Sources of exposure and risk among employees infected with severe acute respiratory coronavirus virus 2 (SARS-CoV-2) in a large, urban, tertiary-care hospital in the United States.

Objective: Hospital employees are at risk of SARS-CoV-2 infection through transmission in 3 settings: (1) the community, (2) within the hospital from patient care, and (3) within the hospital from other employees. We evaluated probable sources of infection among hospital employees based on reported exposures before infection.

Design: A structured survey was distributed to participants to evaluate presumed COVID-19 exposures (ie, close contacts with people with known or probable COVID-19) and mask usage.

Analysing 3429 digital supervisory interactions between Community Health Workers in Uganda and Kenya: the development, testing and validation of an open access predictive machine learning web app.

Background: Despite the growth in mobile technologies (mHealth) to support Community Health Worker (CHW) supervision, the nature of mHealth-facilitated supervision remains underexplored. One strategy to support supervision at scale could be artificial intelligence (AI) modalities, including machine learning. We developed an open access, machine learning web application (CHWsupervisor) to predictively code instant messages exchanged between CHWs based on supervisory interaction codes.

Community health workers in palliative care provision in low-income and middle-income countries: a systematic scoping review of the literature.

Background: Community health workers (CHWs) are currently deployed in improving access to palliative care in a limited number of low-income or middle-income countries (LMICs). This review therefore aimed to document evidence from LMICs regarding (1) where and how CHWs are currently deployed in palliative care delivery, (2) the methods used to train and support CHWs in this domain, (3) the evidence surrounding the costs attached with deploying CHWs in palliative care provision and (4) challenges and barriers to this approach.

Methods: We conducted a systematic scoping review of the literature, adhering to established guidelines.

Community health workers and early detection of breast cancer in low-income and middle-income countries: a systematic scoping review of the literature.

Background: Breast cancer is the leading cause of female mortality in low-income and middle-income countries (LMICs). Early detection of breast cancer, either through screening or early diagnosis initiatives, led by community health workers (CHWs) has been proposed as a potential way to address the unjustly high mortality rates. We therefore document: (1) where and how CHWs are currently deployed in this role; (2) how CHWs are trained, including the content, duration and outcomes of training; and (3) the evidence on costs associated with deploying CHWs in breast cancer early detection.