Decreased risk of HIV-associated TB during antiretroviral therapy expansion in rural Eswatini from 2009 to 2016: a cohort and population-based analysis

Abstract

Objectives: This paper assesses patient- and population-level trends in TB notifications during rapid expansion of antiretroviral therapy in Eswatini which has an extremely high incidence of both TB and HIV.

Methods: Patient- and population-level predictors and rates of HIV-associated TB were examined in the Shiselweni region in Eswatini from 2009 to 2016. Annual population-level denominators obtained from projected census data and prevalence estimates obtained from population-based surveys were combined with individual-level TB treatment data. Patient- and population-level predictors of HIV-associated TB were assessed with multivariate logistic and multivariate negative binomial regression models.

Results: Of 11 328 TB cases, 71.4% were HIV co-infected and 51.8% were women. TB notifications decreased fivefold between 2009 and 2016, from 1341 to 269 cases per 100 000 person-years. The decline was sixfold in PLHIV vs. threefold in the HIV-negative population. Main patient-level predictors of HIV-associated TB were recurrent TB treatment (adjusted odds ratio [aOR] 1.40, 95% confidence interval [CI]: 1.19-1.65), negative (aOR 1.31, 1.15-1.49) and missing (aOR 1.30, 1.11-1.53) bacteriological status and diagnosis at secondary healthcare level (aOR 1.18, 1.06-1.33). Compared with 2009, the probability of TB decreased for all years from 2011 (aOR 0.69, 0.58-0.83) to 2016 (aOR 0.54, 0.43-0.69). The most pronounced population-level predictor of TB was HIV-positive status (adjusted incidence risk ratio 19.47, 14.89-25.46).

Conclusions: This high HIV-TB prevalence setting experienced a rapid decline in TB notifications, most pronounced in PLHIV. Achievements in HIV-TB programming were likely contributing factors.

Keywords: ART expansion; Swaziland; expansion de l’ART; temporal trends; tendances temporelles; tuberculose; tuberculosis.

Figure 1

Temporal trends in TB notifications (absolute numbers vs. rates), overall and stratified by sex and HIV status. [Colour figure can be viewed at http://wileyonlinelibrary.com]

Figure 2

Age‐stratified temporal trends in TB notifications (absolute numbers vs. rates) for HIV‐positive and HIV‐negative cases. [Colour figure can be viewed at http://wileyonlinelibrary.com]

Figure 3

The effect of calendar year on TB notifications in Model‐1 (a) and Model‐2 (b). aIRR, adjusted incidence risk ratio. *Model‐1 considered the entire study period adjusted for the population‐level covariates sex, age and HIV status. **Model‐2 combined HIV status and ART status into one covariate with three categories (HIV‐negative case, PLHIV on ART, PLHIV without ART), but was restricted to the years from 2009 to 2014 as reliable stratified ART coverage estimates were not available for the time period thereafter. [Colour figure can be viewed at http://wileyonlinelibrary.com]

Figure 4

Temporal trends in TB notifications and ART coverage from 2008 to 2016. This graph combines estimates from the study (blue and red lines) and data points obtained from the annual TB report for TB notifications in 2008 [22] and from the Swaziland HIV Incidence Measurement Surveys for ART coverage in 2011 [19,29] and 2016 [30]. Blue line: ART coverage for all age groups combined. Red line: TB notification rates for all age groups combined irrespective of HIV status. DP‐1, data point 1: TB notification per 100 000 population in 2008 [22]. DP‐2, data point 2: ART coverage in ≥18 to 49‐year‐olds in 2011 [19,29]. DP‐3, data point 3: ART coverage in ≥15‐year‐olds in 2016 [30]. [Colour figure can be viewed at http://wileyonlinelibrary.com]