Daniel Reidpath - ORCID: 0000-0002-8796-0420 https://orcid.org/0000-0002-8796-04204pubpub1117

Reidpath, Daniel

Sarran, Christohe

Soyiri, Ireneous

Emerging Health Threats Journal

English

PubMed

University of Hull Institutional Repository

See discussions, stats, and author profiles for this publication at: https://www.researchgate.net/publication/247773001Determinants of asthma length of stay in London hospitals: individual versusarea effectsArticle  in  Emerging Health Threats Journal · December 2011DOI: 10.3402/ehtj.v4i0.11179CITATION1READS143 authors:Some of the authors of this publication are also working on these related projects:Food Microbiology View projectAlcohol Use In Malaysia View projectIreneous N SoyiriUniversity of Hull64 PUBLICATIONS   696 CITATIONS   SEE PROFILEDaniel ReidpathMonash University (Australia)218 PUBLICATIONS   3,108 CITATIONS   SEE PROFILEChristophe SarranMet Office28 PUBLICATIONS   130 CITATIONS   SEE PROFILEAll content following this page was uploaded by Ireneous N Soyiri on 12 February 2017.The user has requested enhancement of the downloaded file.Determinants of asthma length of stay in Londonhospitals: individual versus area effectsIreneous Soyiri1,2*, Daniel Reidpath2 and Christophe Sarran31University of Ghana, Legon, Accra, Ghana; 2MONASH University, Sunway Campus, Malaysia; 3Met Office, Exeter, UKObjectiveTo identify predictors of length of stay (LOS) of asthmaadmissions in London and to model their area and individualeffects.IntroductionAsthma is a chronic condition of public health concernassociated with morbidity, mortality and healthcare utilisation.It disproportionately affects certain ethnic and demographicgroups.MethodsAsthma admission records in London (20012006) were used.Negative binomial regression was used to model the effect ofdemographic (sex, age & ethnic group), diagnostic (primary &secondary diagnosis, method of admission) and temporal (day ofthe week, meteorological season & year of admission) factors onthe LOS, accounting for the random effects of each patient’sattendance, as model ‘I’ and again for area of residence, model‘A’. Akaike information criterion (AIC) was used to compare thetwo models.ResultsThe median and mean asthma LOS over the period of studywere 2 and 3 days, respectively. Admissions increased over theyears from 8308 (2001) to 10,554 (2006), but LOS declinedwithin the same period. Fewer males (48%) than females (52%)were admitted and, the latter had longer LOS compared tomales. Only 5% were primarily diagnosed as predominantlyallergic, whilst >94% were classified as ‘asthma, unspecified’.Younger people were more likely to be admitted than elderly,but the latter had higher LOS (p B0.001). The secondarydiagnosis and method of admission were important diagnosticdeterminants of length of stay, with very marginal differencesbetween the two statistical models (‘I’ & ‘A’). Again, all thetemporal factors were significant determinants of LOS. Overallthe patient cluster model (AIC=239394.8) outperformed thearea model (AIC=247899.9).ConclusionsAsthma LOS is best predicted by demographic, diagnostic andtemporal factors with individual patients as a random effect.KeywordsAsthma; length of stay; spell duration; risk factors; hospitaladmission*I. SoyiriE-mail: soyiriin@yahoo.com(page number not for citation purpose)Table 1. Summary statistics of asthma-related hospital admissions inLondon, 20012006Characteristics N (%)Age (years)B 5 12,420 (12.4)514 10,700 (10.7)1544 16,612 (16.6)4559 7,029 (7.0)6074 5,698 (5.7)] 75 4,309 (4.3)Ethnic GroupWhite 26,230 (46.2)Black 6,604 (11.6)Asian 6,382 (11.2)Mixed/Other 5,780 (10.2)Not stated 11,782 (20.8)Secondary DiagnosisOther diseases of URT 25,053 (44.1)Influenza and Pneumonia 692 (1.2)Other acute lower respiratory infections 6,256 (11.0)Acute upper respiratory infections 70 (0.1)Chronic lower respiratory infections 1,207 (2.1)Lung diseases due to external agents 1,519 (2.7)Other diseases of respiratory system 378 (0.7)Other Non-respiratory system diseases 15,227 (26.8)Missing Values 6,376 (11.2)Method of AdmissionAccident and emergency services 52,074 (91.7)General Practitioner (GP) 2,602 (4.6)Bed bureau 41 (0.1)Consultants out patient clinic 577 (1.0)Other means 1,484 (2.6)Meteorological SeasonSummer 12,340 (21.7)Spring 13,453 (23.7)Autumn 16,800 (29.6)Winter 14,185 (25.0)ISDS 2011 Conference AbstractsEmerging Health Threats Journal 2011. # 2011 I. Soyiri et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.143Citation: Emerging Health Threats Journal 2011, 4: 11179 - DOI: 10.3402/ehtj.v4i0.11179View publication stats

Determinants of asthma length of stay in London hospitals: individual versus area effects

Daniel B. Neill

Karl A. Soetebier

Crossref

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance

Repository@Hull - CRIS

Sarran, Christophe

Queen Margaret University eResearch

Determinants of asthma length of stay in Londonhospitals: individual versus area effectsIreneous Soyiri1,2*, Daniel Reidpath2 and Christophe Sarran31University of Ghana, Legon, Accra, Ghana; 2MONASH University, Sunway Campus, Malaysia; 3Met Office, Exeter, UKObjectiveTo identify predictors of length of stay (LOS) of asthmaadmissions in London and to model their area and individualeffects.IntroductionAsthma is a chronic condition of public health concernassociated with morbidity, mortality and healthcare utilisation.It disproportionately affects certain ethnic and demographicgroups.MethodsAsthma admission records in London (20012006) were used.Negative binomial regression was used to model the effect ofdemographic (sex, age & ethnic group), diagnostic (primary &secondary diagnosis, method of admission) and temporal (day ofthe week, meteorological season & year of admission) factors onthe LOS, accounting for the random effects of each patient’sattendance, as model ‘I’ and again for area of residence, model‘A’. Akaike information criterion (AIC) was used to compare thetwo models.ResultsThe median and mean asthma LOS over the period of studywere 2 and 3 days, respectively. Admissions increased over theyears from 8308 (2001) to 10,554 (2006), but LOS declinedwithin the same period. Fewer males (48%) than females (52%)were admitted and, the latter had longer LOS compared tomales. Only 5% were primarily diagnosed as predominantlyallergic, whilst >94% were classified as ‘asthma, unspecified’.Younger people were more likely to be admitted than elderly,but the latter had higher LOS (p B0.001). The secondarydiagnosis and method of admission were important diagnosticdeterminants of length of stay, with very marginal differencesbetween the two statistical models (‘I’ & ‘A’). Again, all thetemporal factors were significant determinants of LOS. Overallthe patient cluster model (AIC=239394.8) outperformed thearea model (AIC=247899.9).ConclusionsAsthma LOS is best predicted by demographic, diagnostic andtemporal factors with individual patients as a random effect.KeywordsAsthma; length of stay; spell duration; risk factors; hospitaladmission*I. SoyiriE-mail: soyiriin@yahoo.com(page number not for citation purpose)Table 1. Summary statistics of asthma-related hospital admissions inLondon, 20012006Characteristics N (%)Age (years)B 5 12,420 (12.4)514 10,700 (10.7)1544 16,612 (16.6)4559 7,029 (7.0)6074 5,698 (5.7)] 75 4,309 (4.3)Ethnic GroupWhite 26,230 (46.2)Black 6,604 (11.6)Asian 6,382 (11.2)Mixed/Other 5,780 (10.2)Not stated 11,782 (20.8)Secondary DiagnosisOther diseases of URT 25,053 (44.1)Influenza and Pneumonia 692 (1.2)Other acute lower respiratory infections 6,256 (11.0)Acute upper respiratory infections 70 (0.1)Chronic lower respiratory infections 1,207 (2.1)Lung diseases due to external agents 1,519 (2.7)Other diseases of respiratory system 378 (0.7)Other Non-respiratory system diseases 15,227 (26.8)Missing Values 6,376 (11.2)Method of AdmissionAccident and emergency services 52,074 (91.7)General Practitioner (GP) 2,602 (4.6)Bed bureau 41 (0.1)Consultants out patient clinic 577 (1.0)Other means 1,484 (2.6)Meteorological SeasonSummer 12,340 (21.7)Spring 13,453 (23.7)Autumn 16,800 (29.6)Winter 14,185 (25.0)ISDS 2011 Conference AbstractsEmerging Health Threats Journal 2011. # 2011 I. Soyiri et al. This is an Open Access article distributed under the terms of the Creative Commons Attribution-Noncommercial 3.0 Unported License (http://creativecommons.org/licenses/by-nc/3.0/), permitting all non-commercial use, distribution, and reproduction inany medium, provided the original work is properly cited.143Citation: Emerging Health Threats Journal 2011, 4: 11179 - DOI: 10.3402/ehtj.v4i0.11179View publication stats

A spatio-temporal absorbing state model for disease and syndromic surveillance. Under Revision.

Bayesian CAR models for syndromic surveillance on multiple data streams: theory and practice. Inform Fusion.

Bayesian methodology for spatio-temporal syndromic surveillance. Statistical Analysis and Data Mining.

Citation: Emerging Health Threats Journal 2011, 4: 11059 - DOI: 10.3402/ehtj.v4i0.11059Subsidized laboratory testing as an incentive for improved livestock disease reporting Kathy Zurbrigg* Veterinary Science

E-mail: zoujian@iupui.edu (page number not for citation purpose)  ISDS

https://eresearch.qmu.ac.uk/bitstream/20.500.12289/13056/1/13056.pdf

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

International Society for Disease Surveillance Conference 2011: Building the Future of Public Health Surveillance: Building the Future of Public Health Surveillance

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