A Systematic Review and Meta-Analysis on Catastrophic Cost Incurred by Tuberculosis Patients

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El Saeh, Shaimaa Abdulaziz, Esraa Abdellatif Hammouda, and 5 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-409667/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 11 Jan, 2022 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Background: One of the World Health Organization End Tuberculosis (TB) Strategy is to reduce the proportion of TB affected families facing catastrophic costs (CC) to 0% by 2020. CC is defined if total cost related to TB management exceeded 20% of annual pre-TB household income. This study aimed to estimate the pooled proportion (PP) of TB affected households who suffered from CC. Method: A search of the online database through September 2020 was performed. Of 5114 articles, 29 articles were included in meta-analysis. We used R software to estimate the PP at 95% confidence intervals (CIs) using the fixed/random-effect models. Result: The PP of patients faced CC was 43%. Meta-regression revealed that country, drug sensitivity and HIV co-infection were the main predictors. CC incurred by drug sensitive, drug resistant and HIV coinfection patients were 32%, 80%, and 81% respectively. Lower CC incurred by active than passive case finding; 12% versus 42%. Direct cost represented 55% (95% CI 43-66) of the total cost. About 45% of TB-affected household faced catastrophic health expenditure at cut-off point of 10%. Conclusion: There is still a significant proportion of TB patients facing CC, which represent a main obstacle against TB control. PROSPERO registration: CRD42020221283 Health Economics & Outcomes Research Tuberculosis catastrophic cost catastrophic health expenditure coping cost direct cost indirect cost Figures Figure 1 Figure 2 Figure 3 Introduction Tuberculosis (TB) infection is one of the top 10 causes of death. It caused 1.2 million deaths in 2019. TB affects about one-quarter of the world's population[ 1 ]. According to World Health Organization (WHO) report in 2020, WHO region that reported the highest incidence of TB was Africa region (266/10 5 ) corresponding to 2.5 million cases. The South-East Asian region ranked the second (217/10 5 ) corresponding to 4.3 million cases followed by the East Mediterranean region (114/10 5 ) corresponding to 819 thousand case, and by Western Pacific region (93/10 5 ) corresponding to 1.8 million cases. On country-based ranking, number of reported new cases is the highest in India (26%), Indonesia (8.5%), China (8.4%), Philippines (6.0%), Pakistan (5.7%), Nigeria (4.4%), Bangladesh and South Africa (3.6% for each) .[ 2 ] On 26 September 2018, WHO’s End TB Strategy was set and agreed by United Nation to end TB epidemic by 2030, with step wise milestones for 2020, 2025, and 2030. One of these Strategies is to reduce TB incidence rate and deaths by 90% and 95% respectively. It was also recommended to find TB missing cases by “active case finding (ACF) instead of passive case finding (PCF). ACF means systematic identification and screening of people with presumptive TB, in high-risk groups, using tests, examinations or other procedures that can be applied rapidly”, while PCF entails visiting health services for diagnosis[ 3 , 4 ]. In addition, all TB patients or families should not suffer from catastrophic total costs (CTC) due to TB as one of the main obstacles for TB patients to complete their treatment; [ 5 ]. Catastrophic cost is defined as the total direct and indirect costs that reaches or exceed 20% of the pretreatment patient or household’s annual income. [ 5 ]of note, factors that aggravate this catastrophic cost are patient age and sex, socioeconomic status, Human immuno-deficiency virus (HIV) co-infection, and being infected with multidrug-resistant TB (MDR-TB) that does not respond to at least Isoniazid and Rifampicin, the 2 most powerful anti-TB drugs [ 6 ] [ 7 ]. The nominator of catastrophic cost is the summation of direct and indirect costs. The direct cost includes either medical cost (consultation fees, diagnostic tests and treatment) or non-medical cost (transportation, accommodation, increased food needs). Indirect cost includes lost wages due to unemployment; time spent away from work and associated loss of productivity. Moreover, patients also incur large costs in the pre-treatment phase to cover consultations and laboratory tests, symptomatic treatment, antibiotics trial, and hospitalization [ 8 ]. An important segment of the financial hardship is dissaving which means reduced financial strength of a household or engage the household in damaging financial coping strategies. This will reduce the financial capacity and their coping with the financial shocks and cast them into the poverty trap .[ 9 ] Dissaving can take many forms like taking out a loan, taking children out of education, selling assets, reducing consumption to below basic needs to cope with health-related expenditure [ 8 – 10 ]. Consequently, WHO developed the TB patient cost survey to properly assess the total costs and proportion of patients facing catastrophic cost. This tool provide a standardized methodology for cross-sectional surveys in TB affected countries [ 11 ]. Many studies used this cost survey to report catastrophic cost, catastrophic health expenditure, or hardship financing incurred by TB patients [ 12 – 14 ]. Some literatures calculated catastrophic cost for drug sensitive, MDR or HIV co-infection [ 14 – 16 ]. Other studies estimated compared this cost considering adoption of different case finding strategies (ACF versus PCF) [ 17 , 18 ]. In response to this reported catastrophic cost, the Global TB Program endorses social protection initiatives to complement Universal health coverage (UHC) initiatives [ 19 , 20 ]. Examples of social protection interventions include cash transfers, food assistance, disability grants and health insurance. Those global financial supports already exist in most countries, but may not be fully implemented [ 7 ]. At the end, keeping in mind that COVID-19 pandemic may reverse the achieved progress in the TB control as many countries directed their resources toward pandemic containment. In addition, there are no published systematic reviews that report the pooled proportion of patients suffering from catastrophic cost; we aimed to perform this systematic review and meta-analysis to estimate the proportion of catastrophic cost among TB patients and their households in attempt to support the ongoing TB control programs. Method This systematic review and meta-analysis was conducted according to the Preferred Reporting Items of the Systematic Reviews and Meta-Analyses (PRISMA) guidelines [ 21 ]. Data source and search strategy EMBASE, Scops, EBSCO, MEDLINE central/PubMed, ProQuest, Scielo, SAGE, Web of science, and Google scholar databases were searched for articles without timeframe, geographical or language restrictions up to November 20 th , 2020 by two authors ( ShA & NZ) then revised by (RMG& SA). Highly focused and sensitive search strategies were developed by RMG after the approval of PubMed Help Disk. The search terms include (“tuberculosis “OR “Mycobacterium tuberculosis” OR “Koch’s disease” AND “catastrophic cost”). References from relevant studies were screened for supplementary articles. Study selection and data extraction: We aimed to include observational studies, which reported the proportion of patients suffering from catastrophic cost during the intensive (first 2 or 8 months of treatment in DS or MDR respectively) or the continuation phases of TB treatment. The primary endpoint of interest was the proportion of TB affected patients and their households who face catastrophic cost. It was defined as the total direct and indirect costs due to TB reaches or exceed 20% of the patient or household’s annual income [5] . Furthermore, CTC was assessed among patients according to their drug sensitivity as DS or MDR (with or without HIV), and strategy of case finding (ACF versus PCF). Secondary outcomes were the proportion of the direct to the total cost of TB among DS or MDR, with or without HIV, catastrophic health expenditure CHE (defined as direct cost that reaches or exceeds 40% of patients capacity to pay or 10% of their household income [22], and the different coping strategies. Titles and abstracts were screened independently by four authors (AM, ShA, NZ, and EE), who discarded articles not pertinent to the topic. Non-observational studies, case reports, editorial, reviews, letters, and studies that estimated the direct and indirect cost of the population as a one unit not individually were excluded from qualitative analyses but screened for potential additional references. Three other authors (RMG, SA & HE) solved the discrepancies on study judgements. Data extraction and analysis were performed by (RMG, AM, HE) and independently verified by (SA). Data analysis: The proportion of CTC among TB patients was pooled using the random-effects model. To ensure robustness of the model and susceptibility to outliers, pooled data was also analyzed with the fixed-effects model. Heterogeneity was assessed by the Chi-squared test on N-1 degrees of freedom, with an alpha of 0.05 considered for statistical significance and the Cochrane-I-squared (I 2 ) statistic. I2 values of 25%, 50% and 75% were considered to correspond to low, medium and high levels of heterogeneity, respectively. Sources of heterogeneity, for identifying possible effect modifiers on the pooled analyses, were explored using: 1- Sensitivity analysis (leave one out sensitivity analysis, GOSH sensitivity analysis, remove outliers) 2- Subgroup analysis: we categorized the catastrophic cost at 20% for ACF and PCF patients according to country where studies were conducted (inside/outside) India. 3- Met-regression: The impact of country where the survey was conducted (high versus low incidence of TB) [23], quality of the study, sex, and population criteria (drug sensitivity, drug resistant with or without HIV) on the size effect of studies to explain the substantial heterogeneity. The forest plot was used to visualize the degree of variation between studies. All data analysis was performed R software version 4.0.3 using Harrer hand-on guide [24]. Publication bias: Publication bias was investigated by visual inspection of funnel plots, and by Egger’s regression test. Quality assessment The Newcastle-Ottawa Scale (NOS) was used to assess the quality of studies. Studies were classified according to the NOS as: very good studies (9-10 points), good studies (7-8 points), satisfactory studies (5-6 points), and unsatisfactory studies (0-4 points).[25] Results Search results: The flow diagram of the selection process is shown in figure 1. In total of 5114 potentially relevant articles were found after data base search. One additional citation was found through a personal search, of this number, 1922 articles were excluded as duplicates by Endnote X8. After title and abstract screening 3041 article were excluded (201 duplicates found manually, 2840 irrelevant). Two unpublished data were included to the 152 text eligible articles to full text screening, in addition we added 2 articles were added manually. A total of 29 articles were therefore reviewed in detail and included in the analysis. The main characteristics of these studies are summarized in table 1. The inter-rater agreement for inclusion was κ=0.95 and for the quality assessment was κ=0.8 Study characteristics Qualitative synthesis included 29 studies conducted in 15 countries; six studies from India, five from China, four from Indonesia, one study from each of the following countries (Egypt, Zimbabwe, Nepal, Lao PDR, Ghana, Pakistan, Vietnam, Cambodia, Peru, and Cavite), and two studies from each Uganda, and South Africa. Of included studies there were 5 cohort studies [12], [13], [17], [26] &[27] . One mixed methods study [28], while the other 23 studies were cross-sectional. Male sex presentation ranged from 30% [29], to 77% [18]. The sample size ranged from 50 [29], to 1178 [30]. The tool used for estimation of the cost survey were either WHO TB cost survey tool, [5], [15], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40] & [44], or adapted WHO tool to Indonesian context [12] & [41], or structured questionnaire [17], [26], [42], [43], pre-coded interview scheduled [18],or tool of stop TB partnership, [13], [27], [28],or headcount tool [44], or Lumley T. survey [14], or TB coalition tool [16]. On the other hand, there were two studies not mentioned the tool used [29], [45]. The percent of patients facing catastrophic cost at cut off point 20% ranged from 4% in study of Mihir et al, study [46], to 87% in study of Wang et al, [44]. Regarding the percent of MDR-TB patients that facing catastrophic cost, they ranged from (68%), reported by Mullerpattan 2019 to (90%), reported by Collin et al, 2018 however DS-TB patients ranged from 24%, in the study of Gadallah,2018 to 42%, in the study of Rebecca L.Walctt, 2020. The percent of ACF patient facing catastrophic cost ranged from 9% to 44%, however the percent of PCF patient ranged from 29% to 61% [18] & [39]. Hardship financing was discussed only in two studies[14] [45]. Seven studies discussed coping cost [16], [27], [30], [33], [35], [37] & [45]. Regarding the quality score, it was ranged from (3- unsatisfactory) [29] to (9-Very good) [34]. The Good score ranged from 7 to 8 pints, was among thirteen studies [5], [16], [17], [26], [28], [31], [32], [33], [37], [38], [39], [41] & [45]. While Satisfactory score which ranged from 5 to 6 points, was illustrated in the remaining fourteen studies, [12], [13], [14], [15], [18], [27], [30], [35], [36], [40], [42], [43], [44] & [45]. Publication bias: The 29 studies reported the catastrophic cost at 20% were be assessed for the risk of bias by the funnel plot and Eggers’ test [t = -1.188, P-value= 0.24], which revealed the absence of asymmetry and decline the presence of publication bias. Fig. 2 1. Primary outcome 1.1 Catastrophic cost at cut-off point 20% The pooled prevalence of catastrophic cost among 11750 TB patients included in 29 studies at cut-off point of 20% was 43% (95% CI:34-52) with high heterogeneity (I 2 = 99%). Fig. (3) To identify the cause of this substantial heterogeneity we conducted meta-regression. Predictors were sex, country where the study conducted (had high incidence vs none)[23], drug sensitivity (DS or MDR± HIV), and quality of the study. The model was significant P<0.0127, R 2 =51.57%. This model explained more than 50% of the reported heterogeneity. The identified predictors country (high vs low incidence) (β=-0.194, P=0.04) and type of patients regarding drug sensitivity (DS or MDR) and HIV co-infection (β=0.289, P=0.026). In this study, there are multiple main predictors of catastrophic cost like food and nutritional supplements [33-35],travel and transportation [30, 32, 45], age category [26, 28, 32], employment status [26, 32, 36, 41, 44], the socioeconomic status[13, 26, 27, 32, 41, 44, 47], MDR or HIV positive [28, 32, 35, 47], male gender, [26, 27, 44], and duration of hospitalization [13, 28, 32, 44, 45]. 1.2 Coping strategy In response to balance the enormous financial burden they encounter, the TB-affected families may adopt some coping strategies. Borrowing money, taking out loans, pledging gold and jewels, bringing their children out of schools or selling assets are options to compensate the income loss and the high out-of-pocket expenses [37, 45]. All these approaches are referred to as “dissaving” which is the core of the hardship financing dilemma. 1.3 Pooled proportion of catastrophic cost at 20% among different subgroups 1.3.1 Pooled proportion of catastrophic cost at 20% among TB drug sensitive The pooled proportion of patients facing catastrophic cost was 39%, 95CI (28-51%), the reported heterogeneity was 99%. After removing outliers, the pooled proportion of 11 studies recruited 3492 patients dropped to 32%, 95% CI [29 – 35]. The pooled prevalence of DS-TB patients facing catastrophic costs ranged from 24%, 95%CI [19 – 30] in the study of Gadallah,2018 [27] to 42%, 95% CI [35 – 49] in the study of Rebecca L.Walctt, 2020 [13].The heterogeneity of the included studies was as follows; I 2 = 70%, P < 0.01. (Table. 2) 1.3.2 Pooled prevalence according to TB drug resistant With a heterogeneity of 92%, the pooled proportion of TB affected household of MDR patients facing catastrophic cost among 1879 patients was 78%, 95%CI, [86%-86%]. After removing outliers, the pooled proportion of patients facing catastrophic cost among 574 patients with MDR reached 80% 95%CI [74-85%], I 2 = 54%. The highest proportion (90%) reported by Collin et al, 2018[40], while the lowest proportion (68%) reported by Mullerpattan 2019 [29]. (Table. 2) 1.3.3 Pooled proportion of TB-HIV co-infected patients facing catastrophic cost at 20% The pooled proportion of 796 TB patients with HIV facing catastrophic cost at 20% was 76%, 95%CI [ 65 -85%], with a heterogeneity of 88%. After conducting leave-one out sensitivity analysis, the study of Don Mudzengi et al 2017 [16], removed. The heterogeneity dropped to 0% and the pooled proportion patients facing catastrophic cost has increased to 81%, 95%CI [78 – 84] as it illustrated in. (Table.2) 1.3.4 Pooled proportion of TB facing catastrophic cost at 20% through active case finding (ACF) The proportion of patients facing catastrophic cost among 491 patients exposed active case finding ranged from 9%, 95%CI [7-15%] to 62%, 95%CI [45-77%]. After subgroup analysis based on the country where the ACF was implemented (inside/outside India). The pooled proportion was 10% 95%CI [7-14%], I 2 = 0% inside India and 48%, 95CI(25-72%) I 2 86% outside India. (Table.2) 1.3.5 Pooled proportion of TB facing catastrophic cost through passive case finding (PCF) The proportion of patients facing catastrophic cost among 638 patients during passive case finding ranged from 12%, 95%CI [8-17%] to 45%, 95%CI [35-55%]. The pooled proportion was 42%, 95%CI [35-50%]; It is worthy to note that heterogeneity was 94%. We further subdivided the studies according to the studied country (inside/outside) India. The pooled proportion of TB household facing catastrophic cost was 19% 95CI (7-41%), I2=95% while outside India 45 95CI(37-53%), I2=0%. (Table.2) 2. Secondary Outcome 1.1 Proportion of direct cost to the total cost 1.1.1 Pooled prevalence according to drug sensitive The proportion of the mean direct cost to the mean total cost addressed in 6 studies, the pooled proportion of direct to total cost at catastrophic cost of 20% was not calculated as the heterogeneity was high. The proportion was variants, two studies reported similar proportions, Tomeny, 2020[15] & Collins Timire, 2020 [47] with a proportion of 41% and 43% respectively. However, higher proportion 52% reported among Chittamany2020[33] and Nhung, 2018[37]. Two other extreme values reported, 33% by Fuady 2018 [41] and 65% reported by Muttamba, 2020 [30]. 1.1.2 Pooled proportion of direct cost in MDR The proportion of the mean direct cost to the mean total cost at 20% addressed in 7 studies, ranged from 26% in Chittamany, 2020 [33] to 93% in Yang, 2020[32]. Low proportions were observed in Fuady, 2018[41], Tomeny, 2020 [15], and Collins Timire, 2020 [47] with proportion of 32%, 34% and 49% respectively, while high proportion also reported in Muttamba, 2020[30], with 66% and in Nhung, 2018[37] with 68%. The pooled proportion of mean direct to total cost was difficult to assess because of the heterogeneity which wasn’t explained even after a meta-regression performed. 1.1.3 Pooled proportion of direct cost to total cost in case of active case finding (ACF) The pooled proportion of the mean direct cost to the mean total cost was addressed in 3 studies, the pooled proportion of mean direct to mean total cost was 25%, 95%CI [16-37%], I 2 =83%. After conducting leave one out sensitivity analysis, the Suman Chandra Gurung, 2019 [39], was removed, the pooled proportion dropped to 29%, 95%C1 [20-41%] I 2 =55%. (Table.2) 1.1.4 Pooled proportion of direct cost to total cost in case of passive case finding (PCF) The pooled proportion of the mean direct cost to the mean total cost addressed in 4 studies [17, 18, 39, 45], the pooled proportion of mean direct to mean total cost was 37%, 95%C1 [31-42%] I 2 =0%. (Table.2) 1.1.5 Proportion of direct cost to total cost in case HIV and TB co-infection The proportion of the mean direct cost to the mean total cost addressed in 2 studies. Don Mudzengi , 2017[16] and his team showed that the proportion of mean direct cost to the mean total cost was 30% among HIV and TB co-infection patients, while a higher proportion reported in Chittamany, 2020[33] with 59%. As we couldn’t pool the study because of the high un-explained heterogeneity. The pooled proportion of the mean direct cost to the mean total cost addressed in 14 studies, the pooled proportion of mean direct to mean total cost was 55%, 95%CI [43-66%], I 2 = 99%. After conducting outliers removal, study Mihir P. Rpan, 2020 [46] was excluded, the pooled proportion dropped to 51%, 95%CI [43-66%], I 2 = 96%. (Table.2) 2.2 Catastrophic Health Expenditure at 10% & Capacity to Pay at 40% In this study, we have found that there are six studies that also calculated the CHE 10% and the CTP 40%, in addition to their results regarding the CTC 20%. 2.2. 1 Pooled proportion of CHE at 10%: The pooled proportion of the CHE at 10% were studied also among the studies which they calculated CTC 20%. Three studies [ 2, 27, 32] were included with pooled proportion of 45%, 95%CI [35-56%], I 2 = 93%. The result after leave one out sensitivity analysis, Fuady, 2018[ 40 ], has excluded and the heterogeneity has decreased to reach I 2 = 28%, while the pooled proportion has increased to 50%, 95%CI [47-54%]. (Table.2) 2.2.2 Pooled proportion of CTP at 40%: With 63% pooled proportion, 95%CI [40-80%], I 2 = 96%, three studies measured the CTP at 40% [12, 29, 30] and after the sensitivity test the heterogeneity was I 2= 0, while the pooled proportion increased to 70%, 95%CI [64-76%]. (Table.2) Discussion Compared to the unknown data on the proportion of TB-patient affected household facing catastrophic cost in 2015, the GDGs goals set that 0% of household affected by TB have faced these costs by 2020 [ 51 ]. To the best of our knowledge, this is the first article that pooled of the proportion of TB patients or their households who suffered from catastrophic cost. In this meta-analysis 29 surveys conducted in 22 countries recruiting DS-TB, MDR-TB with or without HIV recruited through ACF, PCF. The quality score of the included studies ranged from 3–10. The proportion of patients facing catastrophic cost at a cut-off point 20% was 43%, (32%, 95%CI [ 29 – 35 ] among DS and 80% 95%CI [74–85%] among MDR). TB co-infected with HIV faced the highest catastrophic cost 81%, 95%CI [78–84]. Catastrophic cost was variables according to the strategy of case finding (ACF 12%95%CI [9–16%], versus PCF 42% 95%CI [35–50%]). The direct cost including medical and non-medical cost represented 51%, 95%CI [43–59%] of the total cost. Among drug sensitive and drug resistant TB, the proportion of direct cost to the total cost ranged from (33–65%)[ 15 , 30 , 33 , 37 , 41 , 47 ] and (26%-93%)[ 15 , 30 , 32 , 33 , 37 , 41 , 47 ] respectively. ACF incurred lower catastrophic than PCF 29%, 95%C1 [20–41%] versus 37%, 95%C1 [34–40%]. The direct cost to the total cost among TB and HIV co-infected patients ranged from 30% [ 16 ]-59%[ 33 ]. The CHE was 50%, 95%CI [47–54%], and 70%, 95%CI [64–76%] at 10% of household yearly income and 40% of their capacity to pay respectively. Catastrophic cost In fact, the cost incurred by some patients may be catastrophic and minimal for others. This is based on the household annual income. In the current study, we have included many studies that addressed the catastrophic cost among the TB at different thresholds, points (30%, 25%, 20%, 10% and 5%). Despite absence of robust evidence on the sensitivity of the cut-off point at 20% to reflect the catastrophic cost regardless patients are drug sensitive or resistant. Fuady et al, [12]settled 15% and 30% as more consistent cut-of points for treatment adherence and success respectively. In the current work, the proportion of TB-household patients facing catastrophic cost was 39%, which considered very high compared to the targeted GDGs in 2020 (0)%, more efforts and activities need to be directed to reduce this cost. It is worthy to note that diagnosis and treatment are provided for free in many of the included countries under the umbrella pooled of NTP, however, the treatment related expenditure is still very high. Yadav and his group, [52] illustrated that even with free services for tuberculosis care, 21.3% of the people in their study exposed to hardship financing, advising the need to take into consideration more innovated ways to increase the supported coverage of tuberculosis treatment in the country. The study also suggests the use of hardship financing as an index to measure the effectiveness of tuberculosis control program in the country. It is crucial to decrease the burden of catastrophic cost among the TB patients as it results in poorer treatment outcome. Patients suffer from catastrophic cost had 2-4 times higher odds of treatment failure than those who do not[12]. The latter is due to reduces access to the treating health facility, and treatment completion. Turning to the coping cost, a large proportion of household’s resort to different coping strategies to confront the increased out-of-pocket costs; and to compensate the consequences of income loss. Those coping strategies include selling a property or livestock, taking loans, pledging jewels, dropping their children out of school and cutting down their consumption to below basic needs [7]. Despite pooling of these studies’ outcome yielded substantial heterogeneity, the current study has found that almost 51% of heterogeneity, was mainly because of two predictors, the first was that some studies estimated CTC of DS and patients with MDR with or without HIV together. This factor played a major role in the heterogeneity, as it was clear that the CTC was dramatically higher among patients with HIV. The second predictor was the classification of country where the study was conducted[23]. Two-third of the new cases of TB reported in eight countries of the world, with India foremost the count, followed by Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh and South Africa. Consequently, we divided studies into studies conducted in countries with high versus low incidence. In meta-regression, the country, where the study was conducted was a second major determinant of the different size effect. The reported high incidence of CTC in many countries raised the need for social protection interventions. The most common social protection intervention is the cash transfer or cash assistance; it has already implemented in many countries across the world either conditionally or unconditionally [53]. In such a way, it is supposed that the household can get better access to treatment and food. Other social protection interventions include disability grants, food baskets (food assistance), food or travel vouchers and social insurance[7]. Many countries implemented reimbursement programs to help TB patients to cope with the disease cost. However, these programs prioritize poorer and MDR[54].The effect of this intervention is questionable. At a cutoff point of 20%, two studies have applied and calculated a catastrophic cost before and after reimbursement. Lue et al,2020[42] there reported a slight change on the proportion of CTC; before reimbursement, the CTC was (22%) and declined to 19% after the reimbursement. In contrary, Fuady,2019,[55] showed a higher change in the proportion of CTC after the reimbursement. The intervention program effectively decreased CTC from 44% to 13%. With regards to cash transfer, Wingfield et al, 2016 [56] reported that the proportion of TB household suffered from CTC was 30% and 42% among intervention and control respectively. These findings indicate that this social support is not enough to mitigate the impact of TB. Consequently, household of TB patients should receive sufficient financial support that covers the indirect cost (job lost), and direct cost (transportation, food, accommodation)[57].Of note, this social support should be proportionate to the income lost, this is due to the high variability of the pretreatment income. We speculate that development of newer treatment guidelines for TB of shorter duration would be beneficial. At the bottom, provision of free medication is not sufficient to prevent the catastrophic cost. TB patients should receive transport vouchers, reimbursement schemes and food assistance to reduce or compensate for such catastrophic costs. Furthermore, decentralization of patient supervision (including directly observed therapy), e.g. through community-based or workplace-based treatment [58], can reduce transport costs as well as income loss for patients[59]. As expected, the catastrophic cost among MDR was higher than DS, as DS patients receive treatment for shorter duration (6 months only), while MDR treatment extend to 24 months. Additional cost is incurred by MDR patients like the cost related to prolonged days of work absenteeism, need for daily injection, exposure to more side effects, and need for investigation [60]. Direct cost to total cost The mean total direct cost to the mean total cost was lower than the mean indirect cost among drug sensitive patients, HIV co-infected patients, while it was higher among drug resistant patients. This finding is essential to be considered when reimbursement strategies are implemented. Stakeholders should know which part of patient cost should be compensated. The direct cost dropped significantly if the strategy of active case finding was adopted instead of the passive case finding (29% to 37%) respectively. Determinant of catastrophic cost Of note, it is essential to identify the factors that contribute to catastrophic cost. In this study, there are multiple main predictors of catastrophic cost. The main two components that affect the catastrophic cost are income loss as an impact of being diseased and food and nutritional supplements other than the patients’ regular diet habit addressing the catastrophic cost through increasing the direct non-medical costs [33-35]. Also travel and transportation affect the direct non-medical costs increasing the suffer of TB patients [30]. Age also considered to affect the prevalence of catastrophic cost whether the young age [27] or the old age [32]. Catastrophic health expenditure Out of the 29 studies, only six studies have been included with a clear measurement of the CHE at 10% of their income and 40% of their capacity to pay. It was clear that many studies ignored CHE, despite its importance to understand the impact of this cost on treatment outcome [42]. Two studies assessed the effect of reimbursements intervention on the CHE. Xiang et al, [61] reported a 8% reduction in CHE, however, this reduction was not statistically significant. Similarly, Zhou et al[62] reported that the effect of reimbursement on CHE was minimal, the achieved reduction in CHE was only 12%. In order to decrease the catastrophic expenditures National health financing systems must be designed and implemented, not to allow people to access services when they are needed only, but also to protect households from financial catastrophe, by reducing out-of-pocket spending. In the long run, prepayment mechanisms should be developed, for instance, social health insurance, tax-based financing of health care, or some mix of prepayment mechanisms such as efficient reimbursement or cash intervention. [63] Strength and limitation of the study Our study has many strengths and limitations. Strengths include a comprehensive systematic approach to the existing literature, study selection, data extraction and quality assessment that have all been conducted according to current methodological standards. Furthermore, we included all studies without design, language, or geographical restriction. Moreover, we considered an ample list of outcomes and we compared these outcomes based on the definition, drug sensitivity and HIV infection. The limitation of this study was that different cut-off points were settled by different studies to estimate the proportion of the households facing catastrophic cost using different tools. A major challenge was that different studies estimated the catastrophic cost due to TB regardless drug sensitivity (DS, MDR), co-infection with HIV, case finding strategy (ACF, and PCF). Another point of limitation was that all studies included subjects with confirmed TB. Costs for those ill patients with undiagnosed TB may add a lot to the already estimated values. Furthermore, many of the included studies used the WHO cost survey tool, that include patients only treated in the NTP, omitting patients treated in private sectors who represent a considerable proportion of TB patients. Conclusion About future global policy, our study provides evidence that despite the free TB treatment policy, there is a major proportion of TB patients are still facing catastrophic cost. The proportion of patient facing catastrophic cost is variable according to the type of TB; lowest among DS, higher in MDR, and highest if there is concomitant infection with HIV. Patients exposed to ACF incurred lower cost than those exposed to PCF. The direct cost (medical &non-medical) related to TB is not the only major contributor to the catastrophic cost, indirect cost represents a major contributor that should not be ignored. To sum up, this study paves the way to effective cost mitigation in the context of the End TB Strategy. As it addressed the proportion of TB patients and their households who are suffering from catastrophic cost and its predictors. Obviously, effective management of these predictors will eventually contribute to better community, clinical, financial outcomes [ 64 ]. Now it is clear that, the global health system must do more efforts to achieve the zero catastrophic cost for TB by 2030. Declarations Acknowledgments Many efforts overseas co-operate to finalize this work in this comprehensive way. We would like to thank Dr. Samia laokri and Dr. Charlesbatt, for giving us the opportunity to access their full-texts papers, which helped to make this Meta-analysis comprehensive, and guide us to the most robust outcome. Assistant Professor | Department of Instructional Technology and Learning Sciences, Otah University USA. We would like also, to express our indebtedness and deepest gratitude to Dr. Ramy shaaban, Dr. Suzan, and Dr. Ahmed Mandil (EMRO-WHO) for their great help, which enriched our search. Author contribution Ramy Mohamed Ghazy (RMG) : Grant holder, conceptualized and designed the study, database search, full text screening, data analysis, writing manuscript. Haider El Saeh (HE): Revision of tittle & abstract, revision of the full text screening and data extraction, data analysis and writing manuscript. Shaimaa Abdulaziz (ShA): Database search with full text screening, data extraction, writing manuscript and references manager. Amira Mohamed Elzorkany (AM): Full text screening, data extraction, data analysis and writing manuscript. Heba Khidr (HK): Database search with full text screening Nardin Zarif (NZ): Database search with full text screening, data extraction and writing manuscript. Esraa Abdellatif (EAH): Full text screening, data extraction and writing manuscript. Ehab Elrewany (EE): Full text screening, data extraction and writing manuscript. Samar Abdel-Hafeez (SA): Final decision of the title & abstract screening with the full text screening, and writing manuscript. Funding This research was partially funded by WHO-TDR (SGS 20-28) Conflict of interest All authors have read the criteria set out in the ICMJE form, and they disclosed, there isn`t conflict of interest. References World Health Organization. 10 facts on tuberculosis. 2020 Oct 14, 2020 2021 Feb 20th]; Available from: https://www.who.int/news-room/facts-in-pictures/detail/tuberculosis . world Health Organization. Tuberculosis . 2020 [cited 2021 Feb 19]; Available from: https://www.who.int/news-room/fact-sheets/detail/tuberculosis . World Health Organization, Systematic screening for active tuberculosis: principles and recommendations . 2013: World Health Organization. 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Tables Table 1 Studies that addressed catastrophic cost included in systematic review analysis Author, Year, country Study design Population Criteria + inclusion and exclusion Sample size/Sex/Age Tool used in cost estimation Studied outcome CTC (COP) Predictors of CTC CHE and its predictors Notes Coping cost Quality interpretation Shewade 2018 India(Axshya) [ 17 ] Community based cohort study Sputum + ve pulmonary TB ACF&PCF 3/2016–2/2017 Sample size = 465 Sex: Male = 66% Age (years): 42 ± 17 Structured questionnaire CTC ACF 10.3%&PCF 11.5% at (20%) Predictors: not mentioned ----- ----- Score = 8 Good Muniyandi, 2020, India [ 31 ] Community based /Cross-sectional TB patients PTB/EPTB registered in NTCP 2/2017 -3/2018 Sample size = 384 sex: Male= (67%) Mean age 38.4 ± 16 WHO TB cost surveys CTC + it`s predictors 31% at (20%) Predictors: Lower socioeconomic segments ------ ----- Score = 7 Good Wingfield, 2016,, Peru [ 26 ] Community based /Prospective cohort Any patient treated with the Peruvian national TB control programme DS & MDR (11%) 2/2014–8/2014 Sample size = 876 Sex: male = 59% Age ≥15 years Questionnaire CTC + it`s predictors 39% at (20%) Predictors: Inadequate nutrition, severe TB, hidden costs/adherence ------ ------ Score = 7 Good Muniyandi, 2019, India [ 18 ] Community based/ Cross-sectional TB PT ≥ 15 y of age ACF vs PCF 10/2016–3/2018 Sample size = 336 Sex: Male = (77%) All age pre-coded interview schedule CTC PCF (29%), ACF (9%), at (20%) Predictors: not mentioned ----- ----- Score = 5 Satisfactory Fuady, 2020, Indonesia [ 12 ] Hospital-based/ Cohort Pt ≥ 18 yrs, treatment ≥ 1 Month or completed treatment since < 1 Month DS 7–9/ 2016 Sample size = 252 Sex: Male = (54%) Age ≥ 18 years Tool adapted to the Indonesian context CTC + it`s predictors 46 %, 38%, 33%, 26%, 22%, 17%, at (10%) (15 %) (20%) (25%) (30%) (35%) Predictors: Prolonged treatment, additional visits needed to complete the full treatment course ----- ----- Score = 5 Satisfactory Mullerpattan, 2018, India [ 29 ] Hospital based/Cross-sectional Drug resistant-TB, hospitalized patients MDR, private sector 8/2015–2/2016 Sample size = 50 Sex: Male = 30% Mean age = 30 yrs Not mentioned CTC 68% 78%, at (20%), (10%) Predictors: not mentioned ----- ----- Score = 3 Unsatisfact-ory Lu, 2020, China [ 42 ] Community + Hospital based/Cross-sectional Culture-confirmed pulmonary TB DS 12/2014–12/2015 Sample size = 248 sex: Male (54.9%) Mean Age = 34 (26–49) Standardized questionnaire CTC 22.2%, at (20%) Predictors: not mentioned ----- ----- Score = 6 Satisfactory Prasanna, 2018, India [ 28 ] Community + Hospital based/Mixed methods Newly diagnose, previously treated, PT registered for treatment under NTCP Puducherry district TB and TB + HIV 1/12/2016–31/1/2017 Sample size = 102 sex: Male= (69%) All ages Estimate TB, Patient’s Costs’ developed by the Poverty SWC of the StopTB Partnership CTC + it`s predictors 32.% 49%, at (10%), (20%) Predictors: Age (yrs), HIV status, Hospitalization ----- 38% coping 8% sold household property Score = 8 Good Fuady et al., 2018, Indonesia [ 41 ] Cross-sectional PHCs linked with NTCP( Treated 1 month or finished treatment since < 1 month Not Extra-pulmonary TB TB vs MDR-TB (poor vs non poor) 7–9/2016 Sample size = 346 (282 TB − 64 MDR) Sex: Male = 55% Age: ≥18 yrs Adapted Bahasa Indonesia version CTC + it`s predictors + CHE TB 36% (Poor 43%, Non poor 25%,) MDR-TB 83%, at 20% Predictors: Traval costs, food / nutritional supplementation costs, income loss TB, 22% MDR-TB 84%, at (10%) Predictors: not mentioned ----- Score = 8 Good Yang, 2020, China [ 32 ] Community + Hospital based/Cross sectional Pulmonary TB confirmed by SC RS, RMR, MDR 9–10/2018 Sample size = 672 Sex: Male (64.3%) Median age = 41 WHO patient cost CTC + it`s predictors CHE 46%, 37.1%, 30.2%, at (15%), (20%), (25%) Predictors: Age, Senior school or above, Minimum living security household, Employment status, Household economic status, Patient delay, medical care outside the city, Hospitalization, MDR 59.8%, 42.6%, at (10%), (40%) Predictors: not mentioned ----- Score = 8 Good Chittamany, 2020, Lao PDR [ 33 ] Hospital based/Cross-sectional TB patients on treatment in intensive or continuation phase & recieved ≥ 14 days ttt People ttt under NTCP, Pulm.TB, EPTB, HIV, MDR-TB 12/2018- 1/2019 & 5–6/2019 (DR-TB, TB-HIV) Sample size = 848 Sex: Male= (59.7%) Mean age= (50.4 yrs) WHO CTC + it`s predictors + coping Total 62.6% DS-TB 62.2%, DR-TB 86.7%, TB -HIV Co-inf. 81.1%, at (20%) Predictors: Food & nutritional supplements, income loss, treatment phase, educational status ----- coping 49.9% Score = 8 Good Viney, 2019, Indonesia [ 34 ] Hospital based/Cross- sectional Received treatment ≥ 2weeks All patients, 10/2016–3/2017 Sample size = 457 Sex: Male= (50.6%) Age = 32 year (22–52) standardized WHO questionnaire CTC + it`s predictors 83%, at 20% Predictors: Income loss & nutritional supplements, travel and medical costs after diagnosis ----- ------ Score = 9 Very good Wang, 2020, China [ 44 ] Hospital based/ Cross-sectional TB-MDR finished 1 year of treatment MDR-TB 1–8/ 2018 Sample size = 161 Sex: Male 68.9% Age = 36yrs (26-48yrs) Headcount tool CTC + it`s predictors CHE 87%, at (20%) Predictors: Low household income, absence of students in a family, LOS, male gender, job or productivity loss 68.3%. at (40%) Predictors: not mentioned ----- Score = 5 Satisfactory Muttamba, 2020, Uganda [ 30 ] Hospital-based/Cross-sectional DS-TB & DR-TB ≥ 2 weeks of present treatment) DS & MDR-TB 2017 Sample size = 1178 Sex: Male= (62.7%) All ages WHO TB CTC + it`s predictors Coping cost 53%, at (20%) Predictors: Transport, symptom relieving medications, food, loss of income ----- 48.5% Score = 5 Satisfactory Pedrazzoli, 2018, Ghana [ 35 ] Hospital-based/Cross- sectional Patients received ≥ 2w of treatment DS & DR-TB, HIV 2016 Sample size = 691 Sex: Male= (67.3%) Median age = 41 IQR(29–52) WHO TB CTC + it`s predictors Coping cost 64.1%, at (20%) Predictors: Income loss & nutritional supplements, DR-TB ----- 51.5% Score = 5 Satisfactory Xu, 2019, China [ 43 ] Jospital-based/Cross-sectional DS, pulmonary, under NTP DS-TB (pulmonary) 3–6/ 2017 Sample size = 1147 Sex: Male= (70.7%) Median age = 51 IQR(12–89) Structured questionnaire CTC + it`s predictors 11.7%, at (20%) Predictors: Region, residence, insurance ----- ----- Score = 6 Satisfactory Ikram, 2020, Pakistan [ 36 ] Hospital-based/Cross-sectional diagnosis since > 3 mons Pulmonary & DS, Not AIDS, Hepatitis, or DM TB-patients Not mentioned Sample size = 400 Sex: Male= (47%) Median age = 30 (22–49 .50) WHO generic instrument CTC + it`s predictors 67%, at (20%) Predictors: Availability of paid sick leave, number of follow up visits, Job loss ----- ----- Score = 5 Satisfactory Nhung, 2018, Viet Nam [ 37 ] Community-based/ Cross-sectional study (DS-TB & MDR-TB) including children on ttt > 14 days All ages DS & MDR-TB 7–10/2016 Sample size = 735 Sex: Male= (75.9%) Median age = 47 (IQR 35–58) WHO generic instrument CTC + It`s predictors Coping (Dissaving mechanism) Total 63%, 48%, 35% MDR 98 %, 98 %, 39 %, DS 59.6%, 43% 30%, at (20%),(30%), (40%) Predictors: Purchase special foods, travel, nutritional supplements, and accommodation Total 15 % 7.9% 2.8% MDR 77% 56.2% 21.3% DS 9.5% 3.7% 1.2%, at (10%) (20%) (40%) 25% loan 16% use of savings 5.8% sale of assets − 22% food insecurity 0.7% loss of job 1.6% child interrupted schooling Score = 7 Good Morishita, et al., 2016, Cambodia [ 45 ] Hospital + Community-based/ Cross-sectional comparative New pulmonary TB Patients without unfavorable ttt outcomes & re ttt ACF vs PCF 2012–2013 Sample size = 208 (108 ACF + 100 PCF) Sex: Male ACF, 48.1% PCF, 56%/ Median age: ACF (55 IQR (43.8–68)) PCF (52.5 IQR (45-62.3) --------- CTC + it`s predictors Financial hardship ACF 54.6% 36.1% 24.1% 17.6% PCF 63% 45% 34% 21%, at (10%) (20%) (30%) (40%) Predictors: Time spent for travel - waiting - consultation - hospitalization - ------ ACF & PCF (13.9% − 21% for sale) - all dissaving (46.3% − 52%) - any loan (42.6% − 46%) − 12 ACF & 17 PCF sold livestock Score = 6 Satisfactory McAllister, etal., 2020, Indonesia [ 38 ] Hospital-based/Cross-sectional Newly diagnosed pulmonary TB. Private/ non-private sector 10/2017–1/2019 Sample size = 469 Sex: Male (49.25%) Age: ≥ 18 yrs WHO CTC 38.6% 26.5%21.7%, at (10%) (20%) (25%) Predictors: not mentioned ------ ----- Score = 7 Good Tomeny, 2020, Cavite [ 15 ] Hospital = based/Cross-sectional Patients ≥ 16 yrs on treatment of pulmonary TB DS-TB vs MDR-TB 5–8/2016 Sample size = 194 Sex: Male (66%) Age: ≥ 16 yrs WHO CTC + it`s predictors DS-TB 28% MDR-TB 80%, at (20%), Predictors: Travel, accommodation, nutritional supplement, food ----- ----- Score = 6 Satisfactory Stracker, 2019, South Africa [ 5 ] Hospital-based/Cross-sectional 2 months after diagnosis, > 18 yrs, transferred patients Adults 10/ 2017-1/2018 Sample size = 237 Sex: Male (54%) Age: ≥ 18 yrs WHO tool CTC + it`s predictors 28%, at (20%) Predictors: Transport, treatment, income loss, time lost care-seeking ----- ---- Score = 8 Good Y.Z Ruan, 2016, China [ 14 ] Hospital-based/Cross-sectional MDR-TB 6–8/2012 Sample size = 73 Sex: Male= (48%) All ages Lumley T. Survey CTC + it`s predictors CHE + it's predictors Hardship financing 78%, at (20%) Predictors: Treatment, tests, nutrition, transportation, and accommodation. time loss 74%, at (40%) Predictors: Treatment, nutrition, transportation and accommodation. (62%) Score = 6 Satisfactory Don Mudzengi, 2017, South Africa [ 16 ] Hospital-based/Cross-sectional Diagnosis 3–5 month prior to the interview TB, HIV, or Both 4–10/ 2013 Sample size = 454 Sex: Male (36%) Age: ≥ 18 years TB Coaliation tool CTC coping % Total 60% (10%) TB/HIV 79% 67 % 65% 64% 61% TB only: 55% 53% 47% 47% 45% HIV only: 72% 60% 55% 52% 49%, at (5%), (10%), (15%), (20%), (25%) ----- 15% HIV only, 6% TB/HIV, 8% TB only Score = 7 Good Suman Gurung, 2019, Nepal [ 39 ] Hospital-based/Cross-sectional Adults ≥ 18 yrs, new and relapse TB cases, residents of Nepal New and relapse TB (ACF vs PCF) 4–10/2013 Sample size = 99 Sex: Male= (71%) Age: ≥15 years WHO TB patient costing tool CTC + it`s predictors Total 52% PCF 61% ACF 44%, at (20%) Predictors: Gender, Age, Disease category (new, relapse), Poverty line, Dissaving, Financial and social impact ----- ------ Score = 7 Good Rebecca L. Walctt 2020, Uganda [ 13 ] Hospital-based/Retrospective cohort Adults ≥ 18 yrs, spoke Luganda or English, confirmed active pulmonary TB Newly diagnosed TB 7–9/2017 Sample size = 224 Sex: Male= (60.2%) age: ≥ 18 years Adapted version of Tool to Estimate Patients' Cost (stop TB partnership) CTC + it`s predictors 41.8%, at (20%) Predictors: Hospitalization, experience of coping costs, low income status, age, education, HIV + quit job, female gender ----- ------ Score = 6 Satisfactory Mihir P. Rpan, 2020, India [ 46 ] Cross-sectional Patients ≥ 18 yrs on treatment, registered under public sector Not previously treated. DS pulmonary TB 1/2019 Sample size = 458 Sex: Male= (70%) Median age IQR: 35 (23–50), Adapted WHO costing tool % CTC Coping 14% 7% 5% 4%, at (5% ) (10%) (15%) (20%) Predictors: not mentioned ----- 18% Score = 7 Good Collins Timire, 2020, Zimbabwe [ 40 ] Hospital-based/Cross-sectional survey All ages on treatment for DS/ MDR DS, MDR 23/7–31/-8 2018 Sample size = 900 Sex: Male (56%) Mean age: 36.9 ± 14.7 Adapted WHO costing tool CTC + it`s predictors 80%, at (20%) Predictors: Gender, Age, TB type, treatment phase, treatment delay HIV status, Breadwinner, Income quintile, Location of health facility ------ ----- Score = 5 Satisfactory Gadallah 2018 Egypt [ 27 ] Hospital-based.Prospective cohort New patients attending TBMUs for starting their treatment, have consent TB patients 1–6/2019 Sample size = 257 Sex: Male (61.9%) Mean age: 38.3 ± 14.8 Tool to estimate TB patient cost from gp stop TB partnership CTC + it`s predictors Coping % 22.6% 24.1% 6.6%, at (10%) (20%), (30%) Predictors: Age, Gender Employment Crowding index Governorates Income Coping ----- 11.3% Score = 5 Satisfactory ACF: Active Case Finding; PCF: Passive Case Finding; SP: Smear Positive; TB: Tuberculosis; CTC: Catastrophic total cost; COP: Cut-off point; CHE: Catastrophic Health Expenditure; DS: Drug Sensitive; HB: Hospital Based, HCB: Health care centers Based; LOS: Length of Stay; MDR: Multi Drug Resistant; NTCP: National TB Control Program; PHCB: Public Health Centers Based; RMR: Rifampicin-nonresistant; RS Rifampicin-susceptible; SC: Sputum Culture; SWC: Sub-Working Group; TBMU: Tuberculosis Medical Unit. Table 2 Pooled proportion of catastrophic cost at 20% among drug sensitive, drug resistant, TB-HIV, active & passive case finding patients, direct cost to total cost, and catastrophic health expenditure. 1. Pooled proportion of catastrophic cost at 20% among drug sensitive Study Event Total Proportion 95%CI Weight Fuady,2020 83 252 0.33 [0.27–0.39] 9.30% Wingfield, 2014 295 783 0.38 [0.34–0.41] 12.20% McAllister., 2020 22 83 0.27 [0.17–0.37] 5.10% Gadallah, 2018 62 257 0.24 [0.19–0.30] 8.80% Muniyandi, 2020 141 455 0.31 [0.27–0.35 10.90% Prasanna, 2018 33 102 0.32 [0.23–0.42] 6.20% Fuady, 2018 101 282 0.36 [0.30–0.42] 9.80% Yang, 2020 197 586 0.34 [0.30–0.38] 11.50% Tomeny, 2020 47 169 0.28 [0.21–0.35] 7.70% Stracker, 2019 90 327 0.28 [0.23–0.33] 9.80% Rebecca L. Walctt, 2020 82 196 0.42 [0.35–0.49] 8.80% Random effect model 1153 3492 0.32 [0.29–0.35] Heterogeneity I 2 = 70% 2. Pooled proportion of catastrophic cost at 20% among drug resistant Mullerpattan, 2018 34 50 0.68 [0.53–0.80] 12.50% Fuady, 2018 53 64 0.83 [0.71–0.91] 11.60% Yang, 2020 39 56 0.70 [0.56–0.81] 12.90% Chettamany, 2020 (VIP) 26 30 0.87 [0.69–0.96] 6.60% Wang, 2020 140 161 0.87 [0.81–0.92] 15.00% Pedrazzoli, 2018 50 66 0.76 [0.64–0.85] 13.10% Tomeny,2020 20 25 0.80 [0.59–0.93] 7.30% Collins Timire, 2020 44 49 0.90 [0.78–0.97] 7.80% Y-Z. Ruan, 2016 57 73 0.78 [0.67–0.87] 13.20% Random effect model 463 574 0.80 [0.74–0.85] Heterogeneity I 2 = 54% 3. Pooled proportion of catastrophic cost at 20% among TB and HIV infected patients Chittamany, 2020 100 123 0.81 [0.73–0.88] 17.80% Collins Timire, 2020 450 557 0.81 [0.77–0.84] 82.20% Random effect model 550 680 0.81 [0.78–0.84] Heterogeneity I 2 = 0% 4. Pooled proportion of catastrophic cost at 20% among during active case finding after sub-group analysis Inside India Muniyandi, 2019 10 108 0.09 [0.5 − 0.16] Shewade, 2018 24 234 0.10 [0.7 − 0.15] Fixed effect model 34 342 0.10 [0.07–0.14] Heterogeneity I 2 = 0% Outside India Morishita, 2016 39 108 0.36 [0.27–0.46] Suman Chandra Gurung, 2019 24 39 0.61 [0.45–0.77] Fixed effect model 63 247 0.26 [0.25–0.72] 5. Pooled proportion of direct to total cost at catastrophic cost of 20% among active case finding Morishita, 2016 110.5 399 0.28 [0.23–0.32] 57.70% Shewade, 2018 12 4.5 0.80 0.28–0.99] 4.90% Muniyandi, 2019 18 69 0.26 [0.16–0.38] 37.40% Random effect model 140.5 427.5 0.29 [0.20–0.41] Heterogeneity I 2 = 55% 6. Pooled proportion of direct to total cost at catastrophic cost of 20% among passive case finding Morishiita, 2016 206 535 0.39 [0.34–0.43] 33.6% Shewade, 2018 26.9 28.4 0.94 0.98 − 0.90] 4.2% Muniyandi, 2019 79 227 0.35 [0.29–0.41] 30.1% Suman Chandra Gurung, 2019 131.74 325.3 0.45 [0.35–0.46] 32.1% Random effect model 443.64 1115.7 0.37 [0.31–0.42] Heterogeneity I 2 = 0% 7. Pooled proportion of the direct cost to the total cost Wingfield, 2014 392 961 0.41 [0.38–0.44] 8.1% Muttamba, 2020 400.48 556.58 0.72 [0.68–0.76] 8.0% Gadallah, 2018 89 198 0.45 [0.38–0.52] 7.9% Shewade, 2018 18.1 19.5 0.93 [0.70–0.99] 3.9% Muniyandi, 2020 108.48 451.35 0.24 [0.20–0.28] 8.0% Prasanna, 2018 77.32 234.1 0.33 [0.27–0.39] 7.9% Viney, 2019 1588.43 2585.54 0.61 [0.60–0.63] 8.1% Wang, 2020 6316 8266 0.76 [0.75–0.77] 8.1% Xu, 2019 769.2 839.6 0.92 [0.90–0.93] 7.9% Ikram, 2020 292.76 843.12 0.35 [0.32–0.38] 8.1% Nhung et al., 2018 736 1314 0.56 [0.53–0.59] 8.1% Suman Chandra Gurung, 2019 79.4 286.4 0.28 [0.23–0.33] 7.9% Collins Timire, 2020 620.97 1360 0.46 [0.43–0.48] 8.1% Random effect model 10867.2 16555.2 0.55 [0.43–0.66] Heterogeneity I 2 = 96% 8. Pooled proportion of Catastrophic Health Expenditure at 10% Lu, 2020 132 248 0.53 [0.47–0.60] 26.80% Muttamba, 2020 567 1155 0.49 [0.46–0.52] 73.20% Random effect model 699 1403 0.5 [0.47–0.54] 9. Catastrophic Health Expenditure at 10% & Capacity to Pay at 40% Wang, 2020 110 161 0.68 [0.61 − 0.5] 71.30% Y-Z Ruan, 2016 54 73 0.74 [0.62–0.84] 28.70% Random effect model 164 234 0.7 [0.64–0.76] Heterogeneity I 2 = 0% Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 11 Jan, 2022 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Major revision 09 Sep, 2021 Reviews received at journal 01 Sep, 2021 Reviews received at journal 10 Jul, 2021 Reviewers agreed at journal 10 Jul, 2021 Reviews received at journal 09 Jul, 2021 Reviewers agreed at journal 28 Jun, 2021 Reviewers invited by journal 24 Jun, 2021 Editor assigned by journal 21 Jun, 2021 Editor invited by journal 20 Apr, 2021 Submission checks completed at journal 19 Apr, 2021 First submitted to journal 10 Apr, 2021 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-409667","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":22024572,"identity":"8971d183-e427-45a2-93a8-d0111badef42","order_by":0,"name":"Ramy Mohamed Ghazy","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ramy","middleName":"Mohamed","lastName":"Ghazy","suffix":""},{"id":22024573,"identity":"d57101a6-2ee4-4c31-a4b2-8c267ddca062","order_by":1,"name":"Haider M. El Saeh","email":"","orcid":"","institution":"University of Tripoli","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Haider","middleName":"M. El","lastName":"Saeh","suffix":""},{"id":22024574,"identity":"3f353ca3-2219-4871-94d0-c7ae964cf5a3","order_by":2,"name":"Shaimaa Abdulaziz","email":"","orcid":"","institution":"Ministry of Health and Population","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Shaimaa","middleName":"","lastName":"Abdulaziz","suffix":""},{"id":22024575,"identity":"e1319366-dbfa-402e-b90b-b939b66c4a26","order_by":3,"name":"Esraa Abdellatif Hammouda","email":"","orcid":"","institution":"Ministry of Health and Population","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Esraa","middleName":"Abdellatif","lastName":"Hammouda","suffix":""},{"id":22024576,"identity":"c214138e-1aed-4138-8603-6d0546f768ad","order_by":4,"name":"Amira Elzorkany","email":"data:image/png;base64,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","orcid":"","institution":"Ministry of Health and Population","correspondingAuthor":true,"submittingAuthor":false,"prefix":"","firstName":"Amira","middleName":"","lastName":"Elzorkany","suffix":""},{"id":22024577,"identity":"c1497cc9-31e1-44ea-ac58-15409842468d","order_by":5,"name":"Heba Khidr","email":"","orcid":"","institution":"Ministry of Health and Population","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Heba","middleName":"","lastName":"Khidr","suffix":""},{"id":22024578,"identity":"fe77ae9c-f3c1-4357-a810-5d078d98d62a","order_by":6,"name":"Nardine Zarif","email":"","orcid":"","institution":"Ministry of Health and Population","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Nardine","middleName":"","lastName":"Zarif","suffix":""},{"id":22024579,"identity":"82d2fa1a-c680-4f49-8bf2-f82015e63f46","order_by":7,"name":"Ehab Elrewany","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Ehab","middleName":"","lastName":"Elrewany","suffix":""},{"id":22024580,"identity":"92f33036-b717-4219-a963-249875d96982","order_by":8,"name":"Samar Abd ElHafeez","email":"","orcid":"","institution":"Alexandria University","correspondingAuthor":false,"submittingAuthor":false,"prefix":"","firstName":"Samar","middleName":"Abd","lastName":"ElHafeez","suffix":""}],"badges":[],"createdAt":"2021-04-10 23:14:03","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-409667/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-409667/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-021-04345-x","type":"published","date":"2022-01-11T11:41:23+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":8238159,"identity":"e95a42e6-b4a8-42db-a0b8-3f27be159207","added_by":"auto","created_at":"2021-04-20 18:13:02","extension":"jpg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":80100,"visible":true,"origin":"","legend":"PRISMA flow-charts of studies included in meta-analysis of catastrophic cost/expenditure among patients with tuberculosis. ","description":"","filename":"Fig1.jpg","url":"https://assets-eu.researchsquare.com/files/rs-409667/v1/af5f40184f25e30803f94833.jpg"},{"id":8238157,"identity":"d47f9e29-2b5b-4c78-ac4f-0bf9e36fa7e1","added_by":"auto","created_at":"2021-04-20 18:13:02","extension":"jpg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":31803,"visible":true,"origin":"","legend":"Funnel plot of studies included in estimation of the proportion tuberculosis patients facing catastrophic cost.","description":"","filename":"Fig2.jpg","url":"https://assets-eu.researchsquare.com/files/rs-409667/v1/eff28660ee1e4290010129ad.jpg"},{"id":8238158,"identity":"df43899e-0259-464a-bf26-1d858f83f053","added_by":"auto","created_at":"2021-04-20 18:13:02","extension":"jpg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":165715,"visible":true,"origin":"","legend":"Pooled proportion of catastrophic cost at 20%","description":"","filename":"Fig3.jpg","url":"https://assets-eu.researchsquare.com/files/rs-409667/v1/a2ff8a6bec57866aad3af620.jpg"},{"id":17200106,"identity":"9dee4080-456d-46a5-87e9-ad603781a322","added_by":"auto","created_at":"2022-01-11 11:41:31","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1424418,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-409667/v1/0772a4e2-1bdd-4e27-a238-bde397f1e187.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"\u003cp\u003eA Systematic Review and Meta-Analysis on Catastrophic Cost Incurred by Tuberculosis Patients\u003c/p\u003e","fulltext":[{"header":"Introduction","content":" \u003cp\u003eTuberculosis (TB) infection is one of the top 10 causes of death. It caused 1.2\u0026nbsp;million deaths in 2019. TB affects about one-quarter of the world's population[\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. According to World Health Organization (WHO) report in 2020, WHO region that reported the highest incidence of TB was Africa region (266/10\u003csup\u003e5\u003c/sup\u003e) corresponding to 2.5\u0026nbsp;million cases. The South-East Asian region ranked the second (217/10\u003csup\u003e5\u003c/sup\u003e) corresponding to 4.3\u0026nbsp;million cases followed by the East Mediterranean region (114/10\u003csup\u003e5\u003c/sup\u003e) corresponding to 819 thousand case, and by Western Pacific region (93/10\u003csup\u003e5\u003c/sup\u003e) corresponding to 1.8\u0026nbsp;million cases. On country-based ranking, number of reported new cases is the highest in India (26%), Indonesia (8.5%), China (8.4%), Philippines (6.0%), Pakistan (5.7%), Nigeria (4.4%), Bangladesh and South Africa (3.6% for each) .[\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]\u003c/p\u003e \u003cp\u003eOn 26 September 2018, WHO\u0026rsquo;s End TB Strategy was set and agreed by United Nation to end TB epidemic by 2030, with step wise milestones for 2020, 2025, and 2030. One of these Strategies is to reduce TB incidence rate and deaths by 90% and 95% respectively. It was also recommended to find TB missing cases by \u0026ldquo;active case finding (ACF) instead of passive case finding (PCF). ACF means systematic identification and screening of people with presumptive TB, in high-risk groups, using tests, examinations or other procedures that can be applied rapidly\u0026rdquo;, while PCF entails visiting health services for diagnosis[\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. In addition, all TB patients or families should not suffer from catastrophic total costs (CTC) due to TB as one of the main obstacles for TB patients to complete their treatment; [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]. Catastrophic cost is defined as the total direct and indirect costs that reaches or exceed 20% of the pretreatment patient or household\u0026rsquo;s annual income. [\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e]of note, factors that aggravate this catastrophic cost are patient age and sex, socioeconomic status, Human immuno-deficiency virus (HIV) co-infection, and being infected with multidrug-resistant TB (MDR-TB) that does not respond to at least Isoniazid and Rifampicin, the 2 most powerful anti-TB drugs [\u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e] [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eThe nominator of catastrophic cost is the summation of direct and indirect costs. The direct cost includes either medical cost (consultation fees, diagnostic tests and treatment) or non-medical cost (transportation, accommodation, increased food needs). Indirect cost includes lost wages due to unemployment; time spent away from work and associated loss of productivity. Moreover, patients also incur large costs in the pre-treatment phase to cover consultations and laboratory tests, symptomatic treatment, antibiotics trial, and hospitalization [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. An important segment of the financial hardship is dissaving which means reduced financial strength of a household or engage the household in damaging financial coping strategies. This will reduce the financial capacity and their coping with the financial shocks and cast them into the poverty trap .[\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e] Dissaving can take many forms like taking out a loan, taking children out of education, selling assets, reducing consumption to below basic needs to cope with health-related expenditure [\u003cspan additionalcitationids=\"CR9\" citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e10\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eConsequently, WHO developed the TB patient cost survey to properly assess the total costs and proportion of patients facing catastrophic cost. This tool provide a standardized methodology for cross-sectional surveys in TB affected countries [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e]. Many studies used this cost survey to report catastrophic cost, catastrophic health expenditure, or hardship financing incurred by TB patients [\u003cspan additionalcitationids=\"CR13\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. Some literatures calculated catastrophic cost for drug sensitive, MDR or HIV co-infection [\u003cspan additionalcitationids=\"CR15\" citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Other studies estimated compared this cost considering adoption of different case finding strategies (ACF versus PCF) [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. In response to this reported catastrophic cost, the Global TB Program endorses social protection initiatives to complement Universal health coverage (UHC) initiatives [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e]. Examples of social protection interventions include cash transfers, food assistance, disability grants and health insurance. Those global financial supports already exist in most countries, but may not be fully implemented [\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAt the end, keeping in mind that COVID-19 pandemic may reverse the achieved progress in the TB control as many countries directed their resources toward pandemic containment. In addition, there are no published systematic reviews that report the pooled proportion of patients suffering from catastrophic cost; we aimed to perform this systematic review and meta-analysis to estimate the proportion of catastrophic cost among TB patients and their households in attempt to support the ongoing TB control programs.\u003c/p\u003e "},{"header":"Method","content":" \u003cp\u003eThis systematic review and meta-analysis was conducted according to the Preferred Reporting Items of the Systematic Reviews and Meta-Analyses (PRISMA) guidelines [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e].\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eData source and search strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eEMBASE, Scops, EBSCO, MEDLINE central/PubMed, ProQuest, Scielo, SAGE, Web of science, and Google scholar databases were searched for articles without timeframe, geographical or language restrictions up to November 20\u003csup\u003eth\u003c/sup\u003e, 2020 by two authors ( ShA \u0026amp; NZ) then revised by (RMG\u0026amp; SA). Highly focused and sensitive search strategies were developed by RMG after the approval of PubMed Help Disk. The search terms include (\u0026ldquo;tuberculosis \u0026ldquo;OR \u0026ldquo;Mycobacterium tuberculosis\u0026rdquo; OR \u0026ldquo;Koch\u0026rsquo;s disease\u0026rdquo; AND \u0026ldquo;catastrophic cost\u0026rdquo;). References from relevant studies were screened for supplementary articles.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy selection and data extraction:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe aimed to include observational studies, which reported the proportion of patients suffering from catastrophic cost during the intensive (first 2 or 8 months of treatment in DS or MDR respectively) or the continuation phases of TB treatment.\u003c/p\u003e\n\u003cp\u003eThe primary endpoint of interest was the proportion of TB affected patients and their households who face catastrophic cost. It was defined as the total direct and indirect costs due to TB reaches or exceed 20% of the patient or household\u0026rsquo;s annual income [5] . Furthermore, CTC was assessed among patients according to their drug sensitivity as DS or MDR (with or without HIV), and strategy of case finding (ACF versus PCF).\u003c/p\u003e\n\u003cp\u003eSecondary outcomes were the proportion of the direct to the total cost of TB among DS or MDR, with or without HIV, catastrophic health expenditure CHE (defined as direct cost that reaches or exceeds 40% of patients capacity to pay or 10% of their household income [22], and the different coping strategies.\u003c/p\u003e\n\u003cp\u003eTitles and abstracts were screened independently by four authors (AM, ShA, NZ, and EE), who discarded articles not pertinent to the topic. Non-observational studies, case reports, editorial, reviews, letters, and studies that estimated the direct and indirect cost of the population as a one unit not individually were excluded from qualitative analyses but screened for potential additional references. Three other authors (RMG, SA \u0026amp; HE) solved the discrepancies on study judgements. Data extraction and analysis were performed by (RMG, AM, HE) and independently verified by (SA).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eData analysis:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of CTC among TB patients was pooled using the random-effects model. To ensure robustness of the model and susceptibility to outliers, pooled data was also analyzed with the fixed-effects model. Heterogeneity was assessed by the Chi-squared test on N-1 degrees of freedom, with an alpha of 0.05 considered for statistical significance and the Cochrane-I-squared (I\u003csup\u003e2\u003c/sup\u003e) statistic. I2 values of 25%, 50% and 75% were considered to correspond to low, medium and high levels of heterogeneity, respectively.\u003c/p\u003e\n\u003cp\u003eSources of heterogeneity, for identifying possible effect modifiers on the pooled analyses, were explored using:\u003c/p\u003e\n\u003cp\u003e1- Sensitivity analysis (leave one out sensitivity analysis, GOSH sensitivity analysis, remove outliers)\u003c/p\u003e\n\u003cp\u003e2- Subgroup analysis: we categorized the catastrophic cost at 20% for ACF and PCF patients according to country where studies were conducted (inside/outside) India.\u003c/p\u003e\n\u003cp\u003e3- Met-regression: The impact of country where the survey was conducted (high versus low incidence of TB) [23], quality of the study, sex, and population criteria (drug sensitivity, drug resistant with or without HIV) on the size effect of studies to explain the substantial heterogeneity.\u003c/p\u003e\n\u003cp\u003eThe forest plot was used to visualize the degree of variation between studies. All data analysis was performed R software version 4.0.3 using Harrer hand-on guide [24].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication bias:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003ePublication bias was investigated by visual inspection of funnel plots, and by Egger\u0026rsquo;s regression test.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eQuality assessment\u003c/strong\u003e\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eThe Newcastle-Ottawa Scale (NOS) was used to assess the quality of studies. Studies were classified according to the NOS as: very good studies (9-10 points), good studies (7-8 points), satisfactory studies (5-6 points), and unsatisfactory studies (0-4 points).[25]\u003c/p\u003e"},{"header":"Results","content":"\u003cp\u003e\u003cstrong\u003eSearch results:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe flow diagram of the selection process is shown in figure 1. In total of 5114 potentially relevant articles were found after data base search. One additional citation was found through a personal search, of this number, 1922 articles were excluded as duplicates by Endnote X8. After title and abstract screening 3041 article were excluded (201 duplicates found manually, 2840 irrelevant). Two unpublished data were included to the 152 text eligible articles to full text screening, in addition we added 2 articles were added manually. A total of 29 articles were therefore reviewed in detail and included in the analysis. The main characteristics of these studies are summarized in table 1. The inter-rater agreement for inclusion was \u0026kappa;=0.95 and for the quality assessment was \u0026kappa;=0.8\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStudy characteristics\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eQualitative synthesis included 29 studies conducted in 15 countries; six studies from India, five from China, four from Indonesia, one study from each of the following countries (Egypt, Zimbabwe, Nepal, Lao PDR, Ghana, Pakistan, Vietnam, Cambodia, Peru, and Cavite), and two studies from each Uganda, and South Africa. Of included studies there were 5 cohort studies [12], [13], [17], [26] \u0026amp;[27] . One mixed methods study [28], while the other 23 studies were cross-sectional. Male sex presentation ranged from 30% [29], to 77% [18]. The sample size ranged from 50 [29], to 1178 [30]. The tool used for estimation of the cost survey were either WHO TB cost survey tool, [5], [15], [30], [31], [32], [33], [34], [35], [36], [37], [38], [39], [40] \u0026amp; [44], or adapted WHO tool to Indonesian context [12] \u0026amp; [41], or structured questionnaire [17], [26], [42], [43], pre-coded interview scheduled [18],or tool of stop TB partnership, [13], [27], [28],or headcount tool [44], or Lumley T. survey [14], or TB coalition tool [16]. On the other hand, there were two studies not mentioned the tool used [29], [45]. The percent of patients facing catastrophic cost at cut off point 20% ranged from 4% in study of Mihir et al, study [46], to 87% in study of Wang et al, [44]. Regarding the percent of MDR-TB patients that facing catastrophic cost, they ranged from (68%), reported by Mullerpattan 2019 to (90%), reported by Collin et al, 2018 however DS-TB patients ranged from 24%, in the study of Gadallah,2018 to 42%, in the study of Rebecca L.Walctt, 2020. The percent of ACF patient facing catastrophic cost ranged from 9% to 44%, however the percent of PCF patient ranged from 29% to 61% [18] \u0026amp; [39]. Hardship financing was discussed only in two studies[14] [45]. Seven studies discussed coping cost [16], [27], [30], [33], [35], [37] \u0026amp; [45]. Regarding the quality score, it was ranged from (3- unsatisfactory) [29] to (9-Very good) [34]. The Good score ranged from 7 to 8 pints, was among thirteen studies [5], [16], [17], [26], [28], [31], [32], [33], [37], [38], [39], [41] \u0026amp; [45]. While Satisfactory score which ranged from 5 to 6 points, was illustrated in the remaining fourteen studies, [12], [13], [14], [15], [18], [27], [30], [35], [36], [40], [42], [43], [44] \u0026amp; [45].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePublication bias:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe 29 studies reported the catastrophic cost at 20% were be assessed for the risk of bias by the funnel plot and Eggers\u0026rsquo; test [t = -1.188, P-value= 0.24], which revealed the absence of asymmetry and decline the presence of publication bias. Fig. 2\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1. Primary outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1 Catastrophic cost at cut-off point 20%\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled prevalence of catastrophic cost among 11750 TB patients included in 29 studies at cut-off point of 20% was 43% (95% CI:34-52) with high heterogeneity (I\u003csup\u003e2\u003c/sup\u003e = 99%). Fig. (3) To identify the cause of this substantial heterogeneity we conducted meta-regression. Predictors were sex, country where the study conducted (had high incidence vs none)[23], drug sensitivity (DS or MDR\u0026plusmn; HIV), and quality of the study. The model was significant P\u0026lt;0.0127, R\u003csup\u003e2\u003c/sup\u003e=51.57%. This model explained more than 50% of the reported heterogeneity. The identified predictors country (high vs low incidence) (\u0026beta;=-0.194, P=0.04) and type of patients regarding drug sensitivity (DS or MDR) and HIV co-infection (\u0026beta;=0.289, P=0.026).\u003c/p\u003e\n\u003cp\u003eIn this study, there are multiple main predictors of catastrophic cost like food and nutritional supplements [33-35],travel and transportation [30, 32, 45], age category [26, 28, 32], employment status [26, 32, 36, 41, 44], the socioeconomic status[13, 26, 27, 32, 41, 44, 47], MDR or HIV positive [28, 32, 35, 47], male gender, [26, 27, 44], and duration of hospitalization [13, 28, 32, 44, 45].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.2 Coping strategy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn response to balance the enormous financial burden they encounter, the TB-affected families may adopt some coping strategies. Borrowing money, taking out loans, pledging gold and jewels, bringing their children out of schools or selling assets are options to compensate the income loss and the high out-of-pocket expenses [37, 45]. All these approaches are referred to as \u0026ldquo;dissaving\u0026rdquo; which is the core of the hardship financing dilemma.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3 Pooled proportion of catastrophic cost at 20% among different subgroups\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.1 Pooled proportion of catastrophic cost at 20% among TB drug sensitive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled proportion of patients facing catastrophic cost was 39%, 95CI (28-51%), the reported heterogeneity was 99%. After removing outliers, the pooled proportion of 11 studies recruited 3492 patients dropped to 32%, 95% CI [29 \u0026ndash; 35]. The pooled prevalence of DS-TB patients facing catastrophic costs ranged from 24%, 95%CI [19 \u0026ndash; 30] in the study of Gadallah,2018 [27] to 42%, 95% CI [35 \u0026ndash; 49] in the study of Rebecca L.Walctt, 2020 [13].The heterogeneity of the included studies was as follows; I\u003csup\u003e2 \u003c/sup\u003e= 70%, P \u0026lt; 0.01. (Table. 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.2 Pooled prevalence according to TB drug resistant\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith a heterogeneity of 92%, the pooled proportion of TB affected household of MDR patients facing catastrophic cost among 1879 patients was 78%, 95%CI, [86%-86%]. After removing outliers, the pooled proportion of patients facing catastrophic cost among 574 patients with MDR reached 80% 95%CI [74-85%], I\u003csup\u003e2\u003c/sup\u003e\u003csub\u003e=\u003c/sub\u003e54%. The highest proportion (90%) reported by Collin et al, 2018[40], while the lowest proportion (68%) reported by Mullerpattan 2019 [29]. (Table. 2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.3 Pooled proportion of TB-HIV co-infected patients facing catastrophic cost at 20%\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled proportion of 796 TB patients with HIV facing catastrophic cost at 20% was 76%, 95%CI [ 65 -85%], with a heterogeneity of 88%. After conducting leave-one out sensitivity analysis, the study of Don Mudzengi et al 2017 [16], removed. The heterogeneity dropped to 0% and the pooled proportion patients facing catastrophic cost has increased to 81%, 95%CI [78 \u0026ndash; 84] as it illustrated in. (Table.2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.4 Pooled proportion of TB facing catastrophic cost at 20% through active case finding (ACF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of patients facing catastrophic cost among 491 patients exposed active case finding ranged from 9%, 95%CI [7-15%] to 62%, 95%CI [45-77%]. After subgroup analysis based on the country where the ACF was implemented (inside/outside India). The pooled proportion was 10% 95%CI [7-14%], I\u003csup\u003e2\u003c/sup\u003e= 0% inside India and 48%, 95CI(25-72%) I\u003csup\u003e2\u003c/sup\u003e 86% outside India. (Table.2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.3.5 Pooled proportion of TB facing catastrophic cost through passive case finding (PCF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of patients facing catastrophic cost among 638 patients during passive case finding ranged from 12%, 95%CI [8-17%] to 45%, 95%CI [35-55%]. The pooled proportion was 42%, 95%CI [35-50%]; It is worthy to note that heterogeneity was 94%. We further subdivided the studies according to the studied country (inside/outside) India. The pooled proportion of TB household facing catastrophic cost was 19% 95CI (7-41%), I2=95% while outside India 45 95CI(37-53%), I2=0%. (Table.2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2. Secondary Outcome\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1 Proportion of direct cost to the total cost\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.1 Pooled prevalence according to drug sensitive\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of the mean direct cost to the mean total cost addressed in 6 studies, the pooled proportion of direct to total cost at catastrophic cost of 20% was not calculated as the heterogeneity was high. The proportion was variants, two studies reported similar proportions, Tomeny, 2020[15] \u0026amp; Collins Timire, 2020 [47] with a proportion of 41% and 43% respectively. However, higher proportion 52% reported among Chittamany2020[33] and Nhung, 2018[37]. Two other extreme values reported, 33% by Fuady 2018 [41] and 65% reported by Muttamba, 2020 [30].\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.2 Pooled proportion of direct cost in MDR\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of the mean direct cost to the mean total cost at 20% addressed in 7 studies, ranged from 26% in Chittamany, 2020 [33] to 93% in Yang, 2020[32]. Low proportions were observed in Fuady, 2018[41], Tomeny, 2020 [15], and Collins Timire, 2020 [47] with proportion of 32%, 34% and 49% respectively, while high proportion also reported in Muttamba, 2020[30], with 66% and in Nhung, 2018[37] with 68%. The pooled proportion of mean direct to total cost was difficult to assess because of the heterogeneity which wasn\u0026rsquo;t explained even after a meta-regression performed.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.3 Pooled proportion of direct cost to total cost in case of active case finding (ACF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled proportion of the mean direct cost to the mean total cost was addressed in 3 studies, the pooled proportion of mean direct to mean total cost was 25%, 95%CI [16-37%], I\u003csup\u003e2\u003c/sup\u003e=83%. After conducting leave one out sensitivity analysis, the Suman Chandra Gurung, 2019 [39], was removed, the pooled proportion dropped to 29%, 95%C1 [20-41%] I\u003csup\u003e2\u003c/sup\u003e=55%. (Table.2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.4 Pooled proportion of direct cost to total cost in case of passive case finding (PCF)\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled proportion of the mean direct cost to the mean total cost addressed in 4 studies [17, 18, 39, 45], the pooled proportion of mean direct to mean total cost was 37%, 95%C1 [31-42%] I\u003csup\u003e2\u003c/sup\u003e=0%. (Table.2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e1.1.5 Proportion of direct cost to total cost in case HIV and TB co-infection\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe proportion of the mean direct cost to the mean total cost addressed in 2 studies. Don Mudzengi , 2017[16] and his team showed that the proportion of mean direct cost to the mean total cost was 30% among HIV and TB co-infection patients, while a higher proportion reported in Chittamany, 2020[33] with 59%. As we couldn\u0026rsquo;t pool the study because of the high un-explained heterogeneity.\u003c/p\u003e\n\u003cp\u003eThe pooled proportion of the mean direct cost to the mean total cost addressed in 14 studies, the pooled proportion of mean direct to mean total cost was 55%, 95%CI [43-66%], I\u003csup\u003e2\u003c/sup\u003e= 99%. After conducting outliers removal, study Mihir P. Rpan, 2020 [46] was excluded, the pooled proportion dropped to 51%, 95%CI [43-66%], I\u003csup\u003e2\u003c/sup\u003e= 96%. (Table.2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2 Catastrophic Health Expenditure at 10% \u0026amp; Capacity to Pay at 40%\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn this study, we have found that there are six studies that also calculated the CHE 10% and the CTP 40%, in addition to their results regarding the CTC 20%.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2. 1 Pooled proportion of CHE at 10%:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe pooled proportion of the CHE at 10% were studied also among the studies which they calculated CTC 20%. Three studies [ 2, 27, 32] were included with pooled proportion of 45%, 95%CI [35-56%], I\u003csup\u003e2\u003c/sup\u003e= 93%. The result after leave one out sensitivity analysis, Fuady, 2018[\u003cu\u003e40\u003c/u\u003e], has excluded and the heterogeneity has decreased to reach I\u003csup\u003e2\u003c/sup\u003e= 28%, while the pooled proportion has increased to 50%, 95%CI [47-54%]. (Table.2)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e2.2.2 Pooled proportion of CTP at 40%:\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWith 63% pooled proportion, 95%CI [40-80%], I\u003csup\u003e2\u003c/sup\u003e= 96%, three studies measured the CTP at 40% [12, 29, 30] and after the sensitivity test the heterogeneity was I\u003csup\u003e2=\u003c/sup\u003e0, while the pooled proportion increased to 70%, 95%CI [64-76%]. (Table.2)\u003c/p\u003e"},{"header":"Discussion","content":" \u003cp\u003eCompared to the unknown data on the proportion of TB-patient affected household facing catastrophic cost in 2015, the GDGs goals set that 0% of household affected by TB have faced these costs by 2020 [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. To the best of our knowledge, this is the first article that pooled of the proportion of TB patients or their households who suffered from catastrophic cost. In this meta-analysis 29 surveys conducted in 22 countries recruiting DS-TB, MDR-TB with or without HIV recruited through ACF, PCF. The quality score of the included studies ranged from 3\u0026ndash;10. The proportion of patients facing catastrophic cost at a cut-off point 20% was 43%, (32%, 95%CI [\u003cspan additionalcitationids=\"CR30 CR31 CR32 CR33 CR34\" citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e] among DS and 80% 95%CI [74\u0026ndash;85%] among MDR). TB co-infected with HIV faced the highest catastrophic cost 81%, 95%CI [78\u0026ndash;84]. Catastrophic cost was variables according to the strategy of case finding (ACF 12%95%CI [9\u0026ndash;16%], versus PCF 42% 95%CI [35\u0026ndash;50%]). The direct cost including medical and non-medical cost represented 51%, 95%CI [43\u0026ndash;59%] of the total cost. Among drug sensitive and drug resistant TB, the proportion of direct cost to the total cost ranged from (33\u0026ndash;65%)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] and (26%-93%)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e, \u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e, \u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e, \u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e, \u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e, \u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e] respectively. ACF incurred lower catastrophic than PCF 29%, 95%C1 [20\u0026ndash;41%] versus 37%, 95%C1 [34\u0026ndash;40%]. The direct cost to the total cost among TB and HIV co-infected patients ranged from 30% [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]-59%[\u003cspan citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e]. The CHE was 50%, 95%CI [47\u0026ndash;54%], and 70%, 95%CI [64\u0026ndash;76%] at 10% of household yearly income and 40% of their capacity to pay respectively.\u003c/p\u003e \n\u003cp\u003e\u003cstrong\u003eCatastrophic cost \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eIn fact, the cost incurred by some patients may be catastrophic and minimal for others. This is based on the household annual income. In the current study, we have included many studies that addressed the catastrophic cost among the TB at different thresholds, points (30%, 25%, 20%, 10% and 5%). Despite absence of robust evidence on the sensitivity of the cut-off point at 20% to reflect the catastrophic cost regardless patients are drug sensitive or resistant. Fuady et al, [12]settled 15% and 30% as more consistent cut-of points for treatment adherence and success respectively. In the current work, the proportion of TB-household patients facing catastrophic cost was 39%, which considered very high compared to the targeted GDGs in 2020 (0)%, more efforts and activities need to be directed to reduce this cost. It is worthy to note that diagnosis and treatment are provided for free in many of the included countries under the umbrella pooled of NTP, however, the treatment related expenditure is still very high. Yadav and his group, [52] illustrated that even with free services for tuberculosis care, 21.3% of the people in their study exposed to hardship financing, advising the need to take into consideration more innovated ways to increase the supported coverage of tuberculosis treatment in the country. The study also suggests the use of hardship financing as an index to measure the effectiveness of tuberculosis control program in the country. It is crucial to decrease the burden of catastrophic cost among the TB patients as it results in poorer treatment outcome. Patients suffer from catastrophic cost had 2-4 times higher odds of treatment failure than those who do not[12]. The latter is due to reduces access to the treating health facility, and treatment completion. Turning to the coping cost, a large proportion of household\u0026rsquo;s resort to different coping strategies to confront the increased out-of-pocket costs; and to compensate the consequences of income loss. Those coping strategies include selling a property or livestock, taking loans, pledging jewels, dropping their children out of school and cutting down their consumption to below basic needs [7]. Despite pooling of these studies\u0026rsquo; outcome yielded substantial heterogeneity, the current study has found that almost 51% of heterogeneity, was mainly because of two predictors, the first was that some studies estimated CTC of DS and patients with MDR with or without HIV together. This factor played a major role in the heterogeneity, as it was clear that the CTC was dramatically higher among patients with HIV. The second predictor was the classification of country where the study was conducted[23]. Two-third of the new cases of TB reported in eight countries of the world, with India foremost the count, followed by Indonesia, China, the Philippines, Pakistan, Nigeria, Bangladesh and South Africa. Consequently, we divided studies into studies conducted in countries with high versus low incidence. In meta-regression, the country, where the study was conducted was a second major determinant of the different size effect.\u003c/p\u003e\n\u003cp\u003e\u0026nbsp;The reported high incidence of CTC in many countries raised the need for social protection interventions. The most common social protection intervention is the cash transfer or cash assistance; it has already implemented in many countries across the world either conditionally or unconditionally [53]. In such a way, it is supposed that the household can get better access to treatment and food. Other social protection interventions include disability grants, food baskets (food assistance), food or travel vouchers and social insurance[7]. Many countries implemented reimbursement programs to help TB patients to cope with the disease cost. However, these programs prioritize poorer and MDR[54].The effect of this intervention is questionable. At a cutoff point of 20%, two studies have applied and calculated a catastrophic cost before and after reimbursement. Lue et al,2020[42] there reported a slight change on the proportion of CTC; before reimbursement, the CTC was (22%) and declined to 19% after the reimbursement. In contrary, Fuady,2019,[55] showed a higher change in the proportion of CTC after the reimbursement. The intervention program effectively decreased CTC from 44% to 13%. With regards to cash transfer, Wingfield et al, 2016 [56] reported that the proportion of TB household suffered from CTC was 30% and 42% among intervention and control respectively. These findings indicate that this social support is not enough to mitigate the impact of TB. Consequently, household of TB patients should receive sufficient financial support that covers the indirect cost (job lost), and direct cost (transportation, food, accommodation)[57].Of note, this social support should be proportionate to the income lost, this is due to the high variability of the pretreatment income. We speculate that development of newer treatment guidelines for TB of shorter duration would be beneficial. At the bottom, provision of free medication is not sufficient to prevent the catastrophic cost. TB patients should receive transport vouchers, reimbursement schemes and food assistance to reduce or compensate for such catastrophic costs. Furthermore, decentralization of patient supervision (including directly observed therapy), e.g. through community-based or workplace-based treatment [58], can reduce transport costs as well as income loss for patients[59].\u003c/p\u003e\n\u003cp\u003eAs expected, the catastrophic cost among MDR was higher than DS, as DS patients receive treatment for shorter duration (6 months only), while MDR treatment extend to 24 months. Additional cost is incurred by MDR patients like the cost related to prolonged days of work absenteeism, need for daily injection, exposure to more side effects, and need for investigation [60].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDirect cost to total cost\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe mean total direct cost to the mean total cost was lower than the mean indirect cost among drug sensitive patients, HIV co-infected patients, while it was higher among drug resistant patients. This finding is essential to be considered when reimbursement strategies are implemented. Stakeholders should know which part of patient cost should be compensated. The direct cost dropped significantly if the strategy of active case finding was adopted instead of the passive case finding (29% to 37%) respectively.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDeterminant of catastrophic cost\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOf note, it is essential to identify the factors that contribute to catastrophic cost. In this study, there are multiple main predictors of catastrophic cost. The main two components that affect the catastrophic cost are income loss as an impact of being diseased and food and nutritional supplements other than the patients\u0026rsquo; regular diet habit addressing the catastrophic cost through increasing the direct non-medical costs [33-35]. Also travel and transportation affect the direct non-medical costs increasing the suffer of TB patients [30]. Age also considered to affect the prevalence of catastrophic cost whether the young age [27] or the old age [32].\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCatastrophic health expenditure\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOut of the 29 studies, only six studies have been included with a clear measurement of the CHE at 10% of their income and 40% of their capacity to pay. It was clear that many studies ignored CHE, despite its importance to understand the impact of this cost on treatment outcome [42]. Two studies assessed the effect of reimbursements intervention on the CHE. Xiang et al, [61] reported a 8% reduction in CHE, however, this reduction was not statistically significant. Similarly, Zhou et al[62] reported that the effect of reimbursement on CHE was minimal, the achieved reduction in CHE was only 12%. In order to decrease the catastrophic expenditures National health financing systems must be designed and implemented, not to allow people to access services when they are needed only, but also to protect households from financial catastrophe, by reducing out-of-pocket spending. In the long run, prepayment mechanisms should be developed, for instance, social health insurance, tax-based financing of health care, or some mix of prepayment mechanisms such as efficient reimbursement or cash intervention. [63]\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eStrength and limitation of the study\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur study has many strengths and limitations. Strengths include a comprehensive systematic approach to the existing literature, study selection, data extraction and quality assessment that have all been conducted according to current methodological standards. Furthermore, we included all studies without design, language, or geographical restriction. Moreover, we considered an ample list of outcomes and we compared these outcomes based on the definition, drug sensitivity and HIV infection. The limitation of this study was that different cut-off points were settled by different studies to estimate the proportion of the households facing catastrophic cost using different tools. A major challenge was that different studies estimated the catastrophic cost due to TB regardless drug sensitivity (DS, MDR), co-infection with HIV, case finding strategy (ACF, and PCF). Another point of limitation was that all studies included subjects with confirmed TB. Costs for those ill patients with undiagnosed TB may add a lot to the already estimated values. Furthermore, many of the included studies used the WHO cost survey tool, that include patients only treated in the NTP, omitting patients treated in private sectors who represent a considerable proportion of TB patients.\u003c/p\u003e"},{"header":"Conclusion","content":" \u003cp\u003eAbout future global policy, our study provides evidence that despite the free TB treatment policy, there is a major proportion of TB patients are still facing catastrophic cost. The proportion of patient facing catastrophic cost is variable according to the type of TB; lowest among DS, higher in MDR, and highest if there is concomitant infection with HIV. Patients exposed to ACF incurred lower cost than those exposed to PCF. The direct cost (medical \u0026amp;non-medical) related to TB is not the only major contributor to the catastrophic cost, indirect cost represents a major contributor that should not be ignored. To sum up, this study paves the way to effective cost mitigation in the context of the End TB Strategy. As it addressed the proportion of TB patients and their households who are suffering from catastrophic cost and its predictors. Obviously, effective management of these predictors will eventually contribute to better community, clinical, financial outcomes [\u003cspan citationid=\"CR64\" class=\"CitationRef\"\u003e64\u003c/span\u003e]. Now it is clear that, the global health system must do more efforts to achieve the zero catastrophic cost for TB by 2030.\u003c/p\u003e "},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eAcknowledgments\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eMany efforts overseas co-operate to finalize this work in this comprehensive way. We would like to thank Dr. Samia laokri and Dr. Charlesbatt, for giving us the opportunity to access their full-texts papers, which helped to make this Meta-analysis comprehensive, and guide us to the most robust outcome. Assistant Professor | Department of Instructional Technology and Learning Sciences, Otah University USA. We would like also, to express our indebtedness and deepest gratitude to Dr. Ramy shaaban, Dr. Suzan, and Dr. Ahmed Mandil (EMRO-WHO) for their great help, which enriched our search.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor contribution\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRamy Mohamed Ghazy (RMG)\u003c/strong\u003e: Grant holder, conceptualized and designed the study, database search, full text screening, data analysis, writing manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHaider El Saeh (HE): \u003c/strong\u003eRevision of tittle \u0026amp; abstract, revision of the full text screening and data extraction, data analysis and writing manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eShaimaa Abdulaziz (ShA): \u003c/strong\u003eDatabase search with full text screening, data extraction, writing manuscript and references manager.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAmira Mohamed Elzorkany (AM): \u003c/strong\u003e\u0026nbsp;Full text screening, data extraction, data analysis and writing manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eHeba Khidr (HK): \u003c/strong\u003eDatabase search with full text screening\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNardin Zarif (NZ): \u003c/strong\u003eDatabase search with full text screening, data extraction and writing manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEsraa Abdellatif (EAH): \u003c/strong\u003eFull text screening, data extraction and writing manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEhab Elrewany (EE): \u003c/strong\u003eFull text screening, data extraction and writing manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSamar Abdel-Hafeez (SA): \u003c/strong\u003eFinal decision of the title \u0026amp; abstract screening with the full text screening, and writing manuscript.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis research was partially funded by WHO-TDR (SGS 20-28)\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConflict of interest\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAll authors have read the criteria set out in the ICMJE form, and they disclosed, there isn`t conflict of interest.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eWorld Health Organization. \u003cem\u003e10 facts on tuberculosis.\u003c/em\u003e 2020 Oct 14, 2020 2021 Feb 20th]; Available from: \u003ca href=\"https://www.who.int/news-room/facts-in-pictures/detail/tuberculosis\"\u003ehttps://www.who.int/news-room/facts-in-pictures/detail/tuberculosis\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eworld Health Organization. \u003cem\u003eTuberculosis\u003c/em\u003e. 2020 [cited 2021 Feb 19]; Available from: \u003ca href=\"https://www.who.int/news-room/fact-sheets/detail/tuberculosis\"\u003ehttps://www.who.int/news-room/fact-sheets/detail/tuberculosis\u003c/a\u003e.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, \u003cem\u003eSystematic screening for active tuberculosis: principles and recommendations\u003c/em\u003e. 2013: World Health Organization.\u003c/li\u003e\n\u003cli\u003eSingh, M., et al., \u003cem\u003eAre treatment outcomes of patients with tuberculosis detected by active case finding different from those detected by passive case finding?\u003c/em\u003e Journal of global infectious diseases, 2020. \u003cstrong\u003e12\u003c/strong\u003e(1): p. 28.\u003c/li\u003e\n\u003cli\u003eStracker, N., et al., \u003cem\u003eRisk factors for catastrophic costs associated with tuberculosis in rural South Africa.\u003c/em\u003e The International Journal of Tuberculosis and Lung Disease, 2019. \u003cstrong\u003e23\u003c/strong\u003e(6): p. 756-763.\u003c/li\u003e\n\u003cli\u003eWorld Health Oganization. \u003cem\u003eTuberculosis: Multidrug-resistant tuberculosis (MDR-TB)\u003c/em\u003e. 2018 [cited 2021 Feb 19]; Available from: \u003ca href=\"https://www.who.int/news-room/q-a-detail/tuberculosis-multidrug-resistant-tuberculosis-(mdr-tb\"\u003ehttps://www.who.int/news-room/q-a-detail/tuberculosis-multidrug-resistant-tuberculosis-(mdr-tb\u003c/a\u003e).\u003c/li\u003e\n\u003cli\u003eTanimura, T., et al., \u003cem\u003eFinancial burden for tuberculosis patients in low-and middle-income countries: a systematic review.\u003c/em\u003e European Respiratory Journal, 2014. \u003cstrong\u003e43\u003c/strong\u003e(6): p. 1763-1775.\u003c/li\u003e\n\u003cli\u003eL\u0026ouml;nnroth, K., et al., \u003cem\u003eBeyond UHC: monitoring health and social protection coverage in the context of tuberculosis care and prevention.\u003c/em\u003e PLoS Med, 2014. \u003cstrong\u003e11\u003c/strong\u003e(9): p. e1001693.\u003c/li\u003e\n\u003cli\u003eMadan, J., et al., \u003cem\u003eWhat can dissaving tell us about catastrophic costs? 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Menon, \u003cem\u003eOut of pocket expenditure on tuberculosis in India: Do households face hardship financing?\u003c/em\u003e Indian Journal of Tuberculosis, 2019. \u003cstrong\u003e66\u003c/strong\u003e(4): p. 448-460.\u003c/li\u003e\n\u003cli\u003eBoccia, D., et al., \u003cem\u003eTowards cash transfer interventions for tuberculosis prevention, care and control: key operational challenges and research priorities.\u003c/em\u003e BMC infectious diseases, 2016. \u003cstrong\u003e16\u003c/strong\u003e(1): p. 1-12.\u003c/li\u003e\n\u003cli\u003eSaqib, S.E., M.M. Ahmad, and C. Amezcua-Prieto, \u003cem\u003eEconomic burden of tuberculosis and its coping mechanism at the household level in Pakistan.\u003c/em\u003e The Social Science Journal, 2018. \u003cstrong\u003e55\u003c/strong\u003e(3): p. 313-322.\u003c/li\u003e\n\u003cli\u003eFuady, A., et al., \u003cem\u003eEffect of financial support on reducing the incidence of catastrophic costs among tuberculosis-affected households in Indonesia: eight simulated scenarios.\u003c/em\u003e Infectious diseases of poverty, 2019. \u003cstrong\u003e8\u003c/strong\u003e(1): p. 1-14.\u003c/li\u003e\n\u003cli\u003eWingfield, T., et al., \u003cem\u003eThe economic effects of supporting tuberculosis-affected households in Peru.\u003c/em\u003e European Respiratory Journal, 2016. \u003cstrong\u003e48\u003c/strong\u003e(5): p. 1396-1410.\u003c/li\u003e\n\u003cli\u003eFuady, A., et al., \u003cem\u003eEffect of financial support on reducing the incidence of catastrophic costs among tuberculosis-affected households in Indonesia: eight simulated scenarios.\u003c/em\u003e Infectious diseases of poverty, 2019. \u003cstrong\u003e8\u003c/strong\u003e(1): p. 10.\u003c/li\u003e\n\u003cli\u003eSinanovic, E., et al., \u003cem\u003eCost and cost-effectiveness of community-based care for tuberculosis in Cape Town, South Africa.\u003c/em\u003e The international journal of tuberculosis and lung disease, 2003. \u003cstrong\u003e7\u003c/strong\u003e(9): p. S56-S62.\u003c/li\u003e\n\u003cli\u003eDatiko, D.G. and B. Lindtj\u0026oslash;rn, \u003cem\u003eCost and cost-effectiveness of smear-positive tuberculosis treatment by Health Extension Workers in Southern Ethiopia: a community randomized trial.\u003c/em\u003e PLoS One, 2010. \u003cstrong\u003e5\u003c/strong\u003e(2).\u003c/li\u003e\n\u003cli\u003eOrganization, W.H. and W.E.C.o. Malaria, \u003cem\u003eWHO Expert Committee on Malaria: twentieth report\u003c/em\u003e. 2000: World Health Organization.\u003c/li\u003e\n\u003cli\u003eXiang, L., et al., \u003cem\u003eThe impact of the new cooperative medical scheme on financial burden of tuberculosis patients: evidence from six counties in China.\u003c/em\u003e Infectious diseases of poverty, 2016. \u003cstrong\u003e5\u003c/strong\u003e(1): p. 8.\u003c/li\u003e\n\u003cli\u003eZhou, C., et al., \u003cem\u003eThe effect of NCMS on catastrophic health expenditure and impoverishment from tuberculosis care in China.\u003c/em\u003e International journal for equity in health, 2016. \u003cstrong\u003e15\u003c/strong\u003e(1): p. 172.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, \u003cem\u003eDesigning health financing systems to reduce catastrophic health expenditure\u003c/em\u003e. 2005, World Health Organization.\u003c/li\u003e\n\u003cli\u003eWorld Health Organization, \u003cem\u003eProtocol for survey to determine direct and indirect costs due to TB and to estimate proportion of TB-affected households experiencing catastrophic total costs due to TB.\u003c/em\u003e Geneva: World Health Organization, 2015.\u003c/li\u003e\n\u003c/ol\u003e"},{"header":"Tables","content":"\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab1\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003eStudies that addressed catastrophic cost included in systematic review analysis\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eAuthor, Year, country\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStudy design\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003ePopulation Criteria\u0026thinsp;+\u0026thinsp;inclusion and exclusion\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eSample size/Sex/Age\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eTool used in cost estimation\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eStudied outcome\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCTC (COP)\u003c/p\u003e\n\u003cp\u003ePredictors of CTC\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eCHE and its predictors\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eNotes Coping cost\u003c/p\u003e\n\u003c/th\u003e\n\u003cth align=\"left\"\u003e\n\u003cp\u003eQuality interpretation\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShewade 2018 India(Axshya) [\u003cspan class=\"CitationRef\"\u003e17\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity based cohort study\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSputum\u0026thinsp;+\u0026thinsp;ve pulmonary TB\u003c/p\u003e\n\u003cp\u003eACF\u0026amp;PCF\u003c/p\u003e\n\u003cp\u003e3/2016\u0026ndash;2/2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;465\u003c/p\u003e\n\u003cp\u003eSex: Male\u0026thinsp;=\u0026thinsp;66%\u003c/p\u003e\n\u003cp\u003eAge (years): 42\u0026thinsp;\u0026plusmn;\u0026thinsp;17\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStructured questionnaire\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACF 10.3%\u0026amp;PCF 11.5% at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuniyandi, 2020, India [\u003cspan class=\"CitationRef\"\u003e31\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity based /Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTB patients\u003c/p\u003e\n\u003cp\u003ePTB/EPTB registered in NTCP\u003c/p\u003e\n\u003cp\u003e2/2017 -3/2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;384 sex: Male= (67%)\u003c/p\u003e\n\u003cp\u003eMean age 38.4\u0026thinsp;\u0026plusmn;\u0026thinsp;16\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO TB cost surveys\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e31% at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Lower socioeconomic segments\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e------\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWingfield, 2016,, Peru [\u003cspan class=\"CitationRef\"\u003e26\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity based /Prospective cohort\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAny patient treated with the Peruvian national TB control programme\u003c/p\u003e\n\u003cp\u003eDS \u0026amp; MDR (11%)\u003c/p\u003e\n\u003cp\u003e2/2014\u0026ndash;8/2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;876\u003c/p\u003e\n\u003cp\u003eSex: male\u0026thinsp;=\u0026thinsp;59%\u003c/p\u003e\n\u003cp\u003eAge \u0026ge;15 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eQuestionnaire\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39% at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Inadequate nutrition, severe TB, hidden costs/adherence\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e------\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e------\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuniyandi, 2019, India [\u003cspan class=\"CitationRef\"\u003e18\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity based/ Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTB PT\u0026thinsp;\u0026ge;\u0026thinsp;15 y of age\u003c/p\u003e\n\u003cp\u003eACF vs PCF\u003c/p\u003e\n\u003cp\u003e10/2016\u0026ndash;3/2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;336\u003c/p\u003e\n\u003cp\u003eSex: Male = (77%)\u003c/p\u003e\n\u003cp\u003eAll age\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003epre-coded interview schedule\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePCF (29%), ACF (9%), at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFuady, 2020, Indonesia [\u003cspan class=\"CitationRef\"\u003e12\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/ Cohort\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePt\u0026thinsp;\u0026ge;\u0026thinsp;18 yrs, treatment\u0026thinsp;\u0026ge;\u0026thinsp;1 Month or completed treatment since \u0026lt;\u0026thinsp;1 Month\u003c/p\u003e\n\u003cp\u003eDS\u003c/p\u003e\n\u003cp\u003e7\u0026ndash;9/ 2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;252\u003c/p\u003e\n\u003cp\u003eSex: Male = (54%)\u003c/p\u003e\n\u003cp\u003eAge\u0026thinsp;\u0026ge;\u0026thinsp;18 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTool adapted to the Indonesian context\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46 %, 38%, 33%, 26%, 22%, 17%, at (10%) (15 %) (20%) (25%) (30%) (35%)\u003c/p\u003e\n\u003cp\u003ePredictors: Prolonged treatment, additional visits needed to complete the full treatment course\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMullerpattan, 2018, India [\u003cspan class=\"CitationRef\"\u003e29\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDrug resistant-TB, hospitalized patients\u003c/p\u003e\n\u003cp\u003eMDR, private sector\u003c/p\u003e\n\u003cp\u003e8/2015\u0026ndash;2/2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;50\u003c/p\u003e\n\u003cp\u003eSex: Male\u0026thinsp;=\u0026thinsp;30%\u003c/p\u003e\n\u003cp\u003eMean age\u0026thinsp;=\u0026thinsp;30 yrs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNot mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68% 78%, at (20%), (10%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;3 Unsatisfact-ory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLu, 2020, China [\u003cspan class=\"CitationRef\"\u003e42\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity\u0026thinsp;+\u0026thinsp;Hospital based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCulture-confirmed pulmonary TB\u003c/p\u003e\n\u003cp\u003eDS\u003c/p\u003e\n\u003cp\u003e12/2014\u0026ndash;12/2015\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;248\u003c/p\u003e\n\u003cp\u003esex: Male (54.9%)\u003c/p\u003e\n\u003cp\u003eMean Age\u0026thinsp;=\u0026thinsp;34 (26\u0026ndash;49)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStandardized questionnaire\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.2%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrasanna, 2018, India [\u003cspan class=\"CitationRef\"\u003e28\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity\u0026thinsp;+\u0026thinsp;Hospital based/Mixed methods\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNewly diagnose, previously treated, PT registered for treatment under NTCP Puducherry district\u003c/p\u003e\n\u003cp\u003eTB and TB\u0026thinsp;+\u0026thinsp;HIV\u003c/p\u003e\n\u003cp\u003e1/12/2016\u0026ndash;31/1/2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;102 sex: Male= (69%)\u003c/p\u003e\n\u003cp\u003eAll ages\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eEstimate TB, Patient\u0026rsquo;s Costs\u0026rsquo; developed by the Poverty SWC of the StopTB Partnership\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.% 49%, at\u003c/p\u003e\n\u003cp\u003e(10%), (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Age (yrs), HIV status, Hospitalization\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38% coping\u003c/p\u003e\n\u003cp\u003e8% sold household property\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFuady et al., 2018, Indonesia [\u003cspan class=\"CitationRef\"\u003e41\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCross-sectional PHCs linked with NTCP(\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTreated 1 month or finished treatment since \u0026lt;\u0026thinsp;1 month\u003c/p\u003e\n\u003cp\u003eNot Extra-pulmonary TB\u003c/p\u003e\n\u003cp\u003eTB vs MDR-TB (poor vs non poor)\u003c/p\u003e\n\u003cp\u003e7\u0026ndash;9/2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;346 (282 TB \u0026minus;\u0026thinsp;64 MDR)\u003c/p\u003e\n\u003cp\u003eSex: Male\u0026thinsp;=\u0026thinsp;55%\u003c/p\u003e\n\u003cp\u003eAge: \u0026ge;18 yrs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdapted Bahasa Indonesia version\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u003c/p\u003e\n\u003cp\u003e+ it`s predictors\u003c/p\u003e\n\u003cp\u003e+ CHE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTB 36% (Poor 43%, Non poor 25%,)\u003c/p\u003e\n\u003cp\u003eMDR-TB 83%, at 20%\u003c/p\u003e\n\u003cp\u003ePredictors: Traval costs, food / nutritional supplementation costs, income loss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTB, 22%\u003c/p\u003e\n\u003cp\u003eMDR-TB 84%, at (10%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYang, 2020, China [\u003cspan class=\"CitationRef\"\u003e32\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity\u0026thinsp;+\u0026thinsp;Hospital based/Cross sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePulmonary TB confirmed by SC\u003c/p\u003e\n\u003cp\u003eRS, RMR, MDR\u003c/p\u003e\n\u003cp\u003e9\u0026ndash;10/2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;672\u003c/p\u003e\n\u003cp\u003eSex: Male (64.3%)\u003c/p\u003e\n\u003cp\u003eMedian age\u0026thinsp;=\u0026thinsp;41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO patient cost\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003cp\u003eCHE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e46%, 37.1%, 30.2%, at (15%), (20%), (25%)\u003c/p\u003e\n\u003cp\u003ePredictors: Age, Senior school or above, Minimum living security household, Employment status, Household economic status, Patient delay, medical care outside the city, Hospitalization, MDR\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e59.8%, 42.6%, at (10%), (40%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChittamany, 2020, Lao PDR [\u003cspan class=\"CitationRef\"\u003e33\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTB patients on treatment in intensive or continuation phase \u0026amp; recieved\u0026thinsp;\u0026ge;\u0026thinsp;14 days ttt\u003c/p\u003e\n\u003cp\u003ePeople ttt under NTCP, Pulm.TB, EPTB, HIV, MDR-TB\u003c/p\u003e\n\u003cp\u003e12/2018- 1/2019 \u0026amp; 5\u0026ndash;6/2019 (DR-TB, TB-HIV)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;848\u003c/p\u003e\n\u003cp\u003eSex: Male= (59.7%)\u003c/p\u003e\n\u003cp\u003eMean age= (50.4 yrs)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u0026thinsp;+\u0026thinsp;coping\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal 62.6%\u003c/p\u003e\n\u003cp\u003eDS-TB 62.2%,\u003c/p\u003e\n\u003cp\u003eDR-TB 86.7%,\u003c/p\u003e\n\u003cp\u003eTB -HIV Co-inf. 81.1%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Food \u0026amp; nutritional supplements, income loss, treatment phase, educational status\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ecoping 49.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViney, 2019, Indonesia [\u003cspan class=\"CitationRef\"\u003e34\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital based/Cross- sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eReceived treatment\u0026thinsp;\u0026ge;\u0026thinsp;2weeks\u003c/p\u003e\n\u003cp\u003eAll patients,\u003c/p\u003e\n\u003cp\u003e10/2016\u0026ndash;3/2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;457\u003c/p\u003e\n\u003cp\u003eSex: Male= (50.6%)\u003c/p\u003e\n\u003cp\u003eAge\u0026thinsp;=\u0026thinsp;32\u0026nbsp;year (22\u0026ndash;52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003estandardized WHO questionnaire\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83%, at 20%\u003c/p\u003e\n\u003cp\u003ePredictors: Income loss \u0026amp; nutritional supplements, travel and medical costs after diagnosis\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e------\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;9\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eVery good\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWang, 2020, China [\u003cspan class=\"CitationRef\"\u003e44\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital based/ Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTB-MDR finished 1 year of treatment\u003c/p\u003e\n\u003cp\u003eMDR-TB\u003c/p\u003e\n\u003cp\u003e1\u0026ndash;8/ 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;161\u003c/p\u003e\n\u003cp\u003eSex: Male 68.9%\u003c/p\u003e\n\u003cp\u003eAge\u0026thinsp;=\u0026thinsp;36yrs (26-48yrs)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHeadcount tool\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003cp\u003eCHE\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e87%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Low household income, absence of students in a family, LOS, male gender, job or productivity loss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e68.3%. at (40%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e-----\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuttamba, 2020, Uganda [\u003cspan class=\"CitationRef\"\u003e30\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDS-TB \u0026amp; DR-TB\u0026thinsp;\u0026ge;\u0026thinsp;2 weeks of present treatment)\u003c/p\u003e\n\u003cp\u003eDS \u0026amp; MDR-TB\u003c/p\u003e\n\u003cp\u003e2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;1178\u003c/p\u003e\n\u003cp\u003eSex: Male= (62.7%)\u003c/p\u003e\n\u003cp\u003eAll ages\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO TB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC + it`s predictors\u003c/p\u003e\n\u003cp\u003eCoping cost\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Transport, symptom relieving medications, food, loss of income\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e48.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePedrazzoli, 2018, Ghana [\u003cspan class=\"CitationRef\"\u003e35\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross- sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePatients received\u0026thinsp;\u0026ge;\u0026thinsp;2w of treatment\u003c/p\u003e\n\u003cp\u003eDS \u0026amp; DR-TB, HIV\u003c/p\u003e\n\u003cp\u003e2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;691\u003c/p\u003e\n\u003cp\u003eSex: Male= (67.3%)\u003c/p\u003e\n\u003cp\u003eMedian age\u0026thinsp;=\u0026thinsp;41 IQR(29\u0026ndash;52)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO TB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC + it`s predictors\u003c/p\u003e\n\u003cp\u003eCoping cost\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64.1%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Income loss \u0026amp; nutritional supplements, DR-TB\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e51.5%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXu, 2019, China [\u003cspan class=\"CitationRef\"\u003e43\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eJospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDS, pulmonary, under NTP\u003c/p\u003e\n\u003cp\u003eDS-TB (pulmonary)\u003c/p\u003e\n\u003cp\u003e3\u0026ndash;6/ 2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;1147\u003c/p\u003e\n\u003cp\u003eSex: Male= (70.7%)\u003c/p\u003e\n\u003cp\u003eMedian age\u0026thinsp;=\u0026thinsp;51 IQR(12\u0026ndash;89)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStructured questionnaire\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.7%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Region, residence, insurance\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIkram, 2020, Pakistan [\u003cspan class=\"CitationRef\"\u003e36\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ediagnosis since \u0026gt;\u0026thinsp;3 mons\u003c/p\u003e\n\u003cp\u003ePulmonary \u0026amp; DS, Not AIDS, Hepatitis, or DM\u003c/p\u003e\n\u003cp\u003eTB-patients\u003c/p\u003e\n\u003cp\u003eNot mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;400\u003c/p\u003e\n\u003cp\u003eSex: Male= (47%)\u003c/p\u003e\n\u003cp\u003eMedian age\u0026thinsp;=\u0026thinsp;30 (22\u0026ndash;49 .50)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO generic instrument\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e67%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Availability of paid sick leave, number of follow up visits, Job loss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNhung, 2018, Viet Nam [\u003cspan class=\"CitationRef\"\u003e37\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCommunity-based/ Cross-sectional study\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(DS-TB \u0026amp; MDR-TB) including children on ttt\u0026thinsp;\u0026gt;\u0026thinsp;14 days\u003c/p\u003e\n\u003cp\u003eAll ages DS \u0026amp; MDR-TB\u003c/p\u003e\n\u003cp\u003e7\u0026ndash;10/2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;735\u003c/p\u003e\n\u003cp\u003eSex: Male= (75.9%)\u003c/p\u003e\n\u003cp\u003eMedian age\u0026thinsp;=\u0026thinsp;47 (IQR 35\u0026ndash;58)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO generic instrument\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC +\u003c/p\u003e\n\u003cp\u003eIt`s predictors\u003c/p\u003e\n\u003cp\u003eCoping (Dissaving mechanism)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal 63%, 48%, 35%\u003c/p\u003e\n\u003cp\u003eMDR 98 %, 98 %, 39 %,\u003c/p\u003e\n\u003cp\u003eDS 59.6%, 43% 30%, at (20%),(30%), (40%)\u003c/p\u003e\n\u003cp\u003ePredictors: Purchase special foods, travel, nutritional supplements, and accommodation\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal 15 % 7.9% 2.8%\u003c/p\u003e\n\u003cp\u003eMDR 77% 56.2% 21.3%\u003c/p\u003e\n\u003cp\u003eDS 9.5% 3.7% 1.2%, at\u003c/p\u003e\n\u003cp\u003e(10%) (20%) (40%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25% loan 16% use of savings 5.8% sale of assets \u0026minus;\u0026thinsp;22% food insecurity 0.7% loss of job 1.6% child interrupted schooling\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMorishita, et al., 2016, Cambodia [\u003cspan class=\"CitationRef\"\u003e45\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital\u0026thinsp;+\u0026thinsp;Community-based/ Cross-sectional comparative\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew pulmonary TB\u003c/p\u003e\n\u003cp\u003ePatients without unfavorable ttt outcomes \u0026amp; re ttt\u003c/p\u003e\n\u003cp\u003eACF vs PCF\u003c/p\u003e\n\u003cp\u003e2012\u0026ndash;2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;208 (108 ACF\u0026thinsp;+\u0026thinsp;100 PCF)\u003c/p\u003e\n\u003cp\u003eSex: Male ACF, 48.1% PCF, 56%/\u003c/p\u003e\n\u003cp\u003eMedian age: ACF (55 IQR (43.8\u0026ndash;68)) PCF (52.5 IQR (45-62.3)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e---------\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003cp\u003eFinancial hardship\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACF 54.6% 36.1% 24.1% 17.6%\u003c/p\u003e\n\u003cp\u003ePCF 63% 45% 34% 21%, at (10%) (20%) (30%) (40%)\u003c/p\u003e\n\u003cp\u003ePredictors: Time spent for travel - waiting - consultation - hospitalization -\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e------\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eACF \u0026amp; PCF (13.9% \u0026minus;\u0026thinsp;21% for sale) - all dissaving (46.3% \u0026minus;\u0026thinsp;52%) - any loan (42.6% \u0026minus;\u0026thinsp;46%) \u0026minus;\u0026thinsp;12 ACF \u0026amp; 17 PCF sold livestock\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMcAllister, etal., 2020, Indonesia [\u003cspan class=\"CitationRef\"\u003e38\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNewly diagnosed pulmonary TB.\u003c/p\u003e\n\u003cp\u003ePrivate/ non-private sector\u003c/p\u003e\n\u003cp\u003e10/2017\u0026ndash;1/2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;469\u003c/p\u003e\n\u003cp\u003eSex: Male (49.25%)\u003c/p\u003e\n\u003cp\u003eAge: \u0026ge; 18 yrs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e38.6% 26.5%21.7%, at (10%) (20%) (25%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e------\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTomeny, 2020, Cavite [\u003cspan class=\"CitationRef\"\u003e15\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital\u0026thinsp;=\u0026thinsp;based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePatients\u0026thinsp;\u0026ge;\u0026thinsp;16 yrs on treatment of pulmonary TB\u003c/p\u003e\n\u003cp\u003eDS-TB vs MDR-TB\u003c/p\u003e\n\u003cp\u003e5\u0026ndash;8/2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;194\u003c/p\u003e\n\u003cp\u003eSex: Male (66%)\u003c/p\u003e\n\u003cp\u003eAge: \u0026ge; 16 yrs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDS-TB 28%\u003c/p\u003e\n\u003cp\u003eMDR-TB 80%, at (20%),\u003c/p\u003e\n\u003cp\u003ePredictors: Travel, accommodation, nutritional supplement, food\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStracker, 2019, South Africa [\u003cspan class=\"CitationRef\"\u003e5\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2 months after diagnosis, \u0026gt; 18 yrs, transferred patients\u003c/p\u003e\n\u003cp\u003eAdults\u003c/p\u003e\n\u003cp\u003e10/ 2017-1/2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;237\u003c/p\u003e\n\u003cp\u003eSex: Male (54%)\u003c/p\u003e\n\u003cp\u003eAge: \u0026ge; 18 yrs\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO tool\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Transport, treatment, income loss, time lost care-seeking\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;8\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY.Z Ruan, 2016, China [\u003cspan class=\"CitationRef\"\u003e14\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMDR-TB\u003c/p\u003e\n\u003cp\u003e6\u0026ndash;8/2012\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;73\u003c/p\u003e\n\u003cp\u003eSex: Male= (48%)\u003c/p\u003e\n\u003cp\u003eAll ages\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLumley T. Survey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003cp\u003eCHE\u0026thinsp;+\u0026thinsp;it's predictors\u003c/p\u003e\n\u003cp\u003eHardship financing\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e78%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Treatment, tests, nutrition, transportation, and accommodation. time loss\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e74%, at (40%)\u003c/p\u003e\n\u003cp\u003ePredictors: Treatment, nutrition, transportation and accommodation.\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e(62%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDon Mudzengi, 2017, South Africa [\u003cspan class=\"CitationRef\"\u003e16\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eDiagnosis 3\u0026ndash;5 month prior to the interview\u003c/p\u003e\n\u003cp\u003eTB, HIV, or Both\u003c/p\u003e\n\u003cp\u003e4\u0026ndash;10/ 2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;454\u003c/p\u003e\n\u003cp\u003eSex: Male (36%)\u003c/p\u003e\n\u003cp\u003eAge: \u0026ge; 18 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTB Coaliation tool\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u003c/p\u003e\n\u003cp\u003ecoping %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal 60% (10%)\u003c/p\u003e\n\u003cp\u003eTB/HIV\u003c/p\u003e\n\u003cp\u003e79% 67 % 65% 64% 61%\u003c/p\u003e\n\u003cp\u003eTB only: 55% 53% 47% 47% 45%\u003c/p\u003e\n\u003cp\u003eHIV only: 72% 60% 55% 52% 49%, at (5%),\u003c/p\u003e\n\u003cp\u003e(10%),\u003c/p\u003e\n\u003cp\u003e(15%), (20%), (25%)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15% HIV only,\u003c/p\u003e\n\u003cp\u003e6% TB/HIV,\u003c/p\u003e\n\u003cp\u003e8% TB only\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuman Gurung, 2019, Nepal [\u003cspan class=\"CitationRef\"\u003e39\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdults \u0026ge; 18\u0026thinsp;yrs, new and relapse TB cases, residents of Nepal\u003c/p\u003e\n\u003cp\u003eNew and relapse TB (ACF vs PCF)\u003c/p\u003e\n\u003cp\u003e4\u0026ndash;10/2013\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;99\u003c/p\u003e\n\u003cp\u003eSex: Male= (71%)\u003c/p\u003e\n\u003cp\u003eAge: \u0026ge;15 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWHO TB patient costing tool\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTotal 52%\u003c/p\u003e\n\u003cp\u003ePCF 61%\u003c/p\u003e\n\u003cp\u003eACF 44%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Gender, Age, Disease category (new, relapse), Poverty line, Dissaving, Financial and social impact\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e------\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRebecca L. Walctt 2020, Uganda [\u003cspan class=\"CitationRef\"\u003e13\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Retrospective cohort\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdults\u0026thinsp;\u0026ge;\u0026thinsp;18\u0026thinsp;yrs, spoke Luganda or English, confirmed active pulmonary TB\u003c/p\u003e\n\u003cp\u003eNewly diagnosed TB\u003c/p\u003e\n\u003cp\u003e7\u0026ndash;9/2017\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;224\u003c/p\u003e\n\u003cp\u003eSex: Male= (60.2%)\u003c/p\u003e\n\u003cp\u003eage: \u0026ge; 18 years\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdapted version of Tool to Estimate Patients' Cost (stop TB partnership)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e41.8%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Hospitalization, experience of coping costs, low income status, age, education, HIV\u0026thinsp;+\u0026thinsp;quit job, female gender\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e------\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;6\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMihir P. Rpan, 2020, India [\u003cspan class=\"CitationRef\"\u003e46\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCross-sectional\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePatients\u0026thinsp;\u0026ge;\u0026thinsp;18\u0026thinsp;yrs on treatment, registered under public sector\u003c/p\u003e\n\u003cp\u003eNot previously treated.\u003c/p\u003e\n\u003cp\u003eDS pulmonary TB\u003c/p\u003e\n\u003cp\u003e1/2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;458\u003c/p\u003e\n\u003cp\u003eSex: Male= (70%)\u003c/p\u003e\n\u003cp\u003eMedian age IQR: 35 (23\u0026ndash;50),\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdapted WHO costing tool\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e% CTC\u003c/p\u003e\n\u003cp\u003eCoping\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e14% 7% 5% 4%, at (5% )\u003c/p\u003e\n\u003cp\u003e(10%)\u003c/p\u003e\n\u003cp\u003e(15%)\u003c/p\u003e\n\u003cp\u003e(20%)\u003c/p\u003e\n\u003cp\u003ePredictors: not mentioned\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;7\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eGood\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollins Timire, 2020, Zimbabwe [\u003cspan class=\"CitationRef\"\u003e40\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based/Cross-sectional survey\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAll ages on treatment for DS/ MDR\u003c/p\u003e\n\u003cp\u003eDS, MDR\u003c/p\u003e\n\u003cp\u003e23/7\u0026ndash;31/-8 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;900\u003c/p\u003e\n\u003cp\u003eSex: Male (56%)\u003c/p\u003e\n\u003cp\u003eMean age: 36.9\u0026thinsp;\u0026plusmn;\u0026thinsp;14.7\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eAdapted WHO costing tool\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC + it`s predictors\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e80%, at (20%)\u003c/p\u003e\n\u003cp\u003ePredictors: Gender, Age, TB type,\u003c/p\u003e\n\u003cp\u003etreatment phase, treatment delay\u003c/p\u003e\n\u003cp\u003eHIV status,\u003c/p\u003e\n\u003cp\u003eBreadwinner,\u003c/p\u003e\n\u003cp\u003eIncome quintile,\u003c/p\u003e\n\u003cp\u003eLocation of health facility\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e------\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGadallah 2018 Egypt [\u003cspan class=\"CitationRef\"\u003e27\u003c/span\u003e]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eHospital-based.Prospective cohort\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNew patients attending TBMUs for starting their treatment, have consent\u003c/p\u003e\n\u003cp\u003eTB patients\u003c/p\u003e\n\u003cp\u003e1\u0026ndash;6/2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSample size\u0026thinsp;=\u0026thinsp;257\u003c/p\u003e\n\u003cp\u003eSex: Male (61.9%)\u003c/p\u003e\n\u003cp\u003eMean age: 38.3\u0026thinsp;\u0026plusmn;\u0026thinsp;14.8\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTool to estimate TB patient cost from gp stop TB partnership\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCTC\u0026thinsp;+\u0026thinsp;it`s predictors\u003c/p\u003e\n\u003cp\u003eCoping %\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22.6% 24.1% 6.6%, at (10%)\u003c/p\u003e\n\u003cp\u003e(20%), (30%)\u003c/p\u003e\n\u003cp\u003ePredictors:\u003c/p\u003e\n\u003cp\u003eAge, Gender\u003c/p\u003e\n\u003cp\u003eEmployment\u003c/p\u003e\n\u003cp\u003eCrowding index\u003c/p\u003e\n\u003cp\u003eGovernorates\u003c/p\u003e\n\u003cp\u003eIncome\u003c/p\u003e\n\u003cp\u003eCoping\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e-----\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.3%\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eScore\u0026thinsp;=\u0026thinsp;5\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eSatisfactory\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003ctfoot\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"10\"\u003eACF: Active Case Finding; PCF: Passive Case Finding; SP: Smear Positive; TB: Tuberculosis; CTC: Catastrophic total cost; COP: Cut-off point; CHE: Catastrophic Health Expenditure; DS: Drug Sensitive; HB: Hospital Based, HCB: Health care centers Based; LOS: Length of Stay; MDR: Multi Drug Resistant; NTCP: National TB Control Program; PHCB: Public Health Centers Based; RMR: Rifampicin-nonresistant; RS Rifampicin-susceptible; SC: Sputum Culture; SWC: Sub-Working Group; TBMU: Tuberculosis Medical Unit.\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tfoot\u003e\n\u003c/table\u003e\n\u003cp\u003e\u0026nbsp;\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv class=\"gridtable\"\u003e\n\u003ctable id=\"Tab2\" border=\"1\"\u003e\u003ccaption\u003e\n\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\n\u003cdiv class=\"CaptionContent\"\u003e\n\u003cp\u003ePooled proportion of catastrophic cost at 20% among drug sensitive, drug resistant, TB-HIV, active \u0026amp; passive case finding patients, direct cost to total cost, and catastrophic health expenditure.\u003c/p\u003e\n\u003c/div\u003e\n\u003c/caption\u003e\n\u003cthead\u003e\n\u003ctr\u003e\n\u003cth colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e1. Pooled proportion of catastrophic cost at 20% among drug sensitive\u003c/p\u003e\n\u003c/th\u003e\n\u003c/tr\u003e\n\u003c/thead\u003e\n\u003ctbody\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eStudy\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eEvent\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eTotal\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eProportion\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e95%CI\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eWeight\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFuady,2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e252\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.27\u0026ndash;0.39]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.30%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWingfield, 2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e295\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e783\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.38\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.34\u0026ndash;0.41]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMcAllister., 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e22\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.27\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.17\u0026ndash;0.37]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e5.10%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGadallah, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e62\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e257\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.19\u0026ndash;0.30]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuniyandi, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e141\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e455\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.31\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.27\u0026ndash;0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrasanna, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e102\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.23\u0026ndash;0.42]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFuady, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e101\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e282\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.30\u0026ndash;0.42]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYang, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e197\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e586\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.30\u0026ndash;0.38]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.50%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTomeny, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e47\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e169\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.21\u0026ndash;0.35]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eStracker, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e327\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.23\u0026ndash;0.33]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e9.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRebecca L. Walctt, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e196\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.42\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.35\u0026ndash;0.49]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1153\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e3492\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.32\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.29\u0026ndash;0.35]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;70%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e2. \u003cstrong\u003ePooled proportion of catastrophic cost at 20% among drug resistant\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMullerpattan, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e34\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.53\u0026ndash;0.80]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.50%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFuady, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e64\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.83\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.71\u0026ndash;0.91]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e11.60%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eYang, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.70\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.56\u0026ndash;0.81]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChettamany, 2020 (VIP)\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.69\u0026ndash;0.96]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6.60%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWang, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e140\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.87\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.81\u0026ndash;0.92]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e15.00%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePedrazzoli, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e50\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e66\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.64\u0026ndash;0.85]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.10%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eTomeny,2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e20\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e25\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.59\u0026ndash;0.93]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.30%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollins Timire, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e44\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.90\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.78\u0026ndash;0.97]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY-Z. Ruan, 2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.78\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.67\u0026ndash;0.87]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e13.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e463\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e574\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.80\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.74\u0026ndash;0.85]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;54%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e3. \u003cstrong\u003ePooled proportion of catastrophic cost at 20% among TB and HIV infected patients\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eChittamany, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e100\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e123\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.73\u0026ndash;0.88]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e17.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollins Timire, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e450\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e557\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.81\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.77\u0026ndash;0.84]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e82.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e550\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e680\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.81\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.78\u0026ndash;0.84]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e4. \u003cstrong\u003ePooled proportion of catastrophic cost at 20% among during active case finding after sub-group analysis\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eInside India\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuniyandi, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.09\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.5\u0026thinsp;\u0026minus;\u0026thinsp;0.16]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShewade, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.10\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.7\u0026thinsp;\u0026minus;\u0026thinsp;0.15]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eFixed effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e34\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e342\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.10\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.07\u0026ndash;0.14]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eOutside India\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMorishita, 2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.36\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.27\u0026ndash;0.46]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuman Chandra Gurung, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.45\u0026ndash;0.77]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eFixed effect model\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e63\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e247\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.25\u0026ndash;0.72]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e5. \u003cstrong\u003ePooled proportion of direct to total cost at catastrophic cost of 20% among active case finding\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMorishita, 2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e399\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.23\u0026ndash;0.32]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e57.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShewade, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.80\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u0026ndash;0.99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.90%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuniyandi, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e69\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.26\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.16\u0026ndash;0.38]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e37.40%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eRandom effect model\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e140.5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e427.5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.29\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.20\u0026ndash;0.41]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;55%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e6. \u003cstrong\u003ePooled proportion of direct to total cost at catastrophic cost of 20% among passive case finding\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMorishiita, 2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e206\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e535\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.39\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.34\u0026ndash;0.43]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e33.6%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShewade, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.9\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.94\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.98\u0026thinsp;\u0026minus;\u0026thinsp;0.90]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e4.2%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuniyandi, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e227\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.29\u0026ndash;0.41]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e30.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuman Chandra Gurung, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e131.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e325.3\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.35\u0026ndash;0.46]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e32.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e443.64\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1115.7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.37\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.31\u0026ndash;0.42]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e7. \u003cstrong\u003ePooled proportion of the direct cost to the total cost\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWingfield, 2014\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e392\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e961\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.41\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.38\u0026ndash;0.44]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuttamba, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e400.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e556.58\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.72\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.68\u0026ndash;0.76]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eGadallah, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e89\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e198\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.45\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.38\u0026ndash;0.52]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eShewade, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e18.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e19.5\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.93\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.70\u0026ndash;0.99]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e3.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuniyandi, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e108.48\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e451.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.24\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.20\u0026ndash;0.28]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.0%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003ePrasanna, 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e77.32\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e234.1\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.33\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.27\u0026ndash;0.39]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eViney, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1588.43\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e2585.54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.61\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.60\u0026ndash;0.63]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWang, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e6316\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8266\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.75\u0026ndash;0.77]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eXu, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e769.2\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e839.6\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.92\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.90\u0026ndash;0.93]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eIkram, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e292.76\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e843.12\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.35\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.32\u0026ndash;0.38]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eNhung et al., 2018\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e736\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1314\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.56\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.53\u0026ndash;0.59]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eSuman Chandra Gurung, 2019\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e79.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e286.4\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.28\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.23\u0026ndash;0.33]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e7.9%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eCollins Timire, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e620.97\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1360\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.46\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.43\u0026ndash;0.48]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e8.1%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e10867.2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e16555.2\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.55\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.43\u0026ndash;0.66]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"5\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;96%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e8. \u003cstrong\u003ePooled proportion of Catastrophic Health Expenditure at 10%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eLu, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e132\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e248\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.53\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.47\u0026ndash;0.60]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e26.80%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eMuttamba, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e567\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e1155\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.49\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.46\u0026ndash;0.52]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73.20%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e699\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e1403\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.5\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.47\u0026ndash;0.54]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e9. \u003cstrong\u003eCatastrophic Health Expenditure at 10% \u0026amp; Capacity to Pay at 40%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eWang, 2020\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e110\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e161\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.68\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.61\u0026thinsp;\u0026minus;\u0026thinsp;0.5]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e71.30%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003eY-Z Ruan, 2016\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e54\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e73\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e0.74\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e[0.62\u0026ndash;0.84]\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e28.70%\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eRandom effect model\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e164\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e234\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e0.7\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003e[0.64\u0026ndash;0.76]\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003ctd align=\"left\"\u003e\u0026nbsp;\u003c/td\u003e\n\u003c/tr\u003e\n\u003ctr\u003e\n\u003ctd colspan=\"6\" align=\"left\"\u003e\n\u003cp\u003e\u003cstrong\u003eHeterogeneity I\u003c/strong\u003e\u003csup\u003e\u003cstrong\u003e2\u003c/strong\u003e\u003c/sup\u003e\u0026thinsp;\u003cstrong\u003e=\u0026thinsp;0%\u003c/strong\u003e\u003c/p\u003e\n\u003c/td\u003e\n\u003c/tr\u003e\n\u003c/tbody\u003e\n\u003c/table\u003e\n\u003c/div\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Tuberculosis, catastrophic cost, catastrophic health expenditure, coping cost, direct cost, indirect cost","lastPublishedDoi":"10.21203/rs.3.rs-409667/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-409667/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eBackground: One of the World Health Organization End Tuberculosis (TB) Strategy is to reduce the proportion of TB affected families facing catastrophic costs (CC) to 0% by 2020. CC is defined if total cost related to TB management exceeded 20% of annual pre-TB household income. This study aimed to estimate the pooled proportion (PP) of TB affected households who suffered from CC. \u003c/p\u003e\u003cp\u003eMethod: A search of the online database through September 2020 was performed. Of 5114 articles, 29 articles were included in meta-analysis.\u0026nbsp;\u0026nbsp;We used R software to estimate the PP at 95% confidence intervals (CIs) using the fixed/random-effect models. \u003c/p\u003e\u003cp\u003eResult: The PP of patients faced CC was 43%. Meta-regression revealed that country, drug sensitivity and HIV co-infection were the main predictors.\u0026nbsp;CC incurred by drug sensitive, drug resistant and HIV coinfection patients were 32%, 80%, and 81% respectively. Lower CC incurred by active than passive case finding; 12% versus 42%. Direct cost represented 55% (95% CI 43-66) of the total cost.\u0026nbsp;About 45% of TB-affected household faced catastrophic health expenditure at cut-off point of 10%. \u003c/p\u003e\u003cp\u003eConclusion:\u0026nbsp;There is still a significant proportion of TB patients facing CC, which represent a main obstacle against TB control.\u003c/p\u003e\u003cp\u003ePROSPERO registration: CRD42020221283\u003c/p\u003e","manuscriptTitle":"A Systematic Review and Meta-Analysis on Catastrophic Cost Incurred by Tuberculosis Patients","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2021-04-20 18:13:00","doi":"10.21203/rs.3.rs-409667/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Major revision","date":"2021-09-09T12:44:28+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-09-01T04:54:47+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-07-10T11:28:11+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33cb7fce-f256-40fa-8bb8-2af8e85c1777","date":"2021-07-10T09:39:22+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2021-07-10T01:42:13+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"33485689-61df-4835-8ece-010590722115","date":"2021-06-28T20:18:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2021-06-24T11:05:03+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2021-06-21T15:10:30+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2021-04-20T08:31:17+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2021-04-19T05:58:20+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2021-04-10T23:07:19+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"07a0f755-f388-48ca-9f4f-97cb8f4838b3","owner":[],"postedDate":"April 20th, 2021","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":3779723,"name":"Health Economics \u0026 Outcomes Research"}],"tags":[],"updatedAt":"2022-01-11T11:41:23+00:00","versionOfRecord":{"articleIdentity":"rs-409667","link":"https://doi.org/10.1038/s41598-021-04345-x","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2022-01-11 11:41:23","publishedOnDateReadable":"January 11th, 2022"},"versionCreatedAt":"2021-04-20 18:13:00","video":"","vorDoi":"10.1038/s41598-021-04345-x","vorDoiUrl":"https://doi.org/10.1038/s41598-021-04345-x","workflowStages":[]},"version":"v1","identity":"rs-409667","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-409667","identity":"rs-409667","version":["v1"]},"buildId":"rHA-KDH7Qsr4HCuvH75dn","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}