LEADER 07635nam 22013453u 450 001 9910460169503321 005 20210310222724.0 010 $a92-4-069325-4 035 $a(CKB)3710000000245864 035 $a(EBL)1794219 035 $a(SSID)ssj0001534347 035 $a(PQKBManifestationID)12629887 035 $a(PQKBTitleCode)TC0001534347 035 $a(PQKBWorkID)11493521 035 $a(PQKB)11719020 035 $a(MiAaPQ)EBC1794219 035 $a(OCoLC)889633863 035 $a(Au-PeEL)EBL1794219 035 $a(EXLCZ)993710000000245864 100 $a20140929d2014|||| u|| | 101 0 $aeng 135 $aur|n|---||||| 181 $ctxt 182 $cc 183 $acr 200 10$aUnderstanding and Using Tuberculosis Data$b[electronic resource] 210 $aGeneva $cWorld Health Organization$d2014 215 $a1 online resource (205 p.) 300 $aDescription based upon print version of record. 311 $a92-4-154878-9 327 $aCover; 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 327 $a1.5 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 327 $alinking 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 327 $aAnnex 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 327 $aDisplaying 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? 327 $aWhat if there are no data on something that experts deem as important? 330 $aCountry 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 606 $aTuberculosis -- Epidemiology 606 $aTuberculosis -- Statistics 606 $aTuberculosis 606 $aTuberculosis$xEpidemiology$vStatistics 606 $aTuberculosis$xStatistical methods 606 $aPublic health surveillance 606 $aMycobacterium Infections 606 $aDecision Support Techniques 606 $aStatistics as Topic 606 $aPublic Health 606 $aEpidemiologic Methods 606 $aMedical Informatics Applications 606 $aInvestigative Techniques 606 $aMedicine 606 $aActinomycetales Infections 606 $aHealth Care Evaluation Mechanisms 606 $aQuality of Health Care 606 $aHealth Occupations 606 $aMedical Informatics 606 $aGram-Positive Bacterial Infections 606 $aBacterial Infections 606 $aEnvironment and Public Health 606 $aInformation Science 606 $aHealth Care Quality, Access, and Evaluation 606 $aHealth Care 606 $aBacterial Infections and Mycoses 606 $aDiseases 606 $aData Interpretation, Statistical 606 $aTuberculosis 606 $aEpidemiology 606 $aPublic Health$2HILCC 606 $aHealth & Biological Sciences$2HILCC 606 $aCommunicable Diseases$2HILCC 608 $aElectronic books. 615 4$aTuberculosis -- Epidemiology. 615 4$aTuberculosis -- Statistics. 615 4$aTuberculosis. 615 0$aTuberculosis$xEpidemiology 615 0$aTuberculosis$xStatistical methods 615 0$aPublic health surveillance 615 2$aMycobacterium Infections 615 2$aDecision Support Techniques 615 2$aStatistics as Topic 615 2$aPublic Health 615 2$aEpidemiologic Methods 615 2$aMedical Informatics Applications 615 2$aInvestigative Techniques 615 2$aMedicine 615 2$aActinomycetales Infections 615 2$aHealth Care Evaluation Mechanisms 615 2$aQuality of Health Care 615 2$aHealth Occupations 615 2$aMedical Informatics 615 2$aGram-Positive Bacterial Infections 615 2$aBacterial Infections 615 2$aEnvironment and Public Health 615 2$aInformation Science 615 2$aHealth Care Quality, Access, and Evaluation 615 2$aHealth Care 615 2$aBacterial Infections and Mycoses 615 2$aDiseases 615 2$aData Interpretation, Statistical 615 2$aTuberculosis 615 2$aEpidemiology 615 7$aPublic Health 615 7$aHealth & Biological Sciences 615 7$aCommunicable Diseases 676 $a616.109234 700 $aOrganization$b World Health$0819556 712 02$aWorld Health Organization 712 02$aWorld Health Organization 801 0$bAU-PeEL 801 1$bAU-PeEL 801 2$bAU-PeEL 906 $aBOOK 912 $a9910460169503321 996 $aUnderstanding and Using Tuberculosis Data$92455207 997 $aUNINA