Gender difference in mortality among pulmonary tuberculosis HIV co-infected adults aged 15-49 years in Kenya

Tuberculosis mortality and drug resistance among patients under TB treatment before and during COVID-19 in Burundi: a case-control study

BACKGROUND

The coronavirus SARS-CoV-2 (COVID-19) experience has underscored the consequences of inequalities in health and access to health services across and within countries. Vulnerable population groups have been disproportionately exposed to certain diseases such as tuberculosis (TB) due to service interruptions. The current study aimed to assess TB related mortality and risk of drug resistance during the COVID-19 Pandemic in Burundi.

METHODS

We conducted an incident case-control study on 362 TB patients, with 181 multidrug resistant TB (MDR-TB) patients and 181 drug susceptible TB (DS-TB) patients. These patients under TB treatment between July 11, 2018, and November 11,2022 (18 months before and 18 months during COVID-19). Baseline and drug susceptibility status data were captured at treatment initiation. Mortality during treatment follow-up TB mortality was compared between categories of drug susceptibility, period (before vs during COVID-19) and regimen phase. A multivariate logistic regression was used to show the predictive risk factors. K-Fold cross-validation was used to evaluate the final model.

RESULTS

A half of TB patients was under 40 years old, with majority of them being unemployed, malnourished and lacking food support during TB treatment. Most of them lived in precarious conditions with limited access to healthcare services. The overall TB-related mortality was 16.0% (95% CI: 12.5%- 20.3%) with 15.5% (95%CI: 10.7%-21.8%) in MDR-TB patients and 16.6% (95% CI: 11.6%-22.9%) in DS-TB patients. Stratified by the period, TB related mortality was 15.3% (95%CI: 11.7%-20.9%) before the COVID-19 pandemic and 17.1% (95%C 11.5%-24.6%) during the COVID-19 pandemic. More than a half of deaths in TB patients occurred during intensive phase of treatment. The risk of MDR-TB was significantly higher (p < 0.05) among patients undergoing treatment during the pandemic, those with a low education level, living in rural areas, unemployed, using public transportation, or living in overcrowded households (big family size,a small number of rooms). Additionally, patients with history of TB, previous treatment failure, and close contact with MDR-TB patients were more likely to have MDR-TB. The likelihood of MDR-TB further increased with the cumulative presence of these risk factors on the same TB patient.

CONCLUSION

TB mortality increased during the COVID-19 pandemic, particularly among MDR-TB patients. The odds of MDR-TB encompass a range of socio demographic and clinical factors particularly among economically disadvantaged patients. These findings underscore the need for targeted equity-driven interventions in high-risked populations, especially in the context of emerging outbreaks, in order accelerate TB elimination goals. Additional investigation on TB related mortality should focus on the intensive phase of treatment, which aligns with the 2025 World Health Organization consolidated guidelines on TB diagnosis and control.

Survival Rate and Predictors of Mortality Among TB-HIV Co-Infected Patients During Tuberculosis Treatment at Public Health Facilities in Bahir Dar City, Northwest Ethiopia

Background

Tuberculosis and human immunodeficiency virus co-infection pose a major public health concern, particularly in developing countries. The survival and predictors of mortality were not sufficiently studied among TB-HIV co-infected patients in Ethiopia.

Objective

This study aimed to investigate the survival rate and predictors of mortality among TB-HIV co-infected patients during TB treatment at public health facilities in Bahir Dar, Northwest Ethiopia.

Methods

A retrospective follow-up study was conducted among 401 TB-HIV co-infected patients who were treated for tuberculosis between July 2018 and June 2022 at public health facilities in Bahir Dar city, Ethiopia. Data were collected using a structured checklist from patient charts. Data entry and analysis were done using EpiData 3.1 and Stata version 15, respectively. A Cox proportional Hazard regression model was used to identify predictors of mortality. Predictors with P < 0.05 in the multivariable regression were considered statistically significant.

Results

Among the 401 TB-HIV co-infected patients, 59 (14.7%) died during the follow-up period. Predictors like lower BMI (AHR = 3.00, 95% CI = 1.44, 6.28), extrapulmonary TB infection (AHR = 3.30, 95% CI = 1.50, 7.29), presence of opportunistic infection (AHR = 5.07, 95% CI = 2.55, 10.08), functional status (bedridden: AHR = 4.49, 95% CI = 1.63, 12.33), and adherence to TB treatment (fair = AHR = 2.74, 95% CI = 1.41, 7.20, and poor = AHR = 3.75, 95% CI = 1.52, 9.23) were associated with mortality.

Conclusion

Mortality among TB and HIV coinfected people was high at public health facilities in Bahir Dar city. This result suggested that in order to increase patient survival, it would be necessary to enhance nutritional status, increase adherence to TB treatment, and prevent opportunistic infections.

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.

Tuberculosis poor treatment outcomes and its determinants in Kilifi County, Kenya: a retrospective cohort study from 2012 to 2019

Background

Tuberculosis (TB) is one of the leading causes of deaths in Africa, monitoring its treatment outcome is essential to evaluate treatment effectiveness. The study aimed to evaluate proportion of poor TB treatment outcomes (PTO) and its determinants during six-months of treatment at Kilifi County, Kenya.

Methods

We conducted a retrospective analysis of data from the TB surveillance system (TIBU) in Kilifi County, Kenya from 2012 to 2019. The outcome of interest was PTO (lost-to-follow-up (LTFU), death, transferred out, treatment failure, drug resistance) or successful treatment (cured or completed treatment). We performed time-stratified (at three months follow-up) survival regression analyses accounting for sub-county heterogeneity to determine factors associated with PTO.

Results

We included 14,706 TB patients, their median (IQR) age was 37 (28–50) years and 8,791 (60%) were males. A total of 13,389 (91%) were on first line anti-TB treatment (2RHZE/4RH), 4,242 (29%) were HIV infected and 192 (1.3%) had other underlying medical conditions. During 78,882 person-months of follow-up, 2,408 (16%) patients had PTO: 1,074 (7.3%) deaths, 776 (5.3%) LTFU, 415 (2.8%) transferred out, 103 (0.7%) treatment failure and 30 (0.2%) multidrug resistance. The proportion of poor outcome increased from 7.9% in 2012 peaking at 2018 (22.8%) and slightly declining to 20% in 2019 (trend test P = 0.03). Over two-thirds 1,734 (72%) poor outcomes occurred within first three months of follow-up. In the first three months of TB treatment, overweight ((aHR 0.85 (95%CI 0.73–0.98), HIV infected not on ARVS (aHR 1.72 (95% CI 1.28–2.30)) and year of starting treatment were associated with PTO. However, in the last three months of treatment, elderly age ≥50 years (aHR 1.26 (95%CI 1.02–1.55), a retreatment patient (aHR 1.57 (95%CI 1.28–1.93), HIV infected not on ARVs (aHR 2.56 (95%CI 1.39–4.72), other underlying medical conditions (aHR 2.24 (95%CI 1.41–3.54)) and year of starting treatment were positively associated with PTO while being a female (aHR 0.83 (95%CI 0.70–0.97)) was negatively associated with PTO.

Conclusions

Over two-thirds of poor outcomes occur in the first three months of TB treatment, therefore greater efforts are needed during this phase. Interventions targeting HIV infected and other underlying medical conditions, the elderly and retreated patients provide an opportunity to improve TB treatment outcome.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13690-022-00807-4.

Prediction of tuberculosis cases based on sociodemographic and environmental factors in gombak, Selangor, Malaysia: A comparative assessment of multiple linear regression and artificial neural network models

Background

Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

Methods

The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.

Results

The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R²) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO₂, SO₂, PM₁₀, rainfall, temperature, and atmospheric pressure, with the highest adjusted R² value of 0.47, errors below 6, and accuracies above 96%.

Conclusions

It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.