Evidence map›Paper›PMID 31992222›Full record

ArticleBMC nephrology2020

Prevalence of chronic kidney disease in the Lazio region, Italy: a classification algorithm based on health information systems.

Claudia Marino, Pietro Manuel Ferraro, Matteo Bargagli, Silvia Cascini, Nera Agabiti, Giovanni Gambaro, Marina Davoli

Open access · goldAbstract read
In one paragraph

Article in BMC nephrology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.7field-weighted citation impact, top 31% of its field
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

13 citing papers in PubMed, 19 citations in OpenAlex.

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  3. Cost-effectiveness of a ketoanalogue-supplemented very low-protein diet in CKD.Nephrology, dialysis, transplantation : official publication of the European Dialysis and Transplant Association - European Renal Association · 2025
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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

7 authors at 3 institutions in 1 country.

Claudia MarinoDepartment of Epidemiology Lazio Regional Health Service, Via Cristoforo Colombo, 112, 00147, Roma, Italy. c.marino@deplazio.it.ORCID 0000-0001-7430-6166
Pietro Manuel FerraroU.O.C. Nefrologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli, 8, 00168, Roma, Italy.
Matteo BargagliU.O.C. Nefrologia, Fondazione Policlinico Universitario A. Gemelli IRCCS, Largo Agostino Gemelli, 8, 00168, Roma, Italy.
Silvia CasciniDepartment of Epidemiology Lazio Regional Health Service, Via Cristoforo Colombo, 112, 00147, Roma, Italy.
Nera AgabitiDepartment of Epidemiology Lazio Regional Health Service, Via Cristoforo Colombo, 112, 00147, Roma, Italy.
Giovanni GambaroDepartment of Medicine, Renal Unit, Division of Nephrology and Dialysis, University of Verona, Piazzale Ludovico Antonio Scuro 10, 37134, Verona, Italy.
Marina DavoliDepartment of Epidemiology Lazio Regional Health Service, Via Cristoforo Colombo, 112, 00147, Roma, Italy.
Agenzia Regionale Parchi · ITUniversità Cattolica del Sacro Cuore · ITUniversity of Verona · IT

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundEstimating CKD prevalence is difficult. Information on CKD prevalence is rather scanty in Italy and available figures come from surveys in selected geographical areas. Administrative data have been already demonstrated to be an effective tool in estimating the epidemiological burden of diseases, however there is limited experience in literature as far as CKD is concerned.

methodsThe aim of this study is to develop an algorithm based on regional Health Administrative Databases to identify individuals with CKD and provide estimates of disease prevalence in Lazio Region (Italy); about 5.500.000 inhabitants in 2017. A population-level analysis based on a record-linkage strategy using data from Health Administrative Databases has been applied in Lazio Region. CKD cases were identified between January 1, 2012 and December 31, 2017 using Outpatient Specialist Service Information System, Hospital Discharge Registry, Ticket Exemption Registry and Drug Dispensing Registry. Age-specific and standardized prevalence rates were calculated by gender. CKD cases were classified as higher and lower severity.

resultsThe algorithm identified 99,457 individuals with CKD (mean age 71 years, 55.8% males). The exclusive contributions of each regional source used were: 35,047 (35.2%) from Outpatient Specialist Service Information System, 27,778 (27.9%) from Hospital Discharge Registry, 4143 (4.2%) from Ticket Exemption Registry and 463 (0.5%) from Drug Dispensing Registry; 5.1% of cases were found in all databases. The standardized prevalence rate at December 31, 2017 was 1.76, 2.06% for males and 1.50% for females. The prevalence increased with age, rising from 0.33% (age 0-18) up to 14.18% (age 85+) among males and from 0.25% up to 8.18% among females. The proportion of CKD individuals with lower severity disease was 78.7% in both genders.

conclusionsThe proposed algorithm represents a novel tool to monitor the burden of CKD disease, that can be used by the regional government to guide the development and implementation of evidence-based pathways of care for CKD patients. The high prevalence of people with CKD of lower severity should be carefully considered in order to promote diagnosis and optimal management at early stages.

Indexed as

AlgorithmsDatabases, FactualAdolescentAdultAgedAged, 80 and overChildChild, PreschoolFemaleHealth Information SystemsHumansInfantInfant, NewbornItalyMaleMedical Record LinkageAdministrative dataChronic kidney diseaseDiagnostic algorithmHealth administrative datasetsPrevalence

Identifiers

PMID31992222
PMCPMC6986004
OpenAlexW3006301423

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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.