Economic theory suggests that a binding minimum wage increase may reduce the generosity of employer-sponsored insurance (ESI) or other fringe benefits, yet previous empirical studies reach conflicting conclusions about the existence of a trade-off between minimum wages and ESI. We study whether recent state and federal minimum wage increases affect the level or the source of health insurance coverage for low-income families using the 2005–16 Current Population Survey. Our research design uses state and year fixed effects to isolate within-state minimum wage changes while controlling for Medicaid eligibility and other changes in health policy related to implementation of the Affordable Care Act. Because dependent coverage might also be affected by minimum wage hikes, we examine ESI coverage for both low-wage workers and their dependents. We find robust evidence that minimum wage increases lead to reductions in ESI coverage in families below 300 percent of the federal poverty level, with a nominal $1 increase in the minimum wage reducing the probability of ESI coverage by 0.99 percentage points. Reductions in coverage were observed both for workers and for their dependents.
{"title":"The Effect of the Minimum Wage on Employer-Sponsored Insurance for Low-Income Workers and Dependents","authors":"M. Dworsky, C. Eibner, Xiaoyu Nie, J. Wenger","doi":"10.1086/716198","DOIUrl":"https://doi.org/10.1086/716198","url":null,"abstract":"Economic theory suggests that a binding minimum wage increase may reduce the generosity of employer-sponsored insurance (ESI) or other fringe benefits, yet previous empirical studies reach conflicting conclusions about the existence of a trade-off between minimum wages and ESI. We study whether recent state and federal minimum wage increases affect the level or the source of health insurance coverage for low-income families using the 2005–16 Current Population Survey. Our research design uses state and year fixed effects to isolate within-state minimum wage changes while controlling for Medicaid eligibility and other changes in health policy related to implementation of the Affordable Care Act. Because dependent coverage might also be affected by minimum wage hikes, we examine ESI coverage for both low-wage workers and their dependents. We find robust evidence that minimum wage increases lead to reductions in ESI coverage in families below 300 percent of the federal poverty level, with a nominal $1 increase in the minimum wage reducing the probability of ESI coverage by 0.99 percentage points. Reductions in coverage were observed both for workers and for their dependents.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-07-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"43609902","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
R. Avery, J. Cawley, Julia C. P. Eddelbuettel, Matthew D. Eisenberg, Charlie Mann, A. Mathios
This paper estimates the impact on consumer behavior of a firm’s voluntary disclosure of information. Specifically, we study the impact of Starbucks’ disclosure of calorie information on its menu boards in June 2013. Using a large data set of consumers’ visits to specific restaurant chains, we estimate difference-in-difference models that compare the change in the probability that consumers recently visited Starbucks with the change in the probability that they recently visited a similar chain that did not disclose calorie information: Dunkin’ Donuts. Estimates from difference-in-differences models indicate that we cannot reject the null hypothesis that Starbucks’ disclosure of calorie information had no detectable impact on the probability that consumers patronized Starbucks in the past month. However, we find an effect among men; after Starbucks’ information disclosure, the probability that men visit the chain fell by 1.0 percentage points (10.7 percent). There is no evidence of heterogeneity in effect by education or clinical weight classification. These results are useful for understanding how consumers respond to the voluntary disclosure of information, a decision many firms face.
{"title":"Consumer Responses to Firms’ Voluntary Disclosure of Information","authors":"R. Avery, J. Cawley, Julia C. P. Eddelbuettel, Matthew D. Eisenberg, Charlie Mann, A. Mathios","doi":"10.1086/722269","DOIUrl":"https://doi.org/10.1086/722269","url":null,"abstract":"This paper estimates the impact on consumer behavior of a firm’s voluntary disclosure of information. Specifically, we study the impact of Starbucks’ disclosure of calorie information on its menu boards in June 2013. Using a large data set of consumers’ visits to specific restaurant chains, we estimate difference-in-difference models that compare the change in the probability that consumers recently visited Starbucks with the change in the probability that they recently visited a similar chain that did not disclose calorie information: Dunkin’ Donuts. Estimates from difference-in-differences models indicate that we cannot reject the null hypothesis that Starbucks’ disclosure of calorie information had no detectable impact on the probability that consumers patronized Starbucks in the past month. However, we find an effect among men; after Starbucks’ information disclosure, the probability that men visit the chain fell by 1.0 percentage points (10.7 percent). There is no evidence of heterogeneity in effect by education or clinical weight classification. These results are useful for understanding how consumers respond to the voluntary disclosure of information, a decision many firms face.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48823164","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Even among commercially insured individuals, opioid use disorder is undertreated in the United States: nearly half receive no treatment within six months of a new diagnosis. Using a difference-in-differences specification exploiting the extension of insurance parity requirements for substance use disorder treatment to small-group enrollees in 2014, we find that parity increases utilization of residential treatment but decreases utilization of agonist medications, the standard of care. We find direct interventions to increase access to medication may be more promising: increases in the county-level share of physicians able to prescribe agonists are associated with substitution toward treatment that includes medication.
{"title":"Do Policies to Increase Access to Treatment for Opioid Use Disorder Work?","authors":"E. Barrette, Leemore S. Dafny, Karen Shen","doi":"10.1086/722980","DOIUrl":"https://doi.org/10.1086/722980","url":null,"abstract":"Even among commercially insured individuals, opioid use disorder is undertreated in the United States: nearly half receive no treatment within six months of a new diagnosis. Using a difference-in-differences specification exploiting the extension of insurance parity requirements for substance use disorder treatment to small-group enrollees in 2014, we find that parity increases utilization of residential treatment but decreases utilization of agonist medications, the standard of care. We find direct interventions to increase access to medication may be more promising: increases in the county-level share of physicians able to prescribe agonists are associated with substitution toward treatment that includes medication.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46211865","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We find that Ohio’s “Vax-a-Million” lottery increased first-dose COVID-19 vaccinations by between 50,000 and 100,000, with most of the additional doses occurring during the two weeks between the announcement and the first lottery drawing. We use county-level data and two empirical approaches to provide causal estimates of the lottery in Ohio. First, a difference-in-differences design compares vaccination rates in border counties in Ohio and Indiana before and after the announcement. Second, we use a pooled synthetic control method to construct a counterfactual for each of Ohio’s counties using control counties in Indiana, Michigan, and Pennsylvania. The synthetic control analysis reveals larger increases in vaccination rates in more populous counties. Our estimates imply that Ohio paid about $75 per additional starting dose during this period.
{"title":"The Ohio Vaccine Lottery and Starting Vaccination Rates","authors":"Margaret E. Brehm, Paul A. Brehm, Martin Saavedra","doi":"10.1086/718512","DOIUrl":"https://doi.org/10.1086/718512","url":null,"abstract":"We find that Ohio’s “Vax-a-Million” lottery increased first-dose COVID-19 vaccinations by between 50,000 and 100,000, with most of the additional doses occurring during the two weeks between the announcement and the first lottery drawing. We use county-level data and two empirical approaches to provide causal estimates of the lottery in Ohio. First, a difference-in-differences design compares vaccination rates in border counties in Ohio and Indiana before and after the announcement. Second, we use a pooled synthetic control method to construct a counterfactual for each of Ohio’s counties using control counties in Indiana, Michigan, and Pennsylvania. The synthetic control analysis reveals larger increases in vaccination rates in more populous counties. Our estimates imply that Ohio paid about $75 per additional starting dose during this period.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-06-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47128130","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We use temperature variation within narrowly defined geographic and demographic cells to show that exposure to extreme temperature increases the risk of maternal hospitalization during pregnancy. This effect is driven by emergency hospitalizations for various pregnancy complications, suggesting that it represents a deterioration in underlying maternal health rather than a change in women’s ability to access health care. The effect is larger for black women than for women of other races, suggesting that without significant adaptation, projected increases in extreme temperatures over the next century may further exacerbate racial disparities in maternal health.
{"title":"What to Expect When It Gets Hotter","authors":"Jiyoon Kim, Ajin Lee, Maya Rossin-Slater","doi":"10.1086/714359","DOIUrl":"https://doi.org/10.1086/714359","url":null,"abstract":"We use temperature variation within narrowly defined geographic and demographic cells to show that exposure to extreme temperature increases the risk of maternal hospitalization during pregnancy. This effect is driven by emergency hospitalizations for various pregnancy complications, suggesting that it represents a deterioration in underlying maternal health rather than a change in women’s ability to access health care. The effect is larger for black women than for women of other races, suggesting that without significant adaptation, projected increases in extreme temperatures over the next century may further exacerbate racial disparities in maternal health.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/714359","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"42676629","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The COVID-19 pandemic significantly altered individual behaviors, including the consumption of health care. I study utilization and mortality in the largest integrated health-care system in the United States, the Veterans Health Administration, and find that between the middle of March and the beginning of May 2020, emergency department and inpatient hospital visits declined by 37 percent and 46 percent, and remained 10 percent and 17 percent below expected levels by the end of October. Declines were more pronounced for nonurgent and non-life-threatening conditions, although urgent and life-threatening conditions also dropped by a quarter during the early months. Conditional on arrival at the emergency department, conditions were more severe at presentation. In the first two months of the pandemic, veteran mortality increased by 19.5 percent, yet non-COVID-19 mortality in VA inpatient settings declined. I find suggestive evidence that hospital avoidance may have resulted in higher non-COVID-19 mortality. By focusing on counties with no official COVID-19 deaths by May 19, 2020, I estimate that an upper bound of 7.9 percent of excess veteran deaths in the first two months of the pandemic were due to hospital avoidance.
{"title":"Hospital Avoidance and Unintended Deaths during the COVID-19 Pandemic","authors":"Jonathan Zhang","doi":"10.1086/715158","DOIUrl":"https://doi.org/10.1086/715158","url":null,"abstract":"The COVID-19 pandemic significantly altered individual behaviors, including the consumption of health care. I study utilization and mortality in the largest integrated health-care system in the United States, the Veterans Health Administration, and find that between the middle of March and the beginning of May 2020, emergency department and inpatient hospital visits declined by 37 percent and 46 percent, and remained 10 percent and 17 percent below expected levels by the end of October. Declines were more pronounced for nonurgent and non-life-threatening conditions, although urgent and life-threatening conditions also dropped by a quarter during the early months. Conditional on arrival at the emergency department, conditions were more severe at presentation. In the first two months of the pandemic, veteran mortality increased by 19.5 percent, yet non-COVID-19 mortality in VA inpatient settings declined. I find suggestive evidence that hospital avoidance may have resulted in higher non-COVID-19 mortality. By focusing on counties with no official COVID-19 deaths by May 19, 2020, I estimate that an upper bound of 7.9 percent of excess veteran deaths in the first two months of the pandemic were due to hospital avoidance.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46904265","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Efforts to improve the efficiency of the US health-care system involve both provider payment reform and efforts to give consumers the information they need to choose efficient providers and a financial incentive to do so. An example of the latter type of initiative is tiered cost-sharing. We analyze data from a long-standing tiered cost-sharing system for primary care gatekeeper clinics. These clinics control access to specialists and hospitals and are held accountable for their patients’ total annual risk-adjusted spending on covered health-care services. Consumers choosing higher cost clinics face higher levels of deductibles, copayments, and out-of-pocket maximums. We find that when choosing a primary care clinic, consumers are responsive to the clinic’s tier. Consumers exhibit a high level of inertia, but nonetheless, many clinics voluntarily reduce their fees to move to, or retain placement in, lower cost tiers.
{"title":"Tiered Cost-Sharing for Primary Care Gatekeeper Clinics","authors":"B. Dowd, Tsan-Yao Huang, T. McDonald","doi":"10.1086/714360","DOIUrl":"https://doi.org/10.1086/714360","url":null,"abstract":"Efforts to improve the efficiency of the US health-care system involve both provider payment reform and efforts to give consumers the information they need to choose efficient providers and a financial incentive to do so. An example of the latter type of initiative is tiered cost-sharing. We analyze data from a long-standing tiered cost-sharing system for primary care gatekeeper clinics. These clinics control access to specialists and hospitals and are held accountable for their patients’ total annual risk-adjusted spending on covered health-care services. Consumers choosing higher cost clinics face higher levels of deductibles, copayments, and out-of-pocket maximums. We find that when choosing a primary care clinic, consumers are responsive to the clinic’s tier. Consumers exhibit a high level of inertia, but nonetheless, many clinics voluntarily reduce their fees to move to, or retain placement in, lower cost tiers.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-03-17","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/714360","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"47635599","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
The Current Population Survey provides official estimates of the number of people covered by health insurance and the number of uninsured in the United States. This type of survey data are also used to study the effects of policy changes on health insurance coverage. However, there is evidence that individuals sometimes misreport health insurance coverage, which might bias findings that use survey data. We use new administrative health insurance information from tax data to evaluate health insurance coverage in survey data across several dimensions, including age, income, and state. Our main findings suggest that although overall coverage counts are similar between survey and administrative data across all demographic characteristics, coverage rates and uninsured counts differ because of differences in population size. These similarities mask coverage differences by insurance type. Medicaid coverage is very well reported in tax data, whereas surveys tend to underreport it, especially for low-income individuals and people under the age of 40. Employer-sponsored coverage counts are higher in survey data than in administrative data. Finally, this study provides researchers that use survey data a benchmark for how to adjust Medicaid coverage to align with administratively reported levels.
{"title":"Health Insurance Coverage in Tax and Survey Data","authors":"I. Lurie, James E. Pearce","doi":"10.1086/712213","DOIUrl":"https://doi.org/10.1086/712213","url":null,"abstract":"The Current Population Survey provides official estimates of the number of people covered by health insurance and the number of uninsured in the United States. This type of survey data are also used to study the effects of policy changes on health insurance coverage. However, there is evidence that individuals sometimes misreport health insurance coverage, which might bias findings that use survey data. We use new administrative health insurance information from tax data to evaluate health insurance coverage in survey data across several dimensions, including age, income, and state. Our main findings suggest that although overall coverage counts are similar between survey and administrative data across all demographic characteristics, coverage rates and uninsured counts differ because of differences in population size. These similarities mask coverage differences by insurance type. Medicaid coverage is very well reported in tax data, whereas surveys tend to underreport it, especially for low-income individuals and people under the age of 40. Employer-sponsored coverage counts are higher in survey data than in administrative data. Finally, this study provides researchers that use survey data a benchmark for how to adjust Medicaid coverage to align with administratively reported levels.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/712213","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"60719528","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
A record number of firearm background checks were completed at the onset of the COVID-19 pandemic and during the protests following the murder of George Floyd. Using monthly state-level data, we show that the increase in firearm background check rates in March 2020 and June 2020 differ from previous gun-buying events in at least two important ways. First, the increases in the background check rates surrounding COVID-19 and the George Floyd protests are significantly larger than previous gun-buying events. Second, the gun-buying events of 2020 are nonpartisan; the effect in Republican-leaning states is statistically indistinguishable from the effect in Democrat-leaning states. Our estimates suggest that there were 62 percent more background checks completed between March and August 2020 than expected, which amounts to over 7 million additional background checks. We provide evidence that the recent spikes in background checks are not motivated by gun policy uncertainty and discuss policy recommendations that may alleviate any negative outcomes associated with expanded gun ownership during an unprecedented pandemic.
{"title":"Pandemics, Protests, and Firearms","authors":"Bree J. Lang, Matthew Lang","doi":"10.1086/713035","DOIUrl":"https://doi.org/10.1086/713035","url":null,"abstract":"A record number of firearm background checks were completed at the onset of the COVID-19 pandemic and during the protests following the murder of George Floyd. Using monthly state-level data, we show that the increase in firearm background check rates in March 2020 and June 2020 differ from previous gun-buying events in at least two important ways. First, the increases in the background check rates surrounding COVID-19 and the George Floyd protests are significantly larger than previous gun-buying events. Second, the gun-buying events of 2020 are nonpartisan; the effect in Republican-leaning states is statistically indistinguishable from the effect in Democrat-leaning states. Our estimates suggest that there were 62 percent more background checks completed between March and August 2020 than expected, which amounts to over 7 million additional background checks. We provide evidence that the recent spikes in background checks are not motivated by gun policy uncertainty and discuss policy recommendations that may alleviate any negative outcomes associated with expanded gun ownership during an unprecedented pandemic.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-03-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/713035","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"46705744","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
COVID-19 has led to an abrupt change in time spent at home, with many cities and states implementing official stay-at-home (SAH), or “lockdown,” policies. Using cell phone block-level activity data and administrative 911 and crime data from the City of Chicago, we estimate the effects of the Illinois governor’s SAH order on calls for police service, crimes recorded by police, and arrests made relating to domestic violence. We find that the SAH order announcement increased time spent at home, leading to a decrease in total calls for police service, but a subsequent increase in domestic violence–related calls for police service. However, we find that official reports by police officers and arrests for domestic violence crimes fell by 6.8 percent and 26.4 percent, respectively. Declines in reported domestic violence crimes mirror drops in total reported crimes; however, the reduction for domestic violence crimes is around 5 times smaller than the decline in overall crime rates.
{"title":"COVID-19 and Crime","authors":"L. Bullinger, J. Carr, Analisa Packham","doi":"10.1086/713787","DOIUrl":"https://doi.org/10.1086/713787","url":null,"abstract":"COVID-19 has led to an abrupt change in time spent at home, with many cities and states implementing official stay-at-home (SAH), or “lockdown,” policies. Using cell phone block-level activity data and administrative 911 and crime data from the City of Chicago, we estimate the effects of the Illinois governor’s SAH order on calls for police service, crimes recorded by police, and arrests made relating to domestic violence. We find that the SAH order announcement increased time spent at home, leading to a decrease in total calls for police service, but a subsequent increase in domestic violence–related calls for police service. However, we find that official reports by police officers and arrests for domestic violence crimes fell by 6.8 percent and 26.4 percent, respectively. Declines in reported domestic violence crimes mirror drops in total reported crimes; however, the reduction for domestic violence crimes is around 5 times smaller than the decline in overall crime rates.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":null,"pages":null},"PeriodicalIF":3.7,"publicationDate":"2021-02-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/713787","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48395960","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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