Feasibility and acceptability of undertaking postmortem studies for tuberculosis medical research in a low income country

Introduction

If we are to break new ground in difficult-to-treat or difficult-to-vaccinate diseases (such as HIV, malaria, or tuberculosis), we must have a better understanding of the immune system at the site of infection in humans. For tuberculosis (TB), the initial site of infection is the lungs, but obtaining lung tissues from subjects suffering from TB has been limited to bronchoalveolar lavage (BAL) or sputum sampling, or surgical resection of diseased lung tissue.

Methods

We examined the feasibility of undertaking a postmortem study for human tuberculosis research at Mulago National Referral Hospital in Kampala, Uganda.

Results

Postmortem studies give us an opportunity to compare TB-involved and -uninvolved sites, for both diseased and non-diseased individuals. We report good acceptability of the next-of-kin to consent for their relative's tissue to be used for medical research; that postmortem and tissue processing can be undertaken within 8 hours following death; and that immune cells remain viable and functional up to 14 hours after death.

Discussion

Postmortem procedures remain a valuable and essential tool both to establish cause of death, and to advance our medical and scientific understanding of infectious diseases.

Integration and Validation of a Natural Language Processing Machine Learning Suicide Risk Prediction Model Based on Open-Ended Interview Language in the Emergency Department

BACKGROUND

Emergency departments (ED) are an important intercept point for identifying suicide risk and connecting patients to care, however, more innovative, person-centered screening tools are needed. Natural language processing (NLP) -based machine learning (ML) techniques have shown promise to assess suicide risk, although whether NLP models perform well in differing geographic regions, at different time periods, or after large-scale events such as the COVID-19 pandemic is unknown.

OBJECTIVE

To evaluate the performance of an NLP/ML suicide risk prediction model on newly collected language from the Southeastern United States using models previously tested on language collected in the Midwestern US.

METHOD

37 Suicidal and 33 non-suicidal patients from two EDs were interviewed to test a previously developed suicide risk prediction NLP/ML model. Model performance was evaluated with the area under the receiver operating characteristic curve (AUC) and Brier scores.

RESULTS

NLP/ML models performed with an AUC of 0.81 (95% CI: 0.71-0.91) and Brier score of 0.23.

CONCLUSION

The language-based suicide risk model performed with good discrimination when identifying the language of suicidal patients from a different part of the US and at a later time period than when the model was originally developed and trained.

Stakeholders' Perspectives on the Challenges of Emergency Obstetric Referrals and the Feasibility and Acceptability of an mHealth Intervention in Northern Iraq

The health system in northern Iraq has been weakened by conflict and the internal displacement of over three million people. Mobile phone-based interventions (mHealth) may improve maternal and neonatal health outcomes by enabling emergency referrals, facilitating communication between patients and providers, and improving patient data management; however, they have not been sufficiently studied in conflict-affected settings. We explored stakeholders' perspectives on challenges to obstetric referrals and the feasibility and acceptability of a mobile phone-based application to reduce delays in reaching emergency obstetric care in order to inform its development. We conducted a qualitative study in the Kurdistan region of northern Iraq from May to July, 2018. Using purposive sampling, we carried out 15 semi-structured interviews with coordination actors including healthcare management staff, government health officials, non-government health program managers and ambulance staff. The interviews explored obstetric care delivery, referral processes, mobile phone usage and mHealth implementation strategies. Eleven focus group discussions, which incorporated participatory activities on similar topics, were conducted with ambulance drivers, hospital and primary health center staff. Audio-recorded, transcribed and translated data were coded iteratively to identify emerging concepts, and analyzed thematically. Sixty-eight stakeholders (36 women and 32 men) participated. Challenges regarding the referral system included resource limitations, security concerns, costs and women's reluctance to be transported in male-staffed ambulances. In terms of obstetric care and decision-making, participants noted gaps in communication and coordination of services with the current paper-based system between health care providers, ambulance drivers, and hospital staff. Ambulance drivers reported incurring delays through lack of patient information, poor road conditions, and security issues. A prototype mobile phone application was found to be acceptable based on perceived usefulness to address some of the challenges to safe obstetric care and focused on phone usage, access to information, Global Positioning System (GPS), connectivity, cost, and user-friendliness. However, the feasibility of the innovation was considered in relation to implementation challenges that were identified, including poor connectivity, and digital literacy. Implementation of the app would need to account for the humanitarian context, cultural and gender norms regarding obstetric care, and would require substantial commitment and engagement from policymakers and practitioners.