Who uses telehealth? Setting a usage baseline for the early identification of pandemic influenza activity.
Identifieur interne : 000389 ( Main/Exploration ); précédent : 000388; suivant : 000390Who uses telehealth? Setting a usage baseline for the early identification of pandemic influenza activity.
Auteurs : Elizabeth Rolland-Harris [Royaume-Uni] ; Punam Mangtani ; Kieran Michael MooreSource :
- Telemedicine journal and e-health : the official journal of the American Telemedicine Association [ 1556-3669 ] ; 2012.
Descripteurs français
- KwdFr :
- Adolescent, Adulte, Adulte d'âge moyen, Enfant, Enfant d'âge préscolaire, Facteurs de l'âge, Facteurs sexuels, Facteurs temps, Femelle, Grippe humaine (), Grippe humaine (diagnostic), Grippe humaine (épidémiologie), Humains, Jeune adulte, Maladies pulmonaires (), Maladies pulmonaires (diagnostic), Maladies pulmonaires (épidémiologie), Mâle, Ontario (épidémiologie), Orientation vers un spécialiste, Pandémies (), Santé mondiale, Santé publique (), Surveillance de la population (), Télémédecine ().
- MESH :
- diagnostic : Grippe humaine, Maladies pulmonaires.
- épidémiologie : Grippe humaine, Maladies pulmonaires, Ontario.
- Adolescent, Adulte, Adulte d'âge moyen, Enfant, Enfant d'âge préscolaire, Facteurs de l'âge, Facteurs sexuels, Facteurs temps, Femelle, Grippe humaine, Humains, Jeune adulte, Maladies pulmonaires, Mâle, Orientation vers un spécialiste, Pandémies, Santé mondiale, Santé publique, Surveillance de la population, Télémédecine.
English descriptors
- KwdEn :
- Adolescent, Adult, Age Factors, Child, Child, Preschool, Female, Global Health, Humans, Influenza, Human (diagnosis), Influenza, Human (epidemiology), Influenza, Human (prevention & control), Lung Diseases (diagnosis), Lung Diseases (epidemiology), Lung Diseases (prevention & control), Male, Middle Aged, Ontario (epidemiology), Pandemics (prevention & control), Population Surveillance (methods), Public Health (methods), Referral and Consultation, Sex Factors, Telemedicine (statistics & numerical data), Time Factors, Young Adult.
- MESH :
- diagnosis : Influenza, Human, Lung Diseases.
- epidemiology : Influenza, Human, Lung Diseases, Ontario.
- methods : Population Surveillance, Public Health.
- prevention & control : Influenza, Human, Lung Diseases, Pandemics.
- statistics & numerical data : Telemedicine.
- Adolescent, Adult, Age Factors, Child, Child, Preschool, Female, Global Health, Humans, Male, Middle Aged, Referral and Consultation, Sex Factors, Time Factors, Young Adult.
Abstract
OBJECTIVE
To describe Ontario Telehealth usage for respiratory complaints during normal (i.e., interpandemic) circumstances.
METHODS
Descriptive analyses were conducted on symptom calls of a respiratory nature made to Ontario (Canada) Telehealth during a 25-month period.
RESULTS
Approximately 300,000 calls were made during the period under study, peaking annually in January/February. Calls were above average during the weekend and Mondays (p<0.0001). All-ages consultation rate was 0.21/1,000 (range, 0.11-0.43). Standardized call rates suggested an inverse relationship between age and call rate (except for >65 years of age). During peak activity, weekly telehealth call rates were up to more than twice the weekly mean and up to four times as high as the lowest weekly rate. Highest call rate was for under 5 years old (158.4/1,000). Male rates exceed female call rates in younger age groups; the pattern reversed in older age groups. The relationship between income and call pattern showed that income and call patterns were (1) directly related for under 5 years old, (2) inversely related for callers aged 45 years and above, and (3) bimodal (higher call rates in both the highest and lowest income groups) for callers 5-44 years old.
DISCUSSION
The advent of annual respiratory illness seasons under study here resulted in surge capacity. Data such as these can and should be used for exercises such as seasonal and pandemic forecasting. Also, recent pandemic experience has showed us monitoring both overall exceedances in usage and deviances from established demographic patterns could enhance existing routine surveillance.
DOI: 10.1089/tmj.2011.0110
PubMed: 22381061
Affiliations:
Links toward previous steps (curation, corpus...)
Le document en format XML
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Setting a usage baseline for the early identification of pandemic influenza activity.</title><author><name sortKey="Rolland Harris, Elizabeth" sort="Rolland Harris, Elizabeth" uniqKey="Rolland Harris E" first="Elizabeth" last="Rolland-Harris">Elizabeth Rolland-Harris</name><affiliation wicri:level="3"><nlm:affiliation>Infectious Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, London, UK. erolland@gmail.com</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Infectious Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, London</wicri:regionArea><placeName><settlement type="city">Londres</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Londres</region></placeName></affiliation></author><author><name sortKey="Mangtani, Punam" sort="Mangtani, Punam" uniqKey="Mangtani P" first="Punam" last="Mangtani">Punam Mangtani</name></author><author><name sortKey="Moore, Kieran Michael" sort="Moore, Kieran Michael" uniqKey="Moore K" first="Kieran Michael" last="Moore">Kieran Michael Moore</name></author></titleStmt><publicationStmt><idno type="wicri:source">PubMed</idno><date when="2012">2012</date><idno type="RBID">pubmed:22381061</idno><idno type="pmid">22381061</idno><idno type="doi">10.1089/tmj.2011.0110</idno><idno type="wicri:Area/Main/Corpus">000454</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Corpus" wicri:corpus="PubMed">000454</idno><idno type="wicri:Area/Main/Curation">000454</idno><idno type="wicri:explorRef" wicri:stream="Main" wicri:step="Curation">000454</idno><idno type="wicri:Area/Main/Exploration">000454</idno></publicationStmt><sourceDesc><biblStruct><analytic><title xml:lang="en">Who uses telehealth? Setting a usage baseline for the early identification of pandemic influenza activity.</title><author><name sortKey="Rolland Harris, Elizabeth" sort="Rolland Harris, Elizabeth" uniqKey="Rolland Harris E" first="Elizabeth" last="Rolland-Harris">Elizabeth Rolland-Harris</name><affiliation wicri:level="3"><nlm:affiliation>Infectious Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, London, UK. erolland@gmail.com</nlm:affiliation><country xml:lang="fr">Royaume-Uni</country><wicri:regionArea>Infectious Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, London</wicri:regionArea><placeName><settlement type="city">Londres</settlement><region type="country">Angleterre</region><region type="région" nuts="1">Grand Londres</region></placeName></affiliation></author><author><name sortKey="Mangtani, Punam" sort="Mangtani, Punam" uniqKey="Mangtani P" first="Punam" last="Mangtani">Punam Mangtani</name></author><author><name sortKey="Moore, Kieran Michael" sort="Moore, Kieran Michael" uniqKey="Moore K" first="Kieran Michael" last="Moore">Kieran Michael Moore</name></author></analytic><series><title level="j">Telemedicine journal and e-health : the official journal of the American Telemedicine Association</title><idno type="eISSN">1556-3669</idno><imprint><date when="2012" type="published">2012</date></imprint></series></biblStruct></sourceDesc></fileDesc><profileDesc><textClass><keywords scheme="KwdEn" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Age Factors</term><term>Child</term><term>Child, Preschool</term><term>Female</term><term>Global Health</term><term>Humans</term><term>Influenza, Human (diagnosis)</term><term>Influenza, Human (epidemiology)</term><term>Influenza, Human (prevention & control)</term><term>Lung Diseases (diagnosis)</term><term>Lung Diseases (epidemiology)</term><term>Lung Diseases (prevention & control)</term><term>Male</term><term>Middle Aged</term><term>Ontario (epidemiology)</term><term>Pandemics (prevention & control)</term><term>Population Surveillance (methods)</term><term>Public Health (methods)</term><term>Referral and Consultation</term><term>Sex Factors</term><term>Telemedicine (statistics & numerical data)</term><term>Time Factors</term><term>Young Adult</term></keywords><keywords scheme="KwdFr" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Facteurs de l'âge</term><term>Facteurs sexuels</term><term>Facteurs temps</term><term>Femelle</term><term>Grippe humaine ()</term><term>Grippe humaine (diagnostic)</term><term>Grippe humaine (épidémiologie)</term><term>Humains</term><term>Jeune adulte</term><term>Maladies pulmonaires ()</term><term>Maladies pulmonaires (diagnostic)</term><term>Maladies pulmonaires (épidémiologie)</term><term>Mâle</term><term>Ontario (épidémiologie)</term><term>Orientation vers un spécialiste</term><term>Pandémies 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qualifier="épidémiologie" xml:lang="fr"><term>Grippe humaine</term><term>Maladies pulmonaires</term><term>Ontario</term></keywords><keywords scheme="MESH" xml:lang="en"><term>Adolescent</term><term>Adult</term><term>Age Factors</term><term>Child</term><term>Child, Preschool</term><term>Female</term><term>Global Health</term><term>Humans</term><term>Male</term><term>Middle Aged</term><term>Referral and Consultation</term><term>Sex Factors</term><term>Time Factors</term><term>Young Adult</term></keywords><keywords scheme="MESH" xml:lang="fr"><term>Adolescent</term><term>Adulte</term><term>Adulte d'âge moyen</term><term>Enfant</term><term>Enfant d'âge préscolaire</term><term>Facteurs de l'âge</term><term>Facteurs sexuels</term><term>Facteurs temps</term><term>Femelle</term><term>Grippe humaine</term><term>Humains</term><term>Jeune adulte</term><term>Maladies pulmonaires</term><term>Mâle</term><term>Orientation vers un spécialiste</term><term>Pandémies</term><term>Santé mondiale</term><term>Santé publique</term><term>Surveillance de la population</term><term>Télémédecine</term></keywords></textClass></profileDesc></teiHeader><front><div type="abstract" xml:lang="en"><p><b>OBJECTIVE</b></p><p>To describe Ontario Telehealth usage for respiratory complaints during normal (i.e., interpandemic) circumstances.</p></div><div type="abstract" xml:lang="en"><p><b>METHODS</b></p><p>Descriptive analyses were conducted on symptom calls of a respiratory nature made to Ontario (Canada) Telehealth during a 25-month period.</p></div><div type="abstract" xml:lang="en"><p><b>RESULTS</b></p><p>Approximately 300,000 calls were made during the period under study, peaking annually in January/February. Calls were above average during the weekend and Mondays (p<0.0001). All-ages consultation rate was 0.21/1,000 (range, 0.11-0.43). Standardized call rates suggested an inverse relationship between age and call rate (except for >65 years of age). During peak activity, weekly telehealth call rates were up to more than twice the weekly mean and up to four times as high as the lowest weekly rate. Highest call rate was for under 5 years old (158.4/1,000). Male rates exceed female call rates in younger age groups; the pattern reversed in older age groups. The relationship between income and call pattern showed that income and call patterns were (1) directly related for under 5 years old, (2) inversely related for callers aged 45 years and above, and (3) bimodal (higher call rates in both the highest and lowest income groups) for callers 5-44 years old.</p></div><div type="abstract" xml:lang="en"><p><b>DISCUSSION</b></p><p>The advent of annual respiratory illness seasons under study here resulted in surge capacity. Data such as these can and should be used for exercises such as seasonal and pandemic forecasting. Also, recent pandemic experience has showed us monitoring both overall exceedances in usage and deviances from established demographic patterns could enhance existing routine surveillance.</p></div></front></TEI><pubmed><MedlineCitation Status="MEDLINE" Owner="NLM"><PMID Version="1">22381061</PMID><DateCompleted><Year>2012</Year><Month>07</Month><Day>10</Day></DateCompleted><DateRevised><Year>2018</Year><Month>12</Month><Day>01</Day></DateRevised><Article PubModel="Print"><Journal><ISSN IssnType="Electronic">1556-3669</ISSN><JournalIssue CitedMedium="Internet"><Volume>18</Volume><Issue>2</Issue><PubDate><Year>2012</Year><Month>Mar</Month></PubDate></JournalIssue><Title>Telemedicine journal and e-health : the official journal of the American Telemedicine Association</Title><ISOAbbreviation>Telemed J E Health</ISOAbbreviation></Journal><ArticleTitle>Who uses telehealth? Setting a usage baseline for the early identification of pandemic influenza activity.</ArticleTitle><Pagination><MedlinePgn>153-7</MedlinePgn></Pagination><ELocationID EIdType="doi" ValidYN="Y">10.1089/tmj.2011.0110</ELocationID><Abstract><AbstractText Label="OBJECTIVE" NlmCategory="OBJECTIVE">To describe Ontario Telehealth usage for respiratory complaints during normal (i.e., interpandemic) circumstances.</AbstractText><AbstractText Label="METHODS" NlmCategory="METHODS">Descriptive analyses were conducted on symptom calls of a respiratory nature made to Ontario (Canada) Telehealth during a 25-month period.</AbstractText><AbstractText Label="RESULTS" NlmCategory="RESULTS">Approximately 300,000 calls were made during the period under study, peaking annually in January/February. Calls were above average during the weekend and Mondays (p<0.0001). All-ages consultation rate was 0.21/1,000 (range, 0.11-0.43). Standardized call rates suggested an inverse relationship between age and call rate (except for >65 years of age). During peak activity, weekly telehealth call rates were up to more than twice the weekly mean and up to four times as high as the lowest weekly rate. Highest call rate was for under 5 years old (158.4/1,000). Male rates exceed female call rates in younger age groups; the pattern reversed in older age groups. The relationship between income and call pattern showed that income and call patterns were (1) directly related for under 5 years old, (2) inversely related for callers aged 45 years and above, and (3) bimodal (higher call rates in both the highest and lowest income groups) for callers 5-44 years old.</AbstractText><AbstractText Label="DISCUSSION" NlmCategory="CONCLUSIONS">The advent of annual respiratory illness seasons under study here resulted in surge capacity. Data such as these can and should be used for exercises such as seasonal and pandemic forecasting. Also, recent pandemic experience has showed us monitoring both overall exceedances in usage and deviances from established demographic patterns could enhance existing routine surveillance.</AbstractText></Abstract><AuthorList CompleteYN="Y"><Author ValidYN="Y"><LastName>Rolland-Harris</LastName><ForeName>Elizabeth</ForeName><Initials>E</Initials><AffiliationInfo><Affiliation>Infectious Disease Epidemiology Unit, London School of Hygiene and Tropical Medicine, London, UK. erolland@gmail.com</Affiliation></AffiliationInfo></Author><Author ValidYN="Y"><LastName>Mangtani</LastName><ForeName>Punam</ForeName><Initials>P</Initials></Author><Author ValidYN="Y"><LastName>Moore</LastName><ForeName>Kieran Michael</ForeName><Initials>KM</Initials></Author></AuthorList><Language>eng</Language><PublicationTypeList><PublicationType UI="D016428">Journal Article</PublicationType></PublicationTypeList></Article><MedlineJournalInfo><Country>United States</Country><MedlineTA>Telemed J E 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data</QualifierName></MeshHeading><MeshHeading><DescriptorName UI="D013997" MajorTopicYN="N">Time Factors</DescriptorName></MeshHeading><MeshHeading><DescriptorName UI="D055815" MajorTopicYN="N">Young Adult</DescriptorName></MeshHeading></MeshHeadingList></MedlineCitation><PubmedData><History><PubMedPubDate PubStatus="entrez"><Year>2012</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="pubmed"><Year>2012</Year><Month>3</Month><Day>3</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate><PubMedPubDate PubStatus="medline"><Year>2012</Year><Month>7</Month><Day>11</Day><Hour>6</Hour><Minute>0</Minute></PubMedPubDate></History><PublicationStatus>ppublish</PublicationStatus><ArticleIdList><ArticleId IdType="pubmed">22381061</ArticleId><ArticleId IdType="doi">10.1089/tmj.2011.0110</ArticleId></ArticleIdList></PubmedData></pubmed><affiliations><list><country><li>Royaume-Uni</li></country><region><li>Angleterre</li><li>Grand Londres</li></region><settlement><li>Londres</li></settlement></list><tree><noCountry><name sortKey="Mangtani, Punam" sort="Mangtani, Punam" uniqKey="Mangtani P" first="Punam" last="Mangtani">Punam Mangtani</name><name sortKey="Moore, Kieran Michael" sort="Moore, Kieran Michael" uniqKey="Moore K" first="Kieran Michael" last="Moore">Kieran Michael Moore</name></noCountry><country name="Royaume-Uni"><region name="Angleterre"><name sortKey="Rolland Harris, Elizabeth" sort="Rolland Harris, Elizabeth" uniqKey="Rolland Harris E" first="Elizabeth" last="Rolland-Harris">Elizabeth Rolland-Harris</name></region></country></tree></affiliations></record>Pour manipuler ce document sous Unix (Dilib)
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