Reduction of HIV-associated excess mortality by antiretroviral treatment among tuberculosis patients in Kenya

Clinical Outcomes in Children With Human Immunodeficiency Virus Treated for Nonsevere Tuberculosis in the SHINE Trial

BACKGROUND

Children with human immunodeficiency virus (HIV, CWH) are at high risk of tuberculosis (TB) and face poor outcomes, despite antiretroviral therapy (ART). We evaluated outcomes in CWH and children not living with HIV treated for nonsevere TB in the SHINE trial.

METHODS

SHINE was a randomized trial that enrolled children aged <16 years with smear-negative, nonsevere TB who were randomized to receive 4 versus 6 months of TB treatment and followed for 72 weeks. We assessed TB relapse/recurrence, mortality, hospitalizations, grade ≥3 adverse events by HIV status, and HIV virological suppression in CWH.

RESULTS

Of 1204 children enrolled, 127 (11%) were CWH, of similar age (median, 3.6 years; interquartile range, 1.2, 10.3 versus 3.5 years; 1.5, 6.9; P = .07) but more underweight (weight-for-age z score, -2.3; (3.3, -0.8 versus -1.0; -1.8, -0.2; P < .01) and anemic (hemoglobin, 9.5 g/dL; 8.7, 10.9 versus 11.5 g/dL; 10.4, 12.3; P < .01) compared with children without HIV. A total of 68 (54%) CWH were ART-naive; baseline median CD4 count was 719 cells/mm3 (241-1134), and CD4% was 16% (10-26). CWH were more likely to be hospitalized (adjusted odds ratio, 2.4; 1.3-4.6) and to die (adjusted hazard ratio [aHR], 2.6; 95% confidence interval [CI], 1.2 to 5.8). HIV status, age <3 years (aHR, 6.3; 1.5, 27.3), malnutrition (aHR, 6.2; 2.4, 15.9), and hemoglobin <7 g/dL (aHR, 3.8; 1.3,11.5) independently predicted mortality. Among children with available viral load (VL), 45% and 61% CWH had VL <1000 copies/mL at weeks 24 and 48, respectively. There was no difference in the effect of randomized treatment duration (4 versus 6 months) on TB treatment outcomes by HIV status (P for interaction = 0.42).

CONCLUSIONS

We found no evidence of a difference in TB outcomes between 4 and 6 months of treatment for CWH treated for nonsevere TB. Irrespective of TB treatment duration, CWH had higher rates of mortality and hospitalization than their counterparts without HIV. Clinical Trials Registration. ISRCTN63579542.

Predictors of mortality and survival probability distribution among patients on tuberculosis treatment in Vihiga County, Kenya

Background

Tuberculosis (TB) related mortality remains a serious impediment in ending TB epidemic.

Objective

To estimate survival probability and identify predictors, causes and conditions contributing to mortality among TB patients in Vihiga County.

Methods

A cohort of 291 patients from 20 purposively selected health facilities were prospectively considered. Data was obtained by validated questionnaires through face-to-face interviews. Survival probabilities were estimated using Kaplan-Meier method while Cox proportional hazard model identified predictors of TB mortality through calculation of hazard ratios at 95% confidence intervals. Mortality audit data was qualitatively categorized to elicit causes and conditions contributing to mortality.

Results

209 (72%) were male, median age was 40 (IQR=32-53) years while TB/HIV coinfection rate was 35%. Overall, 45 (15%) patients died, majority (78% (log rank<0.001)) during intensive phase. The overall mortality rate was 32.2 (95% CI 23.5 - 43.1) deaths per 1000 person months and six months' survival probability was 0.838 (95% CI, 0.796-0.883). Mortality was higher (27%) among HIV positive than HIV negative (9%) TB patients. Independent predictors of mortality included; comorbidities (HR = 2.72, 95% CI,1.36-5.44, p< 0.005), severe illness (HR=5.06, 95% CI,1.59-16.1, p=0.006), HIV infection (HR=2.56, 95% CI,1.28-5.12, p=0.008) and smoking (HR=2.79, 95% CI,1.01-7.75, p=0.049). Independent predictors of mortality among HIV negative patients included; comorbidities (HR = 4.25, 95% CI; 1.15-15.7, p = 0.03) and being clinically diagnosed (HR = 4.8, 95% CI; 1.43-16, P = 0.01) while among HIV positive; they included smoking (HR = 4.05, 95% CI;1.03-16.0, P = 0.04), severe illness (HR = 5.84, 95% CI; 1.08-31.6, P = 0.04), severe malnutrition (HR = 4.56, 95% CI; 1.33-15.6, P = 0.01) and comorbidities (HR = 3.04, 95% CI; 1.03-8.97, p = 0.04). More than a half (52%) of mortality among HIV positive were ascribed to advanced HIV diseases while majority of (72%) of HIV negative patients died to TB related lung disease. Conditions contributing to mortality were largely patient and health system related.

Conclusion

Risk of TB mortality is high and is attributable to comorbidities, severe illness, HIV and smoking. Causes and conditions contributing to TB mortality are multifaceted but modifiable. Improving TB/HIV care could reduce mortality in this setting.

Mortality during tuberculosis treatment in South Africa using an 8-year analysis of the national tuberculosis treatment register

In 2011, the South African HIV treatment eligibility criteria were expanded to allow all tuberculosis (TB) patients lifelong ART. The impact of this change on TB mortality in South Africa is not known. We evaluated mortality in all adults (≥ 15 years old) treated for drug-susceptible TB in South Africa between 2009 and 2016. Using a Cox regression model, we quantified risk factors for mortality during TB treatment and present standardised mortality ratios (SMR) stratified by year, age, sex, and HIV status. During the study period, 8.6% (219,618/2,551,058) of adults on TB treatment died. Older age, male sex, previous TB treatment and HIV infection (with or without the use of ART) were associated with increased hazard of mortality. There was a 19% reduction in hazard of mortality amongst all TB patients between 2009 and 2016 (adjusted hazard ratio: 0.81 95%CI 0.80-0.83). The highest SMR was in 15-24-year-old women, more than double that of men (42.3 in 2016). Between 2009 and 2016, the SMR for HIV-positive TB patients increased, from 9.0 to 19.6 in women, and 7.0 to 10.6 in men. In South Africa, case fatality during TB treatment is decreasing and further interventions to address specific risk factors for TB mortality are required. Young women (15-24-year-olds) with TB experience a disproportionate burden of mortality and interventions targeting this age-group are needed.

Mortality and associated factors among adult patients on tuberculosis treatment in Tanzania: A retrospective cohort study

INTRODUCTION

Tuberculosis (TB) is the global leading cause of death from an infectious agent. Tanzania is among the 30 high TB burden countries with a mortality rate of 47 per 100,000 population and a case fatality of 4%. This study assessed mortality rate, survival probabilities, and factors associated with death among adult TB patients on TB treatment in Tanzania.

METHODS

A retrospective cohort study was conducted utilizing case-based national TB program data of adult (≥15 years) TB cases enrolled on TB treatment from January 2017 to December 2017. We determined survival probabilities using the Kaplan-Meier estimator and a Cox proportional hazard model was used to identify independent risk factors of TB mortality. Hazard ratios and their respective 95% confidence intervals were reported.

RESULTS

Of 53,753 adult TB patients, 1927 (3.6%) died during TB treatment and the crude mortality rate was 6.31 per 1000 person-months. Male accounted for 33,297 (61.9%) of the study population and the median (interquartile range [IQR]) age was 40 (30-53) years. More than half 1027 (56.7%) of deaths occurred in first two months of treatment. Overall survival probabilities were 96%, and 92% at 6th and 12th month respectively. The independent risk factors for TB mortality among TB patients included: advanced age ≥ 45 years (adjusted hazard ratio (aHR) = 1.74, 95% confidence interval (CI) = 1.45-2.08); receiving service at the hospital level (aHR = 1.22, 95% CI = 1.09-1.36); TB/HIV co-infection (aHR = 2.51, 95% CI = 2.26-2.79); facility-based direct observed therapy (DOT) option (aHR = 2.23, 95% CI = 1.95-2.72); having bacteriological unconfirmed TB results (aHR = 1.58, 95% CI = 1.42-1.76); and other referral type (aHR = 1.44, 95% CI = 1.16-1.78).

CONCLUSION

Advanced age, TB/HIV co-infection, bacteriological unconfirmed TB results, other referral types, receiving service at facility-based DOT option and obtaining service at the hospital level were significant contributors to TB death in Tanzania. Appropriate targeted intervention to improve TB referral systems, improve diagnostic capacity in the primary health facilities, minimize delay and misdiagnosis of TB patients might reduce TB mortality.

A systematic review of the quality of conduct and reporting of survival analyses of tuberculosis outcomes in Africa

BACKGROUND

Survival analyses methods (SAMs) are central to analysing time-to-event outcomes. Appropriate application and reporting of such methods are important to ensure correct interpretation of the data. In this study, we systematically review the application and reporting of SAMs in studies of tuberculosis (TB) patients in Africa. It is the first review to assess the application and reporting of SAMs in this context.

METHODS

Systematic review of studies involving TB patients from Africa published between January 2010 and April 2020 in English language. Studies were eligible if they reported use of SAMs. Application and reporting of SAMs were evaluated based on seven author-defined criteria.

RESULTS

Seventy-six studies were included with patient numbers ranging from 56 to 182,890. Forty-three (57%) studies involved a statistician/epidemiologist. The number of published papers per year applying SAMs increased from two in 2010 to 18 in 2019 (P = 0.004). Sample size estimation was not reported by 67 (88%) studies. A total of 22 (29%) studies did not report summary follow-up time. The survival function was commonly presented using Kaplan-Meier survival curves (n = 51, (67%) studies) and group comparisons were performed using log-rank tests (n = 44, (58%) studies). Sixty seven (91%), 3 (4.1%) and 4 (5.4%) studies reported Cox proportional hazard, competing risk and parametric survival regression models, respectively. A total of 37 (49%) studies had hierarchical clustering, of which 28 (76%) did not adjust for the clustering in the analysis. Reporting was adequate among 4.0, 1.3 and 6.6% studies for sample size estimation, plotting of survival curves and test of survival regression underlying assumptions, respectively. Forty-five (59%), 52 (68%) and 73 (96%) studies adequately reported comparison of survival curves, follow-up time and measures of effect, respectively.

CONCLUSION

The quality of reporting survival analyses remains inadequate despite its increasing application. Because similar reporting deficiencies may be common in other diseases in low- and middle-income countries, reporting guidelines, additional training, and more capacity building are needed along with more vigilance by reviewers and journal editors.

Survival time among patients who were diagnosed with tuberculosis, the precocious deaths and associated factors in southern Brazil

BACKGROUND

A diagnosis of tuberculosis (TB) does not mean that the disease will be treated successfully, since death may occur even among those who are known to the health services. Here, we aimed to analyze patient survival time from the diagnosis of TB to death, precocious deaths, and associated factors in southern Brazil.

METHODS

We conducted a longitudinal study with patients who were diagnosed with TB and who died due to the disease between 2008 and 2015 in southern Brazil. The starting point for measuring survival time was the patient's diagnosis date. Techniques for survival analysis were employed, including the Kaplan-Meier test and Cox's regression. A mixed-effect model was applied for identifying the associated factors to precocious deaths. Hazard ratio (HR) and odds ratio (OR) with 95% confidence intervals (95% CI) were estimated. We defined p value <0.05 as statistically significant for all statistics applied.

RESULTS

One hundred forty-six patients were included in the survival analysis, observing a median survival time of 23.5 days. We observed that alcoholism (HR=1.55, 95% CI=1.04-2.30) and being male (HR=6.49, 95% CI=1.03-2.68) were associated with death. The chance of precocious death within 60 days was 10.48 times greater than the chance of early death within 30 days.

CONCLUSION

Most of the deaths occurred within 2 months after the diagnosis, during the intensive phase of the treatment. The use of alcohol and gender were associated with death, revealing inequality between men and women. This study advanced knowledge regarding the vulnerability associated with mortality. These findings must be addressed to fill a gap in the care cascades for active TB and ensure equity in health.

Clinical Evaluation of Active Tuberculosis-Related Deaths in Shenzhen, China: A Descriptive Study

Objective

The aim of this study was to assess active tuberculosis-related deaths in Shenzhen city of China to identify major causes of mortality in different age groups.

Patients and Methods

Medical records of mortality cases of patients with active TB diagnosed during 2013-2018 were reviewed. All TB deaths were classified into two broad age groups (the young group: 18-65 years old and the elderly group: >65 years old). Causes of death were analyzed based on medical records.

Results

A total of 279 mortality cases of active TB were reviewed during the study period. Among them, mean age was 54.0±20.5 years old; 80.6% (225/279) were male. There were 5.7% and 4.6% MDR/XDRTB patients in the young and elderly group. Newly treated TB accounted for 89.6% in the young group and 85.1% in the elderly group. Pulmonary TB was a major infection type in both groups (65.1% vs 77.0%). Advanced TB (23.4%) and HIV co-infection (20.8%) were the leading causes of deaths in the young group, but deaths in the elderly group were mostly associated with underlying diseases, including cardiovascular disease (52.9%), diabetes (33.3%), COPD (16.1%) and cancer (11.5%). Malnutrition was a significant condition in both groups (43.2% vs 35.6%). In terms of respiratory complications, bacterial infection was the leading comorbidity in both groups (27.1% vs 18.4%), followed by septic shock (18.2% vs 12.6%) and respiratory failure (12.0% vs 11.5%). There were no significant statistical differences between the two groups.

Conclusion

Our findings suggest that screening for HIV co-infection and early diagnosis of TB is vital in lowering TB-related deaths in young patients. Most deaths in elderly TB patients were caused by underlying health conditions or complications other than TB.

Bayesian hierarchical modeling of joint spatiotemporal risk patterns for Human Immunodeficiency Virus (HIV) and Tuberculosis (TB) in Kenya

The simultaneous spatiotemporal modeling of multiple related diseases strengthens inferences by borrowing information between related diseases. Numerous research contributions to spatiotemporal modeling approaches exhibit their strengths differently with increasing complexity. However, contributions that combine spatiotemporal approaches to modeling of multiple diseases simultaneously are not so common. We present a full Bayesian hierarchical spatio-temporal approach to the joint modeling of Human Immunodeficiency Virus and Tuberculosis incidences in Kenya. Using case notification data for the period 2012–2017, we estimated the model parameters and determined the joint spatial patterns and temporal variations. Our model included specific and shared spatial and temporal effects. The specific random effects allowed for departures from the shared patterns for the different diseases. The space-time interaction term characterized the underlying spatial patterns with every temporal fluctuation. We assumed the shared random effects to be the structured effects and the disease-specific random effects to be unstructured effects. We detected the spatial similarity in the distribution of Tuberculosis and Human Immunodeficiency Virus in approximately 29 counties around the western, central and southern regions of Kenya. The distribution of the shared relative risks had minimal difference with the Human Immunodeficiency Virus disease-specific relative risk whereas that of Tuberculosis presented many more counties as high-risk areas. The flexibility and informative outputs of Bayesian Hierarchical Models enabled us to identify the similarities and differences in the distribution of the relative risks associated with each disease. Estimating the Human Immunodeficiency Virus and Tuberculosis shared relative risks provide additional insights towards collaborative monitoring of the diseases and control efforts.

Mortality Among People With HIV Treated for Tuberculosis Based on Positive, Negative, or No Bacteriologic Test Results for Tuberculosis: The IeDEA Consortium

Background

In resource-constrained settings, many people with HIV (PWH) are treated for tuberculosis (TB) without bacteriologic testing. Their mortality compared with those with bacteriologic testing is uncertain.

Methods

We conducted an observational cohort study among PWH ≥15 years of age initiating TB treatment at sites affiliated with 4 International epidemiology Databases to Evaluate AIDS consortium regions from 2012 to 2014: Caribbean, Central and South America, and Central, East, and West Africa. The exposure of interest was the TB bacteriologic test status at TB treatment initiation: positive, negative, or no test result. The hazard of death in the 12 months after TB treatment initiation was estimated using a Cox proportional hazard model. Missing covariate values were multiply imputed.

Results

In 2091 PWH, median age 36 years, 53% had CD4 counts ≤200 cells/mm³, and 52% were on antiretroviral therapy (ART) at TB treatment initiation. The adjusted hazard of death was higher in patients with no test compared with those with positive test results (hazard ratio [HR], 1.56; 95% confidence interval [CI], 1.08–2.26). The hazard of death was also higher among those with negative compared with positive tests but was not statistically significant (HR, 1.28; 95% CI, 0.91–1.81). Being on ART, having a higher CD4 count, and tertiary facility level were associated with a lower hazard for death.

Conclusions

There was some evidence that PWH treated for TB with no bacteriologic test results were at higher risk of death than those with positive tests. Research is needed to understand the causes of death in PWH treated for TB without bacteriologic testing.

Bayesian modeling of spatiotemporal patterns of TB-HIV co-infection risk in Kenya

Background

Tuberculosis (TB) and Human Immunodeficiency Virus (HIV) diseases are globally acknowledged as a public health challenge that exhibits adverse bidirectional relations due to the co-epidemic overlap. To understand the co-infection burden we used the case notification data to generate spatiotemporal maps that described the distribution and exposure hypotheses for further epidemiologic investigations in areas with unusual case notification levels.

Methods

We analyzed the TB and TB-HIV case notification data from the Kenya national TB control program aggregated for forty-seven counties over a seven-year period (2012–2018). Using spatiotemporal poisson regression models within the Integrated Nested Laplace Approach (INLA) paradygm, we modeled the risk of TB-HIV co-infection. Six competing models with varying space-time formulations were compared to determine the best fit model. We then assessed the geographic patterns and temporal trends of coinfection risk by mapping the posterior marginal from the best fit model.

Results

Of the total 608,312 TB case notifications, 194,129 were HIV co-infected. The proportion of TB-HIV co-infection was higher in females (39.7%) than in males (27.0%). A significant share of the co-infection was among adults aged 35 to 44 years (46.7%) and 45 to 54 years (42.1%). Based on the Bayesian Defiance Information (DIC) and the effective number of parameters (pD) comparisons, the spatiotemporal model allowing space-time interaction was the best in explaining the geographical variations in TB-HIV coinfection. The model results suggested that the risk of TB-HIV coinfection was influenced by infrastructure index (Relative risk (RR) = 5.75, Credible Interval (Cr.I) = (1.65, 19.89)) and gender ratio (RR = 5.81e⁻⁰⁴, Cr. I = (1.06e⁻⁰⁴, 3.18e⁻⁰³). The lowest and highest temporal relative risks were in the years 2016 at 0.9 and 2012 at 1.07 respectively. The spatial pattern presented an increased co-infection risk in a number of counties. For the spatiotemporal interaction, only a few counties had a relative risk greater than 1 that varied in different years.

Conclusions

We identified elevated risk areas for TB/HIV co-infection and fluctuating temporal trends which could be because of improved TB case detection or surveillance bias caused by spatial heterogeneity in the co-infection dynamics. Focused interventions and continuous TB-HIV surveillance will ensure adequate resource allocation and significant reduction of HIV burden amongst TB patients.

Effect of TB/HIV Integration on TB and HIV Indicators in Rural Ugandan Health Facilities

BACKGROUND

The World Health Organization recommends integrating services for patients coinfected with tuberculosis (TB) and HIV. We assessed the effect of TB/HIV integration on antiretroviral therapy (ART) initiation and TB treatment outcomes among TB/HIV-coinfected patients using data collected from 14 rural health facilities during 2 previous TB and HIV quality of care studies.

METHODS

A facility was considered to have integrated TB/HIV services if patients with TB/HIV had combined treatment for both illnesses by 1 provider or care team at 1 treatment location. We analyzed the effect of integration by conducting a cross-sectional analysis of integrated and nonintegrated facility periods comparing performance on ART initiation and TB treatment outcomes. We conducted logistic regression, with the patient as the unit of analysis, controlling for other intervention effects, adjusting for age and sex, and clustering by health facility.

RESULTS

From January 2012 to June 2014, 996 patients with TB were registered, 97% were tested for HIV, and 404 (42%) were HIV-positive. Excluding transfers, 296 patients were eligible for analysis with 117 and 179 from nonintegrated and integrated periods, respectively. Being treated in a facility with TB/HIV integration was associated with lower mortality [adjusted odds ratio (aOR) = 0.38, 95% confidence interval (CI): 0.18 to 0.77], but there was no difference in the proportion initiating ART (aOR = 1.34, 95% CI: 0.40 to 4.47), with TB treatment success (aOR = 1.43, 95% CI: 0.73 to 2.82), lost to follow-up (aOR = 1.64, 95% CI: 0.53 to 5.04), or failure (aOR = 1.21, 95% CI: 0.34 to 4.32).

CONCLUSIONS

TB/HIV service integration was associated with lower mortality during TB treatment even in settings with suboptimal proportions of patients completing TB treatment and starting on ART.

Mortality during treatment for tuberculosis; a review of surveillance data in a rural county in Kenya

Background

Globally in 2016, 1.7 million people died of Tuberculosis (TB). This study aimed to estimate all-cause mortality rate, identify features associated with mortality and describe trend in mortality rate from treatment initiation.

Method

A 5-year (2012–2016) retrospective analysis of electronic TB surveillance data from Kilifi County, Kenya. The outcome was all-cause mortality within 180 days after starting TB treatment. The risk factors examined were demographic and clinical features at the time of starting anti-TB treatment. We performed survival analysis with time at risk defined from day of starting TB treatment to time of death, lost-to-follow-up or completing treatment. To account for ‘lost-to-follow-up’ we used competing risk analysis method to examine risk factors for all-cause mortality.

Results

10,717 patients receiving TB treatment, median (IQR) age 33 (24–45) years were analyzed; 3,163 (30%) were HIV infected. Overall, 585 (5.5%) patients died; mortality rate of 12.2 (95% CI 11.3–13.3) deaths per 100 person-years (PY). Mortality rate increased from 7.8 (95% CI 6.4–9.5) in 2012 to 17.7 (95% CI 14.9–21.1) in 2016 per 100PY (P_trend<0.0001). 449/585 (77%) of the deaths occurred within the first three months after starting TB treatment. The median time to death (IQR) declined from 87 (40–100) days in 2012 to 46 (18–83) days in 2016 (P_trend = 0·04). Mortality rate per 100PY was 7.3 (95% CI 6.5–7.8) and 23.1 (95% CI 20.8–25.7) among HIV-uninfected and HIV-infected patients respectively. Age, being a female, extrapulmonary TB, being undernourished, HIV infected and year of diagnosis were significantly associated with mortality.

Conclusions

We found most deaths occurred within three months and an increasing mortality rate during the time under review among patients on TB treatment. Our results therefore warrant further investigation to explore host, disease or health system factors that may explain this trend.

A decade of antiretroviral therapy in Uganda: what are the emerging causes of death?

BACKGROUND

The roll out of antiretroviral therapy (ART) in Sub-Saharan Africa led to a decrease in mortality. Few studies have documented the causes of deaths among patients on long term antiretroviral therapy in Sub-Saharan Africa. Our objective was to describe the causes of death among patients on long term ART in Sub-Saharan Africa.

METHODS

We used data from a prospective cohort of ART naïve patients receiving care and treatment at the Infectious Diseases Institute in Kampala, Uganda. Patients were followed up for 10 years. All deaths were recorded and possible causes established using verbal autopsy. Deaths were grouped as HIV-related (ART toxicities, any opportunistic infections (OIs) and HIV-related malignancies) and non-HIV related deaths while some remained unknown. We used Kaplan Meier survival methods to estimate cumulative incidence and rates of mortality for all causes of death.

RESULTS

Of the 559, (386, 69%) were female, median age 36 years (IQR: 21-44), 89% had WHO clinical stages 3 and 4, and median CD4 count at ART initiation was 98 cells/μL (IQR: 21-163). A total of 127 (22.7%) deaths occurred in 10 years. The HIV related causes of death (n = 70) included the following; Tuberculosis 17 (24.3%), Cryptococcal meningitis 10 (15.7%), Kaposi's Sarcoma 7(10%), HIV related toxicity 6 (8.6%), HIV related anemia 5(7.1%), Pneumocystis carinii Pneumonia (PCP) 5 (7.1%), HIV related chronic diarrhea 4 (5.7%), Non-Hodgkin Lymphoma 3 (4.3%), Herpes Zoster 2 (2.8%), other 10 (14.3%). The non-HIV related causes of death (n = 20) included non-communicable diseases (diabetes, hypertension, stroke) 6 (30%), malaria 3 (15%), pregnancy-related death 2 (10%), cervical cancer 2 (10%), trauma 1(5%) and others 6 (30%).

CONCLUSION

Despite the higher rates of deaths from OIs in the early years of ART initiation, we observed an emergence of non-HIV related causes of morbidity and mortality. It is recommended that HIV programs in resource-limited settings start planning for screening and treatment of non-communicable diseases.