Victims of human trafficking and exploitation in the healthcare system: a retrospective study using a large multi-state dataset and ICD-10 codes.

Trafficking and exploitation for sex or labor affects millions of persons worldwide. To improve healthcare for these patients, in late 2018 new ICD-10 medical diagnosis codes were implemented in the US. These 13 codes include diagnosis of adult and child sexual exploitation, adult and child labor exploitation, and history of exploitation. Here we report on a database search of a large US health insurer that contained approximately 47.1 million patients and 0.9 million provider organizations, not limited to large medical systems. We reported on any diagnosis with the new codes between 2018-09-01 and 2022-09-01. The dataset was found to contain 5,262 instances of the ICD-10 codes. Regression analysis of the codes found a 5.8% increase in the uptake of these codes per year, representing a decline relative to 6.7% annual increase in the data. The codes were used by 1,810 different providers (0.19% of total) for 2,793 patients. Of the patients, 1,248 were recently trafficked, while the remainder had a personal history of exploitation. Of the recent cases, 86% experienced sexual exploitation, 14% labor exploitation and 0.8% both types. These patients were predominantly female (83%) with a median age of 20 (interquartile range: 15-35). The patients were characterized by persistently high prevalence of mental health conditions (including anxiety: 21%, post-traumatic stress disorder: 20%, major depression: 18%), sexually-transmitted infections, and high utilization of the emergency department (ED). The patients' first report of trafficking occurred most often outside of a hospital or emergency setting (55%), primarily during office and psychiatric visits.

A digital approach to asthma self-management in adults: Protocol for a pragmatic randomized controlled trial.

Asthma self-management can improve symptom control, but adherence to established self-management behaviors is often poor. With adult asthma uncontrolled in over 60% of U.S. cases, there is a need for scalable, cost-effective tools to improve asthma outcomes. Here we describe a protocol for the Asthma Digital Study, a 24-month, decentralized, pragmatic, open-label, randomized controlled trial investigating the impact of a digital asthma self-management (DASM) program on asthma outcomes in adults. The program leverages consumer-grade devices with a smartphone app to provide "smart nudges," symptom logging, trigger tracking, and other features. Participants are recruited (target N = 900) from throughout the U.S., and randomized to a DASM or control arm (1:1). Co-primary outcomes at one year are a) asthma-associated costs for acute care and b) change from baseline in Asthma Control Test™ scores. Findings may inform decisions around adoption of digital tools for asthma self-management. Trial registration:clinicaltrials.gov identifier: NCT04609644. Registered: Oct 30, 2020.

Adaptive Susceptible-Infectious-Removed Model for Continuous Estimation of the COVID-19 Infection Rate and Reproduction Number in the United States: Modeling Study.

BACKGROUND: The dynamics of the COVID-19 pandemic vary owing to local population density and policy measures. During decision-making, policymakers consider an estimate of the effective reproduction number Rt, which is the expected number of secondary infections spread by a single infected individual. OBJECTIVE: We propose a simple method for estimating the time-varying infection rate and the Rt. METHODS: We used a sliding window approach with a Susceptible-Infectious-Removed (SIR) model. We estimated the infection rate from the reported cases over a 7-day window to obtain a continuous estimation of Rt. A proposed adaptive SIR (aSIR) model was applied to analyze the data at the state and county levels. RESULTS: The aSIR model showed an excellent fit for the number of reported COVID-19 cases, and the 1-day forecast mean absolute prediction error was <2.6% across all states. However, the 7-day forecast mean absolute prediction error approached 16.2% and strongly overestimated the number of cases when the Rt was rapidly decreasing. The maximal Rt displayed a wide range of 2.0 to 4.5 across all states, with the highest values for New York (4.4) and Michigan (4.5). We found that the aSIR model can rapidly adapt to an increase in the number of tests and an associated increase in the reported cases of infection. Our results also suggest that intensive testing may be an effective method of reducing Rt. CONCLUSIONS: The aSIR model provides a simple and accurate computational tool for continuous Rt estimation and evaluation of the efficacy of mitigation measures.