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Resta E, Resta O, Costantiello A, Leogrande A. The hospital emigration to another region in the light of the environmental, social and governance model in Italy during the period 2004-2021. BMC Public Health 2024; 24:1880. [PMID: 39009998 PMCID: PMC11247882 DOI: 10.1186/s12889-024-19369-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/01/2024] [Accepted: 07/04/2024] [Indexed: 07/17/2024] Open
Abstract
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.
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Affiliation(s)
| | | | - Alberto Costantiello
- LUM University Giuseppe Degennaro, Strada Statale 100 km 18, Casamassima, Bari, Puglia, Italia
| | - Angelo Leogrande
- LUM University Giuseppe Degennaro, Strada Statale 100 km 18, Casamassima, Bari, Puglia, Italia.
- , LUM Enterprise s.r.l. Strada Statale 100 km 18, Casamassima, Bari, Puglia, Italia.
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An influence of dew point temperature on the occurrence of Mycobacterium tuberculosis disease in Chennai, India. Sci Rep 2022; 12:6147. [PMID: 35413979 PMCID: PMC9005621 DOI: 10.1038/s41598-022-10111-4] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/20/2021] [Accepted: 03/21/2022] [Indexed: 11/28/2022] Open
Abstract
Climate factors such as dew point temperature, relative humidity and atmospheric temperature may be crucial for the spread of tuberculosis. This study was conducted for the first time to investigate the relationship of climatic factors with TB occurrence in an Indian setting. Daily tuberculosis notification data during 2008–2015 were generated from the National Treatment Elimination Program, and analogous daily climatic data were obtained from the Regional Meteorological Centre at Chennai city, Tamil Nadu, India. The decomposition method was adopted to split the series into deterministic and non-deterministic components, such as seasonal, non-seasonal, trend and cyclical, and non-deterministic climate factors. A generalized linear model was used to assess the relation independently. TB disease progression from latent stage infection to active was supported by higher dew point temperature and moderate temperature. It had a significant association with TB progression in the summer and monsoon seasons. The relative humidity may be favored in the winter and post-monsoon. The water tiny dew droplets may support the TB bacterium to recuperate in the environment.
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Duckworth C, Chmiel FP, Burns DK, Zlatev ZD, White NM, Daniels TWV, Kiuber M, Boniface MJ. Using explainable machine learning to characterise data drift and detect emergent health risks for emergency department admissions during COVID-19. Sci Rep 2021; 11:23017. [PMID: 34837021 PMCID: PMC8626460 DOI: 10.1038/s41598-021-02481-y] [Citation(s) in RCA: 28] [Impact Index Per Article: 9.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/27/2021] [Accepted: 11/15/2021] [Indexed: 01/11/2023] Open
Abstract
A key task of emergency departments is to promptly identify patients who require hospital admission. Early identification ensures patient safety and aids organisational planning. Supervised machine learning algorithms can use data describing historical episodes to make ahead-of-time predictions of clinical outcomes. Despite this, clinical settings are dynamic environments and the underlying data distributions characterising episodes can change with time (data drift), and so can the relationship between episode characteristics and associated clinical outcomes (concept drift). Practically this means deployed algorithms must be monitored to ensure their safety. We demonstrate how explainable machine learning can be used to monitor data drift, using the COVID-19 pandemic as a severe example. We present a machine learning classifier trained using (pre-COVID-19) data, to identify patients at high risk of admission during an emergency department attendance. We then evaluate our model's performance on attendances occurring pre-pandemic (AUROC of 0.856 with 95%CI [0.852, 0.859]) and during the COVID-19 pandemic (AUROC of 0.826 with 95%CI [0.814, 0.837]). We demonstrate two benefits of explainable machine learning (SHAP) for models deployed in healthcare settings: (1) By tracking the variation in a feature's SHAP value relative to its global importance, a complimentary measure of data drift is found which highlights the need to retrain a predictive model. (2) By observing the relative changes in feature importance emergent health risks can be identified.
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Affiliation(s)
- Christopher Duckworth
- School of Electronics and Computer Science, University of Southampton, Southampton, UK.
| | - Francis P Chmiel
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Dan K Burns
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Zlatko D Zlatev
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Neil M White
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
| | - Thomas W V Daniels
- Department of Respiratory Medicine, Minerva House, University Hospital Southampton, Southampton, UK
- School of Clinical and Experimental Sciences, Faculty of Medicine, University of Southampton, Southampton General Hospital, LF13A, South Academic Block, Southampton, UK
| | - Michael Kiuber
- Emergency Department, University Hospital Southampton NHS Foundation Trust, Southampton, UK
| | - Michael J Boniface
- School of Electronics and Computer Science, University of Southampton, Southampton, UK
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Immanuel SA, Schrader G, Bidargaddi N. Differences in Temporal Relapse Characteristics Between Affective and Non-affective Psychotic Disorders: Longitudinal Analysis. Front Psychiatry 2021; 12:558056. [PMID: 33692704 PMCID: PMC7938319 DOI: 10.3389/fpsyt.2021.558056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/27/2020] [Accepted: 01/28/2021] [Indexed: 11/13/2022] Open
Abstract
Objective: Multiple relapses over time are common in both affective and non-affective psychotic disorders. Characterizing the temporal nature of these relapses may be crucial to understanding the underlying neurobiology of relapse. Materials and Methods: Anonymized records of patients with affective and non-affective psychotic disorders were collected from SA Mental Health Data Universe and retrospectively analyzed. To characterize the temporal characteristic of their relapses, a relapse trend score was computed using a symbolic series-based approach. A higher score suggests that relapse follows a trend and a lower score suggests relapses are random. Regression models were built to investigate if this score was significantly different between affective and non-affective psychotic disorders. Results: Logistic regression models showed a significant group difference in relapse trend score between the patient groups. For example, in patients who were hospitalized six or more times, relapse score in affective disorders were 2.6 times higher than non-affective psychotic disorders [OR 2.6, 95% CI (1.8-3.7), p < 0.001]. Discussion: The results imply that the odds of a patient with affective disorder exhibiting a predictable trend in time to relapse were much higher than a patient with recurrent non-affective psychotic disorder. In other words, within recurrent non-affective psychosis group, time to relapse is random. Conclusion: This study is an initial attempt to develop a longitudinal trajectory-based approach to investigate relapse trend differences in mental health patients. Further investigations using this approach may reflect differences in underlying biological processes between illnesses.
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Affiliation(s)
- Sarah A. Immanuel
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Flinders Digital Health Research Centre, Flinders University, Adelaide, SA, Australia
| | - Geoff Schrader
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Barossa Gawler Adelaide Hills Fleurieu Local Health Network, Adelaide, SA, Australia
| | - Niranjan Bidargaddi
- College of Medicine and Public Health, Flinders University, Adelaide, SA, Australia
- Flinders Digital Health Research Centre, Flinders University, Adelaide, SA, Australia
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Ben-Assuli O, Heart T, Vest JR, Ramon-Gonen R, Shlomo N, Klempfner R. Profiling Readmissions Using Hidden Markov Model - the Case of Congestive Heart Failure. INFORMATION SYSTEMS MANAGEMENT 2020. [DOI: 10.1080/10580530.2020.1847362] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
Affiliation(s)
- Ofir Ben-Assuli
- Faculty of Business Administration, Ono Academic College, Kiryat Ono, Israel
| | - Tsipi Heart
- Faculty of Business Administration, Ono Academic College, Kiryat Ono, Israel
| | - Joshua R. Vest
- Fairbanks School of Public Health, Indiana University, Bloomington, Indiana, USA
| | - Roni Ramon-Gonen
- The Graduate School of Business Administration , Bar Ilan University, Ramat-Gan, Israel
| | - Nir Shlomo
- The Leviev Heart Center, Sheba Medical Center, Ramat Gan, Israel
| | - Robert Klempfner
- The Leviev Heart Center, Sheba Medical Center, Ramat Gan, Israel
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Bakstein E, Mladá K, Fárková E, Kolenič M, Španiel F, Manková D, Korčáková J, Winkler P, Hajek T. Cross-sectional and within-subject seasonality and regularity of hospitalizations: A population study in mood disorders and schizophrenia. Bipolar Disord 2020; 22:508-516. [PMID: 31883178 DOI: 10.1111/bdi.12884] [Citation(s) in RCA: 8] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
BACKGROUND Seasonal peaks in hospitalizations for mood disorders and schizophrenia are well recognized and often replicated. The within-subject tendency to experience illness episodes in the same season, that is, seasonal course, is much less established, as certain individuals may temporarily meet criteria for seasonal course purely by chance. AIMS In this population, prospective cohort study, we investigated whether between and within-subject seasonal patterns of hospitalizations occurred more frequently than would be expected by chance. METHODS Using a compulsory, standardized national register of hospitalizations, we analyzed all admissions for mood disorders and schizophrenia in the Czech Republic between 1994 and 2013. We used bootstrap tests to compare the observed numbers of (a) participants with seasonal/regular course and (b) hospitalizations in individual months against empirical distributions obtained by simulations. RESULTS Among 87 184 participants, we found uneven distribution of hospitalizations, with hospitalization peaks for depression in April and November (X2 (11) = 363.66, P < .001), for mania in August (X2 (11) = 50.36, P < .001) and for schizophrenia in June (X2 (11) = 70.34, P < .001). Significantly more participants than would be expected by chance, had two subsequent rehospitalizations in the same 90 days in different years (7.36%, bootstrap P < .01) or after a regular, but non-seasonal interval (6.07%, bootstrap P < .001). The proportion of participants with two consecutive hospitalizations in the same season was below chance level (7.06%). CONCLUSIONS Psychiatric hospitalizations were unevenly distributed throughout the year (cross-sectional seasonality), with evidence for regularity, but not seasonality of hospitalizations within subjects. Our data do not support the validity of seasonal pattern specifier. Season may be a general risk factor, which increases the risk of hospitalizations across psychiatric participants.
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Affiliation(s)
- Eduard Bakstein
- National Institute of Mental Health, Klecany, Czech Republic
| | - Karolína Mladá
- National Institute of Mental Health, Klecany, Czech Republic
| | - Eva Fárková
- National Institute of Mental Health, Klecany, Czech Republic.,3rd School of Medicine, Charles University, Prague, Czech Republic
| | - Marian Kolenič
- National Institute of Mental Health, Klecany, Czech Republic.,3rd School of Medicine, Charles University, Prague, Czech Republic
| | - Filip Španiel
- National Institute of Mental Health, Klecany, Czech Republic
| | - Denisa Manková
- National Institute of Mental Health, Klecany, Czech Republic
| | - Jana Korčáková
- National Institute of Mental Health, Klecany, Czech Republic.,3rd School of Medicine, Charles University, Prague, Czech Republic
| | - Petr Winkler
- National Institute of Mental Health, Klecany, Czech Republic
| | - Tomas Hajek
- National Institute of Mental Health, Klecany, Czech Republic.,Department of Psychiatry, Dalhousie University, Halifax, NS, Canada
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Hong WS, Haimovich AD, Taylor RA. Predicting hospital admission at emergency department triage using machine learning. PLoS One 2018; 13:e0201016. [PMID: 30028888 PMCID: PMC6054406 DOI: 10.1371/journal.pone.0201016] [Citation(s) in RCA: 101] [Impact Index Per Article: 16.8] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2018] [Accepted: 07/06/2018] [Indexed: 01/09/2023] Open
Abstract
OBJECTIVE To predict hospital admission at the time of ED triage using patient history in addition to information collected at triage. METHODS This retrospective study included all adult ED visits between March 2014 and July 2017 from one academic and two community emergency rooms that resulted in either admission or discharge. A total of 972 variables were extracted per patient visit. Samples were randomly partitioned into training (80%), validation (10%), and test (10%) sets. We trained a series of nine binary classifiers using logistic regression (LR), gradient boosting (XGBoost), and deep neural networks (DNN) on three dataset types: one using only triage information, one using only patient history, and one using the full set of variables. Next, we tested the potential benefit of additional training samples by training models on increasing fractions of our data. Lastly, variables of importance were identified using information gain as a metric to create a low-dimensional model. RESULTS A total of 560,486 patient visits were included in the study, with an overall admission risk of 29.7%. Models trained on triage information yielded a test AUC of 0.87 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.88) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on patient history yielded an AUC of 0.86 for LR (95% CI 0.86-0.87), 0.87 for XGBoost (95% CI 0.87-0.87) and 0.87 for DNN (95% CI 0.87-0.88). Models trained on the full set of variables yielded an AUC of 0.91 for LR (95% CI 0.91-0.91), 0.92 for XGBoost (95% CI 0.92-0.93) and 0.92 for DNN (95% CI 0.92-0.92). All algorithms reached maximum performance at 50% of the training set or less. A low-dimensional XGBoost model built on ESI level, outpatient medication counts, demographics, and hospital usage statistics yielded an AUC of 0.91 (95% CI 0.91-0.91). CONCLUSION Machine learning can robustly predict hospital admission using triage information and patient history. The addition of historical information improves predictive performance significantly compared to using triage information alone, highlighting the need to incorporate these variables into prediction models.
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Affiliation(s)
- Woo Suk Hong
- Yale School of Medicine, New Haven, Connecticut, United States of America
| | | | - R. Andrew Taylor
- Department of Emergency Medicine, Yale School of Medicine, New Haven, Connecticut, United States of America
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