Combinations of multimorbidity & risk of hospitalisation or death in England during the winter season: population-based study

<h3>Context:</h3> The annual winter season poses substantial challenges to the NHS in England. Those with multiple long-term conditions (MLTC) are more likely to be admitted to hospitals and/or die during this period, yet no research has examined which combinations of conditions are related to death or hospitalisation during the winter season. <h3>Objective:</h3> To describe which combinations of long-term conditions (LTC) are associated with a higher risk of death &amp; hospitalisation during winter amongst adults with MLTC in England. <h3>Study Design &amp; Analysis:</h3> In this population-based study, we used linked primary and secondary care data used over dispersed Poisson regression models to estimate the incidence rate ratios of all-cause hospitalisations and deaths during the winter season (1st December 2021 to 31st March 2022) associated with combinations MLTC, compared to those with no LTC. <h3>Setting/Dataset:</h3> General Practice Extraction Service Data for pandemic planning (GDPPR) database, Hospital Episode Statistics, &amp; Office for National Statistics death registry identified 10 combinations of MLTC most commonly associated with hospital admission or death during the winter season. <h3>Population Studied:</h3> All individuals registered in GDPPR with complete sociodemographic variables, alive at our study start date were alive &amp; aged ≥18 years on 1st December 2021. <h3>Intervention/Instrument:</h3> Data analysis were performed using Python (version 3.7) and Spark SQL (version 2.4.5) on Databricks (version 6.4). Statistical analysis was conducted in R (version 4.0.3). <h3>Outcome Measures:</h3> Total number of all-cause hospital admissions (from HES APC) &amp; deaths (from ONS deaths registry) during the winter season by MLTC per 1000 person-years. <h3>Results:</h3> Complete data were available for 48,253,125 individuals; 15 million (31.2%) had a MLTC. Hospitalisation rate per 1000 person-years was higher amongst people with MLTC, varying by combinations of conditions. There were 96/1000 person-years in those with no LTC vs 1643/1000 amongst those with a combination of cancer, chronic kidney disease (CKD), cardiovascular disease (CVD) &amp; diabetes mellitus. The incidence rate of death was 345/1000 person-years among those with cancer+CKD+CVD+dementia compared with 1/1000-person-years among those with no LTC. <h3>Conclusions:</h3> Hospitalisation &amp; death vary by combinations of MLTC. High-risk combinations need prioritisation &amp; preventive action by clinicians &amp; policy makers to manage winter pressures on the NHS.

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Publication Details

Journal
The Annals of Family Medicine
Published
2024-11-20
DOI
https://doi.org/10.1370/afm.22.s1.6882
Primary Topic
Climate Change and Health Impacts
Type
article
Field-Weighted Citation Impact
0.00
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article

Combinations of multimorbidity & risk of hospitalisation or death in England during the winter season: population-based study

Nusrat Khan, Hajira Dambha‐Miller, Lucy Smith, Clare L. Gillies et al.
The Annals of Family Medicine
Climate Change and Health Impacts
article

Combinations of multimorbidity & risk of hospitalisation or death in England during the winter season: population-based study

Nusrat Khan, Hajira Dambha‐Miller, Lucy Smith, Clare L. Gillies, Nazrul Islam, Amitava Banerjee, Sharmin Shabnam, Francesco Zaccardi
article en

Abstract

<h3>Context:</h3> The annual winter season poses substantial challenges to the NHS in England. Those with multiple long-term conditions (MLTC) are more likely to be admitted to hospitals and/or die during this period, yet no research has examined which combinations of conditions are related to death or hospitalisation during the winter season. <h3>Objective:</h3> To describe which combinations of long-term conditions (LTC) are associated with a higher risk of death &amp; hospitalisation during winter amongst adults with MLTC in England. <h3>Study Design &amp; Analysis:</h3> In this population-based study, we used linked primary and secondary care data used over dispersed Poisson regression models to estimate the incidence rate ratios of all-cause hospitalisations and deaths during the winter season (1st December 2021 to 31st March 2022) associated with combinations MLTC, compared to those with no LTC. <h3>Setting/Dataset:</h3> General Practice Extraction Service Data for pandemic planning (GDPPR) database, Hospital Episode Statistics, &amp; Office for National Statistics death registry identified 10 combinations of MLTC most commonly associated with hospital admission or death during the winter season. <h3>Population Studied:</h3> All individuals registered in GDPPR with complete sociodemographic variables, alive at our study start date were alive &amp; aged ≥18 years on 1st December 2021. <h3>Intervention/Instrument:</h3> Data analysis were performed using Python (version 3.7) and Spark SQL (version 2.4.5) on Databricks (version 6.4). Statistical analysis was conducted in R (version 4.0.3). <h3>Outcome Measures:</h3> Total number of all-cause hospital admissions (from HES APC) &amp; deaths (from ONS deaths registry) during the winter season by MLTC per 1000 person-years. <h3>Results:</h3> Complete data were available for 48,253,125 individuals; 15 million (31.2%) had a MLTC. Hospitalisation rate per 1000 person-years was higher amongst people with MLTC, varying by combinations of conditions. There were 96/1000 person-years in those with no LTC vs 1643/1000 amongst those with a combination of cancer, chronic kidney disease (CKD), cardiovascular disease (CVD) &amp; diabetes mellitus. The incidence rate of death was 345/1000 person-years among those with cancer+CKD+CVD+dementia compared with 1/1000-person-years among those with no LTC. <h3>Conclusions:</h3> Hospitalisation &amp; death vary by combinations of MLTC. High-risk combinations need prioritisation &amp; preventive action by clinicians &amp; policy makers to manage winter pressures on the NHS.

The Annals of Family MedicineVol. 22(Supplement 1)
Openalex Percentile: Top 69%
Climate Change and Health Impacts
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