Age-specific ALS incidence: a dose–response meta-analysis View Full Text


Ontology type: schema:ScholarlyArticle     


Article Info

DATE

2018-04-23

AUTHORS

Benoît Marin, Andrea Fontana, Simona Arcuti, Massimilano Copetti, Farid Boumédiene, Philippe Couratier, Ettore Beghi, Pierre Marie Preux, Giancarlo Logroscino

ABSTRACT

To evaluate the association between worldwide ALS incidence rates and age, using a dose–response meta-analysis. We reviewed Medline and Embase up to July 2016 and included all population-based studies of newly-diagnosed cases, using multiple sources for case ascertainment. A dose–response meta-analysis was performed. A meta-regression investigated potential sources of heterogeneity. Of 3254 articles identified in the literature, we included 41 incidence studies covering 42 geographical areas. Overall, the fit between observed and predicted age-specific rates was very good. The expected variation of ALS incidence with age was characterized, in each study, by a progressive increase in the incidence from the 40s leading to a peak in the 60s or 70s, followed by a sharp decrease. Cochran’s Q test suggested a significant heterogeneity between studies. Overall, estimated patterns of ALS age-specific incidence (at which the peak was reached) were similar among subcontinents of Europe and North America: peak of ALS incidence ranged in these areas between 6.98 and 8.17/100,000 PYFU, which referred to age in the range 71.6–77.4 years. The relationship between age and ALS incidence appeared different for Eastern Asia which was characterized by a peak of ALS incidence at 2.20/100,000 PYFU around 75 years of age. This study confirms the consistency of the age-specific ALS incidence pattern within different subcontinents. Age-specific incidence appears lower in Eastern Asia as compared to Europe and North America. More... »

PAGES

621-634

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s10654-018-0392-x

DOI

http://dx.doi.org/10.1007/s10654-018-0392-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1103601784

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/29687175


Indexing Status Check whether this publication has been indexed by Scopus and Web Of Science using the SN Indexing Status Tool
Incoming Citations Browse incoming citations for this publication using opencitations.net

JSON-LD is the canonical representation for SciGraph data.

TIP: You can open this SciGraph record using an external JSON-LD service: JSON-LD Playground Google SDTT

[
  {
    "@context": "https://springernature.github.io/scigraph/jsonld/sgcontext.json", 
    "about": [
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/11", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Medical and Health Sciences", 
        "type": "DefinedTerm"
      }, 
      {
        "id": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/1117", 
        "inDefinedTermSet": "http://purl.org/au-research/vocabulary/anzsrc-for/2008/", 
        "name": "Public Health and Health Services", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adolescent", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Adult", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Age Distribution", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Aged, 80 and over", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Amyotrophic Lateral Sclerosis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Child", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Child, Preschool", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Incidence", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Infant", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Internationality", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Middle Aged", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Young Adult", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari \u201cAldo Moro\u201d, at \u201cPia Fondazione Cardinale G. Panico\u201d, 73039, Tricase, Lecce, Italy", 
          "id": "http://www.grid.ac/institutes/grid.7644.1", 
          "name": [
            "INSERM, U1094, Neuro\u00e9pid\u00e9miologie Tropicale, 87000, Limoges, France", 
            "Univ. Limoges, UMR_S 1094, Neuro\u00e9pid\u00e9miologie Tropicale, Institut d\u2019Epid\u00e9miologie Neurologique\net de Neurologie Tropicale, CNRS FR 3503 GEIST, 87000, Limoges, France", 
            "CHU Limoges, Centre d\u2019Epid\u00e9miologie de Biostatistique et de M\u00e9thodologie de la Recherche, Limoges, France", 
            "Laboratorio di Malattie Neurologiche, Dipartimento di Neuroscienze, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy", 
            "Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari \u201cAldo Moro\u201d, Bari, Italy", 
            "Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari \u201cAldo Moro\u201d, at \u201cPia Fondazione Cardinale G. Panico\u201d, 73039, Tricase, Lecce, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Marin", 
        "givenName": "Beno\u00eet", 
        "id": "sg:person.01335362427.19", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01335362427.19"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Unit of Biostatistics, IRCCS \u201cCasa Sollievo della Sofferenza\u201d, San Giovanni Rotondo, Italy", 
          "id": "http://www.grid.ac/institutes/grid.413503.0", 
          "name": [
            "Unit of Biostatistics, IRCCS \u201cCasa Sollievo della Sofferenza\u201d, San Giovanni Rotondo, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Fontana", 
        "givenName": "Andrea", 
        "id": "sg:person.0660374013.97", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660374013.97"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Neurology Unit, Department of Medicine, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy", 
          "id": "http://www.grid.ac/institutes/grid.413503.0", 
          "name": [
            "Unit of Biostatistics, IRCCS \u201cCasa Sollievo della Sofferenza\u201d, San Giovanni Rotondo, Italy", 
            "Neurology Unit, Department of Medicine, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Arcuti", 
        "givenName": "Simona", 
        "id": "sg:person.01135627245.34", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01135627245.34"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Unit of Biostatistics, IRCCS \u201cCasa Sollievo della Sofferenza\u201d, San Giovanni Rotondo, Italy", 
          "id": "http://www.grid.ac/institutes/grid.413503.0", 
          "name": [
            "Unit of Biostatistics, IRCCS \u201cCasa Sollievo della Sofferenza\u201d, San Giovanni Rotondo, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Copetti", 
        "givenName": "Massimilano", 
        "id": "sg:person.01241602613.10", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01241602613.10"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "CHU Limoges, Centre d\u2019Epid\u00e9miologie de Biostatistique et de M\u00e9thodologie de la Recherche, Limoges, France", 
          "id": "http://www.grid.ac/institutes/grid.411178.a", 
          "name": [
            "INSERM, U1094, Neuro\u00e9pid\u00e9miologie Tropicale, 87000, Limoges, France", 
            "Univ. Limoges, UMR_S 1094, Neuro\u00e9pid\u00e9miologie Tropicale, Institut d\u2019Epid\u00e9miologie Neurologique\net de Neurologie Tropicale, CNRS FR 3503 GEIST, 87000, Limoges, France", 
            "CHU Limoges, Centre d\u2019Epid\u00e9miologie de Biostatistique et de M\u00e9thodologie de la Recherche, Limoges, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Boum\u00e9diene", 
        "givenName": "Farid", 
        "id": "sg:person.01154245655.41", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01154245655.41"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "CHU Limoges, Service de Neurologie, Centre expert SLA, Limoges, France", 
          "id": "http://www.grid.ac/institutes/grid.411178.a", 
          "name": [
            "INSERM, U1094, Neuro\u00e9pid\u00e9miologie Tropicale, 87000, Limoges, France", 
            "Univ. Limoges, UMR_S 1094, Neuro\u00e9pid\u00e9miologie Tropicale, Institut d\u2019Epid\u00e9miologie Neurologique\net de Neurologie Tropicale, CNRS FR 3503 GEIST, 87000, Limoges, France", 
            "CHU Limoges, Service de Neurologie, Centre expert SLA, Limoges, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Couratier", 
        "givenName": "Philippe", 
        "id": "sg:person.01235521544.26", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235521544.26"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Laboratorio di Malattie Neurologiche, Dipartimento di Neuroscienze, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy", 
          "id": "http://www.grid.ac/institutes/grid.4527.4", 
          "name": [
            "Laboratorio di Malattie Neurologiche, Dipartimento di Neuroscienze, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Beghi", 
        "givenName": "Ettore", 
        "id": "sg:person.01003553116.20", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01003553116.20"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "CHU Limoges, Centre d\u2019Epid\u00e9miologie de Biostatistique et de M\u00e9thodologie de la Recherche, Limoges, France", 
          "id": "http://www.grid.ac/institutes/grid.411178.a", 
          "name": [
            "INSERM, U1094, Neuro\u00e9pid\u00e9miologie Tropicale, 87000, Limoges, France", 
            "Univ. Limoges, UMR_S 1094, Neuro\u00e9pid\u00e9miologie Tropicale, Institut d\u2019Epid\u00e9miologie Neurologique\net de Neurologie Tropicale, CNRS FR 3503 GEIST, 87000, Limoges, France", 
            "CHU Limoges, Centre d\u2019Epid\u00e9miologie de Biostatistique et de M\u00e9thodologie de la Recherche, Limoges, France"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Preux", 
        "givenName": "Pierre Marie", 
        "id": "sg:person.0660503264.75", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660503264.75"
        ], 
        "type": "Person"
      }, 
      {
        "affiliation": {
          "alternateName": "Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari \u201cAldo Moro\u201d, at \u201cPia Fondazione Cardinale G. Panico\u201d, 73039, Tricase, Lecce, Italy", 
          "id": "http://www.grid.ac/institutes/grid.7644.1", 
          "name": [
            "Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari \u201cAldo Moro\u201d, Bari, Italy", 
            "Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari \u201cAldo Moro\u201d, at \u201cPia Fondazione Cardinale G. Panico\u201d, 73039, Tricase, Lecce, Italy"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Logroscino", 
        "givenName": "Giancarlo", 
        "id": "sg:person.01240630110.69", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240630110.69"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1007/s13670-015-0127-8", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1020078875", 
          "https://doi.org/10.1007/s13670-015-0127-8"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00415-009-5448-0", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1024474772", 
          "https://doi.org/10.1007/s00415-009-5448-0"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10654-015-0090-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050403293", 
          "https://doi.org/10.1007/s10654-015-0090-x"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00415-012-6681-5", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1018351276", 
          "https://doi.org/10.1007/s00415-012-6681-5"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s00415-006-0454-y", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1050292921", 
          "https://doi.org/10.1007/s00415-006-0454-y"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1007/s10552-004-5025-x", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1031787925", 
          "https://doi.org/10.1007/s10552-004-5025-x"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2018-04-23", 
    "datePublishedReg": "2018-04-23", 
    "description": "Abstract\nTo evaluate the association between worldwide ALS incidence rates and age, using a dose\u2013response meta-analysis. We reviewed Medline and Embase up to July 2016 and included all population-based studies of newly-diagnosed cases, using multiple sources for case ascertainment. A dose\u2013response meta-analysis was performed. A meta-regression investigated potential sources of heterogeneity. Of 3254 articles identified in the literature, we included 41 incidence studies covering 42 geographical areas. Overall, the fit between observed and predicted age-specific rates was very good. The expected variation of ALS incidence with age was characterized, in each study, by a progressive increase in the incidence from the 40s leading to a peak in the 60s or 70s, followed by a sharp decrease. Cochran\u2019s Q test suggested a significant heterogeneity between studies. Overall, estimated patterns of ALS age-specific incidence (at which the peak was reached) were similar among subcontinents of Europe and North America: peak of ALS incidence ranged in these areas between 6.98 and 8.17/100,000 PYFU, which referred to age in the range 71.6\u201377.4\u00a0years. The relationship between age and ALS incidence appeared different for Eastern Asia which was characterized by a peak of ALS incidence at 2.20/100,000 PYFU around 75\u00a0years of age. This study confirms the consistency of the age-specific ALS incidence pattern within different subcontinents. Age-specific incidence appears lower in Eastern Asia as compared to Europe and North America.", 
    "genre": "article", 
    "id": "sg:pub.10.1007/s10654-018-0392-x", 
    "inLanguage": "en", 
    "isAccessibleForFree": false, 
    "isPartOf": [
      {
        "id": "sg:journal.1095636", 
        "issn": [
          "0393-2990", 
          "1573-7284"
        ], 
        "name": "European Journal of Epidemiology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "7", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "33"
      }
    ], 
    "keywords": [
      "age-specific incidence", 
      "ALS incidence", 
      "population-based study", 
      "age-specific rates", 
      "years of age", 
      "Cochran's Q test", 
      "incidence rates", 
      "case ascertainment", 
      "incidence study", 
      "incidence patterns", 
      "incidence", 
      "significant heterogeneity", 
      "PYFU", 
      "age", 
      "progressive increase", 
      "EMBASE", 
      "MEDLINE", 
      "study", 
      "years", 
      "ascertainment", 
      "association", 
      "geographical areas", 
      "rate", 
      "potential source", 
      "heterogeneity", 
      "patterns", 
      "decrease", 
      "cases", 
      "subcontinent", 
      "increase", 
      "test", 
      "area", 
      "North America", 
      "sharp decrease", 
      "literature", 
      "relationship", 
      "eastern Asia", 
      "peak", 
      "multiple sources", 
      "Asia", 
      "Europe", 
      "consistency", 
      "America", 
      "article", 
      "source", 
      "variation", 
      "expected variation", 
      "fit", 
      "worldwide ALS incidence rates", 
      "ALS incidence rates", 
      "\u2019s Q test", 
      "ALS age-specific incidence", 
      "subcontinents of Europe", 
      "range 71.6", 
      "age-specific ALS incidence pattern", 
      "ALS incidence pattern", 
      "different subcontinents"
    ], 
    "name": "Age-specific ALS incidence: a dose\u2013response meta-analysis", 
    "pagination": "621-634", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1103601784"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1007/s10654-018-0392-x"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "29687175"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1007/s10654-018-0392-x", 
      "https://app.dimensions.ai/details/publication/pub.1103601784"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-01-01T18:48", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220101/entities/gbq_results/article/article_778.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1007/s10654-018-0392-x"
  }
]
 

Download the RDF metadata as:  json-ld nt turtle xml License info

HOW TO GET THIS DATA PROGRAMMATICALLY:

JSON-LD is a popular format for linked data which is fully compatible with JSON.

curl -H 'Accept: application/ld+json' 'https://scigraph.springernature.com/pub.10.1007/s10654-018-0392-x'

N-Triples is a line-based linked data format ideal for batch operations.

curl -H 'Accept: application/n-triples' 'https://scigraph.springernature.com/pub.10.1007/s10654-018-0392-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1007/s10654-018-0392-x'

RDF/XML is a standard XML format for linked data.

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1007/s10654-018-0392-x'


 

This table displays all metadata directly associated to this object as RDF triples.

275 TRIPLES      22 PREDICATES      102 URIs      88 LITERALS      21 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1007/s10654-018-0392-x schema:about N0eea92bd7d84428187161713817f6462
2 N1a7ff0a67dba4d108efea9facfae91a4
3 N4ae91c30f2e948d391b94027c9377638
4 N5a50387e4c98493885bd1d714813fef3
5 N63c96f32758848e6b8783cb1598a266b
6 N6a500ed98c9246ae930738ec1dbd91b0
7 N7adf35266121490c96f7d9bb4c5ae4a3
8 N82761b1e8db24b61a0adbc5f56a3fc4b
9 N8a9798c19f9d423f86500c0353163f11
10 Nbb93419c1c3a45829fd651e493d0bd5f
11 Nc611962341c24b539ca7e468e515672a
12 Ncb2cef8b6bb04f4ca02a33ce2384fab0
13 Ne33b5ab122424aeca0d88cc3d35216c4
14 Nf523006a92544793a1296f41452b44a5
15 anzsrc-for:11
16 anzsrc-for:1117
17 schema:author N71095e069d1246f5a6255911862dad22
18 schema:citation sg:pub.10.1007/s00415-006-0454-y
19 sg:pub.10.1007/s00415-009-5448-0
20 sg:pub.10.1007/s00415-012-6681-5
21 sg:pub.10.1007/s10552-004-5025-x
22 sg:pub.10.1007/s10654-015-0090-x
23 sg:pub.10.1007/s13670-015-0127-8
24 schema:datePublished 2018-04-23
25 schema:datePublishedReg 2018-04-23
26 schema:description Abstract To evaluate the association between worldwide ALS incidence rates and age, using a dose–response meta-analysis. We reviewed Medline and Embase up to July 2016 and included all population-based studies of newly-diagnosed cases, using multiple sources for case ascertainment. A dose–response meta-analysis was performed. A meta-regression investigated potential sources of heterogeneity. Of 3254 articles identified in the literature, we included 41 incidence studies covering 42 geographical areas. Overall, the fit between observed and predicted age-specific rates was very good. The expected variation of ALS incidence with age was characterized, in each study, by a progressive increase in the incidence from the 40s leading to a peak in the 60s or 70s, followed by a sharp decrease. Cochran’s Q test suggested a significant heterogeneity between studies. Overall, estimated patterns of ALS age-specific incidence (at which the peak was reached) were similar among subcontinents of Europe and North America: peak of ALS incidence ranged in these areas between 6.98 and 8.17/100,000 PYFU, which referred to age in the range 71.6–77.4 years. The relationship between age and ALS incidence appeared different for Eastern Asia which was characterized by a peak of ALS incidence at 2.20/100,000 PYFU around 75 years of age. This study confirms the consistency of the age-specific ALS incidence pattern within different subcontinents. Age-specific incidence appears lower in Eastern Asia as compared to Europe and North America.
27 schema:genre article
28 schema:inLanguage en
29 schema:isAccessibleForFree false
30 schema:isPartOf N0f140ebd27ca4f31822d04c3beba8ea3
31 N56462ce159a641ca81b82721bcd39b6b
32 sg:journal.1095636
33 schema:keywords ALS age-specific incidence
34 ALS incidence
35 ALS incidence pattern
36 ALS incidence rates
37 America
38 Asia
39 Cochran's Q test
40 EMBASE
41 Europe
42 MEDLINE
43 North America
44 PYFU
45 age
46 age-specific ALS incidence pattern
47 age-specific incidence
48 age-specific rates
49 area
50 article
51 ascertainment
52 association
53 case ascertainment
54 cases
55 consistency
56 decrease
57 different subcontinents
58 eastern Asia
59 expected variation
60 fit
61 geographical areas
62 heterogeneity
63 incidence
64 incidence patterns
65 incidence rates
66 incidence study
67 increase
68 literature
69 multiple sources
70 patterns
71 peak
72 population-based study
73 potential source
74 progressive increase
75 range 71.6
76 rate
77 relationship
78 sharp decrease
79 significant heterogeneity
80 source
81 study
82 subcontinent
83 subcontinents of Europe
84 test
85 variation
86 worldwide ALS incidence rates
87 years
88 years of age
89 ’s Q test
90 schema:name Age-specific ALS incidence: a dose–response meta-analysis
91 schema:pagination 621-634
92 schema:productId N3471ec4d1799415499c633a33986ca5e
93 N8f78fb3052d74a378857fbd0958f5753
94 Nc7c9438816a84fc5a0f40b5dd13856e2
95 schema:sameAs https://app.dimensions.ai/details/publication/pub.1103601784
96 https://doi.org/10.1007/s10654-018-0392-x
97 schema:sdDatePublished 2022-01-01T18:48
98 schema:sdLicense https://scigraph.springernature.com/explorer/license/
99 schema:sdPublisher N8f049f7abb3c40868196e6fda45257a3
100 schema:url https://doi.org/10.1007/s10654-018-0392-x
101 sgo:license sg:explorer/license/
102 sgo:sdDataset articles
103 rdf:type schema:ScholarlyArticle
104 N0eea92bd7d84428187161713817f6462 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
105 schema:name Humans
106 rdf:type schema:DefinedTerm
107 N0f140ebd27ca4f31822d04c3beba8ea3 schema:issueNumber 7
108 rdf:type schema:PublicationIssue
109 N10ef2417138648ecbb57eac88be01733 rdf:first sg:person.01154245655.41
110 rdf:rest N7dc28bd2785442a98a4dbdabe6132749
111 N1a7ff0a67dba4d108efea9facfae91a4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
112 schema:name Incidence
113 rdf:type schema:DefinedTerm
114 N3471ec4d1799415499c633a33986ca5e schema:name pubmed_id
115 schema:value 29687175
116 rdf:type schema:PropertyValue
117 N3e962a99ac724d9781074341650f3500 rdf:first sg:person.01240630110.69
118 rdf:rest rdf:nil
119 N4ae91c30f2e948d391b94027c9377638 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
120 schema:name Young Adult
121 rdf:type schema:DefinedTerm
122 N56462ce159a641ca81b82721bcd39b6b schema:volumeNumber 33
123 rdf:type schema:PublicationVolume
124 N5a50387e4c98493885bd1d714813fef3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
125 schema:name Adolescent
126 rdf:type schema:DefinedTerm
127 N63c96f32758848e6b8783cb1598a266b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
128 schema:name Aged
129 rdf:type schema:DefinedTerm
130 N6a500ed98c9246ae930738ec1dbd91b0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
131 schema:name Adult
132 rdf:type schema:DefinedTerm
133 N71095e069d1246f5a6255911862dad22 rdf:first sg:person.01335362427.19
134 rdf:rest Nf30c91e0f3814a8dbf6d6918834b9985
135 N7adf35266121490c96f7d9bb4c5ae4a3 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
136 schema:name Child
137 rdf:type schema:DefinedTerm
138 N7dc28bd2785442a98a4dbdabe6132749 rdf:first sg:person.01235521544.26
139 rdf:rest Nc6a90531aab142ab9d8480cc04238c01
140 N82761b1e8db24b61a0adbc5f56a3fc4b schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
141 schema:name Aged, 80 and over
142 rdf:type schema:DefinedTerm
143 N8a9798c19f9d423f86500c0353163f11 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
144 schema:name Amyotrophic Lateral Sclerosis
145 rdf:type schema:DefinedTerm
146 N8f049f7abb3c40868196e6fda45257a3 schema:name Springer Nature - SN SciGraph project
147 rdf:type schema:Organization
148 N8f78fb3052d74a378857fbd0958f5753 schema:name doi
149 schema:value 10.1007/s10654-018-0392-x
150 rdf:type schema:PropertyValue
151 Nbb93419c1c3a45829fd651e493d0bd5f schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Internationality
153 rdf:type schema:DefinedTerm
154 Nc27a7db561084a0fbfe7164340ae563b rdf:first sg:person.01135627245.34
155 rdf:rest Nc782ad6ae197409b8d983897c5c2aa15
156 Nc611962341c24b539ca7e468e515672a schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
157 schema:name Infant
158 rdf:type schema:DefinedTerm
159 Nc6a90531aab142ab9d8480cc04238c01 rdf:first sg:person.01003553116.20
160 rdf:rest Nd3b1e993c9d04283abac4e8930ae727c
161 Nc782ad6ae197409b8d983897c5c2aa15 rdf:first sg:person.01241602613.10
162 rdf:rest N10ef2417138648ecbb57eac88be01733
163 Nc7c9438816a84fc5a0f40b5dd13856e2 schema:name dimensions_id
164 schema:value pub.1103601784
165 rdf:type schema:PropertyValue
166 Ncb2cef8b6bb04f4ca02a33ce2384fab0 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
167 schema:name Age Distribution
168 rdf:type schema:DefinedTerm
169 Nd3b1e993c9d04283abac4e8930ae727c rdf:first sg:person.0660503264.75
170 rdf:rest N3e962a99ac724d9781074341650f3500
171 Ne33b5ab122424aeca0d88cc3d35216c4 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
172 schema:name Child, Preschool
173 rdf:type schema:DefinedTerm
174 Nf30c91e0f3814a8dbf6d6918834b9985 rdf:first sg:person.0660374013.97
175 rdf:rest Nc27a7db561084a0fbfe7164340ae563b
176 Nf523006a92544793a1296f41452b44a5 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
177 schema:name Middle Aged
178 rdf:type schema:DefinedTerm
179 anzsrc-for:11 schema:inDefinedTermSet anzsrc-for:
180 schema:name Medical and Health Sciences
181 rdf:type schema:DefinedTerm
182 anzsrc-for:1117 schema:inDefinedTermSet anzsrc-for:
183 schema:name Public Health and Health Services
184 rdf:type schema:DefinedTerm
185 sg:journal.1095636 schema:issn 0393-2990
186 1573-7284
187 schema:name European Journal of Epidemiology
188 schema:publisher Springer Nature
189 rdf:type schema:Periodical
190 sg:person.01003553116.20 schema:affiliation grid-institutes:grid.4527.4
191 schema:familyName Beghi
192 schema:givenName Ettore
193 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01003553116.20
194 rdf:type schema:Person
195 sg:person.01135627245.34 schema:affiliation grid-institutes:grid.413503.0
196 schema:familyName Arcuti
197 schema:givenName Simona
198 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01135627245.34
199 rdf:type schema:Person
200 sg:person.01154245655.41 schema:affiliation grid-institutes:grid.411178.a
201 schema:familyName Boumédiene
202 schema:givenName Farid
203 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01154245655.41
204 rdf:type schema:Person
205 sg:person.01235521544.26 schema:affiliation grid-institutes:grid.411178.a
206 schema:familyName Couratier
207 schema:givenName Philippe
208 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01235521544.26
209 rdf:type schema:Person
210 sg:person.01240630110.69 schema:affiliation grid-institutes:grid.7644.1
211 schema:familyName Logroscino
212 schema:givenName Giancarlo
213 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01240630110.69
214 rdf:type schema:Person
215 sg:person.01241602613.10 schema:affiliation grid-institutes:grid.413503.0
216 schema:familyName Copetti
217 schema:givenName Massimilano
218 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01241602613.10
219 rdf:type schema:Person
220 sg:person.01335362427.19 schema:affiliation grid-institutes:grid.7644.1
221 schema:familyName Marin
222 schema:givenName Benoît
223 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01335362427.19
224 rdf:type schema:Person
225 sg:person.0660374013.97 schema:affiliation grid-institutes:grid.413503.0
226 schema:familyName Fontana
227 schema:givenName Andrea
228 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660374013.97
229 rdf:type schema:Person
230 sg:person.0660503264.75 schema:affiliation grid-institutes:grid.411178.a
231 schema:familyName Preux
232 schema:givenName Pierre Marie
233 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.0660503264.75
234 rdf:type schema:Person
235 sg:pub.10.1007/s00415-006-0454-y schema:sameAs https://app.dimensions.ai/details/publication/pub.1050292921
236 https://doi.org/10.1007/s00415-006-0454-y
237 rdf:type schema:CreativeWork
238 sg:pub.10.1007/s00415-009-5448-0 schema:sameAs https://app.dimensions.ai/details/publication/pub.1024474772
239 https://doi.org/10.1007/s00415-009-5448-0
240 rdf:type schema:CreativeWork
241 sg:pub.10.1007/s00415-012-6681-5 schema:sameAs https://app.dimensions.ai/details/publication/pub.1018351276
242 https://doi.org/10.1007/s00415-012-6681-5
243 rdf:type schema:CreativeWork
244 sg:pub.10.1007/s10552-004-5025-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1031787925
245 https://doi.org/10.1007/s10552-004-5025-x
246 rdf:type schema:CreativeWork
247 sg:pub.10.1007/s10654-015-0090-x schema:sameAs https://app.dimensions.ai/details/publication/pub.1050403293
248 https://doi.org/10.1007/s10654-015-0090-x
249 rdf:type schema:CreativeWork
250 sg:pub.10.1007/s13670-015-0127-8 schema:sameAs https://app.dimensions.ai/details/publication/pub.1020078875
251 https://doi.org/10.1007/s13670-015-0127-8
252 rdf:type schema:CreativeWork
253 grid-institutes:grid.411178.a schema:alternateName CHU Limoges, Centre d’Epidémiologie de Biostatistique et de Méthodologie de la Recherche, Limoges, France
254 CHU Limoges, Service de Neurologie, Centre expert SLA, Limoges, France
255 schema:name CHU Limoges, Centre d’Epidémiologie de Biostatistique et de Méthodologie de la Recherche, Limoges, France
256 CHU Limoges, Service de Neurologie, Centre expert SLA, Limoges, France
257 INSERM, U1094, Neuroépidémiologie Tropicale, 87000, Limoges, France
258 Univ. Limoges, UMR_S 1094, Neuroépidémiologie Tropicale, Institut d’Epidémiologie Neurologique et de Neurologie Tropicale, CNRS FR 3503 GEIST, 87000, Limoges, France
259 rdf:type schema:Organization
260 grid-institutes:grid.413503.0 schema:alternateName Neurology Unit, Department of Medicine, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
261 Unit of Biostatistics, IRCCS “Casa Sollievo della Sofferenza”, San Giovanni Rotondo, Italy
262 schema:name Neurology Unit, Department of Medicine, IRCCS Casa Sollievo della Sofferenza, San Giovanni Rotondo, Italy
263 Unit of Biostatistics, IRCCS “Casa Sollievo della Sofferenza”, San Giovanni Rotondo, Italy
264 rdf:type schema:Organization
265 grid-institutes:grid.4527.4 schema:alternateName Laboratorio di Malattie Neurologiche, Dipartimento di Neuroscienze, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
266 schema:name Laboratorio di Malattie Neurologiche, Dipartimento di Neuroscienze, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
267 rdf:type schema:Organization
268 grid-institutes:grid.7644.1 schema:alternateName Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari “Aldo Moro”, at “Pia Fondazione Cardinale G. Panico”, 73039, Tricase, Lecce, Italy
269 schema:name CHU Limoges, Centre d’Epidémiologie de Biostatistique et de Méthodologie de la Recherche, Limoges, France
270 Department of Basic Medical Sciences, Neuroscience and Sense Organs, University of Bari “Aldo Moro”, Bari, Italy
271 INSERM, U1094, Neuroépidémiologie Tropicale, 87000, Limoges, France
272 Laboratorio di Malattie Neurologiche, Dipartimento di Neuroscienze, IRCCS Istituto di Ricerche Farmacologiche Mario Negri, Milan, Italy
273 Unit of Neurodegenerative Diseases, Department of Clinical Research in Neurology, University of Bari “Aldo Moro”, at “Pia Fondazione Cardinale G. Panico”, 73039, Tricase, Lecce, Italy
274 Univ. Limoges, UMR_S 1094, Neuroépidémiologie Tropicale, Institut d’Epidémiologie Neurologique et de Neurologie Tropicale, CNRS FR 3503 GEIST, 87000, Limoges, France
275 rdf:type schema:Organization
 




Preview window. Press ESC to close (or click here)


...