Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England
Abstract Background Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of Englan...
| Main Authors: | , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2024-02-01
|
| Series: | BMC Health Services Research |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s12913-024-10716-7 |
| _version_ | 1797275369238167552 |
|---|---|
| author | Rachael Mountain Jo Knight Kelly Heys Emanuele Giorgi Timothy Gatheral |
| author_facet | Rachael Mountain Jo Knight Kelly Heys Emanuele Giorgi Timothy Gatheral |
| author_sort | Rachael Mountain |
| collection | DOAJ |
| description | Abstract Background Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of England since 2017. A key target area has been reducing referrals to outpatient respiratory clinics by upskilling primary care teams. This study aims to explore space-time patterns in referrals from general practice in the Morecambe Bay area to evaluate the impact of the initiative. Methods Data on referrals to outpatient clinics and chronic respiratory disease patient counts between 2012-2020 were obtained from the Morecambe Bay Community Data Warehouse, a large store of routinely collected healthcare data. For analysis, the data is aggregated by year and small area geography. The methodology comprises of two parts. The first explores the issues that can arise when using routinely collected primary care data for space-time analysis and applies spatio-temporal conditional autoregressive modelling to adjust for data complexities. The second part models the rate of outpatient referral via a Poisson generalised linear mixed model that adjusts for changes in demographic factors and number of respiratory disease patients. Results The first year of the Morecambe Bay Respiratory Network was not associated with a significant difference in referral rate. However, the second and third years saw significant reductions in areas that had received intervention, with full intervention associated with a 31.8% (95% CI 17.0-43.9) and 40.5% (95% CI 27.5-50.9) decrease in referral rate in 2018 and 2019, respectively. Conclusions Routinely collected data can be used to robustly evaluate key outcome measures of integrated care. The results demonstrate that effective integrated care has real potential to ease the burden on respiratory outpatient services by reducing the need for an onward referral. This is of great relevance given the current pressure on outpatient services globally, particularly long waiting lists following the COVID-19 pandemic and the need for more innovative models of care. |
| first_indexed | 2024-03-07T15:13:22Z |
| format | Article |
| id | doaj.art-bafde1fc38da48919ae7af34633fab99 |
| institution | Directory Open Access Journal |
| issn | 1472-6963 |
| language | English |
| last_indexed | 2024-03-07T15:13:22Z |
| publishDate | 2024-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Health Services Research |
| spelling | doaj.art-bafde1fc38da48919ae7af34633fab992024-03-05T18:05:14ZengBMCBMC Health Services Research1472-69632024-02-0124111410.1186/s12913-024-10716-7Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, EnglandRachael Mountain0Jo Knight1Kelly Heys2Emanuele Giorgi3Timothy Gatheral4Lancaster Medical School, Lancaster UniversityLancaster Medical School, Lancaster UniversityUniversity Hospitals of Morecambe Bay NHS Foundation Trust, Westmorland General HospitalLancaster Medical School, Lancaster UniversityLancaster Medical School, Lancaster UniversityAbstract Background Promoting integrated care is a key goal of the NHS Long Term Plan to improve population respiratory health, yet there is limited data-driven evidence of its effectiveness. The Morecambe Bay Respiratory Network is an integrated care initiative operating in the North-West of England since 2017. A key target area has been reducing referrals to outpatient respiratory clinics by upskilling primary care teams. This study aims to explore space-time patterns in referrals from general practice in the Morecambe Bay area to evaluate the impact of the initiative. Methods Data on referrals to outpatient clinics and chronic respiratory disease patient counts between 2012-2020 were obtained from the Morecambe Bay Community Data Warehouse, a large store of routinely collected healthcare data. For analysis, the data is aggregated by year and small area geography. The methodology comprises of two parts. The first explores the issues that can arise when using routinely collected primary care data for space-time analysis and applies spatio-temporal conditional autoregressive modelling to adjust for data complexities. The second part models the rate of outpatient referral via a Poisson generalised linear mixed model that adjusts for changes in demographic factors and number of respiratory disease patients. Results The first year of the Morecambe Bay Respiratory Network was not associated with a significant difference in referral rate. However, the second and third years saw significant reductions in areas that had received intervention, with full intervention associated with a 31.8% (95% CI 17.0-43.9) and 40.5% (95% CI 27.5-50.9) decrease in referral rate in 2018 and 2019, respectively. Conclusions Routinely collected data can be used to robustly evaluate key outcome measures of integrated care. The results demonstrate that effective integrated care has real potential to ease the burden on respiratory outpatient services by reducing the need for an onward referral. This is of great relevance given the current pressure on outpatient services globally, particularly long waiting lists following the COVID-19 pandemic and the need for more innovative models of care.https://doi.org/10.1186/s12913-024-10716-7Chronic respiratory diseaseIntegrated careOutpatient referralsRoutinely collected dataSpatio-temporal |
| spellingShingle | Rachael Mountain Jo Knight Kelly Heys Emanuele Giorgi Timothy Gatheral Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England BMC Health Services Research Chronic respiratory disease Integrated care Outpatient referrals Routinely collected data Spatio-temporal |
| title | Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England |
| title_full | Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England |
| title_fullStr | Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England |
| title_full_unstemmed | Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England |
| title_short | Spatio-temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the Morecambe Bay area, England |
| title_sort | spatio temporal modelling of referrals to outpatient respiratory clinics in the integrated care system of the morecambe bay area england |
| topic | Chronic respiratory disease Integrated care Outpatient referrals Routinely collected data Spatio-temporal |
| url | https://doi.org/10.1186/s12913-024-10716-7 |
| work_keys_str_mv | AT rachaelmountain spatiotemporalmodellingofreferralstooutpatientrespiratoryclinicsintheintegratedcaresystemofthemorecambebayareaengland AT joknight spatiotemporalmodellingofreferralstooutpatientrespiratoryclinicsintheintegratedcaresystemofthemorecambebayareaengland AT kellyheys spatiotemporalmodellingofreferralstooutpatientrespiratoryclinicsintheintegratedcaresystemofthemorecambebayareaengland AT emanuelegiorgi spatiotemporalmodellingofreferralstooutpatientrespiratoryclinicsintheintegratedcaresystemofthemorecambebayareaengland AT timothygatheral spatiotemporalmodellingofreferralstooutpatientrespiratoryclinicsintheintegratedcaresystemofthemorecambebayareaengland |