Evidence map›Paper›PMID 36232094›Full record

ArticleInternational journal of environmental research and public health2022

Trends in the Use of Home LTC Services in Large, Medium and Small Municipalities in Italy: Lessons for the Post-COVID-19 Reappraisal.

Carlo Lallo, Marta Pasqualini, Cecilia Tomassini

Abstract read
In one paragraph

Article in International journal of environmental research and public health, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

3 authors.

Carlo LalloDepartment of Economics, University of Molise, Via F. De Sanctis, 86100 Campobasso, Italy.ORCID 0000-0003-3855-5127
Marta PasqualiniDepartment MEMOTEF, Sapienza University of Rome, Via del Castro Laurenziano 9, 00161 Rome, Italy.ORCID 0000-0002-8379-375X
Cecilia TomassiniDepartment of Economics, University of Molise, Via F. De Sanctis, 86100 Campobasso, Italy.ORCID 0000-0002-2819-7779

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Italian Long-Term Care is considered largely inadequate, and the recent COVID-19 pandemic has dramatically exposed its limitations. Public Home Care Services in particular were revealed as under-financed and unable to cover the potential demand for care from the older population. But does the type of municipality and its geographic location play a role in creating or mitigating unmet demand? This is the first study addressing this research question in Italy. Our hypothesis is that older people's care preferences and care possibilities may vary between small, medium and metropolitan areas, as will the organisation, funding and availability of services, and the combination will influence (unmet) demand for public home care services. In this paper, using nationally representative survey data collected by the Italian National Statistical Institute in 2003 and 2016, we investigate changes and differences in the use of public and private home care services among people aged 75 or older in Italy by size of the municipality. Our results reveal inequalities in service use between Northern and Southern areas of the country and in particular between metropolitan areas, medium and small municipalities. Such differences reinforce post-pandemic calls for new investment and changes in the design of the Italian Long-Term Care system.

Indexed as

COVID-19AgedCitiesHumansItalyLong-Term CarePandemicsageing in placecare provisionCOVID-19 pandemicItalyLTCmunicipality sizeregional differences

Identifiers

PMID36232094
PMCPMC9566680

What OpenQuestion holds

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LicenceCC BY
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.