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.

Years of Life Lost (YLL) as a result of death from tuberculosis. Directly age-Standardised Rates (DSR) per 100,000 population Source: Office for National Statistics (ONS) Publisher: Information Centre (IC) - Clinical and Health Outcomes Knowledge Base Geographies: Local Authority District (LAD), Government Office Region (GOR), National, Strategic Health Authority (SHA) Geographic coverage: England Time coverage: 2005-07, 2007 Type of data: Administrative data

ObjectiveTuberculosis (TB) is a major cause of ill health and one of the leading causes of death worldwide. The first step in developing strategies to reduce TB mortality is to identify the direct causes of death in patients with TB and the risk factors for each cause.MethodsData on patients with TB systemically collected from the National Surveillance System of South Korea from January 2019 to December 2020 were included in this study. We analyzed the clinical characteristics associated with TB and non-TB-related deaths, including TB-related symptoms, comorbidities, and radiographic and microbiological findings.ResultsOf the total of 12,340 patients with TB, 61% were males with a mean age of 61.3 years. During the follow-up period, the overall mortality rate was 10.6%, with TB-related deaths accounting for 21.3% of all TB deaths. The median survival time in the TB-related death group was 22 days. TB-related death was associated with older age, lower body mass index (BMI), dyspnea, fever, general weakness, bilateral radiographic patterns, and acid-fast bacilli (AFB)-positive smears. Non-TB-related deaths were associated with older age, male sex, lower BMI, comorbidities of heart, liver, kidney, and central nervous system (CNS) diseases, CNS TB involvement, the presence of dyspnea, general weakness, and bilateral radiographic patterns.ConclusionPatients with high-risk TB must be identified through cause-specific mortality analysis, and the mortality rate must be reduced through intensive monitoring of patients with a high TB burden and comorbidities.

As a serious infectious disease, tuberculosis (TB) is one of the major threats to human health worldwide, leading to millions of death every year. Although early diagnosis and treatment can greatly improve the chances of survival, it remains a major challenge, especially in developing countries. Computer-aided tuberculosis diagnosis (CTD) is a promising choice for TB diagnosis due to the great successes of deep learning. However, when it comes to TB diagnosis, the lack of training data has hampered the progress of CTD. To solve this problem, we establish a large-scale TB dataset, namely Tuberculosis X-ray (TBX11K) dataset. This dataset contains 11200 X-ray images with corresponding bounding box annotations for TB areas, while the existing largest public TB dataset only has 662 X-ray images with corresponding image-level annotations. The proposed dataset enables the training of sophisticated detectors for high-quality CTD. Rethinking Computer-Aided Tuberculosis Diagnosis, Yun Liu*, Yu-H

This dataset provides an extensive view of global population statistics and health metrics across various countries from 2014 to 2024. It combines population data with vital health-related indicators, making it a valuable resource for understanding trends in population growth and health outcomes worldwide. Researchers, data scientists, and policymakers can utilize this dataset to analyze correlations between population dynamics and health performance at a global scale.

Key Features: - Country: Name of the country. - Year: Year of the data (2014–2024). - Population: Total population for the respective year and country. - Country Code: ISO 3-letter country codes for easy identification. - Health Expenditure (health_exp): Percentage of GDP spent on healthcare. - Life Expectancy (life_expect): Average life expectancy at birth in years. - Maternal Mortality (maternal_mortality): Maternal deaths per 100,000 live births. - Infant Mortality (infant_mortality): Deaths of infants under 1 year per 1,000 live births. - Neonatal Mortality (neonatal_mortality): Deaths of newborns (0–28 days) per 1,000 live births. - Under-5 Mortality (under_5_mortality): Deaths of children under 5 years per 1,000 live births. - HIV Prevalence (prev_hiv): Percentage of the population living with HIV. - Tuberculosis Incidence (inci_tuberc): Estimated new and relapse TB cases per 100,000 people. - Undernourishment Prevalence (prev_undernourishment): Percentage of the population that is undernourished.

Use Cases: - Health Policy Analysis: Understand trends in healthcare expenditure and its relationship to health outcomes. - Global Health Research: Investigate global or regional disparities in health and nutrition. - Population Studies: Analyze population growth trends alongside health indicators. - Data Visualization: Build visual dashboards for storytelling and impactful data representation.

Tuberculosis is a communicable chronic disease and one of the top ten causes of death worldwide according to World Health Organization (WHO). With availability of clean and well encoded clinical data from tuberculosis patients, artificial intelligence and machine learning algorithms would be able to transform the management of tuberculosis patients through intelligent prediction and intervention. This dataset contains four hundred and thirty (430) clinical data from patients with tuberculosis at Tuberculosis and Leprosy Hospital, Eku, Delta State, Nigeria. The dataset was gathered through validated and structured questionnaire administered using random sampling after obtaining the patients' consent. The collated dataset was pre-processed and encoded with variables (features) for prediction which include cough, night sweat, breathing difficulty, fever, chest pain, sputum, immune suppression, loss of pleasure, chill, lack of concentration, irritation, loss of appetite, loss of energy, lymph node enlargement, systolic blood pressure and BMI. Prediction of tuberculosis based on the clinical data from patients' features would play an essential role in diagnosis, intervention and management of tuberculosis patient.

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.

Background Unlike pulmonary tuberculosis, there is limited information on delays in diagnosis and treatment initiation in extrapulmonary tuberculosis (EPTB) and their consequences for disease outcomes and mortality. In low- and middle-income countries, most EPTB cases are presumed rather than microbiologically confirmed, which might lead to an underestimation of the mortality rates in EPTB. Objective The study aimed to assess the delays in diagnosis and treatment in EPTB and their association with mortality in a setting with a high prevalence of both HIV and malnutrition. Method We included 106 EPTB patients from Mbeya Zonal Referral Hospital, who were followed up until the completion of their treatment. Patients were classified as having EPTB using a clinical case definition. In total, 37 of 106 (35%) EPTB cases resulted in death. The median (interquartile range) total diagnostic delay for survivors was 59 days (26-136), while for those who died, it was 78 days (32-165). The corresponding median (interquartile range) treatment delay was 66 days (33-140) for survivors and 78 days (27-189) for those who died. None of the differences reached statistical significance when analyzed with non-parametric tests. Surprisingly, 21 patients did not receive TB treatment, but this lack of therapy did not affect mortality or correlate with a longer diagnostic delay. Conclusion We were unable to demonstrate that diagnostic or treatment delays were higher in EPTB patients who died. Furthermore, EPTB patients who did not receive TB treatment did not exhibit higher mortality rates. Further prospective studies with larger sample sizes are needed to better understand the factors contributing to delays in diagnosis and treatment, as well as their potential impact on mortality in EPTB.

"BACKGROUND: Nearly 20% of tuberculosis (TB) patients die within one year, and TB-related mortality rates remain high in Taiwan. The study aimed to identify factors correlated with TB-specific deaths versus non-TB-specific deaths in different age groups among TB-related mortalities. METHODS: A retrospective cohort study was conducted from 2006-2008 with newly registered TB patients receiving follow-up for 1 year. The national TB database from the Taiwan-CDC was linked with the National Vital Registry System and the National Health Insurance database. A chi-squared test and logistic regression were used to analyse the correlated factors related to TB-specific and non-TB-specific deaths in different age groups. RESULTS: Elderly age (odds ratio [OR] 2.68-8.09), Eastern residence (OR 2.01), positive sputum bacteriology (OR 2.54), abnormal chest X-ray (OR 2.28), and comorbidity with chronic kidney disease (OR 2.35), stroke (OR 1.74) or chronic liver disease (OR 1.29) were most likely to be the cause of TB-specific deaths, whereas cancer (OR 0.79) was less likely to be implicated. For non-TB-specific deaths in patients younger than 65 years of age, male sex (OR 2.04) and comorbidity with HIV (OR 5.92), chronic kidney disease (OR 8.02), stroke (OR 3.75), cancer (OR 9.79), chronic liver disease (OR 2.71) or diabetes mellitus (OR 1.38) were risk factors. CONCLUSIONS: Different factors correlated with TB-specific deaths compared with non-TB-specific deaths, and the impact of comorbidities gradually decreased as age increased. To reduce TB-specific mortality, special consideration for TB patients with old age, Eastern residence, positive sputum bacteriology and comorbidity with chronic kidney disease or stroke is crucial. In particular, Eastern residence increased the risk of TB-specific death in all age groups. In terms of TB deaths among patients younger than 65 years of age, patients with HIV, chronic kidney disease or cancer had a 6-10 times increased risk of non-TB-specific deaths."

IntroductionTuberculosis incidence is disproportionately high among people in poverty. Cash transfer programs have become an important strategy in Brazil fight inequalities as part of comprehensive poverty alleviation policies. This study was aimed at assessing the effect of being a beneficiary of a governmental cash transfer program on tuberculosis (TB) treatment cure rates.MethodsWe conducted a longitudinal database study including people ≥18 years old with confirmed incident TB in Brazil in 2015. We treated missing data with multiple imputation. Poisson regression models with robust variance were carried out to assess the effect of TB determinants on cure rates. The average effect of being beneficiary of cash transfer was estimated by propensity-score matching.ResultsIn 2015, 25,084 women and men diagnosed as new tuberculosis case, of whom 1,714 (6.8%) were beneficiaries of a national cash transfer. Among the total population with pulmonary tuberculosis several determinants were associated with cure rates. However, among the cash transfer group, this association was vanished in males, blacks, region of residence, and people not deprived of their freedom and who smoke tobacco. The average treatment effect of cash transfers on TB cure rates, based on propensity score matching, found that being beneficiary of cash transfer improved TB cure rates by 8% [Coefficient 0.08 (95% confidence interval 0.06–0.11) in subjects with pulmonary TB].ConclusionOur study suggests that, in Brazil, the effect of cash transfer on the outcome of TB treatment may be achieved by the indirect effect of other determinants. Also, these results suggest the direct effect of being beneficiary of cash transfer on improving TB cure rates.

Background: Coronavirus disease 2019 (COVID-19) and tuberculosis (TB) are two major infectious diseases posing significant public health threats, and their coinfection (aptly abbreviated COVID-TB) makes the situation worse. This study aimed to investigate the clinical features and prognosis of COVID-TB cases.Methods: The PubMed, Embase, Cochrane, CNKI, and Wanfang databases were searched for relevant studies published through December 18, 2020. An overview of COVID-TB case reports/case series was prepared that described their clinical characteristics and differences between survivors and deceased patients. Pooled odds ratios (ORs) with 95% confidence intervals (CIs) for death or severe COVID-19 were calculated. The quality of outcomes was assessed using GRADEpro.Results: Thirty-six studies were included. Of 89 COVID-TB patients, 19 (23.46%) died, and 72 (80.90%) were male. The median age of non-survivors (53.95 ± 19.78 years) was greater than that of survivors (37.76 ± 15.54 years) (p < 0.001). Non-survivors were more likely to have hypertension (47.06 vs. 17.95%) or symptoms of dyspnea (72.73% vs. 30%) or bilateral lesions (73.68 vs. 47.14%), infiltrates (57.89 vs. 24.29%), tree in bud (10.53% vs. 0%), or a higher leucocyte count (12.9 [10.5–16.73] vs. 8.015 [4.8–8.97] × 109/L) than survivors (p < 0.05). In terms of treatment, 88.52% received anti-TB therapy, 50.82% received antibiotics, 22.95% received antiviral therapy, 26.23% received hydroxychloroquine, and 11.48% received corticosteroids. The pooled ORs of death or severe disease in the COVID-TB group and the non-TB group were 2.21 (95% CI: 1.80, 2.70) and 2.77 (95% CI: 1.33, 5.74) (P < 0.01), respectively.Conclusion: In summary, there appear to be some predictors of worse prognosis among COVID-TB cases. A moderate level of evidence suggests that COVID-TB patients are more likely to suffer severe disease or death than COVID-19 patients. Finally, routine screening for TB may be recommended among suspected or confirmed cases of COVID-19 in countries with high TB burden.

Canada CA: Incidence of Tuberculosis: per 100,000 People data was reported at 5.800 Ratio in 2023. This stayed constant from the previous number of 5.800 Ratio for 2022. Canada CA: Incidence of Tuberculosis: per 100,000 People data is updated yearly, averaging 5.500 Ratio from Dec 2000 (Median) to 2023, with 24 observations. The data reached an all-time high of 6.500 Ratio in 2001 and a record low of 4.600 Ratio in 2010. Canada CA: Incidence of Tuberculosis: per 100,000 People data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Canada – Table CA.World Bank.WDI: Social: Health Statistics. Incidence of tuberculosis is the estimated number of new and relapse tuberculosis cases arising in a given year, expressed as the rate per 100,000 population. All forms of TB are included, including cases in people living with HIV. Estimates for all years are recalculated as new information becomes available and techniques are refined, so they may differ from those published previously.;World Health Organization, Global Tuberculosis Report.;Weighted average;Aggregate data by groups are computed based on the groupings for the World Bank fiscal year in which the data was released by the World Health Organization. This is the Sustainable Development Goal indicator 3.3.2[https://unstats.un.org/sdgs/metadata/].

BackgroundMortality from TB continues to be a global public health challenge. TB ranks alongside Human Immunodeficiency Virus (HIV) as the leading infectious causes of death globally. HIV is a major driver of TB related morbidity and mortality while TB is the leading cause of mortality among people living with HIV/AIDS. We sought to determine excess mortality associated with HIV and the effect of antiretroviral therapy on reducing mortality among tuberculosis patients in Kenya.MethodsWe conducted a retrospective analysis of Kenya national tuberculosis program data of patients enrolled from 2013 through 2014. We used direct standardization to obtain standardized mortality ratios for tuberculosis patients compared with the general population. We calculated the population attributable fraction of tuberculosis deaths due to HIV based on the standardized mortality ratio for deaths among TB patients with HIV compared to TB patients without HIV. We used Cox proportional hazards regression for assessing risk factors for mortality.ResultsOf 162,014 patients included in the analysis, 6% died. Mortality was 10.6 (95% CI: 10.4–10.8) times higher among TB patients than the general population; 42% of deaths were attributable to HIV infection. Patients with HIV who were not receiving ART had an over four-fold risk of death compared to patients without HIV (aHR = 4.2, 95% CI 3.9–4.6). In contrast, patients with HIV who were receiving ART had only 2.6 times the risk of death (aHR = 2.6, 95% CI 2.5–2.7).ConclusionHIV was a significant contributor to TB-associated deaths in Kenya. Mortality among HIV-infected individuals was higher among those not on ART than those on ART. Early initiation of ART among HIV infected people (a “test and treat” approach) should further reduce TB-associated deaths.

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.

Crime and socioeconomic data for the German Reich and mortality statistics for Prussia at county level for 1871 to 1912. Topics: A: variables for the entire German Reich (1047 counties) 1. crime data: a) totals of all convicted for crimes and offences per 100000 b) number convicted due to dangerous bodily injury per 100000 c) number convicted due to simple theft per 100000 2. demographic information: a) totals of population of the age of criminal responsibility in the counties for 1885, 1905 and 1910 b) male German-speaking population in 1900 c) female German-speaking population in 1900 d) male, non-German-speaking population in 1900 e) female, non-German-speaking population in 1900 f) primary ethnic groups in 1900 3. data on urbanization: a) total population of the municipalities with more than 2000 residents per county in 1900 b) population in medium-sized cities per county in 1900 c) population in large cities per county in 1900 d) total population per county in 1900 e) typing the counties in city counties (=1) and districts (=2) in 1900 4. Geographic data a) short designation of all counties (1881 to 1912) b) identification number of all counties listed under 4a) c) surface area of the county in square kilometers in 1900 B: variables for Prussia (583 counties) mortality data for 1885, 1886, 1904, 1905 and 1906: a) totals of deaths (according to sex) for the respective year b) number of deaths due to Tuberculosis (according to sex) for the respective year c) number of deaths due to suicide (according to sex) for the respective year d) number of deaths due to murder and manslaughter (according to sex) for the respective year The variables for the Prussian counties can be compared with the corresponding counties of the German Reich. Kriminalitäts- und sozioökonomische Daten für das Deutsche Reich und Sterbedaten für Preußen, jeweils auf Kreisebene, für die Jahre 1871 bis 1912. Themen: A: Variablen für das gesamte Deutsche Reich (1.047 Kreise) 1. Kriminalitätsdaten: a) Gesamtzahl aller Verurteilten für Verbrechen und Vergehen pro 100.000 b) Zahl der Verurteilten wegen gefährlicher Körperverletzung pro 100.000 c) Zahl der Verurteilten wegen einfachen Diebstahls pro 100.000 2. Demographische Angaben: a) Gesamtzahl der strafmündigen Bevölkerung der Kreise für die Jahre 1885, 1905 und 1910 b) Männliche deutschsprachige Bevölkerung im Jahre 1900 c) Weibliche deutschsprachige Bevölkerung im Jahre 1900 d) Männliche, nicht deutschsprachige Bevölkerung im Jahre 1900 e) Weibliche, nicht deutschsprachige Bevölkerung im Jahre 1900 f) Vorherrschende ethnische Gruppen 1900 3. Daten zur Urbanisierung: a) Gesamtbevölkerung der Gemeinden mit über 2.000 Einwohnern je Kreis im Jahr 1900 b) Bevölkerung in mittelgroßen Städten je Kreis im Jahre 1900 c) Bevölkerung in Großstädten je Kreis im Jahre 1900 d) Gesamtbevölkerung je Kreis im Jahre 1900 e) Typisierung der Kreise in Stadtkreise (=1) und Landkreise (=2) im Jahre 1900 4. Geographische Daten: a) Kurzbezeichnung aller Kreise (1881 bis 1912) b) Identifikationsnummer aller unter 4a) aufgeführten Kreise c) Fläche der Kreise in Quadratkilometern im Jahre 1900 B: Variablen für Preußen (583 Kreise) Sterbedaten für die Jahre 1885, 1886, 1904, 1905 und 1906: a) Gesamtzahl der Verstorbenen (nach Geschlecht) für das jeweilige Jahr b) Zahl der an Tuberkulose Gestorbenen (nach Geschlecht) für das jeweilige Jahr c) Zahl der durch Selbstmord Gestorbenen (nach Geschlecht) für das jeweilige Jahr d) Zahl der durch Mord und Totschlag Gestorbenen (nach Geschlecht) für das jeweilige Jahr Die Variablen für die preußischen Kreise lassen sich mit den entsprechenden Kreisen des Deutschen Reiches vergleichen.

Numbers, percentages and rates of TB cases by country of birth, 2017, Santa Clara County. Source: California Reportable Disease Information Exchange, 2017, data are provisional as of February 12, 2018METADATA:Notes (String): Lists table title, notes and sourcesCountry of birth (String): List of birth countriesNumber (Numeric): Number of TB diagnoses in 2017 Percentage (Numeric): Percentage of TB diagnoses in one birth country among all TB diagnoses in 2017Rate per 100,000 people (Numeric): Number of TB diagnoses per 100,000 people among people from the same birth country

Abstract: Mortality in prisons, a basic indicator of the right to health for incarcerated persons, has never been studied extensively in Brazil. An assessment of all-cause and cause-specific mortality in prison inmates was conducted in 2016-2017 in the state of Rio de Janeiro, based on data from the Mortality Information System and Prison Administration. Mortality rates were compared between prison population and general population after standardization. The leading causes of death in inmates were infectious diseases (30%), cardiovascular diseases (22%), and external causes (12%). Infectious causes featured HIV/AIDS (43%) and TB (52%, considering all deaths with mention of TB). Only 0.7% of inmates who died had access to extramural health services. All-cause mortality rate was higher among prison inmates than in the state’s general population. Among inmates, mortality from infectious diseases was 5 times higher, from TB 15 times higher, and from endocrine diseases (especially diabetes) and cardiovascular diseases 1.5 and 1.3 times higher, respectively, while deaths from external causes were less frequent in prison inmates. The study revealed important potentially avoidable excess deaths in prisons, reflecting lack of care and exclusion of this population from the Brazilian Unified National Health System. This further highlights the need for a precise and sustainable real-time monitoring system for deaths, in addition to restructuring of the prison staff through implementation of the Brazilian National Policy for Comprehensive Healthcare for Persons Deprived of Freedom in the Prison System in order for inmates to fully access their constitutional right to health with the same quality and timeliness as the general population.

Background Delay in the diagnosis of tuberculosis may worsen the disease, increase the risk of death and enhance tuberculosis transmission in the community. This study aims to determine the length of delay between the onset of symptoms and patients first visit to health care (patient delay), and the length of delay between health care visit and the diagnosis of tuberculosis (health service delay). Methods A cross sectional survey that included all the public health centres was conducted in Addis Ababa from August 1 to December 31 1998. Patients were interviewed on the same day of diagnosis using structured questionnaire. Results 700 pulmonary TB patients were studied. The median patient delay was 60 days and mean 78.2 days. There was no significant difference in socio-demographic factors in those who delayed and came earlier among smear positives. However, there was a significant difference in distance from home to health institute and knowledge about TB treatment among the smear negatives. The health service delay was low (median 6 days; mean 9.5 days) delay was significantly lower in smear positives compared to smear negatives. Longer health service delay (delay more than 15 days) was associated with far distance. Conclusions The time before diagnosis in TB patients was long and appears to be associated with patient inadequate knowledge of TB treatment and distance to the health centre. Further decentralization of TB services, the use of some components of active case finding, and raising public awareness of the disease to increase service utilization are recommended.

Abstract

The do-file marital_spouselinks.do combines all data on people's marital statuses and reported spouses to create the following datasets: 1. all_marital_reports - a listing of all the times an individual has reported their current marital status with the id numbers of the reported spouse(s); this listing is as reported so may include discrepancies (i.e. a 'Never married' status following a 'Married' one) 2. all_spouse_pairs_full - a listing of each time each spouse pair has been reported plus summary information on co-residency for each pair 3. all_spouse_pairs_clean_summarised - this summarises the data from all_spouse_pairs_full to give start and end dates of unions 4. marital_status_episodes - this combines data from all the sources to create episodes of marital status, each has a start and end date and a marital status, and if currently married, the spouse ids of the current spouse(s) if reported. There are several variables to indicate where each piece of information is coming from.

The first 2 datasets are made available in case people need the 'raw' data for any reason (i.e. if they only want data from one study) or if they wish to summarise the data in a different way to what is done for the last 2 datasets.

The do-file is quite complicated with many sources of data going through multiple processes to create variables in the datasets so it is not always straightforward to explain where each variable come from on the documentation. The 4 datasets build on each other and the do-file is documented throughout so anyone wanting to understand in great detail may be better off examining that. However, below is a brief description of how the datasets are created:

Marital status data are stored in the tables of the study they were collected in: AHS Adult Health Study [ahs_ahs1] CEN Census (initial CRS census) [cen_individ] CENM In-migration (CRS migration form) [crs_cenm] GP General form (filled for various reasons) [gp_gpform] SEI Socio-economic individual (annual survey from 2007 onwards) [css_sei] TBH TB household (study of household contacts of TB patients) [tb_tbh] TBO TB controls (matched controls for TB patients) [tb_tbo & tb_tboto2007] TBX TB cases (TB patients) [tb_tbx & tb_tbxto2007] In many of the above surveys as well as their current marital status, people were asked to report their current and past spouses along with (sometimes) some information about the marriage (start/end year etc.). These data are stored all together on the table gen_spouse, with variables indicating which study the data came from. Further evidence of spousal relationships is taken from gen_identity (if a couple appear as co-parents to a CRS member) and from crs_residency_episodes_clean_poly, a combined dataset (if they are living in the same household at the same time). Note that co-parent couples who are not reported in gen_spouse are only retained in the datasets if they have co-resident episodes.

The marital status data are appended together and the spouse id data merged in. Minimal data editing/cleaning is carried out. As the spouse data are in long format, this dataset is reshaped wide to have one line per marital status report (polygamy in the area allows for men to have multiple spouses at one time): this dataset is saved as all_marital_reports.

The list of reported spouses on gen_spouse is appended to a list of co-parents (from gen_identity) and this list is cleaned to try to identify and remove obvious id errors (incestuous links, same sex [these are not reported in this culture] and large age difference). Data reported by men and women are compared and variables created to show whether one or both of the couple report the union. Many records have information on start and end year of marriage, and all have the date the union was reported. This listing is compared to data from residency episodes to add dates that couples were living together (not all have start/end dates so this is to try to supplement this), in addition the dates that each member of the couple was last known to be alive or first known to be dead are added (from the residency data as well). This dataset with all the records available for each spouse pair is saved as all_spouse_pairs_full.

The date data from all_spouse_pairs_full are then summarised to get one line per couple with earliest and latest known married date for all, and, if available, marriage and separation date. For each date there are also variables created to indicate the source of the data.
As culture only allows for women having one spouse at a time, records for women with 'overlapping' husbands are cleaned. This dataset is then saved as all_spouse_pairs_clean_summarised.

Both the cleaned spouse pairs and the cleaned marital status datasets are converted into episodes: the spouse listing using the marriage or first known married date as the beginning and the last known married plus a year or separation date as the end, the marital status data records collapsed into periods of the same status being reported (following some cleaning to remove impossible reports) and the start date being the first of these reports, the end date being the last of the reports plus a year. These episodes are appended together and a series of processes run several times to remove overalapping episodes. To be able to assign specific spouse ids to each married episode, some episodes need to be 'split' into more than one (i.e. if a man is married to one woman from 2005 to 2017 and then marries another woman in 2008 and remains married to her till 2017 his intial married episode would be from 2005 to 2017, but this would need to be split into one from 2005 to 2008 which would just have 1 idspouse attached and another from 2008 to 2017, which would have 2 idspouse attached). After this splitting process the spouse ids are merged in.
The final episode dataset is saved as marital_status_episodes.

Analysis unit

Individual

Mode of data collection

Face-to-face [f2f]