Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy
The objective of this study was to determine the relationship between weather conditions and hospital admissions for cardiovascular diseases (CVD). The analysed data of CVD hospital admissions were part of the database of the Policlinico Giovanni XXIII of Bari (southern Italy) within a reference per...
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| Format: | Article |
| Language: | English |
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MDPI AG
2023-02-01
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| Series: | Healthcare |
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| Online Access: | https://www.mdpi.com/2227-9032/11/5/690 |
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| author | Vito Telesca Gianfranco Castronuovo Gianfranco Favia Cristina Marranchelli Vito Alberto Pizzulli Maria Ragosta |
| author_facet | Vito Telesca Gianfranco Castronuovo Gianfranco Favia Cristina Marranchelli Vito Alberto Pizzulli Maria Ragosta |
| author_sort | Vito Telesca |
| collection | DOAJ |
| description | The objective of this study was to determine the relationship between weather conditions and hospital admissions for cardiovascular diseases (CVD). The analysed data of CVD hospital admissions were part of the database of the Policlinico Giovanni XXIII of Bari (southern Italy) within a reference period of 4 years (2013–2016). CVD hospital admissions have been aggregated with daily meteorological recordings for the reference time interval. The decomposition of the time series allowed us to filter trend components; consequently, the non-linear exposure–response relationship between hospitalizations and meteo-climatic parameters was modelled with the application of a Distributed Lag Non-linear model (DLNM) without smoothing functions. The relevance of each meteorological variable in the simulation process was determined by means of machine learning feature importance technique. The study employed a Random Forest algorithm to identify the most representative features and their respective importance in predicting the phenomenon. As a result of the process, the mean temperature, maximum temperature, apparent temperature, and relative humidity have been determined to be the most suitable meteorological variables as the best variables for the process simulation. The study examined daily admissions to emergency rooms for cardiovascular diseases. Using a predictive analysis of the time series, an increase in the relative risk associated with colder temperatures was found between 8.3 °C and 10.3 °C. This increase occurred instantly and significantly 0–1 days after the event. The increase in hospitalizations for CVD has been shown to be correlated to high temperatures above 28.6 °C for lag day 5. |
| first_indexed | 2024-03-11T07:24:22Z |
| format | Article |
| id | doaj.art-423fc3d7eaf54a28a624c3463e69473e |
| institution | Directory Open Access Journal |
| issn | 2227-9032 |
| language | English |
| last_indexed | 2024-03-11T07:24:22Z |
| publishDate | 2023-02-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Healthcare |
| spelling | doaj.art-423fc3d7eaf54a28a624c3463e69473e2023-11-17T07:43:14ZengMDPI AGHealthcare2227-90322023-02-0111569010.3390/healthcare11050690Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern ItalyVito Telesca0Gianfranco Castronuovo1Gianfranco Favia2Cristina Marranchelli3Vito Alberto Pizzulli4Maria Ragosta5School of Engineering, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, ItalySchool of Engineering, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, ItalyInterdisciplinary of Medicine, School of Medicine, University of Bari, Piazza Giulio Cesare 11, 70124 Bari, ItalyFreelance Engineer, Via Mazzini 54, 75025 Policoro, ItalySchool of Engineering, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, ItalySchool of Engineering, University of Basilicata, Viale dell’Ateneo Lucano 10, 85100 Potenza, ItalyThe objective of this study was to determine the relationship between weather conditions and hospital admissions for cardiovascular diseases (CVD). The analysed data of CVD hospital admissions were part of the database of the Policlinico Giovanni XXIII of Bari (southern Italy) within a reference period of 4 years (2013–2016). CVD hospital admissions have been aggregated with daily meteorological recordings for the reference time interval. The decomposition of the time series allowed us to filter trend components; consequently, the non-linear exposure–response relationship between hospitalizations and meteo-climatic parameters was modelled with the application of a Distributed Lag Non-linear model (DLNM) without smoothing functions. The relevance of each meteorological variable in the simulation process was determined by means of machine learning feature importance technique. The study employed a Random Forest algorithm to identify the most representative features and their respective importance in predicting the phenomenon. As a result of the process, the mean temperature, maximum temperature, apparent temperature, and relative humidity have been determined to be the most suitable meteorological variables as the best variables for the process simulation. The study examined daily admissions to emergency rooms for cardiovascular diseases. Using a predictive analysis of the time series, an increase in the relative risk associated with colder temperatures was found between 8.3 °C and 10.3 °C. This increase occurred instantly and significantly 0–1 days after the event. The increase in hospitalizations for CVD has been shown to be correlated to high temperatures above 28.6 °C for lag day 5.https://www.mdpi.com/2227-9032/11/5/690hospital admissioncardiovascular diseasestemperaturedistributed lag non-linear modeltime series decompositionfeature importance |
| spellingShingle | Vito Telesca Gianfranco Castronuovo Gianfranco Favia Cristina Marranchelli Vito Alberto Pizzulli Maria Ragosta Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy Healthcare hospital admission cardiovascular diseases temperature distributed lag non-linear model time series decomposition feature importance |
| title | Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy |
| title_full | Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy |
| title_fullStr | Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy |
| title_full_unstemmed | Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy |
| title_short | Effects of Meteo-Climatic Factors on Hospital Admissions for Cardiovascular Diseases in the City of Bari, Southern Italy |
| title_sort | effects of meteo climatic factors on hospital admissions for cardiovascular diseases in the city of bari southern italy |
| topic | hospital admission cardiovascular diseases temperature distributed lag non-linear model time series decomposition feature importance |
| url | https://www.mdpi.com/2227-9032/11/5/690 |
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