Diagnosis of physical and mental health conditions in primary care during the COVID-19 pandemic: a retrospective cohort study

Abstract

Background: To date, research on the indirect impact of the COVID-19 pandemic on the health of the population and the health-care system is scarce. We aimed to investigate the indirect effect of the COVID-19 pandemic on general practice health-care usage, and the subsequent diagnoses of common physical and mental health conditions in a deprived UK population.

Methods: We did a retrospective cohort study using routinely collected primary care data that was recorded in the Salford Integrated Record between Jan 1, 2010, and May 31, 2020. We extracted the weekly number of clinical codes entered into patient records overall, and for six high-level categories: symptoms and observations, diagnoses, prescriptions, operations and procedures, laboratory tests, and other diagnostic procedures. Negative binomial regression models were applied to monthly counts of first diagnoses of common conditions (common mental health problems, cardiovascular and cerebrovascular disease, type 2 diabetes, and cancer), and corresponding first prescriptions of medications indicative of these conditions. We used these models to predict the expected numbers of first diagnoses and first prescriptions between March 1 and May 31, 2020, which were then compared with the observed numbers for the same time period.

Findings: Between March 1 and May 31, 2020, 1073 first diagnoses of common mental health problems were reported compared with 2147 expected cases (95% CI 1821 to 2489) based on preceding years, representing a 50·0% reduction (95% CI 41·1 to 56·9). Compared with expected numbers, 456 fewer diagnoses of circulatory system diseases (43·3% reduction, 95% CI 29·6 to 53·5), and 135 fewer type 2 diabetes diagnoses (49·0% reduction, 23·8 to 63·1) were observed. The number of first prescriptions of associated medications was also lower than expected for the same time period. However, the gap between observed and expected cancer diagnoses (31 fewer; 16·0% reduction, -18·1 to 36·6) during this time period was not statistically significant.

Interpretation: In this deprived urban population, diagnoses of common conditions decreased substantially between March and May 2020, suggesting a large number of patients have undiagnosed conditions. A rebound in future workload could be imminent as COVID-19 restrictions ease and patients with undiagnosed conditions or delayed diagnosis present to primary and secondary health-care services. Such services should prioritise the diagnosis and treatment of these patients to mitigate potential indirect harms to protect public health.

Funding: National Institute of Health Research.

Figure 1

Number of clinical codes recorded in patient records per week, 2010–20 Clinical codes included all symptoms, observations, diagnoses, prescriptions, operations, procedures, laboratory tests, and administration codes.

Figure 2

Number of diagnostic codes recorded in patient records per week, 2015–20 Counts included all diagnostic codes in patient records, not limited to the four diagnostic categories assessed in this study.

Figure 3

Number of prescription codes recorded in patient records per week, 2015–20 Counts include all prescription codes in patient records, not limited to the individual prescriptions assessed in this study.

Figure 4

Temporal variation in the number of patients with a first diagnosis of type 2 diabetes per month, Jan 1, 2019–May 31, 2020 Number of expected cases (95% CI) was estimated with negative binomial regression models, using data from Jan 1, 2010, to Feb 29, 2020 (inclusive).

Figure 5

Temporal variation in the number of patients with a first prescription for metformin per month, Jan 1, 2019 –May 31, 2020 Number of expected cases (95% CI) was estimated with negative binomial regression models, using data from Jan 1, 2010, to Feb 29, 2020 (inclusive).