In this paper, I examine how patient death affects referrals from referring physicians to cardiac surgeons. I use Medicare data to identify pairs of referring physicians and cardiac surgeons who experience a patient death after a major surgical procedure to examine how these events affect referrals. I construct counterfactuals for affected pairs using pairs that experience a patient death but five quarters in the future. I find that there is a significant decline in the number of referrals and probability of a referral from the referring physician to the cardiac surgeon after the patient's death.
{"title":"Effect of patient death on referrals to cardiac specialists","authors":"Sidra Haye","doi":"10.1002/hec.4840","DOIUrl":"10.1002/hec.4840","url":null,"abstract":"<p>In this paper, I examine how patient death affects referrals from referring physicians to cardiac surgeons. I use Medicare data to identify pairs of referring physicians and cardiac surgeons who experience a patient death after a major surgical procedure to examine how these events affect referrals. I construct counterfactuals for affected pairs using pairs that experience a patient death but five quarters in the future. I find that there is a significant decline in the number of referrals and probability of a referral from the referring physician to the cardiac surgeon after the patient's death.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-19","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"141065221","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Physicians often face tight resource constraints, meaning they have to make trade-offs between which patients they care for and the amount of care received. Studies show that patients requiring many resources disproportionately suffer a loss of care when resources are constrained. This study uncovers whether physicians' attitudes toward prioritization of healthcare predicts poor-health patients' access to care. We combine unique survey data on Danish GPs' preferred prioritization principle with register data on their patients' contacts in general practice. We consider different types of contacts as the required effort could impact the need for prioritization. Our results show variation in GPs' prioritization principles, where a majority prefers a principle that may lead to an unequal distribution of services. We further find that GPs' attitudes toward prioritization predict some poor-health patients' access to general practice. GPs who state they prefer the principle of prioritizing patients in the poorest health state when resources tightened provide more contacts to poor-health patients. The additional contacts are typically high-effort contacts such as annual status meetings and home visits, but also low-effort contacts such as emails. Our findings indicate inequity in poor-health patients' access to care across general practices.
{"title":"Do physicians' attitudes toward prioritization predict poor-health patients' access to care?","authors":"Anne Sophie Oxholm, Dorte Gyrd-Hansen","doi":"10.1002/hec.4843","DOIUrl":"10.1002/hec.4843","url":null,"abstract":"<p>Physicians often face tight resource constraints, meaning they have to make trade-offs between which patients they care for and the amount of care received. Studies show that patients requiring many resources disproportionately suffer a loss of care when resources are constrained. This study uncovers whether physicians' attitudes toward prioritization of healthcare predicts poor-health patients' access to care. We combine unique survey data on Danish GPs' preferred prioritization principle with register data on their patients' contacts in general practice. We consider different types of contacts as the required effort could impact the need for prioritization. Our results show variation in GPs' prioritization principles, where a majority prefers a principle that may lead to an unequal distribution of services. We further find that GPs' attitudes toward prioritization predict some poor-health patients' access to general practice. GPs who state they prefer the principle of prioritizing patients in the poorest health state when resources tightened provide more contacts to poor-health patients. The additional contacts are typically high-effort contacts such as annual status meetings and home visits, but also low-effort contacts such as emails. Our findings indicate inequity in poor-health patients' access to care across general practices.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-14","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hec.4843","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140922039","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Laia Maynou, Alistair McGuire, Victoria Serra-Sastre
This paper examines the effect of new medical technology (robotic surgery) on efficiency gains and productivity changes for surgical treatment in patients with prostate cancer from the perspective of a public health sector organization. In particular, we consider three interrelated surgical technologies within the English National Health System: robotic, laparoscopic and open radical prostatectomy. Robotic and laparoscopic techniques are minimally invasive procedures with similar clinical benefits. While the clinical benefits in adopting robotic surgery over laparoscopic intervention are unproven, it requires a high initial investment cost and carries high on-going maintenance costs. Using data from Hospital Episode Statistics for the period 2000–2018, we observe growing volumes of prostatectomies over time, mostly driven by an increase in robotic-assisted surgeries, and further analyze whether hospital providers that adopted a robot see improved measures of throughput. We then quantify changes in total factor and labor productivity arising from the use of this technology. We examine the impact of robotic adoption on efficiency gains employing a staggered difference-in-difference estimator and find evidence of a 50% reduction in length of stay (LoS), 49% decrease in post-LoS and 44% and 46% decrease in postoperative visits after 1 year and 2 years, respectively. Productivity analysis shows the growth in radical prostatectomy volume is sustained with a relatively stable number of urology surgeons. The robotic technique increases total production at the hospital level between 21% and 26%, coupled with a 29% improvement in labor productivity. These benefits lend some, but not overwhelming support for the large-scale hospital investments in such costly technology.
{"title":"Efficiency and productivity gains of robotic surgery: The case of the English National Health Service","authors":"Laia Maynou, Alistair McGuire, Victoria Serra-Sastre","doi":"10.1002/hec.4838","DOIUrl":"10.1002/hec.4838","url":null,"abstract":"<p>This paper examines the effect of new medical technology (robotic surgery) on efficiency gains and productivity changes for surgical treatment in patients with prostate cancer from the perspective of a public health sector organization. In particular, we consider three interrelated surgical technologies within the English National Health System: robotic, laparoscopic and open radical prostatectomy. Robotic and laparoscopic techniques are minimally invasive procedures with similar clinical benefits. While the clinical benefits in adopting robotic surgery over laparoscopic intervention are unproven, it requires a high initial investment cost and carries high on-going maintenance costs. Using data from Hospital Episode Statistics for the period 2000–2018, we observe growing volumes of prostatectomies over time, mostly driven by an increase in robotic-assisted surgeries, and further analyze whether hospital providers that adopted a robot see improved measures of throughput. We then quantify changes in total factor and labor productivity arising from the use of this technology. We examine the impact of robotic adoption on efficiency gains employing a staggered difference-in-difference estimator and find evidence of a 50% reduction in length of stay (LoS), 49% decrease in post-LoS and 44% and 46% decrease in postoperative visits after 1 year and 2 years, respectively. Productivity analysis shows the growth in radical prostatectomy volume is sustained with a relatively stable number of urology surgeons. The robotic technique increases total production at the hospital level between 21% and 26%, coupled with a 29% improvement in labor productivity. These benefits lend some, but not overwhelming support for the large-scale hospital investments in such costly technology.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hec.4838","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140908552","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Jisoo Hwang, Seung-sik Hwang, Hyuncheol Bryant Kim, Jungmin Lee, Junseok Lee
We utilize the phased rollout of COVID-19 vaccines by exact birth date in South Korea as a natural experiment for testing risk compensation. People may resume face-to-face social activities following vaccination because they perceive lower risk of infection. Applying a regression discontinuity design based on birth date cutoffs for vaccine eligibility, we find no evidence of risk-compensating behaviors, as measured by large, high-frequency data from credit card and airline companies as well as survey data. We find some evidence of self-selection into vaccine take-up based on perception toward vaccine effectiveness and side effects, but the treatment effects do not differ between compliers and never-takers.
{"title":"Risk compensation after COVID-19 vaccination: Evidence from vaccine rollout by exact birth date in South Korea","authors":"Jisoo Hwang, Seung-sik Hwang, Hyuncheol Bryant Kim, Jungmin Lee, Junseok Lee","doi":"10.1002/hec.4837","DOIUrl":"10.1002/hec.4837","url":null,"abstract":"<p>We utilize the phased rollout of COVID-19 vaccines by exact birth date in South Korea as a natural experiment for testing risk compensation. People may resume face-to-face social activities following vaccination because they perceive lower risk of infection. Applying a regression discontinuity design based on birth date cutoffs for vaccine eligibility, we find no evidence of risk-compensating behaviors, as measured by large, high-frequency data from credit card and airline companies as well as survey data. We find some evidence of self-selection into vaccine take-up based on perception toward vaccine effectiveness and side effects, but the treatment effects do not differ between compliers and never-takers.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-10","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hec.4837","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140903545","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
I investigate heterogeneity across occupational characteristics in the effect of retirement eligibility on mental health in the United Kingdom. I use K-means clustering to define three occupational clusters, differing across multiple dimensions. I estimate the effect of retirement eligibility using a Regression Discontinuity Design, allowing the effect to differ by cluster. The effects of retirement eligibility are beneficial, and greater in two clusters: one comprised of white-collar jobs in an office setting and another of blue-collar jobs with high physical demands and hazards. The cluster with smaller benefits mixes blue- and white-collar uncompetitive jobs with high levels of customer interaction. The results have implications for the distributional effect of raising the retirement age.
{"title":"The effect of retirement eligibility on mental health in the United Kingdom: Heterogeneous effects by occupation","authors":"Joe Spearing","doi":"10.1002/hec.4835","DOIUrl":"10.1002/hec.4835","url":null,"abstract":"<p>I investigate heterogeneity across occupational characteristics in the effect of retirement eligibility on mental health in the United Kingdom. I use K-means clustering to define three occupational clusters, differing across multiple dimensions. I estimate the effect of retirement eligibility using a Regression Discontinuity Design, allowing the effect to differ by cluster. The effects of retirement eligibility are beneficial, and greater in two clusters: one comprised of white-collar jobs in an office setting and another of blue-collar jobs with high physical demands and hazards. The cluster with smaller benefits mixes blue- and white-collar uncompetitive jobs with high levels of customer interaction. The results have implications for the distributional effect of raising the retirement age.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-05-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hec.4835","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140855408","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Andrew L. Hicks, Ernst R. Berndt, Richard G. Frank
Changes in the dynamics of prescription drug markets have raised issues regarding whether the United States Bureau of Labor Statistics' (BLS’) Prescription Drug Consumer Price Index (CPI-Rx) has adequately kept up with the evolving marketplace. The CPI-Rx limits its sampling frame to retail outpatient outlets and excludes prescription pharmaceuticals dispensed in non-retail settings such as hospitals, physician/clinic outpatient facilities, and nursing homes. Thus, the CPI-Rx overlooks the increasingly important specialty pharmaceuticals dispensed in non-retail settings, whose transactions are instead captured in the overall hospital and professional services component of the medical care CPI. Specialty drugs now account for about 55% of all U.S. drug spending, double the share of a decade earlier. To the extent specialty drug price growth differs from that of traditional pharmaceuticals, the CPI-Rx could provide an inaccurate measure of overall drug price inflation. We calculate a chained Laspeyres CPI using data from the Merative™ MarketScan® Databases for the years 2010–2019 and IQVIA-designated specialty drugs and offer evidence showing that by not sampling specialty drugs in non-retail settings, the CPI-Rx has understated overall U.S. prescription drug inflation by just under 75 basis points annually. We discuss implications for health care policy and suggest the BLS examine the feasibility of publishing an overall pharmaceutical price index incorporating both traditional and specialty pharmaceuticals dispensed in retail and non-retail settings.
{"title":"Auditing the prescription drug consumer price index in a changing marketplace","authors":"Andrew L. Hicks, Ernst R. Berndt, Richard G. Frank","doi":"10.1002/hec.4836","DOIUrl":"10.1002/hec.4836","url":null,"abstract":"<p>Changes in the dynamics of prescription drug markets have raised issues regarding whether the United States Bureau of Labor Statistics' (BLS’) Prescription Drug Consumer Price Index (CPI-Rx) has adequately kept up with the evolving marketplace. The CPI-Rx limits its sampling frame to retail outpatient outlets and excludes prescription pharmaceuticals dispensed in non-retail settings such as hospitals, physician/clinic outpatient facilities, and nursing homes. Thus, the CPI-Rx overlooks the increasingly important specialty pharmaceuticals dispensed in non-retail settings, whose transactions are instead captured in the overall hospital and professional services component of the medical care CPI. Specialty drugs now account for about 55% of all U.S. drug spending, double the share of a decade earlier. To the extent specialty drug price growth differs from that of traditional pharmaceuticals, the CPI-Rx could provide an inaccurate measure of overall drug price inflation. We calculate a chained Laspeyres CPI using data from the Merative™ MarketScan® Databases for the years 2010–2019 and IQVIA-designated specialty drugs and offer evidence showing that by not sampling specialty drugs in non-retail settings, the CPI-Rx has understated overall U.S. prescription drug inflation by just under 75 basis points annually. We discuss implications for health care policy and suggest the BLS examine the feasibility of publishing an overall pharmaceutical price index incorporating both traditional and specialty pharmaceuticals dispensed in retail and non-retail settings.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-29","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140833778","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
David Glynn, John Giardina, Julia Hatamyar, Ankur Pandya, Marta Soares, Noemi Kreif
There is increasing interest in moving away from “one size fits all (OSFA)” approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules (‘policies’) that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for “standard” and “intensive” blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.
人们越来越关注从 "一刀切(OSFA)"的方法转向分层治疗决策。了解预期疗效和成本效益如何随患者协变量的变化而变化是分层决策的一个关键方面。最近提出的机器学习(ML)方法可以在不预先指定亚组或函数形式的情况下学习结果的异质性,从而构建决策规则("政策"),将个体协变量映射到治疗决策中。然而,这些方法尚未将 ML 估计值整合到决策建模框架中,以反映与政策相关的长期结果并综合多种来源的信息。在本文中,我们提出了一种整合 ML 和决策建模的方法,即在有患者个人数据的情况下,估算特定治疗的生存时间。我们还提出了一种新颖的策略树算法实施方法,利用决策模型输出来定义子组。我们使用 SPRINT(收缩压干预试验)演示了这些方法,比较了 "标准 "和 "强化 "血压目标的治疗效果。我们发现,将 ML 纳入决策模型可影响 OSFA 政策的增量净健康效益 (INHB) 估计值。我们还发现有证据表明,使用基于树状算法定义的亚组对治疗进行分层可以提高 INHB 的估计值。
{"title":"Integrating decision modeling and machine learning to inform treatment stratification","authors":"David Glynn, John Giardina, Julia Hatamyar, Ankur Pandya, Marta Soares, Noemi Kreif","doi":"10.1002/hec.4834","DOIUrl":"10.1002/hec.4834","url":null,"abstract":"<p>There is increasing interest in moving away from “one size fits all (OSFA)” approaches toward stratifying treatment decisions. Understanding how expected effectiveness and cost-effectiveness varies with patient covariates is a key aspect of stratified decision making. Recently proposed machine learning (ML) methods can learn heterogeneity in outcomes without pre-specifying subgroups or functional forms, enabling the construction of decision rules (‘policies’) that map individual covariates into a treatment decision. However, these methods do not yet integrate ML estimates into a decision modeling framework in order to reflect long-term policy-relevant outcomes and synthesize information from multiple sources. In this paper, we propose a method to integrate ML and decision modeling, when individual patient data is available to estimate treatment-specific survival time. We also propose a novel implementation of policy tree algorithms to define subgroups using decision model output. We demonstrate these methods using the SPRINT (Systolic Blood Pressure Intervention Trial), comparing outcomes for “standard” and “intensive” blood pressure targets. We find that including ML into a decision model can impact the estimate of incremental net health benefit (INHB) for OSFA policies. We also find evidence that stratifying treatment using subgroups defined by a tree-based algorithm can increase the estimates of the INHB.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/hec.4834","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140657247","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
This paper identifies the health penalty experienced by girls due to having a brother from endogenous sibling gender composition. We propose a girls-to-girls comparison strategy and rule out the confounding effect from the sibship size, birth interval, and birth order. Employing an instrumental variable approach and data from the Chinese Family Panel Studies, we find that girls with a brother are demonstrably shorter and report poorer health. This “brother's penalty” manifests even prenatally. Alternative explanations, such as birth order disadvantages, are carefully addressed and ruled out. The results hold even after excluding gender-neutral ethnic minorities. This observed penalty is likely attributed to unequal resource allocation within families and potential parental neglect. This penalty is amplified in families with lower income and maternal education, implying resource constraints contribute to gender discrimination. Our findings highlight the importance of addressing intrafamily gender bias for ensuring equal opportunities and health outcomes.
{"title":"The brother's penalty: Boy preference and girls' health in rural China","authors":"Yuli Ye, Qinying He, Qiang Li, Lian An","doi":"10.1002/hec.4833","DOIUrl":"10.1002/hec.4833","url":null,"abstract":"<p>This paper identifies the health penalty experienced by girls due to having a brother from endogenous sibling gender composition. We propose a girls-to-girls comparison strategy and rule out the confounding effect from the sibship size, birth interval, and birth order. Employing an instrumental variable approach and data from the Chinese Family Panel Studies, we find that girls with a brother are demonstrably shorter and report poorer health. This “brother's penalty” manifests even prenatally. Alternative explanations, such as birth order disadvantages, are carefully addressed and ruled out. The results hold even after excluding gender-neutral ethnic minorities. This observed penalty is likely attributed to unequal resource allocation within families and potential parental neglect. This penalty is amplified in families with lower income and maternal education, implying resource constraints contribute to gender discrimination. Our findings highlight the importance of addressing intrafamily gender bias for ensuring equal opportunities and health outcomes.</p><p><b>Clinical trial registration</b>: Not applicable.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-04-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140737920","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
We investigate the effects of regulations governing the practice autonomy of dental hygienists on dental care use with the 2001–2014 Medical Expenditure Panel Survey. We measure the strength of autonomy regulations by extending the Dental Hygiene Professional Practice Index to the years 2001–2014, allowing us to capture changes in regulations within states over time. Using a difference-in-differences framework applied to selected states, we find that relaxing supervision requirements to provide dental hygienists moderate autonomy results in an increase in total dental visits due to greater use of preventive dental care. However, the use of dental treatment decreases when states adopt the highest level of autonomy. Both sets of estimates increase in magnitude when we subset the sample to dental care provider shortage areas. In support of these findings, we show that dental visits shift to dental hygienists in shortage areas when states expand the scope of practice of hygienists, and that there is an increase in tasks performed by hygienists, such as cleanings and dental exams.
{"title":"The effects of dental hygienist autonomy on dental care utilization","authors":"Jie Chen, Chad D. Meyerhoefer, Edward J. Timmons","doi":"10.1002/hec.4832","DOIUrl":"10.1002/hec.4832","url":null,"abstract":"<p>We investigate the effects of regulations governing the practice autonomy of dental hygienists on dental care use with the 2001–2014 Medical Expenditure Panel Survey. We measure the strength of autonomy regulations by extending the Dental Hygiene Professional Practice Index to the years 2001–2014, allowing us to capture changes in regulations within states over time. Using a difference-in-differences framework applied to selected states, we find that relaxing supervision requirements to provide dental hygienists moderate autonomy results in an increase in total dental visits due to greater use of preventive dental care. However, the use of dental treatment decreases when states adopt the highest level of autonomy. Both sets of estimates increase in magnitude when we subset the sample to dental care provider shortage areas. In support of these findings, we show that dental visits shift to dental hygienists in shortage areas when states expand the scope of practice of hygienists, and that there is an increase in tasks performed by hygienists, such as cleanings and dental exams.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-27","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140305402","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Are youths who consume pornography more likely to engage in risky sexual behaviors? Using longitudinal data from the National Study of Youth and Religion and an individual fixed effects strategy, this paper investigates the relationship between pornography use among 13- to 23-year-olds and a range of subsequent risky sexual behaviors. It also estimates a lagged dependent variable model where risky sexual behavior of the previous wave is included as a control. The findings suggest that moderate and frequent pornography use increases the likelihood of engaging in acts such as unprotected sex and having multiple sexual partners. Finally, a heterogeneity analysis by gender reveals that males and females behave differently in response to exposure to pornography, but that is true for only a few indicators of risky sex. The paper's findings provide critical information on determinants of risky sexual behavior and meaningful evidence for the policy debate on government censoring and monitoring online behavior.
阅读色情作品的青少年是否更有可能从事危险的性行为?本文利用《全国青年与宗教研究》(National Study of Youth and Religion)的纵向数据和个体固定效应策略,研究了 13 至 23 岁青少年使用色情制品与一系列后续危险性行为之间的关系。本文还估算了一个滞后因变量模型,将上一波的危险性行为作为对照。研究结果表明,适度和频繁使用色情制品会增加发生无保护性行为和有多个性伴侣等行为的可能性。最后,按性别进行的异质性分析表明,男性和女性在接触色情制品后的行为有所不同,但这只适用于危险性行为的少数指标。本文的研究结果提供了有关危险性行为决定因素的重要信息,并为有关政府审查和监控网络行为的政策辩论提供了有意义的证据。
{"title":"Pornography usage during adolescence: Does it lead to risky sexual behavior?","authors":"Hamida Mubasshera","doi":"10.1002/hec.4825","DOIUrl":"10.1002/hec.4825","url":null,"abstract":"<p>Are youths who consume pornography more likely to engage in risky sexual behaviors? Using longitudinal data from the National Study of Youth and Religion and an individual fixed effects strategy, this paper investigates the relationship between pornography use among 13- to 23-year-olds and a range of subsequent risky sexual behaviors. It also estimates a lagged dependent variable model where risky sexual behavior of the previous wave is included as a control. The findings suggest that moderate and frequent pornography use increases the likelihood of engaging in acts such as unprotected sex and having multiple sexual partners. Finally, a heterogeneity analysis by gender reveals that males and females behave differently in response to exposure to pornography, but that is true for only a few indicators of risky sex. The paper's findings provide critical information on determinants of risky sexual behavior and meaningful evidence for the policy debate on government censoring and monitoring online behavior.</p>","PeriodicalId":12847,"journal":{"name":"Health economics","volume":null,"pages":null},"PeriodicalIF":2.0,"publicationDate":"2024-03-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"140174259","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}