cometadata/datacite-titles-descriptions-related-identifiers huggingface dataset:
DOI: 10.21989/d9/clatyk
title: Replication Data for: Direct oral anticoagulant and antiplatelet combination therapy: Hemorrhagic events in coronary artery stent recipients

description: Using a 40% random sample of the Medicare beneficiaries
enrolled in fee-for-service Medicare Parts A (inpatient), B (outpatient)
and D (prescription) coverage, residing in the U.S., we identified
a cohort of patients receiving inpatient coronary artery stents
in 2010–2012 (Table S1). The year 2010 was selected as the baseline
year because DOACs appeared on the market in 2010. Each
patient’s first stent episode between 2010 and 2012 served as
the index stent episode. Patients dying within 30 days of the index
stent episode were excluded. Patients not enrolled Medicare Part D
or not using prescription benefits (i.e. no prescription fill record) in
the 120 days preceding index stent were also excluded. We also
excluded cancer patients because of their potentially complicated
anticoagulation profile.
DOI: 10.5281/zenodo.14280890
title: Computational library for the Nutrient-Unicellular-Multicellular plankton modeling framework

description: This release is created for submitting "Computational library for the Nutrient-Unicellular-Multicellular plankton modeling framework" to GMD.
DOI: 10.21989/d9/piemr4
title: Replication Data for: Opioid Prescribing for Opioid-Naive Patients in Emergency Departments and Other Settings: Characteristics of Prescriptions and Association With Long-Term Use

description: &lt;p&gt;&lt;b&gt;Study Objective:&lt;/b&gt; We explore the emergency department (ED) contribution to prescription opioid use for opioid-naive patients by comparing the guideline concordance of ED prescriptions with those attributed to other settings and the risk of patients’ continuing long-term opioid use.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;Methods:&lt;/b&gt; We used analysis of administrative claims data (OptumLabs Data Warehouse 2009 to 2015) of opioid-naive privately insured and Medicare Advantage (aged and disabled) beneficiaries to compare characteristics of opioid prescriptions attributed to the ED with those attributed to other settings. Concordance with Centers for Disease Control and Prevention (CDC) guidelines and rate of progression to long-term opioid use are reported.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;Results:&lt;/b&gt; We identified 5.2 million opioid prescription fills that met inclusion criteria. Opioid prescriptions from the ED were more likely to adhere to CDC guidelines for dose, days’ supply, and formulation than those attributed to non-ED settings. Disabled Medicare beneficiaries were the most likely to progress to long-term use, with 13.4% of their fills resulting in long-term use compared with 6.2% of aged Medicare and 1.8% of commercial beneficiaries’ fills. Compared with patients in non-ED settings, commercial beneficiaries receiving opioid prescriptions in the ED were 46% less likely, aged Medicare patients 56% less likely, and disabled Medicare patients 58% less likely to progress to long-term opioid use.&lt;/p&gt;

&lt;p&gt;&lt;b&gt;Conclusion:&lt;/b&gt; 
Compared with non-ED settings, opioid prescriptions provided to opioid-naive patients in the ED were more likely to align with CDC recommendations. They were shorter, written for lower daily doses, and less likely to be for long-acting formulations. Prescriptions from the ED are associated with a lower risk of progression to long-term use.&lt;/p&gt;
DOI: 10.21989/d9/sgphog
title: Exnovation of Low Value Care: A Decade of Prostate-Specific Antigen Screening Practices

description: &lt;p&gt;OBJECTIVES: To examine prostate-specific antigen (PSA) screening practice change in subgroups of men defined in guidelines and in various regions and to identify factors associated with change in screening practices. &lt;/p&gt;

&lt;p&gt;DESIGN: Observational study using serial cross-sections, 2003 to 2013. &lt;/p&gt;

&lt;p&gt;SETTING: National fee-for-service Medicare. &lt;/p&gt;

&lt;p&gt;PARTICIPANTS: Men aged 68 and older eligible for prostate cancer screening. &lt;/p&gt;

&lt;p&gt;MEASUREMENTS: National PSA screening practices in men aged 68 and older from 2003 to 2013 and change in regional screening rates in men aged 75 and older. &lt;/p&gt;

&lt;p&gt;RESULTS: The PSA screening rate in men aged 68 and older was 17.2% in 2003, 22.3% in 2008, and 18.6% in 2013 (p &lt; .001 for all differences); rates ended slightly lower than rates in 2003 only in men 80 and older. Racial disparities in screening became less pronounced over this period. In men aged 75 and older, change in regional screening rates varied widely, with absolute rates growing by 15 per 100 enrollees in some areas and declining by the same amount in others. Areas with high social capital, a measure associated with diffusion of new ideas, were more likely to decline; malpractice intensity and managed care penetration had no effect. &lt;/p&gt;

&lt;p&gt;CONCLUSION: Studying Medicare enrollees over time, we found little reduction in PSA screening and even increases according to race and in some regions. The heterogeneous changes across regions suggest that consistent reduction in the use of low-value care may require change strategies that go beyond evidence and guidelines to include monitoring and feedback on performance.&lt;/p&gt;
DOI: 10.21989/d9/sgphog/ege4ff
title: exclusionmedpar.sas
DOI: 10.21989/d9/sgphog/mw8mdu
title: hospsnf.sas
DOI: 10.21989/d9/sgphog/4f5zna
title: partb2000.sas
DOI: 10.21989/d9/sgphog/dumvxe
title: dod.sas
DOI: 10.21989/d9/sgphog/wh0n1w
title: enrollmtbyHRR.sas
DOI: 10.21989/d9/sgphog/j5bbsm
title: formats.sas
DOI: 10.21989/d9/sgphog/g5f0nc
title: Supplement Exnovation.pdf
DOI: 10.21989/d9/sgphog/lfhbzc
title: exclusionpartb.sas
DOI: 10.21989/d9/sgphog/78y97t
title: socialcapital.sas
DOI: 10.21989/d9/sgphog/gwywzz
title: denom.sas
DOI: 10.21989/d9/sgphog/y6xqry
title: partb.sas
DOI: 10.21989/d9/sgphog/izbdhr
title: PtToMD.sas
DOI: 10.21989/d9/sgphog/di3xmd
title: dualpreviousyr.sas
DOI: 10.21989/d9/sgphog/wt5yc0
title: combineexclusion.sas
DOI: 10.21989/d9/sgphog/7caxud
title: PtToMDmacros.sas
DOI: 10.5281/zenodo.14651959
title: Ovisopis

description: Full set of scoring sheets of male-male interactions with Gryllus ovisopis individuals
DOI: 10.5281/zenodo.14651960
title: Ovisopis

description: Full set of scoring sheets of male-male interactions with Gryllus ovisopis individuals
DOI: 10.21989/d9/sgphog/uey0p6
title: outptvisit.sas
DOI: 10.21989/d9/sgphog/rkq0ld
title: analyticfiles.sas
DOI: 10.21989/d9/sgphog/4774pa
title: familyhx.sas
DOI: 10.21989/d9/sgphog/ddomgs
title: cohort.sas
DOI: 10.21989/d9/sgphog/oqgdnp
title: Exnovation of Low Value.pdf
DOI: 10.21989/d9/cgg10o
title: Replication Data for: Trends in opioid use in commercially insured and Medicare Advantage populations in 2007-16: retrospective cohort study

description: OBJECTIVE: To describe trends in the rate and daily dose of opioids used among commercial and Medicare Advantage beneficiaries from 2007 to 2016. DESIGN: Retrospective cohort study of administrative claims data. SETTING: National database of medical and pharmacy claims for commercially insured and Medicare Advantage beneficiaries in the United States. PARTICIPANTS: 48 million individuals with any period of insurance coverage between 1 January 2007 and 31 December 2016, including commercial beneficiaries, Medicare Advantage beneficiaries aged 65 years and over, and Medicare Advantage beneficiaries under age 65 years (eligible owing to permanent disability). MAIN ENDPOINTS: Proportion of beneficiaries with any opioid prescription per quarter, average daily dose in milligram morphine equivalents (MME), and proportion of opioid use episodes that represented long term use. RESULTS: Across all years of the study, annual opioid use prevalence was 14% for commercial beneficiaries, 26% for aged Medicare beneficiaries, and 52% for disabled Medicare beneficiaries. In the commercial beneficiary group, quarterly prevalence of opioid use changed little, starting and ending the study period at 6%; the average daily dose of 17 MME remained unchanged since 2011. For aged Medicare beneficiaries, quarterly use prevalence was also relatively stable, ranging from 11% at the beginning of the study period to 14% at the end. Disabled Medicare beneficiaries had the highest rates of opioid use, the highest rate of long term use, and the largest average daily doses. In this group, both quarterly use rates (39%) and average daily dose (56 MME) were higher at the end of 2016 than the low points observed in 2007 for each endpoint (26% prevalence and 53 MME). CONCLUSIONS: Opioid use rates were high during the study period of 2007-16, with the highest rates in disabled Medicare beneficiaries versus aged Medicare beneficiaries and commercial beneficiaries. Opioid use and average daily dose have not substantially declined from their peaks, despite increased attention to opioid abuse and awareness of their risks.
DOI: 10.21989/d9/rccnvg
title: Long-Term Implications Of A Short-Term Policy: Redacting Substance Abuse Data

description: We used complete fee-for-service Medicare claims for 2012, the last year of claims available before redaction was implemented. To estimate the impact of redaction, we created a new version of the 2012 cohort by removing any claim that included a substance abuse–related diagnosis or procedure code that was redacted in 2013, as specified by the Research Data Assistance Center(ResDAC). Our analysis used the full 2012claims and the redacted version.We estimated the prevalence of thirteen chronic conditions by age, comparing unredacted and redacted data (exhibit 2). Following previously published work, we constructed chronic conditions based on International Classification of Diseases, Ninth Revision(ICD-9), codes corresponding to a subset of hierarchical classification categories used to risk adjust payments to Medicare Advantage health plans. We focused on a subset of conditions that are relatively common among Medicare beneficiaries or were likely to be affected by redaction.Next, we computed inpatient utilization in the redacted and unredacted data. We estimated inpatient admissions per 100 beneficiaries and inpatient spending per beneficiary, overall and by age (exhibit 3). To characterize how redaction might affect studies that created diagnostic cohorts in inpatient settings, we calculated the population rates of admission per 100 beneficiaries (and, separately, psychiatric hospital admissions) for selected diagnoses likely to be affected(serious mental illness, depression, and hepatitis C) and less likely to be affected (diabetes) by redaction (exhibit 4).
DOI: 10.21989/d9/fpxy3c
title: A comparative analysis of long-term mortality after carotid endarterectomy and carotid stenting.

description: Background:
The value of carotid intervention is predicated on long-term survival for patients to derive a stroke prevention benefit. Randomized trials report no significant difference in survival after carotid endarterectomy (CEA) vs carotid artery stenting (CAS), whereas observational studies of “real-world” outcomes note that CEA is associated with a survival advantage. Our objective was to examine long-term mortality after CEA vs CAS using a propensity-matched cohort.
Methods:
We studied all patients who underwent CEA or CAS within the Vascular Quality Initiative from 2003 to 2013 (CEA, n = 29,235; CAS, n = 4415). Long-term mortality information was obtained by linking patients in the registry to their respective Medicare claims file. We assessed the long-term rate of mortality for CEA and CAS using Kaplan-Meier estimation. We assessed the crude, adjusted, and propensity-matched (total matched pairs, n = 4261) hazard ratio (HR) of mortality for CEA vs CAS using Cox regression.
Results:
The unadjusted Kaplan-Meier estimated 5-year mortality was 14.0% for CEA and 18.3% for CAS. The crude HR of all-cause mortality for CEA vs CAS was 0.75 (95% confidence interval [CI], 0.70-0.81), indicating that patients who underwent CEA were 25% less likely to die before those who underwent CAS. This survival advantage persisted after adjustment for age, sex, and comorbidities (adjusted HR, 0.75; 95% CI, 0.69-0.82). This effect was confirmed on a propensity-matched analysis, with an HR of 0.76 (95% CI, 0.69-0.85). Finally, these findings were robust to subanalyses that stratified patients by presenting symptoms and were more pronounced in symptomatic patients (adjusted HR, 0.69; 95% CI, 0.61-0.79) than in asymptomatic patients (adjusted HR, 0.80; 95% CI, 0.71-0.90).
Conclusions:
During the last 15 years, patients who underwent CEA in the Vascular Quality Initiative have a long-term survival advantage over those who underwent CAS in real-world practice. Despite no difference in long-term survival in randomized trials, our observational study demonstrated a survival benefit for CEA that did not diminish with risk adjustment.
DOI: 10.21989/d9/ame6yx
title: Evaluating Breast Cancer Care Coordination at a Rural National Cancer Institute Comprehensive Cancer Center Using Network Analysis and Geospatial Methods

description: Background: Variation in cancer care coordination may affect care quality and patient outcomes. We sought to characterize the impact of geographic access to and dispersion of cancer care providers on variation in care coordination.
Methods: Using electronic health record data from 2,507 women diagnosed with breast cancer at a National Cancer Institute Comprehensive Cancer Center from April 2011 to September 2015, a breast cancer patient–sharing physician network was constructed. Patient “care networks” represent the subnetworks of physicians with whom the focal patient had a clinical encounter. Patient care networks were analyzed to generate two measures of care coordination, care density (ratio of observed vs. potential connections between physicians), and clustering (extent to which physicians form connected triangles).
Results: The breast cancer physician network included 667 physicians. On average, the physicians shared patients with 12 other physicians. Patients saw an average of 8 physicians during active treatment. In multivariable models adjusting for patient sociodemographic and clinical characteristics, we observed that greater travel burden (&gt;2 hours) and lower geographic dispersion were associated with higher care density (P &lt; 0.05 and P &lt; 0.001, respectively) but lower care network clustering (P &lt; 0.05).
Conclusions: Variation in network-based measures of care coordination is partially explained by patient travel burden and geographic dispersion of care.
Impact: Improved understanding of factors driving variation in patient care networks may identify patients at risk of receiving poorly coordinated cancer care.
DOI: 10.21989/d9/cnbofo
title: Replication Data for: Moneyball in Medicare.

description: The main analyses use national hospital-level data to estimate the effect of a one-decile improvement in measurement. These calculations do not require individual-level data. Therefore, we use national data. We used data from the CMS Hospital Compare and Medicare Impact File to identify hospitals participating in the HVBP program in both FY 2015 and FY 2016. For each of these 2972 hospitals, we evaluated hospital-level performance on the spending and quality measures in the HVBP program using publically available CMS Hospital Compare data (see Table 3 for summary statistics). Additionally, we used the CMS FY 2016 Final Rule and Impact File to obtain actual payment adjustments due to the HVBP program and to estimate case-mix adjusted annual Medicare base payment rates.
DOI: 10.21989/d9/21zhrn
title: Replication Data for: The Association Between Medicaid Expansion and Cardiovascular Interventions: The Michigan Experience

description: We evaluated Michigan's Medicaid expansion as it relates to access and outcomes for 7,558 coronary artery bypass grafting (CABG) operations at 33 hospitals, and 45,183 percutaneous coronary interventions (PCI) at 47 hospitals. We excluded patients &gt;=65 years and those with Medicare, non-Medicaid state or Federal government coverage (e.g., military), or non-US insurance. Clinical data, from each institution’s participation in CABG and PCI collaboratives, represents procedures performed between April 2012 – March 2014 and April 2014 – March 2016 (24 months before/after expansion).

We evaluated changes in access (i.e., number of procedures) by insurance type (private, Medicaid, uninsured), and used risk adjustment models for CABG(3) and PCI(4) to calculate adjusted mortality (CABG: operative mortality; PCI: during the index admission) before and after expansion. G-computation was utilized to estimate the relationship between Medicaid expansion and the number of patients presenting quarterly for revascularization within insurance coverage type (Medicaid, private, uninsured). A generalized estimating equation (GEE) model using autoregressive covariance structure was fitted, including insurance type, calendar quarter, Medicaid expansion, and interaction terms (two-way and three-way interactions among quarter, Medicaid expansion, and insurance coverage type) as predictors.
DOI: 10.21989/d9/fyzzam
title: Overuse and Insurance Plan Type in a Privately Insured Population
title: Health insurance plan type may be an important lever for reducing low-value healthcare use among patients with commercial insurance.

description: OBJECTIVES:
A substantial portion of healthcare spending is wasted on services that do not directly improve patient health and that cause harm in some cases. Features of health insurance coverage, including enrollment in high-deductible health plans (HDHPs) or health maintenance organizations (HMOs), may provide financial and nonfinancial mechanisms to potentially reduce overuse of low-value healthcare services.
STUDY DESIGN:
Using 2009 to 2013 administrative data from 3 large commercial insurers, we examined patient characteristics and health insurance plan types associated with overuse of 6 healthcare services identified by the Choosing Wisely campaign.
METHODS:
We explored associations between overuse and patient characteristics using multivariate logistic regression models, including patient age, gender, enrollment in an HMO, enrollment in an HDHP, an indicator of primary care fragmentation, and number of outpatient visits as explanatory variables.
RESULTS:
Measurement of services highlighted as potential overuse by the Choosing Wisely recommendations revealed low to moderate prevalence, depending on the service. HMO coverage and enrollment in HDHPs were significantly associated with differences in prevalence of all 6 services, albeit differently in terms of the direction of the effects. Primary care fragmentation was significantly associated with higher rates of overuse.
CONCLUSIONS:
Neither HDHPs nor HMO plans, with their closed networks and referral requirements, consistently reduced overuse, although HMO plans were never associated with higher rates of overuse. As policy makers seek levers for reducing low-value healthcare utilization, health insurance plan features may prove a valuable target, although the effect may be complicated by other factors.
DOI: 10.21989/d9/bbgegi
title: Replication Data for: Analysis of the U.S. Patient Referral Network

description: In this paper we analyze the US Patient Referral Network (also called the Shared Patient Network) and various subnetworks for the years 2009–2015. In these networks two physicians are linked if a patient encounters both of them within a specified time-interval, according to the data made available by the Centers for Medicare and Medicaid Services. We find power law distributions on most state-level data as well as a core-periphery structure. On a national and state level, we discover a so-called small-world structure as well as a “gravity law" of the type found in some large-scale economic networks. Some physicians play the role of hubs for interstate referral. Strong correlations between certain network statistics with healthcare system statistics at both the state and national levels are discovered. The patterns in the referral network evinced using several statistical analyses involving key metrics derived from the network illustrate the potential for using network analysis to provide new insights into the healthcare system and opportunities or mechanisms for catalyzing improvements.
DOI: 10.21989/d9/bwklg5
title: Replication Data for: Quality of EOL care for Medicare decedents at minority-serving cancer centers: A retrospective study

description: &lt;p&gt;This project is an update to the Dartmouth Atlas Cancer Report profiling NQF EOL measures using our previously-developed algorithm using the most up-to-date Medicare claims data (2012-Q2 2017). In that algorithm, we categorize hospitals into the following four mutually exclusive types: members of the National Comprehensive Cancer Network (NCCN; n=30); hospitals outside the network that were designated Comprehensive Cancer Centers by the National Cancer Institute (NCI; n=24); hospitals that were not in the network or designated comprehensive cancer centers but that were academic medical centers (n=161); and community hospitals, those institutions not in the above groups (n=4,240). In the current study, we focus on National Comprehensive Cancer Network and Comprehensive Cancer Center hospitals because they set national standards for high quality care.&lt;/p&gt;

&lt;p&gt;The algorithm used to profile cancer centers’ EOL quality follows three steps:&lt;/p&gt;
&lt;ul&gt; 
&lt;li&gt;1) Define the cohort; &lt;/li&gt;
&lt;li&gt;2) Attribute cohort member’s medical care to the hospital providing largest number of hospitalizations for cancer care in the last six months of life; &lt;/li&gt;
&lt;li&gt;3) Calculate EOL quality measures in the six months preceding death for each patient assigned to a National Comprehensive Cancer Network hospital or NCI-designated Comprehensive Cancer Centers. &lt;/li&gt;
&lt;/ul&gt;
&lt;p&gt;The cohort includes fee-for-service Medicare beneficiaries who died at ages 66–99, had continuous inpatient and outpatient Medicare insurance (Parts A, B, and D) in the last six months of life, and had at least one hospital discharge or at least two clinician visits in the last six months of life with cancer diagnosis codes associated with a high risk of near-term death and at least one hospital admission for cancer care in the last six months of life. These criteria exclude patients with many common cancers not associated with a high likelihood of dying in the near term. We calculate NQF measures using our linked Medicare Provider Analysis and Review (MedPAR) files, hospice files, Part B and Part D files.&lt;/p&gt;
DOI: 10.21989/d9/bwklg5/yblqdq
title: toDBF.hrr.extra.2016.sas
DOI: 10.21989/d9/bwklg5/mbof6q
title: toDBF.hrr.dqc.sas
DOI: 10.21989/d9/bwklg5/vrl5wv
title: hospform2016.sas
DOI: 10.21989/d9/bwklg5/bjyb29
title: formats.sas
DOI: 10.21989/d9/bwklg5/g9bgbv
title: toDBF.hsp.extra.2016.sas
DOI: 10.21989/d9/bwklg5/jqmwho
title: ChemoHCPCS.sas
DOI: 10.21989/d9/bwklg5/zqaqsx
title: toDBF.state.dqc.sas
DOI: 10.21989/d9/bwklg5/th86bi
title: RxChemo.sas
DOI: 10.21989/d9/bwklg5/1gd9j7
title: run.2.assign.CCS.2016.sas
DOI: 10.21989/d9/bwklg5/jyjjoe
title: step4.events.dqc.sas
DOI: 10.21989/d9/bwklg5/zaohvx
title: make.finalCCS.dqc.sas
DOI: 10.17632/vsz947w82h
title: Macrogenomic raw data-4

description: Macrogenomic
DOI: 10.17632/vsz947w82h.1
title: Macrogenomic raw data-4

description: Macrogenomic
DOI: 10.21989/d9/bwklg5/cqmgu6
title: assignCCS.step1a.dqc.sas
DOI: 10.21989/d9/bwklg5/fdbeny
title: run.modelhrr.hha.2016.sas
DOI: 10.6084/m9.figshare.28211375
title: Test movie 2

description: Supplementary data of a thesis
DOI: 10.6084/m9.figshare.28211375.v1
title: Test movie 2

description: Supplementary data of a thesis
DOI: 10.21989/d9/bwklg5/01lcpk
title: run.modelhsp.hha.2016.sas
DOI: 10.21989/d9/bwklg5/yzxnta
title: CORRECT.CCS.dqc.sas