Counts Of Admissions Research Articles

Cold temperature-related attributable risk for emergency circulatory hospitalizations, length of stay, and healthcare costs in Hong Kong.

The association of ambient temperature with the length of hospital stays and healthcare costs has not been well-explored. We aimed to examine the association of temperature with emergency hospital admission (EHA) count and length of stay (LOS) for total and cause-specific circulatory diseases and evaluate the temperature-related attributable risk and healthcare costs. This ecologic time series analysis linked the daily circulatory EHA count and LOS from the Hong Kong Hospital Authority with the environmental exposures from the fixed monitoring stations from 2004 to 2019. The generalized linear quasi-Poisson model integrated with a distributed lag nonlinear model was applied to examine ambient temperatures' relative and attributable risks on EHA counts and LOS while adjusting the time-varying confounders. Temperature-related healthcare costs were evaluated based on the estimated attributable number (AN) and charges per attendance or bed day. Among 1276,632 EHAs and 7621,232 bed days of LOS, significant associations of cold temperatures with both EHA counts and LOS for total and cause-specific circulatory diseases were found. The temperature-related attributable fraction (AF) was estimated as 7.98 % (95 % CI: 5.24-10.58 %) for EHA count and 13.09 % (6.80-18.28 %) for LOS for total circulatory diseases. The cold temperature defined as the lower quartile of the ambient temperature range explained more than half of the temperature-related attributable risk and healthcare costs. Cold weather can lead to adverse health outcomes, with considerable pressure on the healthcare system. Longer LOS-associated higher healthcare costs in colder weather may have implications for developing targeted preventions and hospital service planning.

Ambient air pollution and risk of hospital admission due to acute and chronic coronary syndromes: a time-stratified case-crossover study in the 3 largest urban agglomerations in Poland.

Short-term exposure to air pollution may worsen the course of ischemic heart disease (IHD), causing acute and chronic coronary syndromes. This study aimed to assess the risk of hospital admission due to chronic and acute coronary syndromes (ACS) after exposure to various air pollutants in Poland. In this time-series study, the risk of hospital admission due to IHD over 3 days from exposure to several air pollutants was evaluated. A distributed lag nonlinear modelling was used to determine the counts of hospital admissions due to chronic and acute coronary syndromes. Data for the 3 largest urban agglomerations in Poland between 2012 and 2017 were collected. There were 88 467 admissions due to chronic coronary syndromes and 35 403 admissions due to acute coronary syndromes. After accounting adjusting for meteorological variables, the risk of admission due to chronic coronary syndromes increased after the exposure to particulate matter (PM) of aerodynamic diameter no greater than 10 µm (PM10) (cumulative rate ratio per 10 µg/m3, 1.007 [95%CI: 1.002-1.012]), of diameter no greater than 2.5 mm (PM2.5) (1.008 [1.001-1.014]), and nitrogen dioxide (NO2) (1.037 [1.026-1.047]). The risk of admission due to acute coronary syndromes was increased after exposure to PM10 (1.009 [1.002-1.015]) and PM2.5 (1.009 [1.001-1.018]), whereas the association for the gaseous pollutants NO2 and sulfur dioxide was less pronounced: 1.012 (0.998-1.026) and 1.032 (0.998-1.067), respectively. The exposure to PM10, PM2.5, and NO2 is associated with an increased short-term risk of hospital admission due to acute and chronic coronary syndromes.

Association between ambient temperature and increased total length of hospital stay of patients with cardiopulmonary disease in Hong Kong.

While temperature extremes have been shown to be associated with an increased risk of hospital admissions, evidence of their impact on the length of hospital stay, which may capture the lingering effects of temperature extremes, is scarce. We aimed to evaluate the association between daily variation in ambient temperature and daily variation in daily total length of stay (daily TLOS), a composite measure encompassing the daily count of hospital admissions and their corresponding length of hospital stay among cardiopulmonary patients. Additionally, we quantified the burden of TLOS attributable to non-optimal temperatures among Hong Kong's older adult population. We used a generalized linear regression with a distributed lag non-linear model to estimate the association between ambient temperature and daily TLOS. The analysis used 13 years of time-series data (1998-2010) on daily temperature and hospital admissions for cardiopulmonary diseases through accident and emergency departments among Hong Kong's older adult population. We quantified the attributable risk of TLOS by calculating the temperature-related days of hospital stay and the attributable fraction (AF). We recorded a total of 4,095,722 hospital stay days for cardiovascular patients and 4,492,697 days for respiratory patients. We found that both cold and heat were associated with increased TLOS for cardiopulmonary disease. The temperature-related AF was 11.5% (95% empirical CI: 5.3-17.2%) for cardiovascular disease, corresponding to an annual increase of 36,174 days (95% empirical CI: 15,286-57,018). For respiratory disease, the AF was 10.7% (95% empirical CI: 7.1-13.9%), equating to an annual increase of 36,897 days (95% empirical CI: 24,949-49,024) days annually. Extreme temperatures were associated with increased TLOS for cardiopulmonary patients in Hong Kong's older adult population. These findings highlight the need for hospitals to prepare in advance for extreme temperature events by implementing specific measures in terms of human resources and medical resources. In addition, the results provide valuable scientific evidence to support public health policies and inform hospital planning and management.

Risk of Hematuria-related Complications Associated With Anticoagulant and Antiplatelet Medications: A Single-Institution Retrospective Cohort Study

ObjectiveTo evaluate enteral and parenteral anticoagulant and antiplatelet medications and their associated risks of hematuria-related complications. Materials and MethodsThis was a single-institution, retrospective cohort study. Primary outcomes were counts of ED visits, hospital admissions, and urologic procedures to manage gross hematuria that occurred while patients were exposed to anticoagulant/antiplatelet medications. Multivariable negative binomial regression was used to identify incidence density rates and rate ratios for hematuria-related complications associated with each anticoagulant/antiplatelet medication. ResultsAmong 119,528 patients in the study cohort, 10,601 were exposed to rivaroxaban and 108,927 were not exposed to rivaroxaban. Patients who were prescribed rivaroxaban were more likely to be male (55.5% vs. 52.0%, p <.001), of white race (95.9% vs. 92.8%, p <.001), and have higher BMIs (median BMI, 29.3 vs. 28.3, p <.001). Those not exposed to rivaroxaban had lower incidence density rates than those exposed to rivaroxaban for any hematuria-related complication (29.4 vs. 39.3). Exposure to enoxaparin compared to rivaroxaban was associated with higher rates of any hematuria-related complication (adjusted risk ratio (aRR) 2.74, 95% CI 2.43-3.10), emergency department visits related to hematuria (aRR 3.34, 95% CI 2.73-4.11), and hematuria-related hospitalizations (aRR 4.58, 95% CI, 3.70-5.70). All other oral anticoagulant and antiplatelet medications studied were associated with lower risk than rivaroxaban for hematuria-related complications. ConclusionsAmong enteral and parenteral anticoagulant and antiplatelet medications studied, enoxaparin is associated with the highest rates of hematuria-related events. Further work is needed to elucidate the mechanisms by which distinct anticoagulant and antiplatelet medications contribute to hematuria.

Implementation of Opioid Safety Efforts: Influence of Academic Detailing on Adverse Outcomes Among Patients in the Veterans Health Administration.

The Veterans Health Administration (VA) implemented academic detailing (AD) to support safer opioid prescribing and overdose prevention initiatives. Patient-level data were extracted monthly from VA's electronic health record to evaluate whether AD implementation was associated with changes in all-cause mortality, opioid poisoning inpatient admissions, and opioid poisoning emergency department (ED) visits in an observational cohort of patients with long-term opioid prescriptions (≥45-day supply of opioids 6 months prior to a given month with ≤15 days between prescriptions). A single-group interrupted time series analysis using segmented logistic regression for mortality and Poisson regression for counts of inpatient admissions and ED visits was used to identify whether the level and slope of these outcomes changed in response to AD implementation. Among 955 376 unique patients (19 431 241 person-months), there were 53 369 deaths (29 025 pre-AD; 24 344 post-AD), 1927 opioid poisoning inpatient admissions (610 pre-AD; 1317 post-AD), and 408 opioid poisoning ED visits (207 pre-AD; 201 post-AD). Immediately after AD implementation, there was a 5.8% reduction in the odds of all-cause mortality (95% confidence interval [CI]: 0.897, 0.990). However, patients had a significantly increased incidence rate of inpatient admissions for opioid poisoning immediately after AD implementation (incidence rate ratio = 1.523; 95% CI: 1.118, 2.077). No significant differences in ED visits for opioid poisoning were observed. AD was associated with decreased all-cause mortality but increased inpatient hospitalization for opioid poisoning among patients prescribed long-term opioids. Mechanisms via which AD's efforts influenced opioid-related outcomes should be explored.

Extrapolating Sentinel Surveillance Information to Estimate National COVID Hospital Admission Rates: A Bayesian Modeling Approach.

The COVID-19-Associated Hospitalization Surveillance Network (COVID-NET) was established in March 2020 to monitor trends in hospitalizations associated with SARS-CoV-2 infection. COVID-NET is a geographically diverse population-based surveillance system for laboratory-confirmed COVID-19-associated hospitalizations with a combined catchment area covering approximately 10% of the US population. Data collected in COVID-NET includes monthly counts of hospitalizations for persons with confirmed SARS-CoV-2 infection who reside within the defined catchment area. A Bayesian modeling approach is proposed to estimate US national COVID-associated hospital admission rates based on information reported in the COVID-NET system. A key component of the approach is the ability to estimate uncertainty resulting from extrapolation of hospitalization rates observed within COVID-NET to the US population. In addition, the proposed model enables estimation of other contributors to uncertainty including temporal dependence among reported COVID-NET admission counts, the impact of unmeasured site-specific factors, and the frequency and accuracy of testing for SARS-CoV-2 infection. Based on the proposed model, an estimated 6.3 million (95% uncertainty interval (UI) 5.4-7.3 million) COVID-19-associated hospital admissions occurred in the United States from September 2020 through December 2023. Between April 2020 and December 2023, model-based monthly admission rate estimates ranged from a minimum of 1 per 10,000 population (95% UI 0.7-1.2) in June of 2023 to a highest monthly level of 16 per 10,000 (95% UI 13-19) in January 2022.

Hematological Markers as Predictors of ICU Admission in COVID-19 Patients: A Case-Control Study From a Tertiary Hospital.

COVID-19 illness severity ranges from mild- to life-threatening cases necessitating critical care. Rapid prediction of disease severity and the need for critical care support in COVID-19 patients remain essential, not only for current management but also for preparedness in future pandemics. This study aimed to assess hematological parameters as predictors of intensive care unit (ICU) admission and survival in COVID-19 patients, providing insights applicable to a broad range of infectious diseases. A case-control study was conducted at Hospital Raja Perempuan Zainab II, a tertiary referral hospital in Kelantan, Malaysia, from March 2020 to August 2021. Demographics, clinical, and laboratory data were retrieved from patients' medical records. Statistical analyses, including the Chi-square (χ2) test, independent t-tests, and simple and multiple logistic regressions, were used to analyze the data. A receiver operating characteristic (ROC) curve analysis was conducted to assess the accuracy of the predictors. The median age was 51 years, with females comprising 56.7% (n=148) and males 43.3% (n=113). A total of 88.5% of patients were admitted to non-ICU wards, with a mortality rate of 5.7%. Significant differences were observed in the distribution of hematological parameters between ICU-admitted and non-admitted patients. Neutrophil (OR: 23.96, 95% CI: 7.296-78.675) and white blood cell (WBC) count (OR: 36.677, 95% CI: 2.086-644.889) were the most significant predictors for ICU admission and survival, respectively. WBC and neutrophil counts exhibited high predictive value for ICU admission, while WBC, neutrophil, lymphocyte, and immature granulocyte (IG) counts were significant predictors of survival status among COVID-19 patients. These findings underscore the continued relevance of hematological markers in managing severe respiratory infections and improving critical care triage, with implications for current and future healthcare challenges.

Hospitalization risk in multiple myeloma episodes of care.

e23101 Background: The Enhancing Oncology Model (EOM) is a total cost of care (TCOC) program that was initiated in July 2023. Participating groups are expected to reduce expenditures while delivering high-quality care. Hospitalization (IP) is an important quality metric and cost driver in TCOC programs such as EOM. Understanding how cancer treatment may impact IP is essential for success. Multiple Myeloma (MM) treatment has transformed with multi-agent treatment as the standard of care (SOC), routinely incorporating 3 or more drugs used concurrently to maximize response. We examined the impact multi-drug treatment had on IP rates in MM patients. Methods: Using Parts A, B and D Medicare claims data from EOM baseline period (January 2019 - June 2022) for 12 participating practices, we extracted a unique count of chemotherapy drugs and IP admissions attributed to 6-month episodes of care. Dexamethasone (dex) was included even if claims were not found. Planned and cancer-surgery related IP were excluded using the Clinical Classifications Software Planned Admission Procedure Categories and per EOM specifications. IPs were dichotomized as Yes/No. A logistic regression model was developed to evaluate the association between the number of unique drugs and the unplanned IP, adjusting for variables: prior chemo, age at episode start, race, and HCC count. A 3-drug regimen was established as the reference (including dex) based on SOC. Results: 11,613 episodes had a primary diagnosis of MM, with at least one IP occurring in 2,382 episodes. The number of episodes (% IP) per drug count group are 1-2: 6636(16.3%); 3: 3772 (23.6%); 4: 863 (30.8%); 5+: 342 (40.9%). Detailed statistics are provided in Table 1. Compared to a 3-drug regimen, 4+ drugs increased the risk of IP, while episodes with 1-2 drugs had a lower risk of IP after adjusting for age, race, HCC count and prior treatment. Conclusions: Our findings can guide the design of care transforming interventions aimed at avoiding unnecessary IP while using guideline concordant SOC treatments for MM, driving TCOC reduction. Identifying patients with a higher likelihood of IP helps practices target patient monitoring programs. To understand why patients with prior treatment had lower IP identifying maintenance following initial treatment and patients receiving 3 or more drugs after failure of prior therapy, are worth exploring further. CMMI Disclaimer: The statements contained in this document are solely those of the authors and do not necessarily reflect the views or policies of CMS. The authors assume responsibility for the accuracy and completeness of the information contained in this document. [Table: see text]

Interpretable zero-inflated neural network models for predicting admission counts

AbstractIn this paper, we construct interpretable zero-inflated neural network models for modeling hospital admission counts related to respiratory diseases among a health-insured population and their dependants in the United States. In particular, we exemplify our approach by considering the zero-inflated Poisson neural network (ZIPNN), and we follow the combined actuarial neural network (CANN) approach for developing zero-inflated combined actuarial neural network (ZIPCANN) models for modeling admission rates, which can accommodate the excess zero nature of admission counts data. Furthermore, we adopt the LocalGLMnet approach (Richman &amp; Wüthrich (2023). Scandinavian Actuarial Journal, 2023(1), 71–95.) for interpreting the ZIPNN model results. This facilitates the analysis of the impact of a number of socio-demographic factors on the admission rates related to respiratory disease while benefiting from an improved predictive performance. The real-life utility of the methodologies developed as part of this work lies in the fact that they facilitate accurate rate setting, in addition to offering the potential to inform health interventions.

Temperature and hospital admissions in the Eastern Mediterranean: a case study in Cyprus

Exposure to extreme temperatures can trigger a cascade of adverse cardiovascular and respiratory events. However, in Cyprus, a hotspot of climate change in the Eastern Mediterranean region, little is known about the temperature-related cardiorespiratory morbidity risks. We analyzed daily counts of hospital admissions for cardiovascular and respiratory diseases from four general hospitals in three districts in Cyprus from 2000 through 2019. For each district, we fitted time-series quasi-Poisson regression with distributed lag non-linear models to analyze the associations between daily mean temperature (lag 0–21 d) and hospital admissions for cardiorespiratory, cardiovascular, and respiratory diseases. A random-effects meta-analytical model was then applied to pool the district-specific estimates and obtain the national average associations. We analyzed 20 years of cause-specific hospitalization data with a total of 179 988 cardiovascular and respiratory events. The relationships between cardiorespiratory morbidity and temperature were overall U-shaped. During extreme temperature days, 15.85% (95% empirical CI [eCI]: 8.24, 22.40%) excess cardiovascular hospitalizations and 9.59% (95% eCI: −0.66, 18.69%) excess respiratory hospitalizations were attributable to extreme cold days (below the 2.5th percentile). Extreme hot days (above the 97.5th percentile) accounted for 0.17% (95% eCI: 0.03, 0.29%) excess cardiovascular hospitalizations and 0.23% (95% eCI: 0.07, 0.35%) excess respiratory hospitalizations. We found evidence of increased cardiovascular morbidity risk associated with extreme temperatures in Cyprus. Our study highlights the necessity to implement public health interventions and adaptive measures to mitigate the related temperature effects in an understudied region.

Short term exposure to low level ambient fine particulate matter and natural cause, cardiovascular, and respiratory morbidity among US adults with health insurance: case time series study

ObjectiveTo estimate the excess relative and absolute risks of hospital admissions and emergency department visits for natural causes, cardiovascular disease, and respiratory disease associated with daily exposure to fine particulate...

The impact of digital technology in care homes on unplanned secondary care usage and associated costs.

A substantial number of Emergency Department (ED) attendances by care home residents are potentially avoidable. Health Call Digital Care Homes is an app-based technology that aims to streamline residents' care by recording their observations such as vital parameters electronically. Observations are triaged by remote clinical staff. This study assessed the effectiveness of the Health Call technology to reduce unplanned secondary care usage and associated costs. A retrospective analysis of health outcomes and economic impact based on an intervention. The study involved 118 care homes across the North East of UK from 2018 to 2021. Routinely collected NHS secondary care data from County Durham and Darlington NHS Foundation Trust was linked with data from the Health Call app. Three outcomes were modelled monthly using Generalised Linear Mixed Models: counts of emergency attendances, emergency admissions and length of stay of emergency admissions. A similar approach was taken for costs. The impact of Health Call was tested on each outcome using the models. Data from 8,702 residents were used in the analysis. Results show Health Call reduces the number of emergency attendances by 11% [6-15%], emergency admissions by 25% [20-39%] and length of stay by 11% [3-18%] (with an additional month-by-month decrease of 28% [24-34%]). The cost analysis found a cost reduction of £57 per resident in 2018, increasing to £113 in 2021. The introduction of a digital technology, such as Health Call, could significantly reduce contacts with and costs resulting from unplanned secondary care usage by care home residents.

Assessing Safety of Anticoagulation for Atrial Fibrillation in Patients with Cirrhosis: A Real-World Outcomes Study.

In patients with atrial fibrillation (AF) and stroke risk factors, randomized trials have demonstrated that anticoagulation decreases the risk of ischemic stroke. However, all trials to date have excluded patients with significant liver disease, leaving guidelines to extrapolate recommendations. We aim to evaluate the impact of anticoagulation on safety events in patients with AF and cirrhosis. In this retrospective cohort study, we obtained de-identified health record data to extract anticoagulation strategy, comorbidities, prescriptions, lab values, and procedures for a cohort of patients with cirrhosis who develop AF. After selecting a propensity matched population to match patients with various anticoagulation strategies, we tracked data on outcomes for death, transfusion requirements, hospital and ICU admissions. After propensity score weighting and multivariable adjustment, anticoagulation strategy was associated with increased hospital admission count (OR = 1.74 per admission, P < .001), binary risk of hospital admission (OR = 1.54, P = .010) and risk of ICU admission (OR = 1.41, P = .047). We detected no significant differences in mortality, transfusion of blood products, or average length of stay. Direct oral anticoagulant (DOAC) prescriptions were associated with increased binary risk of hospital admission compared to warfarin prescriptions. In a third comparison, DOAC strategy alone was associated with increased hospital admission count (OR = 1.41 per admission, P < .001) and binary risk of hospital admission (OR = 1.52, P = .038) compared to no anticoagulation strategy. Anticoagulation strategy in patients with cirrhosis and AF was associated with increased rate of hospital admission and ICU admission but not associated with increased risk of mortality or transfusion requirement.

Risk association model for atelectasis complication in Mycoplasma pneumoniae pneumonia patients following standardized treatment.

Mycoplasma pneumoniae pneumonia (MPP) is a common disease of childhood pneumonia, and atelectasis is a serious comorbidity. Traditional diagnostic methods for MPP are limited by low accuracy, emphasizing the need for improved diagnostic approaches. This study aimed to establish a predictive scoring model for early detection of MPP complicated with atelectasis following standardized treatment. A total of 572 children were retrospectively enrolled, including 40 patients with MPP complicated by atelectasis despite standardized treatment and 532 patients in the non-atelectasis group. Clinical, laboratory, and imaging data within 24 h of admission were collected, including demographic information and various biomarkers. Multivariate logistic regression analysis was employed to identify risk factors and construct a predictive model, evaluated using receiver operating characteristic (ROC) curve analysis. Significant differences were observed between the MPP complicated with atelectasis group and the non-atelectasis group in terms of age, hospital admission time, fever duration, neutrophil percentage and count, CRP, ALT, and LDH levels (P < 0.05). According to the multivariate logistic regression analysis, length of fever, neutrophil ratio, platelet count, ALT, LDH, age were incorporated into the nomogram. The predictive model exhibited a sensitivity of 87.97% and specificity of 77.50% according to the ROC curve. Our study presents a preliminary risk association model incorporating clinical indicators such as fever duration, neutrophil ratio, platelet count, ALT value, LDH value, and age to aid in the early prediction of atelectasis in children with MPP. Given the methodological limitations, the generalizability of our findings is constrained, and this model should be viewed as an initial framework for clinical assessment rather than a definitive tool.

Frequency of acute renal injury in neonates with respiratory distress syndrome.

Objective: To determine the frequency of acute kidney injury (AKI) in neonates with acute respiratory distress syndrome (RDS). Study Design: Cross-sectional study. Setting: Neonatal Intensive Care Unit (NICU) of National Institute of Child Health, Karachi, Pakistan. Period: January 2023 to June 2023. Material &amp; Methods: A total of 118 neonates of both genders, born below 37 weeks of gestation and having RDS were analyzed. Demographic, clinical and laboratory parameters were noted. Frequency of AKI was determined while final outcome was noted in terms of discharged or mortality. Results: Of 118 neonates, 55.1% were girls. The mean gestational age: 33.11±1.76 weeks, mean admission age 8.30±9.0 hours, and mean birth weight 1.53±0.27 kg. AKI occurred in 50.8% of neonates with RDS. Mortality was reported in 46.6% cases. Factors associated with AKI included grunting at admission (63.3% vs. 43.1%, p=0.028), need for mechanical ventilation (35.0% vs. 12.1%, p=0.003), and platelet count on admission (p=0.042). Factors associated with mortality included lower Apgar score at minute 5 (p&lt;0.001), resuscitation at delivery (p&lt;0.001), grunting at admission (p=0.005), platelet count at admission (p=0.001), leukocyte count at admission (p=0.003), and the need for mechanical ventilation (p&lt;0.001). Conclusion: Very high frequency of AKI (50.8%) in neonates with acute RDS was noted. Mortality was associated with lower Apgar scores, resuscitation at delivery, grunting at admission, low platelet and leukocyte counts, and the need for mechanical ventilation.

Time warping between main epidemic time series in epidemiological surveillance.

The most common reported epidemic time series in epidemiological surveillance are the daily or weekly incidence of new cases, the hospital admission count, the ICU admission count, and the death toll, which played such a prominent role in the struggle to monitor the Covid-19 pandemic. We show that pairs of such curves are related to each other by a generalized renewal equation depending on a smooth time varying delay and a smooth ratio generalizing the reproduction number. Such a functional relation is also explored for pairs of simultaneous curves measuring the same indicator in two neighboring countries. Given two such simultaneous time series, we develop, based on a signal processing approach, an efficient numerical method for computing their time varying delay and ratio curves, and we verify that its results are consistent. Indeed, they experimentally verify symmetry and transitivity requirements and we also show, using realistic simulated data, that the method faithfully recovers time delays and ratios. We discuss several real examples where the method seems to display interpretable time delays and ratios. The proposed method generalizes and unifies many recent related attempts to take advantage of the plurality of these health data across regions or countries and time, providing a better understanding of the relationship between them. An implementation of the method is publicly available at the EpiInvert CRAN package.

Agent-based simulation and child protection systems: Rationale, implementation, and verification

Background and objectiveSimulation models are an important tool used in health care and other disciplines to support operational research and decision-making. In the child protection literature, simulation models are an under-utilized source of research evidence. Participants and settingIn this paper, we describe the rationale for and the development of an agent-based simulation of a child protection system in the US. Using the investigation, prevention service, and placement histories of 600,000 children served in an urban child welfare system, we walk the reader through the development of a prototype known as OSPEDALE. MethodsThe governing equations built into OSPEDALE probabilistically simulate the onset of investigations. Then, drawing from empirical survival distributions, the governing equations trace the probability of subsequent interactions with the system (recurrence of maltreatment, service referrals, and placement) conditional on the characteristics of children, their assessed risk level, and prior child protection system involvement. ResultsAs an initial test of OSPEDALE's utility, we compare empirical admission counts with counts generated from OSPEDALE. Though the verification step is admittedly simple, the comparison shows that OSPEDALE replicates the empirical count of new admissions closely enough to justify further investment in OSPEDALE. ConclusionsManagement of public child protection systems is increasingly research evidence-dependent. The emphasis on research evidence as a decision-support tool has elevated evidence acquired through randomized clinical trials. Though important, the evidence from clinical trials represents only one type of research evidence. Properly specified, simulation models are another source of evidence with real-world relevance.

Impact of childhood 13-valent pneumococcal conjugate vaccine introduction on adult pneumonia hospitalisations in Mongolia: a time series analysis

IntroductionFew studies have assessed the potential indirect effects of childhood pneumococcal conjugate vaccine (PCV) programs on the adult pneumonia burden in resource-limited settings. We evaluated the impact of childhood PCV13 immunisation on adult all-cause pneumonia following a phased program introduction from 2016. MethodsWe conducted a time-series analysis to assess changes in pneumonia hospitalisation incidence at four district hospitals in Mongolia. Adults (≥18 years) that met the clinical case definition for all-cause pneumonia were enrolled. A negative binomial mixed-effects model was used to assess the impact of PCV13 introduction on monthly counts of pneumonia admissions from January 2015—February 2022. We also performed a restricted analysis excluding the COVID-19 pandemic period. All models were stratified by age and assessed separately. Additional analyses assessed the robustness of our findings. FindingsThe average annual incidence of all-cause pneumonia hospitalisation was highest in adults 65+ years (62·81 per 10,000 population) and declined with decreasing age. After adjusting for the COVID-19 pandemic period, we found that rates of pneumonia hospitalisation remained largely unchanged over time. We did not observe a reduction in pneumonia hospitalisations in any age group. Results from restricted and sensitivity analyses were comparable to the primary results, finding limited evidence of a reduced pneumonia burden. InterpretationWe did not find evidence of indirect protection against all-cause pneumonia in adults following childhood PCV13 introduction. Direct pneumococcal vaccination and other interventions should be considered to reduce burden of pneumonia among older adults. FundingPfizer clinical research collaboration agreement (contract number: WI236621).

Admissions for Bronchiolitis at Children’s Hospitals Before and During the COVID-19 Pandemic

The COVID-19 pandemic has been associated with a transient decrease in bronchiolitis hospitalizations compared with prepandemic patterns, but current effects remain unknown. To analyze changes in patterns of bronchiolitis admissions at US children's hospitals during the 2020-2023 bronchiolitis seasons compared with the 2010-2019 seasons. This retrospective cross-sectional study used data from 41 US children's hospitals in the Pediatric Health Information System database. Bronchiolitis has winter-predominant seasonality, so hospitalizations were grouped according to bronchiolitis season (from July through June). This study included all patients aged younger than 2 years admitted with a diagnosis of bronchiolitis between July 1, 2010, and June 30, 2023. Bronchiolitis seasons from July through June between 2010-2011 and 2019-2020 were classified as the prepandemic era, and seasons between 2020-2021 and 2022-2023 were classified as the pandemic era. Data analysis was performed from July 1, 2010, through June 30, 2023. Admission date. The primary outcome was number of hospitalizations for bronchiolitis by season and month. Monthly admission counts from the prepandemic era were transformed into time series and used to train seasonal ensemble forecasting models. Forecasts were compared to monthly admissions during the pandemic era. In this study, there were 400 801 bronchiolitis admissions among 349 609 patients between July 1, 2010, and June 30, 2023. The median patient age was 6 (IQR, 2-12) months; 58.7% were boys and 43.7% were White. Hospitalizations increased gradually during the prepandemic era (median, 29 309 [IQR, 26 196-34 157]), decreased 69.2% (n = 9030) in the 2020-2021 season, and increased 75.3% (n = 51 397) in the 2022-2023 season. Patients in the pandemic era were older than those in the prepandemic era (median, 7 [IQR, 3-14] vs 6 [2-12] months; P < .001). Intensive care unit (ICU) admissions increased from 32.2% (96 245 of 298 535) in the prepandemic era to 36.7% (37 516 of 102 266) in the pandemic era (P < .001). The seasonality of bronchiolitis admissions changed during the pandemic era. Admissions peaked in August 2021 (actual 5036 vs 943 [95% CI, 0-2491] forecasted) and November 2022 (actual 10 120 vs 5268 [95% CI, 3425-7419] forecasted). These findings were unchanged in sensitivity analyses excluding children with complex chronic conditions and excluding repeat admissions. In a sensitivity analysis including all viral lower respiratory tract infections in children aged younger than 5 years, there were 66 767 admissions in 2022-2023 vs 35 623 (31 301-41 002) in the prepandemic era, with the largest increase in children aged 24 to 59 months. The findings of this cross-sectional study suggest that bronchiolitis hospitalizations decreased transiently and then increased markedly during the COVID-19 pandemic era. Patients admitted during the pandemic era were older and were more likely to be admitted to an ICU. These findings suggest that bronchiolitis seasonality has not yet returned to prepandemic patterns, and US hospitals should prepare for the possibility of atypical timing again in 2023.

The Incidence of Hematological Toxicities in Colorectal Cancer Patients Treated With Fluoropyrimidine-Based Regimens at Princess Noorah Oncology Center.

Background Fluoropyrimidine-based regimens are used for the management of colorectal cancer,which is the second most common cancer in Saudi Arabia. We aimed to study the incidence of hematological toxicities in colorectal cancer patients treated with fluoropyrimidineand fluoropyrimidine-based regimens at Princess Noorah Oncology Center, King Abdulaziz Medical City- Jeddah, Saudi Arabia. Methods A retrospective cohort study that included adult colorectal cancer patients who were treated with fluoropyrimidine-based regimens from January 1, 2018 to December 31, 2018 at Princess Noorah Oncology Center, Jeddah, Saudi Arabia was performed. Our primary objective was to determine the incidence of anemia, neutropenia, and thrombocytopenia in colorectal cancer patients treated with fluoropyrimidines and fluoropyrimidine-based regimens. Secondary objectives were to assess the grade of hematological toxicities associated with 5-fluorouracil (5-FU) use and to determine the frequency of unplanned hospital admissions or emergency department (ED) visits after receiving fluoropyrimidine-based regimens. The collected data contained patients' characteristics (weight, height, age, gender, and diagnosis), chemotherapy agents, and hematological toxicity-related findings such as absolute neutrophil count, hemoglobin, platelet count, and number of ED visits or hospital admissions during fluoropyrimidine-based chemotherapy regimens. Results Of the 570 cycles of the fluoropyrimidine-based regimen received by 68 patients, hematological toxicities were observed in 508 (89.1%) cycles, and grade ≥ 3 grade toxicities were found in 46 (8.1%) cycles. The results demonstrated a statistically significant difference in the incidence of grade 3-4 neutropenia between patients who received bolus administration of 5-FU and those who did not (8.5% vs. 2.3% respectively, p=0.025). The incidence of grade 3-4 anemia was higher in the bolus group (11.3%) compared to the group where bolus was omitted (4.6%); however, the difference was not statistically significant (p=0.059). Furthermore, there was no significant difference among the two groups for grade 3 and grade 4 thrombocytopenia (0.0% with bolus given and 0.7% with bolus omission p=1.00). Conclusion Our retrospective study showed that there have been significantly higher grade 3-4 hematological toxicities observed with bolus administration of 5-FU, which confirms the previous reports.