SCAN for Abuse: Electronic Health Record-Based Universal Child Abuse Screening

BACKGROUND

Identification of physical abuse at the point of care without a systematic approach remains inherently subjective and prone to judgement error. This study examines the implementation of an electronic health record (EHR)-based universal child injury screen (CIS) to improve detection rates of child abuse.

METHODS

CIS was implemented in the EHR admission documentation for all patients age 5 or younger at a single medical center, with the following questions. 1) "Is this patient an injured/trauma patient?" 2) "If this is a trauma/injured patient, where did the injury occur?" A "Yes" response to Question 1 would alert a team of child abuse pediatricians and social workers to determine if a patient required formal child abuse clinical evaluation. Patients who received positive CIS responses, formal child abuse work-up, and/or reports to Child Protective Services (CPS) were reviewed for analysis. CPS rates from historical controls (2017-2018) were compared to post-implementation rates (2019-2021).

RESULTS

Between 2019 and 2021, 14,150 patients were screened with CIS. 286 (2.0 %) patients screened received positive CIS responses. 166 (58.0 %) of these patients with positive CIS responses would not have otherwise been identified for child abuse evaluation by their treating teams. 18 (10.8 %) of the patients identified by the CIS and not by the treating team were later reported to CPS. Facility CPS reporting rates for physical abuse were 1.2 per 1000 admitted children age 5 or younger (pre-intervention) versus 4.2 per 1000 (post-intervention).

CONCLUSIONS

Introduction of CIS led to increased detection suspected child abuse among children age 5 or younger.

LEVEL OF EVIDENCE

Level II.

TYPE OF STUDY

Study of Diagnostic Test.

Cost-effectiveness of Increasing Buprenorphine Treatment Initiation, Duration, and Capacity Among Individuals Who Use Opioids

Importance

Buprenorphine is an effective and cost-effective medication to treat opioid use disorder (OUD), but is not readily available to many people with OUD in the US. The current cost-effectiveness literature does not consider interventions that concurrently increase buprenorphine initiation, duration, and capacity.

Objective

To conduct a cost-effectiveness analysis and compare interventions associated with increased buprenorphine treatment initiation, duration, and capacity.

Design and Setting

This study modeled the effects of 5 interventions individually and in combination using SOURCE, a recent system dynamics model of prescription opioid and illicit opioid use, treatment, and remission, calibrated to US data from 1999 to 2020. The analysis was run during a 12-year time horizon from 2021 to 2032, with lifetime follow-up. A probabilistic sensitivity analysis on intervention effectiveness and costs was conducted. Analyses were performed from April 2021 through March 2023. Modeled participants included people with opioid misuse and OUD in the US.

Interventions

Interventions included emergency department buprenorphine initiation, contingency management, psychotherapy, telehealth, and expansion of hub-and-spoke narcotic treatment programs, individually and in combination.

Main Outcomes and Measures

Total national opioid overdose deaths, quality-adjusted life years (QALYs) gained, and costs from the societal and health care perspective.

Results

Projections showed that contingency management expansion would avert 3530 opioid overdose deaths over 12 years, more than any other single-intervention strategy. Interventions that increased buprenorphine treatment duration initially were associated with an increased number of opioid overdose deaths in the absence of expanded treatment capacity. With an incremental cost- effectiveness ratio of $19 381 per QALY gained (2021 USD), the strategy that expanded contingency management, hub-and-spoke training, emergency department initiation, and telehealth was the preferred strategy for any willingness-to-pay threshold from $20 000 to $200 000/QALY gained, as it was associated with increased treatment duration and capacity simultaneously.

Conclusion and Relevance

This modeling analysis simulated the effects of implementing several intervention strategies across the buprenorphine cascade of care and found that strategies that were concurrently associated with increased buprenorphine treatment initiation, duration, and capacity were cost-effective.

Assessing Interventions That Prevent Multiple Infectious Diseases: Simple Methods for Multidisease Modeling

BACKGROUND

Many cost-effectiveness analyses (CEAs) only consider outcomes for a single disease when comparing interventions that prevent or treat 1 disease (e.g., vaccination) to interventions that prevent or treat multiple diseases (e.g., vector control to prevent mosquito-borne diseases). An intervention targeted to a single disease may be preferred to a broader intervention in a single-disease model, but this conclusion might change if outcomes from the additional diseases were included. However, multidisease models are often complex and difficult to construct.

METHODS

We present conditions for when multiple diseases should be considered in such a CEA. We propose methods for estimating health outcomes and costs associated with control of additional diseases using parallel single-disease models. Parallel modeling can incorporate competing mortality and coinfection from multiple diseases while maintaining model simplicity. We illustrate our approach with a CEA that compares a dengue vaccine, a chikungunya vaccine, and mosquito control via insecticide and mosquito nets, which can prevent dengue, chikungunya, Zika, and yellow fever.

RESULTS

The parallel models and the multidisease model generated similar estimates of disease incidence and deaths with much less complexity. When using this method in our case study, considering only chikungunya and dengue, the preferred strategy was insecticide. A broader strategy-insecticide plus long-lasting insecticide-treated nets-was not preferred when Zika and yellow fever were included, suggesting the conclusion is robust even without the explicit inclusion of all affected diseases.

LIMITATIONS

Parallel modeling assumes independent probabilities of infection for each disease.

CONCLUSIONS

When multidisease effects are important, our parallel modeling method can be used to model multiple diseases accurately while avoiding additional complexity.

Dependence of COVID-19 Policies on End-of-Year Holiday Contacts in Mexico City Metropolitan Area: A Modeling Study

Background. Mexico City Metropolitan Area (MCMA) has the largest number of COVID-19 (coronavirus disease 2019) cases in Mexico and is at risk of exceeding its hospital capacity in early 2021. Methods. We used the Stanford-CIDE Coronavirus Simulation Model (SC-COSMO), a dynamic transmission model of COVID-19, to evaluate the effect of policies considering increased contacts during the end-of-year holidays, intensification of physical distancing, and school reopening on projected confirmed cases and deaths, hospital demand, and hospital capacity exceedance. Model parameters were derived from primary data, literature, and calibrated. Results. Following high levels of holiday contacts even with no in-person schooling, MCMA will have 0.9 million (95% prediction interval 0.3-1.6) additional COVID-19 cases between December 7, 2020, and March 7, 2021, and hospitalizations will peak at 26,000 (8,300-54,500) on January 25, 2021, with a 97% chance of exceeding COVID-19-specific capacity (9,667 beds). If MCMA were to control holiday contacts, the city could reopen in-person schools, provided they increase physical distancing with 0.5 million (0.2-0.9) additional cases and hospitalizations peaking at 12,000 (3,700-27,000) on January 19, 2021 (60% chance of exceedance). Conclusion. MCMA must increase COVID-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in early 2021 depends on sustaining physical distancing and on controlling contacts during the end-of-year holiday.

Racial/Ethnic Disparities In COVID-19 Exposure Risk, Testing, And Cases At The Subcounty Level In California

With a population of forty million and substantial geographic variation in sociodemographics and health services, California is an important setting in which to study disparities. Its population (37.5 percent White, 39.1 percent Latino, 5.3 percent Black, and 14.4 percent Asian) experienced 59,258 COVID-19 deaths through April 14, 2021-the most of any state. We analyzed California's racial/ethnic disparities in COVID-19 exposure risks, testing rates, test positivity, and case rates through October 2020, combining data from 15.4 million SARS-CoV-2 tests with subcounty exposure risk estimates from the American Community Survey. We defined "high-exposure-risk" households as those with one or more essential workers and fewer rooms than inhabitants. Latino people in California are 8.1 times more likely to live in high-exposure-risk households than White people (23.6 percent versus 2.9 percent), are overrepresented in cumulative cases (3,784 versus 1,112 per 100,000 people), and are underrepresented in cumulative testing (35,635 versus 48,930 per 100,000 people). These risks and outcomes were worse for Latino people than for members of other racial/ethnic minority groups. Subcounty disparity analyses can inform targeting of interventions and resources, including community-based testing and vaccine access measures. Tracking COVID-19 disparities and developing equity-focused public health programming that mitigates the effects of systemic racism can help improve health outcomes among California's populations of color.

How do Covid-19 policy options depend on end-of-year holiday contacts in Mexico City Metropolitan Area? A Modeling Study

BACKGROUND

With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the largest number of Covid-19 cases in Mexico and is at risk of exceeding its hospital capacity in late December 2020.

METHODS

We used SC-COSMO, a dynamic compartmental Covid-19 model, to evaluate scenarios considering combinations of increased contacts during the holiday season, intensification of social distancing, and school reopening. Model parameters were derived from primary data from MCMA, published literature, and calibrated to time-series of incident confirmed cases, deaths, and hospital occupancy. Outcomes included projected confirmed cases and deaths, hospital demand, and magnitude of hospital capacity exceedance.

FINDINGS

Following high levels of holiday contacts even with no in-person schooling, we predict that MCMA will have 1·0 million (95% prediction interval 0·5 - 1·7) additional Covid-19 cases between December 7, 2020 and March 7, 2021 and that hospitalizations will peak at 35,000 (14,700 - 67,500) on January 27, 2021, with a >99% chance of exceeding Covid-19-specific capacity (9,667 beds). If holiday contacts can be controlled, MCMA can reopen in-person schools provided social distancing is increased with 0·5 million (0·2 - 1·0) additional cases and hospitalizations peaking at 14,900 (5,600 - 32,000) on January 23, 2021 (77% chance of exceedance).

INTERPRETATION

MCMA must substantially increase Covid-19 hospital capacity under all scenarios considered. MCMA's ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.

FUNDING

Society for Medical Decision Making, Gordon and Betty Moore Foundation, and Wadhwani Institute for Artificial Intelligence Foundation.

RESEARCH IN CONTEXT

Evidence before this study: As of mid-December 2020, Mexico has the twelfth highest incidence of confirmed cases of Covid-19 worldwide and its epidemic is currently growing. Mexico's case fatality ratio (CFR) - 9·1% - is the second highest in the world. With more than 20 million residents, Mexico City Metropolitan Area (MCMA) has the highest number and incidence rate of Covid-19 confirmed cases in Mexico and a CFR of 8·1%. MCMA is nearing its current hospital capacity even as it faces the prospect of increased social contacts during the 2020 end-of-year holidays. There is limited Mexico-specific evidence available on epidemic, such as parameters governing time-dependent mortality, hospitalization and transmission. Literature searches required supplementation through primary data analysis and model calibration to support the first realistic model-based Covid-19 policy evaluation for Mexico, which makes this analysis relevant and timely.Added value of this study: Study strengths include the use of detailed primary data provided by MCMA; the Bayesian model calibration to enable evaluation of projections and their uncertainty; and consideration of both epidemic and health system outcomes. The model projects that failure to limit social contacts during the end-of-year holidays will substantially accelerate MCMA's epidemic (1·0 million (95% prediction interval 0·5 - 1·7) additional cases by early March 2021). Hospitalization demand could reach 35,000 (14,700 - 67,500), with a >99% chance of exceeding current capacity (9,667 beds). Controlling social contacts during the holidays could enable MCMA to reopen in-person schooling without greatly exacerbating the epidemic provided social distancing in both schools and the community were maintained. Under all scenarios and policies, current hospital capacity appears insufficient, highlighting the need for rapid capacity expansion.Implications of all the available evidence: MCMA officials should prioritize rapid hospital capacity expansion. MCMA's ability to reopen schools in mid-January 2021 depends on sustaining social distancing and that contacts during the end-of-year holiday were well controlled.

Quantifying Positive Health Externalities of Disease Control Interventions: Modeling Chikungunya and Dengue

Purpose. Health interventions can generate positive externalities not captured in traditional, single-disease cost-effectiveness analyses (CEAs), potentially biasing results. We illustrate this with the example of mosquito-borne diseases. When a particular mosquito species can transmit multiple diseases, a single-disease CEA comparing disease-specific interventions (e.g., vaccination) with interventions targeting the mosquito population (e.g., insecticide) would underestimate the insecticide's full benefits (i.e., preventing other diseases). Methods. We developed three dynamic transmission models: chikungunya, dengue, and combined chikungunya and dengue, each calibrated to disease-specific incidence and deaths in Colombia (June 2014 to December 2017). We compared the models' predictions of the incremental benefits and cost-effectiveness of an insecticide (10% efficacy), hypothetical chikungunya and dengue vaccines (40% coverage, 95% efficacy), and combinations of these interventions. Results. Model calibration yielded realistic parameters that produced close matches to disease-specific incidence and deaths. The chikungunya model predicted that vaccine would decrease the incidence of chikungunya and avert more total deaths than insecticide. The dengue model predicted that insecticide and the dengue vaccine would reduce dengue incidence and deaths, with no effect for the chikungunya vaccine. In the combined model, insecticide was more effective than either vaccine in reducing the incidence of and deaths from both diseases. In all models, the combined strategy was at least as effective as the most effective single strategy. In an illustrative CEA, the most frequently preferred strategy was vaccine in the chikungunya model, the status quo in the dengue model, and insecticide in the combined model. Limitations. There is uncertainty in the target calibration data. Conclusions. Failure to capture positive externalities can bias CEA results, especially when evaluating interventions that affect multiple diseases. Multidisease modeling is a reasonable alternative for addressing such biases.