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. Participants were stratified into high, medium, low, and unknown risk categories based on exposure characteristics and personal protective equipment. Setting: Tertiary-care hospital in Boston, Massachusetts. Participants: Hospital employees with a positive SARS-CoV-2 PCR test result between March 2020 and January 2021. During this period, 573 employees tested positive, of whom 187 (31.5%) participated. Results: We did not detect a statistically significant difference in the proportion of employees who reported any exposure (ie, close contacts at any risk level) in the community compared with any exposure in the hospital, from either patients or employees. In total, 131 participants (70.0%) reported no known high-risk exposure (ie, unmasked close contacts) in any setting. Among those who could identify a high-risk exposure, employees were more likely to have had a high-risk exposure in the community than in both hospital settings combined (odds ratio, 1.89; P = .03). Conclusions: Hospital employees experienced exposure risks in both community and hospital settings. Most employees were unable to identify high-risk exposures prior to infection. When respondents identified high-risk exposures, they were more likely to have occurred in the community.

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. We document the development and validation of the web app and report its predictive accuracy. METHODS: CHWsupervisor was developed using 2187 instant messages exchanged between CHWs and their supervisors in Uganda. The app was then validated on 1242 instant messages from a separate digital CHW supervisory network in Kenya. All messages from the training and validation data sets were manually coded by two independent human coders. The predictive performance of CHWsupervisor was determined by comparing the primary supervisory codes assigned by the web app, against those assigned by the human coders and calculating observed percentage agreement and Cohen's kappa coefficients. RESULTS: Human inter-coder reliability for the primary supervisory category of messages across the training and validation datasets was 'substantial' to 'almost perfect', as suggested by observed percentage agreements of 88-95% and Cohen's kappa values of 0.7-0.91. In comparison to the human coders, the predictive accuracy of the CHWsupervisor web app was 'moderate', suggested by observed percentage agreements of 73-78% and Cohen's kappa values of 0.51-0.56. CONCLUSIONS: Augmenting human coding is challenging because of the complexity of supervisory exchanges, which often require nuanced interpretation. A realistic understanding of the potential of machine learning approaches should be kept in mind by practitioners, as although they hold promise, supportive supervision still requires a level of human expertise. Scaling-up digital CHW supervision may therefore prove challenging. TRIAL REGISTRATION: This was not a clinical trial and was therefore not registered as such.

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. METHODS: We conducted a systematic scoping review and searched eight major databases, as well as the grey literature. We included original studies focusing on the role of CHWs to assist in breast cancer early detection in a country defined as a LMIC according to the World Bank. FINDINGS: 16 eligible studies were identified. Several roles were identified for CHWs including awareness raising and community education (n=13); history taking (n=7); performing clinical breast examination (n=9); making onward referrals (n=7); and assisting in patient navigation and follow-up (n=4). Details surrounding training programmes were poorly reported and no studies provided a formal cost analysis. CONCLUSIONS: Despite the relative paucity of studies addressing the role of CHWs in breast cancer early detection, as well as the heterogeneity of existing studies, evidence suggests that CHWs can play a number of important roles in breast cancer early detection initiatives in LMICs. However, if they are to realise their full potential, they must be appropriately supported within the wider health system.

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. 11 major databases were searched for literature published between 1978 and 2019, as well as the grey literature. FINDINGS: 13 original studies were included, all of which were conducted in sub-Saharan African countries (n=10) or in India (n=3). Ten described a role for CHWs in adult palliative care services, while three described paediatric services. Roles for CHWs include raising awareness and identifying individuals requiring palliative care in the community, therapeutic management for pain, holistic home-based care and visitation, and provision of psychological support and spiritual guidance. Reports on training context, duration and outcomes were variable. No studies conducted a formal cost analysis. Challenges to this approach include training design and sustainability; CHW recruitment, retention and support; and stigma surrounding palliative care. CONCLUSION: Despite relatively limited existing evidence, CHWs have important roles in the delivery of palliative care services in LMIC settings. There is a need for a greater number of studies from different geographical contexts to further explore the effectiveness of this approach.