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Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project

BACKGROUND: Population health planning within a health district requires current information on health profiles of the target population. Information obtained during primary care interactions may provide a valuable surveillance system for chronic disease burden. The Sentinel Practices Data Sourcing...

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Detalles Bibliográficos
Autores principales: Ghosh, Abhijeet, Charlton, Karen E, Girdo, Lisa, Batterham, Marijka
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2014
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077676/
https://www.ncbi.nlm.nih.gov/pubmed/24899119
http://dx.doi.org/10.1186/1471-2458-14-557
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author Ghosh, Abhijeet
Charlton, Karen E
Girdo, Lisa
Batterham, Marijka
author_facet Ghosh, Abhijeet
Charlton, Karen E
Girdo, Lisa
Batterham, Marijka
author_sort Ghosh, Abhijeet
collection PubMed
description BACKGROUND: Population health planning within a health district requires current information on health profiles of the target population. Information obtained during primary care interactions may provide a valuable surveillance system for chronic disease burden. The Sentinel Practices Data Sourcing project aimed to establish a sentinel site surveillance system to obtain a region-specific estimate of the prevalence of chronic diseases and mental health disorders within the Illawarra-Shoalhaven region of New South Wales, Australia. METHODS: In September 2013, de-identified information for all patient interactions within the preceding 24 months was extracted and collated using a computerised chronic disease management program that has been designed for desktop application (Pen Computer Systems Clinical Audit Tool: (™) (PCS CAT)). Collated patient data included information on all diagnosed pathologies and mental health indicators, clinical variables such as anthropometric measures, and patient demographic variables such as age, sex, geographical location of residence and indigenous status. Age-standardised prevalence of selected health conditions was calculated. RESULTS: Of the 52 general practices within the 6 major Statistical Local Areas (SLAs) of the health district that met the inclusion criteria, 17 consented to participate in the study, yielding data on n = 152,767 patients, and representing 39.7% of the regional population. Higher than national average estimates were found for the age-adjusted prevalence of chronic diseases such as obesity/overweight (65.9% vs 63.4%), hypertension (11.9% vs 10.4%) and anxiety disorders (5.0% vs 3.8%), but a lower than national average age-adjusted prevalence of asthma (8.0% vs 10.2%) was also identified. CONCLUSIONS: This proof-of-concept study has demonstrated that the scope of data collected during patient visits to their general practitioners (GPs), facilitated through the Medicare-funded primary health care system in Australia, provides an opportunity for monitoring of chronic disease prevalence and its associated risk factors at the local level. Selection of sentinel sites that are representative of the population being served will facilitate an accurate and region-specific system for the purpose of population health planning at the primary care level.
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spelling pubmed-40776762014-07-02 Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project Ghosh, Abhijeet Charlton, Karen E Girdo, Lisa Batterham, Marijka BMC Public Health Research Article BACKGROUND: Population health planning within a health district requires current information on health profiles of the target population. Information obtained during primary care interactions may provide a valuable surveillance system for chronic disease burden. The Sentinel Practices Data Sourcing project aimed to establish a sentinel site surveillance system to obtain a region-specific estimate of the prevalence of chronic diseases and mental health disorders within the Illawarra-Shoalhaven region of New South Wales, Australia. METHODS: In September 2013, de-identified information for all patient interactions within the preceding 24 months was extracted and collated using a computerised chronic disease management program that has been designed for desktop application (Pen Computer Systems Clinical Audit Tool: (™) (PCS CAT)). Collated patient data included information on all diagnosed pathologies and mental health indicators, clinical variables such as anthropometric measures, and patient demographic variables such as age, sex, geographical location of residence and indigenous status. Age-standardised prevalence of selected health conditions was calculated. RESULTS: Of the 52 general practices within the 6 major Statistical Local Areas (SLAs) of the health district that met the inclusion criteria, 17 consented to participate in the study, yielding data on n = 152,767 patients, and representing 39.7% of the regional population. Higher than national average estimates were found for the age-adjusted prevalence of chronic diseases such as obesity/overweight (65.9% vs 63.4%), hypertension (11.9% vs 10.4%) and anxiety disorders (5.0% vs 3.8%), but a lower than national average age-adjusted prevalence of asthma (8.0% vs 10.2%) was also identified. CONCLUSIONS: This proof-of-concept study has demonstrated that the scope of data collected during patient visits to their general practitioners (GPs), facilitated through the Medicare-funded primary health care system in Australia, provides an opportunity for monitoring of chronic disease prevalence and its associated risk factors at the local level. Selection of sentinel sites that are representative of the population being served will facilitate an accurate and region-specific system for the purpose of population health planning at the primary care level. BioMed Central 2014-06-05 /pmc/articles/PMC4077676/ /pubmed/24899119 http://dx.doi.org/10.1186/1471-2458-14-557 Text en Copyright © 2014 Ghosh et al.; licensee BioMed Central Ltd. http://creativecommons.org/licenses/by/4.0 This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly credited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.
spellingShingle Research Article
Ghosh, Abhijeet
Charlton, Karen E
Girdo, Lisa
Batterham, Marijka
Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project
title Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project
title_full Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project
title_fullStr Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project
title_full_unstemmed Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project
title_short Using data from patient interactions in primary care for population level chronic disease surveillance: The Sentinel Practices Data Sourcing (SPDS) project
title_sort using data from patient interactions in primary care for population level chronic disease surveillance: the sentinel practices data sourcing (spds) project
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4077676/
https://www.ncbi.nlm.nih.gov/pubmed/24899119
http://dx.doi.org/10.1186/1471-2458-14-557
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