A framework for future national pediatric pandemic respiratory disease severity triage: The HHS pediatric COVID-19 data challenge.

Introduction: With persistent incidence, incomplete vaccination rates, confounding respiratory illnesses, and few therapeutic interventions available, COVID-19 continues to be a burden on the pediatric population. During a surge, it is difficult for hospitals to direct limited healthcare resources effectively. While the overwhelming majority of pediatric infections are mild, there have been life-threatening exceptions that illuminated the need to proactively identify pediatric patients at risk of severe COVID-19 and other respiratory infectious diseases. However, a nationwide capability for developing validated computational tools to identify pediatric patients at risk using real-world data does not exist. Methods: HHS ASPR BARDA sought, through the power of competition in a challenge, to create computational models to address two clinically important questions using the National COVID Cohort Collaborative: (1) Of pediatric patients who test positive for COVID-19 in an outpatient setting, who are at risk for hospitalization? (2) Of pediatric patients who test positive for COVID-19 and are hospitalized, who are at risk for needing mechanical ventilation or cardiovascular interventions? Results: This challenge was the first, multi-agency, coordinated computational challenge carried out by the federal government as a response to a public health emergency. Fifty-five computational models were evaluated across both tasks and two winners and three honorable mentions were selected. Conclusion: This challenge serves as a framework for how the government, research communities, and large data repositories can be brought together to source solutions when resources are strapped during a pandemic.

Quantifying the competitiveness of the electronic health record market and its implications for interoperability.

OBJECTIVE: The objective of this study was to quantify both the competitiveness of the EHR vendor market in the United States of America (US) and the degree of fragmentation of individual Medicare beneficiaries' medical records across the differing EHR vendors found in the US healthcare system. METHODS AND MATERIALS: We determined the Part A and Part B Medicare-expenditure weighted market shares of EHR vendors and estimated the rate of attestation of meaningful use (MU) for EHRs among Medicare Part A & B providers from 2011 to 2016. Based on these data we calculated the annual Herfindahl-Hirschman Index to quantify the competitiveness of the EHR market as well as the number of vendors individual Medicare beneficiaries' medical records were stored in for the period 2014-2016. RESULTS: We find that as of 2016 the EHR vendor environment was competitive but trending towards becoming highly concentrated soon. We also found that patient medical records were highly fragmented as only 4.5 % of expenditure-weighted individual Medicare beneficiaries had their MU medical records associated with a single vendor, while 19.8 % of expenditure-weighted beneficiaries had their MU medical records stored in 8 or more vendors. DISCUSSION: These results indicate that there are tradeoffs between EHR market competition, and the challenges associated with achieving interoperability across numerous competing vendors. CONCLUSION: Uncertainty of interoperability among different EHR vendors may make transmission of medical records among different providers challenging, mitigating the benefit of vendor competition. This highlights the critical importance of current interoperability efforts moving forward.

Societal willingness to pay to avoid mortality and morbidity from Clostridioides difficile and carbapenem-resistant Enterobacteriaceae infections in the United States.

BACKGROUND: Clostridioides difficile infection (CDI) is among the most common health care-associated infections in the United States and is increasingly affecting the elderly. Although carbapenem-resistant Enterobacteriaceae (CRE) infections are still relatively uncommon, there are reported increases in the rate of infection for certain strains, such as Klebsiella pneumoniae. This study examines the burden of mortality and morbidity for CDI and CRE infections in the United States and estimates the societal willingness to pay to avoid them. METHODS: We use an analytic model to estimate the number of incident cases and associated health outcomes for CDI and CRE infections. RESULTS: The number of CDI and CRE infection incident cases in the United States in 2016, is estimated at 468,567 and 9,620, respectively. These infections result in a total of 17,630 estimated deaths and 8,624 lost quality-adjusted life years among patients who survive per year. CONCLUSIONS: Given the significant mortality and morbidity from these infections, the estimated societal willingness to pay to avoid them is high at $176.7 billion per year, of which 93.9% ($166.0 billion) is for CDI. Our estimates far exceed the medical care costs for CDIs and CRE infections reported in the literature despite not capturing the additional costs borne by third-party payers. As incident cases increase or resistant strains develop, the societal willingness to pay is also expected to increase.

Promoting Antibacterial Drug Development: Select Policies and Challenges.

BACKGROUND: The development pipeline for antibacterial drugs has not met the demand of hospitals and healthcare providers struggling to cope with increasing problems of antibacterial resistance. Although the challenges associated with antibacterial drug development have been known for some time, previous efforts to address them have not been sufficient. There remains an urgent need for targeted incentives to foster antibacterial drug development while encouraging prudent use. OBJECTIVE: We examine the effects of two types of incentives, a 5-year delay in competition from generics and a lump-sum US$50 million prize payment upon successful US Food and Drug Administration approval, on antibacterial drug company returns. METHODS: We use the decision-tree framework developed in a study for the US Department of Health and Human Services, which models the drug company's decision process as a revenue maximizer under uncertainty. RESULTS: Our results show that, to maximize societal benefit, such incentives need to take into consideration the indication(s) the new antibacterial drug is designed to treat as well as the drug development stage. CONCLUSIONS: Optimal policies should maximize the difference between societal benefit, primarily measured as the reduction in public health burden from the development of a new antibacterial drug that treats an infectious disease while ensuring prudent use, and social cost. Here, we show that the two types of policies examined under-incentivize early-stage developers (i.e., do not achieve the desired outcome) and over-incentivize late-stage developers (i.e., achieve the desired outcome but at a cost that is higher than needed) ceteris paribus.

Key cost drivers of pharmaceutical clinical trials in the United States.

BACKGROUND: The increasing cost of clinical research has significant implications for public health, as it affects drug companies' willingness to undertake clinical trials, which in turn limits patient access to novel treatments. Thus, gaining a better understanding of the key cost drivers of clinical research in the United States is important. PURPOSE: The study which is based on a report prepared by Eastern Research Group, Inc., for the US Department of Health and Human Services, examined different factors, such as therapeutic area, patient recruitment, administrative staff, and clinical procedure expenditures, and their contribution to pharmaceutical clinical trial costs in the United States by clinical trial phase. METHODS: The study used aggregate data from three proprietary databases on clinical trial costs provided by Medidata Solutions. We evaluated per-study costs across therapeutic areas by aggregating detailed (per patient and per site) cost information. We also compared average expenditures on cost drivers with the use of weighted mean and standard deviation statistics. RESULTS: Therapeutic area was an important determinant of clinical trial costs by phase. The average cost of a Phase 1 study conducted at a US site ranged from US$1.4 million (pain and anesthesia) to US$6.6 million (immunomodulation), including estimated site overhead and monitoring costs of the sponsoring organization. A Phase 2 study cost from US$7.0 million (cardiovascular) to US$19.6 million (hematology), whereas a Phase 3 study cost ranged from US$11.5 million (dermatology) to US$52.9 (pain and anesthesia) on average. Across all study phases and excluding estimated site overhead costs and costs for sponsors to monitor the study, the top three cost drivers of clinical trial expenditures were clinical procedure costs (15%-22% of total), administrative staff costs (11%-29% of total), and site monitoring costs (9%-14% of total). LIMITATIONS: The data were from 2004 through 2012 and were not adjusted for inflation. Additionally, the databases used represented a convenience, that is, non-probability, sample and did not allow for statistically valid estimates of cost drivers. Finally, the data were from trials funded by the global pharmaceutical and biotechnology industry only. Hence, our study findings are limited to that segment. CONCLUSION: Therapeutic area being studied as well as number and types of clinical procedures involved were the key drivers of direct costs in Phase 1 through Phase 3 studies. Research shows that strategies exist for reducing the price tag of some of these major direct cost components. Therefore, to increase clinical trial efficiency and reduce costs, gaining a better understanding of the key direct cost drivers is an important step.

The effect of Surgical Care Improvement Project (SCIP) compliance on surgical site infections (SSI).

BACKGROUND: The Surgical Care Improvement Project (SCIP) has developed a set of process compliance measures in an attempt to reduce the incidence of surgical site infections (SSIs). Previous research has been inconclusive on whether compliance with these measures is associated with lower SSI rates. OBJECTIVES: To determine whether hospitals with higher levels of compliance with SCIP measures have lower incidence of SSIs and to identify the measures that are most likely to drive this association. DATA AND METHODS: Analysis of linked SCIP compliance rates and SSIs on 295 hospital groups observed annually over the study period 2007-2010. A hospital group comprises all hospitals sharing identical categories for location by state, teaching status, bed size, and urban/rural location. We used a generalized linear model regression with logistic link and binomial family to estimate the association between 3 SCIP measures and SSI rates. RESULTS: Hospital groups with higher compliance rates had significantly lower SSI rates for 2 SCIP measures: antibiotic timing and appropriate antibiotic selection. For a hospital group of median characteristics, a 10% improvement in the measure provision of antibiotic 1 hour before intervention led to a 5.3% decrease in the SSI rates (P<0.05). Rural hospitals had effect sizes several times larger than urban hospitals (P<0.05). A third-core measure, Timely Antibiotic Stop, showed no robust association. CONCLUSIONS: This analysis supports a clinically and statistically meaningful relationship between adherence to 2 SCIP measures and SSI rates, supporting the validity of the 2 publicly available healthcare-associated infection metrics.

Comparative effectiveness research in practice: the Drug Effectiveness Review Project experience.

AIM: Assess the effect of the Drug Effectiveness Review Project's comparative effectiveness research findings on prescribing behavior independently and in conjunction with a Medicaid preferred drug list. METHOD: We queried prescription drug claims and enrollment information from the 2001-2008 Medicaid Analytic eXtract and Medicaid Statistical Information System for 17 states using a Wilcoxon signed rank test design to evaluate the effects of the Drug Effectiveness Review Project's report release and preferred drug list implementation on ACE inhibitor prescribing behavior at a state level. The primary outcome of interest was the percentage of ACE inhibitor prescriptions that are defined as 'differentiated' based on the content of the Drug Effectiveness Research Program report. RESULTS: The use of differentiated ACE inhibitors increased significantly in states that participated in the Drug Effectiveness Research Program and subsequently implemented a preferred drug list (p < 0.05, one-tailed). However, there was no significant change in utilization in nonparticipating states or in states that participated but did not subsequently implement a preferred drug list. CONCLUSION: Although the publication of comparative effectiveness research findings may not directly influence practice, a preferred drug list can align utilization with clinical evidence. The states that participate in the Drug Effectiveness Review Project and use preferred drug lists have greater utilization of higher quality drugs, making the combination an effective strategy to translate comparative effectiveness research into practice.

Temporal variation in patterns of comorbidities in the medicare population.

It is widely accepted that Medicare beneficiaries with multiple comorbidities (ie, patients with combinations of more than 1 disease) account for a disproportionate amount of mortality and expenditures. The authors previously studied this phenomenon by analyzing Medicare claims data from 2008 to determine the pattern of disease combinations (DCs) for 32,220,634 beneficiaries. Their findings indicated that 22% of these individuals mapped to a long-tailed distribution of approximately 1 million DCs. The presence of so many DCs, each populated by a small number of individuals, raises the possibility that the DC distribution varies over time. Measuring this variability is important because it indicates the rate at which the health care system must adapt to the needs of new patients. This article analyzes Medicare claims data for 3 consecutive calendar years, using 2 algorithms based on the Centers for Medicare & Medicaid Services (CMS)-Hierarchical Conditions Categories (HCC) claims model. These algorithms make different assumptions regarding the degree to which the CMS-HCC model could be disaggregated into its underlying International Classification of Diseases, Ninth Revision, Clinical Modification codes. The authors find that, although a large number of beneficiaries belong to a set of DCs that are nationally stable across the 3 study years, the number of DCs in this set is large (in the range of several hundred thousand). Furthermore, the small number of beneficiaries associated with the larger number of variable DCs (ie, DCs that were not constantly populated in all 3 study years) represents a disproportionally high level of expenditures and death.

The complexity of disease combinations in the Medicare population.

Developing systems of care that address the mortality, morbidity, and expenditures associated with Medicare beneficiaries with multiple diseases would benefit from a greater understanding of the complexity of disease combinations (DCs) found in the Medicare population. To develop estimates of the number of DCs, we performed an observational analysis on 32,220,634 beneficiaries in the Medicare Fee-for-Service claims database based on a set of records containing each beneficiary's Part A and B International Classification of Diseases, 9(th) Revision, Clinical Modification (ICD-9-CM) claims data for the year of 2008. We made 2 simplifying adjustments. First, we mapped the individual ICD-9-CM codes to the Centers for Medicare and Medicaid Services-Hierarchical Conditions Categories (HCC) model that was developed in 2004 to risk adjust capitation payments to private health care plans based on the health expenditure risk of their enrollees. Second, we aggregated beneficiaries with identical HCCs regardless of the temporal order of these findings within the 2008 claims year; thus the DC to which they are assigned represents the summation of their 2008 claims data. We defined 3 distinct populations at the HCC level. The first consisted of 35% of the beneficiaries who did not fall into any HCC category and accounted for 6% of expenditures. The second was represented by the 100 next most prevalent DCs that accounted for 33% of the beneficiaries and 15% of expenditures. The final population, accounting for 32% of the beneficiaries and 79% of expenses, was complex and consisted of over 2 million DCs. Our results indicate that the majority of expenditures are associated with a complex set of beneficiaries.