Finding gaps in TB notifications: spatial analysis of geographical patterns of TB notifications, associations with TB program efforts and social determinants of TB risk in Bangladesh, Nepal and Pakistan

• Published in: BMC infectious diseases Vol. 20; no. 1; pp. 490 - 14
• Main Authors: van Gurp, Margo, Rood, Ente, Fatima, Razia, Joshi, Pushpraj, Verma, Sharat Chandra, Khan, Ahmadul Hasan, Blok, Lucie, Mergenthaler, Christina, Bakker, Mirjam Irene
• Format: Journal Article
• Language: English
• Published: England BioMed Central Ltd 10.07.2020; BioMed Central; BMC
• Subjects: Access to healthcare; Acquired immune deficiency syndrome; AIDS; Autocorrelation; Autoregressive models; Bangladesh - epidemiology; Case notification; Clustering; Datasets; Demography; Disease Notification - statistics & numerical data; Epidemics; Female; Generalized linear models; Geography; GIS; Health care; Health Services Accessibility - statistics & numerical data; HIV; Human immunodeficiency virus; Humans; Identification methods; Indicators; Local planning; Male; Mathematical analysis; Mortality; Nepal - epidemiology; Pakistan - epidemiology; Patients; Population Density; Regression analysis; Sex Ratio; Social Determinants of Health - statistics & numerical data; Spatial Analysis; Spatial data; Spatial dependencies; Spatial variations; Treatment Outcome; Tuberculosis; Tuberculosis - drug therapy; Tuberculosis - epidemiology; Vaccination; Vaccination Coverage - statistics & numerical data; Bangladesh; Nepal; Pakistan; GIS; Spatial analysis; Tuberculosis; Case notification; Access to healthcare
• ISSN: 1471-2334; 1471-2334
• DOI: 10.1186/s12879-020-05207-z

In order to effectively combat Tuberculosis, resources to diagnose and treat TB should be allocated effectively to the areas and population that need them. Although a wealth of subnational data on TB is routinely collected to support local planning, it is often underutilized. Therefore, this study uses spatial analytical techniques and profiling to understand and identify factors underlying spatial variation in TB case notification rates (CNR) in Bangladesh, Nepal and Pakistan for better TB program planning. Spatial analytical techniques and profiling was used to identify subnational patterns of TB CNRs at the district level in Bangladesh (N = 64, 2015), Nepal (N = 75, 2014) and Pakistan (N = 142, 2015). A multivariable linear regression analysis was performed to assess the association between subnational CNR and demographic and health indicators associated with TB burden and indicators of TB programme efforts. To correct for spatial dependencies of the observations, the residuals of the multivariable models were tested for unexplained spatial autocorrelation. Spatial autocorrelation among the residuals was adjusted for by fitting a simultaneous autoregressive model (SAR). Spatial clustering of TB CNRs was observed in all three countries. In Bangladesh, TB CNR were found significantly associated with testing rate (0.06%, p < 0.001), test positivity rate (14.44%, p < 0.001), proportion of bacteriologically confirmed cases (- 1.33%, p < 0.001) and population density (4.5*10-3%, p < 0.01). In Nepal, TB CNR were associated with population sex ratio (1.54%, p < 0.01), facility density (- 0.19%, p < 0.05) and treatment success rate (- 3.68%, p < 0.001). Finally, TB CNR in Pakistan were found significantly associated with testing rate (0.08%, p < 0.001), positivity rate (4.29, p < 0.001), proportion of bacteriologically confirmed cases (- 1.45, p < 0.001), vaccination coverage (1.17%, p < 0.001) and facility density (20.41%, p < 0.001). Subnational TB CNRs are more likely reflective of TB programme efforts and access to healthcare than TB burden. TB CNRs are better used for monitoring and evaluation of TB control efforts than the TB epidemic. Using spatial analytical techniques and profiling can help identify areas where TB is underreported. Applying these techniques routinely in the surveillance facilitates the use of TB CNRs in program planning.