Why do patients bypass the nearest hospital? An empirical analysis for orthopaedic care and neurosurgery in the Netherlands

Varkevisser, Marco; Geest, Stéphanie A. van der

doi:10.1007/s10198-006-0035-0

Cited by 89 publications

(99 citation statements)

References 13 publications

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“…It is also worth emphasizing here that we are working with a large, comprehensive data set that includes admissions data for all hospital departments in a region that incorporates both urban and rural areas, rather than focusing on just one specialty as other authors have done (e.g. [2,37]). Our results can therefore confirm that the expected dependence on travel times and other factors still holds for all hospital admissions in a geographically diverse region.…”

Section: Discussionmentioning

confidence: 99%

“…RP data from the Netherlands are used in [37] and [5], and from the UK in [2], all of which focus mainly on choice of orthopaedic care. Several hospital choice studies use similar data to ours, hospital discharge records, but in a United States setting [10,24], and [35] use Medicare claims data.…”

Section: Background: Discrete Choice Modellingmentioning

confidence: 99%

“…The effects of waiting times on patient choice are studied by several authors. [37] develop a binary choice model to study how waiting times affect the decision to bypass the nearest hospital. They find that the probability of bypassing a hospital decreases by between 2% and 10% when waiting times are less than the average.…”

Section: Background: Discrete Choice Modellingmentioning

confidence: 99%

“…Proximity is especially important for those on low incomes or those who rely on hospital transport. Nonetheless, there is always a trade-off between distance and other factors such as confidence in the treatment and waiting time [37]. In this study we use travel time by car as a proxy for the inconvenience of travel; however, we acknowledge that the assumption that all patients travel by car may introduce inaccuracies, particularly in the urban parts of this study area.…”

Section: Factors Affecting a Patient's Choice Of Hospitalmentioning

confidence: 99%

“…Moreover, effects of patients bypassing smaller local hospitals in favour of more distant larger hospitals, such as teaching hospitals, have been observed in several countries [37]. In this study we use number of beds as a proxy for the size and level of functioning (secondary or tertiary) of hospitals.…”

Section: Factors Affecting a Patient's Choice Of Hospitalmentioning

confidence: 99%

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Patient choice modelling: how do patients choose their hospitals?

Smith

Currie

Chaiwuttisak

et al. 2017

Health Care Manag Sci

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As an aid to predicting future hospital admissions, we compare use of the Multinomial Logit and the Utility Maximising Nested Logit models to describe how patients choose their hospitals. The models are fitted to real data from Derbyshire, United Kingdom, which lists the postcodes of more than 200,000 admissions to six different local hospitals. Both elective and emergency admissions are analysed for this mixed urban/rural area. For characteristics that may affect a patient's choice of hospital, we consider the distance of the patient from the hospital, the number of beds at the hospital and the number of car parking spaces available at the hospital, as well as several statistics publicly available on National Health Service (NHS) websites: an average waiting time, the patient survey score for ward cleanliness, the patient safety score and the inpatient survey score for overall care. The Multinomial Logit model is successfully fitted to the data. Results obtained with the Utility Maximising Nested Logit model show that nesting according to city or town may be invalid for these data; in other words, the choice of hospital does not appear to be preceded by choice of city. In all of the analysis carried out, distance appears to be one of the main influences on a patient's choice of hospital rather than statistics available on the Internet.

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Section: Discussionmentioning

confidence: 99%

Section: Background: Discrete Choice Modellingmentioning

confidence: 99%

Section: Background: Discrete Choice Modellingmentioning

confidence: 99%

Section: Factors Affecting a Patient's Choice Of Hospitalmentioning

confidence: 99%

Section: Factors Affecting a Patient's Choice Of Hospitalmentioning

confidence: 99%

See 3 more Smart Citations

Patient choice modelling: how do patients choose their hospitals?

Smith

Currie

Chaiwuttisak

et al. 2017

Health Care Manag Sci

View full text Add to dashboard Cite

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Simulating the impact of centralization of prostate cancer surgery services on travel burden and equity in the English National Health Service: A national population based model for health service re‐design

et al. 2020

Self Cite

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Introduction:There is limited evidence on the impact of centralization of cancer treatment services on patient travel burden and access to treatment. Using prostate cancer surgery as an example, this national study analysis aims to simulate the effect of different centralization scenarios on the number of center closures, patient travel times, and equity in access. Methods:We used patient-level data on all men (n = 19,256) undergoing radical prostatectomy in the English National Health Service between January 1, 2010 and December 31, 2014, and considered three scenarios for centralization of prostate cancer surgery services A: procedure volume, B: availability of specialized services, and C: optimization of capacity. The probability of patients travelling to each of the remaining centers in the choice set was predicted using a conditional logit model, based on preferences revealed through actual hospital selections. Multivariable linear regression analysed the impact on travel time according to patient characteristics. Results: Scenarios A, B, and C resulted in the closure of 28, 24, and 37 of the 65 radical prostatectomy centers, respectively, affecting 3993 (21%), 5763 (30%), and 7896 (41%) of the men in the study. Despite similar numbers of center closures the expected average increase on travel time was very different for scenario B (+15 minutes) and A (+28 minutes). A distance minimization approach, assigning patients to their next nearest center, with patient preferences not considered, estimated a lower impact on travel burden in all scenarios. The additional travel burden on older, sicker, less affluent patients was evident, but where significant, the absolute difference was very small. Conclusion:The study provides an innovative simulation approach using national patient-level datasets, patient preferences based on actual hospital selections, and personal characteristics to inform health service planning. With this approach, we demonstrated for prostate cancer surgery that three different centralization scenarios

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Health‐seeking behavior and hospital choice in China's New Cooperative Medical System

Brown

Theoharides

2009

Health Economics

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Since the dissolution of the Rural Cooperative Medical System at the end of the commune period, illness has emerged as a leading cause of poverty in rural China. To address the poor state of health care, the Chinese government unveiled the New Cooperative Medical System in 2002. Because local governments have been given significant control over program design, fundamental characteristics of the program vary from one county to the next. These differences may influence the decision to seek health care as well as the choice of hospital conditional on that initial decision. In this paper, we use a nested logit model to analyze household survey data from 25 counties to analyze the determinants of such health-seeking behavior. We find that age, the share of household expenditures allocated to food consumption (a measure of relative income), and the presence of other sick people in the household negatively affect the decision to seek health care while disability has a positive influence. Further, conditional on seeking treatment, the reimbursement scheme in place in each county and the average daily expenditure associated with hospitalization strongly influence hospital choice.

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Why do patients bypass the nearest hospital? An empirical analysis for orthopaedic care and neurosurgery in the Netherlands

Cited by 89 publications

References 13 publications

Patient choice modelling: how do patients choose their hospitals?

Patient choice modelling: how do patients choose their hospitals?

Simulating the impact of centralization of prostate cancer surgery services on travel burden and equity in the English National Health Service: A national population based model for health service re‐design

Health‐seeking behavior and hospital choice in China's New Cooperative Medical System

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