Improving efficiency or impairing access? Health care consolidation and quality of care: Evidence from emergency hospital closures in Sweden

Avdic, Daniel

doi:10.1016/j.jhealeco.2016.02.002

Cited by 62 publications

(61 citation statements)

References 35 publications

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“…The magnitude of the IV effect reported here is, on the other hand, comparable to previous results in the literature, despite the focus here is on health outcomes before hospital treatment whether other settings look health outcomes observed after that. This is in line with the idea that time matters and that, as shown by Avdic (2016), most deaths during cardiovascular emergencies take place before the patients reach the hospital. Jena et al (2017) report an increase in 30-day mortality rate for a cardiovascular episode due to ambulance delay similar to this setting (4.4 minutes longer responses on marathon days, and 3.3 percentage points risk difference in mortality).…”

Section: Resultssupporting

confidence: 86%

“…39 It is assumed that someone undergoing a cardiac event who calls an ambulance will survive for at least one year if the hospital is reached in time. This assumption is supported by Avdic (2016) who shows that the mortality rate of emergency patients is similar at the time of admission to the hospital and one year later.…”

Section: The Value Of One Minutementioning

confidence: 72%

“…8 The elasticity of life-threatening conditions with respect to response time is 0.9 and 5 in the case of mortality. Avdic (2016) shows that most deaths during cardiovascular emergencies take place before the patients are able to get in-hospital medical treatment.…”

Section: Introductionmentioning

confidence: 99%

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It Could Be Worse...It Could Be Raining: Ambulance Response Time and Health Outcomes

Lucchese¹

2020

SSRN Journal

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Ambulance response time to emergency calls is a key indicator of a health system's efficiency although its impact on health is not precisely known. This causal relation is identified by exploiting rainfall at the time of the ambulance run as a shock to responsiveness. The elasticity of the likelihood of a severe cardiovascular condition with respect to response time is 0.9 and that of the likelihood of death before reaching the hospital is 5. Finally, the economic value of time is quantified, and it is shown that improving the ambulance's ability to locate the scene would substantially increase efficiency.

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Section: Resultssupporting

confidence: 86%

Section: The Value Of One Minutementioning

confidence: 72%

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It Could Be Worse...It Could Be Raining: Ambulance Response Time and Health Outcomes

Lucchese¹

2020

SSRN Journal

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“…In public healthcare systems, patients are not allowed to direct the ambulance to a specific hospital, and the ambulance has no discretion to decide where to transport the patient. Differently, in the US, there is some discretion in emergency situations.4 Although we control for elements correlated with local development, such as population density and income levels, some concerns may remain.5 The OLS bias reconciles our findings with both the evidence fromYamashita and Kunkel (2010), who show that hospital proximity has no significant impact on heart disease mortality rates once the socioeconomic characteristics of patients are considered, and the evidence fromAvdic (2016), who assesses the impact of proximity only for the first year after a hospital closure.…”

supporting

confidence: 51%

The life‐saving effect of hospital proximity

Bertoli

Grembi

2017

Health Economics

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SummaryWe provide a new assessment of the effect of hospital proximity in an emergency situation-road-traffic accidents-exploiting the exogenous variation in the proximity to cities that are legally allowed to have a hospital on the basis of their population size. Our instrumental variable results show that a one-standard-deviation increase in the distance to the nearest hospital (5 km) raises the fatality rate by 13.84% at the sample average. This figure is equal to 0.92 additional deaths per 100 accidents. We show that both ordinary least squares and difference-in-differences estimates, common approaches in the literature, provide a downward-biased measure of the true effect of hospital proximity because they do not fully solve spatial sorting problems. Proximity is more important when the level of road safety is low, when emergency services are less responsive, and when the nearest hospital has relatively low quality standards.

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“…For example, some individuals in South Korea cannot have their medical care needs met, due to the lack of geographical accessibility of medical services, although most public healthcare services have been held accountable by the Korean government for a long time [2]. Recent studies found that the probability of mortality after an emergency was affected by the distance from an emergency hospital [3,4]. Social inequity in public health has been a challenging issue in social and community welfare, and many researchers have examined the geographical aspects of such an issue [5,6].…”

Section: Introductionmentioning

confidence: 99%

Analysis of accessibility to emergency rooms by dynamic population from mobile phone data: Geography of social inequity in South Korea

Yun

Kim

et al. 2020

PLoS ONE

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Accessibility of emergency medical care is one of the crucial factors in evaluating national primary medical care systems. While many studies have focused on this issue, there was a fundamental limit to the measurement of accessibility of emergency rooms, because the commonly used census-based population data are difficult to provide realistic information in terms of time and space. In this study, we evaluated the geographical accessibility of emergency rooms in South Korea by using dynamic population counts from mobile phone data. Such population counts were more accurate and up-to-date because they are obtained by aggregating the number of mobile phone users in a 50-by-50 m grid of a locational field, weighted by stay time. Considering both supply and demand of emergency rooms, the 2step floating catchment analysis was implemented. As a result, urban areas, including the capital city Seoul, showed lower accessibility to emergency rooms, whereas rural areas recorded higher accessibility. This result was contrary to the results analyzed by us based on census-based population data: higher accessibility in urban areas and lower in rural. This implies that using solely census data for accessibility analysis could lead to certain errors, and adopting mobile-based population data would represent the real-world situations for solving problems of social inequity in primary medical care. OPEN ACCESS Citation: Yun SB, Kim S, Ju S, Noh J, Kim C, Wong MS, et al. (2020) Analysis of accessibility to emergency rooms by dynamic population from mobile phone data: Geography of social inequity in South Korea. PLoS ONE 15(4): e0231079. https://

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Improving efficiency or impairing access? Health care consolidation and quality of care: Evidence from emergency hospital closures in Sweden

Cited by 62 publications

References 35 publications

It Could Be Worse...It Could Be Raining: Ambulance Response Time and Health Outcomes

It Could Be Worse...It Could Be Raining: Ambulance Response Time and Health Outcomes

The life‐saving effect of hospital proximity

Analysis of accessibility to emergency rooms by dynamic population from mobile phone data: Geography of social inequity in South Korea

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