Risk Adjustment Factor Research Articles

Development of a standardized set of outcomes for autism spectrum disorder: The International Consortium for Health Outcomes Measurement (ICHOM)

BackgroundAutism spectrum disorder (ASD) is characterized by persistent deficits in reciprocal social interaction, communication, and restricted, repetitive, behaviour, that are atypical or excessive for the individual’s age and sociocultural context (Diagnostic and Statistical Manual 5th ed TR.; 2022). The rate of ASD continues to increase and remains a concern due to inequitable access to diagnosis and quality services. A lack of standardization in assessment and treatment presents further difficulty in benchmarking and improving care. MethodThe International Consortium for Health Outcomes Measurement (ICHOM) recruited a Working Group (WG) of 20 subject matter experts, including researchers, clinicians, policy makers, and lived-experience experts to identify a minimum set of measurable outcomes along with case mix variables (i.e., risk adjustment factors). A proposed set of Patient Centered Outcome Measures (henceforth known as the ASD Set) was identified and endorsed using a modified Delphi process to reach consensus. ResultsThe WG took into consideration variation in global health, social care and educational systems were considered to ensure implementation of the ASD Set is feasible. The Set includes a core set of measurable outcomes (i.e., domains) that include core symptoms, daily functioning, accessibility, and support. ConclusionsThe ICHOM ASD Set is a scientifically-sound, internationally vetted tool that will enable assessment of care for individuals with ASD that can facilitate targeted, person-centered clinical and social inclusion strategies. Large scale implementation and use should generate standardized data describing the individual's health and wellbeing that can inform decision-making, quality improvement and best-practices insights.

Unequal Health Risks and Attributable Mortality Burden of Source-Specific PM2.5 in China.

Anthropogenic emissions, originating from human activities, stand as the primary contributors to PM2.5, which is recognized as a global health threat. The disease burden associated with PM2.5 has been extensively documented. However, the prevailing estimations have predominantly relied on PM2.5 exposure-response functions, neglecting the distinct risks posed by PM2.5 from various sources. China has experienced a significant reduction in the PM2.5 concentration due to stringent emission controls. With diverse sources and abundant mortality data, this situation provides a unique opportunity to estimate short-term source-specific attributable mortality. Our approach involves an integrated unequal health risk-oriented modeling in China, incorporating a source-oriented Community Multiscale Air Quality model, an adjustment and downscaling method for exposure measurement, a generalized linear model with random-effects meta-analysis, and premature mortality estimation. Adhering to the unequal health risk concept, we calculated the attributable mortality of multiple PM2.5 sources by determining the source risk-adjusted factor. In this study, we observed varying excess risks associated with multiple PM2.5 sources, with transportation-related PM2.5 exhibiting the most substantial association. An interquartile range increase (7.65 μg/m3) was linked to a 1.98% higher daily nonaccidental mortality. Residential use- and transportation-related PM2.5 emerged as the two principal sources of premature mortality. In 2018, a remarkable 53,381 avoiding deaths were estimated compared to 2013, and over 67% of these were attributed to reductions in coal-dependent sources. Notably, transportation-related PM2.5 emerged as the largest contributor to premature mortality in 2018. This study underscores the significance of a new source-oriented health risk assessment to support actions aimed at reducing air pollution. It strongly advocates for heightened attention to PM2.5 reductions in the transportation sector in China.

Geopolitical risk exposure and stock returns: Evidence from China

This study explores the effect of geopolitical risk on stock returns in China. We identify a significant premium associated with low geopolitical risk exposure: stocks with lower geopolitical risk exposure outperform the ones with higher geopolitical risk exposure in the ensuing months. Our findings suggest that this premium is not attributable to risk-adjusted factors or recognized common pricing factors. Moreover, the robustness of this premium can be demonstrated by a range of alternative settings. Finally, we provide a potential explanation for the low risk exposure premium: geopolitical risk shocks lead to increased investor attention, making investors more inclined to purchase stocks with low risk exposure to hedge against geopolitical risk.

Life-cycle decisions and general equilibrium in the heterogeneous-agent OLG economy

ABSTRACT This study develops a life-cycle model to predict an overlapping generations economy with heterogeneous agents. In our proposed model, ex-ante heterogeneity includes the work probability, survival rate, subjective discount rate, and intertemporal elasticity of substitution. We observe differences in financial decisions and asset accumulation among heterogeneous agents and calculate equilibrium values of aggregate economic variables, such as consumption and capital. For the numerical analyses, we classify the population into four groups based on labour productivity and gender, and investigate the life-cycle behaviours of heterogeneous agents. Retirees in the male group with low labour productivity and low remaining life expectancy have the highest propensity to consume and the highest consumption elasticity for retirement shock compared to other retirees. Workers with relatively high labour productivity have lower risk adjustment factors, indicating that they are less concerned about retirement risk than less productive workers.

Expanding the Role of the Surgical Preoperative Evaluation Clinic: Impact on Risk and Quality Outcome Measures

ObjectiveTo prove that inpatient-adjusted surgical risk and quality outcome measures can be considerably impacted by interventions to improve documentation in the preoperative evaluation (POE) clinic. Patients and MethodsWe designed a quality improvement project with a multidisciplinary team in our POE clinic to more accurately reflect surgical risk and impact expected surgical quality outcomes through improved documentation. Interventions included an improved patient record acquisition process and extensive POE provider education regarding patient comorbidities’ documentation. For patients admitted after their planned operations, POE clinic comprehensive evaluation notes were linked to inpatient History and Physical notes. High complexity patients seen from October 1, 2018 to December 31, 2018 were the preintervention cohort, and the patients seen from January 1, 2019 to December 31, 2019 were the postintervention cohort. ResultsThe primary outcome measures included the total number of coded diagnoses per encounter and the number of coded hierarchical condition categories per encounter. The secondary outcomes included the calculated severity of illness, risk of mortality, case-mix index, and risk-adjustment factor. Postintervention results show statistically significant increases in all primary outcomes with a P<.05. All secondary outcome measures reported positive change. ConclusionOur interventions confirm that a comprehensive POE and thorough documentation provide a more accurate clinical depiction of the preoperative patient, which in turn impacts quality outcomes in inpatient surgical settings. These results are impactful for direct and indirect patient care and publicly reported hospital and provider level performance data.

Impact on outcomes of measuring lactates prior to ICU in unselected heterogeneous critically ill patients: A propensity score analysis.

Elevated blood lactate levels were reported as effective predictors of clinical outcome and mortality in ICU. However, there have been no studies simply comparing the timing of measuring lactates before vs. after ICU admission. A total of 19,226 patients with transfer time ≤ 24 hr were extracted from the Medical Information Mart for Intensive Care IV database (MIMIC-IV). After 1:1 propensity score matching, the patients were divided into two groups: measuring lactates within 3 hr before (BICU group, n = 4,755) and measuring lactate within 3 hr after ICU admission(AICU group, n = 4,755). The primary and secondary outcomes were hospital mortality, hospital 28-day mortality, ICU mortality, ICU length of stay (LOS), hospital LOS, and restricted mean survival time (RMST). Hospital, hospital 28-day, and ICU mortality were significantly higher in AICU group (7.0% vs.9.8%, 6.7% vs. 9.4%, and 4.6% vs.6.7%, respectively, p<0.001 for all) Hospital LOS and ICU LOS were significantly longer in AICU group (8.4 days vs. 9.0 days and 3.0 days vs. 3.5 days, respectively, p<0.001 for both). After adjustment for predefined covariates, a significant association between the timing of measuring lactate and hospital mortality was observed in inverse probability treatment weight (IPTW) multivariate regression, doubly robust multivariate regression, and multivariate regression models (OR, 0.96 [95%CI, 0.95-0.97], OR 0.52 [95%CI, 0.46-0.60], OR 0.66 [95%CI, 0.56-0.78], respectively, p<0.001 for all), indicating the timing as a significant risk-adjusted factor for lower hospital mortality. The difference (BICU-AICU) of RMST at 28- days after ICU admission was 0.531 days (95%CI, 0.002-1.059, p<0.05). Placement of A-line and PA-catheter, administration of intravenous antibiotics, and bolus fluid infusion during the first 24-hr in ICU were significantly more frequent and faster in the BICU vs AICU group (67.6% vs. 51.3% and 126min vs.197min for A-line, 19.6% vs.13.2% and 182min vs. 274min for PA-catheter, 77.5% vs.67.6% and 109min vs.168min for antibiotics, and 57.6% vs.51.6% and 224min vs.278min for bolus fluid infusion, respectively, p<0.001 for all). Additionally, a significant indirect effect was observed in frequency (0.19879 [95% CI, 0.14061-0.25697] p<0.001) and time (0.07714 [95% CI, 0.22600-0.13168], p<0.01) of A-line replacement, frequency of placement of PA-catheter (0.05614 [95% CI, 0.04088-0.07140], p<0.001) and frequency of bolus fluid infusion (0.02193 [95%CI, 0.00303-0.04083], p<0.05). Measuring lactates within 3 hr prior to ICU might be associated with lower hospital mortality in unselected heterogeneous critically ill patients with transfer time to ICU ≤ 24hr, presumably due to more frequent and faster therapeutic interventions.

Selectivity and Market-Timing Performance of Mutual Funds Using Human-Capital Augmented Various Asset-Pricing Models: Evidence from Pakistan

This study demonstrates the selectivity skills and market-timing ability performance of funds mangers through various sophisticated augmented asset pricing models. The results indicate better performance for selectivity and market-timing ability of fund managers but unexpectedly after adjusting size and value factors into the specifications, both demonstrate insignificant findings. It also investigates the nexus between various risk-adjusted factors including market, size, value, momentum and human-capital sixth-factor, with the excess portfolio mutual funds returns using sample of 120 open-ended mutual funds monthly data for the time span of 16 years 2005-2020. The simple and multivariate time-series OLS regression approaches are employed to investigate the nexus between various factors and mutual funds returns. The findings of the study evidenced statistically significant nexus of market, size and value factors with mutual funds returns while insignificant results for human-capital and momentum factors. Moreover, based on GRS test, the average absolute alpha of Income-Growth augmented CAPM is closer to zero therefore, assumed to be the best model among the risk-adjusted completing asset pricing models in mutual funds.

Risk Aware Minimum Principle for Optimal Control of Stochastic Differential Equations

We present a probabilistic formulation of risk aware optimal control problems for stochastic differential equations. Risk awareness is in our framework captured by objective functions in which the risk neutral expectation is replaced by a risk function, a nonlinear functional of random variables that accounts for the controller's risk preferences. We state and prove a risk aware minimum principle that gives necessary and sufficient conditions for optimality of generalized control processes taking values on probability measures defined on a given action space. We show that going from the risk neutral to the risk aware case, the minimum principle is modified by the introduction of one additional real-valued stochastic process that acts as a risk adjustment factor. This adjustment process is explicitly given as the expectation, conditional on the filtration at the given time, of an appropriately defined functional derivative of the risk function evaluated at the random total cost. The control model we employ differs from standard relaxed controls, and we elaborate on the differences, and benefits and drawbacks, of the control types; we further give conditions under which the generalized control can be realized using a strict control process. We present an application of the results for a portfolio allocation problem and show that the risk awareness of the objective function gives rise to a risk premium term that is characterized by the risk adjustment process described above. This suggests uses of our results in e.g. pricing of risk modeled by generic risk functions in financial applications.

Risk Perceptions and Public Acceptance of Autonomous Vehicles: A Comparative Study in Japan and Israel

Autonomous vehicles (AVs) are rapidly transforming the automotive industry due to rising consumer interest in these vehicles worldwide. However, few studies have compared different countries in terms of public acceptance of AVs. This study compares public acceptance of AVs as a function of risk perceptions in two countries leading the AV industry—Japan and Israel. We set our study within the risk-as-feelings framework. In contrast to “risk as analysis,” which invokes factual reasoning to bear on risk assessment and decision making, “risk as feelings” takes affective cues such as the sense of dread and unfamiliarity into judgments of risk. To this end, we conducted two web-based surveys in Japan in 2017 and Israel in 2021. In a between-subjects design, we manipulated introductory video information to portray various combinations of risk factors commonly associated with AVs: system errors, external interferences with car controls (e.g., hacking), and the inability of the AV to cope with unexpected events. Next, participants were surveyed about how they perceive the risks of AVs and other well-known technologies and activities. Results showed that acceptable risk, perceived risk, and perceived benefit of AVs were all generally higher in Israel than in Japan. The opposite pattern was found for a “risk adjustment factor,” suggesting that the Japanese seek more safety before acceptance than Israelis. Furthermore, we conducted a factor analysis on seven risk dimensions, resulting in a two-factor model of dread and unfamiliarity. Cognitive mapping of AVs and other technologies and activities in the two-factor plane revealed that the AV technologies we studied (i.e., AV-car levels 3 and 4; AV-bus levels 3 and 4) have high unfamiliarity risk but moderate dread risk compared to technologies and activities such as smoking, flying, and handguns. After exposure to video-based educational content, unfamiliarity risk was less influential but dread risk—in particular, related to human-made risks—became more influential. The results indicated that manufacturers and policymakers should emphasize mitigating human-made risks instead of focusing on improving public familiarity with AVs to garner trust and improve public acceptance of the technology.

Validation of a deep learning, value-based care model to predict mortality and comorbidities from chest radiographs in COVID-19

We validate a deep learning model predicting comorbidities from frontal chest radiographs (CXRs) in patients with coronavirus disease 2019 (COVID-19) and compare the model's performance with hierarchical condition category (HCC) and mortality outcomes in COVID-19. The model was trained and tested on 14,121 ambulatory frontal CXRs from 2010 to 2019 at a single institution, modeling select comorbidities using the value-based Medicare Advantage HCC Risk Adjustment Model. Sex, age, HCC codes, and risk adjustment factor (RAF) score were used. The model was validated on frontal CXRs from 413 ambulatory patients with COVID-19 (internal cohort) and on initial frontal CXRs from 487 COVID-19 hospitalized patients (external cohort). The discriminatory ability of the model was assessed using receiver operating characteristic (ROC) curves compared to the HCC data from electronic health records, and predicted age and RAF score were compared using correlation coefficient and absolute mean error. The model predictions were used as covariables in logistic regression models to evaluate the prediction of mortality in the external cohort. Predicted comorbidities from frontal CXRs, including diabetes with chronic complications, obesity, congestive heart failure, arrhythmias, vascular disease, and chronic obstructive pulmonary disease, had a total area under ROC curve (AUC) of 0.85 (95% CI: 0.85-0.86). The ROC AUC of predicted mortality for the model was 0.84 (95% CI,0.79-0.88) for the combined cohorts. This model using only frontal CXRs predicted select comorbidities and RAF score in both internal ambulatory and external hospitalized COVID-19 cohorts and was discriminatory of mortality, supporting its potential use in clinical decision making.

How Long-Term Care Quality Assurance Measures Address Dementia in Australia, England, Japan, and the United States

Abstract The prevalence of people with dementia living in long-term care (LTC) is high and rising internationally, and the need to improve LTC for people with dementia is widely recognized. In some countries, LTC quality assurance programs use quantitative measures of LTC quality, and international bodies emphasize the importance of person-centered care and healthy ageing outcomes. To better understand how LTC quality assurance programs address dementia, programs were reviewed in four countries—Australia, England, Japan, and the United States. Quality measures from each program were identified (n = 38) and examined to determine how they address dementia. Most measures did not address dementia, but four risk-adjusted for dementia (antipsychotic use, fractures, falls, mobility), one was dementia-specific (dementia/delirium hospitalizations), and one excluded people with dementia (losing bowel/bladder control). The other 32 measures were calculated equally regardless of the prevalence of dementia among LTC residents. Overall, LTC quality measurement differs internationally, but few measures address dementia. When dementia is addressed in quality measure calculations, it is most often as a risk-adjustor. Risk adjustment can help with attributing performance on these measures to the LTC setting rather than to the types of residents that the setting serves, but risk adjustment factors also are highly amenable to fraud, and thus require ongoing monitoring. Although LTC quality assessment programs and measures can help ensure people with dementia have access to quality LTC, adoption of measures that are meaningful to people with dementia—including measures of person-centered care and healthy ageing outcomes—remains needed.

Unmanaged Pharmacogenomic and Drug Interaction Risk Associations with Hospital Length of Stay among Medicare Advantage Members with COVID-19: A Retrospective Cohort Study.

Unmanaged pharmacogenomic and drug interaction risk can lengthen hospitalization and may have influenced the severe health outcomes seen in some COVID-19 patients. To determine if unmanaged pharmacogenomic and drug interaction risks were associated with longer lengths of stay (LOS) among patients hospitalized with COVID-19, we retrospectively reviewed medical and pharmacy claims from 6025 Medicare Advantage members hospitalized with COVID-19. Patients with a moderate or high pharmacogenetic interaction probability (PIP), which indicates the likelihood that testing would identify one or more clinically actionable gene–drug or gene–drug–drug interactions, were hospitalized for 9% (CI: 4–15%; p < 0.001) and 16% longer (CI: 8–24%; p < 0.001), respectively, compared to those with low PIP. Risk adjustment factor (RAF) score, a commonly used measure of disease burden, was not associated with LOS. High PIP was significantly associated with 12–22% longer LOS compared to low PIP in patients with hypertension, hyperlipidemia, diabetes, or chronic obstructive pulmonary disease (COPD). A greater drug–drug interaction risk was associated with 10% longer LOS among patients with two or three chronic conditions. Thus, unmanaged pharmacogenomic risk was associated with longer LOS in these patients and managing this risk has the potential to reduce LOS in severely ill patients, especially those with chronic conditions.

Developing an International Standard Set of Patient-Reported Outcome Measures for Psychotic Disorders.

The objective of this project was to develop a set of patient-reported outcome measures for adolescents and adults who meet criteria for a psychotic disorder. A research team and an international consensus working group, including service users, clinicians, and researchers, worked together in an iterative process by using a modified Delphi consensus technique that included videoconferencing calls, online surveys, and focus groups. The research team conducted systematic literature searches to identify outcomes, outcome measures, and risk adjustment factors. After identifying outcomes important to service users, the consensus working group selected outcome measures, risk adjustment factors, and the final set of outcome measures. International stakeholder groups consisting of >100 professionals and service users reviewed and commented on the final set. The consensus working group identified four outcome domains: symptoms, recovery, functioning, and treatment. The domains encompassed 14 outcomes of importance to service users. The research team identified 131 measures from the literature. The consensus working group selected nine measures in an outcome set that takes approximately 35 minutes to complete. A set of patient-reported outcome measures for use in routine clinical practice was identified. The set is free to service users, is available in at least two languages, and reflects outcomes important to users. Clinicians can use the set to improve clinical decision making, and administrators and researchers can use it to learn from comparing program outcomes.

Forecasting project duration using risk-based earned duration management

Earned duration management (EDM) is an extension of earned value management (EVM) in which the schedule performance of the project is evaluated using time-based data with no dependency on project cost data. While the use of EDM leads to a more accurate and realistic performance evaluation of the project schedule, it, however, does not differentiate between critical and non-critical activities and their related risk factors. This can lead to misleading information in cases where critical activities are behind schedule. Accordingly, this paper presents a new risk-based earned duration management model (RBEDM) for monitoring and estimating schedule performance of projects considering critical activities only and their associated risk factors. The contribution of this research to the body of knowledge lies in its newly introduced risk adjustment factor (RAFcr) which quantifies the impact of future uncertainties associated with critical activities in estimating project duration at completion. The introduced risk adjustment factor addresses the main drawback of EDM for its reliance only in past performance data. A numerical example from the literature is utilized to highlight the limitations of the existing models and demonstrate the capability of the developed model in monitoring and estimating a more accurate and realistic schedule performance.

A Systematic Review of the National Breast Implant Registry for Application in Korea: Can We Predict "Unpredictable" Complications?

Background and Objectives: Since silicone breast implants were introduced to the market several decades ago, the safety of breast implants has remained controversial. Recently, several studies have explored breast implant-associated anaplastic large-cell lymphoma (BIA-ALCL) and breast implant illness (BII). Several countries have developed national breast implant registries to improve the safety and quality of breast implant surgery. We performed a systematic review of the current status of national breast implant registries and propose a pilot form of an appropriate breast implant registry model for Korea. Materials and Methods: The systematic review was conducted in accordance with the “preferred reporting items for systematic reviews and meta-analyses (PRISMA) pro forma”. PubMed and Google Scholar databases were searched to identify all articles containing information on national breast implant registries. We limited the search to articles written in the English language from 2010 to 2020. Articles were reviewed by two independent authors. Results: A total of 63 articles related to national breast implant registries, registry principles and national breast implant registry annual reports were identified. After reviewing the literature, 25 national breast implant registry-related articles were included in the full-text synthesis. Currently, four countries, The Netherlands, Australia, Sweden, and the UK, have breast implant registries with well-formed sources for big data. Overall, similarities in data points were detected for three categories: implant-related complications, operation details, and device information. However, there were differences for each registry in terms of governance, funding, and capture rate. Conclusion: After reviewing other countries’ experiences, tentative datasets for the Korean Breast Implant Registry (K-BIR) were developed. The K-BIR can improve the quality of breast implant surgery in Korea by providing datasets on overall processes and outcome measures with quality indicators and risk adjustment factors. This approach will register characteristics of patients and monitor breast implants, complications, and surgical procedures to improve the outcomes of breast implant surgery in Korea. In addition, it can be used as a track-and-trace system with automated notifications to patients in the event of a product recall or other safety concerns related to a specific type of implant.Background and Objectives: Since silicone breast implants were introduced to the market several decades ago, the safety of breast implants has remained controversial. Recently, several studies have explored breast implant-associated anaplastic large-cell lymphoma (BIA-ALCL) and breast implant illness (BII). Several countries have developed national breast implant registries to improve the safety and quality of breast implant surgery. We performed a systematic review of the current status of national breast implant registries and propose a pilot form of an appropriate breast implant registry model for Korea. Materials and Methods: The systematic review was conducted in accordance with the “preferred reporting items for systematic reviews and meta-analyses (PRISMA) pro forma”. PubMed and Google Scholar databases were searched to identify all articles containing information on national breast implant registries. We limited the search to articles written in the English language from 2010 to 2020. Articles were reviewed by two independent authors. Results: A total of 63 articles related to national breast implant registries, registry principles and national breast implant registry annual reports were identified. After reviewing the literature, 25 national breast implant registry-related articles were included in the full-text synthesis. Currently, four countries, The Netherlands, Australia, Sweden, and the UK, have breast implant registries with well-formed sources for big data. Overall, similarities in data points were detected for three categories: implant-related complications, operation details, and device information. However, there were differences for each registry in terms of governance, funding, and capture rate. Conclusion: After reviewing other countries’ experiences, tentative datasets for the Korean Breast Implant Registry (K-BIR) were developed. The K-BIR can improve the quality of breast implant surgery in Korea by providing datasets on overall processes and outcome measures with quality indicators and risk adjustment factors. This approach will register characteristics of patients and monitor breast implants, complications, and surgical procedures to improve the outcomes of breast implant surgery in Korea. In addition, it can be used as a track-and-trace system with automated notifications to patients in the event of a product recall or other safety concerns related to a specific type of implant.

Assignment of pre-event ASA physical status classification by pre-hospital physicians: a prospective inter-rater reliability study

BackgroundIndividualized treatment is a common principle in hospitals. Treatment decisions are made based on the patient’s condition, including comorbidities. This principle is equally relevant out-of-hospital. Furthermore, comorbidity is an important risk-adjustment factor when evaluating pre-hospital interventions and may aid therapeutic decisions and triage. The American Society of Anesthesiologists Physical Status (ASA-PS) classification system is included in templates for reporting data in physician-staffed pre-hospital emergency medical services (p-EMS) but whether an adequate full pre-event ASA-PS can be assessed by pre-hospital physicians remains unknown. We aimed to explore whether pre-hospital physicians can score an adequate pre-event ASA-PS with the information available on-scene.MethodsThe study was an inter-rater reliability study consisting of two steps. Pre-event ASA-PS scores made by pre- and in-hospital physicians were compared. Pre-hospital physicians did not have access to patient records and scores were based on information obtainable on-scene. In-hospital physicians used the complete patient record (Step 1). To assess inter-rater reliability between pre- and in-hospital physicians when given equal amounts of information, pre-hospital physicians also assigned pre-event ASA-PS for 20 of the included patients by using the complete patient records (Step 2). Inter-rater reliability was analyzed using quadratic weighted Cohen’s kappa (κw).ResultsFor most scores (82%) inter-rater reliability between pre-and in-hospital physicians were moderate to substantial (κw 0,47-0,89). Inter-rater reliability was higher among the in-hospital physicians (κw 0,77 to 0.85). When all physicians had access to the same information, κw increased (κw 0,65 to 0,93).ConclusionsPre-hospital physicians can score an adequate pre-event ASA-PS on-scene for most patients. To further increase inter-rater reliability, we recommend access to the full patient journal on-scene. We recommend application of the full ASA-PS classification system for reporting of comorbidity in p-EMS.

Multi-objective optimization of crude oil-supply portfolio based on interval prediction data

The optimization of crude oil-supply portfolio is a hot research issue in energy security, which is closely related to the implementation of national strategy and development of economy. Forecasting the demand of crude oil is the basis for portfolio optimization. Therefore, this paper innovatively introduces the decomposition hybrid interval prediction method and proposes a multi-objective programming model in order to provide decision-making support for the formulation of crude oil-supply portfolio scheme. Under the constraints of volume, price and risk, the minimum cost and risk of importing crude oil are achieved. Furthermore, by introducing optimization parameters and risk preference factors, and setting different scenarios for numerical simulation, the results show that (1) decomposition hybrid prediction methods perform better than single prediction methods. (2) As the optimization parameter increases, costs and risks are significantly decreased. Decision-makers can set large parameters to achieve significant optimization of the objective function. (3) The total cost of imported crude oil fluctuates sharply, while the total risk decreases with the increase of risk preference factors under the different scenarios. (4) The fluctuation of price and risk adjustment factors will cause the change of oil-supply portfolio optimization scheme.

PNS408 HOW IS THE ENGLISH NHS PRESCRIPTION DRUGS BUDGET SPENT

In Financial Year (FY) 2018/19, NHS England (NHSE) spent £7.9 billion on prescription drugs prescribed by GPs to their patients. This is 2.7% less than FY 2017/18. This study investigated drivers of this trend at regional and drug-class levels on a risk-adjusted basis and projected the results over a five year time period. Publicly available data was used. Prescription drug and population data published by the NHS were used to derive the per person per month costs. The prescription drug risk-adjustment factors were based on those used in the NHS funding allocation formula. Prescription drug cost and activity trends were analysed at a drug-class (BNF chapter) and regional level. Regions were defined by sustainability and transformation plan (STP) area. Risk-adjustment helped determine what cost we would ‘expect’ based on each STP’s risk profile compared to the English average. Costs were projected over a five-year period under various scenarios, using expected changes in population size and structure published by the NHS. The reduction in expenditure is driven by a combination of lower average costs (3.83% decrease) and lower levels of activity (0.92% decrease). These decreases in total cost are slightly offset by the 1.19% increase in the English population size. The research highlights particular BNF chapters and STP regions that are driving these trends as well as how STP regions’ experience compares to the English average on a risk-adjusted basis. The projection model shows that prescription drug costs can be expected to decrease by 3.6% p.a. over the next five years if historical trends persist. The insights provided by this research can help stakeholders with experience analysis and planning by identifying cost and activity drivers on a population risk-adjusted basis as well as having a view of how demand may develop over the projection period.

1019. Defining electronic patient phenotypes to inform risk-adjustment strategies in hospital antimicrobial use comparisons

BackgroundComparison of antimicrobial use (AU) rates among hospitals can identify areas to intervene for antimicrobial stewardship. Hospital AU interpretation is difficult without risk-adjustment for patient mix. Identifying high- or low-risk patient characteristics, or “electronic phenotypes,” for receipt of antimicrobials using data from electronic health records (EHR) could help define risk-adjustment factors AU comparisons.MethodsWe performed a retrospective study of EHR-derived data from adult and pediatric inpatients within the Duke University Health System from October 2015 to September 2017. Encounters were included if the patient spent time in an inpatient location. The analysis aimed to identify subpopulations that were high- or low-risk for antimicrobial exposure based on EHR data summarized on the encounter level. Antimicrobial days of therapy (DOT) and days present, representing the length of stay (LOS), were defined as in the 2018 NHSN AU Option. Location exposures were defined in binary variables if patients were housed at least 1 day on a hospital unit type. We compared antimicrobial-exposed to unexposed patients as well as DOT among various factors including demographics, location, nonantimicrobial medications, labs, ICD-10 codes, and diagnosis-related groups (DRG).ResultsThe EHR-derived dataset included 170,294 encounters and 204 variables in one academic and two community hospitals; 80,192 (47%) received at least one antimicrobial. Distributions of both LOS and DOT were zero-inflated and skewed by long outliers (figure). Encounters with >=7 DOT made up 63% of total DOT, but only 9% of inpatient encounters. Electronic phenotypes with highest DOT included those with long lengths of stay, older age, exposures to stem cell transplant, pulmonary, and critical care units, and DRG that included transplant, respiratory, or infectious diagnoses. Zero DOT phenotypes included those with short lengths of stay, exposure to labor and delivery wards, medical wards, and DRG that included birth and pregnancy.ConclusionFuture work in defining risk-adjustment factors for hospital AU data comparisons should determine if factors associated with low- or high-risk electronic phenotypes assist in prediction of antibiotic use. Disclosures All authors: No reported disclosures.

Risk-adjusted design basis earthquake: a case study of Tehran megacity

The basic goal of earthquake resistant building design codes is to provide life safety in the event of extreme earthquake loading and to reduce the seismic risk in the long run. In the most building codes including Iranian building code (Standard No. 2800), design basis earthquake is defined as an earthquake with 10% probability of exceedance (PE) in 50 years. Such an approach leads to buildings with unknown reliability, meaning that the probability of exceedance from the specified limit states in the service lifetime is not known. Furthermore, the regions with the same mapped peak ground acceleration (PGADBE) and different hazard curves, may have different seismic risks. Therefore, life safety is not uniformly provided across the entire region. Here, an attempt is made to estimate PGADBEs based on the uniform collapse risk approach for Tehran megacity. The building capacity and PGA hazard curve for 354 grids in Tehran are computed. Then, the risk-adjusted design PGA at different grid points are calculated by optimizing risk integral using the acceptance criteria of 1% in 50 years for ordinary usage buildings (risk category II). The computed risk-adjusted design PGAs vary from 0.33 g in the east to about 0.45 g toward west, south, and north. Meanwhile, the proposed mapped PGADBE in standard 2800 is a constant value of 0.35 g for the whole region. To preserve a uniform collapse risk, risk-adjustment factors to modify the mapped PGADBE are introduced. Furthermore, the maximum considered earthquake (MCE) required to evaluate the acceptance criteria is found to be in the range of 1.5% to 2.5% PE in 50 years. The PGAs with 2% PE in 50 years provide nearly uniform acceptable probability of collapse for buildings classified in risk category II. In addition, it is found that the buildings’ seismic importance factor (I) also needs to be revised from 1.2 to 1.3 for seismic risk category III, and from 1.4 to 1.6 for seismic risk category IV to preserve the 0.5% and 0.25% collapse risk in 50 years, respectively. The outcomes also revealed that the sensitivity analysis with regard to the uncertainty of buildings’ collapse fragility curve can be ignored for buildings in risk categories I and II, while it needs to be performed for buildings in risk categories III, and especially IV. Finally, the estimated PGA hazard curves of the Tehran megacity are approximated by the cubic functions to expedite the future risk assessment studies in Tehran megacity.