ResearchResearch Variations in tuberculosis prevalence, Russian Federation: a multivariate approach Ivan Meshkov,a Tatyana Petrenko,a Olivia Keiser,b Janne Estill,b Olga Revyakina,a Irina Felker,a Mario C Raviglione,c Vladimir Krasnova & Yakov Schwartza Objective To analyse the epidemiological trends of tuberculosis in the Siberian and Far Eastern federal districts, the areas with the highest disease burden in the Russian Federation. Methods We applied principal coordinate analysis to study a total of 68 relevant variables on tuberculosis epidemiology, prevention and control. Data on these variables were collected over 2003–2016 in all 21 regions of the Siberian federal district and Far Eastern federal district (total population: 25.5 million) through the federal and departmental reporting system. We identified the regions with a favourable or unfavourable tuberculosis epidemiological profile and ranked them as low or high priority for specific interventions. Findings The median number of tuberculosis notifications in the regions was 123.3 per 100 000 population (range: 54.5–265.7) in 2003, decreasing to 82.3 per 100 000 (range: 52.9–178.3) in 2016. We found large variations in the tuberculosis epidemiological profile across different regions. The principal coordinate analysis revealed that three aggregated indicators accounted for 55% of the variation. The first coordinate corresponded to tuberculosis prevalence and case notifications in the regions; the second to the severity of the disease among patients; and the third to the percentage of multidrug-resistant tuberculosis among tuberculosis patients. The regions where intervention was most urgently needed were Chukotka Autonomous Okrug, Jewish Autonomous Oblast and Tyva Republic. Conclusion The variability in tuberculosis epidemiology across regions was likely due to differences in the quality of antituberculosis services. Precision in defining necessary interventions, as determined through the principal coordinate analysis approach, can guide focused tuberculosis control efforts. Introduction number of aggregated indicators for each region. Description of the regions’ tuberculosis status in terms of these indicators During the 1990s the tuberculosis epidemic in the Russian would enable us to characterize and compare the epidemiol- Federation worsened due to an economic recession in the ogy of tuberculosis among regions. The results are intended country at that time. National tuberculosis case notifications to inform the development of a unified prevention, care and reached a peak of 90.7 per 100 000 in the population of 146.6 control policy that addresses each region’s specific needs. million in the year 2000. The robust measures to control tu- berculosis taken by the government between 2000 and 2010 Methods led to a reduction in case notifications to 48.3 per 100 000 Data source in the population of 144.5 million by 2017. Despite these measures, the tuberculosis situation in the Asian part of the The study was a retrospective epidemiological analysis cov- Russian Federation remained less favourable compared with ering all tuberculosis cases notified in the Asian part of the the European part. For example, in the year 2017, tuberculosis Russian Federation from 2003 to 2016. case notifications in the Siberian federal district (population: We analysed data obtained from federal and department 19.3 million) and Far Eastern federal district (population: reports for each of the 21 regions in the Siberian and Far 6.1 million) were as high as 83.3 and 86.1 per 100 000 respec- Eastern federal districts which constitute the Asian part of tively.1–4 Identification of the key factors that determine the the Russian Federation. The health authority reports cover all unfavourable epidemic situation in these districts is therefore registered patients with records of all forms of tuberculosis. a top priority for research. The evidence collected can then be A total of 68 variables of tuberculosis incidence, treatment used to plan focused interventions. and outcomes are collected. The method of data gathering Tuberculosis surveillance and monitoring were estab- is identical in all regions of both federal districts. Access to lished in their present form in these two federal districts in the epidemiological data is restricted to clinical specialists 2003 with data pm dozens of variables are collected annually and medical statisticians. The raw data are obtained by re- by the government. We aimed to identify the variables that gional tuberculosis centres from tuberculosis hospitals and are closely correlated and to understand how they interact and outpatient departments at regional, municipal and township contribute to the tuberculosis epidemic in different regions of levels using official ministerial guidelines, and collated for Siberian and Far Eastern federal districts. We used multivari- analysis in Novosibirsk Tuberculosis Research Institute. The ate principal coordinate analysis,5 a method that allowed us to data are regularly checked for consistency and reliability by condense the complex, multidimensional data set into a small experienced research personnel at the Institute who frequently a FSBI Novosibirsk Tuberculosis Research Institute, Ministry of Health of the Russian Federation, Novosibirsk, Okhotskaya street 81A, 630040, Russian Federation. b Institute of Global Health, University of Geneva, Geneva, Switzerland. c Global Health Centre, University of Milan, Milan, Italy. Correspondence to Ivan Meshkov (email: [email protected]). (Submitted: 17 January 2019 – Revised version received: 29 May 2019 – Accepted: 31 May 2019 – Published online: 3 September 2019 ) Bull World Health Organ 2019;97:737–745A | doi: http://dx.doi.org/10.2471/BLT.19.229997 737 Research Tuberculosis in the Asian part of the Russian Federation Ivan Meshkov et al. conduct supervisory visits in the ob- − our case, it was convenient to present served regions. aMij edj the administrative regions as points on X = (1) A detailed description of all ij IQR a three-dimensional scatterplot. variables collected by the federal au- j The epidemiological meaning of a thorities and their calculation rules Where aij is the initial value of variable j coordinate can be understood from the and periods of data collection are for the region i, Medj is a median value value of its correlation with 68 initial 6 available from the data repository. and the IQRj interquartile range of the epidemiological variables, according to These variables are recommended for variable j over all regions, and xij is the the epidemiological meanings of these epidemiological reporting as key fac- final transformed value. variables. The Spearman correlations tors in control of epidemic tuberculo- All collected variables should be between variables and coordinates were sis.7,8 Variables cover a wide range of used to achieve a comprehensive de- calculated for each year of observation. characteristics, including: (i) case no- scription of the difference in the epi- Only strong relationships with correla- tifications and the number of notified demic situation between the regions. It tion coefficients ≥ 0.6 in absolute value tuberculosis cases stratified by age and is possible to choose a suitable metric, were considered. by urban or rural population; (ii) data then to calculate the differences (called We used R programming language, on tuberculosis patients who were distances) between regions and finally version 3.4.0 (R Foundation for Statis- difficult to treat, including those with to construct the matrix of distances. For tical Computing, Vienna, Austria) for multidrug-resistant (MDR) tuber- the present study we chose Manhattan statistical analysis.14 culosis or extensive fibrotic changes distances and calculated the distances and cavities (verified by a medical between the regions using the formula: Results board of experts reviewing difficult cases using clinical and tomography Tuberculosis time trends =− +− ++ − data);9–11 (iii) coverage of tuberculo- dxManh ik11xx ik22xx ... ij xkj The median number of tuberculosis sis diagnostics, including any type (2) case notifications was 123.3 per 100 000 of examination; and (iv) outcomes of population in 2003, ranging from 54.5 to tuberculosis treatment. where i and k are the regions between 265.7 per 100 000 across the 21 regions Data analysis which the distance is calculated, and 1, (Fig. 1). Notifications remained some- 2, … j are the variables. We chose this what stable up to the year 2010, after We used principal coordinate analy- metric because the values of variables for which they decreased in most regions, sis for the study. In this method, pairwise correlation of two regions were to a median of 82.3 per 100 000 in 2016 the differences between regions are mostly identical or slightly different, but (range: 52.9–178.3 per 100 000). represented by a small number of there were several variables with large In the analysis by region, we found coordinates. The method has several differences of values. In this case, the that tuberculosis prevalence (Fig. 2) advantages compared with more com- Manhattan distance will smooth these and mortality (Fig. 3) between the years monly used methods such as principal differences. 2003 and 2016 decreased in all regions component analysis. First, principal To analyse the matrix of the Man- except Chukotka Autonomous Okrug. coordinate analysis has low sensitiv- hattan distances, information from the Case fatalities among the mid-year aver- ity to the presence of