TB forms Pulmonary TB only All TB forms Pulmonary TB only Level 1 Sex Male 0.90 (0.88 to 0.91) 0.90 (0.88 to 0.92) 0.97 (0.92 to 1.02) 0.98 (0.93 to 1.03) Age 40–59 years 0.96 (0.95 to 0.98) 0.98 (0.96 to 1.00) 0.93 (0.87 to 0.98) 0.93 (0.87 to 0.99) >60 years 0.81 (0.79 to 0.84) 0.82 (0.80 to 0.85) 0.75 (0.68 to 0.82) 0.73 (0.67 to 0.81) Schooling 1–4 years 1.00 (0.97 to 1.04) 1.01 (0.97 to 1.04) 0.96 (0.90 to 1.03) 0.94 (0.88 to 1.01) 5–8 years 0.91 (0.88 to 0.95) 0.93 (0.90 to 0.97) 0.87 (0.81 to 0.94) 0.87 (0.80 to 0.94) >8 years 1.17 (1.13 to 1.21) 1.19 (1.15 to 1.23) 0.93 (0.85 to 1.01) 0.93 (0.86 to 1.02) Skin color Black 0.94 (0.91 to 0.96) 0.94 (0.91 to 0.97) 0.95 (0.86 to 1.05) 0.98 (0.88 to 1.08) Brown 0.98 (0.96 to 1.00) 0.99 (0.97 to 1.01) 1.05 (0.98 to 1.13) 1.06 (0.98 to 1.15) Yellow/Indigenous 1.15 (1.10 to 1.20) 1.15 (1.10 to 1.21) 1.07 (0.97 to 1.18) 1.07 (0.97 to 1.19) Level 2 Region of residence North 1.11 (1.08 to 1.14) 1.14 (1.11 to 1.17) 1.05 (0.96 to 1.16) 1.10 (0.99 to 1.21) Northeast 1.10 (1.08 to 1.13) 1.10 (1.08 to 1.13) 1.08 (0.99 to 1.17) 1.12 (1.02 to 1.21) Middle-West 1.09 (1.06 to 1.13) 1.11 (1.07 to 1.15) 0.99 (0.87 to 1.12) 0.99 (0.86 to 1.13) South 0.93 (0.90 to 0.96) 0.95 (0.92 to 0.98) 0.97 (0.86 to 1.08) 0.99 (0.88 to 1.12) Area of residence Rural or peri-urban 1.15 (1.12 to 1.18) 1.15 (1.12 to 1.18) 1.14 (1.07 to 1.20) 1.12 (1.06 to 1.19) Level 3 Deprived of their freedom 1.14 (1.10 to 1.17) 1.14 (1.11 to 1.18) 1.12 (1.04 to 1.22) 1.09 (0.99 to 1.19) Homeless 0.54 (0.48 to 0.60) 0.53 (0.47 to 0.60) 0.73 (0.48 to 1.11) 0.68 (0.42 to 1.10) Healthcare worker 0.99 (0.93 to 1.05) 0.94 (0.88 to 1.01) 1.16 (0.86 to 1.58) 1.08 (0.65 to 1.77) Immigrant 1.09 (0.94 to 1.27) 1.04 (0.87 to 1.24) 1.23 (0.97 to 1.56) 1.32 (0.99 to 1.75) Level 4 Tobacco smoking 0.93 (0.90 to 0.96) 0.93 (0.90 to 0.96) 0.89 (0.81 to 0.98) 0.91 (0.83 to 1.01) Alcohol use disorder 0.85 (0.82 to 0.87) 0.84 (0.81 to 0.87) 0.82 (0.73 to 0.91) 0.81 (0.72 to 0.91) Drug use disorder 0.76 (0.73 to 0.80) 0.77 (0.73 to 0.81) 0.83 (0.69 to 0.99) 0.81 (0.67 to 0.97) Diabetes 1.01 (0.98 to 1.04) 1.01 (0.98 to 1.04) 0.97 (0.89 to 1.07) 0.98 (0.89 to 1.07) Mental health disorder 0.98 (0.92 to 1.04) 0.97 (0.90 to 1.03) 1.04 (0.93 to 1.15) 1.07 (0.97 to 1.18) HIV/AIDS 0.42 (0.40 to 0.44) 0.44 (0.41 to 0.47) 0.46 (0.36 to 0.58) 0.52 (0.41 to 0.66) Level 5 DOT 1.21 (1.19 to 1.23) 1.22 (1.20 to 1.25) 1.17 (1.10 to 1.24) 1.15 (1.08 to 1.22)
- 2 * Adjusted for the determinants that belong to the hierarchical levels above, as well as for the determinants' effects on the same level.
- 3 DOT: directly observed therapy; EPTB: extrapulmonary tuberculosis; HIV/AIDS: human immunodeficiency virus/ acquired immunodeficiency syndrome; n: number of observations; TB: tuberculosis.
- 4 References: treatment outcome (cure), age (<40 years), schooling (no schooling), skin color (white), region of residence (Southeast), area of residence (urban), people deprived of their freedom (no), people living in homeless (no), healthcare worker (no), immigrant (no), and DOT (no).
Cure rates were inversely associated with homeless status (RR 0.54; 95%CI 0.48 to 0.60 to participants with all TB forms and RR 0.53; 95%CI 0.47 to 0.60 to participants with pulmonary TB) among total population. However, among the cash transfer group, this association was not found (RR 0.73; 95%CI 0.48 to 1.11 for participants with all TB forms and RR 0.68; 95%CI 0.42 to 1.10 for participants with pulmonary TB). Having, alcohol use disorder, drug use disorder, or living with HIV/AIDS were associated with decreased cure rates in the models analyzed.
People assigned to DOT were more likely to experience TB cure when compared with those not assigned to DOT in the total studied population (RR 1.21; 95%CI 1.19 to 1.23 in participants with all TB forms and RR 1.22; 95%CI 1.20 to 1.25 in participants with pulmonary TB). This effect was sustained among people in the cash transfer group, however there was an attenuation in the estimates of effect (RR 1.17; 95%CI 1.10 to 1.24 in participants with all TB forms and RR 1.15; 95%CI 1.08 to 1.22 in participants with pulmonary TB).
Table 4 shows that 68% (
Table 4: Distribution and estimates of the direct effect of being beneficiary of governmental cash transfer program on tuberculosis treatment outcomes among people from the Brazilian Notifiable Disease Information System database (SINAN), 2015.
Determinants All TB forms Pulmonary TB only Treatment outcome Not curedn (%) Curedn (%) Totaln (%) Not curedn (%) Curedn (%) Totaln (%) No cash transfer group 7,476 (32) 15,894 (68) 23,370 (100) 6,174 (31) 13,485 (69) 19,659 (100) Cash transfer group 383 (22) 1,331 (78) 1,714 (100) 316 (21) 1,188 (79) 1,504 (100) Total 7,859 (31) 17,225 (69) 25,084 (100) 6,490 (31) 14,673 (69) 21,163 (100) Total population Model Aa RR 1.14 (95%CI, 1.11 to 1.17) RR 1.15 (95%CI, 1.12 to 1.18) Model Bb RR 1.13 (95%CI, 1.10 to 1.16) RR 1.14 (95%CI, 1.10 to 1.17) Model Cc RR 1.13 (95%CI, 1.10 to 1.16) RR 1.14 (95%CI, 1.10 to 1.17) Not assigned to DOT Model Aa RR 1.11 (95%CI, 1.04 to 1.17) RR 1.15 (95%CI, 1.08 to 1.21) Model Bb RR 1.13 (95%CI, 1.07 to 1.20) RR 1.16 (95%CI, 1.10 to 1.23) Model Cc RR 1.12 (95%CI, 1.05 to 1.18) RR 1.15 (95%CI, 1.09 to 1.23) Assigned to DOT Model Aa RR 1.10 (95%CI, 1.07 to 1.13) RR 1.10 (95%CI, 1.07 to 1.13) Model Bb RR 1.08 (95%CI, 1.05 to 1.11) RR 1.08 (95%CI, 1.05 to 1.11) Model Cc RR 1.09 (95%CI, 1.05 to 1.12) RR 1.09 (95%CI, 1.05 to 1.12) ATEdtotal population Coeff. 0.08 (95%IC, 0.05 to 0.10) Coeff. 0.08 (95%IC, 0.06 to 0.11) ATEdnot assigned to DOT Coeff. 0.06 (95%IC, 0.02 to 0.10) Coeff. 0.08 (95%IC, 0.04 to 0.13) ATEdassigned to DOT Coeff. 0.06 (95%IC, 0.03 to 0.09) Coeff. 0.06 (95%IC, 0.03 to 0.09)
- 5 a: Model unadjusted.
- 6 b: Model adjusted by schooling, skin color, healthcare worker, area of residence, region of residence, and comorbidities.
- 7 c: Model adjusted by propensity score, skin color, and region of residence.
- 8 d: Average treatment effect of being beneficiary of governmental social program of cash transfer on cure.
- 9 Coeff.: difference of the effects for beneficiaries of cash transfer program and non-beneficiaries; DOT: directly observed therapy; n: number of observations; RR: risk ratio
- 10 Not Cured: reference category of outcome.
Controlling for confounding variables, there was a positive direct effect of being beneficiary of cash transfer program on cure rates of TB treatment in Poisson models of both studied groups, participants with all TB form and participants with pulmonary TB alone (Table 4). The risk ratio of cure among the cash transfer group was 1.13 (95%CI: 1.10 to 1.16) in both the model adjusted by confounding variables individually and the propensity scored model. DOT moderated the effect of this association [RR 1.16 (95%CI, 1.10 to 1.23) among participants with pulmonary TB not assigned to DOT and RR 1.08 (95%CI, 1.05 to 1.11) among participants with pulmonary TB assigned to DOT].
The average treatment effect of governmental program of cash transfer on TB cure rates, based on propensity score matching, found that being beneficiary of cash transfer improved cure rates by 8% [Coefficient 0.08 (95%CI: 0.05 to 0.10) in participants with TB of any type and Coefficient 0.08 (95%CI: 0.06 to 0.11) to participants with pulmonary TB only]. Also, the balance plots showed an improvement in the comparability between cash transfer groups after matching (Fig 2).
All analyses were replicated with a dataset of people without missing information with did not undergo previous imputation, and they are available as supplementary material (S1, S2, S3 and S4 Tables and S1 Fig).
In a Brazilian longitudinal database of people newly diagnosed with TB in 2015, we identified several determinants of TB treatment outcomes. Among the total population with pulmonary TB only, males, older subjects, those with 5–8 years of schooling, blacks, those living in the Southeast and South region and urban areas were less likely to be cured. Also were less likely to be cured those not deprived of their freedom, homeless, who smoke tobacco, living with alcohol use disorder, substance abuse or drug abuse disorder, HIV AIDS, and those assigned to DOT. However, among the cash transfer group, the association disappeared among males, blacks, region of residence, not deprived of their freedom, and who smoke tobacco products.
Previous assessment of SINAN database found similar associations among TB determinants [[
In general, studies evaluating the determinants of TB treatment outcome among beneficiaries of cash transfer programs are scarce. Torrens et. al. found, among people who were beneficiaries of BFP during TB treatment and after TB treatment, that cure was less likely in older subjects, illiterates, blacks, PLHIV/AIDS, those with unknown HIV status, and those not assigned to DOT [[
In our present study, the effect of some determinants of TB treatment outcome in the total population was not maintained in the cash transfer group, suggesting that there might be an indirect effect of governmental social programs of cash transfer on TB treatment outcome in agreement with previous evidence [[
Our results suggest that there is an independent effect of benefit of cash transfer on improving cure rates of TB among people under TB treatment. This field of research is still incipient, but most of the studies evaluated the role of TB-specific initiatives of cash transfer on cure rates and they also found a positive association [[
With regard to TB-specific initiative, in Peru, people being treated for TB and their household contacts were randomly assigned to socioeconomic support (comprised conditional cash transfers up to $230 United States dollars). The socioeconomic support intervention was feasible and increased the proportion of household contacts of people being treated for TB who initiated TB preventive therapy; and the TB treatment success rate among people with TB [[
To our knowledge, in Brazil, only two previous studies assessed the effect of a TB-sensitive program of cash transfer, the BFP, on TB treatment outcome [[
Catastrophic costs incurred when the total costs due to TB disease exceed 20% of the annual household income. Thus, social protection initiatives, TB-specific or not, appear to be a way to struggle against catastrophic costs, by alleviating the heavy economic burden of TB [[
The consistency between these results strengthens the evidence of a direct effect of BFP on TB treatment outcomes. However, given the nature of the data, we can not rule out the possibility that residual confounding may act in this association. Effect estimates reported by observational studies can be distorted by confounding. Generally, measurement error in a confounder and unmeasured confounders results in partial adjustment, biasing the association of interest in any direction [[
Evaluation based on the SINAN database with people notified with TB, in 2011, revealed that the DOT strategy targets vulnerable populations, as it is designed to do, and that it had an important impact on improving the TB cure rate in these populations [[
In agreement with previous findings [[
The strengths of this study include the large longitudinal database with a countrywide sample, allowing a wide national representativeness. Moreover, we built the analytic models based on a rigorous theoretical model specific to Brazil and used multiple imputation techniques to deal with missing data. In spite of the known variation of missing data between states, and geographical regions, the reasons for such absence are related to health facilities characteristics, leading to a non-differential measurement error which was dealt with through multiple imputation. In the present study, we performed a sensitivity analysis to assess the likelihood of bias due to imputation by replicating our models with a dataset of people with completed information. Results with inclusive confidence intervals allow us to conclude that the main findings are consistent.
While governmental benefit was reported in the same way for beneficiaries and non-beneficiaries, the self-reported information may be a limitation by lowering accuracy and potential misclassification. As this error is likely to be non-differential, it would leave residual confounding. Heterogeneity among non-beneficiaries of cash transfer program also could be a limitation of our study. To overcome this, we used a propensity score approach to indicate the probability of allocation to exposure group and control for different variants of exposure. In addition, the assessments of social and behavioral aspects involve complex causal chains and contextual effects may also interact with these individuals' characteristics altering the outcome determination [[
DOT should be considered in SINAN as the observation of TB medicines taken, by a health professional, at least 3 times a week, in the attack phase, and at least 2 times a week, in the maintenance phase. However, there is no quality control to assure the consistency of this variable and DOT might not be correct classified in some health facilities. In spit of this limitation, is important to highlight that the initiative which led the health professional to assigned a people as under DOT contributed to better treatment outcomes.
Our study suggests that, in Brazil, the effect of benefit of governmental cash transfer on the outcome of TB treatment may be achieved by other determinants, indirect effect. Also, there is suggestion of a direct effect of being beneficiary of cash transfer program on improving TB cure rates. Further studies should focus on elucidating the social and behavioral determinants which may play a role in the association of TB-sensitive interventions and the outcome of TB treatment, as well as on describing all causal paths involved in this association. Also, the effect of TB-specific interventions on the outcome of TB treatment as well as the role of strategies like DOT, with recognized effectiveness, in these association should be assessed.
S1 Table. Distribution of missing data among subjects from the Brazilian Notifiable Disease Information System database (SINAN), 2015. DOT: directly observed therapy; HIV/AIDS: human immunodeficiency virus/ acquired immunodeficiency syndrome; n: number of observations; TB: tuberculosis. (PDF)
S2 Table. Distribution of tuberculosis determinants of subjects with completed data on the Brazilian Notifiable Disease Information System database (SINAN), 2015. DOT: directly observed therapy; EPTB: extrapulmonary tuberculosis; HIV/AIDS: human immunodeficiency virus/ acquired immunodeficiency syndrome; n: number of observations; TB: tuberculosis. (PDF)
S3 Table. Adjusted* risk ratios of hierarchical Poisson regression models of the association of determinants of TB and treatment cure rates to subjects with completed data from the Brazilian Notifiable Disease Information System database (SINAN), 2015*. Adjusted for the determinants that belong to the hierarchical levels above, as well as for the determinants' effects on the same level. DOT: directly observed therapy; EPTB: extrapulmonary tuberculosis; HIV/AIDS: human immunodeficiency virus/ acquired immunodeficiency syndrome; n: number of observations; TB: tuberculosis. References: treatment outcome (cure), age (<40 years), schooling (illiterate), skin color (white), region of residence (Southeast), area of residence (urban), prisoner (no), homeless (no), healthcare worker (no), immigrant (no), and DOT (no). (PDF)
S4 Table. Distribution and estimates of the direct effect of being beneficiary of governmental cash transfer program on tuberculosis treatment outcomes among subjects with completed data from the Brazilian Notifiable Disease Information System database (SINAN), 2015. a: Model unadjusted. b: Model adjusted by schooling, skin color, healthcare worker, area of residence, region of residence, and comorbidities. c: Model adjusted by propensity score, skin color, and region of residence. d: Average treatment effect of being beneficiary of governmental social program of cash transfer on cure. Coeff.: difference of the effects for beneficiaries of cash transfer program and non-beneficiaries; DOT: directly observed therapy; n: number of observations; RR: risk ratio. Not cured: reference category of outcome. (PDF)
S1 Fig. Balance box from propensity score matching of subjects with completed data from the Brazilian Notifiable Disease Information System database (SINAN-2015). (TIFF)
DIAGRAM: Fig 1: Flowchart of study sampling of people from the Brazilian Notifiable Disease Information System database (SINAN), 2015.
DIAGRAM: Fig 2: Balance box from propensity score matching of people from the Brazilian Notifiable Disease Information System database (SINAN-2015).
This article was conducted by the Lab-Epi UFES–Laboratory of Epidemiology at Federal University of Espírito Santo (Brazil), with the collaboration of the National Tuberculosis Program of Brazil, and the Global Tuberculosis Programme of the World Health Organization.
By Barbara Reis-Santos, Writing – original draft; Priya Shete, Writing – review & editing; Adelmo Bertolde, Writing – review & editing; Carolina M. Sales, Writing – review & editing; Mauro N. Sanchez, Writing – review & editing; Denise Arakaki-Sanchez, Writing – review & editing; Kleydson B. Andrade, Writing – review & editing; M. Gabriela M. Gomes, Writing – review & editing; Delia Boccia, Writing – review & editing; Christian Lienhardt, Writing – review & editing and Ethel L. Maciel, Writing – review & editing
