Medicare & medicaid research review最新文献

{"title":"Costs and clinical quality among Medicare beneficiaries: associations with health center penetration of low-income residents.","authors":"Ravi Sharma, Lydie A Lebrun-Harris, Quyen Ngo-Metzger","doi":"10.5600/mmrr.004.03.a05","DOIUrl":"10.5600/mmrr.004.03.a05","url":null,"abstract":"<p><strong>Objective: </strong>Determine the association between access to primary care by the underserved and Medicare spending and clinical quality across hospital referral regions (HRRs).</p><p><strong>Data sources: </strong>Data on elderly fee-for-service beneficiaries across 306 HRRs came from CMS' Geographic Variation in Medicare Spending and Utilization database (2010). We merged data on number of health center patients (HRSA's Uniform Data System) and number of low-income residents (American Community Survey).</p><p><strong>Study design: </strong>We estimated access to primary care in each HRR by \"health center penetration\" (health center patients as a proportion of low-income residents). We calculated total Medicare spending (adjusted for population size, local input prices, and health risk). We assessed clinical quality by preventable hospital admissions, hospital readmissions, and emergency department visits. We sorted HRRs by health center penetration rate and compared spending and quality measures between the high- and low-penetration deciles. We also employed linear regressions to estimate spending and quality measures as a function of health center penetration.</p><p><strong>Principal findings: </strong>The high-penetration decile had 9.7% lower Medicare spending ($926 per capita, p=0.01) than the low-penetration decile, and no different clinical quality outcomes.</p><p><strong>Conclusions: </strong>Compared with elderly fee-for-service beneficiaries residing in areas with low-penetration of health center patients among low-income residents, those residing in high-penetration areas may accrue Medicare cost savings. Limited evidence suggests that these savings do not compromise clinical quality.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-08","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4167229/pdf/mmrr2014-004-03-a05.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32686208","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Affordable Care Act risk adjustment: overview, context, and challenges.","authors":"John Kautter, Gregory C Pope, Patricia Keenan","doi":"10.5600/mmrr.004.03.a02","DOIUrl":"10.5600/mmrr.004.03.a02","url":null,"abstract":"<p><p>Beginning in 2014, individuals and small businesses will be able to purchase private health insurance through competitive marketplaces. The Affordable Care Act (ACA) provides for a program of risk adjustment in the individual and small group markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge and the incentive for plans to avoid sicker enrollees. This article--the first of three in the Medicare & Medicaid Research Review--describes the key program goal and issues in the Department of Health and Human Services (HHS) developed risk adjustment methodology, and identifies key choices in how the methodology responds to these issues. The goal of the HHS risk adjustment methodology is to compensate health insurance plans for differences in enrollee health mix so that plan premiums reflect differences in scope of coverage and other plan factors, but not differences in health status. The methodology includes a risk adjustment model and a risk transfer formula that together address this program goal as well as three issues specific to ACA risk adjustment: 1) new population; 2) cost and rating factors; and 3) balanced transfers within state/market. The risk adjustment model, described in the second article, estimates differences in health risks taking into account the new population and scope of coverage (actuarial value level). The transfer formula, described in the third article, calculates balanced transfers that are intended to account for health risk differences while preserving permissible premium differences.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4214269/pdf/mmrr2014-004-03-a02.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32786816","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Risk transfer formula for individual and small group markets under the Affordable Care Act.","authors":"Gregory C Pope, Henry Bachofer, Andrew Pearlman, John Kautter, Elizabeth Hunter, Daniel Miller, Patricia Keenan","doi":"10.5600/mmrr.004.03.a04","DOIUrl":"10.5600/mmrr.004.03.a04","url":null,"abstract":"<p><p>The Affordable Care Act provides for a program of risk adjustment in the individual and small group health insurance markets in 2014 as Marketplaces are implemented and new market reforms take effect. The purpose of risk adjustment is to lessen or eliminate the influence of risk selection on the premiums that plans charge. The risk adjustment methodology includes the risk adjustment model and the risk transfer formula. This article is the third of three in this issue of the Medicare & Medicaid Research Review that describe the ACA risk adjustment methodology and focuses on the risk transfer formula. In our first companion article, we discussed the key issues and choices in developing the methodology. In our second companion paper, we described the risk adjustment model that is used to calculate risk scores. In this article we present the risk transfer formula. We first describe how the plan risk score is combined with factors for the plan allowable premium rating, actuarial value, induced demand, geographic cost, and the statewide average premium in a formula that calculates transfers among plans. We then show how each plan factor is determined, as well as how the factors relate to each other in the risk transfer formula. The goal of risk transfers is to offset the effects of risk selection on plan costs while preserving premium differences due to factors such as actuarial value differences. Illustrative numerical simulations show the risk transfer formula operating as anticipated in hypothetical scenarios.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4209298/pdf/mmrr2014-004-03-a04.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32777453","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Financial and quality impacts of the Medicare physician group practice demonstration.","authors":"Gregory Pope,&nbsp;John Kautter,&nbsp;Musetta Leung,&nbsp;Michael Trisolini,&nbsp;Walter Adamache,&nbsp;Kevin Smith","doi":"10.5600/mmrr.004.03.a01","DOIUrl":"https://doi.org/10.5600/mmrr.004.03.a01","url":null,"abstract":"<p><strong>Objective: </strong>To examine the impact of the Medicare Physician Group Practice (PGP) demonstration on expenditure, utilization, and quality outcomes.</p><p><strong>Data source: </strong>Secondary data analysis of 2001-2010 Medicare claims for 1,776,387 person years assigned to the ten participating provider organizations and 1,579,080 person years in the corresponding local comparison groups.</p><p><strong>Study design: </strong>We used a pre-post comparison group observational design consisting of four pre-demonstration years (1/01-12/04) and five demonstration years (4/05-3/10). We employed a propensity-weighted difference-in-differences regression model to estimate demonstration effects, adjusting for demographics, health status, geographic area, and secular trends.</p><p><strong>Principal findings: </strong>The ten demonstration sites combined saved $171 (2.0%) per assigned beneficiary person year (p<0.001) during the five-year demonstration period. Medicare paid performance bonuses to the participating PGPs that averaged $102 per person year. The net savings to the Medicare program were $69 (0.8%) per person year. Demonstration savings were achieved primarily from the inpatient setting. The demonstration improved quality of care as measured by six of seven claims-based process quality indicators.</p><p><strong>Conclusions: </strong>The PGP demonstration, which used a payment model similar to the Medicare Accountable Care Organization (ACO) program, resulted in small reductions in Medicare expenditures and inpatient utilization, and improvements in process quality indicators. Judging from this demonstration experience, it is unlikely that Medicare ACOs will initially achieve large savings. Nevertheless, ACOs paid through shared savings may be an important first step toward greater efficiency and quality in the Medicare fee-for-service program.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 3","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-08-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4144360/pdf/mmrr2014-004-03-a01.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32618336","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medicare's hospice benefit: analysis of utilization and resource use.","authors":"Susan Bogasky,&nbsp;Steven Sheingold,&nbsp;Sally C Stearns","doi":"10.5600/mmrr.004.02.b03","DOIUrl":"https://doi.org/10.5600/mmrr.004.02.b03","url":null,"abstract":"<p><strong>Objective: </strong>This work provides descriptive statistics on hospice users. It also explores the magnitude of relative resource use during hospice episodes and whether such patterns vary by episode length for patients who only use routine home care as compared to those who use multiple levels of hospice care. Examining resource use for hospice users who require different hospice levels of care within an episode versus solely routine home care provides insight to the varied resource use associated with the different patient populations (i.e., those who may require steady routine home care across the entire episode versus those who require varied levels of care across the episode).</p><p><strong>Data source: </strong>The analyses were based on a longitudinal analytic file that was constructed from 100% of Medicare claims for hospice users with completed episodes spanning September 1, 2008 through the end of calendar year 2011. In examining resource use for routine home care users and all levels of hospice care, the analyses were restricted to single episode decedents who began their hospice episode on or after April 1, 2010 and whose date of death was on or before December 31, 2011. Daily wage-weighted visit units (WWVUs) were calculated for each patient during their hospice stay. In order to compute a WWVU, one-fourth of the Bureau of Labor Statistics hourly wage rate for each visit discipline (i.e., skilled nursing, medical social services, home health aide, and an average for therapies) was multiplied by the corresponding number of visit units reported on hospice claims.</p><p><strong>Principal findings: </strong>Using enhanced data on the intensity of service use, the results confirm previous research that suggested a curved pattern to service use during a hospice episode. For several measures of resource intensity, service use is more intensive during the initial days in the episode and for the last few days prior to death relative to the middle days of the episode. The pattern becomes more pronounced as episodes increase in length, but is otherwise a similar curve when compared by diagnosis. Thus, the results provide useful information for potential policy discussions about Medicare hospice reform.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-07-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4120783/pdf/mmrr2014-004-02-b03.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32563124","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Measuring coding intensity in the Medicare Advantage program.","authors":"Richard Kronick,&nbsp;W Pete Welch","doi":"10.5600/mmrr2014-004-02-a06","DOIUrl":"https://doi.org/10.5600/mmrr2014-004-02-a06","url":null,"abstract":"<p><strong>Background: </strong>In 2004, Medicare implemented a system of paying Medicare Advantage (MA) plans that gave them greater incentive than fee-for-service (FFS) providers to report diagnoses.</p><p><strong>Data: </strong>Risk scores for all Medicare beneficiaries 2004-2013 and Medicare Current Beneficiary Survey (MCBS) data, 2006-2011.</p><p><strong>Measures: </strong>Change in average risk score for all enrollees and for stayers (beneficiaries who were in either FFS or MA for two consecutive years). Prevalence rates by Hierarchical Condition Category (HCC).</p><p><strong>Results: </strong>Each year the average MA risk score increased faster than the average FFS score. Using the risk adjustment model in place in 2004, the average MA score as a ratio of the average FFS score would have increased from 90% in 2004 to 109% in 2013. Using the model partially implemented in 2014, the ratio would have increased from 88% to 102%. The increase in relative MA scores appears to largely reflect changes in diagnostic coding, not real increases in the morbidity of MA enrollees. In survey-based data for 2006-2011, the MA-FFS ratio of risk scores remained roughly constant at 96%. Intensity of coding varies widely by contract, with some contracts coding very similarly to FFS and others coding much more intensely than the MA average. Underpinning this relative growth in scores is particularly rapid relative growth in a subset of HCCs.</p><p><strong>Discussion: </strong>Medicare has taken significant steps to mitigate the effects of coding intensity in MA, including implementing a 3.4% coding intensity adjustment in 2010 and revising the risk adjustment model in 2013 and 2014. Given the continuous relative increase in the average MA risk score, further policy changes will likely be necessary.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-07-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4109819/pdf/mmrr2014-004-02-a06.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32539648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Ever enrolled Medicare population estimates from the MCBS Access to Care files.","authors":"Jason Petroski,&nbsp;David Ferraro,&nbsp;Adam Chu","doi":"10.5600/mmrr.004.02.a05","DOIUrl":"https://doi.org/10.5600/mmrr.004.02.a05","url":null,"abstract":"<p><strong>Objective: </strong>The Medicare Current Beneficiary Survey's (MCBS) Access to Care (ATC) file is designed to provide timely access to information on the Medicare population, yet because of the survey's complex sampling design and expedited processing it is difficult to use the file to make both \"always-enrolled\" and \"ever-enrolled\" estimates on the Medicare population. In this study, we describe the ATC file and sample design, and we evaluate and review various alternatives for producing \"ever-enrolled\" estimates.</p><p><strong>Methods: </strong>We created \"ever enrolled\" estimates for key variables in the MCBS using three separate approaches. We tested differences between the alternative approaches for statistical significance and show the relative magnitude of difference between approaches.</p><p><strong>Results: </strong>Even when estimates derived from the different approaches were statistically different, the magnitude of the difference was often sufficiently small so as to result in little practical difference among the alternate approaches. However, when considering more than just the estimation method, there are advantages to using certain approaches over others.</p><p><strong>Conclusion: </strong>There are several plausible approaches to achieving \"ever-enrolled\" estimates in the MCBS ATC file; however, the most straightforward approach appears to be implementation and usage of a new set of \"ever-enrolled\" weights for this file.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-06-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077701/pdf/mmrr2014-004-02-a05.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32477640","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Medicare's Physician Quality Reporting System (PQRS): quality measurement and beneficiary attribution.","authors":"Bryan Dowd,&nbsp;Chia-hsuan Li,&nbsp;Tami Swenson,&nbsp;Robert Coulam,&nbsp;Jesse Levy","doi":"10.5600/mmrr.004.02.a04","DOIUrl":"https://doi.org/10.5600/mmrr.004.02.a04","url":null,"abstract":"<p><strong>Purpose: </strong>To explore two issues that are relevant to inclusion of PQRS reporting in a value-based payment system: (1) what are the characteristics of PQRS reports and the providers who file them; and (2) could PQRS provide active attribution information to supplement existing attribution algorithms?</p><p><strong>Design and methods: </strong>Using data from five states for the years 2008 (the first full year of the program) and 2009, we examined the number and type of providers who reported PQRS measures and the types of measures that were reported. We then compared the PQRS reporting provider to the provider who supplied the plurality of the beneficiary's non-hospital evaluation and management (NH-E&M) visits.</p><p><strong>Results: </strong>Although PQRS-reporting providers provide only 17 percent of the beneficiary's NH-E&M visits on average in 2009, the provider who provided the plurality of visits supplied only 50 percent of such visits, on average.</p><p><strong>Implications: </strong>PQRS reporting alone cannot solve the attribution problem that is inherent in traditional fee-for-service Medicare, but as PQRS participation increases, it could help improve both attribution and information regarding the quality of health care services delivered to Medicare beneficiaries.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":"doi: 10.5600/mmrr.004.02.a04"},"PeriodicalIF":0.0,"publicationDate":"2014-06-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077700/pdf/mmrr2014-004-02-a04.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32477639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"Availability and usability of behavioral health organization encounter data in MAX 2009.","authors":"Jessica Beth Nysenbaum,&nbsp;Ellen Bouchery,&nbsp;Rosalie Malsberger","doi":"10.5600/mmrr2014-004-02-b02","DOIUrl":"https://doi.org/10.5600/mmrr2014-004-02-b02","url":null,"abstract":"<p><strong>Objective: </strong>To assess the availability, completeness, and quality of the Behavioral Health Organization (BHO) encounter data in MAX 2009.</p><p><strong>Data source: </strong>The Medicaid Analytic Extract (MAX) 2009.</p><p><strong>Methods: </strong>We compared metrics of reporting completeness and quality for BHOs to similar metrics for six states that primarily cover MH and SA services on a FFS basis. For the IP file, number of encounters per 1,000 person months of enrollment were compared. In the OT file, we examined three completeness measures: the number of claims per PME, number of claims reported per BHO outpatient service user, and the number of OT claims per service user.</p><p><strong>Principal findings: </strong>Out of the 15 states reporting enrollment in BHO plans in MAX 2009, 10 reported complete capitation data. IP encounter data were available in four states (Arizona, Colorado, Florida, and Iowa), compared well to FFS ranges, and appear usable for research. OT data are available for five states, but our analysis suggests data are only sufficiently complete for analysis in Arizona and Iowa.</p><p><strong>Conclusions: </strong>The initial assessment of the availability, completeness and quality of BHO encounter data in MAX 2009 suggests that only limited data are available and usable.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4067041/pdf/mmrr2014-004-02-b02.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32456098","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

{"title":"What influences the awareness of physician quality information? Implications for Medicare.","authors":"Jon Christianson,&nbsp;Daniel Maeng,&nbsp;Jean Abraham,&nbsp;Dennis P Scanlon,&nbsp;Jeffrey Alexander,&nbsp;Jessica Mittler,&nbsp;Michael Finch","doi":"10.5600/mmrr.004.02.a02","DOIUrl":"https://doi.org/10.5600/mmrr.004.02.a02","url":null,"abstract":"<p><strong>Objective: </strong>Examine the factors that are associated with awareness of physician quality information (PQI) among older people with one or more chronic illnesses and the implications for Medicare.</p><p><strong>Data sources/study setting: </strong>Random digit-dial survey of adults with one or more chronic illnesses.</p><p><strong>Research design: </strong>Structural equation modeling to examine factors related to awareness of PQI.</p><p><strong>Results: </strong>Awareness of PQI is low (13 percent), but comparable to findings in general population surveys. Age, race, education, and self-reported health status are associated with PQI awareness. Trust in the Internet as a source of health care information and not trusting one's physician as a source of information both are associated with a greater likelihood of being aware of PQI. Patients with high levels of activation have greater trust in physicians as information sources, but this is not associated with awareness, nor is degree of satisfaction with their care experience.</p><p><strong>Conclusions: </strong>Awareness of PQI among older persons with chronic illnesses is relatively low across all socio-economic and demographic subgroups. Changes in population characteristics over time are unlikely to improve awareness in this population, nor are changes in patient activation or satisfaction with care. Medicare would need a broad-based effort if it wishes to raise PQI awareness among Medicare beneficiaries in the near term. Before undertaking resource-intensive efforts to increase awareness, Medicare may want to consider what level of awareness actually is needed to accomplish the overall objective for PQI transparency, which is raising the quality of care received by beneficiaries. It may be that relatively low levels of awareness are sufficient.</p>","PeriodicalId":89601,"journal":{"name":"Medicare & medicaid research review","volume":"4 2","pages":""},"PeriodicalIF":0.0,"publicationDate":"2014-06-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4054667/pdf/mmrr2014_004_02_a02.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"32439515","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}