Global Tuberculosis Report -- 2012.pdf

2011. On average, 15 data points were retained for analysisper country (standard deviation (SD) of 6.5) from atotal of 1898 usable data points.Reports of TB mortality were adjusted upwards toaccount for incomplete coverage (estimated deaths withno cause documented) and ill-defined causes of death(ICD-9 code B46, ICD-10 codes R00–R99). 1It was assumed that the proportion of TB deaths amongdeaths not recorded by the VR system was the same as theproportion of TB deaths in VR-recorded deaths. For VRrecordeddeaths with ill-defined causes, it was assumedthat the proportion of deaths attributable to TB was thesame as the observed proportion in recorded deaths.The adjusted number of TB deaths d a was obtainedfrom the VR report d as follows:dd a =c(1 – g)where c denotes coverage (i.e. the number of deaths witha documented cause divided by the total number of estimateddeaths) and g denotes the proportion of ill-definedcauses.The uncertainty related to the adjustment was estimatedwith standard deviation SD = d/4[1/(c/(1 – g)) – 1]. Theuncertainty calculation does not account for miscoding,such as HIV deaths miscoded as deaths due to TB.Missing data between existing adjusted data pointswere imputed using log-linear interpolation (or simpleinterpolation in small countries with reports of zeromortality). Trailing missing values were predicted usingexponential smoothing models for time-series. 2 A penalizedlikelihood method based on the in-sample fit wasused for country-specific model selection. Leading missingvalues were similarly predicted backwards to 1990. Atotal of 813 country-year data points were thus imputed.Results from mortality surveys were used to estimateTB mortality in India. Further details are available in the2011 edition of the WHO report on global TB control.3.2 Estimating TB mortality from anecological modelAn out-of-sample, goodness-of-fit, stepwise selectionapproach was used to select an ecological model thatcould predict TB mortality in countries without VR data.The model was based on the time series of VR data reportedto WHO as described above, expressed as counts of TBdeaths and corrected for ill-defined causes of deaths andVR coverage.A population-averaged negative binomial model, withtotal population as the offset converting model outputs1Mathers CD et al. Counting the dead and what they died from:an assessment of the global status of cause of death data. Bulletinof the World Health Organization, 2005, 83:171–177.2Hyndman R et al. Forecasting with exponential smoothing: the statespace approach. Springer Series in Statistics, 2008.to rates, was used to account for the longitudinal structureof the data as well as the observed over-dispersion ofcounts of TB deaths.Ten variables were investigated for inclusion in themodel. These were: the infant mortality rate per 1000 livebirths; gross domestic product per capita; HIV prevalenceamong the general population; the percentage of the totalpopulation aged