Research and analysis

Direct and indirect health impacts of COVID-19 in England: emerging Omicron impacts

Published 4 August 2022

Executive summary

Background

COVID-19 has had significant direct and indirect impacts on the health of the population in England. Our previous papers have examined these effects as the pandemic has evolved (see Initial Estimates of Excess Deaths from COVID-19, 8 April 2020, July to September 2020, December 2020 and September 2021). This iteration focuses on assessing the emerging impacts of Omicron which became the dominant variant in England in mid-December 2021.

This paper gives a high-level overview of the short-term and long-term health harms that have arisen as a consequence of COVID-19 infections and mitigating behaviours. It covers from the start of the pandemic in March 2020 to June 2022, where data permits, and uses the latest available data otherwise. Therefore, the impacts of the first wave of Omicron infection in England (December 2021 to February 2022) are examined, with some coverage of the second wave of Omicron infections which was mainly driven by Omicron sub-lineage BA.2.

Where possible, Omicron impacts have been compared to different waves of COVID-19 infection, particularly the first wave and the wave caused by the Alpha variant given they were associated with some of the largest increases in infection, morbidity and mortality during the pandemic in England. This provides a valuable context in which to analyse the emerging impacts of the Omicron variant on health.

It is worth noting that a ‘wave of infection’ is considered to be a period of increased transmission of disease, although there is no strict definition as to when a wave starts or ends. For graphical purposes, periods in which restrictions were in place have been included as they have generally been implemented during waves of COVID-19 infection. Annex 1 sets out when COVID-19 restrictions were in place, the periods corresponding to the different waves of infection referred to in this report and important vaccine roll-out dates in England. Regional breakdowns or commentary have been provided for many of the impacts.

A ‘compendium’ of data and research sources have been used which reflects the availability of information at the time of production. Readers are encouraged to use the underlying referenced sources to obtain the most up to date information.

This paper has been produced by the Department of Health and Social Care (DHSC) in collaboration with the Office for National Statistics (ONS). This paper also benefits from analytical input from the Health Foundation’s REAL Centre and the clinical expertise of Professor Christian Mallen and colleagues from Keele University, to whom we are extremely grateful.

Overview and key findings

Category A: direct impacts of COVID-19

Mortality and morbidity: infection rates with Omicron have surpassed previous peaks, over a sustained period. However, the numbers dying or requiring hospitalisation due to Omicron have been much lower compared to the first wave of infections and the Alpha wave.

Long COVID: an estimated 2 million people were experiencing Long COVID (self-reported) as of 1 May 2022. High numbers of acute COVID-19 cases since the emergence of the Omicron variant have led to increased Long COVID prevalence.

Category B: impact of COVID-19 on NHS critical care capacity

There has been no discernible increase in the number of COVID-19 positive patients in critical care beds during the Omicron wave of infection. However, increases in staff absence may have led to increased pressure in critical care.

Category C: indirect impacts of COVID-19 on population health due to living through a pandemic and restrictions

Primary care: appointments and referrals were resilient during the Omicron wave of infection. However, lower overall activity across the pandemic has led to ‘missing’ appointments and referrals. A proportion of these may return in the future with patients being in a worse state of health, whilst others will not return for a variety of reasons. During the pandemic there has been a reduction in the diagnosis of new conditions compared to pre-pandemic trends. Whilst the reported incidence of some conditions has returned to pre-pandemic trends, other conditions are still persistently below the pre-pandemic trend.

Staff infections: there has been a wave of NHS staff absences (these include absences from acute trusts and MHLDA trusts) due to Omicron, similar to that seen during the wave of infections caused by the Alpha variant but lower than the first wave of infection. This continues to put pressure on the NHS.

Secondary care: supply constraints during the Omicron wave of infection have led to longer waits for elective and emergency patients. Elective activity remains below the levels delivered prior to the pandemic. Whilst it has been more resilient during the Omicron wave of infection compared to previous waves, there were reductions in activity in December 2021 and January 2022, with activity not returning to pre-Omicron levels until February 2022.

Mental health: the number of referrals and people in touch with mental health services are above pre-pandemic levels and children’s mental health needs continue to grow.

Category D: indirect impacts of COVID-19 on the wider population in the long-run

Social care: adult social care has long-lasting pressures pre-dating COVID-19, including workforce pressures. In many cases these have been exacerbated by the pandemic and may lead to indirect health impacts. It is possible that COVID-19 may have had an impact on access to social care.

Economic impacts: impacts from Omicron have been smaller compared to those seen during previous waves of infection. Unemployment, which would be a negative driver of long-term health, did not spike following the end of furlough, though non-COVID-19 pressures mean that the wider economic climate remains fragile.

Category A: direct impacts of COVID-19

Mortality

Main findings:

• mortality involving COVID-19 has been much lower during the Omicron wave of infection compared to the first wave of COVID-19 infection and the Alpha wave
• the most deprived areas continued to experience greater mortality than the least deprived areas
• deaths involving COVID-19 of care home residents during the Omicron wave have been considerably lower than the first wave of infection and the Alpha wave

The direct impacts of COVID-19 on mortality are measured using the ONS weekly death registration data. Since the third national lockdown ended in March 2021, weekly death registrations involving COVID-19 have been comparatively low. A death ‘involving COVID-19’ here means COVID-19 was mentioned on the death certificate either as a cause of death or an underlying cause, consistent with how the term is used in ONS mortality statistics. More information on the definitions used in those statistics are available in Annex 2.

Figure 1: weekly deaths involving COVID-19 and weekly excess deaths involving COVID-19, England

Source: Office for National Statistics - Death registration data, England, up until week ending 17 June 2022.

During the first national lockdown from late March 2020 to mid-May 2020, the peak number of weekly deaths involving COVID-19 was around 8,000 (to the nearest 100), and there were roughly 5,200 deaths involving COVID-19 per week on average. The magnitudes of these numbers were similar but lower in the tiered intervention system, second and third lockdowns, but during the plan B restrictions of December 2021 to February 2022, the peak weekly deaths involving COVID-19 only just exceeded 1,000, and average weekly deaths involving COVID-19 were around 800.

Table 1: weekly peak and average COVID-19 deaths by periods in which COVID-19 restrictions are in place

	Peak weekly COVID-19 deaths	Average weekly COVID-19 deaths
First national lockdown	8,000	5,200
Tiers and second national lockdown	5,000	2,200
Third national lockdown	7,300	4,900
Plan B restrictions	1,000	800

Source: Office for National Statistics - Death registration data, England, up until week ending 17 June 2022.

Note: the dates in which COVID-19 restrictions are in place are defined in Annex 1.

Mortality by deprivation quintile

Figure 2: absolute number of weekly deaths involving COVID-19, March 2021 onwards, by IMD quintile

Source: Office for National Statistics - death registration data, England, up until week ending 17 June 2022.

The population of England can be split into quintiles of deprivation levels, using the index of multiple deprivation (IMD). A previous report in this series examined inequalities impacts of the pandemic to May 2021. From the end of the third national lockdown in March 2021 until March 2022, the most deprived quintile of the population consistently experienced the highest COVID-19 mortality, and the least deprived quintile experienced the lowest mortality. This pattern has been less clear between March 2022 and June 2022, but the difference in mortality by deprivation level observed up to March 2022 is consistent with the pattern observed earlier in the pandemic. Figure 3 shows the inequality in deaths involving COVID-19 between the 5 quintiles of deprivation, presenting the proportion of all deaths involving COVID-19 which occurred in each deprivation quintile. Whilst the more deprived quintiles experienced greater COVID-19 mortality throughout the pandemic, the inequality between the most and least deprived quintiles has increased since the third national lockdown. A greater proportion of COVID-19 deaths have occurred in the most deprived quintile since March 2021, and a smaller proportion have occurred in the least deprived quintile.

Figure 3: proportion of all deaths involving COVID-19 occurring in each deprivation quintile, by date

Source: Office for National Statistics - Death registration data, England, up until week ending 17 June 2022.

Regional distribution of deaths

The proportion of deaths in each region that involved COVID-19 up to the end of the third lockdown ranged from 12.6% in the south west to 28% in London. Since the end of the third lockdown there have been some changes between the regions but the south west remained the region with the lowest proportion with 4.9% of its deaths involving COVID-19 and London remained the highest proportion with 7.7%.

Figure 4: proportion of all cause deaths that involved COVID-19 in England, by region and date

Source: Office for National Statistics - Death registration data, England, up until week ending 17 June 2022.

Note: not adjusted for age or population.

Mortality in adult social care

Between 14 March 2020 and 3 June 2022, there were 304,747 deaths of care home residents (wherever the deaths occurred) registered in England. Of these, 46,887 (15%) involved COVID-19. During the Omicron wave, deaths of care home residents (wherever the deaths occurred) involving COVID-19 increased but were far fewer than in previous waves of infection. Explanatory factors are likely to include:

• the success of the vaccine programme
• the reduced severity of the Omicron variant in comparison to the previously dominant variant (Delta)

Historically, there has been an increase in the weekly deaths of care home residents (wherever the deaths occurred) in the week ending 7 January, following the seasonal period when death registrations are affected by bank holidays. We are therefore unable to distinguish whether the observed increase in deaths involving COVID-19 at the end of December 2021 is driven by the Omicron wave of infection, or by seasonal trends. An increase in weekly deaths involving COVID-19 occurred in April 2022, however numbers have since decreased.

Figure 5: number of registered weekly deaths of care home residents (wherever the deaths occurred) involving COVID-19 from 14 March 2020 to 3 June 2022, England

Source: Office for National Statistics – deaths involving COVID-19 in the care sector, England and Wales, provisional data, to 1 January 2021, and provisional data from 8 January 2021 to 3 June 2022.

Note: weekly deaths of care home residents are a subset of Figure 1 which represents all weekly deaths involving COVID-19 from ONS death registration data.

The Care Quality Commission (CQC) have also published the number of deaths involving COVID-19 of care home residents notified to the CQC from 10 April 2020. In this data, a similar trend to Figure 5 is observed, whereby during the Omicron period the number of deaths involving COVID-19 occurring in care homes increased, but there were far fewer than in previous waves of COVID-19 infection.

Morbidity

Main findings:

• the hospitalisation of confirmed COVID-19 patients increased with the Omicron wave of infection, where the 7-day average peaked in January and April 2022. Individually, these 2 peaks of hospitalisations were similar to that experienced in the first wave of COVID-19 infections (April 2020) but significantly below that of the Alpha wave (January 2021)
• there is a lower proportion of confirmed COVID-19 patients being treated primarily for COVID-19 after December 2021, when Omicron became the dominant variant, compared to the 6 months prior (June 2021 to November 2021)
• COVID-19 hospital admissions from care homes increased under Omicron, peaking at around half the level of the Alpha wave of infection (January 2021)
• the number of individuals self-reporting Long COVID has been increasing since June 2021, with an estimated 2 million people experiencing self-reported Long COVID (as of 1 May 2022). There has been increases in self-reported Long COVID across all regions, with the north east experiencing the highest prevalence

Hospitalisations of COVID-19 patients

As shown in Figure 6, the daily number of beds occupied by patients confirmed with COVID-19 peaked to 17,100 in January 2022 and 16,600 in April 2022. Respectively, this corresponds to when the original Omicron lineage, BA.1, and sub-lineage BA.2 were dominant in England. These peaks in infection are significantly below the peak in beds occupied with COVID-19 patients when the Alpha variant was dominant in England in January 2021 (34,300), which was the highest recorded since the start of the pandemic, and slightly lower than the number occupied in April 2020 during the first wave of infection (19,000).

Figure 6: daily number of beds occupied by confirmed COVID-19 patients, from 20 March 2020 to 9 June 2022, England

Source: NHS England Statistics – COVID-19 hospital activity, monthly publication of COVID-19 data, Total Beds Occupied COVID up until 9 June 2022.

Hospitalisations by region

Figure 7 shows the daily number of beds occupied with confirmed COVID-19 patients by region. It shows that, over the pandemic, most regions followed the national trend. However, the Midlands, north east and Yorkshire, and the north west had an additional peak in beds occupied by patients with COVID-19 in November and December 2020. The number of beds occupied by patients with COVID-19 fell in the north east and Yorkshire, and the north west, before peaking again in January 2021. This corresponded to the period in which tiering regulations were in place, with some areas within these regions placed into higher tiers (stricter rules). In contrast, the fall in beds occupied by COVID-19 patients in the Midlands was less pronounced, with a greater rise of beds occupied into January 2021.

Compared to previous months (April 2021 to November 2021), the Midlands, north east and Yorkshire, north west and London saw the largest rises in beds occupied by COVID-19 patients between December 2021 and January 2022. The rises are comparable to the increases seen in those regions during the first wave, though less than those seen when Alpha was the dominant COVID-19 variant.

The Midlands, north east and Yorkshire, and the north west also experienced a second peak in COVID-19 infections due to Omicron in early April 2022, which corresponds to when Omicron sub-lineage BA.2 was the dominant variant in England. This was broadly comparable to what these regions saw between November 2021 and January 2022. The south east and south west also experienced a rise in the number of beds occupied with COVID-19 patients in April 2022. However, this second peak was larger for these 2 regions than the peak seen in November 2021 and January 2022.

It should be noted that Figure 6 and the regional breakdowns shown in Figure 7 below are not indicators of bed capacity, nor do they attempt to analyse bed capacity.

Figure 7: daily beds occupied by patients with COVID-19, by region, from 20 March 2020 to 9 June 2022, England

Source: NHS England Statistics – COVID-19 hospital activity, monthly publication of COVID-19 data, Total Beds Occupied COVID, data analysis by DHSC.

Hospitalisations of COVID-19 positive patients: primary reason for treatment

As set out above, Omicron led to a substantial increase in the number of COVID-19 positive patients occupying hospital beds. However, the proportion of COVID-19 patients being treated primarily for COVID-19 declined, from around 75% between June to mid-December 2021 to around 35% by June 2022 (Figure 8). Nonetheless, for some patients, COVID-19 can still present a significant co-morbidity even if it is not the primary reason for hospitalisation. Additionally, significant increases in confirmed COVID-19 patients could impact hospital capacity given infection control measures in place, such as segregating COVID-19 positive patients, see COVID-19 hospital activity on the NHS England website.

Figure 8: total daily beds occupied by confirmed COVID-19 patients split by whether the primary reason for treatment is COVID-19 or something else, from 18 June 2021 to 7 June 2022

Source: NHS England Statistics – COVID-19 hospital activity, Primary Diagnosis Supplements, data analysis by DHSC.

Morbidity in adult social care

Between 1 August 2020 and 11 June 2022, there were over 25,800 reported COVID-19 hospital admissions and COVID-19 diagnoses in hospital from care homes. This reflects morbidity for patients admitted from care homes only and not those who are domiciliary care users (Figure 9).

From early to mid-December 2021, daily COVID-19 hospital admissions from care homes increased, before declining and then stabilising at the end of February 2022. Admissions started to increase again in March 2022, before declining at the start of April 2022. Admissions have started to show a small increase since the start of June 2022. The peaks in the number of reported COVID-19 hospital admissions from care homes during the Omicron wave was considerably lower than the peak experienced during the Alpha wave of infection in January 2021. Explanatory factors are likely to include the success of the vaccine programme, and the reduced relative risk of hospitalisation with Omicron compared to the previously dominant variant (Delta).

Figure 9: total reported COVID-19 daily admissions and diagnoses from care homes in England, from 1 August 2020 to 9 June 2022

Source: NHS England Statistics – COVID-19 hospital activity, daily admissions and beds.

Morbidity – Long COVID

Long COVID is commonly defined as having persisting symptoms lasting at least 4 weeks after an initial SARS-CoV-2 infection. This includes both ongoing symptomatic COVID-19 (4 to 12 weeks) and post-COVID-19 syndrome (12 weeks or more) that cannot be explained by something else (see COVID-19 rapid guideline: managing the long-term effects of COVID-19) on the NICE website. As shown in Figure 10, Long COVID prevalence has increased since June 2021, with an estimated 2 million people reporting experiencing Long COVID in the UK, as of 1 May 2022 (‘Self-reported Long COVID’ relates to responses to the question D5 in the Coronavirus Infection Survey follow-up questionnaire).

Figure 10: estimated number of people living in private households with self-reported Long COVID of any duration in the UK, error bars show 95% confidence intervals

Source: Office for National Statistics – prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 1 June 2022.

As a proportion of the UK population, prevalence of self-reported Long COVID was greatest among people aged 35 to 69 years, females, people living in more deprived areas, those working in social care, teaching and education or health care, and those with another activity-limiting health condition or disability (Figure 11).

Figure 11: estimated percentage of people living in private households with self-reported Long COVID as of 1 May 2022 by duration and demographic characteristics; error bars show 95% confidence intervals

Source: Office for National Statistics – prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK, 1 June 2022.

Emerging evidence on the effectiveness of vaccination before COVID-19 infection suggests that people vaccinated before SARS-CoV-2 infection (with either 1 or 2 doses) are less likely to develop symptoms of Long COVID following infection. In addition, 3 studies suggest that people with Long COVID who are subsequently vaccinated are less likely to report Long COVID symptoms shortly after vaccination, and over longer periods, than people with Long COVID who are not subsequently vaccinated (see The effectiveness of vaccination against Long COVID: A rapid evidence briefing).

Persisting health problems after acute COVID-19 may increase the burden on the healthcare system. A study in Italy found that, compared to controls in the corresponding period of 2019, among those who had been previously admitted to intensive care units (ICU) and medical wards with COVID-19 there was a greater frequency of re-hospitalisation, attendance in hospital A&E and visitation of out-patient clinics (see Impact of the post-COVID-19 condition on health care after the first disease wave in Lombardy, Journal of Internal Medicine). Studies in the USA (see Xie and colleagues, 7 February 2022, Xie and colleagues, 16 February 2022 and Xie and colleagues, 21 March 2022) have found that those who have survived COVID-19 have a significantly greater risk of cardiovascular disease, mental health conditions, and diabetes up to 12 months post infection, compared to controls with no history of COVID-19. In England, there have been 55,687 referrals to an NHS Post-COVID assessment centre in the period between 5 July 2021 and 10 April 2022, of which 49,159 were accepted (this equates to an average of 4,916 per 28 days), however, not all those suffering from Long COVID have received a diagnosis (see NHS England, COVID-19 Post-Covid assessment service statistics on the NHS England website).

Across all regions of the UK, there are an increasing number of people with self-reported Long COVID (Figure 12). As of 1 May 2022, Yorkshire and the Humber has the highest prevalence of Long COVID in England with 3.65% of people living in private households reporting Long COVID of any duration, compared to 2.28% in London which has the lowest prevalence in England. All countries across the UK had prevalence of around 3%, and the differences between countries are not statistically significant (see Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 1 June 2022).

Figure 12: estimated percentage of people living in private households with self-reported Long COVID of any duration; as of 1 May 2022

Source: Office for National Statistics – prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 1 June 2022, analysis by DHSC.

The spread of the Omicron variant of COVID-19 has been accompanied by an increase in Long COVID cases, however the main reason for the increase is likely to be the large number of acute COVID-19 infections over this period. Among adults who were double-vaccinated when first infected with SARS-CoV-2, the odds of reporting Long COVID symptoms 4 to 8 weeks after infection were 49.7% lower in infections compatible with the Omicron BA.1 variant compared to infections compatible with the Delta variant, after adjusting for socio-demographic characteristics. Small sample sizes of those who were triple vaccinated and infected with the Delta variant make it difficult to draw any conclusions, and any differences are not statistically significant (see Prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 1 June 2022).

Figure 13 shows that as of 1 May 2022, an estimated 33% of people currently with self-reported Long COVID had an initial coronavirus infection when Omicron was the main variant of SARS-CoV-2. However, it is difficult to draw conclusions about differences in Long COVID between variants due to the differing time periods in which variants were dominant and due to differences in the time elapsed from the initial COVID-19 infection, which means that people will have had greater opportunity to recover from variants dominant further in the past (Figure 13).

Figure 13: estimated number of people living in private households with self-reported Long COVID of any duration of current cases as of 1 May 2022 by main variant of SARS-CoV-2 at time of first (suspected) coronavirus infection; error bars show 95% confidence intervals

Source: Office for National Statistics – prevalence of ongoing symptoms following coronavirus (COVID-19) infection in the UK: 1 June 2022, analysis by DHSC.

Category B: impact of COVID-19 on NHS critical care capacity

Main findings:

• there has been no discernible increase in the number of COVID-19 positive patients in critical care beds during the Omicron wave of infection. However, increases in staff absences may have led to increased pressure in critical care

Previous publications of the ‘Direct and indirect health impacts of COVID-19’ (see Initial Estimates of Excess Deaths from COVID-19, 8 April 2020, July to September 2020, December 2020 and September 2021) did not identify a national breach in healthcare capacity due to COVID-19. In other words, it estimated that no deaths had resulted from reduced or unavailable critical care services due to COVID-19 impacts. Nonetheless, there was some evidence of local transfer of patients between hospitals due to a lack of bed availability and critical care staff shortages resulting in lower staffing ratios than clinically optimal (see Pett and colleagues (2020). Critical care transfers and COVID-19: Managing capacity challenges through critical care networks and Adapting hospital capacity to meet changing demands during the COVID-19 pandemic).

Whilst the Omicron wave of infection has led to the highest peaks in recorded infection rates since the pandemic began, it is also characterised by lower levels of hospitalisation and mortality compared to the Alpha wave of infection. Indeed, during the Omicron wave of infection, there has been no discernible increase in the number of patients requiring critical care, including mechanical ventilation (MV) beds. Therefore, we estimate that there has been no breach in critical care capacity at a national level during the Omicron wave of infection.

Although the direct impacts of COVID-19 on critical care may be lower with Omicron compared to previous waves of infection, critical care capacity may have been affected indirectly, particularly through NHS hospital staff absences (see Figure 28). In January 2022, the 7-day average NHS absences peaked to 57,000. This is slightly above the January 2021 peak of 53,000, when Alpha was the dominant variant, but considerably below the 7-day average peak in April 2020 (104,000).

Critical care bed occupancy

Figure 14 shows the number of critical care beds occupied by confirmed COVID-19 patients and non-COVID-19 patients, and unoccupied critical care beds. It shows that there has been no discernible increase in critical care beds occupied by patients with COVID-19 during the Omicron wave of infection, whereas with the Alpha wave of infection, when the third lockdown was in place, there was a prominent spike. Across the English regions, there was little impact on the number of critical care beds occupied by COVID-19 patients during the Omicron wave of infection, mirroring the national picture.

Figure 14: 7-day average number of critical care (CC) beds occupied by confirmed COVID-19 patients, non-COVID patients and beds unoccupied.

Source: NHS England - COVID-19 hospital activity, Weekly publication of COVID-19 admissions and bed occupancy data, 19 November 2020 to 9 June 2022, analysis by DHSC. It is important to note that the figures presented are not an indication of the critical care bed capacity across England or by region during the pandemic.

Factors which contribute to the lack of a notable increase in the demand for critical care beds during Omicron are likely the greater population immunity through vaccination, better testing and treatments and reduced relative risk of hospitalisation with Omicron BA.1 compared to the previously dominant variant (Delta), (see COVID-19: Omicron, recent developments, and the likely impact of future variants on the pandemic and Risk assessment for SARS-CoV-2 variant: Omicron VOC-21NOV-01 (B.1.1.529) – 12 January 2022). It is worth noting that there has been no evidence of increases in hospital attendance or admission for Omicron sub-lineage BA.2 compared to the original lineage BA.1, this includes no discernible increase for critical care beds (as can be seen in Figure 14).

Category C: indirect impacts of COVID-19 on population health due to living through a pandemic and restrictions

The COVID-19 pandemic has impacted both the demand for and provision of healthcare. Changes in underlying health needs, such as lower levels of infectious disease during the pandemic, and people’s health seeking behaviour, have impacted the demand for health services and have contributed to declines in activity. Furthermore, health system adaptations to mitigate the spread of COVID-19 on staff and patients, and increased levels of staff absences due to COVID-19, are supply-side factors which have impacted the delivery of healthcare.

Underlying health needs

During the winter months, there is usually increased demand for NHS services to manage health conditions which are caused or exacerbated by the cold or seasonal infectious diseases. This can impact demand for primary care services by increasing the demand for GP appointments. In addition, secondary care can be impacted with increasing numbers of individuals requiring hospital care to deal with respiratory conditions or decompensating other conditions. For example, cold weather and viruses can inhibit blood circulation for those with long-standing vascular diseases.

Norovirus reporting

As seen in Figure 15, there has generally been much lower reporting of norovirus during the pandemic compared to the 5-season pre-pandemic average. Since COVID-19 restrictions were eased, some resurgence has occurred, with higher levels of norovirus reported in 2021/2022 compared to 2020/2021, although it is still below the 5-season average.

Individuals have changed their behaviour in response to COVID-19, both voluntarily and in order to comply with regulations, which is likely to have contributed to reductions in the transmission of norovirus. Other factors that may explain the lower reporting of norovirus in Figure 15 include access to health care services, capacity for testing and, data and surveillance limitations.

Figure 15: norovirus laboratory reports in England in 2020/2021 and 2021/2022, compared to 5-season average

Source: UKHSA – National norovirus and rotavirus bulletin (2021 to 2022 week 12).

Note: first, the 5-season mean is calculated from the 5-season period of 2014/2015 and 2018/2019; second, to capture the winter peak of activity in the reporting period, the norovirus and rotavirus season runs from week 27 in year 1 to week 26 in year 2, that is, week 27 2020 to week 26 2021, July to June.

Influenza rates

In winter, there is usually a spike in the estimated rate of individuals with flu-like illness. However, as seen in Figure 16, influenza-like illness rates have been very low throughout the pandemic including winter 2021/2022. Since April 2020, influenza hospital admission rates have also been very low, particularly compared to pre-pandemic years.

Behavioural change in response to COVID-19, driven in part by legal requirements and guidance, and also successful influenza vaccination campaigns, are likely to have contributed to reductions in influenza infection and hospitalisation rates.

Figure 16: annual flu rate (7-day rolling average), from 2017/2018, and 2021/2022

Source: i-Sense Flu analysis, conducted by DHSC. Data covers 1 July 2017 to 13 March 2022 and the year goes from 1 July to 30 June.

Impact on primary care

Main findings:

• GP consultation rates fell sharply in the beginning of the pandemic and mostly returned to pre-pandemic levels by autumn 2020. Appointments were resilient during the Omicron wave of infection
• analysis by the Health Foundation on incidence rates has found persistent monthly reductions in recorded incidence rates for some conditions, particularly coronary heart disease (CHD), asthma and atrial fibrillation (Afib)
• the number of total referrals, through the e-RS service, dropped sharply in the beginning of the pandemic and returned to pre-pandemic levels by summer 2021. There will be missing referrals that will likely not return due to self-limiting concerns, use of private or secondary care, changes in how GPs refer and deaths due to COVID-19. Referrals were resilient during the Omicron wave of infection, with the reduction in referrals in December 2020 likely reflecting normal seasonal variations

GP consultations rates

Figure 17 shows General Practice (GP) consultations per person, per year from January 2019 to January 2022 and was produced by the Health Foundation’s REAL Centre using data from Clinical Practice Research Datalink (CPRD) and NHS Digital (NHSD) on GP appointments. It shows a sharp reduction in consultation rates at the beginning of the pandemic (March 2020) which return to pre-pandemic levels in autumn 2020. Although consultation rates have since been similar to 2019 (pre-pandemic) levels, if appointments for COVID-19 vaccinations delivered by GP practices are included, then the consultations rates in 2021 were generally higher than 2019. There is no discernible impact of Omicron on consultation rates, with the drop in December 2021 likely reflecting normal seasonal variations.

Figure 17: GP consultations per person per year

Source: analysis conducted by the Health Foundation’s REAL Centre using CPRD Aurum and NHS Digital data. See Annex 3 for background and methodology on the CPRD.

Note: CPRD data is based on a sample of 500,000 patients from January 2016 to January 2022. CPRD does not include appointments for COVID-19 vaccines. NHSD figures are attended appointments published by NHS digital.

GP consultations: proportion delivered face-to-face

Figure 18 shows the proportion of consultations that were estimated to be delivered non-face-to-face, in other words remotely, and was produced by the Health Foundation’s REAL Centre using CPRD and NHS Digital data. It is estimated that around 20% of appointments were delivered remotely before the COVID-19 pandemic, however with the onset of the pandemic and the implementation of infection control measures, face-to-face appointments were largely substituted with telephone appointments. By April 2020, an estimated peak of 70% of consultations were delivered remotely.

The proportion of consultations carried out remotely has since been decreasing over the pandemic, and by the end of 2021, around 50% of consultations were estimated to be conducted remotely (according to CPRD data). The latest NHS Digital statistics show that the proportion of face-to-face appointments was 63% as of April 2022.

Figure 18: proportion of consultations that were non-face-to-face

Source: analysis conducted by the Health Foundation’s REAL Centre using CPRD Aurum and NHS Digital data. See Annex 3 for background and methodology on the CPRD.

Note: CPRD data is based on a sample of 500,000 patients from January 2016 to January 2022. CPRD does not include appointments for COVID-19 vaccines. NHSD figures are attended appointments published by NHS digital.

Diagnosis of long-term conditions

During the pandemic, reduced use of primary care and infection control measures, which impacted some diagnostic testing, are likely to have led to reductions in the diagnoses of some conditions. This comes through in statistics as apparent reductions in incidence of those conditions (‘incidences’ refers to the number of new cases of a disease among a certain group of people during a specific period of time, see glossary on the NICE website).

Using CPRD data, the Health Foundation’s REAL Centre conducted analysis which shows the incidence rates of certain heart conditions and respiratory conditions, as well as strokes and transient ischaemic attacks (TIAs), and diabetes. In this analysis, incidence is calculated per patient that is not already diagnosed with the condition. Given analysis covers January 2016 to December 2021, which precedes the period in which Omicron was the dominant variant, this paper cannot comment on Omicron impacts on incidence rates.

The analysis has found persistent monthly reductions in the recorded incidence rates for some conditions, particularly CHD, asthma and chronic obstructive pulmonary disease (COPD). Other conditions, such as diabetes, have since returned to the pre-pandemic trends following a drop during the first lockdown (March 2020).

It is hard to know the true number of ‘missing’ new diagnoses as we do not know how prevalence has changed over the pandemic. This will be influenced by changes in mortality, migration and lifestyle changes, among other factors. Moreover, patients can live with some conditions undiagnosed for long periods of time, such as COPD or Afib (atrial fibrillation), whilst others have a much shorter life expectancy. Further analysis of these complex dynamics is required to understand the extent of missing new diagnoses.

Incidence per patient: heart conditions

Figures 19, 20 and 21 show considerable drops in the apparent incidence per person of Afib, CHD and heart failure in the beginning of the pandemic. For heart failure^{[footnote 1]}, the incidence per patient seems to have mostly returned to the pre-COVID-19 trend. However, the incidence per person of Afib^{[footnote 2]} and CHD^{[footnote 3]} has been largely below the pre-pandemic trend, suggesting that new diagnoses have not been formally made. Testing for Afib is usually conducted in-person as part of routine checks for elderly patients. Therefore, a potential contributory factor to the lower incidence per patient of Afib, particularly in the beginning of the pandemic, could be the shift to remote GP consultations.

Compared to the 2019 average, between March 2020 and December 2021, there are an estimated 59,000 fewer new cases of atrial fibrillation, 65,000 fewer new cases of CHD and 17,000 fewer new cases of Heart Failure. This corresponds to a ‘missing’ incidence (as a % of estimated prevalence in December 2021) of 4.7% of Afib, 3.3% of CHD and 2.5% of heart failure.

Figure 19: incidence per patient – Afib

Figure 20: incidence per patient – CHD

Figure 21: incidence per patient – heart failure

Source: analysis conducted by the Health Foundation’s REAL Centre using CPRD Aurum data. See Annex 3 for background and methodology on the CPRD.

Note: incidence is calculated per patient that is not already diagnosed with the condition. The trend line in the graph indicates the trajectory of incidence and is produced using regression analysis and data from January 2016 to February 2020. However, the missing incidence (or fewer cases diagnosed) is calculated by comparing incidence to the monthly 2019 average.

Incidence per patient: strokes and transient ischaemic attacks (TIAs)

Figure 22 shows a considerable reduction in the apparent incidence rate of strokes^{[footnote 4]} and TIAs^{[footnote 5]} since the beginning of the pandemic, meaning that fewer patients are being diagnosed with strokes and TIAs compared to pre-pandemic levels. It is hypothesized that at least part of these lower rates may be due to patients avoiding seeking care, especially in the beginning of the pandemic, rather than there being fewer strokes or TIAs (see Stroke Association – Stroke recoveries at risk report and D’Anna et al (2021) - Impact of National Lockdown on the Hyperacute Stroke Care). Whilst the incidence per patient returned closer to the pre-pandemic trend in autumn 2020, the incidence rate fell notably in July 2021 and has since been below the pre-COVID-19 trend.

Compared to the 2019 average, between March 2020 and December 2021, there are an estimated 26,000 fewer new cases of stroke and TIAs. This corresponds to a ‘missing’ prevalence (as a % of estimated prevalence in December 2021) of 2.5%.

Figure 22: incidence per patient – stokes and TIAs

Source: analysis conducted by the Health Foundation’s REAL Centre using CPRD Aurum data. See Annex 3 for background and methodology on the CPRD.

Note: incidence is calculated per patient that is not already diagnosed with the condition. The trend line in the graph indicates the trajectory of incidence and is produced using regression analysis and data from January 2016 to February 2020. However, the missing incidence (or fewer cases diagnosed) is calculated by comparing incidence to the monthly 2019 average.

Incidence per patient: respiratory conditions

Respectively, figures 23 and 24 show sharp reductions in the apparent incidence rates of asthma^{[footnote 6]} and COPD^{[footnote 7]} in the beginning of the pandemic (March 2020). Although incidence rates have been recovering somewhat in the latter part of 2020, they remain mostly below pre-pandemic levels. Part of the reduction in incidence rates can be explained by the restrictions in spirometry testing that were implemented for infection control purposes during the COVID-19 pandemic. In addition, behavioural changes due to the pandemic have led to reductions in air pollution and lower circulation of respiratory viruses which are triggers that cause asthma exacerbations.

Compared to the 2019 average, between March 2020 and December 2021, there are an estimated 107,000 fewer new cases of asthma and 87,000 fewer cases of COPD. This corresponds to a missing prevalence (as a % of estimated prevalence in December 2021) of 1.5% for asthma and 8% for COPD.

Figure 23: incidence per patient – asthma

Figure 24: incidence per patient – COPD

Source: analysis conducted by the Health Foundation’s REAL Centre using CPRD Aurum data. See Annex 3 for background and methodology on the CPRD.

Note: incidence is calculated per patient that is not already diagnosed with the condition. The trend line in the graph indicates the trajectory of incidence and is produced using regression analysis and data from January 2016 to February 2020. However, the missing incidence (or fewer cases diagnosed) is calculated by comparing incidence to the monthly 2019 average.

Incidence per patient: diabetes

Figure 25 shows that there was a sharp drop in the apparent incidence rate of diabetes^{[footnote 8]} in March 2020 with the beginning of the pandemic. By spring 2021, the incidence rate had returned to the pre-pandemic trend.

Compared to the 2019 average, between March 2020 and December 2021, there are an estimated 60,000 fewer cases of diabetes. This corresponds to a missing prevalence (as a % of estimated prevalence in December 2021) of 1.6%.

Figure 25: incidence per patient – diabetes

Source: analysis conducted by the Health Foundation’s REAL Centre using CPRD Aurum data. See Annex 3 for background and methodology on the CPRD.

Note: incidence is calculated per patient that is not already diagnosed with the condition. The trend line in the graph indicates the trajectory of incidence and is produced using regression analysis and data from January 2016 to February 2020. However, the missing incidence (or fewer cases diagnosed) is calculated by comparing incidence to the monthly 2019 average.

NHS referrals through the e-Referral service

Figure 26 shows the number of referrals made per working day in England using NHS e-Referral Service Open Data published by NHS Digital. It shows that the total number of referrals dropped sharply in March 2020, with the beginning of the COVID-19 pandemic, and recovered to pre-pandemic levels by summer 2021. There has been no discernible impact on the number of referrals during the Omicron wave of infection. The short, sharp reduction in referrals seen when plan B restrictions were in place is likely due to normal seasonal variations.

The large reduction in referrals in March 2020 was consistent across all regions. By Summer 2021, most regions had returned to pre-pandemic levels of referrals with the exception of the south west, which has been persistently below its pre-pandemic level.

Figure 26: NHS referrals through the e-Referral service

Source: NHS Digital – NHS e-Referral Service Open Data, October 2019 to February 2022, NHS Digital.

Note: the baseline was constructed using the average number of referrals between 7 October and 29 December 2019, which is in line with the approach used by NHS Digital. This was deemed to be a period in which COVID-19 did not impact referrals. However, caution should be used with this baseline as it is based on only a limited number of weeks and includes Christmas and New Year.

Figure 27 shows referrals per working day, broken down by priority: 2 week wait, urgent and routine referrals. There was a sharp reduction in 2 week wait and urgent referrals during the first national lockdown which recovered to pre-pandemic levels by the autumn of 2020. These referrals have been above pre-pandemic levels since April 2021. Conversely, whilst routine referrals also fell sharply during the first lockdown, and have somewhat recovered, they mostly remain below pre-pandemic levels. It may be the case that some referrals that would previously have been routine are now considered urgent.

Omicron appears to have had no discernible impact on referrals, by priority, although it is hard to determine given normal seasonal variations in referrals.

Figure 27: referrals through the e-Referral service by priority

Source: NHS Digital – NHS e-Referral Service open data, October 2019 to February 2022, NHS Digital.

Missing referrals

The number of missing referrals, in other words referrals we would have expected to happen during the pandemic if we operated at pre-pandemic levels, is mainly driven by routine referrals. We expect some, but not all of these referrals to return.

Reasons why some missing referrals will not return:

• self-limiting conditions which may have since resolved
• patients may have used private healthcare or secondary care
• changes in GP ways of working. There could be increased or better usage of the Referrals Assessment Service (RAS) to obtain specialist advice and triage. Specialist advice could help GPs better manage patients in primary care and triage could help avoid unnecessary referrals. Additionally, given increased referral waiting times and backlogs, some patients who would have traditionally been referred to specialist care may now being managed in primary care for longer
• COVID-19 may have caused the death of some individuals who would have otherwise been referred

Impact on secondary care

This section explores the impact of the pandemic on secondary care.

Main findings:

• there has been a wave of NHS staff absences due to Omicron, similar to that seen during Alpha but lower than the first wave
• COVID-19, delayed discharge and infection controls have also constrained supply in secondary care. Bed occupancy by patients with COVID-19 peaked in early January 2022 and in early April 2022, but at a lower level than in April 2020 and January 2021. A smaller proportion of COVID-19 patients are being treated primarily for COVID-19 than in December 2021
• elective activity remains below the levels delivered prior to the pandemic. Whilst it has been more resilient during the Omicron wave of infection compared to the previous waves, there were further reductions in activity in December 2021 and January 2022. Activity didn’t return to pre-Omicron levels until February 2022

NHS staff absences in acute and MHLDA trusts

Figure 28: 7-day average staff absence of acute and MHLDA (mental health, learning disabilities and autism) trusts, England, from week ending 2 April 2020 to week ending 22 April 2022

Source: NHS England – COVID-19 hospital activity, data analysis by DHSC.

Figure 28 shows NHS staff absences covering acute and MHLDA trusts in England. It shows that the number of staff absent have been consistently higher throughout the pandemic when compared to 2019 (pre-pandemic).^{[footnote 9]} The significant increases on overall staff absence figures are mainly driven by COVID-19 staff absences, particularly during waves of COVID-19 infection.

Up until 19 April 2022, Omicron has caused 2 peaks of staff absences in England. In January 2022, there was a 7-day average peak of 57,000 COVID-19 absences which related to the period in which Omicron BA.1 was the dominant variant in England. This is slightly above the 7-day average peak in COVID-19 staff absences seen in January 2021 (53,000), but considerably below the peak in April 2020 (104,000). In April 2022, there was a further spike in COVID-19 staff absences, with the 7-day average peak reaching 36,000. This corresponds to the period in which Omicron sub-lineage BA.2 was the dominant variant.

The impact of COVID-19 on staff absences has been somewhat mitigated by changes to self-isolation rules, better testing, better resilience and infection control, increased population immunity (from a vaccine, previous infection or both), and the reduced severity of Omicron in comparison to the previously dominant variant (Delta). Even though the risk of hospitalisation due to Omicron is lower and there is a high proportion of the population with antibodies against COVID-19, its high transmissibility has contributed to significant increases in staff absences which causes pressure to the NHS.

NHS staff absence rates

Figure 29: average NHS staff ^{[footnote 10]} absence rates in England, from March 2020 to December 2021

Source: NHS Digital - NHS Sickness Absence Rates, October 2021 to December 2021, Provisional Statistics, data analysis by DHSC.

Figure 29 shows the sickness absence rates^{[footnote 11]} of the NHS workforce in England. Compared to absence rates in 2019, the rates of absence were higher between March to June 2020, September 2020 to February 2021, and April 2021 to December 2021. The increase in absences observed in December 2021 occurred in all regions, although it was more pronounced in London and the north west. The most reported reason for staff sickness in December 2021 was anxiety, stress, depression, or other psychiatric illnesses which accounted for 23.7% of all sickness absence in that month.

The NHS staff survey, which was undertaken in November 2021, before Omicron, showed that staff morale had declined to a 5-year low, following steady improvements between 2017 to 2020. This, in part, represents the impact of increasing work pressures and work stressors.

During the pandemic, infection prevention and control measures were implemented which included isolating COVID-19 patients, additional cleaning, social distancing, and the use of personal protective equipment (PPE). Whilst these controls help protect patients and staff from acquiring COVID-19 and controls transmission, it has also reduced hospital capacity and flexibility. These controls remained in place to April 2022, at which point many were relaxed.

These supply constraints have impacted the productivity of healthcare. Provisional estimates suggest that secondary care productivity in 2021/2022 was 19.4% lower than in 2019/2020 (provisional estimates, based on internal calculations and historic case mix). This figure takes into account the reduction in secondary care activity alongside the increase in planned NHS expenditure, though it assumes costs only move in line with general inflation.

Elective activity

Figure 30 shows admitted elective activity per working day using NHS England data on consultant-led referral to treatment waiting times (RTT). It evidences the significant disruption on the delivery of elective care due to COVID-19. Admitted elective care has hovered below pre-pandemic levels since 2020 and has remained below pre-pandemic trends so far in 2022. Admitted activity fell in December 2021, but didn’t return to pre-Omicron (November 2021) levels until February 2022.

Figure 30: admitted elective activity per working day, from April 2016 to May 2022

Source: NHS England - Consultant-led Referral to Treatment Waiting Times, data analysis by DHSC.

Figure 31 shows non admitted elective activity, which is elective activity that does not require hospital admission, per working day using NHS England RTT data. Whilst there was a significant drop in activity in 2020, non admitted elective activity broadly returned closer to pre-pandemic levels in 2021. Non admitted activity also fell in December 2021 before returning to pre-Omicron levels.

Figure 31: non admitted elective activity per working day, from April 2016 to May 2022, NHS England

Source: NHS England - Consultant-led Referral to Treatment Waiting Times, data analysis by DHSC.

Figure 32 shows the drop and subsequent recovery in admitted elective activity at a regional level between November 2021 and February 2022. It shows that admitted elective activity has been more impacted in certain regions during the Omicron wave of infection. Admitted activity often recovers from a seasonal dip in December by January, but some regions saw almost no recovery in January 2022. The north east and Yorkshire, north west, midlands and the east of England saw particularly little recovery in admitted activity in January 2022.

Figure 32: admitted elective activity per working day by region, from September 2021 to February 2022 compared to same months in 2019/2020

Source: NHS England – Consultant-led Referral to Treatment Waiting Times, data analysis by DHSC.

Figure 33 shows that general surgery, and trauma and orthopaedics were the specialties in admitted elective activity most impacted by COVID-19. It is also evident from the figure that the most common admitted specialities all saw dips in activity in December 2021. Trauma and orthopaedics, and general surgery saw little to no recovery in activity in January 2022, but these specialities broadly returned to pre-Omicron levels by February 2022. However, they still remain well below 2019/2020 levels.

Figure 33: admitted elective activity by most common specialties per working day, from September 2021 to February 2022 compared to same months in 2019/2020

Source: NHS England - Consultant-led Referral to Treatment Waiting Times, data analysis by DHSC.

Consultation-led referral to treatment waiting times data

Figure 34 and 35 show the proportion of patients waiting less than 18 weeks for elective treatment and the proportion waiting 52 weeks or more, respectively. The figures show that although very long waits have remained broadly unchanged since November 2021 (Figure 35), a higher proportion of patients are waiting 18 weeks or more for elective treatment (Figure 34). As of May 2022, 6.61 million patients are waiting for elective care, up from 4.42 million during the first 2 months of 2020 (see NHS England – Consultant-led Referral to Treatment Waiting Times).

It is estimated that 10 million patients (a small proportion of the 10 million missing patients are patients for cancer diagnosis and treatment) have not come forward for treatment over the pandemic, who otherwise may have. If around half of these missing patients eventually return, then we would expect waiting lists to be reducing by around March 2024 (see NHS England and NHS improvement, delivery plan for tackling the COVID-19 backlog of elective care). Patients on waiting lists are waiting longer. The proportion waiting more than 18 weeks and the proportion waiting more than 52 weeks have both increased since 2019.

Figure 34: proportion of patients waiting less than 18 weeks, to May 2022

Source: NHS England – Consultant-led Referral to Treatment Waiting Times, data analysis by DHSC.

Figure 35: proportion of patients waiting 52 weeks or more, to May 2022

Source: NHS England - Consultant-led Referral to Treatment Waiting Times, data analysis by DHSC.

Ambulance and emergency data

Figure 36 shows that A&E attendances have broadly returned to pre-pandemic levels whilst category 1 ambulance incidents are higher than pre-pandemic levels (Figure 37). At the start of the pandemic, emergency attendances had fallen to record lows as people avoided seeking care. By mid-2021, the number of A&E attendances broadly returned to pre-pandemic levels. Category 1 ambulance incidents, which involve life threatening conditions such as cardiac or respiratory arrest, has been substantially above pre-pandemic levels since mid-2021.

Figure 36: number of A&E attendances (per month) to June 2022, NHS England

Source: NHS England – Ambulance Quality Indicators, data analysis by DHSC.

Figure 37: number of category 1 ambulance incidents per month, to June 2022, NHS England

Source: NHS England – Ambulance Quality Indicators, data analysis by DHSC.

Figure 38 shows the proportion of attendances waiting less than 4 hours to be admitted, transferred or discharged. It shows that there was a significant decrease in the proportion of attendances waiting less than 4 hours since mid-2021. Indeed, the proportion of A&E attendances waiting more than 4 hours since December 2021 has been the highest since 2011.

Figure 38: proportion of attendances waiting less than 4 hours to be admitted, transferred or discharged, to June 2022

Source: NHS England - A&E attendances and emergency admissions, data analysis by DHSC.

Figure 39 shows that, since mid-2021, ambulance response times for category 1 incidents have been at their highest levels since 2018, and notably above the 7-minute standard. Response times for less urgent incidents (categories 2, 3 and 4) have also increased. Handover delays for patients waiting in ambulances have fed into longer waiting times for admissions which could impact health outcomes. Indeed, a research study that looks at admissions between 2016 and 2018 found an association between delays in emergency admissions and an increase in all-cause 30 day mortality.

Figure 39: category 1 mean ambulance response time, seconds, to June 2022

Source: NHS England - Ambulance Quality Indicators, data analysis by DHSC.

Impact on mental health

The pandemic has seen increases in underlying need on mental health, at the same time as mental health services have expanded. There are now more people in contact with mental health services than before the pandemic, driven by increases in the number of referrals.

Self-reported mental health and wellbeing has worsened throughout the coronavirus pandemic, particularly for children and young people. Figure 40 shows the increased prevalence of probable mental disorders among children and young people in 2020 (January to October) and 2021 (July to August), compared to 2017 (January to October). Among children aged between 6 and 16, prevalence has increased from 1 in 9 (11.6%) in 2017, to 1 in 6 in 2020 (16.6%) and 2021 (17.4%). Among young people aged between 17 and 19, prevalence has increased from 1 in 10 (10.1%) in 2017 to 1 in 6 in 2020 (17.7%) and 2021 (17.4%).

Figure 40: prevalence of probable mental disorders among children and young people

Source: NHS Digital - mental health of children and young people in England, data to March 2021. Data analysis by DHSC. Data for 20 to 22 year olds from January to October 2017 is not available.

Figure 41 shows the increased prevalence of depression among adults during the pandemic compared to July 2019 to March 2020 (pre-pandemic). Around 1 in 6 (17%) adults experienced some form of depression in summer 2021. This is a decrease since early 2021 (21%) but is still above levels seen before the pandemic (10%) (data is not available to permit us to comment on any trend in depression amongst adults pre-pandemic).

Figure 41: percentage of adults with moderate to severe depressive symptoms in Great Britain

Source: ONS – coronavirus and depression in adults, Great Britain, data to August 2021.

More people are in contact with mental health services, particularly children and young people. NHS England have been expanding access to mental health services, as set out in the NHS Long Term Plan. Services were expanding prior to the pandemic and continue to do so as we emerge from the pandemic.

Figure 42 shows the number of people in contact with mental health services (data includes referrals for learning disabilities, autism and other neurodevelopmental services) at the end of each month, for each age group. More people were in contact with mental health services at the end of February 2022 than before the pandemic. Numbers are furthest above their pre-pandemic level (February 2020) for those aged 0 to 18.

Figure 42: number of people in contact with mental health services at the end of each month

Source: NHS Digital - mental health services statistics, data to February 2022, mental health services Power BI dashboard, data analysis by DHSC.

More people are being referred to mental health services compared to before the pandemic. Figure 43 shows that the number of referrals to mental health services (both adult, and children and young people) are above pre-pandemic levels. Between December 2021 and February 2022, 1,063,797 referrals were made to mental health services, which is up from 970,941 in the 3 months from December 2019 to February 2020.

Figure 43: number of referrals to mental health services each quarter

Source: NHS Digital – mental health services statistics, rolling quarters, data finalised to February 2022 and provisional to March 2022, data analysis by DHSC.

Figure 44 shows that the number of children and young people referred to mental health services has increased since 2017. In February 2022, 96,396 people aged 18 and under were referred to mental health services which is up from 80,555 in the same month in 2020.

Figure 44: number of children and young people referred to mental health services each month

Source: NHS Digital - mental health services statistics, referrals for children and young people Power BI dashboard, data to February 2022, data analysis by DHSC.

Record numbers of children and young people are accessing support for eating disorders. In the year 2021/2022, a total of 12,457 children and young people started treatment, which is an increase of 16% on the year before and up by 55% since before the pandemic.

The pandemic has impacted the NHS’s ability to deliver against waiting time standards, notably for children and young people’s eating disorders, where there have been significant increases in cases coming forward for treatment. Waiting times for children and young people with an eating disorder to start treatment has deteriorated and is yet to recover to pre-pandemic levels due to these levels of demand. In the 3 months to March 2022, only 62% of urgent cases were seen within 1 week and only 64% of routine cases were seen within 4 weeks (see NHS, Children and young people’s eating disorders programme).

However, waiting times for other services have been resilient throughout the pandemic. Waiting times for people experiencing a first episode of psychosis and for the talking therapies programme continue to meet waiting time standards. However, since the onset of the pandemic, providers are reporting an increase in complexity of cases which is evidenced through an increase in average sessions per course of an increasing access to psychological therapies (IAPT) treatment.

Category D: indirect impacts of COVID-19 on the wider population in the long-run

Public health impacts

Main findings:

• increasing or higher risk alcohol consumption increased during the early pandemic and has remained high
• the pandemic has seen a greater proportion of smokers trying to quit, but this has not been accompanied by a corresponding decrease in smoking prevalence

Alcohol consumption

Figure 45 shows that the prevalence of increasing or higher risk alcohol consumption rose during the early pandemic and has persistently remained above pre-pandemic levels. In January 2022, prevalence stood at 17.5%. This increase has been greater for those in manual occupations compared to those in professional to clerical occupations.

Figure 45: increasing or higher risk alcohol consumption prevalence by occupation; the vertical line represents the start of the first national lockdown

Source: UCL alcohol toolkit study, data analysis by DHSC.

In the early stages of the pandemic, there was an increase in the percentage of high-risk alcohol drinkers reporting they made an attempt to cut down for reasons related to weight loss, fitness or a future health concern, as well as for other reasons (Figure 46). However, the percentage reporting that GP advice contributed to an attempt to cut down has been relatively stable over time.

Figure 46: reasons for attempts to cut down alcohol consumption by high-risk drinkers; the vertical line represents the start of the first national lockdown

Source: UCL alcohol toolkit study, data analysis by DHSC.

Smoking

Figure 47 shows that the percentage of adults who were cigarette smokers remained broadly constant from April 2020 to August 2021 at around 15% (3 month moving average). Whilst smoking prevalence fell in October 2021, with 13.4% of adults reporting to be smokers, there was an increase in smoking prevalence between November and December 2021 which was maintained in January and February 2022.

Figure 47: percentage of adults who were cigarette smokers (3-month moving average)

Source: UCL Smoking Toolkit Study, data analysis by DHSC.

The percentage of smokers who tried to stop smoking increased in Spring 2020 and attempts to quit have remained at higher levels than before the pandemic began. However, this has not been accompanied by a corresponding decrease in smoking prevalence.

Figure 48: percentage of those who were smokers in the past year who tried to stop that year (3-month moving average); the vertical line represents the start of the first national lockdown

Source: UCL smoking toolkit Study, data analysis by DHSC.

The percentage of smokers who reported a GP triggered an attempt to quit that year fell in the start of the pandemic and despite fluctuations has remained lower than before the pandemic (3.5% in January 2022 compared to 4.9% in January 2020).

Figure 49: percentage of all those who smoked in the past year who reported a GP triggered an attempt to quit that year; the vertical line represents the start of the first national lockdown

Source: UCL Smoking Toolkit Study, data analysis by DHSC.

Social care impacts

Main findings:

• adult social care has long-lasting pressures pre-dating COVID-19. These include rising demand for social care, workforce pressures, low productivity, availability of care home and home care provision, and financial pressures faced by the care home and home care market. These are discussed in the summary of the evidence review for adult social care reform. In many cases these pressures have been exacerbated by the pandemic

The story in adult social care: COVID-19 has exacerbated existing pressures, particularly workforce pressures

Staff absence rates

During the Omicron wave, staff absence rates for COVID-19-related reasons increased in both care home and domiciliary care settings (Figure 50). Among care home staff, COVID-19-related absences peaked at 2.9% in the week ending 11 January 2022, comparable to levels seen in early 2021. Among domiciliary care staff, COVID-19-related absences peaked at 4.8% in the week ending 4 January 2022, the highest level seen since the start of the timeseries in March 2021. Absence rates in both settings started to increase again in March 2022, following a substantial decrease.

Figure 50: percentage of staff absent due to COVID-19-related reasons, March 2021 to week ending 22 March 2022, in England

Source: DHSC – adult social care in England, monthly statistics: April 2022.

Recruitment and retention issues throughout the pandemic

During the Omicron wave, the vacancy rate amongst care home and domiciliary care providers reached the highest levels seen both during and pre-COVID-19 (March 2020), peaking at 10% in March 2022 (Figure 51). This compares to the pre-COVID-19 (March 2020) rate of 7.6%. These values were correct in Skills for Care’s monthly tracking as of April 2022 but may be revised with future data updates. The sector has also seen a reduction in the number of jobs (filled posts) during this period, following a steady increase in the number of jobs from 2012/2013 and 2020/2021.

Figure 51: vacancy rate trend, all services (care home with nursing, care home without nursing and domiciliary care services), pre-COVID-19 (March 2020) to March 2022, in England. Data correct as of April 2022 – published historical values may be revised with subsequent updates

Source: Skills for Care - vacancy information, monthly tracking.

Note: data from June 2020 to March 2021 is from cohort 1, and from April 2021 to March 2022 is from cohort 2. Quarterly tracking during 2020, monthly from March 2021 onwards.

In a survey of adult social care providers, 81.9% of respondents found it more or much more challenging to recruit staff in October 2021 compared to April 2021 in England, and 70.3% of respondents found it more or much more challenging to retain staff in October compared to April 2021. Amongst care homes, the top 3 reasons believed to be the main cause of staff leaving were better pay outside of the care sector (25.9%), do not wish to be vaccinated (14.7%) and better hours and working conditions outside the care sector (13.4%). Amongst domiciliary care providers these were better pay outside of the care sector (29.1%), better hours and working conditions outside the care sector (11.5%) and feeling burnt out or stress (10.2%).

A survey conducted by the National Care Forum (undertaken over a short period of time, 5 to 10 January 2022, and therefore likely yielding a small number of responses) on the impact of the Omicron variant on social care found that for 97% of respondents, staff had covered extra shifts to cope with reduced staffing levels.

The story in adult social care: it is possible that COVID-19 may have had an impact on access to social care

Access to social care

There are many reasons why individuals who have a need for support with their daily activities may not receive formal or informal support. Some stakeholders call this ‘unmet need’ although there is no agreed definition in either the Care Act 2014 or research literature. Understanding the number of people with social care needs who are not receiving support, and how these numbers are changing over time, is challenging. Due to this, understanding the impact the pandemic has had on the number of people not receiving support for their care needs – and importantly, the drivers of this – is difficult. As there is no conclusive evidence of the scale of unmet need, it is difficult to assess the impact that the pandemic has had on access to social care services. The following evidence suggests that COVID-19 may have had an impact on access to social care:

• concerns related to COVID-19 have led to a decrease in new requests for care. In 2020/2021, new requests for support to local authorities by older people fell from 1.37 million the previous year to 1.34 million. According to The King’s Fund, this reduction is likely to reflect a reluctance to come forward for services during the pandemic. In contrast, requests have increased among working-age adults, from 560,000 (in 2019/2020) to 578,000 (in 2020/2021)

• constraints on care providers because of COVID-19 pressures have led to an increase in refusals of requests for care. The Association of Directors of Adult Social Services (ADASS) estimates that more than 2.2 million hours of commissioned home care could not be provided between 1 January and 31 March 2022 (during the Omicron wave) due to workforce capacity constraints, up from 300,000 hours between 1 February and 30 April 2021. This is despite an increase in home care hours delivered over the same period. Note that ADASS derived these estimates by extrapolating survey responses from a non-random sample of local authorities, so the finding may be an over or under estimate. A National Care Forum survey on the impact of Omicron on social care (undertaken over a short period of time, 5 to 10 January 2022, and therefore likely yielding a small number of responses) found that due to staff shortages, 66% of those home care providers that responded had refused new requests for home care, whilst 43% of care homes that responded had closed their care home to new admissions

Unpaid carers

The number of unpaid carers is likely to have increased during the pandemic. Whilst estimating the number of unpaid carers, and the impact of the pandemic, is challenging, analysis of the understanding society dataset found that two-thirds (67%) of people who reported providing unpaid care during the second wave of the pandemic were not previously providing care. This analysis also found that almost half of carers providing more than 20 hours of care per week during the second wave of the pandemic were not previously providing care (45%). However, a change in the definition of caring outside the household for the COVID-19 survey may be contributing to this increase in the number of unpaid carers observed. There is a range of forthcoming data sources which will provide an updated estimate of the number of unpaid carers in England, including the 2021 Census and future iterations of the Health Survey for England.

Care home occupancy

In March 2022, care home occupancy remained below pre-COVID-19 levels, at 83% for care homes without nursing and 78% for care homes with nursing (Figure 52). These values were correct as of April 2022 but may be revised with future data updates. We identify 3 key factors that are likely to have contributed to this – mortality of residents, reduced care home capacity due to staffing constraints, and a shift in preference from care homes to domiciliary care as a result of perceived infection risks.

Figure 52: care home occupancy, pre-COVID-19 (March 2020) and March 2022, in England. Data correct as of April 2022; published historical values may be revised with subsequent updates

Source: Skills for Care – staffing and occupancy, monthly tracking.

Economic impacts on health

At a macro-level, indicators suggest that the Omicron wave of infection may not have had a significant impact on the economy. It follows that resulting health outcomes, such as chronic conditions, would not have been materially impacted through economic channels. Note that the current emergence of other non-COVID-19 economic pressures may give rise to their own short and long-term health impacts. These pressures include rising inflation and cost of living, labour supply constraints, financial impacts on public services and higher interest rates.

Demand for goods and labour

Total gross value added (GVA) (the contribution made to an economy by 1 individual producer, industry, sector or region) remained stable during the peaks of the omicron and Delta waves of infection (December 2021 and July 2021 respectively). This suggests aggregate demand remained stable in December 2021. This has ensured that demand for labour has remained stable. The UK employment rate in December 2021 remained broadly similar to late November 2021 levels, at 75.5%. During the summer of 2020, when the Delta wave peaked, the employment rate increased slightly from 75.1% in Q2 2020 to 75.3% in Q3 2020. Being in employment can improve people’s health and wellbeing (see Health matters: health and work).

However, some industries have been impacted during the Omicron wave of infection, with GVA in the food service and arts industries declining by 9% in December 2021 compared to the previous month (Figure 53). These industries have since recovered, posting GVA growth of 9% in February 2022. In the 3 months to March 2022, this uptick in demand has led to an additional 27,000 and 5,000 jobs in the accommodation and food service industry and arts, entertainment and recreation industry respectively (see employee jobs by industry data on the ONS website).

Figure 53: percentage change in GVA on previous month in selected industries

Source: ONS - GVA, data analysis by DHSC.

Figure 54 shows that consumer spending fell sharply in December 2021 but it is difficult to attribute this to Omicron as this is also the observed trend in previous years. Spend dampened in July 2021 which may have been due to the impact of the Delta variant. However, there is a trend of spend falling after the first week of every month which may explain some of this pattern. An increase in consumer spending generates growth in aggregate demand in the economy, which may impact health outcomes through the income and employment channels.

Figure 54: consumer spending between February 2021 and January 2022, UK

Source: ONS - UK spending on credit and debit cards, data analysis by DHSC.

Annexes

Annex 1: timeline – restrictions, variants and vaccine roll-out

Restrictions during COVID-19	Start	End
First national lockdown	26 March 2020	13 May 2020
Introduction of tiers and second lockdown	14 October 2020	5 January 2021
Third national lockdown	6 January 2021	7 March 2021
Roadmap	8 March 2021	19 July 2021
Step 1	8 March 2021	N/A
Step 2	12 April 2021	N/A
Step 3	17 May 2021	N/A
Step 4	19 July 2021	N/A
Plan B regulations	10 December 2021	26 January 2022

Main waves of COVID-19 infection referred to in this report: ^{[footnote 12]}

• first wave: estimated to have started in March 2020 and ended at the end of May 2020
• Alpha wave: estimated to have started in December 2020, when the variant became dominant, and ended at the end of April 2021
• Omicron wave: estimated to have started in December 2021, when the variant became dominant. This publication considers the first wave of Omicron infections, as a result of Omicron BA.1, to have started December 2021 and ended in February 2022. This was followed by a second wave of infections when Omicron sub-lineage BA.2 became the dominant variant in the UK at the end of February 2022

It worth noting that infection levels increased with the emergence of other variants not explicitly considered in this report, such as the Delta variant. This report focussed on Omicron impacts and contextualised these impacts using the waves which had the largest impacts (infection, mortality, and morbidity). These were generally the first wave of infection and the Alpha wave.

Other important dates:

• vaccine roll-out – began in December 2020, with all adults eligible for the first dose from 18 June 2021
• booster – all adults eligible for a booster dose from 15 December 2021

The data and interventions referred to in this report all refer to England unless otherwise stated.

Annex 2: category A mortality definitions

Deaths involving COVID-19 and COVID-19 deaths: any registered death where COVID-19 is mentioned on the death certificate, whether the death was due to COVID-19 or COVID-19 was an underlying cause.

Weekly excess COVID-19 deaths: see chart below. When total death registrations in a week exceed the 5-year average number of deaths for that week, excess COVID-19 deaths = total deaths involving COVID-19 or total excess deaths, whichever is lower.

Note the sum of weekly excess COVID-19 deaths over a period does not necessarily equal the total number of excess COVID-19 deaths in the same period.

Five-year average: the 5 years used as a comparator differ depending on the year of deaths data analysed:

• for deaths registered in 2020 and 2021, the 5-year average is based on 2015 to 2019
• for deaths registered in 2022, the 5-year average is based on 2016 to 2019 and 2021

Figure 55: weekly excess COVID-19 deaths calculation

Deaths by region presented in Figure 4 are the percentage of all COVID-19 deaths in England which occurred in each region. We have presented this statistic instead of age-standardised mortality rates or absolute COVID-19 deaths because the 2 periods being compared are of different lengths, and have had very different mortality rates at different times since March 2020. We do not expect the proportion should be equal for each region because of these population size and demographic differences – but can compare if areas have been disproportionately affected between this latest reporting and our previous reporting.

Annex 3: background and methodology note on the Clinical Practice Research Datalink (CPRD) – Real Centre Analysis

This note details the background and methodology for the Clinical Practice Research Datalink (CPRD). Data from the CPRD has been used by the Health Foundation’s REAL Centre in category C of this paper – primary care diagnosis of long-term conditions.

The Clinical Practice Research Datalink (CPRD) is a patient level primary care data set used to explore primary care activity and outcomes from a subset of practices. Unlike the high-level NHS digital appointment data, this patient-level data allows us to match consultations to patients by age, sex, geographical region and whether they have been diagnosed with non-communicable diseases (NCDs). The data provide the most detailed view of the changes that the COVID-19 pandemic and accompanying lockdown has brought about in primary care.

The CPRD Aurum data base (used in this study) contains data from 19 million patients from 738 primary care practices in England. As of September 2018, 7 million (13%) of the English population were alive and registered at a CPRD Aurum Practice (see Wolf et al. (2019)).

Wolf et al. (2019) also detail the data collection process for CPRD Aurum: ‘There are 4 principal GP IT systems (primary care patient management software system) suppliers in England and the largest coverage is provided by EMIS Health (EMIS Web software is used in 56% of English practices). CPRD Aurum encompasses EMIS Web GP practices that have agreed to contribute data to this database on a daily basis’.

CPRD obtains ethics approval annually from the UK’s Health Research Authority (HRA) Research Ethics Committee (REC) (east Midlands – Derby, REC reference number 05/MRE04/87). CPRD is jointly sponsored by the UK government’s Medicines and Healthcare products Regulatory Agency and the National Institute for Health Research (NIHR). As a not-for-profit UK government body, CPRD seeks to recoup the cost of delivering its research services to academic, industry and government researchers through research user licence fees.

CPRD Aurum contains a wide range of diagnostic, prescription and procedural information and its key strengths are its size, coverage and longitudinal follow-up. It is representative of the English population in terms of age, gender, region and measures of deprivation. Studies have also found it to be relatively accurate in recording comorbidities and illness: Persson et al. (2020), Jick et al. (2021) and Persson et al. (2021).

This analysis is based on a sample of 500,000 CPRD Aurum patient identities stratified by age, gender and region. The patients were active and registered in CPRD in an English practice at any time between January 2016 and January 2022. The patient histories, diagnoses and consultations allow for a much deeper understanding of primary care than is currently publicly available. The data is provided by patients and collected by the NHS as part of their care and support. The data is anonymized and only the patient’s government office region is provided as a geographical variable in the CPRD Aurum database. The data were analysed by the REAL Centre in the Health Foundation’s Secure Data Environment that holds ISO 27001 certification.

Scientific approval for this study was given by the CPRD Independent Scientific Advisory Committee (ISAC). The study was approved by the ISAC for CPRD protocol number 20_143.

Consultations

Consultations are defined in CPRD using the same method as in Watt, Sullivan & Aggarwal (2022):

‘We define consultations in CPRD data by a set of rules developed based on 2 variables in the consultations file (Primary care data for public health research) (‘EMIS consultation source identifier’ and ‘Consultation source code identifier’) – these variables contain strings that categorise the patient record input and are selected by the staff member completing the record. In line with the approach taken by Carey et al. 2017 for CPRD Gold data, we use a combination of the consultation code and the category of the record to identify consultations.’

Details are provided in the online supplemental Annex 2 to the same article.

Remote consultations

Within primary care, since the start of the pandemic, many more consultations have been conducted remotely. The information contained in CPRD was sufficient to define consultations that were conducted remotely through variables in the consultation file.

Remote consultations were defined as any consultation from CPRD, as defined above, for which the Consultation source code identifier and the EMIS consultation source identifier contained any of the following phrases:

• consultation via multimedia
• consultation via sms text message
• consultation via telemedicine web camera
• consultation via video conference
• email consultation
• enterprise consultation
• first telephone consultation
• online communication
• other consultation medium used
• telephone call from a patient
• telephone call to a patient
• telephone consultation
• telephone encounter

Diseases

The analysis presented in this paper looked at disease incidence for the following long-term illnesses – atrial fibrillation, coronary heart disease, heart failure, stroke or transient ischaemic attack (TIA), asthma, chronic obstructive pulmonary disease (COPD) and diabetes (types 1 and 2).

A diagnosis of 1 of these conditions was defined by adapting the approach of Barnett et al. (2012), using code lists developed by epidemiologists for an analysis published in the Lancet, Healthy Longevity. See the code lists for many conditions, including those used in this analysis.

A patient is diagnosed with a condition at the time of the first instance of a diagnosis across CPRD. All conditions but asthma are modelled as chronic, life-long conditions. Asthma was considered to have been resolved if the patient has had no new diagnoses or related drug prescriptions for a year.

Any diagnosis that takes place within 12 months of a patient’s registration with a new practice was treated as an existing disease (prevalence) and not a new diagnosis (incidence).

Incidence

Monthly incidence rates were defined as patients with a new diagnosis in each calendar month as a share of ‘at risk’ patients, as in, registered patients that are not already diagnosed with the relevant condition at the beginning of that month.

‘Missing incidence’ are estimated by comparing the incidence rate for each month after March 2020 to the average monthly incidence rate for 2019. The estimated missing incidence are then added up in the post-COVID period to the end of 2021 and uprated to the estimated English population and rounded to the nearest 1,000. To provide context to these figures, they are compared to the estimated prevalence in the CPRD sample.

References

1. Heart failure occurs when the heart is unable to pump blood around the body properly – it is usually the result of a number of problems such as high blood pressure, Afib and CHD. Source: NHS website – Heart Failure. ↩

2. Afib is a condition where normal function (beating) of the heart changes and instead ‘flutters’ – it increases the likelihood of serious disease or fatal cardiac incidents. Source: NHS website – Afib. ↩

3. CHD occurs when the heart’s blood supply is blocked or interrupted by a build-up of fatty substances in the coronary arteries – it is a major cause of death in the UK and cannot be cured – it can however be treated to help manage symptoms and reduce problems such as heart attacks. Source: NHS website – CHD. ↩

4. A stroke is a serious life-threatening medical condition that happens when the blood supply to part of the brain is cut off. It is a medical emergency which requires urgent treatment. Source: NHS website – Stroke ↩

5. A TIA attack, also known as mini-stroke, is caused by a temporary disruption in the blood supply to part of the brain. Source: NHS website – TIA ↩

6. Asthma is a common lung condition which causes occasional breathing difficulties. Simple treatments can help keep symptoms under control. Triggers can include allergies, pollution and infections (such as colds or flu). Badly controlled asthma can negatively impact a patient’s life, lead to lung infections and even delayed growth or puberty in children. Source: NHS website – asthma. ↩

7. COPD refers to a group of lung conditions that cause breathing difficulties, including emphysema (damage to air sacs in lungs) and chronic bronchitis (long-term inflammation of the airways). Without treatment, symptoms get progressively worse and it increases the chance of flare-ups. Source: NHS website – COPD. ↩

8. Diabetes is a lifelong condition that causes a person’s blood sugar level to become too high. It is important to be diagnosed as early as possible as diabetes will get progressively worse if left untreated. There are two main types: type 1, where the body’s immune system attacks and destroys the cells that produce insulin; and type 2, where the body does not produce enough insulin or the body’s cells do not react to insulin. Around 90% of all adults with diabetes have type 2 diabetes. Source: NHS website – diabetes ↩

9. The baseline for 2019 used for the staff absence data is taken from the NHS Sickness Absence Rates, October 2019 to December 2019. The monthly figures have been averaged based on the monthly sick days by full-time equivalent (FTE) staff across the NHS trusts, hospital trusts, ambulance trusts, mental health trusts, regions, clinical commissioning groups. Further noted that the baseline is based on monthly averages whereas the absence numbers are averaged to 7 days. ↩

10. Figure 29 only includes staff absences for acute and MHLDA trusts, whereas this publication on NHS staff absence rates covers more organisations: NHS trusts, hospital trusts, ambulance trusts, mental health trusts, regions, clinical commissioning groups. ↩

11. Sickness absence rate is calculated by dividing the total sickness absence days (including non-working days) by the total days available per month for each member of staff. Uses FTE figures. ↩

12. A ‘wave of infection’ is considered to be a period of increased transmission of disease, although there is no strict definition as to when a wave starts or ends. ↩