A tutorial on the case time series design for small-area analysis View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2022-04-30

AUTHORS

Antonio Gasparrini

ABSTRACT

BackgroundThe increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels.MethodsThis tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level.ResultsThe application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions.ConclusionsThe extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions. More... »

PAGES

129

Identifiers

URI

http://scigraph.springernature.com/pub.10.1186/s12874-022-01612-x

DOI

http://dx.doi.org/10.1186/s12874-022-01612-x

DIMENSIONS

https://app.dimensions.ai/details/publication/pub.1147539745

PUBMED

https://www.ncbi.nlm.nih.gov/pubmed/35501713


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": "Environment", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Humans", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "London", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Small-Area Analysis", 
        "type": "DefinedTerm"
      }, 
      {
        "inDefinedTermSet": "https://www.nlm.nih.gov/mesh/", 
        "name": "Time Factors", 
        "type": "DefinedTerm"
      }
    ], 
    "author": [
      {
        "affiliation": {
          "alternateName": "Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, WC1E 7HT, London, UK", 
          "id": "http://www.grid.ac/institutes/grid.8991.9", 
          "name": [
            "Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15-17 Tavistock Place, WC1H 9SH, London, UK", 
            "Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, WC1E 7HT, London, UK"
          ], 
          "type": "Organization"
        }, 
        "familyName": "Gasparrini", 
        "givenName": "Antonio", 
        "id": "sg:person.01221570054.38", 
        "sameAs": [
          "https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01221570054.38"
        ], 
        "type": "Person"
      }
    ], 
    "citation": [
      {
        "id": "sg:pub.10.1038/nclimate2123", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1047676246", 
          "https://doi.org/10.1038/nclimate2123"
        ], 
        "type": "CreativeWork"
      }, 
      {
        "id": "sg:pub.10.1186/1471-2288-14-122", 
        "sameAs": [
          "https://app.dimensions.ai/details/publication/pub.1002836838", 
          "https://doi.org/10.1186/1471-2288-14-122"
        ], 
        "type": "CreativeWork"
      }
    ], 
    "datePublished": "2022-04-30", 
    "datePublishedReg": "2022-04-30", 
    "description": "BackgroundThe increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels.MethodsThis tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level.ResultsThe application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions.ConclusionsThe extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.", 
    "genre": "article", 
    "id": "sg:pub.10.1186/s12874-022-01612-x", 
    "inLanguage": "en", 
    "isAccessibleForFree": true, 
    "isFundedItemOf": [
      {
        "id": "sg:grant.7870394", 
        "type": "MonetaryGrant"
      }
    ], 
    "isPartOf": [
      {
        "id": "sg:journal.1024940", 
        "issn": [
          "1471-2288"
        ], 
        "name": "BMC Medical Research Methodology", 
        "publisher": "Springer Nature", 
        "type": "Periodical"
      }, 
      {
        "issueNumber": "1", 
        "type": "PublicationIssue"
      }, 
      {
        "type": "PublicationVolume", 
        "volumeNumber": "22"
      }
    ], 
    "keywords": [
      "availability of data", 
      "complex temporal relationships", 
      "use of information", 
      "harmonisation of data", 
      "small area analysis", 
      "different geographical levels", 
      "large datasets", 
      "computational demands", 
      "geographical levels", 
      "time series data", 
      "efficient computational scheme", 
      "case time series", 
      "computational efficiency", 
      "real data case studies", 
      "graphical description", 
      "finer geographical level", 
      "series data", 
      "time series", 
      "time series structure", 
      "small area level", 
      "socio-economic characteristics", 
      "tutorial", 
      "computational scheme", 
      "rich data", 
      "fine geographical resolution", 
      "time series design", 
      "R code", 
      "individual-level analysis", 
      "series structure", 
      "applications", 
      "case study", 
      "important methodological issues", 
      "dataset", 
      "tool", 
      "design", 
      "fine geographical scale", 
      "analytical tools", 
      "complexity", 
      "popularity", 
      "code", 
      "longitudinal structure", 
      "small geographical area", 
      "methodological issues", 
      "scheme", 
      "data", 
      "framework", 
      "time-varying exposure", 
      "vulnerability differences", 
      "extension", 
      "temporal relationship", 
      "information", 
      "flexibility", 
      "availability", 
      "geographical scales", 
      "geographical areas", 
      "series design", 
      "geographical resolution", 
      "longitudinal measurements", 
      "linkage", 
      "health outcomes", 
      "wide range", 
      "methodology", 
      "issues", 
      "valuable analytical tool", 
      "estimation", 
      "demand", 
      "main reason", 
      "efficiency", 
      "example", 
      "step", 
      "definition", 
      "harmonisation", 
      "description", 
      "method", 
      "UK", 
      "short-term associations", 
      "structure", 
      "opportunities", 
      "analysis", 
      "temperature", 
      "use", 
      "levels", 
      "high temperature", 
      "questions", 
      "results", 
      "resolution", 
      "risk", 
      "relationship", 
      "measurements", 
      "area", 
      "series", 
      "mortality risk", 
      "reasons", 
      "cases", 
      "characteristics", 
      "range", 
      "London", 
      "scale", 
      "factors", 
      "outcomes", 
      "assessment", 
      "epidemiological questions", 
      "differences", 
      "study", 
      "association", 
      "epidemiological analysis", 
      "exposure", 
      "individual death", 
      "risk factors", 
      "risk association", 
      "BackgroundThe", 
      "summer", 
      "death"
    ], 
    "name": "A tutorial on the case time series design for small-area analysis", 
    "pagination": "129", 
    "productId": [
      {
        "name": "dimensions_id", 
        "type": "PropertyValue", 
        "value": [
          "pub.1147539745"
        ]
      }, 
      {
        "name": "doi", 
        "type": "PropertyValue", 
        "value": [
          "10.1186/s12874-022-01612-x"
        ]
      }, 
      {
        "name": "pubmed_id", 
        "type": "PropertyValue", 
        "value": [
          "35501713"
        ]
      }
    ], 
    "sameAs": [
      "https://doi.org/10.1186/s12874-022-01612-x", 
      "https://app.dimensions.ai/details/publication/pub.1147539745"
    ], 
    "sdDataset": "articles", 
    "sdDatePublished": "2022-06-01T22:24", 
    "sdLicense": "https://scigraph.springernature.com/explorer/license/", 
    "sdPublisher": {
      "name": "Springer Nature - SN SciGraph project", 
      "type": "Organization"
    }, 
    "sdSource": "s3://com-springernature-scigraph/baseset/20220601/entities/gbq_results/article/article_939.jsonl", 
    "type": "ScholarlyArticle", 
    "url": "https://doi.org/10.1186/s12874-022-01612-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.10.1186/s12874-022-01612-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.10.1186/s12874-022-01612-x'

Turtle is a human-readable linked data format.

curl -H 'Accept: text/turtle' 'https://scigraph.springernature.com/pub.10.1186/s12874-022-01612-x'

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

curl -H 'Accept: application/rdf+xml' 'https://scigraph.springernature.com/pub.10.1186/s12874-022-01612-x'


 

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

205 TRIPLES      22 PREDICATES      146 URIs      136 LITERALS      12 BLANK NODES

Subject Predicate Object
1 sg:pub.10.1186/s12874-022-01612-x schema:about N4221ab09ba654c6b93b7358d2cfd2eb7
2 N42b20c768306418e879edfc8b8faa107
3 N528b0be2897e4332b631aa0acf083ac8
4 Nee9f9c00d9d14a6f879556babd3ed0cf
5 Nef1ddd474aec43aaba6bcc517872cd17
6 anzsrc-for:11
7 anzsrc-for:1117
8 schema:author Nb33a0188b0f841ff8be8b7125aa0c310
9 schema:citation sg:pub.10.1038/nclimate2123
10 sg:pub.10.1186/1471-2288-14-122
11 schema:datePublished 2022-04-30
12 schema:datePublishedReg 2022-04-30
13 schema:description BackgroundThe increased availability of data on health outcomes and risk factors collected at fine geographical resolution is one of the main reasons for the rising popularity of epidemiological analyses conducted at small-area level. However, this rich data setting poses important methodological issues related to modelling complexities and computational demands, as well as the linkage and harmonisation of data collected at different geographical levels.MethodsThis tutorial illustrated the extension of the case time series design, originally proposed for individual-level analyses on short-term associations with time-varying exposures, for applications using data aggregated over small geographical areas. The case time series design embeds the longitudinal structure of time series data within the self-matched framework of case-only methods, offering a flexible and highly adaptable analytical tool. The methodology is well suited for modelling complex temporal relationships, and it provides an efficient computational scheme for large datasets including longitudinal measurements collected at a fine geographical level.ResultsThe application of the case time series for small-area analyses is demonstrated using a real-data case study to assess the mortality risks associated with high temperature in the summers of 2006 and 2013 in London, UK. The example makes use of information on individual deaths, temperature, and socio-economic characteristics collected at different geographical levels. The tutorial describes the various steps of the analysis, namely the definition of the case time series structure and the linkage of the data, as well as the estimation of the risk associations and the assessment of vulnerability differences. R code and data are made available to fully reproduce the results and the graphical descriptions.ConclusionsThe extension of the case time series for small-area analysis offers a valuable analytical tool that combines modelling flexibility and computational efficiency. The increasing availability of data collected at fine geographical scales provides opportunities for its application to address a wide range of epidemiological questions.
14 schema:genre article
15 schema:inLanguage en
16 schema:isAccessibleForFree true
17 schema:isPartOf N08182626b86c4d7187ac55582cdf96f0
18 N3add89cf9c764b25a200a54b5b79cc88
19 sg:journal.1024940
20 schema:keywords BackgroundThe
21 London
22 R code
23 UK
24 analysis
25 analytical tools
26 applications
27 area
28 assessment
29 association
30 availability
31 availability of data
32 case study
33 case time series
34 cases
35 characteristics
36 code
37 complex temporal relationships
38 complexity
39 computational demands
40 computational efficiency
41 computational scheme
42 data
43 dataset
44 death
45 definition
46 demand
47 description
48 design
49 differences
50 different geographical levels
51 efficiency
52 efficient computational scheme
53 epidemiological analysis
54 epidemiological questions
55 estimation
56 example
57 exposure
58 extension
59 factors
60 fine geographical resolution
61 fine geographical scale
62 finer geographical level
63 flexibility
64 framework
65 geographical areas
66 geographical levels
67 geographical resolution
68 geographical scales
69 graphical description
70 harmonisation
71 harmonisation of data
72 health outcomes
73 high temperature
74 important methodological issues
75 individual death
76 individual-level analysis
77 information
78 issues
79 large datasets
80 levels
81 linkage
82 longitudinal measurements
83 longitudinal structure
84 main reason
85 measurements
86 method
87 methodological issues
88 methodology
89 mortality risk
90 opportunities
91 outcomes
92 popularity
93 questions
94 range
95 real data case studies
96 reasons
97 relationship
98 resolution
99 results
100 rich data
101 risk
102 risk association
103 risk factors
104 scale
105 scheme
106 series
107 series data
108 series design
109 series structure
110 short-term associations
111 small area analysis
112 small area level
113 small geographical area
114 socio-economic characteristics
115 step
116 structure
117 study
118 summer
119 temperature
120 temporal relationship
121 time series
122 time series data
123 time series design
124 time series structure
125 time-varying exposure
126 tool
127 tutorial
128 use
129 use of information
130 valuable analytical tool
131 vulnerability differences
132 wide range
133 schema:name A tutorial on the case time series design for small-area analysis
134 schema:pagination 129
135 schema:productId Nae069bb3e355428abfee07f7c34d049a
136 Nd0f4aa5f38ce426387615b0b36a80e2f
137 Neb348305d8e54bc8b027473087a8671d
138 schema:sameAs https://app.dimensions.ai/details/publication/pub.1147539745
139 https://doi.org/10.1186/s12874-022-01612-x
140 schema:sdDatePublished 2022-06-01T22:24
141 schema:sdLicense https://scigraph.springernature.com/explorer/license/
142 schema:sdPublisher N4bf82dba93cb4fe9bbc43a47d0eaa829
143 schema:url https://doi.org/10.1186/s12874-022-01612-x
144 sgo:license sg:explorer/license/
145 sgo:sdDataset articles
146 rdf:type schema:ScholarlyArticle
147 N08182626b86c4d7187ac55582cdf96f0 schema:issueNumber 1
148 rdf:type schema:PublicationIssue
149 N3add89cf9c764b25a200a54b5b79cc88 schema:volumeNumber 22
150 rdf:type schema:PublicationVolume
151 N4221ab09ba654c6b93b7358d2cfd2eb7 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
152 schema:name Time Factors
153 rdf:type schema:DefinedTerm
154 N42b20c768306418e879edfc8b8faa107 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
155 schema:name Environment
156 rdf:type schema:DefinedTerm
157 N4bf82dba93cb4fe9bbc43a47d0eaa829 schema:name Springer Nature - SN SciGraph project
158 rdf:type schema:Organization
159 N528b0be2897e4332b631aa0acf083ac8 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
160 schema:name Humans
161 rdf:type schema:DefinedTerm
162 Nae069bb3e355428abfee07f7c34d049a schema:name dimensions_id
163 schema:value pub.1147539745
164 rdf:type schema:PropertyValue
165 Nb33a0188b0f841ff8be8b7125aa0c310 rdf:first sg:person.01221570054.38
166 rdf:rest rdf:nil
167 Nd0f4aa5f38ce426387615b0b36a80e2f schema:name pubmed_id
168 schema:value 35501713
169 rdf:type schema:PropertyValue
170 Neb348305d8e54bc8b027473087a8671d schema:name doi
171 schema:value 10.1186/s12874-022-01612-x
172 rdf:type schema:PropertyValue
173 Nee9f9c00d9d14a6f879556babd3ed0cf schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
174 schema:name London
175 rdf:type schema:DefinedTerm
176 Nef1ddd474aec43aaba6bcc517872cd17 schema:inDefinedTermSet https://www.nlm.nih.gov/mesh/
177 schema:name Small-Area Analysis
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:grant.7870394 http://pending.schema.org/fundedItem sg:pub.10.1186/s12874-022-01612-x
186 rdf:type schema:MonetaryGrant
187 sg:journal.1024940 schema:issn 1471-2288
188 schema:name BMC Medical Research Methodology
189 schema:publisher Springer Nature
190 rdf:type schema:Periodical
191 sg:person.01221570054.38 schema:affiliation grid-institutes:grid.8991.9
192 schema:familyName Gasparrini
193 schema:givenName Antonio
194 schema:sameAs https://app.dimensions.ai/discover/publication?and_facet_researcher=ur.01221570054.38
195 rdf:type schema:Person
196 sg:pub.10.1038/nclimate2123 schema:sameAs https://app.dimensions.ai/details/publication/pub.1047676246
197 https://doi.org/10.1038/nclimate2123
198 rdf:type schema:CreativeWork
199 sg:pub.10.1186/1471-2288-14-122 schema:sameAs https://app.dimensions.ai/details/publication/pub.1002836838
200 https://doi.org/10.1186/1471-2288-14-122
201 rdf:type schema:CreativeWork
202 grid-institutes:grid.8991.9 schema:alternateName Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, WC1E 7HT, London, UK
203 schema:name Centre for Statistical Methodology, London School of Hygiene & Tropical Medicine (LSHTM), Keppel Street, WC1E 7HT, London, UK
204 Department of Public Health, Environments and Society, London School of Hygiene and Tropical Medicine (LSHTM), 15-17 Tavistock Place, WC1H 9SH, London, UK
205 rdf:type schema:Organization
 




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


...