Hospital admission tendencies caused by day-to-day temperature changes during summer: a case study for the city of Novi Sad (Serbia)

Savić, Stevan; Arsenović, Daniela; Lužanin, Zorana; Milošević, Dragan; Dunjić, Jelena; Šećerov, Ivan; Kojić, Milena; Radić, Ivana; Harhaji, Sanja; Arsic, Miodrag

doi:10.1007/s00484-023-02447-x

Cited by 5 publications

(2 citation statements)

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“…One of the major benefits of this type of deployment was its ability to operate at a low Internet service fee, and ensure high reliability and performance on low-budget hardware [17]. Here, we can The dataset stored on the FKP represents a subset of the data from the original experiment, and is openly available [15]. Metadata describes air temperature data captured hourly from 12 of the urban sites in Novi Sad over a period of 2 years covering 2016 and 2017.…”

Section: Micro Climate Knowledge Portalmentioning

confidence: 99%

Constructing a Searchable Knowledge Repository for FAIR Climate Data

Roantree¹,

Lalić

Savić

et al. 2023

Preprint

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The development of a knowledge repository for climate science data is a multidisciplinary effort between the domain experts (climate scientists), data engineers who's skills include design and building a knowledge repository, and machine learning researchers who provide expertise on data preparation tasks such as gap filling and advise on different machine learning models that can exploit this data. One of the main goals of the CA20108 cost action is to develop a knowledge portal that is fully compliant with the FAIR principles for scientific data management. In the first year, a bespoke knowledge portal was developed to capture metadata for FAIR datasets. Its purpose was to provide detailed metadata descriptions for shareable micro-meteorological (micromet) data using the WMO standard. While storing Network, Site and Sensor metadata locally, the system passes the actual data to Zenodo, receives back the DOI and thus, creates a permanent link between the Knowledge Portal and the storage platform Zenodo. While the user searches the Knowledge portal (metadata), results provide both detailed descriptions and links to data on the Zenodo platform. Our adherence to FAIR principles are documented below: <ul> <li>Findable. Machine-readable metadata is required for automatic discovery of datasets and services. A metadata description is supplied by the data owners for all micro-meteorological data shared on the system which subsequently drives the search engine, using keywords or network, site and sensor search terms.</li> <li>Accessible. When suitable datasets have been identified, access details should be provided. Assuming data is freely accessible, Zenodo DOIs and links are provided for direct data access.</li> <li>Interoperable. Data interoperability means the ability to share and integrate data from different users and sources. This can only happen if a standard (meta)data model is employed to describe data, an important concept which generally requires data engineering skills to deliver. In the knowledge portal presented here, the WMO guide provides the design and structure for metadata.&#160;&#160;&#160;&#160;</li> <li>Reusable. To truly deliver reusability, metadata should be expressed in as detailed a manner as possible. In this way, data can be replicated and integrated according to different scientific requirements. While the Knowledge Portal facilitates very detailed metadata descriptions, not all metadata is compulsory as it was accepted that in some cases, the overhead in providing this information can be very costly.&#160;</li> </ul> Simple analytics are in place to monitor the volume and size of networks in the system. Current metrics include: network count; average size of network (number of sites); dates and size of datasets per network/site; numbers and types of sensors in each site, etc. The current Portal is in Beta version meaning that the system is currently functional but open only to members of the Cost Action who are nominated testers. This status is due to change in Q1/2023 when access will be open to the wider climate science community.&#160;&#160; Current plans include new Tools and Services to assess the quality of data, including the level of gaps and in some cases, machine learning tools will be provided to attempt gap filling for datasets meeting certain requirements. &#160;

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Section: Micro Climate Knowledge Portalmentioning

confidence: 99%

Constructing a Searchable Knowledge Repository for FAIR Climate Data

Roantree¹,

Lalić

Savić

et al. 2023

Preprint

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“…On the other hand, results for California showed that during the warm season (May-September), an increase in the mean apparent temperature was associated with an increase in hospital admissions related to certain cardiovascular diseases (Green et al, 2010). Generally, this association has been confirmed for exposure to both heat and cold, and has been shown to vary by latitude and cause of hospitalization (Schwartz et al, 2004;Phosri et al, 2020;Savić et al, 2023).…”

Section: Introductionmentioning

confidence: 99%

Impact of urban outdoor thermal conditions on selected hospital admissions in Novi Sad, Serbia

Arsenović,

Savić,

Milošević

et al. 2024

Populationyearbook

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Climate change has been recognized as an important issue in public health, with particular concerns being raised about the effects of heat and cold extremes on health, and about seasonal changes over the year and their associations with increased mortality and hospitalizations. This paper explored the relationship between physiological equivalent temperature (PET) and cardiovascular and respiratory hospital admissions in Novi Sad (Serbia) with the aim of assessing the impact of urban outdoor thermal conditions on health. Analysis was performed using daily data on cardiovascular and respiratory hospital admissions by gender covering the period from January 1, 2016 to December 31, 2017. For the same period, PET was calculated using data from two urban meteorological network stations. The association between PET and hospital admissions was examined using a generalized additive model (GAM) combined with a distributed lag non-linearmodel (DLNM). The study found a non-linear relationship between PET and cardiovascular and respiratory hospital admissions, with a larger impact during the cold period of the year. The findings also indicated that under conditions of high PET, the cumulative RR increased for cardiovascular admissions (for males) and respiratory admissions (for females). People with pre-existing respiratory diseases were found to be more vulnerable under conditions of extremely low and moderately low PET, with a greater effect at lag 0–14 days. By contrast, for people with cardiovascular diseases, low PET was linked to a decrease in hospital admissions, with the risk being lowest at lag 0 and 0–3 days.

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