We aim to use an agent-based model to accurately predict the spread of COVID-19 within multiple US state prisons.

We developed a semistochastic transmission model of COVID-19.

Five regional state-owned prisons within North Carolina.

Several thousand incarcerated individuals.

We measured (1) the observed and simulated average daily infection rate of COVID-19 for each prison studied in 30-day intervals, (2) the observed and simulated average daily recovery rate from COVID-19 for each prison studied in 30-day intervals, (3) the mean absolute percentage error (MAPE) of each prison’s summary statistics and the simulated results and (4) the parameter estimates of key predictors used in the model.

The COVID-19 pandemic disparately affected incarcerated populations in the USA, with severe morbidity and infection rates across the country. In response, many predictive models were developed to help mitigate risk. However, these models did not feature the systemic factors of prisons, such as vaccination rates, populations and capacities (to determine overcrowding) and design and were not generalisable to other prisons.

An agent-based model that used geospatial contact networks and compartmental transmission dynamics was built to create predictive microsimulations that simulated COVID-19 outbreaks within five North Carolinian regional prisons between July 2020 and June 2021. The model used the characteristics of an outbreak’s initial case size, a given facility’s capacity and its incarcerated vaccination rate as additional parameters alongside traditional susceptible-exposed-infected-recovered transmission dynamics. By fitting the model to each prison’s data using approximate Bayesian computation methods, we derived parameter estimates that reasonably modelled real-world results. These individualised estimates were then averaged to produce generalised parameter estimates for North Carolina state prisons overall.

Our model had a mean average MAPE score of 23.0 across all facilities, meaning that it reasonably forecasted facilities’ average daily positive and recovery rates of COVID-19. Our model estimated an average incarcerated vaccination rate of 54% across all prisons (with a 95% CI of ±0.12). In addition, the prisons of this study were estimated to be operating at 90% of their capacity on average (95% CI ±0.16). Given the high levels of COVID-19 observed in these prisons, which averaged over one-third positive tests on respective 1-day maxima, we conclude that vaccination levels were not sufficient in curbing COVID-19 outbreaks, and high occupancy levels likely exacerbated the spread of COVID-19 within prisons.

In addition, data gaps in facilities without recorded daily testing resulted in poor spread predictions, demonstrating how important consistent data release practices are in incarcerated settings for accurate tracking and prediction of outbreaks.

The findings of this study better quantify how spatial contact networks and facility-level characteristics unique to congregate living facilities can be used to predict infectious disease spread. Our approach also highlights the need for increased vaccination efforts and potential capacity reductions to mitigate COVID-19 transmission in prisons.

Practice guidelines recommend addressing patient non-medical drivers of health such as access to nutritious food and transportation as part of whole-person care. Emergent electronic health record (EHR)-based tools can enable non-medical needs care coordination, but adoption commonly faces workflow and infrastructure barriers. Targeted implementation support strategies (eg, training, practice facilitation) can enhance technology adoption in healthcare settings, but no prior research has assessed if implementation strategies can improve how care managers use enabling technologies for non-medical needs care coordination. This study will test whether providing implementation support to primary care health centre care management teams improves the adoption of EHR-based enabling technologies to address patients’ non-medical needs.

This hybrid implementation-effectiveness type 2 pragmatic trial has a mixed methods design. The primary outcomes include: (1) Whether patients enrolled in care management programmes have been screened for unmet non-medical health-related needs and (2) Whether patients with identified unmet non-medical health-related needs received a referral to a community organisation to address their need. The secondary outcomes include: (1) Whether referrals for financial-related non-medical needs had a documented outcome in the EHR, such as successful connection to services, service unavailability or other disposition statuses, (2) Whether the referral outcomes indicated ‘successful connection to services’ and (3) Clinical markers including hypertension and diabetes control. Formative evaluation of barriers and facilitators to using EHR tools to conduct non-medical needs screening, referrals and tracking of receipt of services will include semi-structured interviews and a ‘guided tour’ of enabling technology used by care managers. A modified Delphi process will then inform the development of a set of implementation strategies for inclusion in the intervention. The intervention will be piloted in three health centres, refined, then tested in a stepped-wedge cluster-randomised trial in 20 health centres.

We obtained ethics approval for all study activities from Advarra Institutional Review Board (registration number #00000971). Results will be disseminated to Health Centres and Health Centre network nationally at meetings and we will disseminate to researchers via manuscripts in peer-reviewed journals and scientific meetings.

NCT06489002.

IPE is defined as ‘occasions when two or more professions learn with, from and about each other to improve collaboration and the quality of care’2 (p: 6). Its inclusion in preregistration nursing programmes in the UK is a mandatory requirement3 and is believed to benefit both patients and communities,4 particularly in reducing human errors and increasing patient safety.5 Kirkpatrick’s model6...

Individuals living with severe mental illness (SMI) are at a significantly higher risk of mortality. This mixed-methods systematic review identifies and explores factors, including access inequalities to annual health checks (AHCs), for people living with SMI sharing protected characteristics in the UK, as identified in Core20PLUS5.

Mixed-methods systematic review.

MEDLINE, EMBASE, PsycINFO, CINAHL, ASSIA, Google Scholar and the grey literature were searched from 1 January 2004 to 30 January 2025.

Inclusion criteria were adults >18 years of age living with SMI. We included studies of AHCs, short health screening interventions, health promotion interventions, considering or aiming to improve uptake and/or access to screening for people living with SMI. We included mixed-methods and quantitative studies: randomised controlled trials, non-randomised controlled studies, cohort studies, cross-sectional studies and process evaluations. We also included qualitative studies.

Two reviewers independently assessed the evidence for inclusion using the eligibility criteria at title, abstract and at full-text screening. Quality Assessment with Diverse Studies was used for methodological quality. Analysis used Levesque’s Conceptual Framework of Access as an a priori framework and dimensions of equality from Core20PLUS5 and PROGRESS PLUS. Separate and independent quantitative and qualitative narrative syntheses and integration of the evidence from both occurred.

36 studies were included. Five studies applied reasonable adjustments to increase access to AHCs but lacked evaluation, controls and comparisons. 26 studies failed to discuss deprivation or ethnicity and only 6 studies discussed barriers and facilitators of access to AHCs for people of different ethnic, linguistic or cultural backgrounds. There was no evidence for interventions improving access to AHCs. Access focused primarily on dimensions of services, over abilities to access AHCs for people living with SMI.

There are access inequalities to AHCs for people living with SMI sharing protected characteristics. Robust research is urgently needed to identify, modify and ameliorate barriers to the policy recommended AHCs.

CRD42023437905.