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OBJECTIVES: Tuberculosis (TB) is known as a disease of the poor. Despite TB diagnosis and care usually being offered for free, TB patients can still face substantial costs, especially in the context of multi-drug resistance (MDR). The End TB Strategy calls for zero TB-affected families incurring 'catastrophic' costs due to TB by 2025. This paper examines, by MDR status, the level and composition of costs incurred by TB-affected households during care seeking and treatment; assesses the affordability of TB care using catastrophic and impoverishment measures; and describes coping strategies used by TB-affected households to pay for TB care. METHODS: A nationally representative survey of TB patients at public health facilities across Ghana. RESULTS: We enrolled 691 patients (66 MDR). The median expenditure for non-MDR TB was US$429.6 during treatment, vs. US$659.0 for MDR patients (P-value = 0.001). Catastrophic costs affected 64.1% of patients. MDR patients were pushed significantly further over the threshold for catastrophic payments than DS patients. Payments for TB care led to a significant increase in the proportion of households in the study sample that live below the poverty line at the time of survey compared to pre-TB diagnosis. Over half of patients undertook coping strategies. CONCLUSION: TB patients in Ghana incur substantial costs, despite free diagnosis and treatment. High rates of catastrophic costs and coping strategies in both non-MDR and MDR patients show that new policies are urgently needed to ensure TB care is actually affordable for TB patients.

Model parameters. Parameter values for each modelled scenario. (DOCX 19366 kb)

Background In low-incidence countries, clinical experience of tuberculosis is becoming more limited, with potential consequences for patient outcomes. In 2007, the Department of Health released a guidance ‘toolkit’ recommending that tuberculosis patients in England should not be solely managed by clinicians who see fewer than 10 cases per year. This caseload threshold was established to try to improve treatment outcomes and reduce transmission, but was not evidence based. We aimed to assess the association between clinician or hospital caseload and treatment outcomes, as well as the relative suitability of making recommendations using each caseload parameter. Methods Demographic and clinical data for tuberculosis cases in England notified to Public Health England’s Enhanced Tuberculosis Surveillance system between 2003 and 2012 were extracted. Mean clinician and hospital caseload over the past 3 years were calculated and treatment outcomes grouped into good/neutral and unfavourable. Caseloads over time and their relationship with outcomes were described and analysed using random effects logistic regression, adjusted for clustering. Results In a fully adjusted multivariable model (34,707 cases)there was very strong evidence that management of tuberculosis by clinicians with fewer than 10 cases per year was associated with greater odds of an unfavourable outcome compared to clinicians who managed greater numbers of cases (cluster-specific odds ratio, 1.14; 95 % confidence interval, 1.05–1.25; P = 0.002). The relationship between hospital caseload and treatment outcomes was more complex and modified by a patient’s place of birth and ethnicity. The clinician caseload association held after adjustment for hospital caseload and when the clinician caseload threshold was reduced down to one. Conclusions Despite the relative ease of making recommendations at the hospital level and the greater reliability of recorded hospital versus named clinician, our results suggest that clinician caseload thresholds are more suitable for clinical guidance. The current recommended clinician caseload threshold is functional. Sensitivity analyses reducing the threshold indicated that clinical experience is pertinent even at very low average caseloads, which is encouraging for low burden settings. Electronic supplementary material The online version of this article (doi:10.1186/s12916-016-0592-8) contains supplementary material, which is available to authorized users.

Sensitivity analysis â multivariable random effects logistic regression of the association between clinician or hospital caseload and treatment outcome, with different clustering structures. Sensitivity analysis of the impact of different clustering structures on the clinician and hospital caseload models. (DOCX 30 kb)

Sensitivity analyses â multivariable random effects logistic regression of the association between clinician caseload at different thresholds and treatment outcome, post-toolkit. Sensitivity analyses setting the clinician caseload threshold to one and a level designated by recursive partitioning. (DOCX 24 kb)

Treatment outcomes at 12Â months. Table of categorised treatment outcomes. (DOCX 21 kb)

Sensitivity analysis â multivariable random effects logistic regression of the association between clinician caseload and treatment outcomes in a restricted dataset of 10 hospitals. Sensitivity analysis restricted to 4,884 cases from 10 hospitals where the named clinician field was further checked. (DOCX 16 kb)

Sensitivity analysis â stratified multivariable random effects logistic regression of the association between hospital caseload and treatment outcome, adjusted for clinician caseload. Sensitivity analysis of the impact of including clinician caseload in the hospital caseload model. (DOCX 20 kb)

Flow chart of data. Flow chart documenting exclusion from the patient cohort. (DOCX 41 kb)

BCG uptake among infants in England has not been measured since targeted infant vaccination replaced universal schoolchildren vaccination in 2005, mainly because of the challenges in defining denominators. We estimated uptake between 2006 and 2008 by dividing number of BCG doses administered to infants by number of all live births (where BCG vaccination is universal) or ethnic minority/Eastern Europeans live births (where infant-BCG vaccination is selective). Weighted average uptake was 68% (95% CI 65% to 71%), slightly higher in primary care trusts with universal (72% (95% CI 64% to 80%)) than selective (66% (95% CI 61% to 70%)) policy; and also 13% higher in areas vaccinating in postnatal wards compared with community settings.

Introduction: Tuberculosis (TB) disproportionally affects poor people, leading to income and non-income losses. Measures of socioeconomic impact of TB, e.g. impoverishment and patient costs are inadequate to capture non-income losses. We applied impoverishment and a multidimensional measure on TB and non-TB affected households in Zimbabwe., Methods: We conducted a cross-sectional study in 270 households: 90 non-TB; 90 drug-susceptible TB (DS-TB), 90 drug-resistant TB (DR-TB) during the COVID-19 pandemic (2020-2021). Household data included ownership of assets, number of household members, income and indicators on five capital assets: financial, human, social, natural and physical. We determined proportions of impoverished households for periods 12 months prior and at the time of the interview. Households with incomes below US$1.90/day were considered to be impoverished. We used principal component analysis on five capital asset indicators to create a binary outcome variable indicating loss of livelihood. Log-binomial regression was used to determine associations between loss of livelihood and type of household., Results: TB-affected households reported higher previous episodes of TB and household members requiring care than non-TB households. Households that were impoverished 12 months prior to the study were: 21 non-TB (23%); 40 DS-TB (45%); 37 DR-TB (41%). The proportions increased to 81%, 88% and 94%, respectively by the time of interview. Overall, 56% (152/270) of households sold assets: 44% (40/90) non-TB, 58% (52/90) DS-TB and 67% (60/90) DR-TB. Children's education was affected in 31% (56/180) of TB-affected compared to 13% (12/90) non-TB households. Overall, 133(50%) households experienced loss of livelihood, with TB-affected households twice as likely to experience loss of livelihood; adjusted prevalence ratio (aPR=2.02 (95%CI:1.35-3.03)). The effect of TB on livelihood was most pronounced in poorest households (aPR=2.64, (95%CI:1.29-5.41))., Conclusions: TB-affected households experienced greater socioeconomic losses compared to non-TB households. Multidimensional measures of TB are crucial to inform multisectoral approaches to mitigate impacts of TB and other shocks., Competing Interests: Conflicts of interest None declared

Background: Households in low-resource settings are more vulnerable to events which adversely affect their livelihoods, including shocks such as the death of a family member, inflation, droughts and more recently COVID-19. Drug Resistant Tuberculosis (DR-TB) is also another shock that inflicts physical, psychological and socioeconomic burden on individuals and households. We describe experiences and coping strategies among people affected by DR-TB and their households in Zimbabwe during the COVID-19 pandemic, 2020 to 2021., Methods: We conducted 16 in-depth interviews with adults who had just completed or were completing treatment. Interview themes included health seeking behaviour, impact of DR-TB on livelihoods and coping strategies adopted during treatment. We analysed data using thematic analyses., Results: Health seeking from providers outside the public sector, extra-pulmonary TB and health system factors resulted in delayed DR-TB diagnosis and treatment and increased financial drain on households. DR-TB reduced productive capacity and narrowed job opportunities leading to income loss that continued even after completion of treatment. Household livelihood was further adversely affected by lockdowns due to COVID-19, outbreaks of bird flu and cattle disease. Stockouts of DR-TB medicines, common during COVID-19, exacerbated loss of productive time and transport costs as medication had to be accessed from other clinics that were further away. Reversible coping strategies included: reducing number of meals; relocating in search of caregivers and/or family support; spending savings; negotiating with school authorities to keep children in school. Some households had to adopt irreversible coping strategies such as selling productive assets and withdrawing children from school., Conclusion: DR-TB combined with COVID-19 and other stressors pushed households into deeper poverty, and vulnerability. Multi-sectoral approaches that combine health systems, psychosocial and economic interventions are crucial to mitigate diagnostic delays and suffering, and meaningfully support people with DR-TB and their households to compensate the loss of livelihoods during and post DR-TB treatment.