{"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":"7 1","pages":"281 - 305"},"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}
{"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":"7 1","pages":"405 - 426"},"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}
{"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":"7 1","pages":"306 - 332"},"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}
{"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":"7 1","pages":"164 - 184"},"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}
{"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":"7 1","pages":"131 - 163"},"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}
{"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":"7 1","pages":"249 - 280"},"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}
{"title":"THE EVOLVING CONSEQUENCES OF OXYCONTIN REFORMULATION ON DRUG OVERDOSES.","authors":"David Powell, Rosalie Liccardo Pacula","doi":"10.1086/711723","DOIUrl":"10.1086/711723","url":null,"abstract":"<p><p>Recent evidence suggests that the short-term transition of the opioid crisis from prescription opioids to heroin can be attributed to the reformulation of OxyContin, which substantially reduced access to abusable prescription opioids. In this paper, we find that over a longer time horizon, reformulation stimulated illicit drug markets to grow and evolve. We compare overdose trajectories in areas more exposed to reformulation, defined as states with higher rates of nonmedical OxyContin use before reformulation, to less exposed areas. More exposed areas experienced disproportionate increases in fatal overdoses involving synthetic opioids (fentanyl) and nonopioid substances like cocaine, suggesting that these new epidemics are related to the same factors driving the rise in heroin deaths. Instead of just short-term substitution from prescription opioid to heroin overdoses, the transition to illicit markets spurred by reformulation led to growth in the overall overdose rate to unprecedented levels.</p>","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":"7 1","pages":"41-67"},"PeriodicalIF":3.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8460090/pdf/nihms-1666645.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39453109","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Improving the Performance of Risk Adjustment Systems: Constrained Regressions, Reinsurance, and Variable Selection.","authors":"Thomas G McGuire, Anna L Zink, Sherri Rose","doi":"10.1086/716199","DOIUrl":"10.1086/716199","url":null,"abstract":"<p><p>Modifications of risk-adjustment systems used to pay health plans in individual health insurance markets typically seek to reduce selection incentives at the individual and group levels by adding variables to the payment formula. Adding variables can be costly and lead to unintended incentives for upcoding or service utilization. While these drawbacks are recognized, they are hard to quantify and difficult to balance against the concrete, measurable improvements in fit that may be achieved by adding variables to the formula. This paper takes a different approach to improving the performance of health plan payment systems. Using the HHS-HHC V0519 model from the Marketplaces as a starting point, we constrain fit at the individual and group level to be as good or better than the current payment model while <i>reducing</i> the number of variables in the model. We introduce three elements in the design of plan payment: reinsurance, constrained regressions, and machine learning methods for variable selection. The fit performance of our alternative formulas with many fewer variables is as good or better than the current HHS-HHC V0519 formula.</p>","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":"7 4","pages":"497-521"},"PeriodicalIF":3.7,"publicationDate":"2021-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8635414/pdf/nihms-1733043.pdf","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"39949457","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":2,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
{"title":"Substance Use Disorder Treatment Centers and Residential Property Values","authors":"Brady P. Horn, Aakrit Joshi, J. Maclean","doi":"10.1086/713033","DOIUrl":"https://doi.org/10.1086/713033","url":null,"abstract":"Substance use disorders (SUDs) are a major social concern. There is an extensive economic literature estimating the social costs associated with SUDs in terms of health care, labor market outcomes, and crime. However, beyond anecdotal claims that SUD treatment centers (SUDTCs), settings in which patients receive care, reduce residential property values, there is little empirical work on this question. We apply a spatial difference-in-differences model and administrative data to test this relationship. We find that SUDTCs sort into lower-value areas, but once SUDTC selection is addressed, we find no evidence that SUDTCs influence residential property values.","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":"7 1","pages":"185 - 221"},"PeriodicalIF":3.7,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/713033","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"41486477","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}
{"title":"Insurance Coverage, Provider Contact, and Take-Up of the HPV Vaccine","authors":"Brandyn F. Churchill","doi":"10.1086/713037","DOIUrl":"https://doi.org/10.1086/713037","url":null,"abstract":"Human papillomavirus (HPV) is the most common sexually transmitted infection in the United States and the single biggest cause of cervical cancer, as well as certain cancers of the head and throat, anus, vulva, vagina, and penis. Between 2008 and 2012 nearly 40,000 people annually were diagnosed with an HPV-related cancer. Despite these staggering numbers and the existence of a highly effective vaccine, HPV vaccination rates remain low. In this paper, I show that state Medicaid expansions as part of the Affordable Care Act were associated with a 3–4 percentage point increase in the probability that a teenager initiated the HPV vaccine. This relationship appears to have been driven in part by increases in Medicaid coverage, the probability of having a recent checkup, and knowledge about the HPV vaccine. Supporting this pathway, I show that Medicaid expansion states saw increased searches for “pediatrician,” “Gardasil” (a trade name of the HPV vaccine), and “HPV cancer.”","PeriodicalId":45056,"journal":{"name":"American Journal of Health Economics","volume":"7 1","pages":"222 - 247"},"PeriodicalIF":3.7,"publicationDate":"2020-12-15","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1086/713037","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"48397296","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}