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OBJECTIVE: We estimate annual hospital expenditures to achieve high emergency department (ED) pediatric readiness (HPR), that is, weighted Pediatric Readiness Score (wPRS) ≥ 88 (0-100 scale) across EDs with different pediatric volumes of children, overall and after accounting for current levels of readiness. METHODS: We calculated the annual hospital costs of HPR based on two components: (1) ED pediatric equipment and supplies and (2) labor costs required for a Pediatric Emergency Care Coordinator (PECC) to perform pediatric readiness tasks. Data sources to generate labor cost estimates included: 2021 national salary information from U.S. Bureau of Labor Statistics, detailed patient and readiness data from 983 EDs in 11 states, the 2021 National Pediatric Readiness Project assessment; a national PECC survey; and a regional PECC survey. Data sources for equipment and supply costs included: purchasing costs from seven healthcare organizations and equipment usage per ED pediatric volume. We excluded costs of day-to-day ED operations (ie, direct clinical care and routine ED supplies). RESULTS: The total annual hospital costs for HPR ranged from $77,712 (95% CI 54,719-100,694) for low volume EDs to $279,134 (95% CI 196,487-362,179) for very high volume EDs; equipment costs accounted for 0.9-5.0% of expenses. The total annual cost-per-patient ranged from $3/child (95% CI 2-4/child) to $222/child (95% CI 156-288/child). After accounting for current readiness levels, the cost to reach HPR ranged from $23,775 among low volume EDs to $145,521 among high volume EDs, with costs per patient of $4/child to $48/child. CONCLUSIONS: Annual hospital costs for HPR are modest, particularly when considered per child.

High emergency department (ED) pediatric readiness is associated with improved survival in children, but the cost is unknown. We evaluated the costs of emergency care for children across quartiles of ED pediatric readiness. This was a retrospective cohort study of children aged 0-17 years receiving emergency services in 747 EDs in 9 states from January 1, 2012, through December 31, 2017. We measured ED pediatric readiness using the weighted Pediatric Readiness Score (range: 0-100). The primary outcome was the total cost of acute care (ED and inpatient) in 2022 dollars, adjusted for ED case mix and hospital characteristics. A total of 15 138 599 children received emergency services, including 27.6% with injuries and 72.4% with acute medical illness. The average adjusted per-patient cost by quartile of ED pediatric readiness ranged from $991 (quartile 1) to $1064 (quartile 4) for injured children and $1104-$1217 for medical children. The resulting cost differences were $72 (95% CI: -$6 to $151) and $113 (95% CI: $20-$206), respectively. Receiving emergency care in high-readiness EDs was not associated with marked increases in the cost of delivering services.

Objectives. To determine facility-level factors associated with COVID-19 outbreaks in US Immigration and Customs Enforcement (ICE) detention centers. Methods. We obtained COVID-19 case counts at 88 ICE detention facilities from May 6, 2020, through June 21, 2021, from the COVID Prison Project. We obtained information about facility population size, facility type (dedicated to immigrants or mixed with other incarcerated populations), and facility operator (public vs private contractor) from third-party sources. We defined the threshold for a COVID-19 outbreak as a cumulative 3-week incidence of 10% or more of the detained population. Results. Sixty-three facilities (72%) had at least 1 outbreak. Facilities with any outbreak were significantly more likely to be privately operated (P <.001), to have larger populations (113 vs 37; P =.002), and to have greater changes in their population size over the study period (‒56% vs −26%; P <.001). Conclusions. Several facility-level factors were associated with the occurrence of COVID-19 outbreaks in ICE facilities. Public Health Implications. Structural and organizational factors that promote respiratory infection spread in ICE facilities must be addressed to protect detainee health. (Am J Public Health. 2024;114(9):909–912. https://doi.org/10.2105/AJPH.2024.307704) [ABSTRACT FROM AUTHOR]

Background: The potential for selection bias in nonrepresentative, large-scale, low-cost survey data can limit their utility for population health measurement and public health decision making. We developed an approach to bias adjust county-level COVID-19 vaccination coverage predictions from the large-scale US COVID-19 Trends and Impact Survey. Design: We developed a multistep regression framework to adjust for selection bias in predicted county-level vaccination coverage plateaus. Our approach included poststratification to the American Community Survey, adjusting for differences in observed covariates, and secondary normalization to an unbiased reference indicator. As a case study, we prospectively applied this framework to predict county-level long-run vaccination coverage among children ages 5 to 11 y. We evaluated our approach against an interim observed measure of 3-mo coverage for children ages 5 to 11 y and used long-term coverage estimates to monitor equity in the pace of vaccination scale up. Results: Our predictions suggested a low ceiling on long-term national vaccination coverage (46%), detected substantial geographic heterogeneity (ranging from 11% to 91% across counties in the United States), and highlighted widespread disparities in the pace of scale up in the 3 mo following Emergency Use Authorization of COVID-19 vaccination for 5- to 11-y-olds. Limitations: We relied on historical relationships between vaccination hesitancy and observed coverage, which may not capture rapid changes in the COVID-19 policy and epidemiologic landscape. Conclusions: Our analysis demonstrates an approach to leverage differing strengths of multiple sources of information to produce estimates on the time scale and geographic scale necessary for proactive decision making. Implications: Designing integrated health measurement systems that combine sources with different advantages across the spectrum of timeliness, spatial resolution, and representativeness can maximize the benefits of data collection relative to costs. Highlights: The COVID-19 pandemic catalyzed massive survey data collection efforts that prioritized timeliness and sample size over population representativeness. The potential for selection bias in these large-scale, low-cost, nonrepresentative data has led to questions about their utility for population health measurement. We developed a multistep regression framework to bias adjust county-level vaccination coverage predictions from the largest public health survey conducted in the United States to date: the US COVID-19 Trends and Impact Survey. Our study demonstrates the value of leveraging differing strengths of multiple data sources to generate estimates on the time scale and geographic scale necessary for proactive public health decision making. [ABSTRACT FROM AUTHOR]

Background: Compartmental infectious disease (ID) models are often used to evaluate nonpharmaceutical interventions (NPIs) and vaccines. Such models rarely separate within-household and community transmission, potentially introducing biases in situations in which multiple transmission routes exist. We formulated an approach that incorporates household structure into ID models, extending the work of House and Keeling. Design: We developed a multicompartment susceptible-exposed-infectious-recovered-susceptible-vaccinated (MC-SEIRSV) modeling framework, allowing nonexponentially distributed duration in exposed and infectious compartments, that tracks within-household and community transmission. We simulated epidemics that varied by community and household transmission rates, waning immunity rate, household size (3 or 5 members), and numbers of exposed and infectious compartments (1–3 each). We calibrated otherwise identical models without household structure to the early phase of each parameter combination's epidemic curve. We compared each model pair in terms of epidemic forecasts and predicted NPI and vaccine impacts on the timing and magnitude of the epidemic peak and its total size. Meta-analytic regressions characterized the relationship between household structure inclusion and the size and direction of biases. Results: Otherwise similar models with and without household structure produced equivalent early epidemic curves. However, forecasts from models without household structure were biased. Without intervention, they were upward biased on peak size and total epidemic size, with biases also depending on the number of exposed and infectious compartments. Model-estimated NPI effects of a 60% reduction in community contacts on peak time and size were systematically overestimated without household structure. Biases were smaller with a 20% reduction NPI. Because vaccination affected both community and household transmission, their biases were smaller. Conclusions: ID models without household structure can produce biased outcomes in settings in which within-household and community transmission differ. Highlights: Infectious disease models rarely separate household transmission from community transmission. The pace of household transmission may differ from community transmission, depends on household size, and can accelerate epidemic growth. Many infectious disease models assume exponential duration distributions for infected states. However, the duration of most infections is not exponentially distributed, and distributional choice alters modeled epidemic dynamics and intervention effectiveness. We propose a mathematical framework for household and community transmission that allows for nonexponential duration times and a suite of interventions and quantified the effect of accounting for household transmission by varying household size and duration distributions of infected states on modeled epidemic dynamics. Failure to include household structure induces biases in the modeled overall course of an epidemic and the effects of interventions delivered differentially in community settings. Epidemic dynamics are faster and more intense in populations with larger household sizes and for diseases with nonexponentially distributed infectious durations. Modelers should consider explicitly incorporating household structure to quantify the effects of non-pharmaceutical interventions (e.g., shelter-in-place). [ABSTRACT FROM AUTHOR]

The article discusses the cost-effectiveness of population-wide screening for chronic kidney disease (CKD). The authors conducted a sensitivity analysis and found that reducing the cost of screening tools and primary care visits resulted in more favorable cost-effectiveness ratios for screening. They also noted that including the benefits of SGLT2 inhibitor therapy in preventing nonfatal cardiovascular events would likely further enhance the cost-effectiveness of CKD screening. The article provides specific cost estimates for different age groups and screening intervals. [Extracted from the article]