Understanding and Using Tuberculosis Data [[electronic resource]]

• Record Nr.: UNINA9910460169503321
• Autore: Organization World Health
• Pubbl/distr/stampa: Geneva, : World Health Organization, 2014
• ISBN: 92-4-069325-4
• Descrizione fisica: 1 online resource (205 p.)
• Disciplina: 616.109234
• Soggetti: Tuberculosis -- Epidemiology; Tuberculosis -- Statistics; Tuberculosis; Tuberculosis - Epidemiology; Tuberculosis - Statistical methods; Public health surveillance; Mycobacterium Infections; Decision Support Techniques; Statistics as Topic; Public Health; Epidemiologic Methods; Medical Informatics Applications; Investigative Techniques; Medicine; Actinomycetales Infections; Health Care Evaluation Mechanisms; Quality of Health Care; Health Occupations; Medical Informatics; Gram-Positive Bacterial Infections; Bacterial Infections; Environment and Public Health; Information Science; Health Care Quality, Access, and Evaluation; Health Care; Bacterial Infections and Mycoses; Diseases; Data Interpretation, Statistical; Epidemiology; Health & Biological Sciences; Communicable Diseases; Electronic books.
• Lingua di pubblicazione: Inglese
• Formato: Materiale a stampa
• Livello bibliografico: Monografia
• Note generali: Description based upon print version of record.
• Nota di contenuto: Cover; Contents; Acknowledgements; Introduction; Abbreviations; Chapter 1 Analysis of aggregated TB notification data; 1.1 Aggregated notification data: what are they?; 1.2 Assessment and assurance of the quality of aggregated TB notification data; Data validation at data entry; Data validation after data entry; 1.3 Analysis of aggregate data; Rationale for analysis of trends; 1.4 Examples of analysis of trends; Notifications by time; Notifications by age; Notifications by sex; Notifications by place; Notifications by place and time; reasons for changes in notification rates over time; 1.5 Limitations of aggregated notification data1.6 Summary; References; Annex 1 TB surveillance data quality standards with examples; Chapter 2 Analysis of case-based TB notification data; 2.1 Case-based notification data: what they are and why are they important; Steps in case-based data analyses; 2.2 Developing an analytic plan; 2.3 Preparing the dataset; Data cleaning; Addressing missing data; Identifying outliers; De-duplication of datasets; Re-coding variables; linking datasets Sex Age (years) (Original, Continuous Variable Age Group (Recoded, Categorical Variable 0-25 years=1 26-50 years=2 >50 years=3 Height (m) (Original, Continuous Variable) Weight (kg) (Original, Continuous Variable) BMIFinalizing the dataset; 2.4 Data analysis: conducting and interpreting descriptive analyses; Univariate and bivariate analyses; Rates and trends; Other descriptive analyses; Other types of information used for further examination of data; 2.5 Data analysis: conducting and interpreting more complex analyses; 2.6 Communicating findings; 2.7 Conclusion; References; Annex 2 Analytic plan exampleAnnex 3 Example of multivariable analysis to assess risk factors for loss to follow-up; Chapter 3 Using genotyping data for outbreak investigations; 3.1 Genotyping data: an overview; Introduction; Purpose and uses of genotyping; Intended audience; 3.2 Preparation of data; Differentiating TB strains; Identifying and naming clusters; 3.3 Analysing outbreaks; Excluding false-positive cases; Epidemiological links; Drug resistance patterns; Previous episodes of TB; Presenting epidemiological links between cases; 3.4 Analysing large clusters; Displaying time, person and place3.5 Limitations of genotyping data; 3.6 Special considerations for genotyping in high TB burden settings; 3.7 Conclusion: using genotyping data for public health; References; Chapter 4 Analysis of factors driving the TB epidemic; 4.1 Ecological analysis; What can be explained with ecological analysis?; 4.2 TB incidence; 4.3 Using ecological analysis to understand TB epidemics; 4.4 Conceptual framework for ecological analysis; What if certain key information is unavailable for all domains?; How should we prioritize the domains and indicators to include?; What if there are no data on something that experts deem as important?
• Sommario/riassunto: Country health information systems provide a rich source of data on ; the burden of diseasecaused by tuberculosis (TB) and the effectiveness of programmatic efforts to reduce thisburden both of which are crucial for public health action. However the available dataare often underused or not used at all. At least in part this may reflect the absence ofclear guidance on recommended approaches to the analysis of such data. This handbookis designed to address this gap through detailed practical examples of the analysis of TBsurveillance data in particular TB notification data data from surveillance o