Reducing loss to follow-up during treatment for drug-resistant tuberculosis

Machine learning algorithms using national registry data to predict loss to follow-up during tuberculosis treatment

BACKGROUND

Identifying patients at increased risk of loss to follow-up (LTFU) is key to developing strategies to optimize the clinical management of tuberculosis (TB). The use of national registry data in prediction models may be a useful tool to inform healthcare workers about risk of LTFU. Here we developed a score to predict the risk of LTFU during anti-TB treatment (ATT) in a nationwide cohort of cases using clinical data reported to the Brazilian Notifiable Disease Information System (SINAN).

METHODS

We performed a retrospective study of all TB cases reported to SINAN between 2015 and 2022; excluding children (< 18 years-old), vulnerable groups or drug-resistant TB. For the score, data before treatment initiation were used. We trained and internally validated three different prediction scoring systems, based on Logistic Regression, Random Forest, and Light Gradient Boosting. Before applying our models we splitted our data into training (~ 80% data) and test (~ 20%) sets, and then compared the model metrics using the test data set.

RESULTS

Of the 243,726 cases included, 41,373 experienced LTFU whereas 202,353 were successfully treated. The groups were different with regards to several clinical and sociodemographic characteristics. The directly observed treatment (DOT) was unbalanced between the groups with lower prevalence in those who were LTFU. Three models were developed to predict LTFU using 8 features (prior TB, drug use, age, sex, HIV infection and schooling level) with different score composition approaches. Those prediction scoring systems exhibited an area under the curve (AUC) ranging between 0.71 and 0.72. The Light Gradient Boosting technique resulted in the best prediction performance, weighting specificity and sensitivity. A user-friendly web calculator app was developed ( https://tbprediction.herokuapp.com/ ) to facilitate implementation.

CONCLUSIONS

Our nationwide risk score predicts the risk of LTFU during ATT in Brazilian adults prior to treatment commencement utilizing schooling level, sex, age, prior TB status, and substance use (drug, alcohol, and/or tobacco). This is a potential tool to assist in decision-making strategies to guide resource allocation, DOT indications, and improve TB treatment adherence.

Factors associated with loss to follow-up before and after treatment initiation among patients with tuberculosis: A 5-year observation in China

Background

Loss to follow-up (LTFU) is a significant barrier to the completion of anti-tuberculosis (TB) treatment and a major predictor of TB-associated deaths. Currently, research on LTFU-related factors in China is both scarce and inconsistent.

Methods

We collected information from the TB observation database of the National Clinical Research Center for Infectious Diseases. The data of all patients who were documented as LTFU were assessed retrospectively and compared with those of patients who were not LTFU. Descriptive epidemiology and multivariable logistic regression analyses were conducted to identify the factors associated with LTFU.

Results

A total of 24,265 TB patients were included in the analysis. Of them, 3,046 were categorized as LTFU, including 678 who were lost before treatment initiation and 2,368 who were lost afterwards. The previous history of TB was independently associated with LTFU before treatment initiation. Having medical insurance, chronic hepatitis or cirrhosis, and providing an alternative contact were independent predictive factors for LTFU after treatment initiation.

Conclusion

Loss to follow-up is frequent in the management of patients with TB and can be predicted using patients' treatment history, clinical characteristics, and socioeconomic factors. Our research illustrates the importance of early assessment and intervention after diagnosis. Targeted measures can improve patient engagement and ultimately treatment adherence, leading to better health outcomes and disease control.

High burden of childhood tuberculosis in migrants: a retrospective cohort study from the Thailand-Myanmar border

BACKGROUND

Tuberculosis (TB) is a leading cause of morbidity and mortality in children but epidemiological data are scarce, particularly for hard-to-reach populations. We aimed to identify the risk factors for unsuccessful outcome and TB mortality in migrant children at a supportive residential TB programme on the Thailand-Myanmar border.

METHODS

We conducted retrospective analysis of routine programmatic data for children (aged ≤ 15 years old) with TB diagnosed either clinically or bacteriologically between 2013 and 2018. Treatment outcomes were described and risk factors for unsuccessful outcome and death were identified using multivariable logistic regression.

RESULTS

Childhood TB accounted for a high proportion of all TB diagnoses at this TB programme (398/2304; 17.3%). Bacteriological testing was done on a quarter (24.9%) of the cohort and most children were diagnosed on clinical grounds (94.0%). Among those enrolled on treatment (n = 367), 90.5% completed treatment successfully. Unsuccessful treatment outcomes occurred in 42/398 (10.6%) children, comprising 26 (6.5%) lost to follow-up, one (0.3%) treatment failure and 15 (3.8%) deaths. In multivariable analysis, extra-pulmonary TB [adjusted OR (aOR) 3.56 (95% CI 1.12-10.98)], bacteriologically confirmed TB [aOR 6.07 (1.68-21.92)] and unknown HIV status [aOR 42.29 (10.00-178.78)] were independent risk factors for unsuccessful outcome. HIV-positive status [aOR 5.95 (1.67-21.22)] and bacteriological confirmation [aOR 9.31 (1.97-44.03)] were risk factors for death in the secondary analysis.

CONCLUSIONS

Children bear a substantial burden of TB disease within this migrant population. Treatment success rate exceeded the WHO End TB target of 90%, suggesting that similar vulnerable populations could benefit from the enhanced social support offered by this TB programme, but better child-friendly diagnostics are needed to improve the quality of diagnoses.

Predictors of loss to follow-up among adult tuberculosis patients in Southern Ethiopia: a retrospective follow-up study

Background

Loss to follow-up (LTFU) from tuberculosis (TB) treatment and care is a major public health problem as patients can be infectious and also may develop a multi-drug resistant TB (MDR-TB). The study aimed to assess whether LTFU differs by the distance TB patients travelled to receive care from the nearest health facility.

Methods

A total of 402 patient cards of TB patients who received care were reviewed from March 1–30, 2020. The Kaplan-Meir curve with the Log-rank test was used to compare differences in LTFU by the distance travelled to reach to the nearest health facility for TB care. The Cox proportional hazard regression model was used to identify predictors. All statistical tests are declared significant at a p-value< 0.05.

Results

A total of 37 patients were LTFU with the incidence rate of 11.26 per 1000 person-months of observations (PMOs) (95% CI: 8.15–15.53). The incidence rate ratio was 12.19 (95% CI: 5.01–35.73) among the groups compared (those who travelled 10 km or more versus those who travelled less than 10 km). Age ≥ 45 years (aHR = 7.71, 95% CI: 1.72, 34.50), educational status (primary schooling, aHR = 3.54, 95% CI: 1.49, 8.40; secondary schooling, aHR = 2.75, 95% CI: 1.08, 7.03), lack of family support (aHR = 2.80, 95% CI: 1.27, 6.19), nutritional support (aHR = 3.40, 95% CI:1.68, 6.89), ≥ 10 km distance to travel to a health facility (aHR = 6.06, 95% CI: 2.33, 15.81) had significantly predicted LTFU from TB treatment and care.

Conclusions

LTFU from adult TB care and treatment was 12 times higher among those who travelled ≥10 km to reach a health facility compared to those who travelled less. To retain adult TB patients in care and ensure appropriate treatment, health professionals and other stakeholders should give due attention to the factors that drive LTFU. We suggest identifying concerns of older patients at admission and those who travel long distance and establish social support platforms that could help people to complete TB treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12889-022-13390-8.

Impacts of Medical Security Level on Treatment Outcomes of Drug-Resistant Tuberculosis: Evidence from Wuhan City, China

BACKGROUND

Drug-resistant tuberculosis (DR-TB) is an increasingly serious global issue. DR-TB has a lower success rate and more severe interruption of treatment than ordinary tuberculosis. Incomplete treatment not only reduces recovery rate in DR-TB patients but also increases the spread of DR-TB. Optimizing medical security policies for DR-TB can reduce the economic burden of patients and can thereby improve treatment success rate.

METHODS

Patients with DR-TB who were registered in Wuhan Center for Tuberculosis Control and Prevention from January 2016 to December 2019 were selected as research subjects. General descriptive statistical analysis methods were used in analyzing patients' treatment outcomes and medical security compensation rate. The binary logistic regression was used in analyzing the impacts of medical security level on treatment outcomes of DR-TB.

RESULTS

A total of 409 DR-TB patients were included in the study, and the refusal rate was 12.47%. The treatment success rate was only 37.09% for patients who started treatment and had treatment outcomes. The total out-of-pocket expenses (OOPs) per capita for DR-TB patients were 13,005.61 Chinese yuan. The outpatient effective compensation ratio (ECR) of DR-TB patients was only 51.04%. The outpatient ECR of DR-TB with subsidies of public health projects (SPHPs) were nearly 80% higher than those without SPHP. high outpatient ECR helped optimize treatment outcomes (P < 0.001, OR = 1.038). The inpatient ECR had no effect on patients' treatment outcomes (P = 0.158, OR = 0.986).

CONCLUSION

Many DR-TB patients did not receive complete treatment. The key breakthrough point in improving DR-TB treatment outcomes is to optimize the outpatient medical insurance compensation policy. Including the costs of DR-TB in expenses for severe diseases in outpatient care is recommended, and financial investment should be appropriately increased to ensure the high coverage ratio of subsidies for public health projects.