Operational Challenges in Diagnosing Multi-Drug Resistant TB and Initiating Treatment in Andhra Pradesh, India

Revised National TB Control Programme (RNTCP), Andhra Pradesh, India. There is limited information on whether MDR-TB suspects are identified, undergo diagnostic assessment and are initiated on treatment according to the programme guidelines.To assess i) using the programme definition, the number and proportion of MDR-TB suspects in a large cohort of TB patients on first-line treatment under RNTCP ii) the proportion of these MDR-TB suspects who underwent diagnosis for MDR-TB and iii) the number and proportion of those diagnosed as MDR-TB who were successfully initiated on treatment.A retrospective cohort analysis, by reviewing RNTCP records and reports, was conducted in four districts of Andhra Pradesh, India, among patients registered for first line treatment during October 2008 to December 2009.Among 23,999 TB patients registered for treatment there were 559 (2%) MDR-TB suspects (according to programme definition) of which 307 (55%) underwent diagnosis and amongst these 169 (55%) were found to be MDR-TB. Of the MDR-TB patients, 112 (66%) were successfully initiated on treatment. Amongst those eligible for MDR-TB services, significant proportions are lost during the diagnostic and treatment initiation pathway due to a variety of operational challenges. The programme needs to urgently address these challenges for effective delivery and utilisation of the MDR-TB services

Source of Previous Treatment for Re-Treatment TB Cases Registered under the National TB Control Programme, India, 2010

BACKGROUND: In 2009, nearly half (289,756) of global re-treatment TB notifications are from India; no nationally-representative data on the source of previous treatment was available to inform strategies for improvement of initial TB treatment outcome. OBJECTIVES: To assess the source of previous treatment for re-treatment TB patients registered under India's Revised National TB control Programme (RNTCP). METHODOLOGY: A nationally-representative cross sectional study was conducted in a sample of 36 randomly-selected districts. All consecutively registered retreatment TB patients during a defined 15-day period in these 36 districts were contacted and the information on the source of previous treatment sought. RESULTS: Data was collected from all 1712 retreatment TB patients registered in the identified districts during the study period. The data includes information on 595 'relapse' cases, 105 'failure' cases, 437 'treatment after default (TAD)' cases and 575 're-treatment others' cases. The source of most recent previous anti-tuberculosis therapy for 754 [44% (95% CI, 38.2%-49.9%)] of the re-treatment TB patients was from providers outside the TB control programme. A higher proportion of patients registered as TAD (64%) and 'retreatment others' (59%) were likely to be treated outside the National Programme, when compared to the proportion among 'relapse' (22%) or 'failure' (6%). Extrapolated to national registration, of the 292,972 re-treatment registrations in 2010, 128,907 patients would have been most recently treated outside the national programme. CONCLUSIONS: Nearly half of the re-treatment cases registered with the national programme were most recently treated outside the programme setting. Enhanced efforts towards extending treatment support and supervision to patients treated by private sector treatment providers are urgently required to improve the quality of treatment and reduce the numbers of patients with recurrent disease. In addition, reasons for the large number of recurrent TB cases from those already treated by the national programme require urgent detailed investigation

Piloting Upfront Xpert MTB/RIF Testing on Various Specimens under Programmatic Conditions for Diagnosis of TB & DR-TB in Paediatric Population

India accounts for one-fifth of the global TB incidence. While the exact burden of childhood TB is not known, TB remains one of the leading causes of childhood mortality in India. Bacteriological confirmation of TB in children is challenging due to difficulty in obtaining quality specimens, in the absence of which diagnosis is largely based on clinical judgement. While testing multiple specimens can potentially contribute to higher proportion of laboratory confirmed paediatric TB cases, lack of high sensitivity tests adds to the diagnostic challenge. We describe here our experiences in piloting upfront Xpert MTB/RIF testing, for diagnosis of TB in paediatric population in respiratory and extra pulmonary specimens, as recently recommended by WHO.Xpert MTB/RIF testing was offered to all paediatric (0-14 years) presumptive TB cases (both pulmonary and extra-pulmonary) seeking care at public and private health facilities in the project areas covering 4 cities of India.Under this pilot project, 8,370 paediatric presumptive TB & presumptive DR-TB cases were tested between April and-November 2014. Overall, 9,149 specimens were tested, of which 4,445 (48.6%) were non-sputum specimens. Xpert MTB/RIF gave 9,083 (99.2%, CI 99.0-99.4) valid results. Of the 8,143 presumptive TB cases enrolled, 517 (6.3%, CI 5.8-6.9) were bacteriologically confirmed. TB detection rates were two fold higher with Xpert MTB/RIF as compared to smear microscopy. Further, a total of 60 rifampicin resistant TB cases were detected, of which 38 were detected among 512 presumptive TB cases while 22 were detected amongst 227 presumptive DR-TB cases tested under the project.Xpert MTB/RIF with advantages of quick turnaround testing-time, high proportion of interpretable results and feasibility of rapid rollout, substantially improved the diagnosis of bacteriologically confirmed TB in children, while simultaneously detecting rifampicin resistance

Building a tuberculosis-free world: The Lancet Commission on tuberculosis

___Key messages___ The Commission recommends five priority investments to achieve a tuberculosis-free world within a generation. These investments are designed to fulfil the mandate of the UN High Level Meeting on tuberculosis. In addition, they answer

Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990–2021: a systematic analysis for the Global Burden of Disease Study 2021

BACKGROUND Regular, detailed reporting on population health by underlying cause of death is fundamental for public health decision making. Cause-specific estimates of mortality and the subsequent effects on life expectancy worldwide are valuable metrics to gauge progress in reducing mortality rates. These estimates are particularly important following large-scale mortality spikes, such as the COVID-19 pandemic. When systematically analysed, mortality rates and life expectancy allow comparisons of the consequences of causes of death globally and over time, providing a nuanced understanding of the effect of these causes on global populations. METHODS The Global Burden of Diseases, Injuries, and Risk Factors Study (GBD) 2021 cause-of-death analysis estimated mortality and years of life lost (YLLs) from 288 causes of death by age-sex-location-year in 204 countries and territories and 811 subnational locations for each year from 1990 until 2021. The analysis used 56 604 data sources, including data from vital registration and verbal autopsy as well as surveys, censuses, surveillance systems, and cancer registries, among others. As with previous GBD rounds, cause-specific death rates for most causes were estimated using the Cause of Death Ensemble model-a modelling tool developed for GBD to assess the out-of-sample predictive validity of different statistical models and covariate permutations and combine those results to produce cause-specific mortality estimates-with alternative strategies adapted to model causes with insufficient data, substantial changes in reporting over the study period, or unusual epidemiology. YLLs were computed as the product of the number of deaths for each cause-age-sex-location-year and the standard life expectancy at each age. As part of the modelling process, uncertainty intervals (UIs) were generated using the 2·5th and 97·5th percentiles from a 1000-draw distribution for each metric. We decomposed life expectancy by cause of death, location, and year to show cause-specific effects on life expectancy from 1990 to 2021. We also used the coefficient of variation and the fraction of population affected by 90% of deaths to highlight concentrations of mortality. Findings are reported in counts and age-standardised rates. Methodological improvements for cause-of-death estimates in GBD 2021 include the expansion of under-5-years age group to include four new age groups, enhanced methods to account for stochastic variation of sparse data, and the inclusion of COVID-19 and other pandemic-related mortality-which includes excess mortality associated with the pandemic, excluding COVID-19, lower respiratory infections, measles, malaria, and pertussis. For this analysis, 199 new country-years of vital registration cause-of-death data, 5 country-years of surveillance data, 21 country-years of verbal autopsy data, and 94 country-years of other data types were added to those used in previous GBD rounds. FINDINGS The leading causes of age-standardised deaths globally were the same in 2019 as they were in 1990; in descending order, these were, ischaemic heart disease, stroke, chronic obstructive pulmonary disease, and lower respiratory infections. In 2021, however, COVID-19 replaced stroke as the second-leading age-standardised cause of death, with 94·0 deaths (95% UI 89·2-100·0) per 100 000 population. The COVID-19 pandemic shifted the rankings of the leading five causes, lowering stroke to the third-leading and chronic obstructive pulmonary disease to the fourth-leading position. In 2021, the highest age-standardised death rates from COVID-19 occurred in sub-Saharan Africa (271·0 deaths [250·1-290·7] per 100 000 population) and Latin America and the Caribbean (195·4 deaths [182·1-211·4] per 100 000 population). The lowest age-standardised death rates from COVID-19 were in the high-income super-region (48·1 deaths [47·4-48·8] per 100 000 population) and southeast Asia, east Asia, and Oceania (23·2 deaths [16·3-37·2] per 100 000 population). Globally, life expectancy steadily improved between 1990 and 2019 for 18 of the 22 investigated causes. Decomposition of global and regional life expectancy showed the positive effect that reductions in deaths from enteric infections, lower respiratory infections, stroke, and neonatal deaths, among others have contributed to improved survival over the study period. However, a net reduction of 1·6 years occurred in global life expectancy between 2019 and 2021, primarily due to increased death rates from COVID-19 and other pandemic-related mortality. Life expectancy was highly variable between super-regions over the study period, with southeast Asia, east Asia, and Oceania gaining 8·3 years (6·7-9·9) overall, while having the smallest reduction in life expectancy due to COVID-19 (0·4 years). The largest reduction in life expectancy due to COVID-19 occurred in Latin America and the Caribbean (3·6 years). Additionally, 53 of the 288 causes of death were highly concentrated in locations with less than 50% of the global population as of 2021, and these causes of death became progressively more concentrated since 1990, when only 44 causes showed this pattern. The concentration phenomenon is discussed heuristically with respect to enteric and lower respiratory infections, malaria, HIV/AIDS, neonatal disorders, tuberculosis, and measles. INTERPRETATION Long-standing gains in life expectancy and reductions in many of the leading causes of death have been disrupted by the COVID-19 pandemic, the adverse effects of which were spread unevenly among populations. Despite the pandemic, there has been continued progress in combatting several notable causes of death, leading to improved global life expectancy over the study period. Each of the seven GBD super-regions showed an overall improvement from 1990 and 2021, obscuring the negative effect in the years of the pandemic. Additionally, our findings regarding regional variation in causes of death driving increases in life expectancy hold clear policy utility. Analyses of shifting mortality trends reveal that several causes, once widespread globally, are now increasingly concentrated geographically. These changes in mortality concentration, alongside further investigation of changing risks, interventions, and relevant policy, present an important opportunity to deepen our understanding of mortality-reduction strategies. Examining patterns in mortality concentration might reveal areas where successful public health interventions have been implemented. Translating these successes to locations where certain causes of death remain entrenched can inform policies that work to improve life expectancy for people everywhere. FUNDING Bill & Melinda Gates Foundation

Mycobacterium tuberculosis polyclonal infections through treatment and recurrence.

BackgroundMixed/polyclonal infections due to different genotypes are reported in Tuberculosis. The current study was designed to understand the fate of mixed infections during the course of treatment and follow-up and its role in disease pathogenesis.MethodsSputum samples were collected on 0,1,2,3,6,12 and 24 months from 157 treatment-naïve patients, cultures subjected to Drug-Susceptibility-testing (MGIT 960), spoligotyping, MIRU-VNTR and SNP genotyping. All isolated colonies on thin layer agar (7H11) were subjected to spoligotyping.FindingsOne thirty three baseline cultures were positive (133/157, 84.7%), 43(32.3%) had mixture of genotypes. Twenty-four of these patients (55.8%) showed change in genotype while six showed different drug-susceptibility patterns while on treatment. Twenty-three (53.5%) patients with polyclonal infections showed resistance to at least one drug compared to 10/90 (11.1%) monoclonal infections (PConclusionsThe coexistence of different genotypes and change of genotypes during the same disease episode, while on treatment, confirms constancy of polyclonal infections. The composition of the mixture of genotypes and the relative predominance may be missed by culture due to its limit of detection. Polyclonal infections in TB could be a rule rather than exception and challenges the age-old dogma of reactivation/reinfection