Scaling up investigation and treatment of household contacts of tuberculosis patients in Brazil: a cost-effectiveness and budget impact analysis

Background

In Brazil, investigation and treatment of tuberculosis infection (TBI) in households contacts (HHC) of TB patients is not a priority. We estimated the cost-effectiveness and budget-impact of scaling-up an enhanced HHC management in Brazil.

Methods

We conceptualized a cascade-of-care that captures how HHC of tuberculosis patients are investigated in Brazil (status quo) and two enhanced strategies for management of HHC focusing on: (1) only tuberculosis disease (TBD) detection and, (2) TBD and TBI detection and treatment. Effectiveness was the number of HHC diagnosed with TBD and completing TBI treatment. Proportions in the cascades-of-care were derived from a meta-analysis. Health-system costs (2019 US$) were based on literature and official data from Brazil. The impact of enhanced strategies was extrapolated using reported data from 2019.

Findings

With the status quo, 0 (95% uncertainty interval: 0-1) HHC are diagnosed with TBD and 2 (0-16) complete TBI treatment. With strategy(1), an additional 15 (3-45) HHC would be diagnosed with TBD at a cost of US$346 each. With strategy(2), 81 (19-226) additional HHC would complete TBI treatment at a cost of US$84 each. A combined strategy, implemented nationally to enhance TBD detection and TBI treatment would result in an additional 9,711 (845-28,693) TBD being detected, and 51,277 (12,028-143,495) more HHC completing TBI treatment each year, utilizing 10.9% and 11.6% of the annual national tuberculosis program budget, respectively.

Interpretation

Enhanced detection and treatment of TBD and TBI among HHC in Brazil can be achieved at a national level using current tools at reasonable cost.

Funding

None.

The project for objective measures using computational psychiatry technology (PROMPT): Rationale, design, and methodology

Introduction

Depressive and neurocognitive disorders are debilitating conditions that account for the leading causes of years lived with disability worldwide. However, there are no biomarkers that are objective or easy-to-obtain in daily clinical practice, which leads to difficulties in assessing treatment response and developing new drugs. New technology allows quantification of features that clinicians perceive as reflective of disorder severity, such as facial expressions, phonic/speech information, body motion, daily activity, and sleep.

Methods

Major depressive disorder, bipolar disorder, and major and minor neurocognitive disorders as well as healthy controls are recruited for the study. A psychiatrist/psychologist conducts conversational 10-min interviews with participants ≤10 times within up to five years of follow-up. Interviews are recorded using RGB and infrared cameras, and an array microphone. As an option, participants are asked to wear wrist-band type devices during the observational period. Various software is used to process the raw video, voice, infrared, and wearable device data. A machine learning approach is used to predict the presence of symptoms, severity, and the improvement/deterioration of symptoms.

Discussion

The overall goal of this proposed study, the Project for Objective Measures Using Computational Psychiatry Technology (PROMPT), is to develop objective, noninvasive, and easy-to-use biomarkers for assessing the severity of depressive and neurocognitive disorders in the hopes of guiding decision-making in clinical settings as well as reducing the risk of clinical trial failure. Challenges may include the large variability of samples, which makes it difficult to extract the features that commonly reflect disorder severity.

Trial Registration

UMIN000021396, University Hospital Medical Information Network (UMIN).