Patient mobility for cardiac problems: a risk-adjusted analysis in Italy

The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021

The following article presents an analysis of the impact of the Environmental, Social and Governance-ESG determinants on Hospital Emigration to Another Region-HEAR in the Italian regions in the period 2004-2021. The data are analysed using Panel Data with Random Effects, Panel Data with Fixed Effects, Pooled Ordinary Least Squares-OLS, Weighted Least Squares-WLS, and Dynamic Panel at 1 Stage. Furthermore, to control endogeneity we also created instrumental variable models for each component of the ESG model. Results show that HEAR is negatively associated to the E, S and G component within the ESG model. The data were subjected to clustering with a k-Means algorithm optimized with the Silhouette coefficient. The optimal clustering with k=2 is compared to the sub-optimal cluster with k=3. The results suggest a negative relationship between the resident population and hospital emigration at regional level. Finally, a prediction is proposed with machine learning algorithms classified based on statistical performance. The results show that the Artificial Neural Network-ANN algorithm is the best predictor. The ANN predictions are critically analyzed in light of health economic policy directions.

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.

Inter-Regional Patients' Migration for Hospital Orthopedic Intensive Rehabilitation: The Italian Experience

BACKGROUND

Following the introduction of administrative federalism in the Italian National Health Service, inter-regional patients' mobility has become increasingly relevant because, in addition to being an indirect index of the quality of care, it has important economic and financial implications. This study aimed to evaluate the fulfillment of the need for hospital orthopedic intensive rehabilitation on site and care-seeking patients' migration to other regions.

METHODS

From 2011 to 2019, the data of intensive orthopedic rehabilitation extracts from the Hospital Discharge Cards provided by Italian Ministry of Health were analyzed. We studied the hospital networks of every Italian region (catchment areas). The epidemiological flows of inter-regional mobility were analyzed with Gandy's Nomogram, while the financial flows were analyzed through Attraction Absorption and Escape Production Indexes.

RESULTS

Gandy's Nomogram showed that only Piedmont, Lombardy, A.P. of Trento, E. Romagna, Umbria and Abruzzo had good public hospital planning for intensive orthopedic rehabilitation, with a positive balance for all studied periods. Lombardy, E. Romagna, Piedmont, Veneto and Latium have absorbed approximately 70% of all financial flows (about EUR 60.5 million).

CONCLUSIONS

Only six regions appear to be able to satisfy the care needs of their residents, with a positive epidemiological and financial balance for all studied periods.

Analysis of the Characteristics of Cross-Regional Patient Groups and Differences in Hospital Service Utilization in Beijing

When medical diagnostic difficulties occur at local hospitals, seeking high-quality services across regions becomes a priority for many patients. Traditional statistical methods in health care are unable to account for spatial characteristics such as outflow place or distributions of disease type and patient ages in the context of an increasing number of cross-regional groups; thus, these methods are incapable of studying service utilization differences among hospitals. From a geographic perspective, we analyzed the spatial characteristics of cross-regional patient groups who travelled from other places to Beijing and the spatial decay patterns in the actual service utilization of different hospitals in Beijing by using geographic calculations, geographic visualizations, and distance decay functions. We found the following results: (1) It is feasible to study patients’ cross-regional mobility from a geographical perspective. Through interdisciplinary integration, we can explore laws and conclusions that cannot be examined by traditional statistical methods in health care. (2) The characteristics of cross-regional patients who travelled from other places to Beijing were as follows: (a) Most patients came from northern China, and neoplasm treatment was the main demand of cross-regional patients; (b) patients 40–65 years old were the main cross-regional treatment group, and the average age of patients from northern regions and certain eastern coastal cities was relatively high. (3) The exponential distance decay function was the best of all five distance decay functions in fitting the distribution of cross-regional patient mobility to hospitals of different levels, types, and functional areas. The results of applying this function and the centrality calculation method showed that hospital service utilization was least affected by distance and that average radial distances (AR) were greatest in tertiary hospitals (distance decay coefficient β = 0.0786, AR = 664.70 km), traditional Chinese medicine hospitals (β = 0.0752, AR = 743.52 km), and hospitals in urban extension areas (β = 0.0782, AR = 693.29 km). Our results can serve as a reference for research concerning the allocation of medical resources and patients’ choices regarding medical treatment.

Inter-Regional Hospital Patients' Mobility in Italy

Background: The federalization of the Italian National Health Service (NHS) gave administrative, financial, and managerial independence to regions. They are in reciprocal competition according to the “quasi-market” model. A network of independent providers replaced the state monopoly. The NHS, based on the Beveridge model in which citizens are free to choose their place of treatment, was consolidated. The aim of our research was to analyze the fulfillment of need for hospital services on site and patients’ migration to hospitals of other regions. Material and Methods: We analyzed data from 2013 to 2017 of Hospital Discharge Cards (HDCs) provided by the Ministry of Health. The subjects of the analysis (catchment areas) were the hospital networks of every Italian region. The study of flows was developed through Internal Demand Satisfaction, Attraction, Escape, Attraction, Absorption, and Escape Production indexes. Graphic representations were produced using Gandy’s Nomogram and Qgis software. Results: In the studied period, the mean number of mobility admission was 678.659 ± 3.388, with an increase of 0.90%; in particular, the trend for ordinary regime increased 1.17%. Regions of central/northern Italy have attracted more than 60% of the escapes of the southern ones. Gandy’s Nomogram showed that only nine regions had optimal public hospital planning (Lombardy, Autonomous Province of Bolzano, Veneto, Friuli V.G., Emilia-Romagna, Tuscany, Umbria, Latium and Molise). Conclusion: The central/northern regions appear more able to meet the care needs of their citizens and to attract patients than the southern ones.

Clustering patient mobility patterns to assess effectiveness of health-service delivery

Background

Analysis of patient mobility in a country not only gives an idea of how the health-care system works, but also can be a guideline to determine the quality of health care and health disparity among regions. Even though determination of patient movement is important, it is not often realized that patient mobility could have a unique pattern beyond health-related endowments (e.g., facilities, medical staff). This study therefore addresses the following research question: Is there a way to identify regions with similar patterns using spatio-temporal distribution of patient mobility? The aim of the paper is to answer this question and improve a classification method that is useful for populous countries like Turkey that have many administrative areas.

Methods

The data used in the study consist of spatio-temporal information on patient mobility for the period between 2009 and 2013. Patient mobility patterns based on the number of patients attracted/escaping across 81 provinces of Turkey are illustrated graphically. The hierarchical clustering method is used to group provinces in terms of the mobility characteristics revealed by the patterns. Clustered groups of provinces are analyzed using non-parametric statistical tests to identify potential correlations between clustered groups and the selected basic health indicators.

Results

Ineffective health-care delivery in certain regions of Turkey was determined through identifying patient mobility patterns. High escape values obtained for a large number of provinces suggest poor health-care accessibility. On the other hand, over the period of time studied, visualization of temporal mobility revealed a considerable decrease in the escape ratio for inadequately equipped provinces. Among four of twelve clusters created using the hierarchical clustering method, which include 64 of 81 Turkish provinces, there was a statistically significant relationship between the patterns and the selected basic health indicators of the clusters. The remaining eight clusters included 17 provinces and showed anomalies.

Conclusions

The most important contribution of this study is the development of a way to identify patient mobility patterns by analyzing patient movements across the clusters. These results are strong evidence that patient mobility patterns provide a useful tool for decisions concerning the distribution of health-care services and the provision of health care equipment to the provinces.

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.

Short-term adjusted outcomes for heart failure

PURPOSE

Heart failure (HF) is recognized as a major problem in industrialized countries. Short-term adjusted outcomes are indicators of quality for care process during/after hospitalization. Our aim is to evaluate, for patients with principal diagnosis of HF, in-hospital mortality and 30-day readmissions for all-causes using two different risk adjustment (RA) tools.

METHODS AND RESULTS

We used data from the hospital discharge abstract (HD) of a retrospective cohort of patients (2002-2007) admitted in Tuscan hospitals, Italy. Considered outcomes were in-hospital mortality and readmission at 30 days. We compared the All-Patients Refined Diagnosis Related Groups (APR-DRG) system and the Elixhauser Index (EI). Logistic regression was performed and models were compared using the C statistic (C). examined records were 58.202. Crude in-hospital mortality was 9.7%. Thirty-day readmission was 5.1%. The APR-DRG class of risk of death (ROD) was a predictive factor for in-hospital mortality; the APR-DRG class of severity was not significantly associated with 30-day readmissions (P>0.05). EI comorbidities which were more strongly associated with outcomes were nonmetastatic cancer for in-hospital mortality (odds ratio, OR 2.25, P<0.001), uncomplicated and complicated diabetes for 30-day hospital readmissions (OR 1.20 and 1.34, P<0.001). The discriminative abilities for in-hospital mortality were sufficient for both models (C 0.67 for EI, C 0.72 for APR-DRG) while they were low for 30-day readmissions rate (C 0.53 and 0.52).

CONCLUSIONS

Age, gender, APR-DRG ROD and some Elixhauser comorbidities are predictive factors of outcomes; only the APR-DRG showed an acceptable ability to predict hospital mortality while none of them was satisfactory in predicting the readmissions within 30 days.

Factors influencing inpatients' satisfaction with hospitalization service in public hospitals in Shanghai, People's Republic of China

OBJECTIVE

The aim of this study was to observe the current status of inpatient satisfaction and analyze the possible factors influencing patient satisfaction during hospitalization.

METHODS

A cross-sectional investigation was conducted to obtain basic information about inpatient satisfaction, and statistical methods were used to describe and analyze the data. A total of 878 questionnaires were included in this study. A 5-point Likert scale rating was employed to assess items related to hospitalization care. Nonparametric tests and ordinal logistic analysis were used to explore the relationship between predictors and the patients' overall satisfaction.

RESULTS

Among the respondents, 89.75% were satisfied overall with the service they received during hospitalization, while 0.57% reported dissatisfaction. Inpatient demographic characteristics such as sex of the patients, occupation, age, and residence had significant associations with satisfaction, while monthly income and marital status did not. Additionally, the statistical outcome indicated that doctors' and nurses' service attitudes, and expenditure and environment were found to have an impact on the inpatient satisfaction ratings, with odds ratio of 2.43, 3.19, and 2.72, respectively.

CONCLUSION

This study emphasizes the influence of sex of the patients, the service attitudes of the doctors and nurses, and expenditure and environment on inpatient satisfaction. An increase in satisfaction ratings concerning the areas of doctors' and nurses' service attitudes, and expenditure and environment can improve the overall satisfaction levels. Responsible health management departments should pay attention to patient satisfaction and improve the quality of relevant health services, thus ultimately enhancing inpatients' hospitalization experiences.

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.