Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol

Publication Date:
2018-05-22
Publisher:
BMJ Publishing
Electronic ISSN:
2044-6055
Topics:
Medicine
Keywords:
Open access, Respiratory medicine
Published by:
http://group.bmj.com/
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autor Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
beschreibung Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks. Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km x 5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland. Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype. Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.
citation_standardnr 6263423
datenlieferant ipn_articles
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insertion_date 2018-05-22
journaleissn 2044-6055
publikationsjahr_anzeige 2018
publikationsjahr_facette 2018
publikationsjahr_intervall 7984:2015-2019
publikationsjahr_sort 2018
publisher BMJ Publishing
quelle BMJ Open
relation http://bmjopen.bmj.com/cgi/content/short/8/5/e023289?rss=1
schlagwort Open access, Respiratory medicine
search_space articles
shingle_author_1 Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
shingle_author_2 Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
shingle_author_3 Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
shingle_author_4 Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
shingle_catch_all_1 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
Open access, Respiratory medicine
Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks. Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km x 5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland. Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype. Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.
Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
BMJ Publishing
2044-6055
20446055
shingle_catch_all_2 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
Open access, Respiratory medicine
Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks. Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km x 5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland. Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype. Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.
Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
BMJ Publishing
2044-6055
20446055
shingle_catch_all_3 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
Open access, Respiratory medicine
Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks. Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km x 5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland. Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype. Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.
Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
BMJ Publishing
2044-6055
20446055
shingle_catch_all_4 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
Open access, Respiratory medicine
Introduction Asthma has a considerable, but potentially, avoidable burden on many populations globally. Scotland has some of the poorest health outcomes from asthma. Although ambient pollution, weather changes and sociodemographic factors have been associated with asthma attacks, it remains unclear whether modelled environment data and geospatial information can improve population-based asthma predictive algorithms. We aim to create the afferent loop of a national learning health system for asthma in Scotland. We will investigate the associations between ambient pollution, meteorological, geospatial and sociodemographic factors and asthma attacks. Methods and Analysis We will develop and implement a secured data governance and linkage framework to incorporate primary care health data, modelled environment data, geospatial population and sociodemographic data. Data from 75 recruited primary care practices (n=500 000 patients) in Scotland will be used. Modelled environment data on key air pollutants at a horizontal resolution of 5 km x 5 km at hourly time steps will be generated using the EMEP4UK atmospheric chemistry transport modelling system for the datazones of the primary care practices’ populations. Scottish population census and education databases will be incorporated into the linkage framework for analysis. We will then undertake a longitudinal retrospective observational analysis. Asthma outcomes include asthma hospitalisations and oral steroid prescriptions. Using a nested case–control study design, associations between all covariates will be measured using conditional logistic regression to account for the matched design and to identify suitable predictors and potential candidate algorithms for an asthma learning health system in Scotland. Findings from this study will contribute to the development of predictive algorithms for asthma outcomes and be used to form the basis for our learning health system prototype. Ethics and dissemination The study received National Health Service Research Ethics Committee approval (16/SS/0130) and also obtained permissions via the Public Benefit and Privacy Panel for Health and Social Care in Scotland to access, collate and use the following data sets: population and housing census for Scotland; Scottish education data via the Scottish Exchange of Data and primary care data from general practice Data Custodians. Analytic code will be made available in the open source GitHub website. The results of this study will be published in international peer reviewed journals.
Soyiri, I. N., Sheikh, A., Reis, S., Kavanagh, K., Vieno, M., Clemens, T., Carnell, E. J., Pan, J., King, A., Beck, R. C., Ward, H. J. T., Dibben, C., Robertson, C., Simpson, C. R.
BMJ Publishing
2044-6055
20446055
shingle_title_1 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
shingle_title_2 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
shingle_title_3 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
shingle_title_4 Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
timestamp 2025-06-30T23:35:00.448Z
titel Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
titel_suche Improving predictive asthma algorithms with modelled environment data for Scotland: an observational cohort study protocol
topic WW-YZ
uid ipn_articles_6263423