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Observational Study
. 2023 Apr 27;73(730):e332-e339.
doi: 10.3399/BJGP.2022.0353. Print 2023 May.

Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records

Cathy Qi  1 , Tim Osborne  1 , Rowena Bailey  1 , Alison Cooper  2 , Joe P Hollinghurst  1 , Ashley Akbari  1 , Ruth Crowder  3 , Holly Peters  4 , Rebecca-Jane Law  5 , Ruth Lewis  6 , Deb Smith  2 , Adrian Edwards  2 , Ronan A Lyons  1
Affiliations
Observational Study

Impact of COVID-19 pandemic on incidence of long-term conditions in Wales: a population data linkage study using primary and secondary care health records

Cathy Qi et al. Br J Gen Pract. .

Abstract

Background: The COVID-19 pandemic has directly and indirectly had an impact on health service provision owing to surges and sustained pressures on the system. The effects of these pressures on the management of long-term or chronic conditions are not fully understood.

Aim: To explore the effects of COVID-19 on the recorded incidence of 17 long-term conditions.

Design and setting: This was an observational retrospective population data linkage study on the population of Wales using primary and secondary care data within the Secure Anonymised Information Linkage (SAIL) Databank.

Method: Monthly rates of new diagnosis between 2000 and 2021 are presented for each long-term condition. Incidence rates post-2020 were compared with expected rates predicted using time series modelling of pre-2020 trends. The proportion of annual incidence is presented by sociodemographic factors: age, sex, social deprivation, ethnicity, frailty, and learning disability.

Results: A total of 5 476 012 diagnoses from 2 257 992 individuals are included. Incidence rates from 2020 to 2021 were lower than mean expected rates across all conditions. The largest relative deficit in incidence was in chronic obstructive pulmonary disease corresponding to 343 (95% confidence interval = 230 to 456) undiagnosed patients per 100 000 population, followed by depression, type 2 diabetes, hypertension, anxiety disorders, and asthma. A GP practice of 10 000 patients might have over 400 undiagnosed long-term conditions. No notable differences between sociodemographic profiles of post- and pre-2020 incidences were observed.

Conclusion: There is a potential backlog of undiagnosed patients with multiple long-term conditions. Resources are required to tackle anticipated workload as part of COVID-19 recovery, particularly in primary care.

Keywords: COVID-19; anxiety; chronic disease; diagnosis; primary health care.

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Conflict of interest statement

All authors completed the ICMJE uniform disclosure form at http://www.icmje.org/disclosure-of-interest/ and declare: no support from any organisation for the submitted work; Adrian Edwards declares a role as Director of the Wales Covid-19 Evidence Centre as part of university employment, receiving no further payments; no financial relationships with any organisations that might have an interest in the submitted work in the previous 3 years; no other relationships or activities that could appear to have influenced the submitted work. All other authors have declared no competing interests.

Figures

Figure 1.
Figure 1.
Study flowchart: numbers presented are number of diagnoses (number of individuals). Data were extracted in two ways: (a) via using a ‘diabetes algorithm’ to identify individuals diagnosed with type 1, type 2, or undetermined type diabetes, (b) via using International Classification of Diseases (version 10) and Read codes to identify individuals diagnosed with one or more of 17 conditions (including diabetes mellitus). For (a), the identification algorithm selected the earliest diagnosis date per individual. For (b), the number of diagnoses refers to the number of unique diagnosis dates available, where a diagnosis date is defined as having one or more diagnosis codes recorded on that day. The final dataset included the earliest recorded diagnosis date for each individual per condition. CHD = coronary heart disease. CKD = chronic kidney disease. COPD = chronic obstructive pulmonary disease. IBD = inflammatory bowel disease. PEDW = Patient Episode Database for Wales. PVD = peripheral vascular disease. TIA = transient ischaemic attack. WLGP = Welsh Longitudinal General Practice.
Figure 2.
Figure 2.
Monthly observed number of diagnoses per 100 000 population from 2015 to 2021 for 17 long-term conditions and three diabetes subtypes (type 1/type 2/undetermined). For 2020 and 2021, monthly predicted number of diagnoses per 100 000 are also shown with 95% confidence intervals indicated by the shaded region. Monthly observed data are overlaid with 3-month rolling averages (solid line). CKD = chronic kidney disease. COPD = chronic obstructive pulmonary disease. PVD = peripheral vascular disease. TIA = transient ischaemic attack.
Figure 2.
Figure 2.
Monthly observed number of diagnoses per 100 000 population from 2015 to 2021 for 17 long-term conditions and three diabetes subtypes (type 1/type 2/undetermined). For 2020 and 2021, monthly predicted number of diagnoses per 100 000 are also shown with 95% confidence intervals indicated by the shaded region. Monthly observed data are overlaid with 3-month rolling averages (solid line). CKD = chronic kidney disease. COPD = chronic obstructive pulmonary disease. PVD = peripheral vascular disease. TIA = transient ischaemic attack.
See this image and copyright information in PMC

References

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