The geography of hospital admission in a national health service with patient choice

Inter-regional patient mobility in decentralised Spain: Explaining regional budget imbalances

Inter-regional patient mobility represents both a resource and a challenge for the organization and financing of health systems, particularly in decentralised countries. We use cross-sectional time series regression analysis to test the determinants of imbalances in regional funds to finance inter-regional patient mobility for the 17 Spanish regions for the period 2014-2020. The findings indicate that highly specialised health centres and bilateral agreements partly explain the budget imbalance from inter-regional patient referrals, while local tourism partly explains the budget imbalance from non-referred patient mobility. Developing effective national schemes to compensate net patient recipient regions would be fundamental to addressing territorial imbalances.

Simulation analysis of an adjusted gravity model for hospital admissions robust to incomplete data

BACKGROUND

Gravity models are often hard to apply in practice due to their data-hungry nature. Standard implementations of gravity models require that data on each variable is available for each supply node. Since these model types are often applied in a competitive context, data availability of specific variables is commonly limited to a subset of supply nodes.

METHODS

This paper introduces a methodology that accommodates the use of variables for which data availability is incomplete, developed for a health care context, but more broadly applicable. The study uses simulated data to evaluate the performance of the proposed methodology in comparison with a conventional approach of dropping variables from the model.

RESULTS

It is shown that the proposed methodology is able to improve overall model accuracy compared to dropping variables from the model, and that model accuracy is considerably improved within the subset of supply nodes for which data is available, even when that availability is sparse.

CONCLUSION

The proposed methodology is a viable approach to improve the performance of gravity models in a competitive health care context, where data availability is limited, and especially where a the supply nodes with complete data are most relevant for the practitioner.

Factors associated with patients' mobility rates within the provinces of Iran

BACKGROUND

The absence of a referral system and patients' freedom to choose among service providers in Iran have led to increased patient mobility, which continues to concern health policymakers in the country. This study aimed to determine factors associated with patient mobility rates within the provinces of Iran.

METHODS

This cross-sectional study was conducted in Iran. Data on the place of residence of patients admitted to Iranian public hospitals were collected during August 2017 to determine the status of patient mobility within each province. The sample size were 537,786 patients were hospitalized in public hospitals in Iran during August 2017. The patient mobility ratio was calculated for each of Iran's provinces by producing a patient mobility matrix. Then, a model of factors affecting patient mobility was identified by regression analysis. All the analyses were performed using STATA14 software.

RESULTS

In the study period, 585,681 patients were admitted to public hospitals in Iran, of which 69,692 patients were referred to the hospital from another city and 51,789 of them were admitted to public hospitals in the capital of the province. The highest levels of intra-provincial patient mobility were attributed to southern and eastern provinces, and the lowest levels were observed in the north and west of Iran. Implementation of negative binomial regression indicated that, among the examined parameters, the distribution of specialist physicians and the human development index had the highest impact on intra-provincial patient mobility.

CONCLUSION

The distribution of specialists throughout different country areas plays a determining role in patient mobility. In many cases, redistributing hospital beds is impossible, but adopting different human resource policies could prevent unnecessary patient mobility through equitable redistribution of specialists among different cities.

Improving inpatient and daycare admission estimates with gravity models

Growing healthcare costs have been accompanied by increased policymakers’ interest in the efficiency of healthcare systems. Network formation by hospitals as a vehicle for consolidation and achieving economies of scale has emerged as an important topic of conversation among academics and practitioners. Within networks, consolidation of particular specialties or entire campuses is expected and encouraged to take place. This paper describes the main findings of an effort to build gravity-type models to describe patient choices in inpatient and daycare hospital facilities. It analyzes the distance decay effects as a function of car travel times and great-circle distance, and it offers a method for inclusion of university hospitals. Additionally, it reviews the impact of driving and transit accessibility on hospital attraction and reviews the differences in distance decay for patient age groups and hospitalization types. In the described application, the best models achieve a Mean Absolute Percentage Error of around 10% in non-metropolitan areas, and 14.5% across different region types. Results in metropolitan areas suggest that latent factors unrelated to proximity and size have a significant role in determining hospital choices. Furthermore, the effects of relative driving and transit accessibility are found to be small or non-existent.

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

BACKGROUND

Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.

METHODS

We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the weighted interval score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

RESULTS

All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.

CONCLUSIONS

Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

Comparative assessment of methods for short-term forecasts of COVID-19 hospital admissions in England at the local level

BACKGROUND

Forecasting healthcare demand is essential in epidemic settings, both to inform situational awareness and facilitate resource planning. Ideally, forecasts should be robust across time and locations. During the COVID-19 pandemic in England, it is an ongoing concern that demand for hospital care for COVID-19 patients in England will exceed available resources.

METHODS

We made weekly forecasts of daily COVID-19 hospital admissions for National Health Service (NHS) Trusts in England between August 2020 and April 2021 using three disease-agnostic forecasting models: a mean ensemble of autoregressive time series models, a linear regression model with 7-day-lagged local cases as a predictor, and a scaled convolution of local cases and a delay distribution. We compared their point and probabilistic accuracy to a mean-ensemble of them all, and to a simple baseline model of no change from the last day of admissions. We measured predictive performance using the Weighted Interval Score (WIS) and considered how this changed in different scenarios (the length of the predictive horizon, the date on which the forecast was made, and by location), as well as how much admissions forecasts improved when future cases were known.

RESULTS

All models outperformed the baseline in the majority of scenarios. Forecasting accuracy varied by forecast date and location, depending on the trajectory of the outbreak, and all individual models had instances where they were the top- or bottom-ranked model. Forecasts produced by the mean-ensemble were both the most accurate and most consistently accurate forecasts amongst all the models considered. Forecasting accuracy was improved when using future observed, rather than forecast, cases, especially at longer forecast horizons.

CONCLUSIONS

Assuming no change in current admissions is rarely better than including at least a trend. Using confirmed COVID-19 cases as a predictor can improve admissions forecasts in some scenarios, but this is variable and depends on the ability to make consistently good case forecasts. However, ensemble forecasts can make forecasts that make consistently more accurate forecasts across time and locations. Given minimal requirements on data and computation, our admissions forecasting ensemble could be used to anticipate healthcare needs in future epidemic or pandemic settings.

How to identify the drivers of patient inter-regional mobility in beveridgean systems? Critical review and assessment matrix for policy design & managerial interventions

Decentralized, tax-funded health systems like Italian and Spanish ones reveal relevant internal patient flows, raising concerns in terms of equity, budget imbalances, and unexploited economies of scale at the regional and organizational level. However, policymakers lack effective tools to rapidly identify the causes of patient outflows in Beveridgean healthcare systems. We address the gap by conducting a critical review of the drivers of patient mobility. Elaborating on existing knowledge, we propose a concise, versatile assessment matrix to help policymakers in understanding the most relevant causes of mobility. Specifically, we identify three main categories of drivers: insufficient service availability, poor (perceived) quality, and regulatory issues. We include appropriate indicators to identify each driver, or mix of drivers. For each of them, we also propose specific policy and organizational responses. The applicability of the model is proven by an empirical test using the Italian national hospital discharge database for all inter-regional inpatient mobility flows. In addition to adding to previous contributions on mobility drivers by creating a model that informs policymakers' understanding and actions, the paper provides an innovative approach to patient mobility by proposing a model that, for the first time, primarily focuses on the clinical discipline of the flows.

What has driven the spatial spillover of China's out-of-pocket payments?

Background

Even though China launched a series of measures to alleviate several financial burdens (including health insurance scheme, increased government investment, and so on), the economic burden of health expenditure has still not been alleviated. Out-of-pocket payments (OPPs) show not only a time correlation but also some degree of spatial correlation. The aims of the current study were thus to identify the spatial cluster of OPPs, to investigate the main factors affecting variation, and to explore the spatial spillover sources of China’s OPP.

Methods

Global and local spatial autocorrelation tests were validated to identify the spatial cluster of OPPs using the panel data of 31 provinces in China from 2005 to 2016. The Spatial Durbin Model, established in this paper, measured the spatial spillover effect of OPPs and analyzed the possible spillover sources (demand, supply, and socio-economic factors.

Results

OPPs were found to have a significant and positive spatial correlation. The results of the Spatial Durbin Model showed the direct and indirect effects of demand, supply, and socio- economic factors on China’s OPPs. Among the demand factors, the direct and indirect correlation (elasticity) coefficients were positive. Among the supply factors, the direct and indirect effects of the share of primary health beds on residents’ OPPs were negative. The ratio of health technicians in hospitals to those in primary health institutions on per capital OPPs had a significant indirect effect. Among the socio-economic factors, the direct effects of GDP, government health expenditure, and urbanization on OPPs were found to be positive. There were no significant indirect effects of socio-economic factors on OPPs.

Conclusion

This paper finds that China’s OPPs are not randomly distributed but, overall, present a positive spatial cluster, even though a series of measures have been launched to promote health equity. Socio-economic factors and those associated with demand were found to be the main influences of variation in OPPs, while demand was seen to be the driver of the positive spatial spillover of OPPs, whereby effective supply could inhibit these positive spillover effects.

Electronic supplementary material

The online version of this article (10.1186/s12913-019-4451-0) contains supplementary material, which is available to authorized users.

Effect of patient choice and hospital competition on service configuration and technology adoption within cancer surgery: a national, population-based study

Background

There is a scarcity of evidence about the role of patient choice and hospital competition policies on surgical cancer services. Previous evidence has shown that patients are prepared to bypass their nearest cancer centre to receive surgery at more distant centres that better meet their needs. In this national, population-based study we investigated the effect of patient mobility and hospital competition on service configuration and technology adoption in the National Health Service (NHS) in England, using prostate cancer surgery as a model.

Methods

We mapped all patients in England who underwent radical prostatectomy between Jan 1, 2010, and Dec 31, 2014, according to place of residence and treatment location. For each radical prostatectomy centre we analysed the effect of hospital competition (measured by use of a spatial competition index [SCI], with a score of 0 indicating weakest competition and 1 indicating strongest competition) and the effect of being an established robotic radical prostatectomy centre at the start of 2010 on net gains or losses of patients (difference between number of patients treated in a centre and number expected based on their residence), and the likelihood of closing their radical prostatectomy service.

Findings

Between Jan 1, 2010, and Dec 31, 2014, 19 256 patients underwent radical prostatectomy at an NHS provider in England. Of the 65 radical prostatectomy centres open at the start of the study period, 23 (35%) had a statistically significant net gain of patients during 2010–14. Ten (40%) of these 23 were established robotic centres. 37 (57%) of the 65 centres had a significant net loss of patients, of which two (5%) were established robotic centres and ten (27%) closed their radical prostatectomy service during the study period. Radical prostatectomy centres that closed were more likely to be located in areas with stronger competition (highest SCI quartile [0·87–0·92]; p=0·0081) than in areas with weaker competition. No robotic surgery centre closed irrespective of the size of net losses of patients. The number of centres performing robotic surgery increased from 12 (18%) of the 65 centres at the beginning of 2010 to 39 (71%) of 55 centres open at the end of 2014.

Interpretation

Competitive factors, in addition to policies advocating centralisation and the requirement to do minimum numbers of surgical procedures, have contributed to large-scale investment in equipment for robotic surgery without evidence of superior outcomes and contributed to the closure of cancer surgery units. If quality performance and outcome indicators are not available to guide patient choice, these policies could threaten health services' ability to deliver equitable and affordable cancer care.

Funding

National Institute for Health Research.

Spatial effects in hospital expenditures: A district level analysis

We use spatial econometric methods to analyse spillovers in hospital expenditures across Health Districts of the Emilia-Romagna Region (Italy). We estimate spatial models that allow for global spillovers and distinguish between the expenditures associated with potentially inappropriate hospitalizations and those associated with complex medical procedures. We also investigate the relative contribution of geographical and institutional proximity in explaining spatial dependence, by explicitly modelling different connectivity structures and exploiting them to build alternative spatial weight matrices. We find that interactions largely differ between types of expenditures, with positive spatial effects for potentially inappropriate admissions, the effect being generally not significant for high-complexity expenditure. Relying on the estimated direct and indirect effects, we also test for the presence of spatial spillovers across districts. Finally, the paper draws policy implications for the public health planner.

Patient Mobility for Elective Secondary Health Care Services in Response to Patient Choice Policies: A Systematic Review

Our review establishes the empirical evidence for patient mobility for elective secondary care services in countries that allow patients to choose their health care provider. PubMed and Embase were searched for relevant articles between 1990 and 2015. Of 5,994 titles/abstracts reviewed, 26 studies were included. The studies used three main methodological models to establish mobility. Variation in the extent of patient mobility was observed across the studies. Mobility was positively associated with lower waiting times, indicators of better service quality, and access to advanced technology. It was negatively associated with advanced age or lower socioeconomic backgrounds. From a policy perspective we demonstrate that a significant proportion of patients are prepared to travel beyond their nearest provider for elective services. As a consequence, some providers are likely to be “winners” and others “losers,” which could result in overall decreased provider capacity or inefficient utilization of existing services. Equity also remains a key concern.

Does Experience Rating Improve Obstetric Practices? Evidence from Italy

Using inpatient discharge records from the Italian region of Piedmont, we estimate the impact of an increase in malpractice pressure brought about by experience-rated liability insurance on obstetric practices. Our identification strategy exploits the exogenous location of public hospitals in court districts with and without schedules for noneconomic damages. We perform difference-in-differences analysis on the entire sample and on a subsample which only considers the nearest hospitals in the neighborhood of court district boundaries. We find that the increase in medical malpractice pressure is associated with a decrease in the probability of performing a C-section from 2.3 to 3.7 percentage points (7-11.6%) with no consequences for medical complications or neonatal outcomes. The impact can be explained by a reduction in the discretion of obstetric decision-making rather than by patient cream skimming.

Regional incentives and patient cross-border mobility: evidence from the Italian experience

BACKGROUND

In recent years, accreditation of private hospitals followed by decentralisation of the Italian National Health Service (NHS) into 21 regional health systems has provided a good empirical ground for investigating the Tiebout principle of "voting with their feet". We examine the infra-regional trade-off between greater patient choice (due to an increase in hospital services supply) and financial equilibrium, and we relate it to the significant phenomenon of Cross-Border Mobility (CBM) between Italian regions. Focusing on the rules supervising the financial agreements between regional authorities and providers of hospital care, we find incentives for private accredited providers in attracting patient inflows.

METHODS

The analysis is undertaken from an institutional, regulatory and empirical perspective. We select a sample of five regions with higher positive CBM balance and we examine regional regulations governing the contractual agreements between purchasers and providers of hospital care. According to this sample, we provide a statistical analysis of CBM and apply a Regional Attraction Ability Index (RAAI), aimed at testing patient preferences for private/public accredited providers.

RESULTS

We find that this index is systematically higher for private providers, both in the case of distance/boundary patients and of excellence/general hospitals.

CONCLUSION

Conclusions address both financial issues regarding the coverage of regional healthcare systems and equity issues on patient healthcare access. They also raise concerns on the new European Union (EU) directive inherent to patient mobility across Europe.

Traveling for care: inter-regional mobility for aortic valve substitution in Italy

Patient flows across the regions of the Italian National Health Service can shed light on patient mobility, including cross-border flows within the European Union. We used 2009 data on 11,531 NHS admissions for aortic valve replacement operations to measure the extent of inter-regional patient mobility and to determine whether resident and non-resident patients differ. We also investigated whether public and private hospitals behave differently in terms of attracting patients. For this major cardio-surgical intervention, patient mobility in Italy is substantial (13.6% of total admissions). Such mobility mainly involves patients moving from southern to northern regions, which often requires several hundred kilometers of travel and a transfer of financial resources from poorer to richer regions. Patients admitted in the regions where they reside are older than those admitted outside their regions (69.2 versus 65.6, p<0.0001), and stay in hospital approximately 0.7 days longer (14.7 versus 14.0, p=0.017). Compared to public hospitals, private hospitals are more likely to admit non-resident patients (OR between 2.1 and 4.4). The extent and direction of patients' mobility raise equity concerns, as receiving care in locations that are distant from home requires substantial financial and relational resources.

Dispersal of antibiotic-resistant high-risk clones by hospital networks: changing the patient direction can make all the difference

BACKGROUND

Patients who seek treatment in hospitals can introduce high-risk clones of hospital-acquired, antibiotic-resistant pathogens from previous admissions. In this manner, different healthcare institutions become linked epidemiologically. All links combined form the national patient referral network, through which high-risk clones can propagate.

AIM

To assess the influence of changes in referral patterns and network structure on the dispersal of these pathogens.

METHODS

Hospital admission data were mapped to reconstruct the English patient referral network, and 12 geographically distinct healthcare collectives were identified. The number of patients admitted and referred to hospitals outside their collective was measured. Simulation models were used to assess the influence of changing network structure on the spread of hospital-acquired pathogens.

FINDINGS

Simulation models showed that decreasing the number of between-collective referrals by redirecting, on average, just 1.5 patients/hospital/day had a strong effect on dispersal. By decreasing the number of between-collective referrals, the spread of high-risk clones through the network can be reduced by 36%. Conversely, by creating supra-regional specialist centres that provide specialist care at national level, the rate of dispersal can increase by 48%.

CONCLUSION

The structure of the patient referral network has a profound effect on the epidemic behaviour of high-risk clones. Any changes that affect the number of referrals between healthcare collectives, inevitably affect the national dispersal of these pathogens. These effects should be taken into account when creating national specialist centres, which may jeopardize control efforts.

Geographic distribution of healthcare resources, healthcare service provision, and patient flow in Japan: a cross sectional study

Healthcare systems in developed countries are facing the challenge of dealing with changing social structures as a result of rapidly aging populations. This study examines the relationship among the geographical distribution of healthcare resources, healthcare service provision, and interregional patient flow in Japan. A cross-sectional study was performed using data from healthcare-related public surveys conducted in 2008, together with social, economic, and environmental variables. The geographical units of analysis were 348 Secondary Healthcare Service Areas, which provide and manage most healthcare services in Japan. The equity of the distribution of physicians among hospitals and clinics was evaluated using the Lorenz curve and the Gini coefficient. Multiple regression analysis was used to examine the relationships between the inpatient flow ratio and selected variables. Next, the 348 Secondary Healthcare Service Areas were divided into tertiles according to the inpatient flow ratio, and differences among these variables were examined using Bonferroni's correction for multiple comparisons. The Gini coefficient for physician distribution among hospitals was 0.209 and was 0.165 among clinics. Multiple regression analysis showed that hospital physician density, the elderly ratio, and hospital bed density were all correlated with the inpatient flow ratio (β = 0.396, -0.576, 0.425, respectively; R(2) = 0.622, all ps < 0.001). Healthcare resources were significantly more scarce in the lowest tertile (outflow group) than in other groups in both hospitals and clinics. The provision of healthcare services was also imbalanced among tertiles. Our results imply that there is a need for reconstituting the geographical distribution of healthcare resources in Japan. Further research and healthcare-related databases are also needed to facilitate the creation of a more balanced geographical distribution and of a more effective healthcare system in Japan.