Project Tycho datasets contain case counts for reported disease conditions for countries around the world. The Project Tycho data curation team extracts these case counts from various reputable sources, typically from national or international health authorities, such as the US Centers for Disease Control or the World Health Organization. These original data sources include both open- and restricted-access sources. For restricted-access sources, the Project Tycho team has obtained permission for redistribution from data contributors. All datasets contain case count data that are identical to counts published in the original source and no counts have been modified in any way by the Project Tycho team. The Project Tycho team has pre-processed datasets by adding new variables, such as standard disease and location identifiers, that improve data interpretabilty. We also formatted the data into a standard data format.

Each Project Tycho dataset contains case counts for a specific condition (e.g. measles) and for a specific country (e.g. The United States). Case counts are reported per time interval. In addition to case counts, datsets include information about these counts (attributes), such as the location, age group, subpopulation, diagnostic certainty, place of aquisition, and the source from which we extracted case counts. One dataset can include many series of case count time intervals, such as "US measles cases as reported by CDC", or "US measles cases reported by WHO", or "US measles cases that originated abroad", etc.

Depending on the intended use of a dataset, we recommend a few data processing steps before analysis:

• Analyze missing data: Project Tycho datasets do not inlcude time intervals for which no case count was reported (for many datasets, time series of case counts are incomplete, due to incompleteness of source documents) and users will need to add time intervals for which no count value is available. Project Tycho datasets do include time intervals for which a case count value of zero was reported.
• Separate cumulative from non-cumulative time interval series. Case count time series in Project Tycho datasets can be "cumulative" or "fixed-intervals". Cumulative case count time series consist of overlapping case count intervals starting on the same date, but ending on different dates. For example, each interval in a cumulative count time series can start on January 1st, but end on January 7th, 14th, 21st, etc. It is common practice among public health agencies to report cases for cumulative time intervals. Case count series with fixed time intervals consist of mutually exxclusive time intervals that all start and end on different dates and all have identical length (day, week, month, year). Given the different nature of these two types of case count data, we indicated this with an attribute for each count value, named "PartOfCumulativeCountSeries".

Tuberculosis death rate is the estimated number of deaths from tuberculosis among HIV-negative people, expressed as the rate per 100,000 population. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.

BACKGROUND: Tuberculosis (TB) has killed more people than any other infection since records began. The Sustainable Development Goals and the World Health Organisation “End TB” Strategy prioritise key targets for reducing mortality due to TB. However, there seems to be limited research evidence available to inform how this target of reducing TB mortality may best be achieved. OBJECTIVES: We aim to describe and categorise the published literature concerning mortality due to TB and then to review, critically appraise and synthesise the evidence that interventions decrease mortality due to TB. METHODS: The Pubmed database will be searched. Screening and selection of eligible publications will be made by 2 independent reviewers and a third will be asked to resolve any discrepancies. Key information from selected publications will be extracted using a shared cloud-based spreadsheet. Quantitative assessments of the impacts of trial interventions on TB mortality will be extracted and synthesised using meta-analysis, if possible. When appropriate, the quality of trial evidence will be assessed.This systematic review and meta-analysis is registered with the PROSPERO database (CRD42023387877). CONCLUSIONS: We will review the current published evidence concerning TB mortality and how it may best be prevented. We aim to clarify research gaps and also to synthesise evidence in order to guide future policy and research.

The indicator measures the standardised death rate of tuberculosis, HIV and hepatitis (International Classification of Diseases (ICD) codes A15-A19_B90, B15-B19_B942 and B20-B24). The rate is calculated by dividing the number of people dying due to selected communicable diseases by the total population. Data on causes of death (COD) refer to the underlying cause which - according to the World Health Organisation (WHO) - is "the disease or injury which initiated the train of morbid events leading directly to death, or the circumstances of the accident or violence which produced the fatal injury". COD data are derived from death certificates. The medical certification of death is an obligation in all Member States. The data are presented as standardised death rates, meaning they are adjusted to a standard age distribution in order to measure death rates independently of different age structures of populations. This approach improves comparability over time and between countries. The standardised death rates used here are calculated on the basis of the standard European population referring to the residents of the countries. Copyright notice and free re-use of data on: https://ec.europa.eu/eurostat/about-us/policies/copyright

WONDER online databases include county-level Compressed Mortality (death certificates) since 1979; county-level Multiple Cause of Death (death certificates) since 1999; county-level Natality (birth certificates) since 1995; county-level Linked Birth / Death records (linked birth-death certificates) since 1995; state & large metro-level United States Cancer Statistics mortality (death certificates) since 1999; state & large metro-level United States Cancer Statistics incidence (cancer registry cases) since 1999; state and metro-level Online Tuberculosis Information System (TB case reports) since 1993; state-level Sexually Transmitted Disease Morbidity (case reports) since 1984; state-level Vaccine Adverse Event Reporting system (adverse reaction case reports) since 1990; county-level population estimates since 1970. The WONDER web server also hosts the Data2010 system with state-level data for compliance with Healthy People 2010 goals since 1998; the National Notifiable Disease Surveillance System weekly provisional case reports since 1996; the 122 Cities Mortality Reporting System weekly death reports since 1996; the Prevention Guidelines database (book in electronic format) published 1998; the Scientific Data Archives (public use data sets and documentation); and links to other online data sources on the "Topics" page.

BackgroundTuberculosis (TB) is a worldwide public health problem, especially in countries that also report high numbers of people living with HIV (PLWH) and/or diabetes mellitus (DM). However, the unique features of persons with TB-HIV-DM are incompletely understood. This study compared anti-TB treatment (ATT) outcomes of diabetic and non-diabetic TB/HIV co-infected patients.MethodsA nationwide retrospective observational investigation was performed with data from the Brazilian Tuberculosis Database System among patients reported to have TB-HIV co-infection between 2014 and 2019. This database includes all reported TB cases in Brazil. Exploratory and association analyses compared TB treatment outcomes in DM and non-DM patients. Unfavorable outcomes were defined as death, treatment failure, loss to follow-up or recurrence. Multivariable stepwise logistic regressions were used to identify the variables associated with unfavorable ATT outcomes in the TB-HIV population.ResultsOf the 31,070 TB-HIV patients analyzed, 999 (3.2%) reported having DM. However, in these TB-HIV patients, DM was not associated with any unfavorable treatment outcome [adjusted Odds Ratio (aOR): 0.97, 95% CI: 0.83–1.12, p = 0.781]. Furthermore, DM was also not associated with any specific type of unfavorable outcome in this study. In both the TB-HIV group and the TB-HIV-DM subpopulation, use of alcohol, illicit drugs and tobacco, as well as non-white ethnicity and prior TB were all characteristics more frequently observed in persons who experienced an unfavorable ATT outcome.ConclusionDM is not associated with unfavorable TB treatment outcomes in persons with TB-HIV, including death, treatment failure, recurrence and loss to follow up. However, consumption habits, non-white ethnicity and prior TB are all more frequently detected in those with unfavorable outcomes in both TB-HIV and TB-HIV-DM patients.

This is the microdataset used in the paper "SMS nudges as a tool to reduce Tuberculosis treatment delay and pretreatment loss to follow-up. A randomized controlled trial". We fielded two SMS interventions in three Cape Town clinics to see their effects on whether people returned to clinic, and how quickly. One was a simple reminder; the other aimed to overcome “optimism bias” by reminding people TB is curable and many millions die unnecessarily from it. Recruits were randomly assigned at the clinic level to a control group or one of the two SMS groups (1:2:2). In addition to estimating effects on the full sample, we also estimated effects on HIV-positive patients.

The data shows the statistics of different item-wise reports on a cumulative yearly basis in states up to the sub-district level in Kerala. It included 1) Ante Natal Care (ANC) - Antenatal care (ANC) is a means to identify high-risk pregnancies and educate women so that they might experience healthier delivery and outcomes. 2) Deliveries - The delivery of the baby by the pregnant women 3) Number of Caesarean (C-Section) deliveries - Caesarean delivery (C-section) is used to deliver a baby through surgical incisions made in the abdomen and uterus. 4) Pregnancy outcome & details of new-born - The records kept of the pregnancy outcome along with the details of new-born 5) Complicated Pregnancies - The different pregnancies that were not normal and had complications 6) Post Natal Care (PNC) - Postnatal care is defined as care given to the mother and her new-born baby immediately after the birth of the placenta and for the first six weeks of life 7) Reproductive Tract Infections/Sexually Transmitted Infections (RTI/STI) Cases - The records of reproductive tract infections along with the records of the sexually transmitted cases 8) Family Planning - The different methods used by families to keep track of family 9) CHILD IMMUNISATION - The records of child immunisation which are records of vaccination 10) Number of cases of Childhood Diseases (0-5 years) - The records of the number of cases of childhood diseases within the age of 5 years old 11) NVBDCP - The National Vector Borne Disease Control Programme (NVBDCP) is one of the most comprehensive and multi-faceted public health activities in the country and concerned with the prevention and control of vector-borne diseases, namely Malaria, Filariasis, Kala-azar, Dengue and Japanese Encephalitis (JE). 12) Adolescent Health - The record of the conditions of adolescent health 13 ) Directly Observed Treatment, Short-course (DOTS) - Directly observed treatment, short-course (DOTS, also known as TB-DOTS) is the name given to tuberculosis (TB) control strategy recommended by the World Health Organization 14) Patient Services - Patient Services means those which vary with the number of personnel; professional and para-professional skills of the personnel; specialised equipment, and reflect the intensity of the medical and psycho-social needs of the patients. 15) Laboratory Testing - A medical procedure that involves testing a sample of blood, urine, or other substance from the body. Laboratory tests can help determine a diagnosis, plan treatment, check if the treatment works, or monitor the disease over time. 16) Details of deaths reported with probable causes - The reports of deaths recorded with possible reasons are given in a detail 17) Vaccines - The reports of vaccines which are recorded 18) Syringes - It is the number of syringes that are used and recorded 19) Rashtriya Bal Swasthaya Karyakram (RBSK) - Rashtriya Bal Swasthya Karyakram (RBSK) is an important initiative aiming at early identification and early intervention for children from birth to 18 years to cover 4 'D's viz. Defects at birth, Deficiencies, Diseases, Development delays, including disability. 20) Coverage under WIFS JUNIOR - The coverage of the Weekly Iron Folic Acid Supplementation Programme for children six to one 21) Maternal Death Reviews (MDR) - A maternal death review cross-checks how the mother died. It provides a rare opportunity for a group of health staff and community members to learn from a tragic – and often preventable. 22) Janani Shishu Suraksha Karyakaram (JSSK)- This initiative provides free and cashless services to pregnant women, including standard deliveries and caesarean operations. It entitles all pregnant women in public health institutions to free and no-expense delivery, including caesarean section.

Background

Tuberculosis is one of the top ten causes of death globally and the leading cause of death from a single infectious agent. Eradicating the Tuberculosis epidemic by 2030 is one of the top United Nations Sustainable Development Goals. Early diagnosis is essential to achieving this goal because it improves individual prognosis and reduces transmission rates of asymptomatic infected. We aim to support this goal by developing rapid and sensitive diagnostics using machine learning algorithms to minimize the need for expert intervention.

A single-molecule fluorescence immunosorbent assay was used to detect the Tuberculosis biomarker lipoarabinomannan from a set of twenty clinical patient samples and a control set of spiked human urine. Tuberculosis status was separately confirmed by GeneXpert MTB/RIF and cell culture. Two machine learning algorithms, an automatic and a semiautomatic model, were developed and trained by the calibrated lipoarabinomannan titration assay data and then tested against the ground truth patient data. The semiautomatic model differed from the automatic model by an expert review step in the former, which calibrated the lower threshold to determine single molecules from background noise. The semiautomatic model was found to provide 88.89% clinical sensitivity, while the automatic model resulted in 77.78% clinical sensitivity.

The semiautomatic model outperformed the automatic model in clinical sensitivity as a result of the expert intervention applied during calibration and both models vastly outperformed manual expert counting in terms of time-to-detection and completion of analysis. Meanwhile, the clinical sensitivity of the automatic model could be improved significantly with a larger training dataset. In short, semiautomatic, and automatic Gaussian Mixture Models have a place in supporting rapid detection of Tuberculosis in resource-limited settings without sacrificing clinical sensitivity.

BackgroundTuberculous meningitis (TBM) is the most severe form of tuberculosis (TB) and can be difficult to diagnose and treat. We aimed to describe the clinical presentation, diagnosis, disease spectrum, outcome, and prognostic factors of patients treated for TBM in China.MethodsA multicenter retrospective study was conducted from 2009 to 2019 enrolling all presumptive TBM patients referred to Xijing tertiary Hospital from 27 referral centers in and around Shaanxi province, China. Patients with clinical features suggestive of TBM (abnormal CSF parameters) were included in the study if they had adequate baseline information to be classified as “confirmed,” “probable,” or “possible” TBM according to international consensus TBM criteria and remained in follow-up. Patients with a confirmed alternative diagnosis or severe immune compromise were excluded. Clinical presentation, central nervous system imaging, cerebrospinal fluid (CSF) results, TBM score, and outcome—assessed using the modified Barthel disability index—were recorded and compared.FindingsA total of 341 presumptive TBM patients met selection criteria; 63 confirmed TBM (25 culture positive, 42 Xpert-MTB/RIF positive), 66 probable TBM, 163 possible TBM, and 49 “not TBM.” Death was associated with BMRC grade III (OR = 5.172; 95%CI: 2.298–11.641), TBM score ≥ 15 (OR = 3.843; 95%CI: 1.372–10.761), age > 60 years (OR = 3.566; 95%CI: 1.022–12.442), and CSF neutrophil ratio ≥ 25% (OR = 2.298; 95%CI: 1.027–5.139). Among those with confirmed TBM, nearly one-third (17/63, 27.0%) had a TBM score 60 years) have higher TBM scores or CSF neutrophil ratios, have signs of disseminated/miliary TB, and are at greatest risk of death. In general, more effort needs to be done to improve early diagnosis and treatment outcome in TBM patients.

BackgroundHIV-TB co-infection poses a significant public health threat, notably in sub-Saharan Africa including Ethiopia. Despite this public health problem, studies in Ethiopia regarding the mortality of HIV-TB co-infection patients have been inconsistent, and the overall estimate of mortality was not determined. Accordingly, this meta-analysis aims to assess the magnitude of mortality and predictors among HIV-TB co-infected patients in Ethiopia.MethodsA search of the literature was conducted from three databases (PubMed, Global Index Medicus, and CINHAL), and other sources (Google Scholar, Google, Worldwide Science). All observational studies that reported the mortality of HIV-TB co-infected patients in Ethiopia were included. Joanna Briggs Institute’s (JBI) quality appraisal checklist was used to assess the quality of studies. Effect sizes were pooled using the random effects model. Heterogeneity was assessed using Cochrane Q and I2 test statistics, and the prediction interval was determined. Subgroup analysis was conducted by region. To examine the presence of an influential study, a sensitivity analysis was done. Egger’s test was used to check publication bias. A non-parametric trim and fill analysis was carried out.ResultsA total of 886 studies were identified, using database searches and keywords. Of these, 37 met the criteria for inclusion. The pooled proportion of mortality among HIV-TB co-infected patients was found to be 18.42% (95% CI:14.27–22.57). In the subgroup analysis, the highest mortality was observed in the Tigray region at 31.86% (95% CI: 7.69–56.03), and the lowest mortality was reported in two general studies in Ethiopia 11.95 (95% CI: 4.19–19.00). From the examined 20 predictors, only four predictors such as Anaemia (HR = 2.25, 95% CI: 1.65–3.07), Poor adherence to ART (HR = 2.42, 95% CI: 1.39–4.21), not taking co-trimoxazole preventive therapy (HR = 1.87, 95% CI: 1.28–2.73), and extrapulmonary tuberculosis (HR = 1.23, 95% CI: 1.01–1.51) were significant predictors.ConclusionsIn Ethiopia, 18.42% of HIV-TB co-infected patients died. Anaemia, poor adherence, not taking CPT, and extrapulmonary tuberculosis were found to be significant predictors. Hence, the concerned stakeholders need to expand and strengthen the HIV-TB collaborative services and attention should be given to patients presented with the aforementioned predictors.Trial registrationThis meta-analysis has been registered in PROSPERO with registration number CRD42023466558.

Sociodemographic and clinical characteristics associated with death in cases of TB/HIV coinfection, 2009–2013, Porto Alegre, Brazil.